esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Tracked In-Pipe Robot's Obstacle-Crossing Performance
## 3 Results
### Simulation Analysis of Passing an Obstacle  
Based on the maximum obstacle-crossing height determined through analysis, the robot was modeled and imported into ADAMS (Automatic Dynamic Analysis of Mechanical Systems) for simulation of its obstacle-crossing performance. The simulation was conducted in three scenarios, with obstacle heights set at 5 mm, 7.5 mm, and 10 mm to evaluate the robot's ability to pass over these obstacles. Results of the simulation are presented below.  
Figures 5-7 illustrate centroid velocity diagrams of the robot as it traversed obstacles with heights of 5 mm, 7.5 mm, and 10 mm, respectively. These figures reveal that the front wheels of the robot's crawler began to climb the obstacle at approximately 4.5 s, while the rear wheels started to pass over the obstacle at around 6.8 s. Comparing these three scenarios, it is evident that when the robot encountered a 5 mm obstacle, the \(Z\)-direction velocity exhibited significant fluctuations, but these were short-lived, whereas velocities along the \(X\)- and \(Y\)-axes remained relatively stable. The overall duration of velocity changes along all three axes was brief. In contrast, when traversing a 7.5 mm obstacle, the robot experienced substantial fluctuations in both \(Z\)- and \(Y\)-axis velocities, with a longer duration. Compared to the 5 mm obstacle, the robot took significantly longer to fully cross the 7.5 mm obstacle, completing the traversal at 9 s. When attempting to cross a 10 mm obstacle, the robot's velocities in all three axes fluctuated severely from the moment the front wheels of the crawler engaged with the obstacle, and these fluctuations persisted throughout the simulation. Ultimately, the robot was unable to surmount the 10 mm circular obstacle.  
Figure 5: The centroid velocity when passing a 5 mm obstacle.  
Figure 8: The velocity of the robot’s wheels when passing a 5 mm obstacle.  
Figure 6: The centroid velocity when passing a 7.5 mm obstacle.  
Figure 7: The centroid velocity when passing a 10 mm obstacle.  
Figure 11-13 illustrate the centroid displacement of the robot while traversing obstacles of 5 mm, 7.5 mm, and 10 mm, respectively. These figures indicate that the robot's movement was directed along the negative \(Z\)-axis. When encountering the 5 mm obstacle, the robot exhibited minor fluctuations in displacement, while more pronounced fluctuations were observed at the 7.5 mm obstacle. Despite these fluctuations, the robot successfully traversed both obstacles. However, when attempting to pass the 10 mm obstacle, the robot's \(Z\)-axis displacement ceased to change at around 4.5 s and remained constant for the duration of the simulation. This result confirms that the robot is unable to surmount a 10 mm obstacle.  
Figure 11: The 5 mm centroid displacement.  
Figure 10: The velocity of the robot’s wheels when passing a 10 mm obstacle.  
Figure 9: The velocity of the robot’s wheels when passing a 7.5 mm obstacle.
###### Abstract  
Accurate surface water mapping is crucial for rationalizing water resource utilization and maintaining ecosystem sustainability. However, the diverse shapes and scales of water bodies pose challenges in automatically extracting them from remote sensing images. Existing methods suffer from inaccurate lake boundary extraction, inconsistent results, and failure to detect small rivers. In this study, we propose a dual-stream parallel feature aggregation network to address these limitations. Our network effectively combines global information interaction from the Swin Transformer network with deep local information integration from Convolutional Neural Networks (CNNs). Moreover, we introduce a deformable convolution-based attention mechanism module (D-CBAM) that adaptively adjusts receptive field size and shape, highlights important channels in feature maps automatically, and enhances the expressive ability of our network. Additionally, we incorporate a Feature Pyramid Attention (FPA) module during the advanced coding stage for multi-scale feature learning to improve segmentation accuracy for small water bodies. To verify the effectiveness of our method, we chose the Yellow River Basin in China as the research area and used Sentinel-2 and Sentinel-1 satellite images as well as manually labelling samples to construct a dataset. On this dataset, our method achieves a 93.7% F1 score, which is a significant improvement compared with other methods. Finally, we use the proposed method to map the seasonal and permanent water bodies in the Yellow River Basin in 2021 and compare it with existing water bodies. The results show that our method has certain advantages in mapping large-scale water bodies, which not only ensures the overall integrity but also retains local details.  
attention mechanism; swin transformer; convolutional neural networks; feature pyramid attention; Yellow River Basin +
Footnote †: journal: Medical Imaging  
1
# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 2 Related Work
### Data Association  
Data association poses a pivotal challenge in multi-target tracking, as it necessitates accurately linking detected targets with existing trajectories. Various techniques have been employed to address this challenge, including the Hungarian algorithm, Joint Probabilistic Data Association (JPDA), and Multiple Hypothesis Tracking (MHT). However, conventional methods often rely heavily on the targets' movement patterns and visual features for association, which occlusions and significant similarities among targets can challenge.  
Deep learning methods have introduced innovative approaches to the problem of data association. [PERSON] et al. (2019) proposed a graph neural network-based association method that enhances robustness by constructing a relationship graph among the targets. Concurrently, [PERSON] et al. (2019) introduced the Tracktor algorithm, simplifying the association process by integrating detectors and trackers, thereby enabling direct data association on detection outputs. These advancements demonstrate the potential of deep learning techniques to improve multi-target tracking systems.  
Furthermore, multi-sensor fusion technology is crucial in data association, facilitating information integration from diverse sensors, such as cameras, LiDAR, and millimeter-wave radar. This integration leads to a more detailed and accurate understanding of the environment, thereby enhancing the precision and stability of the data relationships. For instance, [PERSON] et al. (2019) developed a vehicle tracking technique that integrates cameras and LiDAR. This approach ensures robust vehicle surveillance in complex traffic scenarios by harnessing the precise distance measurements from LiDAR and the rich visual details captured by cameras.
## 5 Simulation Results and Analysis
### Radar Performance Analysis  
The radar performance evaluation is provided in two figures: the first figure compares the desired LFM waveform for radar and the far-field synthesized waveform, whereas the second gives the detection probability (pD) versus the SNR.  
The waveform synthesized in the radar direction is shown in Figure 9. As shown in Figure 9 (upper), the synthesized radar waveform and the desired radar waveform seem almost identical, which validates the efficiency of the proposed scheme. However, there  
Figure 8: The waveform modulus per iteration showing the constant modulus constraint is satisfied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Waveform Modulus** & **Normalized Waveform Error/dB** \\ \hline FFRED-0\% & Non-constant & \(-\)320.08 \\ FFRED-10\% & Constant & \(-\)34.08 \\ FFRED-40\% & Constant & \(-\)113.56 \\ MNO & Non-constant & \(-\)312.06 \\ IO & Constant & \(-\)39.40 \\ IO-AW & Constant & \(-\)40.90 \\ ADMM-based (Proposed) & Constant & \(-\)35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different methods for normalized waveform error.  
are small differences between the waveforms, shown in Figure 9 (lower). These differences or sample errors are defined as  
\[\mathbf{e}_{R}=\mathbf{x}_{R}-\mathbf{a}(\theta_{R})\mathbf{S}. \tag{40}\]  
Figure 10 shows the graph of detection probability plotted against the SNR. The probability of a false alarm is set as \(10^{-4}\). For comparison, the pD versus SNR graphs of other methods are also provided. As the figure shows, FFRED-40% [56] has the best pD, which is almost the same as that of the desired LFM. However, the graph of the proposed method is so close that the difference becomes visible upon zooming in on the plots. At the same time, the proposed method provides better pD than that of the directly normalized method [26].
## Appendix A Python Code to Compute Stress and Derivatives  
The implementation using Python of Equations (16) to (22) is proposed in Figure 11, where the arguments of the function stressAndDerivatives are xi for the \(\overrightarrow{\xi}\) vector, deps for \(\hat{\xi}\), Act for the activation function and dAct for its derivative.  
```
1 defstressAndDerivatives(xi,deps,Act,dAct):
2x=(xi-xi-xi0)/Dxi
3y1=w1.dot(x)+b1
4 yf1=Act(y1)
5y2=w2.dot(yf1)+b2
6 yf2=Act(y2)
7 Sig=Dsig*(w.dot(yf2)+b)+sig0
8 dSig=Dsig*((w1.T).dot((w2.T).dot(w.T*dAct(y2))*dAct(y1)))/Dxi
9 dSig[1]=dSig[1]/deps
10 returnSig,dSig
```  
The network architecture is defined by the numpy arrays w1, w2, w, b1, b2 and b, which are global variables in the proposed piece of code. The other variables xi0, Dxi, sig0 and Dsig correspond to the quantities \(\overrightarrow{\xi}_{0}\), \(\Delta\overrightarrow{\xi}\), \(\sigma_{0}\) and \(\Delta\sigma\), respectively.  
Line 2 in Figure 11 corresponds to Equation (16). Lines 3 and 4 correspond to Equations (17) and (18) and concern the first hidden layer, while lines 5 and 6 correspond to Equations (19) and (20) and concern the second hidden layer. Finally, line 7 computes the flow stress \(\sigma\) conforming to Equation (21) and lines 8 and 9 compute the three derivatives of the flow stress conforming to Equation (22). The stress Sig and the three derivatives array dSig are returned as a tuple at line 10.
## 3 Results and Discussion
### Statistical Analysis
Moving on to the perceived usefulness for sustainability of the previous practices, no significant differences were found across either the role of the participants or the size of the companies. There seems to be a consensus in the opinions expressed by the participants, regardless of their role or the size of their company. In other words, as shown in Section 3.1, there are practices that employees of software development companies believe could help companies to be sustainable and other practices in which they seem to have fewer expectations. Regarding the questions on the sustainability of the software development process that the participants apply in their companies, we only found significant differences in two items. On the one hand, participants with a technical role were less inclined (OR = 0.398, 0.171-0.924) to believe that problems with the use of energy were more difficult to discover, diagnose, and/or fix than other performance problems in comparison with participants with a non-technical role. On the other hand, the fact that participants took into account the different factors involved (use scenarios, environment, hardware, etc.) when the use of energy was being evaluated depended on whether those who did so were technical staff (OR = 0.385, 95% CI 0.148-1.000) or non-technical staff. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Set**} & \multirow{2}{*}{**\#Q**} & \multicolumn{2}{c}{**Profile**} & \multicolumn{2}{c}{**Size**} \\ \cline{3-6}  & & **Sig.** & **OR, 95\% CI** & **Sig.** & **OR, 95\% CI** \\ \hline \multirow{8}{*}{First} & 1 & 0.054 & 0.395, 0.151–1.031 & **0.018** & **0.328, 0.127-0.843** \\  & 2 & 0.569 & 1.567, 0.331–7.420 & 0.873 & 1.135, 0.240–5.374 \\  & 3 & 0.125 & 2.118, 0.803–5.584 & 0.642 & 0.801, 0.314–2.045 \\  & 4 & 0.795 & 0.860, 0.276–2.680 & **0.010** & **0.159, 0.033-0.754** \\  & 5 & **0.030** & **0.398, 0.171–0.924** & 0.076 & 0.474, 0.207–1.088 \\  & 6 & **0.009** & **0.324, 0.138–0.763** & 0.154 & 0.543, 0.234–1.261 \\  & 7 & 0.076 & 0.471, 0.204–1.088 & 0.154 & 0.543, 0.234–1.261 \\  & 8 & **0.017** & **0.357, 0.152–0.839** & 0.587 & 1.256, 0.551–2.862 \\  & 9 & 0.747 & 0.850, 0.316–2.284 & 0.561 & 0.743, 0.272–0.208 \\  & 10 & 0.123 & 0.500, 0.206–1.213 & **0.023** & **2.866, 1.142–7.193** \\  & 11 & 0.361 & 0.682, 0.300–1.552 & 0.753 & 1.141, 0.503–2.589 \\  & 12 & 0.584 & 0.704, 0.199–2.488 & 0.984 & 0.987, 0.279–3.489 \\  & 13 & 0.896 & 0.875, 0.118–6.486 & 0.395 & 0.381, 0.038–3.803 \\  & 14 & **0.044** & **0.419, 0.179–0.984** & 0.051 & 0.436, 0.188–1.012 \\  & 15 & 0.248 & 0.279, 0.028–2.784 & 0.861 & 1.195, 0.161–8.861 \\  & 16 & **0.000** & **0.089, 0.019–0.421** & 0.860 & 0.907, 0.307–2.681 \\  & 17 & 0.788 & 1.160, 0.393–3.427 & **0.017** & **0.219, 0.058–0.830** \\  & 18 & 0.484 & 0.429, 0.038–4.895 & 0.661 & 0.583, 0.051–6.663 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results of the statistical tests.
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 1 Introduction
In this paper, an LED converter with an output ripple eliminator is proposed to remove the double line frequency flicker of a T8 LED tube, while providing high power efficiency, high PF, low THD and precise output current regulation. Due to the addition of the output ripple eliminator, the power efficiency has been slightly reduced by an average of 2.7%. Nevertheless, the power efficiency is still more than 87% and light flicker is almost removed. The proposed LED converter is separated into AC and DC driving circuit and designed to mount in a G13 base and G13 base dummy. Because of the size limitations, we propose a practical method for product manufacturing by locating the output ripple eliminator on the LED module. In Section 2, the theory of the output ripple eliminator operation is explained. In Section 3, design specifications of the proposed LED converter are represented in detail. In Section 4, a 10 W prototype is introduced and experimental results are discussed. Finally, conclusions are given in Section 5. Figure 1: The block diagram of typical single-stage buck-boost light-emitting diode (LED) converter.
# A Thermopile-Based Colorimetric Temperature Measurement Method for Arbitrary Bandwidth
## 3 Mathematical Proof of Temperature Measurement Methods
### Thermopile-Based Colorimetric Thermometry with Arbitrary Bandwidth
\begin{cases}K_{1}(T)=\int_{\lambda_{a}}^{\lambda_{b}}M_{0}(\lambda,T)G_{1}( \lambda)d\lambda\\ K_{2}(T)=\int_{\lambda_{c}}^{\lambda_{d}}M_{0}(\lambda,T)G_{2}(\lambda)d\lambda \end{cases} , \tag{21}\]  
clearly, \(K_{1}(T)>0\), \(K_{2}(T)>0\), \(K_{1}(T)K_{2}(T)>0\), thereby according to Equation (20), the sign of \(\frac{d\ln O(T)}{dT}\) is determined solely by \(D(T)\). Discussing \(D(T)\) from Equation (20) individually:  
\[ \begin{array}{ll}D(T)&=\int_{\lambda_{c}}^{\lambda_{d}}M_{0}(\lambda_{1},T)G _{2}(\lambda_{1})d\lambda_{1}\int_{\lambda_{a}}^{\lambda_{b}}M_{0}(\lambda_{2},T)G_{1}(\lambda_{2})C_{2}{\lambda_{2}}^{-1}T^{-2}d\lambda_{2}\\ &-\int_{\lambda_{a}}^{\lambda_{b}}M_{0}(\lambda_{2},T)G_{1}(\lambda_{2})d \lambda_{2}\int_{\lambda_{c}}^{\lambda_{d}}M_{0}(\lambda_{1},T)G_{2}(\lambda_{ 1})C_{2}{\lambda_{1}}^{-1}T^{-2}d\lambda_{1}\\ &=\iint\limits_{D}C_{2}T^{-2}\Big{(}{\lambda_{2}}^{-1}-{\lambda_{1}}^{-1} \Big{)}M_{0}(\lambda_{1},T)M_{0}(\lambda_{2},T)G_{2}(\lambda_{1})G_{1}(\lambda _{2})d\lambda_{1}d\lambda_{2},\end{array}  \tag{22}\]  
observing Equation (22), it is evident that the sign of \(D(T)\) is also determined solely by \({\lambda_{2}}^{-1}-{\lambda_{1}}^{-1}\). Therefore, when the wavelength ranges of \([\lambda_{a},\ \lambda_{b}]\) and \([\lambda_{c},\ \lambda_{d}]\) do not overlap, \(Q(T)\) with respect to \(T\) is strictly monotonic. This means that regardless of the widths of the infrared bands absorbed by the two thermopiles, as long as the wavelength ranges of these bands do not overlap, the ratio \(Q(T)\) of the electric signals with respect to \(T\) is strictly monotonic. The temperature can be determined by using the ratio of electrical signals and the temperature relationship curve calibrated with a blackbody furnace. This method is based on the principle of colorimetric thermometry and allows for temperature measurement even when the spectral emissivity of the object is unknown. This method effectively reduces the impact of atmospheric media and background radiation on measurement results. In addition, compared to traditional narrow-band colorimetric thermometry, it utilizes thermopiles as infrared detectors, significantly lowering costs. Moreover, it covers a wider range of absorption bands, providing richer spectral information. It does not require expensive optical systems with complex manufacturing processes, thereby reducing the size of the temperature measurement system.
# 25 Years of Bluetooth Technology
## 2 Bluetooth Technologies
### Bluetooth Low Energy  
Bluetooth Low Energy (BLE) PHYsical layer (PHY) is a reduced and optimized version of Bluetooth BR PHY. While the BR PHY hops over 79 channels (with the possibility of reducing to 20 channels) and implements discovery over 32 channels, the BLE PHY takes 37 channels, while it performs device discovery on three channels. Since BLE goes via fewer channels while performing discovery, efficiency is achieved, resulting in quick connection establishment (compared to Bluetooth BR/EDR). BLE spacing of channels is 2 MHz in contrast to BR's 1 MHz, and this reduces demands on RF filtering.  
BLE connections are identical to sniff sub-rating mode in BR. Therefore, BLE has an energy-efficient method of keeping connectivity while minimizing active radio usage. BLE allows Integrated Circuit (IC) vendors to perform optimizations that are otherwise challenging with BR/EDR Bluetooth. These optimizations allow single-mode chips to be more energy efficient than dual-mode or classic chips. Comparatively, BLE profiles are layered over Generic Attribute Profile (GATT), using the GATT/ATT protocol. In contrast, Bluetooth BR/EDR profiles generally state their protocols, which provides better adaptability, however, makes the implementation more complex [3].  
There are three layers in the BLE stack: Controller, Host, and Application. The Physical and the Link layer comprise the Controller. The upper layer functionalities, the Logical Link Control and Adaptation Protocol (L2 CAP), the Attribute Protocol (ATT), the Generic Attribute Profile (GATT), the Security Manager (SM) and the Generic Access Profile (GAP) are located in the Host. Host Controller Interface (HCI) enables communication between the Host and the Controller. Applicationsreside above the Host layer. Figure 6 illustrates the BLE protocol stack. The L2 CAP layer performs segmentation; reassembly of packets. Device communication is defined by the ATT and the SM implements authentication, pairing and key distribution.  
Data to be transmitted is kept in a GATT server database. This data can be relocated to the GATT client. The application dictates whether the GATT server/client is kept on the master or the client node. GATT specifies a framework for communication between GATT server and client databases.  
Based on the hierarchy (_profile-service_, _characteristics-attributes_)), the information is organized on the GATT server. Attributes may be defined as small, addressable GATT data entities.  
A service includes features and is characterized via specific characteristics. Service groups are described using a _profile_. GAP identifies ways of discovering other BLE devices and describes four functions: advertiser, scanner or initiator, master and slave. Advertising, scanning, and initiating modes are defined in the discovery process to search BLE devices. The scanner searches only for the advertiser while the initiator requests a connection with the advertiser [8].
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 2 The PCA NN Anomaly Detection Approach
### Contaminated Time Series Identification  
The first step of the approach aims to identify the time series with anomalies based on the reconstruction errors obtained using PCA and Algorithm 1. In this section, we detail the process these new features undergo. We assign an anomaly score to each reconstruction error, whose computation is handled by a neural network (NN) (see [4]). We also numerically demonstrate that our NN has an advantage over a naive approach in terms of being able to assess more accurately the propensity of a time series to be contaminated.
# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
## 5 Results  
### Spatial Patterns of Vegetation  
Figure 3 shows the spatial distribution of average annual GPP and NPP for the study period (2004-2018). Both GPP and NPP show a strong latitudinal gradient increasing from low to high latitudes. Exceptionally, the most southern region, where site F is located, exhibits average annual GPP and NPP similar to those in the northern regions. Table 2 shows average annual GPP and NPP for the vegetation and forest types indicated in Figure 1c,d. The results reveal that the greatest GPP is found for broadleaved forests (1.6, 1.8, and 2.0 kg m\({}^{-2}\) y\({}^{-1}\) for EBF, HDBF, and LDBF, respectively), while the smallest GPP is found for sparse vegetation and rainfed crops, both presenting GPP = 0.6 kg m\({}^{-2}\) y\({}^{-1}\). The greatest forest NPP is also found for broadleaved forests (0.4, 0.5, and 0.8 kg m\({}^{-2}\) y\({}^{-1}\) for EBF, HDBF, and LDBF, respectively). Needle-leaved forests reach NPP values up to half of the broadleaved forests NPP (0.2 kg m\({}^{-2}\) y\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**V Vegetation Type** & **GPP (kg m\({}^{-2}\) y\({}^{-1}\))** & **NPP (kg m\({}^{-2}\) y\({}^{-1}\))** \\ \hline SPV & 0.6 (0.3) & \\ GRA & 0.8 (0.3) & \\ SHR & 0.7 (0.2) & \\ CRO & 0.6 (0.3) & \\ ICRO & 1.4 (0.4) & \\ EBF & 1.6 (0.5) & 0.4 (0.3) \\ LDBF & 2.0 (0.3) & 0.8 (0.2) \\ HDBF & 1.8 (0.4) & 0.5 (0.2) \\ LENF & 1.2 (0.3) & 0.20 (0.16) \\ HENF & 1.1 (0.3) & 0.20 (0.14) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average annual GPP and NPP. Only pixels with coherent classification between Figures 1c and 1d were used in the calculations of the means and the standard deviations (between brackets). SPV (sparse vegetation), GRA (grasslands), SHR (shrutblands), CRO (rainfed croplands), ICRO (irrigated crops), EBF (evergreen broadleaved forest), LDBF (low-altitude deciduous broadleaved forest), HDBF (high-altitude deciduous broadleaved forest), LENF (low-altitude evergreen needle-leaved forest), and HENF (high-altitude evergreen needle-leaved forest).