esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RTI Data Acquisition Methods
### Laser Methods  
Terrestrial Laser Scanning (TLS), also termed LiDAR (Light Detection And Ranging), utilises directed lasers to scan the visible tunnel intrados, generating dense 3D point clouds (Figure 2a) at up to 1 \(\times\) 10\({}^{6}\) datapoints per second [40; 41; 42; 43]. Visible light impulses reflect with variable intensity informing relative distances. However, datapoints lack classification labels and do not penetrate the subsurface. This makes segmentation of tunnel features challenging [44], but does permit direct insight into subsurface condition (e.g., profile distortions indicate abnormal strains) [45]. Pursuit of TLS integration with counterpart penetrating NDI methods marks an emerging avenue of long-term research. We note that the development of a standardised, efficient and reliable method to perform the essential alignment of multiple point cloud datasets--to form a unified digital environments--will be a key milestone for innovators to achieve before practical deployment becomes mainstream RTSSI practice.  
Returning to standalone laser methods, we found TLS-RTI studies and commercial contractors most commonly deploy FAROR(r) FOCUS scanning modules [46; 47; 48; 49] (Figure 2b) or the Z+F Profiler(r) 9012 [50; 51; 52] to assimilate RGB optical photography for improved end-user navigational ease in recovered point clouds. Noteworthy innovations include an automated deformation detection assembly [11], which utilises a novel Circular Laser Scanning System (CLSS), highlighting the practicality of adopting circular sensing arrays that complement natural tunnel curvature.  
Notable innovation is showcased in the Tunnel Monitoring and Measurement System (TMMS) developed by [52]. The prototype visualisation framework utilises a Z+F Profiler(r) 9012 mounted on a bespoke rail trolley (Figure 2c) to pass RGB LiDAR tunnel point clouds and a 'roaming video' feed of intrados condition to an engineer's tablet PC. The developed hardware bares strong similarities with a similar mobile TLS apparatus used in [49] employing a FARO X330 scanner. Validation trials in China's Zhengzhou Metro network demonstrate practical deployment capability but also relay that primary functions of ingress and cross-sectional deformation detection suffer noteworthy accuracy and stability reduction when applied to non-circular tunnel profiles (e.g., horseshoe, elliptical, etc.). TMMS therefore flags the importance adaptability in the design of new RTSSI solutions for wide-scale deployment, particularly on older rail networks (e.g., UK) which adopt multiple'standard' tunnel cross-section variants.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 3 Circular Grid Pattern  
### Circular Grid Pattern Shape  
The tensile specimen used in this study applied the KS B 0801 standard [35], as shown in Figure 6. The material used was STS (stainless steel) 304 with a thickness of 3 mm. In this context, T represents thickness, L is the gauge length, r is the parallel length, R is the radius, and B is the grip width, which are 3 mm, 50 mm, 60 mm, 20 mm, and 20 mm, respectively. The specimen was processed using laser cutting.  
This paper proposes a circular grid pattern to replace the speckle pattern used in traditional DIC. The proposed circular grid pattern can measure strain from the gauge length to the local strain at the fracture site. As shown in Figure 7, the pattern consists of grids with circles arranged in configurations, such as 1 \(\times\) 5, 3 \(\times\) 5, 1 \(\times\) 7, and 5 \(\times\) 7. The  
Figure 5: AI model structures VGG [32], ResNet50 [33], and MobileNetV2 [34].  
Figure 6: Tensile specimen diagram.  
pattern position is based on the center of the parallel section of the tensile specimen. There must be at least one row, and at least three circular markings must be formed in the tensile direction. It should consist of at least two circles indicating the gauge length and one or more positioned at the specimen center. However, because fractures do not consistently occur at the center, at least five circular markings should be formed in the tensile direction to account for the fracture location. Accordingly, the 1 \(\times\) 5 circular grid is the minimum pattern proposed in this study, and the minimum circle size can be selected on the basis of the material strain and camera specifications. This is a crucial factor because of the area within the camera frame where the tensile specimen deforms must be specified, considering both the initial state and the deformation. The tensile specimen moves further from the camera as it strains, and the extracted tensile specimen area becomes a low-resolution image as the camera moves away. Hence, it is necessary to examine the minimum pixel size of the circle that can be calculated for strain.
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 2 Related Work
### Object Detection  
In recent years, Convolutional Neural Networks (CNNs) have been widely applied to object detection tasks, with the evolution of detection frameworks primarily distinguishing between single-stage and two-stage detectors. Common two-stage detectors include the Faster R-CNN [6] series algorithms, which generate candidate boxes and then classify and refine their positions. Two-stage detectors typically achieve better accuracy but may compromise on speed. On the other hand, single-stage algorithms such as YOLO [2; 3; 4; 5] (You Only Look Once) and SSD [17; 18] (Single-Shot Multibox Detector) directly detect and locate objects in images using a single neural network model. These models are known for their speed and simple design but may face challenges in precise localization and detecting small objects. Notably, Facebook's DETR [19] integrated Transformers into the object detection framework, eliminating the need for post-processing like NMS and outperforming Faster R-CNN in metrics, albeit showing poorer performance in detecting small objects. TPH-YOLOv5 [20] introduced Transformers into the detection head and applied it in drone capture scenarios. Subsequently, [PERSON] et al. [21] optimized this detection head. Considering the application scenarios of foggy weather detection tasks, this paper adopts YOLOv5s due to its significant advantages in real-time processing, end-to-end training, and stability.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Pan-Tilt-Zoom Attacks  
Pan-Tilt-Zoom (PTZ) is a functional characteristic of a surveillance camera that can zoom in and out, and change the view of the camera to horizontal (right, left) and vertical (up, down) angles. Camera models utilize stepper motors built into them and employ PTZ data protocols to achieve this functionality. The PTZ data protocols use RS-422 or RS-485 links, as well as Ethernet and Wi-Fi channels. PTZ-capable cameras can be controlled by keyboard shortcuts or with a specially designed joystic using PTZ commands. A compromised PTZ camera can send data to the external attacker along with the PTZ coordinates [33]. When a user is using a mobile application to watch a live feed from the camera through a cloud server, then all the PTZ requests are routed through cloud servers to the camera. If this communication is carried out after an interval of every few seconds, an attacker who is intercepting this communication may not be able to decode the PTZ data but can precisely find the interval after which communication is happening. The attacker can then launch an attack that matches with the communication interval, thus avoiding detection [34].
## 4 Experiments
### Foreground Detection  
The input video frame was segmented into foreground and background by the SuBSENSE algorithm, and multiple foreground objects were extracted. SuBSENSE [15; 20] combines the color and local binary-similarity pattern features to detect moving objects. This method outperformed all previously tested state-of-the-art unsupervised methods on the CDnet [30] dataset. As a famous  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Frame Dimension** & **FPS** & **Number of Frames** & **Objects** \\ \hline video 1 & 320 \(\times\) 240 & 24 & 1677 & Pumping unit, person \\ video 2 & 352 \(\times\) 288 & 24 & 1708 & Pumping unit, person, vehicle \\ video 3 & 640 \(\times\) 840 & 24 & 1643 & Pumping unit, person \\ video 4 & 640 \(\times\) 480 & 24 & 4031 & Pumping unit, person, vehicle \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of video datasets.  
Figure 5: Fine-tuning GoogLeNet.  
benchmark dataset, CDnet provides ground truths for all video frames that range over diverse detection challenges such as dynamic background and various lighting conditions. Based on its excellent performance, SuBSENSE was used to extract the moving objects. Figure 6 presents the results of background subtraction. As can intuitively be seen, the segmentation results of SuBSENSE outperformed other methods. In foreground detection, several parts of the pumping unit were normally detected as the foreground rather than the whole pumping unit. The reason is that pumping units have a large scale along with periodic rotation in surveillance scenes. Some parts of the pumping unit are judged as background by background-subtraction methods.  
Pumping unit surveillance is a long time supervision; therefore, the background subtraction method has to address the light condition changes. In order to further verify the foreground extraction ability of the background subtraction method in light condition changes, a long term video was tested. Figure 7 shows the background subtraction results in the variant light conditions. As can intuitively be seen, the region of foreground detection of the pumping unit is less sensitive to the changing light. The experimental results show that SuBSENSE is able to eliminate the interference caused by light condition gradual changes.  
Figure 6: Comparisons of foreground-segmentation results. (**a**) Input images; (**b**) SuBSENSE; (**c**) Gaussian mixture model (GMM); (**d**) kernel-density estimation (KDE); (**e**) ViBe.
## 1 Introduction
We note that this paper focuses on continuous PDFs of arbitrary shapes and does not consider data-driven approaches which define a PDF through its moments without any further assumptions about it [31; 32]. Due to a number of desirable properties, e.g., granularity, interpolation, and quadrature stability, and nestedness, weighted Leja nodes have been getting increasing attention in the context of approximation and UQ [33; 34; 35; 16; 30; 36]. Nonetheless, with the exceptions of [34; 37; 38] where beta distributions are considered for the parameters, the use of Leja nodes has been limited to uniform and log-normal parameter distributions. The contribution of this paper is exactly to start filling this gap with the application of weighted Leja interpolation for possibly high-dimensional systems with arbitrary parameter PDFs. To that end, we present here the use of weighted Leja rules in the context of sparse Lagrange interpolation for approximation and UQ purposes in a comprehensive and self-consistent way. Section 2 summarizes the main theory related to weighted Leja interpolation. In Section 2.1 we first present a general overview of the UQ setting in which Leja interpolation will be applied. Then, in Section 2.2 we recall the basics of univariate Lagrange interpolation, both standard and hierarchical. Unweighted and weighted Leja rules along with their properties are presented in Section 2.3, while in in Section 2.4 the extension of Lagrange interpolation to higher dimensions, along with a dimension-adaptive, Leja-based scheme for the construction of sparse approximations and its use for UQ purposes, is presented. Numerical experiments which verify the suitability of the Leja interpolation scheme for physical systems with arbitrarily distributed stochastic input parameters are presented in Section 3. In the same section, the adaptive weighted Leja interpolation UQ method is compared against a well-known adaptive gPC algorithm based on least angle regression (LAR). We conclude with a discussion on the overall findings of this work and on possibilities for further research in Section 4.
## 1 Introduction
The results showed that PI-FLC presented reductions in the overshoot and better performance than the other controllers. [PERSON] and [PERSON] [28] and [PERSON] et al. [29] used the Matlab environment in order to simulate the orientation of a heliostat by using an FLC with two different rule bases, comparing the output response with a PI, a PID, a PI-FLC and a neural controller. The results showed a better output response for the FLC compared with the other controllers. Additionally, the FLC with fewer rules showed a better output response to step changes than the FLC with the bigger rule base. [PERSON] et al. [30] simulated the position control of two DC motors in order to modify the orientation of a heliostat by using a PID controller self-adjusted by an FLC. The FLC adjusts each PID controller gain by using an individual rule base of forty-nine rules and the error and change of error values. The results showed a faster output response and a smaller overshoot than a classic PID controller. [PERSON] and [PERSON] [31] applied an FLC to control the orientation of a lab-scale heliostat with two stepper motors by using a micro-step driver. The position of the heliostat is obtained by using image processing of the reflected solar radiation on the target. The results showed a good performance of the FLC; however, there are tracking errors when the system cannot process the image due to passing clouds. Nevertheless, the works cited above have only been presented in simulations and small-scale models. Considering the aforementioned, the objective of this paper is to describe the design and implementation of a two-axis STS for the orientation control of a real-scale azimuth-elevation heliostat by using an FLC implemented on a low-cost microcontroller-based embedded system. The comparison between the FLC and a PID controller has also been done.
## 3 Results
### Special Role of PRI in the SIF-GPP Relationship for Corn and Soybean
#### 3.2.3 Impact of PRI on the SIF-GPP Relationship under Different Stress Conditions  
In this study, in order to better explore the impact of PRI on the SIF-GPP relationship under different stress conditions, we considered two stress indicators, including CWSI and VPD. CWSI can reflect the stressed status of crops in response to water deficit. Water deficit affects crops' physical-physiological activities (e.g., photosynthesis and transpiration), among which the stomatal conductance is an important adaptation mechanism to environment. Additionally, we used VPD to represent the degree of dryness of the air.  
Figure 6a,b shows that for both corn and soybean, PRI falls with increasing CWSI. In reaction to stress, a plant will dissipate more energy absorbed by chlorophyll molecules through NPQ. The reduction in energy allocated for photosynthesis or the stomatal responses to stress may be the causes of GPP downregulation. As \(SIF_{t}\) or NPQ are a component of the light reactions other than those that are related to the dark reactions, the incongruuity of light and dark reactions is a potential factor that could influence the GPP estimation based on \(SIF_{t}\) and PRI. Despite a slight decline of PRI with VPD for soybean, PRI showed an obvious decreasing tendency with increasing VPD for corn (Figure 6c,d).  
Figure 5: Relationships between PRI and \(SIF_{t}\) for corn ((**a**) for half-hourly data; (**c**) for daily data) and soybean ((**b**) for half-hourly data; (**d**) for daily data). The Pearson correlation coefficient and the significance level were given. A significance level of 0.01 is represented by **. The color bar indicates the magnitude of GPP. The solid black line is the fitted curve.  
In addition, using correlation coefficients, we also analyzed the impacts of CWSI and VPD on the relationships between GPP and \(SIF_{t}\) as well as PRI (Figure 7). For corn and soybean, the GPP-\(SIF_{t}\) relationship exhibited a gradually downward trend along with CWSI (Figure 7a,b). The GPP-\(SIF_{t}\) relationships for corn exhibited a more pronounced decline than those for soybean with an increase in CWSI. The GPP-PRI relationship resembled a flat lying \"S\" shape with increasing CWSI. When CWSI was higher than 0.6, there was a downward tendency in these correlation coefficients. Figure 7c,d also depict the impact of VPD on the relationship of GPP to PRI and \(SIF_{t}\).  
Figure 6: The first row shows violins of the PRI at 0.1 intervals of CWSI (**(a)** for corn, (b) for soybean), and the second row represents violins of the PRI at 0.5 intervals of VPD (**(c)** for corn, (**d**) for soybean).  
Figure 7: The influences of CWSI on the relationships between GPP and \(SIF_{t}\) as well as PRI for corn (**(a)** and soybean (**(b)**. (**c,d**) represent the effects of VPD on the relationships between GPP and \(SIF_{t}\) as well as PRI for corn and soybean, respectively. The blue line shows the GPP-\(SIF_{t}\) relationship, the red line shows the GPP–PRI relationship. CWSI was distinguished as a 0.1 interval. VPD was distinguished as a 0.5 interval.
## 2 Materials and Methods
### Voltammetric Analysis  
The analyses were carried out according to previous experiments [41]. The samples were prepared in 50 mL flasks, adding 8 mL of ethanol (96% \(\mathrm{v}/\mathrm{v}\)), the required volume of biolubricant, 4 mL of buffer (NaH\({}_{2}\)PO\({}_{4}\)/H\({}_{3}\)PO\({}_{4}\) 0.5M, pH = 2.5), and 2 mL of tensioactive (0.8% cetylmethylammonium bromide, CTAB), to obtain the required PG and/or TBHQ concentrations. Afterwards, it was diluted to 50 mL with ultrapure water, treating the sample with ultrasound for 4 min for a suitable homogenization. Finally, the sample was placed in the electrochemical cell (with a previous deoxygenation for 3 min) and CV or DPV voltammograms were registered. The measure was carried out by using a voltammetric device (\(\mu\)Autolab, ECO Chemie, Utrecht, The Netherlands) coupled to a 663 VA-Stand Met Rohm unit (Herisau, Switzerland) with a three-electrode system: a working electrode (glassy carbon), a reference electrode (Ag/AgCl), and an auxiliary electrode (Pt) with a measuring cell. The glassy carbon was cleaned with a piece of cotton  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Details** & **Ref.** \\ \hline Viscosity and Cold Filter & A Cannon-Fenske viscometer was used, & \\ Plugging Point (CFPP) & controlling temperature at 40 \({}^{\circ}\)C. For CFPP, the & [49; 50] \\ Density & A densiteter was used for this determination. & [51] \\  & FAME content was analyzed by using a gas & \\  & chromatograph (Varian 3900, Varian, Palo Alto, & \\ FAME content & CA, USA) coupled to a flame ionization detector & [52] \\  & (FID). Main FAMEs, such as methyl oleate, & \\  & linoleic,ionate, palmitate, and stearate. & \\ Acid value & According to UNE-EN 14104 standard. & [53] \\  & Iodine value & According to UNE-EN 14111 standard. & [54] \\  & Oxidation stability & Rancimat method was used, at 110 \({}^{\circ}\)C. & [55; 56] \\  & Flash and fire points & Cleveland open cup method was used. & [57] \\  & Marvel of four seasons lettuce (_Lactuca sativa_) & \\  & was selected for this test (lettuce is a recurring & \\  & species in this kind of tests, as observed in & \\  & references), adding 20 mL of water as control & \\  & sample, 25\% and 50\% solution of WCO & \\ Phytotoxicity test & biolubricant. Afterwards, the lettuce samples & \\  & were visually assessed, including chlorophyll & \\  & content by using a chlorophyll meter (SPAD 502 & \\  & Plus, Konica Minolta, Tokyo, Japan). & \\  & Measurements were carried out in different & \\  & leaves on different days for one week. & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main characterization tests for WCO biodiesel and biolubricant.  
after each measurement, using N,N-dimethyl formamide (DMF) for 2 min and Milli-Q water for 30 s.
# Development and Verification of a Simulation Model of an Automated Assembly Line
## 2 Application of Simulation Models
### Tecnomatix Plant Simulation  
Tecnomatix Plant Simulation is a discrete event simulation program [17]. With its help, it is possible to create a digital twin for the real process [18; 19]. Simulation software from Siemens is used to simulate production processes and systems as well as logistics systems.  
The program can be used at all stages related to the process and system, from planning and design, through simulation and verification, to implementation and management [20]. Plant Simulation is a good tool for modeling complex digital models of various types of systems [21]. The simulation program allows you to quickly test many experiments and scenarios without the participation of a real model and then analyze and optimize existing, real processes. Thanks to the simulation, it is possible to test many scenarios of the process that is in the design phase and then choose the best solution to implement a sustainable production process [22]. It is possible to optimize the flow of material, facilitate the effective use of resources, and streamline logistics processes [23].  
Modeling takes place in a 2D and 3D environment. Plant Simulation uses a hierarchical structure in which even the most complex simulations can be easily edited and analyzed [24]. Copied objects, thanks to their hierarchical structure, inherit properties from each other. This makes it possible to copy the finished simulation model to a new simulation [25]. Tecnomatix Plant Simulation can be connected to a PLC [26]. The developed simulation model can be combined with virtual reality using Go VR. This function allows you to connect to HMD or VR glasses [27; 28].  
The benefits that can be obtained thanks to the use of Plant Simulation software include [23; 29; 30; 31]:  
* Quick design and optimization of the technological process;
* Assembly planning and verification;
* Verification of the use of robots and automated devices;
* More efficient arrangement of machines and warehouses;
* Better quality management;
* Analysis and reduction in energy consumption;
* Maximum use of production machines;
* Faster achievement of positive results and definition of consequences;
* Testing innovative strategies in a secure virtual environment;
* Quick identification of the source of the problem in the production area;
* Reducing the investment risk by using quick verification in the simulation.  
Tecnomatix Plant Simulation supports product lifecycle management. The possibility of continuous analysis of processes, thanks to the use of simulation, allows for continuous improvement and thus contributes to the success of companies.