esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 4 Conclusions  
This study proposes a method for automatically measuring dimensional features using image processing techniques using machining feature models. First, the ROI of the part is extracted through the machining features of the model. Next, a local image coordinate system is established for measuring the end face to reduce the impact of machining patterns on measurement accuracy. Based on measuring the local coordinates of the part edges, an improved ROI edge detection is used to obtain the diameter of the measuring hole and the distance feature from the center to the center edge. This paper introduces line and circle detection methods based on the Hough transform, which realizes the positioning of part edges and circular holes and establishes dimensional measurement relationships. Finally, the effectiveness of this method was verified by experiments.  
1. The developed image acquisition system can perform real-time image capture during the manufacturing process of parts. By utilizing methods such as ROI extraction, image deblurring, denoising, and edge detection, the system can complete detection within 0.16 s, achieving clear workpiece contours and excellent detection speed.
2. Compared to the measurements of a coordinate measuring machine, the developed measurement method in this study achieved a relative accuracy of 97% for straightness and 96% for roundness.
3. Experimental results show that the relative accuracy between the inner groove measured in this study and the measurement results of the coordinate measuring machine reached 99%. In addition, the detection results were analyzed and compared, summarizing the factors that affect the detection accuracy of this method.  
Figure 14: Edge detection time comparison chart.  
**Author Contributions:** Z.L.: project administration, funding acquisition, writing review and editing. W.L. and G.S.: visualization. L.Z.: Intelligent Manufacturing, Condition Monitoring. Y.R.: visualization. Y.S.: CAD/CAM, optimization algorithm. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Tianjin under Grant 22 JCQNJC01000.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Results and Discussions
### Evaluation of Accuracy and Reliability of the Present Algorithm
lines with different markers, while the overall performances of all 80 selected shadow maps is indicated with dotted lines. From the plots, it can be concluded that \(G_{3}\) has the highest order of accuracy and regression rate--this means it has the highest detection accuracy for cracks--whereas \(G_{5}\) has the lowest order of accuracy and regression rate, and the other groups' accuracy and regression rates are in between these values. In general, the differences in performance curves between the ten groups of 3D shadow maps are due to the differences in noise in each group. Nevertheless, the overall performance of the proposed method is satisfactory. The accuracy, regression rate and F-score of the overall performance are 93.53%, 92.04% and 91.18%, respectively. The result confirmed that the proposed method can automatically identify corrosion cracks on pipelines with satisfactory performance regardless of the image backgrounds and conditions. In engineering applications, most studies focus on using artificial neural networks to process images taken from structures such as pipelines, bridges, and tunnels. However, even when we use deep learning models such as CNN, this method usually does not have sufficient generalization to reflect the fact of structural health status. By combining the 3D-SM with CNN, the 3D shadow maps' information can be extracted and then fed into CNN for training and testing. This combination will be a useful tool to solve actual crack identification problems of complex structures in mechanical and civil engineering.
# Cross-Platform UAV Swarm Key Management in Denied Environments
###### Abstract  
As resources provided by single unmanned aerial vehicles (UAVs) are limited, we propose a cross-platform UAV swarm key management scheme for task scenarios in denied environments. In denied environments where the communication link is open and the UAV nodes may go invalid, secure communication is often at stake. To solve this problem, we propose a key management scheme which, based on the Chinese remainder theorem (CRT) and the Hash function, constructs a swarm key by combining the local key and the session key to reduce the overhead of individual UAV nodes in the swarm. Meanwhile, the swarm head node constructs broadcast messages according to the key update needs, which reduces the overhead of the member nodes, improves the efficiency of key updating, and fulfills the key establishment and updating of the UAV swarm. Experiments show that our proposed scheme has forward and backward security and can defend against collusion attacks and replay attacks; our method was compared with other methods on the MIRACL cryptographic library in Visual Studio 2019, and it was found that our method has a lower computing and communication overhead, provides a solution to cross-platform key management of UAV swarms in denied environments, and ensures safe communication of UAVs in the swarm.  
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A _Applied_  
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for message transmission. In addition, because of the limited resources of individual UAV nodes, the UAVs are delivered through the delivery platform to exchange information and regroup into a new task swarm according to the environment and task demand. However, the UAV swarm is susceptible to security threats and attacks [6; 7] in the open, wireless, and denied environment in the sky. In particular, during cross-platform regrouping and variations in the network topology, the attackers can easily launch attacks by intercepting, tampering with or forging data. Therefore, it is crucial to encrypt the communication links of UAVs to ensure security and confidentiality of data transmission when the UAV swarm is performing tasks in a denied environment [8]. Therefore, here we propose a cross-platform key management scheme for a UAV swarm in denied environments, with a vision to achieve secure and efficient management of UAV keys in the denied environment and improve the efficiency of the UAV swarm in fulfilling tasks.
## 2 Materials and Methods
### Data Measurement Equipment  
EEG data from the FCz, Pz, O1, and O2 positions were measured based on the international 10-10 system using a wireless portable EEG device (PolymateMini AP108, Miyuki Giken Co., Ltd., Tokyo, Japan) and dry electrodes (Unique Medical Co., Ltd., Tokyo, Japan). The horizontal and vertical EOGs were measured from electrodes that were placed next to the lateral annulus of and above the eyebrow of the participant's left eye. The ground and reference electrodes were placed on the left and right mastoid, respectively. The sampling frequency of all signals was 500 Hz.
## 4 Results and Discussion
### Quality Assessment  
Table 11 lists the scores of each selected study against the Quality Criteria QC1 to QC7, and the final column shows the normalized score. Reference [66] is ranked 1 st according to the set Quality Criteria (QCs), achieving the maximum score in 6 out of the 7 QCs. References [71; 76] have accrued the lowest scores in QCs and, consequently, are the lowest in the rank.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **QC1:** & **QC2:** & **QC3:** & **QC4:** & **QC5:** & **QC6:** & **QC7:** & **Total** & **Normalized** \\ \hline  & **Publication** & **Prunewe** & **Proof of** & **Dataset** & **Study** & **Citations** & **Future** & & \\  & **Venue** & **Concept**  
Further analysis of the top five studies [46; 66; 80; 109; 111] reveals that, except for one study [80]--which only partially fulfilled QC4 due to the use of a smaller dataset--all the other studies fully met QC1 to QC4 and QC7. These studies have been published in reputable venues, have presented and tested their frameworks on well-known and large datasets, and have highlighted future research directions. Whereas two studies [66; 80], fully met QC5 by discussing the limitations of their study, none of the other studies stated any limitations. Similarly, studies [109; 111] fully met QC6 with good citation rates per year; however, the rest of the studies only partially fulfilled QC6.  
Upon analyzing the bottom three studies [71; 76; 119], it can be observed that none of the studies fully met any of the given QCs. Only QC2 and QC3 have been partially met by all the studies, as these studies either propose an incomplete framework or have not detailed the proof of concept within the study. QC4 was only partially met by reference [119] by utilizing a basic dataset; neither of the other two studies mentions datasets. All of these studies were also not published in a reputable venue, received few or no citations per year, and did not discuss any future research directions, hence not meeting QC1, QC6, and QC7.
# Experimental and Geant4 Simulation Study of MURA Mask for Scintimammography
## 3 Results and Discussion
### Geant4 Benchmark  
The Geant4 setup including the breast model (red), the MURA mask (blue) and the imaging detector is shown in Figure 2.  
However, five experimental and simulated imaging scenarios are shown in Figures 3 and 4. We see the point spread function and tumor located at positions 8, 17, 19 and 21 of the anthropomorphic breast projections and corresponding decoded images, denoted as (a), (b), (c), (d) and (e), respectively.  
The comparison of the measured and the calculated normalized PSF in terms of profile distributions allowed us to conclude their similarities. Moreover, the average and the standard deviation values were found to be \(0.39\pm 0.37\) and \(0.41\pm 0.38\) for the experimental and the simulation procedures, respectively, showing an error of 6% that can be explained by systematic and experimental related errors. First, a good agreement between the simulations and measurements is achieved.  
Figure 4: Five Geant4 simulated (up) and decoded (down) images: (**a**) PSF, (**b**) location 8, (**c**) location 17, (**d**) location 19 and (**e**) location 21. The same scale and legend remains valid for other subplots, as given in (**a**).  
Figure 3: Five measured (up) images and decoded (down) experimental images: (**a**) PSF, (**b**) location 8, (**c**) location 17, (**d**) location 19 and (**e**) location 21. The same scale and legend remains valid for other subplots, as given in (**a**).  
Figure 2: Geant4 setup visualization including the breast model (red), the MURA mask (blue), the imaging detector (orange) and the scattered photons trajectories (green lines). Zoomed out phantom and mask were also shown.  
Thus, the developed Geant4-based program can correctly predict MURA mask scintillammography. Second, The spot position, shown in the decoded image, is directly related to the tumor position within the phantom. Hence, as location 19 is located near the central axis of the mask and below location 21 and over location 17, as seen in Figure 1, subfigures (a), (c), (d) and (e) confirm such a positioning in the decoded images. Additionally, location 8, situated on the right side of location 19, obeys the same conclusions. Finally, we can safely expand our work by simulating other scenarios.
## 4 Result and Discussion
### EVS Charging Behavior  
The effect of the owner preferences based on EPTD and OPR as well as the wind power generation on arbitrary EVs charging process is presented in this section. Three EVs are chosen at different plugged-in times, the EVs numbered as EV#i, EV#j, and EV#k. Moreover, the SOC and operating charging power are presented for each EV.  
Table 2 gives the initial parameter for the three arbitrary EVs, SOC is 40% for all selected EVs however, they have different plugged-in times. Each EV has a different owner preference variable. Figure 17a,b displays the operating power and SOC of EV#i respectively. In the case of no control (Case 1), the battery starts charging at the plugged-in time 09:23 whereas the battery stops charging and gets disconnected from the grid when the SOC reaches 90%.  
However, in Case 2, with the owner preferences considered, the proposed DSM shows a flexible charging process and capable to perform discrete charging. EV#i is delayed and set at an Idle state as shown in Figure 17a,b due to the real-time ETR not meeting the OPR in this duration of time considering the EPTD limit.  
In Case 3, with the integration of wind power generation, the battery is charged in two sessions. The first session starts at the plugged-in time of 09:23 and stops at 11:17 whereas the second session starts at 13:53 and reaches the required SOC at 16:00.  
Similarly, for EV#j and #k, the charging behavior is affected by the DSM as depicted in Figures 18 and 19 respectively. EV#j is in Idle state two times in case 2 and one time in case 3 when wind power has been integrated. Furthermore, EV#j is connected to the grid in case 3 after it has been sent to the waiting list two times in case 2.  
Figure 17: EV#\(i\) charging behavior at different case studies.  
Figure 18: _Cont._
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 7 Effects of Ohmic Heating on Quality of Food Products  
Modern food processing has given considerable attention to treating food with electrical techniques [67], including ohmic heating, which depends on the passage of alternating electric current through the food, which generates heat inside the food [29]. This technology provides fast and uniform heating and results in less thermal damage to the food. Therefore, a high-quality product can be manufactured with minimal structural or nutritional or sensory changes as a result of a short treatment time [18]. Electrical, thermophysical, and rheological properties of foods have an important role in uniform heating. It is necessary to point out the importance of potential electrochemical reactions at the contact surface between food and electrodes as well as possible non-thermal effects of the electric field that depend on treatment conditions [68]. The electric field of ohmic heating can cause various changes in the quality and biological processes of food, such as the inhibition of enzymes and microorganisms, the degradation of heat-sensitive compounds, changes in cell membranes, viscosity, pH, color, and rheological characteristics [69]. Table 2 reports some of the effects of ohmic heating on the quality and characteristics of different foods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Foods** & **Electrical Properties** & **Effect of Ohmic Heating** & **References** \\ \hline \multirow{4}{*}{Milk} & \multirow{4}{*}{0.39 S/m} & \multicolumn{2}{c}{Inhibition of aerobic and thermophilic bacteria, there} \\  & & & was no difference in the degrees of protein denaturation. \\  & & & A deadly thermal effect for microorganisms and another \\  & & & non-thermal effect due to the electric current. Very \\  & & & effective for milk pasteurization without the additional \\  & & & degradation of protein. \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Fresh solid food \\ (potatoes, carrots, and apples) \\ \end{tabular} } & \multirow{4}{*}{1100 V/m; 2200 V/m; 3300 V/m).} & \multicolumn{2}{c}{A significant of ohmic heating on the texture of solid} \\  & & & food. The hardness of the samples decreased with the \\ \cline{1-1}  & & & treatment time and resulted in structural damage, even \\ \cline{1-1}  & & & though the food material used in the study had a low \\ \cline{1-1}  & & & electrical conductivity. \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Meat and its production \\ \end{tabular} } & \multirow{4}{*}{0.8 S/m and 1.6 S/m} & \multicolumn{2}{c}{The quantity of the product is higher while maintaining \\  & & the sensory quality and improving the color compared \\ \cline{1-1}  & & to the traditional method, preventing the growth of \\ \cline{1-1}  & & microbes effectively. Temperature of 100 \({}^{\circ}\)C was able to \\ \cline{1-1}  & & cause the toxic changes in meat and meat products due \\ \cline{1-1}  & & to direct contact with electrodes and meat products. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of ohmic heating on some types of foods.
## 4 Overview of DSRC, LTE C-V2X, and 5G NR V2X
### LTE C-V2x  
C-V2X, or cellular vehicle-to-everything, commonly referred to as LTE-V2X due to its foundation on time division long-term evolution (TD-LTE) 4G cellular technology, is a prominent communication framework designed to enhance vehicular connectivity and road safety. It was developed by 3 GPP as an alternative to DSRC.  
C-V2X enables direct and reliable wireless communication among vehicles (V2V), between vehicles and infrastructure (V2I), and even with pedestrians and other vulnerable road users (V2P), thus forming a comprehensive V2X ecosystem. This technology leverages the existing cellular network infrastructure to provide low-latency, high-throughput data exchange, enabling vehicles to exchange critical information such as traffic conditions, emergency alerts, and real-time sensor data. C-V2X holds significant promise in advancing intelligent transportation systems, reducing traffic accidents, and enabling future autonomous driving applications, making it a focal point of research and development in the field of connected vehicles and smart transportation.  
C-V2X is regarded as the most powerful competitor to 802.11p [34] and, consequently, many studies have investigated their comparative performances. Comparison at the link level showed that C-V2X could improve the link budget over IEEE 802.11p by around 7 dB [34]. In addition, the study also found the coverage of LTE V2X to be larger than DSRC under the same velocity. Furthermore, owing to the modulation technique used by LTE V2X, it is less susceptible to noise, which allows it to guarantee a more reliable communication link than DSRC. A more detailed performance comparison between the two technologies is covered in the next section below.
## 2 Materials and Methods
### Definition of the Problem
However, the absence of adequate semantic understanding remains a challenge in many current models, which, although excellent at identifying contextual patterns, lack deep semantic knowledge. This gap, at the intersection of symbolic knowledge and deep learning, forms the core of the problem studied. For this, it is essential to highlight recent developments that have marked the field. Models like BERT and GPT-3 have revolutionized natural language understanding, offering improved text comprehension and generation capabilities. These models are based on capturing extensive contexts and generating coherent and relevant responses. Additionally, there is significant progress in integrating semantic understanding into NLP models, allowing these systems better to understand the subtleties and nuances of human language. Advances in NLP have led to innovative, practical applications, such as machine translation, sentiment analysis, and automatic text generation, which have significantly improved accuracy and naturalness. These recent developments underscore the importance of continuing to explore and improve the capabilities of PLN. At the same time, we recognize the need for a balanced approach that integrates symbolic knowledge and deep learning to address human language's complexities effectively. A process flow encapsulating semantic integration in deep learning models has been structured to address semantic challenges in deep learning. As illustrated in Figure 1, the procedure begins with a text input that undergoes preprocessing and tokenization steps. Subsequently, an essential semantic integration step is introduced, which infuses the model with a semantic understanding of the text. An improved attention mechanism has also been incorporated that weights inputs based on their context and inherent meaning, unlike traditional approaches. This optimized mechanism is fed to the deep learning model, culminating in the final model output. Figure 1: Semantic integration process flow in deep learning models.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 3 Numerical Simulation of Tunnels and Landslides
### Interaction between Landslide and Tunnel  
During the tunnel excavation process, the landslide is continuously disturbed, which intensifies its deformation. Spatially, the tunnel is located on the sliding surface. A penetrating plastic zone forms on the sliding surface, and the landslide's mass slides along this plastic zone. Figure 11 shows a schematic diagram of the shear strain change after tunnel excavation. During the initial stage of excavation, the shear strain first appears near the crown on the right side of the tunnel. The increased shear strain increment means that the sliding potential along the region becomes larger. Subsequently, the maximum shear strain increment of the whole landslide mass accumulates on the sliding surface, forming a continuous maximum shear strain increment zone. Landslide failure often occurs in the section with the highest shear strain, and by analyzing the shear strain increment, the weakest points in the tunnel and landslide can be identified.  
Tunnel excavation increases the shear strain increment of the shallow sliding surface, and in turn, landslide displacement greatly affects tunnel excavation. Therefore, to effectively prevent landslide and tunnel deformation, it is necessary to propose technical solutions that can stabilize landslide slip, limit tunnel deformation, and control local tunnel stress. To strengthen tunnels and prevent landslide and tunnel deformation, strong engineering support structures such as anti-slide piles are usually used.