esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Dielectric ceramic capacitors have attained considerable attention due to their energy storage performance in the field of advanced high/pulsed power capacitors. For such a purpose, configurationally disordered composite material engineering, with the substitution of suitable oxide cations at a single lattice site have demonstrated a strong dielectric relaxor phase with the ability to show high performance capacitive properties. Herein, two prominent high-entropy ceramics systems (Ba\({}_{0.2}\)Na\({}_{0.2}\)K\({}_{0.2}\)A\({}_{0.2}\)Bi\({}_{0.2}\))TiO\({}_{3}\) (with A = La and Mg) were fabricated to evaluate their structural, ferroelectric and dielectric properties. XRD patterns and Rietveld refinement of the XRD analysis confirmed the cubic structure \(Pm\overline{3}m\) space group of the ceramics. The relative dielectric analysis of Ba\({}_{0.2}\)Na\({}_{0.2}\)La\({}_{0.2}\)Bi\({}_{0.2}\)TiO\({}_{3}\) (BNKLBT) and Ba\({}_{0.2}\)Na\({}_{0.2}\)K\({}_{0.2}\)Mg\({}_{0.2}\)Bi\({}_{0.2}\)TiO\({}_{3}\) (BNKLBT) ceramics were demonstrated with relaxor ferroelectric behavior having diffusion coefficients of 1.617 and 1.753, respectively. Moreover, BNKLBT and BNLMBT ceramics presented better stored energy density (1.062 J/cm\({}^{3}\) and 0.8855 J/cm\({}^{3}\), respectively) and high energy conversion efficiency (80.27% and 82.38%, respectively) at an electric field of 100 kV/cm. The results clearly demonstrate that such high-entropy configured ceramics have the potential to be used in efficient energy storage devices.  
high entropy; relaxor ferroelectrics; energy conversion efficiency; dielectric capacitors 1
# Memory Efficient Implementation of Modular Multiplication for 32-bit ARM Cortex-M4
## 1 Introduction
During the transition and migration from pre-quantum to post-quantum, IoT devices should support both PKC algorithms according to the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) committee (i.e., hybrid PKC protocol) [15]. There are also several security levels (e.g., 128-bit brute-force attack, 192-bit, and 256-bit);;, thus a number of PKC implementations should be considered. In particular, a lightweight PKC implementation can be achieved by optimized multi-precision multiplication implementation and optimized multi-precision squaring implementation. These methods are important for the deployment of cryptography for practical applications. In Table 1, both multi-precision multiplication implementation and multi-precision squaring implementation are compared. The fastest performance is achieved by [14]. For the 256-bit ECC implementation (e.g., NIST P-256 and Curve25519), 772 bytes are required for the multiplication and squaring implementations. For the finite field operation, modular reduction is also needed, and it is usually a similar size of multiplication (i.e., 452 bytes) by using Montgomery reduction. To support SIKEp751, an additional 5,768 bytes are used for multiplication and squaring. Compared with other approaches, the proposed method is highly memory-efficient, requiring only 584 bytes. Moreover, the implementation is already parameterized, which supports all ECC, RSA, and SIKE protocols. By supporting all PKC protocols, the hybrid PKC protocol can be utilized. For this reason, the memory-efficient implementation can be a practical solution and the proposed method only satisfies this requirement.
## 2 Related Work
### Text Classification Method Using Attention Mechanism and Joint Network  
Although text classification methods based on CNN or RNN are highly effective, incorporating the knowledge of the document structure into the model architecture can achieve an improved representation. [PERSON] et al. [13] proposed the hierarchical attention network (HAN) in this context. HAN utilizes word-level and sentence-level attention mechanisms. This mechanism enables the model to focus on crucial content within the document, thereby better integrating the context to assign higher weights to keywords and sentences, ultimately resulting in an excellent performance. [PERSON] et al. [14] introduced the Attention-XML model, which effectively leverages the most relevant multiple labels from an extensive label set to annotate a given text. This approach efficiently addresses the long-tail problem of labels, significantly improving the classification performance of each label. [PERSON] et al. [15] proposed a multi-task multi-granularity attention network. By combining coarse-grained classifiers and fine-grained classifiers, data with category intersections are effectively learned. At the same time, the gradient control module controls the gradient propagation of the two-level granular learner to suppress the interference caused by irrelevant features, achieving a good classification performance. Related methods [16] using a multi-head attention or connection attention mechanism achieved a great score in recognition tasks.  
In addition, the joint network method shows effectiveness in text classification tasks: the architectures of CNNs, RNNs, and attention networks can be combined to form joint networks. In [PERSON] et al. [17], the CNN was utilized to extract a sequence of higher-level phrase representations, which were subsequently fed into an LSTM to obtain the sentence representation. [PERSON] et al. [18] introduced TextRCNN, building upon the TextCNN model. This model utilizes the RNN mechanism to capture sequential dependencies and CNN to extract crucial local features. [PERSON] et al. [19] proposed BLSTM-2 DCNN based on TextRCNN, which utilizes BLSTM to capture long-term sentence dependencies and 2D convolution with 2D max pooling operations to obtain the representation of the entire sentence.  
However, a certain distance limitation will always exist in handling the correlations within text sequences, regardless of whether it is an attention or joint model due to the lack of an effective learning mechanism for long-distance dependencies. The learning capability of the model is always constrained by various factors, such as the window size, preceding hierarchical representation, and model gradients.
## 4 Experimental Validation and Results Analysis  
### Irradiance Uniformity Measurement and Evaluation  
To improve the spatial energy uniformity of the solar illumination module, a composite beam shaping structure is proposed, combining a large-core homogenizing optical fiber and an apodizer. The homogenizing fiber operates based on multimode interference, effectively eliminating interference fringes and modal modulation from the laser source. The apodizer suppresses high spatial frequency components at the beam edges and reconstructs the near-field intensity distribution, enabling the formation of a quasi-flat-top irradiance profile in the far field. The apodization optics consists of three main components: a collimating lens, a spatial aperture, and a focusing lens. The key structural parameters are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Description** & **Variables** & **Description** \\ \hline \(L_{\text{source}}(\lambda,P)\) & the original laser spectrum output & \(\alpha\) & surface distribution coefficient \\ \(U_{\text{L}}\) & spectral uniformity & \(G(\theta_{i},\varphi_{i},\theta_{r},\varphi_{r})\) & geometric shadowing function \\ \(L_{\text{solar}}(\lambda)\) & the solar spectrum output & \(\text{P}_{1}\sim\text{P}_{7},\text{q}_{1}\sim\text{q}_{4},\text{g}_{0}\) & fitting coefficient in \(g(\theta_{i},\varphi_{r})\) \\ i & index of wavelength points & \(\text{k},\text{c}\) & fitting coefficient in \\ j & index of power levels & \(\varepsilon\) & micro-surface _BRDF_ \\ A, B, g & fitting coefficient in BRDF model & TIS & Total Integrated Scatter \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._To quantitatively evaluate the far-field irradiance uniformity of the homogenization structure, a measurement setup was constructed as follows. A vertical detection plane was positioned at the output aperture of the solar illumination module. A Thorlabs PM100D digital optical power meter (detector model: S145C, with an active aperture of 11.8 mm and spectral response range of 800-1700 nm) was used for energy acquisition. The power sensor was mounted on a two-dimensional precision motorized translation stage. The scanning area covered a circular region with a diameter of 200 mm, and the sampling step size was set to 10 mm in both the x and y directions to obtain the irradiance distribution over the plane.  
Figure 6 shows the experimentally measured far-field energy distribution. As seen from the figure, the beam exhibits good symmetry and smoothness, forming an overall quasi-flat-top profile. Based on the irradiance uniformity evaluation Formula (14),  
\[U_{E}=1-\frac{E_{\text{max}}-E_{\text{min}}}{E_{\text{max}}+E_{\text{min}}} \tag{14}\]  
where \(E_{\text{max}}\) and \(E_{\text{min}}\) respectively represent the maximum and minimum irradiance values within the 200 mm test area, the calculated far-field irradiance uniformity of the system reaches 85.8%. Compared to the theoretical design target of 90%, the deviation is less than 5%, indicating that the proposed homogenization optical path demonstrates excellent uniformity retention and strong engineering feasibility.
## 4 Discussion
These errors may lead to both false detections (e.g., identifying a pass where none occurred) and missed events, particularly in situations involving subtle or low-velocity passes. Given that the ball tracking system may vary in precision depending on factors such as camera calibration, occlusions, or frame rate, the robustness of the algorithm is inherently tied to the quality of the positional data. This dependency should be considered when interpreting the results, especially in comparative studies across different data providers or match contexts. Future improvements could involve integrating contextual variables (e.g., player-ball distance, directionality) to reduce reliance on velocity thresholds alone.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 2 Related Work
### Video and Multimedia Content Descriptors  
The problem of quantitatively assessing the vast amounts of data in modern multimedia content has been addressed for decades, first for the purpose of content indexing and retrieval and later for content-based recommender systems (CBRS). We hypothesize that similar dimensionality reduction tools originally used for indexing and CBRS can also be used to determine the impact of content on multimedia engagement scales.  
Many user-based multimedia classification systems utilize high-level content features, such as genre, director, cast, tags, text reviews, and similar meta-information [19]. These metadata annotations are scarce, particularly for video advertising, and implicit metadata available through computational analysis of the content are preferred. Therefore, the main goal of the presented research is to relate an individual's measurable content engagement to the computable features of the multimedia content consumed. We begin our presentation of potential feature sets with a standardized and widely used set of low-level descriptors for visual multimedia content, a subset of which can be algorithmically extracted.
## 4 Experimental Results and Analysis  
### Experimental Environment and Data  
We built a three-perspective adaptive cheating behavior determination system using the Python 3.7 language and PyTorch 1.12.1 framework under the Ubuntu 18.04 system. The server hardware configuration used for training each model consisted of an NVIDIA TITAN XP GPU and Intel Xeon 10 CPU. The gaze direction recognition dataset consisted of 3000 images from 17 students in a simulated online examination environment, as well as some public data on conventional gaze direction. As shown in Figure 10, taking one of the participants as an example, the nine gaze directions corresponding to the dataset are upleft, left, downleft, up, normal, down, upright, right, and downright. Considering the influence of lens reflection on gaze direction recognition, 8 of 17 students wore glasses. To overcome the influence of different pupil colors on gaze direction classification, 750 images from the MPIIFaceGaze dataset [41] were added to the 2250 self-built images.  
The cheating tool detection model in this paper includes a desktop perspective abnormal object detection model and a glasses perspective abnormal object detection model. The types and appearances of cheating tools vary from different perspectives. Therefore, we established an overhead perspective abnormal object dataset and a horizontal perspective abnormal object dataset. The overhead perspective abnormal object dataset includes 7600 images of books, paper, and mobile phones, as shown in Figure 11a. The horizontal perspective abnormal object dataset includes 8899 images of books, paper, mobile phones, and people, as shown in Figure 11b.  
Figure 10: Gaze direction dataset.  
Figure 9: Adaptive three-perspective cheating behavior determination model driven by gaze direction.  
The three-perspective cheating behavior determination dataset used in this article was created from monitoring images of 17 students in a simulated online exam environment. The dataset includes 5687 sets of images from three perspectives--horizontal, face, and overhead--with 70% of the dataset used for training and 30% for testing. Figure 12 shows several examples of data collection.
## 2 The Formulation of the Multi-Material Topology Optimization Problem  
### Design Variable  
The formulation of the multi-material topology optimization problem based on the mapping-based interpolation function is briefly introduced here for the completeness of this paper. The design domain is usually discretized into finite elements, and each element is assigned a density \(x_{e}\) to determine the material distribution in the topology optimization of continuum structures. Only one design variable is used for the single-material problem, and it is easy to model the interpolation function. The design variable of single materials can be expressed as \(x=\left[x_{1},x_{2},\cdots x_{n-1},x_{n}\right]^{T}\), where \(n\) is the number of elements used to discretize the design domain. While there are multiple materials that participate in the structural design for multi-material topology optimization problems, the number of design variables for multi-materials is equal to the number of materials multiplied by the number of elements, which can be expressed as:  
\[x=\left[\begin{array}{cccc}x_{1}^{1}&x_{2}^{1}&\cdots&x_{NM-1}^{1}&x_{NM}^{1 }\\ x_{1}^{2}&x_{2}^{2}&\cdots&x_{NM-1}^{2}&x_{NM}^{2}\\ \vdots&\vdots&&\vdots&\vdots\\ x_{1}^{NE-1}&x_{2}^{NE-1}&\cdots&x_{NM-1}^{NE-1}&x_{NM}^{NE-1}\\ x_{1}^{NE}&x_{2}^{NE}&\cdots&x_{NM-1}^{NE}&x_{NM}^{NE}\end{array}\right] \tag{1}\]  
where \(NE\) as the superscript of design variables denotes the number of finite elements and \(NM\) as the subscript of design variables denotes the multiple materials.
## 4 Experimental Determination of the Light Radiation Pattern of Commercial Vehicle Rear Lights
### Discussion on Measurement Results  
In line with what could be expected, these experimental determinations have shown that the vehicles' rear lighting systems do not follow a Lambertian light distribution pattern. The explanation for this is that usually, the vehicle lights have irregular reflective surfaces behind each individual LED and/or small lenses in front of them, to keep the radiation pattern in line with the safety regulations.  
The experimental determinations showing the irradiance distribution patterns for the two vehicles are illustrated in Figure 5. The measured values for irradiance range as follows: from 10.46 \(\upmu\)W/cm\({}^{2}\) to 0.06 \(\upmu\)W/cm\({}^{2}\) for taillights with LEDs, from 66.4 \(\upmu\)W/cm\({}^{2}\) to 0.18 \(\upmu\)W/cm\({}^{2}\) for stolights with LEDs, from 10.47 \(\upmu\)W/cm\({}^{2}\) to 0.08 \(\upmu\)W/cm\({}^{2}\) for taillights with incandescent bulbs, and from 317.5 \(\upmu\)W/cm\({}^{2}\) to 2.17 \(\upmu\)W/cm\({}^{2}\) for stolights with incandescent bulbs. As expected, the light irradiation pattern has the highest values right behind the vehicle, and gradually decreases as the distance increases or as the measurement angle increases. The irradiance values also show that the brake lights have a higher intensity compared to the taillights, indicating their suitability for use in Emergency Electronic Brake Light (EEBL) applications, since the higher irradiance will provide an increased communication distance and enhanced resilience. Additionally, these determinations indicate that the irradiance of LED-based vehicle stop lights is significantly lower than the one of incandescent rear lighting systems. This fact indicates that the optical power of LED lighting systems could be further increased, contributing to an improved perception and to enhanced VLC performances.  
Figure 5: _Cont._Because these measurements were made while orientating the irradiance meter toward the center of the vehicles, in the real-world scenario when two vehicles are situated on different lanes while having the same direction, the quantity of light that reaches the photosensitive element is influenced by the incidence angle, as expressed in Equation (11). Thus, in such cases, the light that is actually received is lower than the values determined and illustrated in Figure 5. However, as the inter-vehicle distance increases, the incidence angle decreases, reducing the effect of vehicle misalignment.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple
### Optimization of Permanent Magnet and Iron Bridge  
The topology and location of permanent magnets and the iron bridges are also critical to the electric performances, especially for the torque output performances. First of all, several key parameters of the rotor PM are studied below. The key parameters of the rotor PM are \(R_{2}\), \(w_{rib}\), \(h_{rib}\), and \(w_{b}\), as can be found in Figure 1. The \(w_{b}\) and \(h_{rib}\) are directly related to the permanent magnet flux leakages, while  
Figure 5: Influence of different rotor pole arc offset distance on the cogging torque.  
Figure 6: Influence of different rotor pole arc offset distance on torque ripple at rated operation.  
Figure 7: The variation of cogging torque and output torque with the rotor pole arc offset distance.  
\(w_{rib}\) and \(R_{2}\) show more influences on q-axis inductances, thus torque output capabilities. The torque performances were obtained by 2-D FEA, as shown in Figure 8, where the torque ripple coefficient \(k_{rip}\) is defined as  
\[k_{rip}=\frac{\textit{torque ripple}}{\textit{average torque}} \tag{6}\]  
1. As shown in Figure 8a, the average torque changed slightly with the variation of \(R_{2}\). Thus, the influence of \(R_{2}\) on the torque output capabilities was negligible. However, the torque ripples and thus \(k_{rip}\) varied greatly with \(R_{2}\). When \(R_{2}\) was 52.5 mm, the output torque ripple was the smallest and tended to be stable, with a value of 70 Nm and a \(k_{rip}\) of 5.8%, which is acceptable in heavy-load traction vehicles.
2. As shown in Figure 8b, the output torque was almost unchanged with the increase of distance between the poles, i.e., \(w_{rib}\), because the back-EMF and inductances remained unchanged. As for the torque ripple, it achieved the peak value of 51.2 Nm and \(k_{rip}\) of 7.9% when \(w_{rib}\) was 16 mm, and exhibited a nonlinear trend with the variation of \(w_{rib}\). The lowest \(k_{rip}\) was 4.18% when \(w_{rib}\) was 18 mm, which is much lower than the case of 16 mm. This can be explained, as when the \(w_{rib}\) is changed, the pole arc coefficient and the air-gap magnetic field harmonic content changes significantly.
3. As shown in Figure 8c, both the output torque and torque ripple reduced linearly with the increase of \(h_{rib}\); an apparent variation was found in the torque ripple especially. The average output torque was 897.1 Nm and the \(k_{rip}\) was 8.78% when \(h_{rib}\) was 10 mm; meanwhile, they were 863.2 Nm and 3.8% respectively, when \(h_{rib}\) was 12 mm. Comparing those two cases, it was found that with the cost of 3.8% reduced torque output, the \(k_{rip}\) could be reduced by 45.6%.
4. Figure 8d shows that \(w_{b}\) had a negligible influence on torque ripples, since it mainly provides necessary mechanical strength, and will enlarge the permanent magnet leakage fluxes, and should be designed as small as possible.  
Figure 8: \(Cont.\)
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 4 Performance Evaluation
### Cold-Start Performance Comparison (RQ3)  
To evaluate the performance of the proposed TDDRMF framework in cold-start environments, this experiment focuses on single-step prediction under a limited training data scenario. The proportion of training data is set to 5%, simulating a cold start condition. The results are summarized in Table 5.  
In the cold-start scenario for multi-step prediction, ARIMA achieves the best RMSE performance; however, its results exhibit significant instability. In particular, the model struggles to adapt to sparse data or evolving trends in certain scenarios, resulting in unreliable \(R^{2}\) values. This highlights that while ARIMA excels in short-term time series modeling, its robustness and applicability are limited in dynamic or complex environments.  
TDDRMF achieves the second-best prediction accuracy and demonstrates superior stability and adaptability compared to other matrix factorization-based methods. This performance can be attributed to the synergistic effects of nuclear norm regularization and temporal regularization, which effectively capture key microservice dependencies and  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & **MF + T** & **MF + T + P** & **MF + T + P + N** & **TDDRMF** \\ \hline
**RMSE** & 0.9312 & 0.7137 & 0.7115 & **0.6870** \\
**MAE** & 0.8237 & 0.5524 & 0.5532 & **0.5282** \\
**\(R^{2}\)** & 0.9529 & 0.9672 & 0.9670 & **0.9703** \\
**Train Time (s)** & 8030.86 & 6267.20 & 6587.21 & **831.46** \\
**Pred Time (s)** & 0.91 & 0.70 & 0.59 & **0.42** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results (500-step).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Metric** & **TDDRMF** & **TAMF** & **TRMF** & **ARIMA** & **RF** & **LSTM** \\ \hline
**RMSE** & 0.3042 & 0.4379 & 0.3343 & **0.2016** & 0.3106 & 0.4908 \\
**\(R^{2}\)** & 0.9740 & 0.9711 & 0.9733 & \(-4.5\times 10^{20}\) & **0.9987** & 0.9767 \\
**Train Time (s)** & **2.13** & 19.39 & 18.98 & 251.36 & 121.61 & 2268.53 \\
**Pred Time (s)** & **0.008** & 0.009 & 0.009 & 3.59 & 0.682 & 1.952 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cold-start performance at 5% training data (10-step).  
multi-step temporal dynamics. Additionally, TDDRMF exhibits the highest computational efficiency, with both the shortest training and shortest prediction times among all models, making it highly suitable for resource-constrained or real-time scenarios.