esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In comparison to conventional human-driven vehicles (HVs), connected and automated vehicles (CAVs) provide benefits (e.g., reducing travel time and improving safety). However, before the period of fully CAVs appears, there will be a situation in which both HVs and CAVs are present, and the traffic flow pattern may differ from that of a single class (e.g., HV or CAV). In this study, we developed a multi-class traffic assignment problem (TAP) for a transportation network that explicitly considered mixed modes (e.g., HV and CAV). As a link's travel time is dependent on the degree of mixed flows, each mode required an asymmetric interaction cost function. For TAP, the multi-class user equilibrium (UE) model was used for the route choice model. A route-based variational inequality (VI) formulation was used to represent the multi-class TAP and solve it using the gradient projection (GP) algorithm. It has been demonstrated that the GP algorithm is an effective route-based solution for solving the single-class user equilibrium (UE) problem. However, it has rarely been applied to solving asymmetric UE problems. In this study, the single-class GP algorithm was extended to solve the multi-class TAP. The numerical results indicated the model's efficacy in capturing the features of the proposed TAP utilizing a set of simple networks and real transportation networks. Additionally, it demonstrated the computational effectiveness of the GP algorithm in solving the multi-class TAP.  
Keywords:autonomous vehicle; gradient projection; mixed modes; traffic assignment
## 2 Challenging Open Issues
### Other Open Issues
#### 2.2.6 Node Heterogeneity  
In WBSNs, sensor nodes are most likely heterogeneous. Each sensor node with its specific application has different requirements and constraints. Each node might have a different size and different capacities in terms of computation, communication and energy capacity. They also might have different Quality of Service (QoS) requirements, depending on what application they are intended for. One application might require a higher data rate, while another might be tolerant to a long delay.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Transmission Overhead  
A simulation message is composed of a PHY preamble of 8 bytes, a MAC header of 21 bytes, an IPv6 header of 6 bytes compressed, a fragment header of 3 bytes, the RA  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cryptography** & **(HMAC-SHA-256** & **SHA-256** & **LiCi- 128** & **AES-CBC-256** \\ \hline Time (\(ms\)) & 0.0617 & 0.0311 & 0.02 & 0.1253 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of the execution time.  
Figure 8: An analysis of various group-based handover schemes.  
figure based on the frame format of 26 bytes [35], and the RA/RS header of 26 bytes [34]. An additional overhead is associated with the fast authentication in the handover header and the 22-byte RS header. As shown in Table 8, the total energy cost associated with fast authentication during a handover is compared. According to [36], transmission has a significantly higher energy cost than local computation. To prolong the lifespan of the 6 LoWPAN network, it is necessary to minimize the number of messages. Based on Table 8, comparing SL_GAS to SGMS, SL_GAS achieves a reduction in energy consumption of 6.59% for groups of 10 nodes and 7.07% for groups of 50 nodes.
## 3 Materials and Methods
### Main Controlling Factors of Reservoir Formation
#### (2) Moderate Dolomitization  
The process of dolomitization, where dolomite replaces calcite (or aragnite and high-magnesium calcite), is known as dolomitization. In the study area, the best reservoir spaces and hydrocarbon-bearing layers within the Changxing Formation carbonate rocks are predominantly dolostones formed by dolomitization. Among these, dolostones with dissolution pores exhibit the best porosity and permeability properties, indicating that the development of reservoirs is closely related to the extent of dolomitization. The Changxing Formation reservoirs on the northern side of the platform depression have been less affected by dolomitization, predominantly consisting of limestone-type reservoirs. In contrast, dolomitized reservoirs are more common in the southern side of the platform depression, particularly in the areas of wells W061-1, W117-, W118-, W118-1, and S18. The southern section of the region shows a significantly higher dolomite development compared to the northern section, where dolomite thickness is relatively thin, and dolomitization is primarily developed in the second segment (Figure 10).
## 2 Non-Symmetric Cross-Section
### Net Shear Stress  
The shear stress due to transverse shear and axial torsion is to be investigated here. A torsional shear flow analysis requires that a longitudinal axis passing through E in Figure 1b receives a net torque:  
\[T=F_{y}\;e_{x}-F_{x}\;e_{y} \tag{4}\]  
where \(e_{x}\) and \(e_{y}\) are the perpendicular distances from \(F_{y}\) and \(F_{x}\) to E, as shown in Figure 1b.  
Note (1): For the axially symmetric channels considered here, with a vertical web and horizontal flanges, the line of action of \(F_{x}\) passes through E, and therefore, \(e_{y}=0\) in Equation (4).  
Note (2): If forces \(F_{x}\) and \(F_{y}\) do not lie at G, then \(e_{x}\) and \(e_{y}\) are replaced by their respective perpendicular distances from E.  
Note (3): If the applied transverse forces are inclined to \(x\) and \(y\), the shear flow Equation (2a) employs component forces \(F_{x}\) and \(F_{y}\) that are referred to the shear centre.  
Figure 2: Channel section (**a**) with (**b**) swept area at shear centre E.  
Consider shear flow \(q\) arising from \(T\) applied at E for a thin-walled, cantilever beam section with \(x\)-axis symmetry in Figure 1a [1]. The Wagner-Kappus contribution [10; 11] to the total torque \(T\) employs shear flow \(q\) as follows:  
\[T_{w}\ =\ \mathrm{I_{c}}\ q\ R_{t}\ \mathrm{d}s\ =\ \mathrm{E}\ \left(\mathrm{d}^{3}\ \theta/\mathrm{d}z^{3}\right)\ \mathrm{I_{c}}\ R_{t}\ \left[\int_{0}^{s}(2A_{E})\ t\ \mathrm{d}s\right]\ \mathrm{d}s\] (5a) where \[R_{t}\] is the perpendicular distance from E to the mid wall position and \[c\] is the full length of the mid-wall perimeter co-ordinate, \[s\]. Shear flow \[q\] in Equation (5a) for a uniformly thin section is given as \[q=E\left(\mathrm{d}^{3}\theta/\mathrm{d}z^{3}\right)\int_{s}\left(2A_{E}\right) \ t\ \mathrm{d}s=Et\left(\mathrm{d}^{3}\theta/\mathrm{d}z^{3}\right)Q\] (5b) where \[Q=\int_{s}2A_{E}\ \mathrm{d}s=\int_{s}\left(\omega-\varpi\right)\ \mathrm{d}s\] (5c) in which \[\varpi\] is twice the swept areas between E and each median point, 1, 2, 3... 7, in the cross-section. The total (applied) torque at length position \[z\] is the sum of the Wagner and St Venant torque contributions to the total (applied) torque: \[T=GJ\left(\mathrm{d}\theta/\mathrm{d}z\right)-\mathrm{E}\ \Gamma_{1}\ \mathrm{d}^{3}\theta/\mathrm{d}z^{3}\] (6a) for which the solution to the twist rate is [1] \[\mathrm{d}\theta/\mathrm{d}z=[T/(GJ)][1-\cosh\ [\mu(L-z)]/\cosh\ (\mu L)]\] (6b) This gives the derivative \[\mathrm{d}^{3}\theta/\mathrm{d}z^{3}\] in Equation ( 6a ) as \[\therefore\ \mathrm{d}^{3}\theta/\mathrm{d}z^{3}=-(T/GJ)\ [\mu^{2}\mathrm{cosh}\mu(L-z)/\cosh\ \mu L]\] (6c) where \[J=\sum\mathrm{d}t^{3}/3\] is the polar second moment of area for a thin-walled section composed of rectangles, as are all channel, T and I sections.  
It will be seen that the shear flow distribution arising from each source (shear and torsion) may be overlaid graphically to provide a net shear stress distribution (\(\tau=q/t\)) for each length position \[z\] in a beam of a given cross-section.
## 2 Antennas with a Single Reconfigure Feature
### Polarization-Reconfigurable Antennas  
Antennas with polarization reconfigurability can offer exceptional multipath fading reduction. With this type, an antenna can be switched to different modes [36]. Most investigations in the polarization-reconfigurable antennas are concerned with switching between right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) at a desired frequency [37], although linear polarization (vertical or horizontal modes) is also studied [38; 39]. Employing modified structures such as slot, slit, parasitic structures, and truncated corners of the main radiator along with embedded active elements are the most popular methods to obtain polarization-reconfigurability [40; 41]. Adopting active structures such as meta-surfaces and feeding networks can also provide polarization  
Figure 3: (**a**) Antenna configuration S\({}_{11}\) results and (**b**) the radiation patterns for different diode states [30]. Reproduced with permission from [30], Copyright IEEE, 2018.  
reconfigurability [42]. Other techniques, such as using reconfigurable external polarizers or phase shifters, can also be useful [43].  
In [44], a simple and new design of a polarization reconfigurable antenna with a C-shaped slot was introduced for 2.2-2.8 GHz 4G applications. Its configuration, shown in Figure 4a, consists of a circular radiation patch with a C-shaped slot and two diodes across the concentric circular slot on the radiation patch. As can be observed from Figure 4b, the antenna can switch between vertical and horizontal linear polarizations (VP/HP) modes as well as between LHCP and RHCP circularly polarized modes, and is applicable for use in 5G wireless communications. In [45; 46], two designs of polarization reconfigurable patch antennas of compact size were reported for 5G wireless systems and their characteristics were investigated.
## 4 Discussion
### Limitations  
There are several limitations to this study. First, the research was conducted exclusively on professional boxers, which limits the generalizability of the findings to athletes from other sports disciplines. Extending the study to include different sports would help determine whether plyometric training produces similar effects across various athletic contexts. Additionally, the study only examined an eight-week training period, so the long-term effectsof glyometric training remain unknown. Future research should explore the sustainability and long-term benefits of glyometric training over extended periods. Another important limitation is the relatively small sample size, with only 10 athletes per group, which may have reduced the statistical power and affected the reliability of the results. Increasing the sample size in future studies could enhance the robustness of the findings and improve their applicability. Furthermore, this study focused solely on physical performance parameters, without considering psychological factors such as motivation, mental state, or recovery, which may also influence training outcomes. Incorporating these variables could provide a more comprehensive understanding of the effects of glyometric training.  
One of the primary limitations of this study is the absence of a non-intervention control group. While both intervention groups demonstrated improvements in key performance metrics, it is not possible to definitively attribute these changes solely to the glyometric training protocols. Given that participants continued their regular boxing sessions and were in the preparatory phase of their competitive season, adaptations may also reflect the cumulative effects of overall training load, seasonal progression, or natural performance fluctuations. Additionally, participants' prior training experience (average > 5 years) may have influenced their responsiveness to the intervention. Future studies should consider including a true control group and more closely monitoring external variables such as training volume, intensity, and competition calendar to better isolate the effects of specific interventions.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Criteria  
We evaluate E3 AF using the following criteria:  
1. _Network Lifetime_[26] is the period from the start of network operations to the first failed node due to battery depletion.
2. _Coverage Ratio_ is very essential in our deployment method. That is reasonable since our main goal is to increase network coverage. The coverage ratio is defined as the number of zones covered with at least one sensor node. In this context, the highest priority zones should be covered as much as possible. \[\begin{split} Coverage Ratio=\frac{\sum\limits_{i=1}^{n}Zp_{i}}{ \sum\limits_{j=1}^{m}Zp_{j}}\end{split}\] (36) where \(m\) is the total number of zones, \(Zp_{i}\) is the zone's \(i\) priority, and \(n\) is the number of covered zones.
## 4 Implementation
### Results  
The test results of the FairMOT-DLA-34 model proposed in this paper showed IDF1 11.0%, MOTA 44.1%, MOTP 0.393, and IDSW 1 as shown in Table 5. On the other hand, the test results of the FairMOT-YOLOv5s model showed IDF1 14.6%, MOTA 43.8%, MOTP 0.400, and IDSW 10. Comparing the test results of the two models, the FairMOT-DLA-34 model showed relatively good performance in IDF1, MOTA, and MOTP, but the FairMOT-YOLOv5s model showed more errors in ID switches (IDSW).  
Figures 7 and 8 show the test results using FairMOT-DLA-34 and FairMOT-YOLOv5s models. Figure 7 compares the fish detection results of the two models in the same frame. The FairMOT-YOLOv5s model accurately detected the fish in the 1290 th frame, whereas the FairMOT-DLA-34 model failed to detect the fish in the same frame. This suggests that the model using the YOLOv5s backbone has higher detection accuracy.  
Figure 8 compares the fish ID tracking results of the two models in the same frame [19,21]. The FairMOT-YOLOv5s model accurately tracked the fish ID until the 1137 th frame, whereas the FairMOT-DLA-34 model failed in ID tracking in the same frame. This shows that the model using the YOLOv5s backbone has superior performance in ID tracking as well.  
In conclusion, the FairMOT-DLA-34 model generally showed superior performance in detection and ID tracking but had relatively more errors in ID switches. In contrast, the FairMOT-YOLOv5s model showed relatively good performance in detection accuracy and ID tracking, and fewer errors in ID switches. Therefore, this paper suggests that the FairMOT-YOLOv5s model is more suitable when ID tracking performance is more important.
###### Abstract  
Combinations of data augmentation methods and deep learning architectures for automatic pancreas segmentation on CT images are proposed and evaluated. Images from a public CT dataset of pancreas segmentation were used to evaluate the models. Baseline U-net and deep U-net were chosen for the deep learning models of pancreas segmentation. Methods of data augmentation included conventional methods, mixup, and random image cropping and patching (RICAP). Ten combinations of the deep learning models and the data augmentation methods were evaluated. Four-fold cross validation was performed to train and evaluate these models with data augmentation methods. The dice similarity coefficient (DSC) was calculated between automatic segmentation results and manually annotated labels and these were visually assessed by two radiologists. The performance of the deep U-net was better than that of the baseline U-net with mean DSC of 0.703-0.789 and 0.686-0.748, respectively. In both baseline U-net and deep U-net, the methods with data augmentation performed better than methods with no data augmentation, and mixup and RICAP were more useful than the conventional method. The best mean DSC was obtained using a combination of deep U-net, mixup, and RICAP, and the two radiologists scored the results from this model as good or perfect in 76 and 74 of the 82 cases.  
pancreas; segmentation; computed tomography; deep learning; data augmentation 2020
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 4 Methodology
### Individual Feature Projector  
The individual feature projector in the EPEM framework considers two different aspects when obtaining node feature representations: common features and unique features of links. Common features refer to the association formed purely based on the topological structure of links between nodes, without considering the potential attribute differences among nodes. Unique features encapsulate the special characteristics of different link types.  
We firstly approach heterogeneous nodes as if they were homogeneous nodes concerning their structure, focusing solely on the structural attributes between nodes, and we leverage node2 vec to derive link feature representations, which serve as common features for nodes \(i\) and \(j\), denoted as \(x_{i}\) and \(x_{j}\), respectively. Subsequently, we employ PME to generate type-specific priori feature representations for nodes \(i\) and \(j\), designated as \(X_{i}\) and \(X_{j}\), respectively. This encompasses the attribute information intrinsic to each link type and serves as the individual feature of distinct link types. Ultimately, to emphasize the distinctive traits of various link types and reinforce the capacity to effectively discriminate between them, we fuse the individual features and common feature representations of nodes via orthogonal projection as follows.  
\[Proj(a,b)=\frac{a\cdot b}{|b|}\cdot\frac{b}{|b|} \tag{1}\]  
where \(Proj(a,b)\) is a projection function which can joint vector \(a\) and \(b\).  
According to the aforementioned function \(Proj(a,b)\), we obtain the projection representation \(X_{i}^{-}\) of node \(i\) as follows.  
\[X_{i}^{-}=Proj(X_{i},x_{i}) \tag{2}\]  
where \(X_{i}\) represents the unique feature representation and \(x_{i}\) represents the common one, respectively. The individual projection representation \(X_{i}^{*}\) of each node \(i\) is obtained by orthogonal projection function as follows.  
\[X_{i}^{*}=Proj(X_{i},X_{i}-X_{i}^{-}) \tag{3}\]  
Finally, by aggregating the unique and common feature representations, we obtain the feature representation \(R_{l}\) of link \(l=(i,j)\) by combining the projection feature representations of nodes \(i\) and \(j\) in (4).  
\[R_{l}=X_{i}^{*}*X_{j}^{*} \tag{4}\]  
Figure 1: The framework of the proposed EPEM.
## 5 Conclusions  
We studied the effect of QW number on EL properties in AlGaN-based DUV-LEDs. An increase in QW number could decrease the electron current overflow, which resulted in the improvement of band emission around 265 nm and a decrease in the parasitic peak around 304 nm. As a result, the light output power was increased. However, the increment of output power from 5 QWs to 10 QWs was less than that from 10 QWs to 40 QWs, which was very different from what has been reported for blue and near-UV LEDs. From the comparison of LEDs with and without a 1 nm AlN EBL for the same QW numbers, we could conclude that the efficiency of the EBL was better than that of the QW-number increase for suppressing the electron current overflow. The simulation showed that the use of a thin EBL increased the barrier height for electron overflow and significantly decreased the electron current in p-type layers.  
Writing--Original draft preparation with analysis, S.T.; APSYS simulation, G.C.; Experiment and writing--review, J.Z.; Supervision and writing--review, T.E.  
This research received no external funding.  
This work was partly supported by the National Natural Science Foundation of China (No. 61474133, 61504071), Beijing Municipal Natural Science Foundation (4182046), and the research project of Beijing University of Chemical Technology (YY1701, buctrc201802).  
The authors declare no conflict of interest.  
Figure 6: The band structures of LEDs with (**a**) 3 QWs; (**b**) 10 QWs and (**c**) 15 QWs without an EBL under an injection current of 120 mA.
## 2 Communication Middleware Technology Trend for Smart Factory  
The evolution of the computer environment has made it possible to link various manufacturing facilities in the manufacturing process. As a result, various types of communication methods have emerged, and various communication services are being expanded. These communication services are specialized according to the characteristics of the manufacturer. There is a problem of using a protocol dependent on the manufacturer. To solve this problem, standardized communication middleware technologies that can be applied to various industries have been developed to eliminate direct data exchange between devices and provide autonomous and efficient data communication.  
The main core technology of communication middleware is data-oriented, high-reliability, real-time data distribution technology. That is, for facilities made by various manufacturers to be controlled/managed in an integrated environment, standardized industrial communication protocols are needed [7].  
The current industrial communication protocol has evolved from the existing serial communication to an Ethernet-based communication method. To unify the Ethernet communication method and increase the data interoperability between devices, various international standardization organizations are converging demands for industrial communication protocol standardization. By applying these requirements, communication middleware, such as Data Distribution Service (DDS), and communication protocols, such as OLE Process Control (OPC) and MTConnect, were standardized. This chapter describes the characteristics of DDS, OPC, and MTConnect.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 5 Simulations and Discussion
### Simulation Description
#### 5.1.1 Simulation 1: Unidirectional Communication  
In this simulation, the communication between the source and destination DANH was unidirectional. The source DANH sent \(N\) (\(N=10\), \(20\),..., \(100\)) unicast frames to the destination DANH. Networkframes (including network traffic frames and control frames) were recorded to validate and compare the network performance.
## 4 Challenges and Future Directions  
The blockchain-integrated IoUT ensures security, consensus, traceability, privacy, and transparency, which makes it a possible solution to many of the challenges faced in an underwater environment. However, integrating blockchain in such a resource-constrained environment indeed poses certain challenges as mentioned below.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Motivation for Using Blockchain for the IoUT** & **Challenges of Blockchain–IoUT Integration** \\ \hline Environmental Monitoring & -To provide authenticated and distributed access to the users of the data collected -To guarantee the security and privacy of nodes participating in underwater communication & -Deployment Cost \\ \hline Disaster Management & -To make a reliable and secure acoustic communication underwater & -Energy Consumption -Computation Complexity \\ \hline Localization & -To ensure the privacy of location information -To share the massive amount of data collected in a secured manner -Only the participating nodes can view the location -To avoid data tampering, the immutability of blockchain can help & -High demand for power -Memory and computational capacity \\ \hline Defense & -Securing the data source -To secure the communication between the participating nodes -To validate the transactions & -Migrating from legacy systems -Scalability \\ \hline Underwater Exploration & -To ensure the transparency of the information collected and shared -To ensure traceability by providing a fully auditable and valid ledger of transactions -To share the data in a secure manner & -Scalability issues -Ocean governance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the applications of and motivations for blockchain for the IoUT and the challenges.  
Figure 5: Use cases of the blockchain-enabled IoUT