esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Formulation  
Consider three-dimensional double diffusive convection inside a cubical enclosure filled with incompressible viscous electrically conducting micropolar nanofluid Al\({}_{2}\)O\({}_{3}\)/water as shown in Figure 1. The uniform magnetic field is applied perpendicularly to the differentially heated cavity walls. The fluid thermo-physical properties are supposed to be constant except the density in the buoyancy force term which is modeled by Boussinesq approximation. The base fluid (i.e., water) and nanoparticles are assumed to be in thermal equilibrium state. The thermo-physical properties of the nanofluid are listed in Table 1.  
The viscous dissipation, radiation and Joules heating effects are judiciously neglected. It is also assumed that the magnetic Reynolds number is as small as the induced magnetic field is neglected. Using the dimensionless variables below: \(\mathrm{x}=\frac{\mathrm{x}^{\prime}}{\mathrm{L}}\), \(\mathrm{y}=\frac{\mathrm{y}^{\prime}}{\mathrm{L}}\), \(\mathrm{z}=\frac{\mathrm{z}^{\prime}}{\mathrm{L}}\), \(\mathrm{t}=\frac{\mathrm{a}\mathrm{t}^{\prime}}{\mathrm{L}^{2}}\)\((\mathrm{U}_{1},\mathrm{U}_{2},\mathrm{U}_{3})=\frac{(\mathrm{U}^{\prime}_{1}, \mathrm{U}^{\prime}_{2},\mathrm{U}^{\prime}_{3})\mathrm{L}}{\alpha}\), \(\mathrm{T}=\frac{\mathrm{T}^{\prime}-\mathrm{T}^{\prime}_{\mathrm{c}}}{ \mathrm{T}_{\mathrm{h}}-\mathrm{T}^{\prime}_{\mathrm{c}}}\), \(\mathrm{C}=\frac{\mathrm{C}^{\prime}-\mathrm{C}^{\prime}_{1}}{\mathrm{C}_{ \mathrm{h}}-\mathrm{C}^{\prime}_{1}}\), and \((\mathrm{h}_{1},\mathrm{h}_{2},0)=\frac{(\mathrm{h}_{1},\mathrm{h}_{2}^{ \prime},0)\mathrm{L}^{2}}{\alpha}\). The microtriction vector \(\mathrm{h}^{\prime}\) can be expressed as \(\mathrm{h}^{\prime}=(\mathrm{h}^{\prime}_{1},\mathrm{h}^{\prime}_{2},0)\) ([24; 25; 26]). The vorticity-vector potential formulation is employed in order to eliminate the pressure term which is delicate to treat. The \(\mathrm{\overrightarrow{\omega}}\) and \(\mathrm{\overrightarrow{\psi}}\) are, respectively, defined by the following relations: \(\mathrm{\overrightarrow{\omega}}=\
abla\times\mathrm{\overrightarrow{U}}\) and \(\mathrm{\overrightarrow{U}}=\
abla\times\mathrm{\overrightarrow{\psi}}\). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Pure Water** & **Alumina (Al\({}_{2}\)O\({}_{3}\))** \\ \hline Pr & 6.2 & \\ \(\mathrm{\hat{\varrho}}\) (kg/m\({}^{3}\)) & 997.1 & 8933 \\ C\({}_{\mathrm{p}}\) (J/kg\(\cdot\)K) & 4179 & 385 \\ k (W/m\(\cdot\)K) & 0.613 & 401 \\ \(\mathrm{\hat{\beta}}\) (K\({}^{-1}\)) & 21.10\({}^{-5}\) & 1.67.10\({}^{-5}\) \\ \(\mathrm{\hat{\sigma}}\) (\(\mathrm{\Omega}\)-m)\({}^{-1}\) & 0.05 & 1.10\({}^{-10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermo-physical properties of pure water and Alumina (Al\({}_{2}\)O\({}_{3}\)) [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Figure 1: Physical model with coordinate system. Using the above hypothesis, the dimensionless governing equations [23; 41; 43] are:  
\[\
abla^{2}\overset{\rightarrow}{\psi}=-\overset{\rightarrow}{\omega} \tag{1}\]  
\[
## 4 Finite Element Modeling
### Elements Used in FE Modeling  
For the 3D heat flow analysis, a four-node isoparametric quadrilateral element (BQ4 HT) is commonly used to define the convective interface boundaries. The exposed surfaces of the concrete slab subjected to heat convection, as shown in Figure 9, are modeled using this four-node quadrilateral element. The concrete was discretized through a 20-node isoparametric solid brick element (CHX60). The contact interface between the bottom face of the concrete slab and ground base is modeled using an eight-node element (CQ481) with interface elements of zero-thickness. The accuracy of the FE analysis depends on the size and characteristics of the element in the mesh. The element size is taken as 30 mm based on the mesh sensitivity analysis.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 4 Results
### Load Separation  
The final case studied in this work concerns a harmonic response of the system with an additional distribution transformer. To evaluate the differences, we energized Substation D by means of an underground cable with its sending end connected to the busbar of Substation A (see Figure 1). The estimated length of this cable connection is about 2 km. Next, a reference Triphase harmonic load was moved from Substation A to Substation D. In Figure 27, we only present results for 5 th harmonic voltage measured synchronously at all three LV test bays.  
Figure 27: Change of 5 th harmonic voltages as result of load separation. Test sequence B.7—public grid supply. (**a**) triphase-emulated harmonic load connected to Substation D; (**b**) triphase-emulated harmonic load connected to Substation A.  
Whilst harmonic voltage at Substation A dropped after removing dominant injection source, we observe that 250 Hz harmonic trend measured at Substation D is visually higher in comparison with local harmonic voltage values when a disturbance source was connected to the distribution Substation A. Naturally, background harmonic levels also evolved in time, but, certainly, the additional impedance of 2 km cable equivalent boosted harmonic voltages at this substation. Furthermore, the transformer of Substation D is characterized by 0.0295 \(\Omega\) fundamental frequency impedance.
###### Abstract  
With the increasing penetration of Internet of Things devices and distributed energy resources in the next-generation distribution network, the efficient energy management for system operation are facing new challenges. One reason is that the large-scale resources cannot be all connected to the supervisory control and data acquisition system, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. In this work, neural network are used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network.  
distribution network; prosumers; fog-cloud computing; artificial intelligence technology; Internet of Things (IoT) devices; energy management
## 7 Evaluation and Discussion
### Experimental Setup  
#### 7.1.1 Benchmarks  
We evaluated _VMGuards_ with six real-life applications running on the Windows operating system (Windows 7, Microsoft Corporation, Redmond, WA, USA). Table 1 describes the key information of these applications. The code segments to be protected in these applications represent some commonly used algorithms and operations, including mathematical calculations, compression, location positioning, recursion, message sending and receiving, and web browsing. The length of the protectedcode segment varies from four instructions to 363 instructions, with runtime ranging from 0.34 us to 39 ms.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 2 Theoretical Foundations
### NoSQL  
NoSQL is a generic term, and stands for \"Not Only SQL\". It represents a category of database management systems. It refers to any data storage method used for storing and querying data that does not utilize the traditional relational database management system model [20]. They are especially useful in cases where the strict complexity of relational databases is unneeded, and a significantly higher data throughput is desired. They also offer other benefits such as the ability to be horizontally scalable and, therefore, will not cause the same operational efforts as relational database management systems [21]. In general, there are four main categories of NoSQL database models, including key-value-stores, document-based stores, graph-based stores, and column-oriented stores. In this paper, we mainly focus on graph-based stores. Graph-based NoSQL databases uses relationships and nodes to represent the stored data. With this characteristic, they are especially fitting for Knowledge Graphs, since they are able to represent the natural structure of Knowledge Graphs efficiently [20; 22]. With the efficient way of storing and querying data, NoSQL databases are valuable in an LLM context. With the higher flexibility, they are also more fitting at handling the complex data structures that are used by LLMs than other databases.
## 3 Comparison of Power Generation of Wind Farm between MPPT and MPG
### Optimal Pitch Angle and Tip Speed Ratio Selection at Various Wind Directions  
Based on the layout of the wind farm (as shown in Figure 5), the wake effect appears within the four symmetrical areas with the wind direction of 258\({}^{\circ}\)-270\({}^{\circ}\), 270\({}^{\circ}\)-282\({}^{\circ}\), 78\({}^{\circ}\)-90\({}^{\circ}\), and 90\({}^{\circ}\)-102\({}^{\circ}\). The wind direction of 270\({}^{\circ}\) and 90\({}^{\circ}\) suffer the most severe wake effect due to the highest overlap area, while in the wind direction range between 282\({}^{\circ}\)-78\({}^{\circ}\) and 102\({}^{\circ}\)-258\({}^{\circ}\), no wake effect can be expected because no turbine is located in the wake area of the wind farm. In situations where the wind directions result in there not being any wake effect on the wind farm, the WT\({}_{1}\) and WT\({}_{2}\) both can be controlled by using the MPPT method.  
In the cases of the wind directions at 270\({}^{\circ}\), 276\({}^{\circ}\), and 282\({}^{\circ}\), the pitch angle and tip speed ratio of WT\({}_{1}\) can be optimized by using the MPG control, and its corresponding rotor speed and active power are shown in Figure 9(a). It is noted that the optimized pitch angle and tip speed ratio of WT\({}_{1}\) is the same with the MPPT method at the wind direction of 282\({}^{\circ}\), as there is no energy loss caused by WT\({}_{1}\) at this wind direction. Meanwhile, the optimized total active power values of the wind farm at the wind directions of 270\({}^{\circ}\), 276\({}^{\circ}\), and 282\({}^{\circ}\) are shown in Figure 9(b).
## 3 Methodology
### UAVIC Studies in Laboratory Conditions
#### 3.1.3 Image Acquisition  
A series of digital images are acquired during load implications at different load levels to process in DIC [29]. Image acquisition during loading condition is done simultaneously with DSLR camera and drone camera as shown in Figure 4. Dial gauge is also placed prior to loading at the bottom centre of the beam to measure the deformation that occurred due to load implication. Similarly, images for the rest of the beams are also acquired at specific load levels. The camera images are acquired using a Nikon D5600 (Nikon, Tokyo, Japan) bearing a CMOS sensor of 24.2 MP with a focal length of 18 mm and 1920 \(\times\) 1080 resolution, and it has 60 fps video recording capabilities. The drone image acquisition is achieved by employing DJI Phantom4 Pro V2.0 (DJI, Shenzhen, China) that is equipped with CMOS 20 MP sensor with a focal length of 8.8 mm and resolution of 5472 \(\times\) 3648 pixels, and it has 60 fps video recording capabilities. The UAV is a quadcopter with a designed flight time of 30 min and a maximum weight of 1375 g. It is a relatively cheaper and commonly used UAV in many engineering applications, due to its onboard sensor capabilities. Both the cameras are set at 3 m away from the target beam for imaging.  
Figure 3: Specimens with random speckle pattern.
# Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm
## 1 Introduction
[28] designed an automatic detection method combining CNNs, sliding windows, and Otsu thresholding. It enables pixel-level segmentation and the accurate measurement of crack width and spalling areas on plastered masonry surfaces. Although YOLOv8 has shown strong performance in various crack detection tasks, its application to stone slabs--with their high-texture and curved crack patterns--still faces several challenges. These include the following: (1) limited ability to model nonlinear curved cracks, (2) loss of edge details during upsampling, and (3) poor performance of existing instance segmentation methods on high-resolution, large-area cracks. Therefore, targeted improvements to YOLOv8 are needed to meet the complex demands of stone crack detection. YOLOv8 is selected as the backbone due to its established deployment in industrial defect-detection pipelines and its favorable accuracy-throughput balance for high-resolution stone imagery. The architecture's modularity further enables targeted, task-specific enhancements without compromising overall stability. This study proposes an enhanced YOLOv8-Seg framework with the following optimizations: First, it replaces the backbone with a lightweight U-NetV2 architecture, introducing dynamic feature calibration and multi-scale fusion to improve micro-crack representation. Second, it incorporates dynamic snake convolution (DSConv) in the detection head to align more effectively with curved crack boundaries. Third, it introduces the DySample module in place of traditional upsampling, using learnable offsets to adapt to irregular crack structures while preserving edge details and reducing artifacts. The proposed method is trained on a high-resolution ST dataset of stone cracks. Ablation studies are conducted to evaluate the effectiveness of each module, and comparisons are made with mainstream models such as U-Net, DeepLabV3, and Attention U-Net. Results demonstrate that the model performs robustly across different stone textures and backgrounds, showing strong potential for industrial applications. Deployment in stone slab manufacturing plants demonstrates the method's efficacy, where integration with conveyor-belt systems achieves real-time micro-crack detection. This application reduces false rejections by approximately 30% for high-texture stones, significantly improving production yield.
## 2 Materials and Methods
### Mapping UHIs and UHI Intensity and Impact Assessment  
UHIs are related to the temperature difference between cities and their adjoining countryside areas [55]. Urbanization and meteorology increase the risk of UHI formation and their effects due to the evolution in urban heat, because of electrical energy demands within urban areas [56]. UHI severity represents the variance among UHI mean temperatures and the mean temperatures of non-urban spots [57]. Landsat data were employed for UHI recognition in this study [58]:  
\[UHI\ Intensity\ (``\mathrm{C}\mathrm{C})=T_{u}-T_{r} \tag{9}\]  
\[LST>\mu+0.5\times\delta\ referred\ to\ UHI\ area\]  
\[0<LST\leq\mu+0.5\times\delta\ referred\ to\ non-UHI\ area\]  
where \(T_{u}\) represents the temperature over the built-up area, \(T_{r}\) represents the non-urban (adjoining) heat, and \(\mu\) and \(\delta\) represent temperature average and standard deviation within the location, respectively. The presence of a UHI is denoted by positive values, (i.e., the city heat is greater than the heat of the countryside). In contrast, when urban heat is much less than non-urban heat, negative UHI intensity values are produced, suggesting an urban cool island. The temperatures in Equation (9) could be air, radiant, or surface temperatures. The impact of UHIs is generally estimated according to these temperatures (air, radiant, or surface) so that UHI influences can be specifically determined [59]. Throughout a hot period, for instance in summertime, water bodies in the city, like waterways, pools, and watercourses, might additionally impose cooling influences within the nearby built-up spots. The influence of cooling is brought on via evaporation and the consumption of heat [60].
## 2 System Design
### Data Center
#### 2.4.2 Introduction to Functions  
(1). Online List: The online list shows the detailed information of the module, such as the IP address, the login time and the data update time after logging in the data center. The state of the GPRS/GSM module would be examined and the online list could be cleaned by the right-click shortcuts.  
Figure 5: The communication of the GPRS/GSM module.  
(2). Framework: The framework, a key of the data center, manages the module access, the categories of modules and the data reception. Each project is a unit to which one module belongs. To acquire the project data requires the addition of the corresponding DLL dynamic data analysis. The compiled DLL file could be put into the catalog of the executing program in the server. The GPRS/GSM module may modify the service life and the access permission to make the module more conveniently to be used by the project.  
(3). Data Management: The module data of each project can be viewed. The data management interface is extensible, which makes it easier and simpler to display data.  
(4). Setting the Data Center: The startup options of the data center include the default auto-running mode and the manual configuration mode. These two options make the startup more convenient. Furthermore, they both can meet different network environments, because the data center will be operated according to the user's options once the server restarts.  
Figure 6: The working flow of the data center.
## 6 Experimental Results
### DWT Results  
As shown in Figure 23, the three-phase voltage data for both healthy and ITSCF conditions are obtained under test conditions of 1500 rpm and 25% load. The \(V_{q}\) is then derived using Equation (16). Despite Figure 23b, actual product testing reveals that the \(V_{q}\) waveform of ITSC motors exhibits significantly larger amplitude variations and a higher harmonic content compared to healthy motors, attributed to the impact of inter-turn short circuits. The \(V_{q}\) voltage data is analyzed using DWT, and the number of DWT levels is defined based on Equation (27), where \(f_{c}\) is 10,000 Hz and \(f_{s}\) is 100 Hz, resulting in a DWT level of \(n_{ls}=6\). The corresponding frequency ranges are shown in Table 2. The wavelet coefficients (details) obtained from the analysis exhibit varying temporal resolutions across different frequency bands, with higher frequency bands demonstrating finer temporal resolution. Furthermore, IDWT is used to reconstruct the waveforms at the resolution of each frequency range, as shown in Figures 24 and 25, which display the IDWT-reconstructed signals for both healthy and ITSC conditions. In this experiment, an 8-pole test motor is used, meaning the twice-frequency at 1500 rpm is 200 Hz. As shown in Table 2, this falls within the D\({}_{5}\) frequency range. Although DWT and IDWT separated the information for each frequency range, Figures 24 and 25 contain excessive data, making it difficult to directly observe error characteristics. To facilitate fault diagnosis, further quantification of the information in each frequency band is necessary.  
Figure 22: IPMSM ITSC Experimental Platform.  
Figure 23: The \(V_{q}\) waveform: (**a**) healthy; (**b**) ITSCF.  
Figure 24: Healthy voltage IDWT reconstruction at 1500 rpm, 25% load.  
Figure 25: ITSCF voltage IDWT reconstruction at 1500 rpm, 25% load.
## 4 Experiments
### Match and Mismatch Conditions  
Originally, for an initial experimental set-up we used the data available for Sets 1 and 2 together (21 speakers). This preliminary experiment is made to observe the behaviour of the speaker identification rate in mismatch conditions. First of all, we divided the data into Neutral (N) and Stressed (S) speech and experimented training with one type of speech, testing with the other and then mixing both types. The results in terms of accuracy--the percentage of audio segments correctly classified--can be found in Table 3. In order to get reliable results these experiments were repeated 50 times, where in each repetition the data used for testing was chosen randomly, excluding samples used for training.  
As a first conclusion, matched settings are better than mismatched ones as expected. When training with neutral utterances and testing with stressed ones, accuracy decreases in more than a 15% with respect to match settings, so it seems proved that stressed speech does have different characteristics compared to neutral speech that affect SI. On the contrary, when training with stressed utterances of speech and testing with neutral ones, the decrease in accuracy with respect to the matched setting is not that important (5%) comparing it to the reversed case, leading us to think that stressed speech could be sparse data in which neutral speech could be contained but not vice versa. About the mixed conditions experiments, the accuracy reached a 96.05%, achieving a good result for this particular task.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 4 The Performance Analysis of Dual-Hop Single-Relay System
### Spectral Efficiency  
SE provides a measure of the system's capacity which is the amount of information transmitted over a given bandwidth. In the context of the adaptive discrete rate, it represents the ratio of the transmission bit rate (\(R_{b}\)) to the bandwidth (\(W\)), which is expressed by [29]  
\[S=\frac{R_{b}}{W}=\sum_{i=1}^{N}a_{i}\log_{2}M_{i}=\sum_{i=1}^{N}i\,a_{i}, \tag{15}\]  
where  
\[a_{i} =P_{r}\{\gamma_{i}\leq\gamma_{I}\,<\,\gamma_{i+1}\}=\int_{ \gamma_{i}}^{\gamma_{i+1}}f_{\gamma_{I}}(\gamma)d\gamma\] \[=F_{\gamma_{I}}(\gamma_{i+1})-F_{\gamma_{I}}(\gamma_{i}). \tag{16}\]The SE of the dual-hop single-relay (DH-SR) system can be then given as  
\[S =\sum_{i=1}^{N}i\left[F_{\gamma_{I}}(\gamma_{i+1})-F_{\gamma_{I}}( \gamma_{i})\right]\] \[=N-\sum_{i=1}^{N}F_{\gamma_{I}}(\gamma_{i})=\sum_{i=1}^{N}\overline {F}_{\gamma_{I}}(\gamma_{i}). \tag{17}\]
## 3 Approximation Using Linearization
However, after studying a wide range of temperature and humidity conditions, the choice of 14 dB is the best compromise. A significant change of the air attenuation is seen when the results for different atmospheric conditions are compared in Figure 5. Two quite different examples are shown. In the first example, the temperature is 0 \({}^{\circ}\)C, RH 20%, and the air attenuation is pronounced at 500 Hz and 1 kHz. The knee point of the approximation is reached at or before 3 s (Figure 5a). The air attenuation at 500 Hz is high enough to show a non-linear behavior like that at higher frequencies, but the knee point for this frequency is at a lower value of attenuation, \(\delta L_{\text{k}}\) = 6 dB. When the temperature is 20 \({}^{\circ}\)C and RH 80% (Figure 5b), the non-linear attenuation is pronounced at 4 kHz and 8 kHz, but not so much at the lower frequencies. Figure 5: Air attenuation in octave bands and approximation with the air attenuation of two specific frequencies as in Figure 4. (**a**) Temperature 0 \({}^{\circ}\)C and 20% RH. (**b**) Temperature 20 \({}^{\circ}\)C and 80% RH.