esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Thermal Field  
To satisfy the thermal boundary conditions in Equation (17), we introduce a Fourier-type solution as follows:  
\[T=\sum\limits_{m=1}^{\infty}\sum\limits_{n=1}^{\infty}T_{mn}(z)\sin(P_{m}x)\ \sin(P_{n}y). \tag{20}\]  
where \(P_{m}=m\pi/a\) and \(P_{n}=n\pi/b\). Moreover, the thermal conductivity coefficients related to the GPLs distribution pattern are determined as:  
\[UD:\ \frac{K_{c}}{K_{m}}=1+D \tag{21a}\] \[GPL-X:\ \left\{\begin{array}{ll}\frac{K_{c}}{K_{m}}=1+2D(1-2\frac{Z}{h})&0\leq Z \leq\frac{h}{2}\\ \frac{K_{c}}{K_{m}}=1+2D(-1+2\frac{Z}{h})&\frac{h}{2}\leq Z\leq h\end{array}\right.\] (21b) \[GPL-O:\ \left\{\begin{array}{ll}\frac{K_{c}}{K_{m}}=1+4D(\frac{Z}{h})&0\leq Z \leq\frac{h}{2}\\ \frac{K_{c}}{K_{m}}=1+4D(1-\frac{Z}{h})&\frac{h}{2}\leq Z\leq h\end{array}\right. \tag{21c}\]  
Inserting Equations (20) and (21a) into Equation (16a) and solving the equation analytically in its final form, we obtain the following expression of temperature gradient for GPLRC rectangular plates with a uniform distribution of GPLs.  
\[T_{\rm mn}=\mathrm{C}_{11}\mathrm{e}^{\sqrt{\Lambda_{11}}Z}+C_{22}\mathrm{e}^ {-\sqrt{\Lambda_{11}}Z} \tag{22}\]  
where \(\mathrm{C}_{11}\),\(\mathrm{C}_{22}\), and \(\mathrm{A}_{11}\) are arbitrary constants determined with appropriate enforcement of the thermal surface boundary conditions (see more details in Appendix B).  
At the same time, by combining Equations (20), (21b) and (21c), the heat conduction differential Equation (16b) reduces to the following hypergeometric equation:  
\[(\mathrm{A}_{1}Z+\mathrm{A}_{2})\frac{\partial^{2}T_{\rm mn}(z)}{\partial Z^{ 2}}+(\mathrm{A}_{3}Z+\mathrm{A}_{4})\frac{\partial T_{\rm mn}(z)}{\partial Z} +(\mathrm{A}_{5}Z+\mathrm{A}_{6})T_{\rm mn}(z)=0 \tag{23}\]with \(\text{A}_{1},\text{A}_{2},\ldots,\text{A}_{6}\) being constant coefficients depending on the pattern of GPLs distribution (see Appendix B). The analytical solution of Equation (23) takes the following form:  
\[T_{\text{mn}}=C_{1}e^{\alpha_{1}}\text{KummerM}(\beta_{1},\beta_{2},\beta_{3})( \text{A}_{2}+\text{A}_{1}\text{Z})^{\alpha_{2}}+\text{C}_{2}e^{\alpha_{1}} \text{KummerU}(\beta_{1},\beta_{2},\beta_{3})(\text{A}_{2}+\text{A}_{1}\text{Z })^{\alpha_{2}} \tag{24}\]  
where the Kummer's function, also known as the confluent hypergeometric function of the first kind, is a solution to a Kummer's differential equation. In addition, KummerU and KummerM functions represent two special types of Kummer function.  
As the thermal behavior of a structure depends on its thermo-mechanical properties, it is worth noticing in Figure 3 that the temperature distribution in thermoelastic solutions is completely different from a uniform or harmonic distribution.
## 3 Results and Discussion
### Rotation Angle  
The rotation angle is measured using the method illustrated in Figure 6 and calculated by using Equation (1):  
\[tan\varphi=\frac{h}{l}, \tag{1}\]  
where, \(\varphi\) is the rotation angle of the nut, \(h\) is the linear displacement, and \(l\) is half of the length of the nut side.  
Figure 4: The preload variations of all bolted joints under cyclical temperature changes from 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C.  
Figure 5: The preload variations of all bolted joints under cyclical temperature changes from 10 \({}^{\circ}\)C to 80 \({}^{\circ}\)C.  
The changes of the rotation angle of the nuts under cyclical temperature changes from 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C are shown in Figure 7. The glass nut rotated by about 0.2 degrees in the first cycle. It had a very small rotation of 0.26 degrees at the end of the experiment. The ABS-2 nut did not rotate through the experiment. However, the PLA nut rotated significantly by about 2.4 degrees after the first cycle. Hence, the nut kept rotating slightly through the test and by approximately 2.9 degrees after the final cycle of the temperature changes.  
The changes of the rotation angle of the nuts under cyclical temperature changes from 10 \({}^{\circ}\)C to 80 \({}^{\circ}\)C are shown in Figure 8. It can be noticed from the figure that the ABS-2 nut rotated by 0.73 degrees in the initial cycle and did not rotate any further, and the same scenario was observed with both glass and PLA nuts. In other words, after the initial cycle, glass and PLA nuts rotated in the loosening direction by 8.38 degrees and 3.64 degrees, respectively, and their values kept constant by the end of the test.  
Figure 6: The method for measuring the rotation angle of the nut in the loosening direction.  
Figure 7: The rotation angle variations of all bolted joints under cyclical temperature changes from 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C.
# Chemical and Sensory Evaluation of Silicone and
## 3 Experimental Section
### Analysis Methods  
#### 3.3.1 Methoxypyrazine Analysis  
Commercial IBMP (97%), SBMP (99%) and IPMP (97%) and deuterated IBMP (99%) were purchased from Sigma Aldrich (Sigma Aldrich Inc., St. Louis, MO, USA) and MilliQ (EMD Millipore, Billerica, MA, USA) water was used for dilutions. Sodium hydroxide (NaOH 99.8%) was purchased from Sigma Aldrich while NaCl (99%) was purchased from Caledon Laboratories Ltd (Caledon, ON, Canada). The analytical method used for MPs was that of [PERSON] et al. (2014). The method yielded good linearity of standard curves for all three MPs (\(r^{2}>0.99\)) and satisfactory repeatability and recovery values. The limit of detection was 1-3 ng/L for all MPs, while the limit of quantification was \(<\)5 ng/L for IBMP, and \(<\)3 ng/L for SBMP and IPMP. MP analyses were performed using an Agilent 7890A Gas-Chromatograph (GC) coupled with an Agilent MS 5975 Mass Spectrophotometer (MS) (Agilent Inc., Santa Clara, CA, USA). The GC was equipped with a Dean Switch (Agilent Inc.) that allowed the flow between the two columns of the multi-dimensional system. The first column was a HP5 MS column (30 m, 0.25 mm I.d., 0.25 mm film thickness) (J&W Scientific, Folsom, CA, USA) and the second column was a DBWAX column (30 m, 0.25 mm I.d., 0.25 mm film thickness) (J&W Scientific). The column temperature was held at 70 \({}^{\circ}\)C then ramped to 100 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min, further ramped to 130 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min and to 240 \({}^{\circ}\)C at a rate of 35 \({}^{\circ}\)C/min where it was held for 8 min. The front inlet was set to splitless mode and held at 250 \({}^{\circ}\)C under 23.2 psi pressure. The purge flow was turned on after 3 min with a flow to the split vent of 50 mL/min. The dean switch was programmed for two heart cuts, the first between min 10.1 and min 13.5, the second between min 14.1 and min 14.88. The MSD interface was held at 240 \({}^{\circ}\)C and the temperature of the ion source was 200 \({}^{\circ}\)C. The carrier gas was helium, with an initial flow of 0.8 L/min. Identification was performed using Selective Ion Monitoring (SIM). Mass channels were \(m/z=137\) (quantifying) and 152, for IPMP, 138 (quantifying) and 124 for SBMP, 124 (quantifying) and 151 for IBMP. Mass channels for the internal standard d\({}_{3}\) IBMP were \(m/z=127\)(quantifying) and 154. Quantitation of the compounds was performed by interpolating of the peak area for each compound of interest with the internal standard peak area, based on calibration curves established using the commercial pure compounds and the commercial Merlot wine used throughout the experiments. Quantification of each compound was performed using Chemstation software (MSD E.02.00493, Agilent Technologies). Analyses were performed in duplicate.
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 3 Methods
### Segmentation Algorithms
#### 3.2.4. Canny Edge Segmentation  
Segmentation methodologies built upon the Canny edge detector allow locating contours in medical images. It is the main reason to include this algorithm in this application. The Canny method is a well known edge detection technique [27; 31]. In our implementation, two different options (manual and automatic) for segmentation are used. The first option determines the interval where the threshold search should be conducted.  
A manual edge detection algorithm requires as an input parameters the original grayscale image, a sigma value corresponding to the standard deviation of the Gaussian filter to be applied, and a threshold range. Manual segmentation is focused on different steps. First, a Gaussian filter is applied to smooth the image and to remove noise and artefacts. Then, the intensity gradients of the image are calculated, and, afterwards, non-maximum suppression is applied to remove any spurious response in the edge detection stem. Subsequently, a double threshold techniques are used to determine potential edges. Finally, the detection of edges is carried out by suppressing all the other edges that are weak and not connected to strong edges.  
A second edge implementation for automatic edge detection is used based on the approach proposed by [PERSON] [32]. This method provides a criterion to reduce, in a significant way, the number of initial values to be considered as threshold candidates compared to manual segmentation. The proposed method can be applied to any feature image provided by an edge detector upon which hysteresis has been implemented. In this case, the inputs required by this algorithm include the original image (in greyscale), a sigma value corresponding to the standard deviation of the Gaussian filter to be applied, and a set of candidate pairs organized  
Figure 4: Example of the calculated \(Tmax\) as a threshold value obtained from the maximum value in a histogram display.  
in a \(M\)x2 matrix where \(M\) is the number of candidates. These candidates are chosen in an unsupervised manner. Users should select the choice of local extremes as the candidates for the hysteresis thresholds. Then, the automated algorithm is capable of identifying the optimum pair of hysteresis thresholds based on the distribution of grey levels in the input image.
# Thermal Exchange and Skid Resistance of Chip Seal with Various Aggregate Types and Morphologies
###### Abstract  
Steel slag, the by-product of steelmaking, is a desirable alternative material for natural aggregate. However, there are few studies applying steel slag in the preventive maintenance of asphalt pavements, especially chip seal. The main objective of this study is to explore the feasibility of applying steel slag in chip seal and the effect of steel slag on the thermal exchange and aggregate retention properties. Furthermore, the surface features, including texture depth and skid resistance. of chip seal were also evaluated. The results show that the thermal exchange performances of chip seal vary with aggregate types. The ranking of the chip seal samples according to the cooling rate places ferrochromium (FER) slag as the fastest and basic oxygen furnace (BOF) slag as the slowest, with the basalt (BS) falling in between. The use of FER slag can make the chip seal resume traffic about ten minutes earlier than original samples. The skid resistance and texture depth of FER slag meet the requirements of the specification, although they are less than those of ordinary aggregates. Moreover, FER slag has a better aggregate retention performance than BOF slag and BS due to its spherical particles and alkaline surface. The application of steel slag in chip seal can recycle industrial waste, reduce the consumption of natural resources and promote economic pavement maintenance technology.  
chip seal; thermal behavior; aggregate morphology; skid resistance; aggregate loss Article
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods
### Computational Models
#### 2.2.5 Definition of Effective SAR volume  
The temperature elevation is mainly affected by the SAR value when larger than \(1/e\) of peak SAR [49]. [PERSON] et al. estimated the heat diffusion length in terms of the length, where the amplitude of heat decreases to \(1/e\)[43, 50]. [PERSON] et al. derived an analytic formula to estimate the thermal time constant in a one-dimensional homogeneous model for plane wave exposure [49]. [PERSON] et al. proposed the effective area of the SAR pattern as an evaluation index of temperature elevation, which is defined by the area where SAR is larger than \(1/e\) of the peak value in the averaging area using a multilayer cubic model [51]. The effective SAR volume \(V_{off}\) [cm\({}^{3}\)] is defined as a metric where SAR is  
Figure 1: Exposure scenarios for computational (**a**) rat and (**b**) human head models (TARO). The separations between the antenna and the rat and human models are 5 mm and 5–25 mm, respectively.  
larger than \(1/e\) of the \(SAR_{max}\) to consider both the depth and the spread on the model surface. Here, \(SAR_{max}\) was treated as a value excluding the SAR voxels, and had the highest SAR value of 0.01% (see Appendix A).
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
###### Abstract  
Medical teleconsultation was among the initial use cases for early telepresence research projects since medical treatment often requires timely intervention by highly specialized experts. When remote medical experts support interventions, a holistic view of the surgical site can increase situation awareness and improve team communication. A possible solution is the concept of immersive telepresence, where remote users virtually join the operating theater that is transmitted based on a real-time reconstruction of the local site. Enabled by the availability of RGB-D sensors and sufficient computing capability, it becomes possible to capture such a site in real time using multiple stationary sensors. The 3D reconstruction and simplification of textured surface meshes from the point clouds of a dynamic scene in real time is challenging and becomes infeasible for increasing capture volumes. This work presents a tightly integrated, stateless 3D reconstruction pipeline for dynamic, room-scale environments that generates simplified surface meshes from multiple RGB-D sensors in real time. Our algorithm operates directly on the fused, voxelized point cloud instead of populating signed-distance volumes per frame and using a marching cube variant for surface reconstruction. We extend the formulation of the dual contouring algorithm to work for point cloud data stored in an octree and interleave a vertex-clustering-based simplification before extracting the surface geometry. Our 3D reconstruction pipeline can perform a live reconstruction of six incoming depth videos at their native frame rate of 30 frames per second, enabling the reconstruction of smooth movement. Arbitrarily complex scene changes are possible since we do not store persistent information between frames. In terms of mesh quality and hole filling, our method falls between the direct mesh reconstruction and expensive global fitting of implicit functions.  
telepresence; real-time reconstruction; medical consultation 20231310199. [[https://doi.org/10.3390/app131810199](https://doi.org/10.3390/app131810199)]([https://doi.org/10.3390/app131810199](https://doi.org/10.3390/app131810199))  
10 July 20237 August 20237 August 202316 August 202311 September 2023
# Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020
## 4 Discussion
### Positive Role of National Land Policies  
In the past 20 years, ecological projects and land management policies implemented in China have played a significant and positive role in SOC changes, including the Three-North Protective Forest Project, the Return of Farmland to Forests and Grassland Project, soil and water conservation ecological restoration, comprehensive management of rocky desertification control, conservation tillage, and returning straw to the fields, among others [23; 75; 79]. Public statistics show that the Three-North Project completed afforestation of 9.7057 million hectares from 2001 to 2020; and the return of a total of 34.33 million hectares of farmland to forests and grasslands was implemented nationwide from 1999 to 2019 (as noted in the white paper \"Twenty Years of Returning Farmland to Forest and Grassland in China (1999-2019)\", issued by the State Forestry and Grassland Administration). By 2020, the national soil erosion area was 269.27 km\({}^{2}\), a decrease of 86.7 km\({}^{2}\) compared with 1999; by 2016, the area of rocky desertification in karst areas was 100,700 km\({}^{2}\), a decrease of 28,920 km\({}^{2}\) compared with 2005 [85].  
These policies promote SOC accumulation mainly through anthropogenic land management measures such as afforestation and vegetation restoration. Studies have shown that large-scale changes in land use after returning farmland to forest significantly increased soil carbon stocks in China [86]. [PERSON] et al. (2023) found that large-scale afforestation in northern China contributed about 913 Tg C, of which 26% originated from the fixation of SOC [75]. A study by [PERSON] et al. (2022) found that vegetation restoration increased SOC stocks in the Loess Plateau's SOC storage by 16.12 Tg C [23]. In addition, conservation tillage and straw return are important management practices for agricultural land in China [87], and studies have shown that conservation tillage and straw return can significantly increase the rate of soil carbon sequestration [88]. Similarly, these conclusions have been proved in the practice of other countries, such as the planting pattern model of crop rotation and fallow adopted in the United States, and the implementation of land management measures such as afforestation and straw return implemented in European countries, which also promote the recovery and accumulation of organic carbon [89; 90; 91].
## 4 Implementation and Results
### Study Area  
The area that was chosen for implementing our proposed framework is the state of Aceh, located in the north of Sumatra (Indonesia). The study area is depicted in Figure 3, which shows the state of Aceh, which is known for severe deforestation and a high oil palm plantation density and a lot of oil palm concessions. Indonesia was known to have the highest deforestation rate in the world.  
We gathered data related to deforestation events between 1988 and 2020, a land cover map, Sentinel-1 Google Earth and Bing aerial imagery, maps of yearly distances to oil palm plantations from 1988 to 2020, a list of oil palm concessions, and a list of roads.  
We used the well-known normalized difference vegetation index (NDVI) to define deforested areas. The NDVI is an indicator of vegetation health, because degradation of  
Figure 3: Study area: the state of Aceh in Indonesia.  
ecosystem vegetation, or a decrease in green, would be reflected in a decrease in NDVI value. The decrease in NDVI values may indicate a degradation of vegetation cover (i.e., deforestation) [56].  
We obtained a raster map where each pixel depicts the day and year of deforestation. The darker pixels represent more recent deforestation events, while the brighter pixels depict less recent deforestation events. The resulting raster map can be seen in Figure 4.  
The map above shows the deforestation dates pixel by pixel, which were then grouped together if they are directly adjacent or they have the same day of deforestation.  
We then created a deforestation and water map by extracting oil palm and water pixels from the land cover map. Water and oil palm features can be seen in Figure 5. We implemented the extraction process using numpy and rasterio Python libraries. Python libraries are very useful for carrying out image processing to extract or enhance information useful for mapping purposes.
## 4 Result and Discussion
### Prediction and Validation
#### 4.1.4 Predictions via Random Forest (RF)  
The hyperparameters tuned for this algorithm are the number of estimators (to initiate the total number of decision trees) and the evaluation criteria (to measure the error difference between the predicted and observed values). The plots for the train and test data are shown in Figure 7 for predicted vs. observed pressure loss. The calculated RMSE and MAE for train data is 4.7 kPa and 1.74% with R\({}^{2}\) = 0.99, whereas, for test data RMSE and MAE is 9.09 kPa and 3.4% with R\({}^{2}\) = 0.986. The prediction for train data is observed to be excellent due to cloud formation around the best fit line till 200 kPa. A slight distortion is observed along the best fit line for the test dataset. The data points lie in close approximation to the best fit line and drifts further much after 200 kPa.
## 3 Results and Discussion
### Comparison of Vibration Impact Using SWD Scales
  
However, in the case of vibrations induced by mining tremors in the underground mine, they should be classified into zone II of the SWD-I scale (Figure 15) and the effects of the impact should be described as harmless to the building structure. However, an accelerated wear of the building and initial cracks in the coatings and plaster, on the corners of walls, facets, etc., can be expected.
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Photovoltaics Integrated in Vehicles and Ships  
The reduction in PV costs and higher penetration of EV are the main driving forces behind vehicle-integrated photovoltaics developments. However, this application can have several variations depending on the (i) type of vehicles (light-duty, heavy-duty, camper, etc.), (ii) use of energy (for extended range, refrigeration, etc.), and (iii) PV technology (Si, III-V, organic, etc.).  
Apart from the above-mentioned parameters, the climatic conditions also play a significant role in vehicle-integrated photovoltaics. A recent study performed on a commuter car and a light delivery van, even though it does not take into account shading, suggests that the average annual solar range (mileage) of a photovoltaic electric vehicle with 454 W\({}_{\text{P}}\) vehicle-integrated photovoltaics can amount between 12% (worst climatic conditions for PV) and 35% (best climatic conditions for PV). The respective range for a delivery van with 649 W\({}_{\text{P}}\) vehicle-integrated photovoltaics is between 9% and 23% (considering a 51% higher annual mileage versus the car driving pattern) [55]. A crucial parameter requiring extensive research is the shading patterns and effects for vehicle-integrated photovoltaics [56].  
Vehicle-integrated photovoltaics applications are expected to reach TRLs between 6 and 8 by 2025 [19]. More technology details can be found in the dedicated paper in this Special Issue.  
The tightening of environmental standards, especially in costal regions, has increased the interest of the maritime transport industry for the use of renewable energy sources [57]. The use of a PV system in combination with battery storage and a conventional diesel engine can reduce the CO\({}_{2}\) emissions of the maritime transport sector, especially during port times, when the overall energy demand is considerably reduced [58; 59]. This application is still in a demonstration and research phase.