esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Global Optimization Algorithms
#### 2.1.2 Hybrid-BH-RRT (HYBRID)  
The HYBRID algorithm [37] combines the underlying principles of two methods: BH and RRT [32]. RRT is a popular algorithm in the robotics community, originally developed to plan the motions of robotic systems, and subsequently extended and applied to explore the conformational space of molecules [34; 39]. RRT performs an iterative stochastic process that, starting from a given state, incrementally builds a tree that tends to cover the reachable regions of the search space. The tree's growth is guided by an intrinsic \"Voronoi bias\", enabling it to expand rapidly into unexplored regions. The idea of the HYBRID algorithm is to interleave exploration stages following the BH heuristic (i.e., digging into low-energy basins) and stages applying the RRT approach (i.e., biasing the search towards unexplored regions).  
The pseudo-code in Algorithm 3 presents the main steps of HYBRID. The implementation applied in this work involves a mechanism similar to the multistart-BH, in which the exploration starts from different initial configurations sampled by the function SampleRoot. The number of initial configurations is defined in the parameters \(P\) and tested inside the function MaxNumberRoots. Then, the algorithm follows the main steps of RRT: (line 6) a conformation \(q_{\text{rand}}\) is randomly sampled in the whole conformational space; (line 7) the nearest conformation \(q_{\text{near}}\) to \(q_{\text{rand}}\) among the already explored minima contained in \(\mathcal{T}\) is selected; (line 8) the basic tree expansion step in the original RRT algorithm is replaced by an execution of the monotonic-BH algorithm starting from the selected \(q_{\text{near}}\). Since the probability of a conformation \(q_{\text{near}}\) to be selected for expansion is proportional to the volume of its associated Voronoi cell (i.e., the subset of points in the conformational space closer to \(q_{\text{near}}\) than to any other conformation contained in the tree), the tree tends to grow towards the least explored regions of the space. Note that the parameters used for the monotonic-BH within the HYBRID algorithm should ensure that the number of iterations of perturbation and local minimization stages is relatively small compared to the number of iterations within the multistart-BH, so that the efficiency of the exploration via the coupling of the BH with the RRT algorithm will be enhanced.
## 5 Evaluation and Translation Results
### Experiment Results and Discussion
[11], which back-translated the remaining 372k target language sentences of 672k training data to generate 372k pseudo-source sentences directly with no segmentation; the 372k pseudo-source sentences, together with the 372k target sentences, expanded the parallel corpus from 300k sentence pairs to 672k sentence pairs. The experiment of this method aimed to confirm the effectiveness of applying our proposed methods to the augmented data by the back-translation method of [PERSON] et al. [11]. \"300k + mono (372k) + P2\" represents the combination method of \"Baseline 2 + mono (372k)\" (672k training data) and \"Proposed 2\". \"300k + mono (372k) + P2\" back-translated 522k and 501k from the \"Baseline 2 + mono (372k)\" (672k training data), so that the numbers of sentence pairs were increased from 672k to 2287k and 2223k in both directions. The proposed methods obtained nearly identical and better results of BLEU and TER scores than in the case of baseline methods on the development-test data and test data. These comparison experiments demonstrate that our proposed method can augment the extended data by the other corpus augmentation methods to yield better translation performance. In the future, we plan to combine the proposed methods with other augmentation approaches, as our results suggest it may be more beneficial than only back-translation. The salient benefits of the proposed method are that it requires no monolingual data and that, without changing the neural network architecture, our method can generate more pseudo-parallel sentences. Moreover, it can be combined with other augmentation methods.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 3 Coupling Mechanism Design and Array Switching Control Strategy
### Transmitting Array Switching Control Strategy Based on Receiving Coil Position Estimation
#### 3.2.2 Simulation Verification
At this time, \(Z_{\text{max}}\) = \(Z_{\text{(1, 2)}}=Z_{\text{(2, 3)}}\). According to the principle of transmitting array switching control, the transmitting array is switched to the transmitting coil TX(2, 3) for power supply. After that, \(dx\) and \(dy\) continue to increase, \(Z_{\text{(1, 3)}}>Z_{\text{(2, 3)}}\). At this time, \(Z_{\text{max}}=Z_{\text{(1, 3)}}\), and the transmitting array is switched to the transmitting coil TX(1,3) for power supply. After that, \(dx\) and \(dy\) continue to increase, \(Z_{\text{max}}=Z_{\text{(1, 3)}}\), and there is no need to switch the power supply coil. The simulation results show that when the two-dimensional offset of the receiving coil RX occurs, the transmitting array switching control strategy based on the position estimation of the receiving coil proposed in this paper can achieve the optimal transmitting coil switching control.
## 2 Background and Related Works
### Myocardial Damage Detection  
Segmentation of the myocardial scar has been widely addressed in many works, where methods have been mainly validated under pathological datasets. Thus, before segmenting the scar area, myocardial abnormality identification by visual inspection is performed, as in n-SD [4] and FHWM [10] methods. By means of this approach, the lesion's search is only guaranteed in the abnormal myocardium, reducing potential false positives in healthy scans. Despite the fact that these methods are able to better control the false positive rate, one of the drawbacks is the required expert interaction. Under this scenario, the development of an automatic method that could deal with healthy patients as well is highly desired. Devising such a method based on intensity myocardial profiles could be conducted by characterizing healthy and abnormal myocardium histograms. In previous works, healthy and scarred myocardial tissue distributions have been well described [15; 16; 17]. According to [15], a Rayleigh distribution might appropriately model the normal tissue, while hyper-enhanced infarcted areas can suitably be modeled by a Gaussian one. Thus, the whole myocardium histogram consists on the resulting distribution obtained from the overlapped healthy and abnormal tissues. For the sake of simplicity, assumption of both distributions as Gaussian models has been extensively used [8; 18; 19; 20]. [PERSON] et al. [15] proposed the use of information criteria (Akaike and Bayesian ones) for histogram characterization. Thus, by assessing the best histogram model fit a normal or abnormal myocardium could be identified. However, the main limitation of this approach regards the expectation-maximization algorithm performance. Due to the considerable intensity overlap in diseased cases, the method may sub-optimally estimate the parameters of both tissue (healthy and abnormal) distributions. Moreover, in small myocardial lesions,the scarred tissue distribution is obscured by the healthy one, turning the method inaccurate. These limitations suggest that the problem could be better addressed by extracting more complex features (and not only intensity-based ones). In this sense, some initial attempts using deep learning were performed in CT scans for detecting coronary artery stenoses [21].
## 3 Materials and Methods
### Method
#### 3.2.1 Data Retrieval Techniques
To identify the results of the streaming data, the URL of the username was set as [[https://Twitter.com/](https://Twitter.com/)]([https://Twitter.com/](https://Twitter.com/))(username) (access on 30 May 2020 until 30 April 2021), while Twitter.com/(username) was used for archive data (access on 1 May 2021 until 30 September 2021). The program was set to save the retrieved data in MongoOb. A subprogram, Sheet.py, was constructed for saving in MongoOb. (Attachment S4 in Supplementary Materials). Sixth, the program was run regularly, particularly for data streaming acquisition. Twitter has a data acquisition quota limit of a maximum of 50 data every 15 min; therefore, data collection was repeated accordingly. Data acquisition stops when the Twitter platform has detected that data retrieval has exceeded the limit set. Archive data acquisition was performed using a list of 2492 Twitter usernames that resulted from the acquisition of streaming data. Data acquisition at the initial stage was repeated several times; however, this was not performed for usernames in the archive data acquisition after the same data were obtained. In accordance with the rules for the maximum radius of data coverage, which is 40.2 km, it was assumed that the entire city of Serang, measuring 20.6 km \(\times\) 25.25 km, could be covered, as shown in Figure 3. At the initial stage, an attempt was made to collect Twitter streaming data using 1 as the reference point for capturing the entire research area at once. The time interval for data acquisition was performed at the same relative time. An attempt was then made to collect data from numerous reference points to capture the entire research area, section by section. Since this revealed more results, using multiple reference points to acquire data was continued. In referring to many reference points, the researchers used the coordinate points of the location of the ward office in each zone, according to subdistrict areas, as shown in Figure 5. The ward offices were: 1. Kasemen, 2. Taktakan, 3. Serang, 4. Cipocokjaya, 5. Walantaka, and 6. Curug.
## 3 Methods
### Data Collection and Preprocessing  
The dataset used in this study was sourced from Amazon (www.amazon.com), one of the world's largest e-commerce platforms that provides ingredient information on most food product detail pages. This study focused on collecting information for breakfast products, covering seven major subcategories: Applesauce and Fruit Cups, Breakfast and Cereal Bars, Breakfast Syrups and Toppings, Cereals, Instant Breakfast Drinks, Pancake, Waffle, and Baking Mixes, and Toaster Pastries. Because a food product in the market cannot selectively determine who buys it, we used all available Amazon reviews for each product, representing the base of consumers who purchased the item. We gathered both basic product information and user reviews for each item. The basic information includes product category, name, website rating, and ingredient list, while user reviews contain the review text and corresponding user ratings. Data collection was conducted in 2021, encompassing 1062 breakfast products sold through official channels and 58,206 user reviews.  
A total of 672 products provided ingredient lists. During the processing of these lists, it was first necessary to disambiguate ingredients of the same type. Some ingredients, although expressed differently in text, refer to the same entity--for example, variations in capitalization (e.g., \"Apple\", \"apple\", and \"APPLE\") or common names versus scientific names (e.g., \"Vitamin E\" vs. \"tocopherols\")--and thus require standardization. Some lists include additional notes on function, such as \"BHT and BHA added to preserve  
Figure 1: Overall research framework.  
freshness\", which need to be merged with simplified descriptions. Others use a hierarchical format, where a more detailed formulation appears in parentheses (e.g., \"CRISP RICE (RICE FLOUR, SUGAR, BARLEY MALT, SALT)\"), requiring these items to be broken down to the most granular level. Because manufacturers often lack standardized ingredient description practices, this study manually standardized, disambiguated, and numbered the ingredient lists to improve the accuracy of ingredient-product relationships. As a result, 1012 unique ingredients from the 672 products were compiled for subsequent heterogeneous information network construction and representation analysis.
## 4 Numerical Results  
### Case Descriptions  
An actual 20 kV distribution network in China, including two 110-kV substation nodes (i.e., node 1 and node 53, represented by orange solid point), fifty-three 20- kV load nodes, and 53 lines, is used to verify the effectiveness of the proposed model and methods. The total active load of the distribution network is 272.85 MW. The topology of the distribution network is shown in Figure 3, and the dotted lines in Figure 3 represent the tie lines in the distribution system. The voltage fluctuation range of node \(i\) at the dispatching period \(t\) is set to be \([0.95U_{i}^{N},1.05U_{i}^{N}]\).  
To analyze the influence of the number of DG connection nodes on the accommodation capacity and flexibility of the distribution network, two scenes with different DG connection nodes are set up, as shown in Table 1. Table 1 shows that only node 41 is a WF node, and there are more PV nodes in scene A than in scene B. The maximum accommodation capacity of DG in the distribution network in this area (i.e., the maximum connection capacity of DG) is calculated by the proposed CLPSO algorithm and mixed strategy Nash equilibrium. The basic parameters of the CLPSO algorithm used in this paper are as follows: the number of particles is set to 30; each acceleration factor is 1.494; the upper and lower limit of inertia weight are set to 0.7 and 0.2, respectively; and the maximum number of iterations is set to 100.  
Figure 3: Topology of distribution network. Note: PV = photovoltaic energy; WF = wind farms.  
Considering the uncertainties of DG, three typical WF output scenarios and four typical PV output scenarios are generated based on measured data. Each scenario is divided into six dispatching periods. The standardized output curve of each dispatching period is shown in Figure 4.
# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 2 Related Work
In [20], the authors propose the use of opportunistic networks nodes and their computing features as a way of sampling of data and elaboration by involving other users in a distributed fashion without intervening in a central processing node.
## 3 Simulation of Underwater Electric Field Communication
### CSMA/CA Channel Backoff Algorithm Design and Simulation Analysis
Design and Implementation of Underwater Electric Field Multi-Node Communication System Based on Raspberry Pi  
Based on the previous section, it can be inferred that the collision problem in AUV channels can also be solved in the same way using CSMA/CA. Therefore, this article adopts Raspberry Pi for the design and implementation of the CSMA/CA protocol. Currently, Raspberry Pi 4B can be equipped with 4 GB of memory, has four USB ports and a Gigabit Ethernet port, and can be connected to a network cable, mouse, and keyboard. It also features an HDMI video interface, an analog video signal TV interface, and a camera interface with stereo headphone output. All these functions are integrated into a small-sized motherboard, and only a screen and keyboard are required to achieve full PC functionality, such as hardware operation, software programming, video playback, etc. Due to its low price, Raspberry Pi is widely used and greatly improves the portability of the underwater electric field communication system as the main control center [34].
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 2 Theoretical Background
### Convolutional Neural Network  
Each Thermal image consists of non-linear features and are stored digitally in RGB format. Simple Neural Networks are not able to generalize complex patterns in images. Convolutional Neural Networks (CNN) learns to recognize differences and patterns in images. CNN [30] consists of - Convolution, Max Pooling, Flattening, and ANN layers. The primary purpose of convolution is to find features in an image using a feature detector and put them into a feature map.
## 5 Results and Discussion
### Streamer-to-Flame Transition  
With the method described in the previous sections, the distribution with lambda of the time to obtain visible flames after the start of discharge was found, as can be seen in Figure 13. Firstly, in each of the tested cases, a visible flame was generated before the end of the discharge. On average, at \(\lambda=1.0\), flames appeared between the 12 th and 13 th frames after the discharge start; at \(\lambda=1.2\), they appeared between the 13 th and the 15 th frames, while, in the \(\lambda=1.4\) case, they appeared between the 14 th and the 15 th frames. Note that such values refer to the average, while the standard deviation, not reported in the graph, was found to be pretty constant, i.e., between 1.7 and 2.3 for all cases. Focusing on each quadrant, a monotonic non-decreasing relationship between lambda and the transition average frame was found.  
It is worth remarking that the difference of max three frames denotes about 0.23 CAD; whatever the tested lambda, the corona igniter was able to ignite the mixture proficiently, before the end of the discharge. These results assess the corona effectiveness in terms of quickness in flame kernel generation, as well as with moderate and medium/lean mixtures [15]. Note that no further information regarding combustion evolution was provided from the optical point of view, since this analysis was focused on the flame onset detection. Such results can be found in other works [11; 14; 15; 32].  
Finally, at constant lambda, no relationships were found between streamer variability in motored conditions and the transition frame. This means that, despite a certain variability in the streamers generated from each tip, the discharge mechanism was effective, thanks to the combined thermal and chemical effects [17; 34]. The streamer variability, instead, was one of the factors responsible for the engine CCV, with a strong effect, particularly in the first 5% of MFB [32], which can be investigated with other optical techniques such as the flame probability presence [49].  
Figure 13: Distribution of the frame aSOD in which streamer-to-flame transition occurs. Bars refer to the average value (among the 63 events recorded for each test case).
## 5 Discussion  
In our methodology, two key parameters influence the outcome of the algorithm: the number of splines used during the initial and secondary curve estimations. Initially, a relatively small number of splines, ranging between 6 and 10, is chosen to capture the general trend of the curved text. This approach allows us to approximate the overall form of the distortion without overly fitting minor variations that could detract from the general trend. Subsequently, a higher number of splines, typically between 9 and 15, is utilized to fit the curve of the semi-processed text more precisely. This number is carefully selected to be high enough to conform closely to the modified text curvature, yet not so high as to cause overfitting and introduce unwanted distortions.  
Notably, the latter stage of the process does not employ the Bresenham line drawing algorithm but instead adjusts pixel columns vertically, enhancing the alignment without distorting the text. It has been observed that using too few splines in the initial phase can result in a noticeable tilt in the letters, while an excessively high number can lead to overfitting, where the text becomes unnaturally distorted. The choice of spline number thus plays a crucial role in balancing the accuracy of the fit against the risk of overfitting, ensuring that the text remains legible and well-aligned.  
As previously mentioned, not all scenarios necessitate the complete dual-step processing approach. There are particular cases, such as text that is only minimally distorted as if it was lightly slanted or arched, which can be adequately rectified through simple vertical adjustments alone. This streamlined approach involves less computational complexity. Examples of these types of minimally distorted text are illustrated in Figures 9 and 10, respectively.  
The selection of the appropriate version of our proposed algorithm is ultimately determined by the user based on their specific needs and the characteristics of the images they are working with. This flexibility allows users to tailor the approach to optimize performance for their particular applications, whether they require handling of minor distortions with less intensive processing or more adjustments for severely distorted texts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Warping**} & \multirow{2}{*}{**DW**} & **Word Error Rate w/o** & **Character Error Rate w/o** & **Word Error Rate w/** & **Character Error Rate w/** \\  & & **Rectification** & **Rectification** & **Rectification** \\ \hline \(y=-x\) & 99.86\% & 0.9440 & 0.5063 & 0.1552 & 0.0237 \\ \(y=x^{2}\) & 99.86\% & 1.3352 & 0.8339 & 0.3973 & 0.0620 \\ \(y=-x^{3}\) & 99.88\% & 1.1067 & 0.6613 & 0.1838 & 0.0318 \\ \(y=x^{4}\) & 99.92\% & 1.7962 & 0.7910 & 0.3772 & 0.0575 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics evaluating the rectification process via OCR performance under various warping function scenarios. The table includes the dewarping evaluation measure (DW), as well as the word error rate (WER) and character error rate (CER), before and after processing the input images.
## 2 Materials and Methods
### Model Performance Evaluation  
For machine learning models, comparisons were performed using accuracy, recall, precision and F1. The concordance index was used to evaluate the predictive performance of the model. For the Cox-PH and DeepSurv models, the predictive performance of the models was assessed using the Harrell C-index; the specificity and sensitivity of the models were assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC); the accuracy of the models was assessed using calibration curves; and the clinical utility and net benefit of the models were assessed using decision curve analysis (DCA).
## 2 Materials and Methods
### Release of Organic Matter by the Biosorbent  
The release of organic matter from the biosorbent to ultrapure water was evaluated for 1, 2, and 5 bean peels. This evaluation was made to determine the adsorbent dosage that can be used without introducing a significant amount of organic matter into the solution. Therefore, the different number of peels (1, 2, or 5) were put in contact with 50 mL of ultrapure water into Erlenmeyer flasks for 1 h, 2 h, 4 h, and 6 h, and for similar experimental conditions to those chosen to perform this work. After each time of contact, aliquots of water were taken to be analyzed by UV-Vis spectroscopy. Considering absorbance values lower than 0.05 A.U. as the admissible value for the introduction of organic matter into the water [31], it was found that only the experiments using one peel, corresponding to a mass of 0.044 g of biosorbent, in contact with 50 mL of water, attained such requisite (see Supplementary Materials Figure S1). When the number of peels increased to 2 or 5, absorbance levels significantly exceeded the 0.050 A.U. target, indicating a significant and undesirable increase in the organic matter release. The extension of the contact time between the biomaterial and the solution did not result in significant changes in the absorbance values obtained. This suggests that the critical factor influencing the organic matter release is primarily the mass of the biosorbent rather than the duration of contact. Therefore, in this work, the adsorbent dosage was fixed at a mass corresponding to one peel (0.044 g) in 50 mL of water for all experiments, ensuring that the biosorption process consistently operated with low organic matter release.