esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Method Validation  
In this study, for each of the methods, the optimized parameters to extract THM components were evaluated to assess: calibration linearity, reproducibility and limits of detection and quantification. THM standards were prepared by serial dilution to give a set of aqueous standards for analysis that included concentrations ranging lower and higher of the required analytical calibration range (3 to 800 \(\mathrm{\SIUnitSymbolMicro g}\cdot\mathrm{L}^{-1}\)). For precision, triplicate analysis on each sample for each method was carried out to estimate component stability on the fiber and reproducibility of the HS-sampling scheme employed. The analysis was repeated randomly over the sampling period and the results were compared, via GC peak areas. Periodically, a spiked standard (10 mg: \(\mathrm{mL}^{-1}\) of 1-2, dibromopropane) was used as an internal calibration marker to assess any bias in runs or methods. No variation (<2%) or loss of any of the 6 THM component peak areas from the fiber were noted. See Table 1. In addition, for TWA studies, a zero-sink test was performed to check for THM component loss from the fiber after absorption and before injection, though negligible change was found. Results show that acceptable correlation coefficients (\(R^{2}\): 0.966-0.998) were obtained using the 65 \(\mathrm{\SIUnitSymbolMicro m}\) PDMS/DVB fiber for particular THM components covering a linear range of over two orders of magnitude. A linear regression method of least squares was used to ascertain linearity. From Table 2 it is seen that overall the calibration curves achieved for the DI-SPME method gave better \(R^{2}\) values and slightly lower detection limits than for HS- or TWA- methods. However, for all methods the \(R^{2}\) obtained using a 10-min (8 min extraction and 2 min desorption) period were satisfactory for quantitation at occurring levels and were similar to those obtained by EPA501.3 method [15]. The LOD and limit of quantitation (LOQ) were calculated from the S/N, where LOD = 3 \(\times\) S/N, using a 10 \(\mathrm{\SIUnitSymbolMicro g}\)/L standards [16]. The LODs vary for individual THMs and methods but generally fall within a 2.6-5.1 \(\mathrm{\SIUnitSymbolMicro g}\cdot\mathrm{L}^{-1}\), which are low enough to detect THMs  
Figure 3: A home built continuous flow system used TWA optimization. in natural water samples, (Table 1). The results obtained for total THMs were similar to Newfoundland and Labrador, Canada provincial data from previous years for the fresh water ponds [4].
## 4 Analyzing Experimental Results
### Edge Device Deployment  
In Figure 9, we show the specific process of edge deployment.  
For practical applications, we need to deploy the model within some edge devices to facilitate real-time detection, and in some engineering projects, the detection of video sources. Therefore, we deployed the model to edge devices and performed image detection, as well as video detection to measure the detection results of the model. The NVIDIA Jetson AGX Xavier edge device was used by us for the edge deployment of the model. We used a mobile device to take some pavement crack images in a jpg format for testing the  
Figure 8: Comparison of evaluation metrics of each algorithm model.  
Figure 9: Automatic crack detection.  
model effect in the edge device. We also took a 12 s video of the pavement crack, which was used to emulate the engineering practical application scenario. The test results showed that the crack detection algorithm that we proposed can be deployed on the edge device and achieve the effect of real-time crack detection. In Figure 10, we showed some crack detection effects in the edge device.  
When deployed within edge devices for detection, the inference time can reach 5.1 ms per image, and when tested on images, it can detect cracks in the images very well.  
We showed the crack detection under cloudy and rainy weather conditions in Figure 11, where Figure 11 is the result tested by the unmodified model, and Figure 11 is the result detected by our proposed model in this environment. From the displayed results is the image, it can be seen that our proposed model also had a good detection effect in this environment, and the missed detection situation was greatly improved.  
We showed the effect of crack video detection in Figure 12. We achieved a frame rate of 89 FPS when using the edge device for video detection, which met the demand for real-time detection in the project.  
Figure 11: Detection performance under rainy conditions. (**a**) the result tested by the unmodified model. (**b**) the result detected by our proposed model.  
Figure 10: Edge device picture detection.
## 3 Building Load Type
### Classify Based on Building Type
#### 3.2.4 Commercial Buildings  
Commercial buildings, as the core spaces for people to conduct business activities, are notably characterized by their continuously running equipment, significant internal heat dissipation, and relatively high energy consumption per unit area. In Europe, commercial buildings account for nearly 30% of total electricity consumption, while in the United States, this proportion exceeds 35% [94]. This underscores the importance and urgency of optimizing and reducing energy use in commercial buildings.  
The energy consumption of commercial buildings is influenced by a range of internal and external factors, such as weather conditions and building occupancy. These factors intermingle, resulting in complex nonlinear patterns of energy consumption [95]. Compared to office buildings, the load of commercial buildings is not only influenced by outdoor weather parameters and the building envelope but also by numerous complex factors that are difficult to monitor and predict online, such as personnel density, personnel behavior, and sales activities [96]. Therefore, the load characteristics of commercial buildings exhibit strong nonlinearity, randomness, and fluctuation. A schematic diagram of the load forecasting process for commercial buildings is shown in Figure 9.  
Given these characteristics, short-term load forecasting for commercial buildings holds higher practical value and guiding significance compared to medium and long  
Figure 8: Office buildings sensor placement [91].  
term forecasting. To achieve accurate load forecasting, the industry has widely adopted advanced forecasting methods, including machine learning and deep learning [97; 98]. However, the energy systems in commercial buildings are often intricate and complex, posing significant challenges to the assessment and prediction of load demand. Therefore, while pursuing efficient energy use, it is also crucial to continuously explore and innovate to address these challenges.
## 4 Summary and Conclusions
* It was found that with an increase in the MgO content in the material, the \(I_{C}\) decreases (with increasing MgO content in castable, the castable's resistance to slag increases), while with the decrease in the mass ratio C/S in the slag, the \(I_{C}\) increases, i.e., the aggressive character of the slag increases. * Insights on in situ secondary MgAl\({}_{2}\)O\({}_{4}\) formation mechanism and its correlation with the corrosion resistance study were conducted of spinel-containing refractory castables. * The Ca\({}^{2+}\) ions that most deeply diffuse into the material form a continuous zone of CA\({}_{2}\) and CA\({}_{6}\), which prevents further degradation of the material. As the C/S ratio decreases, this zone is deeper in the material. J.R.: methodology, writing--original draft preparation, visualization, data curation, investigation, software; K.W.-T.: FactSage calculations; R.P. : sample preparation; D.M. : conceptualization, methodology, validation, formal analysis, resources, writing--review and editing, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript. This study was funded by The National Centre for Research and Development (Poland) within the framework of LIDER VIII project No. LIDER/5/0034/L-8/16/NCBR/2017 (Recipient: [PERSON]). The sponsor had no role in the design, execution, interpretation, or writing of the study. The authors declare no conflict of interest. Figure A2: (**A–D**) In the center is a schematic diagram of the phases present in the castables-slag S2 sample after corrosion. In the corners, there are photos of SEM micro-areas, as well as selected EDS.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 5 Implementation
### Efficient Computations of the Spherical Harmonics \(Y_{l}^{m}(\theta,\phi)\)  
The spherical harmonics are related to the associated Legendre polynomials, from which they inherit many properties. In particular, they can be computed recursively. We have used the following recurrences:  
**Proposition 3**.: _For non negative integers \(l\) and \(m\) with \(0\leq m\leq l\) the following relationships hold: (i) For \(l>1\) and \(0\leq m<l-1\):_  
\[Y_{l}^{m}(\theta,\phi)=\sqrt{\frac{(2l+1)(2l-1)}{(l+m)(l-m)}} \cos\theta Y_{l-1}^{m}(\theta,\phi)\\ -\sqrt{\frac{(2l+1)(l+m-1)(l-m-1)}{(2l+3)(l+m)(l-m)}}Y_{l-2}^{m} (\theta,\phi).\] (35) (ii) For \(l>0\) and \(m=l-1\):_  
\[Y_{l}^{l-1}(\theta,\phi)=\sqrt{2l+1}\cos\theta Y_{l-1}^{l-1}(\theta,\phi).\] (36) (iii) For \(l>0\) and \(m=l\):_  
\[Y_{l}^{l}(\theta,\phi)=-\sin\theta\sqrt{\frac{2l+1}{2l}}\mathrm{e}^{i\phi}Y_{ l-1}^{l-1}(\theta,\phi). \tag{37}\]  
_With the initialization \(Y_{0}^{0}(\theta,\phi)=1/\sqrt{4\pi}\), Equations (35)-(37) provide an efficient scheme for computing all spherical harmonics at angles \((\theta,\phi)\)._
## 5 Empirical Results
### Long-Range Dependence and Multifractality
In contrary, the multifractal spectrum will have a long right tail when the time series have a multifractal structure that is insensitive to the local fluctuations with small magnitudes [37]. Figure 7 depicts the multifractal spectrum for ship flow sequences of three ports with different time scales. According to Figure 7, the multifractal spectrum of ship flow sequences for all ports can be divided into two sections. However, the spans of the multifractal singularity are different, implying that they have different multifractality strengths. For the daily ship flow sequences, the port of Rotterdam has the lowest multifractal strength, while the port of Singapore has the highest multifractal strength. Furthermore, the shape of multifractal spectrum of daily ship flow for Singapore shifts to the right and the spectrum is  
Figure 6: Hurst exponents of ship flow sequences calculated by Multifractal Detrended Fluctuation Analysis (MF-DFA) for \(q=[-10:10]\). slightly right-skewed, indicating that the scaling behavior of small fluctuations dominates the fluctuation of the daily ship flow for Singapore port. Compared with the daily ship flow time series, the multifractal spectra of Shanghai and Rotterdam obtained from weekly ship flow sequences shifted to the left and the spectrum is slightly left-skewed. This indicates that the scaling behavior of large fluctuations dominates the fluctuation of the weekly ship flow for Shanghai and Rotterdam port. According to the multifractal theory, the strength of multifractality can be characterized by the span of the multifractal singularity strength function in Equation (6). The bigger the \(\Delta\alpha\) is, the stronger the degree of multifractality becomes. Table 4 presents the quantitative strength of multifractality of all the ship flow sequences of the three ports. It can be seen that the degree of multifractality of weekly series is the strongest for all three ports, followed by the monthly time series, and the daily time series has the weakest multifractality. The monthly ship flow sequence of Singapore port has the highest degree of multifractality. An interpretation for this result is that the weekly ship flow sequence of Singapore is very sensitive to the changes of various influencing factors, and it is very hard to predict. Therefore, compared with the forecast of the ship traffic flows of days and months, the weekly forecast is the most difficult for the ship flow at the container port. This shows that the multifractal method is essential for the analysis of ship flow sequences. Figure 7: Multifractal spectra for ship flow sequences of three ports with different time scales.
## 3 Methodology
### Data Preprocessing  
3.2.1. APK Feature Extraction  
Android applications are packaged as APK (Android Package) files, which are compressed archives containing various elements essential for app execution. For malware analysis, we extract three critical files from each APK, as illustrated in Figure 1a:  
* Dalvik Executable (classes.dex): Contains compiled bytecode executed by the Android Runtime (ART) or Dalvik Virtual Machine (DVM), representing the app's functional logic.  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Stage** & **Description** & **Main Output** \\ \hline Feature Extraction & Extract DEX, Manifest, and Resources components from APK files and map them to RGB channels & RGB image that encodes structural and semantic information \\ Classification & Apply YOLO11 architecture with multi-scale feature fusion for malware prediction & Real-time binary and multi-class prediction \\ Interpretability & Generate post hoc visual explanations using Grad-CAM, HiRes-CAM, and other CAM-based methods & Heatmaps showing discriminative regions; interpretability scores (faithfulness, robustness, localization, sparsity) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of YoloMal-XAI framework stages.  
* Manifest (AndroidManifest.xml): Declares permissions, components (activities, services), and interactions with the Android system. Malware often exploits this file to request sensitive privileges or hide background services.
* Resources (resources.arsc): Stores compiled UI elements, strings, and themes. Malicious applications may modify these to carry out phishing or deceive users.  
These files were selected due to their documented significance in Android malware behavior. The manifest's permissions are widely recognized as strong static indicators [29], while UI resources are often manipulated for deceptive intent [30]. Native libraries (.so) and assets were excluded: the former are platform-dependent, frequently obfuscated, and inconsistent across samples [31]; the latter primarily contain non-functional media that introduce noise. Including them reduced model performance in preliminary tests.
## 2 Edge-of-Field Technologies
### Vegetated Buffer Strips
[135] pioneered this investigation and documented the effect of a tile drain on the retention of P by a VBS. The study demonstrated that the tile drain helped to prevent inundation of the system during periods of high water runoff. In relation to P retention, it showed that the concentrations of total P and DRP decreased at the outlet of the tile drain after the P load passed through the system. However, there was no evidence of P accumulation in the system. A promising alternative technology combining a ditch-like pond with a slightly inundated VBS to retain P from agricultural drainage discharge was recently proposed by [PERSON] et al. [136]. This design allowed the drainage water to discharge into the system without bypassing it. The study showed that the system retained from 31 to 69% of the P load on average per month, despite the low ratio of the system area to the agricultural catchment area of around 0.1%. Moreover, the system also proved to effectively retain DRP, most likely by plant uptake in the pond and by sorption through the VBS. The design was suggested as a modification to the conventional VBSs when targeting high P loads from tile drains. In line with this, [PERSON] and [PERSON] [129] recommended the use of CWs integrated with VBSs as a second treatment stage in order to properly treat drainage discharge. The experiences from previous studies indicate, therefore, that VBSs may be viewed as a complement to design alternatives attempting to attain acceptable P retention in tile-drained agricultural catchments. The alternative systems should primarily avoid the bypass of P loads and enhance the retention mechanisms for DRP (e.g., by using substrates with adequate infiltration level and P sorption capacity).
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 3 Results and Discussion
### Analysis of Emulsion Flooding
#### 3.2.2 Effect of Water Cut of Emulsion  
Figure 21 displays the flow distribution of high- and low-permeability pipes in the process of emulsion displacement with different water cuts when the permeability contrast was 2.03. As shown in Figure 21, at 30% water cut of the emulsion, the ability of the emulsion to block the high-permeability pipe was demonstrated when the emulsion was injected with 0.9 PV, the low-permeability pipe was activated, and the shut-off capacity of the emulsion disappeared when the total injection was 2.0 PV. At 40% water cut in the emulsion, the low-permeability pipe was activated when the emulsion was injected with 0.65 PV, whereas, at 1.54 PV in total, the emulsion shut-off capacity disappeared. At 50% water cut of emulsion, when injected with 0.6 PV, the emulsion began blocking the high-permeability pipe and started the low-permeability pipe, whereas the emulsion shut-off capacity disappeared when the total injection was 1.16 PV. When the total injection amount was 1.16 PV, the emulsion plugging capacity disappeared to the maximum. At 60% water cut of emulsion, the low-permeability pipe was activated after injection of 0.35 PV of emulsion, and the shut-off capacity of emulsion disappeared after total injection of 0.69 PV. From the fractional flow under the condition of emulsion flooding with different water cuts, it can be seen that, when the water cut of the emulsion increased, the shut-off capacity of the emulsion increased, but the plugging time was shorter. The reverse phenomenon of the fluid entry profile occurred in emulsion flooding with 60% water cut.  
Figure 22 shows the changes in water cut in the produced liquid and oil displacement efficiency after emulsion flooding plus water flooding under different emulsion water cuts.  
Figure 21: Influence of water cut of emulsion on fractional flow: (**a**) 30%; (**b**) 40%; (**c**) 50%; (**d**) 60%.  
As shown in Figure 22, when the water cut of the produced liquid was 40% to 60%, the water cut of the produced liquid increased slowly with the increase in oil displacement efficiency, indicating that the emulsion played a role in plugging the high-permeability layer at this production stage, whereby the emulsion with water cuts of 40% to 60% had the best plugging ability.
## 5 Experimental Results and Analysis
### The Loading Conditions of a Given 1 Rad Step Signal  
Figure 16 illustrates that the ADRC controller demonstrates poor convergence when a 10 N load is suddenly applied within 0.1 s during the experiment. In contrast, the RBF-ADRC controller shows improved convergence. Notably, the FRBF-ADRC controller surpasses both the ADRC and RBF-ADRC controllers in terms of its convergence performance.
## 4 Discussion  
The present study systematically reviewed the occurrence of GP between the perceived widths of the maxillary anterior teeth among populations in different geographic regions of the world. The null hypothesis that GP would not be consistently present between the perceived widths of the maxillary anterior teeth among populations in different geographic regions was accepted. The review determined that GP manifests only in a small percentage of the population and, therefore, cannot be reliably used to determine tooth width for pleasing aesthetic dental rehabilitation. Since the introduction of GP in dentistry, over 52 [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68] case-control studies have been carried out worldwide to investigate the existence of the golden proportion between the perceived widths of the maxillary anterior teeth. A majority of these studies (\(n=27\)) were carried out in the South Asian region, with most in India (\(n=16\)), followed by Pakistan (\(n=7\)). The mean percentage on the right side for the apparent width of CI-to-LI was found to be greatest in the Pakistani population (27.68%) [16; 17; 30; 31; 32] as compared to the Banglades population (17%) [45], the Nepalese (14.28%) [40; 41; 42] or the Indian population (13.61%) [18; 21; 22; 33; 34; 35; 36; 37; 38; 39; 40; 41]. In addition, lower mean percentages were found for Ca-to-LI among populations; 26.68% (Pakistan), 12.68% (Banglades), 4% (Nepal) and 8.60% (India). Interestingly, different results were found between the apparent widths of CI-to-LI-to-Ca when comparing the left to the right side [30; 45]. These differences in tooth proportions may be due to various ethnic and racial backgrounds. Another notable reason could be the assessment method used to record the GP and the sample size among the studies in the South Asian population. [PERSON] et al. [16], in a pilot study on 44 patients, utilized dental cast and grid analysis for assessing tooth proportions and observed 66% of the results. While the 15 included studies performed evaluations with 2D photograph analysis [17; 21; 22; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 45] and direct clinical measurement of teeth [35; 36; 38; 39]. In addition, 12 studies [22; 30; 32; 34; 35; 36; 38; 39; 40; 41; 43; 44; 45] had a sample size of more than 100. This heterogeneity in the assessment methods and sample size could have influenced the observed review outcomes. The range of GP in the South Asian population for CI-to-LI was 13.61-27.68% and Ca-to-LI was 4.0-26.68%. Whereas local proportion for CI-to-LI was 40.84-74.50% and Ca-to-LI was 42.76-91% on the right side, CI-to-LI was 41.41-75.50% and Ca-to-LI was 31.41-90%
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### PV Cell Models
#### 2.1.1 Single-Diode Model (SDM)  
The SDM is widely utilized in the academic and research literature to extract the parameters of PV cells and panels. The SDM's equivalent circuit is seen in Figure 1, as the diagram depicts a circuit with photocurrent, a diode, a shunt, and series resistance. The diode is connected in parallel with the shunt resistance, where the shunt resistance is utilized to consider the leakage current, while the series resistance accounts for ohmic losses. Equation (1) describes mathematically the equivalent circuit. It is obtained using Kirchhoff's laws and used to calculate the output current [36].  
\[I=I_{ph}-I_{sd}\bigg{[}exp\bigg{(}\frac{V+IR_{s}}{nV_{t}}\bigg{)}-1\bigg{]}- \frac{V+IR_{s}}{R_{sh}} \tag{1}\]The photogenerated current is denoted by \(I_{ph}\), whereas the reverse saturation current is denoted by \(I_{sd}\); \(n\) represents the ideality factor of the diode, while the shunt resistance and series resistance are represented by \(R_{sh}\), and \(R_{s}\), respectively. The thermal voltage \(V_{t}\) is calculated as \(V_{t}=k\cdot T/q\), where \(q\) is the elementary electric charge with a value of 1.60217646 \(\times\) 10\({}^{-19}\) C, and the Boltzmann constant is represented as \(k\) with a value of 1.3806503 \(\times\) 10\({}^{-23}\) J/K. Lastly, \(T\) denotes the temperature of the photovoltaic cell. In order to ensure consistency in the computations for the extraction of parameters for both PV cells and panels, it is advised to use the same values for \(q\) and \(k\).  
In the case of the SDM, there are five parameters that need to be extracted from the single-diode cell, which are \(I_{ph}\), \(I_{sd}\), \(n\), \(R_{sh}\), and \(R_{s}\).