esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### BioEP/FF Composites' Characterization  
#### 2.3.1 Mechanical Properties  
Flexural properties of BioEP resin and BioEP/FF composites were obtained at room temperature in a universal test machine Ibertsel ELIB 30 (S.A.E. Ibertset, Madrid, Spain) equipped with a 5 kN load cell following the guidelines of the ISO 178. Rectangular samples with dimensions \(80\times 40\times 4\) mm\({}^{3}\) were subjected to a three-point bending flexural test with a crosshead speed of 5 mm min\({}^{-1}\). At least five specimens of each composition were tested, and characteristic average values were calculated.  
Impact-absorbed energy of different samples was obtained in a 1-J Charpy's impact pendulum from Metrotec S.A. (San Sebastian, Spain) as indicated in ISO 179:1993. The values of the impact-absorbed energy of each sample were calculated as the average of the energies obtained for five different specimens.  
Shore D hardness values of BioEP resin and BioEP/FF composites were obtained with a Shore D hardness durometer model 676-D from J. Bot Instruments S.A. (Barcelona, Spain) according to ISO 868. At least five different measurements were taken at room temperature, and average values were calculated.  
Figure 1: Images corresponding to (**a**) raw flaxseeds (FS); (**b**) flaxseed flour (FF) obtained by grinder (FF); (**c**) flaxseed flour (FF) obtained by ultracentrifuge (FFF).
## 3 Preliminaries Knowledge  
In this paper, a directed network is regarded as a simple, directed graph \(G=(V,E)\), where \(V=(v_{1}\,\ v_{2}\,\ldots,\ v_{n})\) is the set of nodes, and \(E\) is the links table, each link \((i,j)\) denotes a relationship from \(v_{i}\) to \(v_{i}\).  
**Definition 1** (The undirected graph and the directed graph).: _As shown in Figure 1, the Figure 1a is an undirected graph, while the Figure 1b represents a directed graph, where each edge denotes a relationship from one node to another node. In the Figure 1b, the edge (\(v_{1}\), \(v_{4}\)) denotes a link relation from node \(v_{1}\) to node \(v_{4}\)._  
**Definition 2** (Neighboring directed graph).: _For two directed graphs \(G_{1}=(V_{1},E_{1})\), \(G_{2}=(V_{2},E_{2})\), if \(|V_{1}\oplus V_{2}|+|E_{1}\oplus E_{2}|=1\), where \(\bigoplus\) is Exclusive--OR operation, we can say \(G_{1}\) and \(G_{2}\) are neighbors._  
As shown in Figure 2, compared with the Figure 2b, Figure 2a has one more different node with three directed edges. So the Figure 2a,b are neighboring graphs.  
**Definition 3** (Differential Privacy).: _For all outputs \(\mathrm{S}\) belong to Range(Z), if we can obtain the result as follows:_  
\[P_{r}[Z(Ga)\in S]=e^{\epsilon}\times P_{r}[Z(Gb)\in S] \tag{1}\]  
_where Ga and Gb are neighbors, \(\epsilon\) is a privacy preservation level, we can see that the algorithm \(Z\) satisfies \(\epsilon\)-differential privacy._  
_In order to achieve \(\epsilon\)-differential privacy, we must perturb the outputs of queries in two ways, which include the Laplace mechanism and the exponential mechanism._  
**Definition 4** (Laplace Mechanism).: _For a sequence of queries \(F\): \(G\)\(\rightarrow\)\(G\), if the following holds:_  
\[Z(G)=F(G)+Lap(\Delta f/\epsilon) \tag{2}\]  
Figure 1: The undirected graph and the directed graph.  
Figure 2: The example of neighboring graph of node.  
_where \(\mu=0\), \(b=\Delta f\epsilon\) and Lap(\(\Delta f\epsilon\)) represents the Laplace noise, the way that makes an algorithm \(Z\) satisfies \(\epsilon\)-differential privacy by adding Laplace noise is the Laplace mechanism._  
_In the Laplace mechanism, the Laplace noise distribution is shown in Equation (4)._  
\[n(x)=1/2b*exp(-|x-\mu|/b) \tag{3}\]  
_where \(\mu\) is a position parameter, \(b\) denotes a scale parameter, and \(x\) is a random variable._  
**Definition 5** (Exponential Mechanism).: _Given a dataset \(D\), an output range \(T\), a privacy budget \(\epsilon\), and a utility function \(U\): (D, \(t\)) \(\rightarrow\) R, a mechanism \(M\) that selects an output \(t\in T\) with probability proportional to \(exp(\frac{<U(D,t)}{2\Delta U})\) satisfies \(\epsilon\)-differential privacy._  
**Definition 6** (Parallel composition properties).: _Given a sequence of algorithms \(\{A_{1},\)\(A_{2},\)\(\dots,\)\(A_{n}\}\), and each algorithm \(A_{i}\) satisfies \(\epsilon_{i}\) differential privacy, if these algorithms are applied independently on a disjoint subset of the input database \(D\), this data process is called the parallel composition properties of differential privacy, which satisfies \(\max\)\(\epsilon_{i}\) differential privacy._
# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 1 Introduction
Finally, the fusion test results and shortcomings were analyzed, and the future development direction of BDS-5G signals was proposed. The structure of the paper is as follows: Section 2 describes the mathematical model of BDS-5G fusion, Section 3 introduces the multiobjective particle filter algorithm for the model solution, Section 4 describes our experimental methods and results in detail, and Section 5 provides a conclusion and plan for future work.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 9 Efficient Signature Verification in Post-Quantum VerITAS
### Proof System for the Lattice Hash
#### 9.3.3 Polynomial Encoding for Efficient Proofs  
The goal of the matrix-vector product proof is to verify that \(h=A\cdot v\). This is accomplished using [PERSON] algorithm combined with a sum-check protocol. The steps are as follows:  
To enable efficient zero-knowledge proofs, vectors are encoded as polynomials. Given a vector \(v=(v_{0},\dots,v_{m-1})\), it is encoded as a polynomial \(v(X)\), such that:  
\(v\big{(}\omega^{i}\big{)}=v_{i}\) for \(i\in[m]\), where \(\omega\) is a primitive \(m\)-th root of unity.  
This encoding is performed by the following function:  
\[F^{m}\to F^{<m}[X], \tag{15}\]  
which maps a vector of length \(m\) to a polynomial of degree less than \(m\).  
We use polynomials for efficient proofs, as they leverage algebraic properties, such as interpolation and evaluation, to enable efficient zero-knowledge proofs. These properties allow for compact representations and fast verification of computations. Also, any vector \(v\) can be uniquely represented as a polynomial of degree \(<m\), ensuring a one-to-one correspondence between vectors and their polynomial encodings. This property is crucial for maintaining the integrity and correctness of the proofs.
## 2 Methods
### Information Sources and Search Strategy  
The search for articles was conducted in the PubMed, PEDro, and Scopus databases. Additionally, we performed a non-systematic search of the first five pages on Google Scholar. We used combinations of the following keywords in our article search.: \"_VR_\", \"_virtual reality_\", \"_virtual rehabilitation_\", \"_virtual environment_\", \"_XR_\", \"_extended reality_\", \"_X-reality_\", \"_cross reality_\", \"_enhanced reality_\", \"_mixed reality_\", \"_MR_\", \"_augmented reality_\", \"_AR_\", \"_immersive technology_\", \"_immersive environment_\"\", \"_immersive technologies_\", \"_terminology_\", \"_definition_\", \"_terms_\", \"_taxonomy_\", \"_framework_\"\", \"_musculoskeletal_\", \"_musculo-skeletal_\", \"_orthopedic_\", \"_orthopedic_\", \"_orthopedic_\", \"_chronic pain_\", \"_persistent pain_\", \"_neck pain_\", \"_back pain_\", \"_knee_\", \"_hip_\" _ankle_\", \"_shoulder_\", \"_elbow_\", \"_tendinopathy_\". Articles examining the use of VR for musculoskeletal conditions were also derived from data extraction conducted during the development of recent meta-analyses on the use of VR for musculoskeletal issues and associated chronic pain authored by the researchers [37; 38]. Furthermore, we conducted a non-systematic search using the Google search engine to identify commercially available VR solutions (hardware and software). In addition to synonyms for VR and musculoskeletal conditions (outlined in beforementioned keywords), the following keywords were used when searching for commercially available VR solutions: _HMD_, _head-mounted display_, _technology_,hardware, software, tracker_. Relevant information about VR technology was collected also by examining the websites of various VR companies listed on websites of associations such as _XR4 REHAB_. These associations and organizations were identified through Google searches using keywords such as: _organizations, networks, associations, societies_, alongside the previously mentioned VR-related terms. Furthermore, some associations and VR technological solutions were introduced to the authors during international conferences on virtual rehabilitation, such as the <<_World Congress on Virtual Rehabilitation 2024>>_ held in Amsterdam. The search process is presented in Figure 1.
# A Strong Noise Reduction Network for Seismic Records
## 4 Conclusions  
This paper addresses the suboptimal denoising performance of seismic signal processing techniques on seismic signals with very low SNRs by introducing a novel denoising model. Our model significantly enhances the feature extraction and the ability to distinguish between signal and noise through the integration of Inception modules and a coordinate attention (CA) mechanism within the U-Net architecture. Training and testing using the STEAD dataset have demonstrated the model's superior denoising capabilities across various seismic noise conditions. Compared to conventional methods and other deep learning models, our model achieves a notable increase in the mean SNR and correlation coefficient, while also reducing the RMSE.  
Furthermore, the application of our model to real seismic data has effectively improved the precision of identifying the first arrival of seismic phases. Additional testing on the \"Di Ting\" dataset further confirms the model's robust generalization capabilities. This research not only advances the adaptability and accuracy of earthquake early warning systems but also enhances their practical value in complex real-world settings. We believe that these outcomes will have a profound impact on the field of seismology and provide new directions for the development of future seismic signal processing technologies. In the future, we will continue to deeply analyze the characteristics of various types of noise in seismic data so that our model can remove other types of noise and improve its generalization ability.  
All the authors made significant contributions to this work. Formal analysis, T.S., W.R., X.T. and G.P.; investigation, T.S., X.J., W.R. and X.T.; methodology, [PERSON], X.J. and W.R.; prepared the training dataset, [PERSON] and L.X.; writing original draft, [PERSON], [PERSON], [PERSON] and L.X.; editing, [PERSON], [PERSON] and W.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Science Foundation of Sichuan Province (grant number 2022 NSFSC1110), Doctoral Fund of Southwest University of Science and Technology (grant number 192x7159) and National Natural Science Foundation of China (NSFC, No. U23A20651).  
Not applicable.  
The data presented in this study are openly available at [32; 33].  
The authors declare no conflicts of interest.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 3 Simulation of Ship's Wake Bubble Laser Dual-Mode Detection Based on Monte Carlo
### Simulation of Laser Illumination Imaging Characteristics of Ship Wake Bubbles
The imaging perspective reveals more pronounced changes compared to variations in backscattering, which can compensate for the limited change in backscattering amplitude when bubble size varies. Therefore, it may be possible to counter ships' evasion of underwater weapons by discriminating changes in bubble size characteristics. In summary, when the bubble target size, bubble layer thickness, and bubble number density gradually increase, the photons in the bubble-receiving lens area are more and more concentrated, the number of photons in the circumferential area increases, and the position changes are more random. This can be confirmed by the above ship's wake backscattering simulation, which proves the feasibility of the imaging detection of bubbles. The echo  
Figure 9: Bubble passive imaging characteristics under different bubble size changes: (**a**) 40 \(\upmu\)m; (**b**) 60 \(\upmu\)m; (**c**) 80 \(\upmu\)m; (**d**) 300 \(\upmu\)m; (**e**) 400 \(\upmu\)m;(**f**) 500 \(\upmu\)m.  
characteristics of the ship's wake bubbles in the two detection modes are different. Both of them have their own advantages and can make up for each other. The integration of the two detection modes can further exert their advantages. The Design and Signal Processing Method of Laser Dual-Mode Fusion Detection System for Ships' Wakes
# Signal Separation Based on Knowledge Representation
## 3 Methodology
### Loss Function  
Followed by Equation (2), the overall loss function consists of two components. One part is the reconstruction loss of the observed signal, and the other part is the knowledge-guided loss. The specific expressions are as follows:  
\[L=L_{rec}+\lambda L_{k} \tag{17}\]  
where \(\lambda\) is the empirical parameter, \(L_{rec}\) is the reconstruction loss of the observed data, and \(L_{k}\) is the knowledge-guided loss. The goal is to minimize the residual difference between the reconstructed signal and observation record; therefore, \(L_{1}\) loss is used as the reconstruction loss function and \(L_{rec}\) can be concisely written as  
\[L_{rec}=\left\|X-\widehat{X}\right\|_{1} \tag{18}\]  
As introduced in Section 2.3, the application of knowledge representation involves scenarios where knowledge representation can be directly observed. In such cases, the expression for the knowledge-guided loss function is as follows:  
\[\begin{split}&\mathbf{K_{u}}=\{a_{i}|1\leq i\leq q,1\leq q\}\\ &\mathbf{K_{d}}=\{\beta_{i}|1\leq i\leq p,1\leq p\}\\ &\Omega_{u}(\mathbf{K_{u}},\mathbf{\tilde{S}}_{u})=\min\{\
abla_ {\alpha_{i}}*\mathbf{\tilde{S}}_{u}|1\leq i\leq p,1\leq p\}\\ &\Omega_{d}(\mathbf{K_{d}},\mathbf{\tilde{S}}_{d})=\min\{\
abla_ {\beta_{i}}*\mathbf{\tilde{S}}_{d}|1\leq i\leq q,1\leq q\}\\ & L_{k}=\left\|\Omega_{u}(\mathbf{K_{u}},\mathbf{\tilde{S}}_{u}) \odot\Omega_{d}(\mathbf{K_{d}},\mathbf{\tilde{S}}_{d})\right\|_{F}^{2}\end{split} \tag{19}\]  
Based on physical processes, the guided loss function formed by the knowledge representation derived from partial differential equations is as follows:  
\[L_{k}=\left\|\mathbf{K_{p}}-\mathbf{\tilde{S}}_{p}\right\|+\left\|\mathbf{K_{ s}}-\mathbf{\tilde{S}}_{s}\right\| \tag{20}\]  
Finally, the guided loss function formed by the knowledge representation derived from data-driven approaches, based on similarities in features within the data and differences in features between different target sources, is expressed as follows:  
\[\begin{split} L_{k}&=\sum_{i=1}^{N}a_{i}L_{\text{ cos}}(\mathbf{K_{i}},\mathbf{\tilde{S}}_{i})+\sum_{1=1}^{n}c_{i}|\mathbf{K_{i}}- \mathbf{\tilde{S}}_{i}|\\ &+\sum_{i=1}^{N}\sum_{j=1}^{N}\int_{j\
eq i}^{N}b_{j}\big{(}1-L_{ \text{cos}}(\mathbf{\tilde{S}}_{i},\mathbf{\tilde{S}}_{j})\big{)}\end{split} \tag{21}\]  
where \(a_{i}\), \(b_{i}\), and \(c_{i}\) are weight factors that balance the contribution of each term in the loss function, and \(L_{\text{cos}}\) represents the cosine similarity loss function in Equation (10).
## 1 Introduction
The role of radiation in thermal runaway propagation in a  
Figure 1: Configuration and thermal runaway reactions for a typical LCO LIB. pack of cylindrical cells has been investigated computationally [31]. These results have shown that neglecting radiation leads to a prediction of onset in the trigger battery but an erroneous prediction of no thermal runaway propagation. In addition, thermal radiation has often been combined with the effects of conduction and convection in studying the triggering of thermal runaway [30; 32], leading to ambiguity on the specific role of radiative heat transfer. A radiative shielding method to minimize thermal runaway propagation has been presented, where the fraction of heat flux due to radiation in a battery pack during thermal runaway is also evaluated [33]. More recently, our experiments have demonstrated battery-to-battery variability in thermal runaway [34], even among batteries with the same chemistry and near-identical initial conditions. It is hence expected that variation from radiation in the triggering cell can lead to different modes and paths for thermal runaway propagation in a pack, which necessitates study on the specific role of radiative heat transfer. The literature cited above clearly highlights the importance of radiative heat transfer and the need for a comprehensive investigation of how radiative heat transfer impacts thermal runaway propagation. This work presents an investigation of the role of radiative heat transfer in thermal runaway propagation when an LIB is exposed to fire and other heat sources. A limiting condition for triggering thermal runaway by radiative heat transfer between two closely packed cylindrical cells is computationally investigated. Results indicate the existence of complex nonlinear coupling between chemical reactions and thermal radiation. A key novelty of the work is that we have identified the non-monotonic impact of radiation on thermal runaway triggering and have mapped out the regime diagram under both low- and high-temperature limits. We have shown that, depending on conditions, radiation can either facilitate or mitigate thermal runaway. When the triggering cell is at a relatively low temperature, we have further shown that pre-runaway chemistry plays an increasingly significant role. We extend our investigation to battery packs and practical conditions where radiative blocking effects and localized hot spots may be important. Results presented here highlight the importance of radiative heat transfer in thermal runaway propagation and can help guide future efforts towards mitigation of thermal runaway in safe electrochemical energy storage systems.
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 3 NetSel-RF Model
### Modeling
In this matrix, each column represents the number of predictions per class, while each row represents the instances in the real class. Here, TPR and FRP represent the proportion of APs selected correctly and incorrectly, respectively. Table 5 presents the performance evaluation results of the five algorithms mentioned above according to metrics derived from the confusion matrix and \(T_{\text{C}}\). These results show that all algorithms have a higher TPR than than 90%. Again, RF and ARF behave better than the other evaluated algorithms, reaching percentages of around 99.7% and 99.57%, respectively. Furthermore, RF and ARF have a low FPR of about 0.29% and 0.43%, respectively. In addition, RF and ARF reach a high precision of around 99.68% and 99.69%, respectively. \(T_{\text{C}}\) is low for all tested algorithms, ranging between 0.66 and 5.146 ms. HT and SVM obtain the shortest  
From the cross-validation and confusion matrix analysis, we concluded the following: (a) none of the algorithms achieve the best results for all the metrics presented, (b) RF and ARF algorithms outperform the other algorithms regarding accuracy and MCC; and (c) the algorithms that obtained better performance in the TPR metric result in a high \(T_{\text{C}}\) and vice-versa. Considering these conclusions, we decided to use RF for network selection due to it being the model with the best balance between accuracy, MCC, and \(T_{\text{C}}\). Thus, we built NetSel-RF by using RF. In the next section, we compare NetSel-RF with SSF and AHP-TOPSIS.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Rez  
Concurrent transmissions in WPANs can outperform standard serial TDMA transmissions in mmWave networks. A randomized exclusive region (REX) was created primarily in response to concurrent transmission and to investigate spatial multiplexing and resource management concerns for mmWave WPANs [50]. If there are fewer interferences, the REX protocol can outperform the traditional serial TDMA transmissions. The number of active flow requests in the WPAN, which the PNC of the protocol is aware of, is first determined. The proposed algorithm chooses a flow at random that has the fewest number of slots available. Afterwards, the algorithm checks the remaining active flows to determine whether they satisfy the concurrent transmission requirements. Following that, the scheme allocates slots one by one and arranges the flows accordingly. This procedure is repeated until all flows are scheduled.  
* Advantages: REX is one of the first protocols to address the resource management issues in mmWave WPANs, and its scheme execution is unexpectedly simple. It utilizes a concurrent transmission scheme and as a result it outperforms the traditional serial TDMA transmission.
* Disadvantages: In the protocol, the authors created their model based on the free space path loss. This free space path loss model, which calculates the interference level and the received signal strength, is not ideal for indoor WPANs, since signal reflections would also create interferences. Moreover, the computational complexity is high for the REX protocol.
# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 3 Proposed Autoscaling Framework
### Managed Resource
#### 3.1.2. Collector Component  
In time series forecasting, a multivariate prediction model usually consists of multiple feature values at each time step. The collector component collects the resource utilization data of multiple features such as the CPU, memory, disk I/O, and network received throughput. All the collected data are sent to the monitor server of the autonomic manager.