esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis
### Throughput  
As observed in Figure 10, an increase in the number of selfish nodes in the network results in a gradual decrease in the network throughput. This is a result of fewer data packets being delivered to the desired destination. When comparing the networks without the reputation model to the networks with the reputation model, the same trend of a reduction in the network throughputs is observed. However, the networks with the reputation model have a better network throughput performance. The improved network throughputs could be a result of fewer nodes utilizing the available bandwidth after the selfish nodes are blacklisted.  
The reputation model also increases the proportion of packets that reach the final destination by re-routing the packets to avoid blacklisted nodes. Additionally, since selfish nodes are blacklisted when detected through the computed reputation values, the act of ignoring their route requests means that they  
Figure 10: Throughput for networks experiencing an increase in selfish nodes.  
Figure 9: Routing overhead for networks experiencing an increase in deceitful nodes.  
are not able to send their own data packets. Thus, a possible reduction in packet collision may most likely improve the network throughput.  
Similarly, when the throughput of the networks with the reputation model and the networks without the reputation model are compared in the presence of an increased number of deceitful nodes, as observed in Figure 11, as the number of the deceitful nodes increases, the network throughputs decrease due to many fewer data packets being delivered to their respective destinations. Similarly, the improved network throughput observed in the network with the reputation model may also be a result of the ability of the network to isolate the deceitful nodes. The reputation model ensures that only nodes in the white-list and watch-list participate in network activities and results in reduced packet collision. It is also observed that the deceitful nodes reduce that network throughput more than the selfish nodes due to the behaviours, _i.e._, continuously dropping data packets presented for forwarding.
## 3 Example Test and Result Analysis
### Example Testing Process  
The length of the inverted arch of the tunnel is K13 + 060-K13 + 170. During the monitoring period, the inverted arch cracks changed significantly. There were eight cracks in the mileage section. The cracks mainly extended from small distances to large distances along the lining centerline, and the rest of the cracks extended along the lining centerline to the ditches on both sides. The mileage filling layer of this section had obvious uplift, and some of the cracks had obvious dislocation. The sizes of the cracks were different. The maximum crack size was 20 cm, and its length was about 110 m. The method described in this paper was used to monitor the uplift deformation of the tunnel inversion in the K13 + 060-K13 + 170 section.  
The example test process was as follows:  
Equipment use: Use the Leica Scan Station2 3D laser scanner to collect 3D point cloud data of the soft rock tunnel invert, set the laser safety level to 3R, and use the dual scanning window to generate 360\({}^{\circ}\) at most \(\times\) with a 270\({}^{\circ}\) scanning field angle. The scanner is equipped with a CCD digital camera and supporting software Cyclone 2023.0.2 x64 to set the camera exposure at random under the pulse ranging method.  
Point cloud slicing: Select eight public marker points in the inverted arch of the soft rock tunnel in the K13 + 060-K13 + 170 section from the top, bottom, left, and right angles, and use the automatic matching method of public marker points and the improved Rodrigues parameter method to splice the collected 3D point cloud data of the soft rock tunnel inverted arch. Through the Mallat algorithm, reconstruct and process the 3D point cloud data after splicing. According to the reconstructed 3D point cloud data, carry out plane fitting using the overall least square method. Through the principal component analysis method, estimate the normal vector of the fitting plane to find the best datum plane of the soft rock tunnel inverted arch. Extract the slice point cloud by taking the distance from the best datum plane to the soft rock tunnel inverted arch point cloud as a fixed value.  
Point cloud data collection: Collect the point cloud data from the top, bottom, left, and right angles, extract the geometric parameters of the slicing point cloud, and regularly obtain the displacement, deformation, stress, and other data of the soft rock tunnel invert every 30 min. The acquisition results are shown in Figure 2.  
According to Figure 2a-d, this method can effectively collect point cloud data from four angles in the inverted arch of soft rock tunnel (up, down, left and right) to provide data support for subsequent deformation monitoring of the inverted arch of the soft rock tunnel.
# Comparison of CAD Software for Designing Cellular Structures for Additive Manufacturing
## 4 Discussion
### AM of Designed Lattice Structures  
The additive manufacturing of lattice structures presents a challenge in the AM toolchain. Finding suitable AM processes that are able to manufacture strut-based and surface-based lattice structures can be demanding. These structures are primarily produced through the LPBF of metals and sometimes polymers, which do not require support structures. Many AM processes, such as MEX and VPP, are capable of producing strut-based lattice structures without the need for support structures. However, surface-based lattice structures may require additional support. It is a fact that for all additive manufacturing processes, the parts produced differ significantly from the original design models. In many cases, this behavior is observed in the LPBF of metals [70; 71]. As a result, the current research is focused on predicting and virtually correcting defects in the CAD model prior to manufacturing by means of numerical simulations and statistical models. This approach ensures the production of both accurate and mechanically robust parts [70; 72]. However, additional fundamental research needs to be conducted to achieve predictive models that automatically correct process parameters and help the user achieve the desired mechanical properties of the manufactured parts.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 4 Cell Adhesion Molecules
### Cell-Cell Adhesion Molecules  
#### 4.2.1 Immunoglobulin Superfamily  
The immunoglobulin superfamily (IgSF) has over 765 diverse members and is one of the largest groups of proteins found in the human body [79]. The most common members of the IgSF are T cell receptor complex proteins, major histocompatibility complex class 1 and 2 molecules, virus receptors and cell surface glycoproteins [79]. Almost all members of the superfamily are classed as single-pass transmembrane proteins, comprising an extracellular domain with one or multiple Ig-resembling repeats and intracellular domains of varying length [80]. Other members can be tethered to the plasma membrane of the cell through a glycosylphosphatidylinositol (GPI) unit. IgSF components, especially Ig domains, can promote both homophilic and heterophilic cell adhesion, while specialised linker proteins couple the IgSF component to cytoskeletal regions [80]. One of the major functions of the IgSF is to promote cell-cell adhesion. In particular, a subgroup of the IgSF that executes this role is the nectin family, which promotes homophilic or heterophilic cell-cell attachment in various tissues [81].
## 3 Problem Statement  
### Distance-related Probabilistic Sensor Detection Model  
Rather than the binary detection model, and in a seek for more realism, we consider as in [14] and [15] a Probabilistic Sensor Detection Model. More precisely, we assume that event detection ability of a sensor diminishes as its distance to the sensed point increases. Using this model, a _confidant_ and _maximum_ sensor monitoring circles are defined. If any event (event _(1)_ in Fig.1) occurs within the _confident_ circle, then event detection probability is considered as equal to \(1\). If the event (event _(2)_ in Fig.1) occurs outside the _confidant_ circle but within the _maximum_ circle, then the detection probability decreases with the distance. Finally, when the distance is larger than the radius of the _maximum_ circle, then the event (event _(3)_ in Fig.1) is no longer detected. Based on the above assumption, we consider, as in [16], the following expression of the general detection probability \(P\) of a sensor \(s\) at an arbitrary point \(p\):  
\[P(s,p)=\left\{\begin{array}{rl}1&\mbox{if }\|sp\|\preceq 1\\ \frac{\alpha}{\|sp\|^{\beta}}&\mbox{if }1\prec\|sp\|\preceq R_{max}\\ 0&\mbox{if }R_{max}\prec\|sp\|\end{array}\right. \tag{1}\]  
where \(R_{max}\) is the radius of the _maximum_ circle, \(\alpha\) is a sensor technological related parameter and \(\beta\) is an event characteristic-dependant parameter. Finally, \(\|sp\|\) is the Euclidean distance between the sensor \(s\) and the point \(p\). We believe that this model is more realistic than the binary model. Therefore, we assume it works with this new model for our deployment problem, which we formulated in the next section.
## 3 Experiments and Results
### Experiment 3  
This experiment was based on Set 3. Equation (8) is at the core of general calibrated measurements using the non-uniform model and relies on the angle \(\alpha\). Experiment 3 was conducted to investigate the accuracy of the estimation of \(\alpha\) from an image. This experiment is especially important, given the presence of non-linear distortion in flexible endoscopy [14]. Angle differences between adjacent lines from Set 3 (Figure 3) were estimated, and then they were subtracted from their true value (i.e., 15\({}^{\circ}\)). Figure 11 presents boxplots of this error for different working distances. Running a one-way analysis of variance (ANOVA) did not indicate any significant effect of working distance. Therefore, all measurement errors were combined into a single group. The overall angle estimation error had the value of \(-0.03^{\circ}\)\(\pm\) 0.6\({}^{\circ}\) (average \(\pm\) std). Consequently, central angles can accurately be estimated from acquired images. This result may seem contradictory with a previous finding, suggesting significant errors in the estimation of angles from flexible endoscopes [31], and hence requires further explanation. The proposed method relies on central angles; however, the work of [31] was based on a general angle. Considering the radial nature of the non-linear distortion, lines passing through the center do not experience bending and curving. Therefore, the central angles can be measured very accurately.
## 4 Discussion
The results of present study show the stability of soft tissue in the pre-operative period and after a minimum of one year of follow-up. There was no significant recession of the midfacial mucosa; the recession of the mesial papillary height was 0.32 mm, and the recession of the distal papillary height was 0.10 mm. The mean horizontal change of the labial mucosa was 0.32 \(\pm\) 0.83 mm. [PERSON] et al. reported that the midfacial recession was 0.53 mm in average in patients who received single immediate implant and provisionalization in the anterior maxilla during the first year of function [8]. [PERSON] et al. evaluated the esthetic outcome by analyzing the dimensional changes of the midfacial soft tissue as a result of using the immediate placement and restoration protocol, and reported that advanced soft tissue recession (>1 mm) was observed in 11% of the cases [39]. [PERSON] et al. evaluated changes in the contour of the labial soft tissue after an immediate post-extraction implant placement and provisionalization. During a one-year follow-up period, the labial soft tissue contour showed  
Figure 5: The Reconstruction of the Emergence Profile and Gingival Architecture with the Equi-Gingival Abutment Margin. Figure 6: The Customized Titanium Anatomic Abutment Schematic Representation. a continuous alteration. The total mean change was 0.62 mm, mainly in the first three months [40]. The PES value before treatment was 11.16, and at one year of follow-up, 10.25. There was a slight decrease in each measurement and the existing esthetic status was found to have a significant effect on the outcome. In the present study, a favorable outcome was reported, which implies that the immediate restoration with the anatomic abutments provides predictable results regarding the stability of the soft tissue. The evaluation of the stability of the soft tissue was intended to help support the scientific basis for the immediate restoration with a CAD/CAM customized titanium anatomic abutment using a guided surgery. With a retrospective study design, the present clinical study was limited by its small sample size, a relatively short observation period, and a lack of hard tissue evaluation. Future studies with a larger sample size, longer follow-up periods, and a prospective clinical trial design are needed to further support the findings of the present study.
## 5 Results and Discussion
### Water Environment
In order to discuss the influence of seawater on slurry penetration, the soil samples were saturated by slurry without CMC, and three types of water environment with different seawater content (0%, 25%, and 100%) were considered in the tests. The electrical resistivity of soil samples with different slurry concentrations is shown in Figure 7. The electrical resistivity of the soil samples under deionized water conditions was much larger than that under seawater conditions. It indicated that the soil electrical resistivity decreased with the increase in seawater content. Since the amount of free ions in seawater is much greater than that in slurry, the slurry penetration is more difficult to change the soil electrical resistivity. Hence, it is very difficult to evaluate the slurry penetration in offshore engineering with soil electrical resistivity. Figure 5: Electrical resistivity of the slurry and sand samples (0.5-2 mm) with different slurry concentrations. Figure 8 shows the change in soil formation factors under different water environments. The formation factors of soil samples increased with the decrease in seawater content. The soil samples with different seawater contents presented a similar change where the corresponding formation factors increased with the increase in slurry concentration. Due to the large amount of free ions in seawater, the soil mixed with seawater was less sensitive to the slurry concentration. Moreover, when slurry penetrates into the soil under a seawater environment, those cations in the seawater can neutralize some of the anions on the surface of bentonite particles. The bentonite particles then flocculated into aggregates and deposited rapidly. As shown in Figure 9, the sedimentation of bentonite particles increased the pore spaces of soil samples, which can promote the migration of free ions in soil pores, resulting in a decrease in the formation factor. The reaction of cations and anions between seawater and bentonite can also contribute to the formation of filter cakes and the increase in the bleeding rate of slurry [33]. Figure 6: Formation factor of sand samples (0.5-2 mm) with different slurry concentrations.
## 5 Simulation Results and Discussion
### Comparative Study
#### 5.2.2 Computational Efficiency and Robustness  
Addressing a large and complex system can increase the time consumption of any algorithm. Therefore, we used execution time to judge the computational efficiency of the optimization technique. The results shown in Tables 2, 4, 6, 8 and 10, with their corresponding figures, Figures 5, 7, 9, 11 and 13, respectively, prove that the proposed RLGWO requires shorter CPU time to obtain the minimum fuel cost when compared to other reported techniques, except for in the cases of Test 1 and Test  
cost in all cases. GWO is considered a straightforward optimization technique that obviates the need for the initialization of input parameters and has demonstrated its superiority in several optimization problems. The robust tolerance-based adjust searching direction mechanism and opposite-based learning combined with GWO allow it to improve the convergence rate and generate promising search agents. Future work for this research will include applying RLGWO in solving complex real-world power system problems.  
Conceptualization, T.-C.T. and C.-C.L.; methodology, T.-C.T.; software, C.-C.L.; formal analysis, T.-C.T. and C.-C.K.; writing--original draft preparation, T.-C.T.; writing--review and editing, T.-C.T.; visualization, C.-C.L.; supervision, C.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We would like to acknowledge project NSTC112-2622-8-011-011-TE1 for the assistance in our research.  
The authors declare no conflict of interest.
###### Abstract
The ground truth of the TQU-SLAM benchmark dataset is constructed by hand, including the camera coordinates in the real-world coordinate system, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world. * We experiment with the TQU-SLAM benchmark dataset for estimating VO based on PySLAM [15; 22] with the features extracted such as ORB [16], ORB2 [17], SIFT [4], SURF [5], BRISK [7], AKAZE [18], and KAZE [18], or features extracted from a DL model such as VGG [19], DPVO [20], and TartanVO [21]. * The results are analyzed and compared with the TQU-SLAM benchmark dataset with many types of traditional features and features extracted from DL models. The structure of our paper is organized as follows: Related studies are presented in Section 2. Section 3 introduces the TQU-SLAM benchmark dataset and the process of building ground-truth data of the camera's moving trajectory. Section 4 presents the testing of the TQU-SLAM benchmark dataset with traditional features and features extracted from DL. The experiments, results, and challenges of VO estimation of the TQU-SLAM benchmark dataset are shown in Section 5. Finally, conclusions and future research are presented in Section 6.
## 2 Traditional Matrix Structural Analysis (MSA)
Therefore, an exact element stiffness matrix for the axial-loaded Timoshenko beam-columns is required for the stability analysis of the Timoshenko beam-column system. \[[\mathbf{K_{TB,lst}}]=\left[ \begin{array}{cccc}\frac{12}{1+\varphi}\frac{ EI}{L^{3}}&\frac{6}{1+\varphi}\frac{EI}{L^{2}}&-\frac{12}{1+\varphi}\frac{EI}{L^{3}}& \frac{6}{1+\varphi}\frac{EI}{L^{2}}\\ &\frac{4+\varphi}{1+\varphi}\frac{EI}{L^{2}}&-\frac{6}{1+\varphi}\frac{EI}{L^{ 2}}&-\frac{2\varphi}{1+\varphi}\frac{EI}{L}\\ &&\frac{12}{1+\varphi}\frac{EI}{L^{3}}&-\frac{6}{1+\varphi}\frac{EI}{L^{2}}\\ \text{sym.}&&\frac{4+\varphi}{1+\varphi}\frac{EI}{L}\\ \end{array} \right] \tag{4}\]  
\[\left[\mathbf{K_{TB,Ekhande}}\right]=\left[ \begin{array}{cccc}T_{\text{Ek}} \frac{EI}{L^{3}}&Q_{\text{Ek}}\frac{EI}{L^{2}}&-T_{\text{Ek}}\frac{EI}{L^{3}}&Q _{\text{Ek}}\frac{EI}{L^{2}}\\ &S_{\text{Ek}}\frac{EI}{L}&-Q_{\text{Ek}}\frac{EI}{L^{2}}&C_{\text{Ek}}\frac{EI }{L}\\ &&T_{\text{Ek}}\frac{EI}{L^{3}}&-Q_{\text{Ek}}\frac{EI}{L^{2}}\\ \text{sym.}&&S_{\text{Ek}}\frac{EI}{L}\\ \end{array} \right]\qquad \begin{array}{cccc}T_{\text{Ek}}=\frac{1}{1+\varphi}T _{\text{EB}}\\ &Q_{\text{Ek}}=\frac{1}{1+\varphi}Q_{\text{EB}}\\ &S_{\text{Ek}}=\frac{4+\varphi}{4(1+\varphi)}S_{\text{EB}}\\ &C_{\text{Ek}}=\frac{2-\varphi}{2(1+\varphi)}C_{\text{EB}}\\ \end{array}  \tag{5}\]  
Figure 1: An axial-loaded beam–column with element-end displacements and forces. where \(\varphi\) is a dimensionless cross-sectional property of Timoshenko beams, which is defined based on the ratio of the flexural rigidity and the shear rigidity [6; 18]. It is noted that this cross-sectional factor \(\varphi\) corresponds to the extent of the shear deformation effect in the Timoshenko beam-column. \[\varphi=\frac{12 EI}{k_{\mathrm{s}}GAL^{2}}. \tag{6}\]
# Engineering Applications of Adaptive Kalman Filtering Based on Singular Value Decomposition (SVD)
###### Abstract  
This paper presents the results of applying the new mechanization of the Kalman filter (KF) algorithm using singular value decomposition (SVD). The proposed algorithm is useful in applications where the influence of round-off errors reduces the accuracy of the numerical solution of the associated Riccati equation. When the Riccati equation does not remain symmetric and positive definite, the fidelity of the solution can degrade to the point where it corrupts the Kalman gain, and it can corrupt the estimate. In this research, we design an adaptive KF implementation based on SVD, provide its derivation, and discuss the stability issues numerically. The filter is derived by substituting the SVD of the covariance matrix into the conventional discrete KF equations after its initial propagation, and an adaptive estimation of the covariance measurement matrix \(R_{k}\) is introduced. The results show that the algorithm is equivalent to current methods in terms of robustness, and it outperforms the estimation accuracy of the conventional Kalman filter, square root, and unit triangular matrix diagonal (UD) factorization methods under ill-conditioned and dynamic applications, and is applicable to most nonlinear systems. Four sample problems from different areas are presented for comparative study from an ill-conditioned sensitivity matrix, navigation with a dual-frequency Global Positioning System (GPS) receiver, host vehicle dynamic models, and distance measuring equipment (DME) using simultaneous slant range measurements, performed with a conventional KF and SVD-based (K-SVD) filter.  
adaptive Kalman filter applications; singular value decomposition filtering; computer round-off errors; control systems; navigation; GNSS; DME +
Footnote †: journal: Applied
## 2 Data and Methods
### Collocation and Validation Methodology  
All (RSS70, RSS40, JPL and HYCOM) salinity datasets were collocated with saildrone observations. Data collocations were made using Xarray's interp method for multidimensional interpolation of variables [37]. The Xarray nearest-neighbour interpolation routine was used to match the locations and times of the saildrone product with the nearest location and time available in the averaged model and satellite salinity products. Only collocations within 24 h and 25 km were included, and the closest points in space were collocated before the closest points in time.  
For the SMAP datasets, the L2 orbital data were collocated with the saildrone data using the Pyresample kd-tree resample_nearest method and SciPy spatial kd-tree method for quick nearest-neighbour lookup [38; 39]. The multiple saildrone data points that matched with a unique SMAP observation were averaged to a single saildrone observation, providing a single collocation matchup. To compare the HYCOM model and SMAP data to the saildrone observations, the standard deviation of the difference (STD), the mean difference and the Spearman correlation coefficient between observed (saildrone) and each of the estimated (HYCOM, JPL, RSS40, and RSS70) salinity products were calculated. The L2 JPL and RSS datasets were preferred for statistical comparison to the saildrone data since these datasets have minimal spatial and temporal averaging as compared to the L3 datasets. Therefore, the analysis presented in the discussion uses the L2 datasets (Section 4).