esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Configuration
### SSRR Analysis  
The behavior of the material or unit-cell with negative permittivity (\(\varepsilon\)) and permeability (\(\mu\)) simultaneously in certain frequency ranges is called metamaterial (MTM). In 1967, [PERSON] investigated left-handed MTMs (LHMs) and their properties [34]. The refractive index of the medium is measured as being negative due to negative \(\mu\) and negative \(\varepsilon\). In this section, we have discussed the S-parameter response and refractive index of the SSRR unit-cell.  
The SSRR unit-cell is printed on an FR4 substrate (\(h=1.6\) mm, \(\varepsilon_{r}=4.3\), \(tan\)\(\delta=0.025\)) to analyze its performance. The unit-cell design and analysis were carried out using CST-MWS. For this design, it can be observed from Figure 2 that the perfect boundary conditions (PBCs) are used on the unit-cell that comprises of only one SSRR, having two sides (along the \(y\)-axis) as a perfect electric conductor (PEC), and the other two (along the \(z\)-axis) as a perfect magnetic conductor (PMC). For the wave ports, the other two sides of the unit-cell (along the \(x\)-axis) are used for excitation and radiation purposes as highlighted in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Dimension (mm)** \\ \hline \(E\) & 40 mm \\ \(F\) & 28 mm \\ \(G\) & 14 mm \\ \(H\) & 2.6 mm \\ \(W\) & 5 mm \\ \(S\) & 0.5 mm \\ \(X\) & 0.5 mm \\ \(Y\) & 1 mm \\ \(Z\) & 2.5 mm \\ \(P\) & 6 mm \\ \(R\) & 11 mm \\ \(T\) & 0.9 mm \\ \(Q\) & 13 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed antenna dimensions.  
Figure 1: Proposed triple band UWB antenna with dual notch (**a**) Top-layer (**b**) SSRR Unit-cell (**c**) Bottom-layer.  
The \(S_{11}\) and \(S_{12}\) behavior of the SSRR unit-cell is illustrated in Figure 3a. The unit-cell is resonating between the frequency range of 5.45-7.4 GHz with a return-loss below -10 dB. This unit-cell is inserted into the proposed antenna to develop an operational band at 5.45-6.77 GHz and a rejection band at 4.2-5.45 GHz, respectively. These S-parameter values have been used to plot the graph of refractive index (\(n\)). The \(n\) of the SSRR unit-cell is depicted in Figure 3b. From Figure 3b, it has been observed that the unit-cell has a negative \(n\) between the frequency range of 4.2-5.45 GHz. This result reflects that the proposed SSRR unit-cell shows MTM properties and helps to provide a notch-band between the above frequency range by inserting it to the radiating patch of the proposed antenna.  
Figure 3: Performance analysis of SSRR unit-cell (**a**) S-parameter response (**b**) Refractive index response.  
Figure 2: Perfect boundary condition and excitation applied to single unit-cell using CST-MWS.
## 4 Hardware Implementation Design
### I/Os Timing Protocols  
As seen in Figure 5, the mechanism has a 4 CPU cycle delay between the time an uncompressed datum is processed and the time the corresponding compressed equivalent is ready at the output. Therefore, and based on the lookup table at that point, datum 0\(\times\)FFD8 corresponds to 0\(\times\)39A requiring 10 bits according to the lookup table, datum 0\(\times\)000 corresponds to 0\(\times\)3A5 also corresponding to 10 bits and so on.  
Figure 4: Compression module block data flow.  
Figure 3: Compression module block diagram.  
The same timing process is followed until the end of the block as indicated in Figure 6, where the cycled datum is the 500 th (i.e., last) datum of the block corresponding to the corresponding cycled datum of data out pin.
## 7 Case Study and Comparison with the Field Data
### Results and Comparison  
According to the semi-analytical solution of unsteady groundwater flow outside the pit with a suspended waterproof curtain established in this paper, an analysis was conducted on the conceptual model. Figure 12 presents the time-depth groundwater level decline of an observation well outside the excavation pit's cut-off curtain. The pit is situated in an area of the Yangtze River floodplain, and preliminary pumping tests have shown hydraulic connectivity between the site's confined layer and the Yangtze River. There are seasonal variations in the Yangtze River water level, which could significantly affect groundwater levels around the pit during the stages of dewatering. However, the established semi-analytical solutions, based on the assumption of constant-head lateral boundaries, cannot account for such complex time-variable boundary conditions. In order to mitigate the influence of this type of time-dependent boundary, observation well data were selected from the end furthest from the Yangtze River for comparative analysis.  
It can be observed that, at this specific location, the groundwater level decreases with prolonged pumping time and tends towards a stable state. The figure also compares mea  
Figure 11: Conceptualized computational model for excavation pit with a suspended cut-off wall.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Calculation Parameters** & **Value** \\ \hline Length of the calculation model \(L\) & 200 m \\ Thickness of the aquifer \(M\) & 30 m \\ Depth of the waterproof curtain embedded \(l_{c}\) & 20 m \\ Half-width of the foundation pit \(B\) & 18 m \\ Unit flow rate of dewatering wells \(Q\) & 25 m\({}^{2}\)/day \\ Horizontal permeability coefficient \(K_{x}\) & 17.3 m/day \\ Vertical permeability coefficient \(K_{z}\) & 17.3 m/day \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation parameters of conceptualized dewatering project.  
Figure 12: Groundwater drawdown on the outside of the cut-off curtain in the excavation pit obtained from the presented semi-analytical solution, finite element simulation and filed data.  
sured values with numerical results based on finite element simulations using quadrilateral elements, with refinement near the pumping wells and waterproof curtain locations. Overall, the presented semi-analytical and numerical simulation results are in good agreement, and they generally match the measured values from the observation well.
## 2 MAP-DBSCAN Method
### Performance Evaluation  
#### 2.4.1 ETAS Framework  
The efficiency of the method to correctly identify spatio-temporal correlated seismicity is evaluated on a simulated ETAS catalog, where the underlying structure of the clusters is known a priori. Additionally, we aim to demonstrate the performance of the method compared to widely used clustering algorithms. In particular, our approach is compared with the Nearest-Neighbor (NN), Gardner and Knopoff (GK) window-based and Reasenberg (RB) link-based algorithms. A detailed review on each one of them is given in Appendix B.  
The ETAS model belongs to a wide class of branching processes where the occurrence rate of earthquakes, known as intensity function, depends on the history of all previous seismicity and consists of two parts given by  
\[\lambda(t,\mathbf{x}=(x,y)/H_{t})=\mu(\mathbf{x})+K\sum_{j:t_{j}<t,x_{j}\in \Sigma_{0}}e^{a(m_{j}-m_{c})}g(t)f(\mathbf{x}), \tag{1}\]  
where \(j\) runs over all past earthquakes with magnitude larger than or equal to \(m_{c}\). The intensity function is evaluated at each event that occurred during the time interval \([t_{in},T]\) and inside the target region \(\Sigma\subseteq\Sigma_{0}\). However, events in the broader region \(\Sigma_{0}\) and time interval \([t_{0},T]\), with \(t_{0}<t_{in}\), can be considered triggering events, therefore, they should be included in the evaluation of \(\lambda(t,\mathbf{x})\). The first term of the right-hand side of Equation (1) expresses the background seismicity \(\mu(\mathbf{x})\) which is assumed stationary in time (mother events) and clustered in space due to the fault network geometry. The latter term represents the space-time-dependent seismicity (daughter events) expressed through the following empirical laws:  
* the productivity law, \(Ke^{a(m_{j}-m_{c})}\), which gives the number of aftershocks triggered by a main shock with magnitude \(m_{j}\);
* the modified Omori law, \(g(t)=(p-1)c^{(p-1)}(t-t_{j}+c)^{-p}\), with \(p>1\), which describes the temporal decay of aftershocks;
* the spatial distribution of aftershocks, \(f(\mathbf{x}/M)=\frac{q-1}{\pi d(m_{j})^{q-1}}[\|\mathbf{x}-\mathbf{x}_{j}\| _{2}^{2}+d(m_{j})]^{-q}\) with \(q>1\), and \(d(m_{j})=d_{0}10^{\gamma(m_{j}-m_{c})}\), which assumes an isotropic distribution of aftershocks around the main shock.  
Each mother event generates its daughters (first generation), the daughters generate their own descendants (second generation), and so on. In this way, a cluster is defined as a sequence of events with a common mother event (first event in the cluster). There are also single events, i.e., mother events without any subsequent triggered earthquake.
## 5 Classification of Harmonic Emissions Profiles  
The definition and the categorization of the 360 emission profiles for the 3 rd harmonic current (c.f. Figure 5) were required for the development of the classification system for harmonic emission profiles using machine learning methods. The given requirements of pre-known classes allow for the use of supervised learning algorithms, such as binary tree structure classifiers, in combination with support vector machines (SVM) [25]. SVMs have been proven to be suitable in terms of classification rate and adaptiveness to the given  
Figure 5: Classes of emission profiles for the 3 rd harmonic current; min–max normalized 50 th percentile time series for 40 measurements in LV grids within three groups: working days, Saturdays, and Sundays/holidays for all phases L1 to L3.  
classification problems. SVMs have been successfully applied for large amounts of data [26] and a wide range of applications in power systems, from energy theft detection [27], islanding, and grid fault detection [28] to classification of power quality disturbances [29; 30; 31; 32; 33]. For continuous PQ parameters, such as harmonics, the present applications rarely go beyond the generation of static reports and the comparison with respective limits. An automated classification system can support network operators with the identification of consumer configurations and the early detection of fundamental changes in the harmonic emission behavior at a particular site. This way it can extract valuable information about the PQ characteristics of a grid, which can serve as basis for further optimization of planning and operation in future grids.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 4 Experimental Results and Analysis
### The Maximum Embedding Rate for the Tested Images  
We take ten standard images, including three publicly available image \"Lena\", \"Airplane\", \"man\", and seven images selected from the BOWS-2 database [41] to illustrate the pure embedding rate ofthe proposed method. For convenience of description, we name these seven images F\({}_{1}\), F\({}_{2}\),..., F\({}_{7}\), as shown in Figure 7. The maximum pure embedding rates of ten images under different values of \(m\) are shown in Table 1. From Table 1, we can see that in some cases, the embedding rate of some images is very low or even 0 when the value of \(m\) is small. This is mainly because the number of embeddable pixels will be few for moderate smooth images when the value of \(m\) is small. Meanwhile, it will generate more pixels to be marked. If the edged pixels are not enough to accommodate the auxiliary information, we do not embed secret bits in this scenario. Thus, the embedding rate is 0. For an image, when five different values of \(m\) are utilized, there are five different \(PER\); and finally the largest one of the five \(PER\) is selected as the maximum \(PER\) of the image. It can be seen that different images may have different values of \(m\) when the maximum \(PER\) is obtained, which mainly depends on the smoothness of an image. For example, in the ten tested images, images F\({}_{1}\) and F\({}_{2}\) are smoother than other images, and the maximum pure embedding rates obtained when \(m\) = 3 were 3.5123 bpp and 3.0972 bpp, respectively. In general, the smoother the image, the more accurate the predicted pixel values will be; we can use a smaller value of \(m\) to obtain the maximum \(PER\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & \(m\) **= 3** & \(m\) **= 4** & \(m\) **= 5** & \(m\) **= 6** & \(m\) **= 7** & **Maximum** \\ \hline
**Lena** & 0 & 0.5862 & **1.7964** & 1.4202 & 0.7404 & **1.7964** \\
**Airplane** & 0 & 1.4800 & **1.8318** & 1.3911 & 0.7298 & **1.8318** \\
**Man** & 0 & 0 & 1.1140 & **1.3256** & 0.7335 & **1.3256** \\
**F1** & **3.5123** & 2.9023 & 2.2132 & 1.4860 & 0.74
## 2 Linearization of the Power Deficit Calculation
### Proposed Aggregation Method for Calculating the Resulting Average Power \(P_{i}\)  
An alternative method for calculating the total energy deficit has been evaluated, where instead of considering the speed deficit for each turbine, the power deficit is considered.  
**Linear Aggregation of Power Deficits (LAPD)**  
\[P_{i}(u,\theta) = Pc(u)-\delta P_{i}(u,\theta)\quad\text{with}\] \[\delta P_{i}(u,\theta) = \sum_{j\
eq i}\delta P_{j\to i}(u,\theta) \tag{13}\] \[\delta P_{j\to i}(u,\theta) = Pc(u)-Pc(u-\delta v_{j\to i}(u,\theta)) \tag{14}\]  
and  
\[\mathbb{E}[P]=\frac{\text{AEP}}{T}=\sum_{i=1}^{nt}\sum_{u=u_{cl}}^{u_{cn}} \sum_{\theta=1}^{360}\left(Pc(u)-\delta P_{i}(u,\theta)\right)\;\text{f}(u, \theta)\Delta u\Delta\theta= \tag{15}\]  
\[=\sum_{i=1}^{nt}\sum_{u=u_{cl}}^{u_{cn}}Pc(u)\sum_{\theta=1}^{360}\;\text{f} (u,\theta)\Delta u\Delta\theta-\sum_{i=1}^{nt}\sum_{u=u_{cl}}^{u_{cn}}\sum_{ \theta=1}^{360}\sum_{j\
eq i}\delta P_{j\to i}(u,\theta)\;\text{f}(u, \theta)\Delta u\Delta\theta \tag{16}\]  
\[=ntP_{1T}^{\text{AEP}}-\mathbb{E}[\delta P]\quad\text{with}\quad\mathbb{E}[ \delta P]=\sum_{i=1}^{nt}\sum_{j\
eq i}\sum_{u=u_{cl}}^{u_{cn}}\sum_{\theta=1}^ {360}\delta P_{j\to i}(u,\theta)\;\text{f}(u,\theta)\Delta u\Delta\theta \tag{17}\]  
By summing over the range of speeds \(u\) and directions \(\theta\), the average power deficit for each turbine (\(\mathbb{E}[\delta P_{i}]\)) can be easily determined as follows:  
\[\mathbb{E}[\delta P_{i}]=\sum_{j\
eq i}\mathbb{E}[\delta P_{j\to i}]=\sum_{j \
eq i}\sum_{u=u_{cl}}^{u_{cn}}\sum_{\theta=1}^{360}\delta P_{j\to i}(u, \theta)\;\text{f}(u,\theta)\Delta u\Delta\theta \tag{18}\]  
yielding  
\[\mathbb{E}[P]=\frac{\text{AEP}}{T}=nt\;P_{1T}^{\text{AEP}}-\mathbb{E}[\delta P ]\quad\text{with}\quad\mathbb{E}[\delta P]=\sum_{i=1}^{nt}\mathbb{E}[\delta P _{i}] \tag{19}\]  
as in (5).  
To sum up, this new aggregation method proposes through (18) that, in the case of multiple wakes affecting a single turbine, the average power deficit experienced by turbine \(i\) in the farm can be obtained as the sum of all individual power deficits caused by the interactions between this turbine and each of the others.  
This has a significant implication: assuming a certain wind distribution \(\mathrm{f}(u,\theta)\), for each pairwise interaction, the power deficit \(\mathbb{E}[\delta P_{j\to i}]\) only depends on the distance and relative orientation between the two turbines. Therefore, a 2D LUT can be precomputed offline to obtain this deficit for any feasible separation \(s_{i,j}\) (e.g., between 4 and 45 diameters) and relative orientation between turbines \(i\) and \(j\) (between 1 and 360 deg).
## 2 Physics of Hall Thruster Erosion
### Hall Thruster Plasma  
Shown schematically in Figure 2, the Hall thruster forms and sustains plasma with crossed electric and magnetic fields within an annular ceramic discharge chamber. An external power supply establishes an electric field along the discharge axis, and a set of solenoids generates a magnetic field that points along the discharge radius. The electric field is established between a positively biased anode and a negatively biased emitting hollow cathode. The crossed electric and magnetic fields force electrons emitted by the cathode into a gyrating azimuthal Hall drift inside the discharge chamber. Neutral propellant (typically xenon) fed through the anode collides with the electrons to form ions that are subsequently accelerated into the thruster exhaust by the axial electric field. Charge quasineutrality is maintained in the resulting plume because some emitted electrons follow expelled ions rather than enter the discharge chamber. The ionized propellant is not captured in a Hall drift inside the discharge chamber because, though large enough to magnetize electrons, the magnetic field does not have sufficient strength to magnetize the comparatively heavy ions [1; 2; 3; 4; 5; 6; 7; 8].  
Representative peak electric and magnetic field values inside the discharge chamber are 400 V/cm and 200 G, respectively. The plasma density is typically 10\({}^{17}\)-10\({}^{19}\) m\({}^{-3}\), electron temperature generally remains below 100 eV, and the peak ionization fraction exceeds 10%. The ion-neutral collision frequency is on the order of 10-100 kHz, and the total electron collision frequency is 1-10 MHz [3; 6; 7; 21].  
Erosion rates are greatest near the thruster exit plane in the so-called propellant ionization and ion acceleration zones, where ion density and energy are largest due to axial peaks in the electric and magnetic fields [7; 22; 23]. The radial distribution of the electric field in these regions is influenced by the magnetic field configuration, discharge chamber wall boundary conditions, charged particle densities, and plasma turbulence [7; 8]. These factors complicate the idealized scenario presented in the preceding description and can cause ions to accelerate into the walls of the discharge chamber, rather than into the thruster plume. Estimates vary, but some work has shown that as many as 30-40% of Hall thruster ions are accelerated into the thruster walls [5].  
A substantial source of this wall acceleration is the plasma sheath that forms to connect the quasineutral bulk plasma to the discharge chamber walls. Because the walls are insulating, the sheath must form to enforce a zero net current boundary condition at the wall. Electrons reach the wall first, so the sheath resists further electron current to the wall by establishing a potential drop from the bulk plasma to the wall. Ions formed sufficiently close to the wall or transported to the wall region by collisions or local electric fields are accelerated into the wall by the sheath potential gradient [7; 8; 24; 25]. Figure 3 provides a simplified illustration of this effect.
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 2 Literature Review
### Machine-Learning Based Techniques
#### 2.1.2 PPG-Based Recognition Method  
These studies used PPG signals for human recognition. In [7], a model based on Parse SoftMax Vector and k-Nearest Neighbor (K-NN) was proposed. Experiments were performed on BIDMC, MIMIC, and CapnoBase datasets and achieved a recognition rate of 99.95%, 97.21%, and 99.92%, respectively. In another study, the authors of [8] used A discrete cosine transform (DCT) for feature extraction. Then, the extracted features have been used as input for some machine-learning techniques like Decision Tree, KNN, and Random Forests. The achieved accuracy of the algorithms was 93%, 98%, and 99%, respectively. The authors of [9] proposed an authentication method that depends on a continuous wavelet transform (CWT) and direct linear discriminant analysis (DLDA). The method was tested on different datasets and achieved an equal error rate (EER) of 0.5-6%. The authors of [10] proposed an authentication method based on non-fiducial features and used Neural Networks (NN) and SVM for classification. To optimize the classification, a Genetic Algorithm was used. The method achieved 100% accuracy. Another study [11] evaluated fiducial and non-fiducial approaches to feature extraction for PPG-based authentication using both supervised and unsupervised machine learning methods. The experimental results achieved an accuracy of 99.84% based on non-fiducial feature extraction.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 3 Experimental Results and Discussion
### Datasets  
Experimentation has been carried out on two widely used action recognition datasets: HMDB51 and UCF101. HMDB51 was developed by Serre Lab of Brown University [30]. The videos are taken from YouTube and movies. It contains 51 different categories with 6800 videos approximately.  
Each action category contains at least 100 videos with reasonable discrepancy. The actions in the dataset can be grouped into five categories: facial movements (chew, laugh, talk, smile), body movements (clap hands, walk, wave, jump, pull up, cartwheel, etc.), facial movement with objects (smoke, eat, drink), body movements with objects (brush hair, sword, pour, catch, ride bike, etc.) and human interactions (fencing, kick, shake hands, etc.). Figure 3 shows some images from these action categories. The HMDB51 is considered as one of the most challenging datasets due to poor quality videos and substantial camera motions.  
UCF101 is an extension of the UCF50 dataset [31], developed by the University of Central Florida. The videos are taken from YouTube. The dataset contains 101 realistic human action categories. With more than 13,000 videos in the dataset, the UCF101 offers a large diversity in terms of actions. The action classes in the dataset are divided into five types: body motion, sports, human to object interaction, human-human interaction and playing of musical instruments. Each action category is divided into 25 groups each having four to seven clips. The mean clip duration is around 7.21 s. The resolution of videos is 320 \(\times\) 240 with a frame rate of 25 fps. Some sample frames from these videos are given in Figure 4.  
For these two datasets three training and testing splits are provided, which are used as a standard evaluation scheme. The average accuracy of these training-testing splits is used for performance evaluation.