esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Four-Point Bending Test Results  
As shown in Figure 11, the slope of the load-deflection curves decreases, while the angle-ply orientation increases toward 90\({}^{\circ}\) or CLT. Therefore, the grain orientation of the inner layers is confirmed to affect the stiffness of the panels. Regarding the data from the testing shown in Table 4, the apparent bending stiffness of the panels is improved by 33% from CLT to DCLT \(\pm\) 10\({}^{\circ}\). This indicates that grain orientation had a statistically significant (\(p\)-value of 0.009) effect on the apparent bending stiffness of the DCLT panels. Table 4 also summarizes the difference percentages (%) between the bending stiffness properties of DCLT panels with different grain orientations in inner layers.  
The beams were loaded to failure, and the first cracks started to form outside of the center third and closer to the ends due to rolling shear failure. This region provides the low secondary stresses originating from load heads or bearing supports, and with increasing load, the deformation propagates along the length of the panel toward the supports, where eventually the combination of the shear and bending stresses resulted in the final fracture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{EI_{App.}}\)**(N-mm\({}^{2}\times\mathbf{10^{11}}\))**} & \multicolumn{4}{c}{**Grain Orientation**} \\ \cline{2-6}  & \(\mathbf{10^{\circ}}\) & \(\mathbf{20^{\circ}}\) & \(\mathbf{40^{\circ}}\) & \(\mathbf{70^{\circ}}\) & \(\mathbf{90^{\circ}}\) \\ \hline Mean & 2.28 & 2.14 & 1.95 & 1.76 & 1.71 \\ \hline \% Increase to CLT & 33\% & 25\% & 14\% & 3\% & – \\ \hline COV (\%) & 2.62 & 5.93 & 3.37 & 7.42 & – \\ \hline Min & 2.23 & 2.00 & 1.90 & 1.67 & 1.71 \\ \hline Max & 2.35 & 2.26 & 2.02 & 1.91 & 1.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bending stiffness properties for different grain orientations obtained from experimental tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{EI_{App}}\)**(N-mm\({}^{2}\times\mathbf{10^{11}}\))**} & \multicolumn{4}{c}{**Grain Orientation**} \\ \cline{2-6}  & \(\mathbf{10^{\circ}}\) & \(\mathbf{20^{\circ}}\) & \(\mathbf{40^{\circ}}\) & \(\mathbf{70^{\circ}}\) & \(\mathbf{90^{\circ}}\) \\ \hline Handbook Values & 1.45 & 1.39 & 1.31 & 1.21 & 1.18 \\ \hline MoE of the Layers & 1.74 & 1.68 & 1.58 & 1.43 & 1.40 \\ \hline \% Increase to CLT & 23\% & 18\% & 12\% & 3\% & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bending stiffness properties for different grain orientations obtained from [PERSON]’s theory.
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Radionuclide \({}^{198}\)Au  
[PERSON] et al. [81] provided an overview of AuNPs with different forms (nanospheres, nanodisks, nanorods, and cubic nanocages) incorporating radioactive Au. Studying a murine EMT6 breast cancer model, \({}^{198}\)Au-labeled AuNPs were imaged by the Cerenkov signal. Both \({}^{198}\)Au nanospheres and nanodisks were found on the surfaces of the tumors, while \({}^{198}\)Au nanorods and nanocages were located inside the tumors. \({}^{198}\)Au was used to functionalize the prostate tumor-specific epigallocatechin-gallate (EGCg) with AuNPs (\({}^{198}\)AuNP-EGCg) to deliver a targeted therapy to the prostate gland using the \(\beta\) emission. Pharmacokine studies showed about 72% of \({}^{198}\)AuNP-EGCg uptake in SCID mice bearing PC-3 tumors 24 hours after the intratumoral injection. The therapeutic effect was a significant inhibition of the tumor growth, with an 80% decrease of tumor volumes after 28 days [136].
## 2 Methods
### Data Preprocessing  
After obtaining the ALS point cloud, the data are subjected to point cloud filtering, elevation normalization, subplot division, point cloud denoising, down-sampling, and coordinate normalization, and the whole process is shown in Figure 2. The specific steps of data preprocessing are as follows. (1) Point cloud filtering. The cloth simulation algorithm [28] is adopted to filter the point cloud, and the point cloud is divided into two types: ground points and non-ground points. (2) Elevation normalization. CloudCompare software is used to normalize the elevation of the point cloud to remove the influence of terrain relief. (3) Divide sub-plot. The sample plot is divided into 25 m \(\times\) 25 m sub-plots. (4) Point cloud denoising. The residual outliers and branches at the sub-plot boundaries are removed by artificial visual denoising. (5) Down-sampling. Grids of size 0.5 m \(\times\) 0.5 m are divided on the xoy plane, and the highest point in each grid is taken as the sampling point, and these sampling points are smoothed with a mean smoothing filter. (6) Coordinate normalization. After the coordinate normalization operation of the sub-plots, the data preprocessing is completed.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation  
On the PTLI dataset, we verified the effectiveness of our proposed method on the ice thickness measurement. Additionally, on the PTLI scene dataset, the overall performance of our approach was evaluated using the confusion matrix. The segmentation results from the proposed method are compared with ground truth images. The proposed method aims to classify transmission line icing pixels on ROI as true positive, false negative, or false positive, as represented by accuracy, precision, and recall.
## 3 Discussion of Experimental Result
### State of the Art Aspects
This manuscript presents a \(2\times 2\) array of a broadband MPA with low cross-polarization levels. The presented structure has several features. First, a combination of PF and AC methods has been used to feed this structure. PF allows us to control impedance characters and achieve a higher impedance matching. The experimental results indicate that the operating bandwidth of this MPA array is 29.8%. On the other hand, the aperture coupling method has helped us to provide a very effective mechanism for suppressing cross-polarization without the need to design complex power distribution networks. The reduced cross-polarization is studied for a 3-D half-power beamwidth space. The measured results show that the cross-polarization level for the presented MPA is less than 30 dB in the whole operating impedance bandwidth. The designed array can be easily used as a subarray to design larger arrays. This does not need to increase the number of PCB layers to design larger arrays. The proposed array does not require the use of blind screws or even internal connections and its fabrication process is simple.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 3 Methods
### Methods for Efficient Computation
#### 3.3.1 Random Projection  
Random projection ([PERSON] et al., 2017) is a dimension reduction technique that can transform an \(n\)-dimensional vector into a \(k\)-dimensional vector (\(k\ll n\)), while preserving the critical information of the original vector. The idea behind of random projection is the Johnson-Lindenstrauss lemma ([PERSON], 1961). That is, for any vectors \(\{x,x^{\prime}\}\in X\) from a set of \(m\) vectors in \(X\subset\mathbb{R}^{n}\) and for \(\epsilon\in(0,1)\), there exists a linear mapping \(f:\mathbb{R}^{n}\to\mathbb{R}^{k}\) such that the pairwise distances of vectors are almost preserved after projection in the sense that:  
\[(1-\epsilon)\|x-x^{\prime}\|_{2}^{2}\leq\|f(x)-f(x^{\prime})\|_{2}^{2}\leq(1+ \epsilon)\|x-x^{\prime}\|_{2}^{2},\]  
where \(k>8\ln(m)/\epsilon^{2}\). It is known that when the original dimension \(n\) is large, a random projection matrix \(P\in\mathbb{R}^{k\times n}\) can serve as the feature mapping \(f\) in the lemma, since random vectors in \(\mathbb{R}^{n}\) tend to be orthogonal to each other as \(n\) increases [18].  
Motivated by the above phenomenon, we use random projection to find a vector that satisfies (3) instead of calculating the eigenvalue decomposition to solve (4). The idea is that if the number of random vectors is sufficiently large, the maximum value of the Fisher's criterion calculated by random projection can approximate the behavior of a true solution.  
Furthermore, random projection makes it unnecessary to explicitly store \(\hat{S}_{w}\) and \(\hat{S}_{b}\) since we can simply compute the denominator and numerator of (3) as follows:  
\[w^{T}\hat{S}_{w}w=\sum_{i=1}^{c}\frac{1}{m_{i}}\sum_{j=1}^{m_{i}}w^{T}(x_{i,j }-\overline{x}_{i})(x_{i,j}-\overline{x}_{i})^{T}w,\]  
\[w^{T}\hat{S}_{b}w=\sum_{i=1}^{c}\frac{m_{i}}{\sum_{j=1}^{c}m_{j}}w^{T}( \overline{x}_{i}-\overline{x})(\overline{x}_{i}-\overline{x})^{T}w,\]  
where \(w\) is a random unit vector drawn from \(\mathcal{N}(0,1)\). This technique is critical for dealing with high-dimensional data, such as images, in a memory-efficient way. In our experiments, ten random projection vectors were sufficient in most cases to accurately estimate our quality measures.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 1 Introduction  
Traditionally, infrastructure monitoring is performed using geodetic techniques such as precise levelling, tachymetry, and extended long-term observations of static Global Navigation Satellite System (GNSS) observations [1; 2; 3]. All these techniques are precise, but they are also cost-consuming and provide data for a sparse network of points. With the advent of Synthetic Aperture Radar (SAR) satellite missions, such as ESR [4], Envisat [5], ALOS [6], TerraSAR-X [7], and Sentinel-1 [8], observations from SAR satellites have become intensively used for monitoring deformation based on their wide coverage of space and time capabilities.
## 1 Introduction
Graphene has a thermal conductivity of \(5000\) W/(mK) in the plane direction and \(30\) W/(mK) in the longitudinal direction [21]. Yet, simply using graphene oxide as a thermally conductive and wave-absorbing filler for packaging material is impractical for several reasons. Firstly, due to its large specific surface area, a very small amount of graphene oxide will have a huge impact on the fluidity of the compounds [22]. It has been reported that pure graphene filling can only achieve a maximum of \(-7\) dB of reflection loss [19]. Secondly, graphene oxide, working as a dielectric loss absorber and exhibiting a high reflection of microwaves, may emit microwave pollution to the surroundings [22]. To improve this, many attempts have been made to combine GO with magnetic particles. [PERSON] et al. [23] developed Fe/GO nanocomposites by inserting Fe\({}^{3+}\) into GO followed by a reduced reaction in \(H_{2}\). The maximum reflection loss of \(9\) dB was at \(11\)-\(18\) GHz. [PERSON] et al. [24] synthesized GO/Fe\({}_{3}\)O\({}_{4}\)/ iron phthalocyanine composites using a facile one-step solvothermal method. They observed the maximum microwave absorption of \(-27.92\) dB at \(10.8\) GHz. [PERSON] et al. [25] fabricated n-doped GO/MnCo\({}_{2}\)O\({}_{4}\) nanocomposites using a facile hydrothermal method followed by an annealing process, and the reflection loss was observed to fall in the range of \(-90\) to \(-77\) dB. Nevertheless, the synthesis processes of the above studies are not practical for scaling up production. Selecting a suitable magnetic particle and finding a feasible way to produce the compound is important for rapid application. FeSiAl, as a type of soft magnetic alloy, has become widely used in the microwave absorption field for its excellent magnetic  
Figure 1: 5G base station (**a**) without wave-absorbing and heat conducting measure, (**b**) with wave absorbing layers and FIP sealing strips. properties such as high saturation magnetization and high eddy current loss causing by its high permeability in the low-frequency range [26; 27; 28]. However, its high conductivity, along with a low Snoek limit at a high frequency, limit its application [29; 30; 31]. In this study, a novel epoxy-based 5G base station packaging material with comprehensive properties of high wave-absorbing ability and high thermal conductivity ability was introduced, in which GO/FeSiAl particles were made and modified by \(\gamma\)-aminopropyl triethoxysilane in gentle solvent conditions. By combining these two particles, an impedance match can be formed, improving its high-frequency range wave-absorbing ability [30]. Meanwhile, graphene has a large specific surface area and can act as an anti-settling agent [32], providing another application advantage. Our work provides a simple and practical way to produce multifunctional 5G base station packaging materials for application at a large scale, which has an advanced reflection loss (RL) of \(-48.28\) dB and a wide effective range of 3.6 GHz within the range of operating frequency of 5G antennas of 2.575-2.645 GHz at a small thickness (2 mm).
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 4 Autonomous Orbit Determination Simulation
### Analysis of Cramer-Rao Lower Bound
Consequently, 30 days is chosen as the orbit determination duration for the subsequent simulation.
# Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease
###### Abstract  
We propose here a spectroscopic method to diagnose and differentiate inflammatory bowel diseases (IBD), such as ulcerative colitis (UC) and Crohn's disease (CD) with pediatric onset, in a complete noninvasive way without performing any duodenal biopsy. In particular, the Raman technique was applied to proteic extract from fecal samples in order to achieve information about molecular vibrations that can potentially furnish spectral signatures of cellular modifications occurring as a consequence of specific pathologic conditions. The attention was focused on the investigation of the amide I region, quantitatively accounting the spectral changes in the secondary structures by applying deconvolution and curve-fitting. Inflammation is found to give rise to a significant increasing of the nonreducible (trivalent)/reducible (divalent) cross-linking ratio \(R\) of the protein network. This parameter revealed an excellent marker in order to distinguish IBD subjects from non-IBD ones, and, among IBD patients, to differentiate between UC and CD. The proposed methodology was validated by statistical analysis using the receiver operating characteristic (ROC) curve.  
Keywords:inflammatory bowel disease; diagnosis; Raman spectroscopy; ROC curve +
Footnote †: journal: Applied Sciences
## 2 Preliminaries
### Hurst Parameter  
LRD is characterized by the Hurst exponent \(H\). It means that there is a strong coupling between values at different times. The coupling effect characterized by the autocorrelations functions (ACF) and power spectrum density (PSD) obey the power law, hyperbolically decaying, while the conventional assumptions are based on the exponentially decaying effects. To quantify the level of the coupling, the Hurst exponent and many valuable Hurst exponent estimators have been provided to more accurately characterize the LRD time series.  
The rescaled range (R/S) method is one of the time-domain analysis of Hurst parameter defined as follows [16]:  
\[E[\frac{R(n)}{S(n)}]_{n\rightarrow\infty}=Cn^{H}, \tag{3}\]  
where \(E(\cdot)\) denotes the expected value of the observations, \(R(n)\) is the range of the first \(n\) values, \(S(n)\) is their standard deviation, and \(C\) is a constant. Alternatively, [PERSON]'s maximum likelihood estimator (MLE) and wavelet analysis are using periodogram based analysis in the frequency domain [17].  
LRD processes appear in many contexts, which is characterized by the Hurst parameter \(H\) (\(0<H<1\)). The phenomenon of LRD can be observed in hydrology, finance, economics and so on. Unlike with a stationary process, the autocorrelations between observations of a long memory series are slowly decaying to zero. However, according to the survey paper [18], Hurst estimators can be significantly affected by trend and seasonality.  
LRD or long memory process can be defined by the autocovariance \(\gamma(k)\) in the time domain, or defined by the power spectrum \(P(f)\) in the frequency domain. The power law is observed in the long memory ARFIMA(\(p,d,q\)) process \(\{X_{t}\}\) with \(d\in(-1/2,1/2)\setminus\{0\}\), since the asymptotic behaviour of the autocovariance function \(\gamma(\cdot)\) is given by [19]  
\[\gamma(k)\simeq Ck^{2d-1}(k\rightarrow\infty). \tag{4}\]  
In particular, if \(0<d<0.5\), then \(\{X_{t}\}\) is a long-memory process, or long range positive dependence, since \(\sum_{k=0}^{\infty}|\gamma(k)|=\infty\)[20]. The process is said to exhibit intermediate memory (anti-persistence) (Also known as a mean-reverting process), or long-range negative dependence, for \(-0.5<d<0\). In addition, the process is non-stationary for \(|d|\geq 0.5\), as it possesses infinite variance and first order difference is needed to get the stationary series. Therefore, the fractional order difference filter is the first step of processing long memory ARFIMA models [21].  
Besides, the fractional order difference (integral) coefficient \(d\) has a closed relation with Hurst parameter and characteristic exponent \(\alpha\):  
\[d=H-1/\alpha, \tag{5}\]  
where \(H\) is the Hurst parameter and \(\alpha\) is from the \(\alpha\)-stable distribution, which will be introduced in the next subsection. The Hurst parameter of fGn is related to \(d\) by the Equation (5).
## 3 Data Analysis and Results
Along with the entropic parameters \(q\) and \(T_{c}\), which represent the transition point from the non-additive to additive range in every aftershock series, the parameters of each mainshock and its aftershock sequence are summarized in Table 1.  
Next, the interevent time distribution is calculated for each aftershock sequence, and a \(Q\)-exponential function fitting up to a value \(T_{c}\), yielding to the \(Q\) and \(q\) parameters, respectively. In all cases that we study, we observe a deviation from the \(Q\)-exponential function for high values of time \(T\), with \(T>T_{c}\). Additionally, using the estimated \(Q\) value from the prior analysis, the \(Q\)-logarithmic function of \(P(\succ\)T) as a function of \(T\) is constructed. The range of interevent times, provided by Equation (12), where \(ln_{Q}P(\succ\)\(T)\) vs. \(T\) is a straight line, is then specified along with its correlation coefficient. The transition from NESP to BG statistical physics is indicated by the deviation from linearity at \(T_{c}\). This demonstrates that in the immediate aftermath of the mainshock, the system is controlled by NESP, whereas as the aftershock sequence develops at \(T>T_{c}\), the system is controlled by BG statistical mechanics. Figure 2 presents a flowchart of the process behind the non-extensive statistical physics model flowchart implemented in the present work.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 3 Fault Reconstruction Based on a Hybrid Multi-Objective Genetic Algorithm
### Genetic Coding for Satellite Numbering  
Suppose the number of satellites in a given swarm is \(N\), and the set of numbers is \(S=\left\{s_{1},s_{2},\cdots,s_{N}\right\},s_{i}\in N\). At a certain moment, \(N_{F}\) faulty satellites are detected, the set of which is \(\mathbf{S_{F}}=\left\{s_{F1},s_{F2},\cdots,s_{FN_{F}}\right\},s_{Fi}\in S\). Then, the set of nonfaulty satellites is \(\mathbf{S_{H}}=\left\{s_{H1},s_{H2},\cdots,s_{HN_{H}}\right\},s_{Hi}\in(S- \mathbf{S_{F}})\), and \(N_{H}=N-N_{F}\).  
Since it is necessary to determine the number of satellites participating in the reconstruction, integer coding is used. The decision variable \(\mathbf{X}=\left[x_{1},x_{2},\cdots,x_{N_{F}}\right]\) is a vector composed of satellite numbers participating in the reconstruction. The genetic code is a one-to-one correspondence between the \(x_{i}\) and \(s_{Fi}\), which is shown in Figure 5:To prevent the repetition of variable \(x_{i}\) in the process of cross-mutation, the process draws on the principle of solving the traveling salesman problem. During initialization, the domain of \(\mathbf{X}\) is set to \(\mathbf{S_{H}}\), which can be expressed as:  
\[x_{i}\bigg{\{}\begin{array}{l}\in\mathbf{S_{H}},\text{ if }s_{Fi}\text{ is replaced}\\ =0,\text{ if }s_{Fi}\text{ is not replaced}\end{array} \tag{9}\]  
In the formula, \(i=1,2,\cdots,N_{F}\).
## 2 Concepts and Theory
### Experimental Requirements FCS-MPM  
In order to implement FCS in combination with MPM there are primarily three major technical requirements in addition to equipment enabling laser scanning MPM [2; 22]. The first being the requirement of time-resolved data acquisition. In this paper we focus on TCSPC technology; but there are alternative solutions one being hard-ware correlation. The second is the requirement of high NA objective lenses. As discussed, earlier MPM imaging generally utilized long working distance objective lenses with NA in the range 0.7-1.0; however, for FCS NA >1.0 is preferred. Oil immersion objective lenses are not preferable for FCS measurements for biological applications due to the refractive index mismatch which leads to distortion of the PSF and excitation volume [21; 30]. In this paper we compare two objective lenses in order to elucidate the importance of choice of objective lens, also imposing a constraint to the applicability of the method. The third major requirement is the possibility to eliminate stray light and background signal. Despite seeming an easy task, an optics lab comprises several components with light emitting diodes potentially interfering with the measurements. Thus, meticulous care must be taken to ensure complete removal of all sources of light disturbances.