esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microstructure of Concrete
#### 3.2.4 Chloride Ion Content in Concrete  
Figure 15 plots the variation curve of chloride ion content with erosion depth at day 28 for the four groups of concrete. It can be seen that the chloride ion content within the concrete decreased as the depth of erosion increased. The difference in the chloride ion content in the first layer and the second layer within the concrete was greater because the salt solution caused salt crystals to adsorb to the concrete surface, resulting in a higher chloride ion concentration in the first layer. As the only cementitious material in the OC group consisted of cement, the pores between the mortar could only be filled by cement particles during the hydration process, which had a poor filling capacity, resulting in more pores in the concrete. The fly ash and slag were complementary to the cement particles due to their different particle sizes, filling the voids between the concrete mortar matrix, which improved the denseness of the concrete and its ability to resist the penetration of external chloride ions. As shown in Figure 15, the content of chloride ions within the same depth of the four groups of concrete was ranked as OC > F10S20 > F15S15 > F15S20, indicating that as the content of mineral admixture increased, the chloride ion content within the same depth of concrete decreased and the resistance of concrete to erosion by chloride ions was enhanced [43]. Moreover, when the content of slag was 20%, with the increase in the fly ash, the content of chloride ions within the concrete decreased, and when the total mineral admixture was 30%, increasing the content of fly ash and decreasing the content of slag were beneficial to enhance the resistance of concrete to erosion by chloride ions.  
Figure 14: Pore diameter distribution of concrete at different ages: (**a**) the OC group; (**b**) the F15S20 group.  
Figure 15: Variation curve of Cl\({}^{-}\) content with depth in concrete on the 28 th day.
## 3 Results: Discussion and Comparison
Figure 6: 2D ERT inverse models obtained starting from the 3D models of prism-shaped resistive blocks, with different values for the orthogonal width \(s\) of the prism, where (**a**) \(s/l=\infty\) (2D model) and (**b**–**j**) from \(s/l=5\) to \(s/l=1\) (cube model). The orthogonal distance \(d\) between the electrode line and the projection on the surface of the center of the cube was equal to zero for all the inversions. The solid black line indicates the boundaries of the prism. Instead, considering the central sections of the 3D ERT in Figure 7, \(\mathbf{P}_{1}\) (black circles) increased irregularly as \(s/l\) increased, approaching 1 when \(s/l\geq 5\). It follows that, considering the returned resistivity value, a prismatic structure could be considered comparable to a two-dimensional structure when the ratio between the length of the prism and the side of its section was at least equal to five. \(\mathbf{P}_{3}\) (black squares) showed the opposite trend to \(\mathbf{P}_{1}\), indicating that the overestimation of the anomalous area decreased as the perpendicular dimension s increased, tending to double the real section when s tended toward infinity. The test results for the model in Figure 2 clearly show that when the 2D ERT crossed perpendicular to a 3D resistive structure, the resulting anomaly became similar to that of a 2D structure having the same vertical section as its orthogonal length increased. The resistivity anomaly produced became comparable with that of the homologous two-dimensional block only when the orthogonal length of the block was at least five times the side of its section. Figure 7: Central sections \((d=0)\) extracted from 3D ERT inverse models obtained starting from the 3D models of prism-shaped resistive blocks. (**a**) \(s/l=\infty\) (2D model) and (**b–j**) from \(s/l=5\) to \(s/l=1\) (cube model). The orthogonal distance \(d\) between the section and the projection on the surface of the center of the cube was equal to zero for all the sections. The solid black line indicates the boundaries of the prism.
## 2 Materials and Test Methods
### Test Methods  
Specimens with a diameter of 50 mm and a height of 100 mm, as presented Figures 3 and 4, were prepared using the dry deposition method to achieve the target relative density specified in Table 2 for both monotonic triaxial (MTX) and resonant column (RC) tests (Figure 4). Tests were carried out in dry conditions; therefore, no porewater pressure (PWP) was measured. To prevent the dispersion of the sandy specimens, a vacuum pressure ranging from 10 to 30 kPa was applied to the samples via the pipe at the top cap prior to the application of confining stress (refer to Figure 3a,b). Subsequently, the samples were isotropically consolidated at the mean principal stress indicated in Table 2.  
The RC tests employ a pneumatic stress-controlled system, which utilizes an electric motor to generate shear strain (torsion) at the specimen's top cap. The RC test is based on the theory of the wave propagation of prismatic bars. The testing apparatus not only automatically applies the necessary confining pressure (\(\sigma_{3}\)) via the control panel depicted in Figure 4b but also independently measures and records shear stress (\(\tau\)) and strain (\(\gamma\)) at the specimen's top cap, as shown in Figure 4c. The resonant frequencies of the excited torque range from 60 to 160 Hz for fine sands (S1 to S4) and from 80 to 180 Hz for clean sand. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{3}{*}{**Sand No**} & **Uniformity** & **Coff. Of** & **Average** & **Specific** & **Fine** & **Maximum** & **Minimum** & **Unified** \\  & **Coeff.** & **Curvature**, & **Diameter,** & **Gravity,** & **Content FC** & **Coid Ratio,** & **Void Ratio** & **Soil Class.** \\  & **Cu** & **Cu** & **Cu** & **D50** & **G\({}_{\rm s}\)** & **Content FC** & **e\({}_{\rm max}\)** & **e\({}_{\rm min}\)** & **System** \\  & **[-]** & **[-]** & **[mm]** & **[-]** & **[-]** & **[-]** & **[-]** & **(USCS)** \\ \hline S1 & 27.0 & 6.0 & 0.52 & 2.65 & 16 & 0.960 & 0.590 & SC \\ S2 & 6.20 & 2.9 & 0.38 & 2.64 & 11 & 0.830 & 0.500 & SW \\ S3 & 13.6 & 3.4 & 0.12 & 2.67 & 28 & 1.020 & 0.600 & SC \\ S4 & 25.0 & 1.5 & 0.08 & 2.70 & 45 & 1.230 & 0.670 & SC \\ S5 & 6.00 & 1.28 & 0.63 & 2.64 & 0 & 0.674 & 0.415 & SW \\ \hline \end{tabular}
\end{table} 
Table 1: Index properties of the tested granular materials. Figure 2: Tested grain size distribution curves. In the MTX tests, the hydraulic system was employed to apply \(\sigma_{3}\) isotropically, as illustrated in Figure 3c. To ensure the acquisition of precise stress-strain curves during the tests, a minimum rate of 0.05 mm/min was selected at the shearing stage, owing to the data-reading capabilities of the S-type load cell in the apparatus. The tests were conducted until an axial strain of 15% was achieved (Figure 3d). Table 2 presents the initial conditions and testing program for both types of tests, as well as the measured modulus values obtained from each test.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
###### Abstract  
Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with this issue. Ground-based radars can detect Earth orbiting debris down to a few square centimeters and therefore constitute a major building block of a space debris monitoring system. New radar sensors are required in Europe to enhance capabilities and availability of its small radar network capable of tracking and surveying space objects and to respond to the debris increase expected from the New Space economy activities. This article presents ATLAS, a new tracking radar system for debris detection located in Portugal. It starts by an extensive technical description of all the system components followed by a study that estimates its future performance. A section dedicated to waveform design is also presented, since the system allows the usage of several types of pulse modulation schemes such as LFM and phase coded modulations while enabling the development and testing of more advanced ones. By presenting an architecture that is highly modular with fully digital signal processing, ATLAS establishes a platform for fast and easy development, research, and innovation. The system follows the use of Commercial-Off-The-Shelf technologies and Open Systems which is unique among current radar systems.  
radar; sst; ssa; space debris; debris; leo; tracking; doppler; eusst; ATLAS +
Footnote †: journal: 9 March 2021
## 5 Experimental Simulation Analysis  
### Energy Efficiency of the RSSST-SR-DCSK System  
In the RSST-SR-DCSK system, the length of the reference signal within the current symbol period is R, and the length of the information signal is \(\beta(\beta=P\times R)\).  
In the RSST-SR-DCSK system, taking \(N\) as 2 bits as an example, since the reference signal is formed using 4 scramblers and selecting one route from them using a 2-dimensional information bit \(b_{k}b_{k+1}\), the overall signal length of the system is \(4R+PR\).  
The ratio DBR of transmitted data energy to transmitted bit energy in a frame for a 2-bit RSST-SR-DCSK system is given using  
\[DBR_{RSST-SR-DCSK}=\frac{E_{data-RSST-SR-DCSK}}{E_{b-RSST-SR-DCSK}}=\frac{R+ \beta}{4R+\beta}=\frac{R+PR}{4R+PR}=\frac{P+1}{P+4} \tag{33}\]  
The signal energy within a frame for a DCSK system is given by:  
\[DBR_{DCSK}=\frac{E_{data-DCSK}}{E_{b-DCSK}}=\frac{\beta}{2\beta}=\frac{1}{2} \tag{34}\]  
Figure 9 illustrates a comparison of the energy utilization rates between RSST-SR-DCSK and DCSK systems. The graph indicates that when \(P=2\), the performance of SR-ODBR-DCSK and DCSK systems is identical, with both having a DBR of 0.5. However, when \(P>2\), the RSST-SR-DCSK system demonstrates significantly better energy utilization compared to the DCSK system. As \(P\) increases, the DBR of the RSST-SR-DCSK system approaches 0.95. Consequently, the RSST-SR-DCSK system exhibits higher energy efficiency in comparison to the traditional DCSK system.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters  
Drones' global market is continuously increasing year on year, especially in the agriculture sector. Figure 3 shows the agricultural drone global market size, estimated to be 6.11 billion USD in 2024 and forecasted to grow to 17.9 billion USD by 2028 and 23.78 billion USD by 2032 [36; 37]. The Compound Annual Growth Rate (CAGR) measures an invest  
Figure 2: Rotary-wing UAV types.  
ment's annual growth rate over a period of time, as defined by (1), and is projected to be 30.8% by 2028 and 18.5% by 2032.  
\[\sqrt{\frac{V_{f}}{V_{\rm s}}}-1 \tag{1}\]  
where \(V_{f}\) is the final value, \(V_{\rm s}\) is the starting value, and \(P\) is the period in years.  
Many commercial drones are available in the market for smart agriculture applications, with rotary-wing drones being the most popular. This subsection specifically explores various types of commercial quadcopters suitable for smart agriculture. A comparison between these quadcopters is presented in Table 2, based on parameters such as weight, battery type, maximum flight time, maximum operating speed, and the equipped camera.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 1 Introduction
### Main Contributions  
The previous works considered mainly a sub-connected hybrid architecture for single and multi-user narrowband systems. The works for wideband sub-connected hybrid architectures are very scarce in the literature and they mainly focus on solutions for the downlink considering OFDM modulations. To the best of our knowledge, sub-connected hybrid approaches for uplink of multi-user wideband mmWave massive MIMO systems have yet to be addressed in the literature. Therefore, in this paper we aim to fill this gap and design an efficient hybrid multi-user equalizer combined with a pure analog precoder for sub-connected uplink mmW massive MIMO single-carrier frequency division multiple access (SC-FDMA) systems. We consider single RF UTs employing a low complexity, yet efficient, analog precoder approach based on the knowledge of partial channel state information (CSI), i.e., only a quantized version of the average angle of departure (AoD) of each cluster is considered. The hybrid multi-user linear equalizer employed at the BS is optimized by using the bit-error-rate (BER) as a metric over all the subcarriers. We assume that the digital part of the equalizer is computed on a per subcarrier basis while the analog part is constant over the subcarriers. The analog domain hardware constraints considerably increase the complexity of the corresponding optimization problem. To simplify it, the merit function is first upper bounded, and by leveraging the specific properties of the resulting problem, we show that the analog equalizer may be computed iteratively over the RF chains by assigning the users in an interleaved fashion to the RF chains, using a dictionary built from the array response vectors. The results show that the performance penalty of the sub-connected multi-user equalizer approach to the fully connected counterpart decreases as the number of RF chains increases.
# Predicting Compressive Strength of
## 4 Results and Discussion
The average RMSE values (over 10 folds) of predicting the CSRE samples' compressive strength achieved by each method are presented in Table 4.  
It can be noticed (based on the results presented in Table 4) that the proposed DCNN with a regression model based on an artificial neural network, applied on all the obtained features (4096), outperforms the other regression models when compared. This is due to the use of all the deep features obtained from the DCNN, unlike the PCA, which reduces the quantity of the features. However, using a large number of features it is a time-consuming process. To compare the proposed DCNN prediction model with other state-of-the-art traditional computer vision methods, the selection of the features from the SEM images was made with the use of the following algorithms: histogram of oriented gradients (HOG) [40], local binary pattern (LBP) [41], and scale-invariant feature transform (SIFT) [42]. The values of RMSE calculated for the compressive strength (of CSRE samples) predictions made with the use of these traditional features extraction methods are presented in Table 5. \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **No. of Features** & **RMSE (MPa)** \\ \hline DCNN & 4096 & 1.5 \\ Random forest with PCA on the deep features & 572 & 3.1185 \\ Linear regression with PCA on the deep features & 572 & 2.5057 \\ \hline \end{tabular}
\end{table} 
Table 4: Average root mean square error (RMSE) of predicting the compressive strength of CSRE samples using the proposed DCNN and the deep features obtained. \begin{table}
\begin{tabular}{c c c} \hline
**Fold No.** & **Covariance** & **Correlation Coefficient** \\ \hline Fold 1 & 8.742 & 0.857 \\ Fold 2 & 10.08 & 0.897 \\ Fold 3 & 9.588 & 0.914 \\ Fold 4 & 9.827 & 0.915 \\ Fold 5 & 9.385 & 0.853 \\ Fold 6 & 10.21 & 0.934 \\ Fold 7 & 9.411 & 0.910 \\ Fold 8 & 8.654 & 0.896 \\ Fold 9 & 8.622 & 0.890 \\ Fold 10 & 9.353 & 0.914 \\ \hline Average & 9.39 & 0.898 \\ \hline \end{tabular}
\end{table} 
Table 5: RMSE of compressive strength (of CSRE samples) predictions achieved in a random forest and linear regression, fed with the features extracted traditionally from the SEM images. It can be noticed (see Table 5) that none of the traditional methods could handle the prediction better than the proposed DCNN. This may be attributed to the representative power of the deep features obtained by the proposed model compared to that of the handcrafted low-level features (HOG, LBP, and SIFT). A similar effect is achieved by many researchers, e.g. in [43; 44; 45].
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Representation Methods
#### 5.2.2 Frequency-Based Vector  
The production of malware variants nowadays is an easy task for the reason that there are various obtainable malicious software code libraries and online tools to reuse and alter the existing malicious codes and then introduce new variants. As a result, the frequency (number of occurrence times) of each extracted feature in each malware sample, such as API calls and opcodes, can be used to identify the similarity between malicious behaviors that belong to multiple variants of the same malware family [44].  
To construct the frequency-based vectors, the occurrence times of the extracted features must be considered. Based on the assumption that there are differences between malware and benign programs in the occurrence numbers of their performed functions, frequency-based vectors have been used to represent the differences between malicious and legitimate activities. Therefore, the most frequent features have to be determined for both malware and benign classes [102]. In their study, ref. [70] was concerned about VBasic-based malware, so the VBscript samples were looked up to identify specific functions, methods, and keywords along with their number of occurrences to construct the feature vector. The authors of [44] extracted API calls using n-gram techniques, and then the frequency-based vectors which consist, of the occurrences of each n-gram, were constructed to represent the file characteristics.  
Despite the fact that using a frequency-based approach to represent malware on graphs yields discriminating patterns [103], the performance of frequency-based malware detection models is hampered by obfuscation techniques such as dead code insertion, which is capable of updating the distribution of the feature [104].
## 3 Results
### Effect of Calibration Method on Repeatability of HF DPOAEs  
To examine if one calibration method results in less variability between measurements with probe removal in newborns, the average absolute difference in DPOAE levels between the two trials is plotted. If DPOAE levels are identical between trials the difference would be zero. To be included in this difference plot, newborns had to have data for each trial tested for at least one calibration method. While the number of observations is less than when considering only one trial, visual and descriptive analyses were still performed. As seen in Figure 3, the DPOAE level variability from trial 1 to 2 is similar for both FPL, light-blue short-dashed line, and SPL, black solid line, calibrations. At the highest frequencies tested (12-16 kHz), there is a trend that FPL shows slightly less variability in DPOAE levels across the two trials compared to SPL with an average absolute difference between trials of 2.31 versus 3.87 dB, respectively. The greatest variability between the two trials occurred at the two lowest frequencies tested for each calibration method.
## 3 Setup for Downscaling Simulations
### Settings for the Observations  
The observations are generated based on the hydraulic properties. The initial condition is set as pressure head is \(-\)10 m. The total simulation time is 120 d, and the observation interval is 3 d. In order to investigate the influence of boundary conditions and on the inversion results, we set up three models with three different specific boundary conditions as follows: constant pressure head boundary (Model A in Figure 2a), variable flux boundary (Model B in Figure 2b), and lateral flow boundary (Model C in Figure 2c); other parameters not mentioned can be found in Figure 2.  
In the case of determining the coarse-scale and fine-scale grids, we want to use as few observations as possible to obtain reasonable parameter estimates, so we set up a set of different numbers of observations to investigate their effects on the inverse results. This investigation is conducted in the case of a 32 \(\times\) 32 coarse-scale grid, which is a trade-off between efficiency and accuracy addressed in a later section. In the case of a coarse-scale grid of 32 \(\times\) 32, if there are observations on all grids there will be 1024 observations, here we gradually and randomly reduce the number of observations by taking \(N\) equal to 1024, 974, 424, 124, 74, 24, 14, 9, 4 for nine cases.  
Totally, three boundary conditions and different magnitudes of decline in scale, together with different degrees of soil heterogeneity, yield to the 66 numerical experiments in this study. The combinations are shown in Figure 3.  
In order to measure the performance of the inverse method on these experiments, the normalized RMSE is used as a measure of agreement between observed and modeled values:  
\[\text{NRMSE}=\frac{\sqrt{\frac{1}{M}\sum_{j=1}^{M}\bigl{(}\widetilde{Y}_{\text {sim}}^{j}-Y_{\text{obs}}^{j}\bigr{)}^{2}}}{\widetilde{Y}_{\text{max}}- \widetilde{Y}_{\text{min}}}, \tag{13}\]  
where \(M\), denotes the total number of soil water pressure head measurements or spatially distributed saturated hydraulic parameters; \(\widetilde{Y}_{\text{sim}}\) and \(\widetilde{Y}_{\text{obs}}\) denote the model simulated and observed value; \(\widetilde{Y}_{\text{max}}\) and \(\widetilde{Y}_{\text{min}}\) signify the maximum and minimum soil water pressure head measurement, respectively.  
Figure 2: Schematic diagrams of the 2D section models with different boundary conditions. (**a**) Model A. (**b**) Model B. (**c**) Model C.