esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Given its high redox potential and simultaneous generation of ferric coagulating species, K\({}_{2}\)FeO\({}_{4}\) was successfully applied to the treatment of dye wastewater. The OD removal efficiency reached 99.7% when 56.4 mg/L of K\({}_{2}\)FeO\({}_{4}\) was used for 15 min of contact time. The LGB removal efficiency was 99.0% using 70.5 mg/L of K\({}_{2}\)FeO\({}_{4}\) over 30 min of contact time. The optimized pH was found to be acidic (pH 3), while contact time had no significant effect on the removal efficiency of NaOCl. Moreover, K\({}_{2}\)FeO\({}_{4}\) exhibited a superior performance in OD and LGB removal, compared to NaOCl as Cl\({}_{2}\) and FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O as Fe (III).  
K\({}_{2}\)FeO\({}_{4}\) has the combined effect of oxidation and coagulation, which is useful and important for improving water quality. In comparison with the conventional two-step unit processes (oxidation and coagulation), K\({}_{2}\)FeO\({}_{4}\) is expected to produce a reduced footprint for wastewater treatment. K\({}_{2}\)FeO\({}_{4}\) treatment for azo dye removal has the potential to replace the conventional two-step process as a cost-effective method by synthesizing the K\({}_{2}\)FeO\({}_{4}\) salts on an industrial scale. The K\({}_{2}\)FeO\({}_{4}\) process has a possible application for wastewater treatment plants to treat dye-containing effluent on a real scale.  
Investigation, M.J., S.J.; Formal analysis, M.J.; Methodology, M.J.; Data curation, J.L.; Writing original draft, M.J., J.L.; Funding acquisition, S.J.; Supervision, A.J., S.J.; Writing-review& editing, A.J., S.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1A6A3A04004335), the Brain Korea 21 FOUR Project in the Education and Research Center for Infrastructure of Smart Ocean City (i-SOC Center), and Pusan National University Research Grant, 2019.  
Not applicable.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 6 Experimental Results  
### Method  
In the experiment, first we made the quadrotor take off manually, and once the target was in view, the flight mode was automatically switched to the position hold mode. The quadrotor will fly to a setpoint relative to the target original point and keep its position. Since the relative position between the target and quadrotor is used for position-based visual servoing, the quadrotor will keep flying to a setpoint during the target's movement.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
## 5 Conclusions  
In this study, a side metal structure was utilized to improve the gain of the source antenna used in metasurface applications. By employing a simple sidewall (SW) and side plate (SP) structure, the gain of the source antenna was enhanced by 4.63 dB. Electromagnetic simulations were conducted to analyze the gain variations corresponding to each structure. The proposed SP structure was implemented on the substrate used for fabricating the patch antenna, eliminating the need for additional complex structures. A metallic cross-type slot transmitarray was employed to verify the source antenna's suitability for metasurface applications. Although the experimental results were limited due to constraints in the testing environment, further validation is expected in future studies.  
In the study of transmitarray antennas, the profile of the entire structure is greatly affected by the source antenna. In this study, the gain of a patch antenna classified as low-gain was improved to increase its usability as a source antenna of a transmitarray antenna. Improving the gain of patch source antennas for metasurface studies is a significant endeavor, but further study is also essential for generating source waves with diverse beam patterns. Due to the nature of metasurfaces, there are demands in various applications for beam steering, multi-beam generation, and signal enhancement in specific directions. Therefore, continued research on the side metal structure proposed for the source antenna in this study could maximize metasurface performance and expand its applicability to various communication and radar systems.  
Conceptualization, S.P., J.L. and H.B.; methodology, J.L.; validation, H.B.; formal analysis, H.B.; investigation, H.B.; resources, S.P.; data curation, H.B.; writing--original draft preparation, H.B.; writing--review and editing, H.B.; visualization, H.B.; supervision, S.P.; project administration, S.P. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004), and This work was supported by the Soonchunhyang University Research Fund.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Authors declare no conflicts of interest.
# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 4 Method  
### Problem Formulation  
Semantic segmentation methods regard vehicle smoke detection as a pixel-wise prediction task, which usually takes expensive computational costs [26]. To overcome the inherent limitation of the segmentation method, we leverage a block-wise recognition strategy for vehicle smoke detection, which lies between semantic segmentation and object detection in granularity. Specifically, images were gridded into \(R\) rows and \(C\) columns to obtain \(R\times C\) image blocks and recognizing on block-wise. Compared with the segmentation method, the block-wise recognition strategy reduces computational costs significantly more than the segmentation strategy. Suppose an image with pixel size \(W\times H\), and \(X\) is the feature extracted through the model. The formulation of the segmentation method can be written as follows:  
\[P_{i,j,:}=f^{ij}(X),s.t.i\in[1,W],j\in[1,H] \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Type** & **Scenario** & **Total Samples** & **Annotation** & **Available** \\ \hline VSD [23] & image & wild & 24,217 & category & public \\ RF dataset [24] & image & wild & 12,620 & bounding boxes & public \\ Synthetic-Smoke [21] & image & - & 3000 & pixel-level & private \\ LaSSoV-video [25] & video & road vehicle & 163 & temporal segments & public \\ LaSSoV [25] & image & road vehicle & 75,000 & bounding boxes & public \\ \hline
**PoVSSeg**  & image & road vehicle & 3962 & polygon & public \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison between existing datasets and ours.  
Figure 3: Some samples from existing datasets.  
where \(f^{ij}\) is the classifier to predict the vehicle smoke at the position \((i,j)\), and the \(P_{i,j,:}\) are the corresponding probability vector. In a similar way, the block-wise prediction method can be formulated as follows:  
\[P_{i,j,:}=f^{ij}(X),s.t.i\in[1,C],j\in[1,R]. \tag{2}\]  
Therefore, the computation reduction ratio (\(r\)) can be expressed as:  
\[r=\frac{W\cdot H}{C\cdot R}. \tag{3}\]  
Taking the setting of the DB-Net as an example, the input size of the image is \(960\times 540\), and the size of the gridding blocks is \(60\times 34\). The computation reduction ratio approximately achieved 254.
## 5 Discussion  
To detect vessels, we introduced an approach that integrates four algorithms. The Hough transform algorithm detects accurately shapes (extraneous lines, circles, edges, etc.) and occlusions in an image. It takes into account multiple transformations that can slow down the detection process. The image is converted into a binary map by means of the OSTU-based adaptive threshold segmentation method.  
The weighted morphological filtering approach is very useful for binary images. It handles various shapes and patterns in image analysis. This algorithm is also good for edge detection and noise cancellation. However, it does not perform well on images with high noise, and it can smooth the edges too much, resulting in a loss of fine detail. This is confirmed by the results presented in Table 1. It can be seen that after filtering, there were still about 10 corrugated bands in the image. To solve these limitations, the neighbor-based adaptive fast median filtering method was used. This method preserves the edges and removes impulse noise from the image. It can be seen in Table 2 that after filtering, there were fewer than five corrugated bands in the image. However, it is less effective when noise takes up large parts of the image, and it can blur fine details and textures. The connected domain calculation based on moment features is used due to its high vessel tracking efficiency; it extracts and analyzes the connected components of an image. It provides useful statistical properties for shape analysis. However, it is susceptible to segmentation errors and noise in the image.  
Each algorithm has its advantages and limitations. Therefore, we believe that by combining different algorithms with different limitations and advantages, the resulting model can be more efficient.
## 1 Introduction
[24] used continuous glass fiber-reinforced PLA and investigated the effects of process parameters such as printing temperature, speed, layer height, and fiber volume fraction. The mechanical properties were explored using tensile and three-point bending tests. The experimental results showed that the mechanical properties of continuous glass fiber-reinforced PLA were better than those of pure PLA. When the fiber volume fraction was about 5.21% and 6.24%, the tensile and flexural strengths of the specimens were increased by 400% and 204%, respectively. In order to improve the quality of FFF parts, many research works have been carried out by researchers in the last decade. It has been confirmed that process parameters do have a significant impact on the quality of the manufactured product. However, there are many process parameters of FFF that affect the mechanical properties and dimensional accuracy of a part. In order to fully understand the influence of various processing parameters on the parts printed by using FFF technology, it is essential to select a suitable experimental design and analysis method to take full advantage of all parameter combinations, besides an extensive analysis process. In the past, there has been a lot of research on the effect of printing parameters on the surface roughness, dimensional accuracy, and mechanical properties of parts printed using FFF. Commonly used DOE significant experimental methods that can optimize process parameters are the Taguchi method [13; 15; 25] full factorial design [26; 27], analysis of variance [28], bacterial forging techniques [29], and fuzzy logic [30]. In this paper, an independent extrusion type 3D printer is selected. The Taguchi method was used to scientifically select the factors investigated, and then full factorial analysis was used to investigate the ultimate printing parameters of three factors: fiber printing temperature, fiber printing layer thickness, and fiber printing speed, as well as the effects of their interactions on the strength of the printed parts. The findings were verified again in supplementary tests, and the effect of fiber overlap was excluded. Finally, the interfacial morphology of printed parts with different layer thicknesses was investigated using ultra-deep field microscopy and specimen fracture forms.
## 5 Discussion
### Assessing the Results
Purely data-driven techniques can reveal unexpected patterns and adapt well to complex environments, but they often lack transparency and may pose difficulties for understanding the reasoning behind specific findings. Integrating both approaches can provide a solid interpretive foundation while also embracing complexity and flexibility. This combination ensures not only that patterns are recognized, but also that the insights are interpretable and supported by established theoretical underpinnings. As a manager, relying on a hybrid strategy increases confidence in the results, supports informed decision-making, and ultimately contributes to a more robust, practical understanding of the data's implications for strategic planning. If combining methods is not feasible, selecting a single approach would depend largely on the decision-making context and the specific goals at hand. If a manager needs to understand the underlying causes and justify decisions with a clear, theoretically grounded explanation, statistical methods would be the better choice. They provide a robust framework for inferring relationships, testing hypotheses, and ensuring that decision-makers can communicate the reasoning behind certain recommendations to stakeholders. This interpretability is often essential in environments where accountability and transparency are paramount. A practical choice might be a decision-tree-based ensemble method such as a random forest. This approach offers a balance between complexity and interpretability. Random forests are capable of handling large datasets, complex feature interactions, and nonlinear patterns. They typically do not require strict assumptions about the data distribution and tend to be robust against outliers and noise. While less transparent than simple statistical models, random forests still provide more interpretability than many other data mining methods such as deep neural networks. For example, feature importance rankings and partial dependence plots can help managers understand which factors are most influential, even if the model itself is relatively complex.
## 2 Methodology
### Average Suppression Temperature (T) and Dissociation Enthalpies (\(\Delta H_{\text{diss}}\))  
The average suppression temperatures (\(\overline{\text{T}}\)) of the DESs were calculated to determine the thermodynamic CO\({}_{2}\) hydrates suppression capability of the Deep Eutectic Solvents tested in this work. The following equation (1) was implemented to determine the suppression temperature (\(\Delta T\)), as used by [PERSON] et al. [15].  
\[T=\frac{\Delta T}{n}=\frac{\sum_{i=1}^{n}\left(T_{0,pi}-T_{1,pi}\right)}{n} \tag{1}\]  
where \(T_{0,pi}\) represents the equilibrium temperature of studied gas in a blank sample, while \(T_{1,pi}\) is the equilibrium temperature of CO\({}_{2}\) gas in the presence of DES. The values of both dissociation temperatures should be attained at the same pi. The \(n\) denotes the number of pressure points taken into consideration for the study, which was in the range of 2 to 4 MPa.  
The gas hydrate dissociation enthalpies denoted as \(\Delta\text{H}_{\text{diss}}\) could be determined by using the commonly used Clausius-Clapeyron equation, according to [PERSON] et al. [15]. The HLVE data obtained from experiments were plotted as \(\ln P\) against \(1/T\), and the slope was considered as the left-hand side of the Equation (2).  
\[\frac{d\ln P}{d\frac{1}{T}}=\frac{\Delta\text{H}_{\text{diss}}}{zR} \tag{2}\]  
where \(P\) and \(T\) denote the equilibrium pressure and temperature, respectively, \(R\) represents the universal gas constant. The z is the compressibility factor of the gas involved in the study, while \(\Delta\text{H}_{\text{diss}}\) signifies the enthalpy of gas hydrates dissociation. The average suppression temperature (\(\overline{\text{T}}\)) and dissociation enthalpies (\(\Delta\text{H}_{\text{diss}}\)) of the formed DESs were analyzed for further understanding of the gas inhibition mechanism.
# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 3 Proposed Scheme
### Bayesian Ridge Distribution Estimation  
We aim to improve the performance of the LOPUB algorithm by using perturbed Bloom filters as representative features of the data, similar to LOPUB. The central server receives samples with noise from a specific distribution, which can be estimated using linear regression \(M=\beta\mathbf{y}+\vartheta\). The candidate bit matrix \(M\) is created using Bloom filters, which guarantee that the features extracted by the central server will be the same as those extracted by the user. BRR Algorithm 2 unfolds through a sequence of steps. Initially, the central server acquires perturbed Bloom filters sent by users. Subsequently, the server undertakes the computation of the original count for each bit within \(k\) attributes, estimating the accurate count for each. Following this, a candidate bit matrix \(M\) is meticulously constructed for \(k\) attributes, as illustrated in Figure 1. The BRR model is then fitted, employing the Bloom filters \(M\) and the associated counts \(\mathbf{y}\). Finally, the probability of the target is determined by normalizing \(\beta\), the estimated coefficients derived from the regression model, through division by their sum. Algorithm 2 improves performance and privacy preservation by using perturbed Bloom filters as representative features and by estimating the distribution using BRR.  
Figure 2: Comparison of the posterior mode of \(\beta\) under a Gaussian prior (**Left**) and Laplacian prior (**Right**).
```
\(U_{j}\): \(j\)th attribute (\(1\leq j\leq k\)) \(\Omega_{j}\): domain of (\(1\leq j\leq k\)) \(g_{j}^{i}\): perturbed bloom filter (\(1\leq i\leq N\)) (\(1\leq j\leq k\)) \(f\): flipping probability \(\mathcal{H}\): set of hash functions for \(U_{j}\) D: a subset of attributes \(U_{j1},\ldots,U_{jk}\) \(P_{r}(A_{D})\): joint probability distribution of \(k\) attributes specified by \(D\) for each \(j\in D\)do  for each \(b=1,\ldots,m_{j}\)do  compute \(\hat{y}_{j}[b]=\sum_{i=1}^{N}\hat{s}_{j}^{i}[b]\)  compute \(y_{j}[b]=(\hat{y}_{j}[b]-fN/2)/(1-f)\) endfor \(\mathcal{H}_{j}(\Omega_{j})\) Bloom filters are constructed by the server using \(\mathcal{H}\) endfor \(\mathbf{y}=(y_{1}[1],\ldots,y_{1}[m_{1}]\ldots y_{k}[1],\ldots,y_{k}[m_{k}])\) \(M=(\mathcal{H}_{1}(\Omega_{1})\times\ldots\times\mathcal{H}_{k}(\Omega_{k}))\) Candidate bit matrix is created \(\boldsymbol{\beta}=\text{BayesianRidgeRegression}(M,\mathbf{y})\) return\(P_{r}(A_{D})=\boldsymbol{\beta}/\sum\boldsymbol{\beta}\)
```  
**Algorithm 2** Bayesian Ridge Regression \(k\)-way Joint Probability Distributions
## 2 Materials and Methods
### NIR Reflectance Spectral Acquisition  
NIR spectroscopy captures the absorption and scattering characteristics of biological tissues when exposed to near-infrared light. Absorption features are mainly attributed to chemical constituents such as water, sugars, and proteins, while scattering is influenced by tissue density, cell structure, and surface morphology [19,20]. Compared with the transmission mode, the reflectance mode is more sensitive to changes in the surface and subsurface layers, which makes it suitable for detecting mechanical bruising in fruits [21,22]. Therefore, the reflectance mode was selected in this study to enhance the sensitivity to bruise-induced surface and near-surface tissue variations.  
Figure 1: Custom-designed prune impact damage device, impact process, and representation of bruised samples. The red “\(\times\)” marks indicate the locations where the bruises were artificially induced.  
In this study, a reflectance-mode Vis-NIR spectral acquisition system was constructed, as illustrated in Figure 2. The system consists of a PG2000-Pro back-thinned spectrometer (Ideaoptics, Shanghai, China; wavelength range: 367-1052 nm; SNR: 800:1), a 100 W HL-100 halogen light source (Shanghai Ideaoptics), and a Y-type bifurcated fiber optic probe (Guangzhou Ruike Photoelectric Technology Co., Ltd., Guangzhou, China). The illumination path was arranged such that the light passed through the Y-probe and illuminated the prune sample within a light-shielding box. The reflected signal was collected by the second branch of the fiber probe and transmitted to the spectrometer. All data were recorded using Morpho 3.2 software.  
To stabilize sample positioning and reduce variability, each prune was placed in a custom fruit holder made of high-density foam, shaped to fit the fruit's curvature. Preliminary experiments were conducted using a control-variable approach to determine the optimal acquisition parameters by minimizing spectral area variation and standard deviation across multiple test points, as recommended in prior optical system optimization studies [23,24]. Based on these trials, the finalized acquisition settings were the following:  
* Integration time: 100 ms;
* Smoothing level: 5;
* Fiber-sample distance: 18 cm (spot size slightly larger than the prune's cross section);
* Acquisition region: equatorial zone of the fruit.  
During formal acquisition, reflectance spectra were collected at two symmetrical points on the equatorial region of each fruit. For damaged samples, one spectrum was taken from the bruise site, and the other from the opposite side. For intact samples, two equatorial points 180\({}^{\circ}\) apart were selected to simulate real inspection conditions. The average of the two spectra was calculated and used as the representative spectrum for subsequent modeling.
# The Impact of Silver Nanoparticles on Detntinal Tubule Penetration of Endodontic Bioceramic Sealer
## 2 Material and Methods
### The Characterization Process of AgNPs  
The analysis of AgNPs was carried out using scanning electron microscopy (SEM) (JEOL, JSM-7610F Schottky Field Emission, Hitachi, Ibaraki, Japan) and transmission electron microscopy (TEM) (JEOL, JEM, 1400 Plus, Hitachi, Ibaraki, Japan) to assess the particle size, morphology, distribution, and degree of agglomeration. The AgNPs powder, with a particle size of 20 nm, was obtained from Zhengzhou Dongyao Nano Materials Co., Ltd. (Zhengzhou, China). The same procedures were implemented for the BC sealer combined with AgNPs.
## 3 Results and Discussion
### Selection of the Crucial Operational Extraction Process Variables
Ethanol, on the other hand, has a relatively low environmental impact, is categorized as GRAS (generally recognized as safe), and is proved to be a good choice for extracting natural compounds from plant materials [63; 64]. Hence, in the case of the tested CS material, ethanol could be a more advantageous extraction solvent compared to methanol. Analyzing the effects of extraction process variables for ethanol (Figure 3), it can be seen that the most significant for the antioxidant activity and TPC in the CS extracts were the ratio of ethanol to CS matrix and the concentration of ethanol. The SMR was positive, while the solvent concentration had a negative effect on the extraction of antioxidant compounds. Both the extraction time and the use of ultrasounds were not statistically significant variables in the model. These results suggest that extended extraction time has a limited impact on the recovery of phenolic compounds from CS and most of these compounds are extracted at short times. Similar to this study, the extraction time was a less pronounced variable during the extraction of phenolic compounds from spent coffee grounds in the works of [PERSON] and [PERSON] [65]. Low impact of ultrasound assisted extraction could be explained by the possibility of the generation of highly reactive hydroxyl radicals, which could cause degradation or oxidation of phenolic compounds [66]. Therefore, in the next stage, it was decided to carry out the extraction for 30 min with the use of the laboratory shaker with water bath. Figure 2: Pareto charts showing the effects of the tested extraction factors during the first stage of experimental design (type and concentration of the used solvent, SMR, extraction time, and type of used extraction apparatus) on the antioxidant activity measured by the ABTS assay (**a**) and TPC determination (**b**).