esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Among these schemes, the plasma-based all optical [17; 34] and laser electron collision schemes [17; 30; 36] are the most fascinating for current laser intensities and upcoming laser electron QED experiments at high power laser platforms around the world [3; 18; 37; 38; 39; 40]. When a high-energy electron (an electron bunch) collides with a high-intensity laser, the photons can be produced via the NCS process. For a high-intensity laser \(a_{0}\gg 1\), where \(a_{0}\) is the normalized laser intensity, the photon formation length is significantly smaller than the laser wavelength and the laser field can be approximated as a constant field for photon formation. The process can be addressed using the locally constant field approximation (LCFA) [41]. In the laser-based photon production schemes, the energy and polarization of the photon can be controlled by either incoming seed electron or incident laser parameters. Recently, several proposals have been put forward to detect vacuum birefringence using GeV-level highly polarized photons [31]. In this study, we investigated the generation of a linearly polarized \(\gamma\) photon with GeV energy by an intense laser colliding with an unpolarized electron bunch. We simulated the photon emission process of the laser-electron collision using QED particle-in-cell (PIC) simulation, and the photon polarization process was calculated by applying the QED theory. We considered incoming electron bunches with various initial energies within the range of 1-10 GeV, which can be obtained in present day laser wakefield acceleration experiments [9; 42]. The results indicated that the production rate and energy of the produced photons could be enhanced by increasing the laser intensity and electron initial energies. However, high laser intensity and initial beam energy could not yield a high photon polarization degree. In particular, the photon polarization degree reached its maximum (approximately 63%) with an energy exceeding 1 GeV in the case of a low laser intensity of \(a_{0}=50\) with an initial electron beam energy of 5 GeV. The rest of this paper is organized as follows: In Section 2, we present the simulation model, simulation parameters, and simulation results for the laser-electron bunch collision process. In Section 3, we first briefly review and present key equations for the photon polarization process and develop a QED calculation model of the photon polarization. Subsequently, the results of QED calculations for different electron energies and laser intensities are presented and their physical implications are discussed. Comparisons of the results obtained from PIC simulation and QED calculation are presented in the final subsection of Section 3. Finally, the main conclusions are presented in Section 4.
## 1 Introduction
Condition-based maintenance is proposed to perform daily maintenance according to the needs of machines and components, as well as predictive and health management using metrics to monitor the evolution of component wear. However, most of this series of neural networks combined with life prediction methods are based on the application of bearings, batteries, and so on. The combination of direct-drive electro-hydraulic servo valves is rarely applied, and the influence of different influencing factors, such as temperature and differential pressure, on their service life still needs to be better understood. As can be seen from the above literature, there needs to be more research in the current industry on the performance degradation and life prediction of the spool sleeve of direct-drive electro-hydraulic servo valves, especially in combination with information age neural networks for their life prediction. In addition, it is unclear how the performance degradation and life prediction pattern of the direct-drive servo valve spool sleeve changes under different parameters such as differential pressure, flow rate, and opening degree. Since direct-drive electro-hydraulic servo valves are one of the core control elements in aerospace or remote control, further performance degradation and life prediction of the spool valve sleeve for direct-drive electro-hydraulic servo valves is of great significance in the whole field of electro-hydraulic industrial management, based on a previous study of the erosion and wear pattern of direct-drive electro-hydraulic servo valves [24]. This paper starts from a direct-drive electro-hydraulic servo valve, uses the performance degradation law obtained from the predecessor for experimental verification, then combines the experimental data to design a prediction model for the dynamic prediction of its performance degradation index, and finally obtains its law.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Lightweight Cryptographic Solutions
#### 10.2.2 RL for Cybersecurity  
Reactive learning improves response and decision-making processes in the face of cyberattacks. RL algorithms acquire knowledge by interacting with their surroundings and obtaining feedback through incentives or punishments according to their activities. Real-time defensive adaptation, policy optimization, and autonomous detection and response to anomalies are all possible using reinforcement learning in cybersecurity applications. Reactive learning works especially well in complex and dynamic contexts like smart grids, where threats change quickly, and proactive defenses are needed to protect vital infrastructure.
# Congestion Control in CoAP Observe Group Communication
###### Abstract  
The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition.  
Internet of Things; constrained application protocol; wireless sensor networks; congestion control; observing resource; group communication +
Footnote †: journal: Sensors
# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
###### Abstract
Both methods rely on a post-processing operation called non-maxima suppression (NMS), which can effectively eliminate redundant detections but is hard to train and computationally expensive. To address this problem, a new algorithm flavor that focuses on keypoints estimation [13; 20] is proposed. A representative approach, CenterNet [20], first predicts the object center point and then regresses other properties such as object size and orientation. Since bounding boxes are not the direct output, NMS is not needed, which speeds up training. CenterNet has been successfully applied to the dataset under investigation and achieved the state-of-the-art (SOTA) performance. Inspired by the prior efforts, we propose a deep neural network (DNN) architecture for object detection named recursive composite network with a bi-directional feature pyramid. Since the detector part of our model adopts the same idea as CenterNet, we name our model RCBi-CenterNet. The proposed network contains a dual-backbone feature extractor [14] and a bi-directional feature pyramid network (BiFPN) [21] block to extract and fuse cross-scale features. In addition, the model utilizes feedback connections [22] to recursively send output features back to the dual-backbone network to repeat the feature extraction process. This way, an image is looked at and processed twice or more. Due to the inheritance of CenterNet, our model can directly predict the absolute 3D pose of objects without bounding boxes. The contributions of this work are summarized as follows:  
Figure 1: Vehicle object detection: (**a**) raw image; and (**b–e**) vehicle objects labeled by 2D, 3D bounding boxes, keypoints, and 3D masks. * We propose a DNN architecture named RCBi-CenterNet to tackle the object detection problem by predicting the absolute pose of a detected vehicle object in autonomous driving. The model is powered by a recursive composite feature extractor with a BiFPN module to effectively extract, fuse, and represent image features. * We conducted extensive experiments to justify the design choices of augmentation methods and the optimal backbone. In addition, we evaluated the effect of each integrated module via an ablation study. The overall performance of RCBi-CenterNet outperforms CenterNet by 2.16%, 2.76%, and 5.24% in Top 1, Top 3, and Top 10 mean average precision (mAP), respectively. Our method can serve as a credible benchmark for future research in center point-based objection detection. We open-sourced our code at [[https://github.com/YixinChen-AI/RCBi_centernet](https://github.com/YixinChen-AI/RCBi_centernet)]([https://github.com/YixinChen-AI/RCBi_centernet](https://github.com/YixinChen-AI/RCBi_centernet)) (accessed on 2 June 2020) for public access.
## 4 Discussion
When comparing the two LSTM-based architectures, we found that the UNet-LSTM-UNet architecture has similar performance to the LSTM-UNet architecture alone, with non-significantly different precision (\(p\) value = 0.96), Dice coefficient (\(p\) value = 0.99), recall (\(p\) value = 0.61), accuracy (\(p\) value = 0.99) and AUC (\(p\) value = 0.65), indicating that increased parameters alone are not guaranteed to significantly improve performance. In fact, when factoring in the cost of increased evaluation time by the UNet-LSTM-UNet model, our work suggests that the LSTM-UNet approach is superior to the higher capacity model. Additionally, while the LSTM-based models demonstrate greater segmentation performance than the UNet alone, the evaluation time for a 30-slice volume (184.8 or 241.7 s) was 100-200 times longer than the projection segmentation (1.6 s). These differences indicate a trade-off between segmentation performance and speed inherent in the two architectures. Whether the time cost of LSTM-based models is a significant barrier for real-world segmentation and outweighs the benefit of greater fidelity performance will be an important consideration when implementing these models for research or clinical use in the future. This work is not without limitations. Our sample size represents 177 volumes collected from a limited cohort of 18 patients. We note that this is a substantial number of volumes and on a greater or similar scale to previously published OCTA vessel segmentation datasets [47] and that theoretically the computer vision task of classifying pixels from grayscale images should be agnostic to the individual patient identity or disease state. However, more studies will be needed to ensure these results generalize to a greater subject population and perform consistently across glaucoma and control eyes separately. The design of studies to ensure no differences in model segmentation performance of glaucoma and control eyes is especially important if these tools are intended to be utilized to quantify biomarkers between the two disease states. Additionally, as our ground-truth labels were derived from annotating the 2D projection of each volume, rather than 3D annotations with masks for each slice, there is the possibility of human judgement impacting our model's training and performance. However, any error in manual labeling resulting from tracing the 2D projection would more likely bias our results towards the 2D-2D UNet alone, yet our 3D-2D approach remains significantly superior.
## 2 Related Works
Other works such as [26] studied the effect of depth on CNN models with two variations of depths, while the authors of [27] examined the role of depth in CNNs, and another study [28] studied optimization in deep CNNs where the authors observed that increasing depth increases representational power while increasing width smoothes the optimization landscape. A paper by [19] looked at the potential pitfall of adaptive gradient methods finding solutions that generalize worse than SGD despite having the same training loss, and argued against the increasing use of adaptive gradient methods, specifically ADAM, by the deep learning community in all scenarios. The authors of that paper also observed that adaptive learning rate methods often exhibit faster initial progress during training, but their learning performance quickly reaches a critical saturation point on the test set. This has led to some researchers advocating the use of both adaptive methods such as ADAM at the initial stages of training and switching to non-adaptive SGD at a later stage to improve generalization [29]. However, the works of [30] observed that with sufficiently tuned hyperparameters, adaptive methods would never underperform momentum or SGD, while [31] concludes that with sufficiently tuned hyperparameters, standard optimizers such as SGD and ADAM would never underperform state-of-art optimizers for large batch size settings. However, it all comes at the expense of higher computing costs. While all of these works acknowledged that optimizers do impact the performances of different neural network architectures across various theoretical and empirical settings, they did not provide an explicit connection on the effect that different optimizers have had on the increasing complexity of CNN architectures in terms of varying width, depth and width/depth, which is the focus of the current work. Table 1 is a summary of the most relevant works referenced in this study in terms of the models, optimizers, image dataset used and their contributions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Model** & **Optimizer** & **Image Dataset** & **Contribution** \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{Fully connected CNN} & \multirow{3}{*}{SGD, momentum} & CIFAR-10, MNIST, and SVHN & Double descent risk curve that reconciles the bias-variance tradeoff curve and observed behaviors of modern machine learning models \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{ResNet, CNN} & ADAM, SGD, momentum & CIFAR10 and ImageNet32 & Deep networks may have an increased risk of poor generalization on testing data sets \\ \cline{3-5}  & & & & Deep and wide models may have similar overall accuracy, but they do exhibit distinctive error patterns and variations across different classes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant works.
# Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters
## 2 Mechanism of Pipeline across Landslides  
In past studies on pipeline mechanics across landslides, uniform landslide load and parabolic load distributions have been validated [13; 17]. However, there are limitations in fully accounting for the sudden nature of landslides and the entire stress-strain response of pipelines. The fourth-power parabolic curve of displacement [18], due to its robust validation, has been widely used in recent years. As shown in Figure 1, this section proceeds with research based on this theory.  
Soil displacement is symmetrical to the \(Y\)-axis, calculated as  
\[\left\{\begin{array}{l}D_{x}=\frac{2D_{c}}{w_{1}^{2}}(x-w_{1})^{2}-\frac{D_{ c}}{w_{1}^{4}}(x-w_{1})^{4},0\leq x\leq w_{1}\\ D_{x}=\frac{2D_{c}}{w_{2}^{2}}(x+w_{2})^{2}-\frac{D_{c}}{w_{2}^{2}}(x+w_{2})^{ 4},-w_{2}\leq x\leq 0\end{array}\right. \tag{1}\]  
where \(D_{x}\) is the soil displacement at position \(x\) from the landslide center, m; \(D_{c}\) is the soil displacement at the landslide center, m; \(w_{1}\) and \(w_{2}\) are the distances from the two ends of the landslide to its center, m. Substituting into the pipeline differential equation gives  
\[EI\frac{d^{4}\gamma_{1}}{dx^{4}}-T\frac{d^{2}\gamma_{1}}{dx^{2}}+k_{1}\gamma_{ 1}=k_{1}(\frac{2D_{c}}{x_{1}^{2}}(x-w_{1})^{2}-\frac{D_{c}}{x_{1}^{4}}\Big{(} x-w_{1})^{4}\Big{)} \tag{2}\]  
\[q(x)=k_{1}(\gamma_{1}-D_{x}) \tag{3}\]  
Solving the equation yields  
\[\begin{array}{l}\gamma_{1}(x)=e^{a_{1}x}(c_{1}\text{cos}\beta_{1}x+c_{2} \text{sin}\beta_{1}x)+(c_{3}\text{cos}\beta_{1}x+c_{4}\text{sin}\beta_{1}x)\\ e^{-a_{1}x}\frac{D_{c}}{w_{1}}[(x-w_{1})^{4}-\frac{24 EI}{k_{1}}+\frac{12T}{k _{1}}\bigg{(}(x-w_{1})^{2}+\frac{24T^{2}}{k_{1}^{2}}\bigg{]}\end{array} \tag{4}\]  
where \(a_{1}=\sqrt{\sqrt{k_{1}/4 EI+T/4 EI}}\) and \(\beta_{1}=\sqrt{\sqrt{k_{1}/4 EI-T/4 EI}}\), and given that \(T\leq 2\sqrt{k_{1}EI}\), \(E\) is the modulus of elasticity, N/m\({}^{2}\); \(I\) is the moment of inertia, m\({}^{4}\); \(\gamma_{1}\) is the deflection at position \(x\) from the landslide center, m; \(q(x)\) is the lateral soil pressure at that position, KN; and \(T\) is the axial tension, KN. Similarly for non-landslide affected pipelines, the solution is  
\[EI\frac{d^{4}\gamma_{2}}{dx^{4}}+k_{2}\gamma_{2}=0 \tag{5}\]  
\[q(x)=k_{2}\gamma_{2} \tag{6}\]  
\[\gamma_{2}(x)=e^{-\lambda x}(c_{5}\text{cos}\lambda x+c_{6}\text{sin}\lambda x) \tag{7}\]  
where \(\lambda=\sqrt[4]{k_{2}/4 EI}\), and constants \(c_{1}\text{-}c_{6}\) can be determined using boundary conditions.  
Figure 1: Landslide and displacement distribution: (**a**) landslide model and (**b**) fourth-power parabolic curve.
## 2 Method
### Time Step and Periodic Repeatability  
To specify the optimum time step in the unsteady simulations, a series case with different time steps was simulated. A characteristic time \(T_{c}\) defined by Equation (3) is employed as base of the time step:  
\[T_{c}=\ \frac{c}{U_{\infty}} \tag{3}\]  
It has the same significance as the Courant, Friedrichs and Levy criterion (CFL) number in numerical simulations. The time steps of \(0.1\mathrm{T}_{c}\), \(0.05\mathrm{T}_{c}\), \(0.025\mathrm{T}_{c}\), \(0.0125\mathrm{T}_{c}\) and \(0.00625\mathrm{T}_{c}\) (denoted as dt1 to dt5, respectively) are used to examine the time step sensitivity.  
As shown in Figure 4, when the time step dt4 = \(6.25\times 10^{-5}\) s, the time cost and accuracy are acceptable for the present study. This is consistent with dynamic stall cases applied by other researchers [17; 38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grid** & **Suction Surface** & **Pressure Surface** & **Trailing Edge** & **Total Grids** \\ \hline G1 & 50 1  & 50 & 11 & 25,753 \\ G2 & 100 & 100 & 11 & 45,387 \\ G3 & 150 & 150 & 11 & 67,238 \\ G4 & 200 & 200 & 11 & 89,070 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cases for grid sensitivity examination.  
Figure 4: Comparison of computed \(C_{l}\) with different time steps.  
Figure 3: Comparison of computed (**a**) \(C_{l}\) and (**b**) \(C_{d}\) with different grid densities during the 4 th cycle of pitching motion.  
Periodic repeatability is also examined, as shown in Figure 5. It shows that values tend to be well reproducible from the third cycle. That is why the other cases are computed only for 4 periods.
## 2 Materials and Methods
### Data Augmentation  
To prevent overfitting in the training of the deep learning model, we utilized the following three types of data augmentation methods: conventional method, mixup [16], and RICAP [17]. Although mixup and RICAP were initially proposed for image classification tasks, we utilized them for segmentation by merging or cropping/patching labels in the same way as is done for images.  
Conventional augmentation methods included \(\pm 5^{\circ}\) rotation, \(\pm 5\%\) x-axis shift, \(\pm 5\%\) y-axis shift, and 95%-105% scaling. Both image and label were changed by the same transformation when using a conventional augmentation method.  
Figure 1: Illustration of the deep U-net model. The number of downsampling and upsampling is 6 in the deep U-net. Except for the last convolution layer, dropout and convolution layer are coupled. Abbreviations: convolution layer (conv), maxpooling layer (maxpool), upsampling and convolution layer (up conv), rectified linear unit (ReLU).  
Mixup generates a new training sample from linear combination of existing images and their labels [16]. Here, two sets of training samples are denoted by \((x,y)\) and \((x^{\prime},y^{\prime})\), where \(x\) and \(x^{\prime}\) are images, and \(y\) and \(y^{\prime}\) are their labels. A generated sample \((x^{\#},y^{\#})\) is given by:  
\[x^{\#} = \lambda x+(1-\lambda)x^{\prime} \tag{1}\] \[y^{\#} = \lambda y+(1-\lambda)y^{\prime} \tag{2}\]  
where \(\lambda\) ranges from 0 to 1 and is distributed according to beta distribution: \(\lambda\)_-Beta_(\(\beta\), \(\beta\)) for \(\beta\in(0,\,\infty)\). The two samples to be combined are selected randomly from the training data. The hyperparameter \(\beta\) of mixup was set to 0.2 empirically.  
RICAP generates a new training sample from four randomly selected images [17]. The four images are randomly cropped and patched according to a boundary position (\(w\), \(h\)), which is determined according to beta distribution: \(w\)_-Beta_(\(\beta\), \(\beta\)) and \(h\)_-Beta_(\(\beta\), \(\beta\)). We set the hyperparameter \(\beta\) of RICAP to 0.4 empirically. For four images to be combined, the coordinates \((x_{k},\,y_{k})\) (\(k\) = 1, 2, 3, and 4) of the upper left corners of the cropped areas are randomly selected. The sizes of the four cropped images are determined based on the value \((w,h)\), such that they do not increase the original image size. A generated sample is obtained by combining the four cropped images. In the current study, the image and its label were cropped at the same coordinate and size.