esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This work presents a novel intelligent control system based on a Genetic Neuro-Fuzzy tool to optimize and improve the performance of a seawater reverse osmosis desalination plant (SWRO) on board a marine vessel. This investigation pays special attention to minimizing energy consumption to improve the energy efficiency of this marine installation. The system analyzes measurements of different variables--seawater pH, seawater conductivity, permeate flow rate, permeate conductivity, and total energy consumed--in order to provide the most appropriate value of permeate flow rate control and operating pressure of the high-pressure pump (HPP). This intelligent method allows the plant to achieve output values nearer to the desired setpoints set by the plant operators.  
desalination; genetic neuro-fuzzy; energy efficiency; intelligent control; soft computing techniques 2023  
[PERSON], [PERSON]; [PERSON]; [PERSON], Plasencia, G.N.M. Energy Efficiency Optimization in Onboard SWRO Desalination Plants Based on a Genetic Neuro-Fuzzy System. _A.C. elegans_, 2023, 13, 3992. [[https://doi.org/10.3390/app1306392](https://doi.org/10.3390/app1306392)]([https://doi.org/10.3390/app1306392](https://doi.org/10.3390/app1306392))  
1 Higher Polytechnic School of Engineering (EPSI), University of La Laguna, Avda. Francisco Larroche s/n, 38071 Santa Cruz de Tenerife, Spain 1  
2 Water Department, Canary Island Institute of Technology (ITC), Playa de Pozo Izquierdo s/n, 35119 Santa Lucia, Spain 2
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 4 Implementation Details and Results
### Transformation Profile Generation Module Implementation  
#### 4.2.1 GTP Generation  
Geometric Transformation Profile (GTP) is a transformation matrix to extract the screen area from the image captured by a camera. To get this matrix, the screen with the highest brightness is first captured and inverted into a grayscale. Then, the projection and non-projection areas are separated into 0 and 1 values by the appropriate threshold. The process for determining the threshold is as follows:  
1. Performs K-means clustering on the brightness of the pixels to find two clusters: black and white [28,29].
2. Finds the maximum and minimum brightness values of the two clusters.
3. Sorts the values and taking the average of the second- and third-ranked values as the threshold.  
The threshold set by the above procedure will be in the range where brightness occurs least frequently in the original image. Figure (a)a is a histogram of brightness values, and if you follow the procedure, the threshold can be determined automatically. Figure (b)b is a result of binarization by automated thresholding. As a result of this binarization, all pixels in the screen area form the shape of a white square (generally, a trapezoid). To find four vertices of the square, the Hough transform [30,31] is applied to extract the line segments of the square and calculate their intersection points. Figure (c)c shows the edges of the screen area detected by the Hough transform. Finally, the transformation matrix is derived by mapping those vertices into a rectangular area of the size of 320 \(\times\) 180. Figure (d)d shows the automated result of the above process.
## 3 Analysis of Shield Tunneling Parameters  
To ensure the representativeness and accuracy of tunneling parameter research, it is crucial to assess whether the selected parameters adequately capture the characteristics of shield tunneling in tandem with the strata properties. Additionally, the chosen parameters should comprehensively reflect the variations in shield tunneling. Tailored to the project's specifics, the key parameters for statistical analysis include the average values of the slurry pressure, grouting pressure, grouting amount, unearthed amount, total thrust, cutter torque, cutter speed, and excavation speed.  
During the tunneling process, the shield recorded 36 data items every 5 s, accumulating approximately 7.19 million data points. Focusing on the on-site excavation conditions, the study concentrates on rings 56 to 730 as the primary research objects. This meticulous selection ensures a robust examination of the tunneling dynamics within the specific context of the project.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 5 Conclusions  
In this paper, different analytic approaches are considered to investigate the interference for the LTE-M underground and ground systems, respectively. The deterministic results obtained by using the MCL method for the LTE-M underground system show that when LTE-M adopts the leaky cable radiation pattern, the LTE FDD UE has negligible small interference with LTE-M system. When LTE-M uses the directional antenna radiations pattern, the LTE FDD UE interfering with the LTE-M system requires a certain safe distance and some measures to avoid interference, such as frequency band isolation, space isolation, changing the transmission power, antenna gain, and received signal bandwidth. In this case, the interference of LTE FDD UE to LTE-M TE is negligibly small. When LTE FDD UE interferes with LTE-M BS, raising the received signal bandwidth by 5 MHz, it improves the anti-interference capability of LTE-M by 2.88 dB, and adding the 5 MHz guard interval improves the anti-interference capability of LTE-M by 13.65 dB. The throughput loss of the LTE-M ground system is simulated using the MC method, and the simulation results show that the ACIR required by LTE FDD UE to interfere with LTE-M TE is typically less than 35 dB, with nearly no interference caused. The main interference comes from the LTE FDD UE interfering with LTE-M BS. The anti-interference capability improves by around 5.75 dB when the number of users is reduced by 50, and anti-interference performance improves  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Interference Link**} & \multicolumn{3}{c}{**Offset D**} & \multicolumn{3}{c}{**Number of UE/Set**} \\ \cline{2-9}  & **0** & **0.5R** & **R** & **50/Set 1** & **50/Set 2** & **100/Set 1** & **100/Set 2** \\ \hline UE-BS & 38.76 & 49.25 & 55.08 & 50.07 & 54.82 & 56.61 & 59.79 \\ \hline UE-TE & 28.94 & 30.35 & 34.36 & 17.15 & 23.26 & 29.12 & 34.86 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The value of ACIR (dB) at 5% throughput loss.  
by about 3.96 dB when Set 1 is chosen over Set 2. Moreover, it is shown that specific steps, such as lowering the offset \(D\) and selecting appropriate power control parameters based on the number of UE, must be taken in order to reduce throughput loss.  
Conceptualization, H.F. and G.Z.; data curation, H.F. and X.W.; formal analysis, H.F.; funding acquisition, G.Z. and X.Z.; investigation, H.F. and X.W.; methodology, H.F. and G.Z.; project administration, H.F. and A.S.; resources, H.F. and G.Z.; software, H.F.; validation, H.F.; writing--original draft, H.F.; writing--review and editing, H.F., A.S., G.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (61871261) and Natural Science Foundation of Shanghai (22 ZR1422200).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Case Study
### The Reliability and Correlation Coefficients of Slip Pathways  
In this study, for constructing the response surface functions of the elements in each slip pathway, 50 finite element analyses were implemented. The coefficients of determination of the constructed response surface functions are all above 0.85, which confirms that ignoring interaction effects does not significantly compromise accuracy. Based on the elements that make up each slip pathway, the reliability indices of the three slip pathways of the dam slope were calculated using the step-by-step equivalent linearization method: the reliability index of the upstream dam slope slip pathway 1 is 6.94, the reliability index of the upstream deep slip pathway 2 is 6.87, and the reliability index of the downstream deep slip pathway 3 is 7.44. The correlation coefficients between the three slip pathways are shown in Table 3. From the data listed in the table, it can be seen that among the three slip pathways, the upstream dam slope slip pathway 1 and the upstream deep slip pathway 2 have a high correlation, with a correlation coefficient of 0.89. This is because both slip pathways mainly pass through the upstream embankment area, and their high correlation is reasonable. Since the upstream dam slope slip pathway 1 and the downstream deep slip pathway 3 are located on the upstream and downstream sides of the dam, respectively, their correlation is low, with a correlation coefficient of 0.33. Similarly, the correlation between the upstream deep slip pathway 2 and the downstream deep slip pathway 3 is also low, with a correlation coefficient of 0.26.
# Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments
## 2 Methods
### Lane-Changing Trajectory Model  
The vehicle is assumed to be travelling in a straight line and at the same longitudinal speed before changing lanes. To improve comfort, it is necessary to reduce longitudinal and transverse fluctuations. As such, the vehicle's transverse velocity, longitudinal displacement, lateral displacement and longitudinal acceleration are all zero at the initial moment.  
Figure 1: Architecture of the proposed lane-changing trajectory planning system.  
\[\left\{\begin{array}{l}x_{0}=0\\ y_{0}=0\\ \dot{y}|_{x=0}=0\\ \dot{y}|_{x=0}=0\\ y_{t}=w\\ x_{t}=l\\ \dot{y}|_{x=t}=0\\ \dot{y}|_{x=t}=0\\ \dot{y}|_{x=0}=0\\ \ddot{y}|_{x=t}=0\end{array}\right. \tag{8}\]  
where \(\left.\overset{\cdot}{y}\right|_{x=0}\) and \(\left.\overset{\cdot}{y}\right|_{x=t}\) are the lateral jerk at the start and end of the lane change, respectively. From the above formula, the reference path of the seventh-degree polynomial is calculated as:  
\[y_{7}=35\frac{w}{l^{4}}x^{4}-84\frac{w}{l^{5}}x^{5}+70\frac{w}{l^{6}}x^{6}-20 \frac{w}{l^{7}}x^{7} \tag{9}\]  
Comparing the lateral displacements under the constraints of a longitudinal vehicle speed of 72 km/h, a longitudinal distance of the obstacle ahead of \(l=50\) m and a lateral deflection of the vehicle \(w=3.75\) m, the lateral displacements under the third, fifth and seventh order polynomial planning are determined. The result is shown in Figure 2.  
The reference transverse angular velocity and lateral acceleration of the fifth polynomial are relatively smaller and simpler to calculate than those of the seventh order polynomial. In summary, the fifth polynomial provides both comfortable and efficient trajectories for changing lanes. Figure 2d shows fifth polynomial reference lane change  
Figure 2: (**a**) Comparison of different polynomial reference paths; (**b**) Comparison of lateral acceleration. (**c**) Comparison of reference yaw rate; (**d**) Lateral reference displacement of fifth order at different speeds.  
trajectories at different speeds. If the longitudinal speed of the vehicle is constant, the only variable of this expression is \(t_{f}\), so the analytic formula can be rewritten as:  
\[\left\{\begin{array}{l}x(t)=ut\\ y(t)=10\frac{w}{t_{f}}t^{3}-15\frac{w}{t_{f}}t^{4}+6\frac{w}{t_{f}}t^{5}\end{array}\right. \tag{10}\]  
In the geodetic coordinate system, the longitudinal speed of the vehicle remains constant when changing lanes, that is: \(u_{x0}=u_{x}(t)=u\). Due to \(x^{n}=u^{n}t^{n}\), \(l^{n}=u^{n}t^{n}_{f}\), divided by \(u^{n}\) to get Formula (10), where \(w\) is lane width, \(t\) is lane change time, and \(t_{f}\) is lane change end time. Different lane changing end times lead to different trajectories. The optimal lane changing trajectory will be selected from the fifth polynomial trajectory cluster.
## 6 Discussion  
By constructing extra-wide-lane observations to expedite ambiguity convergence, triple-frequency RTK has gained extensive research attention in recent years. However, many studies are based on mixed-frequency models (i.e., using both dual-frequency and three-frequency observations), mainly due to the limited availability of triple-frequency satellites during the research. With the provision of global services by BDS3 and Galileo, the number of available triple-frequency satellites has significantly increased. Therefore, in this paper, we conducted RTK with triple-frequency-only observations for six medium baselines. Experimental results show that triple-frequency-only RTK has little difference in positioning accuracy, fixing rate, and convergence time compared to the mixed-frequency model. However, the computation time of triple-frequency-only method is significantly shorter than the mixed-frequency model, which is beneficial for high-frequency on-embedded terminals.  
Compared to the RTK results in the high-latitude Tokyo area in [17], this study uses medium baselines at lower latitudes, where the double-difference ionosphere and troposphere delay residuals are more pronounced. However, their impact on wide-lane ambiguity resolution can still be ignored. Furthermore, for the 60 km baseline, this study's convergence performance using the GPS/Galileo/BDS three-system combination is significantly better than [17], which uses GPS/Galileo, indicating that the BDS can enhance multi-system RTK performance.  
Although there are the encouraging findings revealed by this paper, there are also limitations. First, the baselines we used are still situated at mid-latitudes, and the performance of the RTK method proposed in this paper in low-latitude regions remains to be evaluated. This article employs static station simulation for RTK positioning, without considering the performance of medium baseline RTK positioning in kinematic scenarios for vehicles. Moreover, due to the high quality of the observations, there is currently a great emphasis on the convergence time of RTK, with less attention given to its fixed reliability. In future work, further experiments and analysis will be conducted to address these issues.
## 2 Background  
### Technical Features  
From a technical point of view, parameters used to describe light and lighting need to previously be defined to explain the modifications made to the calculations. The term color temperature (CT) is applied to highly selective radiators, such as standard illuminant A, when the light of this source has the same, or nearly the same, chromatic coordinates as a blackbody radiator at certain temperature. Meanwhile, the correlated color temperature (CCT) of a light radiator is not exactly equal to any of the chromaticities of a blackbody radiator, and a CCT may be found for a selective radiator by taking the nearest chromaticity match. It is computed by the iso-temperature lines for the evaluation of a CCT for a selective source; these lines are short straight lines crossing the Planckian locus perpendicularly. Light sources with high CCT seem \"cool\", and those with low CCT look \"warm\". A color rendering index (CRI) is a quantitative measure of the ability of a light source to reveal the colors of various objects faithfully in comparison with a natural or standard light source. The chromaticity coordinates (x,y) of a color are the ratios of each tristimulus value of the color to their sum. A diagram in which any one of the three chromaticity coordinates is plotted against any other is called a chromaticity diagram [26].  
In visual photometry, spectral irradiance incident on the corneal plane is measured in the wavelength range of 380 nm to 780 nm. The CIE recommends, among others, two standard sources to be used in colorimetry specifications. One of them is the standard daylight D65 constructed to represent natural daylight having a CCT of 6500 K and, the other, the standard illuminant A intended to represent typical, tungsten-filament lighting with relative SPD, is that of a Planckian radiator at a CT of approximately 2856 K.  
The CCT is recurrently described in lighting specifications, making it freely available and frequently used as the most important parameter to control lighting design. Since visual evaluation is important, not only irradiance spectra, but luminance or illuminance levels for these types of nonvisual quantifications, retinal irradiance is the most relevant parameter. Even the combination of the spectral irradiance provided by artificial sources and natural daylight, including light reflected from the surfaces or a scene, might need to be accurately measured to determine its age-related circadian effects.
## 3 Offline Optimization and Adaptation Under Nonlinear Task Constraints
### Constraints Definitions in Joint Space and Cartesian Space
#### 3.2.4 Repellers  
The robot needs to avoid obstacles in the environment while performing tasks. Constraints between the robot and obstacles are called repeller constraints. Repellers constraints are imposed not only on the robot end effector but also on any other danger points on the robot link. In this paper, we choose three segments on the robot link in each direction: front, back, left and right, and use cylinders to envelop obstacles. Considering we only need to consider danger situations where the robot is apt to collide, we define the danger distance to express the repellers constraints. For three segments in four directions, we compute the shortest distance between segments on the robot link and the axis segment, with the minimum distance \(d_{\min}\) found afterward. If the minimum distance is greater than the safety radius \(R_{\text{safe}}\) of obstacles, the danger distance \(d_{\text{danger}}\) is 0, and if else, the danger distance is computed as \(d_{\text{danger}}=R_{\text{safe}}-d_{\min}\). We formulate the repellers constraint function as follows:  
\[c_{k,t}=\begin{cases}0,d_{\min}\geqslant R_{\text{safe}}\\ R_{\text{safe}}-d_{\min}(T(\mathbf{q}_{t}(\mathbf{\mu}_{t},\mathbf{\sigma}_{t}))),d_{\min} <R_{\text{safe}}\end{cases} \tag{28}\]  
Furthermore, we express the one-side inequality constraint to show the probability that the minimum distance is greater than the safety radius:  
\[P_{\mathbf{\mu}_{t},\mathbf{\sigma}_{t}}(c_{k,t}\leqslant 0)\geqslant\alpha_{k,t} \tag{29}\]  
The distribution of \(c_{k,t}\) is nonlinear in \((\mathbf{\mu}_{t},\mathbf{\sigma}_{t})\). We thus utilize our moment-matching method with Equation (23) to obtain the mean and variance of \(c_{k,t}\). The CDF of \(c_{k,t}\) is then computed using Gaussian distributions to denote the repellers constraint probability with Equation (29).
## 5 Predictive Simulation and Analysis
### Support Vector Machine (SVM)
#### 5.2.2 SVM Parameter Optimization and Kernel Function  
For a given sample, SVM penalty factor \(C\), selection of the kernel function and the determination of kernel parameter \(\delta\) are key issues affecting SVM learning ability and generalization ability. The purpose of introducing penalty factor \(C\) into SVM is to deal with fault tolerance at fault points near hyperplane boundaries. The penalty factor of an appropriate value can improve the system's robust fault tolerance. On the contrary, overfitting or underfitting will result in classification problems.  
The kernel function is very important to SVM. It makes raw data linearly separable and avoids the difficulty of a dimension disaster. There are many types of SVM kernel functions. The commonly used kernel functions include the linear kernel function, the polynomial kernel function, and the radial basis function (RBF) kernel function, respectively:  
\[K\left(x_{i},x_{j}\right)=x_{i}^{T}x_{j}, \tag{16}\]  
\[K\left(x_{i},x_{j}\right)=\left(x_{i}^{T}x_{j}+1\right)^{\delta}, \tag{17}\]  
\[K\left(x_{i},x_{j}\right)=\exp\left(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2 \delta^{2}}\right) \tag{18}\]  
For a linear kernel, the form of a function is simple. Therefore, it is more suitable for dealing with the linear separable problems. For the polynomial kernel function, its function form is more complex, and requires extensive calculation time. For the RBF  
(1) RMSE  
\[\text{MSE}=\sqrt{\frac{\sum_{i=1}^{n}\text{P}_{i1}-\text{P}_{i2}}{n}}, \tag{19}\]  
(2) R\({}^{2}\)  
\[\text{R}^{2}=\frac{\left[\sum_{i=1}^{n}\left(\text{P}_{i2}-\overline{\text{P}}_{i 2}\right)\left(\text{P}_{i1}-\overline{\text{P}}_{i1}\right)\right]}{\sum_{i=1}^ {n}\left(\text{P}_{i2}-\overline{\text{P}}_{i2}\right)\sum_{i=1}^{n}\left( \text{P}_{i1}-\overline{\text{P}}_{i1}\right)} \tag{20}\]  
where, P\({}_{i2}\) and P\({}_{i1}\) are the fatigue life value predicted by the model and the measured value, respectively, and n is the total number of test data.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 4 Case Studies of Multi-Channel and Multi-Hop QoS
### Implications of Results  
Setting up the QoS model requires making some assumptions about network characteristics, though that is a challenging task. For example, when \(P(int)=1\) is used in the case study, it is assumed that an adversary would be able to intercept all critical data in that link, whereas when \(P(int)=0.1\), it is assumed that an adversary is not likely to intercept any critical data in that link. When \(P(dcr)=1\), the assumption implies that no encryption is used, and when \(P(dcr)=0\), the assumption implies a somewhat unrealistic perfectly strong encryption algorithm and strong password are used to protect the data. These assumptions further imply knowledge about the adversary's fullest capabilities and intentions. Typically, those cannot be fully known. Despite that, the adversary's capabilities and intentions must be considered and included in the probabilistic aspect of these metrics. This model provides a framework to estimate what is possible within the current state-of-the-art and under a set of operational characteristics.  
Another complicating factor is that adversarial intentions or capabilities may rapidly change or may be influenced by different situations. Because the QoS model reflects a single moment in time, it may not capture those dynamic changes, or even the technical and environmental conditions within the network. New information, refined research, or updated environmental and systemic conditions may be used to update the model. These iterations point to the need for a model that is able to capture both the single snapshot in time and the dynamic, time-varying characteristics. The differences in static and dynamic results highlight the need for a simulation environment, which is the focus of future work. This simulation environment requires significant investment in understanding the network architectures and supporting protocols. Simulating the adversarial actor will provide further insight into a realistic perception of security.