esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
As shown in Figure 4a,b the mixture explosiveness assessment was carried out considering pressure values P\({}_{\text{max}}\) and the explosion pressure rise rate (dP/dt)\({}_{\text{max}}\)[34]. To show the spectral radiation characteristics, the intensity (I) was recorded and analyzed as it is shown in Figure 5. Parameter I\({}_{0}\) is defined as the value at which the radiation intensity deviates from the baseline (I\({}_{0}\) is 110% of the initial radiation intensity) [35]. The parameter t0 shows the time necessary to reach the maximum value of radiation intensity.  
According to the received experimental data, the ignited methane of stoichiometric concentration in chamber produced maximal explosion pressure P\({}_{\text{max}}\) = 0.83 MPa and the rate of pressure rise was 42.05 MPa/s. When the explosive combustion of KS (KC) grade coal dust was registered in the Dzerzhinsky mine site, the maximum explosion pressure in the chamber was 0.79 MPa and the pressure rise rate was 34.62 MPa/s.  
The results were analyzed using the application software. According to the obtained data, the graphs of changes in the pressure of methane and coal dust explosion were plotted against the time of explosive combustion of the mixture.  
Figure 4: **(a)**Pressure-time curve based on CH\({}_{4}\) ignition (9.5% Vol.). (b) Pressure-time curve based on the results of the coal dust ignition (concentration 200 g/m\({}^{3}\), dispersibility 63–94 microns).  
The results of experimental studies of radiation intensity measurement of methane-air and dust-air mixture explosion carried out by the electron-optical method are presented in the form of a dependence diagram, Figure 6.  
The relative intensity dependence on the wavelength shows that wavelength distribution of radiation energy differs from the [PERSON]'s type of distribution. The graph (Figure 6) clearly shows two peaks at the wavelengths \(\lambda_{1}\) = 383.5 nm and \(\lambda_{2}\) = 620 nm. These peaks appeared when the banded spectrum of molecules and radicals overlaid the continuous radiation spectrum of the heated gas.  
The peak point in the range of \(\lambda\) = 390 nm can be identified with band system of the radical CH radiation [36], and the one in the range of \(\lambda\) = 620 nm-with the band system of the molecule \(C_{2}\) radiation (Swan system). The band system of molecule \(C_{2}\) is also in the range of \(\lambda_{1}\) = 380 nm.  
Figure 5: Maximum intensity of spectral radiation against time.  
Figure 6: Spectral energy distribution at maximum radiation of methane-dust-air mixtures.  
An intense series of bands are observed in the methane combustion spectrum, caused by the OH radical emission in the ultraviolet range of the spectrum \(\lambda_{1}\) = 306 nm. The maximum radiation in the range of 900 nm should be identified with methane-air continuous combustion spectrum. In this case, the temperature, that was determined by the curve peak in accordance with Wien's displacement law, is \(T\) = 2610 K, which correlates well with the data received by spectrometric methods [37].
# Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
## 3 Methodology
### The Updating Procedure of the Policy Network  
The parameters \(\theta_{p}\) of the policy network are updated following the Policy Gradient [14], which is a reinforcement learning approach directly optimizing the policy network by gradient descent. The policy network takes the state vectors \(s(\cdot,\cdot)\) of the randomly sampled unobserved edges as inputs and outputs the actions \(a(\cdot,\cdot)\) for them. After finishing a sampling period, the policy network obtains a reward \(R\). In this sampling period, we define the objective function of the policy network as follows:  
\[J(\theta_{p})=\sum_{(v,v^{\prime})\in E^{s}}\pi_{\theta_{p}}\big{(}s(v,\;v^{ \prime}),a(v,\;v^{\prime})\big{)}R \tag{6}\]  
where the \(E^{s}\) is the set of the randomly sampled unobserved edges in this sampling period.  
To use the gradient descent, the derivative of the objective function is calculated as follows:  
\[\
abla_{\theta_{p}}J(\theta_{p})=\sum_{(v,v^{\prime})\in E^{s}}R\
abla_{ \theta_{p}}\pi_{\theta_{p}}\big{(}s(v,v^{\prime}),a(v,v^{\prime})\big{)} \tag{7}\]  
The purpose of the training of the policy network is to maximize the expected reward. Therefore, its parameters \(\theta_{p}\) are updated following the gradient descent [14]:  
\[\theta_{p}=\theta_{p}+\alpha\
abla_{\theta_{p}}J(\theta_{p}) \tag{8}\]  
where the \(\alpha\) is the learning rate of the policy network.  
Armed with the introduction of the negative sample selector, the negative sampling procedure in this work is described in Algorithm 1.  
```
INPUT: Network \(G\) and number of required negative samples \(N\) OUTPUT: Negative sample set \(E_{train}^{ng}\)
1. Initialize the negative sample set \(E_{train}^{ng}=[\;]\)
2. While\(\left|E_{train}^{ng}\right|<N:\)
3. Randomly sample an unobserved edge \((v,v^{\prime})\) from \(U-E_{train}^{pos}\)
4. if\((v,v^{\prime})\in E_{train}^{ng}\):
5. Continue
6. Obtain the state vector \(s(v,v^{\prime})\) of the edge \((v,v^{\prime})\) following Equation (1)
7. Feed the state vector \(s(v,v^{\prime})\) into the policy network
8. Obtain the action \(a(v,v^{\prime})\) for the edge \((v,v^{\prime})\)
9. if\(a(v,v^{\prime})=1\):
10. Append the edge \((v,v^{\prime})\) to \(E_{train}^{ng}\)
11. Record the \(s(v,v^{\prime})\) and \(a(v,v^{\prime})\) for the update of the policy network
```  
**Algorithm 1**The negative sampling procedure with the negative sample selector
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
###### Abstract  
Drivers of heavy trucks are exposed to large amounts of vibration which can lead to serious health risks. Many suspension systems/methods can be used to isolate these transmitted vibrations, such as vehicle suspension systems, cabin suspension systems and seating suspension systems. The central idea of the work is to identify the research gaps and raise our future research questions in this specific area. The novelty of this paper is proposing a model predictive controller for active vibration control of seating suspension systems. A systematic literature review of the existing work of the vibration control of seating suspension systems has been conducted. Various control techniques that are used in the seating suspension systems have been summarized and evaluated. This paper focusses on the biodynamic model of the driver and seat for the first step needed in the design of the seating suspension system. Then, it illustrates the different types of the system vibration controls and their performance evaluation methods. At the end, the paper details several active seating suspension systems including their actuation system structures and control algorithms which are used in the heavy vehicle trucks.  
active seat suspension; biodynamic model; ride comfort; vibration control methods; model predictive control +
Footnote †: journal: Applied Sciences
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.1 Intensity Modulation Scheme to Reduce Background Spectrum  
As shown in Figure 19b, the BOCDA output spectrum includes the hill-like or the mountain-like background spectrum, besides the signal spectrum corresponding to the correlation peak position. The origin of the background spectrum is caused at both sides of the correlation peak position, where the side lobes are generated by the LD FM modulation. Considering the SOCF technology, we proposed and studied a way that was named the \"intensity modulation scheme\" to reduce the side lobes [51]. By the FM modulation on the LD with a sinusoidal waveform, the time-averaged LD power spectrum has peaks at both sides of the spectrum shape, as shown in Figure 9b. The shape of the time-averaged LD power spectrum and the coherence function shape is also roughly related by a Fourier transform in the BOCDA case. Then, the side lobes could be reduced by manipulating the time-averaged LD power spectrum shape, so that the power at both edges of the spectrum is reduced. We have successfully reduced the mountain-like background by the intensity modulation scheme. A detailed description is shown in the next section.
## 4 Results of the Measurements and Analyses
### Correlation Analysis  
The Minitab tool has a \"Matrix plot\" function that allows you to make a matrix plot showing the correlation between selected parameters described in the HoQ (Figure 3). Such a matrix shows how the indicators depend on each other. The parameter describing the interdependence of the two studied quantities is called the linear correlation coefficient. The correlation coefficient between two parameters will always be between \(-1\) and \(1\). The sign \"-\" denotes the negative influence of one quantity on the other. The value of \"0\" in the discussed interval indicates that there is no relationship between the examined parameters. The correlation coefficient rxy is described by the general equation of the form,  
\[r_{xy}=\frac{1}{n-1}\sum_{i=1}^{n}\frac{x_{i}-\hat{x}}{\sigma_{x}}.\frac{y_{i} -\hat{y}}{\sigma_{y}}=\frac{1}{n-1}\sum_{i=1}^{n}\frac{(x_{i}-\hat{x})\cdot(y _{i}-\hat{y})}{\sigma_{x}\cdot\sigma_{y}} \tag{1}\]  
where \(r_{xy}\)--correlation coefficient, i.e., the level of connection between two selected parameters, \(n\)--number of the measurement results, \(x_{i}\), \(y_{i}\)--successive measured values of  
Figure 5: Regression analysis illustrating the effect of void spaces on the Rth value together with the \(p\)-value: (**a**) for TR_A (Total Void), (**b**) for TR_A (Individual Void), (**c**) for TR_B (Total Voids), (**d**) for TR_B (Individual Voids). parameters \(x\) and \(y\), and standard deviation \(\sigma_{x}\), \(\sigma_{y}\) from the mean value of the measurements of parameters \(x\) or \(y\). In the Minitab, using the Matrix Plot function described above and the results of the measurements presented in Table 1, the relations were obtained between the individual thermal parameters of the tested transistors selected in the HoQ analysis and the size of individual and total voids measured. Referring to the Equation (1), the size of the void corresponds to the value \"\(y\)\" in this equation and the remaining parameters correspond to the value \"x\". The values of the correlation coefficient between the described parameters and the size of the voids are presented in Table 2.  
Referring to the previously discussed regression analyses, to determine the simple regression of one variable (and the trend line), we use the least squares method [39]. The coefficient of convergence or determination R\({}^{2}\) is determined, which in turn determines the percentage of variation for the dependent variable, which then can be explained by an explanatory variable in the regression model. This coefficient measures how well the regression model fits the data. It can be determined as the square of the correlation coefficient r from where it also takes the used name R\({}^{2}\). Due to this parameter, there may be interesting values of the correlation coefficient r, with absolute values around the value r = 0.7. It is not a hard limit, but an empirical value which may indicate the dependence of
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 3 System Control
### Vehicle-to-Grid Control  
Figure 6 depicts the vehicle-to-grid control in which the BSBD and GSBD converter operates in boost and inverter mode, respectively, to feedback the power into the grid. The V2G mode control starts with discharging of the EV batteries. In this operation, the EV batteries are connected with the BSBD DC-DC converter interfaces the GSBD converter. Firstly, the EV battery voltage level is boosted with the help of battery discharge control to achieve the voltage level at the interfaced DC-link voltage. The DC-link voltage control loop provides the reference current for the active power generation to supply the grid in the battery discharge control.  
Figure 4: Grid-side bidirectional totem pole PFC control in G2V mode.  
Figure 5: Battery-side bidirectional converter control in buck/charging mode.  
Figure 3: Block diagram of grid-to-vehicle charging control.  
During the discharge process, the EV batteries voltage decreases. Hence, the increment in reference current is necessary to maintain the constant active power generation. Therefore, along with the voltage control loop, a current control loop is also cascaded in battery discharge control, as shown in Figure 7. This discharging operation provides DC-link voltage to operate the grid-side bidirectional converter.  
The obtained DC-link voltage acts as an input to the GSBD converter, which works as an inverter in V2G mode. With the help of inverter control, the inverter converts the stored DC power of EV batteries into the AC power to supply back to the grid. The inverter control includes a PLL, voltage control, and current control loop, which obtain in synchronously rotating or \(dq\) reference frame. A \(dq\) reference frame consists of a direct (\(d\))-axis and quadrature (\(q\))-axis, which deals with active and reactive power control. Furthermore, in inverter control, PLL synchronizes the generated AC current and voltage with the grid current and voltage. Additionally, in voltage control loop, the obtained \(V_{DC-link}\) is compared with the predefined \(V_{DC-link}^{ref}\) and passed through the proportional-integral (PI) controller. The output of the PI controller provides the active current reference (\(I_{d}^{ref}\)) to generate active power and for unity power factor to keep the reactive current reference (\(I_{q}^{ref}\)), zero. Furthermore, the current control loop generates gate pulses to trigger the inverter to generate the AC power, as shown in Figure 8. The generated AC power is transferred to the grid in V2G mode.  
Figure 6: Block diagram of vehicle-to-grid or discharging control.  
Figure 7: Battery-side bidirectional DC-DC converter control in boost/discharging mode.  
Finally, the designed parameters using system control are utilized to develop a 7.7 kW bidirectional charging and discharging infrastructure. The charging or G2V and discharging or V2G mode performances were verified in MATLAB/Simulink environment which is presented in the next section.
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 3 Methodology  
### Preliminaries  
#### 3.1.1 Adversarial Attack  
Let \(\mathcal{M}:\mathcal{X}\rightarrow\mathcal{Y}\) represent a DNN that maps the raw domain \(\mathcal{X}\) to the target domain \(\mathcal{Y}\). In this paper, we limit the DNN to image classification, indicating that \(\mathcal{X}\) corresponds to a number of images, and \(\mathcal{Y}\) is a set of classes. Given a sample \(\mathbf{x}\), an attacker aims to construct a perturbation \(\delta\) that the perturbed sample, also called adversarial example, \(\mathbf{x}^{\prime}=\mathbf{x}+\delta\) successfully deceives \(\mathcal{M}\), i.e., \(\mathcal{M}(\mathbf{x}^{\prime})\
eq\mathcal{M}(\mathbf{x})\). Specifically, in the untargeted attack scenario, this causes the model's classification to deviate from the original label, and in the targeted attack scenario, this causes the model to classify the samples to the target label.  
White-box adversarial attacks produce adversarial examples directly on the target model, which does not work for black-box attacks. To realize successful black-box attacks, a common strategy utilizes a substitute model to generate adversarial examples, which are then used to attack the black-box models, leveraging transferability. Black-box models with various structures are used to simulate the possible target models.  
For compactness, let \(\mathcal{M}_{0}\) denote the substitute model and \(\mathcal{M}_{1},\mathcal{M}_{2},\ldots,\mathcal{M}_{B}\) denote \(B\) black-box models that are independent of \(\mathcal{M}_{0}\). The structures of these models can be different from each other. In the targeted attack scenario, the goal of the attack is to construct adversarial examples with \(\mathcal{M}_{0}\) such that these adversarial examples are misclassified as a specified incorrect class by \(\mathcal{M}_{i}\), \(1\leq i\leq B\). For the untargeted attack scenario, the output is not limited to a particular incorrect class.  
Mathematically, given a sample \(\mathbf{x}\), generating the adversarial example \(\mathbf{x}^{\prime}\) with \(\mathcal{M}_{0}\) can be formulated as the following problem:  
\[\mathbf{x}^{\prime}=\mathbf{x}+\delta=\arg\max_{\mathbf{x}^{\prime}}\ \mathcal{L}\big{(}\mathbf{x}^{\prime},\mathbf{y};\mathcal{M}_{0}\big{)} \tag{1}\]  
subjected to \(\|\delta\|_{p}\leq\epsilon\), where \(\mathcal{L}(\mathbf{x}^{\prime},\mathbf{y};\mathcal{M}_{0})\) is the loss function and \(\epsilon\) is a threshold given in advance. Existing adversarial attack methods often use cross-entropy loss. The strength of perturbation is constrained by the \(L_{p}\) norm to control the visual differences between the adversarial examples and clean ones. Existing adversarial attacks typically use \(L_{0}\)[20], \(L_{2}\)[9; 21], and \(L_{\infty}\)[9; 10; 21] to control the perturbation in the spatial domain.
## 4 Results and Discussion
### Results and Analysis
#### 4.3.2 Performance Evaluation on Different Subsets of Economic Behavior Data  
This experiment was designed to evaluate the generalization capability and response stability of different models when applied to various dimensions of economic behavior data in a large-scale setting. By constructing four specialized subsets--product pricing records, sales feedback, browsing behavior, and inventory status--the performance of each model under different types of information structures was assessed to determine its adaptability and prediction effectiveness.  
As shown in Tables 4-7 and Figure 5, the proposed method consistently outperformed all baselines across the sub-tasks. It achieved the highest total profit gain and average reward on the sales feedback dataset while maintaining lower action variability and higher correlation scores in the browsing behavior and inventory status datasets, demonstrating strong robustness in handling unstructured behavior data. From a theoretical standpoint, XGBoost, although capable of modeling structured features, lacked the temporal representation and contextual modeling abilities necessary for sequential prediction, resulting in the weakest performance across all subsets. BERT captured semantic relationships to a certain extent, but the inconsistency between its pretraining objectives and temporal dynamics hindered its sequential adaptability. The LSTM-based model benefited from its recurrent architecture for learning local dependencies but suffered from memory decay when modeling long-range behavioral cycles. In contrast, the transformer-based model leveraged its global attention mechanism to model nonlinear dependencies over extended time horizons, resulting in performance improvements. The DQN model constructed an iterative optimization path via reinforcement learning, providing advantages in sparse-feedback environments. The proposed method, which combines multi-head attention structures, delayed-return estimation, and sparse-feedback enhancement, achieved synergistic improvements in representation learning, value estimation, and policy stability, thereby enhancing prediction accuracy and consistency across heterogeneous behavior data subsets.  
Figure 5: \(R^{2}\) curves of different models.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 6 Results and Analysis  
This section summarizes the main results obtained in the evaluation and comparison of the different proposed methods. Initially the battery modeling is established by an optimization process on the battery ECM, and then the SOC estimations can be compared across the different methods.
## 6 Research and Analysis of the Obtained Results
### Test No. 1
In addition, as a function of the distance to 5 m, EKF histogram columns occur most often. DR and USBL histograms are flattened as a function of distance. This proves that the incidence of very scattered distance values (up to 15 m) is highly probable. The statistical parameters of the positioning errors using EKF, DR methods and the USBL system are presented in Table 4.  
The results in Table 4 clearly show that the best values of the statistical parameters were obtained by the EKF method.
## 1 Introduction
This study focuses on a Geographic Information System (GIS)-based assessment of the suitability of land use in the Apuseni Mountains (specifically the Somesul Mic watershed), taking into account the changing limitations caused by climate change. This project aims to assess the suitability of diverse land parcels for different uses (pasture, grassland, orchards). Its objective is to provide a rational framework that can assist land use planning and policy-making in the region. By including climate scenarios in the GIS-based assessment, it becomes possible to conduct a proactive study that confirms land use decisions are not only successful in the present, but also capable of coping with future climatic changes [64; 65]. This study addresses two main research directions. The primary objective is to implement a GIS model to assess the suitability of the land in the research region for use as grassland, pasture, and orchards. This model utilizes a geodatabase that includes characteristics such as topography, soil type, climate variables, and water supplies, providing a precise evaluation of potential sites for different uses. The second research direction is centered around analyzing the favorability classes of different land uses in the present day. The validation of the GIS model is achieved through a comparison with land parcels that are used according with the classification provided by the model. Furthermore, this study investigates modifications in favorability categories within the context of climate change, using a statistical analysis of the effects of the expected changes in average annual temperature and precipitation fluctuations for the time frame of 2020-2100. A GIS land favorability assessment is based on a careful analysis of the environmental variables present in the research area. These conditions include factors like slope, altitude, temperature, and precipitation [66], as well as soil qualities such as texture, pH, gleiss, and useful graphic volume [67]. The investigation of climate change in agriculture from an economic perspective is essential, as agriculture serves as the fundamental support system for many economies, especially in underdeveloped areas [68]. A decrease in crop production can result in an escalation in food costs, heightened volatility in the market, and inflation [69]. Inclement weather conditions such as droughts, heatwaves, and floods inflict direct harm upon crops and livestock, leading to substantial economic losses for farmers, governments, and the insurance industry [70]. A better understanding of climate-related risks allows for more accurate cost assessments and informed decision-making. If left unchecked, the cumulative effects of climate change on agriculture could lead to long-term economic decline, particularly in economies heavily dependent on agriculture [71]. This is why we consider a study such as this one useful, because it will provide a concrete picture of current agricultural suitability and model how this suitability will change in the future under climate change.