esa-sceva/satcom-chunk-collection huggingface dataset:
# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 4 Experimental Results and Applications
### Method Accuracy Verification and Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**PDR**} & \multicolumn{2}{c}{**SURF + RANSAC**} & \multicolumn{2}{c}{**Coordinate System Conversion**} \\ \cline{3-8}  & & **RMSE** & **Offset Value** & **RMSE** & **Offset Value** & **RMSE** & **Offset Value** \\ \hline
1 & 12 & 2.104 & \([-572.66,408.70]\) & 2.138 & \([-572.51,408.55]\) & 2.672 & \([-539.63,442.98]\) \\
2 & 12.5 & 1.288 & \([-568.57,401.85]\) & 1.309 & \([-578.05,411.19]\) & 2.408 & \([-539.63,442.98]\) \\
3 & 13 & 1.414 & \([-568.88,401.50]\) & 2.273 & \([-560.02,396.42]\) & 3.400 & \([-539.63,442.98]\) \\
4 & 13.5 & 1.673 & \([-548.88,427.80]\) & 2.236 & \([-546.42,439.11]\) & 1.703 & \([-539.63,442.98]\) \\
5 & 14 & 1.511 & \([-535.88,433. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**FDR**} & \multicolumn{2}{c}{**SURF + RANSAC**} & \multicolumn{2}{c}{**Coordinate System Conversion**} \\ \cline{3-7}  & & **RMSE** & **Offset Value** & **RMSE** & **Offset Value** & **RMSE** & **Offset Value** \\ \hline
1 & 12 & 1.580 & [\(-\)581.30, 400.18] & 3.460 & [\(-\)650.86, 429.19] & 1.603 & [\(-\)539.63, 442.98] \\
2 & 12.5 & 2.000 & [\(-\)527.25, 454.23] & 7.000 & [\(-\)764.14, 464.32] & 1.914 & [\(-\)539.63, 442.98] \\
3 & 13 & 1.825 & [\(-\)565.46, 396.90] & 7.071 & [\(-\)435.08, 357.04] & 2.000 & [\(-\)539.63, 442.98] \\
4 & 13.5 & 1.224 & [\(-\)551.95, 410.41] & 8.940 & [\(-\)429.81, 405.41] & 2.041 & [\(-\)539.63, 442.98] \\
5 & 14 & 1.802 & [\(-\)561.50, 448.63] & 5.000 & [\(-\)538.63, 401.97] & 2.031 & [\(-\)539.63, 442.98] \\
6 & 14.5 & 2.041 & [\(-\)541.24, 455.38] & 10.198 & [\(-\)474.47, 446.21] & 1.914 & [\(-\)539.63, 442.98] \\
7 & 15 & 1.612 & [\(-\)541.24, 436.27] & 161.500 & [\(-\)1297.60, 351.95] & 2.049 & [\(-\)539.63, 442.98] \\
8 & 15.5 & 2.738 & [\(-\)551.37, 453.16] & 485.060 & [\(-\)2139.85, 802.55] & 1.732 & [\(-\)539.63, 442.98] \\
9 & 16 & 1.414 & [\(-\)538.85, 447.02] & 918.190 & [\(-\)2657.80, 1029.11] & 2.291 & [\(-\)539.63, 442.98] \\
10 & 16.5 & 1.264 & [\(-\)538.85, 445.33] & 1500.000 & [\(-\)2657.80, 1029.11] & 1.732 & [\(-\)539.63, 442.98] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Registration results of three methods in vector maps of Shaxi. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{3-7}  & & **Beijing** & **Ganzhou** & **Shaxi** \\ \hline \multirow{3}{*}{Remote sensing map} & PDR & 1.86 & 2.49 & 1.68 \\  & SURF + RANSAC & 2.56 & 3.52 & 1.96 \\  & coordinate system conversion & 4.19 & 3.35 & 1.95 \\ \hline \multirow{3}{*}{Vector map} & PDR & 1.66 & 2.4 & 1.75 \\  & SURF + RANSAC & 586 & 1116.36 & 310.64 \\ \cline{1-1}  & coordinate system conversion & 1.68 & 1.96 & 1.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RMSE of registration for three methods in six scenarios.
# Hygroscopicity and Morphology of Bio-Based Boards--The Influence of the Formulation
## 3 Test Methods
### Internal Structure and Porosity  
Three cylindrical samples of each composition (A4, A5, A6, H4, H5, H6, AH4, AH5, and AH6), with diameter = 10 cm and h = 4 cm (Figure 2d-f), were used to analyze their internal structures and porosities using X-ray computed tomography. For this purpose, a CT system was used. The apparatus (Figure 3a) was assembled in-house (byPhysics and Astronomy Department of the University of Bologna) as a multipurpose CT system and consisted of a Bosello XRG120.IT conventional X-ray tube, a Varian PaxScan 2520D flat-panel X-ray detector, and a Physik Instrument manipulation axis set with an M-038 micrometric sample rotation stage and M413.3 PD for detector translation. The samples were placed with one face over the rotation stage in a horizontal position.  
Two or three samples, separated with expanded polystyrene boards (which are transparent to X-rays), were placed vertically on the rotation platform, one on top of the other (Figure 3b), to optimize the scanning time. A standard scanning protocol for the system was used: 900 projections over a 360-degree angle; a 4-frame average for each projection; and low magnification (M = 1.115). The voltage (70 kV), current (6.5 mA), and detector frame rate (3 fps) were chosen according to the sample size and material after an initial evaluation of the X-ray absorption and signal outcome in the detector. No filtration was used. The overall scanning time was 1 h.  
After scanning each group of samples, CT slices were obtained via raw data elaborations with the in-house software PARREC, EXE_2023_01_26_PARREC_BHC_AV, (implementing a standard version of the filtered back-projections cone-beam Feldkamp algorithm [59]). One of the great strengths of the performed X-ray computed tomography was the large number of reconstructed slices, which ensured the reproduction of the entire internal structures of the samples at a high resolution.  
Slices produced with the reconstruction software were imported into the ImageJ/Fiji 1.53c freeware image processing software for a more convenient data visualization and further elaboration via a segmentation and pore classification process (Section 4.2). The three-dimensional rendering of the reconstructed X-ray-scanned objects and the first simple overall porosity measurements were performed by importing CT slices with the VGStu-dioMax 2.1 commercial software (Volume Graphics).  
Figure 3: X-ray computed tomography: (**a**) employed apparatus; (**b**) three samples separated with expanded polystyrene boards placed on the rotation platform.  
It is important to underline that although the samples employed for the tomography were not the same as those employed for the hygroscopic analysis (described in the following Section 3.3), the results were considered significant to carry out a comparison between the internal structures and hygroscopicity as all the samples were produced at the same time with the same mixtures of the aggregate-adhesive, production process, and conditions.
## 3 Optimum Parameters Selection
### Quantization Effects  
So far, the analysis has been performed using floating point representation. It is worth to evaluate the impact of signal quantization in this process, since analog signals are sampled using analog-to-digital converters (ADC) with finite number of bits, and usually the higher the sampling frequency, the  
Figure 8: RFI mitigation performance using the Symlet 3 wavelet transform and a sinusoidal RFI for the heuristic SURE threshold method as a function of the correlation length (in samples) of the noise. Sequence length \(2^{16}\), decomposition level = 12.  
smaller the number of bits. To test the impact of quantization in the performance of the above-described algorithms, signals (noise + RFI) have been quantized using a variable number of bits from 4 to 16, and the ADC window has been set to \(\pm 3.5\cdot\sigma\). Enlarging the ADC window truncates less the input signal, better capturing the tails of the noise probability density function and -eventually- the RFI that may be present, but reduces the number of sampling levels per \(\sigma\), which degrades the quality of the signal representation. Reducing the ADC window the input signal is truncated and the detected power is smaller than it actually is. With an ADC window of \(\pm 3.5\cdot\sigma\), the detected power is 1% less (\(0.99\cdot\sigma^{2}\)) the true input power.  
Figure 9 shows the simulation results for a sinusoidal RFI and the Symlet 3 wavelet transform. For large _INR_, the interference signal is so large that is clipped by the ADC and noise power error saturates to 1 (recall that noise variance is normalized to \(\sigma^{2}=1\)). Only for _INR_\(<10^{-3}\), the effect of the number of bits used in the quantization can be distinguished, with better performance when a larger number of bits are used in the ADC. The reason for obtaining good results with a moderate number of bits is due to the fact that noise power is estimated after integration of thousands or millions of samples (sampling frequency times the integration time). [PERSON] _et al_. [26] demonstrated that for a \(\pm 4.05\cdot\sigma\) ADC window and 8 bits quantization, the estimated antenna temperature error was smaller than 0.01 K, suitable for sea surface salinity measurements, while coarser quantization schemes can be used for less demanding applications.  
Figure 9: RFI mitigation performance using the Symlet 3 wavelet type for a sinusoidal signal as a function of the _INR_ for the heuristic SURE threshold method as a function of the number of bits of the analog-to-digital converter (ADC) from 4 to 16, assuming a sampling window of \(\pm 3.5\cdot\sigma\) to minimize signal clipping in the absence of RFI. Sequence length \(2^{16}\), decomposition level \(=12\). At high _INR_ values of RFI clipping occurs. For larger ADC sampling windows RFI is better sampled, but noise signals suffer more from discretization effects due to the smaller number of samples per \(\sigma\).
## 4 Positioning Model
For the discrete-time standard KF, the prediction step at epoch \(t\) is performed as [88]:  
\[\hat{\mathbf{x}}_{t}(-) =\mathbf{F}_{t-1}\hat{\mathbf{x}}_{t-1}(+) \tag{18}\] \[\mathbf{P}_{t}(-) =\mathbf{F}_{t-1}\mathbf{P}_{t-1}(+)\mathbf{F}_{t-1}^{T}+\mathbf{ Q}_{t-1} \tag{19}\]  
where \(\hat{\mathbf{x}}_{t}\) is the estimate of \(\mathbf{x}_{t}\), \(\mathbf{F}_{t-1}\) is the state transition matrix matrix \(\mathbf{R}\) as well as the new correlation matrix describing correlation between measurement noise and process noise \(\mathbf{S}\) are given by formulas:  
\[\mathbf{H}_{t}^{*} =\mathbf{H}_{t}-\mathbf{\Psi}_{t}\mathbf{H}_{t-1}\mathbf{F}_{t-1}^{ -1} \tag{21}\] \[\mathbf{S}_{t} =\mathbf{Q}_{t-1}(\mathbf{F}_{t-1}^{-1})^{T}\mathbf{H}_{t-1}^{T} \mathbf{\Psi}_{t}^{T}\] (22) \[\mathbf{R}_{t}^{*} =\mathbf{R}_{t}+\mathbf{\Psi}_{t}\mathbf{H}_{t-1}\mathbf{F}_{t-1 }^{-1}\mathbf{Q}_{t-1}(\mathbf{F}_{t-1}^{-1})^{T}\mathbf{H}_{t-1}^{T}\mathbf{ \Psi}_{t}^{T} \tag{23}\]  
The measurement VC matrix must be presented as a function of white (uncorrelated) measurement noise VC matrix \(\mathbf{N}\) and time correlated noise VC matrix \(\mathbf{M}\) in the form:  
\[\mathbf{R}_{t}=\mathbf{M}_{t-1}+\mathbf{N}_{t}+\mathbf{\Psi}_{t}\mathbf{N}_{t- 1}\mathbf{\Psi}_{t}^{T} \tag{24}\]  
and the decomposition of \(\mathbf{V}\) into \(\mathbf{M}\) and \(\mathbf{N}\) can be done as [88]:  
\[\mathbf{M}_{t} =\mathbf{\Psi}_{t}\mathbf{V}_{t}\mathbf{\Psi}_{t} \tag{25}\] \[\mathbf{\Gamma}_{f_{1},f_{2}} =\rho_{f_{1},f_{2}}\mathbf{I}_{k} \tag{16}\]0  
The solution for the updated state is then computed using a gain matrix \(\mathbf{K}\) defined as:  
\[\mathbf{\Gamma}_{f_{1},f_{2}} =\rho_{f_{1},f_{2}}\mathbf{I}_{k} \tag{16}\]1 \[\mathbf{\Gamma}_{f_{1},f_{2}} =\rho_{f_{1},f_{2}}\mathbf{I}_{k} \tag{16}\]2 (28) \[\mathbf{\Gamma}_{f_{1},f_{2}} =\rho_{f_{1},f_{2}}\mathbf{I}_{k} \tag{16}\]3  
The modified KF was used to obtain the float solution (approximate coordinates and real-valued DD ambiguities) with its VC matrix. The final baseline solution was obtained after fixed integer ambiguity using Integer Least-Squares (ILS) estimates and updating float solution. Note that the presented algorithm taking into account the time-correlation of observations can be simply reduced to a standard Kalman Filter when a time-correlation coefficient is assumed to be zero. A detailed description of the data set used to evaluate the empirical stochastic model impact on the positioning model solution is presented in Section 5.
## 3 Experiment 1: Frequency Dependence of Vibration-Weight Illusion
### Results
#### 3.5.3 Correlation between Grip Force and Perceived Heaviness at Different Frequencies  
The changes in the grip force (Figure 3A) and perceived heaviness (Figure 3B) exhibited a positive rank correlation coefficient of 0.66 (\(p=1.24\)\(\times\)\(10^{-9}\)). This correlation indicates that the stimulus conditions with the larger increase in the grip force were perceived to be heavier.  
To investigate the influence of the time intervals to the average grip force on the results, we calculated a rank correlation coefficient with different time interval conditions, namely  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(p\)**-Value (\(t\)-Value)** & **60 Hz** & **100 Hz** & **200 Hz** & **300 Hz** & **None** \\ \hline
30 Hz & 0.582 (161.0) & 0.020 (176.0) & 0.086 (171.5) & 0.004 (181.5) & 0.001 (186.0) \\
60 Hz & - & 0.069 (172.5) & 0.134 (171.5) & 0.006 (182.5) & 0.002 (186.5) \\
100 Hz & - & - & 0.616 (161.0) & 0.021 (176.5) & 0.001 (187.0) \\
200 Hz & - & - & - & 0.082 (172.5) & 0.005 (183.0) \\
300 Hz & - & - & - & - & 0.057 (172.0) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of heaviness ranks. \(p\)-values and (\(t\)-values) between vibration conditions in Task 1-2 (Wilcoxon signed-rank test with Bonferroni correction).  
Figure 3: Results of Experiment 1. Stimuli with five different frequencies were used and their amplitudes were 3 dB higher than the thresholds for the weight illusion at each frequency. (**A**) Increase in grip force for five vibratory frequencies. (**B**) Ranks of perceived heaviness. A higher rank number indicates a perception of greater heaviness. * and ** indicate significant differences at the 5% and 1% levels, respectively. There was a positive correlation between the increase in grip force and perceived heaviness (\(\rho=0.66\), \(p=1.24\)\(\times\)\(10^{-9}\)).  
when the time intervals before and after the stimulus onset were \(-1\) s to \(0\) s and \(1\) s to \(2\) s. The correlation coefficient was 0.65 (\(p\) = 3.73 \(\times\) 10\({}^{-9}\)), which is close to the result (\(\rho\) = 0.66) with intervals of \(-0.5\) s to \(0\) s and \(1\) s to \(1.5\) s.
## 3 Key Techniques
### Geometric Model and Material Mapping Method
#### 3.1.1 Solid Model Establishment Method in Blender  
The information available from FE models consists of geometric information (including the coordinates of element nodes and sections) and material information about different elements. After data preparation, a physics engine model for large deformation analysis is established in Blender, as shown in Figure 2.  
The element establishment method in Blender is different from that in FE software. In Blender, \"primitive\" mesh shapes should be established first, then all the elements should be transformed from the \"primitive\" mesh shapes. However, as the shape and position of \"primitive\" mesh shapes do not coincide with the corresponding FE elements, the shape and position transformations are performed to get the target elements in FE models. Blender provides several \"primitive\" mesh shapes to begin modeling, including cubes, planes and cylinders [43]. Because a cube is a shape that best approximates engineering components and FE model meshes, it is selected as the basic mesh shape in Blender. The geometric model mapping is then performed in two steps.  
* Step 1: Shape and position transformation (including translation and rotation). The shape and position transformations are performed on the cube mesh provided in Blender [43], to create geometric objects with the same shapes and positions as the elements in the FE model. The rotation method shown in Figure 3 is used to model inclined elements in FE models. Specifically, the rotation angle is obtained by first determining the endpoint coordinates of the longest edge of an FE element (line segment \(l_{1}\)) and the projection of \(l_{1}\) in the XOY plane in the global coordinate system (line segment \(l_{2}\)). The angle \(\alpha\) between \(l_{2}\) and the X axis, and the angle \(\beta\) between \(l_{2}\) and \(l_{1}\), are then calculated using these endpoint coordinates. Subsequently, the shape-transformed object is rotated by angle \(\alpha\) around the Z axis in the global coordinate system. The object is then rotated by angle \(\beta\) around the Y' axis in the rotated coordinate system. The rotated coordinate system is the local coordinate system of the cube after the first rotation, where the X'O' axis is parallel to \(l_{2}\).
* Step 2: Add rigid bodies to the cubes. Convert the cubes into rigid bodies after the shape and position transformations to obtain the rigid body model for the Bullet physics engine simulation.  
Figure 2: Solid model establishment method in Blender.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
Electrical grid systems are nonlinear and their dynamics are complicated, so only few studied have been worked on FDIA in AC Smart-grids. In order to distinguish a set of sensors at attack risk which are adequate to make an unpredictable attack; in this regards, in the paper [26], a FDIA in AC power system is investigated according to linearization around the aim state, assuming which state estimation is taken via a particular method, that can operate very accurately. Dual physical-attacks and cyber-attacks on electrical networks are presented and analyzed in Reference [27], and also this method is expanded to a simple scheme of AC load flow in Reference [28]; besides, cyber-physical-attacks on electrical grid system has been used based on the FDIA mechanism in paper [29]. In addition, to guard against invalid, false, spoiled or bad data and adversarial injections, a robust SE on AC system has been studied [30]; (for more information and a review on this topic, interested readers can refer to Reference [21]); nevertheless, there is no guarantee of detecting stealthy injections in the system. As can be seen from the analyzed results in Section 4, even if robust SE techniques are recruited, cyber-attacks (based on FDI occurred in AC systems) cannot be detected by Bad Data Detector (BDD). A robust cubature Kalman filter (RCKF) is considered to retrieve the dynamic state estimation (DSE) of generator agents which have been attacked by malicious data [31],where primary, two various kinds of cyber-attacks containing denial of service attacks (DSA) and FDIA are analyzed and also introduced into the generator's DSE by a technique of combining the attack vectors through the obtained data, and secondly, the CKF and RCKF methods are adopted to the DSE with the aim of obtaining cyber-attacks with different complicated levels; eventually, the efficiency of the mentioned technique are compared and analyzed. In addition, the other FDI attack detection method for AC-SE is illustrated in [32]. In the abovementioned way, when the state vectors are manipulated by malicious data, their spatial and temporal datum correlations may deviate from those in the same operating conditions. This mentioned procedure could take these inconsistencies through evaluating temporal sequential forecasted system states via using wavelet transform (WT) as inputs of deep neural network (DNN). In addition, several published papers are considered in Table 1.  
The sustainable development concept is one the principle objectives that countries strictly follow to achieve better performance of life [37]. The sustainability of cyber security in power systems is a significant concern today. The paper evaluated the FDI attack impact of a smart island and considered FDIA concepts for the sustainability of the smart island. Data were collected from an AC-MG with three renewable resources. The results
# Destructive M6.2 Petrinja Earthquake (Croatia) in 2020--Preliminary Multidisciplinary Research
## 3 Macroseismic Investigations  
The Mediterranean region is seismically active due to the northward convergence (4-10 mm/yr) of the African plate with respect to the Eurasian plate along a complex plate boundary.  
As discussed earlier, the strongest earthquake that occurred before the Petrinja 2020 event was the earthquake that occurred on 8 October 1909, the epicentre of which was located 9 km north of Pokupsko (the Kupa Valley earthquake). The magnitude was 5.8, and the hypocentral depth was 14 km. The effects it caused were evaluated according to the intensity of the VIII MCS. Extensive macroselismic analysis enabled the development of a macroselismic isoselism map (Figure 2). The quake area was elongated in the NW-SE direction, and the earthquake was also strongly felt in Zagreb, where it knocked down many chimneys.  
The M6.2 earthquake, which hit the area around Petrinja on 29 December 2020, was felt throughout Croatia and in the surrounding countries, with the highest intensity estimated  
Figure 2: Isoselism map of the Kupa Valley earthquake on 8 October 1909—macroselismic intensities were assessed according to the European Macroselismic Scale (EMS) [7].  
One part of the results was obtained by automatic processing of questionnaires, and the other was obtained on the basis of data that were collected in the field. The intensities, the values of which are marked in various colours (see the legend), are rated according to the European Macroseismic Scale (EMS). Unprocessed questionnaires are indicated with blue circles.  
In contrast to the macroseismic isoseismal map of the 1909 event (Figure 2), the highest intensities extend in the NE-SW direction (Figure 3). This direction is almost perpendicular to the direction of the maximal intensity isoseist (pleistocene) in the case of the Kupa Valley 1909 earthquake.  
The quick moment tensor solutions (QMTSs) that were released online (CSEM-EMSC, [9]) immediately after the foreshock, mainshock and strongest aftershock (Figure 4) indicated that all the events were caused by a clear strike-slip movement of the subvertical fault(s).  
Figure 3: (a) A preliminary intensity map for the M6.2 Petrinja 2020 earthquake, which was obtained by automatic processing of questionnaires (circles) and by a macroseismic field survey (preliminary values, which are marked by hexagonal shapes): and (b) Intensities in the wider Petrinja area, which is indicated by a rectangle in (**a**) [8].