esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Evaluation of Overall Residual Security and Privacy Risk Acceptability
### Conduct Vulnerability Scanning/Penetration Testing  
Vulnerability scans and penetration testing are very different from each other, but both serve important functions for evaluating the implemented controls. A vulnerability scan only discovers known vulnerabilities; it does not attempt to exploit a vulnerability but instead only confirms the possible existence of a vulnerability. An organization can conduct vulnerability scanning using an automated tool with some manual support. Table A2 in Appendix C lists popular tools for vulnerability scanning. Penetration testing is a security testing approach which identifies exploitable vulnerabilities of a system, or of individual components of a system. Penetration testing requires specialized skills, higher budgets and more time than vulnerability scanning. An organization can conduct penetration testing by forming a team of people within the organization who have the technical expertise to and/or on-board external resources with the required expertise to conduct penetration testing. Table A3 in Appendix C lists some penetration testing tools. To conduct vulnerability scanning and/or penetration testing, an organization should conduct the following steps:  
* Define the scope of the vulnerability scanning and/or penetration testing. The scope will include:
* List of application use-cases.
* List of assets.
* List of threats and vulnerabilities for which countermeasures are implemented.
* Select the tools to be used to conduct the testing.
* Include external expertise (if required).
* Collect the results for review.
* Document the overall residual security and privacy risk acceptability in a report including:
* Date of the testing.
* Name of people/organization who performed the scanning and/or testing.
* Scope of the testing.
* List of tools used for conducting the testing.  
Figure 11: Steps for evaluating the overall residual security and privacy risk acceptability.
## 4 Corrosion Protection Measures
### Cathodic Protection
#### 4.2.3. Drainage Protection of Cathodic Protection  
In an environment with stray currents, applying cathodic protection to protected structures by eliminating stray currents is called drainage protection. There are four common drainage methods.  
(1) Direct drainage  
When the polarity of stray current interference potential is stable, the protection body and interference source can be directly connected by cable to eliminate stray current. This method is simple and feasible, but it also has some disadvantages. Take stray currents caused by urban rail transit as an example; if the pipe ground potential is lower than the rail and ground potential, the current on the rail will flow into the pipe, causing corrosion.  
(2) Polar drainage  
When the polarity of the stray current interference potential alternates between positive and negative, the stray current can be discharged back to the interference source through a series of diodes. Due to the unidirectional conduction performance of the diodes, only the stray current is allowed to be discharged in the positive direction, while the negative direction is reserved for cathodic protection. This method is widely used at present.  
(3) Forced drainage  
The above two methods can only protect the protective body when discharging, whereas the protective body is in a natural corrosion state when it is not discharging; thus, the advantages of forced drainage are obvious. Forced drainage means drainage through a rectifier. A discharge current is used for protection under intense stay current, while this system is used to supply protection current to place the protection body into a cathode protection state. A constant potential meter is usually used for forced discharge, and it is better to leave a small amount of protection current output when there is discharge protection.  
(4) Grounding drainage H\({}_{2}\)O  
Grounding drainage is conducted to change the connection object of the drain cable so that one end of the drain cable changes from the running rail to the auxiliary anode. This method is convenient to use, but the auxiliary anode needs to be replaced regularly.  
To sum up, as an important part of cathodic protection, discharge protection is often arranged according to the specific conditions of the site instead of using a discharge method. In actual situations, cathodic protection schemes in which auxiliary anode, ICCP, and grounding discharge are arranged at the same time are often used.
## 5 Conclusions  
In this paper, we have investigated the channel characteristics of a galvanic-type IBC based on the transfer function derived from our analytical quasi-static EM model. Both time and frequency domain experimental results evidence good agreements with the model calculations, which demonstrate the  
Figure 8: Performance of BPSK, QPSK and 8 PSK _versus_ data rate in simulation and theory at S = 11 cm.  
Figure 9: Performance of BPSK, QPSK and 8 PSK _versus_ SNR in simulation and theory at S = 11 cm.  
benefits of using an analytical transfer function in analyzing IBC channel characteristics. The high pass channel characteristic suggests that a galvanic-type IBC can be suitably operated at least in hundreds of kHz of carrier for lower attenuation in sub-MHz frequency region and maintain reasonably good data rates. Moreover, the harmonic distortion in the IBC channel time domain analyses is around 96% for baseband transmission with the 500 Hz input square wave; while it reduces to around 41.9% for passband transmission, close to the ideal case of considering equipment ability of four harmonics. The results suggest that passband transmission can achieve low distortion even without channel equalization. Considering power efficiency, bandwidth efficiency and hardware complexity through theoretical calculation and simulation, QPSK is the optimum modulation method among BPSK, QPSK and 8 PSK modulation schemes for reasonable BER at the low transmitted power of \(-25\) dBm in passband transmission. The calculation, experimental and simulation results in this work have provided useful information for transmission method and modulation scheme selection in the IBC system design. In the future, we will continue to research on the optimum IBC method in terms of power, bandwidth and cost.  
The work was supported by The Science and Technology Development Fund of Macau (FDCT) under Grant 014/2007/A1, Grant 063/2009/A, and Grant 024/2009/A1; the Research Committee of the University of Macau, under Grant RG072/09-10S/MPU/FST; the National Natural Science Foundation of China under Grant 61201397; the Natural Science Foundation of Fujian Province under Grant 2011J05077; the Program of International S&T Cooperation under Grant S2013 GR0188. The authors would like to express heartfelt thanks to the financial support from the Science and Technology Development Fund of Macau, the Research Committee of the University of Macau, the Funds of Fujian Provincial Department of Science and Technology, the Natural Science Foundation of Fujian Province and the National Natural Science Foundation of China.
## 1 Introduction  
Microwave photonics (MWP) [1; 2; 3; 4] is an emerging interdisciplinary area that investigates the interaction between microwave and optical waves, for widespread applications ranging from defense applications, such as radar [5; 6] and electronic warfare systems [7], to civil applications, such as wireless [8; 9] and satellite [10] communications, imaging [11] and instrumentation [12]. Thanks to inherent advantages of optics, such as high speed, broad bandwidth and reduced electromagnetic interference, various novel photonic techniques have been developed for efficient generation, distribution, control, processing and analysis of microwave signals [13], thus enhancing and enablingnew functionalities that are very complex or challenging to achieve only in the microwave domain due to the limited speed and bandwidth of electronic devices. Recent research efforts within the area of microwave photonics include photonic generation [14] and processing [15,16] of microwave and millimeter-wave signals, photonically assisted phased array antennas [17,18], radio-over-fiber systems [19,20], arbitrary microwave waveform generation [21,22,23], and photonic analog-to-digital conversion (ADC) [24,25]. Most recently, integrated microwave photonics [26,27] has become an emergent topic aiming to reduce cost, power consumption, as well as to improve performance and robustness of MWP systems by incorporating discrete MWP components in photonic integrated circuits (PICs) [28, 29, 30].  
Dispersive Fourier transformation (DFT) [31,32], also known as real-time Fourier transformation [33,34,35] or frequency-to-time mapping [36,37], uses large chromatic dispersion to map the spectrum of a broadband ultrashort optical pulse into a stretched time-domain waveform. With the help of high-speed time-domain modulation and detection technology, DFT enables ultrafast pulse-by-pulse spectroscopic measurement and manipulation. With this unique capability, DFT has become a powerful tool for high-throughput and real-time measurement where traditional instruments fall short [32,38].  
DFT has also been widely employed in a diverse range of microwave photonics applications, such as microwave arbitrary waveform generation [23,39], microwave spectrum sensing [40,41], photonic ADC [24,42,43], real-time spectroscopy [44,45,46,47], optical sensor interrogation [48,49], and ultrafast continuous imaging [50,51]. In this paper, we review the recent development that we have made in this area. The paper is organized as follows. In Section 2, we introduce the theory and implementation of DFT. Its widespread applications in microwave photonics systems are discussed in Section 3, with emphasis on real-time spectroscopy, microwave arbitrary waveform generation, and microwave spectrum sensing. Finally, in Section 4, we outline the current limitations and potential future research directions for DFT-based microwave photonics applications.
## 3 The VTP-GCN Model
### Graph Representation Based on Raw Vehicle Motion Data
Since the historical state of the vehicle can affect its future trajectory, and the future trajectory cannot affect the past historical state of the vehicle, we define the temporal graph as a spare directed graph \(G_{tmp\_i}=\big{\{}G_{tmp\_i},j\big{|}\forall i\in\{1,2,\cdots,N\}\big{\}}\), where \(G_{tmp\_i}=\big{\{}V_{tmp\_i},E_{tmp\_i}\big{\}}\) represents the temporal graph of the \(i_{th}\) vehicle, and \(V_{tmp\_i}=\big{\{}v_{t}^{i}\big{|}\forall t\in 1,2,\cdots t_{h}\big{\}}\) is the set of all historical time instants. \(E_{tmp\_i}=\big{\{}e_{kq}^{i}\big{|}\forall k,q\in\{1,2,\cdots,h_{h}\}\big{\}}\) is the set of all temporal edges, where \(e_{kq}^{i}=1\) if \(v_{k}^{i}\) is connected to \(v_{q}^{i}\), that is, the time instant \(k<q\), and \(e_{kq}^{i}=\)0 if \(v_{k}^{i}\) is not connected to \(v_{q}^{i}\). The movement status of a vehicle at different historical moments has different impacts on its future movement trajectory. How to accurately mark this temporal interaction intensity is crucial to generate accurate predictions of vehicle trajectories. With the proposal of Transformer networks, the self-attention mechanism has received widespread attention. With the introduction of the Transformer network, the self-attention mechanism has received widespread attention, as it can automatically capture and learn the similarity of features between different moments. Inspired by this, this study uses the self-attention mechanism to model the temporal interaction intensity of vehicles at different historical moments, then constructs the temporal weighted adjacency matrix \(A_{tmp\_i}\in\mathbb{R}^{k_{h}\times t_{h}}\):  
\[Q_{tmp\_i}=\phi(V_{tmp\_i},W_{Q}) \tag{7}\]  
\[K_{tmp\_i}=\phi(V_{tmp\_i},W_{K}) \tag{8}\]  
\[A_{tmp\_i}=\text{softmax}(\frac{Q_{tmp\_i}K_{tmp\_i}^{\text{T}}}{\sqrt{d_{k }}}) \tag{9}\]  
where \(Q_{tmp\_i}\) and \(K_{tmp\_i}\) are the query and key of the self-attention mechanism, respectively, \(\phi()\) denotes linear transformation, \(W_{Q}\) and \(W_{K}\) are the weights of the linear transformation, and \(d_{k}\) is a scaled factor. By stacking \(A_{tmp\_i}\), \(A_{tmp\_2}\), \(\ldots\), \(A_{tmp\_N}\), we can obtain the weighted adjacency matrix of the temporal graph \(A_{tmp}\in\mathbb{R}^{k_{h}\times t_{h}\times N}\).
## 3 Results
### Correlation Analysis  
A correlation analysis was performed to assess how the biological parameters measured behaved with respect to each other (Figure 9). The most noticeable differences were observed with body weight, where a stronger correlation was detected between body weight and several of the satiety questions. For example, a slightly negative correlation was detected in the placebo group between body weight and the question \"How hungry do you feel?\" immediately after breakfast as well as 1 h afterwards, whereas a strong positive correlation was detected in the experimental group. This could be interpreted in the case of the placebo group that less body weight correlated with more hunger after the meal, while in the experimental group, less body weight correlated with less hunger after breakfast. Similar results were observed regarding the questions \"How satisfied do you feel?\" and \"How much could you eat now?\" In these cases, a stronger negative correlation was detected in the experimental group compared to the placebo. Overall, this could suggest that in the experimental group, those losing more weight equally felt less hungry after the meals, and therefore a stronger satiety effect (due to the product's effect) was perceived by the subjects.  
Figure 8: Fat, bone, and muscle mass analysis by DEXA scan. * \(p<0.05\) vs. baseline, *** \(p<0.01\) vs. baseline, *** \(p<0.001\) vs. baseline, *** \(p<0.001\) vs. baseline, *** \(p<0.001\) vs. placebo.
## 4 Routing Using SPWO Algorithm
### The SPWO Algorithm
```
Input: \(C_{\text{best}},C_{\text{mean}},X,D\) Output: \(C(v+1)\) Initialize the population Compute fitness measure while\(v\leq v_{max}\)do for(\(n=1\), \(2\),..., \(p\)) ; \(p\)-number of subject offered Do for each student ifC = best then  Update Score by Equation (18) elseifC = good then  Update Score by Equation (19) else  Update Score by Equation (35) endif ifC = average then  Update Score by Equation (36) else  Modify the Updated Score by Equation (37) endif  Check boundary Analyze class score Re-perform evaluation end for endwhile  Return best solution
```  
**Algorithm 1** Pseudo code of proposed SPWO algorithm [41]  
Proposed Student Psychology, Whale Optimization Integrated Deep Maxout Network for Breast Cancer Detection in IoT  
Cancer is a critical health problem worldwide. Among different categories of cancers, breast cancer results in higher cases among women. Hence, this research is modeled to develop a method named SPWO-based Deep maxout network for classifying breast cancer in a network paradigm. The nodes acquire the information about patients and are routed toward the sink node using the SPWO algorithm. The nodes are allowed to acquire medical information about patients in the IoT framework and the collected data are routed towards the sink node using a hybrid optimization algorithm. The proposed hybrid algorithm, named SPWO, is devised by inheriting the features of the Student Psychology based Optimization (SPBO) with Whale Optimization Algorithm (WOA). The process of routing is effectively made by assuming fitness factors, namely energy, delay, link quality, distance, and trust. The features are selected from input data and, based on their features, classification is performed using the deep learning framework. The fitness measures employed in the routing process are energy, distance, link quality, delay, and trust. With selected features, the classification of breast cancer is performed by utilizing an organizational approach that is tuned by the created optimization algorithm. Figure 3 portrays a schematic representation of the proposed approach. Routing is an important phenomenon in IoT, such that it has the facility to handle the dynamic nature of network topology. It is more significant to select the best routing path to transfer data packets such that the procedure of routing is performed by the proposed SPWO approach. The features associated with SPBO are inherited by WOA to derive a hybrid solution to accomplish the task of data routing in an IoT network. The proposed algorithm is highly efficient at routing the data packets as it effectively eliminates local optima and attains a higher convergence by moving the solution toward global optima. Let us consider the dataset as \(E\) with \(h\) number of input data specified by Equation (38):  
\[E=\{Z_{1},Z_{2},\ldots,Z_{u},\ldots,Z_{h}\}. \tag{38}\]  
Here, \(E\) implies the dataset, \(Z\) denotes input data, \(Z_{u}\) illustrates data located at the \(u\)th index. Figure 3: Portrays Schematic Representation of the Proposed Model.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 1 Introduction
Each group member must be verifiably assured that the data they are forwarding is, indeed, from one of the members of the group that is currently active and transacting. The SMs in the group need to be updated about the group's membership status, and active nodes, periodically. The security information that is relayed to the members of the group must reach only the validated members of the group. This is the setting for the discussion and addresses how a secure scheme is provided for a group of SMs that have a multi-hop path within the group, to reach the gateway SM. The rest of the paper is organized as follows. In Section 2 we present a literature review for key management and authentication solutions for NANs. Section 3 describes the network model for a smart grid and NAN. Section 4 proposes key management for NAN. Section 5 discusses the implementation and analyses the security features of the proposed scheme. Section 6 concludes the paper with a summary of results and future work. Figure 1: Various segments of the smart grid (HAN, NAN, WAN) and the interconnectivity between them.
## 4 Results and Discussion
### Effects of the Injection Timing on Engine Performance
#### 4.2.2. \(\rm NO_{x}\) and \(\rm Soot\) Emission Characteristics  
Figure 15 shows the trend of in-cylinder NO and soot mole fraction with the crank angle at different injection timings. Figure 16 shows the influence of injection timing on  
Figure 14: Spray development process with different fuel injection timings.  
the average in-cylinder temperature. From Figure 15a, it can be found that the in-cylinder NO mole fraction is very low at the beginning of fuel injection, and it can be observed from Figure 16 that as the oxidation process proceeds, the area near the perimeter of the spreading flame forms a high-temperature environment, which is favorable for NO production. This period is also the time when the oxygen content inside the cylinder is relatively high. The longer the fuel and mixture remain in this zone, the higher the NO\({}_{\text{x}}\) concentration will be. The in-cylinder temperature constantly drops with injection timing delays, and the NO\({}_{\text{x}}\) emission follows suit. To be more specific, the peak in-cylinder temperature drops 16.8% when the injection timing delays from 695 \({}^{\circ}\)CA to 713.7 \({}^{\circ}\)CA. However, the in-cylinder temperature and NO\({}_{\text{x}}\) emission both marginally increase when the injection timing is delayed to 715 \({}^{\circ}\)CA, with the NO\({}_{\text{x}}\) emission being at its lowest for the injection timing at 713.7 \({}^{\circ}\)CA. Figure 15b illustrates that the peak soot mole fraction decreases significantly as the injection timing is advanced. However, the injection timing corresponding to the peak mole fraction is 713.7 \({}^{\circ}\)CA, indicating that the injection timing close to 713.7 \({}^{\circ}\)CA is most beneficial for the generation of soot precursors.  
Figure 17 illustrates the NO\({}_{\text{x}}\) and soot emission characteristics at different fuel injection timings, and soot emission characteristics refer to the soot mole fraction at the moment of exhaust valve opening. The mole concentration of NO\({}_{\text{x}}\) is lowest near the injection timing at 713.7 \({}^{\circ}\)CA, and the mole concentration of soot is lowest near the injection timing at 700 \({}^{\circ}\)CA. The mole concentration of soot increases significantly with the delayed injection timing. When the fuel injection timing is 713.7 \({}^{\circ}\)CA, the corresponding actual soot emission is already very low. As can be seen in the shaded area of Figure 17, the engine emission performance is optimal when the injection timing is between 710 \({}^{\circ}\)CA and 715 \({}^{\circ}\)CA. In  
Figure 16: In-cylinder temperature.  
Figure 15: Trend of the average mole fraction of NO and soot in the cylinder with the crank angle. (a) NO mole fraction. (b) Soot mole fraction.  
summary, when the blending ratio of PODE is around 15% and the injection timing is between 710 \({}^{\circ}\)CA and 715 \({}^{\circ}\)CA, the emissions are within the optimal range.
# Reliable Internet of Things: Challenges and Future Trends
## 6 Future Opportunities and Challenges
### Machine Learning Techniques  
As future IoT applications will generate a large amount of data, intelligent machine learning techniques are needed to analyze the data and get useful insights to improve the reliability of IoT [141]. Many machine learning techniques can be useful for IoT applications. We provide an overview of few machine learning techniques and their use cases in IoT in Figure 7.  
Regression-based techniques are useful for predicting many important parameters in IoT [142]. One application of regression is to predict the data traffic and load on the network. By accurately predicting the traffic load, optimal resource allocation can be realized. Moreover, traffic prediction can be used to develop efficient congestion control techniques. Another application of traffic prediction is optimal load balancing for fog computing IoT networks. Using the predicted load (in terms of the number of tasks received for computation) on different fog nodes, tasks can be fairly distributed to the fog nodes.  
Vehicle density prediction in an IoV can also utilize regression techniques. Infrastructure Road Side Units can collect large data of vehicle mobility and predict future vehicle densities at different times of the day. Another key application of regression techniques is the energy prediction of different IoT nodes. The energy of IoT nodes depends on factors such as node's idle time duration, transmission time duration, and transmission power. A robust prediction of these parameters allows IoT applications to take corrective actions. An accurate energy prediction technique help design robust energy-aware algorithms that can enhance the lifetime of IoT nodes and also reduce chances of total energy depletion.  
Reinforcement learning techniques can also be used in IoT for better resource allocation and load balancing [143]. Using reinforcement learning algorithms, optimal actions such as transmission power selection, cache placement in the fog nodes, and task offloading ratios can be figured out. The reward function in these algorithms can be based on wireless interference, network sum-rate, task offloading time and cache access time.  
Classification techniques such as k-nearest neighbor and decision trees, etc., can be used to solve challenges such as anomaly detection for enhanced security. Malicious nodes can launch attacks on IoT nodes in the form of false data sharing or jamming signals.  
Figure 7: Future opportunities of using Machine learning for IoT.  
Thus, it is vital to detect abnormal traffic to maintain the reliability of the IoT network. Another use of classification techniques is for transmitter-receiver distance estimation. Techniques such as k-means clustering can be used in this regard to find how far the receiver is located [144]. In addition, data traffic can be classified into various classes based on their QoS requirements to design priority algorithms.