esa-sceva/satcom-chunk-collection huggingface dataset:
# Goldmann Tonometry and Corneal Biomechanics
###### Abstract  
Glaucoma is the second cause of irreversible blindness in the world. Intraocular pressure (IOP) is a recognized major risk factor for the development and progression of glaucomatous damage. Goldmann explanation tonometry (GAT) is internationally accepted as the gold standard for the measurement of IOP. The purpose of this study was to search for correlations between Goldmann tonometry and corneal mechanical properties and thickness by means of in vitro tests. IOP was measured by the Goldmann explanation tonometer (GOP), and by a pressure transducer inserted in the anterior chamber of the eye (TIOP), at increasing pressure levels by addition of saline solution in the anterior chamber of enucleated pig eyes (n = 49). Mechanical properties were also determined by inflation tests. The GAT underestimated the real measurements made by the pressure transducer, with most common differences in the range 15-28 mmHg. The difference between the two instruments, highlighted by the Bland-Altman test, was confirmed by ANOVA, normality tests, and Mann-Whitney's tests, both on the data arranged for infusions and for the data organized by pressure ranges. Pearson correlation tests revealed a negative correlation between (TIOP-GIOP) and both corneal stiffness and corneal thickness. In conclusion, data obtained showed a discrepancy between GIOP and TIOP more evident for softer and thinner corneas, that is very important for glaucoma detection.  
Goldmann tonometry; intraocular pressure; glaucoma; inflation tests; pig eyes; corneal stiffness +
Footnote †: journal: Applied Sciences  
doi: 10.3390/app11094025
## 3 Results
### Acoustic Emission Testing Based on Uniaxial Experiments  
The AE characteristics are closely related to the changes in microstructure and crack behavior within the rock. The closure, expansion, aggregation, and propagation of cracks in basalt specimens during uniaxial loading can be inferred from the acquisition of AE signals. The acoustic emission (AE) characteristics of basalt were evaluated under uniaxial compression. The testing utilized a six-channel acoustic emission (AE) system (model PCI-2) from the Physical Acoustics Corporation (PAC), which was equipped with Mini300 AE sensors. To mitigate background noise interference, a threshold amplitude of 40 dB was set, ensuring that only AE signals with amplitudes above 40 dB were recorded by the transducer. As shown in Figure 11, the AE activity for basalt specimens without microwave radiation is primarily concentrated in the post-peak stage of loading. However, microwave radiation significantly alters this behavior, with AE counts occurring more frequently across all stages of loading in specimens that have undergone microwave radiation. This increase in AE activity can be attributed to the fact that microwave radiation promotes the formation and development of microfractures within the rock. As the microwave radiation time increases, the AE active period of the basalt specimens expands during uniaxial loading. Additionally, higher microwave power further amplifies this effect, resulting in a more pronounced increase in the AE active period compared to lower microwave power. This suggests that both the duration and intensity of microwave radiation significantly contribute to the advancement of microfracture activity within the rock.  
Figure 11: AE counts of basalt specimens under uniaxial loading.
## 8 Results of the Optimization Study  
A Kriging-based surrogate multi-objective genetic algorithm [19] was successfully employed to complete 22 optimization cases. This approach efficiently achieved the targeted objectives with a reduced number of design parameters, as summarized in Table 3. The results indicate that the optimization process converged towards global optima for both enhanced engine performance and reduced NO\({}_{\text{x}}\) emissions, particularly for design iterations 21 and 22.  
Figure 14: CFD-PIV Vector Field Result Comparison at \(-\)70 [CA].  
However, further analysis revealed that Design 22 exhibited a tendency towards knocking, as indicated by the abnormal heat release profiles shown in Figure 15. In contrast, Design 21 displayed no signs of critical heat release, highlighting its superior combustion stability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Hole Count** & \begin{tabular}{c} **Injection Angle** \\ **(deg)** \\ \end{tabular} & \begin{tabular}{c} **pIMEP** \\ **(bar)** \\ \end{tabular} & \begin{tabular}{c} **PFP** \\ **(bar)** \\ \end{tabular} & \
\begin{tabular}{c} **NO\({}_{\mathbf{x}}\)** \\ **(mg)** \\ \end{tabular} &
\begin{tabular}{c} **Knock Index (\(d\)/\(d\)\(\theta\))\({}_{max}\)** \\ **[bar/\({}^{\circ}\)CA]** \\ \end{tabular} \\ \hline Case 1 & 3 & 17.99 & 4.42 & 149.54 & 10.72 & 13.3 \\ \hline Case 2 & 4 & 15.84  
To quantitatively assess knock tendency, the maximum pressure rise rate \(\left(dP/d\theta\right)_{max}\) was employed as a knock index. This parameter is commonly used to capture high-frequency pressure oscillations associated with abnormal combustion in hydrogen-fueled engines. As shown in Table 3, Design 21 exhibited a relatively low \(\left(dP/d\theta\right)_{max}\) value of 11.5 [bar/\({}^{\circ}\)CA], confirming its favorable combustion characteristics compared to higher values observed in other cases, such as that Design 22 has 36.2 [bar/\({}^{\circ}\)CA].  
Additionally, the burned fuel mass fraction analysis demonstrated that combustion in the baseline case did not complete within +50 [CA] after the top dead center (TDC), whereas Design 21 achieved complete combustion of the injected hydrogen within this interval.  
Equivalence ratio distribution analyses, illustrated in Figure 16 above, further support these findings. In the baseline (no-hole) configuration, the air-fuel mixture near the spark plug region remained excessively lean. Conversely, Design 21 achieved an equivalence ratio of approximately 0.6 in the spark vicinity, ensuring sufficient flammability and improved ignition reliability.
## 3 Experiments and Result Analysis
### Results and Analysis
#### 3.3.1 Comparison of the Dimensionality Reduction Methods  
Firstly, for the dimensionality reduction, the reconstruction error curves of SAE and NC-SAE in the training processes are shown in Figure 5. It can be seen that the reconstruction error of NC-SAE is slightly less than that of SAE. Moreover, the standard deviation within the same crop class were calculated and plotted in Figure 6A,B for different categories. The six main crops (i.e., lentil, spring wheat, field pea, canola, barley and flax) which have relatively larger cultivated areas shown in Figure 6a were chosen to evaluate the standard deviation. Meanwhile we also choose six easily confused crops shown in Figure 6b (i.e., durum wheat, oat, chemical fallow, mixed hay, barely, mixed pasture) for performance evaluation. We can see that the standard deviation of the proposed method NC-SAE is the smallest. Therefore, a better crop classification performance is expected by using the features that extracted through NC-SAE.  
Figure 5: The reconstruction error curves of NC-SAE and SAE.  
Additionally, by using CNN classifier, the _OA_, VA, Kappa coefficients and CPU time performances for different dimension reduction methods are listed in Table 4, and the predicted results of the classifier and their corresponding error maps are illustrated in Figure 7. In this experiment, the size of input data for CNN classifier was set to \(15\times 15\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Classification Performance**} \\ \cline{2-5}  & **VA (\%)** & **OA (\%)** & **Kappa (\%)** & **CPU Time** \\ \hline PCA & 80.63 & 87.92 & 85.23 & 0.4521 s \\ LLE & 81.18 & 88.03 & 85.41 & 16.8427 s \\ S-SAE & 91.29 & 94.24 & 93.03 & 5.5383 s \\ NC-SAE & 90.26 & 94.23 & 93.05 & 0.5193 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the classification performance using CNN classifier under various data compression schemes.  
Figure 6: The selected crops and their standard deviations of compressed data. (**a**) and (**b**) are the distribution maps of six selected crops, (**a**) the six crops with the largest cultivated area, (**b**) the six crops that are difficult to discriminate; (**A**) and (**B**) illustrate the standard deviation within the selected crops in (**a**) and (**b**), respectively.  
We can see that the dimensionality reduction methods of S-SAE and NC-SAE are both superior to PCA and LLE. For the CNN classifier, the OA and Kappa of S-SAE and NC-SAE are approximately 6-8% higher than PCA and LLE. The performances of S-SAE and NC-SAE are nearly equal. However, keep in mind that these two neural networks have different structures. The proposed NC-SAE is a single-layer network, while the S-SAE uses three auto-encoders to sequentially perform the feature compression. Comparing the CPU time that required computing the compressed features, it can be seen that NC-SAE takes almost one tenth as long as S-SAE.
## 2 Materials and Methods
### NGAL Measurements  
The peritoneal NGAL was measured in all PD patients with both techniques described below. Peritoneal laboratory-based NGAL was measured by the conventional BioPorto test (BioPorto Diagnostics, 2900 Hellerup, DK), using a particle-enhanced turbidimetric immunoassay for the quantitative determination. The assay range is between 50 and 3000 ng/mL. Values \(\geq\) 200 ng/mL are indicative of peritonitis [13].  
In addition, peritoneal NGAL was tested by the novel POCT (NGAL dipstick- NGALds, BioPorto Diagnostics, 2900 Hellerup, DK). NGALds is a rapid assay for the semi-quantitative evaluation of NGAL levels in biological fluids by colorimetric strips (NGALds, BioPorto Diagnostics, 2900 Hellerup, DK). This test is an antibody sandwich lateral flow dipstick test. Color-categorized NGAL levels were 25 ng/mL-50 ng/mL-100 ng/mL-150 ng/mL-300 ng/mL-600 ng/mL and were reported in the user guide. For the reproducibility of the data, NGALds were determined by two distinct trained lab operators on all PD patients in a blinded manner.
# Inferring Urban Social Networks from Publicly Available Data
## Appendix D Results for the City of Sabaudia  
In this section, we report all plots and tables for the city of Sabaudia, which are by all means analogous to those already presented for Florence and Viterbo.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{\(\mu=5\)} \\
**Population** & \(\mathbf{D(u,v)}\) & \(\langle\)**dist**\(\rangle\) & \(C\) & \(\rho\) & **\# Comp.** & **Giant \%** \\ \hline homogeneous & \(1\) & \(6.37\) & \(2.55\)e-04 & \(-0.002\) & \(149.2\) & \(99.3\)\% \\ homogeneous & \(d(u,v)^{-0.5}\) & \(6.36\) & \(2.43\)e-04 & \(0.002\) & \(155.1\) & \(99.3\)\% \\ homogeneous & \(d(u,v)^{-2}\) & \(6.49\) & \(0.0017\) & \(0.04\) & \(181.4\) & \(99.1\)\% \\ real & \(1\) & \(6.37\) & \(2.47\)e-04 & \(-5.67\)e-04 & \(155.6\) & \(99.3\)\% \\ real & \(d(u,v)^{-0.5}\) & \(6.25\) & \(2.95\)e-04 & \(0.06\) & \(277.1\) & \(98.7\)\% \\ real & \(d(u,v)^{-2}\) & \(6.93\) & \(0.0013\) & \(0.4\) & \(3356.8\) & \(80.9\)\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: (**Sabaudia**) Main features of the friendship graph \(G_{F}\) as the population type, \(D(u,v)\) and \(\mu\) vary, for constant \(f_{u}\) and \(S\). \(\langle\)dist\(\rangle\) is the average path length, \(C\) is the global clustering coefficient, \(\rho\) is the degree assortativity, “# comp.” denotes the number of connected components, and “giant %” denotes the percentage of nodes in the giant component.  
Figure A16: (**Sabaudia**) Degree distributions of friendship graphs with \(\mu=5\) and corresponding Poisson distributions with \(\lambda=5\). The data distribution lines show the mean, whereas the shaded areas around the lines represent the 95% confidence interval. The Kullback–Leibler divergence between the Poisson and the empirical distribution are (left to right, top to bottom): 5.3e-06, 5.8e-06, 1.2e-05, 0.001, 0.0001, 0.05.  
Figure A18: (**Sabaudia**) Distribution of the size of the connected components other than the giant for friendship graphs with \(\mu=5\) and \(\mu=10\). The lines show the mean, whereas the error bars (with “caps”) represent the 95% confidence interval computed over each set of runs.  
Figure A19: (**Sabaudia**) Overview of the urban social graph for the city of Sabaudia. Each plot shows the average with confidence interval for 10 independent runs with the same configuration. (**a**) Size of the largest 50 connected components. (**b**) Size of the largest 50 clusters (Louvain). (**c**) Degree distribution with Lognormal fit. (**d**) Geographical distance between adjacent vertices. (**e**) Mean and max intra-cluster geographical distances. (**f**) Mean distance to all other vertices vs. Degree.  
Figure A20: (**Sabaudia**) Modularity of the obtained clustering structure—distribution for 10 independent runs per configuration.  
Figure A21: (**Sabaudia**) Adjacency matrix of the social graph with nodes (people) ordered by age-group. In both cases, \(f_{u}\sim 1+\mathcal{LN}(\ln(2),0.25)\), \(\mu=10\), and \(D(u,v)=d(u,v)^{-\beta}\), but \(\beta\) varies between the two figures.
## 3 Empirical Study
According to respondents' answers to Q7 in Table 2, PIRs easily sway their original opinions before they believe they have adequate information, while some AISs are quite confident (Q7 Table 2: PIRs = mean 9.02, AISs = mean 5.41, \(t\) = 20.5992, \(p\) < 0.001), with 26.26% of AISs seldom or never change their mind after interacting with others. Additionally, PIRs become nearly as confident as PIRs if they acquire necessary information from authoritative sources such as opinion leaders (Q8 Table 2: mean 5.21, Q7 Table 2: AISs = mean 5.41, \(t\) = 1.2462, \(p\) > 0.1). Fourth, platform users mainly talk about the platform after interaction with managers, service providers or other users of the platform. According to respondents' answers to Q9 in Table 2, 96.91% of users talk about the platform with nonusers after only these interactions, and only 4.95% may spread WOM in other situations. Fifth, opinion leaders are effective in persuading others and improving the information amount and self-efficacy of others who have limited knowledge. According to respondents' answers to Q10 and Q11 in Table 2, opinion leaders are more persuasive (Q11 Table 2: OL = mean 8.90, NL = mean 5.03, \(t\) = 9.7080, \(p\) < 0.001), which might be the result of their higher notoriety and expertise. Sixth, opinion leaders are particularly willing to spread WOM. According to respondents' answers to Q10 and Q12 in Table 2, opinion leaders are more involved in interactions and share the platform with more nonusers than nonleaders do (OL = mean 8.8, NL = mean 4.84, \(t\) = 10.0366, \(p\) < 0.001)  
Seventh, PIRs cannot become new opinion leaders without the help of opinion leaders. According to the respondents' answers to Q13 in Table 2, nearly all of the PIRs completely denied the possibility of becoming opinion leaders without being educated, while 67.68% of AISs said that they were able to become opinion leaders by themselves (\(t\) = 14.1443, \(p\) < 0.001).
## 5 Price-and-Branch FMP Solution Algorithm
### Algorithm Description
Otherwise, adding \(c^{*}\) to DFMP, i.e., to \(\mathcal{C}\), will in general decrease the maximum of \(W\) in (4a) because the dual polytope of DFMP(\(\mathcal{C}\cup\{c^{*}\}\)) is a proper subset of the polytope of DFMP(\(\mathcal{C}\)) (because those solutions of the latter problem that contain \(\lambda^{*}\) do not belong to the polytope of DFMP(\(\widehat{C}\))). Thus, since for a given \(\mathcal{C}\), the optimum \(W^{*}\) of the dual is equal to the optimum \(T^{*}\) of the linear relaxation of FMP, adding \(c^{*}\) to FMP will in general decrease the optimum of FMP. To summarize, the following c-set generation algorithm can be used in order to solve the linear relaxation of FMP for the full list of c-sets. **CGA: c-set generation algorithm Step-1:** Define an initial feasible list \(\mathcal{C}\) of c-sets. Step-2:** Solve the dual master problem DMP(\(\mathcal{C}\)) using an LP solver to obtain optimal dual variables \(\lambda^{*}\). Step-3:** Solve the pricing problem PP(\(\lambda^{*}\)) using a MIP solver. If the maximum of the objective function \(P^{*}=P(c^{*},\lambda^{*})\) is strictly greater than 0, then add the resulting c-set \(c^{*}\) to the c-set list \(\mathcal{C}\) (\(\mathcal{C}:=\mathcal{C}\cup\{c^{*}\}\)) and go to Step-2. Step-4:** Otherwise stop: the resulting c-set list \(\mathcal{C}^{*}\) is sufficient to solve the linear relaxation of FMP to global optimality. For completeness, Algorithm 1 shows the pseudocode of CGA, where the following functions are used:  
* \(define\_initial\_feasible\_cSets\_list()\)--the function that generates and returns an initial feasible list \(\mathcal{C}\) of c-sets
* \(solve\_DMP(\mathcal{C})\)--the function that solves the dual master problem DMP(\(\mathcal{C}\)) and returns optimal dual variables \(\lambda^{*}\)
* \(solve\_PP(\mathcal{C})\)--the function that solves the pricing problem PP(\(\lambda^{*}\)) and returns a c-set \(c^{*}\)  
```
1:procedureCGA
2:\(\mathcal{C}:=define\_initial\_feasible\_cSets\_list()\);
3:\(new\_cSet\_generated:=true\);
4:while(\(new\_cSet\_generated=true\))do
5:\(\lambda^{*}:=solve\_DMP(\mathcal{C})\);
6:\(c^{*}:=solve\_PP(\lambda^{*})\);
7:if\(P(c^{*},\lambda^{*})>0\)then
8:\(\mathcal{C}:=\mathcal{C}\cup\{c^{*}\}\);
9:else
10:\(new\_cSet\_generated:=false\);
11:endif
12:endwhile
13:endprocedure
```  
**Algorithm 1** CGA: c-set generation algorithm  
A simple feasible initial c-set list (i.e., a list \(\mathcal{C}\) for which FMP(\(\mathcal{C}\)) is feasible) that can be used for Step-1 is the family of all c-sets with only one node broadcasting to all its neighbors, with a fixed transmission power level. Finally, for solving the main problem, we use the so called price-and-branch method (see [36]), i.e., we first apply CGA and then solve the basic MIP formulation (3) for the family \(\mathcal{C}^{*}\) obtained from CGA. For that, we apply a MIP solver.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 2 Finite Element Simulation
### Structural Composition and Heat Transfer Principles of QFN Package  
The constituents of the QFN package encompass epoxy resin-based molding compound, die, die attach, thermal pad, solder, lead, and gold wires, as depicted in Figure 1. During operational conditions, heat transfer within the chip transpires through three modes: conduction, convection, and radiation, where conduction and convection emerge as the predominant mechanisms. The dissipated heat predominantly traverses the following paths to disperse into the surrounding environment: (1) A portion of the heat travels from the die to the molding compound, subsequently propagating from the molding compound's surface to the surroundings; (2) A portion of the heat travels from the die to the thermal pad, subsequently diffusing within the pad and eventually transferring to the PCB; (3) The remaining heat is transmitted through gold wires to the leads around the package and further transferred to the PCB through solder joints.  
However, due to the relatively low thermal conductivity of the molding compound, a significant proportion of the heat generated by the die is transmitted to the PCB. Subsequently, this heat diffuses gradually from the PCB's surface into the surrounding environment. Nevertheless, owing to the relatively lower thermal conductivity of the PCB itself, the efficiency of heat transfer from the bottom surface of the PCB to the surrounding environment is relatively diminished.  
Figure 1: QFN package structure mounted on a PCB.
###### Abstract  
UV-C LEDs, which offer short-wavelength characteristics and serve as an alternative to traditional UV mercury lamps, represent a new light source for applications in space decontamination and surface disinfection. This paper presents the design and development of a UV-C LED lamp driver circuit configured to operate with a DC-input voltage source for sterilization and germicidal purposes. The primary circuit integrates a modified buck converter with a flyback converter, resulting in an innovative single-stage, single-switch DC-DC power converter. Additionally, the proposed electronic driver recovers energy stored in the transformer's leakage inductors, enhancing overall circuit efficiency. A prototype driver circuit with a 3.3 W power rating (10 V/330 mA) is developed for a UV-C LED lamp intended for sterilization and germicidal applications with a DC-input voltage source. The experimental results from the prototype circuit, tested at an 18 V DC input, confirm the functionality of the proposed electronic driver for UV-C LED sterilization and germicidal lighting. Additionally, the circuit achieves efficiency exceeding 91%.  
converter; driver; UV-C LED lamp; sterilization and germicidal applications
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 3 Conventional and Deep Learning-Based Approaches
### Deep Learning Applied to FER  
Deep learning [90; 91] is a machine learning technique theorized for the first time in the 1980s [92] but only lately considered in practice because it needs a large amount of labeled data and considerable processing power. In recent years, deep learning techniques have been employed successfully in a wide range of tasks including the recognition of facial expressions, a difficult problem for machine learning, since people can show their feelings in very different ways. Deep Neural Networks (DNN) have been used to classify images of the human face into emotion categories in an end-to-end approach and overcome the difficulties of the traditional methods, reaching a level of recognition accuracy higher than that of man in some activities.  
Automatic deep FER includes three different steps: pre-processing, deep feature learning, and deep feature classification [13]. Pre-processing is necessary before training the neural network to learn essential features for recognition; some examples are image cropping, rotation correction, data augmentation, and spatial normalization. Completed this preliminary phase, one of the deep learning techniques, particularly CNN or RNN, is applied for FER, and the given face is classified into one of the basic emotion categories. Using deep networks, feature extraction and their classification are performed in an end-to-end way [93], while in the traditional methods these two last phases are independent. Alternatively, the neural network can be used for feature extraction only, and then independent classifiers, such as support vector machine (SVM), can be applied to the extracted representations.
# Method for Volume of Irregular Shape Pellets Estimation Using 2D Imaging Measurement
## 3 Results  
The reliability of the evaluation of pellet volume depends on the shape of a pellet. An evenly shaped, not fractured pellet that is resistant to mechanical impact can be evaluated precisely, using approximation of a circle shape. However, in reality we see many irregularly shaped pellets. Therefore, their evaluation using 2D imaging decreases the reliability of the method. Discrepancy is introduced with irregularities of the pellet's surface. For developing a method of the evaluation of volume of irregularly shaped pellets, primary analysis of production is necessary, because the shape of surface irregularities must be evaluated.