esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
developed a reliable method which was performed in terms of the modulation transfer function, noise power spectrum, and noise equivalent quanta under different \(L_{p}\)-norm priors in sparse-view X-ray CT reconstruction, and concluded that the best noise-resolution performance is achieved when \(p\) is between 0.8 and 1 [37]. Although using the \(L_{p}\) norm leads to non-convex optimization problems, it might allow efficient image reconstruction with a few projection datasets for radiation dose reduction [38]. In this study, we explore the quaternion total variation and \(L_{p}\) quasinorm relaxation to improve the sparsity exploitation of FTV; our proposed method is referred to as the quaternion fractional TV with \(L_{p}\) quasinorm (FTV4L\({}_{p}\)), which is efficiently solved through plug-and-play alternating direction method of multipliers (ADMM) [39; 40; 41; 42; 43] in conjunction with non-convex \(p\)-shrinkage mapping. The novelty of our work is three-fold. First, the FTV4L\({}_{p}\) method is far less restrictive than the TV4 and FTV4 methods for infrared image reconstruction; it not only shows good performance in terms of detail preservation by incorporating high-order image derivatives, but also achieves accurate measurement of the sparsity potential from the regularity prior. Second, an efficient iterative algorithm is proposed to optimize the plug-and-play ADMM with a fast and stable convergence result. Third, fast and efficient closed-form solutions are investigated and derived for computationally complex sub-minimization problems using fast Fourier transforms (FFT). The remainder of this paper is organized as follows. Section 2 briefly introduces the FTV method, TV4 method, and constrained TVp minimization method. Section 3 describes the proposed method as well as the fast plug-and-play ADMM algorithm for infrared image SRR. Then, in Section 4, our experiments and results are described. Finally, Sections 5 and 6 present our discussion and conclusions, respectively.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 3 Satellite Development Lessons
### Payload Integration  
Proper interface documentation is mandatory in multi-partner collaborations as soon as possible and must be managed and reviewed for the project's duration. As the ESTCube team had worked together with payload providers since the beginning of the project, we saw that most complications came from the bad interface specifications.  
Even though the payload interface requirements were agreed upon at the beginning of the project, none of the payload teams could deliver everything as initially planned. As we did not review the status of payload interface readiness, this led to problems during the final stages of payload integration. To fix many payload problems, we had to outsource our existing developers to other payload teams. Our team developed parts of the payloads, software, and hardware, reducing our development capabilities temporalily.  
Due to unforeseen funding problems, the initial high-speed communications system payload was dropped late in the development process of the satellite. We solved this by finding a COTS replacement solution and procuring it using reserved funding.  
The CGP system supplier could procure a suboptimal system for the ESTCube-2 mission because we could not afford a custom configuration development quoted by the manufacturer. Due to this, the team decided to use the CGP system only for the ESTCube-2 RWs desaturation, which will be used to spin up the satellite to deploy the IPB tether using centrifugal force as the nozzles of the CGP system face in the bottom Z-axis direction (see Figure 7). This is a suboptimal solution as this cold-gas propulsion system has been designed for LEO satellite deorbiting rather than attitude control. As the system is not mission-critical, we decided to keep using this system, as finding a different one would require a major satellite redesign. The simulations made by the AOCS team show that the spin-up of the satellite is possible in this way [19; 20; 45; 46].For an optimal solution, nozzles pointing in the Y-axis direction could be used for both RW desaturation and satellite spin-up (see Figure 7).
## 3 The Integrated Sensing and Communication Platform
### Communication Service Requirements  
As discussed in detail in Section 1, in order to avoid fires and explosions in the presence of faults and gas leakages, it is necessary to promptly communicate to the CC any anomalous concentration of gases. In the considered system, this kind of data is referred to as UPs. Accordingly, UPs are characterized by stringent requirements in terms of reliability and latency. More specifically, in the scenario at hand we have identified as minimum requirements a packet loss rate (PLR) and end-to-end latency (l) of 0.1% and 500 ms, respectively. Moreover, basing on the required resolution of the data collected from the sensors, the UPs payload is set to 20 bytes. Basing on these requirements, we have limited our LoRaWAN system to use only SF7-10, since with SF11 and SF12 the time on air exceeds 500 ms [29]. Finally, packets retransmissions are not allowed for UP packets which, as such, can be transmitted in UNCONF mode only.
## 3 Aim, Materials, and Methods
Emergency broadcasting plays a crucial role in disseminating national policies, managing emergencies effectively, governing society, and constructing spiritual civilization by bridging information gaps at local levels while ensuring precise mobilization. It also facilitates effective communication channels that foster innovative development within rural spiritual civilization construction efforts. In line with this emphasis on rural revitalization, Gansu Province has issued its own opinions advocating accelerated progress in constructing rural emergency management information systems based on implementing Spirit of Central Document No. 1 in 2022 according to recommendations from the CPC Emergency Management Committee. China aims to empower rural emergency management through digital technologies by extending smart fire control measures into rural areas while also expediting the implementation and utilization of an information-based monitoring system. According to the theory of emergency management, this paper establishes the framework of a rural intelligent emergency management model. The principles of emergency management and collaborative governance theory are seamlessly integrated, effectively harnessing the strengths of government, medical institutions, enterprises, and other departments to achieve synergistic effects. Drawing on the key concepts from crisis theory regarding reduction force, we construct the fundamental framework for a rural natural disaster intelligent emergency management model. This includes an integrated intelligent perception mode encompassing both aerial and ground-based observations, a command and scheduling mode that leverages communication networks enhanced by multiple elements, an emergency material scheduling mode based on machine learning algorithms, and a dynamic deduction approach for hybrid transportation scheduling under complex disasters, as well as a rescue team scheduling mode that integrates multidimensional resources (Figure 1). The fundamental architecture of the intelligent emergency management mode for rural natural disasters encompasses the integrated intelligent perception mode of \"sky and ground\", the command and scheduling mode that enhances communication network plurality, the machine learning-based emergency material scheduling mode, the dynamic deduction-based hybrid transportation scheduling mode under complex disasters, and the multi-dimensional resource integration-driven rescue team scheduling mode, as depicted in Figure 1.
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
## 6 Results  
### Evaluations  
This experiment verified the capacity proportional to the number of processors. For every configuration, 1000 tests were conducted on the comparison method. Every experiment casually generates a DAG task. The standardized worst-case maximum CT is used  
Figure 4: Illustration of Hierarchical scheduling.  
Figure 5: The architecture of virtualization platform.  
as an index. Figure 6 shows the worst-case maximum CT of the existing method and the proposed method under the different numeral of processors on the DAG generated at \(p=8\). When \(m\leq 4\), rta-cpf provides similar results to the classical bound, that is, most of its results are the upper bound of the classical bound. This is because the parallelism of DAG is limited when the numeral of cores is small, so every NC-joint has a long worst-case CT (in Formula (3)). This leads to a lower \(\alpha_{i}\) limit (a higher \(\beta_{i}\) limit) for every supplier, so the worst-case maximum CT is longer. With the further increase of \(m\), rta-cpf becomes effective (\(m=6\)), and in the case of \(m=7.8\), it exceeds the classical limit by \(15.7\%\) and \(16.2\%\) (and as high as \(31.7\%\) and \(32.2\%\)) on average. In this case, more workloads can be executed in parallel with the CP, that is, the increase of \(\alpha_{i}\) and the decrease of \(\beta_{i}\). Therefore, rta-cpf leads to stricter results by explicitly considering this workload, thus safely reducing the interference on the CP. Similar results were obtained in the comparison between rta-cpf-eo and He2019, in which rta-cpf-eo provided a shorter worst-case CT approximation when \(m\geq 4\), for example, it was as high as \(11.1\%\) and \(12.0\%\) when \(m=7\) and \(m=8\), respectively. We note that the execution order of joints in the two methods will also affect the limit of analyzing the worst case. We compare the scheduling and analysis methods respectively. In short, when \(m=7\), rta-cpf provides similar results to it, and is superior to it when \(m=8\). The result shows the usefulness of the proposed method.
## 2 Numerical Methods and Target Model  
### Large Eddy Simulation  
In this section, LES is briefly introduced. LES with the Smagorinsky-Lilly model was used to solve the governing equation predicting the external flow and acoustic field around a train body. LES directly computes only large eddies, and hence a low-pass spatial filter was applied to the conservation equations to formulate the unsteady governing equations for large scale motion. The three-dimensional, compressible, unsteady LES governing equations with the turbulent viscosity of the Smagorinsky-Lilly model can be written in the forms below:  
\[\begin{array}{c}\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_ {i}}\left(\rho u_{i}\right)=0\\ \frac{\partial}{\partial t}\left(\rho u_{i}\right)+\frac{\partial}{\partial x_ {j}}\left(\rho u_{i}^{\ast}\bar{u}_{j}\right)=\frac{\partial}{\partial x_{i}} \left(\alpha_{ij}\right)-\frac{\partial  
\[\mu_{t}=\rho L_{s}^{2}\bigg{|}\widetilde{S}\bigg{|}\textit{ where, }\bigg{|}\widetilde{S}\bigg{|}=\sqrt{2\widetilde{S}_{ij}\widetilde{S}_{ij}} \tag{2}\]  
where \(\overline{S}_{ij}\) is the rate of the strain tensor and \(L_{s}\) is the mixing length, which represents the length scale of turbulence components smaller than the grid size. These terms can be written in the following form.  
\[\widetilde{S}_{ij}=\frac{1}{2}\Bigg{(}\frac{\partial\widetilde{u}_{i}}{ \partial\widetilde{x}_{j}}+\frac{\partial\widetilde{u}_{j}}{\partial\widetilde {x}_{i}}\Bigg{)} \tag{3}\]  
\[L_{s}=min(\kappa d,C_{s}\Delta),\Delta=V^{1/3} \tag{4}\]  
where \(\kappa\) is the von Karman constant, d is the distance to the nearest wall, \(C_{s}\) is the Smagorinsky constant, and \(\Delta\) is the local grid size. The discretization methods are listed in Table 1. The LES was numerically realized using the commercial software ANSYS Fluent (version: 18.0).
## 2 Materials and Methods
### Primary Dataset Preparation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Description** & **Possible Values** \\ \hline _F1—Domain length_ & Number of symbols in the host domain name. & Number, [7... 38] * \\ \hline _F2—Top domain length_ & Number of symbols in the top domain name. & Number, [2... 13] * \\ \hline _F3—Presence of prefix “www”_ & Presence of the prefix “www” in the active URL of the online shop. & \(\{0,1\}\) \\ \hline _F4—Number of digits_ & Number of digits in the URL. & Number, [0... 4] * \\ \hline _F5—Number of letters_ & Number of letters in the URL. & Number, [11... 39] * \\ \hline _F6—Number of dots (.)_ & Number of dots in the URL. & Number, [1... 3] * \\ \hline _F7—Number of hyphens (-)_ & Number of hyphens in the URL. & Number, [0... 4] * \\ \hline _F8—Presence of credit card payment_ & Presence of payment methods, which offer the consumer the option to pay using credit cards. & \(\{0,1\}\) \\ \hline _F9—Presence of money back payment_ & Presence of payment methods, which offer the consumer the option of getting their money back. & \(\{0,1\}\) \\ \hline _F10—Presence of cash on delivery payment_ & Presence of payment methods, which allow the consumer to pay for goods once they are received. & \(\{0,1\}\) \\ \hline _F11—Presence of crypto currency_ & Presence of the ability to use cryptocurrencies for payments. & \(\{0,1\}\) \\ \hline _F12—Presence of free contact emails_ & Indication of whether public e-mail services are used for contact e-mail. & \(\{0,1,2,3\}\) \\ \hline _F13—Presence of logo URL_ & Indication of whether the website uses its own favicon, which is associated with the online shop logo and is shown in the browser’s address bar. & \(\{0,1\}\) \\ \hline _F14—SSL certificate issuer organization_ & The ID of the organization of the SSL certificate issuer: 1—Cloudflare, Inc., 2—Let’s Encrypt, 3—Sectigo Limited, 4—Panel, Inc., 5—GoDaddy,com, Inc., 6—Amazon, 7—DigiCert, Inc., 8—GlobalSign nv-sa, 9—Google Trust Services LLC, 10—ZeroSSL, 11—other organization. & \(\{0,1\}\) \\ \hline _F15—Indication of young domain_ & Shows whether the domain is young, registered 400 days ago or later. Due to data protection, not all domain owners provide a date of registration; such domains are identified using a special value ’hidden’. The domain registration date comes from the WHOIS database. & \(\{0,1,2\}\) \\ \hline _F16—Presence of TrustPilot reviews_ & Indicates whether the website has at least one review on the TrustPilot platform. & \(\{0,1\}\) \\ \hline _F17—Presence of SiteJaber reviews_ & Indicates whether the website has at least one review on the SiteJabber platform. & \(\{0,1\}\) \\ \hline _F18—Presence in the standard Tranco list_ & Indicates whether the domain of the website is included in the standard Tranco list based on the average number of visits. & \(\{0,1\}\) \\ \hline \hline \end{tabular}  
* Value ranges in the used dataset.  
\end{table} 
Table 1: Description of the features used in this study.
## 2 Materials and Methods
### Classification
* Bagging classifier Bagging classifiers [38] are ensemble meta-estimators that apply basic classifiers to random subsets of the original dataset before aggregating their predictions (either by vote or average) to generate a final prediction. A meta-estimator that incorporates randomness into the building technique of a black-box estimator (e.g., a decision tree) can be used to reduce the variance of a black-box estimator. In this study, the base estimator is a linear SVM, and the number of base estimators is 10. * Adaboost classifier An AdaBoost classifier [39] is a meta-estimator that begins by fitting a classifier on the original dataset before fitting consecutive copies of the classifier on the same dataset, but it modifies the weights of incorrectly classified cases such that subsequent classifiers focus more on challenging circumstances. In this study, the base estimator used is the CART decision tree, and the number of base estimators is 100. * Neural networks (NN) The multilayer perceptron (MLP) [39] is a type of artificial neural network that is used to simulate complicated functions. It has three or more layers of nodes, including an input layer, one or more hidden levels and an output layer, as shown in Figure 3. MLP, in contrast to conventional techniques, does not need any previous assumptions about the distribution of training data, which eliminates the impact of data distribution on performance. The activation function converts input to output. A cost function is used to determine the best parameter values. To enhance the model, the network is run many times. The backpropagation method aids in the learning of NN parameters. In this study, to determine the best parameters, the Adam optimizer is utilized. In the hidden layers, the rectified linear unit (ReLU) activation function is employed, and, in the output layer, the sigmoid activation function is used to offer a prediction between 0 and 1, with a value of larger than 0.5 indicating malignancy and a value of less than 0.5 indicating benignity.
## 5 Variational Bayesian
### Parameters Estimation Based on VB
#### 5.2.2 Parameter Estimation of vM Distribution  
We can get the noisy heading of AUV by the angle sensor. we build a vector which constitutes the cosine set and sine set of AUV heading, i.e., \(\mathbf{L}=\left[cos\left(\psi_{v,k}\right),sin\left(\psi_{v,k}\right)\right]_{1: K}^{T}\). The cosine set and sine set of MRS heading is obtained by:  
\[\mathbf{L}=\left[\frac{\mathbf{x}_{k}\left(4,1\right)}{\sqrt{\mathbf{x}_{k}\left(6,1 \right)^{2}+\mathbf{x}_{k}\left(4,1\right)^{2}}}\ \frac{\mathbf{x}_{k}\left(6,1\right)}{\sqrt{\mathbf{x}_{k}\left(6,1\right)^{2}+\mathbf{x}_ {k}\left(4,1\right)^{2}}}\right]^{T} \tag{27}\]  
The goal is to infer the posterior distribution for the mean direction \(\mu\) and concentration parameter \(\lambda\) given the set \(L\). The generative model is given by the joint distribution of all random variables as:  
\[p\left(\mathbf{L},\mathbf{\mu},\lambda\right)=p\left(\mathbf{L}|\mathbf{\mu},\lambda\right)p \left(\mathbf{\mu},\lambda\right) \tag{28}\]  
We assume that a vM-Gamma distribution as the prior over \(\mu\) and \(\lambda\) as  
\[p\left(\mathbf{\mu},\lambda\right)=p\left(\mathbf{\mu}|\lambda\right)p\left(\lambda \right)=vM\left(\mathbf{\mu}|\mathbf{s}_{0},\beta_{0}\lambda\right)G\left(\lambda|a_{ 0},b_{0}\right) \tag{29}\]  
The conjugate conditional distribution of \(\mu\) given \(\lambda\) is a vM distribution. The choice of the Gamma distribution for \(p\left(\lambda\right)\) is reasonable, and more specific introduction are provided by [24]. \(G\left(\lambda|a_{0},b_{0}\right)\) is a Gamma density function with the shape parameter \(a_{0}\) and the inverse scale parameter \(b_{0}\). The specific expression is:  
\[G\left(\lambda|a_{0},b_{0}\right)=\frac{1}{\Gamma\left(a_{0}\right)}b_{0}^{a_{0 }}\lambda^{a_{0}-1}e^{-b_{0}\lambda} \tag{30}\]  
where \(\Gamma\left(\cdot\right)\) denotes the Gamma function, \(a_{0}\) and \(b_{0}\) are regarded as prior parameters. The derivation process and relevant formulas of VB algorithm for calculating the parameters of VM distribution are shown in Appendix B.
## 4 Applications in Photonic Devices
### Passive Components
The substrate (green) material is ITO, the cladding layer (pink) material is PDMS, and the core layer (red) material is PMMA. **(e)** SEM images of the MZI blood glucose sensor, including the 1 \(\times\) 2 MMI coupler, 2 \(\times\) 1 MMI coupler, input/output ports, and curved waveguides. The structure is symmetric, with a core size of 2952 \(\upmu\)m \(\times\) 130 \(\upmu\)m [130]. Reprinted (adapted) with permission from [130]. Copyright 2024 American Chemical Society. The continued development of these passive components benefits significantly from advances in polymer science, with new materials and processing methods expanding the range of achievable functionalities and applications. The ability to combine different polymer systems and incorporate responsive elements opens new possibilities for creating adaptive and multifunctional photonic devices.
## 2 Study Area and Datasets
#### Data Used  
Two separate RS images were used in this study, taken in February 2011 (Landsat 7 image with 30 m spatial resolution) and February 2021 (Sentinel 10 m spatial resolution). The selected images have been classified as free from haze and clouds. The linear artifacts observed in the 2011 LULC map are due to the scanline corrector malfunction in the Landsat 7 satellite imagery. After the failure of Landsat 7's scanline corrector in 2003, all subsequent images contained these distinctive linear gaps. However, these artifacts were addressed by applying standard correction techniques to reconstruct the image as accurately as possible without compromising data integrity. The corrected images maintained their overall spatial accuracy, which was essential for consistency in calculating LULC metrics. Additionally, these artifacts do not reflect any systematic bias in our analysis, as the correction techniques used ensure that these gaps did not alter or bias the spatial structure and metrics of the LULC classes in the 2011 map. A summary of the satellite data used is shown in Table 1.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 2 Preliminaries
### Decoding of Traditional Special Nodes
#### 2.2.1 Fast Decoding of a Rep Node  
The log-likelihood ratio (LLR) sum of a Rep node with a code length of \(N_{S}\) can be calculated as follows:  
\[S_{q}=\sum_{i=1}^{N_{S}}\ln\frac{\Pr(y_{i}|0)}{\Pr(y_{i}|1)}, \tag{8}\]  
where \(S_{q}\) is the LLR sum and \(\Pr\) denotes probability. The decoding result of Rep is 0 when \(S_{q}\geq 0\) and 1 otherwise.
## 2 Theoretical Framework and Network
### Mode Classifier
The loss function adopts the Cross Entropy Loss function, and the model adjusts the weight parameters through backpropagation of the loss function, calculates the gradient values of each layer of the network, and uses the Adam optimizer with small batch gradient descent to regularize the parameters. Through multiple iterations and updates to weights and biases, the parameters of the model are gradually optimized, resulting in a decrease in the value of the cost function and helping the model converge better to the global optimum. Finally, the established OAM detection model is used to complete the modal detection task of distorted vortex beams. In addition, detection accuracy is defined as the ratio of the number of correctly detected samples to the total number of detected samples to measure system performance [25], expressed as  
\[Accuracy=\frac{\sum\limits_{m=1}^{M}f(m)}{M}\times 100\% \tag{7}\]  
\[f(m)= \begin{cases}1&l_{q}=l_{q}^{*}\\ 0&l_{q}\
eq l_{q}^{*}\end{cases}  \tag{8}\]  
where \(M\) is the total number of samples, \(l_{q}\) is the true label of the distorted vortex beam, and \(l_{q}^{*}\) is the predicted label by the model. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Layer** & **Kernel Numbers** & **Kernel Size** & **Stride** & **Padding** & **Activation Function** \\ \hline Conv1 & 48 & 11 \(\times\) 11 & 4 & 2 & MP (3 \(\times\) 3) + ReLU \\ Conv2 & 128 & 3 \(\times\) 3 & 1 & 2 & MP (3 \(\times\) 3) + ReLU \\ Conv3 & 192 & 3 \(\times\) 3 & 1 & 1 & ReLU \\ Conv4 & 192 & 3 \(\times\) 3 & 1 & 1 & ReLU \\ Conv5 & 128 & 3 \(\times\) 3 & 1 & 1 & MP (3 \(\times\) 3) + ReLU \\ Conv6 & 128 & 3 \(\times\) 3 & 1 & 1 & MP (3 \(\times\) 3) + ReLU \\ Conv7 & 128 & 3 \(\times\) 3 & 1 & 1 & ReLU \\ FC1 & 2048 & - & - & - & Dropout(0.5) + ReLU \\ FC2 & 2048 & - & - & - & Dropout(0.5) + ReLU \\ FC3 & 15 & - & - & - & ReLU \\ \hline \end{tabular}
\end{table} 
Table 2: Structure of the Transfer-OAM Classifier.