esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 On-Site Monitoring and Analysis of Surface Deformation Law
### Analysis and Comparison of Transverse Ground Settlement  
In 1969, [PERSON] [12] proposed a method for predicting surface settlement based on the relationship between ground loss and surface deformation derived from extensive tunnel and underground construction data. This model was later refined by incorporating correction coefficients \(\alpha\) and \(\beta\), resulting in the modified empirical Peck formula. The formula for calculating the settlement value \(S\) at any point along the ground surface transverse to the tunnel axis is given as [24]:  
\[S(\mathrm{x})=\alpha S_{\mathrm{max}}.\exp\left(-\frac{\mathrm{x}^{2}}{2( \beta  
where: \(S_{max}\) is the maximum ground surface settlement (mm); \(\alpha\) is the correction coefficient for maximum surface settlement; \(x\) is the horizontal distance from the calculation point to the tunnel centerline (m); \(i\) is the settlement trough width (m); \(\beta\) is the correction coefficient for the width of the settlement trough; \(k\) is the coefficient related to soil properties (typically 0.63-1.0); \(H\) is the burial depth to tunnel centerline (m); \(R\) is the tunnel radius (m); \(n\) is an empirical coefficient (generally 0.8-1.0); \(V\) is the formation loss rate, which is taken as 0.5% in this paper; and \(D\) is the tunnel diameter, m.  
In this project, both the replacement and overlying soils are cohesive. Therefore, the coefficient values are selected based on the ranges recommended by [PERSON] [25] and [PERSON] [26]: \(\alpha\) = 0.9, \(\beta\) = 0.87. Substituting these into the equation yields \(i\) = 3.37 m for section CDBC719 and \(i\) = 3.02 m for section CDBC709.  
Using the above parameters, modified Peck fitting curves were generated and compared with both simulation and field monitoring results. The settlement comparisons for the two monitoring sections are shown in Figure 11.  
Analysis of Figure 11 shows that the monitoring data, numerical simulation results, and Peck fitting curves align well for both sections, all exhibiting the typical Gaussian distribution. The settlement curves form a \"V\" shape, with symmetric settlement on either side of the tunnel centerline and maximum settlement occurring at the centerline itself. Because CDBC709 is located at the edge of the reinforcement zone (i.e., the interface between reinforced and unreinforced zones), the settlement there is slightly greater than that at CDBC719. Nevertheless, settlement at both sections remains within control limits, further confirming that the simulation and modified Peck formula accurately predict surface settlement. The error measurement RMSE between the CDBC-719 monitoring value and the theoretical value and the analog value were 0.81 and 0.39, respectively, and the error measurement RMSE between the CDBC-709 monitoring value and the theoretical value and the analog value were 0.78 and 0.55, respectively.
## 4 Results and Discussion
### Adsorption Characteristic Curve  
In order to obtain the methane adsorption characteristic curves, the volumes of the adsorbed methane on coals under different pressures at the temperature of 30 \({}^{\circ}\)C were calculated according to the adsorption constants of the Langmuir model. Then, the adsorption potentials and adsorption spaces under different pressures at 30 \({}^{\circ}\)C were calculated. Following this, the adsorption characteristic curves were obtained (Figure 4). The fitting equations were obtained based on the adsorption characteristic curves as given by Equations (7)-(10). It can be seen that the univariate cubic equation could satisfactorily fit the relationship between the adsorption potential and the adsorption space with an R\({}^{2}\) of 0.9998.  
\[\varepsilon_{\rm{XW1}}=1.43\times 10^{4}-4.22\times 10^{5}w+7.88\times 10^{6 }w^{2}-6.90\times 10^{7}w^{3}\quad\text{R}^{2}=0.9998 \tag{7}\]  
\[\varepsilon_{\rm{XW2}}=1.41\times 10^{4}-3.92\times 10^{5}w+6.64\times 10^{6 }w^{2}-5.22\times 10^{7}w^{3}\quad\text{R}^{2}=0.9998 \tag{8}\]  
\[\varepsilon_{\rm{WH}}=1.42\times 10^{4}-3.77\times 10^{5}w+6.21\times 10^{6 }w^{2}-4.77\times 10^{7}w^{3}\quad\text{R}^{2}=0.9998 \tag{9}\]  
\[\varepsilon_{\rm{ZK}}=1.42\times 10^{4}-4.9\times 10^{5}w+1.05\times 10^{7}w^{2}-1.04\times 10^{8}w^{3}\quad\text{R}^{2}=0.9998 \tag{10}\]  
In the present study, the maximum adsorption potential of around 12,000 J/mol and the maximum adsorption space of 0.059-0.008 cm\({}^{3}\)/g (Figure 3) were obtained. There was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(V_{\rm L}\) **(cm\({}^{3}\))** & \(P_{\rm L}\) **(MPa)** & \(\text{R}^{2}\) \\ \hline XW1 & 30.54 & 1.10 & 0.9573 \\ XW2 & 33.59 & 1.28 & 0.9825 \\ WH & 34.56 & 1.20 & 0.9782 \\ ZK & 26.67 & 1.23 & 0.9659 \\ \hline \hline \end{tabular} Note: Dry basis.  
\end{table}
Table 2: The Langmuir constants of the samples.  
Figure 4: Predicted adsorption characteristic curve of methane on coal samples using adsorption potential theory.  
a significant difference in the adsorption characteristic curves among coals from coal mines and the drilling well. Compared to drilled coal samples, the coal samples collected from coal mines had larger adsorption spaces at the same adsorption potential. This phenomenon may be due to the significant differences in the sample collection and processing process.
## 3 Results and Discussion  
### Elevation of the Pipe Centerline  
#### 3.1.1 Detection of Abnormal Elevation Data  
A total of 70% of the labeled data is training data and 30% is testing data. Model evaluation is performed by evaluating the accuracy of abnormal sample prediction and the accuracy of all prediction samples. The confusion matrix in Figure 4 is the result of the classification model for abnormal data, and it shows that the algorithm classifies most of the data on the testing set correctly, and only 4 data are misjudged out of 200 data, among which 1 out of 11 abnormal data is judged as correct data. The precision and accuracy of the prediction reached 91% and 98%. It can be used to identify the remaining data.
# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 1 Introduction  
Energy consumption in buildings represents 32% of total final energy consumption [1]. This is also a hot research topic with several publications [2; 3; 4; 5]. The energy savings process can be greatly increased with user's participation since, in most cases, they can change local parameters like heating/cooling or lights. In this context, IoT real-time sensing data represents an important step toward consumption reduction via the identification of local patterns and, in some cases, promoting automatic interactions to reduce costs [6]. In this research, we integrate people as part of the process with the following two complementary Things2 People approaches: (1) where real-time sensor data is integrated into a 3D building user interface and (2) using predictions of energy consumption and costs related to users' possible actions.  
The knowledge about the predictions of energy consumption in buildings is essential for producers and consumers. IoT real-time information on energy, temperature, and humidity can be stored and correlated with external events to identify consumption patterns. Since the heating/cooling system is responsible for a significant percentage of the energy consumption in the building, the prediction also gives essential information about possible savings if we increase or decrease the inside temperature by as much as one degree.  
To facilitate Things2 People interaction, we developed an IoT building management system, where measurements were performed and represented in building information models (BIMs), where userreal-time interactions and automatic actions can be predicted regarding heating/cooling systems. In this research, we describe one of our contributions toward Things2 People, like the 3D IoT data representation and personalized prediction using past local data.  
Because building energy consumption prediction depends on a variety of factors, starting with random human behaviour, IoT implementation with associated data analytics platforms helps by reducing dependent variables, but to improve accuracy and precision, data analytics and associated dashboards are essential tools to extract knowledge from data for the identification of waste. Correlation with external events also has an important role, and predictions can help in savings actions.  
Predictions algorithms, using locally collected data, can be very important to change user behaviour, and taking into account that energy suppliers can result in a rough prediction of community energy needs for heating or cooling. Figure 1 illustrates our major work outputs and developed modules. The IoT platform developed in previous work [6] gives real-time data to people through a 3D interface and feeds the developed predictive process for the personalized forecast. The system is also used to generate alerts when it detects abnormal behaviours or consumptions.
###### Abstract  
The introduction of sparse code multiple access (SCMA) is driven by the high expectations for future cellular systems. In traditional SCMA receivers, the message passing algorithm (MPA) is commonly employed for received-signal decoding. However, the high computational complexity of the MPA falls short in meeting the low latency requirements of modern communications. Deep learning (DL) has been proven to be applicable in the field of signal detection with low computational complexity and low bit error rate (BER). To enhance the decoding performance of SCMA systems, we present a novel approach that replaces the complex operation of separating codewords of individual sub-users from overlapping codewords using classifying images and is suitable for efficient handling by lightweight graph neural networks. The eigenvalues of training images contain crucial information, such as the amplitude and phase of received signals, as well as channel characteristics. Simulation results show that our proposed scheme has better BER performance and lower computational complexity than other previous SCMA decoding strategies.  
sparse code multiple access (SCMA); deep learning (DL); signal detection; bit error rate (BER) +
Footnote †: journal: _entropy_
# Research on a Simplified Estimation Method for Wheel Rolling Resistance on Unpaved Runways
## 2 Theory and Methods
### Rolling Resistance Calculation Model Based on Wheel-Soil Interaction Mechanism
#### 2.1.2 Computational Model for Wheel Resistance  
On unpaved soil surfaces, pneumatic wheels exhibit mechanical behavior analogous to rigid wheels under vertical loading [24]. Based on [PERSON]'s foundational assumptions:  
1. Soil reaction forces at the wheel-soil interface are strictly radial in direction;
2. The magnitude of these radial forces equals the normal pressure measured in plate-sinkage tests at equivalent depths.  
These postulates enable the derivation of force equilibrium equations from the simplified interaction model illustrated in Figure 1[25], where the horizontal force component corresponds to the total rolling resistance:  
\[F=b\int_{0}^{\theta_{0}}\sigma R\sin\theta\mathrm{d}\theta \tag{5}\]  
\[W=b\int_{0}^{\theta_{0}}\sigma R\cos\theta\mathrm{d}\theta \tag{6}\]  
where \(W\) denotes vertical load (N), \(F\) represents rolling resistance (N), \(\sigma\) signifies the radial pressure (kN/m\({}^{2}\)), \(b\) indicates wheel contact width (m), and \(R\) is the wheel radius (m).  
By combining the rolling resistance model (Equation (9)) with the maximum sinkage depth expression (Equation (10)), the consolidated equation for wheel resistance on unpaved surfaces is derived as follows:  
\[F=\frac{1}{(3-n)^{\frac{2n+2}{3n+1}}(n+1)^{\frac{1}{2n+1}}\left(\frac{k_{c}}{b}+ k_{\varphi}\right)^{\frac{1}{2n+1}}}\left(\frac{3W}{\sqrt{D}}\right)^{\frac{2n+2}{ 2n+1}} \tag{11}\]
## 3 Numerical Modelling and Analysis of Ultrasonic Inspection of Grounded Flat Steel
### Inverse Transmission Characteristics of SH0 Wave and DAC Curve
Figure 10: (**a**) The relationship between echo amplitude and corrosion defect depth, (**b**) the relationship between return wave amplitude and corrosion defect length, and (**c**) the relationship between echo amplitude and corrosion defect width. Figure 9: DAC curves for different lengths of corrosion defect. The reflection of the grounding flat steel defect echo becomes more pronounced with the increase in the depth of the defect, as reflected by the change in the displacement amplitude of the defect echo signal. Specifically, the defect reflection echo's displacement amplitude increases with the defect depth. Furthermore, the echo signal's displacement amplitude rises with the corrosion defect's length. However, the defect width has almost no effect on the echo amplitude; as the corrosion defect width increases, the echo amplitude shows no significant variation trend. In summary, the defect echo is positively correlated with the defect depth and length, while the defect width has little effect on the echo amplitude.
## 3 Results and Discussion
### Methodology Tests
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **How Rate** & **Sampling** & **Air** & **Conc. of** & **Conc. in** & \(\delta^{\rm 199}\)**Hg** & \(\delta^{\rm 200}\)**Hg** & \(\delta^{\rm 204}\)**Hg** & \(\delta^{\rm 204}\)**Hg** & \(\delta^{\rm 204}\)**Hg** & \(\delta^{\rm 204}\)**Hg** \\  & **(L min\({}^{\rm-}\))** & **(M)** & **(M)** & **(M)** & **(M)** & **(M)** & **(M)** & **(M)** & **(M)** \\ \hline Noyaki, 24 March & 75 & 0.9 & 4.2 & 3.1 & 3.7 & \(-\)0.58 & \(-\)0.58 & \(-\)1.01 & \(-\)0.93 & \(-\)1.40 \\ Noyaki, 6 April & 78 & 0.8 & 3.5 & 1.0 & 1.4 & \(-\)1.14 & \(-\)0.69 & \(-\)1.25 & \(-\)1.39 & 1.19 \\ Background air, & 54 & 2.1 & 6.7 & 1.1 & 0.8 & \(-\)0.05 & 0.44 & 0.35 & 1.06 & 1.30 \\ Background air, & 80 & 4.2 & 20.1 & 3.0 & 0.7 & \(-\)0.02 & 0.13 & \(-\)0.06 & 0.16 & 0.49 \\ The average of the & NA.\({}^{\ddagger}\) & NA.\({}^{\ddagger}\) & NA.\({}^{\ddagger}\) & NA.\({}^{\ddagger}\) & NA.\({}^{\ddagger}\) & 0.8 & \(-\)0.04 & 0.28 & 0.14 & 0.61 & 0.89 \\ Outdoor air & 75 & 22.3 & 100.2 & 17.6 & 0.9 & \(-\)0.12 & 0.08 & 0.01 & 0.29 & 0.48 \\ Laboratory air & 100 & 6.0 & 36.0 & 23.2 & 3.2 & \(-\)0.12 & \(-\)0.11 & \(-\)0.22 & \(-\)0.16 \\ Clean room air & 100 & 24.0 & 144.0 & 17.0 & 0.6 & \(-\)0.23 & \(-\)0.20 & \(-\)0.45 & \(-\)0.40 & \(-\)0.59 \\ \hline \hline \end{tabular}  
* The concentrations of sample extracts were determined by the preanalysis of the extracts using CV-MC–ICP–MS.
* IGM concentrations in the air were determined according to the extract concentrations, the volume of each extract, and the sampled air volume. \({}^{\ddagger}\) Not applicable.  
\end{table} 
Table 4: Results of the overall methodology tests. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample ID** & \(\delta^{\rm 199}\)**Hg & \(\delta^{\rm 200}\)**Hg & \(\delta^{\rm 201}\)**Hg & \(\delta^{\rm 202}\)**Hg & \(\delta^{\rm 204}\)**Hg & \(\Delta^{\rm 199}\)**Hg & \(\Delta^{\rm 200}\)**Hg & \(\Delta^{\rm 204}\)**Hg** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Noyaki, 24 March & \(-\)0.73 & \(-\)0.82 & \(-\)1.33 & \(-\)1.35 & \(-\)2.03 & \(-\)0.39 & \(-\)0.14 & \(-\)0.31 & \(-\)0.01 \\ Noyaki, 6 April & \(-\)2.53 & \(-\)0.72 & \(-\)0.84 & \(-\)1.18 & 1.10 & \(-\)2.24 & \(-\)0.13 & 0.05 & 2.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Background-corrected \(\delta^{\rm x}\)Hg and \(\Delta^{\rm x}\)Hg for TGM from the open field burning (Noyaki). mass isotope showed strong MDF. The calculated \(\Delta^{\rm x}\)Hg for these isotopes showed this trend more clearly (Table 6). The unusual MIF of \({}^{200}\)Hg found in the atmospheric precipitation of Peterborough, Canada [22] may be explained by this process, but the strong MDF found for the odd-numbered masses here does not explain their observations. Again, thorough evaluation is needed of these kinetic isotope effects.
## 2 Point Cloud Data Registration Method
### Verification of the Methodology
#### 2.2.2 Fine Registration  
After applying SIFT feature points to perform coarse registration, fine registration utilizing the K-D tree improved ICP algorithm and LM improved ICP algorithm were performed respectively, of which, the LM improved ICP algorithm can solve the problem of slow convergence of the classic ICP algorithm when the Gauss-Newton approximation error is large [44]. The registration results of the two algorithms are shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature Point Type** & **Quantity** & **Accuracy Rate** & **Elapsed Time** \\ \hline SIFT feature point & 39 & 100\% & 20.37 s \\ SURF feature point & 41 & 98.6\% & 27.82 s \\ Harris feature point & 36 & 91.7\% & 25.98 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative indicators for feature point extraction.  
Figure 2: Feature point extraction effect: (**a**) initial point clouds, (**b**) SIFT feature points, (**c**) SURF feature points, (**d**) Harris feature point.  
The advantages and disadvantages of the two algorithms were evaluated by the elapsed time and RMSE; the results are as shown in Table 2. As can be seen from Table 2, the K-D tree improved ICP algorithm was better than the LM improved ICP algorithm. Therefore, it is reasonable and feasible to use the K-D tree improved ICP algorithm to perform fine registration.
## 1 Introduction
It was found to be useful in accelerating tooth movement [12], thanks to its cell-mediated alveolar bone remodeling induction; it also appeared to have a positive impact on the reduction of orthodontic treatment-related pain due to its analgesic, anti-inflammatory and biostimulant effects [13], and minimized anchoring loss during orthodontic treatment [14]. Research indicates that laser light promotes the proliferation of osteoclasts, osteoblasts, and fibroblasts, thereby influencing bone remodeling and accelerating tooth movement. The underlying mechanism behind this acceleration involves ATP production and the activation of cytochrome C. Studies [15; 16; 17] have demonstrated that low-energy laser irradiation enhances tooth movement speed through the RANK/RANKL pathway and the expression of the macrophage colony-stimulating factor and its receptor. PBMT influences the release of inflammatory mediators in periodontal tissues, enhances collagen synthesis, and stimulates the proliferation of osteoblasts and osteoclasts. This process promotes bone remodeling through the receptor activator of nuclear factor-kBligand (RANKL)/RANK/osteoprotegerin pathway, ultimately accelerating orthodontic tooth movement [18; 19; 20; 21]. Some studies have investigated the application of photobiomodulation therapy on impacted teeth, primarily focusing on its efficacy in reducing postoperative inflammation and pain. However, these investigations did not assess its potential role in enhancing or accelerating tooth movement [22]. Despite the well-documented ability of low-level laser therapy to stimulate orthodontic tooth movement, its stimulation effect on impacted teeth movement--particularly palatatly impacted maxillary canines--has not yet been thoroughly explored. Further research is warranted to evaluate the potential of PBMT as an adjunctive tool in facilitating the guided eruption of impacted elements. In this case report, a patient with bilateral palatal impaction of the maxillary canines underwent surgical conventional exposure of both teeth. Following the surgery, only one of the canines received photobiomodulation therapy with a low-level laser, in accordance with a split-mouth protocol. Orthodontic traction was avoided on both dental elements to assess and compare only the extent of spontaneous eruption movement, without introducing variables that might interfere with these assessments. The objective of this study was to evaluate potential differences in eruption rate and extent between the canine treated with photobiomodulation therapy (PBMT) and the untreated canine in order to investigate the stimulatory effects of PBMT on tooth eruption.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 3 Discussion  
### Phenotyping  
The SR phenotype was reported for the first time in 1987 in slow-ripening nectarines derived from the cultivar Fantasia that mature approximately 2.5 months after commercially harvested, normal ripening Fantasia, and this slow ripening phenotype did not present flesh color and firmness changes [12]. These slow ripening nectarines failed to produce normal ethylene levels for at least one month after harvest and their ethylene production peaks were significantly reduced [24]. Conversely, the SR siblings displayed a more significant accumulation of soluble solids and less tittatable acidity than regular ripening fruit [12]. Similar results were observed in the V\(\times\)V population (Figure 1). Significant differences were obtained in size, color, and firmness between the NR and SR phenotypes. These changes were identified in the S2 development stage between T3 and T4 (65-87 DASP), the period in which the process of endocarp lignification (pit hardening) begins, and fruit growth was consequently reduced [4]. At S3, no fruit growth was observed in the SR siblings, suggesting fruit development stagnation in stage S2.  
The expression profile of the candidate gene _NAC072_ was analyzed (Figure 1). One peak of expression was observed during fruit development at T2 (51 DASP). This period was identified as the transition from S1 (first fruit growth period) to S2 (endocarp lignification), and is also the period when the embryo starts to develop the seed [4]. Furthermore, as mentioned before, the differences between the NR and SR phenotypes were observed between T3 and T4. For these reasons, it seems possible that _NAC072_ id involved in fruit, seed, or embryo development, and its absence may cause the slow ripening phenotype.
## 2 Methods and Materials
### Online Correction Method: Continuous Radiation
#### 2.2.2 Analytic Online Correction Method--Continuous Beams  
We define the ionization current \(I(t)\) in this case as:  
\[I(t)=\frac{e\ \dot{D}\ m}{W} \tag{18}\]  
We will assume that the variations in time of the dose rate \(\dot{D}\) are sufficiently slow as to keep the equilibrium charge carrier distributions across the detector at each time \(t\).  
The current in the detector is given by:  
\[I_{col}(t)=I(t)\ f(I(t)), \tag{19}\]  
where \(I_{col}(t)\) is the readout current, \(I(t)\) is the ionization current, and \(f\) the collection efficiency. As \(\dot{D}(t)\propto I(t)\), we can write the collection efficiency as a function of \(I(t)\).  
We can use the two-voltage-method, or any other method, to determine the collection efficiency at a reference dose rate, \(\dot{D}_{ref}\sim I_{ref}\), and the value of \(b\), which we will refer to as \(b(\dot{D}_{ref})\equiv b(I_{ref})\equiv b_{ref}\). The collection efficiency at any other dose rate/ionization current can be written as:  
\[f(I(t))=\left(1+b_{ref}\frac{I(t)}{I_{ref}}\right)^{-1} \tag{20}\]  
\(I(t)\) is not known, but \(I_{col}(t)\) is measured, and by using Equation (19) we can write:  
\[f(I(t))=\left(1+b_{ref}\frac{I_{col}(t)}{I_{ref}\ f(I(t))}\right)^{-1} \tag{21}\]  
This equation can be solved to obtain an expression for the collection efficiency as a function of the measured ionization current \(I_{col}(t)\):  
\[f(I(t))=\left(1-\frac{b_{ref}\ I_{col}(t)}{I_{ref}}\right)=\left(1-k_{ref}^{c }\ I_{col}(t)\right) \tag{22}\]  
where \(k_{ref}^{c}=b_{ref}/I_{ref}\) in continuous irradiation. With this expression, we can correct the readout current as:  
\[I_{corrected}(t))=\frac{I_{col}(t)}{f(I(t))} \tag{23}\]