esa-sceva/satcom-chunk-collection huggingface dataset:
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 2 Materials and Methods
### Density Model
#### 2.4.2 Density Analysis Procedure  
Figure 3 shows the flowchart of the spatial-temporal density analysis procedure. First, an area is defined and grid cells of a certain size are created. Next, the collected AIS data is pre-processed for density analysis. This step divides the large dataset to facilitate data processing. The dynamic and static data of each individual files are based on the same MMSI number. Next, the AIS data for analysis are extracted by filtering under multiple conditions such as data period, analysis area, ship type, and ship size. The preprocessed AIS data in csv format are then imported into a relational database that handles data importing, analysis, and lookup operations. The preprocessed AIS data are point data, including the ship-position information and various attributes of ship information.  
A spatial analysis on a continuous 2D space requires a geometric scheme and a coordinate system. As the spatial coordinate system, this study adopts the EPSG:4326 spatial coordinate system of the European Petroleum Survey Group (EPSG) standard developed for mapping, surveying, and geodetic data storage. EPSG:4326 is based on the World Geodetic System 1984 (WGS84) and is used for global position system (GPS) data.  
For each given MMSI, every two-consecutive ship position can be connected by a line. As each trajectory point contains a timestamp record, the occupancy time along a line can be determined by calculating the time (in h) difference between two consecutive points. Moreover, the length of each line (in km) can be obtained from the spatial coordinates. By intersecting the created lines with the cells and using the time and length properties along  
Figure 3: Flowchart of the spatial-temporal density analysis.  
each line, one can determine the density of the occupancy time of a ship in a given cell over a given timespan.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 9 Threat Analysis
### Results Related to Poisoning Attacks  
The simulations for verifying the performance and the robustness introduced by the secure aggregator consider three different configurations: zeroing, clipping, and clipping followed by zeroing. Two distinct experiments were conducted, each involving a single or two clients engaged in a poisoning attack through label flipping. Figure 18 shows the results obtained for the network CNN in the case of one poisoned client, while Figure 19 refers to the case of two poisoned clients. The full results are available in [73]. The primary outcomes derived from the analysis of these figures can be summarized as follows.  
The best results in terms of FPR and FNR with both one and two poisoned clients are achieved by a combination of clipping and zeroing. This approach reduces the percentage of FP caused by poisoning by 1% with both one and two poisoned clients. Moreover, the findings indicate a reduction in the proportion of FN resulting from poisoning, amounting to 2% in the case of a single poisoned client and 3.5% in instances involving two poisoned clients. The results of clipping followed by zeroing are similar to results obtained with only clipping; the real difference is that by using zeroing, the server can detect which clients t In summary, although FL inherently preserves data privacy by design, it is necessary to take additional measures to fully secure the FL environment. Establishing secure connections between clients and the server is essential to safeguard against potential confidentiality and integrity threats. Furthermore, employing secure aggregator methods such as zeroing and clipping enhances the system's resilience against data poisoning attacks, ensuring the integrity of the collaborative learning process.
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 4 Stiffness Mapping Method for Compliant Teleoperation
### Stiffness Identification System at the Human Arm Endpoint  
The stiffness identification experimental system at the human arm endpoint consists of three modules: the KUKA iiwa robot module (KUKA, Germany), the JR3 six-axis force sensor module (JR3 Inc., Canada) and the TrignoTM Wireless sEMG system module (Delsys Inc., USA), as shown in Figure 6. The robot module measures the displacement at the human arm endpoint caused by the random disturbance. The six-axis force sensor JR3 is used to detect the force applied by the operator hand. The sEMG electrodes on the operator arm detect the muscle electrical signals of human arm while the operator demonstrates different behaviors with different stiffness. In the experiment for stiffness identification, the operator holds the interface handle mounted at the end of Kuka iiwa, and applies appropriate force to cause the robot to move within a small scope. With the measureddisplacement, force and sEMG signals, the stiffness mapping matrix at the human arm endpoint can be identified.
## 3 Results
### LUCs in the Southern Jiangsu Urban Agglomeration  
As shown in Table 5 and Figure 3, the land use in the study area is primarily composed of farmland, water areas, and built-up land. Among these, farmland remains the largest land use type, accounting for 65.88% of the total area in 2000, though its proportion decreased to 54.21% by 2020. Built-up land and water areas follow, constituting 22.64% and 18.39% of the total area in 2020, respectively.  
Figure 4 provides a more intuitive visualization of the significant land use changes over the 20-year period. There is a continuous increase in the area of unused land and built-up land, while farmland and forest land exhibit notable declines. Specifically, the area of built-up land expanded rapidly, increasing from 2634.99 km\({}^{2}\) in 2000 to 6365.49 km\({}^{2}\) in 2020, with its proportion rising from 9.37% to 22.64%. This substantial growth is closely  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Area (km\({}^{2}\))**} \\ \hline
**Land Use Types** & **2000** & **2005** & **2010** & **2015** & **2020** \\ \hline Farmland & 18,527.12 & 17,158.07 & 15,998.75 & 15,292.73 & 15,244.25 \\ Forest land & 1644.88 & 1539.38 & 1528.63 & 1350.66 & 1341.11 \\ Grass land & 1.66 & 6.15 & 13.33 & 1.97 & 0.43 \\ Water area & 5313.54 & 5684.58 & 5757.60 & 5592.33 & 5170.63 \\ Unused land & 0.11 & 0.34 & 0.39 & 0.46 & 0.41 \\ Built-up land & 2634.99 & 3733.79 & 4823.61 & 5884.16 & 6365.49 \\ \hline \hline \end{tabular}
\end{table}
Table 5: LUCs of the study area.  
linked to urbanization and industrialization, reflecting the conversion of large tracts of agricultural land and natural areas into urban and infrastructural use.  
Conversely, farmland experienced a significant reduction, decreasing from 18,527.12 km\({}^{2}\) in 2000 to 15,244.25 km\({}^{2}\) in 2020, with its share declining from 65.88% to 54.21%. This decline can be attributed to the conversion of agricultural land into urban areas, as well as shifts in economic structure that have reduced agricultural activities. Additionally, water areas showed an initial increase but an overall decreasing trend, from 5313.54 km\({}^{2}\) in 2000 to 5170.63 km\({}^{2}\) in 2020, with their proportion dropping from 18.89% to 18.39%. This change may be attributed to urbanization and regional policies. Specifically, between 2000 and 2005, the implementation of the \"Yangtze River-to-Tai Lake\" policy led to a temporary increase in water area. However, subsequent urban development and the \"South-to-North Water Diversion\" project contributed to the reduction in water areas.
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 2 SC-SA Cipher
### Randomness Testing
#### 3.6.3 Low-Density 80-Bit Keys  
Use a fixed plaintext of all zeros (1,048,576 bits);  
Include an 80-bit key block of all 0 s;  
Obtain 80 key blocks with a single bit set to 1 and the remaining 79 bits set to 0 (each bit position from 1 to 80);  
Obtain 3160 key blocks with two bits set to 1 and the remaining 78 bits set to 0 (each possible combination of two bits within eighty bits).  
Encrypt the fixed plaintext with each of the 3240 keys, resulting in corresponding ciphertext blocks of 1,048,576 bits (128 KB).  
High-Density 80-Bit Keys  
Use a fixed plaintext of all zeros (1,048,576 bits);  
Include a key block of all 1s;  
Obtain 80 key blocks with a single bit set to 0 and the remaining 79 bits set to 1 (each bit position from 1 to 80);  
Obtain 3160 key blocks with two bits set to 0 and the remaining 78 bits set to 1 (each possible combination of two bits within eighty bits).  
Encrypt the fixed plaintext with each of the 3240 keys, resulting in corresponding ciphertext blocks of 1,048,576 bits (128 KB).  
According to paper [25], we can determine that at a significance level of 0.01, if the sample consists of 128 sequences, the rejection rate should not exceed 4.657. For other sample sizes, the corresponding maximum number of rejections can be calculated. The parameter information is shown in Table 4.  
Through the analysis of the 0/1 balance of the sequences, it can be concluded that the SC-SA scheme passed the randomness test, with the actual number of rejections being 0 in all tests.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Procedure of Step 1 for MSSOE  
A flowchart presenting the several steps of the proposed method based on the sequential switch opening technique is provided in Figure 1. First, all switches were closed to perform a meshed system. Then, the power losses in the system for each open switch were determined. Next, we selected a switch with the smallest power loss as a first tie-switch. The rest tie-switches were selected respectively based also on the smallest power loss and maintained radial system. With this proposed method, all switches in the same loop of the previous tie-switches were completely removed from the search space of possible switches to be tie-switches for further iterations. However, all switches in the same loops were not removed from possible tie-switches in the existing method. An optimal radial topology was finally provided.  
Moreover, the detailed procedure of step 1 for MSSOE is described as follows:  
Step 1-1: Read the data of the system.  
Step 1-2: Close all ties switches and find out all the switches in loops.  
Step 1-3: Open a switch in a loop of topology and calculate power loss.  
Step 1-4: Check the opened switch in the loop based on minimum loss and delete all switches in the loop.  
Figure 1: Flowchart of step 1 for modified sequential switch opening and exchange (MSSOE).  
Step 1-5: Repeat steps 1-3.  
Step 1-6: Find minimum loss satisfied objective function in Equation (1)  
Step 1-7: Repeat steps 1-2 to 1-5 until Equation (4) is satisfied.
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 2 Methodology
### Recurrent Neural Network (RNN) Model
#### 2.3.1 Dense Layers  
The dense layer represents the processing units that integrates a classic deep learning model. In the scientific literature it is common to find models that use linear functions to map an input to an specific output, however, in the case of signal processing where a specialized treatment of noise must be performed, such as when working with EMG signals as is done in this research, the literature recommends the use of activation functions of the hyperbolic tangent type. The vectorized implementation of the dense layer can be seen in Equation (2).  
\[a^{(l)}=tanh(W^{(l)}a^{(l-1)}+b^{(l)});\qquad a^{(0)}=X \tag{2}\]  
where:  
\(a^{(l-1)}\) represents the previous layer activations. In the case of the first layer, they are equal to the input \(X\) which contains the value of each EMG channel.  
\(W^{(l)}\) represents the current layer weights.
## 4 Results
### Respondents with Asthma or COPD  
All 30 respondents who had been previously diagnosed with asthma, reported that they had experienced symptoms of asthma, such as wheeze, chest tightness, coughing or shortness of breath, in the past 12 months (Figure 2). Most respondents (21/29; 72.4%) visited their GP at least once in the past 12 months due to worsening or out of control asthma symptoms, while only eight (27.6%) did not consult their GP. Most (24/30; 80.0%) did not have to go to hospital or the emergency department in the past 12 months due  
Figure 1: Participants’ satisfaction with the mobile lung function testing service. to worsening or out of control asthma symptoms, although four respondents (13.3%) had a single hospital presentation in the past 12 months, one respondent (3.3%) had two presentations, and another respondent (3.3%), who lived remotely in Narrowin, had 12 hospital presentations in the past 12 months for these reasons. Only eight (26.7%) respondents with asthma had an Asthma Action Plan and three had referred to it in the past 12 months to manage their asthma. Most (7/8; 87.5%) felt confident to use their Asthma Action Plan. At baseline, most respondents (22/30; 73.3%) had ACT scores \(\leq\) 19, indicating their asthma was poorly controlled (mean 16.6; range from 8-25) (Table 3). Although there was some improvement in the ACT score at follow-up (mean 18.0; range 6.0-25.0), 16/31 respondents had ACT scores \(\leq\) 19. In comparing each variable from baseline to follow-up, while there were statistically significant changes for some variables, the sample size for most variables were small (Table 3). Interestingly, not all respondents who identified having asthma in the baseline questionnaire were diagnosed with asthma after the lung function test (5/30; 16.7%). Following the lung function test, 6/31 (19.4%) respondents, who did not indicate they had asthma at baseline, reported they had asthma, two of whom had an ACT score \(<\) 19. Of the 11 respondents who reported having been diagnosed with COPD at baseline, five (45.4%) had experienced COPD exacerbations in the past 12 months (Figure 3). In addition, two respondents reported that the exacerbations impacted their usual activities and that they had been admitted to hospital (one of the respondents had experienced both, i.e., the exacerbations impacted their usual activities and that they had been admitted to hospital). Following the lung function test, a total of 13 respondents indicated they had COPD; 2 respondents, who did not report having COPD at baseline, indicated that they were diagnosed with COPD. Most (8/11; 72.7%) were unaware of the severity of their COPD. Figure 2: Respondents’ agreement with statements related to asthma. Compared to baseline results, the mean overall St George Score, the Symptom Score and Impact Score decreased at follow-up. However, the mean St George Activity Score increased at follow-up, suggesting more limitations of activity (Table 3). The summary scores shown in Table 4 were compared between participants attending the metropolitan and rural clinics, and there appeared to be no significant differences.
## 2 Data and Methodology
### Overview of the Research Region and Data Source
#### 2.1.2 Data Source  
As shown in Table 1, the primary dataset underpinning the Net Primary Productivity (NPP) analysis in this study was sourced from the MOD17A3 HGF.006 dataset, which offers annual NPP data. This dataset, with a spatial resolution of 500 m, spans from 2000 to 2020 and is readily available through the Google Earth Engine (GEE) platform ([[https://earthengine.google.com](https://earthengine.google.com)]([https://earthengine.google.com](https://earthengine.google.com))). The integration of this dataset into GEE leverages the platform's advanced data processing capabilities, enabling the efficient handling and analysis of extensive satellite imagery and datasets. GEE facilitates the processing and examination of large-data volumes, allowing for a detailed exploration of NPP temporal and spatial patterns across the expansive study area. The inclusion of the MOD17A3 HGF.006 dataset within GEE highlights the platform's value in advancing environmental analysis, offering researchers the necessary tools for in-depth ecosystem productivity evaluations.  
Figure 1: Study area.  
This research utilized a monthly temperature dataset developed via Gaussian Process Regression (GPR) from weather station data, notable for its detailed 1 km spatial resolution. Such resolution offers an in-depth understanding of temperature fluctuations in the region, including monthly averages, maximums, and minimums, thereby providing significant data support for analyzing the area's thermal dynamics [27]. Similarly, the precipitation dataset, created with the \"anusplin4 4\" spatial interpolation software, with its 1 km spatial resolution, supplies precise and comprehensive precipitation information for the study [28]. Both temperature and precipitation datasets were subjected to meticulous adjustments, including cropping and resampling, to conform to the 500 m spatial resolution of the NPP data. These harmonized datasets played a crucial role in calculating heterotrophic respiration and facilitating partial correlation analysis with Net Ecosystem Productivity (NEP).  
Furthermore, this research incorporated geospatial vector data and land-cover classification products spanning from the years 2000 to 2020, sourced from the Resource and Environment Data Center of the Chinese Academy of Sciences ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))). The amalgamation of these varied and high-resolution datasets with sophisticated analytical techniques constituted the foundation of this study, substantially enriching our comprehension of carbon dynamics within the \"Three North\" region.
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 4 Experimental Setup  
The overall NMR rotation sensor experimental setup is shown in Figure 4. The experimental setup consists of the NMR sensor prototype, the turntable, the laser control system, and the sensor control circuit. The NMR sensor prototype and the breadboard are mounted on the turntable. The laser control system (TED 200C and LDC 200C) is used to produce the pumping and probing laser beam. The laser beam is induced into the prototype by the optical fiber.  
The comagnetometer signal (\(S_{pd}\)) is collected by the photodetector. Then, the signal is sampled and converted to a digital signal by the pre-amplifier circuit. To process the  
Figure 4: Experimental setup of the NMRRS.  
digital signal and the electrical signal, the sensor control circuit using a digital signal processor (DSP, TMS 320F28335) and a field-programmable gate array (FPGA, Xilinx Kintex7 XC7K480T) has been implemented to control the magnetic field in the NMR rotation sensor.  
As is shown in Figure 4, the signal is parametrically modulated at the alkali Larmor frequency (100 kHz range). The input signal is demodulated (32-bit CORDIC rotate algorithm) by a lock-in amplifier (LIA) to obtain the oscillation signal. By a low-pass filter, the oscillation signals of the Xe isotopes are separated and the phase of the oscillation signals are detected by the phase detector (32-bit CORDIC vector algorithm). The magnetic field estimation method based on the traditional PLL controller and the proposed synchronous estimation method based on LESO are implemented in the FPGA. The PLL or LADRC will keep the Xe isotopes resonant with the Larmor frequency by adjusting the frequency of the numerically controlled oscillator (NCO). Additionally, the estimation algorithms can obtain the magnitude of the magnetic field according to the frequencies of the NCO. The photograph of the experimental platform which consists of the control system and the experimental prototype is shown in Figure 4.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Data Exploration and Analysis  
The meteorological dataset thus obtained contains dirty data. The messy data were cleaned up using data cleaning approaches in Python and R used for implementing the models. The first exploratory data analysis focused on various methods of cleaning up data. The summary statistics of the response variable (WS_62_mean) is shown in Table 2.  
Table 2 shows that the distribution of the wind speed measured at 62 m WT hub is right skewed and platykurtic, as seen in the skewness and kurtosis values, hence not normally distributed. Another reason for the non-normality assertion is that the mean and median have different values. Time series plot, density, QQ (normal quantile to quantile) and box plots in Figure 2 all show that the response variable (wind speed at 62 m) distribution is not normally distributed.  
Figure 1: Vredendal map location (Source: [[https://www.weather-forecast.com/locationmaps/Vredendal.10.gif](https://www.weather-forecast.com/locationmaps/Vredendal.10.gif)]([https://www.weather-forecast.com/locationmaps/Vredendal.10.gif](https://www.weather-forecast.com/locationmaps/Vredendal.10.gif))).  
Figure 2: Diagnostic plots for the response variable, WS_62_mean.  
We also visualised the variations in the wind speed measured at 62 m altitude of the WT to envisage the possibility of forecasting it. The visualisation was carried out using box plots, shown in Figure 3. As shown in Figure 3, various patterns that are expected from the unpredictable nature of the wind speed are visible. There are no clear patterns visible when visualised in terms of the weeks in the year and the days of the month, other than the availability of the wind speed. However, the hours of the day and the months of the year show some clear patterns. Seasonal variation can be inferred from the months of the year box plots, and a trend can be seen from the hours of the day box plots. The 10 th month corresponding to the peak of the spring season (in South Africa) has the largest wind speed, as seen in the month of the year box plots. The wind speed is seen to progress from the early hours of the day to the later hours of the day. The box plots show that wind speed is available throughout the days, however in varying and unpredictable quantity. This further shows how viable the wind speed is for generating renewable energy.