esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Proposed Method for Estimating Surface Albedo
\begin{table}
\begin{tabular}{r l l l l l l l l l} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Time**}} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**NDVI**} & \multicolumn{1}{c}{**Albedo**} & \multicolumn{1}{c}{**Albedo**} & \multicolumn{1}{c}{**Difference**} & \multicolumn{1}{c}{**Difference**} & \multicolumn{1}{c}{**Difference**} & \multicolumn{1}{c}{**Difference**} \\  & & & **SURFRAD** & \multicolumn{1}{c}{**ABOVE**} & \multicolumn{1}{c}{**SURFRAD–**} & \multicolumn{1}{c}{**SURFRAD–**} & \multicolumn{1}{c}{**SURFRAD–**} & \multicolumn{1}{c}{**SURFRAD–**} & \multicolumn{1}{c}{**SURFRAD–**} \\  & & & **SURFRAD** & \multicolumn{1}{c}{**ABOVE**} & \multicolumn{1}{c}{**V01**} & \multicolumn{1}{c}{**V02**} & \multicolumn{1}{c}{**V03**} & \multicolumn{1}{c}{**V04**} \\ \hline
8 June 2016 & BND & 0.2444 & 0.1214 & 0.164 & \(-0.0398\) & \(-0.0823\) & \(-0.0676\) & \(-0.102\) & \(-0.2288\) \\
25 October 2014 & BND & 0.2790 & 0.0786 & 0.184 & \(-0.1053\) & \(-0.1320\) & \(-0.0928\) & \(-0.1333\) & \(-0.2726\) \\
13 November 2015 & BND & 0.2919 & 0.1250 & 0.149 & \(-0.0239\) & \(-0.0578\) & \(-0.0586\) & \(-0.1026\) & \(-0.2209\) \\
4 September 2013 & BND & 0.294 & 0.1120 & 0.163 & \(-0.0509\) & \(-0.0534\) & \(-0.0532\) & \(-0.0133\) & \(-0.2135\) \\
1 November 2015 & FPK & 0.3041 & 0.1214 & 0.167 & \(-0.0455\) & \(-0.0448\) & \(-0.0438\) & \(-0.0897\) & \(-0.2093\) \\
30 July 2016 & FPK & 0.3216 & 0.1394 & 0.192 & \(-0.0525\) & \(-0.0549\) & \(-0.0591\) & \(-0.0699\) & \(-0.2185\) \\
25 July 2014 & FPK & 0.3732 & 0.0940 & 0.2 & \(-0.1059\) & \(-0.0887\) & \(-0.0726\) & \(-0.1170\) & \(-0.2483\) \\ \hline
24 September 2013 & FPK & 0.4169 & 0.1031 & 0.177 & \(-0.0738\) & \(-0.0548\) & \(-0.0465\) & \(-0.0941\) & \(-0.2250\) \\
28 March 2015 & SKF & 0.4470 & 0.1036 & 0.184 & \(-0.0803\) & \(-0.0454\) & \(-0.0934\) & \(-0.0938\) & \(-0.2355\) \\
7 November 2015 & SKF & 0.4727 & 0.0940 & 0.22 & \(-0.1259\) & \(-0.0920\) & \(-0.0939\) & \(-0.1143\) & \(-0.2642\) \\
15 November 2016 & BND & 0.4990 & 0.1376 & 0.162 & \(-0.0243\) & \(-0.0952\) & \(-0.0647\) & \(-0.1031\) & \(-0.2031\) \\
9 November 2016 & SKF & 0.4999 & 0.0948 & 0.212 & \(-0.1171\) & \(-0.0842\) & \(-0.0641\) & \(-0.1080\) & \(-0.2575\) \\
12 In this way, the combined methodologies proposed conform an adaptative methodology for the estimation of surface albedo in each pixel based on the NDVI, providing more accurate data for mapping and monitoring the surface albedo.
###### Abstract  
There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the need to apply complex features extraction or data augmentation processes. Therefore, this paper proposes a visualized malware multi-classification framework to avoid false positives and imbalanced datasets' challenges through using the fine-tuned convolutional neural network (CNN)-based TL models. The proposed framework comprises eight different FT CNN models including VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2. First, the binary files of different malware families were transformed into 2D images and then forwarded to the FT CNN models to detect and classify the malware families. The detection and classification performance was examined on a benchmark Malimg imbalanced dataset using different, comprehensive evaluation metrics. The evaluation results prove the FT CNN models' significance in detecting malware types with high accuracy that reached 99.97% which also outperforms the performance of related machine learning (ML) and deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset.  
Keywords:cybersecurity threats; malware visualization; detection; classification; deep learning; machine learning; CNN; transfer learning; fine-tuning; VGG16 +
Footnote †: journal: Applied Sciences
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 3 Case Study  
The area of USCB which is in southern Poland and the Ostrava-Karvina region in the Czech Republic covers about 7400 km\({}^{2}\), of which 5800 km\({}^{2}\) are in Poland (Figure 8). The Polish part along with the Lower Silesian Basin and Lublin Basin provides 92% of the needed electricity and 89% of the heat for Poland according to the World Energy Council [48]. The USCB has been intensively exploited since the nineteenth century, when coal and lignite extraction strengthened the industrialisation, followed by its peak in Eastern European countries during the post-Second World War period [49]. Nowadays, coal regions still have ongoing mining, which provides a number of workplaces for about 185,000 employees in the EU [50] and brings together a large population of people. Consequently, the USCB is one of the most densely populated zones in Poland (Figure 8). Since 1970, almost 40% of coal mining activities have been located under an urbanised area, where about 600 km\({}^{2}\) are affected by intensive underground coal extraction each month [17].  
An example of the severe consequences of mining activity in the USCB area and, particularly, in the vicinity of the town of Bytom, where 28 resident houses were evacuated in 2011 due to severe damage in the building construction was reported [20]. The surface subsidence occurs in relation to the currently active mines and is occurring over the abandoned mines [51]. The current and residual subsidence depends on the technology for inactive shaft preservation, the depth and thickness of the exploited layers, and the local geological conditions.
# Hybrid Optimization Method for Correcting Synchronization Errors in Tapping Center Machines
## 3 Related Research
### Uniform Design and Regression Analysis  
The UD method proposed by [PERSON] and [PERSON] [17; 18] was used to design and plan experiments in which the test points were evenly distributed within the test range. A uniform layout is expressed as \(U_{n}(n^{s})\), where \(U\) is the uniform layout, \(n\) is the level number, and \(s\) is the factor number. Table 1 shows the distribution of a \(U_{6}(6^{6})\) uniform layout.  
In the UD method, data collected in experiments were used for model building, and data analysis was performed using regression analysis. Regression analysis is a statistical method for analyzing relationships between input variables and output variables. Its main purposes are understanding the relationships among independent variables and dependent variables and then building a mathematical model for predicting dependent variables. Depending on the complexity, a regression analysis can be classified as simple regression and multiple regression. Simple regression uses a single independent variable to predict a dependent variable, and multiple regression explores how a dependent variable is related to multiple independent variables.  
The unary linear regression equation expresses the relationship between a dependent variable and an independent variable:  
\[Y=\beta_{0}+\beta_{1\times 1}+\varepsilon \tag{2}\]  
where \(\beta_{0}\) is a constant, \(\beta_{1}\) is a regression coefficient, and \(\varepsilon\) is an error.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(U_{6}(6^{6})\) & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
6 & 6 & 5 & 4 & 3 & 2 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: The \(U_{6}(6^{6})\) uniform layout.  
Figure 1: Measurements used for the analysis of screw teeth.  
A multiple linear regression equation expresses the relationship of a dependent variable to multiple independent variables:  
\[Y=\beta_{0}+\beta_{1\times 1}+\beta_{2\times 2}+\beta_{3\times 3}+\dots+\beta_{m \times m}+\varepsilon \tag{3}\]  
where \(\beta_{0}\) is a constant, \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\),..., \(\beta_{m}\) is a regression coefficient, and \(\varepsilon\) is an error.  
Regression analysis is often used for the interpretation of experimental data and prediction. For interpretation, the regression equation was calculated from the obtained experimental data. The equation was then used to determine the contribution of each independent variable to the dependent variable. A common application of regression analysis is determining the significance of the independent variable to the dependent variable. Since the regression equation is linear, regression analysis can be used for prediction, i.e., to estimate how an independent variable will change a dependent variable. That is, after regression analysis, a regression model is used to predict a future change in a dependent variable, it can be used to determine how to compensate or respond to the change.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study
### Comparative Experiments
(**a**) shows a comparison of weekly load prediction results, and (**b**) presents the load prediction results for one specific day.
## 2 Processing and Preparation of Water-Saturated Fissured Sandstone Specimens and SHPB Test Device
### SHPB Test Device and Principle  
The test was conducted using the Split Hopkinson Pressure Bar (SHPB) test device of the State Key Laboratory of Deep Coal Mine Mining Response and Disaster Prevention and Control (as shown in Figure 4). The device consists of an impact loading system, a data acquisition system, and a speed measuring device system. The bars in this device are machined from 40 Cr high-strength alloy steel with a modulus of elasticity of up to 210 GPa, and the diameter of the press bars is 50 mm. To apply dynamic shock loads, we utilize a high-pressure nitrogen gas cylinder as a pulse source to form a shock wave by rapidly releasing the gas. Specifically, we generated a high-speed shock wave by introducing a compressed gas inside the shock rod, followed by a rapid opening of the release valve, which caused the gas to expand rapidly. The impulse load thus generated acts on the specimen, triggering the dynamic response of the rock under high strain rate conditions. The entire test process was monitored by a high-speed data acquisition system to record and analyze the strain waveform of the specimen.  
Based on the basic assumptions of the SHPB test principle [29; 30], three important dynamic mechanical parameters of rock specimens, namely stress \(\sigma(t)\), strain \(\varepsilon(t)\), and strain rate \(\dot{\varepsilon}(t)\), can be calculated as shown in Equation (1).  
\[\begin{array}{c}\sigma(t)=\frac{E_{0}A_{0}}{2A_{S}}[\varepsilon_{I}(t)+ \varepsilon_{R}(t)+\varepsilon_{T}(t)]\\ \varepsilon(t)=-\frac{\mathrm{\Gamma}_{S}}{\mathrm{\Gamma}_{S}}\int_{0}^{ \infty}[\varepsilon_{I}(t)+\varepsilon_{R}(t)-\varepsilon_{T}(t)]dt\\ \dot{\varepsilon}(t)=-\frac{\mathrm{\Gamma}_{S}}{\mathrm{\Gamma}_{S}}[ \varepsilon_{I}(t)+\varepsilon_{R}(t)-\varepsilon_{T}(t)]\end{array} \tag{1}\]  
Figure 4: SHPB test device sandstone specimen placement diagram.  
Figure 3: XRD patterns of water—saturated fissured sandstone specimen.  
Figure 2: Flow chart of preparation of water-saturated fractured sandstone specimen.  
where \(A_{0}\), \(A_{S}\) is the cross-sectional area of compression bar and specimen; \(E_{0}\), \(C_{0}\) is the modulus of elasticity of compression bar material and longitudinal wave velocity; \(L_{S}\) is the length of the specimen; \(\varepsilon_{I}(t)\),\(\varepsilon_{R}(t)\),\(\varepsilon_{T}(t)\) is the incident, reflected, and transmitted strains; and the compressive stress and strain are taken as the positive direction.
# A Novel Method to Measure the Powder Consumption of Dental Air-Polishing Devices
## 2 Materials and Methods
### Investigated Devices  
In this investigation, different device types were evaluated, i.e., Table-top and Handy devices. The Table-top devices were measured in the sub-gingival mode (low pressure and gentle powder). These conditions were the most representative conditions for an advanced minimally-invasive treatment procedure. Three Table-top devices were used. Table-top E1 was an AIRFLOW(r) PROPHYLAXIS MASTER device (ref: FT-229, Electro Medical Systems SA, Nyon, Switzerland), operated at a pressure setting of 3/10 using  
Figure 1: Powder flowmeter; first, the powder passes through a laser beam. The light emitted by the powder is then measured with a spectrometer.  
AIRFLOW(r) PLUS powder (Erythiritol, ref: DV-082, Electro Medical Systems SA, Nyon, Switzerland). This represents the recommended setting when exposed dentine is involved in the treatment. Table-top N2 was a VARIOS COMBI PRO device (ref: Y1002843, NSK-Nakanishi Inc, Kanuma Tochigi, Japan) used with the Perio kit (ref: Y1003042, NSK-Nakanishi Inc, Kanuma Tochigi, Japan), operated at a setting of 3/10 using PERIO MATE powder (Glycine, ref: Y900938, NSK-Nakanishi Inc, Kanuma Tochigi, Japan). Table-top M3 was a COMBI TOUCH device (ref: 04000103, Mectron s.p.a, Carasco, Italy) using the PERIO powder chamber and MECTRON SENSITIVE powder (Glycine, Mectron s.p.a, Carasco, Italy). This device has no pressure setting options.  
Three Handy devices were also measured in a sub-gingival compatible mode (with a 3-bar dental chair pressure setting and gentle powder). Handy E1 was an AIRFLOW(r) HANDY 3.0 PERIO device (ref: FT-221, Electro Medical Systems SA, Nyon, Switzerland) using AIRFLOW(r) PLUS powder (Erythiritol, DV-082, Electro Medical Systems SA, Nyon, Switzerland). Handy N2 was a PERIO MATE (ref: Y1002655, NSK-Nakanishi Inc, Kanuma Tochigi, Japan) operated using PERIO MATE powder (Glycine, ref: Y900938, NSK-Nakanishi Inc, Kanuma Tochigi, Japan). Handy M3 was a STARJET (ref: 05150075007, Mectron s.p.a, Carasco, Italy) using MECTRON SENSITIVE powder (Glycine, Mectron s.p.a, Carasco, Italy). In these tests, the handy devices were fixed perpendicularly to the ground, with the handpiece axis directed 30\({}^{\circ}\) down. A summary of all of the devices is provided in Table 1.  
All of the Table-top devices were operated with a regular pedal press of 25 s and a 5 s pedal release. The Handy devices, which are devoted to quick treatments, were operated with a regular pedal press of 12 s and a 6 s pedal release. This time setup was chosen to increase the number of pedal presses, as the test duration was very short, depending on the device.
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
## 3 Results
### Contrast  
To test the robustness of the beamformers in terms of phase aberration. A lab-made cyst phantom is used to compare image contrast under different beamformers. This phantom has a random background and two anechoic cysts. The left cyst is centered at (x, z) = (\(-\)8, 22) mm and the right one is centered at (x, z) = (4, 18) mm. The anechoic cysts are surrounded by randomly placed scatterers, the attenuation coefficient is 0.6 dB/MHz/cm and the speed of sound within the phantom is 1539-1550 m/s.  
The beamformed images are shown in Figure 6. For CR and CNR calculation, the measured cyst region is enclosed in the white circle and the background in the white rectangle in Figure 6. The background region with uniform speckle patterns shares the same centerline as the measured cyst and contains no particular bright reflectors. The detailed CR and CNR values are given in Table 2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **DAS** & **NDSB** & **Comp1** & **Comp2** \\ \hline Lateral resolution [mm] & 2.04 & 2.35 & 1.37 & 1.40 \\ Axial resolution [mm] & 0.54 & 0.54 & 0.43 & 0.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lateral and axial resolution for point target.  
Figure 4: B-mode images of a point target. (**a**) DAS image (**b**) NDSB image (**c**) Comp1 image (**d**) Comp2 image.  
Figure 5: Lateral and axial profile of beamformed point target. (**a**) lateral profiles (**b**) axial profile.  
From the beamformed images, we can find cyst region enclosed by the white circle gets cleaner after compounding. In Table 2, Comp2 has the better CR performance which is 11.1 dB higher than DAS and 13.0 dB higher than NDSB. CNR value also validates the improvement in image contrast. Comp2 gives the best CNR value which provides 3.9-dB and 4.0-dB improvement compared to DAS and NDSB, respectively.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 4 Experiments and Analysis
### Pre-processing  
ECG signal is easy to be interfered by several kinds of noises when collected. The common types of ECG signal noise are power frequency interference, baseline drift, and EMG noise [33]. Among them, EMG noise has a significant impact on ECG signal.  
The MIT-BIH noise stress test database [34] offers EMG noise recording when assembling the ECG signal. The noise recordings were made using physically active volunteers and standard ECG recorders, leads, and electrodes; the electrodes were placed on the limbs in positions in which the subjects' ECGs were not visible. The recordings are created by the script nstdbgen- using clean recordings from the MIT-BIH, to which calibrated amounts of noise from record 'em' were added using nst- [26].  
According to the requirement of Signal to Noise Ratio (\(SNR\)), ECG signal and noise of certain amplitude can be superposed. Here, two groups of ECG data with EMG noise, which \(SNR\) is 24 dB and 12 dB, are assembled as shown in Figure 2. There are fluctuations on original signals combined with EMG noises, and waves seems less smoothly with \(SNR\) declining.  
Several steps are conducted in noise reduction. First of all, the DC components are eliminated by subtracting the mean value. Baseline drift can be removed by a 8.33 ms width median filter. An 341 th order low-pass filter using a Hanning window, whose cut-off frequency is 40 Hz, is used to remove power frequency interference and EMG noise. An 341 th order high-pass filter using a  
Figure 2: Effect of EMG noise on ECG signal. (**a**) Original signal; (**b**) ECG signal with EMG noise (\(SNR\) = 24 dB); (**c**) ECG signal with EMG noise (\(SNR\) = 12 dB).  
Hanning window, whose cut-off frequency is 0.5 Hz, is used to remove some other low-frequency noise. Figure 3 demonstrates the ECG signal before and after denoising.  
As shown in Figure 3, after denoising, the P-wave waveform, which is seriously disturbed by EMG noise, is significantly improved. Adaptive differential threshold method [35] is then conducted for QRS wave detection.  
After detecting the R-peak in every QRS complex, the single heartbeat was segmented such that it was centered around the R peak, as shown in Figure 4. The segmentation length is 556 ms.  
Figure 4: Single heartbeat after denoising.  
Figure 3: ECG signals before and after denoising.
## 2 Methodology  
In order to use the multi-temporal PolSAR data to classify crops, a neural network NC-SAE was employed to compress the data, and then a novel crop discrimination network with multi-scale features (MSCDN) was constructed to achieve the crop classification. The flowchart of the whole study method is shown in Figure 1, which mainly includes three steps: polarization feature decomposition, feature compression and crop classification.
# Experiments on the State Boundary Surface of Aeolian Sand for Road Building in the Tengger Desert
## 1 Introduction
[33; 34; 35] studied the characteristics of deformation, strength and failure of aeolian sand in the Tengger Desert, China at different spatial stress states, different densities and multiple confining pressures. All in all, the unique mechanical properties of aeolian sand have attracted increasing popularity, but some other aspects, such as the critical state, phase transformation and dilatancy of aeolian sand at different stress states and stress paths, need to be studied further. In particular, the determination of its state boundary surface is rarely reported. In this paper, a series of triaxial drained and undrained tests on the medium-density specimens were conducted to obtain the unique relationship of the state boundary surface of the aeolian sand taken from the Tengger Desert in China. The mechanical responses are measured at different confining pressures. The critical state lines, phase transformation lines and dilatancy characteristics are determined. According to the unique relationship between the stress state and the volume state, the state boundary surface in the _p-q-e_ space is established. The test results improve our understanding of the mechanical response and helps to establish a reasonable constitutive relationship for aeolian sand.
## 3 Results
### Cost-Benefit Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tank** & **Costs, \(\upxi\)** & **Water-Saving,** & **Annual \(E\), \%** & **NPV** & **PBP, Year** \\
**Volume, m\({}^{3}\)** & **Costs, \(\upxi\)** & **m\({}^{3}\)/Year** & **Annual \(E\), \%** & **(20 Years), \(\upxi\)** & _PBP, Year_ \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{2654} & 106 (min.) & 28.3 & 1150 & 16 \\  & & **138 (av.)** & **36.8** & **2292** & **13** \\  & & 165 (max.) & 43.9 & 3240 & 11 \\ \hline \multirow{3}{*}{9} & \multirow{3}{*}{2863} & 111 (min.) & 29.4 & 1092 & 16 \\  & & **144 (av.)** & **38.3** & **2279** & **13** \\  & & 173 (max.) & 46.1 & 3335 & 12 \\ \hline \multirow{3}{*}{12} & \multirow{3}{*}{3017} & 111 (min.) & 29.4 & 938 & 17 \\  & & **147 (av.)** & **38.9** & **2219** & **14** \\  & & 181 (max.) & 48.0 & 3438 & 12 \\ \hline \multirow{3}{*}{15} & \multirow{3}{*}{3362} & 111 (min.) & 29.4 & 593 & 18 \\  & & **149 (av.)** & **39.5** & **1945** & **15** \\  & & 187 (max.) & 49.6 & 3308 & 12 \\ \hline \multirow{3}{*}{18} & \multirow{3}{*}{3516} & 111 (min.) & 29.4 & 439 & 19 \\  & & **150 (av.)** & **39.8** & **1841** & **15** \\  & & 193 (max.) & 51.2 & 3368 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Net present value (_NPV_) and payback period (_PBP_) values related to each tank size for the RWH system. Nevertheless, the volume of a tank equal to 12 m\({}^{3}\) represented the highest \(NPV\) = 68698, a higher annual \(E\) = 90.4%, and higher water savings compared to smaller tanks. For example, the largest analyzed reservoir (18 m\({}^{3}\)) had returned within 9 years (bolded) but its annual efficiency was the highest (92.4%). That is why the 12 m\({}^{3}\) tank was chosen for the combined system's effective operation. It is worth mentioning that the combined system's average annual efficiency, depending on the size of the reservoir, was in a wider range (from 87.1% to 92.4%) than was the case of the system with only rainwater recovery (from 36.8% to 38.8%).
## 3 Experimental Results and Discussion
### Effect of Joints on Cutting Force
The \(R^{2}\) value was 0.445, and the \(F\)-value exceeded 10, with a \(p\)-value of 0.000. These results indicate that the regression curve is statistically significant at the 99% confidence level. According to the regression curve, the  
Figure 7: Variation of \(FC_{m}\) with distance from joint plane: (**a**) \(p=6\) mm, (**b**) \(p=9\) mm, and (**c**) \(p=12\) mm. normalized \(FC_{m}\) reaches its minimum when the ratio \(d/J_{s}\) is between 0.2 and 0.3. This suggests that joint effects are most pronounced when d falls within this range, regardless of joint spacing. In contrast, at \(d/J_{s}=0.5\), the normalized \(FC_{m}\) approaches 1 and exhibits low variability. This indicates that joint effects are minimal when cutting is performed at the midpoint between adjacent joint planes. At \(d/J_{s}=0.1\), the normalized \(FC_{m}\) values are distributed near 1, suggesting that the influence of joints is limited even when the cutting path is located very close to a joint plane. Therefore, it can be inferred that joint-induced reductions in cutting force are most significant when the cutting path is located between joints, rather than directly adjacent to one.