esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Complete Islands Mapping Results  
As shown in Figure 3, the water depth inversion results for the four study areas within the 0-20 m depth range are presented. The figure displays both the overall water depth inversion results for each island and the detailed characteristics of local areas, comparing them with the water depth inversion results based on Sentinel-2 data proposed by [PERSON] et al. It can be observed from the figure that the water depth results obtained from the two methods are generally similar. However, in some edge areas of the islands, the results from this study appear to be slightly lower, which may be related to the presence of cloud cover and wave effects at the edges of the islands. Nevertheless, upon examining the local details of the islands, it becomes evident that this study's method, due to its higher spatial resolution, is able to depict underwater topographic features more clearly and coherently, showcasing richer terrain information.  
Figure 3: Complete bathymmetric mapping results based on Sentinel-2 and GF-1 inversions for the four study areas (**top**), detailed bathymmetric results for localized areas (**middle**), and original imagery (**bottom**); the black boxes indicate the specific location of the localized results on the complete island and the solid black lines indicate the location of the profiles used for analysis in Figure 4.  
To demonstrate more intuitively the effect of the improved resolution, a profile was selected from each island in each study area to compare the differences in the bathymetric results obtained by inversion using Sentinel-2 data and GF-1 data. As shown in Figure 4, it can be seen from the profiles that although the overall trend of the bathymetry results obtained by the two methods is similar, the results obtained based on the GF-1 data in this study have a significantly higher density of points in the profile, which is able to depict the fluctuating characteristics of the local bathymetry changes in a more detailed way compared with the Sentinel-2 inversion results.
## 3 Simulation and Experiment Process
### Experimental and Signal Capturing  
An experimental study was conducted to validate the effectiveness of the proposed method using the self-designed test rig shown in Figure 8a. It includes the LS-PMSM, sensor and powder break. Magnetic powder brake offers a variable mechanical load by adjusting the voltage. Two one-hp, four-pole LS-PMSMs were considered to generate data under four levels of starting load (0, 0.5, 1 and 1.5 Nm). The first motor was considered as healthy and taken as a standard for evaluation with the faulty motor. The second motor was faulty, with one BRB, as illustrated in Figure 8b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Rated Power (HP) & 1 & Starting Torque (Nm) & 2.3 \\ Rated Voltage (V) & 415 & Number of Poles & 4 \\ Rated Frequency (Hz) & 50 & Connection & Y \\ Rated Torque (Nm) & 4.8 & Number of stator Slots & 24 \\ Rated Speed (rpm) & 1500 & Number of Rotor Slots & 16 \\ Rated Current (A) & 1.3 & Permanent Magnet & N38H \\ \hline \hline \end{tabular}
\end{table}
Table 2: Motor specifications.  
Figure 7: Cross-section of LS-PMSM with mesh plotting.  
A schematic diagram of the experimental test setup including the data acquisition technique and computer interfacing is shown in Figure 9. The signal of the stator current was collected noninvasively while the system was on operation. Two categories of signal exist: transient and stationary. A transient signal is the signal obtained during motor start-up. It is selected as a feature source and analyzed in the time-frequency domain. The sampling frequency is a vital component in capturing of the transient signal and it is a significant point that should be taken into account.  
According to [PERSON] criterion, implementation of this method needs a very high sampling frequency, since the majority of the features exist in the low-frequency section [33]. The sampling frequency of 5 ks/s can provide a good resolution for the analysis of the transient signal here. In each case, 40 tests were performed; hence, 320 data sets in total were acquired for each analysis.
## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection  
In this subsection, we explain how the GA is implemented to select the features of multilabel Arabic text. We explore the fitness function, the feature representation, and the genetic operators of the GA. Due to the GA adopting the binary representation, we represent the processed text features as binary string bits vectors using both Count Vectorizer and Tfidf Vectorizer. The f1_score cost function mentioned in Table 4 is the fitness function for the metaheuristic GA method. In addition, the classification models are trained on the population and children of the GA using the cross-validation technique and evaluated based on the cost function, the type of cost function, and the cost function improvement. Because the task is classification, increasing the value of the cost function improvement is considered as an indication of the best value of the fitness function. The micro-averaged aggregation of the f1-score is used for the type of cost function. The initial values of the other hyper-parameters in Tables 4 and 5 are used for implementing the metaheuristic GA-feature selection module. Algorithm 2 clearly presents the pseudocode of the GA operations and steps to select the best features and best model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline classification\_models & ETC, RFC, and LRC \\ n\_jobs & \(-1\) \\ random\_state & \(1\) \\ cost\_function & f1\_score \\ cost\_function\_type & micro-averaged \\ cost\_function\_improvement & increase \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selected default values of metaheuristic hyper-parameters.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyper-Parameter** & **Description** \\ \hline pickle\_path & Path where base model \\ \hline base\_model\_list & List of machine learning algorithms to be trained \\ \hline vector\_list & Type of text vectors from sklearn to be used \\ \hline feature\_list & Type of features to be used for ensembling \\ \hline method & Which method you want to specify for metaheuristics \\  & feature selection \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline number of generations & 100 \\ number of population & 150 \\ probability of crossover & 0.9 \\ probability of mutation & 0.1 \\ run\_time & 120 min \\ \hline \hline \end{tabular}
\end{table}
Table 5: Selected default values of genetic algorithm hyper-parameters.  
We apply two types of representation, namely CountVectorizer and TfidfVectorizer. The experiments compare the performance of single-task label classification with feature selection and without feature selection and grid search optimization algorithm. We explore the experimental results and compare our method with existing work in the discussion section.
## 1 Introduction
### Background of an \"Estimation Procedure\"
#### 1.1.2 Unsaturated Shear Strength
In silts and silty clays, the shear strength increased to a constant value and then decreased slightly. In clays or expansive soils, the shear strength increases as the suction increases over a much wider suction range than that of sand or silt. They also proposed shear strength equations based on the average effective stress and suction over a wide suction range. All the previously mentioned studies concluded the nonlinearity of \(\phi^{\text{b}}\) and the need for further research with different methodologies to determine the shear strength of soil under unsaturated conditions, employing various approaches. This is due to the fact that tests to measure suction are expensive, sensitive, complex, and require a sophisticated laboratory testing system. SWCC is widely used to relate the shear strength of soil to a given water content and suction, as well as to the air-entry value or residual suction obtained from this curve [32]. Models for calculating soil shear strength can be divided into two main categories: fitting or empirical equations and estimating equations. Empirical equations fit the trend of a dataset using fitting parameters, whereas estimation equations are based on shear strength parameters under saturated conditions and additional variables that relate them to unsaturated conditions [1]. Table 3 summarizes some of the equations by category. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Type of** & **Shear Strength Equation** & **Parameters** \\ \hline
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 3 Results and Discussion
### S-Parameters and Antenna Efficiency  
The simulated reflection coefficients for Ant1 and Ant2 are plotted as a function of frequency in Figure 5, and the reflection coefficients are greater than \(-6\) dB in all operating frequency bands. Next, Figure 6 shows the measured reflection coefficient values for Ant1 and Ant2. As is evident from the figures, the simulation findings are quite similar to the test results. The proposed antenna exhibits near-ideal measured 6 dB impedance bandwidths (3:1 VSWR) of 5.71% (at 3400-3600 MHz), 5.40% (at 3600-3800 MHz), and 6.59% (at 4400-4700 MHz). Figure 7a shows the simulated coupling values for various antenna element combinations, while Figure 7b shows the corresponding measured values. The isolation amplitude is greater than 11 dB in the covered 5G NR band (N77/N78/N79).  
Figure 4: Fabricated prototype of the proposed eight-port MIMO antenna system.  
Figure 5: Simulated reflection coefficient variation with frequency for Ant1 and Ant2.  
Figure 8 shows the planned antenna measurement setup in a microwave-protected far-field anechoic chamber. Meanwhile, the simulated radiation efficiency values for Ant1 and Ant2, which were found to be greater than 87%, are shown in Figure 9.  
Figure 8: Testing environment of the proposed antenna in an anechoic chamber.  
Figure 6: Variation of measured reflection coefficient values with frequency Ant1 and Ant2.  
Figure 7: Coupling among different antenna pairs. (**a**) Simulated values of coupling among different antenna pairs. (**b**) Measured values of coupling among different antenna pairs.
## 2 Circular Cavity Metasurface Antenna with a Diameter of 0.25 \(\lambda_{0}\)
### Description of the Reference Antenna: A Circular Patch Embedded in a Circular Cavity  
The reference antenna structure is represented in Figure 3. It consists of a circular metallic cavity of external diameter \(b\), inner diameter \(a\), and height \(h_{c}\). The cavity is entirely filled with a dielectric material of relative permittivity \(\varepsilon_{r}\) glued to the cavity bounds (no glue is considered in Section 2.4.: \(g_{t}\) = 0 mm, but \(g_{t}\) = 0.2 mm is realistic for practical cases). A circular patch of diameter \(d\) is printed on the upper face of the dielectric material. The patch is fed by a coaxial probe located at a distance \(f_{x}\) from the antenna center.
## 4 Discussion and Results  
### Comparison of Fuzzy Markov and Traditional Methods  
There are many risk and reliability assessment methods, but the fuzzy Markov method proposed in this paper can solve the problems that other methods cannot solve. Table 6 shows the comparison between the results obtained by the traditional techniques and the fuzzy Markov method. The \"indicates the function that the method has, and \"\(\times\)\" indicates the function that the method does not have.  
It can be seen from Table 6 that fuzzy Markov can not only analyze risks, reliability, availability and maintainability, but also obtain unknown failure data. In addition, the analysis of uncertainties is also superior to other methods.
## 4 Model Applied to Polyp Segmentation
### Results of Polyp Segmentation  
Firstly, an evaluation of the proposed segmentation method in this paper was conducted based on the Dice and mIoU evaluation metrics. Table 6 quantitatively presents the performance of FuzzyTransNet and eight comparative methods on three colon polyp datasets. All evaluation metrics in the table are the averaged results of a sixfold cross-validation. It can be observed that FuzzyTransNet achieved favorable performance on the Kvasir and ClinicDB datasets. On the Kvasir dataset, FuzzyTransNet achieved Dice and mIoU metrics of 92.9% and 87.5%, respectively, which were 1.1% and 0.7% higher than those of TransFuse. On the ClinicDB dataset, FuzzyTransNet achieved Dice and mIoU metrics that were 0.8% and 0.5% higher than those of TransFuse. It is noteworthy that TransFuse performed the best among the eight segmentation networks, as seen in the table. However, FuzzyTransNet did not achieve the optimal performance on the ColonDB dataset. This can be attributed to the fact that the ColonDB dataset consists of 300 images collected from 15 colonoscopy video sequences, leading to a relatively limited amount of effective data due to the presence of multiple similar images. Consequently, while all segmentation networks exhibited a noticeable decrease in performance on the ColonDB dataset compared to the Kvasir and ClinicDB datasets, there was an improvement in the Dice coefficient for FuzzyTransNet compared to TransFuse.
## 3 Results
### Light Use Efficiency  
For the MODIS GPP algorithm, the values of the maximum light use efficiency (\(\varepsilon_{\max}\)) depend on the types of land cover. Each land cover type corresponds to a constant value of \(\varepsilon_{\max}\). Equations (1) and (3) in Section 2.1 were utilized to obtain the inferred \(\varepsilon_{\max}\) for each land cover type, which were then compared with MOD17A2 \(\varepsilon_{\max}\) directly (Figure 12 and Table 6). The results show that the inferred \(\varepsilon_{\max}\) were higher than MOD17A2 \(\varepsilon_{\max}\) by 60% for MF, 74% for ENF, 11% for EBF, 143% for Crop and 37% for Grass, respectively. However, in the case of a single site, the inferred \(\varepsilon_{\max}\) were not invariably higher than MOD17A2 \(\varepsilon_{\max}\). For example, the inferred \(\varepsilon_{\max}\) for DHS and NMG were less than the MOD17A2 \(\varepsilon_{\max}\).  
Figure 11: University of Maryland (UMD) land cover classification (MCD12Q1 Collection 5.1, Land Cover Classification Type 2) for the eight sites. The red columns indicate the actual land cover at each site.  
Figure 12: Eight-day tower estimated GPP (gC\(\cdot\) m\({}^{-2}\). 8-day\({}^{-1}\)) against the product of GLASS FPAR, tower PAR, VPD\({}_{\rm s}\) and T\({}_{\rm s}\) for: (**a**) Mixed forests; (**b**) Evergreen needleleaf forests; (**c**) Evergreen broadleaf forests; (**d**) Croplands; and (**e**) grasslands. APAR (Absorbed Photosynthetic Active Radiation) = FPAR \(\times\) PAR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site (Biome)** & **Inferred \(\epsilon_{\rm max}\)** & **MOD17A2 \(\epsilon_{\rm max}\)** & **Bias** \\ \hline CBS (MF) & 1.68 & 1.05 & \(-\)0.63 \\ QYZ (ENF) & 1.67 & 0.96 & \(-\)0.71 \\ DHS (EBF) & 1.07 & 1.27 & 0.20 \\ XSBN (EBF) & 1.59 & 1.27 & \(-\)0.32 \\ YC (Crop) & 2.53 & 1.04 & \(-\)1.49 \\ HB (Grass) & 1.75 & 0.86 & \(-\)0.89 \\ NMG (Grass) & 0.67 & 0.86 & 0.19 \\ DX (Grass) & 0.91 & 0.86 & \(-\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the inferred \(\epsilon_{\rm max}\) and MOD17A2 \(\epsilon_{\rm max}\) at the eight sites.
## 3 Results and Discussion
### The Most Influential Documents
In order to enable multiple requests for priority messages to a traffic signal controller of priority-eligible vehicles, such as emergency vehicles, transit buses, commercial trucks and pedestrians, the authors formulated a request-based mixed-integer linear program (MILP). The proposed control method was compared with state-of-practice transit signal priority (TSP) under the optimized signal timing plans using the microscopic simulation technique. The simulations highlighted that the proposed control model is able to reduce average bus delay, average pedestrian delay and average passenger car delay, especially for highly congested conditions with a high frequency of transit vehicle priority requests.
### Aim, Objectives, and Scope  
This review paper aims to elucidate the current state of knowledge regarding techniques for return temperature reduction in DH systems and the associated technical considerations. The need for this review is underscored by the significant implications for both new and existing DH systems, highlighting the importance of return temperature reduction in system design and implementation.  
The objective of this paper is to provide a comprehensive analysis of the relevant literature organized around the theme of return temperature reduction. This review is uniquely structured into several key sections: DH network, heat storage, control strategies, heat source, system-level optimization, and application methodologies. This paper represents the first survey of its kind, integrating a detailed review of contemporary practices with a thorough synthesis of methodologies across various stages of implementation.  
The goal is to provide a holistic overview of the current state of knowledge, offering a detailed synthesis of existing methods and their interactions. By considering these elements within a unified framework, this review aims to demonstrate how the integration of individual contributions leads to a cumulative effect on overall return temperature reduction. A holistic approach, as an additional focus, recognizes the interdependence of key system components, ensuring that improvements in one area complement advancements in others rather than being considered in isolation. By adopting this system-wide perspective, this review highlights how a coordinated optimization of DH networks, heat storage, control strategies, and heat sources enhances overall system efficiency, facilitating long-term sustainability and improved energy performance.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
The term \"real-time\" refers to the localization, tracking, and positioning systems that have been practically implemented, tested, and can be used in real-time implementation. Furthermore, limited the inclusion of the selected research to systems for indoor environment applications. Since many technologies can be used to build indoor positioning systems, the focus was on scientific research and systems that operate with ultra-wideband technology, especially those used for mobile object tracking applications such as mobile robots and unmanned aerial vehicles (UAVs). Accordingly, the inclusion and exclusion criteria were as follows:  
* Real-time systems, positioning algorithms, and filters for a real-time system.
- Systems for indoor application.
- Can be used for mobile object tracking (robots, UAVs, etc.).
- Complete IPS/RTLS system including hardware, software, and monitoring server (object coordinates, tracking, and positioning can be monitored).
* Simulation, theoretical research, and algorithms; UWB manufacturing research.
- Systems for outdoor environments.
- Systems cannot be used for mobile object applications.
- Partial work (incomplete system; cannot implement real-time tracking and monitoring; work for solving a partial issue).
- Works that do not include any information about the UWB components used.  
Based on the inclusion criteria, 147 papers were included that met the aims of the proposed study. Figure 2 illustrates the flowchart of the system selection process.  
Figure 2: Flowchart of the selection process.
# Children's Functional Clothing: Design Challenges and Opportunities
## 4 Functional Textiles
### Functional Textiles for Improved Protection and Wellbeing
#### 4.1.1 Antimicrobial Textiles  
Among the most sought-after and extensively investigated attributes in functional textiles are antimicrobial properties. According to [PERSON] et al. [40], antimicrobial textiles can either eradicate microorganisms (biocide) or inhibit or reduce their growth (biostatic). The authors suggest that, for clothing application, biostatic fabrics are preferable, preserving skin flora without adverse effects. On the other hand, biocidal fabrics, causing direct microorganism mortality, find greater recommendations for medical or environmental applications.  
Perspiration is a natural and anticipated occurrence during daily clothing use. To protect fabrics or garments from the generation and persistence of odours, including those caused by fungi, bacteria, and other microorganisms, the integration of antimicrobial textiles is essential [35].  
The antimicrobial property has been a growing research topic in the textile field, with a broad spectrum of studies investigating the achievement of this property in fabrics. Various approaches have been investigated by researchers to develop and enhance antimicrobial textiles, including the incorporation of metal nanoparticles (MNPs), particularly silver nanoparticles (AgNPs) [25; 39], dyeing with plant-derived dyes [26; 42], the incorporation of essential oils into fibres [24], and the utilisation of materials such as cyclodext and their derivatives, lignin, and chitosan [40].  
Blending antimicrobial textiles with design elements like colours and prints has the potential to contribute to innovation and competitiveness within the children's segment of the textile industry. However, there might be limitations in terms of awareness and utilisation of such products [40].
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing  
In this section our proposed protocol Event Segregation based Delay sensitive Routing is introduced and salient features of ESDR are explained. Directed Graph (digraph) theory is employed to serve as model for network topology in ESDR. Deployed nodes map to vertices and links between nodes represent edges of digraph. In digraph \(\mathcal{G}=(\mathcal{N},\mathcal{A})\) vertices \(\mathcal{N}\) are connected via edges or arcs \(\mathcal{A}\). Where \(\mathcal{N}\) is the set of all deployed nodes. We denote the set of event sensing or data generating nodes by \(\mathcal{E}\), the set of broadcasting nodes by \(\mathcal{B}\) and the set of courier nodes by \(\mathcal{C}\).  
There are some applications where only a fraction of all the deployed nodes are the data generating nodes, i.e., the members of \(\mathcal{E}\) while the remaining nodes are the members of set \(\mathcal{B}\). While both the set \(\mathcal{E}\) and \(\mathcal{B}\) are disjoint and subset of set \(\mathcal{N}\). There are some other (regular data gathering) applications in which all nodes are equally responsible for data sensing and forwarding tasks. In such cases the set \(\mathcal{E}\) is equal to set \(\mathcal{B}\) and both sets are subset of set \(\mathcal{N}\).  
We have considered the second case in which all nodes can be both the data sensing and forwarding nodes. The upcoming subsections provides the details of the phases involved in the protocol operations. These are summarized in the following points.  
* In first subsection network initialization and node deployment is explained.
* Motivation for event segregation and its positive impacts on network are explained in this subsection.
* Improvements in terms of forwarding function calculation process and holding time calculation process is explained in subsequent subsections.  
* Mobility of mobile sink is also impacted by event segregation approach. Mobility of deployed courier nodes in ESDR is explained in this subsection.