esa-sceva/satcom-chunk-collection huggingface dataset:
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 2 Related Work
### Graph-Based HOIR  
Many researchers have advocated the use of graph-based method for HOIR [36]. [PERSON] et al. [37] constructed a fully connected graph with the detected humans and objects as nodes. The initial undirected graph was then pruned to obtain an HOI graph containing only those edges that connect human and object nodes. For robust feature extraction from the human and object nodes, the authors employed two different attention-based networks that modeled global and local contexts respectively. They used the V-COCO and HICO-DET datasets to prove that their system performed better than other state-of-the-art methods. These two datasets were also used by [PERSON] et al. [38], who proposed a novel graph-based interactive reasoning model called interactive graph. Their approach exploited the interactive semantics among visual targets. Their model inferred HOIs using instance features and dynamically parsed pairwise interactive semantics among visual targets by integrating two-level in-Graphs, i.e., scene-wide and instance-wide in-Graphs. The proposed framework was end-to-end trainable and free from costly annotations, such as human pose. Although these two methods proved their effectiveness on image datasets, [PERSON] et al. [39] used video datasets too. They proposed a hybrid approach that uses GCN and hierarchical recurrent neural networks (RNNs), for recognizing human-object interactions in videos. Their approach did not rely on hand-crafted features. Instead, they used pure visual features derived from a re-trainable off-the-shelf network to represent the inputs.  
[PERSON] et al. [40] detected and recognized human-object interactions in images and videos using the graph parsing neural network (GPNN). For a given scene, their GPNN model inferred a parse graph that included the HOI graph structure represented by an adjacency matrix and the node labels. Within a message passing inference framework, the proposed GPNN iteratively computed the adjacency matrices and node labels. [PERSON] et al. [41] used the few-shot learning (FSL) approach for HOIR, which means using only a few samples to perform the task. However, it is difficult to do so and therefore, the traditional FSL methods do not perform well in complex HOI scenes. Thus, the authors proposed theuse of dynamic graph-in-graph networks (DGIG-Net) for achieving better results. They built a knowledge reconstruction graph to learn the latent representations for different HOI categories and a dynamic relation graph which integrated both constructible visual nodes and dynamic task-oriented semantic information.
## 3 Related Studies and Motivation
### Reinforcement-Learning-Based Network Routing
However, related studies have not considered switches that fail in the routing paths, and studies on applying DQN when transferring requested data blocks, and parity blocks during the decoding of erasure coding remain insufficient. Therefore, DQN-EC leverages DQN, a type of deep-reinforcement-learning-based Q-Learning, to collect network data and yield efficient routing transmission paths.
###### Abstract
[27] utilized ATEGs in air coolers, suggesting ATEGs are more suitable than FTEGs for fluid operations. This adaptability of ATEGs in diverse applications further reinforces the need for targeted research in TEGs to exploit their full potential in waste heat recovery. [PERSON] et al. [28] fabricated tubular thermoelectric components via melt-spinning and spark plasma sintering to validate these discoveries, achieving substantial power generation. Their work signifies a critical step in transitioning TEG technology from laboratory settings to real-world applications. [PERSON] et al. [29] manufactured ring-shaped components for tubular structures using a current-assisted sintering technique with a custom mold and diffusion barrier. Innovation in manufacturing processes such as these is crucial for the practical deployment of advanced TEGs. [PERSON] et al. [30] researched concentric heat exchangers to enhance overall heat transfer and reduce pressure drop in ATEGs, increasing net power production by 65%. This research contributes to the efficiency of TEGs and economic viability by enhancing their power output. A range of studies have explored the performance of tubular or annular thermoelectric generators (TEGs) in various applications. [PERSON] [31] and [PERSON] [32] both highlight the importance of environmental factors and thermal contact quality in TEG performance, with [PERSON] [32] specifically focusing on the fabrication of tubular TEGs. [PERSON] [33] and [PERSON] [34] propose design improvements for TEGs, with [PERSON] [33] introducing a novel concentric cylindrical TEG system and [PERSON] [34] using the Taguchi method to optimize TEG performance. [PERSON] [35] and [PERSON] [36] delve into the technical aspects of TEGs, with [PERSON] [35] analyzing the influence of various factors on TEG performance and [PERSON] [36] discussing the potential of high-temperature thermoelectric materials. [PERSON] [37] and [PERSON] [38] both investigate the long-term performance and stability of TEGs, with [PERSON] (2010) [37] focusing on a commercially available TEG and [PERSON] [38] examining a TEG system used in diesel engines. Finally, [PERSON] et al. [39] focused solar energy 20-fold onto combined FTEG/ATEG systems, achieving a 161.68 W power output, 86.5% thermal efficiency, and 1345 L of heated water per day. Integrating solar energy with TEGs opens new avenues for sustainable energy solutions, combining renewable energy sources with waste heat recovery. This research significantly contributes to enhancing the efficiency of thermoelectric waste heat exchangers. By incorporating advancements in materials science, design, and application-specific innovations, our study presents a novel module for efficient fluid heat recovery. This module, which integrates full-ring elements within a tubular thermoelectric generator (TTEG), combines the advantages of form and contact area found in annular thermoelectric generators (ATEGs) (Figure 2). The design offers a compact, practical solution for thermal energy recovery from fluids, contributing to the ongoing efforts in sustainable energy technologies.
# Impact of Multiple Beams and Mobility-Based Beam Alignment Error on Millimeter Wave Communication +
## 5 Conclusions  
In this paper, we analyze the impact of multiple beams and beam misalignment error due to UE random movement on the uplink CP of mmwave cellular network. We  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Parameter** & **Value** \\ \hline \(f_{c}\) & Frequency & 28 GHz \\ B & channel bandwidth & 500 MHz \\ \(\sigma^{2}\) & noise power & \(-77\) dBm/Hz \\ N\({}_{L}\) & Nakagami fading parameter & 3 \\ \(\alpha_{L}\) & path loss exponent & 2 \\ C\({}_{L}\) & path loss intercept & 61.4 dB \\ \(\beta\) & blockage parameter & 0.0071 \\ \(\tau\) & SSB periodicity & 20 ms \\ \(v\) & UE velocity & [3; 30; 120] km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for numerical analysis [6; 12].  
Figure 1: Impact of cell radius and UE velocity on SINR coverage probability for different BS beams.  
Figure 2: Impact of UE beams on SINR coverage probability for different BS beams.  
derive mathematical expressions for SINR CP to determine the effect of BS and UE beams, probability of beam misalignment, UE velocity and cell radius. The results demonstrate that an optimum BS beam exists that maximizes CP at different UE velocities and cell radii. Moreover, increasing the number of beams at UE is found to have the best coverage performance due to increased beamforming gain.  
[PERSON] formulated the system design, developed the theoretical model, performed numerical analysis and wrote the manuscript. [PERSON] and M.I.A. verified the analytical methods and findings, provided useful feedback and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Geographic, Geological and Archaeological Background  
Features 8 and 10 (Figures 1 and 2) are located at the southern edge of the relatively flat karst plateau close to Oprtalj/Portole, at an average height of about 400 m a.s.l. (Figure 1, nn. 8 and 10). They are divided by a deep narrow valley of an ephemeral watercourse which drains into the Mirna/Quieto river. This latter perennial river and its wide valley flanks the southern side of the investigated area and represents an important waterway which has connected coastal Istria with the inlands since prehistoric times [25].  
The plateau is made of Cretaceous limestone, dolomite and breccia and overlooks southward a hilly sector made of marks and sandstones (Eocene Flysch deposits; [24,26,27]) divided by the Mirna valley (Figure 2).  
The overall area is rich in archaeological features consisting of prehistoric, protohistoric and Roman sites ([23] and references therein). Close to the two investigated structures, the main evidence in the area is represented by the Laganisi cave, occupied for a long period spanning from Neolithic to the Roman times (around 5000 BC--4 th century AD), which was investigated in recent years ([28]; Figure 1, Laganisi).  
On the contrary, the two features here analysed were unknown before the recent publication of the results of the ALS survey [23], as well as two other protohistoric fortified structures located in their surroundings. Feature 9 is an irregular fortification (about 130 \(\times\) 60 m in size) located immediately west of site 10 and at the edge of the scarp of a deep narrow valley placed between them (Figure 1, feature 9). Feature 7 is an elliptical structure, about 80 m large and showing an additional south-western wall, located north of a limestone quarry between Vizintini and Sorgi villages (Figure 1, feature 7).  
Hundreds of similar fortified settlements, located on hilltops and protected by imposing dry-stone ramparts, mark the landscape of the north-eastern Adriatic region between the Trieste Karst plateau, the Istria peninsula and the Dalmatian islands. These villages were settled for many centuries, in some cases up to the arrival of the Romans in the area in the early 2 nd century BC, and shared homogenous traits in pottery assemblages and funerary practices [29]. Among these are burial mounds widely documented in the whole region with negligible differences and generally attributed to Early and Middle Bronze Age (ca. 2200-1350 BC). Other, larger protohistoric hillforts known in the area by previous research carried out in the late nineteenth and early twentieth century are located close to  
Figure 2: Position of features 8 and 10 and geological map of the investigated area. 1: Flysch deposits (Eocene); 2: Rudist limestone, dolomite and breccia (Cretaceous); 3: Quaternary deposits (mainly Holocene). Modified after [PERSON] et al. [24].  
the investigated sites, at Kukuj, Oprtalj, Sv. Jelena, Cebranjak, Beninici, Sv. Stippan and perhaps Gradinje and Vizintin ([30]; Figure 1).
## 2 System Model  
### RSMA Architecture  
To better understand how RSMA works, we use a \(k\)-user MIMO system to demonstrate the process, as shown in Figure 1. First, all users' messages are divided into two parts: public and private parts, which would be \(\left\{M_{1}^{\text{pub}},M_{2}^{\text{pub}},\cdots,M_{k}^{\text{pub}}\right\}\) and \(\left\{M_{1}^{\text{pri}},M_{2}^{\text{pri}},\cdots,M_{k}^{\text{pri}}\right\}\) in the figure. Then, all the common messages are combined together to obtain one new message \(M^{\text{pub}}\) whereas \(k\) private messages remain untouched. By encoding, we can obtain \(k+1\) streams \(\left\{s_{\text{pub}},s_{1},\cdots,s_{k}\right\}\). Stream \(s_{\text{pub}}\) will be encoded again by a public precoder \(w_{\text{pub}}\) shared among all users. As for the private streams, each of them will be encoded by its own precoder \(\left\{w_{1},w_{2},\cdots,w_{k}\right\}\) accordingly. Finally, by superimposition, we will get the transmitted signal \(w_{\text{pub}}\text{\text{\According to reference [9], RSMA is the bridge that connects spacial division multiple access (SDMA) and PD-NOMA. In SDMA, users are assigned with different beamforming precoders. Multi-user interference is discarded as noise. The performance of SDMA heavily depends on the orthogonality of users' channels and the number of antenna.  
In PD-NOMA, users are distinguished by different power. And in a multi-user system, the \(k\)-th user needs to perform \((k-1)\) times SIC to restore its own message. That means multi-user interference needs to be fully decoded. PD-NOMA's performance highly relies on the discrepancy of users' channel gains and the perfect SIC operation.  
Unlike these two extreme approaches, RSMA partially decodes the public stream and partially discards the private streams interference. This design allows it to achieve larger capacity. Moreover, the performance of RSMA depends on the precoder design and does not need channels to be orthogonal or significantly different. Therefore, RSMA is less limited than the other two. In addition, the one-time SIC in RSMA greatly reduces the complexity at receiver end. The advantages of RSMA could be summarized as follows.  
* It has a larger achievable rate region, which means it allows more connections;
* It has no requirements for users' channel condition, so it could be applied to more scenarios;
* It does not need user pairing or ordering for SIC, which reduces the complexity and delay;
* It only needs one-time SIC operation. Complexity is reduced again and the error accumulation is avoided.  
Figure 1: RSMA architecture.
###### Abstract  
To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network's malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.  
WSN; cluster head; sensor node; self-configuration; self-diagnosis; self-maintain
# The Virial Effect--Applications for SF\({}_{6}\) and CH\({}_{4}\) Thermal Plasmas
## 3 Virial Coefficients  
The tool presented in this study is also aimed at determining initial compositions for kinetic studies, in applications where the pressure can reach bar values of several tens. The developments, taking into account the pressure correction, are now presented. Virial coefficients are parameters that are used to describe the real behavior of a fluid. For this description, the second- (B(T)) and third-(C(T)) order coefficients could be used, and they were expressed using intermolecular potential:  
\[B(T)=-2.\pi.N_{a}.\int\limits_{0}^{\infty}f(r_{12}).r_{12}^{2}.dr_{12} \tag{23}\]  
\[C(T)=-\frac{8.\pi^{2}.N_{a}^{2}}{3}.\int\limits_{0}^{\infty}\int\limits_{0}f( r_{12}).f(r_{13}).f(r_{23}).r_{12}.r_{13}.r_{23}.dr_{1}.dr_{2}.dr_{3} \tag{24}\]  
where \(f\big{(}r_{ij}\big{)}=\exp\left[-\frac{u\left(r_{ij}\right)}{k_{B}.T}\right]-1\) is the Mayer function, \(u\left(r_{ij}\right)\) is the potential for interactions between two molecules, and \(r_{ij}\) are the coordinates of molecules \(i\), relative to molecule \(j\).  
For a mixture, the virial coefficients can be expressed by Equations (25) and (26):  
\[B(T)_{mixture}=\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}B_{ij}(T).x_{i}.x_{j} \tag{25}\]  
\[C(T)_{mixture}=\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\sum\limits_{k=1}^{m} C_{ijk}(T).x_{i}.x_{j}.x_{k} \tag{26}\]  
Figure 3: Evolution of molecular species: air; P = 1 bar \(-\) LTE. Comparison of our results with [PERSON]. [PERSON] [5].  
where  
* \(m\) is the number of gas for mixing;
* \(x_{i}\), \(x_{j}\), and \(x_{k}\) are the respective molar fractions of species \(i\), \(j\), and \(k\);
* \(B_{ij}\) is the binary interaction parameter;
* \(C_{ijk}\) is the ternary interaction parameter.
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 2 Occlusion Vehicle Target Segmentation and Recognition Scheme Design
### Design of Vehicle Component Feature Mask Module Based on Swin Transformer
#### 2.2.1 Swin Transformer Unit
In addition, a Layer Norm layer is introduced before each MSA layer and MLP layer, and residual joins are used overall. The calculation process of the Swin Transformer Block is shown in Equation (8). \[\left\{ \begin{array}{l}\widetilde{D}_{l}=W-MSA[LN(D_{l-1})]+D_{l-1}\\ D_{l}=MLP[LN(\widehat{D}_{l})]+\widehat{D}_{l}\\ \widetilde{D}_{l+1}=SW-MSA[LN(D_{l})]+D_{l}\\ D_{l+1}=MLP[LN(\widetilde{D}_{l+1})]+\widehat{D}_{l+1}\end{array} \right. \tag{8}\]  
In the Equation (8), \(D_{l}\) and \(D_{l+1}\) are the regional features of the output vehicle components at different levels, \(\widetilde{D}_{l}\) and \(\widetilde{D}_{l+1}\) represent the output of (S)W-MSA and \(l\)th \(MLP\). \(MLP\) is a multi-layer perceptron, \(LN\) is normalization operation, \(W-MSA\) is the window attention mechanism, and \(SW-MSA\) is the moving window attention mechanism.
## 2 Related Works
### High-Frequency Details Recovery  
Most CNN-based models adopt MSE as the loss function. Because MSE treats every pixel equally, it tends to produce over-smoothed results. Thus, the key objective of image SR is to recover the missing high-frequency details.  
To address the over-smoothing issue, the gradient prior is widely applied in reconstruction- [4; 27; 30] and CNN-based MRI SR methods [33; 34; 35]. Image gradient provides the exact positions and magnitudes of high-frequency image parts, which are important for improving the accuracy of super-resolution performance. Two approaches are commonly used to embed the image gradient prior into CNNs:  
1. The image gradient is employed as a regularization item in the loss function. In a correctly restored image, the edges and texture (related to the image gradients) should be accurate. The regularization term, which is induced by additional sources of information, helps recover high-frequency details. \(\mathcal{L}=\mathcal{L}_{MSE}+\mathcal{L}_{G}\), where \(\mathcal{L}_{G}\) is defined as \[\mathcal{L}_{G}=\|G(\hat{x})-G(x)\|_{2}^{2}\] (4) in which \(G(\cdot)\) denotes the gradient detector, and \(G(x)\) is the gradient magnitude of image \(x\).
2. The alternative approach to incorporating image gradient in the SR process is to concatenate the gradient maps with the input LR image \(y\) as a joint input \([y,G(y)]\) of the network. Thus, the mapping function is \[\hat{x}=F(y,G(y);\Theta)\] (5) the above approaches implicitly assume that the input or loss function is where the gradient information should be incorporated. However, the positions of high-frequency details are not explicitly explored in the process of image reconstruction. In traditional CNN-based SR, the intermediate layers just attempt to restore the image blindly.  
To encourage the network to focus on the image high-frequency details, we design a gradient-guided Resblock, which is illustrated in Figure 2b. The gradient-guided Resblock is based on Resblock, while the result of the intermediate layer is modulated using gradient information.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 2 System Model
### Interference Model
\(A_{fixed},\ A_{super}\) denote the amplitude of constant frequency interference and sweep frequency interference signals, \(f_{fixed}\) represents the set of constant frequencies, \(f_{sweep}(k)\) signifies the set of frequencies at time \(k\) for sweep frequency interference, and \(Sa(\cdot)\) denotes the sampling function. The constant frequency interference mode is characterized as a bandwidth-limited random process with a central frequency of \(f_{fixed}\), while the sweep frequency interference mode is a random process with frequencies varying over time. The hybrid frequency interference mode combines both constant frequency interference and sweep frequency interference, making it a more complex random process. As a result, external malicious interference exhibits high randomness and instability. Combined with the extremely limited prior knowledge of the interference signals, achieving efficient and accurate prediction and estimation is challenging. This renders the electromagnetic environment faced by the relay drone exceptionally complex and severe, posing significant challenges to communication performance. The dynamic interference process is illustrated in Figure 2.  
Under the context of the above interference modes, the expression for the SINR of the relay drone working on the \(f_{n}^{Raw}\) channel, receiving signals from cluster drone \(i\), can be reformulated as follows:  
\[ \begin{array}{l}SINR_{i;R}=\frac{P^{f}(k)H_{i}^{w}(k)}{P_{T}}=\frac{P^{f}(k)G_ {i}G_{r}(\Lambda^{C})^{2}}{(4\pi D(k)^{E}_{R})^{2}P_{T}}\ i\in M,\ \forall k\\ P_{T}=Y(f,f_{x})(P^{f}H_{1}^{*}(P^{L}(L^{D}+\theta)+P^{NL}(L^{D}+\delta+ \theta))/S)+n_{0}B_{n}^{Raw}\\ L^{D}=32.45+20\text{lg}(D(k)^{R}_{f})+20\text{lg}(f_{n}^{Raw})\ D(k)^{R}_{Fi}= \left\|Q(k)-F_{i}(k)\right\|_{2}\end{array}  \tag{9}\]  
In this context, \(S=\{0,1,...,F^{I}\}\) represents the number of channels affected by interference, and \(Y(f,f_{x}^{Raw})\) denotes the channel interference indicator function, which takes  
Figure 2: Schematic diagram of the dynamic interference process in the channel. a value of 0 or 1. When \(f=f_{n}^{Rauv}\), this signifies that the \(f_{n}^{Rauv}\) channel is experiencing interference, \(Y(f,f_{n}^{Rauv})=1\). Conversely, when \(f\
eq f_{n}^{Rauv}\), this indicates that the \(f_{n}^{Rauv}\) frequency channel is not subject to interference, \(Y(f,f_{n}^{Rauv})=0\).