esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Evaluation of the Original Image-Based Model  
When training for a total of 10 epochs, the training and validation loss and accuracy for Model 1 at each epoch are visualized in the Model 1 section of Figure 4. Although some variability was observed during the initial epochs, both training and validation losses gradually decreased while accuracy increased, eventually stabilizing as the number of epochs increased. The validation graph in Figure 4 clearly indicates that the highest validation accuracy was achieved at the 10 th epoch.  
Furthermore, the test accuracy obtained from the model trained with the original images matches the results presented for Model 1 in Table 2. In Tables 2 and 3, the highest validation accuracy is also observed at the 10 th epoch, and the model, whose parameters were saved at this epoch, achieved a test classification accuracy of approximately 99.65% when evaluated on the original images. This value is higher than the previous research for lettuce growth stage classification in smart aquaponics using K-Nearest Neighbor (KNN), which showed the most effective model among Logistic Regression (LR) and Linear Support Vector Machine (L-SVM), with a classification accuracy of 91.67% [42].
## 3 Results
### Muscle Activities
#### 3.2.2 Load Mass Effects  
The mean EMG signals of the hip, knee, and ankle extensor muscles increased generally as the backpack load masses increased. Compared to walking without backpack loads, the increases were statistically significant for the GM and GA during walking with 30-kg loads (GM, \(p=0.000091\) and GA, \(p=0.00091\)) and for the RF during walking with 30 kg (\(p=8.86\times 10^{-7}\)) (Figure 4). The increases in the HA and AT were not statistically significant (\(p>0.0072\)). Compared to walking without loads at the same grade, the mean EMG of the GM increased by 5% to 173% with a 30-kg backpack load, the HA increased by \(-5\%\) to 26%, the RF increased by 104% to 172%, the AT increased by 0% to 35%, and the GA increased by 15% to 61% (the data are shown in Table S3).  
The muscle activity of the hip extensors (GM and HA) increased as the backpack load mass increased, as shown in Figure 5a. The maximum muscle activity of the GM increased greatly from 0.66 to 1.24 as the backpack load mass increased from 0 to 30 kg. The maximum muscle activity of the HA increased slightly from 0.63 to 0.81 as the load mass increased.  
The knee extensor muscle RF increased greatly in muscle activation as the backpack load mass increased (Figure 5b). The maximum muscle activation of the RF increased greatly from 0.67 to 1.60 during the early stance stage as the load mass increased from 0 to 30 kg across all the slope walking.  
The ankle extensor muscle GA increased greatly in muscle activation from 0.70 to 1.07 as the backpack load mass increased from 0 to 30 kg, and the dorsiflexion muscle AT increased slightly, from 0.50 to 0.58 (Figure 5c). The muscle activity demonstrated that the ankle extensor muscles were activated more than the flexor muscles during slope walking with backpack loads.
## 2 Materials and Methods
### Method
#### 2.2.3 Colour Measurement  
A Minolta CR-400 Chroma Meter (Konica Minolta, Inc., Tokyo, Japan) was utilized for reflection colour measurements. This instrument evaluates the CIE Lab colour characteristics by plotting the ratio of three different wavelength lights within the CIE Lab colour space. The colour data are quantified by the device [21]. Calibration of the instrument was performed using a standard white etalon. Each sample was measured in triplicate on a white plate under consistent illumination to minimize potential interference. The chroma metre's measuring head was positioned directly on the surface of the treated meat samples. A Minolta CR-400 colorimeter was used with an 8 mm aperture, a 2\({}^{\circ}\) observer, an illuminant D65, and a pulsed xenon lamp as the default light source. During the measurement, a glass cover with a light shield tube (CR-A33a, Konica Minolta, Tokyo, Japan) was placed over the aperture. The convex protective glass plate prevents the sample from entering the instrument and facilitates cleaning between measurements. The instrument was calibrated with a white tile standard before each analysis. The illumination of the samples was uniform and continuous throughout the measurements, which were performed on a white tray between white walls. However, it is important to highlight that the environment could not influence the colour measurement, because the measuring head was directly in contact with the surface of the samples so that no light other than the light from the chroma metre was shining on the measured spots. The colour attributes assessed included redness/greenness (a*), yellowness/blueness (b*), and lightness (L*). Additionally, the hue angle (h(ab)) was calculated as a significant colour attribute using the following formula [22]:  
\[\text{h\_ab}=\text{arctan (b*)/(a*)} \tag{1}\]
## 3 The Optimized Kinematic Modeling Method
### Traditional Kinematic Modeling of the Mobile Manipulator Robot  
For the mobile manipulator robot in Figure 1, the manipulator subsystem is composed of two links and six motors. To establish a mathematical model, the subsystem can be simplified to six joints and seven links according to the standard Denavit-Hartenberg (SDH) method. Then, the position and posture of the end tool of the manipulator subsystem can be solved by the homogeneous matrix transformation between adjacent coordinate frames. Assuming the mobile platform moves on the horizontal plane, since the mobile platform is driving by the rear wheels, the position and posture of the midpoint of the driving wheels can be solved by the movement along the \(x\)-axis and \(y\)-axis and the rotation around the \(z\)-axis of the world frame. Therefore, the control variables of the entire mobile robotic arm system can be specifically expressed as  
\[q=[q_{m},q_{a}]^{T}=[d_{x},d_{y},q_{z},q_{1},q_{2},......,q_{n}]^{T} \tag{1}\]  
where \(q\), \(q_{m}\), and \(q_{a}\) indicate the control variables of mobile manipulator subsystem, the mobile platform, and manipulator subsystem, respectively; \(d_{x}\) and \(d_{y}\) indicate the distance moved along the \(x\)-axis and \(y\)-axis; \(q_{z}\) indicates the rotation angle around the \(z\)-axis of the world frame; and \(q_{i}\) indicates the rotation angle of the \(i\) point in the manipulator subsystem.  
In practical tasks, the matrix transformation from the midpoint coordinate frame of the driving wheels to the center of gravity of the mobile platform is fixed. Moreover, the transformation matrix from the mobile-fixed frame to the base-fixed frame does not change. According to the SDH method, we can obtain the position and posture of the end-effector of the mobile manipulator in the world frame, which is expressed as  
\[{}^{o}T_{e}={}^{o}T_{c}\cdot{}^{c}T_{b}\cdot{}^{b}T_{1}\cdot.........\cdot{}^{ n-1}T_{n}\cdot{}^{n}T_{e} \tag{2}\]  
where \({}^{o}T_{c}\) indicates the position and posture of the center of gravity of the mobile platform in the world frame, \({}^{c}T_{b}\) indicates the position and posture of the manipulator's base in the body-fixed frame, \({}^{b}T_{1}\) indicates the homogeneous transformation matrix from the 1-th joint to the base-fixed frame, \({}^{n}T_{e}\) indicates the homogeneous transformation matrix from the end-fixed frame to the \(n\)-th joint-fixed frame, and \({}^{i-1}T_{i}\) indicates the homogeneous transformation matrix from the \(i\)-th joint-fixed frame to the \((i-1)\)-th joint-fixed frame.
## 2 Materials and Methods
### Module Two: Multi-Level Spatial Aggregation Strategy (MSAS)
\(OCP\) selects the maximum feature value within each region through max pooling, highlighting the salient features of the target. It applies spatial attention weights to calculate the relationships between features in different regions, further strengthening the connections between features, enabling the model to more accurately capture and recognize important features; \(F_{ocp}^{k}\) represents the feature map processed through object-based context pooling (OCP). Finally, features from different spatial ranges are aggregated using a feature concatenation method. The formula for this is as follows:  
\[F_{final}=Concat(F_{ocp}^{1},F_{ocp}^{2},F_{ocp}^{3},F_{ocp}^{6}) \tag{14}\]  
In the formula, \(F_{final}\) represents the final comprehensive feature map and \(Concat\) denotes the feature map concatenation operation. This multi-level feature processing operation enables the model to better distinguish and recognize various targets, especially in scenes with rich textures and complex backgrounds. In summary, in RS image segmentation, the multi-scale context aggregation module we designed, by integrating compact \(3\times 3\) convolutions, sparse self-attention mechanisms, and object-based context pooling, effectively integrates features of different scales and spatial ranges. This approach optimizes computational efficiency and retains key information. This method significantly enhances the model's ability to segment RS data with rich textures and complex backgrounds.
## 3 The Proposed TETSO Framework
The process of re-positioning of the particles continues until a certain completion condition is fulfilled. As stated in Section 2, the TET mechanism is a ternary scheduler with three decision levels. Our proposed approach, instead of using heuristics, chooses the decision levels automatically using multi-objective particle swarm optimization and improves the selection criteria of threshold values that feed into the ternary state estimation. In this model, an open-loop and event-based architecture is considered when a remote sensor transfers its measurement to the fusion center exclusively in case a specific event happens. The TETSO framework modifies the PSO algorithm by adding sets of thresholds representing the TET approach. The sets of thresholds are defined as follows for iteration \(k\), where \(\Pi_{L,k}\) is the left set of threshold values and \(\Pi_{W,k}\) is the right set:  
\[\Pi_{L,k} = \left\{\Delta_{1,k},\Delta_{2,k},\ldots,\Delta_{m,k}\right\}\ \ \text{for}\ j=1\ldots m,\] \[\Pi_{W,k} = K_{k}\cdot\Pi_{L,k}\ \ \text{for}\ j=1\ldots m \tag{7}\]  
where \(m\) is the number of thresholds in each set, \(K_{k}\) is a coefficient whose value changes randomly with each iteration, and each \(\Delta_{j,k}\) is randomly defined at each iteration within a  
Figure 1: Flowchart of the general PSO algorithm. predefined range. The simulations section will define the specific ranges of these random values. It should be noted that the TETSO framework will look for Pareto optimal solutions that will satisfy the following constraints in the sets of thresholds, which are described as follows for \(\Pi_{L,k}\) and \(\Pi_{W,k}\), respectively,  
\[ \begin{array}{ll}\text{Constraint 1}:&\Delta_{j,k}\ >\ \Delta_{j,k-1}\ \text{ for }j=1\ldots m\\ \text{Constraint 2}:&\Delta_{j+1,k}\ >\ \Delta_{j,k}\ \text{ for }j=1\ldots m-1\end{array}  \tag{8}\]  
Constraint 1 ensures that the values of the thresholds in \(\Pi_{L,k}\) and \(\Pi_{W,k}\) increase with the next iteration of \(k\), and constraint 2 guarantees that the values in each set remain sorted. The aforementioned set values of the thresholds are received by the objective function of the TETSO framework as input. The framework attempts to increase the amount of quantized data communication and simultaneously decrease the point-valued and set-valued data transmission, which lowers the communication overhead and the cost [14].
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 3 Materials and Methods
### Integration of Advanced Technologies in Risk Assessment  
The integration of advanced technologies, including Artificial Intelligence (AI), Geographic Information Systems (GIS), and drones, into the ALOHA software framework is paramount for elevating ammonia leak risk assessments. While ALOHA is a powerful tool in its own right, the complexity of modern industrial hazards necessitates an adaptive approach to crisis management.  
AI can transform data analysis by rapidly processing vast amounts of information, yielding real-time insights into gas dispersion and potential impact zones. This capability is crucial for making informed decisions during emergencies. Coupling AI with GIS providesa spatial dimension, allowing emergency responders to visualise hazardous areas clearly and strategically allocate resources.  
Furthermore, the deployment of drones equipped with advanced sensors enhances situational awareness, offering immediate aerial data on environmental conditions and gas concentrations. This integration allows for timely interventions and precise assessments of affected areas, improving response strategies significantly.  
Embracing these technological advancements not only streamlines ALOHA's operational capabilities but also fortifies public safety and environmental protection. As the risks associated with ammonia leaks continue to evolve, it is essential for ALOHA to adapt, ensuring that emergency services can respond swiftly and effectively to mitigate potential hazards.
## 5 Results and Discussion
In addition, the proposed methodology has reasonably estimated the suspension deflection values, and the values have never exceeded the maximum value, which is better than the results presented in [21]. Figure 5 illustrates the control signal \(u(t)\), i.e., the actual force applied by the actuator. Note that the proposed robust predictive methodology considers the failures at the sensor and reacts by increasing the force needed. As a result, the proposed robust controller has improved the vehicle's ride comfort while satisfying all hard constraints, including both suspension deflection and actuator saturation constraints, even though the system is subject to an actuator fault. Figure 5: The control signal. Figure 6: The proposed sensor fault and its estimation. Figures 6 and 7 present the fault estimation results, which show that the stability of the closed-loop system is guaranteed under external disturbances and sensor faults. The estimation results show that the proposed methodology has excellent estimation performance and efficiently tolerates sensor faults. Table 2 summarises the RMS comparison between the proposed robust approach and the method discussed in [21]. The comparison describes the RMS errors of the vehicle's dynamic responses in terms of the suspension stroke, body acceleration, and tire dynamic load under random road excitation. Table 2 presents the maximum values reached in body acceleration, suspension deflection, and tire deflection with the estimated RMS errors. The results suggest that the proposed robust predictive controller has better performance than the passive suspension system discussed in [21] when the system is subject to sensor faults and external disturbances. In addition, the proposed FTPC structure has the ability to improve the ride quality and road-holding performance under different types of active suspension constraints and in the presense of external disturbances and sensor failures. Note that the illustrated performance of the proposed fault predictive controller is due to the following:  
The proposed predictive control structure is well adapted for LPV models such as LPV representations for automotive suspension systems. In addition, the external disturbances and faults in the output channel are already considered in the predictive control design procedures, which means that the controller is designed to meet both the stability of the vertical motion of the car's body and the passenger's isolation from vibrations caused by rough roads. The proposed predictive controller has the ability to anticipate any errors caused by external disturbances and sensor failures, which eventually helps to reduce the impact of these errors while upper limits are imposed on the dynamic force of the motor. Note that the upper limits are used to avoid discomfort to passengers and damage to the vehicle's components.
## 2 Materials and Methods
### QM/MM Calculations  
The structural model for hybrid quantum mechanics/molecular mechanics (QM/MM) calculations was based on the crystal structure of \(Cr\)ChR2 (PDB entry: 6 EID [10]) with an all-trans conformation of the retinal protonated Schiff base (RSBH\({}^{+}\)). Titractable residues were modeled corresponding to the experimental pH and considering their local environment. Based on spectroscopic studies, the residue E90 was modeled in the neutral form and the counterions of RSBH\({}^{+}\) (E123 and D253) were taken as negatively charged. The variants of \(Cr\)ChR2, T127A and T127S, were prepared with PyMol [20].  
In order to account for the effect of T127 and its variants, we used a large QM region that comprised the RSBH\({}^{+}\) side chains of E123, T127, A127, S127, C128, D253, K257, and two water molecules in the close vicinity of RSBH\({}^{+}\). The hydrogen link atom was placed at the QM/MM boundary between the C8 and C\(\varepsilon\) atoms of K257, and between the C\(\alpha\) and side chain atom of remaining residues. The QM region was treated at the B3 LYP/cc-pVDZ level of theory [21]. Corrections for the dispersion effect were included with D3/B-J damping variant [22]. All the remaining atoms were described at the MM level using the AMBER ff14 SB force field [23]. The vibrational frequencies were calculated at the same level of theory. A scaling factor of 0.97 was used [24]. However, the QM region in frequency calculations did not include the water molecules. Furthermore, the QM/MM model was used to calculate the excitation energies using the simplified TD-DFT approach developed by [PERSON] and coworkers [25]. All QM/MM calculations were carried out using ChemShell interfaced with the quantum chemistry program Orca with DL_POLY module for the force field [26].
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## Principles of Coil Guns
### Electrical Model  
At each stage of the coil gun, a \(\dfrac{4700}{n}\)\(\mu\)F, 450 V capacitor \(C\) is discharged in the inductance \(L\) using a controlled switch based on a MOSFET Transistor as shown in Figure 13. In this \(LC\) circuit, resistor \(R\) must be considered because its value is not negligible at all due to the important number of loops in the coil. \(R=\dfrac{\rho L}{S}\) can be evaluated or measured, where \(\rho\) is the resistivity of copper, \(L\) the total length of the coil wire and \(S\) the surface of a wire section. This leads to the differential Equation (1) where \(L\) is not constant, but depends on the plunger position and on the coil current. Considering that, Equation (1) must be solved by numerical simulation.  
\[\dfrac{d^{2}U_{C}}{dt^{2}}+\dfrac{R}{L}\dfrac{dU_{C}}{dt}+\dfrac{1}{LC}U_{C}=0 \tag{1}\]  
Figure 11: _FEMM 4.2_ model: flux density.  
Figure 12: _FEMM 4.2_ mesh with its boundary.  
As shown in Figure 14, _FEMM_ 4.2 simulations shows that \(L\) inductance varies by a factor 20 from \(L=13\) mH to \(L=253\) mH in a 1 coil kicking system.  
The inductance value depends on the plunger position and the saturation of the magnetic circuit due to the current in the coils. In Figure 14, discrete values of the inductance are calculated for positions of the plunger (Figure 15) varying from \(x=-100\) mm to 60 mm by increment of 5 mm, and for coil currents varying from 1 A to 200 A by increment of 50 A.  
It is important to note that:  
* Inductance value \(L\) increases as the plunger enters the coil, is maximized when the plunger centre is aligned with the coil centre, and decreases after that. This is because the magnetic field is well guided when the plunger is inside the coil with a low air gap.
* The inductance value is highly dependent on the coil current. For a current \(I=1\) A, \(L\) varies, depending on plunger position, from \(L=13\) mH to \(L=253\) mH whereas for a current \(I=100\) A, \(L\) varies only from \(L=13\) mH to \(L=24\) mH. There is an important difference between maximal values because at a low current, magnetic material is not saturated, leading to a high inductance value. In contrast, there is no difference between minimal values because when the plunger is outside the coil, the air gap is so important that it leads to a huge reluctance in the air gap part which prevents saturation of the magnetic circuit.  
Figure 14: Variation of the inductance value depending on the plunger position and the coil current.  
Figure 13: Electric circuit.
## 2 Materials and Methods
### The Proposed MSFNet
#### 2.3.2 Hierarchical Spectral Semantic Fusion Module  
The limited spectral resolution of multispectral images poses a significant challenge in capturing the fine-grained spectral variations induced by plant diseases, thereby constraining the model's discriminative capacity in disease region identification. Existing methods typically rely on the final feature layer of deep networks for classification, leveraging high-level spectral semantics and spatial features. However, relying solely on high-level features lacks the support of low-level spectral details, which are crucial for preserving subtle spectral variations within diseased regions. Spectral features at different levels encode a progressive transition from local details to global semantics, and utilizing only a single-level representation fails to fully exploit the hierarchical spectral information, ultimately constraining the accuracy of disease region classification.  
To address this issue, a novel hierarchical spectral semantic fusion module was proposed (as shown in Figure 5), employing a pyramid architecture with an attention mechanism to effectively integrate spectral semantic information across different feature levels, thereby enhancing the model's perception of hierarchical spectral semantics. As illustrated in Figure 4, the feature maps extracted by different ResBlocks in MSFNet form a pyramid structure, where each scale-specific feature map represents a distinct level within the hierarchical pyramid. To enhance the discriminative capability of key spectral-spatial features, the extracted multi-level feature maps are first processed by the Convolutional Block Attention Module (CBAM), which strengthens feature representation through channel and spatial attention mechanisms. The attention-refined features are propagated along two paths: one path continues bottom-up feature extraction by being fused with the next ResBlock output, thereby constructing the hierarchical pyramid structure, while the other undergoes global average pooling to generate a spectral feature vector at that level. These vectors from different levels are then concatenated to integrate spectral information across multiple layers. The final fused feature vector effectively aggregates hierarchical spectral semantics, providing a richer spectral representation for disease region classification.  
As depicted in Figure 6, the CBAM consists of sequential channel and spatial attention mechanisms. The channel attention mechanism employs an excitation/compression strategy to enhance the focus on key spectral features, while spatial attention adaptively assigns weights to different spatial positions to highlight disease-related regions. Given input feature maps for channel and spatial attention denoted as \(C_{F}\) and \(S_{F}\), the channel attention weighting formulation is shown in Equation (1), and the spatial attention computation is shown in Equation (3). By incorporating the CBAM, the discriminative power of spectral-spatial features is significantly enhanced.  
\[SA(S_{F})=sigmoid\{Cov_{\gamma,\gamma}[MaxPool(S_{F})+AvgPool(S_{F})]\} \tag{3}\]