esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Requirements and Considerations for Selection of Waste-to-Energy Technologies
It can be seen that waste-to-energy technologies such as incineration, gasification, and pyrolysis have higher energy potentials than others, but they also have higher plant capacities, ranging from 900 to 1300 tons of waste per day for incineration and gasification. Additionally, the capital cost of these technologies is much higher, being in the range of USD 30-180 million for incineration, USD 50-80 million for plasma gasification, and USD 16-90 million for pyrolysis. Meanwhile, the O and M cost ranges approximately from USD 80 to 120 per ton of waste for incineration and from USD 80 to 150 per ton of waste for plasma gasification and pyrolysis. It has been observed that since sanitary landfills have the lowest capital and O and M costs, most Southeast Asian countries practice it as a major waste disposal method [1]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Indicators**} & \multicolumn{4}{c}{**Most Suitable (\(\backslash\)); Moderate (M); Not Suitable (\(\times\))**} \\ \cline{2-6}
**Technical Parameters** & \multicolumn{4}{c}{**Waste-TO-ENERGY Technologies**} \\ \cline{2-6}  & **Anaerobic Digestion (AD)** & **Incineration** & **Pyolysis** & **Plasma** & **Landfill  
Additionally, the waste management cost for sanitary landfills in developing countries ranges from 10 to 45 USD per ton [12]. The emissions from an incineration plant are 1.6 kg of CO\({}_{2}\) equivalent per kWh, 191.2 g of NOx per ton of waste processed, and 94.6 g of SO\({}_{2}\) per ton of waste processed. Dioxin emissions from incineration plants have stringent regulations in other developing and developed countries, being limited to 1 ng/m\({}^{3}\) toxic equivalent [32; 54]. In the case of the thermo-chemical treatment of MSW via incineration, gasification, and pyrolysis, a facility distance of up to 10 km for the exposed population for all MSW incinerators still gives negative health impacts in the long-term [34]. The disposal of bottom ash and fly ash from incinerators into landfills remains a serious environmental issue due to the presence of 8-12% ferrous metals and 0.5-1.5% non-ferrous metals in such ash [32]. Figure 8: Comparison of different waste-to-energy technologies in term of **(a)** plant capacity and energy potential **(b)** capital cost **(c)** operation and maintenance (O and M) cost, and **(d)** planning-to-commissioning cost, and **(e)** emissions [15; 35; 55]. Note: Capital cost, O and M cost, and planning-to-commissioning cost are based on plant capacity. However, in the case of AD, although it is not as energy efficient as the other methods, which can yield high energy potentials with high environmental costs, the produced digestate may be beneficial to rural countries. The digestate contains a high proportion of nutrients and can be used as a fertilizer in agriculture and horticulture to avoid the use of chemical fertilizer.
## 4 Computational Experiments
### Parameter Tuning  
We have performed a preliminary tuning with the aim to select the most appropriate combination of parameters:  
* the activation function;
* the number of hidden layers;
* the training algorithm;
* the learning rate;
* the maximum number of iterations in the training phase;
* the number of zones.  
Table 2 summarizes the investigated values for each parameter.  
Our datasets contained approximately 6-700 instances with 50 customers each: 90% has been assigned to the training set, while the remaining 10% to the test set. The best results, in terms of strength of captured relationships, were obtained by the following neural network settings: three layers, hyperbolic tangent activation function, five neurons in the hidden layer, LBFGS solver and constant learning rate.  
As far as customer aggregation is concerned, it was necessary to project customers locations to a new planar reference system. According to the EPSG projection, we used the Transverse Mercator Projection for London and the Lambert Zone II Projection for Paris.  
For London, 8 clusters gave the best results in terms of coefficient of determination (\(R^{2}\)), whilst for Paris 6 zones were the best case for neural network performance. Tables 3 and 4 summarize the neural network mean errors (in minutes) for each zone. The \(R^{2}\) scores (=0.53 for the London instances and =0.60 for the Paris instances) suggest a moderate effect size.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Possible Values** \\ \hline Activation Function & tanh, logistic, relu \\ Number of Hidden Layers & (3) (4) (5) (10) (15) (20) (30) (40) (3,3) (4,4) (5,5) (10,10) (15,15) (20,20) (30,30) (40,40) \\ Solver & sgd, lbfgs, adam \\ Learning Rate & constant, inverse scaling, adaptive \\ Max. iterations & 200, 300, 400, 500 \\ Number of zones & 5, 6, 7, 8, 9, 10, 11, 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Investigated parameter values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zone** & **Mean Error** & **Mean Absolute Error** & **Standard Error** \\ \hline
1 & 7.68 & 36.78 & 55.16 \\
2 & \(-\)4.61 & 29.23 & 37.19 \\
3 & 8.32 & 26.94 & 35.51 \\
4 & \(-\)1.93 & 27.34 & 36.87 \\
5 & \(-\)2.68 & 28.78 & 46.21 \\
6 & 8.69 & 56.68 & 69.21 \\
7 & 2.54 & 24.60 & 32.31 \\
8 & 6.68 & 54.00 & 64.84 \\ \hline
**Average** & 3.09 & 35.54 & 47.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean errors in the London instances.
## 5 Discussion
This limitation constrains the application of the LM-OBF algorithm to the post-processing and identification stages of data, making it unsuitable for real-time parameter estimation scenarios involving magnetic dipole sources. In addition, the accuracy of magnetic anomaly data itself significantly influences the LM-OBF algorithm's accuracy in estimating the parameters of magnetic dipole sources. For example, the data obtained during total field scalar measurements includes the superimposed anomalies of the geomagnetic background field and the target magnetic source. Accurately separating the target magnetic anomaly from the geomagnetic background anomaly, ensuring that the data contains only the magnetic anomalies generated by the target magnetic source, will profoundly affect parameter estimation accuracy under actual measurement conditions. This separation can lead to significant deviations between the actual and estimated values. Therefore, developing more accurate field separation strategies or investigating parameter estimation methods based on gradient and tensor data will hold greater practical significance in the future.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 2 Related Work
### Development and Basic Principle of CenterNet  
With the emergence of Feature Pyramid Networks [31] and Focal Loss [30], the detectors based on the anchor-free network become a research hotspot and exhibit their good performance. CornerNet [32] was first reported in 2018 and realized anchor-free detection by regarding the object as a pair of corner points. Authors designed two heatmap branches to predict the top-left corner point and bottom-right corner point, respectively. In order to settle the problem of no local visual feature for the location of corners, the corner pooling module was proposed. The detector achieved the highest detection accuracy at that time. Inspired by CornerNet, Keypoint Triplets [35] detected top-left corner, bottom-right corner and center point of the object as triplets, while ExtremeNet [47] detected the top-most, left-most, bottom-most, right-most, and center point of the object. Both these methods build on the same robust keypoint estimation network. However, they require a combinatorial grouping stage after keypoint detection, which significantly reduces the detection speed.  
CenterNet simply extracted the center-point for each object, and then regressed the size and the location of the bounding box. The input image was feed into a Fully Convolution Network to generate a heatmap branch, in which peaks corresponded to target centers. Offset branch predicted the location offsets of the center pixel while size branch predicted the height and weight of the bounding box. CenterNet assigned anchor based on center point, while adjacent points were lowered in sequence, thus there was no need to set thresholds between foreground and background classification. The recognition speed of CenterNet was much better than that of anchor-based detectors, since it eliminated the need for multiple anchors.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### Reliability Evaluation Method  
An exponential reliability function is frequently used to calculate the reliability of general components and systems [59]. Being a univariate function, the reliability function varies with time. The failure rate \(\lambda\) is another parameter for the reliability function. Equation (3) provides the failure probability density function.  
\[f(t)=\lambda e^{-\lambda t} \tag{3}\]  
Based on Equation (3), integration can be used to determine reliability--the likelihood that a failure will not happen before its time--as illustrated by the following Equation (4).  
\[R(t)=1-\int_{0}^{t}f(t)\,dt=e^{-\lambda t} \tag{4}\]  
In order to create an FT for the system, events are examined using appropriate logic gates (AND or OR). This allows for an assessment of the fault contribution of individual components. The output probability of \"AND\" and \"OR\" gates is computed using probability theory.  
\[P_{AND}=\prod_{i=1}^{n}p_{i} \tag{5}\] \[P_{OR}=1-\prod_{i=1}^{n}(1-p_{i}) \tag{6}\]  
where \(p_{i}\) is the failure probability of the event that corresponds to the logic gate's input.
## 2 Materials and Methods
### Data Sets
#### 2.3.2 Drought Indicator: The Standardized Precipitation Evapotranspiration Index (SPEI)  
Due to the integration of precipitation (P) and potential evapotranspiration (PET) for monitoring drought [52], SPEI achieves remarkable results in eco-hydrological applications [53]. Because of the lagged effect of water on vegetation, 3-month SPEI data covering the period from 2003 to 2015 were selected from the SPEIbase v.2.4 product ([[https://spei.csic.es/database.html](https://spei.csic.es/database.html)]([https://spei.csic.es/database.html](https://spei.csic.es/database.html)) (accessed on 27 January 2025), [54]). The average of 3-month SPEIs in June, July and August were defined as the summer SPEI. Different drought levels were divided based on SPEI (e.g., humid: SPEI \(\geq\) 1.0, normal: \(-\)1.0 \(<\) SPEI \(<\) 1.0, moderate drought (MD): \(-\)1.5 \(<\) SPEI \(\leq\)\(-\)1.0; severe drought (SD): \(-\)2.0 \(<\) SPEI \(\leq\)\(-\)1.5; extreme drought (ED): SPEI \(\leq\)\(-\)2.0).  
Figure 2: Processing flow for estimating ET and GPP using MODIS products.
## 2 Materials and Methods
### Analysis of Volatile Compounds  
The quantitative and qualitative analysis of volatile compounds in EVOOs was performed by headspace-solid phase microextraction (HS-SPME), followed by gas chromatography-mass spectrometry (HS-SPME-GC/MS). The sampling of headspace volatile compounds by SPME was applied as follows: Three grams of VOO were placed in a 20 mL vial and tightly capped with a polytetrafluoroethylene (PTFE) septum. The internal standard (2-methyl propylacetate) was added to the sample at a concentration of 9.8 mg/kg. The vial was left for 10 min at 35 \({}^{\circ}\)C to allow for equilibration of the volatiles in the headspace. After equilibration, the SPME fiber (a 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) with a length of 2 cm (StableFlex, Supelco, Inc., Bellefonte, PA, USA) was exposed to the vapor phase for 30 min to sample the volatile compounds. Then, gas chromatography-mass spectrometry analysis was performed.  
The analyses of the volatile compounds were carried out with an Agilent Technologies GC 7890B equipped with a \"Multimode Injector\" (MMI) 7693A (Agilent Technologies, Santa Clara, CA, USA) and a thermostated PAL3 RSII20 autosampler equipped with a fiber conditioning module and an agitator (CTC Analytics AG, Zwingen, Switzerland). The detection system was an Agilent 5977B single quadrupole GC/MSD with an EI Extractor (XTR) source (Agilent Technologies, Santa Clara, CA, USA). The volatiles adsorbed by the fiber were thermally desorbed in the hot GC injector port, which was set in splitless mode, for 5 min at 250 \({}^{\circ}\)C. The volatile compounds were separated on a DB-WAXetrum column (50 m, 0.32 mm i.d., 1 \(\upmu\)m film thickness) (Agilent Technologies, Santa Clara, CA, USA) using helium as the carrier gas at a constant flow rate of 1.7 mL/min. The GC oven heating program started at 35 \({}^{\circ}\)C; this temperature was held for 4 min, then increased to 150 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, increased to 180\({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min, held for 2 min, increased to 210 \({}^{\circ}\)C at a rate of 11 \({}^{\circ}\)C/min, and this temperature was held for 13.77 min. The total analysis time was 55 min. The temperature of the transfer line was fixed at 215 \({}^{\circ}\)C. The MSD was operated in electron ionization (EI) mode, with an ionization energy of 70 eV, in scan mode, with scanning in the mass range of \(m/z\) 25-350 a.m.u. at a scan rate of 4.3 scan/s and in SIM mode for improving the detection limits. The MS source and the MS quad temperatures were 190 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The volatile compounds were identified by comparing their mass spectra and retention times with those of authentic reference compounds and with the spectra in the NIST 2014 mass spectral library. The volatile compounds were quantified using calibration curves for each compound by internal standard calculation, and the results were expressed in \(\upmu\)g/kg of oil.
# Quantitative Evaluation of the Effectiveness of Erbium Glass Laser Therapy for Acne Scars
###### Abstract  
Background: Acne scarring presents a significant esthetic and psychological concern, commonly classified into atrophic and hypertrophic types. Effectively managing these lesions often involves the use of therapeutic strategies such as laser treatments, dermat abrasion, and fillers. This study investigates the efficacy of 1550 nm erbium glass laser therapy in the treatment of atrophic acne scars through a quantitative assessment. Material and Methods: Participants with mild to moderate atrophic acne scars received two sessions of fractional erbium glass laser therapy at one-month intervals. Skin density and epidermal thickness were measured using a high-frequency ultrasound device (DUB SkinScanner), while the Antera 3D imaging system facilitated a comprehensive analysis of skin parameters, including texture, volumetric depressions, and pigmentation. Results: The use of this therapy led to significant improvements across multiple parameters. Skin density and epidermal thickness increased. Significant reductions were observed in fold depth, pore volume, and depression volume, indicating enhanced smoothness and minimized scar appearance. Improvements in texture roughness and pigmentation contributed to a visually coherent skin surface. Conclusions: Fractional erbium glass laser therapy effectively ameliorates the appearance of atrophic acne scars by increasing skin density, reducing dermal depressions, and improving texture and pigmentation uniformity. The Antera 3D system and high-frequency ultrasound device demonstrated high efficacy in capturing subtle changes, supporting its value in clinical applications for optimizing treatment parameters.  
skin imaging; texture; ultrasonography; acne scars +
Footnote †: journal: Applied sciences
# A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best-Worst Method
## 4 Experimental Studies
### Example 2  
In this example, we considered three DMs with four criteria. The first and fourth criteria were assumed to be the best and worst criteria, respectively. The preference values for comparisons with multichoice parameters are depicted in Table 5.  
In Table 5, the last row is the multichoice response sets with \(o_{13}=\{8,\ 9\}\), \(o_{14}=\{3,\ 4\}\), and \(o_{23}=\{3,\ 4\}\). The data provided in the last row were used for solving the model of Example 2. The formulated model is presented in Equations (37)-(44).  
\[\text{Min}\ \ \ \xi\] (37) subject to \[W_{1}-W_{2}\times 2\leq\xi,\ \ W_{1}-W_{2}\times 2\geq-\xi\ \ \ \forall j \tag{38}\] \[W_{1}-W_{3}\times(z^{13}+8)\leq\xi,\ \ W_{1}-W_{3}\times(z^{13}+8) \geq-\xi\ \ \ \forall j\] (39) \[W_{1}-W_{4}\times(z^{14}+3)\leq\xi,\ \ W_{1}-W_{4}\times(z^{14}+3) \geq-\xi\ \ \ \forall j\] (40) \[W_{2}-W_{3}\times(z^{23}+3)\leq\xi,\ \ W_{2}-W_{3}\times(z^{23}+3) \geq-\xi\ \ \ \forall j\] (41) \[W_{4}-W_{3}\times 2\leq\xi,\ \ W_{4}-W_{3}\times 2\geq-\xi\ \ \ \forall j\] (42) \[\sum W_{j}=1,\ \ W_{j}\geq 0,\ \ \forall j,\ \ \xi\geq 0\] (43) \[z^{13}=0,1\ \ z^{14}=0,1\ \ z^{23}=0,1 \tag{44}\]  
The optimal values \(\xi^{*}\)=0 and \(z^{13}=1\), i.e., \(o_{13}=9\); \(z^{14}=1\), i.e., \(o_{14}=4\); and \(z^{23}=0\), i.e., \(o_{23}=3\). The value of \(o_{BW}=9\) as \(z^{13}=1\). The corresponding CR becomes equal to 0. The weights and ranks of the criteria are provided in Table 6.  
In Table 6, the orders of the ranks obtained from the proposed model and by the method of [PERSON] et al. [36] for the criteria are the same, i.e., \(C_{1}>C_{2}>C_{4}>C_{3}\). There is a slight difference in the criteria weights. The CR obtained using the developed approach is 0, which is far better than that of [PERSON] et al. [36] approach, i.e., 0.040 and 0.045 for Models 1 and 2, respectively. The optimal weights, ordering, and CR of the criteria for Example 2 are presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(\mathbf{W_{1}}\) & \(\mathbf{W_{2}}\) & \(\mathbf{W_{3}}\) & \(\mathbf{W_{4}}\) & **CR** \\ \hline [PERSON] et al. [36] Model 1 & 0.553 & 0.226 & 0.066 & 0.155 & 0.040 \\ [PERSON] et al. [36] Model 2 & 0.551 & 0.227 & 0.065 & 0.157 & 0.045 \\ Proposed approach & 0.534 & 0.267 & 0.067 & 0.134 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal weights and CR of criteria for Example 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{o_{12}}\) & \(\mathbf{o_{13}}\) & \(\mathbf{o_{14}}\) & \(\mathbf{o_{23}}\) & \(\mathbf{o_{43}}\) \\ \hline \(DM1\) & 2 & 9 & (3, 4) & 4 & 2 \\ \(DM2\) & 2 & (8, 9) & 4 & 4 & 2 \\ \(DM3\) & 2 & 8 & 4 & (3, 4) & 2 \\ \hline Multichoice & 2 & (8, 9) & (3, 4) & (3, 4) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Three DMs’ pairwise comparison data for Example 2.
# Potential Effects of Photobiomodulation Therapy on Human Dental Pulp Stem Cells
## 2 Materials and Methods
### Cell Cycle  
To determine the effect of PBM on hDPSC growth, cell-cycle analysis was performed to examine the effect of laser treatment on cell-cycle progression. First, hDPSCs were seeded in 6-well plates with a seeding density of 2 \(\times\) 10\({}^{5}\) cells/well for 24 h. Next, cells in groups T6 to T9 were exposed to laser treatments in various serum conditions for 48 h (Table 1). Cells were then trypsinized, centrifuged, collected, and washed with PBS. After 48 h, cells were harvested and pelleted by centrifugation, then cells were fixed by adding 200 \(\upmu\)L of 100% absolute methanol for 30 min. Following fixation, cells were centrifuged and washed twice with cold PBS. Then, cells were stained with 300 \(\upmu\)L of fluorochrome solution containing 4 \(\upmu\)g/mL PI and 2 \(\upmu\)g/mL RNASe A (Invitrogen, San Diego, CA, USA) for 30 min in the dark at room temperature; following that, 200 \(\upmu\)L of cold PBS were added to the samples. Finally, samples were analyzed with FACS DIVA software version 8, using BD FACS Canto II flow cytometer instrument (BD, NJ, USA). Data interpretation was performed using Flowlogic software version 7.3 (Inivai, Melbourne, Australia).  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Treatment Group** & **Power (W)** & **Time (s)** & **Energy (J)** & **Spot Size (cm\({}^{2}\))** & **Irradiance (W/cm\({}^{2}\))** & **Surface Area (cm\({}^{2}\)) (6 or 96-Well Plate)** & **Fluence (J/cm\({}^{2}\))** \\ \hline
**T1** & 0.1 & 30 & 3 & 0.8 & 0.125 & 1.086 & 2.76 \\ \hline
**T2** & 0.2 & 15 & 3 & 0.8 & 0.25 & 1.086 & 2.76 \\ \hline
**T3** & 0.1 & 20 & 2 & 0.8 & 0.125 & 1.086 & 1.84 \\ \hline
**T4** & 0.2 & 12 & 2.4 & 0.8 & 0.25 & 1.086 & 2.21 \\ \hline
**T5** & 0.1 & 10 & 1 & 0.8 & 0.125 & 1.086 & 0.92 \\ \hline
**T6** & 0.3 & 10 & 3 & 0.8 & 0.375 & 9.6 & 0.3125 \\ \hline
**T7** & 0.4 & 10 & 4 & 0.8 & 0.5 & 9.6 & 0.416 \\ \hline
**T8** & 0.8 & 3 & 2.4 & 0.8 & 1 & 9.6 & 0.25 \\ \hline
**T9** & 1 & 3 & 3 & 0.8 & 1.25 & 9.6 & 0.3125 \\ \hline \end{tabular}
\end{table}
Table 1: Photobiomodulation therapy (810 nm) was used in the treatment of hDPSCs (6-well and 96-well plates) at different laser power levels and different exposure time intervals, after 24 h of seeding to perform MTT assay (groups T1–T9) and apoptosis assay (groups T6–T9).
## 4 Materials and Methods
### Production of Desulfated LMWH  
The selective desulfation of 2-O- and 3-O-sulfates of LMWH was performed according to the protocol of [PERSON] et al. [40]. First, 5 mg of enoxaparin was dissolved in 30 \(\upmu\)L of deionized water. Next, 10 \(\upmu\)L of 2 M NaOH was added to reach a final NaOH concentration of 0.5 M, and the solution was mixed, frozen in liquid nitrogen and lyophilized overnight. Subsequently, the solution was neutralized with 2 M acetic acid and buffered to pH 7.2 by the addition of 450 \(\upmu\)L of phosphate buffered saline (PBS; pH 7.2; 10 mM phosphate buffer and 137 mM NaCl). The solution was desalted for 1.5 days in a 0.5 mL float-a-lyzer (500 Da molecular weight cut-off) against 500 mL of deionized water (the water was exchanged two times). The desalted solution was frozen in liquid nitrogen and lyophilized for 2 days. The dried GAG was weighed out and dissolved in deionized water. The degree of desulfation was assessed by measuring the heparin activity using an anti-factor Xa (Kinetichrome) assay (Iduron; Cheshire, UK). Heparin's anti-Xa activity was determined enzymatically using a chromogenic peptide substrate that produces a chromophore (405 nm) whose wavelength is inversely related to the activity of heparin in the test sample. Using the desulfation protocol, a 77.8% reduced activity of dsLMWH was observed compared with commercial LMWH. Additionally, strong anion exchange chromatography was performed to compare the disaccharide composition, as described in a recent study [41]. In short, dsLMWH and LMWH were digested using heparinase III and the samples were subjected to strong anion exchange chromatography. By comparing the retention times to commercially available disaccharide standards (Iduron) with different degrees of sulfation, the disaccharide composition could be determined, and the peak areas were compared relatively between dsLMWH and its control. It should be noted that no 3-O desulfated disaccharide standard exists, thus only 2-O-desulfation can be monitored here. The changes in relative molar disaccharide composition depicted a 45% reduction in 2-O-sulfations, with the UA2S-GlcNAc86S (38% reduction) disaccharide being the most affected.