esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Training of CAE-Based Defect Detectors Using Structural Noise
## 2 Contrasts between the Classic Usage of Autoencoders and Ours
### Alternative Use of Autoencoders in Our Research  
Our study re-explored autoencoders used to restore altered pictures [9; 10; 11], but, the restored pictures are then used to detect the alterations that were present within the original images. These networks are trained on flawless images and are able to reconstruct defective images that are input during the test phase. The encoder adjusts its weights to reduce data effectively, while the decoder adjusts its to restore encoded data as closely as possible to their original versions. As the autoencoder will have been accustomed to process pictures that are not defective, it will not treat faulty ones differently and will try to reform them as if they were flawless. The output will be an approximation of the unimpaired version of the flawed part, because the encoder is fit to regular data and codes anything as such, while the decoder handles the seemingly \"flawless\" encoded data normally. In this way, the differences between the input and the output can be counted as irregularities.  
As shown in Figure 2, the architecture of our CAE is divided into two sequences of layers: convolutional and max pooling layers for the encoding, convolutional and upsampling layers for the decoding.  
Figure 1: Structure of a classical autoencoder.  
Since defective data are uncommon, being able to prepare these NNs from regular data is an advantage: it is laborious to form proper/relevant training datasets from scarce resources. In addition, it is known that the reduction/restoration ability of convolutional autoencoders is improved by the addition of noise to the input layer and the hidden layer [12; 13]. Therefore, our study intended to determine what kind of noise should be used to get the best performances. Experiments involving no noise, Gaussian noise and a noise made of a known structure were conducted.  
The Figure 3 presents typical flawed pieces and their flawless counterparts. These pictures are for illustration purposes; no actual restoration was performed, these are results sought in our study.
# Integrated Information in Relational Quantum Dynamics (RQD)
## Appendix B  
### Numerical Benchmarks of \(\Phi\) for Three Canonical 4-Qubit States  
To provide concrete examples of the integrated-information measure \(\Phi\), we performed an exact bipartition search (all \(\binom{4}{2}=6\) balanced cuts) on three well-known 4-qubit pure states: GHZ, cluster, and W. In each case, we set  
\[D_{QJS}(\rho\|\sigma)=S\Big{(}\tfrac{\rho+\sigma}{2}\Big{)}-\tfrac{1}{2}\,S( \rho)-\tfrac{1}{2}\,S(\sigma)\quad\text{(entropies in bits)},\]  
and then  
\[\Phi(\rho)=\min_{\mathcal{AB}}D_{QJS}\big{(}\rho\,\|\,\rho_{A}\otimes\rho_{ B}\big{)},\]  
where \(\rho_{A}=\operatorname{Tr}_{B}\rho\), \(\rho_{B}=\operatorname{Tr}_{A}\rho\).  
Interpretation: \(\mathbf{GHZ_{4}}\) achieves the maximum possible \(\Phi\approx 1\) bit under balanced cuts, reflecting its fully non-local coherence. \(\mathbf{Cluster_{4}}\), with entanglement concentrated on nearest neighbors, yields an intermediate \(\Phi\approx 0.63\) bits for the natural split into two linked pairs. \(\mathbf{W_{4}}\), whose single excitation is delocalized, is best \"cut\" as one qubit versus the other three, giving the lowest \(\Phi\approx 0.46\) bits.  
Hence the ordering \(\Phi(\operatorname{GHZ_{4}})\ >\ \Phi(\operatorname{Cluster_{4}})\ >\ \Phi( \operatorname{W_{4}})\), quantitatively matches intuitive expectations of multipartite correlation strength.
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## 1 Introduction  
### Motivation  
The Power-Flow (PF) is one of the main computational tools in power system analysis. It solves the nonlinear model of a power system for a given power generation/consumption profile. Nowadays, the Newton-Raphson (NR) is still the most widely used technique for solving the nonlinear equations involved in the PF problem. This is because of its simplicity, easy codification in standard programming languages, acceptable robustness, and quadratic convergence. These features typically bring superior characteristics with respect to (w.r.t.) other conventional PF solvers.  
The PF problems are customary categorized as well or ill-conditioned. Conventionally, a PF problem is said to be ill-conditioned when its solution by using standard techniques such as NR is hardly reachable [1]. This has supposed an important barrier for the wide application of Newton-like techniques; nevertheless, this issue has not been very problematic due to most PF cases being well-conditioned. However, ill-conditioned cases are becoming more frequent [2]. Solving these kinds of problems is still challenging due to the difficulties showed by standard methods or the low degree of efficiency manifested by most of the available robust PF solution techniques. In this context, developing novel efficient and robust PF solution approaches is still interesting, relevant, and demanding.
## 3 Results and Discussion
### Flow around the SD7003 Airfoil
#### 3.2.3 U-Net Model Training  
In this section, we present the results obtained using an alternative approach, as explained in Section 2.4. We trained a U-Net model using the data from the two angles of attack for which field inversion was performed. The inputs to the U-Net model are the converged results of the original model, including velocity, pressure, temperature, and turbulent viscosity fields. The U-Net model then generates a turbulent viscosity field, which is compared with the corrected field obtained through field inversion optimization.  
In this approach, the U-Net model leverages the converged results of the original SA model to determine the correct turbulent viscosity field, which is then imposed in the solution. After imposing this field in the solution, we allow the other fields to evolve to achieve an improved steady solution.  
In Figure 15, (b) and (d) show the corrected turbulent viscosity obtained from the U-Net model, while (a) and (c) represent the velocity magnitude after imposing this corrected turbulent viscosity in the RANS equations. The turbulent viscosity from the U-Net model is in agreement with that obtained from field inversion, as shown in Figures 8a and 10a.  
Table 6 provides a detailed comparison of the results in terms of CL and CD. Since the data were optimized to match CL, the CL values obtained in this simulation align closely with those from the optimization. For \(\alpha=4^{\circ}\), the CD value is consistent with the optimized value but still differs from the experimental data. For \(\alpha=8^{\circ}\), the CD is higher compared to the experimental data. This discrepancy is expected, as the turbulent viscosity field was specifically optimized to match CL. Nevertheless, in the case of \(\alpha=4^{\circ}\), the CD shows an improvement compared to the original model.  
We use the \(\alpha=6^{\circ}\) case to test the performance of the U-Net model outside the training data. In Figure 16, we show the velocity and turbulent viscosity fields. From the velocity field, it is evident that the separation moves towards the leading edge compared to the \(\alpha=4^{\circ}\) case. More detailed results are shown in Table 7. The results from the U-Net model still show a discrepancy in CL similar to the original SA model, but this time CL is overestimated. Notably, the CD value in this case is close to the LES value.  
Figure 15: Results of the U-Net-augmented Spalart–Allmaras model for the SD7003 test case. (**a**) Velocity magnitude and (**b**) turbulent viscosity at \(\alpha=4^{\circ}\). (**c**) Velocity magnitude and (**d**) turbulent viscosity at \(\alpha=8^{\circ}\).
# A FEAST Algorithm for the Linear Response Eigenvalue Problem
## 1 Introduction
As a result, one can compute a large number of eigenpairs at once on modern computing architectures, while typical Krylov- or Davidson-type solvers fail in this respect because they require orthogonalization of any new basis vector with respect to the previously-converged ones. The basic idea of the FEAST algorithm is based on the contour integral function:  
\[f(\alpha)=\frac{1}{2\pi\iota}\oint_{\mathcal{C}}(\mu-\alpha)^{-1}d\mu\]  
to construct bases of particular subspaces and obtain the corresponding approximate eigenpairs by the Rayleigh-Ritz procedure where \(\mathcal{C}\) is any contour enclosing the set of wanted eigenvalues. The corresponding theoretical analysis was established in [18]. In particular, it was shown that the FEAST algorithm can be understood as an accelerated subspace iteration algorithm. Due to the advantage of the FEAST algorithm in parallelism, it has been well received in the electronic structure calculations community. Additionally, the improvements and generalizations of FEAST algorithm have been active research subjects in eigenvalue problems for nearly a decade. For example, in [19], [PERSON], [PERSON], and [PERSON] presented the FEAST eigensolver for non-Hermitian problems by using the dual subspaces for computing the left and right eigenvectors. In [20], the FEAST algorithm was developed to solve nonlinear eigenvalue problems for eigenvalues that lie in a user-defined region. Some other work for the FEAST method can be found in [21; 22; 23; 24]. Motivated by these facts, in this paper, we will continue the effort by extending the FEAST algorithm to LREP. The rest of the paper is organized as follows. In Section 2, some notations and preliminaries including the canonical angles of two subspaces and basic results for LREP are collected for use later. Section 3 contains our main algorithm, the implementation issue, and the corresponding convergence analysis. In Section 4, we present some numerical examples to show the numerical behavior of our algorithm. Finally, conclusions are drawn in Section 5.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Soft Error Robustness Verification and Comparison  
To evaluate the soft-error resilience of the proposed EWS-16T cell, a double-exponential current model was employed to simulate SEUs. The injected transient current I(t) is described by the following equation [7].  
\[I(t)=I_{0}\bigg{(}e^{-\frac{L}{t_{0}}}-e^{-\frac{L}{t_{0}}}\bigg{)} \tag{1}\]  
Here, \(I_{0}\) is approximated from the total deposited charge \(Q\) using the following equation.  
\[I_{0}=\frac{Q}{\tau_{\rm{A}}-\tau_{\beta}} \tag{2}\]In this model, \(Q\) denotes the total deposited charge. The parameters \(\tau_{a}\) and \(\tau_{\beta}\) represent the junction collection time constant (200 ps) and the ion track establishment time constant (50 ps), respectively. Experimental results show that when 300 fC of charge is individually injected into sensitive nodes \(Q\) and S0, the proposed EWS-16T cell successfully restores its original state in both cases, demonstrating excellent tolerance to SEUs. Figure 12 shows the SEU scenarios for nodes \(Q\) and S0, respectively, where the node voltages recover to their original stable levels over time.  
To further evaluate multi-node soft-error resilience, charge was simultaneously injected into pairs of sensitive nodes. The EWS-16T cell successfully recovered in all combinations except when both \(Q\) and S0 were struck simultaneously. In this case, simultaneous disturbance of the internal feedback loop prevented recovery. However, the proposed cell addresses this limitation through a layout that ensures sufficient spacing between sensitive transistors, thereby suppressing charge-sharing-induced simultaneous inversions.
## 3 Materials and Methods
### Vineyard Experimental Design  
In 2021 and 2022, a set of experiments that involved leaf removal were conducted in the commercial vineyards of Chateau Langes, which are located at 119 deg25' N, 39 deg76' E, within Jieshishan county. These vineyards have a 0.3% incline gradient in the western direction and predominantly comprise sandy loam soil. The study examined _Vitis viunifera_, L. cv Cabernet Sauvignon and Marselan cultivars, both of which are self-rooted and grown using the pruning training system called modified vertical spur positioning (M-VSP). The grapevine rows were arranged in a nearly south-north orientation and spaced at 2 m \(\times\) 1 m (vine \(\times\) row). The investigation with Cabernet Sauvignon grapes was conducted over a two-year period (2021 and 2022), whereas the experimentation with Marselan grapes was limited to the 2022 growing season.  
For the study, a group of 24 grapevines with uniform growth within a designated row were selected. The vines were divided into four experimental groups, each based on the timing of leaf-removal interventions. Six basal leaves were removed to completely expose the grapes. Leaf removal occurred during specific periods, including at the hard green berry stage (designated as LR1), at the beginning of veraison (LR2), 25 days after veraison (LR3). A control group underwent no leaf removal. Each row was a cluster of biological replicates, resulting in a total of three replicative rows. During the commercial harvest period, 300 individual berries were collected randomly from each biological replicate. Out of these, 50 berries were subjected to physicochemical analysis, while the rest were frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C until the analysis was completed.
# Detecting and Mitigating Attacks on GPS Devices
## 2 Existing Surveys
The techniques highlighted in [26] employed various types of optimization-based approaches, including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), simulated annealing (SA), pigeon-inspired optimization (PIO), Cuckoo Search (CS) algorithms, Dijkstra's Algorithm, Differential Evolution (DE), and Grey Wolf Optimization (GWO). The authors of [26] also discussed various DL-based approaches, including reinforcement learning (RL) and deep reinforcement learning (DRL). These algorithmic approaches can be applied to any navigation-related problem; however, the survey focused on path planning and optimization applications. The authors identified federated learning (FL) as a top future research direction, where AI model training occurs in a distributed manner across multiple devices using local datasets. Additional identified future research directions included the need for improved energy consumption, reduced computational power requirements, improved fault handling, and the need for AI-based solutions for physical threat avoidance. The primary contribution of this paper is that we cover all aspects of Tables 1 and 2. There are many high-quality surveys in the literature; however, they typically focus on niche areas of the problem space. This paper covers all relevant aspects of GPS security, including threats, attack detection, and attack mitigation for jamming and spoofing. This paper also comprehensively describes positioning and navigation in GNSS-denied environments.
# Remediation Opportunities for Arsenic-Contaminated Gold Mine Waste
## 4 Phytoremediation
### Co-Application of Soil Amendments
#### 4.2.1 Organic Amendments  
A study by [PERSON] et al. [13] investigated phytostabilisation and the impacts of three different biochar amendments on mine tailings. Biochar is a carbonaceous material derived from pyrolysis methods that involve heating organic material (in a range of 500-900 \({}^{\circ}\)C in a limited oxygen environment) [13; 130; 131]. The organic matter used to produce biochar is typically biomass, sewage sludge, agricultural waste, and compost [13; 131]. Biochar has a porous structure, high surface area, and other physiochemical properties that improve soil characteristics such as carbon and water holding capacity (Table 5) [131]. The mine tailings had high As and Pb concentrations (1587.1 and 3642.7 mg/kg, respectively), a pH of 6.5, and were low in nutrients such as total carbon (0.3%) and total nitrogen (0.01%) [13]. A greenhouse pot experiment was implemented over 100 days involving 10 treatments, three biochar types (_Hibiscus cannabinus_ core, sewage sludge, and chicken manure) at three biochar concentrations (0.4%, 1%, and 3% \(w\)/\(w\)) and one control (mine tailings only) [13]. The results showed that overall plant shoots and roots increased in biomass and As shoot concentrations decreased by 54.9-77.5% [13]. Sewage sludge biochar applied at 3% concentration provided the best results for plant growth [13]. Plant height was 12.1 cm, and root length was 370.9 cm, compared to the control values of 8.1 cm and 112.4 cm for plant height and root length, respectively [13]. Sewage sludge biochar did not immobilise As; further focus is needed on the potential As release during increased biochar application rates [13]. Arsenic leaching as a result of organic soil amendments was also reported by [PERSON] et al. [42]. [PERSON] et al. [42] investigated mine waste soil amended with compost and biochar then monitored the effects on pore water and aqueous soil extracts. It was reported that As leaching was a concern and potential risk for further environmental pollution [42]. Arsenic leaching into pore water was >2500 ug 1\({}^{-1}\) (World Health Organisation drinking water standard is 10 ug 1\({}^{-1}\)) and was found to be related to pH, dissolved organic carbon, and soluble P [42]. However, arsenic toxicity was reduced most effectively when compost and biochar were combined, and soil nutrients increased [42]. A study by [PERSON] et al. [132] researched the effects of combined biochar and iron-sulphate-assisted phytoremediation. [PERSON] et al. [132] also reported an increase in soil nutrients such as increased pH, electrical conductivity (EC), and reduced metal(loid) concentrations in soil pore water. However, there was no improvement in plant growth, which is essential for phytoremediation projects. [PERSON] et al. [42] concluded that field trials to further understand As leaching and effects of organic amendments are needed. Biochar should be investigated further to assess As leaching and its ability to improve soil properties and plant growth.
## 3 Operation of the European Transmission System
### Awareness of System States  
For any power system, knowing its status is of primary importance. The different states of a power system depend on the size of the network, the levels of load/frequency risk, as well as the necessary actions to limit the propagation of faults in the network [32]. There are four system state situations:  
Normal State: It is the operating state in which the system is within the limits of its safe operation, in steady state and after the occurrence of any emergency, considering the effect of remedial actions that are available [30].  
Alert State: It is the operating state in which the system is within its safe operating limits, but an emergency has been identified for which the remedial actions are not enough to maintain the normal operating state.  
Emergency State: It is the operating condition in which the safe operating limits of the system have been violated and at least one of the operating parameters is outside its limit.  
Blackout State: It is the system's condition where part or all of the transmission system is out of service.  
In Figure 2, all the above system states are illustrated.  
Finally, the Restoration State is defined as the system state in which all actions are aimed at restoring operation of the transmission system and maintaining the safe operation of the system after a blackout or emergency state.
# Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory
## 1 Introduction
[PERSON] focused on Guangdong Province, analyzing the correlations between hazard indices and the maximum daily rainfall and highest wind speeds recorded during typhoons [18]. [PERSON] et al. employed a combination of Bayesian models and spatial conditional regression models to analyze the relationship between typhoon locations and cumulative rainfall during typhoon periods in Taipei City [19]. However, when only a single factor is considered in risk assessment, such as the precipitation index used by most of the above scholars, the risk of tropical cyclones may be underestimated. Existing research indicates that risk assessments of tropical cyclone hazards predominantly focus on high-intensity events such as typhoons. These studies often assess individual typhoons and provincial areas, with less emphasis on larger-scale research. Additionally, as tropical cyclones continue to move inland after making landfall, their impact extends beyond coastal cities to inland regions--an aspect that has not been sufficiently explored spatially. In light of the shortcomings in previous research, this study is based on tropical cyclones that made landfall in mainland China and the Hainan region in the period from 1949 to 2023. Utilizing hazard risk theory and classical extreme value theory, this research focuses on data obtained from various meteorological stations from 1997 to 2021 during the peak cyclone period (June to October). This includes wind speed, rainfall, collected elevation, and vegetation cover data. Analytical techniques such as the Analytic Hierarchy Process and ArcGIS spatial analysis are employed to analyze the spatiotemporal distribution of landfalling tropical cyclones, assess the comprehensive risk of tropical cyclone hazards, and delineate risk regions. The primary aim of this study is to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, thereby providing references for hazard prevention and mitigation in affected areas.
## 3 Background
### Power Sub-System Overview  
The electrical power sub-system is required to manage, store, and generate electrical energy and distribute this to consuming components and sub-systems [23]. Typically, the electrical power system will account for approximately a third of the total space-craft mass. Commonly used power generation sources include Photovoltaic Cells, solar arrays, and thermonuclear power generators, although these are commonly found on much larger craft [23].  
Power storage is typically performed by rechargeable lithium-ion batteries; close attention should be paid to this component during thermal design [26]. A power management and distribution module (PMAD), often referred to as a battery management system is employed to control the control of power draw to the instrumentation and sub systems. This device is often engineered to meet specific mission requirements; however, off-the-shelf components are readily available [21]. As a CubeSat is constrained by weight and volume it is vital to select components with a high specific power to mass ratio [23]. Figure 6 illustrates a simplistic overview of a CubeSat Power subsystem.