esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 3 Three-Band Optimization Results
### Final Optimization Results  
After optimization, the structural parameters of L1-L8 were as shown in Figure 9a. The final structure of the three-band multispectral camera is shown in Figure 9b. The total mass of the support was reduced from the initial 0.72 kg to 0.50 kg through topology optimization and further reduced to 0.43 kg after parameter optimization, achieving a total weight reduction rate of 40.3%. Under gravity conditions, the deformation in the Z-direction was optimized, improving from an initial maximum deformation of 5.52 nm to 5.34 nm. Although the first-order fundamental frequency slightly decreased, it still remained above 100 Hz.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7** & **L8** \\ \hline Upper/mm & 4.0 & 6.0 & 3.0 & 36.0 & 44.0 & 5.5 & 48.0 & 7.5 \\ Lower/mm & 1.5 & 4.0 & 3.5 & 32.0 & 40.0 & 3.5 & 44.0 & 3.0 \\ Initial/mm & 3.0 & 5.0 & 5.0 & 33.0 & 41.0 & 5.0 & 45.0 & 6.0 \\ Parameter & \(1.6\pm 0.1\) & \(4.1\pm 0.1\) & \(3.1\pm 0.2\) & \(33.1\pm 0.0\) & \(41.2\pm 0.0\) & \(3.0\pm 0.1\) & \(45.0\pm 0.0\) & \(3.1\pm 0.2\) \\ optimization/mm & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Optimization of dimensions for preceding and succeeding parameters.  
Figure 8: The iteration curve (black line: iteration curve; red line: convergence curve).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Mass/kg** & **Frequency/Hz** \\ \hline Initial & \(0.72\pm 0.01\) & \(546.04\pm 8.19\) \\ Topology optimization & \(0.50\pm 0.03\) & \(492.55\pm 4.48\) \\ Parameter optimization & \(0.43\pm 0.02\) & \(217.89\pm 2.32\) \\ Z-Deformation for topology optimization & & \(6.80\pm 0.15\) nm \\ Z-Deformation for parameter optimization & & \(5.34\pm 0.02\) nm \\ \hline \end{tabular}
\end{table}
Table 3: Performance analysis before and after structural optimization.
## 2 Hot Topics
### Durability  
Most existing AEMWE components, especially membranes and catalysts, are not robust enough considering industrial targets due to chemical and mechanical degradation. This scenario is prone to change with intensive research and a few promising alternatives.  
The causes of AEMWE performance losses include membrane degradation. It is well-known that the hydroxide anions attack the positively charged cations in the AEM, neutralizing them [4]. This leads to a loss of anion-exchange functional groups and, consequently, a decrease in OH\(-\) conductivity generating higher ohmic resistances [19]. Additionally, continued thermal and chemical degradation of ionomers could reduce the connection to the catalysts, decreasing the reaction rate [13]. The ionomer ionic conductivity is better using a high number of ion-exchange groups, but this situation intensifies the water uptake originating the ionomer dissolution at higher temperatures. The catalyst activity may also become worse through catalyst dissolution, particle coalescence (catalyst agglomeration), Ostwald ripening, support corrosion, and adsorption of ionomer moieties [7].  
[PERSON] et al. [20] demonstrated a good performance, considering industrial parameters using Sustainion(r) membranes. Durability tests exhibited a stable performance of 1 A.cm\({}^{-2}\) at 1.85 V with a degradation rate below 1 mV/h over 10,000 h, leading to an estimated lifetime of about 20 years. Moreover, the authors reported that it is possible to increase the mechanical stability of a Sustainion(r) membrane through the addition of zirconia to the polymer matrix.  
[PERSON] et al. [11] determined that stability is intrinsically related to the system. The authors pointed out that a reliable prediction of membranes' lifetime is necessary. Since durability tests usually need up to 10,000 h, it is necessary to develop ASTs, as was performed for PEM Fuel Cells. Durability tests using ASTs to determine components' aging are crucial to attaining the industrial standard requirements. Overall, despite stability being recognized as a bottleneck, there is weak knowledge about the lifetime of optimized systems.  
Bearing in mind catalyst stability, they must have a lifetime of more than 1000 h and keep their efficiency when operating at high current densities and under repeated on-off cycles from RES [13].
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 2 Theoretical Background
### Challenges
Nonetheless, CYGNSS provides quite fine spatial resolutions (from hundreds of meters to several kilometers, depending on the coherence, incidence angle, elevation, and orientation) with frequent revisit times (several hours to few days), and its measurements are highly sensitive to the topography, surface roughness, and vegetation changes [23]. Thus, even successive observations along the CYGNSS track can have largely different values due to the spatial variations in these land geophysical parameters. Figure 1 illustrates this phenomenon by inspecting the CYGNSS observations after the Hurricane Florence landfall on North Carolina, USA. It shows the mean of the uncalibrated CYGNSS SNR values from 14-18 September 2018 that are averaged per SMAP grid pixels (roughly 36 km \(\times\) 36 km). The zoomed-in version of one of the grids demonstrates the actual CYGNSS data where spatial and temporal variability of the CYGNSS measurements even within a SMAP pixel is apparent. It is evident from Figure 1 that the CYGNSS mission, or GNSS-R in general, offers a sufficiently high spatio-temporal resolution which can help improve hydrological and agricultural applications. Therefore, the detailed information from this resolution gets lost due to any spatial gridding and/or temporal averaging while developing a CYGNSS-based SM retrieval methodology. For instance, previous SM retrieval attempts gridded multiple CYGNSS data points into larger grids (such as 36 km \(\times\) 36 km SMAP EASE-grid) even though the coherent reflections over land are considered [22; 28]. Regarding the methodological challenges and the retrieval complexities described so far, regression techniques can be practical for the CYGNSS-based SM retrieval problem instead of pure explicit solution of the physical model shown in Section 2. In principle, such techniques are based on fitting a regression model between the known SM values from a reference data set (such as SMAP, SMOS, or in situ SM networks) and the CYGNSS observations (possibly in conjunction with ancillary data), and exploiting this model to perform future SM estimations. There have been previous efforts conducted to obtain variations of linear regression models [22; 28]. As [PERSON] et al. [28] state, however, linear regression approaches may be too simplistic to deal with the nonlinear dependence of the CYGNSS observations on SM and the other land geophysical parameters (SM, vegetation canopy, topography, surface roughness, and soil texture). For instance, large local variations between NDVI and topography occur at very high resolutions (few tens of meters) [46]. Such a high spatial variation of parameter correlations, combined with diverse sensitivity of CYGNSS DDMI to different parameters, would make linear regression approaches perform poorly.
# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 2 Linkage Structure Calculation  
To analyze the mechanism and kinematics of the gripper finger structure, the \"vector loop method\" was adopted to analyze the linkage mechanisms with various joints. Using this method, the equations of motion of the fingers were calculated. Through the vector loop method, the position, velocity, acceleration, and even the dynamic responses of a gripper structure, whether it is a single- or multi-degree-of-freedom mechanism, can be systematically analyzed. The linkage structure must contain multiple degrees of freedom to fulfill the object adaptivity of the gripper. A four-bar linkage structure was adopted in this study to provide such mobility. The topology of the four-bar linkage structure, which serves as the fundamental element for forming a 2-D planar system, is shown in Figure 4.  
Joint \(P_{1}\) is set as the fixed origin point of the Cartesian X-Y coordinates, as shown in Figure 4. Each linkage vector \(\overset{\rightharpoonup}{r_{i}}\), where \(i=1\), \(2\), \(3\), and \(4\), has a corresponding length \(\overline{r_{i}}\) defined by Equation (1):  
\[\begin{array}{rcl}\overline{r_{1}}&=&\overline{P_{1}P_{4}},\text{ with }\theta_{1}\text{ from the positive }x\text{-axis, }\overset{\rightharpoonup}{r_{1}}&=& \overline{P_{1}P_{4}}\\ \overline{r_{2}}&=&\overline{P_{1}P_{2}},\text{ with }\theta_{2}\text{ from the positive }x\text{-axis, }\overset{\rightharpoonup}{r_{2}}&=& \overline{P_{1}P_{2}}\\ \overline{r_{3}}&=&\overline{P_{2}P_{3}},\text{ with }\theta_{3}\text{ from the positive }x\text{-axis, }\overset{ \rightharpoonup}{r_{3}}&=&\overline{P_{2}P_{3}}\\ \overline{r_{4}}&=&\overline{P_{4}P_{3}},\text{ with }\theta_{4}\text{ from the positive }x\text{-axis, }\overset{ \rightharpoonup}{r_{4}}&=&\overline{P_{1}P_{3}}\end{array} \tag{1}\]  
The vector loop method sums up the vectors in a vector loop, and it can be expressed as:  
\[\overset{\rightharpoonup}{r_{2}}+\overset{\rightharpoonup}{r_{3}} = \overset{\rightharpoonup}{r_{1}}+\overset{\rightharpoonup}{r_{4}} \tag{2}\]  
Figure 4: Example of a four-bar linkage structure.  
Figure 3: Section view of the model gripper.  
This vector equation is then decomposed into vector components along the X and Y directions. The equation for the angle can then be derived in the form expressed in Equations (3) and (4) along the X- and Y-axes, respectively:  
\[X:-r_{1}\text{cos}\theta_{1}+r_{2}\text{cos}\theta_{2}+r_{3}\text{cos}\theta_{3 }-r_{4}\text{cos}\theta_{4}\ =\ 0 \tag{3}\]  
\[Y:-r_{1}\text{sin}\theta  
After determining all the angles and linkage lengths, the positions of any point on the finger structure can be calculated using trigonometric functions.
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 2 Collaborative Channel Perception Algorithm
### Model Establishment  
Perceiving the channel state of the data link network is the key to improving the accuracy of message transmission decisions and reducing the probability of message exchange conflicts [6; 16; 17; 18]. To enhance the accuracy and robustness of channel state perception, the data link network employs a distributed and decentralized network topology. Each node within the network counts the number of traffic pulses in the channel and shares this information with other nodes. The network architecture for perceiving the channel state of the data link is shown in Figure 1.  
As shown in Figure 1, the channel state perception of the data link network mainly includes four stages, which are described below.  
Stage 1: Data Collection. Each node in the data link network can acquire the channel state perception data and characteristic parameters of other nodes by receiving the broadcast messages.  
Stage 2: Node Weight Allocation. The evidence theory is used to analyze the node characteristic parameters. Nodes' channel perception capabilities can be scientifically evaluated. Based on this evaluation, reasonable weights can be allocated.  
Stage 3: Calculation of Channel Status Perception Results. A weighted data fusion method is employed to calculate the channel state perception results, to quantify the current channel status.  
Stage 4: Message Transmission Decision. The priority threshold of the data link message is compared to the channel status perception results. The comparison result serves as the basis for determining whether to send the data link message [7; 19; 20].
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 4 Formulation for Optimal Solution
### Optimal Path Planning Problem  
The optimal path planning issue for mobile sinks, which may be categorized as NP-hard optimization problems, is one of the challenges. In order to fulfill the optimal mobile sink route, it must take into account the network delay and cost. A specified set of the best points (locations) in the monitored field must be visited by the best path, and each location must be visited once. As a result, it is clear that there are several limits that must be taken into account. Let us start with the environmental restriction. WSNs are often deployed in hostile environments, which exposes the sensor nodes and mobile sink to failure and causes the network to malfunction. So, the mobile sink must not cross through danger area. Moreover, the environmental effect on urgent data routing should be lowered as much as possible. To guarantee reliable transmission in harsh environments, such data must not be sent across dangerous places.
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 2 Materials and Methods
### Data Processing and Chemometrics  
All chemometrics analyses were conducted using Matlab software with partial least squares (PLS) and support vector machine (SVM) Toolbox (ver. 2012b, Mathworks, Inc., Natick, MA, USA, 2012). PLS regression with full leave-one-out cross-validation (LOOCV) was used for development of content prediction models of soy contents. With this method, samples were all used for each model training to gain the highest robustness. In order to determine and compare the proficiency of predictive models, root mean square error of calibration and cross-validation (RMSEC and RMSECV) as well as the coefficient of determination of calibration and cross-validation (\(R_{\mathrm{c}}\)\({}^{2}\) and \(R_{\mathrm{cv}}\)\({}^{2}\)) were used. SVM classification method was also used for discriminating different soy products in ground beef.
## 2 System Model
### Cognitive Radio Wireless Sensor Network Energy Consumption Model
#### 3.3.2 Processing Unit Energy Consumption  
The processing unit executes functions such as sensor data logging, data aggregation and processing. Energy consumption for sensor data logging is due to amount of energy consumed for reading and writing a packet of data into the memory. Let \(P_{rd}\) denote the power consumption for reading a packet  
Figure 4: Block diagram of a cognitive radio sensor node.  
from the memory, \(P_{wr}\) denotes power consumption for writing a packet into the memory then, the energy consumption for data logging can be expressed as [45]:  
\[E_{log}(B_{lg})=B_{lg}(P_{rd}+P_{wr}) \tag{9}\]  
Energy consumption for data aggregation and processing are mainly derived from microcontroller energy losses which occur as a result of switching and leakage currents. The total energy consumption for processing or aggregating \(B_{ap}\) bits of data packet can be expressed as [46]:  
\[E_{ap}\big{(}B_{ap},N_{cy}\big{)}=B_{ap}N_{cy}C_{av}V_{s}^{2}+B_{ap}V_{s}\left( {I_{o}e^{\frac{V_{g}}{n_{p}V_{t}}}}\right)\left({\frac{N_{cy}}{f}}\right) \tag{10}\]  
where \(N_{cy}\) denotes the number of clock cycles per task, \(I_{o}\) denotes the leakage current, \(C_{av}\) denotes average number of capacitance switches per cycle, \(f\) denotes the frequency of the sensor, \(n_{p}\) denotes a constant parameter defined by the processor, \(V_{s}\) and \(V_{t}\) denote the source and terminal voltage, respectively [45].
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Damage Detection
#### 4.4.1 Damaged Roof Comparison  
In Table 4, the top predictions for MobileNet and EfficientNet correctly classify the object as a _damaged roof_, and MobileNet achieves the higher confidence score of 90.93% for that predicted class. The bounding box location in Figure 6 predicted by EfficientNet more accurately encompasses the entire _damaged roof_ structure in comparison to the bounding box location in Figure 6 predicted by MobileNet. On the other hand, the top two predictions made by ResNet are inaccurately classified as _structural damage_, and the bounding box location in Figure 6 is also inaccurate due to the different image features associated with the _structural damage_ class. The same applies to the third prediction made by ResNet of the _flood damage_ class and the third prediction made by EfficientNet of the _structural damage_ class.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **ResNet** & \multicolumn{2}{c}{**MobileNet**} & \multicolumn{2}{c}{**EfficientNet**} \\ \hline  & **Score** & **Type** & **Score** & **Type** & **Score** & **Type** \\ \hline \#1 & 28.12\% & structural damage & 90.93\% & damaged roof & 62.85\% & damaged roof \\ \#2 & 21.99\% & structural damage & 32.73\% & damaged roof & 47.72\% & damaged roof \\ \#3 & 12.47\% & flood damage & 21.75\% & damaged roof & 15.97\% & structural damage \\ \hline \end{tabular}
\end{table}
Table 4: Summary of confidence scores and associated object categories for the top three predictions made on the image of a _damaged roof_ for each of the three models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **ResNet** & \multicolumn{4}{c}{**MobileNet**} \\ \hline Precision & Recall & F1-score & Precision & Recall & F1-score \\ \hline
0.75 & 0.85 & 0.79 & 0.87 & 0.89 & 0.88 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of flood damage classification using transfer learning on the basics of ResNet and MobileNet.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 3 Preliminaries and Background
### Hamming Distance  
Hamming distance is a metric for measuring the difference between two strings of equal length. For binary strings \(A\) and \(B\) of length \(m\), it is defined as the total number of positions where the corresponding bits differ. Hamming distance can be computed through simple additive aggregation (summing bit differences), which, in a secret sharing framework, is more efficient than computing Dice/Jaccard similarity functions that require secure intersection operations (typically involving complex or high overhead secure multiplications).
## 2 Datasets and Methods
### Methods
#### 2.3.4 Analysis of the Climate Conditions on the CUE Extremes  
We used the partial correlation method to assess the correlations between CUE and climate factors. The method explored the correlations between CUE and individual factors while eliminating the influence of remaining climate factors [10; 11]. The datasets were detrended before conducting partial correlation analysis.  
To determine the climate conditions in forming CUE extremes, we separately averaged the climate factors' anomalies in positive and negative CUE extreme events at the pixel level for the four seasons. Additionally, we averaged them with the latitudes to obtain the general climate conditions for CUE extremes.
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 2 Related Work
### Subgraph Embedding  
Methods based on subgraphs play an important role in network representation learning. Generally, existing works associated with subgraphs can be divided into two categories: learning the embeddings of subgraphs and using subgraphs to learn node embeddings.  
(1) Learning the embeddings of subgraphs: Most graph embedding models focus on the distributed representation of nodes, while subgraph embedding models are different. Some network mining tasks like community detection and graph classification require mining the properties of the graph/subgraph. Sub2 vec [25] learns feature representations of arbitrary subgraphs. It extends Paragraph2 vec [40] to learn subgraph embedding while preserving distance between subgraphs. Subgraph2 vec [26] leverages local information obtained from neighborhoods of nodes to learn latent representations of rooted subgraphs present in large graphs. After learning subgraph representations, these models can do well in community detection and graph classification tasks.  
(2) Using subgraphs to learn node embeddings: Unlike the methods mentioned above, the second category aims to get node embeddings, and it is commonly used on heterogeneous graphs. PTE [27] projects the input heterogeneous network into several homogeneous and bipartite networks, which is the extension of LINE. M2 GRL [28] first learns a separate graph representation for each view of data and then models cross-view  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Areas** & **Methods** \\ \hline \multirow{3}{*}{Network Embedding} & GraRep [21], HOPE [22], DeepWalk [3], Node2 vec [4], \\  & SDNE [23], GCN [7], GAT [8], Metapath2 vec [24], \\  & HIN2 Vec [16], HAN [12], HetGNN [14] \\ \hline \multirow{2}{*}{Subgraph Embedding} & Sub2 vec [25], Subgraph2 vec [26], PTE [27], M2 GRL [28] \\ \hline \multirow{2}{*}{Dynamic Network Embedding} & dynnode2 vec [29], EvolveGCN [30], CTDNE [31], \\  & HG2 Img [32], DySAT [33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical methods in three areas.  
relations between different graphs. The model treats the intra-view task as a representation learning problem on a homogeneous graph. Using subgraphs can take advantage of semantic information, and it is a good way to deal with the heterogeneity of graphs. The MSGCN model generates subgraphs according to the edge types. Additionally, the node embeddings of each subgraph are aggregated to obtain the final node embeddings.
## 5 Finite Element Analysis and Shear Capacity Checking Calculation
### Stress Cloud Diagram and Hysteresis Curve Analysis  
The size of the preassembled unit cast in place uniformly resemble each other. The stress cloud for each joint model is available in Figure 15. The findings reveal that the longitudinal bars at the beam end tend to yield before the joint area's stirrups, which aligns with the real test results. The joint area's stirrups in the ZP-2 specimen yield before the longitudinal bars at the beam end, aligning yet again with the experimental results.  
Figure 14: Modeling and meshing. (**a**) XJ-1, (**b**) ZP-1, ZP-2, ZP-3, (**c**) ZP-4, (**d**) reinforcing cage, (**e**) meshing.  
Figure 15: Stress cloud diagram. (**a**) XJ-1 joint, (**b**) ZP-1 joint, (**c**) ZP-2 joint, (**d**) ZP-3 joint, (**e**) ZP-4 joint.  
Table 8 clearly shows that the overall hysteresis curve trend from the specimen, as shown in the finite element simulation, is substantially synonymous with the actual test. The peak points for each load also mostly align with the testing data.  
As shown in Figure 16, the finite element simulation hysteresis curve of the five nodal specimens is basically consistent with the overall change trend of the test hysteresis curve, and the peak points of each loading are basically consistent with the test data. Compared with the finite element simulation hysteresis curve, the test hysteresis curve has a large slip section in the later stage of loading, and the pinch phenomenon is obvious. This is because ABAQUS is difficult to fully simulate the bond slip between steel bars and concrete, so the hysteresis curve is relatively full. At the initial stage of loading, due to the gap between the loading device and the joint specimen, the displacement change speed is faster than the strain change speed. The finite element simulation is based on a simulation analysis under ideal conditions, so the initial stiffness of the finite element simulation hysteresis curve is significantly larger than that of the test hysteresis curve.