esa-sceva/satcom-chunk-collection huggingface dataset:
# Simple Approach for Evaluation of Abrasive Mixing Efficiency for Abrasive Waterjet Rock Cutting
## 2 Abrasive in the Focus
### Distance between Abrasive Particles in the Focus  
In the focus, the abrasive particles move and are independently located in each space, as shown in Figure 1. The volume occupied by a single particle (\(V_{s}\)) depends on the volume inside the focus (\(V_{focus}\)) and the total number of abrasives within the focus (\(N_{p,f}\)):  
\[V_{s}=V_{focus}/N_{p,f}=\frac{\pi}{4}d_{f}^{2}m_{p}/\dot{m}_{a}t_{f}, \tag{6}\]  
where \(d_{f}\) is the focus diameter and \(l_{f}\) is the focus length.  
Assuming that the abrasives are uniformly distributed and are spherical-shaped with a volume \(V_{s}\), they are all circumscribed. Thus, the distance between the centers of \(V_{s}\) is the diameter of \(V_{s}\). Considering the abrasive diameter (\(d_{p}\)), the DBP becomes:  
\[\text{DBP}=\sqrt[3]{V_{s}\text{-}6/\pi}-d_{p}. \tag{7}\]  
Substituting Equation (6) into Equation (7), the DBP in the focus is represented by the AFR, the focus geometry, the abrasive diameter and mass, and the terminal velocity, as follows:  
\[\text{DBP}=\sqrt[3]{\frac{3}{2}d_{f}^{2}l_{f}m_{p}/\dot{m}_{a}t_{f}}-d_{p}. \tag{8}\]  
The main energy of the abrasive waterjet for material removal comes from the accelerated abrasive particles. In terms of the AFR and the terminal velocity, the kinetic energy of the abrasive per second (\(E_{a}\)) can be expressed as:  
\[E_{a}=\frac{1}{2}\dot{m}_{a}v_{t}^{2}. \tag{9}\]  
Based on kinetic energy per unit second (\(E_{a}\)) and cutting depth (\(D\)), the energy required to cut a unit depth (\(\varepsilon\)) as a function of the DBP is assumed to be:  
\[E_{a}/D=\varepsilon=\alpha(\text{DBP})^{\beta}, \tag{10}\]  
where \(\alpha\) and \(\beta\) are an empirically determined constant and an exponent, respectively, and \(\varepsilon\) is the energy required to cut one unit depth of rock.  
The cutting depth is affected by the mixing efficiency. As shown in Equation (8), it is possible to demonstrate the relationship between the kinetic energy removal and the experimental results in consideration of the focus geometry, the AFR, and the WFR. This model can also be expressed by the pump pressure using Equation (1). Through this study, the mixing variables can be expressed comprehensively. This model defines the energy in unit time, rather than the mixing time \(t_{f}\), to intuitively show the mass and velocity of the abrasive. At a high AFR, the kinetic energy decreases as the terminal velocity decreases, whereas \(t_{f}\) increases. However, a limitation of this approach is that the ideal case of abrasive-fluid behavior is assumed in the developed model. The mixing is greatly affected by the turbulent flow, the viscosity of the fluid, and the physical properties of the abrasive as well as collision and friction. Therefore, the empirical relationship between the simple theoretical approach and the experimental results is shown in this study. Eventually, further studies regarding the effect of geometrical and frictional loss of both water and abrasive within the focus are required to overcome this limitation.
# A Petri Net Approach for Business Process Modeling and Simulation
## 2 Fundamental Concepts
### Enterprise Resource Planning (ERP)  
An ERP is a set of software modules integrated with a common central database. The database collects information from many divisions and departments of the company, intervening in a large number of key business processes related to manufacturing and production, finance and accounting, sales and marketing, human resources, etc. The application layout supports most of an organization's BPs [14].  
The main characteristic of an ERP is that the database is centralized, meaning that all the modules that make it up (sales, finance, accounting, production control, payroll, etc.) record and take information from the same database. In addition, as they are network-based systems, the information is available immediately.  
The advantages of using an ERP include the following [15]:  
1. Unification: allows all the functions and departments of the company to be unified in a single framework, which facilitates communication and collaboration between the different teams.
2. Automation: although it is not a feature of ERPs, repetitive and well-structured tasks can be automated, avoiding errors and saving time in their execution.
3. Information in real time: because ERPs are systems that work in a network, information can be updated in real time to facilitate decision making.
4. Efficiency and productivity: ERPs are based on the best practices of the industry; thus, when a company implements a system of this type, it can anticipate improved efficiency and increased productivity.
5. Cost reduction: costs can be reduced through improved efficiency.
6. Control and visibility: management decision-making is optimized, along with issues related to different business processes, thanks the availability of reliable, timely, and truthful information.
7. Regulatory Compliance: compliance with applicable laws and regulations is assured by maintaining accurate and up-to-date records.
8. Scalability: ERP systems are scalable and can be adapted as the company grows or changes.  
The use and implementation of these systems requires the necessary infrastructure for its application and training for personnel in the correct obtaining, use, administration, and interpretation of the information contained in it. Each implementation is unique, and may require modifications in order to meet particular business requirements [14].
## 4 SENSED-SG Design and Protocols
### SENSED-SG Network Layer
To overcome this situation, messages intended to be received by all neighboring nodes, are not dispatched via the phase-lock mechanism, but instead they are continuously transmitted during a full duty cycle period, ensuring all neighbors have received the message. Since broadcasting techniques lead to higher power consumption, our routing protocol makes use of broadcast messages only when strictly necessary. Besides, it is worth mentioning that in order to not neglect relaying tasks, a specific buffering system has been implemented: if a node receives a message from a neighbor to relay it and the node under consideration is trying to dispatch its own message, this node will store the other neighbor's message until it can be sent at the appropriate moment, determined by the phase-lock mechanism. This mechanism greatly reduces the number of messages lost in more active networks.
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 3 Read Algorithm
### Improvement of Read Disturbance
found that a short \"pre-turn on\" pulse, just before applying the read voltage to the selected WL, will share the potential of the channel boosting area of the adjacent WL and the negative boosting area of the selected WL, such that the read disturbance is improved even in various worst program patterns [67]. However, this method can also cause read performance degradation by incurring extra read time. To further improve the above problems, a reverse read scheme was proposed [44; 62] (Figure 9). In conventional methods, the read voltage to the selected WL is applied sequentially from the low read level (R1) to the high read level (R3). Conversely, the proposed method reads from the high read level (R3) to the low read level (R1) on the selected WL. In this case, when a high read level is applied, the channel potential difference between selected WL and adjacent WLs decreases and the corresponding transverse electric field decreases. Besides this, during read phases R1 and R2, an electron was pulled from the selected WL to the adjacent unselected WLs\({}^{\prime}\) channel due to potential difference, and so the transverse electric field is also reduced and the occurrence of HCI-related read disturbance can be reduced again. Reading the Vt first for the highest read level can also reduce the time required to reach the target voltage, therefore reducing WL setup time [42].
## 3 Results
### Keyword Co-Occurrence and Clustering
#### 3.4.1 Keyword Co-Occurrence Network and Evolutionary Pattern
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Count** & **Centrality** & **Year** & **Keywords** \\ \hline
1 & 24 & 0.06 & 2013 & assistive technology \\
2 & 20 & 0.13 & 2002 & visually impaired \\
3 & 14 & 0.11 & 2002 & wearable technology \\
4 & 11 & 0.03 & 2002 & navigation system \\
5 & 11 & 0.07 & 2019 & sensor \\
6 & 8 & 0.02 & 2019 & user experience \\
7 & 8 & 0.08 & 2016 & warning alerts \\
8 & 8 & 0.02 & 2017 & pedestrian safety \\
9 & 8 & 0.06 & 2016 & traffic safety \\
10 & 7 & 0.25 & 2013 & collision avoidance \\
11 & 7 & 0.30 & 2016 & performance \\
12 & 7 & 0.11 & 2017 & obstacle avoidance \\
13 & 6 & 0.19 & 2018 & vulnerable road users \\
14 & 6 & 0.16 & 2018 & object detection \\
15 & 6 & 0.16 & 2018 & accident prevention \\
16 & 6 & 0.14 & 2016 & age differences \\
17 & 6 & 0.21 & 2014 & electronic travel aids \\
18 & 6 & 0.02 & 2013 & haptic feedback \\
19 & 5 & 0.18 & 2017 & perception \\
20 & 5 & 0.04 & 2016 & obstacle detection \\
21 & 5 & 0.13 & 2018 & cyclist safety \\
22 & 5 & 0.03 & 2017 & virtual reality \\
23 & 5 & 0.02 & 2021 & augmented reality \\
24 & 5 & 0.00 & 2020 & connected vehicle communication \\
25 & 4 & 0.14 & 2016 & environment \\
26 & 4 & 0.17 & 2017 & blind navigation \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The top 26 keywords with the highest frequency and centrality in the wearable technology for the VRU safety field. Figure 6 displays the 14 keywords with the strongest citation burst in the field of wearable technology for VRU safety. This analysis reveals a clear three-stage evolution of research hotspots: the early stage (2002-2015) is dominated by foundational concepts, with \"wearable technology\" and \"electronic travel aids\" bursting simultaneously in 2014, primarily targeting basic navigation assistance for the visually impaired, while \"navigation system\" established itself as a continuous research direction throughout the entire period; the middle stage (2016-2020) exhibits transitional characteristics, as the burst cycle of \"wearable technology\" ended and \"obstacle detection\" rapidly gained attention, with research content diversifying into specific wearable forms such as \"smart helmets\" and \"head-mounted display,\" while the research population expanded to various vulnerable users, reflected in the sustained interest in keywords like \"age differences,\" \"blind navigation,\" and \"pedestrian safety\"; the recent stage (2021-2025) demonstrates a system integration trend, prioritizing \"user experience,\" with \"augmented reality\" serving as a platform for technology integration, while \"navigation system\", despite being an early concept from 2002, experienced a significant resurgence with a citation burst beginning in 2023, forming a complementary cluster with emerging \"obstacle avoidance\" and \"attention guidance\" technologies, marking a significant evolution from isolated technological exploration to comprehensive safety solutions. Figure 5: Keyword co-occurrence network for wearable technology in VRU safety.
# Automatic 2D Floorplan CAD Generation from 3D Point Clouds
## 1 Introduction  
The creation of 2D floorplan computer-aided designs (CADs) of the existing buildings is a challenging task in the architecture, engineering, and construction (AEC) industry, as the as-built condition of buildings can differ from original plans due to undocumented renovations. The creation of an indoor model from existing building has been widely researched with building information modeling (BIM) and has been increasingly requested in various applications [1]. Currently, BIM processes are well established for new buildings, but most existing buildings are not maintained, refurbished, or deconstructed with BIM [2]. The advantage of the implementation of BIM in existing buildings is significant, especially for restoration, documentation, maintenance, quality control, and energy/space management.  
Thus, the most important aspect is the creation of BIM without a prior model, as existing buildings usually do not have a model. To create BIM for existing buildings, 3D reconstruction is required, and a key aspect of 3D reconstruction is wall geometry modeling because it forms the basis for other elements of buildings. Currently, these objects are created manually based on large unstructured 3D point clouds acquired from the built structure. However, this process is labor-intensive and is prone to human error. Another challenge related to automated reconstruction is that 3D point clouds contain various types of clutter, such as furniture, and 3D point clouds lack semantic information [3].  
In order to resolve these issues, we propose a fully automated method to create 2D floorplan CADs from 3D point clouds without a prior model. In the proposed method, we generate 3D reconstruction model of walls based on assumption of Manhattan-World (MW) buildings, which assumes that mostman-made structures can be approximated by planar surfaces that are parallel to one of the three principal planes of a common orthogonal coordinate system [4].  
We then generate a 2D floorplan CAD based on the 3D reconstruction of the elements. In detail, to reconstruct the wall geometry, the proposed algorithm initially detects planes from 3D point clouds and classifies them as belonging to floors, ceilings, or walls. A 2D image which contains only wall candidate points is then created and wall lines are detected. There are several line-detection algorithms, such as RANSAC [5], the Hough transform [6], and the least-square (LS) method [7]. In this paper, we propose an efficient line-detection algorithm for wall detection.  
This paper is organized as follows. In Section 2, the background and related works are presented. Section 3 presents the proposed method, including the steps of data preprocessing, floor segmentation, wall detection, and 2D floorplan CAD generation. In Section 4, experimental results are discussed, including the evaluation metrics and datasets used. Finally, the concluding remarks follow in Section 5.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 3 Traffic Status Prediction
### Short-Term Traffic Prediction  
Research on traffic prediction has been mostly restricted to short-term prediction. According to a definition provided by [PERSON] et al. [11], short-term traffic predictions are made to predict a period of time in the future that can range between a few seconds and a few days. The authors listed two reasons why short-term traffic prediction has become so dominant in the traffic prediction models field. The first reason is the availability of traffic data that represents a short period of time. The second reason is the availability of many traffic data analytical models that can be used to explore the data. However, traffic data that are collected every 10 s or less is meaningless and not useful for short-term traffic prediction [12]. A number of studies claimed that collecting traffic conditions every 15-30 min would be more effective for prediction results [13; 14]. A study by [PERSON] et al. [15] on short-term traffic speed prediction provided a comparison between four prediction methods under different data collected in time windows that ranged from 1 min up to 30 min [15]. The study proposed a seasonal discrete grey model (SDGM) and compared the prediction accuracy with the seasonal autoregressive integrated moving average (SARIMA) model, artificial neural network (ANN) model, and support vector regression (SVR) model. The findings of this study show that the prediction accuracy increases when the targeted time window is more than 10 min, while the prediction of a time window that is less than 10 min suffers from instability. Additionally, the study shows that the SARIMA model's performance had the highest error indicator in the prediction results. A probable explanation regarding these results is that SARIMA cannot capture the variation characteristics of the traffic data in a small time window.
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 3 Theoretical Analysis of Torque Characteristic of TFM
### Electromagnetic Torque
When the high order harmonic component of Equation (1) is neglected, the air-gap flux density can be simplified as:  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]1  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]2  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]3  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]4  
where \(B_{\rm max}\) is the maximum value of air-gap flux density, \(B_{\rm min}\) is the minimum value of air-gap flux density, and \(k\) is the modulated factor of armature MMF field. The imaginary current of permanent magnet [21] can be expressed as:  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]5  
where \(B_{\rm r}\) is the residual magnetism of permanent magnet. Then, the electromagnetic force of each pair permanent magnet and the torque of single phase TFM can be written as:  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]6  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]7  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]8  
\[B=B_{0}+B_{n}\text{sin}\left(\frac{n\tau\tau x}{\tau_{\rm m}}\right), \tag{1}\]9  
where \(B_{\rm al}\) is the air-gap flux density at the position of left boundary of permanent magnet, \(B_{\rm ar}\) is the air-gap flux density at the position of right boundary of permanent magnet, \(p\) is the pole-pair number of TFM, \(\theta\) is the relative angle when the rotor rotates. Then, the electromagnetic torque of single phase TFM can be written as:  
Figure 2:0  
Figure 2:1  
where \(T_{\rm max}\) is the maximum of the single-phase torque, and \(D_{\rm a}\) is the diameter of air-gap. When the initial phase of armature current \(\varphi\) is zero, the amplitude of the single-phase torque reaches the maximum value according to Equation (16), and the electromagnetic torque of each phase can be respectively written as:  
\[\left\{ \begin{array}{l}T_{\rm A}=0.5T_{\rm max}\left[1-\cos 2\theta\right]\\ T_{\rm B}=0.5T_{\rm max}\left[1-\cos\left(2\theta+2\pi/3\right)\right]\\ T_{\rm C}=0.5T_{\rm max}\left[1-\cos\left(2\theta+4\pi/3\right)\right]\end{array}  \right., \tag{18}\]  
and the total electromagnetic torque is:  
\[T=T_{\rm A}+T_{\rm B}+T_{\rm C}=1.5T_{\rm max}. \tag{19}\]  
From Equation (19), the total electromagnetic torque is a constant if the cogging torque and high order harmonics of air-gap flux density are neglected. However, the cogging torque and harmonics of air-gap flux density actually exist in the motor, which will lead to the generation of torque ripple.
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 1 Introduction
The contributions of this paper are itemized as follows. * **Achieving the purpose of cooperative data collection. ** The proposed CDCA partitions the data collection task into \(k\) subtasks through benefit calculations. The mobile sinks can cooperatively collect data along the constructed paths. * **Prolonging network lifetime. ** The proposed CDCA considers the forwarding workload of each sensor, selects the \(k\) sensors with the largest forwarding workloads and then partitions a tree with \(n\) sensors into \(k\) subtrees. This can minimize the energy consumption of sensors with maximal energy consumption and hence prolong the network lifetime. * **Balancing the workloads of sensors for packet forwarding in each subset. ** The proposed mechanism considers the length cost between any two consecutive CPs. Therefore, the established route allows the mobile sink to visit more collection points. Compared with weighted rendezvous planning (WRP) [15], the proposed CDCA distributes the workloads of data forwarding to more collection points, prolonging the network lifetime. * **Maintaining a stable cycle for the rendezvous opportunities between each local mobile sink and global sink. ** The proposed mechanism adjusts the velocities of the mobile sinks to ensure that each local mobile sink and the global sink can have stable rendezvous time periodically. The remainder of this work is organized as follows. Section 2 reviews the existing works related to this study. Section 3 illustrates assumptions, network environment and the problem formulation of the proposed approach. Section 4 presents the proposed mechanism. The experimental results of the proposed mechanism are proposed in Section 5. Finally, Section 6 offers a conclusion and future works.
## 1 Introduction
### Conclusions for the SWS-SYS Project  
Although Brayton batteries are a widespread research topic, there are no comprehensive systematic studies on their topology. Only individual systems or a few different systems are compared, but not the many different possibilities for the exact design regarding the introduction or extraction of heat from the system. While the systems considered in the literature include heat exchangers at various points in the system, a rationale for the specific design is not provided.  
The aim of the present work is therefore to conduct a comprehensive structural concept study with the aim of obtaining Brayton batteries that are simple yet efficient with few components. For this purpose, two different stages of development are considered: compressors at the state of the art (450 \({}^{\circ}\)C compressor outlet temperature) and a realistic and near-term achievable development stage for compressors (625 \({}^{\circ}\)C compressor outlet temperature) with moderate investment costs. Above 625 \({}^{\circ}\)C, cooling of the rear compressor blades or the use of very cost-intensive nickel alloys, as in gas turbines, is necessary [31; 32]. Although the use of reversible turbomachinery, i.e., using compressors and expanders for both the charging and discharging processes, involves a significant reduction in components, this is not investigated here. Firstly, this is not the current state of the art, and secondly, there are no model descriptions of the physical behavior that would not require detailed design details [33]. Furthermore, it is questionable whether such units would actually lead to cost reductions, as the construction of such machines, assuming high efficiencies in both directions, would certainly be significantly more complex.  
The objectives of the study are to identify favored system designs through a comprehensive techno-economic evaluation of system configurations. One focus is on identifying additional flexibility options (e.g., sector coupling, industrial processes, district heating extraction, cooling provision, waste heat utilization, etc.) compared to pure electricity storage operations.