esa-sceva/satcom-chunk-collection huggingface dataset:
However, as only two nodes exist on one element edge, only linear interpolation can be realized, and the deformation cannot be measured accurately when the element exhibits complex deformation. More importantly, when the displacement field near or on the curved edges needs to be measured, Q4-DIC cannot cover the AOI very effectively, owing to the straight element edges. To improve on this, [PERSON] et al. [12] introduced 8-node quadrilateral elements into the DIC model (abbreviated as Q8-DIC). High-order shape function interpolation with three nodes on each edge was used in this technique, enabling the Q8 element to be mapped to an element with second-order curved edges. Hence, with this method, quadratic interpolation can be achieved, and the displacement of points close to or even on the curved edges can be measured. In this study, the capability of Q8-DIC with spatial continuity in boundary deformation measurement was explored and highlighted for specimens with non-uniform deformation. The measurement results were compared with those from the extrapolation and half-subset methods, both of which are based on conventional subset-based DIC. The method was verified using simulated tests and effectively applied to actual experiments. The results demonstrated that the calculation accuracy of Q8-DIC in deformation measurement near curved edges was much higher than those of the extrapolation and half-subset methods. The remainder of this paper is organized as follows. In Section 2, the basic principles of the conventional subset-based methods and their deficiencies in terms of boundary deformation measurement are briefly explained. Section 3 compares the accuracy of the three methods in measuring the deformation near curved edges using simulated speckle images. In Section 4, the application of the three methods in a real experiment is presented. Finally, several concluding remarks are provided.
###### Abstract  
Radio Occultation (RO) is pivotal for profiling the neutral and ionized atmosphere, with the PlanetQ mission, via its GNOMES satellites, striving to establish an advanced atmospheric observing system. However, an assessment of the spatiotemporal distributions of PlanetQ observations and comparisons with reliable datasets are lacking. This study addresses this gap by examining the temporal and spatial distribution of RO observations from PlanetQ during its initial 198 operational days in 2023, alongside comparisons with COSMIC and Numerical Weather Prediction (NWP) models. Data from GN02, GN03, and GN04 satellites, yielding 1099, 1313, and 1843 RO events per day, respectively, were analyzed. The satellite constellation's observations demonstrate a generally well-distributed pattern, albeit minor deficiencies in equatorial and polar regions. Single-profile comparisons with COSMIC data reveal strong correlations for pressure, temperature, Water Vapor Pressure (WVP), and refractivity profiles, with temperature exhibiting larger variations (RMSE = 1.24 \({}^{\circ}\)C). Statistical analyses confirm statistically insignificant differences between the PlanetQ and COSMIC profiles at the same spatio-temporal coordinates. Comparisons with NWP models show slight differences with GFS, with overall RMSE values of 0.23 mb (WVP), 0.6 mb (pressure), 1.3 (refractivity), and 1.5 \({}^{\circ}\)C (temperature). However, assessments against GFS/ECMWF models indicate overall compatibility, with insignificant differences between PlanetQ profiles and model observations.  
GNSS radio occultation; PlanetQ; COSMIC; GFS; ECMWF 2024 14, 4179. [[https://doi.org/10.3390/app14104179](https://doi.org/10.3390/app14104179)]([https://doi.org/10.3390/app14104179](https://doi.org/10.3390/app14104179))
## 5 Results and Discussion
### RQ1: What Cyber-Attacks in the MSCN Have Been Reported in the Academic Literature?
#### 5.2.1 Practices to Manage Information Security
[25] discussed the specific challenges and solutions for implementing these technologies in maritime environments, emphasizing the need for tailored solutions to overcome the limited bandwidth and intermittent connectivity.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Novelty and Contribution
#### 1.3.1 Enhanced Pilot-Based Channel Estimation with Hadamard Sequences  
* Unlike traditional pilot sequences used in LIS systems [10], our method employs structured Hadamard sequences that are specifically optimized for low-SNR conditions, reducing cross-interference and improving channel estimation accuracy.
* Unlike conventional pilots, Hadamard sequences enable perfect orthogonality even under multipath fading, leading to more stable channel estimation.
## 5 Proposed Channel Model
### Free Space Path Loss  
The free space path loss (FSPL), described as a function of the elevation angle between UAV and receiver, is given by  
\[L_{FSPL}=20 log\frac{\Delta h}{\text{sin}\theta}+20 logf\ -\ 27.55 \tag{11}\]  
where \(\Delta h=\text{h}_{\text{UAV}}-\text{h}_{\text{r}}\) is the height difference between transmitter (UAV) (\(\text{h}_{\text{UAV}}\)) and receiver (hr), measured in kilometers, and f is the frequency, measured in MHz.
## 3 Methodology
### Stratified SMOTE Algorithm  
The different magnitudes of training samples have always been an unavoidable problem affecting the accuracy of deep learning models. To solve the imbalance of land cover samples, the traditional approach is to increase the number of small samples by upsampling and force the order of magnitude of the small samples to the same order of magnitude as the large samples. The common method is the SMOTE algorithm. In a nutshell, a new sample is generated for a few classes by means of \"interpolation\" [51]. The algorithm idea can be summarized in that for each sample x of a few classes, the distance from all samples in the minority sample set \(S_{min}\) is calculated by the Euclidean distance standard, and the k-nearest neighbor is obtained. For each minority class sample x, several samples are randomly selected from their k nearest neighbors. Assuming that the nearest neighbor is \(X_{n}\), for each \(X_{n}\), a new sample is constructed according to Formula (6) using the original sample.  
\[X_{new}=\ x\ +\ rand\ (0,\ 1)|x-xn|, \tag{6}\]  
However, for the category samples of land cover classification, the sample sizes of different categories are very different. For example, a sample of the cultivated land category can reach the order of 300,000, while the sample size of the urban category is only 6000. When using the SMOTE algorithm, the city category sample will be increased from 6000 to 300,000. Among the results, the city's 294,000 samples were obtained by SMOTE. It is very likely that the original characteristics of the city category will be lost, resulting in inaccuracies in the classification results [69].  
To improve the inaccuracy of the classification caused by the excessive sample increase from the SMOTE algorithm, we used a sampling method called the stratified SMOTE algorithm to calibrate the sample [70]. The sample set obtained through the method and the sample set obtained through the traditional SMOTE algorithm are input into the model for training, and the results of the prediction are compared. The stratified SMOTE algorithm yields better results than does the traditional SMOTE  
Figure 4: Cultivated land cover percentage distribution.  
algorithm. The number of small sample sizes can be increased within a reasonable range. This result will be discussed later.  
The specific operation of the stratified SMOTE algorithm is to first count the number of samples in each category. Then, according to the order of magnitude of the samples, they are divided into three layers. The first layer is farmland, grassland, woodland and bare land. The second layer is shrubs. The third layer is wetland, water bodies, tundra, city and ice and snow. Then, the SMOTE algorithm is used in each of the three layers, and the sample set for the model training is obtained.
# Electrohydrodynamic Propeller as One of the Alternatives to Replace Conventional Propulsion Systems
## 1 Introduction
The Silent Ventus is a revolutionary UAV developed and constructed in 2023 by Undefined Technologies. The latest version is comprised of two electrodes with a difference between the electrode potentials of 40 kV. It is powered by a Li-Ion battery, which in the latest prototypes facilitates a levitation lasting longer than 4 min. As for the noise levels, the UAV is significantly more silent than conventional vehicles, as it produces 70 dB of sound. The MIT EAD Airframe V2 glider, developed by MIT researchers, is the first flying glider prototype with an EHD propeller. Its length is 2 m, its wingspan is 5.14 m, and its total weight is 2.45 kg. It is powered by Li-ion batteries installed in its front part. The electrodes are of opposite polarities, and the voltage is applied to their clips through a two-way Cockroft-Walton multiplier. The distance between the electrodes is 50 mm; the difference in the potentials of its two electrodes is 40.3 kV; the maximum thrust generated by eight EAD thrusters arranged in an array in terrestrial conditions is 3.2 N; and the highest potential velocity is 4.8 m\(\cdot\)s\({}^{-1}\). The operating time, which is limited by the battery's capacity, is 90 s [20]. The key advantage of EHD propulsion, compared to conventional propulsion devices, is its ability to produce a thrust that, albeit of a lower intensity, lasts much longer. That may facilitate a spacecraft to reach a higher speed in the long run. The key advantage is the time effect of the acting force, expressed by the impulse of the force, which is higher by several orders than that of any conventional propelling system used in vehicles of this type. Thanks to the latest advancements taking place in the field of material sciences, including the production of novel alloys, ultra-light materials, energy storage systems, and related software and hardware, the weights of propulsion devices have significantly decreased and are now reaching a value at which the thrust produced by an ion engine generates enough kinetic energy for a vehicle to move in atmospheric conditions.
# The Evaluation Method of Particle Clustering Phenomena in RAP
## 3 Methodology
### Extraction Test  
According to the Chinese specification JTG E20-2011 T0722, which is \"Standard Test Methods of Asphalt and Asphalt Mixtures for Highway Engineering\", the centrifugal separation method is used to extract the RAP. This extraction test is often used to determine the aggregate gradation or binder content of asphalt mixtures. But different from the traditional method of extraction test, in this study, the extraction test was aimed at the size of each of the RAP particles, including 2.36 mm, 4.75 mm, 9.5 mm, 13.2 mm, 16 mm and 19 mm. Then the extraction test was carried out separately. The main process of the experiment included the  
* Carrying out the crushing test at room temperature, and the rotation number of Los Angeles abrasion tester is set to 50 r, 100 r, 200 r, 300 r.
* After the test, taking out the samples and sieving, weighing the mass of the remaining RAP materials on the sieve;
* Calculating the index of Crushing-Loss rate following Equation (2): \[CL_{i}=\frac{m_{1i}-m_{2i}}{m_{1i}}\times 100\%\] (2)  
where \(CL_{i}\) = Crushing loss rate of the \(i\)th particle size; \(m_{1i}\) = The mass of the \(i\)th particle before test, kg; \(m_{2i}\) = The mass of the remaining RAP particles on the \(i\)th sieve, kg.
## 4 Experimental Results
### Interpret the Cross-Validated Results  
The next step was to assemble sets of 10 prediction patterns to obtain via rank-based statistics the corresponding target and uncertainty patterns. The term \"target\" refers to what we are looking for as a validated prediction result. The term \"uncertainty\" refers to the stability of it. Indeed, the prediction pattern is the most informed prediction because it is using all information available, all the 70 active occurrences as DSP. However, we do not know its prediction capability. This is estimated by generating the target pattern via iterative cross validations using whichever strategy we can apply.  
Figure 5 shows the uncertainty patterns obtained applying the five models to the database, the **70 at** as DSP and **ulpACDIS** as ISPs. To obtain the target patterns, rank-based statistics was used to select for each pixel the corresponding median rank of the 10 prediction patterns generated by the iterative cross-validation process **70 atm7**. The display of the target patterns is not provided here due to the extreme similarity with the corresponding prediction patterns in Figure 3 when using the same color legend. More informative are the uncertainty patterns in Figure 5, generated by ranking the ranges of ranks around the median ranks of the target pattern. The wider is the range the higher is the uncertainty of the corresponding target pattern (and consequently also of the prediction pattern).  
Figure 5: Uncertainty patterns, all using **70 atm7**_ulpACDIS** as cross-validation process, from the different models FZ, LI, LO, LR, and BP in (**a**–**e**), respectively. Black patches are the oversized trigger zones of the 70 active landslides. Colors in the legend indicate groups of ranks of % of SA in ascending order.  
We can use the same color legend for the uncertainty ranks as for the prediction or target ranks. The uncertainty ranks relate to all target ranks from high to low. For uncertainty, however, the desirable ranks are the lower ones, indicating lower uncertainties. Obviously, if a high target rank corresponds to a high uncertainty rank, it is considered less credible than one corresponding to a low uncertainty rank. Figure 5 shows areas of high uncertainty, higher ranks, to the North in all the five uncertainty patterns. As to those along the valley edges, they are visible only in Figure 5a,b,d, for FZ, LI, and LR, respectively. Other areas of high uncertainty to the south are common to all the five patterns in the illustration.  
At this point, it becomes important to study the relationships between target and corresponding uncertainty ranks with the 70 active landslide occurrences, **70 at**, from the **70 atm7_ulpACDIS** process of cross validation.
## 4 Conclusions  
Data from the present investigation based on a sequential extraction procedure and XRD analyses revealed that paper-ash is an effective additive for the immobilization process in remediation of Cd, Pb, and Zn in contaminated soils. Sulfate added to the soil did not affect the remediation efficiency. In the remediated soil composites, the Cd, Pb, and Zn were transferred from the easily soluble, highly mobile soil fractions to the sparingly soluble fractions of soil composite. Consequently, their concentrations in the water-soluble fraction were reduced far below the limit values for inert materials (Cd concentration was reduced from 0.8 to 0.0005 mg/kg, Pb concentration from 0.8 to 0.003 mg/kg, and Zn concentration from 60 to 0.4 mg/kg). A significant decrease in the Cd, Pb, and Zn concentrations was also observed in the exchangeable fraction of the soil composite (Cd concentration was reduced from 1.5 to 0.0005 mg/kg, Pb concentration from 8 to 0.1 mg/kg, and Zn concentration from 40 to 0.5 mg/kg). The mechanism of immobilization for the Cd, Pb, and Zn with paper-ash involved the formation of hydration products from the paper-ash, which resulted in the precipitation of insoluble Cd, Pb, and Zn hydroxides, while through the carbonation process, Cd, Pb, and Zn were stabilized in insoluble carbonates. The present study revealed the high efficiency of the remediation with paper-ash, which is related to the stability of the hydroxide and carbonate mineral phases of Cd, Pb, and Zn, formed after the remediation. Regarding the environmental impacts of soil remediation with paper-ash, it was demonstrated that the Cd, Pb, and Zn in the remediated soil composites do not present an environmental hazard. To the best of our knowledge, such a comprehensive investigation using complementary analytical methodologies allowed us to understand the mechanisms of Cd, Pb, and Zn immobilization in geotechnical composites made from contaminated soil and paper-ash and their environmental impacts has not yet been performed. The results of this study provide novel findings and contribute important knowledge to strategies for effective remediation of soils contaminated with Cd, Pb, and Zn by means of immobilization with paper-ash. The new findings can contribute to more sustainable management and use of waste materials generated in the paper and pulp industry for the remediation and rehabilitation of degraded areas. New knowledge can also initiate an industrial symbiosis of a closed-loop system that can be established locally. From the scope of demands to achieve long-term efficiency of the soil remediation procedures with the immobilization approach, in addition to the laboratory experiments, there is a need for performing similar field tests, which will be a topic of our next investigations.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.2 Artificial Neural Networks (ANNs)  
ANNs were used to capture intricate patterns within the data and identify non-linear relationships between input parameters and employee capability. A feedforward neural network (FNN) consisting of multiple layers, including an input layer, at least one hidden layer, and an output layer, was deployed [46; 47].  
In forward propagation, the network computes the predicted output (ypred) based on the input features (X) and the weights and biases of the neurons. The mathematical representation of forward propagation is as follows:  
Input Layer : The input layer simply passes the input features (X) to the hidden layer.  
Hidden Layer: The hidden layer computes the weighted sum of the four inputs, applies an activation function, and passes the result to the output layer. This can be represented as follows for the \(i\)-th neuron in the hidden layer:  
\[Y=z_{i}=\sum_{j=1}^{4}(w_{ij}x_{j})+b_{i}\] \[a_{i}=f(z_{i}) \tag{3}\]  
\(z_{i}\) is the weighted sum for the \(i\)-th neuron in the hidden layer.  
\(w_{ij}\) is the weight connecting the \(j\)-th input feature to the \(i\)-th neuron.  
\(x_{j}\) is the \(j\)-th input feature.  
\(b_{i}\) is the bias for the \(i\)-th neuron.  
\(f(\cdot)\) is the activation function applied to \(z_{i}\) to compute \(a_{i}\), the activation of the neuron.  
Output Layer: The output layer computes the final predicted output. The activation function used in the output layer is the identity function (linear activation)  
\[y_{pred}=a_{i} \tag{4}\]  
Mean Squared Error (MSE) was deployed as the loss function  
\[L=\frac{1}{n}\sum_{i=1}^{n}(y_{pred}-y_{i})^{2} \tag{5}\]  
\(n\) is the number of data points.  
\(y_{pred}\) is the predicted output of the \(i\)-th data point.  
\(y_{i}\) is the actual target value for the \(i\)-th data point.  
Backpropagation was used to compute the loss gradients concerning the network's weights and biases. These gradients are then used to update the weights and biases through gradient descent, which involves adjusting the weights to minimize the loss.  
The weight updates are typically carried out using gradient descent, where the weights (\(w_{ij}\)) are updated as follows:  
\[w_{ij}=w_{ij}-\alpha\frac{\partial L}{\partial w_{ij}} \tag{6}\]  
where \(\alpha\) is the learning rate, and \(\frac{\partial L}{\partial w_{ij}}\) is the gradient of the loss concerning the weight \(w_{ij}\).
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 2 Observations and Data Processing
### Data Comparison Method  
As the buoy data can only be used to detect the SWH of a small area, in order to significantly compare the CYGNSS estimation value with the buoy data and the satellite altimetry data, the data need to be matched in the same dimension [18]. The matching method is as follows: using the average value of buoy observation data 30 min before and after the satellite transit time (60 min total) as a buoy comparison value, while using the SWH estimation data estimated from the specular point within 50 km of the buoy station, as the CYGNSS matching data [19]. The spatial resolution of the CYGNSS SWH estimate obtained in this paper is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), which are down-sampled to match the satellite altimeter data spatial (1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)). The matching process is shown in Figure 5.  
In order to ensure the quality of satellite and on-site observation, satellite observation data and on-site observation data are also subject to the following quality control: when performing SWH data averaging, data outside the range of 3 times the standard deviation is excluded. The number of the buoy observation data used for the average should be more than three.  
After matching, this paper uses the deviation, root mean square error and correlation coefficient to make a detailed evaluation of the matching results. Their definitions are as follows:  
\[\text{bias}=\frac{1}{N}\sum_{i=1}^{N}(A_{i}-B_{i}), \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(A_{i}-B_{i})^{2}}, \tag{5}\]  
\[R=\frac{\sum_{i=1}^{N}\left(A_{i}-\overline{A}\right)\left(B_{i}-\overline{B} \right)}{\sqrt{\sum_{i=1}^{N}\left(A_{i}-\overline{A}\right)^{2}\sum_{i=1}^{N }\left(B_{i}-\overline{B}\right)^{2}}}, \tag{6}\]  
where N represents total number of matching points, Ai represents CYGNSS SWH estimates, \(\overline{A_{i}}\) represents the average of estimates, Bi represents the buoy or satellite altimeter data compared to the estimated data, and \(\overline{B_{i}}\) represents the average of the matching data.  
Figure 4: Trend of satellite elevation angle and SNR on 1 April 2017.