esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Currently, silicon and silicon-based composite materials are widely used in microelectronics and solar energy devices. At the same time, silicon in the form of nanoscale fibers and various particles morphology is required for lithium-ion batteries with increased capacity. In this work, we studied the electrolytic production of nanosized silicon from low-fluoride KCl-K\({}_{2}\)SiF\({}_{6}\) and KCl-K\({}_{2}\)SiF\({}_{6}\)-SiO\({}_{2}\) melts. The effect of SiO\({}_{2}\) addition on the morphology and composition of electrolytic silicon deposits was studied under the conditions of potentiostatic electrolysis (cathode overvoltage of 0.1, 0.15, and 0.25 V vs. the potential of a quasi-reference electrode). The obtained silicon deposits were separated from the electrolyte residues, analyzed by scanning electron microscopy and spectral analysis, and then used to fabricate a composite Si/C anode for a lithium-ion battery. The energy characteristics of the manufactured anode half-cells were measured by the galvanostatic cycling method. Cycling revealed better capacity retention and higher coulombic efficiency of the Si/C composite based on silicon synthesized from KCl-K\({}_{2}\)SiF\({}_{6}\)-SiO\({}_{2}\) melt. After 15 cycles at 200 mA\(\cdot\)g\({}^{-1}\), material obtained at 0.15 V overvoltage demonstrates capacity of 850 mAh\(\cdot\)g\({}^{-1}\).  
lithium-ion battery; silicon; halide melt; electrodeposition; silicon fibers; nanotubes; nanoneedles; composite anode +
Footnote †: journal: selected  
Academic Editor: [PERSON]  
Received: 25 October 2021  
Accepted: 16 November 2021  
Published: 19 November 2021  
###### Abstract  
Currently, silicon and silicon-based composite materials are widely used in microelectronics and solar energy devices. At the same time, silicon in the form of nanoscale fibers and various particles morphology is required for lithium-ion batteries with increased capacity. In this work, we studied the electrolytic production of nanosized silicon from low-fluoride KCl-K\({}_{2}\)SiF\({}_{6}\) and KCl-K\({}_{2}\)SiF\({}_{6}\)-SiO\({}_{2}\) melts. The effect of SiO\({}_{2}\) addition on the morphology and composition of electrolytic silicon deposits was studied under the conditions of potentiostatic electrolysis (cathode overvoltage of 0.1, 0.15, and 0.25 V vs. the potential of a quasi-reference electrode). The obtained silicon deposits were separated from the electrolyte residues, analyzed by scanning electron microscopy and spectral analysis, and then used to fabricate a composite Si/C anode for a lithium-ion battery. The energy characteristics of the manufactured anode half-cells were measured by the galvanostatic cycling method. Cycling revealed better capacity retention and higher coulombic efficiency of the Si/C composite based on silicon synthesized from KCl-K\({}_{2}\)SiF\({}_{6}\)-SiO\({}_{2}\) melt. After 15 cycles at 200 mA\(\cdot\)g\({}^{-1}\), material obtained at 0.15 V overvoltage demonstrates capacity of 850 mAh\(\cdot\)g\({}^{-1}\).
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 2 Materials and Methods
### Prior Work  
This section introduces the theoretical basis of LDPC encoding and decoding, the minimum sum decoding algorithm, the model-driven deep learning method, and the improved methods of shared parameters and codebook-based weight quantization. Our proposed LDPC long-code decoding networks are based on these contents.  
LDPC codes (N,k) consist of k information bits and (N-k) parity bits. They offer advantages such as low decoding complexity and parallel decoding capability while approaching the Shannon limit. It is suitable for application to unmanned aerial vehicle communication. The parity-check matrix of LDPC [38] exhibits strong sparsity, containing only a few non-zero elements. The information is encoded using an LDPC encoder [39] to obtain codewords, denoted as \(\mathrm{y}=\mathrm{x}\cdot\mathrm{G}\), where \(\mathrm{G}\) is the generation matrix. When codewords pass through the channel, they are affected by noise. The decoder takes the maximum likelihood ratio of the signal affected by noise as its input. Through the continuous updating of the check matrix and variable matrix, that is, the iterative process of variable nodes (VNs) and check nodes (CNs) constantly updating and transmitting messages to each other, the LDPC iterative decoding algorithm realizes the channel decoding process. The flow of the LDPC iterative decoding algorithm is shown in Figure 3.  
The LDPC iterative decoding algorithm includes the belief propagation (BP) algorithm, the min-sum (MS) algorithm, etc. While the BP algorithm exhibits excellent performance, it comes with high complexity. On the other hand, the MS algorithm simplifies the expression for updating check node information, reducing the complexity of the decoding algorithm.  
Figure 2: Air-to-air data transmission link model.  
However, it sacrifices some performance. In this paper, we improve the min-sum decoding algorithm to enhance its decoding performance while reducing complexity. This modification allows the decoding algorithm to meet the requirements of UAVs and be deployed on UAVs, ensuring the reliability of communication data transmission in UAVs.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 2 Low-Power Rectifiers
### Matching Network Enhancement  
The input impedance of the diode is highly sensitive to both input power and frequency at low input power levels. The matching network design becomes critical to ensure the entire input power is delivered to the diode. DC-load matching can also considerably improve efficiency, especially under extremely low-power conditions. However, the matching network itself may introduce additional insertion losses. Therefore, multiple aspects can be addressed to enhance the performance of the matching network.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study
### Result  
We present the experimental results of the proposed method on the ScanNet [19] and 3 DMatch [20] datasets in Table 3. To further investigate the stability of the method, we report the performance comparison between our method and PointMBF [24] across varying frame intervals in Table 4.  
From Table 3, it can be observed that our method demonstrated superior performance on the majority of metrics, particularly on key metrics, where we achieved reductions of 26.9% in average rotational error, 17.2% in average translational error, and 21.7% in average Chamfer error compared to PointMBF [24] on the 3 DMatch [20] dataset. Furthermore, on the ScanNet [19] dataset, our method achieves reductions of 32% in average rotational error, 28.5% in average translational error, and 23.4% in average chamfer error compared to PointMBF [24]. Our experimental results were obtained under strictly identical experimental conditions as those of PointMBF [24].  
From Table 4, it can be observed that as the frame interval increased, our method consistently outperformed PointMBF [24] at each frame interval. To further validate this result, we provide the visualization in Figure 3, which clearly illustrates the trend of the data. In practical applications, the frame intervals of RGBD images are typically not fixed. Therefore, this experimental result strongly demonstrates the potential advantages of our method in engineering applications, particularly in handling data with irregular frame intervals, where it maintained a stable and superior performance.  
Figure 3: Rotation/Translation accuracy and error with different frames apart. On the left, **pr5** represents the accuracy of PointMBF when the rotation error is less than 5\({}^{\circ}\), **ot5** represents the accuracy of our methods when the rotation error is less than 5\({}^{\circ}\), **prm** represents the mean rotational error of PointMBF, and **orm** represents the mean rotational error of our methods. On the right, **pt5** represents the accuracy of PointMBF when the translation error is less than 5 cm, **ot5** represents the accuracy of our methods when the translation error is less than 5 cm, **ptm** represents the mean translational error of PointMBF, and **orm** represents the mean translational error of our methods and others that can be deduced by analogy.
# A New Soil Conditioner for Highly Permeable Sandy Gravel Stratum in EPBs
## 3 Results and Discussion
### Permeability Test
#### 3.3.2 Effect of Polymer Type and Concentration on Permeability of Modifier  
In this study, the effect of high molecular polymer type, including Kapom, Guar, Xanthan, Carboxymethyl cellulose Sodium (CMC), Hydroxyethyl cellulose (HEC) and Polyacrylamide (PAM), on the impermeability of coarse sand was investigated. Among them, the solubility of Xanthan is lowest, 0.5 wt%, which was used as the concentration of various polymers to study the effect of polymer type on the impermeability property. Therefore, coarse sand is conditioned by 30% modified slurry consisting of B, A, S and X (i.e., 0.5 wt% polymer) and their volume ratio is 10:1:2:2. The permeability tests under 2 bar  
Figure 7: (a) Effect of B/A injection ratio on seepage flow. (b) Effect of sodium silicate content on B/A seepage flow.  
air pressure of conditioned coarse sand were performed and the results were shown in Figure 8a. It is observed that the improvement degree of impermeability of coarse sand with addition of HEC is the greatest compared to that of the sand sample with the addition of other polymers. The order of improvement performance degree of polymer from high to low is HEC, CMC, Kapom, PAM, Guar, Xanthan.  
In order to study the effect of HEC concentration on the impermeability of coarse sand, coarse sand samples were prepared by adding different concentrations of HEC (i.e., 0-4 volume concentration with 0.5 interval). Permeability tests were conducted under 2 bar air pressure. The results were shown in Figure 8b. It is observed that the higher the HEC concentration, the greater the obtained impermeability of conditioned coarse sand. When the HEC proportion is 2, the volume of fluid per unit time is the lowest (<1 mL/min). However, with the HEC proportion is 2-4, the impermeability of coarse sand shows a reversed trend.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 3 Hyper-Heuristic Approach for VRP
### Vehicle Routing Problem with Time Windows  
The vehicle routing problem with time windows (VRPTW) is an extension of the VRP, defined in [44]. The VRPTW includes an additional constraint, which is that every customer \(v\in V\) must be serviced within a given time window \([e_{v},l_{v}]\). If a vehicle arrives earlier it must wait for the window opening time; if the vehicle arrives after the end of the time window, the solution is not valid. Every customer has the following parameters defined:  
* ready time \(e_{v}\)--window opening time;
* due date \(l_{v}\)--window closing time;
* service time \(\delta_{v}\)--time needed for the customer to be serviced;
* demand \(d_{i}\)--customer capacity;
* geographical data.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Blockchain's Secondary Cryptography on Privacy
#### 3.2.3 Ring Signatures for Achieving Blockchain Privacy  
Numerous kinds of signatures are present in cryptography. However, to achieve anonymity in blockchain networks, ring signatures and its variants are used. Ring signatures, introduced in 2001 [69], work on the idea of involving various network participants to form a ring and create a signature based on the private key of ring creator and public keys of other participants in the ring. Doing this will reveal to the verifiers that one of the participants have signed the transaction without giving out the information of who exactly has signed the transaction. Thereby achieving anonymity and unforgeability [70]. Ring signatures were extended [71] to traceable ring signatures and adopted for the formation of Ring-Coin. In this case, anyone impersonating another person in the ring to sign the same message will risk revealing his identity immediately. This idea was further deployed for prevention of double-spending attack in blockchain and thereby became the basis of CryptoNote [72] with a slight modification.  
A ring signature-based scheme was proposed [73] to strengthen the privacy in blockchain networks. This work combined ring signatures with elliptic curve cryptography for privacy enhancement. The study does not describe any experimentations performed for evaluation; however, it gives mathematical proofs testifying that the proposed mechanism was efficient.
### Background
Likewise, OpenFedLLM [28] enabled training a GPT-style model on decentralized clinical text data, employing techniques to reduce communication cost and handle non-IID (not independent and identically distributed) data across nodes [29]. To further bolster privacy, researchers have combined FL with techniques such as differential privacy--adding calibrated noise to model gradients--and secure multi-party computation or homomorphic encryption to prevent any single party from reconstructing the data of another [30; 31; 32]. Big Data in Clinical AI.The effective use of big data frameworks has become essential for modern CDSS platforms, especially in emergency and high-throughput clinical settings. Frameworks such as Apache Spark and Hadoop [33] have been adopted for large-scale analytics on EHRs, enabling fast retrieval of historical cases, patient stratification, and cohort identification [34; 35]. For example, [PERSON] et al. developed the SERA algorithm, which utilizes both structured data and unstructured clinical notes to predict and diagnose sepsis, achieving high predictive accuracy up to 12 h before onset [36]. Additionally, big data solutions underpin scalable storage and indexing of clinical narratives, with Elastic-search and Solr frequently used to enable fast semantic search over unstructured clinical text [37]. **Digital Twins and Continuous Data Integration. ** Digital twin frameworks have been explored for patient monitoring and personalized care [38], especially with the rise of Internet of Things (IoT) medical devices (e.g., wearable vital sign monitors). For example, Eclipse Ditto [39] is an open-source platform that manages digital twin state and synchronization with IoT data streams. In a CDSS context, digital twins can mirror the status of each patient, integrating live sensor readings, lab results, and other data, and trigger alerts or recommendations when certain criteria are met. Recent studies have integrated digital twins with event-driven architectures: Node-RED [40] workflows subscribing to events (e.g., a sudden spike in heart rate) and then invoking clinical decision logic [41; 42]. **Emerging Technologies for Real-Time Clinical Data. ** Emerging technologies such as Flying Ad Hoc Networks (FANETs), characterized by decentralized, dynamic, and rapidly deployable aerial nodes, offer promising solutions for healthcare-related applications, particularly in emergency scenarios requiring rapid data collection and dissemination. FANETs can significantly enhance the responsiveness and coverage of real-time monitoring systems by facilitating flexible, high-speed communications in remote or disaster-affected areas [43].
## 6 Similarity Evaluation
### Local-Link-Focused (LLF) Method  
In the first proposed metric, chances are that the posture of a chain of links (such as an arm chain consisting of an upper-arm link and a lower-arm link) of the robot in the world frame look will very similar to that of the human, but the corresponding values of the link correlation coefficients will nevertheless be quite low because the mother link in the robot chain (such as the upper-arm link in an arm chain) may deviate from the direction of the corresponding human link in the world frame. For example, the directions of some robot mother links may be modified considerably to maintain balance, and, thus, all of the child links will be affected. Therefore, the local posture similarity cannot be properly assessed when using the WBF method. However, the local motions of certain links will be more important than whole-body motions under some circumstances. For example, human observers will pay more attention to the arms and upper torso of the demonstrator when they wish to learn arm movements in a dance class. Therefore, to enable the similarity metric to reflect the effects of local postures, a link vector of the human or the robot should be transformed into the link frame of its own mother link. When calculating the correlation coefficient of two vectors, the two link frames of the mother links of this pair of corresponding vectors are translated and rotated to coincide with each other and are unified as one frame. Meanwhile, the link vectors are translated and rotated with the link frames of their mother links, respectively. Then, the correlation coefficient between one robot vector and the corresponding human vector in the unified frame, \(r_{L}\), is  
\[r_{L}=\cos\alpha_{L}, \tag{45}\]  
where \(\alpha_{l}\in[0,\pi]\) denotes the angle between two corresponding vectors in their unified frame.  
Thus, the correlation coefficient between the postures of the robot and the human using the LLF evaluation method is  
\[R_{\rm L}=\frac{\sum\limits_{l=1}^{10}r_{l}}{10}=\frac{\sum\limits_{l=1}^{10} \cos\alpha_{l}}{10}, \tag{46}\]  
where \(R_{\rm L}\) is also the similarity index of the LLF evaluation method.  
In the experimental section, the two metrics proposed here will be applied to evaluate the similarities of whole-body and local-link features, respectively.
## 5 Conclusions and Future Work  
The data-driven class of inertial pedestrian navigation techniques faces the issue of low navigation accuracy brought on by straightforward network models. To solve this problem, the novel deep neural network RBCN-Net is proposed. On the dataset VOIMU used in this paper, networks including CNN, LSTM, ResNet18, ResNet50 and others are built and evaluated against RBCN-Net. The navigation accuracy is greatly increased with RBCN-Net, which also roughly matches the real trajectory.  
The shortcoming of this paper is that the obtained trajectories are trajectories in two-dimensional planes, and are not adapted to indoor maps. Some unforeseen pedestrian behaviors, such as collisions, emergency turns, etc., cannot be handled by it. As a result, we will continue to develop our work to provide precise indoor localization on any mobile device, regardless of measurement units, users, or settings.  
Methodology, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); Software, J.Z. and B.Z.; Validation, Y.Z. ([PERSON]); Formal analysis, Y.Z. ([PERSON]); Data curation, J.Z. and W.M.; Writing--original draft, Y.Z. ([PERSON]) and J.Z.; Writing--review & editing, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Lightweight Stacked Hourglass Network for Human Pose Estimation
## 4 Experiments and Results
### Effect of the Dilation Scale  
The dilated convolution in our residual block used zero padding equal to the dilation value \(D\) to fit the size of the input and output. Therefore, to check the effect of zero padding on the pose estimation problem according to dilation size, the dilation sizes of \(D_{1}=2\) and \(D_{2}=3\) and the increased sizes of \(D_{1}=3\) and \(D_{2}=5\) (proposed in this work) were compared (Table 4).  
The receptive field of the \(3\times 3\) dilated convolution extended by \(D=2,\;3,\;\text{and}\;5\) was the same as the standard convolution using kernels of \(5\times 5,\;7\times 7,\;\text{and}\;11\times 11\), respectively. In this experiment, optimum dilation enhanced the performance of pose estimation; however, when the dilation size became too large, too much zero padding caused the network to fail to learn the spatial features, resulting in a loss of the ability to localize joints. In the human pose estimation problem, it was confirmed that zero padding due to the size of the receptive field and dilation had a significant effect on performance, while the optimum dilation size was determined to be \(D_{1}=2\) and \(D_{2}=3\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method**\(\times\)**Stack** & **[EMAIL_ADDRESS] (Mean)** \\ \hline Ours (\(D_{1}=2,\;D_{2}=3\)) [\(\times 1\)] & 86.12 \\ Ours (\(D_{1}=3,\;D_{2}=5\)) [\(\times 1\)] & 85.67 \\ Ours (\(D_{1}=2,\;D_{2}=3\)) [\(\times 2\)] & 87.89 \\ Ours (\(D_{1}=3,\;D_{2}=5\)) [\(\times 2\)] & 87.19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different dilation scales with the MPII validation dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Residual Block** & **\# Params** & **[EMAIL_ADDRESS]** \\
**Architecture** & & & **(Mean)** \\ \hline Hourglass (Original) & \
\begin{tabular}{c} Bottleneck (Original) \\ (Figure 5a) \\ \end{tabular} & 6.7M & 87.72 \\ \hline Ours &
\begin{tabular}{c} Bottleneck (Original) \\ (Figure 5a) \\ \end{tabular} & 6.7M & 88.68 \\ \hline Ours & Ours (Figure 5d) & 3.9M & 87.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different network architectures with the MPII validation dataset. (# Params is the total number of parameters in a dual-stack hourglass network).