esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Priority-Based Clustering Scheme
#### 3.3.3 Phase 3: Scheduling Algorithm for Inter-CH and Relay  
Based on the operations of inter-CHs in Phase 2, this subsection briefly describes a conceptual design of relay scheduling for the multi-relay case. Considering the priority for each relay node, the approach described in Phase 2 can also be applied to schedule the relays. For instance, referring to Figure 7, there are two inter-CHs in Intensive Care Unit. Therefore, the weight of relay \(i,w_{R_{1}}\), is equal to the summation of the corresponding \(L_{i}\) (e.g., \(w_{R_{1}}=L_{1}+L_{2}=17\)). The weights of relay for four wards are shown in Figure 9. Accordingly, weight calculation can be applied to decide the transmission priority by adding the priority levels of its corresponding inter-CHs, as shown in Figure 10.  
Based on the operations in Phases 2 and 3 and considering the weights of signals, TDMA protocol assigns the transmission slots among the base station and relay nodes, which is used to schedule the data to transmit from CHs to inter-CHs and from inter-CHs to relay. The setup phase and the steady state phase in the TDMA schedule has been proposed in [13]. Every slot may have a different size, which may depend on the parameter \(w_{R_{i}}\), which means if a relay is associated with inter-clusters with high priority or many members in the inter-cluster, the time slot duration will be adaptively adjusted. Moreover, for inter-cluster scheduling, members with higher priority levels are allowed to transmit their data more frequently for monitoring the high-risk patients. As shown in Figure 9, \(\sum_{R_{i}}=44\), then we allocate the corresponding size of the time slot for each relay (e.g., the percentage of time slot for \(R_{1}\) is about 38.64% (17/44) of the transmission time). Accordingly, the scheduling  
Figure 8: Inter-CHs scheduling.  
Figure 10: Relay scheduling.  
Figure 9: Network clustering and inter-CH selection.  
method sets the high-level CH to transmit more frequently, and the inter-CH will fusion the whole data then transmit to relay.
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 6 Polyphenols as Antimicrobial Agents
### Gallic Acid  
Gallic acid (3,4,5-trihydroxybenzoic acid), first discovered in 1786 by [PERSON] [124], is a secondary metabolite that occurs in several plants, vegetables, nuts, and fruits. It has the chemical formula C\({}_{7}\)H\({}_{6}\)O\({}_{5}\), and it is composed of one benzene ring with three contiguous OH groups at the 3, 4, and 5 positions, as well as one carboxyl group at position 1. This polyphenol is nowadays considered of great importance due to its vast range of biological properties [124], mostly imparted by the hydroxyl groups [101], comprising antioxidant, anti-inflammatory, anti-dengue, anti-platelet, anti-apoptotic, antimicrobial, anti-arteriosclerosis, anti-tumor and anti-cancer activities [124; 125; 126; 127]. Gallic acid was also found to decrease melanogenesis to overcome pigmentation and protect cells from UV-B or ionizing irradiation, which is why it is employed as an essential ingredient in many cosmetic formulations [127]. Furthermore, this compound also holds GRAS status, granted by the FDA [128].  
Concerning the antimicrobial properties of gallic acid, it is thought to be active against microorganisms due to the three hydroxyl groups in its molecule, as mentioned before. It was demonstrated [129; 130] that this polyphenol has antimicrobial action against the following bacteria: _Salmonella typhimurium_, _E. coli_, _S. aureus_, _Listeria innocua_, _Helicobacter pylori_, _Campylobacter_ spp., and _Pseudomonas_. According to these studies, gallic acid has the capacity to degrade bacterial cell membranes, which leads to irreversible permeability alterations, rupture, hole formation, and a decrease in negative surface charge. The leakage of vital intracellular components is a result of this activity. Moreover, some reported MICs range from 0.5 to 8 mg/mL for _E. coli_, _Streptococcus mutans_, _P. aeruginosa_, _S. aureus_ and _Listeria monocytogenes_[131; 132; 133].
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 4 Experimental Results
### Experiment 4  
The ARE values of the BUS images which have been filtered in Experiment 3 were calculated in this experiment as shown in Table 6. From the results of Table 6, it is obvious that the BF with LF outperforms other filters in the metrics of ARE. However, for the images with different degrees of speckle, the ARE value indicates that the LF fluctuates more significantly compared to the BF. The WTF follows BF and LF, but its MR (1/VMR) value is very high (see Table 5). Therefore, the WTF cannot be directly suitable to the filtering process of BUS images. The WJD and TV also did a good job. In spite of good VMR, their poor LRD values lead to poor performance in tumor segmentation accuracy. Although the PMD can make the filtered image obtain higher edge retention (see Table 5), the ARE of PMD is the second worst among those filters. Taken as a whole, the PMD may not be an ideal choice for increasing tumor region segmentation accuracy. Obviously, the WF with high MR (or low VMR) values and the worst ARE is not recommended in filtering process of BUS images (see Tables 5 and 6). In other words, when the homogeneous region is seriously polluted by additive noise, it is likely that the segmentation can be corrupted, thus we tend to use filtered images with high VMR (1/MR) value.
# Exploring Effective Bio-Cover Materials for Mitigating Methane Emission at a Tropical Landfill
## 2 Methodology
### Evaluation of Bio-Cover Performance  
The performance of bio-cover was evaluated in terms of methane loading rate (MLR) and methane removal rate (MRR) using the following equations.  
\[MLR\ =\ \frac{Q\times C_{i}}{V_{m}} \tag{1}\]  
\[MRR\ =\ \frac{Q\times(C_{i}-C_{o})}{V_{m}} \tag{2}\]  
where \(Q\) is the gas flow rate (m\({}^{3}\)/d), \(C_{i}\) and \(C_{o}\) are the inflow and outflow methane concentrations (mol/m\({}^{3}\)), and \(V_{m}\) is the material volume in the corresponding section of the bio-cover unit (m\({}^{3}\)). Average and standard deviation of the \(MLR\) and \(MRR\) in each bio-cover section were determined on a weekly basis during the wet and dry operation periods.  
The methane mass leaving the bio-cover units was also quantified using close-flux chamber methodology on a weekly basis. The procedures for the methane emission rate (MER) measurement are described in [PERSON] et al. [23]. An acrylic chamber (0.2 m diameter, 0.25 m height) was temporally installed at the surface of bio-cover materials during which the increasing rate of methane concentration in the chamber was determined. The MER was then calculated using the following equation.  
\[MER\ =\ \frac{V}{A}\left(\frac{dC}{dt}\right) \tag{3}\]  
where \(MER\) = methane emission rate (mol/m\({}^{2}\)/d), \(V\) = chamber volume (m\({}^{3}\)), \(A\) = foot-print area of chamber (m\({}^{2}\)), and \(dC/dt\) = linear slope of methane concentration over time (mol/m\({}^{3}\)/d).  
The outflow methane mass over time (\(MER\) multiplied by bio-cover unit area) was then calculated and presented as the percentage of methane mass loading into the bio-cover  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Range** & **Average** \\ \hline pH & 8.6–8.9 & 8.76 \\ EC (us/cm) & 256–262 & 259 \\ BOD (mg/L) & 100–200 & 150 \\ COD (mg/L) & 1300–2000 & 1725 \\ TKN (mg/L) & 50.4–70 & 68 \\ NH4+ (mg N/L) & 2.24–5.6 & 4.5 \\ NO2 – (mg N/L) & 1.615–1.664 & 1.639 \\ NO3 – (mg N/L) & 44.64–52.63 & 46.47 \\ PO4\({}^{3-}\) (mg P/L) & 0.249–0.818 & 0.418 \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics of leachate.  
unit (\(Q\times C_{\text{J}}\)). The difference between inflow (loading) and outflow methane mass was considered as the removal in the bio-cover unit.
# High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images
## 2 Related Works  
### Absolute Moment Block Truncation Coding (AMBTC)  
In 1984, AMBTC was introduced by [PERSON] and [PERSON] [17]. The main steps of AMBTC consist of encoding and decoding procedures and are explained in detail as follows:  
The Encoding Procedure  
Step 1. Segment the images into a collection of \(t\times t\) non-overlapping blocks.  
Step 2. Calculate the mean \(\mathbf{\mu}\) and the standard deviation \(\mathbf{\alpha}\) by using (1) and (2), respectively. Assume that \(p_{ij}\) is the value of pixel in the block's position \((i,j)\).  
\[\mu=\frac{1}{t\times t}{\sum_{i=1}^{t}{\sum_{j=1}^{t}{p_{ij}}}} \tag{1}\]  
\[\alpha=\sqrt{\frac{{\sum_{j=1}^{t\times t}{\left|{{p_{ij}}^{2}-\mu^{2}}\right|} }}{t\times t}} \tag{2}\]  
Step 3. Create the bitmap value \(bm=\left\{{{m_{ij}}|{m_{ij}}\in\{0,1\},1\leq i,j\leq t}\right\}\) by using (3):  
\[{m_{ij}}=\begin{cases}1,if{p_{ij}}\geq\mu\\ 0,if{p_{ij}}<\mu\end{cases} \tag{3}\]  
The Decoding Procedure  
Step 1. Calculate two quantizers, i.e., high quantizer \(\mathbf{hq}\) and low quantizer \(\mathbf{lq}\), using (4) and (5), respectively.  
\[{hq}=\mu-\alpha\sqrt{\frac{q}{t\times t-q}} \tag{4}\]  
\[{lq}=\mu+\alpha\sqrt{\frac{t\times t-q}{q}} \tag{5}\]  
where \(\mathbf{q}\) is the quantity of \(p_{ij}\) which is greater than or equal to the mean value \(\mathbf{\mu}\).  
Step 2. Create the reconstructed block \(rm=\left\{{{r_{ij}}|{r_{ij}}\in\{{hq},{lq}\},1\leq i,j\leq k}\right\}\) by using (6):  
\[{rm_{ij}}=\begin{cases}{hq},if{m_{ij}}=1\\ {lq},if{m_{ij}}=0\end{cases} \tag{6}\]  
In order to provide a detailed understanding of the AMBTC compression method, we provide a numerical example as follows. Figure 1 depicts the illustration of AMBTC. First, Figure 1a depicts the original image block size of 4 \(\times\) 4. Next, we computed the block means \(\mu\) and standard deviation \(\alpha\) by using (1) and (2), respectively. In this example, the value of \(\mu=78\) and \(\alpha=19\). Next, we generate the bitmap in the encoding procedure using (3). The result is shown in Figure 1b. Finally, we calculate the higher and lower quantization levels, \(hq\) and \(lq\), using (4) and (5), respectively. In this example, \(hq=105\) and \(lq=62\). Finally, the reconstructed image block generated in the decoding procedure using (6) is shown in Figure 1c.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 1 Introduction
### Computational Domain Discretization  
Advances in aerospace, power engineering [19; 20], automotive [21; 22], civil engineering [6; 23; 24], medicine [25; 26] and other numerous branches of the industry could not have been achieved without the application of computational methods as a research tool. The primary and critical activity in numerical modelling is the computational domain discretization. It consists of dividing the analysed geometry into numerous small control volumes (elements).
# Benchmarking Message Queues
## 2 Background
### Apache Kafka [10]  
Apache Kafka is an open-source distributed streaming platform that is designed for high-throughput, fault-tolerant, and scalable real-time data streaming. It provides a messaging system that allows applications to publish, subscribe, and process streams of records in a fault-tolerant and distributed manner.  
At its core, Kafka is based on a publish-subscribe model, where producers send records to topics and consumers subscribe to those topics to receive the records. Kafka stores the records in a distributed and fault-tolerant manner, enabling them to persist and be replicated across multiple nodes or clusters.  
One of the key features of Kafka is its ability to handle large volumes of data and a high message throughput. It is known for its horizontal scalability, allowing it to handle millions of messages per second. Kafka achieves this high throughput by leveraging a distributed architecture that partitions data across multiple brokers and allows for parallel processing.  
Kafka uses topics to categorize and organize messages. Producers publish records on specific topics, and consumers can subscribe to one or more topics to consume the records. Topics can be divided into multiple partitions, which allows for parallel processing and enables high scalability. Additionally, Kafka retains messages for a configurable period of time, allowing consumers to consume messages at their own pace.
## 2 Theoretical Background
### Fisher Score- Based Feature Selection Method  
As mentioned above, a number of features can be extracted according to the behavior of the statistical method. However, the extracted features are not all useful for the classification, which might be redundant or irrelevant. It is essential to select the significant features and discard irrelevant features to improve the accuracy of the classification. Meanwhile, low dimensionality is beneficial to simultaneously reduce the computation burden and improve the efficiency of the classification algorithm. Various methods, such as Laplacian Score, Fisher score, Relief, Wilcoxon rank, and Gain Ratio, are valid for feature selection.  
The Fisher score is one of the most popular algorithms for ranking the priority of features [27,28]. As a supervised algorithm, the Fisher score ranks extracted features by the Fisher criterion. In this method, the extracted features can be quantified in discriminative scores for different classes. For instance, the data set \(\{f_{ik}(1),f_{ik}(2),\,\ldots\,f_{ik}(N)\}\) is defined as the \(k\)th feature in the \(i\)th class. First, the mean value \(u_{ik}\) and standard deviation \(\sigma^{2}_{ik}\) of the \(k\)th feature are calculated, as follows:  
\[\begin{split} u_{ik}=\frac{1}{N}\sum\limits_{p=1}^{N_{i}}f_{ik}\\ \sigma^{2}_{ik}=\frac{1}{N}\sum\limits_{p=1}^{N_{i}}(f_{ik}-u_{ik })^{2}\end{split} \tag{8}\]  
Afterwards, the Fisher score value \(FS_{ij}\) between the \(i\)th and the \(j\)th class can be quantified, as follows:  
\[\begin{split} FS_{ij}^{(k)}=\frac{\sum\limits_{k=1}^{C}\left(f_{ ij}^{k}-u_{i}^{k}\right)^{2}}{\sum\limits_{k=1}^{C}\sum\limits_{yi\in k}\left(f_{ ij}^{k}-u_{i}^{k}\right)^{2}}\end{split} \tag{9}\]  
As observed from the Fisher score expression, a large value represents a highly relevant correlation of the kth feature between the \(i\)th and the \(j\)th classes. A larger Fisher score indicates that the corresponding feature reveals more relevant distinction between the classes. The feature has a high priority to be chosen for the training model.
## 3 Experiment
### Experimental Results
#### 3.4.3 Epochs Effect  
Figure 9 shows the average training epochs required for generating the channel model of the LSTM network and the improved transformer network at 50, 200, and 400 data points. It can be seen that the transformer architecture requires fewer training epochs than the LSTM-based architecture, and fits the free space path loss model relatively faster.
## 3 Application of Drive-by Methodology on the Railway Track Subsystem
### Rail Supporting Components' Condition-Based Assessment
To include spatial information for the location of the damage, it was proposed to divide the track into 20 m stretches, for which the spectrum was calculated individually. The feasibility of the method was demonstrated based on a simplified vehicle model composed of a sprung mass and the track modeled as a beam on an elastic foundation. Promising results were obtained but validation against more field factors and real field tests is still needed. Still concerning damage to track interlayers, a recent study by [PERSON] et al. [64] further explored the detection of damage associated with track loss of stiffness, particularly hanging sleepers, through the use of the filtered bogie displacement (FBD) response calculated by the integration of acceleration signals. Firstly, a numerical study, based on a 2D 10 DOF vehicle model and a three-layer track model, was conducted to demonstrate the good correlation between the FDB and the track's loaded profile. These numerical findings were then experimentally confirmed by measurements taken on a passenger train. Then, the authors demonstrated that a localized track loss of stiffness results in a bump on the displacement signal, which can be used as a damage indicator. The methodology was also tested considering uncertainties in the train speed and measurement noise, and despite the amplitude of the FBD being significantly affected by these parameters, it was demonstrated  
Figure 10: 2D interaction model for track foundation damage assessment [61]. that it can still be used as a reliable damage feature as long as the average value of several train passes is considered.
# Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet +
## 3 Experimental Verification Based on Vibration Signals
### Experimental Outcome
#### 3.2.4 Performance Evaluation with Various Loads  
Machines and their bearings have to operate under a variety of load conditions in the field or in real-world applications. It is more difficult to diagnose faults when the vibration signal's characteristics alter in response to variations in load. Vibration signals under various loads of 0 kW (0 hp), 0.746 kW (1 hp), 1.492 kW (2 hp), and 2.238 kW (3 hp) are used to evaluate the proposed system after training. The achieved findings are highly efficient, with accuracy ranging from 98.10% to 99.60%. The results are shown in Figure 15. The results obtained indicate that the proposed system performs superiorly under various loading situations.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 2 Overview and Problems of RPL
### Problems
It is too subjective and cannot alter the corresponding weighting factors in a timely manner according to the changes of the network. In Ref. [18; 19; 20; 21], the authors use the additive method to composite several routing metrics to improve the network performance. Different simulation results with different weighting factor distribution schemes are given. However, similar to [15], all of them have no definite weighting factors distribution theory analysis and basis. The weighting factor of each metric is determined based on subjective experiences and experimental results instead of rigorous weighting distribution theory. In conclusion, RPL and its improvements based on composition metrics in the additive way have the following problems:  
1. Packet queue length in buffer, end-to-end delay, residual energy ratio of nodes, number of hops, and expected transmission count (ETX) all have an important influence on routing selection. However, the recent improvements of RPL only consider two or three routing metrics. So, the optimal routes are hard to select, and the network performance is affected to some extent. 2. It is too subjective that the weighting factor of each routing metric is decided by the personal experiences of experts. There is no definite analysis and basis about weighting factors distribution theory of each routing metric used in composition metric. 3. The weighting factors of routing metrics cannot be dynamically adjusted according to network changes. Therefore, the network performances are affected to a certain extent. To solve these problems, RPL-CGA, a novel improved algorithm of RPL based on a chaotic genetic algorithm is presented. By adopting the newly-proposed chaotic genetic algorithm to optimize the weighting factor of each routing metric in composition metric to assess candidate parents (neighbors) all sidedly, RPL-CGA will choose the optimum candidate parent as the preferred parent (the next hop) and achieve significant improvement on network performance of LLNs in the aspect of average end-to-end delay, average success rate, etc.
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 2 Study Area and Datasets
### Dataset and Pre-Processing  
This study utilized two Landsat 8 images from the USGS Earth Explorer website, captured on 31 August and 2 October 2021. The former helped in creating a land use and land cover map (LULC), and the latter was used for SOM modelling aligned with soil sample collection timing. For precision, images underwent radiance conversion and reflectance transformation via the FLAASH module in ENVI 5, then were georeferenced using WGS 84 UTM Zone 38 North. To generate the SOM model, 13 variables were used (Table 1). They influenced the SOM model, confirming their significance in previous studies [15; 26]. A 30 m resolution DEM from the SRTM satellite, corrected to UTM-WGS84, was incorporated. The study also utilized multiple Landsat-8 bands and indices like the normalized difference vegetation index (NDVI) [16], soil-adjusted vegetation index (SAVI) [27], and brightness index (BI) [15] known to correlate with SOM [15]. The spatial resolution of those bands along with the indices is 30 m.  
From 27 to 29 September 2021, 96 soil samples were gathered in Batifa, distributed over 24 plots. Pre-fieldwork, ArcGIS facilitated stratified random sampling, and post-collection, the samples were subjected to a 24-h period of air drying, after which they underwent a meticulous cleaning procedure to remove any extraneous materials, including stones, weeds, and roots. A grinding and sieving was performed using a 2-mm sieve that was used ordinarily for the determination of soil properties. Then, in the stage of organic matter estimation, the soil was sieved through a 0.5 mm sieve according to the procedure of [PERSON] and [PERSON] [28; 29]. In many countries, the Tyurin method is used instead of  
Figure 1: A map displaying the study area: (**a**) Iraq; (**b**) Duhok Governorate; (**c**) Batifa region with location of sample plots; and (**d**) example of a plot with soil samples locations as it is a zoom-in part through arrows for one sample plot.  
the Walkley-Black method [30]. However, in this study, we employed the Walkley-Black method, which is more commonly utilized in professional settings. The northern region was excluded due to the current ongoing military conflicts between the Kurdistan Workers' Party and Turkey.
## Appendix B Special Remarks on the Moving Head
### Control of the Pan and Tilt Axis  
The initial commissioning of the device showed that the tilt axis has an even higher angular range; however, the exact limits were not specified, resulting in inaccurate movements. Therefore, the range has been software limited to 0-180\({}^{\circ}\), which proved to be accurate. Furthermore, the full range of the DMX channels is not used. The largest angle that still results in movement is 65,535 \(-\) 255 = 65,280. Any value larger than 65,280 does not change the position. The right side superscript determines the format in which the values are stored (\({}^{\circ}\) for degrees, \({}^{DMX}\) for the DMX channel notation, and no superscript for decimal values).  
\[pan^{\circ}=pan\cdot\frac{540^{\circ}}{65280}\qquad tilt^{\circ}=tilt\cdot \frac{180^{\circ}}{65280} \tag{1}\]  
\[pan=pan^{\circ}\cdot\frac{65280}{540^{\circ}}\qquad tilt=tilt^{\circ}\cdot \frac{65280}{180^{\circ}} \tag{2}\]  
The resulting 16-bit values from Equations (1) and (2) have to be split into two 8-bit values for their respective DMX channels, called \(pan\), \(finepan\), \(tilt\) and \(finetilt\), as shown in Equations (3) and (4).  
\[pan^{DMX}=(pan\ \&\ 0 xFF00)\gg 8\qquad finepan^{DMX}=pan\ \&\ 0x00 FF \tag{3}\] \[tilt^{DMX}=(tilt\ \&\ 0 xFF00)\gg 8\qquad finetilt^{DMX}=tilt\ \&\ 0x00 FF \tag{4}\]