esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems
## 5 Conclusions  
In this work, we propose a novel Edge-Driven Multiple Trajectory Attention Model (E-MTAM) to address VRPs of different scales. Our model is built upon the existing encoder-decoder architecture and employs a deep reinforcement learning approach for routing, eliminating the reliance on manually designed rules. We introduce three pivotal innovations to further improve the routing performance of the model. First, we integrate edge information into the encoder via an edge-driven multi-head attention block, enhancing the model's ability to capture the graph's topological structure. Second, we combine visitation information with graph embeddings to incorporate dynamic updates, enabling the decoder to adapt to real-time changes in the graph. Finally, we employ a multi-decoder architecture with a regularization term, encouraging the generation of diverse trajectories and thereby fostering a more comprehensive exploration of the solution space.  
We conducted extensive experiments on three types of routing problems: TSP, CVRP, and OP. We evaluate the performance of E-MTAM against a range of traditional and DRL-based methods. The comparison results demonstrate that our proposed E-MTAM significantly outperforms a variety of alternative approaches. Furthermore, generalization experiments demonstrate that our model exhibits stronger generalization capabilities than the baseline method on larger-scale problems. Finally, ablation studies validate the effectiveness of the three key improvements we introduced. Pertaining to future works, we acknowledge that there remains a gap between our E-MTAM and exact algorithms. On the one hand, the utilization of graph topology information in our model is still not fully optimized. Therefore, we will focus on exploring the underlying structure of the graph to enhance this aspect. On the other hand, we plan to incorporate other novel techniques to further optimize our encoder-decoder framework and to extend this solution to more complex variants of the VRP.  
Conceptualization, D.Y. and B.O.; methodology, B.O.; software, Q.G. and Z.Z.; validation, D.Y., B.O. and Q.G.; formal analysis, Z.Z.; investigation, Z.Z.; resources, Q.G.; data curation, B.O.; writing--original draft preparation, B.O.; writing--review and editing, D.Y.; visualization, Q.G.; supervision, H.C.; project administration, H.C.; funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62306232; the Natural Science Basic Research Program of Shaanxi Province under Grant No. 2023-JC-QN-0662; and the State Key Laboratory of Electrical Insulation and Power Equipment under Grant No. EIPE23416.  
**Informed Consent Statement**: Not applicable.  
Data Availability Statement: Data are contained within the article.  
We thank Xi'an Jiaotong University for helping us with the Article Processing Charge for the publication of this article in open access.  
The authors declare no conflicts of interest.
###### Abstract  
Driving is based on effective navigation. When using a navigation device, the user interface, the amount and quality of the underlying data and its representation all effect the quality of navigation. This study evaluates whether drivers in three different countries consider these devices to be useful and what functionality they would prefer. An online questionnaire was used to assess built-in navigation systems. The findings from 213 respondents show that current car GPS are overloaded with features. Regardless of country, drivers simply require more basic functionality in the interface. It was also noted that the embedded functions in these devices are not fully utilized. In addition, many people use the navigation service to enter a new address while the car is moving. It may be worth examining how this option can be better implemented.  
car navigation; built-in GPS; user experience; GPS interface +
Footnote †: journal: _Applied_  
The authors recognize the implicit quandary in using navigation devices of all kinds. When navigating in an environment without electronic assistance, people learn its spatial configuration as a side effect of wayfinding. This is especially true when using a larger map that provides an overall perspective of the environment. A variety of cognitive processes are involved when self-navigating with a map. For example, mental rotation is needed in order to adapt the map to the current view of the environment [1]. The process of planning a route from start to finish requires an entire array of cognitive skills. When people move without navigation assistance, objects in the environment are noted and compared to mental visualizations for confirmation [2]. This all helps to form a mental map that can be the basis for future navigation.  
It should also be noted that a more general definition of \"GPS\" is used here. Formally, the acronym refers to the Global Positioning System, first used for the U.S.' satellite network, called Navstar. Therefore, the term \"car/built-in GPS\" would technically refer only to devices that use satellites from the U.S. system. Global Navigation Satellite Systems (GNSS) refers to all satellite positioning systems that have been implemented by different countries throughout the world. Most current position-finding devices use a combination of GNSS, WiFi and cell-phone-tower triangulation methods to determine a user's location. To most people, GPS simply means \"position-finding,\" without consideration or understanding of how it is actually achieved.  
This paper begins by relating the historic progression of navigation devices. It then examines previous research on their effectiveness, spatial knowledge acquisition and location-based services (LBS). Finally, the questionnaire is presented and results are statistically analyzed. This leads to the presentation of a proposed user interface that can help in the design of future car navigation systems.
# Pricing Strategies of Logistics Distribution Services for Perishable Commodities
## 3 Model Construction of the Optimal Decision under Different Pricing Strategies
### Optimal Fixed Pricing Model
The relation \(\frac{\partial P_{F}{}^{*}}{\partial h}=\frac{1}{2}>0\) is established. Namely, \(P_{F}{}^{*}\) is a monotonically increasing function on \(h\). \(\square\)  
Under the fixed pricing model, it can be seen from the above properties that the pricing of perishable commodities is determined by many factors, such as consumer service satisfaction, the value attenuation parameters of perishable commodities, and the cost of perishable commodities. If the decay parameter of perishable goods is larger, the price of logistics distribution should be lower and the satisfaction degree of consumers should be higher. The price of logistics distribution should also be higher due to the higher storage cost of perishable goods. However, in real life, the fixed pricing model often makes consumers less satisfied with logistics services, which leads to a reduction in the number of perishable goods that consumers decide to distribute, and the profits of logistics service providers will be reduced.
# Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia
###### Abstract  
As part of the global endeavor to encourage sustainable urban growth and lower carbon emissions, Hail City is leading the way in implementing cutting-edge technologies with which to improve its urban infrastructure. Initiatives for energy resilience and the environment heavily rely on shifting to electric vehicles (EVs). This work describes the strategic planning required to implement a network of solar charging stations and analyzes the parameters that affect this, supporting cleaner transport options. In addition to meeting the growing demand from an increased number of EVs, constructing a network of solar charging stations positions the city as a leader in integrating renewable energy sources into urban areas. A solar electric vehicle charging station (EVCS) will also be designed. This study highlights a competitive attitude in establishing international standards for sustainable practices and critically examines the technical factors affecting the required charging stations. Regarding the latter, the following results were obtained. The ideal number of station slots is 200. Less efficient vehicles with higher consumption rates require a more comprehensive charging infrastructure, and increasing the charging power leads to an apparent decrease in the number of stations. The influence of battery capacity on the required NSs is limited, especially at charger power values above 30 kWh. By taking proactive measures to address these factors, Hail City hopes to improve its infrastructure effectively and sustainably, keeping it competitive in a world where cities are increasingly judged on their ability to adopt new technology and green projects. A solar station was designed to supply the EVCS with a capacity of 700.56 kWp.  
car charging station; electric vehicle; renewable energy sources; urban area; solar energy +
Footnote †: journal: Applied Sciences
## 2 Technical Backgrounds
### Threat Modeling on Authentication  
5G communication is particularly vulnerable compared to the previous environment due to its structural characteristics. This section addresses the threats that may arise during the second authentication process in 5G environments: NAS signaling storm and diversity of access mechanism.  
Converging with the IoT paradigm, the 5G communication network is expected to cover billions of heterogeneous devices and interact with them [18]. Interaction between devices requires mutual authentication. This authentication process occurs at the 3 GPP NAS(Non-Access Stratum) layer. However, the packet data used in bearer activation [19] and location update for authentication may cause a NAS signaling storm [20]. NAS signaling storm means that the NAS layer service becomes difficult to operate correctly due to a large amount of traffic in the network environment. An attacker can exploit this phenomenon maliciously. An attacker can maliciously drive the NAS layer's traffic by requesting re-authentication by constantly modifying device information, or by continuously generating unnecessary authentication signals [16]. NAS signal traffic is affected by the number of devices connected to the network, which means that as the network expands, the entire network may become vulnerable. Also, encryption-based tunnel setup such as bearer activation and IPSec further increases the cost of network signals. These features can be regarded as a structural problem of the centralized core network. Therefore, the 5G network environment is required to prevent structural DoS attacks that can be applied to signal traffic planes in the process of performing authentication between multiple devices.  
Also, 5G networks provide connectivity to small cells such as femtocells and picocells, as well as general networks for connectivity between various devices [21]. These secondary open-access networks are used to increase the overall capacity of the network and have various advantages in terms of low cost and indoor coverage. However, the secondary network of various devices may not support sufficient strength of security due to the performance spectrum of each device [15]. Low-power devices used in open-access networks may not encrypt for availability during data communications and may use weak authentication mechanisms. Integration with open secondary networks requires a low complexity and efficient handover authentication mechanism to optimize power consumption [8].
# An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening
## 4 Results and Discussion
### Surface Integrity Parameters Along the Tooth Profile  
Figure 21 shows the surface topography distributed along the tooth profile. This study divides the surface integrity parameters along the tooth profile into three regions for investigation: the tooth tip, the pitch circle, and the tooth root. The assessment of surface topography is conducted by analyzing the 3D roughness parameter Sa to evaluate the surface quality at different gear surface positions. At an initial shot velocity of 60 m/s, the surface topography along the tooth profile was calculated using S110 shots under the same shot peening duration. The surface topography at all three positions exhibited distinct shot peening characteristics; the area near the tooth tip had fewer pits, the pitch circle showed a greater number of larger diameter pits, and the area near the tooth root had smaller diameter pits. The surface roughness Sa near the tooth tip was 0.507 \(\upmu\)m, at the pitch circle it was 0.523 \(\upmu\)m, and near the tooth root it was 0.481 \(\upmu\)m. It was observed that the Sa value at the tooth root was the smallest, indicating the best surface quality, with the formation of smaller pits. This is primarily attributed to the lower impact velocities of the shots at the tooth root.  
Figure 21: Surface topography of different regions of the tooth surface. (**a**) Gear tip area. (**b**) Gear pitch circle area. (**c**) Gear root area.  
Figure 20: Impact velocity distribution at the gear root area.  
Figure 22 shows the results of residual stress. At the tooth tip, the pitch circle, and the tooth root, the surface residual compressive stresses were \(-\)837.00 MPa, \(-\)874.41 MPa, and \(-\)855.44 MPa, respectively, with the highest surface residual compressive stress occurring at the pitch circle. The maximum residual compressive stresses at the tooth tip, pitch circle, and tooth root were \(-\)1345.23 MPa, \(-\)1350.75 MPa, and \(-\)1332.59 MPa, respectively, showing only minor variations across different positions. The depths at which the maximum residual compressive stresses occurred were 27 \(\upmu\)m, 29 \(\upmu\)m, and 30 \(\upmu\)m at the tooth tip, pitch circle, and tooth root, respectively, indicating little change with the gear surface position. The effective depth of residual stress influence was approximately 70 \(\upmu\)m at all three locations: the tooth tip, pitch circle, and tooth root. The residual stress trends at these three positions were consistent, with the surface residual stress initially increasing, then decreasing from a depth of 30 \(\upmu\)m, and eventually stabilizing around \(-\)100 MPa. The analysis of residual stress indicates that there is no significant difference in residual stress across different positions on the gear surface.
## 3 Controller Design
### The Nonlinear GPC with Economic Objective  
The objective of this section is to design a nonlinear GPC controller that directly accounts for economic objectives. This is achieved designing a one-layer RTO-GPC controller. To derive the non-linear predictive control algorithm the future trajectory of the system is assumed to be known. State-space model (2) matrices may be re-calculated for the future using the future trajectory. The resulting state-space model may be seen as a time-varying linear model and for this model the controller is designed. In the proposed strategy, due to the presence of \(f_{co}\), the objective function Equation (3) is not a quadratic function of the manipulated variables of the optimization problem that defines the controller. Thus, the control problem turns into an NLP, which may result difficult to solve.  
Then, assuming that the vector of the control action is changed to \(u+\Delta u\), the first order approximation of the gradient of the economic function  
\[F_{coo}=f_{coo}(u,\hat{y}) \tag{16}\]  
can be represented as follows:  
\[\xi_{u+\Delta u}=D+G\Delta\bar{u} \tag{17}\]  
\[D=\frac{\partial F_{coo}}{\partial y}K_{P}+\frac{\partial F_{coo}}{\partial u} \tag{18}\]  
\[G=K_{P}^{T}\frac{\partial^{2}F_{coo}}{\partial y^{2}}K_{P}+\frac{\partial F_{ coo}}{\partial y}(\frac{\partial^{2}F_{coo}}{\partial y\partial u})+\frac{ \partial^{2}F_{coo}}{\partial u^{2}} \tag{19}\]  
where \(K_{P}=\frac{\partial y}{\partial u}\) corresponds to the process gain.  
In the Equation (17), \(\Delta\bar{u}=u(k+m-1|k)-u(k-1|k)\) is the total move of the input vector, \(D\) is the gradient vector at the present time and \(G\) is the Hessian of the economic function with respect to the inputs. The gradient vector \(\xi_{(u+\Delta u)}^{T}\) can be considered as a deviation vector, which is equivalent to considering that the gradient of the economic function is zero at the optimum. Thus, \(f_{coo}\) can be approximated by a quadratic function as  
\[f_{coo}=\xi_{(u+\Delta u)}^{T}\xi_{(u+\Delta u)}. \tag{20}\]  
**Remark.**_In the unconstrained economic optimization, the operating point where the gradient \(\xi\) is equal to zero corresponds to a local maximum (when \(G<0\)) or local minimum (when \(G>0\)) of the economic function. However, when the constraints of the control problem are active, the optimum corresponds to the point where the reduced gradient of the economic function is equal to zero. The reduced gradient is obtained through the projection of the gradient on the tangent space of the active constraints._
## 4 Discussion
### Limitations  
While this study offers valuable insights into the effects of age, training experience, and anthropometric characteristics on motor performance, several limitations should be acknowledged when interpreting the findings.  
First, the sample was limited to male handball players within a specific age range, which restricts the generalizability of the results to broader populations, such as female athletes, younger age groups, or players at different competitive levels. Additionally, due to the relatively small sample size, subgroup analyses based on player positions could not be conducted. Although all participants were from the same team and trained under the supervision of the same coaching staff following a standardized training program, this homogeneity does not allow for comparisons across positional roles or playing levels. Second, the cross-sectional design of the study limits the ability to draw causal inferences. While relationships between variables were identified, longitudinal research is needed to better understand how motor performance evolves in relation to age, training background, and anthropometric changes. Third, only basic anthropometric data (height, body weight, and BMI) were included in the analysis. More detailed body composition variables, such as body fat percentage and lean body mass, were not assessed due to logistical constraints. This limited the ability to explore physiological explanations for performance outcomes in greater depth. Fourth, psychological and cognitive factors--such as motivation, decision-making ability, and mental fatigue--were not considered in this study. These factors may influence motor tasks, particularly agility and explosive power, and should be integrated into future research for a more comprehensive understanding of performance. Finally, the study included only male participants, which limits the applicability of the findings to female athletes. Future studies should aim to include more diverse samples that account for gender differences and provide comparative insights across sexes, age groups, and competition levels.  
In light of these limitations, future research is encouraged to adopt longitudinal designs, incorporate more detailed physiological and psychological assessments, and recruit larger, more heterogeneous samples to enhance the generalizability and depth of findings in the field of sports performance.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 3 WANs: Time Data Measurement and Analysis  
Delay and offset measurements in a WAN connecting two cities apart by 67 miles physical distance and 15 hops in network distance were conducted. The heterogeneous communication network includes mainly fiber-optics-based networks and various wireless networks such as Wi-Fi towards the endpoints. Four different network traces that represented different network conditions ranging from good to fair were selected. In this paper, the detailed measurement and analysis of the first two traces depicting deteriorating network conditions have been showcased, skipping traces from good network conditions. However, the results for all four traces have been summarized.
# Immunomodulatory Properties of Probiotics and Their Derived Bioactive Compounds
## 5 Probiotic Derived Bioactive Compounds and Immunity
### Bacteriocins
#### 5.1.3 Bacteriocins as Immunomodulatory Molecules
Moreover, _Lactobacillus plantarum_ genes encoding production or secretion of bacteriocins [113] were reported to enhance production of IL-10 over IL-12 and possibly TNF-a induction in DCs [114] and in PBMCs [115]. \begin{table}
\begin{tabular}{c c c c} \hline
**Cell Type** & **Compound** & **Immunomodulatory Effect** & **References** \\ \hline \multirow{2}{*}{IECs} & \(\begin{array}{c}\text{Sublancin}\\ \text{Bacteriocins}\end{array}\) & \(\begin{array}{c}\text{Inhibition of NF-\(\kappa\)B activation}\\ \text{Simulation of host immunity as signaling peptides}\end{array}\) & [90] \\ \cline{2-4}  & \(\begin{array}{c}\text{Nisin}\\ \text{Sublancin}\end{array}\) & \(\begin{array}{c}\text{INI}\\ \text{IL-12}\text{ increase}\end{array}\) & \(\begin{array}{c}\text{IL-10}\\ \text{INI-1}\text{2}\text{, IL-6, TNF-\(\alpha\), and NO production, TLR, NF-\(\kappa\)B,}\\ \text{and MAPK signaling pathways modulation}\end{array}\) & [110] \\ \hline \multirow{2}{*}{DCs} & \(\begin{array}{c}\text{\emph{L}}\text{\emph{ plantarum}}\\ \text{bacteriocin-like}\end{array}\) & \(\begin{array}{c}\text{Genes encoding bacteriocin secretion enhance IL-10}\\ \text{over IL-12}\end{array}\) & \(\begin{array}{c}\text{[114]}\end{array}\) \\ \cline{2-4}  & \(\begin{array}{c}\text{Nisin}\\ \text{Sublancin}\end{array}\) & \(\begin{array}{c}\text{CD4+ and CD8+ T cell proliferation,}\\ \text{macrophages}/\text{monocytes}\end{array}\) & [103; 104] \\ \cline{2-4}  & \(\begin{array}{c}\text{Nisin}\\ \text{Nisin Z}\end{array}\) & \(\begin{array}{c}\text{IL-18}\text{, IL-6, IL-8, MCP-1, Gro-\(\alpha\) secretion,}\\ \text{TNF-\(\alpha\) suppression}\end{array}\) & [105] \\ \cline{2-4}  & \(\begin{array}{c}\text{Acidocin A}\\ \text{\emph{L}}\text{\emph{ plantarum}}\\ \text{bacteriocin-like}\end{array}\) & \(\begin{array}{c}\text{Cytokines- chemokines production}\\ \text{Genes encoding bacteriocin secretion enhance IL-10}\end{array}\) & [112] \\ \cline{2-4}  & \(\begin{array}{c}\text{perptide}\end{array}\) & \(\begin{array}{c}\text{Genes encoding bacteriocin secretion enhance IL-10}\\ \text{over IL-12}\end{array}\) & [115] \\ \hline \end{tabular}  
* IECs: intestinal epithelial cells, NF-\(\kappa\)B: nuclear factor-\(\kappa\)B, NETs: neutrophil extracellular traps, IL-8: Interleukin-8, IL-12: Interleukin-12, IL-16: Interleukin-19, IL-6: Interleukin-6, TNF-\(\alpha\): Tumor necrosis factor-\(\alpha\), NO: nitric oxide, TLR: Toll-like receptor, MAPK: mitogen-activated protein kinase, DCs: dendritic cells, _L. plantarum. Lactobacillusilus plantarum_, IL-10: Interleukin-10, MCP-1: monocyte chemoattractant protein-1, Gro-\(\alpha\): Growth regulated oncogene-alpha, PBMCs: peripheral blood mononuclear cells.  
\end{table} 
Table 1: The effects of probiotic derived bacteriocins on immune cells.