<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"DFC: Device-free human counting through WiFi fine-grained subcarrier information."
Abstract:"A device-free human counting (DFC) algorithm that uses fine-grained subcarrier information from WiFi devices, called channel state information (CSI), to count the number of people in indoor environments is proposed. The DFC algorithm extracts the features of average attenuation and variation of CSI amplitudes caused by human motions, and puts the features into a training process to improve the counting accuracy. Through a bootstrapping process, the DFC can estimate the number of people standing in the middle of a WiFi link by constructing a probability model with the CSI signals at a receiver side. With this human counting capability, the DFC can support the efficient monitoring and automatic control of electrical devices (e.g. air conditioner, heater, bulb, and beam projector) indoors. Through a real implementation and experiments, it is shown that the DFC algorithm outperforms the state-of-the-art DFC algorithm based on RSSI in indoor environments with human mobility. For a dynamic-target case in a meeting room, for example, DFC can predict the number of people in an indoor space with an accuracy about 98% at best.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [1].
DFC: Device-free human counting through WiFi fine-grained subcarrier information
A device-free human counting (DFC) algorithm that uses fine-grained subcarrier information from WiFi devices, called channel state information (CSI), to count the number of people in indoor environments is proposed. The DFC algorithm extracts the features of average attenuation and variation of CSI amplitudes caused by human motions, and puts the features into a training process to improve the counting accuracy. Through a bootstrapping process, the DFC can estimate the number of people standing in the middle of a WiFi link by constructing a probability model with the CSI signals at a receiver side. With this human counting capability, the DFC can support the efficient monitoring and automatic control of electrical devices (e.g. air conditioner, heater, bulb, and beam projector) indoors. Through a real implementation and experiments, it is shown that the DFC algorithm outperforms the state-of-the-art DFC algorithm based on RSSI in indoor environments with human mobility. For a dynamic-target case in a meeting room, for example, DFC can predict the number of people in an indoor space with an accuracy about 98% at best.