esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sensitivity Analysis  
The PBL schemes implemented in the WRF model are divided into two categories, local and non-local schemes. Since their performance depends on the geographical area under study and the prevailing atmospheric conditions, it cannot be identified with certainty that a given PBL scheme fits better than others for every geographical region in a general sense [76]. In this study, five different PBL schemes were tested to assess which one allows the best simulation of the wind field over the whole of Iran. The five PBL schemes tested here were: (i) Yonsei University (YSU) [77; 78], (ii) Mellor, Yamada and Janjic (MYJ) [79; 80], (iii) Mellor-Yamada-Nakanishi-Nino (MYNN2.5) [81], (iv) Quasi-Normal Scale Elimination (QNSE) [82], and (v) Asymmetric Convective Model Version 2 (ACM2) [83].The PBL schemes aim to capture the exchange of heat, moisture, and momentum between the surface and the atmosphere. Below, a brief overview of the five PBL schemes and their differences is given:  
1. The YSU scheme is widely utilized for its ability to handle a diverse range of atmospheric conditions. It employs a non-local closure approach and incorporates both local and non-local mixing processes within the boundary layer. The YSU scheme's notable feature is its use of a prognostic equation for turbulent kinetic energy. 2. The MYJ scheme combines elements of local and non-local closures and adopts an eddy diffusivity approach to represent turbulent mixing. This scheme also includes a counter-gradient term to account for buoyancy effects. 3. The MYNN2.5 scheme is an advanced extension of the MYJ scheme, aiming to enhance the representation of the vertical structure of the boundary layer. It introduces additional prognostic equations for turbulent kinetic energy and incorporates a higher-order closure for sub-grid-scale turbulence. 4. QNSE Scheme: In contrast to the previous schemes, the QNSE scheme adopts a different approach based on quasi-normal scale elimination. It explicitly solves equations for turbulent kinetic energy and its dissipation rate, allowing for a more precise representation of boundary layer processes. The QNSE scheme effectively captures the effects of both local and non-local turbulent mixing. 5. ACM2 Scheme: Designed specifically for weather and climate prediction models, the ACM2 scheme focuses on representing convective processes within the boundary layer. It employs a multi-plume approach to simulate convective updrafts and downdrafts, enabling a more realistic representation of convective phenomena.
# Narrowband-Internet of Things Device-to-Device Simulation: An Open-Sourced Framework
###### Abstract  
Insurrowband-Internet of Things (NB-IoT) displays high-quality connectivity underpinned by fifth-generation (5G) networks to cover a wide array of IoT applications. The devices' development and integration into different smart systems require permanent control, supervision, and the study of power consumption models to determine the performance of the network topology and allow for the measurement of the efficiency of the network topology's application. This paper reports on an architecture and open-sourced simulation that was developed to study NB-IoT in Device-to-Device (D2D) mode, which includes the Physical (PHY), network, and application layers, as well as a queuing model, the model for uplink and downlink delays, the throughput, the overall NB-IoT D2D network performance, and the energy consumption based on the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Our results prove that the suggested framework contributes to a reduction in power consumption, a minimization of queuing delays, a decrease in communication cost, a reduction in inter-cluster collisions, and the prevention of attacks from malicious nodes. Consequently, the framework manages the battery's State of Charge (SOC), improves the battery's State of Health (SOH), and maximizes the whole network lifetime. The proposed framework, the code of which has been open-sourced, can be effectively used for scientific research and development purposes to evaluate different parameters and improve the planning of NB-IoT networks.  
narrow band; Internet of Things; simulation; simulator; path loss; energy consumption; power profile; adaptive clustering hierarchy; channel scheduling; device-to-device (D2D) communication 202121824 [[https://doi.org/10.3390/s21051824](https://doi.org/10.3390/s21051824)]([https://doi.org/10.3390/s21051824](https://doi.org/10.3390/s21051824))  
1
# Architectural Design for Digital Twin Networks
## 6 Tackling Challenges in DTN Implementation  
As discussed in Section 3.3, many of the challenges faced by DTNs are not unique to them but are interconnected and often amplify one another. Several of these challenges, as outlined in Table 2, have already been addressed to some extent within the proposed architecture. For instance, the incorporation of interfaces and mapping concepts significantly enhances Interoperability, facilitating communication between different DTNs.  
However, while improvements have been made, challenges such as Interoperability and others remain unresolved in certain areas and require targeted strategies to address them effectively. The following subsections will detail specific strategic approaches to mitigate these challenges and enhance the functionality and reliability of DTNs.
## 3 Kinematic Analysis
### Principle of Pose Calculation
#### 3.1.2 Rotation Matrix and Euler Angles  
The Euler angles (\(Z\)-\(Y\)-\(X\)) in robotics are adopted to describe the attitude of the shield machine relative to the coordinate frame \(\{A\}\). Thus, the rotation matrix can be written as Equation (2) [25; 26]:  
\[\tensor{A}{B}\mathbf{R}_{3\times 3}=\mathbf{R}\mathbf{P}\mathbf{Y}(\phi,\theta,\psi)=\left[ \begin{array}{cc}c\phi c\phi c\phi\phi\phi\phi-s\phi c\psi&c\phi s\theta c\psi +s\phi s\psi\\ s\phi c\theta&s\phi s\theta s\psi+c\phi c\psi&s\phi s\theta c\psi-c\phi s\psi\\ -s\theta&c\phi s\psi&c\theta c\psi\end{array}\right] \tag{2}\]  
where \(\psi\), \(\theta\), \(\phi\) denotes roll angle, pitch angle, and yaw angle of the shield machine, respectively; c and s denote cosine and sine, respectively.  
An arbitrary vector, \(\tensor{B}{h}=\left[\begin{array}{cc}x_{h}&y_{h}&z_{h}&0\end{array}\right]^ {T}\), in coordinate frame \(\{B\}\) can be represented as \(\tensor{A}{h}=\tensor{B}{T}\tensor{B}{h}\) in coordinate frame \(\{A\}\).  
Therefore, any pose of the shield machine can be described by a matrix, \(\tensor{A}{B}\mathbf{T}\), containing six unknown pose variables,(\(x\), \(y\), \(z\), \(\psi\), \(\theta\), \(\phi\)).  
Figure 1: Schematic diagram of driving mechanism and the layout of coordinate systems [24]. 1: cutter head; 2: backplane of the middle shield; 3: front hinge of the \(i\)th cylinder; 4: cylinder sleeve; 5: flange plate attached to the shield; 6: cylinder rod; 7: rear hinge of the \(i\)th cylinder; 8: segment ring; 9: shield shell.
## 3 Overview of Short-Range and Long-Range Communication
### NR-V2X Use Cases  
The success of 5G NR, in practical terms, is largely dependent on its ability to meet the demands of specific services and advanced use cases. The main goal of the NR-V2X standard is to support use cases with ultra-high reliability, ultra-low latency, precise positioning, and high throughput requirements that may not be achievable by LTE-V2X. For this, both short-range direct and long-range NR-V2X can be used depending on the use case. While LTE-V2X is focused on basic safety services, NR-V2X can be used for advanced safety services and cooperative and connected autonomous driving. Table 2 shows the use case requirements including reliability, latency and range for various use cases as in 3 GPP Technical Specification 22.186 [19].
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 3 Elements of Frequency Selective Surfaces
### Class 1: N-Pole or Center-Connected Elements  
The first group is represented by an array of tri-poles, where the elements consist of three concentric, thin monopoles that share a common center point. The tri-poles can be packed closely to reduce the spacing between elements, which enhances their bandwidth. The group also contains the Jerusalem cross, which is the oldest building block of FSSs. The Jerusalem cross comprises two crossing dipoles, which have small orthogonal sections at the ends. Also, the Jerusalem cross has superior stability as compared to the tri-pole; as in tri-poles, the angle of incidence changes both the bandwidth and harmonics [111].
## 3 Results and Discussion
### Adsorption Thermodynamics
The Gibbs free energy, RS, and WW demonstrated positive \(\Delta\)G\({}^{\circ}\) values at the adsorption temperatures of 20-40 \({}^{\circ}\)C with 0 \(<\) RS \(<\) WW. Thus, these biosorbents are not favourable for MB adsorption. Despite CH having a positive \(\Delta\)G\({}^{\circ}\) value at the adsorption temperatures of 20-30 \({}^{\circ}\)C, it has relatively high MB removal (60-65 wt.%). Additionally, the \(\Delta\)G\({}^{\circ}\) values for CH decreased with following adsorption temperature order: T40 \({}^{\circ}\)C \(<\) 0 \(<\) T30 \({}^{\circ}\)C \(<\) T20 \({}^{\circ}\)C, which indicates that a higher temperature is favourable for MB removal with CH due to the nature of endothermic adsorption. Conversely, negative \(\Delta\)G\({}^{\circ}\) values were obtained for LD \(<\) HH \(<\) 0. This indicates that MB removal could be a thermodynamically favourable process as a result of the spontaneous nature of the adsorption for these biosorbents [7, 66]. This also explains why LD, HH, and CH have higher MB adsorption capacities compared to RS and WW (Table 2). Furthermore, the decreasing \(\Delta\)G\({}^{\circ}\) for HH and CH with increasing adsorption temperature suggests that the adsorption process not only increases in spontaneity at higher temperatures [66] but also becomes more favourable with increasing temperature [7].
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 1 Introduction  
Volume reconstruction from serial sections of biological tissue [1; 2] has attracted considerable attention from the neuroscientific community in recent years. However, due to distortions caused by sectioning, microscopic image registration, which aims to recover the 3D continuity of serial sections, is a key problem.  
Several 3D registration methods [3; 4; 5; 6; 7] have been proposed for serial section images; however, because reliable correspondences could be extracted only from adjacent section images, these methods always select one of the section images as a reference and then perform forward or backward image registration sequentially for each pair of neighboring images. While these sequential methods alleviate the difficulty of 3D registration, they introduce error accumulation and propagation. Moreover, they all have high time consumption, making them inappropriate for simultaneously registering large numbers of section images.  
The correct positions of the first and last section images can be determined by taking photos of the top and bottom surfaces of the samples before sectioning. Assuming that the positions of the firstand last section images have been correctly adjusted, if the optimal transformations of the remaining section images could be simultaneously estimated, no error would accumulate or propagate. Therefore, it is reasonable to consider that simultaneous registration for serial section images can be achieved under the condition that the positions of the first and last section images are fixed. This constrained simultaneous registration problem is completely new and has not previously been solved.  
To address the above problem, we propose a novel noniterative method that simultaneously estimates the optimal rigid transformations for serial section images while maintaining the positions of the first and last section images unchanged. We prove that our algorithm can obtain an optimal solution under ideal conditions. In addition, even under nonideal situations, we experimentally show that our approach remains strongly robust to noise. Finally, our method outperforms the state-of-the-art methods that are widely used in modern volume reconstruction tasks with regard to both speed and accuracy.
## 3 Results and Discussion
### Properties of Composite Portland Cements (CPCs) Manufactured by 20% Slag
#### 3.2.4 Effect of Blaine  
The higher grinding times that were required in case of CPCs manufacturing compared to OPC for Blaine 4500 cm\({}^{2}\)/g, Table 6, indicate higher energy requirements. In view of economic and environmental considerations, grinding targeting to lower Blaine level was indicatively examined. For this purpose, two CPCs with 20% slag were selectively prepared at 3650 cm\({}^{2}\)/g blaine using slags 4-2a and 5 having the lowest and highest iron oxide content, respectively. According to the obtained results (Table 9, Figure 9), although the compressive strengths of the produced cements are lower than the corresponding ones at 4500 cm\({}^{2}\)/g, as expected, they still fall inside the EN-197-1 [17] cement specifications. In fact, cement with 4-2a slag conforms to the limits of the high strength cement category 52.5 N even at the lower Blaine. These results point out that even at lower Blaine the produced slag could be used up to 20% in cement mass replacing clinker. The reduced grinding time employed in case of the lower compared to the higher Blaine, corresponds to reduced manufacturing energy requirements. In case of commercial use, cement Blaine and thus grinding time depends on the requirements of the construction application for which it is intended, namely the strength specs. Nevertheless, we should not forget that despite the additional grinding time required for the preparation of composite cements, the economic benefit to the cement producer is still significant, due to the substitution of 21% of the mass of clinker by slag, which is an energy upgraded material and does not need to go through the high energy demanding firing stage in the rotary kiln during the production of cement.  
\begin{table}
\begin{tabular}{c c} \hline
**Slag** & **Expansion (mm)** \\ \hline – & 0.0 \\
3-1a & 0.5 \\
3-2a & 0.5 \\
4-1a & 0.5 \\
4-2a & 0.5 \\
5a & 0.5 \\
3-1b & 0.5 \\
3-2b & 0.5 \\
4-1b & 1.5 \\
4-2b & 1.5 \\ \hline \end{tabular}
\end{table}
Table 8: Expansion of manufactured CPCs (20% slag).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Slag** & **Grinding Time** & \multicolumn{4}{c}{**Compressive Strength (MPa)**} \\ \hline  & & **1 Day** & **2 Days** & **7 Days** & **28 Days** \\ \hline – & 55 & 15.60 & 23.80 & 37.10 & 53.00 \\
4-2a & 69 & 16.50 & 24.00 & 36.80 & 53.75 \\
5a & 60 & 13.80 & 21.50 & 32.00 & 46.30 \\ \hline \end{tabular}
\end{table}
Table 9: Compressive strength and grinding time of manufactured CPCs (20% slag) at Blaine 3650 cm\({}^{2}\)/g, w/c: 0.5.
# An Initial Case Study of the Computational Modeling of Cord-Rubber Segments
## 2 Materials and Methods  
### Theoretical Formulations  
#### 2.1.1 Cord-Rubber Segment Description  
The cord-rubber segment studied in this paper is part of a flexible coupling used in railway vehicles (Figure 1). Couplings, as parts of the drivetrain in railway vehicles, allow, in addition to torque transmission, mutual angular rotation and mutual movement between the input and output shafts they connect in the axial and radial directions. Couplings also reduce the transmission of vibrations caused by traveling over bumps or shocks during rapid changes in torque.  
Based on the requirements for size, built-in dimensions and the amount of displacement, gear, lamellar and flexible couplings (with metal-rubber or cord-rubber segments) are most often used in technical practice.  
Figure 1: The cord–rubber flexible coupling used in railway vehicles and its components: (**a**) entire cord–rubber flexible coupling; (**b**) cord–rubber segment; (**c**) cord bundle.  
The cord-rubber flexible coupling consists of cord-rubber segments composed of steel bashing on which a textile cord is coiled. Rubber is then vulcanized on the segments. The transmission of the torque is mainly provided by the textile cord, loaded in tension. Rubber has a protective function primarily in terms of mechanical and chemical damage. It transfers the load between individual cords and serves as a damping element. The dimensions of the cord-rubber segment investigated in this study are shown in Figure 2.  
The materials of the individual cord-rubber segment components and the cord parameters are summarized in Tables 1 and 2, respectively.
# Longitudinal Jitter Analysis of a Linear Accelerator Electron Gun
## 2 Longitudinal Jitter Measurements  
After emission from the gun, electrons were bunched by SHB1, SHB2 and the buncher. The current at the exit of the gun and A0 accelerator was measured by two Beam Current Transformers (BCTs); 1 st BCT and 2 nd BCT, which were displayed on oscilloscope at the control room. Therefore, we could measure the beam current and the interval between the produced bunches. Figure 2 is a schematic diagram of the beam current and timing measurement scheme in the pre-injector.  
EVG and EVR are event generator and event receiver of timing system when a single beam was bunched and measured by a 6 G bandwidth oscilloscope as displayed in Figure 3. The measurement system could measure a bunch length of \(\sim\)10 ps level [9]. The spot on the profile monitor produced by the analysis magnet was captured, as shown in Figure 4. The profile is taken under conditions at a single beam with 10 ps beam width (which is the best bunching efficiency). Then the time interval between BCT1 and BCT2 was observed and measured in Figure 5, which was about 21.5 ns with tolerance less than 100 ps. The calibration ratio of BCT1 was 10.95, while BCT2 was 9.87. The signal pulse height of the BCT1 was less than that of the BCT2 in the measurement with an oscilloscope, but their actual beam currents were about 10 A and 9 A, respectively, therefore, the beam bunch efficiency was about 90% [10].  
Figure 2: Beam current and timing measurement scheme in the pre-injector.  
If instability exists in the pre-injector cells due to gun trigger timing jitter [11], the injection phases of the beam to the SHBs are not suitable, and the signal pulse height of the BCT2 is substantially decreased compared to the normal situation. Figure 6 is one of the jitter situations which describes BCT1 and BCT2 as unstable. The BCT1 timing jitter is \(\sim\)20 ps, and the timing jitter in the BCT2 is \(\sim\)10 ps. Meanwhile, beam injection rate is decreased or fluctuates.  
Figure 4: Beam profile image measured by a profile monitor.  
Figure 5: Signal waveforms measured by the Beam Current Transformers (BCTs).  
Figure 3: Single waveform picked up by a BPM with an oscilloscope.
# Analysis of Powertrain Loading Dynamic Characteristics and the Effects on Fatigue Damage
## 2 Collection and Analysis of Test Loading Spectra
### Data Preprocessing
#### 2.2.2 Effective Band Estimation and Sampling Rate  
To determine the effective band of the torque signals, a test was implemented with the engine running on the highest speed. The engine output shaft torque was measured with its sampling rate set to 1000 Hz. The power spectrum densities (PSD) of the test data are shown in Figure 2.  
Figure 1: Rotary telemetry torque measurement system: (**a**) system configuration; (**b**) installation of the system.  
Figure 2: Power spectrum densities (PSD) of the highest speed test data.  
The power within the band of \([0,f_{\rm c}]\) and the total power are calculated as follows:  
\[P_{\rm c}=\int_{0}^{f_{\rm c}}G(f)\mathrm{d}f,P=\int_{0}^{\infty}G(f)\mathrm{d}f. \tag{1}\]  
when \(c=\frac{P_{\rm c}}{P}\geq 0.995\), it is believed that the effective band is included in \([0,f_{\rm c}]\)[18].  
By setting \(f_{\rm c}\) to 150 Hz, the checking value of \(c\) was 0.995 for the torque on the engine output shaft, and 0.997 for the torque on the transmission output shaft, respectively. So the conclusion was that 0-150 Hz contained the effective band of the signals.  
According to reference [17], the sampling rate needs to be at least 22.5 times the signal harmonic frequency, i.e., 3375 Hz, to achieve 1% accuracy in signal peak value.
## 2 Proposed Method  
Our proposed method is a matrix factorization-based fusion method, which requires first transforming the 3D hyperspectral image data into 2D matrix data and then performing the corresponding calculations. Usually, we denote the target HR-HSI as \(\mathcal{Z}\in\mathbb{R}^{W\times H\times L}\), with \(W\times H\) pixels and \(L\) bands; the LR-HSI involved in the fusion as \(\mathcal{H}\in\mathbb{R}^{w\times h\times L}\), with \(w\times h\) pixels and \(L\) bands; and the HR-MSI as \(\mathcal{M}\in\mathbb{R}^{W\times H\times l}\), with \(W\times H\) pixels and \(l\) bands, and their mode-3 matricization matrices are \(\mathbf{Z}\in\mathbb{R}^{L\times WH}\), \(\mathbf{H}\in\mathbb{R}^{L\times mH}\), and  
Figure 1: Flowchart for achieving super-resolution using a fusion approach.  
\(\mathbf{M}\in\mathbb{R}^{l\times WH}\), respectively. We denote a three-dimensional tensor as \(\mathcal{X}\in\mathbb{R}^{l_{1}\times l_{2}\times l_{3}}\), its \((i,j,k)\) elements as \(\mathbf{X}_{ijk}\) or \(\mathbf{x}_{ijk}\), and its Frobenius norm as \(\|\mathcal{X}\|_{F}=\sqrt{\sum_{ijk}\left|x_{ijk}\right|^{2}}\).
## 2 Research Methodology
### TVD-MacCormack Numerical Scheme  
Following [PERSON] et al. [25] and [PERSON] et al. [26; 27], (1) splits into two sets of one-dimensional equations according to the operator-splitting method, as follows:  
\[\frac{\partial\overrightarrow{Q}}{\partial t}+\frac{\partial \overrightarrow{f}}{\partial x}=\overrightarrow{S}, \tag{3a}\] \[\frac{\partial\overrightarrow{Q}}{\partial t}+\frac{\partial \overrightarrow{g}}{\partial y}=\overrightarrow{H} \tag{3b}\]Next, we apply a numerical discretization procedure to (3a) within the MacCormack framework ((3b) follows similarly), which includes two successive steps during each marching time step,  
\[\left(\overset{\rightarrow}{Q_{i}}^{n+1}\right)^{\prime}=\overset{ \rightarrow}{Q_{i}}-\left(\overset{\rightarrow}{f_{i}}-\overset{ \rightarrow}{f_{i-1}}\right)\cdot\Delta t/\Delta x+\overset{\rightarrow}{S_{i }}^{n}\cdot\Delta t, \tag{4a}\] \[\overset{\rightarrow}{Q_{i}}^{n+1}=\frac{1}{2}\left[\left(\overset{ \rightarrow}{Q_{i}}^{n+1}\right)^{\prime}+\overset{\rightarrow}{Q_{i}}^{n}- \left(\left(\overset{\rightarrow}{f_{i+1}}^{n}\right)^{\prime}-\left( \overset{\rightarrow}{f_{i}}^{n}\right)^{\prime}\right)\cdot\Delta t/\Delta x +\left(\overset{\rightarrow}{S_{i}}^{n+1}\right)^{\prime}\cdot\Delta t\right]+ \overset{\rightarrow}{T}_{i}, \tag{4b}\]  
where the subscript and superscript are the spatial and temporal indices, respectively. Note that, different from the traditional MacCormack scheme, a symmetric five-point TVD term [25; 28; 29], \(T_{i}\), is inserted into the corrector step to suppress large oscillations caused by steep gradients in the calculation; this term is expressed as follows:  
\[\overset{\rightarrow}{T}_{i}=\left[G\left(r_{i}^{+}\right)+G\left(r_{i+1}^{-} \right)\right]\cdot\left(\overset{\rightarrow}{Q_{i+1}}^{n}-\overset{ \rightarrow}{Q_{i