esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### SSN-Based Structural Optimization and CFD Validation  
The optimal solution identified by the GA is the individual [\(-0.006\), \(-0.008\), \(0.014\), \(-1\), \(-1\), 1] with the best fitness value, 31,800 s. This indicates that nozzle 1 and nozzle 2 are used as outlets (condition \(-1\)), while nozzle 3 serves as an inlet with an antioxidant additive (condition 1). The corresponding SSN configuration is shown in Figure 7.  
Based on the optimal flow distribution, CFD simulations were conducted to observe the mixing behavior of the antioxidant additive. Figure 8 presents snapshots of antioxidant distribution at various time intervals.  
The corresponding volume fraction curves of the antioxidant at the three monitoring points are shown in Figure 9.  
Figure 8: Temporal evolution of antioxidant distribution inside the tank.  
Figure 7: Optimal diesel conditioning path based on SSN structure.  
As seen in Figures 8 and 9, the antioxidant becomes increasingly uniform over time. In early stages (e.g., 3600 s and 7200 s), concentration gradients are evident. By 25,200 s to 31,800 s, the distribution becomes nearly homogeneous, indicating effective mixing. Both 2D and 3D views show diminishing gradients along axial and radial directions. The curves in Figure 9 further confirm this trend: initially, growth rates vary among points (with sensor 2 closest to the injection region), but later, all curves converge, indicating successful homogenization of the antioxidant throughout the tank.
## 7 Discussion and Open Questions
### Risk Analysis in Digital Twin-Based Validation Frameworks
#### 7.4.1 Risks Associated with Model Fidelity and Validation Gaps  
A primary risk arises from discrepancies between digital models and their physical counterparts. Inaccurate or incomplete simulation models can lead to false confidence in system behavior, particularly for safety-critical functions such as adaptive headlamp control. These modeling errors may result from simplified or outdated environmental representations (e.g., lighting conditions, weather effects), insufficient coverage of edge cases and rare operating scenarios, and limited representation of sensor inaccuracies or hardware variability [43].  
Examples are as follows:  
* An adaptive headlamp system model used in a DT may simplify environmental factors such as fog density or wet road reflectivity. In a real-world scenario, the system might fail to correctly adjust beam patterns under heavy fog, causing glare to oncoming drivers. If this edge case is absent or poorly represented in the simulation, the false sense of system reliability could lead to hazardous conditions post-deployment;
* Simulation of sensor noise: If a camera sensor model does not accurately represent the noise characteristics caused by low light or dirty lenses, the system's object detection reliability may be overestimated in the virtual tests;
* Failure to model hardware aging: An LED light's luminance may degrade over time, but if the model only represents ideal initial conditions, maintenance schedules based on DT predictions may be inaccurate.  
Failure to identify these gaps can cause undetected system faults, impacting vehicle safety and regulatory compliance. Rigorous model validation against empirical data and continuous model updates are necessary to minimize these risks [44].  
Although it is possible to include all of these effects in high-fidelity models within the DT framework, organizations must ultimately determine an appropriate balance between the development effort and the anticipated return on investment. Beyond a certain level of complexity, further model refinement may no longer be economically justifiable.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 2 Related Work
[25] introduce a deep attention selective network to solve image classification tasks. [PERSON] et al. [26] explore attention-based recurrent networks for speech recognition. [PERSON] et al. [27] make a great improvement in the image caption task by X-Linear Attention. A recent work of [PERSON] et al. [28] designs the squeeze-and-excitation module (SEnet) to exploit the inter-channel relationship. Based on SEnet, [PERSON] et al. [7] exploit both spatial and channel-wise attention. In addition, introduce the convolutional block attention module (CBAM), which can be used as a plug-and-play module for pre-existing CNN-based architectures. These two modules [7; 28] have shown the great importance of channel attention. However, due to videos having a temporal structure, the global structure of the channel has not been taken seriously. We combine the advantages of channel attention and soft attention to exploit the global structure for each channel. Based on these previous works above, the model proposed in this paper considers the attention mechanism comprehensively from temporal and channel. Furthermore, the semantic concepts are embedded in the decoder and the semantic vector is reconstructed to fully use the semantic information. This can be seen as a novel loss for generating accurate captions.
## 6 Verification of the Proposed Protocols
### Formal Analysis of DDSec Protocol  
The protocol is simulated using insecure public channel and processes representing the entities (!processUEA()) for \(UE_{A}\), (!processUEB()) for \(UE_{B}\) and (!processUEB()) for \(gNB\). The security properties we are interested are secrecy, mutual authentication, authorization and secrecy on communication between the entities. In consideration with adversary, we formally analyze the protocol, and there were attacks on the protocol, as shown in Figure 6; hence, the protocol is not secure. ProVerif results show that the secrecy query holds rand_b, UA, UB, d1, Acap and skuea, skueb does hold, but secrecy of rand_a does not. In addition, the authentication of \(UE_{A}\) to \(UE_{B}\) does hold that is non-injective and injective agreements, but \(UE_{B}\) to \(UE_{A}\) does not hold. Thus, \(UE_{B}\) may end the protocol thinking it is talking to \(UE_{A}\), while \(UE_{A}\) never runs the protocol with \(UE_{B}\).  
Event beginUEA means that \(UE_{A}\) has completed the protocol and \(UE_{A}\) received Message 6 and sent Message 7 Event beginUEB means that \(UE_{B}\) sent Message 6. All the parameters of the protocol are taken as arguments by these events: A, B, pkueb, rand_a, rand_b must verify the ID and respond to a nonce with a nonce. If the arguments match, then data are sent, otherwise it sends authentication failure and terminates the communication. We would like to prove the correspondence for_UE\({}_{A}\)_ and _UE\({}_{B}\)_ authentication; event(endUEB(A, B, K, rand_a, rand_b)) ==> event (beginUEA(A, B, K, rand_a,rand_b)) and inj-event (endUEB(A, B, K, rand_a, rand_b)) ==> inj-event(beginUEA(A, B, K, rand_a, rand_b)).  
The direct proof of this correspondence in ProVerif does not hold. We also try to prove and conclude the desired correspondence by noticing that event which has ub, d1, rand_a, rand_b, Ts_1 as argument cannot be executed before ua, d1, rand_a has been sent. That is, before rand_a request is has been executed with rand_a, rand_b response and which does not hold.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 4 Results and Discussion
### Nondominated Front Generation  
The non-dominated front, also called Pareto Front (PF), is composed of all Pareto-optimal solutions as shown in Figure 2. Thirty points of randomly distributed buoys are selected to generate the experiment result. Considering the problem is a two-objective optimization, each point on the PF does not dominate the other points, which indicates the solutions lie in the PF achieving high performance on communication and radar simultaneously. From Figure 2, we can see that all points are smoothly distributed on the curve with the horizontal axis from \(6.1\times 10^{-4}\) m\({}^{2}\) to \(7.4\times 10^{-4}\) m\({}^{2}\) and the vertical axis from \(4.68\times 10^{8}\) bps to \(4.81\times 10^{8}\) bps. It is worth noting that these values are not unique, which indicates that different buoy distributions and numbers or other parameters such as the location of the UAV will result in different final PF values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Height of the UAV \(H_{U}\) & 100 m \\ \hline Target Location 1 & \([500,300]^{T}\) m \\ \hline Target Location 2 & \([400,700]^{T}\) m \\ \hline Minimum data rate requirement \(R_{\text{th}}\) & 1 Mbps \\ \hline Reference channel gain g\({}_{0}\) & \(-60\) dB \\ \hline AWGN power \(\sigma^{2}\) & \(-110\) dBm \\ \hline Probability of mating parents selection \(\delta\) & 0.9 \\ \hline Localization accuracy threshold \(\eta_{\text{max}}\) & 3 \(\times 10^{-3}\)m\({}^{2}\) \\ \hline Minimum distance between UAV and & \\ target \(D_{\text{min}}\) & 300 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 2: The non-dominated front obtained by the CRMOP algorithm.
## 6 Experimental Results and Discussion
### Experimental Damage Observations and Crack Patterns
#### 6.1.1 Specim S1A  
Figure 10 provides a photographic summary of the evolution of crack patterns and CFRP fabric damage at the end-yielding region of S1A. Local debonding of the CFRP at  
the hinging region started at 0.6% drift while loading downward and manifested itself in the form of a short vertical (flexural) hairline crack starting at the tip of the FRP U-wrap (i.e., web-slab interface), at the location of the dominant flexural-shear concrete crack. This crack expanded vertically during higher drift levels, creating a continuous vertical tear along the fabric at a distance of 15-20 cm (6-8 in) from the beam-column interface. The first yield of the longitudinal reinforcement was also recorded at 0.6% drift by the SGs closest to the support. Widening of this first vertical tear across the CFRP started at 1.5% drift (Figure 10a) and continued through successive cycles. Bulging of the concrete surface underneath the CFRP, resulting from extensive spalling of the cover concrete at the hinging region, eventually led to the complete detachment of a strip of the CFRP U-wrap during the second cycle at 3.0% drift, while loading downward (Figure 10c). This was closely followed, during the first cycle at 4.0% drift in upward loading, by a sudden fracture of the adjacent strip of CFRP at one of the web corners (Figure 10d). Detachment of the CFRP exposed widely open flexure-shear cracks in the concrete underneath (Figure 10e). Peak capacity was reached during the first cycle at 3.0% drift. Only 2% load-carrying capacity was lost in the downward direction following the detachment of the first strip of CFRP (between the second and third cycle at 3.0%), but the fracture of the second strip caused a very abrupt collapse with a dramatic 37% decrease in strength in the downward-loading direction accumulated during the three cycles at 4.0% drift. The cycles at 4.0% and 6.0% drift were characterized by progressively severe deterioration at the hinge, with intense crushing and abrasion of the core concrete, detachment of big concrete blocks, and buckling of the top and bottom longitudinal reinforcement.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 2 Preliminaries
### UV Network Coverage  
#### 2.3.1 Basic Concept  
Network coverage is a reference standard that reflects the communication service quality provided by the UV communication network. In the UV NLOS communication network, network coverage means that each node achieves the coverage effect of the target area through cooperative communication. In this article, the Boolean coverage model is used, as shown in Figure 4. The Boolean perception model is an idealized model, ignoring the decreasing process from 0 to 1. Since this model has only two states of being covered and not being covered, it is also called the 0-1 model.  
Assuming that the coverage radius of the node \(O\) is \(R\), the coverage quality of node \(O\) at a certain point \(X\) is as [15]  
\[p(s)=\left\{\begin{array}{cc}1,&d(o,x)<R\\ 0,&d(o,x)\geq R\end{array}\right., \tag{5}\]  
where \(p(s)\) represents whether the point \(O\) can cover the point \(X\), and \(d(o,x)\) represents the distance between the \(X\) and the \(O\).
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
###### Abstract
The key differences between our work and [5] are summarized as follows. In [5], the impact of group size was analyzed from various perspectives, and the optimal group size was derived to minimize average AoI and bounded AoI. However, the optimization was performed under a fixed PER constraint, implying that the blocklengths of the status packet and the coded packet are determined by their respective PERs. Departing from [5], we treat blocklengths and group size as control variables to be optimized, thereby further exploiting the potential for AoI reduction. Since blocklengths and group size are coupled, we jointly optimize them to minimize the average PAoI and propose joint blocklength and group size (JBG) optimization. Furthermore, we adopt the average PAoI as a performance metric to evaluate the freshness of status updates from the worst-case perspective. The contributions of this paper are summarized as follows. * We present a closed-form formula for the average PAoI in a two-hop status update system with joint packet coding in the finite blocklength regime. * Based on this formula, we jointly optimize the first-hop blocklength, second-hop blocklength, and group size to minimize the average PAoI. * Through numerical studies, we validate the effectiveness of our analysis and optimization. In particular, the results show that the proposed scheme reduces the average PAoI by around 8% compared to the existing approach, with more significant gains observed under poor wireless channel conditions. The remainder of this paper is organized as follows. Section 2 reviews related work. Section 3 describes the system model. In Section 4, we analyze the average PAoI. Based on this analysis, Section 5 details the procedure for minimizing the average PAoI. Numerical results are provided in Section 6. Section 7 discusses the applicability of our framework to fading channels and other key AoI metrics, such as the average and variance of AoI. Finally, Section 8 concludes the paper.
## 2 Materials and Methods
By taking the difference of Equations (5) and (6) and combining with Equations (1), (2) and (4), the slant TEC can be calculated as shown in Equation (7):  
\[STEC=\frac{{f_{1}}^{2}{f_{2}}^{2}}{40.28{\left({f_{1}}^{2}-f_{2}^{2}\right)}} \cdot(\lambda_{1}\Phi_{1}-\lambda_{2}\Phi_{2}+\lambda_{1}N_{1}-\lambda_{2}N_{2 }), \tag{7}\]  
where \(\Phi_{1}\) and \(\Phi_{2}\) are the carrier phase observables, \(\lambda_{1}\) and \(\lambda_{2}\) are the carrier wavelengths, and \(\lambda_{1}N_{1}\) and \(\lambda_{2}N_{2}\) are the integer ambiguity of the carrier waves. Owing to the presence of unknown integer ambiguity, only the relative variations in TEC with a high degree of precision can be ascertained, taking into account that earlier CODE's GIM maps are produced with the time gap 2 h, which is a relatively small-time window [43]. This time scale is enough to capture some short-term changes in TEC, but it will not lose important long-term trends more; thus, the two-hour sliding average approach was employed in this study. The smoothed TEC value is calculated by averaging TEC measurements within a two-hour window (one hour before and after the specific time instant). This technique could effectively eliminate the typical trend of TEC fluctuations and irrelevant noise, facilitating the emphasis of relative TEC changes, which is applied for the subsequent research of seismic disturbances.
## 1 Introduction
Since then, many other joint inversion studies based on the cross-gradient constraint theory have been carried out. Joint inversion algorithms of more than two physical properties have been realized and widely used. [PERSON] and [PERSON] extended cross-gradient joint inversion to three-dimensional gravity and magnetic data [12]. [PERSON] et al. proposed a joint inversion framework containing multiple datasets of three-dimensional magnetotelluric, gravity and seismic refraction data, and summarized the advantages and disadvantages of both cross-gradient structural coupling and physical coupling [13]. [PERSON] et al. developed a joint inversion algorithm of receiver functions and magnetotelluric data to determine the crustal and mantle structure beneath central Namche Barwa in the eastern Himalayan syntaxis [14]. [PERSON] et al. applied the cross-gradient constraint to the joint inversion of three-dimensional airborne electromagnetic data and gravity data [15]. [PERSON] et al. combined magnetotelluric and wide-angle seismic reflection/refraction inversion to detect crustal structures and Moho discontinuities [16]. However, the three-dimensional joint inversion of the resistivity method and TDIP has not been realized. In this research, a three-dimensional joint inversion algorithm of the resistivity method and TDIP based on the cross-gradient constraint was developed by utilizing L-BFGS as the inversion tool. In the joint inversion process, the resistivity and polarizability models were iteratively updated alternately, and the cross gradient was used to constrain the inversion, thus improving the reliability and consistency of the inversion results. The stability and reliability of the algorithm were tested in three synthetic inversion cases. Then, the algorithm was applied to groundwater detection work, and geologically sound results were obtained, which verifies the practicability of the algorithm.
## 3 Routing Maintenance and Reconstruction Algorithm
### Algorithm Timing  
PLC only allows one node to send data at any given time, so it is necessary to present a reasonable timing strategy. Otherwise, a channel conflict will seriously affect the performance of the system. Using the data transmission timing of the nodes in a subnet, the artificial cobweb routing algorithm (ACRA) is compared with the clustered simple polling (CSP, developed from the general polling method used to solve the \"silent node\" problem in the PLC-access network) algorithm mentioned in reference [12] through simulation. The interval time of communication between two peripheral nodes \(T_{WT}\) in the ACRA is defined as (1). \(T_{P}\) represents the manage processing delay of one peripheral node; \(T_{T}\) is the signal transmission time among nodes; \(T_{R}\) indicates the communication redundancy time. Generally, the signal transmission time \(T_{T}\) in a power line is fast enough to be ignored:  
\[T_{WT}=T_{p}+T_{T}+T_{R} \tag{1}\]  
Based on the protocol and the experimental results [16,17], \(T_{WT}\) is assumed to be 150 ms in this paper. Finally, the communication effectiveness of a cobweb structure is confirmed using data collision and the channel utilisation percentage. Figure 7a shows the timing of the ACRA, in which the horizontal axis is time, the single-layer cobweb consists of n nodes, node n is the central node, the rest of the nodes are peripheral nodes, and every node sends a data message in turn, according to the smallest to largest logical ID, to central node of its subnet with an interval time \(T_{WT}\). After receiving messages from all nodes, the central node then sends the messages to the central node or BS. This process avoids the channel data collision caused by the occupation of a channel by several nodes at the same time. Figure 7b shows the timing of CSP using five nodes. A node of medium distance will communicate with the BS through a closer node as a relay. Without constant relay nodes and strict timing, the five nodes will find the close node by sending random messages, resulting in a large amount of data collision in the channel. The corresponding simulation result is described in detail in the fourth part of this paper.
Effect of WC Nanoparticles on the Microstructure and Properties of WC-Bronze-Ni-Mn Based Diamond Composites  
[PERSON]  
1 College of Construction Engineering, Jilin University, Changchun 130026, China; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (Q.M.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (X.C.)  
[PERSON]  
1 College of Construction Engineering, Jilin University, Changchun 130026, China; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (Q.M.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (X.C.)  
[PERSON]  
1 College of Construction Engineering, Jilin University, Changchun 130026, China; [EMAIL_ADDRESS] (S.L.  
of diamond tools [14; 15; 16; 17]. However, the actual applicability of these matrices based diamond tools for different rocks is unsatisfactory because they wear out faster than diamond grits when processing hard and abrasive rocks. These metals also catalyze the reaction of diamond (sp3) to graphite (sp2) at elevated temperature, which reduces adhesion between diamond and matrix [18; 19; 20].  
Development of metal matrix composites reinforced by nanoparticles is a promising way to meet the actual engineering requirement on mechanical and tribological properties. Metal matrix composites reinforced by nanoparticles have promising properties, which is more suitable for a large number of functional and structural applications than other metal matrix strengthening methods, such as solution strengthening, work hardening and precipitation strengthening [14]. Recently, some researchers investigated metal matrix reinforced with nano-particles (nanotubes), finding out that adding nanosized reinforcement improved hardness, bending strength and wear resistance of matrix [21; 22; 23; 24; 25; 26; 27; 28]. Ceramic (Al\({}_{2}\)O\({}_{3}\), ZrO\({}_{2}\), TiC, TiB\({}_{2}\), WC, etc.) nanoparticles can be used as reinforcing phase. But Al\({}_{2}\)O\({}_{3}\), ZrO\({}_{2}\), TiC and TiB\({}_{2}\) nanoparticles have low wettability with bonding phase bronze, which decreases composites properties [7; 13; 22; 23; 24; 25; 26]. WC nanoparticles have excellent wettability with bronze and have good properties, namely high hardness, high wear resistance, good thermal and chemical stability. So it has been used in enhancing Ni, Fe and Co based matrix composites for cutting tools and modifying coatings for high wear resistance tools [13; 22; 29; 30; 31].  
In this work, nano-WC was introduced into WC-Bronze-Ni-Mn based diamond composites for the first time. The effects of nano-WC addition on the microstructures, mechanical properties, and wear resistance of WC-Bronze-Ni-Mn based diamond composites were investigated and discussed. The aim is to seek optimal nano-WC addition concentration to meet the requirement of mechanical properties and wear resistance.