esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Effects of Temperature Gradient  
Soil temperature is a critical factor influencing moisture transfer in landfill covers [46]. Figure 2a illustrates the temperature distributions on the 30 th day under different waste temperature conditions. As the waste temperature increases, the temperature gradient along the cover profile becomes more pronounced. Due to the heat exchange between the cover surface and the atmosphere [37], the soil temperature increment induced by waste temperature increases with depth. When the waste temperature increases from 20 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, the corresponding surface temperature rises from 17.2 \({}^{\circ}\)C to 19.6 \({}^{\circ}\)C. Under various temperature gradients, the surface temperature consistently remains lower than the air temperature. This phenomenon is primarily due to heat loss from water evaporation within the cover (see Equation (7)), which is consistent with the findings reported by [PERSON] et al. [47]. Figure 2b illustrates a significant decrease in the volumetric water content (VWC) within the cover. This reduction is primarily attributed to substantial water loss due to surface evaporation [45]. The evaporation rate of the cover layer increases with the temperature gradient (Figure 2c). As the temperature at the bottom increases from 20 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, the evaporation rate increases from 1.1 mm/d to 1.8 mm/d, indicating that the temperature gradient enhances the moisture transfer in the cover. However, despite exhibiting the highest evaporation rate, Case WT50 shows a significantly higher VWC compared to Case WT20 and Case WT35. In addition, Case WT35 maintains a VWC close to that of Case WT20, despite experiencing a higher rate of surface evaporation. This is likely due to the increased vapor flux entering the cover at higher temperature gradients [26], thereby enhancing moisture replenishment. Soil temperature is a crucial factor affecting the CH\({}_{4}\) oxidation capacity of landfill cover. Optimal temperatures can enhance the activity and growth rates of CH\({}_{4}\)-oxidizing bacteria, thereby significantly improving the CH\({}_{4}\) oxidation efficiency [10]. Figure 3 shows the influence of temperature gradient on the CH\({}_{4}\) oxidation efficiency. Overall, the CH\({}_{4}\) oxidation efficiency of the cover increases with the temperature of the waste. This is due to the fact that the soil temperature in the 0.3 m depth of the compost-amended cover layer remains below 30 \({}^{\circ}\)C. Within this temperature range, the CH\({}_{4}\) oxidation rate of the cover soil is positively correlated with the soil temperature [14]. In addition, the CH\({}_{4}\) oxidation efficiency of the cover increases over time under different temperature gradients. This may be attributed to the reduced moisture content in the shallow soil of the cover due to continuous evaporation, which enhances the diffusion of atmospheric O\({}_{2}\) into the cover, thereby increasing the rate of CH\({}_{4}\) oxidation [9].
# An Assisted Numerical Simulation Diagnosis Method for Atherosclerosis Based on Hemodynamics
## 3 Calculation Results and Analysis
### Effect of Lipid Concentration Distribution on Atherosclerosis  
The root cause of atherosclerotic lesions was the excessive accumulation of lipids in the blood vessel walls. The accumulation of lipids in blood vessel walls was directly related to the distribution of lipid concentrations in blood. The distribution of lipid concentration in blood was not always uniform, but had a certain regularity, which showed important significance to explain the focus selectivity of atherosclerosis.  
Unlike wall osmosis, the distribution of particles in the main stream of blood vessels was not uniform. The annular concentration vacuum region appeared near the external wall of the carotid artery. There was a high concentration area near the vacuum area; this phenomenon was especially significant when the flow was large. Under the condition of maximum mass flow, a high concentration ring area was approximately formed near the bifurcation wall. A region of low particle concentration formed on the surface of the inner bend, while the particle concentration near the outer surface of the bend was relatively high. This was because particles were deflected by centripetal force during the movement of the bend. From the perspective of particle concentration distribution, it was not conducive to the accumulation of lipid particles on the inner wall of curved blood vessels, which had a certain hindering effect on the formation of atherosclerotic plaques. Especially when the flow became larger, a higher concentration ring area would be formed near the wall surface. There was no significant difference between the high concentration area in the bend and the outer wall. Near the inner wall, the concentration gradually decreased, and there was a low concentration area. The particles began to move away from the inner wall of the bend and aggregated toward the outer wall. At low flow velocity, the concentration near the inner wall was significantly lower, and at high flow velocity, the low concentration area decreased, but the high concentration ring area still existed.
## 3 Results
### Influence of the Caffeic Acid on the Biosensors Responses
From the data presented, it appears that the SPCE/PB/HRP biosensor has the highest sensitivity when the anodic peak current is used as an input parameter. The detection limit of 0.90 uM is better than the value obtained for other biosensors (not containing the electrons mediator) developed in this study. The SPCE/PB/HRP biosensor sensitive characteristics in comparison with other biosensors reported in the literature when caffeic acid is detected are presented in Table 2.  
Figure 6: (**a**) Overlapping cyclic voltammograms of SPCE/PB/HRP biosensor, immersed in different solutions with variable concentrations of caffeic acid (electrolyte support was \(10^{-2}\) M PBS pH 7-\(10^{-2}\) M hydrogen peroxide solution); (**b**) plot of linear calibration of the SPCE/PB/HRP biosensor for caffeic acid based on anodic peak currents; and (**c**) plot of linear calibration of the SPCE/PB/HRP biosensor for caffeic acid based on cathodic peak currents. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensitive Material** & **Detection Technique** & **Linearity Range (\(\mu\)M)** & **Limit of Detection (\(\mu\)M)** & **Reference** \\ \hline PEDOT-Tyr & CV & 10–300 & 4.33 & [50] \\ MoS2-GQDs-TvL & CV & 0.38–10 & 0.32 & [51] \\ GNP@MnO2/Lac & Amperometry & 5–320 & 1.9 & [52] \\ GCE/TvL & Amperometry & 4–55 & 4 & [53] \\ AgCl/Ag-Pt/polyethersulfone & Amperometry & 2–14 & 1 & [54] \\ membrane/Lacase & & & & \\ Polyethersulfone & Amperometry & 5–35 & 0.88 & [55] \\ membranes/Lacase & & CV & 5–74 & 0.9 & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Enzymatic biosensors used to detect caffeic acid. As can be observed in Table 2, the SPCE/PB/HRP biosensor possesses better or comparable performance characteristics with other biosensors reported in the literature. However, the advantages of this type of biosensor are ease of preparation, use of a natural extract in which the enzyme retains its biocatalytic activity, high sensitivity, and low cost. Therefore, the SPCE/PB/HRP biosensor has enough good performance characteristics to be used in practice for the detection of caffeic acid in real samples.
## 2 Smart Grid Architecture
### Threat Model and Security Requirements  
In smart grid networks, we consider both passive attacks (e.g., eavesdropping) and active attacks (e.g., data manipulation or false data injection) by inside and outside adversaries. When defending against these attacks, the following security properties are required for secure smart metering.  
1. Data confidentiality (user privacy): Individual power usage of a smart meter is considered the private information of the owner. Thus, it should not be revealed to the concentrator or other meters on the routing path during data aggregation and delivery, and passive attacks such as eavesdropping should thus be defended against.
2. Data integrity: Dishonest or compromised smart meters in the network could manipulate the intermediate metering data during aggregation, causing inaccurate aggregation results. Thus, manipulation of the aggregate by active inside attack from the compromised meters should be detected by the concentrator.
3. Sender authentication: Defending against any active outside attacks, such as false data injection attack by outside adversaries, the concentrator should be able to ensure the authenticity of the smart meters' identities on the routing path that have contributed to the data aggregation.
## 2 Chebyshev Graph Convolutional Neural Network Modeling Method
### Feature Extraction Module Based on Fused Graph Neural Network
#### 2.2.2 Introduction of Multi-Head Graph Attention Mechanism  
To accurately capture the local features in the variable working conditions data, the graph attention network (GAT) is incorporated as an intermediate layer to thoroughly account for the node neighborhood features and dynamically adjust the weight representations [16]. The node feature update process of the GAT is shown in Figure 2(a) and comprises two key steps: attention coefficient calculation and weighted summation.  
(1) Attention coefficient calculation: For node \(i\), the correlation coefficient between node \(j(j\in N_{i})\) is calculated individually, as shown in Equation (11). The linear mapping increases the dimensionality of the node features to enhance feature representation, concatenates the transformed features of the nodes, and maps the resulting high-dimensional features to the correlation coefficient. The correlation calculation between nodes \(i\) and \(j\) is completed through learnable parameters \(W\) and mapping functions. The correlation coefficient is normalized to obtain the attention coefficient. The calculation is shown in Figure 3b and Equation (12).  
\[e_{ij}=a(Wh_{i}\parallel Wh_{j}),j\in N_{i} \tag{11}\]  
\[\alpha_{ij}=\frac{\exp(LeakyReLU(e_{ij}))}{\sum\limits_{k\in N_{i}}\exp(LeakyReLU (e_{ik}))} \tag{12}\]  
Here, \(e_{ij}\) is the correlation coefficient between nodes \(i\) and \(j\); \(W\) is a shared parameter; \(h_{i}\) and \(h_{j}\) represent the features of nodes \(i\) and \(j\), respectively; \((Wh_{i}\parallel Wh_{j})\) represents the concatenation of the transformed features of nodes \(i\) and \(j\); \(e_{ik}\) is the correlation coefficient between nodes \(i\) and \(k\); \(k\) denotes the \(k\)-th neighbor node of node \(i\); and \(N_{i}\) is the number of neighboring nodes of node \(i\), including node \(i\) itself.  
(2) Weighted Summation: The attention coefficient is employed to perform a weighted fusion of the features, resulting in new node features. The features obtained from different attention heads are concatenated to yield new node features, as illustrated in Figure 3c and expressed in Equation (13).  
Here, \(h_{i}^{\prime}\) is the new feature of node \(i\) that integrates the domain features; \(K\) is the number of multi-head attention heads; \(\alpha_{ij}^{K}\) is the attention coefficient between nodes \(i\) and \(j\) computed by the \(k\)-th attention head, following the previously described calculation steps; and \(W^{K}\) is the weight matrix for the input linear transformation corresponding to the \(k\)-th attention head.  
\[h_{i}^{\prime}=\mathop{\parallel}\limits_{K=1}^{K}\sigma(\sum\limits_{j\in N _{i}}\alpha_{ij}^{K}W^{K}h_{j}) \tag{13}\]
## 4 Discussion
This pathway is important during cartilage development, and its activation in adult cartilage tissue leads to hypertrophy, the initiation of calcification and tissue degradation via an increased expression of ECM-degrading components. Several studies investigating the MSC and HA ability to repair cartilage defects were done in the following animal models: intra-articular administration of MSC with HA has been proven to be beneficial in a spontaneous OA Hartley strain guinea pig model through articular cartilage regeneration [40]. Meanwhile, another study conducted using an ACL rat model suggested that multiple injections might be needed due to the temporary effect of MSC with HA therapy, as, after 6 weeks, a significantly better International Cartilage Repair Score was evident when compared to the control group, but none was observed after 12 weeks [41]. MSC and HA combination therapy on an induced chondral defect porcine animal model showed improved cartilage healing both histologically and morphologically at 6 and 12 weeks compared to the control groups [42]. The cartilage repair effect of the MSC and HA combination therapy observed from these studies was similar to the results shown in our study. Healthy knee synovial fluid contains HA with various molecular weights (MW) [43], which suggests that administration using a combination of high and low MW HA in the same manner might provide better results by mimicking the normal physiological properties. Some studies on KOA treatment showed that combined high and low MW HAs resulted in greater improvements in patient conditions compared to either high or low MW HA-only treatments [44; 45]. Therefore, a high and low MW HA combination was used in this study. More studies need to be conducted to provide evidence on whether there is a combined high and low MW HA superior efficacy compared to high or low MW HA-only for OA attenuation, which can also be applied to hUC-MSC therapy. Conceptualization, J.-L.W. ; supervision W.L. ; methodology, T.B.-C.W., K.-Y.L. and C.-H.C.; validation, P.-C.W. and C.-W.H. ; investigation and formal analysis, K.-Y.L., C.-H.C., R.L., Y.-Z.L. and W.-Y.C. ; writing--original draft preparation, T.B.-C.W., K.-Y.L. and R.L. and writing--review and editing, J.-L.W. and W.L. All authors have read and agreed to the published version of the manuscript. This work was funded by Meridigen Biotech Co., Ltd, Tokyo, Japan. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of National Cheng Kung University Hospital (IRB No. A-BR-104-045, 8 October 2015). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data that support the findings of this study are available from the corresponding author, [[PERSON]], upon reasonable request. The authors declare no conflict of interest.
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 2 Formalizing Risk
### Risk  
With all the components of risk defined, the _pointwise risk_ can be defined as the formally correct application of (1):  
\[r(T,P,B,Q,x,y)=h(T,P,B,x,y)\cdot e(P,B,x,y)\cdot v(P,B,Q,x,y) \tag{7}\]  
where \(T\in\mathcal{T}\), \(P\in\mathcal{P}\), \(B\in\mathcal{B}(P)\) are defined as in (6), and \(Q\in\mathcal{Q}(P,B)\) is the resilience of the individual exposed element.  
The risk associated with a specific area \(A\subseteq\Omega\) can then be obtained by integration:  
\[\begin{split} R(A,T,P,B,Q)&=\int_{A}r(T,P,B,Q,x,y )\mathrm{d}x\mathrm{d}y=\\ &=\int_{A}h(T,P,B,x,y)e(P,B,x,y)v(P,B,Q,x,y)\mathrm{d}x\mathrm{d} y,\end{split} \tag{8}\]  
and the total risk over the entire domain is thus \(R(\Omega,T,P,B,Q)\).  
The relationship between the input variables and hazard, exposure, vulnerability, and risk is illustrated in Figure 1.  
We observe that (7) matches (1), as intended, and [2] reasoning can be applied to our definition of risk on each subset of \(\Omega\) that has a (spatially) uniform hazard, exposure and vulnerability, making (1) a zero-order (piecewise constant) approximation of the more complete (8). Equation (8) on the other hand can also be interpreted as describing risk as the expected (loss of) value for the random variable \(e\cdot v\) with probability density \(h\), in line with the approach used to quantify hazard in engineering [8]. Indeed, (8) is already frequently used in its discrete form (and without explicit mention of \(T,P,B,Q\)) in the literature (see, e.g., [11; 12]).
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 4 Results and Discussion
### Fetal Monitor Model  
To obtain the dynamic model of the fetal monitor, a calibrated signal generator was used to generate an input signal \(u_{k}\) for obtaining an output \(y_{k}\) from the fetal monitor device used in this study. With these data and applying the least-squares method described in Section 3.1.1, a second-order linear deterministic model \(l=2\) was estimated, the ARX model parameters were \(\hat{\theta}=[1.1381,-0.2829,\ 0.0985,-0447,0.0907]\). The dynamic model response of the fetal monitor is shown in Figure 8.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 5 Guided Image Filtering
### OpenCL Code
#### 5.2.6 Fixed-Point Calculations  
One way to limit the resource usage and allow larger radius values is to use fixed-point arithmetic. The filtering input consists of floating-point variables, which results in a higher resource usage. Not only the number of DSPs will increase, the overall logic usage will increase as well. We also implemented a kernel using fixed-point variables. The bit lengths of the integer and fractional parts of the fixed-point numbers can be determined based on the intensity range of the input data as well as  
Figure 8: Shift register of an image with \(r=1\) and \(w=8\). The sliding window for pixel 9 is indicated in blue. In the next clock cycle, the red pixel will be loaded in the shift register in local memory.  
a minimal fractional precision. For the guided image filter, determining the intensity range is quite straightforward, as the input image and the guidance image are typically stored in an 8-bit per pixel representation. However, the calculation requires intensities to be scaled and summed and to avoid numerical overflow, it is best to over-dimension the bit lengths. We performed an analysis of the output and intermediate value ranges for the example case. As a result, we used 26 bits for the integer part and 6 bits for the fractional part in the fixed-point representation. The conversion is done in line using the code depicted in Listing 5. Since the code is pipelined, there is no conversion penalty. On the other hand, the integer calculations require much less area on the FPGA. This allows the selection of a larger radius in the guided image filter.  
```
1 intfixed_point=(int)(floating_point_input*(1<<fractional_bits));
2...
3 floatfloating_point_output=((float)fixed_point)/(1<<fractional_bits);
```  
Listing 5: In-line fixed-point conversion code.  
The accuracy of the fixed-point implementation is verified at three levels. First, the selection of the place of the decimal point is based on an evaluation of the intermediate results so that there is no underflow or overflow. Second, the filtered images show no visual differences and third, the fixed-point and floating-point results are compared by computing \(NormRel\_L2\), i.e., the relative error for the L2 norm of differences between pixel values of the two images. This gives \(NormRel\_L2<3\%\).
## 4 Experiments and Results
### Visualization Results and Analysis  
The segmentation outputs of various common techniques exhibit distinct patterns when visualized in the Vaihingen dataset. As shown in Figure 5, in the DeepLabv3 and GLNet, there are problems where large independent areas cannot be accurately segmented due to interference from boundary details. In contrast, GLSNet excels in choosing segmentation boundaries. For the stacking of small target objects, GLSNet has proven to be more precise in segmenting these objects compared to GLNet and DeepLabv3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Network Architectures** & **mIoU [\%]** & **Memory [MB]** & **FPS [f/s]** \\ \hline GLNet (baseline) [20] & 71.6 & 1865 & 0.05 \\ Attention (DANet) [47] & 71.4 & 1510 & 0.02 \\ Attention (transformer) [39] & **72.4** & **1414** & **1.14** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Changes in mIoU and GPU memory usage for different fusion module designs  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Network Architectures** & **mIoU [\%]** & **Memory [MB]** & **FPS [f/s]** \\ \hline GLNet (baseline) & 71.6 & 1865 & 0.05 \\ Shallow–Deep & 72.4 & 1414 & 1.14 \\ Shallow–Shallow & 71.9 & **1044** & **1.34** \\ Deep–Deep & **72.6** & 2903 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Changes in mIoU and GPU memory usage for different network architecture models.
## 5 Conclusions  
This paper investigates the Belief Propagation List (BPL) decoding algorithm for polar codes in unmanned aerial vehicle (UAV) communication scenarios in 6G, with a particular focus on path selection criteria based on information geometry. Firstly, a review of the traditional BPL decoding algorithm for polar codes is provided, highlighting the shortcomings of its path selection criteria in terms of error correction performance. Subsequently, this paper introduces the theoretical framework of information geometry to mathematically model and analyze the soft information in BPL decoding. The concept of the soft information centroid is proposed, along with a path selection criterion based on this centroid. The feasibility of this method is analyzed, and geometric distance is adopted as the weight for this method. Simulation results demonstrate that, compared to the traditional BPL algorithm, this method exhibits higher decoding accuracy, reduces hardware overhead, and better adapts to the requirements of UAV communication.  
The research in this paper offers new theoretical explanations and optimization methods for the BP list decoding algorithm of polar codes, providing valuable insights for further improving the decoding performance of polar codes. It also provides robust support for the application of polar codes in complex scenarios such as UAV communication in 6G. Future research can be conducted along the following two directions:  
1. Investigate the architectural design of the algorithm, converting the parallel algorithm proposed in this paper into a serial one;
2. The weight selection method based on geometric distance proposed in this paper is an empirical value and has room for optimization.  
**Author Contributions:** This research was accomplished by all the authors: [PERSON] and [PERSON] conceived the idea, performed the analysis, and designed the scheme; [PERSON] and [PERSON] conducted the numerical simulations; [PERSON], [PERSON] and [PERSON] co-wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data used to support the findings of the current study are available from the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.