esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The widely used Bouc-Wen-Baber-Noori (BWBN) hysteresis model, although effective in simulating hysteresis behaviors, does not account for variations in the pinching region of hysteretic behaviors. This can negatively impact the accuracy of the BWBN model in simulating structural responses and damage mechanisms in structures such as reinforced concrete (RC) and timber, which exhibit highly pinched hysteresis behavior when damaged by earthquakes. This paper introduces a BWBN model with varying pinching region characteristics (BWBN-VP model) which can degrade pinching stiffness and increase pinching effects under seismic loads. Unlike the original BWBN model using constant pinching stiffness (\(k_{p}\)), this modified new model, inspired by real-world structural damage, improves structural damage detection, identifiability, and analysis in real-world scenarios. Model validation uses experimental data from three RC column tests with different failure modes and hysteresis loop shapes, resulting in an \(\sim\)0.98 correlation coefficient between the experimental and simulated responses. Further validation uses real-world seismic data from a six-story RC building and achieves an average correlation of \(\sim\)0.97 with a minor 2.5% difference in the peak restoring forces compared to direct measurements. The proposed BWBN-VP model also accurately and realistically captures damage to both the elastic and pinching stiffness values of the building, with an average difference of \(\sim\)4%. Results confirm that the BWBN-VP model, compared to the original, more accurately predicts hysteretic responses, especially in Shear Failure (SF) modes. Therefore, the BWBN-VP model, superior in simulating highly pinched behaviors in RC and timber structures, would be an advanced tool for resilient seismic design and Structural Health Monitoring (SHM).  
B 20 November 2021  
6 January 2025  
11 January 2025  
13 January 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON] A Hysteresis Model Incorporating Varying Pinching Stiffness and Spread for Enhanced Structural Damage Simulation. _Appl. Sci._**2025**, 15, 724. [[https://doi.org/10.3390/app15020724](https://doi.org/10.3390/app15020724)]([https://doi.org/10.3390/app15020724](https://doi.org/10.3390/app15020724))  
**Copyable**: (c) 2025 by the authors.  
Liconses MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation
### Impact of Deadline  
To analyze the performance of the proposed and previous scheme with respect to the deadline. We take a short deadline from 1 to 3 while the numbers of flows and clusters are fixed to 6 and 6 respectively. In Figure 4a, the acceptance rate of the proposed and previous schemes are compared with respect to the deadline = 1. In the proposed scheme, the clusters that have either IntraSend or IntraRecv flows can make use of both IntraRecv and IntraSend time-slots. In contrast to the proposed scheme, the previous schemes provide a lower acceptance rate. Because in the previous scheme, although clusters have only outgoing or incoming flows the IntraSend and IntraRecv scheduling are not utilizing the empty IntraRecv and IntraSend time slots respectively. In Figure 4b,c, the deadline increases to 2 and 3 respectively. As the deadline increases the acceptance rate also increases in both schemes. However, the acceptance of the proposed scheme is higher than the previous scheme. As the proposed  
Figure 3: Flow diagram of proposed scheme.  
scheme are utilizing both the IntraSend and IntraRecv time slots. While the previous schemes utilize the IntraSend time slots and did not utilize the IntraRecv empty time slots.
# Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective
## 2 Literature Review  
### Driving Variables of HSR System Resilience Improvement  
Many scholars have turned their attention to issues related to the HSR system. To date, the majority of studies have focused on the application of technology, accessibility, external influence, risk management, and competitive advantage. Several studies have tested the validity of one single variable for improving HSR system resilience. For example, [PERSON] and [PERSON] (2018) explored the possibility of staff repairing damagedparts through remote manipulation, so as to measure the degree of human contribution to system resilience [23]. [PERSON] et al. (2018) reviewed various kinds of sensors and their application in resilience monitoring [22]. [PERSON], [PERSON], and [PERSON] (2019) discussed the influence of decentralization on resilience [24]. [PERSON] and [PERSON] (2015) proposed that maintenance can improve the resilience of complex technological and organizational systems [25].  
System resilience is closely related to system performance during disturbance [26; 27]. Figure 1 shows hypothetical system performance curves during disturbance, HP(t), and during normal function, BP(t). The area between BP(t) and HP(t) can be used to evaluate the level of system resilience loss [28].  
System resilience includes multiple attributes. According to the Multidisciplinary Center for Earthquake Engineering Research (MCEER), resilience is composed of four dimensions: robustness, redundancy, resourcefulness, and rapidity [29]. [PERSON] (2009) divided resilience into \"soft\" and \"hard\" measures. Specifically, hard resilience is reflected in structures and institutions, determining the possibility of system collapse. Soft resilience refers to the ability of a system to absorb strain and recover after disturbance [30]. [PERSON] et al. (2011) proposed that a system with survivability, viability, and conviviality can minimize the impact of disasters [31]. [PERSON] et al. (2017) quantified engineering resilience using the combination of two attributes, i.e., reliability and recoverability [32]. [PERSON] (2020) proposed six attributes of system resilience, i.e., vulnerability, response, survivability, recovery, mitigation, and preparedness [4]. Although there are different categories of system resilience attributes, they are all related to system ontology and the connotation of resilience. This study identified the driving variables of HSR system resilience improvement from two aspects, i.e., HSR system ontology-related variables and resilience attribute-related variables.
# Blockchain-Based E-Voting Mechanisms: A Survey and a Proposal +
## 3 Comparative Analysis
### Trends in Blockchain-Based E-Voting  
Referring to Table 1, our analysis of blockchain-based e-voting research has surmised several trends. Public blockchains are predominantly favored for e-voting systems, followed by private and hybrid types. Public blockchains offer transparency and wider accessibility, essential elements in voting systems. A variety of cryptographic methods are used, with public-key cryptography being the most prevalent. Public-key cryptography is often complemented with other techniques, including ECDSA, digital signature requirements, zero-knowledge proofs, RSA and Elliptic Curve Cryptography. This diversity in cryptographic techniques suggests a focus on ensuring security and voter privacy. Regarding vote counting, there is a balance between third-party counting systems, self-tallying, and hybrid methods. While self-tallying systems are notably popular, offering a degree of automation and decentralization, hybrid systems provide a more controlled environment, potentially enhancing the integrity of vote counting. Voter anonymity is prioritized in most systems, aligning with the blockchain's ability to offer anonymity alongside traceability. This feature proves critical in preserving the confidentiality of votes. Auditability and verifiability are almost universally integrated, indicating a trend towards creating systems where election results can be independently verified and audited, bolstering the trust in the electoral process. The uniqueness of votes is a standard feature, ensuring one person, one vote. However, fairness, representing equal access and representation, is less emphasized, indicating an area for future development. We also discover some emerging technologies in this study. The use of post-quantum cryptography and the incorporation of advanced cryptographic techniques like zero-knowledge proofs and ring signatures indicate a proactive approach to future-proofing against challenges, such as those posed by quantum computing. These trends reflect the dynamic and evolving nature of blockchain-based e-voting systems and emphasizing security, transparency, and the integrity of the voting process. The diversity in the approaches taken by various systems suggests ongoing experimenting to find the optimal balance these critical features.
## 3 Experiments and Results
### Performance Evaluation Metrics of the Network Model
#### 3.2.1 Precision  
Precision is used to measure the proportion of true positive samples among all the samples predicted as positive by the model, reflecting the model's ability to control false positive (FP) instances. The formula for calculating precision is  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{6}\]  
where \(TP\) represents true positives, and \(FP\) represents false positives.  
Figure 7: Illustration of MPDIoU Bounding Box and IoU Overlap. (Note: The green box is the ground-truth, the yellow box is the prediction, the gray area shows IoU overlap, and the points/lines represent reference points and distances used in MPDIoU.).
## 3 Results and Discussion
### Results of PLS Image Discrimination of South Korean and Chinese Rice  
A PLS image algorithm for geographical origin discrimination was developed. The development process of the image processing algorithm is shown in Figure 7. Binarized images were obtained to remove the background of the HSI at a 489.4 nm wavelength. From that point, the images of all wavelength bands were masked for feature extraction of the rice samples. Regression coefficients of the PLS model were applied to all wavelength band images, followed by acquisition of the PLS images. The South Korean and Chinese rice samples were discriminated from the obtained binary image by applying a threshold value to the PLS image.  
Figure 6: Geographical origin discrimination results for the PLS-DA models developed using (**a**) no pre-treatment, (**b**) baseline pre-treatment, (**c**) maximum normalization, (**d**) smoothing pre-treatment, (**e**) Savizky-Golay first-order derivative, and (**f**) Savizky-Golay second-order derivative.  
Figure 8 illustrates the PLS image of geographical origin discrimination between the South Korean and Chinese rice samples using the Savitzky-Golay first-order derivative at a pixel dimension of \(30\) mm \(\times\) 30 mm. As shown in Figure 8, the PLS image is a potential technology for discriminating the South Korean and Chinese rice origins. The edge of the rice sample--the boundary between the sample cell and the rice sample--shows a higher pixel value. This may be because the edge portion of the rice in the sample cell has less overlap than the inside portion. Moreover, the fluorescence signal of the sample cell and the fluorescence signal of the rice are simultaneously affected. As shown in Figure 8, the white value of the South Korean sample shows the effect of the sample cell; thus, it may be appropriate to exclude the edge value to avoid the effect of the cell on the geographical origin discrimination.
## 2 Materials and Methods
### Deep Learning Models
#### 2.5.2 LSTM Cell  
Long short-term memory (LSTM) cells have a different architecture than simple cells. Their structure is in Figure 4, and the set of equations required is in Equation (12). Three gates compose the cell: forget gate, input gate, and output gate.  
The processing starts on the lower part with \(x_{(t)}\) and the previously generated \(h_{(t-1)}\). They are used to calculate \(f(t)\) (left-hand side), via the \(\sigma(x)\) function in Equation (12b).  
Figure 4: Default structure of an LSTM cell, based on [27]. It has a combination of \(\sigma(x)\) and \(\text{tanh}(x)\) functions, to create three main gates (forget, input, output) and so generate the short-term \(h_{(t)}\) and long-term \(c_{(t)}\) vectors.  
Figure 3: The basic structure of an RNN. On the left of the figure are the changes through time, and on the right of the figure is a static version. \(x_{t}\) represents a sample at time \(t\), \(A\) is a neuron with a certain activation function (usually tanh) that behaves like an ANN’s neurons, and \(h_{t}\) is not only the output at time \(t\) but also the input at time \(t+1\), with its respective sample \(x_{t+1}\).  
This controls the forget gate to determine which information of the long-term state should be erased, as a \(\sigma(x)\) function is used, and its values range from 0 (erase all) to 1 (erase nothing). On the other hand, \(x_{(t)}\) and \(h_{(t-1)}\) are also used to calculate \(i_{(t)}\) and \(g_{(t)}\), as shown in (13a) and (13d), respectively. This consists of the input gate, where the \(\sigma(x)\) function in \(i_{(t)}\) controls the information of \(g_{(t)}\) that should be included in the long-term state. The addition of the forget gate and input gate is calculated in \(c_{(t)}\), as shown in Equation (12e), which is further used in the output gate with \(o_{(t)}\), which controls how the information from the long-term state should be read and output. The element-wise multiplication of both \(o_{(t)}\) and \(c_{(t)}\) makes the output \(y_{(t)}\), which is equal to \(h_{(t)}\) (used in the next iteration as \(h_{(t-1)}\)), as calculated in Equation (12f).  
\[i_{(t)}=\sigma(W_{xi}^{T}x_{(t)}+W_{hi}^{T}h_{(t-1)}+b_{i}) \tag{12a}\] \[f_{(t)}=\sigma(W_{xf}^{T}x_{(t)}+W_{hf}^{T}h_{(t-1)}+b_{f})\] (12b) \[o_{(t)}=\sigma(W_{xo}^{T}x_{(t)}+W_{ho}^{T}h_{(t-1)}+b_{o})\] (12c) \[g_{(t)}=\tanh\left(W_{xg}^{T}x_{(t)}+W_{hg}^{T}h_{(t-1)}+b_{g}\right)\] (12d) \[c_{(t)}=f_{(t)}\otimes c_{(t-1)}+i_{(t)}\otimes g_{(t)}\] (12e) \[y_{(t)}=h_{(t)}=o_{(t)}\otimes\tanh\left(c_{(t)}\right) \tag{12f}\]
## 5 Simulation Studies
### Quality Indices  
In order to evaluate the analyzed geometric configurations of the robot and control system solutions from the point of view of the accuracy of tracking control and energy efficiency, quality indices are adopted.  
The accuracy of the tracking control by the wheels' controller is assessed based on the maximum angular velocities' errors of individual wheels and their average for all \(n\) wheels using the following formulae:  
\[\mathsf{e}_{\omega imax}=\max_{t\in\{0,\,T\}}(|\mathsf{e}_{\omega i}|),\quad \mathsf{e}_{\omega imax}=\sum_{i=1}^{n}\mathsf{e}_{\omega imax}/n. \tag{47}\]  
The integrals for the errors (47) are also calculated in relation to the simulation period \(T\) (s) for individual wheels and their average for all \(n\) wheels from the following relationship:  
\[E_{\omega i}=\sqrt{\frac{1}{T}\int_{0}^{T}\left(\mathsf{e}_{\omega i}\right) ^{2}\mathrm{d}t},\quad\ E_{\omega}=\sum_{i=1}^{n}E_{\omega i}/n. \tag{48}\]In terms of assessing the accuracy of movement using the pose controller, the following quality indices are applied, analogous as before, separately with respect to the distance error \({}^{R}e_{d}\) and course \({}^{R}e_{\varphi}\):  
\[{}^{R}e_{kmax}=\max_{t\in(0,\,T)}\left(\left|{}^{R}e_{k}\right|\right),\,\,E_{k }=\sqrt{\frac{1}{T}\int_{0}^{T}\left({}^{R}e_{k}\right)^{2}\mathrm{d}t}, \tag{49}\]  
where \(k\in\{d,\varphi\}\).  
To assess the energy efficiency, the following quality indices relating to electrical power and electrical energy consumed by the robot drives are introduced:  
\[p_{imax}=\max_{t\in(0,\,T)}(\mathrm{pos}(p_{i})),\,\,\,\,\,\,\,p_{max}=\sum_{i=1 }^{n}p_{imax}/n, \tag{50}\]  
\[E_{Di}=\int_{0}^{T}\mathrm{pos}(p_{i})\mathrm{d}t,\,\,\,\,\,\,\,E_{D}=\sum_{i= 1}^{n}E_{Di}/n, \tag{51}\]  
where function  
\[\mathrm{pos}(p_{i})=\begin{cases}0&\text{if }p_{i}\leq 0,\\ p_{i}&\text{otherwise}\end{cases} \tag{52}\]  
allows one to take into account the lack of electric energy recuperation.  
As before, for the quality indices \(p_{imax}\) (W) and \(E_{Di}\) (J), their averages are calculated for all \(n\) wheels.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results
### End-to-End Delay and Packet Inter-Arrival Time  
In Figure 10 the end-to-end delay and packet inter-arrival time for each of the transmitter nodes using SDN WISE-TSCH and Orchestra can be seen. The nodes have different combinations of flows assigned, as shown in Figure 3. The schedulers SDN WISE-TSCH in Figure 10a and Orchestra in Figure 10b achieve the same behaviour as in the simulation, where SDN WISE-TSCH has a minimum end-to-end delay of 50 ms for the three hops to the destination. This behaviour is typical of optimised scheduling, since it allows the source-to-destination cells between the different nodes to be linked. In Orchestra, where the cells are assigned locally so that the transmission of each hop is done in different slotframes, queuing occurs, causing the delays to increase considerably. In this case, with a slotframe of only 7 timeslots SDN WISE-TSCH achieves an end-to-end delay reduction of 75% with respect to that obtained by Orchestra. For this node, the packet inter-arrival time is constant in Figure 10c SDN WISE-TSCH, remaining at 70 ms. This, together with the low end-to-end delay, means that between source and destination there is a constant packet flow, and no delays occur at any point in the network. In contrast, Orchestra in Figure 10d shows variations of up to 380 ms. The results for node 9, which has the highest saturation case with all three flows, are shown in Figure 10e-h. SDN WISE-TSCH Figure 10e maintains the minimum end-to-end (50 ms) in the three flows due to the complete allocation of resources.  
In contrast, Orchestra Figure 10f presents delays for all flows of up to 4000 ms and a high number of losses. This is due to the congestion of the queues, where Orchestra cannot configure enough bandwidth for the flows generated by the node. Because of this, the PIAT is stable in SDN WISE-TSCH Figure 10g, where it stays close to the packet generation period, while in Orchestra this lack of bandwidth is reflected with a high dispersion Figure 10h. Node 10, has the flows with the lowest bandwidth requirement, so the end-to-end delay for the two protocols in Figure 10i and Figure 10j, is very similar, with small exceptions that are due to the RPL traffic that is sent by the unicast queues. In this case the PIAT is also seen to be more bounded Figure 10k and Figure 10l, so the performance at this node is very similar for the two case studies.  
Figure 9: Testbed using OpenMote B.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 1 Introduction
Section 2 describes the construction and properties of the 2D wavelength/time permutation vectors' codes. Section 3 presents the architecture of the transmitter and receiver of the proposed code. Section 4 analyzes the system performance. The numerical and simulation results are presented in Sections 5 and 6, respectively. Finally, the conclusions are drawn in Section 7.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 7 Run-Time Analysis and Implementation  
We tested the presented algorithm on meshes with various complexities. In particular, we performed the speedup analysis of the parallel algorithm to our implementation of the sequential version given [26]. Our experiments were done on an AMD FX 6300 6-Core with 32 GB memory. The algorithm was implemented in C++, and the Windows platform was used.  
We test our parallel algorithm with two datasets: the AIM@SHAPE Repository as well as the MeshDeform dataset available in [82]. The initial attempt did not give us an increase of performance  
Figure 17: Obtaining a unit gradient scalar field.  
over the sequential algorithm for most of the meshes available in the above datasets. We tested our algorithm on high resolution meshes in order to take advantage of the parallel implementation. Specifically, we uniformly increase the resolution of the meshes available these two datasets to 200 k. On the AIM@SHAPE library our implementation gave us a minimum speedup of 3.6, a maximum one of 4.3, and an average speedup of 3.8 on six cores. Using this, we obtain a 63% average parallel efficiency. One the MeshDeform dataset we obtained a minimum speedup of 2.9, a maximum one of 3.9, and an average speedup of 3.5 on six cores. This dataset gives a 42% average parallel efficiency on six cores. Figure 18 provides the details. The \(x\)-axis represents the number of processes, and the \(y\)-axis shows the speedup.  
Figure 19 shows a few examples of the meshes that we utilized in our tests above, along with their corresponding Reeb graphs.
## 4 Queuing Model for Baseline Algorithm
### State-Space Decomposition  
Let us decompose system state space (1) into smaller subsets for better analysis. Note that \(N-m\) represents the maximum number of 2-users that can jointly transmit elastic traffic. The partitioning of set \(\mathcal{S}\) into subsets is based on two factors: the possibility of resource reallocation and the ratio of users transmitting traffic to those waiting in buffers. We define \(\mathcal{S}_{\delta}\) as the states where resource reallocation occurs upon receiving a signal and \(\overline{\mathcal{S}_{\delta}}\) as the states in which it does not occur. Hence, \(\mathcal{S}\) can be represented as a union of these two disjoint subsets:  
Figure 5: Signals resulting and not resulting in resource reallocation between the service providers.  
Moreover, we know that \(m\) is the parameter used in the reallocation management policy to determine the size of the 1-slice. Thus, we can express \(\mathcal{S}\) as a union of subsets corresponding to different values of \(m\):  
\[\mathcal{S} =\bigcup_{m=0}^{N}\mathcal{S}(m),\quad\mathcal{S}(m)=\Big{\{}(i,n_ {1},n_{2})\in\mathcal{S}:\;\;i=m\Big{\}},\] \[\mathcal{S} =\bigcup_{m=0}^{N}\mathcal{S}_{\delta}(m)\cup\overline{\mathcal{ S}}_{\delta}(m).\]  
Furthermore, we can divide subset \(\mathcal{S}_{\delta}(m)\) into two more subsets: \(\mathcal{S}_{\delta}^{1}(m)\) and \(\mathcal{S}_{\delta}^{2}(m)\), where \(\mathcal{S}_{\delta}^{1}(m)\) represents states in which the size of the 1-slice is increased upon receiving a signal and \(\mathcal{S}_{\delta}^{2}(m)\) represents states in which the size of the 2-slice is increased upon receiving a signal.  
\[\mathcal{S}_{\delta}(m)= \mathcal{S}_{\delta}^{1}(m)\cup\mathcal{S}_{\delta}^{2}(m),\] \[\mathcal{S}_{\delta}^{1}(m) =\Big{\{}(i,n_{1},n_{2})\in\mathcal{S}(m):\;\;n_{1}>i,\;\;n_{2}<N -i\Big{\}},\] \[\mathcal{S}_{\delta}^{2}(m) =\Big{\{}(i,n_{1},n_{2})\in\mathcal{S}(m):\;\;n_{1}<i,\;\;n_{2}>N -i\Big{\}}.\]  
Finally, in the case where there are enough free resources to serve waiting users, the slices are increased either by the number of waiting users or by all free resources. Thus, the subset of \(\mathcal{S}_{\delta}^{k}(m)\) can be represented as the following union:  
\[\mathcal{S}_{\delta}^{k}(m)= \mathcal{S}_{\delta}^{k1}(m)\cup\mathcal{S}_{\delta}^{k2}(m),\] \[\mathcal{S}_{\delta}^{k1}(m) =\Big{\{}(i,n_{1},n_{2})\in\mathcal{S}_{\delta}^{k}(m):\;\;n_{1} +n_{2}\leq N\Big{\}},\] \[\mathcal{S}_{\delta}^{k2}(m) =\Big{\{}(i,n_{1},n_{2})\in\mathcal{S}_{\delta}^{k}(m):\;\;n_{1} +n_{2}>N\Big{\}}.\]  
Figure 6: Queueing system.