esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling for Media Function Virtualization
## 4 System Model and Algorithm
### System Model  
A media service \(s\) is represented using a DAG denoted by \(\mathcal{G}=(\mathcal{F},\mathcal{L})\), where \(\mathcal{F}\) is the set of VMFs and \(\mathcal{L}\) is the set of virtual links. The bandwidth requirement, in bytes per hypercycle, on a virtual link is denoted by \(bw_{f_{i},f_{j}}\). Here, the hypercycle time is equal to the frame interval, i.e., \(|C|\times T_{c}=T_{frame}=1/R_{frame}\). Thus, bandwidth requirement per hypercycle for \((f_{i},f_{j})\) is equal to the frame size, e.g., an FHD (1920 \(\times\) 1080 or 2 K) @30 fps video stream with YCbCr color space and 4:2:2 sub-sampling, the bandwidth requirement per frame is 5184000B (5 MB).  
The MFVi network is also denoted with a directed graph \(G^{I}=(N,E)\), where \(N\) and \(E\) denote the set of physical nodes and links, respectively. The subset of physical nodes includes server nodes (\(N_{c}\subset N\)) with computational resources to host VMFs. The available bandwidth in cycle \(c\) on physical link \((n_{u},n_{v})\) is denoted by \(bw_{n_{u},n_{v}}^{c}\).  
Deploying a media service in an MFV environment involves mapping of the service's VMF-FG onto the MFVi network. In the problem of VMF-FG scheduling, given a VMF-FG \(\mathcal{G}\) and MFVi \(G^{I}\), (i) the VMFs should be assigned to the server nodes (\(\alpha:\mathcal{F}\to N_{c}\)), (ii) the virtual links should be mapped to the physical paths (\(\gamma:\mathcal{L}\to\mathtt{Phhs}\)), and (iii) the virtual links should also be assigned a packet transmission schedule (\(\omega:\mathcal{L}\to\mathbb{R}^{|C|}\)). As shown in Figure 3, the CO block has a view of MFVi network \(G^{I}\); the scheduler in the CO block takes VMF-FG \(\mathcal{G}\) and \(G^{I}\) as input and solves the VMF-FG scheduling to generate a solution. The solution (\(\alpha\), \(\gamma\), and \(\omega\)) is then used to configure MFVi, e.g., assign VMFs to servers, segment routing configuration for CSQF schedule, etc. Table 1 lists the various parameters, procedures, and variables involved in the ILP model and heuristic.
# GTMesh: A Highly Efficient C++ Template Library for Numerical Schemes on General Topology Meshes
## 4 The GTMesh Library
### Problem Formulation and Discretization  
Let \(\Omega\subset\mathbb{R}^{d}\), \(d\in\{2,3\}\) be a bounded polyhedral domain and \(\mathcal{J}=(0,T_{\text{end}})\) be the time interval, where \(T_{\text{end}}\) is the final time. The evolution of temperature \(T\) in \(\mathcal{J}\times\Omega\) is governed by the problem  
\[\frac{\partial T}{\partial t} =\Delta T \text{in }\mathcal{J}\times\Omega, \tag{18}\] \[T|_{\partial\Omega} =T_{\text{wall}} \text{on }\mathcal{J}\times\partial\Omega,\] (19) \[T|_{t=0} =T_{\text{ini}} \text{in }\Omega, \tag{20}\]  
where (18) is the heat equation (with heat conductivity equal to 1, for simplicity), (19) is a constant Dirichlet boundary condition and (20) is the initial condition. Given a mesh \(\mathcal{T}\) covering \(\Omega\) (recall Definition 1), we integrate (18) over each control volume \(K\in\mathcal{T}\), apply the Gauss-Green theorem and use FVM approximations to arrive at  
\[\int_{K}\frac{\partial T}{\partial t}(t,\mathbf{x})\text{d}\mathbf{x} =\int_{\mathcal{E}_{K}}\
abla T(t,\mathbf{x})\cdot\text{d}S \forall K\in\mathcal{T}, \tag{21}\] \[\downarrow \downarrow\] \[m(K)\frac{\text{d}T_{K}}{\text{d}t}(t) =\sum_{\sigma\in\mathcal{E}_{K}}m(\sigma)F_{K,\sigma} \forall K\in\mathcal{T}, \tag{22}\]  
where:  
* \(T_{K}\) is the approximation of \(T(t,\mathbf{x}_{K})\);
* \(F_{K,\sigma}\) is the approximation of \(\
abla T(t,\mathbf{y}_{\sigma})\cdot\mathbf{n}\) defined as \[F_{K,\sigma}=\begin{cases}\frac{T_{L}-T_{K}}{|\mathbf{x}_{L}-\mathbf{x}_{K}|}&\sigma= \bar{K}\cap\bar{L},L\in\mathcal{T},\\ \frac{T_{\text{wall}}-T_{K}}{|\mathbf{y}_{\sigma}-\mathbf{x}_{K}|}&\sigma\subset \partial\Omega;\end{cases}\]
* \(\mathbf{x}_{K},\mathbf{x}_{L}\) are the centers of the volumes \(K\), \(L\), respectively;
* \(\mathbf{y}_{\sigma}\) is the center of \(\sigma\).  
The semidiscrete scheme (22) represents a system of ODEs in the form (17), which is solved by the RKM solver described in Section 5.4.
## 3 Results
### The Bacillary Postbiotics Inhibit Dendritic Swarming Pattern in E. coli  
One of the mechanisms controlling biofilm formation is related to swarming motility regulated by the quorum-sensing (QS) system [31; 32; 33]. Bacteria may slide on semi-solid agar to generate different types, including a dendritic pattern of swarming motility [33]. The swarming motility is a QS-controlled phenotype that is usually driven by flagellated motion on semi-solid surfaces [34]. We tested the effect of collected postbiotics on the _E. coli_  
Figure 1: Effect of Bacillary postbiotics on physiology of bacterial pathogens. (**a**) Effect of AP and CSP on _E. coli_ and _S. aureus_ biofilm formation. Biofilms were generated on polystyrene surfaces in the presence or absence of the postbiotics, visualized, and quantified by crystal violet staining method. The graph shows the means \(\pm\) SEMs of three measurements. \(\texttt{{}^{***}}\)\(p<0.001\) vs. the non-treated controls. (**b**) Fluorescent microscopic images of bacterial biofilms on polystyrene surfaces treated with either AP or CSP (5% \(v/v\)). Scale bar: 200 \(\upmu\)m. (**c**) The diameter of _E. coli_. swarm area in AP or CSP treated and non-treated controls. The graph shows the means \(\pm\) SEMs of three measurements. \(\texttt{{}^{***}}\)\(p<0.001\) vs. the non-treated controls. (**d**) Effect of AP or CSP (5% \(v/v\)) on dendrite swarming motility of _E. coli_. Scale bar: 2 cm.  
colony expansion characterized by a dendritic pattern of swarming motility (Figure 1c,d). The _E. coli_ cells, however, failed to produce the dendrite pattern when AP or CSP was added to the semi-solid agar (Figure 1d). The finding hints at the ability of the postbiotics to restrain swarming motility in _E. coli_. Further work is warranted to understand if the swarming inhibition is related to the QS quenching.
###### Abstract  
The aim of this study was to analyze the biomechanical effects of a conventional molar uprighting spring on a mesially tilted mandibular second molar through a novel iterative finite element (FE) approach in order to estimate clinical treatment duration. A new model was developed utilizing data from previous canine retraction experimental studies to aid in correlating each individual FE iteration with its real-time value. Another model consisting of a 30\({}^{\circ}\) mesially titled mandibular second molar and a conventional molar uprighting spring was developed to evaluate kinetic and kinematic responses. The iterative FE simulation of the treatment was then carried out. The molar uprighting simulation was completed in 180 iterations, which was equivalent to almost 12 weeks of clinical treatment time. The average stress-normalized bone remodeling velocity was found to be \(\sim\)0.9 \(\upmu\)m/(KPa-day). During the simulation, the spring initially produced a 15 N-mm (\(\sim\)1530 g-mm) counterclockwise moment responsible for molar uprighting. The mandibular second molar showed 3.75 mm of distal movement, 2.15 mm of vertical extrusion, and a 22\({}^{\circ}\) counterclockwise rotation at the end of the treatment. This study provides a foundation for the future estimation of biomechanical responses as a function of treatment time in various orthodontic applications using iterative FE simulations.  
m eisally tilted molar; conventional uprighting spring; orthodontics; biomechanics; tooth movement rate; bone remodeling velocity; iterative finite element analysis +
Footnote †: journal: Applied sciences
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 4 Computer Vision System
### Two-Dimensional (2D) and Three-Dimensional (3D) Vision  
In mushroom harvesting, 2D vision is primarily used to identify mushrooms by assessing their size, shape, color, and texture. This information helps the harvester determine which mushrooms are ready to be picked. In contrast, 3D vision provides spatial orientation and position, allowing for precise depth perception. This is essential for robotic systems to accurately grasp and pick mushrooms without causing damage. Additionally, 3D vision aids in estimating the mushroom's volume and ensuring the picking mechanism interacts with mushrooms at the correct angle and depth.  
Both 2D and 3D camera systems are widely used in mushroom identification, monitoring, and quality assurance. Commercially available RGB cameras, including those in smartphones and digital devices [11], capture 2D images. In the past, video recorders, monochromatic cameras, and webcams were commonly used for these tasks [26; 27; 28; 29; 78; 83; 85]. Today, more advanced cameras like the IP67 network cameras and smartphone cameras are popular in mushroom harvesting applications [80; 81; 86]. For 3D vision, RGB-D cameras, which combine RGB imaging with depth measurement, are commonly used. Popular 3D cameras include Intel Realsense and Microsoft Kinect [73; 82]. The quality of images captured by these cameras is significantly influenced by the lighting conditions, impacting the accuracy of image processing [27]. To address this, various lighting setups, including linear and circular sources, controlled illumination, and arrays of lights, are used. While fluorescent lights were once common, LEDs have become the preferred choice in recent years.  
The selection between 2D and 3D vision systems greatly influences the accuracy of mushroom detection and localization. Studies have demonstrated that 3D vision systems generally provide higher accuracy due to their ability to capture depth information, which is crucial for precise spatial orientation. For example, [PERSON] et al. [74] effectively utilized low-cost RGB-D sensors for detection and 3D pose estimation, achieving high accuracy in both laboratory and farm environments. Similarly, [PERSON] et al. [73] reported significant effectiveness in mushroom detection and pose estimation using a template-based approach with 3D point clouds.  
Conversely, 2D vision systems, while useful for identifying surface features like color and shape, often face challenges with depth perception and occlusion, impacting their overall accuracy. Traditional 2D methods like those employed by [PERSON] [26; 27] and [PERSON] et al. [29] achieved detection accuracies of 84% and 86%, respectively. However, the lack of in-depth information can limit precise localization, which is essential for automated harvesting.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 4 Results
### The Overall Average 3D-RMS  
Figure 15 summarizes the overall average 3D-RMS of SLR-only orbit determination for all nine BDS satellites, where the colours blue, cyan, yellow, and red refer to the overall average 3D-RMS of 3-, 5-, 7-, and 9-day solutions. For GEO C01, the 3D-RMS of orbit overlaps is about five times better than that of the comparison with WUM orbits. The difference indicates that there must be some large systematic errors between SLR-only and microwave-based orbit determination of GEO C01 satellite, as seen in Figure 6. Interestingly, for C01, the average RMS of the 3-day arc solution is optimal among the four multi-day arc solution strategies. This is synthetically affected by the fixed orbit yaw mode of GEO, the frequent manoeuvers to adjust satellite attitude, the sparse SLR observations, and the limited visibility of SLR sites. In addition, the deterioration of the dynamic model parameters is an important factor with the lengthening of the multi-day arc solutions. Among the three IGSO satellites, the 3D-RMS of C10 are as abnormal as C01 for different multi-day arc solutions. With the increase in the arc length, the accuracy of SLR-only orbit determination improves steadily for IGSO C08 and especially for C13. The 3D-RMS of 9-day arc solutions can be significantly better than 2 m for C13. The regional coverage in the shape of '8' is typical characteristic of IGSO satellites, so that the observation of SLR sites are also regional because of this limitation, which is bound to reduce the quantity of the SLR observations, as well as weaken the geometric distribution of SLR sites.  
Table 5 shows the maximum and minimum values of the 3D-RMS for three type of BDS satellites derived from the orbit overlaps and the comparison with WUM orbits in the half year of 2019. In all five MEO satellites, the accuracy of multi-day solutions has steadily improved from 3-day to 9-day arc solutions. The 3D-RMS of C11 is better than 1.55 m for 3-day arc solutions and 0.49 m for 9-day arc solutions. Among them, the accuracy of C11 is the best, followed by C21, C30, and C29, while that of C20 is the worst. There are three periods for which more than 10 days of SLR observations  
Figure 14: The results of SLR-only orbit determination for C30.  
are missing for C20, as shown in Figure 2. When we calculate the orbit of the multi-day arc with the moving time window method, the missing data over 10 consecutive days will undoubtedly have a great impact on the SLR-only orbit determination, especially the data around the beginning and end of each interruption. Overall, the accuracy of orbit overlaps is better than that of the comparison with WUM orbits, except for the GEO C01 satellite. The BDS IGSO are characterized by the RMS values higher by a factor of about 3-5 than MEO orbits. The 9-day arc solutions are the best for BDS IGSO and MEO satellites, and their optimal 3D-RMS is 1.89 m and 0.49 m for the orbit overlaps, and 1.85 m and 0.55 m for the comparison with WUM orbits.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
###### Abstract  
In this work, we present the design of a mitigation scheme for jamming attacks integrated to the routing protocols MPH, AODV, and DSR. The resulting protocols are named MPH-M (Multi-Parent Hierarchical - Modified), AODV-M (Ad hoc On Demand Distance Vector - Modified), and DSR-M (Dynamic Source Routing - Modified). For the mitigation algorithm, if the detection algorithm running locally in each node produces a positive result then the node is isolated; second, the routing protocol adapts their paths avoiding the isolated nodes. We evaluated how jamming attacks affect different metrics for all these modified protocols. The metrics we employ to detect jamming attack are number of packet retransmissions, number of CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) retries while waiting for an idle channel and the energy wasted by the node. The metrics to evaluate the performance of the modified routing protocols are the throughput and resilience of the system and the energy used by the nodes. We evaluated all the modified protocols when the attacker position was set near, middle and far of the collector node. The results of our evaluation show that performance for MPH-M is much better than AODV-M and DSR-M. For example, the node energy for MPH-M is 138.13% better than AODV-M and 126.07% better than DSR-M. Moreover, we also find that MPH-M benefits much more of the mitigation scheme than AODV-M and DSR-M. For example, the node energy consumption is 34.61% lower for MPH-M and only 3.92% and 3.42% for AODV-M and DSR-M, respectively. On throughput, the MPH protocol presents a packet reception efficiency at the collector node of 16.4% on to AODV and DSR when there is no mitigation mechanism. Moreover, MPH-M has an efficiency greater than 7.7% with respect to AODV-M and DSR-M when there is a mitigation scheme. In addition, we have that with the mitigation mechanism AODV-M and DSR-M do not present noticeable modification. However, MPH-M improves its efficiency by 8.4%. We also measure the resilience of these algorithms from the average packet re-transmissions perspective, and we find that MPH-M has around a 15% lower change rate than AODV-M and DSR-M. The MPH-M recovery time is 5 s faster than AODV-M and 2 s faster than DSR-M.  
Wireless Sensor Networks; jamming; routing algorithms +
Footnote †: journal: Sensors
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 5 Global SFM-PMVS Pipeline  
In this section, we review the whole 3D reconstruction pipeline. The SFM-PMVS method consists of the following steps: (1) extract features and match features; (2) construct a view graph; (3) compute camera motions using rotation averaging and translation averaging; (4) obtain the sparse structure and optimize it with bundle adjustments, then finally compute the dense point cloud with PMVS.  
**Step 1: feature extraction and feature matching.**  
We use SIFT [40] to extract image features, which is a scale-invariant feature descriptor with advantages in terms of stability, efficiency, and abundance (a large number of features even though the object is extremely small). The quality of an image may be greatly reduced because of poor contact and spectral distortion in underwater scenes, whereby SIFT can extract enough features. The 128-dimensional feature descriptor obtained by the SIFT algorithm can be used to match features between two images. We use KNN-match (afeature matching method based on the K nearest neighbor algorithm [41] provided by opencv) to obtain matching points, and filter the matched points using the essential matrix.  
**Step 2: View graph construction.**  
We construct the view graph by finding the epipolar geometry (internal projective relationship between two views, which depends on the relative position and camera's internal parameters) between images. In the view graph, each vertex represents a camera and each edge means that relative motions between two cameras can be estimated. In our strategy, we use the rotation angle to represent the weight of the edge and construct a minimum spanning tree as the final view graph.  
**Step 3: Robust Rotation Averaging and Translation Averaging.**  
After step 1 and step 2, we can begin to compute the global rotation with the relative rotations. As described in Section 4.2, we use the LTS-L1 RA method to compute global rotations. Then, we use the ACRANSAC method for the camera pose registration described in [6] to fulfill the translation averaging.  
**Step 4: Triangulation, Bundle Adjustment, and PMVS.**  
Through the robust estimation of global rotations and poses in step 3, we can recover the scene structure from the motion. In this step, we compute the 3D point positions via triangulation and then refine the structure and motions via bundle adjustment. After this, in order to provide a better visual effect, we use the PMVS [42] toolkit to obtain a dense point cloud.
## 3 Materials and Methods
### Structural Semantic Feature Extraction
#### 3.2.1 EDLines Line Feature Detection  
Presently, the EDLines algorithm is one of the most popular line detection algorithms. Compared with the traditional line feature extraction methods, such as Hough [31] and LSD [32], EDLines can detect the line features in an image with high accuracy. The principle of the algorithm is to use the edge drawing algorithm to generate a clean and continuous edge pixel chain and then generate a straight-line segment based on the edge pixels. This algorithm is efficient and fast. In comparison, the detection speed of EDLines is 10 times higher than that of LSD.
## 4 Experimental Design and Results
### Findings and Analysis from the Experiments
#### 4.3.1 Accuracy  
Throughout the model's training and testing phases, we conducted 10 iterations and generated an accuracy curve based on the achieved accuracy in each iteration, as depicted in Figure 9. The model demonstrated outstanding training performance, with a final training accuracy of 96.04%.
## 3 Results
### Analysis of Sports Injury Rate  
The sports injury rate for the three months prior to the training (five days a week, for 2 h a day, totaling 120 h) was used as a pre-test in this study. The 12-week experiment served as the post-test (168 total training hours for the experimental group, and 120 total training hours for the control group). The independent-sample \(t\)-test revealed that there was no significant difference between the pre-test results of the two groups (t = \(-\)0.79, \(p\) < 0.05).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Vertical Jump** & **Lateral Jump** & **Horizontal Jump** & \multirow{2}{*}{**\(F\)-Value**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{\(\boldsymbol{\eta^{2}}\)} \\  & **M \(\pm\) SD** & **M \(\pm\) SD** & **M \(\pm\) SD** & & \\ \hline JD (m) & 0.58 \(\pm\) 0.03 & 1.66 \(\pm\) 0.08 & 1.15 \(\pm\) 0.05 & 1758.37 * & \textless{}0.05 & 0.99 \\ R-GRF (N) & 2119 \(\pm\) 263 & 2040 \(\pm\) 134 & 1909 \(\pm\) 277 & 18.81 * & \textless{}0.05 & 0.57 \\ T-PRF (\(\theta\)) & 90.0 \(\pm\) 0.0 & 66.4 \(\pm\) 0.9 & 56.7 \(\pm\) 1.6 & 6465.28 * & \textless{}0.05 & 0.98 \\ \hline \hline \end{tabular} Abbreviations: Jump distance is denoted as JD. Resultant force during the step-back is represented as R-GRF. Take-off resultant force trajectory is indicated as T-PRF (\(\theta\)). Data are presented as the mean \(\pm\) standard deviation (M \(\pm\) SD). F-test results are presented as F-values (\(p\)-values), * \(p\) < 0.05.  
\end{table}
Table 6: Analysis of differences in three types of jumping abilities in the post-test of the experimental group.  
Figure 1: Average numbers of goals scored in the pre- and post-tests for the three types of jump shots. EG is the experimental group, and CG is the control group. * There is a significant difference between the pre-test and post-test in the experimental group.  
However, a significant difference in the post-test results was found (t = \(-\)5.44 *, \(p\) < 0.05). The statistical results indicated that the sports injury rate index in the experimental group dropped to 6% after the PTP intervention. This suggests that the strength enhancement in the experimental group contributed to a reduction in the sports injury rate as shown in Table 7.
## 2 Materials and Methods
### EIS and Polarization Curves  
Involving the under static condition, a PJZ100 radiometer analytical potentiostat controlled by Tacussel corrosion analysis software model (Voltamaster 4) was employed. A three electrode cell, thermostatically stabilized at 308 K, is used to perform electrochemical tests. The working electrode is a 1 cm\({}^{2}\) disc made from one of the previously mentioned coupons. A saturated calomel electrode SCE was used as reference electrode. With the same surface area as the working electrode, a platinum electrode was used as auxiliary electrode. Seeking to define the open circuit potential E\({}_{\text{OCP}}\), the working electrode is immersed for 30 min in the test solution to achieve a steady state OCP. At E\({}_{\text{OCP}}\) and a frequency ranged between 100 KHz and 0.1 Hz, the EIS experiments were conducted with 10 points per decade. Nyquist plots were established using this measurement results. Once the EIS was finished and after 5 min of E\({}_{\text{OCP}}\), the polarization measurement of the tested solutions were scanned from \(-800\) to \(-200\) mV by applying a 1 mV s\({}^{-1}\) as a scan rate [21].  
The inhibition efficiency for EIS and polarization measurements was calculated using, respectively, Equations (7) and (8):  
\[E_{R}\%_{\circ}=\frac{R_{t(inh)}-R_{t}}{R_{t(inh)}}\times 100 \tag{7}\]  
\[E_{i}\%_{\circ}=\frac{l_{corr}^{0}-I_{corr}}{l_{corr}^{0}}\times 100 \tag{8}\]  
where \(R_{t(inh)}\) and \(R_{t}\) (respectively, \(I_{corr}\) and \(l_{corr}^{0}\)) are the charge transfer resistances (respectively, current densities) for the inhibited and uninhibited medium.  
Further technical and theoretical details on the electrochemical methods and weight loss measurement were published by our lab members in their works [22; 23; 24; 25; 26; 27].