esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Damage in Structures with Masonry Infill Panels  
It is important to characterize the failure of masonry infill panels. To do so, the results of post-earthquake surveys were consulted, where it was determined that global plasticization in confined masonry buildings occurs with drifts of approximately 0.5% and 0.9% [9]. In a numerical study [4], the authors reported the following drifts for confined masonry buildings: for formal buildings, drifts of 0.4% and 0.8% correspond to the maximum response and collapse, respectively, while for informal buildings, these drifts are 0.25% and 0.5%, respectively. On the other hand, the sudden loss of stiffness that affects the overall stiffness of buildings is achieved at very low floor drifts. The authors [15] have indicated that for a drift of 0.1%, 50% of the stiffness of the walls is lost, while for a drift of 0.5%, up to 84% of the stiffness of the walls is lost.  
The type of failure of confined masonry buildings designed according to industry standards shows adequate resistance to strong seismic actions, as was demonstrated in [16], in which a value of up to 0.8 g was reached in shaking table tests, producing global collapse through the weak ground floor mechanism. In terms of damage to confined masonry buildings, two main types must be distinguished: out-of-plane stress failures and in-plane stress failures. For this last type of failure, numerous researchers have proposed certain displacements or drifts associated with certain behavioral states [6].  
On the other hand, in [29], it was proposed that for interstory drifts between 0.4% and 1.5%, the masonry is expected to fail, while in [30], it was considered that cracking occurs in masonry walls at lower drifts of approximately 0.25%. An extreme state of damage in masonry walls has been reported in [5], indicating that the experimental results show that the bricks undergo crushing when an interstory drift of 1.4% occurs. This value is very close to that indicated in [6], where a value of 1.5% is set for the limit at which the masonry collapses. The improvement in the behavior of masonry walls can be achieved  
Figure 3: Confined masonry model (**a**) masonry infill panel and frame scheme, (**b**) compression-tension struts and (**c**) shear strut. Adapted from [27; 28].  
by increasing the presence of confining elements (especially columns) and placing steel meshes in the seat of the masonry walls [15]. Finally, in [9], the maximum lateral resistance of confined masonry structures subjected to the action of earthquakes was reached at values between 0.5% and 0.9%.  
The following section provides a concise and precise description of the numerical results, their interpretation, and the conclusions that were drawn.
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.9 Model Complexity vs. Dataset Size  
The intricate nature of DDPM may lead to overfitting on smaller datasets, where the model might capture noise as if it were genuine signal information [107; 108].
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Performance Evaluation and Trade-Off Analysis  
The performance of the proposed framework is evaluated using several key metrics.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 5 Performance Evaluation
### Average Objective Function  
In this section, we present the simulation results to show the behavior of the objective function across different iterations. Figure 5 shows the objective function given in Equation (5) for four different particle sizes across different iterations. The PSO is an iterative algorithm that generally provides the best solution after a certain number of iterations. The stopping criteria (in terms of iterations) depend on the objective function and constraints of the given problem. We plot the results for low traffic and high traffic scenarios as depicted in Table 3. It is clearly visible through the two line plots that 12 particles is the most suitable population size for our problem. Therefore, the rest of the simulation results are carried out using a population size of 12-particles. In addition, the proposed scheme shows quick convergence towards the optimal solution for both scenarios. For example, the proposed scheme serves more than 95% of SUs' requests for low traffic as shown in Figure 5 and approximately 90% of SUs' requests for the high traffic case, as shown in Figure 6. Hence, the proposed scheme optimally manages the heterogeneous traffic of SUs in a smart-building.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Low Traffic**} & \multicolumn{2}{c}{**High Traffic**} \\ \hline
**Class** & **Members** & **Class** & **Members** \\ \hline Video & 2 & Video & 5 \\ Voice & 4 & Voice & 5 \\ Web & 4 & Web & 10 \\ Ivideo & 5 & IVideo & 10 \\ Ismoke, ICO\({}_{2}\) & 4 & ISmoke, ICO\({}_{2}\) & 5 \\ NM & 1 & ICO\({}_{2}\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: QoS parameters for different SUs.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Values** \\ \hline Power (P) & 35 dBm \\ Noise variance (\(\sigma^{2}\)) & 0.1\(\sim\)0.65 \\ Channels & 100 \\ PU activity & 0.0\(\sim\)0.6 \\ Population size & 12 \\ PSO acceleration coefficients & 2 \\ PSO inertia weight & 0.72 \\ \([-Vmax,Vmax]\) & \([-100,100]\) \\ Sensing interval & 0.1 ms \\ Modulation scheme & MQAM \\ Constellation size & 4 \\ History slots & 30 \\ History partitions & 3 \\ Weights \([\omega_{1},\omega_{2},\omega_{3}]\) & \([0.6,0.25\ 0.15]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Handover Management in 5G Vehicular Networks
## 5 Simulation Setup and Results
### Study of a Simulation Snapslot
For the latter two schemes, the algorithms considered for the HO initiation and the network selection processes were the Velocity-Aware Handover (VAH) [35], the Fuzzy AHP TOPSIS (FAT) [53], the Fuzzy AHP SAW (FAS) [53], the Fuzzy AHP MEW (FAM) [53], and the Fuzzy AHP ELECTRE (FAE) [54]. The number of hops that each signaling message performs (Table 6), as well as the size in bytes of each message (Table 7) according to [55] were considered for the evaluation. In particular, the signaling cost of the discussed methodology was estimated using Formula (42). In this formula, the \(P_{f}\) parameter indicates the connection failure probability, which is supposed to be equal to 0.5 [55]. Furthermore, the \(n\) parameter indicates the number of available PoAs. \[ \begin{split} Cost_{proposed}&=\frac{P_{f}}{1-P_{f}} \cdot H_{MN,MAG}\cdot(M_{1}+M_{8}+M_{9}+M_{14}+M_{15})\\ &+2\cdot H_{MAG,LMA}\cdot(M_{12,16}+M_{13,17})\\ &+H_{MAG,MIIS}\cdot(M_{2}+M_{3})+H_{MAG,MAG}\cdot(n\cdot(M_{4} \\ &+M_{5})+M_{6}+M_{7}+M_{10}+M_{11}+M_{18}+M_{19}))\end{split}  \tag{42}\]  
Similarly, the signaling cost of the FMIPv6 was estimated using Formula (43), while the corresponding cost of the eFPMIPv6 was estimated using Formula (44). \[ \begin{split} Cost_{FMIP6}&=\frac{P_{f}}{1-P_{f}} \cdot H_{MN,MAG}\cdot(M_{1}+M_{4}+M_{5}+M_{8}+M_{9}\\ &+M_{20}+M_{21})+3\cdot H_{MAG,LMA}\cdot(M_{12,16}+M_{13,17})\\ &+H_{MAG,MIIS}\cdot(M_{2}+M_{3})+H_{MAG,MAG}\cdot(n\cdot(M_{5b }\\ &+M_{5c})+M_{6}+M_{7}+M_{10}+M_{11}+M_{18}+M_{19})\end{split}  \tag{43}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Communicating Components** & **Number of Hops** \\ \hline \(H_{MN,MAG}\) & \(1\) \\ \(H_{MAG,LMA}\) & \(1\) \\ \(H_{MAG,MIIS}\) & \(1\) \\ \(H_{MAG,MAG}\) & \(2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The hops between the components of the considered topology. \begin{table}
\begin{tabular}{c c} \hline \hline
**Vehicle** & **Selected Network** \\ \hline
1 & LTE-A Pro FD-MIMO Macrocell 3 \\
2 & LTE-A Pro FD-MIMO Macrocell 4 \\
3 & LTE-A Pro FD-MIMO Macrocell 3 \\
4 & WiMAX Macrocell 5 \\
5 & WiMAX Macrocell 6 \\
6 & LTE Macrocell 7 \\
7 & LTE-A Pro FD-MIMO Macrocell 2 \\
8 & LTE-A Pro FD-MIMO Macrocell 1 \\
9 & WiMAX Macrocell 5 \\
10 & WAVE 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The network that each vehicle selects. \[ \begin{split} Cost_{eFPMIP6}&=\frac{P_{f}}{1-P_{f}}\cdot H_{ MN,MAG}\cdot(M_{1}+M_{4}+M_{5}+M_{8}+M_{9}\\ &+M_{14}+M_{15})+2\cdot H_{MAG,LMA}\cdot(M_{12,16}+M_{13,17})\\ &+H_{MAG,MIIS}\cdot(M_{2}+M_{3})+H_{MAG,MAG}\cdot(n\cdot(M_{5b}\\ &+M_{5c})+M_{6e}+M_{7e}+M_{18}+M_{19})\end{split}  \tag{44}\]  
The signaling costs obtained from each scheme are presented in Figure 21. In particular, the proposed scheme achieved a lower signaling cost than the one achieved from the FMIPv6 and eFPMIPv6 schemes, since these schemes exchange additional signaling messages during the HO execution.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 3 Methodology: Mapping Study
### Planning
#### 3.1.4 Classification Scheme and Data Extraction
**Exact Algorithms:** They guarantee that the solution found is the best possible, that is, the optimal solution, although they have the disadvantage that they invest a lot of time in execution [26]. 2. **Heuristic Algorithms:** They are approximate techniques capable of obtaining reasonable quality solutions in a limited time but without being able to certify that these solutions are optimal [26]. 1. **Based on Sampling:** They need a mathematical representation of the workspace. These methods then sample nodes or cells, usually stochastically. 2. **Node-Based:** Define the workspace as a graph or a grid, where the path is defined between grid nodes. 3. **Bio-inspired:** They present the path planning of UAVs through natural phenomena, so they are not accurate enough. 4. **Multiple:** It means that there can be more than one heuristic algorithm. (e) **Others:** It means that other heuristic algorithms may exist.
# Improving Time Study Methods Using Deep Learning-Based Action Segmentation Models
## 1 Introduction  
Industry 4.0 marks a transformative shift in manufacturing, characterized by the integration of machines and humans within complex Cyber-Physical Systems (CPS) driven by extensive data collection through sensor networks [1, 2]. In this advanced technological landscape, the agility of the system increasingly depends on skilled and trained employees [3], necessitating a re-evaluation of the interaction dynamics between machines and humans [1, 4]. A pivotal aspect of this evolution is the efficiency of the human factor, which continues to significantly impact the overall productivity of manufacturing systems. This necessitates the monitoring and quantification of human efficiency, for which time-based metrics have emerged as a good indicator [5]. Traditional time study methodologies, including the use of stopwatches or Predetermined Time Standards (PTS) [5], alongside tools like worksheets or video recordings [6], have been the norm. However, these methods exhibit inefficiencies, such as the inability for real-time analysis in manual observation processes, limiting the scope and frequency of analyses [7]. Furthermore, manual video analysis is time-consuming, often requiring two to five hours for one hour of footage, and it is prone to subjectivity and inconsistency [6].  
The field of computer vision, particularly action segmentation or detection from video input, offers a promising solution to these challenges [8]. This approach entails the automated recognition and duration estimation of human activities, with a potential to upgrade work productivity analysis and aid in production planning and load balancing.  
Traditional machine learning algorithms, that were dominant in human activity recognition until a few years ago [8; 9], required extensive domain knowledge and time investment for feature engineering. In contrast, deep learning methods circumvent manual feature extraction through the hierarchical learning of complex data representations, although they demand significant data and computational resources [10].  
The deficiencies of existing time study methods and technological trends are the motivation for researching artificial intelligence techniques aimed at automating the recognition and estimation of the duration of human activities in manufacturing processes. This study aims to explore the application of deep learning models to enhance time study methodologies, by developing multiple action segmentation models and evaluating their efficiency on real manufacturing data. To achieve this goal, a sample was collected from a real manufacturing process, which consists of nine work activities. During the video recording of the process, the work activities were performed by four subjects on three different types of products, while the recording itself was performed from two different viewpoints. A total of 27 different models have been developed, which differ with respect to recording viewpoint, feature extraction method, and model architecture responsible for action segmentation.
# Algorithmic Advances for 1.5-Dimensional Two-Stage Cutting Stock Problem
## 3 Problem Formulation
### Two-Stage Master Problem  
The previous model represents the most general case, which can be easily adapted to the two-stage version. In this case, the master problem formulated using the matrix form becomes as follows:  
minimize \[\mathbf{c}_{1}^{T}\mathbf{x}_{1}+\mathbf{c}_{2}^{T}\mathbf{x}_{2}\] (5) subject to \[A_{11}\mathbf{x}_{1}+A_{12}\mathbf{x}_{2}=\mathbf{0}\] (7) \[A_{22}\mathbf{x}_{2}\geq\mathbf{b}_{2}\] (8) \[\mathbf{x}_{1}\geq\mathbf{0}\text{, integer}\] (9) \[\mathbf{x}_{2}\geq\mathbf{0}\text{, integer}\] (10)  
In this formulation, note that the demand vector for the first stage, \(\mathbf{b}_{1}\), is set to zero. This is because the intermediate orders produced at the first stage must be entirely consumed at the second stage; partial consumption would result in significant waste and logistical issues due to leftovers. Therefore, a set partitioning constraint is enforced at the first stage (7) to ensure that all intermediate stock produced is fully utilized, while at the second stage, a set covering constraint (8) is enforced to meet the customer demands for finished rolls. A schedule with the least number of different intermediate sizes is most desirable, as it minimizes both complexity and waste in the production process.  
The difficulty in solving this model derives from the multitude of unknown cutting patterns and intermediate stock orders, corresponding to the columns and rows of \(A\) matrix, respectively. Generating the entire matrix is impractical because the number of cutting patterns grows exponentially with the number of orders.
## 5 Discussion
### Comparison with Previous Studies  
To delve deeper into the investigation, Table 12 displays the outcomes of this study alongside numerical models from the historical record, as well as the Gaussian Process Regression Approach introduced by [PERSON] et al. (2018). To facilitate a more comprehensive comparison, an additional parameter referred to as Nash-Sutcliffe Efficiency (NSE), as defined in Equation (9), was employed alongside the \(R^{2}\). The findings clearly highlight the superiority of the suggested models in contrast to the pre-existing historical models.  
\[NSE=1-\frac{\sum_{t=1}^{T}(Q_{o}^{t}-Q_{m}^{t})^{2}}{\sum_{t=1}^{T}(Q_{o}^{t}- \overline{Q}_{o})^{2}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**[PERSON] [4]**} & \multirow{2}{*}{**[PERSON] [3]**} & **Gaussian Process Regression Approach** & **This Study (Best Greedy-Box Method),** & **This Study (Hybrid Method),** & **This Study Approach (GPA-ENN)** \\ \hline \(R^{2}\) & 0.902 & 0.894 & 0.940 & 0.975 & 0.995 \\ NSE & 0.815 & 0.727 & 0.842 & 0.975 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of the results of this study with literature models.  
Figure 10: Results of different developed models of (**a**) MLR, (**b**) GP, (**c**) CART and (**d**) GA-ENN, for different data points.  
In the equations, \(T\) is the number of data points, and \(Q_{o}^{t}\) and \(Q_{m}^{t}\) are the actual and predicted output of \(i\)th sample of the data, respectively; \(\overline{Q}_{o}\) is the averaged actual output of the data.
## 5 Conclusions  
The SCSSR system proposed in this paper can enhance and extract bearing fault characteristics under a strong noise background. The main conclusions are as follows:  
1. For large-parameter signals, combined with the variable-scale method, the SCSSR system can detect weak signals.
2. SOA is used to determine model parameters of the SCSSR system with the output SNR as its fitness function.
3. Simulation and engineering data show that the SCSSR has better filtering performance and higher output SNR than the traditional SR method.  
Although this article makes the characteristic frequency of the useful signal obvious, it still relies on the output SNR as a measure of SR. Future work should focus on how to identify the detection of unknown characteristic frequency signals.  
Methodology, W.D. and L.L.; software, C.C.; validation, L.L. and Y.Y.; data curation, L.L. and Y.Y.; writing--original preparation, L.L.; writing--review and editing, Y.Y. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Liaoning Province under Grant 2019 ZD0112, and in part by the National Natural Science Foundation of China under grants 61771087 and 51605068, Traction Power State Key Laboratory of Southwest Jiaotong University under grant TPL2002, the Open Project Program of State Key Laboratory of Mechanical Transmissions of Chongqing University under grant SKLMT-KFKT-201803, and Liaoning BaiQianWan Talents Program.  
The authors declare no conflict of interest.
# Cloud Storage Data Verification Using Signcryption Scheme
## 2 Preliminaries
### [PERSON] [14] proposed a signcryption scheme that provides public verifiability for data verification.  
* **ComGen**. Given the security parameter \(k\) and \(n\). Two cyclic groups \((G_{1},+)\) and \((G_{2},\cdot)\) of the same prime order \(p>2^{k}\), a generator \(P\) of \(G_{1}\), a bilinear map \(e:G_{1}\times G_{1}\to G_{2}\), three hash functions \(H_{1}:\{0,1\}^{*}\to Z_{p},H_{2}:G_{1}^{3}\to\{0,1\}^{n}\) and \(H_{3}:\{0,1\}^{k}\), and an symmetric encryption scheme \((E,D)\). Then, \(I=\{k,n,G_{1},G_{2},P,e,H_{1},H_{2},H_{3},E,D\}\).
* **KeyGen**. Every user picks his private key \(SK_{U}\) from \(Z_{p}^{*}\) randomly and uniformly. Then, he computes his public key \(PK_{U}=SK_{U}P\).
* **Signcrypt**. Given a message \(m\in\{0,1\}^{*}\), the recipient's public key \(PK_{R}\) and the sender's private key \(SK_{S}\). The sender computes:
* pick \(r\leftarrow^{R}\{0,1\}^{n}\) and compute \(u=(H_{1}(m)+SK_{S}+r)^{-1}\ mod\ p\).
compute \(U=uP\in G_{1},V=r\oplus H_{2}(U,PK_{R},uPK_{R})\) and then \(W=E_{\pi}(m||PK_{S})\) where \(\kappa=H_{3}(r)\). Finally, form the signcryptext \(C=(U,V,W)\).  
* by recipient upon receiving \((U,V,W)\).
* parse \(C\) as \((U,V,W)\) and compute \(r=V\oplus H_{2}(U,PK_{R},SK_{R}U)\).
* compute \(m||PK_{S}=D_{\kappa}(W)\) where \(\kappa=H_{3}(r)\).
* if \(e(U,(H_{1}(m)+r)P+PK_{S})=e(P,P)\), then return the message \(m\); otherwise return \(\bot\) means unsigncryption failure.
* **Public Verifiability**. The recipient wants to prove that sender actually signcrypted a message \(m\) to the trusted third party (TTP). So, the recipient forwards \((m,U,r,PK_{S})\) to the TTP. Then, TTP accepts the proof if this equation is valid \(e(U,(H_{1}(m)+r)P+PK_{S})=e(P,P)\).  
ZSS signcryption by [14] gives the public verifiability property. Unfortunately, it lacks two other desirable properties, privacy-preserving, and blockless verification, for several reasons. First, the user must forward the original message \(m\) to the TTP. By doing so, users' data might leak to the TTP. Second, in the ZSS signature generation, the author included a random value \(r\). So, the user also needs to pass the \(r\) value to the TTP. The fact that the TTP will be able to compute a symmetric encryption key, \(\kappa\), by using \(H_{3}(r)\) makes it dangerous. The original message \(m\) can thus be decrypted by the TTP, endangering data privacy. Third, the verifier needs to download message \(m\) to do data verification, which is inefficient, especially if the size of message \(m\) is huge. Therefore, we proposed an improved version of the ZSS signature scheme that provides three desirable properties, as explained in Section 1.
## 1 Introduction
However, the proposed method uses a sixth-order coupled differential equation, and the present method can efficiently analyze the dynamic characteristics of such structures for various boundary conditions. Moreover, a numerical method that can accurately analyze the axial displacement effect on bending-dominated modes has not been studied. To reduce the model error of finite elements, modeling updating techniques based on the finite element method are being studied to predict the dynamic behavior of beam structures [29; 30; 31]. Some researchers [32; 33; 34] have analyzed the dynamic characteristics of sandwich beams using the transfer matrix method (TMM). The coupling of axial and bending displacements was not considered in [32]; a discrete lumped mass system [33] and discretized state equations [34] were used. The method proposed in this study is different from the ones used in the above studies. The stubborn shortcomings, as well as the advantages of the transfer matrix method have been discussed in previous works [35; 36]. The use of a simple precaution to overcome the non-physical numerical instability occurring in the calculation of high-order frequencies is discussed [36]. This study aims to analyze the dynamic behaviors of sandwich beams with a soft core, and the natural frequencies and mode shapes of beams with different boundary conditions (i.e., clamped-free (C-F), clamped-clamped (C-C), clamped-pinned (C-P), and pinned-pinned (P-P) end conditions) were successfully computed using the transfer matrix method. The accuracy of the predicted results of the proposed method is compared with the natural frequencies obtained from earlier studies [26; 27]. For the sandwich beam, it is assumed that the mass of the core layer can be ignored, and only the coupling of the axial and bending displacements is considered. The Euler-Bernoulli beam theory is used for the top and bottom face layers, and only the shear deformation is considered for the core layer. The continuity conditions between the core and face layers are as follows. The proposed beam model assumes that no delamination occurs between layers for free vibration. The bending of the core layer is caused by the bending displacements of the face layers; however, the bending stiffness of the core is ignored. Therefore, the shear deformation of the core, caused by the bending of the face layers, is assumed by making the difference in the axial displacements of the face layers. In addition, an analytical method that can distinguish the effects of the face and core strain energies on the natural frequencies of soft-core sandwich beams is proposed. This is deduced from the relationship between the maximum normal strain and kinetic energies. Some physical phenomena related to the dynamic behavior of sandwich beams are revealed using the analytical method.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Frequency Bands in 5G  
The 5G standard is a revolutionary step in the field of wireless communication, utilizing Frequency Range 1, Frequency Range 2, and New Radio Unlicensed (NR-U) to cater to a wide array of requirements and scenarios. In the following, we provide a summary of 5G NR frequency bands.