esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 3 System Model and Problem Formalization
### Task Offloading Problem Formalization  
The delay of vehicle task offloading is influenced by several factors. By referring to the formula for calculating the offloading delay to the RSU or locally in [17], we derive the formulas for calculating the offloading delay to the BS, RSU, and locally.  
In the proposed system, the tasks can be assigned to the RSU, BS and local for computation. Denote \(\alpha\) as the proportion of the task allocated to the RSU, \(\theta\) represents the proportion of the task allocated to the BS, \(\gamma\) denotes the proportion of the task allocated to the local. The data size of the task is \(s\). Assume that the vehicle's transmission power is fixed at \(P_{v}\). The CPU frequency for task processing at the RSU is \(f_{r}\), at the BS is \(f_{b}\), and when processing tasks locally, it is \(f_{v}\). Let the number of CPU cycles required to complete the computation task be \(C=ks\), where \(k\) represents the computational complexity of the task. The main notations are summarized in Table 1.
## 3 LDEM Simulation  
### Model Description and Calibration  
The simulated LDEM model consists of a cube with 300 mm edges, composed of cubic modules with edge length \(L=4\) mm, resulting in 75 modules along each axis. The model's vertical displacement is fixed at the solid's lower face (the horizontal plane at \(y=-150\) mm), which corresponds to the area in contact with the hydraulic press's lower steel plate in the experiments, while its upper face (the horizontal plane at \(y=+150\) mm) suffers a prescribed vertical displacement to simulate the experimental load. This arrangement is illustrated in Figure 11a.  
Figure 10: Partial counts related to: (**a**) frontal face of the cube (Lunitek sensors L1 and L2, Mistras sensor M1) and; (**b**) back face of the cube (Lunitek sensors L5 and L6, Mistras sensor M2) (see Figure 4b).  
The material had the following mechanical properties: mass density \(\rho=\)2400 kg/m\({}^{3}\), Young's modulus \(E=\) 40 GPa, and fracture energy \(G_{f}=\) 200 N/m. Considering \(d_{eq}=\) 0.045 m and using expression (5), the constitutive law's critical strain \(\varepsilon_{p}\) (Figure 3b) is:  
\[\varepsilon_{p}=\left[200/\left(40.10^{9}\times 0.045\right)\right]^{0.5}=3.33 \times 10^{-4} \tag{7}\]  
Thus, the expected upper bound for the global tensile failure stress is \(E\varepsilon_{p}=\) 13.3 MPa, which could be reduced by about 50% due to the interaction between the structure's boundary conditions and the random distribution of the fracture energy. On the other hand, the compressive strength reached during the experiment was 45.75 MPa. In general, quasi-brittle materials tend to present failure global tensile stresses close to 10 times lower than the global compressive stress. Then, for this example, the tensile stress is about 5 MPa, which is compatible with the upper estimated here.  
Regarding the 3D random field for determining \(G_{f}\), the adopted correlation length (\(L_{c}\)) was \(L/2\) for all three directions. The coefficient of variation, \(CV_{Gf}\), was taken as 50%. For further information about the methodology followed in the calibration, see [43].
# Novel Predictive Model of the Debonding Strength for Masonry Members Retrofitted with FRP
## 2 Shear Strength Contribution of FRP  
The shear strength of FRPs is dependent on several factors, e.g., width of the FRP strip (\(b_{p}\)), thickness of the FRP strip (\(t_{p}\)), tensile strength of the masonry block (\(f_{ut}\)), Young's modulus of the FRP (\(E_{p}\)), width of the masonry block (\(b_{m}\)), and bonded length (\(l_{b}\)).  
Table 1 shows several bond strength models which can be utilized for masonry. To improve the models for utilizing with masonry, the compressive strength (\(f_{cm}^{\prime}\)) is represented as a function of the tensile strength (\(f_{ut}\)) of masonry in \(MPa\) units as:  
\[f_{ut}=0.53\sqrt{f_{cm}^{\prime}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Equation (Units: N, mm)** & **References** \\ \hline [PERSON] & \(P_{\max}=l_{b}\,b_{p}(6.13-\ln l_{b})\) & [40] \\ \hline [PERSON] & \(\begin{array}{l}P_{\max}=2.68\times 10^{-5}\left(f_{cm}^{\prime}\right)^{0.2} \,E_{p}\,t_{p}l_{e}(b_{p}+7.4)\\ l_{e}=1.89\left(E_{p}\,l_{p}\right)^{0.4}\,if\,l_{b}>l_{e}\,:\,l_{e}=l_{b} \\ \end{array}\) & [41] \\ \hline [PERSON] & \(\begin{array}{l}P_{\max}=0.93\left(f_{cm}\right)^{0.44}l_{e}\,b_{p}\\ l_{e}=0.125\left(E_{p}\,l_{p}\right)^{0.57}\,if\,l_{b}>l_{e}\,:\,l_{e}=l_{b} \\ \end{array}\) & [41] \\ \hline [PERSON] & \(\begin{array}{l}P_{\max}=\left(0.5+0.08\sqrt{0.01E_{p}\,l_{p}/f_{ut}}\right) \,L_{e}\,b_{p}f_{ut}/2\\ l_{e}=100\,\mathrm{mm}\end{array}\) & [41] \\ \hline [PERSON] & \(\begin{array}{l}P_{\max}=\left\{\begin{array}{l}0.64k_{p}\,b_{p}\sqrt{E_{p} t_{p}f_{ut}}\\ 0.64k_{p}\,b_{p}\sqrt{E_{p}t_{p}f_{ut}}\,\alpha\end{array}\right.\begin{array}{ l}l_{b}\geq l_{e}\\ l_{b}<l_{e}\end{array}\right.\\ \end{array}\) & [42] \\ [PERSON] & \(\begin{array}{l}\alpha=\left(\frac{l_{e}}{l_{e}}\right)\left(2-\frac{l_{e} }{l_{e}}\right)\end{array}\) & \(\begin{array}{l}l_{e}=\sqrt{\frac{E_{p}t_{p}}{2f_{ut}}}\\ \sqrt{2f_{ut}}\end{array}\) & [42] \\  & \(k_{p}=\sqrt{1.125\frac{2-b_{p}/b_{p}}{1+b_{p}/400}}\) & \\ \[PERSON] & \(\begin{array}{l}P_{\max}=1.45\varphi_{p}^{0.268}f_{ut}^{0.6}\sqrt{l_{per}E_{ p}l_{p}l_{b}}\\ \varphi_{f}=1/(2+b_{p})\,\mathrm{for\,EB\,case}\end{array}\) & [43] \\  & \(\begin{array}{l}L_{per}=2+b_{p}\end{array}\) & \\ \hline [PERSON] & \(\begin{array}{l}P_{\max}=13.69\varphi_{f}^{0.84}f_{ut}^{0.9}\sqrt{l_{per}E_{ p}l_{p}l_{b}}\\ \varphi_{f}=1/(2+b_{p})\,\mathrm{for\,EB\,case}\end{array}\) & [44] \\  & \(\begin{array}{l}L_{per}=2+b_{p}\end{array}\) & [40] \\ \hline [PERSON] & \(\begin{array}{l}P_{\max}=110.2\times 10^{-6}\,E_{p}\,t_{p}l_{e}\,b_{p}\\ l_{e}=e^{6.134-0.58\times\ln\left(E_{p}\,t_{p}\right)}\quad\left(\text{unit of $E_{p}$ is GPa}\right)\end{array}\) & [40] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical models for the evaluation of the (fiber-reinforced polymer) FRP-masorny bond strength.
# Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
## 2 Materials and Methods
### Study Sites and Forest Data Collection
#### 2.1.2 Znp--Zahamena National Park, Madagascar  
Zahamena National Park (ZNP) comprises a total area of 64.37 km\({}^{2}\), with 42.30 km\({}^{2}\) of National Park and 22.10 km\({}^{2}\) of Strict Nature Reserve (similar protection status as BTP, located at 17\({}^{\circ}\)37\({}^{\prime}\)45\({}^{\prime\prime}\) S, 48\({}^{\circ}\)43\({}^{\prime}\)30\({}^{\prime\prime}\) E). ZNP is isolated in an area of Madagascar that has virtually no infrastructure (roads, utilities, other facilities) and only little tourism. First established as a Strict Nature Reserve in 1927, its boundaries were amended a few times before the August 1997 Malagasy government decree that established its present-day boundaries. However, it was not until 2001 that ZNP achieved its current national park status [24]. Because of its high biodiversity, 85% endemism, and disappearing ecosystem type, ZNP is one of the six national parks that comprise the \"Rainforests of the Atsinanana\", which became a United Nations Educational, Scientific and Cultural Organisation (UNESCO) World Heritage Site in 2007 [25; 26]. ZNP is approximately 30 km north-west of BTP, has a similar climate, and is believed to contain a similar floral community composition (There are currently no published studies that contain a full listing of the tree species in Zahamena National Park) (see Supplementary A for more detail). The park comprises several rainforest ecosystems types, including lowland (where study plots were located), mid-elevation rainforest and mountainous rainforest. The study inventory plots were located on the eastern slopes of ZNP, in lowland rainforest.  
Due to its isolated proximity and the political turmoil that Madagascar has experienced in recent decades, there is little or no enforcement of ZNP's boundaries in terms of protection from encroachment, poaching, or burning (e.g., subsistence agriculture). In 2010, UNESCO designated the \"Rainforests of Atsinanana\" as \"In Danger\" [27]. Currently, there is no published information on the state of the primary forest or how heavily the boundaries of ZNP have been logged or burned.
## 7 Discussion and Analysis
### Performance and Computational Efficiency Trade-Offs  
While our proposed approach demonstrates superior accuracy for emotion recognition in children with autism, its practical deployment in intervention settings necessitates the evaluation of its computational efficiency alongside performance benefits.  
1. Computational Complexity Comparison  
Vision transformers and convolutional neural networks represent different approaches to feature extraction with distinct computational profiles. According to [PERSON] et al. [39], ViT models exhibit quadratic computational complexity O (n\({}^{2}\)) with respect to token count, creating substantial overhead for image processing. For standard \(224\times 224\) pixel inputs with \(16\times 16\) patches, this results in processing 196 token sequences through multiple transformer layers.  
The CvT architecture addresses this limitation through a hierarchical design that progressively reduces spatial resolution [40]. Their analysis demonstrates that CvT-13 achieves higher accuracy than ViT-B/16 while requiring approximately 20% fewer parameters (20 M vs. 86 M) and 30% less computational overhead (4.5 G vs. 17.6 G FLOPs). This efficiency gain stems from two key factors as follows: convolutional token embedding that downsamples spatial dimensions and convolutional projection layers that introduce inductive biases beneficial for visual tasks.  
2. Efficiency-Performance Balance in Practice  
Hybrid architectures combining convolutional operations with transformer elements consistently demonstrate favorable efficiency-performance trade-offs. [PERSON] et al. [41] showed that such models achieve up to 5\(\times\) faster inference speed compared to pure transformer approaches at comparable accuracy levels. Similarly, [PERSON] et al. [42] demonstrated that compact transformer designs incorporating convolutional principles can maintain competitive performance while significantly reducing parameter count.  
These findings align with our experimental results, where the CvT model outperformed the pure transformer approach (ViT) by 31.12% in accuracy for facial expression recognition. This substantial performance gap, combined with the inherent efficiency advantages of the CvT architecture, underscores the value of our approach for specialized applications like autism emotion recognition.  
3. Multimodal Fusion Considerations  
Our multimodal fusion approach necessarily requires additional computational resources compared to single-modal methods. However, the substantial accuracy improvement (90.73% for fusion versus 79.12% and 83.47% for individual modalities) represents a compelling trade-off for clinical applications where recognition precision directly impacts intervention quality. As [PERSON] et al. [32] noted, models employing hierarchical designs with local processing elements typically achieve a more favorable computation-accuracy balance for complex vision tasks.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 2 Materials and Methods
### Light Curve Representation Methods
#### 2.1.3 Variational Auto-Encoders  
The variational auto-encoder (VAE) is a stochastic auto-encoder (AE) learned in a probabilistic fashion based on the variational lower bound or evidence lower bound. The VAE framework [29] is extended to work with uniform time series on the variational recurrent auto-encoder (VRAE) proposed by [PERSON] [30]. The motivation behind this is that VAEs are deep generative models trained on an unsupervised scenario, learning latent variable representation as it trains. These variables are learned through the observed distribution, so they are built to adapt to the variations in the behavior of the data. This is the main difference of standard (deterministic) AEs that learn an invariable specific point for the input pattern [31].  
The use of the VRAE on different time series applications is associated with anomaly detection [32; 33; 34], with the objective of detecting outliers. It usually compares the reconstructed (or generated) input with the original values and sets some threshold of tolerance to normal behavior. The sampled values from the latent distribution have smooth transitions [29], so the reconstructed data should reduce the bias of the specific patterns [34]. In addition, a VAE for generating transit-shape light curves as a data augmentation technique was presented by [PERSON] et al. [35].  
In the work of [PERSON] et al. [36], the importance of disentangled representations for _better_ unsupervised learning was discussed--_better_ in the sense that the representation should contain the information of the data in a compact and interpretable structure, among other points. In [37], a good representation was defined as a representation that discovers and disentangles some underlying factors in variation that the data may reveal. These should be explanatory factors of the data that are a priori unknown and need to be learned. [PERSON] et al. [36] showed that the VAE regularization also aims to achieve this.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Part 2: Domain Data Preparation and Splitting
#### 2.4.3 Spectrum Creation  
Since the rotor unbalance exhibits a clear pattern in the frequency domain, time-domain signal samples were transformed into spectra using a Fast Fourier Transform (FFT). Rotational speed was included as an additional scalar feature, concatenated to the FFT feature vector, providing context for learning speed-dependent vibration patterns.
## 4 Experimental Studies
### Results Analysis and Discussion
#### 4.3.3 Statistical Results of Solutions  
(1) Wilcoxon rank sum test: A Wilcoxon rank sum test [50] is a nonparametric sample test that is based on the rank order of samples and not the average value, which is used to compare the difference between the distribution positions of two populations to which two independent samples belong. In this paper, the nonparametric Wilcoxon rank sum test was used to measure the statistical significance difference between the proposed MLDMS-PSO and the other comparison algorithms. In this experiment, the distribution of the results obtained by the MLDMS-PSO algorithm and the other PSO variants on all the benchmark functions through 51 simulation runs on the 10-D, 30-D, and 50-D problems were compared, in which its significance level was 0.05. The statistical results are recorded in Table 4. The symbols \"N+\", \"N\(-\)\", and \"N\(=\)\" indicate the number that MLDMS-PSO was significantly better than, significantly worse than, and almost the same as the corresponding competitor algorithm, respectively. The comprehensive performance (_CP_) was equal to the sum of \"N+\" minus the sum of \"N\(-\)\". Table 4 shows that MLDMS-PSO was significantly better than the other PSO variants on most 10-D, 30-D, and 50-D problems. This view is also supported by the fact that all values of \"N+\" were greater than \"\(N-\)\". In addition, the _CP_ of all the comparison algorithms was positive, which also showed that the new proposed MLDMS-PSO had the best comprehensive performance. HCLDMS-PSO performed well on the 30-D and 50-D test functions with _CP_ values of 3 and 2, respectively, and its performance was close to that of MLDMS-PSO. However, in the 10-D problems, its performance was significantly weaker than MLDMS-PSO. (2) Friedman Test: In this paper, the Friedman test of the mean was used to compare the overall performance of MLDMS-PSO and the other comparison algorithms on different benchmark functions. The Friedman test results of the 10-D, 30-D, and 50-D problems are given in Table 5, listing each algorithm and its ranking (ascending order). Finally, a comprehensive ranking based on the mean of the results was used to measure the overall performance of all of the algorithms. From Table 5, we can see that the proposed MLDMS-PSO algorithm in this paper ranked first in the 10-D, 30-D, and 50-D problems and obtained a better overall performance than the other comparison algorithms, which was the same as the results obtained by the Wilcoxon rank sum test. In detail, TSLPSO and CCPSO-ISM ranked behind MLDMS-PSO in the 10-D problems, and HCLDMS-PSO and PSO-DLS ranked second and third in the 30- and 50-dimensional problems, respectively. This is because the HCLDMS-PSO and PSO-DLS could obtain better population diversity during the evolution than TSLPSO and CCPSO-ISM, strengthening the exploration ability of the algori
# LMI Pole Regions for a Robust Discrete-Time Pole Placement Controller Design
## 2 Preliminaries and Discrete-Time Pole Region Problem Formulation  
The basic aim of the control design is to modify system dynamics so that the corresponding closed loop system reaches the required performance. The considered uncertain linear dynamic system can be described by its continuous-time or discrete-time model. When the discrete-time controller is applied, it can be designed for a continuous-time system model and then recalculated to a discrete-time one. However, such an approach requires a sufficiently small sampling period, otherwise the closed loop system performance can be significantly distorted, [17]. It is therefore advantageous to stick to the discrete-time system representation. In this paper we consider the uncertain discrete-time system described in state space by a system of difference equations, written in a matrix form as follows:  
\[x(k+1)=A(\infty)x(k)+B(\infty)u(k), \tag{1}\]  
where \(x(k)\in R^{n}\), \(u(k)\in R^{m}\) are system state and control vectors, respectively. Matrices \(A(\infty),B(\infty)\) belong to a convex polytopic uncertainty domain with \(N\) vertices  
\[(A(\infty),B(\infty))\in\{(A(\infty),B(\infty))=\sum_{i=1}^{N}\infty_{i}\;( A_{i},B_{i}),\sum_{i=1}^{N}\infty_{i}=1,\alpha_{i}\geq 0\}. \tag{2}\]  
It is assumed that all states are accessible for the state feedback control  
\[u(k)=Kx(k). \tag{3}\]
## 1 Introduction
Previous studies have considered travel time, localization accuracy, reproduction of the assumed route, interpretation of control signals, and interference as the main criteria for evaluating navigation. However, most of these works did not take into account the impact of the control system on energy consumption. Research to date has primarily focused on refining vehicle control algorithms, while comparatively little attention has been given to comparing the operational parameters of guided vehicles using different sensors for optical navigation. The authors' research represents an important contribution to the study of the effectiveness of various AGV navigation systems. Unlike previously presented studies, the authors emphasized energy consumption as an additional factor in evaluating a vehicle's navigation system, which constitutes the primary novelty of this work.
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## Data and Methodology  
### In-Situ Rain Data  
The in-situ meteorological stations of the very reliable [30] dense and well-distributed network operated by the Cyprus Department of Meteorology were used for ground validation. Daily and hourly complete data records from 136 rain gauges were used for the study period (see Figure 1). These gauges are distributed in such a way so as to cover the whole study area, including not only coastal, urban and agricultural areas, but also the hilly and mountainous areas. It is worth noting that data underwent quality control prior to the present analysis.  
The average annual total precipitation increases up the southwestern windward slopes from 450 mm to nearly 1100 mm at the top of the central massif. On the leeward slopes amounts decrease steadily northwards and eastwards to between 300 and 350 mm in the central plain and the flat southeastern parts of the island [34].  
The annual average rainfall, covering the period 1951-1980, is 480 mm, exhibiting a decreasing trend in the last 30 years [34]. Furthermore, rainfall in the warmer months contributes little or nothing to the annual precipitation amounts.  
It should be noted at this point that data from four meteorological stations are provided by the Cyprus Department of Meteorology to be incorporated in the TRMM and GPM estimates. These four stations are not representative of the country as a whole. Furthermore, they barely make up 3% of the data used herein, hence, the are not expected to have an impact on the objectivity of the study.
# Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network
## 3 Method
### Loss Function  
The training data are a pair of images composed of a sketch and corresponding paintings; as shown in Figure 3. We use the loss function to define the gap between the generated image and the real paintings, and the model is then trained by minimizing the loss function. We use both L1 loss and adversarial loss to train the generative model.  
\[\mathcal{L}_{L1}(G)=\mathbb{E}_{x,y\sim p_{data}(x,y),z\sim p_{z}(z)}[\|y-G(x, z)\|_{1}] \tag{6}\]  
L1 loss is a good way to reconstruct low-frequency information, while adversarial loss can reconstruct high-frequency information very well, so it can produce realistic images by combining L1 loss (Equation (6)) and adversarial loss (Equation (2)). Our final objective is below.  
\[G^{*}=\arg\min_{G}\max_{D}\mathcal{L}_{cGAN}(G,D)+\lambda\mathcal{L}_{L1}(G) \tag{7}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{6}{c}{**Discriminative Model**} \\ \hline
**Name** & **Kernel Size** & **Stride** & **Pad** & **Norm** & **Activation** \\ \hline Conv1 & \(4\times 4\times 3\times 64\) & 2 & 1 & - & Leaky \\ Conv2 & \(4\times 4\times 64\times 128\) & 2 & 1 & BN & Leaky \\ Conv3 & \(4\times 4\times 128\times 256\) & 2 & 1 & BN & Leaky \\ Conv4 & \(4\times 4\times 256\times 512\) & 2 & 1 & BN & Leaky \\ Conv5 & \(4\times 4\times 512\times 512\) & 2 & 1 & BN & Leaky \\ Conv6 & \(4\times 4\times 512\times 512\) & 2 & 1 & BN & Leaky \\ Conv7 & \(4\times 4\times 512\times 1\) & 2 & 1 & BN & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discriminative model network architectures. Conv means Convolutional layer; BN means Batch Normalization; Leaky means leaky rectified linear unit.
## 2 Literature Review
### ETMS Regional--Sweden  
This system was developed in cooperation with the International Union of Railways (UIC); the Swedish Transport Administration, Trafikverket; and with the Bombardier company [18]. ERTMS Regional is a simpler and cheaper version of the ERTMS/ETCS L1 and L2 systems as a suitable option for low-traffic lines. The designed system is operated on a single track of a 143 km overall length with five stations and a density of sixteen trains per day. The UIC plans further extension of the track to a total length of 565 km. The basic principle is a periodic (every 6 s) transmission of information about the train position to the Train Control Center--TCC--which grants the train drive permits. Information about the train position is obtained from an odometer; the position is further specified by means of balises installed at stations and on the track with a fixed spacing of five kilometres. Mobile network GSM-R and GSM are used for the information transfer.