esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Safety Determination of CLRF
#### 3.3.2 Red Blood Cell Hemolysis Test  
In addition to the chicken embryo chorioallantoic membrane test, the erythrocyte hemolysis test is also a classic safety test to check sample irritation. Figure 2B,C show a bar graph of the hemolysis rates of CW and CLRF at different concentrations. The centrifuge tubes from left to right are, in order of volume fraction, 100%, 80%, 60%, 40%, 20% and 10% of the sample solution, positive control for complete hemolysis and negative control. When the volume fraction was greater than 60%, the erythrocyte hemolysis rate was around 40% for CW and 30% for CLRF, and when the volume fraction was less than 40%, the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Haemolysis**} & \multicolumn{8}{c}{**Numbering**} & \multirow{2}{*}{**ES**} \\ \cline{3-3} \cline{5-8}  & & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{2}{*}{**Positive control (0.1 mol/L**} & Bleeding & 2 & 2 & 2 & 2 & 2 & 2 & \\  & Vascular coagulation & 2 & 1 & 1 & 2 & 1 & 2 & 12 \\  & Vasolysis & 1 & 1 & 1 & 1 & 1 & 1 & \\  & Bleeding & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Vascular coagulation & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Vasolysis & 0 & 0 & 0 & 0 & 0 & 0 & \\  & Bleeding & 0 & 0 & 0 & 0 & 0 & 0 & \\  & Vascular coagulation & 0 & 0 & 0 & 0 & 0 & 0 & \\  & Visolysis & 0 & 0 & 0 & 0 & 0 & 0 & \\  & Bleeding & 0 & 0 & 0 & 0 & 0 & 0 & \\
**CLRF** & Vascular coagulation & 0 & 0 & 0 & 0 & 0 & 0 & \\  & Vasolysis & 0 & 0 & 0 & 0 & 0 & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of Eye Irritation of Chicken Chorioallantoic Membrane.  
Figure 2: Safety measurements of CW and CLRF: (**A**) Vascular presentation and response plots before and after representative CAM spiking for CW and CLRF; (**B**) Erythrocyte hemolysis rates for different volume fractions of CW; (**C**) Erythrocyte hemolysis rates for different volume fractions of CLRF. In the photographs in Figure 2B and C, the red colour of the solution in the centrifuge tube correlates positively with erythrocyte haemolysis. The supernatant of the centrifuge tube is colourless and clear with a red precipitate at the bottom, indicating low or no haemolysis of the sample. CW: Coix seed water extract; CLRF: Coix seed _Lactobacillus reuteri_ fermentation broth. Each value is expressed as mean \(\pm\) SD (\(n=3\)).  
erythrocyte hemolysis rate was significantly reduced to less than 3% for both CW and CLRF. Experiments have shown that CW is more irritating than CLRF at high volume fractions, and that both CW and CLRF are less irritating at low volume fractions.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
###### Abstract  
Autonomous navigation and localization are the foundations of unmanned intelligent systems, therefore, continuous, stable, and reliable position services in unknown environments are especially important for autonomous navigation and localization. Aiming at the problem where GNSS cannot continuously localize in complex environments due to weak signals, poor penetration ability, and susceptibility to interference and that visual navigation and localization are only relative, this paper proposes a GNSS-aided visual dynamic localization method that can provide global localization services in unknown environments. Taking the three frames of images and their corresponding GNSS coordinates as the constraint data, the GNSS coordinate system and world coordinate system transformation matrix are obtained through horn coordinate transformation, and the relative positions of the subsequent image sequences in the world coordinate system are obtained through epipolar geometry constraints, homography matrix transformations, and 2D-3D position and orientation solving, which ultimately yields the global position data of unmanned carriers in GNSS coordinate systems when GNSS is temporarily unavailable. Both the dataset validation and measured data validation showed that the GNSS initial-assisted positioning algorithm could be applied to situations where intermittent GNSS signals exist, and it can provide global positioning coordinates with high positioning accuracy in a short period of time; however, the algorithm would drift when used for a long period of time. We further compared the errors of the GNSS initial-assisted positioning and GNSS continuous-assisted positioning systems, and the results showed that the accuracy of the GNSS continuous-assisted positioning system was two to three times better than that of the GNSS initial-assisted positioning system, which proved that the GNSS continuous-assisted positioning algorithm could maintain positioning accuracy for a long time and it had good reliability and applicability in unknown environments.  
visual navigation and positioning; GNSS; assisted positioning; BA optimization; horn coordinate transformation +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 4 Proposed Distributed Extended Kalman Filter Based 3D Jammer's Localization Schemes
### Distance Ratio Based Distributed Extended Kalman Filter (DEKF-DR) for the 3D Localization
\right] \tag{37}\]  
Therefore, the first derivative of jamming power with respect to jammer's position at time \(k\) will be resulted in the following expressions:  
\[\frac{\partial P_{IB}}{\partial x_{J}}=C_{JB}\frac{x_{B}\widehat{x}_{I_{k-1}} }{(x_{B}+\widehat{x}_{I_{k-1}})^{2}+(y_{B}+\widehat{y}_{I_{k-1}})^{2}+(z_{B}+ \widehat{z}_{I_{k-1}})^{2}} \tag{38}\]  
\[\frac{\partial P_{JE}}{\partial x_{J}}=\frac{\partial P_{IB}}{\partial x_{J}} \;\beta_{k-1} \tag{39}\]  
\[\frac{\partial P_{EB}}{\partial x_{J}}=\frac{\partial P_{IB}}{\partial x_{J}} \;(1-\beta_{k-1}) \tag{40}\]  
\[\frac{\partial P_{IB}}{\partial y_{J}}=C_{JB}\frac{y_{B}\widehat{y}_{I_{k-1}} }{(x_{B}+\widehat{x}_{I_{k-1}})^{2}+(y_{B}+\widehat{y}_{I_{k-1}})^{2}+(z_{B}+ \widehat{z}_{I_{k-1}})^{2}} \tag{41}\]  
\[\frac{\partial P_{JE}}{\partial y_{J}}=\frac{\partial P_{IB}}{\partial y_{J}} \;\beta_{k-1} \tag{42}\]  
\[\frac{\partial P_{EB}}{\partial z_{J}}=\frac{\partial P_{IB}}{\partial z_{J}} \;(1-\beta_{k-1}) \tag{43}\]  
\[\frac{\partial P_{EB}}{\partial z_{J}}=\frac{\partial P_{IB}}{\partial z_{J}} \;(1-\beta_{k-1}) \tag{44}\]  
where,  
\[C_{JB}=\frac{10n}{ln10} \tag{47}\]  
Thus, the proposed DEKF-DR algorithm for the 3D localization as explained in Algorithm 1 can be implemented while using the Equations (13)-(17) with the aid of Equations (37)-(47). ```
1: Set the system threshold value = \(\gamma\). 2: Initialize time index \((k)\) the boundary node index \((i)\) and the neighbor node index \((n)\). 3:Input:\(\mathbf{x}_{k-1}\), \(\mathbf{P}_{k|k-1}\mathbf{z}_{k}\). 4:Output:\(\mathbf{x}_{k}\). 5:repeat
6:\(k=k+1\). 7: Capture the \(P_{I_{B_{k}}}\). 8: Estimate \(P_{NB}\). 9: Compute the \(\beta_{k}\) using Equation (5). 10: Compute the \(P_{I_{E_{k}}}\) and \(P_{EB_{k}}\) using Equations (8) and (9). 11:\(\widehat{\mathbf{x}}_{\mathbf{k}|k-1}\leftarrow\mathbf{f}(\widehat{\mathbf{x}}_ {k-1|k-1})\). 12:\(\mathbf{P}_{k|k-1}\leftarrow\mathbf{A}_{k}\mathbf{P}_{k-1|k-1}\mathbf{A}_{k}^ {T}+\mathbf{Q}_{k}\). 13:\(\mathbf{z}_{\mathbf{k}}\leftarrow\mathbf{h}(\mathbf{x_{k}})+\mathbf{v_{k}}\). 14: Estimate \(d_{I_{B_{k}}}\) using Equation (1). 15:\(d_{I_{E_{k}}}\leftarrow\beta_{k}\,d_{I_{B_{k}}}\). 16:\(d_{EB_{k}}\leftarrow(1-\beta_{k})\,d_{I_{B_{k}}}\). 17:\(\mathbf{K}_{\mathbf{k}}\leftarrow\mathbf{P}_{\mathbf{k}|k-1}\mathbf{H}^{T}_{ \mathbf{H}}(\mathbf{H}_{\mathbf{k}}\mathbf{P}_{k|k-1}\mathbf{H}^{T}_{\mathbf{ k}}+\mathbf{R}_{\mathbf{k}})^{-1}\). 18:\(\widehat{\mathbf{x}}_{\mathbf{k}}\leftarrow\widehat{\mathbf{x}}_{\mathbf{k}|k -1}+\mathbf{K}_{\mathbf{k}}(\mathbf{z}_{\mathbf{k}}-\mathbf{h}(\widehat{ \mathbf{x}}_{\mathbf{k}|k-1}))\). 19:\(\mathbf{P}_{\mathbf{k}}\leftarrow\mathbf{P}_{\mathbf{k}|k-1}-\mathbf{K}_{ \mathbf{k}}\mathbf{H}_{\mathbf{k}}\mathbf{P}_{\mathbf{k}|k-1}\)
20:until [Final time \(k_{f}\)]
```  
**Algorithm 1** Pseudo-code of DEKF-DR
## 2 Microbial Processes of Anaerobic Digestion
### Acetogenesis  
The accumulation of electron sinks, such as lactate, ethanol, propionate, butyrate and higher VFAs, may promote an increased hydrogen concentration in the medium, which cannot be consumed directly by the methanogens and should be further degraded by the obligate hydrogen producing acetogenic bacteria. This process is referred to as acetogenesis. Syntrophobacter (propionate-utilizing acetogens) and Syntrophomonas (butyrate-utilizing acetogens) represent the major acetogens. These groups of bacteria are strictly anaer as they rely on the acetyl coenzyme A pathway, containing highly oxygen sensitive enzymes. The acetogenic bacteria can degrade the electron sinks to acetate, carbon dioxide and hydrogen, and this transition is fundamental for the successful production of biogas. During this degradation process, the anaerobic oxidation of butyrate and propionate has only to occur in syntrophic association with H\({}_{2}\)-utilizing methanogens, which consume H\({}_{2}\) and CO\({}_{2}\) for methane (CH4) production, thus preventing the accumulation of H\({}_{2}\).
# Wireless Home Assistive System for Severely Disabled People
###### Abstract  
A lot of people with severe disabilities such as amyotrophic lateral sclerosis, motor neuron diseases, cerebral palsy, stroke, and spinal cord injury with intubation always have different degrees of communication problems. Therefore, it is very important to develop an effective and easy use assistive communication system for the severely disabled. In this study, a wireless home assistive system (WHAS) with different types of assistive input accessories sensors, Morse code translator, and human machine interface is developed and tested to help the severely disabled communicate with people and machines. A Morse code translator is implemented as an assistive communication core device to facilitate the input of the severely disabled. For the proposed human-machine interfaces, personal computer-based alternative augmentative communication is developed for patients to communicate with other people easily. To promote the quality of life, the home appliance control interface is developed for the severely disabled to directly control the functions of home appliances by themselves. The experimental results showed that the proposed WHAS is practical and feasible. Therefore, the proposed approach can help severely disabled individuals effectively interact with their surroundings.  
amyotrophic lateral sclerosis; motor neuron diseases; cerebral palsy; stroke; spinal cord injury; information communication technology; home automation Article
# Hardware Acceleration of Tsunami Wave Propagation Modeling in the Southern Part of Japan
## 3 Numerical Results
### Digital Bathymetry  
In Figure 3 we show the digital bathymetry of the water area under consideration (the region of Shikoku Island and Kii Peninsula, south Japan). It was developed in [13] on the basis of data produced by the Japan Oceanographic Data Center (JODC) [16].  
We conducted 12 numerical experiments for model tsunami sources (indicated as _Si-i_ in Figure 3). The sources are of the same shape with elevation and subsidence parts (details are given in the next subsection). The selected shape of the initial sea surface displacement has four different positions along the coast (indicated with subscript \(i\) = 0, 1, 2, 3) and three values of offshore distance (with the indication \(j\) = \(a\), \(b\), \(c\)).  
In Figure 3, only the sources closest to the coast, _Si-a_ (\(i\) = 0,..., 3), and those more distant from the coast, _Si-c_ (\(i\) = 0,..., 3), are presented. Their positive wings (parts) are outlined in pink, and negative wings (water surface depression) are outlined in yellow. The respective middle-posed sources _Si-b_ (\(i\) = 0,..., 3) are located between sources _Si-a_ and _Si-c_.  
The digital bathymetry and, correspondingly, the grid for computation are as follows.  
The size of the computational domain is \(3000\times 2496\) points (approximately \(842\times 556\) km).  
The grid steps in the west-east and south-north directions are 0.003 and 0.002 arc degrees (which means 280.6 and 223 m, respectively).  
The computational domain covers the area between \(131^{\circ}\) and \(140^{\circ}\) east longitude and between \(30.01^{\circ}\) and \(35^{\circ}\) north latitude.  
According to the stability condition, the time step used in the computations was 0.5 s.  
Mostly, regular rectangular computational grids are used for tsunami numerical modelling. The grid could also be matched with one of a geography projection. We took into consideration the spherical shape of the Earth by decreasing the grid step with respect to longitude for larger values of latitude. Matching the grid to geographic coordinates, we suppose that the length of one arc degree at latitude \(\lambda\) is equal to the length of one equator arc degree multiplied by \(\cos(\lambda)\).  
Using the advantages of the proposed Calculator, it takes only 25 s to compute tsunami wave propagation over the water area in Figure 3. In practice, the travelling time of a real wave through this water area is 3200 s.  
During the numerical experiments, we moved an artificial tsunami source along the Tokai-Nankai deep water trench. Note that, according to [1], a strong seismic event is expected in this area.  
Figure 3: Digital bathymetry around Kii Peninsula and Shikoku Island (Japan) [13]. The positions of tsunami model sources are indicated. Pairs of ellipses, indicated as _Si-a_ and _Si-c_ (\(i\) = 0, 1, 2, 3), show the positions of the artificial tsunami sources.  
The authors discussed the disastrous tsunami that happened in this region nearly 300 years ago and provided a reconstruction of the tsunami source based on historical records (see Figure 4).
## 4 Proposed Methodology
### Token Embeddings Phase  
The BERT model of the proposed approach used a structure called WordPiece to partition the input source-code blocks into word tokens [34]. The WordPiece structure divides each input source code into subwords called tokens. As the midpoint between words and characters, word particles retain their linguistic meaning. Thus, even with a small-sized vocabulary, it can be successful in the case of out-of-vocabulary word fragments (Figure 2).  
In the first process for source-code vulnerability detection in the proposed approach, code slices were extracted from the source-code files using the tokenizer training phase. Code fragments are candidates for syntax- and semantics-based vulnerabilities in particular. The open-source code-analysis tool joern was used to parse the code slices and source-code files and to extract the control flow graph corresponding with each code. In addition,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Access** & **Samples** & **Vuln Code** & **Vulnenable Rate (\%)** \\ \hline Document Management System & Private & 540,223 & 2539 & 0.469 \\ Student Information System & Private & 1,131,201 & 5345 & 0.472 \\ Personal Information System & Private & 389,870 & 1320 & 3.385 \\ Quality Management System & Private & 123,892 & 1672 & 1.349 \\ nopCommerce [32] & Public & 675,120 & 4450 & 0.659 \\ Miniblog Core [33] & Public & 412,678 & 1802 & 0.436 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**CVE Code** & **Description** & **Samples** \\ \hline CVE-2023-36899 & ASPNET Elevation of Privilege Vulnerability & 3420 \\ CVE-2023-36560 & ASPNET Security Feature Bypass Vulnerability & 1253 \\ CVE-2023-36588 & ASPNET Core Security Feature Bypass Vulnerability & 4610 \\ CVE-2023-36038 & ASPNET Core Denial of Service Vulnerability & 570 \\ CVE-2023-35391 & ASPNET Core Signal R and Visual Studio Information Disclosure Vulnerability & 3190 \\ CVE-2023-33170 & ASPNET and Visual Studio Security Feature Bypass Vulnerability & 230 \\ CVE-2015-6099 & Cross-Site Scripting (XSS) Vulnerability in ASP-NET & 3500 \\ CVE-2014-4075 & Cross-Site Scripting (XSS) Vulnerability in System & 588 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution and description of vulnerabilities according to CVE codes.  
Figure 2: The tokenizer process of raw C-Sharp code.  
user-defined variable and function names were mapped to symbolic names by preserving the original name of the keywords in the source-code language.
## 3 Industrial Consumption, BESS, and Grid Modelling
### Industrial Consumption Model  
The industrial consumption is modelled as a PQ load, i.e., it consumes the desired active and reactive power independently of the voltage at the PCC. Hence, it needs to be modelled as a controlled current source, as depicted in Figure 3. The inputs of the model, named as P and Q in Figure 3, correspond to the 24-hour active and reactive power profile on a 1-second basis.  
The Simulink diagram in Figure 3 shows, from left to right, the inputs of the model P and Q together with the measured voltage (Vabc). From those inputs, the complex values of the phase currents are calculated, and then redirected to the corresponding current controlled source. A resistor with a very high value is also connected in parallel, which acts as a snubber. At last, the industrial consumption model is linked to the rest of the model via connectors A, B, C, and N.
## 3 Directional Antennas Suitable for WSN Nodes
The second prototype [35; 36] uses a group of four identical antennas containing two quarter-wavelength L-shaped slot antenna elements which are disposed in a symmetrical planar structure of 10 \(\times\) 10 cm\({}^{2}\). \begin{table}
\begin{tabular}{c c c c} \hline
**Research** & **Frequency** & \multicolumn{2}{c}{**Antenna’s Structure**} & **Note Platform** \\ \hline [PERSON] and Kalis [30] & 868 MHz & \begin{tabular}{c} Two horizontal or vertical wire antennas and a \\ reflective SPDT switch \\ \end{tabular} & SensorDVB \\ \hline [PERSON] [31; 32] & 2.4 GHz & \begin{tabular}{c} Electronically switched parasitic element antenna \\ \end{tabular} & TMote Sky \\ \hline [PERSON] _et al._[33] & 2.4 GHz & \begin{tabular}{c} A box-like structure of four coaxially fed planar \\ patch antennas \\ \end{tabular} & TelosB \\ \hline [PERSON] _et al._[34] & 2.4 GHz & Active cylindrical frequency selective surface & VirtualSense \\ \hline [PERSON] _et al._[12] & 2.4 GHz & \begin{tabular}{c} Radiation structure made of eight microstrip \\ antennas using rectangular two-element patch \\ antenna arrays and a vertical half-wavelength \\ dipole antenna \\ \end{tabular} & STM32W-EXT \\ \hline [PERSON] _et al._[35; 36] & 2.4 GHz & \
\begin{tabular}{c} Four identical antennas, containing two \\ quarter-wavelength L-shaped slot antenna \\ elements, disposed in a symmetrical \\ planar structure \\ \end{tabular} & STM32W-EXT \\ \hline [PERSON] _et al._[37] & 2.4 GHz &
\begin{tabular}{c} 6-Sectored antennas having an overlap of \\ 120 degrees in azimuth \\ \end{tabular} & Nano-Qplus \\ \hline \end{tabular}
\end{table} 
Table 2: Directional antenna prototypes for WSNs. Another directional antenna prototype for WSN nodes was developed and evaluated in a fully directional neighbor discovery protocol by [PERSON] _et al._[37]. For this, they equipped five sensor nodes, developed on a Nano-Qplus hardware platform, with low-cost 6-sectored antennas having an overlap of 120 degrees in azimuth. Although the research in developing directional antennas suitable for WSN nodes is in the early phases, the results obtained so far are encouraging. This allows us to envisage new models of wireless communications between sensor nodes, endowed with higher security.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 3 Concepts of Functional and Stochastic Atmospheric Constraints
### Existing Atmospheric Products
#### 3.1.2. Zenith Path Delay Products  
Using ground-based GNSS information, tropospheric products are generated from IGS ACs. Horizontal gradient components and 5-min interval estimates of zenith path delay (ZPD) are included  
Figure 2: Analyses for 24-h and 10-min convergence for different existing GIM products using 3 days of 24-h observations from MGEX multi-GNSS stations.  
in the products. Using over 350 IGS GNSS stations, ZPD estimates are generated and made available daily per site. Precipitated water vapour extracted from estimated ZPDs are included in the products due to surface pressure and temperature measurements made at GNSS site locations. Given that the tropospheric products are generated using IGS final satellite orbits and Earth Orientation Parameters (EOP) products, they are therefore available approximately three weeks after the day of observation. Different methodologies are employed in the generation of the IGS combined tropospheric solution which potentially impacts the accuracy of the products. The production and dissemination of these products since inception has been the focus of three ACs, namely the Jet Propulsion Laboratory (JPL, Pasadena, CA, USA), German Research Centre for Geosciences (GFZ) and United States Naval Observatory (USNO, Washington, DC, USA) [34; 35; 36; 37; 38; 39].
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 3 Ethical Considerations
### Data Management Frameworks  
Various data management frameworks guide hospitals in protecting patient privacy during predictive analytics, offering structured approaches to data management throughout its lifecycle. These frameworks ensure that patient data are treated safely and ethically. Some key frameworks include:  
* Health Importability and Accountability Act: This organization is a comprehensive United States federal law designed to protect the privacy and security of patient health information. It sets standards for data protection that hospitals must follow to ensure patient information is handled confidentially and securely [20];
* General Data Protection Regulation: This organization is a European Union regulation that imposes strict guidelines on the collection, use, and storage of personal data, including patient data. Hospitals operating in the European Union or processing data from EU citizens are required to comply with \"General Data Protection Regulation\" to protect individual privacy and data security [21].  
They offer essential guidelines and standards that support hospitals in managing patient data responsibly and safeguarding patient privacy in the context of predictive analytics.
## 4 The Proposed Method
### Architecture of the Proposed Method  
This algorithm is a new method that deals with noisy images without reference images. The architecture of algorithm can be divided into two parts. In the first part, DTCWT enhanced by IFOA (IFOA-DTCWT) is applied to remove noise. Since there are no reference images, the denoised images can only compare with the noisy images to estimate the denoising performance. The IFOA searches the noise variance randomly, and a new wavelet coefficient is gained through the bivariate shrinkage function with the noise variance. Then, the denoised image is obtained through the inverse DTCWT of the new wavelet coefficient. The process continues until the optimized denoised image is acquired. After being denoised by IFOA-DTCWT, many processing areas of the square shape are remained in the denoised image. Then in the second part, bilateral filter is applied to smooth the area and enhance the edge information of the image at the same time. The combination of the two methods acquired good performance. The architecture is shown in Figure 3.