esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Amorphous Oxide Modified Zeolite Composite Catalysts
### Amorphous Silica Decorated Zeolite Composite Catalysts
After careful comparison, SiO\({}_{2}\)-CLD modification is regarded as the preferred method for industrial preparation of the catalysts for the selective toluene disproportionation process, and the zeolite  
Figure 1: **(a)** Scheme of selective toluene disproportionation reaction on amorphous silica decorated zeolite composite catalysts; **(b)** illustration of the external surface active sites of the zeolite modified by polysiloxane PDMS. Reproduced from Reference [77]. Copyright 2007 Elsevier. composites decorated with amorphous silica are among the most suitable catalysts. Recently, we have made detailed studies on the method [77], only to find that by SiO\({}_{2}\)-CLD modification, polysiloxane is first adsorbed on ZSM-5 via Van der Waals force and hydrogen bond interactions (Figure 1b). The deposition of polysiloxane as silica on ZSM-5 is a combined process of the degradation of the acid-catalyzed cracking with the thermal pyrolysis, followed by oxidation at high temperature. SiO\({}_{2}\)-CLD modification mainly occurs on the external surface of zeolite, and does not destroy the framework structure of ZSM-5. Besides, most of the acidic framework bridging hydroxyls and non-framework hydroxyls remain after SiO\({}_{2}\)-CLD modification, while the silanol hydroxyls of the zeolite almost disappear. On the other hand, the amount of acid of the modified ZSM-5 is less than that of the parent ZSM-5, and it drops gradually when the content of modified SiO\({}_{2}\) increases, but the acidic strength distribution of ZSM-5 is unchanged during modification. After four-cycle modification, the catalytic activity of external surface is reduced by almost 92%, making the external surface of the modified almost inactive, while the acidity and activity of the inner pores of the zeolite composite catalyst remain at almost the same levels as those of the parent ZSM-5. Therefore, SiO\({}_{2}\)-CLD with polysiloxane is an ideal modification for obtaining the high shape-selective ZSM-5 catalyst. In 2007, we successfully developed a new amorphous silica decorated zeolite composite catalyst which we called SD-01 [78,79]. This catalyst has been successfully applied in pilot plants by the Tianjing and Yangzi petrochemical corporations. It should be noted that small ZSM-5 crystallite particles (less than 200 nm) are used here for preparation of this catalyst. Although it is more difficult to modify the external surface by polysiloxane on small crystallites than on large ones, this catalyst has lower benzene/xylene ratio (less than 1.4) than the catalysts made from large zeolite crystallites (above 1.4), because it has shorter diffusion path of small crystals and leads to lower decomposition of xylene to benzene.
# Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations
## 3 Proposed Method
### Data Aggregation and FIR Smoothing  
Gaze data representing one user watching one video is stored. We denote with:  
* \(i\in[1...N_{subjects}]\) the subject \(i\), where \(N_{subjects}\) is the total number of participants;
* \(j\in[1...N_{ideos}]\) the \(j\)-th video, where \(N_{ideos}\) is the total number of different videos;
* \(k\in[1...N_{session}]\) the \(k\)-th session, where \(N_{session}\) is the total number of session performed by the subjects.  
First of all, for each \(i,j,k\), the extracted \(N\) gaze tracks are concatenated in one vector \(S(i,j,k)\) in the following way:  
\[S(i,j,k)=[x(1),\ldots,x(N),y(1),\ldots,y(N),z(1),\ldots,z(N)] \tag{4}\]  
If the gaze in a frame \(n\) is not detected, then the triplet \((x(n),y(n),z(n))\) takes values \((0,0,0)\). Since videos have different lengths, zero padding is used in all videos (except the longest one) to force all the vectors having the same length. The vector \(S(i,j,k)\) is created for each video, subject and (eventually) session, obtaining a sparse matrix \(D\) of size \((N_{subjects}\cdot N_{viideos}\cdot N_{session})\times 3N\). In the case of input data not divided in different sessions (see Section 4), then simply \(N_{session}=1\). The last part of this processing step aims at filtering data using a Savitzky-Golay Finite Impulse Response (FIR) filter to perform smoothing [53]. In particular, as detailed by [PERSON] in [54], a polynomial function of the N-th degree, \(p(n)\), is fit on the data by minimizing the sum of squared residual with the original sequence of samples, \(x[n]\), on a window of size \(2M+1\). Therefore, a set of \(N+1\) polynomial coefficients \(a_{k}\) is found for every sample in \(x\) using Equations (5). The idea behind this process is to perform least-squares polynomial smoothing to \(p(n)\) with a set of coefficients that minimize the mean-square approximation error \(\epsilon_{n}\), defined as in Equation (6), for the group of input samples centered at \(n=0\).  
\[p(n)=\sum_{k=0}^{N}a_{k}n^{k} \tag{5}\]  
\[\epsilon_{n}=\sum_{n=-M}^{M}(p(n)-x[n])^{2}=\sum_{n=-M}^{M}(\sum_{k=0}^{N}a_{ k}n^{k}-x[n])^{2} \tag{6}\]
## 3 Results and Discussion
### Device Characterization  
Figure 10 left, illustrates the main structure of the PSC device and on the right is a photograph of the PSC fabricated in this work. The current density-voltage (J-V) characteristics curves of the PSCs were obtained by employing an ABET Sun3000 sunlight simulator set at AM 1.5 G (100 mW/cm\({}^{2}\)). The overall sample size was 2.5 \(\times\) 2.5 cm\({}^{2}\).  
The different layers of the PSC are illustrated in Figure 11. It shows a detailed schematic structure of a typical device composed of FTO/C-TiO\({}_{2}\)/M-TiO\({}_{2}\) doped with FK209 as the ETL/Perovskite CsI\({}_{0.05}\)[(FAPbI\({}_{3}\))\({}_{0.85}\) (MAPbBr\({}_{3}\))\({}_{0.15}\)J\({}_{0.95}\)/Spiro-MeOTAD as the HTL and an Au back electrode. Figure 11 illustrates an SEM of a cross-section of the complete perovskite solar cell. The optimized perovskite solar cell comprises of a 70-nanometer thick C-TiO\({}_{2}\) layer and a 150-nanometer thick M-TiO\({}_{2}\) doped with FK209 cobalt film. The  
Figure 8: Photoluminescence spectra of perovskite films deposited on M-TiO\({}_{2}\) doped with FK209, FK209/Li\({}^{+}\) and lithium.  
Figure 9: Photoluminescence decay time of perovskite deposited on M-TiO\({}_{2}\) doped with FK209, FK209/lithium and lithium and measured using fluorescence PL decay lifetime.  
active perovskite layer thickness was 350 nm, followed by a 180-nanometer thick spiro-MeOTAD, and finally a 70 nm of the electron beam evaporated gold as the backside electrodes for the complete solar cell device.
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 6 Data Analysis and Interpretation
While CCA is used to identify correlated patterns between two sets, PCA focuses on capturing the overall variance within a single set [77; 81]. When dimensionality reduction is required, PCA is typically preferred over CCA [93]. However, both techniques can be employed for feature extraction. Moreover, to differentiate between healthy and color-deficient individuals, frequency features are usually used. After recording the EEG signal, frequency analysis, such as Fourier transform or fast Fourier transform (FFT), is applied to identify the presence of frequency components corresponding to the stimuli [77]. Using machine learning algorithms makes it possible to automatically extract meaningful patterns and features from EEG signals, aiding in the classification process as they can handle the complex nature of EEG data [94]. For example, the SVM algorithm has been used in [79] as it aims to find a hyperplane that separates data points of different classes with the largest margin [95]. It is capable of capturing complex relationships and making accurate predictions. Another classifier, such as the KNN classifier, has been employed in some studies [79; 82] as it is simple, does not require explicit model training, and can handle \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Filter** & **Fc** \\ \hline
[77] & BPF & 0.3–40 Hz \\ \hline
[81] & BPF & 0.5–20 Hz \\ \hline
[85] & Online BPF & 2–100 Hz \\ \cline{2-3}  & Offline notch filter & 48–52 Hz \\ \hline
[82] & LPF & 40 Hz \\ \cline{2-3}  & HPF & 1.5 Hz \\ \hline
[87] & BPF: 4 th order & 3–45 Hz \\ \cline{2-3}  & Notch filter & 60 Hz \\ \hline
[88] & BPF: 12 th order & 5–55 Hz \\ \hline
[89] & BPF: 12 th order & 4–85 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Filter parameters in existing studies. both numerical and categorical features [96]. Overall, most studies, such as [77; 80; 87], have used MATLAB for analysis and classification purposes.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Conceptual Design
This rotator is installed on an antenna tower at least 6 m high and on a tall building to reduce obstructions from surrounding structures. It can track LEO satellites from low elevation angles and receive signals from airborne groups at greater distances. Additionally, there is an omni-directional antenna installed on the tower, designed as an ultra-wideband omni-directional antenna. This antenna is used to track signals from terrestrial sources and satellites without an accurate two-line element set (TLE), and for multiple applications, such as receiving ADS-B signals from airplanes. 3. SDR Server: This server acts as a collection point for raw data received from the antennas and controls the rotator's operation through remote commands from users. 4. Classroom: This is a learning space used alongside the ground station, connected to the SDR server to receive satellite data and control signal reception, such as center frequency and bandwidth, as well as the rotator. Besides the classroom, the system is also designed to allow online users to connect to the SDR server and receive signals via a web browser. For the detailed design of each antenna connected to the SDR server, as shown in Figure 6, the numbers correspond to the numbers in Table 4. Each antenna is equipped with a surge protection system (Figure 6, item 20) to guard against lightning strikes before entering a filter to select the desired frequency. For UHF, a bandpass filter (BPF) in the 400-438 MHz range is used (Figure 6, item 2); for VHF, a BPF in the 136-150 MHz range is used (Figure 6, item 5); the wideband grid antenna uses a high-pass filter (HPF) at 600 MHz for LDPA (Figure 6, item 11); the dish antenna uses a BPF that is a specifically designed SAW filter at a center frequency of 1688 MHz (Figure 6, item 16); and for the discone antenna, no filter is installed because it is designed to receive signals over a wide frequency range. Figure 5: Conceptual design of satellite ground station. The signal then enters the low-noise amplifier (LNA), which amplifies the signal, with a bias-T providing power to the LNA. This entire system is installed closest to the feed or antenna to ensure that the amplified signal has the least noise possible. The data is then transmitted through an LMR-400 cable (Figure 6, item 24), which is designed to be as short as possible, to the software-defined radio (SDR) devices (Figure 6, item 19) for data collection on the SDR server. For connecting all devices to the computer, USB isolation is used to reduce noise and protect the equipment from electrical shorts, enhancing the stability of the connection. The SDR server system is designed to receive radio frequencies from all antennas simultaneously, but it still has limitations with antenna orientation on the two rotator units (Figure 6, items 12 and 17). This setup allows a maximum of one to two satellites to be tracked at the same time.
## Results and Discussion
### Micelle Preparation and Characterization  
As described in the experimental section (Figure 2), micelles were prepared by incorporating PtTFPP into an amphiphilic block copolymer and injecting into distilled water under high-speed stirring. The tetrahydrofuran (THF) was removed by dialysis. The particle sizes of the micelles were measured by DLS with average diameters of about 40.0-203.8 nm (Figure 3), which depends on the polymers used. The more hydrophobic segments of the polymer, the larger the size of the micelles. By observing the stability of the micelles (Figure S1), the micelles were found to be stable at 4 \({}^{\circ}\)C for at  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Product-**} & \multirow{2}{*}{**Molar Ratio \({}^{a}\)**} & \multirow{2}{*}{**Yield**} & \multicolumn{3}{c}{**GPC**} & \multicolumn{3}{c}{\({}^{1}\)**H-NMR**} & \multirow{2}{*}{**Mass ratio \({}^{b}\)**} & \multirow{2}{*}{**CMC** (\(\upmu\)**g/mL)} \\ \cline{5-6}  & & & **M\({}_{\mathbf{a}}\)** & **M\({}_{\mathbf{w}}\)** & & & & \\  & & & **(g/mol)** & **(g/mol)** & **PDI** & **(g/mol)** & & \\ \hline P1 & 1:224 & 72.4\% & 13610 & 17800 & 1.31 & 33300 & 1:2.33 & 3.10 \\ P2 & 1:112 & 45.6\% & 11940 & 12680 & 1.27 & 18700 & 1:0.87 & 2.49 \\ P3 & 1:56 & 44.0\% & 8970 & 9960 & 1.11 & 13600 & 1:0.36 & 3.31 \\ \hline \hline \end{tabular}  
* \({}^{a}\) Feed molar ratio of 4-arm poly(ethylene glycol) (PEG) to \(\varepsilon\)-caprolactone (CL). \({}^{b}\) Molar ratio of hydrophilic chain to hydrophobic chain determined by \({}^{1}\)H NMR.  
\end{table}
Table 1: Materials’ properties of **P1**, **P2**, and **P3**. GPC: gel permeation chromatography.  
least 24 days with little change in the sizes or physical properties. The concentration of PtTFPP in the micelles was determined by lyophilizing micelles, re-dissolving in tetrahydrofuran, measuring the absorption spectrum of PtTFPP, and calculating from the standard curve of PtTFPP in THF. The detailed polymer concentration and PtTFPP concentration in the micelles were provided in Table 2.  
For amphiphilic polymers, CMC is an important concern. The CMCs of the polymers **P1**-**P3** were measured to be from 2.5 to 3.3 \(\upmu\)g/mL (Figure S2). These CMCs are compatible with other amphiphilic block copolymers [46; 47]. These micelles containing PtTFPP have high storage stability. At 37 \({}^{\circ}\)C, at least 95% of the PtTFPP was still in the micelles after five days of dialysis.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 3 SON Applications
### The Key SON Applications Leading to Full Automation
#### 3.2.2 SON--ANR  
Possibly the most common self-optimization application is ANR (Automatic Neighbor Relations), which replaces the manual process of neighbor cell configuration and handover optimization previously described by creating automatically and in real time the most optimal neighbor relation list for a cell operating in either technology (2G/3G/4G/5G) and in relation to either technology as well. The application runs both for operational and new cells added into the network and it is a key use case for B5G development since recent publications analyze and propose hybrid solutions for ANR implementation in 5G networks [64; 72].  
SON ANR applications are mainly used to create intra-frequency (same technology and same frequency) and inter-frequency (same technology and different frequency) neigh  
Figure 7: Self-configuration and self-optimization process.  
bor lists since these are of high importance; however, Inter-RAT (Radio Access Technology, different technology and different frequency) relations can be optimized too. ANR applications act by deleting existing redundant neighbor relations that either are not used or exist with cells out of the coverage area and they add important neighbor relations according to the status of the network at a given time. This means that even if a neighbor relation is deleted because it has low usability rates, the same one might be added again after a certain period since subscribers moving around the coverage area of a cell might need this relation again so that handovers in either technology are successful. All cells have a maximum allowed number of neighbor relations that can be set and ANR applications are initially tuned during the initial SON system tuning process to keep the specific rule according to the policy that each network operator demands. The ANR operation is repeated multiple times until the neighbor list is perfectly optimal according to real-time metrics and according to the subscriber's mobility and distribution.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 3 Methods
### Score-Analysis
#### 3.5.2 Gaussian Mixture Model  
To handle disjoint score distributions, a method based on a Gaussian Mixture Model (GMM) [19] is employed. Commonly used in classification and clustering problems, GMMs are probabilistic models that assume data is generated from a finite mixture of Gaussian distributions with unknown parameters, a reasonable assumption in most scenarios [34].  
Score distributions are modeled as mixtures of two Gaussian curves. Labeling each score becomes a classification problem among two classes \(C_{1}\) and \(C_{2}\), representing abnormality and normality, respectively. The problem is defined as uncovering the value of \(P(C_{1},C_{2}|s)\) for each score value \(s\), which can be obtained by [PERSON] rule  
\[P(C_{i}|s)=\frac{P(s|C_{i})P(C_{i})}{P(s)},\quad i\in 1,2, \tag{9}\]  
where \(P(s|C_{i})\) is the likelihood of score \(s\) belonging to class \(C_{i}\), \(P(C_{i})\) the priors for each class and \(P(s)\) the evidence. The threshold is defined as the boundary that better separates both curves, which describes the point of maximum uncertainty. Evidence \(P(s)\) for each score is calculated according to  
\[P(s)=P(s|C_{1})P(C_{1})+P(s|C_{2})P(C_{2}). \tag{10}\]  
Combining Equations (9) and (10) leads to the conclusion that for a score \(s\) be classified as anomalous, it must hold inequality  
\[P(s|C_{1})P(C_{1})>P(s|C_{2})P(C_{2}). \tag{11}\]  
Such is known as the [PERSON] classification rule which provides the desired boundary.  
The GMM is defined as the sum of the two Gaussian distributions, i.e., \(\alpha_{1}\mathcal{N}(Y|\mu_{1},\sigma_{1}^{2})+\alpha_{2}\mathcal{N}(Y|\mu_{ 2},\sigma_{2}^{2})\). An Expectation-Maximization algorithm [39] is used to determine the values of parameters \(\alpha_{i}\), \(\mu_{i}\) and \(\sigma_{i}^{2}\). In the current study, GMM is employed with the aid of the available R package nclust[40].  
The GMM strategy can handle discontinued score distributions, however, it assumes the existence of an outlier cluster which may not always be appropriate. Thus, both [PERSON]'s and GMM strategies should be contemplated.
# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
## 4 Literature on LMD Vehicle Routing
### Sidekicks
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Problem Nature** & **Important Considerations** & **Objective(s)** & **Sample Size** & **Solution Method** \\ \hline \multirow{4}{*}{[38]} & & - & Grouping the customers, & & & & \\  & & & then using a drone to & & & & \\  & & & reach them & & & & \\  & & - & Trucks as conveyors of & Minimize cost of & & & \\  & & & drones & distance traveled and & - & 100 or 500 customers & \\  & & - & Drones’ capacity affects & waiting time of the & - & 10-100 drones & \\  & & - & customer grouping & main vehicle (AV) & - & 8 delivery scenarios & Heuristic (greedy \\  & & & - & Drones perform limited & & & \\  & & travel based on their & & & & \\  & & & charge & & & \\ \hline \multirow{4}{*}{[39]} & & & - & Main vehicle can deliver & & & \\  & & & products & & & \\  & & - & Main vehicles and & & & & \\  & & & sidekicks should join & Minimize the total & - & 8 customer nodes & Mixed-integer \\  & & & before returning to the & truck arrival time & - & 1–2 trucks & problem (MIP) \\  & & & depot & & & \\  & & - & Two types of sidekick & & & & \\  & & & evaluated & & & \\ \hline \multirow{4}{*}{[50]} & & & - & Customers clustered to & & & \\  & & & intermediate depots & & & \\  & & - & Two-echelon VRP: a van & & & & \\  & & & as main and drone as & & & \\  & & & sidekicks & & & \\  & & - & Different vans can go to & & & \\  & & & each intermediate depot & & & \\  & & & - & Sidekicks should join vans & & & \\  & & & to come back to depot & & & \\ \hline \multirow{4}{*}{[112]} & & & - & Main vehicles and & & & \\  & & & sidekicks should join & & & \\  & & & before returning to the & & & \\  & & & depot & & & \\  & & - & Sidekicks can join the & & & \\  & & & maintain vehicle in different & & & \\  & & & intermediate depots & & & \\  & & & intermediate depots & & & \\  & & & & intermediate depots & & \\ \hline \multirow{4}{*}{[113]} & & & - & Main vehicles and & & & \\  & & & - & Main vehicles and & & \\  & & & sidekicks come back & & & \\  & & & independently. & & & \\  & & - & Both trucks and sidekicks & & & \\  & & & can deliver parcels & & & \\  & & - & Sidekicks should meet a & & & \\  & & & truck for each delivery & & & \\  & & & and can ride them in & return time & & \\  & & & some parts of the path & & & \\  & & & Intermediate depots can & & & \\  & & & be met many times by & & \\  & & & trucks or sidekicks & & & \\  & & - & Minimized the latest & & & \\  & & & return to the depot & & & \\ \hline \multirow{4}{*}{[115]} & & & - & There are limited main & & & \\  & & vehicles and sidekicks & & & \\  & & - & Each intermediate depot & & & \\  & & & is considered as a & & \\  & & & micro-hub & & & \\  & & & (volume) and & & \\  & & drones, as well as battery & & & \\  & & life limitations for drones, & & & \\  & & are considered & & & \\  & & - & Each drone joins back to & & \\  & & the van at the same & & & \\  & & location after delivery & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Literature on delivery using sidekicks.