esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### CH\({}_{4}\) Diffusivity and Permeability by Measurements and MIP Data  
The air permeability data using helium gas of 12 samples is listed in Table 4. The permeability most samples are in the range of 0.1-1.6 mD except HDG 6# (3.68 mD), YQ-YK 15# (4.02 mD) and GJ-DQ 4# (0.012 mD). High-pressure CH\({}_{4}\) diffuse rate data on all 12 coal samples has been modeled using the unipore diffusion model (Equation (3)). As shown in Table 4. The magnitude of CH\({}_{4}\) effective diffusion coefficient (De) is 10\({}^{-8}\)-10\({}^{-7}\), and the correlation coefficients are in the range of 0.79-0.98. The raw pressure vs. time of methane diffuse on coals (a: HDG 6#; b: HYC 4#; c: LL-HJG 5#; d: LL-DP 10#; e: GJ-DQ 4#; f: YQ-WK) are shown in Figure 3. The diagrams show that the pressure decreases rapidly within the initial 1000 s and then the diffusion process approaches the equilibrium state  
Figure 2: Cumulative intrusion volume vs. intrusion pressure for six coal samples with different coal ranks.  
Figure 4 demonstrates the comparison of results from the theoretical calculation and experimental approach by CH\({}_{4}\) diffusion, which shows that effective diffusion coefficient acquired by these two approaches is consistent except for sample LL-SL 5# (_da_ = 26.12). The results of these two approaches for most samples are distributed equally near the straight line, which may support the validity of the theoretical method to determine the effective diffusion coefficient by MIP data. However, the effective diffusion coefficient calculated in this work is slightly higher than the experimental result by CH\({}_{4}\) diffusion. This phenomenon may be due to the CH\({}_{4}\) adsorption occurring in coal when a diffusion experiment wascarried out. The surface adsorption layer makes CH\({}_{4}\) diffusion lag, while De calculated in this work without considering the gas adsorption phenomenon [32; 44]. Therefore, a higher effective diffusion coefficient was acquired. For permeability, the theoretical calculation and experimental permeability by CH\({}_{4}\) are distributed equally near the straight line with the slope equal to 1. This result means that the validity of the theoretical derivation method of determining coal permeability by mercury porosimetry data is reliable.  
Figure 4: Comparison of effective diffusion coefficients, permeability obtained by theoretical calculation with experimental results. (**a**) demonstrates that the comparison of effective diffusion coefficient from the theoretical calculation and experimental approach by CH\({}_{4}\) diffusion, which shows that the effective diffusion coefficient calculated in this work is slightly higher than the experimental result by CH\({}_{4}\) diffusion; (**b**) shows that the theoretical calculation and experimental permeability by CH\({}_{4}\) are distributed equally near the straight line with the slope equals to 1.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 2 Related Work  
Currently, the implementation of time synchronization mostly adopts centralized [23; 24] or distributed manners [9; 10]. In centralized ones, a reference node is selected and spreads its time to synchronize other network nodes rapidly. In contrast, distributed approaches do not require a specific reference node, and the local node updates its clock status robustly according to the neighbor's information. Furthermore, many time synchronization approaches have been studied in the literature for different requirements, e.g., precision [25; 26], consumption [7; 8; 27], robustness [4; 9; 10], security [14], and so on. For any requirements, security is essential for sophisticated time information acquisition. Threshold detection [15; 16; 19; 20], message filtering [21; 22], and encryption [17; 18] techniques are common defense measures in secure time synchronization. We present the review of the secure time synchronization methods as follows.
## 3 Results and Discussion
### Microstructure of Geopolymers
#### 3.2.4 Porosity  
Porosity is an important parameter affecting the mechanical properties of materials. The pore volume and average pore diameter of the geopolymer materials prepared using an 8 M NaOH activator and varying amounts of calcium hydroxide were measured using the BET method and are shown in Figure 7, along with the compressive strength of the corresponding materials. It is evident that the geopolymer prepared without the presence of calcium hydroxide had the highest observed pore volume and average pore diameter. As the content of calcium hydroxide was increased up to 60 g, the pore volume, as well as the average pore diameter of the specimens, continuously decreased, indicating that the presence of calcium hydroxide made the materials more densely structured. This is  
Figure 6: FT-IR spectra analysis of materials prepared with 8 M NaOH activator and different amounts of Ca(OH)\({}_{2}\): (a) 0 g, (b) 15 g, (c) 30 g, (d) 45 g, (e) 60 g, and (f) 75 g.  
because calcium hydroxide not only participates in the geopolymerization process in an alkaline environment but also forms crystalline phases or precipitates that act as fillers, adding particles that fill the observed holes, voids, and gaps in the material [2]. However, when more than 60 g of Ca(OH)\({}_{2}\) was added, the pore volume and average pore diameter increased. This can be explained by the excessive Ca(OH)\({}_{2}\) particles forming deposits or precipitates, resulting in a loose internal structure and higher porosity of materials. Although it is well-known that compressive strength should increase when materials become denser, increasing the amount of calcium hydroxide from 45 g to 60 g resulted in a decrease in compressive strength despite the decrease in pore volume and pore diameter. Therefore, it can be concluded that both porosity and the formation of N-A-S-H/C-S-H/C-A-S-H during the polymerization process play a crucial role in the mechanical properties of materials.
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 3 Analytical Solutions to Peristatic Problems
### Clamped-Clamped Beam  
The clamped-clamped beam, Figure 8a, can be considered as a simply supported beam subjected to bending moments \(M_{0}\) and \(M_{L}\) at the boundaries, as shown in Figure 8b, if the two rotational constrains are released. By applying [PERSON]'s theorem, we obtain  
Figure 6: Clamped–simply supported beam: (**a**) original problem; (**b**) equivalent simply supported beam with applied moment.  
Figure 7: Beam subjected to external moment: (**a**) moment applied at \(x_{0}\); (**b**) force couple applied at \(x_{0}\).  
\[\begin{split} M_{0}&=\frac{6}{L^{2}}\int\limits_{0}^{L} \left[\int_{0}^{x}p(a)(x-a)\mathrm{d}a\right]\mathrm{xd}x-\frac{4}{L}\int\limits _{0}^{L}\left[\int\limits_{0}^{x}p(a)(x-a)\mathrm{d}a\right]\mathrm{d}x\\ M_{L}&=-\int\limits_{0}^{L}p(x)(L-x)\mathrm{d}x+ \frac{6}{L^{2}}\int\limits_{0}^{L}\left[\int\limits_{0}^{x}p(a)(x-a)da\right] x\mathrm{d}x-\frac{2}{L}\int\limits_{0}^{L}\left[\int\limits_{0}^{x}p(a)(x-a) \mathrm{d}a\right]\mathrm{d}x.\end{split} \tag{31}\]  
With the PD solution for simply supported beam (27) and (31), the PD solution for the clamped-clamped beam can be cast as  
\[\begin{split} w(x)&=\frac{2\delta^{2}}{LEI}\sum \limits_{n=1}^{\infty}\int\limits_{0}^{L}\left[p(x)+M_{0}\delta^{\prime}(x-0)+ M_{L}\delta^{\prime}(x-L)\right]\sin\!\frac{n\pi x}{L}\mathrm{d}x\times\\ &\qquad\times\left[\int\limits_{-\delta}^{\delta}\frac{1}{\delta^ {2}}\left(1-\cos\!\frac{n\pi_{\xi}^{\xi}}{L}\right)\mathrm{d}\xi\right]^{-2} \sin\!\frac{n\pi x}{L}\\ &=\frac{2\delta^{2}}{LEI}\sum\limits_{n=1}^{\infty}\left[\int \limits_{0}^{L}p(x)\!\sin\!\frac{n\pi x}{L}\mathrm{d}x+\int\limits_{0}^{L}M_{ 0}\delta^{\prime}(x-0)\!\sin\!\frac{n\pi x}{L}\mathrm{d}x+\int\limits_{0}^{L}M _{L}\delta^{\prime}(x-L)\!\sin\!\frac{n\pi x}{L}\mathrm{d}x\right]\\ &\qquad\times\left[\int\limits_{-\delta}^{\delta}\frac{1}{\delta^ {2}}\left(1-\cos\!\frac{n\pi_{\xi}^{\xi}}{L}\right)\mathrm{d}\xi\right]^{-2} \sin\!\frac{n\pi x}{L}\\ &=\frac{2\delta^{2}}{LEI}\sum\limits_{n=1}^{\infty}\left\{\int \limits_{0}^{L}p(x)\!\sin\!\frac{n\pi x}{L}\mathrm{d}x+M_{0}\!\left[-\int \limits_{0}^{L}\frac{n\pi}{L}\delta(x-0)\!\cos\!\frac{n\pi x}{L}dx\right]\\ &\qquad+M_{L}\!\left[-\int\limits_{0}^{L}\frac{n\pi}{L}\delta(x- L)\!\cos\!\frac{n\pi x}{L}\mathrm{d}x\right]\right\}\left[\int\limits_{- \delta}^{\delta}\frac{1}{\delta^{2}}\!\left(1-\cos\!\frac{n\pi_{\xi}^{\xi}}{L} \right)\!\mathrm{d}\xi\right]^{-2}\sin\!\frac{n\pi x}{L}\\ &=\frac{2\delta^{2}}{LEI}\sum\limits_{n=1}^{\infty}\left\{\int \limits_{0}^{L}p(x)\!\sin\!\frac{n\pi x}{L}\mathrm{d}x-\frac{n\pi}{L}\!\left[M _{0}+M_{L}(-1)^{n}\right]\right\}\!\left[\int_{-\delta}^{\delta}\frac{1}{ \delta^{2}}\!\left(1-\cos\!\frac{n\pi_{\xi}^{\xi}}{L}\right)\!\mathrm{d}\xi \right]^{-2}\!\sin\!\frac{n\pi x}{L}.\end{split} \tag{32}\]
## 4 Numerical Examples
### Inversion Analysis of a High Earth-Rockfill Dam Using the Step-by-Step Method
#### 4.2.3 Further Study on Material Parameter Optimization  
From the variation range of each parameter, it can be seen that the material parameter \(K\) has the most obvious trend of change. Figure 9 is the trend diagram of \(K\) and the third principal stress with construction periods. It can be seen from Figure 9 that \(K\) is unchanged when the third principal stress is small, \(K\) begins to increase significantly and keeps a high value when the third principal stress keeps increasing, and \(K\) starts to decrease at the end of construction periods. Thus, Equation (10) can be proposed to represent this phenomenon:  
\[\left\{\begin{array}{ll}K_{1}=ample1\times K\;,&S3\leq 500\;\text{ kPa}&\text{Stage1}\\ K_{2}=ample2\times K_{1}-(ample2-1)\times K_{1}\times\left(\frac{S3}{p_{a}}-4 \right)^{-2}\;,&500\;\text{kPa}<\!\!S3\leq 2000\;\text{kPa}&\text{Stage2}\\ K_{3}=K_{2 last}\times\left(\frac{S3}{p_{a}}-19\right)^{-2}+ample3\times K\times \left(1-\left(\frac{S3}{p_{a}}-19\right)^{-2}\right)\;,&S3>\!\!2000\;\text{kPa}& \text{Stage3}\end{array}\right. \tag{10}\]  
where \(K\) is the material parameter used for designing; _ample1_, _ample2_, and _ample3_ are scale factors; \(S3\) is the third principal stress; and \(K_{2 last}\) is the material parameter obtained at the end of Stage2.  
A new constitutive model (called the variable parameter constitutive model in this paper) can then be obtained by adding Equation (10) in the Duncan-Chang constitutive model. The vertical compression values in the bottom of the dam can be found in Figure 10. The relative error of the compression values using the variable parameter constitutive model is 9.74%, which is smaller than that of the conventional inversion (14.4%) and larger than that of the step-by-step inversion (4.6%). Considering that more time is needed when using step-by step inversion and enough calculation accuracy cannot be reached using conven  
Figure 8: Comparison of compression values of the bottom of the core wall.  
Figure 9: The trend diagram of K and the third principal stress with construction periods.  
tional inversion, the variable parameter constitutive model proposed in this article might be more suitable for the calculation of earth-rockfill dams during construction periods.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 1 Introduction
[34] demonstrated bulk density prediction within the range of 1.02 to 2.01 g/cm\({}^{3}\), achieving an R\({}^{2}\) value of 0.46. Despite its cost and time effectiveness [31; 35], vis-NIR spectroscopy does pose a challenge due to its reliance on specialized equipment, a limitation that mirrors the constraints associated with X-ray microtomography, thus impeding its widespread adoption in general agricultural fields. What is necessary for soil density analysis by digital images is to separate voids and soil areas, and thresholding can be used for this purpose. Thresholding is an image processing method mainly used to distinguish between an object and a background in an image [36]. In a soil image, the soil solids are the objects and the voids are the background. [PERSON] [24] and [PERSON] et al. [15] quantified and analyzed soil cracks by performing thresholding on soil images. [PERSON] et al. [14] and [PERSON] et al. [37] also performed thresholding to analyze the void distribution. Each researcher applied the automatic thresholding method provided by the image processing software or, if necessary, determined the threshold value based on the pixel intensity distribution curve and performed the analysis. The most important factor in thresholding is to determine the threshold value. There are various methods for thresholding; however, there is no standard related to soil analysis [38]. Since there is no standard for thresholding methods for soil analysis, the accuracy of the soil density analysis may vary depending on the selection of the thresholding method. Therefore, it is necessary to determine which of the various thresholding methods is suitable for soil density analysis, or to propose an image analysis method suitable for density analysis. Therefore, this study aims to investigate thresholding methods suitable for soil image-based density estimation using common digital cameras. For this purpose, soils from agricultural fields were prepared under various water content and dry density conditions and digital images were acquired. The digital images of the soil underwent thresholding using the results of simple thresholding at different threshold values and representative automatic thresholding methods such as the Otsu method and Triangle method. Through the analysis of the threshold images, the limitations of the existing thresholding methods in predicting soil density were analyzed, and based on this, a new analysis method suitable for predicting soil density, the void area curve method, was proposed. The void area curve method is a new analysis method that can overcome the limitations of the existing analysis methods, whose accuracy may vary depending on the selection of the threshold value, and can be widely used for soil density analysis.
## 3 Results
### Computational Model Setup
#### 3.2.2 Introduction to Model Meshes  
For the convenience of calculation, we set the mesh as hexahedral mesh. As shown in Figure 8. Hexahedral meshes have regular shapes, and the geometric characteristics of each element are relatively simple and uniform. Thanks to this advantage, hexahedral meshes can better fit the geometric shape of the object, effectively reducing numerical errors caused by irregular mesh shapes. When aiming for the same calculation accuracy, compared with other types of meshes such as tetrahedral meshes, hexahedral meshes generally require fewer elements. This is because hexahedral meshes can fill the space more efficiently, and each element can undertake more computational tasks.  
Given that the number of hexahedral meshes is relatively small, the memory space required for storing mesh information and intermediate calculation results will also be correspondingly reduced. This characteristic is particularly crucial in large-scale numer  
Figure 8: Hexahedral mesh.  
Figure 7: Plot of average winter wind speed and direction versus air quality index (AQI).  
ical simulations, as it can lower the requirements for computer hardware configuration, enabling simulations to be carried out smoothly on more common computing devices.
## 2 Materials and Methods
### Prediction Objective  
A relevant dataset for the prediction task was obtained from the Psychiatry Department of the University Medical Center Utrecht (UMCU) in The Netherlands. This psychiatry department consist of six inpatient units, where patients with different medical histories are admitted, each with their own focus on different patient populations, diagnoses, and treatments. The department delivers secondary care to patients with severe but general psychiatric symptoms, and tertiary care to patients with more complex symptomatology or comorbidities, ensuring a diverse population. Admissions from all six units between 2013 and 2016 were included in the dataset, resulting in a total of 2521 admissions from 1796 unique patients, including readmissions and transfers between different units. In all six units, mandatory reporting of violence incidents by one of the health care professionals involved in the incident took place. Typically, these incidents concerned violence from patients directed at staff or at other patients, including both verbal and physical aggression. In the relevant time period, a total of 1267 violent incidents were reported. After excluding incidents that did not involve a patient that was admitted at the time of the incident (\(n=19\)), for example incidents that involved visitors rather than the patient or incidents that happened after dismissal of a patient, a total of 1248 incidents remained. Some descriptive statistics of the dataset per unit can be found in Table 1.  
We define the prediction objective as follows: predict for which admissions a violence incident will occur in the first 30 days, based on clinical texts that were written up to and including the first day of admission. Since in many admissions, relevant information was not discovered and written down until the day of admission, especially in acute admissions, the prediction task did not include violence incidents on the day of admission. Although an number of incidents that was not negligible occurred on the day of admission, exploratory analysis showed that at that point in time, insufficient data was available to make a prediction. The interval of 30 days was furthermore chosen so that the prediction was specific, while the majority of incidents were included in the prediction, given that the mean duration of admission was 40.3 days. In total, 81.9% of incidents happened during the first 30 days of admission, and it could be verified in Figure 2 that the amount of incidents diminished over time. To be in line with literature on this topic, we will use the Area under Curve (AUC) of the Receiver Operator Curve to report the performance of the prediction.
###### Abstract  
The relationship between absorption and gas concentration was studied using a diffuse integrating cavity as a gas cell. The light transmission process in an arbitrary diffuse cavity was theoretically derived based on a beam reflection analysis. It was found that a weak absorption condition must be satisfied to ensure a linear relationship between absorbance and gas concentration. When the weak absorption condition is not satisfied, a non-linear relation will be observed. A \(35\times 35\times 35\) cm diffuse integrating cavity was used in the experiment. Different oxygen concentrations were measured by detecting the P9 absorption line at 763.8 nm, based on tunable diode laser absorption spectroscopy. The relationship between the absorption signals and oxygen concentration was linear at low oxygen concentrations and became non-linear when oxygen concentrations were higher than 21%. The absorbance value of this transition point was 0.17, which was considered as the weak absorption condition for this system. This work studied the theoretical reason for the non-linear phenomenon and provided an experimental method to determine the transition point when using a diffuse integrating cavity as a gas cell.  
diffuse integrating cavity; TDLAS; gas detection; non-linearity Article
## 1 Introduction
### Objectives
#### 1.1.2 Accuracy Assessment  
Map accuracy is characterized in terms of positional and thematic accuracy [36]. High resolution imagery can be well registered because man-made and natural landmarks lead to visual verification for change events [5]. Thematic accuracy of a classification or change model is usually assessed by calculating the prediction efficiency for some withheld or newly collected training data [36, 49, 50, 51]. For a simple change classifier, observed locations can be mapped as having (1) changed; (2) not changed; (3) changed erroneously; or (4) not changed erroneously. Counts of these quantities are displayed in a confusion matrix with the main diagonal elements representing correctly classified observations [36, 52] and the non-zero off-diagonal counts representing the two types of errors (Figure 1). Locations labeled as change erroneously are the commission errors, while erroneous non-changed locations (locations that actually did change but were not mapped as such) are the omission errors.  
The job of a statistical classifier is to find a set of attributes that minimizes these classification errors [53]. However, these types of errors are not always viewed with equal weight. In the language of receiver operating curves, a classifier has a high sensitivity (is called a liberal classifier) if it correctly classifies a high percentage of observed changes [37]. A classifier that correctly attributes non-changing observations has a high specificity or is referred to as a conservative classifier. High sensitivity usually comes with higher commission errors and high specificity is accompanied by more omission errors. Therefore, there is a tradeoff for optimizing a classifier for either sensitivity or specificity [37]. Optimization is achieved by altering the minimum accepted threshold for labeling an analysis unit as a certain class. The simple threshold is usually 50% for a two class model or the reciprocal of the number of classes for models with more than two classes. When the risks associated with the two types of error are not equal, thresholds can be altered to minimize the cumulative risk instead of the simple error rate.  
Combined change detection uses a threshold set on some image difference across times, such as overall brightness, to assess how much of a difference is enough to be considered a change. For example, in a recent paper on high resolution change detection [54], the authors suggested using the distribution of the normalized difference vegetation index (NDVI) differences between dates as a measure for change, specifically, they suggested using the standard deviation as a threshold for assigning a polygon as changed. Setting a high threshold will mean less area will be mapped as change, but should have lower commission errors and, thus, higher omission errors. Conversely, setting a low difference threshold, accepting a low probability of change as change-should capture more change, but will also increase the erroneous area of mapped change, _i.e._, commission errors.