esa-sceva/satcom-chunk-collection huggingface dataset:
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 3 Results  
### Typhoon Frequency and Landfall Intensity  
A total of 829 typhoon events made landfall or passed within 500 km of Vietnam and the Philippines from 1970 to 2018. However, only 128 events (15%) were associated with reports of rice damage. For Vietnam, 303 typhoons occurred from 1970 to 2018, with 68 typhoons (22%) associated with reports of rice damage. For the 68 typhoons that damaged rice, the location of typhoon genesis was evenly split between the Pacific Ocean (52%) and the South China Sea (43%). A total of 27 out of these 68 typhoons (40%) happened between 2010 and 2018. The next time period with the highest number of typhoon-induced rice damage was from 1980 to 1989 (17 typhoons). There was no agricultural loss data reported from typhoons from 1970 to 1979 (Figure 1a; Table 1). Of the 303 typhoons that affected Vietnam during our study, the number of typhoons with information on rice damage ranged from 6% for typhoons with no landfall (6 out of 100) to 31% for Category 1 events (15 out of 49) to 32% for typhoons with the intensity of a tropical storm (47 out of 147; Figure 1c). Based on the landfall wind speeds of the typhoons that caused rice damage, 47 typhoons (69%) were tropical storms, while another 15 typhoons (22%) were Category 1 events. The remaining six typhoons caused rice damage without making landfall.  
For the Philippines, a total of 526 typhoons made landfall or passed within 500 km of the Philippines from 1970 to 2018. Of these, 60 typhoons (11%) had reports of rice damage. Most of these typhoon events (97%) originated in the Pacific Ocean, and 42 events (70%) occurred between 2010 and 2018. In contrast, only nine typhoons had caused rice damaged in the preceding 10 years (i.e., 2000-2009). Like Vietnam, there was no data from 1970 to 1979 (Figure 1b; Table 1). Out of the 526 typhoons that affected the Philippines, the number of typhoons with information on rice damage was 5% for typhoons with no LF, 10% for TS, 15% for Category 1, 22% for Category 2, 42% for Category 3, and 50% for Category 4 (Figure 1d). The sole Category 4 event that damaged rice during our study period was Typhoon Megi in 2010, while rice damage from the other Category 4 typhoon, Typhoon Haiyan in 2013, was amalgamated with other crops, and we had to exclude this information as we focused solely on damage to rice crops in this paper. Of the 60 typhoons that damaged rice in the Philippines, 28% were tropical storms (\(n\) = 17), while 25% were Category 1 typhoons (\(n\) = 15). Our findings showed that eight Category 2 typhoons (13%), 10 Category 3 typhoons (17%), and 1 Category 4 typhoon (2%) had damaged rice during the study period. Lastly, 9 out of 60 typhoons (15%) did not make landfall, but the accompanying rains and strong winds damaged rice plants.
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
## 3 Numerical Methods
### Governing Equations and Turbulence Models
#### 3.1.1 Unsteady RANS approach  
For the 32 Hz oscillation, the unsteady RANS (URANS) approach was used because URANS can predict unsteady flow structures.  
Conservation of mass (continuity equation):  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}\Big{(}\rho U_ {j}\Big{)}=0 \tag{3}\]  
Conservation of momentum:  
\[\rho\bigg{(}\frac{\partial U_{i}}{\partial t}+U_{j}\frac{\partial U_{i}}{ \partial x_{j}}\bigg{)}=-\frac{dP}{dx_{i}}+\rho g\delta_{i3}+\frac{\partial}{ \partial x_{j}}\bigg{(}\mu\frac{\partial U_{i}}{\partial x_{j}}-\rho\overline {u_{i}u_{j}}\bigg{)} \tag{4}\]  
Conservation of energy:  
\[\rho c_{p}\bigg{(}\frac{\partial\overline{T}}{\partial t}+U_{j}\frac{ \partial\overline{T}}{\partial x_{j}}\bigg{)}=-\frac{\partial}{\partial x_{j }}\bigg{(}-\kappa\frac{\partial\overline{T}}{\partial x_{j}}+\rho c_{p} \overline{u_{j}T^{\prime}}\bigg{)} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency (Hz)** & **0** & **2** & **16** & **32** \\ \hline Sr & 0 & 0.0314 & 0.2513 & 0.5027 \\ \(B\) & 0 & 0.04 & 0.05 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(B\) values in Equation (2) in terms of frequency and Strouhal number (Sr).  
Six Reynolds stresses, \(-\overline{\rho uu}\), \(-\overline{\rho uw}\), \(-\overline{\rho uw}\), \(-\overline{\rho vw}\), \(-\overline{\rho vw}\), and \(-\overline{\rho ww}\), from Equation (4) and \(\overline{T^{\prime}u}\), \(\overline{T^{\prime}v}\), and \(\overline{T^{\prime}w}\) from Equation (5) need to be modeled [23]. A closure for these equations can be obtained using Boussinesq hypothesis, which is expressed as follows [24]:  
\[-\overline{\rho u_{i}^{\prime}u_{j}}^{\prime}=\mu_{l}\bigg{(}\frac{\partial u _{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}-\frac{2}{3}\frac{ \partial u_{k}}{\partial x_{k}}\delta_{ij}\bigg{)}-\frac{2}{3}\rho k\delta_{ij} \tag{6}\]  
where \(\mu_{t}\) represents the turbulent viscosity, and it is modeled as follows [24]:  
\[\mu_{t}=\frac{\rho C_{\mu}k^{2}}{\varepsilon} \tag{7}\]
###### Abstract  
When evaluating the shear strength of rock mass discontinuities, certain challenges arise due to the difficulty in quantifying the roughness characteristics of surfaces and the strength of asperities. Recent research has focused on enhancing techniques for assessing these characteristics and exploring the application of laser scanning to aid in evaluating discontinuity features. The analysis of reflectivity values (\(l\)) obtained through a laser scanner survey presents an efficient method for assessing mechanical characteristics, such as joint compressive strength (\(l\)CS). Reflectivity measurements demonstrate correlations with Schmidt hammer rebound values (\(r\)). The laser scanner technique would enable the measurement of \(l\)CS without the direct application of the Schmidt hammer on rocks in areas where rebound values (\(r\)) measurements are unavailable. The use of a laser scanner allows for the acquisition of high-precision geometrical information concerning the 3D roughness and anisotropy of rock surfaces. In this study, an innovative technique was introduced that utilizes laser scanner data from six previous experimental surveys conducted on rock formations in Southern Italy. This technique facilitates the evaluation of roughness profiles, considering potential variations along kinematically admissible sliding directions, allowing for the estimation of the Joint Roughness Coefficient (\(J\!RC\)). This new methodology aids in evaluating the parameters of [PERSON]'s equation to determine the strength characteristics of rock mass discontinuities.  
laser scanner; rock mass; discontinuity shear strength; JCS; JRC; reflectivity; roughness profiles +
Footnote †: journal:  
range that varies between 100 and 1000m, with scanning speeds ranging from 2000 to 100,000 points per second. The analysis aimed at determining the discontinuity planes and their orientation can be carried out either manually or automatically on the Digital Terrain Model (DTM) [7]. In the first case, the operator manually selects, for each plane, a sufficient number of points to determine its orientation, including dip and dip direction. The second option relies on an automated approach to extract discontinuity planes from a dense Digital Surface Model (DSM) of a rock face obtained through laser scanning and computes their parameters [4; 8].  
There are several software applications that perform the tasks of detecting planes and determining their equations. These software tools essentially differ in the algorithms they implement. The simpler ones are limited to determining the plane equations using the least squares method applied to points subjectively selected by the operator. More sophisticated methods are based on segmentation algorithms capable of identifying different planes within the point cloud, as well as detecting and automatically removing outliers. [9].
## 6 Experiment
### Evaluation Metrics
#### 6.4.3 Network Overhead Ratio  
This parameter (that we will call OVH, for short), shows the ratio of the messages that were relayed to the network that did not reach their destination with respect to the number of messages that did do it. Equation (9) shows this definition:  
\[OVH=\frac{\#\text{relayed\,\,msgs}-\#\text{delivered\,\,msgs}}{\#\text{delivered\,\,msgs}}. \tag{9}\]  
The impact of OVH in the network is directly in the resource usage on the entire network. Ideally, this value should be minimized to reduce the problems related to poor bandwidth allocation, such as network congestions and consequential delays and disruptions.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 5 Experiments and Results
### Discerning the Inner Workings of the Proposed SAM2-UNet Architecture
#### 5.1.1 Mean Intersection over Union (mIoU)  
The mIoU metric is great for segmentation tasks because it provides a balanced assessment of model accuracy by measuring the overlap between predicted and actual regions. It accounts for both false positives and false negatives, making it reliable even when the classes are imbalanced. The metric'mIoU' (mean Intersection over Union) is widely recognized in benchmark evaluations and serves as a standard for comparing the performance of various models. It is also well suited for multi-class segmentation, capturing performance across all categories. Thus, the mIoU is simple, interpretable, and effective for evaluating image segmentation quality [10].  
\[\text{mIoU}=\frac{1}{N}\sum_{i=1}^{N}\frac{|A_{i}\cap B_{i}|}{|A_{i}\cup B_{i}|}\]
## 5 Advanced MPPT Techniques
### Metaheuristic MPPT Techniques  
Metaheuristic MPPT techniques provide advanced solutions for optimizing PV systems, especially in environments where traditional methods may fall short. Their ability to handle complexity, dynamic conditions, and multi-objective scenarios makes them a valuable tool in maximizing PV system performance [67]. They can be classified as optimization-based and bio-inspired MPPT algorithms. Figure 11 illustrates the general outline of the flowchart for metaheuristic MPPT techniques.
# Parallel Ghost Imaging with Extra Large Field of View and High Pixel Resolution
###### Abstract  
Ghost imaging (GI) facilitates image acquisition under low-light conditions through single pixel measurements, thus holding tremendous potential across various fields such as biomedical imaging, remote sensing, defense and military applications, and 3D imaging. However, in order to reconstruct high-resolution images, GI typically requires a large number of single-pixel measurements, which imposes practical limitations on its application. Parallel ghost imaging addresses this issue by utilizing each pixel of a position-sensitive detector as a bucket detector to simultaneously perform tens of thousands of ghost imaging measurements in parallel. In this work, we explore the non-local characteristics of ghost imaging in depth, and by constructing a large speckle space, we achieve a reconstruction result in parallel ghost imaging where the field of view surpasses the limitations of the reference arm detector. Using a computational ghost imaging framework, after pre-recording the speckle patterns, we are able to complete X-ray ghost imaging at a speed of 6 min per sample, with image dimensions of 14,000 \(\times\) 10,000 pixels (4.55 mm \(\times\) 3.25 mm, millimeter-scale field of view) and a pixel resolution of 0.325 um (sub-micron pixel resolution). We present this framework to enhance efficiency, extend resolution, and dramatically expand the field of view, with the aim of providing a solution for the practical implementation of ghost imaging.  
ghost imaging; compressed sensing; X-ray +
Footnote †: journal: Applied Sciences
# Computer-Aided Detection of Hypertensive Retinopathy Using Depth-Wise Separable CNN
### Major Contributions  
In this study, a CAD-HR system is designed to overcome the concerns that have been discussed above. This system makes use of a \(DSC\) model with residual connection and a LSVM for classification between HR and non-HR. The CAD-HR system has made several significant contributions, which are mentioned here.  
1. This paper develops a new CAD-HR system to recognize HR eye-related disease based on a residual connection and \(DSC\) model LSVM.
2. To effectively extract features from HR and non-HR photos, a pre-trained CNN model that is lightweight and based on a \(DSC\) network is developed. Our utilization of a depth-wise separable CNN for feature extraction in a HR classification challenge is a first to our knowledge. To deal with a real-time context, the proposed feature extraction is more extensive.
3. For automatic HR classification, we employed a 75-25% train-test split utilizing the linear SVM machine learning classifier. The efficiency and performance of Linear SVM make it a popular choice, especially when working with limited data sets.
4. Extensive experiments were conducted by employing several statistical metrics on two publicly available and one proprietary benchmark, namely DRIVE, DiaRetDB0, and Imam-HR. A full comparative study comparing the suggested strategy to other existing DL approaches is presented.
5. The proposed CAD-HR system outperforms other transfer learning (TL) based architectures in recognizing HR.
## 2 System Overview
### VLC Transmitter  
The transmitter mainly includes a modulation module, a light-emitting diode (LED) driving module, and a light source. After the field-programmable gate array (FPGA) processes the data, the visible light signal transmission circuit converts the electrical signal into a visible light signal, and transmits it to the underwater channel. Due to the serious attenuation of the underwater optical communication channel, a proper communication scheme must be designed to ensure that the UWVLC achieves a high transmission rate and long transmission distance.  
We chose non-return-to-zero on-off keying (NRZ-OOK) as the signal modulation method of the system, where binary OOK directly modulates the binary digital baseband signal to the LED for visible light emission. When the binary digital baseband symbol is \"1\", the driving circuit controls the LED to turn on and emit light; when the binary digital baseband symbol is \"0\", the driving circuit controls the LED to turn off. Using this modulation method greatly reduces the design complexity of the modulation and demodulation circuit.  
The LED driving circuit used in this work adds a carrier drawing-out circuit based on the basic common-source driving circuit to extract the remaining carriers in the junction capacitance when the LED is turned off, as shown in Figure 2a. The simple common-source circuit for the transistor MOS2 is used for driving the LED. The part surrounded by the dotted line is a carrier drawing-out part. U1 controls MOS1, and the signal of the inverse phase operation onto MOS2 is sent to MOS1. Hence, MOS1 is turned off at the time of the LED lighting and is turned on at the time of the LED turning off. Since both terminals of  
Figure 1: The basic composition diagram of optical–acoustic cooperative communication system.  
the LED are short-circuited at the time of turning off, an LED remaining carrier is drawn out.  
A commercial blue LED with a central wavelength of 450 nm is used as the light source for its large diffuse emission angle. Figure 3b shows the spatial distribution of the lights with royal blue and white, respectively. It can be seen that the blue light has a larger emission angle. In the underwater environment, the communication modem will inevitably have a certain jitter due to the influence of the water flow, and the large emission angle of the LED can reduce the probability of communication interruption and improve the robustness of optical communication.
## 2 Materials and Methods
### Instrumentation  
All sensors used for measurements and data acquisitions are shown in the PFD of the installation in Figure 1a. The characteristics of the measurement devices are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **NovecTM649** & **0.8 NovecTM649/0.2 HFE7000** & **HFE7000** \\ \hline Type & Dry & Dry & Dry \\ Molar mass (g/mol) & 316 & 283.2 & 200.1 \\ Normal boiling point (\({}^{\circ}\)C) & 49.1 & 41.2 & 34.2 \\ Normal latent heat (kJ/kg) & 88.1 & 99.6 & 133.9 \\ Critical temperature (\({}^{\circ}\)C) & 168.7 & 165.6 & 164.6 \\ Critical pressure (bar) & 18.7 & 20.3 & 24.8 \\ Ozone Depletion Potential & 0 & 0 & 0 \\ Global Warming Potential & 1 & 107 & 530 \\ Inflammatory & Nonflammable & Nonflammable & Nonflammable \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main properties of the considered fluids.  
The working fluid and the heat source and cooling source loops are equipped with Type-K thermocouples to measure temperatures between the various components.  
The working fluid circuit is equipped with absolute pressure sensors (APS) at the turbine inlet and outlet to measure the two pressure stages within the installation.  
Volumetric flow rate measurements of hot and cold circuits were performed using electromagnetic flow meters (EFMs). The mass flow rate of the working fluid is determined by the energy balance at the condenser of the experimental installation.  
The electrical power produced at the turbine was measured by means of a wattmeter.
## 2 Materials and Methods
### Size and Zeta Potential Measurement  
The size and zeta potential of PRKCA siRNA and PTEN plasmid complexed with CA NPs were measured using Malvern Zeta Sizer Nano (United Kindom). The NPs, PRKCA siRNA+NPs, PTEN+NPs and PRKCA siRNA+PTEN+NP complexes were formed by adding 4 \(\upmu\)L of 1 M CaCl\({}_{2}\), 1 nM siRNA against PRKCA and 1 \(\upmu\)g of PTEN plasmid into 1 mL of DMEM (pH 7.4), followed by incubation at 37 \({}^{\circ}\)C for 30 min. After that, 10% FBS was added in order to stop further formation of NPs complexes which were subsequently maintained on ice till measurement was done with Zetasizer. The data was analyzed using Zetasizer software 6.20 and all samples were measured in duplicates, with the values presented as mean \(\pm\) SD.
# Topological Navigation and Localization in
## 4 Development and Testing Methodology
### Validation in a Real Mining Operation  
In order to carry out the final stages of development, the equipment had to be tested in its real operation environment, where the last design assumptions and algorithms needed to be validated, and the presence of personnel from the operation site were required.  
In the case of the automation system described here, after test site validation, the LHD was transported to a real sublevel stopping mine in the north of Chile, where the development team, the OEM, and the mine personnel coordinated the final system validation and tests.
## 2 Materials and Methods
### Prototype Experiments for Validation  
The proposed prototype was tested in a city used as a case study, the city of Popayan. Popayan is a medium-sized city in the southwest of Colombia (Latin America), it has an extension of 512 km\({}^{2}\) (including rural and urban areas) and a population of approximately 300,000 inhabitants. Considering the choice of LoRa as the communication technology for the prototype, a coverage analysis of this technology was performed for the city, using the QGIS (Quantum Geographic Information System) tool. QGIS was used because it has a plugin that allows the estimation of radio coverage taking into account demographic data and city buildings [30]. The study was performed in order to find an adequate location for the gateways and the total number of these devices that would be necessary to cover the city.  
Once the coverage analysis was performed, a suitable location in the city was determined to develop the experiments. The objective of the experiments was to validate sending and receiving messages from and to the modules located in the transit vehicles (using four of these devices), determiningparameters such as signal level, distance reached, percentage of packets received, and operation of the data security layer.  
Section 5 of this document explains in detail the design and results of experiments.
## 1 Introduction
As such, it holds great potential for monitoring the physiological status and water stress of vegetation [40]. SIF (W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) is a kind of long-wave signal emitted by vegetation with 600-800 nm range after absorbing energy under sunlight [41; 42]. An increasing number of SIF datasets are now available from ground-based, airborne, and satellites sources [37]. These datasets are derived from satellite platforms and instruments such as Greenhouse Gases Observing Satellite (GOSAT) [43; 44], Global Ozone Monitoring Experiment-2 (GOME-2) [45; 46], Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) [47; 48; 46], the Orbiting Carbon Observatory-2 (OCO-2) [49], etc. Utilizing these datasets, various inversion algorithms have been proposed [50; 51]. Two kinds of inversion algorithms are recently used, namely, the inversion algorithm based on the physical model [43; 48] and data-driven algorithms [45; 46]. However, global SIF products typically have coarser spatial resolution, such as GOME-2 (40 \(\times\) 80 km\({}^{2}\)), GOSAT (10 \(\times\) 10 km\({}^{2}\)), and SCIAMACHY (30 \(\times\) 240 km\({}^{2}\)). To achieve global 0.05-degree products, downscaling methods have been employed [52]. To acquire spatially continuous and high-resolution SIF products, these studies employed techniques that either interpolate missing data or integrate machine learning or deep learning approaches with auxiliary data [50; 53; 54; 55; 56]. However, for regional-scale studies of the carbon cycle and crop growth, SIF products with higher resolution are needed. The random forest (RF) downscaled method was employed in this study to meet the regional drought requirements and enhance the spatial resolution of data, resulting in an improved spatial resolution of 1 km for SIF data. The feasibility of utilizing SIF in drought monitoring was investigated by establishing and applying the SIF index to monitor drought conditions in Henan Province. The objectives of this study are as follows: (1) to introduce a downscaled method based on RF and obtain spatially resolved SIF data at a resolution of 1 km; (2) verify the SIF result after downscaling using different methods; and (3) monitor and assess long-term drought in Henan Province using the 1 km spatial resolution-downscaling SIF.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 3 Experimental Results  
This section shows the experimental results for desktop- and VR-based listening experiments. The anchor signal could be unambiguously identified by each subject (externalization score = 0), therefore its rating is not shown in the following figures. A Shapiro-Wilk test revealed that the data for each condition (BRIR type) did not all have a normal distribution [21]. As a result, non-parametric statistical tests were performed for analyzing the experimental results. A Friedman test was used to analyze the significant effect of BRIR types on externalizing ratings [21]. Furthermore, Wilcoxon tests were performed to analyze significant differences in externalization results between BRIR types, and \(p\)-values were adjusted using the Bonferroni correction [21].