task_name stringclasses 13
values | dataset_name stringclasses 13
values | id stringlengths 4 47 | data_type stringclasses 4
values | input_ts dict | input_text stringclasses 593
values | gt_text stringclasses 75
values | gt_result dict | meta_data unknown |
|---|---|---|---|---|---|---|---|---|
RadSeg | RadSeg | RadarSeg_3083_test_sub_12 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3083_test_sub_12.npy",
"length": 87,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -3
} |
RadSeg | RadSeg | RadarSeg_9073_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9073_test_sub_1.npy",
"length": 36,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": -15
} |
RadSeg | RadSeg | RadarSeg_3173_test_sub_32 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3173_test_sub_32.npy",
"length": 271,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 4
} |
RadSeg | RadSeg | RadarSeg_4986_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4986_test_sub_3.npy",
"length": 50,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -9
} |
RadSeg | RadSeg | RadarSeg_3441_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3441_test_sub_1.npy",
"length": 60,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 10 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": 10
} |
RadSeg | RadSeg | RadarSeg_2213_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2213_test_sub_3.npy",
"length": 104,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 1
} |
RadSeg | RadSeg | RadarSeg_1762_test_sub_4 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1762_test_sub_4.npy",
"length": 43,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 17
} |
RadSeg | RadSeg | RadarSeg_3638_test_sub_2 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3638_test_sub_2.npy",
"length": 171,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -6 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -6
} |
RadSeg | RadSeg | RadarSeg_8276_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8276_test_sub_1.npy",
"length": 283,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 13
} |
RadSeg | RadSeg | RadarSeg_5780_test_sub_9 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5780_test_sub_9.npy",
"length": 61,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 19 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 19
} |
RadSeg | RadSeg | RadarSeg_6363_test_sub_0 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6363_test_sub_0.npy",
"length": 157,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -15
} |
RadSeg | RadSeg | RadarSeg_3762_test_sub_5 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3762_test_sub_5.npy",
"length": 289,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": -14
} |
RadSeg | RadSeg | RadarSeg_8272_test_sub_6 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8272_test_sub_6.npy",
"length": 130,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 4 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 4
} |
RadSeg | RadSeg | RadarSeg_3503_test_sub_5 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3503_test_sub_5.npy",
"length": 135,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -7
} |
RadSeg | RadSeg | RadarSeg_8639_test_sub_5 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8639_test_sub_5.npy",
"length": 86,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": 8
} |
RadSeg | RadSeg | RadarSeg_1026_test_sub_15 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1026_test_sub_15.npy",
"length": 189,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -17 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -17
} |
RadSeg | RadSeg | RadarSeg_5021_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5021_test_sub_3.npy",
"length": 199,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -10
} |
RadSeg | RadSeg | RadarSeg_8578_test_sub_8 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8578_test_sub_8.npy",
"length": 312,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 18
} |
RadSeg | RadSeg | RadarSeg_8355_test_sub_8 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8355_test_sub_8.npy",
"length": 168,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 8
} |
RadSeg | RadSeg | RadarSeg_2890_test_sub_6 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2890_test_sub_6.npy",
"length": 97,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 3 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": 3
} |
RadSeg | RadSeg | RadarSeg_6302_test_sub_10 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6302_test_sub_10.npy",
"length": 60,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -5
} |
RadSeg | RadSeg | RadarSeg_5200_test_sub_11 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5200_test_sub_11.npy",
"length": 195,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": -14
} |
RadSeg | RadSeg | RadarSeg_6791_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6791_test_sub_7.npy",
"length": 145,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": 5
} |
RadSeg | RadSeg | RadarSeg_4082_test_sub_12 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4082_test_sub_12.npy",
"length": 172,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 15
} |
RadSeg | RadSeg | RadarSeg_4312_test_sub_10 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4312_test_sub_10.npy",
"length": 68,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -15 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -15
} |
RadSeg | RadSeg | RadarSeg_6931_test_sub_8 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6931_test_sub_8.npy",
"length": 262,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": 9
} |
RadSeg | RadSeg | RadarSeg_8315_test_sub_12 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8315_test_sub_12.npy",
"length": 255,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -13 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -13
} |
RadSeg | RadSeg | RadarSeg_915_test_sub_6 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_915_test_sub_6.npy",
"length": 254,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 14
} |
RadSeg | RadSeg | RadarSeg_8676_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8676_test_sub_1.npy",
"length": 272,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 18 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": 18
} |
RadSeg | RadSeg | RadarSeg_5209_test_sub_14 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5209_test_sub_14.npy",
"length": 160,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 8
} |
RadSeg | RadSeg | RadarSeg_4829_test_sub_2 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4829_test_sub_2.npy",
"length": 272,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": -14
} |
RadSeg | RadSeg | RadarSeg_3890_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3890_test_sub_7.npy",
"length": 79,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": -10
} |
RadSeg | RadSeg | RadarSeg_2261_test_sub_15 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2261_test_sub_15.npy",
"length": 206,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -19 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -19
} |
RadSeg | RadSeg | RadarSeg_1778_test_sub_8 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1778_test_sub_8.npy",
"length": 229,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 13
} |
RadSeg | RadSeg | RadarSeg_826_test_sub_9 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_826_test_sub_9.npy",
"length": 288,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -20 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -20
} |
RadSeg | RadSeg | RadarSeg_3181_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3181_test_sub_3.npy",
"length": 314,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -7
} |
RadSeg | RadSeg | RadarSeg_1462_test_sub_5 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1462_test_sub_5.npy",
"length": 77,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 11
} |
RadSeg | RadSeg | RadarSeg_2233_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2233_test_sub_3.npy",
"length": 199,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 16
} |
RadSeg | RadSeg | RadarSeg_630_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_630_test_sub_7.npy",
"length": 43,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 8 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 8
} |
RadSeg | RadSeg | RadarSeg_6536_test_sub_6 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6536_test_sub_6.npy",
"length": 45,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 15
} |
RadSeg | RadSeg | RadarSeg_8475_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8475_test_sub_1.npy",
"length": 279,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 13
} |
RadSeg | RadSeg | RadarSeg_4925_test_sub_10 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4925_test_sub_10.npy",
"length": 229,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -9
} |
RadSeg | RadSeg | RadarSeg_9075_test_sub_8 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9075_test_sub_8.npy",
"length": 72,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -9 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": -9
} |
RadSeg | RadSeg | RadarSeg_4624_test_sub_12 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4624_test_sub_12.npy",
"length": 121,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -10
} |
RadSeg | RadSeg | RadarSeg_2089_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2089_test_sub_7.npy",
"length": 292,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -16
} |
RadSeg | RadSeg | RadarSeg_9189_test_sub_14 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9189_test_sub_14.npy",
"length": 237,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -4 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": -4
} |
RadSeg | RadSeg | RadarSeg_4445_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4445_test_sub_1.npy",
"length": 186,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 14 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 14
} |
RadSeg | RadSeg | RadarSeg_5963_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5963_test_sub_7.npy",
"length": 234,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 5 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": 5
} |
RadSeg | RadSeg | RadarSeg_162_test_sub_10 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_162_test_sub_10.npy",
"length": 236,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -5
} |
RadSeg | RadSeg | RadarSeg_6114_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6114_test_sub_3.npy",
"length": 39,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -5 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -5
} |
RadSeg | RadSeg | RadarSeg_4172_test_sub_20 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4172_test_sub_20.npy",
"length": 117,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": -7
} |
RadSeg | RadSeg | RadarSeg_1425_test_sub_6 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1425_test_sub_6.npy",
"length": 282,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": -7
} |
RadSeg | RadSeg | RadarSeg_8688_test_sub_4 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8688_test_sub_4.npy",
"length": 209,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": 13
} |
RadSeg | RadSeg | RadarSeg_7191_test_sub_8 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7191_test_sub_8.npy",
"length": 63,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 11
} |
RadSeg | RadSeg | RadarSeg_7627_test_sub_0 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7627_test_sub_0.npy",
"length": 168,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -18 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": -18
} |
RadSeg | RadSeg | RadarSeg_6680_test_sub_14 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6680_test_sub_14.npy",
"length": 157,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 13 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 13
} |
RadSeg | RadSeg | RadarSeg_2039_test_sub_1 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2039_test_sub_1.npy",
"length": 95,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": 9
} |
RadSeg | RadSeg | RadarSeg_3637_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3637_test_sub_3.npy",
"length": 205,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": 17
} |
RadSeg | RadSeg | RadarSeg_2084_test_sub_5 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2084_test_sub_5.npy",
"length": 254,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": -3
} |
RadSeg | RadSeg | RadarSeg_5235_test_sub_0 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_5235_test_sub_0.npy",
"length": 86,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -3 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -3
} |
RadSeg | RadSeg | RadarSeg_8205_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8205_test_sub_7.npy",
"length": 77,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 11 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 11
} |
RadSeg | RadSeg | RadarSeg_2723_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2723_test_sub_3.npy",
"length": 135,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 12 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 12
} |
RadSeg | RadSeg | RadarSeg_4817_test_sub_3 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4817_test_sub_3.npy",
"length": 88,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -7 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -7
} |
RadSeg | RadSeg | RadarSeg_4879_test_sub_24 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4879_test_sub_24.npy",
"length": 47,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -10 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -10
} |
RadSeg | RadSeg | RadarSeg_1954_test_sub_0 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1954_test_sub_0.npy",
"length": 212,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -16 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": -16
} |
RadSeg | RadSeg | RadarSeg_4449_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_4449_test_sub_7.npy",
"length": 35,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 20
} |
RadSeg | RadSeg | RadarSeg_3191_test_sub_12 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3191_test_sub_12.npy",
"length": 234,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -8 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -8
} |
RadSeg | RadSeg | RadarSeg_6608_test_sub_18 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_6608_test_sub_18.npy",
"length": 307,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 17 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": 17
} |
RadSeg | RadSeg | RadarSeg_3192_test_sub_0 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3192_test_sub_0.npy",
"length": 202,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": 9
} |
RadSeg | RadSeg | RadarSeg_7754_test_sub_19 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_7754_test_sub_19.npy",
"length": 40,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 15 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": 15
} |
RadSeg | RadSeg | RadarSeg_1551_test_sub_7 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_1551_test_sub_7.npy",
"length": 142,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of -14 dB and belongs to one of the following radar categories: Barker codes, Polyphase ... | This signal is classified as Coherent Unmodulated Pulse Trains. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Coherent Unmodulated Pulse Trains"
]
} | {
"snr": -14
} |
RadSeg | RadSeg | RadarSeg_9755_test_sub_0 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_9755_test_sub_0.npy",
"length": 159,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 1 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Barker codes"
]
} | {
"snr": 1
} |
RadSeg | RadSeg | RadarSeg_3954_test_sub_12 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_3954_test_sub_12.npy",
"length": 289,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 20 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Polyphase Barker codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Polyphase Barker codes"
]
} | {
"snr": 20
} |
RadSeg | RadSeg | RadarSeg_2848_test_sub_18 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_2848_test_sub_18.npy",
"length": 166,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 9 dB and belongs to one of the following radar categories: Barker codes, Polyphase Ba... | This signal is classified as Linear Frequency-Modulated (LFM) pulses. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Linear Frequency-Modulated (LFM) pulses"
]
} | {
"snr": 9
} |
RadSeg | RadSeg | RadarSeg_8831_test_sub_15 | npy | {
"num_channel": 2,
"channel_detail": [
"real",
"imag"
],
"path": "Electromagnetic-RadSeg-Coding_Scheme-Classification/raw_input/RadarSeg_8831_test_sub_15.npy",
"length": 169,
"timestamps": [],
"fs": 3200000
} | Please assist with a radar segment classification task. The dataset consists of pulsed radar signals with signal-to-noise ratios (SNRs) ranging from -20 dB to 20 dB, at a resolution of 0.5 dB. The provided signal segment has an SNR of 16 dB and belongs to one of the following radar categories: Barker codes, Polyphase B... | This signal is classified as Frank codes. | {
"class_list": [
"Barker codes",
"Polyphase Barker codes",
"Frank codes",
"Linear Frequency-Modulated (LFM) pulses",
"Coherent Unmodulated Pulse Trains"
],
"gt_class": [
"Frank codes"
]
} | {
"snr": 16
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QPSK_6_000143 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_6_000143.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QPSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QPSK"
]
} | {
"split": "eval",
"mod": "QPSK",
"snr": 6,
"sample_index_in_group": 143
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_AM-DSB_-6_000156 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_-6_000156.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is AM-DSB. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"AM-DSB"
]
} | {
"split": "eval",
"mod": "AM-DSB",
"snr": -6,
"sample_index_in_group": 156
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_GFSK_-4_000034 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000034.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is GFSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"GFSK"
]
} | {
"split": "eval",
"mod": "GFSK",
"snr": -4,
"sample_index_in_group": 34
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_GFSK_-4_000133 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-4_000133.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is GFSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"GFSK"
]
} | {
"split": "eval",
"mod": "GFSK",
"snr": -4,
"sample_index_in_group": 133
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_PAM4_-10_000140 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-10_000140.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is PAM4. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"PAM4"
]
} | {
"split": "eval",
"mod": "PAM4",
"snr": -10,
"sample_index_in_group": 140
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QAM64_-2_000063 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-2_000063.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QAM64. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QAM64"
]
} | {
"split": "eval",
"mod": "QAM64",
"snr": -2,
"sample_index_in_group": 63
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_BPSK_6_000033 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_6_000033.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is BPSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"BPSK"
]
} | {
"split": "eval",
"mod": "BPSK",
"snr": 6,
"sample_index_in_group": 33
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_CPFSK_0_000079 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_0_000079.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is CPFSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"CPFSK"
]
} | {
"split": "eval",
"mod": "CPFSK",
"snr": 0,
"sample_index_in_group": 79
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QPSK_4_000144 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_4_000144.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Based on the given in-phase and quadrature components, predict the corresponding modulation format. Candidate classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QPSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QPSK"
]
} | {
"split": "eval",
"mod": "QPSK",
"snr": 4,
"sample_index_in_group": 144
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QAM16_-18_000108 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_-18_000108.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QAM16. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QAM16"
]
} | {
"split": "eval",
"mod": "QAM16",
"snr": -18,
"sample_index_in_group": 108
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_8PSK_10_000127 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_8PSK_10_000127.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is 8PSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"8PSK"
]
} | {
"split": "eval",
"mod": "8PSK",
"snr": 10,
"sample_index_in_group": 127
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QAM16_0_000196 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM16_0_000196.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QAM16. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QAM16"
]
} | {
"split": "eval",
"mod": "QAM16",
"snr": 0,
"sample_index_in_group": 196
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_GFSK_-8_000076 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_GFSK_-8_000076.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is GFSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"GFSK"
]
} | {
"split": "eval",
"mod": "GFSK",
"snr": -8,
"sample_index_in_group": 76
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QPSK_2_000171 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QPSK_2_000171.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QPSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QPSK"
]
} | {
"split": "eval",
"mod": "QPSK",
"snr": 2,
"sample_index_in_group": 171
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_CPFSK_-20_000002 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-20_000002.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | I need you to perform automatic modulation classification on the provided radio signal sample. Select the correct class from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is CPFSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"CPFSK"
]
} | {
"split": "eval",
"mod": "CPFSK",
"snr": -20,
"sample_index_in_group": 2
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_WBFM_-12_000033 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_-12_000033.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is WBFM. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"WBFM"
]
} | {
"split": "eval",
"mod": "WBFM",
"snr": -12,
"sample_index_in_group": 33
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_AM-SSB_-20_000131 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-SSB_-20_000131.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Given this two-channel I/Q sequence, identify the modulation scheme. The answer must be one of: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is AM-SSB. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"AM-SSB"
]
} | {
"split": "eval",
"mod": "AM-SSB",
"snr": -20,
"sample_index_in_group": 131
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_AM-DSB_10_000166 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_10_000166.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is AM-DSB. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"AM-DSB"
]
} | {
"split": "eval",
"mod": "AM-DSB",
"snr": 10,
"sample_index_in_group": 166
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_AM-DSB_12_000197 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_AM-DSB_12_000197.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Determine the most likely modulation type for the provided signal sequence. Choose from: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is AM-DSB. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"AM-DSB"
]
} | {
"split": "eval",
"mod": "AM-DSB",
"snr": 12,
"sample_index_in_group": 197
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_WBFM_18_000016 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_WBFM_18_000016.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is WBFM. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"WBFM"
]
} | {
"split": "eval",
"mod": "WBFM",
"snr": 18,
"sample_index_in_group": 16
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_PAM4_-20_000128 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_PAM4_-20_000128.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Given the sampled I/Q data, recognize which digital or analog modulation scheme is used. The available classes are: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is PAM4. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"PAM4"
]
} | {
"split": "eval",
"mod": "PAM4",
"snr": -20,
"sample_index_in_group": 128
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_BPSK_-6_000163 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_BPSK_-6_000163.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please infer the modulation category of this I/Q signal. Only choose from the following modulation types: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is BPSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"BPSK"
]
} | {
"split": "eval",
"mod": "BPSK",
"snr": -6,
"sample_index_in_group": 163
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_CPFSK_-14_000026 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_CPFSK_-14_000026.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please classify this I/Q sample for automatic modulation recognition. Output one of the following labels: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is CPFSK. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"CPFSK"
]
} | {
"split": "eval",
"mod": "CPFSK",
"snr": -14,
"sample_index_in_group": 26
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QAM64_0_000080 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_0_000080.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please analyze the given I/Q signal sample and classify its modulation type. Choose one label from the following classes: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QAM64. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QAM64"
]
} | {
"split": "eval",
"mod": "QAM64",
"snr": 0,
"sample_index_in_group": 80
} |
RadioML | RadioML2016.10A | RML2016.10A_eval_QAM64_-6_000199 | npy | {
"num_channel": 2,
"channel_detail": [
"I",
"Q"
],
"path": "Electromagnetic-RadioML-Radio_Modulation_Classification/raw_input/RML2016.10A_eval_QAM64_-6_000199.npy",
"length": 128,
"timestamps": [],
"fs": 1000000
} | Please examine the signal characteristics and classify the modulation type into one of these categories: 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM. | The modulation type is QAM64. | {
"class_list": [
"8PSK",
"AM-DSB",
"AM-SSB",
"BPSK",
"CPFSK",
"GFSK",
"PAM4",
"QAM16",
"QAM64",
"QPSK",
"WBFM"
],
"gt_class": [
"QAM64"
]
} | {
"split": "eval",
"mod": "QAM64",
"snr": -6,
"sample_index_in_group": 199
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.