esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development
### The Metric, Energy-Momentum Tensor, and Field Equations
#### 2.1.1 Energy-Momentum Tensor  
We now follow the approach of [PERSON] [29; 30; 31; 32], reported as Bondian observers of the GR [9]: a local observer is located, according to the Minkowski perspective, in motion with the fluid charged with velocity -\(\omega\); for such an observer following the radial direction (the components of the energy-momentum tensor are given by the energy density, the electric energy density, the unpolarized energy density, and the pressure represented by the symbols \(\rho\), \(\mu\), \(\varrho\), and \(p\), respectively), Equation (10) will represent the covariant energy-momentum tensor.  
\[T_{\
u\mu}\ =\ (\rho\ +\ p)u_{\mu}u_{\
u}\ -\ pg_{\mu\
u}\ +\ \epsilon l_{\mu}l_{\
u}\ +\ E_{\mu\
u} \tag{10}\]  
where density \(\rho\), pressure \(p\), metric tensor components \(g_{\mu\
u}\), radiation flux \(\epsilon\), null vector \(l_{\mu}\), and electromagnetic tensor \(E_{\mu\
u}\) are identified. For this paper,  
As shown above, the comma (\(\centerdot\)) is the partial derivative concerning the indicated coordinate. Equation (13) represents the velocity of matter.  
\[\frac{dr}{du}\ =\ \frac{V}{r}\frac{\omega}{1\ -\ \omega} \tag{13}\]  
When the fluid is adiabatic, it possesses a diagonal energy tensor in covariant form \((\rho,p_{r},p_{t},p_{t})\) defining \(\hat{\rho}\ =\ \rho\ +\ \hat{\mu}\), \(p_{r}\ =\ p\ -\ \hat{\mu}\), which is the same for an anisotropic fluid [16; 17]. It is clear that, in the presence of electric charge, local anisotropy occurs. On the contrary, in the absence of electric charge, the Pascalian character of matter, i.e., isotropic fluid, is recovered. The variables \(P_{t}\) and \(P_{r}\) are the tangential and radial pressures, respectively.  
In addition, by defining the mass function as shown in Equation (14),  
\[m\ =\ \frac{1}{2}\Big{(}r\ -\ Ve^{2\hat{\rho}}\Big{)} \tag{14}\]  
the so-called Missner-Sharp mass generalization [33; 34] is obtained. When there is charge, it is expressed as  
\[m\ =\ m\ +\ \frac{C^{2}}{2r} \tag{15}\]
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 4 The Variable-Step Iteration Algorithm for Efficient Determination of Optimal Rotation Angle
### The Selection of Iteration Step  
The convergence analysis of Formula (15) is as follows. Let the rotation angle error of the \(l\)th iteration be  
\[v(l)=\Delta\varphi(l)-\Delta\varphi_{opt} \tag{19}\]  
It can be written as  
\[v(l+1)=v(l)+\mu(l)\cdot(-\frac{\partial e}{\partial\Delta\varphi}\bigg{|}_{ \Delta\varphi=\Delta\varphi_{l}}) \tag{20}\]  
When the iteration is convergent, it can be derived that  
\[\underset{\Delta\varphi\rightarrow\Delta\varphi_{  
For the four-array central circular array antenna that is commonly used for navigation receivers, due to the symmetry and periodicity of the array, there would be  
\[\max[|v(l)|]=\frac{\pi}{12} \tag{23}\]  
if the initial value is selected reasonably. Considering that \(v(l)\) and \(\frac{\partial e(l)}{\partial\Delta\varphi(l)}\) are both positive or negative, the iteration step should satisfy:  
\[0<\mu(l)<|\frac{\pi}{6}/\frac{\partial e(l)}{\partial\Delta\varphi(l)}| \tag{24}\]  
In observing Figure 3b, the extreme values in \(e(l)\) appear in the location of \(\Delta\varphi=19^{\circ}\) and \(\Delta\varphi=47.7^{\circ}\), where only the former one is the optimal rotation angle. So, the iteration step also needs to meet the demand of being positively related to residual power. Then the iteration step is further required as:  
\[\mu(l)=y(l)/\sum\limits_{k=1}^{K}p_{\hat{h}}\times c_{itr} \tag{25}\]  
where input interference power \(\sum\limits_{k=1}^{K}p_{\hat{h}}\) normalizes the residual power \(y(l)\). \(c_{itr}\) is a coefficient related to the iteration speed, whose empirical value can be selected by offline statistics of \(\frac{\partial e(l)}{\partial\Delta\varphi(l)}\). The statistics of \(\frac{\partial e(l)}{\partial\Delta\varphi(l)}\) is determined by implementation scenarios. This is discussed in Section 5.
## 3 Results
### Forest Classification and Management  
Drones are playing a key role in forest area classification and management. Drones equipped with advanced remote sensing capabilities and programmed with deep learning algorithms, that are able to enhance the accuracy and efficiency of forest area classification and management, particularly in areas threatened by deforestation [74]. The use of drones with multispectral sensors can eliminate some of the major limitations associated with satellite imagery, particularly when dealing with tiny plants such as native desert vegetation [45]. The use of consumer-grade cameras mounted on drones offers an affordable and accurate option of investigating tree crowns across specific areas [69]. Through the drone's aerial system imagery, researchers conducted tree crown mapping and quantified percent canopy mortality [70]. [PERSON] et al. [51] presented a method to combine drone and satellite images to produce landscape-scale map of flowering dynamics so that to better understand the condition of forest ecosystems. Research found that drones equipped with sub-metre resolution capabilities, are vital in accurately mapping vegetation in spatially heterogeneous landscapes, offering enhanced precision in training dataset construction and filling gaps where other high-resolution data is unavailable [68]. Furthermore, drones mounted with colour sensors can be used to detect some vegetation diseases in forests, such as pine wilt [76].
## 3 Results and Discussion
### Optimization of the Extraction Conditions Using BBD
#### 3.2.3 Validation of the Model  
To verify the suitability of the model equation, the optimum response values were used (66 \({}^{\circ}\)C, 205 W, and 2.6 h) for a test extraction. The average extraction yield was 8.97 \(\pm\) 0.36%, which was similar to the predicted best extraction yield (9.17%). These results indicated that the model was adequate for polysaccharide extraction.
## 4 Proposal Development
### Gateway  
The function of the gateway is to forward and receive the information arriving from the end nodes via LoRa wireless communication to the IoT server using standard IP internet protocol [36].  
Figure 5a illustrates the electronic design of the gateway that has an ESP32 microcontroller as the central coordinator module; it is an IoT device with Wi-Fi and Bluetooth support integrated on a single development board. In the present prototype, an ESP32 DEVKIT MCU was used, which has an on-board USB serial converter and a micro-USB port for power supply, integrated 4 MB flash memory, GPIOs with PWM function, I2C, SPI, AD/DA converters, among other functions [37]. The ESP32 DEVKIT MCU allows sending and receiving information via the integrated Wi-Fi chip to and from the user interface over the IP network. For communication with the end nodes, the Heltec Wi-Fi LoRa 32(V2) module was used, which has the integrated SEMTECH SX1276 LoRa chip and the ESP32 programmable microcontroller and incorporates a 0.96-inch OLED display. The chip operates at a frequency of 902-928 MHz with a receive sensitivity of \(-127\) dBm, an omnidirectional UHF antenna and a gain of 2 dBi [18]. With the Heltec Wi-Fi LoRa 32(V2) modules, 2 independent LoRa wireless channels were created, one for transmitting and one for receiving data. The synchronous information flow between the ESP32 DEVKIT microcontroller and the Heltec Wi-Fi LoRa 32(V2) MCUs was achieved with I2C digital communication, generating a functional gateway for LoRa Full Duplex communications. Power supply took place with the HP 120 VAC charger at 1.6 A/60 Hz, the DC 18.5 V/3.5 A output, and a DC-DC LM2596 module configured to provide 5 V to the gateway MCUs.  
A PCB board was designed for the synergy of the electronic components that are protected against external IP65 weather conditions with a custom-made housing printed with 3D PLA thermoplastic as presented in Figure 5b.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 4 Challenges of PV-DT
### Communication  
DT implementation relies on bidirectional communication between both the PT and the DT. Several authors have reviewed and investigated the challenges posed to DT development in the area of communication [118; 119; 120].  
In the context of PV-DT, communication challenges are largely due to the remote location of PV installations and the need to transmit large data, including images, with very low latency. Due to efficient land resource management, the large systems that mostly benefit from DT integration are found in very remote areas like desert regions, water bodies for floating systems, vertical systems along the freeway remote rural non-agricultural land sites, and more. Bidirectional data transfer in such areas requires adequate 5G/6G internet coverage and connectivity. This is not always the case because the 5G/6G base stations are currently only intended for densely populated areas with high communication and data traffic. This is done to optimize the cost-benefits and reduce the communication base stations' average energy consumption (ensure carbon neutrality) during the operational phase. For instances such as these, both IoT to cloud or IoT-IoT data transmission become ineffective [121; 122].  
Considering the above and the need for real-time image processing requirements in PV-DT, the DT operators are beginning to look into reliable satellite-based alternatives such as Starlink [123]. While this is a suitable solution, it drastically increases the cost of system maintenance compared to when 5G/6G is used.
# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 6 Experiments and Results
### TableBank  
TableBank [64] is a unique dataset that comprises three types of documents, i.e., Latex, Word, and a mixture of Latex and Word documents. It has a separate dataset for each document type. We used a smaller train-test split for training, which was defined by the current state-of-the-art approach [8]. This allowed us to perform a direct comparison of our results with their results. We performed finetuning of HybridTabNet on each of the three datasets in the TableBank dataset.  
Table 3 summarizes the results of HybridTabNet on TableBank dataset. It achieved the highest F1-score of 0.980 on the 0.5 IoU threshold in Latex documents. There was only a slight drop in the f1-score of HybridTabNet from IoU thresholds of 0.6 to 0.8, which shows that its performance was not limited to lower IoU thresholds. However, at the IoU threshold of 0.9, there was a significant drop in the performance of our approach. Similarly, in Word and a mixture of Latex and Word documents, the F1-score almost remained constant from 0.5 to 0.8. For the IoU threshold of 0.9, the F1-score of Word was similar to lower thresholds, i.e., from 0.5 to 0.8. Conversely, for the mixture of Latex and Word, the F1-score at 0.9 was low.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **IOU Threshold** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{6}{*}{HybridTabNet} & 0.5 & 0.882 & 0.997 & 0.936 \\ \cline{2-5}  & 0.6 & 0.882 & 0.997 & 0.936 \\ \cline{2-5}  & 0.7 & 0.882 & 0.997 & 0.936 \\ \cline{2-5}  & 0.8 & 0.879 & 0.994 & 0.933 \\ \cline{2-5}  & 0.9 & 0.870 & 0.983 & 0.926 \\ \cline{2-5}  & W.Avg & **0.878** & **0.993** & **0.932** \\ \hline \hline \end{tabular}
\end{table}
Table 2: HybridTabNet results ICDAR-17 POD results with a deformable ResNeXt-101 backbone. W.Avg denotes weighted-average of the respective measure on the IoU threshold.  
Figure 5: HybridTabNet results on the ICDAR-2017 dataset. (**a–c**) True positive, False positive, and False negative results, respectively. The blue outline shows the predicted table, and the red outline highlights the ground truth table.
## 5 Results
### Coverage Analysis
Meanwhile, when the _fv_ decrease to 5 m/s, we can identify some combinations of _fh_ and _dc_ that result in an optimal connection (_fh_ = 24 m and _dc_ = 25 m). For the lowest _fv_, several combinations allow good and optimal connectivity. Finally, for the scenario with the lowest density of nodes (one node every 5000 m\({}^{2}\)), we can identify some combinations that allow optimal connection even with the highest _fv_ with _fh_ equal or smaller than 100 m. For the other _fv_ (15 to 1 m/s), several combinations of _fh_ and _dc_ ensure that the drone can act as a mobile gateway and gather the data of nodes placed in the soil. In case that none of the combinations of the given parameters (_fh_, _fv_, and node density) with different _dc_ values offer us a good (yellow) or optimal (green) solution, it will be necessary to evaluate if the possibility to modify the given parameters (fixed parameters) exists. For example, decreasing the _fv_ or increasing the _fh_ can be useful to ensure communication; meanwhile, we maintain the original purpose of the drone. These adjustments must be carefully considered to guarantee that the original purpose of the drone surveillance of the crop is possible. To do so, it will be necessary to evaluate  
Figure 12: Time in coverage when a drone flies at 5 m/s and different _dc_. the loss of image resolution due to the increment in the _flt_ and the reduction of overlapping between obtained images due to _flt_. Figure 13: Summary of communication in different scenarios: gray is no coverage (NC), red is not enough time in coverage. Otherwise, in case there is no possibility to change the given parameters, it will be necessary to use some other communication process in the nodes placed on the soil. For example, it will be possible to use a clustering algorithm to communicate all the gathered data from the cluster to the cluster head, and this cluster head sends the data to the node. This will minimize the time consumed to send the data, as fewer processes of communication establishment must be done. Nonetheless, this solution is limited to the coverage of WiFi antennas of nodes placed in the soil, and it can only be applied in cases with high node densities.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
These hybrid models can be categorised either as  
* Single-level, comprising a standalone model, such as OpenMP, MPI, or CUDA;
* Dual-level (X + Y), comprising two programming models to improve parallelism, such as MPI + OpenMP [10; 11; 12] or OpenMP + CUDA [13];
* Tri-level (MPI + X + Y) [14], comprising three distinct programming models to enhance parallelism. However, the latter two programming models may produce new errors for a plethora of reasons, unlike each of its individual constituent models. Several studies have shown that each programming model boasts different features and serves different purposes. For instance, the purpose of OpenACC is to make the parallel programming process of heterogeneous CPU and GPU systems more straightforward [15], while OpenCL(tm) is designed to utilise the computing power of GPUs to improve the productivity of GPU applications. However, OpenCL(tm) may actually decrease productivity as it is complex and difficult to programme [16]. OpenMP, on the other hand, is commonly used in systems and architectures that have shared memory. It provides excellent parallelism at both coarse- and fine-grained levels, as well as seamlessly integrating with a variety of hardware, platforms, systems, and programming languages via its application programming interface (API), and, specifically, its compiler directive streamlines the process of creating multiprocessing shared-memory programmes. Meanwhile, other programming models, such as CUDA [9], allow code to access data from any memory address, which provides developers with some details, such as the kernel boot time mapping of threads, the memory transferred from the host to the device, parallelism, and the temporary storage of data. The compute unified device architecture (CUDA), which is also used in parallel computing, is commonly programmed using the Fortran and C/C++ programming languages. As such, it supports the use of GPUs and offers significant support in this regard. Parallel models not only require more effort to test but also produce more parallel errors. Furthermore, when parallel models are combined into one application, the causes of these errors change during run-time as the models integrate. As present, there are no compilers capable of detecting these types of run-time errors. Therefore, tri-level programming models currently do not have testing tools that are capable of detecting their run-time errors. As such, the purpose of this present study was to develop a parallel hybrid testing tool using the C++ programming language and a combination of static and dynamic testing methods to effectively test and detect errors both in real time and during run-time by analysing the source code of systems comprising MPI + OpenMP + CUDA programming models, as parallel hybrid techniques may increase the testing efficiency and detect more errors.
# Study of Sea Fog Environment Polarization Transmission Characteristics
###### Abstract  
Sea fog is a particular kind of atmospheric aerosol that often poses hidden risks to ship navigation, ocean exploration, human productivity, and life. In light of the aforementioned issues, this research conducted a thorough investigation of the polarization transmission properties in a sea fog environment. We studied the physical characteristics of sea fog and established a polarized radiative transfer model based on RT3/PolRadtran (polarized radiative transfer) in a sea fog environment based on the theory of Mie scattering. The effects of wavelength, polarization state, sea fog concentration, and salt content on the polarization degree were simulated by using the polarization transport model. An indoor sea fog simulation device was designed and built. The simulation test results were compared with the experimental test results from many aspects, and the existing errors were analyzed so that they could be mutually verified for the overall trend. According to the modeling results, distinct polarization states of light exhibit evident depolarization as the sea fog concentration rises. Overall, circularly polarized light has superior polarization-maintaining properties compared to linearly polarized light under the same contrast settings. The penetrating impact of incoming light in the visible range improves with increasing wavelength, and the amount of salt in sea fog has some bearing on the degree of polarization.  
M +
Footnote †: journal: Medical Imaging  
10.3390/app1278892  
Mie scattering; optical thickness; polarization; sea fog; transmission
# Free and Forced Wave Motion in a Two-Dimensional Plate with Radial Periodicity
## 2 Stepwise WFE Approximation of a Radially Periodic Plate  
A lossless and linear elastic plate with radial periodicity is considered. The plate consists of an infinite repetition of a sequence of annuli of the same width with alternating properties, as shown in Figure 1a. The lattice constant, or the characteristic length of the unit cell, is here defined in the radial direction and denoted by \(\overline{R}\).  
Figure 1: (**a**) radially periodic structure; (**b**) slice of the radially periodic structure; (**c**) approximation in piecewise Cartesian periodic waveguides.  
In order to apply the WFE method, a slice of the plate is taken, as shown in Figure 1b. Since the periodicity is in the radial direction, the angle of the slice can be arbitrarily small. The slice is approximated as a piecewise rectangular waveguide in Cartesian coordinates, as shown in Figure 1c. A finite number of segments is assumed. These segments are numbered increasingly in the radial direction from 1 to \(n\), where \(n\) is the cell distant \(\overline{r}\) from the centre. Wave characteristics of each of these rectangular segments are obtained using the WFE approach as described in Section 2.1. In the model, the left nodes of the first cell are shifted at an arbitrarily small distance \(R_{0}\) from the origin of the coordinates; the inner part, \(0<r<R_{0}\), is uniform and homogenous.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 3 Results
### Estimating the Impact of Floods on Land  
The total area of the Cigher Basin is 84,540 hectares. Of this area, 37% is allocated to arable land, primarily in the northern half of the basin along the middle and lower courses of the rivers. Forests account for 32% of the area, mainly located in the southern half of the basin along the upper courses of the rivers, while grasslands occupy 14% of the total area, found at the forest edges or interspersed with arable land (Figure 9). Other land use categories are present in smaller proportions.  
From its origin point to the confluence with the Crisul Alb River, the Cigher River, with a total length of 64.2 km, flows through forested areas in its upper section and agricultural land in its middle and lower sections (Figure 9). The river also passes through the localities of Seleus, Moroda, Silindia, and Taut.  
In this study, various hydraulic simulation scenarios were developed. For the scenario assuming a maximum flow rate of 60 m\({}^{3}\)/s, the floodable area was estimated to have an approximate width of 100 m along both banks of the river. Hydraulic modeling was applied to a defined segment of the river, for which the floodable area was estimated. Based on the initial modeling hypothesis, it was assumed that the estimated floodable area for the analyzed segment remains consistent along the entire course of the river. Consequently, land use categories affected by flooding were identified and analyzed to assess the potential impact on these areas.  
It was found that, at a flow rate of 60 m\({}^{3}\)/s, the Cigher River floods a total of 871 hectares, distributed as follows: 392 hectares of arable land, 231 hectares of forested areas, 202 hectares of grasslands, and 46 hectares of built-up land.  
The results of the flood extent analysis were obtained for a river section equipped with protective dikes. In the absence of these defense structures, the area of zones affected by flooding could be significantly larger due to the unrestricted flow of water into adjacent lands.  
Figure 9: Land use in the Cigher Basin (processed based on [54; 59]).