esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Privacy-Preserving Federated Learning
#### 3.5.2 Local Aggregation at Edge Nodes
The weight \(w_{i}\) of device \(i\) is set as a weighted combination of priority \(P_{\text{priority},i}\), and data volume \(n_{i}\) represented as  
\[w_{i}=\frac{P_{\text{priority},i}\cdot n_{i}}{\sum\limits_{j\in D_{\text{ selected}}}P_{\text{priority},j}\cdot n_{j}+\psi} \tag{48}\]  
where \(D_{\text{selected}}\) is the set of devices selected to participate in the upload. \(\sum\limits_{j\in D_{\text{selected}}}P_{\text{priority},j}\cdot n_{j}\) is the sum of weighted priorities and data volumes of all devices participating in aggregation, which is used for normalization to ensure that the weights of all devices are between \(0\) and \(1\), avoiding excessive weight for any one device. \(\psi\) is a small constant used to avoid division by zero. The local aggregation of model parameters uploaded by devices at the edge node is represented as  
\[\theta_{\text{local}}=\sum\limits_{i\in D_{\text{selected}}}w_{i}\cdot\theta_{i} \tag{49}\]  
where \(w_{i}\) is the weight of device \(i\), and \(\theta_{i}\) is the local model parameters uploaded by device \(i\).
## 2 Materials and Methods
### Specim Preparation and Test Methods
#### 2.3.3 Exposed Aggregate Area Ratio Analysis  
The exposed aggregate area ratio represents the percentage of the unit surface area of EAC occupied by exposed coarse aggregates, serving as a critical indicator for characterizing the surface texture of EAC. According to the Technical Guidelines for Construction of Highway Cement Concrete Pavements (JTG/T F30-2014) [23], the exposed aggregate area ratio on EAC surfaces should, optimally, be controlled between 65% and 75%.  
Given the distinct color contrast between the exposed black basalt coarse aggregates and the surrounding off-white cement mortar on the EAC slab surface, the exposed aggregate area ratio can be obtained using digital image processing techniques [31] via MATLAB R2022b software. The specific processing steps are as follows: (1) The captured surface image of EAC slab was subjected to grayscale conversion, yielding the grayscale frequency distribution histogram shown in Figure 3; (2) Considering the inevitable presence of residual cement paste stains on exposed aggregate surfaces, causing localized off-white discoloration, a skewed normal distribution was employed to characterize the grayscale frequency distribution of coarse aggregates, while a normal distribution was adopted for the cement mortar due to its relatively minor color contamination. Based on the above analysis, a weighted combination function of skew-normal and normal distributions was utilized to fit the grayscale frequency data of EAC slab image, with the specific mathematical formulation detailed in Equation (1) and the fitting curve results demonstrated in Figure 3; (3) In the fitting results, the integral area under the skew-normal distribution curve within the 0-255 grayscale range was calculated as the exposed aggregate area ratio  
Figure 2: Exposed aggregate depth test.  
of the EAC slab. Figure 4 shows the pre- and post-grayscale processing results of a typical exposed aggregate slab surface image.  
\[f(x)=\frac{2a}{\sigma_{1}}\phi(\frac{y-\mu_{1}}{\sigma_{1}})\Phi(\lambda\cdot \frac{y-\mu_{1}}{\sigma_{1}})+\frac{b}{\sqrt{2\pi\sigma_{2}}}\exp[-\frac{1}{2}( \frac{x-\mu_{2}}{\sigma_{2}})^{2}] \tag{1}\]  
where \(f(x)\) represents the grayscale value; \(\phi(\cdot)\) denotes the probability density function of the standard normal distribution, while \(\Phi(\cdot)\) denotes its cumulative distribution function; parameters \(\mu_{1}\), \(\sigma_{1}\) and \(\mu_{2}\), \(\sigma_{2}\) correspond to the mean and standard deviation of the skew-normal distribution and normal distribution, respectively; \(\lambda\) serves as the skewness parameter for the skew-normal distribution; and \(a\) and \(b\) represent the weighting coefficients for the two distributions.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 2 The Considered Class of Flexible Job Shops and Their Representation
### Total Energy Consumed  
Here, for a given machine assignment \(j\), the energy consumption is considered composed of the energy \(PE_{ikj}\) necessary to process operations with machines \(l_{ikj}\) and the energy \(TE_{ikl_{aij}}\) to transport processed products from a machine \(l_{ikj}\) to the successor machines \(l_{i\!ij}\) with \(h\) in \(\Gamma_{ik}\). Then, the energy used in processing job \(i\) within machine assignment \(j\) can be written as  
\[E_{ij}=\sum_{k=1}^{S_{i}}PE_{ikj}+\sum_{k=0}^{S_{i}}\sum_{h\in\Gamma_{ik}}TE_{ iI_{aij}}\;\;\;\;\;\;i=1\text{ to }n,\;j=1\text{ to }|A| \tag{8}\]  
This expression does not consider the energy consumed by idling machines. Depending on the machine and the durations of its idling periods, it may be interesting to save energy to turn them off when they are not in use rather than leaving them on. Restarting a machine can not only result in a temporary overconsumption of energy but also in a delay of its availability. In this study, it is assumed, since it is generally the case in industrial workshops, that the energy consumption of the idling machines is negligible compared to the processing and transfer energies. So, it does not seem necessary to explicitly introduce this component of the energy spent in a mathematical formulation of the scheduling problem, especially since this would lead to increased complexity resulting from the new variables and constraints to be introduced. What can be achieved--once a solution to the overall scheduling problem has been obtained that minimizes the main components of the consumed energy, whether this solution is exact or approximate--is, in a second step, to evaluate the idle periods of each machine and decide whether to turn them off or keep them on until their next use. This is the kind of problem that could be solved by an expert system using artificial intelligence techniques to exploit both the characteristics of the scheduling solution and the energy performance of each machine.
## 4 Obstacle Avoidance Planning
### Artificial Potential Field Function
#### 4.1.2 Repulsion Potential Field Function  
The distance between the obstacle and the end-effector is inversely proportional to the repulsive potential field. The closer the distance is, the stronger the potential field is, and vice versa. When the manipulator is close to the obstacle infinitely, the repulsion potential field tends to infinity. When the distance between them is greater than the distance affected by the repulsion potential field, the obstacle no longer affects the pickup manipulator. That is to say, the repulsion potential field becomes zero. Thus, the repulsion potential field function can be expressed as:  
\[\left\{\begin{array}{l}U_{rep}(X)=\frac{1}{2}\varepsilon(\frac{1}{d(X,X_{obs })}-\frac{1}{d_{0}})^{2}\quad,X-X_{obs}\leq d  
In the formula, \(\varepsilon\) refers to repulsion potential field gain factor, \(X_{obs}\) refers to the position of obstacles in the work environment, \(d_{0}\) refers to the distance affected by the repulsion potential field.  
The repulsion force of the obstacle on the pickup manipulator is generated by the repulsion potential field. It is the negative gradient of the repulsion potential field function and repels the pickup manipulator away from the obstacle. The function expression is:  
\[F_{rep}(X)=-\
abla U_{rep}(X)=\left\{\begin{array}{l}\varepsilon(\frac{1}{d (X,X_{obs})}-\frac{1}{d_{0}})\frac{1}{(X-X_{obs})^{2}}\frac{\partial d(X,X_{ obs})}{\partial X},X-X_{obs}\leq d_{0}\\ 0\hskip 28.452756 pt,X-X_{obs}>d_{0}\end{array}\right., \tag{27}\]
## 3 Results and Discussion  
### The Surface Condition of Magnesium during Corrosion  
The micrographs of the magnesium electrode surface in the first half-hour of corrosion in an electrolyte of 0.5 mol/L magnesium sulfate are presented at Figure 1. Before immersion into the electrolyte, the magnesium surface had already been covered with a film formed by surface preparation in the atmosphere, and at washing after grinding, the film possessed a marked thickness (Figure 1a). After one minute of corrosion, the surface equalize became noticeable due to the growth of the film, and its cracking starting herewith became noticeable too (Figure 1b).  
\begin{table}
\begin{tabular}{c c c} \hline
**Electrolyte** & **Electrical Conductivity (\(\gamma\), mSm/sm)** & **pH** \\ \hline
0.5 mol/L Na\({}_{2}\)SO\({}_{4}\) & 47.23 & 5.95 \\
0.5 mol/L MgSO\({}_{4}\) & 27.98 & 5.62 \\
1 mol/L MgSO\({}_{4}\) & 39.75 & 5.83 \\
2 mol/L MgSO\({}_{4}\) & 42.80 & 6.16 \\ \hline \end{tabular}
\end{table}
Table 1: Electrical conductivity and pH of solutions for corrosion studies.  
After 5 min of immersion, cracking increased and, on the surface, a new layer, also subjected to cracking, began to form; the film thickness in the cracks after 5 min may be estimated as units of micron (Figure 1c). Further, after 20 min, the cracks were gradually tightened and the second more incoherent layer was built up from above, as it had been reported in many works [3; 4; 5; 12; 16]. The observed cracking of the corrosion films demonstrated above for magnesium sulfate solutions in some investigations was associated with impact of hydrogen evolution [5].  
In magnesium sulfate solutions of higher concentrations, corrosion was rapidly accelerated and in a minute the film appeared thicker and more incoherent than half an hour in a solution of 0.5 mol/L MgSO\({}_{4}\) (Figure 2). In 2 mol/L MgSO\({}_{4}\) solution, after a minute, the surface of the film became fretted as though it was subjected to dissolving.  
Figure 2: Morphologies of the magnesium electrode surface after 1 min of corrosion in MgSO\({}_{4}\) solution of different concentrations: (**a**) 1 mol/L and (**b**) 2 mol/L.  
In sodium sulfate solutions, the film growth was less expressed and its slight cracking occurred after 30 min of immersion in the electrolyte (Figure 3). However, formation of the upper crystalline layer occurred as well as in magnesium sulfate after 20 min of exposure of the sample in solution.  
During corrosion, the pH of the solution changes in accordance with the main reaction as magnesium hydroxide accumulates in the solution, resulting in it equilibrating to 7.76 and 8.33 for 0.5 mol/L of sodium and magnesium sulfate, respectively, after half an hour of exposure.  
This difference corresponds to an almost tenfold increase in the content of magnesium hydroxide in magnesium sulfate solutions over sodium sulfate solutions. With the same area of the electrodes and the volume of the solution, taking into account the type of surface on the micrographs, we can talk about a much higher rate of corrosion in magnesium sulfate.
## 6 The 5G Spectrum Standardization
### Spectrum Allocation and Assignment  
A sufficient radio spectrum in the low-, mid-, and high-frequency bands is essential for the deployment of a 5G network to support the many use cases and applications. The 5G standard separates two carrier frequency ranges known as FR1 (sub-6 GHz with TDD and FDD), and FR2 (mmWave band, 23-53 GHz with TDD) [155; 156]. While some of these frequencies will be allocated particularly for the deployment of the 5G network, other frequencies will need to be obtained from existing communication services. In [154], the comprehensive list of available frequency ranges for the 5G FR1 and FR2 frequencies are presented, where frequencies in FR1 support TDD, FDD, and Supplemental Downlink/Uplink (SDL UL/DL); frequencies in FR2 support only TDD, which uses the same frequencies for transmission from both the device and the base station but are coordinated to transmit at different times to reduce interference. By contrast, with FDD, the base station and the device each transmit data using a separate frequency.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Groundwater Scarcity/Water Stress** & **Groundwater Recharge/GWS** \\ \hline
2003 & Water reduction in Arequipa and Tacna. & Madre de Dios remains stable. \\
2004 & Slight reduction in Arequipa and Moquegua. & Puno remains unchanged. \\
2005 & Decline in Moquegua and Tacna. & Madre de Dios stable. \\
2006 & Severe reduction in Arequipa. & Puno and Cusco maintain reserves. \\
2007 & Marked scarcity in Arequipa and Tacna. & Madre de Dios remains unchanged. \\
2008 & Critical conditions in Tacna and Moquegua. & Cusco remains stable. \\
2009 & Severe deficiencies in Moquegua and Tacna. & Puno and Cusco remain unchanged. \\
2010 & Water crisis in Arequipa and Tacna. & Madre de Dios remains stable. \\
2011 & Ongoing crisis in Arequipa and Moquegua. & Puno and Cusco remain stable. \\
2012 & Severe reduction in Tacna and Arequipa. & Madre de Dios maintains normal levels. \\
2013 & Severe crisis in Arequipa and Moquegua. & Cusco remains stable. \\
2014 & Extreme reduction in Tacna and Arequipa. & Madre de Dios remains stable. \\
2015 & Low reserves in Arequipa and Tacna. & Madre de Dios stable. \\
2016 & Lower storage in Moquegua and Arequipa. & Puno and Cusco remain unchanged. \\
2017 & Low levels in Tacna and Arequipa. & Madre de Dios and Apurimac maintain storage. \\
2018 & Reduction in Moquegua and Puno. & Arequipa remains in crisis. \\
2019 & Low groundwater availability in Moquegua & Puno remains stable. \\ and Tacna. & & Cusco and Madre de Dios remain stable. \\
2020 & Severe groundwater crisis in Arequipa and Tacna. & Puno remains stable. \\
2021 & Continued scarcity in Arequipa and Moquegua. & Madre de Dios remains stable. \\
2022 & Crisis in Arequipa and Tacna. & Cusco and Puno maintain stable groundwater levels. \\ \hline \end{tabular}
\end{table} 
Table 6: Annual groundwater variation at Southern Zone (2003–2023).
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Motion Estimation Methods
#### 2.4.1 Speckle Tracking  
Speckle tracking uses focused images (radio frequency or envelope detected, channel or beamsum) with cross-correlation to track motion between pairs of frames. It is assumed that the speckle patterns remain largely correlated for small displacements of the receiving array [27] and can be matched. For the lateral estimation, envelope detection was performed on the beamsum data to reduce axial variations and performed all operations on a Cartesian grid.  
Given a 2-D reference region (or kernel) from the first plane wave image, normalized cross correlation was used to search laterally in the second plane wave image for the best matching position as shown in Figure 4b. The observed motion in the image is opposite to the motion of the transducer. The parameters of this estimation (kernel size and depth) are explored in the simulation section. For phantom imaging, the kernel and search region were chosen to be 10 \(\times\) 10 mm at a depth of 20 mm (30 mm for cases with added tissue) with a 2.5 mm lateral search region in each direction.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 3 System Composition
### Hard Devices and System Software  
The hard devices are used to realize the communication between each module and install the system software. The main functions of the software for real-time tunnel deformation monitoring system include measurement, control, communication, and data storage. Considering the multi-system operation, cross platform, and compatibility of the system software requirements, LabVIEW [34] 2010 by NI (Austin, TX, USA) is adopted as the software development platform. The communication between the laser transmitter module, image acquisition module, sound and light alarm module, and hard devices and system software is wireless. The flow chart of the system software is shown as Figure 8.  
Figure 8: Flowchart of the system software.
## 4 Overall Control Strategy of _Lc_-Hapf  
Figure 3 shows the overall control block diagram of the LQRC and LQRIC for the _LC_-HAPF. Firstly, both \(i_{Lx}\) and \(i_{cx}\) are transformed into \(d\)-\(q\)-\(0\) coordinates by Park transformation, where the \(\omega_{1}t\) is obtained by the PLL. The reference compensation current in the \(d\)-axis \(i_{cd}^{*}\) is derived from the high pass filter (HPF). Then, the compensation current error \(\varepsilon_{h}\) is obtained by \(\varepsilon_{h}=i_{ch}-i_{ch}^{*}\). After that, the \(\varepsilon_{h}\) can be transformed into \(v_{imvh}\) by the LQRC or LQRIC, and the \(v_{imvh}\) is converted back into the \(a\)-\(b\)-\(c\) coordinate, which is \(v_{imv}\)\({}^{*}\). It will be applied into the PWM so as to generate the inverter trigger signals \(T_{1x}\) and \(T_{2x}\) to control the IGBTs in the VSI of _LC_-HAPF. Finally, the VSI outputs \(v_{imvx}\) to the PPF branch in Figure 1, in which \(i_{cx}\) injects into the power grid and compensates the reactive power and harmonics current.  
In this paper, a comparative study of the compensation for _LC_-HAPF with different controllers will be demonstrated in order to verify the effectiveness of the LQRCI for _LC_-HAPF. The HCC and PCC are applied to the _LC_-HAPF for the comparative study, and their control block diagrams and parameter designs will be mentioned in the following sections.
## 2 Materials and Methods
### Data Preprocessing  
To ensure the data's quality and reliability, we performed systematic pre-processing of our dataset. Features were categorized by their missing rates as low (<30%), medium (30-60%), or high (>60%). Rather than immediately eliminating features with moderate missing rates, we retained all features for initial statistical screening. This decision aimed to preserve potentially informative features during the early screening phase, before applying stricter inclusion thresholds in modeling.  
For analyses requiring complete data, we employed multiple imputation by chained equations (MICE) [37] with predictive mean matching [38]. The imputation quality was assessed by comparing the pre- and post-imputation distributions, correlation structures, and statistical relationships with cognitive status, ensuring that the process did not significantly alter the underlying data patterns.  
To address the class imbalance between the cognitively normal (CN) and cognitively impaired (CI) groups, we incorporated class weights into the modeling process, assigning weights proportional to the inverse class frequencies.
## 3 Physical-Layer Network Coding Based on Block Markov Superposition Transmission
### Genie-Aided Lower Bound Analysis  
BMST codes exhibit a notable advantage in that their performance can be effectively characterized through genie-aided lower bounds, which are obtained using a genie-aided decoder. The basic idea behind the genie-aided decoder is stated as follows:  
For any given \(\ell\geq 0\), we assume the existence of a genie that provides the decoder with complete information about all layers except the \(\ell\)-th decoding layer. In other words, the decoder has perfect knowledge of the partial information sequences \(\underline{\mathbf{u}}^{\prime}=(\mathbf{u}^{(0)},\cdots,\mathbf{u}^{(\ell-1)},\mathbf{u}^{( \ell+1)},\cdots,\mathbf{u}^{(L-1)})\). Under this assumption, it follows that the BER performance of the standard BMST decoder cannot surpass that of the genie-aided decoder. Consequently, the performance of the proposed DC algorithm is lower-bounded by the iterative decoding process between two genie-aided decoders. In our simulations, the genie-aided decoder is implemented by setting \(L=1\), which simplifies the analysis while preserving the essential characteristics of the bound. Theoretical derivations and performance descriptions of the genie-aided decoder are detailed in [21].
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 3 Interference Management
### Recent Advances in Interference Management
#### 3.1.1 Interference Avoidance
[PERSON] et al., in [40], proposed a radio resource allocation scheme for multihop D2D links that is based on interference avoidance techniques in LTE-A networks. The authors stated that the concept is to expand the components of the key enabling technology of a single-hop D2D to multi-hop D2D communications. The results demonstrate that by using the proposed mechanisms and proper power control, the established D2D links can reuse the cellular band and still produce minimum interference for the cellular communication.
## 3 Results and Discussion
### Implications for Radiometric Surveys and Radionuclide Scanning  
The observed correlations have significant implications for radiation monitoring and assessment technologies. Airborne or drone-borne vehicles equipped with gamma detectors [28; 29; 30] are effective for covering large areas, such as quarries or mines. Given the correlations between radon and thoron exhalation rates or surface emissions with radioelement activities, drones can be utilized to map radiation levels and identify high-activity zones efficiently. This capability is crucial for environmental monitoring and safety in areas with potential radon and thoron exposure.  
Handheld gamma detectors are well-suited for localized monitoring at distribution centers, markets, and residential or workplace settings. The strong correlation between the I Index and the dose rate indicates that handheld devices can provide accurate measurements of radiation levels corresponding to radon and thoron activities. Regular use of these devices can help pinpoint high-radiation areas and ensure that safety measures are in place to mitigate potential health risks associated with elevated radiation exposure. Potential drawbacks of using handheld gamma detectors include potential instrument calibration issues, and the high sensitivity of some instruments, which necessitates adequate lead shielding to prevent overestimating gamma dose rates and uranium, thorium, and potassium concentrations.  
Cross-Validation of Data from Handheld Gamma Detector (GT40) and Drone-Based Surveys with D230A Detector  
As part of a supplementary research project [30], drone-based surveys were conducted to map gamma radiation variations across four quarries in County Donegal, Ireland: one sandstone, two quartzite, and one granite quarry. A D230A detector mounted on a drone was utilized for these surveys. Before the drone surveys, a GT40 handheld gamma detector (in assay measurement mode) was employed to collect dose rate, uranium, thorium, and potassium activity data at specific locations.  
Table 4 presents the results of a cross-validation analysis of radioelement data (see Supplementary Table S3) collected using both handheld and drone-borne surveys, employing the Pearson correlation test (\(p<0.05\)). Overall, there is a good agreement between the datasets collected using both methods. Dose rates exhibit a strong correlation (R\({}^{2}\) = 0.73), while the highest agreement (R\({}^{2}\) = 0.79) was observed for thorium values. This enhanced agreement for thorium is likely due to its higher activity at the source, minimizing the impact of the background radiation effect. It is noteworthy that regression methods were applied to the drone-based gamma data to interpolate values between flight lines and generate smooth surface maps. Therefore, it is expected that the interpolation of data may have slightly affected (decreased) the correlation coefficients between the data.