esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case Study: Faults in the Lithuanian Distribution Grid
#### 3.2.1 Correlation with Grid Reliability Indexes
As has been mentioned in Section 1, one of the reasons is the absence of massive and fully automated (digitalized) PQ monitoring systems in the world [5]. The second reason is the not clearly (universally) defined boundaries between listed PQ events. A summary of the interruption time thresholds defined by European countries can be found in [50] (p. 24), and some countries do not define all types of interruptions. In Lithuania, the boundary between short and long interruption is 3 min, but the boundary between a transient and short interruption is not set. EN 50160:2010 limits voltage sag duration up to 1 min; however, a 1 min voltage sag whose residual voltage is, for example, 0.1-0.2 p.u. will probably has a similar effect as 1 min interruption (i.e., when the residual voltage is up to 0.05 p.u.). Nevertheless, it is obvious that if operation of ACR is successful, a customer feels a voltage sag/transient interruption or at least short interruption, and it is highly desirable to avoid outage minutes under successful operation of ACR. Since ACR impact has been evaluated with Lithuanian distribution grid data, the situation can be understood through the prism of the methodology of Section 2.3 (Table 15). Firstly, the fewer minutes are lost during ACR events, the higher is the success of ACR (see Equation (4)): the lowest success coefficient (0.9567) belongs to the 35 kV grid in 2016, however, as has been mentioned earlier, the influence of the 35 kV grid on total SAIDI is very low--up to 5% (SAIDI including ACR was 1.4311, excluding ACR it was 1.3691). Secondly, the ACR ratio, defined by Equation (5), is given in the brackets as a supplementary characteristic: the higher the ACR ratio, the lower is its impact on SAIDI, hence the more significant is the success of ACR.
## Nomenclature  
\(\rho\)Density [kg\(\cdot\)m\({}^{-3}\)]  
\(u\), \(u\)Velocity [m\(\cdot\)s\({}^{-1}\)]  
\(p\)Pressure [Pa]  
\(\tau\)Viscous stress tensor [N\(\cdot\)m\({}^{-2}\)]  
\(\mu\)Dynamic viscosity [Pa\(\cdot\)s]  
\(\mu_{\rm t}\)Turbulent dynamic viscosity [Pa\(\cdot\)s]  
\(\lambda\)Bulk viscosity [Pa\(\cdot\)s]  
\(\mathbf{I}\)Identify tensor [N\(\cdot\)m\({}^{-2}\)]  
\(e\)Internal (thermal) energy [J]  
\(\kappa\)Thermal conductivity [W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\)]  
\(T\)Temperature [K]  
\(k\)Turbulent kinetic energy (TKE) [J\(\cdot\)kg\({}^{-1}\)]  
\(\sigma_{k}\)Turbulent Prandtl number for \(k\) [m\({}^{2}\cdot\)s\({}^{-1}\)]  
\(R\)Gas constant [J\(\cdot\)mol\({}^{-1}\)K\({}^{-1}\)]  
\(\sigma_{\varepsilon}\)Turbulent Prandtl number for \(\varepsilon\) [m\({}^{2}\cdot\)s\({}^{-1}\)]  
\(\varepsilon\)Turbulent kinetic energy dissipation rate [J\(\cdot\)kg\({}^{-1}\)s\({}^{-1}\)]  
\(G_{\rm K}\)Generation of turbulent kinetic energy due to mean velocity gradients  
\(C_{1e}\), \(C_{2e}\), \(C_{\mu}\)Constants (dimensionless)  
\(\omega\)Specific dissipation rate of turbulent kinetic energy [s\({}^{-1}\)]  
\(\sigma_{\omega}\)Turbulent Prandtl number for \(\omega\) [m\({}^{2}\cdot\)s\({}^{-1}\)]  
\(\alpha\), \(\alpha\), \(\beta\), \(\beta^{*}\)  
Empirical coefficients  
Superscript \(T\)Matrix transformation
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 4 System Architecture
### Flow Diagrams of the Processes
#### 4.3.3 Image Capture Process  
The computer at the control room keeps a record of all the sensor data received. When the data from one of the nodes exceed or is lower than expected values, the computer at the control room forwards the location of the node to the UAV. Since the nodes are static the coordinates of each node are known. The UAV then takes off and moves towards the node that reported the data [25]. When it is over the node, it starts taking pictures of the leaves and the area around. The UAV takes two pictures over each initial location and then rotates horizontally for 40\({}^{\circ}\) and takes pictures again. The process
# A Blockchain-Based Auction Framework for Location-Aware Services
## 3 The Proposed Blockchain-Based Auction Framework
### Post-Auction
#### 3.3.4. Violation Dispute and the Witness Group  
Once a violation claim is received, the submitted claim will then be transferred to the Witness Group (WG). This group consists of several participants who are responsible for disputing any violation claim. The concept of employing witnesses has been used in several application domains [20]. Witnesses are third-party participants, neither seller nor buyer. A witness is assumed to be trustworthy, and the selection of such participants must ensure the satisfaction of any regulation imposed by the government. The process of selecting the members of the witness group is outside the scope of this work, and we adopt the assumption that they are fair and always act honestly.  
The members of the witness group have access to the auction agreement details, including the uploaded images. Such images are accessible using their hash values that are stored on-chain. The members of this group use their private keys to decrypt the images' hash values. They must process each submitted claim within a pre-determined time interval (\(t\)). After reviewing the claim details, each member of this group can confirm or reject the received claim. Once the time deadline (\(t\)) is reached, the outcome with the highest votes will be selected as the response for the claim. When the violation claim is confirmed and if the claim owner is the buyer, any scheduled payment starting from the claim date will be canceled. In comparison, if the claim owner is the seller, besides canceling the auction agreement, any insurance amount paid by the buyer will be transferred to the seller.  
Furthermore, rejected claims will not result in terminating the period of the auction contract. For any confirmed claim, the violator will be given a chance to challenge the outcome during a pre-determined time interval (\(C_{t}\)). In this situation, the witness group can either confirm the previous judgment or announce a new final outcome.  
The members of the witness group are rewarded for their service, and part of the collected fees during the auction process can be used for rewarding purposes. The decentralization nature of this process depends on the number of members in the witnesses group. Thus, increasing this group's size supports the proposed system's decentralization aspects.
## 2 Methodology
### Boundary Conditions  
Boundary conditions of the present numerical wave tank are presented as follows.  
1. At the inlet boundary, the velocity for the water is given according to the wave theory, while the air velocity is zero. The pressure is set as a zero normal gradient boundary condition.
2. At the outlet of the domain, the velocities for both water and air are set to zero. The zero normal gradient condition is applied for the pressure.
3. At the top boundary, the velocity for the inflow is calculated from the flux in the patch-normal direction. The velocity for the outflow is set as the zero normal gradient boundary condition. The patch pressure \(p_{p}\) on the top boundary is obtained by subtracting the dynamic pressure from the total pressure, expressed as follows, \[p_{p}=p_{0}-0.5|\mathbf{u}|^{2}\] (17) where \(p_{0}\) is total pressure and \(\mathbf{u}\) is the mean velocity vector.
4. The no-slip condition is employed in four boundaries, i.e., front, back, bottom, and the cylinder's surface, where the velocity is zero. Meanwhile, the wall functions are employed on these boundaries. The distance of the first layer center to the wall is 0.002 D, where D is the diameter of the cylinder. It ensures that the dimensionless wall distance \(y^{+}\) for the present simulation is at the range of 40-200.
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 2 Methodology
### Vee Model  
The V model is a basic model of traditional systems engineering. According to NASA [29] and INCOSE [57] systems engineering handbooks, technical processes can be organized and managed according to this model [59]. The V model shows the stages of the life cycle that are executed through levels, but carrying out verification and validations at the end of each stage in order to ensure that failures are not propagated in later stages, to finally verify that the user's requirements have been fulfilled. In this way, then, the diagram begins in the upper left part with the definition of user requirements and ends in the upper right part with the confirmation of such requirements. The left side explains the requirement-based design process from top to bottom, while the right side explains the validation and integration process from bottom to top [60]. Part of the model used on the ROV Pionero500 is shown in Figure 3, focusing on development and production stages, while concept and operation stages are addressed deeply in [54]. The outcome of such stages is mostly common for both software and hardware systems.  
Figure 1: Pionero500 system’s simplified functional architecture [54,58].  
Figure 2: Pionero500 modular hardware architecture presented in [54]. Three main groups can be identified: subsea systems (which include the vehicle itself and all its components), topside systems (components that are at the surface station), and the umbilical cable that connects the first two systems. Power box, CPU box, and mux are required for basic operation, while modularity allows using more boxes, i.e., sampler box, to expand the ROV capabilities.  
The model defines stakeholders interactions (Figure 3). This is useful for validation on both stages, development and production, and at every implementation level vertically; for instance, think of architectural design that must be validated with stakeholders, as well as its implementation in production stage. One of the main elements of the model are the baselines, that are established during the early stages of the project and must be fulfilled at the end of each stage, indicating the beginning of the next one, as it happens for hardware and software systems. Prognostic and problem-solving actions and tools are specified as detailed as possible with the resources available when developing the model (lower part of the diagram in Figure 3). Regarding software development, engineering design selection processes applied to choose an architecture and then producing a prototype were specified among such activities at the development stage. Production stage tools in this context comprise typical software tests applied to both hardware and software systems, such as unit testing (UT) and quality assurance (QA).
## 3 Results and Discussion  
### Density and P-Wave Interval Transit Time  
The bulk density of the rock samples is listed in Figure 4, where it can be noted that the bulk density of the samples ranged from 2.17 to 2.50 g\(\cdot\)cm\({}^{-3}\) with an average of 2.35 g\(\cdot\)cm\({}^{-3}\). Additionally, there was a certain range of variation in the bulk density of different samples, and the change was less than 0.36 g\(\cdot\)cm\({}^{-3}\). The difference in bulk density may be caused by the difference in the mineral composition, mineral content, and porosity of samples from the Permian Lucaogou Formation in the Junggar Basin. The P-wave interval transit time of the rock samples is shown in Figure 5 and was between 60.96 and 94.22 \(\upmu\)s/ft for the samples, with a mean value of 74.84 \(\upmu\)s/ft. There is a certain range of variation in the P-wave interval transit times of different samples, and the change is less than 33.62 \(\upmu\)s/ft. The difference in the P-wave interval transit time may be due to the difference in the pore structure of samples from the Lucaogou Formation of Permian in the Junggar Basin. The correlation between the density and the P-wave interval transit time is depicted in Figure S5 (within the Supplementary Data). The P-wave interval transit time decreases with increasing density, with a correlation coefficient of 0.5895. The results of the study are in consistent with the conclusions of previous research [16].
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
###### Abstract  
The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellite Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Parana, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis.  
remote sensing in hydrology; precipitation; performance evaluation; GPM Article  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Biosystems Engineering Department, College of Agriculture \"Luiz [PERSON]\"--University of Sao Paulo (ESALQ/USP), Av. Padua Dias, 11, Piracicaba 13418-900, Brazil; [EMAIL_ADDRESS] (H.C.B.); [EMAIL_ADDRESS] (S.N.D.); [EMAIL_ADDRESS] (I.Z.G.)  
22 January 2021  
23 February 2021  
28 February 2021
## 1 Introduction
Research on the topic has consistently emphasized the critical role of synchronization stability in the performance of chaos-based systems. For instance, studies on systems such as the Vilnius chaos oscillator highlight the importance of robust synchronization methods to withstand noise, parameter deviations, and environmental variability [33]. This is particularly crucial in applications of chaos-based communication, where synchronization directly impacts the system's ability to securely and accurately transmit data [34; 35]. Our previous studies have examined key synchronization parameters, including synchronization time and noise immunity [35]. These investigations provided valuable insights but were often limited to single configurations. Additionally, research into analog-discrete synchronization has explored different configurations, highlighting the potential for hybrid systems to achieve robust synchronization under specific conditions [36; 37]. In this work, we extend this research by testing chaos oscillator synchronization in noisy environments to evaluate its resilience and behavior in various configurations. The collected experimental data were processed and analyzed in MATLAB R2023a to assess synchronization performance under different noise conditions. Based on these findings, we propose an approach for quadrature chaos shift keying (QCSK) communication system construction, where the system's core is a chaos oscillator and secure communication is ensured through chaotic synchronization between the drive and response oscillators. QCSK utilizes chaos shift keying (CSK) to encode the data stream, with quadrature amplitude modulation applied to enhance spectral efficiency. This approach offers improved resistance to noise and eavesdropping compared to conventional CSK schemes. This study aims to evaluate the performance of the QCSK system and determine its design particularities by assessing synchronization noise immunity across different configurations and analyzing the bit error rate (BER) in the QCSK system. The research addresses the limitations of prior studies by providing a comprehensive evaluation of synchronization methods, bridging the gap between theoretical models and practical applications, and advancing the field of chaos-based secure communications. This article is organized into five sections. Section 2 introduces the Colpitts and Vilnius chaos oscillators and provides an overview of chaotic synchronization. Section 3 investigates the noise immunity of synchronization between chaotic oscillators across different configurations. Section 4 examines the QCSK communication system, evaluating its noise immunity. Finally, Section 5 concludes the study.
## 2 Methodology
### Object Detection and Tracking  
Object detection expresses the minimum range of an object in an image as a bounding box composed of four rectangular coordinates. Because the bounding box can convert the position of the visually expressed object into data, the local image processing of the object is conducted within the bounding box image. Thus, the center point of the bounding box can be defined as the only single pixel coordinate where the target object exists in the image. Among various object detection techniques of region-based CNNs (R-CNNs), YOLO-v3 [29] demonstrates object detection speed that is more than 100 times faster than that of Fast R-CNNs [30], thanks to two model structure updates from YOLO-v1 [31]. In addition, the YOLO-v3 model has the advantage of being able to learn universal objects well, so it has been recently adopted in various object recognition models in the construction field [32; 33; 34].  
Tracking is focused on matching detected objects in neighboring frames. Vision-based object tracking algorithms include mean-shift-based, optical-flow-based, and Kalman-filter-based techniques based on image processing methods. The Kalman-filter-based technique is used for object tracking because it yields high accuracy, has a higher speed than the deep learning method, and requires no additional training.  
The basic principle of the Kalman-filter-based object-tracking technique is to (1) predict the motion of a target object and then (2) correct the newly extracted location through the predicted motion to detect the same object in successive frames. Different tracking techniques  
Figure 1: Flow chart of generating proximity visualization.  
are derived using the method for predicting the movement of an object or correcting the location information [35; 36; 37]. In this study, the velocity of detected objects is calculated for consecutive frames and formulated for the object position during the prediction stage. In addition, the predicted object is corrected based on the intersection-over-union score of the Gaussian covariance, considering the optical features of the detected objects.
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 3 Materials and Methods
### Post-Processing  
The output of the deep-learning model is fine-tuned by implementing a post-processing stage. If two regions with the same label exist, the larger region is identified as the desired region, while the smaller region is categorized as unwanted. Initially, we identify border pixels using chain coding. Subsequently, we detect neighboring pixels of each border pixel using 8-connectivity. We only consider those neighboring pixels with intensities differing from those of the unwanted portion. As shown in Figure 6, we observe three cases that need to be addressed.  
_Case-I:_ If all the neighbor pixels are 0 (background), then dissolve the unwanted portion into the background.  
_Case-II:_ If the neighbor pixels consist of background pixels and pixels of a specific label, then perform the following:  
* First, ignore background pixels.
* Then, dissolve the unwanted portion into label pixels.  
_Case-III:_ If there exist two different labels in the neighboring pixels, then perform the following:  
* Count the number of border pixels that lie in both labels.
* The most frequent label is the winner.
## 3 Results and Discussion
### Influence of Shadow Effects on Indoor VLC Systems under Optimized Layouts of Light Sources  
To solve the above problems, a control variable method was used to optimize the layout of four LED light sources, and then the influence of the total shadow generated by human obstacles on the indoor visible light communication system before and after the optimized layouts of the light sources were compared and analyzed.  
Figure 9 shows the variation in the mean square error of illuminating with the distance L between four LEDs and the wall. As seen from the figure, when L = 0, that is, when the four LEDs were located at the four corners of the ceiling, the illumination in the four corners of the room was much larger than that in the center of the room, and the mean square error of illumination was also larger. As the distance L between the LED and the wall gradually increased, the mean square error of illumination on the receiving plane decreased first and then increased. When L = 0.9 m, the mean square error of illumination on the receiving plane reached a minimum value of 90.29 lx, and the distribution of illumination on the receiving plane was the most uniform.  
Figure 10 shows the illumination distribution of the receiving plane before and after the optimized layout of the light source, respectively. Before optimization, the illuminance distribution of the system ranged from 328 lx to 1065 lx, and the mean square error was 139.51 lx. After optimization, the illumination of the receiving plane of the system was between 441 lx and 921 lx, and the mean square error was 90.29 lx, both of which met the requirements of international lighting standards. However, after optimization, the illumination distribution of the receiving plane was more uniform.  
Figure 11 shows the illumination mean square error and SNR mean square error data of the receiving plane of the system under different numbers of human obstacles before and after the optimal layout of the light source, respectively. As seen from the two figures, both the mean square error of the illumination and SNR on the receiving plane increased with an increase in the number of obstacles before and after the optimized layout of the light source, and the mean square error of the illumination and SNR after the optimized layout were generally smaller than those without the optimized layout of the light source. In Figure 11a, when the number of obstacles was 16, the mean square error of the illumination after optimization was reduced by 58.3 lx compared with that before optimization. In Figure 11b, when the number of obstacles was 16, the mean square error of the optimized SNR was reduced by 0.49 dB compared with that before optimization. This shows that optimizing the layout of the light source could reduce the influence of the shadow effect caused by human body obstacles on the system's performance. In addition, there was also  
Figure 10: Illuminance distribution of the receiving plane. (**a**) Before the optimized layout; (**b**) After the optimized layout.  
the phenomenon of an inverse increase in shadow areas caused by the randomness of the obstacle position.