esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
## 3 Improvement Methods and Improved Models
### Adding NWD to the Loss Section  
As mentioned earlier, small object defects account for one-third of the total dataset. However, in traditional object detection tasks, detecting small objects often comes with difficulties. Small objects, due to their small size and sparse information, are easily affected by factors such as noise and background interference, resulting in inaccurate detection results. To address this issue, [PERSON] et al. [29] proposed a new metric called normalized Wasserstein distance (NWD) to measure the similarity between small object bounding boxes. Based on this, NWD is embedded into the loss function of label assignment, non-maximum suppression, and anchor-based detectors to replace the original IoU metric, leading to a new small object detector.  
This module extracts small objects from the image. Since these objects are generally not strictly rectangular, the module calculates based on the bounding boxes. Within each bounding box, the pixel weights decrease progressively from the center toward the edges. This models each target bounding box as a two-dimensional Gaussian distribution, with the mean and variance determined by the position and size of the target. For each extracted target, the Wasserstein distance based on optimal transport theory is used to calculate the distribution distance between it and other targets. The distance matrix is then normalized to eliminate the influence of scale factors, making the distances between different targets comparable and better reflecting the similarity of small objects. This yields a new measurement of normalized NWD. Finally, based on the normalized distance matrix, object detection is performed, as shown in Figure 4.  
Compared to the original algorithm, incorporating the NWD detector and using Gaussian Wasserstein distance as a similarity measure allows for more accurate differentiation between small objects and the background. By normalizing the distance matrix, the influence of size factors can be effectively eliminated, enabling comparability between different targets and more accurate classification of small objects. This enhances the accuracy and robustness of detection. Modeling small objects based on Gaussian distributions, using the object bounding boxes, provides better adaptability to objects of various scales.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 4 Coordinate Recognition Algorithm
### Example  
As an example, we took an area with a longitude ranging from 120.6 to 122.9 East and a latitude ranging from 24.8 to 25.4 North. We collected 267,858 geographic points, which were check-in places obtained from a well-known social network service platform, in this area [25]. This range also contained the capital of Taipei City. We used the city as the targeted area and the check-in places as the targets to calculate the inner points of this city. Figure 4 provides the distribution of 10,000 targets that were randomly selected from the 267,858 points. The results related to the layouts of target distributions in this study, such as those shown in Figures 4 and 5, and others, were obtained by using Ajax and the Google Maps API [31]. The city's polygon \(P\) had 2054 points that were selected along the outermost area, as shown in Figure 5a. A dataset \(R\) was obtained by using RectOfPy with the polygon P as input, where \(R\) contained an \(x_{\min}\) value of about 121.457, an \(x_{\max}\) value of about 121.666, a \(y_{\min}\) value of about 24.960, and a \(y_{\max}\) value of about 25.210. Figure 5b provides the corresponding shape, which formed four points (\(r_{0}\)(=\(x_{\min}\), \(y_{\min}\))), \(r_{1}\)(=(x_{\max}\), \(y_{\min}\)), \(r_{2}\)(=(x_{\max}\), \(y_{\max}\))), and \(r_{3}\)(=(x_{\min}\), \(y_{\max}\)))) that were the four vertices of a rectangle, i.e., the minimal rectangle covering polygon \(P\). Figure 5b provides the shape of this rectangle.  
Then, by using RectPtnToSquare, this rectangle could be divided into a set SS of squares. Figure 6 shows that rectangle \(R\) was divided into a square set \(SS\) with \(7\times 6\) squares. For each square \(S\) of \(SS\), the space-overlap status of \(P\) was calculated through SarInPy. When square \(S\) was an inner square of or a square intersecting with polygon \(P\), the inner points of square \(S\) were calculated through the corresponding SELECT statement. If \(S\) was an inner square, the inner points of square \(S\) were also the inner points of polygon \(P\) and were directly stored in the set \(GP\) without confirmation through PtInPy. If \(S\) was a  
Figure 4: An area as an example for evaluating the targeted points in a city.  
Figure 5: Polygon of the city and a minimal rectangle covering the polygon. (**a**) Polygon of the city; (**b**) Minimal rectangle covering the polygon.  
square that intersected, the points that PtInPy confirmed as inner points of \(P\) were stored in the set \(GP\). Finally, the set \(GP\) contained all inner points of \(P\). In this example, there were 89,209 inner points in the targeted city. Figure 7 provides the distribution of the 10,000 inner points that were randomly selected from 89,209 points.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 2 Description of the Problem and Concept of the Solution
### Reinforcement Learning
#### 2.3.1 Q-Learning and Deep Q-Learning  
Q-Learning is a form of temporal difference learning, where the goal is to estimate the optimal action values by learning a parameterized action-value function \(Q(s,a;\theta_{t})\)[13]. In a standard Q-Learning algorithm, after taking an action \(a_{t}\) in state \(s_{t}\) and observing the immediate reward \(r_{t}\) and a new state \(s_{t+1}\), the target action value is calculated using the Bellman's equation as follows:  
\[Y_{t}^{Q}=r_{t}+\gamma\max_{a}Q(s_{t+1},a;\theta). \tag{3}\]  
The parameter is updated using the stochastic gradient descent method to modify the current value \(Q(s_{t},a_{t};\theta)\) towards the target value \(Y_{t}^{Q}\).  
In Deep Q-Learning, a multi-layered neural network is used to approximate the action-value function. For each state \(s\), the network outputs a vector of action values \(Q(s,\cdot;\theta)\) of length \(card(A)\), where \(\theta\) are the parameters of the network. To mitigate value estimation errors, [PERSON] et al. [14] introduced a dual-network approach. They utilized the online network for making updates and the target network to estimate the target value \(Y_{t}^{Q}\).
## 3 Experimental Set-Up
### Signal Acquisition and Experimental Conditions  
A pair of bearings is included inside each axle box, supporting the rotating wheels. Three uni-axial accelerometers were placed at each axle box of the wheelset as indicated in Figure 2a, three on the right side (RS) and three on the left side (LS) oriented in three directions: left vertical (LV) accelerometer, left longitudinal (LL) accelerometer, and left axial (LA) accelerometer; right vertical (RV) accelerometer, right longitudinal (RL) accelerometer, and right axial (RA) accelerometer, as presented in Figure 2b. The accelerometer model is CMSS-RAIL-9100 with sensitivity of 100 mV/g, frequency range 0.52 Hz-8 kHz and resonance frequency 25 kHz, the accelerometer is coupled to the conditioner system SKF Multilog IMX-R, that was connected to a computer with the software SKF @ptitude Observer. This conditioner system is for industrial use and reduces the possibility of the signals having noise or interference. The sampling frequency was 12.8 kHz for a time of 1.2 s. The experimental conditions for load, speed, the rotation direction, and crack depths in the railway axle (see Figure 2) are presented in Table 2 for the four experimental conditions; at least 60 samples were acquired for each condition. We named the data obtained from each accelerometer: right-hand accelerometer data set (RD), and for the left and accelerometer data set (LD). Crack faults were artificially generated by an abrasive grinding process. Further details of the experimental conditions can be found in [7].  
\begin{table}
\begin{tabular}{l l} \hline \hline
3 loads & - 4, 10, y 16 tons \\ \hline
2 speeds conditions & - 20 km/h \\  & - 50 km/h \\ \hline  & - Left vertical (LV) accelerometer, left longitudinal (LL) accelerometer, \\
6 vibration measurement placements & left axial (LA) accelerometer \\  & - Right vertical (RV) accelerometer, right longitudinal (RL) accelerometer, \\  & right axial (RA) accelerometer \\ \hline Rotational directions & - Clockwise and counterclockwise \\ \hline  & - Healthy (Normal) \\
4 fault conditions, see Figure 2 & - Crack level 1 (e = 5.7 mm) \\  & - Crack level 2 (e = 10.9 mm) \\  & - Crack level 3 (e = 15 mm) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions.  
Figure 1: Components of the bed test. (**a**) Back view of the test bed. (**b**) Front view of the test bed.
## 2 Direct Scattering Problem and Subspace Migration
### Subspace Migration Imaging Algorithm
Then, for sufficiently large \(N\) and \(k\), \(\mathfrak{F}_{\text{SUB}}(\mathbf{x})\) can be represented as_  
\[\mathfrak{F}_{\text{SUB}}(\mathbf{x})=\bigg{|}\sum_{m=1}^{M}\bigg{(}J_{0}(k| \mathbf{x}-\mathbf{y}_{m}|)+\frac{\mathcal{D}(\mathbf{x}-\mathbf{y}_{m})}{ \theta_{N}-\theta_{1}}\bigg{)}\bigg{|},\]  
_where the term \(\mathcal{D}(\mathbf{x}-\mathbf{y}_{m})\), which degrades the imaging performance, is given by_  
\[\mathcal{D}(\mathbf{x}-\mathbf{y}_{m})=4\sum_{s=1}^{\infty}\frac{i^{s}}{s}J_{ s}(k|\mathbf{x}-\mathbf{y}_{m}|)\sin\frac{s(\theta_{N}-\theta_{1})}{2}\cos \frac{s(\theta_{N}+\theta_{1}-2\phi_{m}+2\pi)}{2}.\]  
Notice that, in the full-view problem, the range of incident and observation directions is \(\theta_{N}-\theta_{1}=2\pi\) so that the term \(\mathcal{D}(\mathbf{x}-\mathbf{y}_{m})\) can be removed. This means that the scatterers \(\Gamma_{m}\) can be retrieved via the map of \(\mathfrak{F}_{\text{SUB}}(\mathbf{x})\) in a full-view problem. Therefore, for a proper identification of scatterers, it is natural to consider the generation of complete elements of MSR matrix from limited-view data. Unfortunately, one needs prior information of scatterers to generate missing elements so that direct generation of the elements is very difficult. Motivated by this difficulty, we consider the generation of the MSR matrix \(\mathbb{M}\) in a full-view configuration from the MSR matrix \(\mathbb{L}\) in a limited-view configuration: for an even number \(N\), generate complete elements \(\Delta S_{j,l}\), \(j,l=1,2,\cdots,N\) of \(\mathbb{M}\) from the following MSR matrix \(\mathbb{L}\in\mathbb{C}^{N/2\times N/2}\):  
\[\mathbb{L}= \begin{bmatrix}\Delta S_{1,1}&\Delta S_{1,2}&\cdots&\Delta S_{1,N/ 2}\\ \Delta S_{2,1}&\Delta S_{2,2}&\cdots&\Delta S_{2,N/2}\\ \vdots&\vdots&\ddots&\vdots\\ \Delta S_{N,1}&\Delta S_{N,2}&\cdots&\Delta S_{N/2,N/2}\end{bmatrix} .\]
###### Abstract
* Simulation results demonstrate the effectiveness of our proposed scheme in regulating offloading decisions and resource allocation when there is a significant difference in the data size of the offloaded tasks. When the tasks are of regular size, our scheme obtains the minimum delay as the compared baseline scheme. The remainder of this paper is organized as follows. Section 2 discusses the related work. Section 3 shows the details of the proposed system model. Section 4 introduces the optimal solution based on the KKT conditions and CD. Finally, Section 5 presents the simulation results and analyses, and we conclude our work in Section 6.
# Intra-Day Variability Observations of Two Dozens of Blazars at 4.8 GHz
## 3 Results and Discussion
### Discussion  
We have tried to cross-correlate the parameters listed in Tables 1 and 2. No correlation is found between the IDV strength (\(m^{\prime}\)) and either redshift or Galactic latitude, as shown in Figure 3a,b, with correlation parameters listed in Table 3. Three strong IDVs appear in area of relatively lower flux density \(<S><\) 0.7 Jy as shown in Figure 4.  
Figure 4: The modified modulation index (\(m^{\prime}\) in per cent) versus average flux density (\(<S>\), in Jy) of the target sources observed in session 1 (blue circle) and session 2 (red square). Arrows indicate from session 1 to session 2 for 11 common target sources.  
Figure 3: (**a**) The modified modulation index (\(m^{\prime}\) in per cent) versus redshift (\(z\)) of the target sources observed in session 1 (blue circle) and session 2 (red square); (**b**) the modified modulation index (\(m^{\prime}\) in per cent) versus Galactic latitude of the target sources observed in session 1 (blue circle) and session 2 (red square).  
For 11 common target blazars in Tables 1 and 2, there is no significant correlation between the changes of IDV strength \(m^{\prime}\) and the changes of average flux density \(<S>\) from session 1 (in February) to session 2 (in March), as the arrows shown in Figure 4 and the comments in Section 3.2.  
The detection of an IDV or not from one epoch to another is dependent on many factors, e.g., the changes of physical properties of scattering plasma as well as the changes of radio core of AGN for the ISS model [4], and the telescope sensitivity/uncertainty, calibration procedure/precision, and also the definition of an IDV detection (slightly different criteria from different research groups). We can see that some of our detections (non-detections) of IDV have non-detections (detections) in previous observations e.g., in [4] as shown in Tables 1 and 2, these occur mostly for weak or marginally detected IDVs. The non-detection of IDV in our session 1 but a weak IDV in session 2 for 1156 + 295 might be partly due to the higher residual uncertainty (\(m_{0}=0.64\)) of observation in session 1 than (\(m_{0}=0.45\)) in session 2. Except for the observation sensitivity (including weather and interferences) effects and the issue of different definition criteria (or confidence level) of IDV, it is believed that the ISS-induced IDVs are often intermittent in time, e.g., the strong IDV of 0917 + 624 has been quenched after year 2000 [38], although it shows weak IDV in this paper.  
In the ISS models, the variability timescale of around a day, requires the angular size of radio-emitting component from sub-milliarcsecond to microarcsecond for the blazar emission to be scintillated by a nearby plasma so as to show an IDV (see [4, 19, 39]). Emission from the extended or long jet of a blazar is less possible to show an IDV. However, a detailed VLBI-structure and core fraction analysis for these blazars along with the brightness temperature of core components from the high-resolution RadioAstron data is planned in the forthcoming paper.
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 2 Related Work
### Dynamic VI-SLAM
DS-SLAM [9] used a multithreaded system with a deep semantic segmentation network, SegNet, to perform motion object segmentation in an independent thread. Similar to DS-SLAM [9], DynaSLAM [10] used Mask R-CNN for motion object segmentation and identifies data points with significant depth or angle differences as dynamic points. SLAMANTIC [11] integrated semantic information into a geometric, feature-based VSLAM using Mask R-CNN to detect objects and construct semantic maps. In this method, the confidence measurement for each feature point considers both semantic labels and combined values of reprojection error, photometric error, and depth error. Dynamic-VINS [12] used YOLOv3 [27] for real-time motion object detection and employed depth thresholds and IMU data for motion consistency checks to preserve static background points within the bounding box. Although deep learning-based methods can effectively detect dynamic objects at the semantic level, they incur substantial computational overhead and struggle in scenarios such as (a) the presence of undefined dynamic objects and (b) partially visible dynamic objects due to occlusion. In these situations, deep learning networks may fail to detect dynamic objects effectively, limiting the applicability of corresponding dynamic SLAM approaches. In comparison to the existing literature on event-based SLAM and dynamic VI-SLAM, our proposed method introduces several key differences. First, unlike many prior event-based approaches that do not address dynamic environments, our method explicitly handles dynamic scenes by introducing a novel dynamicity weighting mechanism and alternating optimization framework. Second, our approach incorporates event camera data and fuses them with traditional image inputs, offering improved robustness in dynamic environments. Furthermore, we address the challenges posed by temporarily stopping dynamic objects and their associated uncertainties, which have not been fully considered by previous methods. These distinctions are elaborated in detail in the following chapters:  
* In Section 3, we describe the differences in pipeline design, including the introduction of dynamicity weights and alternating optimization. * In Section 4, we present quantitative and qualitative comparisons with existing methods, demonstrating the superior performance of our approach in dynamic scenes. * In Section 6, we highlight the advantages and limitations of our method in handling dynamic environments. These sections provide a comprehensive view of how our method differs from, and improves upon, existing research.
## 3 Effect of Fixed-Altitude Heterogeneous LEO Constellations on CIT  
In order to investigate the effectiveness of increasing LEO satellite observations at a fixed altitude on the accuracy of the CIT inversion, we fix a certain altitude (300 km, 500 km, 800 km, 1000 km) and analyze the effects of different numbers of heterogeneous constellations on the CIT inversion results, including the geometrical distribution of the observed rays and the accuracy of the tomography results.
## 3 Results and Discussion
### Adsorption Tests in Multi-Step Mode: Aerogels (A) vs. Cryogels (C)
The adsorption kinetics showed that this configuration allowed to use the surface and the inner mesoporosity of three-dimensional structures; indeed, the dye is initially adsorbed on the surface and, then, through the diffusion process, it is adsorbed on the active sites present in the inner structure [25; 26; 27; 28; 29]. According to the literature, the most used configuration for adsorption process is represented by batch-mode; for this reason, particles are most frequently used as adsorbent materials since they allow an effective contact between solids and liquid medium due to their large surface area. However, time-consuming processes are required to separate the two phases [14; 15; 16; 28; 29]. Therefore, the multi-step mode has a good potential as an adsorption process, since this configuration allows the dye to occupy all the available sites and a three-dimensional structure, characterized by a large mesoporosity (aerogels), can be used and easily separated before regeneration. Moreover, this approach is not energy-consuming compared with other adsorption processes. Figure 6: Adsorption kinetics of cryogel samples analyzed in the experiments of Set C (Cryogels) performing a multi-step adsorption (T = 25 \({}^{\circ}\)C, flow rate = 0.1 mL/min) and using three different amounts of cryogels: 150 mg (black line), 200 mg (red line) and 300 mg (blue line). The experimental points were fitted by the continuous lines reported in Figures 3-6. The equation that was used to provide these curves was the following one:  
\[\mathrm{y}=\mathrm{A_{1}e^{-\frac{x}{H}}+y_{0}}\]  
The best aerogel candidate was regenerated using a 1 M solution of ascorbic acid. After CV desorption, the aerogels were used for other 3 cycles of adsorption, and they preserved a 93% of the original adsorption capacity.
## 1 Introduction
[21] presented a study on the development length of steady flows of Oldroyd-B and Phan-Thien-Tanner (PTT) fluids through a two-dimensional rectangular channel and concluded that the development length determined for the Oldroyd-B fluid varies exponentially with \(Wi\) and linearly for the linear PTT model; they also concluded that higher entry lengths are predicted with increasing \(Wi\) (at fixed \(Re\)). To remove the unstable numerical effect of the singularity at the entrance corner, a continuous inlet velocity profile is used in both works of [PERSON] et al. [20] and [PERSON] et al. [21]. This regularised profile can affect the true development length, so in the work of [PERSON] [22] the log-conformation formulation [23; 24; 25] is used, which reduces the rate of increase of the stresses and thus avoids the need to introduce artificial inlet velocity profiles. It should be noted that the development of a correlation for the prediction of the development length of such complex fluids is still difficult due to the high number of parameters involved and the fact that it is model dependent. Here we follow the work of [PERSON] [22], where a detailed analysis of the development length of the linear PTT model is performed. We extend his work to the exponential and generalised PTT models [22; 26; 27]. This work is organised as follows. First, we present the differential equations to be solved and their numerical solution. In Section 3, we present the geometry and the meshes. In Section 4, we perform a validation of the numerical method and the meshes, using Newtonian benchmark results. In Section 5, we present and discuss the results for viscoelastic fluids. The paper ends with the main conclusions in Section 6.