esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Verification of the Recovery Scheduling  
To verify the recovery scheduling process, representative simulations are conducted. In this section, simulations are carried out in a scenario of 100 km \(\times\) 100 km and the trajectory of the mother aircraft is fixed. The sample distance of the mother aircraft trajectory is 0.1 km. The velocity of each UAV is 100 m/s and the curvature bound is 2 km\({}^{-1}\). The velocity of the mother aircraft is 150 m/s and the recovery time cost \(\tau\) of each UAV is 60 s. For more intuitive simulation results, three UAVs are set to be recovered by the mother aircraft. The settings of the UAVs, including the initial position and the initial direction, are given in Table 3.  
Figure 9 shows the simulation results of the recovery scheduling. As seen in Figure 9a-c, the UAVs are simply recovered at the minimum value for their recovery time windows. When the mother aircraft recovers all of the UAVs, the recovery scheduling is executed and the optimal recovery sequence is (2,3,1). In Figure 9d, the recovery trajectory of UAV 1 is different to that in Figure 9a. The recovery process of UAV 1 is influenced by UAV 3 since the recovery process cannot be performed instantly. Figure 10 intuitively illustrates the timeline of the recovery process. In particular, the recovery time window of UAV 3 consists of two regions due to the geometric relationship between its initial position and the recovery position. Moreover, the results show that the minimum value of the recovery time window of UAV 1 is occupied by the recovery time cost of UAV 3; hence, UAV 1 is immediately recovered after completing the recovery process of UAV 3. In this case, the recovery scheduling process is simple, but it can be difficult when the size of the UAV swarm increases. The recovery processes of the UAVs can influence each other. In addition, UAVs with multi-region recovery time windows do not necessarily need to be recovered urgently and other UAVs that are in need should be preferentially taken into account. As discussed in the previous sections, the GA is adopted to deal with the large-scale scheduling problem.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Index**} & **Path Length Coverage Interval of Path** & **Path Length Coverage Interval of Path Homotopy** \\  & **Homotopy 1 (km)** & **2 (km)** \\ \hline (a) & [0, 19.63] & [12.56, \(+\infty\)) \\ (b) & [0, 19.70] & [12.56, \(+\infty\)) \\ (c) & [0, 19.76] & [12.56, \(+\infty\)) \\ (d) & [0, 19.83] & [12.56, \(+\infty\)) \\ (e) & [0, 26.79] & [12.56, \(+\infty\)) \\ (f) & [0, 26.84] & [12.56, \(+\infty\)) \\ (g) & [0, 26.89] & [12.56, \(+\infty\)) \\ (h) & [0, 26.94] & [12.56, \(+\infty\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Path length coverage interval.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**UAV Index** & **Initial Position (km)** & **Initial Direction (Degree)** \\ \hline
1 & (8,52) & 11.25 \\
2 & (80,65) & 56.25 \\
3 & (10,30) & \(-90\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial settings of the UAVs.
# Electronic Life Cycle Monitoring System for Various Types of Lead Acid Batteries
## 2 The Experimental Setup and Methodology  
An electronic monitoring system was developed to measure and evaluate the characteristics of various types of lead-based batteries (non-deep cycle battery, deep cycle battery, GEL battery and AGM battery). The aforementioned types of batteries with a capacity of 40 Ah and a working voltage of 12 V were used for the research. The coulombic efficiency of this type of battery is about 80%. The general diagram of the monitoring system outlining the concept is presented in Figure 1.  
Figure 1: General scheme of the battery life cycle monitoring system.  
The system consists of a main test stand with the electronic smart control, a subsystem of relays for charging and discharging batteries and the battery load. The monitoring system has the ability to collect data and record it in the database. Smart control of the monitoring system is ensured by ATmega328 series microcontrollers. The high-performance Microchip 8-bit AVR(r) RISC-based microcontroller combines 32 KB ISP Flash memory with read-while-write capabilities, 1 KB EEPROM, 2 KB SRAM, 23 general purpose I/O lines, 32 general purpose working registers and three flexible timer/counters with compare modes. The device operates between 1.8 and 5.5 V. MEAN WELL HDR-100-24 power sources were used for the battery charging process. ACS711-5 and LM2596S-based IC voltage and current converters were used to ensure the required charging mode of the batteries. MCP3422 meters were used to measure the charging and battery voltage. RS485 modules were used to ensure communication between individual parts of the monitoring system and between the system and the computer application. The program code of the system was written using the programming language C. Meanwhile, the Delphi programming language was used to create the application for data display on the computer screen, and the MySQL database was used to store the data.
## 3 Path Following Control Strategy
### Composite Control Strategy
#### 3.3.2. Adaptive Compensation Control Design  
Define \(\Psi_{1}=E_{\Omega}^{T}P\left(t\right)\), \(\Psi_{2}=E_{\Omega}^{T}P\left(t\right)G_{33}\), \(\Psi_{3}=E_{\Omega}^{T}P\left(t\right)G_{22}\), \(\Psi_{4}=E_{\Omega}^{T}P\left(t\right)G_{11}\), \(G_{11}=\left[0_{2},G_{1}\right]^{T}\), \(G_{22}=\left[0_{2},G_{2}\right]^{T}\), \(G_{33}=\left[0_{2},G_{3}\right]^{T}\). For unknown dynamics, RBFNN with \(l\) nodes is employed to approximate \(\Delta f\left(X\right)\), and the corresponding controller is selected as:  
\[\mu_{m}=G_{4}\left(X\right)v_{m} \tag{38}\]where \(v_{m}=\Delta\hat{f}\left(X\right)=\hat{W}^{T}S\left(X\right)\). The update law with \"\(\kappa\)-correction\" is taken to be:  
\[\dot{\hat{W}}=\sigma_{1}\left(\Psi_{3}S\left(X\right)-\kappa_{1}\left(\hat{W}-W_ {0}\right)\right) \tag{39}\]  
where \(\sigma_{1}\), \(\kappa_{1}\) represent positive constants, \(W_{0}\) is the initial value, \(\hat{W}\) is the estimate of \(W\), and the estimation error is \(\hat{W}=\hat{W}-W\).  
Then, an LESO [38] is constructed to estimate external disturbance, which can be written as:  
\[\begin{cases}\dot{\zeta}_{1}=-\varpi_{1}\zeta_{1}-\varpi_{1}^{2}X_{2}-\varpi_ {1}\left(f\left(X_{2}\right)+\Delta\hat{f}\left(X\right)+b\mu\right)\\ \hat{d}=\zeta_{1}+\varpi_{1}X_{2}\end{cases} \tag{40}\]  
where \(\hat{d}\) denotes the estimate of \(d\); the estimate error is \(\hat{d}=\hat{d}-d\); \(\zeta_{1}\) is the state; \(\varpi_{1}>0\) is the observer gain. Define \(\hat{d}=\eta_{s}\), the compensation controller \(\mu_{l}\) is given as \(\mu_{l}=G_{0}\left(X\right)\eta_{s}\).  
Then, a robust term is developed to eliminate the estimation errors of NN and LESO, which is designed as:  
\[\mu_{r}=G_{0}\left(X\right)v_{r} \tag{41}\]  
where \(v_{r}=\hat{\vartheta}\operatorname{sgn}\left(\Psi_{3}\right)\). The corresponding adaptive law is:  
\[\dot{\hat{\vartheta}}=\sigma_{2}\left(\Psi_{2}-\kappa_{2}\left(\hat{\vartheta }-\vartheta_{0}\right)\right) \tag{42}\]  
where \(\sigma_{2}\), \(\kappa_{2}\) are two positive constants.  
In order to handle rudder saturation, the following auxiliary dynamic system is introduced:  
\[\dot{\Theta}=\left\{\begin{array}{ll}-K_{\Theta}\Theta-\frac{\left|\Psi_{3} \cdot\Delta\mu\right|+0.5\Delta\mu^{2}}{\left|\Theta\right|^{2}}\cdot\Theta+ \Delta\mu,&\left|\Theta\right|\geq\chi_{1}\\ 0,&\left|\Theta\right|<\chi_{1}\end{array}\right. \tag{43}\]  
where \(\Theta\) is the auxiliary state vector, \(\Delta\mu=\mu-\mu_{0}\), \(K_{\Theta}\) is a positive design parameter, and \(\chi_{1}\) is a positive parameter.  
Therefore, the total control law is summarized as:  
\[\mu_{0}=\hat{\mu}+\hat{\mu}+\mu_{n}-\mu_{r}-\mu_{m}-\mu_{l} \tag{44}\]  
where \(\mu_{n}=K_{n}\Theta\), and \(K_{n}\) is a positive design parameter.
## 3 Results
### Spectral Decomposition of the High and Low-Frequency Data  
The output of spectral decomposition is characterized by more localized anomalies, which increases the details of interpretation and makes it possible to obtain detailed results from the seismic data. Figure 21 demonstrates the improvement of the enhanced data over the original. It shows the results of a generalized spectral decomposition (GSD) applied on both the original and enhanced seismic data. For a fair comparison, we used the RGB (red, blue, and green) blending algorithm to mix frequency volumes of 10 Hz, 30 Hz, and 40 Hz derived from both data. Time slices of the respective seismic volumes were taken at different time depths to visualize the geologic information in the data. Spectral decomposition is a tool for better imaging and mapping temporal bed thickness and geological discontinuities within 3D seismic surveys [18], and it aids in seismic interpretation by analyzing the variation of amplitude spectra and phase spectra. Amplitude spectra delineate thin-bed variability via spectral notching patterns that can be used to map the subtle stratigraphic changes such as channel systems [28]. It can be observed from the figures below that the original seismic data has smeared and fuzzy appearance, whereas the features in the improved data are sharp and distinct. Figure 21 shows a clastic depositional system in a predominantly fluvial-delta environment in Inas Field, offshore Malay Basin. Although the traces of some geologic features are faintly visible in Figure 21a, it is hard to interpret the geologic history prevalent in the field. As indicated in Figure 21b below, we were able to interpret the entire depositional system using the enhanced data. It was easier to see the geologic stories in the data. The whole depositional system is visible, and how the river channel prograded from the hinterland, through the coastal plain to the delta plain, is seen. Subtle features such as the barrier complexes, alluvial fan system, crevasse splay, point bar system, coastal planes, and chute-cut oxbow lakes are well resolved with their correct geomorphologies conspicuously shown.  
Figure 20: Sections from seismic data showing the difference between bandlimited and enhanced data. (**A**) bandlimited data, (**B**) enhanced data.
###### Abstract  
This study aimed to compare the physiologic effects of regular water consumption to those of electrolyte drink consumption in exercise capacity and recovery after exhaustive exercise. The participants were 10 healthy young men who exercised on a treadmill before and after receiving regular water and an electrolyte drink (3 RINK) four weeks later. A 250-mL fluid volume was ingested 30 min before exercise and immediately after. Body composition, water metabolizing hormones, and body electrolytes were analyzed at rest (R), immediately after exercise (P0), and 1 h after exercise (P1). Moreover, serum lactic acid levels were measured to determine recovery. Total body water, intracellular, and extracellular water levels were higher after consuming 3 RINK at P0 than at R. There was no interaction effect between the types of fluids and antidiuretic hormone, aldosterone, and renin levels. Hematocrit levels showed an interaction effect between the type of fluid and period. Sodium levels were significantly different between the different types of fluids at P0 and P1. Finally, an interaction effect was noted between each type of fluid and serum lactate levels. Thus, 3 RINK intake before and after exhaustive exercise increased body capacity to retain water, improved exercise ability, and reduced exercise-related fatigue.  
exercise; electrolyte; body water homeostasis; antidiuretic hormone Article
## 5 Results and Discussion
### Alive Nodes Number  
Figure 5a-d illustrate the number of active nodes in each cycle of a network comprising 100 nodes. In this context, the term \"active nodes\" refers to the count of live nodes, i.e., nodes that still have remaining energy. Conversely, the number of death nodes corresponds to the count of nodes that have depleted their energy and are no longer operational.  
The increased number of living nodes for the EPOA-CHS algorithm when compared to the other four algorithms demonstrates its better energy consumption performance. This suggests that the nodes' energy consumption is distributed more evenly. The number of surviving nodes is a crucial factor, but it is not the only one that determines the network's lifespan. Data transmission and network connectivity depend on the existence of a feasible path linking the remaining nodes to the BS.  
For the network to remain connected, it is crucial to have a sufficient number of surviving nodes, but the effectiveness and balance of energy consumption are equally important. The EPOA-CHS technique effectively maintains network connectivity and achieves energy balance, contributing to the network's extended lifetime.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 5 Results and Discussion
### Model Validation
In addition, by comparing overall performance measures models, we found that the hybrid system outperformed detection and prediction models in all performance metrics, as in Table 9.  
The proposed hybrid model solved the locality problem of the prediction model through the fusion of real time weather data and detection of rock-fall events. As a result, the hybrid model can work in various locations of rock-fall. Therefore, this model can be used in reducing the rock-fall risk globally for any site. It can also be used as a road site unit in intelligent transportation systems in urban areas.
###### Abstract  
The effect of cultivar and environmental variations and their interaction on anthocyanin components of strawberry were assessed for six cultivars grown in five locations from North to South of Europe in two different years. To evaluate the impact of latitude- and altitude-related factors, daily mean (T\({}_{\text{mean}}\)), maximum (T\({}_{\text{max}}\)) and minimum (T\({}_{\text{min}}\)) temperature and global radiation accumulated for 3, 5, 10 and 15 days before fruit sampling, was analyzed. In general, fruits grown in the south were more enriched in total anthocyanin and pelargonidin-3-glucoside (pel-3-glc), the most abundant anthocyanin in strawberry. Principal component analysis (PCA) provided a separation of the growing locations within a cultivar due to latitudinal climatic differences, temporary weather changes before fruit collection and cultivation technique. PCA also depicted different patterns for anthocyanin distribution indicating a cultivar specific reaction on the environmental factors. The linear regression analysis showed that pel-3-glc was relatively less affected by these factors, while the minor anthocyanins cyanidin-3-glucoside, cyanidin-3-(6-O-malonyl)-glucoside, pelargonidin-3-rutinoside and pelargonidin-3-(6-O-malonoyl)-glucoside were sensitive to T\({}_{\text{max}}\). The global radiation strongly increased cya-3-mal-glc in 'Frida' and pel-3-rutin in 'Frida' and 'Florence'. 'Candonga' accumulated less pel-3-glc and total anthocyanin with increased global radiation. The anthocyanin profiles of 'Gariguette' and 'Clery' were unaffected by environmental conditions.  
annbreviations; _Fragaia \(\times\) ananassa_; latitude; temperature; global radiation; cultivar \(\times\) environmental interaction
# Dynamic Modeling of Motorized Spindle System with Unbalanced Mass and Spindle Inclination
## 2 Dynamic Modeling  
The structure diagram of a high-speed motorized spindle is shown in Figure 1. Its main parts include a rotor spindle, shell, stator, and bearings. Figure 1b shows the structure diagram of a motorized spindle-bearing system.  
Compared with other methods, the whole transfer matrix method (WTMM) has more efficient computing power and more extensive application capabilities [23; 24]. So, in this paper, it is used to analyze the unbalanced mass fault and spindle inclination fault of a motorized spindle.  
According to the WTMM, a motorized spindle is divided into subsegments. By lumped mass and lumped moment of inertia method, as shown in Figure 1c, every subsegment is simplified into two rigid discs which are connected by massless elastic shaft. The unit is formed by a disc and a shaft, and two units are connected by the node. Each node has four effective parameters: section deflection (radial displacement) \(y\), section rotation angle \(\theta\), section bending moment \(M\), and section shear force \(Q\). The vector composed of these four effective parameters is the cross-sectional state vector, which is recorded as  
\[\mathbf{Z}=\begin{bmatrix}y&\theta&M&Q\end{bmatrix}^{T} \tag{1}\]
## 1 Introduction
LSTMs, in contrast, are designed for sequential data processing, leveraging memory cells to capture long-range temporal dependencies [8, 21]. Despite their strengths, LSTMs are prone to overfitting and can still face gradient challenges, common pitfalls inlearning, especially when computational complexity and training time are not adequately managed [8,10,22]. Additionally, their performance can degrade when processing noisy or irregular signals, which may impair the network's ability to learn long-term relationships and cause instability during training [21]. Recent research has explored hybrid network architectures combining CNNs and LSTMs to leverage the spatial and temporal features of EMG signals [22, 23, 24, 25, 26]. For instance, a CNN-LSTM model achieved a mean recognition accuracy of 90.55% on a dataset of 306 healthy subjects, including EMG signals for five gestures and nine-axis inertial measurement unit (IMU) data [22]. While this approach enables automatic biosignal feature extraction without extensive manual design, it requires segmentation and postprocessing [22,25]. However, its applicability in the subacute stage of stroke remains unclear, as EMG and IMU signals from post-stroke patients are often unreliable due to neuromuscular impairments [2,13,17,27]. Another example involved gesture decoding for a hemiplegic patient, where DNNs assessed upper limb motor states for neuro rehabilitation [26]. Given the demand for precise, early, and efficient rehabilitation approaches, and the challenges of unique post-stroke muscle tone patterns for collection and training, as well as the constraints of low-channel signal data for low-latency applications and feature engineering, we leveraged CNNs and LSTMs in a lightweight hybrid network for subacute hand gesture recognition [2,8,22,24]. Based on the literature review, a 1D CNN-LSTM model has not been tested on acute or subacute stroke patients' muscle signals; previously, such studies have primarily been conducted on healthy individuals [2,8,9]. In this paper, we pioneer the evaluation of a 1D CNN-LSTM model that classifies volitional paretic hand gestures using EMG data from subacute stroke patients. To address the limitations of EMG data collection in early-stage stroke, we introduce a novel bilateral data fusion approach that incorporates signals from both the affected and unaffected upper limbs during training. This architecture captures the spatial and temporal dynamics of diverse paretic EMG signals and remains effective with limited data. Our objective was to test whether DL could leverage atypical EMG patterns to accurately recognize paretic hand gestures. By incorporating bilateral input (i.e., additional training data from non-paretic gestures), our proposed model, optimized through targeted hyperparameter tuning, demonstrates the potential for advancing stroke neurorehabilitation and monitoring.
# The Vibration Isolation Design of a Re-Entrant Negative Poisson's Ratio Metamaterial
## 4 Bandgap Properties of N-NPRM  
The bandgap is a distinctive property of metamaterials in the field of vibration control. Therefore, in this section, the bandgap performance of T-NPRM and N-NPRM is analyzed to reveal the influence of the design parameters on the bandgap behavior. This work aims to provide a foundation for the bandgap design of the proposed metamaterials.  
According to the basic equation of elastic dynamics, without the damping and body force, the propagation of elastic waves in two-dimensional periodic structures can be written as [43]  
\[\rho(r)\ddot{u}(r)=\
abla[\lambda(r)+2\mu(r)][\
abla u(r)]-\
abla\times[\mu(r) \
abla\times u(r)] \tag{3}\]  
where \(u\) is the displacement vector, which is related to the position vector \(r=(x,y)\). \(\ddot{u}\) is a second derivative of the displacement with respect to time. \(\
abla=(\frac{\partial}{\partial x},\frac{\partial}{\partial y})\) denotes the differential operator, and \(\rho\) is the mass density. The symbols \(\lambda\) and \(\mu\) are the Lame constants, which can represent [PERSON]'s modulus \(E\) and [PERSON]'s ratio \(\
u\) as  
\[E=\frac{\mu(2\mu+3\lambda)}{\mu+\lambda},\;\
u=\frac{\lambda}{2(\mu+\lambda)} \tag{4}\]  
According to the Bloch theorem, the displacement vector can be expanded as  
\[u(r,k)=u(r)e^{-L(kr-\omega t)} \tag{5}\]  
where \(\omega\) and \(t\) mean the angular frequency and time, respectively. \(L\) equals \(\sqrt{-1}\), and \(k\) is the wave vector of the 2D lattice. The displacement vector \(u\) is a periodic function with the same periodicity as the periodic structure, which follows the relation  
\[u(r)=u(r+R) \tag{6}\]  
in which \(R\) is the lattice vector. Combined with Equation (6), and considering the relationship between the node displacement vectors on the cell boundary in the space domain, Equation (5) can be rewritten as  
\[u(r+R,k)=u(r)e^{-L(kr-\omega t)} \tag{7}\]  
By substituting Equation (7) into Equation (3), the wave\(-\)governing equation can be transformed into the eigenvalue equation  
\[\left(K-\omega^{2}M\right)U=0 \tag{8}\]  
where \(K\) and \(M\) are the stiffness and mass matrices, respectively. \(U\) is the displacement column vector of all nodes.
# Rotation Invariant Graph Neural Network for 3D Point Clouds
## 7 Discussion
### Classification
#### 7.1.2 Synthetic Dataset with Noise  
An interesting observation appears when looking at the rotation noise results for the models trained with position noise. RGCNN again dropped in accuracy for rotations larger than 10 degrees, whereas all the other networks remained stable. While the graph computed from the distances between the points remained the same, the coordinates of the points used in the first convolution changed, essentially creating 'new' point clouds that the network was not trained on. This appears in both Figures 12(b) and 13(b). The other methods either place the input point cloud in a canonical position, on which the model was trained, or create new rotation-invariant features instead of the position (Gram matrix). This means that the rotation-invariant networks are now robust to large noise, while maintaining the same performance regardless of rotation noise.  
Networks trained with rotation noise improved their accuracy results. RGCNN now has a sharp drop in performance at 20 degrees, whereas all the rest have higher performance than the networks trained without noise.  
Lastly, for networks trained with occlusion noise, the performance is much more stable. The Gram matrix rotation-invariant network still has much better results on the dataset without occlusion, hence this method is vulnerable to occlusion noise.