esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present study deals with the experimental research findings for the characteristics of ignition (ignition delay times, minimum ignition temperature) and combustion (maximum combustion temperature, concentration of anthropogenic emission), as well as theoretical calculations of integral environmental, economic, and energy performance indicators of fuel compositions based on coal processing waste with the most typical types of biomass (sawdust, leaves, straw, oil-containing waste, and rapeseed oil). Based on the results of the experiments, involving the co-combustion of biomass (10% mass) with coal processing waste (90% mass) as part of slurry fuels, we establish differences in the concentrations of NOx and SOx in the gaseous combustion products. They make up from 36 to 218 ppm when analyzing the flue gases of coal and fuel slurries. Additionally, the values of relative environmental, economic, and energy performance indicators were calculated for a group of biomass-containing fuel compositions. The calculation results for equal weight coefficients are presented. It was shown that the efficiency of slurry fuels with biomass is 10%-24% better than that of coal and 2%-8% better than that of filter-cake without additives. Much lower anthropogenic emissions (NOx by 25%-62% and SOx by 61%-88%) are confirmed when solid fossil fuels are partly or completely replaced with slurry fuels.  
biomass; slurry fuels; co-combustion; anthropogenic emissions; relative efficiency indicators of fuels +
Footnote †: journal: Applied Sciences
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 5 Simulation Inputs and Results
### Simulation Results  
#### 5.2.1 Charging Power Comparison--With and without Smart EMS  
The results of simulated power demand for the EVCS are presented in Figure 9. The blue line represents the temporal profile of total charging power without implementing EMS, PV, and ESS. The red line illustrates the power profile during the summer months when the proposed EMS is applied. The green line depicts the power profile during non-summer months with the implementation of the EMS. (The overlapping portions of the red and green lines are displayed in green). The results demonstrate that upon implementing the control strategies proposed in this study, uniform current control can be achieved during EV charging, leading to a smoother power demand profile for the charging station. This not only contributes to the preservation of EV battery life but also facilitates a reduction in the required contracted capacity of the EVCS.
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 3 Languages in the Study  
A language family is a collection of languages that share a common ancestor. English, for instance, is a member of the Indo-European (IE) language family. Our target languages are South Slavic languages with very few labeled examples for sentiment analysis tasks. Languages within the same family typically share a subset of vocabulary and typological characteristics. In the context of languages from the same family tree, typological characteristics, such as word order, morphology, and phonology, refer to shared structural features that these languages exhibit despite their historical divergence. Cognates [25], which are sets of words in different languages that have been directly inherited from an etymological ancestor in a common parent language, are one such phenomenon. For instance, the Proto-Slavic word \(\mathrm{nokt}\) (night) has equivalents in other languages, such as \(\mathrm{nob}\) (\(\mathrm{noc}^{\ell}\)) (\(\mathrm{Russian}\)), \(\mathrm{niv}\) (\(\mathrm{nric}\)) (\(\mathrm{Ukrainian}\)), \(\mathrm{nob}\) (\(\mathrm{noc}\)) (Belarusian), \(\mathit{noc}\) (\(\mathrm{Polish}\), Czech, Slovak), \(\mathit{noc}\) (\(\mathrm{Slovene}\)), \(\mathrm{nob}\) (\(\mathrm{Serbo}\)-\(\mathrm{Croatian}\)), \(\mathrm{nob}\) (\(\mathrm{nosht}\)) (\(\mathrm{nolk}\)) (\(\mathrm{nok}\)) (\(\mathrm{Macedonian}\)).  
The language family is subdivided into branches that are categorized as subsets. For instance, one of the branches of IE, Balto-Slavic, has a Slavic branch that is subdivided into West, South, and East subgroups [26]: Russian, Belarusian, and Ukrainian (in the East group); Polish, Czech, and Slovak (in the West group); Bulgarian and Macedonian (eastern dialects of the South group); and Serbo-Croatian and Slovene (western dialects of the South group). We chose to concentrate on three West Slavic languages (Czech, Slovak, and Polish), three South Slavic languages (Croatian, Slovene, and Bulgarian), and one East Slavic language (Russian). Czech and Slovak have the highest degree of mutual intelligibility, followed by Croatian and Slovenian [27]. Except for Bulgarian and Russian (which use the Cyrillic script), all languages use the Latin alphabet. Russian has a complex case system, whereas Bulgarian has lost almost all of its case declensions [28]. The Slavic language family is illustrated in Figure 1.
# BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
## 3 Results
### Spondylolisthesis Prediction  
We evaluated the spondylolisthesis prediction experiments by using three classifiers. The accuracies of the prediction are shown in Table 10. SVM achieved the highest accuracy in the testing set of 95.14% in the AP view and 92.26% in the LA view. The prediction results are classes of pairs of adjacent vertebrae. The spondylolisthesis class indicates the slippage of the upper verteb in each pair, as shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**View**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-4}  & & **Training Set** & **Testing Set** \\ \hline \multirow{2}{*}{Decision Tree} & AP & 100.00\% & 92.77\% \\  & LA & 100.00\% & 77.38\% \\ \hline \multirow{2}{*}{XGBoost} & AP & 100.00\% & 94.87\% \\  & LA & 100.00\% & 86.41\% \\ \hline \multirow{2}{*}{SVM} & AP & 99.95\% & 95.14\% \\  & LA & 96.48\% & 92.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracies of spondylolisthesis prediction.  
Figure 19: Spondylolisthesis prediction: **(a)** input points; **(b)** ground truth; **(c)** prediction.  
Figure 18: Detected corner points of patients with and without diseases: (a) normal; (b) anterolisthesis; (c) retrolisthesis.
## 5 Conclusions  
In the present study on six groups of 3D-direct-printed aligners post-cured with a nitrogen generator for, respectively, 15 min, 20 min, 25 min, 30 min, 40 min, and 50 min, and one group cured for 30 min without the nitrogen generator, and taking into consideration 20 reference points, the local aligner thickness was assessed with a micro-CT scanner. No direct association was found between curing time and thicknesses. Nitrogen generator usage showed a significant effect on thickness. With a 60\({}^{\circ}\) printing inclination, and following the prescribed centrifugation process, a local thickness increase at the front teeth (with a median value of +0.15 mm at the central point of the incisal edge of +0.25 mm at the central point of the facial surface) was observed. Limitations of the present study include the following: First, the nitrogen effect was not investigated for all curing times as it was not our primary goal; in order to consider this factor, a much larger sample size would be required (two groups per curing time). Secondly, no biomechanical analysis was taken into account, and the impact of increased thickness in the front still needs to be studied. Eventually, the study was limited to a single printer/resin combination and future studies are needed to validate if the same patterns will be observed.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132111952/s1](https://www.mdpi.com/article/10.3390/app132111952/s1)]([https://www.mdpi.com/article/10.3390/app132111952/s1](https://www.mdpi.com/article/10.3390/app132111952/s1)), File S1: Plots: boxplots showing the local thickness values at various reference points for the different curing times and graphs of the correlation analysis between thickness and time; File S2: AlignerItaly_Results.xlsx.  
Conceptualization, M.M. and S.D.; methodology, K.B.; software, R.K.; validation, M.M. and A.L.; formal analysis, S.D.; investigation, R.K.; resources, K.B.; data curation, S.D.; writing--original draft preparation, S.D., A.C. and G.B.; writing--review and editing, M.M., A.C., D.D. and K.B.; visualization, R.K.; supervision, D.D. and K.B.; project administration, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement:Publicly available datasets were analyzed in this study. This data can be found here: File S2: AlignerItaly_Results.xlsx.  
The authors wish to thank [PERSON] for her valuable assistance in producing the aligners used in the present study.
## 3 RS Applications in Ocean
### Ocean Tide (OT)
#### 3.5.1 Optical  
Optical satellite images have been extensively exploited to study different aspects of OT mainly due to the availability of open-access imagery and proper temporal and spatial resolutions. For example, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI), Automated Water Extraction Index (AWEI), Modified Soil-Adjusted Vegetation Index (MAAVI), and Enhanced Vegetation Index (EVI) generated from optical satellite images have been applied to delineate tidal flats by subtracting the classified images of the high and low tides [263; 275; 276]. Waterlines, which are important for studying changes in tidal flats and coastlines [277], can also be efficiently extracted using high spatial resolution optical satellite imagery. Appropriate spectral bands for waterline extraction must be selected based on various conditions. For instance, the NIR, SWIR, and TIR bands are efficient for flood tide [278; 279].  
Spatial analysis and edge detection methods in optical satellite images have also been applied to locate and map tidal channels. For instance, box-counting, a widely used fractal method, is a spatial analysis method used to identify linear features such as streams and coastlines. Moreover, a multilevel knowledge-based approach can extract channel edges using multiscale edge detection algorithms in optical images [266]. The low-level processing uses edge detection algorithms (e.g., the Sobel and Prewitt operators) to discover channel edges and then associates neighboring nonparallel edges together to form channels [266]. For example, [PERSON] et al. [280] proposed a fractal analysis of tidal channels using box-counting and contiguity methods for Landsat-5 data to derive the degree of geomorphological control on a tidal channel network in the Bahia Blanca Estuary, Argentina.  
Finally, optical satellite data have been widely used for tidal wetland classification. To this end, various spectral and textural indexes, along with advanced ML algorithms, have been employed [272; 273; 281; 282; 283].
## 3 Shielding of Electromagnetic Field
At this point, the law of reflection occurs again, and part of the electromagnetic wave's energy is reflected back into the material, while a part passes out of the shielding material. The reflected portion continues to travel through the shielding material against the direction of the original incident wave until it again reaches the vacuum/shielding material interface. For simplification, many texts mention [2,4,13] that the reflection coefficient \(R\) includes these multiple reflections, meaning the value of \(R\) represents the total reflection of the electromagnetic wave. The absorption coefficient can be expressed through modification as [2]:  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]3  
where \(t\) represents the thickness of the shielding material, and \(\delta\) is the depth of penetration of the electromagnetic wave into the material. The penetration depth can also be calculated according to the formula [2]:  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]4  
where \(\mu\) is the permeability, which includes the permeability of the shielding material, and \(\sigma\) is the specific conductivity of the shielding material. The reflection coefficient \(R\) is determined according to [2]:  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]5where \(\mu_{R}\) is the relative permeability of the shielding material, and \(\varepsilon_{0}\) is the permittivity of a vacuum. The previous formulas apply when the intensities of the electric and magnetic fields are given in basic units. If the value of the transmitted signal is set in logarithmic units, the shielding effectiveness \(SE\) is determined according to Equations (17) and (18) [2]:  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]6  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]7  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]8  
\[Z_{0}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}} \tag{2}\]9  
where \(E_{2}\) is the intensity of the electric field at a certain point in the shielded space, \(E_{1}\) is the intensity of the electric field impinging on the shielding barrier, \(H_{2}\) is the intensity of the magnetic field at a certain point in the shielded space, \(H_{1}\) is the intensity of the magnetic field impinging on the shielding barrier, \(V_{2}\) is the voltage of the electromagnetic wave at a certain point in the shielded space, \(V_{1}\) is the voltage of the electromagnetic wave impinging on the shielding barrier or wall, \(P_{2}\) is the power of the electromagnetic wave at a certain point in the shielded space, and \(P_{1}\) is the power of the electromagnetic wave impinging on the shielding barrier or wall.
## 3 High-Performance LU Decomposition
### Implementation  
#### 3.2.1 Partial Refactorisation in NICSLU  
During the implementation of the partial refactorisation based on a factorisation path, it is important to carefully consider the data structures and ordering methods of NICSLU [30] in order to properly compute the factorisation path on the **LU** matrix. On the one hand, NICSLU uses the compressed sparse row (CSR) matrix format for internal storage. This means that the matrix's nonzero elements are only saved row-wise in a vector. Moreover, another vector stores the indices of all elements of the first vector that are the first element of a row. This saves memory space for nonzero elements and allows efficiently accessing the matrix in a row-wise manner. This has to be considered when computing the column-wise part of the algorithm.  
On the other hand, the ordering methods applied by NICSLU have to be taken into account during the determination of the factorisation path. As mentioned in Section 2.2, NICSLU uses standard AMD and MC64 in the preprocessing step. In addition, NICSLU performs partial pivoting in the factorisation step. Given these reorderings during preprocessing and factorisation, an entry in the original matrix can be permuted to a different location in the **LU** matrix. Hence, to obtain the corresponding permutations, preprocessing and a regular factorisation have to be executed first. These permutations can be applied to the locations of numerically varying entries in the original matrix and, as a result, the locations of numerically varying entries in the **LU** matrix are obtained. Based on the locations in the **LU** matrix, the factorisation path for partial refactorisation is determined.  
After the determination of the factorisation path, the routine that actually computes the partial refactorisation of the **LU** matrix can be called. According to the factorisation path, NICSLU iterates over the subset of columns and only refactors these columns. After this step, the implementation is the same as the original and can be used as such, e.g., calling the solve routine for RHS solving. Additionally, it should be remarked that NICSLU's parallel interface was not employed in this work, since it did not yield any significant performance improvements in the test cases.  
If the partial pivoting choice with its corresponding permutation is kept while a subset of matrix entries varies numerically throughout the simulation, this may deteriorate the numerical stability of subsequent LU factorisations. To address this problem, the authors of [11] proposed a pivot validity test, which ensures that the pivoting choice remains feasible. In the presented work, pivot validity testing was adopted by checking whether the pivot element remained above a certain threshold. This testing was incorporated into our NICSLU based refactorisation method. However, the pivot validity test was always passed, and consequently, it had no impact on the considered use cases. Nonetheless, the integrated pivot validity test shall ensure the numerical stability of our proposed method when applying it in other use cases.
## 2 Field Monitoring Project  
### General Situation  
The Gangkou double-arched tunnel was excavated by the new Austrian tunnelling method (NATM). The NATM is a construction technique which takes rock bolts and shotcrete as main supporting measures and emphasizes on timely supporting to control both deformations and stress relaxation of surrounding rocks and carries out field monitoring of the stresses and deformations of surrounding rocks and supporting structures to guide the design and excavation of tunnels. The Gangkou tunnel is 500 m long and it is located between K89+680 and K90+180 in the Jinhua-Wenzhou highway. According to the engineering geologic investigation data, the lithology in the tunnel region is hard and dense tuff with blocky structures as well as good anti-weathering ability. The discontinuities are well-developed in the rock masses in the tunnel region including a fault and tectonic joints. The fault, oriented 225/115, is located approximately in the middle of the tunnel line. Although the rock masses in the fault zone and its affected zone are quite fragmentized and the rock quality designation (RQD) in the area ranges from 25% to 30%, the width of the fault is no more than 0.5 m, hence it has only a limited influence on the stability of the rock mass in this area. In addition, there are three sets of shear joints, the orientations of which are respectively 39/52, 230/70 and 90/75. The groundwater mainly consisting of the Quaternary pore phreatic water and the bedrock fissure water is of shortage. Figure 1 presents the longitudinal and selected cross sections of the Gangkou double-arched tunnel.  
According to the well-known RMR classification system of rock masses [26] and analysis on the data obtained from the geological prospecting, the surrounding rock masses in that area can be classified into three categories: class III, class IV and class V. Detailed physical and mechanical parameters of these three different classes of rock masses are shown in Table 1.
# O-Band and C/L-Band III-V Quantum Dot Lasers Monolithically Grown on Ge and Si Substrate
## 3 InAs QD Microcavity Lasers on (111)-Faceted Hollow Si (001) Substrate
### InAs QD Microdisk Laser on Si Substrate  
Furthermore, InAs/GaAs quantum-dot microcavity laser on exact Si (001) via (111)-faceted Si hollow structure has been demonstrated. By using a (111)-faceted Si (001) hollow substrate, GaAs buffer and dislocation filter layer were epitaxially grown to reduce the threading dislocations in the subsequent QD laser structure's growth. The active layer consisting of five periods of InAs/GaAs dot-in-a-well (DWELLs) structure was sandwiched between Al\({}_{0.35}\)Ga\({}_{0.65}\)As claddings with a 600-nm-thick Al\({}_{0.65}\)Ga\({}_{0.35}\)As sacrificial layer. The structural characterization of the QD active layer has been presented in earlier work [69]. The schematic diagram and corresponding SEM image of the laser structure are shown in Figure 8. The microdisk size of 4 \(\upmu\)m is chosen for laser characterization. The L-L characterization of Si-based microdisk laser is shown in Figure 9, with a threshold pump power of approximately 380 \(\upmu\)W, which is similar to those on the GaAs substrate. The inset of Figure 9 shows the log-log plot of the L-L curve, revealing an 'S-shaped' nonlinear transition.  
Figure 10 shows the emission spectrum under an optical pump power of 1.8 mW for an O-band InAs/GaAs QD microdisk laser. The laser spectrum shows a full-width-half-maximum (FWHM) of  
Figure 8: Schematic diagram and SEM image of InAs QD microdisk laser structure.  
Figure 9: L-L curve of InAs/GaAs QD microdisk laser grown on (111)-faceted hollow Si (001) substrate; inset: double log plot of the S-curve.  
0.37 nm, which indicates a cold cavity quality factor (Q) of 3645. The spacing between the adjacent modes in the same radial order agrees well with the calculated free spectral range (18 nm) for the first-order mode of the microdisk laser. The QD microcavity laser on the CMOS-compatible Si (001) substrate could potentially be implemented in the application of low-power integrated light sources in the near future.
## 3 Research Results--Analysis and Discussion
### Uniaxial Compressive Strength (UCS)
#### 3.1.3 Effects of Alternative Chloride-Based Stabilisers on UCS  
Chloride-based reagents--ferric chloride, calcium chloride, and potassium chloride--were considered as alternative stabilisers, and with the aim of determining the optimal content of this type of reagent, clayey soil samples with the addition of 1%, 2%, and 3% reagent were tested.  
For all the considered chloride-based reagents, the measured UCS values were significantly lower than the values achieved with the addition of conventional stabilisers; their application, however, certainly resulted in an increase in soil strength, which is in the range of values achieved with the application of stabilisers based on waste materials (Figure 4).  
With the addition of 1% **ferric chloride**, the test results obtained 3, 7, and 28 days after the treatment are quite similar, whereas, with the addition of 2% and 3% stabiliser, the UCS values increased more significantly with time. It was also observed that with the addition of 3% ferric chloride, higher UCS values were achieved compared to the samples with 1% of this stabiliser added. However, compared to the natural state (205 kPa), the maximum improvement was registered with the addition of 2% ferric chloride (369 kPa).  
On the other hand, with the addition of **calcium chloride** or **potassium chloride**, the samples treated with 1% stabiliser resulted in higher UCS values than the samples with the addition of 3% stabiliser. It was noted that the values after 28 days with the addition of 1% calcium chloride (371 kPa) and 2% calcium chloride (391 kPa) are very close. Even closer UCS values were registered with the addition of 1% potassium chloride (337 kPa) and 2% potassium chloride (342 kPa). It should be noted that with the addition of 1% potassium chloride after 7 days since the treatment, a higher value (321 kPa) was obtained than with the addition of 2% of this stabiliser (297 kPa).  
In the case of each of the considered chloride-based stabilisers, the maximum value of UCS was obtained with the addition of 2% stabiliser 28 days after the treatment, which leads to the conclusion that the optimal content of chloride as a reagent in the soil mixture can be considered 2%.  
For the sake of comparison and determining the best-performing binder type, a graphic interpretation showing the UCS performance for the natural clayey soil and the best-performing soils using conventional, waste-based, and chloride-based stabilisers is presented in Figure 5. Conventional stabilisers proved to be the most effective stabilisers in terms of improving the uniaxial compressive strength of the clayey soil, between which cement stands out slightly.  
Figure 5: Comparison of the effects of the examined conventional, waste, and alternative materials on the soil UCS value.