esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, we propose a flexible hardware platform that is intended for an evaluation of hybrid terrestrial-satellite access performances. Also, we introduce an intelligent selection of network access using the predictions performed by long short-term neural networks (LSTMs) based on the measured SNR (Signal-to-Noise Ratio). Appropriate channel models are used in the simulation process, generating SNR data for both satellite and terrestrial network channels. The goal of our proposal is to create a simulation framework that can be adjusted to the broader scope of use cases and scenarios. For this reason, we have included in our framework components like an intelligent controller that can aid in network access selection, hardware boards that can set link delays or be used for hardware accelerations, different channel models support, etc. By modifying appropriate components, the framework can be adjusted to the desired scenario. In this paper, a demonstration is performed for a UDP streaming-based session where an intelligent selector, based on SNR measurements, selects better access that provides higher bandwidth. UDP streaming is selected because, in this scenario, the obtained results focus on intelligent selection performance, avoiding the impact of flow and congestion control mechanisms of TCP-based flows. The results show that the proposed platform enables the fast simulation and estimation of performances of the proposed intelligent controller. Also, we show that the intelligent controller enables the optimal usage of resources. There are two main contributions of this paper. The first is the design of an intelligent session controller that utilizes LSTMs to estimate the condition of both network accesses based on SNR measurements. The second contribution is the design of flexible hardware-based simulation platform that is intended for the inspection of high-level based multi-access approaches and protocols. It is important to note that the framework is designed to be flexible, so all modules and components can be modified and adjusted to different scenarios. Also, most of the simulation setups focus on terrestrial wireless networks, while in this paper, we focus on a hybrid satellite-terrestrial combination of wireless networks. Additional contributions include performance testing for UDP-based streaming sessions and performance measurements using the proposed hardware-based simulation platform.
## 3 Image Sensor: Power Consumption Analysis
### Timing Profile for Running Mode Basic Operations  
We have measured the time required to capture and deliver one image with each of the available image sizes. For that purpose, we have used the high precision 16 bit timer to perform accurate time measurements (microsecond resolution). All time measurements were repeated 100 times providing the average value with a 95% confidence interval. As previously shown at Figure 4, running mode is composed of six basic steps described as follows:  
* CAM_Connect: It powers on the camera and establishes connection through the USART0 port.
* CAM_Config: Once the camera is connected and ready, this process configures the camera with the proper image parameters (image size, capture mode, _etc._).
* CAM_Capture: This operation sends the capture command to the camera. After capturing the image in its internal memory, the camera performs JPEG compression with a fixed quality level.
* T_Get_Slice: It represents the downloading process of the compressed image from camera. It is performed in chunks we called image slices. As mentioned before, the size of each slice is fixed at 240 bytes, with the exception of the last one. This operation quantifies the total time required to download all the image slices from camera.
* T_Send_Slice: This process is related with the wireless transmission to the control station of all slices belonging to the captured image. This operation is interlaced with the previous one, so after downloading one slice (Get_Slice) from camera, this process starts its wireless transmission (Send_Slice). So, it represents the total time required to deliver all the image slices.
* CAM_Power_Off: Before exit running mode, the camera is powered off to save power.  
In Figure 5, we show the latency of the running mode operations. As it can be seen, the time spent on receiving the sequence of slices corresponding to the compressed image (T_Get_Slice), and sending them to the central station (T_Send_Slice) represents a significant portion of the total time. In particular, T_Send_Slice takes 32.17% of the total time for the smallest image size (about 5 slices on average) and 62.18% for the largest image size (about 40 slices on average). Take into account that the number of packets (slices) resulting from the JPEG compression for a particular image format size, will depend on the characteristics and features of the captured image content (textures, details, _etc._).  
Notice that operations not devoted to managing image slices like initialization, configuration, capture and image compression, are independent of the selected image size (all of them are completed in 0.365 s approx.).
## 4 Challenges and Future Research Directions
### 3D Network Coverage for UAVs and Monitoring Robots  
It is very challenging to achieve three-dimensional (3D) network coverage for UAVs and monitoring robots in 6G-enabled smart grids with respect to fault monitoring in transmission lines and towers. The enhanced data rate, low latency, and reliable wireless network coverages are needed for UAVs and monitoring robots. Moreover, mobility is another big challenge for uninterrupted communication between ground offices and UAVs/robots [7].  
UAV swarm-incorporated cell-free wireless communication is a potential solution for 3D network coverage regarding the monitoring of UAVs and robots [7]. Moreover, UAV-based base stations can also provide network coverage in remote areas where transmission lines and towers are located [7]. A number of ongoing studies are required on AI-based UAV swarms that can provide more effective network coverage and transmission line monitoring. Moreover, a number of future studies on UAVs to everything (U2X) and cell-free 6G wireless communication are required to overcome the mobility issue of monitoring UAVs/robots [130].
## 3 Methodology
### Accuracy Assessment  
Training of our model was controlled using a 10-fold cross-validation (CV) method. An accuracy assessment was done using the validation data set. Three evaluation indices, namely the overall accuracy (OA), Kappa coefficient (Kappa) and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. OA determines the algorithm's overall efficiency and can be calculated by dividing the total number of correctly labeled samples by the total number of the testing samples [64]. The Kappa indicates the degree of agreement between the ground truth data and the predicted values [65]. The F1 score was calculated from the precision and recall of the test, where the precision is the number of correctly identified positive results divided by the number of all positive results, including those not identified correctly. The recall is the number of correctly identified positive results divided by the number of all samples that should have been identified as positive [66; 67]. The no-information rate (NIR) is the largest proportion of the observed classes. We also calculated producer accuracy for all the classes. The producer's accuracy represents the probability that a reference sample is correctly identified in the classification map. Table 3 shows a representation of a confusion matrix for two classes:  
\[\text{OA}=(\text{A}+\text{C})/(\text{A}+\text{B}+\text{C}+\text{D}) \tag{4}\]  
\[\text{Recall}=\text{A}/\text{A}+\text{C} \tag{5}\]  
\[\text{Precision}=\text{A}/\text{A}+\text{B} \tag{6}\]  
\[\text{F1 score}=(1+\beta^{2})(\text{precision}*\text{recall})/(\beta 2*\text{ precision}+\text{recall}) \tag{7}\]  
Suppose that there is response \(y_{i}\) and covariates \(x_{i}\) for i = 1... n, and some loss function \(L\). The no-information error rate of a model \(f\) is the average loss of \(f\) over all combinations of \(y_{i}\) and \(x_{i}\):  
\[\text{NIR}=\frac{1}{n^{2}}\sum_{i=1}^{n}\ \sum_{j=1}^{n}L(y_{i},f(x_{j})) \tag{8}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Predicted**} & \multicolumn{2}{c}{**Reference**} \\ \cline{2-3}  & **Event** & **No Event** \\ \hline event & A & B \\ No event & C & D \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for \(2\times 2\) matrix.
# Research Progress and Applications of 2D Antimonene
## 2 Theoretical Research and Experimental Methods
### Epitaxial Growth  
In addition to mechanical exfoliation and liquid phase exfoliation, epitaxial growth is often used for multilayer and single-layer growth of two-dimensional materials. Epitaxial growth includes the bottom-up growth pattern, which is also useful for the experimental preparation of antimonene.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 4 Methodology
### Research Materials and System Configuration  
We solved the complex healthcare scheduling operation model using the _Gurobi Optimizer_[30], version 10.0.1, which is a powerful optimization solver. The optimization program ran on an Intel(r) Core(tm) i5-6200U CPU @ 2.30 GHz with 2 physical cores and 4 logical processors for efficiency. Table 1 shows the research's system configuration and model information. Our sophisticated _mixed-integer linear programming model_ has 90,101 rows, 1220 columns, and 1,801,420 nonzeros. Despite its complexity, the solver performed well in two optimization techniques. The initial optimization technique yielded five healthcare resource allocation and staff scheduling strategies. The model found an ideal solution with an _objective value_ of 844.0, measuring resource allocation efficiency and effectiveness. The _best-bound score_, representing the objective function's theoretical lower limit, was 844.0, demonstrating the solution's accuracy. The difference between the _best objective value_ and _best-bound score_ was 0.0%, a key model performance parameter. Since the optimal solution and the lower bound of the objective function were identical, the solution was the best within the tolerance limitations.  
Furthermore, we optimized the model again to find more subtle solutions. The best of three plausible solutions has an objective value of 539.0, a substantial increase from the first. As with the initial method, the best-bound score matched the objective value, demonstrating the solver's accuracy. The _best objective value_ and _best-bound score_ were 0.0% apart, demonstrating that the solution was optimal within tolerance. Moreover, branching, a complicated optimization phase, explored 33 nodes and executed 782 simplex iterations. Cutting plane approaches improved convergence and solution quality. Cover cutbacks, mixed integer rounding (MIR), zero-half cuts, relaxation-induced neighborhood search 235 (RINS), and bilinear quadratic pair (BQP) cuts were among these. These methods improved solver speed and precision, which aided optimization.  
The two model procedures demonstrated our approach's robustness and efficacy. The Gurobi solver's powerful optimization algorithms can address challenging healthcare problems, including resource allocation, staffing, and patient assignment. These findings suggest that healthcare professionals may boost efficiency and quality.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline CPU model & Intel® CoreTM i5-6200U CPU @ 2.30 GHz \\ Instruction set & [SSE2 | AVX1 AVX2] \\ Thread count & 2 physical cores, 4 logical processors, using up to 4 threads \\ \hline \hline \end{tabular}
\end{table}
Table 1: System configuration and model information.
###### Abstract  
Lycopene exhibits a broad spectrum of biological activities with potential therapeutic applications. Despite its established antioxidant and anti-inflammatory properties, the molecular basis for its pharmacological actions remains incompletely defined. Here we investigated the molecular targets, pharmacodynamic feasibility, and tissue-specific expression of lycopene targets using a computational pharmacology approach combined with affinity and protein-protein interaction (PPI) analyses. Lycopene-associated human protein targets were predicted using a Swiss target screening platform. Molecular docking was used to estimate binding affinities, and concentration-affinity (CA) ratios were calculated based on physiologically relevant plasma concentrations (75-210 nM). PPI networks of lycopene targets were constructed to identify highly connected targets, and tissue expression analysis was assessed for high-affinity targets using protein-level data from the Human Protein Atlas database. Of the 94 predicted targets, 37% were nuclear receptors and 18% were Family A G Protein Coupled Receptors (GPCRs). Among the top 15 high-affinity targets, nuclear receptors and GPCRs comprised 40% and 26.7%, respectively. Twenty targets had affinities < 10 uM, with six key targets (_MAP2K2_, _SCN2A_, _SLC6A5_, _SCN3A_, _TOP2A_, and _TRIM24_) showing submicromolar binding. CA ratio analysis identified _MAP2K2_, _SCN2A_, and _SLC6A5_ as pharmacodynamically feasible targets (CA > 1). PPI analysis revealed 32 targets with high interaction and 9 with significant network connectivity. Seven targets (_TRIM24_, _GRIN1_, _TRIK1_, _FGFR1_, _NTRK3_, _CHRNB4_, and _PIK3 CD_) showed both high affinity and centrality in the interaction network. The expression profiling of submicromolar targets revealed widespread tissue distribution for _MAP2K2_ and _SCN3A_, while _SCN2A_, _TOP2A_, and _TRIM24_ showed more restricted expression patterns. This integrative analysis identifies a subset of lycopene targets with both high affinity and pharmacological feasibility, particularly _MAP2K2_, _SCN2A_, and _TRIM24_. Lycopene appears to exert its biological effects through modulation of interconnected signalling networks involving nuclear receptors, GPCRs, and ion channels. These findings support the potential of lycopene as a multi-target therapeutic agent and provide a rationale for future experimental and clinical validation.  
Lycopene; network pharmacology; inflammation; molecular docking; systems biology
# Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach
## 5 Conclusions  
This paper proposes a novel weak fault detection approach for tapered rolling bearing based on the augmented Huber nonconvex penalty regularization (AHNPR) approach. The purpose of this paper is to overcome the following two problems: (1) The traditional convex regularizer (e.g., L1-norm) may underestimate sparse coefficients of the underlying signal; and (2) the convexity of the objective function cannot be guaranteed using traditional separable non-convex penalties regularizers.  
In this work, we proposed an AHNPR for solving the low-rank matrix approximation (LRMA) problem (or inverse problem). Compared to the LFLO algorithm, where the singular values would be calculated by a soft-threshold function, the proposed method does not underestimate singular values. The AHNPR is known to estimate singular values more accurately. In addition, an efficient algorithm using the forward-backward splitting (FBS) technique to solve the proposed OCF is derived. Finally, the effectiveness of the proposed method is demonstrated by the simulated vibration signal practical rolling bearing under severe additive background noise.  
A possible future direction of research involves the use of AHFs, which are focused on multi-fault diagnosis under variable speed or variable harsh running environments.  
**Author Contributions:** Algorithms improvement, programming, experimental analysis and paper writing were done by Q.L. Review and suggestions were provided by S.Y.L. All authors have read and approved the final manuscript.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (Grant No. CUSF-DH-D-2017059 and BCZD2018013) and the Research Funds of World-tech Transmission Technology (Grant No. 12966 EM). The authors wish to express their sincere gratitude for this support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: The diagnosis results of right-side and left-side generated by the asymmetric convex penalty regularization (ACPR) method. (**a**) the sparse component of right-side of input shaft generated by the ACPR method; (**b**) the envelop spectrum of the sparse component of right-side; (**c**) the sparse component of left-side of input shaft generated by the ACPR method; (**d**) envelop spectrum of the sparse component of left-side.
## 3 Results  
### Effects of Y Addition on the \(\alpha\)-Al Phases  
Figure 2 is the SEM image of the matrix without Y addition. Figure 3 represents the optical micrographs of as-cast ADC12 alloys with various Y additions, and demonstrates a substantial difference in the microstructure in terms of the size and morphology of the \(\alpha\)-Al phases. As shown in Figures 2 and 3, the outline of the \(\alpha\)-Al phases of the alloy without Y addition was not clear. The eutectic silicon exhibited either a coarse needle or plate shape. The Al\({}_{2}\)Cu and iron-rich \(\gamma\) phase show the plate-like and skeleton-like attributes, respectively. In comparison, the \(\alpha\)-Al phases of the alloy containing 0.1 wt% Y had a very clear outline and the SDAS value decreased from 48.5 to 33.5 \(\upmu\)m. In accordance with Figure 3b,c, the \(\alpha\)-Al primary phase exhibited an ellipsoidal shape and the SDAS value further reduced to 20.3 \(\upmu\)m when Y addition was 0.2 wt%. This phenomenon means that the \(\alpha\)-Al phases were further refined due to the addition of Y. However, as shown in Figure 3d, the refined \(\alpha\)-Al primary phase increased when the amount of Y was further increased up to 0.3 wt%.  
According to the Hume-Rothery rules, almost no solid solubility can be formed in the alloy unless the atomic radius difference between the solvent and the solute is <15%. As we know, the atomic radius of Y and Al are 0.182 and 0.143 nm, respectively. The atomic radius difference is 27%. Hence, the Y element has difficulty entering the crystal lattice of primary phase \(\alpha\)-Al. Instead, the rare earth element concentrates in the front of the solid-liquid interface. This phenomenon restricts the growth of \(\alpha\)-Al grains and leads to a strong constitutional undercooling that promotes the nucleation of grains [11; 12]. On the other hand, rare earth Y may react with O and Al\({}_{2}\)O\({}_{3}\) to produce Y\({}_{2}\)O\({}_{3}\) according to [PERSON] et al. [8]. The lattice structure of Y\({}_{2}\)O\({}_{3}\) was similar to the lattice of the \(\alpha\)-Al phases. Y\({}_{2}\)O\({}_{3}\) could act as the nucleus of \(\alpha\)-Al and increase the number of \(\alpha\)-Al dendrites, resulting in a decrease in \(\alpha\)-Al dendrite size.
# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
## 4 Experiment
### Conducted Experiments and Results
#### 4.3.3 Experiment 3
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Machine-** \\ **Learning** \\ **Matcher** \\ \end{tabular} & \multirow{2}{*}{**Accuracy**} & \begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **Machine-** \\ **Learning** \\ **Matcher** \\ \end{tabular} } & \multirow{2}{*}{**Tested**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} } \\ \cline{5-8}  & & & & & & & \\ \hline \multirow{4}{*}{SVM (linear)} & 2-1 & 100\% & 71.20 s & & 2-1 & 99.90\% & 117.01 s \\  & 2-2 & 100\% & 19.41 s & & 2-2 & 98.55\% & 30.01 s \\  & 3-1 & 100\% & 91.77 s & SVM (radial) & 3-1 & 98.55\% & 132.89 s \\  & 4-1 & 99.98\% & 100.73 s & & 4-1 & 96.85\% & 137.91 s \\  & 5-1 & 99.97\% & 107.26 s & & 5-1 & 96.68\% & 150.48 s \\  & 2-1 & 100\% & 98.86 s & & 2-1 & 92.29\% & 95.61 s \\  & 2-2 & 99.96\% & 24.41 s & & 2-2 & 94.18\% & 27.14 s \\ SVM (poly) & 3-1 & 99.85\% & 113.06 s & SVM (sigmoid) & 3-1 & 89.70\% & 115.24 s \\  & 4-1 & 99.54\% & 116.40 s & & 4-1 & 97.67\% & 124.69 s \\  & 5-1 & 99.37\% & 126.23 s & & 5-1 & 88.63\% & 150.90 s \\  & 2-1 & 91.37\% & 0.86 s & & 2-1 & 98.50\% & 209.74 s \\  & 2-2 & 93.14\% & 0.64 s & & 2-2 & 97.18\% & 101.35 s \\ Naive Bayes & 3-1 & 88.56\% & 1.13 s & Random forest & 3-1 & 96.22\% & 225.83 s \\  & 4-1 & 85.64\% & 1.31 s & & 4-1 & 95.20\% & 233.55 s \\  & 5-1 & 84.22\% & 1.51 s & & 5-1 & 94.34\% & 234.48 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of tested matchers varies with different machine-learning classifiers. Figure 11: Structures of the proposed classifier and tested matchers. first layer, as illustrated in Figure 11) using an SVM with sigmoid kernel function is 92.29%, the accuracy of matcher 5-1 (a machine-learning matcher in a tree-structured classifier with five child clusters in the first layer, as illustrated in Figure 11) drops to 88.63%. It is expected that the difficulty of classification increases with the classification type, resulting in lower accuracy. Likewise, with regard to training time, it can be observed that the training time for all machine-learning classifiers augments as the number of child clusters increases, which implies an increase in the number of categories for classification, such as a binary problem with two child clusters. Notably, the least training time required was for an SVM with a linear kernel function compared to SVM with different kernel functions. Based on accuracy and training time, the authors arrive at the conclusion that SVM with a linear kernel function is the best-performing machine-learning matcher.
# Double Additive Margin Softmax Loss for Face Recognition
## 4 Experiment
### Implementation Settings  
#### 4.1.1 Datasets  
**Training Datasets**. The CASIA-WebFace [7] dataset used for training consists of 49,4414 color face images from 10,575 classes.  
**Test Datasets**. LFW dataset [17] contains 13,233 web-collected images from 5749 different identities, with large variations in pose, expression and illuminations. CFP dataset [18] consists of 500 subjects, each with 10 frontal and 4 profile images. The evaluation protocol includes frontal-frontal (FF) and frontal-profile (FP) face verification, each having 10 folders with 350 same-person pairs and 350 different-person pairs. In our experiments, we employ to test the performance the most challenging subsets CFP-FP which contains images of celebrities in frontal and profile views, CPLFW [20] and CALFW [21] which have higher pose and age variations with same identities from LFW dataset. Specific details of the three datasets above are shown in Table 1 and some example images from CFP-FP, CPLFW and CALFW are given in Figure 3-5, respectively.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Datasets** & **Identity** & **Image** \\ \hline CPLFW [20] & 5749 & 11625 \\ CALFW [21] & 5749 & 12174 \\ CFP-FP [18] & 500 & 7000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Face datasets for training and testing.  
Figure 3: Samples of the same people in the CFP-FP.  
**Data Prepossessing.** We adopt the data preprocessing method used in [14; 22] to detect faces and facial landmarks in images and align them. Then, we crop the aligned face images and resize them to \(112\times 112\), and proceed to perform the normalization for the cropped face images by subtracting 128 and dividing 128.  
**Dataset Overlap Removal**. To develop open-set evaluations, we use the overlap checking code provided by [PERSON] [22] to get ride of the overlapped subjects between the training dataset of CASIA-WebFace and the testing dataset of LFW.