esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
## 3 Results and Discussion
### Seismic Response Analysis of Bridge
#### 3.1.3 Pier Shear Force  
The shear force-time histories of pier P2 are shown in Figure 12. The peak shear forces of pier P2 of the non-isolated bridge and the isolated bridge in the X-direction are 876.4 kN and 1186.5 kN, respectively. The isolation bearing did not decrease the shear force of the pier but increased the peak shear force of pier P2. The increasing percentage of the peak shear force is 35.4%. It is shown that the isolation bearing may not be suitable for the near-fault ground motion. In the Z-direction, the isolation bearing decreases the peak shear force of pier P2 from 1009.4 kN to 634.7 kN. The decreasing percentage of the peak shear force is 37.1%.
## Appendix A Sort I Error Analysis  
Consider the sort I error, i.e., the transmitter sends \(\mathbf{c}_{i}\), yet \(\mathbf{y}\
otin\mathcal{T}_{\mathsf{K}}\) for every \(i\in\mathsf{K}\). The sort I error probability is given by  
\[P_{\varepsilon,1}(i,\mathsf{K}) =\Pr\bigl{(}\mathbf{Y}(i)\in\mathcal{T}_{\mathsf{K}}^{ \varepsilon}\bigr{)}=\Pr\left(\mathbf{Y}(i)\in\Big{(}\bigcup_{j\in\mathsf{K} }\overline{\tau}_{j}\Big{)}^{\varepsilon}\right)\] \[\stackrel{{(a)}}{{=}}\Pr\left(\mathbf{Y}(i)\in\bigcap_ {j\in\mathsf{K}}\overline{\tau}_{j}^{\varepsilon}\right)\stackrel{{ (b)}}{{\leq}}\Pr\bigl{(}\mathbf{Y}(i)\in\overline{\tau}_{i}^{ \varepsilon}\bigr{)}=\Pr\left(T(\mathbf{Y}(i),c_{i})>\left\lfloor n\delta_{ \beta}\right\rfloor\right), \tag{11}\]  
where \((a)\) follows by _[PERSON]_'s law, i.e., \(\left(\bigcup_{i\in\mathsf{K}}\overline{\tau}_{i}\right)^{\varepsilon}= \bigcap_{i\in\mathsf{K}}\overline{\tau}_{i}^{\varepsilon}\) and \((b)\) holds since \(\bigcap_{j\in\mathsf{K}}\overline{\tau}_{j}^{\varepsilon}\subset\overline{ \tau}_{i}\). Now, observe that  
\[\Pr\left(T(\mathbf{Y}(i),c_{i})>\left\lfloor n\delta_{\beta}\right\rfloor \right)\stackrel{{(a)}}{{=}}\Pr\left(d_{\mathrm{H}}(\mathbf{Y}(i),\mathbf{c}_{i})>\left\lfloor n\delta_{\beta}\right\rfloor\right)\stackrel{{ (b)}}{{=}}\sum_{l=\left\lfloor n\delta_{\beta}\right\rfloor+1}^{n}\binom{ n}{l}\varepsilon^{l}(1-\varepsilon)^{n-l}, \tag{12}\]  
where \((a)\) follows by \((52)\) and \((b)\) holds by \((12)\). In order to bound (12), we proceed to apply the bound provided in (10) given in Lemma A4: Observe that  
\[\frac{l}{n}=\frac{\left\lfloor n\delta_{\beta}\right\rfloor+1}{n}>\frac{n \delta_{\beta}}{n}=\delta_{\beta}\stackrel{{(b)}}{{>}}\varepsilon, \tag{13}\]where \((a)\) follows, since \(x<\lfloor x\rfloor+1\) for real \(x\) and \((b)\) holds by (50). On the other hand,  
\[\frac{l}{n}=\frac{\left\lfloor n\delta_{\beta}\right\rfloor+1}{n}\leq\frac{\max \left\lfloor n\delta_{\beta}\right\rfloor+1}{n}\stackrel{{(a)}}{{< }}\frac{\left\lfloor n\max\left(\varepsilon+\beta(1/2-\varepsilon)\right) \right\rfloor+1}{n}\stackrel{{(b)}}{{<}}\frac{\left\lfloor n/2 \right\rfloor+1}{n}\stackrel{{ n\geq 3}}{{<}}1,\] (A4)  
where \((a)\) follows by (50) and \((b)\) holds since \(\varepsilon+\beta(1/2-\varepsilon)\) is upper bounded by the boundary value of \(\varepsilon\), i.e., where \(\varepsilon=1/2\). Observe that the last inequality in (A4) holds for sufficiently large \(n\).
## 1 Introduction
However, most of the studies focused on the comparative analysis of the models to increase the predictive accuracy of traffic accidents on land, and studies predicting the occurrence of maritime traffic accidents were found to be insufficient. Only a few studies have been investigated for the development of marine traffic accident prediction models with machine learning techniques. [PERSON] and [PERSON] [18] developed a machine learning-based model for predicting marine port accidents focused on container terminals. [PERSON] et al. [19] predicted accidents at Korean container terminals using machine learning techniques. However, there were no relevant studies using deep learning techniques. To the best of our knowledge, therefore, our study represents the first attempt to use a deep learning approach to forecast marine accidents. The purpose of this study was to build a model that can predict the frequency of marine accidents using LSTM by utilizing statistical data on marine accidents as a fundamental study to predict the risk of marine accidents. We also aimed to find the best LSTM models to forecast marine accidents by navigators' watch duty time with different setups and to see if the LSTM models outperform the traditional statistical analysis methods. In order to achieve our objectives, we built four different LSTM models and compared the proposed LSTM models with an autoregressive integrated moving average (ARIMA) model, which is one of the commonly used models for time series forecasting. Through this LSTM-based prediction model, the risk of future marine accidents could be anticipated in advance using certified marine accident statistics data, and a monitoring system could be effectively established. The remainder of the paper is organized as follows: We first describe how the data were collected and preprocessed for this study. Next, the prediction model and the research methodology utilized in this study are presented, followed by the results. Lastly, the findings and implications of this study are discussed.
# Train Scheduling with Deep Q-Network: A Feasibility Test
## 3 Problem Description  
We consider a train scheduling problem on single-track unidirectional railway. Let \(\mathcal{S}=\{1,\ldots,S\}\) be the set of stations, and all trains are assumed to begin their trips from station 1 and end at station \(S\). There are two types of trains--local trains and express trains--denoted by \(\mathcal{T}_{l}\) and \(\mathcal{T}_{e}\), respectively. The difference in type is due to the stopping patterns of trains. Local trains stop at every station, while express trains stop only at the predetermined set of stations, denoted by \(\mathcal{S}\subseteq\mathcal{S}\). We assume that all express trains have the same set of stopping stations. If there are only local trains, no overtaking is occurred. If not, an express trains can overtake its preceding normal trains at a predefined set of stations, denoted by \(\mathcal{S}_{o}\), in which a sidetrack is prepared.  
We consider the following operational environment.  
1. There exists a safety headway, say \(h\). Therefore, a train arriving (departing) at a station can arrive at (depart from) the station \(h\) time units after its immediate predecessor.
2. For two consecutive stations \(s\) and \(s^{\prime}\), where \(s^{\prime}=s+1\), there are minimum transit times between \(s\) and \(s^{\prime}\) for local and express trains, denoted by \(l^{l}_{ss^{\prime}}\) and \(l^{e}_{ss^{\prime}}\), respectively.
3. If a train stops at station, it should stay at least \(d\) time units.
4. Overtaking only occurs between local and express trains. Any overtaking between the same type of trains is not allowed.  
We assume that the departing sequence \(\pi\) of trains at station 1 is given. Thus, \(\pi(g)\in\mathcal{T}:=\mathcal{T}_{l}\cup\mathcal{T}_{e}\) is the train which departs the initial station \(g\)th for \(g=1,2,\ldots,|\mathcal{T}|\). Conversely, \(\pi^{-1}(i)\) is the departing order of train \(i\in\mathcal{T}\) at the initial station. We also assume that there are departure times \(\bar{D}_{i1}\) for \(i=1,\ldots,|\mathcal{T}|\) after which train \(i\) can depart from station 1. Let us consider the departure times \(\bar{D}_{is}\) and arrival times \(\bar{A}_{is}\) for \(i=1,\ldots,|\mathcal{T}|\) and \(s=2,\ldots,\mathcal{S}\) obtained by scheduling all trains separately. This schedule is possibly infeasible as it can violate (E1) and (E4). However, this schedule is the most desirable one and is easily obtainable, and therefore we regard it as our target schedule, i.e., we want to find a schedule which is as close as possible to the target schedule.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
###### Abstract  
Sparse code multiple access (SCMA) has been considered as an efficient technique to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks. However, the conventional uniform quantization of the message passing algorithm (MPA) for the SCMA detection induces a significant bit error ratio (BER) performance degradation. In this sense, we propose a new quasi-uniform quantization scheme that can efficiently handle the dynamic range in the exchange of messages. To accelerate the convergence of conventional Max-log MPA, the Sub-log MPA is considered by using the latest updating messages at the current iteration. Simulation results show that the proposed quasi-uniform quantization method can significantly improve the BER performance of the SCMA decoder without modifying the resource nodes' and variable nodes' update rules in both the additive white Gaussian noise and the Rayleigh frequency selective channels, as compared to the uniform quantizer.  
bit error ratio; message passing algorithm; quasi-uniform quantization; SCMA 1
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Application Level
#### 3.6.4 Access Control Lists  
Access control lists (ALCs) define at the device level which users or systems are authorized to access a particular IoT device [80]. This scheme-preconditioned access control ensures that device operations and data access are limited to authorized individuals, thus reducing the chance of malicious accesses by adversaries.  
Role-based access control, or RBAC, is a flexible access-control model that assigns appropriate privileges to users anchored on their roles [81]. With the RBAC model in place, sensitive data and device features are only available to any roles that need the information. This highly centralized privilege control effectively alleviates the risk of data leakage and unauthorized accesses. Even if a user's credentials are compromised, a threat actor can only gain access to resources allowed by that user's role, thereby mitigating security risks.  
Figure 8: A list of sample data security APIs and their security features.
## 2 Materials and Methods
### Variational Formulation
#### 2.2.2 Temporal Discretization  
The temporal discretization of the equation occurs using the finite difference scheme known as Crank-Nicolson [9; 11]. This method gives rise to an unconditionally stable scheme of order two and has been widely used. It consists of taking the average instant between two subsequent instants; that is, using the time-centered difference \(t^{n+\frac{1}{2}}\), obtaining the following approximations:  
\[\left.\frac{dc_{j}}{dt}\right|_{t^{n+\frac{1}{2}}}=\frac{c_{j}^{n+1}-c_{j}^{n}}{ \Delta t},\ \ \ \ \ c_{j}^{n+\frac{1}{2}}=\frac{c_{j}^{n+1}+c_{j}^{n}}{2},\]  
\[M^{n+\frac{1}{2}}=\frac{M^{n+1}+M^{n}}{2},\ \ \ W^{n+\frac{1}{2}}=\frac{W^{n+1}+W^{n}}{2},\ \ \ H^{n+\frac{1}{2}}=\frac{H^{n+1}+H^{n}}{2},\ \ \ N^{n+\frac{1}{2}}=\frac{N^{n+1}+N^{n}}{2}.\]  
Replacing \(t\) in (19) by \(t^{n+\frac{1}{2}}\), we have  
\[M^{n+\frac{1}{2}}\left[\frac{dC_{h}^{n+\frac{1}{2}}}{dt}\right]+W^{n+\frac{1} {2}}\left[C_{h}^{n+\frac{1}{2}}\right]=H^{n+\frac{1}{2}}+N^{n+\frac{1}{2}}.\]  
Thus,  
\[M^{n+\frac{1}{2}}\left[\frac{C_{h}^{n+1}-C_{h}^{n}}{\Delta t}\right]+W^{n+ \frac{1}{2}}\left[\frac{C_{h}^{n+1}+C_{h}^{n}}{2}\right]=H^{n+\frac{1}{2}}+N^ {n+\frac{1}{2}}.\]  
Factoring the equation by taking out \(C_{h}^{n+1}\) and \(C_{h}^{n}\) and multiplying everything by \(\Delta t\), the solution to the system of differential equations (19) can be approximated by the solution of the following system:  
\[\left[M^{n+\frac{1}{2}}+\frac{\Delta t}{2}W^{n+\frac{1}{2}}\right]\cdot C_{h}^ {n+1}=\left[M^{n+\frac{1}{2}}-\frac{\Delta t}{2}W^{n+\frac{1}{2}}\right]\cdot C _{h}^{n}+\Delta t\left(H^{n+\frac{1}{2}}+N^{n+\frac{1}{2}}\right). \tag{21}\]  
The system (21) is solved iteratively, in time, starting from an initial condition. Furthermore, some inner products must be calculated to construct the M, W, H, and N matrices, as defined in (20). To this end, the \(\varphi\) functions used in the process are defined below.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 6 Conclusions  
This paper proposes a MAC protocol for the ad hoc network using IBFD communications, which is a state-of-the-art MAC for wireless communications named AdHoc-FDMAC. The proposed MAC has been found to have the capability to improve the throughput significantly compared to a recent publication in this area. The proposed AdHoc-FDMAC also outperforms the conventional counterpart in terms of throughput and routing time. As we have to handle a huge volume of traffic, we need to incorporate IBFD communications in all kinds of wireless networks. Therefore, this AdHoc-FDMAC will play a vital role in the future ad hoc networks. In this paper, we have considered the symmetric data traffic and AODV routing algorithm. In the future, this research can be extended for the asymmetric data traffic. In addition, other proactive and reactive routing algorithms can be incorporated in the future research on the MAC protocol design for ad hoc networks.  
This ADHoc-FDMAC protocol is not fully compatible with the present IEEE standard. IBFD is a new technology and the existing IEEE standard does not support the IBFD communications. Therefore, the existing wireless communication devices (such as laptops, mobile phones, WiFi routers, etc.) are not able to perform this IBFD communication as well, as this MAC protocol cannot be implemented in these existing devices. To support the huge data in our limited bandwidth, IBFD is one of the best potential technologies to be implemented in the upcoming new devices. On the other hand, this AdHoc-FDMAC is applicable to all the applications that are provided by the existing AdHoc networks, but this proposed MAC has a higher capability to handle more data than the existing one.  
Conceptualization, network model and protocol design, M.A.R. and M.A.A.; methodology, M.A.R., M.M.R. and M.A.A.; mathematical analysis, M.A.R. and M.A.A.; simulation, M.A.R.; writing and editing of the manuscript, M.A.R., M.M.R. and M.A.A.; supervision and funding, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Khulna University Research Cell (KURC Project FY 2020-21).  
Data Availability StatementData are available from the authors, on request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Behavior Study of the MSAMT  
#### 3.3.1 Effect of the Clusters Number  
The clusters number \(n\) is an important parameter in the MSAMT, and in order to study its impact on the MSAMT performance, we compared the outcomes of the MSAMT  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Problem** & **Dimension** & **Optimum** & **Property** \\ \hline F1 & Ellipsoid & 10, 20, 30, 50, and 100 & 0 & Unimodal \\ F2 & Rosenbrock & 10, 20, 30, 50, and 100 & 0 & Multimodal with narrow valleys \\ F3 & Ackley & 10, 20, 30, 50, and 100 & 0 & Multimodal \\ F4 & Griewank & 10, 20, 30, 50, and 100 & 0 & Multimodal \\ F5 & Rastrigin & 10, 20, 30, 50, and 100 & 0 & Multimodal \\ F6 & Shifted Rotated High Conditioned & 10, 20, 30, 50, and 100 & \(-450\) & Unimodal, shifted, rotated, and \\  & Elliptic Function & & & non-separable \\ F7 & Shifted Rotated Ackley’s Function with Global & 10, 20, 30, 50, and 100 & \(-140\) & Multimodal, shifted, rotated, and \\  & Optimum on Bounds & & & non-separable \\ F8 & Shifted Rastrigin & 10, 20, 30, 50, and 100 & \(-330\) & Multimodal, shifted and separable \\ F9 & Shifted Rotated Rearrigin & 10, 20, 30, 50, and 100 & \(-330\) & Very complicated multimodal \\ F10 & Shifted Rotated Expanded Scaffer’s F6 (Shifted Rosenbrock’s Function) Function & 10, 20, 30, 50, and 100 & \(-300\) & Multimodal, shifted, and \\ F11 & Rotated Hybrid Composition Function 1 & 10, 20, 30, 50, and 100 & 120 & Very complicated multimodal \\ F12 & Rotated Hybrid Composition Function 2 & 10, 20, 30, 50, and 100 & 10 & Multimodal, rotated, and \\  & Rotated Hybrid Composition Function 2 with a Narrow Basin for the Global Optimum & 10, 20, 30, 50, and 100 & 10 & Very complicated multimodal \\ F14 & Rotated Hybrid Composition Function 2 with the Global Optimum on the Bounds & 10, 20, 30, 50, and 100 & 10 & Multimodal, non-separable, and \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test functions used in the experiment study.  
variants with varying \(n\) values across the 10, 20, and 30 dimensions of the 14 test problems, as illustrated in Table 11.  
Table 11 displays the results of the algorithm analysis, which demonstrated that different variants performed significantly differently when faced with the same test problems. For example, the MSAMT with \(n=2\) outperformed the other variants on test problems such as F1, F4, and F5, the MSAMT with \(n=5\) performed well on the F7 test function, and when \(n=6\), the MSAMT outperformed on test functions such as F9 and F10. Comparing the results of all variants, it was found that the MSAMT with \(n=2\) exhibited a superior performance on most of the test problems. Therefore, \(n=2\) was used in this paper.
## 3 Hierarchical Blockchain-Empowered Cloud-Edge Orchestrated FL Architecture
### The Main Elements in HBCE-FL Architecture  
The proposed HBCE-FL architecture contains four main elements: trusted authority, local client, edge blockchain, and cloud server.  
* Trusted authority: A trusted authority, often controlled by a government department or industry federation, is a fully trusted entity crucial for system initialization. It securely communicates with edge servers via reliable connections. Edge servers and IoT devices must register with this entity to gain system legitimacy. The trusted authority defines HBCE-FL specifics, such as access control policies, user rights, data structures, parameter lifecycles, etc.
* Local client: Any IoT device has the capability to generate data. The local client is responsible for processing and training these data locally, subsequently encrypting the resulting model parameters (knowledge) using its designated key. The encrypted text is delivered to the nearest edge server.
* Cloud server: The cloud aggregation layer, consisting of independent servers with varied security levels, hosts diverse learning tasks. Upon receiving sanitized ciphertext from edge servers, cloud servers decrypt using private keys, aggregating plaintext to update the model.
* Edge blockchain: The edge blockchain layer comprises many edge servers, such as communication base stations, responsible for connecting smart devices to cloud servers and handling message sanitization and forwarding. Identity authentication for FL participants, key distribution, and message sanitization is performed through smart contracts. The edge server is considered a semi-trusted entity, meaning it accurately executes pre-configured algorithms but maintains a level of curiosity about the privacy of the forwarded messages. Due to its interest in the information transmitted within the network and for facilitating subsequent operational analyses, it may anticipate acquiring additional information from the forwarded messages. While it does not inherently engage in activities that compromise the system or user interests, there exists a possibility of exploitation by malicious actors, thereby resulting in the compromise of user privacy.  
Figure 1: The architecture of our proposed HBCE-FL.
## 3 Projective Rigidity
### Plucker Coordinates and Extensors
\(P_{U^{*}}\) is also called the _dual extensor_ of \(P_{U}\). It is well known that the dual Plucker coordinate vector of \(U\) is the same as the Plucker coordinate vector of \(U\), except for a reordering of the coordinates and some sign changes. Note that for a basis \(\{w_{1},\dots,w_{d+1-k}\}\) of \(U^{*}\), the basis vectors of \(U\) and \(U^{*}\) satisfy  
\[w_{i}\cdot u_{\ell}=0\qquad i=1,\dots,(d+1-k);\,\ell=1,\dots,k.\]  
So if we consider the linear system  
\[w_{i}\cdot x=0\qquad i=1,\dots(d+1-k)\]  
and think of the \(w_{i}\) as hyperplanes, then it follows that each of these hyperplanes contains \(U\). Hence \(U\) can be represented as the subspace spanned by the \(u_{i}\) or as the subspace obtained by intersecting the hyperplanes \(w_{i}\). Let \(\mathbb{V}^{k}\) denote the \({d+1\choose k}\)-dimensional space spanned by \(\{u_{1}\vee\dots\lor u_{k}\mid u_{1},\dots,u_{k}\in\mathbb{R}^{d+1}\setminus\{ 0\}\}\). For \(X=x_{1}\vee\dots\lor x_{k}\) and \(Y=y_{1}\vee\dots\lor y_{\ell}\), the _join_ of \(X\) and \(Y\) is defined as the \((k+\ell)\)-extensor \(x_{1}\vee\dots\lor x_{k}\lor y_{1}\vee\dots\lor y_{\ell}\). Note that the support of \(X\lor Y\) is the span of the union of the supports of \(X\) and \(Y\), provided that \(\{x_{1},\dots,x_{k},y_{1},\dots,y_{\ell}\}\) is linearly independent. Otherwise \(X\lor Y=0\). For \(X\) and \(Y\) as above, with \(k+\ell\geq d+1\), we define the _meet_ of \(X\) and \(Y\) to be  
\[X\wedge Y=\sum_{\sigma}\text{sgn}(\sigma)[x_{\sigma(1)},\dots,x_{\sigma(d+1- \ell)},y_{1},\dots,y_{\ell}]x_{\sigma(d+2-\ell)}\lor x_{\sigma(d+3-\ell)} \vee\dots\lor x_{\sigma(k)},\]  
where the brackets denote determinants and the sum is taken over all permutations \(\sigma\) of \(\{1,\dots,k\}\) such that \(\sigma(1)<\sigma(2)<\dots<\sigma(d+1-\ell)\) and \(\sigma(d+2-\ell)<\sigma(d+3-\ell)<\dots<\sigma(k)\). Each such permutation \(\sigma\) is called a shuffle and the expression for \(X\wedge Y\) above is known as the _shuffle formula_. Note that if \(X\) and \(Y\) are non-zero and the union of \(X\) and spans the whole space, then the support of \(X\wedge Y\) is the intersection of the supports of \(X\) and \(Y\). The operations of join and meet are dual to each other in the sense that if we interchange \(\vee\) and \(\wedge\) then we must interchange \(\vee^{k}\) with the space \(*\vee^{d+1-k}\) of dual extensors.