esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model for Urban Area Air Quality Analysis Based on CFD Simulations
### Algorithm-Based Interpretation of the CFD Analysis  
For the interpretation of the results, different 3D software is used. After solving eight analyses, the results are exported as a point cloud, where each point is represented with various properties such as temperature, velocity, pressure, and more. Out of these properties, our method only considers velocity values calculated from x, y, and z vectors. These point clouds are then imported into McNeel Rhinoceros 3D [41]. The software has a built-in plug-in named Grasshopper, further enhancing its capacity for analyzing and representing the results.  
The surface envelope of the urban block is divided into smaller parts. Parts of 3D mesh marked as suitable for windows of the object are divided into rectangular polygons and from each of the polygons, a box is created. The base of the box is the polygon itself and the height of the box is 1 m in front of the polygon. This box represents the potential for natural ventilation for windows hosted by the observed wall. The results of operations inside the box are interpreted as results that are assigned to the contact segment of the mesh. The algorithm for the process is presented in Figure 4.  
The next step involves the observation of points with velocity information that we acquired earlier. For each wind direction, collisions are checked for each box and the point cloud. Points inside the box are considered for further calculation. From their velocities, the average speed is calculated for each box and for each wind direction. If the value is a smaller value than 0.15 m/s, then it is marked as 0, and it is marked with 1 in other cases. It is represented as 8 binary numbers for each mesh segment; these are formulated as a set \(\mathrm{S_{i}}=[n_{1},n_{2},n_{3},n_{4},n_{5},n_{6},n_{7},n_{8}]\) for segment \(\mathrm{M_{k}}\).  
Figure 3: Dimensions of the computational domain.  
If all numbers are 0, the overall performance is 0. If all numbers are 1, the overall performance is 1. In other cases, the performance is calculated from the wind direction using the following equations.  
\[\varphi_{\mathrm{i}}=\frac{\varepsilon_{\mathrm{i}}}{\sum_{\mathrm{i}=1}^{8} \varepsilon_{\mathrm{i}}} \tag{1}\]  
\[\mathrm{R_{k}}=\sum_{\mathrm{i}=1}^{8}n_{\mathrm{i}}\times\ \varphi_{\mathrm{i}} \tag{2}\]  
In Equation (1), \(\varphi_{\mathrm{i}}\) represents the relative percentage of the wind direction frequency. It is calculated once per location. A variable represents relative frequencies of wind directions, that are obtained through weather data such as in Table 1. \(\mathrm{R_{k}}\) is a percentage of the stagnant air distribution throughout the year for mesh segment \(\mathrm{M_{k}}\). A percentage value of 100 can be translated as perfect airflow through most of the year near the observed mesh. This result is then assigned to the part of the surface mesh segment connected to the box.  
Figure 4: Algorithm for interpretation of CFD simulation results. The output is a set of binary numbers correlated to wind directions.
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 4 Algorithm Simulation and Analysis
### Cluster Head Distribution and Stability Analysis  
To evaluate the balance and stability of cluster head (CH) selection under varying node densities, we simulate the average number of cluster heads per round and their variance over time for the LEACH, HEED, and LEAD-RP protocols. A stable clustering mechanism should maintain an optimal number of cluster heads with low variance, ensuring consistent data aggregation and minimized energy consumption. Figure 4 illustrates the number of cluster heads per round for \(N=300\), demonstrating that LEAD-RP consistently maintains  
Figure 5: Surviving node count vs. running rounds under the LEACH, HEED, and LEAD-RP protocols for \(N=100\) and \(N=300\).  
Figure 6: Total data transmission capacity comparison for different protocols with network sizes \(N=100\) and \(N=300\).  
a near-optimal number of CHs throughout the simulation. In contrast, LEACH shows significant fluctuations due to its probabilistic selection, leading to instability in dense networks. HEED performs better but still lacks adaptiveness to energy heterogeneity.
## 3 Results
### Risk of Bias  
The risk of bias across studies was assessed using the QUADAS-2 tool, covering four key domains: Patient Selection, Index Test, Reference Standard, and Flow and Timing. Overall, studies demonstrated low-to-moderate risk, with some notable areas of concern:  
* Patient Selection: Most studies had low risk and high applicability regarding patient selection, reflecting the target population of adolescents with idiopathic scoliosis undergoing spinal fusion. However, two studies presented a high risk of bias due to selection methods that may limit generalizability.
* Index Test: Motion capture protocols were generally consistent, showing low risk and good applicability. Nonetheless, a few studies showed moderate risk and uncertain applicability due to variations in data acquisition protocols, impacting comparability.
* Reference Standard: The majority adhered to valid reference standards for assessing postoperative functional outcomes, yielding low bias and high applicability. However, some studies showed unclear applicability due to variations in the criteria used for outcome classification, which could affect uniformity in results interpretation.
* Flow and Timing: This domain exhibited the most significant variability, with specific studies rated as high risk due to incomplete follow-up or inconsistent assessment timing, potentially impacting longitudinal robustness.  
The majority of studies demonstrated moderate methodological quality based on the QUADAS-2 assessment, with limitations primarily related to sample size heterogeneity, standardization of MCA protocols, and variations in follow-up duration.
# Anxiety Detection System Based on Galvanic Skin Response Signals
## 2 Background
### Challenges in Anxiety Detection  
Addressing social anxiety, which is a common mental health condition, poses several challenges that can hinder the development of effective detection and treatment methods. As discussed in the literature [26], the inherent complexity of social anxiety and ethical considerations surrounding its study make it difficult to research and develop effective interventions. The subtle and frequently internalized nature of social anxiety symptoms makes it difficult to identify using conventional methods. In addition, ethical considerations surrounding the collection of personal data and potential for stigmatization further complicate research efforts [26].  
Developing a universally effective emotion-recognition system remains a challenge due to the inherent variability in individual emotional expression and the diverse ways in which different brains manifest emotions. Researchers advocate for the use of compact and user-friendly sensors to capture emotional cues and obtain reliable results [27].  
Challenges pertaining to computational algorithms and physiological signals include the effect of individual living habits on the collection of physiological indicators, which potentially hinders the accuracy of mental health detection systems. In addition, relying solely on a single indicator poses limitations because different data categories reflect distinct aspects of mental health. Thus, integrated signal collection is required to realize accurate screening [28].  
Intersubject data variance also poses a significant challenge in emotion recognition, and researchers have focused on developing novel machine learning (ML) algorithms, particularly deep learning architectures, to address this complexity. For example, data annotation, preprocessing, splitting, and multimodal fusion techniques have been explored [10]. Acquiring high-quality physiological signals also faces considerable challenges, e.g., noise, artifacts from body movements, participant variability, and low-graded signals. Thus, establishing stable laboratory setups, carefully selecting stimuli, and properly training subjects are crucial factors to address these challenges. Furthermore, generalizability to new datasets is also a critical issue that must be considered [10].
## 4 Experiment and Discussion  
### Experimental Setup  
The experimental setup includes software and hardware. As shown in Figure 5, the hardware device consists of the target DSP processor and a PC. The software contains the DSPUIM tool written in JAVA and the DSP Electronic Design Automation (EDA) tool. The software runs on a PC with an AMD Core Ryzen7-5800 CPU operating at 3.20 GHz, with a memory of 16 GB and an operating system of Windows 10 64-bit. Texas Instruments (TI) is the DSP manufacturer with the highest market share; it is reasonable to add it to the research object list. We selected 14 different TI DSPs processors picking up from six series, i.e., C28x, C54x, C64x, C64x+, C66x, and C674x.
# Enhanced Drug Screening Efficacy in Zebrafish Using a Highly Oxygen-Permeable Culture Plate
## 1 Introduction
Zebrafish (_Danio rerio_) is an ideal vertebrate model extensively used in traditional developmental biology and has recently gained prominence in human disease research, including metabolic disorders such as obesity and diabetes [12,13], neurodegenerative diseases like Parkinson's disease [14,15], cardiovascular disease [16], and kidney disorders such as glomerulopathy [17]. Zebrafish are effective tools for drug screening owing to their distinct biological and practical benefits. As small tropical freshwater fish, zebrafish are genetically manageable and share approximately 70% of their genome with humans, including orthologs for approximately 84% of the genes associated with human diseases. Zebrafish embryos develop rapidly and are optically transparent, enabling real-time, non-invasive observation of internal organs and drug-induced phenotypic changes. Zebrafish can be bred in large numbers and maintained at a low cost, making them well-suited for high-throughput screening in multi-well plate formats. Notably, their functional organ systems, including cardiovascular, nervous, immune, and metabolic systems, are similar to those of mammals. This similarity enables the efficient assessment of drug effectiveness and toxicity, with substantial translational relevance. Because the early embryonic stages are not heavily regulated by animal welfare laws, rapid testing of compounds can be performedwith fewer ethical concerns. These characteristics highlight the potential of zebrafish as a highly efficient, cost-effective, and predictive model for drug discovery and biomedical research [18; 19; 20]. In the current study, we aimed to evaluate the application of InnoCell(tm) plates in zebrafish development and drug screening. We first assessed the impact of the high oxygen permeability of the plate on zebrafish embryos under both normal and oxygen-deprived conditions and compared the results with those obtained using conventional PS plates. Furthermore, we investigated the minimum culture medium volume required in 96- and 384-well plates to support normal development and effective drug activity.
## 4 Numerical Simulation of the Formation Effect of Grout Curtain in Ore-Rock Contact Zone
### Single-Hole Grouting Curtain Body Formation Effect  
#### 4.2.1 Single-Hole Grouting Calculation Model  
To investigate the impact of the curtain body's creation, a 3D porous medium model measuring 10 m \(\times\) 10 m \(\times\) 40 m is created. The ore-rock contact zone is situated between y = 15 m and y = 25 m, with a dip angle of 45\({}^{\circ}\). The grouting holes, which have a diameter of 10 cm and a depth of 30 m, are positioned in the center. The model may be seen in Figure 7. The model is partitioned into 295,091 cells using a quadrilateral meshing technique. The exit borders define the upper and lower boundaries of the model, while the no-flow boundaries define the left and right boundaries. The model parameters are displayed in Table 1, while the model itself is depicted in Figure 7.  
Figure 5: Morphology of stone body under different water pressures.  
Figure 6: Changes in slurry pressure and velocity under different water pressures.  
Figure 7: Calculation model of single-hole grouting. (**a**) Model structure (**b**) Model meshing.
## 3 Results and Discussion  
### Isolate G34 Characteristics  
Microscopic examination of the Gram-stained _P. jordanii_ strain G34 revealed that the bacteria are Gram-negative rods (Figure 1), a common characteristic of many _Pseudomonas_ species [34]. Moreover, _P. jordanii_ strain G34 appeared to possess capsules but were non-endospore formers. The presence of capsules around the bacteria indicates potential adaptations for survival in various environments, possibly aiding in protection against environmental stresses [35]. Clearly, the bacteria cells were non-motile, as assessed by using the depression slide technique and the agar slant tubes, although some flagellar genes were identified during genome sequencing, but several Flagellin genes were missing. As to their arrangement, bacteria were seen as singles, confirmed microscopically by the Gram stain and the hanging drop methods.  
_Pseudomonas jordanii_ strain G34 is catalase positive, which can break down toxic hydrogen peroxide into water and oxygen (Figure 1). Catalase plays a crucial role in enhancing bacterial survival under oxidative stress conditions [36]. Also, _P. jordanii_ strain G34 is oxidase positive, which can oxidize a colorless reagent into purple color. _Pseudomonas jordanii_ strain G34 is indole positive because the red ring appeared at the top after the addition of a reagent and was able to produce indole-3-acetic acid (IAA), a growth-promoting hormone. The capability to produce IAA suggests a potential role for _P. jordanii_ strain G34 in enhancing plant growth and development through hormone-mediated mechanisms [37]. For citrate, bacteria utilize citrate as a source of carbon, and the pH was reduced to change the color from blue into green. The bacteria cannot ferment glucose, as shown in the KIA test (Figure 1).
## 2 Materials and Methods
### SCC Mix Design  
A total of 9 SCC mixes were designed, which always incorporated 100% coarse RA, in order to maximize the amount of this aggregate, thus increasing the sustainability of the SCC containing RA [30]. Three mixes were designed with each aggregate powder, each incorporating a different content of fine RA by volume: 0%, 50%, and 100%. The fundamental target of the design of each mix was to achieve a slump flow between 750 mm and 850 mm (slump-flow class SF3 according to EN 206 [47]). For this purpose, the mix compositions were initially obtained from Eurocode 2 [49], after which they were empirically adapted when the nature of the aggregate powder or fine aggregate was modified:  
* All the mixes incorporated the same amount of coarse RA, cement, and admixtures.
* The amount of water was increased when increasing the fine RA content or adding RA powder to balance their high-water absorption levels (Table 1), so that the effective water-to-cement ratio was equal to 0.40 in all mixes. The effective-water-to-cement ratio was calculated according to the 15-min water absorption levels (Table 1), as the mixing process of SCC lasted for 15 min.
* The use of larger-sized aggregate powders (limestone powder 0/0.5 mm and RA powder 0/0.5 mm) required increased amounts of aggregate powder compared to the mixes made with limestone filler <0.063 mm, proportionally adjusting the amount of fine aggregate. This action was necessary to achieve the fine particle content for the required slump flow [38]. The composition of each mix is shown in Table 2 with the following system of labels:
* Aggregate powder: Limestone Filler (\(F\)); Limestone powder (\(L\)); RA powder (\(R\)).
* Percentage content of fine RA: 0% fine RA (\(0\)); 50% fine RA (\(50\)); 100% fine RA (\(100\)).
## 3 Results and Findings
### Results of Empirical Study
#### 3.2.4 Quantitative Analysis
* The lack of mutual understanding significantly impacted the endogenous construct by satisfying the given criteria with a \(T\)-value of 1.772 at \(P\) is 0.04 with a path coefficient value of 0.117. * The cultural difference significantly impacted CRM by satisfying the given criteria with a \(T\)-value of 1.712 at \(P\) is 0.04 with a path coefficient value of 0.113. From the above results, it is clear that H1, H2, H3, H4, H5, H7, H9, H10, and H11 were statistically significant, but H6 and H8 did not support the above-mentioned criteria and were not statistically significant. * The endogenous construct, i.e., CRM values of R2 being 0.83, was statistically very significant. The value of R2 is acceptable if it is \(<\)=0.5 [58]. Six global fitness values calculated for the complete model evaluation using WrapPLS 6.0 show that if it satisfies the following measurements, the model is statistically significant. * \(P\)-values of APC, ARS, and AARS equal to or less than 0.05 are acceptable [48]. * It is mentioned in [82] that the average adjusted R-squared (AARS) is generally less than the average adjusted R-squared (AARS). * Both average block VIF (AVIF) and average full colinearity VIF (AFVIF) are acceptable if they are less than or equal to 5 and ideally if they are equal to or less than 3.3 [83]. The hypothesis testing of the conceptual framework is shown in Figure 5. This research study followed the given measurements [86] by having \(P\) values of APC, ARC, and AARS less than 0.05. The value of AARS was 0.825, whereas the value of ARS was 0.834; both were attaining the recommended measurements [87]. Furthermore, AVIF and AFVIF were also calculated in this current study. As a result, AVIF and AFVIF introduced the latest dimensions that upgrade the assessment of the model's all-inclusive descriptive quality [88]. Based on the suggested measurements, this research study outcome verifies that AVIF and AFVIF satisfied the given criteria, as both values were less than 3.3. Furthermore, it demonstrates that both these values are ideally satisfied. Therefore, the evaluation of the structural model is also significant accordingly.
# RF Energy Harvesting Wireless Communications:
## 2 Urban and Semi-Urban RF Environment
### Power Restriction  
The higher frequency EM ionizing radiation, such as ultraviolet rays (\(8\times 10^{14}\) to \(3\times 10^{16}\,\mathrm{Hz}\)), X-rays (\(3\times 10^{16}\) to \(3\times 10^{19}\,\mathrm{Hz}\)), and gamma rays (\(>\)\(10^{19}\,\mathrm{Hz}\)), can break DNA molecules. For safety concern, the non-ionizing band that is between \(30\,\mathrm{kHz}\) and \(300\,\mathrm{GHz}\) is usually used for telecommunications [26]. Although the non-ionizing RF band is relatively safe to the body, an RF wave at sufficiently high power can burn tissues by converting energy into heat. To protect human from strong RF waves, there are several regulations on the maximal RF power density allowed in the air. According to the Federal Communications Commission (FCC) document published in 2013 [27], the advised maximal permissible exposure (MPE) of RF density to general public is \(1\,\mathrm{mW/cm^{2}}\) on frequencies 1-100 GHz, as depicted in Figure 2.  
In addition to the safety concern, the RF transmission power is further restricted to avoid interference for space reuse, especially in cellular and WiFi networks. As a consequence, the RF power density in most of the public area is much lower than the Maximum Permissible Exposure (MPE) limitation. Specifically, early results published in [28] show that in the very high frequency (VHF) and ultra high frequency (UHF) TV bands, the RF radiation power is lower than \(0.1\,\mathrm{\SIUnitSymbolMicro W}/\mathrm{cm^{2}}\) for 95% population in US cities; only less than 1% population receive exposures higher than \(1\,\mathrm{\SIUnitSymbolMicro W}/\mathrm{cm^{2}}\). The recent study tested in four US cities (Raleigh, NC; Spokane, WA; Wichita Falls, TX; and Colville, WA) also verifies that, in the common frequency bands, the strength of radio waves is below \(1\,\mathrm{\SIUnitSymbolMicro W}/\mathrm{cm^{2}}\) over 90% area of the tested cities [29].
## 1 Introduction
### Advantages of Using CR in WSNs
#### 1.4.1 Efficient Spectrum Utilization and Spaces for New Technologies  
The electromagnetic spectrum is a precious gift of Nature. The amount of available useable spectrum bands cannot be increased but they can be used more efficiently. With the exception of industrial, scientific and medical (ISM) radio bands, one requires a license from the government of the respective country to utilize the radio bands. Owing to the high cost associated with spectrum licensing, many researchers and hardware manufacturers have focused on developing devices for ISM bands. Therefore, ISM bands are overcrowded limiting the development of new technologies [13]. On the other hand, many licensed spectrum bands are either underutilized or unutilized [14]. Cognitive radio wireless sensors can use the unutilized spectrum, called white spaces, without disturbing the license holders. Unlicensed users can use those bands with little or no cost, so that more technologies can be developed for these bands. Table 2 lists the frequency bands available for ISM applications, as defined by ITU-R (RR Nos. 5.138 and 5.150) [15].
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## Abbreviations  
\begin{tabular}{l l l} \hline \hline
**Symbol** & **Definition** & **Value (Unit)** \\ \hline \(N\) & Number of satellites & 288 \\ \(p\) & Number of orbital planes & 12 \\ \(F\) & Phasing parameter & - \\ \(j\) & Orbital plane index & - \\ \(l\) & Order within orbital plane & - \\ \(\Omega\) & Right ascension of ascending node & 0-\(2\pi\) \\ \(\
u\) & Initial true anomaly & 0-\(2\pi\) \\ \(S\) & Number of satellites on an orbital plane & 24 \\ \(v_{r}\) & True radial velocity & variable (m/s) \\ \(\rho\) & Slant distance between a satellite and & variable (m) \\  & a ground IoT device & \\ \(t\) & Time variable & - \\ \(\Delta t\) & Simulation time step & 5 (s) \\ \(f_{\rm d},f_{r}\) & Classical \& relativistic Doppler shift & - (Hz) \\  & frequency & \\ \(c\) & Speed of light & 299,792,458 (m/s) \\ \(f\) & Center operating frequency & 2 (GHz) \\ \(\mu_{\rm d}\) & True Doppler shift frequency & - (Hz) \\ \(\sigma_{\rm d}\) & Standard deviation of Doppler error & - (Hz) \\ \(\Phi\) & Azimuth angle of arrival & - (\({}^{\circ}\)) \\ \(\Theta\) & Off-nadir angle of arrival & - (\({}^{\circ}\)) \\ \(\Lambda_{S}\) & Latitude of the source (ground IoT device) & - (\({}^{\circ}\)) \\ \(\phi_{S}\) & Longitude of the source (ground IoT device) & - (\({}^{\circ}\)) \\ \(\mathbf{x_{s}}\) & Position vector & - \\ \(\mathbf{M}\) & Coordinate transformation matrix & refer to (6) \\ \(\mathbf{x_{S}}\) & Satellite coordinate in ECEF frame & - \\ \(\mathbf{x_{G}}\) & Ground IoT device coordinate in ECEF frame & - \\ \(\kappa\) & Concentration parameter in Kent & - \\ distribution & & \\ \(\beta\) & Ovalness parameter in Kent distribution & - \\ \(I_{3}\) & Identity matrix of size 3 & - \\ \(\sigma_{\Phi}\) & Standard deviation of azimuth AoA error & - (\({}^{\circ}\)) \\ \(\sigma_{\Theta}\) & Standard deviation of off-nadir AoA error & - (\({}^{\circ}\)) \\ \(\mathbf{x}\) & State vector (latitude and & - (\({}^{\circ}\)) \\  & longitude of ground IoT device) & \\ \(\mathbf{Z}^{(k)}\) & Doppler and AoA measurement vector & - \\ \(k\) & Discrete-measurement index & - \\ \(g_{1}(.)\) & Doppler likelihood function & - \\ \(g_{2}(.)\) & AoA likelihood function & - \\ \(\mu_{\Theta}\) & True azimuth angle of arrival & - (\({}^{\circ}\)) \\ \(\mu_{\Theta}\) & True off-nadir angle of arrival & - (\({}^{\circ}\)) \\ \(p(\mathbf{Z}^{(k)}|\mathbf{x})\) & Joint likelihood of Doppler and AoA & - \\ \hline \hline \end{tabular}