esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Fall Detection Application
### Experimental Results  
Typical time domains of the IF signals resulting from the four different motions are shown in Figure 5. We generated a spectrogram for each type of motion, as can be seen in Figure 6. These spectrograms contain valuable information related to human motion characteristics. The results are obtained consistently for a specific target but depend on the target's characteristics, such as height, and the system's characteristics, such as the transmission frequency and the viewing angle.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 3 Methodology
### Preprocessing
#### 3.3.1 Feature Selection  
JSON objects represent the enterprise dataset that stores event data, including the appliance identification (ID), uniform resource locator (URL), Internet Protocol (IP) addresses, event description, event impact, downloaded file hash values, port numbers, and timestamps. Information entropy [55] was used to divide attributes into three types: numerical, categorical, and signature. Subsequently, security alert attributes with better generalization performance were carefully selected and used as features to represent alerts from the enterprise dataset.
## 3 Results
### Results within the Same Visit  
For every type of signal and visit, 50% of the data is used for training and testing is carried out with the remaining information in the visit. The random sample selection is the same in ND, FD, and SD. This process gives an idea of how the system configuration works within different user visits. In Table 7 we can see that results are 0 or close enough to 0. As expected, the highest values are calculated under Ex, due to its higher variability. FD is the one with the lowest EER values. SD provides good results under S but performs poorly in the remaining visits.  
Figure 5 represents DET graphs for ND, FD, and SD when using Ex in training as an example. As seen in the previous results, FD has the best performance, followed by ND and SD. In the three types of signal, the FNMR reaches higher percentages than the FMR, meaning that false negatives are more common than false positives. In a biometric environment, this could lead to the need of repeating the recognition process even if the user is whom they say to be.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **R1** & **S** & **R2** & **Ex** \\ \hline ND & 0.10 & 0.13 & 0 & 0.17 \\ \hline FD & 0 & 0 & 0 & 0.07 \\ \hline SD & 0.20 & 0 & 0.20 & 0.77 \\ \hline \end{tabular}
\end{table}
Table 7: EER (%) results for every type of signal under the same visit.  
Figure 5: Detection Error Trade-off (DET) for Ex as train and test set.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Methods for Comparison
#### 5.2.3 Foa  
Ref. [44] shows that the first four channels of the FOA are considered. This step involves convolving the signals of the last three channels with the one in the first channel, assembling the convolved result, and then taking the real and imaginary parts of the new result as input. In order to better compare the effectiveness of different inputs, all methods are performed by using the same network structure.
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Fluctuation Test
#### 4.3.7 Kane Damper Tuning
The result was obtained by finding the three indices where each of the three angular velocities X, Y, and Z met. The index of the longest settling time of the X, Y, and Z angular velocities was noted. The note was made because the ACS would be seen as converged when all the desired angular velocities had been achieved. The index of total settling time was contrasted as the algorithm increased Id and c. When a lower index was found, it would take over the previous one and note the respective Id and c values. Figure 44 below presents a pseudocode that illustrates the logic that was mentioned. The tuner's performance was measured by the time it took for the system to reach the target conditions. The time of convergence was determined by finding the point where the average of the previous \"_n_\" values was within a \"_m_\" percent range of the desired angular speeds. Moreover, to ensure the stability of the solution, the final value was checked to see if it stayed in the convergence range. For the tuner, 5 and 20 were selected as the values of \"_n_\" and \"_m_\". See Figure 45 below. The MATLAB(r) code was executed at a zoom level of 3. The MATLAB(r) optimization code produced the best values for Id and c, which are shown in Table 8 below. Using these values, the controller achieved its convergence in 21 min. A 21 min convergence time satisfies requirement 2.1. Figure 39.0 Pseudocode to find optimal Id and c. \begin{table}
\begin{tabular}{c c} \hline \hline
**Id** & **c** \\ \hline
0.07 & 0.0025 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final Id and c calculated to optimize the CubeSat Kane damper. Figure 43: Borderline search region without expansion. The red square symbolizes a decision not to expand the search region, but to confirm a boarder result by searching within the convergence field.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
###### Abstract  
With the long-term dependence of humans on ore-based energy, underground mines are utilized around the world, and underground mining is often dangerous. Therefore, many underground mines have established networks that manage and acquire information from sensor nodes deployed on miners and in other places. Since the power supplies of many mobile sensor nodes are batteries, green communication is an effective approach of reducing the energy consumption of a network and extending its longevity. To reduce the energy consumption of networks, all factors that negatively influence the lifetime should be considered. The degree constraint minimum spanning tree (DCMST) is introduced in this study to consider all the heterogeneous factors and assign weights for the next step of the evaluation. Then, a genetic algorithm (GA) is introduced to cluster sensor nodes in the network and balance energy consumption according to several heterogeneous factors and routing paths from DCMST. Based on a comparison of the simulation results, the optimization routing algorithm proposed in this study for use in green communication in underground mines can effectively reduce the network energy consumption and extend the lifetimes of networks.  
green communication; underground mine; DCMST; GA; heterogeneous network +
Footnote †: journal: Sensors
# A Supernet-Only Framework for Federated Learning in Computationally Heterogeneous Scenarios
## 4 Experiments
### Evaluation of the Training Pipeline
#### 4.2.1 Evaluation of the Main Pretraining Phase  
In Section 3.2, we recommend the SPRQ method for the main pretraining phase. Fixing the warm-up training method as DARTS, we used SPRQ, DARTS, and standard optimization methods during the pretraining phase. Pretraining and warm-up were conducted on the ImageNet1k dataset, with the supernet's loss and accuracy on the validation set during pretraining shown in Figure 4a,b. Subsequently, FL was performed on the CIFAR-100, CIFAR-10, and CINIC-10 datasets, and the accuracy of different clients was evaluated using the respective validation sets. The mean and standard deviation of client accuracies are shown as error bars in Figure 4c,d.  
As shown in Figure 4a,b, during the pretraining phase, SPRQ did not outperform DARTS or Normal in terms of loss and accuracy. The Normal group showed the best convergence ability and final precision. However, despite the additional randomness introduced, SPRQ demonstrated convergence comparable to DARTS and Normal and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**AdaptiveFL**} & \multicolumn{2}{c}{**Supernet Only**} \\  & **Best** & **Full** & **Best** & **Max** \\ \hline CIFAR-100 & 53.75 & 14.03 & 56.91 & 53.34 \\ CIFAR-10 & 87.36 & 76.64 & 88.81 & 88.64 \\ CINIC-10 & 74.72 & 59.15 & 78.05 & 75.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of performance gaps between the largest and best structures. Comparisons of the full-size and best-performing models in AdaptiveFL with max and best sub-networks in the Supernet-Only framework without pretraining. Full and max refer to the largest network configurations (both matching ResNet-18), while best indicates the sub-network or pruned network achieving the highest test accuracy.  
achieved the highest average accuracy across all clients with the smallest standard deviation in FL, as shown in Figure 4c,d. Conversely, the Normal training method performed poorly during FL. The Normal group had the lowest average client performance and the largest performance standard deviation.  
Furthermore, as shown in Table 7, SPRQ consumed significantly less time during pretraining due to frequent early exits.
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 3 System Overview and Identity Resolution
### System Environment  
In this paper, an edge network was deployed across multiple devices on the production floor to monitor their statuses. The devices are represented as \(F=\{f_{1},f_{2},...,f_{n}\}\), and \(n\) is the quantity of devices in the production workshop. The recorded data are transmitted to an edge server, denoted as \(E\), and a unique ID number is assigned to each device using identity resolution techniques. Real-time state awareness and sequence prediction are performed by the edge server. A cloud server is employed to construct and train the state awareness and sequence prediction models. The proposed framework is carried out in time periods, denoted as \(T=\{t_{1},t_{2},...,t_{m}\}\). The analysis is mainly carried out on the series data of the previous time period in order to predict the series data of the next time period. This enables accurate prediction of a device's status and maintenance requirements. Table 1 presents the notation and meaning of the parameters in the proposed framework.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BPS & Blind Phase Search \\ CD & Chromatic Dispersion \\ CPR & Carrier Phase Recovery \\ DAC & Digital to Analog Converter \\ DDPLL & Decision-Directed Phase-lock-loop \\ DFRA & Distributed Raman Fiber Amplifier \\ DSO & Digital Storage Oscilloscope \\ DSP & Digital Signal Processing \\ ECL & External Cavity Laser \\ EDFA & Erbium Doped Fiber Amplifier \\ HD-FEC & Hard-Decision Forward Error Correction \\ IQ & In-phase and Quadrature-phase \\ LO & Local Oscillator \\ OBPF & Optical Bandpass Filter \\ PolMux & Polarization Multiplexing \\ PPG & Pulse Pattern Generator \\ PSD & Power Spectrum Density \\ RIN & Relative Intensity Noise \\ RPN & Relative Phase Noise \\ SSMF & Standard Single Mode Fiber \\ QPSK & Quadrature Phase Shift Keying \\ QPSKP & QPSK Partitioning \\ QAM & Quadrature Amplitude Modulation \\ VOA & Variable Optical Attenuator \\ WDM & Wavelength Division Multiplexer \\ \end{tabular}
## 4 Discussion
### Limitations of the Study  
Topographic characterization studies extend beyond the scope of geomorphometry and terrain analysis, frequently establishing significant connections with other subfields of earth science. Examples encompass tectonics, hydrology, ecology, and geology [42; 43]. However, acquiring information related to hydrogeology, tectonics, and fault data in the study area poses significant challenges. Integration of this data would enhance the precision and richness of geomorphological characterization.  
The utilization of ALOS DEM and GF-2 data in this study remains to be explored in a more detailed investigation of topographic feature identification. Multi-source, multi-modal data yield a more comprehensive set of terrain feature information than individual datasets, as they support, complement, and correct each other. Subsequent research en deavors can leverage multi-source and multi-mode data, conduct varied comparisons, and choose the most suitable data source based on specific research requirements.  
Theoretically, this study offers insights and methods applicable to surface feature identification and the analysis of geometric tectonic evolution within a similar complex environment. However, the specific applicability requires thorough practical validation.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 5 Physical Structure of Sport WT
### Textile Structures  
Textile-based WT is typically created by embedding conductors, sensors, special fibers, and other electronic components into the fabric using techniques such as weaving [82], embroidery [83; 84; 85], or knitting [86]. Among these systems, the most common and straightforward method is to add sensors to the textile surface. This can be achieved by directly sewing or gluing a typical sensor onto the fabric surface. Transmission lines required for the sensors to function are applied to the textile using conductive threads or a cable-drawing method. Another method in textile-based sensor applications is to impart sensor properties to the textile surface through various modifications [87]. These modifications can be executed internally or externally. Internal modification involves chemically or mechanically modifying the textile surface during the production of cationic yarn or fibers, making the textile surface sensitive to external factors. This way, the textile fiber or thread itself becomes a sensor. External modification refers to post-production processes such as dyeing, various coating methods, or lamination, which transform the textile surface into a sensor.  
Internal and external modifications can be applied during production, but internal modifications encounter difficulties in interacting with electronic circuits and have lower sensitivity. In this regard, external modifications are more functional and practical. Internal modifications result in a more homogeneous structure since they are applied to the entire textile surface, and factors such as sliding, bending, and stretching behave uniformly across the surface. On the other hand, external modifications do not exhibit similar homogeneous behavior on the textile surface as they cause local changes [88]. Additionally, discomfort can occur more easily when there is a stiffer structure in that area when worn. Both internal and external modifications result in a change in the performance of the sensor surface when their properties deteriorate over time due to washing.
# Implementing and Testing a U-Space System: Lessons Learnt
## 2 Interfaces  
### Web App  
Operators and pilots need a graphical interface to visualize the information and instructions from and send requests to the U-space. An Application Programming Interface (API) was provided so that operators could implement the display of dialogues and information in their GCS. Figure 2 shows this option, chosen by Arquimea. To do this, operators must have access to the source code of the GCS software, which is not always possible, and this integration can be expensive and take some time (both operators during the project used GCS software developed by themselves). Moreover, embedding more elements could result in a cluttered screen, which works against the usability principles. As an alternative, to overcome these problems, DLR developed a Web app to display the dialogues and information from the U-space services. It requires a dedicated screen, something to which the flight crews were reluctant. However, another benefit is that one member of the flight crew can take care of the U-space communications while the pilot in command (PIC) flights manually or monitors the flight status, reducing the cognitive workload of the pilot, which is important in the case of flying swarms. This was the option chosen by INTA (Figure 3).
## 3 Proposed Methodology
### _Rfo-Svm_
#### 3.5.3 Genetic Algorithm-Based Data Allocation
Crossover Rate: Typically set between 60% and 80%, to ensure sufficient crossover while maintaining some genetic integrity of the parents. Mutation Rate: A lower mutation rate of 1% to 5% is used to prevent excessive randomness, which can lead to divergence of the population from optimal regions. Elitism: Preserving the best individuals from one generation to the next ensures that the gains obtained are not lost, which is critical for maintaining the quality of solutions. Fitness Evaluation  
After generating offspring solutions through crossover and mutation, their fitness values need to be evaluated based on the cost function and represented as  
\[F=f_{costi} \tag{31}\]  
where \(F\) is the fitness value of the individual solution, and \(f_{costi}\) is a function that calculates the cost based on the allocation of data fragments. The fitness values of the final population are plotted as a function of the number of iterations, providing insights into the optimization progress and convergence of the Genetic Algorithm. These operators allow the Algorithm 2 to explore the search space and converge towards better solutions over generations to allocate the fragmented data using RFO-SVM algorithm. The fitness function for data allocation assigns a fitness score to measure the quality of a sites or nodes, which involves allocating data fragments. A population of individuals is randomly initialized, and the Genetic Algorithm is applied iteratively until the termination criteria are met, i.e., all fragmented data are allocated based on the total allocation cost as the fitness function. ```
Input: Dataset, RFO parameters, SVM parameters and GA parameters Output: Fragments and optimal allocated database. Step 1: Pre-process the input dataset to handle missing data. Step 2: Optimize SVM parameters using the RFO algorithm: Step 2a. Initialize the RFO algorithm parameters. Step 2b. Set the optimization objective as maximizing the accuracy metric. Step 2c. Perform the RFO algorithm to search for optimal parameter values for SVM. Step 3: Obtain the optimized SVM parameters (\(\gamma,\ k\) and \(C\)). Step 4: Apply the RFO-SVM model for data fragmentation: Step 4a. Analyze the features and target label of the dataset. Step 4b. Classify the data based on the class labels using SVM with the optimized parameter values obtained from the RFO algorithm. Step 4c. Calculate the accuracy metric to evaluate the classification performance. Step 5: Perform data allocation using GA: Step 5a. Allocate the classified data fragments obtained from the RFO-SVM model. Step 5b. Initialize the GA parameters for optimization. Step 5c. Define the fitness function as the total allocation cost. Step 5d. Initialize a population of individuals, each representing a possible allocation. Step 5e. Iterate until a termination condition is met (e.g., selecting nodes or sites for fragmented data): Step 5f. Select the allocation with the lowest fitness score. ```  
**Algorithm 2** Fragmentation and Allocation Algorithm
## 3 Main Results
### Model-Free Distance-Prescribed Performance Control Design  
When \(z^{L}(k)-z(k)\) enters the error constraint range of the safe distance \(d_{safe}(k)\), i.e., \(\phi_{d}(k)\in\Phi,\phi_{d}(0)\
eq 0,\psi_{d}(\cdot)=\phi_{d}^{-1}\), it is necessary to consider the distance control of the vehicle, where \(z(k)\) is the output of the system. To design the controller, define the distance-tracking error:  
\[\mathfrak{e}_{d}(k)=z(k)-z^{L}(k)+d_{safe}(k)+\psi_{d}(k) \tag{8}\]  
In this case, the vehicle dynamics model described in Equation (1) can be transformed into  
\[\left\{\begin{array}{rl}\dot{z}(k)&=v(k),\\ \dot{v}(k)&=\frac{1}{m}F(k)-f(\eta(k),v(k))\end{array}\right. \tag{9}\]  
According to the PI-funnel control theory [32], design a vehicle distance-prescribed performance control algorithm:  
\[F_{d}(k) = -\kappa_{d}^{1}(k)\mathfrak{e}_{d}(k)-\kappa_{d}^{2}(k)\mathfrak{ e}_{v}(k) \tag{10}\] \[\kappa_{d}^{1}(k) = \frac{\phi_{d}(k)}{1-\phi_{d}(k)|\mathfrak{e}_{d}(k)|}\] (11) \[\kappa_{d}^{2}(k) = \frac{\phi_{v}(k)}{1-\phi_{v}(k)|\mathfrak{e}_{v}(k)|} \tag{12}\]where \(\phi_{d}(k)\in\phi_{1}\) is the prescribed performance function for the distance error. This algorithm can ensure that \(\epsilon_{d}(k)\) is limited within the preset range:  
\[\mathcal{R}_{\phi_{d}}:=\big{\{}(k,\epsilon_{d}(k))\in\mathbb{R}_{\geq 0}\times \mathbb{R}\big{|}\phi_{d}(k)|\epsilon_{d}(k)|<1\big{\}} \tag{13}\]  
**Remark 1**.: _Because of the strict requirement of distance control to ensure \(z(k)-z^{L}(k)\geq d_{safe}(k)\), the definition of distance error \(\epsilon_{d}(k)\) has an additional term of \(\psi_{d}(k)\) compared to the definition of speed error \(\epsilon_{v}(k)\), and the control objective is \(d_{safe}(k)<z(k)-z^{L}(k)<d_{safe}(k)+2\psi_{d}(k)\)._
## 5 Results and Discussion
### Simulation Results
#### 5.1.2 Scenario 2: Only Danger Area Information  
This section tests the operational effects of the three algorithms in the scenario containing only danger area information (Scenario 2). Figure 9 compares the results of PSO, FFA and SHH algorithms in scenario 2. The cost functions in scenario 2 are time, danger area, and curvature. The path is as short as possible on a smooth and bumpless basis.  
After running 200 times under the same conditions, the results of the three algorithms were analyzed. Table 3 shows the statistics of PSO, FFA and SHH algorithms in scenario 2 running under fixed conditions for 200 times. In terms of computing time, the SHH also had the greatest advantage, with an average computing time of 0.6217 s, while the average computing time of PSO and FFA were 2.4215 s and 1.2700 s respectively. The final ultimate average cost value of SHH was 306.4266, while the total average cost value of PSO and FFA were 588.3817 and 322.0653 respectively. This indicates the SHH algorithm can find the best optimal performance for the proposed model in scenario 2. The specific time cost and curvature cost of the SHH algorithm were more advantageous than the results obtained by the PSO and FFA. In addition, the map cost value in the cost function was 0, which indicated the visible paths had no collision with the environment obstacles.  
Figure 9: Comparison of the path planning results of PSO, FFA and SHH algorithms in scenario 2.