esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wood-Based Panels
### Adhesives in WBP Production  
The adhesives used in WBP production can be classified as natural or synthetic, and further categorized into thermosetting and thermoplastic resins. Among synthetic adhesives, the focus is on thermosetting resins, particularly amino resins (such as urea-formaldehyde (UF) and melamine-urea-formaldehyde (MUF)), phenol-formaldehyde resins (PF), and isocyanate-based resins (pMDI), which are commonly used in wood composite production [28; 29].  
While UF resins are widely used due to their low cost, adaptability to curing conditions, and fast curing, there are also known limitations to their use. They are more susceptible to degradation under humid or acidic conditions and slowly release formaldehyde from the finished panel [28; 29; 30; 31]. To mitigate formaldehyde emissions, adjusting the formaldehyde/urea ratio during resin synthesis is a common practice.  
MUF resins, when compared to UF resins, exhibit higher moisture resistance, which makes them more appropriate for humid conditions in interior applications or covered outdoor applications. Although the incorporation of up to 40% of melamine in the formation of MUF resin increases the moisture resistance of the wood products in which it is used, it also results in higher costs, as melamine is less affordable than urea [28].  
PF resins are highly stable adhesives, displaying significant resistance to hydrolysis degradation and minimal formaldehyde emissions, particularly in comparison to UF resins [28; 29; 30; 31]. These resins find extensive use in the manufacture of exterior panels like construction plywood and oriented-strand board. However, their higher cost and the tendency to yield panels with reduced mechanical properties remain noteworthy drawbacks.  
Isocyanate-based resins represent a more costly but highly durable option for exterior-grade adhesives, predominantly utilized in the production of OSB. The absence of formaldehyde and other VOC emissions upon curing is a major advantage of this adhesive system [31]. However, challenges arise due to pMDI's strong adhesion to the press plates or belts, necessitating the use of appropriate release agents, as well as its associated handling risks [32].  
A summarized comparison of these different synthetic thermosetting resin systems listing their applications, advantages, and disadvantages, is presented in Table 1.  
Figure 2: Schematic diagram of the medium-density fiberboard production process. Adapted from [10; 18].  
Bio-based resins are natural adhesives that should exhibit similar performance to synthetic resins while being derived from natural and organic sources. These resins can be based on proteins, tannins, carbohydrates, oils, and liquefied cellulosic materials [31]. Although interest in these adhesives has increased in recent years, their impact on the market is still far from surpassing that of synthetic resins.
## 4 Performance Evaluation of Improved SOSK-Means
### Datasets
#### 4.1.2 The Real-Life Datasets  
The real-life datasets consisted of the following: eight shape sets (Aggregation, Compound, D31, Flame, Jain, Path-based, R15 and Spiral datasets); ten datasets from the UCI repository (Breast, Glass, Iris, Jain, Leaves, Letter, Thyroid, Wdbc, Wine and Yeast); two Mopsi locations datasets (Joensuu and Finland); two RGB images datasets (Housec5 and Housec8); one Gray-scale image blocks; and two miscellaneous datasets (T4.8k and Two moons). The real-life datasets had varying clusters, ranging from 2 to 256, their di  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Datasets** & \multicolumn{1}{p{56.9 pt}}{**Number of Data Objects**} & \multicolumn{1}{p{56.9 pt}}{**Dimension of Data**} & \multicolumn{1}{p{56.9 pt}}{**Number of Clusters**} & \multicolumn{1}{p{56.9 pt}}{**Dataset Types**} & \multicolumn{1}{p{56.9 pt}}{**References**} \\ \hline
**Aggregation** & 788 & 2 & 7 & Shape sets & [64; 69] \\ \hline
**Breast** & 699 & 9 & 2 & UCI dataset & [64; 70] \\ \hline
**Bridge** & 4096 & 16 & 256 & Grey-scale image blocks & [64; 71] \\ \hline
**Compound** & 399 & 2 & 6 & Shape sets & [64; 72] \\ \hline
**D31** & 3100 & 2 & 31 & Shape sets & [64; 73] \\ \hline
**Flame** & 240 & 2 & 2 & Shape sets & [64; 74] \\ \hline
**Glass** & 214 & 9 & 7 & UCI dataset & [64; 70] \\ \hline
**Housec5** & 34,112 & 3 & 256 & RGB Image & [64; 71] \\ \hline
**Housec8** & 34,112 & 3 & 256 & RGB Image & [64; 71] \\ \hline
**Iris** & 150 & 4 & 3 & UCI dataset & [64; 70] \\ \hline
**Jain** & 373 & 2 & 2 & Shape sets & [64; 75] \\ \hline
**Leaves** & 1600 & 64 & 100 & UCI dataset & [64; 70] \\ \hline
**Letter** & 20,000 & 16 & 26 & UCI dataset & [64; 70] \\ \hline
**Joensuu** & 6014 & 2 & 4 & Mopsi locations & [64; 76] \\ \hline
**Finland** & 13,467 & 2 & 4 & Mopsi locations & [64; 76] \\ \hline
**Path-based** & 300 & 2 & 3 & Shape sets & [1; 64] \\ \hline
**R15** & 600 & 2 & 15 & Shape sets & [64; 77] \\ \hline
**Spiral** & 312 & 2 & 3 & Shape sets & [64; 78] \\ \hline
**Thyroid** & 215 & 5 & 2 & UCI dataset & [64; 70] \\ \hline
**T4.8k** & 8000 & 2 & 3 & Miscellaneous & [64; 79] \\ \hline
**Two moons** & 10,000 & 2 & 2 & Miscellaneous & [80; 81] \\ \hline
**Wdbc** & 569 & 32 & 2 & UCI dataset & [64; 70] \\ \hline
**Wine** & 178 & 13 & 3 & UCI dataset & [64; 70] \\ \hline
**Yeast** & 1484 & 8 & 10 & UCI dataset & [64; 70] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The characteristics of the 24 real-life datasets.  
mensions ranged between 2 and 64, and the number of data objects in the datasets ranged between 150 and 34,112.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 3 Building Blocks
### Beam Splitters  
There are three kinds of beam splitters, i.e., the multimode interference (MMI) beam splitters, star couplers, and directional couplers. The MMI splitters equally distribute input power into each output channels [40,59,65,70,71,75,75]. The star coupler has a smaller footprint [12,35,42,85], which is especially useful for many output channels. Phase alignments are necessary to compensate for the phase error accumulations due to the path-length mismatch in a star coupler [42]. Compared with MMI splitters, star couplers usually have a larger insertion loss [81].  
The directional couplers evanescently transfer power from the bus waveguide to the row waveguides and from each row waveguide to the output emitter array. By varying the coupling length, the same amount of power can be set at all the emitters [10,16,17,86,87]. Optical directional couplers can be used to achieve compact large-scale OPAs [16]. To take advantage of the low loss and high-power handling properties of Si\({}_{3}\)N\({}_{4}\) and the excellent modulation performance of Si, hybrid OPA chips with Si\({}_{3}\)N\({}_{4}\) and Si waveguide layers were realized, which are vertically coupled to each other with adiabatic linear tapers working as directional couplers [88,89]. The directional coupler structures can also be exploited for testing the optical crosstalk between the adjacent [90], the second nearest, and even the third-nearest waveguides. As another example, the wavelength-dependent directional coupler was also used for separating the 1.599 \(\upmu\)m signal wavelength from the 980 nm pump wavelength [77].
###### Abstract  
A solar dish concentrator system has a large windward area and heavy structural mass, and under the action of wind loads and self-weight loads, foundation settlement can easily occur and cause the column (the azimuth axis) to tilt. Upon tilting, the azimuth axis is no longer perpendicular to the horizontal plane, causing a tracking error in the service of the solar dish concentrator system. In this paper, a tracking error model of a solar dish concentrator system is established based on the rigid body motion theory, which considers the azimuth axis tilt error. In this model, a radial angle and tangential angle parameters are used to describe the azimuth axis's tilt angle and tilt direction. Under the tilt error of the azimuth axis, we analyze, in detail, the initial tracking position of a solar dish concentrator system, the system operation area, and the variation rule of tracking performance in long-term operation. The results show that under the azimuth axis tilt error of the solar dish concentrator system, the deviation of the initial tracking position of the solar dish concentrator system in the horizontal or vertical plane will reduce its tracking performance and the stability of tracking performance compared with the initial tracking position being due east. The tracking performance of a solar dish concentrator system and its stability are better in areas with a relatively low latitude. In different areas with close latitude, the tracking performance of the solar dish concentrator system and its stability are better, particularly with lower longitudes. During a whole year operation period, the tracking performance of an solar dish concentrator system in the first quarter and the fourth quarter is relatively better, and its stability in June and July is relatively better. This work can provide a theoretical basis for the installation, debugging, and error control of solar dish concentrator systems.  
s 2022, 15, 3261. [[https://doi.org/10.3390/en15093261](https://doi.org/10.3390/en15093261)]([https://doi.org/10.3390/en15093261](https://doi.org/10.3390/en15093261))  
1
## 2 Materials and Methods
### Classification  
We used the whole set of extracted radiomic features to predict the following GGO characteristics estimated by the expert radiologists:  
* Multifocal GGO;
* Presence of Crazy Paving;
* Presence of Consolidation;
* Roundish GGO;
* Peripheral GGO;  
Additionally, the two clinical outcomes:
* PREDI-CO score;
* Patient survival.  
Not all the above scores were available for all the patients. The GGO characteristics were reported for only 78/82 patients, while the clinical outcomes for only 72 of them. We restricted the score classification on these two subsets of data.  
The considered scores are all binary values (True/False), representing the presence/absence of the corresponding characteristic. The only exception is given by the PREDI-CO score, which, by definition, allows an incremental set of values: PREDI-CO score values range from 0 (minimal risk) to 9 (maximal risk). The 47% of patients report a PREDI-CO score of 0 or 1, leading to an overrepresentation of these 2 labels. We grouped multiple labels into the same class to overcome this issue: we applied the cutoff of 1 to dichotomize the PREDI-CO score values into two classes.  
We applied a feature selection procedure to filter out redundant and non-informative values for each classification. This step is required to improve the classification performances (remotion of possible confounders) and to make the obtained results more interpretable from a clinical point of view. The selection was performed using a Fisher Exact and a \(\chi^{2}\) tests selecting the 3 features with the highest significance (lower p-values). Both the tests require categorical data, so we dichotomized the features according to their medians.  
We used the set of filtered features as input to 4 different classification algorithms to predict the various GGO characteristics and clinical outcomes:  
* Logistic Classifier (L1 penalty);
* (Logistic) Ridge Classifier (L2 penalty);
* K-Nearest Neighbors;
* Random Forest Classifier.  
The performances of these 4 methods give us an insight into the structure of the data: KNN methods are strongly local, Random Forest relies on the separability of the samples but does not include co-linearities between variables, and linear models (Logistic classifier and Ridge classifier) strongly rely on linear dependencies between the observed values and the predictions.  
The classification performances were estimated according to the following metrics:  
* Precision
* Sensitivity* F1 score
* Balanced accuracy  
\[BA=\frac{Sensitivity+Specificity}{2}\]  
using a leave-one-out cross-validation strategy.  
The numerosity of the labels of each characteristic is strongly unbalanced (e.g., only 10% of patients show the presence of consolidations) for every characteristic, except for Crazy Paving and PREDI-CO score (after the dichotomization). To compensate for this, we weighted the classification performances of each algorithm according to the inverse of the class frequency.  
We used the scikit-learn [65] Python package for the implementation of all the analyses.
## 2 Materials and Methods
### Universal GNSS Channel Design Decisions
#### 2.1.7 Power Consumption and Resource Usage  
Table 3 summarizes both the power consumption, as obtained with the Xilinx ISE XPower software, and the FPGA resource usage for different tracking channel complexities, leading to the proposed universal channel. The power consumption percentages presented herein are taken relatively to the \"BPSK with FDMA\" reference implementation, assessing the overhead associated with the implementations derived with added feature sets.  
It can be seen that the quiescent power is relatively constant across all implementations, and may be attributed to the chip itself, leaving the dynamic power as a more meaningful comparison metric. The single-component MBOC implementation consumes 33% more power, while the dual-component (data and pilot) requires twice as much, _i.e._, 66% increase compared to the reference BPSK implementation.  
For each implementation, the absolute number of resources and associated percentage (_vs._ available) are presented. As a result, the proposed optimizations led to a dual-component MBOC universal channel of complexity comparable to that of two traditional BPSK reference channels,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Resources in xcetwx55-10 ff148** & **Available** & **BPSK w/FDMA** & **L2C** & & **BOC** & **Single MBOC** & **Dual MBOC** \\ \hline Dynamic Power (mW) & 25.1 & 100\% & 25.1 & 100\% & 29.6 & 118\% & 33.4 & 133\% & 41.8 & 166\% \\ Qaicscent Power (mW) & 860 & 100\% & 860 & 100\% & 860 & 100\% & 861 & 100\% & 861 & 100\% \\ Total Power (mW) & 885 & 100\% & 885 & 100\% & 890 & 10\% & 894 & 10\% & 903 & 102\% \\ Slices & 24576 & 651 & 200\% & 765 & 31\% & 943 & 88\% & 1018 & 1410 & 32\% \\ Slice Phiops & 49152 & 775 & 16\% & 918 & 1.9\% & 1118 & 2.3\% & 1186 & 2.4\% & 1476 & 30\% \\
4 input LUTs & 49152 & 908 & 1.8\% & 1127 & 2.3\% & 1456 & 3.0\% & 1554 & 3.2\% & 1223 & 4.3\% \\ as logic & 894 & 1113 & & 1436 & 1528 & 2091 & & & & \\ ashift registers & 14 & 14 & 20 & 26 & 32 & & 32 & \\ FIFO16/RMB16s & 320 & 2 & 60\% & 2 & 0\% & 2 & 0\% & 3 & 69\% & 4 & 13\% \\ DSP4s & 512 & 11 & 2.1\% & 11 & 2.1\% & 17 & 3.3\% & 17 & 3.3\% & 29 & 57\% \\ \hline Max. number of single channels & 3 & 7 & 26 & 26 & 24 & 24 & 12 & \\ Max. number of dual channels & 18 & 16 & 13 & & 12 & 17 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Universal Channel Resources for Different Feature Sets.  
but with a lot more flexibility. For flexibility and maintainability, the universal channel has been implemented with VHDL configurations that can easy be changed to enable or not several feature sets.
# Potential Effects of Photobiomodulation Therapy on Human Dental Pulp Stem Cells
###### Abstract  
Background: A significant clinical technology enabling the use of stem cells in dentistry is Photobiomodulation (PBM). The current study aimed to investigate various PBM treatment variables on human dental pulp stem cell proliferation, survival, and changes in cell cycle progression. Methods: Human third molar samples were collected, and human Dental Pulp Stem Cells (hDPSCs) were isolated, expanded, seeded, and cultured in media containing 0, 1, 5, or 10% FBS. PBM treatments using an 810 nm diode laser at various doses were performed 24, 48, and 72 h after seeding. Cell proliferation and apoptosis were assessed. A two-way ANOVA test was performed among all treatment groups, followed by multiple comparison tests (Tukey's test) performed between each treated group and its control untreated group. Results: After 24 h, only cells cultured in 10% FBS showed a significant (_p_ < 0.005) increase in the proliferation rate of cells treated with T7 (0.4 W \(\times\) 10 s). After 48 h, hDPSCs cultured in both 5% and 10% FBS showed a significant (_p_ < 0.005) increase in the proliferation rate of cells treated with T7 (0.4 W \(\times\) 10 s) as compared to the untreated control. After 72 h, only cells cultured in 10% FBS showed a significant increase in the proliferation rate of the cells treated with T9 (1 W \(\times\) 3 s) (_p_ < 0.005) and T7 (0.4 W \(\times\) 10 s) (_p_ < 0.00005). Conclusions: Low-power laser therapy at a wavelength of 810 nm induced hDPSC proliferation at the following parameters: power output 0.4 W, irradiance 0.5 W/cm\({}^{2}\), fluence 0.416 J/cm\({}^{2}\), exposure time 10 s.  
Keywords:apoptosis; dentistry; hDPSCs; laser; MTT assay; photobiomodulation; proliferation +
Footnote †: journal: _Sciences_
## 3 Results
### Advantages of the RSVI  
Based on four vegetation indices representing different ecological significance, we developed an RSVI that demonstrated consistent variations in monitoring and assessing vegetation dynamics compared to the initial four indices (Figure 5). It exhibited greater sensitivity to dry-wet variations under both drought and nondrought conditions (Figures 12 and 13). Furthermore, the RSVI we constructed exhibited a mean correlation  
Figure 12: Two-dimensional scatter plots of vegetation in the 2012 Central Asian region within grids affected by drought in relation to temperature, precipitation, and TVDI changes: \(\Delta\)VI represents the difference between the average vegetation index (VI) during the 2012 growing season and the reference year (average VI from 2011 to 2013). Similarly, \(\Delta\)TVDI represents the difference between the average temperature vegetation dryness index (TVDI) during the 2012 growing season and that during the reference year (the average TVDI from 2011 to 2013). Additionally, \(\Delta\)Pre and \(\Delta\)Tem represent the differences between the average precipitation and temperature in 2012 and those in the reference year (average precipitation and temperature from 2011 to 2013).  
Figure 13: Two-dimensional scatter plots of vegetation in grids in the 2012 Central Asian region where no drought occurred in relation to temperature, precipitation, and TVDI changes: \(\Delta\)VI represents the difference between the average vegetation index (VI) during the 2012 growing season and the reference year (average VI from 2011 to 2013). Similarly, \(\Delta\)TVDI represents the difference between the average temperature vegetation dryness index (TVDI) during the 2012 growing season and that during the reference year (the average TVDI from 2011 to 2013). Additionally, \(\Delta\)Pre and \(\Delta\)Tem represent the differences between the average precipitation and temperature in 2012 and those in the reference year (average precipitation and temperature from 2011 to 2013).  
of 0.5 with the TVDI. The significance test (\(p<0.05\)) revealed that 81% of the raster cells achieved statistically significant correlations, indicating the strong potential of the RSVI in research related to vegetation and drought relationships (Figure 14).
## 1 Introduction
Moreover, Uniaxial compression tests and high-speed camera technology analyses on grouted bodies with varying fracture dip angles showed that the strength of grouting reinforcement body initially decreased and then increased with increasing fracture dip angles, while the failure modes transitioned from axial splitting to shear failure, revealing the failure mechanisms under different stress conditions [21; 22; 23]. These findings reveal the failure mechanisms of grouted bodies under different stress conditions, offering theoretical support for engineering practices. The aforementioned research on fractured rock grouting reinforcement bodies has predominantly focused on normal temperature environments, with limited studies examining the effects in seasonally frozen regions. As grouting technology continues to develop and be applied in these regions, more grouting projects will be subjected to the impact of freeze-thaw cycles. Therefore, it is crucial to investigate the physical and mechanical properties of grouted reinforcement bodies under freeze-thaw cycles in seasonally frozen environments. This paper investigates grouting reinforcement bodies with varying crack dip angles as the primary research focus. Through a series of laboratory tests, including freeze-thaw cycling tests, nuclear magnetic resonance (NMR) tests, and uniaxial and triaxial compression tests, this research examines the grouted sandstone from multiple perspectives,ranging from the micro-scale pore structure to the macro-scale mechanical properties. The study provides a quantitative analysis of the micro-pore evolution and mechanical property changes of the grouting reinforcement body during freeze-thaw cycles. Additionally, the influence of crack dip angle on the failure modes of the grouting reinforcement body is also explored. The findings of this study will offer theoretical support for the design and safe construction of grouting projects in cold regions. They will also serve as a valuable reference for the long-term monitoring and maintenance of grouting projects in cold regions, which is conducive to the development of more monitoring scientific plans and maintenance strategies to ensure the safe operation of the projects.
## 2 Materials and Methods
### MTT Viability Assay  
The effect of _S. triloba_ crude extracts on U2 OS, SKOV3, and HepG2 cell viability was evaluated using the 3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolzolium bromide (MTT) colorimetric assay. Viable cells reduce the yellow MTT reagent (Serva, Germany) reagent to a purple formazan salt, which can be quantified using a spectrophotometer [32]. Cells were seeded at a density of 5000 cells/well in a 96-well plate, and cultured overnight to facilitate attachment. Cell viability was measured after a fixed time point of 48 h. After incubation, the treatment was discarded and replaced by 100 uL of fresh DMEM supplemented with 20% MTT (5 mg/mL) per well and incubated for 4 h. The MTT solution was then discarded and replaced by 100 uL of DMSO (Sigma Aldrich, USA) to dissolve the formazan crystals and the color change quantified using the SPECTROstar-Nano microplate reader (BMG LABTECH), with wavelength parameters set to 570 nm. Cell viability was determined as a percentage of the absorbance of treated cells relative to the absorbance of untreated cells.  
HepG2 cells were eliminated from further analysis since treatment with _S. triloba_ ethanol extract yielded negligible effect on cell viability. All dose-response curves were generated after transformation and normalization of MTT viability data in GraphPad Prism 6.01 software [33], using Equation (1) (log[inhibitor] vs. normalized response--Variable slope).  
\[\text{Y}=\frac{100}{\left(1+10^{((\text{Log}\text{ LC50--X})\times\text{ Hill slope})}\right)} \tag{1}\]  
where **X** = log of the concentration of the treatment, **Y** = % viability.