esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### Optimization Framework
#### 4.3.3 Hierarchical Solution Strategy  
The three-layer optimization architecture ensures scalability and real-time responsiveness through time-scale separation:  
* _Local layer:_ Station-level optimization \(\min_{P_{i}}\sum w_{k}f_{k}^{i}(P_{i})\) with equipment constraints, operating at fast timescales (seconds to minutes).
* _Regional layer:_ Cluster coordination via \(P_{i}^{k+1}=P_{i}^{k}+\eta\sum_{j\in\mathcal{N}_{i}}(P_{j}^{k}-P_{i}^{k})\) for load balancing, acting at intermediate timescales (minutes to hours).
* _Global layer:_ Weight adaptation \(\mathbf{w}^{k+1}=\mathbf{w}^{k}-\alpha\
abla L(\mathbf{w}^{k})\) using gradient-based meta-optimization, operating at slower timescales (hours to days).  
This hierarchical decomposition guarantees convergence through Lyapunov stability analysis while maintaining operational constraints across all system layers. The separation of timescales reduces communication overhead and ensures robustness to network delays and intermittent connectivity between charging stations. For effective implementation, key parameters require careful calibration: the coordination step size \(\eta\in(0,1)\) should be selected based on network topology, with smaller values for densely connected clusters to prevent oscillation; the meta-learning rate \(\alpha\) should follow an adaptive schedule, starting with \(\alpha\approx 10^{-2}\) and gradually decreasing to \(10^{-4}\) to ensure convergence to stable weight configurations.  
The system incorporates adaptive mechanisms to handle dynamic operating conditions. During periods of high network volatility or rapid changes in EV arrival/departure patterns, the algorithm automatically increases regional layer update frequency while temporarily freezing global weight updates until system metrics stabilize. Conversely, during periods of predictable demand, the system can reduce computational load by extending the interval between regional coordination steps. This adaptability ensures robust performance across varying operational scenarios while preserving computation resources.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 4 Channel Estimation for the Core Network: Link eNB-to-Relay Node
### Interpolation Methods
#### 4.1.2 BCI Method  
Another effective interpolation method is the BCI, which employs 1D Cubic Interpolation (CI). Considering the mathematical model developed in [31; 32], CI can expressed as  
\[\hat{\mathbf{H}}_{c_{d}^{i}s_{d}^{i}}^{ij}=\frac{3L_{p}\hat{\beta}^{2}-\hat{ \beta}^{3}}{L_{p}^{3}}\hat{\mathbf{H}}_{c_{p+1}^{i}s_{p+1}^{i}}^{ij}+\frac{L_{ p}^{3}-3L_{p}\hat{\beta}^{2}+2\beta^{3}}{L_{p}^{3}}\hat{\mathbf{H}}_{c_{p}^{i}s_{p} ^{i}}^{ij}+\frac{\hat{\beta}^{2}(\beta-L_{p})}{L_{p}^{3}}m_{c_{p+1}^{i}s_{p+1}^ {i}}+\frac{\beta(\beta-L_{p})^{2}}{L_{p}^{3}}m_{c_{p}^{i}s_{p}^{i}}, \tag{7}\]  
where \(\beta\) are the local variables, which are defined by \(\beta=(c_{d}^{i},s_{d}^{i})-(c_{p}^{i},s_{p}^{i})\) on the interval \((c_{p}^{i},s_{p}^{i})\leq(c_{d}^{i},s_{d}^{i})\leq(c_{p+1}^{i},s_{p+1}^{i})\). \(L_{p}\) denotes the data length between two consecutive pilot symbol positions. Furthermore, in (7), \(m_{c_{p}^{i}s_{p}^{i}}\) represents the slope of the interpolant at \((c_{p}^{i},s_{p}^{i})\), which should be determined in an accurate way to obtain the best performance. In this sense, in Algorithm 1, we present the Piecewise Cubic Interpolation (PCHIP), which permits us to find \(m_{c_{p}^{i}s_{p}^{i}}\), as in [33; 34]. \(\delta_{p}\) denotes the first-order difference of \(\hat{\mathbf{H}}_{c_{p}^{i}s_{p}^{i}}^{ij}\). Nevertheless, the Cubic Spline [32] is another algorithm to calculate \(m_{c_{p}^{i}s_{p}^{i}}\), the main difference of which, with respect to PCHIP, is that it has a continuous second derivative.  
```
Input :\(\hat{\mathbf{H}}_{c_{p+1}^{i}s_{p+1}^{i}}^{ij}\), \(\hat{\mathbf{H}}_{c_{p}^{i}s_{p}^{i}}^{ij}\), \(L_{p}\) Output :\(m_{c_{p}^{i}s_{p}^{i}}^{ij}\), \(\delta_{p}=\frac{\hat{\mathbf{H}}_{c_{p+1}^{i}s_{p+1}^{i}}^{ij}-\hat{\mathbf{H}} _{c_{p}^{i}s_{p}^{i}}^{ij}}{L_{p}}\) if\(\delta_{p}==-\delta_{p-1}\) || \(\delta_{p}\
eq 0\)&& \(\delta_{p-1}\
eq 0\)then \(m_{c_{p}^{i}s_{p}^{i}}^{ij}=0\) ; else \(m_{c_{p}^{i}s_{p}^{i}}=\frac{2\delta_{p-1}\delta_{p}}{\delta_{p-1}+\delta_{p}}\) ;  end if
```  
**Algorithm 1**PCHIP Method
# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 6 Experiments and Results
### Comparison with State-of-the-Art Approaches
#### 6.6.3 ICDAR-13  
Table 6 shows the results of HybridTabNet on the ICDAR-2013 [62] dataset. The current state-of-the-art approach [8] already achieved the perfect f1-score of 1.0 at the 0.5 IoU threshold. Our approach achieved an f1-score of 1.0 at the 0.5 IoU threshold, which is state-of-the-art performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**IOU**} & \multirow{2}{*}{**W.Avg**} \\ \cline{3-6}  & & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline \multirow{4}{*}{ICDAR-2019-TrackA-Modern} & TableRadar [63] & – & 0.969 & 0.957 & **0.951** & 0.897 & 0.940 \\  & NLPR-PAL [63] & – & **0.979** & **0.966** & 0.939 & 0.850 & 0.927 \\  & Cascade-TabNet [8] & – & 0.943 & 0.934 & 0.925 & 0.901 & **0.901** \\  & Ours & **0.953** & 0.942 & 0.933 & 0.927 & **0.901** & 0.928 \\ \hline \multirow{4}{*}{ICDAR-2017-POD} & Fast Detectors [1] & – & 0.921 & – & 0.896 & – & – \\  & PAL [1] & – & 0.960 & – & 0.951 & – & – \\  & GOD [12] & – & **0.971** & – & **0.968** & – & – \\  & DeCNT [7] & – & 0.968 & – & 0.952 & – & – \\  & Ours & **0.936** & 0.936 & **0.936** & 0.933 & **0.923** & **0.932** \\ \hline \multirow{2}{*}{ICDAR-2013} & Cascade-TabNet [8] & 1.0 & – & – & – & – & – \\  & Ours & **1.0** & – & – & – & – & – \\ \hline \multirow{2}{*}{TableBank(Latex)} & Cascade-TabNet [8] & 0.966 & – & – & – & – & – \\  & Ours & **0.980** & **0.980** & **0.978** & **0.971** & **0.934** & **0.9661** \\ \hline \multirow{2}{*}{TableBank(Word)} & Cascade-TabNet [8] & 0.949 & – & – & – & – & – \\  & Ours & **0.970** & **0.967** & **0.965** & **0.964** & **0.962** & **0.965** \\ \hline \multirow{2}{*}{TableBank(Both)} & Cascade-TabNet [8] & 0.943 & – & – & – & – & – \\  & Ours & **0.974** & **0.972** & **0.970** & **0.967** & **0.949** & **0.965** \\ \hline \multirow{4}{*}{Marmot} & DeCNT [7] & 0.895 & – & – & – & – & – \\  & CDeC-Net [9] & 0.952 & – & – & 0.840 & 0.769 & – \\  & Ours & **0.956** & **0.953** & **0.948** & **0.936** & **0.901** & **0.935** \\ \hline \multirow{4}{*}{UNLV} & GOD [12] & 0.928 & – & – & – & – & – \\  & CDeC-Net [9] & 0.938 & 0.883 & – & – & – & – \\  & Ours & **0.944** & **0.931** & **0.931** & **0.919** & **0.807** & **0.898** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of HybridTabNet on f1-scores with previous state-of-the-art methods. Our proposed method achieved state-of-the-art performance on every dataset except ICDAR-2017-POD. W.Avg denotes the weighted-average of the respective measure on the IoU threshold.
## 3 Results
### Diversity of 3 DPLM Production Methods  
Table 2 demonstrates the diverse 3 DP technologies and materials and varied times and costs involved in 3 DPLM production. Fused filament fabrication (FFF) was the most common 3 DP technology identified in this review (50%), followed by stereolithography (SLA) (13.6%) and selective laser sintering (SLS) (13.6%). These correlated with the frequently utilised 3 DP materials, including acrylonitrile butadiene styrene (ABS) (22.7%), polylactic acid (PLA) (22.7%), and Polymide 12 (PA12, commonly known as nylon) (13.6%). Transparent silicone/silicone-like materials were used in 10 articles (45.5%) to represent liver parenchyma.  
The imaging protocols, and segmentation and processing methods used also varied across the included articles. Nevertheless, 3 DPLM accuracy was generally adequate for clinical use, with studies reporting CT-validated dimensional error of <0.7-2% [31; 37; 39] or no greater than a few millimetres [17; 22; 31]. The majority of studies (68.2%) created 3 DPLMs using CT imaging data. Two studies exclusively used MRI datasets as per the preoperative protocol [9; 34], and three studies used a combination of CT and MRI [11; 14; 31] datasets to segment their 3D models without distinction or comment.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 3 Technical Architecture and Solution
### Embeddings RAG  
The embedding pipeline follows a standard architecture, based on two processes as well: one process for indexing, based on embeddings, and one for querying, retaining its modularity across multiple applications in different contexts. During indexing, we used JSON files containing text content, document titles, and source URLs, using the raw content for embeddings generation and storing them into Neo4J as an embedding variable. Before indexing, during this process, the documents undergo cleansing to remove unnecessary whitespace, empty lines, and specific unwanted substrings.  
Following cleaning, the documents are embedded using the embedding-ada-002 model from OpenAI [40] to create semantic vector representations, making similarity-based retrieval efficient and accurate. However, different embedding models can be adopted based on preference. Finally, the embedded documents are indexed into the Neo4J database, configured to use cosine similarity for optimal retrieval performance.  
The query process, responsible for generating responsive answers to user queries and employed with a RAG approach using GPT-4.1 chat completion, starts by embedding the user's query and retrieving the most relevant documents from the Neo4J document store. To improve the relevance of results, a reranker model with a default of five documents (intfloat/simlm-msmarco-reranker) refines the top retrieved documents before passing them to the prompt construction stage.  
Afterwards, a dedicated prompt template, presented in Table A4 in Appendix A, was designed to enforce strict grounding in the retrieved documents, ensuring that the generated answers cite only information present within the selected sources. Furthermore, each source is referenced using inline HTML links in the response, enhancing transparency and traceability. Finally, the structured prompt is passed to a language model, which generates a coherent and well-structured answer. The modularity of this system ensures that various open-source and proprietary models (e.g., Hugging Face models, OpenAI GPT, Anthropic Claude, Azure ChatGPT, and Coherence) can be seamlessly integrated based on project requirements.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 1 Introduction
Both equations show that the Young's modulus of a microcantilever's material is a key factor for sensitivity and mass resolution, such that a large Young's modulus increases the resonant frequency of the microcantilever, which, in turn, increases the mass resolution and sensitivity of a microcantilever-based biosensor. Under dynamic mode operation, the actuation mechanism of the microcantilever plays a critical role. The most used methods include magnetic, electrostatic, thermoelectric, optothermal, and piezoelectric actuation [17]. Piezoelectric actuation offers several advantages, including suitability for miniaturization, high efficiency, absence of electromagnetic noise generation, and non-flammability [18]. Among piezoelectric materials, lead (Pb)-zirconium (Zr)-titanate (Ti) (PbZr\({}_{x}\)Ti\({}_{1-x}\)O\({}_{3}\) (PZT)) films are commonly used because they exhibit some of the highest piezoelectric coefficients and strong electromechanical coupling factors [19]. However, because of the toxicity of lead (Pb), alternative materials, such as aluminum nitride (AIN), zinc oxide (ZnO), and barium titanate (BaTiO\({}_{3}\)), have been explored for use in MEMS piezoelectric film applications. AlN presents an electromechanical coupling coefficient of \(\sim\)0.23, a high breakdown voltage (\(>\)1.2 \(\times\) 10\({}^{6}\) V\(\cdot\)cm\({}^{-1}\)), a high resistivity (\(\sim\)10\({}^{12}\)\(\Omega\cdot\)cm), a low loss tangent, and a high signal-to-noise ratio [20; 21; 22; 23]. The purpose of the research presented in this article is to implement a methodology for studying the structural performance of UNCD microcantilevers actuated piezoelectrically, using AIN as the piezoelectric layer, for biosensing applications. The simulations were carried out using the commercially available software COMSOL Multiphysics Version 4. The results presented in this article provide valuable insight into the advantages of using UNCD-based microcantilevers for biosensing applications. The biosensor design is based on the structure proposed in reference [13], which features a stepped microcantilever with a portion of the beam left uncovered to allow particle adhesion on the UNCD surface, as shown in Figure 2. This uncovered region, referred to as the active area, can be functionalized for the selective binding of specific biomolecules, taking advantage of the excellent biocompatibility of the UNCD surface--an attribute demonstrated in multiple chapters of a recently published book edited by [PERSON] [24]. Additionally, a silicon (Si) microcantilever with the same design parameters was modeled to compare its performance with that of the UNCD-based microcantilevers.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 5 Discussion
### Experimental Results  
In this subsection, we discuss the performance of the proposed system. Because the security level and system performance exhibit a tradeoff relationship, the operation costs for data storage and retrieval will increase when the proposed scheme is adopted. To solve this issue, we measured the performance time for each process and demonstrated that the processes would be performed within a reasonable duration. For the experiments, we use forced expiratory volume data from a spirometry device.  
For the performance analysis, we used two computers with Intel(R) Core(TM) i5-8400 CPU @2.80 GHz, 8 GB of RAM, and Ubuntu Linux 18.04 LTS. These computers are used as proxy servers and client computers, respectively. In addition, pairing-based cryptography library 0.5.14 [48] and OpenSSL 1.1.1f library were used for PRE and AES cryptography, respectively. In this experimental analysis, we measured the operation times for the data storage and data retrieval processes.  
First, we analyzed the results of the data storage process. This process involves a digital signature, data encryption using the AES, and symmetric key encryption using the PRE. For the digital signature, a digital signature algorithm is used. To operate AES encryption, we selected AES256. The sizes of the EHRs ranged from 128 kB to 64 MB, and 100 tests were conducted for each data size. The average time required for each data size is shown in the blue graph of Figure 6.  
Next, we discuss the time required for data retrieval. This process involves symmetric key decryption using PRE, AES decryption, and the signature verification procedure. The experimental environment is the same as that for data storing. Moreover, to measure the operation time, we conducted 100 tests for each data size and calculated the average value. The experimental results for data retrieval are shown in the orange graph of Figure 6.  
As shown by the graphs in Figure 6, we can confirm that the operation times for saving or loading the EHR are less than 1 s if the size of the record is not greater than 64 MB. Consequently, we conclude that, except for a case involving a large EHR, data storage and retrieval can be completed within a reasonable duration. In addition, we can verify whether the operation time is proportional to the EHR size, such that the computation costs for the PRE used for encrypting and decrypting the symmetric key will not affect the  
Figure 6: Operation times for data storage and data retrieval.  
entire operation duration, even though the PRE is a relatively heavy cryptography scheme compared with other security schemes.
## 3 System Architecture
### FSO Channel Modeling
To mitigate this, larger receiver apertures are beneficial as they help collect more of the incoming beam and reduce signal loss. This effect, known as aperture averaging, improves system reliability by minimizing intensity fluctuations at the receiver. Additionally, beam divergence impacts the received power, especially in longer-range FSO links. A higher divergence angle results in increased spreading loss, reducing the power density at the receiver and leading to higher BER. By optimizing the beam width and receiver aperture size, the system can effectively counteract these losses, enhancing overall link performance. We note that our model assumes ideal alignment (no pointing error) between transmitter and receiver, which maximizes the collected optical power. In practice, pointing errors (jitter) can cause misalignment losses that compound turbulence effects [47]. To mitigate scintillation and beam wander, aperture averaging is employed in the receiver design: using a larger receiver lens helps to average out intensity fluctuations across its surface [47]. These considerations are taken into account in the link budget and design, even though the baseline model uses the Gamma-Gamma distribution for turbulence-induced fading. Adaptive power control and spatial diversity techniques counteract transmission losses, fortifying the stability of the link. Additionally, LDPC decoding plays a crucial role in lowering the bit error rate by correcting errors that arise from atmospheric turbulence. Through its iterative approach incorporating soft decision feedback, the decoder substantially boosts error resilience, enabling the receiver to reconstruct the transmitted data accurately.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 5 Evaluation Results
### Effect of \(1/\lambda_{C}\)  
Figure 6 shows the effect of the average duration \(1/\lambda_{C}\) of the connection between the ground station and the observation satellite on the average delay \(\zeta_{D}\) to deliver the observation to the destination. From Figure 6, it can be observed that the average delay of RNA-OTS decreases as \(1/\lambda_{C}\) increases. This is explained in the following: A large duration \(1/\lambda_{C}\) of the connection between the ground station and the observation satellite indicates that the observation satellites can transmit the observations to the ground station most of the time without conducting the delay action. In addition, because a large duration \(1/\lambda_{C}\) indicates that observation satellites can transmit the observations to the ground station most of the time, the load of the relay satellite does not increase significantly (i.e., short queuing delay in the relay satellite), which leads the decreased delay \(\zeta_{D}\) in delivering the observation to the destination.  
Figure 4: Effect of the target utilization of the computational resource.  
Figure 5: Effect of the target utilization of the storage resource.  
Figure 6: Effect of the average duration of the connection between the ground station and the observation satellite on the average delay.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
###### Abstract  
The time synchronization protocol is indispensable in various applications of wireless sensor networks, such as scheduling, monitoring, and tracking. Numerous protocols and algorithms have been proposed in recent decades, and many of them provide micro-scale resolutions. However, designing and implementing a time synchronization protocol in a practical wireless network is very challenging compared to implementation in a wired network; this is because its performance can be deteriorated significantly by many factors, including hardware quality, message delay jitter, ambient environment, and network topology. In this study, we measure the performance of the Flooding Time Synchronization Protocol (FTSP) and Gradient Time Synchronization Protocol (GTSP) in terms of practical network conditions, such as message delay jitter, synchronization period, network topology, and packet loss. This study provides insights into the operation and optimization of time synchronization protocols. In addition, the performance evaluation identifies that FTSP is highly affected by message delay jitter due to error accumulation over multi-hops. We demonstrate that the proposed extended version of the FTSP (E-FTSP) alleviates the effect of message delay jitter and enhances the overall performance of FTSP in terms of error, time, and other factors.  
Keywords:time synchronization; clock; performance analysis; message delay jitter; wireless sensor networks +
Footnote †: journal:
# Intelligent Target Design Based on Complex Target Simulation
## 5 Conclusions  
The continuous improvement of scientific and technological means of military warfare has made it particularly important to design and simulate various targets for assessing the performance of ground air defense systems. An intelligent target design based on complex target simulation was proposed in this paper to accurately simulate various types of targets. The main conclusions are as follows:  
1. The corner reflector could simulate the RCS distribution characteristics of the target sharp angle by changing the size and angle between the two metal plates on both its sides.
2. The Luneburg ball reflector could simulate the RCS distribution characteristics of the target stationary space angle by changing the size and position of metal reflectors.  
Figure 8: Radar cross-section distribution characteristics of the corner reflector and Luneburg ball array.  
Figure 9: Radar cross-section distribution characteristics of an F-22 fighter jet.  
3. The classification and intelligent shifting of several models using the fuzzy clustering algorithm and single-chip microcomputer is the focus of future research on intelligent target design for complex target simulation.
4. In future research, a certain type of aircraft will be simulated using the variable structure angle reflector and its array form, as well as the Luneburg ball reflector. However, it is necessary to further consider how to construct the hybrid array of the variable angle reflector and Luneburg balls and to predict and evaluate the accuracy of the simulated target. At the same time, the distribution characteristics of the RCS simulating a certain range of pitch angles should be considered for accurate radar target detection, and an error analysis should be made.  
Conceptualization, X.W.; methodology, J.H.; investigation, S.Y.; formal analysis, H.G.; software, J.H.; Resources, C.Y.; validation, W.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Ministry of Education of the People's Republic of China] grant number [2019020940001].  
Not applicable.  
The authors declare no conflict of interest.