esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Future Work  
One of the exciting findings of this research was that reasonable predictions could be made with as little as a single question. A goal for future work is to investigate this further, and determine the minimum amount of data required to provide an accurate assessment, as reducing assessment lengths could reduce usage friction and create an ever more seamless experience.  
As stated above, the most surprising result of the data was the model's lack of improvement when combining multiple modalities. Future work is planned which will investigate this further by examining different audio media including spectrograms, implementing various fusion techniques, and/or determining whether this result was an outcome of the dataset itself.
# Assessment of Characteristics of Acid Mine Drainage Treated with Fly Ash
## 3 Results and Discussion
### XRD Analysis of Fly Ash  
The mineral phase resulting peaks (Figure 3) were compared to the Joint Committee on Powder Diffraction Standards (JCPDS) data. The crystalline phases present in the fly ash consisted of quartz (SiO\({}_{2}\)), mullite (Al\({}_{6}\)SiO\({}_{2}\)), hematite (Fe\({}_{2}\)O\({}_{3}\)) and calcium oxide (CaO). Similar results (material rich in quartz, mullite, hematite and quicklime) were noted in  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sampling Location**} & \multicolumn{6}{c}{**Parameters**} \\ \cline{2-9}  & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **FeO** & **MgO** & **MnO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** \\ \cline{2-9}  & \multicolumn{6}{c}{**Concentration in \%**} \\ \hline Anpara TPP & 45.9 & 27.7 & 17.4 & 3.2 & 2.9 & 1.6 & 0.6 & 0.7 \\ Renusagar TPP & 40.5 & 23.0 & 16.1 & 8.1 & 8.0 & 2.2 & 1.4 & 0.7 \\ Shaktinagar TPP & 44.7 & 26.3 & 15.6 & 7.9 & 1.7 & 2.1 & 1.3 & 0.4 \\ Vindhyachal TPP & 44.0 & 20.7 & 14.9 & 8.4 & 5.8 & 4.8 & 1.2 & 0.2 \\ \hline \hline \end{tabular} Note: TPP = thermal power plant.  
\end{table}
Table 2: Characteristics of the fly ash samples.  
the XRD analysis of a fly ash sample from a coal-fired boiler's electrostatic precipitators, generally in bituminous fly ashes [22].
## 2 Materials and Methods  
### Dataset  
In this study, a total of 2775 images of maize leaf pests and disease were collected, including 724 disease images and 2051 insect pest images. The disease images included three categories of diseases: northern leaf blight (NLB), northern leaf spot (NLS), and grey leaf spot (GLS). The insect pest images included five types of pests: maize borer, aphid, red spider, armyworm, and peach borer. Figure 1 provides example images for various pests and diseases.  
Figure 1: Images of some samples of pests and diseases in the dataset. (**a**) Northern leaf blight, (**b**) northern leaf spot, (**c**) gray leaf spot, (**d**) red spider, (**e**) maize borer, (**f**) armyworm, (**g**) aphids, (**h**) peach borer.  
The dataset was annotated using the LabelImg image annotation tool, which generates an XML file containing object information that is saved in PASCAL VOC format along with the corresponding image file. The dataset was divided into an 80% training set, a 10% verification set, and a 10% test set. The original image was cropped and scaled by affine transformation to 512 \(\times\) 512 pixels as input to the model. The distribution of the data samples is presented in Table 1.  
CenterNet model is an anchor-free box detection algorithm proposed by [PERSON] et al. [11], which takes the object as a key point to estimate and then returns to other object attributes. First, the CenterNet model generates a feature map of the input RGB image through a feature extraction network and then sends the feature map to a detection network. The detection network consists of three branches that predict the heatmap of the object, the offset of the center point, and the width and height of the object.
## 5 Discussion  
Underwater is an unattended area such that battery replacement is costly. If an underwater network can be managed with only a subset of active nodes, we can extend the average battery life of the nodes. The number of required active nodes for an application varies depending on the type of applications. Thus, executing a specific application can be considered a requirement. Instead of initializing all the nodes in the underwater network, it would be more efficient to initialize as many nodes as required for an application. For this purpose, we proposed the application-based partial initialization (API) protocol in this paper. To the best of our knowledge, the API is the first initialization-only protocol that initializes only a portion of the nodes according to applications, and repeats the same initialization process until the application requirement is satisfied by considering unreliable underwater acoustic channel conditions. To apply the API for a UWASN, we also investigated its feasibility via simulations. We checked the similarity of data statistics between the API and the conventional FI (full initialization). The results showed that the similarity fluctuates in the beginning but saturates to 90% as the data accumulates. In addition, we analyzed the performance of the API and compared it to that of the FI. As a result, the initialization time and message overhead performances are noticeably improved when the FI is replaced by the API. The worse the channel is, and the smaller the application requirement (i.e., the required number of active nodes) is, the more remarkable the guaranteed performance superiority of the API is compared to the FI. Therefore, considering the bad channel conditions and high energy-efficient requirements of underwater networks, the API can be effectively applied to a UWASN since it is compatibly applied to any protocol. Moreover, the concept of the API can be employed to the IoUT where massive data sensing and gathering is necessary under energy-restricted underwater environments, and various applications require different levels of partial initialization.  
The authors collaborated upon developing the main idea of partial initialization and designing the protocol specified in this manuscript. For the analysis of the proposed protocol, [PERSON] mainly built the system level simulator, took simulation experiments, and investigated several performance measures. [PERSON] and [PERSON] also collaborated upon analyzing simulation results and writing this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from National R&D Project \"Development of the wide-area underwater mobile communication systems\" funded by the Ministry of Oceans and Fisheries, Korea (PMS4330).  
The authors declare that there is no conflict of interest.
## 1 Introduction  
There is a growing interest and demand for traditional Korean medicine (TKM), traditional Chinese medicine (TCM), and Kampo medicine (KM) worldwide, especially in East Asian countries. In general, they are composed of two or more different herbal medicines and have both multi-component and multi-target features. Because they are known to have few side effects, traditional medicines have the advantage in that they can be used widely to treat and prevent various diseases [1, 2, 3]. To manage the quality of TKM, TCM, and KM for their continued use, standardization studies are required.  
Insamyangpye-tang (ISYPT) is a traditional Korean medicinal formula comprised of 13 different types of herbal medicines (_Bupleurum fulatum_ L., _Morus alba_ L., _Poria cocos_ Wolf, _Schisandra chinensis_ Baill., _Fritillaria thunbergi_ Miq., _Prunus armeniaca_ L., _Poncirus trifoliate_ Rafin., _Platycodon grandiforum_ A.DC., _Panax ginseng_ C.A.Mey., _Equus asimus_ L., _Glycyrrhiz valensis_ Fisch., _Zingiber officinal_ Rosc., _Ziziphus jujuju_ Mill.). ISYPT is an herbal prescription representative of those used to treat lung-related diseases, such as pulmonary tuberculosis and bronchitis [4].  
The main constituents of herbal medicines comprising ISYPT are triterpene saponins, saikosaponin A, C, and D from _B. falcatum_[5]; stilbenoids, mulberroside A, and flavones, kuvuromon G and morusin from _M. alba_[6]; landserma-type triterpenoids, pachymic acid from _P. cocos_[7]; lignans, schiarandol A and gomisin A from _S. chinensis_[8]; alkaloids, peimine and peiminine from _F. thunbergi_[9]; cyanogenic glycoside compounds, amygdala from _P. armeniaca_[10]; flavonoid glucosides, naringin, didymin, and poncir  
In the present study, for the first time to our knowledge, a method of analysis for quality control of ISYPT to assure consistency was developed using a high-performance liquid chromatography-photodiode array detection (HPLC-PDA) system for simultaneous measurement of eight marker compounds. The eight marker compounds selected were mulberroside A (MBRSA) as found in _M. alba_, liquirtiti apioside (LQRTA), liquirtioside (LQRTS), and glycyrrhizic acid (GCRZA) as found in _G. uralensis_, naringin (NRG), didymin (DDM), and poncirin (PCR) as found in _P. trifoliate_, and schizandrol A (SZDRA) as found in _S. chinensis_.
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 4 MDRM-Based Reliability Analyses
### The Procedure of Reliability Analysis for Slope  
The reliability index and probability failure of the slope was determined through combining MDRM and FOSM. MDRM is adopted to approximate the high dimensional and implicit limit state function. FOSM is used to estimate the reliability index and probability failure of slope. In this process, the Gauss quadrature scheme was used to build the input integration grid and compute the statistics moment. The procedure was shown in Figure 1 and is explained below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Gauss Rule** & \(\mathbf{k}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) \\ \hline \multirow{2}{*}{Gauss-Legendre} & \(w_{k}\) & 0.2369 & 0.47863 & 0.5689 & 0.4786 & 0.2369 \\  & \(x_{k}\) & \(-0.9062\) & \(-0.5385\) & 0 & 0.5385 & 0.9062 \\ \hline \multirow{2}{*}{Gauss-Hermite} & \(w_{k}\) & \(1.13\times 10^{-2}\) & 0.2221 & 0.5333 & 0.2221 & \(1.13\times 10^{-2}\) \\  & \(x_{k}\) & \(-2.8570\) & \(-1.3556\) & 0 & 1.3556 & 2.8570 \\ \hline \multirow{2}{*}{Gauss-Laguerre} & \(w_{k}\) & 0.5218 & 0.39867 & \(7.59\times 10^{-2}\) & \(3.61\times 10^{-3}\) & \(2.34\times 10^{-5}\) \\  & \(x_{k}\) & 0.2636 & 1.4134 & 3.5864 & 7.0858 & 12.641 \\ \hline \end{tabular}
\end{table}
Table 1: Some commonly used Gauss points and its weight for Gauss quadrature.  
Step 1: Collect the slope engineering information, such as geological conditions, project scale, geomaterial mechanical parameters, in situ stress, etc.; determine the numerical or analytical model of slope stability analysis.  
Step 2: According to the information collected in Step 1, determine the random variables and their statistics property.  
Step 3: Construct the input integration grid for MDRM based on Gauss point and compute the corresponding safety factor of a slope using a numerical or analytical method.  
Step 4: Compute the statistics moment of safety factor based on the MDRM model.  
Step 5: Determine the reliability index and probability failure of a slope using FOSM.  
The above procedure includes slope stability analysis (computing slope safety factor, determining the input grid, constructing MDRM model, computing moment based on MDRM and computing the reliability index. Obviously, input grid can be easily obtained based on Table 1. Then, the MDRM and moment can be obtained through repeating slope stability analysis. Finally, Equation (16) was used to determine the reliability index. The proposed method is perfectly consistent with slope stability analysis in practice.
###### Abstract  
There is a growing interest in the security of underwater communication with the increasing demand for undersea exploration. In view of the complex composition and special optical properties of seawater, this paper deals with a performance analysis for continuous-variable quantum key distribution (CVQKD) over an underwater link. In particular, we focus on analyzing the channel transmittance and detection efficiency based on Monte Carlo simulation for different water types, link distances and transceiver parameters. A comparison between the transmittance obtained by simple Beer's law and Monte Carlo simulation reveals that the transmittance of underwater link may be severely underestimated in the previous underwater CVQKD research. The effect of the receiver aperture and field of view (FOV) on detection efficiency under different water types is further evaluated based on Monte Carlo. Simulation results show that the transmission distance of the underwater CVQKD system obtained by Monte Carlo simulation in pure sea water, clear ocean water and coastal ocean water is larger than that obtained by Beer's law, while the key rate of the system in all types of water is smaller than that obtained by Beer's law because the size and FOV of the receiver aperture are taken into account. By considering the practical system parameters, this paper establishes a comprehensive model for evaluating the security of underwater CVQKD systems with different system configurations.  
continuous variable quantum key distribution; underwater channel; Monte Carlo
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 4 Processing of Small Berries and Their Effects on Bioactive Compounds
### Pasteurization
Cold atmospheric-pressure plasma is a low-cost emerging non-thermal process that can be successfully used to pasteurize fruit and vegetable juices. Although cold plasma has a negative effect on the most bioactive compounds of fruit and vegetable juices, due to reactive species generated during cold plasma treatment, the content of some phytochemicals (e.g., anthocyanins and flavonoids) is dependent on the treatment time and oxygen concentration [107]. For example, in blueberry juice, the content of anthocyanins, antioxidant activity, and vitamin C decreased with increasing exposure to cold plasma treatment and oxygen concentration, while the content of phenolic compounds increased [108]. In chokeberry juice, the samples treated with cold plasma samples presented a similar content of anthocyanins to untreated juice, while the level of polyphenols increased by about 9% with respect to the control sample [109]. Reactive species and ultraviolet photons, induced during cold plasma treatment, caused breakage of covalent bonds and induced chemical reactions that resulted in the breakdown of cell membranes and further release of phenolic compounds [109].
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 3 Stratifying Privacy-Preserving Solutions
### Access Control and Authorization  
Access control and authorization mechanisms control data access and ensure that only authorized entities can retrieve and manipulate patient data. These mechanisms focus on controlling and managing permissions and privileges to protect sensitive information. These access control mechanisms include the following:  
1. Role-Based Access Control (RBAC): RBAC assigns access permissions based on predefined roles and responsibilities. It ensures that users access data and system functionalities based on their assigned roles, reducing the risk of unauthorized access [15].
2. Attribute-Based Access Control (ABAC): Taking into consideration many factors, such as the user's characteristics, external elements, and environmental conditions, ABAC permits the user access based on their attributes and policy rules.
3. Consent Management Systems: These provide mechanisms for patients to control their data, as they assist in collecting, acquiring, managing, using, and sharing data, delegating the responsibility to patients to make knowledgeable decisions [16].
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 2 Method
### Development of ANN Model Architecture
#### 2.2.3 Determination of Modeling Cycle Number  
Since ANNs randomly split the data into training and testing sets during the modeling process, each cycle can result in different data partitions. As a result, each cycle may generate a unique model that fits the training data well but produces varying error values when tested on the test data. Therefore, it is crucial to determine how many modeling cycles are needed for the results to converge to a \"typical\" level of reliability. To investigate the impact of different data partitions on ANN accuracy, various numbers of modeling cycles were tested in this study to ensure model error convergence.  
The ANN model was trained with varying cycle counts, ranging from 10 to 100. The model error for each cycle count, along with the 95% confidence interval, was calculated, as shown in Figure 8. The results indicated that the model error for both training and test data converged at 70 modeling cycles. Therefore, a minimum of 70 modeling cycles is required to achieve reliable and consistent results in ANN model training.  
Figure 8: ANN Model error for samples covering 90% of the box dimensions commonly used in the industry.  
Figure 7: Model error (loss) of running extra epochs of BCS prediction for one modeling cycle with real data (395 data points).  
A summary of the ANN architecture parameters, including their selection rationale and optimal values, is presented in Table 2.
## 3 Modeling of Path Planning  
The conventional path planning method mainly relies on discretizing the environment and using the A* and other search algorithms to obtain the path between the start point and the end point. This obtained path is the absolute shortest path at the high resolution of the environment map. Although the path obtained by the A* and other search algorithms is the absolute shortest path, either it is not necessarily a smooth path due to the low-resolution environment map, or those methods would consume much time to search in the high-resolution map. It is difficult to track the unsmooth path for AUVs due to its ontology structure design. In recent years, many scholars have begun to study the path planning method that satisfies the requirements of the smoothness, and the shortest path simultaneously. In this section, the A* algorithm was first used to find the global path, and then modeled the local path planning based on the global path planning, considering the time, turning radius, obstacles, and ocean current constraints. The authors provided a detailed description of the problem mathematical definition and the optimization criteria for the local path planning.
## 3 Results
### Evaluation of ORCHIDEE iWUE* and LUE Average
#### 3.1.2 Average LUE during 1982-2010  
The spatial pattern of LUE is similar to that of GPP (data not shown). Maximum LUE values are found in the central African forest where the PAR is lower (because of clouds), whereas low LUE values are observed in Sahelian and African southern zones where PAR is high. The MTE-LUE average over grasslands and savannas is less than \(0.7\,\mathrm{gC}\cdot\mathrm{M}\mathrm{J}^{-1}\cdot\mathrm{APAR}\), with a maximum value of \(2.5\,\mathrm{gC}\cdot\mathrm{M}\mathrm{J}^{-1}\cdot\mathrm{APAR}\) in some grid cells. In the central African forest, average MTE-LUE reaches up to \(1.3\,\mathrm{gC}\cdot\mathrm{M}\mathrm{J}^{-1}\cdot\mathrm{m}^{-2}\).  
Figure 1: 1982–2010 iWUE* average (gC \(\cdot\mathrm{hPa}\cdot\mathrm{mm}^{-1}\cdot\mathrm{m}^{-2}\)). (**A**) iWUE* calculated from MTE-GPP and ET products and WFDEI climate forcing data; (**B**) iWUE* simulated with the ORCHIDEE 11-layer version; (**C**) with ORCHIDEE 2-layer version  
APAR (maximum value \(2.6\;\mathrm{gC\cdot MJ^{-1}\cdot APAR}\)). The ORCHIDEE-simulated LUE matches the MTE-LUE better in the 11-layer version than the 2-layer version. The later overestimates LUE across the entire continent (Figure 2C). Taking grasslands as an example, the 11-layer version estimates LUE around \(0.73\;\mathrm{gC\cdot MJ^{-1}\cdot APAR}\) against \(0.9\;\mathrm{gC\cdot MJ^{-1}\cdot APAR}\) for the 2-layer, and \(0.7\;\mathrm{gC\cdot MJ^{-1}\cdot APAR}\) for the MTE-LUE.
## 1 Introduction
### Contributions  
This paper proposes a method to gather information from a given environment and find an energy-saving mission trajectory for the HAUV to cover this target space. The core contributions of this work are:  
1. Presenting a framework of multidomain path planning for HAUVs, including information clustering, path planning trajectory generation, and unknown obstacle avoidance in static environments and environments with current fields.
2. Addressing a heuristic algorithm with a weighted edge for multidomain path planning based on general large neighborhood searching and k-means.
3. Presenting a trajectory generation method combining B-spline with KD tree. The output trajectory is ensured to be smooth and safe, and conform to different constraints, underwater or aerial.
4. Presenting a method to avoid random unknown obstacles based on the scapegoat tree and KD tree.  
The remainder of this paper is organized as follows: Section 2 formulates the mathematical models and presents the theory of path planning, replanning, and trajectory generation methods. Section 3 gives a detailed description of the path planning and replanning algorithm. In Section 4, the trajectory generation scheme is outlined. The experimental results and discussion is proposed in Section 5. Finally, in Section 6 our findings are concluded, and avenues of future work are presented. The mathematical symbols denoted in this paper are as summarized in Table 1.
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 5 Exploratory Data Analysis
### Model Evaluation  
The most popular performance indicators were used in this study to evaluate the effectiveness of the various strategies. These include the F1 score, precision, recall, and confusion matrix. The confusion matrix for classification issues consists of the four scenarios depicted in Table 4: true (T.P.) positive rate, true negative (T.N.) rate, false positive (F.P.), and false negatives (F.N.) rate. The accuracy of a sample's classification or prediction is measured as a percentage of all samples. Equation (10) is used to calculate the metric sensitivity. Similar calculations can be made for other performance metrics such as accuracy, F-measure, and specificity using Equations (9)-(13).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[Recall/Sensitivity=\frac{TP}{TP+FN} \tag{10}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{11}\]  
\[Precision=\frac{TP}{TP+FP} \tag{12}\]  
\[F-Score=\frac{Precision*Recall}{Precision+Recall} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Prediction Condition**} \\ \hline
**Actual Conditions** & **Positive** & **Negative** \\ \hline Positive & True Positives (T.P.s) & False Negatives (F.N.s) \\ Negative & False Positives (F.P.s) & True Negatives (T.N.s) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for evaluating model’s performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Model** & **Parameters** & **Range** & **Best Values** \\ \hline \multirow{6}{*}{Logistic Regression} & penalty & \{11, 12, elasticnet, none\} & L2 \\  & C & Default 1.0 & 1.38 \\  & Solver & \{’newton-cg’, ’bfgs’, \\  & multi\_class & ’liblinear’, ’sag’, ’saga’\} & Lbfgs \\  & multi\_class & \{’auto’, ’ovr’, ’multinomial’\} & ovr \\ \hline Random Forest (R.F.) & n\_estimators & \{0, 75, 100, 200, 300\} & 75 \\  & max\_features & \{’auto’, ’sqrt’, ’log2’\} & auto \\  & max\_depth & \{5, 10, 15, 20, 25\} & 5 \\  & criterion & \{’gini’, ’entropy’\} & ’gini’ \\ \hline XGBoost & n\_estimators & \{50, 75, 100, 200, 300\} & 100 \\  & learning\_rate & \{0.3, 0.01, 0.01, 0.05\} & 0.3 \\  & max\_depth & \{5, 10, 15, 20, 25\} & 10 \\  & gamma & \{0.5, 1, 1.5, 2\} & 1 \\  & booster & \{’gbtree’, ’gblinear’\} & ’gbtree’ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized hyperparameters for ML classifier models.