esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result  
### Daytime LST Anomalies Extracted from Landsat 8 Data  
Based on Landsat 8 daytime LST data from 2013 to 2023, average LST values for each winter season in the study area were extracted (Figure 3a-k). The results indicate that LST anomaly zones are primarily located in the central basin and western foothill regions, exhibiting a persistent high-temperature distribution pattern over multiple years. The average LST and standard deviation for each winter season (Figure 4b) reveal interannualtemperature variation trends, with a peak of 17.53 \({}^{\circ}\)C in 2015 and a minimum of 9.33 \({}^{\circ}\)C in 2018, while the standard deviation remains generally stable. The multi-year average LST for all winter seasons from 2013 to 2023 (Figure 3l) ranges from \(-\)14.79 \({}^{\circ}\)C to 31.71 \({}^{\circ}\)C, with a mean value of 8.46 \({}^{\circ}\)C.  
NDVI values in the study area range from \(-\)0.54 to 0.56, with an average of 0.17, indicating a gradual increase in vegetation coverage from the central region toward the surrounding areas (Figure 5a). Areas with NDVI < 0 are identified as water bodies (Figure 5b). The extracted water bodies are primarily concentrated in valley areas and surrounding mountainous regions, aligning with the known river network and providing support for surface temperature correction.  
Figure 3: Time series of daytime winter average LST in the Yangbajing–Yangyi Basin from 2013 to 2023, based on Landsat-8 data acquired between November and March. Subfigures (**a–k**) show the average LST for each winter season from 2013 to 2023, while subfigure (**l**) presents the multi-year average LST across all winters. LST values are represented using a blue-to-red color gradient, where blue indicates lower temperatures and red indicates higher temperatures. Units are in degrees Celsius.  
As shown in Figure 3a, LST decreases with elevation, with a temperature difference of approximately 20 \({}^{\circ}\)C between the highest and lowest elevation zones. The elevation-corrected image is presented in Figure 6b. After correction, the LST range was adjusted to \(-\)29.15 \({}^{\circ}\)C to 27.18 \({}^{\circ}\)C, with a mean of 6.2 \({}^{\circ}\)C and a standard deviation of 6.6 \({}^{\circ}\)C. Using the corrected data, the local block threshold method was applied to extract daytime high-temperature LST anomaly regions (Figure 6c). The identified anomalies are primarily distributed along fault zones and correspond well with known geothermal hotspot locations.
## 4 Conclusions  
Soil improvement relying on enzymes is found promising in enhancing the strength and stiffness properties of cohesive clays while improving their adsorption abilities. Efficacy of EICP method on adsorption and desorption of soils spiked with combination of heavy metals and for individual case(s) gave encouraging results. The following conclusions are drawn from the study:  
* Heavy metal adsorption of soils improved followed by enzyme treatment with E2 outperforming E1 and E3. When heavy metal ions were spiked in the soils individually the order of sorption was Ni \(>\) Pb \(>\) Cd for red soil and black soil at initial concentration of 100 mg/L and at dilution ratio of 1:100. Ni sorption increased by seven-fold at an initial concentration of 100 mg/L. Further at a dilution ratio of 1:100, Ni sorption was found to increase 10 manifolds for red soil and 15 manifolds greater for black soil with enzyme treatment. * When heavy metals were simultaneously spiked in the soils, competition of heavy metal sorption was observed and it portrayed that heavy metals with higher ionic radius and electronegativity exhibited greater affinity in occupying adsorbent surface. Black soil exhibited comparatively greater ability in sorbing metal ions onto its surface compared to red soil. * Desorption studies on simultaneously spiked heavy metals gave an understanding that a probable formation of heavy metal carbonates (PbCO\({}_{3}\), CdCO\({}_{3}\) and NiCO\({}_{3}\)) on the soil surfaces takes place after CaCO\({}_{3}\) interacts with the spiked heavy metal ions as the pH value increases. These arbonates are insoluble and reduce the toxicity of heavy metals. * Marked reduction in permeability values was observed with EICP treatment and this phenomenon was more pronounced with E2. Non-fat milk powder in E2 facilitates effective CaCO\({}_{3}\) precipitation due to the presence of casein protein causing this reduction in permeability. For red soil, permeability values reduced from \(5.3\times 10^{-10}\) cm/s to \(1.12\times 10^{-10}\) cm/s with E2 treatment at 21 days of curing period. For black soil, permeability reduced from \(7.83\times 10^{-11}\) cm/s to \(6.31\times 10^{-11}\) cm/s with E2 treatment. * Swell characteristics significantly improved upon enzyme treatment. The reduction in swell pressure was three-folds with red soil and three and a half folds with black soil with E2 treatment 21 days of curing period. Calcite precipitates at nucleation sites cause improved contact points between soil grains enabling them to withstand the swell pressures. EICP method has proven effective in addressing the problems posed by expansive soils and for remediation of heavy metals. This promising method can be opted for in-situ applications to reduce the toxicity levels of brownfields. EICP method is an environment friendly, sustainable, high on green ratings, low on carbon emissions and practical for field applications and can be a promising substitute for conventional mechanical stabilization techniques.
## 3 Path Loss Models
### UMa Path Loss Models
#### 3.2.2 3 GPP Model  
The LOS UMa PL is measured based on the break point distance and the ABG large-scale path-loss model. Similarly, UMa NLOS PL is measured using the ABG model for a standard deviation of 7.8 dB [17].
## 5 Discussion
### Performance of VS and ER Models
These errors combine to generate a background error from which the groundwater model simulations add uncertainty associated to the inherent natural variability of environmental variables, the possible smoothing and bias introduced using fitting functions, and the mapping errors due to adopted spatial functions. In the case of aquifer geometry definition, the use of geophysical techniques does not cancel nor reduce its input error, but it generates a numerical data base (M and P data sets) that enables to deduce the error produced during data inversion. This error can be measured when M and P data are compared, and becomes an advantage comparatively to the sole use of raw geological data and qualitative, large-scale geological mapping for the purpose of segregating the error of aquifer geometry. Normalized errors (CVMAE, CVRMSE, CVSTD) of VS and ER models are in the 0.01-0.09 range (Table 7). In particular, CVSTD is 0.01 for VS models and varies in the 0.05-0.08 range for ER models. These magnitudes are lower than those that can be deduced from the VS data sets reported in similar MASW experiences [59; 63; 64] and the normalized errors of ER models in diverse ERT surveys [68; 69; 70] experiences. These values can be used as manageable quotas of the error that can be segregated from overall groundwater models uncertainty without substantially affecting its simulations accuracy.
# Mechanical Integrity Analysis of a Printed Circuit Heat exchanger with Channel Misalignment
## 1 Introduction
The existence of a cope hole was found to influence the peak stress surrounding the transition zone [22]. From all the previous research mentioned above, there has not been a study on the misalignment effect in PCHEs under high-temperature conditions. For this reason, this study investigates the structural integrity of the proposed geometry and the effect of the misalignment condition along the diffusion bonding line on the structural integrity of the PCHE. The structural integrity is represented by the utilization factor, which is the ratio of stress intensity to the maximum allowable stress for design conditions. Besides the utilization factor, displacement is also a main observed variable, which further includes the displacement value and displacement behavior under the applied load. ASME Section III is taken as the reference construction code. The obtained stress distributions are used to evaluate the compliance with design criteria by using ASME Boiler and Pressure Vessel Code (BPVC), Section III. This code contains specific rules for construction of nuclear facility components. ASME Section III, Division 5 [23] is taken as the reference construction code, specifically Subsection HB, which covers metallic pressure boundary components. Subsection HB consists of Subpart A (HBA) for low-temperature service components and Subpart B (HBB) for elevated-temperature service components. Table 1 shows the values of maximum temperature for various classes of permitted materials. Subpart HBB is applicable for materials that is used at temperatures above \(T_{max}\). The maximum temperature of liquid sodium in SFR applications is 528 \({}^{\circ}\)C. Hence, subpart HBB is  
Figure 1: The schematic illustration of the printed circuit heat exchanger (PCHE). chosen as the construction code for this application. These rules are applicable to class A components, regardless of the type of contained fluid, such as water, steam, sodium, helium, or any other process fluids. The obtained stress distribution will be compared to the design loading parameters.
## 1 Introduction
As a solution, a global mapping XCO\({}_{2}\) dataset derived from GOSAT XCO\({}_{2}\) retrievals, which has been demonstrated to be accurate in characterizing the spatio-temporal variations of XCO\({}_{2}\) at the global scale [19, 20], can be made to investigate the detailed seasonal cycle pattern of XCO\({}_{2}\). On the other hand, satellite observations of terrestrial biosphere have been widely used in studying the terrestrial vegetation activities resulting in terrestrial carbon uptake and release. Vegetation indices and parameters, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), and gross primary production (GPP), indicate the strength of photosynthesis activity related to terrestrial CO\({}_{2}\) uptake [21, 22, 23], while land surface temperature (LST) has an impact on the respiration rate and evaporation of terrestrial biosphere [24]. For a better understanding of where and how strong the biosphere's seasonal activities influence the seasonal cycle pattern of XCO\({}_{2}\), this study proposes a data-driven approach to assess the impacts from terrestrialbiosphere-atmosphere interactions on the seasonal cycle pattern of XCO\({}_{2}\) using satellite retrievals of XCO\({}_{2}\) observed by GOSAT and parameters related to terrestrial biosphere activities observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) from June 2009 to May 2014. To use the satellite XCO\({}_{2}\) retrievals, which are irregularly distributed in space and time and have many gaps, a data-driven mapping approach based on spatio-temporal geostatistics is applied to fill the gaps and generate a gridded, mapping XCO\({}_{2}\) dataset [19; 20] with a resolution of 1\({}^{\circ}\) by 1\({}^{\circ}\) in space, and three days in time (hereafter referred to as GM-XCO\({}_{2}\)). The satellite observations of terrestrial biosphere, including NDVI, EVI, LAI, GPP, and LST from MODIS, are collected and re-gridded into the same spatial resolution with the mapping XCO\({}_{2}\) dataset. In Section 2, we describe all the used data and methods. Results of XCO\({}_{2}\) SCA from GOSAT, correlation analysis of seasonal cycle between XCO\({}_{2}\) and terrestrial biosphere parameters, and seasonal cycle phase lags between XCO\({}_{2}\) and biosphere activities indicated by NDVI are presented in Section 3. Discussions on the uncertainty of GM-XCO\({}_{2}\), and possible impact of LST on seasonal variation of XCO\({}_{2}\) on tropical land are presented in Section 4, followed by conclusions in Section 5.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 4 The System Hardware and Software Implementation Options
### Brief Overview of System Hardware Implementation Issues  
The chaotic systems, as well as chaotic communication systems, may be hardware-implemented by using different kinds of embedded systems, for example: Arduino, Raspberry Pi and FPGAs.  
Digital implementation of the four-dimension autonomous hyperchaotic system on a Field Programmable Gate Array (FPGA) for creating a chaotic secure communication system using a XILINX VERTEX5 chip recently reported by [PERSON] et al. [14]. Bit error rate analysis in FPGA/FPAA based communication systems by using Arduino in a Matlab/Simulink environment with Sprott chaotic generator performed by [PERSON] and [PERSON] in [15]. The approach of chaotic system simulation in the Arduino platform is described by [PERSON], et al. in [16]. Dual-channel Chaotic Generator of Mackey-Glass system is implemented on Arduino Due in [17] by [PERSON] et al. A Raspberry Pi 3 implementation of Lorenz Chaotic System with estimation by Hybrid Jaya-Powell Algorithm is described by [PERSON], et al. in [18]. This small overview defines several validated options of chaotic system hardware implementation, while a synergetic observer is a resource-intensive computational algorithm and requires additional research to find a suitable platform. The most promising and universal approach is to program a designed synergetic observer in FPGAs. For simulation, the resulting closed-loop system can be represented in a form suitable for numerical simulation. FPGA implementation techniques for chaotic systems are described by [PERSON], et al. [20], by [PERSON] et al. [19] and in other related papers. Notably, [PERSON], et al. [20] demonstrate the implementation of Forward Euler and Runge-Kutta numerical methods on an FPGA. [PERSON] et al. [19] demonstrate synchronization techniques examples applied to 22 chaotic oscillators. These implementation examples may be used in a hardware implementation of the designed system from Sections 3.5 and 3.6.
## 2 Problem Formulation
### Control Objective  
For the satellite equipped with a sun sensor and a TAM as attitude sensors, the solar vector cannot be obtained in the shadow zone of the orbit, which makes it difficult to achieve a sun-oriented attitude in the shadow zone of the orbit. The spinning of the satellite provides an effective way to solve this problem [27]. According to the angular momentum theorem, the orientation of the spin axis of the satellite in the inertial space will remain unchanged when the satellite is free from external torque or the external torque is small. Sun-oriented spin-stabilized attitude control is a specific satellite attitude control mode, in which the satellite spins around a given vector fixed to the satellite, and the spin axis is aligned with the solar vector at the same time; i.e.,  
\[\left\{\begin{array}{l}\mathbf{S}_{t}^{b}=\mathbf{e}^{b}\\ \mathbf{\omega}_{bi}^{b}=\omega_{spin}\mathbf{S}_{t}^{b}\end{array},\right. \tag{5}\]  
where \(\mathbf{e}^{b}\) is the given vector fixed to the satellite; \(\mathbf{S}_{t}^{b}\) is the unit vector pointing from the satellite to the sun, and \(\omega_{spin}\) is a given spinning angular rate.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Channel Response Models under Test
#### 4.2.3 Abrupt Phase Shift Channel  
Reference [6] shows several cases in which important phase shift conditions occur in real field situations. As modulation of each PRIME subcarrier is a phase differential modulation between carriers that are adjacent in frequency, analyzing how the standard behaves on a phase abrupt channel is particularly important.  
This filter is specifically designed to have a \(\pi\)_rad_ phase shift at 62.5 kHz (considering the nominal sampling frequency of 250 kHz). The phase of the frequency response is represented in Figure 5. It is a linear phase filter except for the abrupt phase change.  
The filter also presents a small amplitude glitch in the phase shift point, which attenuates two carriers 20 dB and one single carrier 40 dB. Figure 6 represents the module of the filter response.  
The channel with abrupt phase shift is defined by the impulsional response \(h[n]\) shown in Table 3. It is a FIR filter of order 31. Figure 7 represents this impu  
Figure 5: Frequency Response Phase of the abrupt phase change channel.  
Figure 6: Module of the frequency response of abrupt phase change channel.
## 4 Results
### Myocardial Abnormality Detection
#### 4.1.2 Classifier Evaluation  
The AUC value obtained on the ROC analysis was 0.95 \(\pm\) 0.03 for the proposed classifier (Figure 3). The results obtained under 100-random splits scenarios show high performance stability and low variance. Since the classifier will be used to decide whether or not the segmentation lesion search algorithm should be applied in each image, it is not equally important to have false positive or negative detections. Thus, each pathological image misclassified as a healthy one will not be assessed by the segmentation algorithm and their damaged areas will be lost from the analysis. On the other hand, misclassified healthy images into pathological ones might tend to produce an over-segmentation of the lesion. Under this scenario the classifier was set up for assuring high-sensitivity performance. When moving the decision rule threshold for addressing this goal we obtained for sensitivities of 90%, 92.5%, 95% and 97.5% corresponding specificity values of 90%, 85.4%, 73.3% and 57.3%.  
The last experiment of this section involved a ROC permutation analysis. There were consistent AUC distribution differences between permuted and un-permuted data, which showed statistical significance (\(p<0.05\), paired \(t\)-Student test). For the random dataset configurations there was no permutation outperforming in AUC terms the original data configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Se** & **Sp** & **Acc** \\ \hline Fine-tuned VGG19 & 84.41 (11.02) & **93.89 (6.79)** & 89.15 (5.36) \\ Our method & **88.11 (6.54)** & 93.15 (4.84) & **90.63 (4.32)** \\ \hline \hline \end{tabular}  
* Se: Sensitivity; Sp: Specificity; Acc: Accuracy.  
\end{table}
Table 2: Mean (standard-deviation) performance metrics obtained for the explored classifiers under the 100-random splits validation.  
Figure 3: ROC curve obtained after 100-random splits for the proposed classifier. The solid black line represents the mean AUC performance obtained, while the red area represents the variability AUC interval (mean SD). ROC: Receiver Operating Characteristic; AUC: Area Under the Curve.
## 3 Numerical Results
### Uncertainty Analysis of NLS Equation with Initial Value
#### 3.1.2 Sensitivity Analysis  
In uncertainty quantification analysis, another concern is the sensitivity for initial value of random input to output variation. We use the Sobol index [35; 36; 37] to analyze the contribution of each input variable to the output variance. Figure 4 displays the main and total sensitivities box plots of the estimators repeated 100 times under different computational budgets. We note that the MFMC estimator can accurately predict the contribution of each input variable compared with the standard MC estimator. When the low-fidelity model is used, the sensitivity index of the estimator decreases, and the variance of the estimator is also reduced.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **100 Samples** & **500 Samples** & **1000 Samples** \\ \hline High-fidelity (FD) \(f^{(1)}\) & 212.3323 & 218.7528 & 232.9878 \\ Low-fidelity (POD + DEIM) \(f^{(2)}\) & 317.5544 & 336.1835 & 337.2713 \\ Ratios \((\sigma_{1}/\sigma_{2})/\%\) & 0.6686 & 0.6507 & 0.6908 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of sample variances by 100, 500 and 1000 samples, respectively.  
Figure 3: (a) Comparison the estimated MSE of our MFMC estimators from 100, 500 and 1000 samples, respectively; (b) Proportion plot of the number of model calculations \(m\) for MFMC estimator when the sample numbers are 100, 500 and 1000.
## 3 Results  
### Physical Performance Demands across Rotations  
Selected running variables are presented in Tables 1 and 2. When total distance was observed, no significant interaction was reported between position (F (8, 1766) = 0.08; \(p\) = 0.9; \(\eta^{2}\) = 0.001; small) and rotation (F = (8, 1766) = 0.5; \(p\) = 0.8; \(\eta^{2}\) = 0.002; small). A significant interaction between position (F (2, 541) = 9.8; \(p\)\(\leq\) 0.001, \(\eta^{2}\) = 0.05, small) and high-speed distance (m). However, when each rotation was considered, no interaction was reported (F (8, 1766) = 2; \(p\) = 0.7; \(\eta^{2}\) = 0.008; small). When positions were considered, a significant main effect was observed. Defenders were reported to cover significantly less high-speed distance (108 \(\pm\) 56) when compared to the midfield (149 \(\pm\) 65; d = 0.7; large) and forwards (157 \(\pm\) 73; d = 0.8; large). Forwards were also reported to cover significantly more high-speed distance than the midfield (149 \(\pm\) 65; d = 1.1; large). There was significant interaction between position (F (2, 1766) = 15; \(p\)\(\leq\) 0.001; \(\eta^{2}\) = 0.02; small) and sprint distance (m); however, when each rotation was considered, no interaction was reported (F (8, 1766) = 0.9; \(p\) = 0.5; \(\eta^{2}\) = 0.004; small). When positions were considered, a significant main effect was observed. Defenders were reported to cover significantly less sprint distance (27 \(\pm\) 8) when compared to the midfield (30 \(\pm\) 10; d = 0.3; medium) and forwards (34 \(\pm\) 9; d = 0.8; large). The was significant interaction between position (F (2, 1766) = 11;\(p\leq 0.001\); \(\eta^{2}\) = 0.01; small) and maximal speed (km\(\cdot\)h\({}^{-1}\)); however, when each rotation was considered, no interaction was reported (F (8, 1766) = 1.0; \(p\) = 0.5; \(\eta^{2}\) = 0.004; small). Defenders (22.9 \(\pm\) 0.5) were likely to achieve a lower maximal speed (km\(\cdot\)h\({}^{-1}\)) when compared to the forwards (23.2 \(\pm\) 0.7; d = 0.5; medium) and midfielders (23.0 \(\pm\) 0.8; d = 0.2; medium). A significant interaction was observed across position (F (2, 1766) = 0.5; \(p\) = 0.04; \(\eta^{2}\) = 0.004; small) and rotation (F (8, 1766) = 0.8; \(p\) = 0.7; \(\eta^{2}\) = 0.007; small) for maximal speed (%). Defenders (77 \(\pm\) 2) were likely to achieve a lower maximal speed (%) when compared to the forwards (79 \(\pm\) 2; d = 1.0; medium) and midfielders (79 \(\pm\) 2; d = 1.0; medium).