esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A flexible Approach to a Reachable Network Topology with a Minimum Connection Degree
### A Full-Reachable WSN Based on an Adaptive Transmission Range with Minimum Node Degree
Instead, reachability is a better index of network topology to reasonably evaluate the performance. Figure 8: _Cont._  
Figure 10: A comparison of simulation results for energy savings with node degrees ranging from 1 to 8 with different node densities. Figure 9: A comparison of simulation results for the transmission range with node degrees ranging from 1 to 8 with different node densities. The node distribution is the critical factor to determine the relation between node degree and network reachability. In nature, the network reachability is increased as the node degree increasing. In this study, the adjustment of the transmission range with a minimum node degree is proposed to guarantee a full reachability network topology. There is no isolated node in a full reachability network topology. In other word, in this network topology, any pair of nodes is able to deliver data each other. In order to achieve this goal, a proper node degree is required. How to obtain a proper node degree to establish a full reachability network topology is a significant issue. In this study, the proposed algorithm first determines the node degree and adjusts the transmission range to make all nodes with this degree. The nodes will construct the links with other nodes based on this derived transmission range. The expected node degree will be adjusted continually until the full reachability network topology is established. All nodes in a network will meet the expected node degree to establish network topology. The network reachability is higher if the expected node degree is increased. When the full reachability network topology is established, the increment of expected node degree is unable to affect the network reachability. The network patterns with uniform distribution are attempted to establish a full reachability network topology in this study. And the needed node degree is about 3 or 4 to establish a full reachability network topology for these patterns. Hence, the network reachability is changed obviously in the node degree less than 3. The change of network reachability is insignificant when the node degree greater than 4. Naturally, the network reachability is increased as the node degree increases. As varying with the node distributions, there exists a threshold of node degree affecting the network reachability. In the studied cases, most big jumps occur when the expected node degree is changed from 2 to 3. That means the threshold of node degree is between 2 and 3. Upon the node degree exceeding the threshold, the network reachability approaches to full reachability. This finding benefits the parameter configuration in real applications of the proposed scheme. Regarding to the impact of node distribution on the threshold, we will explore in future work.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 5 Multi-Gateway-Based Protocol
### Sensor Node Distribution Phase  
Sensors are distributed randomly over the surveillance area to accumulate some interesting phenomenon, like frequency of trembles in an earthquake-prone area or a change in the temperature of the area during the night. A gateway node acts as a central control system within two cluster regions. It initiates a request for accumulating the information. The gateway node broadcasts a control HELLO packet, and in reply, sensor nodes transmit their location information to the gateway. The gateway node calculates the distance of each sensor from itself and from the sink. If the sensor node is within the cluster region which is covered by the gateway node, it is in the range of the gateway node. The gateway node informs the sensor nodes about their region number by using their location information. It saves the resulting information into every sensor data table. The data table includes the sensor's residual energy, identification number (ID), its distance to the sink and gateway, and its location information. In a hierarchical sensor network CHs are responsible for data retrieving, aggregation, and transmission. All sensors and the gateway node have information about the location of CHs; they send data or a query packet to CH directly instead of flooding the entire network with HELLO packets.
## 1 Introduction
The proposed architecture incorporates several innovative techniques, such as a thermometer-coded capacitor array layout for improved matching, DWA-based linearization for the feedback DAC, and an RC calibration scheme that maintains accurate frequency response across process, voltage, and temperature (PVT) variations. These optimizations collectively result in a power-efficient receiver achieving \(-95\) dBm sensitivity and \(54.2\) dBc image rejection while consuming only \(4.08\) mW, making it particularly suitable for battery-operated IoT sensor nodes in the 2.4 GHz band. Figure 1: IoT applications utilizing low-power CMOS receivers in the 2.4 GHz ISM band.
## 2 Results and Discussion
### Adsorption of Iron Species on the Flax Fiber Surface  
To gain insight into the role of iron species in the Fenton process, XPS profiles were obtained to investigate the impact of various iron sulfate levels on the amount of iron adsorbed by the FFs (Figure 1). In this step, the fibers were soaked in FeSO\({}_{4}\) (_aq_) solutions (0.1, 1, 2.5, and 5 wt.%) for 1 h [24]. The results revealed that, by increasing the concentration of iron from 0.1 to 2.5 wt.%, there was an increase in the amount of iron adsorbed on the fiber surface [24]. In Figure 1, the XPS results indicate that there was no significant presence of iron on the surface of the flax fiber at either 0.1% or 1% FeSO\({}_{4}\) (_aq_). Therefore, the concentration of iron sulfate is considered as one of the key factors in the modified Fenton reaction for the treatment of flax fibers. Based on the findings of the XPS experiment, it was observed that there was no increase in the amount of iron adsorbed on the surface of the fiber as the iron sulfate concentration increased from 2.5% wt.% to 5% wt.% (Table 1).  
Figure 1: Wide-scan XPS spectra of flax fibers after exposure to an aqueous solution of iron sulfate at variable concentrations (wt.%).
# Cost-Effective Design Modification of a Sleeve Bearing with Large Bearing Clearance
## 3 Numerical Simulation
### Bearing Models
#### 3.3.1 The Fluid Film Force of the UGB and LGB
\[h(\psi)=0.5C_{p}+e_{j,x}\ cos(\psi)+e_{j,\ y}\ sin(\psi)-r_{p}cos(\psi-\psi_{c}) \tag{7}\]  
where \(e_{j,x}\) is the trajectory of the journal center at the \(j\)-node in the \(X\)-axis, \(e_{j,y}\) is the trajectory of the journal center at the \(j\)-node in the \(Y\)-axis, \(r_{p}=0.5\big{(}C_{p}-C_{b}\big{)}\) is the pad preload, \(\psi\) is the angular displacement measured from the negative \(X\)-axis, and \(\psi_{c}\) is the angular displacement measured from the negative \(X\)-axis to the minimum film thickness (for this case, \(\psi_{c}=\)0.5\(\pi\)). The bearing forces (\(f_{x}\), \(f_{y}\)) are calculated by integrating the pressure distribution over the fluid film domain using Equation (8):  
\[\left[ \begin{matrix}f_{x}\\ f_{y}\end{matrix} \right]=\int_{0}^{2\pi}\int_{0}^{L}p\left[ \begin{matrix}\cos \psi\\ \sin\psi\end{matrix} \right]\ (Rd\psi)dz \tag{8}\]  
To solve the Reynolds equation and calculate the pressure distribution under the bearing, a finite element method was employed and solved using MATLAB R2020a. In this method, the domain was subdivided into smaller elements, and the solution was estimated using local trail functions. Further description of the method is available in [23]. For our case, the pressure at the boundaries was assumed to be zero. Furthermore, a four-node bilinear quadrilateral element was chosen, and the bearing surfaces were discretized into \(Ne\times Me=120\times 40\) elements, where \(Ne\) and \(Me\) are the number of elements in the circumferential and axial direction, respectively. \[\mathbf{K_{p}P}=\mathbf{K_{U_{x}}U_{x}+K_{h}\dot{h}} \tag{9}\]  
where \(\mathbf{p}\) is the pressure vector, \(\mathbf{K_{p}}\) is the pressure fluidity component \(\mathbf{K_{U_{x}}}\) is the shear fluidity component, and \(\mathbf{K_{h}}\) is the squeeze component.
## 4 Flight Test Results and Discussion
### Latency Time Analysis
#### 4.2.1 Lvelihood of Location Analysis  
For this analysis, the flight test data were grouped based on the livelihood of each location, namely, a suburban or rural area. The resulting boxplots for the broadcast time, process time, query time, latency time, and interval time are shown in Figure 12.  
The broadcast time shown in Figure 12a indicates that the average value in the suburban area (0.33 s) was a little bit higher than for the rural area (0.3 s) and the data variation was larger in the suburban area. In contrast, the query time shown in Figure 12c indicates the average value in a rural area (1 s) was a little bit higher than in the suburban area (0.9 s), and the query time had a larger variation in the rural area.  
Since the query time was measured based on a normal 4G connection on the ground, the result was in line with the characteristic of 4G signal quality, where the suburban area had better signal quality than the rural area [45]. However, most of the broadcast times were measured based on a 4G signal at a certain height above the ground. Since the rural area had a better line of sight to the 4G tower, the broadcast time was better than in the suburban area. This phenomenon is mentioned in other research about 4G signal measurement in the suburban area in Malaysia [46].  
The process time shown in Figure 12b indicates that the rural area had slightly larger average and variation values. The livelihood should not affect the process time since the system uses the same application and computer hardware. Since the process time was large when a detection event happened, this indicates that the flight test in the rural area had more frequent or longer warning events.  
In total, the latency time shown in Figure 12d was higher in rural areas with averages of 1.48 s and 1.40 s for the suburban area with more variation in the rural area. Similarly, the interval time in Figure 12e shows the same trend as the latency time because the interval time depends on the latency time, as mentioned in Section 2.3.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 3 Motivation  
The method that comes to mind first for outdoor navigation is, of course, GNSS. The typical accuracy of smartphone's code-based GNSS under an open sky is 4.9 m [42]. There are methods such as carrier phase enhanced GPS (CPGPS), Real-Time Kinematic Positioning (RTK), Continuously Operating Reference Stations (CORS), GDGPS, TDRSS, PPP-AR, etc. that can increase the accuracy of GNSS localization, but these typically add to cost and complexity, require subscription to auxiliary services, or do not operate in real-time [9]. There has also recently been interest in alternate methods for localization given the susceptibility of GNSS to jamming and spoofing [43].  
In the experiments reported here, the positions determined using the BGU method with RTT ranging have smaller errors than those of the smartphone's code-based GNSS. In Figure 1, the blue curve is the Cumulative Distribution Function (CDF) for the error in expected value of the probability distribution of the RTT measurements, while the red curve is the CDF for the error in the positions reported by the smartphone's GNSS. These CDFs are based on 6140 measurements in a hotel/condo complex described in Section 11.  
Figure 1: Cumulative Distribution Function (CDF) for position errors (in meters) using (i) RTT and the expected value of the Bayesian Grid Update method (BGU) (blue) and (ii) smartphone GNSS (red). The mean and RMS error are smaller for the BGU results than for the smartphone’s GNSS. The CDFs are based on measurements in a hotel/condo complex described in Section 11.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 1 Introduction
This work introduces the SAR-Based Robust Satellite Technique-FLOOD (SAR-RST-FLOOD), a novel approach that utilizes SAR data to enable the accurate and timely detection of flooded areas. RST-FLOOD has been successfully applied to optical sensors data, such as those acquired by the Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS), to detect and monitor flood events by exploiting the typical behavior of water in the visible-near-infrared spectral region [26; 27; 28]. It is a multi-temporal approach aimed at the preliminary characterization of the investigated signal at the pixel level in unperturbed conditions, and then the identification of any signal transient as statistical deviation from these \"normal\" conditions [29]. In this study, the RST algorithm was applied for the first time to SAR data, particularly to backscattered signals, to identify flooded areas. One of the key advantages of the RST approach is its robustness against various environmental factors such as vegetation, topography, and weather conditions. On the other hand, one of the main limitations that has hampered the application of RST to SAR data so far is the computational effort, in terms of both data storage and processing, needed to analyze long-term series of backscatter signals over large areas. In this study, we addressed this issue by leveraging the remarkable capabilities of the Google Earth Engine (GEE) cloud-based geospatial processing platform [30], which allowed us to implement our methodology in a streamlined and efficient manner. GEE revolutionizes the field of geospatial analysis by offering an intuitive and user-friendly interface, allowing us to explore and process data with ease while obtaining results at both regional and global scales [30; 31; 32; 33]. One of the key advantages of GEE is its ability to handle massive datasets, thereby facilitating informed decision-making and comprehensive analysis [26]. A remarkable feature of GEE is its vast geospatial data repository, which includes constantly updated Sentinel-1 GRD (\"Ground Range Detected\") data [14]. The availability of analysis-ready SAR datasets in GEE has greatly simplified the complex SAR pre-processing procedures, opening new avenues for SAR-based geospatial analysis. SAR-RST-FLOOD was implemented in GEE, allowing us to develop an automatic method that can be applied anywhere. To validate the effectiveness of the proposed algorithm, extensive experimental evaluations were conducted using Sentinel-1 SAR data from different satellite missions, including the Copernicus Emergency Management System (CEMS) Mapping database [34]. The results demonstrate that the algorithm is highly accurate in detecting flooded areas and can provide timely information to aid disaster response and management efforts.
## 3 Interaction Enhanced ICA
### Artificial Imperialist  
This section proposes the idea of artificial imperialist, and describes how it is used in Interaction Enhanced ICA. Let \(\boldsymbol{p}^{1}\), \(\boldsymbol{p}^{2}\), \(\ldots\), \(\boldsymbol{p}^{m}\) denote the imperialists in current population, listed in ascending order of their costs. An artificial imperialist \(\boldsymbol{p}^{a}\) is constructed as the weighted sum of all imperialists, as shown below.  
Figure 2: Interaction Enhanced ICA.  
\[\textbf{p}^{a}=\sum_{i=1}^{m}w_{i}\textbf{p}^{i} \tag{10}\]  
\[w_{i}=\frac{(0.9)^{i}}{\sum_{j=1}^{m}(0.9)^{j}} \tag{11}\]  
The weights of these imperialists form a geometric sequence with ratio 0.9. A better imperialist has a larger weight. Intuitively, the artificial imperialist combines the niche of all imperialists, and allows exploration of global optimum efficiently. To employ artificial imperialists in Interaction Enhanced ICA, the steps in Figure 3 are used for the interaction step in Figure 2. If the artificial imperialist \(\textbf{p}^{a}\) is better than the weakest imperialist \(\textbf{p}^{m}\), then \(\textbf{p}^{a}\) becomes the new imperialist of empire \(m\), and \(\textbf{p}^{m}\) is simply discarded. We denote the Interaction Enhanced ICA using Artificial Imperialist as ICAAI.  
ICAAI only incurs one more calculation of the objective function per generation than the original ICA does. However, the time complexity remains the same for both methods.
## 3 Results
### Assessment Methods for scoliosis Diagnosis  
The goal of conducting a systematic review is to reduce the likelihood of potential biases by thoroughly searching and examining all published papers. All the characteristics and design studies in diagnosing scoliosis, such as the imaging modality/instrumentation, the parameter/landmark evaluated, the software or tools used, the assessment mechanism, and the plane/view used, have an influence on the accuracy and precision of the study. These variables have an impact on the method that must be considered in the evaluation of both the outcomes and inferences. Table 3 presents the variables utilized by the reviewed articles of the assessment method for scoliosis diagnosis. These data can help to provide supplementary information by comparing methods in scoliosis diagnosis studies. The instrumentation or imaging modality is crucial in capturing the structure of the spine to obtain good image quality because it affects the accuracy of scoliosis diagnosis. Six out of eighteen studies used common and conventional imaging modalities, such as X-rays, computed tomography (CT), and ultrasounds [18; 19; 20; 28; 33; 34], while other researchers [21; 22; 23; 24; 25; 26; 27; 29; 30; 31] used uncommon instrumentation, like rasterstereography, cameras, EOS imaging, scanners, and 3D laser profilimeters. Special mention goes to [PERSON] et al. [32], as the study did not collect data as images that used electromyogram (EMG) and ground reaction force (GRF) data. The critical aspect of the diagnosis of scoliosis deformities is the parameters or landmarks evaluated during the assessment, as they are utilized as a metric that determines the existence of scoliosis in a person. The gold-standard parameter that current clinicians use is the Cobb angle, which [21; 27] utilized in their studies. Study [18] provided no evaluated parameter, as the study used image processing for the assessment, whereas the authors constructed new parameters or improvised from the current parameter to evaluate scoliosis. The new parameters used for scoliosis evaluation are the vertebrae as a landmark [22; 24], rasterstereographic measurements [25], the center of laminae (COL) [28], the Scolioscan angle [29], a digital image-based postural assessment (DIPA) [31], the bending asymmetry index (BAI) [33], EMG and GRF [32] data, and a 3D scoliosis angle [34]. Three possible approaches have been employed for the assessment method of scoliosis diagnosis where five scholarly articles devoted to the study of artificial intelligence [18; 19; 25; 27; 32], nine articles studied image processing [20; 22; 24; 26; 28; 31; 33; 34], and four articles studied building a system to diagnose scoliosis [21; 23; 29; 30]. Digital image processing has become the most common form in the medical field, as it is the most efficient and cheapest method. Previous studies have tested scoliosis assessment by using various image processing techniques, including 3D reconstructions of the spine and rib cage [24], segmentation and superimposition [26], the COL [28], photogrammetry [31], the BAI
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 4 Implementation and Experiments
### Hyperparameters  
Empirical parameters are used as candidate values for the model. A manual random search method is used to find the best hyperparameter combinations, taking into consideration the small corpus size and potential overfitting. We observed the effects of altering these values over the performance in order to develop the best possible model given the available corpus size. For example, parameters such as embedding size, batch size, and number of units are varied. Moreover, we explored multi-layer variants of each of the neural network models investigated. Table 4 provides a summary of the used hyperparameters and associating values.  
The models are optimized using Nadam, which is an Adam optimizer [35] variant with Nesterov momentum [36]. We left the parameters of this optimizer at their default values as recommended by Keras documentation [37]. Training is stopped when the validation loss stops decreasing over two epochs.  
Moreover, we optimized the model by minimizing cross-entropy between the predicted label distributions and the annotated labels. However, our dataset is imbalanced because there are far more correct words than incorrect words. Therefore, we defined and used a weighted binary cross entropy function [38]. This function allowed trading off between recall and precision by adjusting the weight of positive errors in comparison to negative errors.
# Hepatoprotective Effect of Alpinetin on Thioacetamide-Induced Liver Fibrosis in Sprague Dawley Rat
## 3 Results
### Microscopically Investigation of Liver  
The control group presented regular liver with hepatic cords, sinusoidal spaces, and a principal vein. Liver cells have integral well-maintained cytoplasm and prominent nuclei. Livers were free from pathological aberration, and their slices were observed with even cellular construction. The histological structures of liver parenchyma tissues were integral (Figures 3 and 4).  
structure and style. (**B**) TAA group exhibited physical injury, asymmetrical renewing lobules with a condensed fibrotic septum, proliferation of bile ducts, and existence of inflammation. (**C**) TAA + Silymarin liver with minor inflammation and fibrosis. (**D**) TAA + 30 mg/kg Alpinetin group exposed moderately well-maintained hepatic cells, a small range of necrosis, and a thin fibrotic septum. (**E**) TAA+ 60 mg/kg Alpinetin group presented partly conserved hepatocytes and minor zones of insignificant necrosis (H&E stain 20\(\times\)).  
In the TAA control group, histology proved liver damage was demonstrated through the presence of inflammation besides necrotic tissue debris in corresponding hepatic sections. The construction of hepatic tissue is changed by fibrous septa arranged collagen connection among hepatic triangles outlining minor and big re-forming nodules of necrotic tissue. The nodules bounded via dense fibrous connective tissue separated the liver into virtual lobules. The representations of cytoplasmic vacuolation, bile vessel propagation, hepatic cell deterioration, necrosis, a gathering of inflammatory cells, extended portal areas, and collagen confession were observed.  
The Silymarin-fed group revealed significantly minor pathological changes as correlated to a widespread liver injury initiated in the hepatotoxic group. Silymarin prevented inflammation, cellular permeation, liver cell death, then fibrous connection material pro  
Figure 4: Histopathology of hepatic slices. (**A**) The usual group exhibited standard structure. (**B**) TAA control group displays propagation of bile ducts, thick rubbery septum, besides collagen threads. (**C**) TAA + Silymarin group exhibits slight leathery septa and collagen filaments. (**D**) TAA + 30 mg/kg Alpinetin group demonstrates reasonable fibrous septum and uneven restoring nodules. (**E**) TAA + 60 mg/kg Alpinetin group displayed a slightly tough septum besides collagen threads ([PERSON]’s trichrome stains 20\(\times\)).  
duction displayed via the TAA control group. Therefore, hepatic cells were well-conserved, approximately usual liver lobular construction by central veins besides burning liver strings. Outcomes confirmed the protective roles of Silymarin in contrast to the TAA-induced liver impairment. Microscopically inspection of liver sections from rats fed with Alpineti discovered diminished grades of fibrosis; however, a lesser necrotic tissue facilities parenchyma, considerable stages of typical parenchymal construction with funny fibrous, and minor amounts of cytoplasmic vacuolization and nucleic damage.