esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Brightness Preference  
The findings related to brightness preferences are detailed in Figure 5. For electronic reading brightness preference, both 150 lx and 200 lx illumination levels in the 3000 K group were significantly favored over the 50 lx level. In the 4000 K group, the illumination levels of 100 lx, 150 lx, and 200 lx all received significantly higher preference scores compared to 50 lx, indicating a potential preference for higher brightness levels. Similarly, in the 5000 K group, the 150 lx and 200 lx levels were significantly preferred over the 50 lx level. Notably, the preference for the 4000 K setting was significantly greater than that for the 3000 K setting when comparing across groups.  
Regarding brightness preferences for paper reading, the 200 lx setting in the 3000 K group was significantly more preferred than the 50 lx setting. In the 4000 K group, the 150 lx level was significantly favored over the 50 lx level. Within the 5000 K group, both 150 lx and 200 lx levels achieved significantly higher preference scores than the 50 lx level. Furthermore, when comparing across groups, the preference score for the 4000 K setting was significantly higher than that for the 5000 K setting, highlighting a clear overall preference for the 4000 K lighting condition in terms of brightness for paper reading.  
Figure 4: Reading experience preference results: (**a**) digital reading; (**b**) paper reading. Statistical significances \(p<0.05\) marked with *.  
Figure 5: Brightness preference results: (**a**) digital reading; (**b**) paper reading. Statistical significances \(p<0.05\) marked with *.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 6 Conclusions  
LEO satellite mega-constellations are becoming increasingly important for providing high-performance Internet access with global coverage. The position of the LEO satellites is constantly changing, and phased array antennas allow orientation to be adjusted without any physical movement. They also allow multiple satellites to be used simultaneously. Consequently, the operating costs are greatly reduced compared to conventional parabolic antennas, as the space requirements are significantly reduced, and the installation and maintenance costs are also lower [8]. This paper introduces a new workflow for modelling and designing large phased array antennas for LEO satellite communication gateways. Phased array antennas from SpaceX, Telesat, OneWeb, and Amazon were developed using this methodology. Originally, patch antennas were developed for both transmission and reception. Various phased array configurations were then tested to determine the optimal physical layout and number of radiating elements. The transmission antenna was modeled by using a circular distribution, and the reception antenna with a hexagonal distribution. The specific values are summarized in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Transmission**} & \multicolumn{2}{c}{**Reception**} \\ \cline{2-6}  & **EIRP (dBW)** & \begin{tabular}{c} **Number of** \\ **Elements** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity** \\ **(dBm)** \\ \end{tabular} & \
\begin{tabular}{c} **Gain (dB)** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Elements** \\ \end{tabular} \\ \hline SpaceX & 68.4 & 4000 & \(-113.1\) & 46.9 & 8910 \\ \hline Telesat & 75.9 & 9600 & \(-102.5\) & 46.5 & 7956 \\ \hline OneWeb & 63.2 & 2150 & \(-114.1\) & 51.5 & 26,791 \\ \hline Amazon & 62.3 & 1950 & \(-106.9\) & 50.2 & 19,927 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results’ summary.  
Figure 20: Reference parameters used for calculating the area used in reception.  
**Funding:** This work has been partially supported by Grant PID2021-127712 OB-C21 funded by MCIN/AEI/10.13039/501100011033 and, by \"ERDF a way of making Europe\", the Spanish Ministry of Science, Innovation and Universities (RTI2018-099189-B-C22) and by Universidad de Las Palmas de Gran Canaria by the PIFULPGC2020-2 ING-ARQ3 grant.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Proposed Framework
### Process-Mining Model
#### 4.1.1 Process Discovery  
Process discovery entails utilising an event log as an initial data source and generating a model that effectively captures the observed behaviour within the log. The objective of process discovery techniques extends beyond merely constructing models that depict the control flow of activities; they also encompass uncovering additional dimensions, such as revealing the social network connections among the resources involved in executing these activities [53]. The utilisation of process discovery techniques is highly valuable for gaining insights into real-world processes.  
_Process discovery algorithm:_ Let \(I\) denote the set of process discovery algorithms. In a formal sense, a process discovery algorithm can be defined as a function that maps an event log to a process model. Specifically, for each \(i\in I\), we have \(\mathsf{D}_{i}:L\mapsto\mathcal{N}\), where \(L\) represents the set of event logs and \(\mathcal{N}\) represents the resulting process model. The primary objective is to ensure that the discovered process model accurately reflects the observed behaviour within the event log. Figure 6 shows a model of log \(L\) discovered using a simple discovery algorithm, _Alpha Miner_. _Alpha Miner_, one of the first and simplest process  
Figure 5: Proposed framework.  
discovery algorithms, is utilised for its capability to discover a process model that is easier to understand compared with other discovery algorithms, which may generate complex models.
# CT Scanning of Structural Characteristics of Glacial Till in Moxi River Basin, Sichuan Province
## 4 Results
### Meso-Structure Characteristics of Intact Glacial Till
#### 4.1.3 Particle Shape  
The particle shape, as one of its meso-structure characteristics, has a certain impact on the macroscopic mechanical properties of glacial till. Roundness and aspect ratio are commonly used in engineering to quantitatively analyze the shape of particles. The equation for particle roundness and aspect ratio is as follows [27]:  
\[\varphi=\frac{l_{r}}{l}=\frac{\sqrt{4\pi S}}{l}=3.545\frac{S}{l} \tag{1}\]  
In Equation (1): \(\varphi\)--particle circularity; \(l\)--circumference length equal to particle area; \(l\)--particle circumference; \(S\)--particle area.  
According to Equation (1), the circularity of a circle is 1, the circularity of a square is 0.886, the circularity of a rectangle with an aspect ratio of 3 is 0.767, the circularity of a rectangle with an aspect ratio of 10 is 0.51, and the circularity of an equilateral triangle is 0.777.  
\[\alpha=\frac{L}{W}=\frac{L^{2}}{S} \tag{2}\]  
In Equation (2): \(\alpha\)--particle aspect ratio; \(L\)--particle maximum chord length; \(W\)--the short side length of a rectangle with the same area as the particle and the long side \(L\).  
According to Equations (1) and (2), the X-section, Y-section and Z-section CT scanning pictures of the glacial till sample are selected for statistical analysis. The statistical results are shown in Figure 16 and Table 2.  
Figure 16: Particle shape statistics of glacial till samples: (**a**) X-section circularity statistics; (**b**) X-section aspect ratio statistics; (**c**) Y-section circularity statistics; (**d**) Y-section aspect ratio statistics; (**e**) Z-section circularity statistics; (**f**) Z-section aspect ratio statistics.  
Figure 16 and Table 2 show that the average circularity of the particles in the longitudinal and transverse sectional directions of the glacial till sample is 0.836 and 0.802, and the average aspect ratio is 2.5 and 3.7, respectively. It can be seen that the shape of the particles in the glacial till sample is generally an irregular rectangular shape with a side-to-length ratio of 2 to 4; however, there are differences in the shapes of the particles in the longitudinal section and the transverse section, and they are closer to round in the longitudinal section direction.
# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
### Amplitude and Phase Synthesis  
This type of synthesis allows for the realization of directive lobes with \"strongly controllable\" levels of secondary lobes. This technique is effective for applications in adaptive networks; however, it is computationally intensive. We will apply the excitation weights (amplitude and phase) of the antenna array that have been previously optimized. By controlling the sources in both amplitude and phase, the antenna array can produce a radiation pattern with a desired shape, allowing the main lobe to be oriented in the desired direction while minimizing the levels of the secondary lobes [11; 12; 13].  
For this type of synthesis, the array factor is described by the following Equation (4):  
\[AF(\theta,\phi)=2\sum_{n=1}^{\frac{N}{2}}\alpha_{n}\cos(\beta a(\sin(\theta) \cos(\phi-\phi_{n})-\sin(\theta_{0})\cos(\phi_{0}-\phi_{n}))+\phi_{n}) \tag{4}\]  
where \(\alpha_{n}\) represents the amplitude \(\in[0,1]\) and \(\phi_{n}\) represents the phase \(\in[-\pi,\pi]\).  
The array factor for a network of 16 antennas is described by the following Equation (5):  
\[AF(\theta,\phi)=2\sum_{n=1}^{8}\alpha_{n}\cos(\beta a(\sin(\theta)\cos(\phi- \phi_{n})-\sin(\theta_{0})\cos(\phi_{0}-\phi_{n}))+\phi_{n}) \tag{5}\]  
When applying both amplitude and phase synthesis (see Figure 2), we observe a significant decrease in the levels of the secondary lobes in the radiation patterns from \(-8\) dB to \(-28\) dB, with results closely matching the desired radiation [14; 15; 16].  
Figure 1: Electronic-scanning of the space with a secondary lobe level of \(-8\) dB for a circular antenna array of 24 elements.
###### Abstract
The above-mentioned advantages allow the use of only one instrument to determine the final shape of the root canal system in a safe way without excessive risk of intracanal separation [1; 3]. This could not be performed with complete rotation because of the risk of fracture, which is severely reduced with the reciprocation. Despite this, manufacturers suggest the single use of this type of instrument since all mechanical stresses arising from shaping are concentrated on a single instrument and not spread among more instruments in a sequence [3]. Regarding this, the knowledge of the mechanical resistance of reciprocating single-file and their wear resistance is fundamental to obtaining an in-depth comprehension of their mechanical limits, in order to avoid as much as possible their intracanal separation and using them as effective as possible, reducing the waste and discard of several instruments. The most suitable method to analyze the wear resistance of endodontic instruments is through scanning electron microscopy (SEM), which allows the observation at higher magnification in order to detect any surface alteration such as microfractures, metal defects, deformations, blunt cutting edges, disruption of cutting edges, presence of debris, pitting and tip flattening [10; 11]. According to this, the aim of this study was to assess and compare the wear resistance of two recently introduced single-file reciprocating instruments (OneRECI (Coltene, MicroMega, Besancon, France)) and EdgeOne Fire (EdgeEndo, Albuquerque, NM, USA) with two other well-known martensitic single-file reciprocating instruments Reciproc Blue (VDW, Munich, Germany) and WaveOne Gold (Dentsply Maillefer, Ballaiques, Switzerland) through an SEM observation by comparing surface defects before and after the simulated use of the instruments in resin lower molars.
## 3 Science Data Product Performance
### The CrIS/ATMS Instruments and Science Data Products
#### 3.2.1 The NUCAPS CrIS/ATMS EDR Products
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Clear (Cloud \textless{} 50\%)**} & \multicolumn{3}{c}{**Cloudy (Cloud \textgreater{} 50\%)**} \\
**Temperature Profile** & \multicolumn{2}{c}{**Moisture Profile**} & \multicolumn{2}{c}{**Temperature Profile**} & \multicolumn{2}{c}{**Moisture Profile**} \\
**Layer** & **P (R) K** & **Layer** & **P (R) \%** & **Layer** & **P (R) K** & **Layer** & **P (R) \%** \\ \hline
1–30 & 1.5 (1.5) & 100–300 & 22.7 (35) & 30–300 & 2.1 (1.5) & 100–400 & 39.8 (40) \\
30–300 & 1.1 (1.5) & 300–600 & 24.9 (35) & 300–700 & 2.3 (1.5) & 400–600 & 35.6 (40) \\
300–Surf & 1.3 (1.6) & 600–Surf & 22.7 (20) & 700–Surf & 3.0 (2.5) & 600–Surf & (20) \\ \hline \hline \multicolumn{7}{l}{\({}^{*}\) S-NPP NUCAPS temperature and moisture profile performance and requirements (in parenthesis) summary.} \\ \end{tabular}
\end{table} 
Table 10. S-NPP NUCAPS temperature/moisture profile performance and requirements \({}^{*}\). Figure 12. JPSS products captured severe California fire in November 2018: **(a)** VIIRS Active Fire; **(b)** VIIRS AOD; **(c)** CrIS/NUCAPS CO at 500 mb; **(d)** CrIS/NUCAPS CO at 700 mb. (Figures from JSTAR Mapper website [[https://www.star.nesdis.noaa.gov/jpss/mapper](https://www.star.nesdis.noaa.gov/jpss/mapper)]([https://www.star.nesdis.noaa.gov/jpss/mapper](https://www.star.nesdis.noaa.gov/jpss/mapper))).
## 2 Experimental Setup and Procedure  
### Sample Preparation  
For the investigation of the impact of plasma pre-treatment on the adhesiveness of cemented glass components plane samples made of float glass with a diameter of 15 mm were used. In total, 10 samples were prepared in order to realise 5 cemented doublets. The samples were cleaned classically using ethanol and lens-cleaning paper. After such cleaning, the samples were plasma treated as described in more detail in Section 2.2. Subsequently, two samples were cemented to a doublet using a commercial UV-curable optical adhesive photopolymer (Norland Optical Adhesive NOA63, Norland Products Inc.). This adhesive is especially designed for the cementation of optical components made of glass. As usually performed in optics manufacturing, cementing was performed manually at room temperature by applying a drop of cement--with a constant volume for all samples--to the surface of one part and subsequent contacting and pressing of the other part at a constant contact pressure. Final curing of the cement was then carried out by storing the doublets in an UV-oven for 60 min. After the actual cementing procedure, three different kinds of storing were applied as specified in detail in Table 1.  
After storing, the adhesive pull strength (APS) of cemented samples was determined by the direct pull method as defined in [33] using an APS tester (PosiTest AT-A, DeFelsko Corp.). In order to evaluate the impact of plasma pre-treatment, such measurements were performed at samples with and without plasma pre-treatment where the non-treated reference samples were also stored as specified in Table 1.
## 3 ITS Architecture and Security Challenges
The data in ITS meet all the characteristics of Big data, which is a precondition for applying Artificial Intelligence (AI). Its application into security-critical systems such as ITS must be carefully considered, as it is very vulnerable to a number of cyberattacks [17; 18; 19; 8; 15]. Table 1 summarizes the security issues addressed in this document, their respective architectural layers and the possible cyberattacks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture Layer** & **Security Issue** & **Cyberattack** \\ \hline \multirow{3}{*}{Perception layer} & Configuration and initialization of & Denial-of-Service; \\  & the devices during manufacturing; & Spoofing; \\  & Internal vehicular network design; & Internal vehicle network attack; \\ \hline \multirow{3}{*}{Network layer} & \multirow{3}{*}{Anonymous authentication in} & Sybil Attacks; \\  & & Denial-of-Service; \\  & & Man-in-the-Middle; \\ \cline{1-1}  & & Eavesdropping; \\ \cline{1-1}  & & Routing attacks; \\ \cline{1-1}  & & Identity attack; \\ \cline{1-1}  & & Timing attack; \\ \hline Support layer & Fog defense; & Attack against Fog; \\ \hline \multirow{3}{*}{Application layer} & Complicated data model; & Data poisoning; \\  & AI defense. & Environmental Perturbations; \\ \cline{1-1}  & & Policy manipulation. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Intelligent Transportation Systems (ITS) architecture, cybersecurity issues and attacks.
## 4 Simulation Studies
### Deep Neural Network Design Validation  
We evaluated the proposed two ANN (auto-designed DNN and RNN) on driving dataset of ACC. The size of this dataset is 146,565. First, we sampled eight data for the process of auto-design DNN. Figure 7 shows the auto-design process of DNN using NAS. It has eight batch sizes and 5000 epochs with a 0.2 learning rate. The number of neurons including DNN has increased by 14 in early stage, and  
Figure 6: Auto-design process of DNN using NAS.  
it has an accuracy lower than 0.8. However, the number of neurons has decreased by six and it shows that oscillatory increased up to 10. When it has increased to 10, accuracy of this DNN is about 0.9. After 10 neurons, the number of neurons has decreased gradually. On the other hand, accuracy has settled down within about 0.9. The number of neurons has decreased and settled down to four or six.  
The auto-designed DNN architecture shown in Figure 7 has four or six neurons and a fully connected architecture with one hidden layer. Then, we simulated some DNN architecture trained with full dataset to validate this result. Figure 8 shows results of loss rate with respect to the number of neurons. The architecture with five or six neurons shows the minimum loss, meanwhile it has a similar loss rate. The architecture that has lower number of neurons is a much better architecture since the number of neurons affect the calculation speed. Figure 9 shows the loss of auto-designed DNN with five neurons. The average loss rate of this architecture is 0.016811.  
Figure 8: Loss rate with respect to the number of neurons.  
Figure 7: Auto-design process of DNN using NAS.  
Since the human driving dataset has sequential data, we compared auto-designed DNN with RNN. The RNN architecture with a five sequence length is implemented. Figure 10 shows loss of RNN. The average loss rate of this architecture is 0.029620.  
Table 2 presents average loss rate of each of architectures. DNNs with 5 neurons and 6 neurons have almost same loss rate. However, DNN with 5 neurons is optimal architecture because the number of neuron affects calculation speed. And loss rate of RNN is lower than auto-designed DNN, it is regarded as discrete data like sudden brake.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & **DNN with** & **DNN with** & **DNN with** & **DNN with** & **DNN with** & **RNN with 5** \\  & **1 Neuron** & **2 Neurons** & **3 Neurons** & **4 Neurons** & **5 Neurons** & **6 Neurons** & **Sequences** \\ \hline Loss & 0.021684 & 0.019931 & 0.019142 & 0.019627 & 0.016811 & 0.016753 & 0.02962 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average loss rate of each of the architectures.  
Figure 10: Loss of RNN.  
Figure 9: Loss of auto-designed DNN.
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods
### Cybersecurity Vulnerability Evaluation
#### 2.2.3 Resource Management and YARN Access Controls  
The Resource Manager was introduced in Hadoop 2.0 as a component of YARN. It oversees the division of processing load on Data Nodes by using the Node Manager, Application Masters, and JVM container daemons.  
YARN obeys the file permissions settings of the HDFS and uses the identity of the user (by default) to interact with the file system. YARN considers only the files that the user owns to execute the YARN program and does not perform any privileged action. It is possible, however, to specify a different user such that the YARN Resource Loader interacts with HDFS using their rights. When Hadoop is accessed by multiple users, separate instances of the Resource Loader should be created (one per user) instead of assigning additional permissions or groups to one user. This ensures that ACs in the HDFS is applied per user. However, if impersonation is used with a proxy server, the Resource Loader might (and will typically) return restricted files that should not be seen by the user.  
To control permissions to submit jobs to YARN, ACs can be configured in the context of queues and pools for job submission schedule. For example, when Spark users submit their jobs, they are added to the job scheduler after authentication and AC-related decisions. YARN has three types of schedulers: a capacity scheduler (default), first-in-first-out (FIFO), and a fair scheduler. By default, the permissions to submit jobs in the yarn-site.xml configuration file are open (e.g., set to all using an asterisk).  
A more user-friendly and flexible approach to managing job scheduling ACs, rather than configuring XML files, is for the system administrators to use Apache Ranger. There are Apache Ranger YARN plugins that can manage policies on users that are allowed to submit jobs to YARN. These policies enforce rules on who can submit to the YARN queue.  
When the AC of the YARN scheduler is not configured, the cluster can be subject to a wide variety of unauthorized job submissions. Several unauthorized cryptocurrency mining programs have been detected in Hadoop clusters, such as DemonBot. A more destructive threat is ransomware which can destroy a large-scale cluster dataset by encrypting the data and withholding the decryption key [50; 51].
# PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions
## 5 PV-Powered EV Charging Station Simulation Results and Analyses  
This paper presents two case studies for the PV-powered charging stations: PV parking shade for one private charger and PV parking shade for nine chargers at the workplace. The two cases were simulated under the same solar irradiation profile. Regarding the EVs, lithium-ion batteries were considered and it was assumed they have the same battery capacity of 50 kWh, while the driving characteristics and charging profiles covered a daily needed charge of 2-6 kWh, as described in Section 3, but they are not exclusive.  
For all scenarios, the following assumptions were considered:  
* Charging station location is in Compiegne, France, where the yearly average solar irradiation is not very high;
* PV panel is Sunpower SPR X21-345 with 21% efficiency under STC;
* Mounting position is fixed and optimized as follows: slope angle 38\({}^{\circ}\) and azimuth angle \(-2^{\circ}\);
* System loss was estimated at 14% system loss;
* Lead-acid batteries were considered for the stationary storage and its limits were chosen as 20% and 80% for \(SOC_{S,min}\) and \(SOC_{S,max}\) respectively.  
With the objective of determining preliminary requirements and feasibility conditions of PV-powered EV charging stations that may bring some PV benefits, the following subsections present and analyze several scenarios as well as simulation results.