esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
## 1 Introduction
### Scope of Routing Protocols in Sensor Networks
This means that information may be easily collected from the network, such as sensor-generated data, link quality metrics, neighbor node tables, and other variables that may be used to locate routes and for performance analysis and network optimization. If a node sends a packet to the destination or collector node, it searches for its parents in its Neighbor Table, selects a route, and sends the message. This process is repeated hop by hop until the destination. The sink node knows the complete topology of the network through frequent query packets at times previously established in the network nodes. These packets are sent through the hierarchical branches and request each node to send its information to the coordinator node. This protocol takes advantage of the proactive controlled route maintenance but combines the agility of having more than one route per node. This makes it more versatile and adaptable to other topologies. ZTR is a simple protocol that establishes parent-child links with the nodes always carrying information to their parent. This protocol features a tree topology, is easy to implement, fast, and is proactive. ZigBee networks require at least one full-function device. That is, a robust device acting as a network coordinator, but the final nodes of the star may have low specifications to reduce costs. Before transmitting to a channel, the node must measure the energy level in a specific channel. This measurement only indicates if the channel is busy, but the node is not able to identify whether that energy corresponds to another device under the IEEE 802.15.4 standard. Here, the child node would be the one that most recently entered the network. The parent node is the node that has given the child access to the network. In this way, parent-child links are created, but each child can only have one parent. Some of the advantages of ZTR are balance between cost per unit, battery expenditure, and implementation complexity to achieve an appropriate cost-performance ratio for the application.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
###### Abstract  
In this paper, we propose and experimentally demonstrate an orthogonal subblock division multiple access (OSDMA) scheme for orthogonal frequency division multiplexing with index modulation (OFDM-IM)-based multi-user visible light communication (MU-VLC) systems, where both single-mode index modulation (SM-IM) and dual-mode index modulation (DM-IM) are considered. In order to overcome the low-pass frequency response and the light-emitting diodes (LED) nonlinearity issues of practical MU-VLC systems, OSDMA is employed together with discrete Fourier transform spreading (DFT-S) and interleaving. The feasibility and superiority of the proposed scheme have been successfully verified via both simulations and hardware experiments. More specifically, we evaluate and compare the peak-to-average power ratio (PAPR) performance and the bit error rate (BER) performance of OFDM-SM-IM, DFT-S-OFDM-SM-IM, OFDM-DM-IM and DFT-S-OFDM-DM-IM without and with interleaving. Experimental results show that remarkable distance extensions can be achieved by employing DFT spreading and interleaving for both SM-IM and DM-IM in a two-user OSDMA-VLC system.  
visible light communication (VLC); orthogonal frequency division multiplexing (OFDM); index modulation (IM); orthogonal subblock division multiple access (OSDMA)
## 4 Simulation Results and Analysis
### Total Energy Cost  
Figure 12 describes the comparison of the four schemes in terms of total energy cost. Which shows that the energy cost of the DMR and CoDMR schemes is lower than the counterpart schemes. The reason of the lowest energy cost of the DMR algorithm is the balance energy utilization of the entire network, which enables the nodes to forward maximum information over the low cost of energy. While the reason of maximum energy cost in the DBR scheme, is the transmission of heavy packets from the highest depth source node to the lowest surface node, which increases the energy cost. Likewise, the CoDMR scheme also reduces the energy cost simply by enforcing a threshold that, a node close to the targeted minor sink node forwards the information in a non cooperative manner. While the nodes away from the minor sink nodes forward the information in cooperative fashion. These strategies reduce the overall network energy cost and forwards maximum information to the major sink node. Secondly, in CoDMR scheme, placing the minor sink nodes at balance position in the network decreases the information flooding due the to long multi-path routing. In CoDBR scheme, the contribution of two relay nodes and long propagation path increase the total energy cost. At round 200, the energy cost of the DBR and CoDBR protocols is maximum and has the values of approximately 4000 J and 4500 J, respectively. While at the same number of round, the energy cost of the DMR and CoDMR schemes is much lower than the  
Figure 11: End-to-end delay in second.  
counterpart schemes and has the values of 2000 J and 2500 J relatively. This shows that the proposed schemes are energy efficient than existing schemes.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks  
Unmanned aerial vehicles (UAV) have been widely utilized in military operations, disaster relief tasks, and wild-area monitoring for perception, surveillance, material delivery and communication purposes due to their high mobility, flexibility, and relatively low-cost characteristics. In 6G scenarios, the role of UAVs is irreplaceable, serving as aerial network nodes providing ubiquitous network connections in the lower-altitude airspace between terrestrial networks and satellite networks. For the future vision of 6G networks that have extended coverage on the ground, in the air, and in space, realizing a space-air-ground integrated communication network by means of utilizing UAVs and other low-altitude aerial platforms is of the utmost significance.  
In 6G NTN scenarios, UAVs are capable of performing various communication tasks, which include serving as base stations, communication relays, and user equipment (Figure 3. When mounted with communication equipment, UAVs can be employed as flying base stations or communication relays to maintain communication services to ground users in case of crippled or congested ground base stations. In contrast, from the recent literature, UAVs can also be considered as aerial nodes, connecting to ground and non-terrestrial networks, referred to as cellular-connected UAVs, which have drawn more and more attention from research scientists. Cellular-connected UAVs are promising in a number of applications such as surveillance, autonomous driving, parcel delivery, post-disaster rescue, and communication enhancement. Previous works have placed great emphasis on UAV-assisted cellular communications. However, in recent studies, UAVs connected to communication infrastructure serving as cellular-connected platforms are also drawing attention.  
In addition, in scenarios that require wide-area communication coverage, a swarm of interconnected UAVs through Flying Ad Hoc Networks (FANETs) are utilized to integrate with terrestrial infrastructure to extend network coverage, as has been studied by a variety of researchers. UAV communications have a variety of advantages, ranging from line-of-sight links to dynamic deployment, which is promising in terms of enabling space-air-ground-sea connections. However, a variety of common problems still need in-depth investigation to support all kinds of UAV applications concerning NTN communications. In UAV NTN communication, place deployment, flight trajectory optimization, and resource allocation in mobile edge computing are hot topics under profound discussion.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 2 Mission Design
### Constellation Design  
Satellite constellations are typically designed with similar altitude, eccentricity, and inclination so that the effect of perturbations will be approximately the same on each satellite. Such a design will preserve the geometry of the constellation, especially in CubeSat, where there is limited fuel onboard for station keeping. Another advantage is that satellites at constant altitude will have the same resolution and steady signal strength for communication [12; 17; 18]. The Walker constellation is the most popular type of circular orbit geometry to satisfy the above considerations. The Walker constellation pattern is used in most studies to design a global coverage problem [19; 20].  
Given the mission requirements, a Walker constellation will be well-suited for our constellation system design. A Walker constellation consists of a total number of satellites (\(T\)) in circular orbits with the same period and inclination. The pattern of the constellation consists of evenly spaced satellites (\(S\)) in each of the orbital planes (\(P\)) specified so that \(T=P\times S\). The ascending nodes of the orbital planes are also evenly spaced over a range of right ascensions of the ascending node (RAAN). The study implemented a Walker Delta constellation pattern with one interplane spacing and 360\({}^{\circ}\) RAAN spread.
# Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf
## 3 Stability Prediction Model  
### Predictive Modeling  
The modeling steps for the random forest approach are summarized as follows:  
1. Data acquisition and preprocessing: obtain a dataset containing features and target variables and perform preliminary data processing.
2. Data splitting: divide the preliminarily processed dataset into a training set and a test set using an appropriate sampling method, according to a certain ratio (this study  
Figure 4: Correlation coefficient heatmap of the variables. This figure displays a correlation coefficient heatmap of four variables in the dataset: the shock wave speed (SPEED), compressive strength (obc), shear strength (\(\tau\)), and damage level (Damage). The color of each cell in the heatmap represents the strength of the correlation between the two corresponding variables, with redder colors indicating stronger positive correlations and bluer colors indicating stronger negative correlations. The number in each cell displays the specific correlation coefficient, which ranges from \(-1\) to \(1\), where \(1\) represents perfect positive correlation, \(-1\) represents perfect negative correlation, and \(0\) represents no linear correlation.  
uses an 8:2 ratio for data partitioning). Randomly select samples from the training set to construct multiple decision trees. The results of all of the decision trees can then be integrated through voting or averaging to generate prediction results.
3. Model evaluation and optimization: use the test set to evaluate the model performance by considering the accuracy, precision, recall, and other indicators; based on the evaluation results, adjust the model parameters to achieve the final model optimization.  
Using Python version 3.8.20, the model was developed with the RF classification algorithm, leveraging geological parameters (the dimensions of the roadway, which was used in Section 2.1.1 for calculating the shock wave velocity) of a mined-out area and mechanical parameters (the compressive strength \(\sigma\)bc and the shear strength \(\tau\)) of the closed wall as features to predict the impact of the shock wave from roof fall under various conditions. The model employs several scikit-learn modules, such as the ensemble module for the random forest classifier, the neural_network module for the multilayer perceptron (MLP) classifier, and the model_selection module for splitting the dataset into training and test sets, performing cross-validation, and tuning. The metrics module is used to compute the model accuracy and confusion matrices for evaluation. Further hyperparameter optimization enhanced the performance of the prediction model, demonstrating that the RF algorithm, especially after optimization, exhibits an excellent predictive capability. In this study, there is no mathematical derivation for the machine learning model, so only the RF algorithm is introduced.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Food Products** & **Pulse Energy (J/cm\({}^{3}\))** & **Treatment Time (sys) \#** & **Logia Reductions** & **Reference** \\ \hline Apple juice & & & \(\sim\)47 of _E. coli_ & \\  & & & 1.39 of _L. innocata_ & \\ \hline Orange juice & 28 & 8 s & About 1 forb.h. _L. innocata_ and _E. coli_ & [66] \\ \hline Milk & & & 1.06 of _E. coli_ & \\  & & & 0.51–0.84 of _L. innocata_ & \\ \hline Apple juice & 5.1 & 1.52 & 3.9 of _E. coli_ & [90] \\ \hline Orange juice & 5.1 & 2.81 s & 2.42 of _E. coli_ & [91] \\ \hline Apple juice & 5.1 & 300 s & 4.9 of _E. coli_ & [92] \\ \hline Plums, & & & 1.1–1.5 of unreophiles & \\ tomatoes, cultflowers, sweet peppers & 5.4 & 0-200 s & 1.3–1.8 of _B. cereus_ & [93] \\ \hline Fresh-cut watermelons & 12 & - & 2.3\(\circ\) of _E. coli_ & [94] \\ \hline Fresh-cut mushrooms & 12 & - & 3 of _E. coli_ & \\  & & & 2 of _L. innocata_ & [54] \\ \hline Milk (9.8\% total solids) & 8.4 & & 2.5\(\circ\) _E. coli_ _ACC_ 25922 & \\ \hline Concentrated milk (45\% total solids) & 8.4 & & \(<\) 1 of _E. coli_ _ATCC_ 25922 & \\ \hline Skin milk & 14.9 & - & 3.4 of _E. coli_ _ATCC_ 25922 & [95] \\ \hline Milk (2\% rat) & 14.9 & & \(>\)2.5\(\circ\) _E. coli_ _ATCC_ 25922 & \\ \hline Whole milk & 14.9 & & \(>\)2.5\(\circ\) _E. coli_ _ATCC_ 25922 & \\ \hline Ground spices (caraway, black pepper and red pepper) & 10 & - & 0.8 in ground caraway and black pepper and 1 in ground red pepper for _B. subtilis_ & [50] \\ \hline Shrimp & & & 2.4 of _L. innocyledons_ & \\ \hline Salmon & 12.1 & 1380 s & 2.1 of _L. innocyledons_ & [56] \\ \hline Flatfish filtes & & & 1.9 of _L. innocyledons_ & \\ \hline Ready-To-Eat dry cured meat & & & 1.5–1.8 of _L. innocyledons_ and _S. enterica_ serovar Typhimurium & [96] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 1 Introduction
A thorough comparison with established SVM and XGBoost algorithms demonstrates that the ionospheric scintillation detection model presented in this study outperforms them. The Sparrow Search Algorithm, with its robust global optimization capabilities, is employed to optimize some of the hyperparameters in XGBoost. These refinements enhance the model's performance and operational efficiency, reducing the likelihood of overfitting in the detection classification task. The innovative aspect of this study lies in the utilization of an optimized XGBoost algorithm for ionospheric scintillation detection (which is subsequently applied to global regions, especially the polar and equator regions), as well as its quantitative analysis and comparison of machine learning algorithm performance with regard to accuracy, precision, recall, and F-Score. The remainder of this paper is structured as follows: Section 2 provides a detailed description of the XGBoost model, as well as an overview of the Sparrow Search Algorithm and how it can optimize XGBoost. Section 3 outlines the development of the GNSS RO dataset for the detection of ionospheric scintillations and presents the experimental procedure and corresponding evaluation criteria used for our quantitative analysis of the results. Finally, the results obtained are scrutinized, and different machine learning algorithms are compared. Section 4 summarizes the findings and provides a prospective outlook on future studies.
## 5 Signal Approximation and Estimation  
As seen in Figure 1, the proposed scheme has three main sections: (a) preprocessing, (b) resampled signal approximation and estimation, and (c) fault classification. Preprocessing was explained in the previous section. In the second section, resampled RMS signals are approximated and estimated under two main subsections: (i) modeling the normal resampled RMS signal and (ii) estimating the resampled RMS signals under normal and abnormal conditions. The following steps are used for signal modeling: an AR design for resampled RMS normal signal modeling, improvement of the robustness and performance from the AR technique using defined uncertainty as an external input that is ARXU technique, (i.e., ARXU), and solving nonlinear bearing signal behavior using the combination of MBR and ARXU to design AMRXU.
## 4 Conclusions  
In this paper, SJAs with chevron orifices were tested. The orifices differed in the number of chevrons and the edges of cutouts were sharp or rounded (see Figure 2 and Table 1). The time-mean reaction force and the noise of actuators were measured. The Reynolds number was in a range of 1000-12,000.  
The classic chevron (case a) orifices decreased the electrical efficiency of the actuator even about 0.5% (percentage points), at the real power \(P\ \geq 1\) W. The efficiency increase even 0.4% in case b. This shows, that the rounded cutouts in the chevron orifice can increase the efficiency of the actuator. The efficiency rise was small but this may indicate differences in the SJ formation.  
The SPL was measured at the power of the actuator above 5 W. The noise reduction was obtained at the chevron number equal to 7 or more. The SPL was higher in cases 2a and 3a than in cases 2b and 3b. In case 4, the impact of rounded cutouts was unclear, but the SPL reduction was obtained only at \(P=8\) W. The received results are congruous to the results from other papers.  
The paper showed that the chevron orifice with rounded cutouts can increase the efficiency of the actuator and can reduce the SPL more than a classic chevron orifice. It is very important due to the lot of noise generated by the SJA. In the case of investigated actuators, the SPL was in the range of \(49\leq\text{SPL}\leq 58\) dB.  
This work was supported by the National Center for Research and Development, Poland. Grant No.: LIDER/6/0024/L-10/18/NCBR/2019.  
Not applicable.  
All data generated or used during the study are available from the corresponding author by request.  
The authors declare no conflict of interest.
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing
#### 2.1.2 The Dynamic Structure of 6G  
With the increasing population that will connect to 6G networks, it is envisaged that more and more user devices, autonomous driving vehicles, drones, satellite networks, unmanned aerial vehicles, and IoT devices will be connected to the network in a highly dynamic way. The aim is to design a dynamic and intelligent 6G topology, together with critical services such as uMBB, ULBC, and mULC, which are targeted to be reached in 6G networks. This will significantly increase the complexity of the network. Moreover, the dynamic nature of the network often increases routing and maintenance, and this will cause a weakening in routing performance. At this point, it is hoped that more intelligent and foresighted systems will help to overcome this complexity. AI and deep AI approaches have critical potential in this area and are the first approaches that come to mind.
## 6 Case Study and Discussion
### Simulation Results
#### 6.3.3 Case 3  
In Case 3, the numbers of EVBCDIs in three microgrids are fixed to be the same with Case 1, and installation capacities of RESs are optimized. The numbers of EVCDIs for RMG1, RMG2, and OBMG are 10, 10, and 20 respectively. Optimal WT installation capacities for RMG1, RMG2, and OBMG are 80 kW, 40 kW, and 360 kW. The installation capacity of PV modules for RMG1, RMG2, and OBMG are 180 kW, 0 kW, and 400 kW. The 2-day operation profiles of RMG1 and OBMG in Case 3 are shown in Figure 8.  
Figure 7: Dispatching results in Case 2: (**a**) Dispatching profiles of RMG1; (**b**) Dispatching profiles of OBMG.  
According to the figures, we can see that the profiles of RMG1 are not much different. The installation capacities of RESs in RMGs and OBMG have been optimized. For the RMGs, electricity prices of the distribution grid are low and it is not beneficial to sell energy to the grid. Therefore,  
Figure 8: Dispatching results in Case 3: (**a**) Dispatching profiles of RMG1; (**b**) Dispatching profiles of OBMG.  
the redundant installation of RESs is decreased, and less energy are sold to the grid while the load demands of RMGs are still satisfied. For OBMG, the electricity prices of the distribution grid are high, and it is beneficial to sell extra energy to the grid. Therefore, more RESs are installed to sell more energy to the grid, and the microgrid earns more from selling energy.  
In Case 3, the total cost of one year is $161,090.30. The costs for RMG1, RMG2, and OBMG are $55,138.10, $36,348.80, and $69,603.40, respectively.
## 4 Results and Discussion
### Quantitative Interpretation  
The Euler Deconvolution and power spectrum analyses are used for quantitative interpretation to estimate any subsurface structure. The Euler deconvolution is used for resolving the 3D components of structural elements while the power spectrum analysis is used for estimating the subsurface source depth and possible interfaces.
## 4 Conclusions  
Comprehensive evaluation and testing of the ICT laparoscopy system showed that the prototype is promising for revolutionizing laparoscopic surgery. The system's intuitive interface and ability to integrate with surgical workflows seamlessly address key challenges surgeons face, particularly in the context of solo surgery and the anticipated exacerbation of the surgeon shortage.  
The system's positive reception in usability and performance, demonstrated through tests on both phantom and porcine models and evaluations by clinical experts, highlights its potential to improve surgical outcomes while reducing technical challenges. Specifically, the system's accurate motion tracking is notable. There is minimal delay between controller action and camera response. Favorable evaluations of key critical functionalities, such as the foot switch operation and camera driving speed and range, also highlight its effectiveness in addressing critical needs in laparoscopic surgery.  
Figure 9: Captured pictures of the laparoscopic camera movements in the abdominal cavity prototype test. The direction of the camera switches according to the movement of the controller.  
Furthermore, some safety tests that complied with medical electrical equipment standards were completed to confirm performance and safety potential in a clinical environment.  
In summary, our ICT laparoscopic system comprises a novel groundbreaking device that employs motion tracking of the surgeon for precise camera control, as evidenced by our latest prototype. Additionally, the ICT laparoscopic system contains an integrated camera-cleaning mechanism, which could further revolutionize surgical efficiency. This innovation streamlines the surgical process by eliminating the need for manual camera adjustments and cleaning, thereby facilitating solo surgery. These advancements hold great promise for enhancing surgeons' capabilities and optimizing surgical outcomes, ultimately benefiting patients. Future studies should prioritize refining system functionality, addressing areas for improvement, and conducting rigorous clinical trials to validate its efficacy in real-world surgical scenarios.