esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results

Table 5: Percent improvement of the prediction ability of the tuned model, compared to the reference values guaranteeing MPC controller performance and non-regularized model prediction ability. Figure 7: Examples of regularized GA model prediction based on real-time lake validation data; (**a**) 5-steps ahead prediction, (**b**) 10-steps ahead prediction, (**c**) validation data sequence. Table 6 presents the percentage STD values in relation to the model parameter values. They should not exceed 10% in order to get a good model, and exceeding the value of 50% makes the model unreliable. The GA tuning method decreased their percentage values, and only one of them was bigger than 10% but still remained less than 50%. These percentage values guaranteeing model reliability were not obtained during the conventional identification procedure. A significant improvement in the quality of the model was achieved during GA tuning for \(b_{1}\)-\(b_{4}\) coefficients. Percentage STD values for the rest of the coefficients were comparable in both the conventional identification and GA tuning method. Model division for future MASS predictive control requires averaging the estimated parameters values. During the identification procedure we obtained one model for each data set, and the controller needs one model for each working point. Figure 10 presents results of averaging the parameters. The model output was compared with validation \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Percentage STD Values** & **Percentage STD** \\  & **of GA Model [\%]** & **Values of Model [\%]** \\ \hline \(a_{1}\) & 6.75 & 4.52 \\ \(a_{2}\) & 3.34 & 2.54 \\ \(a_{3}\) & 1.66 & 1.34 \\ \(a_{4}\) & 0.66 & 0.54 \\ \hline \(b_{1}\) & 3.29 & 539.94 \\ \(b_{2}\) & 4.66 & 120.85 \\ \(b_{3}\) & 27.68 & 24.74 \\ \(b_{4}\) & 7.55 & 19.76 \\ \hline \(k_{1}\) & 3.05 & 2.69 \\ \(k_{2}\) & 3.54 & 3.49 \\ \(k_{3}\) & 3.47 & 4.54 \\ \(k_{4}\) & 3.75 & 5.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage STD values in relation to the model parameter values. Figure 9: Regularized GA model and conventional model 10-step ahead prediction comparison based on real-time lake validation data. -like trial data. The model fit decreased as the prediction horizon elongated. The model fit for short-term prediction (a) reached almost 90% and fell to almost 70% for mid-term prediction (b). Long-term prediction (c) is rarely used in MASS control systems. From the obtained result, the mapping rotational speed changing tendency was sufficient. Results presented in Figure 10 prove that it is justified to regularize the identified model using real validation data. This avoids the occurrence of high-frequency disturbances related to the identification of the mathematical relationships that constitute the mathematical model, instead of the dynamics of the ship.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 2 Motivation and Main Methodology
### Construction of Real-Time Attribute Decision-Making Model of Multirotor Based on TOPSIS
#### 2.4.1 Construction of the Attribute-Strategy Matrix and Weight Matrix
\(D(B,C)\) is a vague value; \(B\) and \(C\) are distances, \(D(B,C)=\sqrt{\big{(}t_{B}-t_{C}\big{)}^{2}+(f_{B}-f_{C})^{2}};J(B,C)\) is a vague value, \(B\) and \(C\) are projections, \(J(B,C)=\frac{t_{B}\cdot t_{C}+(1-f_{B})\cdot(1-f_{C})}{\sqrt{t_{C}^{2}+(1-f_{ C})^{2}}}\). Different candidate flight strategies have different degrees of satisfaction for each attribute, and decision matrix elements are used. The evaluation value of \(r_{ij}\) can be obtained by the weight of different flight strategies under each attribute. \[\omega_{ij}^{0}=\frac{W\big{(}r_{ij},r_{0j}\big{)}}{\sum_{i=1}^{5}W\big{(}r_ {ij},r_{0j}\big{)}}. \tag{3}\]  
By converting \(\omega_{ij}^{0}\) using the rows, we obtain the weight of each attribute under different candidate flight strategies, as given by Equation (4). \[\omega_{ij}=\frac{\omega_{ij}^{0}}{\sum_{j=1}^{5}\omega_{ij}^{0}}. \tag{4}\] \begin{table}
\begin{tabular}{c c c c} \hline
**Abbreviation** & **Grade** & **Vague Value Range** & **Typical Vague Value** \\ \hline Absolute (AH) & Absolutely high & [1, 1] & [1, 1] \\ Very high (VH) & Very high & [0.9, 1] & [0.9, 0.95] \\ Height (h) & High & [0.85, 0.9] & [0.8, 0.9] \\ Higher (FH) & Fairly high & [0.7, 0.85] & [0.7, 0.85] \\ Medium (MH) & Medium high & [0.6, 0.8] & [0.6, 0.8] \\ Medium (m) & Medium & [0.5, 0.5] & [0.5, 0.5] \\ Medium low (ML) & Medium low & [0.45, 0.6] & [0.4, 0.6] \\ Low (FL) & Fairy low & [0.3, 0.45] & [0.3, 0.45] \\ Low (l) & Low & [0.15, 0.3] & [0.2, 0.3] \\ Very low (VL) & Very low & [0, 0.15] & [0.1, 0.15] \\ Absolute low (AL) & Absolutely low & [0, 0] & [0, 0] \\ \hline \end{tabular}
\end{table} 
Table 1: Vague set language variables representing weight. By taking the attribute weights as elements, the decision matrix can be constructed as follows. The attribute matrix \(\mathbf{AB}\) is of the same scale as \(\mathbf{W}\). \[\mathbf{W}= \begin{bmatrix}\omega_{11}&\cdots&\omega_{1g}\\ \vdots&\ddots&\vdots\\ \omega_{s1}&\cdots&\omega_{sg}\end{bmatrix} , \tag{5}\]  
where \(\omega_{ij}\) is the attribute weight, \(1\leq i\leq s\), \(1\leq j\leq g\), and \(\sum_{j=1}^{g}\omega_{ij}=1\).
# Deep Q-Network Algorithm-Based Cyclic Air Braking Strategy for Heavy-Haul Trains
## 2 Model Construction for Heavy-Haul Trains
### Running Constraints  
The aim of this research on the circulating air braking of a heavy-haul train on a long and steep downhill section is essentially to solve a multi-constraint and multi-objective optimization problem. Considering the actual requirements of train driving control and model design, the running constraints set in this research are as follows:  
(1) Air-refill time: When a train operates on a long and steep downhill section, it necessitates the adoption of cyclic braking for speed control. To guarantee sufficient braking force in the next braking cycle, sufficient time should be ensured for refilling the air pipe to its maximum pressure [26]. In other words, the release phase must not be shorter than the minimum air-filling time \(T_{a}\), as stated in the operating guidelines.  
\[t_{j+1}^{b}-t_{j}^{r}\geq T_{a}, \tag{9}\]  
In the above formula, \(T_{a}\) is closely related to the formation of the train and the pressure drop within the train air pipe. For predetermined train parameters, the air-filling time needs to be ascertained.  
(2) Speed limit: To ensure safety, the speed of the heavy-haul train cannot exceed the speed limit \(\overline{V}\) at any point on the lengthy and steep downhill line stretch. This value often depends on the underlying framework of the railway line or the provisional setup. Additionally, the speed of the train should be greater than the minimum air brake release speed \(V_{min}^{r}\). The specified limit is designated as 40 km/h for a 20,000-ton heavy-haul train formation [19]. Therefore, the speed should meet the following requirement:  
\[V_{min}^{r}\leq v\leq\overline{V}, \tag{10}\]  
(3) Electric braking force: Heavy-haul trains are equipped with both an electric braking system and an air braking system. When the train engages electric braking, the braking current is regulated by adjusting the series excitation resistance of the electric braking system, so as to generate a continuous electric braking force to slow down the running speed of the heavily loaded train. In actual operation, the constraint is the maximum electric braking force [27], so the relative output ratio of the electric braking force should satisfy  
\[0\leq l^{d}\leq 1, \tag{11}\]
## 2 Nonlinear Consolidation  
### Nonlinear Compressibility  
On the basis of consolidation theory, the relation of void ratio and effective stress in soil is described as [38]  
\[\partial e=-a_{v}\partial\sigma^{\prime} \tag{1}\]  
where \(e\) is the void ratio, \(\sigma^{\prime}\) is the effective stress, and \(a_{v}\) is the compressibility.  
According to the relation of \(e\)-log\(\sigma^{\prime}\)[38] shown in Figure 1, the increment of void ratio can be calculated using Equation (2) for overconsolidation and Equation (3) for normal consolidation:  
\[de=-C_{s}d(\log\sigma^{\prime})=-\frac{C_{s}}{2.303}\frac{d\sigma^{\prime}}{ \sigma^{\prime}} \tag{2}\]  
\[de=-C_{c}d(\log\sigma^{\prime})=-\frac{C_{c}}{2.303}\frac{d\sigma^{\prime}}{ \sigma^{\prime}} \tag{3}\]  
where \(de\) is the increment of void ratio, \(C_{s}\) is the recompression index, \(C_{c}\) is the compression index, and \(d\sigma^{\prime}\) is the increment of effective stress.  
From Equations (1)-(3), the nonlinear compressibility, which can be calculated using the compression index or the recompression index, is obtained and expressed as  
\[a_{v}=\begin{cases}\frac{C_{s}}{2.303\sigma^{\prime}}&\text{Overconsolidation}\\ \frac{C_{c}}{2.303\sigma^{\prime}}&\text{Normal Consolidation}\end{cases} \tag{4}\]  
Figure 1: The diagram of \(e\)-log\(\sigma^{\prime}\) relation.
## 1 Introduction
This synergistic integration enables GLARA to effectively bridge global semantic consistency and local behavioral dynamics, leading to more accurate and adaptive recommendations. Experimental results on two benchmark datasets demonstrate that our model significantly outperforms state-of-the-art baselines, especially in long-tail recommendation and dynamic preference modeling scenarios. The main contributions of this work are summarized as follows:  
* We propose GLARA, a novel recommendation framework that combines a Virtual Relational Knowledge Graph (VRKG) with a Graph Attention Network (GAT) to address both global semantic sparsity and local dynamic preference modeling. * We design a hierarchical co-optimization architecture, where the global layer employs a Local Weighted Smoothing (LWS) strategy to align semantically related node embeddings, and the local layer uses attention-based interaction modeling to capture time-sensitive user interests. * We conduct extensive experiments on two benchmark datasets (Last.FM and MovieLens-1M), and the results demonstrate that our model consistently outperforms state-of-the-art baselines in terms of both accuracy and robustness, particularly in long-tail and cold-start scenarios.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Deep Belief Network  
The deep belief network (DBN) is a deep learning model that was introduced by [PERSON] et al. in 2006 [61]. It is a probabilistic generative graphical model created to overcome the limitations of traditional models, such as slow convergence and convergence to a local minimum, among others.  
A DBN is composed of restricted Boltzmann machines (RBMs), in which each one presents two layers, visible and hidden. In this case, each hidden layer acts as a visible layer to the next RBM. The first two layers present indirect connections that create an associative memory. On the other hand, the lower layers present direct connections [62].  
The training process follows a greedy approach so that each layer is adjusted until the global optimum is found, starting from the visible layer of the RBM. Then, the hidden layer is tuned, and its outputs are used as inputs to the next RBM layer until the end of the network [63].
# Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction
## 2 Review of General Studies of Unusual Animal Behavior in Association with EQs  
\"Unusual animal behavior\", which has been known since a long time ago as a macroscopic phenomenon, is regarded as a classical branch of short-term EQ prediction [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20], even though its mechanism is poorly understood [14; 21]. Based on many anecdotal and retrospective data in different countries, [PERSON] (2001) [21] summarized unusual animal behaviors in terms of the three important plots: (i) EQ magnitude (M) versus D, (ii) log\({}_{10}\) T versus M, and (iii) the occurrence histogram of log\({}_{10}\) T using two parameters (epicentral distance (D) of an anomaly and lead time (T)). He found not only short-term precursors, but also medium-term (with a lead time of a few months) and imminent (a lead time of the order of one day) precursors. Further, he suggested different candidates of animal sensory mechanisms including not only conventional mechanical (ground motion), chemical, but also electromagnetic environmental changes, and he paid more attention to electromagnetic effects.  
In an attempt to better understand the animal sensory mechanism, [PERSON] (2013) [22] performed an extensive comparison of [PERSON]'s statistical results on general unusual animal behavior with those of various seismo-electromagnetic effects. Electromagnetic effects include radio emissions in different frequency ranges (DC, ULF (ultra-low frequency)/ELF (extremely low frequency), VLF (very low frequency)/LF (low frequency), HF (high frequency) bands) and seismo-atmospheric and seismo-ionospheric perturbations described by many scientists during the last few decades (as summarized in [PERSON] and [PERSON] (2008) [6] and [PERSON] and [PERSON] (2010) [2]). In this study, he paid particular attentios to the three plots by [PERSON] [21] mentioned above, and he came to the conclusion, by extensive comparison of temporal evolutions, that lower frequency radio emissions (such as ULF lithospheric radiation and ULF/ELF atmospheric radiation) are a plausible candidate as a sensory mechanism of animals among many possibilities, suggesting a close link between unusual animal behavior and electromagnetic effects.  
Several works on the observations of unusual animal behavior followed [23; 24; 25; 26; 27; 28], and further a laboratory experiment was performed by [PERSON] et al. (2010) [29], who applied the ULF/ELF radio waves on lizards resulting in a behavioral change of the lizards, giving support to [PERSON]'s (2013) [22] conclusion.
## 1 Introduction
The contribution of this work is that the final proposed system does not require the selection of a ROI as others methods have reported in the literature [18, 20, 23, 24, 25, 26, 34]. In addition, to the best of our knowledge, this is the first time that a CNN is trained using tagged frames as inhalation and exhalation, instead of a raw respiratory rate signal used to train other CNN strategies [18]. Our tagging strategy for training the CNN uses only two classes and is simple to implement. Table 1 puts into context our proposal with respect to the other important works in the literature. The paper is organized as follows. Section 2 presents the methodology of the proposed system including the description of the motion magnification technique, the two training strategies, and the respiratory rate measuring method. Section 3 presents the experimental protocol and the results obtained from the trained CNN. Finally, conclusions are given in the last section. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & **Choice of ROI** & \multirow{2}{*}{**Reference**} & **Signal to estimate de RR** & \multirow{2}{*}{**Method**} & **Nb of Subjects** & **Metric Error** \\ \hline [PERSON] et al. [29] & \multirow{2}{*}{Manually} & Visual (from Mag. & Frame Binary signal, & Amplitude & \multirow{2}{*}{4} & \multirow{2}{*}{MAE} \\  & & Video) & Inhalation–Exhalation & & & \\ \hline [PERSON] et al. [20] & \multirow{2}{*}{Manually} & Electronic Device & Respiratory Signal & Filtering on original video & \multirow{2}{*}{10} & \multirow{2}{*}{MAE, BA} \\  & & & Video) & Inhalation–Exhalation & Amplitude & & \\ \hline [PERSON] et al. [30] & \multirow{2}{*}{Manually} & Visual (from Mag. & Binary signal & Amplitude & \multirow{2}{*}{1} & MAE, BA, \\  & & Video) & Inhalation–Exhalation & & & SCC, PCC \\ \hline [PERSON] [24] & \multirow{2}{*}{Automatic} & Visual (from Mag. & \multirow{2}{*}{Respiratory Signal} & Amplitude & \multirow{2}{*}{10} & \multirow{2}{*}{MAE} \\  & & Video) & & & & \\ \hline [PERSON] et al. [25] & Automatic & Electronic Device & Respiratory Signal & Phase magnification & 6 & RMSE \\ \hline [PERSON] et al. [26] & \multirow{2}{*}{No ROI} & Visual (from Mag. & Binary signal & Phase magnification, & \multirow{2}{*}{1} & \multirow{2}{*}{MAE} \\  & & Video) & Inhalation–Exhalation & Peak detections & & \\ \hline [PERSON] et al. [18] & \multirow{2}{*}{Automatic} & Electronic Device & Respiratory Signal & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & Frame difference & \multirow{2}{*}{25} & \multirow{2}{*}{MAE} \\  & & & CNN & & & \\ \hline Our proposal & No ROI & Visual (from Mag. & Binary signal, & Amplitude & \multirow{2}{*}{10} & \multirow{2}{*}{MAE, BA, \\  & & Video) & Inhalation–Exhalation & Magnification CNN & & \\  & & & & & PCC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Principal motion detection works to detect respiratory activity.
## 5 Experimental Validation  
### Scale Model of the IRW Vehicle  
Experimental validation is crucial for verifying the efficacy of the active steering control strategy in real-world deployments. We designed a scale model of an IRW vehicle according to [PERSON]'s similarity laws [27] at a scale factor of 1:5, as shown in Figure 10a. The electromechanical structure of the scale vehicle comprises two sets of independently rotating wheelsets and subframes, 600 W PMSMs as drive motors, right-angle gear reducers, and primary and secondary suspension systems. Each IRW, mounted on a subframe through axle boxes, connects to the subframe via primary suspension. The drive motors exert control torque through gear reducers, combing traction and steering. The car body is connected to the subframe through secondary suspension and equipped with onboard devices such as controllers, a data acquisition PC, motor drives, and power inverters.  
Figure 9: Curved line cosimulation results (\(V_{0}\) = 150 km/h, \(R_{c}\) = 1500 m). (**a**) Lateral displacement of the wheelsets, (**b**) Yaw angle of the wheelsets, (**c**) Wear number of the wheelsets, (**d**) Controller output.  
Each subframe of the scale model is equipped with two laser displacement sensors that track the movement of the IRW. The sensors measure the wheelset's lateral displacement and yaw angle, defined as the lateral position and angular deviation of the wheelset's geometric center from the track's centerline, consistent with the approach used in [2]. Wheel rotation is captured by 12-bit incremental encoders. The differentials of displacement, yaw angle, and speed difference are essential for the controller and are determined by applying backward difference calculations to the measured values. This method approximates the derivatives of these variables in discrete-time signals. The MARL algorithm is deployed on an NVIDIA Jetson Nano embedded computer. The policy networks for the two collaborating agents are trained using the PER-MADDPG algorithm within the simulated environment of the scale IRW model, as illustrated in Figure 10. The inference applications of deep neural network models are optimized using TensorRT to enable real-time control of the GPU during scaled vehicle operation.
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
## 4 Results
### Simulation Results  
#### 4.3.1 Efficiency  
Figure 7 shows the efficiency metric collected among all test runs. The \(X\) axis is a table size (a number of entries), and the \(Y\) axis is the efficiency measured as a success rate.  
\[E=\frac{N_{successful}}{N_{complete}} \tag{10}\]where \(N_{complete}\) is a total number of complete (defragmented) IP packets and \(N_{successful}\) is a number of IP packets successfully defragmented by the given configuration (e.g., 16 entries one-way).  
Efficiency data on Figure 7 demonstrates that higher bandwidth imposes more \"stress\" to the model, which results in lowering the efficiency (e.g., lowest score being 0.8 for \(R=0.9\) vs. 0.92 for \(R=0.1\)) but at the same time reduces the spread of the results.  
The main observation is that the efficiency results support the research hypothesis: the IP defragmentation algorithm is highly efficient with relatively small table size. If the one-way table is excluded from the consideration, the efficiency of 95% is reached with the table size of 128.  
The second conclusion is that there are diminishing returns from increasing the associativity beyond 4. Technical challenges that haunt the highly associative memories increase the relevance of minimizing this parameter.
## 3 Proposed System Architecture
### Object Detection
#### 3.1.1 Mixture of Gaussians (MoG) Model  
In this part, the mixture of Gaussians (_MoG_) using the low-rank matrix factorization model [12] is selected to perform foreground and background segmentation. Using this model, knowledge from previous frames is learned and updated frame by frame. The probabilistic model of _MoG_ noise distribution in the low-rank matrix factorization form in each successive frame is briefly introduced in Equation (1):  
\[x_{i}^{t}\sim\prod_{k=1}^{K}\left(x_{i}^{t}\Big{|}(u_{i})^{T}v_{i},\sigma_{k}^{ t}\right)^{2^{i}_{k}},z_{i}^{t}\sim\text{Multi}\Big{(}z_{i}^{t}\big{|}\Pi\Big{)} \tag{1}\]  
where \(x_{i}^{t}\) signifies the \(i\)th pixels of \(x_{t}\), \(k=1\ldots,K\) represents the number of Gaussians, N means the number of random variables, \(u_{i}\) means the \(i\)th row vectors of low-rank properties; \(U\)(basis), and \(v_{i}\) are the \(V\)(coefficient) matrices. In addition, \(\sigma\) and \(\Pi\) are the variances and mixture rate, respectively. Multi refers to the multinomial distribution. Then, the _MoG_ model is utilized by implementing the expectation maximization (_EM_) algorithm on a new frame sample \(x_{t}\) for updating parameters for foreground and background [12]. However, the resultant _MoG_ cannot give the optimal solution for background subtraction, and ghost effects occur around the foreground object. In real-life video sequences, much redundant data occurs, as when foreground objects move very slowly or remain in place for a long period. In such situations, the system cannot always recognize a person as the foreground when the person comes into the frame and sits there for a long time. This has been a recurrent problem, but to address this issue, graph theory is used to refine the foreground.