esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Path-Planning Approach
### Boundary Conditions  
The simulations were performed at Busan port, with the satellite capture as in Figure 7. The geometry of this port was simplified as Figure 8. Considering the geometry of this port, two cases of berthing were selected to investigate the path-plan-generating system.  
Case 1: Parallel berthing task in a 30.5 \(\times\) 202 m water area. In this case, the ship's initial states were generated randomly, as described in Table 6. The berthing point was assumed, as shown in Table 7. The berthing task can be considered a success if the ship state is in the range of the values described in Table 7. The boundary area is described as a Figure 8a and Table 8.  
Case 2: Perpendicular berthing task in a 42 \(\times\) 170.5 m water area. In this case, the ship's initial states were generated randomly, as described in Table 9. The berthing point was assumed, as shown in Table 10. The berthing task can be considered a success if the ship state is in the range of the values described in Table 10. The boundary area is described as a Figure 8b and Table 11.  
Figure 7: Reward coefficients.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value (Exploration Rate c)** \\ \hline Step [0–5000] & 0.5 \\ Step [5000–10,000]: & 0.4 \\ Step [10,000–15,000] & 0.3 \\ Step [15,000–20,000] & 0.2 \\ Step [20,000–50,000] & 0.1 \\ \hline \end{tabular}
\end{table}
Table 5: Exploration rates.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline \(x_{0}\)\((m)\) & \([-20,20]\) \\ \(y_{0}\)\((m)\) & \([-10,10]\) \\ \(\psi_{0}\)\((^{\circ})\) & \([-5,5]\) \\ \(u_{0}\)\((knot)\) & \(1\) \\ \(v_{0}\)\((knot)\) & \(0\) \\ \(r_{0}\)\((^{\circ}/s)\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Initial state values (case 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline \(x\)\((m)\) & \(180\)\(\pm\)\(2\) \\ \(y\)\((m)\) & \(20\)\(\pm\)\(0.5\) \\ \(\psi\)\((^{\circ})\) & \(0\)\(\pm\)\(3\) \\ \(u\)\((m)\) & \(0\)\(\pm\)\(0.1\) \\ \(v\)\((m)\) & \(0\)\(\pm\)\(0.05\) \\ \(r\)\((^{\circ}/s)\) & \(0\)\(\pm\)\(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Target point (case 1).  
Figure 8: Geometry of the port and berthing situation: (**a**) parallel berthing task (case 1) and (**b**) perpendicular berthing task (case 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline \(x\)\((m)\) & \([-20,20]\) \\ \(y\)\((m)\) & \([-10,10]\) \\ \(u_{0}\)\((knot)\) & \(1\) \\ \(v_{0}\)\((knot)\) & \(0\) \\ \(r_{0}\)\((^{\circ}/s)\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Initial state values (case 2).
## 2 General Background Review
### Cyber Resilience  
The term cyber resilience first appeared in published research in 2009 [50], making it relatively recent compared to related concepts like cybersecurity, which has been in use since 1980 [51]. Cyber resilience refers to the ability to prepare for, withstand, and recover from cyber disruptions while maintaining critical operations, particularly in those systems, organizations, missions, or business processes whose functioning and/or service delivery rely heavily on information technology (IT) systems [11; 52]. Unlike cybersecurity and cyber defense, which focus on prevention, cyber resilience encompasses response, and recovery [53].  
Critical components of cyber resilience include preparedness [54; 55], adaptability [8; 13; 54; 56; 57; 58; 59], robustness [8; 54; 58; 60], recovery [12; 57; 56; 57; 58; 59], response [12; 57], flexibility [8; 56; 59; 60], and redundancy [8; 12; 55; 56; 57; 58; 58; 59].  
Most of the critical components of cyber resilience are interconnected and fundamentally rely on effective risk identification and assessment. Specifically, preparedness involves identifying potential threats and vulnerabilities to develop proactive measures and contingency plans [54; 55]. Adaptability is the ability to modify and evolve strategies and systems in response to changing threat landscapes [8; 13; 54; 55; 56; 57; 58; 59]. Robustness refers to the capacity to maintain core functions during a cyber incident, requiring a thorough understanding of risks to fortify CIs [8; 54; 58; 60]. Recovery focuses on restoring operations quickly and effectively after a disruption, guided by pre-identified risks and vulnerabilities [12; 13; 54; 56; 57; 58; 59]. Response is the immediate reaction to a cyber incident, which must be planned based on anticipated risks [12; 57]. Flexibility allows an organization to shift resources and strategies as threats evolve, informed by continuous risk assessments [8; 56; 59; 60]. Finally, redundancy ensures that alternative resources and systems are in place to support critical functions in case of failures, necessitating prior identification of single points of failure and associated risks [8; 12; 55; 56; 57; 58; 59].  
Exploring the recent literature and standards reveals that some methods and frameworks now integrate resilience and business continuity with traditional risk management practices. An example is the ISO 223 XX series, which emphasizes continuity and resilience, aiming to prepare organizations for potential disruptions and enhance their ability to recover from unforeseen events [61; 62; 63; 64]. Another example is PIMs, which are used widely to quantify the risk and strategic planning to increase adaptability [65], and efficiently address vulnerabilities [66; 67]. Moreover, the COSO ERM Framework integrates risk management with strategic objectives [24], ensuring that risk is considered when making high-level decisions. Finally, specialized standards also address risk management in specific sectors, offering tailored guidelines, such as ISO 14971:2019 for medical devices [47].
## 3 Results and Discussion
### Optimisation of Square-Wave Voltammetry  
Herein, it was observed that finding the optimum working parameters for peak separation is crucial. These parameters included potential step (\(E_{\text{step}}\)), frequency (\(f\)), and potential amplitude (\(E_{\text{ampl}}\)). For SWV, it is known that the setting of \(E_{\text{step}}\) and \(f\) defines the final scan rate (\(\
u\)), which may have the main effect on the peak separation. The whole optimisation of SWV parameters is presented in Figure S3. It was found that the potential step has the main effect on peak separation when the values higher than 1 mV caused significant peaks overlapping. The other two parameters did not have any effect on peak separation. The peak current responses only increased with higher values of these two parameters. For \(E_{\text{ampl}}\) values higher than 25 mV, frequency 25 Hz, and potential step 1 mV, there was no effect found on peak separation. Therefore, these values were chosen as optimum for the construction of calibration curves for different concentration ratios.
## 3 The Proposed Scheme
### The EHR Migration Phase  
In this phase, the server \(V\) will behave according to the delegated agreement file \(Agree_{V->W}\) with the signature \(S_{U}\), and it prepares these messages as follows.  
* First, it selects a random \(r_{V}^{*}\) and computes \(C_{V}=AE_{pky}(r_{V}^{*})\). It then forms the message tuple \((C_{V},S_{U},S_{HCA},EHR_{U}\oplus r_{U}^{\prime\prime},Date)\) for the server of the desired migration hospital \(W\). When the system of \(W\) has received this migration agreement from \(V\), it verifies this message tuple and decrypts \(C_{V}\) to obtain the challenge random number. If all signatures and parameters are valid, it generates the response random number \(r_{W}^{*}\oplus H_{2}(r_{V}^{*}+1)\) and returns it to the server \(V\).
* When \(V\) receives \(r_{W}^{*}\oplus H_{2}(r_{V}^{*}+1)\), it decrypts it with \(r_{V}^{*}+1\). If it is valid, it can obtain \(r_{W}^{*}\) and computes the response random number \(H_{2}((r_{W}^{*}+1)\oplus r_{U}^{\prime\prime})\), \((r_{W}^{*}+1)\oplus r_{U}^{\prime\prime}\) for the server of hospital \(W\). Finally, \(W\) receives \(H_{2}(r_{W}^{*}+1\oplus r_{U}^{\prime\prime})\), \((r_{W}^{*}+1)\oplus r_{U}^{\prime\prime}\) from \(V\). It also verifies the message to check if it was returned by the real \(V\). If true, it decrypts \((r_{W}^{*}+1)\oplus r_{U}^{\prime\prime}\) to obtain \(r_{U}^{\prime\prime}\), computes the session key \(ssk_{V,W}=H_{1}((r_{W}^{*}+1)||(r_{V}^{*}+1))\), and decrypts \(EHR_{U}\oplus r_{U}^{\prime\prime}\) to fetch the patient's EHR file \(EHR_{U}\) with the random \(r_{U}^{\prime\prime}\).
* After performing mutual authentication successfully, \(V\) can also use the session key \(ssk_{V,W}\) to generate an cihper-text, such as \(E_{ssk_{V,W}}(EHR_{U})\), for the rest of the data transmission of the patient \(U\)'s EHR. Figure 2 shows the scenario of this phase.  
Figure 1: The migration registration phase.
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
## 3 Establishing a Degenerate Model
### Parameter Estimation  
Assume the data form \(\left\{X_{ij;}i=1,2,\ldots,m;j=1,2,\ldots,n\right\}\), where \(X_{ij}\) represents the performance degradation value obtained from the \(j\)-th sample of the \(i\)-th measurement and the initial performance degradation is 0.  
For the model parameters, the maximum likelihood estimation method can be used to obtain the estimation values \(\hat{\mu}\) and \(\hat{\sigma}\) of the parameters \(\mu\) and \(\sigma\). The increment of the degradation amount follows the normal distribution \(N\left(\mu\Delta t,\sigma^{2}\Delta t\right)\), so using the long-life product degradation data \(\left\{X_{ij;}i=1,2,\ldots;j=1,2,\ldots\right\}\), the performance degradation increment between the measurement times of the \(j\)-th product is  
\[\Delta z_{ij}=X_{i+1,j}-X_{i,j}(i=1,2,\ldots,m;j=1,2,\ldots,n) \tag{9}\]  
From the incremental data of product degradation, the likelihood function of the unknown parameters \(\mu\) and \(\sigma\) can be obtained.  
\[L(\mu,\sigma)=\prod\limits_{j=1}^{n}\prod\limits_{i=1}^{m}\frac{1}{\sqrt{2\pi \Delta t\sigma}}\exp\left[-\frac{\left(\Delta z_{ij}-\mu\Delta t\right)^{2}}{2 \sigma^{2}\Delta t}\right] \tag{10}\]  
Using equations  
\[\left\{\begin{array}{l}\frac{\partial\ln(L(\mu,\sigma))}{\partial\mu}=0\\ \frac{\partial\ln(L(\mu,\sigma))}{\partial\sigma}=0\end{array}\right. \tag{11}\]  
can obtain the estimation of \(\hat{\mu}\) and \(\hat{\sigma}\):  
\[\left\{\begin{array}{l}\hat{\mu}=\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1 }^{n}\Delta z_{ij}}{\mu\sum\limits_{i=1}^{n}\Delta z_{ij}}\\ \hat{\sigma}=\sqrt{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\frac{\left( \Delta z_{ij}-\mu\Delta t_{j}\right)^{2}}{\Delta t_{ij}}}\end{array}\right. \tag{12}\]  
Substituting the obtained parameter estimates into the formula, the corresponding failure distribution function can be obtained.
# Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT
###### Abstract
The SDN has brought a new approach to the domain of communication networks. It allows network engineers and administrators to install, control, manage, and modify networks. In the SDN network, the gateway is directly connected to the internal network, where all security mechanisms are either installed in the application layer (controlled by the controller for traffic forwarding to the appropriate security device) or installed in the controller layer. Another main difference from a traditional network is that the controller controls every node in the network and can block the nodes' traffic or forward it to a specific path. According to [PERSON] et al. [21], IoT orchestration with SDN can lessen the security susceptibility of IoT devices. These SDN-enabled frameworks can enhance the potential of the underlying highly dynamic and heterogeneous environment of the IoT network. Deploying the malware-detection module on the control plane of the SDN has several benefits. First, the control plane represents the core centralized intelligence of the SDN and maintains the global view of the entire underlying network. Moreover, all the forwarding decisions are made in the control plane. Secondly, the resource-constrained IoT devices can simply be leveraged from the additional computational task. Furthermore, it allows the use of the antimalware system in a highly manageable and scalable way. Finally, it can be customized and extended to any commercial SDN controller [15]. Machine learning approaches, as opposed to the traditional rule-based programming approach, learn from the dataset gathered from prior experiences to tackle complicated issues. This procedure is completed by using machine learning techniques, including supervised, unsupervised, and reinforcement learning. For instance, [PERSON] et al. [15] proposed a hybrid deep learning-driven SDN-enabled Internet of medical things (IoMT) malware-detection module to be deployed on the control plane of the SDN. In this article,
* we discuss an IoT and SDN network orchestration for better security and the vulnerability holes even with the SDN networks,
* we perform a systematic review of the related work to identify the gaps in the previous work done on the same public datasets that we used to propose a new approach that performs better depending on different criteria,
* we reimplement already existing traditional machine learning algorithms and pre-trained deep learning models to observe their performance in terms of model evaluation parameters, memory requirement, and computational time requirements, and
* we propose a lightweight deep learning approach that has higher performance in terms of accuracy, precision, recall, F1 score, less computational time, and fewer memory requirements. The paper is structured as follows. The related works and previous studies on this topic are presented in Section 2. In Section 3, the dataset, employed approach, and evaluation metrics are covered. In Section 5, the experimental results and comparison to the existing approach are discussed.
# RoPT: Route-Planning Model with Transformer
## 5 Experiments
### Experiment Settings  
Environment. The machine used in the experiments is equipped with a 2.50 GHz Intel(R) Xeon(R) Platinum 8255C CPU, and an NVIDIA GTX 3080 GPU with 24 GB VRAM. Our model RoPT is developed on Python 3.8 and PyTorch 1.13.  
Hyper-parameter. The datasets are partitioned in an 8:1:1 ratio into training, validation, and test sets. The average number of neighbors of each node in the Chengdu dataset is 4.34, while that of Porto is 2.39. The dimensions of the initial feature matrix of a road network \(M_{A}\) is set as 128. The number of layers of the GCN is set as 4. The output dimensions of each layer are 64, 128, 256, and 256. The number of heads in the encoder of the Transformer is set as 8, whose number of layers is set as 3, and the dimension is set as 256. The number of layers of the MLP corresponding to the decoder is also set as 3, and the dimension is 256. These hyper-parameters conform to those used in the baselines which follow the best settings listed in the references. The analysis of choosing 4 as the number of layers of the GCN is detailed as described in Section 5.6.  
Evaluation Metric. For the route-planning task, we adopt the standard performance metrics [1; 2; 3; 6; 32]:  
(1) Acc, (2) Recall, (3) F1, and (4) Acc@K, (k = 1).  
The first three metrics are used to evaluate the performance of a recommended full route, and the fourth Acc@K is used to evaluate the performance of a recommended next node. Their calculation formulae are shown in Formulae (25)-(27):  
\[Acc=\frac{|R\cap R^{*}|}{|R^{*}|}\;Recall=\frac{|R\cap R^{*}|}{|R|} \tag{25}\]  
\[F1=\frac{2*Acc*Recall}{Acc+Recall} \tag{26}\]  
\[Acc@k=\frac{1}{|R^{*}|}{\sum_{i=1}^{|R^{*}|}1(rank_{i}<k)} \tag{27}\]  
Here, \(R\) is a route recommended with model PoPT, \(R^{*}\) is the corresponding real route, the intersection of these two routes represents the set of common nodes, the symbol similar to the absolute value represents the cardinality of the intersection, and \(rank_{i}\) is the position of the \(i\)-th recommended node ranked with the transfer probability in the current reachable node set.
## 2 Contemporary Model- and Data Driven (Crowd) Forecasting Methods
### Data-Driven Forecasting of Traffic
The'simplest' version of a machine learning technique that incorporates dynamic features of the past realisations of traffic patterns is the auto-regressive integrated moving average (ARIMA) model ARIMA (e.g., [47]). This type of model forecasts future realisations of traffic flow variables based on their correlation with historic realisations, the difference between the current and historic realisations and the moving average of the most recent realisations. ARIMA models are generally trained using historic data regarding one or more features of traffic. The memory of an ARIMA is limited to a certain period in time. More sophisticated machine learning techniques, such as recurrent neural networks (RNN) with and/or without the long short-term memory (RNN-LSTM) or gated recurrent units (RNN-GRU), have been introduced to overcome some of the shortcomings of the shallow neural networks, namely their lack of memory. The inclusion of memory allows RNN networks to incorporate the ordering of events. Thatis, an RNN can distinguish between the arrays [1; 2; 3; 4] and [1; 2; 3; 4] even though the numbers within the two arrays are similar. In recent years, the popularity of RNN-based solutions for the prediction of macroscopic as well as microscopic properties of traffic has increased hand over hand (e.g., [15; 48; 49; 50]). Comparisons of the performance of RNN-based solutions to shallow neural network and ARIMA models illustrate that RNN-based solutions are better equipped to predict traffic properties [48; 49]. Moreover, the performance difference between the two main RNN-based solutions is negligible. The dependencies in traffic are highly non-linear and the current movement pattern of an individual is related to its past actions. Consequently, to predict crowd movements a forecasting method is needed that can handle non-linear dependencies and has a memory. Table 1 presents and overview of the methods discussed in the previous paragraphs, and has categorised them with respect to these two properties. Given the properties of traffic, it can be concluded that we need to apply a technique that falls in the lower right corner of the table.
# A Minimal GBT Model for Distortional-Twist Elastic Analysis of Box-Girder Bridges
## 3 A Minimal GBT Model  
A rectangular, \(b\times h\), double-symmetric box-girder is considered (Figure 1). Its cross section is made of four thin elements: the bottom/upper flanges \(i=1,3\), of thickness \(t_{1}=t_{3}\), and the right/left webs \(i=2,4\), of thickness \(t_{2}=t_{4}\). A simple model is formulated, aimed at capturing the main mechanical behavior of the twist-distortional behavior. The task is accomplished by following the GBT approach, in which just two modes are considered: (i) the _torsional mode_, in which the cross section keeps its shape unaltered, and (ii) the _distortional mode_, in which the cross section is deformed. According to Equation (3), the local displacement field is expressed as follows:  
\[\left(\begin{array}{c}u(z,s)\\ v(z,s)\\ w(z,s)\end{array}\right)=\theta(z)\left(\begin{array}{c}U_{t}(s)\\ V_{t}(s)\\ W_{t}(s)\end{array}\right)+\varphi(z)\left(\begin{array}{c}U_{d}(s)\\ V_{d}(s)\\ W_{d}(s)\end{array}\right) \tag{10}\]  
where the \(s\)-functions are the components of the two modes, to be properly chosen, and \(\mathbf{a}=(\theta(z),\varphi(z))^{T}\) are the unknown amplitude functions, measuring the magnitude of twist \(\theta\) and distortion \(\varphi\).  
To define the amplitudes, a general _inextensional_ transformation of the box-girder is considered (by ignoring, at this stage, compatibility at corners), in which the \(x\)-axis rotates of an angle \(\psi_{x}\) and the \(y\)-axis of an angle \(\psi_{y}\), both positive counterclockwise (Figure 2a). By decomposing the transformation in its skew-symmetric (Figure 2b) and symmetric (Figure 2c) parts, the twist \(\theta\) and the distortion \(\varphi\) are uniquely determined as follows:  
\[\theta=\frac{1}{2}\big{(}\psi_{x}+\psi_{y}\big{)},\qquad\varphi=\frac{1}{2} \big{(}\psi_{x}-\psi_{y}\big{)} \tag{11}\]  
together with their inverse \(\psi_{x}=\theta+\varphi\), \(\psi_{y}=\theta-\varphi\).  
To define the modes, a suitable in-plane field \(U(s),V(s)\) is chosen, as illustrated ahead. From this, the warping component \(W(s)\) is derived by exploiting the internal constraint condition \(\gamma_{zs}(z,s)t(s)=\phi(z)\), borrowed by the [PERSON] theory. Since, in a generic mode, \(\gamma_{zs}=u_{z}+w_{s}=(U(s)+W^{\prime}(s))a^{\prime}(z)\), the constraint entails \((U(s)+W^{\prime}(s))t(s)=\text{const}{=:}\,Q\). This equation can be integrated to furnish  
\[W=-\int\limits_{0}^{s}U(s)ds+Q\int\limits_{0}^{s}\frac{ds}{t(s)}+C \tag{12}\]  
where \(C\) is a further integration constant. The previous result is particularized to the two modes.  
Figure 1: Box-girder: (**a**) geometry; (**b**) element numbering and local systems of coordinates.
## 3 Materials and Methods
### Security Metrics for Satellite Networks  
This study utilizes three evaluation methods from TV-HARM to assess the security posture of satellite networks. The network centrality measure evaluates the importance of the nodes linked to threat vectors, while the vulnerability score, using CVSS/CVE data, assesses the risks and impacts of vulnerabilities for a quantitative security evaluation. Finally, attack impact metrics comprehensively assess the network's overall security state.