esa-sceva/satcom-chunk-collection huggingface dataset:
# GTMesh: A Highly Efficient C++ Template Library for Numerical Schemes on General Topology Meshes
## 4 The GTMesh Library
### Algorithms
#### 4.3.5 Calculation of Element Centers  
The previous algorithms work with the mesh as a topological object, i.e., they do not use the coordinates of vertices. Now, we will describe the algorithms that calculate some significant geometrical properties of the mesh. We begin with an algorithm calculating the center points of all objects in the mesh, which is crucial for implementing cell-centered FVM schemes.  
The center of each element is calculated as an average of the positions of the centers of all the connected sub-elements. For example, the center of a cell is an average of the centers of the cell's faces. The advantage of this approach is the reduction of the depth of iteration over the mesh (see the example of the algorithm in Figure 6). Because the algorithm uses the previously calculated values, the computation may speed up against the calculation of the center as the average of the positions of the connected vertices. For example, in the case of dimension \(d_{\mathscr{T}}=2\), it is sufficient to visit the sub-elements of dimension \(1\). The scheme of the algorithm is as follows:  
1. set the dimension \(d=1\);
2. for all elements \(e\in\mathscr{T}^{d}\), calculate the center of \(e\) as \(\boldsymbol{x}_{e}=\frac{1}{\left|N^{d-1}(e)\right|}\sum_{f\in N^{d-1}(e)} \boldsymbol{x}_{f}\) by a loop over sub-elements of \(e\);
3. if \(d<d_{\mathscr{T}}\) then \(d=d+1\), and go to step \(2\); else, stop the algorithm and return the result.  
In a 2D mesh, this algorithm returns the same result as the average of the positions of the connected vertices. However, in a 3D mesh, the results of both algorithms may differ if there are faces with different numbers of vertices. The computeCenters() function calculates the centers of all elements in the mesh, using the algorithm presented above.
# Development of a Multiplex PCR Assay to Monitor Living Modified Cottons in South Korea
## 3 Results and Discussion
### Detection of Single Event Using 8 Primer Pair Sets  
To confirm the specificity of eight primer pairs first, PCR analysis using one event genomic DNA was performed. Genomic DNA of CRMs (eight events and one non-LM) were used. As a result, the mixture of eight primer pairs showed a high level of event specificity as indicated by only a single  
\begin{table}
\begin{tabular}{c c c c} \hline
**Event** & **Primer Name** & **Sequence (5’-3’)** & **Product Size (bp)** \\ \hline GHB614 & GHB614-F & CAAATACACTTGAGCAGCTTCGT & 119 \\ GHB614-R & GCAGGCCATGCAAGCTTTTAAA & & \\ MON88913-F & CAAATTACACCTTAAAGCTACCCAATATAC & & \\ MON89913 & MON8913-R164 & CGAGCACAGCCCTCGAGAAGCTCTGA & 164 \\ MON15985 & MONN15985-F & GTTACTAGATGCGGAGATCC & 214 \\ MONN15985-R214 & GCCGTCAGGAAAAAGCTTCGTGACA & & \\ LLCOTTON25-F26 & CCTACACTTGTTGACCACCACCACT & & \\  & LOCOTION25-R276 & GCCGCCTTGTACAACCACCACT & 276 \\ MON1445 & MONN1445-2-F & GGAGTAGAGAGCATCAGATCAAACC & 389 \\ MON1445-2-R389 & CGTGGCCTTCATAGCCGAGCTT & & \\ GHB119-FP & CGAGCTCTAAATCCCATACCCA & & \\ GHB119 & GHB119-408 & CATTATTACACACAACAACAACAATAC & 450 \\  & 281-3006-F & CTCATTGCCTGATCCATGATGATG & 518 \\
281-3006 & 281-3006-R518 & CGTTGCTCATATCCAGGAGGCTT & \\  & MONN531-F270 & GGTACGATGATGAGCCGTACTCTT & \\ MON531 & MON531-R629 & GTGACCTCTCTGCAGAGACACCTTAACC & 629 \\ ACP1 & ACP1-F & ATGTGATGGCTTGAGAGAAGA & 76 \\ (Cotton endogenous) & ACP2-R & CITGAACACGTTTGATGATG & 76 \\ \hline \end{tabular}
\end{table}
Table 1: New oligonucleotide primers used to detect eight LM cotton events.  
Figure 2: Schematic representations of the PCR primer positions for multiplex PCR of eight LM cotton events. Sites and direction of primers are indicated by black arrows. All primer pairs were designed with the DNA flanking region of the cotton genome and inserted DNA cassette. (LB: Left border; RB: Right border; F: Forward primer; R: Reverse primer; gray pentagon: Promoter; white square: Encoding gene; gray square: 3’ _terminator_).  
band for each separate event (Figure 3). All the bands of PCR products showed the expected size for each of the eight candidate events. Those results indicated that the 8 newly designed primer pairs were not amplified to a non-specific site, but only to a desired position.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead
### Positioning and Localization  
Positioning and localization are critical for any FSO communication system, to ensure accurate alignment and the establishment of a reliable link [67]. For NTNs, this is even more essential, due to the non-stationary position of airborne nodes [122]. To address the positioning challenges faced by UAVs, a combination of Global Navigation Satellite Systems (GNSS), Inertial Navigation System (INS), and ground-based reference stations can be employed. However, factors like urban canyons and electromagnetic interference in urban environments can affect the reliability of GNSS.  
Precise positioning and localization are fundamental prerequisites for ensuring reliable communication. Challenges such as signal blockage due to terrain, buildings, or clouds, and the need to track the movements of non-stationary UAVs/HAPSs underscore the importance of real-time positioning solutions. Integrating GNSS technology with INS can offer redundancy and improved accuracy, ensuring uninterrupted communication in scenarios where GNSS signals may be temporarily unavailable. However, several challenges and limitations exist. One major concern is GNSS signal availability, which can be disrupted due to various factors, such as signal jamming, atmospheric conditions, or satellite failures. In scenarios where GNSS signals are temporarily unavailable, relying solely on INS may lead to a gradual degradation of accuracy over time, due to inertial sensor errors accumulating. To address this, alternative positioning systems like terrain-based navigation or celestial navigation can be considered as backups.  
Terrain-based navigation relies on topographical features for localization, but it may not be suitable for all environments. Celestial navigation, using stars or celestial bodies, can provide absolute positioning but requires clear skies. Therefore, while GNSS and INS integration enhances accuracy, ensuring robustness in the face of GNSS signal disruptions remains a significant challenge for FSO-based NTNs, necessitating the development of reliable backup systems. These backup systems become crucial for maintaining navigation accuracy and network reliability, especially when GNSS signals are temporarily unavailable due to unforeseen circumstances or deliberate interference.
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions
### Network Performance Parameters--Discussion
#### 4.2.2 Total Energy Consumption  
Figure 9 shows total energy consumption of the proposed protocols in comparison to EEDBR and CARQ. Energy consumption of E-ACE, ACE and CARQ is greater than EEDBR due to more allowed retransmissions. ACE and E-ACE show same energy consumption with almost same throughput (refer Figure 10). CARQ and EEDBR show less energy consumption because they have low throughput and high packet drop (refer Figure 11). Less throughput contributes to low energy consumption because less packets are transmitted.  
Figure 8: Network lifetime.  
In later time slots, energy consumption of the four protocols is almost same because ACE and E-ACE have very few alive nodes to send data to sink. EEDBR has 238 nodes alive at 300 s, whereas, ACE and E-ACE have not more than 100 alive nodes.  
Figures 8 and 9 show the drawback of E-ACE. These figures also show the tradeoff between energy consumption and reliability. More number of retransmission causes surplus energy consumption, thereby, reducing the network lifetime.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Recognition
#### 5.3.1 Character/Non-Character Classifier  
This stage is used to identify whether the segmented image is a character or not. Training of the neural network is divided into 1000 positive data elements for numbers and 1000 negative elements for non-numbers. The training dataset is fed to a feed-forward neural network with 6 layers; a single input layer with a size 1600 representing the size of the input image after resizing, four hidden layers in different sizes and finally an output layer with one activation unit. The output activation unit represents whether a character is recognized (1) or not (0). To reduce the non-linearity of the output of neurons, the sigmoid activation function is used.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.10 Tree Loss Detection and Validation  
Tree loss detection is based on the analysis of difference in vegetation heights (\(\Delta\)MNH) and based on the criteria of tree height and tree crown area. The change in vegetation height is calculated using Equation (3):  
\[\Delta\text{MNH}=\text{MNHt2}-\text{MNHt1} \tag{3}\]  
where t1 and t2 represent the two selected acquisition dates (November 2018 and April 2019, respectively).  
Negative values correspond to vegetation losses. Thus, the average value of the tree crown area allows us to discriminate between what corresponds to tree losses.  
The tree height distribution maps in April 2019 and tree loss during the period of November 2018 to April 2019 were evaluated based on the photointerpretation of a sample of 511 observation points categorized as tree loss (total of 107 points), stable forest (total of 200 points), and stable nonforest (total of 204 points). Accuracy indicators, such as overall accuracy and kappa index, were finally calculated.
## 2 Categorized Overview of Papers (Based on the Areas of Focus)  
### Category 1: Retail and Customer Analysis  
These papers collectively contribute to the field of data-driven applications and algorithm development. The first paper presents a data-driven recommendation system for retail, emphasizing tailored product recommendations and marketing strategies [8]. The second paper addresses the challenge of handling imbalanced data streams with concept drift, introducing the HSDW-MI algorithm [9]. The third paper focuses on electric load classification in industrial scenarios, highlighting the effectiveness of an equilibrium optimizer-based feature selection method [10]. These papers collectively demonstrate the versatility and impact of data-driven approaches in enhancing decision making and addressing real-world challenges across various domains.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Enhancing Genomic Data Interpretation  
One of the primary applications of AI in cancer genomics is enhancing the interpretation of sequencing data. AI-powered models analyze WES, WGS, and targeted gene panels to distinguish PV from polymorphisms, enabling targeted therapies. Deep learning models, such as DeepVariant (Google, Mountain View, CA, USA) and DeepSEA (developed by researchers at Stanford University and Princeton University), enhance the accuracy of genomic analyses by improving variant calling and regulatory element identification [76]. DeepVariant, uses convolutional neural networks (CNNs) to transform raw sequencing reads into high-accuracy variant calls, reducing false positives in genomic data interpretation. DeepSEA, on the other hand, predicts the functional impact of non-coding variants by analyzing DNA sequence patterns and chromatin accessibility, aiding in the identification of regulatory elements that influence gene expression in cancer [77]. AlphaFold, a leading deep learning model, enhances protein structure prediction by analyzing amino acid distances and peptide bond angles [78].  
Beyond known pathogenic variants, AI algorithms stand out for detecting rare genetic variants and novel alterations that may influence cancer progression or treatment resistance.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 4 Numerical Results
### Feature Importances When the Transmitter is at 35 m  
The feature importances for the 35 m case can be found in Figure 5.  
Figure 5: Feature importances, as estimated when the transmitter is placed at 35 m, via two machine learning models: (**a**) XGBoost; (**b**) Random Forest. The feature called _LOS_3b_ is ranked as the most important.  
According to both models, the same set of three features was found to be the most important for this height. However, the inner ranking between the three features changed: _LOS_3b_ was clearly the most important, indicating that over-rooftop diffraction is the primary propagation mechanism for this height of the transmitter. Moreover, the importance of _Buildings_ fell, since the contribution of multiple reflections decreased.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Distance-Based Adaptive Transmission Methods  
In prior approaches, the use of transmission energy was not economical. In our proposed technique, we produced an idea to efficiently limit energy usage by ranging transmission energy on different depth levels. This approach mainly restricts the transmission energy to beneficial usage. We use adaptive energy levels that are devised, based on the distance between the source node and master node. In previous practices, two types of mechanisms were followed. First, the source node sends neighbor requests in its range with power P, and in response, the neighboring PFNs send Acknowledgments back to the source containing all required information. Secondly, when the source node receives Acknowledgments from its neighbors, it compares their depth parameter, broadcasts data packet with power P to all neighbors while setting the awaiting timer based on their depth information. At this stage, the node closer to the source might need less transmission energy from the source. However, it is transmitted towards the neighbor with the same power needed to transmit over R which is unacceptable in a scenario of optimizing energy consumption. To address this issue, we restrict additional energy costs that the network bear for data transmission in the close neighborhood of the source. For this purpose, we use the technique where the transmission energy assignment is based on the distance between the source node and master node. The larger distance between the source and master nodes means greater transmission energy and vice versa.  
Figure 4: Number of Nodes in Each Region.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## Materials and Methods
### Physical Characterization  
The crystalline structure of Zn-Al LDHs and its nanocomposites were measured by Rigaku powder X\(-\)ray diffraction RINT 2200 using CuK\(\alpha\) (filtered) radiation (\(\lambda\) = 0.154 nm) at 40 kV and 20 mA between 1.8 and 70\({}^{\circ}\). The functional groups of the solid materials were detected through Fourier Transform Infrared Spectroscopy (FT-IR) as KBr discs in the range of 425-4000 cm\({}^{-1}\) by Horiba FT-720.  
Figure 1: Schematic presentation of experimental setup and graphite anode exfoliation.  
Imaging of the nanostructures was accomplished by scanning electron microscopy (SEM) with JEOL: JSM-6330F, (15 kV/12 mA). Thermogravimetric Analysis (TGA) and Differential Thermal Analysis (DTA) were employed to investigate the thermal behavior of the samples. TGA\(-\)DTA were carried out up to 1000 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min in flow of gases using a Seiko SSC 5200 apparatus.  
The Barrett-Joyner-Halenda (BJHI) and Brunauer\(-\)Emmett-Teller (BET) methods were adopted to determine the material surface area, pore size distribution, and pore volume. Diffuse reflectance technique was used to measure the optical parameters of the nanomaterials by using a UV/VIS/NIR Shimadzu 3600 spectrophotometer. The spectrophotometer was equipped with an integrating sphere attachment (ISR-603) to measure solid materials, and the thickness of the sample was 3 mm. Barium sulfate was used as the reflectance standard.
## 5 Results and Discussion
### Performance Evaluation of MTLA  
As one of the most important components proposed in this paper, it is necessary to verify the superiority of the MTLA module for feature extraction and fusion. Therefore, this experiment compares the performance of MTLA, TA [55], CBAM [56], the global attention mechanism (GAM) [67], efficient channel attention (ECA) [68], efficient multi-scale attention (EMA) [69], and multi-scale large kernel attention (MLKA) [70] on the C1 dataset. The performance comparison results of MTLA and other attention modules are presented in Table 7.  
Based on the experimental results in Table 7, the following conclusions can be drawn: (1) Not all attention modules can improve model accuracy, as these attention modules mostly process both temporal and spatial dimensions through convolution, which is not suitable for time-series prediction. (2) Compared with TA, the multi-scale features capture information at various levels, which allows the model to capture both detailed and global patterns. (3) The proposed MTLA model leads to the best performance, and the fusion of attention across temporal, spatial, and feature dimensions allows the model to flexibly focus on key information in the data, enhancing the richness and accuracy of feature representation and leading to more accurate and stable predictions.
## 2 Materials and Methods
### Optical Patterning: Holographic Recording Setup  
An off-axis holographic lens can be recorded by using the setup shown in Figure 2. Two collimated beams are arranged such that they overlap on the photopolymer layer, then a suitable lens is inserted into one of the beams such that it focuses the beam before it reaches the photopolymer as shown. This produces a diverging beam to interfere with the collimated reference beam and a HOE lens is recorded. The numerical aperture of the lens determines the divergence of the beam and the range of the angles of incidence at the photopolymer. This kind of recording was demonstrated previously with both cylindrical and spherical lenses [24]. However, in this instance, a cylindrical lens was used so that the beam only diverged in one axis (horizontal). This simplified the modeling and experimental testing since the grating characteristics varied in only one axis. In the blue sensitive photopolymer formulation optimization work, simple gratings were recorded, and the set-up was identical except for the fact that both beams were collimated, as the cylindrical lens was not present. For some characterization work, a 532 nm and a 473 nm laser were used but for recording in the dual-sensitized layer, an argon ion was used so that both the 514 nm and 488 nm laser lines could be used in the same optical setup.  
When recording lenses, the range of angles of incidence which the diverging beam produces determines the inter-beam angles for the interference pattern, and consequently, the spatial frequency and slant at each location of the photopolymer. The cone of rays will be the same (assuming negligible aberrations) for recordings at both green and blue recording wavelengths. However, since the spatial frequency also depends on the wavelength of the incident light, this will lead to a different interference pattern for each recording.
## 2 Materials and Methods
### Analytical Methods  
#### 2.3.1 Analysis of Aromatic Compounds  
Each sample of 0.75 g of beetroot powder was prepared for head space extraction and dissolved in 4.25 mL of distillated water and then transferred to vials (20 mL) which were closed with a silicone septum. Sampling was performed using DVB/CAR/PDMS fiber (1 cm long) at a 60 \({}^{\circ}\)C temperature and the absorption time was 5 min in an SPME system. Before the absorption of volatiles on the SPME fiber, 1 \(\mu\)L of internal standard (1,2-dichlorobenzene, 0.01% solution in methanol) was added to each sample. The fiber was inserted into the GC/S injector, where after 2 min desorption took place. The apparatus used in the experiment was GCMS-QP2010 (Shimadzu Corporation, Kyoto, Japan). The separation of aroma compounds was performed with the use of Stabilwax (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, polyethelene glycol) from Restek. The carrier gas was helium and the column flow was 0.58 mL/min. The column temperature program was as follows: initial temperature 60 \({}^{\circ}\)C (for 10 min) at a rate of 5 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, and 200-250 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min (holding time 5 min at 250 \({}^{\circ}\)C). The total time of the analysis was 55 min. The interface and injector temperatures were 25 \({}^{\circ}\)C. The ion source temperature was 250 \({}^{\circ}\)C and the ionization energy 70 eV. The internal standard 1,2-dichlorobenzene was used to semi-quantify volatile compounds. Identification of aroma compounds was made on the basis of mass spectra libraries (NIST, Gaithersburg, USA 2008, [PERSON] 175) and from literature data. The analysis was conducted twice for each material.
###### Abstract  
For the last two decades, the American GPS and Russian GLONASS were the basic systems used in global positioning and navigation. In recent years, there has been significant progress in the development of positioning systems. New regional systems have been created, i.e., the Japanese Quasi-Zenith Satellite System (QZSS) and Indian Regional Navigational Satellite System (IRNSS). A plan to build its own regional navigation system named Korean Positioning System (KPS) was announced South Korea on 5 February 2018. Currently, two new global navigation systems are under development: the European Galileo and the Chinese BeiDou. The full operability of both systems by 2020 is planned. The paper deals with a possibility of determination of the user's position from individual and independent global navigation satellite system (GNSS). The article is a broader concept aimed at independent determination of precise position from individual GPS, GLONASS, BeiDou and Galileo systems. It presents real time positioning results (Real Time Kinematic-RTK) using signals from Galileo satellites only. During the test, 14 Galileo satellites were used and the number of simultaneously observed Galileo satellites varied from five to seven. Real-time measurements were only possible in certain 24-h observation windows. However, their number was completed within 6 days at the end of 2017 and beginning of 2018, so there was possible to infer about the current availability, continuity, convergence time and accuracy of the RTK measurements. In addition, the systematic errors were demonstrated for the Galileo system.  
GPS; Galileo; Multi-GNSS precise positioning; convergence time +
Footnote †: journal: Sensors