esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When a potential difference is applied between the electrodes, an electric field is established in the dielectric fluid, causing electrons to migrate toward the anode and positive ions toward the cathode. This movement results in collisions with molecules and particles in the dielectric fluid, generating energy and forming a plasma channel. Electrical discharges occur through this plasma channel, heating the workpiece and causing localized melting and re-solidification of the material [41; 42; 43]. The dielectric fluid helps absorb and remove the heat generated by the discharges from both the tool electrode and the workpiece [44]. The characteristics of the resulting coating depend on the applied polarity, machine parameters, dielectric fluid, and the tool electrode used [45; 46]. Previous studies have investigated the effects of EDM processing on Ti-6 Al-4V, analyzing surface morphology, roughness, hardness, and modifications induced by different machining parameters [47; 48]. These parameters were explored in terms of their influence on surface characteristics, and different dielectric fluids were evaluated for their role in modifying surface properties. However, none of them assessed the in vitro biocompatibility of EDM-treated Ti-6 Al-4V, particularly when using fluorine- and calcium/phosphorus-based dielectric fluids. To date, no studies have conducted a cytotoxicity evaluation of EDM-modified Ti-6 Al-4V using these dielectric fluids [49,50]. This study expands upon previous research by introducing an innovative approach that combines two polarity conditions and distinct dielectric fluids, containing fluorine/phosphorus and calcium/phosphorus, to modify the Ti-6 Al-4V alloy through the EDM process. Furthermore, it presents, for the first time, an in vitro cytotoxicity evaluation of the surfaces obtained with these dielectric fluids applied under both polarity conditions. These findings contribute to a better understanding of how these parameters may influence the surface characteristics of Ti-6 Al-4V for biomedical implants applications. To build on these insights, this work offers a comprehensive and novel analysis of EDM-modified titanium surfaces. The influence of dielectric composition and tool polarity was systematically investigated under controlled conditions. Multiple characterization methods were employed, including optical microscopy, surface roughness measurements, microhardness testing, contact angle analysis, and in vitro cytotoxicity assays. This combination of techniques allowed for a detailed evaluation of morphological, mechanical, physicochemical, and biological properties. The integrative approach not only confirms the surface changes promoted by EDM but also establishes a solid framework for optimizing titanium-based implants for biomedical applications.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 5 Results
### Signal-to-Noise Ratio Impact on Radar Sensing
#### 5.2.1 Peak-to-Noise-Floor Ratio  
The peak-to-noise-floor ratio (PNFR) is defined as follows [37]:  
\[PNFR=20\log_{10}\left(\frac{A_{reflection}}{A_{noise}}\right), \tag{21}\]  
where \(A_{reflection}\) (in dB) denotes the peak magnitude of the radio signal reflected by the moving target in the RVM matrix, and \(A_{noise}\) (in dB) denotes an estimated average noise floor magnitude of the RVM matrix. The average noise floor magnitude is estimated by taking a small square (45 \(\times\) 45 points of RVM) where only noise appears and calculating its average value.  
In Figure 9, PNFR is presented together with the root mean square error (RMSE) of the velocity estimation based on the Aboutanios and Ye method [34], as a reference. It can be seen that the PNFR becomes constant at 80 dB of the input SNR. Higher values do not result in a better PNFR; moreover, the RMSE velocity becomes constant at about 50 dB of the input SNR, so it is clear that the increase in PNFR is not reflected in a more precise velocity measurement. PRS-2-2-2-RES, PRS-4-4-2-RES, and PRS-6-6-2-RES are the configurations with the best PNFR performance, reaching about 83 dB. These configurations also have the highest PNFR for all levels of SNR. The next best are PRS-12-12-1-RES, PRS-6-6-1-RES, PRS-4-4-1-RES, and PRS-2-2-1-RES, which reach a maximum PNFR of approximately 77 dB. The two worst-performing configurations are PRS-12-1-2-RES and PRS-12-1-1-RES. They stand out from the rest, with the first one achieving a maximum PNFR of 74 dB, and the second one achieving only 70 dB. According to the velocity estimation, all configurations, apart from PRS-12-1-2-RES and PRS-12-1-1-RES, achieve similar performance for a wide range of SNRs. All of them achieve RMSE values under \(10^{-3}\) m/s, with the two worst configurations reaching this later than the others. In general, we see only a slight difference between configurations with one and two PRS resources. Allocating two times more resource elements for PRS does not guarantee sufficient gain in terms of PNFR. More detailed analysis of velocity estimation is presented in the next subsection.
# Candidate Waveforms for ARoF in Beyond 5G
###### Abstract  
5G mobile networks aim to support a large variety of services with different and demanding requirements. To achieve this, analog radio over fiber (ARoF) fronthaul along with millimeter-wave (mmWave) cells is a strong candidate to be part of the 5G architecture. Very high throughput can be achieved by using mmWave signals due to the large available bandwidths, which combines well with the advantages of employing ARoF technology. Nevertheless, combined mmWave and ARoF systems face a particular challenge as the impacts of both channels--such as high free-space path loss, phase noise, chromatic dispersion, and other degrading effects--affect the signal without the possibility for intermediate restoration. The selection of the signal waveforms plays an important role in reducing these defects. In addition, waveforms are one of the keys in the physical layer available towards satisfying the requirements for 5G and beyond. In this manuscript, several key requirements are presented to determine the merit of candidate waveform formats to fulfill the 5G requirements in the mmWave ARoF architecture. An overview of the different suitable waveforms for this architecture is provided, discussing their advantages and disadvantages. Moreover, a comprehensive comparison in terms of different requirements is also presented in this paper.  
5G; ARoF; mmWave; DSP; waveform; modulation; OFDM +
Footnote †: journal: Applied Sciences
## 3 Results
### Regeneration  
To evaluate the reusability of BS-GMA-EDA-Q and BS-GMA-PEHA-Q, the adsorption-desorption cycles were repeated five times using 1 M HCl as the elution reagent. Figure 10 shows that BS-GMA-EDA-Q and BS-GMA-PEHA-Q have a negligible decrease in adsorption capacity after five adsorption-desorption cycles, indicating that BS-GMA-EDA-Q and BS-GMA-PEHA-Q were stable and recyclable for phosphate removal.  
Figure 10: The adsorption–desorption cycles of BS-GMA-EDA-Q and BS-GMA-PEHA-Q for phosphate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & \multicolumn{2}{c}{**Max Adsorption**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**Reference**} \\  & **Capacity (mg/g)** & & \\ \hline woody sawdust nanoparticles & 50 & 7 & [41] \\ \hline polypyrrole-coated sawdust & 17.33–30.39 & & [42] \\ \hline amine-crosslinked Shaddock Peel & 59.89 & 3 & [43] \\ \hline diethylamine modified Cellulose & 22.88 & 6.8 & [44] \\ \hline modified sugar-case bagasse & 21.3 & 7 & [45] \\ \hline wheat straw anion exchanger & 52.80 & & [46] \\ \hline Amine crosslinked tea waste & 98.72 & 6 & [47] \\ \hline BS-GMA-EDA-Q & 85.25 & 7 & This paper \\ \hline Modified aleppo pine sawdust & 116.25 & & [40] \\ \hline BS-GMA-PEHA-Q & 152.21 & 7 & This paper \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of adsorption capacity of BS-GMA-EDA-Q and BS-GMA-PEHA-Q with other available sawdust.
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion
### Baseline Models  
In this research, to evaluate the performance of the proposed GCN-BiLSTM model, we select three typical models for controlled experiments. Autoregressive Integrated Moving Average (ARIMA) [27] belongs to the classical mathematical statistics method, which is good at dealing with temperature data with prominent non-stationary fluctuation characteristics. LSTM [15] is a commonly used model in time series processing research. It can learn the temporal dependence of temperature from historical data and has good performance in handling data with time series features. The Deep Feedforward Network (DFN) [28] is a DL method that can learn the relationships between meteorological elements and temperature to perform accurate predictions.  
ARIMA: This model converts a nonstationary time series into a stationary time series, and then regresses the lag values of the dependent variables, the present values, and lag values of the random errors to deal with time series forecasting problems.  
LSTM: The model explores the dependencies between sequence elements by transferring internal states. The gating mechanism is applied to solve the defect of the RNN gradient update.  
DFN: DFN consists of an input layer, an output layer, and multiple hidden layers. There is no backward feedback in the network, and the signal propagates unidirectionally from the input layer to the output layer.
## 2 Materials and Methods
The initial position of electron (\(x^{\prime},y^{\prime},z^{\prime}\)) is set at \((0,0,14.5\lambda_{0})\), so the collision center of the NITS process (i.e., the maximum value of the electron motion amplitude) is guaranteed to be at the coordinate origin. The time-dependent electromagnetic field generated by a moving charged particle can be deduced from the Lienard-Wiechert potentials [23]; thus, the radiated power per unit solid angle can be calculated as follows:  
\[\frac{dP(t)}{d\Omega}=\left[\frac{|\mathbf{n}\times[(\mathbf{n}-\mathbf{u})\times d_{t_{e }}\mathbf{u}]|^{2}}{(1-\mathbf{n}\cdot\mathbf{u})^{6}}\right]_{t^{\prime}} \tag{16}\]  
where the subscript \(t^{\prime}=t_{e}+R^{\prime}-\mathbf{n}\cdot j\) indicates the equation should be calculated at the delay time \(t\). \(R^{\prime}\) is the distance between the observer and interaction center and \(\mathbf{j}\) is the electron's displacement. The power of radiated pulse \(dP(t)/d\Omega\) is normalized by \(e^{2}\omega_{0}^{2}/4\pi c\), and the radiation direction \(\mathbf{n}\) is as follows:  
\[\mathbf{n}=(\sin\theta\cos\phi,\sin\theta\sin\phi,\cos\theta) \tag{17}\]  
where \(\theta\) is the polar angle and \(\phi\) is the azimuth angle shown in Figure 1.  
By solving the ordinary differential Equations (14) and (15) with the 8-9 Runge-Kutta-Fehlberg method (RKF89) along with a large-scale GPU parallel solution on MATLAB, the numerical solution of the electron's position, velocity and acceleration are recorded for each step, and the radiation with different states in time and space can be obtained from Equation (16).
# A Vulnerability Index for Multimodal Transportation Networks: The Case of Korea
## 3 Methodology
### Definition of Vulnerability Functions
#### 3.3.2 Vulnerability of Group Nodes  
The function for analyzing the network vulnerability of group nodes on a regional scale is as follows. This allows for the calculation of vulnerability values for each region within the network. Specifically, it evaluates how vulnerable a particular region is in terms of its connections to external regions. In other words, it indicates the degree to which the nodes within a region are connected to nodes in other regions, determining the region's vulnerability.  
\[v(T)=\mid T\mid-\mid N(T)\mid \tag{3}\]  
where \(\mid T\mid=\) Number of nodes in the set \(T\);  
\(N(T)=\) A set of neighboring nodes connected to the nodes in set \(T\);  
\(\mid N(T)\mid=\) Size of neighboring nodes whose set is connected to the outside.  
Here, \(v(T)\) represents the vulnerability of the group node set \(T\), and \(\mid N(T)\mid\) indicates the number of neighboring nodes connected externally to the set. This formula evaluates how connected or disconnected a group of nodes is compared to other groups in the network. If the nodes in set \(T\) are well connected externally, the vulnerability value will be high. Conversely, if external connections are limited, the vulnerability value will be low.  
* If \(v(T)>0\), the region is relatively isolated and in a vulnerable state within the network. This means the nodes within the set lack sufficient external connections, making the region vulnerable. In transportation networks, poorly connected regions may lead to disruptions in traffic flow, social mobility, and economic activities between regions.  
* If \(v(T)<0\), the region is well connected to its surroundings, indicating lower vulnerability within the network. Nodes within the set act as strong connection hubs, reducing their susceptibility to external disruptions. Such regions play a key role in supporting inter-regional connectivity within the network.  
As a result, we analyzed the structural characteristics and regional vulnerabilities of Korea's transportation network using the vulnerability evaluation results for both single and group nodes. Nodes with positive vulnerability values in the single-node vulnerability function were classified as highly vulnerable nodes within the network, often corresponding to bottleneck points in traffic flow. Regions with negative vulnerability values in the group-node vulnerability function were identified as major connection hubs, supporting the overall network stability.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 4 Proposed Solution
### Design Objectives  
The design of SSBI aims to address and satisfy the following list of requirements within our proposed implementation:  
* Prevent financial data leakage when third-party agencies promote financial products or KYC services, and eliminate user's fraud calls and phishing messages by trusting DID and VCs.
* Trusted identity owner since we consider the active account bank user, which means the KYC process is to completed in advance with a unique DID.
* Achieve a high level of assurance using Veramo key management combined with tamper-resistant secret key storage, by relying on a payment card to prevent theft and unauthorized usage, with potential enhancement to use a smart card with embedded biometric fingerprint authentication.
* Ensure validity of identity and proofs by asserting the revocation status on-chain using Veramo APIs.
* Strong and multifactor authentication against attacks, such as replay, Man-in-The-Middle, and theft attacks, through the usage of a banking card, PIN, and biometric card.
* User-controlled identity and data sharing. The user is able to manage one or more identities, including their attributes, using an open system that prohibits third parties  
Figure 2: NFC banking card sample equipped with a fingerprint.  
to restrict its usage. The user is able to select and authorize related parties to access these identities by enabling access to subsets of identity attributes. User consent can be given, and the related party is able to request access to the identity attribute of an end user. The attributes must be retrievable without a direct communication channel between the user and the party.
* The identity data owner is able to revoke access at any time.
* A secure and seamless user experience is achieved by combining the trusted issued banking cards for cryptographical identity operations. Furthermore, we propose new form factors such as stickers, tags, and biometric payment cards.
* An identity and secret key recovery mechanism is proposed where the key part is saved by the issuing bank while the passphrase is memorized by the user.
# A Supernet-Only Framework for Federated Learning in Computationally Heterogeneous Scenarios
## 4 Experiments
### Evaluation of Supernet-Only Framework
#### 4.1.1 Evaluation of the Supernet-Only Framework Under Ring-Based Federated Learning  
We first evaluate the Supernet-Only framework using ring-based FL [12] under simulated resource-constrained conditions. The supernet is pretrained on ImageNet1K [8] using the method recommended in Section 3.2. Federated datasets are divided into fourteen parts to simulate private datasets owned by fourteen clients with different performance levels. The variable module counts allowed for each client simulate the computational constraints. In the start of the federated learning phase, the first client of each federated cluster obtain the model from a provider. Then, each client trains the model using private data and passesthe updated model to the next client following training until the last client passes the model to the first one. Thus, the cycle goes on, simulating ring-based FL.  
Federated training is conducted for 20 epochs with a batch size of 64, using a learning rate of \(1\times 10^{-4}\) and the Nadam optimizer with default settings. Following the related work, we select the public datasets CIFAR-100, CIFAR-10 [9], and CINIC-10 [10] for evaluation. CIFAR-10 and CIFAR-100 each provide 50,000 training images and 10,000 validation images, while CINIC-10 provides 90,000 images for training and 90,000 for validation. To simulate online learning, the random generator is not turned off during inference, leading to some performance fluctuations in the Max Trainable and Max Runnable groups. After the completion of the federated learning process, the mean and variance of the accuracy for all clients on the validation set are as shown in Table 1. The mean reflects the federated training performance of the model on heterogeneous clients under various data conditions. The variance of accuracy among all clients indicates the variability in performance within the group.  
As shown in Table 1, our method achieved the best performance across all datasets, benefiting from its enhanced federated training capabilities. In the experiments on CIFAR-100, which has only 600 samples per class and presents greater challenges for model generalization, our method demonstrated the most significant improvement. However, in the federated training on CIFAR-10 and CINIC-10, the gaps between our method and the Max Trainable group are narrow, whereas the performance gaps between our method and the Max Runnable are significant. Across all datasets, Peaches, along with both the No Federated and Grouped settings, exhibited inferior overall performance due to its limited ability to share desensitized information. Furthermore, the No Federated group, which had the least sharing, performed the worst, despite each client being able to use the strongest model it could afford.
## 7 Review of Security and Privacy-Preserving Schemes in VANETs
### k-Anonymity Schemes  
Researchers have proposed k-anonymity schemes for VANETs. In [32], a k-anonymity scheme has been proposed where k vehicles in a region are assigned the same PN for communicating with the RSU. This ensures that an attacker cannot associate a message with a specific vehicle. An attacker can only detect that a group of nodes are receiving the messages but cannot determine which one in particular. The source and destination anonymity cannot be guaranteed as VANETs are inherently changing all the time, therefore, it is difficult to identify the source and destination. In k-anonymity schemes, when a message has to be delivered to a vehicle, the region is flooded with the message to ensure that it reaches the destination vehicle. However, in such a multi-hop scheme there is a clear problem of the scarce bandwidth resource utilization. The network might be flooded by the same message, causing congestion which is highly undesirable in VANETs.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 6 Experiments and Results
### ADS-B Data Spatiotemporal Griding Modeling Experiments  
After establishing the data representation relationship of airspace grid spatio-temporal information, a MongoDB-based grid database was created for airspace management, using the grid spatiotemporal code as the primary key of the database to make full use of the query, and retrieval of the string code. The table structure is designed as follows: each subdivision element maps a subdivision element code and multiple aircraft objects (there can be multiple aircrafts in a grid at the same time); thus, they can be organized by grid data and aircraft object data. The multiscale grid aviation database is created using the multiscale prefix feature of the grid. The ADS-B data design for the database table structure is shown in Table 7.  
The results of the coding conversion time for different data volumes--20%, 40%, 60%, 80%, 100%--are shown in Table 8.  
It is observed that ADS-B data conversion for the entire day, using the coding conversion method outlined in Section 4, takes only a hundred milliseconds for conversion on the order of ten million data points, at various ratios of the complete data volume, and the cost of coding conversion is less. Thus, the coding conversion time advantage is more obvious  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Database (Table) Structure** & **Note** \\ \hline \{ & \\  “GeoSOT-AS Code”:[ & Planar code \\ PCode: N49F461611434217 & Time code \\ Hcode: 1 1011010 & Tcode: 0 1001001 110011 \\ ], & \\ “data\_exisit”:1, & Data source identifier \\ “dataid”:603e3a3 fb9a39c8 dbd9245e8 & \\ “data”:[ & \\ Attribute\_I:[ & information (I) property class \\ “B”:104.373 & Longitude \\ “L”:29.7987 & Latitude \\ “H”:359.7 & QNE \\ “Direction”:315 & \\ “T”: 2020-04-26T00:00:00Z & ISODate \\ “v”:680 & Speed \\ ] & \\ Attribute\_S:[ & situation(S) property class \\ “Fnum”:CCA4392 & Flight number \\ “Type”:A320 & \\ “DA”:ZGSD & Departure airport \\ “AA”:ZUUU & Arrival airport \\ ] & \\ \(\cdots\) & \\ ] & \\ \hline \hline \end{tabular}
\end{table}
Table 7: ADS-B information structure table under GeoSOT-AS framework.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Data Amount** & 20\% & 40\% & 60\% & 80\% & 100\% \\ \hline
**Conversion time (ms)** & 0.02 & 0.13 & 1.15 & 12.91 & 109.89 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Coding conversion time results.  
when the data volume is lower due to the machine performance, and it is clear from the above analyses that the method is less time costly and more operable.