esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Relevant Basic Theories
#### 2.2.4 Ensemble Deep Learning  
Ensemble deep learning aims to better capture complex data distributions and improve model generalization performance by combining the outputs of multiple deep learning models, thereby diminishing the impact of random changes. The success factors of ensemble learning can be categorized into the following aspects [36; 37]:  
1. Data sample techniques used in training: Enhancing data quality by introducing other data features or using different data sampling techniques, such as bootstrapping and cross-validation, thereby increasing the robustness and generalization performance of the ensemble.
2. Types of basic models: Selecting different types of basic models can increase the diversity of the ensemble and improve overall performance--for example, models like decision trees, support vector machines, and neural networks.
3. Diversity of basic models: Ensuring differences among basic models to avoid overfitting. This can be achieved by adjusting model parameters, selecting different feature subsets, or using different training algorithms to increase model diversity.
4. Methods of combining basic models: This includes voting methods and meta-learning approaches. The main ensemble learning methods include Bagging, Boosting, Stacking, and Ensemble Pruning. This paper employs the Stacking approach, which involves multiple basic models and a meta-model. Through parallel ensemble techniques for generating basic learners, it fully leverages the strengths of different models and obtains the final result through a meta-learning fusion method.  
The ensemble deep learning strategy employed in this paper involves training different configurations of the same basic model with a variety of data samples. This approach is unique to ensemble deep learning and allows for the training of the same basic model with different structures on diverse data samples, combining the advantages of different configurations. This method can enhance the predictive performance and robustness of the model.
# Improvement of Radiant Heat Efficiency of the
###### Abstract  
This study presents the application of additive manufacturing (AM) technology to a W-type INCONEL radiant tube (RT) used to improve its radiant heat efficiency. Appropriate dimensions of honeycomb structure were determined from finite element (FE) analysis and the resulting increase in radiant heat was computed. The honeycomb patterns on the RT surfaces were printed using the directed energy deposition (DED) method. Radiant heat efficiency of a prototype RT with a honeycomb pattern printed was examined in a pilot furnace emulating the continuous annealing line (CAL). Finally, soundness of the prototype RT was tested on-site on the actual the CAL of No. 3 CGL in POSCO Gwangyang Steel Works. The results revealed that partial FE analysis, which predicts the amount of radiant heat by partially modeling the RT structure rather than modeling the entire RT structure, is suitable for overcoming the limitation of the computer memory capacity and calculating the design parameters of honeycomb patterns. The DED is suitable for printing honeycomb patterns on RT with large and curved surfaces. The average amount of gas consumed to maintain 780 \({}^{\circ}\)C and 880 \({}^{\circ}\)C for 1440 min was reduced by 10.42% and 12.31%, respectively. There were no cracks and no gas leaks on the RT surface in an annual inspection over three years.  
additive manufacturing; radiant heat efficiency; radiant tube; directed energy deposition; Inconel; fuel consumption Article
## 3 Sparse Representation Framework for Speckle Reduction
### Performance Estimation  
The reconstructed denoised image using the proposed algorithm were compared with the original image. Two image quality metrics were used for quantitative performance measurements: peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM) [54]. Peak signal-to-noise ratio is defined as:  
\[PSNR=10\log_{10}\frac{N_{\max}}{\frac{1}{MN}\sum_{n=1}^{N}\sum_{m=1}^{M}\left|x (n,m)-\widehat{x}\left(n,m\right)\right|^{2}}, \tag{12}\]  
where \(N_{\max}\) represents the maximum fluctuations in the input image. Here, \(N_{\max}\) = (\(2^{n}-1\)), \(N_{\max}\) = 255, when the components of a pixel are encoded using eight bits. \(N\) denotes the number of pixels processed, \(x(n,m)\) is the original signal, and \(\widehat{x}\left(n,m\right)\) is the recovered image signal. In _MSSIM_, the structures of the two images are compared after normalizing the variance and subtracting the luminance as follows:  
\[MSSIM=\frac{1}{N}\sum_{i=1}^{N}\left[l\left(\widehat{x},x\right)\right]^{ \alpha}\cdot\left[c\left(\widehat{x},x\right)\right]^{\beta}\cdot\left[s\left( \widehat{x},x\right)\right]^{\gamma}, \tag{13}\]  
where \(l\left(\widehat{x},x\right)\) denotes luminance, \(c\left(\widehat{x},x\right)\) denotes contrast, and \(s\left(\widehat{x},x\right)\) denotes structure comparison functions. Further, \(\alpha\), \(\beta\), and \(r\) are weighted parameters that are used to adjust the relative importance of the three components.
# Demonstration of High-Speed Optical Transmission at 2 \(\upmu\)m in Titanium Dioxide Waveguides
## 2 Design and Fabrication of the Photonic Structure
### Modal Analysis  
Here, we focus our attention on two strip waveguides (Figure 2a): an \(8.0\)-\(\upmu\)m wide waveguide and a subwavelength \(1.6\)-\(\upmu\)m wide waveguide. The \(8\)-\(\upmu\)m wide waveguide is clearly multimode with nine TE modes and three TM modes. Considering a refractive index of \(1.44\) for the glass substrate and \(2.41\) for the TiO\({}_{2}\) layer [17], Figure 2b shows that the first TE and TM modes in the multimode (MM) waveguide have an effective index of \(1.87\) and \(1.49\), respectively. Note that the TM mode is clearly much less confined than the TE mode, as can be seen in Figure 2(b2), but over the length considered, the lossy behavior of the TM mode has very low impact, as demonstrated in the following sections. The subwavelength \(1.6\)-\(\upmu\)m wide waveguide is single-mode (SM) with an effective index of the TE mode around \(1.78\), shown in Figure 2c.
# On the Stability of Graphene-Based Aqueous Dispersions and Their Performance in Cement Mortar
## 3 Results and Discussion
### Ultrasonic and Mechanical Tests  
In Figure 5a-c, the results of the ultrasonic test are shown. In the top panel, Gr-TG exhibits an increase in the \(E_{d}\) values with rising concentration. This outcome suggests that the Gr-TG additive leads to a reduced porosity in the cement matrix, which might enhance strength. The trend for the other two additives is less distinct.  
The flexural strength of the doped samples, shown in Figure 5d-f, is comparable to that of the reference mortar up to 0.1% by weight of cement of graphene-based nanomaterials. However, a different outcome can be observed at higher concentrations. Mortars doped with Gr-TG (upper panel) do not show a significant variation in the flexural strength, whereas the increase in the amount of Gr-SG (middle panel) and GO (lower panel) leads to a loss of 19% and 33%, respectively.  
In Figure 5g-i, the compressive strengths of Gr-TG (upper panel), Gr-SG (middle panel), and GO (lower panel) are reported. The addition of 0.3 wt% of Gr-TG and Gr-SG increases the compressive strengths of mortars by 15% and 12%, respectively.  
A further increase of graphene-based nanomaterials, within the investigated range, does not provide a beneficial effect on the compressive strength. Mortars doped with GO show a similar trend.  
Overall, the results show that at lower concentrations of graphene-based nanomaterials, a slight improvement in compressive strength and a negligible effect on flexu  
Figure 5: Results of: (**a–c**) ultrasonic, (**d–f**) flexural strength, (**g–i**) compressive strength tests, plotted as a function of the additive concentration. Higher panels are referred to as Gr-TG; middle panels to Gr-SG; lower panels to GO.  
ral strength are observed, regardless of the type of graphene-based nanomaterials used. The improvement can be ascribed to both nucleation and filling effects [5; 35; 56]. Graphene-based nanomaterials, when properly dispersed in the cementitious matrix and in adequate amounts, act as nucleation sites for C-S-H, thus improving the microstructure through densification of the hydration products. The results obtained under flexural tests can instead be explained by the fact that graphene-based nanomaterials, based on their dimensions and high aspect ratio, can only provide a strengthening effect in the cement paste but fail in transferring the stresses or bridging the microcracks that form in the interfacial transition zone (ITZ) or between sand particles, as they are only larger than the calcium silicate hydrate (C-S-H) particles [5]. At higher concentrations, a limited decline in mechanical properties is observed, regardless of the type of graphene-based nanomaterials used, as a result of their agglomeration that promotes a weakening of the cement matrix, as supported by FESEM analysis (see Section 4).
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 2 Materials and Methods
### Search Strategy and Preliminary Search Outcomes
Studies conducted in Asia are more than 4150, in Europe more than 1750, in the Americas almost 1200, 500 in Africa, and 200 in Oceania. In Europe, the country with the largest number of studies is Italy (more than 200), followed by Spain. We also classified publications versus publishers. Elsevier is the one with the highest record count, with 1230 papers (20.3%). Then, there is MDPI with 14.9%, Springer Nature with 13.1%, and IEEE with 8.1%. Wiley, Frontiers MediaSa, and Taylor & Francis follow, as shown in Table 1.  
Figure 4: Histograms showing the geographical distribution of papers indexed in the Web of Science (WoS) by country of application during the period 2015–2024. Left: summary per continent. Right: the top 10 countries in the list. Figure 3: Recurrence of the algorithms in the papers indexed in the Web of Science (WoS) during the period 2015–2024. The search results illustrate the distribution of record counts across different publication titles. The journals _Remote Sensing_ and _Frontiers in Plant Science_ have the highest record count, with 196 papers each (3.2%). Following closely are _Computers and Electronics in Agriculture_ with 2.8% and _Multimedia Tools and Applications_ with 1.8%. Other prominent titles include _IEEE Access_, _Agronomy Basel_, _Plants Basel_, _Scientific Reports_, _Agriculture Basel_, and _Sensors_, as shown in Table 2.  
The data can also provide an overview of the distribution of records across different Sustainable Development Goals (SDGs), of which \"03 Good Health and Well Being\" stands out as the most prevalent goal, representing 43.3% of all records, followed by \"13 Climate Action\" with 23.1%, \"15 Life on Land\" with 18.2%, and \"02 Zero Hunger\" with 13.1%. These high percentages indicate a significant focus on health, environmental, and climate-related themes in the dataset. Other SDGs contribute to the dataset, albeit to a lesser extent (Figure 5). \begin{table}
\begin{tabular}{l l} \hline \hline
**Publisher** & **Count** \\ \hline Elsevier & 1230 \\ MDPI & 904 \\ Springer Nature & 793 \\ IEEE & 488 \\ Wiley & 293 \\ Frontiers Media Sa & 274 \\ Taylor \& Francis & 151 \\ Nature Portfolio & 84 \\ Oxford Univ Press & 63 \\ Amer Chemical Soc & 61 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Publication count in the Web of Science (WoS) per publisher in the period 2015–2024, showing the top 10 publishers in the list. \begin{table}
\begin{tabular}{l l} \hline \hline
**Publication Title** & **Count** \\ \hline _Frontiers in Plant Science_ & 196 \\ _Remote Sensing_ & 196 \\ _Computers and Electronics in Agriculture_ & 167 \\ _Multimedia Tools and Applications_ & 112 \\ _IEEE Access_ & 102 \\ _Agronomy Basel_ & 83 \\ _Plants Basel_ & 72 \\ _Scientific Reports_ & 72 \\ _Agriculture Basel_ & 67 \\ _Sensors_ & 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Publication count in the Web of Science (WoS) per journal in the period 2015–2024, showing the top 10 titles in the list.
# Influence of Cryogenic Grinding on the Nutritional and Antinutritional Components of Rapeseed Cake
## 2 Materials and Methods
### Determination of Glucosinolates  
The content of glucosinolates was determined according to the standard ISO method [29] using HPLC with sinigrin monohydrate (potassium allyl-glucosinolate monohydrate) as an internal standard. After extraction and desulfation of glucosinolates, an Agilent Technology HPLC 1200 system with DAD detector and Phenomenex C18 column (Kinetex 150 mm \(\times\) 4.6 mm, 2.6 \(\upmu\)m, 100 A) was used for separation and identification of each component. The prepared sample (20 \(\upmu\)L) was injected into the system. The mobile phases for the separation of glucosinolates were water (solvent A) and 20% (\(v/v\)) acetonitrile solution in water (solvent B), and the gradient used was as follows: 0-1 min 100% A; 1-21 min 100-0% A; 21-26 min 0-100% A; 26-31 min 100% A. The flow rate was 1 mL/min throughout the run. The column temperature was 30 \({}^{\circ}\)C. Chromatograms were recorded at a wavelength of 229 nm, and UV spectra were also recorded throughout the analysis time. The identification of each component was performed by comparing the relative retention times with those in the aforementioned ISO method, and for their quantification the internal standard was used, taking into the account response factors for each component given in the same ISO standard method.
## 4 Simulation Experiment Results and Discussion
### BER Performance Comparison of MP-WFRFT Parameters Based on the VPAPM Scheme  
Firstly, the scanning characteristics of a single parameter in the scale vector are investigated for the VPAPM scheme, with the results presented in Figure 12. Secondly, under the same parameter scanning interval, the effects of MP-WFRFT parameter settings on the BER performance are explored in the \(\cos\gamma\) and \(\sin\gamma e^{\parallel\varphi}\) directions, and the results are illustrated in Figure 13a-f.  
The following conclusions can be drawn from Figure 12:  
1. Changing the parameter \(m_{0}\) in the horizontal and vertical directions has no impact on the BER performance of the received signal, as shown in Figure 12a.
2. From Figure 12b-d, it is observed that the BER performance for receiver \(m_{2}\) is the best, followed by \(m_{3}\), while \(m_{4}\) has the worst performance.
3. The effects of parameters \(n_{0}\), \(n_{1}\), \(n_{2}\), and \(n_{3}\) on the BER performance at the receiver are nearly the same, as shown in Figure 12e-h.  
The analysis of Figure 13 reveals that the BER performance curves for \(\alpha\), \(MV1\), and \(NV1\) in the \(\cos\gamma\) direction change significantly, whereas those in the \(\sin\gamma e^{\parallel\varphi}\) direction show only minor variations for \(\beta\), \(MV2\), and \(NV2\), despite the same parameter scanning interval. Compared to the \(\sin\gamma e^{\parallel\varphi}\) direction, the \(\cos\gamma\) direction is more effective in enhancing the system's anti-interception performance.  
The above two sets of simulations demonstrate that the MP-WFRFT algorithm, when applied to the VPAPM scheme, exhibits robust parameter anti-detection capabilities. Even when the VPM method is known, there are still eighteen free parameter combinations that need to be scanned. If the interceptor is also unfamiliar with the signal transformation method, the difficulty of demodulating the intercepted signal becomes even more substantial.  
Figure 12: _Cont._Figure 12: BER curves of scale vectors scanning characteristic of VPAPM scheme.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### Robustness Enhancement of Kalman Prediction
#### 4.4.2 Data Processing Thread  
According to the above mathematical principle of length and area measurement, the corresponding data processing thread was designed. After receiving, the serial interrupt program captures the valid information frame. Then the data processing thread detects the validity of the information frame and extracts the spatiotemporal information from the information frame, and then stores the coordinate information. At the same time, the sampled information is packaged and sent to the OLED screen display thread for user monitoring. The sampling flow chart in the data processing thread is shown in Figure 8.  
Considering the drift of data information and preventing the device from oversampling, the distance between the collected position coordinates and the previous position coordinates is calculated by using the large circle distance formula during each storage, and detecting whether the distance is greater than a certain threshold. If it is greater than the threshold, it is stored.  
In order for the user to end the sampling, they must press the corresponding button. At this time, the thread will process, calculate the area, and draw the user's stepping point trajectory according to the collected data.
## 3 Results and Discussion
### Phytochemical Characterization and Biological Activities
#### 3.3.2 Bioactivity: Antioxidant, Antihypertensive, and Antiproliferative Effects
ramossissima_ samples was evaluated through of the ACE inhibition assay. The ACE inhibitors are commonly used in therapy for the treatment of hypertension and cardiovascular diseases [123; 124]. IC\({}_{50}\) values obtained for the dried plants were significantly different from the IC\({}_{50}\) value of the fresh plant. However, IC\({}_{50}\) values of dried plants did not show significant differences between them, indicating that the oven-drying process does not have a significant impact on the compounds that are responsible for the inhibition of the ACE enzyme. In the same manner, both dried plants demonstrated similar antiproliferative effects in colorectal cancer cells (HT29) (Figure 3). Fresh and dried extracts had the ability to impair HT29 cell proliferation in a dose-dependent manner (Figure 3a-c), with an EC\({}_{50}\) value of 15.82 mg/mL dw, 17.56 mg/mL, and 17.24 mg/mL for the fresh, oven-dried and freeze-dried plants, respectively (Figure 3d). It is important to mention that the EC\({}_{50}\) values determined in this work did not present cytotoxic effects in confluent Caco-2 cells, a cell model widely used to evaluate the effect of chemicals and food bioactive compounds in intestinal function. The similar bioactive response in terms of the antiproliferative and anti-hyperglycemic effects among both drying samples could be attributed to their similar composition in bioactive compounds, namely quercetin-3-O-glucose, neochlorogenic acid, ferulic acid-glucoside, caffe acid-glucuronide-glucoside, and 3,5-dicaffeoylquinic acid (Table 4). Accordingly, it has been reported that quercetin glycosides have a significant impact on ACE inhibition, in which quercetin-3-O-glucose is the most effective ACE inhibitor among the flavonoids' glycosides [125; 126]. Quercetin glucosides also have an antiproliferative activity in human cancer cells with inhibitory effects on proliferation of HT29 cells [14; 127]. Similarly, caffeoylquinic acids are able to reduce HT29 cell viability, promoting specific changes in the cell cycle, and increase the apoptosis rate [11; 128; 129], and their derivatives are found to be effective as natural ACE inhibitors [126; 130; 131]. In addition, the synergistic effects between quercetin glycosides and caffeoylquinic acids in the dried extracts from _S. ramossissima_ may also have contributed to similar antihypertensive and antiproliferative activities. Phenolic extracts from halophyte plants and their isolated polyphenols have been studied in a number of cancer cell lines and antihypertensive models [132; 133; 134; 135; 136]. Extracts from halophytes, such as _Saliconia herbacea_ seed cells, showed potent antioxidant activity and selective cytotoxic effects against HT29 and HCT116 human colon adenocarcinoma [132]. In addition, _Artemisia scoparia_, _Cocos nucifera_, and _Tribulus terrestris_ showed ACE inhibitory activity [135; 136; 137; 138; 139].
# Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method
## 4 Expanded Experimental Data Through the NSF Method
### Internal Forces Based on the NSF Method  
In the investigation of CFST arch supports, the changing characteristics of internal forces could help researchers understand the working behavior and failure mechanism of the supports more comprehensively, so the internal forces are tried to be constructed through expanded stress data based on the NSF method. Due to the application of the lateral restraints, the cross sections of the supports mainly undertake axial force and in-plane bending moment, and these internal forces could be obtained through the integral of the sectional stresses. Here, the axial force is calculated by summing the product of longitudinal stress and area for each element, and the in-plane bending moment is achieved through summing the product of longitudinal stress, vertical distances and area for each element, just as Equations (12) and (13)  
\[N_{j}=\int_{A}\sigma\mathrm{d}A=\sum_{A}\sigma_{ij}A_{i} \tag{12}\]  
Figure 5: The comparison between experimental and interpolated strain of CFST arch support-D.  
\[M_{j}=\int_{A}\sigma y\mathrm{d}A=\sum_{A}\sigma_{ij}y_{i}A_{i} \tag{13}\]  
where \(N_{j}\) is the axial force at \(j\)th load step, \(\sigma_{ij}\) is the longitudinal stress of the \(i\)th element at \(j\)th load step, \(A_{i}\) is the area of the \(i\)th element, \(M_{j}\) is the in-plane bending moment at \(j\)th load step, \(y_{i}\) is vertical distances of the \(i\)th element from the neutral axis.  
The internal forces calculated by the stress fields would have certain changes with the variations of mesh sizes, however, when the mesh is small enough, the error of results could be ignored, which would not affect the analysis of stressing state of the supports. Hence, adopting the appropriate mesh sizes, the internal forces could be calculated based on the NSF method, so as to study the working behavior of the supports.