esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Cognitive Load Management (Based on Cognitive Load Theory)
#### 4.2.2 Enhancing Essential Elements to Increase Germane Load  
In a VR language learning setting, reducing distractions is paramount. However, it is just as vital to spotlight elements that directly aid in mastering the language, thereby bolstering the relevant cognitive load. The relevant cognitive load pertains to the mental energy expended on assimilating and processing new data, which translates to linguistic material in this scenario.  
In a VR framework, this can be realized by underscoring pivotal linguistic components, terms, and cultural subtleties. For instance, lucid visual indicators or recurring motifs can assist learners in recognizing and retaining fresh terms. Likewise, engaging simulations that echo everyday situations can be crafted to emphasize distinct grammatical facets, ensuring that learners grasp and utilize them in a realistic context.  
Sound-based tools such as distinct pronunciation aids or diverse dialect can be merged to facilitate auditory comprehension and accent mastery. By delivering on-the-spot feedback within these simulations, learners can promptly rectify errors, solidifying the accurate linguistic patterns.  
It is also advantageous to weave cultural aspects into the VR landscape. By submerging learners in settings that faithfully represent a culture, they attain a more profound grasp of the societal backdrop of the language, rendering their educational voyage more comprehensive and resonant. Fundamentally, a learner's mental capacity is adeptly channeled by enhancing these crucial components, refining the educational trajectory within the immersive VR domain.
## 3 Data-Driven Robust Scheduling Method
### Data-Driven PSO Algorithm with Uncertain Parameters
#### 3.2.1 Encoding Scheme of AJSSP  
In the design of heuristic algorithms, the term _chromosome_ represents the encoding of a solution. A reasonable chromosome representation is the primary task for the successful application of heuristic algorithms to solve actual production scheduling problems. In order to make the PSO algorithm applicable to the discrete AJSSP, we propose a discrete chromosome coding and initialization method based on the adjacency matrix to represent sequential constraints of the product.  
To make the description about the adjacency matrix easier to follow, we assume that one product (i.e., product A) will be scheduled, and its assembly structure is shown in Figure 1. First, we establish the corresponding adjacency matrix \(A=\left(a_{ij}\right)_{n\times n}\) of product A to represent the sequential constraints (see Figure 4). The row and column numbers of the matrix \(A\) represent the numbers of all parts/subassemblies to be scheduled, and A\({}_{0}\) is a virtual node that represents the end of assembly. In the adjacency matrix \(A\), if the value of element \(a_{ij}\) is not equal to 0, it means that A\({}_{i}\) is a lower level part of A\({}_{j}\), i.e., A\({}_{j}\) can only start after all operations of A\({}_{i}\) are completed. The value of \(a_{ij}\) represents the number of operations of part A\({}_{i}\) that have not yet been processed. In the initialization and repair process of DPSO, one can continuously search and update the adjacency matrix \(A\) to avoid generating an infeasible schedule.  
Figure 3: Diagram of the proposed DPSO algorithm framework.  
A discrete chromosome representation decodes a schedule of AJSSP with a positive integer string O, which defines the permutation of jobs, i.e., parts and subassemblies. The O-string consists of a sequence of job numbers. The number of job \(i\) occurs \(n_{i}\) times in the O-string, where \(n_{i}\) is the number of operations included in job \(i\). Therefore, the lengths of the O-string is equals to the sum of all operations of all jobs to be scheduled. Table 2 shows a feasible chromosome of product A. In the decoding process, the O-string is converted to a sequence of operations, and then each operation is assigned to the prespecified machine.
# Performance-Based Analysis of Single-Layer Cylindrical Steel Reticulated Shells in Fire
## 4 Fire Temperature Field Analysis of the Cylindrical Latticed Shells  
### Temperature Calculation of the Steel Members  
Latticed shells are comprised of thin-walled circular steel tubes with a high \(F/V\), which causes the heat transferred to the steel members in and around the same section to, basically, be the same. It is assumed that the temperature of each point in the same cross section of the steel members is the same without the consideration of the heat conduction inside the members. Therefore, the temperature rise of the steel members can be calculated by simplified calculation method proposed in the Chinese Code: the Technical specifications for the fire protection of steel structures in buildings [21]. For the light steel members with uniform temperature distribution, the iterative method is widely used in the calculation of temperature rise. The iterative calculations for the temperature of steel members are carried out by Equations (4)-(7):  
\[T_{s}(t+\Delta t)=K\cdot\frac{1}{\rho_{s}\cdot c_{s}}\cdot\frac{F}{V}[T_{g}(t) -T_{s}(t)]\cdot\Delta t+T_{s}(t) \tag{4}\]  
\[\Delta T_{s(t+\Delta t)}=K\cdot\frac{1}{\rho_{s}\cdot c_{s}}\cdot\frac{F}{V}[T _{g}(t)-T_{s}(t)]\cdot\Delta t \tag{5}\]  
\[K=\alpha_{r}+\alpha_{c} \tag{6}\]  
\[\alpha_{r}=\frac{\varepsilon_{r}\cdot 5.67\times 10^{-8}}{T_{g}-T_{s}}\cdot[(T _{g}+273)^{4}-(T_{s}+273)^{4}] \tag{7}\]  
where \(T_{s}\) is the temperature of the steel tubes in the latticed shells (\({}^{\circ}\)C); \(\Delta T_{s}\) is the temperature increment in the steel tubes (\({}^{\circ}\)C); \(T_{g}\) is the air temperature around the steel tubes in the latticed shells (\({}^{\circ}\)C); \(\Delta t\) is the time step, which is 5 s in this paper; \(K\) is the heat transfer coefficient (W/(m\({}^{\circ}\)C)); \(\rho_{s}\) is the density of steel (7850 kg/m\({}^{3}\)); \(C_{s}\) is the specific heat capacity of steel (600 J/(kg\({}^{\circ}\)C)); \(F\) is the area of the steel tube units exposed to fire (m\({}^{2}\)/m); \(V\) is the volume of the steel tube units exposed to fire (m\({}^{3}\)/m); \(F/V\) is section shape coefficient(m\({}^{-1}\)); \(\alpha_{c}\) is the radiation heat transfer coefficient; \(\varepsilon_{r}\) is the overall heat radiance, where \(\varepsilon_{r}\) = 0.5; and \(\alpha_{c}\) is the convective heat transfer coefficient, where \(\alpha_{c}\) = 25 W/(m\({}^{2.\circ}\)C).  
We calculated the air temperature of the latticed shell near the steel pipe tubes in different fire scenarios using FDS. Iterative calculations of the temperature of the steel tubes were carried out through the MATLAB program, which was written based on the simplified calculation method presented above.
# Shear Strength Analysis and Slope Stability Study of Straight Root Herbaceous Root Soil Composite
###### Abstract
The shear test results of the root soil composite provide a basis for the analysis of slope stability. Scholars like [PERSON] et al. employed numerical simulation methods to explore erosion resistance and soil reinforcement effects of root-soil composites, considering shear strength and vegetation growth periods [33]. [PERSON] used finite element software to simulate the influence of the root system on slope stability under different conditions such as root spacing, growth cycle, and lateral root angle [34]. [PERSON] et al. determined shear strength indicators through indoor direct shear tests of root soil composites, which were consistent with the results of finite element simulation direct shear tests. The infinite slope theory model has been used to analyze the slope stability when different distribution forms of roots act on sliding surfaces [35]. Slope instability occurs when shear stress in the slope surpasses the soil shear strength. Slope instability and failure often lead to crack formation, expansion, and aggregation. Field tests for slope instability can be challenging, and numerical simulations offer an effective alternative [36]. The key factors affecting plant slope stability include the shear and tensile properties of root-soil composites and root distribution patterns. In addition to triaxial shear tests, indoor direct shear tests are also commonly used to determine the shear strength of soil-root composites. However, discrepancies between artificially determined shear failure planes and the actual weakest shear planes may occur. Therefore, this study employs a triaxial apparatus for shear strength tests of undisturbed soil and determines the corresponding influencing factors. The impact of different root contents and depths on plant root reinforcement is considered. More specifically, consolidated undrained tests were conducted using a triaxial compression apparatus on intact powdery clay samples with and without the _Tagetes eercta_ root systems at different sampling depths. We performed significance testing, correlation analysis, and finite element simulations to assess the effectiveness of slope protection under different working conditions. The results revealed the stress-strain relationship, the contribution of different factors, and the influence of the variables on the slope stability of _Tagetes eercta_ root-reinforced soil for different soil moisture contents and root contents. The study comprehensively evaluated the efficacy of _Tagetes eercta_ root system reinforcement on powdery clay slopes, providing a scientific basis for studying and applying the erosion resistance of plant root systems and soil reinforcement effects.
## 2 Strategy  
PPP is a powerful technique that provides centimeter or decimeter level positioning accuracy with a single receiver. No more corrections are needed for PPP except for satellite orbit and clock products [8], unless the integer ambiguity resolution is to be achieved. The accuracy of satellite orbit and clock products is crucial for PPP and has essential impact on both convergence time and positioning accuracy. As one of the analysis centers of IGS MGEX campaign [9], Wuhan University (WHU) determines orbits and clocks for GPS, GLONASS, Galileo, and BDS routinely. The strategy of precise orbit and clock determination for WHU MGEX products and their quality have been presented in [10,11]. The MGEX products of WHU (indicated as WUM) are used in this study.
# 3D Shape Generation via Variational Autoencoder with
## 2 Related Work
### 3D Shape Generation with Signed Distance Functions  
The signed distance function (SDF) is a data representation that has recently gained popularity in the computer vision and graphics domain due to its ability to overcome certain limitations of voxels, point clouds, and meshes when used as input data for CNNs. SDF represents the structural relationship of distances on 3D surfaces and has been used to train various deep learning algorithms, such as GANs, VAEs, and AD algorithms, to generate detailed 3D mesh-based shapes. In the field of 3D mesh-based shape generation, [PERSON] and [PERSON] (2016) proposed a 3D-GAN-based hierarchical detail enhancing mesh shape generation using SDF data representation. They generated SDF fields on a gridded domain to create higher-quality polygon mesh surfaces, but their network was limited by being driven by uninformative random vectors and trained only on 3D data. [PERSON] et al. (2017) proposed a 3D-VAE-GAN-based framework that mapped point clouds directly to other 3D shape representations. [PERSON] et al. (2018) proposed SDF-StyleGAN, which extends the popular generative model for 2D images, StyleGAN, to the 3D domain using an implicit SDF-based approach. The network generates new shapes by sampling from the learned SDF representation and then extracting the surface mesh. SDF-StyleGAN is trained using a combination of adversarial and reconstruction losses to produce high-quality 3D shapes with controllable attributes. However, 3D-VAE-SDFRaGAN has an advantage in generating a greater variety of shapes by interpolating between different SDF representations. In contrast, our proposed 3D-VAE-SDFRaGAN framework is trained on both 2D images and 3D models concurrently to enhance the quality of 3D mesh-based shape generation and stabilize the training process. To date, none of the current research studies use a 3D-VAE-GAN framework with a relativistic average GAN loss function to map 2D directly to 3D mesh-based shapes. Here we offer a comparative analysis between the proposed model and related works in 3D shape generation with signed distance functions.
## 3 Technology Overview  
In this section, we will introduce the technology used in this paper. Specifically, we will discuss the three main components of our platform for performance evaluation--Ansible, MySQL, and Grafana--with a focus on Ansible. This will give us a firm understanding of why these components were selected and how they contributed to the final platform and our research. At the time of writing, the latest versions available were Ansible 2.17.1, MySQL 9.0.0, and Grafana 9.5.20. Our custom Docker image used for testing is based on Ubuntu 24.04, with added packages for testing purposes (iperf3 3.16, netperf 2.7.0).
# Electromagnetic Field Variation of ELF Near-Region Excited by HED in a Homogeneous Half-Space Model
## 3 Analytical Approximate Expression of the Field Component  
### Quasistatic Approximate Propagation Conditions  
Under the condition that the propagation distance satisfies \(k_{2}\rho<<1\), the Fourier-Bessel function integral in the expression of the ELF electromagnetic field at or near the boundary of the two layers of media has discrete terms. The distribution of divergent poles is shown in Figure 3.  
Figure 3: The geometric shape of \(\gamma_{1}\) (related to the Sommerfeld integral) due to the excitation of the horizontal electric dipole: (**a**) corresponding pole and integral path of the Sommerfeld integral; (**b**) integral path when \(k_{2}<<k_{1}\); (**c**) geometric diagram of \(\gamma_{1}\); (**d**) triangular quantitative relationship.  
In order to evaluate the half-space Sommerfeld integral using the complex function method, the integrand must be divided into single-valued branches; that is, several slits must be made in the complex plane of \(\lambda\), and the multivalued functions must be specified clearly and reasonably. For this reason, we take the radical function \(\gamma_{1}=(k_{1}{}^{2}-\lambda^{2})^{1/2}\) as an example and first analyze its amplitude change on the complex plane; if \(k_{1}=\beta_{1}+j\alpha_{1}\), \(\lambda=\xi+j\eta\), then  
\[\gamma_{1}=\sqrt{(\beta_{1}^{2}-\alpha_{1}^{2})-(\xi^{2}-\eta^{2})+2j(\alpha_ {1}\beta_{1}-\xi\eta)} \tag{1}\]  
On the isoaxial hyperbola \(\xi\eta=\alpha_{1}\beta_{1}\), the amplitude angle of \(\gamma_{1}\) in Equation (1) is divided into three parts, as shown in Figure 3c: the amplitude angle of \(\gamma_{1}\) near the virtual axis is 0, the amplitude angle of \(\gamma_{1}\) on the dotted line is \(\pi/2\), and the amplitude angle of \(\gamma_{1}\) away from the virtual axis is \(\pi\). If two symmetric slits are made over the entire \(\lambda\) complex plane, then \(\gamma_{1}\) in the \(\lambda\) complex plane is singly analytic.  
Under the same condition, these integrals diverge in the traditional sense. For example, the multivalued function \(D(\lambda)=k_{1}{}^{2}\gamma_{2}+k_{2}{}^{2}\gamma_{1}\) included in the Sommerfeld integral has four zeros on the Riemann surface. \(\gamma_{1}\) is the propagation parameter in the seawater medium of region 1, and \(\gamma_{2}\) is the propagation parameter in the air medium of region 2. Therefore, two sets of symmetrical slits are created on the \(\lambda\) complex plane, as shown in Figure 3a. Here, slits parallel to the imaginary axis were used to simplify the calculation. According to the loop integral theory of the complex variable function [27], the integral path along the real axis of the Sommerfeld integral can be converted into an integral along the slits \(C_{1}\) and \(C_{2}\).
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
## 2 RM Decoder Based on AD
### Hyperparameters  
Table 3 lists the hyperparameters used for training the RM decoder. Let \(\mathbf{y}=\left\{0,1\right\}^{N}=\left(y_{0},y_{1},\cdots,y_{j},\cdots,y_{N-1 }\right)=\left(0,0,\cdots,1,\cdots,0\right)\) be the one-hot encoded vector for the message \(\mathbf{m}=\left(m_{0},m_{1},\cdots,m_{k}-1\right)\), where \(j=\sum_{l=0}^{k-1}m_{l}2^{l}\) is the decimal value for \(\mathbf{m}\), and all bits of \(\mathbf{y}\) are zeros, except the value of one at the \(j\)-th bit. Let \(z_{i}\) be the \(i\)-th output of the FC layer. The cross-entropy is given by:  
\[L(\mathbf{y},\mathbf{z})=-\sum_{i=0}^{N-1}[y_{i}\log z_{i}+(1-y_{i})\log(1-z_ {i})] \tag{1}\]  
is used as the loss function, and the Adam optimizer is used for model training. The training data set was \(2^{k}\times 10^{5}\), the epoch was \(10^{2}\), and the batch size was set to \(10^{4}\). Normalized validation error (NVE) of [19]  
\[\text{NVE}=\frac{1}{S}\sum_{s=1}^{S}\frac{BER_{AD}(\rho_{t},\rho_{\sigma s})}{ BER_{FHT}(\rho_{\sigma s})} \tag{2}\]  
Figure 4: Message estimation of \(\mathfrak{R}(2,4)\) from the output of FC layer.  
Figure 3: Structure of RM decoder based on AD.  
is used to select the appropriate signal-to-noise (SNR) ratio for model training, where \(\rho_{t}\) and \(\rho_{v,s}\) are the SNRs for the training set and the \(s\)-th validation set, respectively, and \(S\) is the number of all possible different validation sets. \(BER_{AD}(\rho_{t},\rho_{v,s})\) is the bit error rate when the RM decoder with AD is trained at \(\rho_{t}\) and evaluated at \(\rho_{v,s}\). Table 4 shows the NVE values at different training SNRs, from 0 dB to 7 dB with 1 dB intervals, and we observe that NVE = 0.945 at \(\rho_{t}=1\) dB is the lowest value among them. Thus, the training SNR is set to 1 dB.
## 4 Experimental Validation and Analysis
### Evaluation Metrics  
To measure the effectiveness of the prediction model proposed, evaluation indicators, including root mean square error, mean absolute error, coefficient of determination (\(R^{2}\)), and performance score function (Score), were introduced to assess the model's performance from four dimensions. The calculation method for the RMSE [23] is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}, \tag{12}\]  
The RMSE penalizes overestimation and underestimation errors in predicting the RUL of the engine equally, whereas Score [24] penalizes these errors differently. Therefore, to enhance the accuracy of model evaluation, the performance score function was introduced.  
\[\text{Score}=\left\{\begin{array}{l}\sum_{i=1}^{n}\left(e^{\frac{-(\hat{y_{i }}-y_{i})}{13}}-1\right),\hat{y_{i}}-y_{i}<0\\ \sum_{i=1}^{n}\left(e^{\frac{\hat{y_{i}}-y_{i}}{10}}-1\right),\hat{y_{i}}-y_{ i}>0\end{array}\right., \tag{13}\]  
The mean absolute error (MAE) [25] represents the dispersion level among data samples, and its calculation formula is as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i}^{n}|\hat{y_{i}}-y_{i}|, \tag{14}\]  
The smaller the predicted values for the RMSE, MAE, and Score, the better the model's predictive performance. For the \(R^{2}\), the closer its value is to 1, the better the fitting degree of the prediction model.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y_{i}})^{2}},R\in[0,1], \tag{15}\]  
In the equation, \(n\) represents the total number of engines, \(\hat{y}\) represents the predicted RUL value for the \(i\)-th engine, and \(y_{i}\) represents the true RUL value for the \(i\)-th engine.  
Figure 5: Distribution of data sample lengths.
# Clipping Noise Compensation with Neural Networks in OFDM Systems
## 3 Results  
In this section, the learned weight matrices of the NN are first presented, and patterns are recognized in them to provide a nice interpretation of their behavior. Then, the MSE of reconstructed signals and the final BER of the proposed scheme are provided. The performance is compared to that of the method based on Compressed Sensing (CS) [18] to illustrate the effectiveness of our approach. Our reason for choosing [18] is because it provides a state-of-the-art performance. There exist more up-to-date algorithms [27; 28], which are modified from [18], to cope with more complicated nonlinear effects or to utilize more side information. They are not included here to keep the comparison straightforward.
## 3 Method
### MDRF Block  
Bearing fault diagnosis typically involves time-series signals that require capturing both short- and long-range temporal dependencies. Although Transformer-based models have proven effective for this purpose, their high computational cost often limits their deployment in real-time or edge computing scenarios. To address these limitations, we propose a convolution-based MDRF block that enables efficient temporal modeling while maintaining low computational complexity.  
The MDRF block employs parallel 1D convolutions with different kernel sizes and dilation rates to construct a multi-scale receptive field. This design captures temporal features at various scales, reducing information loss that might occur in single-scale architectures. Structurally, four MDRF blocks--MDRF-block 1 through MDRF-block 4--are incorporated into SpikeCAN. Specifically, MDRF-block 1 uses three parallel 1D convolution (Conv1d) layers with kernel sizes \(k\in\{3,5,7\}\) and dilation factors \(D\in\{1,2,4\}\) applied to the batch-normalized input. The outputs are summed element-wise and passed through a PLIF neuron to generate spike activations:  
\[\text{RP}_{7} = \text{Conv1d}\big{(}\text{BatchNorm}(x_{0}),\,k=7,\,D=4\big{)}, \tag{14}\] \[\text{RP}_{5} = \text{Conv1d}\big{(}\text{BatchNorm}(x_{0}),\,k=5,\,D=2\big{)},\] (15) \[\text{RP}_{3} = \text{Conv1d}\big{(}\text{BatchNorm}(x_{0}),\,k=3,\,D=1\big{)},\] (16) \[\text{RP}_{\text{add}} = \text{RP}_{7}+\text{RP}_{5}+\text{RP}_{3},\] (17) \[\text{MDRF}_{1} = \text{PLIF}\big{(}\text{RP}_{\text{add}}\big{)}. \tag{18}\]In contrast, MDRF-blocks 2, 3, and 4 are designed to be lighter in computational cost. Each of these blocks utilizes two convolution branches with kernel sizes of 3 and 5 and corresponding dilation factors of 1 and 2. For each block, the outputs are batch-normalized, fused via element-wise addition, and passed through a PLIF neuron to produce spike-based outputs. This structure allows efficient local feature extraction while preserving compatibility with event-driven SNN processing.  
Additionally, each of these blocks is followed by a MaxPooling operation to reduce the temporal resolution, helping to lower the computational load and mitigate feature redundancy. Formally, for each input \(X_{j}\in\{X_{2},X_{9},X_{10}\}\), the computation proceeds as:  
\[\text{RP}_{5} =\text{Conv1d}\big{(}\text{BatchNormNorm}(X_{j}),\,k=5,\,D=2\big{)},\quad j\in\{2,\,9,\,10\}, \tag{19}\] \[\text{RP}_{3} =\text{Conv1d}\big{(}\text{BatchNormNorm}(X_{j}),\,k=3,\,D=1\big{)},\quad j\in\{2,\,9,\,10\}.\] (20) \[\text{RP}_{\text{add}} =\text{RP}_{5}+\text{RP}_{3},\] (21) \[\text{MDRF}_{i} =\text{PLIF}\big{(}\text{RP}_{\text{add}}\big{)},\quad i\in\{2, 3,4\}. \tag{22}\]  
In this manner, MDRF-block 1 extracts richer temporal representations using three convolution branches, while MDRF-blocks 2 through 4 offer computationally efficient alternatives optimized for real-time applications.