esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The very important part of this study was selecting optimal hyperparameters for utilized models. Below the results of grid search for hyperparameter optimization across four models are presented. For XGBoost, the optimized parameters were:  
1. Sampling predictors (colsample_bytree): 0.8
2. Sampling observations (subsample): 0.6
3. The learning rate (learning_rate): 0.1
4. The maximum depth of a tree (max_depth): 3
5. The number of trees (n_estimators): 200
6. Minimum loss reduction required to make a further partition on a leaf node of the tree (gamma): 0.3
7. Regularization parameter (reg_alpha and reg_lambda): 0 and 2 For Random Forest:  
1. The number of trees (n_estimators): 30
2. Maximum depth of each tree (max_depth): 30
3. Minimum number of samples that must be present in a leaf node (min_samples_leaf): 1
4. Minimum number of samples required to split an internal node (min_samples_split): 10
5. Whether to bootstrap samples or not (bootstrap): True  
For Support Vector Machine:  
1. The regularization strength (C): 1
2. Decision boundaries (gamma): 1
3. The type of kernel function (kernel): linear  
For CatBoost:  
1. The maximum depth of the individual decision trees (depth): 4
2. The number of boosting iterations (iterations): 100
3. Regularization term that prevents overfitting (l2_leaf_reg): 7
4. The step size for updating the model during each iteration (learning_rate): 0.05  
The hyperparameters defined for the designed neural network were:  
1. Learning rate (lr): 0.001
2. Optimizer (optimizer_fn): Adam
3. Loss function: binary crossentropy
4. batch size: 32  
and for TabNet:  
1. Learning rate (lr): 0.02
2. Optimizer (optimizer_fn): Adam
3. batch size: 32
4. Width of the decision prediction layer (n_d): 8
5. Width of the attention embedding for each mask (n_a): 8
6. Number of steps (n_steps): 3  
The selected classification models were evaluated using standard performance metrics such as accuracy, recall, precision, and F1-Score. The results of the evaluation of CatBoost, XGBoost, SVM, Random Forest, and neural networks are presented in Table 1. The evaluation of ensemble models using voting is presented in Table 2, while Table 3 presents the  
In the case of machine-learning algorithms implemented for disease classification, it is desired to have a deeper insight on their performance among selected groups of patients. In this study, the results of ML is compared with the DLCN score that allows the assigning of patients into four groups based on the likelihood of FH. Moreover, a DLCN score above 5 is a reliable threshold for recommending genetic testing [31, 32]. In the dataset used in this study, all patients had DNA results towards FH regardless of the DLCN score. Therefore, for comparison, the diagnostic accuracy of the DLCN score was assessed by calculating the ratio of patients with a score above 5 to those confirmed with FH through genetic testing. Moreover, the selected model performance was evaluated in consideration of the FH likelihood groups based on the DLCN score. This evaluation is presented in Table 4.
# Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
## 1 Introduction  
In an era marked by rapid advancements in robotics and autonomous systems, the impact of these technologies on various sectors is evident [1, 2]. One domain undergoing significant transformation due to these innovations is the modern warehouse. With the integration of robotics, Internet of things (IoT) devices, and real-time data analytics, warehouses are evolving into efficient, data-driven entities [3, 4]. Specifically, the deployment of UGVs is revolutionizing how warehouses operate.  
However, as warehouses scale in both size and technological complexity, new challenges emerge. Two primary issues dominate current discussions: the efficient navigation and task allocation for UGVs [5, 6, 7, 8] and the strategic placement of power stations and batteries to support their continuous operation [9]. An inappropriately placed power station can create workflow disruptions, resulting in decreased UGV operational times and overall inefficiency.  
This study addressed the intertwined challenges of UGV task allocation and battery placement in the warehouse setting. Our contributions are as follows:  
1. A novel approach to improve UGV operations within modern warehouses by strategically placing batteries and power stations.
2. Extensive simulations in realistic warehouse settings to validate our approach.  
By focusing on these aspects, we aimed to significantly reduce the time UGVs spend traveling and performing associated sub-operations in warehouses. We validated our approach using extensive simulations set in warehouse environments with real-world characteristics.  
Regarding the structure of this paper, Section 2 delivers a review of relevant studies that resonate with our core theme. The essence of our proposed algorithm is given in Section 3. Section 4 dives deep into the empirical evidence, elucidating our evaluation methodology. Section 5 explores the intricate challenges of modern warehouse management and the drawbacks of approximate cellular decomposition (ACD) [10] and emphasizes the complexities and hurdles in achieving optimization. Lastly, Section 6 presents a recapitulation of our discoveries, culminating with forward-thinking conclusions.
## 5 Conclusions  
In conclusion, despite the limited certainty of the data, dental caries risk was considerably increased by low serum vitamin D levels. The risk of dental caries decreased linearly with an increase in vitamin D concentration, and this association was dose-dependent. A 10 nmol/L increase of 25[OH] D levels was significantly associated with a 3% lower risk of dental caries, according to the linear dose-response analysis. Hence, clinically, serum vitamin D level and dental caries status can be seen as markers and predictors for each other, especially in high-risk groups. It is suggested that high-quality studies, e.g., controlled trials or prospective cohort studies with large sample sizes could be an appropriate way to design future research and produce reliable results.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13179883/s1](https://www.mdpi.com/article/10.3390/app13179883/s1)]([https://www.mdpi.com/article/10.3390/app13179883/s1](https://www.mdpi.com/article/10.3390/app13179883/s1)), Table S1: Search strategy, last updated April 2023; Table S2: List of excluded articles; Table S3: Quality Assessment of Included Articles (Modified Newcastle-Ottawa Quality Assessment Scale); Table S4: The GRADE quality of evidence; Table S5: The association of different vitamin D levels on dental caries forms the nonlinear dose-response meta-analysis (odds ratio and 95% confidence interval).  
[PERSON] and [PERSON] contributed to study design, literature search, screening of manuscripts, and drafting the paper. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] assessed risk of bias and quality of evidence. [PERSON] and [PERSON] commented on the manuscript, and revised the study. All authors were involved in the final approval version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 6: Possible biological mechanisms of vitamin D in caries prevention: A. Enhances absorption of calcium and phosphorus from the intestine. B. Regulates the deposition of adequate amount of calcium and phosphorus in the tooth structure during development. C. Helps remineralization of tooth surface after demineralization. D. Antimicrobial effects against cariogenic bacteria.
# Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach
## 2 Methodology
### Methods  
#### 2.3.1 Crash Spatial Aggregation Algorithm  
To find the RCHC, we should aggregate crashes in roads as the count of traffic crashes on the road. First, we add a fields (<crash_count>, <crashlist >) of the road DBF table. Second, we calculate the attributes (<crash_count>, <crashlist>) for each road. To determine whether the crash is on a road, the following two premises shall be considered.  
1. As the accuracy of crash global positioning system (GPS) coordinate has a positioning error of approximately 10 m [34]. A crash occurs on the road if its coordinates are within 10 m of the buffer of the linear road considering the positioning error of crash.
2. As the Interstate Highway standards for the U.S. Interstate Highway System use a 12 foot (3.7 m) standard lane width [35], a crash occurring on the road can be determined if its coordinates are within 3.7 m of the buffer of the road.  
Under above premises, we can determine the crash is on the road if the geometric and attribute relationships between crash and road meet both conditions 1 and 2 (means geometric and attribute integrated matching). If the crash cannot match any road by geometric and attribute integrated matching, the crash can be spatially aggregated to the road meet both conditions 3 and 4 (means spatial fuzzy matching).  
1. Condition 1: The shortest distance between traffic crash and road less than 47 m (consider 10 lane roads and GPS positioning accuracy) order by the distance.
2. Condition 2: Attribute matching between name of road and location of crash.
3. Condition 3: The shortest distance between traffic crash and road is less than 10 m (consider GPS positioning accuracy).
4. Condition 4: The shortest distance between traffic crash and road is minimum in all datasets.  
We use qt platform to realize the crash spatial aggregation algorithm in PostGIS/PostGreSQL database following the conditions. Figure 2 shows the definition of main data types. Figure 3 shows the main structure of the algorithm.  
Figure 2: Definition of main data types.
# Effect of Temperature, pH, and Reaction Duration on Microbially Induced Calcite Precipitation
## 4 Conclusions  
In this study, the effects of temperature, pH of medium, and the reaction duration on calcite precipitation were evaluated for two microbial species (_S. saprophyticus_ and _S. pasteurii_). The amount of precipitated calcite was quantified indirectly by measuring the pressure of CO2 gas, which is generated as a result of the reaction between calcite and HCl. The following conclusions were drawn from the test results:  
1. The optimal conditions for calcite precipitation for both the species were identical: the optimum pH of the urea-CaCl2 medium was 7 and the optimum temperature during precipitation was 30 \({}^{\circ}\)C. The precipitation of _S. saprophyticus_ was five times more than that of _S. pasteurii_ under identical conditions.
2. Both microbial species were strongly influenced by the temperature of the environment during precipitation, showing a significant decrease with varying temperatures. The least precipitation was measured at a temperature of 50 \({}^{\circ}\)C, which was as little as 30-40% of that seen at the optimal temperature (30 \({}^{\circ}\)C).
3. _S. saprophyticus_ was found to be less sensitive to a change in pH of the urea-CaCl2 medium than _S. pasteurii_; the decrease in precipitation by varying the pH was less than 20% for _S. saprophyticus_. On the other hand, pH significantly affected the precipitation of _S. pasteurii_, reducing it by 60%.
4. For _S. saprophyticus_, a meaningful increase in precipitation was observed between the reaction durations of 2 and 3 days, but little difference was measured for longer durations, indicating that maximum precipitation occurred within 3 days. Likewise, most of the precipitation occurred within 3 days for _S. pasteurii_.  
G.K. performed the laboratory experiments; J.K. analyzed testing results; H.Y. designed the study and prepared for the manuscript.  
National Research Foundation of Korea: 2016R1C1B2013478.  
This work was supported by National Research Foundation of Korea (NRF) funded by Ministry of Science, ICT & Future Planning (NRF-2016R1C1B2013478), and by 2017 Hongik University Research Fund.  
The authors declare no conflict of interest.
# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 5 Experimental Analysis
### Segmentation Experiment and Results
Image 1 (000019) was selected as an example, and the PSNR value of the image, \(k=8\), was 21.3771, which was significantly better than other comparison algorithms. The SSIM value of the image was 0.7333, which was also slightly better than other algorithms, indicating that the image distortion is low and ensures the image quality after segmentation. Under the premise of maintaining the segmentation effect, it can be seen from Figure 4 that the running time of the proposed scheme was shorter than that of other algorithms, especially in the case of a large number of thresholds. When \(k=8\), the algorithm segmentation running time was shortened from 3.7 s to 2.8 s, which decreased by more than 30%, which greatly improved the running speed. To verify the universality of the algorithm in this paper, we randomly selected 100 images from the PASCAL 2012 dataset for parameter comparison tests. The experimental results show that our method ia faster and more accurate than other algorithms in terms of PSNR, SSIM, FSIM, and runtime parameters. Experimental results show that the proposed method can improve the segmentation accuracy and segmentation efficiency. Tables 6 and 10 show the fitness values obtained by different algorithms under different thresholds. The segmented image is shown in Figures 6 and 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric Name** & **k** & **WOA** & **BOA** & **PSO** & **OURS** \\ \hline \multirow{8}{*}{PSNR} & 2 & 13.2343 & 14.7236 & 14.9630 & **15.8237** \\ \cline{2-6}  & 4 & 14.5273 & 14.9263 & 15.4328 & **15.9417** \\ \cline{2-6}  & 6 & 16.3862 & 16.9732 & 17.7237 & **19.6872** \\ \cline{2-6}  & 8 & 18.4687 & 19.2433 & 20.2934 & **21.6278** \\ \hline \multirow{8}{*}{SSIM} & 2 & 0.4327 & 0.4938 & 0.5019 & **0.5823** \\ \cline{2-6}  & 4 & 0.4825 & 0.5349 & 0.5835 & **0.6132** \\ \cline{2-6}  & 6 & 0.5926 & 0.6419 & 0.6723 & **0.6835** \\ \cline{2-6}  & 8 & 0.6324 & 0.6638 & 0.6924 & **0.7247** \\ \hline \multirow{8}{*}{FSIM} & 2 & 0.6842 & 0.7154 & 0.7369 & **0.7437** \\ \cline{2-6}  & 4 & 0.7147 & 0.7519 & 0.7830 & **0.8039** \\ \cline{2-6}  & 6 & 0.7422 & 0.7751 & 0.7984 & **0.8241** \\ \cline{2-6}  & 8 & 0.7645 & 0.7957 & 0.8226 & **0.8469** \\ \hline \multirow{8}{*}{Running Time} & 2 & 1.35 & 1.35 & 1.35 & **1.28** \\ \cline{2-6}  & 4 & 1.64 & 1.65 & 1.64 & **1.52** \\ \cline{1-1} \cline{2-6}  & 6 & 2.85 & 3.12 & 2.97 & **2.25** \\ \cline{1-1} \cline{2-6}  & 8 & 3.82 & 4.83 & 3.94 & **2.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average of 100 image segmentation results. Figure 4: Algorithm running time (Images a–f in Figure 3). Figure 5: Algorithm running time (Images g–l in Figure 3). Figure 6: When threshold \(k=8\), the original image and the segmented image of different algorithms are shown (images a–\(t\) in Figure 3). Figure 7: When threshold \(k=8\), the original image and the segmented image of different algorithms are shown (images g-l in Figure 3).
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 3 Fractal Reflectarray Antennas
### Fractal Reflectarray Elements with Multifunctional Operation
As shown in [50], the reflectarray cell offers several advantages, including a simpler and thinner structure and smaller unit cell sizes at both operating frequencies (0.49\(\mathrm{\SIUnitSymbolMicro}\) at 27 GHz and 0.58\(\mathrm{\SIUnitSymbolMicro}\) at 32 GHz), which preserves the ability to direct the main beam at wide scan angles. In [50], the full-phase variation was measured for both polarizations at both operating frequencies (Figure 13b). Additionally, a dual-band/dual-polarized prototype was designed and tested, demonstrating the unit cell's flexibility in providing arbitrary beam directions/shapes at each frequency for both polarizations (see Figure 13c).
## 6 Complexity Analysis  
In this section, we discuss the online computational requirements of the proposed scheme compared to the conventional solutions as well as ANN approach. We focus on the on-line computational demand since the training algorithms are offline-based, rendering the deployment phase as the critical stage for computation. SLM performs the highest amount of computations and generally requires \(U\) IFFT operations, \(U\) CM computations and \(U\) check operations for the selection of the sequence having the lowest CM value. For all other schemes, the time-domain OFDM signal is first generated through the application of an IFFT operation to the modulated data sequence, \(X[k]\)\(\forall\)\(k=1,2,\ldots,\eta N\), which typically requires \((N/2)log_{2}(N)\) complex multiplications and \(Nlog_{2}(N)\) complex additions.  
Each iteration of the ICF algorithm requires one clipping operation followed by an FFT operation to zero out the frequency-domain OoB radiations and finally an IFFT operation to convert back to the time domain. ICF thus requires \(L\) clipping operations as well as a total of \(2 LNlog_{2}(N)\) and \(LNlog_{2}(N)\) complex addition and multiplication operations, respectively. SCF processes the signal in a single iteration and requires one clipping operation and a total of \(N(2 log_{2}(N)+2)\) and \(N(log_{2}(N)+1)\) complex addition and multiplication operations, respectively.  
Each module of ANN performs \(2N\) real multiplications and \(2N\) real additions in each hidden layer as well as \(N\) real multiplications and \(N\) real additions in the output layer. Thus, ANN requires a total of \(10N\) real multiplications and \(10N\) real additions. The output layer as well as each hidden layer of the proposed SNN-CF scheme performs \(4N\) real multiplications and \(4N\) real additions, resulting in a total of \(12N\) real multiplications and \(12N\) real additions. Finally, unlike conventional approaches, neither ANN nor SNN-CF requires additional checking or clipping operations. Therefore, neural network-based approaches provide significantly less computation requirements in general, rendering the real-time operation of such schemes much more favorable.  
The computational cost of the investigated schemes are summarized in Table 1. The complexity of both ANN and the proposed SNN-CF schemes are based on computations in \(\mathbb{R}\), the set of real numbers. However, the remaining schemes operate in the complex domain. Since one complex addition is equivalent to two real additions and one complex multiplication is equivalent to four real multiplications and two real additions, the equivalent complexities of ANN and SNN-CF are \(\mathcal{O}(6.67N)\) and \(\mathcal{O}(8N)\), respectively. Although compared to ANN the proposed SNN-CF scheme requires a slight increase in computational cost within the same asymptotic bound on complexity, it can potentially boost the system performance in terms of PAPR reduction and better controlled OoB emissions while simultaneously providing a lower BER for high-order modulations.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 5 Experiments and Analysis of Results
### Experimental Results and Analysis
#### 5.3.3 Interpretability Analysis  
As the Hurst exponent provides a measure of predictability, the self-similarity of the time-series corresponding to different time steps varies. The larger the Hurst exponent, the more the value of mapping on the sigmoid function tends to 1. The specific forget and retention values of the LSTM are controlled by the current input and the implicit state of the previous step through the sigmoid function, and the forget gate is in the memory state. As shown in Figure 8 above, when the step size was 70, the output value of the forget gate was 0.46, which is the corresponding value in the figure, and the value of the sequence was 0.67. With the Hurst exponent was added before the linear transformation, the value after linear transformation was closer to 1 (which is the corresponding point in Figure 9). From the above, it can be seen that, when using this value to guide the model training before prediction, the FPK-Net model had the best prediction when the step size was 70. Therefore, we believe that, at this time, by incorporating prior knowledge, the LSTM can efficiently and meaningfully forget some input information from the previous step, and the network traffic sequence with larger exponents can be calculated before attempting to build the prediction model. In addition, one can also focus on sequences with large Hurst exponents, as network traffic with long time scales has self-similarities that can be regularly found, which can save time and effort while allowing for more accurate predictions.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model
### Different Hardware Support  
Since the model has an elaborate set of parameters, adapting the model to different hardware while maintaining an equal level of accuracy is a burdensome task. However, at the cost of a slight decrease in accuracy, the model can easily be simplified in order to apply it to different hardware. For example, one can set the duration of short states to zero (e.g., TxDataDelayStart and RxAckOffsetStart) and only update the states that have the most impact on consumption. Alternatively, the duration can be estimated instead of measured as most durations will be very similar to the ones presented in this article. Furthermore, the consumption of the CPU and radio does not have to be measured: these  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **CPU State** & **Radio State** \\ \hline TxDataOffsetStart & Active & Sleep \\ TxDataOffset & Sleep & Sleep \\ TxDataPrepare & Active & Idle \\ TxDataReaday & Sleep & Idle \\ TxDataDelayStart & Active & Idle \\ TxDataDelay & Sleep & TX \\ TxDataStart & Active & TX \\ TxData & Sleep & TX \\ RxAckOffsetStart & Active & Sleep \\ RxAckOffset & Sleep & Sleep \\ RxAckPrepare & Active & Idle \\ RxAckReadedy & Sleep & Idle \\ RxAckListenStart & Active & Idle \\ RxAckListen & Sleep & Listen \\ TxProc & Active & Sleep \\ Sleep & Sleep & Sleep \\ \hline \hline \end{tabular}
\end{table}
Table 7: States in a TxDataRxNodck slot.  
values can be found in the data sheet of the manufacturer. The resulting model will be slightly less accurate, but no or only a few additional measurements have to be made to use this model to simulate the charge drawn by other TSCH hardware.