esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Neutron Experiment
SIRT-MWTDM algorithm has the fastest descent speed at the beginning and the lowest final RMSE value from Figure 14a. The results show that the SIRT-MWTDM algorithm has a faster convergence speed and the gray value of the reconstructed image is closer to the ideal gray value. Figure 14b presents the SSIM curve for each iteration. SIRT-MWTDM algorithm has the fastest rising speed at the beginning and the largest final SSIM value from Figure 14b. When the three iterative algorithms (SIRT, SIRT-TV, SIRT-WTDM) are used to reconstruct the Shepp-Logan phantom from spare projections, they take about 100 iterations to achieve convergence, while the SIRT-MWTDM algorithm in this paper only needs about 50 iterations to achieve convergence. The RMSE and SSIM curves of the reconstruction process in Figure 6 for 30 projections of the circular phantom are shown in Figure 15. Similarly, we can find from Figure 15 that as the number of iterations increases, the SIRT-MWTDM algorithm has the smallest RMSE and the largest SSIM. All algorithms reach convergence in about 50 iterations. Comparing Figures 14 and 15, it can be found that the number of iterations to achieve convergence is not the same when the same algorithm is used to reconstruct different phantoms. In contrast, our method requires a small number of iterations to reach convergence. The image reconstructed by our method in this paper can obtain the smallest RMSE and the largest SSIM for the same number of projections. Figure 7:0 The RMSE and SSIM change curves of reconstructed images with different algorithms for 30 projections of the Shepp–Logan phantom. (**a**) RMSE; (**b**) SSIM.
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 3 Precise Registration Algorithm Based on Curvature and FPFH Features  
The iterative closest point algorithm is the most commonly used method in precise cloud registration, which tends to fall into a local optimum when dealing with large-scale point clouds and noisy point clouds. Therefore, on the premise of completing the abovementioned rough registration process to obtain good initial values of R and T, this paper proposes an ICP fine registration algorithm based on curvature and FPFH features. The specific process is as follows:  
1) The octree algorithm and ISS feature point combination algorithm are used to sample the point cloud, and the curvature idea is used to obtain the feature point set of the point cloud, thus reducing the complexity of algorithm calculation.  
2) A new method of selecting nearest neighbor points is defined. After the rough registration, the point clouds are, respectively, named \(P_{1}(x_{1},x_{2},\cdots,x_{n})\) and \(Q_{1}(y_{1},y_{2},\cdots,y_{m})\), and any \(p_{l}(a_{0},b_{0},c_{0})\) in \(P_{1}\) is selected to find the three points \(q_{h}(a_{h},b_{h},c_{h})\), \(q_{i}(a_{i},b_{i},c_{i})\), and \(q_{j}(a_{j},b_{j},c_{j})\) with the closest Euclidean distance on \(Q_{1}\). These three points form a minimum neighborhood space, and its barycenter coordinate is \(q_{l}(a,b,c)\). Equation (13) is the calculation method of the barycenter coordinates of the minimum neighborhood unit, and \(p_{l}\) and \(q_{l}\) are recorded as a pair of neighboring points.  
Figure 10: Extraction of feature points on the edge of the point cloud.  
\[\left\{\begin{array}{l}a=\frac{a_{h}+a_{i}+a_{j}}{3}\\ b=\frac{b_{h}+b_{i}+b_{j}}{3}\\ c=\frac{c_{h}+c_{j}}{3}\end{array}\right. \tag{13}\]  
3) Kd-tree is used to search the nearest neighbors of points, which improves the searching efficiency of point sets.  
4) Calculate the FPFH characteristics of point \(p_{l}\) and neighboring points. The corresponding point set is determined by the calculated value, the number of correct matching points is increased, and the accuracy of point cloud registration is improved.  
5) The quaternion method is used as an iterative solution until the error condition is met or the maximum number of iterations is reached, and the calculation is stopped.  
Compared with the original ICP algorithm, the improved ICP algorithm puts forward a new method to determine the nearest neighbor, which increases the calculation of the FPFH eigenvalue, which seems to increase the workload. However, in the actual registration process, the point cloud is processed by curvature features, and the original Euclidean distance judgment condition is replaced by comparing the FPFH features of points, which increases the number of correct matching pairs and greatly improves the registration accuracy of point clouds.
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 3 Probe Test and Validation
### Isolated Voltage Probe as an Oscilloscope Probe
#### 3.1.1 Monitoring an Electricity Generation System (Ward Leonard)  
In this first verification process, the presented differential and isolated voltage probe was connected to a Ward Leonard system, comparing the results achieved to those obtained using two commercial differential probes. Figure 11 shows a photograph of the experimental setup, including the connection of a Chauvin Arnoux DP25 and a Tektronix P5205 high voltage differential probe as reference commercial devices, together with the ad hoc design presented in this work.  
Figure 11: Experimental setup to test the proposed probe using a Ward Leonard system.  
Figure 10: (**a**) PCB of the developed low-cost differential probe. (**b**) Box container of the proposed low-cost differential probe.  
Figure 12a shows the results of measuring a 150 V amplitude and 16 Hz frequency AC signal in one of the three phases in a Ward Leonard system; Figure 12b shows the voltage at the same phase line for a voltage amplitude of 230 V and a frequency of 33 Hz. In both images, the cyan signal corresponds to the voltage acquired using the Tektronix P5205 probe, the blue signal is the measure obtained using the Chauvin Arnoux DP25 probe, while the yellow signal presents the measure acquired using the proposed differential isolated probe. In Figure 12c, the green signal is the current measured in the same conditions as in Figure 12b using a current probe TCP202 from Tektronix.  
As Figure 12 shows, the most significant difference between the results obtained by the commercial probes and the proposed one consists in a discrepancy in the value of amplitude measured by our probe. this can be explained as an error in the gain circuit of the probe, which can be easily compensated either by hardware or software.  
It should be noted that for frequencies close to those of the mains grid (Figure 12a,b), the error in amplitude measurement is reduced, so that the performance of the proposed low-cost design is very similar to that of commercial, costly probes.
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 3 Proposed Solution
### Delegation Process
#### 3.2.3 Phase 3--Transaction Validation & Block Generation  
Block transactions update the trust values of all nodes in a network, including malicious nodes. All transactions are sent to the validator node or a delegated node, which will generate a block. This scheme is conducted based on the OLSR protocol, where transactions are disseminated through MPR nodes. Each node n will send an encrypted transaction (n, transaction)prKey\({}_{n}\), where transactions are encrypted by the private key of n.  
If a malicious node is revealed by DCFM, it will receive a negative TV and be eliminated from the network. However, one node could erroneously assign a negative value to another node, even if it is not an adversary. To prevent this issue, transactions related to malicious node information must be validated by neighbors before they are sent to the delegate node. The attacker information and any inconsistencies generated by the attacker must be reported through two-hop neighbors because attackers can claim that two-hop neighbors of a victim are their neighbors in NIA. Specifically, a report message (v, x, ReportAttack)prKey\({}_{n}\) is sent, where the victim ID (v), attacker ID (x), and ReportAttack (identified inconsistencies) are encrypted by the victim's private key, as described in Figure 8. This message is sent by piggybacking in hello message until two-hop range since the malicious  
Figure 7: Demonstration of Phase 2.  
hello message contains the two-hop neighbors of the victim. If the neighboring nodes agree on the transaction, they will reply (i, AckReport)prKey\({}_{i}\) and the transaction will be validated. Since the attacker can also include the fake nodes, to get agreement from all nodes is impossible. Moreover, the node requesting the agreement could also be the attacker which is trying to isolate a good node. Thus, if half of the neighbors included in attacker's hello agrees with the report, the transaction is validated. In addition, \"agree\" means that they don't have connection with that attacker even though it claims that they are its neighbors. Thus, every node is evaluating with the same criteria to agree or not.  
Only nodes with TVs above \(\theta\) will send normal TV transactions that are not attack transactions. The delegate node will count the number of nodes voting for a particular transaction. Based on the count, the delegate will determine the transaction sequence and generate a block. Consequently, the delegate will broadcast the new block (dl, Block)prKey\({}_{d}l\) through MPR nodes in the network. When all nodes receive the block, each node replies with a confirmation message (n, BlockAck)prKey\({}_{n}\). If the majority of nodes agree with the new block, each node chains it to a local blockchain.
## 2 Materials and Methods
### Early Multi-Branch Fusion of Spatial-Temporal Joint Attention GCN
#### 2.2.1 Multi-Branch Input Features and Fusion  
The 2 s-AGCN model was the first model to fuse multiple streams at the decision layer. Subsequently, many multi-stream models have been developed for skeleton-based action recognition. However, the decision-layer fusion approach needs to handle various data inputs by independently training multiple models, which significantly increases the complexity of the model. Consequently, this approach cannot be applied to end-to-end training processes. In this study, we developed an architecture for early multi-branch fusion, in which temporal and spatial convolution are performed on each branch to extract  
Figure 1: Schematic of the joint reconstruction. (**a**) Kinect V2 Joint reconstruction, (**b**) Azure Kinect Joint reconstruction. Both of these simplifications are based on decreasing the number of joints on the head and the hands.  
Figure 2: STJA-GCN model structure (The two numbers in each block denote input and output channels, and /2 represents a stride of 2. BN: BatchNorm, SGC: Spatial Graph Convolution, TC: Temporal Convolution, GAP: Global Average Pool, FC: Fully Connected Layer).  
the respective features, which are then fused and imported to the spatial-temporal graph convolutional layer. This process both diversifies input features and simplifies the required computations. Therefore, the proposed architecture can process more input data and exhibits improved performance.  
Let \(X=\left\{x\in\mathbb{R}^{C_{in}\times T_{in}\times V_{in}}\right\}\) be the set of the 3D body joint coordinate series corresponding to the input spatial-temporal graph of the skeleton STG, where Cin, Tin, and Vin are the input coordinates, number of frames, and number of joints, respectively. The corresponding set of joint positions can be expressed as \(R=\left\{r_{i}|i=1,2,\ldots V_{in}\right\}\), where  
\[r_{i}=x[:,:,i]-x[:,:,c], \tag{2}\]  
with \(c\) being the index of the central spinal joint. Thus, the input of the joint position feature consists of both \(X\) and \(R\).  
The set of motion velocity features is composed of fast motions \(F=\left\{f_{t}|t=1,2,\ldots T_{in}\right\}\) and slow motions \(S=\left\{s_{t}|t=1,2,\ldots T_{in}\right\}\), where  
\[\begin{split} f_{t}&=x[:,t+2,:]-x[:,t,:],\\ s_{t}&=x[:,t+1,:]-x[:,t,:]\end{split} \tag{3}\]  
The set of skeleton features contains the joint length \(L=\left\{l_{i}|i=1,2,\ldots V_{in}\right\}\) and joint angle \(A=\left\{a_{i}|i=1,2,\ldots V_{in}\right\}\). The length (\(l_{i}\)) and angle (\(a_{i,w}\)) of each joint can be calculated as  
\[\begin{split} l_{i}&=x[:,:,i]-x\Big{[}:,:,i_{adj} \Big{]},\\ a_{i,w}&=arccos\left(\frac{l_{i,w}}{\sqrt{l_{i,x}^{2 }+l_{i,y}^{2}+l_{i,z}^{2}}}\right)\end{split} \tag{4}\]  
where \(i_{adj}\) is the joint adjacent to the \(i\)-th joint, and \(w\in\left\{x,y,z\right\}\) is the 3D coordinates of the joint.
# An Analysis of Mechanical Properties for Ultrasonically Welded Multiple C1220-Al1050 Layers
## 3 Results of the Experiment
### Evaluation of Mechanical Joint Performance
#### 3.1.2 Fracture Sequence  
Ultrasonic multi-layer welding has three welds, and even if they are simultaneously tensioned to measure shear strength, they cannot be fractured at same time. The difference in strength between each weld and the material causes sequential fracture and the fracture location of the weld in the form of stress is shown as Figure 6. Because stress is related to the weldability, the better the weldability and the greater the force to withstand external forces that can be guaranteed by higher stress. Four fracture sequences were inferred through this experiment, and the fracture sequence, as shown in Table 3, could be predicted by the differences of stress at the fracture points. In Figure 6, \(\sigma_{ai}\) is tensile stress of indentation at the aluminum upper sheet and \(\sigma\_\)al is shear stress in the aluminum base material. The shear stresses of weld zones from 1 to 3 are \(\tau_{ab}\), \(\tau_{bc}\), and \(\tau_{cd}\).  
In case 1 of Table 3, the vibrational energy is transferred from the horn to the anvil. In this process, while the four base materials overlap, the two intermediate materials play the role of attenuation of energy transfer. Therefore, it was confirmed that the weldability of the overlapping materials was in the order of weld 1 \(>\) weld 2 \(>\) weld 3. In fact, as a result of the shear tensile test, as shown in Figure 8a, it was confirmed that the weld zone was fractured in the order of 3-2-1. Case 2 is the breaking sequence shown in Figure 8b, and case 3 is the breaking pattern of Figure 8c,d. In addition, in case 4, the strengths of the weld zones 1, 2, and 3 were higher than that of the aluminum base material, and it was fractured at the position of the aluminum base material.  
Figure 9: Four kinds of fracture types in a multi-step shear strength test. **(a)** Interface fracture at all weld zones, **(b)** Combination fracture of Al indentation and interface, **(c)** Combination fracture of Al indentation, Al base metal and interface, **(d)** Combination fracture of Al indentation and Al base metal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Stress Condition**} & \multicolumn{3}{c}{**Fracture Sequence**} \\ \cline{3-5}  & & **First** & **Second** & **Third** \\ \hline
1 & \(\sigma_{ai}>\sigma_{al}\geq\tau_{ab}\equiv\tau_{bc}\equiv\tau_{cd}\) & Weld zone 3 & Weld zone 2 & Weld zone 1 \\
2 & \(\sigma_{al}>\tau_{ab}\equiv\tau_{bc}\equiv\tau_{cd}>\sigma_{ai}\) & Al indentation zone & Weld zone 2 & Weld zone 3 \\
3 & \(\tau_{ab}\equiv\tau_{cd}>\sigma_{al}>\tau_{bc}>\sigma_{ai}\) & Al indentation zone & Weld zone 2 & Al base metal \\
4 & \(\tau_{ab}\equiv\tau_{bc}\equiv\tau_{cd}>\sigma_{al}>\sigma_{ai}\) & Al indentation zone & Al base metal & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four kinds of fracture type.
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 3 Methodology
### Influence Factors  
#### 3.2.1 Bulkhead Additive Thrust and Friction on Jacking Machine  
In front of the jacking machine, the condition of the surrounding soils can be further divided into the shearing region and the compression region, as shown in Figure 4. Equations (7)-(9) based on [PERSON]'s solution only considered the normal compression induced by the bulkhead additive thrust. The shearing behavior of soils at the boundary between region \(i\) and region \(iii\) has not been considered. During the compression of soil in front of the jacking machine, the shearing behavior will apply a friction force against the bulkhead additive thrust, then mitigate the ground movement. The same phenomenon also exists between the jacking machine and surrounding soils. The surrounding soils will reach the failure criterion becoming soft under the shearing effect. The smaller friction coefficient of the softened surrounding soils will decrease the ground movement induced by the friction force. The modified ground movement caused by the bulkhead additive thrust and friction on jacking machine can be expressed as:  
\[{u_{1}}^{\prime}=\alpha_{p}u_{1} \tag{19}\]  
\[{u_{2}}^{\prime}=\alpha_{f}u_{2} \tag{20}\]  
Figure 3: Calculation schematic of [PERSON]’s solution.  
where, \(\alpha_{p}\) is the shearing disturbance coefficient caused by bulkhead additive thrust, and \(\alpha_{f}\) is the shearing disturbance coefficient caused by friction, which are supposed to have a relationship with the cohesion of the construction soil layer and the bulkhead additive thrust or the friction between jacking machine and surrounding soils. The empirical equations for these two coefficients can be expressed as:  
\[\alpha_{p}=\frac{c}{p} \tag{21}\]  
\[\alpha_{f}=\frac{c}{f} \tag{22}\]  
where, \(c\) is the cohesion of the construction soil layer.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 6 Numerical Results
### Block Encoding Time  
A similar comparison was made for the encoding time measurements. Figure 7 presents the total encoding time of a single block for different parity-check matrices. The measured time involved the data preparation, calculation of parity bits \(\mathbf{p_{1}}\), \(\mathbf{p_{2}}\), and gathering of the output vector. We included results for standard LDPC encoding with the index representation (orange bar), as well as the proposed efficient QC-LDPC encoding (blue bar). A significant encoding time reduction was visible in the case of the developed efficient QC-LDPC algorithm implementation in comparison with the LDPC with the index matrix representation, as well as the Reed-Solomon (RS) encoders. This implementation thus has potential for the reduction in energy consumption in solutions that use sleep modes after the communication of a message from an IoT node. The relation of the working mode time to the sleep mode time can be reduced if the block encoding time is reduced.  
Figure 6: Memory utilization of the implemented encoders.  
Figure 7: Single block encoding time periods.  
Basing on the timing measurements, we estimated that energy saving that can be achieved for the proposed encoder in comparison with the Reed-Solomon encoding. For this estimation, the following assumptions were made, reflecting real operation of the microcontroller STM32 used for experiments:  
* The voltage supplied to the circuit is 3.6 V.
* The current in the active mode with 80 MHz clock is 33.5 mA.  
We calculated the normalized energy needed for encoding a single bit of the QC-LDPC codes as well as the other reference encoders. The results are presented in Figure 8. Then, we subtracted the results, obtaining the potential saving that can be achieved, expressed in [\(\mu\)J/bit]. These results are presented in Table 3.
# Recent Lipid Membrane-Based Biosensing Platforms
## 1 Introduction
A quick glance at the history of the field reveals that the 1990's witnessed a dedicated and intense research towards stable lipid membrane constructs [1]. Efforts included a variety of fabrication procedures, supports, and materials [10]. However, as the structural stability of the membrane was increasing, its ability to reproduce the natural membrane function was decreasing and so did its analytical supremacy (high sensitivity and selectivity, low detectability, and fast response times) [10]. Nano-tools and new engineering concepts are now used in device engineering in order to simulate adequately membrane energetics and provide platforms that address certain requirements for practical implementation:  
* Adequate robustness for long-term stability under storage and operation. * Simple and non-expertise demanding reproducible fabrication. * Stable membrane state, preferably liquid crystalline, that allows lateral mobility. * Stable and reproducible membrane morphology, preferably defect-free for easier control on ion transport. * Adequate hydration in the hydrophilic region in order to support protein-based interactions. * Compatible coupling to transducers. * Fit-for-purpose detection strategy that will ensure optimal detection without catastrophic perturbations of the lipid organization. * Carefully managed strength of immobilized biology in order to avoid leakage but maintain functionality. Lipid membrane technology adsorbed rapidly the scientific and technological output of many fields in order to evolve into the diverse cluster of today (Figure 2). This paper presents the electroanalytical aspects of artificial membranes, the processes and tools used for building membrane platforms, the current challenges and the future trends. What is expected in the near future is the market availability of these lipid-based sensing platforms for research and practical implementation. Figure 2: The science and technology input that enabled lipid membrane technology to progress from freely suspended fragile bilayers (black lipid membranes) to rugged nanopatterned sensing platforms.
## 5 Nanoindentation of Diffusion-Bonded Interfaces
### Aluminium/Steel  
Diffusion bonding has been investigated as a means of joining aluminium alloys to steels for use in automobile bodies to reduce weight, fuel consumption and CO\({}_{2}\) emissions [52; 53]. However, the formation of Al-Fe intermetallic compounds can present difficulties in bonding these materials. Al-Fe intermetallics are also formed in other joining processes (e.g., spot welding, laser welding, friction stir welding and brazing). Many of these Al-Fe intermetallics exhibit high hardness--for example, the monoclinic Al\({}_{3}\)Fe, which is 7.57-9.98 GPa, and the orthorhombic Al\({}_{5}\)Fe\({}_{2}\), which is 9.81-11.36 GPa [65]--although they are also very brittle owing to their limited number of slip systems [53]. Consequently, they are detrimental to achieving a bond of sufficient strength [19]. Indeed, the tensile strength of the joint has been found to decrease with increasing intermetallic layer thickness [66]. Although the bond strength can be improved by the addition of silicon (Si) and/or copper (Cu) [51], it is important to understand the nature of the bond that has formed, most notably the phases present and their mechanical properties. Nanoinindentation has been used to characterise the Al/Fe bond and the properties of any interfacial phases in a number of studies [51; 52; 53]. [PERSON] et al. [51] compared the interfaces of steel bonded to Al with those formed in steel bonded to three other Al alloys with elevated levels of Si and/or Cu. Bonding was carried out at temperatures of between 500 \({}^{\circ}\)C and 575 \({}^{\circ}\)C and durations of between 2.5 and 30 min. Electron probe microanalysis (EPMA) and selected area electron diffraction (SAED) were used to characterise the interfacial regions. It was found that, in the base Al/steel interface, two Al-Fe intermetallic compounds had formed: Al\({}_{5}\)Fe\({}_{2}\) on the steel side and Al\({}_{3}\)Fe on the Al side. When the steel was bonded to the Al alloys containing higher Si and/or Cu contents, the reaction layer