esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Major Components of Network Slicing System
### Advanced Deep Learning Module
Moreover, the dataset has 12 attributes related to resource utilization such as the timestamps (ms), CPU cores (number of virtual cores), CPU usage (MHz), CPU capacity (MHz), CPU capacity (percentage), memory requested (KBs), memory usage (KBs), memory usage (%), received throughput(KBs), transmitted throughput(KBs), disk writeup throughput (KBs) and disk size (GBs). Thus, we trained our model at this dataset and our deep learning model can predict the future state of the resources and network operator used these predicted statistics for better resource management, i.e., scaling up or scaling down the resources. Moreover, update manager of the IBN system updates the resources' statistics in our IBN policy store. Additionally, the IBN system considers these statistics from the database while preparing and designing user intents. It designs the slice intent by looking at the statistics of the resources, if there are enough resources then process the slice request otherwise rejected. Thus, in this way IBN system can easily scale up and down the resources; in case of failure, it will notify the IBN system and request to update the resources. In the future, we have plan to used reinforcement learning with the GAN model in our system for better QoS, slice admission, slice control, and slice management. On the other hand, the training and testing accuracy of the deep learning model is presented in Figure 8, which shows almost 87% training accuracy and 82% testing accuracy. However, our GAN shows satisfactory performance while predicting VNFs resources--CPU, memory, etc.
## 5 Conclusions  
This paper mainly models and solves the problem of multi-channel power allocation under a one-shot confrontation scenario and repeated confrontation scenario. For the problem of multi-channel power allocation under a one-shot confrontation scenario, we model the problem based on the BG model, and design the DO-SINR algorithm with the number of channels as the optimization objective to solve the approximate Nash equilibrium of the game between the two parties. The results obtained in this way are more in line with the application requirements of satellite communication. Concerning the problem of multi-channel power allocation under a repeated confrontation scenario, we transform the problem into an online shortest path problem with a graph structure to transform the problem-solving process more intuitively, and then design the Exp3-U algorithm based on the graph structure to solve the resource allocation problem. Experiments show that our algorithm can minimize the expected regret of communication parties during the online confrontation while maintaining good operation efficiency. The two game problems constructed in this paper are the common forms in the confrontation scenario. Our research and analysis can simulate the confrontation scenario of the actual satellite communication power allocation.  
Figure 8: Comparison of Exp3-U algorithm and Exp3-OE algorithm in running time.  
**Author Contributions:** Conceptualization, M.Z. and J.C.; methodology, M.Z., S.C., and J.L.; validation, S.C. and J.L.; resources, J.L. and Z.H.; data curation, M.Z. and Z.H.; writing--original draft preparation, M.Z.; writing--review and editing, S.C., J.L. and M.Z.; supervision, J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grants No. 61806212, No. 61702528, and the Postgraduate Research Innovation Project of Hunan Province No. CX20210011.  
**Data Availability Statement:** Our code will be available at [[https://github.com/mw-zou/power-allocation](https://github.com/mw-zou/power-allocation)]([https://github.com/mw-zou/power-allocation](https://github.com/mw-zou/power-allocation)) (accessed on 1 October 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest and there is nothing else that needs to be declared.
## 1 Introduction
Nonetheless, it is important to note that FBG sensors retain significant potential for multi-site measurement applications within compact measurement environments. Moreover, the investigation of FBG subsidence sensors utilizing conventional FBGs for the simultaneous measurement of multiple parameters has not been conducted. The primary distinction between the current tilt measurement study and the similar previous study [39] lies in the following point: the prior research utilized only two FBGs to ascertain both the tilt angle and the tilt direction. This study explores a modular system comprising two FBG subsidence sensors designed for the simultaneous measurement of water level and inclination while also investigating the feasibility of concurrently measuring various water temperatures. It is important to clarify that the upper limit of the measurement range and the measurement sensitivity of water level and tilt are not the primary focus of this study. Concentrating on these aspects would shift attention towards the design of sensor packaging rather than the measurement of multiple parameters. Furthermore, the FBG subsidence sensors were skewed in deployment to enhance the sensitivity of tilt measurements. Additionally, the measurement system can be integrated with the FSO, thereby potentially increasing the system's flexibility for practical applications. Consequently, this study contributes to the cost optimization and efficacy of related monitoring and measurement processes.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 2 Problem Space and Related Work
This provides ease for researchers to use our virtual machine setup to dive straight into testing and data generation without wasting their time and effort building the virtual setup. Table 1 shows a list of previous HTTP adaptive video streaming databases that are widely used in research within QoE. Our generated database of encoded videos contains 6 source segment division videos and 120 different resolution videos. Our database comes with a pre-configured P4 software defined network with a server and multiple clients for testing and evaluating QoE with DASH reference player. Its main contribution is that it monitors the network and all its ports for recording DASHF reference server packets and evaluating client data. It then generates training-ready clean data for ML usage purposes. This was performed to ease the experimentation with big data and data mining for use in ML and the understanding of multimedia network environment. Furthermore, QoE is widely discussed and predicted in multiple aspects within the use of different features and multiple joint classifiers [17; 18; 19; 20; 21]. Our approach is unique in its data generation. We depend on our test bed to generate the network data required, and based on that network data, we use machine learning algorithms to predict the generation of MOS and all other features.In addition, we analyse the most effective feature from the network data that directly affects the MOS and prioritize it in our test bed. Thus, the data generated from the test bed is always training-ready to test on more algorithms.
# Electrospun Nanomaterials for Energy Applications: Recent Advances
## 10 Textiles for Energy Saving through Personal Thermal Management  
In the latest years, the need for limiting GHG emissions has greatly boosted the development and utilization of renewable carbon-free energy sources. Cooling and heating systems take up an appreciable fraction of the global energy demand and significantly contribute to enhancing the GHG emissions [266; 267]. Hence, another important action against climate changes and extreme weather can be the reduction of these energy consumptions through the improvement of energy efficiency. Taking into consideration the residential and commercial buildings, the principal energy demand originates from indoor cooling and heating. However, a large portion of the energy spent on this purpose is wasted for empty spaces and inanimate objects. For a long time, the approach commonly followed to cope with this problem has consisted of trying to improve the building thermal isolation through the use of innovative materials and suitable design.  
In recent years, a new and complementary approach has emerged, intended to profit from the smaller thermal mass of human beings compared with buildings. This approach is centered on providing cooling or heating only in the immediate environment around the human body, to minimize wasted energy and, thus, enhance efficiency [268]. [PERSON]'s group has devoted several efforts in the field of personal thermal management (PTM) [266; 267; 268; 269]. The group has developed wearable textiles with a superior cooling [266; 270] or heating effect [269] with respect to common commercially available textiles, able to mitigate the energy demand for outdoor cooling or indoor heating. In particular, [PERSON] et al. [271; 272] have utilized the co-axial electrospinningning technique to prepare phase-change polyethylene glycol/cellulose acetate (PEG/CA) fibers with great potential in PTM. In the ultrafine composite core-shell fibers, CA and PEG act as a supporting fiber-template material and as a model phase-change material, respectively. The electrospun fibrous material exhibits good thermo-regulating capability. For further information on electrospun phase-change fibers see ref. [8; 273].
# SmartX Multi-View Visibility Framework
## 2 Background and Requirements of Flow-Centric Visibility
### OF@TEIN Playground as an SDN-Enabled Multisite Cloud Playground  
Aligned with Future Internet testbeds, we established OF@TEIN Playground, which is an SDN-enabled multisite cloud playground [8; 10]. OF@TEIN Playground connects ten international sites spread over nine Asian countries to dynamically provide dedicated resources for performing various miniaturized experiments to the developers. In order to automatically build, operate, and utilize multisite playground, we have a SmartX playground tower (playground tower), which provides a logical space in a centralized location by following the concept of monitor and control tower. Playground tower systematically covers various functional requirements of operating multisite playground by employing several entities as depicted in Figure 1. First, ID center is responsible for OpenStack-based keystone authentication and provides OpenStack Horizon Web UI. The provisioning center (pro center) is responsible for remote installation and configuration of multi-site playground resources. The visibility center (vis center) covers playground visibility and provides panoramic visualization support. The security center (sec center) and SDN controllers take care of the security challenges of keeping the playground safe and inter-connects playground sites by using virtual extensible local area network (VXLAN) tunnels.  
At OF@TEIN playground, distributed SmartX boxes [10] hosts the virtual machine (VM) instances of OpenStack cloud [11] and controls virtual networking overlay among multiple sites. Playground developers request OpenStack VM instances and virtual local area network (VLAN) isolated overlay virtual networking from distributed SmartX boxes to perform various diversified networking experiments [12]. Distributed SmartX boxes support multi-tenancy by hosting any number  
Figure 1: OF@TEIN playground as a software-defined networking (SDN)-enabled multisite cloud.  
of dedicated virtual entities for each tenant. By using five virtual switches at each SmartX box, we implement required virtual networking features. Three virtual switches: br-int (integration bridge), brvlan (VLAN tagging bridge), and br-ex (external bridge) are used by the cloud portion of SmartX box. Two virtual switches: br-dev (developer's switch) and br-cap (operator switch) are used to enable SDN capability at the playground. Developer switch is sliced based on flowspace allocation under FlowVisor OpenFlow controller and utilized by several developers simultaneously. Operator switch provides output ports for the developer switch, which is mapped into several VXLAN-based tunnel ports for inter-site connections. Additionally, the underlay network for the playground is spread across several research and education (R&E) networks under distinct administrative domains. For instance, an interconnection between any two sites of the playground involves from three to seven heterogeneous underlay networks.
## 2 Materials and Methods  
### Dataset  
This retrospective observational study analyzed match-related data from all 380 matches of the 2022-23 French Ligue 1 season. The league spanned from 5 August 2022 to 3 June 2023, with a winter break from 13 November 2022 to 27 December 2022 due to the FIFA World Cup. The competition involved 20 teams playing each other twice, once at home and once away (double round robin), resulting in 38 matchdays. The top five teams qualified for UEFA competitions: the first two for the UCL group stage, the third for the UCL third qualifying round, the fourth for the Europa League group stage, and the fifth for the Europa Conference League play-off round. Additionally, the team, which finished in 13 th place, qualified for the Europa League group stage as the 2022-23 Coupe de France winners. As the league transitioned to an 18-team format for the 2023-24 season, four clubs were relegated without play-offs at the conclusion of the 2022-23 campaign.  
Match data were obtained from a public-accessed football statistics website, \"whoscored.com\" [37], which utilizes data resources provided by Opta Stats Perform Company (London, UK). A study by [PERSON] et al. [38] assessed the inter-operator reliability of this company's tracking system and found it acceptable. Their research demonstrated very good agreement (weighted kappa values of 0.92 and 0.94) between independent operators coding team match events with an average difference in event timing of 0.06 \(\pm\) 0.04 s.  
After a rigorous screening process considering data availability and alignment with previous research [39; 40], the following 33 technical performance-related match actions and events were selected for each match and team: total match goals; total shots count; shots on target; shots off target; shots blocked; attempts in open play; attempts in counterattack; attempts in set pieces; shot directions (left/middle/right); shot zones in the 6-yard box; shot zones in the 18-yard box; shot zones out of the box; ball possession (%); touches; crosses; long balls; total passes; total successful passes; pass success (%); short passes; through balls; dribbles attempted; dribble success (%); total aerial duels; aerial duels success (%); aerial duels offensive; aerial duels defensive; tackles attempted; tackle success (%); corners; attack from the corridor (left/middle/right). Definitions of these variables can be found in previous studies [38; 40] as well as in the glossaries of whoscored and Opta [41; 42]. Additionally, the variable \"LOCATION\" (home/away) of the match was added to the tabular dataset.
## Abbreviations  
The notations are introduced as follows:  
\(G\): The shear modulus, Pa  
\(K\): The bulk modulus of coal, Pa  
\(E\): The Young's modulus, Pa  
\(K_{S}\): Bulk modulus of coal skeleton  
\(V_{m}\): Molar constant of gas  
\(R\): Universal gas constant, J/mol-k  
\(P_{i}\): Gas pressure, MPa  
\(T\): Temperature of the coal, K  
\(\theta_{PY}\): Stress coefficient caused by gas pressure  
\(\theta_{PX}\): Stress coefficient caused by gas adsorption  
\(\theta_{T}\): Coefficient of thermal stress  
\(q_{g}\): Seepage velocity(m\({}^{3}\)/s)  
\(I_{i}\): Diffusion component of single phase  
\(D_{i}\): Diffusion coefficient of single phase, m\({}^{2}\)/s  
\(m_{i}\): Gas mass of each component, kg  
\(c_{i}\): Molar concentration of a single component, mol/m\({}^{3}\)  
\(k\): Permeability, m\({}^{2}\)  
\(Q_{S}\): Source term, kg/(m\({}^{3}\)\(\cdot\)s)  
\(M_{i}\): Molar mass of each component, kg/mol  
\(dQ_{H}\): Heat source for thermal expansion  
\(dU\): The internal energy per unit volume  
\(C_{V}\): Specific heat at constant volume of coal seam  
\(Q_{dis}\): Differential heat source.  
\(V_{S0}\): Initial volume of coal skeleton  
\(\Delta V_{S}\): The coal skeleton volume changes  
\(\Delta V_{SP}\): Bulk expansion and deformation of coal caused by pressure  
\(\Delta V_{ST}\): Bulk expansion and deformation of coal caused by temperature  
\(k_{0}\): The initial permeability, m\({}^{2}\)  
\(\
u\): Poisson's ratio  
\(\beta\): Thermal expansion coefficient, K\({}^{-1}\)  
\(\delta_{ij}\): Kronecker function  
\(a_{i}\): Langmuir volume constant, m\({}^{3}\)/kg  
\(b_{i}\): Langmuir pressure constant, Pa\({}^{-1}\)  
\(\rho_{c}\): Density of coal, kg/m\({}^{3}\)  
\(\varepsilon_{T}\): Thermal expansion strain  
\(\varepsilon_{PY}\): Strain caused by gas pressure  
\(\varepsilon_{PX}\): Gas strain  
\(\varepsilon_{ij}\): Train component  
\(u_{ij}\): Displacement component  
\(\alpha\): Biot coefficient  
\(\sigma_{ij}\): Stress tensor(\(i,j=1,2\))  
\(\mu_{i}\): Viscosity coefficient of single phase  
\(\rho_{i}\): Density of a single component, kg/m\({}^{3}\)  
\(\varphi\): Porosity  
\(\rho_{a}\): Density of gas at standard conditions, kg/m\({}^{3}\)  
\(\eta\): Thermal conductivity of coal  
\(\varphi_{0}\): Initial porosity of coal  
\(\mathbf{e}\): Volumetric strain of coal  
\(d_{e}\): Effective diameter of particle
## 3 Overview of Pseudo-Dynamic Testing and Result Analysis  
As shown in Figure 6 below, a full-size two-story test frame modeled from existing RC structures with non-seismic details in Korea was subjected to pseudo-dynamic testing using the pseudo-dynamic testing system developed in the present study. As a result, the effect of the developed CFT-MF system, when applied to existing RC structures, was examined and verified, especially as to its seismic retrofitting performance, i.e., restoring force characteristics, stiffness reinforcement, and seismic response control.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strengthening Method** & **Element** & \
\begin{tabular}{c} **Number of** \\ **Reinforcement** \\ \end{tabular} & **Unit Cost (USD)** & **Total Cost (USD)** &
\begin{tabular}{c} **Cost-Benefit Ratio** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{2}{*}{RC shear wall} & Concrete shear wall & 8 & 28,351 & \multirow{2}{*}{385,875} & \multirow{2}{*}{-} \\ \cline{2-2} \cline{5-5}  & Micro pile & 10 & 15,907 & & \\ \hline CFT-MF & CFT-MF & 8 & 40,982 & 327,856 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cost evaluation between the CFT-MF and the conventional adding infill shear wall strengthening methods.  
Figure 6: Illustration of the pseudo-dynamic testing system developed in the present study.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 5 Results and Discussion  
To evaluate the effectiveness of the proposed method, a series of experiments were conducted using three datasets of varying complexity, containing 2, 13, and 47 gesture classes, respectively. The objective was to assess the influence of optical flow integration on the performance of the BiLSTM model across various metrics, including accuracy, precision, recall, F1-score, and inference time. The results provide a comprehensive overview of how the inclusion of motion information affects classification performance, especially in the context of dynamic gestures.  
In addition to internal benchmarking, our method was compared with recent state-of-the-art approaches in sign language recognition, particularly those utilizing YOLO-based frameworks. [PERSON] et al. [26] developed a system based on YOLOv5 and YOLOv8 for the detection of American Sign Language (ASL) letters. Their custom-trained model achieved a precision of 95%, recall of 97%, and mean average precision ([EMAIL_ADDRESS]) of 96% on their  
Figure 8: Obtained metrics after completed YOLOv8-Pose training: (**a**) validation pose loss; (**b**) mAP50-95(P); (**c**) mAP50-95(B).  
ASL alphabet dataset, demonstrating strong real-time detection performance. In another study, [PERSON] et al. [27] introduced a bilingual recognition system using YOLOv11 to simultaneously detect letters from both Bangla Sign Language (BdSL) and ASL. Their model was trained on a dataset comprising 9556 annotated images across 64 different signs. Evaluation of the system yielded a precision of 99.12%, an average recall of 99.63%, and high mAP values over 30 training epochs, making the approach suitable for real-time communication technologies.  
Compared to these object detection approaches, our BiLSTM-based model emphasizes temporal dynamics by integrating keypoint sequences and optical flow. The experimental results indicate that this method provides competitive performance in binary classification tasks and meaningful accuracy in larger multi-class setups, though with some decline in precision due to inter-class similarity. Nevertheless, the integration of motion features proves valuable, offering a distinct advantage in capturing gesture transitions over time. These findings highlight the complementary strengths of temporal sequence modeling compared to spatial object detection in the domain of sign language recognition.
## 3 Methodology
### Models Training  
To enhance efficiency and reduce training time, transfer learning is employed by utilizing pre-trained models in object detection and adapting them for drone detection. To analyze the performance of the models in the context of the problem, the dataset is divided between testing and validation. These subsets include 40,000 images for training and 5000 images for validation. Initial testing is performed locally, and various batch sizes (2, 4, 8, 16) are evaluated for all models until the size that shows the best final performance is identified. In addition, the effect of using up to eight workers to measure the processing speed is evaluated to avoid GPU overload, which in some cases requires a reduction in the number of workers.  
Regarding the optimizer, the two most common options are evaluated: Adam (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent). Although SGD is more stable than Adam [65] and tends to generalize faster, some work like [66] suggests that hyperparameters may be the reason that adaptive algorithms such as Adam fail to generalize. In our case, Adam yields superior results in terms of accuracy; therefore, it is selected, despite requiring a longer training period.  
Once these configurations are determined, server training commences, during which the initial hyperparameters are configured, including the learning rate and batch size, based on the insights obtained from the preliminary tests. This training is conducted using the complete database, which comprises a distribution of 70% images for \"train\" (162,090), 15% for \"validation\" (34,733), and the remaining 15% for \"test\" (34,734).  
During training, after each epoch, the results are validated to monitor the model's progress. If overfitting is detected, the hyperparameters are adjusted to mitigate this issue. In instances where performance begins to decline, reverting to previous configurations is considered to prevent losses in training efficiency and prediction accuracy.