esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of DEG Profiling by qRT-PCR Analysis  
To validate the accuracy and reliability of the RNA-Seq results, five genes involved in the terpenoid pathway were selected for qRT-PCR analysis. A linear regression analysis showed a significantly positive correlation, with a coefficient of 0.8228, \(p<\) 0.01 (Figure 9b). The qRT-PCR results revealed that the RNA-Seq data were reliable.  
The 1-deoxy-D-xylulose-5-phosphate pathway is crucial to monoterpene biosynthesis. As a rate-limiting enzyme, DXS is responsible for the catalysis and condensation of glyceraldehyde-3-phosphate and pyruvate into 1-deoxy-D-xylulose-5-phosphate [19]. At harvest, the expression levels of _VviDXS_ genes (_VIT_09s0002g02050, VIT_05s0020g02130_ and _VIT_00s0218g00110_) were upregulated in the L-treated grapes, which correlated well with the total monoterpene profiles (Figures 9a and S2). Previous study indicated that  
Figure 8: Weighted genes co-expression network analysis (WGCNA) of DEGs. (**a**) Hierarchical clustering tree using the topological overlap dissimilarity, tree branches have been colored by module membership. (**b**) Module-monoterpene relationship. Each row represents a module, and the number of genes in each module is shown on the left. Each column represents a specific monoterpene profile (total of their free and bound forms). The left panel shows 10 modules and the right panel is a color scale for module trait correlation from \(-1\) to 1. Each cell was colored based on the statistical significance and labeled with two numbers. The upper number indicates the correlation coefficient and the lower number indicates the \(p\)-value. (**c**) Heatmap plot of topological overlap in the gene network. In the heatmap, each row and column corresponds to a gene, a light color denotes low topological overlap, and progressively darker red denotes a higher topological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram and module assignment are shown along the left and top.  
DXR is a rate-limiting enzyme [38]. The _VviDXR_ (_VIT_17s00009g08390_) expression level was higher in L- and M-treated grapes during ripening, which correlated well with the most bound monoterpene profiles. Compared with the H-treated grapes, the L- and M-treated grapes had higher _VviGPPS_ expression levels, which positively correlated with the monoterpene accumulation levels at harvest. The TPSs are responsible for the conversion of GPPS into various terpen [39]. Here, six _TPS_ genes had higher transcript levels in the H-treated grapes than in the L- and M-treated grapes during growth and development (Figure 9a), which was consistent with some of the free monoterpene profiles.  
Figure 9: _Cont._
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 6 Numerical Experiments
### Result of Numerical Experiment
#### 6.2.2 Improvements in Information Sharing for the Efficiency of Relief Activities  
1. \"Reconnaissance and rescue\" (the one-level method) always has the worst transportation efficiency in the total flight time, finishing time, and waiting time, especially when the Gini coefficient is large. This is because in \"reconnaissance and rescue,\" there is no information communication between the aircraft dispatchers and shelters. In this case, aircraft dispatchers need to keep sending aircraft to collect information. There are some cases where the aircraft travels without any injured persons, which leads to worse total flight time, finishing time, and waiting time. When the Gini coefficient is large enough, although there may be very few injured in some shelters, dispatchers still need to keep sending aircraft for further updates on the situation in the shelter. This results in a greater waste in the flight resources.  
Figure 9: Simulation results.  
2. Dynamic routing (Level three) is better than both methods in Level two, especially when the D/C is smaller and the Gini coefficient is larger. This is because, in this case, a single flight to a destination cannot make the best use of the aircraft capacity, and the total number of injured does not match the total capacity of the aircraft fleet. Moreover, the multiple-destination method is not very flexible. Thus, dynamic routing is the best option.
# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 3 Materials and Methods
### Sentiment Analysis Approach
#### 3.4.1 Process for Sentiment Analysis  
Sentiment analysis tasks commonly use classification models such as machine learning (ML) and deep learning (DL) models to categorize the sentiment of text data as positive or negative. In our study, we chose to use a Bi-directional Long Short-Term Memory model because of its proven success in sentiment analysis tasks [39].  
The first step in the model development process was to partition the dataset into three portions: a training set, a validation set, and a test set.  
Figure 3: The process of Bi-LSTM.  
Figure 2: The structure of the LSTM.  
The process employed for conducting sentiment analysis through employment of a Bi-LSTM model is delineated in Figure 4. The following steps were pursued in order to accomplish this task:  
* Data Preprocessing: The first step is to clean and preprocess the text data (Section 3.2).
* Glove Embedding: In this step, the tokenized words are converted into dense vectors of fixed size.
* Bi-LSTM Model Architecture: The next step is to build a Bi-LSTM model architecture. The input to the model is the sequence of word vectors generated in the previous step. The model consists of two LSTM layers, one in forward and one in backward direction. The output of the LSTM layers is concatenated and passed through a fully connected layer with a sigmoid activation function to obtain the final sentiment label.
* Training the Model: The model is trained on the labeled dataset. The loss function used is cross-entropy, and the optimizer used is Adam. In order to apply the dropout regularization technique, a dropout layer is included in the model with a dropout rate of 0.5. This means that in each iteration of the training process, half of the neurons in the previous layer will be randomly deactivated.
* Model Evaluation: Once the model is trained, it is evaluated on a test dataset to measure its performance.
* Prediction: The final step is to use the trained model to predict the sentiment label for new text data. The input text is preprocessed and converted into word vectors, which are then passed through the Bi-LSTM model to generate the predicted sentiment label.
## 3 Results and Discussion
### Selection of LMO Monitoring Sites and LM Cotton Survey
Furthermore, based on classification analysis of LMO collection sites from 2014 to 2017, survey sites of natural environments near stockbreeding farms and transportation routes were expanded, and the number of LM cotton samples collected was substantially increased in 2018. Most LM cotton possessed herbicide- and insect-resistance traits. Except for 2014, the single event MON531 was detected in all periods, and stack events such as MON15985\(\times\)MON88913 and MON88913\(\times\)281-3006 were discovered every year. In particular, MON15985\(\times\)MON88913 accounted for approximately 39% of all identified LM cotton (Table 2). These results could be helpful for managing LMO-released environments through physical elimination and/or herbicide or pesticide application depending on the specific LMO events identified in unintentionally released LMOs.
# Thai Rice Vinegars: Production and Biological Properties
## 3 Results and Discussion
### Biological Properties of Rice Vinegars
#### 3.3.4 Determination of Antioxidant Activity
The industrial process resulted in nutrition loss from raw materials, so the antioxidant activity of traditional vinegar was more effective [31]. Many studies have described the relationship between total phenolic content and antioxidant activity. In previous reports, the DPPH scavenging activity of phenolic compounds were positively correlated with the amount of hydroxyl groups [45]. These phenolic compounds were found in rice grain fermented to rice vinegar. In comparisons between unpolished (or colored) rice and polished (or colorless) rice vinegar, the results indicated that the unpolished rice vinegas exhibited higher antioxidant activity due to the high amounts of total phenolic content. These findings were consistent with a previous study of dihydroferulic acid (DFA) and dihydroInapic acid (DSA), ferulic acid and sinapic acid isolated from Japanese unpolished rice vinegar (Kurosu) and polished rice vinegar (Komesu), which were described as antioxidative compounds. These antioxidant compounds from Kurosu extract contained higher levels than in Komesu; therefore, Kurosu showed higher antioxidant activity. It has been suggested that unpolished rice vinegar is more advantageous than polished rice vinegar for antioxidative use [10]. Thus, these antioxidative compounds of rice vinegas were found in the rice grain. Particularly, colored rice (or pigmented rice), such as black glutinous and black fragrant rice have large contents of anthocyanin. Many researchers have reported the antioxidant properties of anthocyanin from pigmented rice. Therefore, colored rice has shown more antioxidant activity than white (or colorless) rice [12; 13]. Consequently, these phenolic compounds influence the antioxidant activity of Thai rice vinegar products. Furthermore, the influence of storing and aging on the DPPH radical scavenging activity of Zhenjiang aromatic vinegar (Chinese sticky rice vinegar) was investigated. Two years of vinegar storage provided higher antioxidant activity than fresh-made vinegar, but the total phenolic content was not significantly different. During storage, melanoidins (the brown polymer) were formed by the Maillard reaction, making the color of the vinegar become darker. The vinegar melanoidins were important for antioxidant activity [11].
## 4 Design of a Reinforced-Concrete Conical Foundation
### Material and Reinforcement  
To manufacture such a foundation block, heavy concrete of class B20 is used, which has high strength and resistance to loads, as well as durability under environmental conditions. Concrete must be frost-resistant and moisture-resistant, especially when laid in areas with high groundwater. High-quality concrete, in combination with reinforcement, protects the foundation from cracking and deformation, especially under the influence of dynamic and shear loads.  
The selection of reinforcement is based on calculations of expected loads, including vertical and horizontal forces. Recommended reinforcement:  
Vertical bars made of reinforcing steel grade A500C with a diameter of 16-25 mm. They are distributed along the height of the foundation and fixed at the base of the cone, and with a 20 cm overhang at the sharp end of the cone.  
Transverse clamps for fixing vertical rods--made of reinforcing steel with a diameter of 8-12 mm with a step of 15-20 cm, decreasing as they approach the base of the cone.  
Additionally, mesh reinforcement at the base of the cone increases shear resistance.  
In the lower part of the foundation, where significant tensile forces occur, the reinforcement pitch is reduced, to compensate for the loads.  
A denser mesh is used in the base to increase rigidity and stability under pressure on the ground. The width of the mesh side at the base of the cone is proposed to be 15-20 cm of A1 reinforcement--8-12 mm in diameter. This ensures sufficient reinforcement density to increase shear strength and prevent the concrete cracking under the load. In the case of increased loads or in the presence of aggressive conditions, the reinforcement pitch can be reduced to 100-150 mm, to strengthen the reinforced mesh of 10-16 mm.  
For conical reinforced-concrete foundation blocks, the protective layer of concrete must ensure durability and protection of reinforcement from corrosion. When designing conical foundation blocks for industrial purposes, a protective layer of 40-50 mm is recommended, to ensure reliability and durability. If necessary, an anti-corrosion coating for reinforcement is used if the foundation is installed in an aggressive environment.  
During the design, calculations are made for resistance to possible horizontal displacements and shear forces, which is especially important when using a cone with a deepened sharp end.  
Figure 14 shows examples of reinforcement of a conical block having a height of 500 mm and with a cone base diameter of 1000 mm (Figure 14a), as well as a conical foundation with a pile-stand with a total length of 1780 mm and a base diameter of 1200 mm (Figure 14b). Such a project, of a reinforced-concrete conical foundation, ensures the stability of industrial equipment and structures, reducing deformation and increasing the service life of the system.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 4 Results and Discussion
### Channel Coding and Interleaving in LoRa
#### 4.1.2 Forward Error Correction  
FEC is used for controlling errors in data transmission over unreliable or noisy communication channels. In LoRa, Hamming FEC is used with a variable codeword size ranging from 5 to 8 bits [13]. Furthermore, the data size per codeword is set to 4 bits, which allow defining the coding rate as \(\frac{4}{4+CR}\), where \(CR\in\{1,\ldots 4\}\) is the code rate or is also the number of redundancy bits.  
Figure 3: Proposed receiver architecture.
## 2 Materials and Methods
### AR Depletion by CRISPR/Cas9 Genome Editing  
The CRISPR/Cas9 system was used to generate SNU719 cells with the depletion of _AR_. The sgRNA sequence was designed using the web tool of the Centre for Organismal Studies Heidelberg (COS): CCTop ([[https://crispr.cos.uni-heidelberg.de/](https://crispr.cos.uni-heidelberg.de/)]([https://crispr.cos.uni-heidelberg.de/](https://crispr.cos.uni-heidelberg.de/))) accessed on 1 February 2021. The following sgRNA sequence was utilized to target the AR gene: 5\({}^{\prime}\)-CACCgTGCACCTTCATCCTTAGCTCA-3\({}^{\prime}\). The sgRNA targeting human AR (NC_000023.11) was designed and inserted into the LentiCRISPRv2 vector (catalog number 52961, Addgene, Watertown, MA, USA). HEK293T cells were then cotransfected with the LentiCRISPRv2 or LentiCRISPRv2-AR sgRNA plasmid, along with lentiviral packaging plasmids (psPAX2 and pMD2.G), using TurboFect Transfection Reagent (R0531, Thermo Fisher Scientific), and incubated for 72 h.  
The viral supernatants were collected, filtered, and used to infect SNU719 cells for 24 h. Afterward, the supernatants were replaced with fresh RPMI medium, and the cells were treated with 0.5 \(\upmu\)g/mL puromycin every 2 to 3 days. Knockout efficiency was validated by analyzing protein expression. The resulting SNU719 cells with AR depletion were designated as SNU719_AR(\(-\)) cells (Supplementary Figure S1A,B). SNU719 cells transfected with a lentiviruss containing only the LentiCRISPRv2 vector were named SNU719_AR(+) cells.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 4 Multi-Channel Routing in WSNs
### Static vs. Dynamic Channel Assignment  
Multi-channel communication in a single-radio WSN can be of two types: static and dynamic. In the static type, \(k\) available channels are distributed over the nodes in the network, such that all nodes in a flow have the same channel. Nodes that are on the same channel form a subtree. Thus the scheme partitions the whole network into \(k\) vertex-disjoint subtrees as shown in Figure 5b. Although this channel assignment scheme reduces the average overhearing, it does not allow the nodes to control their energy consumptions with respect to their _varying_ energy resources, which is our goal for balancing the remaining battery capacity of the nodes and thereby maximizing the lifetime of the network. To achieve this, we need a _dynamic_ channel assignment scheme which allows nodes to choose their channels _dynamically_ to balance the energy consumption of its neighbors so as to balance their residual battery capacities. The drawback of such dynamic channel assignment is the _channel switching_ time and the additional energy consumption due to the switching. However, such channel switching time and energy consumption is shown to be negligible for CC2420 radios in [45]. The authors show that the average switching time for a transmitting node is about 0.34 ms and the additional energy  
Figure 5: Illustration of the benefit of using multiple channels in reducing overhearing. (**a**) A typical single-channel tree based WSN is depicted, where each node experiences overhearing of transmissions from all nodes within its range. The goal of this work is to develop a multi-channel tree for such WSNs to extend its lifetime, as illustrated using multiple colors representing channels in (**b**,**c**). The channel assignment in (**b**) tries to maintain the same channel assignment in each path, whereas that in (**c**) has different channels on the same path, requiring frequent channel switching. Nodes in (**b**,**c**) have fewer neighbors on the same channel in comparison to that in (**a**), implying lower overhearing.  
consumption caused by channel switching is no more than 2% when 10\({}^{4}\) packets with 43 bytes of each are sent out continuously. Therefore in this paper, our key objective is to develop _a dynamic channel selection scheme to balance the residual battery capacities of the sensor nodes_.
## 2 Materials and Methods
### Mechanical Testing  
Both prototypic weld inserts have a shank with an undercut to prevent the weld insert from falling out when handling the composite component before joining by REW. Push-out tests were carried out on TPC specimens with embedded weld inserts before welding in order to investigate whether the handling strength is guaranteed. For this purpose, the weld inserts were loaded against the embedding direction. The push-out tests were performed on a universal testing machine (see Figure 5a). The size of the TPC test specimens was \(100\times 100\) mm\({}^{2}\) and the weld inserts were positioned in the centre. The test specimens were clamped between steel plates with a clearance hole diameter of 18 mm. The load was applied to the weld insert by a pressure pin at a constant crosshead velocity of 1 mm/min.  
After welding the TPC sheets to steel sheets, the welded joints were investigated by chisel tests in accordance with [21] to verify the chosen welding parameters. Chisel test as well as microsections were conducted on quadratic specimen (\(45\times 45\) mm\({}^{2}\)) with the weld inserts placed in the centre. Joint strength tests of welded TPC-steel-joints were conducted on single-lap shear specimen in accordance with [22] at quasi-static load. Before welding, the TPC specimen with embedded weld inserts were cut from size \(160\times 100\) mm\({}^{2}\) to \(115\times 45\) mm\({}^{2}\). The geometry of the specimen is shown in Figure 5b. The tests were performed on a high-rigid universal testing machine (Zwick Z100, by ZwickRoell AG, Ulm, Germany) at a testing velocity of 10 mm/min. The displacement was measured locally in the area of the joint  
Figure 4: Two-dimensional (2D) axisymmetric model used for the simulation of the resistance element welding process.  
Figure 5: (a) Schematic illustration of the push-out test setup, (b) geometry of single-lap shear specimen (\(l_{o}\): overlap length); 1: pressure punch, 2: weld insert, 3: TPC specimen, 4: exchangeable steel clamping, 5: base steel plates, 6: steel specimen.  
using long-stroke extensometers. The extensometers were positioned each at a distance of 22 mm from the joint centre. The tests ended in the complete separation of the specimen.
# Utilization of Machine Learning Algorithms
###### Abstract  
Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithms in strengthening HIV testing worldwide. This systematic review aimed to evaluate how effectively ML algorithms can enhance the efficiency and accuracy of HIV testing interventions and to identify key outcomes, successes, gaps, opportunities, and limitations in their implementation. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A comprehensive literature search was conducted via PubMed, Google Scholar, Web of Science, Science Direct, Scopus, and Gale OneFile databases. Our of the 845 identified articles, 51 studies were eligible. More than 75% of the articles included in this review were conducted in the Americas and various parts of Sub-Saharan Africa, and a few were from Europe, Asia, and Australia. The most common algorithms applied were logistic regression, deep learning, support vector machine, random forest, extreme gradient booster, decision tree, and the least absolute shrinkage selection operator model. The findings demonstrate that ML techniques exhibit higher accuracy in predicting HIV risk/testing compared to traditional approaches. Machine learning models enhance early prediction of HIV transmission, facilitate viable testing strategies to improve the efficiency of testing services, and optimize resource allocation, ultimately leading to improved HIV testing. This review points to the positive impact of ML in enhancing early prediction of HIV spread, optimizing HIV testing approaches, improving efficiency, and eventually enhancing the accuracy of HIV diagnosis. We strongly recommend the integration of ML into HIV testing programs for efficient and accurate HIV testing.  
machine learning; algorithms; HIV testing; predictive modeling; support vector machine; XGBoost; LASSO; random forest; deep learning; efficient; accurate +
Footnote †: journal: _Algorithms_  
1 Faculty of Health Sciences, South African Medical Research Council/University of Johannesburg (SAMRC/UJ) Pan African Centre for Epidemics Research (PACER) Extramural Unit, Johannesburg 2006, South Africa;[EMAIL_ADDRESS] (E.P.); [EMAIL_ADDRESS] (R.N.P.-M.)