esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 3 Pipeline for 3D Mesh Reconstruction
### Depth Information Patching  
The inherent limitations of camera technology result in depth images often containing significant artifacts, such as holes, reflections, and other sources of error. To mitigate these issues, we integrate a bilateral filter into the processing pipeline.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Sensors** & **3D Pose** & **Loop Closure** & **Map Type** &
\begin{tabular}{c} **Localization** \\ **Performance** \\ \end{tabular} & **CPU Load** \\ \hline \multirow{2}{*}{Gmapping [6]} & 2D LiDAR, & & & Grid Map & Medium & High \\  & Odometry & & & Pointcloud Map & Good & Medium \\ \hline LOAM [21] & 3D LiDAR, IMU * & \(\surd\) & & Pointcloud Map & Good & Medium \\ \hline \multirow{2}{*}{Cartographer [22]} & 2D/3D LiDAR, & & & & & \\  & IMU *, Odometry * & \(\surd\) & \(\surd\) & Grid Map & Good & High \\ \hline ORB-SLAM3 [20] & Camera, IMU * & \(\surd\) & \(\surd\) & Pointcloud Map & Excellent & High \\ \hline \hline \end{tabular} Note: The “*” means optional. The “\(\surd\)” symbol in the table indicates that the corresponding condition is met, while a blank space signifies non-applicability or non-compliance.  
\end{table}
Table 2: Comparison of SLAMs.  
A bilateral filter [23] is a noise-smoothing and edge-preserving filter governed by range and spatial parameters. This filtering method not only enhances the quality of depth maps by reducing noise and preserving structural details but also effectively minimizes the occurrence of holes, thereby improving the accuracy of localization.  
To evaluate the effectiveness of this approach, we conducted experiments using ORB-SLAM3 on the TUM RGB-D dataset [24]. Figure 2 presents the proposed method, which incorporates a bilateral filter, in comparison to the standard ORB-SLAM3 algorithm, as summarized in Table 3. The results demonstrate a substantial improvement in absolute pose error (APE), with a 39% reduction in APE root mean square error (RMSE) for the fr2/largeloop dataset. These findings are further illustrated in Figures 3 and 4.
## 3 Results and Discussion  
### Mechanical Properties of Epoxy Asphalt During the Curing Process  
To evaluate the changes in the tensile mechanical properties of epoxy asphalt during curing, tensile tests were performed on epoxy asphalt cured at 60 \({}^{\circ}\)C for different times, and the tensile strength and elongation at break were measured. The tensile strength and elongation at break of the epoxy asphalt specimens with different curing times are shown in Figure 1. In the early curing stage, the tensile strength in the epoxy resin was relatively low. As the curing time increased, the tensile strength of the epoxy asphalt increased rapidly. The rate of increase in the tensile strength decreased after 36 h, and the tensile strength stabilized at approximately 4 MPa after 72 h. The increase in epoxy asphalt strength may be related to the formation of an epoxy asphalt network structure, which is confirmed by the micromorphology characterization below. The reaction between the epoxy resin and the curing agent increases the cohesion of epoxy asphalt via the formation of a crosslinked network. It has been shown that the formation of a crosslinked epoxy asphalt network can reduce the elongation at break of epoxy asphalt [21]. The test results show that the elongation at break of epoxy asphalt decreased rapidly in the early curing stage. The rate of decrease in the elongation at break slowed down after 48 h, and the elongation at break stabilized at approximately 500% after 96 h. In summary, at the initial curing stage, the tensile strength of epoxy asphalt was relatively low, and the elongation at break was relatively high. As the curing reaction proceeded, the gradual formation of the crosslinked network was accompanied by an increase in the tensile strength and a gradual decrease in the elongation at break. After 96 h, the tensile strength and the elongation at break stabilized in conjunction with the formation of a relatively stable crosslinked epoxy asphalt structure.  
To compare the mechanical properties of the cured epoxy asphalt and the pure epoxy resin, tensile tests were conducted after curing at 60 \({}^{\circ}\)C for 96 h (by which time the two materials were completely cured). The test results are compared in Figure 2. Compared to that of pure epoxy resin, the tensile strength of fully cured epoxy asphalt was 27% lower, whereas the elongation at break was 79% higher. Thus, compared to the structure of pure epoxy resin, the three-dimensional (3D) network structure of epoxy asphalt resulted in a better deformation performance. This result is consistent with the findings of a previous report [23].  
Figure 1: Mechanical properties of epoxy asphalt with different curing times.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 2 Related Works  
Existing works on RF tomographic imaging can be classified into vector-based and tensor-based approaches. The vector-based approaches [16; 17] aim at estimating a spatial distribution of the shadowing loss in 2D regions of interest. They are not able to infer three-dimensional regions due to the fact that spatial structures of the signal data are ignored. Therefore, researchers have proposed the tensor-based approaches [1; 4] to infer three-dimensional spaces. The tensor-based compressed sensing [1] uses the tensor nuclear norm (TNN) [18] to extend RF tomographic problems to the three-dimensional case. This approach has a high computational complexity and error rate. [PERSON] et al. [4] exploited the transform-based tensor model [19] to explore three-dimensional spatial structures for higher accuracy and speed. Our work aims at accelerate [PERSON]'s work on GPUs to make it practical for real-time scenarios and bigger tensors.  
GPUs have massive parallelism and high memory bandwidth, and many existing research works [20; 21; 22; 23; 24] have demonstrated the benefit of utilizing GPUs to accelerate general purpose computing. Due to the high dimensions of tensors, tensor computations are often computation-intensive and time consuming. Recently, GPUs have been increasingly adopted to accelerate diverse tensor computations. Some works focused on accelerating specific tensor operations including tensor contraction [25; 26], factorization [27], transpose [28; 29], and tensor-matrix multiplication [30]. These works propose parallel tensor algorithms specifically optimized for the GPU architectures. GPUENSOR [31] is a parallel tensor factorization algorithm that splits a tensor into smaller blocks and exploits the inherent parallelism and high-memory bandwidth of GPUs. To handle dynamic tensor data, GPUENSOR updates its previously-factorized components instead of recomputing them from the raw data. Sparse tensor-times-dense matrix multiplication is a critical bottleneck in data analysis and mining applications, and [32] proposed an efficient primitive on CPU and GPU platforms. Different from these works, our work considers the tensor sensing based on the transform tensor model for RF tomographic imaging.
## 4 Numerical Experimental Simulation and Analysis
### Introduction to Test Models  
To evaluate the effectiveness of the proposed G-Pre preconditioning algorithm and to simplify the testing process, we selected a total of four asymmetric matrix solver models with varying scales. These models were used to assess the solving performance of the G-Pre algorithm. The relevant information for each model is summarized in Table 1.
## 5 Results and Discussions  
Simulation case studies were performed in both space-based and ground-based tracking scenarios. The results obtained are presented and discussed in the following sections. However, it has to be noted that the current work focuses on estimating the RSO position and the associated errors. Assuming that the tracked RSO moves in a circular orbit, the equations used to estimate RSO velocity from position estimates are defined in [94]. Assuming that the RSO is moving freely in a circular orbit, then its motion can be governed by classic orbital dynamics, as:  
\[\tilde{\mathbf{r}}_{\mathrm{t}}=\frac{-\mu}{\left|\mathbf{r}_{\mathrm{t}} \right|^{3}}+\mathrm{f}_{\mathrm{i}} \tag{60}\]  
where:  
\(\mathbf{r_{\mathrm{t}}}\) = the position vector of the RSO in ECI frame,  
\(\mathrm{f}_{\mathrm{i}}\) = orbital perturbations that take into account J2 perturbations and drag.  
The velocity of the RSO can be calculated by integrating Equation (60).
## 4 Experimental Setup
### Signature Image to Covariance Matrix  
We briefly review the process that maps an offline signature (i.e., the content of a digital image \(\mathbf{Img_{X}}\)) to its corresponding covariance matrix \(\mathbf{X}\). The mapping commences by the preprocessing step, which comprises the thresholding and thinning originally proposed in [72] and is then utilized in other research efforts such as [52; 55; 56]. For each handwritten signature image, the thinning algorithm of [72] detects the optimal thinning level (OTL), a parameter which has been found to affect the verification results of a number of datasets in an optimal way, including the ones that are currently employed [37]. Therefore, the \(I_{p}\) image, i.e., the result of the preprocessing stage, is derived by employing the individual OTL at the thinning stage, and then the corresponding covariance matrix \(\mathbf{X}\) is extracted accordingly. A feature map of ten (i.e., \(d=10\)) image planes derived from \(\mathbf{Img_{X}}\) comprises the corresponding image filters, \(\left[I_{p},I_{x},I_{y},I_{xx},I_{xy},I_{yy},\sqrt{{I_{x}}^{2}+{I_{y}}^{2}}, \tan^{-1}\left(I_{y}/I_{x}\right),x,y_{y}\right]\), in which \(I_{p}\) is the result of the preprocessing stage of the \(\mathbf{Img_{X}}\); \(I_{x},I_{y},I_{xx},I_{yy}\) are the first- and second-order derivatives of \(I_{p}\); \(x,y\), are the normalized coordinates (by their maximum number of rows and columns of the image bounding box) of the signature pixels; and \(\sqrt{(\cdot)}\) and \(\tan^{-1}(\cdot)\) denote the gradient magnitude and direction (normalized in radians). The corresponding covariance matrix \(\mathbf{X}\in\mathbf{P_{10}}\) is evaluated only on the pixels that belong to the signature trace of the thinning preprocessing step. In the unlikely case that \(\mathbf{X}\) is not strictly SPD, an additional regularization term, \(10^{-4}\mathbf{I}_{10}\), where \(\mathbf{I}_{10}\) is the identity matrix, is added to \(\mathbf{X}\) in order to ensure it; i.e., \(\mathbf{X}\leftarrow\mathbf{X}+10^{-4}\times\mathbf{I}_{10}\). Figure 5 presents the visual output of the preprocessing stage (\(I_{p}\)) along with the outputs produced by the applied filters. Regarding the use of specific image filters, we report the following: We tried to utilize other types of image filters on the \(I_{p}\) image that have been suggested in the literature for other image recognition and computer vision applications. For example, we created and experimented with covariance matrices that utilize the following:  
* A family of Gabor filters in different directions and frequencies. * A family of difference of Gaussians (DoG). In addition, we applied a tactic in which for each signature pixel a 5 \(\times\) 5 window creates a 25-dimensional intensity feature. However, the currently exploited 10 dimensional filters (also utilized in our previous research work) strongly suggest that this is a robust image filter set for signature verification. Most likely, this is due to the nature of the signature, as it is made from handmade strokes depicted as sparse image lines of variable durations and curvature (p. 2, [56]).
###### Abstract  
The present study investigated the effectiveness of employing a patient simulator with an integrated visual feedback mechanism to improve the light-curing skills of dental students. A total of 44 third-year dental students were randomly divided into a control group (\(n=22\)) and a training group (\(n=22\)). Both groups light-cured a simulated restoration in tooth 27 in a patient simulator (MARC Patient Simulator, BlueLight Analytics, Halifax, Canada) by using the same curing device for 10 s. Delivered irradiances were recorded in real time by the built-in spectrophotometer. After measuring the baseline irradiances for both groups, the training group received detailed light-curing instructions and hands-on training with immediate visual feedback using the patient simulator. The irradiance of the training group was re-measured after the training. Both groups then attended a 26-day preclinical course, which involved placing 30 composite restorations. Upon completion of this course, the light-curing performance of both groups was re-assessed. The data were statistically analyzed using the Wilcoxon signed-rank test, Friedman's ANOVA, and the Mann-Whitney U-test at an overall level of significance of \(\alpha=0.05\). At baseline, the control and the training group delivered statistically similar irradiances with similar data scattering. In the training group, data scattering was considerably reduced after the hands-on training with the patient simulator. After the 26-day preclinical course, the irradiance of the training group was significantly higher and considerably less scattered compared to the control group. In conclusion, training with the patient simulator improved the light-curing performance of the dental students, mainly by helping them to deliver light energy more consistently.  
d 2020 10, 8269; doi:10.3390/app10228269
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Energy  
Satellite observation may exert impacts on the following three components in energy system: demand, supply, and distribution. Accordingly, the CDR relative data sets may delve deeper into these components, and link them with other factors, such as population and/or gross domestic product.  
Among an exhaustive list of energy supplies, altogether 9 kinds, 5 of them can be potentially assessed using remote sensing technologies, including biomass, solar [87], wind, hydro, and heat transfer [115]. Wind energy, for example, transforms the kinetic energy of wind into electricity using rotating a turbine. Key variables to estimate wind energy are wind speed and direction, which can be measured from several onsite and space-borne instruments. A wind climatology data set (or CDR) could greatly help select the sites for a wind farm. Similarly, satellite imagery and CDR products can help in the exploration of oil, gas, and minerals.  
According to a report of Global Science & Technology, Inc. Greenbelt, MD, USA [116] in 2007, power distribution systems were damaged with dry conditions and Santa Ana winds, and as a result, fueled massive fires burning 13% of San Diego Gas & Electric's service territory. The utility was found liable and the company paid $2 billion in hundreds of settlement cases. Afterward, they created an early warning and operational maintenance system using historical climate data. In a similar dry condition during 2014, the system saved the utility billions of dollars in avoided liability.
# Transparent Redirections in Content Delivery Networks
###### Abstract  
In today's age of digital media and social networks, delivering content to a massive scale of recipients became one of the main challenges. Load Balancers and Content Delivery Networks are used to distribute content to multiple locations, which increase the scalability of services and decrease the load on content origins. Both technologies rely on redirections. Redirections have not received a significant amount of attention in the recent years; however, they do impose some limitations. In this article, we propose a transparent redirection mechanism, which exploits the versatility of Software Defined Networking. The redirection method is achieved by handing off existing TCP sessions without any required modifications to the endpoints. This article demonstrates how the proposed redirection mechanism can be adopted in Content Delivery Networks and Load Balancing scenarios. The performance of the solution is thoroughly tested and compared to existing legacy solutions.  
Software Defined Networking; TCP session handoff; Content Delivery Networks; redirections 1 Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 842 16 Bratislava, Slovakia; [EMAIL_ADDRESS] (R.B.); [EMAIL_ADDRESS] (I.K.)1
## 2 A Simple Example Problem and Methods
### Signed-Particles  
Historically, the basic similarities between the concepts and notions of classical mechanics and quantum mechanics in phase space motivated the development of particle methods for solving the Wigner equation almost three decades ago [24]. Different particle approaches have been developed since then [25; 26; 27], which focus mainly on the electrostatic problem in a scalar potential gauge. In particular, the concept of signed-particles, which has matured for 15 years [28], is based on the application of stochastic approaches to solving integral equations by converting them to different integral forms of the Wigner equation. The concept is not a single, unique particle model, but comprises a set of particle attributes which can be combined and modified to develop suitable algorithms, specific for the physical aspects of the particular problem. Some of these attributes are, however, fundamental and give rise to a heuristic picture of quantum mechanics in terms of particles [29]. Basic attributes are particle sign, particle generation, and particle annihilation. In a signed-particle approach, point-like particles are enabled with classical features, such as drift over Newtonian (field-less) trajectories but carry the quantum information by their positive or negative sign. The mean value of a generic physical quantity \(A\), represented by a phase space function, is evaluated by the \(\sum_{n}\text{sign}(n)A_{n}\) for all particles \(n\) in a desired region, where \(\text{sign}(n)\) is the sign of the \(n\)-th particle and \(A_{n}\) the corresponding value of the physical quantity. During the evolution, each particle generates couples of one positive and one negative particle, according to rules dictated by the Wigner potential, and propagate in space by distinct but fixed momentum (no acceleration). Particles with opposite sign which meet in the phase space annihilate each-other since they have a common probabilistic future but opposite contribution to the physical averages. Proof of concept simulations, showing that [PERSON]'s second law can be reproduced by generation/annihilation of un-accelerated signed-particles [29], have been conducted, and also for two- and three-dimensional problems [30]. This allows to prove that annihilation is able to reproduce classical ballistic behavior in the quantum case, where the applied electric field shows constant or linear behavior. Also, there aren't abrupt variations of the electric potential, like the one introduced by the dopant, that (exceeding quadratic dependence) generate quantum behavior.  
Figure 1: The two-slit electron problem: simulation setup.
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 12 Security Issues concerning the Data Link Layer  
As WSNs are broadcast in nature, several vulnerabilities might arise as a result of attacks that can easily disrupt, modify, or insert malicious data. There are several types of attacks that affect the data link layers and change the flow of traffic. At the data link layer, flow management is a strategy for ensuring that data flows correctly from the source to the recipient and is an issue of design. The transmitter must
# Microfabrication Bonding Process Optimization for a 3D Multi-Layer PDMS Suspended Microfluidics
## 2 3D Suspended Polymeric Microfluidic Resonator Fabrication (SPMF\({}^{3}\))
### Fabrication Issues
#### 2.3.1 Air-Vent PDMS Fabrication Method  
As mentioned during the fabrication process, microchannel (MLU, MLB) and nozzle layers (NL) are placed on a glass slide during bonding, as shown in Figure 6a. Therefore, air bubbles may be trapped between layers during the bonding step, resulting in microfluidics dysfunction. In order to avoid this issue, two air-vent channels were designed and fabricated during the molding step, as shown in Figure 7. These channels help vent the trapped air bubbles out and consequently increases the bonding strength.  
In order to optimize the air-vent channel's dimensions, three suspended microfluidic systems with and without air-vent channels were fabricated and bonded. In this experiment, air-vent channels of 300 \(\upmu\)m and 500 \(\upmu\)m were integrated into the microchannel layer. As shown in the results, the air bubble is highly trapped in the fabrication without air-vent channels, which causes complete fabrication failure and unusable microfluidics, as shown in Figure 8.  
However, when an air-vent channel of 300 \(\upmu\)m was fabricated within the microchannel layer, the trapped air bubble transformed into multiple smaller bubble where there were no bubbles near the air-vent channels, which means that trapped air near these channels was taken out. Increasing the air-vent channel size to 500 \(\upmu\)m removed most of the air bubbles and cleared all critical regions around the microchannel where the bonding strength was required. There is still a tiny portion of the trapped air left that is not in a critical region and can be removed with an extra air-vent channel in that zone.  
Figure 8: The effect of air-vent microchannel on tapped air-bubble size; (a) microchannel layer without air-vent channel; (b) microchannel layer with 300 \(\upmu\)m air-vent channel; (c) microchannel layer with 500 \(\upmu\)m air-vent channel.  
Figure 7: Schematic view of the microchannel layer of SPMF\({}^{3}\) with two air-vent microchannels.