esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Subject of Research and System Analysis  
The measurement system consisted of the following modules:  
* A set of UWB beacons based on TWR technology;
* A phone with a designed mobile application dedicated to iOS (an iPhone 13 with U1 chipset was used in this research) enabling distance measurement using UWB beacons. The mobile application enables entering additional information about the environment, such as the measured distance using a measuring tape, or turning on/off system options, e.g., CameraAssist;
* The reference system used in this study was the JetRacer mobile platform equipped with LIDAR, in two main cases, static and dynamic;
* A REST API server enabling communication between the mobile application, the JetRacer platform, and the MySQL database;
* Two databases--CoreData (collecting data when the mobile platform was not involved in the measurements) and MySQL (collecting data during dynamic tests using the Jetracer platform); the databases collected distance measurements as well as environmental information.
* The diagram of the measurement system used in this research is shown in Figure 1.
* In addition, the following elements were used to conduct the research:
* GTEM (Gigahertz Transverse Electro Magnetic) cell for simulating LoS/NLoS conditions. GTEM is the chamber that simulates free-space conditions for electromagnetic wave propagation and is utilized to test the electromagnetic interference and immunity of electronic devices. Overall, GTEM cells are valuable tools in the field of electromagnetic compatibility, providing a convenient and efficient way to conduct testing in a controlled environment;
* Various obstacles such as concrete walls, glass walls, and wooden doors;
* A designed test environment, which consisted of rooms in a residential building, to best replicate the everyday situation; a snapshot of the test room using the SLAM (simultaneous localization and mapping) algorithm is presented in Figure 2.
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 3 Method  
### Data  
Red giants, a category of stars characterized by their slow evolutionary rate, are prevalent throughout the entire evolutionary process of the Milky Way galaxy. These stars carry extensive information about the evolution of both individual stars and the galaxy as a whole, making them ideal candidates for studies on stellar and galactic evolution. Consequently, this research focuses on red giants as the primary subject of study.  
The LAMOST DR8 survey data, by analyzing stellar spectra, provided atmospheric parameters for 1,033,787 stellar spectra and determined the \(\alpha\)-element abundance, effective temperature, surface gravity, and abundance of 12 individual elements through the label-transfer method based on CNN. [PERSON] et al. [51] provided 640,986 entries with mass parameters for red giant star spectra. The SDSS DR17 [6] survey data provided 733,901 stellar spectra data, including age parameters.  
This study utilized the Tool for Operations on Catalogues And Tables (TOPCAT) ([[http://www.starlink.ac.uk/topcat/](http://www.starlink.ac.uk/topcat/)]([http://www.starlink.ac.uk/topcat/](http://www.starlink.ac.uk/topcat/)), accessed on 24 January 2024) [56] software (version: 4.8-7) to intricately cross-match data possessing identical Fiber pointing right ascension (RA) and Fiber pointing declination (DEC) parameters across the LAMOST DR8, [PERSON] et al. [22], and SDSS DR17 datasets. This rigorous process successfully identified a dataset comprising 41,590 red giants. Owing to the CNN in LAMOST DR8 employing training samples from SDSS, the derived dataset features a synthesis of parameters: \(\alpha\)-element abundance, effective temperature, surface gravity, and chemical abundance from LAMOST DR8, mass parameters as per [PERSON] et al. [49], and age labels attributed to SDSS DR17. Table 1 presents a list of parameters contained in the red giant star data.
## 2 Background on Technologies for RSBD Processing and Related Works
### RSBD Specification  
RS techniques have been broadly used in many environmental applications, such as AP monitoring [1] and climate change monitoring. Satellites are the primary equipment for the measurement. They use sensors within various temporal, spatial, and spectral resolutions. Satellites continuously pass by unique polar or geostationary orbits. In this investigation, we applied RS techniques to monitor the AQ and climate changes in NRT [2]. We collected data from various organizations, satellites, and instruments within different spatial, temporal, and spectral resolutions, as illustrated in Section 3.1. In our case study, satellite data occur with high-velocity attainment of 40,000 daily files with an average latency of 30 min [3]. These data continuously increase the storage space by 60 GB per day. The collected data are stored in a scientific file format, particularly the NetCDF, HDF5, and BUFR [4].  
Consequently, RSBD management turns out to be a challenging problem to be tackled. Satellites produce persistent data with high velocity, which cannot be continuously stored inside a usual storage system [5]. Thus, it is necessary to develop a model determining which RSBD to keep and which one to remove. Finally, RSBD processing involves mathematical skills in probability and statistics to integrate deep learning, machine learning, and neural network algorithms to extract new insights.
## 4 Joint Parameters Optimization
### Joint Optimization of Pico CRE Bias and PBS Density Optimization  
Based on the aforementioned parameter optimization algorithms, each parameter is just optimized with the other one being fixed, which in fact cannot reach these parameters' global optimization due to the fact that these parameters are affected by each other. Therefore, combining the CBO Algorithm and PDO Algorithm, we further propose a heuristic pico CRE bias and PBS density joint optimization algorithm to address the objective Equation (15), which is as shown in Algorithm 3.  
```
1. Initialization:

(1) Initialize the network scenario and the values \(\lambda_{u}\), \(\lambda_{m}\), \(B_{p}\) and \(\rho_{p,m}\), where \(B_{p}\in(0,25]\) and \(\rho_{p,m}\in(0,30]\).

(2) Let \(EE^{*}=0\) represent the initial optimal value of the network EE. Initialize algorithm iteration number \(N_{loop}=0\). Given a tolerance \(\varepsilon>0\).

(3) Denote \(B_{p}^{*}\) as the optimized PBS CRE bias. Denote \(\rho_{p,m}^{*}\) as the optimized ratio of PBS density to MBS density. Denote \(\lambda_{p}^{*}\) as the optimized PBS density.

2. Calculate the suboptimal CRE bias according to CBO Algorithm \(B_{p}^{sub\_opt}=\underset{B_{p}}{\text{arg max}}\,EE(B_{p})\left|{}_{ \lambda_{u},B_{p}^{sub\_opt}}\right.\)

\(B_{p}=B_{p}^{sub\_opt}\)

3. Calculate the suboptimal ratio of PBS density to MBS density according to PDO Algorithm \(\rho_{p,m}^{sub\_opt}=\underset{p,m}{\text{arg max}}\,EE\left(\rho_{p,m} \right)\left|{}_{\lambda_{u},B_{p}^{sub\_opt}}\right.\)

\(\rho_{p,m}=\rho_{p,m}^{sub\_opt}\)

\(EE^{sub\_opt}=EE(B_{p}^{sub\_opt},\rho_{p,m}^{sub\_opt})\)

4. Termination of the loop if\(\left|{}_{\text{${EE^{sub\_opt}-EE^{*}}$}}\right|>\varepsilon\)then \(EE^{*}=EE^{sub\_opt}\), \(N_{loop}=N_{loop}+1\), go to step 2 else \(B_{p}^{*}=B_{p}\), \(\rho_{p,m}^{*}=\rho_{p,m}\) \(\lambda_{p}^{*}=\lambda_{m}\rho_{p,m}^{*}\), \(EE^{*}=EE^{sub\_opt}\) endif
```  
**Algorithm 3**Joint Pico CRE Bias and PBS Density Optimization (JBPDO) Algorithm.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 1 Introduction
### AC/DC Power Flow for Hybrid AC/DC Grids with MT-HVDC Systems  
There have been some relevant surveys about power system operation considering PF problem solutions [7; 8]. In traditional AC systems, the PF can be controlled through three hierarchical levelswhich are, HLI (generation level), HLI (generation and transmission levels), and HLIII (generation, transmission, and distribution levels). Considering hierarchical levels and the market analysis, the Independent Electricity System Operator (IESO) can only control the generated power on the power system, and based on that, control the PF to the next hierarchical level(s). The integration of MT-HVDC systems to the existing AC grids leads to increasing the region of controllability of the hybrid AC/DC grids. This fact is due to the capability of controlling active and reactive PF by each converter station in MT-HVDC systems [1, 4, 9]. Therefore, MT-HVDC systems can change the PF patterns, and from the IESO and Transmission System Operator (TSO) perspectives, these changes in the pattern may cause significant issues in hybrid AC/DC grids. Moreover, due to the temporary or permanent outages of the components in hybrid AC/DC grids, the dynamic behavior of nonlinear components can change the PF and enhance the risk of instability in the entire hybrid AC/DC grids [9, 10, 11].
## 3 Materials and Methods  
### Site Conditions  
The data used in this study were collected during the spring of 2020, on a day that was partly cloudy but mostly sunny. During data collection, the temperature remained at a pleasant average of 19 degrees Celsius, while wind speed remained constant at 5 m per second. The location is characterised by spring weather, with average temperatures varying between 20 and 30 degrees Celsius, and wind speeds generally below 5 m per second. These climatic conditions favoured data acquisition and ensured the accuracy of the results.  
The study was carried out over a three-hour period in a large rain-fed agricultural area. In this area the presence of trees is scarce, except in the plots located to the east, which contain vineyards and olive groves that were not the subject of our analysis. The topography of the region shows minimal variations, with a difference of less than 2 m from north to south, making it gentle and practically flat. This topography provided optimal conditions for data collection, greatly simplifying our research.  
To mitigate possible risks (although they were not considered particularly relevant in our study area, such as collisions of the drone with topographic elements or vegetation), a digital elevation model (DEM) of the area was previously elaborated. In this flight the most essential geometric criteria for photogrammetric applications [13] were applied, which guaranteed the obtaining of high-quality cartographic data and a Ground Sampling Resolution (GSD) of 4 centim per pixel.  
DEM generation was carried out using a DJI Mavic 2 Pro drone equipped with a 1-inch CMOS sensor, flying at an altitude of 150 m above ground level (AGL). This approach ensured a stable and safe flight, which allowed us to obtain accurate and reliable results for our research.  
In addition, a short electromagnetic sounding campaign was conducted to assess the possible thickness of the Tertiary strata [14,15]. This campaign consisted of three electromagnetic soundings arranged in a 75 \(\times\) 75 m profile.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 6 Open Issues
### End-to-End Delay  
In SDVNs, the number of controllers utilized can have a significant impact on the end-to-end delay experienced by OBUs (On-Board Units) in the network. If only a few controllers are used, OBUs that are far away from the controlling RSUs (Road-Side Units) may experience significant route setup delays. This is because the controllers need to communicate with the RSUs and other controllers to establish routes, which can lead to long delays for OBUs that are farther away from the controllers. Delay-sensitive VANET applications require a minimal end-to-end delay to function correctly, so it is crucial to minimize the delay experienced by OBUs during the deployment of controllers. It is important to note, however, that latency is a trade-off between the number of controllers and the number of processors. OBUs can be improved by increasing the number of controllers, but the overhead and complexity of the network may also increase.  
To address this trade-off, network designers need to carefully consider the number and placement of controllers in the network. They must balance the need for minimal end-to-end delay with the need to reduce the overhead and complexity of the network. One approach is to use distributed control, where the control plane is distributed across multiple controllers, each responsible for a specific area of the network. This approach can help minimize the delay experienced by OBUs while reducing the overhead and complexity of the network. In summary, the number and placement of controllers in SDVNs can have a significant impact on the end-to-end delay experienced by OBUs. In designing SDVNs, it is important to consider the trade-off between a number of controllers and latency, as well as the need to reduce overhead and complexity while maintaining minimal end-to-end delay. Distributed control can be an effective approach for minimizing delay while reducing network overhead and complexity.
## 4 Point Cloud Segmentation
### Plane Detection  
Following the completion of supervoxelization, the system began segmentation of the scene. The scene was first divided into two parts: planar and non-planar point clouds. The purpose of this was to remove planes and thereby increase the efficiency of object segmentation. Furthermore, the planes were considered complete point groups that did not require further segmentation. In this study, planes were identified using the RANSAC method [34], in which the plane equation of three random points was calculated, and then other points were tested to see whether they belonged to this plane. When a set number of points did belong to a particular plane, they were considered a plane into themselves. Plane detection results are shown in Figure 5.  
Figure 4: Flow chart showing the process of point cloud segmentation.
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 4 Proposed Method
### Model Compilation and Training  
The hybrid model used the binary cross-entropy loss function together with the Adam optimizer, which provides an adaptive learning rate during the optimization process for model training. We used accuracy, precision, recall, and additional classification metrics as performance evaluation tools. We used manual tuning and validation performance metrics for determining the best values of dropout rates and other model parameters. An experimental evaluation of multiple training runs tested numerous combinations between dropout values from 0.2 to 0.7 and learning rates. The selected dropout value for best validation accuracy and overfitting reduction is determined as 0.5. This experimental series aided in determining optimal hyperparameters, which created a solid equilibrium between generalization and training stability but did not include automated grid or random search processes. Using the training dataset (X train, Y train), the model completed 100 epochs at a batch size of 8 before accepting test data (X test, Y test) for validation. We established training parameters to optimize learning while ensuring robust validation through verbose output tracking. Through the embedding layer, the model develops important textual features. A combination of GRU and LSTM provides excellent performance in identifying lengthy dependencies while keeping computations efficient. Dropout mechanisms assist the model in achieving better generalization, along with preventing overfitting behavior. The Adam Optimizer provides an automatic mechanism to optimize learning rates to achieve improved training stability. Binary Cross-Entropy Loss: Ideal for binary classification (spam vs. ham). An 8-item batch size maintains training stability and prevents memory from becoming unnecessarily high. Additionally, 100 Epochs allows sufficient training iterations without excessive overfitting.
# Economic Impact Analysis of the Application of Different
## 5 Discussion  
All performance models presented here were established with scientifically proven methods. Thorough understanding of observed pavement construction, thorough measuring methods, and sampling frequency, all prerequisites for establishing pavement deterioration models were met. The nature of APT and long-term pavement monitoring produces evolution curves of similar shapes, reflecting the familiar degradation initiation and progression phase. In this sense, the presented deterioration equations are all valid. The main variation is the \"steepness\" of the progress. As demonstrated, these changes may seem negligible at first glance, but in reality, when incorporated in the PMS, they produce significant aberration in economic results. Road administration decision making is usually heavily influenced by, if not completely dependent on, project economic indicators. In this sense, errors in deterioration models translate into faulty decision making. Wrong projects can be chosen at the wrong time, producing sub-optimal repair planning and economic loss to society. This is shown in Table 3, in which the breaking point of project effectiveness fluctuates with different evolution equations used for the calculation.  
* ATP functions related to Equations (1) and (2): In the economic efficiency analysis, these functions are very similar, producing similar economic results when used in the CBA. In model simulation A (average traffic load), the _IRR_ variation interval is 0.6%. In model simulation B (heavy traffic loaded), _IRR_ variation is 0.19%. _NPV_ variation for model simulations A and B is EUR 2336 and EUR 8052, respectively. Thus, the _IRR_ difference is negligible (less than 1%). However, if the decision is based on _NPV_ in model simulation B, the variation produced by different performance models will be 25.41%.
* Functions related to Equations (3)-(5): These equations, derived from Long-term pavement monitoring of pavements in operation, are also similar for both model simulations. For model A, the _IRR_ variation ranges from 1.7% to 7.8%, and for _NPV_, from EUR 2068 to EUR 9334. For model simulation B, _IRR_ variation ranges from 4.41% to 6.07% and _NPV_ from EUR 16,000 to EUR 21,509. As in ATP functions, this variation is in line with the assumption that change in deterioration models can have a considerable impact.  
Figure 12: _NPV_ for model B.
## 2 Methods
### Sample Limitations  
SAXS requires homogeneous or monodisperse samples to obtain unambiguous interpretation. In analyzing solution scattering, improper solvents or background subtractions can introduce artifacts that can affect the derived parameters [43; 44]. At high concentrations, intermolecular interactions make data interpretation more difficult [45]. Dilute series are required to extrapolate to infinite dilution [46; 47]. X-ray-induced degradation can damage biological samples [48; 49]. Cryo-cooling the sample during testing can reduce degradation but can prevent the examination of the conformations at elevated temperatures [49; 50; 51]. Reducing the exposure time can also reduce the amount of damage but results in poor-quality data. Oscillating the sample through the beam and rastering the beam across the sample reduces radiation damage. In translating the laboratory results into clinical applications, difficulty in accessing a large number of patient tissues will affect the reproducibility of these diseased tissues.  
Figure 1: **(a)** Comparison of the experimental set-up for wide- and small-angle X-ray scattering measurements; F is the sample-to-detector distance. **(b–d)** The three classes of scattering patterns discussed in the paper, (**(b)** fiber, **(c)** lamellae [34], and solution), showing both the sample and the corresponding scattering pattern.
## 4 Experimental Results  
### 10 m Walking Test  
Figures 7 and 8 show the real-time gait results and control states from the first experiment of the 10-m walking test for a hemiplegic patient and an able-bodied individual, respectively. In each chart, the vertical lines indicate when the proposed gait controller recognized the user's gait intention and switched from the control state to the pre-swing state, marking the start of the swing phase. According to the results of Figures 7 and 8, 12 swings occurred in the 10 m walk of the person with hemiplegia, and 7 swings were noted for the able-bodied individual with the controller generating assistive force (negative damping torque, ND) at the correct moments to aid knee joint actuation.  
The negative damping coefficient \(b\) applied to the gait assistance of the person with hemiplegia was set at 0.11 and 0.28 mNms/deg for the SWF and SWE states, respectively. As shown in Figure 8, for the able-bodied individual who did not require assistive force, the output of the negative damping torque was set to zero, but only 650 mA pre-flexion torque was applied.
However, a limitation of this investigation was that all the included studies used PET systems that are diagnostically inferior to current PET/CT systems that have CT-based attenuation correction. In a single PET system, it is also more difficult to localize anatomically small lesions compared to PET/CT. Therefore, a new systematic review is required to analyze PET/CT data. In 2014, [PERSON] et al. [11] focused only on one and the most common aggressive subtype of NHL, the DLBCL, and showed that [18]FDG PET/CT has 88.7% pooled sensitivity and 99.8% pooled specificity for detecting BMI. In a meta-analysis conducted by [PERSON] et al. [12] in 2011, a separate population for aggressive and indolent NHL patients reported a pooled sensitivity and specificity equal to 74% and 84%, respectively for the detection of BMI in aggressive NHL and 46% and 93%, respectively for indolent NHL. However, two drawbacks were found in the study by [PERSON] et al. [12], namely, that the included studies used either a single PET unit or a PET/CT, and they did not use a standard reference as no biopsy results were available in most of the studies and they were forced to instead rely on other diagnostic procedures that affect the interpretation of the results. The value of [18]FDG PET/CT in diagnosing BMI in patients with NHL is still a timely question, and remains a subject of debate in the literature. The purpose of this study was to systematically review published data on the value of [18F]FDG PET/CT in diagnosis of BMI for NHL patients compared to BMB.