esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrical Circuits Simulator in Null-Flux Electrodynamic Suspension Analysis
## 2 Unveiling the Inner Workings: Exploring the Principles and Model of an EDS System
### The Computational Algorithm  
The flowchart depicted in Figure 3 provides a comprehensive illustration of the sequential steps involved in this work, which are elaborated upon in subsequent sections. The primary objective is to present an overview of the implemented computational algorithm and the specific order of software utilization.  
The input for this algorithm encompasses the physical parameters of the coils, specifically those outlined in Table 1, along with variables such as train speed, lateral air gap, and the train's relative \"height\" concerning the guideway coils.  
Figure 3: Calculation flowchart of this simulation model.  
inductances of the loops of the same null-flux coil and its resistance were calculated, utilizing [PERSON]'s formula and [PERSON]'s second law, respectively [41].  
The software responsible for numerically calculating mutual inductances between onboard and guideway coils, including the \(x,y,\) and \(z\) derivatives of these inductances, receives the above inputs and generates output tables. These tables are a basis for computing the electrical circuit simulator's drag, levitation, and orientation forces.  
The electrical circuit simulator, in turn, utilizes these pre-calculated tables as input data. By considering the mutual inductances as a function of time, the simulator models the magnetic coupling between the coils, effectively emulating real-world scenarios on a test track. As a consequence of this interaction, an induced current arises in the 8-shaped coil.  
The interplay between the induced current and the original current from the SC coil, which serves as the source of the magnetic field, generates magnetic forces along the three Cartesian axes. The outputs of the electrical circuit simulator are the induced current, and these forces, which can be presented in tabular or graphical formats, catering to the user's preferences.  
As a result of its numerical iterative nature, this computational algorithm substantially reduces the computational effort required, compared to conventional finite element simulations. This characteristic is visually demonstrated in Figure 4, which presents the hardware specifications employed in this study. Since it was a simulation work, a conventional desktop computer, and a laptop were utilized for the computational simulations. It is important to note that no specific hardware was developed in this research, as the focus was on the computational modeling and analysis of the EDS system. The significance of this work lies in its ability to streamline the research process for those investigating electrodynamic levitation phenomena.
## 4 Organized Classification of Sports Wearables  
Considering the constant growth of the wearable device market, it provides a continuous response to consumers' changing needs, while at the same time, it limits the possibility of definitive classifications.  
Generally, wearable smart devices are classified into the following four categories by the International Electrotechnical Committee (IEC):  
* Near-body electronics. Devices that operate in close proximity to the body without direct skin contact, such as smart glasses, wireless earbuds, and augmented or virtual reality devices. These use short-range sensors and communication technologies to interact with the environment and user, providing contextual information or enhancing sensory experience.
* On-body electronics. Devices embedded in clothing or accessories, including smart clothing and jewelry, which use textile-integrated sensors or embedded microelectronics to track movement and biometrics.
* In-body electronics. Implantable devices, which are inserted into the body to monitor internal physiological parameters using biosensors that can measure things like glucose levels and heart rhythm.
* Electronic fabrics. Fabrics embedded with electronic components and sensors, capable of detecting various forms of physical and environmental interaction through conductive threads and flexible circuits.  
Following these four categories, the list below reflects the wearable devices currently used by athletes:  
* Watches: basic, smart.
* Hearables: earphones, earbuds, headsets.
* Smart clothing: smart shoes, bras, suits (jacket, trousers), shirts, pants, socks.
* Smart jewelry: bracelets, necklaces, brooches, rings, analog watches, fitness jewelry.
* Head-mounted displays: AR HDMs, VR HDMs, mixed HDMs.
* Glasses: smart, AR.
* Wearable cameras.
* Body sensors.
* Implantable.
* Ingestible.
* Tattooable.
* Exoskeletons: active, passive.
* Location trackers.
* Gesture control.  
We have attempted to categorize wearable devices into a variety of categories in this article, so that we may create a comprehensive classification system that can catalog their use in sports as comprehensively as possible.  
Based on the available literature, we believe that we are proposing a classification system that reflects the current usage of wearable devices by athletes.
## 2 Materials and Methods
### Feature Extractors  
Neural networks, as universal function approximators, have proven highly effective in a range of machine learning tasks due to their ability to approximate complex, non-linear relationships between input data and the target output. The Universal Approximation Theorem [35] demonstrates that a neural network with at least one hidden layer can approximate any continuous function to a desired accuracy, given sufficient parameters and non-linear activation functions. This adaptability makes neural networks particularly well suited for modeling financial data, where the relationships between market signals and asset returns are often non-linear and time-varying.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Market Behavior** & **AROONOSC** & **RSI** & **CCI** & **CMO** & **MFI** & **Williams \%R** & **STOCHF** \\ \hline \multirow{2}{*}{Overbought} & \(>\)50 (Uptrend) & \(>\)70 & \(>\)+100 & \(>\)50 & \(>\)80 & \(>\)\(-\)20 & \(>\)80 \\ \hline \multirow{2}{*}{Oversold} & \(<\)\(-\)50 (Downtrend) & \(<\)30 & \(<\)\(-\)100 & \(<\)\(-\)50 & \(<\)20 & \(<\)\(-\)80 & \(<\)20 \\ \hline \multirow{2}{*}{High Volatility} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} \\ \hline \multirow{2}{*}{Low Volatility} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} \\ \hline \multirow{2}{*}{Bullish Divergence} & Price \(\downarrow\), & & & & & & \\  & AROON \(\uparrow\) & & & & & & \\ \hline \multirow{2}{*}{Bearish Divergence} & Price \(\uparrow\), & & & & & & \\  & AROON \(\downarrow\) & & & & & & \\ \hline \multirow{2}{*}{Bearish Divergence} & Price \(\uparrow\), & & & & & & \\  & AROON \(\downarrow\) & & & & & & \\ \hline \multirow{2}{*}{Momentum Confirmation} & \multirow{2}{*}{\(>\)0 (Uptrend)} & \multirow{2}{*}{\(>\)50 (Bullish)} & \(>\)+100 (Uptrend) & \(>\)00 (Bullish) & \(>\)-50 (Bullish) & \(\%\)K \(>\)\%D \\ \hline \multirow{2}{*}{Trend Reversal} & Crosses 0 (Up/Down) & \multirow{2}{*}{Crosses 50} & Crosses +100/\(-\)100 & Crosses 0 & Crosses & Crosses & Crosses \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interpretation of technical indicator values and market behavior.  
Figure 2: Technical Indicators for S&P 500 for April 2020.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### Route Evaluation Parameters
#### 3.1.3 The Cache Remaining Rate  
The data transmission process in event-driven WSNs has prominent \"many-to-one\" characteristics which could cause network congestion, increase the delay, and reduce the transmission success rate during traffic bursts. Because the cache queue occupancy can reflect node load status, the routing protocol should consider this property to avoid the higher cache occupied node. Moreover, it should transfer the traffic load to the idle or lower cache occupied node to realize the fair utilization of wireless network resources. Thus, the remaining cache \(r_{c}(j)\) of the candidate node \(j\) is expressed as follows:  
\[r_{c}(j)=\frac{1-Q_{j}}{Q_{max}},j\in F(i) \tag{7}\]  
Here, \(Q_{j}\) is the latest cache queue length of node \(j\), and \(Q_{max}\) is the cache's max length.  
When selecting the next-hop from the forward region of node \(i\), the preferred node has a more significant cache remaining amount than the mean length. For this reason, based on the _logistic_ function, we introduce the \(f_{\delta}(x)\) function, which can be expressed as  
\[f_{\delta}(x)=\frac{1}{1+\left(\frac{1-x}{1-\delta}\right)^{2}},x\in[0,1] \tag{8}\]  
Assume \(x\) represents the cache remaining amount of node \(j\), \(\delta\) represents the average cache remaining number of nodes from the forward neighbor set, and \(num[F(i)]\) is the number of node \(j\)'s forwarding neighbor.  
\[\delta=\overline{r_{c}(j)}=\frac{1}{num[F(i)]}\bullet\sum_{j\in F(i)}r_{c}(j) \tag{9}\]  
As shown in Equation (8), the value of the function \(f_{\delta}(x)\) increases with the increase in \(x\), and the inflection point is the mean of \(\delta\). Moreover, the rate of change for function \(f_{\delta}(x)\) near the mean \(\delta\) will be significantly greater than the rate of change at both endpoints of x. This will make the nodes with \(f_{\delta}(x)\) values greater than the mean more dominant in routing decisions. Combined with the cache remaining amount and function \(f_{\delta}(x)\), the cache rate of the node \(j\) is:  
\[r_{fc}(j)=f_{\overline{r_{c}(j)}}[r_{c}(j)] \tag{10}\]  
The cache rate can effectively represent the node load. Therefore, in the process of packet transmission, the routing protocol tries to select the node with a larger cache remaining rate to minimize the loss of packets in the congested node to ensure the success rate of data collection.
# Experiment on Cultivation Performance of Plant Fiber-Based Degradable Film in Paddy Field
## 3 Results and Analysis
### Film Degradation  
It can be seen from Table 3 that the cracks of SM began to appear during the blossom fruit period. ZM began to crack in the panicle development stage, but there were significant differences in degradation rates in different years. The degradation rate of ZM in 2016 was faster than that in 2015 and 2017. Moreover, the degradation rate of ZM in the blossom fruit period in 2015 to 2017 reached the grade of 3, 4, and 3, respectively. The ZM had begun to show a small area crack at the position of transplanting  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Treatment**} & **Yield** & **Film Dosage** & **Film Cost [b]** & **Output Value [c]** \\  & & **(kg.hm\({}^{-2}\))** & **[a] (kg.hm\({}^{-2}\))** & **(yuan\(\cdot\)hm\({}^{-2}\))** & **(yuan\(\cdot\)hm\({}^{-2}\))** \\ \hline \multirow{3}{*}{2015} & ZM & 1251.62a & 750 & 3750 & 4255.508a \\  & SM & 1173.93a & 90 & 1080 & 3991.362b \\  & CK & 1142.67b & 0 & 0 & 3885.078b \\ \hline \multirow{3}{*}{2016} & ZM & 1462.55a & 750 & 3750 & 5001.921a \\  & SM & 1390.89a & 90 & 1080 & 4756.8438b \\ \cline{1-1}  & CK & 1352.37b & 0 & 0 & 4625.1054b \\ \hline \multirow{3}{*}{2017} & ZM & 859.87a & 750 & 3750 & 2665.597a \\  & SM & 812.61a & 90 & 1080 & 2519.091a \\ \cline{1-1}  & CK & 782.34b & 0 & 0 & 2425.254a \\ \hline \hline \end{tabular}  
* The basis weight of plant fiber-based film and plastic film mulching was 75 and 9 g/m\({}^{2}\), respectively; [b] The unit price of plant fiber-based film and plastic film mulching was 5.0 and 12.0 yuan-kg\({}^{-1}\), respectively; [c] The unit price of rice in 2015, 2016, and 2017 was 3.4, 3.42, and 3.1 yuan-kg\({}^{-1}\), respectively.  
\end{table}
Table 2: Effect of different films mulching on rice quality.  
Figure 6: Effect of different films mulching on rice yield.  
rice seedlings in the panicle development stage after paddy mulching cultivation. At the blossom fruit period, most of the ZM had been degraded into fragments without large film existing, which reached the grade of film degradation rates 3 and 4. Although the degradation rate and degradation cycle of various degradable films are different due to the differences in raw material composition, additives, manufacturers, production requirements, and induction period design, the degradation process is basically similar mainly due to the influences of meteorological factors and soil environment [29; 30; 31].
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 4 Results
### Training and Testing Process  
We employed the cross-entropy loss function and the AdamW optimizer, coupled with a polynomial learning rate adjustment strategy and linear warm-up at the beginning, which aids in effectively smoothing the convergence and stabilizing the model during the initial training phases. Throughout the training process, we closely monitored the loss function values to assess the model's convergence. When the model's performance became stable, and the loss values exhibited no significant fluctuations, training was concluded. In the testing stage, we conducted a thorough evaluation using comprehensive metrics, including mean intersection over union (_MIoU_) [41], mean accuracy (_MAcc_) [42], and pixel accuracy (_PAcc_) [43], to assess the model's overall performance on the test dataset. These metrics offered profound insights into the model's applicability and effectiveness. These formulas define the evaluation metrics:  
\[MIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{FN+FP+TP} \tag{9}\]  
\[MAcc=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{FP+TP} \tag{10}\]  
\[PAcc=\frac{TP+TN}{TP+TN+FP+TN} \tag{11}\]  
where \(k\) represents the number of categories. Due to cloud detection being a binary classification problem, \(k=1\) during the computation process. _TP_ (True Positive) signifies the count of pixels correctly predicted as clouds by the model, _TN_ (True Negative) represents the count of pixels correctly predicted as background, _FP_ (False Positive) stands for the count of misclassified pixels predicted as clouds, and _FN_ (False Negative) denotes the count of missed pixels predicted as background.
# Synthesis of High-Precision Sub-Micron CaCO\({}_{3}\) Anticancer Drug Carriers from Coral Remains
## 2 Materials and Methods
### Exploration of Optimal Parameters for the Synthesis of Vaterite
#### 2.4.3 EG Concentration  
Ethylene glycol (EG) played a critical role in this study due to its high viscosity, which decelerates ion diffusion and inhibits the growth of CaCO\({}_{3}\). We investigated various EG concentrations, including 0%, 25%, 50%, and 85%, to determine their impact on the synthesis of CaCO\({}_{3}\).
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
## 3 Results  
The performance of the proposed models for streamline curvature sensitization was assessed by computing three flow configurations with large streamline curvature, which present challenges for conventional RANS models. Specifically, we considered the 2D infinite serpentine passage flow, the 2D U-turn duct flow, and the 2D periodic hill flow. The predictions were compared with the relevant DNS, experimental, and LES data. To demonstrate the advantages of the proposed models more convincingly, the results of the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model and the SST \(k\)-\(\omega\)-CC model are incorporated for comparison.
# Innovation and Application of the Type III Secretion System Inhibitors in Plant Pathogenic Bacteria
## 4 Conclusions and Perspectives
Thus, besides the capacity for disease management, the impact of T3 SS inhibitors on the host microbiomes and the host plants should be monitored and evaluated. Another challenge for the T3 SS or any other virulence inhibitors is a successful implementation in the field. Unlike laboratory conditions, most commercially grown plants face fluctuating environmental conditions, such as sunlight, precipitation, and temperature. Therefore, it is important to evaluate the stability and efficacy of T3 SS inhibitors under various environmental conditions. For example, a recent study showed that oxytetracycline and kasugamycin could be degraded by sunlight and early evening application is suggested to maximize the efficacy in controlling fire blight in the field [163]. In 2014 and 2015, we conducted field assays using a phenolic T3 SS inhibitor, _trans_-4-phenylcinnamic acid, against fire blight on apple trees. Our results showed that it reduced blossom blight with an efficacy similar to kasugamycin at a concentration of 5 mM ([PERSON] and [PERSON], unpublished data), suggesting that application of T3 SS inhibitor is a promising alternative method for controlling fire blight in the field. Meanwhile, trial application and evaluation of T3 SS inhibitors on other bacterial plant diseases are planned. With more new discoveries, these antibiotic alternatives are expected to make agriculture more prepared for the upcoming challenges from continuously evolving pathogens. All authors contributed to drafting or revising the manuscript, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work. All authors have read and agreed to the published version of the manuscript. This project was supported by grants from the United States Department of Agriculture National Institute of Food and Agriculture (nifa.usda.gov) grant no. 2016-67030-24856, 2017-51106-27001, and 2020-70006-32999, the Research Growth Initiative of the University of Wisconsin-Milwaukee, the UWM Research Foundation (Catalyst Grant in Advanced Automation), and T3 Bioscience LLC. This research is partially sponsored by T3 Bioscience LLC (T3 B) and may lead to the development of products which may be licensed to T3 B. In accordance with my ethical obligation as a researcher, I am reporting that I have financial and business interests in T3 B. I have disclosed those interests fully to the publisher and have in place an approved plan for managing any potential conflicts arising from this arrangement with Research Office of University of Wisconsin, Milwaukee and University of Wisconsin, Milwaukee Research Foundation.
# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 5 Discussion  
The UnifiedCut model presents significant strengths in word segmentation for Thai, Burmese, and Khmer scripts. A key advantage lies in its simplicity and efficiency: by employing a single-layer transformer encoder, UnifiedCut maintains a streamlined architecture that accelerates processing, making it considerably faster than state-of-the-art models such as DeepCut and AttaCut. Additionally, UnifiedCut demonstrates robust performance in recognizing out-of-vocabulary (OOV) words and identifying new and meaningful terms effectively. Its high accuracy across both word and syllable segmentation tasks further underscores its versatility and robustness across various southeast Asian languages.  
Figure 3: Speed comparison between Thai word segmenters on Wisesight’s training set.  
Compared to dictionary-based methods, UnifiedCut has distinct advantages. While dictionary-based approaches can be efficient and precise for known vocabulary, they often struggle with OOV words and depend heavily on comprehensive and up-to-date lexicons. In contrast, UnifiedCut leverages neural network-based multi-head attention, enabling it to generalize better by learning patterns directly from data rather than relying on static dictionaries. This flexibility allows UnifiedCut to adapt more easily to dynamic language changes and linguistic variations.  
Despite these strengths, UnifiedCut faces certain limitations, particularly in cross-domain applications. Its accuracy and recall decline when applied to datasets from different domains, highlighting challenges in generalization. This limitation suggests that while UnifiedCut excels with data closely aligned to its training set, its effectiveness diminishes in unfamiliar linguistic contexts. Additionally, although UnifiedCut performs well in OOV word recognition, improvement levels vary across different datasets, revealing inconsistencies in handling diverse language data.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Smart Transportation Systems  
The advancement of smart transportation relies on distributed intelligence for real-time traffic optimization, vehicle coordination, and safety management. Edge computing enables the local processing of vast streams of vehicular data, facilitating dynamic route adjustments, congestion mitigation, and intelligent traffic signal control. Cloud services aggregate large-scale mobility data, providing long-term analytics for infrastructure planning and predictive modeling [106; 107].  
Vehicle-to-everything (V2X) communication ensures seamless interaction between vehicles, roadside units, and cloud platforms, enabling low-latency data exchange crucial for collision avoidance and autonomous navigation. The increasing integration of AI enhances decision-making by predicting traffic patterns, optimizing resource allocation, and enabling cooperative driving strategies. While edge nodes process time-sensitive data for immediate action, cloud resources refine long-term mobility insights, ensuring a balance between real-time responsiveness and large-scale intelligence [108; 109; 110].  
The deployment of autonomous vehicles intensifies the demand for ultra-reliable, low-latency processing. Edge-based AI algorithms support real-time sensor fusion and adaptive control, minimizing reliance on distant cloud servers. As mobility ecosystems become more interconnected, FL enables collaborative AI model training across distributed vehicle networks while preserving data privacy, improving real-time decision accuracy, and enhancing safety standards [111; 112; 113].