esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Clustering, Classification, and Quantification of Coal Based on Machine Learning
### Clustering Models
#### 4.1.1 K-Means Clustering  
The K-means clustering divides all samples into K clusters only according to the LIBS spectra. K samples are randomly selected as K initial cluster centers, and then the distance between the first sample that needs to be assigned and each cluster center is calculated, and finally, the sample will be assigned to the closest cluster. Once the first sample is determined, each cluster center will be recalculated until the termination condition is met. The termination condition can be that all samples have been assigned to the cluster center, or the cluster center no longer changes.  
The key parameter of the K-means clustering is the K value (number of clusters). The K-means clustering is highly dependent on the initial cluster distribution, which may induce the calculation non-convergence. K values will be determined by the elbow method, and the silhouette coefficient mentioned in Section 3.5 can be used to evaluate the model performance.  
The elbow method calculates the sum of the distortion degree for each cluster, where the distortion degree is the sum of the squared distance from each sample point to the clustering center. If the distortion degree is small, the points in the cluster are close to each other; otherwise, the points will be more dispersed. As the K value increases, the number of samples contained in each cluster decreases, and the distortion degree for the cluster becomes smaller. However, when the K value increases to a certain extent, the distortion degree has no obvious improvement. Thus, the elbow point is where the slope suddenly slows down, and the K value can be considered the best parameter under this condition.  
The number of input variables of K-means clustering is up to two-dimensional features; thus, the 54 variables mentioned in Section 3.3 are reduced to 2 principal components by PCA. The results of the elbow method under different K values are shown in Figure 5a. When K is equal to 4, the slope of the curve slows down, which can be determined as the elbow point. Hence, coal can be divided into four clusters with totally different LIBS spectra as shown in Figure 5b.  
The results showed that the silhouette coefficient of the training set and the test set was 0.621, and for the prediction set was 0.663. The silhouette coefficient for a dataset is close to 1, which indicated that the results of K-means clustering were reasonable. K-means clustering will be able in the future to estimate the category and the proximate analysis data for an unknown sample based on the LIBS spectra and current database.
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 3 WSN Topology  
In this paper, a WSN for precision agriculture was used and a hierarchical topology was adopted for this WSN. It is composed of a set of balanced clusters. Each cluster is formed by a set of sensor nodes managed by a Cluster Head (CH). The sensor nodes collect and transmit data to the CHs in one hop. The CHs receive and process the data transmitted by the nodes, then transmit the processed data to the Base Station (BS) with a single hop. In general, clustering [41] has been proven to be an effective energy-saving method in WSNs.
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
## 3 Results
### Baseline and Experiment Energy Efficiencies  
The introduction of plants and increased thermal mass reduced the TW's overall efficiency, affecting both daytime absorption and nighttime dissipation (Table 5). Despite these reductions, the green TW remained a viable solution, even with year-round use.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compared**} & \multicolumn{3}{c}{**Daytime Yearly Averages**} & \multicolumn{3}{c}{**Nighttime Yearly Averages**} \\ \cline{2-7}  & **Temperature**, & **Relative** & **Absolute** & **Temperature**, & **Relative** & **Absolute** \\  & \({}^{\circ}\)**C** & **Humidity**, \%** & **Humidity**, \(\mathbf{g/m^{3}}\)** & \({}^{\circ}\)**C** & **Humidity**, \% & **Humidity**, \(\mathbf{g/m^{3}}\) \\ \hline Baseline year & & & & & & \\ (empty TW) & 35.79 & 29.66 & 10.04 & 18.41 & 50.84 & 8.93 \\ \hline Experiment year & & & & & & \\ (water+plants in TW) & 27.33 & 41.47 & 10.93 & 18.93 & 53.52 & 9.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Day and night average air temperatures and humidities of the Trombe wall.  
Figure 5: Trombe wall air temperatures during a cloudy day in July. The solid and dashed lines depict the measured and modeled temperatures during the baseline and experimental year (separated by color) days: 17 July 2021 and 18 July 2022. The dotted lines depict the outside temperatures recorded during the same day.. The areas in yellow and cyan show the solar radiation during both years for reference. Scatterplots in the corner depict the correlations between the model and the measurements with their 95% confidence ellipses.  
Dissipation efficiency was halved during the experimental year (EY), exhibiting seasonal variability due to changes in water and plant masses compared to the consistently high values observed during BY. Absorption efficiency, which was highest during autumn and winter in BY, declined more significantly under cooler conditions in EY. However, spring and summer absorption efficiencies remained relatively unchanged, indicating the potential for optimizing TW performance during the warmer months by incorporating plants during this period.  
Cloudy days consistently showed higher absorption efficiencies during both years, as generally, lower air temperatures allow the system to absorb the incoming radiations better throughout the day's duration.
## 3 Test Results and Analysis
### Test Analysis
#### 3.2.3 Analysis of Ultimate Flexural Bearing Capacity  
As shown in Figure 9, compared with the BFRP bar concrete beam L-0-0, addition of the bamboo fiber in the beam L-0-30 had little effect on the ultimate flexural bearing capacity, showing a small decrease. This is because the ultimate flexural bearing capacity of the test beam is determined by the crushing of the concrete in the compression zone and is affected by the compressive strength of the concrete. When the bamboo fiber is added into the plain concrete specimen, its compressive strength decreases slightly (as can be seen from \(f_{cu}\) in Table 5), so the ultimate flexural bearing capacity of the specimen decreases slightly. The flexural ultimate bearing capacity of beam L-1-0 was significantly improved when the steel wire mesh was in the whole beam section, and its ultimate flexural bearing capacity was increased by 16.8%. This is because the steel wire mesh binding was attached to the bottom side and the left and right sides of the rectangular section of the steel frame. The longitudinal steel wires on both sides have the same effect as the longitudinal compression steel bars, thereby enhancing the compressive bearing capacity of the cross section and further enhancing the specimen's ultimate flexural bearing capacity. However, when bamboo fiber and steel wire mesh are composite in concrete, the improvement of the ultimate flexural bearing capacity is still not ideal, and the reason is the same as the reason for the reduction of crack resistance under composite action.  
Figure 8: Comparison of test beam deformation under different loads.
## 5 Conclusions and Future Directions  
In the last years, there has been a growing interest in TDDSs, with a number of experimental trials especially dedicated to patients with neurodegenerative diseases. The initial results are encouraging and definitely demonstrate the safety of these delivery systems, thus opening the way for better therapeutic opportunities for AD and PD patients at later stages. However, research in the field has focused attention on the relevance of oral health in patients with neurodegenerative disease, as an essential condition to successfully approach novel TDDSs-based therapies. In fact, it seems clear that these patients may have poor oral hygiene with several active infectious foci and tooth loss because of the inability to perform usual daily activities, such as brushing their teeth. Accordingly, there is a need for early identification of these conditions--which seriously impact on the patient's prognosis [81; 82]--and for attempting adequate preventive strategies. Neurodegenerative patients should perform regular dental checkups and receive frequent reminders for professional hygiene. Moreover, the patient's caregivers should be appropriately trained [83; 84] but also dentists, social and health workers should be made aware of the importance of oral hygiene in these patients [85; 86], developing preventive and rehabilitative measures aimed at ensuring a good standard of living, early targeted interventions, adequate timing for dental care, and tailored treatment plans [87; 88; 89; 90]. All these aspects will be critical for the successful impact of the above-mentioned IntelliDrug and Orafuse systems. Moreover, it should be reminded that oral mucosa might represent a valuable source of disease biomarkers, as new studies suggest [91; 92; 93; 94].  
Conceptualization, M.B., P.G., P.B., T.S. and A.D.F.; Methodology, M.B., P.G., T.S. and A.D.F.; Investigation M.B., P.G. and A.T.; Data Curation, M.B., P.G. and A.T.; Writing--Original Draft Preparation M.B., P.G. and A.T.; Writing--Review & Editing M.B. and P.G; Supervision A.S., M.B., P.G., P.B., T.S., M.B. and P.G. have contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
_Article_  
**Application of Advanced Measuring Methods for**  
**Identification of Stresses and Deformations of**  
**Automotive Structures**  
**[PERSON] \({}^{1}\)\({}^{\bigoplus}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3,*}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{4}\)\({}^{\bigoplus}\)**  
\({}^{1}\) Department of Applied Mechanics and Mechanical Engineering, Faculty of Mechanical Engineering, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia; [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (P.L.)  
\({}^{2}\) Department of Biomedical Engineering and Measurement, Faculty of Mechanical Engineering, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia; [EMAIL_ADDRESS]  
\({}^{3}\) Department of Construction and Transport Engineering, Faculty of Mechanical Engineering, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia  
\({}^{4}\) Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia; [EMAIL_ADDRESS]  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +421-55-602-2360  
**Abstract:** In the automotive industry, 3D laser scanning is the most frequently used method to check the geometry of the shape and dimensions of a body in individual stages of production, where the scanned model is compared in a computer model. This procedure identifies excessive deformations around the roof antenna of an SUV. The manufacturer assumed that these deformations occurred during the antenna assembly process. An analysis of possible causes of deformation occurrence was performed based on the comparison of results obtained by numerical and experimental modelling. Experimental measurements using tensometry performed on a painted and unpainted roof structure showed the effect of paint reinforcement on deformations around the antenna. Based on the analysis of results obtained directly under operational load, it was found that the installation of the antenna was carried out on the already deformed roof plate and the installation process itself was not the primary cause of the identified excessive deformations. The presented results of deformation and stress analysis document deeper connections between the measured displacements of 3D scanning and the technological process of component production itself. The use of the achieved results should especially help designers, calculators, technologists as well as production quality controllers.  
**Keywords:** 3D scanning; stress analysis; tensometry; numerical modelling; residual stresses
## 3 Experimental Section
### Adsorption Experiments  
The pharmaceutical Sumetrolim(r) tablet, manufactured by Egiis Pharmaceuticals PLC, Hungary, contained 400 mg of sulfamethoxazole. An accurately weighed 0.753 g sulfamethoxazole tablet was transferred to 1000 mL volumetric flask and dissolved, to achieve a concentration of 3 mM/L. The solution was then diluted to obtain different solutions of sulfamethoxazole with the required initial concentrations (2.5 mM/L, 2 mM/L, and 1 mM/L, respectively).  
Tetracycline was purchased from Arena Group SA, Romania. A total of 1332 g of tetracycline was dissolved in 1 L of deionized water to prepare the tetracycline solution with an initial concentration of 3 mM/L, and was then diluted to prepare additional solutions with the required tetracycline concentrations (2.5 mM/L, 2 mM/L, and 1 mM/L, respectively).  
The influence of different parameters on adsorption of sulfamethoxazole and tetracycline onto the three synthesized polymeric adsorbents was studied. To study the effect of the contact time, initial concentration, and temperature on adsorption, the experiments were carried out with samples of 0.2 g adsorbent in 25 mL of 3 mM/L, 2.5 mM/L, 2 mM/L, and 1 mM/L sulfamethoxazole and tetracycline solution, respectively. The suspensions were shaken at 180 rpm for 24 h using a Julabo SW22 shaker (JULABO GmbH, [PERSON], Germany), at four different temperatures (298 K, 308 K, 313 K, and 318 K).  
The solutions were tested hourly for the first 8 h. Every hour, a sample of 2 mL of solution was analyzed to determine the antibiotic compound uptake, using UV spectrophotometry. To determine the residual concentrations of antibiotics, we used a Shimadzu UV mini 1240 UV-VIS spectrophotometer (Shimadzu, Duisburg, Germany). The measurements were conducted at wavelengths of 257 nm for sulfamethoxazole solutions and 357 nm for tetracycline solutions, respectively.
## 5 Discussion  
The experiments demonstrate that the proposed algorithm significantly enhances depth estimation efficiency, consuming less time compared to the [PERSON] and [PERSON] algorithms. This is primarily attributed to the application of GPU and parallel acceleration during the dense matching phase. In terms of depth quality, the algorithm improves depth pixel density and reduces the error pixel ratio and root mean square error by considering microlens blur features and geometric consistency checks. Furthermore, the application of blur feature transfer and prior knowledge of planar geometric structures further optimizes  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Our** & **Zeller** & **BLADE** & **Number of Laser Point Clouds** \\ \hline \multirow{3}{*}{HR260-Indoor} & **18.39\%** & 5.50\% & 1.16\% & 1,074,988 \\  & **7.88\%** & 5.38\% & 1.28\% & 1,959,513 \\  & **9.67\%** & 8.52\% & 1.47\% & 1,305,758 \\ \hline \multirow{3}{*}{R32-Indoor} & **5.12\%** & 3.74\% & 2.47\% & 2,579,291 \\  & **5.22\%** & 3.27\% & 2.31\% & 2,582,835 \\  & **5.44\%** & 4.58\% & 1.13\% & 2,588,814 \\ \hline \multirow{3}{*}{R12-Indoor} & 2.57\% & 1.68\% & **3.16\%** & 5,271,516 \\  & 2.58\% & 1.07\% & **3.17\%** & 5,231,268 \\ \cline{1-1}  & 2.38\% & 1.14\% & **2.61\%** & 5,085,259 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Depth completeness indicator results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Our** & **Zeller** & **BLADE** \\ \hline \multirow{3}{*}{HR260-Indoor} & **2.18 s** & 4.12 s & 132.76 s \\  & **1.81 s** & 6.34 s & 129.20 s \\  & **2.43 s** & 3.58 s & 126.36 s \\  & **3.17 s** & 3.33 s & 82.54 s \\ R32-Indoor & **2.66 s** & 2.79 s & 76.32 s \\  & **2.71 s** & 3.56 s & 79.55 s \\  & **4.29 s** & 8.29 s & 65.11 s \\ R12-Indoor & **3.13 s** & 6.54 s & 58.03 s \\  & **4.77 s** & 7.98 s & 67.82 s \\ \hline \hline \end{tabular}
\end{table}
Table 8: Depth completeness efficiency results.  
the depth estimation results. Across different dataset scenarios, the proposed algorithm also exhibits advantages in terms of the completeness of depth scene recovery.
# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 2 Methods  
As shown in Figure 1, the CFD method and deep learning method were employed for the rapid prediction and optimization of the wind environment around buildings. The schematic of the study includes four steps: data generation, model training, rapid prediction, and optimization. Initially, CFD simulations were conducted to simulate the wind field around the buildings and to generate data. The wind field data was then transformed into a dataset suitable for model training based on the CNN model. In this step, the U-net model was implemented using Python programming language and the Tensorflow open-source platform, encompassing processes such as model construction, training, and prediction. The model was then utilized for the prediction of the flow field under different building layouts and incoming flow conditions. Finally, the optimal combination of building layouts and incoming flow conditions was determined.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods
### Defect Detection
#### 2.3.2 Denoising and Defect Localization  
The dissimilarity computation of reconstructed images and the defect localization method are as follows:  
In Figure 4c, a significant amount of noise is present, giving rise to spurious defects, necessitating image denoising and grayscale conversion. This paper employs the enhanced FT algorithm [46] for denoising and grayscale conversion, replacing the FT algorithm's filter with a mean filter. The processing outcome is depicted in Figure 4d. Finally, by binarizing the image, the defect detection results are obtained, as illustrated in Figure 4e.
## 3 Results
### Cytotoxicity Assessment
Data are the means \(\pm\) SD of three independent experiments; each consisting of three replicates per test group: *** \(p\) = 0.0001 * \(p\) < 0.01. Both PE and FPE might boost skin cell viability in vitro. HaCaT cells showed the largest pro-proliferative effect at a concentration of 2.5% (1.375 mg/mL) for FPE, with a vitality of 119.6%, and the most ideal concentration for PE was 1.0% (0.48 mg/mL), with a viability of 114.4%. In the case of HDF cells, the best concentration for both FPE and PE was 2.5% (corresponding to 1.375 and 1.196 mg/mL), with viability of 113.2% and 109.5%. Keratinocytes reported a maximum viability of 127.5% at a concentration of 1.0% (0.55 mg/mL) after using FPE. In the case of PE, we recorded the maximum cell activity (111.5%) at a concentration of 0.1% (0.048 mg/mL), but as the concentration increased, the viability gradually decreased, reaching 66.1% at a concentration of 10.0% (4.8 mg/mL). In the case of fibroblasts, all of the tested FPE concentrations of 0.1-10.0% (0.055-5.5 mg/mL) increased viability when compared to control cells. At a concentration of 0.1%, we reported the highest viability (122.1%); however, as the concentration of FPE increased, the viability gradually declined, reaching 108.2% at a concentration of 10.0%. In the case of PE, doses up to 2.5% (up to 1.196 mg/mL) had a favourable effect on viability. Comparing the results of both experiments, it is possible to conclude that FPEs have a greater favourable influence on the vitality and activity of the examined cells, as evidenced by a stronger pro-proliferative effect and lower cytotoxicity (Figures 3-6).
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 5 Test Signal Verification
### Noise Reduction Effect Analysis  
As seen in Table 4, the noise reduction effect of VMD-WT (Soft) is slightly superior to that of WT and EMD-WT (Soft) for measured signals. Compared to the VMD-WT (Soft) noise reduction algorithm with default parameters, the signal-to-noise ratio of the proposed method in this paper is increased by 13.01%, and the root-mean-square error is reduced by 9.26%. All noise reduction evaluation indices remain optimal. Figure 9 is a comparison diagram of the waveforms of different denoising methods for the measured signal. It can be observed that after denoising processing using the method proposed in this paper, the signal waveform curve is smooth, the original shape of the actual signal is retained, and the degree of fitting is higher. Therefore, the experimental results for the measured signal demonstrate that the RIME-VMD-IWT algorithm proposed in this paper can effectively filter out actual noise, and the noise reduction effect is more significant.  
Figure 9: Measured signal waveform comparison.
## 2 Materials and Methods
### Methodology
#### 2.5.3 The Importance of SLN and NMP  
The importance of all input variables was determined using the RF method based on the combination of A3. Additionally, to further verify the importance of NMP and SLN in the ML model, A0 and A3 were used for a comparison to evaluate the effectiveness based on the RF method from site to site. The NE1, NE2, and NE3 sites are in close proximity to each other, but they vary in terms of data size and moisture conditions. All data from three sites were selected to verify the role of SLN and NMP. The three sites are separated, and site-to-site verification is chosen between adjacent sites. The purpose is to evaluate whether the role of SLN and NMP is still maintained despite such differences. Three scenarios were set up for verification as follows:  
1. NE1 for training and NE2 for testing: these two sites exhibit similar moisture levels, yet possess varying data quantities, and the larger data set is utilized to validate the smaller data set.
2. NE1 for training and NE3 for testing: the water conditions at the two sites differ, and the smaller data set is validated using the larger data set.
3. NE3 for training and NE2 for testing: due to the varying water conditions at the two sites, the smaller data set is employed to validate the larger data set.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Input Variable Combination** \\ \hline A0 & NDVI + Tmean + Tmin + Tmax + SSR \\ A1 & NDVI + Tmean + Tmin + Tmax + SSR + SLN \\ A2 & NDVI + Tmean + Tmin + Tmax + SSR + NMP \\ A3 & NDVI + Tmean + Tmin + Tmax + SSR + SLN + NMP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input variable combinations for comparison.