esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation  
We performed several simulation experiments to test the performance of the proposed QoS controller, using the same network configuration illustrated in Figure 2. We present the obtained results as a function of the Packet Error Rate (PER) experienced by subscribers, i.e., the fraction of packets lost by subscribers. Note that the PER is determined by both the channel BER as well as the level of interference traffic. To easily control the number  
Figure 8: Activation of the QoS controller in hitrate-constrained mode.  
Figure 7: Activation of the QoS controller in latency-constrained mode.  
Figure 9: Activation of the QoS controller in autonomous mode.  
of lost packets at the application layer, we programmed the simulator setting an ideal channel (with no losses and no interference traffic) and we forced the UDP layer at each subscriber to drop a specific number of packets, therefore triggering retransmissions at the application layer. We find this approach more practical than carefully setting the lower layers parameters (BER and interference traffic) to reach a specific PER. Moreover, such an approach allows us to shadow the complexity of the lower layers of the protocol stack, allowing us to obtain results at the application layer, which are valid regardless of the specific choices performed at the lower layers (e.g., MAC+PHY configuration, level of interference, signal quality and BER, etc.). Therefore, in each simulation performed, we set the average PER \(\bar{p}\) of the network, assigning to each subscriber a specific PER value drawn from a normal distribution with mean \(\mu=\bar{p}\) and standard deviation \(\sigma\).  
During all tests, the simulation is performed by setting one publisher to send periodically one 1400-byte message every second, for a total duration of 5 min. For the first 120 s of the execution, the controller is left shut down to show the behavior without its adjustment. After 120 s the controller starts its execution by checking the metrics of the previous messages and adjusting the QoS of the subscribers, if needed. Finally, to avoid continuous change of the QoS of a single subscriber, the controller can not change the QoS of the same subscriber in two consecutive intervals.
## 2 Materials and Methods
### Bioinformatic Analysis of High-Throughput 16S rDNA Sequencing Data  
The raw bacterial 16S rDNA V3/V4-specific fragment sequences in FASTQ format were processed by Trimmomatic v0.22 [52] to remove low-quality reads. Optimized paired-end reads were assembled using FLASH v1.2.11 [53]. Shorter-than-normal reads and reads containing ambiguous or single repetitive bases were discarded to improve assembly accuracy. At the same time, UCHIME v4.1 [54] was used to detect and remove chimeric sequences. The high-quality 16S rDNA sequences generated afterwards were processed by VSEARCH v2.14.2 [55] to define operational taxonomic units (OTUs), with sequences sharing at least 97% identities being clustered into the same OTU. Representative 16S OTU sequences were selected by QIIME v1.9.1 [56] and used for BLAST searches against Greengenes v13.5 [57] and SILVA Release 138 [58] databases. BLAST results with confidence intervals larger than 0.7 were used for phylum/genus/species annotation using the Ribosomal Database Project (RDP) v11.4 Classifier [59].
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 3 Materials and Methods  
Self-learning algorithms are enabled by models inspired by the human brain. These models let machines to learn from data and gradually enhance their performance over time. Machine learning algorithms have grown in relevance across a wide range of industries, including healthcare. They are very useful for identifying disorders by utilizing medical databases. Many firms use these tools to forecast diseases early on and improve medical diagnostics. This study seeks to overcome the issues involved with skin cancer diagnosis and categorization by establishing a complete strategy.  
This research focuses on addressing the challenges associated with skin cancer detection and classification by developing a comprehensive approach. The motivation behind the research is to improve the accuracy, efficiency, and accessibility of skin cancer diagnosis by incorporating advanced techniques and algorithms. The research builds on the Efficient Net B0 model, which is noted for its lightweight construction and strong picture categorization performance. To assess the effectiveness of our approach, we compare the performance of the Efficient Net B0 model with other widely used models, including EfficientNet B7, VGG-16, and AlexNet. These models represent state-of-the-art architectures in the field of image classification. The comparison encompasses evaluation metrics such as accuracy, precision, recall, and F1-score. Transfer learning is used to utilize pre-trained weights and improve skin cancer-classification accuracy. The Principal Component Analysis (PCA) technique is used for feature extraction, which reduces dataset dimensionality while keeping key properties. This helps to capture underlying information, simplify calculations, and decrease overfitting. The Adaboost technique improves classification accuracy by iteratively training weak classifiers and applying greater weights to misclassified examples. The combined strength of numerous weak classifiers improves overall performance and overcomes difficulties in correctly categorizing skin lesions. For classification and prediction, the model utilizes SVM, a supervised learning algorithm that creates a hyperplane to separate different classes in the dataset. By mapping the input data into a higher-dimensional space and finding the optimal hyperplane, SVM maximally separates the classes. As shown in Figure 1, the model architecture consists of multiple algorithms working in tandem to achieve robust and accurate skin cancer diagnosis.  
Through our research, we aim to develop a robust and accurate skin cancer diagnosis and categorization system by combining the power of the Efficient Net B0 model, PCA algorithm, Adaboost algorithm, and SVM algorithm. The use of these new approaches has the ability to enhance early detection, increase diagnosis accuracy, and eventually contribute to better treatment results for people with skin cancer.
## 2 Analysis of the State of the Art
### Device Models
#### 2.3.1 Composite Capabilities/Preference Profiles  
Composite Capabilities/Preference Profiles [64] (CC/PP) is a W3C standard system to express user and device capabilities. Using CC/PP a user will be able to show a specific preference or disability. For example, even though a user's device can display millions of colors, perhaps the user can just distinguish between a small set of colors. The necessity of this system stems from the wide range of web and ubiquitous devices available in the market. These devices have more and more multimedia and Web capabilities. This makes troublesome to Web content providers to service their content to these devices keeping usability and user satisfaction [62].  
However, managing a large number of devices is not a new problem. Many approaches have been proposed in the literature to tackle this situation. Most of them are based on content management. This approach considers different presentation alternatives. Depending on the client device, a presentation configuration is served. This way, in the content serving process there are two options: (a) the server chooses which is the best configuration for the device, and (b) the client decides what to do with the content. This approach is very easy to be performed since every device identifies itself against the server.  
CC/PP is based on profiles. A profile contains components and attributes. Each component has, at least, one attribute, and each profile has at least one component. The main components are: the hardware platform, the software platform and single applications, e.g., a browser. Attributes can contain one or many values. For example, in case of the \"hardware platform\" component we can find the attributes _displayWidth_ and _displayHeight_. These attributes have a single value. CC/PP uses RDF (Resource Description Language) as formal language to build these profiles. Table 3 shows several advantages and drawbacks of this approach.
## 2 Modeling and Optimization Method
### BSCN Model and Optimization
#### 2.2.2 Optimization Process  
The genetic algorithm is a bionic algorithm for solving complex global optimization based on [PERSON]'s natural evolution theory and genetic variation theory [42]. It draws on natural selection and natural genetic mechanisms in the biological world. It conducts a random search in the solution space based on probability theory and finally finds the optimal solution to the problem [43; 44]. A genetic algorithm is a mature optimization algorithm with unique advantages for solving multi-variable optimization problems. It can save the time and cost of experimentation and provide more accurate results.  
Figure 6 shows the optimization process of the structural parameters of the BSCN. The BSCN optimization variables' parameter combinations were converted to the genetic algorithm to identify the search space for processing, and chromosome coding was realized for the parameter data. The constraint function created each generated initial population within the range of values. In the optimization process of genetic algorithms, fitness is an important indicator used to evaluate individual performance. This study selected the objective function as the fitness function to evaluate the fitness.
# Nano-/Microcapsules, Liposomes, and Micelles in
## 4 Food Applications
\begin{table}
\begin{tabular}{c c c c} \hline
**Polysaccharide** & **Nanoparticle** & **Application** & **Reference** \\ \hline \multirow{4}{*}{Chitosan} & nanocapsule & Functional food & [117] \\  & nanocapsule & Food preservative & [118] \\  & micelle & Food packaging & [141] \\  & nanocapsule & Food packaging & [119,120] \\ \hline \multirow{2}{*}{Carboxymethylcellulose} & nanocapsule & Vitamin carrier & [116] \\  & nanocapsule & Carrier of & [60] \\ \hline \multirow{4}{*}{Starch} & micelle & Functional food & [133] \\  & micelle & Food packaging & [141] \\  & micelle & Carrier of bioactive & [80,81] \\  & micelle & compounds, supplements & \\ \hline \multirow{2}{*}{Hyaluronic acid} & micelle & Carrier of & [132] \\  & micelle & bioactive compounds & \\  & nanocapsule & Food packaging & [142] \\  & nanocapsule & and protection & \\ \hline \multirow{4}{*}{Alginate} & liposome & Carrier of bioactive & \\  & liposomes & compounds, functional food & \\ \cline{1-1}  & & Carrier of bioactive & \\ \cline{1-1}  & nanocapsule & compounds, food & [143] \\ \cline{1-1}  & & packaging, sensor & \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of polysaccharides and nanostructures and their application in food technology.
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 5 Simulations, Experiments, and Results
### Gazebo-ROS Simulation  
We validate our framework's ability to transfer to robotic systems in human environments with three high-fidelity Gazebo simulations. The implementation details can be found in Figure 5. CONCERTS receives a static map, a search region, the number of teammates and their initial positions. The search server manages the entropy map and manages the path planning. Each agent is managed via ROS and receives a path before executing the search. The first environment is a 10 m \(\times\) 20 m simulation of an apartment in the Anna Hiss Gymnasium Robotics Facility at The University of Texas. The second is a 40 m \(\times\) 50 m simulation of the area surrounding a home, and the final environment is a 100 m \(\times\) 100 m town that has been struck by natural disaster. In the home outdoor environment, the team performs robust search and initiates re-planning when one teammate fails. This scenario is depicted in Figure 6. In (a), three agents begin the search and on the left frame a top-down view of the map in Rviz is shown. Each of 3 agents is assigned a set of waypoints in colors red, green and blue. Soon after starting, the blue agent fails, and re-planning occurs to generate new waypoints and assign each agent to them. The new clustering results are shown in in (b). Here, the yellow regions are those that have been searched by the team. On the other hand, in the case of the disaster map, the team takes advantage of prior target knowledge (see Figure 7). Figure 7a shows the visualization of the four-robot search for an injured person. The team is searching for a missing person, and it is given that the person is likely to be near a building. These contexts are represented as particle filters, shown by the four red particle sets. This particle set represents a belief of the target location and is updated based on the team sensor information using Equation (2). When we use this prior information, our path planner is designed to visit waypoints close to the target belief. As a result, as shown in Figure 7, it is shown that each first point of the planned route is set as points near the building. The target location is given by the cyan circles near the destroyed set of homes. Figure 7b shows a top-down view zoomed in over the homes with the target location indicated in the Gazebo environment. In the case of using such an initial guess, if the guess is correct, the target can be found early, but in the opposite case, it may take a long time. Videos demonstrating these scenarios can be found at the project website.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
###### Abstract
This issue of standardization and incompatible devices has greatly affected the smooth and speedy roll-out of newly invented telemedicine projects. Other challenges and research issues include data security  
Figure 1: Telemedicine Systems  
and privacy, safety of patients in case of wearable devices, and QoS requirements. These are important areas that require attention by researchers to enhance the deployment of telemedicine in addressing the rising demand and cost of medication. The specific contributions of this work include:  
1. An overview of telemedicine and the evolution of telecommunications technology;
2. A review of the various standards and protocols present in telemedicine, with a focus on the current trends;
3. Identification of several deployment challenges along with the state-of-the-art in this research area. A general outline of the rest of this article is provided here. Section 2 presents a review of major research areas, with the aim of identifying state-of-the-art technologies along with existing implementation constraints. Section 3 provides an overview of telemedicine, while Section 4 introduces the evolution of communication technologies for telemedicine. Some use cases of telemedicine were presented in Section 5 also types of information transmitted in Section 6. Communications standards and protocols used in telemedicine are highlighted in Section 7. Finally, Section 8 outlines the current research challenges and future directions involved in harnessing robust, secure, efficient, and high-quality communications technologies for telemedicine systems, and we conclude the review in Section 9.
## 4 Results and Discussion
### Flight Control System Assessment of 60 Flights  
According to the process above, the health assessment of 60 flights is carried out in this subsection, and the average value of the health index obtained from each simulation is taken as the health index of that flight. Then, the health index of the four channels and flight control system are obtained, which are shown in Figures 9 and 10:  
Figure 8: PDF of flight control system health index.  
Figure 10: Health index of flight control system.  
Figure 9: Health index of each channel.  
It can be seen that the health index for 60 flights under uncertain conditions is mostly distributed around 0.75. Among the 60 flights, the distribution of flights in different health states is shown in Figure 11 below:  
From the above figure, it can be seen that the system in 29 flights is operating well, is slightly degraded in 31 flights and no flight is in severe decline or fault. In addition, flights 34 and 51 have a larger decline. Table 6 lists the health indices of the four channels of these two flights.  
Furthermore, for these 60 flights, the assessment results under certain conditions and uncertain conditions are compared in Figure 12 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flight** & **Channel** & **Health Index** \\ \hline \multirow{4}{*}{34} & Flap channel & 0.7500 \\ \cline{2-3}  & Pitch channel & 0.5114 \\ \cline{2-3}  & Roll channel & 0.6963 \\ \cline{2-3}  & Yaw channel & 0.5026 \\ \hline \multirow{4}{*}{51} & Flap channel & 0.7516 \\ \cline{2-3}  & Pitch channel & 0.5597 \\ \cline{2-3}  & Roll channel & 0.7318 \\ \cline{2-3}  & Yaw channel & 0.5621 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Health index of each channel of flight 34 and flight 51.  
Figure 11: Health status of different flights.  
Figure 12: Comparison of assessment results with and without uncertainty.
# A Survey on Fault Diagnosis of Rolling Bearings
## 1 Introduction
This survey discusses not only traditional methods based on signal processing and analysis but also machine learning and artificial intelligence methods, including feature extraction/reduction methods, deep learning methods, and evolutionary learning methods, to present a relatively full picture of this field. In addition, this survey summarizes commonly used datasets, existing limitations/challenges, and future research trends to provide researchers with useful guidance. The structure of the survey is task-based, including tasks for fault detection and fault type recognition. The organization of this paper is as follows. The primary fault forms of rolling bearings and the major research topics of rolling bearing fault diagnosis are presented in Section 2. Then, Sections 3 and 4 review the typical works on rolling bearing fault detection and fault type recognition, respectively. Section 5 summarizes datasets and the limitations/challenges of existing methods and discusses future research trends in rolling bearing fault diagnosis. Finally, Section 6 draws the conclusions. Figure 1: The number of publications on rolling bearing fault diagnosis from 2011 to 2021.
## 2 Method
### The Four-Dimensional Tracking Algorithm  
In Section 2.3, three possible cases are described in detail, and the solutions of four DOFs are given, respectively. However, the underground pipeline is invisible, and the relative position of the tracking receivers and the pipeline may be one of the above three cases or a combination of the three cases. Therefore, this paper further proposes a comprehensive algorithm to achieve high-precision and 4-D tracking of the PIG in any relative position case. The block diagram of the 4-D tracking algorithm is shown as Figure 4, which is divided into three steps as follows:  
* Step 1: The pitch angle of the pipeline is assumed to exist, similar to the pitch angle case. Using (17) and (18), the pitch angle is calculated and the tracking of the DOF \(z\) is achieved. (16) is usedto compensate the magnetic field distortion caused by the pitch angle, and the radial distance difference between the dual receivers corresponding with (20) is updated.
* Step 2: The lateral offset of the pipeline is assumed to exist, similar to the lateral offset case. Referring to (11) and (12), the lateral offset is calculated and tracking of the DOF \(x\) is achieved. It is worth noting that \(z\) in (11) and (12) needs to be subtracted from \(\Delta z\) in the solution in Step 1. As in Step 1, the magnetic field distortion caused by the lateral needs to be compensated by (13). The radial distance between the dual receivers needs to be updated with (14).
* Step 3: Referring to (19), the sign-integration of the compensated magnetic field can be obtained. Using the MFSI method in (8) and (15), the tracking of the DOF \(y\) is achieved. At the same time, the DOF \(x\) can be updated.
## 3 Results and Discussion
### Changes in Littoral Plant Waste
#### 3.1.1 Macronutrient Content  
As it has been demonstrated in many studies [24; 25; 26; 27] in the process of vermicomposting earthworms modify physical, chemical, and biological properties of organic waste. The value of the vermicompost depends on many factors, such as type and origin of organic waste, temperature, humidity and aeration of vermiculture, earthworm species, and more. Thus, before starting the process of vermicomposting it is important to determine the physicochemical properties of waste, and after the completion of  
Figure 1: Duration of vermicomposting of plant waste. Figure 2: Plant waste processed in vermireators (**I**), the obtained vermicomposts (**II**), vermicomposts under magnification of x\({}^{\prime}\) (**III**). Species code: _C. demersum_ (**C**), _l. pseudacorus_ (**I**), _T. latifolia_ (**T**). vermicomposting--to analyze these properties in vermicompost with regard to its usefulness as a fertilizer. Physicochemical properties of plant waste and vermicomposts are presented in Tables 1 and 2. As a result of vermicomposting, changes in pH value between the initial biomass and the obtained manure were noted (Table 1). Values of pH of plant waste diminished from alkaline (7.71-7.79) to slightly acidic (6.32-6.47) (\(p<0.05\)). According to [PERSON] et al. (2018), a decrease in pH value in vermicompost may be associated with decomposition of organic matter that leads to the formation of ammonium ions NH4\({}^{+}\) and huminic acids. Electrical conductivity (EC) of vermicomposts was higher compared to the initial biomass (Table 1). Significant changes were noted in vermicactors of groups (I) and (C) (\(p<0.05\)). The increase in EC value could be a result of release of various mineral ions, such as phosphates, ammonium ions, potassium ions and others ([PERSON] et al., 2018). A significantly increased macroelement content compared to the utilized plant biomass was also observed in the vermicomposts (Table 1). After 70 days of vermicomposting, a significant decrease was recorded in C/N ratio of the vermicomposts compared to the initial waste biomass (in vermicreactors (I) and (C)--a decrease by 55.7% (\(p<0.05\)) and 57.5% (\(p<0.05\)), respectively (Table 1). C/N ratio is most often used as an indicator of vermicompost maturity that implies the degree of waste mineralization and stability. Depending on the degree of advancement of vermicomposting process, loss of carbon in the form of CO2 occurs due