esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Location  
The research study focused on the South African transportation system due to the high traffic volume and density of vehicles in Southern Africa. The traffic data were obtained from signalized road intersections between Johannesburg and Pretoria in Gauteng province. The chosen road intersections used for this study are known to have the highest traffic volume and traffic density of vehicles in South Africa. Based on the information obtained from the Ministry of Transportation, these road intersections sometimes experience more than half a million vehicles depending on the periods of the day [6].  
The road intersections consist of several lanes (Figure 3a-d), with the maximum lanes being four and the minimum being two. They are all moving in a northbound direction depending on the vehicle's direction. The location and features of these road intersections are explained in Table 1:  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Road Intersections** & **Date** & **Distance (m)** & **Direction** & \begin{tabular}{l} **Number of** \\ **Lanes** \\ \end{tabular} & \
\begin{tabular}{l} **Speed Limit** \\ **(Km/h)** \\ \end{tabular} &
\begin{tabular}{l} **Number of** \\ **Vehicles** \\ \end{tabular} \\ \hline Road Intersection 1 & 15 July 2019–27 July 2019 & 13.5 & Northbound & 4 & 120 & 12,067,153 \\ Road Intersection 2 & 15 July 2019–29 July 2019 & 10.40 & Northbound & 3 & 120 & 18,340,250 \\ Road Intersection 3 & 15 July 2019–29 July 2019 & 8.0 & Northbound & 4 & 120 & 11,444,024 \\ Road Intersection 4 & 15 July 2019–29 July 2019 & 4.60 & Northbound & 2 & 120 & 16,151,125 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of the Signalized Road Intersections.  
Figure 2: Collection of traffic datasets.  
Traffic flow data were collected for fifteen days between 15 July 2019 and 29 July 2019 using installed video cameras at road intersections and video cameras and roadside detectors. Figure 4 illustrates the traffic-data input and output used for the ANFIS and ANFIS-GA models.
## 3 Problem Modeling
_Start and end of routes:_ Each UAV that departs from the depot (Node 0) should come back to the depot. \[(x_{i,j}^{k}>0)\Rightarrow(\sum_{i\in N_{m,l}}\ x_{0,i}^{k}=\sum_{i\in N_{m,l} }\ x_{i,0}^{k}=1),\ \ k=1\ldots K\enspace. \tag{6}\]  
Constraint (6) make sure that each UAV departs from the depot and comes back to the depot. The sum of all the edges which starts from depot and sum of all the edges which returns to depot should be equal to one. _Energy:_ Each UAV has a maximum energy capacity of \(P_{max}\), and in flight, it is not possible to consume more than the max energy capacity. \[\sum_{i\in N_{m,l}}\sum_{j\in N_{m,l}}x_{i,j}^{k}\Big{(}P_{i,j}^{k}\Big{)} \Big{(}t_{i,j}\Big{)}\leqslant P_{max},\ \ k=1\ldots K. \tag{7}\]  
\[P_{i,j}^{k}=\frac{1}{2}C_{D}AD(\
u u_{i,j}^{l})^{3}+\frac{\Big{(}(ep+\ f_{i,j}^{k})g \Big{)}^{2}}{Db^{2}\
u u_{i,j}^{l}}. \tag{8}\]  
where \(C_{D}\) is the aerodynamic drag coefficient, \(A\) is the front facing area, \(ep\) is the empty weight of the UAV, \(D\) is the density of the air, \(b\) is the width of the UAV and \(g\) is gravitational acceleration ([PERSON] et al. 2020). The air speed of a UAV \(va_{i,j}^{l}\) is defined in the following way:  
\[va_{i,j}^{l}=\sqrt{\Big{(}vg_{i,j}cos\delta_{i,j}-vw_{l}cos\theta_{l}\Big{)}^ {2}+\Big{(}vg_{i,j}sin\delta_{i,j}-vw_{l}sin\theta_{l}\Big{)}^{2}}. \tag{9}\]  
The main question for this study to focus on is: _is it possible to find a sequence of UAVs' sub-missions, (which are determined by decision variables: \(S_{n,m,l}\), \(RL_{m,m}lRL_{n,m,l}\), \(x_{i,j}^{k}\), \(y_{i}^{k}\), \(c_{i}^{k}\), \(f_{i,j}^{k}\)) of given UAV fleet, which maximize the customer satisfaction (1) (customer demand (\(D_{i}\)) fulfilment) under changing weather conditions considering energy constraints of UAVs (7), ensuring collision avoidance (3), and satisfying the constraints (2),(4)-(6))?_
###### Abstract
The effect of topical fluoride on the prevention of caries depends on its constant presence in the oral cavity and also relies on the patient's oral hygiene [20]. Studies have indicated that laser treatment can work synergistically with the application of topical fluoride to further enhance its inhibitory effect [11; 21]. Many studies have shown the effect of treatment combining irradiation by carbon dioxide laser at different wavelengths and topical fluoride application on inhibiting tooth demineralization [17; 22; 23; 24; 25; 26; 27]. A study demonstrated that the combined use of a CO\({}_{2}\) laser and sodium fluoride can significantly increase the uptake of fluoride ions [28]. Another study reported that the combined treatment resulted in the transformation of hydroxyapatite to fluorapatite, which is more resistant to acid dissolution [29]. A clinical trial reported that a treatment combining CO\({}_{2}\) laser irradiation and sodium-fluoride application significantly inhibited the formation of fissure caries in molars after 12 months [30]. Moreover, this combined treatment greatly enhanced the remineralization of enamel [31]. As the 9300 nm carbon dioxide laser has only been on the market for a relatively short period of time, there are far less studies using this equipment in comparison to the conventional 10,600 nm CO\({}_{2}\) laser. The result and the quality of these studies varies, and no consensus has been reached regarding the effect of the 9300 nm carbon dioxide laser and fluoride on the prevention of caries. As individual study cannot provide strong enough evidence to draw a generalized conclusion, we conducted a systematic review and meta-analysis of all the published studies in the literature. We aimed to provide clinicians and researchers with preliminary evidence. This systematic review and meta-analysis are the first to verify the pooled effect of data from studies that compare treatments combining fluoride application and 9300 nm CO\({}_{2}\) laser irradiation with a separate application for the prevention of caries. No similar review so far can be found in the literature.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 2 Methodology  
### Proposed Converter  
Figure 1 displays the proposed active clamp boost converter, which is constructed by one conventional boost converter combined with one auxiliary circuit. The former is built up by one input inductor \(L_{in}\) and one main switch \(S_{1}\), along with one body diode \(D_{s1}\), one parasitic capacitor \(C_{s1}\), one output diode \(D_{o}\), and one output capacitor \(C_{o}\). The latter is constructed by one auxiliary switch \(S_{2}\) along with one body diode \(D_{s2}\), one parasitic capacitor \(C_{s2}\), one resonant inductor \(L_{r}\), and one active clamp capacitor \(C_{c}\). The load is represented by one output resistor \(R\). In addition, Figure 2 displays the equivalent circuit of the circuit shown in Figure 1, based on the assumption that the values of \(C_{o}\) and \(C_{c}\) are large enough to be regarded as ideal voltage sources, whereas the value of \(L_{in}\) is large enough to be viewed as an ideal current source.
## 3 Examples  
### Homogeneous Model  
To verify the correctness of the generalized finite difference method for solving the elastic wave equation, a two-dimensional homogeneous medium model was set (2400 m \(\times\) 2400 m), with a P-wave velocity of 4000 m/s, an S-wave velocity of 2300 m/s and a density of 2000 kg/m\({}^{3}\). The dominant frequency of the Ricker wavelet was 20 Hz, and the source was located at the center of the model. For comparison, all sources were loaded onto the vertical component of the displacement. The generalized finite difference method was compared with both the analytical solution and the staggered grid finite difference method (SGFD). The node distribution was consistent with the rectangular grid of the staggered grid finite difference method, and the spacing of the grid (nodes) was 10 m.  
To ensure the stability of the simulation, the time step was set at 0.5 ms, and the total computation time at 1.0 s. The forward modeling was performed using a 2 nd-order  
Figure 5: Wave field computed by GFDM, (**a**) X component (without attenuation boundary); (**b**) Z component (without attenuation boundary); (**c**) X component (45 layers of Cerjan boundary); (**d**) Z component (45 layers of Cerjan boundary).
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 4 Discussion
### Temporal Adaptability and Regional Specialization  
A distinctive feature of our methodology is its ability to recognize and leverage regional specialization through time-varying weights. The heatmap visualizations of provincial weights reveal clear hierarchical patterns that align with the underlying strengths of each region's forecasting systems. The substantial shift in weights from Jiangsu's dominance in summer months (0.30-0.35) to increased Anhui contribution in winter (0.30-0.35) demonstrates the methodology's responsiveness to seasonal changes in provincial load characteristics.  
Moreover, the weight trajectories show important temporal variations throughout the day, with significant differences between peak and non-peak periods. Shanghai's weights consistently remain lower during daytime hours (08:00-18:00) and higher during early morning hours (00:00-06:00), reflecting the changing reliability of its forecasts throughout the day. This temporal adaptability enables our methodology to effectively respond to the evolving operational conditions that characterize complex power systems, particularly those with high renewable energy penetration.  
The multi-temporal scale analysis further confirms this adaptability, revealing that weighted fusion maintains superior performance across forecasting horizons ranging from ultra-short-term (1 h) to medium-term (168 h). The consistent outperformance across all temporal scales provides compelling evidence for the robustness of our methodology in diverse operational contexts.
## 7 Simulation Study
### Simulation Results  
The following figures demonstrate the working and performance of LNR-RCTD algorithm.  
Figure 3a-c represent the naive, underutilize-marked, and optimized versions of the mesh topology, respectively. In Figure 3c, the red circled nodes are those nodes whose all incident links are marked as underutilized, as shown in Figure 3b, but after applying LNR-RCTD, optimization these nodes could not be released. This is due to our simulation's computational limitations, as we only compute and store the first 100 alternative paths for each marked link from its parent node (where underutilized link occur) and then compute the QoS parameters of each path one by one and compare them with the actual QoS demands. If some alternative path is found, then that flow will be shifted on new path, otherwise, that marked link remains intact.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Application** & **Bitrate (kb/s)** & **Delay (s)** & **PLR** \\ \hline Audio (CBR) & Speech, high quality music & 128 & \textless{}2 & \textless{}1\% \\ Video (CBR) & Real-time video, surveillance & 384 & \textless{}2 & \textless{}2\% \\ Data & Bulk data transfer & \textless{}384 & NA & 0\% \\ \hline \end{tabular}
\end{table}
Table 6: QoS requirements.  
Figure 3: **(a)** 25 nodes’ naive topology; **(b)** 25 nodes’ marked topology; **(c)** 25 nodes’ optimized topology.  
Figure 4a-d represent the naive, underutilized-marked, optimized, and redundant paths after optimization, respectively. In Figure 4b, the bold and red links shows the underutilized links. After applying LNR-RCTD optimization, four nodes and 87 links are identified. In Figure 4c, the nodes' having no incident links are identified as extra nodes'. Redundancy is one of the major design objectives in WMNs, and our LNR-RCTD optimization also preserves the redundancy of WMNs by only removing the extra resources. Figure 4d represents the redundant available paths for each network flow ID after optimization. Figure 5a-d represent the naive, underutilized-marked, optimized, and redundant paths after optimization, respectively. After applying LNR-RCTD optimization, 11 extra nodes and 171 links are identified as extra.  
The following Figure 6a,b represent the identified extra wireless mesh backbone nodes and links, respectively. The presented results are the averages of 100 simulations for the same node density. The Figure 6a,b demonstrate that a lot of physical resources are being wasted. From these figures we can calculate % of wasted resources for 100 nodes. The calculation is given as follows:  
Figure 4: **(a)** 35 nodes’ naive topology; **(b)** 35 nodes’ marked topology; **(c)** 35 nodes’ optimized topology; **(d)** Path redundancy.
## 3 Results
### Current Density and Electrical Potential Distributions  
The electrical current density based on various simulation frequency spectrum using EA models is shown in Figure 8.  
Figure 8: The electrical current density based on various simulation frequency samples for (**A**) border and (**B**) centered placed EA models.  
Figure 7: The impedance variation for the quasi-static and transient simulation based on various conductivities of the ST. The results for the quasi-static and transient simulations based on different frequency values are recorded for the odd-numbered electrode contacts (from E1 to E15).  
The current density variation versus defined arclength at the border and at the center of the ST based on various frequency samples is shown in Figure 8A,B, respectively. There is a significant variation in current density based on EA models. The induced current density is higher when the border EA model is used compared to the center-placed EA model. The shorter simulation waveforms resulted in higher induced current density at the border of the ST. In particular, the current density for the 100 MHz simulation is relatively higher compared to the remaining for both EA models. This is more pronounced for the border-placed EA model.  
The electrical potential distribution based on the QS and TS methods on the outer wall of the ST are shown in Figure 9A,B, respectively. The results are recorded for the TS methods based on the selected samples.  
The electrical potential variation across different electrodes was recorded in both magnitude and contour lines at the outer wall of the ST for both QS and TS. Although the electrical potential variation has limited spread over the outer wall of the ST using the QS method, this is a relatively spread wide area of the outer wall. It is shown that the electric potential is finely distributed with relatively higher values over the outer wall of the ST when higher frequency values are used based on the TS method. Also, the higher induced electrical potential variation is measured in the vicinity of the electrode and the far region of the electrode when the higher frequency-based simulation is used.
# Development of an Insect-like Flapping-Wing Micro Air Vehicle with Parallel Control Mechanism
## 3 Control System  
### Attitude Control  
The number and performance of sensors that FMAV can carry are limited due to the size and weight. To achieve the flight of the vehicle with an onboard controller and battery, gyroscopes and accelerometers were adopted to obtain the attitude of FMAV. Therefore, the attitude of FMAV in flight can be solved by the Newton-Euler Equation (10).  
\[\mathbf{J}\dot{\mathbf{\omega}}=\mathbf{M}-\mathbf{\omega}\times\mathbf{J}\mathbf {\omega} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Flapping frequency & 25 Hz \\ wingspan & 21 cm \\ weight & 28 g \\ battery capacity & 250 mah \\ power & 11 W \\ max lift & 34 gf \\ \hline flight control board (7\%) & battery (18\%) \\ wires (4\%) & skeleton (12\%) \\ control mechanism (24\%) & wings (1\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main properties of the FMAV prototype.  
Figure 11: The mass distribution of the FMAV.  
Figure 10: The physical drawing of the prototype.  
where \(\mathbf{J}\) is the inertia matrix; \(\dot{\mathbf{\omega}}\), \(\mathbf{\omega}\) are the angular acceleration and velocity vector; and \(\mathbf{M}\) is the total external torque vector.  
The \(\mathbf{M}\) is produced by the control mechanism changing stroke plane rotation and wing twist. Therefore, it can be divided into two parts, where \(\mathbf{M_{s}}\) indicates the torque produced by rotating the stroke plane, which can be expressed as Equation (11), where the lift is defined as \(\mathbf{T}\) and the input is defined as \(\mathbf{U}=(\alpha,\beta,\gamma)^{T}\). When \(\alpha\), \(\beta\) are small angles, \(\mathbf{M_{s}}\) is proportional to \(\alpha,\beta\), respectively.  
\[\mathbf{M_{s}}=\left[\begin{array}{c}Th_{1}\cos(\beta)\sin(\alpha)\\ Th_{1}\sin(\beta)\\ 0\end{array}\right]=\left[\begin{array}{cc}Th_{1}&&\\ &Th_{1}&\\ &&0\end{array}\right]\left[\begin{array}{c}\alpha\\ \beta\\ \gamma\end{array}\right]=\mathbf{K_{s}U} \tag{11}\]  
\(\mathbf{M_{t}}\) indicates the torque produced by the movement of the trailing edge. With reference to the previous research [19], we can assume that there is a linear relationship between \(\mathbf{M_{t}}\) and movement of trailing edge. Therefore, \(\mathbf{M_{t}}\) can be expressed as Equation (12).  
\[\mathbf{M_{t}}=\left[\begin{array}{c}k_{p}h_{2}\sin\alpha\\ k_{r}h_{2}\sin\beta\\ k_{\gamma}\gamma\end{array}\right]=\left[\begin{array}{cc}k_{p}h_{2}&&\\ &k_{r}h_{2}&\\ &&k_{y}\end{array}\right]\left[\begin{array}{c}\alpha\\ \beta\\ \gamma\end{array}\right]=\mathbf{K_{t}U} \tag{12}\]  
The external torque vector \(\mathbf{M}\) can be expressed as the sum of \(\mathbf{M_{s}},\ \mathbf{M_{t}}\) and is shown in Equation (13).  
\[\mathbf{M}=\mathbf{M_{s}}+\mathbf{M_{t}}=(\mathbf{K_{t}}+\mathbf{K_{s}}) \mathbf{U} \tag{13}\]
# Evaluation of Snowboarding Helmets in Mitigation of the Biomechanical Responses of Head Surrogate
###### Abstract  
Traumatic brain injury (TBI) during snowboarding sports is a major concern. A robust evaluation of existing snowboarding helmets is desired. Head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and associated brain responses (brain pressure, equivalent (von Mises) stress, and maximum principal strain) of the head are a predominant cause of TBI or concussion. The conventional snowboarding helmet, which mitigates linear acceleration, is typically used in snow sports. However, the role of conventional snowboarding helmets in mitigating angular head kinematics is marginal or insignificant. In recent years, new anti-rotational technologies (e.g., MIPS, WaveCel) have been developed that seek to reduce angular kinematics (i.e., angular velocity, angular acceleration). However, investigations regarding the performance of snowboarding helmets in terms of the mitigation of head kinematics and brain responses are either extremely limited or not available. Toward this end, we have evaluated the performance of snowboarding helmets (conventional and anti-rotational technologies) against blunt impact. We also evaluated the performance of newly developed low-cost, silica-based anti-rotational pads by integrating them with conventional helmets. Helmets were mounted on a head surrogate-Hybrid III neck assembly. The head surrogate consisted of skin, skull, dura mater, and brain. The geometry of the head surrogate was based on the GHBMC head model. Substructures of the head surrogate was manufactured using additive manufacturing and/or molding. A linear impactor system was used to simulate/recerate snowfield hazards (e.g., tree stump, rock, pole) loading. Following the ASTM F2040 standard, an impact velocity of 4.6 \(\pm\) 0.2 m/s was used. The head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and brain simulant pressures were measured in the head surrogate. Further, using the concurrent simulation, the brain simulant responses (i.e., pressure, von Mises stress, and maximum principal strain) were computed. The front and side orientations were considered. Our results showed that the helmets with anti-rotation technologies (i.e., MIPS, WaveCel) significantly reduced the angular kinematics and brain responses compared to the conventional helmet. Further, the performance of the silica pad-based anti-rotational helmet was comparable to the existing anti-rotational helmets. Lastly, the effect of a comfort liner on head kinematics was also investigated. The comfort liner further improved the performance of anti-rotational helmets. Overall, these results provide important data and novel insights regarding the performance of various snowboarding helmets. These data have utility in the design and development of futuristic snowboarding helmets and safety protocols.  
conventional helmet; anti-rotational helmets; biomechanical response; head surrogate; silica pad helmet; comfort liner +
Footnote †: journal: Applied sciences
## 3 Fuzzy Kernel Nearest Feature Line Embedding (FKNFLE)
### Kernelization of NFLE  
The kernelization function adopted in this study was inspired by that in [15]. Let \(\phi:x\in X\rightarrow\phi\left(x\right)\in H\) be a nonlinear mapping from a low-dimensional space to a high-dimensional Hilbert space \(H\). The mean squared distance for all training points to their NFLs in the Hilbert space is written as follows:\[\sum_{i}\left\|\phi\big{(}y_{i}\big{)}-L_{m,n}\left(\phi\big{(}y_{i} \big{)}\big{)}\right\|^{2}\right)=\sum_{i}\left\|\phi\big{(}y_{i}\big{)}-\sum_{j }M_{i,j}\phi\big{(}y_{j}\big{)}\right\|^{2}\] \[=tr\left(\phi^{T}\big{(}Y\big{)}\big{(}I-M\big{)}^{T}\big{(}I-M \big{)}\phi\big{(}Y\big{)}\right) \tag{6}\] \[=tr\left(\phi^{T}\big{(}Y\big{)}\big{(}D-W\big{)}\phi\big{(}Y \big{)}\right)\] \[=tr\Big{(}w^{T}\phi\big{(}X\big{)}L\phi^{T}\big{(}X\big{)}w\Big{\}}\]  
Then, the object function in Equation (6) is minimized and expressed as a Laplacian matrix. The eigenvector problem of kernel NFLE in the Hilbert space is expressed as:  
\[\left[\phi\big{(}X\big{)}L\phi^{T}\big{(}X\big{)}\right]w=\lambda\left[\phi \big{(}X\big{)}D\phi^{T}\big{(}X\big{)}\right]w \tag{7}\]  
To extend NFLE to its kernel version, the implicit feature vector, \(\phi(x)\), does not need to be obtained explicitly. The dot product expression of two samples is exclusively applied in the Hilbert space with a kernel function as follows: \(K\big{(}x_{i},x_{j}\big{)}\!=\!\left\langle\phi\big{(}x_{i}\big{)},\phi\! \big{(}x_{j}\big{)}\right\rangle\). The eigenvectors of Equation (7) are represented by the linear combinations \(\phi\big{(}x_{1}\big{)}\), \(\phi\big{(}x_{2}\big{)}\),..., \(\phi\big{(}x_{N}\big{)}\). The coefficient \(\alpha_{i}\) is \(w\!=\!\sum_{i=1}^{N}\alpha_{i}\phi\big{(}x_{i}\big{)}\!=\!\phi\big{(}X\big{)}\bm {a}\) where \(\mathbf{a}\!=\!\left[\alpha_{1},\alpha_{2},\ldots,\alpha_{N}\right]^{T}\!\in R^{N}\). Then, the eigenvector problem is as follows:  
\[KLK\alpha=\lambda KDK\alpha\,. \tag{8}\]  
Let the coefficient vectors, \(\alpha^{1},\alpha^{2},\ldots,\alpha^{N}\), be the solutions of Equation (8) in a column format. Given a testing point, \(z\), the projections onto the eigenvectors, \(w^{k}\), are obtained as follows:  
\[\Big{(}w^{k}\cdot\phi\big{(}z\big{)}\Big{)}\!=\sum_{i=1}^{N}\alpha_{i}^{k} \left\langle\phi\big{(}z\big{)},\phi\big{(}x_{i}\big{)}\right\rangle=\sum_{i =1}^{N}\alpha_{i}^{k}K\big{(}z,x_{i}\big{)}\,, \tag{9}\]  
where \(\alpha_{i}^{k}\)  is the \(i\)th element of the coefficient vector, \(\alpha^{k}\). The kernel function RBF (radial basis function) is used in this study. Thus, the within-class and between-class scatters in a kernel space are defined as follows:  
\[\mathbf{S}_{w}^{\phi}=\sum_{k=1}^{N_{C}}\Bigg{(}\sum_{\phi\big{(}x_{i}\big{)} c_{k}}\sum_{L_{m,n}\in F_{k}\big{(}\phi\big{(}x_{i}\big{)},c_{k}\big{)}} \Big{(}\phi\big{(}x_{i}\big{)}\!-L_{m,n}\big{(}\phi\big{(}x_{i}\big{)}\big{)} \Big{)}\Big{(}\phi\big{(}x_{i}\big{)}\!-L_{m,n}\big{(}\phi\big{(}x_{i}\big{)} \big{)}\Big{)}^{T}