esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Robustness to Noise
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **67 th Slice** & \multicolumn{3}{c}{**96 th Slice**} \\ \hline
**Model** & **Iteration** & **Time (s)** & **Iteration** & **Time (s)** \\ \hline MICO & 9 & 4.79 & 8 & 3.97 \\ BCFCM & 26 & 25.77 & 23 & 22.56 \\ FLICM & 44 & 6.61 & 40 & 5.83 \\ RC\_LCI & 11 & 3.83 & 9 & 3.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The iterations and CPU time of MICO, BCFCM, FLICM, and RC_LCI. Figure 3: The segmentation results of RC_LCI compared with MICO, BCFCM, and FLICM on real images: (**a**) original real brain MRI; (**b**) segmentation results of MICO; (**c**) segmentation results of BCFCM; (**d**) segmentation results of FLICM; and (**e**) segmentation results of RC_LCI. where \(S_{1}\) and \(S_{2}\) are two segmentation results. \(|\cdot|\) shows the number of pixels in the corresponding area. The more accurate the segmentation results, the higher the JS. The JS coefficient for GM, WM, and CSF acquired from MICO, BCFCM, FLICM, and RC_LCI are shown in the boxplots of Figure 4. As shown in Figure 4, RC_LCI had the highest JS compared with the other three models and had no outliers in its JS, which revealed that RC_LCI not only had better robustness to noise but also higher accuracy than the other three models. Both visual and quantitative evaluations showed that the RC_LCI model was more robust to different noises and could obtain more accurate results than the other models, whether considering synthetic or real images.
# Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge
## 6 Experiments and Analysis
### Data Transmission under Distributed Environment  
READ is a distributed algorithm. In a distributed environment, data will be transmitted between each observation node and the global server, including:  
* REC from observation node to the global server;
* Candidate super points from the global server to each observation node;
* The LE set of candidate super points from each observation node to the global server.  
In the above data, the size of REC is fixed. The size of candidate super points and LE in transmission depends on the number of candidate super points. From the running process of READ, it can be seen that the candidate super points generated by READ when running in a single node environment are the same as those generated when running in a distributed environment. Therefore, the number of candidate super points generated at runtime under a single node can be used to determine the size of data transmission between observation nodes and the global server in a distributed environment.  
Table 4 lists data transmission between each observation node and the global server. The number of candidate super points is the number of candidate super points produced by REC. The size of candidate super points is multiplied by 4 bytes (each IPv4 address size is 4 bytes); the size of candidate super points' LE is multiplied by \(2^{11}\) bytes (LE contains \(2^{14}\) bits, \(2^{11}\) bytes). The total amount of data transmitted is the sum of the size of REC, the size of candidate super point and the size of LE of candidate super points. The master data structure size is the sum of REC and LEV. The percentage of transmitted data is the ratio of the total amount of transmitted data to the size of the master data structure. From Table 4, it can be seen that the average amount of data transmitted by READ between the global server and each observation node is not more than 7.5 MB, which only occupies less than 2.3% of the total size of master data structure.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 3 Field Experiment of the Path Planning Techniques on the Fish Farm  
This work is intended to validate the conclusions derived from our simulations by implementing these algorithms onto drone hardware. We apply the three methods to pond coordinates in the Southern Illinois University Aquaculture Research Center and returns a set of routes for each method. These routes are then loaded onto a set of three drones, and power measurements from the drones are recorded after they fly their missions to conduct their measurements.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty  
[PERSON]  
1 School of Mechanical Engineering, Shandong University, Jinan 250061, China; [PERSON]_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (Q.S.)1 School of Mechanical Engineering, Shandong University, Jinan 250061, China; [PERSON]_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (Q.S.)1 School of Mechanical Engineering, Shandong University, Jinan 250061, China; [PERSON]_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (Q.S.)1 School of Mechanical Engineering, Shandong University, Jinan 250061, China; [PERSON]@mail.  
a lumped disturbance term was adopted to represent the combined effect of the parameter uncertainty and external disturbance. Moreover, most of these previous works [5; 6; 7; 8; 9; 10; 11; 12; 13; 14] made the assumption that the lumped disturbance term was smooth, slowly varying and bounded. Actually, the parameter uncertainty is coupled with the system states. High frequency chattering, which is the main drawback of the DSMC, always exists in the system states. Hence, the aforementioned slow varying assumption may not hold. Moreover, it also is inappropriate to mix the parameter uncertainty with external disturbance since they have different characteristics.  
This paper reconsiders and reevaluates the parameter uncertainty along with its compensation in the DSMC system. Unlike existing similar works, this paper exhibits the following two merits simultaneously:  
1. The stability of a DSMC system with parameter uncertainty is reevaluated analytically. Different from previous methods, parameter uncertainty is no longer regarded as part of the lumped disturbance but analyzed separately. The coupling relationship between parameter uncertainty and system state is considered.
2. The performance of six state-of-the-art disturbance compensators including NSDE, OSDE, TSDE, SDC, DDC, and DTDO for parameter uncertainty compensation is comprehensively studied. The theoretical bases of the compensators are presented and compared in detail.  
The remaining parts of this paper are organized as follows. The closed-loop stability of a DSMC system with parameter uncertainty is analyzed in Section 2. The comparative investigations of six state-of-the-art disturbance compensators are carried out in Section 3. Section 4 presents a series of simulation investigations and discussions on the control performance. Section 5 summarizes this paper.
## 4 Pca-Elm  
Figure 2 indicates the approach to the integration of PCA and ELM, which is split into three segments. Segment 1 is the selection of the input dataset. A thermal power plant is built with huge machines and other equipment, where a lot of parameters will affect their efficiency. The efficiency of the machines and equipment affects the efficiency of the thermal power plant. A boiler's efficiency is purely based on parameters such as the inlet steam pressure, inlet steam temperature, outlet steam pressure, and outlet steam temperature, etc. So, a dataset consisting of the values of these parameters is selected as the input data. The principal component analysis is performed in Segment 2 on the selected input data. The principal components i.e., a new dataset with reduced dimensions, are set as the input to ELM. Segment 3 details ELM development and execution. The new data obtained by Segment 2 is further divided into training and testing datasets. The ELM model is simulated, and errors are calculated.  
The testing accuracy of the developed model can be determined from several errors such as the Mean Square Error--mean of the square of the resulted error; Mean Absolute Error--mean of the absolute values of the error; Root Mean Square Error--square root of the mean square error; Mean Absolute Percentage Error--percentage value of the mean absolute error. For \(K\) number of samples, the actual and predicted value for each sample \(u\) are given by \(y_{u}\), \(\hat{y}_{u}\), respectively. Then, the errors are determined using Equations (21)-(24). At last, the network model with the least errors is preferable.  
\[\text{Mean Squared Error}\;(\text{MSE})=\frac{1}{K}\sum(y_{u}-\hat{y}_{u})^{2} \tag{21}\]  
\[\text{Mean Absolute Error}\;(\text{MAE})=\frac{1}{K}\sum(y_{u}-\hat{y}_{u}) \tag{22}\]  
\[\text{Root Mean Squared Error}\;(\text{RMSE})=\sqrt{\frac{1}{K}\sum(y_{u}-\hat{y}_{u}) ^{2}} \tag{23}\]  
\[\text{Mean Absolute Percentage Error}\;(\text{MAPE})=\frac{100}{K}\sum\left|\frac{y_{u}- \hat{y}_{u}}{y_{u}}\right| \tag{24}\]
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Solution of URLLC Allocation Problem
```
Input \(\mathbf{\Phi}^{m}\in\left\{\Phi_{e}^{*},\Phi_{u}^{*},\Phi_{e,u}^{*}\right\}\) Output Optimal solution \(\mathbf{P}_{L}^{m^{*}}(\mathbf{\Phi}^{m}),\mathbf{I}^{m^{*}}(\mathbf{\Phi}^{m}), \mathbf{k}^{m^{*}}(\mathbf{\Phi}^{m})\) Procedure for\(\mathbf{\Phi}^{m}\in\left\{\Phi_{e}^{*},\Phi_{u}^{*},\Phi_{e,u}^{*}\right\}\)do /* solve problem \(P2\)*/  initialize \(\mathbf{I}^{m}\) and \(\mathbf{P}_{L}^{m}\), and calculate \(\mathbf{I}^{\max,m}\); \(\mathbf{C}_{ub}=\left\{C_{ub}^{(1)},\cdots,C_{ub}^{(U)}\right\}\); /*descending order*/ \(\mathbf{\beta}_{e}^{m}=\left\{\mathbf{\beta}_{e}^{m(1)},\cdots,\mathbf{\beta}_{e}^{m(E)}\right\}\); /*ascending order*/ for\(l=1:L^{m}\)do \(c_{l}^{temp}=0\); for\(e=1:E\)do \(l_{e,l}^{temp}=\left\lfloor\frac{c_{l}-c_{l}^{temp}}{c_{l}(P_{ub})}\right\rfloor\); if\(l_{e,l}^{temp}\leq l_{e,m}^{\max,m}\)then \(l_{e,l}^{m}=l_{e,l}^{temp}\); boolean = 1;break; else \(l_{e,l}^{m}=l_{e}^{\max,m}\); \(c_{l}^{temp}=l_{l}^{temp}+l_{e,l}^{m}\ast c_{l}(P_{eb})\) ifboolean = 1 then \(p_{l}^{m}=\frac{\sum\limits_{e=1}^{E}I_{e,l}P_{ub}}{\sum\limits_{e=1}^{E}I_{e, l}}\);  update \(\mathbf{I}^{\max,m}\); else end if  get the optimal solution \(\mathbf{P}_{L}^{m^{*}}(\mathbf{\Phi}^{m}),\mathbf{I}^{m^{*}}(\mathbf{\Phi}^{m}),\mathbf{ k}^{m^{*}}(\mathbf{\Phi}^{m})\)  end for
```  
**Algorithm 3** Heuristic URLLC allocation algorithm based on RIS
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Optimization of Culture Conditions for Biofoculant Production
#### 3.2.4 Effect of Initial pH of Production Medium on Biofloculant Production  
The initial pH of the culture medium does not only have an effect on the electric charge of the cells and the oxidation-reduction potential but also affects nutrient absorption and enzymatic reaction within cells [49]. Hence, the effect of initial pH of the culture medium on biofloculant production was examined at pH values ranging from 3 to 11 and the results are presented in Figure 4. As can be seen from the Figure, the biofloculant was significantly produced within a pH range of 7.0-9.0 with the optimum flocculating activity (80.6%) being observed at pH 7.  
At both acidic pH (3-6) and high alkaline pH (10-11) ranges, flocculating activity was significantly reduced with no flocculating activity being observed at pH 11. Initial medium pH 7 was then used for  
Figure 4: Effect of initial medium pH on biofloculant production.  
Figure 3: Effect of inoculum size on biofloculant production.  
all subsequent experiments, which is a neutral pH suggesting that large amounts of acid and alkali used to adjust pH would be saved [22]. Consequently, production costs would be anticipated to be lower in the event of applying _Bacillus pumilus_ in biofoculant production.  
[PERSON] et al. [50] and [PERSON] et al. [51] observed similar neutral pH values for biofoculants produced by _Halomonas_ sp. OKOH and _Aspergillus flavus_, respectively. A biofoculant produced by _Klebsiella_ pneumonia also reached its highest flocculating activity at pH 7 [14]. However, the initial pH of the medium required for optimum biofoculant production differs with different organisms. The optimum production of biofoculants by _Bacillus_ xn12 and _Streptomyces_ xn17 strains was observed at pH 5 [52]. For biofoculant production by _Arthobacter_ sp. B4, it was observed that the highest flocculation was attained at a pH value above 12 [53].
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 2 Mutual Induction Model: The Static Aspects  
### Preliminary Considerations  
In IPT applications, the mathematical modeling approach of high-frequency electromagnetic coupling, with lumped parameters, allows two categories of parameters to be distinguished; these describe two different classes of phenomena: static phenomena and dynamic phenomena.  
The parameters that are capable of modeling static phenomena are those that have a negligible dependence on the excitation frequency and are only able to vary their own value consequent to an induced variation of the intrinsic geometric configuration of the application. Among these are the parameters of self-inductance \(L_{ii}\) and \(L_{jj}\), mutual-inductance \(L_{ij}\) and \(L_{ji}\) and, consequently, the coupling coefficient \(K_{ij}\).  
On the other hand, parameters classified as dynamic are those that are able to describe phenomena highly dependent on the excitation frequency of the primary coil. These are the ferrite core losses and the copper losses in the wound wires.  
This section presents an in-depth analysis of the governing equations and modeling techniques used during the research to define the static parameters, while Section 3 discusses the most fitting mathematical models available in the literature for the estimation of the dynamic aspects.
## 1 Introduction
Furthermore, a signal timing optimization model that considers both efficiency and fairness is constructed. Finally, the effectiveness of the model is validated using case studies, and sensitivity analysis is conducted on intersection saturation. The Technical roadmap is shown in detail in Figure 1. The first section of the paper summarizes the current research status in related fields domestically and outlines the main research content. The second section delves into the fairness of each phase of the Webster model from both theoretical and simulation perspectives. The third section introduces a fairness evaluation function based on information entropy, upon which a signal optimization model for low-saturation intersections considering delay and fairness is built. In the fourth section, the effectiveness of the model is analyzed using case studies, and sensitivity analysis is conducted on intersection saturation. Figure 1: Technical roadmap.
# Review of Autonomous Intelligent Vehicles for Urban Driving and Parking
## 3 The Concepts of Autonomous Driving
### Parking  
If a reservation is not made in advance, the autonomous vehicle will assist by using sensors installed to scan for an empty car lot to prevent the driver from driving past an empty lot. [PERSON] et al. [104] categorized empty lot detection into seven methods: (1) ultrasonic sensor-based, (2) short-range radar sensor-based, (3) image processing, (4) motion stereo-based method, (5) binocular stereo vision-based method, (6) light stripe projection-based method, and (7) scanning laser radar-based. Ultrasonic sensors and radar sensors work in the same way by detecting the distance between the sensor and targeted object based on the data collected to determine the occupancy status [104; 105; 106; 107]. The concept of image processing is similar to car park monitoring, as discussed earlier. The only difference is the camera is installed on the vehicle body. Stereo-based methods first construct a 3D map [108; 109] then designate the target position. The light stripe projection-based method recognizes 3D information by analyzing the light stripe made by a light plane projector reflected from objects [110].  
Finally, after identifying an empty parking lot, the autonomous vehicle completes the final task by maneuvering itself into the lot. Although there are several different types of parking lots such as parallel, perpendicular, and fishbone parking [111; 112], autonomous parking usually consists of two steps: (1) optimal path planning and (2) path following/tracking [112; 113; 114]. Using data collected from a combination of sensors, the optimal path planning algorithm begins by generating a suitable collision-free path from a given starting point to a required position within the parking lot that satisfies all kinematic constraints. The parking control scheme then adopts a step-by-step control strategy that compares the current position and requires the position to choose the steering action at the current position [115].  
Maneuvering a vehicle into a parking spot of limited space is often challenging, especially for novice drivers, and carries the risk of expensive damage [116; 117]. Due to this problem, novice drivers are reluctant to try parking in smaller lots and cruise around for another empty lot. This contributes to additional air pollution, fuel consumption, and congestion [114].
###### Abstract
They have been used in several application domains, such as education, business, finance, cultural heritage, health and military training. In particular, in the healthcare domain the aim is to introduce innovative methods in the care, general health and rehabilitation processes, where the patient is less dependent on professionals. Serious games can be designed to bring about some behavior change in the patient, whether it is for prevention, treatment or for information about the disease. The general goal of this work is to contribute to the research in the field of early MCI detection. Since MCI is a characteristic precursor of AD and other neurodegenerative conditions, early diagnosis is critical to restrain cognitive deterioration through preventive and rehabilitation measures. In the relevant literature, one can find numerous references to studies where serious games are utilized to support cognitive screening [12] or even rehabilitation [13] in a more engaging and fun way [14]. However, the specific objective of this work is to answer the research question of whether game performance data gathered during playing several sessions of serious games that were specifically designed for cognitive assessment and training of elderly people can be utilized to create machine learning (ML) models that could accurately classify users to the right cognitive state. The ultimate goal would then be, to make use of these models to classify new users to distinct cognitive levels judging by their in-game performance. The challenges that must be addressed in order to build such a model and to provide a service that would enable access of such a model for new data, were also investigated in this work.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 3 Background of V2X  
### NR-V2X Communication Overview  
V2X communication encompasses interactions between vehicles and their surroundings, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Network (V2N), as shown in Figure 1. Its primary goals are enhancing road safety, optimizing traffic management, and supporting autonomous driving by ensuring high reliability, low latency, and high capacity through advanced wireless technologies.  
NR-V2X, developed as the successor to LTE-V2X, addresses the stringent requirements of autonomous driving with key technological advancements [29]. It supports both mmWave and sub-6 GHz bands, enabling ultra-high-speed data transmission necessary for real-time communications in complex vehicular networks [30]. Additionally, its dynamic subcarrier spacing ensures adaptability to varying communication environments, while dual resource allocation modes--distributed and base station-based--provide flexibility in areas with limited infrastructure.  
NR-V2X introduces significant improvements over LTE-V2X, including advanced scheduling algorithms that reduce delays, improved retransmission mechanisms such as Hybrid Automatic Repeat reQuest (HARQ), and enhanced interference management [31; 32]. Furthermore, the addition of new physical channels, such as the Physical Sidelink Feedback  
Figure 1: C-V2X Communication types: Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Pedestrian (V2P) communications.  
Channel (PSFCH), ensures reliable groupcast and unicast communication, particularly in challenging or densely populated environments. These innovations make NR-V2X a critical technology for the future of autonomous driving and advanced vehicular networks.
### Types of Antenna Systems Used in Underground Mining
#### 1.2.5 Multiband Antennas  
[PERSON] et al. [47] designed three microstrip antenna for coal mining applications. The first antenna consists of five branches, and the size and position of all of the branches was optimized. The second antenna was optimized by adding an inverted L-shaped branch on the top of the trident antenna, whereas the third antenna consists of an inverted L-shaped structure in the ground plane. The simulated and measured results cover the frequency bands of 2.51-2.68, 3.40-3.60, and 4.80-4.90 GHz. [PERSON] et al. [48] proposed a novel dual-band antenna design using a genetic algorithm for ISM bands covering 2.45 GHz and 5.8 GHz. The AMC reflector consists of sixteen unit cells and is used to improve the antenna radiation characteristic and reduce the absorption of electromagnetic radiation in the human body. [PERSON] et al. [49] presented an ultra-wideband (UWB) antenna for under-ground UWB applications. The antenna has a patch ring and is fed with a coplanar waveguide. The antenna was optimized to achieve a bandwidth from 2.8 GHz to 12 GHz. The proposed antenna can be used for UWB applications in the underground environment, since UWB exhibits attractive merits such as wide bandwidth, simple structure, low cost, and easy fabrication.