esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Impedance Analysis of Inter-Turn Fault of Induction Motor
### Impedance Analysis with Y Connection  
When an ITF occurs in phase A, the terminal connection is considered to analyze the impedance for the Y connection, as shown in Figure 2. The impedance analysis is conducted for the terminal connection configuration. When the two phases are connected as shown in Figure 2a-c, the terminal impedance can be expressed as follows:  
\[Z_{a-b}=Z_{c-a}=Z_{f}+Z_{h},\ \ Z_{b-c}=2Z_{h} \tag{5}\]  
where \(Z_{a-b}\), \(Z_{c-a}\), and \(Z_{b-c}\) denote the terminal impedances corresponding to the connections between phases A and B, C and A, and B and C, respectively. Since the fault impedance is lower than the healthy impedance, as shown in Equation (4), the terminal impedance comparison is as follows.  
\[Z_{a-b}-Z_{b-c}=Z_{f}-Z_{h}<0\ \ \rightarrow\ \ Z_{a-b}=Z_{c-a}<Z_{b-c} \tag{6}\]  
From Equation (6), the specific terminal impedance is greater than the other terminal impedances. If an ITF occurs in a different phase, the specific terminal impedance increases when the two corresponding phases are connected.  
Figure 1: Configuration of inter-turn fault in IM: (a) Y connection; (b) \(\Delta\) connection.  
The impedance for the terminal connection between phase A and the midpoint of phases B and C, denoted as \(Z_{a\text{-}bc}\) and shown in Figure 2f, is calculated as in Equation (7).  
\[Z_{a\text{-}bc}=Z_{f}+Z_{h}\,||\,Z_{h}=Z_{f}+\frac{1}{2}Z_{h} \tag{7}\]  
Similarly, the impedances for connections _b-ca_ and _c-ab_, represented in Figure 2d,e, are given by Equation (8).  
\[Z_{b-ca}=Z_{c-ab}=Z_{h}+Z_{f}\,||\,Z_{h}=Z_{h}+\frac{Z_{f}Z_{h}}{Z_{f}+Z_{h}} \tag{8}\]  
A comparison between (7) and (8) reveals that the impedance of the _a-bc_ configuration is greater than those of the _b-ca_ and _c-ab_, as summarized in Equation (9).  
\[\begin{split}& Z_{a-bc}-Z_{b-ca}=\frac{\left(Z_{f}-Z_{h}\right) \left(2Z_{f}+Z_{h}\right)}{2\left(Z_{f}+Z_{h}\right)}<0\\ & Z_{a-bc}<Z_{b-ca}=Z_{c-ab}\end{split} \tag{9}\]  
From Equation (9), the specific impedance of a given terminal connection configuration is lower than that of the other configurations. If a fault occurs in phase B or C, the impedance becomes either maximum or minimum, depending on the specific terminal connection. Table 1 presents the maximum and minimum impedance values for each fault phase and terminal connection when an ITF occurs in phase A with the Y connection.
## 3 Operating Characteristics Analysis and Parameter Design
### Parameter Design
#### 3.3.2.  
Capacitor Design  
As shown in Figure 3, in modes 3 and 4, capacitor \(C\) is in the charging state. In modes 1 and 2, \(C\) is in the discharging state. The charging time of \(C\) is complementary to the energy storage time of \(L\), which is \((1-D)T\). Therefore, the ripple of capacitor voltage \(U_{C}\) can be calculated as  
\[\Delta U_{C}=\frac{1}{C}\int_{0}^{(1-D)T}i_{L}dt=\frac{P_{o\_inv}}{U_{dc}C}(1 -D)T \tag{20}\]  
Based on Equations (5) and (20), the ripple coefficient of \(U_{C}\) is  
\[r_{UC}=\frac{\Delta U_{C}}{U_{C}}=\frac{P_{o\_inv}(1-D)^{2}T}{U_{dc}^{2}DC} \tag{21}\]  
The maximum allowable ripple coefficient of the capacitor voltage is defined as \(r_{UC\_max}\). When the inverter outputs the maximum power \(P_{o\_inv\_max}\) and the voltage boost duty cycle reaches \(D_{\max}\), the condition that \(r_{UC\_max}\geq r_{UC}\) should be met. Therefore, the range of values for capacitor \(C\) can be calculated as  
\[C\geq\frac{P_{o\_inv\_max}(1-D_{\max})^{2}T}{U_{dc}^{2}D_{\max}r_{UC\_max}} \tag{22}\]
## 5 Experimental Results
### Experimental Results
This situation may be due to an undetected difference between the simulation and experimental setup. GWO has the best experimental performance and the second-best simulation results; thus, it is concluded that a FOFPID controller with a GWO parametric solution performs better than the same controller with either IWO, BBO, or PSO. Figure 11: Experimental results of the star contour tracking by using IWO algorithm, (**a**) position tracking error on \(X-\)axis, (**b**) position tracking errors on master and slave \(Y-\)axes, (**c**) synchronous error, and (**d**) X–Y plane tracking trajectory. Figure 12: _Cont_. Figure 12: Experimental results of the circle contour tracking by using GWO algorithm, (**a**) position tracking error on X\(-\)axis, (**b**) position tracking errors on master and slave Y\(-\)axes, (**c**) synchronous error, and (**d**) X\(-\)Y plane tracking trajectory. Figure 13: Experimental results of the circle contour tracking by using IWO algorithm, (**a**) position tracking error on X\(-\)axis, (**b**) position tracking errors on master and slave Y\(-\)axes, (**c**) synchronous error, and (**d**) X\(-\)Y plane tracking trajectory. Figure 14: Experimental results of the bow contour tracking by using GWO algorithm, **(a)** position tracking error on \(X-\)axis, **(b)** position tracking errors on master and slave \(Y-\)axes, **(c)** synchronous error, and **(d)** X–Y plane tracking trajectory. Figure 15: _Cont_. Figure 16: Experimental results of the heart contour tracking by using GWO algorithm, (**a**) position tracking error on X\(-\)axis, (**b**) position tracking errors on master and slave Y\(-\)axes, (**c**) synchronous error, and (**d**) X\(-\)Y plane tracking trajectory. Figure 15: Experimental results of the bow contour tracking by using IWO algorithm, (**a**) position tracking error on X\(-\)axis, (**b**) position tracking errors on master and slave Y\(-\)axes, (**c**) synchronous error, and (**d**) X\(-\)Y plane tracking trajectory. Table 6.0 Experimental results of the heart contour tracking by using IWO algorithm, (**a**) position tracking error on X–axis, (**b**) position tracking errors on master and slave Y–axes, (**c**) synchronous error, and (**d**) X–Y plane tracking trajectory. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **ATE (\(\mu\)m)** & **TESD (\(\mu\)m)** & **TIAE** \\ \hline \multicolumn{4}{c}{Circle} \\ \hline PSO & 12.33468 & 19.06461 & 1000.13697 \\ IWO & 22.55877 & 39.85242 & 1018.96457 \\ BBO & 13.65512 & 15.22419 & 1009.078 \\ GWO & 7.93084 & 8.69547 & 998.86251 \\ \hline \multicolumn{4}{c}{Heart} \\ \hline PSO & 17.05340 & 23.22570 & 1011.88779 \\ IWO & 23.88164 & 36.07943 & 1015.39364 \\ BBO & 18.09240 & 27.08182 & 1008.25875 \\ GWO & 9.09414 & 16.49567 & 994.75943 \\ \hline \multicolumn{4}{c}{Star} \\ \hline PSO & 19.17965 & 21.18270 & 997.62259 \\ IWO & 23.93769 & 29.72738 & 1007.70703 \\ BBO & 21.82199 & 24.02363 & 1009.45364 \\ GWO & 14.13845 & 15.14002 & 993.72111 \\ \hline \end{tabular}
\end{table} 
Table 6: The experimental results of all trajectories.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 3 System and Signal Model for the Receivers
### Iterative MRC and EGC FDE Receivers (IB-DFE Receivers)  
It is known that the MRC and EGC receivers are sub-optimal, and their performance normally improves by adopting an iterative approach. In IB-DFE receivers, for a given \(i\)th iteration (see Figure 3), the output samples are given by [5; 17]:  
\[\widetilde{S}_{k}^{(i)}=\sum F_{k}^{(I,i)}Y_{k}^{(I)}-B_{k}^{(i)}\tilde{S}_{k} ^{(i-1)} \tag{10}\]  
where \(\tilde{S}_{k}^{(i-1)}\) is the DFT of the \((i-1)\)th iteration's hard-decision block \(\tilde{s}_{n}^{(i-1)}\), which is associated with the transmitted time-domain block \(s_{n}\).  
Moreover,  
\[\widetilde{s}_{n}^{(i)}=\gamma^{(i)}\cdot s_{n}+\epsilon_{n}^{eq(i)} \tag{11}\]  
where \(\epsilon_{n}^{eq(i)}\), represents the overall error, which includes both channel noise and residual intersymbol interference, and  
\[\gamma^{(i)}=\frac{1}{N}\sum_{k=0}^{N-1}\sum_{l=1}^{L}F_{k}^{(I,i)}H_{k}^{(I)} \tag{12}\]  
From [10; 15], it is shown that the optimum feedforward and feedback coefficients are given by:  
\[F_{k}^{(I,i)}=\frac{H_{k}^{(I)*}}{\beta+(1-(\rho^{(i-1)^{2}}))\sum_{l^{\prime }=1}^{L}\left|H_{k}^{(I^{\prime})}\right|^{2}} \tag{13}\]  
\[B_{k}^{(i)}=\rho^{(i-1)}\left(\sum_{l^{\prime}=1}^{L}F_{k}^{(I^{\prime},i)}H_{ k}^{(I^{\prime})}-1\right), \tag{14}\]  
with  
\[\rho^{(i)}=\frac{E\left[s_{n}^{*}\tilde{s}_{n}^{(i)}\right]}{E\left[\left|s_{ n}\right|^{2}\right]} \tag{15}\]  
Based on the characteristics of each frequency, we could use MRC or EGC to perform frequency-domain processing on SC-FDE signals. The residual interference levels, however, can still be significant, particularly for modest values of T/R. The iterative interference canceller (receiver) shown in Figure 3 suggests as a solution to use the following process:  
\[\widetilde{S}_{k}=Y_{k}-C_{k}\widetilde{S}_{k}, \tag{16}\]  
where \(C_{k}\) defines the interference cancellation matrix, defined as [24]  
\[C_{k}=H_{k}B_{k}-\text{I}, \tag{17}\]  
and where I stands for an \(R\times R\) identity matrix.  
Figure 3: Block diagram of IB-DFE receivers.
# Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
## 3 NACF Framework
### Model Framework Introduction
For example, in a _2-hop_ aggregation scenario in Figure 2, the first iteration updates the \(e_{1}\) to \(e_{1}^{[1]}\), and the \(e_{u}\) is updated to \(e_{u}^{[1]}\), which we call the first-order representation of entity. In the  
Figure 2: Neighborhood aggregation in KG. second iteration, the \(e_{u}^{[1]}\) is updated to \(e_{u}^{[2]}\), which we call the second-order representation of entity. The specific sub-aggregation \(Agg\) will be described in the Section 3.3. 3. Prediction  
When the neighborhood aggregation of \(e_{u}\) is completed, the user's embedding entity \(e_{u}^{[n]}\) and the item's entity \(e_{i}\) perform a point multiplication to generate a prediction score. Finally, this score will be normalized by the sigmoid function to the predicted click rate \(y(u,i)\). The NACF framework is shown in Algorithm 1:  
```
Input:\(U\):set of user; \(I\):Set of item; \(M\):User-item interaction matrix; \(G_{<E,R>}\):Knowledge graph; \(n\):depth of Neighborhood; \(d\):dimension of embedding; Output:\(F\):Prediction function
1 Embedding \(U\) into \(G_{<E,R>}\) and initialize \(E,R,w\) as trainable parameters;
2 whileNACF not convergedo
3 for\((u,i)\in M\)do
4\(N_{e_{u}}^{n}=\sum_{k=1}^{n}H_{e_{u}}^{k}\) for\(h=1,...,n\)do
5 for\(e_{j}\in N_{e_{u}}^{(n-h)}\)do
6\(e_{j}^{[h]}\gets Agg(u,i)\left(N_{e_{j}},e_{j}^{[h-1]}\right)\)
7 end for
8
9 end for
10\(e_{u}\gets e_{u}^{[n]}\) Calculate predicted probability \(y(u,i)=1/\left(1+e^{-e_{u}\cdot e_{i}}\right)\); Calculate loss value; Update parameters by gradient descent;
11
12 end for
13 return\(F\)
14
15 end for
```  
**Algorithm 1**NACF algorithm.
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 3 Motivation  
Based on the previous analysis, in supply chain attacks, it is crucial to quickly identify security patches for vulnerability fixes in a large number of code change submissions. At present, related work has been carried out in this field, but it mainly has the following problems:  
1. Related work mainly regards commit messages and code changes as ordinary text, and conducts research based on text classification technology in natural language processing. Although these methods have been proved their effectiveness to a certain extent through experiments, they have not really learned the essential information of security patches. As described in Section 2.1, we may reasonably come to the conclusion that the key of source code patch analysis is to learn the syntax and semantics of code patches, rather than simply treating them as ordinary text. Intuitively, how to learn the control dependency and data dependency in the source code is crucial for learning the essential information of security patches.
2. In recent years, the methods of code2 vec and seq2 vec have provided a new direction for this field. They build an AST abstract syntax tree by parsing the source code, and convert it into a text sequence by expanding the paths between the leaf nodes. The related work [10] is carried out on this basis, and conducts experiments in the JAVA datasets. However, in the experiment of C source code, we find that the generated AST syntax tree is too large, and this method has certain limitations. This means that the AST abstract syntax tree parsing of C source code will obtain a large number of text sequences after expansion. Moreover, it is difficult to learn the patch information completely and effectively from the results of sampling randomly(extracting a small number of text sequences from a large number of sequences). Meanwhile, it is difficult to directly learn the changes of the data dependencies and control dependencies via the path of the child nodes of the AST abstract syntax tree.
3. Currently in the work of security patch identification, the main ensemble methods are vector concatenation and voting algorithms. However, these ensemble methods are too subjective and do not take into account consistency and complementaritybetween different features. Therefore, they do not have the interpretability of the identification results. In the field of patch analysis, how to make use of consistency and complementarity between different views, to find the inherent pattern of patch commit to improve the effectiveness of security patch identification is deemed scientific in this paper. Therefore, we may reasonably come to the conclusion that, in addition to the identification results, what should be known is the confidence and interpretability of the identification results.
# Advanced Technologies in Optical Wireless Communications
## 3 Visible-Light Positioning (VLP)  
VLP is another important form of OWC, often considered a subset of VLC. In VLP, light signals emitted from LEDs are used to determine the position of objects or users within an indoor environment. By analyzing the properties of the received light signals, such as the received signal strength (RSS), angle of arrival (AoA), or time of flight (ToF), VLP systems can achieve higher accuracy in indoor localization compared to their RF counterparts, such as the global positioning system (GPS) [3]. Another advantage of VLP is that it can be seamlessly integrated with VLC, using the same LED infrastructure for both indoor positioning and wireless data transmission.  
In this Special Issue, we present three articles on the topic of VLP. Since VLP systems typically require multiple transmitters and photodetectors for triangulation calculations, Contribution 6 adapts this configuration to support multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) transmissions. Like MIMO used in RF wireless systems, this approach aims to enhance either data transmission rates or the reliability of received signals. Contribution 7 also investigates the integration of VLC and VLP. A key contribution of this work is the use of a solar cell array, rather than conventional photodetectors, for signal detection. An experimental setup is implemented to demonstrate the system's performance in terms of both data transmission and positioning accuracy. To achieve a more accurate positioning system using AoA, Contribution 8 studies the performance of an angular diversity receiver with aperture structures. This design was originally proposed in [7] for VLC MIMO communications and later adapted for a VLP system in [8]. As an extension of [8, 9], this work focuses on optimizing the layout of the LEDs to improve the positioning accuracy. These improvements are demonstrated through simulations, considering the Cramer-Rao bound (CRB) as the performance metric.
## 3 X-NETAD: The Idea
### A Push/Pull Dissemination Approach  
Generally speaking, the choice between pull-based and push-based approaches should be taken according to four main metric and parameters: (i) the delay affecting the information distribution; (ii) the energy consumption of the device; (iii) the complexity of the client and server applications; and (iv) the unicast or broadcast nature of the communications. In fact, a push-based approach is suitable for broadcast communications, since it allows one to increase the network efficiency. As mentioned in Subsection 3.3.1, in X-NETAD, a hybrid push/pull paradigm is used. In fact, the communications between PUs and SUs use a push-based approach, while the communications between AS and PUs employ a pull-based approach.  
A pull-based approach for the AS/PUs communications has been adopted for the following motivations.  
* The communications between AS and PUs are unicast, since (i) 3G networks do not allow broadcast communications and (ii) each PU should receive different information according to its position.
* The information distributed in X-NETAD are not time-critical, and thus, there is no need to use a push-based approach in order to minimize the delay.
* The energy consumption is one of the main concerns with mobile applications, and it is well known that a 3G connection has a high energy consumption. By adopting a pull-based approach, the mobile application can schedule the request for traffic updates without the need of keeping a 3G connection active all the time. Moreover, the application can adapt the intervals between consecutive queries according to the behavior of the vehicles. For example, if the vehicle is not moving, there is no need to look for traffic updates. Similarly, if the battery of the smartphone is depleting, the application can decide to reduce the update frequency.
* A pull-based approach significantly increases the complexity of the server, since it has to cope with a potentially large number of concurrent requests--current techniques, however, should not make this aspect a limitation.  
In the case of the PUs/SUs communications, the scenario is totally different. In fact, in this case, the PU transmits the same information to all the SUs and, therefore, it is far more efficient to employ broadcast communications instead of a series of unicast communications. Moreover, in VANETs, the nodes cannot turn off their radio interfaces in order to save energy, because of the following critical issues that affect VANETs: (i) short-lived connections; (ii) highly dynamic topology; and (iii) harsh channel conditions. This prevents a pull-based approach from leading to significant energy savings.  
The above reasons motivate the decision for adopting a push-based paradigm, which is also considered in recent works, such as [9].
## 4 A UPPAAL-Based Formal Methodology for the UEMS
### The Formal Specification and Verification of the Ulems
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Formal Specification** & **Informal Description** & **Result** \\ \hline A[]Gw.C\_SUCCEED imply C\_Info.QoS \textgreater{} 0 & Verify if the control is successful when the publisher receives correct ACK message & PASS \\ and C\_Ack.RC == Accepted and & including ’Accepted’ return code and the & \\ C\_Ack.MsgId == C\_Info.MsgId & identical’MsgId’. & \\ \hline A[]Sck.PUBACK\_C imply C\_Info.QoS \textgreater{} 0 & Verify if the subscriber responds to the control when QoS \textgreater{} 0. & PASS \\ \hline A[]Gw.RESEND imply Gw.tG\textgreater{}T\_outand & Verify if the publisher resends control & \\ C\_Info.QoS \textgreater{} 0 & message that QoS \textgreater{} 0 when it does not receive correct ACK during allowed time. & PASS \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The formal verifications of the control behavior. Figure 8: Timed automata model of measure message: (**a**) publisher; (**b**) subscriber. the differentiation between the old firmware and the targeted firmware. The process of the remote firmware upgrade is depicted in Figure 10.  
The formal specifications of upgrade behavior based on timed automata model are depicted as Figure 11 (where the publisher is instantiated as Gw and the subscriber is instantiated as Sck). As illustrated in Figure 10, to generate the upgrade file, the Gw would firstly request the software version of the Sck. After the Sck returns the version information and is ready for upgrade, the Gw starts to transmit segmented upgrade file. After the transmission of upgrade file is completed, the Gw would deliver a check code of the upgrade file to the Sck. The Sck would then check the received check code with the local check code. If the two check codes are equivalent, the upgrade of the Sck is successful. The formal verifications of the time automata are illustrated in Table 7. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Formal Specification** & **Informal Description** & **Result** \\ \hline E\(\textless{}\)\textgreater{}Gw.UPD\_FIN & Verify if the upgrade success can be & \\  & eventually achieved in some condition. & \\ \hline A\(|\)[Gw.PUB\_UPD\_INFO imply & Verify if the publisher keeps publishing & \\ U\_Info.Msgld \(\leq\) U\_Info.Len & upgrade data until all upgrade data have & \\  & been transmitted. & \\ \hline A\(|\)[(Gw.UPD\_FIN and Sck.UPD\_SUCCESSED) & Verify if the upgrade process is successful & \\ imply (U\_CC\_Ack.Result == Succeed and & when local generated check code of terminal & \\ local\_CC == U\_Info.CC) & is equal to the value received from & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The formal verifications of the upgrade behavior. Figure 9: Timed automata model of control message: (**a**) publisher; (**b**) subscriber. Figure 11: _Cont_. Figure 10: The flow of remote firmware upgrade.
# A Dynamic and Adaptive Cybersecurity Governance Framework
## 2 Review of Existing IT and Cybersecurity Governance Frameworks
### Iso/Iec 27014  
The International Organization for Standardization (ISO) and the International Electro Technical Commission (IEC) issued ISO/IEC 27014 to provide standards and guidelines for the governance of IT systems. It is an integral part of the ISO/IEC 27000 series, which mainly provides best practices, guidelines, and standards that support organizations to secure their business and information assets [19; 20; 21]. As shown in Figure 3, the ISO 27014 governance standard has about five processes: evaluate, direct, monitor, communicate, and assure the security of the IT system. It also has five principles, as shown in Figure 4, such as establishing enterprise-wide IS, following a risk-based approach, creating a security-aware environment across the enterprise, setting directions for investment decisions, and reviewing and evaluating performance concerning the business outcome. Moreover, the standard provides a direction for integrating and aligning IT strategies with an enterprise's overall strategy (ISO/IEC 27014, 2013).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **COBIT 5** & **COBIT 2019** \\ \hline _Governance processes_ & 37 processes & 40 processes \\ \hline _Governance principles_ & Five principles & Six principles \\ \hline _Governance framework principles_ & absent & added \\ \hline _Performance measurement_ & \begin{tabular}{l} Based on ISO/IEC 33000 \\ (0-5 level) \\ \end{tabular} & \begin{tabular}{l} CMMI performance \\ measurement scheme is used \\ \end{tabular} \\ \hline _Design factors_ & Not included & \
\begin{tabular}{l} Are included (contextual, \\ strategic, and tactical) \\ \end{tabular} \\ \hline _Enablers_ & Not included & Are renamed as components \\ \hline _Alignment with other standards_ & Not applicable &
\begin{tabular}{l} Aligns with other security \\ standards, frameworks, and \\ protocols \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of COBIT 5 and COBIT 2019.  
Finally, ISO/IEC 27014:2013 provides guidance and standards on the governance parts of how to govern information systems and their security aspects so that they can direct, monitor, evaluate, and communicate at strategic, tactical, and operational levels the information security related functions and activities within the organization.  
The major limitations of ISO/IEC 27014 are presented as follows; since it has a lot of requirement checklists for implementation and auditing purposes, organizations are reluctant to implement it. It is also expensive and requires high skill IT and security professionals for both auditing and implementation. It also involves a lot of time and resources for the implementation.  
Figure 3: ISO/IEC 27014 and ITIL V4 governance principles.