esa-sceva/satcom-chunk-collection huggingface dataset:
# BDS-3 Time Group Delay and Its Effect on Standard Point Positioning
## 3 Experimental Data and Processing Strategies
### Bds-3
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Scheme** & **B1I** & **B3I** & **B1C** & **B2a** & **B1I/B3I** & **B1C/B2a** \\ \hline \multirow{3}{*}{E} & tgd-corr & 1.08 & - & 1.18 & 1.13 & 1.53 & 1.66 \\  & non-corr & 3.38 & 1.15 & 3.35 & 2.84 & 6.13 & 4.89 \\  & Improvement (\%) & 68.0 & - & 64.8 & 60.2 & 75.0 & 66.1 \\ \hline \multirow{3}{*}{N} & tgd-corr & 1.29 & - & 1.48 & 1.60 & 1.67 & 1.85 \\  & non-corr & 4.19 & 1.57 & 4.16 & 3.59 & 5.76 & 4.88 \\  & Improvement (\%) & 69.2 & - & 64.4 & 55.4 & 71.0 & 62.1 \\ \hline \multirow{3}{*}{U} & tgd-corr & 2.73 & - & 2.91 & 4.18 & 2.96 & 3.58 \\  & non-corr & 5.37 & 3.54 & 5.96 & 5.09 & 5.42 & 6.31 \\ \cline{1-1}  & Improvement (\%) & 49.2 & - & 51.2 & 19.8 & 45.4 & 43.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The 41-day mean RMS values of the SPP solutions with/without TGD correction in different BDS-3 frequency combinations from the six selected stations (m). Figure 25: Improvements in the E, N and U components from the “tgd-corr” scheme compared to those from the “non-corr” scheme for different stations. Note that the 41-day mean RMS at each station with and without TGD correction is obtained first. The improvements from the “tgd-corr” scheme is then calculated and compared to those from the “non-corr” scheme. Figure 10, we can clearly conclude that the global PDOP value for BDS-3 is less than that for BDS-2, which further supports our previous conclusions. The number of BDS-3 satellites has increased in Europe, making the correction of the TGD parameters more significant for the BRCH stations. With the rapid development of BDS-3, it now has a global network of 20 satellites and provides global services. We also expect that a better performance from BDS-3 can be reached when the global system is completed in 2020.
# A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements +
## 3 Generative Model and MOHS Algorithm
In [12], the constrained-domination concept works as follows: A solution \(x\) is said to be constrained-dominated a solution \(y\) if one of the following conditions is satisfied:  
* Solution \(x\) is feasible and solution y is infeasible. * Solutions \(x\) and \(y\) are both infeasible; however, solution x has a smaller constraint violation than \(y\). * Solutions \(x\) and \(y\) are both feasible, but solution \(x\) dominates solution \(y\). In the constraint tournament method, first, non-constrained-dominance fronts \(f_{1},\ f_{2},..,f_{k}\) are determined in such a way that the constrained-domination principle is employed instead of the regular domination principle. Note that set \(f_{1}\) is the set of feasible non-dominated solutions in the population. In the constraint tournament selection, two solutions \(x\) and \(y\) are randomly chosen from the population. Between \(x\) and \(y\), the one with a lower front is preferred. If solutions \(x\) and \(y\) are both in the same front, then the one with a higher crowding distance is the winner. Now, we are ready to give the computational flow of the proposed MOHS algorithm as follows:  
Procedure MOHS:  
Step 1: Create a random harmony population \(P_{0}\) of size HM and set \(g=0\). Step 2: Apply harmony search operators to \(P_{0}\) and obtain offspring population \(Q_{0}\) of size HM. Step 3: If the stopping criterion is satisfied, stop and return \(P_{g}\). Step 4: Set \(R_{g}=P_{g}\cup Q_{g}\). Step 5: Using the fast non-dominated sorting algorithm, identify the non-dominated fronts \(f_{1},\ f_{2},..,f_{k}\) in \(R_{g}\). Step 6: For \(i=1,..,k\), do the following steps:  
Step 6.1: Calculate crowding distance of the solutions in \(f_{i}\). Step 6.2: Create \(P_{g+1}\) as follows:  
* \(if\ \left|P_{g+1}\right|+\left|f_{i}\right|\leq HM\), then set \(P_{g+1}=P_{g+1}\cup f_{g}\)
* \(if\ \left|P_{g+1}\right|+\left|f_{i}\right|>HM\), then add the least crowded \(N-\left|P_{g+1}\right|\) solutions from \(f_{i}\) to \(P_{g+1}\). Step 7: Use crowded tournament selection to select a harmony from \(P_{g+1}\). Apply harmony search operators to \(P_{g+1}\) and obtain offspring harmony population \(Q_{g+1}\) of size HM. Step 8: Set \(g=g+1\) and go to Step 3.
## 2 Materials and Methods
### Test Equipment and Methods
#### 2.3.2 ERT Tests Apparatus and Procedure  
The two-dimensional forward modeling for the high-density resistivity method was performed using Res2 DMOD (3.03.06) software, which is specialized for 2D DC resistivity  
Figure 3: Schematic diagram of the freezing–thawing test apparatus.  
forward modeling. In this study, the 2D finite difference method was applied for forward analysis [34]. Based on geological data from the study area, a simplified 2D electrical model was designed as shown in Figure 4. Detailed model parameters are listed in Table 2. Four electrode array configurations were used for the forward simulations: the Wenner \(\alpha\), Wenner \(\beta\), Wenner \(\gamma\), and Schlumberger arrays. Each configuration employed 73 electrodes with an electrode spacing of 1.5 m. After removing outliers from the simulated forward data, resistivity inversion was conducted using Res2 DINV (3.57) software. The root mean square error (RMSE) was used as the inversion quality metric. To obtain an optimal resistivity model, a total of seven iterations were performed on all simulated datasets [35].  
To investigate the distribution and evolution characteristics of seasonally frozen soil in the study area, the field ERT survey was conducted using the EDJD-3 resistivity measurement system, manufactured by Chongqing Dingfeng Geological Exploration Instrument Co., Ltd. (Chongqing city, China). During the test, two parallel survey lines (A-A\({}^{\prime}\) and B-B\({}^{\prime}\)) with a spacing of 10 m were arranged in the study area. Considering the environmental characteristics of the study region, the Wenner four-electrode array was employed, with an electrode sequence of A, B, M, N and an electrode spacing of 5 m. The resistivity was calculated according to Equation (2):  
\[\rho_{s}=K\cdot\frac{\Delta U}{I} \tag{2}\]  
where \(\rho_{s}\) is the apparent resistivity, \(K\) is a geometric factor related to the electrode configuration of the high-density method, \(\Delta U\) is the potential difference, and \(I\) is the current.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Parameter** & **Description** & **Resistivity (\(\Omega\cdot\)m)** \\ \hline I & \(\rho_{1}\) & First layer medium, thawed layer, & 200 \\  & & 2 m parallel strata. & \\ II & \(\rho_{2}\) & Second layer medium, freezing & \\  & & transition zone, 3–8 m inclined strata. & \\  & & Third layer medium, seasonally & \\ III & \(\rho_{3}\) & frozen soil layer, & 2000 \\  & & 4–6 m inclined strata. & \\ IV & \(\rho_{4}\) & Fourth layer medium, bedrock layer, & \\  & & 20 m depth. & 800 \\ \hline \end{tabular}
\end{table}
Table 2: Model parameters.  
Figure 4: Forward model used for 2D resistivity simulation.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
###### Abstract  
Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-_means_ clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.  
adaptive solar power forecasting; machine learning; k-_means_; BPNN; LARS
## Results and Discussion
### Visualization of Carrageenan Aulteration Levels
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Wavelengths (nm)**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Cross-validation**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{3-8}  & & \(\mathbf{R_{c}}^{2}\) & **RMSEC** & \(\mathbf{R_{cv}}^{2}\) & **RMSECV** & \(\mathbf{R_{p}}^{2}\) & **RMSEP** & **RMSEP** \\ \hline RC-R-PLSR & 411, 466, 521, 755, & 0.89 & 0.56 & 0.87 & 0.61 & 0.85 & 0.99 & 3.02 \\  & 835, 889, 918, 939, 978 & & & & & & & \\ \hline RC-A-PLSR & 409, 425, 444, 521, 582, & 0.89 & 0.55 & 0.87 & 0.62 & 0.85 & 0.93 & 3.20 \\  & 621, 763, 840, 893, 939 & & & & & & & \\ \hline RC-KM-PLSR & 409, 423, 438, 460, 493, & 0.89 & 0.57 & 0.85 & 0.65 & 0.84 & 0.99 & 3.02 \\  & 525, 578, 763, 842, 987 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of multispectral PLSR models developed by wavelengths selected using three spectral units.
# Object Detection-Based Video Compression
## 3 Proposed Methods
### CU-Based QP-Control  
A CTU is the basis unit of a coding tree, and is separated into a quad tree, triple tree, or binary tree according to the RDO process, which consists of the optimal CU partitions. Figure 4 shows the object-detection results detected by the bounding boxes in the 186 th picture of the 832 \(\times\) 480 BQMall sequence, where six people and three handbags were detected. In the proposed CU-based QP-control method, the index, or flag, of the detected object is embedded in each CU. When the CUs are encoded, the QP values are controlled by whether an object is included in the CU. In this case, a lower QP value is allocated to the detected objects, and a higher QP value is allocated to the background to improve PSNR values of the detected objects. The CU partitioning results of the CU-based QP control corresponding to the detected objects in the 186 th picture of the BQMall sequence are shown in Figure 5. The CUs allocating low QP values are highlighted with red color blocks, and the CUs allocating high QP value are highlighted with white color blocks in the BQMall sequence, where the CU is determined to be encoded with a low QP when the CU partially includes an object. Even if an object occupies only a part of a CU, the CU is recognized as including the object in the proposed method. As an example, the upper left and upper right large binary CUs over the hat of the woman on the left with the handbag are compressed by a low QP in the picture because the detected woman (object) takes up a large area in Figure 4. In addition, when the accuracy of object recognition is low in YOLO, it is determined that there is no object in the CUs in the proposed method. In Figures 4 and 5, the accuracy of object recognition is low for the person in the center of the image in the bakery, so the person is determined to be part of the background in YOLO, as shown  
Figure 4: Example of the object-detected 186 th picture in the 832 \(\times\) 480 BQMall sequence.
## 3 Results and Discussion  
### Influence on Oil Content and Oil Yield  
Irrigation was associated with an increase of oil content (on dry- and on the fresh-weight basis) in both years of investigation but there was no significant difference among applied watering regimes, while the percentage of water and dry matter in olive paste did not change (Table 3).  
[PERSON] et al. [43] reported that oil content in the olive fruit related to the deficit irrigation treatments (30% and 60% of full irrigation) was lower than in the full irrigation treatment of Arbequina cultivar during the olive fruit development, while after fruit maturation, no significant difference among different irrigation treatments were determined. Increase of irrigation induces changes in the chemical composition of the olive fruit typical for an advanced maturation stage [18] and higher accumulation of oil in the fruits could be one of them. Consequently, an increase in oil content in the olive fruit influenced an increase in the oil yield obtained (Table 3). In 2020, the increase was the highest in the T\({}_{3}\) treatment (full irrigation), while in 2019 there was no significant difference among irrigated treatments (Table 3). [PERSON] et al. [44] also did not find that water availability influenced oil yield in the case of spring deficit irrigation (25-75% ET\({}_{\text{c}}\)) compared to fully irrigated Genovese cv. olive trees. Likewise, [PERSON] et al. [45] did not find differences among rainfed, RDI, 100%, and 125% ET\({}_{\text{c}}\) in oil yield in a study conducted in low density olive orchards in Spain.  
Even all applied irrigation strategies have shown great influence on oil yield, considered as economically the most important parameter of olive oil production, in T\({}_{1}\) and T\({}_{2}\) treatments, significant savings of water needed for irrigation were observed compared to full irrigation (Table 1).
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADC Analog to Digital Converter  
ADS Attitude Determination Subsystem  
COMM Telecommunications subsystem of Ten-Koh  
CCU Communications Control Unit (of Ten-Koh COMM)  
CFRP Carbon Fiber Reinforced Polymer  
CFRTP Carbon Fiber-Reinforced Thermoplastic  
ECU Experiment Control Unit  
EPS Electrical Power Subsystem  
GFRP Glass Fiber Reinforced Polymer  
GNSS Global Navigation Satellite System  
GOSAT Greenhouse gases Observing SATellite  
GPIO General-Purpose Input Output  
CDD Charged Particle Detector, one of Ten-Koh payloads  
COTS Commercial off-the-shelf  
DLP Double Langmuir Probe, one of Ten-Koh payloads  
I2C Inter-Integrated Circuit (protocol)  
JAXA Japan Aerospace Exploration Agency  
Kyutech Kyushu Institute of Technology  
LEO Low Earth Orbit  
LEOP Launch and Early phase (of the Ten-Koh mission)  
MCU Main Controller Unit (of Ten-Koh OBC)  
MGS Main Ground Station  
OBC Onboard computer  
OCP Over-Current Protection  
PCB Printed Circuit Board  
PIC Programmable Integrated Circuit  
PL Payload  
RTC Real Time Clock  
SPI Serial peripheral interface (protocol)  
TTL Transistor-transistor logic  
UHF Ultra-High Frequency  
UCP Ultracapacitor  
UV Ultraviolet
# A Survey of CNN-Based Network Intrusion Detection
###### Abstract  
Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail.  
convolutional neural network; CNN; network security; intrusion detection; deep learning 2022  
128 June 2022  
18 August 2022  
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## 4 Proposed Algorithm for Multi-Agent Configuration Change
### Reward Function
2 Initialize: current policies \(\pi^{n}_{t_{0}}\) (\(\theta^{n}_{t_{0}}\leftarrow\theta^{n}\)), target critic \(Q_{\overline{w}^{n}}(\overline{w}^{n}\gets w^{n})\)
3 Initialize: learning rate \(l_{\alpha}\)
4 whileIter\(K_{iter}\leq K_{itermax}\) and not ReachTargetPositiondo
5 SetCircularBuffer \(D=\{\}\) and SetEmptyList \(\eta=[]\)
6 InitializeActorRNN \(h^{1}_{0,\tau},h^{2}_{0,\tau}\ldots h^{n}_{0,\tau}\)
7 InitializeCriticRNN \(h^{1}_{0,V},h^{2}_{0,V}\ldots h^{n}_{0,V}\)
8 foran episode \(t=1\) to \(T\)do
9 each agent \(i(1,2,\ldots,n)\)
10 ProbabilityObtain \((p^{i}_{t},h^{i}_{t,\tau})\leftarrow\pi^{u}_{t}(o^{i}_{t}\big{|}h^{i}_{t-1,\tau };\theta)\)
11 ActionSelection \(\mathbf{u}_{t}\gets p^{i}_{t}\)
12 ValueFunctionObtain \((v^{i}_{t},h^{i}_{t,V})\gets V(s^{i}_{t},h^{i}_{t-1,V};\phi)\)
13 endfor
14 ActionExecution \(\mathbf{u}_{t}\leftarrow\pi^{n}_{t}(a^{n}_{t}|s^{n}_{t})\), ObservationObtain \(\to r_{t},s_{t+1},o_{t+1}\)
15 TrajectoryObtain \(\tau=\{s_{t},\mathbf{o}_{t},\mathbf{h}_{t,\tau},\mathbf{h}_{t,V},\mathbf{u}_{t},\mathbf{r}_{t}\}\)
16 AdvantangeCompute (SEM) \(\gets A^{n}(s^{n},\mathbf{a}^{n})\), RewardCompute \(\leftarrow\hat{Q}^{n}(s^{n},\mathbf{a}^{n})\)
17 TrajectorySplit \(\rightarrow\tau/L\), DataStore \(\big{\{}\hat{Q}^{n}(s^{n},\mathbf{a}^{n}),A^{n}(s^{n},\mathbf{a}^{n}),a^{n},\mathbf{o}^{n} \big{\}}_{n=1}^{n}\to D\)
18 Randomize \(\rightarrow D^{\prime}\)
19 for\(k=1\) to \(M\)do
20\(B\gets D^{\prime}\) with all agent data
21 GradientCompute \(\rightarrow\Delta\theta^{n}=\big{\{}\
abla_{\theta^{n}}f(r_{k}(\theta^{n}),A ^{n}_{k})\big{\}}_{k=1}^{M/B}\)
22\(\rightarrow\Delta w^{n}=\big{\{}\
abla_{w^{n}}(Q^{n}_{k}-V_{\underline{w}^{n} }(\theta^{n}_{k}))^{2}\big{\}}_{k=1}^{M/B}\)
23 for\(b\leftarrow\) recurrent data blockdo
24 Update RNN \(\to h^{i}_{\pi},h^{i}_{V}\)
25 endfor
26 endfor
27 AdAMUpdate \(\theta^{n}_{t_{0}}\leftarrow\theta^{n}\leftarrow\) GradientAscent \(\overline{w}^{n}\gets w^{n}\leftarrow\) GradientDescent
29 Empty \(\rightarrow\)\(D^{\prime}\)
30 endwhile
```  
**Algorithm 2**Improved Multi-Agent Proximal Policy  
**Remark 5**. : _The way of using cost functions is technically to measure undesirable system behavior or penalize errors; however, the non-zero values would be produced under certain conditions, especially when the sudden stall occurs, which can lead to sparse reward signals. Furthermore, this approach would not resolve the nonlinear problems associated with the multi-agent system and could compromise robustness by excessively minimizing the cost function._
# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 3 Methodology
### Bilingual Keystroke Dynamics Dataset (BKSD)  
Datasets are integral to learning systems. Not only do they allow researchers to compare various classifiers, they also aid in answering specific questions put forth by researchers.  
The Bilingual Keystroke Dynamics Dataset (BKSD) is a publicly available dataset (BKSD can be found at [[https://github.com/ntwaiiry/BKSD](https://github.com/ntwaiiry/BKSD)]([https://github.com/ntwaiiry/BKSD](https://github.com/ntwaiiry/BKSD)) (accessed on 17 October 2023)) collected during the period January 2022 to April 2022 between 8 a.m. and 2 p.m. As much as possible, we controlled various factors that affect keystroke dynamics, such as age, gender, dominant hand, and time of day. Participants were screened to select bilingual English- and Arabic-proficient typists from King Saud University, Riyadh, Saudi Arabia, in the Diriyah Campus. All typists were right-handed female students aged from 19 to 24 years old.  
The total number of participants enrolled was 102, which we divided into two groups of 51 each, one for typing a password, and one for typing a phrase. Each participant entered the password or phrase a total of 400 times in each language, and a keylogger running in the background captured the timings of keystrokes: the system time for each keydown-keyup sequence for each key pressed. Each password or phrase attempt is recorded on one line for each user. The number of participants and the number of features collected in BKSD are in line with other datasets in the literature.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 4 Experiments
### Evaluation Metrics  
The algorithms based on SLAM were primarily evaluated using the Evaluation of Odometry (EVO) evaluation tool. In this paper, ATE was used to assess localization accuracy, and the per-frame computation time (in milliseconds) was used to evaluate real-time performance. ATE quantifies the Euclidean distance between the estimated trajectory's positions and the corresponding positions in the ground truth trajectory, typically described by calculating the root mean square error (RMSE), mean error (Mean), and standard deviation (SD) of the distances. Mathematically, ATE can be expressed as follows:  
\[\text{ATE}=\sqrt{\frac{1}{\text{n}}\sum_{i=1}^{n}\lVert\text{p}_{\text{i}}- \hat{\text{p}}_{\text{i}}\rVert^{2}} \tag{1}\]  
In Equation (1), n represents the number of trajectory points, p\({}_{\text{i}}\) denotes the position of the i-th point in the ground truth trajectory, and \(\hat{\text{p}}_{\text{i}}\) denotes the position of the i-th point in the estimated trajectory.  
RMSE is a commonly used metric for measuring the error between the estimated trajectory and the ground truth trajectory, and it calculates the square root of the average of the squared errors. A smaller RMSE indicates that the estimated path is closer to the actual ground truth path. The mean represents the average error between the estimated trajectory and the ground truth trajectory, reflecting the central tendency of the errors. The SD measures the dispersion of errors, indicating how much the errors deviate from the mean error. A larger SD signifies that the errors are more spread out, while a smaller SD indicates that the errors are more concentrated.