esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Study Results
#### 3.2.5 VR Task  
In 13 studies the VR environments were used exclusively for the different relaxation tasks [35; 46; 47; 48; 49; 51; 52; 53; 54; 55; 56; 57; 58]. Only in three studies were both stress induction and relaxation performed in the VR environment [31; 50; 59]. In the vast majority of studies, natural landscapes [35; 46; 48; 50; 52; 55], forest environments [31; 51; 56], and/or urban environments [48; 49; 51] were used as a means for relaxation. In addition, three studies focused on the design of office spaces [58; 59] or the design of home-work residential spaces [57].  
Although all the VR environments described in the studies were highly immersive, in most cases participants could only immerse themselves in the virtual world as observers, i.e., they could look around or move around. Only in two studies were the participants explicitly instructed not to move so as to avoid EEG artifacts [54; 55]. However, in two studies, the VR environment was designed so that environmental features would respond to the participant's behavior. In these studies, biofeedback tasks were used for relaxation; in one study, the breathing exercise was supported via the movement of a virtual breathing cloud [53], and in the other study, the cloudiness of the night sky changed in response to the biofeedback parameter [47]. Typically, VR relaxation tasks lasted between 5 and 15 min (mean: 8.3 min). An exception was the study of [PERSON] and [PERSON] [35], in which participants completed several sessions of relaxation exercises in different VR environments based on cognitive behavioral therapy.  
In most studies, different VR environments were compared for their relaxation effects [31; 35; 46; 48; 51; 53; 55; 56; 57; 58; 59; 65]. Three studies performed relaxation tasks [52] or biofeedback tasks [47; 50] in both VR environments and on computer screens. Moreover, one study directly compared the relaxation effects between a real and identically designed virtual environment. This study involved the comparison of a real and virtual color therapy room, where identical chromotherapy tasks were performed for relaxation [54].
# DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes
## 4 Experiments and Results
### Ground-Truth Collection  
Similar to the method in [7; 25], a Harris detector [19] and LK tracker [29] are used to collect the ground truth on the intensity image. For two adjacent intensity images, we use the original Harris detector to detect corners on the first image with the shortest distance of 5, and then take the LK tracker to track them on the second image. Considering that new corners may appear in the second image, after tracking, we continue to detect new corners on the second image. If the distance between detected new corners and tracked corners is less than 5 pixels in the second image, the two corners are considered the same corner. Then all corners will be tracked continuously.  
The frame rate of the intensity image is about 25 Hz. We approximately consider that the corners move in a uniform linear motion between two adjacent images. Then, based on the segment generated by the motion of this corner (denote as \(l\)), as shown in Figure 8, we can construct two concentric cylinders and calculate the distance from the event corners to \(l\). If the distance is less than or equal to 3.5 pixels, we consider it as true event corner and label it as the ground truth. For an event whose distance is greater than 3.5 but less than 5, we consider it to be a wrong corner and label it as a false event corner.  
Figure 7: Event camera datasets: DAVIS240C datasets [12]. (**a–d**) show four scenarios of different complexity used in the evaluation of our method.
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
## 2 Systematic Literature Review (SLR) Protocol
### Study Selection Criteria  
The main objective of the research selection process was to identify relevant literature addressing the defined research questions while excluding any irrelevant material. To this end, inclusion and exclusion criteria were applied; these encompassed research papers that built upon earlier relevant studies. In stage 1, we took the first 1000 items from the second search phase and combined them with the 3039 entries from the first search phase to create 4039 entries. In stage 2, 170 duplicate entries were eliminated. After stage 2, articles were removed in accordance with their titles (3126), abstracts (581), and complete texts (118), respectively. In the end, (44) research articles were chosen. Studies that were unrelated to established research topics were eliminated using the inclusion and exclusion criteria. The following definitions describe the inclusion/exclusion criteria:  
Figure 2: Systematic Literature Review method.  
**Inclusion criteria:**  
* All publications that present a novel method for ML/DL-based DDoS attack detection.
* Research that exclusively pays attention to ML/DL techniques
* Studies involving related topics while differing in crucial elements are incorporated as separate primary studies
* Research that responds to the study's questions
* Research building on earlier relevant research
* Articles released between 2018 and 2022.  
**Exclusion criteria:**  
* Articles not written in English
* Research unrelated to this study's topic
* Review papers, editorials, discussions, data articles, brief communications, software publications, encyclopedias, posters, abstracts, tutorials, works-in-progress, keynotes, and invited talks
* Articles that do not provide a sufficient amount of information
* Duplications of other research.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 2 System Model
When Doppler shifts increase significantly, the periodic correlation processing stage in Figure 1 previously described by (3) results in  
\[r_{n}=\sum_{h=0}^{H-1}\left[\sum_{m=0}^{N_{\text{PBS}}-1}\left(s_{\left(m-n_{ \Delta h}\right)_{N}}\text{e}^{\text{j}2\pi f_{\text{D}_{h}}mT_{\text{c}}} \right)s_{\left(-\left(n-m\right)\right)_{N}}^{*}\right]\alpha_{h}+\sum_{m=0}^{ N_{\text{PBS}}-1}z_{m}s_{\left(-\left(n-m\right)\right)_{N}}^{*}. \tag{6}\]  
The pulse compression in this equation can be interpreted as resulting from the periodic cross-correlation function (PCCF) of the pseudorandom sequence \(\mathbf{s}\) and its Doppler-shifted version or simply as the PACF of \(\mathbf{s}\) under Doppler shift, which is defined as \(\mathbf{R}_{\mathbf{s}\mathbf{s}\mathbf{s}}\in\mathbb{R}^{N_{\text{PRBS}}\times 1}\) and has its \(n\)th element expressed as [17]  
\[R_{\mathbf{s}\mathbf{s}\mathbf{s},n}=\sum_{m=0}^{N_{\text{PRBS}}-1}\left(s_{m} \text{e}^{\text{j}2\pi f_{\text{D}_{h}}mT_{\text{c}}}\right)s_{\left(-\left(n- m\right)\right)_{N}}^{*}, \tag{7}\]  
in which \(R_{\mathbf{s}\mathbf{s}\mathbf{s},n}\in\mathbb{R}\) and the index \(h\) of the radar targets has been omitted for the sake of simplicity. Based on previous studies in the literature, it is expected that the effectively compressed PRBS \(\mathbf{R}_{\mathbf{s}\mathbf{s}\mathbf{s}}\) will present increasing sidelobe levels along with the Doppler shift \(f_{\text{D}}\)[17]. In this context, parameters for assessing the degradation of the PACF of typically adopted PRBSs in PMCW radar systems are presented in Section 3.
## 4 Methodology
### Dynamic Threshold  
Using a fixed threshold to identify anomalies in the forecasting outputs usually results in many false positives in the anomaly-free sequence. The forecasting network can capture the general trend of the time series data but still has a high deviation value in the anomaly-free part if \(\Big{\|}T_{fw_{l}}-T_{fw_{l}}\Big{\|}_{2}\) is used to calculate deviation. The issue is that the original data is increasing too fast, and the forecasted data is not sharp enough to catch up. Due to the high slope in those areas, a slight differentiation results in a high deviation value, as shown in Figure 6b. Hence, a new way to calculate the threshold and identify the error is introduced to achieve higher precision in the anomaly detection of liquid level in mold.  
The standard deviation and mean are used to calculate the threshold for every sliding window, marked as \(\sigma(w)\) and \(\mu(w)\). Dynamic thresholds \(th\big{(}T_{w_{l}}\big{)}\) are defined as follows:  
\[th\big{(}T_{w_{l}}\big{)}=\beta_{1}\sigma(T_{w_{l}})+\beta_{2}\mu\big{(}T_{w_{ l}}\big{)}+\epsilon \tag{13}\]  
The first item \(\beta_{1}\sigma(w)\) is the threshold that controls the deviation. The second item controls the offset threshold. Figure 6b shows that the threshold changes according to the window. Compared to the fixed threshold RAE uses, a dynamic threshold guarantees a lower false-positive rate without using post-processing.  
The Euclidean distance is used to calculate the differences between the forecasted sequence and the actual sequence in a sliding window.  
Figure 6: Dynamic threshold vs. fixed threshold. The red rectangle marks a normal area. The green rectangle marks an abnormal area. The dynamic threshold can identify anomalies more precisely than the fixed threshold. (**a**) shows the FEG\(-\)AE’s forecasting network output, and (**b**) shows the Euclidean distance between the forecasted result and the actual data. (**c**) shows the L1 data error between the original and forecasting data. A fixed threshold is more likely to identify a false negative result in the red rectangle and a false negative result simultaneously in the green rectangle area.  
An anomaly is found if \(d_{w}\) is higher than the compared window's threshold, meaning the distance deviation between the forecasted and original values is larger than the threshold. The method can identify the anomaly more precisely. A visual comparison between the fixed and dynamic thresholds is shown in Figure 6.
## 2 Related Work
A regular hexagonal grid deployment scheme, which can achieve the optimal coverage and connectivity as mentioned earlier, is presented as our basic deployment scheme. Based on the data transmission and energy consumption analysis under retransmission, the optimal sensor deployment is obtained by maximizing the lifetime under the constraints of coverage, connectivity and data success transmission rate. As the retransmission rate is highly related to the transmission distance, the optimal sensor deployment under retransmission is quite different from the one in which retransmission is not considered. Moreover, our scheme can guarantee the coverage, connectivity and data success transmission rate, which may not be achieved simultaneously in previous works.
## 3 Results and Discussion  
### Response Surface Method  
The main purpose of the ship resistance experiment is to provide a decision basis for the selection of the actual ship's power equipment because the ship resistance is directly related to the power of the ship's power device. Based on the results of the analysis of drag factors, the response surface method was chosen to further study the effects of ship speed, displacement and appendages on actual ship power.  
Response surface methodology is an optimization method that combines experimental design and statistics, including many experimental and statistical techniques, such as experimental design, mathematical model building, model testing, and combinatorial condition optimization [24; 25; 26]. Through the regression fitting of the process and the drawing of the response surface and the contour line, the response value of each factor can be analyzed intuitively and conveniently. The data processing software Design-Expert version 12 was used to analyze the relationship between the actual ship's power and the ship  
Figure 4: Correlation analysis of influential factors for ship resistance (**a**) When the ship model’s speed is constant, the correlation coefficients between the other four factors; (**b**) When the displacement of the ship model is constant, the correlation coefficients between the other four factors.  
speed, displacement, and appendages. A comprehensive, visual model and optimization results can be given by the professional analysis of experimental data using the software.  
The range of values for each variable needs to be determined before the analysis. According to the above experimental description, it can be seen that the speed of the ship model ranges from 0.44 m/s to 2.06 m/s. The displacement of the ship model is determined to be 0.36 t to 0.41 t. The value is 1 if the ship model is equipped with all appendages, because ship appendages cannot be quantified effectively. The value is 0 without appendages. The value ranges from 0 to 1 if the ship is configured with several appendages. Therefore, the test scheme based on the Design Expert software is shown in Table 3.  
According to the experimental data, the polynomial model is fitted using Design Expert software, and the response equation can be expressed as follows:  
\[P=-390.4-115.1x+2088.4y-18.9z+132 xy+1.2 xz+65 yz+94x^{2}-2702y^{2}-5.9z^{2} \tag{10}\]  
where \(P\) is the power of the actual ship, kw; \(x\) is the speed, m/s; \(y\) is the displacement of the ship model, \(t\); and \(z\) stands for appendages. From the response equation, it can be seen that the influence of ship speed, displacement, and appendages on the actual ship power constitutes a quadratic polynomial model in mathematics.
# A Machine Learning View on Momentum and Reversal Trading
## 3 Experiment Setup
### Backtesting  
In the experiment, we examined different observation and holding periods to investigate the momentum and reversal effects.  
At first, prediction models were built based on the above-proposed machine learning approaches. Then, we conducted backtestings by different models for the testing data. We tried different combinations of observation and holding periods for each model. Finally, the paper trading returns, as indicated in (8), and the Sharpe ratios, as shown in (9), were calculated and compared carefully.  
\[R_{p}=\ \frac{P_{t}}{P_{0}}-1, \tag{8}\]  
\[SR=\frac{\sum_{i=1}^{n}\left(R_{H}^{i}\right)/n-R_{f}/365}{\sqrt{\frac{1}{n-1} \sum_{i=1}^{n}\left(R_{H}^{i}-\overline{R}\right)^{2}}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Feature Sets**} \\ \hline amplitude & market\_cap & mom\_ps & rev\_amplitude & rev\_roc \\ amplitude\_std & market\_cap\_std & mom\_ps\_std & rev\_amplitude\_std & rev\_roc\_std \\ cci & mom\_amplitude & mom\_roc & rev\_ci\_cap & rev\_tumor \\ change & mom\_amplitude\_std & mom\_roc\_std & rev\_ci\_cap\_std & rev\_tumor\_std \\ cir\_cap & mom\_ci\_cap & mom\_tumor & rev\_current\_trn & rev\_yield\_dispersion \\ cir\_cap\_std & mom\_ci\_cap\_std & mom\_tumor\_std & rev\_current\_trn\_std & rev\_yield\_dispersion\_std \\ close & mom\_current\_trn & mom\_yield\_dispersion & rev\_lb & roc \\ current\_trn & mom\_current\_trn\_std & mom\_yield\_dispersion\_std & rev\_lb\_std & roc\_std \\ current\_trn\_std & mom\_lb & obv & rev\_market\_cap & rsi \\ ema & mom\_lb\_std & open & rev\_market\_cap & sat \\ high & mom\_market\_cap & pb & rev\_pb & sma \\ hurst & mom\_market\_cap\_std & pb\_std & rev\_pb\_std & turnover \\ kdj\_slow\_d & mom\_pb & pcf & rev\_pcf & turnover\_std \\ kdj\_slow\_k & mom\_pb\_std & pcf\_std & rev\_pcf & vol\_change \\ lb & mom\_pcf & pe & rev\_pe & volume \\ lb\_std & mom\_pcf\_std & pe\_std & rev\_pe\_std & willr \\ low & mom\_pe & ps & rev\_ps & yield\_dispersion \\ macd & mom\_pe\_std & ps\_std & rev\_ps\_std & yield\_dispersion\_std \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature extraction.  
where \(p_{0}\). is the intial Net Asset Value (NAV) of the portfolio, whose value was set to 1.0 at the beginning of time, while \(p_{t}\) is the NAV at the end of time \(t\). \(R_{H}^{i}\) is the daily return on the \(i\)th day, while \(R_{f}\) is the risk-free rate.  
Since transaction costs are very important factors that may affect the investing return dramatically in real-world trading, we had to take into account these costs in the backtesting for the better market trading simulation. The transaction costs and risk-free rate are listed in Table 3.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 3 Methodology
### Speed Observation  
Speed observations were conducted to analyze the travel behavior of vehicles at the studied intersection. A sample of 20 vehicles was observed for each direction (north and south) during green signal phases. The observations were conducted over a fixed distance of 150 m, and the speed of each vehicle was measured using a stopwatch. The recorded speeds were compiled in Table 3. The analysis revealed that 40% of the observed vehicles traveled within the speed range of 20 km/h to 27 km/h. Additionally, 42.50% of vehicles were observed to be traveling at speeds ranging from 28 km/h to 36 km/h, while 17.5% were traveling between 36 km/h and 39 km/h, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bound**} & \multirow{2}{*}{**Vehicles (Veh/h)**} & \multicolumn{3}{c}{**Movement (Veh/h)**} \\ \cline{3-5}  & & **Right** & **Straght** & **Left** \\ \hline North & 1793 & 225 & 679 & 889 \\ East & 1033 & 814 & 157 & 62 \\ South & 1508 & 244 & 1228 & 36 \\ West & 1052 & 28 & 132 & 892 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic volume data at the studied signalized intersection.  
Figure 3: Traffic signal setup of the signalized intersection in Balgat, Ankara. Source: PTV VISSIM model.  
The speed characteristics of AVs were investigated through simulations, considering the full penetration of each autonomous behavior and their interaction with human-driven passenger vehicles. The optimal speed was determined by observing how AVs interacted with human-driven vehicles under different traffic conditions, particularly in a congested environment. Although a desired speed was set in the simulation, the model naturally adjusted the actual speeds of AVs based on the surrounding traffic, considering factors like vehicle acceleration, deceleration, and lane-changing possibilities. This approach allowed us to identify the effective operating speed of AVs in real-world-like traffic scenarios. The analysis revealed that the speed of AVs, both in isolated scenarios and when mixed with human traffic, is within the range of 27 to 31 km/h. These results are depicted in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vehicle** & **Distance** & \multicolumn{2}{c}{**North-Bound**} & \multicolumn{2}{c}{**South-Bound**} \\ \cline{2-5}
**No.** & **(m)** & **Time (s)** & **Speed (km/h)** & **Time (s)** & **Speed (km/h)** \\ \hline
1 & 150 & 18.68 & 28.91 & 27.69 & 19.50 \\
2 & 150 & 15.61 &
## 3 Algorithm Flow
### Infrared Polarization Image Fusion
#### 3.2.2 High- and Low-Frequency Components' Decomposition of PI and PF Images  
The PI and PF images acquired are then decomposed into high- and low-frequency components using the Haar wavelet transform. As the Haar wavelet transform is an orthogonal wavelet with both symmetry and finite support, it serves as the only orthogonal wavelet that possesses these characteristics. These images come with a typical two-dimensional discrete signal, thus we extend the one-dimensional discrete wavelet transform to a two-dimensional function in order to achieve the two-dimensional discrete wavelet transform of the image. In two-dimensional contexts, each two-dimensional wavelet can be expressed as the product of two one-dimensional wavelets, resulting in 4 separable scale functions, which enable the measurement of grayscale variations within the image along different directions. \(\varphi(x,y)\) is the low-frequency scale change, \(\Psi^{H}(x,y)\) is the high-frequency horizontal edge change, \(\Psi^{V}(x,y)\) is the high-frequency vertical edge change, and \(\Psi^{D}(x,y)\) is the high-frequency diagonal change.
## 4 Industry 4.0 Concept in Railway Transportation
### Predictive Maintenance
Thermal cameras are used to collect thermal imaging data about rails and the catenary-pantograph system, and a fuzzy logic system utilising complex membership functions is used for image processing and related condition monitoring. Similar work related to track condition monitoring was carried out in [193], wherein the support vector machine (SVM) approach was used to model track geometry deterioration with a prediction accuracy over 70% reported in the paper. On the other hand, the authors of [194] utilised measurements from a ground-penetrating radar and compared the conventional frequency analysis (FFT) approach with the AI approach using long short-term memory (LSTM) neural networks and convolution neural networks (CNNs) for the prediction of hazardous conditions such as railway track fouling and deformation, with both AI methods performing remarkably well. Another example of CNN utilisation for predictive maintenance would be the analysis of features of vibration signals for the purpose of early fault detection, as shown in [185]. Figure 14 illustrates the ability of the CNN used in [185] to extract and classify key features of different types of early mechanical faults of rotational equipment with respect to normal operating conditions. The distribution of test samples extracted from input signals and the output layer for the CNN are obtained by means of an appropriate visualisation software tool, which represents the results in a two-dimensional space of non-dimensional variables [185]. The results show good potential for predictive maintenance in railways when vibration measurements are readily available using on-board accelerometers, whose deployment was presented in [113]. Figure 14: An example of early fault detection results obtained by means of CNN [185]. Figure 13: Example topologies of wireless sensor networks on-board a freight train [114]. With the introduction of battery-based and hybrid propulsion in railway vehicles, specific aspects of battery energy storage system maintenance also need to be considered, which can be carried out within a multi-criteria optimisation framework [195]. Moreover, the three-dimensional (3D) reference architecture model from Industry 4.0 can be adapted to describe intelligent high-speed railways in terms of their (i) intelligent features, (ii) system levels, and (iii) life cycles, wherein intelligent features encompass different aspects of the railway vehicle and infrastructure maintenance [196].