esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Quantitative description of heat dissipation in multi-layered nanostructures and thin layers is a challenging task. One of the most popular methods is time-domain thermo-reflectance (TDTR) [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. For superlattices, the strategy for investigating their thermal properties using the TDTR method relies on measuring thermal conductivity. The other cross-plane and in-plane thermal parameters (thermal diffusivity and thermal boundary resistance) are calculated based on this parameter. However, in order to measure the thermal conductivity in the TDTR, method, researchers need to use the heat capacity of the bulk material. A preliminary theoretical investigation, used an elastic continuum model, suggested that the heat capacity in the very thin silicon membranes is the same as in bulk silicon [15]. For superlattices, this relation is more complex. However, frequency-domain measurements are simple and less expensive and can measure all thermal transport properties. The benefit of using frequency domain methods over time-resolved methods is their unique profilometry capability. This relies on the observation that one can relate the origin of a signal at a given frequency to the depth or penetration length of the thermal wave (i.e., thermal diffusion length). This allows the thermal conductivity as a function of depth to be probed by varying the frequency. Another major advantage of frequency domain methods is that they are simpler to implement experimentally and do not require the use of expensive femtosecond lasers. There are many frequency-domain methods, such as photoacoustic [16; 17], photothermal beam reflection [18; 19], thermoreflectance [20; 21] photothermal infrared radiometry [22; 23; 24; 25; 26; 27; 28; 29; 30], and modulated free carrier absorption [31; 32].  
Recently, we reported on cross-plane thermal transport properties of superlattices and thin films by using a frequency-domain PTR (photothermal infrared radiometry) method [24]. We demonstrated that it is possible to estimate values of the cross-plane thermal parameters for one-dimensional heat flow. In this paper, we study thin-layer and superlattice samples using both frequency domain methods, PTR and thermoreflectance (FDTR), at different temperatures. The purpose of this study is to demonstrate that both methods are complementary and that all in-plane and cross-plane heat transport properties can be fully measured using both methods. All parameters are then used for the experimental validation of formulas calculating thermal transport properties in superlattice in different temperature.
## 2 Materials and Methods
### Statistical Analyses  
The normality of the distribution of the data sets was assessed by the Shapiro-Wilk test.  
According to the data distribution, in order to compare the differences among different seasons, summer (S) and fall (F) 2018, and winter (W) and spring (Sp) 2019 within each vegetation cover (P: pine; HO: holm oak; BL: black locust; H: herbs) for both the soil abiotic and biotic properties the ANOVA tests were performed.  
In order to compare the differences between unburnt (UB) and burnt (B) soils within each vegetation cover (P, HO, BL, and H) for both the soil abiotic and biotic properties, the \(t\)-tests were performed.  
In order to highlight the variability of the data during the investigated period and due to fire occurrence, the coefficients of variations (CVs) were calculated as the ratio between standard deviation and mean value for each enzymatic activity.  
In order to assess the linear significant impacts of soil abiotic properties on soil biotic ones, multiple linear regressions were carried out with lm package of R 4.0.3 programming. The responses of soil biotic properties (HA, DHA, and \(\beta\)-GLU), defined as dependent variables to soil abiotic properties (pH, WC, total C, and N concentrations, OM, and available fraction of Al, Ca, Cu, Fe, Mg, Mn, and Na and Pb), defined as independent variables, were performed. To perform the multiple linear regressions, collinearity among independent variables was checked and the collinear variables were excluded from the model. Before performing the multiple linear regressions, the linearity of the data, the independence, the homogeneity, and the normality of residuals were tested. The models were considered significant at least for \(p<0.05\).  
In order to identify the relationships among soil enzymatic activities and abiotic properties within each vegetation cover, four Principal Components Analyses (PCA) were performed. In addition, to highlight the significant differences among different seasons (S, F, W, and Sp), and between unburnt and burnt soils within the same vegetation cover, permutational multivariate analyses (PERMANOVA) were performed.  
All the statistical analyses were performed using the R 4.0.3 programming environment with vegan package [36]. All the statistical analyses were considered significant at least for \(p<0.05\).  
The graphs were created with SigmaPlot12 software (Jandel Scientific, San Rafael, CA, USA).
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods
### Feature Fusion
#### 2.3.2 R-IoU, Optical Flow and Appearance Feature Fusion Model  
The model fuses three features: the R-IoU feature, the optical flow feature and the appearance feature obtained from ReID. As shown in Formula (10), When both the appearance cosine distance \(d^{\text{cos}}_{i,j}\) and the fusion model distance \(d^{\text{Fusion}}_{i,j}\) are less than the given thresholds, we use the appearance cosine distance as the new fused distance \(\widehat{d}^{\text{cos}}_{i,j}\): Otherwise, we set the new fused distance \(\widehat{d}^{\text{cos}}_{i,j}\) to 1. Then, we use the minimum value between the spatiotemporal fusion model \(\widehat{d}^{\text{cos}}_{i,j}\) and the new distance \(\widehat{d}^{\text{cos}}_{i,j}\) as the final value for the elements in the cost matrix \(C\). Our formula for the R-IoU and optical flow feature fusion combined with ReID can be expressed as follows:  
\[\widehat{d}^{\text{cos}}_{i,j}=\left\{\begin{array}{l}d^{\text{cos}}_{i,j}, \left(d^{\text{cos}}_{i,j}<\theta_{\text{emb}}\ \right)\cap\left(d^{\text{SFusion}}_{i,j}<\theta_{\text{fusion}}\ \right)\,\quad C_{i,j}=\min\left\{d^{\text{Fusion}}_{i,j}, \widehat{d}^{\text{cos}}_{i,j}\ \right\}\\ 1,\ \text{otherwise}\end{array}\right. \tag{10}\]  
where \(C_{i,j}\) represents the \((i,j)\) element of the cost matrix \(C\), \(d^{\text{cos}}_{i,j}\) represents the appearance cosine distance of the target within the prediction box and the detection box, \(\widehat{d}^{\text{cos}}_{i,j}\) is the new distance score after fusion, \(d^{\text{Fusion}}_{i,j}\) is the distance score after fusion of R-IoU and optical flow motion features. \(\theta_{emb}\) and \(\theta_{fusion}\) are both set thresholds, where we set both \(\theta_{emb}\) and \(\theta_{fusion}\) to 0.5. Through this process, we can obtain a fusion model that simultaneously integrates R-IoU, optical flow features, and appearance features. For the cost matrix \(C\), the Hungarian algorithm can be used to find the optimal one-to-one matching results between detection boxes and prediction boxes. This model will be applied to the first association stage of the tracking framework. The R-IoU, optical flow and appearance feature fusion model is shown in Figure 3.  
Based on the aforementioned content, we can obtain our spatiotemporal feature information model (i.e., the R-IoU, optical flow and appearance feature fusion model for the first association stage, and the R-IoU and optical flow feature fusion model for the second association stage). This model will be used in the matching and association module  
Figure 3: Feature fusion model.  
of the tracking framework in Section 2.4 to associate spatiotemporal features of targets across consecutive frames.
## 3 An Integrated Tolerance Theory for the Kinematic Accuracy Analysis of Landing
### Analysis of Variance and \(S/n\) Ratios for Tolerance Analysis  
Robust design involves Analysis of variance (ANOVA) and \(S/N\) analysis. ANOVA is a technique for analyzing the way how a variable is affected by different types and combinations of factors. The main parameters of an ANOVA are the Sum of Squared Deviations, Mean Squared Deviations, Contributions, and Confidences. By the analysis, the main factors and their effects are identified. Then, Taguchi analysis is carried out and \(S/N\) ratios of control factors are obtained.  
\(S/N\) ratio is an index of robustness in experiment designs. In other words, A higher \(S/N\) ratio is preferred because a high value of \(S/N\) ratio implies that the signal is much higher than the uncontrolled noise factors.  
After ANOVA (mainly including the Sum of Squared Deviations, Contribution, and Confidence) and \(S/N\) analysis, the optimum setting of control factors is determined and a robust tolerance design can be achieved.  
The purpose of kinematic accuracy analysis is to find a robust mechanism insensitive to the tolerances and clearances. This coincides with the main ideology of robust design using the Taguchi method. Naturally, the indexes of robust design are considered to be applied in the area of mechanism sensitivity analysis.
## 2 Materials and Methods
### Statistical Analysis and Selection of Modeling Approach  
One-way ANOVA tests were performed to check statistical significance of the variables listed in the Table 1. In order to perform one-way ANOVA, it was necessary to confirm the normality assumption of the observed data for adhesion forces. The observed data were transformed using 10 base logarithm and plotted as shown in Figure 1. It was found that the observed data transformed well, as the \(R^{2}\) value was found as 0.988. Then, the one-way ANOVA test was performed and shown in Table 2.  
Figure 1: Normal Q-Q plot confirming normality of adhesion forces (\(R^{2}=0.989\)).  
The one-way ANOVA test showed that there was no significant effect in the adhesion forces in using (i) different percentage of CNT, (ii) different percentage of SBs and SBSs, and (iii) SWNT and MWNT since the \(p\)-values are more than 0.05. However, some significant results were found for the different sample status, i.e., fresh, aged and moisture-damaged samples and different chemical compositions, namely Silicon Nitrate (Si\({}_{3}\)N\({}_{4}\)), Ammonia (NH\({}_{3}\)), Hydroxide (OH), Carboxylic (COOH) and Methyl (CH\({}_{3}\)). Nonetheless, such statistical analysis does not yet confirm and reveal inherent relations among variables with the adhesion forces which can predict the adhesion forces with satisfactory accuracy. Thus, it was necessary to devise a different approach for modeling adhesion forces. Hence, Artificial Neural Network technique was deployed with two widely used modifications namely ensemble learning and hierarchical modeling.
## 8 Numerical Example
### An Elastoplastic Structure  
In this study, a bilinear inelastic hysteretic behavior model of SDOF is considered (Figure 9). The lumped mass of \(m=4000\) kg, the elastic stiffness of \(k_{e}~{}=~{}10^{5}\) N/m and the hardening stiffness of \(k_{h}~{}=~{}10^{4}\) N/m are assumed for the system. The initial natural frequency of the system, based on the initial structural properties, is found to be \(\omega_{0}~{}=~{}\sqrt{k_{e}/m}~{}=~{}5\) rad/s, while the initial structural period \(T_{0}\) is 1.25 s. The yielding strength of the elastoplastic model is taken to be \(R_{T}~{}=~{}R_{C}~{}=~{}5000\) N for both the compression and tension. The undamped system is subjected to an earthquake record of CHY028 with a peak ground acceleration of 0.5 g. It should be mentioned that CHY028 is a near-fault ground motion record recorded by the Central Weather Bureau under the Taiwan Strong Motion Instrumentation Program, during the main shock of the Chi-Chi earthquake (Figure 10). The numerical results are obtained for the different values of \(p=0.5\) and \(1\), and are shown in Figures 11 and 12, respectively. NEM (0.005), AAM (0.02), CVM (0.02, 1) and CVM (0.02, 0.5) are used in this problem. The numerical solutions obtained from NEM (0.005) are considered as the reference solutions for comparison. In the Figures 11 and 12, the numerical solutions obtained from AAM (0.02), CVM (0.02, 0.5) and (0.02, 1) coincide with the reference solutions that are computed by NEM (0.005). As shown in Figures 11 and 12, the system experiences highly nonlinear hysteretic behavior. The results of this example thoroughly confirm that CVM can be used to solve a highly nonlinear system.  
Figure 8: Displacement response for the nonlinear damped SDOF system.  
Figure 9: An SDOF with a bilinear inelastic behavior.
# The Analysis of Periodic Order in Monolayers of Colloidal Superballs
## 2 Theory  
In SAXS the scattered intensity is the product of the form factor and the structure factor:  
\[I(\vec{q})=P(\vec{q})S(\vec{q}), \tag{1}\]  
where \(\vec{q}\) is the scattering vector. The form factor \(P(\vec{q})\) is determined by the shape and size of the individual particles. For uniform spherical particles the form factor is radially symmetric and equal to:  
\[P(\vec{q})=P(q)=\frac{3[\sin(qR)-(qR)\cos(qR)]}{(qR)^{3}}, \tag{2}\]  
where \(q\) is the length of the scattering vector and \(R\) is the radius of the spherical particle. Due to the symmetry of the spherical shape, the form factor does not depend on the particle orientation. For non-spherical particles, however, the form factor does depend on the orientation of the particle. While most anisotropic shapes do not have an analytical solution, their form factor can always be calculated numerically.  
The structure factor \(S(\vec{q})\), on the other hand, is determined by the structural assembly of the particles. Consider an ideal two-dimensional lattice with lattice points at distance vectors of:  
\[\vec{r}=m\vec{a}_{1}+n\vec{a}_{2}, \tag{3}\]  
where \(m\) and \(n\) are integers and \(\vec{a}_{1}\) and \(\vec{a}_{2}\) are the primitive lattice vectors. The structure factor of any structural assembly is equal to:  
\[S(\vec{q})=\frac{1}{N}\left|\sum_{n=1}^{N}e^{i\vec{q}\cdot\vec{r}_{n}}\right|^ {2}, \tag{4}\]  
where \(N\) is the total number of particles and \(\vec{r}_{n}\) is the distance vector of the \(n\)th particle. For an ideal two-dimensional lattice the structure factor is non-zero at:  
\[\vec{q}=h\vec{b}_{1}+k\vec{b}_{2}, \tag{5}\]  
for any value of \(q_{2}\). Here, \(h\) and \(k\) are the Miller indices, \(\vec{b}_{1}\) and \(\vec{b}_{2}\) are the primitive reciprocal basis vectors and \(q_{2}\) is the component of the scattering vector that is parallel to the incoming X-ray beam. The angle between these primitive reciprocal basis vectors is defined as \(\beta\). The primitive reciprocal basis vectors are related to the primitive lattice vectors as follows:  
\[\vec{b}_{1}=2\pi\frac{\vec{a}_{2}\times\vec{n}}{|\vec{a}_{1}\times\vec{a}_{2} |},\vec{b}_{2}=2\pi\frac{\vec{n}\times\vec{a}_{1}}{|\vec{a}_{1}\times\vec{a}_ {2}|}, \tag{6}\]  
where \(\vec{n}\) is a vector normal to the two-dimensional lattice plane. Finally, the angle between the primitive lattice vectors is \(\alpha=180^{\circ}-\beta\).
# Conceptual Design of an Extended Reality Exercise Game for the Elderly
## 5 Conclusions  
This study conducted a conceptual design of an exercise game, _VRrowing_, for the elderly. Based on previous studies and related work, we summarised and analysed the characteristics of elderly physical health and VR exercise games, as well as their game elements, activities and tasks, and gamification techniques. We also found evidence that physical activity could help improve the physical and mental health of the elderly. VR exercise games are a workout method with positive health effect, and the elderly have a positive attitude towards it.  
According to the above reviews (on common fitness themes, using postures, game elements, and game tasks of such exercise VR games for the elderly), as well as the design principles (including exercise games, interaction, and the user interface), we designed an in-home VR rowing game, _VRrowing_, for elderly exercise, with several boat sports' options and levels for users' different needs to target workout parts and physical conditions. In addition, _VRrowing_ considers the effect of body and mind on each other and designs a variety of game tasks (with benefits for both mental and physical health) to potentially match the mental training needs and enhance the fun and excitement. To adapt to different player types and modes' preferences, _VRrowing_ allows multi-mode, multi-users with multi-roles, which also helps mental health and social communication.  
This work illustrates the related studies review and conceptual design of VR exercise games for the elderly. Although the details are limited in how to implement the application, it still could be a reference for researchers and developers who work in VR applications and evaluations in the healthcare area. In future work, the research will deepen to continue to study of the application of VR techniques to seniors' health, their challenges and opportunities, and how to promote strengths and avoid weaknesses to provide better VR game software for elderly health. In addition, practically implementing and evaluating _VRrowing_ based on this conceptual design would be the other main future user study, implementing the demo version of VRrowing to test its main features or as the object to measure the feasibility of evaluation methods for such a game.  
This research received no external funding.  
The authors declare no conflict of interest.
# Error Averaging Effect in Parallel Mechanism Coordinate Measuring Machine
## 2 Measuring Principle of 3-PUU PCMM
In this study, we only focus on the motion errors of sliders that would affect the positional errors of the probe and, therefore, assume all structural parameters to be constant. In such a case, the probing position in space \((x_{p},\,y_{p},\,z_{p})\) can be simplified to the function of sliders positions as below. \[\left\{ \begin{array}{l}x_{p}=f_{1}(x_{1},x_{2},x_{3})\\ y_{p}=f_{2}(x_{1},x_{2},x_{3})\\ z_{p}=f_{3}(x_{1},x_{2},x_{3})\end{array} \right. \tag{3}\]  
Equations (2) and (3) are measuring models that are also called positive solution in parallel mechanism. The positive solution is the key in analyzing the working space of the probe and establishing the error measuring model. The working space refers to the set of points in space wherein the trigger probe can reach. The measuring accuracy is closely related to the position of the probe tip within the working space. For a specified measuring accuracy, however, only within a certain range of working space can the accuracy of the PCMM achieve the required level. This range is called the measurement space. The measurement space is expected to be as large as possible and is largely dependent upon the accuracy of each slider's motion and each structural parameter. Based on the required working space of parallel mechanism and the accuracy index as target, the optimization design with respect to all structural parameters of the PCMM was conducted [10; 11]. In this study, the motion errors of each slider were measured, and the error averaging effect to the probing position is analyzed in the following sections.
# In Vitro Accuracy of Two Different Electronic Apex Locators Under Various Conditions
## 2 Materials and Methods  
### Teeth Selection and Preparation  
This study design was similar to previously published studies and has been described well [7; 18; 19; 20].  
The study protocol was evaluated and approved by the Local Ethics Committee of School of Dentistry University of Zagreb. Forty multi-rooted human teeth extracted and collected for orthodontic or surgical reasons were used in this study. The number of samples used in the study was determined by the number of samples in previous studies, which is usually 40 samples for in vitro research of this type. The teeth were stored in 2% chloramine solution. The teeth were radiographed, and those with a complicated anatomy, external root resorption, immature roots, root fractures, and severe curvature were excluded from the study. The tissues remaining on the external root surface were removed using a periodontal curette. All teeth were cut horizontally with a high-speed diamond burr to provide a consistent reference point for future measurements. In each multi-rooted tooth, one canal was chosen and marked with nail polish.  
The root canals were prepared by one operator (W.D.) experienced in clinical practice. After access to the cavities was created, the canal orifices were enlarged with a Gates Glidden drill, type G ISO 50-150 (Komet Dental, Lemgo, Germany), and a Protaper Hand Use/Shaping File SX (Densply/Mailleffer, Mont-sur-Lausann, Switzerland) in a crown down technique. Canals were irrigated with 1% sodium hypochlorite and the excess solution was removed with cotton pellets and paper points.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 4 Experiments and Results
### Subjective Evaluation of Experimental Results
#### 4.1.2 Testing the Effectiveness of the Proposed Method in Bright Regions  
For the highlighted areas close to the light sources in the images, the pixel value was generally high. Examples include the girl's dress in Figure 16, the river in Figure 17, the hand in Figure 21, etc. These areas were inherently highly brightness and did not require much enhancement. Figures 16-22 show the enhancement results of all comparison algorithms and the proposed method on different images. For high pixel value areas, each comparison algorithm caused a different degree of blurring, while the proposed model retained more complete information.  
Figure 22: Enhanced results of “Church” image. **(a)** Original image; **(b)** BIMEF; **(c)** MF; **(d)** LIME; **(e)** SR; **(f)** Ret-Net; **(g)** FFM; **(h)** LECARM; **(i)** SDD; **(j)** Proposed.  
For example, in Figure 16, for the skirt in the red box area, BIMEF, MF, LIME, SR, FFM, LECARM and SDD adopted the same strategy for all areas when enhancing the image, regardless of the pixel values of the image itself, resulting in excessive exposure and blurred image details. The BEF transform function of our proposed method enhanced the brighter region slowly or not, and was able to improve the image brightness while maintaining the detail information, so the content in the region could be preserved completely. In Figure 22, the window above the church had a high pixel value and, therefore, did not require much enhancement. However, BIMEF, MF, LIME, SR, FFM, LECARM and SDD were enhanced for every pixel value of the whole image, thus losing some information. The method proposed in this paper retained the original content of the window area to a large extent.