esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Tests
### Step Height Measurement in \(Z\) Direction  
The step height in the \(Z\) direction was formed by the difference of two class 1 gauge blocks having calibrated lengths of 2.000022 mm and 5.000024 mm respectively, as shown in Figure 9. They were  
Figure 8: Schematic diagram of \(X\)-\(Y\) non-orthogonal error.  
located together on the surface of the optical flat (the flatness error is less than 30 nm), which is fixed on the 3D stage. Six points on one surface of the gauge block (3) were measured by the developed micro CMM. A reference plane of it was then calculated by the least squares method. For the gauge block (2), the coordinate of the center point of the upper surface was measured. Then, the step height of the two gauge blocks is the distance between the center point and the reference plane. According to this method, the step height of the two gauge blocks was measured 15 times. The average value of the 15 repeated measurements was 3.000006 nm. The standard deviation (\(\sigma\)) was 21 nm. The average value 3.000006 mm of 15 repeated measurements is very close to the difference 3.000002 mm between the two calibrated values. During the measuring process, the triggering direction of the probe was in the vertical direction, so there was no need to compensate the probe radius.  
The step height measurement errors are mainly from the interferometer (26 nm, \(k\) = 2), the residual Abbe error (30 nm, \(k\) = 2), the repeatability error of the probe (30 nm, \(k\) = 2), the error from reflection mirror (50 nm, \(k\) = 2). Neglecting other errors sources, the measurement uncertainty is about 71 nm (\(k\) = \(-\)2).
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 2 Indoor Multi-Robot Formation Platform
### Platform Architecture and Components  
Our multi-robot formation platform consists of three components--an indoor global-positioning system, the robot hardware, and the multi-robot communication technology. Figure 1 shows the architecture and the components of the multi-robot formation platform, which are the following:  
1. A personal computer to monitor the indoor global-positioning system, which is employed to collect and record the robots' pose.
2. The modem of the Marvelmind Indoor Navigation System connected with PC through Universal Serial Bus.
3. A Universal Serial Bus (USB) server to link the Dashboard with PC.
4. The Marvelmind dashboard is the dashboard of Marvelmind Indoor Navigation System.
5. A mobile beacon of the Marvelmind Indoor Navigation System mounted on the Robot, which is used to receive position information and send to the robot's controller.
6. A Wi-Fi communication module ESP8266 installed on each robot, to transmit and receive data as the Transmission Control Protocol(TCP) client.
7. The mobile robot equipped with a microcontroller and other sensors.  
Figure 1: The architecture and the components of the multi-robot formation platform.  
A Wi-Fi communication module ESP8266 connected to the PC, to transmit and receive data as the TCP server.
* USB-TTL (Transistor-Transistor Logic) is used to build a connection between communication module ESP8266 and the PC.
* The data collection application is connected to the PC through I/O API and developed in LabVIEW software, for collecting the robots' poses and recording them in data files.
* The MPU-6050 devices combine a 3-axis gyroscope and a 3-axis accelerometer on the same silicon die, together with an onboard Digital Motion Processor, which is the server for the robots' orientation.
# Eight-Port Modified E-Slot MIMO Antenna Array with Enhanced Isolation for 5G Mobile Phone
## 3 Results and Discussions
### Radiation Performance  
The 3.5 GHz band 2D radiation patterns for Ant. 1-4 are shown in Figure 8. The solid lines denote the simulated results, while the dotted lines are the measured results. It is quite evident that antenna 1-4, at both \(\Phi=90^{\circ}\) and \(\theta=0^{\circ}\), have strong radiation in almost all the planes. Despite minor differences between the measured and simulated results, there is a general trend between the two findings, and the tiny discrepancy is related to the test settings. In conclusion, variation in maximum radiation orientations results in a diverse pattern, which contributes to the general diversity performance of the MIMO antenna.  
Figure 6: Simulated and measured scattering parameters (**a**) reflection parameters and (**b**) transmission parameters value.  
Figure 7: Measured (**a**) antenna total efficiency and (**b**) antenna gain.
## 4 The Optimal Path of Energy Consumption (OPEC)  
### Mathematical Description of Path Energy Consumption  
**Definition 2**.: _Path Set, denoted as P. The source node is \(v_{0}\), the destination node is \(v_{n}\), the alternate sequence \(v_{0}\)\(\ldots\)\(v_{i}v_{j}\)\(\ldots\)\(v_{n}\) is a path from \(v_{0}\) to \(v_{n}\), let P= {\(r_{1}\), \(r_{2}\), \(r_{3}\), \(\ldots\), \(r_{k}\)}, so P is the collection of all paths from \(v_{0}\) to \(v_{n}\)._  
**Definition 3**.: _Path Transmission Energy Consumption, denoted as E. The power consumption required to transfer a packet through \(r_{i}\) is formulated as:_  
\[E(r_{i})=\sum_{0}^{n}a_{ij} \tag{1}\]  
Figure 1: Distributed sample of wireless body area network sensors.  
Figure 2: The network topology of Figure 1.  
The larger the \(E(r_{i})\), the greater the energy required to transfer the data using the path, and vice versa.  
**Definition 4**.: _Path Cost, denoted as C. The cost of the node \(v_{i}\) is formulated as:_  
\[f(v_{i})=\frac{1}{c_{i}} \tag{2}\]  
The cost of the path \(r_{i}\) is the sum of the cost of all nodes in the path and is formulated as:  
\[C(r_{i})=\sum_{i=0}^{n}f(v_{i}) \tag{3}\]  
With the decrease of the residual energy of the node, the cost of transmitting the data of the node will be increased, the \(C(r_{i})\) when the path \(r_{i}\) containing the node \(v_{i}\) also increases, and vice versa.  
**Definition 5**.: _Path Energy Difference Ratio, denoted as \(\eta\). In the nodes of path \(r_{i}\), the node with the largest remaining energy is max and the node with the smallest energy is min, the path \(r_{i}\) energy difference ratio \(\eta\) is formulated as:_  
\[\eta(r_{i})=\frac{c_{\min}}{\alpha_{\min}\times c_{\max}} \tag{4}\]  
\(\eta(r_{i})\) indicates the degree of energy difference between the maximum energy node \(v_{max}\) and the minimum node \(v_{min}\) in the path \(r_{i}\), and the larger the \(\eta\), the smaller the difference is, the better the energy consumption of the path \(r_{i}\), otherwise the worse. \(\alpha_{\min}\) is the importance level factor of \(v_{min}\), used to adjust \(\eta\), when \(v_{min}\) is very important or the energy will be exhausted, you can increase \(\alpha_{\min}\), making \(\eta\) smaller. At the same time, in order to protect the node which residual energy has reached its may easily cause some nodes to run out of energy, the path will not be utilized.
# Optimization and Diversification of Cryptocurrency Portfolios: A Composite Copula-Based Approach
## 3 Data Analysis and Empirical Results
### Modelling the Residual Dependencies Using Vine Copula  
Econometric models coupled with vine copula are commonly used in the field of multivariate modelling of financial returns. The GARCH marginal time series model is first fitted to each cryptoasset returns and standardised residuals are formed. These residuals are subsequently transformed to marginally uniform data using parametric probability integral transformation and used as inputs for the selection of the appropriate bivariate  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline  & **BTC** & **LEO** & **LINK** & **FIL** & **THETA** & **TUSD** & **WAVES** & **ONE** \\ \hline BTC & 1 & 0.124 & 0.609 & 0.374 & 0.505 & \(-\)0.087 & 0.465 & 0.564 \\ LOE & 0.124 & 1 & 0.138 & 0.035 & 0.077 & 0.055 & 0.100 & 0.115 \\ LINK & 0.609 & 0.138 & 1 & 0.371 & 0.487 & \(-\)0.062 & 0.474 & 0.446 \\ FIL & 0.374 & 0.035 & 0.371 & 1 & 0.287 & \(-\)0.051 & 0.263 & 0.265 \\ THETA & 0.505 & 0.077 & 0.487 & 0.287 & 1 & \(-\)0.054 & 0.410 & 0.339 \\ TUSD & \(-\)0.087 & 0.055 & \(-\)0.062 & \(-\)0.051 & \(-\)0.054 & 1 & \(-\)0.050 & \(-\)0.014 \\ WAVES & 0.465 & 0.100 & 0.474 & 0.263 & 0.41 & \(-\)0.050 & 1 & 0.370 \\ ONE & 0.564 & 0.115 & 0.446 & 0.265 & 0.339 & \(-\)0.014 & 0.370 & 1 \\ \hline \end{tabular}
\end{table}
Table 6: Correlation coefficients.  
Figure 4: Dendrogram of the cryptoassets.  
copula. The Alkaike information criterion (AIC) is used to select the copula that best fits the data.  
At the first step the univariate AR(1, 1)-GJR-GARCH(1, 1) model with Student-t innovations is fitted to the log return series of each cryptoasset to extract standardised residuals and their independence are verified using the Ljund-Box test. Subsequently, the empirical probability integral (since the data size is large) is used to transform the standardised residuals to obtain marginally uniform data (iid residuals) for the estimation of copula. These steps are repeated for the eight cryptoassets to obtain a multivariate matrix of uniform marginal. The selected bivariate copula and their estimated Kendall's tau for the eight cryptoassets are recorded in Table 7 for C-vine and Table 8 showing different strengths and signs of pairwise dependencies.
## 2 Materials and Methods
The set of features was derived based on the following parameters: histogram (9 features including histogram mean, histogram variance, histogram skewness, histogram kurtosis, and percentiles at 1%, 10%, 50%, 90%, and 99%), gradient (5 features such as absolute gradient mean, absolute gradient variance, absolute gradient skewness, absolute gradient kurtosis, and the percentage of pixels with a nonzero gradient), run length matrix (5 features across 4 directions: run length nonuniformity, gray level nonuniformity, long run emphasis, short run emphasis, and the fraction of the image in runs), co-occurrence matrix (11 features across 4 directions and 5 inter-pixel distances: angular second moment, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, and difference entropy), autoregressive model (5 features: pa  
Figure 1: Illustration of the selection of cancellous bone tissue area for the study (red box). Figure 2: Imaging samples of tissue from healthy patients and tissue with osteopororotic changes in their original size. rameters \(\Theta\)1, \(\Theta\)2, \(\Theta\)3, \(\Theta\)4, and standard deviation), and Haar wavelet (24 features: wavelet energy calculated at 6 scales across 4 frequency bands LL, LH, HL, and HH) [33]. For each sample, 290 features were obtained. Features with constant values for each sample were eliminated. After reduction, 267 features remained. These were ranked by importance, from the most important to the least important. Figure 3 shows visualizations of the feature value distribution for the most important and least important features.
## 2 Theory and Models
### Second Order Characteristics  
The normal, or nonconjugate, dyadic cross correlation function (DCCF) of a voltage and current can be defined as a function of two time variables, \(t_{1}\) and \(t_{2}\):  
\[R_{vl}(t_{1},t_{2})=\mathbb{E}[v(t_{1})i^{*}(t_{2})], \tag{9}\]  
where \(\mathbb{E}\) denotes expectation and the asterisk * in the superscript marks the complex conjugation.  
As long as the signals representing the voltage and current have been bounded to be deterministic, this literally means that:  
\[v(t)=\mathbb{E}[v(t)],\ i(t)=\mathbb{E}[i(t)], \tag{10}\]  
and their DCCF can be written with the expectation operator omitted:  
\[R_{vl}(t_{1},t_{2})=v(t_{1})i^{*}(t_{2})=v(t_{1})i(t_{2}). \tag{11}\]  
Besides, the second equal sign is possible, owing to the fact that both the voltage and current are real valued functions.  
Since both the voltage and current are periodic functions, their DCCF is also a periodic function, sharing the same period, \(T\), in both its argument:  
\[\forall(n_{1},n_{2})\in\mathbb{Z}^{2}:R_{vl}(t_{1},t_{2})=R_{vl}(t_{1}+n_{1}T,t_{2}+n_{2}T). \tag{12}\]  
Having taken the two-dimensional Fourier transform over the pair of time variables, \((t_{1},t_{2})\), one obtains the function of the pair of frequency variables \((f_{1},f_{2})\):  
\[S_{vl}(f_{1},f_{2})=\int\limits_{-\infty}^{+\infty}\int\limits_{-\infty}^{+ \infty}R_{vl}(t_{1},t_{2})\exp[-j2\pi(f_{1}t_{1}-f_{2}t_{2})]dt_{1}dt_{2}. \tag{13}\]  
The substitution of (11) into (13) and further factorization of its exponential kernel will lead to the spectral representation  
\[S_{vl}(f_{1},f_{2})=V(f_{1})I^{*}(f_{2}), \tag{14}\]  
which is also referred to as Loeve bifrequency cross spectrum. The frequency variables \((f_{1},f_{2})\) are the pairwise counterparts of the time variables \((t_{1},t_{2})\).  
Some formal difficulties may be pointed out: while the periodic functions \(v(t)\) and \(i(t)\), jointly producing \(R_{vl}(t_{1},t_{2})\), are substituted into (13), since they do not belong to the \(L^{1}\) space, integration is carried out over the whole \(\mathbb{R}^{2}\). However, we consider that this operation can be carried out in a generalized sense, and described with rigor enough for engineering applications. As long as (14) remains valid, the formal substitution of the Fourier series (3) and (4) into it yields:  
\[S_{vi}(f_{1},f_{2})=\sum_{k_{1}=-\infty}^{+\infty}\sum_{k_{2}=-\infty}^{+\infty}M _{v}^{(k_{1}F)}\Big{[}M_{i}^{(k_{2}F)}\Big{]}^{*}\delta(f_{1}-k_{1}F,f_{2}-k_{2} F), \tag{15}\]  
where a two-dimensional Dirac delta function can be treated as a product of two one-dimensional Dirac delta functions:  
\[\delta(f_{1},f_{2})=\delta(f_{1})\delta(f_{2}), \tag{16}\]  
wherever it may be considered suitable for subsequent formal transformations [45].
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 4 OS-Based Mitigation Techniques
### Protecting the ELF Binaries  
We have already seen in the previous section that ASLR provides randomization-based protection to protect against ROP like attacks. However, it also faces limitation and can be bypassed with a bit of effort. Therefore, to provide more robust protection there exist other mitigation including position independent executable (PIE) and relocation read-only (RELRO) that harden the binaries itself.
## 9 Fundamental Concepts Underlying Adaptive Filters  
The use of adaptive filters in the field of EEG signal processing marks a significant step forward, offering an efficient mechanism to address and remove artifacts while keeping valuable neural information intact [106]. These filters, distinguished by their ability to automatically adapt to signal changes, represent a breakthrough in EEG data cleaning. Among the various technologies available, Least Mean Squares (LMS) and Recursive Least Squares (RLS) filters stand out for their effectiveness and versatility, offering a sophisticated approach to the challenge of isolating and eliminating interference without compromising the original signal. The following description will focus exclusively on these two filters, exploring their characteristics, operating mechanisms, and application contexts. Through this focus, we aim to highlight how the LMS filter, with its simplicity and effectiveness, and the RLS filter, known for its speed and precision, are instrumental in significantly improving the quality of EEG analyses, contributing to the development of neuroscientific research and to the refinement of diagnostic processes in the clinical setting.
###### Abstract  
The demand for internal nondestructive testing and inspection techniques is rapidly increasing. Using a continuous wave (CW) terahertz (THz) imaging system, we demonstrate that the internal defects in cross-linked polyethylene (XLPE) plates for power cable insulation can be detected. In a coherent detection scheme based on photomixers, which serve as the THz emitters and receivers, the change of phase occurring with the defects inside the XLPE plates is distinctly measured by the change in the amplitude of the transmitted THz waves. According to the two-dimensional images of THz waves transmitted through the XLPE plates, defects of up to 0.5 mm size located inside the XLPE plates can be detected by the internal nondestructive examination method based on CW-THz waves. This technique will be useful for internal nondestructive testing and inspection of insulation materials that require high resolution in various industries, including the automobiles, electronics, and electrical power industries.  
**Keywords:** nondestructive examination; XLPE insulation; terahertz imaging; power cable
## 2 Methodology
### Characterization of the Adsorbent Solid  
GAC was characterized by N\({}_{2}\) adsorption (BET/BJH), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and pH\({}_{\text{PCZ}}\) analyses. N\({}_{2}\) adsorption, XRD, and FTIR were performed before and after caffeine removal under the most suitable conditions for adsorption and the combined process. The pH\({}_{\text{PCZ}}\) was determined using the methodology of Regalbutto and Robles [42]. For N\({}_{2}\) adsorption, a pore size and area analyzer (Quantachrome NOVA 4200e, Quantachrome, Boynton Beach, FL, USA) was used. The specific area was obtained by using the BET method [43], while the pore volume, diameter, and distribution were determined by using the BJH method [44]. XRD was studied using the Bruker D2 Phaser equipment with Cu \(\alpha\)K radiation, with a step size of 0.05\({}^{\circ}\), time per step of 1.0 s, and a scanning range from 5\({}^{\circ}\) to 75\({}^{\circ}\) (20). FTIR was analyzed using the Perkin Elmer equipment (Frontier model) with an analysis region from 400 to 4000 cm\({}^{-1}\), 16 scans, and a resolution of 4 cm\({}^{-1}\), to visualize the change in functional group characteristics of the solid surface before and after adsorption.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 4 Model Trajectories
### Time Series of Separation Distance, Skill Scores and Standard Deviation
Figure 8: Time series of the separation distance (m) between the observed and model drifter #3 (**a**), #4 (**b**) and #5 (**c**) trajectories of WD-REF experiments with wind drag contributions of 0.1, 0.5 and 1.0%. Figure 9: Time series of the skill score and the standard deviation of the distance (km) between the observed and model drifter trajectories of the different experiments (see the Table 1 and the legend): (CPL (**a**,**b**), WD-CPL (**c**,**d**) and WD-REF (**e**,**f**). These differences were illustrated even better by the standard deviations of the WD-CPL experiments (Figure 9d). The standard deviations of D#5 stayed almost constant through the integration, at about 0.5 and 5 km, respectively, for CPL and WD-CPL plus 0.1% (Figure 9). Adding 0.5% windage yielded a higher standard deviation for all experiments. By assuming 1.0% windage, a trend of the systematic increase was observed for all drifters. Neither of the WD-CPL experiments (in Figure 9c,d) improved the skill scores and reduced the deviation between the trajectories of the Lagrangian model and observations by additionally considering the windage contribution to the Eulerian currents obtained by the coupled wave-circulation ocean model. Sensitivity to the windage contribution was also determined for the REF runs (Figure 9e,f). The skill score and standard deviation of WD-REF were better than those of WD-CPL for all experiments. On the other side, the skill scores/standard deviations were lower/higher than those of CPL. Only by considering 0.5% windage of WD-REF are the _ss_ and _dd_ similar to the CPL. It was demonstrated in [53] that simulated trajectories obtained by considering the wind drag of current velocities or the Stokes drift's contribution showed better skills against the observations than without the corrections. They postulated that considering 1.0% windage or adding the contribution of the Stokes drift gives almost identical results. Our sensitivity analysis, however, showed that the CPL experiments performed best. From the rest of the experiments, only WD-REF with 0.5% windage provided similar skill. The model experiments showed that using Eulerian velocity estimated by considering Stokes-Coriolis and the sea state momentum provided similar results.
## 4 Conclusions  
We have successfully demonstrated the photo-sintering technique for the CZTS thin film solar cell. CZTS NPs were achieved by hot injection and a screen printing method was used to apply the CZTS ink on the substrate. The photo-sintered CZTS thin film displayed a kesterite structure through XRD measurement, and a Cu-poor and Zn-rich composition for production of CZTS solar cells was also demonstrated. Raman spectrum and FE-SEM images demonstrated that the morphology and crystallization were improved after treatment. Finally, the fabricated solar cell device with photo-sintered CZTS thin films achieved 1.01% cell efficiency at an energy density of 2.96 J/cm\({}^{2}\). The photo-sintering technique by light irradiation proves to be an important sintering tool for replacing the conventional thermal annealing process on the firm or flexible substrate with a fast and nontoxic process. Although the fabricated device displays low efficiency compared to the previous thermal sintering method, its advantages--fast processing time, low process temperature, adoptable for large areas, and suitable for flexible substrate--can open paths to various applications. The performance of the device can be enhanced by additional analysis and optimization of surface roughness, different thickness, and photo-sintering conditions of the CZTS layer. The analysis of photoluminescence spectra for reducing recombination is also necessary.  
Study design, [PERSON], [PERSON] and J.S.; experiments, J.B.; data analysis, [PERSON] and B.H.; writing--original draft, V.M.H.C. and H.J., writing--review and editing, [PERSON] and J.L.; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Electric Power Corporation (Grant number: R17 XA05-1) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1061615).  
Not applicable.  
The data that support the findings of this study are available upon reasonable request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Voc (V)**} & **Isc** & **Fill Factor** & **Efficiency** & **Rsh (\(\Omega\))** & **Rs** \\  & & **(mA/cm\({}^{2}\))** & **(\%)** & **(\%)** & **(\%)** & **(\(\Omega\)\(\bullet\)cm\({}^{2}\))** \\ \hline
2.96 J/cm\({}^{2}\) & 0.224 & 5.913 & 32.789 & 1.01 & 378.713 & 26.417 \\ \hline \end{tabular}
\end{table}
Table 3: The measured performance of the photo-sintered CZTS thin film solar cell with CdS and ZnS buffer layers.  
Figure 7: A J–V curve of the photo-sintered CZTS thin film solar cell.