esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Factors Influencing the Entrance and Exit Delamination Factors
#### 3.1.2 Influence of Feed Rate on the Entrance and Exit Delamination Factors  
Figure 6 shows the morphology of the entrance and exit at different feed rates, and Figure 6 shows the entrance and exit delamination factors at different feed rates. With the increasing feed rate, the thrust force increased gradually, and the delamination factor of the entrance and exit also increased, as shown in Figure 7. At the entrance, with the increase of the feed rate, the pushing force of the chisel edge was greater than the shearing force, and part of the fiber was torn apart. At the exit, as the feed rate increased, the drill bit moved quickly across the workpiece, and the cutting edge did not have enough time to cut through the internal glass fibers. Therefore, a large number of fibers were torn apart and pulled out of the resin.  
Figure 5: Entrance and exit delamination factors at different cutting speeds. (**a**) Effect of cutting speed on thrust force at different feed rates; (**b**) Entrance delamination factors at different cutting speeds; (**c**) Exit delamination factors at different cutting speeds.  
Figure 6: Morphology of entrance and exit at different feed rates (\(V_{c}=85\) m/min).  
Obviously, the delamination factor was directly related to the feed rate and cutting speed.
## 3 Model Verification and Discussion
### Effects of Toth Tip Chipping
It is simple to deduce that assembly errors increase the reduction of TVMS under tooth tip chipping. Why Chip_1 decreases the healthy installation is due to single tooth contact when Chip_1 happens. As shown in Figure 17a, the larger the chip area, the greater the reduction in unloaded transmission errors. The OQ and RT are the moments of the affected regions by Chip_1 and Chip_2, respectively. For instance, the maximum reduction in unloaded transmission errors in Chip_1 is 48.3884 \(\upmu\)rad, which is smaller than the 131.3402 \(\upmu\)rad observed in Chip_2. The maximum contact stress of Chip_1 (see in Figure 17b) is 1204.7 MPa and Chip_2 (see in Figure 17b) is 1324.7 MPa. Additionally, it is apparent that the contact moment affected by Chip_1 (OQ) is less than that of Chip_2 (RT). Figure 13: The clearances and distributed force between potential contact points at moment C: (**a**) the clearances; (**b**) the distributed force. Figure 14: Unloaded transmission error of different assembly errors considering tooth tip chipping: (**a**) \(\Delta\)AP = \(-0.5\) mm; (**b**) \(\Delta\)AG = \(0.5\) mm; (**c**) \(\Delta\)E = \(0.5\) mm; (**d**) \(\Delta\)E = \(-0.4^{\circ}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Moment F** & **Moment G** & **Moment H** & **Moment I** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Chip\_1 & \(0\) & \(-0.43\%\) & \(-0.8\%\) & \(-0.24\%\) \\ \hline Chip\_2 & \(2.31\%\) & \(-4.92\%\) & \(-4.39\%\) & \(-1.94\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reduction of TVMS of two chip areas at different moments under E = 0.5 mm.
# Two NEH Heuristic Improvements for Flowshop Scheduling Problem with Makespan Criterion
## 4 Second Improvement Method NEH-SS2
### Results and Discussion
proposed a greedy randomized heuristic based on the NEH heuristic, called GGNEH [19], which is similar to the work presented here, except that they randomly chose one partial sequence from a set of five in the second loop. The results obtained with the proposed improvement methods, with NEH-SS2 (3), are compared in Table 5 with some recently developed state-of-the-art methods, such as NEH-D [20], NEHKK1 [30], NEHKK2 [31], NEHLP1 [29], and NEHFF [32]. As can be seen in Table 5, all these methods improve NEH performance compared to [PERSON]'s benchmark. The new proposed NEH improvement method outperforms all existing methods with an ARPD value of 2.42, achieving the best performances for 11 of the 12 sizes of problems. Figure 3 graphically presents these results by benchmark problem size. It can be noted that this method significantly stands out from the already existing results for large problems, and particularly when the number of machines increases. The outstanding performance of this heuristic for the 50/10 problem size is due to the particularity of Ta_034 and Ta_037, dealt with in Table 2 and remarkably solved with the NEH-SS1 method. In addition, it can also be seen with the dotted line, for which the ordinate secondary scale is available on the right of the figure, that the performance of NEH-SS2(3) averages between 10% and 25% for large problem instances, thus confirming the interest in, and the efficiency of, the methods presented in this paper. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**J/M** & **NEH** & **NEH-D** & **NEHKK1** & **NEHKK2** & **NEHLP1** & **NEHFF** & **NEH-SS2 (3)** \\ \hline
**20/5** & 3.30 & 2.48 & 2.73 & 2.65 & 2.29 & 2.36 & 2.58 \\
**20/10** & 4.60 & 4.13 & 4.31 & 4.31 & 4.15 & 4.73 & **3.61** \\
**20/20** & 3.73 & 3.70 & 3.41 & 3.41 & 3.30 & 3.34 & **3.14** \\
**50/5** & 0.73 & 0.73 & 0.59 & 0.66 & 0.92 & 0.56 & **0.27** \\
**50/10** & 5.07 & 4.80 & 4.87 & 4.83 & 5.15 & 4.69 & **2.53** \\
**50/20** & 6.65 & 6.24 & 6.41 & 6.37 & 6.21 & 6.11 & **5.78** \\
**100/5** & 0.53 & 0.49 & 0.40 & 0.42 & 0.42 & 0.36 & **0.27** \\
**100/10** & 2.21 & 1.96 & 1.77 & 1.86 & 2.17 & 1.62 & **1.43** \\
**100/20** & 5.34 & 5.01 & 5.28 & 5.30 & 5.02 & 5.09 & **4.47** \\
**200/10** & 1.26 & 1.01 & 1.17 & 1.12 & 0.97 & 0.93 & **0.87** \\
**200/20** & 4.41 & 3.88 & 4.17 & 4.24 & 4.07 & 3.78 & **2.83** \\
**500/20** & 2.07 & 1.70 & 2.02 & 2.00 & 1.76 & 1.71 & **1.33** \\ \hline
**Avg.** & 3.32 & 3.01 & 3.09 & 3.10 & 3.04 & 2.94 & **2.42** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ARPD values of different improvement methods compared to [PERSON]’s benchmark.
# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
## 2 Theoretical Foundation
### Border Following  
An image is not simply a random and insignificant collection of pixels but represents a meaningful arrangement of regions and objects. The human brain has the remarkable ability to organize these pixels into something that makes sense in the human world, even in the presence of noise. Previous research has identified various factors, such as similarity, proximity, continuity, symmetry, parallelism, closeness, and familiarity, as the key elements influencing the human perception of an image. Understanding and modeling these factors are crucial in capturing the perceptual grouping process [8].  
One strategy commonly used to capture the perceptual grouping process is contour detection. Quantifying the presence of boundaries at specific locations in an image through local measurements is a widely employed approach in contour detection. These local measurements can provide valuable information about the presence and strength of boundaries, enabling the identification and segmentation of objects in the image. Various techniques and algorithms have been developed to enhance contour detection and improve the accuracy of boundary localization [9].  
Accurately identifying boundary localization plays a crucial role in border following, a fundamental technique in processing digitized binary images. Border following has been extensively studied due to its wide range of applications, such as picture recognition, picture analysis, and image data compression [6]. Furthermore, by accurately tracing the boundaries of objects within an image, border-following techniques contribute to various computer vision tasks, enabling efficient and reliable analysis and interpretation of visual data.
# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
## 5 Conclusions  
This study introduces an augmented reality-based surgical simulator designed for robotic-assisted minimally invasive pancreatic surgery, integrating a novel parallel robot, AI-driven force prediction, and real-time haptic feedback. The AI model accurately estimates applied forces with a mean absolute error of 0.0244 N, while the force-feedback system, operating with a 65 ms latency, ensures real-time interaction. Compared to existing simulators, this system eliminates the need for physical force sensors, enhancing adaptability and improving spatial awareness in surgical training and procedural planning.  
Future improvements will focus on expanding the AI training dataset for better generalization, refining biomechanical tissue modeling, and conducting further clinical validation. Overall, the system advances robotic-assisted surgery training by providing a highly realistic and interactive learning environment, addressing key limitations in current surgical simulation platforms.  
Conceptualization, D.P., N.A.H., G.R., B.G., A.C. and D.C.; data curation, N.A.H., A.C., C.R. and B.G.; formal analysis, D.P., N.A.H., D.C. and C.V.; funding acquisition, D.P.; investigation, D.P. and C.V.; methodology, D.P., G.R. and B.G.; project administration, D.P.; resources, C.V.; software, G.R.; supervision, D.P., C.V. and B.G.; validation, N.A.H., B.G., A.C. and C.R.; writing--original draft, G.R.; writing--review and editing, B.G., A.C. and C.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the project New smart and adaptive robotics solutions for personalized minimally invasive surgery in cancer treatment--ATHENA, which was funded by the European Union--NextGenerationEU and Romanian Government, under the National Recovery and Resilience Plan for Romania, contract no. 760072/23.05.2023, code CF 116/15.11.2022, through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, investment I8.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Literature Review
In this case, the attacker uses a standard device attack (weaknesses in the operating system or software of the device) to get remote access to the server from their computer. The attackers' goal is to take total control of the machine that is being attacked. Infrastructure for smart grids and substations is managed and automated by SCADA devices, which could have security flaws. This attack necessitates little network expertise because the device's documentation is accessible. A hacker can easily take complete control of a compromised machine. The application layer's responsibility, availability, and integrity are all at risk [64]. Because it enables utility companies, customers, and producers to communicate automatically and in both directions via smart grid networks, advanced metering infrastructure has grown to be a critical part of the developing smart grid industry. Smart meters are high-tech devices that, in contrast to conventional meters, record a variety of information about a person's energy use, energy production, energy status, and diagnostics [65]. For purchasing, managing and watching user appliances, and troubleshooting, this data is really helpful. These data transfers all take place across a wide area network and are all kept in data centers that are hosted in the cloud. A centralized system may result in problems including a single point of failure, the potential for manipulation, and the loss of sensitive data. Performance, availability, and response time may be affected if more users connect to the same server. Smart meters and electric vehicles in smart grid systems also save a lot of information about payments and energy use [66]. These details and data are frequently disseminated to other businesses for monitoring, billing, and trading. Sharing a lot of data in such a complicated system; however, offers major privacy problems since middlemen, intermediaries, and trusted third parties might divulge private data on identities, locations, patterns of energy output and consumption, energy profiles, charging, or discharging quantities. The situation is made worse by the mistrust that exists between consumers and manufacturers. Because of this, it could be challenging for centralized parties to win the trust of producers and customers by being truthful and open. It is a difficult effort to develop a decentralized AMI system that is dependable, private, and safe. Research on AMI and blockchain.
## 4 Experiments
### Emotional Speech Databases  
EMO-DB is one of the most frequently used emotional speech databases in SER, and it is a free public database [47] consisting of seven emotions, namely anger, happiness, neutrality, sadness, fear, boredom and disgust. The utterances in this database were created by ten (five male and five female) professional actors by speaking ten sentences in seven emotional states. We chose four emotions, namely anger, happiness, neutral, and sadness, for our experiments. The main reason for this was that these four emotions share similar acoustic features, and it is hard to discriminate these emotions in the valence dimension [26; 48; 49]. Moreover, these emotions are easily accessible in almost all other emotional speech databases. The number of utterances in each emotion category is different, meaning that the data are unbalanced. The number of utterances in the anger emotion is almost double that of the rest of the chosen emotions (anger (127), happiness (69), neutrality (78), and sadness (60)). To reduce data imbalance between numbers of instance in each emotion class, we randomly chose 78 utterances for anger emotion, and the number of utterances for the rest of the chosen emotions remained the same as mentioned above.  
The IEMOCAP database is also popular and widely used to evaluate the proposed new methods as well as to analyze acoustic features in the SER [50]. It comprises both audio and visual data. This database is divided into five different sessions, and each session includes acted dialogues performed by two people. Each utterance has three categorical labels that are labeled by three annotators. We chose four emotions, namely anger, happiness, neutrality and sadness, and the utterances in each emotion category were chosen via the majority win method. This means that the utterance was selected when it was labeled with the same emotion by at least two annotators out of 3. We randomly chose 705 utterances for each selected emotion category to avoid an imbalanced data problem. Information about the databases, emotion categories and the number of files in each emotion category are given in Table 2.
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 3 RMCSat Mission
### Performance Requirements and Constraints  
RMCSat performance requirements are driven by the frequency detection and accuracy required, and by the radio noise environment of the satellite. The primary payload must be able to receive and record three solar frequencies simultaneously (2695 MHz, 2785 MHz, 2815 MHz). It must also be able to detect variations in these flux density signals to a satisfactory threshold. For the observations to be scientifically relevant, the same accuracy assigned to DRAO F10.7 readings is used. DRAO gives its published observations an accuracy of 1 fsu or 1%, whichever is greater [1]. Such measurements are heavily influenced by the gain pattern of the main antenna beam and the boresight angle of the antenna which, in turn, define the pointing requirements for the attitude determination and control system (ADCS).
## 4 Experiments
### Datasets  
We experimented with the learnable positional embedding-based GCNNs performance on six benchmark datasets: FashionMNIST [41], CIFAR10 [42], SVHN [43], CAR196 [44], CUB200-2011 [45], and EuroSAT [46]. First, FashionMNIST is a grayscale image dataset of 10 kinds of fashion products with a size of 28 \(\times\) 28 and consists of 60,000 training data and 10,000 testing data. Next, CIFAR10 is an RGB color-based image dataset of 10 categories with a size of 32 \(\times\) 32, consisting of 50,000 training data and 10,000 testing data. The SVHN is an RGB street house-number image dataset divided into 10 numeric categories of 32 \(\times\) 32 size and consists of 73,257 training data and 26,082 testing data. In addition, CAR196 is an RGB image dataset of 196 types of automobile brands with various sizes, consisting of 8041 training data and 8144 testing data. In the following dataset, we adjusted the image size equally to 224 \(\times\) 224. Further, CUB200-2011 is an RGB image dataset of 200 kinds of birds of various sizes, consisting of 5794 training data and 8994 testing data. In this case, we adjusted the image sizes to 64 \(\times\) 64 and 128 \(\times\) 128. Finally, EuroSAT is a dataset of 27,000 Sentinel-2 satellite RGB-based 64 x 64 images divided into 10 categories. We split this dataset into 80% training data (approximately 21,600) and 20% testing data (approximately 5400).  
In this paper, we compare the graph-represented superpixel image classification results with the state-of-the-art paper using the same dataset. Both FashionMNIST and CIFAR10 are compared with RAG-GAT [8], DISCO-GCN [35], and HGNN [10], whereas SVHN and EuroSAT are compared to RAG-GAT and HGNN, respectively. Moreover, CAR196 and CUB200-2011 compare the results of the IMGCN-LPE with the baseline models (ChebNet [29], GCN [5], and GAT [30]) because we could not find a paper that provided results on the same datasets.
# Analysis of the Impact of the New Two-Lane Shield Tunnel Underpass on the Existing Tunnels
## 3 Numerical Simulation of Underpass Construction
### Analysis of Simulation Results  
During tunnel penetration, the shield tunneling disturbs the overlying soil layer, which in turn causes stress redistribution in the surrounding rock of the existing tunnel. This results in additional stress and deformation in the existing tunnel. The vertical displacements of the left and right tunnel arches, as well as the arch bottom, are analyzed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name of Soil** & **Thickness/m** & **Heaviness/(kN-m\({}^{-3}\))** & **Cohesion/(kPa)** & **The Angle of Internal** & **Modulus of Elasticity/(MPa)** & **Poisson’s Ratio** \\ \hline Plain fill & 2.5 & 17 & 10 & 15 & 8 & 0.3 \\ Gravel & 4 & 20 & 0 & 36 & 12,000 & 0.28 \\ Fully weathered & 10 & 22 & 38 & 15 & 20,000 & 0.3 \\ dolomite & & & & & & \\ Strongly weathered & 20 & 22 & 45 & 16 & 35,000 & 0.3 \\ dolomite & & & & & & \\ Moderately & & & & & & \\ differentiated & 23.5 & 23 & 70 & 30 & 50,000 & 0.3 \\ dolomite & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geotechnical physical and mechanical index parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Name** & **Weight (kN\(\cdot\)m\({}^{-3}\))** & **Modulus of Elasticity/(GPa)** & **Poisson’s Ratio** \\ \hline Shield shell & 78 & 250 & 0.2 \\ Pipe sheet & 22.5 & 20 & 0.3 \\ Grouting & 24 & 21 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of structural materials.  
at different construction stages of the shield tunnel. Von Mises analyses were performed on the arch top, arch bottom, and both sides of the arch girdle of the existing tunnel during the six construction stages.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 4 Pending Issues
### Spatial Resolution  
Dense point clouds with a large spatial resolution both in the horizontal and vertical directions are one of the cornerstones of object detection. While detectivity at long range has been achieved even for objects with low reflectivities, the reliable identification of objects at significant distances needs enhancement, as shown by the trend in larger and larger spatial resolution [137]. A rule-of-thumb  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline  & **PIN** & **APDs** & **SPADs** & **MPPCs** & **PMTs** \\ \hline Solid state & Yes & Yes & Yes & Yes & No \\ \hline Gain (typ) & \(1\) & Linear (\(\approx\)200) & Geiger (\(10^{4}\)) & Geiger (\(10^{6}\)) & Avalanche (\(10^{6}\)) \\ \hline Main advantage & Fast & Adjustable & Single photon & Single photon & Gain, UV \\  & & gain by bias & detection & counting & detection \\ \hline Main disadvantage & Limited for & Limited gain & Recovery time & Saturable, bias & Bulky, low QE, \\  & low SNR & & & voltage & high voltage, \\  & & & & dependence & magnetic fields \\ \hline \hline \end{tabular} Note: these are typical values for reference. Individual components evolve continuously and may be different at the time of publishing or from specific providers.  
\end{table}
Table 4: Summary: main features of photodetectors for lidar.  
number says objects above 10 cm can hit the bumper of some cars, and braking or evasion at highway speeds needs at least detection of hazards at 150 m. This brings on a spatial resolution of 0.67 mrad for detecting a point on the object. If five to ten points are considered for reliable identification of an object, then spatial resolution in all directions needs to be reduced to just 67 mrad, a number which is really demanding. Although cumulative detection procedures, statistics and machine learning may help to improve detection [138], it is still clear that larger spatial resolutions, especially along the vertical axis, will be required if autonomous vehicles need to drive at fast speeds on highways. Approaches with a spatial resolution close to 1 mrad in the horizontal and vertical directions preserving real-time operation start to be available (Figure 13) [68].
# Joint Power Charging and Routing in Wireless
###### Abstract  
The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.  
WRSNs; charging efficiency; routing; GA; heuristic algorithm +
Footnote †: journal: Sensors