esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Recommendation Approaches
#### 2.4.2 Quality-Based Non-Personalized Recommendation (Q)  
In this approach, OER recommendations are generated exclusively based on the pedagogical quality of the OER measured with the LORI evaluation model, as explained in Section 2.3. Therefore, these recommendations are non-personalized since neither the profile of the users nor their actions are taken into account for their generation. Thus, the OER that users are viewing at a certain moment are also not taken into account. A system capable of suggesting OER to the users according to this quality-based non-personalized recommendation approach was implemented for the study. This system generates lists of N recommended OER by sorting by quality scores all OER in the dataset and randomly selecting N OER among the ones in the top quartile (i.e., among the 25% of OER with highest quality in the dataset). One option for this approach could have been to directly suggest the top N OER (i.e., always recommend the OER with more quality in the dataset). However, we decided to introduce some randomness in the recommendation process in order to evaluate a more realistic approach.  
There were two main reasons for including this non-personalized recommendation approach in the user study. On the one hand, this approach gathers evidence on the usefulness of this kind of recommendations, which can be useful to determine the benefits for OER repositories of providing users with suggestions such as \"the top ten selections of OER\" or \"the five best OER of the year\". Although it is recognized that non-personalized recommendations can be useful in some scenarios, these types of recommendations are not often addressed by recommender systems research [41]. In fact, no study has ever examined the usefulness of non-personalized OER recommendations based exclusively on pedagogical quality indicators. On the other hand, this approach provides another baseline to be used for comparison purposes.
## 5 Computational Analysis
### Computational Model  
#### 5.1.1 Governing Equations  
Conservation of mass and momentum is governed by the continuity and momentum equations individually for solids and gas phases. Two fluid model approach is used for multiphase modeling. The volume of gas (air) and solid phase (particle) is defined by Equation (1a,b).  
\[v_{a}=\int_{v}\alpha_{a}dv \tag{1a}\]  
Figure 11: Bed Height rise for different distributor plates for three superficial air velocity.  
Figure 12: Initial Bed height rise fluidization phenomena for different distributor plates (3.0\({}^{\prime}\)U\({}_{\text{mf}}\), L/D = 1.16).  
\[\
u_{p}=\int\limits_{v}\alpha_{p}d\
u \tag{1b}\]  
Volume fractions of all the phases are equal to unity and expressed by the relation below:  
\[\sum\limits_{m=1}^{n}\alpha_{m}=1 \tag{2}\]  
where \(\alpha_{m}\) are the volume fractions of all the phases up phase n.  
The continuity equations with time for gas, as well as particle phases, are expressed as follows:  
\[\frac{\partial}{\partial t}(\alpha_{a}\rho_{a})+\
abla\cdot(\alpha_{a}\rho_{a }\overset{\rightarrow}{V_{a}}) \tag{3}\]  
\[\frac{\partial}{\partial t}(\alpha_{p}\rho_{p})+\
abla\cdot(\alpha_{p}\rho_{p }\overset{\rightarrow}{V_{p}}) \tag{4}\]  
The following equations represent the conservation of momentum for the gas and solid phases.  
\[\frac{\partial}{\partial t}(\alpha_{a}\rho_{a}\overset{\rightarrow}{V_{a}} )+\
abla\cdot(\alpha_{a}\rho_{a}\overset{\rightarrow}{V_{a}}\overset{ \rightarrow}{V_{a}})=-\alpha_{a}\
abla p_{a}+\
abla\cdot\overline{\overline{ \overline{a}}}+\alpha_{a}\rho_{a}\overset{\rightarrow}{g}+\beta_{ap}( \overset{\rightarrow}{V_{a}}-\overset{\rightarrow}{V_{p}}) \tag{5}\]  
\[\frac{\partial}{\partial t}(\alpha_{p}\rho_{p}\overset{\rightarrow}{V_{p}})+ \
abla\cdot(\alpha_{p}\rho_{p}\overset{\rightarrow}{V_{p}}\overset{ \rightarrow}{V_{p}})=-\alpha_{p}\
abla p_{p}+\
abla\cdot\overline{\overline{ \overline{p}}}+\alpha_{p}\rho_{p}\overset{\rightarrow}{g}+\beta_{ap}( \overset{\rightarrow}{V_{p}}-\overset{\rightarrow}{V_{a}}) \tag{6}\]  
where \(\overset{\rightarrow}{V_{a}}\) and \(\overset{\rightarrow}{V_{p}}\) are air and particle phase velocities, respectively. Also \(\overline{\overline{\overline{\overline{u}}}}_{a}\)\(\overline{\overline{\overline{p}}}\) are the gas and particle phase strain tensor, respectively and is expressed as  
\[\overline{\overline{\overline{\overline{u}}}}=\alpha_{a}\mu_{a}(\
abla \overset{\rightarrow}{V_{a}}+\
abla\overset{\rightarrow}{V_{a}}+\alpha_{a }(\lambda_{a}-\frac{2}{3}\mu_{a}))\
abla\cdot\overset{\rightarrow}{V_{a}} \overset{\rightarrow}{I} \tag{7}\]  
\[\overline{\overline{\overline{p}}}=\alpha_{p}\mu_{p}(\
abla\overset{ \rightarrow}{V_{p}}+\
abla\overset{\rightarrow}{V_{p}})+\alpha_{p}(\lambda _{p}-\frac{2}{3}\mu_{p}))\
abla\cdot\overset{\rightarrow}{V_{p}} \overset{\rightarrow}{I} \tag{8}\]
## 3 Methodology
### Machine Learning Methods
#### 3.4.3 Random Forest  
Random Forest (RF) is an ensemble learning technique utilized for classification and regression tasks, enhancing prediction accuracy by combining multiple decision trees [55]. Each tree is trained on random subsets of both data and features, which mitigates the risk of overfitting. The bootstrapping method generates numerous unique samples, adding diversity to the model. Due to its resilience to noise, Random Forest performs effectively with large datasets. Additionally, it can assess the importance of features, aiding in the identification of the most significant variables for analysis. The following regression equation is used for localization:  
\[\hat{P}=\frac{1}{n}\sum_{i=1}^{n}T_{i}(X) \tag{15}\]  
where: \(T_{i}(X)\) is prediction of the i-th tree for input data \(X\), n is the total number of trees in a forest. Listed below are the hyperparameters employed for Grid Search optimization:  
* Number of trees (n_estimators): {10, 50, 100, 200};
* Maximum tree depth (max_depth): {None, 10, 20, 30, 40, 50};
* Minimum number of samples required to split a node (min_samples_split): {2, 5, 10};
* Minimum number of samples required in a leaf (min_samples_leaf): {1, 2, 4};
* Bootstrap sampling (bootstrap): {True, False}.
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 4 System Implementation
### Software Development Tools  
The software tools used in the development of the prototypes of the G-MoMo applications include the following:  
\(\circ\)  Vue JS Framework  
The front-end of the native G-MoMo applications were developed using the Vue JS framework. Vue is a progressive JavaScript framework for building simplistic user-interface design. It is a model-view-view model (MVVM) patterned framework for building a lightweight front-end of the applications [85; 86]. Vue JS enables the creation of fast and reliable applications, improves the application's development efficiency, maintains and integrates with other libraries and existing projects, and has a well-thought-out architecture to increase performance and reduce memory consumption [85; 87; 88].  
\(\circ\)  Python  
The backend is implemented using Python. Python is an interpreted high-level, general-purpose, and object-oriented programming language designed to rapidly prototype complex applications. It has an extensive and comprehensive standard library, platform-independent, and automatic memory controls [89].  
\(\circ\)  MySQLThe backend databases are created using MySQL, an open-source database management system used to organize, store, retrieve and manage records. MySQL is chosen because of its compatibility with different operating systems, scalability, robustness, accessibility, reliability, backup and recovery utilities, flexibility to implement many features to improve system performance, encryption and decryption functions to protect sensitive data. It also handles large databases efficiently and delivers reliable and high-performance applications [89; 90; 91].  
Twilio programmable SMS  
Twilio is a cloud communication platform that allows users to send and receive text messages with the help of its web service API [92]. The G-MoMo applications send mobile money IT support staff, agents, and the user's phone number to the Twilio API to send OTP messages. The OTP sent from Twilio via SMS to IT support staff, agents, and users during authentication are four (4) digits and is valid for only 60 s. The G-MoMo applications automatically detect the OTP upon receipt, and the Twilio API also keeps track of the sent OTP messages.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 4 Experimental Analysis and Evaluation  
To evaluate the effectiveness and performance of our proposed approach, we implemented a prototype and compared its performance of the RLBC algorithm with other routing algorithms. In terms of effectiveness evaluation, it is mainly compared with the state-of-the-art reinforcement learning-based routing algorithm, the trust-based algorithm, and our original blockchain-based algorithm. For performance evaluation, we compared our system with the traditional PoW-based blockchain system to reflect the performance of our system in terms of delay, consumption and throughput.
# Adaptive Relay Selection Scheme by Using Compound Channel
## 1 Introduction
However, the wireless network that is suitable for communication cannot always maintain the same channel condition, so outage occurrence is inevitable. Therefore, a new criterion is needed to select relays for different condition of the network. This paper proposes an adaptive relay selection scheme to obtain the diversity gain through the optimal compound channel. To enhance the entire performance of system, the proposed scheme suggests a new algorithm consisting of two selection stages. Specifically, each stage of a new algorithm considers the outage probability at relays and diversity gain of compound channel. Furthermore, the selected relays use an antenna selection algorithm to improve the channel capacity of different users. This paper is organized as follows. Section 2 presents the system model, Section 3 introduces the conventional schemes. Section 4 describes the algorithm and advantage of the proposed scheme. Simulation results are shown in Section 5. Finally, Section 6 describes the concluding contents.
## 3 Results
### Geographical Distribution of Natural and Man-made (Technological) Disasters
#### 3.1.2. In-Depth Analysis of Disaster Distribution by Country with Comprehensive Supporting Data
India follows with 776 natural disaster events (49.08% of its total), with frequent occurrences of epidemics, floods, and droughs. The Philippines reported 698 natural disaster events (74.41% of its total), dominated by storms, floods, and droughs. Indonesia had 621 natural disaster events (70.73% of its total), primarily floods, earthquakes, and droughs. Bangladesh experienced 363 natural disaster events (61.73% of its total), with floods, droughs, and storms being the most common. Japan reported 389 natural disaster events (83.84% of its total), with wildfires, earthquakes, and storms as the leading types. Mexico had 304 natural disaster events (63.20% of its total), with wildfires, floods, and epidemics being the most prevalent. Pakistan had 254 natural disaster events (50.40% of its total), mainly floods, wildfires, and storms. Brazil reported 283 natural disaster events (61.26% of its total), with the most common being epidemics, wildfires, and droughs (Table 3 and Figures 4-6).
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 4 Discussion  
The classification of mountain pines in an alpine environment was tested using optical satellite imagery with different spatial resolutions--PlanetScope (3 m) and Sentinel-2 (10 m)--and additional textural (GLCM), topographical (elevation, slope and aspect) and canopy height (CHM) features.  
Figure 9: Landscape metrics for mountain pine stands in the Sarntal Alps, South Tyrol, based on final classification result (PS + GLCM\({}_{\text{MAX}}\) + Topo + CHM). **(a)** Surface area covered by mountain pines at different altitudes; **(b)** relative land cover of mountain pines at different altitudes; **(c)** surface area covered by mountain pines by cardinal direction; **(d)** surface area by mountain pine patches at different patch sizes.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### One-Dimensional Examples
#### 3.1.1 1D Diffusion
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Reference Variables in Physical-Scale and Lattice-Scale**} \\ \hline
**Reference Variables** & **Physical-Scale** & **Lattice-Scale** \\ \hline \(m_{o}\) & 71,520 kg & 1000 mu \\ \hline \(x_{o}\) & 2 m & 10 lu \\ \hline \(t_{o}\) & 3.4635 \(\times 10^{4}\) s & 600 ts \\ \hline \(\Theta_{o}\) & 373 K & 1 \({}^{\star}\) tu \\ \hline \multicolumn{3}{c}{Scaling units between SI and lattice systems} \\ \hline Primary dimension & Physical-scale & Lattice-scale \\ \hline Mass & 1 kg/kg & 0.014 mu/kg \\ \hline Length & 1 m/m & 5 lu/m \\ \hline Time & 1 s/s & 0.017 ts/s \\ \hline Temperature & 1 K/K & 2.68 \(\times\)\(10^{-3}\) tu/K \\ \hline \multicolumn{3}{c}{Converting properties between scales} \\ \hline Property & Physical-scale & Dimensionless-scale & Lattice-scale \\ \hline Length (\(l_{L}\)) & 2 m & 1 & 10 \({}^{\star}\) lu \\ \hline Cold temperature (\(T_{c}\)) & 298 K & 0.799 & 0.799 tu \\ \hline Hot temperature (\(T_{k}\)) & 373 K & 1 & 1 tu \\ \hline Density (\(\rho\)) & 8940 kg/m\({}^{3}\) & 1 \({}^{\star}\) & 1 \({}^{\star}\) mu.lu\({}^{-3}\) \\ \hline Specific heat (\(C_{p}\)) & 385 J/kg.K & 4.3068 \(\times\)\(10^{13}\) & 1.19 \(\times\)\(10^{10}\) lu\({}^{2}\).ts\({}^{-2}\).tu\({}^{-1}\) \\ \hline Thermal conductivity (\(k\)) & 397.5 W/m.K & 4.3068 \(\times\)\(10^{13}\) & 1.99 \(\times\)\(10^{9}\) mu.lu.ts\({}^{-3}\).tu\({}^{-1}\) \\ \hline Thermal diffusivity (\(\alpha\)) & 1.1549 \(\times\)\(10^{-4}\) m\({}^{2}\)/s & 1 \({}^{\star}\) & 0.1667 lu\({}^{2}\).ts\({}^{-1}\) \\ \hline Uniform heat generation (\(q\)) & 1 \(\times\)\(10^{5}\) W/m\({}^{3}\) & 1.1619\(\times\)\(10^{14}\) & 5.3791 \(\times\)\(10^{7}\) mu.lu\({}^{-1}\).ts\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Converting data for diffusion problem. The values with \({}^{\star}\) represent assumed values. Figure 5: Numerical and analytical solutions of the steady-state temperature distribution along the rod; (**a**) solutions at the lattice-scale (the analytical solution is achieved using Equation (73) at the lattice-scale), and (**b**) solutions at the physical-scale (the numerical solution is achieved by the LBM then transformed to the physical-scale using the scaling units from Table 4).
## 2 System Architecture
### Server  
A server is used to provide the Windows communication foundation (WCF) web-service and a database for remote image backup. We developed three web-services: upload, history, and real-time. The upload web-service is used for the transmission of information to the server, including inspection point number, check-in time, time of intruder detection, and images of intruder. Remote users can access historical data by scan time and monitor the robot in real-time. The database was built using Microsoft SQL server 2008, and the web-service was built using Visual Studio C# 2010.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 2 Problem Description and Mathematical Approach
### Spacecraft Dynamics  
The state of the spacecraft in the polar reference frame is defined by the variables \(\{r,\,\theta,\,u,\,v\}\), where \(u\) (or \(v\)) is the radial (or transverse) component of the vehicle inertial velocity. The equations of motion are  
\[\dot{r} = u,\quad\dot{\theta} = \frac{v}{r},\quad\dot{u} = -\frac{\mu_{\odot}}{r^{2}}\,+\,\frac{v^{2}}{r}\,+\,\mathbf{a}\, \cdot\,\hat{\mathbf{t}}_{r},\quad\dot{v} = -\frac{u\,v}{r}\,+\,\mathbf{a}\,\cdot\,\hat{\mathbf{t}}_{\theta} \tag{8}\]  
where \(\mu_{\odot}\) is the Sun's gravitational parameter. The initial conditions on a circular orbit of radius \(r_{0}\) are  
\[r(t_{0})=r_{0},\quad\theta(t_{0})=0,\quad u(t_{0})=0,\quad v(t_{0})=\sqrt{ \frac{\mu_{\odot}}{r_{0}}} \tag{9}\]  
where it is assumed, without loss of generality, that the spacecraft initially orbits the Sun in a counterclockwise direction as is illustrated in Figure 1 (see the green arrows).  
The differential Equations (8) and (9) can be rewritten in dimensionless form using the definition of \(\widetilde{r}\) and introducing the dimensionless velocity components \(\widetilde{u}\) and \(\widetilde{v}\), defined as  
\[\widetilde{u}=\frac{u}{\sqrt{\mu_{\odot}/r_{0}}},\quad\widetilde{v}=\frac{v} {\sqrt{\mu_{\odot}/r_{0}}} \tag{10}\]  
and the dimensionless time  
\[\widetilde{t}=\frac{t}{\sqrt{r_{0}^{3}/\mu_{\odot}}} \tag{11}\]  
where \(\widetilde{t}_{0}=0\) is the initial time. The dimensionless equations of motion are  
\[\widetilde{r}^{\prime} = \widetilde{u} \tag{12}\] \[\theta^{\prime} = \frac{\widetilde{v}}{\widetilde{r}}\] (13) \[\widetilde{u}^{\prime} = -\frac{1}{\widetilde{r}^{2}}+\frac{\widetilde{v}^{2}}{\widetilde {r}}+\tau\,\frac{\beta}{2\widetilde{r}}\left(1+\cos^{2}\alpha_{n}\right)\] (14) \[\widetilde{v}^{\prime} = -\frac{\widetilde{u}\,\widetilde{v}}{\widetilde{r}}+\tau\,\frac{ \beta}{2\widetilde{r}}\,\cos\alpha_{n}\,\sin\alpha_{n} \tag{15}\]  
where the prime symbol denotes a derivative taken with respect to \(\widetilde{t}\), while the initial conditions (9) become  
\[\widetilde{r}(\widetilde{t}_{0})=1,\quad\theta(\widetilde{t}_{0})=0,\quad \widetilde{u}(\widetilde{t}_{0})=0,\quad\widetilde{v}(\widetilde{t}_{0})=1. \tag{16}\]  
Since the dimensionless Equations (12)-(15) and the initial conditions (16) do not depend on \(r_{0}\), the results of the trajectory design discussed in the next subsection will be independent of the radius of the circular parking orbit. As a result, the numerical results obtained from the proposed approach can be easily adapted to a wide range of heliocentric mission scenarios.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Hybrid Vision Transformer Architecture
#### 3.1.1. Rotationally Aware Patch Embedding  
In a standard ViT, an input image \(\mathrm{I}\in\mathbb{R}^{H\times W\times C}\), where \(H,W,\) and \(C\) represent the height, width, and number of channels, respectively, is divided into a sequence of flattened image patches \(x_{p}\in\mathbb{R}^{N\times(P^{2}\cdot C)}\), where \(P^{2}\) is the patch size, and \(N=\frac{H\times W}{P^{2}}\) is the number of patches. To incorporate rotational invariance, we modify the patch extraction process to be rotationally aware. Each patch is not only extracted based on its spatial location but also adjusted according to the dominant orientation \(\theta_{p}\) of the local features within that patch. The rotationally aware patch embedding is defined as:  
\[z_{p}=E(R(x_{p}\theta_{p}))+E_{pos}(p), \tag{1}\]  
where \(R(x_{p}\theta_{p})\) is the rotated version of the patch \(x_{p}\) by angle \(\theta_{p}\), \(E\) (\(\cdot\)) is the linear embedding function, \(E_{pos}(p)\) is the positional encoding of the patch's original position \(p\). The rotational adjustment \(R(x_{p}\theta_{p})\) ensures that each patch is aligned with the dominant orientation, making the model more robust to rotational variations in the data.
# Nozzle Design of Plug-and-Play Passive Pre-Chamber Ignition Systems for Natural Gas Engines
## 2 Methodology and Specification
### Experimental Setup (Figure 2)  
The bench test was carried out on the natural gas engine WP15 NG, mass-produced by Weichai Power Co., Ltd. The test engine specifications are shown in Table 2. The in-line 6-cylinder engine adopts a turbocharger, intercooler, and EGR, maintaining stoichiometric combustion through an electronic control unit. A direct current dynamometer is used to control the engine speed and torque. The natural gas consumption, intake flow, and air-fuel ratio are closed-loop controlled by INCA software. Six Kistler piezoelectric sensors measure the pressure signals in the respective cylinder with a crank angle resolution of 0.1\({}^{\circ}\)CA. More information on the test bench can be found in the literature. Benefiting from the plug-and-play design of the passive PC, the quick retrofit of different schemes improves the efficiency of the test evaluation. To mimic realistic natural gas combustion, there is 14.6% EGR maintained in the natural gas-air mixtures before combustion. A fresh fuel-air mixture contains 72.3% N\({}_{2}\), 22.2% O\({}_{2}\), and 5.5% CH\({}_{4}\), while the EGR contains 72.2% N\({}_{2}\), 0.3% O\({}_{2}\), 15.3% CO\({}_{2}\), and 12.2% H\({}_{2}\)O in mass fraction.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case** & **d (mm)** & \(\alpha\) (\({}^{\circ}\)) & \(\beta\) (\({}^{\circ}\)) \\ \hline
1 & 1.3 & 130 & 10 \\
2 & 1.3 & 130 & 0 \\
3 & 1.3 & 130 & 20 \\
4 & 1.3 & 120 & 10 \\
5 & 1.3 & 140 & 10 \\
6 & 1.0 & 130 & 10 \\
7 & 1.6 & 130 & 10 \\ \hline \end{tabular}
\end{table}
Table 1: The test cases for passive PC nozzle research.  
Figure 1: Design parameters of the passive PC nozzle, including orifice diameter d, vertical angle \(\alpha\), and lateral angle \(\beta\).  
The operating conditions of the six-cylinder commercial vehicle engine were selected according to the Worldwide Harmonized Heavy-Duty Vehicles Test Procedure, as shown in Table 3, with 1100 rpm and 2000 N\(\cdot\)m representing the typical operating conditions. The sensitivity of different PC configurations to operating conditions can be evaluated by varying the engine speed and load. Cylinder pressure signals were recorded with 0.1\({}^{\circ}\) crank angle resolution and averaged over 50 consecutive engine cycles. The combustion duration was defined as the crank angle difference between 10% and 90% of the mass fraction burned.
# Effects of Refiner Plates with Different Fillings on TMP Properties
## 1 Introduction
The low-intensity zone is located at the outer edges of the plate and has a smoother and less aggressive pattern than the high-intensity zone. This zone is responsible for refining any remaining fibers and producing a smooth and even surface on the pulp. The low-intensity zone is associated with lower energy consumption and plate wear. The specific design and composition of the three zones may vary depending on the specific application and performance requirements of the pulp mill. However, the concept of dividing the refiner plate into three zones is a common approach used in TMP refining to optimize pulp quality and efficiency [6; 7]. It is important to study the refining effects of refiner plates with different fillings on TMP properties in order to optimize the performance of the refining process and the quality of the resulting pulp. Refining is a critical step in the pulp and paper production process in which wood chips or other raw materials are mechanically treated to break down the fibers and improve their properties. This knowledge can help pulp mills select the optimal refiner plate design for their specific application to achieve the desired pulp quality and production efficiency. Additionally, by optimizing the refining process via the selection of appropriate refiner plates, mills will be able to improve the efficiency of the process, reduce energy consumption, and extend the lifespan of their equipment. In this study, two TMP refiner plates with high and low intensity were designed to compare the refining effects on TMP properties to ensure efficient and high-quality pulp production in the pulp and paper industry. These plates differed in the number of bars, the dimensions of the bars, and the design of the three refining zones on the plate.