esa-sceva/satcom-chunk-collection huggingface dataset:
# Sex Prediction Based on Mesiodistal Width Data in the Portuguese Population  
[PERSON]  
1 Clinical Research Unit (CRU), Centro de Investigacao Interdisciplinar Egas Moniz (CiiEM), Egas Moniz, CRL, 2829-511 Caparica, Almada, Portugal; [EMAIL_ADDRESS] (J.A.N.); [EMAIL_ADDRESS] (V.M.); [EMAIL_ADDRESS] (J.J.M.); [EMAIL_ADDRESS] (A.S.D.)  
[PERSON]  
2 Laboratorio de Ciencias Ferensese s  
Teeth are the hardest organ in the human body and very important in postmortem identification procedures. Although pelvic and cranial bones can be more accurate in identifying sex, they are rarely in optimal condition in extreme cases, such as natural disasters or mass graves, which may prevent accurate estimation through them. Teeth are considered quite useful in these scenarios as they are often recovered intact [3; 4; 5; 6]. However, there may be some setbacks that prevent dental eruption of teeth useful for forensic identification [7; 8; 9; 10].  
Accurate sex prediction is a key step in creating a postmortem forensic profile as it excludes approximately half the population [11]. Several studies state that teeth have a high degree of sexual dimorphism [2; 4; 5; 12; 13]. Generally, male teeth are larger than female teeth, however data are not consensual and reverse dimorphism also occurs [4; 12]. Sexual dimorphism may vary between different populations, possibly due to variations in the environment, available food resources, or genetic pool [3; 14].  
The most usual way to obtain data is from dental casts using a digital caliper. There are several measures to take into account and their analysis may be performed through direct comparison of measures, statistical analyses, or indexes.  
Only two previous studies presented potential predictive sex models for Portuguese populations using dental measurements. [PERSON] et al. (2010) [1], using upper canine-to-anine teeth, rendered a combination of incisors mesiodistal and canine diagonal distances. As the proposed model was confined to only six teeth, it lacks a complete teeth analysis. On the other hand, [PERSON] et al. (2015) [12] employed the mandibular canine index [15] with a modest success rate of 64.2%, concluding that this index should be restrictively applied to the Portuguese scenario in sex identification.  
Given the lack of robust sex identification models for the Portuguese population, we aimed to use cast models of a previously studied sample ([PERSON] et al., 2018) [16] to develop a new sex prediction model based on mesiodistal width measures. We hypothesize that there is a sex-based teeth dimorphism in this population and it is distinguishable through a predictive model; therefore our null hypothesis is that such dimorphism may not exist.
## 3 Results and Discussion
### Nanocoated Fabric Pull-Out Properties
#### 3.2.4 Single and Multiple Nanocoated Fabric Intra-Yarn Shear Strength Results  
Single- and multiple-yarn intra-yarn shear strength values of nanocoated p-aramid fabric are provided in Table 4. Additionally, Figure 9 shows the single and multiple intra-yarn shear strengths of various nanocoated p-aramid fabric structures.  
As seen in Table 4 and Figure 9, the single and multiple intra-yarn shear strength of nanocoated p-aramid fabric varies from 0.020 MPa to 0.287 MPa. The single intra-yarn shear strength of the BPO exhibited a notable increase of 34.67% compared to the MPO and was five times and 3.60 times higher than the GPO and KPO structures, respectively. In contrast, the single-yarn intra-yarn shear strength of the GPO was observed to be 28.57% lower than that of the KPO. Furthermore, the two-yarn intra-yarn shear strength of the BPO was found to be significantly greater, exceeding those of the MPO, GPO, and KPO structures by factors of 2.66, 5.83, and 4.15, respectively. Conversely, the two-yarn pull-out force of the GPO was 28.85% lower than that of the KPO. Additionally, the three-yarn intra-yarn shear strength of the BPO was exceptionally higher, surpassing the MPO, GPO, and KPO structures by 48.71%, 463%, and 293% times, respectively. On the other hand, the three-yarn intra-yarn shear strength of the GPO was 36.73% lower than that of the KPO. Additionally, the three-yarn intra-yarn shear strength of the BPO was 2.84 times superior compared to its single-yarn intra-yarn shear strength.  
Considering the stage I pull-out phenomenon, single and multiple intra-yarn shear strengths are enhanced due to the incorporation of nano B\({}_{4}\)C and MWCNTs. This is attributed to the interaction between nanoparticles and the fabric structure's interlacement  
Figure 9: Single- and multiple (two ends and three ends)-yarn intra-yarn shear strengths for various nanocoated p-aramid fabric structures.  
regions, as well as filament surfaces, which leads to an increase in friction-based shear resistance between the yarns [67]. Conversely, the addition of nano-graphene platelets, as explained in the pull-out mechanism, induces shear failures at the platelet-platelet and filament-platelet interfaces, likely due to their smooth nano surfaces, resulting in a reduction in intra-yam shear strength [66].  
It is important to note that key parameters affecting intra-yarm shear strength include fabric directional density [29], interlacement pattern [29], fabric dimensions [65], and directional yarn linear densities [32]. More significantly, in line with the objectives of the study, the effects of various nano-additives and yarn ends on the local intra-yarn shear strength, particularly on the pull-out force applied to the warp and the angular strength in the warp-filling interlacement regions, were analyzed through the collection of data and subsequent evaluation.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm
#### 5.1.5 Feature Reduction  
Feature reduction in AI algorithms reduces data by eliminating less relevant features while keeping essential information. Such reduction produces smaller models, simpler computations, less memory, and less data movement within the FPGA. It can enhance the performance, resource, and power efficiency of FPGA implementations in WMDs. By decreasing the number of features, the AI model becomes less complex, leading to faster processing times, which is vital for real-time monitoring [94]. However, this may reduce accuracy, and thus careful feature selection is essential. Fewer features also mean reduced memory and computational load, allowing for smaller, less powerful FPGAs and extending battery life. Additionally, lower power consumption reduces heat generation, improving user comfort.  
The authors of [95] reduced the area and power consumption in their single-lead ECG processor by using a reduced feature set of four (RFS4) features. Using only four morphological features simplifies the computations, thereby reducing the computations, area, and system power consumption. In [96], the authors also used a feature reduction approach in their low-power stress detector to reduce the computational complexity, memory access, and resources and implement a low-power design with 82 mW of consumption. The authors of [81; 97] also used a similar approach, feature selection, in epileptic seizure detection for power reduction. Employing a lower number of features for prediction with a given confidence can reduce computational complexity. Additional features can be activated only when the desired confidence level is not achieved. This strategy minimizes the overall computational complexity, thereby reducing power consumption.  
Channel selection [94] and weight pruning [98] are other approaches used to reduce computational load and power.
## 4 Performance Analysis of Proposed Algorithm
### Time Complexity  
The initialization phase is a constant-time operation and therefore has complexity of \(O(1)\). Arrival times for PUs and SUs are generated using a Poisson distribution \(O(N)\), where \(N\) is the total number of users (both PUs and SUs). To determine the type of arrival and enqueue the user \(O(1)\) per user, the following conditions are considered. If the buffer is full, the process of discarding the user also has a constant complexity of \(O(1)\) per user. Dequeuing the first user and updating the total waiting times have a constant complexity of \(O(1)\). Updating the remaining service time for users in the buffer is considered as \(O(K)\), since each user in the buffer might need an update. The process of repeating until all users are processed has complexity \(O(N)\). Calculating average waiting times involves simple arithmetic operations and has a constant complexity of \(O(1)\). Therefore, the overall complexity associated with generating arrival times is \(O(N)\), while that associated with enqueuing and processing users is \(O(N.B)\) and that associated with calculating averages is \(O(1)\). Hence, combining these steps, the overall time complexity of the algorithm is found to be \(O(N)+O(N.B)+O(1)=O(N.B)\).
## 3 Results and Discussion  
### Synthetic Pathways of the Catalysts  
#### 3.1.1 Molecular POMs with Organic Cations  
In order to compare the catalytic activity of homogeneous catalysts and their supported version, POMs (based on H\({}_{3}\)PMo\({}_{12}\)O\({}_{40}\) and H\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\) as heteropolyacid (HPA) precursors) surrounded with organic moieties were synthetized, mimicking the surface of the targeted support. 1-butyl pyridinium bromide (BuPyrBr) or butyl-methyl imidazolium bromide (BmImBr) reacted with HPAs in water in an OrgBr/HPA ratio of 3:1 with H\({}_{3}\)PMo\({}_{12}\)O\({}_{40}\) and 4:1 in the case of H\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\) (Figure 2). Salts precipitate and are isolated as colored powders in ca. 95% yield. POMs with the organic cations **(Org)\({}_{3}\)PMo\({}_{12}\)O\({}_{40}\)** and **(Org)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)** (Org = BuPyr or BmIm) are the molecular versions of the grafted ones.
## 2 Equivalent Model of Spiral Corrugated FG-CNTRC Cylindrical Shells
\begin{array}{l}A_{11}=\overline{A}_{11}\cos^{4}\theta+2\big{(}\overline{A }_{12}+2\overline{A}_{66}\big{)}\sin^{2}\theta\cos^{2}\theta+\overline{A}_{2 2}\sin^{4}\theta,\\ A_{12}=\big{(}\overline{A}_{11}+\overline{A}_{22}-4\overline{A}_{66}\big{)} \sin^{2}\theta\cos^{2}\theta+\overline{A}_{12}\big{(}\sin^{4}\theta+\cos^{4} \theta\big{)},\\ A_{16}=\big{(}\overline{A}_{11}-\overline{A}_{22}-2\overline{A}_{66}\big{)} \sin\theta\cos^{3}\theta+\big{(}\overline{A}_{11}-\overline{A}_{22}+2 \overline{A}_{66}\big{)}\sin^{3}\theta\cos\theta,\\ A_{22}=\overline{A}_{11}\sin^{4}\theta+2\big{(}\overline{A}_{12}+2\overline{A}_{ 66}\big{)}\sin^{2}\theta\cos^{2}\theta+\overline{A}_{22}\cos^{4}\theta,\\ A_{26}=\big{(}\overline{A}_{11}-\overline{A}_{22}-2\overline{A}_{66}\big{)} \sin^{3}\theta\cos\theta+\big{(}\overline{A}_{11}-\overline{A}_{22}+2 \overline{A}_{66}\big{)}\sin\theta\cos^{3}\theta,\\ A_{66}=\big{(}\overline{A}_{11}+\overline{A}_{22}-2\overline{A}_{12}-2 \overline{A}_{66}\big{)}\sin^{2}\theta\cos^{2}\theta+\overline{A}_{66}\big{(} \sin^{4}\theta+\cos^{4}\theta\big{)},\\ D_{11}=\overline{D}_{11}\cos^{4}\theta+2\big{(}\overline{D}_{12}+2\overline{D}_ {66}\big{)}\sin^{2}\theta\cos^{2}\theta+\overline{D}_{22}\sin^{4}\theta,\\ D_{12}=\big{(}\overline{D}_{11}+\overline{D}_{22}-4\overline{D}_{66}\big{)} \sin^{2}\theta\cos^{2}\theta+\overline{D}_{12}\big{(}\sin^{4}\theta+\cos^{4} \theta\big{)},\\ D_{16}=\big{(}\overline{D}_{11}-\overline{D}_{22}-2\overline{D}_{66}\big{)} \sin\theta\cos^{3}\theta+\big{(}\overline{D}_{11}-\overline{D}_{22}+2 \overline{D}_{66}\big{)}\sin^{3}\theta\cos\theta,\\ D_{22}=\overline{D}_{11}\sin^{4}\theta+2\big{(}\overline{D}_{12}+2\overline{D}_ {66}\big{)}\sin^{2}\theta\cos^{2}\theta+\overline{D}_{22}\cos^{4}\theta,\\ D_{26}=\big{(}\overline{D}_{11}-\overline{D}_{22}-2\overline{D}_{66}\big{)} \sin^{3}\theta\cos\theta+\big{(}\overline{D}_{11}-\overline{D}_{22}+2\overline{D}_ {66}\big{)}\sin\theta\cos^{3}\theta,\\ D_{66}=\big{(}\overline{D}_{11}+\overline{D}_{22}-2\overline{D}_{12}-2\overline{D}_ {66}\big{)}\sin^{2}\theta\cos^{2}\theta+\overline{D}_{66}\big{(}\sin^{4}\theta+ \cos^{4}\theta\big{)}.\end{array} \]  
Clearly, the spiral corrugated FG-CNTRC cylindrical shells are equivalent to the non-corrugated FG-CNTRC cylindrical shells characterized by stiffness matrix terms \(A_{ij}\), \(D_{ij}\) in Equation (9).
## 2 Materials and Methods
### Structure Equation Model  
Structure equation modeling (SEM) is a general statistical technique, which combines factor analysis and multiple regression, used to examine direct and indirect effects among the interactions between multiple factors [(29)]. SEM can be used to interpret information on the observed correlations of groups of organisms, to test and evaluate complex causal relationships [(30; 31)]. We constructed SEMs using the R package, _lavaan_ (50), applying the maximum-likelihood method to estimate the model. Among the multiple indexes available to assess the fit of model, we chose a normalized \(\chi^{2}\)-test (chi-squared/degrees of freedom), comparative fit index (CFI) and root-mean square error of approximation (RMSEA) [(32)]. The normalized \(\chi^{2}\)-test is used to test whether the relationship structured in model is reasonable for the data. CFI is used to test if the model fits the data, where \(>\)0.9 is good, and RMSEA measures how closely the model reproduces data patterns, where lower values are better [(33)].  
Based on the time-lag relationship found in the correlation analysis, we formulated SEM by setting PsnNet three months prior (_t-3_) as the predictor, rodent population density (_t_) as the mediator, and the incidence of HFRS four months later as the dependent variable. Our model tested the hypothesis that the variation between correlated PsnNet and rodent population density could provide plausible explanations for HFRS outbreak.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 4 Experimental Results  
### Datasets for Evaluation  
Testing of the proposed approach was performed on five benchmark datasets:  
* MSRA-B [31] comprises 5000 images and contains a single object mostly around the center position along with the bounding-box label.
* DUT-OMRON [32] includes 5168 images with complex backgrounds and a variety of content. Pixelwise ground-truth annotations are also available.
* The PASCAL-S [33] dataset contains 850 complex images. Eye-fixation records, and nonbinary and pixelwise annotations are also available.
* HKU-IS [34] comprises 4447 multiple distant objects. In these, a minimum of one object is present at the image boundary. Less difference between background and foreground makes these images more complex.
* DUTS [35] is very large Sod dataset containing 5019 test images and 10,553 training images. Many models use this dataset for training.
## 5 Simulation Result and Discussion
### Real-Time Frequency Domain Identification Simulation
In the above diagram, it can be seen that the initial value error obtained after batch processing is very small, and the identification result is stable in the subsequent recursive process, which can show that the identification is rapid, the convergence is great, and the error is small. In order to explain the identification accuracy more clearly, after the recursion starts, a time interval with stable identification results is selected to average the relative error of parameter identification. Table 2 shows the aerodynamic coefficient identification results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Mean Relative Deviation (\%)** \\ \hline \(C_{ma}\) & \(-0.9084\) \\ \(C_{m\delta}\) & \(-0.4896\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative error of real-time aerodynamic parameter identification. Figure 7: Comparison of Flight Data Before and After Detrending and Reference Standard Curve. The error of the aerodynamic parameter identification results is within 1%, which meets the accuracy requirements of real-time control adjustment.
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### Scheduling and Energy Consumption  
Only one paper fits this category, where the UAV trajectory is fixed since the UAV repeatedly leaves the base station and visits a subset of sensor nodes (with different priorities) to collect data from them. Particularly, ref. [87] considers age-optimal UAV scheduling with battery recharging. In addition to the consideration of capacity, the advantage here is that it is possible for the UAV to embark on multiple trips. In the considered system model involving a base station, a limited-battery-capacity UAV, and a set of sensor nodes, the authors studied optimal scheduling with a sensor-node-specific AoI cost function. They provided a system complexity analysis and proved that the problem is NP-hard in its general form. However, the uniform scenario problem was proven to be tractable. To solve the problem, the authors deployed the graph labeling concept to develop a polynomial-time algorithm that can be used to tune the trade-off between computational effort and solution optimality using a simple control parameter. Via simulations, the authors evaluate the performance of the algorithm by comparing it with greedy-scheduling-based solutions. The results show that the proposed algorithm outperforms the greedy scheduling approach.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 6 Conclusions  
(1) The equivalent algorithm for predicting coal seam gas content is based on the correlation between the basic gas emission rate of the equivalent mining layer and the coal seam gas content. The prediction method fully considers the differences in the development degree of coal seam and surrounding rock pores and their gas occurrence, and the prediction results are sufficiently close to the original coal gas content.  
(2) This method divides the equivalent layer into two categories: sandstone zone and clay zone. The sandstone zone shares the relevant parameters of fine sandstone, while the clay zone is still divided into clay rock and thick mudstone values. The influencing factor options only consider natural factors such as coal seam occurrence and geological structure. The residual gas content is determined using the existing measurement parameters or recognized experimental parameters of the coal seam.  
(3) Based on the prediction method proposed in this article, intelligent auxiliary software for mine gas forecasting has been developed, which can easily obtain the contour map of gas content in the entire coal seam, providing useful assistance for coal mine safety production.  
(4) Compared with the gas geological method, the method proposed in this article can automatically correct the changes in actual gas content monitoring values, and the accuracy of prediction results is very good. It is not only theoretically reasonable but also has high practical value.  
(5) As long as the exploration data of the mine is sufficient and the prediction area is not limited by the progress of the tunnel engineering, the prediction method in this article can more reasonably represent the distribution characteristics of deep coal seam gas content, and effectively guide the formulation of medium and long-term gas prevention and control plans for the mine.  
Writing--review and editing, Y.Z.; writing--original draft preparation, H.C.; methodology, K.C., J.W., and L.Z.; formal analysis, H.C.; grammar revision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [Grant No. 52274118 and No. 52274194 and No. 52474220].  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company Kailuan (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### Adsorption Performance  
#### 3.2.1 Effect of Resin Dosage  
The influence of resin dosage on the removal of high-concentration m-cresol (20 g L\({}^{-1}\)) was investigated to evaluate the adsorbent's applicability under realistic industrial wastewater conditions. As the XDA-1G resin dosage increased from 0.05 g to 1.0 g, the removal efficiency significantly improved, reaching over 98% at the highest dosage (Figure 7). This increase is attributed to the greater availability of active adsorption sites with increasing resin mass, which enhances m-cresol uptake from solution. However, the adsorption capacity (q, mg g\({}^{-1}\)) exhibited an inverse trend, decreasing with increasing resin dosage. The maximum adsorption capacity of 700.8 mg g\({}^{-1}\) was observed at the lowest resin dosage (0.05 g).  
This behavior is commonly associated with adsorbent overdosing, where the excess number of binding sites results in reduced solute-to-sorbent ratios, leading to underutiliza  
Figure 7: Effect of resin dosage on m-cresol removal performance. Variation in removal efficiency (%) and adsorption capacity (mg g\({}^{-1}\)) of XDA-1G resin as a function of resin dosage, illustrating the trade-off between available binding sites and adsorption efficiency.  
tion of the available surface area. In addition, at higher dosages, particle agglomeration may occur, reducing the effective surface area and limiting intraparticle diffusion, particularly under high-solute conditions. The observed trends are closely related to the physicochemical characteristics of the XDA-1G resin. Nitrogen adsorption-desorption analysis revealed a high BET surface area (1439 m\({}^{2}\) g\({}^{-1}\)) and a hierarchical pore structure, enabling efficient molecular transport and site accessibility. Furthermore, FTIR and XPS analyses confirmed the presence of functional groups, such as hydroxyl, carbonyl, and ether groups, which facilitate m-cresol adsorption through hydrogen bonding and \(\pi\)-\(\pi\) interactions. These findings highlight the XDA-1G resin's capacity to maintain high adsorption performance even at elevated m-cresol concentrations, which are representative of coal-based industrial effluents. The results underscore the importance of balancing adsorbent dosage to optimize both removal efficiency and material utilization.