esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Emerging Technology Categorization
### Data Acquisition and Analytics  
The operation management and scheduling in both manufacturing and construction industries conventionally rely on manual methods or outdated simulation and planning software packages. The emerging data acquisition and analytics technologies improve the process of data collection, data analysis, and the communication of process-performance results. These measures increase efficiency in controlling entire project processes such as design, engineering, planning, purchasing, as well as factory floor functions such as materials handling and quality control. The subcategories include:  
1. Sensing technology. Data acquisition is the process of collecting real world physical conditions and converting them into numeric values that can be manipulated by computers. Commonly applied sensing technologies include photogrammetry, digital imaging, laser scanning, GPS sensors, and industrial sensors (i.e., sensors commonly applied in industrial automation processes such as temperature sensors and pressure sensors). These sensors can be applied to collect real-time vision, kinematic, or energy data throughout the project [25].
2. IoT system. IoT (Internet of things) is composed of numerous connected devices that rely on sensory, communication, networking, and information processing technologies [20; 21]. The key technologies that enable the IoT network are wireless sensor networks (WSN) devices, such as RFID and Bluetooth. Other technologies that can integrate with IoT systems include barcodes, industrial sensors and actuators, location-based services, and wearables [30].
3. Optimization and simulation. Various optimization algorithms are used to predict uncertainties, progress, and risks of the construction projects [21; 26]. The collected information from the project can be processed by simulation software to model the behavior of machines, products, and workers [25]. The optimization and simulation results enable the problem prediction, configuration costs reduction, and quality improvement of the industrialized projects.
4. Advanced data analytics. Advanced data analytics are used to extract useful information from vast amounts of data generated from interconnected systems [20]. The specific advanced data analytic techniques that can be used in the construction industry include data management techniques (e.g., data mining, data classification, and data storage) and artificial intelligence techniques (e.g., machine learning and deep learning). The appearance of third-party Cloud computing systems with friendly interfaces and high levels of security and reliability lowers the threshold to using these technologies.
## 4 Dataset Development
### Setting up the Simulation Environment  
The simulation environment was created using Blender, the Cycles ray-tracing engine, for its ability to produce realistic, physically accurate simulations. This level of realism ensures that the synthetic data closely mimic real-world conditions, thereby improving model robustness and reliability. Simple reflection models often fail to account for optical phenomena such as diffraction, scattering, and the intricate interplay of light with micro-surface textures. In contrast, Blender's comprehensive simulation environment allows for the accurate replication of these effects, providing a more robust and realistic dataset.  
The Cycles engine has been validated in numerous scientific studies [49; 50; 51] for its ability to simulate complex optical phenomena. Additionally, the open-source nature of Blender, its extensive online support, and the convenience of programming through Python make it an ideal choice for automated dataset generation. Despite some limitations with caustics and computational cost, Cycles provides a balanced trade-off between usability and precision, making it suitable for our purposes. By leveraging the advanced features of Blender's Cycles engine, we ensure that the synthesized data appear realistic.  
A fixed camera, fixed pattern and surface settings were configured to replicate a realistic scenario for deflectometry, including the use of an orthogonal sinusoidal fringe pattern. This pattern is projected onto a specular object, and the reflected fringes are recorded by a camera. A substantial number of images were captured under these settings, providing a robust foundation for training based on fringe patterns. Figure 3 illustrates this environment for visual clarity.
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 2 Materials and Methods
### Cyber Security  
As every software solution requires connecting to the internet and using interfaces to interact and communicate with other devices, the described solution could also be vulnerable to cyber attacks. However, several security tools, concepts, and mechanisms were applied to ensure safety on a high level. First of all, firewall rules on the edge device, MOXA UC-8100, were applied. Whole inbound network traffic was blocked and then gradually opened for appropriate ports and IP addresses. IP addresses of Azure servers are known [65]. The number of ports through which communication with the cloud takes place is also known (8883-MQTT). Therefore, proper firewall rules could be applied. The connection with Azure IoTHub is secured using TLS/SSL. A comprehensive description of communication details related to IoTHub could be found in Microsoft documentation [66]. MOXA UC-8100 also communicates with PLC using the Modbus TCP protocol. The IP addresses of PLC and Modbus TCP port (502) are known, so another suitable firewall rule could be written. Nevertheless, the described actions were taken to improve security and minimize the risk of security incidents.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 2 Background and Motivation
### Inter-Network Interference  
As previously mentioned, our considered scenario contains many one-to-one networks, each of which includes an End Device (ED) and the AP. Figure 1 shows an example with three networks. In each WLAN, the distance between the ED and the AP is so close that the neighbor networks are within the ED's carrier sensing range. There are \(ED_{1}\) and \(ED_{3}\) in the carrier-sensing range of \(ED_{2}\). On the other hand, \(ED_{1}\) and \(ED_{3}\) have the positional relationship where they cannot detect communication with each other. Therefore, \(ED_{1}\) or \(ED_{3}\) can transmit when the other is transmitting. \(ED_{2}\) cannot decrement the BT because of transmission detection of \(ED_{1}\) or \(ED_{3}\). It is seen from Figure 2 where transmission opportunity of \(ED_{2}\) decreases.
# Modeling Epithelial Homeostasis and Perturbation in Three-Dimensional Human Esophageal Organoids
## 2 Materials and Methods
### Quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR)  
The total RNA was isolated using GeneJET RNA Purification Kit (Thermo Fisher Scientific), and the cDNA was synthesized using High Capacity cDNA Reverse Transcription kits (Applied Biosystems, Waltham, MA, USA) according to the manufacturer's instructions. qRT-PCR was performed with paired primers for _HBEGF_-F (5\({}^{\prime}\)-ATCGTGGGGCTTCTCATGT TT-3\({}^{\prime}\)) and _HBEGF_-R (5\({}^{\prime}\)-TTAGTCCATGCCCAACTTT-3\({}^{\prime}\)), _GAPDH_-F (5\({}^{\prime}\)-GGAGC GAGATCCCCAAAAT-3\({}^{\prime}\)) and _GAPDH_-R (5\({}^{\prime}\)-GGCTGTTGTCATACTTTCTCATGG-3\({}^{\prime}\)) using Power SYBR Green PCR Master Mix (Applied Biosystems). All the reactions were carried out on the StepOnePlus Real-Time PCR System (Applied Biosystems). The relative _HBEGF_ level was normalized to _GAPDH_, serving as an internal control gene.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 1 Introduction
The contribution of this work lies in the novel deployment method proposed, wherein multiple satellites are launched as a single integrated unit and subsequently disengaged and dispersed in a hierarchical manner. This is accompanied by the development of generalized algorithms for constellation phasing, which determine the phase times, required \(\Delta v\) values, and corresponding disengagement timings to achieve the desired orbital spacing of constellations deployed from the many-in-one configuration. Following this introduction, the paper is divided into three main sections. The first section defines the problem through a set of assumptions and outlines the nomenclature and conventions adopted throughout the work. It also introduces the disengagement and phasing equations, which are adapted for the proposed deployment method and used to assess its effectiveness. The second section presents the methodology, detailing the algorithms developed for an enumerative simulation of the deployment process. The third section provides two case studies, one for PocketQube-class satellites and another for CubeSat-class satellites, in which the disengagement sequence is evaluated under the influence of attitude control inaccuracies. The paper concludes with a discussion of the proposed methodology, including a comparison with the Planet Labs deployment model and an assessment of the approach's advantages and limitations.
# Geomechanical Basis for Assessing Open-Pit Slope Stability in High-Altitude Gold Mining
## 2 Materials and Methods
### Analysis of Fracture Orientations and Weakness Planes  
The presence of weakness planes in the near-slope rock mass--such as persistent joints, fault slip surfaces, and bedding planes--significantly reduces slope stability. Although individual rock fragments may exhibit a high strength, the shear strength of a fractured rock mass is several times lower, especially along such weakness planes. Therefore, the assessment of slope stability in fractured rock masses requires a case-specific approach. The orientation of discontinuities relative to the slope surface determines the location and geometry of potential failure planes and influences the selection of the appropriate slope stability calculation model. A methodology is proposed for studying the orientation elements of rock fractures using terrestrial laser scanning, as follows:  
At the open-pit site, a scanning point is selected near the slope, where a tripod is first installed with a prism reflector, followed by a total station to determine the coordinates of the station point. A laser scanner is then mounted at the same point to perform scanning of the slope rock mass. The scanning location is chosen to ensure maximum visibility of the exposed structural surface of the slope. The distance from the scanner to the rock mass should not exceed 800 m; in this case, it is within 200 m (Figure 3). The scanner is activated and oriented toward a known survey control point. The surface of the pit slope and the structural features of the rock mass are scanned with a defined scanning resolution. The scanned data are then processed on a computer using the Maptek I-Site Studio software, which generates a point cloud overlaid with photographic imagery. The resulting 3D electronic model of the rock mass surface is processed to determine the orientation parameters of fractures and discontinuities, including dip and strike angles, the sizes of structural blocks formed by fractures, and the geometric characteristics of the slope benches and pit walls. The post-processing of the scan results for the near-slope rock masses is carried out using the Maptek I-Site Studio software package. By employing specialized fracture analysis tools within the software, fracture systems are identified and block sizes are estimated for use in the rock mass strength classification. The results of fracture analysis at the observa  
Figure 3: Laser scanning of the southern pit wall using a terrestrial laser scanner. tion stations, obtained through both geological compass measurements and terrestrial laser scanning, demonstrate a discrepancy of only 3-4%, which is considered acceptable. The orientation of fractures in bench slopes is studied using a geological compass or a terrestrial laser scanner. The scanned data are processed in the Maptek I-Site Studio environment, which allows for the identification of fracture systems and estimation of block dimensions for further assessment of rock mass strength.
## 2 Problem Statement
### Process Analysis  
Two types of process analyses are considered here: equilibrium and dynamic. Equilibrium state analysis examines the steady-state conditions where all system variables remain constant over time. For this analysis, the input (\(u\)) is assumed to be zero, and all-time derivatives within the set of ordinary differential equations (ODEs) (4) are considered to be zero. This simplification transforms the ODEs into a set of nonlinear static equations which can then be solved analytically. Given the nominal parameter values \(D_{a}=0.072\), \(\gamma=20\), \(H=8\), and \(\beta=0.3\), the process exhibits three equilibrium states (or points): \(x_{A}=[0.144,\,0.886]^{T}\), \(x_{B}=[0.447,\,2.752]^{T}\), and \(x_{C}=[0.765,\,4.705]^{T}\). These equilibria are characterized by complex dynamic behavior. While \(x_{A}\) and \(x_{C}\) are stable equilibria, \(x_{B}\) is unstable. Figure 2 illustrates these equilibria on a phase diagram. Dynamic analysis investigates the process's behavior in response to an abrupt input change. Figure 3 depicts the output response of the CSTR to step changes in the control input (\(u\)), demonstrating a clear dependence of response speed and steady-state gain on the magnitude of \(u\). These nonlinear characteristics of the CSTR may hinder the effectiveness of a conventional PID controller with fixed gains across its entire operating range.  
Figure 2: Phase portrait and three equilibria.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Tools and Frameworks
#### 3.4.3 Conceptual and Methodological Frameworks  
Numerous conceptual and methodological frameworks have been introduced in MMLA research to guide the design, interpretation, and implementation of multimodal systems. These frameworks support diverse objectives, from modelling collaborative learning and personalising feedback to structuring the collection and integration of heterogeneous data. Examples include domain-specific models like the KNIGHT framework for personalised learning analytics and FESSA for sentiment-based student feedback, as well as more generalisable structures like CLM and DUET for analysing collaborative dynamics. Some frameworks are tailored for mobile or embodied contexts, such as Mobile Multimodal Learning Analytics (MOLAM) and MaTHiSiS, while others like transmodal analysis (TMA) and transmodal ordered network analysis model (T/ONA) offer theoretical underpinnings for analysing transmodal and ontological dimensions. Table 3 summarises the main frameworks, their intended purposes, and associated studies.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Tool** & **Purpose** & **Works** \\ \hline EfficientNetB2 & Deep learning architecture used for emotion recognition by analysing video and audio inputs & [76] \\ iMotions & Integrated platform for synchronised recording and analysis of multimodal data such as eye-tracking, physiological signals, and facial expressions & [37; 58] \\ Kinect & Motion sensing and body-tracking tool used for gesture recognition, movement capture, and embodied learning analysis in classroom and game environments & [114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 1
## 4 Experiment Results and Discussion
### Test Results  
Figure 9 shows the test scenario for the evaluation of the SRL-based CACC system. The host vehicle uses a millimeter-wave radar to measure the relative velocity and inter-vehicle distance. The acceleration of the preceding vehicle is sent to the host vehicle via V2V communication. Vehicle-following tests in low-speed driving situations were conducted on a flat, straight road on our campus. To validate the adaptability of the proposed control strategy, time headways of 0.8 s, 1.0 s, and 1.2 s are tested. The safe distance at standstill is set as 5 m.  
The test results with different time headways for the SRL-based CACC system are shown in Figures 10-12. The corresponding maximum, average, and variance of the inter-vehicle distance error are listed in Table 5. Although the velocity of the preceding vehicle fluctuates between 15 and 30 km/h,  
Figure 8: Acceleration distribution for the data samples (**a**) and simulation results (**b**).  
Figure 9: Vehicle-following test scenario.  
the host vehicle can always follow the velocity of the preceding vehicle accurately. The inter-vehicle distance changes according to the desired distance. Distance error occurs when the velocity of the preceding vehicle fluctuates but remains in an acceptable range. Normally, the actual distance is larger than the desired distance when the preceding vehicle accelerates and smaller than the desired value when the preceding vehicle decelerates due to the slight hysteresis of the change of the host vehicle's velocity. The distance error and its variance decrease when a larger time headway is used, as listed in Table 5. In addition, the acceleration of the host vehicle is manipulated by the SRL controller to fluctuate closely with respect to that of the preceding vehicle. In conclusion, the effectiveness of the SRL-based CASC system is validated by vehicle-following tests with satisfactory performance.  
Figure 10: Test results with an 0.8-s time headway: (**a**) velocity, (**b**) acceleration, and (**c**) distance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Headway (s)** & **Maximum (m)** & **Average (m)** & **Variance** \\ \hline
0.8 & 3.41 & \(-\)0.43 & 2.01 \\
1.0 & 2.54 & \(-\)0.12 & 1.24 \\
1.2 & 2.50 & \(-\)0.10 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum, average, and variance of distance error, with the real driving cycle.  
Figure 11: Test results with a 1.0-s time headway: (**a**) velocity, (**b**) acceleration, and (**c**) distance.  
Figure 12: _Cont._
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 3 Acknowledgement-Based Opportunistic Routing Protocol
### Neighbor Discovery and Network Creation  
When the network is started, all nodes tune their radio interface on the same communication channel. This allows each node in the network to be able to receive and broadcast information and to discover its neighborhood. Neighbor discovery helps to build the network and allows nodes to select available channels. All nodes in the network, except the sink, have to discover their 1-hop, 2-hop and 3-hop neighbors. Neighborhood discovery is done using periodic beacon frames broadcast during the start-up phase of the network. Each node builds its 1-hop neighbor list on the reception of beacon frames from its neighbors. Each node includes the list of its 1-hop neighbors in the beacon frame. This way receiving nodes can build the list of their 2-hop neighbors using the neighbors lists of their 1-hop neighbors. The list of 2-hop neighbors is also included in the beacon frames, which will be used by receiving nodes to build their list of 3-hop neighbors. At the end of the neighborhood discovery process, each node in the network except the sink has a list of its 1-hop, 2-hop and 3-hop neighbors. In the network scenario presented in Figure 1, the 1-hop, 2-hop and 3-hop neighbor lists of node 8 are coloured in green and yellow.  
In this version of ABORt, we assume that the number of nodes in the network does not increase once the network starts up. Nodes integration after the network set up phase is out of the scope of this paper.
Sheet beam EIO (SBEIO) is used to solve the problem of relatively small electron beam channels caused by high operating frequency, which affect electron beam transmission [13; 14]. At the same operating frequency, the sheet beam's cross-sectional area is larger than that of a traditional pencil electron beam, resulting in an increased beam-wave interaction area and an enhanced power capacity [15]. As described in the aforementioned studies, they are all based on a single operating frequency band, achieving excellent performance such as high power or wide bandwidth within a single band. However, utilizing two coupled extended interaction circuits to achieve dual-band operation is highly beneficial for expanding the application scope of EIOs, as well as reducing manufacturing complexity and cost.  
Compared with traditional structures, this structure achieved effective connection and energy output by coupling two adjacent circuits through the same coupling slot. The structure of this article is organized as follows: In Section 2, the physical model of EIO interaction is designed and optimized. In Section 3, PIC simulations are conducted, and the results are analyzed. Finally, conclusions are drawn in Section 4.
## 3 Connectivity in Wireless Ad Hoc and Sensor Networks
### Percolation in Infinite Graphs
Designing a large network to achieve full connectivity is much more demanding than designing to ensure that a large percentage of nodes are connected, with an associated increase in energy requirements. This is quantified by simulation in [35] and [36] and analytically in [37]. The largest connected component of a network is called the giant component. In the context of wireless ad hoc networks, the size of the giant component has been evaluated under a wide range of assumptions [36]. Specifically, analytical bounds are known [37] for the minimum transmission range to achieve a giant component that contains more than a given number of nodes. Percolation theory can also be applied to 1-dimensional (1-D) networks, such as sensor networks following rivers. Percolation is much more limited in 1-D, since there is much less variety of possible paths than in higher dimensions. However, some fundamental results are known. For example, the asymptotic connectivity probability of a 1-D network exhibits a strong zero-one law even if the locations of all the nodes independently follow an arbitrary identical distribution, as long as this distribution admits a non-vanishing density function [38]. The zero-one law indicates that the maximal spacing between adjacent nodes converges in probability to a scaling which is dependent on the total number of nodes \(n\), and the density function \(f\), which all the nodes follow. Note that this result is important as it provides a deterministic estimate of the critical transmission range. The application of percolation to the connectivity of 1-D networks has been surveyed by [PERSON] and [PERSON] [39].
## 3 Results and Discussions
### Case Study  
As a case study illustrating the location problem, critical infrastructures in the city of Pittsburgh, Pennsylvania, were analyzed, which has a metropolitan area population of 2.3 million. We concentrate on the center section regarding the metro area, studying a 9 km \(\times\) 9 km section centered on downtown. In addition, the four infrastructures of water, cellular communications, healthcare, and emergency shelter were studied and discussed.