esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment and Validation of Finite Element Model
### Contact Interaction  
Surface-to-surface contact interactions refer to the contact between two deformable surfaces or between a deformable and a rigid surface [25]. Methods for surface-to-surface contact include contact discretization and tracking techniques. The finite sliding tracking method is the most commonly used, as it accommodates arbitrary separation, sliding, and rotation between contact surfaces, making it suitable for scenarios involving large relative sliding or rotation. Compared with node-to-surface discretization, surface-to-surface discretization better prevents the nodes of the master surface from penetrating the slave surface. Therefore, this study employs finite sliding tracking and surface-to-surface discretization methods for simulation. The tangential and normal mechanical properties of the contact follow the penalty function and hard contact, respectively. Surface-to-surface contact is used to simulate the interactions between adjacent segments, between the bolt contact section and segment concrete, between the bolt washer and segment concrete, and between the bolts and bolt washers [28]. The tangential and normal mechanical properties of the contact between the segments follow the penalty function and hard contact, with a friction coefficient of 0.55. Similarly, the tangential and normal mechanical properties of the contact between the bolt contact section and the segment concrete, as well as between the bolt washer and the segment concrete, also adhere to the penalty function and hard contact, with a friction coefficient of 0.15. The friction coefficient between the bolts and bolt washers is set to 0.2 [13]. The interactions between the bolt anchoring section and the segment and between the reinforcement and the segment are simulated using the embedded region. Ties are applied to constrain the nuts and bolt washers, the bolt washers and the concrete, and the bolt sleeves and the concrete bolt hole walls [29; 30]. The specific contact relationships are depicted in Figure 5.  
Figure 5: Contact relationship of the segmental joint.
## 8 System Feasibility Discussion
### Feasibility of the Aircraft-SB System Compared with Aircraft-Satellite Systems
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Operational Aspect** & **Aircraft–SB System** & **Aircraft–Satellite Systems** \\ \hline \multirow{2}{*}{Coverage diameter} & \(\approx\)1200 km per SB @ 20 km altitude and 1o & \(\approx\)1880 km @ 550 km satellite altitude \\  & minimum aircraft cruise elevation angle. & with 25\({}^{\circ}\) minimum elevation angle. \\ \hline \multirow{2}{*}{Basestation (SB or satellite) altitude} & \multirow{2}{*}{20 km.} & \multirow{2}{*}{340–1300 km.} \\  & & \\ \hline Visibility duration & \(\approx\)90 min per SB. & Few minutes per satellite \\ \hline Handover initiation & Due to aircraft motion & Mainly due to satellite motion \\ \hline \multirow{2}{*}{Atmospheric attenuation} & Less than satellite system especially at high frequencies. & Greater than SB system due to longer communication distance. \\ \hline \multirow{2}{*}{System control and management.} & Simple and adaptable to flight traffic areas. & Complicated to the high-speed movement of satellites and continuous handover processes. \\ \hline \multirow{2}{*}{Basestation maintenance and upgrading} & Available as SB can be brought back to ground and fly again. & Not available. \\ \hline \multirow{2}{*}{Operational frequencies} & 5G frequencies and future proposed mm-wave bands [30]. & 12–18 GHz, 26.5–40 GHz, 40–75 GHz. [8] \\ \hline \multirow{2}{*}{Service control strategy} & National, regional, and global service control. & Global service control only. \\ \hline Propagation delay & Tenths of milliseconds & Few milliseconds \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between aircraft–SB and aircraft–Starlink satellite networks. Figure 19: Propagation delay variation with basestation altitude at different minimum elevation angles. Table 3 compares between the FSL of the aircraft-SB link and the aircraft-LEO mobile satellite one. At the same elevation angle, the aircraft-SB link always has less FSL. For example, the minimum elevation angle of 25\({}^{\circ}\) for Starlink satellites at 340 km provides more FSL of at least 23.8 dB compared to an aircraft-SB link. The link of the aircrafts that make handover at the tangential tropopause height in SB network are subjected also to less FSL by at least 3.4 dB which increases to 13 dB compared to the FSL of Starlink satellites at 1200 km altitude.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 5 Conclusions
### Comparison of Morris and Sobol Methods for Sensitivity Analysis  
The Morris method provides a computationally efficient screening tool to identify sensitive parameters and determine their influence on increasing or decreasing water volume. However, it does not quantify higher-order parameter interactions. The Sobol method, while computationally more demanding, offers a detailed variance-based assessment, allowing for a comprehensive evaluation of combined parameter effects. These findings highlight the complementary nature of both methods, supporting a hybrid approach to urban flood modeling that balances efficiency and analytical depth.
## 4 Response Reconstruction for an Actual Continuous Bridge  
In this section, the proposed method was validated with monitoring data from an actual continuous concrete bridge. Firstly, an overview of the monitoring system and the bridge is provided. Following this, the architecture of the BP-ANN for response reconstruction is detailed. Finally, the method's effectiveness was confirmed through two case studies which accounted for varying temperature and traffic conditions.
## 3 Methodology
### The Proposed Metaheuristic-Based Feature and Hyperparameter Optimization
#### 3.4.1 HPSO Optimizer  
Initialization:  
The algorithm begins by randomly initializing particle positions \(x_{i}\) for analog parameters from a uniform distribution \(x_{i}\sim U(0,1)\) and for Boolean parameters from a Bernoulli distribution \(x_{i}\in\{0,1\}\). Velocities \(v_{i}\) are also initialized, where \(x_{i}\) is the position of the \(i^{th}\) particle, and \(v_{i}\) is the velocity of the \(i^{th}\) particle.  
Velocity Update Equation:  
For each particle \(i\), the velocity update equation is given by:  
\[v_{i}(t+1)=\omega v_{i}(t)+c_{1}r_{1}(p_{i}-x_{i}(t))+c_{2}r_{2}(g-x_{i}(t)) \tag{1}\]  
where \(\omega\) is inertia weight, \((c_{1},c_{2})\) are acceleration coefficients that control the influence of the particle's best position \(p_{i}\), and the global best \(g\) and \((r_{1},r_{2})\) are random values.  
Position Update:  
In HPSO, we introduce a hybrid approach to manage both continuous and Boolean parameters. The position update differs for analog and Boolean parameters.  
For analog parameters:  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{2}\]  
For Boolean parameters, the sigmoid transformation is applied to map velocities \(v_{i}(t)\) to a range between 0 and 1.  
\[S(v_{i}(t))=\frac{1}{1+e^{-v_{i}(t)}} \tag{3}\]  
Positions are updated based on the sigmoid output, where the positions are adjusted according to the likelihood of switching between binary states. This action is achieved by applying a threshold on the sigmoid-transformed velocities. The activation value \(a_{i}\) for each boolean parameter can be defined as:  
\[a_{i}=2\cdot|S(v_{i}(t))-0.5| \tag{4}\]This activation value \(a_{i}\) guides the decision of whether a binary feature should be flipped (0 to 1 or 1 to 0) based on the following equation:  
\[x_{i}(t+1)=x_{i}(t)\oplus(r_{3}<a_{i}) \tag{5}\]  
where \(r_{3}\sim U(0,\ 1)\) is a random variable for each boolean parameter, and \(\oplus\) is the logical XOR operation.  
Thus:  
\[x_{i}(t+1)=\begin{cases}1,&\quad if\ \ r_{3}<a_{i}\\ 0,&\quad Otherwise\end{cases} \tag{6}\]  
Fitness Evaluation:  
The fitness of each \(i^{th}\) particle is evaluated using the objective function \(F(x_{i})\). The personal best \(p_{i}\) found by the \(i^{th}\) particle is updated if the current fitness is better:  
\[p_{i}(t+1)=\begin{cases}\ x_{i}(t+1),&\quad if\ \ F(x_{i}(t+1))<F(p_{i}(t))\\ p_{i}(t),&\quad otherwise\end{cases} \tag{7}\]  
Then, the global best position \(g\) found by the swarm is updated as:  
\[g(t+1)=min(F(p_{i}(t+1)))\quad\ \ \forall i \tag{8}\]
## 1 Introduction
Therefore, there is a need for alternative compounds with selective biofilm inhibitory potential targeting _S. mutans_ without altering the oral microflora as well as having minimal side effects. One such compound is mangiferin, a major component of _Mangifera indica_, which belongs to the Anacardiaceae family, and has been documented to have a promising pharmacological profile [19; 20]. Extracts of _Mangifera indica_ have been used in herbal medicine for various indications [21; 22]. Studies also reported an anti-adherence effect of the extracts of _Mangifera indica_ on early and late plaque bacterial colonizers [23; 24]. Periodontitis is caused by organisms, which colonize biofilm, therefore the action of _S. mutans_ in attracting various bacteria strains such as _Aggregatibacter actinomycetemcomitans_, could induce inflammatory reactions [25]. [PERSON] et al., [21], observed that orally administered magniferin was effective in the treatment of experimental periodontitis in rats. In addition, the antimicrobial activity of mangiferin against _S. mutans_ GS-5, a cariogenic strain, has been documented [26]. In addition, other compounds of natural origin, such as biacalm and palmitoylethanolamide, were found to be effective in the treatment of periodontitis [27]. Based on the previous studies that showed the effectiveness of mangiferin in the treatment of experimental periodontitis in rats and the inhibition of the activity of cariogenic strain _S. mutans_, we hereby further evaluate its antibiofilm potential. We hypothesize that it might be an effective natural agent against dental plaque development by inhibitingglucansucrase activity. Therefore, the present investigation evaluated the anti _S. mutans_ activity of mangiferin and its inhibitory activity against glucansucrase using an enzymatic assay. Also, by using computational and biological assays, we investigated the antibacterial activity of mangiferin against planktonic _S. mutans_ and its binding potential to glucansucrase. We also evaluated the inhibitory potential of mangiferin on glucansucrase activity and its ability to form biofilms. Furthermore, the impact of mangiferin on the expression of the virulence genes, namely _gtB_, _gtfC_, _gtfD_, _gbrB_, and _comDE_, was determined. This was undertaken with a view to ascertaining the anti _S. mutans_ biofilm formation by mangiferin, a naturally available and less toxic substance than the current available pharmaceutical products. The null hypothesis to reject is that there is no significant difference between the mangiferin treated group and control group.
# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
## 3 Problem Formulation
### Types of Failure  
The failed radiation pattern formula (6) is used for different types of failures, such as random type failure, row type failure, column type failure and group type failure, for 8 \(\times\) 8 and 6 \(\times\) 6 array configurations in the planar array. For the 8 \(\times\) 8 planar array, 16 elements are failed, and for 6 \(\times\) 6, 12 elements are failed. The number of failed elements for both array sizes is kept between 20% and 33.33% of the total number of elements, as correcting a huge number of elements will make the antenna system weak  
For the random type of failure in an 8 \(\times\) 8 array, 16 locations, i.e., [1, 3, 12, 17, 20, 26, 27, 35, 41, 46, 48, 50, 52, 53, 55, 60] are failed (non-functional), as shown in Figure 4; the faulty elements in the figure are unshaded, and the functional elements are shaded in black.  
Next, for the row type of failures, 2 rows of an 8 \(\times\) 8 planar array are failed. For this purpose, all the elements in row 2 and row 5 are failed, which adds up to 16 elements. In Figure 5 below, the row type of failure for the 8 \(\times\) 8 planar array is shown.  
Figure 3: Initial Radiation Pattern for 8 \(\times\) 8 planar array without any faulty elements.  
For the column type of failures, 2 columns and 16 elements in an 8 \(\times\) 8 planar array are failed. Here, all the elements in columns 2 and column 5 are failed. Figure 6 shows the column type of failure for the 8 \(\times\) 8 planar array.  
Figure 4: Random type failure for 8 \(\times\) 8 planar array.  
Figure 5: Row type failure for 8 \(\times\) 8 planar array.  
For the group type of failures, 16 elements in a form of 4 groups with 4 elements (\(2\times 2\)) are failed. Figure 7 represents the group type of failure in the \(8\times 8\) planar array.  
For the \(6\times 6\) planar array, similar types of failure were used but with 12 failed elements in total for each type of failure. The types of failure are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Failure Type** & \
\begin{tabular}{c} **16 Failed Elements** \\ **(8 \(\times\) 8 Array)** \\ \end{tabular} &
\begin{tabular}{c} **12 Failed Elements** \\ **(6 \(\times\) 6 Array)** \\ \end{tabular} \\ \hline Random & Random locations & Random locations \\ Row & Rows 2, 5 & Rows 2, 5 \\ Column & Columns 2, 5 & Columns 2, 5 \\ Group (\(2\times 2\)) & 4 groups & 3 groups \\ \hline \hline \end{tabular}
\end{table}
Table 1: The \(8\times 8\) and \(6\times 6\) planar array failure types.  
Figure 6: Column type of failure for \(8\times 8\) planar array.  
Figure 7: Group type of failure for \(8\times 8\) planar array.
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 4 Results and Discussion
### Structural Interpretation of PS Deformation Periodicity
The velocities--\(v\) [mm/year]--are consistent with those obtained directly from the MT-InSAR analysis (see Figure 12b), and the thermal expansion coefficients \(\alpha\) are consistent with those expected for prestressed concrete bridges. These results allow us to conclude that the magnitude of the periodic displacements observed through this technique is consistent with the expected temperature response for this bridge. Figure 13: Displacements of (**a**) the north cantilever and (**b**) the south cantilever of Pier 4 in the floodplain; displacements of (**c**) the north cantilever and (**d**) the south cantilever of Pier 9 in the riverbed; (**e**) location of PSs on Pier 4, and (**f**) location of PSs on Pier 9. Now, let us focus on the part of the bridge characterised by green PSs in Figures 10 and 11, indicating deformation velocities between \(-1.5\) and \(1.5\) mm/year. Figure 13a,b show the displacements of the north and south cantilevers of Pier 4 in the floodplain, while Figure 13c,d show the displacements of the north and south cantilevers of Pier 9 in the riverbed. We can observe that all displacement time series exhibit a significant periodicity with an annual period, consistent with the periodicity of temperature variations. However, there is an opposite sign in the displacement variation between the PSs in opposite cantilevers of Pier 4: when the northern cantilever displacements increase, the southern cantilever displacements decrease, and vice versa. Regarding the observed displacements of the cantilever of the riverbed Pier 9, periodicity is present but less pronounced on the northern side than on the southern side. However, unlike Pier 4, where a deflection trend is clearly visible, displacements of Pier 9 seem to be affected by other phenomena in addition to temperature variation. We will discuss this in Section 4.4.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 5 System Performance Evaluation
### Update Frequency
#### 5.1.2 Experimental Validation  
For the validation of the proposed system, three different slot sizes are used (see Table 7). (i) The UWB slot is used for all UWB communication and includes the time of the transmission of a ranging-final message together with all device specific delays for instruction, SPI-communication and radio turnover times. (ii) A second slot is used for the synchronization, and contains a payload of 60 bytes, which includes a header of 12 Bytes, 8 Bytes for synchronization and room for ranging with 20 anchors in one superframe. (iii) The final slot is used for reporting and requires only 22 Bytes. The instruction delays are hardware dependent and are measured using a logic analyzer. The experimental measured update frequency for the different MAC implementations are shown in Table 6. These results are hardware dependent and can be further improved, e.g., by using DMA (Direct Memory Access) to further optimize instruction timings.
## 3 Narrative Review
### Imaging Findings of Orbital Myositis
published a more in-depth analysis of the patients previously examined by [PERSON] and added two additional cases, confirming that the typical presentation of orbital myositis is a thickening of the affected extraocular muscles, often involving the myotendinous junction and surrounding fat with contrast enhancement. A significant highlight of this study is the case of a 54-year-old woman with type 2 diabetes mellitus (DM2) who presented with double vision while reading, without any associated orbital pain or ocular inflammation. Laboratory tests, including assessments of thyroid function, autoantibodies, inflammationmarkers, and anti-acetylcholine receptor antibodies, were all within normal limits. Orbital MRI revealed an enlargement and contrast enhancement of the right superior rectus, leading to a diagnosis of orbital myositis, which was further confirmed by the patient's prompt improvement with prednisone (60 mg/day). This is the first documented case of orbital myositis linked to DM2, indicating a possible causal relationship due to the increased prevalence of autoimmune diseases among individuals with DM2 [19]. Another case of IOM associated with poorly controlled diabetes mellitus has been described in the literature and was related to a 58-year-old male patient with bilateral idiopathic myositis. Orbital MRI revealed high signal intensity in both extraocular muscles. Steroid therapy is recognized for its positive impact on extraocular myositis, particularly when initiated early, as it reduces the risk of lasting complications. Conversely, delaying treatment can lead to recurrences and may require prolonged steroid use. In this case, short-term steroid pulse therapy was effective and resulted in favorable outcomes, likely due to the early initiation of treatment [20]. In a recent study by [PERSON] et al., it was found that MRI enhancement varies depending on the stage of inflammation, i.e., it is pronounced in the acute phase, while in the chronic or sclerosing forms, hypointensity is observed on T2-weighted images [21]. Ultrasonography can also play a role as it has shown homogeneous low-reflective enlargement, which is diagnostic of myositis [22]. A summary of the main radiological features of IOM can be found in Table 2.
## 2 Vein Pattern Identification System
### Feature Extraction Process
#### 2.3.1 Global Hand Geometry Feature Extraction  
The goal of the hand geometry feature extraction stage is to find the fingertips and concave points (valleys) joining fingers. The obvious characteristic of the tip and the concave point of the fingers is the extremal curvature of the contour around the hand. Therefore, most methods of finding the distal and concave points of the fingers are obtained from the curvature of the hand contour [17; 18; 50]. Fingertip points can also be identified using the convex hulls of the binary palm image. Once the fingertip point is located, the concave point is further identified by measuring the distance between the midpoint of each fingertip point to the concave point, known as the convexity defection [50; 51]. In this research, we exploit the radius transform by measuring the distance between the hand centroid to the hand contour. The fingertip and the concave point yield the maximal and minimal radius distance, respectively. To find the hand centroid, a distance map method is applied. As shown in Figure 4, the point with maximal distance represents the hand centroid. Figure 5 demonstrates the robustness of this centroid detection with various hand geometric transformations. Once the centroid is located, the Euclidian distance between the centroid and the hand contours--the so-called radius distance--is computed using Equation (1).  
\[D_{ki}=\sqrt{\left(Q^{x}-P_{ki}^{x}\right)^{2}+\left(Q^{y}-P_{ki}^{y}\right)^{ 2}} \tag{1}\]  
where \(P_{ki}=\left\{P_{ki}\right\}_{i=1}^{I}\) is the hand contour extracted from each image consisting of points, \(P_{ki}=\left\{P_{ki}^{x},P_{ki}^{y}\right\}\) are the coordinates of these hand contour points, \(Q=\left\{Q^{x},Q^{y}\right\}\) are the coordinates of the reference point and \(D_{ki}\) is the distance between the centroid and hand contour.  
As shown below, Figure 6a shows a centroid detected with the distance transform process. Figure 6b shows a plot of the radius distance. From the graph of radius distance, the fiducial points, including fingertip and concave points, can be located by finding the extremal points. The fingertip points will be the five points of maximal radius distance, denoted respectively as T1-T5, while the concave point will be the four minimal radius distance points, denoted respectively as V1-V4 (local minima point, \(\mathrm{V_{n}}=\mathrm{Min(T_{n}:T_{n+1})}\)). The detected fingertip and the concave points are shown in Figure 6c.  
Figure 4: Fiducial points including fingertip and concave point extraction.
## 2 Background
### Operational Power System Use Cases
#### 2.4.3 Use Case #3: DFR Sampling Error  
Automated fault detection and identification algorithms, such as those in [3; 7; 8; 9; 10; 11; 12], assume the DFR is correctly sampling the continuous waveforms. However, methods for ensuring accurate measurement and correct sampling are needed. This use case focuses on the latter and is facilitated by the frequency histogram. Like the clock drift mentioned in Section 2.2.2, the DFR may impose other unintentional anomalies onto the recorded, sampled waveform that interfere with fault detection and prediction. Clock skew can manifest in frequency, where the frequency behavior drifts outside the nominal operating frequency tolerance. This frequency tolerance is \(\pm\)0.05 Hz for the data used herein.