esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 2 Methodology
### Loss Function
#### 2.4.2 Trajectory Smoothing Loss  
For trajectory smoothing in video stabilization, a composite loss function is designed to integrate three key aspects: local smoothness, path fidelity, and historical continuity. Additionally, an alignment loss is introduced to ensure consistency of the motion-compensated images.  
1. Local smoothness loss: This term enforces smooth and gradual transitions in the trajectory over a given time window, preventing abrupt changes. Let \(\hat{P}_{smooth}(t)\) represent the smoothed trajectory at time \(t\). The local smoothness loss is defined as \[L_{local}=\alpha_{s}\sum_{t=T-n}^{T+n-1}\sum_{k=-l}^{l}\left\|\hat{P}_{ smooth}(t)-\hat{P}_{smooth}(t-k)\right\|^{2}\] (21) where \(l\) denotes the local smoothness window radius, \(n\) is the sliding window length, and \(\alpha\) is a weighting parameter that adjusts the contribution of this term.
2. Path fidelity loss: To prevent excessive smoothing that may distort the motion path, the path fidelity loss encourages the smoothed trajectory to remain as close as possible to the original, unsmoothed trajectory \(P_{\text{raw}}(t)\): \[L_{fidelity}=\beta_{s}\sum_{t=T-n}^{T+n-1}\left\|\hat{P}_{smooth}(t)-P_{raw}(t )\right\|^{2}\] (22) where \(\beta\) is a parameter controlling the importance of this term.
3. Historical continuity loss: To maintain coherence between past smoothed trajectories and the current computation, particularly at sequence boundaries, the historical continuity loss is defined as \[L_{historical}=\gamma_{s}\sum_{t=T-n}^{T-1}\left\|\hat{P}_{smooth}(t)-P_{ smooth}(t)\right\|^{2}\] (23) where \(P_{\text{smooth}}(t)\) represents the previously computed smoothed trajectory, and \(\gamma\) controls the influence of this term.
4. Alignment loss: To ensure proper alignment after motion compensation and image warping, an alignment loss is designed as follows: \[L_{align}=\lambda_{s}\frac{1}{n}\sum_{t=1}^{n}\frac{1}{N}\sum_{i=1}^{N}\left\| I_{t,warp1}(x_{i};H_{t,1})-I_{t,warp2}(x_{i};H_{t,2})\right\|^{2}\] (24)where \(I_{I,\text{warp1}}\) and \(I_{I,\text{warp2}}\) represent the images warped through stabilization and stitching, respectively. \(N\) is the total number of pixels, \(x_{i}\) denotes pixel positions, and \(\lambda_{s}\) is a weighting factor.  
Combining these components, the total trajectory smoothing loss function is given:  
\[L_{total}=L_{local}+L_{fidelity}+L_{historical}+L_{align} \tag{25}\]  
This comprehensive loss function design ensures the realism of video content while effectively smoothing the camera motion trajectory. It minimizes unnatural jumps introduced by the smoothing process, thereby improving overall video stability and visual consistency. Through the multi-level loss function design, the model not only learns to transform unstable videos into stabilized outputs but also effectively handles parallax effects and fast-moving objects in dynamic scenes. This enables superior overall stabilization performance under a multi-task joint optimization framework.
## 3 Results and Discussions  
### Validation of the Numerical Methodologies  
Firstly, we compare the simulation results with the available experimental data of a petroleum fishery. An Infrared thermographer is used to detect the temperature inside the reformer. Figure 2 compares the average temperatures at the outer surfaces and the hydro gen yields at the outlets of the catalyst tubes using the prototype and the periodic model. It can be seen that the results calculated from the real reformer agree better with the experimental data than that from the periodic model. The simulation error for the average temperatures at the outer surfaces of the catalyst tubes using the prototype is within 4%. On the other hand, the periodic model predicts much lower temperatures. Using the prototype, the predicted hydrogen yields are around 0.67 while the experimental hydrogen yields are around 0.69. The simulation error for the hydrogen yields at the catalyst tube outlets using the prototype is within 3%. On the other hand, the periodic model predicts very low hydrogen yields of around 0.13 due to its much lower predicted temperatures.  
Table 1 compares the average temperatures at the front wall and the back wall of the reformer. It can be seen that the results using the prototype agree better with the experimental data while the periodic model yields much lower temperatures. This is because the prototype has the same boundaries as the real reformer while the periodic model has  
Figure 2: Comparison of the catalyst tube temperatures and hydrogen yields using the prototype and the periodic model: (**a**) Average temperatures at the outer surfaces of the catalyst tubes; (**b**) Hydrogen yields at the outlets of the catalyst tubes.  
unconfined spaces for heat dissipation at the left- and right-hand-side boundaries, which leads to lower temperatures. The errors of the prototype simulation at the front wall and the back wall are 3.5% and 4.5%, respectively. On the other hand, the errors caused by the periodic model simulation are 22.9% for the front wall and 23.6% for the back wall, respectively.
# Investigation of Rain-On-Snow Floods under Climate Change
## 2 Materials and Methods
### Selection of the Rain-On-Snow Floods and Methods Used for the Analysis  
ROS floods were identified using the discharge threshold that was determined depending on the information about actual flooding at the location of the selected discharge gauging stations. This information was provided by the Slovenian Environment Agency that is responsible for flood forecasting in Slovenia and uses these threshold values for issuing flood warnings. When the discharge at a particular location reaches the threshold value, the yellow warning is issued, which indicates that flooding can occur. We defined flood events based on the selected threshold values at individual discharge gauging stations. Because the range of discharge values using climate projections is not exactly the same as the range for the historical discharge data, we used the discharge-return period relationship using the historical data to define the corresponding return period (T) at which flooding occurs. This relationship was defined using the generalized extreme value (GEV) distribution and by applying the L-moments method for parameter estimation, which means that the flood-frequency analysis (FFA) procedure was performed. A more detailed description of the procedure can be found in, e.g., [30]. Furthermore, the FFA was also carried out using simulated discharge values by applying the climate projections. Based on the return period value (T) defined in the previous step (i.e., historical discharge data) we defined new corresponding discharge thresholds for all three case studies considered. In order to extract only ROS floods, the criteria for snow melting were defined, i.e., only flood events with melt values in any of the CemaNeige GR6J elevation zones larger than 0.1 mm/day were selected. Thus, ROS floods were defined based on the two criteria (i.e., snow melting and discharge above the threshold). Additionally, we checked if high discharge values were associated with rainfall events that might have taken place in the range of a few days before the high discharge value. In the case when ROS-flood duration was longer than one day, only the day with a maximum discharge was considered for further analysis.  
The methodology proposed by [31] was applied in order to investigate seasonal characteristics of ROS floods. An example of the application of the selected methodology can be found in [32; 33]. We calculated the mean direction of the flood date (MD) (i.e., day of the year) using all ROS floods in the selected period for each case study and four considered periods. Moreover, based on all ROS floods in a specific period, strength of seasonality r [0,1] was calculated.
# Automatic Roadway Features Detection with Oriented Object Detection
## 3 System Overview
### Roadway Features Extraction  
As presented in Section 2.1, there are various features affecting roadway safety. To automate the process of roadway safety features detection, a rule-based module that automatically scores safety features is adopted based on literature in addition to the local environment that affects roadway safety. Figure 4 lists the roadway safety-related objects that are considered as input to the module. Due to lack of roadway safety data in developing countries, previous research attempted developing safety indicators for decision makers such as traffic safety equity [55]. Ten features were the criteria of the roadway safety rule-based module that scores and evaluate roadway safety features, where each feature was considered to have either a high, a medium, or a low safety score based on its impact on road safety expressed as a key safety indicator (KSI). As an example, the 'traffic condition' feature can be either fluid, dense, or congested based on speed and traffic density. A congested traffic condition leads to a low safety score and heavy traffic leads to more crashes on weekdays [56]. Moreover, the 'area type' feature can be either be rural, semi-urban, or urban based on buildings density. A rural area leads to a low safety score. 'Median' feature describes whether the road is divided or undivided based on the existence of a left barrier and left curb. 'Roadside barrier' feature is present based on the existence of a fence or right barrier objects. 'Existing marking', 'traffic signs', 'roadside barriers' and 'light poles' features have a positive safety impact, and therefore a higher \"score\". Moreover, 'existing billboards', 'pedestrian waiting area', and 'heavy vehicle' feature leads have a negative safety impact and therefore are given a lower \"score\". The weighted average of safety scores over the different features can be calculated for a road segment resulting in an estimate to the road segment safety. To summarize, we use a rule-based method of weighted averaging the safety scores over the different road features in a road segment to estimate its safety score. The score is a contentious number normalized to be from 0 to 1 (for a very safe segment).  
Figure 3: Samples of data augmentation. (**a**) Shows original image sample, and from (**b–e**) the effect of each augmentation method is shown; (**b**) horizontal flipping, (**c**) rotation, (**d**) adding noise, (**e**) changing brightness, and (**f**) shows the final augmentation pipeline result. (**g–j**) show four additional different output samples from the augmentation pipeline.
## 4 Discussion
### Thick and Thin Lens  
Some of the articles published using the NIMO system with contact lenses include information about their physical characteristics, such as thickness and curvature, to enable them to be measured [9; 27; 28]. However, in published works with intraocular lenses [12; 13] this information does not appear, and it is not indicated whether the procedure was performed with the lens being treated as a thin or a thick lens. Our experience has demonstrated that it is highly challenging, if not impossible, to obtain these data from laboratories due to patents and the protection of related information. Consequently, we investigated whether the outcomes from the NIMO, when measuring an intraocular lens as a thin lens or a thick lens, would differ and thus necessitate certain conditions for measurement.  
The measurements conducted using both procedures, repeated ten times each, showed similar results throughout the entire optical size except for the central zone. The observed differences in the central zone are in line with the previously evaluated points in this study and can be attributed to the loss of precision exhibited by the NIMO TR1504 system within the central 0.5 mm (radius 0.25 mm). This makes sense as the central thickness of an IOL is lower than that of the natural eye lens while the refracting power of the IOL surface is roughly the same as that of the natural eye lens. In a standard eye model (Arizona Eye Model, for example [31]) the difference between the back vertex power and the principal plane's power is already small (around 0.42 D), so for a thinner lens with roughly the same curvature radius, this difference would be even smaller.
## 3 Numerical Algorithm for Solving the Resulting Eikonal PDE
### Alternating Sweeping Strategy for Godunov-Based Scheme  
To speed up the convergence of the high-order Godunov-based scheme, the philosophy of alternating sweeping strategy and Gauss-Seidel iterations [28; 29] are used as follows. The newest available values for \(z\) are adopted when we calculate the derivatives \(p_{m,n}^{-}\), \(p_{m,n}^{+}\), and \(q_{m,n}^{-}\), \(q_{m,n}^{+}\). Simultaneously, the iterations of the scheme do not only sweep in a single direction, but in the four alternating directions as follows: (a) from upper left to lower right; (b) from lower left to upper right; (c) from lower right to upper left; (d) from upper right to lower left. As can be clearly demonstrated that varied values \(z_{m\pm 2,n}\), \(z_{m\pm 1,n}\) and \(z_{m,n\pm 2}\), \(z_{m,n\pm 1}\) will be taken into account according to the current sweeping direction within the preceding cycle.  
The high-order Godunov-based scheme accelerated by an alternating sweeping strategy is summarized in the following way:  
1. _Initialization_: On the boundary \(\partial\Omega\) we set the grid points to be accurate values, i.e., \(z_{m,n}^{0}=\varphi_{m,n}\), which are unchanged during the iterations of the scheme. The approximated solution from the first-order Godunov-based scheme is applied as the initial guess for all other grid points.
2. _Iterations with Alternating Sweepings Orderings_: We calculate \(z_{m,n}^{new}\) at \((i+1)\)th iteration, updated by the rules (19) using Gauss-Seidel iterations with the following four alternating sweepings orderings: \[\begin{array}{l}\bullet\quad\quad m=1:r,\ n=1:c;\\ \bullet\quad\quad m=r:1,\ n=1:c;\\ \bullet\quad\quad m=r:1,\ n=c:1;\\ \bullet\quad\quad m=1:r,n=c:1.\end{array}\]
3. _Convergence Test_: If \(\left\|z^{i+1}-z^{i}\right\|_{L^{1}}\leq\delta\), where \(\delta>0\) is a known threshold value, the scheme stops and converges, or else comes back to (2). In this paper, \(\delta=10^{-5}\) is taken.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
## 5 Results
### Effect of Radial Geometric Dimension  
A magnetorheological brake utilizes both axial and radial magnetic flux to generate braking torque. The rotor of the brake in the proposed work is a dual disc configuration and the MR fluid acts as the force on the disc in a shear mode. In this disc brake, the torque is easily affected by the centrifugal force in a superficial direction. In particular, radial dimensions affect the magnetic field that acts on the disc surface and a very weak magnetic field is exerted axially to both sides of the rotor. Additionally, torque enhancement can be achieved by increasing either the axial or the radial dimensions of the rotor. The extended rotor's radial dimensions create a greater active area of the fluid, thus producing higher shear stress and, subsequently, higher braking torque.  
Design configurations for different pole head geometries varied due to the compactness of the brake. Analysis for each pole head shape was conducted from 27.0 mm to 29.0 mm, which is the maximum radial width to the midpoint of the pole head. As shown in Figure 10, for all pole head configurations, the obtained torque (16.09 N-m) is maximum at a radial width of 27.0 mm, and at 27.5 mm, the torque is (15.73 N-m). For a 28.0 mm radius to the midpoint of the pole head, the obtained torque is (15.34 N-m), which is the maximum. In all the pole head profiles, the maximum output torque (16.27 N-m) was obtained at 28.5 mm. At a higher radial dimension of 29.0 mm, the torque (15.98 N-m) slightly decreased due to the active chain area of MR fluid penetrating the magnetic flux to the outward direction of the brake.
# Effect of Physical Exercise on the Release of Micorparticles with Angiogenic Potential
## 4 Methods of Detection of Microparticles  
Flow cytometry (FC) is one of the most common methods of detection, quantification, and size evaluation of MPs [39]. In addition, FC allows the phenotypic characterization of MPs using fluorescent antibodies against the diverse antigens expressed by the cell of origin [40]. However, FC has some limitations. For example, MPs with a size ranging from 0.1 to 0.4 \(\upmu\)m are too undetected by most cytometers [41]. In addition, the type of instrument, settings, and sample preparation procedures can cause a high level of variability of the results [42].  
The nanoparticle tracking analysis (NTA), on the other hand, is able to detect vesicles smaller than those recognized by FC. Such type of analysis correlates the speed of movements, tracked using a microscope, with the size of MPs [43]. Nevertheless, NTA is unable to discriminate vesicles derived from cells from other particles of small dimensions such as high-density lipoprotein (HDL) [44]. Fluorescent nanoparticle tracking analysis (F-NTA), diversely from NTA, is based on the fluorescence of the particles [45].  
Other techniques to analyze the EVs are based on image analyses obtained by optical and electron microscopy. Optical microscopy permits to assess the size and morphological features of MPs and of apoptotic bodies, while cannot provide information on their phenotype. This kind of information can, anyway, be obtained by fluorescent microscopy labeling MPs with fluorescent probes that recognize specific surface markers [41]. However, this approach does not permit to specify the size of the EVs [41]. An accurate morphological analysis of MPs is obtained by transmission electron microscopy (TEM), that represents the most used method to assess the composition, morphology, size, and membrane structure of MPs [34]. However, TEM requires a considerable amount of time for both preparation and analysis of the sample.  
Western blotting (WB) is a commonly used technique that allows to identify the cellular origin of MPs [46]; anyway, this methodological approach presents some important limitations: indeed, it requires a large amount of MPs to be performed and the analysis needs to be completed with complementary methods such as NTA or TEM [47, 48].  
Finally, another method for MPs detection is the enzyme-linked immunosorbent assay (ELISA). This is also a quantitative analysis, that measures MPs indirectly: indeed, such method relies on the binding of MPs to specific conjugate-antibodies adsorbed to a well plate [49]. ELISA permits the specification of MPs subtypes, but fresh plasma is indispensable, because the freezing-thawing process leads to an increase of annexin-binding MPs [50].  
However, is important to note that most of the studies analyzing MPs in exercise settings utilize the flow cytometric approach [17, 51, 52, 53, 54, 55, 56].
# Pushover Analysis on Infill Effects on the Failure Pattern of Reinforced Concrete Frames
## 4 Conclusions  
Based on the pushover analyses, the following conclusions are made:  
1. The columns' shear forces calculated by FEM results are 1.56 to 5.02 times larger than those calculated by design codes. Shear failure of a column may appear in frames with infilled walls. This shall be considered in the design of the column.
2. Infilled walls exhibit a significant effect on column bending moments, however, this has not been sufficiently addressed in the current design code and practice. According to the FEM analysis, bending moments of some columns are shown to be 6.1%-30.9% larger than those obtained from the design codes. It will be inherently unsafe according to the standard design approach. The influence of infills on the location of the inflection point should be taken into account when using the inflection point method.
3. Amplified factors of the sum of column bending moments are normally used for the purpose of achieving a \"strong column weak beam\" design philosophy. However, most of the available design codes neglect the role of cast in situ slabs. Analysis results show that the effective slab width will be particularly reduced by the existence of infills. Combined action of slabs and infills should be taken into account in the design. According to the FEM results of the current model, the effective slab width is recommended to be equal to \(b+4t\) for interior joints and \(b+2t\) for exterior joints with fulfilled infills, whereas the effective slab width is recommended to be equal to \(b+6t\) for interior joints and \(b+3t\) for exterior joints with half-filled infills, respectively.
4. The required ratio of column to beam strength is reduced when infill exists, i.e., particularly the full-filled case, which indicates that the infilled wall has changed the behavior of the frame substantially. If the effective width is considered, the columns could become \"stronger\" than presumed. Owing to some limitations, the above analysis demonstrated only partial parameters' effects on the behavior of reinforced concrete frames, with or without infills, in the context of structural seismic design, particularly relevant to \"strong column weak beam\" philosophy. More parameters, such as different material models and properties, openings, and axial load ratios, etc., shall be taken into consideration in further investigations to present a comprehensive understanding of the gaps between the current design standard and the practice.  
The research is financially supported by the National Natural Science Foundation of China (Grant No. 51508289, 51478231, and 51608288) and the Taishan Scholar Priority Discipline Talent Group program funded by the Shandong Province. [PERSON] carried out the simulation and analyzed the FEA results. [PERSON], [PERSON] and [PERSON] gave suggestions on establishment of finite element model and theoretical calculation of the paper. The authors declare no conflict of interest.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 6 Hardware Simulation  
To further test the performance of the adaptive tracking method, a hardware simulation testing platform is set up, and the overall testing platform is shown in Figure 16. The Spirent GSS8000 simulator [24; 25] is used to generate the GPS L1 signal. GPS IF data is collected by an acquisition board, whose front-end bandwidth is chosen as 40 MHz, and it is driven by the TCXO referred above. The sampling frequency is chosen as 24 MHz. The simulator also provides a reference PVAT solution which can be used to derive true acceleration and angular velocity measurements through an inverse mechanization. The simulated biases of gyro and accelerometer are \(\delta\omega_{ib}^{b}=10^{\circ}/\mathrm{h}\) and \(\delta f_{ib}^{b}=1\) mg, respectively. The output frequencies of the inertial sensor and GPS software receiver are 100 Hz and 25 Hz, respectively.  
Figure 14: The output of PD (**left**) and the corresponding PLI (**right**) when \(p=50\) m/s\({}^{3}\).  
Figure 15: The output of PD (**left**) and the corresponding PLI (**right**) when \(p=200\) m/s\({}^{3}\).  
Two trajectories have been simulated for the performance test: Trajectory 1 has a relatively lower dynamic of 10 g maximum acceleration, while Trajectory 2 has a higher dynamic of 50 g maximum acceleration and 20 g/s maximum jerk. Two other improved INS-aided PLL tracking methods presented in [8; 15] which are mentioned before are compared with the proposed adaptive tracking method.