esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparative Analysis and Results
### Demonstrating the Excellence of MetaSSI  
Self-sovereign authentication in the Metaverse presents cost-effectiveness over other authentication mechanisms through various means. Firstly, it eliminates intermediaries, enabling users to manage their digital identities directly, thus reducing the costs associated with third-party services. Secondly, it cuts down infrastructure expenses by leveraging decentralized technologies like blockchain, which diminishes the need for centralized infrastructure maintenance. Additionally, MetaSSI ensures enhanced security through cryptographic techniques and decentralized identifiers, mitigating risks of data breaches and compliance costs. It also enhances user experience by empowering users to control their digital identities, leading to decreased support costs related to forgotten passwords. Scalability is another advantage, as self-sovereign authentication can naturally grow with the Metaverse, avoiding substantial additional expenses. Moreover, it opens up monetization opportunities through new models of data sharing, creating revenue streams for both users and service providers. Despite the potential initial implementation costs, the long-term benefits in terms of cost-effectiveness, security, scalability, and user experience outweigh these investments, especially in the dynamic landscape of the Metaverse.  
MetaSSI proves to be time-effective in the Metaverse due to several key factors. Firstly, it facilitates faster onboarding processes by streamlining identity creation and management, thus reducing the time typically spent on lengthy registration procedures. Additionally, its simplified authentication workflows eliminate the need for users to remember multiple passwords, enabling swift authentication through single-click actions or biometric verification. Efficient account recovery further saves time by empowering users to independently recover their accounts using decentralized identifiers and cryptographic keys, bypassing lengthy verification steps and support interactions. Real-time updates and access control capabilities enable users to manage their personal information promptly and efficiently, reducing administrative overheads and enabling swift interaction with digital environments. Furthermore, self-sovereign authentication can be seamlessly integrated with emerging technologies like augmented reality (AR) and virtual reality (VR), ensuring frictionless authentication experiences within immersive digital environments. Overall, by reducing authentication-related friction and streamlining identity management processes, MetaSSI optimizes the user experience in the Metaverse, saving time for both users and service providers.
## 3 Numerical Solution Scheme
 \tag{47}\]\[r_{N}= T_{N}^{k}+(1-\psi)\Delta tf_{N}^{k}+\psi\frac{\Delta t}{(C_{e-h})_{N}^{k+1 }}\times \tag{48}\] \[\left(S_{N}^{k+1}+2(E_{g})_{N-\frac{1}{2}}^{k+1}\times\frac{J_{N-1} ^{k+1}}{\Delta z}+\frac{(C_{e-h})_{N}^{k+1}}{(\tau_{e-p})_{N}^{k+1}}(T_{a})_{N}^ {k+1}-\left(\frac{\partial n}{\partial t}\right)_{N}^{k+1}(E_{g})_{N}^{k+1}\right.\] \[-n_{N}^{k+1}\left\{\left(\frac{\partial E_{g}}{\partial n}\right) _{N}^{k+1}\left(\frac{\partial n}{\partial t}\right)_{N}^{k+1}+\left(\frac{ \partial E_{g}}{\partial T_{a}}\right)_{N}^{k+1}\left(\frac{\partial T_{a}}{ \partial t}\right)_{N}^{k+1}\right\}\right).\]  
Such a system can be resolved with respect to \(\left\{T_{i}^{k+1}\right\}_{i=1}^{N}\) by using the well-known tridiagonal matrix algorithm [41]. We denote the electronic temperature calculated with assumption (36) as \((T_{i}^{k+1})^{(1)}\), showing with \"\((1)\)\" the first correction step. This result allows to calculate the corrected new values of functions in list (35):  
\[[A_{i}^{k+1}]^{(1)}=A_{i}(T=[T_{i}^{k+1}]^{(1)}). \tag{49}\]  
For an improved precision, the corrected new values of \(n\) and \(T_{a}\) can be calculated using the semi-implicit approach (instead of the explicit scheme, Equations (17) and (18), used for the predictor):  
\[\left[n_{i}^{k+1}\right]^{(1)}=n_{i}^{k}+(1-\psi)\Delta t\left(\frac{\partial n }{\partial t}\right)_{i}^{k}+\psi\Delta t\left[\left(\frac{\partial n}{ \partial t}\right)_{i}^{k+1}\right]^{(1)}, \tag{50}\]  
\[\left[(T_{a})_{i}^{k+1}\right]^{(1)}=(T_{a})_{i}^{k}+(1-\psi)\Delta t\left( \frac{\partial T_{a}}{\partial t}\right)_{i}^{k}+\psi\Delta t\left[\left( \frac{\partial T_{a}}{\partial t}\right)_{i}^{k+1}\right]^{(1)}. \tag{51}\]  
In turn, the corrected values allow to calculate \((T_{i}^{k+1})^{(2)}\) from Equation (38) and so on. Owing to its similarity with predictor-corrector methods, we call it a \"predictor-corrector\" algorithm. With this approach, Equation (19) can be rewritten in the following form,  
\[(a_{i})^{(l)}(T_{i-1}^{k+1})^{(l+1)}+(b_{i})^{(l)}(T_{i}^{k+1})^{(l+1)}+(c_{i })^{(l)}(T_{i+1}^{k+1})^{(l+1)}=(r_{i})^{(l)}, \tag{52}\] \[l=0,1,\ldots,\]  
where index \((l)\) shows the current step of correction and \((l)=(0)\) means the value is old, i.e., taken at time step \(k\). This procedure continues until the difference between last two corrected values of electronic temperature is less than the demanded precision:  
\[\sum_{i=1}^{N}\left[(T_{i}^{k+1})^{(l+1)}-(T_{i}^{k+1})^{(l)}\right]<\epsilon. \tag{53}\]  
It takes up to 200 corrections to reach the chosen precision of \(\epsilon=10^{-6}\) K during the laser pulse action, whereas when the laser is ended, 5 corrections are usually enough. The chosen numerical setup is discussed in the next section.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 2 Preliminaries and State of the Art
### Indoor Positioning Technologies
RFID, although being passive and making use of energy harvesting, also has a limited reading range for communication [28; 35] or synchronization [29]. A promising hybrid method makes use of the backscattering technology refined in RFID systems, and directly communicates the received acoustic signals back [41]. In [27], the acoustic signal coming from a CMUT transceiver is modulated around the carrier, making it vulnerable to self-interference and prohibiting multiple access when several mobile nodes are present. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Technology**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Technique**} & \multirow{2}{*}{**Accuracy [cm]**} & \multirow{2}{*}{**Ultra-Low Power**} & \multirow{2}{*}{
\begin{tabular}{c} **Energy** \\ **Harvesting** \\ \end{tabular} } \\  & [12] & RSSI & 75 & \(\checkmark\) (passive) & \(\checkmark\) \\ RFID/ML & [13] & RSSI & 10 & \(\checkmark\) (passive) & \(\checkmark\) \\ RFID/odometry & [11] & RSSI & 5 & \(\checkmark\) (passive) & \(\checkmark\) \\ RFID/odometry & [14] & PoA & 4 & & \\ \hline UWB & [16] & TDoA & 11.4 & & \\ UWB/RFID & [17] & ToA & 13.2 & \(\mathbf{\chi}\) (6.6 mW idle) & \\ UWB/RFID & [15] & ToF & \(<\)10 & & \\ \hline \multirow{6}{*}{Acoustic} & [30] & TDoA & 22 & & \\  & [23] & ToF & 0.05 & & \\ \cline{1-1}  & [31] & TDoA & 20 & & \\ \cline{1-1}  & [32] & TDoA & 50 & & \\ \cline{1-1}  & [33] & TDoA & 3 & \(\checkmark\) (100 mW) & \\ \cline{1-1}  & [34] & TDoA & \(<\)0.5 & & \\ \cline{1-1}  & [24] & ToF, TDoA & \(<\)24 & & \\ \cline{1-1}  & [24] & AoA & \(<\)400 & & \\ \hline \multirow{6}{*}{RF-Acoustic} & [22] & ToF & \(<\)1 & \(\mathbf{\chi}\) (150 mW) & \\ \cline{1-1}  & [39] & RSS, AoA & \(<\)50 & & \\ \cline{1-1}  & [25; 26] & ToF & \(<\)0.2 & \(\mathbf{\chi}\) (21.7 mW) & \\ \cline{1-1}  & [27] & ToF & \(<\)1 & \(\checkmark\) (passive) & \(\checkmark\) \\ \cline{1-1}  & [28] & ToF & 13 & \(\checkmark\) (81 mW) & \(\checkmark\) \\ \cline{1-1}  & [29] & ToF & 5 & \\ \cline{1-1}  & [35] & TDoA & \(<\)0.5 & \(\mathbf{\chi}\) (26.4 mW) & (\(\checkmark\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative summary of indoor positioning systems.
## 3 Results and Discussion
### Recommendations and Limitations on Land Use
#### 3.3.2 Land Use Limitations Degree Mapping for the SE Margin of the Ria de Arosa
Where risks are lower or not recognized, activities such as urban expansion and industrial developments (industrial parks or factories) may be carried out. These locations are found in a large sector of the interior of the eastern margin, connecting the municipalities of Villanueva de Arosa and Cambados and continuing under the mouth of the Umia River, east of the Umia-Grove Bay. These areas are also recognized along the southern margin of the Castrove Peninsula, north of Portonovo. The El Grove peninsula concentrates optimal areas for anthropogenic activities on the interior margin of the town of El Grove and around the outskirts of the Mexilloneira beach. As indicated at the beginning of this section, this tool classifies the territory into categories based on high, medium, or low land use constraints, allowing for a rapid understanding of potential restrictions in each parcel. It was developed to simplify the complex information represented in the land use recommendation and limitation mapping (Figure 9) by summarizing the combined data on conservation quality and natural hazards into three general levels. The assignment of each constraint level is consistent with the data used and the logic of qualitative overlay: areas with high constraints correspond to sites where both high conservation value and natural hazards are present; medium-constraint areas show one of the two limiting factors (e.g., significant risk but low natural value or vice versa); and low-constraint areas lack distinctive environmental elements or clear hazard presence. The results align with the source data, indicating that this tool does not introduce additional information or anomalies. The Boolean analysis carried out using the combination matrix [21] to determine land use limitation levels has proven to be highly effective. This approach simplifies the output while preserving the traceability of how each zone was categorized. Admittedly, grouping the results into three classes involves a certain degree of simplification, which can obscure some of the detail embedded in the process. However, we consider this approach appropriate for land-use planning purposes, as it opens new pathways for communicating complex spatial information. The resulting map is clear and accessible, allowing even non-specialist audiences to understand it with ease. As shown, the land use limitation degree mapping is directly applicable to sustainable territorial planning and management: areas of high constraint may be designated for strict conservation or only light, non-intrusive uses; medium-constraint areas could support moderate uses or development subject to environmental assessment and mitigation measures; and low-constraint areas are the most suitable for urban growth or low-risk industrial projects. In this way, the gradient of restrictions offered by the map helps to balance development and conservation, serving as an objective reference for regulating land use according to each zone's intrinsic capacity and vulnerability.
## 2 Materials and Methods
### Machine Learning
#### 2.2.3 Random Forest  
Random Forest (RF) is a regression technique that combines the performance of multiple Decision Tree (DT) algorithms to classify or predict the value of a variable [34; 35; 36; 37]. That is, when RF receives an (x) input vector made up of the values of the different evidential features analyzed for a given training area, RF builds a number K of regression trees and averages the results. After K, such trees \(\{\mathrm{T(x)}\}_{1}^{\mathrm{K}}\) are grown, and the RF regression  
\[\mathrm{Y_{r}}=\sum_{j=1}^{K}f(D_{ij})xT_{j} \tag{12}\]  
Here, \(f(D_{ij})\) is the kernel function of the K-nearest neighbors (K observed data with the lowest distance from the real time predictor), which is calculated based on distance amounts (D\({}_{\mathrm{rt}}\)) as follows:  
\[f\big{(}D_{ij}\big{)}=\frac{1/D_{ij}}{\sum_{j=1}^{K}1/D_{ij}} \tag{13}\]  
In the K-nearest neighbor algorithm, the amount of predictor weights (\(\omega\) in Equation (11)) and number of neighbors (K) affect the final results; therefore, their optimum amounts should be calculated to achieve the most appropriate results [39]. The K-Nearest Neighbors (KNNs) algorithm makes predictions by averaging the values of the (k) nearest neighbors to a given data point. During model training, the value of (k) (the number of neighbors) is optimized using k-fold cross-validation to improve model accuracy.  
In this study, GNSS measurement data will be analyzed using the selected algorithms to estimate velocity accuracy. The dataset, divided into training and test subsets, will be utilized to assess model performance. This approach aims to predict the sigma value of velocity based on the sigma values of coordinates derived from each measurement.
###### Abstract  
Public administration frequently deals with geographically scattered personal data between multiple government locations and organizations. As digital technologies advance, public administration is increasingly relying on collaborative intelligence while protecting individual privacy. In this context, federated learning has become known as a potential technique to train machine learning models on private and distributed data while maintaining data privacy. This work looks at the trade-off between privacy assurances and vulnerability to membership inference attacks in differential private federated learning in the context of public administration applications. Real-world data from collaborating organizations, concretely, the payroll data from the Ministry of Education and the public opinion survey data from Asia Foundation in Afghanistan, were used to evaluate the effectiveness of noise injection, a typical defense strategy against membership inference attacks, at different noise levels. The investigation focused on the impact of noise on model performance and selected privacy metrics applicable to public administration data. The findings highlight the importance of a balanced compromise between data privacy and model utility because excessive noise can reduce the accuracy of the model. They also highlight the need for careful consideration of noise levels in differential private federated learning for public administration tasks to provide a well-calibrated balance between data privacy and model utility, contributing toward transparent government practices.  
public administration; federated learning; differential privacy; membership inference attacks Article
## 4 Instrument Design and Key Technologies
### EMIS and TSI Make Use of the Space Cryogenic Absolute Radiometer (SCAR)
#### 4.1.2 Total Solar Irradiance  
Total Solar Irradiance (TSI) is a kind of electrical substitution radiometer; the detector is alternately exposed to a radiant energy source (solar irradiance) and then, to known internal electrical heating. The temperature increase due to the absorbed solar irradiance is then compared to the same temperature increase due to a precisely measured equivalent electrical power. In order to improve the measurement uncertainty, the detector thermal compensation of TSI is investigated. The thermal conduction structure is optimized by the blackbody cavity design. Based on the application of a temperature bridge, the temperature drift of the heat sink is compensated to improve measurement repeatability.  
The TSI calibration on the satellite is designed to improve the long-term stability of TSI measurements, as shown in Figure 4. The SCAR is also the benchmark of TSI calibration onboard the satellite. The SCAR and TSI are installed on the precision Sun tracker. The Sun Sensor (SS) is used to obtain the position of the Sun. Apertures suppress stray light in the TSI measurements. The irradiance scale of the TSI is traced to the SCAR via simultaneous solar observations. The system deviation is corrected by the real-time correction based on a super stable voltage reference. The TSI calibration on the satellite improves the absolute measurement accuracy and long-term stability.
# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 4 Model Construction and Result Analysis
### Random Forest Model  
The random forest regression (RF) model is composed of multiple decision trees, each built independently during the training phase. The final prediction is obtained by averaging the predictions from all individual decision trees. To enhance the model's fitting ability, the data are normalized and standardized. The optimal model and its parameters are identified through a grid search. The grid search intervals and model hyperparameter settings are detailed in Table 6.
## 4 Discussion
There are studies that have been conducted on the use of sonication in food products to inhibit _S. cerevisiae_ and bacteria by using ultrasound model systems [41; 42; 43; 44]. In the present study, the values of TPC for the US pasteurized PJ increased at all experimental points which means all factors (temperature, treatment time, and US power on/off position) were statistically significant. The higher levels of TPC are able to be explained with improvements in the extraction efficiency of the ultrasound process and this process might assist to release of bound form of phenolic acids with the help of the cavitation effect. These results obtained from our study are consistent with other researches [45; 46]. The US treatment resulted in an average 1-2 log reduction of the yeast and mold count. Yeast and mold count obtained from at low temperature and the US power-off position were close to each other: The reason is that increasing temperature and the US treatment affect the growth of microorganisms. Our results are close or similar to a previous study that observed the inhibition effect of ultrasound treatment on _Saccharomyces cerevisiae_ in pomergnate juice [47]. There were no significant statistical changes in turbidity, \({}^{\circ}\)Brix value, and color a* of US treated samples. These results are similar to a study reported by [PERSON], [PERSON] [48] in red grape juice, and in blackberry juice by [PERSON], [PERSON] [49]. Integrated non-thermal processes offer the potential to reduce the disadvantages of each method and increase the effectiveness of the method [50]. In the present study, the UV and US treatment were combined and the PJ was pasteurized with this integrated method. Since the UV+US treatment more effectively preserved the physicochemical, biochemical, and microbial properties of PJ samples, this method can be used not only fruit juice but also other fluid pasteurization. Compared with the conventional method, our results show that the UV+US treatment enhanced the quality parameters of the pomergnate juice.
## 3 Results and Discussion  
### Cytotoxicity Analysis  
Cytotoxicity was evaluated using the MTT assay, testing three concentrations of acacia leaf extract prepared in culture medium (150, 250, and 500 \(\upmu\)g/mL). This method detects cellular changes at the metabolic level ([PERSON] et al., 2018). The MTT assay involves the conversion of the yellow dye [3-(4,5-dimethylthiazol-2-yl)]-2,5-diphenyltetrazololium bromide] to the purple dye formazan, catalyzed by mitochondrial reductase [28; 29]. No cytotoxicity was observed (Table 1). The increase in viability was not sufficiently statistically significant for us to be able to state that cell viability percentage increases with the increase in concentration of extracts, except for the AL species.  
As far as we know, no study has evaluated cytotoxicity in acacia leaves, so it is impossible to establish a comparison. It was observed that the extracts are not cytotoxic, which indicates that they have no adverse effects on cell viability or cell structures. This observation suggests a potential safety profile for the extracts tested in the context of cytotoxicity, which is an encouraging finding for their potential applications in various contexts.
## 5 DoA Estimation Techniques
### Multiple Signal Classification (MUSIC) Algorithm  
To detect frequencies in a signal, the multiple signal classification algorithm (MUSIC) performs an eigen decomposition on the covariance matrix of the received signal vector \(\mathbf{r}\)[5; 24]. The process utilizes the correlation matrix \(\sigma_{v}^{2}\mathbf{I}\), where, \(\mathbf{I}\) is the identity matrix and, therefore, expressed as below:  
\[Cov_{M}(\mathbf{r})=\mathbf{A}\;E[\mathbf{s}\mathbf{s}^{H}]\;\mathbf{A}^{H}+ \sigma_{v}^{2}\mathbf{I}, \tag{22}\]  
In Equation (22), the eigenvectors of the covariance matrix define the signal subspace, which is represented by the column space of matrix \(\mathbf{A}\), and the noise subspace, represented by matrix \(\mathbf{V}_{n}\). The estimator function takes advantage of the orthogonality between the signal and noise subspaces, and has peaks that correspond to the DoA of each signal, as expressed below:  
\[P_{MUSIC}(\theta)=\frac{1}{\mathbf{A}^{H}\mathbf{V}_{n}\mathbf{V}_{n}^{H} \mathbf{A}}, \tag{23}\]  
where \(P_{MUSIC}(\theta)\) obtains a significant value when the value of angle \(\theta\) (i.e., part of matrix \(\mathbf{A}\)) corresponds to the DoA of one of the signals.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 3 Result
### Surface Shape Error  
The surface shape of the off-axis parabolic surface after finish grinding was measured using a three-coordinate measuring machine. The results are shown in Figure 6. There is an error between the measured surface shape and the theoretical profile surface shape, and it is found that as the \(x\) value increases, the measured surface shape changes from above the theoretical profile to below the theoretical profile, and the surface shape is different.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 4 Neural Network Optimization Technology Based on FPGA
### Memory and Access Optimization
#### 4.3.3 Standardized Data Access and Storage  
A large amount of data operation and frequent data access are the problems that neural networks must encounter when they are deployed on portable systems such as FPGA. In the optimization design of neural networks that are based on FPGA, FPGA is usually used as a coprocessor, that is, the CPU writes the instructions to the memory, and then the FPGA reads and executes the instructions from the memory unit, and following this, writes the calculation results to the memory. Therefore, by standardizing data access, the read and write efficiency of data can be improved, and the actual bandwidth upper limit can be increased.  
In the aspect of standardizing data access, scholars often cut the feature map into small data blocks stored in discontinuous addresses for feature mapping to standardize data access patterns. [PERSON] et al. [113] standardized data access by partitioning storage space and separately accessing sub-modules of each memory space in order to improve effective memory bandwidth. [PERSON] et al. [114] matched the limited memory space on FPGA by data partitioning, eliminated edge connections of high nodes by merging common adjacent nodes, and normalized data access by reordering densely connected neighborhoods effectively, which increased data reuse and improved storage efficiency. Some scholars have used custom data access paths in order to ensure the timely provision of parameters and intermediate data in model inference, as well as to make full use of local memory in order to improve inference efficiency and reduce the amount of external memory access, thus improving bandwidth utilization [115].