esa-sceva/satcom-chunk-collection huggingface dataset:
# Scene Equipment Saving and Loading Method for Digital Twin Workshop
## 5 Results and Discussion
### Data Saving and Loading Efficiency
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline
**Content** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Average** \\ \hline Temperature & & & & & & & & & & & & \\ Saving & 1289 & 1321 & 1300 & 1311 & 1303 & 1303 & 1295 & 1310 & 1305 & 1309 & 1304.6 \\ Downloading & 567 & 740 & 769 & 753 & 800 & 593 & 794 & 850 & 892 & 618 & 737.6 \\ Loading & 412 & 420 & 405 & 398 & 405 & 399 & 421 & 430 & 409 & 399 & 409.8 \\ Uploading & 290 & 322 & 236 & 292 & 307 & 307 & 291 & 332 & 297 & 333 & 300.7 \\ \hline Vibration & & & & & & & & & & & \\ Saving & 1162 & 1139 & 1134 & 1135 & 1138 & 1129 & 1133 & 1137 & 1130 & 1130 & 1136.7 \\ Downloading & 1298 & 1317 & 1305 & 1530 & 1245 & 1007 & 917 & 1423 & 1109 & 1057 & 1220.8 \\ Loading & 397 & 406 & 411 & 412 & 424 & 410 & 417 & 412 & 404 & 401 & 409.4 \\ Uploading & 543 & 442 & 541 & 496 & 475 & 615 & 582 & 687 & 640 & 580 & 560.1 \\ \hline Abrasion & & & & & & & & & & & \\ Saving & 3024 & 3045 & 3038 & 3032 & 3055 & 3052 & 3035 & 3028 & 3029 & 3025 & 3036.3 \\ Downloading & 1261 & 1295 & 1171 & 1234 & 1651 & 1532 & 1655 & 1807 & 1823 & 1613 & 1504.2 \\ Loading & 464 & 467 & 475 & 471 & 464 & 470 & 452 & 462 & 459 & 485 & 466.9 \\ Uploading & 497 & 554 & 559 & 649 & 727 & 668 & 621 & 508 & 460 & 442 & 568.5 \\ \hline \end{tabular}  
Unit in the table: ms.  
\end{table} 
Table 10: Test results for typical composite data saving and loading efficiency tests based on B/S architecture. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Architecture** & **Data Type** & **Data Volume** & **Saving** & **Loading** & **Downloading** & **Uploading** \\ \hline DTWorks & Temperature & 1,000,000 & 480.7 & 249 & - & - \\ DTWorks & Vibration & 1,000,000 & 613.9 & 258.8 & - & - \\ DTWorks & Abrasion & 1,000,000 & 1344.6 & 408.4 & - & - \\ \hline B/S & Temperature & 1,000,000 & 1304.6 & 409.8 & 737.6 & 300.7 \\ B/S & Vibration & 1,000,000 & 1136.7 & 409.4 & 1220.8 & 560.1 \\ B/S & Abrasion & 1,000,000 & 3036.3 & 466.9 & 1504.2 & 568.5 \\ \hline \end{tabular}  
Unit in the table: ms.  
\end{table} 
Table 11: Comparison results of the efficiency for saving and loading typical composite data.
# Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
## 2 Related Work
### Image Enhancement  
Image enhancement aims to improve image quality [17]. Image enhancement methods can be divided into traditional methods and deep learning methods.  
Figure 1: **(a)** The architecture of Dual-branch Multimodal Fusion Network (DMFNet). AP represents average pooling. Embedding represents an embedding layer aimed at extracting emotional features. **(b)** The architecture of the U-Shape Reconstruction Network (URNet). FCB is composed of convolution operation and Gaussian kernel; DEB is enhanced in detail by Contour Enhancement (CE) and High-frequency Filtering (HF).  
Traditional methods primarily rely on various detection techniques and filters to achieve image enhancement. This includes employing traditional edge detection [18], histogram equalization [19; 20], filters [21], and other techniques to improve image contrast and edge features. [PERSON] et al. [22] incorporated edge perception and detail modulation factors into the cost function of GIF, aiming to enhance image details, effectively suppress image noise, and improve image quality. [PERSON] et al. [23] utilized superpixel grouping and principal component analysis (PCA) of similar patches within superpixels to preserve local image structures and achieve image denoising. [PERSON] et al. [24] applied a hybrid genetic algorithm and particle swarm optimization to select sequence filters for image composite applications, achieving image noise filtering.  
Based on deep learning, methods involve training deep neural networks to learn the intricate mapping relationships between images to tackle complex image enhancement tasks and achieve superior performance [25]. [PERSON] et al. [26] proposed a transformer network based on enhanced medical image denoising. [PERSON] et al. [27] utilized deformable, learnable kernels and stacked convolutional architectures to extract more representative noise features, achieving noise enhancement in images. [PERSON] et al. [28] compared various convolutional networks to deliver faster, more accurate results. They determined the optimal dataset resolution and combined it with image enhancement algorithms to enhance performance.  
For our study focusing on limited driver facial expression data, traditional methods have relatively lower computational resource requirements compared to deep learning approaches. This enables them to effectively handle driver facial expression data. Additionally, traditional methods circumvent the need for extensive annotated data and computational resource utilization associated with deep learning approaches.
## 1 Introduction
utilized artificial neural networks to identify mutual inductance [21], while [PERSON] et al. adopted support vector regression (SVR) to identify mutual inductance and load resistance [22]. However, their data collection and testing are based on a single experimental setup, limiting the ability to demonstrate model transferability. Additionally, these methods increase data collection costs and pose challenges for the adaptation of WPT systems to multiple coils. It is worth noting that most identification methods identify a single parameter or two parameters based on the known parameters on both the primary and secondary sides. Traditional observation approaches suffer from several limitations, such as high-frequency sweeping, slow observation speed, and the inability to observe actual the secondary side are resonant frequency. This paper presents a new method to address these challenges by simultaneously identifying three critical parameters in a WPT system, namely, load resistance, mutual inductance, and actual resonant frequency. The proposed method simplifies the identification process by only requiring input current and voltage values at three predetermined operating frequencies. Observations can be made using the gradient descent algorithm and can be quickly executed on a DSP. Moreover, the primary-side variable capacitor, comprising a standard capacitor and a switch-controlled capacitor (SCC) connected in series, can adjust the equivalent capacitance to compatibly accommodate receivers even though their resonant frequencies are unknown or have drifted. Section 2 will illustrate the parameter identification model. In Section 3, the experimental prototype will be constructed to validate the effectiveness of the proposed method. Finally, a conclusion will be drawn in Section 4.
## 3 Results
### Validation with the Human Activity Data  
There is a close and complex relationship between human activities and land use. Many researchers have demonstrated that the dynamic characteristics of human activity are essential for revealing land use function [49; 60; 61]. To this end, this study selected population heatmap data provided by SmartSteps ([[http://www.smartseps.com/](http://www.smartseps.com/)]([http://www.smartseps.com/](http://www.smartseps.com/)), accessed on 30 July 2020), a subsidiary of China Unicom, one of China's largest mobile operators [62]. The data were aggregated from the mobile phone signaling data in 2019 recorded with individual spatiotemporal travel information, with a spatial resolution of 250 m.  
We calculated the average population of each grid in the study area over a 24 h day and distinguished between urban areas and urban-rural fringes. Figures 7 and 8 show the population distribution of urban areas and urban-rural fringes at four different timestamps (6 a.m., 11 a.m., 3 p.m., and 10 p.m.), respectively. Using 6 a.m. as the baseline, urban areas showed significant population increases during daytime hours (+35.75% at 11 a.m., +37.67% at 3 p.m.) and a moderate increase in the evening (+11.23% at 10 p.m.). In contrast, the urban-rural fringe exhibited much more stable patterns, with only slight variations throughout the day (+3.90% at 11 a.m., +4.01% at 3 p.m., +4.26% at 10 p.m.).  
Figure 5: Distribution of the building characteristics for three land types.  
Figure 6: Distribution of the POI characteristics for three land types.  
These quantitative patterns reflected the distinct functional characteristics of different areas. Urban areas, dominated by non-agricultural industries with concentrated working hours, showed obvious tidal patterns due to daily commuting flows. The urban-rural fringe, with its mixed functions and higher proportion of agricultural activities, maintained more balanced activity patterns. Additionally, factors such as traffic infrastructure, demographics, and environments contributed to the more pronounced diurnal activity differences in urban areas, while urban-rural fringes maintained relatively stable activity patterns.  
Figure 7: Population dynamics in urban areas in a single day: (**a**) 6 a.m., (**b**) 11 a.m., (**c**) 3 p.m., (**d**) 10 p.m.
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 2 Tunnel Lighting Controls: Strategies and Technologies
### Manual Control Strategy  
Manual control is the earliest and most straightforward method employed in tunnel lighting. This simple strategy allows users to turn an individual lamp or a group of lamps on or off using a manual switch or dim the intensity to intermediate light levels with dimming regulators inside the tunnel. Nowadays, advanced tunnel lighting systems provide new flexible ways for users to control the lighting level right from their computer screens and smartphone applications. The manual control strategy scheme is shown in Figure 1.
###### Abstract  
In the present work, the effect of welding current and electrode negative (EN) ratio on microstructure and mechanical properties of square groove butt joint produced by alternating current gas metal arc welding (AC-GMAW) process has been investigated. The experiments were conducted using different combination of welding current ranging from 150 A to 250 A with three distinct EN ratios i.e., 0%, 30% and 50% on 5 mm thick steel plates at a constant joint gap of 1 mm and welding speed of 10 mm/s. The effect of current and EN ratio on welding soundness was evaluated through optical microscopy, tensile tests and micro-hardness mapping. The results revealed that, at higher values of currents, an increase in EN ratio resulted an increase in penetration depth. It was found that at a current of 250 A and a 50% EN ratio, the deep penetration was obtained. Tensile test results showed that the tensile strength properties improved with increasing current. Furthermore, the micro-hardness distribution was not strikingly affected by changing the EN ratio.  
AC-GMAW; EN ratio; thick plate; mechanical properties +
Footnote †: journal: Applied Sciences
## 3 Results
### Model Results
#### 3.2.1 Hyper-Parameter Tuning
The 'learning_rate' regulates the step size for weight updates during boosting, where lower values improve stability and accuracy at the cost of requiring more boosting iterations [44]. At the same time, 'n_estimators' define the number of boosting rounds, influencing the model's learning capacity [45]. Regularization parameters such as'reg_alpha' and'reg_lambda' further stabilize the model by penalizing large coefficients, improving robustness against noise and overfitting [46]. Lastly,'subsample' introduces randomness by specifying the fraction of training data used for each tree, which enhances generalization, particularly in large datasets [47]. Based on the tuned hyper-parameters, this study develops six XGBoost models and calculated SHAP values to examine the relative importance and impacts of independent variables on elderly pedestrian traffic accident frequency and deaths, respectively. The overall accuracy was relatively higher when modeling elderly pedestrian traffic accident frequency, rather than deaths (Table 5).
###### Abstract  
Background: Tai Chi Chuan (TCC) is popular in exercise, but little research has been done on limb and joint strength after the exercise. This study aimed to investigate whether TCC players have different limb and joint strengths in the upper and lower limbs from non-Tai Chi Chuan (NTCC).  
Methods: Twenty TCC players who have at least one year of experience playing TCC were compared with a group of 25 NTCC participants. A force sensor was used to measure forces in the lower and upper limbs. Maximum forces produced by the lower limbs was measured during a standing position with straight legs or were measured in a seated position. The maximum forces produced by the upper limbs were measured in a standing position at elbow and shoulder levels. Results: The forces of the knee extensors in the TCC group were significantly higher (7.4%) than in the NTCC group; however, the forces of the knee flexors in the TCC group were significantly lower than the NTCC group. The heel-force in hip extension in the NTCC group was significantly higher (9.3%) than in TCC; the toe-force in the plantar flexion in the NTCC was significantly higher than in the TCC group. There were no significant differences between the two groups in the hand-forces in the upper limbs.  
Conclusions: TCC routine movements strengthen the muscles related to knee extension, which are mainly related to bending knee performance during TCC exercise. For the healthy people between 42 and 78 years old, TCC benefits the enhancement of the strength for knee extension but not for the knee flexion, ankle plantar flexion and hip extension. There is no significant difference between the TCC and NTCC groups on the strength of upper limbs and joints. As a whole, the effect of Tai Chi Chuan exercise on the strengths in the limbs and joints is compromised. In the future, a study with larger sample size than this study is highly recommended.  
force sensor; Tai Chi Chuan; joint strength; lower limbs; upper limb
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 4 Multiple MPTCP Connections  
Now, we extend our solution to the case of \(N\) MPTCP sources sharing \(K\) paths. Each subflow \(i\) of an MPTCP connection \(n\) over the \(K\) paths has a sending rate \(\lambda_{i}^{n}\), and the following constraint should hold:  
\[\sum_{n=1}^{N}\sum_{i=1}^{K}\lambda_{i}^{n}\leq B. \tag{23}\]  
The throughput obtained by the \(n\)th MPTCP connection is the sum of the throughputs for all of its subflows and is given by:  
\[\theta^{n}=\sum_{i=1}^{K}\theta_{i}^{n}(\mathbf{\lambda}^{n}). \tag{24}\]  
where \(\mathbf{\lambda}^{n}\) is the vector \([\lambda_{1}^{n},\lambda_{2}^{n},\ldots,\lambda_{K}^{n}]\) and  
\[\theta_{i}^{n}(\mathbf{\lambda}^{n})=V\Bigg{(}\frac{\lambda_{i}^{n}}{\sum_{m=1}^{ N}\sum_{j=1}^{K}\lambda_{j}^{m}}\Bigg{)}\text{(recall Equation (\ref{eq:n-1}))}. \tag{25}\]Therefore, the utility of the \(n\)th MPTCP connection is:  
\[U^{n}(\lambda)=\sum_{i=1}^{K}\Big{(}\frac{\lambda_{i}^{n}}{\sum_{n=1}^{N}\sum_{j= 1}^{K}\lambda_{j}^{m}}-\hat{p}\lambda_{i}^{n}\Big{)}. \tag{26}\]  
Observe that the net utility in Equation (26) is computed as the sum of the utilities of each subflow. This decoupling approximation is motivated by the findings in realistic network settings [10], as each network path \(i\) has its own capacity (bandwidth) and prices (independent of each other); therefore, let \(C\) be the vector with the \(i\)th element being a finite constant \(C_{i}\). Then, \(B=\sum C_{i}\), and for each \(i\), we have the following constraint:  
\[\sum_{n=1}^{N}\lambda_{i}^{n}\leq C_{i}. \tag{27}\]  
Due to this constraint, the target loss and delay of RSF MPTCP are attainable iff the _aggregate achievable loss/delay is lower than the target_.  
Observe that when applying the KKT conditions to the optimal response at the normalized equilibrium, there are \(K\) Lagrange multipliers. Clearly, our objective is to find a vector of \(K\) Lagrange multipliers such that the Nash equilibrium for the relaxed game will be an equilibrium for the primary game, where the constraints will be satisfied along with the conditions for the complementary equation. If diagonal strict concavity holds for each subflow and/or for each path separately, then Theorem 4 in [18] guarantees the existence and uniqueness of the equilibrium.
## 4 Event-Triggered Invocation
### Design Methodology  
The schematic diagram of the event-triggered invocation mechanism is depicted in Fig. 6. With a structure similar to event-based controllers [31], there are basically two parts in this invocation mechanism [28], the event detector and the feedback scheduling algorithm. The event detector is time-triggered with a period of \(T_{ED}\), while the feedback scheduling algorithm is triggered by the _execution-request_ event issued by the event detector.  
The design of the event detector is a key issue for implementing the event-triggered invocation mechanism. The major role of the event detector is to decide under what conditions the system needs to execute the feedback scheduling algorithm. Intuitively, when the DMR is in or close to a steady state, there is no need to execute the feedback scheduling algorithm. If the DMR deviates significantly from  
Figure 6: Schematic diagram of event-triggered invocation.  
the desired level, it becomes mandatory to run the feedback scheduler to adjust system parameters. In this paper the following condition is used for issuing the _execution-request_ event:  
\[\mid\rho(k)-\rho_{r}\mid\geq\delta \tag{6}\]  
According to (6), the feedback scheduling algorithm will be executed if and only if the absolute difference between the actual DMR and its desired level is no less than a specific threshold \(\delta\). In this way, the disadvantages of the time-triggered invocation mechanism with respect to response speed and overhead are avoided. Furthermore, the negative effect of measurement noise on the DMR may be alleviated naturally.  
There are two important parameters, \(T_{ED}\) and \(\delta\). Generally speaking, choosing these parameters demands careful trade-offs between quick response and low overhead. Thanks to the small amount of computations of (6), it is possible to assign quite a small period \(T_{ED}\) to the event detector to achieve quick response while keeping the feedback scheduling overhead small. The magnitude of the measurement noise should be taken into account when deciding the value of \(\delta\). A \(\delta\) value that is slightly bigger than the magnitude of measurement noise could be used to reduce the times of execution of the feedback scheduler, which results in smaller overheads.
## 2 Materials and Methods
### Sentiment Scoring and Statistical Analysis
For each group, a lexicon is formed from categories of words from three canonical sentiment analysis lexicons: General Inquirer (GI), National Research Council of Canada (NRC) Emotion Lexicon, and Linguistic Inquiry and Word Count (LWIC) Text Analysis Tool [70; 71; 72]. If the 20 PANAS traits were defined by individual lexicons instead of these nine aggregate groups, then the number of words in each lexicon would be too small to produce reliable sentiment analysis data [73]. The traits included in PANAS, their aggregated groupings, and the specific words in GI, NRC, and LWIC which form each lexicon subgroup are shown in Appendix A.1[74]. For a given PANAS group, a message (i.e., tweet or narrative) is assigned a value of 1 if it contains any word in the corresponding PANAS lexicon. If it does not contain a word in the specified PANAS lexicon, then it is given a value of 0 for that group. A message with a 1 indicates that the message does contain some sentiment related to the specified PANAS trait. A message with a 0 indicates that the message does not contain any sentiment related to the specified PANAS trait. This is an automated and repeatable approach to sentiment scoring; as such, there is no human or evaluator interpreting the semantic meaning of a message and/or assigning it a sentiment score. Average scores, standard deviations, and sample sizes are collected for each tweet and narrative with respect to each PANAS group. Sums of 1- and 0-scoring tweets and narratives are tallied for each PANAS group and utilized for statistical significance testing. The null hypothesis is provided in Hypothesis 0 and the alternative hypothesis is provided in Hypothesis 1. The form of the hypotheses remains the same for both the chi-squared and [PERSON]'s exact test. We utilize an alpha level \(\alpha=0.05\) as our cutoff point to determine statistical significance for each test. Calculated \(p\)-values less than this alpha level provide evidence in support of rejecting the null hypothesis while \(p\)-values higher than this level fail to reject the null hypothesis in favor of the alternative hypothesis. **H0. **_There is_ no _association between the source of the narrative and calculated sentiment between Group 1 (e.g., ChatGPT-generated narratives) and Group 2 (e.g., real tweets) for a given PANAS category._  
**H1. **_There_ is _an association between the source of the narrative and the calculated sentiment between Group 1 (e.g., ChatGPT-generated narratives) and Group 2 (e.g., real tweets) for a given PANAS category._
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### With Nanotechnologies
#### 2.2.1 Nanoclays  
Nanoclay [76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109] is the general term used to refer to mineral clays with a phosilicate or laminar structure in the order of nm and surfaces of 50-150 nm or more. Despite nanoclay being commonly used, we must take into account that not all nanoclay dimensions are nanosized. Some clay dimensions classified as nanoclays are microsize due to agglomerations or their structure, such as motmorillonite laminar, which is nano in one laminar dimension but micro in the other. The mineral base can be synthetic or natural, and it is hydrophilic. The clay surface can be modified with specific compounds to improve its affinity and to make it compatible with, for example, polymers. The surface area of nanoclays is very large, around 750 m\({}^{2}\cdot\)g\({}^{-1}\). When small amounts of these materials are incorporated into polymeric matrices, the result is known as a nanocomposite. Most of the dyes used in the textile industry are anionic, which is why achieving better adsorption performance can be expected from anionic nanoclays.  
The nanoclays studied in this review present several advantages, such as their natural abundance, low cost, and high adsorption capacity. When such nanoclays act as inorganic hosts for organic compounds, they improve organic host properties to confer better fastness from chemical and natural environments. These nanoclays can be chemically synthesized to improve their physico-chemical characteristics. Another firm advantage of chemical synthesis is that there is no purification step, which is necessary with natural nanoclays to improve adsorption capacity.  
Silicon and oxygen are common in all mineral clays. When combined with other elements, such as Al, Mg, Fe, Na, Ca, and K, and given the many ways in which these elements can be linked, they generate a large number of possible configurations. One important distinction in mineral clay properties is the ability of some to change volume by adsorbing water molecules from other polar ions in their structure. This is called the swelling property [110]. In the following, the most significant clays used for the recovery of wastewater in the textile industry will be described.