esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Conceptual Model  
Figure 1 presents a conceptual model outlining a strategy for effectively managing UPFCs and PSSs in response to cyber-physical attacks. As depicted in this diagram, an all-encompassing model needs to possess the ability to flexibly harness all available resources within the network. This includes the ability to regulate power flow, optimize generation costs, and address technical aspects of the power system in a real-time and continuous manner through a hierarchical control framework. It is crucial to emphasize that this approach considers not only economic objectives but also technical parameters of the network, such as power system oscillations and various technical aspects like losses and voltage deviations.  
Figure 1: Schematic of conceptual model for coordinated control of UPFCs and PSSs.  
To enhance clarity on the issue, as illustrated in Figure 1, when a cyber-physical attack such as a line outage or generator outage occurs in any area of the power system, such as area 2, it can disrupt the power and energy flow paths in other transmission lines. This disruption can push the power system into a non-optimal state in terms of both technical and economic operations. In some cases, it might even drive the system to an unstable point. To address this, the proposed approach utilizes real-time and online data (including frequency, voltage, and power) from Data Measurement Units (DMUs) installed at each bus within the system. Operating within a hierarchical control framework, this approach sends appropriate and practical control signals to the unified power flow controller (UPFC) and power system stabilizers (PSSs). The goal is to protect and stabilize the system against cyber-physical attacks, considering both the technical aspects (such as voltage and frequency deviations) and economic aspects (such as generation costs).  
The following definitions of the symbols in Figure 1 should be noted:  
\(V_{s}\): sending-side voltage in area 1;  
\(V_{R}\): receiving-side voltage in area 2;  
\(P_{s}\): sending-side active power in area 1;  
\(P_{R}\): receiving-side active power in area 2;  
\(V_{d}^{upfc}\): d-axis reference voltage of shunt controller in the UPFC;  
\(V_{d}^{upfc}\): q-axis reference voltage of shunt controller in the UPFC;  
\(P^{*}\): active power reference of series controller in the UPFC;  
\(Q^{*}\): reactive power reference of series controller in the UPFC;  
\(V_{C}\): voltage induced into the power line by the series converter of the UPFC;  
\(L\): Equivalent inductance in the receiving side of the power network;  
\([K_{i}]_{UPFC}\): Coefficient controller of the UPFC.  
\([K_{i},T_{i}]_{PSS}\): coefficient controller of the PSS.
# An Overview of Reinforcement Learning Methods for Variable Speed Limit Control
## 3 Research and Implementation
### Results of Research Questions  
Studies listed in Table 1 have shown that RL-VSL has been extensively studied as a promising alternative for solving the VSL optimization problem. In this subsection, the overview of each particular study is given, starting with the earliest ones. Studies in [17,18] have demonstrated formulations of the VSL problem as RL problems and eventually solved the VSL problem using RL algorithms. Later on, in [17], the so-called Reinforcement-Markov Average Reward Technique (R-MART) algorithm was used to manage VSL. The state space for the learning algorithm is described with four discrete traffic density values describing the free flow, lightly congested, moderately congested, and heavily congested traffic flow. Actions are defined by a set of eight elements with values ranging from 44 to 129 km/h that increase or decrease by the constant amount 12 km/h. Although the algorithm itself is similar to QL, it differs in that the value in the Q-function \(Q(s,a)\) is obtained as the expected mean of the rewards collected for each time step. In general, the R-MART algorithm uses the concept of long-term mean rewards instead of deferred rewards, as is the case with QL. The reward function is formulated towards the minimization of the Total Time Spent (\(TTS\)). In addition to measuring the effectiveness of the proposed solution, the vehicle emission gas \(CO_{2}\) was measured, highlighting the correlation between vehicle speeds influenced by VSL. This parameter was not included in the reward function as objective. The research is significant because it was made on the model of the wider traffic city network of the city of Sioux Falls (SD, USA), while other research is mainly based on the analysis of the effect of RL based VSL on a specific isolated segment of an urban motorway. Using the R-MART based VSL controller, a reduction of 18% for \(TTS\) and about 20% less exhaust gas \(CO_{2}\) was achieved compared to the case without VSL (static speed limit 72 km/h). The results show that the problem of smooth changes in the output values of VSL controllers (speed limits), that occasionally oscillate and at some point two adjacent limits take large speed differences, which can affect safety, is not solved. Later on, in [18], the QL algorithm is used to learn to manage VSL in order to optimize traffic flow on motorways. To describe the agent's environment, the state vector \(\vec{s}\) contains six components: two previous actions (speed limits) and currently measured speeds at four consecutive sections around
# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 3 Detection Algorithm  
### Image Preprocessing  
It is necessary to appropriately preprocess an image to improve the effectiveness of subsequent operations, before performing operations such as convolution and segmentation on it. From the typical infrared images shown in Figure 2, it can be seen that there are many isolated bright and dark spots on the complex sea surface under sunlight. In this paper, a Gaussian filter is used to smooth the image, since Gaussian filtering is a classic method for dealing with such salt-and-pepper noise. Additionally, gamma correction is employed to adjust the grayscale of the image and enhance its visibility. The formula for gamma correction is as follows:  
\[f(i,j)=g(i,j)^{\gamma} \tag{7}\]where \(g(i,j)\) represents the grayscale value of the original image pixel, \(f(i,j)\) represents the corrected grayscale value of the pixel, and \(\gamma\) is the correction coefficient. A commonly used value for \(\gamma\) is 1/2.2, which is a classic value in gamma correction.  
In Figure 8, image (a) and image (b) correspond to two distinct scenes. Images (a1) and (b1) represent the results obtained after applying a Gaussian filter to their respective original images, while (a2) and (b2) depict the outcomes following gamma transformation performed on (a1) and (b1). Through a careful examination and analysis of the two sets of images in Figure 8 and their preprocessing results, several observations can be made. After the application of the Gaussian filter, the images exhibit a smoother appearance, with a suppression of high-frequency bright and dark spots. Although this process may introduce a certain degree of blurring to the image edges, it does not significantly impact the subsequent detection process, as the focus of this paper lies on regions characterized by a relatively uniform grayscale and specific areas of interest. Furthermore, after gamma correction, the contrast of the images is enhanced. In the original images, background regions such as mountains and dark stripes that grayscale values similar to the target region exhibit improved differentiation following the correction.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Modulation
#### 3.1.2 Sigfox  
Sigfox implements the DBPSK modulation in the uplink and Gaussian Frequency Shift Keying (GFSK) in the downlink.  
In Sigfox, sensors may select the symbol rate on a per-message basis and according to regional profile permissions. For example, the European configuration allows two bitrates: 100 and 600 bps. Similarly to NB-Fi, lower rate results in lower bandwidths used for the frame transmission, lower maximal power at which the communication is possible, and wider maximal transmission range, but higher frame durations.  
According to the specification [21], the downlink sensitivity equals \(-126\) dBm, while the uplink sensitivity specified on Sigfox support's official website [27] equals \(-135\) dBm.
## 1 Introduction
### Present Study  
The current study aims to address a gap in the existing literature by exploring the influence of failure type and operational proficiency on performance and monitoring behavior during unexpected events in flight tasks.  
In particular, the following hypotheses are studied.  
1. Higher levels of operational proficiency will result in faster resolution of failures. However, due to the unpredictable nature of system-malfunction failures, the facilitating effect of proficiency on resolution speed may not be as pronounced as in the case of system-limit failures.
2. Visual monitoring-related metrics, such as fixation duration, saccade duration, fixation rate, etc., will vary across different types of failures.  
Based on the aforementioned content, this study presents a flight simulation experiment aimed at quantitatively comparing the differences in performance and monitoring behavior between automation system-limit and system-malfunction failures, considering different levels of operational proficiency. Due to the previous lack of distinction between system-limit and system-malfunction failures, this study specifically considers four levels of operational proficiency to examine whether the impact of proficiency level on performance and behavior is consistent across the two types of failures.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Part 3: Scenario Parameters
#### 2.5.2 Severity and Types of Noise  
Both strong and weak versions of each noise type were embedded in the raw signal samples in different scenarios. The specific settings used for noise generation were as follows:  
* Weak WGN: The Mean was set to 0, and the Standard Deviation was 0.005. Strong WGN: The Mean was 0, and the Standard Deviation was 0.01.
* Weak EN: Amplitude was 0.005. Strong EN: Amplitude was 0.02.
* Weak IN: The number of impulses was randomly selected between 1 and 5, and the Amplitude Range was between 0.05 and 0.1. Strong IN: The number of impulses was randomly selected between 3 and 8 and the Amplitude Range was between 0.1 and 0.2.
* Weak and Strong Variants of CN: Combination of WGN, EN, and IN at their respective weak or strong levels.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sstep** & \multicolumn{4}{c}{**Amount of Data Samples Created per Dataset**} & \multicolumn{4}{c}{**Total per Class (D/E)**} & \multicolumn{2}{c}{**\% of**} \\ \cline{2-9} \(\times\) **4096** & **0D/0E** & **1D/1E** & **2D/2E** & **3D/3E** & **4D/4E** & **Class 0** & **Class 1** & **Data** \\ \hline
10 & 479/123 & 562/123 & 584/123 & 579/123 & 593/123 & 479/123 & 2318/492 & 10\% \\ \hline
4 & 1273/325 & 1467/325 & 1273/324 & 1459/325 & 1436/325 & 1273/325 & 5635/1299 & 25\% \\ \hline
1 & 5409/1382 & 5400/1381 & 5409/1378 & 5403/1381 & 5400/1381 & 5409/1382 & 21,612/5521 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation step (Sstep) and amount of generated data samples per recorded dataset, the total amount of data used per class for binary classification, and % of data utilized for each Sstep.  
The parameters for generating weak and strong noise variations were determined through a preliminary prescreening process aimed at defining meaningful boundaries of noise severity for model evaluation. For each noise type, a range of parameter values was systematically tested to identify a lower threshold where noise began to degrade accuracy and an upper threshold beyond which classification became unreliable. Additionally, selected parameters were required to result in classification accuracies for both ML and TL models within an interpretable range of approximately 50% to 95% across all Combined Noise scenarios. This process also revealed which noise levels had negligible impact and which exceeded model capacity, providing a practical basis for defining weak and strong noise settings for analysis across scenarios. Examples of the generated noise signals, vibration signals with embedded strong noise, and their spectra are shown in Figure 5.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## 3 Results
### Pohang 2017 Earthquake: Analysis of in Situ Observations  
#### 3.2.1 In Situ Underwater Ambient Noise Measurement  
The measurement survey of underwater ambient noise was conducted using an underwater glider with a single hydrophone from 13 to 17 November 2017 from Ulgin to the Goseong area, along the east coast of South Korea in Figure 7. The underwater glider was launched at Ulgin 13 November 05:04 (UTC) and recovered at Goesong 17 November 06:20 (UTC) using the R/V Eodo of Korea Institute Ocean Science and Technology. The underwater glider moved vertically between depths of about 10 m and 200 m. During the survey, the glider advanced at an average horizontal speed of 0.02 m/s, logging data while repeatedly taking profiles between 10 and 200 m depth. The glider drifted when it surfaced between dives to transmit CTD data and Global Positioning System (GPS) data to the shore station through a satellite connection. The average surface current calculated from the glider's GPS data was northward with a velocity of 0.35 m/s.  
A single omni-directional hydrophone (HTI92 MIN from High Tech with a sensitivity of \(-168\) dB re 1V/1 uPa) was mounted on the underwater glider. We synchronized the time of the hydrophone with the underwater glider before launched the glider. The signal was digitized by the internal acoustic data logger at a 24 kHz sample rate with 16-bit resolution and 30 dB gain. Each file was approximately 255 MB in size, 60 min long; a total of 18.3 GB was stored on the 128 GB internal flash memory during the 4-day survey. No acoustic data were transmitted in real time due to the large size of the files. All recorded data were processed using MATLAB software after the recovery of the glider. Recordings from the survey produced a one-dimensional time series of sound level spanning approximately 150 km from Ulgin to Geseong. During the survey, on 15 November 05:29:32 (UTC), a magnitude  
Figure 6: Measurement noise levels during underwater glider operation at sea: (**a**) Fin, and (**b**) Pumped Conductivity-Temperature-Depth profiler (CTD).  
5.5 earthquake occurred at Pohang (epicenter 36.12\({}^{\circ}\)N, 129.36\({}^{\circ}\)E) [23; 24]. Underwater sounds resulting from the momentary earthquake were observed in the glider's acoustic record.
# An Overview of Fog Computing and Edge Computing Security and Privacy Issues
## 4 Security and Privacy Issues of Fog Computing
### Attacks in Fog Computing
Denial of Service (DoS): This is a type of attack is a famous attack that could affect many environments including but not limited to fog computing in which fake data is sent towards the fog nodes by attackers and these nodes are flooded with innumerous fake requests so that they remain unavailable for the legitimate users. Such attacks use up resources of the network such as bandwidth, battery, time, etc. which leads to the performance degradation of fog network as they have limited resources. 3. Man in the Middle: Man-in-the-middle is an attack where the attacker stands in between the communicating nodes to overhear and steal the useful communication of the genuine users without them knowing as they think the data is being exchanged with the legitimate receivers directly. A temporary scenario is created by the attacker in this case where he comes in between the communicating parties. 4. Collusion: This type of security attack consists of a two or more groups that collude cooperatively to mislead and cheat the legitimate users. In order to increase the impact of the attack, these group trick and attack a group of fog nodes or IoT nodes or fog nodes with IoT nodes or IoT nodes with cloud nodes. 5. Impersonation: In impersonation attack, as the name shows, the attacker acts like a genuine server to trick the legitimate users by offering fake or malicious services to them to make them believe that they are communicating with the real fog node or server. This way, the attacker steals all the confidential data from the legitimate user's system without their consent. 6. Virtual Machine Attack: Virtual machine attack is an attack where a hacker secretly takes control over the hypervisor that forms a virtual environment within a virtual machine. There are 4 different modes of attack on a virtual machine: guest to host, virtual machine to virtual machine manager, virtual machine to virtual machine, and virtual machine manager outside attacks. 7. Side-channel Attack: This is an attack in which the cryptography of the device is unlocked by gathering information about the applied cryptographic algorithm. 8. Session Hijacking: In session hijacking, the attacker intercepts and hijacks the user session in order to get access to the user confidential data and services. Table 1 shows several security attacks toward Fog computing with some related contribution related to these attacks in the literature. Based on the type of security threat any fog computing attack land in one of three fields which are network services where the attacker aims to modify the way the network is formed or causing failure in delivery of packets in the network, Data processing attacks which affect distribution, protection and content of the data in the network and finally device privacy attacks where the attacker aim to affect the privacy of the actual device including location, usage and identity privacy. Figure 4 shows the main three areas of attacks in fog computing. Figure 4: Security areas in fog computing.
## 2 Methodology and Data
### Proposed Forecasting Framework
#### 2.1.4 The Novel Hybrid NAR-SVR Model  
The hybrid NAR-SVR model proposed in this study is built upon a fundamental and innovative concept: the concept of double prediction. This approach involves two parallel processes aimed at improving forecasting accuracy. Firstly, an a priori estimate is generated for the observed variable using an NAR network. Simultaneously, the relationships within the time series of prediction errors, produced by the NAR network, are explored to apply the SVR technique and predict the system error.  
Mathematically, let \(X_{t}\) represent the time series of the observed variable and \(e_{t}\) denote the prediction error. When the NAR network is applied to \(X_{t}\), as indicated in expression (2), an a priori prediction \(\widehat{X_{t}}\) is obtained, as outlined in Equation (9):  
\[\widehat{X_{t}}=\sum_{j=1}^{L}w_{j\phi}\phi\left(\sum_{i=1}^{p}w_{i,j}X_{t-i}+ \theta_{j}\right)+\theta_{o} \tag{9}\]  
Additionally, by employing the SVR model derived in Equation (8) to forecast the error generated by the system, and considering \(\overrightarrow{e}_{j}\in\mathbb{R}^{n}\) as vectors comprising \(n\) successive elements of \(e_{t}\), we can obtain the error prediction, as illustrated in Equation (10):  
\[\widehat{e_{t}}=g(\overrightarrow{e}_{i})=\sum_{j=1}^{N}(\alpha_{j}-\alpha_{ j}^{*})k(\overrightarrow{e}_{i},\overrightarrow{e}_{j})+b \tag{10}\]  
Therefore, considering that the system error is defined by (11)  
\[X_{t}-\widehat{X_{t}}=e_{t}, \tag{11}\]  
and substituting (9) and (10) into (11), the final expression of the hybridized NAR-SVR forecasting model obtained is (12)  
\[X_{t}=\sum_{j=1}^{L}w_{j\phi}\phi\left(\sum_{i=1}^{p}w_{i,j}X_{t-i}+\theta_{j }\right)+\theta_{o}+\sum_{j=1}^{N}(\alpha_{j}-\alpha_{j}^{*})k(\overrightarrow {e}_{i},\overrightarrow{e}_{j})+b. \tag{12}\]  
The training process of the NAR-SVR model involves a two-step approach. Initially, a preliminary training phase is conducted using the NAR model to generate the error series. Subsequently, the SVR model is trained using this error series, capitalizing on the information extracted by the NAR network.  
By combining the strengths of both the NAR and SVR models, the proposed NAR-SVR hybrid model provides a novel and powerful framework for time series prediction. This approach not only leverages the NAR network's ability to capture complex nonlinear relationships within the data but also utilizes SVR to effectively correct the prediction errors, leading to enhanced forecasting performance.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## The Proposed Scheme
### Watermark Extraction Process  
The extraction process can be implemented without the original image but feature keys, which can be regarded as the semi-blind watermark scheme. The flow diagram of the watermark extraction process is shown in Figure 3.  
Figure 3: Flow diagram of the watermark extraction process.  
The concrete steps are demonstrated as follows:  
1. The attacked image \(I_{\text{a}}\) is performed with SIFT, and the feature points \(P_{\text{a}}\) in the attacked image can be obtained accordingly.
2. In the embedding process, the feature points in watermarked image are recorded as keys \(P\). In this step, keys \(P\) should be stored in a matrix and transmitted along with the watermarked image for feature matching with the feature points \(P_{\text{a}}\). As can be seen in Figure 4, two sets of feature points in two images are selected as matching keypoints, and lines are drawn between two keypoints in two images separately.
3. After the feature matching process finished, RST attacks can be corrected with the angle, coordinates, and scales, which is illustrated concretely in Section 3.3. Then, the RST corrected image \(I_{\text{c}}\) is obtained for watermark extraction.
4. Three-level DWT is performed on the corrected image \(I_{\text{c}}\), and then LL\({}_{3}\), LH\({}_{3}\), HL\({}_{3}\), and HH\({}_{3}\) bands are obtained. Similar to the embedding process, the LL\({}_{3}\) sub-band \(I_{\text{LL\({}_{3}\)w}}^{*}\) is selected for the SVD transform in the next step.
5. SVD is performed on the corrected sub-band using Equation (9), and \(S_{\text{ww}}^{*}\) is recorded for the next step.  
\[I_{\text{LL\({}_{3}\)w}}^{*}=U^{*}S_{\text{ww}}^{*}V^{*\text{T}}. \tag{9}\]
6. \(S_{\text{w}}^{*}\) is retrieved back by Equation (10):  
\[S_{\text{w}}^{*}=U_{\text{w}}S_{\text{ww}}^{*}V_{\text{w}}^{\text{T}}. \tag{10}\]
7. The extracted watermark is generated for the LL\({}_{3}\) sub-band, which is shown in Equation (11):  
\[W_{\text{e}}^{*}=(S_{\text{w}}^{*}-S)/\alpha. \tag{11}\]
8. The extracted watermark is decrypted by the Arnold transform with key \(T\) recorded in the embedding process.  
So far, the watermark extraction process is finished.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 3 Materials and Methods  
A framework for the selection of features using distributed FCMs is proposed in the current paper. Figure 1 depicts the overall workflow of the proposed method. In this method, the input dataset is used to construct a distributed FCM model, which is elaborated in Section 3.1. Section 3.2 explains how the constructed FCM is then used for feature selection, and the selected features are then passed onto the classification model for evaluation, as described in Section 3.3.