esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 3 Methods
### State-Exchanging Long Short-Term Memory (SE-LSTM)
#### 3.1.1 SE-LSTM Process  
The idea of SE-LSTM is to extend the standard LSTM model, which updates the current time step's cell state, as formulated in Equation (1). Theoretically, the cell state contains information learned from the previous time steps. Beyond this general process, before we updated the new cell state \(c_{t}\) with the previous cell state \(c_{t-1}\), we extracted the previous time step's cell state of both streams, taking advantages of Keras API [44], which allowed us to access LSTM states within the layer. Then, as explained in Section 3.1.2, we did element-wise averaging of both state values and modified and updated the \(c_{t}\) using this additional value. That averaged value was called the State-exchanging Package, denoted by \(P_{t}\). Finally, we have to decide what we are going to output as the next cell state. However, this output will be a filtered version based on our cell state. In this step a first sigmoid layer runs and decides what information is needed to be output; then it runs the cell state through _tanh_ and multiplies it by the sigmoid gate's output information. So, during this process, the layer only outputs the information we decided to.  
Equations (1)-(6) express the operations performed by LSTM unit, where, \(i_{t}\), \(f_{t}\), \(o_{t}\), \(z_{t}\) represent the input gate, forget gate, output gate, and cell gate, respectively. \(c_{t}\) and \(h_{t}\) are memory and output activation at time \(t\). Equations (2), (3), (5) and (6) are the formulas for the forget gate, cell gate, output gate, and hidden state, respectively, of a standalone stream. The modified cell state of the proposed method, \(P_{t}\), is given by Equation (11).  
Figure 1: Proposed two-stream, RGB and optical flow-based state-exchanging in Long Short-Term Memory (SE-LSTM).  
\[i_{t}\ =\ \sigma(x_{t}w_{xi}+h_{t-1}w_{hi}+c_{t-1}w_{ci}+w_{ihuis}), \tag{1}\]  
\[f_{t}\ =\ \sigma\Big{(}x_{t}w_{xf}+h_{t-1}w_{hf}+c_{t-1}w_{cf}+w_{fuis}\Big{)}, \tag{2}\]  
\[z_{t}\ =\ \tanh(x_{t}w_{xz}+h_{t-1}w_{nz}+w_{zhis}), \tag{3}\]  
\[c_{t}\ =\ z_{t}\otimes i_{t}+c_{t-1}\otimes f_{t}\, \tag{4}\]  
\[o_{t}\ =\ \sigma(x_{t}w_{x0}+h_{t-1}w_{ho}+c_{t-1}w_{co}+w_{obuis}), \tag{5}\]  
\[h_{t}\ =\ o_{t}\otimes\tanh(c_{t}), \tag{6}\]
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
## 2 Methodology of the MPM
### Discretization of the Governing Equations  
As shown in Figure 1, the MPM discretizes the continuum into a series of material points, which carry various physical quantities such as mass, velocity, energy, stress, and strain. Since each material point has a fixed amount of mass, the mass conservation (Equation (1)) for the material points is automatically satisfied. In the discretized format of the MPM, the material density can be expressed as  
\[\rho(x_{i})=\sum_{p=1}^{n_{p}}m_{p}\delta\big{(}x_{i}-x_{ip}\big{)} \tag{5}\]  
where \(n_{p}\) is the total number of material points, \(m_{p}\) is the mass of the material region where material point \(p\) is located, \(x_{ip}\) is the coordinate of material point \(p\), and \(\delta(x)\) is the Dirac Delta function, which satisfies  
\[\int\limits_{-\infty}^{+\infty}\delta(x)dx=1\delta(x)=0(x\
eq 0) \tag{6}\]  
Substituting Equation (5) into the virtual work equation of Equation (4), we obtain the following summation form:  
\[\sum_{p=1}^{n_{p}}m_{p}\ddot{u}_{ip}\delta u_{ip}+\sum_{p=1}^{n_{p}}m_{p} \sigma_{ij}^{s}\delta u_{ip,j}-\sum_{p=1}^{n_{p}}m_{p}b_{ip}\delta u_{ip}- \sum_{p=1}^{n_{p}}m_{p}\overline{t}_{ip}^{s}h^{-1}\delta u_{ip}=0 \tag{7}\]  
The parameters of a material point, such as displacement \(u_{ip}\), virtual displacement \(\delta u_{ip}\), and velocity \(\delta u_{ip,j}\), can all be obtained by interpolating the corresponding parameters at the background grid nodes \(l\) using the interpolation shape functions \(N_{l}\big{(}x_{p}\big{)}\). For example, any variable \(r_{p}\) can be expressed as  
\[r_{p}=\sum_{l=1}^{N_{n}}r_{l}N_{l}\big{(}x_{p}\big{)} \tag{8}\]  
Substituting the interpolated displacements, virtual displacements, and velocities into the summation form of the virtual work Equation (7), we obtain the motion equation for node \(l\) as follows:  
\[p_{il}=f_{il}^{int}+f_{il}^{ext},x_{l}\
otin\Gamma_{N} \tag{9}\]  
where in the equation, \(p_{il}\) represents the momentum of grid node \(l\) in the \(i\)-direction, and \(f_{il}^{int}\) and \(f_{il}^{ext}\) represent the internal and external forces at the grid node, respectively.
## 3 Markov Chain Monte Carlo Algorithms
### Transitional Markov Chain Monte Carlo (TMCMC)
#### 3.2.3 Implementation and Algorithmic Steps  
TMCMC starts with sample generation from a prior by any sampling methods at first transition stage, i.e., \(j=0\) and \(q_{j}=0\). As transition continues (\(j>0\), \(q_{j}>0\) but \(<0\)), \(q_{j}\) can be calculated based on previously known \(q_{j-1}\) using Equations (18)-(20). Then, intermediate distribution \(p(\mathbf{D}|\theta)^{\delta_{j}}\) can be defined. From here, the MH algorithm is applied to generate \(N\) samples. To perform resampling at each transition, the normalized plausibility weight is formulated as  
\[\widetilde{\omega}(\theta_{i})=\frac{p(\mathbf{D}|\theta_{i})^{q_{j}-q_{j-1}} }{\sum_{i}^{N}p(\mathbf{D}|\theta_{i})^{q_{j}-q_{j-1}}}. \tag{21}\]  
Equation (21) allows the resampling of \(\theta_{i}\) with probability \(\widetilde{\omega}(\theta_{i})\) based on available samples from previous intermediate distribution, resulting samples serve as starting MH sampling. The candidate samples are generated from normal distribution with mean \(\theta_{i}\) and the covariance matrix \(\Sigma\) that facilitates automatic parameter tuning during MH sampling. The covariance matrix \(\Sigma\) is centered at the current samples and expressed as a scaled covariance matrix of intermediate distribution:  
\[\Sigma=\beta^{2}\sum_{i=1}^{N}\widetilde{\omega}(\theta_{i})\left(\left( \theta_{i}-\sum_{i=1}^{N}\theta_{i}\widetilde{\omega}(\theta_{i})\right) \cdot\left(\theta_{i}-\sum_{i=1}^{N}\theta_{i}\widetilde{\omega}(\theta_{i}) \right)^{T}\right), \tag{22}\]  
in which \(\beta\) is a scaling factor that can be determined according to different criteria. One can define \(\beta\) as a constant of 0.2 [19] or adaptively tune \(\beta\) based on acceptance rates across iterations [48; 49]. For the sake of simplicity, \(\beta\) is chosen as 0.2 throughout all examples. \(\sum_{i=1}^{N}\theta_{i}\widetilde{\omega}(\theta_{i})\) is the sample mean of weighted samples. Finally, samples are updated recursively by acceptance-rejection with a probability  
\[\alpha_{1}=\min\left\{1,\frac{p\left(\theta_{i}^{\star}\big{|}\mathbf{D} \right)}{p(\theta_{i}|\mathbf{D})}\right\}, \tag{23}\]  
where \(p\left(\theta_{i}^{\star}\big{|}\mathbf{D}\right)\) is the posterior value at new sample \(\theta_{i}^{\star}\) while \(p(\theta_{i}|\mathbf{D})\) is calculated at \(\theta_{i}\). Throughout all the TMCMC procedures, the tempering parameter is tuned and all steps are repeated until \(q_{j}=1\). At final stage, available samples are asymptotically distributed as the posterior \(p(\theta|\mathbf{D})\). The key advantage of TMCMC lies in its use of intermediate distributions to avoid the direct sampling of the posterior. At each transition stage, TMCMC generates N samples from a new proposal, enhancing efficiency. Through reweighting, resampling, and the classical MH algorithm, TMCMC avoids the burning-in period, as samples at each stage closely follow the estimated posterior [49]. The algorithmic description of TMCMC is summarized in Algorithm 2. ```
Initialization: N: the No.
## 3 Proposed Framework
### Offline Training Phase
#### 3.1.4 Track Classifications
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Time (s)** & **Track Cluster** & **Label** & **Ego Cluster** & **Ego x Position (m)** & **Ego y Position (m)** \\ \hline
0.1000 & 4 & 0 & 0 & 218.0021 & 44.5398 \\
0.2000 & 4 & 0 & 0 & 218.0051 & 44.5382 \\
0.3000 & 4 & 0 & 0 & 218.0092 & 44.5362 \\
0.4000 & 4 & 0 & 0 & 218.0013 & 44.5405 \\
0.5000 & 4 & 0 & 0 & 217.9974 & 44.5423 \\
0.6000 & 4 & 0 & 0 & 217.9942 & 44.5441 \\
0.7000 & 4 & 0 & 0 & 217.9893 & 44.5475 \\
0.8000 & 4 & 0 & 0 & 217.9881 & 44.5494 \\
0.9000 & 4 & 0 & 0 & 217.9875 & 44.5504 \\
1.0000 & 4 & 1 & 5 & 217.9887 & 44.5512 \\ � & � & � & � & � & � \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of interaction data between the ego vehicle and Track 14. Each row represents a frame-level observation, including time, cluster indices, interaction label, and ego vehicle position. During the final stage of offline training phase, we classify tracks based upon the interaction between the clustered states of the ego vehicle and the clusters of the tracks. The interaction between an ego vehicle and a dynamic track cluster should be classified as dynamic, while the interaction with a static track cluster should be classified as static. The relative velocities of the ego vehicle and the track clusters determine whether a track is static or dynamic. This is achieved by measuring the difference between the ego vehicle's velocity and those of each track cluster over a period of time. The relative velocities are plotted in a histogram (see Section 4), and the mean plus one standard deviation (\(\mu+\sigma\)) of the plotted values is used as a threshold to distinguish static and dynamic tracks. This helps to classify interactions in a robust manner, with dynamic velocities appearing above the threshold and static velocities appearing below it. This thresholding method, which is based on the mean and standard deviation, is chosen due to its statistical robustness. It not only considers the central tendency of relative velocities but also accounts for the variability in the data, enabling adaptive classification across heterogeneous environments. By setting the decision boundary dynamically, the approach becomes less sensitive to outliers and better suited to generalize across diverse driving contexts. We validated its effectiveness across two datasets, i.e., iCAB and KITTI, achieving classification accuracies of 87% and 82%, respectively, with F1 scores of 91% and 85% (see Section 4). These metrics confirm the reliability of the method across varying scenarios, including structured urban environments and dynamic traffic conditions.
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 5 Cyber-Physical System
### Data Infrastructure: Data Streaming and Persistence Middleware
#### 5.2.1 Data Streaming  
The data streaming service makes it possible the interaction between the SPVS and the virtual world interactive model. Such an interaction was implemented by adopting the Message Queuing Telemetry Transport (MQTT) protocol that enables the publish/subscribe paradigm. The efficiency of MQTT at an industrial scale has been demonstrated in previous studies [83]. In this regard, an instance of message broker was made available to support the communication between MQTT publishers and subscribers. Clients subscribe to MQTT topics to secure sensor data payloads, ensuring timely data reception, concurrent with the publishing activities. Incoming messages undergo a deserialization process, transforming payloads into manipulable JSON objects for data extraction and subsequent processing.  
On the service side, the software module of the case study architecture that is delegated to support data streaming between services is called _Data Connector_. The Data Connector operations flowchart is reported in Figure 9. So, all the services of the framework that must be enabled for the data streaming needs to integrate such a module. In addition,  
Figure 8: Dynamic 3D model with 2 degrees of freedom (Dof) that is used to visualize the current gradient of the PV module [45; 82].  
the data streaming service is responsible for managing communication via the message broker and the reference database instance, as well as acquiring data from the SPVS and the Energy DT's services. This result is achieved by using an approach to a data fetching request that can be configured to be synchronous or asynchronous, as needed. In this way, it is possible to integrate a controller to the software components of the edge device which can regulate the data transmission rate by monitoring specific indicators related to the state of the system, such as the battery level of charge. A specific service of the middleware called _persistence service_ intercepts the MQTT traffic between the SPVS and the virtual world interactive model in order to persist the corresponding data entities.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 2 Osdma with DFT-Spreading and Interleaving for Bandlimited MU-VLC
### The Review of OFDM-IM
#### 2.1.2 OFDM-DM-IM  
For OFDM-DM-IM, the constellation symbols from the constellation set \(\mathcal{M}_{A}=[S_{1}^{A},S_{2}^{A},\ldots,S_{M_{A}}^{A}]\) corresponding to mode 1 is transmitted on the selected \(k\) subcarriers, while the constellation symbols from the constellation set \(\mathcal{M}_{B}=[S_{1}^{B},S_{2}^{B},\ldots,S_{M_{B}}^{B}]\) corresponding to mode 2 is transmitted on the non-selected \(N-k\) subcarriers, where \(M_{A}\) and \(M_{B}\) are respectively the sizes of \(\mathcal{M}_{A}\) and \(\mathcal{M}_{B}\), and \(\mathcal{M}_{A}\cap M_{B}=\phi\). The mapping table of OFDM-DM-IM for \(N=2\) and \(k=1\) is shown in Table 2. Moreover, the LLR value with \(\beta\)-th subblock for OFDM-DM-IM signal is given by  
\[\begin{split}\lambda_{\beta}^{\eta}&=\ln(k)-\ln(N -k)+\ln\left(\sum_{i=1}^{M_{\text{A}}}\exp\left(-\frac{1}{N_{0}}\left|y_{ \beta}^{\eta}-S_{i}^{A}\right|^{2}\right)\right)\\ &-\ln\left(\sum_{j=1}^{M_{\text{R}}}\exp\left(-\frac{1}{N_{0}} \left|y_{\beta}^{\eta}-S_{j}^{B}\right|^{2}\right)\right),\end{split} \tag{3}\]  
The spectral efficiency per subblock for OFDM-DM-IM with \(M_{A}\)-QAM and \(M_{B}\)-QAM constellations can be obtained as follows  
\[SE_{\text{OFDM-DM-IM}}=\frac{b_{\text{i}}+b_{\text{c}}}{N}=\frac{\left|\log_{2 }(C(N,k))\right|+k\log_{2}(M_{\text{A}})+(N-k)\log_{2}(M_{\text{B}})}{N}, \tag{4}\]
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 2 Literature Review
### Heterogeneous Graph Representation Learning  
Heterogeneous graphs, which include more than one kind of nodes and edges, are more prevalent in real-world scenarios and can contain more kinds of relations within the graph. The goal of representation learning in heterogeneous graphs is to obtain node representation vectors while preserving the graph heterogeneity. To achieve high-quality node representation vectors in heterogeneous graphs, several studies have proposed methods for node-embedding learning. For example, HAN [28] uses meta-paths and hierarchical attention mechanisms to capture relations at both the node and meta-path levels, from the perspectives of nodes and semantic information. HGT [29] integrates the Transformer architecture with graph theory, designing different information aggregation methods for nodes based on their relations. MAGNN [30] aggregates node information through various meta-paths.  
The core concept of contrastive learning is to achieve better representation vectors by bringing positive samples closer together and negative samples farther apart. Some studies have applied contrastive learning to heterogeneous graph neural networks, thereby enhancing downstream tasks. For example, HeCo [31] leverages the network structure and meta-paths of graphs to construct different views and uses contrastive learning to align positive samples; HGCML [32] constructs different views solely through meta-paths; HGMAE [33] enhances model performance by using meta-paths, node attribute-level masking mechanisms, and position predictions; and IHGCL [34] generates different node views through various meta-paths and improves the quality of node representation vectors using different methods of contrastive learning. MEOW [35] is proposed to address the limitations of the neglecting of meta-path contexts and the inability to distinguish between hard and false negative samples. Two views are proposed to aggregate and encode meta-paths and a weighted contrastive loss to differentiate negative samples. As data sparsity and domain differences exist in cross-domain recommendation, HGCCDR [36] constructs heterogeneous graphs, and leverages intra- and inter-domain augmentation strategies and contrastive learning to align domain-independent features. HGCL [37], a robust self-supervised framework for heterogeneous graphs, uses a reciprocal contrastive mechanism to address noise in node attributes and topology by introduce two corresponding reviews. MTDG [38] leverages four encoders to capture knowledge in local, global, long-term, and short-term situations, respectively. The authors also propose single-contrastive and cross-contrastive learning to optimize the learned embeddings. M2 CHGNN [39] leverages a two-level attention mechanism with contrastive learning. Meta-paths and meta-structures are used to capture complex relations within heterogeneous graphs and a contrastive learning framework aligns these views.
## 4 All Dielectric Unit Cell  
### Working Principle  
Dielectric-only cells can be used to physically implement the phase shift \(\varphi_{TA}(\rho)\) of a transmitarray element. These cells behave as an effective index medium adding a particular delay on the transmitted ray. The effective index medium \(\eta_{eff}\) is related to the effective relative dielectric constant as \(\eta_{eff}^{2}=\epsilon_{r,eff}\), and the delay introduced by the cell is computed as:  
\[\varphi(\rho)=\frac{2\pi f\sqrt{\epsilon_{r,eff}}}{c_{0}}t \tag{4}\]  
where \(f\) is the operational frequency, \(c_{0}\) is the speed of light in a vacuum, and \(t\) is the length of the cell.  
Hence, the implementation of the phase delay \(\varphi(\rho)\) by a cell may be done by controlling the \(\epsilon_{r,eff}\) of the cell. One approach is to use dielectric cells based on the combination of two spatially uniform and isotropic materials with dielectric constant \(\epsilon_{r1}\) and \(\epsilon_{r2}\). If the first material hosts the second material as Figure 4 shows, the effective dielectric constant of the assembled cell only depends on \(\epsilon_{r1}\) and \(\epsilon_{r2}\), but not on the geometry [21; 22].  
The effective dielectric constant can be calculated as:  
\[\epsilon_{r,eff}=(\epsilon_{r2}-\epsilon_{r1})G-\epsilon_{r1} \tag{5}\]  
Figure 4: Example of dielectric cells based on the combination of two spatially uniform and isotropic materials with dielectric constants \(\epsilon_{r1}\) and \(\epsilon_{r2}\).  
where \(G\) is the volume fraction of the second material over the whole volume of the assembled cell. The complexity of the cell can be reduced if one of the dielectrics is air (\(\epsilon_{r2}=\epsilon_{0}\)); thus, only one dielectric is needed to accomplish (5).
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 4 Collision Risk Assessment  
A dynamic collision risk assessment model based on fuzzy theory was developed in this study to assess the collision risk of operational activities during teleoperation. Specifically, the collision risk assessment method consists of three steps: selecting the assessment factors, obtaining the assessment factors, and calculating collision risk in real time using fuzzy theory.
## 8 Issues and Recommendations for Future Research
### Security and Privacy Preservation  
As a result of IoV's open deployment nature, it could be intercepted by potential attackers resulting in degradation of IoV services. We need to revise security and privacy at different layers (e.g., physical, link, network, session, and application). Blockchain-based solutions are used to provide security and are a suitable fit due to their decentralized nature, as mentioned in Section 7.3.4. However, in specific use cases, such as in connected and autonomous vehicles, vehicles perform collaborative perception and collaborate using collective perception messages (CPMs) to improve road safety and efficiency. Attackers may target CPMs and send false information that jeopardizes safety. Current standardization efforts do not yet include vehicle misbehavior specifications for advanced V2X services, such as collaborative perception (CP) [126].
## 3 Numerical Simulation  
The objective of this section is to assess the effectiveness of CP and GED for output-only modal identification, and to compare the relative strengths and weaknesses of these two BSS methods. The dynamic responses of the suspension bridge mock-up were generated through a series of numerical simulations. Utilizing the aforementioned algorithms, both CP and GED were employed to extract the modal parameters of the suspension bridge under white-noise excitation.
## 2 Formulation of the 2030 Optimization Model
### Constraints
\[|P_{i,t}-P_{i,t-1}|\leq R_{i}\,\ i=1\ldots 8 \tag{6}\]  
(4) The power output from each technology is lower than or equal to its winter/summer availability (\(A_{t,i}\)) multiplied by nominal capacity (\(\overline{P_{i}}\)); see Equation (7). For the nuclear power plants (technology 4 in Table 1), it was assumed that they operate with at least 60% of their total capacity. \[P_{i,t}\leq\overline{P_{i}}\times A_{t,i},\ i=1,\ldots,3,5,\ldots,8 \tag{7}\]  
(5) The electric vehicles' (EVs) annual electricity consumption over 2030 was fixed at 21.28 TWh, [26]. Equation (8) states that the non-heating demand at time \(t\)--i.e., (\(E_{\text{NH},t}\))--of a certain day is the sum of the EVs' power demand (2429 MW), and the real half-hourly electricity demand in 2010 (\(ED_{2010,t}\)) minus the 10% contribution of electrical heaters in 2010 [24]. \[E_{\text{NH},t}=2429+ED_{2010,t}-0.1\times\alpha_{t}\times H_{t} \tag{8}\]  
(6) At time \(t\) of a certain day, the power output of all generators (\(i=1\ldots 8\)) and CHP units must be equal to \(E_{\text{NH},t}\) (obtained from Equation (8)), and the power consumption for heating purposes (\(Elec4 Heat_{t}\)):  
\[\sum_{i=1}^{8}P_{i,t}+P_{t}^{\text{Wind}}+P_{t}^{\text{Solar}}+P_{t}^{\text{ CHP}}+P_{t}^{\text{Shedding}}=E_{\text{NH},t}+Elec4 Heat_{t} \tag{9}\]  
\[Elec4 Heat_{t}=E_{k,t}\ \text{and}\ k=\text{direct electric heaters, ASHPs, and GSHPs}\]  
The power flow between GB and France (\(P_{8,t}\),GW) can take positive and negative values. At each half-hour, if the price in GB (\(\pi_{\text{GB},t}\)) is greater than that of France (\(\pi_{\text{FR},t}\))then electricity is transmitted to GB and vice versa. In addition, the electricity flow to GB via IC1 has a fixed price of \(\pi_{\text{IC1}}(\E/\text{MWh})\). \[P_{\text{g,t}}\times(\pi_{\text{FR,t}}-\pi_{\text{GB,t}})\leq 0 \tag{10}\]  
The LP problem consisting of the objective function--Equation (1)--and constraints Equations (2)-(10) was solved for each day of 2030 by MATLAB linear programming solver (linprog) for various CO\({}_{2}\) taxes (50, 60, 70, 80, and \(\E 90/\text{ton CO}_{2}\)) and IC1 electricity import prices (\(\pi_{\text{IC1}}\) = 20, 40, 60, 80, \(\E 100/\text{MWh}\)).