esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality for PCB Component Identification and Localization
## 3 Research Aims  
The primary aim of this study is to evaluate the effectiveness of augmented reality (AR), specifically using the Microsoft(tm) HoloLens(tmtm) 2, in enhancing the accuracy, efficiency, and usability of PCB component identification tasks. To this end, we developed and tested a spatially anchored AR system and compared its performance to traditional PDF-based schematic interpretation.  
We formulated the following hypotheses:  
**H1.**: _AR significantly improves component identification accuracy compared to traditional PDF-based methods._  
_Rationale: AR overlays minimize the cognitive gap between schematic interpretation and physical action, reducing identification errors._  
**H2.**: _AR-based identification is faster than traditional methods._  
_Rationale: Direct spatial guidance and visual cues provided by AR reduce the time needed to locate and confirm components._  
**H3.**: _User experience is more positive with AR than with traditional methods, particularly for novice users._  
_Rationale: AR interfaces are more intuitive and reduce the learning curve, which is especially beneficial for users with limited prior experience._  
These hypotheses guide our investigation into how AR can be effectively integrated into electronics workflows for both training and real-time task execution.
## 4 Results and Discussion
### Preliminary Optimization Results  
At the beginning of the optimization, 150 individuals are generated randomly, some of which are show in Table 3 as examples. Pareto optimal frontier is depicted in Figure 4, and every point on the picture is a non-dominated solution. The TOPSIS approach is conducted to evaluate these solutions. Some of the solutions which obtain the highest normalized scores are listed in Table 4 in descending order. Comparing Table 4 with Table 3, it can be noticed that solutions are improved by NSGA-II significantly and distribution is more centralized. The first solution in Table 4, as known from the TOPSIS approach, is the optimum design of PCHE under the conditions of the present paper.  
The effect of the design parameters on the heat transfer area and pressure drop of the hot side are analyzed, respectively. While one parameter is analyzed by varying its value, the others remain constant. Sensitivity analysis is conducted to confirm and explain the rationality of the optimization result. In the pictures in this section, blue curves represent the heat transfer area and red curves represent the pressure drop. When the same parameters on two sides are analyzed, solid curves represent the variables of the hot side and dash lines represent the variables of the cold side.  
Figure 4: Pareto optimal frontier for heat transfer area and pressure drop of PFHE.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(N_{hot}\) & \(L_{w}\) (m) & \(\begin{array}{c}l_{s,hot}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}l_{s,cold}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}s_{hot}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}s_{cold}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}H_{hot}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}H_{cold}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}t_{hot}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}t_{cold}\\ \text{(mm)}\end{array}\) & \(\begin{array}{c}A\\ \text{(m^{2})}\end{array}\) & \(\begin{array}{c}\Delta p\\ \text{(kPa)}\end{array}\) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Specific individuals in first generation.
## 3 Results
### Sensor Calibration  
As can be seen from Figure 7a,b, both regressions were linear in nature with an R\({}^{2}\) determination coefficient close to 1, which indicates a strong correlation among the data analyzed. Since the calibration was carried out using the comparative method, the uncertainty of the data acquisition system was the same as that of the digital multimeter, which was \(\pm 2\%+0.00001\) A for the current, and \(\pm 0.5\%+0.01\) V for the voltage.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Source** & \(\mathbf{I_{\text{ph,ref}}}\) [A] & \(\mathbf{I_{\text{s,ref}}}\) [A] & \(\mathbf{n}\) [-] & \(\mathbf{R_{\text{s}}}\) [\(\mathbf{\Omega}\)] & \(\mathbf{R_{\text{sh,ref}}}\) [\(\mathbf{\Omega}\)] & **Notes** \\ \hline
[49] & 0.7608 & \(3.23\times 10^{-7}\) & 1.4812 & 0.03638 & 53.72 & Monocrystalline \\
[50] & 4.7103 & \(1.48\times 10^{-5}\) & 1.3410 & 0.8020 & 364.51 & Polycrystalline \\
[50] & 4.7063 & \(8.32\times 10^{-4}\) & 1.3110 & 0.1620 & 121.31 & Monocrystalline \\
[47] & 8.2288 & \(2.32\times 10^{-10}\) & 0.9774 & 0.3448 & 150.69 & Multicrystalline \\
[51] & 5.44 & \(1.41\times 10^{-4}\) & 1.96 & 0.0024 & 6.66 & Double single-diode model \\
[51] & 4.31 & \(8.88\times 10^{-5}\) & 1.93 & 0.0025 & 27.81 & Double single-diode model \\
[51] & 5.22 & \(1.30\times 10^{-4}\) & 1.92 & 0.0025 & 16.11 & Double single-diode model \\
[51] & 4.88 & \(5.84\times 10^{-5}\) & 1.84 & 0.0029 & 6.53 & Double single-diode model \\ This study (bounds) & [0, 10] & \([10^{-12},10^{-8}]\) & [0.5, 2.0] & [0.01, 1.0] & [100, 300] & Used for optimization \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of parameter bounds used in this work with values reported in the literature.  
To ensure the sustained accuracy and reliability of the data acquisition system over time, we recommend implementing a schedule for periodic recalibration. As a best practice for field instrumentation, this procedure should be conducted annually, or more frequently if the system is deployed in harsh thermal environments. Recalibration involves repeating the initial validation process, where the DAQ's measurements are compared against a certified reference multimeter, to compensate for any measurement drift arising from the long-term effects of component aging and thermal cycling.
## 3 Results  
### Effect of MSS and DF on In Vitro Rumen Fermentation and Methanogenesis  
The effects of MSS and DF supplementation on in vitro rumen fermentation and methanogenesis are shown in Figure 1 and Table 2. The addition of MSS alone (40 mg/kg) or DF (7 and 14 mmol/L) significantly increased 24 h total gas production (_p_ < 0.05), whereas the addition of only MSS (80 mg/kg) and the co-addition of MSS and DF caused a linear reduction (_p_ < 0.05). The addition of MSS and DF, alone and in combination, was found to significantly decrease methane concentration and production (_p_ < 0.05). When V was the substrate, methane production of was higher than for F; however, the addition of 7 mmol/L DF and 80 mg/kg MSS with V as the substrate showed low methane production compared to control cultures (_p_ < 0.01), so did cultures with F as the substrate (80 mg/kg MSS) (_p_ < 0.05). Compared to the control group, MSS and DF supplementation alone or in combination significantly increased propionate production and decreased acetate:propionate (A:P) (_p_ < 0.05). The highest propionate production and lowest A:P values were all observed in cultures supplemented with the combination of 14 mmol/L DF and 80 mg/kg MSS, in both substrates.
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
## 4 Discussion  
During the last decades, MIs have been introduced effectively as TADs in order to distalize maxillary molars or the entire maxillary dentition [47; 48; 49]. Miniscrew implants are globally utilized by orthodontists because they can provide sufficient and reliable anchorage, expressing a popular solution to the above-mentioned problems [19; 50; 51; 52]. Nevertheless, treatment outcome depends on various biomechanics as well as the appropriate selection of the non-compliance appliance. Some of them need additional laboratory constructions that seem to be less tolerated by patients providing complicated adaptations or activations by the clinician wasting valuable time on the treatment outcome [18; 53; 54; 55; 56]. In addition, some appliances can provide unilateral and bilateral maxillary molar distalization, establishing a precious tool in the hands of experts.  
Non-compliance distalization appliances supported by mini-implants have a global impact on daily orthodontic practice, offering valuable clinical use while many studies have justified their dentoalveolar and skeletal effects [23; 38; 39].  
The existing systematic review was performed to evaluate the efficiency and effectiveness of the noncompliance appliances for maxillary molar distalization in patients with Class II malocclusion.
## 3 Results
### Estimation of Isoprene Emissions Using PRI and MEGAN  
PRI was significantly and negatively correlated with isoprene emissions during the increasing phase (Figure 5). PRI explained 39 and 63% of the variance in isoprene emissions in 2011 and 2012, respectively. The slope of the relationship was similar (ANCOVA, \(p>0.1\)) for both years but the intercept differed, with lower emission for the same PRI values in 2012.  
Figure 6: Relationship between isoprene emission and the results of the isoprene emissions estimated by the MEGAN model for the MoFLUX site during the increasing phase for 2011 and 2012. The dotted line is the 1:1 line.  
Figure 7: Comparison of the seasonal variation of isoprene emissions, PRI, LUE, emissions estimated by MEGAN and temperature for 2011 and 2012. The solid lines are spline fits. Vertical dotted lines show the period of maximum isoprene emissions in 2011 and 2012.  
The seasonal PRI inflection point in the decreasing phase coincided with the emission peaks in both 2011 and 2012 and was thus sensitive to the advance of the emission peak in 2012. The MEGAN estimates and temperature coincided with the emission peak in 2011 but not 2012, when the measured peak was earlier (Figure 7).  
We randomly selected 50% of the data (\(n=34\)) from the increasing phase for both 2011 and 2012 together as a training set for creating two models to estimate isoprenoid emission, one model using MEGAN and another using MEGAN and PRI. The remaining 50% of the data (\(n=33\)) was used for validation. This procedure was repeated 1000 times. Complementing MEGAN algorithms with PRI produced more precise estimates (slopes were 0.89 using MEGAN and 0.98 using MEGAN+PRI) of isoprene emission (Figure 8). Both estimates had the same coefficient of correlation with measured isoprene fluxes, but the model combining MEGAN and PRI was more sensitive to higher emissions, indicated by the better adjustment of the model to the 1:1 line.
## 4 Discussion
### LST versus \(T_{\text{air}}\)  
Even though there was evidence within our dataset that ten-day averages of daytime LST > \(T_{\text{air}}\) for most sites and PFTs, the implications for the GPP simulations were muted. This is perhaps not surprising because temperature is not the sole, or even principal, driver of variation in LUE. VPD influences LUE because of the universal response of stomata [44]--which progressively close to restrict transpiration, and hence photosynthesis, as VPD increases. As a consequence, VPD imposes important limitations on GPP [45]. Inductive multivariate modelling (avoiding a priori assumptions) applied to a daytime dataset for a forest in Germany [46] found the most important non-radiative drivers of variation in NEE to be VPD, \(T_{\text{air}}\) and wind direction--soil water content ranked in the lowest category for explanatory power. That same ranking of effects (VPD > Temperature > Soil moisture) has found support in recent modelling experiments [47].  
Just as light conditions at the crown are not representative of conditions lower down the canopy, so with temperature. Temperature deltas are influenced by height [48]. If we are right to assume that remotely sensed LST can mimic the temperature of the leaf canopy, then we might expect the advantages of adopting LST rather than \(T_{\text{air}}\) to be most pronounced where the leaf area index is low (but not less than 1, see below). Our findings lend support to that expectation.  
Daytime overpasses, typically mid-morning for these European sites, may not accurately reflect average conditions over the course of the daylight hours. On the other hand, many studies have reported a midday depression of photosynthetic activity [45] and so an overpass in mid-morning may often coincide with optimal conditions for photosynthesis. Might model performance be improved if we adopted a daytime average of LST rather than a single snapshot (as here)? Currently, Sentinel3 provides two overpasses in the diurnal cycle: day and night. An averaging methodology is required that will account for daylight hours as influenced by season and latitude. Early attempts (not presented here) proved unsatisfactory and must be developed further. Utilising a suite of different sensors including the Advanced Very High-Resolution Radiometer (offering increased temporal resolution and shorter revisit intervals) could help to resolve the diurnal cycle.
## 3 Results and Discussion
### Effect of BZ25 Addition on Bas
#### 3.7.3 Correlation Analysis  
As shown in Figure 6, red indicates a positive correlation, while blue indicates the opposite. A positive correlation indicates promoting effect, and a negative correlation indicates inhibiting effect. TBA was significantly positively correlated with TVC of NMB and GUTU09 VC of NMBB, with correlation coefficients of 0.878 and 0.808. Bas in natto are mainly metabolized by _Bacillus subtilis_[30]. Figure 6A shows that NK was directly produced by GUTU09. TVC of GUTU09 in NMB promotes the content of TBA, spermine, and tyramine but decreases cadaverine and spermidine. Spermine and tyramine contribute a relatively high proportion of TBA, but cadaverine reduces TBA, which was its metabolic transformation by GUTU09. Figure 6B showed that although BZ25 does not produce NK, it also promoted GUTU09 to produce more NK. BZ25 and GUTU09 promoted each other and jointly resisted changes in unfavorable environments. The VC of GUTU09 in NMBB was positively correlated with tyramine and spermine, negatively correlated with cadaverine and spermidine, and not correlated with putrescine. Comparing Figure 6A,B, it can be found that the addition of BZ25 changes the correlation between indicators. For example, when GUTU09 was fermented alone, TBA, spermine, and NK were positively correlated with pH, but the double-bacteria fermentation of GUTU09 and BZ25 was just right on the contrary. PH drops due to the acid-producing action of BZ25. PH influences some important factors affecting BA formation, namely, enzyme activity and bacterial growth [14]. Both Bas of NMBB and NMB were mainly produced by the metabolism of GUTU09, and this correlation was reduced by the addition of BZ25.  
Figure 6: Pearson correlation coefficients between different indexes in natto. NMB (**A**); NMBB (**B**); GUTU09 VC refers to the viable count of GUTU09 in the NMBB; BZ25 VC refers to the viable count of BZ25 in the NMBB. TVC: total viable count; NK: nattokinase; TA: titratable acidity; “_\"\"_p_ \(<\) 0.05 was significantly different; “_\"\"_p_ \(<\) 0.01 was extremely significantly different.
## 3 Monte Carlo Track Chemistry Simulations
Figure 1 schematically illustrates the total penetration lengths into SCW at 400 \({}^{\circ}\)C and 25 MPa (\(\rho=0.167\) g/cm\({}^{3}\)) for these protons. The chemistry observed under these conditions represents an average over the proton energies, from their initial energies \(E_{p}\)(i = 1 to 3) down to zero. To avoid complexities arising from variations in the moving protons' energies, we used a simplifying approximation, saying that the energies of the three considered recoil protons remained constant as they traversed the water medium. The constant track average energy values, \(\overline{E}_{p}\), were determined using the SRIM software [45] and our own Monte Carlo track structure simulations, as previously detailed [46]. At 25 MPa, these energies were \(\sim\)0.722, 0.230, and 0.080 MeV at 300 \({}^{\circ}\)C; 0.715, 0.233, and 0.086 MeV at 350 \({}^{\circ}\)C; 0.731, 0.242, and 0.086 MeV at 400 \({}^{\circ}\)C; 0.746, 0.249, and 0.086 MeV at 500 \({}^{\circ}\)C; and 0.756, 0.257, and 0.083 MeV at 600 \({}^{\circ}\)C. The corresponding LETs for these protons, as determined by our Monte Carlo code, are \(\sim\)24.0, 46.1, and 57.5 keV/\(\upmu\)m at 300 \({}^{\circ}\)C; 20.2, 38.9, and 48.7 keV/\(\upmu\)m at 350 \({}^{\circ}\)C; 5.32, 10.1, and 12.9 keV/\(\upmu\)m at 400 \({}^{\circ}\)C; 2.88, 5.39, and 6.98 keV/\(\upmu\)m at 500 \({}^{\circ}\)C; and 2.17, 4.14, and 5.50 keV/\(\upmu\)m at 600 \({}^{\circ}\)C, respectively. To reproduce the effects of 2 MeV neutrons, we simulated short track segments (typically \(\sim\)15-150 \(\upmu\)m) for each of the first three generated recoil protons. Within these segments, the energy and LET of the protons remained well-defined and nearly constant. Consequently, our model calculations provided 'track segment' yields for a well-defined LET over time. We chose the number of individual proton histories (usually \(\sim\)10-100, based on proton energy) to minimize statistical fluctuations in the chemical yield averages while still meeting acceptable computational time limits. We then calculated the neutron yields of the various radiolytic species by summing the corresponding weighted \(G\) values for each recoil proton, according to Equations (7) and (8). It is important to note that radiation chemical yields are traditionally quantified as \(G\) values, where \(G\)(X) represents the total number of species X formed or destroyed per 100 eV of absorbed energy. In this paper, \(G\) values are expressed in units of molecules per 100 eV. For conversion to SI units, 1 molecule/100 eV \(\approx\) 0.10364 \(\upmu\)mol/J [10].
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 3 Materials and Methods  
In this section, we describe the Fast Dynamic Dim Target Tracking Approach (FDDTA) in detail, presenting the algorithmic framework. For the feature extraction module, we propose a channel attention mechanism that fuses the position-wise information. We then use the channel information and position information to generate weights to complete the rescaling of the original features and improve the feature extraction capability. For the similarity measurement module, a regional candidate network RPN is referenced to use the extracted features for similarity measurement to generate the current frame prediction frames of the target. This effectively eliminates duplicate redundant predictor frames and retains the information of the predictor frame with the highest confidence.  
The algorithmic framework shown in Figure 2 is based on the Siamese network and consists of a position information attention module, a similarity measurement module, a feature extraction network module, and a prediction frame generation module.  
Firstly, the target data and the search area were introduced into the feature extraction network to obtain the three layers of features. The features were up-dimensioned to a unified dimension; then, the three layers of features were imported in pairs into the similarity measurement module to obtain classification scores and regression scores for the three sets of features at different depths. To obtain the predicted frames for the current frame, we finally weighted and fused the classification and regression scores to calculate the total classification and regression response scores.
## 3 Results
### Surface Modification of Poly(S/DVB)HIPE  
In order to confirm the primary coating layer (consisting of three bilayers of alternating layers of two polyions, PDADMAC(+)/PSS(-)), UV-Vis spectrophotometer was employed. The number of increased layer of the polyelectrolytes for each layer within poly(S/DVB)HIPE was monitored by a UV-Vis spectrophotometer with the absorption of light at 670 nm. The intensity of absorbance of the top surface and cross section of poly(S/DVB)HIPE increased with the increase in the number of layers from two to six layers, as shown in Figure 3a,b. Six layers of PDADMAC/PSS were sufficient to successfully modified the surface of the poly(S/DVB)HIPE. Figure 4 showed that Congo red can react with cation (PDADMAC or amine coating) on the surface (1, 3, 5, and 7 layers). Methylene blue can react with anion (PSS) on the surface (2, 4, and 6 layers). The homogenous coating inside and outside poly(S/DVB)HIPE was confirmed (Figure 4). The results of the photographs of poly(S/DVB)HIPE with dye are illustrated. It was observed that the depth of color increases with the number of layers (see Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Poly(S/DVB)** & **Surface Area** & **Compressive** & **Young’s** & **T\({}_{\rm d}\)** & **Residue Yield** \\
**HIPE** & **(m\({}^{2}\)/g)** & **Strength** & **Modulus** & **(\({}^{\circ}\)C)** & **(\%)** \\ \hline
80:20 & 22.39 \(\pm\) 10.30 & 0.13 \(\pm\) 0.02 & 1.79 \(\pm\) 0.44 & 373.79 & 8.18 \\
20:80 & 189.40 \(\pm\) 16.14 & 0.29 \(\pm\) 0.09 & 3.59 \(\pm\) 1.12 & 432.80 & 24.81 \\
0:100 & 363.06 \(\pm\) 149.77 & 0.30 \(\pm\) 0.03 & 5.41 \(\pm\) 1.29 & 440.98 & 32.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical, mechanical, and thermal properties of poly(S/DVB)HIPEs prepared using a different S/DVB ratio.  
Figure 4: Photograph of cross-section of poly(S/DVB)HIPE modified surface: primary coating contain PDADMAC/PSS; PDADMAC on top of surface (**1**), (**3**), and (**5**); PSS on top of surface (**2**), (**4**), and (**6**) and secondary coating contain TEPA or PEI (**7**) as termination coating.  
Figure 3: Absorbance-number of layer curves for PDAD/PSS deposited 1.0 M NaCl: (**a**) top surface of poly(S/DVB)HIPE; (**b**) cross section of poly(S/DVB)HIPE.  
The study was followed by morphology of unmodified and modified poly(S/DVB)HIPEs. As shown in Figure 5, the micrographs showed similar images before and after the modification with amine solutions. It can be concluded that amine solution coated on poly(S/DVB)HIPEs had little influence on the pore structure, as they neither ruptured or blocked the porosities of the polyHIPEs.