esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preprocessing  
Data preprocessing is a critical step in preparing the collected sensor data for analysis. The min-max normalization was applied to scale the features to a fixed range--[0, 1]. This technique is essential to ensure that all features contribute equally to the machine learning models, especially those sensitive to the input data scale.  
The formula for min-max normalization is as follows:  
\[x^{\prime}=\frac{x-x_{min}}{x_{max}-x_{min}}\]  
where \(x\) is the original value, \(x^{\prime}\) is the normalized value, \(x_{min}\) is the minimum value of the feature, and \(x_{max}\) is the maximum value of the feature. By applying this normalization, all features are scaled proportionally, which helps mitigate the bias toward features with larger ranges and improves the overall performance of the machine learning models.
# Brake Light Detection Algorithm for Predictive Braking
## 5 Discussion
### Future Improvements to the Algorithm  
While conducting the experimental tests, it was noted that the algorithm suffered from some issues common to most machine vision algorithms, such as glares and reflections caused by sunlight. Future tests could include more sunny data with the sun being at different heights. The angle of the sun affects the glares and reflections captured by the camera, thus it could affect the accuracy of the algorithm. Nevertheless, it was noticed that the presented approach was rather robust towards rain. Raindrops on the windshield or light fog did not noticeably impact the performance of the algorithm. More application-specific issues in the tests included detected vehicles that were sideways, and different types of emergency vehicles that significantly differ from normal passenger vehicles in appearance. The presented test results highlight that the algorithm featuring the preprocessing displays a bias towards false negatives.  
As stated earlier, on some occasions the algorithm reacts to vehicles that are sideways, as seen in Figure 9. These false positives, in the picture marked with (e), occur quite rarely. This type of false identification could be solved by using other methods for vehicle rear detection. When utilizing other methods for vehicle rear recognition, attention should be focused on robust detection rates as well as on a fast algorithm run-time. One option could be a vehicle orientation analysis with a 3D bounding box estimation, separating different sides of the vehicle, thereby relaying only the rear to the brake light algorithm.  
The conducted experiments were measured among real traffic on relatively straight roads. Sharp turns or steep inclinations were not featured in the gathered dataset. In all of these cases, the detection can be affected as the vehicle ahead may move out of the region of interest in the camera view. In future research, these types of scenarios could be recognized utilizing sensor data of the steering angle or the vehicle accelerometer. This type of approach would solve such cases to some extent.
# Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design
###### Abstract  
Differential evolution (DE) is one of the best evolutionary algorithms (EAs). In recent decades, many techniques have been developed to enhance the performance of this algorithm, such as the Improve Self-Adaptive Differential Evolution (ISADE) algorithm. Based on the analysis of the aspects that may improve the performance of ISADE, we proposed a modified ISADE version with applying the Gauss distribution for mutation procedure. In ISADE, to determine the scaling factor (F), the population is ranked, then, based on the rank number, population size, and current generation, the formula of the Sigmoid function is used. In the proposed algorithm, F is amplified by a factor which is generated based on Gaussian distribution. It has the potential to enhance the variety of population. In comparison with several reference algorithms regarding converging speed and the consistency of optimal solutions, the simulation results reveal the performance of the suggested algorithm is exceptional.  
differential evolution; Gauss distribution; mutation procedure; optimization algorithm 2023
## 3 Test Results
### Load-Displacement Curve Analysis
#### 3.4.2 Load-Displacement Curve of SRRAC Columns  
According to Figure 11, as the temperature increase, the axial load-displacement curve of the SRRAC columns tends to be flat, and the value of the peak point gradually decreases. Loading process of SRRAC columns mainly includes elastic stage, elastic-plastic stage, stiffness strengthening stage, descending stage and residual stage. The characteristics of each stage are as follows:  
During the elastic stage, the elastic deformation of the section steel and concrete is coordinated, and there is no crack in the concrete surface.  
The elastic-plastic stage of specimens is as follows: The cracks on the concrete surface emerge and develop continuously after the load exceeds the elastic limit. And then, section steel and longitudinal reinforcement yield gradually. The load-displacement curve at this stage is nonlinear. And bond cracks appear at this stage, therefore, the bond slip between section steel and concrete is more obvious.  
The stiffness strengthening stage of specimens is as follows: The load-displacement curve of the specimen appears obvious curvature rising stage before reaching the elastic limit when the exposure temperature of specimen is greater than 600 degrees centigrade. Because the evaporation of free water in the internal void of concrete makes the concrete loose. The loose concrete becomes dense with the increase of compressive load during the loading process. Therefore, the stiffness of the specimen has been improved.  
During the descending stage of specimens, the bearing capacity of the specimen decreases obviously after exceeding the limit load.  
The residual stage of specimens is as follows: The load decreases gently with the increase of displacement. The longitudinal reinforcement and concrete cover have basically not provided bearing capacity at all. Residual strength is provided by section steel and core-concrete.  
Figure 10: Load-displacement curve of rectangular prisms specimens.
# Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids
## 3 System Models
### Algorithm Improvement under Static Nodes
#### 3.3.4 Description of Algorithm Flow  
The SERP protocol algorithm is described as follows: First, initialize the parameters after completing the arrangement of sensor nodes and base station nodes. Then, calculate \(R_{i}\), the cluster radius of each node and ratio of cluster heads \(p\), the optimal cluster head ratio according to Formulas (10) and (12). Determine \(T(s_{i})\), the cluster head selection threshold for each node by the distance factor \(D_{f}\) and the energy factor \(E_{f}\), select the cluster head by comparing it with a randomly generated random number within the range of 0-1, and update the G set. Then the cluster heads broadcast the information of the selected cluster heads, and the non-cluster head node searches for the nearest cluster head within its cluster radius \(R_{i}\) to send a join request. After completing the stage of cluster establishment, proceed with the data transmission stage. The nodes within the cluster send data to the cluster head according to the time slots as scheduled by TDMA. The nodes may shut down themselves and sleep while waiting for the time slot. Complete the algorithm operation if the energy of all the nodes within the system area is depleted or the number of running rounds reaches the maximum after completing the data transmission stage. Otherwise, continue to start a new round of cluster establishment from the calculation of the cluster radius and other parameters. The algorithm flow chart of the SERP protocol is shown in Figure 4.
## 3 Routing Protocols for UWSN
### Protocols Featuring Energy Efficiency
#### 3.4.3 Sparsity-Aware Energy efficient clustering (SEEC)  
In schemes like DBR and EDBR, two things are considered while choosing the next forwarding node during routing. These include how close the nodes are to the surface and their remaining power. This creates several coverage gaps which affects the life span of the network badly. Thus, for improving network lifetime along with stability and energy efficiency [PERSON] et. al propose a unique scheme called SEEC [61].  
Here, depth finding modules are attached to every randomly scattered sensor node of the network. The whole network region is divided into a few sub regions of equal areas. The denser regions of the network are sorted out on the basis of a novel algorithm for finding the denser regions. Similarly, with another dedicated algorithm the sparse areas are also found out. Sinks are used to collect the data from the sparse areas. On the other hand, a clustering scheme is employed to extract the data from the denser areas. On the basis of lower depth and high residual energy, one of the nodes from each cluster is selected as the cluster head. This head has to be the closest node to the water surface in compared to the cluster members. Moreover, its residual power has to be more than the average residual power of the clustering nodes. Data transmission in dense region is completed in three layers. At first the data is collected and aggregated from the member nodes. After that, it is sent to their respective heads. At last, all the heads of the clusters send this data to the nearby static or mobile sink. In the case of the sparse region, two moving sinks are used to extract the data from the nodes. For better understanding, these two sinks are supposedly being designated as MS1 and MS2. One of these two sinks (MS2) remains fixed at the most sparse areas. Other sink-MS1 travels from the topmost sparse areas to the least sparse areas in every round. It is mentionable that region of MS2 is not under the consideration of MS1 mobility and MS2 remain fixed in its position until the death of each node of the region. After that MS2 moves to the topmost sparse region for the rest of the sparse nodes. These sinks always choose the center position of the whole region to travel. By this they can cover the maximum number of nodes within their transmission range. Mobile sinks move from one sparse region to another sparse region periodically and eventually collect the data from all the sparse regions. SEECemploys the moving sinks for the sparse areas where it uses clustering schemes for the dense network. This makes this scheme to be more energy efficient than other two schemes like DBR and EEDBR. Moreover, SEEC also shows a longer life-span and give a better packet delivery ratio than these two mentioned schemes. More over, clustering technique gives SEEC a better stability period but it has lower throughput than DBR and EEDBR.
## 3 Results and Discussion
### Calculation of Intensification Indices
Again, the \(k_{\text{i}}\) values determined in the model for FPCR remained constant in the FPCR + \(Y_{\text{cat}}\) and CPC model, confirming the validity of presented semiempirical models. The applied modelling approach assumed the calculations of \(Y_{\text{RD}}\) and \(Y_{\text{cat}}\) that are specific for upgraded reactor geometry and photocatalyst formulation enhancement, respectively. This means that calculated intensification indices should be the same in default settings. The uniformity of calculated \(Y_{\text{RD}}\) and \(Y_{\text{cat}}\) (Table 2) confirmed the validity of applied approach. The calculated intensification indices show a good match independent of the studied model solution. The \(Y_{\text{RD}}\) and \(Y_{\text{cat}}\) were found to be 6.77 and 1.64 with reasonably low standard deviation. The value of \(Y_{\text{RD}}\) suggests that intensification by reactor design is the correct choice for optimization and further scale-up of reactors engaged in solar photocatalysis. Having in mind only 4.11% of available UV irradiation from natural sunlight, the capture and multiplication of incident irradiation in the reaction space utilizing CPC-based reactors are general design goals. The \(Y_{\text{cat}}\) values suggested approximately 60% better photocatalytic efficiency due to the sensitization effect from CNT in film formulation. Both intensification choices, either by reactor design or photocatalyst formulation, are necessary for real-scale solar photocatalysis applications. The presented methodology for calculation of intensification indexes is simple, robust and applicative in further research. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Solution** & **CT** & **HQ** & **RS** & **CT+HQ** & **CT+RS** & **HQ+RS** \\ \hline \(k_{\text{i}}\), s\({}^{-1}\)W\({}^{-0.5}\)m\({}^{1.5}\) & & & & & & \\ CT & \(3.1\times 10^{10}\) & - & - & \(4.0\times 10^{10}\) & \(4.1\times 10^{10}\) & - \\ HQ & - & \(4.4\times 10^{10}\) & - & \(4.7\times 10^{10}\) & - & \(4.7\times 10^{10}\) \\ RS & - & - & \(4.9\times 10^{10}\) & - & \(8.1\times 10^{10}\) & \(8.2\times 10^{10}\) \\ \hline \(\mu_{\text{UWB}}\), **m\({}^{-1}\)** & & & \(2.0\times 10^{7}\)[40] & & & \\ \hline \(\mu_{\text{UWB}}\), **m\({}^{-1}\)** & & & \(7.3\times 10^{6}\)[40] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Kinetic model parameters. Figure 7: Comparison of experimental data and model values for degradation kinetics CT, HQ and RS in single pollutant model solutions (**a**–**c**) and binary mixtures (**d**–**f**) in FPCR and CPC using TiO\({}_{2}\)-GF and TiO\({}_{2}\)/CNT-GF (marked in legend). Models FPCR, FPCR+\(Y_{\text{cat}}\) and CPC relate to Equations (2)–(4), respectively.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
###### Abstract
An RIS enables enhanced signal coverage and quality in obstructed environments, which is a critical limitation in UAV-based communication due to dynamic channel blockages. The passive nature of RISs makes them a low-power, lightweight solution, which is essential for mobile UAV platforms that have stringent energy and payload constraints. The high speeds of UAVs and vehicles lead to rapid changes in channel conditions, character  
Figure 1: RIS-enhanced 6G NTN architecture integrating satellites, UAV-based aerial relays, and terrestrial base stations for seamless connectivity in dynamic environments. ized by significant Doppler shifts and short coherence times [11, 12]. This requires channel estimation techniques capable of tracking these rapid variations, which is particularly challenging given the large number of passive RIS elements [13]. Traditional channel estimation methods often fail in these scenarios, and there is a need for efficient and accurate channel estimation, as imperfect channel state information (CSI) can significantly degrade the performance of RIS-assisted systems [14]. Static optimization techniques, which are calculated offline, are not suitable for high-mobility scenarios, and there is a need for real-time adaptation to changing channel conditions, which requires dynamic optimization algorithms [15]. This paper aims to address this gap by proposing a comprehensive framework for the optimization of RIS-assisted NTNs in high-mobility scenarios. We focus on the following key aspects:  
* Joint optimization of RIS phase shifts: We develop an algorithm to optimize RIS phase shifts in real time, considering the dynamic nature of NTNs/UAVs and user mobility. * Performance metrics: We evaluate system performance using multiple metrics, including SINR, throughput, energy efficiency, outage probability, and latency. * Scenario-based analysis: We analyze the system performance under various real-world scenarios, serving dense ground, indoor, or aerial user populations in dynamic 3D environments. The remainder of this paper is organized as follows. Section 2 presents related work and identifies the gaps in the field of UAVs that our work addresses. Section 3 describes the system model and the formulation of problems. Section 4 presents the proposed optimization framework. Section 5 discusses the simulation results and performance analysis. Finally, Section 6 concludes the paper and outlines future research directions.
## 2 Materials and Methods
### Experimental Procedures  
This study comprises three experimental procedures, as shown in Figure 1. First, we selected wavelengths in NIR range that correspond to the second overtone absorptions of OH and CH\({}_{2}\) groups  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Calibration Set (120 Samples)**} & \multicolumn{4}{c}{**Prediction Set (60 Samples)**} \\ \cline{2-9}  & **Min** & **Max** & **Mean** & **S.D.** & **Min** & **Max** & **Mean** & **S.D.** \\ \hline Weight (g) & 120 & 167 & 145 & 5.8 & 116 & 155 & 140 & 6.5 \\ Vitamin C (mg/100 g) & 45 & 60 & 53 & 2.5 & 45 & 60 & 52 & 2.3 \\ \hline \hline \end{tabular} S.D.: Standard Deviation.  
\end{table}
Table 1: Statistics of the weight and vitamin C of Indian jujube.  
in vitamin C (1000-1650 nm) as probing light sources. The probing light sources were constructed using a parallel-polarized halogen light source and NIR bandpass filters. The central wavelength of the bandpass filters were varied in 50 nm intervals, which allowed us to determine the optimal combination of wavelengths for vitamin C determination.  
In the second step, a predictive model for the nondestructive measurement of vitamin C in Indian jujube was constructed. The optimal wavelengths provided in Step 1 were parallel-polarized and designed to be incident at the Brewster angle (i.e., \(\theta_{\text{i}}=\theta_{\text{B}}\)) on the 120 Indian jujube samples of the calibration set. HPLC analysis was then performed to determine the vitamin C contents of these samples, while the PLS algorithm was used to relate the reflectance of the fruits' flesh with their vitamin C contents to construct a predictive model for the vitamin C contents of Indian jujube fruits in the calibration set.  
In the third step, the predictive model that was created using the calibration set was used to perform nondestructive vitamin C measurements on fruits in the prediction set. The reflectances of the 60 Indian jujube samples in the prediction set were substituted in the aforementioned predictive model to predict the vitamin C value of each Indian jujube fruit. Destructive vitamin C measurements (HPLC analysis) were then performed to obtain the actual vitamin C values of the fruits in the prediction set. The coefficient of prediction (r\({}_{\text{P}}^{2}\)) and root-mean-square error of prediction (RMSEP) of the predicted values were then calculated.
## 3 Results and Discussion
### Comparison with Kriging-Based Optimization  
For comparison, the Kriging-based optimization approach is used to tackle the heat generation performance optimization problem (which is called KBO for short in this paper). Both the PLCB-based adaptive Kriging optimization method (PLCB-AKO for short) and KBO employ the fast LHD to generate the initial sample points.  
When using the KBO technique, sample sizes of 30, 40, and 50 are set, and the starting sample size for Kriging in the suggested PLCB-AKO approach is set at 20. LHD is used to select sample points for Kriging modeling in KBO. To assess the precision of the Kriging approximation at the last iteration, three metrics are calculated: (1) relative root mean square error (RRMSE), (2) relative maximum absolute error (RMAE) and (3) relative average absolute error (RAAE). An estimate is more accurate if RRMSE and RAAE are smaller. On the other side, a big RMAE indicates that there is a localized sector of the design space where there is poor approximation. RMAE is therefore anticipated to be as minimal as is feasible. The three error measurements mentioned above are stated as follows:  
\[RRMSE=\frac{1}{STD}\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{{{(y_{i}-\hat{y}_{i} )}^{2}}}},RMAE=\frac{1}{STD}\text{max}|y_{i}-\hat{y}_{i}| \tag{19}\] \[RAAE=\frac{1}{N\times STD}\sum\limits_{i=1}^{N}{{{(y_{i}-\hat{y} _{i})}^{2}}},STD=\sqrt{\frac{1}{N-1}\sum\limits_{i=1}^{N}{{{(y_{i}-\overline{y })}^{2}}}}\,i=1,\ldots,N\]  
in which \(N\) is the total number of validation points; \(\overline{y}\) and \(y_{i}\) stand for the observed mean values and the actual responses, respectively, while \(\hat{y}_{i}\) indicates the predicted value.  
Table 2 shows the prediction accuracy of the Kriging approximation using various sample sizes in the KBO technique. It is obvious from Table 2 that the prediction accuracy of the Kriging approximation rises as the sample size grows.  
Figure 7: Outlet temperature and heat-generating power of different schemes.  
Table 3 presents the PLCB-AKO approach's prediction accuracy during the most recent iteration, during which nine new samples were added using PLCB. It is proved that the proposed PLCB-AKO strategy used far fewer sample points, which results in far fewer computer simulations being run and a significant reduction in computing load.
## 3 The Proposed Optimization Procedure
### Procedure of GARS Algorithm  
To sum up, the whole optimization strategy followed by the proposed GARS can be illustrated through the following steps:  
1. Initialization of parameters \(N_{pop}\), \(p_{cr}\), \(p_{m}\), \(D_{max}\), \(N_{best}\), \(N_{BRS}\), \(\alpha\);
2. Generation of initial random population of chromosomes;
3. Application of crossover operator, chosen between Position Based and Two Point Crossover, to a couple of chromosomes chosen through roulette-wheel selection;* Generation of the new population after crossover operator through the insertion of the two best chromosomes individuated between parents and offspring: the two individuals with best values of fitness are introduced in the population;
* Evaluation of \(p_{m}\). If it is verified go to Step 6, else go to Step 7;
* Application of mutation operator, chosen among two different operators: Allele Swapping and Block Swapping. The operator is applied randomly to a chromosome of the population;
* Application of BRS procedure to the \(N_{best}\) best individuals of the population;
* Population control: a mutation operator is applied on duplicates exceeding \(D_{max}\).;
* Updating of the current population, then return to Step 3.
_Appl. Sci._**2015**, \(5\), 1773-1789; doi:10.3390/app5041773  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**On the Design of an Elegant CFO Estimate System with the Assistance of Pilots for OFDM Transmission**  
**[PERSON] \({}^{1}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) Department of Electronics Engineering, National United University, No. 2 Lien-Da, Miaoli 360, Taiwan; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Optoelectronics and Communication Engineering, National Kaohsiung Normal University, No. 62, Shenjhong Road, Yanchao, Kaohsiung 824, Taiwan  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-7717-2930 (ext. 7716); Fax: +886-7605-1146.  
Academic Editor: [PERSON]  
_Received: 6 October 2015 / Accepted: 8 December 2015 / Published: 12 December 2015_  
**Abstract:** We present an improved line search method of the carrier frequency offset (CFO) estimate technique for OFDM systems aided with pilots. Unlike the conventional approaches, prior knowledge of channel order is unnecessary for the presented scheme, because we apply an interpolation technique for obtaining the extended channel frequency response. The search mechanism of the proposed approach is primarily based on the criterion that CFO can be found by discovering the frequency that achieves the minimum value of the well-defined channel residual energy. In addition, we substitute the line search method with an iterative approach to reduce the proposed estimator's complexity. Analytical and simulation results have been conducted to verify the efficacy of the proposed schemes in this paper.  
**Keywords:** orthogonal frequency division multiplexing (OFDM); carrier frequency offset (CFO); pilot-based CFO estimation; interpolation; iterative