esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results for the Light Field Capturing and Reconstruction
### Experiments for Optical Reconstruction of the Captured Light Field  
Not only to capture 3D information with the proposed method but also to display 3D object optically, we adopted holographic display to reconstruct 3D information of the proposed capturing system. Figure 8 shows the top view of holographic display system for verifying reconstructed 3D information. Fast spatial light modulator (SLM) was used to display holographic information. The SLM, which was implemented by a digital micro-mirror device (DMD), could control the amplitude of the incident wave by toggling micro-size mirrors very quickly. The DMD was a V-9501 model of Vialux. In the principle of Fourier hologram, an amplitude hologram can be reconstructed to a complex hologram. The computer generated hologram can be made from intensity and depth information of each pixel. To enhance quality of the reconstructed hologram, random phases were wrapped on each frame of the hologram were and timely multiplexed [31]. Because the aspect ratio of DMD was  
Figure 8: The 360-degree light field capturing setup and experimental results. (**a**) The implemented capturing system with two plane mirrors; (**b**) Captured light field image focused on the center perspective and processed with chroma-key technique; (**c**) The depth map of the light field calculated by the proposed algorithm; (**d**) Refocused image for left perspective; (**e**) Refocused image for center perspective; and, (**f**) Reconstructed mesh with color information.  
not square, reconstructed hologram in the Fourier plane shrank along horizontal direction without compensation. Anamorphic Fourier optical system, which was composed of three cylindrical lenses in Figure 9, was implemented to optically correct the distorted pixel structure and image [32]. The focal lengths of cylindrical lens 1 and 3 were 200 mm and the focal length of cylindrical lens 2 was 100 mm. The distance between adjacent cylindrical lenses was the focal length of cylindrical lens 2. Figure 10 shows the accommodation cue of the reconstructed holograms. The letter of front and rear were located on the front and rear positions of the objects.
## 2 Network Piping System; Flow Distribution Calculation
### A Hydraulic Model  
The Renouard formula; Equation (1) best fits a natural gas distribution system built with polyvinyl chloride (PVC) pipes [19; 20]. Computed pressure drops are always less than the actual drop since the maximal consumption occurs only during extremely severe winter days [21; 22].  
\[f=\Delta\overline{p^{2}}=p_{1}^{2}-p_{2}^{2}=4810.\frac{\rho_{r}L\cdot Q^{1.82 }}{D^{4.82}} \tag{1}\]  
where \(f\) is a function of pressure, \(\varrho_{r}\) is relative gas density (dimensionless), \(\rho_{r}=0.64\), \(L\) is the pipe length (m), \(D\) is the pipe diameter (m), \(Q\) is flow (m\({}^{3}\)/s), and \(p\) is pressure (Pa).  
As shown in Appendix A, other formulas are used in the case of waterworks systems [23; 24] and ventilation networks [7].  
Regarding the Renouard formula (Equation (1)), one has to be careful since the pressure drop function, \(f\), does not relate pressure drop, but actually the difference of the quadratic pressure at the input and the output of the pipe. This means that \(\sqrt{\Delta\overline{p^{2}}}=\sqrt{p_{1}^{2}-p_{2}^{2}}\) is not actually pressure drop despite using the same unit of measurement, i.e., the same unit is used as for pressure (Pa). The parameter \(\sqrt{\Delta\overline{p^{2}}}\) can be noted as a pseudo-pressure drop. In fact, the gas is actually compressed, and hence that volume of the gas is decreased, and then such a compressed volume of the gas is conveying with a constant density through the gas distribution pipeline. The operating pressure for a typical distribution gas network is \(4\times 10^{5}\) Pa abs, i.e., \(3\times 10^{5}\) Pa gauge, and accordingly the volume of the gas decreases four times compared to the volume of the gas in normal (or standard) conditions. Pressure in the Renouard formula is for normal (standard) conditions.  
The first derivative \(f^{\prime}\) of the Renouard relation (Equation (2)), where the flow is treated as a variable, is used in the Hardy Cross method.  
\[f^{\prime}=\frac{\partial f(Q)}{\partial Q}=1.82\cdot 4810.\frac{\rho_{r}L \cdot Q^{0.82}}{D^{4.82}} \tag{2}\]  
First assumed gas flow in each pipe is listed in the third column of Table 1. The plus or minus sign preceding flow indicates the direction of flow through the pipe for the particular loop [18; 25]. A plus sign denotes counterclockwise flow in the pipe within the loop, while the minus sign clockwise. The loop direction can be chosen to be clockwise or counterclockwise (in Figure 1, all loops are counterclockwise).
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 5 Estimation and Analysis of Inter-system Biases
### Intra-day Variation of ISB  
Through the epoch by epoch filtering calculation, the time series of inter-system bias were obtained, among them, G-R represents the inter-system bias between GPS and GLONASS, and G-E represents the inter-system bias between GPS and GALILEO, and G-C represents the inter-system bias between GPS and BDS.The intra-day variation of the ISB parameters was analyzed after determining their respective mean values. Figure 9 show the time series of the ISB parameters for the G-R, G-E and G-C systems of each station on the 150 th day of 2018 (after deducting their mean values), respectively. As can be seen, the variation of the parameters among the three groups of ISB parameters was relatively stable within one day. The ISB parameter values of most stations were within \(\pm\)1 ns, and the standard deviations of the three groups ISB were all less than 0.6 ns. Among them, the ISB parameter between the GALILEO and GPS systems were the most stable and the standard deviation was the smallest, which may be related to the GALILEO system's good signal quality.  
Figure 8: G + C + R + E system positioning error statistics.
# Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field
## 2 Principle of Methods  
### Derivation of Control Equation  
When the medium temperature change caused by thermal radiation is ignored and only the influence of heat conduction and heat convection is considered, the governing equation of temperature field can be written as follows [12]  
\[\
abla\cdot(\lambda\
abla T)-\
abla\cdot(\mu_{\varpi}T\mathbf{v})+A=\mu\frac{ \partial T}{\partial t}. \tag{1}\]  
In Formula (1), \(\lambda\) represents the thermal conductivity \((W/\mathrm{m}\cdot\mathrm{{}^{\circ}C})\), \(\mu_{\varpi}\) represents the volumetric heat capacity of the fluid \(\left(J/\mathrm{m}^{3}\cdot\mathrm{{}^{\circ}C}\right)\), \(v\) represents the velocity of the fluid \((\mathrm{m}/\mathrm{s})\), A represents the heat generation rate \(\left(W/\mathrm{m}^{3}\right)\), and \(T\) represents the temperature field \((\mathrm{{}^{\circ}C})\). According to the superposition of the temperature field, we can divide Equation (1) into background field and anomalous field, respectively, to solve, in which the background field is generated by a uniform medium or a layered medium, and its governing equation is  
\[\
abla\cdot\left(\lambda_{p}\
abla T_{p}\right)+A_{p}=0. \tag{2}\]  
In Formula (2), \(\lambda_{p}\) is the background thermal conductivity, \(T_{p}\) is the background temperature field, and \(A_{p}\) is the background heat generation rate. For the uniform medium and the layered medium, the thermal conductivity is constant in the horizontal direction (x direction) and only changes in the z direction. By subtracting Equation (2) from Equation (1), we obtain the governing equation for the anomalous field  
\[\
abla\cdot\left(\lambda_{p}\
abla T_{s}\right)+\
abla\cdot\left(\lambda_{s} \
abla T\right)-\
abla\cdot\left(\mu_{\varpi}T\mathbf{v}\right)+A_{s}=0. \tag{3}\]  
In Formula (3) \(T_{s}\) denotes anomalous temperature field, \(T_{s}=T-T_{p}\), \(\lambda_{s}\) is anomalous thermal conductivity. \(\lambda_{s}=\lambda-\lambda_{p}\), and \(A_{s}\) is anomalous heat generation rate, \(A_{s}=A-A_{p}\).
## 2 Explicit Element Geometric Transformation
### Analysis of the Transformation
\right)p^{0} \tag{14}\]  
implies \((e_{1})_{(k+1)\text{mod}n}=\omega_{1}(e_{1})_{k}\), \(k\in\{0,1,...,n-1\}\), which says that node \(p_{(k+1)\text{mod}n}\) of the eigenpolygon \(e_{1}\) can be obtained by rotating the preceding node \(p_{k}\frac{360}{n}\) degrees around the origin of coordinates. It is not difficult to understand that the remaining eigenpolygons can be generated by the expression \((e_{m})_{(k+1)\text{mod}n}=\omega_{m}(e_{m})_{k}\), \(k\in\{0,1,...,n-1\}\). The properties above lead to the following crucial result building the foundation for the simultaneous mesh untangling and smoothing approach to be introduced in the next section. Theorem 3.2: _The proposed element geometric transformation can regularize arbitrary polygonal elements after enough iterations._  
Proof of Theorem 3.2: The size of a transformed element can differ significantly from that of the initial element after the stretching operation. This could be easily overcome by scaling the transformed element after each transformation step with respect to the centroid. From a theoretical point of view, using the inverse of \(\mu_{1}\) as scaling factor is the best choice. Of course, the mean edge length preserving scaling scheme is used in practice. The sequence of scaled transformed polygonal elements using the inverse of \(\mu_{1}\) as scaling factor:  
\[p_{s}^{l}=e_{0}+\sum_{k=1}^{n-1}(|\frac{\mu_{k}}{\mu_{1}}|)^{l}e_{k}=e_{0}+e_{ 1}+\sum_{k=2}^{n-1}(\underbrace{|\frac{\mu_{k}}{\mu_{1}}|}_{<1})^{l}e_{k} \tag{15}\]  
converges to \(p_{s}^{\infty}=\lim\limits_{l\to\infty}p_{s}^{l}=e_{0}+e_{1}\), which represents the counterclockwise oriented regular polygonal element centered at the centroid of the initial polygonal element. The speed of convergence for a single polygonal element depends on the ratio \(\rho=\max\limits_{2\leq k\leq n-1}\{|\mu_{k}/\mu_{1}|\}<1\). The eigenvalues depend only on scaling factor \(\lambda\), so does the ratio \(\rho\). For triangular elements, it holds that \(\mu_{1}=1+\sqrt{3}\lambda\), \(\mu_{2}=1-\sqrt{3}\lambda\), and it is obvious that \(\rho\) achieves a minimum of \(0\) when \(\lambda\) is equal to \(\sqrt{3}/3\). As for quadrilateral elements, it holds that \(\mu_{1}=1+2\lambda\), \(\mu_{2}=1\), \(\mu_{3}=1-2\lambda\), and it is easy to obtain that \(\rho\) achieves a minimum of \(1/3\) when \(\lambda\) is equal to \(1\). In a word, the optimal scaling factor \(\lambda\) for a single polygonal element can be found through theoretical analysis. Nevertheless, numerical tests are usually needed to find the appropriate scaling factor in practical mesh optimization problems, which may involve elements of different types and sizes.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 2 Literature Review
### Wireless Communication Feature  
In WSN localization, wireless communication features are used to provide input to the estimation algorithm. Wireless communication features have a distinctive characteristic of a wireless device that contains measurement that is essential for position estimation. Typically, the distance measurement is extracted from these communication features. The communication features include hop count, propagation time, received signal phase/angle and received signal strength. In the hop count, the distance is measured from the number of node-to-node hop count. Propagation time utilizes the Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Return Time of Flight (RToF), while received signal phase/angle uses Angle of Arrival (AoA), RSP and interferometry techniques [6]. In received signal strength [7], RSS and Channel State Information (CSI) techniques are used. These features are then sent to a localization estimation algorithm to estimate the actual position of a blind node.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 5 Proposed Solution
### Tools and Technologies
#### Asynchronous Operations  
* _CompleteFuture_ was introduced in Java 8 and provides a robust interface for asynchronous code development, allowing the chaining of multiple steps of the computation process and abstracting multiple aspects of thread management. Compared to its predecessor, the _Future_ interface, it offers a more fluid, non-blocking design and supports other ecosystem advances such as lambda expressions [6].
* _ExecutorService_ allows for granular control over concurrency and asynchronous task execution. It allows the allocation, configuration, and utilization of dedicated thread pools, which can improve application performance in critical scenarios.
* _Mono_ and _Flux_ are dedicated types that support the reactive and functional approach, as well as straightforward handling of data streams. The interfaces are based on the Project Reactor library [5], leveraged to support the development of the WebFlux component in the Spring ecosystem [13].
* _Schedulers_ are the primary mechanism enabling concurrency management in the reactive model, allowing for enhanced flexibility and granular control through the selection of execution contexts and thread pools such as _boundedElastic_, parallel, immediate, and single, the appropriate selection is imperative for maintaining low resource consumption and high performance.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 5 Conclusions and Future Perspectives
### Applications in Reactor Optimization  
Empirical models and semi-empirical models: The reaction kinetics of SCWO has developed from empirical models to semi-empirical models and then to DCKMs. For empirical and semi-empirical models developed based on the experimental results, their wide applications are limited by experimental conditions. Hence, for experimental device amplification, empirical and semi-empirical models can only be used for the amplification of similar reactors in the same experimental range.  
DCKM models: Widely available DCKMs are urgently needed for the development of new reactors and the optimization of reactor structures. Since the coupling of complex DCKMs and flow model sharply increased the computing time, the development of simplified mechanism models that are still representative of the chemical reaction process is also an urgent matter. They can substantially reduce the chemical and mathematical complexity of DCKMs. The reduced mechanism eventually makes it possible to use fundamental, mechanism-based kinetics models combined with computational fluid dynamics in reactor engineering studies.
## 3 Results
### Cytotoxicity Assessment with MTT Assay in HepG2 Cells with GNPs  
As mentioned in the Materials and Methods section, the confluent or grown cancer cells (HepG2) were used as a control, while a number of different concentrations (1-100 ug/mL) with GNPs were used as treated samples, exposed for 24 h incubation. Cytotoxicity was examined by a set and prescribed MTT assay method. The obtained data show that the viability of HepG2 cells is reduced with the interaction of GNPs and is dose- or concentration-reliant. In HepG2 cells, cell viability according to MTT assay was observed for GNPs-1 at 24 h as 101%, 96%, 86%, 69%, 61%, 48%, and as 17% (Figure 5) for concentrations of 1, 2, 5, 10, 25, 50, and 100 ug/mL, respectively (\(p<0.05\) for each concentration). Similar trends were found in samples of GNPs-2, once treated with HepG2 cells. The viability of HepG2 cells assessed by MTT assay for GNPs 2, at 24 h, was 102%, 98%, 90%, 83%, 77%, 53%, and 46% (Figure 5) for concentrations of 1, 2, 5, 10, 25, 50, and 100 ug/mL, respectively (\(p<0.05\) for each concentration).  
The viability of HepG2 cells is significantly influenced by GNPs-3. The MTT assay result at 24 h was observed at 100%, 99%, 94%, 87%, 79%, 67%, and 55% (Figure 5) for concentrations of 1 ug/mL, 2 ug/mL, 5 ug/mL, 10 ug/mL, 25 ug/mL, 50 ug/mL, and 100 ug/mL, respectively (\(p<0.05\) for each concentration). The received cytotoxicity data of HepG2 cells show that initially the GNPs do not have much effect in reducing cancer cells, but once the concentration of GNPs upsurges, cytotoxicity was significantly influenced and diminished.  
Figure 4: Morphological changes in HepG2 cells exposed to GNPs-1 (**A**), GNPs-2; (**B**), and GNPs-3 (**C**) for 24 h. Cell images were obtained via an inverted microscope at 20\(\times\) magnification. Scale bar = 1 mm.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 3 Trade-offs in Wireless Network Design
### Data Rate  
Data rate is directly correlated with the available bandwidth and thus frequency band, on one hand, and with modulation and coding scheme on the other. More bandwidth enables higher data rates, while modulation techniques and coding schemes can further contribute to the achievable data rate by encoding more data into the signal. Unlicensed wireless technologies operate either in sub-GHz frequency bands (400 MHz, 800-900 MHz), in 2.4 GHz or in 5 GHz. Sub-GHz technologies generally (although not universally) use narrower channels (few hundred kHZ) than those in GHz frequency bands (22 MHz Wi-Fi, 2 MHz 802.15.4) and thus have more limited data rates than the GHz ones.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.9 Generative AI-Based Formalisms  
Generative models, including Large Language Models (LLMs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs), learn implicit knowledge from data without an explicit formalized structure. While they achieve high-quality outputs, accuracy may vary, particularly with limited or biased training data. They also lack precise encoding of rules or constraints found in other formalisms to capture data patterns and nuances. Covering a wide range of data patterns, generative models might struggle with rare or outlier patterns due to biases in training data. On the other hand, their generalizability surpasses neural-based approaches due to a two-step learning approach, initially gathering general knowledge from vast data and fine-tuning for specific use cases.  
In addition to limited explainability, they also lack explicit certainty or confidence expression in generated outputs, requiring proper prompt-based context to improve output quality and resolve conflicting or ambiguous information. Generative models might offer confidence scores, yet their accuracy in reflecting true confidence levels varies. Integrating new knowledge requires retraining or fine-tuning, challenging incremental learning without forgetting previous knowledge.  
In summary, these models excel in generating content, displaying scalability, coverage across patterns, and generalization. However, they lack explicit handling of uncertainty, struggle with explainability and expandability, and are susceptible to data biases.
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 4 Results
### Experimental Overview  
We verified the experimental data of the test site, hardware system and research algorithm. In the experiment, the car with the integrated navigation system was installed as a reference to obtain the trajectory data and generate the 3D point cloud. These values were compared to the 3D point cloud generation method based on multi-sensor fusion. In the integrated navigation system, the GNSS obtained the satellite observation information, and the IMU obtained the three-axis attitude angle. The GNSS reference station was manually erected to improve positioning accuracy. The GNSS and IMU integrated navigation track measurement system [25] had a lateral deviation accuracy of 2 mm and a vertical deviation measurement accuracy of 2 mm.  
This section of the track consisted of straight and curved sections. Four CPIII control pile points with different distances were selected as control points on the line to verify the correction effect and accuracy at different distances. The control points were measured  
Figure 17: Point cloud generation overall effect.  
by a high-precision Leica total station and polar coordinate method. At the same time, satellite receivers were installed at a Leica total station and outside the site to facilitate the orientation of the total station and the conversion between satellite coordinate data and the engineering coordinate system. Figure 19 shows the overall distribution of targets, Figure 20 shows the target photo (left) and the point cloud extraction target (right).
###### Abstract  
Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM\({}_{10}\)), fine particulate matter (PM\({}_{2.5}\)), ozone (O\({}_{3}\)), sulfurrous acid gas (SO\({}_{2}\)), nitrogen dioxide (NO\({}_{2}\)), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.  
comprehensive air-quality index; deep learning model; kriging; network method Article
## 2 Materials and Methods
### Weather Stations  
Weather stations have historically been the first devices for the collection of climatic data on the ground and are still among the most reliable for most of the world. The weather stations that were taken into consideration for this research were: Monte Bove Sud, 13.19 E 42.91 N (1917 m.a.s.l.), Monte Prata, 13.21 E 42.87 N (1816 m.a.s.l.) and Pintura di Bolognola, 13.23 E 42.99 N (1360 m.a.s.l.) (Figure 1). The data from these three weather stations were obtained from the Regional Weather-Hydro-Pluviometric Information System of the Civil Protection of the Marche Region. The data from the above weather stations have a daily step and were collected from 2010 to 2020, as earlier data were not available. From these weather stations, data on precipitation, prevailing wind direction, wind speed and ground snow level were extracted because they could be useful for assessing possible problems in precipitation amounts related to other climatic variables. In addition, data from other rain gauges, 27 in total, (Figure 1) close to the three mountain weather stations under investigation were collected from the above-mentioned Institution's database in order to obtain interpolations of rainfall in the area [1; 19]. All weather stations have undergone a thorough data quality control process [20].  
Figure 1: Geographic map of central Italy; blue dot for the mountainous weather stations under investigation, red dot for interpolation rain gauges.