esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Technologies for Detecting Falls
### Internet of Things  
The Internet of Things (IoT) is a relatively new technology that has a great potentiality for developing a fall detection system. To construct fall diagnosis systems, this emerging technology could supply data processing, communication channels, and smart sensors [14]. IoT also offers powerful processing and storage capabilities, as well as providing services to the other layers of edge and cloud computing [15; 16]. Edge, as well as fog computing, can be used to detect falls. Edge devices process data and are located near other devices and users. On the other hand, fog nodes are located near local networks and other system infrastructure.  
Figure 2: Fall detection overall procedure system using machine learning (ML) algorithms.  
Wireless communication systems such as 5G comprise both physical and software virtual network functions [17]. To begin with, 5G is expected to become a significant and generic communication technology for the Internet of Things. It brings the potential to transmit data at fast speeds and with low latency, which could aid the development of IoT systems for fall detection [18]. Second, passive sensing technologies can be implemented using 5G cellular. Unlike other types of RF-sensing systems, such as Wi-Fi and radar, the 5G wireless network may be used as a pervasive sensing method in both indoor and outdoor environments for fall detection. Intelligent systems or networks powered by IoT and deep learning could be utilized for a variety of ubiquitous sensing and smart monitoring systems, allowing older people to live independently and with a high quality of life [19].
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection  
Over time, several improvements have been incorporated into the LEACH protocol considering various parameters to enhance network performance [73]. Researchers have mainly focused on enhancing the CH selection criteria and the clustering process that prove to conserve energy more efficiently.
###### Abstract  
The current study aimed at evaluating an untreated pig manure, firstly for its suitability for soil amendment in combination with an agricultural/bio-solid biochar, and secondly for its potential to be used for adsorption of hazardous species, replacing expensive activated carbons. Column soil leaching experiments were designed to simulate field conditions, and physical, chemical and mineralogical analyses were performed for raw materials and/or leachates. For activated carbon production, the manure was gasified by steam or carbon dioxide at high temperatures. Biochars were analyzed for organic and mineral matter, structural characteristics and organic functional groups. Activation by steam or carbon dioxide greatly enhanced specific surface area, reaching values of 231.4 and 233.3 m\({}^{2}\)/g, respectively. Application of manure to the soil promoted leaching of nitrates and phosphates and raised COD values of water extracts. Biochar addition retained these ions and reduced COD values up to 10 times at the end of the three-month period. The concentrations of heavy metals in the leachates were low and, in the presence of biochar in soil blends, they were significantly reduced by 50-70%. The manure presents a significant potential for adsorption of various pollutants or improvement of soil amendment if carefully managed.  
manure; leaching; soil; biochar; gasification; active carbon Article
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 4 Discussion  
### Interpretation of Key Findings  
The results of this systematic review highlight three main insights. First, smartwatches demonstrate promising capabilities in detecting stress and monitoring mental health through physiological signals such as heart rate variability, electrodermal activity, and motion data. In studies, several machine learning algorithms, including SVM, Random Forest, and LSTM, have achieved moderate to high accuracy (AUROC > 0.75) in predicting stress levels, validating the feasibility of real-time emotion detection with wearable devices.  
Second, the practical adoption of smartwatches for health monitoring depends not only on technical accuracy but also on user perceptions of comfort, intrusiveness, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Algorithm** & **Features Used** & **Ground Truth** & **AUROC/Accuracy** \\ \hline [PERSON] et al. [3] & SVM & HR, HRV, EDA & Objective + & 0.79/0.72 \\ [PERSON] et al. [57] & Random Forest & HRV, Skin Temp., & & \\  & EDA, Accel. & & Expert labels & 82.5\% \\ [PERSON] et al. [21] & LSTM & HR, HRV & Self-report + & \\ [PERSON] et al. [58] & Decision Tree & HR, HRV, Skin & & \\  & Conductance & & Lab-induced & 78.2\% \\ [PERSON] et al. [59] & CNN + SVM & HRV, Resp. Rate, & & \\  & & Motion & Emotion annotation & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of ML model performance in smartwatch-based stress or mental health studies.  
Figure 5: Performance of ML models in smartwatch-based stress/mental health studies, including accuracy and AUROC where available. This figure illustrates the variability of performance metrics across different studies [3, 21, 57, 58, 59].  
usefulness. As shown in studies such as [PERSON] et al., the perceived ease of use and device ergonomics significantly influence adoption intentions. These findings align with established technology acceptance models and suggest that even high-performance algorithms may have limited impact if devices are not perceived as wearable or trustworthy.  
Third, our findings suggest that personalization improves performance in stress detection. Studies that tailored algorithms to individual baseline physiology showed higher predictive power compared to generic models. This reinforces the need for user-specific adaptive systems in mental health applications rather than a one-size-fits-all approach.  
In general, these findings underscore the dual importance of algorithmic robustness and user-centered design in achieving the full potential of smartwatch-based mental health tools.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 3 Proposed Speech Separation Algorithm
### Speech Separation According to Different Mixing Condition  
Based on the results of PCA and positioning algorithm at each frequency bin, our algorithm starts to separate the mixed signal and there are three cases which are described in detail as followings.
## 4 Photon Diagnostics Systems
### Invasive Photon Diagnostics Systems
#### 4.2.2 Undulator Commissioning Spectrometer  
This spectrometer analyses spontaneous radiation from one or few undulator segments to measure their individual undulator parameter K [58; 59]. These measurements are necessary for an independent measurement and setting of all undulator segments with \(\Delta\)K/K \(<\) 10\({}^{-4}\) and to further adjust the individual phase shifters in-between undulators. The filter chambers of the systems at SASE1, SASE2 and SASE3 contain five filter foils of Al, Mo, Cu, Ni and Al with a diameter of 30 mm and varying thicknesses for attenuation and also for spectroscopy by scanning across their K-edges. The monochromator itself, called K-mono, contains two Si channel-cut crystals which can be used in two- or four-bounce geometry. The Bragg angle range is 7\({}^{\circ}\) to 55\({}^{\circ}\) to cover an energy range from 2.5 keV to 16 keV with Si (111) (7.5 to 48 keV with Si (333)). The resolution is \(\Delta\)E/E = 2 \(\times\) 10\({}^{-4}\) for Si (111) (10\({}^{-5}\) for Si (333)). The crystals are retracted in horizontal direction from the beam. Detection is realized by a photodiode or the highly sensitive SR-imager (see below).
# A Study on the Feasibility of Natural Frequency-Based Crack Detection
## Appendix D Determining the Threshold for Measurable Frequency Change  
Whether the frequency change \(\Delta\omega=\omega_{0}-\omega_{s}\) is measurable largely depends on the equipment being used. In general, vibration measurement can be divided into two categories: contact type and non-contact type. For the contact type, the sensor (e.g., an accelerometer) is attached to the testing object to capture the vibration amplitude and frequency, while for the non-contact type, optical technique (e.g., laser Doppler vibrometer and position sensitive detector) is frequently used due to its contact-free nature and high speed. Table A3 lists typical experimental examples of frequency measurement in the literature. It can be seen that both contact type and non-contact type are capable of frequency measurement with high precision. Although both frequency result and displacement result can be obtained via the non-contact technique, the necessary equipment is more expensive than that needed by the contact type. The non-contact technique maintains a higher accuracy in high-frequency ranges, while for a frequency range below 1000 Hz, its superiority over the contact technique in accuracy is not apparent. Therefore, the contact type is still a popular option. Based on Table A3 and other relevant literature, one is safe to say that in the frequency range of 0-1000 Hz, a frequency change of 0.1 Hz can be measured confidently using a recently launched device.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Contact Type** & **Ref.** & **Equipment Information** & **Test Object** & **Frequency/Displacement Range** & **Precision** \\ \hline \multirow{8}{*}{Contact} & \multirow{4}{*}{[34]} & Accelerometer (Honeywell Sensotec 00-F82-03) & \multirow{4}{*}{A cracked two-story steel frame} & \multirow{2}{*}{0–500 Hz} & Frequency resolution: \\  & & Data logger (Vishay Micro-Measurements) & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{Contact} & \multirow{4}{*}{[35]} & Accelerometer (Hø
## 4 Flight Test Results and Discussion
### Latency Time Analysis  
To evaluate the performance of the developed UTM monitoring application, the system time latency was analyzed. The time latency indicates how fast the data traveled from the UAV until it was shown in the UTM monitoring application. Since there were some outlier data in the flight test, specifically in the broadcast time and interval time, the data was filtered with a time threshold of 10 s. The outlier data of the broadcast time can be seen in flight test records 6 and 7 in Figures 11 and 13 of Appendix A. Meanwhile, the outlier data of the interval time can be seen in flight test records 4 and 5 in Figures 14 and 15 of Appendix A. There were three categories used for the analysis of the latency time, which were based on the livelihood of the location and the flying condition of the Remote ID modules.
## 2 The Basic Principle of RF-WOA-XGBoost
### Extreme Gradient Boosting Algorithm (XGBoost)  
XGBoost optimizes the solution method of the objective function based on the Boosting algorithm, reducing the number of computations [29; 30]. The decision tree is used as the base learner, combining multiple weak classifiers to form a strong classifier. By continuously learning from the residuals of previous learning results and the true values, the learning accuracy is improved [31].  
The expression for the prediction result obtained by the base learner is:  
\[\hat{y}_{i}\ =\ \sum_{k\ =\ 1}^{K}f_{k}(x_{i})\ \ \ f_{k}\epsilon F \tag{1}\]  
In the formula, \(f_{k}\) represents the decision tree model obtained from the \(k\)-th iteration, \(\hat{y}_{i}\) represents the prediction result from the \(k\)-th learning process, \(x_{i}\) represents the \(i\)-th sample data, \(K\) represents the total number of trees, \(F\) represents the set of all tree models, and the expression for the objective function is:  
\[Obj(\theta)\ =\ \sum_{i\ =\ 1}^{i}l(y_{i},\hat{y}_{i})+\sum_{k\ =\ 1}^{K}\Omega(f_{k}) \tag{2}\]  
\[\Omega(f_{k})\ =\ \gamma T+\frac{1}{2}\lambda\|\omega\|^{2} \tag{3}\]  
In the formula, \(l\) represents the loss function, and \(l(y_{i},\hat{y}_{i})\) represents the difference between the predicted result and the true value. \(\Omega(f_{k})\) represents the regularization term to prevent overfitting. In the \(k\)-th learning iteration, the results from the previous \(k-1\) iterations are retained. \(\gamma\) and \(\lambda\) are regularization coefficients, \(T\) represents the number of leaf nodes in the current decision tree, and \(\omega\) represents the weight vector for the nodes. In each iteration, the base classifier \(f_{n}\) is added.
## 4 Prototype Implementation and Experiment
### Hopping Performance Analysis  
#### 4.2.1 Foot Trajectory Verification  
By substituting the optimization results of the hopping mechanism into Equations (5) and (6), the movement posture of the hopping mechanism in the energy storage phase can be obtained, as shown  
Figure 11: Comparison of simulation results for HR-A and HR-B. (**a**) The deformation velocity of the tension spring. (**b**) Key positions in the simulation. \(l_{i}\) means the length of the tension spring.  
Figure 12: Robotic prototype. (**a**) Front view. (**b**) Side view.  
in Figure 13a. Point \(A\) of the connecting link \(P_{11}P_{12}\) was selected as the reference point to obtain the foot trajectory curve.  
As shown in Figure 13a, the theoretical foot trajectory is a straight line perpendicular to the X-axis, and meets the requirements of DR.3-DR.6. In addition, the stretching deformation of the optimized hopping mechanism during the energy storage phase can reach \(\Delta l\) = 96 mm, which is longer than the height of the whole hopping robot RHop.  
To obtain the foot trajectory of the prototype, the body of the hopping robot was fixed. A paint pen was installed at the center of the foot of the robot, thereby enabling the trajectory to be drawn on the scale paper. In the scale paper, the horizontal and vertical distance of each grid was 1 mm. Figure 13b shows the experimental process.  
The comparison of the theoretical and the experimental foot trajectories are depicted in Figure 14. Specific comparison results are listed in Table 3.  
Figure 14: Comparison of the foot trajectory between theory and experiment.  
Figure 13: (**a**) Theoretical movement posture of the hopping mechanism at the energy storage phase. (**b**) Experimental process diagram of trajectory reproduction at energy storage phase. (⃝) represents the initial position; ⃝ and ⃝ represents the general position; ⃝ represents the end position.  
It can be seen that the foot trajectory obtained from the experiment is an approximate straight line perpendicular to the x-axis, which differs by about 3 mm from the theoretical trajectory in the vertical direction, and the maximum horizontal deviation of the trajectory is about 1.4 mm.  
The main reasons for the deviation are concluded as follows:  
1. Insufficient accuracy of 3D printing manufacture, which makes it hard to achieve a completely symmetrical structure during assembly.
2. Vibration in the experiment.
## 2 The Method of Typical Scenarios Partitioning and Load Distribution Matching
### The Method of Typical Scenarios Partitioning  
In the previous section, it is concluded that the control scheme of reactive power in distribution network is determined by the network topology and load distribution. Then, how to make fully and effectively use of the two types of data in historical database has become the key to solve reactive power optimization problem. This paper presented a typical scenarios partitioning method, containing typical topology scenarios partitioning and typical load scenarios partitioning.  
Firstly, typical topology scenarios partitioning method is presented. The change of topology in distribution network is achieved by the cooperation of section switches and tie switches. Although the combination of different switch states can produce many kinds of network topologies, it can be seen from the historical operating data that the difference of the duration of different network topologies in one year is obvious, and the number of typical topologies that has long duration is limited. Therefore, select topologies that has long duration in historical database as the typical topology scenarios, and other topologies are treated as special scenarios.  
Secondly, the method of typical load scenarios partitioning is as follows. For some load that have strong regularity and change smoothly, the load fluctuates around its typical load curve during a specific operating cycle; while the data quantity is large enough, this type of load obeys a normal distribution approximately around its typical load curve in a specific scenario in statistically, so the similar load distributions can be found in the typical load scenario corresponding to the load distribution to be optimized. As it appears different load characteristics in different seasons, weekdays and weekends, it can be simply divided into eight typical load scenarios preliminarily, that is, weekdays and weekend in four seasons. Besides, different types of load are affected by different factors, so the eight typical load scenarios can be further refined according to the weather, holidays and other factors, to increase the number of typical scenarios. For example, typical scenarios of residential load in summer can be further subdivided based on temperature and humidity; national legal holidays also have great influence on the commercial load, so it can be subdivided into the scenario of Spring Festival, May Day, National day and other scenarios. The refinement of typical load scenarios can not only improve the accuracy of load distribution matching, but also improve the computing time of the method. The typical daily load curves of the eight scenarios in a certain region are shown in Figure 1.
## 3 Modeling and Control
### Milli/Microbot Magnetic Actuation Algorithm
#### 3.2.2 Displacement Actuation  
As shown in Algorithm 2, the displacement of the milli/microrobot is controlled by changing the magnetic field gradient in the case where the magnetic moment of the milli/microrobot is in the same direction as the magnetic field. The key to the displacement actuation of the milli/microrobot is to consider the force of the milli/microrobot in the liquid environment and accurately control the force it receives. To decouple rotation DOF and translation DOF, the direction of the magnetic field is set to the desired orientation of the milli/microrobot (the direction of the magnetic moment of the milli/microrobot assuming the milli/microrobot is a magnetic carrier) so that the milli/microrobot exerts the desired torque simultaneously.  
```
Input:\(B_{des}\in\mathbb{R}^{3}\), \(F_{des}\in\mathbb{R}^{3}\) Result:\(I\in\mathbb{R}^{3}\) Objective Function:\(\min f(i)=\sum_{i=1}^{8}i_{i}^{2}\) Subject to: \(\beta I\geq B_{des}\) \(\left(\begin{array}{c}M^{T}\beta_{x}\\ M^{T}\beta_{y}\\ M^{T}\beta_{z}\end{array}\right)I=F_{des}\) \(0\leq i_{n}\leq I_{max}\) End
```  
**Algorithm 2** Optimization for Displacement Actuation
## 3 Materials and Methods
### Patch Generation  
Since all the underwater images and annotations are 3D RGB and have different sizes of resolutions, segmentation of these images may lead to high computational costs. To reduce the cost of computation by avoiding resizing operations considering pixel loss, in the pre-training stage the images and annotations were transformed to patches of 256 \(\times\) 256 \(\times\) 3 from the original sizes, resulting in different numbers of patches for each original image. After extracting the patches for the input images and annotations, patches that do not contain any class information were removed from both the annotation list and their corresponding input patch list. Finally, the 256 \(\times\) 256 \(\times\) 3 input patches containing images and annotations were given as input for the encoder region for network architectures.
## 2 The Online Dynamic Balance System Design for Double-Face Correction
### Discussion on the Balance Accuracy Problem of the Balance Disk Mechanism  
#### 2.3.1 The Position Error Problem  
According to the working principle in Figure 3, the tooth number of the balance disk is designed to be 72, the reachable positions of the balance disk include \(0^{\circ}\), \(5^{\circ}\),..., \(355^{\circ}\). The nearest and reachable position will be considered as the expected position of the balance disk when the online calculated value is not equal to any reachable positions, so the maximum value of the position error is \(2.5^{\circ}\). Because of the position error of the balance disk, the equivalent unbalance in a correcting face will not be offset by the composed correction. The residual unbalance is unavoidable and its derived principle is shown in Figure 5.  
In Figure 5, \(\overset{\rightarrow}{Q_{10}}\) is the equivalent unbalance vector in face 1 from spindle system, \(\overset{\rightarrow}{U_{1}}\) and \(\overset{\rightarrow}{U_{2}}\) are the correcting vectors of balance disks 1 and 2 in theory, \(\overset{\rightarrow}{Q_{1}}\) is the synthetic correcting vector in theory. \(\overset{\rightarrow}{U_{1}^{\prime}}\) and \(\overset{\rightarrow}{U_{2}^{\prime}}\) are the correcting vectors of balance disks 1 and 2 in practice, \(\overset{\rightarrow}{Q_{1}^{\prime}}\) is the correcting vector in practice, \(\Delta\alpha\) is the position error of balance disk 1, \(\Delta\beta\) is the position error of balance disk 2, \(\overset{\rightarrow}{Q_{e1}}\) is the residual unbalance. Based on the geometrical relationship in Figure 5, the residual unbalance can be expressed as Equation (3).  
\[\overset{\rightarrow}{Q_{e1}}=2Q_{1}\cos(90^{\circ}-\frac{\Delta\beta-\Delta \alpha}{4})\angle\frac{\beta-\alpha}{2}-\frac{3(\Delta\beta-\Delta\alpha)}{4} -90^{\circ} \tag{3}\]  
It can be seen from Equation (3), the amplitude of residual unbalance will be maximum when the sign of \(\Delta\alpha\) and \(\Delta\beta\) are opposite. For example, if \(\Delta\alpha=\Delta\beta=2.5^{\circ}\), the maximum amplitude of residual unbalance is \(0.0219\)\(Q_{1}\), and this error is acceptable in most cases.