esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Preprocessing  
The first preprocessing step was to atmospherically correct the datasets. The ASTER L1 and Landsat-8/OLI L1T images were atmospherically corrected using ENVI's 5.5 FLAASH tool. The Sentinel-2B MSI L1C image was atmospherically corrected using the Sen2 Cor processor version 2.8 within ESA's SNAP software [101], which resulted in 12 spectral bands. All datasets were then resampled to 30-m spatial resolution and spatially subset (Figure 3) to the caldera of Nisyros. Finally, in order to exclude from further processing pixels with dense vegetation, the Normalized Difference Vegetation Index (NDVI) (Table 1) was calculated for each one of the three datasets and the respective mask was applied to each one of them in order to retain only pixels with NDVI values less than 0.3 (bare soil to sparse vegetation pixels). Finally, we co-registered the datasets using the ASTER image as reference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ASTER [61]** & ** Landsat-8/OLI [61]** & **Sentinel-2 MSI [61]** \\ \hline \((0.807\ \mu\mathrm{m}{-}0.661\ \mu\mathrm{m})\) & \((0.864\ \mu\mathrm{m}{-}0.6546\ \mu\mathrm{m})\) & \((0.865\ \mu\mathrm{m}{-}0.665\ \mu\mathrm{m})\) \\ \hline \((0.807\ \mu\mathrm{m}{+}0.661\ \mu\mathrm{m})\) & \((0.864\ \mu\mathrm{m}{+}0.6546\ \mu\mathrm{m})\) & \((0.865\ \mu\mathrm{m}{+}0.665\ \mu\mathrm{m})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: NDVI formulation for ASTER, Landsat-8/OLI, and Sentinel-2 MSI images expressed in wavelengths.
## 6 Conclusions  
The prediction of the precise behavior of sand, particularly in cyclic loading, is one of the issues that is currently the subject of many studies, mainly because of the occurrence of the liquefaction phenomenon and due to the need to forecast and safely design geotechnical structures. In particular, it is important to investigate the cyclic behavior of sand and accurately simulate the soil's behavior in different loading conditions. The constitutive models within the generalized plasticity frameworks are among the most advanced and successful constitutive models used to simulate the sand behavior. However, the equation of the memory parameter \(H_{M}\), suggested in these models to consider the loading history, cannot display the cyclic behavior of sand soils well. Therefore, in this study, a new equation for the coefficient \(H_{M}\) has been proposed so as to improve the generalized constitutive model predictions.  
In order to evaluate the performance of the constitutive model with the modified coefficient \(\mathrm{H_{M}}\), various data were prepared from two different sands under one-way and two-way cyclic triaxial tests on undrained sands. By comparing the performance of the generalized constitutive model before and after \(\mathrm{H_{M}}\) modification, and also by comparing them with experimental data, it was observed that the modified coefficient \(\mathrm{H_{M}}\) can have a significant effect on improving the predictions of this constitutive model. In other words,  
Figure 13: Comparison of the constitutive model predictions with the undrained cyclic triaxial data based on the Shahrekord sand with \(\mathrm{e}=0.668\) for initial mean effective stress 300 kPa after modification of the coefficient \(H_{M}\).  
its efficiency has been assessed in accordance with the laboratory data. The results of the study show that the proposed modified equation of \(H_{M}\) allows for predictions of sand behavior to be significantly improved.  
Conceptualization, H.H.; methodology, M.O.; software, M.O.; validation, R.K. and R.J.; formal analysis, H.H.; investigation, M.O.; data curation, R.K.; visualization, H.H.; writing--original draft preparation, M.O.; writing--review and editing, R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Dataset available upon request from the authors.  
The authors declare no conflicts of interest.
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 3 Results
### Optimization of Anchor Positions  
The surface plots (Figures 4-7) suggest that the variables studied are not constant but have a gradient with the variation in anchor positions. From the numerical results and experimental measurements of cable tension, we observe that the functions \(\overrightarrow{T_{s}}\), \(\overrightarrow{F}\), \(\overrightarrow{M_{A}}\), and \(\overrightarrow{M_{C}}\) are convex within the studied range of anchor point positions. Hence, they have a unique minimum value, which is evident from the plots; see Figures 4-7. It is possible to define a minimization problem to realize optimal anchor positions. From the results of the simulation, shown in Figures 4-7, it can be observed that the minimum values for each of the variables, \(\overrightarrow{T_{s}}\), \(\overrightarrow{F}\), \(\overrightarrow{M_{A}}\), and \(\overrightarrow{M_{C}}\), are at different anchor positions. To find the optimal anchor point positions, two cases were considered.
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 1 (\(t_{0}<t<t_{1}\), Figure 6a)  
At the time \(t=t_{0}\), the main power switch \(S_{m1}\) is turned on, and the auxiliary switch \(S_{a1}\) is turned off. At this time, the primary voltage \(v_{p1}\) across the transformer magnetizing inductor is equal to \(V_{in}\), so the current \(i_{Lm1}\) through magnetizing inductor rises linearly. The secondary voltage \(v_{s1}\) of transformer is \(V_{in}/n\), so the forward diode \(D_{1}\) is forward biased, and the freewheel diode \(D_{2}\) is reverse biased. The input power is transmitted to the inductor \(L_{1}\) and the load \(R_{\rm o}\) via the transformer \(T_{x1}\) and the diode \(D_{1}\). The equivalent circuit is as shown in Figure 6a. The voltage \(v_{ds1}\) across the main switch \(S_{m1}\) and the current \(i_{Lr1}\) through the leakage inductor can be expressed as  
\[v_{ds1}(t)\ =\ 0, \tag{8}\]  
\[i_{Lr1}(t)\ =\ i_{Lm1}(t)+\frac{i_{D1}(t)}{n}\ =\ i_{Lm1}(t)+\frac{I_{Lo}}{n}, \tag{9}\]  
where \(I_{\rm Lo}\) is the average current of the output inductor \(L_{\rm o}\) in one switching period. At the time \(t=t_{1}\), the main switch \(S_{m1}\) is switched off, and the circuit operation enters the next state.  
Figure 5: The theoretical timing diagram of key components in one switching period.  
Figure 6: \(Cont.\)Figure 6: \(Cont.\)
## 2 Materials and Methods
### Signal Processing
#### 2.3.2 Phase Aberration Correction  
Phase aberrations caused by bone curvature, bone tilt and material inhomogeneities are corrected for signals reflected from the backside cortical bone interface. Three phase-aberration correction (PAC) methods are used: (1) The curved bone surface geometry results in different round-trip travel times compared with the flat bone model. A time-shift correction based on the periosteal bone surface geometry (hereinafter called'surface time correction' ST), was used to correct for the additional ultrasound wave propagation paths in the water due to the bone curvature. The ST correction used the concept of refraction compensation proposed by [PERSON] et al. [24]. Further details are summarized in Figure 15 in Appendix C. (2) For tilted bone models, the reflected wavefront exhibits a tilt with respect to the beam axis (Figure 14(c)). To correct the phase aberration caused by surface inclination, an autocorrelation function (ACF) analysis on the reflected backside echoes was performed (Figure 15(b)-d in Appendix C). (3) Local variations of the sound velocity caused by material inhomogeneities lead to small fluctuations of the transit time measured at individual receiver elements and subsequently to a distortion of the summed signal;therefore, the following method was used to estimate the backside focus depth. The arrival times for all receiver elements was estimated using a cross-correlation (CC) method. The receiver channel that measured the highest signal amplitude was used as the reference signal. The inter-element arrival times exhibit either a concave, flat, or a convex shape, depending on the distance of the beam focus relative to the backside bone interface. A second-order polynomial was fitted to the inter-element arrival times, and the confocal focus depth was determined by finding the zero-crossing point of the second order fit coefficients (Figure A3e,f in Appendix C). This zero-crossing point was used to determine \(\Delta F_{z}\), and to estimate \(\mathit{Ct.Th}^{MF}\) and \(\mathit{Ct.v}_{11}^{MF}\) using Equation (2).
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 4 The Proposed Separable Data Hiding System
### Data Hiding  
In data hiding phase, a data hider embeds additional information bits into the encrypted image, \(I^{\prime}\). The detailed embedding steps are as follows.  
Step 1: Before embedding, \(Q\) bits of information are permuted by using the data-hiding key; let \(A(1)\), \(A(2)\),..., \(A(Q)\) be the permuted information bits to be embedded.  
Step 2: Then, according to the data-hiding key, the data hider pseudo-randomly selects \(Q\) pixels from the encrypted image. The selection process is the same as that in the data hiding phase of the joint system. Let \(I^{\prime}(1)\), \(I^{\prime}(2)\),..., \(I^{\prime}(Q)\), be the \(Q\) selected pixels in the encrypted image.  
Step 3: The \(t\) th bits of the \(I^{\prime}(1)\), \(I^{\prime}(2)\),..., \(I^{\prime}(Q)\), pixels are collected, where \(t\geq 7\). Then, the information bits are embedded by replacing the \(t\) th bits of the corresponding selected pixels with \(A(1)\), \(A(2)\),..., \(A(Q)\). Herein, the information bits are concealed in the most significant bits or the second most significant bits. Then, the embedded encrypted image \(I^{\prime}\) is sent to the receiver.  
Figure 6: Block diagram of the proposed separable data hiding system.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 4 Implementation  
The performance of 2 LCCM relies on the design of the replica selection approach at the network layer and the congestion control algorithm at the transport layer. Therefore, this section develops an introduction to these two components, in addition to describing the module and packet header design of the transport layer protocol.
# Mobility-Multihoming Duality
## 3 Identifier Locator Network Protocol (ILNP)  
It is clear from our discussion in Section 2 and Table 1 that the use of addressing and a particular _binding_ of an instance of an address value to an object are important in enabling a particular approach. We see in Table 1 that the IP address is overloaded in its semantics in order to enable some form of redirection (e.g., tunnelling in LISP) or indirection (e.g., mappings from the EID to the RLOC in LISP). An address value can play the role of a (global) Identifier or a (global) Locator (as in MIPv6). The Identifier has no topological semantics, but it is a name that uniquely labels a host. A Locator is a name that does have topological significance, and it is used for routing (and/or forwarding).  
In our approach, we explicitly recognise this dichotomy of address usage semantics. With the Identifier Locator Network Protocol (ILNP), we change the addressing model so that the _Node Identifier (NID)_ values and _Locator (L64)_ are clearly defined without overloading IP address semantics.  
In our discussion, we use the term _name_ as a general label referring to, \"a set of bits that has a specific semantic and syntax at a given layer in the protocol architecture and is bound to a specific object in that protocol architecture\". So for example, a \"network name\" is a routing prefix (the upper 64 bits of the IPv6 address) for IPv6--a Locator (L64) for ILNP--and the host part of the address (lower 64 bits) is a unique \"node name\" for a host--a Node Identifier (NID) in ILNP (see Section 3.2).
## Abbreviations  
The following abbreviations are used in this manuscript:  
AAA & Abdominal Aortic Aneurysms  
AD & Aortic Dissection  
ATAA & Ascending Thoracic Aortic Aneurysm  
AV & Aortic Valve  
BAV & Bicuspid Aortic Valve  
BC & Boundary Condition  
CFD & Computational Fluid Dynamics  
CSM & Computational Solid Mechanics  
CT & Computed Tomography  
CVD & Cardiovascular Diseases  
DNS & Direct Numerical Simulations  
DTAA & Descending Thoracic Aortic Aneurysms  
FDM & Finite Difference Method  
FEM & Finite Element Method  
FL & False Lumen  
FSI & Fluid-Structure Interaction  
FVM & Finite Volume Method  
GPU & Graphical Processing Unit  
LES & Large Eddy Simulations  
ML & Machine Learning  
MRI & Magnetic Resonance Imaging  
OSI & Oscillatory Shear Index  
PRISMA & Preferred Reporting Items for Systematic Reviews and Meta-Analyses  
PWS & Peak Wall Stress  
PWV & Pulse Wave Velocity  
RANS & Reynolds-Averaged Navier-Stokes  
RLBM & Regularised Lattice Boltzmann Method  
ROM & Reduced Order Model  
RPIM & Radial Point Interpolation Method  
SPH & Smoothed Particle Hydrodynamics  
STJ & Sinotubular Junction  
TAV & Tricuspid Aortic Valve  
TAWSS & Time-Averaged Wall Shear Stress  
TL & True Lumen  
VFM & Virtual Flux Method  
WS & Wall Stress  
XFEM & Extended Finite Element Method
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 3 Results and Discussion  
### Postharvest Weight Loss  
Weight loss increased during storage, especially with UVA treatment and UVA-Chito treatment (Figure 1). Within 15 days, the blackcurrant weight loss percentage was above 14%. Chitosan combined with UVA treated fruit's weight loss was not significantly different from UVA treatment on day 15, although the chitosan with UVA had slightly less weight loss than the UVA treatment on assessment days prior to day 15. The control treatment weight loss was intermediary among these four treatments. By day 15, the weight loss percentage for the control treatment was approximately 3%. The chitosan treatment itself reduced the blackcurrant weight loss to a large extent. Within 15 days, weight loss was less than 2% of the total weight. Therefore, chitosan had positive effects on blackcurrant weight loss control during storage. Contrastingly, UVA light treatment increased the weight loss, with or without chitosan coating after storage.  
Transpiration and respiration were responsible for the majority of the water loss during fruit postharvest storage [30]. Chitosan itself could reduce the transpiration of fruits [31]. This effect was also observed in this study. UVA treatment effects, on the other hand, were based on its irradiation parameters (dose, irradiation intensity) [32]. UVA (315-400 nm, 100 umol m\({}^{-2}\) s\({}^{-1}\)) could accelerate the ripening of sweet oranges [33]. Low UVA intensity (10 umol m\({}^{-2}\) s\({}^{-1}\)) could enhance the postharvest quality of fresh-cut vegetables, but the effect was not observed with high-UVA-intensity treatment (100 umol m\({}^{-2}\) s\({}^{-1}\)) [34]. In this study, UVA increased the water loss with or without chitosan, which suggested that the appropriate exposure of UVA for blackcurrant berries might need further studies.
## 3 Results and Discussion
### Impacts of Filtering
#### 3.1.3 Distance
This study tracked large, reflective metal back walls of semi-trailers, with cross-sectional areas exceeding 8 square meters. The back walls of semi-trailers are larger than those of other vehicles and presumably facilitate higher-quality detections. Measuring distances to smaller vehicles should be tested. While the maximum distance range may be greater when detecting trucks than passenger cars, situations may exist where trucks are less favorable. For instance, when traversing sharp curves at short distances, the radar cross-section changes as truck back walls change angles in relation to the sensors, as opposed to being located perpendicular to them. Some military ships and aircraft are deliberately made from planar surfaces joined at sharp angles to achieve radar stealth. Similarly, truck back walls are also two-dimensional planar surfaces. In curves, reflected signals may scatter away from the receivers and cause data loss. This may also occur if the preceding truck is located outside the main lobe of the radar antenna. Such diffraction effects were reported in [38].
## 7 Method Application 2: Iron Losses Computation  
The IM analysis rotating the rotor allows for the variation of the flux density in each element, with the rotor position, to be derived. The flux-density behavior in the iron elements is then processed to derive the hysteresis and eddy current losses.  
In Figure 12, the flux-density behavior in several parts of the machine is shown, and the presence of the high frequency component is evident. The fast variations of the flux density cause serious losses located especially in the stator and rotor tooth tips, as shown in Figure 13.  
Figure 11: Measurements comparison about average torque and stator current. The measurements have been carried out according to the standard for grid-connected IMs’ efficiency computation: keeping constant the voltage, the load is increased from 25% to 150% of the rated one.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order of Harmonic** & \
\begin{tabular}{c} **Without Rotor** \\ **Skewing** \\ **[Nm]** \\ \end{tabular} &
\begin{tabular}{c} **With Rotor** \\ **Skewing** \\ **[Nm]** \\ \end{tabular} \\ \hline
0 & 40.1 & 39.1 \\
6 & 0.32 & 0.38 \\
30 & 0.74 & 0.03 \\
36 & 0.51 & 0.25 \\
180 & 1.35 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Torque harmonics comparison.  
The high frequency components in the flux density are mainly due to the stator and rotor MMF slot harmonics and to the slot openings, in particular the stator ones.  
There are two main methods for the losses computation with the element-by-element technique:  
1. Steinmetz method: Once the flux-density behavior during an electric period has been computed, the Fourier analysis is performed, and the Steinmetz formulation of the losses is applied to each flux density harmonic in each element [10].
2. Bertotti method: Here, the hysteresis losses are imposed to be linked only to the main harmonic of flux density; on the other hand, the eddy current losses are computed considering the square of the flux-density time derivative. Such a method provides a sort of instantaneous value of the iron losses [9].  
Figure 12: Normal and tangential flux-density variation in several parts of the machine: the high frequency variation of the induction is very clear.  
Figure 13: Distribution of the iron losses density in a squirrel cage IM; the most critical parts are the stator and rotor tooth tips.
## 2 Radio Planning Aspects
### Frequency Bands  
For the existing TETRA network, the frequency band for RADIECARM is 380-385 MHz (uplink, UL) and 390-395 (downlink, DL), which is the band reserved for PPDR systems in Europe [13].  
For the new LTE network, several possibilities are available for the frequency band. ECC Report 218 [2] develops the necessary conditions to create a harmonized European framework for the implementation of future BB-PPDR systems. This report proposes the concept of 'flexible harmonization' to enable an efficient implementation of BB-PPDR systems. The frequency bands identified as candidates for harmonization are:  
* 400 MHz (410-430 MHz and 450-470 MHz)
* 700 MHz (694-790 MHz)  
In our analysis, the frequency bands 699-716 MHz (UL) and 729-746 MHz (DL) were used. These frequency bands meet spectrum regulations for public safety users and broadband services laid out in Spain's National Table of Frequency Allocations. Moreover, they are E-UTRA operating bands according to the ETSI [14; 15].
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
###### Abstract  
Computer networks are facing threats of ever-increasing frequency and sophistication. Encryption is becoming the norm in both legitimate and malicious network traffic. Therefore, intrusion detection systems (IDSs) are now required to work efficiently regardless of the encryption. In this study, we propose two new methods to improve the efficacy of the Cisco Cognitive Threat Analytics (CTA) system. In the first method, the efficacy of CTA is improved by sharing of intelligence information across a large number of enterprise networks. In the second method, a four variant-based global reputation model (GRM) is designed by employing an outlier ensemble normalization algorithm in the presence of missing data. Intelligence sharing provides additional information in the intrusion detection process, which is much needed, particularly for analysis of encrypted traffic with inherently low information content. Robustness of the novel outlier ensemble normalization algorithm is also demonstrated. These improvements are measured using both encrypted and non-encrypted network traffic. Results show that the proposed information sharing methods greatly improve the anomaly detection efficacy of malicious network behavior with bad base-line detection efficacy and slightly improve upon the average case.  
intrusion detection systems; global reputation model; anomaly detection; intelligence sharing scheme; CTA-IDS; machine learning Article
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.3.4 Data Processing  
The single mesh models were created for every set of points obtained by the above-listed methods, which formed the upper terrain of selected heaps. Points bounding the heap and points on the ground plate have formed the lower terrain. Filtration removed erroneously generated (AP) and measured (TLS) points using Microstation software VSi\({}^{\oplus}\) with Terrascan v.13\({}^{\oplus}\). Such modified point clouds are referenced and were the starting basis for the creation of final mesh models and further analysis. As the reference surfaces at both heaps, mesh models obtained by the TLS were considered.  
For the purpose of compliance rate description of the compared surfaces, the Z coordinate differences were calculated. Applying the AP and GNSS methods, obtained data were compared with the reference model (TLS).  
Residuals represent the vertical difference between the Z value in the data file and the interpolated Z value on a reference surface at every position \(X\), \(Y\) of the data file point. In this case, the bilinear interpolation method was used.  
Figure 6: GCP (5001–5006) scheme with SfM model background.  
The formula used to compute a residual value is  
\[Z_{res}\ =\ Z_{dat}-Z_{ref}, \tag{1}\]  
where: \(Z_{res}\) is the difference value, \(Z_{dat}\) is the \(Z\) value in the compared data file, and \(Z_{ref}\) is the interpolated \(Z\) value on the surface at each \(X\), \(Y\) point coordinate on the reference surface.  
The standard deviation \(\sigma\) of the data file is the square root of the variance of the file and is, in general, calculated by the formula  
\[\sigma\ =\ \sqrt{\frac{1}{(n-1)}\sum_{i\ =\ 1}^{n}{(z_{i}-\overline{z})^{2}}}, \tag{2}\]  
where: \(n\) is the number of observations, \(z_{i}\) is the data value, and \(\overline{z}\) is the mean.  
Creation of the cross-sections was performed by the Surfer(r) software. For both heaps, three vertical sections parallel to each other were created (Figure 7). They were situated parallel to the longest dimension of the heap in the heap centre and its side slopes. In each section, about 120 data points were evaluated without removing outliers. As the reference surface, the model surveyed by the TLS method was chosen.