esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Path-Planning Techniques for Data Harvesting  
The path-planning algorithms employed in ocean data harvesting diverge significantly from conventional problems. As depicted in Figure 9, the distribution of the path-planning methodologies is presented. In the process of data collection, AMVs are required to access multiple sensor nodes while concurrently optimizing data transmission efficiency. As a result, conventional point-to-point path-planning methodologies, typified by heuristic paradigms such as A*[52; 53], D*[54; 55], and theta*[56; 57; 58], as well as sampling-based techniques including Rapidly-exploring Random Trees (RRT*)[59; 60; 61] and Probabilistic Roadmaps (PRMs)[62; 63], show a relative scarcity of application within this field. Contrastingly, meta-heuristic techniques, such as genetic algorithms (GA)[64; 65], and colony optimization (ACO)[66; 67], particle swarm optimization (PSO)[68; 69], simulated annealing (SA)[70; 71], alongside AI-driven approaches exemplified by Q-learning and deep reinforcement learning, exhibit a remarkable ability to accommodate the intricate nature of the problem formulation. Notably, these methodologies not only present a capacity to optimize diverse sets of objective functions but also show an adeptness at navigating the complex constraints. Therefore, they find extensive use in ocean data collection. Additionally, some researchers have also explored the application of online path planning and coverage path-planning techniques for data collection, yielding promising results. In this section, we will thoroughly review these novel approaches, discuss their application scenarios, and evaluate their strengths and limitations. Figure 8: Distribution of research groups around the world. A summary of the existing research is presented in Table 2.  
Figure 9: Summary of the path-planning techniques (based on the articles searched in Section 2, we eliminated some literature including the review papers, papers with irrelevant contents, and papers with poor/vague presentation).
# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
###### Abstract  
Drilling, one of the most used machining processes, has wide application in different industrial fields. Monitoring the system health and operation status of the drilling process is essential for maintaining production efficiency. In this study, a convolutional neural network (CNN), a deep-learning method, is applied to the defect diagnosis of drill bits. Four drill bits with different health conditions were used to drill holes in an aluminum block, and a vibration sensor collected the signals. Vibration spectrograms generated using short-time Fourier transform were applied to a 2D CNN algorithm, and they were then reconstructed into a 1D data set and applied to a 1D CNN algorithm. The input data size was reduced significantly compared to the raw vibration data after the data-reconstruction process. As a result, the 2D CNN process shows a diagnostic accuracy of 97.33%. On the other hand, the 1D CNN provides a diagnostic accuracy of 96.6%, but it only requires 2/3 of the computational time required by the 2D CNN.  
defect diagnosis; convolutional neural network; condition monitoring +
Footnote †: journal: Applied sciences  
0000-00  
for bearing fault diagnosis [6]. [PERSON] et al. demonstrated the use of variational modal decomposition (VMD) for feature extraction in mechanical fault diagnosis [7].  
Many of the methods mentioned above have achieved decent classification and diagnostic accuracy, but the feature extraction process highly depends on the experimenters' experience and the original data. The same feature extraction process and classification method may not be able to provide an accurate result when using different workloads and setups. To address this problem, [PERSON] et al. conducted comparative experiments in their recent research [8]. In their results, it shows that convolutional neural network (CNN) has higher adaptability than support vector machine (SVM), multi-layer perceptron (MLP), and deep belief network (DBN). In recent years, among different deep-learning algorithms, CNN has gained massive interest due to its wide range of practicality and high accuracy [9; 10].
## 2 Materials and Methods
### Determination of VOCs Using Gas Chromatography-Mass Spectrometry (GC-MS)  
The analysis of volatile organic compounds (VOCs) in potatoes was performed using headspace solid-phase microextraction (HS-SPME) combined with GC-MS (7890B GC, 5977B MS; Agilent Technologies, Santa Clara, CA, USA). Samples of 1.5 g were accurately weighed into 20 mL glass vials containing 3 mL of saturated salt water, and then, 20 \(\upmu\)L 2-methyl-3-heptanone (0.816 \(\times\) 10\({}^{-2}\)\(\upmu\)g/\(\upmu\)L) was added to each vial as an internal standard. The vials were immediately placed in a heating block to equilibrate for 5 min at 40 \({}^{\circ}\)C, and then, a CAR/DVB/ PDMS SPME fiber (50/30 \(\upmu\)m) (Stableflex(tm), Santa Clara, CA, USA) was exposed to the headspace for 30 min at 40 \({}^{\circ}\)C. Volatiles were desorbed from the SPME fiber at 250 \({}^{\circ}\)C for 5 min in the injector in splitless mode, and helium was used as the carrier gas at a flow rate of 1.0 mL/min. A DB-5 MS column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness; Agilent, Santa Clara, CA, USA) was used, and the column temperature was set at 40 \({}^{\circ}\)C (held for 3 min), increased to 150 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and then increased to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min (held for 10 min). MS was performed with an ion source temperature of 230 \({}^{\circ}\)C, a mass range of \(m/z\) 30-500 and an electron ionization energy of 70 eV.
Electrical stimulation is mainly performed with PEMF, and in some cases with DC, revealing both anti-inflammatory and pro-inflammatory effects with low-frequency electromagnetic field stimulation [21, 22]. Furthermore, reduced proliferation, higher apoptosis rates, and altered migration behavior of macrophages were observed under PEMF and DC [23, 24, 25]. Due to the mentioned problem of monophasic stimulation, analyses with alternating electrical fields are necessary. Studies regarding human macrophages and osteoclasts are as crucial as studies on osteoblasts in understanding fundamental bone remodeling processes under electric stimulation. Indirect and direct co-culture systems allow the investigation of the interaction and interplay between these cell types [26]. These cellular interactions could even be shown in an osteoid model, i.e., non-mineralized bone tissue [27]. In general, secreted cytokines and released factors modify the cell reaction of each cell type; thus, the crosstalk can change cell behavior in comparison to mono-cultured cells [28]. [PERSON] et al. revealed the importance of a co-culture system between osteoblasts and osteoclasts while observing the influence of PEMF [17]. Changes in differentiation and the bone-resorption capability of osteoclasts were observed after incubating cells with a medium derived from PEMF-stimulated osteocytes [29]. Therefore, in our present study, the effect of electrically stimulated human osteoblasts on osteoclast formation processes was investigated. The aim of our study was to gain a better understanding of the effects of cytokines released by electrically stimulated osteoblasts with alternating electric fields on osteoclastogenesis (Figure 1). Consequently, the indirect influence of electric stimulation on the viability and differentiation of PBMCs and osteoclast-progenitor cells was investigated. Study data should help to understand the activation of PBMCs and pre-osteoclasts in the periphery of electrically active bone implants by releasing cytokines from osteoblasts in direct proximity to the electrical field.
# A Micro-Segmentation Method Based on VLAN-VxLAN Mapping Technology
## 3 Proposed Method
### Micro-Segmentation
#### 3.1.3 Evaluation of the Effect of Differential Segmentation
\[IdAC_{v}=\frac{2\times|\{e_{jk}\}|}{Ni\times(Ni-1)};v\in V,v_{j},v_{k}\in Ni,e_ {jk}\in A, \tag{3}\]  
\[OdAC_{v}=\frac{2\times|\{e_{lm}\}|}{No\times(No-1)};v\in V,v_{l},v_{m}\in No,e_{lm}\in A. \tag{4}\]  
The IdAC of a node is calculated using Equation (3), where \(Ni\) represents the set of predecessor nodes or in-degree nodes of the node. The term \(\left|\{e_{jk}\}\right|\) denotes the number of directed edges connecting the in-degree nodes of a given node, thus indicating the degree of neighbourhood relationships between them. Similarly, the OdAC of a node is calculated using Equation (4), where \(No\) represents the set of successor nodes or out-degree nodes of the node. The term \(|\{e_{lm}\}|\) denotes the number of directed edges connecting the out-degree nodes of the node, reflecting the degree of neighbourhood relationship among them. In this study, the IdAC and OdAC were utilized as metrics to quantify network aggregation. Both IdAC and OdAC range between 0 and 1, with larger values indicating greater network aggregation. By examining the combined IdAC and OdAC values for all nodes in a network, the difficulty of a lateral attack can be assessed. Notably, nodes with a clustering coefficient of 0 and a large degree value represent routers, whereas those with a clustering coefficient of 0 and a small degree value (less than or equal to 2) have only one neighboring node, typically another router. In a fully connected network, where all nodes have an aggregation coefficient of 1, the probability of a successful lateral attack is high. Table 3 presents the aggregation values for each node in Figure 6.  
The impact of micro-segmentation on network connectivity was also assessed. Prior to micro-segmentation, a node directed edges to all hosts within the same subnet, while connections to hosts outside the subnet required gateway device traversal. Consequently, a subnet with \(n\) hosts had outbound and inbound degrees of \(n\), resulting in \(n^{2}\) directed edges. With the implementation of micro-segmentation, direct host access was limited to the gateway device, resulting in each host asset having an outbound and inbound degree of 1. As a result, the number of directed edges reduced to \(2n\) within the original subnet. This reduction in directed edges resulted in decreased network connectivity and increased difficulty for attackers to achieve their attack objectives on the intranet.
# Factorial Numbers and Their Practical Applications
## Application of Factorial Numbers in Permutation Problems
### Permutation Arithmetic  
Factorial numbers can be used to perform addition and subtraction operations on permutations. Moreover, they make it possible to perform this on two levels. At the first level, permutations are converted into factorial numbers, and addition or subtraction operations are performed on them. Then the resulting factorial numbers are converted into permutations, representing the final result of the arithmetic operation performed on the permutations. For example, if the permutations 2 1 3 0 and 1 2 0 3 are being added, then their factorial numbers 2 1 1 0 and 1 0 0 0 are obtained, and then the sum is calculated as 2 1 1 0 + 1 0 0 0 = 3 1 1 0. After that, the factorial number 3 1 1 0 is transformed into the permutation 3 1 2 0. This means that the sum of the permutations is 2 1 3 0 + 1 2 0 3 = 3 1 2 0.  
The second level is arithmetic operations at the level of natural numbers. Then, after the first level of transformation of permutations into factorial numbers, they are converted to the corresponding numbers of the natural number system and the required arithmetic operations are performed on them. The results of these operations are converted first into factorial numbers and then into permutations, representing the final result of performing arithmetic operations on the original permutations. For example, the permutation 3 1 2 0 is divided by permutation 0 2 3 1. Then, the first level of the division operation contains the factorial permutation numbers 3 1 1 0 and 0 1 1 0. Then, they are converted to the corresponding natural numbers 2, 1 and 3. Dividing the number 21 by 3 gives the number 7, which is converted to the factorial number 1 0 1 0. This number, in turn, according to the algorithm proposed above, is converted into permutation 1 0 3 2. This is the result of dividing permutation 3 1 2 0 by 0 2 3 1.
## 3 Materials and Methods
### Methodology  
The communication module was designed using SSAIL technology. On a physical level, the 3D MID concept (electronics embedded in free-shape polymeric components) could aid the sensor network deployment [18]. This technology utilising laser treatment and selective electroless metal deposition is a promising approach to producing sensor nodes and energy-harvesting systems combined in a single body, allowing for more shape, size, and material freedom [2; 3]. The IoT network for temperature and humidity monitoring using an asynchronous web server are customized as follows:* Design and deployment of an asynchronous web server using the following technologies: ESP32 programmed with Arduino IDE, hosting server and domain name, and hypertext pre-processor (PHP) script to insert data into the MySQL database and display it on the web page in charts.
* Estimation of the power consumption of a sensor node and analysis of its performance during various operating modes.
* Prototyping the sensor node with data management and energy-harvesting subsystems.  
A user communicates through the internet with a base station directly or indirectly connected with all sensor nodes distributed in the area to be monitored. Figure 1 depicts a typical design of a WSN [19].
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 7 Conclusions  
In this work, we have presented an IEEE 802.15.6-based overlay network that allows the LTE network to support massive M2M traffic with priorities over PRACH. The PM2M overlay operates by dedicating a number of available preambles to the physical layer of the overlay network; the remaining preambles can be used for LTE-prescribed random access by regular LTE UEs. We have modelled the performance of the overlay, as well as the mutual interference of H2H and PM2M traffic when both are present in the LTE cell and the performance of the PM2M overlay with and without the errors in the clear channel sense performed during the backoff countdown. Our results indicate, first, that the H2H traffic still enjoys fair access to the PRACH despite the presence of the PM2M overlay. Second, the PM2M overlay is capable of accommodating up to about 1500 M2M devices in a single cell with default LTE capacity. Third, the backoff error reduces this capacity by about 12% compared to the case with perfect channel sensing. In both cases, the PM2M overlay is capable of providing sufficient differentiation between low- and high-priority traffic classes given the backoff period. Our results show that including backoff error decreases overlay capacity by approximately 12% compared to the perfect clear channel assessment. We have also evaluated priority differentiation in the overlay network using different sizes of backoff windows. Therefore, we could consider a higher priority traffic class for more critical mMTC applications, such as vehicular safety, and a lower priority traffic class for applications with less stringent requirements, such as crowd sensing, thus making it suitable for a wide range of smart city applications.  
**Author Contributions:** [PERSON] did the conceptualization and designed the methodology. [PERSON] did the investigation and adapted the methodology under the supervision of [PERSON]. Validation was performed by [PERSON] and [PERSON]. Original draft was written by [PERSON], and subsequently reviewed and edited by [PERSON] and [PERSON].  
**Funding:** This research was funded by National Science and Engineering Research Council of Canada (NSERC) through Discovery Grant program.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Experiments and Results
### Fuzzy Target Detection  
Optical remote sensing image is usually photographed outdoors in a high angle shot via satellite and aerial sensors, and thus, there may be fuzzy targets, which are caused by cloud cover, shadow noise, and cluttered backgrounds. In addition to comparing the performance of the algorithms in the remote sensing image train and test dataset, the CISPNet and SSD algorithms are used to detect fuzzy images. The detection results are shown in Figure 9. We can clearly see that our proposed CISPNet is beneficial to the detection of fuzzy targets. This is because that the context information scene perception (CISP) module and feature fusion module (FFM) are added in CISPNet, which can more comprehensively understand the feature information from context information so that it can better distinguish between the background and the fuzzy targets, and determine the location of fuzzy targets through the contextual information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Aspect Ratios**} & \multicolumn{3}{c}{**Metric/\%**} & \multirow{2}{*}{**mAP**} \\ \cline{2-2} \cline{4-5}  & **Aircraft** & **Oiltanker** & **Ship** & \\ \hline
1 & 59.24 & 65.26 & 51.47 & 58.66 \\
1/3,1,3 & 64.44 & 69.75 & 75.25 & 69.81 \\
1/3,1/2,1,2,3 & 78.63 & 79.24 & 77.63 & 78.50 \\
1/3,1/2,2/3,1,3/2,2,3 & 80.28 & 81.56 & 79.17 & 80.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study: Effects of different aspect ratios on the remote sensing image dataset tests.
## 3 Experimental Results
### Eye Diagram Analysis  
We then observed eye diagrams of the differential signal from the perspective of the time domain. We generated S4P files for the MDCRP structure filter board and the reference board using their measurement results. These S4P files were inputted into ADS schematic tools for eye diagram simulation. The input parameter setting generated 211 - 1 pseudo-random binary sequence (PRBS), the differential signal at the input end was fed into \(\pm\)0.5 V, the bit rate was 20 Gb/s, and the rise time was 10 ps.  
The eye diagram simulation result of the reference board is shown in Figure 10a, and the eye diagram simulation result of the MDCRP structure filter board is shown in Figure 10b. It can be observed that the two eye diagrams of the structures show changes in their differential signals. The eye diagrams of the two structures are somewhat different, but the differential signal eye diagrams are very good for both. This proves that the addition of the MDCRP structure filter does not influence the quality of the differential signal transmission. The relevant parameters (the maximum eye height, maximum eye width, and jitter) are given in Table 2.  
Figure 9: Comparison of the measured results between the MDCRP structure filter board and the reference board. (**a**) Output voltage of the differential signal. (**b**) Output voltage of CM noise.  
It can be observed that the eye diagrams of the two structures show some differences when subjected to a numerical comparison, but both maintain good results in terms of their characteristics. This verifies once again that the MDCRP structure filter has good differential signal integrity and can maintain differential signal transmission quality. The transmission rate can reach 20 Gb/s in high-speed data transmission. These results prove that the proposed MDCRP structure filter can support HDMI 2.0 bus capabilities.
## 2 Materials, Process, and Characterizations
### Electrical Charging/Ionization Process  
To create piezoelectric properties in our porous polymer films, a high voltage was applied perpendicular to the material surface to charge the encapsulated gas in the foams (air) and create electrical macro-dipoles. To charge the porous copolymer samples, a high-resolution sputter coater coats the film with a silver electrode on both surfaces (12 nm thick) under high vacuum, and then subjects it to DC electric fields E of 10 kV/mm.  
High voltage was delivered by a voltage amplifier of gain 1000 (TREK10/10 B, Trek Inc., Lisses, France). Input was controlled by a function generator (Agilent 33220A, Santa Clara, CA, USA). The sample had a surface area of 400 mm\({}^{2}\), and the electrical charges were injected for a duration of 15 min. Figure 1 gives a schematic representation of the electrical ionization process. The electrical charging process was performed in silicone oil. The silicone oil used in the experiment is a dielectric liquid consisting of polydimethylsiloxane with a purity specially controlled for use in electrical engineering.
## 3 Results and Discussion
### Examining the Effects of Independent Input Variables  
By using the Qnet2000 software program, the analysis of the contributions for the independent input variables was accomplished [60]. Figure 4 presents the analysis of the independent input variables contribution by the ANN model (4-20-1). As per Figure 4, the horizontal force that was assessed by the ASABE form (DD) had the biggest impact on the modified horizontal force when using the new--Fi-parameter and the working field criterion: 36.10% and 28.05%, respectively. The modified horizontal force was less affected by the soil moisture content and soil texture norm parameters by 14.66% and 21.19%, respectively.  
Figure 4: Analysis of the independent input variables contributions by the ANN model (4-20-1).
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 6 Discussion
(‘X’ and circle represent mean and outlier, respectively). The most commonly used color spaces in the available state-of-the-art bleeding detection algorithms for CE images are RGB, HSV, YIQ, YCbCr, CIE L*a*b*, CIE XYZ, K-L, etc. Among them, from the above review, we can see that the RGB color space had a slightly higher recall value and achieved lower variance for accuracy, recall, and specificity. A computer-aided system also improved the execution speed because no color conversion operations are required when using the RGB color space. So, for practical use, the RGB color space is the best option as there is no need to convert the data into other color domains. For the feature extraction method, the combined global and local feature extraction domain showed greater detection accuracy compared to the individual domains, which makes it more suitable for practical use. For bleeding detection in CE images, most of the literature proposed ML algorithms, which included SVM, KNN, PCA, MLP, NN, Random Tree, Random Forest, etc. Also, the greatest number of articles on DL used CNNs (FCN, SegNet, U-Net, DenseNet, ResNet50, VGGNet, and MobileNet). From the above review of the literature, the KNN and CNN algorithms outperformed the other algorithms. For ML algorithms, the color channel intensity values overlapped between bleeding and non-bleeding pixels. Meanwhile, the DL approach does not require a separate feature extraction stage and it extracts features automatically, which is more effective for practical use.