esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Fly Tower Modeling for Preliminary Acoustic Predictions in Opera Houses
## 3 The Case Study
### Effects of the Fly Tower Depth and Absorptive Characteristics on the RT Trend  
Starting from the fly tower height and the width defined in Section 2, three different Depths are considered: \(1/3\) of the max stage Depth, \(2/3\) of the max stage Depth, and the max stage Depth itself. For each of them, the fixed absorption coefficients of the floor and back wall, as well as three steps increasing the absorption coefficients for the ceiling and the lateral walls for low, middle, and high values, are studied (Table 4 and Figure 12).  
Figure 11: Simulated and measured T30 (**a**) and C80 (**b**) average values.  
The T30 trend is characterized by a higher sensitivity by changing the absorption coefficients from low to middle absorption coefficients in the fly tower, especially at central frequencies, from 500 to 2000 Hz. By increasing the Depth from 2/3 to the max Depth, the T30 does not create JND by changing the absorption coefficients from M.A. to H.A.  
Figure 12: T30 trend in stage empty conditions by increasing the absorption coefficients from low (L.A) to medium (M.A.) and high (H.A.) absorption for three different stage Depths: 1/3 max Depth (**a**), 2/3 max Depth (**b**), and max Depth (**c**).  
From the data represented in the figures above, it is deduced that, with the increase in the absorption coefficients from the absorption coefficient M.A. and from the 2/3 stage Depth, the Reverberation Time trend sensitivity is reduced, and the JND is not present. To better justify the simplified fly tower active volume suggested in Section 2, the T30 trend as the average of the stalls' values and of the boxes is reported (Figure 14).  
As reference absorption coefficients for the defined fly tower active volume, equal values for the ceiling and the lateral walls are chosen, as data from real heavy vapour curtains [39; 59], which are within the interval of the previously defined M.A. and H.A. coefficients. The stage and back wall ones remain the same (Table 5).  
Figure 14: T30 trend in stage empty conditions by increasing the stage Depth for three different low (L..A), medium (M.A.), and high (H.A.) absorption conditions: in the stalls (**a**,**c**,**e**) and in the boxes (**b**,**d**,**f**).
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 3 Results
### Vineyards Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Vintage** & \multicolumn{3}{c}{**NJVI**} & \multicolumn{3}{c}{**NDRE**} \\
**Mean** & **SD** & **CV** & **Mean** & **SD** & **CV** \\ \hline
2017 & 0.548 & 0.548 & 0.363 & 0.347 & 0.136 & 0.391 \\
2018 & 0.580 & 0.581 & 0.302 & 0.375 & 0.124 & 0.332 \\
2019 & 0.573 & 0.573 & 0.288 & 0.369 & 0.116 & 0.314 \\
2020 & 0.554 & 0.554 & 0.325 & 0.359 & 0.124 & 0.346 \\
2021 & 0.540 & 0.540 & 0.335 & 0.347 & 0.124 & 0.356 \\
2022 & 0.537 & 0.537 & 0.302 & 0.350 & 0.114 & 0.324 \\ \hline \end{tabular}
\end{table} 
Table 4: NDVI and NDRE reassume per vintage from the filtered dataset (n = 2304). Finally, the autocorrelation matrix between the numerical features is depicted in Figure 11. Analysing the correlation between similar features and observing some preliminary PCA plots, slope emerged as strongly correlated with elevation. At the same time, any contribution given by perimeter is almost provided by area and the number of vertices. Moreover, as the Italian peninsula has a northwest-to-southeast orientation, longitude and latitude are negatively related to the county's bisector. Because the geographical location of a vineyard is mostly affected by the climate, as shown in Figure 5, no relevant temperature or rainfall gradients are detectable along the longitudinal vector. Thus, a longitude effect may have biased the result because of its negative correlation with latitude. Slope, perimeter, and longitude were excluded from the PCA analysis. The slope effect was independently assessed as \"topography\" in the ANOVA.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 4 Results
### Functional and Motor Performance Assessments  
The evaluation of functional and motor performance revealed significant improvements across different therapy groups (\(p<0.001\)). The 10 m walk test results demonstrated notable differences in maximal walking speed (\(p<0.001\)). The greatest improvements were observed in the Exergame and Cycling groups. In the first trial, the mean speed ranged from 0.67 m/s to 0.93 m/s, while in the second trial, it increased significantly, reaching a range of 1.12 m/s to 1.32 m/s (\(p<0.001\)), indicating improved gait efficiency post-intervention. Functional independence, measured using the Functional Independence Measure (FIM), also showed significant improvement (\(p<0.001\)). The total FIM score increased across all therapy groups, with motor-related FIM scores rising from 39-49 (baseline) to 67-78 (post-intervention), demonstrating improved motor function. The Falls Efficacy Scale revealed a significant decline in fall-related fear post-intervention (\(p<0.001\)), with scores improving from 74-89 (baseline) to 48-56 (post-intervention), reflecting increased confidence in balance and mobility. The Berg Balance Scale (BBS) exhibited substantial progress, particularly in the Exergame and Cycling groups (\(p<0.0001\)), with scores increasing from 23-26 to 36-38 post-intervention. A comprehensive summary of all pre- and post-intervention re  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**EX**} & \multicolumn{2}{c}{**CY**} & \multicolumn{2}{c}{**AG**} & \multicolumn{2}{c}{**ROB**} & \multicolumn{2}{c}{**All**} \\ \hline Variable & Mean & SD & Mean & SD & Mean & SD & Mean & SD & Mean & SD \\ Age (y) & 68.04 & 4.71 & 69.72 & 4.53 & 68.12 & 4.36 & 69.48 & 4.82 & 68.84 & 4.61 \\ Height (cm) & 173.16 & 6.69 & 173.84 & 6.28 & 174.24 & 7.34 & 173.48 & 5.68 & 173.7 & 6.44 \\ Mass (kg) & 74.32 & 7.56 & 76 & 7.93 & 74.08 & 8.37 & 74.32 & 6.45 & 74.68 & 7.54 \\ BMI & 24.80 & 2.29 & 25.19 & 2.74 & 24.45 & 2.85 & 24.78 & 2.59 & 24.81 & 2.60 \\ Hoehn–Yahr & 2.32 & 0.48 & 2.44 & 0.51 & 2.44 & 0.51 & 2.36 & 0.49 & 2.39 & 0.49 \\ L-dopa & 816.4 & 116 & 806.00 & 109 & 838.60 & 123 & 838.40 & 118.1 & 824.9 & 116 \\ UPDRS-MEDL & 18.84 & 3.18 & 18.88 & 3.11 & 19.40 & 4.69 & 18.96 & 3.05 & 19.02 & 3.53 \\ Schwab and England & 71.60 & 9.43 & 70.00 & 10.8 & 71.20 & 10.1 & 70.40 & 8.89 & 70.80 & 9.71 \\ Beck & 12.36 & 2.75 & 12.68 & 3.24 & 12.44 & 2.84 & 12.44 & 2.84 & 12.48 & 2.88 \\ PDQ39 average & 46.52 & 8.70 & 48.40 & 11.9 & 48.5 & 9.27 & 47.64 &sults, including functional and motor outcome measures across the four groups, is provided in Table 2.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 2 Problem Statement  
### Virtual Linkage  
A mechanical linkage is an assembly of rigid bodies connected by joints. Meanwhile, a rigid body is defined as a group of point masses fixed by the constraint that the distances between every pair of points must be constant [26]. A joint is a connection between two bodies and imposes constraints on their relative movement. In other words, a linkage is also a collection of points which belong to different rigid bodies.  
Prior to analyzing a linkage directly, each rigid body component is discussed first. In a single rigid body, particles can be thought of as stationary with respect to a certain frame of reference. If the particles in a rigid body are replaced with movable elements, we obtain the definition of virtual structure proposed in [19], which can be seen below.  
**Definition 1:**_Virtual Structure_  
_A virtual structure is a collection of elements, e.g., robots, which maintain a (semi-) rigid geometric relationship to each other and to a frame of reference [19]._  
The definition of virtual linkage also can be given by replacing particles of a linkage with movable elements and controlling them to act as the original linkage. An intuitive solution to this problem is to model each link as a virtual structure and define a virtual linkage as a collection of virtual structures connected by virtual joints.  
**Definition 2:**_Virtual Joints_  
_A virtual joint is a connection between two virtual structures and imposes constraints on their relative movement._  
**Definition 3:**_Virtual Linkage (VL)_  
_A virtual linkage is an assembly of virtual structures (named \"virtual link\") connected by virtual joints._  
The principle of the virtual structure and virtual linkage is illustrated in Figure 2. In the generation of a virtual linkage, the existence of the virtual joints has enabled relative movement between cascaded virtual structures. Therefore, different geometric shapes are possible to be presented using the same virtual linkage by controlling each virtual joint at different values.  
Figure 2: Principle of virtual structure and virtual linkage.
## 4 Discussion
### Tip  
Tip information is one of the key factors in the straight wire technique. Tooth inclination in the meso-distal plane greatly affects the correct expression of the inbuilt torque information, and also affects the relative tooth dimension and the related Bolton index [4; 18]. However, tip expression is one of the main factors involved in anchorage loss and tip information is usually expressed early in treatment [2]. It is indeed surprising how only a few studies have addressed the issue of bracket tip fabrication accuracy.  
According to the results of our study, in the evaluated bracket systems, all mean tip values were within the tolerance limit established by the ISO 27020:2019 norm. When the different batches were analyzed separately, one system in one of the three batches presented values that exceeded the tolerance limits. Other authors who analyzed the tip inbuilt in the bracket slot found greater deviations than those in the current study. [PERSON] et al. (2014) in vitro tested 60 Roth prescription, pre-adjusted metal brackets, with a bracket slot size of 0.022 in,, from three different manufacturers and found a variation in mean tip angulation between \(2.13^{\circ}\) and \(3.90^{\circ}\), between the different systems [4]. The magnitude of the variation can be considered significant since the theoretical tip value is \(5^{\circ}\) in the Roth prescription.  
[PERSON] et al. (2002) evaluated 220 MBT prescription, maxillary central incisors from four different manufacturers and found tip values significantly different from those declared in three brackets series from three different manufacturers, with a value range of \(3.29^{\circ}\) to \(7.13^{\circ}\) while the declared value was \(4^{\circ}\). The authors underline that, despite the large range found in the total sample, the brackets from \(3\text{M}^{\text{TM}}\) and Dentaurum\({}^{\text{TM}}\) displayed smaller ranges and standard deviation when compared to the other manufacturers [2]. Analyzing their results according to the ISO 27020:2019, we can state that in their sample only these two brands respected the given tolerance limits. [PERSON] et al. (2015) assessed a pool of 0.022 in. metal brackets, MBT prescription from six different manufacturers and found a mean tip value ranging from \(1.67^{\circ}\) to \(6^{\circ}\), the mean actual value being significantly different from those declared in all the evaluated bracket series [1]. In their sample, the Agile Mini\({}^{\text{\textregistered}}\) Bracket from \(3\text{M}^{\text{TM}}\) presented a mean value closer to the declared (\(4.33^{\circ}\)) and within the tolerance limits. Generally speaking, according to the previously cited author, the largest deviations were observed in locally available bracket brands and the smaller in brackets from worldwide providers. This could reflect a better validation of the manufacturing process and quality check in industries with a greater background.
# A Decentralized Receiver in Gaussian Interference
## Appendix B Achievability of Bounds
Taking the log,  
\[\log\left(\sum_{\hat{m}\
eq\hat{m}}P(\mathcal{E}_{\hat{m}n,S})\right) \tag{105}\] \[\leq TR+t\cdot(H(\mathbf{U}_{S}|\mathbf{U}_{SC},(\mathbf{W}_{Q,n})_{n})+ \varepsilon)-t\cdot(t\cdot\sum_{n\in S}r_{n}-\varepsilon)-t\cdot\left(I(\mathbf{X };\mathbf{U}|(\mathbf{W}_{Q,n})_{n})-3\varepsilon\right)+\varepsilon\] (106) \[\leq t\cdot(H(\mathbf{U}_{S}|\mathbf{U}_{SC},(\mathbf{W}_{Q,n})_{n})-t\cdot\sum_{n \in S}r_{n}+tR-I(\mathbf{X};\mathbf{U}|(\mathbf{W}_{Q,n})_{n})+6\varepsilon)\] (107) \[\leq t^{2}\cdot(I(\mathbf{Y}_{S};\mathbf{Z}_{S}(\mathbf{\rho}-\mathbf{1}\cdot \varepsilon)|\mathbf{Z}_{SC}(\mathbf{\rho}-\mathbf{1}\cdot\varepsilon))-\sum_{n\in S}r_{n}+ R-I(\mathbf{X};\mathbf{Z}(\mathbf{\rho}-\mathbf{1}\cdot\varepsilon))+8\varepsilon/t). \tag{108}\]  
Equation (108) follows by using Lemma 2 on \(H(\mathbf{U}_{S}|\mathbf{U}_{SC})/t\) and using reasoning identical to Equations (101)-(103) on \(I(\mathbf{X};\mathbf{U}|(\mathbf{W}_{Q,n})_{n})\). Recall that \(R\) was chosen so that \(R\leq R_{G}(\mathbf{\rho}-\mathbf{1}\cdot\varepsilon)-\lambda=I(X;\mathbf{Z}(\mathbf{\rho}-\mathbf{ 1}\cdot\varepsilon))-\lambda\). Then, Equation (108) will approach \(-\infty\) as \(t\to\infty\) (thereby \(P(\cup_{\tilde{m},S}\mathcal{E}_{\hat{m},S})\) approaches \(0\)) if all \(S\subseteq[0:N]\) satisfy:  
\[I(\mathbf{Y}_{S};\mathbf{Z}_{S}(\mathbf{\rho}-\mathbf{1}\cdot\varepsilon)|\mathbf{Z}_{SC}(\mathbf{\rho} -\mathbf{1}\cdot\varepsilon))<\lambda+\sum_{m\in S}r_{m}-8\varepsilon. \tag{109}\]  
By assumption that \(\mathbf{\rho}\in\mathcal{R}_{DC}^{\lambda}(\mathbf{r})\) and for small enough \(\varepsilon\), then Equation (109) holds for each \(S\). Since all error events approach \(0\), then a rate of \(R_{G}(\mathbf{\rho}-\mathbf{1}\cdot\varepsilon)-\lambda\) is achievable. For small enough \(\varepsilon\), by lower semi-continuity of mutual information \(R_{G}(\mathbf{\rho}-\mathbf{1}\cdot\varepsilon)-\lambda\) can be made arbitrarily close to \(R_{G}(\mathbf{\rho})-\lambda\).
# Impact of Implant Design and Bone Properties on the Primary Stability of Orthodontic Mini-Implants
###### Abstract  
This study investigated the correlation between bone characteristics, the design of orthodontic mini-implants, the pull-out force, and primary stability. This experimental in vitro study has examined commercial orthodontic mini-implants of different sizes and designs, produced by two manufacturers: Tomas-pin SD (Dentaurum, Ispringen, Germany) and Perfect Anchor (Hubit, Seoul, Korea). The total number of 40 mini-implants were tested. There are two properties that are common to all tested implants--one is the material of which they are made (titanium alloy Ti-6 Al-4V), and the other is the method of their insertion. The main difference between the mini-implants, which is why they have been selected as the subject of research in the first place, is reflected in their geometry or design. Regardless of the type of implant, the average pull-out forces were found to be higher for a cortical bone thickness (CBTC) of 0.62-0.67 mm on average, compared to the CBTC \(<\) 0.62 mm, where the measured force averages were found to be lower. The analysis of variance tested the impact of the mini-implant geometry on the pull-out force and proved that there is a statistically significant impact (\(p<\) 0.015) of all three analyzed geometric factors on the pull-out force of the implant. The design of the mini-implant affects its primary stability. The design of the mini-implant affects the pulling force. The bone quality at the implant insertion point is important for primary stability; thus, the increase in the cortical bone thickness increases the value of the pulling force significantly.  
mini-implants; primary stability; geometry analysis; bone scan; pull-out test +
Footnote †: journal: Applied Sciences
## 4 I-NMF: Fusion of CIDAE and O-NeuMF-p  
On the one hand, although O-NeuMF-p performs better than NeuMF-p, it still does not solve the problem of data sparsity. On the other hand, although CIDAE can alleviate the problem of data sparsity, CIDAE only utilizes the idea of a single-layer neural network(AE) and does not fully exploit the ability of deep neural networks to learn potential features. Therefore, this paper fuses CIDAE and O-NeuMF-p with reference to the idea of ensemble learning. We name the fused model the I-NMF (Imputation-Neural Matrix Factorization) model, which combines the advantages of CIDAE and O-NeuMF-p. The I-NMF model can not only alleviate the problem of data sparsity, but also fully exploit the ability of deep neural networks to learn potential features, and I-NMF is also robust. Figure 3 shows the structure of I-NMF.  
It is worth noting that there was a paper [24] combining the AE algorithm and the MF algorithm. The paper [24] uses the AE's feature layer as the initial value of the hidden vector of MF, but the combined model is still linear, like MF. The idea of this paper is not the combination of the feature layer, but purely the combination of the prediction layer, which guarantees the integrity of CIDAE and O-NeuMF-p, so that both models can play their own advantages.
## 11 Proposed Threat Model and Proof of Resistance
### Resistance to Phishing Attacks  
To demonstrate resistance to phishing attacks, we show that the proposed framework incorporates robust authentication and verification mechanisms to prevent unauthorized access and data breaches. The framework ensures that user credentials and sensitive information are securely transmitted and stored, minimizing the risk of phishing attacks [12].  
Proof.: The proposed framework employs strong cryptographic techniques, secure communication protocols, and multi-factor authentication to establish a secure channel between users and the system. These measures protect against phishing attempts by verifying the integrity and authenticity of the communication channels, ensuring that users interact only with legitimate components of the framework. Therefore, the proposed framework provides resistance to phishing attacks.
## 3 Results
### Pomario Test Site
#### 3.2.2 NE Side Area  
Figure 9a shows an enlarged mean LOS displacement map of the CSK ascending dataset over the NE side of the Pomarico village. Targets exhibiting stronger local displacements are aligned on two sections of the road, the former upslope, close to the village, and the latter further downslope. Targets show mean displacement rates ranging between \(-2\) and \(-10\) mm/y (Figure 9a), and the corresponding time series show continuous and linear displacements starting from 2015 (Figure 9c,d).  
Figure 9: (**a**) Enlargement of the mean LOS displacement rate map derived by processing the COSMO-SkyMed ascending dataset over an area NE of the Pomarico village; (**b**) map of targets selected according to the \(FE\) index with a threshold of 0.5; (**c**) displacement time series (black dots) and its replicas obtained by subtracting \(\lambda/2\) (yellow dots) and \(\lambda\) (red dots), corresponding to the target labeled P1 in panel (**a**); (**d**) displacement time series (blue crosses) and its replicas obtained by subtracting \(\lambda/2\) (yellow crosses) and \(\lambda\) (red crosses), corresponding to the target labeled P2 in panel (**a**); (**e**) displacement time series corresponding to target P1 (black squares) and P2 (blue crosses) after correction of phase unwrapping errors. The PAI landslide risk map by the Basilicata section of the Southern Apennine District Basin Authority [27; 28] is plotted with colored lines delimiting the risk classes areas according to the colormap in the figure.  
Figure 9b shows the targets selected through the \(FE\) index according to (3). As in the example reported in Section 3.1.1, the \(FE\) index allows selecting targets with interesting nonlinear displacement trends, such as those sketched in Figure 9c,d, that refer, respectively, to the targets labeled P1 and P2 in Figure 9b. In both cases, like in the example in Section 3.1.1, the displacement time series are affected by discontinuities due to phase unwrapping errors, which can be corrected by exploring their replicas obtained by subtracting integer multiples of the range of ambiguity (\(\lambda/2=16.5\) mm). For example, in Figure 9c, black dots represent the original displacement values of target P1, orange dots represent the displacements obtained by subtracting the range of ambiguity (\(\lambda/2\)), which accounts for the first discontinuity occurring around March 2014, and red dots represent the displacements obtained by subtracting twice the range of ambiguity (\(\lambda\)), which accounts for the second discontinuity occurring around April 2015. The displacement time series corrected by the phase unwrapping errors are sketched in Figure 9e in black squares for target P1 and blue crosses for target P2. Now the two targets, without discontinuities, show similar nonlinear trends of alternating periods of accelerating and decelerating displacements.
## 2 Materials and Methods
### Levelling and GNSS Measurements  
In the AOI, there is an observation line that is 4250 m long. It extends north-east to south-west approximately along the longitudinal axis of the LW502 longwall (Figure 1). It consists of 114 observation points permanently stabilized in the ground.  
Due to the expected range of revealing mining influences, geodetic observations were carried out on the section from point No. 1 in the north to point No. 40 in the south  
Figure 1: Area of Interests (AOI) localization with the scheme of observation points and exploitation of coal longwalls LW501 and LW502 in seam 215.  
(red points in Figure 1) from June 2021 to August 2022. During this period, the mine's measuring department performed 5 series of geometric levelling measurements (the dates of the measurements are listed in Table 1). The levelling measurements were related to the three reference benchmarks of the GIGANT levelling network, which are beyond the range of the mining exploitation influence, and the heights of the points were determined in the PL-EVRF2007-NH altitude system. Height measurements were carried out using a Leica LS15 level using carbon fiber code staffs. The average error m\({}_{\text{H}}\) in determining the height of the observation line measurement points is m\({}_{\text{H}}\)\(\leq\) 0.015 m. Levelling measurements were supplemented with independent satellite measurements with a GNSS receiver. Satellite observations were made in August 2022 with Spectra Precision SP60 receiver in the GNSS RTN technology, using correction corrections from the national system of ASG-EUPOS reference stations. The coordinates of the points were determined in the PUWG2000 zone 6 system (Poland CS2000 zone 6; PL-EVRF89-2000, EPSG: 2177) and the PL-EVRF2007-NH altitude system. The average error in determining the position of points using the ASG-EUPOS system corrections is m\({}_{\text{XY}}\)\(<\)\(\pm\) 0.03 m in the horizontal plane and m\({}_{\text{H}}\)\(<\)\(\pm\) 0.05 m for the height [48].