esa-sceva/satcom-chunk-collection huggingface dataset:
# Mg,Sr-Cosubstituted Hydroxapatite with Improved Structural Properties
## 3 Results and Discussion
### Ion Exchange of Mg in Sr-HA Samples: \"Subsequent Mg Substitution\"
#### 3.2.3 Evaluation of the Mechanical Properties of MgSr-HA  
The process performance strictly depends on the chemical-physical properties of the powder, including its stoichiometry, because these, in turn, affect slurry concentration and stability. Low solid contents yield weak architectures, whereas high solid contents make the viscosity of the slurry unsuitable to obtain 3D porous bone substitutes [36; 44].  
For a given synthetic Sr-HA powder, the microstructure and the mechanical properties of the resulting porous scaffolds are strictly dependent on the forming/shaping process used. A decrease in total porosity of sponge-derived sintered Sr-HA scaffolds from 60 vol% to 45 vol% increased their compressive strength from 2 to 5 MPa [36]. These values, regardless, fall within the wide range of values reported for human bone (femur: 4-11 MPa and proximal tibia 2-8 MPa [45]).  
Porous samples produced by direct foaming generally present improved compressive strength, compared to those prepared by sponge impregnation: tenfold higher values were even found for HA samples with 70 vol% of porosity (5.7 vs. 0.5 MPa), whose performance was positively tested in sheep mandibular sites [26]. Higher compressive strength for foamed samples (about 23.3 \(\pm\) 2.5 MPa), compared to the sponge-derived ones (about 4.5 MPa), was again obtained with sintered Sr-HA with porosity of 45 vol%.  
In addition, a preliminary test revealed that the ion-exchange-based conversion into MgSr-HA left the average compressive strength of the scaffolds (22.4 MPa) likely unaltered. Figure 6 shows two representative curves of Sr-HA foams before and after Mg ion exchange that reflect the typical behavior of porous apatite samples, where the jagged curve is indicative of the progressive rupture of the ceramic struts constituting the porous architecture.  
Keeping the compressive strength value quite unmodified after ion exchange represents an additional benefit of the process because a whole range of Sr-HA products (e.g., macro-granular fillers and scaffolds with different architectures) can be generated from the same Sr-HA powder, with the possibility to convert them into MgSr-HA by just including an additional ion-exchange step.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 2 Research Methodology
### Complete Pipeline of the Proposed Methodology
#### 2.1.6 DenseNet121  
Dense Convolutional Network (DenseNet) is a CNN model that connects each layer to every other layer in a feed-forward style. The feature maps of all previous layers are used as inputs for each layer, and their feature maps are used as inputs into all subsequent layers. DenseNet has many compelling advantages: it improves the problem of vanishing gradient, strengthens propagation of topography, inspires topography reuse, and considerably decreases the number of limits [36]. To encode the pictures, we utilized DenseNet-121. We started with DenseNet-121 that was already trained on ImageNet and refined it on our medical dataset to achieve better results. The pictures were rescaled to 224\(\times\)224 and normalized with the standard deviation and mean of ImageNet to coordinate the necessities of DenseNet-121 and how it was already trained on ImageNet. We utilized binary cross-entropy loss function and sigmoid as an activation function. We increased the number of training epochs from 3 to 10; however, no significant improvement in validation performance was observed. We also tried to unfreeze some layers of our base prototype and train it, but it increased our time complexity; therefore, we maintained the original fine-tuned base model. We tried to run DenseNet121 without maximizing transfer learning on a GPU-founded scheme with 3047 concepts s, but training without transfer learning on the full set of 3047 concepts proved computationally infeasible, requiring almost one month to complete three epochs, primarily due to the extreme class imbalance and model complexity. We also implemented the ChexNet prototype to achieve satisfactory results, but it did not achieve satisfactory performance [37].
## 3 Wind Tunnel Test of Section Model
### VIV Responses of Aerodynamic Measures of Deflector Plate
#### 3.3.4 Web Deflector Plate  
The web deflector plate was added to the original cross section and arranged symmetrically on both outsides of the webs along the truss girder and was in the form of a right-angled deflector plate. The position and shape of the web deflector plate are shown in Figure 17.  
After the web deflector plate was added, the relationship between the vertical and torsional VIV amplitudes and the wind speed under attack angles of +3\({}^{\circ}\) and +5\({}^{\circ}\) was determined (Figure 18).  
It can be seen from Figure 18 that when the wind attack angle was +3\({}^{\circ}\), the vertical and torsional peak amplitudes of the UD working condition decreased compared with those of the OS working condition. After setting the web deflector plate on the original section, the peak amplitude of vertical VIV decreased from 75.54 mm to 66.83 mm, and the vertical VIV locking range also changed from 5.24 m/s-6.72 m/s to 5.43 m/s-6.67 m/s. The peak amplitude of torsional VIV decreased from 0.181\({}^{\circ}\) to 0.164\({}^{\circ}\), and the torsional VIV major locking range also changed from 12.27 m/s-18.06 m/s to 12.11 m/s-17.13 m/s. At a +5\({}^{\circ}\) attack angle, the peak amplitude of vertical VIV decreased from 98.23 mm to 82.44 mm, and the vertical VIV locking range also changed from 5.01 m/s-6.69 m/s to 5.03 m/s-6.62 m/s after setting the web deflector plate on the original section. The peak amplitude of torsional VIV decreased from 0.211\({}^{\circ}\) to 0.180\({}^{\circ}\), and the torsional VIV major locking range also changed from 13.75 m/s-19.49 m/s to 13.45 m/s-18.32 m/s. The right-angled web deflector plate can suppress vertical and torsional VIV at +3\({}^{\circ}\) and  
Figure 17: Working condition of web deflector.  
\(+5^{\circ}\) wind attack angles, reduce the peak amplitude of VIV, and shorten the length of the VIV locking range. When the wind attack angles were \(+3^{\circ}\) and \(+5^{\circ}\), the suppression effect on the maximum amplitude of vertical VIV reached 11.53% and 16.07%, respectively, and the suppression effect on the maximum amplitude of torsional VIV reached 9.39% and 14.69%, respectively.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### Study Area and Data
#### 2.1.1 Ground Data  
The SPI was calculated using ground-based precipitation data from 60 observation stations provided by the automated synoptic observing system (ASOS) of the Korea Meteorological Administration (KMA). Agricultural drought conditions were evaluated based on the nationwide water reserve rates of agricultural reservoirs. Although the data are divided based on the headquarters name, branch name, and facility name, in this study, the average value of the water reserve rate was calculated for 167 cities, municipalities, and districts. Of these 167 local governments, 135 had water reserve data and 32 did not. The first water source for each local government was used to calculate a hydrological drought index based on public water, and in the case of areas for which the first water source is a river rather than a dam or reservoir, river flow data at drought prediction, warning, and monitoring points for each major river were used. One hundred and twenty-two local governments could secure data on dam water levels and river flow to monitor hydrological drought; no data were available for the remaining twenty-two.
# SSCL-TransMD: Semi-Supervised Continual Learning Transformer for Malicious Software Detection
## 4 Experiment
### Experimental Analysis
#### 4.2.3 Model Parameter Sensitivity Analysis  
The baseline weight \(\lambda_{base}\) in the memory replay component determines the adaptive weight \(\lambda_{new}\), which is the core parameter to address sample imbalance. In this section, \(\lambda_{base}\) is successively set to 0.2, 0.4, 0.5, 0.6, and 0.8 for binary classification and multi-classification experiments. Only the metrics after four rounds of mixed-sample training are recorded. The experimental results are shown in Figure 6.  
By observing Figure 6a,b, it can be found that, as \(\lambda_{base}\) increases, the model's classification accuracy initially increases and then decreases. The highest classification accuracy is achieved when \(\lambda_{base}\) is 0.5, indicating that sample balance is best achieved when \(\lambda_{base}=0.5\). Therefore, the default value of \(\lambda_{base}\) in the training process of the SSCL-TransMD detection model is set to 0.5.  
The iteration number \(T\) in the similarity calculation component is a key parameter for calculating the similarity of pseudo-labels. In this section, we set \(T\) to 60, 80, 100, 120, 140, and 160 for binary classification experiment training and multi-classification experiment  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Labeled**} & **First Input of** & **Second Input of** & **Third Input of** & **Fourth Input of** \\  & & **Mixed Samples** & **Mixed Samples** & **Mixed Samples** & **Mixed Samples** \\ \hline SSCL–Transformer & 0.8618 & 0.8441 & 0.8119 & 0.7846 & 0.7311 \\ \hline SSCL–TransMD & 0.8549 & 0.8537 & 0.8492 & 0.8482 & 0.8406 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results for ablation experiments.  
Figure 5: The impact of pseudo-labels provided by LLGC. Pseudo-labels are not utilized in SSCL–Transformer.  
training. We only record the metrics after four rounds of mixed sample training, and the experimental results are shown in Figure 6c,d.  
By observing Figure 6c,d, it can be observed that, as the parameter \(T\) increases, the model's classification accuracy shows an upward trend. However, after 120 iterations, the classification accuracy does not show a significant improvement, indicating that the accuracy of pseudo labels is already close to the true labels at this time. Considering the increasing number of unlabeled samples in subsequent inputs, the iteration number can be appropriately increased. In this paper, considering that the scale of each piece of input unlabeled sample data is similar in SSCL-TransMD detection model, the default value of \(T\) is set to 120.
# Automatic Identification of Landslides Based on Deep Learning
## 4 Results and Discussion
### Training  
The training effect of the deep learning model is closely related to the accuracy of the data set, suitable parameters, and training methods. Therefore, in this study, we selected three models, U-Net, DeepLab v3+, and PSPNet, and used two different classification networks as the backbone network of each model; the backbone network selection of the model is shown in Table 3. We record these models as U-Net (VGG), U-Net (ResNet50), DeepLab v3+ (MobileNet), DeepLab v3+ (Xception), PSPNet (MobileNet), and PSPNet (ResNet50).  
In the experiment, we set and adjusted the training parameters uniformly for all the models, as shown in Table 4. The input image size is fixed at 473 \(\times\) 473. The classification of pixel types is landslide and background. The model training adopts the method of freezing training, which divides the training into two stages: freezing and unfreezing. In the freezing stage, the backbone of the model is frozen, and the feature extraction network does not change. At this time, 50 rounds of fine-tuning are performed on the network. The video memory occupied in the freezing phase is small, so the batch_size and learning rate are set larger. In the unfreezing stage, the backbone network of the model is unfrozen, and the feature extraction network will change; at this time, the network is trained for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Probability of Execution** & **Specific Operations** \\ \hline random rotation & 50\% & rotate 20\({}^{\circ}\), +90\({}^{\circ}\), \(-90^{\circ}\) \\ left-right flipping & 100\% & flip the image left and right \\ image cropping & 100\% & original image 0.7\(\times\) dimension \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data augmentation.  
100 rounds. Due to a large amount of video memory occupied, the batch_size is set to 0.5 times that of the frozen phase, and the learning rate is reduced.  
In addition, during model training, we use the pre-trained weights obtained in the VOC dataset as the initial parameters of the model and set dice_loss to balance the number of training categories. We set focal_loss to balance positive and negative samples and use the NumPy form to give different loss weights to the background and landslides so that the model focuses on landslide pixels. In order to improve the recognition effect, we set the downsample_factor to 8, but it will also occupy much memory; therefore, in order to reduce the occupation of video memory, we do not use aux_branch by default, do not use multi-threading to read data and use the early stop strategy to save computing resources.  
The hardware environment of this experiment: GPU: 4*NVIDIA Tesla K80, CPU: 32*Intel (R) Xeon (R) CPU E5-2620 v4 @ 2.10 GHz, OS: CentOS 8.3. The software environment of this experiment: CUDA 11.2, Python 3.6, PyTorch 1.10.1, Tensorflow 2.2.0.
# Planning MAR Schemes Using Physical Models: Comparison of Laboratory and Field Experiments
## 3 Results
### Usability of Results for Water Flow Estimation through Laboratory Experiments  
The RMSE and NSE in Figure 2 showed that the representativeness of laboratory values for the FIU was limited. We conducted an analysis for scenario 1 for predictors that could increase the statistical representativeness looking into regression functions as well as multipliers and addends. As tension values for the wet phase are relatively constant, an addend was calculated for the difference between mean laboratory and FIU values. This value was calculated to be 0.1 kPa (upper layer) and \(-0.1\) kPa (lower layer) for the tank values and 0.9 kPa (upper layer) and 1.4 kPa (lower layer) for the column values. These coefficients were added to all laboratory tension values of the wet phase.  
For the dry phase, a regression analysis was conducted comparing different regression models (linear, quadratic, and cubic), multipliers, and addends. The linear regression model always produced the smallest RMSE. The regression function comparing upper layer tension in tank and FIU was \(y=1.6+0.7x\) and \(y=2.1+0.6x\) for the column and FIU, respectively. Applying the addend and regression function, the NSE was improved in both cases, for the tank from 0.8 to 0.9 and for the column from \(-0.4\) to 0.9 (compare Figure 2), indicating a very good match between the FIU and the adapted laboratory data. The RMSE decreased from 0.5 to 0.4 kPa for the tank experiment and from 1.2 to 0.4 kPa for the column measurement (Supplementary Figure S2).  
The regression functions and addends retrieved from analyzing scenario 1 were further used to adapt the measured laboratory data of scenario 2 to assess their representativeness for scenarios with differing boundary conditions. In this case, the climate and the length of the cycles changed (Table 1).  
The results depicted in Figure 7 show a good representativeness of the calculated predictors. Before, the laboratory data was visibly below the FIU measurements but through utilization of the regression function, the measurements could be well adapted to fit the FIU values. For the drying phase, most FIU values lay within the confidence interval of the laboratory values that were adapted by the predictors. The difference mapped in Figure 7 was calculated by subtracting the FIU and the adapted laboratory tension values. For the drying phase, the difference shows values close to zero. This was also represented in the RMSE, which decreased significantly from 1.1 to 0.3 kPa (tank) and from 1.7 to 0.5 kPa (column). The biggest errors were obtained during the wetting phase and the rapid shifts from infiltration to drying and vice versa.  
Figure 6: Flow time between soil surface and 0.7 m depth for (**a**) scenario 3 and (**b**) scenario 4. Scenarios were aborted when overflow was detected, leading to different numbers of cycles for the setups (adapted from [PERSON] et al. [27]).
## 2 Materials and Methods  
### Study Area, Water Sources, and Sample Collection  
The water samples were collected from Sibiu county in Romania, which is located in the Transylvanian Depression, crossed in part by the Southern Carpathians [28]. In the mountainous area of the county, the geological substratum is dominated by the presence of crystalline shales, while in other areas gravels, sands, clays, marls, limestones and tuffs can be found, with landslides occurring frequently [28; 29]. The relief is highly diversified, comprising mountain masss and hills, which are predominant [28], but also glacial valleys, foothills, depressions, and stepped plains [28; 29]. Erosion, torrentiality, and surface runoff are processes frequently encountered in the subalpine and alpine areas [28]. The region is rich in deposits of salt, methane gas, chloride, bromide and iodide mineral waters, and various building rocks [28; 29]. The average annual rainfall is between 606.7 and 1300 mm [28]. A moderate continental climate predominates, but in some parts of the county a mountain climate is also encountered [28].  
In Sibiu County, regular monitoring of the water quality intended for human consumption is carried out by the Public Health Directorate through its own accredited laboratories, in order to meet the national and international legal requirements. The audit and control monitoring of the authorized suppliers include the establishment of sampling points, sample collection, transportation, and analysis which are performed by specialized personnel. An extensive database was extracted from lab reports between 2017 and 2021, namely, 65 samples analyzed from surface (54%) and groundwater (23% subsurface and 23% deep sources). Samples were collected annually from 13 locations illustrated in Figure 1, between January and November. The diversity of water sources within the supply zones and the pre-established periodicity of sampling were considered eligible criteria for the present study. In this respect, the following drinking water sources were selected: surface water from the Cibin River (Sibiu); surface water from the Avrig River (Avrig); surface water from the Tarnava Mare River (Agnita, Medias); surface water from the Sadu River (Cisnadie, Sadu); surface water from the Tiliscuta River (Tilisca); water from captured springs (Aciliu, Paltinis); water from drilled wells (Sacel, Talmaciu, Dumbraveni); and water from mixed sources: captured springs and drilled wells (Seica-Mare).  
The investigated 65 samples selected for the present study meet the requirements of data synchronization in terms of time, location, and water quality indicators (physical, chemical, and radiological).
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 2 Materials and Methods
### Multinomial Distributions Based Method  
In [6], a rank distribution is calculated by counting the rank of the constructed \(\Lambda\) from the received bitstream \(N\) times. Using the distribution, the probability of occurrence of the rank distribution can be modeled by multinomial distribution as follows:  
\[P_{l\times l}=\frac{N!}{x_{0}!x_{1}!x_{2}!x_{3}!}P_{0}^{x_{0}}P_{1}^{x_{1}}P_{2 }^{x_{2}}P_{3}^{x_{3}} \tag{1}\]  
where \(P_{s}(s\leq 3)\) follows Table 1, \(x_{s}\) represents the counted number that the rank of the constructed \(\Lambda\) equals to \(l-s\), and \(N\) is the total number of counting ranks, \(N=x_{0}+x_{1}+x_{2}+x_{3}\). Because values of the considered \(s\) range from 0 to 3, note that \(P_{3}\) equals \(1-(P_{0}+P_{1}+P_{2})\). \(P_{l\times l}\) represents the distribution similarity between the length of two matrices consisting of random and received signals, respectively. It is used to decide whether the received signals consist of random signals or codewords.  
When the distribution is the same as that shown in Table 1, \(P_{l\times l}\) equals 1. This means that the distribution is from \(\Lambda\). If the distribution is far different from that shown in Table 1, \(P_{l\times l}\) decreases to 0 and we can consider that the elements of matrices are codewords.
# On Beamforming with the Single-Sideband Transform
## 4 Comparisons and Simulations  
In the simulations (code available at [[https://github.com/VCurtarelli/py-ssb-ctf-bf](https://github.com/VCurtarelli/py-ssb-ctf-bf)]([https://github.com/VCurtarelli/py-ssb-ctf-bf](https://github.com/VCurtarelli/py-ssb-ctf-bf)) (accessed on 21 August 2024)), we employed room impulse responses that were generated using the RIR generator [29] and signals that were selected from the SMARD database [30]. In all cases, we used \(L_{D}=L_{Y}\) and \(\Delta=0\); with this, we disregarded any non-causal frames and considered as many frame samples as the RIR samples. The room's dimensions were \(4\times 6\times 3\) m (width \(\times\) length \(\times\) height), with a reverberation time of 0.3 s. The device composed of the loudspeaker plus sensors was centered at \((3,4,1)\) m, comprising \(M=8\) sensors arranged in a circular array with a radius of 8 cm. All sensors were omnidirectional with a flat frequency response, with the reference being the sensor at \((3,192,1)\) m. The positions and signals used for the sources are shown in Table 1. The room's layout is described in Figure 1, where in green we have the desired source (assumed to be omnidirectional), and in red the device, with the 8 sensors and the loudspeaker in the center.  
We set the input Signal-to-Noise Ratio (iSNR) for the white Gaussian source to gSNR = 30 dB, and initially, the input Signal-to-Echo Ratio (iSER) for the interfering loudspeaker source to iSER = \(-15\) dB. The loudspeaker interference was labeled as echo,given that it was a feedback path between the source and the sensors, which were all mounted on the same device.  
Hamming windows were used for the transform with an overlap of 50%, and all signals were resampled to the desired sampling frequency of 16 kHz. Unless stated otherwise, \(N=32\) samples per window were used in the windowing processes. The regularization parameter was empirically set to \(\alpha=1\times 10^{-4}\), just enough to control the gain in SNR. Although the developments allowed for a time-variant beamformer, we designed a single filter for the whole signal, favoring a faster processing time and ease of comparing the results.  
We compare filters obtained via the two transforms for varying conditions of the signals and variables considered. In all plots, the STFT results are presented in red lines with squares, and the SSBT results in green lines with triangles. The results for an accurate _a priori_ RFR are in lighter continuous lines, and those for the estimated RFR via Equations (10) and (18) are in darker dotted lines. For simplicity, the STFT for an accurate RFR is labeled STFT-A, and for an estimated RFR, it is STFT-E. The same notation applies to SSBT-A and SSBT-E.