esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Implementations
### Managerial Implications  
The managerial implications of this research are substantial. For businesses, particularly those in the online entertainment industry, the findings provide actionable insights for enhancing customer-centric strategies. By recognizing the pivotal role of customer satisfaction in driving loyalty, companies can focus on improving customer satisfaction to foster long-term customer relationships and brand advocacy.  
To increase customer satisfaction and, ultimately, loyalty, businesses must focus on utilitarian and hedonistic aspects. Marketers should prioritize the delivery of functional benefits and enjoyable experiences to build strong customer relationships. These strategies are crucial as they directly enhance customer satisfaction, which, in turn, promotes loyalty.  
Although social value may not directly influence satisfaction or loyalty, its significant impact on trust suggests that fostering a sense of community and social belonging can be beneficial for building trust. Therefore, social elements must be incorporated into marketing efforts to enhance the overall perceived value and trust in the brand. The fact that different dimensions of perceived value influence trust emphasizes the importance of tailoring marketing and service efforts to enhance these value dimensions. Furthermore, the absence of trust as a mediator suggests that businesses should focus on building trust directly with customers rather than relying on trust as an intermediary factor.
## 3 Results
### Results and Analysis
#### 3.2.2 Atmospheric Stratification Delay
Figure 7c,d demonstrate that the neutral atmospheric delay gradually decreases as the number of stratifications increases, eventually stabilizing at a fixed value. Overall, the impact of stratification on atmospheric delay is minimal, with the delay deviations between different stratification levels being less than 0.02 m. Similarly, the discrepancy between gf1 and gf7 imagery remains below 0.02 m. Additionally, the ionospheric delay of GF-3 imagery is significantly smaller than the neutral atmospheric delay, with the ionospheric delay being within 0.3 m, while the neutral atmospheric delay exceeds 2 m. Furthermore, atmospheric delays vary across different imagery types, with both the ionospheric and neutral atmospheric delays of gf7 being smaller than those of gf1. It can be observed that the discrepancy between gf1 and gf7 imagery is minimized when the ionosphere is divided into five layers and the neutral atmosphere into three layers. Therefore, this stratification approach was adopted to compute the atmospheric delay for all the experimental imagery. The satellite positions were obtained using both the vector-autonomous intersection method and the ephemeris data interpolation method. The atmospheric delays calculated by these two approaches were compared to evaluate the effectiveness of the proposed method for atmospheric delay correction, as shown in Table 5 and Figure 8.  
Figure 7: Atmospheric delamination delay. (**a**) gf1 ionospheric delay; (**b**) gf7 ionospheric delay; (**c**) gf1 neutral atmospheric delay; (**d**) gf7 neutral atmospheric delay. Table 5 presents the atmospheric delay values obtained using the vector-autonomous intersection method and the ephemeris data interpolation method, while Figure 8 provides a visual comparison between the two methods. In Table 5, \(\Delta R_{atm\_ref}\) represents the atmospheric delay value obtained using the ephemeris data interpolation method, and \(\Delta R_{atm\_vec}\) represents the atmospheric delay value obtained using the vector-autonomous intersection method. In comparison, the results obtained from the two methods are essentially consistent. Among the 10 experimental datasets, only the gf2 image shows a minor error of 0.0001 m, which is almost negligible. This indicates that the method proposed in this study is effective. Additionally, atmospheric delays vary between imagery from different study areas. Within the same study area, atmospheric delays also differ between image groups, with delays in group A being smaller than those in group B, and delays in group C being smaller than those in group D. However, the atmospheric delay values within each group are nearly identical. In summary, the vector-autonomous intersection method proposed in this study can effectively replace the traditional method for atmospheric delay error extraction. It overcomes the challenges of atmospheric correction for images in the absence of satellites and ephemeris parameters, making a significant contribution to SAR atmospheric correction research.
# Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter
## 3 Methods and Experimental Design
### Finite-Element Single-Factor Simulation Experiment
#### 3.1.2 Finite-Element Modelling
Considering the actual cutting process, in which the serrated surface of the cutter interacts with the cutting surface of the shrub, the shrub eventually undergoes failure and fracture. Therefore, surface-to-surface contact erosion (ERODING_NODES_TO_SURFACE) was selected. The static friction coefficient was set to 0.1, the dynamic friction coefficient to 0.2, and all other parameters were kept at their default settings. The CONTROL and DATABASE settings are key components that directly influence finite-element simulation reliability. CONTROL allows for adjusting parameters such as time step and energy to ensure accuracy and stability. In this study, the keywords \"ENERGY\", \"HOURGLASS\", \"TERMINATION\", and \"TIMESTEP\" were included in the simulation model. \"ENERGY\" ensures energy conservation, \"HOURGLASS\" prevents numerical instability, \"TERMINATION\" sets the end time based on the bush diameter  
Figure 6: Mesh division. (0.4-0.6 s), and \"TIMESTEP\" controls the time step (initial value: 0, scale factor: 0.9, and computed step: \(-1\times 10^{-7}\)). DATABASE settings manage the output format and simulation result contents, such as stress and strain distributions. The keywords \"ASCII_option\", \"BINARY_D3 PLOT\", and \"EXTENT_BINARY\" were used to export the results in multiple formats for further analysis. After importing the final finite-element model into LS-Run, the results were visualised using LS-PrePost 2024 software. As shown in Figure 7, it illustrates the cutting process for A.H. at a rotational speed of 1750 r/min, a feed speed of 60 mm/s, and a shrub diameter of 20 mm. Four key moments during the process are shown, from the initial contact with the shrub to the complete cutting. Throughout the cutting process, the maximum stress on the tool was 240.416 Mpa, which is well below the yield limit of the tool material (440 Mpa).
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 2 Materials and Methods  
### Overview  
The MARVIS system consists of four modules, as illustrated in Figure 1. The phantom-tracking module (PhTM) detects the phantom in the real-time video stream and determines its 3D position and orientation using data stored in the model database (MDB). The position of the surgeon's head is estimated using a marker-based optical head-tracking module (HdTM). Images of internal structures projected by the projection module (PrM) are transformed (in terms of perspective and deformation) considering the relative positions of the phantom and operator's head. The rendered images are visible on the surface of the phantom, which is connected using an electronic structure interaction module (SIM) to detect the physical (electrical) contact of the surgeon's tool with the internal structures of the phantom. In the next subsection, we detail each module and its role in data processing.  
Tracking the phantom relies on its contours acquired through a tracking camera. Contour tracking has lower computational complexity than comparison based on 3D models, which usually requires components such as smart-pixel cameras [25], time-of-flight cameras [26], or at least two standard cameras to get the spatial coordinates of each point [27]. A set of contours is created for the digital liver model (using edge detection on a binary silhouette of a digital model, as shown in the left column of Figure 2) for a range of rotations (\(\pm 60^{\circ}\) with respect to the \(X\) axis, \(\pm 45^{\circ}\) with respect to the \(Y\) axis). A resolution of \(2^{\circ}\) per axis is related to the degrees of freedom of a real liver. For each angle (a combination of rotations around two axes on the horizontal plane, perpendicular to the tracking-camera axis), a contour-based shape descriptor (CSD) is calculated and stored in the MDB. A CSD analysis for the physical phantom is performed by the user during training and allows estimation of its orientation in real time.
# Influence of Coolant Properties and Chip Former Geometry on Tool Life in Deep Drilling
## 1 Introduction
For larger diameters, replaceable elements (plates and guide surfaces) are widely used nowadays. The drill pipe is typically composed of high-quality stainless steel with a V-shaped groove engraved into it for chip removal. The drill pipe is connected to the drill head with silver solder. All-carbide gun drills are produced for the smallest diameters. Their head and pipe are composed of a single piece of carbide. This instrument has significantly higher stiffness. Only the invention of the all-carbide gun drill made it possible to produce gun drills with a diameter of less than 2 mm and allowed the use of higher feed rates. The fastening shank is used to clamp the tool in the holder. The fastening of the gun drill depends mostly on the type of machine tool used. In conventional machines, the milling cutter-lathe's greatest advantage is ensured by using hydrostatic clamping, in which the fastening shank is smooth and facilitates quick replacement while maintaining the best stiffness and clamping force. Figure 1: Description of the gun drill. Long-term research in this area shows that the main parameters that significantly affect the tool life in the deep drilling process are the type of tool, the machine used, the material of the machined part, the geometry of the tool, and the cooling medium used. It is the last two parameters that are the subject of our research, as the manufacturers provide a fairly wide range of recommended values for the parameters of the cooling medium, such as concentration, pressure value, and temperature of the cooling medium. The goal is to find the optimal parameter of these parameters in order to obtain the maximum service life of the tool while maintaining the high economic efficiency of the deep drilling process. The request to carry out the research came from the tool sellers themselves, in order to supplement the methodology of the deep drilling process and maximize the life of the tools used.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 3 Vulnerabilities  
In this section we review relevant vulnerabilities, which involve wireless physical-layer attacks such as sniffing, jamming and wormhole attacks as listed in Table 2. Furthermore, key-extraction attacks as well as energy attacks are reported.
## 6 Conclusions  
This paper investigated the EE performance of a downlink CF-mMIMO-assisted LEO SATCOM system under the impact of NPAs. In the proposed system with multiple satellites and multiple GUs, we developed a user-centric SC strategy to establish the connectivity between the APs and GUs. Additionally, we formulated a hybrid precoding optimization problem aimed at maximizing EE for the partially connected architectures. To solve the problem, we first took the product of tightly coupled analog and digital matrices as a single fully digital precoder. Due to the fractional form of the EE expression, an auxiliary variable was introduced to transform the problem into a linear programming framework. Furthermore, we employed the WMMSE framework and a gradient descent method with [PERSON]'s accelerated momentum to address the equivalent fully digital problem. Finally, we derived the analog and digital precoders by minimizing the Euclidean distance between the fully digital precoder and the hybrid precoder. Simulation results confirmed the convergence of the proposed WMMSE-based algorithm and demonstrated significant EE performance gains in the CF-mMIMO-assisted LEO SATCOM system. Recently, there has been growing interest in the research on programmable metasurface technology within satellite communication systems. This technology has the potential to further reduce hardware costs and demonstrates significant advantages in coverage enhancement [35; 36]. In the context of future 6G networks, it is expected to drive the evolution of CF architectures towards higher energy efficiency and intelligence. We will pay more attention to this technology in future research.  
Conceptualization, X.M. and Y.Z.; methodology, X.M. and Y.Z.; software, X.M., Y.Z. and L.L.; validation, Y.Z. and L.L.; formal analysis, Y.Z. and L.L.; investigation, X.M. and Y.Z.; resources, X.M., Y.Z. and L.L.; writing--original draft preparation, X.M. and Y.Z.; writing--review and editing, Y.Z., L.L. and Z.Z.; funding acquisition, L.L. and Z.Z.; project administration, Y.Z. and L.L.; supervision, L.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2022 YFB2902100, in part by the National Science Foundation of China under Grant 62401271, in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (NUST) under Grant 2023r134, and in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 24 KJB510019.  
Figure 7: EE versus \(N_{RF}\) with \(P=24\) dBW for \(N=16\) and \(N=64\) with different transmit architectures.
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Performance Measures**} \\ \cline{2-10}
**Deep Features** & \multicolumn{2}{c}{**REFUGE**} & \multicolumn{2}{c}{**RIM-ONE**} & \multicolumn{2}{c}{**ACRIMA**} & \multicolumn{2}{c}{**ORGIA**} & \multicolumn{2}{c}{**DRISTI-GS1**} \\ \cline{2-10}
**Descriptors** & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} \\ \hline AlexNet & 93.00 & 88.07 & 86.84 & 92.63 & 91.51 & 95.47 & 76.41 & 80.46 & 80.00 & 79.89 \\ \hline GoogleNet & 93.75 & 87.81 & 84.21 & 90.38 & 90.57 & 90.57 & 74.87 & 75.23 & 70.00 & 72.49 \\ \hline InceptionV3 & 92.25 & 90.31 & 63.16 & 74.04 & 95.28 & 95.28 & 78.97 & 81.39 & 73.33 & 70.90 \\ \hline XceptionNet & 93.25 & 90.26 & 78.95 & 81.09 & 91.04 & 94.90 & 75.90 & 76.12 & 73.33 & 68.78 \\ \hline ResNet-50 & **94.75** & **90.66** & 81.58 & 91.67 & 93.40 & 98.39 & 75.38 & 80.12 & 70.00 & 62.96 \\ \hline SqueezeNet & 92.00 & 84.83 & 81.58 & 96.15 & 94.81 & 98.45 & 78.46 & 80.06 & 80.00 & 78.84 \\ \hline ShuffleNet & **93.75** & **92.24** & **89.47** & **97.44** & 92.45 & 97.99 & 74.36 & 77.56 & 86.67 & 83.07 \\ \hline MobileNet & 92.75 & 89.25 & 86.84 & 94.23 & 92.92 & 96.48 & 77.44 & 81.30 & 76.67 & 85.71 \\ \hline DenseNet & **93.50** & **93.94** & 84.21 & 89.74 & **96.23** & **98.75** & 78.46 & 81.92 & 70.00 & 77.25 \\ \hline InceptionResNet & 92.00 & 88.78 & 78.95 & 91.99 & 92.45 & 96.63 & **78.46** & **82.06** & **83.33** & **91.53** \\ \hline NasNet-Large & 93.25 & 90.81 & 86.84 & 93.59 & 91.04 & 95.92 & 74.87 & 79.23 & 83.33 & 80.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: P2: Deep activated features from eleven pretrained networks and SVM for glaucoma classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Performance Measures**} \\ \cline{2-5}  & **Accuracy** & **Dice Coefficient** & **IoU** \\ \hline DeepLabv3+ ResNet18 & 99.70\% & 90.95\% & 83.56\% \\ DeepLabv3+ + ResNet50 & 99.64\% & 88.78\% & 80.26\% \\ DeepLabv3+ + XceptionNet & 99.71\% & 91.39\% & 84.48\% \\ DeepLabv3+ + InceptionResNet & 99.72\% & 91.29\% & 84.21\% \\ DeepLabv3+ + MobileNet & **99.70\%** & **91.73\%** & **84.89\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical performance of various deep semantic algorithms for two classes segmentation between OD and non-OD.
## 2 Literature Review
### Classification of WBAN Routing Protocol
#### 2.1.4. Temperature-Aware Routing  
In body sensor network designing, interference and absorption of antenna radiation are the crucial tasks. In WBAN, the human body can be affected by the radiation emitted for the sensors implant in or on the human body [35]. In [36], authors presented a model for the effect caused by implanted sensors on the human body during communication. On the basis of observation, authors have proposed a rate controlled recommendation model for WBAN designing. On the other hand, contrary to rate control, during the designing of Intra-WBAN, thermal-aware routing techniques are very important to consider. Various thermal-aware routing protocols have been proposed to avoid the temperature-rise  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Methods** & **Objective** & **Limitations** \\ \hline \multirow{3}{*}{Co-CESStat [20]} & Energy efficient routing & \multirow{3}{*}{Energy utilization is high.} \\  & protocol withhigh network & \\ \cline{1-1}  & throughput and lifetime. & \\ \cline{1-1}  & Support dynamic routing. & \\ \hline \multirow{2}{*}{MEPF [21]} & Minimize energy of node with & \multirow{2}{*}{Network latency is high.} \\  & less transmission power. & \\ \hline \multirow{2}{*}{RSSI [22]} & High energy consumption with QoS & \\ \cline{1-1}  & service & \\ \hline \multirow{2}{*}{DARE [32]} & Minimize energy consumption & \multirow{2}{*}{Lead distributed in not} \\ \cline{1-1}  & of node and diminish the node & \\ \cline{1-1}  & hotspot. & \\ \hline \multirow{2}{*}{ESR [33]} & Improve patient mobility and traffic & \\ \cline{1-1}  & load. & \\ \hline \multirow{2}{*}{SIMPLE [34]} & Energy consumption is balanced & \multirow{2}{*}{Packet drop is high.} \\ \cline{1-1}  & among all nodes. High network & \\ \cline{1-1}  & throughput. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: State-of-the-art analysis of energy-aware routing approaches.  
and radiation that cause damaging effects to the human body. The existing thermal-aware routing protocols presented in the literature are \(M^{2}E^{2}\)[37], RE-ATTEMPT [38], (TMQoS) [39], M-ATTEMPT [38], ATTEMPT [38], HPR [40], THSR [41], RAIN [42], LTRT [43], LTR [44], ALTR [44], and TARA [45]. Table 2, summarized the pros and cons of the existing temperature-aware routing protocol. Table 2, summarized the pros and cons of the existing temperature-aware routing protocol.
## 3 Experimental Results and Analysis
### Experiments on Remote-Sensing HSI Datasets
The Indian Pines dataset, acquired using the AVIRIS sensor (NASA Jet Propulsion Laboratory, Pasadena, CA, USA), includes 220 spectral bands with a spatial resolution of 145 \(\times\) 145 pixels. Some bands are significantly affected by atmospheric absorption and water vapor, leading to complex mixed noise interference. In these real-world datasets, due to the absence of noise-free reference images, the network trained on the ICVL dataset is directly transferred for use in these scenarios.
# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 1 Introduction  
Highways are important to the research and application of CVIS in intelligent highways [1; 2]. Deploying multiple RSUs along the highway ensures vehicles can access the Internet more easily. RSUs can also collect and forward observation data within their range. Some RSUs are built in remote areas, such as mountain forests or the Gobi, which makes Internet connectivity difficult and costly [3]. Therefore, isolated RSUs must carry data through mobile vehicles to reach the RSU access range to the backbone network and then upload the data. RSU observation data include traffic conditions, environmental monitoring, natural disasters, and animal activity information [4; 5; 6]. RSU collects monitoring data in a timely manner, and different data types have different delay deadline requirements [7]. High-speed moving vehicles cause the time-varying network topology of vehicles. An unnecessary impact is caused when the data cannot be fed back to the relevant departments before the delay deadline. Therefore, the main goal of this paper is to determine the optimal scheduling strategy considering that monitoring data have different deadlines and data queues are stable, minimizing the end-to-end average delay.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 1 Introduction
To solve these problems, we propose an SP scheme based on a fuzzy-analytical hierarchical process (FAHP) and attempt to conduct research on the SP scheme for a multi-objective in DDCs. Especially, we focus on multi-objectives in energy efficiency and performance improvement. We also provide analysis of metrics related to efficiency for performance, efficiency for energy, and cost. The evaluation results show the effectiveness of the proposed scheme. Our contributions are as follow. 1. Categorization of metrics to improve energy efficiency and performance,
2. The SP decision whether or not based on the fuzzy system,
3. Selection for target machine with multi-metrics based on the analytical hierarchical process (AHP) system through the calculation of weights. The rest of this paper is organized as follows. Section 2 provides an analysis on related work to solve the current challenges. In Section 3, we propose an SP system and scheme with steps. In Section 4, we evaluate and analyze the proposed system and scheme in terms of the efficiency of performance and energy in DDCs. Lastly, we present our conclusion.
## 4 Performance Test of SOADS
### Performance Test of Anomaly Evaluator  
In order to evaluate the performance of anomaly scoring, the number of sequences in the event stream was increased from 1000 to 10,000 by 1000, and sequence alignment was performed on the sequences in the stream and 11 prepared patterns shown in Table 2. The sequence length in the event stream was randomly generated from 15 to 20 corresponding to the process model. The prepared patterns were extracted from the previous experiments described in Sub-Section 4.4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pattern ID** & **Pattern** & **Length** \\ \hline P1 & A1A5A9A13A17B1B5B9B17B21C1D1E1F1G1 & 15 \\ P2 & A3A5A10A14A19B4B8B16B20B24C1D4E4G4 & 14 \\ P3 & A4A7A10A14A20B3B7B13B19B23C8D3E4G5 & 13 \\ P4 & A1A7A9A16B2B6B11B18D2E4F3G4 & 12 \\ P5 & A1A18B3B7B19B23C9D2E5F6G6 & 11 \\ P6 & A3A10A18B21B8B2C10D1E4G3 & 10 \\ P7 & A10A14A18B16B20B24C5E2F7 & 9 \\ P8 & A3A6A19B14B19C2D25F & 8 \\ P9 & A3A8A15B4B8B15E1 & 7 \\ P10 & A6A9B6D1F7C4 & 6 \\ P11 & A1A13B9B17C3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subsequence patterns for Anomaly Evaluator experiment.  
Figure 14: Process of experiment to assess the performance of the Frequent Subsequence Miner.  
Figure 15: Result of Frequent Subsequence Miner experiment. (**a**) Result of frequent subsequence miner test; (**b**) Details of test result.  
Experimental results show that the anomaly scoring time increases linearly with the number of sequences in the stream, as shown in Figure 16 below. Increasing linearly rather than exponentially means ensuring stable performance as server resources expand. In addition, it is very good to use sequence alignment for anomaly scoring because it is very fast in less than 3 s to analyze 10,000 sequences even in a small number of clusters.