esa-sceva/satcom-chunk-collection huggingface dataset:
# A Full Technological Traceability System for Extra Virgin Olive Oil
## 3 Results and Discussion
### Economic Analysis
The producers that choose to implement a traceability system almost certainly want to demonstrate to the consumers that their products are authentic and produced, transported and sold in a defined  
Figure 14: Percentage impact of the TTS application on the EVOO market price as a function of the olive production per tree. amount and within a certain time. This ensures the consumer that the products have not been re-labeled with different data. A forger is unlikely willing to implement a technology that can reduce (even if not to eliminate) potential fraud. Thus, the TTS is partially based on a trust between producer and consumer. Indeed, on one hand, the producer gains and archives georeferenced information relative to his or her olive orchard that is useful to improve its management strategy in light of a precision farming philosophy (e.g., optimizing a site-specific fertilization plan). On the other hand, the consumers of course benefit from the traceability information reported on the final product. Note that the information that is readable, e.g., on the bottles, is representative of a set of trees in a given area significative for a certain orchard. As mentioned above, producers could obtain several advantages in terms of information helpful for the following precision management strategies [22] regarding:  
* Production yield;
* Pruning indications;
* Pests and diseases/treatments;
* Fertilization;
* Irrigation. As a further result, the net value of the orchard could increase on the market due to the historical information on the management of each single tree. Another advantage owing to this traceability system is related to the manufacturer. Indeed, thanks to the use of blockchain systems, it represents a strong tool to guarantee the EVOO origin. As a result, a manufacturer could promote his or her product with marketing strategies that, on the one hand, provide a guarantee to the consumer, and on the other retain consumer loyalty to the company, which in turn can get a higher price, given the guarantee obtained. As [PERSON] et al. [6] pointed out, the consumers are very attentive and inclined towards the new traceability technologies of EVOO and are willing to pay up to the 17.8% more than the conventional sale price.
###### Abstract  
Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (XGBoost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the XGBoost model's performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. The proposed temperature detection method offers higher accuracy and versatility, contributing to more efficient detection of abnormal ship temperatures at night.  
infrared image; ship image segmentation; blurred edges; small targets; XGBoost algorithm +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### CO\({}_{2}\) Modeling Results  
Figure 15 shows a log plot of well 15/9-F1A, highlighting the Utsira Formation. After applying fluid substitution to the Utsira Formation and generating synthetic seismic traces for each CO\({}_{2}\) saturation state, a clear fluid effect can be seen.  
There is a sharp decrease in \(V_{p}\) (fizz-gas effect) when CO\({}_{2}\) is introduced into a porous medium, whereas the change in velocity is minimal when the CO\({}_{2}\) saturation is greater than 20%. Figures 16-18 summarize the modeling results. Between 0 and 0.1 CO\({}_{2}\) saturation, there is a dramatic decrease in the P-wave velocities (Figure 16a), with a subtle difference observed for higher saturation. With increasing CO\({}_{2}\) saturation, the rock bulk modulus (K\({}_{\text{sat}}\)) decreases. Consequently, the P-wave velocity decreases when the bulk density (b) decreases. At a CO\({}_{2}\) saturation of 0.1%, the P-wave velocity increases subtly, due to a decrease in density.  
Figure 15: Petrophysical analyses—Zone of Interest (ZoI).  
Figure 16: (**a**) P-wave velocity change versus CO2 saturation. An abrupt drop in P-wave velocity values, especially between 0 and 0.2 CO2 saturation, and (**b**) S-wave velocity change versus CO2 saturation. CO2 saturation has a direct relationship with S-wave velocity.  
In contrast, the increase in S-wave velocity is directly related to CO\({}_{2}\) saturation (Figure 16b). Since the shear modulus remains constant and is unaffected by fluid substitution, the change in S-wave velocity depends only on the bulk density variations.  
Figures 17 and 18 summarize our modeling results, while Figure 19a,b show the synthetic seismogram and stack results. The distinction in the synthetic traces is most  
Figure 17: Cross plot of AI versus PR color-coded by V\({}_{clay}\). The arrow illustrates the decreasing CO\({}_{2}\) effect, showing a sharp effect from 0 to 0.2 CO\({}_{2}\) saturation, while a more subtle effect is observed for higher CO\({}_{2}\) saturation values.  
Figure 18: Fluid modeling results from left to right: lithology, porosity, saturation, depth density, \(V_{p}\), \(V_{s}\), AI, and PR tracks. The arrow shows the decreasing CO\({}_{2}\) effect.
## 3 Results
### Mechanical Characterization  
Prior to the compression test, the scaffolds' morphology was assessed in terms of porosity and pore size by the methods described in our previous work [38]. Non-significant differences were obtained between the non-treated, plasma-treated and aloe vera coated samples in regard to these parameters. The average pore size and porosity of all groups of samples were \(480\pm 66\)\(\upmu\)m and \(61\pm 5\%\), respectively. As shown in Figure 2, no significant differences were obtained in terms of compressive modulus or compressive yield strength between the different groups of scaffolds tested. Regarding the compressive modulus, the values obtained were \(31.60\pm 7.97\) MPa for non-treated scaffolds and \(28.52\pm 3.53\) MPa for the plasma-treated ones. The compressive modulus of aloe vera coated samples  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group of Scaffolds** & **O \%** & **C \%** & **O/C** \\ \hline PLA & \(33.89\pm 1.87\) & \(66.11\pm 1.87\) & \(0.51\pm 0.04\) \\ PLA plasma & \(35.44\pm 1.05\) & \(61.89\pm 2.53\) & \(0.57\pm 0.03\) \\ PLA aloe vera pH 3 & \(17.13\pm 1.99\) & \(82.14\pm 1.40\) & \(0.21\pm 0.03\) \\ PLA aloe vera pH 4 & \(28.83\pm 0.79\) & \(70.60\pm 1.78\) & \(0.41\pm 0.02\) \\ PLA aloe vera pH 5 & \(34.09\pm 1.30\) & \(65.91\pm 1.30\) & \(0.52\pm 0.03\) \\ \hline \end{tabular}
\end{table}
Table 1: XPS results, including the oxygen and carbon content of the surface and the O/C ratio of the non-treated, plasma-treated and coated samples analyzed.  
Figure 1: Water contact angle values for the non-treated, plasma-treated and coated samples obtained by compression molding (\({}^{**}\) p \(<\) 0.01 and \({}^{***}\) p \(<\) 0.001), compared to the group of non-treated polylactic acid (PLA) samples.  
at pH 3, 4 and 5 were 29.79 \(\pm\) 10.36 MPa, 25.07 \(\pm\) 8.04 MPa and 36.11 \(\pm\) 11.16 MPa, respectively. These values are in the range of values reported for cancellous bone (20-500 MPa) [39].
## 2 Laboratory Data and Field Measurements
### Laboratory Data  
Consolidation tests were conducted following ASTM D3550 [40] specifications using ring samples on the undisturbed samples. Afterberg limits were performed according to the ASTM D4318 [41] standard. The reported plasticity index is shown in Figure 1. In addition, the direct shear test, in accordance with ASTM D3088 [42] specifications, indicated that the average friction angle ranged between 20 and 35 degrees, and the cohesion varied within the 200 to 300 and 400 to 500 psf for the clayey sand and lean clay/fat clay soils, respectively. Las Vegas soil stratigraphy consists of seven main soil types including clayey Sand (SC); silty sand (SM); lean clay with traces of calcite or gravel (CL); fat clay (CH); sand and gravel (GP, GM, GC); and cemented layers, such as cemented sand and gravel. The characteristics of each mentioned soil type can vary within the depth in the site location.  
Consolidated undrained (CU) tests, along with unconfined compressive strength (UCS) tests, were performed on calcite core samples at the project sites to determine the strength properties of these deposits. Considering practical issues, the Shelby tubes in the soil containing stiff gravel layers and highly cemented deposits were not used. In this regard, the samples were acquired using a California modified ring sampler (split spoon sampler). The average unconfined compressive strength  
Figure 1: Measured plasticity index variation based on the depth values in the project.  
of the caliche deposits following the results of the UCS tests was reported as 8 ksi. Following the UCS results, the existing caliche layers were categorized in hard to very hard range (competent) deposits. Figure 2 presents the measured UCS values based on the depths of the acquired core samples for the caliche following the four available geotechnical reports in this study. The saturated triaxial tests were conducted by applying 14 psi confining pressure with a constant strain rate of 0.03 in the evaluation procedure of the caliche properties.
## 4 Coordinate System Conversion Algorithm-based Depth Estimation
### Implementation of the Camera Image and 3D Model Space Mapping Automation SW Module  
If the focal length (the distance between the camera lens and image sensor) is known, the projection conversion relationship between the camera sensor coordinates and world coordinates can be expressed in a homogeneous coordinate system, as shown in Equation (4) [25].  
\[\begin{bmatrix}x*w\\ y*w\end{bmatrix}=\begin{bmatrix}fr_{1}X+fr_{2}Y+fr_{3}Z+ft_{x}\\ fr_{4}X+fr_{5}Y+fr_{6}Z+ft_{y}\\ r_{7}X+r_{8}Y+r_{9}Z+t_{z}\end{bmatrix} \tag{4}\]  
For \(r\) and \(t\) in Equation (4), the values of the external transformation matrix obtained in Table 4 are used. The focal length \(f\) is the physical distance in mm. In contrast, the focal length \(f_{x},f_{y}\) an internal factor obtained in the camera calibration step, is a value in pixels obtained by multiplying the actual focal length by the pixel size per mm of each image sensor element. Therefore, if the actual focal length \(f\) is unknown, it can be obtained by multiplying the value \(f_{x}\) or \(f_{y}\) obtained through camera calibration by the length in mm per pixel of the image sensor. In this study, substituting 1 for the \(f\) value in Equation (4), \((x,y)\) was used as a coordinate on the normal coordinate system at 1 mm from the camera.  
If both sides of Equation (4) are divided by \(w\) and each term is rearranged based on the X and Y unknowns, the equation can be re-expressed in the form of a binary linear system, as shown in Equation (5). If the \(Z_{w}\) value is then set to 0 and calculated, the intersection point with the floor plane can finally be obtained.  
\[\begin{cases}(x_{u}r_{7}-fr_{1})X_{w}+(x_{u}r_{8}-fr_{2})Y_{w}=fr_{3}Z_{w}+ft_{ x}-x_{u}r_{9}Z_{w}-x_{u}t_{z}\\ (y_{u}r_{7}-fr_{4})X_{w}+(y_{u}r_{8}-fr_{5})Y_{w}=fr_{6}Z_{w}+ft_{y}-y_{u}r_{9} Z_{w}-y_{u}t_{z}\end{cases} \tag{5}\]
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 4 Simulation Results
### Case D
In a conventional power assignment scheme, the maximum available power (\(3\) dBm) is assigned to the base station, i.e., the gateway. For simplicity, let's only take the case of direct LOS; the users at location A, B and C are receiving interference of \(-46.7688\) dBm, \(-49.5413\) dBm and \(-46.7688\) dBm, respectively. With our power optimization algorithm, the same users receive \(-51.266\) dBm at location A, \(-54.0386\) dBm at location B, and \(-51.266\) dBm at location C, when they are in direct LOS with G1. These results illustrate that our power optimization algorithm improve the performance when co-channel interferences exist and the users receive much less interference from neighboring zones. Our proposed algorithm sets the transmission power to \(-1.4973\) dBm for G1. This gives \(4.479\) dBm lower interference at these reference points.
# Can Biochar Improve the Sustainability of Animal Production?
## 5 Manure Storage and Treatment
### Biochar and Manure Composting
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Type of Biochar** & **Type of Waste \({}^{\star}\)** & **Pyrolysis Tem.** & **Dosage** & **Emission Impact** & **Reference** \\ \hline Pine chips & PL & 400 \({}^{\circ}\)C & 20\%  & 18, 25, and 35\% reduction NH\({}_{3}\) for 3, 5, and 10\% dosages, respectively & [PERSON] et al. [109] \\ \hline Wood & DS & 500–700 \({}^{\circ}\)C & 17\%  & 40.4\% and 56.8\% reduction NH\({}_{3}\) for 10\% & [PERSON] et al. [111] \\ \hline Wood & PL & 500–700 \({}^{\circ}\)C & 38\%  & 65–75\% reduction N\({}_{2}\)O and CH\({}_{4}\) & [PERSON] et al. [113] \\ \hline Rice husk & PL & 500 \({}^{\circ}\)C & 10, 20\%  & 29.5\% reduction total N\({}_{2}\)O & [PERSON] et al. [115] \\ \hline Oak & PL & 650 \({}^{\circ}\)C & 3\%  & No reduction CH\({}_{4}\) and H\({}_{2}\)S & [PERSON] et al. [116] \\ \hline Woodchip & PL & - & 5, 10\% [FOOTNOTE:]Footnote *: GW: green waste; PL: poultry litter; DS: manure-based anaerobic digestion solids; SM: swine manure; and AM: animal mortality. [ENDFOOTNOTE] & 7.4\% CO\({}_{2}\) increase & [PERSON] et al. [117] \\ \hline \end{tabular}
\end{table} 
Table 2: Implications of biochar for emission control during composting. Adapted from [PERSON] et al. (2017).
# Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media
## 3 Staggered-Grid Finite Difference
### Stability Condition  
Equation (1) can be expressed as,  
\[\frac{\partial\text{u}}{\partial t}=\text{Au}, \tag{7}\]  
where \(\text{u}=\Big{(}\text{v}_{\text{x}},\text{v}_{\text{y}},\text{v}_{\text{z}}, \text{P}_{\text{s}},\tau_{\text{x}\text{x}\text{v}}^{\text{s}},\tau_{\text{y} \text{y}}^{\text{s}},\tau_{\text{z}\text{z}}^{\text{s}},\tau_{\text{x}\text{y} }^{\text{s}},\tau_{\text{z}\text{z}}^{\text{s}},\tau_{\text{y}\text{z}}^{\text {s}}\Big{)}^{\text{T}}\). A is determined by the matrix L and D\({}^{\alpha e}\) (the expression for A is given by Equation (A21) of Appendix B). Based on the matrix eigenvalue analysis method [24; 43; 44], we derive the stability condition for our acoustic-solid coupled wave equations for TTI media (the details are shown in Appendix B):  
\[0 \leq\frac{\Delta t^{2}}{\rho}\Big{(}D_{11}\Delta x^{2}+D_{44}\Delta y^{2}+D_{ 55}\Delta z^{2}+2D_{15}\Delta x\Delta z\Big{)}\left[\sum_{n=1}^{N}|a_{n}| \right]^{2}\leq 1, \tag{8}\] \[0 \leq\frac{\Delta t^{2}}{\rho}\Big{(}D_{44}\Delta x^{2}+D_{22} \Delta y^{2}+D_{66}\Delta z^{2}+2D_{46}\Delta x\Delta z\Big{)}\left[\sum_{n=1} ^{N}|a_{n}|\right]^{2}\leq 1,\] (9) \[0 \leq\frac{\Delta t^{2}}{\rho}\Big{(}D_{55}\Delta x^{2}+D_{66} \Delta y^{2}+D_{33}\Delta z^{2}+2D_{35}\Delta x\Delta z\Big{)}\left[\sum_{n=1} ^{N}|a_{n}|\right]^{2}\leq 1, \tag{10}\]  
where \(a_{n}\) are difference coefficients and D\({}_{ij}\) is the stiffness coefficient of TTI media.  
Setting \(\Delta x=\Delta y=\Delta z=h\), the stability condition is simplified as  
\[\sqrt{5}\frac{\Delta\text{t}}{\text{n}}\sqrt{\frac{\text{S}}{\rho}}\leq\frac {1}{\sum_{n=1}^{\text{N}}|a_{n}|}, \tag{11}\]  
where \(S=\max\{D_{11},D_{22},D_{33},D_{44},D_{55},D_{66},D_{15},D_{35},D_{46}\}\). Therefore, we can determine the appropriate temporal and spatial step to ensure a stable calculation process and high-precision data with the physical parameters of the model.  
Figure 2: 3D staggered grid and parameter definition diagram. The stresses and the pressure field are defined at the standard grid points. The velocity components and Thomsen parameters of the acoustic-elastic coupled media are defined on the staggered grid points.
## 4 Results
### Experimental Dataset  
The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used to evaluate the effectiveness of our method. In the SOCs (standard operating conditions) of MSTAR dataset, there are 10 targets collected at the depression degrees of 15\({}^{\circ}\) and 17\({}^{\circ}\), respectively. Since classification performance on SAR target images are easily influenced by the depression angle, for a more robust validation, the dataset with 17\({}^{\circ}\) depression angle is used for the training, while the one with 15\({}^{\circ}\) is reserved for the inference phase. The specific information on the dataset we use is shown in Table 1. The SAR images and the corresponding optical images of the ten types of targets in the MSTAR dataset are shown in Figure 6.  
For the CIL experiments, these 10 classes are separated into two parts: classes for basic learning and classes for incremental learning. The classes for basic learning are used to train the network to ensure it possesses sufficient feature extraction and classification capabilities. After that, the stages for incremental learning are utilized to assess the methods' incremental learning capabilities. Considering both aspects, we select 6 classes as basic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Name** & **Serial** & \begin{tabular}{c} **Training Set** \\ **(17\({}^{\circ}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Testing Set** \\ **(15\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **CIL** \\ **Stages** \\ \end{tabular} \\ \hline ZSU\_23\_4 & d08 & 299 & 274 & stage 5 \\ ZIL131 & E12 & 299 & 274 & stage 4 \\ T72 & 132 & 232 & 196 & stage 3 \\ T62 & A51 & 299 & 273 & stage 2 \\ D7 & 13015 & 299 & 274 & \\ BTR70 & \(\zeta\)71 & 233 & 196 & \\ BTR60 & 7532 & 256 & 195 & Basic Learning \\ BRDM2 & E-71 & 298 & 274 & (stage 1) \\ BMP2 & 9563 & 233 & 195 & \\
2S1 & B01 & 299 & 274 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data distribution of MSTAR SOCs.  
Figure 6: Instances of 10 target classes in the MSTAR dataset. (SAR images and their corresponding optical image).  
training classes and the remaining four as incremental learning classes. During each CIL stage, the network is incrementally trained with one new class, resulting in five stages, as shown in Table 1. Based on this CIL process, the comparison experiment and the ablation experiments are conducted to evaluate the effectiveness of our method.
# Random PWM Technique Based Two-State Markov Chain for Permanent Magnet Synchronous Motor Control
## 2 Conventional SVPWM Analysis
### Harmonic Analysis of the SVPWM Output  
The SVPWM with a fixed switching frequency exists with dual time variables determined by the carrier and modulating waveforms: the carrier waveform determined by the carrier frequency \(f_{c}\) and the carrier initial phase \(\theta_{c}\), and the modulating waveform determined by the fundamental frequency \(f_{0}\) and the fundamental initial phase \(\theta_{0}\). By applying the method of double Fourier analysis to analyze the harmonic components of the SVPWM output voltage, the spectral expression of the voltage series output from phase A of the two-level voltage source inverter can be obtained as [22]:  
\[\begin{array}{rl}u_{ao}=&V_{dc}M\cos(2\pi f_{0}t+\theta_{0})\\ &+\frac{2V_{dc}}{\pi}\sum\limits_{m=1}^{\infty}\frac{1}{m}J_{0}\left(m\frac{ \pi}{2}M\right)\sin\!m\frac{\pi}{2}\cos m(2\pi f_{c}t+\theta_{c})\\ &+\frac{2V_{dc}}{\pi}\sum\limits_{m=1}^{\infty}\sum\limits_{n=-\infty}^{+ \infty}\left\{\begin{array}{l}\frac{1}{m}J_{n}\left(m\frac{\pi}{2}M\right) \sin\!\left[\left(m+n\right)\frac{\pi}{2}\right]\times\\ \cos[m(2\pi f_{c}t+\theta_{c})+n(2\pi f_{0}t+\theta_{0})]\end{array}\right\} \end{array} \tag{7}\]  
where \(J_{n}(\cdot)\) denotes the nth order Bessel function; \(m\) and \(n\) represent the index coefficients of the carrier frequency and modulating wave frequency, respectively. The first row is the sinusoidal fundamental component with the same amplitude as the modulated wave; the second row is the fundamental harmonic, that is, the integer multiple frequencies of the fundamental frequency; the third row is the carrier harmonic cluster, each harmonic cluster contains the carrier harmonic and its sideband harmonic, and the harmonic frequency is the linear combination of the carrier frequency and the fundamental frequency, \(m\!f_{c}+\pi f_{0}\).  
The Bessel functions in the formula indicate that the amplitude of the carrier harmonics rapidly decays as the carrier order increases, and the energy of each carrier harmonic cluster is mainly concentrated at the carrier harmonic and its first few sideband harmonics, resulting in obvious noise spikes at the carrier harmonics. Figure 3 shows a schematic diagram of the spectrum before spreading. Since the carrier frequency is fixed, the harmonic energy is mainly distributed around the integer multiples of the carrier frequency, and the spectrum appears as discrete spectral lines.  
Figure 2: Voltage space vector diagram.