esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Nevertheless, these series of experiments only studied the dynamic effect of moisture and suction in a short sand column of less than 1 m, and this did not cover the entire suction profile of sandy soil (approximately 0-2 m). Even though these dynamic SWRC studies allow the quantification of suction and moisture under the transient flow condition, they are not able to provide information on global suction/moisture profiles due to the smaller experimental scale. To collect information on both global suction and moisture profiles, the experimental set-up should be large enough to cover the sandy soil suction profile, and the moisture content profile should also be logged within a smaller time step as well. Therefore, the spatial time domain reflectometry (spatial TDR) developed by the Soil Moisture Group (SMG) at the University of Karlsruhe [17; 18; 19] was applied to the experimental studies of the non-uniqueness of the SWRC on a sand column experiment and levee model [13; 20]. However, in these previous experimental investigations, a high air entry (HAE) ceramic disk, having higher hydraulic resistivity than sand, was placed under the specimen to build up the hydraulic connectivity between the hanging column reservoir and specimen. This might also result in alleviation of moisture seepage velocity and, additionally, reduce the significance of the dynamic response of suction and moisture redistribution. With the aim of mitigating the shortcomings of these previous soil column tests, a novel experimental platform is developed in this study. In this experimental platform, the spatial TDR sensors are installed in sand columns to measure the TDR waveformsconsistently during one-step gravitational drainage experiments. Taking advantage of this valuable moisture measuring technique, the moisture profile along a 240-cm sand column can be calculated using an inverse modeling method developed by SMG [17; 18; 19]. With this powerful inverse technique, and by consistently logging TDR traces along the soil strata, a dynamic moisture profile with high spatial and temporal resolution can be achieved. With the addition of temporal logging of soil suction using a tensiometer along different elevations of the soil column, it is able to determine not only the point-wise SWRC at varied elevations but also the global suction/moisture profile under transient flow conditions. In this study, this novel laboratory-scale experimental set-up is presented. Furthermore, some preliminary outcomes of the gravitational drainage test are presented to demonstrate the capabilities of this experimental platform and eventually compared to the conventional hanging column method with discussions.
## 1 Introduction
KMA also developed a COMS meteorological data-processing system (CMDPS) for the successful operation and efficient use of COMS data [33]. Various meteorological variables, such as cloud cover, aerosol optical depth, SST and LST are being derived automatically by CMDPS from COMS level-1b data and auxiliary data. [PERSON] _et al_. [34] improved the LST retrieval algorithm from COMS data based on the day and night conditions. However, the evaluation results with Moderate Resolution Imaging Spectroradiometer (MODIS) LST data showed that it has a systematic positive bias caused by prescription errors in the emissivity [35]. Therefore, we redeveloped the LST retrieval algorithm in this study. The objective of this study is to introduce the LST retrieval algorithm being operated by KMA's National Meteorological Satellite Center as part of CMDPS. Section 2 describes how the LST retrieval algorithm was developed from two of the COMS thermal channel datasets using simulated data with Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4) over a wide range of surface and atmospheric conditions. Section 3 describes the estimation process used for generating surface emissivity data for the COMS two thermal channels. Section 4 gives an example of LST data retrieved from the COMS data and discusses the validity of these results by comparing them to MODIS LST data. A summary of this study is given in Section 5.
## 3 Extended UML Model with Graph Information--Colored UML
### Extension of Traditional UML Class Diagram
#### 3.1.2 Representation of Classes in Colored UML  
In UML, the relationships between classes include generalization (also known as inheritancy), association, aggregation, composition, and dependency; additionally, a solid circle is also defined to denote \"more than one\". Both aggregation and composition are special cases of association, which specify a whole-part relationship between classes, which will not be separately defined by a graph in colored UML.  
In the colored UML representation, class inheritance is denoted as a triangle and a connecting line from a subclass to a superclass filled with green (RGB: (0, 255, 0)). Specifically, the connecting line is 3 pixels thick and solid. Composition is represented by an arrow with a purple (RGB: (160, 32, 240)) solid rhombus at the root, from the combining class to the combined class, and the thickness and line type of the connecting line are the same as the inheritance relationship. Aggregation is represented by an arrow with a purple (RGB: (160, 32, 240)) hollow rhombus at the root, from the aggregating class to the aggregated class, and the thickness and line type of the connecting line are the same as the inheritance relationship. \"More than one\" is represented by a solid dot with a diameter of 5 pixels. When the dot is at the head of the arrow, it means to combine or aggregate multiple objects. Dependency is represented by a pink-filled (RGB: (255, 192, 203)) dashed arrow with a sharp angle from the depending class to the depended class, and the thickness of the connecting line is the same as the inheritance relationship.
## 3 Results and Discussion
### Resistance to Sweat Exposure  
It was observed that one-time wetting of the heating insert (variant B) with artificial sweat during its operation did not cause disturbances in its functioning. There were no significant differences in either the power or electrical resistance of the insert before wetting (in new state), after wetting with artificial sweat, or after drying the insert. The electrical power of a new heating insert before exposure to artificial sweat (1.95 W) was comparable to the power of the insert after its wetting and drying (1.89 W) (Table 8). Electrical resistance was also at similar levels: 6.92 \(\Omega\) for a new insert vs. 6.98 for an insert moistened with artificial sweat and dried. It was found that the heating insert was not damaged after wetting with acidic artificial sweat, and its operation was not disturbed. Similar research was conducted by [PERSON] et al. [37], who analyzed the influence of sweat on the reliability of joints and sensors in e-textiles. In their experiments, these researchers used the same type of acidic sweat, prepared according to the international standard ISO105-E0, to investigate the accelerated aging of joints in soldered samples. In contrast, the methodology of experimental aging with sweat was different. [PERSON] et al. [37] soaked the samples in a sweat solution for half an hour every day over twenty-four days. Similarly, they noted that the electrical resistance of joints in soldered samples during sweat-induced aging was low and stable for both sweat types without significant differences between sample types. The results for adhesive-bonded samples showed that electrical resistance grows during sweat-induced aging. However, after an initial increase, the values remained relatively stable.  
Figure 11: Thermal resistance of fabric systems with a heating insert depending on the number of washing cycles.
# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline
## 5 Experiment
### Performance on PoseAMBench  
Based on the design principles of the pose ambiguity benchmark described earlier, we evaluate the performance of three methods on the PoseAMBench dataset, where the 2D and 3D skeleton-based action features are both generated by VitPose [17]. Since the labels in all three sets are binary (0 or 1) and thresholds significantly affect similarity score classification, we determine the optimal threshold for each method by testing 0.1 increments of similarity scores, enabling accurate comparison of their performance.  
As shown in the Table 2, for the Viewpoint-Variation Set, the 3D skeleton-based method and the RotJoint-based method achieve accuracies of 84.6% and 93.5%, respectively, both of which are significantly higher than the performance of the 2D skeleton-based method. This indicates that both 3D-based methods demonstrate some resistance to viewpoint interference when assessing pose similarity.  
Figure 5: Relationship between the results of (**a**) 3D skeleton-based and (**b**) RotJoint-based methods and human opinions on pose similarity, presented in the form of a scatter plot with a third-degree polynomial fit.  
In the Shape-Variation Set and Torsional-Pose Set, the RotJoint-based method outperforms the others, highlighting its ability to effectively measure pose similarity even in the presence of body shape variations and limb torsion.  
To compare the impact of different rotation parameter choices, we conducted an ablation study. In the first stage, we extracted features from identical pose images using axis-angle, quaternion, and rotation matrix representations. In the second stage, we employed two methods for similarity estimation: (1) computing the difference followed by normalization (to maintain consistency with the referenced work), and (2) calculating the cosine similarity. We then computed the average accuracy across the three sets. As shown in Table 3, the results indicated minor differences among representations, with our chosen approach achieving the best performance on the benchmark. Additionally, the parameter exhibits a relatively small data footprint.  
These results clearly demonstrate the superior performance of the RotJoint-based pipeline compared to traditional skeleton-based models. The RotJoint-based approach consistently shows higher correlation coefficients across all benchmarks, particularly excelling in handling body shape variations, viewpoint changes, and limb twisting. These findings validate the robustness and precision of our pipeline under diverse conditions, confirming its efficacy in addressing action ambiguity.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 3 Problem Formulation and Analysis
### Zone 2
#### 3.2.1 Primary Phase  
In Mode 2, the carrier frequency was taken to be 28 GHz (mmWave band). Here, direct communication takes place among the D2D users. Now, let us assume that \(R_{t}\) is the radius of coverage for the D2D users in Zone 2. Thus, from Equation (7), it can be stated that the SINR at the receiver D2D userin Zone 2 should be above the predefined threshold value for seamless operation to take place.  
\[\gamma_{D}^{2}=\frac{P_{D}|h_{D2D}|^{2}R_{t}^{-\alpha}}{P_{C}|h_{C2D}|^{2}d_{C2D} ^{-\alpha}+N_{0}} \geq\gamma_{th}\] \[\implies R_{t}^{-\alpha} \geq\frac{(P_{C}|h_{C2D}|^{2}d_{C2D}^{-\alpha}+N_{0})\gamma_{th}}{ P_{D}|h_{D2D}|^{2}} \tag{14}\] \[\implies R_{t} \leq\left[\frac{P_{D}|h_{D2D}|^{2}}{(P_{C}|h_{C2D}|^{2}d_{C2D}^{ -\alpha}+N_{0})\gamma_{th}}\right]^{1/\alpha}=R_{t}^{*}\]  
This equation gives us the expression for the radius of coverage of the D2D users for direct communication in Zone 2 operating in Mode 2.  
If \(R_{t}\leq R_{t}^{*}\), direct communication takes place among D2D users in Mode 2.  
If \(R_{t}>R_{t}^{*}\), mode switching takes place from Mode 2 to Mode 3 and communication between D2D users in Mode 3 occurs through the aid of the two-way DF relay node. The operation of D2D communication in Mode 3 through the assistance of the DF relay node is elaborated in the following subsection.
# Effects of Pulsed Pressure Curing on Beef Quality
## 3 Results and Discussion
### Sensory Evaluation: Trained and Consumer Panel  
Sensory attributes of beef with different curing treatments were assessed by a trained panel, and their results are presented in Figure 3. The findings revealed that there was no apparent difference in the aroma, juiciness, or color of beef treated using various curing techniques (\(p\) > 0.05). PPC could significantly improve the texture of beef owing to the changes in meat tissue caused by the alternating of pressure, which broke down the muscle fibers and decreased the sheer force of beef. There were significant differences in overall texture among the treatments with different curing methods (\(p\) < 0.05), and this modification could tenderize the meat [33]. There were significant differences (static curing, vacuum curing, pressurized curing, pulsed pressure curing) in the saltiness of beef treated with different curing methods (\(p\) < 0.05), and PPC could significantly improve the saltiness of beef, which was due to the changes in the meat tissue and the expansion of intercellular spaces due to the alternating changes of pressure, which effectively promoted the curing solution absorption [24]. Results from consumer acceptability tests of the same meals also indicated no difference in the aroma and juiciness of beef treated using various curing techniques (Table 2). PPC had the best overall approval rating. In general, PPC treatment improved the flavor of the meat and was worthy of future investigation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Curing Methods** & **Hardness (g)** & **Springiness (mm)** & **Chewiness (g)** & **Cohesiveness** \\ \hline SC & 5112.74 \(\pm\) 236.78  & 1448.58 \(\pm\) 121.21  & 0.54 \(\pm\) 0.02  \\ VC & 4125.45 \(\pm\) 212.67  & 0.76 \(\pm\) 0.03  & 1227.26 \(\pm\) 113.37  & 0.59 \(\pm\) 0.02  \\ PC & 4556.94 \(\pm\) 196  & 0.78 \(\pm\) 0.04  & 1328.43 \(\pm\) 98.51  & 0.63 \(\pm\) 0.02  \\ PPC & 3892.97 \(\pm\) 158.77  & 0.85 \(\pm\) 0.05  & 987.56 \(\pm\) 57.51  & 0.67 \(\pm\) 0.04  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Texture of beef with different curing treatments.  
Figure 3: Effect of beef with different curing treatments on the sensory attributes as evaluated by a trained panel. (SC = static curing, VC = vacuum curing, PC = pressurized curing, PPC = pulsed pressure curing).
# Sage-Husa Algorithm Based on Adaptive Double Forgetting Factors
## 4 Conclusions  
This paper addresses the challenge of inadequate filtering accuracy and divergence in the EKF algorithm when noise statistical characteristics are unknown, which limits its practical applicability in dynamic systems. By building on the Sage-Husa algorithm with forgetting factors, this study introduces key innovations to improve robustness and adaptability. Specifically, the proposed approach updates the statistical characteristics of measurement noise and process noise while ensuring the positive definiteness of the measurement noise covariance and the semi-positive definiteness of the process noise covariance. The re-estimation of measurement noise characteristics is dynamically adjusted through a comparative analysis of prior and theoretical residuals. Furthermore, the introduction of dual forgetting factors, with an adaptive forgetting factor derived using a windowing method combined with an exponential form, enhances stability by maintaining values within a range of 0.95 to 1. Simulation results conducted on one-dimensional and multi-dimensional state variables demonstrate the superiority of the proposed AEKF algorithm. Compared to the standard EKF and Sage-Husa algorithms with various forgetting factors, the AEKF algorithm significantly improves filtering accuracy across different dimensions and conditions. These advancements not only address the limitations of traditional EKF approaches but also strengthen the practical applicability of the proposed algorithm in complex, real-world scenarios where noise characteristics are often unknown or dynamically changing.  
**Author Contributions:** Conceptualization, W.L.; methodology, W.L.; software, M.Z.; validation, M.Z.; formal analysis, W.L.; investigation, W.L.; resources, W.L.; data curation, M.Z.; writing--original draft preparation, W.L.; writing--review and editing, M.Z. and H.F.; supervision, W.L.; project administration, W.L.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China grant number 51979210.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 DC Fault Current Analysis of Hybrid MMC Considering FRT Control
### Control Saturation Effect  
In practical engineering implementations of hybrid MMC, control saturation influences both transient control characteristics and fault current behaviors. The \(M_{dc}\) constraints define the permissible operational boundaries for the converter's DC voltage regulation. Failure to account for the saturation effect may drive the \(M_{dc}\) parameter beyond its designated operational threshold, thereby rendering the system inoperable and leading to significant deviations in the analytical modeling of control response and fault current characteristics [25].  
Control saturation phenomena in hybrid MMCs manifest dynamically through both hierarchical control layers: the voltage regulation outer loop and current tracking inner loop (Figure 5). Under outer-loop saturation conditions, the associated responses become negligible, prompting system transition into current-dominant operational mode. Consequently, fault current characteristics can be derived through Equations (11)-(15), with \(I_{dc}\)_/_changing to predefined saturation boundaries.  
Inner loop saturation corresponds to \(M_{dc}\) attaining its operational boundary thresholds. Building upon preceding analytical findings, enhanced PI control gains prove effective in accelerating response characteristics during FRT operations. This control strategy expedites \(M_{dc}\) convergence to saturation boundaries. Under inner loop saturation conditions, \(U_{e}\) attains a static operational state, wherein fault current are mathematically characterized through the following formulation:  
\[L\frac{di_{dc}}{dt}+Ri_{dc}=\delta U_{dcn} \tag{24}\]  
where \(\delta\) denotes the saturation threshold of \(M_{dc}\). Then, the fault current is obtained as follows:  
\[i_{dc}(t)=I_{0}e^{-\frac{\beta}{2}t}+\frac{\delta U_{dcn}}{R}\left(1-e^{-\frac {\beta}{2}t}\right) \tag{25}\]  
As demonstrated in (25), fault current characteristics decouple from PI regulator coefficients upon attainment of inner loop saturation thresholds. In this operational regime, the current magnitude becomes predominantly dictated by system parameters and \(M_{dc}\)'s predefined saturation boundary constraints. Equation (25) indicates that decreasing \(\delta\) leads to lower fault currents. This reduction occurs because the hybrid MMC's maximum negative-level output capacity is augmented, resulting in a decreased output voltage from the converter. The proposed methodology enables the precise prediction of the fault current, while accounting for the operational constraints of the hybrid MMC under practical operating conditions.  
When the fault current decreases below a specific threshold, \(M_{dc}\) returns within its limit, indicating the end of control saturation. At this point, the current satisfies the following relationship:  
\[I_{dc}(s)(sL+R)-LI_{0}=\left[\frac{I_{dc}}{s}-\frac{I_{dc}(s)}{I_{dcn}}\right] \left(k_{pi}+\frac{k_{ii}}{s}\right)U_{dcn} \tag{26}\]
## 4 Results and Discussion
### Spatial Autocorrelation of Elements
#### 4.1.1 Univariate Spatial Autocorrelation of Individual Elements
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Global Moran’s I**} & \multicolumn{2}{c}{**Global Geary’s C**} & \multicolumn{2}{c}{**Global Getis–Ord’s G**} \\ \cline{2-7}
**Variable** & **1** & \(p\)**-Value (\(\times\)10\({}^{-16}\))** & **C** & \(p\)**-Value (\(\times\)10\({}^{-16}\))** & **G** & \(p\)**-Value (\(\times\)10\({}^{-16}\))** & **E(G) (\(\times\)10\({}^{-9}\))** \\ \hline log10(Au) & 0.059 & \(<\)2.2 & 0.949 & \(<\)2.2 & 0.009 & \(<\)2.2 & 5.1 \\ log10(Sn) & 0.439 & \(<\)2.2 & 0.563 & \(<\)2.2 & 0.014 & \(<\)2.2 & 6.7 \\ log10(Ag) & 0.430 & \(<\)2.2 & 0.570 & \(<\)2.2 & 0.013 & \(<\)2.2 & 9.0 \\ log10(As) & 0.451 & \(<\)2.2 & 0.551 & \(<\)2.2 & 0.012 & \(<\)2.2 & 11.0 \\ log10(Sb) & 0.608 & \(<\)2.2 & 0.394 & \(<\)2.2 & 0.014 & \(<\)2.2 & 13.0 \\ log10(Bi) & 0.438 & \(<\)2.2 & 0.564 & \(<\)2.2 & 0.019 & \(<\)2.2 & 52.0 \\ log10(Co) & 0.423 & \(<\)2.2 & 0.578 & \(<\)2.2 & 0.009 & \(<\)2.2 & 0.6 \\ log10(Cu) & 0.468 & \(<\)2.2 & 0.535 & \(<\)2.2 & 0.012 & \(<\)2.2 & 5.3 \\ log10(La) & 0.259 & \(<\)2.2 & 0.740 & \(<\)2.2 & 0.009 & \(<\)2.2 & 0.9 \\ log10(Pb) & 0.500 & \(<\)2.2 & 0.500 & \(<\)2.2 & 0.011 & \(<\)2.2 & 5.1 \\ log10(Zn) & 0.530 & \(<\)2.2 & 0.468 & \(<\)2.2 & 0.010 & \(<\)2.2 & 1.1 \\ log10(W) & 0.365 & \(<\)2.2 & 0.638 & \(<\)2.2 & 0.015 & \(<\)2.2 & 24.0 \\ log10(Mo) & 0.430 & \(<\)2.2 & 0.572 & \(<\)2.2 & 0.012 & \(<\)2.2 & 4.0 \\ log10(Nb) & 0.181 & \(<\)2.2 & 0.817 & \(<\)2.2 & 0.009 & \(<\)2.2 & 0.8 \\ log10(Cd) & 0.459 & \(<\)2.2 & 0.539 & \(<\)2.2 & 0.012 & \(<\)2.2 & 4.0 \\ \hline \hline \end{tabular} \(p\)-value \(<\) 0.05 means that the indicator passes the statistical significance test.  
\end{table} 
Table 3: Univariate global spatial autocorrelation indicators for 15 elements in the study area. Moran's I and the local Geary's C are similar; however, the local Getis-Ord's G covers a broader space, especially for Sb, As, Cu, and Co. Compared with local Geary's C and local Getis-Ord's G, we can identify points with HH, LL, LH, and HL clustering with a precise meaning for each category from the local Moran's I. Therefore, we chose the LMIC results to mine the association rules of various elements. Figure 7: Voronoi diagrams of local Moran’s I indicators for major elements. Figure 8: LISA significance map of local Moran’s I indicators for major elements.