esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Research Methodology
#### 3.2.4 Validation, Accuracy Analysis, and Constraints  
In all the works, great importance was attached to model validation--most often by comparing prediction results with experiments or accurate simulations. In Refs. [85; 86], the ANN method was verified on a series of Sandia flames, and in Refs. [23; 87; 88], the agreement of the results was confirmed with experimental data for gasification gases and thermophysical properties of biodiesel. Ref. [89] used mean square error (MSE) to select the most accurate model for predicting fuel spraying, with the authors stressing that a low MSE does not always guarantee correctness outside the training range. Similarly, in Ref. [90], where an ANN was used to predict the heat transfer coefficient of aviation fuels, attention was paid to the trade-off between error and the risk of overlearning. In Refs. [91; 92], MSE was used as a criterion for estimating the composition of carbon, obtaining the best results for the RF model. In Refs. [86; 93; 94], the use of PCA as a feature selection method contributed to the improvement of the performance of neural networks, especially in the analysis of biomass co-firing. Ref. [95] is an overview of ML applications in thermochemical biomass conversion--it includes combustion, gasification, pyrolysis, and torrefraction. The role of artificial neural networks and hybrid models in the prediction and optimization of complex processes is particularly emphasized. A novelty of recent years is the use of the ability of acoustic waves to extinguish and control flames from various sources and fuels [96; 97; 98; 99; 100; 101], as well as infrasound techniques in power engineering [102; 103]. In practice, the data obtained can be useful for practical model training.
## 4 Discussion
### AUC-ROC Analysis
Ultimately, ResNeXt-101 and EfficientNet-B3 stand out as the top performers, with EfficientNet-B3 achieving the most reliable predictions across all the defect classes. Figure 10: AUC-ROC curves: (**a**) ResNet-18; (**b**) ResNet-50; (**c**) ResNet-101; (**d**) ResNeXt-50; (**e**) ResNeXt-101; (**f**) EfficientNet-B3. Figure 11 shows examples of the model predictions compared to the actual values. In some cases, the model correctly identified the defect class, such as crack and efflorescence. However, in some more complex examples, the model only identified one of the defect classes and ignored the other part. Also, the background was correctly identified, which shows that the model is able to distinguish between the defect and the background. There were some examples of a completely successful prediction, where all defect classes were correctly identified. These results indicate that the model performs well in most cases, but they show that detecting mixed defects can be challenging. From a practical standpoint, architectures such as ResNet-18 and EfficientNet-B3 are expected to perform well in real-time or resource-constrained environments due to their low computational overhead. In contrast, deeper models like ResNeXt-101 may offer higher accuracy but at the cost of slower inference and higher memory usage. These considerations are important for future deployment in field applications such as drone-assisted bridge inspections.
# Multi-Parametric Imaging of Etruscan Chamber Toms:
## 2 Materials and Methods
### Radon and Thoron Soil Gas Data Acquisition  
Radon is a noble gas and is the only naturally occurring radioactive gas. It has three isotopes: 222-Rn (Rn, half-life: 3.8 days), 220-Rn (Th, half-life: 55 s), and 219-Rn (Attinon, An, half-life: 3.96 s). Rn is a short-lived decay product derived from the 238-U decay series, while Th derives from the 232-Th decay series. Because of its low mobility, it needs a gas carrier, such as CO\({}_{2}\), to move from underground to the surface. The relatively short half-life of Th makes it useful in discriminating sectors with very fast soil-gas transport and/or Th-rich mineral outcrops. Thus, Rn soil gas measurements were performed along a regular grid of 20 samples (labeled from GC1 to GC20), approximately 2 m apart in order to cover the flat area (approximately 300 m\({}^{2}\)) where the CCR2, GPR2, and ERT profiles were acquired (see Figure 3). Moreover, an additional nine samples were collected along the CCR1 profile (labeled SGCl to SGC9). The soil Rn and Th measurements were carried out using a RAD7 Durridge(r) radon meter. The RAD7 measures concentrations of both 222-Rn (Rn) and 220-Rn (Tn) in the gas phase by exclusively collecting Rn and then counting the alphas emitted by the decay of their respective daughter nuclides 218-Po (t1/2 = 3.04 min)and 216-Po (t1/2 = 0.145 s). The soil Rn and Th were pumped from the ground at a depth of 40-50 cm using an internal pump. A desiccant (drierite) trap and an inlet filter were used to protect the detector ionization chamber from reaching a soil humidity greater than 10% [44]. The Rn and Tn measurements require approximately 15 min of pumping in the field in order to reach equilibrium with the measured daughter nuclide 218-Po. The acquired data were statistically and graphically processed to identify the geochemical lineaments, background values, and anomalies. An objective approach to the anomalous threshold estimation was obtained using probability graphs [45]. This procedure entailed approximating segments of a probability curve (or identifying inflection points) by straight lines and then selecting threshold values at abscissa levels that corresponded to intersections of these \"linear\" segments. In the simplest case, a single threshold would define two populations (background and anomalous values). However, if the two populations are not clearly separated, they may overlap in an interval defined by two bounding threshold values. Classed post maps of Rn, Th, and their ratio were developed for data visualization and to study spatial patterns. These maps group the data into discrete classes that represent different populations (i.e., background, low anomalous values, high anomalous values, and outliers) distinguished on the probability plots.
# Mechanical Properties Analysis of WAAM Produced Wall Made from 6063 Alloy Using AC MIG Process
## 3 Results and Discussion
### Microstructure Characterization
Figure 5 shows a comparative microstructural analysis of four different WAAM-fabricated samples (samples 11 to 14) at a higher magnification. All samples exhibit predominantly equiaxed grain structures with visible grain boundaries and varying densities of pores or inclusions. The samples produced with AC polarity show a clear trend: sample 11 has clean grain boundaries, fine equiaxed grains and minimal porosity. Sample 12 (balanced) has slightly coarser grains with occasional gas porosity. Sample 13 shows coarse grains, higher porosity and sometimes irregular grain boundaries. In contrast, sample 14, produced with DC MIG, displays the coarsest microstructure, with numerous gas pores the size of the grains, incomplete fusion and uneven solidification. Higher EN polarity directs more heat into the filler wire rather than the base material, resulting in a higher melt rate but lower net heat input into the wall. This promotes faster cooling and solidification, which in turn favors grain refinement and the formation of equiaxed microstructures. The observed refinement of grains with increasing EN ratio is consistent with previously reported results [15], where higher negative polarity promoted directional solidification and favored the formation of equiaxed grains.
# The Potential of Cannabidiol in the Management of Oral Infections
## 5 CBD Delivery Methods for Oral Infections: Current Strategies and Therapeutic Potential
### Localized Drug Delivery Systems
#### 5.3.1 Nanoparticle-Based Delivery  
The encapsulation of CBD into nanoparticles (e.g., polymeric or lipid-based) enhances its solubility, stability, and cellular uptake [69; 70; 71; 72; 73]. These systems can be incorporated into gels or solutions for application in periodontal pockets. Studies report enhancedantimicrobial activity against pathogens such as _Porphylomonas gingivalis_ and _Streptococcus mutans_ along with the modulation of local inflammation [74].
## 2 Circuit Implementation
### The Continuous-Time Input Common-Mode Current-Feedback Loop  
A block diagram of the proposed continuous-time CM input current-feedback loop is shown in Figure 3. The proposed circuit is implemented to clamp unwanted PLI in the saturation range of the input transistor, which can normally be amplified by the current-reused CFIA. The main idea of the proposed circuit is that the sourcing and sinking feedback current can limit the input displacement  
Figure 2: Block diagram of the proposed overall structure.  
current caused by PLI if an input CM voltage is out of the bias range between bias voltages, BIAS_P and BIAS_N. Through this procedure, an input CM voltage including PLI is clamped.
## 4 SoilWorks Numerical Simulation
### Discussion of Results  
The analysis of the final excavation stage in SoilWorks and the actual deformation curve data of the wall were compared to determine if the wall deformation curve fell within a reasonable estimated range. In this study, a maximum deformation range of \(\pm\)10% based on actual monitoring data was considered reasonable for both case studies. The results obtained are shown in Figure 22a,b. The following discussion is based on the analysis results:  
1. A sensitivity analysis of the effective friction angle in SoilWorks was conducted. The results indicate that \(Mmax\) and \(Dmax\) are more sensitive to a smaller friction angle in the sandy and clayey (drained) layers; however, in the clayey (undrained) layer, the sensitivity to the friction angle is lower. Furthermore, comparing the sensitivity analysis results with PLAXIS, it was found that SoilWorks generally exhibits lower sensitivity.
2. A sensitivity analysis of the soil elastic modulus in SoilWorks was conducted. The results show that in both the sandy and clayey (drained and undrained) layers, the elastic modulus values of the soil have a greater influence on \(Mmax\) and \(Dmax\). Comparing the sensitivity analysis results with PLAXIS, it was found that SoilWorks exhibits lower sensitivity than PLAXIS for \(Dmax\), while the sensitivities of \(Mmax\) and \(Favg\) are comparable between the two.
3. From the sensitivity analysis, it can be concluded that the friction angle and soil elastic modulus are two parameters that have a relatively high sensitivity to the displacement of the retaining wall.
4. Consistent with the previous PLAXIS analysis, when the bottom of the wall reaches a certain depth into rock or gravel layers (more than 1.5 m), there is no horizontal displacement observed at the bottom of the wall; therefore, in the analysis, horizontal displacement at the bottom of the wall was restrained. The analysis results demonstrate consistency with the actual monitoring data in terms of the maximum deformation location and the trend of the wall displacement curve, indicating that this basic assumption in the analysis is reasonable.
5. A feedback analysis was conducted using Case 1, followed by validation using Case 3. The results indicate that under the assumption of \(N=100\) for the second layer of gravel, within the soil elastic modulus range \(2450N\)\(\sim\)\(3430N\) (kN/m\({}^{2}\)), a reasonable estimation of the maximum deformation and its occurrence location during the final excavation stage can be achieved under the conditions of gravel layers in the Xindi  
Figure 21: Displacement vector diagram of final excavation stage of Case 3 (SoilWorks).
## 2 Methodology
### Field Data
#### 2.2.1 Cdom  
CDOM absorption was measured in field experiments using a variety of instruments and protocols. Examples include in-line filtered (generally 0.2 \(\upmu\)m) flow-through systems outfitted with ac-9 or ac-S absorption and attenuation meters (Wet Labs) and processed to \(a_{g}(\lambda)\)[49; 50; 51], as well as discrete sampling and filtration for bench-top spectrophotometry [52], or in liquid capillary waveguides [53]. Unfortunately, SeaBASS metadata did not historically specify which methods or protocols were used in data collection or processing, but more recently (since approximately 2012), investigators have been required to submit ancillary documentation, such as instrument calibration records, and encouraged to submit documentation retroactively.  
CDOM data measured in situ (i.e., with ac-9 or ac-S instruments; 33.5% of the preliminary CDOM dataset) were subject to particulate and bubble contamination, especially in experiments in which an automated in-line flow valve switched between filtered and unfiltered water presenting the opportunity for unfiltered water to reside in the plumbing during CDOM data collection. To identify and eliminate particle contamination, any CDOM records with a notable (i.e., \(\geq\)0.006 m\({}^{-1}\)) increase in absorption at 676 nm (a phytoplankton absorption peak) above the absorption curve from 650-715 nm were considered contaminated and removed (109 records).  
Nonlinear, least squares minimization was used to fit \(a_{g}(\lambda)\) to Equation (1) for calculating slopes of all hyperspectral \(a_{g}\) into seven spectral ranges: 275-295 nm, 290-600 nm, 300-600 nm, 350-400 nm, 350-600 nm, 380-600 nm, and 412-600 nm. Multispectral ac-9 data were fitted for slope using the six wavebands in the 412-555 nm range. To reduce outliers and noisy data, any CDOM slope data found to be outside of the range 0.005-0.05 nm\({}^{-1}\) were considered unrealistic and eliminated, together with the \(a_{g}(\lambda)\) data used to calculate them. This accounted for only 125 hyperspectral records in the 300-600 nm range (spectrophotometric), but nearly 6,000 records in the 412-600 nm range (predominantly flow-through). To further reduce outliers and noisy records, \(S_{g}\) and \(a_{g}\) data were eliminated where \(S_{g}\) in any slope range was greater than two standard deviations from the median for the entire database, or where they were outside the 2 nd and 98 th percentiles. This reduced the database of CDOM by nearly 11,000 records. In addition, 460 records were removed for \(a_{g}(676)>0.1\) m\({}^{-1}\), \(a_{g}(715)>0.05\) m\({}^{-1}\), or an average \(a_{g}(\lambda>680\) nm) \(>0.05\) m\({}^{-1}\), and an additional 1,057 CDOM records with extreme outliers (\(>\)4 standard deviations from the median) in the red (\(\lambda>620\)) were eliminated.
## 4 Experimental Analysis
### Experimental Setup
#### 4.2.1 Data Pre-Processing  
DWT is used by researchers extensively in the fields of signal and image processing for the decomposition of a signal into multiple frequency bands. DWT provides multi-resolution analysis and time-frequency localization of the signal [33,54]. A series of high-pass and low-pass filters are applied to a time-series signal such as EEG. The signal is divided into varying sizes of time windows, and the filter is applied to each window to extract approximation coefficients (ACs) and detail coefficients (DCs). The ACs can be further divided iteratively using the same process to obtain finer frequency bands. The high-frequency and low-frequency components of the signal are obtained by stretching or compressing the mother wavelet [55]. In the first iteration of DWT, the EEG signal is simultaneously filtered using low-pass (LP) and high-pass (HP) filters to extract approximation and detail coefficients. The outputs of LP and HP are denoted in Figure 5 as Ac-1 and Dc-1, respectively. The same process is repeated at each step, and the approximation coefficient is further divided into sub-bands.  
In this paper, DWT has been used at the pre-processing stage to decompose the EEG signal using the Daubechies-4 (DB-4) wavelet function to extract five EEG bands: gamma (30-60 Hz), beta (15-30 Hz), alpha (8-15 Hz), theta (4-8 Hz) and delta (0-4 Hz). The mother wavelet DB-4 is considered suitable for analyzing the EEG signal in epilepsy diagnosis due to the orthogonal shape of the wavelet that resembles the spike waves in EEG signal [56,57]. The DWT approximation and detail coefficients of the data segments labeled as \"seizure\" and \"no-seizure\" in the CHB-MIT dataset are depicted in Figure 6a and 6b, respectively.  
The epilepsy classification model employed in this paper uses an RNN-based neural network by decomposing the EEG signal into five frequency sub-bands (gamma, beta,  
Figure 5: Decomposition of EEG signal with 4-Level DWT.  
alpha, theta, and delta) and by extracting statistical features (skewness, kurtosis, mean, and standard deviation) from each frequency sub-band. The proposed RNN-based classification model has been implemented on two widely used, publicly available datasets that contain EEG that are recorded from epileptic patients.
## 3 Materials and Methods
### Hybrid User Study with Domain Experts
#### 3.3.4 Collected Data and Analysis  
Both qualitative and quantitative data was collected during the study. The qualitative data was collected through a semi-structured interview and observations, while the quantitative data was collected via online pre- and post-questionnaires, created with the LimeSurvey tool.  
In the pre-questionnaire, participants shared their expectations on the COVE-VR system based on the video they watched. This was the first part of the SUXES questionnaire, which was used to access the differences in expectations and experiences with VR technology [62]. The post-questionnaire consisted of six parts: (1) experiences with the system (as the 2 nd part of the SUXES), (2) self-designed statements on collaboration, (3) workload via raw SIM-TLX [63], (4) self-designed statements on the perception of VR technology, (5) statements on immersion for VR-participants, adopted from Presence Questionnaire [64],  
Figure 5: Screenshots from the test procedure: (**1**) streamed VR image from the VR-participant’s point-of-view, (**2**) streamed video of the VR-participants, (**3**) Teams chat activity.  
and (6) background information. In this paper, we present the data from the 1 st, 2 nd, 4 th and 6 th sections.  
The semi-structured group interview was conducted at the end of the study by the main facilitator to raise discussion on the usability of the system, the asymmetry between VR and Teams and related collaboration practices; the participants were asked to turn on their cameras during the interview (Figure 6). The interview script consisted of five sections, covering (1) general UX, (2) VR system's usefulness and efficiency for industrial tasks, (3) experiences of asymmetric use between VR and Teams, (4) roles and tasks in asymmetric collaboration, and (5) improvements for the COVE-VR platform and asymmetric collaboration practices. Additionally, observation forms with pre-defined questions were used to collect observation-based data systematically.  
The collected data was analyzed in collaboration with academic and industrial researchers. A descriptive statistical analysis was performed over quantitative data because of the relatively small sample size. The observations and interviews were analyzed using thematic qualitative data analysis [65]; the gathered data was categorized by themes with affinity diagrams. The most popular themes were multi-user and teamwork interaction, roles in asymmetric interaction, remote participant UX, VR tools, and complimenting teamwork with voice commands.
## 2 Materials and Methods
### Volatile Compound Analysis of Lafun Samples and Raw Cassava Roots
#### 2.7.2 Gas Chromatography Analysis  
The volatile compounds were extracted by solid-phase microextraction (SPME) using a 50/30 um DVB/CAR/PDMS Stableflex fibre (Supelco, Poole, UK). The prepared samples were equilibrated for 10 min at 35 \({}^{\circ}\)C with agitation (500 rpm). The SPME fibre was then exposed to the headspace for 30 min, followed by desorption in the GC injection port (splitless) at 250 \({}^{\circ}\)C. An Agilent 5975C Series GC/MSD coupled to an Agilent 7890A gas chromatograph (Agilent, Santa Clara, CA, USA) was used, equipped with a Zebron DBWax column (30 m \(\times\) 250 um \(\times\) 0.5 um, Phenomenex, Macclesfield, UK). The oven was held at 40 \({}^{\circ}\)C for 5 min, and the temperature was subsequently increased to 250 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C min\({}^{-1}\) and held at 250 \({}^{\circ}\)C for 5 min. The carrier gas was helium at a flow rate of 0.9 mL min\({}^{-1}\). Mass spectra were recorded in electron impact mode at an ionisation voltage of 70 eV and a source temperature of 230 \({}^{\circ}\)C. A scan range of _m_/_z_ 20-280 with a scan time of 0.69 s was employed, and the data were collected and stored using the ChemStation system. Volatiles were identified through the comparison of spectra and linear retention indices (based on C5-C26 alkane series) with those obtained from authentic compounds. Semi-quantitation was carried out based on the quantification of a key ion multiplied by a factor to account for the contribution from all ions (the total ion chromatogram). These peak areas were compared with those of the internal standard using a response factor of 1.
# Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
## 4 Problem Formulation and Algorithm Development
### Accelerated Proximal Gradient (APG)  
In addition to ADMM, the accelerated proximal gradient (APG) approach has been widely utilized to solve \(\ell_{1}\)-norm regularization problem because of its fast convergence and excellent performance [32; 33]. It is developed for the following unconstrained optimization problem:  
\[\text{minimize }F(x)+G(x), \tag{31}\]  
with variable \(x\). In the APG algorithm, when the second function is \(\ell_{1}\)-norm, namely \(G(x)=\lambda\|x\|_{1}\), the update of \(x\) is obtained by the soft-thresholding operator, given by  
\[x_{k+1}=\mathbf{T}_{\lambda/L}(\beta_{l}-\frac{1}{L}\
abla F(\beta_{l})). \tag{32}\]  
For our problem, let \(F(\boldsymbol{\alpha},\mathbf{n},\mathbf{H})=\|\mathbf{\hat{y}}-\mathbf{H}( \mathcal{W}^{\mathrm{T}}\boldsymbol{\alpha}+\mathbf{n})\|_{2}^{2}\) and \(G(\boldsymbol{\alpha})=\lambda_{1}\|\boldsymbol{\alpha}\|_{1},G(\mathbf{n})  
```
Objective function: \(\lambda_{1}\|\mathbf{\alpha}_{WT,FT}\|_{1}+\lambda_{2}\|\mathbf{H}\|_{1}+\lambda_{3}\| \mathbf{n}\|_{1}+\|\mathbf{y}-\mathbf{H}(\mathcal{W}_{WT,FT}^{\mathrm{T}}\mathbf{ \alpha}_{WT,FT}+\mathbf{n})\|_{2}^{2}\) Outputs: Estimates of \(\mathbf{n}\), \(\mathbf{\alpha}\) and \(\mathbf{H}\) Initialization:\(l=0\), \(\mathbf{n}_{0}=\mathbf{n}_{-1}\), \(\mathbf{\alpha}_{0}=\mathbf{\alpha}_{-1}\), \(\mathbf{H}_{0}=\mathbf{H}_{-1}\)\(t_{0}=1,t_{-1}=0\) Repeat \(\beta_{I}^{\mathbf{n}}=\mathbf{n}_{l}+\frac{t_{-1}}{t_{1}}(\mathbf{n}_{l}- \mathbf{n}_{l-1})\) \(\beta_{I}^{\mathbf{n}}=\mathbf{\alpha}_{l}+\frac{t_{-1}}{t_{1}}(\mathbf{\alpha}_{l}- \mathbf{\alpha}_{l-1})\) \(\beta_{I}^{\mathbf{n}}=\mathbf{H}_{l}+\frac{t_{-1}}{t_{1}}(\mathbf{H}_{l}- \mathbf{H}_{l-1})\) \(\mathbf{n}_{l+1}=\mathbf{T}_{\lambda_{1}/t_{1}}(\beta_{I}^{\mathbf{n}}- \
abla_{\mathbf{n}}F(\beta_{I}^{\mathbf{n}},\beta_{I}^{\mathbf{n}},\beta_{I}^ {\mathbf{n}})/L)\) \(\mathbf{\alpha}_{l+1}=\mathbf{T}_{\lambda_{2}/L}(\beta_{I}^{\mathbf{n}}-\
abla_{ \mathbf{n}}F(\beta_{I}^{\mathbf{n}},\beta_{I}^{\mathbf{n}},\beta_{I}^{ \mathbf{n}})/L)\) \(\mathbf{H}_{l+1}=\mathbf{T}_{\lambda_{2}/L}(\beta_{I}^{\mathbf{n}}-\
abla_{ \mathbf{n}}F(\beta_{I}^{\mathbf{n}},\beta_{I}^{\mathbf{n}},\beta_{I}^{ \mathbf{n}})/L)\) \(t_{l+1}=\frac{1+\sqrt{1+4t_{l}^{2}}}{2}\) Until\(l>T\) {maximum iteration} or \(\|\mathcal{W}^{\mathrm{T}}(\mathbf{\alpha}_{l+1}-\mathbf{\alpha}_{l})\|_{2}<\delta\) {predefined threshold}.
```  
**Algorithm 2** Accelerated Proximal Gradient (APG) based approach.