esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Feature Selection
#### 3.3.1 Measurement of Feature Contribution  
In DJSS, dispatching rules are usually described as priority function. Given a priority function of \(r(\mathbf{t}),r\in R\), where \(\mathbf{t}=(t_{1},t_{2},\cdots t_{n})\) is the vector of features, then the removal of feature \(t_{i}\) from \(r(\mathbf{t})\) is defined as \(r(\mathbf{t}|t_{i}=1)\); that is, the feature removed is fixed as constant 1. In addition, the following situation may happen after removing a feature from priority function:  
(1) If feature \(t_{i}\) is not a variable of \(r(\mathbf{t})\), then \(r(\mathbf{t})\) remains the same after the removal, e.g., for a dispatching rule whose priority function is \(r(a,b,c,d,e)=(a-b)/(a-b)+c/d\), after removing feature \(e\), the priority function remains the unchanged;  
(2) If feature \(t_{i}\) is one of the irrelevant variables of \(r(\mathbf{t})\), then \(r(\mathbf{t})\) is simplified after the removal, but the nature of the expression does not change, e.g., if a dispatching rule's priority function is \(r(a,b,c,d,e)=(a-b)/(a-b)+c/d=c/d+1\), which is equivalent to \(c/d\), then the features \(a\) and \(b\) are irrelevant, and removing them would simplify the priority function while maintaining its phenotypic behavior;  
(3) If feature \(t_{i}\) is one of the relevant variables of \(r(\mathbf{t})\), then \(r(\mathbf{t})\) will be changed after the removal, e.g., if a dispatching rule's priority function is \(r(a,b,c,d,e)=c/d+1\), then after removing feature \(c\) and \(d\), the priority function will be changed.  
Based on the discussion of the above three situations, the contribution of each feature \(t_{i}\) to a dispatching rule \(r(t)\) was defined as the fitness difference before and after \(t_{i}\) is removed from \(r(t)\), as shown in Equation (11).  
\[C^{r}_{t_{i}}=fitness(r|t_{i}=1)-fitness(r) \tag{11}\]
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 1 Introduction
The robust Student's \(t\) filter (STF) replaces the Gaussian probability distribution in the Gaussian filtering framework with the \(t\)-distribution to obtain robustness [26]. [PERSON] indicated that the STF required information regarding the degrees-of-freedom and scale matrix of the \(t\)-distribution, corresponding to measurement noise [27]. However, it is difficult to determine the relevant parameters of the \(t\)-distribution in advance. Another approach is to use generalized great likelihood estimations to revise the updating process of KF to obtain robustness [28]. The performances of robust filters based on generalized great likelihood estimations mainly depend on robust kernel functions, which restrict the anomalous measurements [29]. The most widely used robust filter adopts a Huber kernel function that combines minimum 11 and 12 norm estimation techniques [30]. Gaussian kernel function is another robust kernel function, namely, the maximum correntropy criterion (MCC). Since the weight function is smaller for the same residual, the MCC-based KF has more robustness than the Huber-based KF [31]. In this paper, we develop a non-linear, multiple-step, randomly delayed, robust filter and refer to as the multiple-step, randomly delayed, dynamic-covariance-scaling cubature Kalman filter (MRD-DCSCKF). The time update of MRD-DCSCKF is derived from the third-degree, the spherical-radial cubature rule, and the multiple-step, randomly delayed system model. The proposed filter adopts a multiple-step, randomly delayed filtering framework to weaken the impact of delayed measurements on the estimation accuracy of the filter, and it suppresses outliers with a dynamic-covariance-scaling kernel, which incorporates the advantages of the Huber kernel and Gaussian kernel. The remaining part of the paper is organized as follows. In Section 2, some preliminaries are briefly reviewed. Section 3 presents the DCS kernel. Section 4 presents the derivation of MRD-DCSCKF. Section 5 introduces the vision-based spacecraft-relative navigation model. The simulation is reported in Section 6. Finally, conclusions are drawn in Section 7.
## 3 Examples
### Undulating Interface Model  
The SGFD model will cause false diffraction due to the stepped grid when dealing with undulating interfaces. These diffraction waves will affect the accuracy of elastic wave modeling. The GFDM can simulate an elastic wave based on nodes adapting to the change of an undulating interface, so there will be no stepped grid diffraction. When the undulating interface model, as shown in Figure 10, was used, the velocity and density of the upper layer of the model were 4000 m/s, 2300 m/s, and 2.4 g/cm\({}^{3}\), respectively, those of the middle layer were 5000 m/s, 2800 m/s, and 2.5 g/cm\({}^{3}\), and those of the lower layer were 6000 m/s, 3500 m/s, 2.6 g/cm\({}^{3}\). The time sampling interval was 0.5 ms, and the total computation time was 1.0 s. The source was located at (1000 m, 0 m), and the dominant frequency of the Ricker wavelet was 20 Hz. When the 4 th-order 21 nodes GFDM was used, the proposed node generation algorithm was applied to discretize the model, with the node radius at 10 m, 12 m, and 15 m for the first, second, and third layers, respectively. For comparison, the SGFD model was used based on regular grids with a grid spacing of 10 m. The local grid distribution of the SGFD model is shown in Figure 11a, in which the stepped grid can be seen clearly, while the local node distribution of the GFDM is shown in Figure 11b, in which the nodes are directly and accurately distributed on the interface. It can be seen from the seismic records depicted in Figure 12a,b that there are a lot of diffracted waves behind the primary reflected wave when modeled by SGFD, while diffraction waves are practically non-existent in the GFDM record (Figure 12c,d). The results demonstrate that the forward modeling using GFDM avoids the effect of stepped grid diffraction, and is suitable for the forward modeling of a formation with undulating interfaces.  
Figure 10: Velocity model of undulating interface: (**a**) longitudinal wave velocity; (**b**) shear wave velocity; (**c**) density.  
Figure 9: Seismic records at different interface depths: (**a**) X component; (**b**) Z component.  
records (Figure 14a,b), while there are almost no scattered waves in the GFDM records (Figure 14c,d). This shows that the method used in this study is suitable for application to such complex models without any sharp lateral changes. Moreover, the GFDM method does not require an extension of the irregular boundary to a rectangular boundary, as it can calculate the wavefield by directly setting nodes on the irregular boundary.
## 5 Methods
### Application of CNN-LSTM for Artifact Removal  
The concept of using our proposed CNN-LSTM for artifact removal is illustrated in Figure 4. The network receives input from one EEG signal channel and five additional channels that provide information about muscle activity. We assume that the CNN-LSTM outputs a single channel of cleaned EEG data, free from artifacts.  
A key challenge in the presented approach was the effective training of the CNN-LSTM network. To address this, we opted to use data generated in a manner that mimics  
Figure 4: Concept of the proposed CNN-LSTM for artifact removal.  
Figure 3: Procedures employed in our study on cleaning EEG signals.  
raw EEG and EMG signals. This strategy allows for the creation of a wide and diverse set of examples, essential for successful network training. To achieve this, the following steps were undertaken:  
1. Generating EEG signals that model brain activity.
2. Generating EMG signals that represent muscle artifacts.
3. Developing augmented EEG recordings containing muscle artifacts.  
Figure 5 illustrates the schematic of the EMG signal generation process and how these signals are utilized to create EEG recordings with muscle artifacts. The CNN-LSTM was trained to output clean EEG signals, free from disturbances caused by muscle artifacts. The effectiveness of the CNN-LSTM in cleaning raw EEG signals largely depends on the quality and representativeness of the training data. The augmented EEG and EMG signals created must accurately replicate the genuine properties of real signals, including their time-frequency characteristics. Additionally, it is crucial that the training data exhibit maximum diversity--for both EEG signals and muscle artifacts. Such diversity is essential as it enables the CNN-LSTM to generalize effectively and recognize various types of artifacts in raw EEG data.
## 3 Results and Discussion  
### Study of Coffee Silverskin  
#### 3.1.1 DSC Study of Coffee Silverskin  
To characterize the thermal behavior of the samples of silverskin, differential scanning calorimetry (DSC) and thermogravimetry (TG) under nitrogen and oxygen flow were performed. The DSC curve presented in Figure 1 shows the thermal transitions of the silverskin sample which occur between -60 \({}^{\circ}\)C and 450 \({}^{\circ}\)C at a heating rate of 5 \({}^{\circ}\)C/min under a nitrogen atmosphere.  
Considering the DSC curve course, three endothermic and two exothermic events can be observed. An early event observed at 70 \({}^{\circ}\)C is associated with water evaporation from the sample. From the DSC profile, two other transitions that occurred at higher temperatures can be observed. These thermal events present at 125 and 240 \({}^{\circ}\)C are related to the presence of disaccharides in the studied material. It is known that coffee silverskin is a material containing carbohydrates, with polysaccharides as the most abundant components [13]. This coffee by-product is a rich source of total fiber, including soluble dietary fiber and insoluble dietary fiber with high amounts of cellulose, hemicellulose and lignin. According to results published by [PERSON] et al. [4], cellulose and hemicellulose are present in silverskin in significant amounts of 40.45% (_w/w_) of the composition of a dry weight basis. Another fraction that is represented by 28.58% (_w/w_) is lignin. On the DSC curve course of the studied silverskin, two main exothermic peaks can be observed with a maximum at 265 and 340 \({}^{\circ}\)C. It can be concluded that they represent the above-mentioned components, namely hemicellulose and cellulose. The obtained results are in agreement with the experimental data presented by [PERSON] et al. [36] and [PERSON] et al. [37], who observed a similar tendency in the study of hemicellulose, cellulose and lignin pyrolysis. According to data given in the literature, thermal transitions of hemicellulose and cellulose are present at temperatures range of 220-315 and 315-400 \({}^{\circ}\)C, with maximum of peaks found at 268 and 355 \({}^{\circ}\)C, respectively. According to the results obtained by [PERSON] et al. [38], cellulose, lignin and hemicellulose decompose at temperatures ranging between 240-350, 280-500 and 200-260 \({}^{\circ}\)C, respectively. Based on the data obtained in this study and presented by other researchers, it can be concluded that hemicellulose is the least thermostable and lignin is the most thermostable component in studied material.
# Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU
## 2 Materials and Methods  
The goal of Q8S is to provide a tool for researchers and developers to test code, evaluate algorithms, and train machine learning models on a heterogeneous Kubernetes cluster running on emulated hardware. Users of Q8S should be able to specify what hardware should be emulated and how many nodes should be deployed. Given all configurations, Q8S should be able to request the required nodes in an OpenStack environment, set up the emulation software, and deploy Kubernetes such that users can start using the cluster as soon as Q8S is done.  
We have captured these goals in the form of the following functional requirements:  
* Required resources are requested from an existing OpenStack environment.
* The specifications of a node type on the to be emulated cluster can be set by the user, including the following:
* System architecture, e.g., x86 or ARM.
* CPU speed and number.
* Memory size.
* Storage size.
* The user can specify the number of nodes per type.
* The hardware of every worker node is emulated. This also includes any nodes that might run the same architecture as the host system.
* After successful completion of Q8S, the user is provided with a working Kubernetes cluster, which meets the provided specifications and can execute real workloads.
* No additional privileges beyond regular user permission in the existing OpenStack environment are required.  
Some components of a heterogeneous Kubernetes cluster might be installed outside of a data center, for example, IoT devices collecting or presenting data close to the user. The connectivity to such devices may be limited or even unstable, requiring specialized scheduling and autonomous actions from such nodes. Therefore, we consider the support for network bandwidth and latency as well as simulating failures in network connectivity as relevant features for an extensive emulation of a heterogeneous Kubernetes cluster. However, support for bandwidth, latency, and network failures are not included in the version of Q8S presented here but will be added in a future release.
## 1 Introduction  
Deep-sea-derived fungi inhabiting extreme sea environments have proven to be a new source for a wide array of structurally intriguing and biologically active secondary metabolites [1; 2; 3]. As of December 2019, about 700 new secondary metabolites were reported from deep-sea-derived fungi, such as terpenoids, polyketides, alkaloids, and steroids. Some of the metabolites featured a broad range of biological activities, for example, cytotoxicity [4; 5] as well as antiviral [6; 7], antibacterial [8; 9], anti-inflammatory [10], and antiallergic effects [11]. In our continuing efforts to discover new or bioactive secondary metabolites from deep-sea-derived fungi [12; 13; 14; 15; 16], the fungus _Aspergillusydowii_ MCCC 3A00324, isolated from the South Atlantic Ocean deep-sea sediment (2246 m), attracted our attention due to the abundantly metabolic profile obtained by HPLC and TLC analysis (Figure S1, Supplementary Materials). A literature retrieval discovered that 52 out of 59 new compounds were isolated from the marine-derived _Aspergillusydowii_ fungus. The bisabolane-type sesquiterpenoids [17; 18; 19; 20; 21; 22; 23; 24], xanthones [18; 23; 25], diphenyl ethers [20; 26; 27; 28], and diketopiperazine alkaloids [29; 30; 31] were common secondary metabolites of the _A. sydowii_,some of which exhibited a wide range of pharmacological activities. To date, only three undescribed polyketides, namely asperentin B [32], 2,3,5-trimethyl-6-(3-oxobutan-2-yl)-4H-pyran-4-one, and (2_R_)-2,3-dihydro-7-hydroxy-6,8-dimethyl-2-[(_E_)-propyl] chromen-4-one [33], have been reported from the deep-sea-derived _A. Sydowii_. On the basis of the seldomly carried out chemical investigation and the abundantly metabolic profile of the _A. Sydowii_ MCCC 3A00324, a chemical study of the MCCC 3A00324 fungus was carried out. In our previous research, 17 undescribed and 10 known sesquiterpenoids were obtained from the fermented cultures of the fungus [34]. In our continuous research on this fungus, eleven compounds (**1-11**) (Figure 1), including one novel (**1**) and one new (**2**) monoterpenoids as well as two new polyketides (**3** and **4**) were discovered. Compounds **1** and **2** were the first representatives of the osmae-related monoterpenoids discovered from the fungi, while **3** was the first example of the \(\alpha\)-pyrone derivatives with two phenyl units at C-3 and C-5, respectively. As terpenids usually possess anti-inflammatory activities [35; 36; 37], all compounds were subsequently evaluated for their anti-inflammatory effects. As a result, compound **6** exhibited strong inhibitory nitric oxide production in lipopolysaccharide-activated BV-2 microglia cells with a 94.4% inhibition rate at 10 uM. Herein, the isolation, structure elucidation, and anti-inflammatory activities of these metabolites as well as the biosynthetic pathway of **1** and **2** are reported.
### Network Architecture and Requirement of Uwsn
#### 1.3.2 2D-Uwsn Architecture  
A network with a cluster of sensor nodes (cluster) placed underwater is referred to as a two-dimensional (2D) UWSN design [11]. There is a cluster head for each cluster (also called anchor node). Because they are grounded at the water's surface, the clusters remain permanent. Each cluster member collects data and transmits it to the anchor node. The anchor node collects and passes data from all of its member nodes to the surface buoyant nodes. The communication in a 2D-UWSN is carried out in two dimensions: Firstly, each cluster member interacts with its anchor node through a horizontal communication connection, and secondly, the anchor node interacts with the surface buoyant node through a vertical communication link.
## 3 Results and Discussion
### Nitrate (\(\text{NO}_{3}\))  
NO\({}_{3}\) was not detected on cycles A, B and C, because denitrification was only occurring on cycle D (Figure 3d), leading to a very small number of calibration samples available for developing the Raman-based PLS model to predict NO\({}_{3}\) concentrations. Nevertheless, the reference values were broadly spanned through the investigated concentration range (0.3-3.3 mgN L\({}^{-1}\); Figure 2d), allowing to obtain predictions of NO\({}_{3}\) concentration with high accuracy (RMSECV = 97.7% and RPDCV = 6.7) simply requiring three LVs and the mean centering of the spectral data (no additional pre-processing needed) in the 1080.4-1069.4 cm\({}^{-1}\) range (Table 1). In fact, according to the UV resonance Raman spectra of nitrate solutions, symmetric N\(-\)O stretching vibrations were reported to produce strong bands at 1044 cm\({}^{-1}\) for NO\({}_{3}\)[18]. The possibility of implementing Raman-based real-time  
Figure 3: Representation of the prediction capabilities of the Raman-PLS models developed for (**a**) total carbohydrates; (**b**) carbon dioxide (CO\({}_{2}\)); (**c**) ammonia (NH\({}_{3}\)); (**d**) nitrate (NO\({}_{3}\)); (**e**) polyhydroxyalkanoate (PHAs); (**f**) phosphate (PO\({}_{4}\)); (**g**) total organic content (TOC); (**h**) total phosphorus (total P); (**i**) total suspended solids (TSSs); (**j**) volatile fatty acids (VFA); and (**k**) volatile suspended solids (VSSs). For each parameter and calibration sample, the analytical values measured by reference methods (\(\blacktriangle\)) are represented along with the corresponding parameter values predicted by the respective cross-validation model (\(\Circuit\)). The pre-processing strategy, spectral regions, and number of latent variables used in the development of the PLS models are indicated in Table 1, for each studied parameter, along with the statistical cross-validation results. Vertical dashed lines separate the sample sets from each studied cycle, corresponding to the SBR operational days 73 (cycle A; samples 1–13); 79 (cycle B; samples 14–26); 85 (cycle C; samples 27–39); and 101 (cycle D; samples 40–52).  
monitoring of NO\({}_{3}\) in a photo-BNR reactor would contribute to control denitrification efficiency, by adapting the length of the anoxic phase, for example, and consequently, guaranteeing that no NO\({}_{3}\) is present when organic carbon is fed. The simultaneous presence of NO\({}_{3}\) and organic carbon promotes the growth of heterotrophic denitrifying organisms, which compete for carbon with PAOs and lead to photo-BNR failure over the time [2,46].
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 4 Simulation and Experimental Results  
The proposed A-MPC based inverter control method has been validated through simulation and experiments. Firstly, a series of simulations through MATLAB/Simulink are conducted to compare the performances of common FCS-MPC based inverter control method and the proposed method. In order to take the simulation and experiments to a close agreement, the simulation model of the three-phase inverter is established according to the practical experiment parameters, which is listed as Table 1.  
As mentioned, in existing FCS-MPC based inverter control methods, the switching variables are directly selected according to the cost function by comparing all the possible variables, known as exhaustive method. There are three obvious drawbacks mentioned above. To address these three issues, this paper proposes A-MPC inverter current control method. The proposed method can overcome these problems effectively.  
In order to validate it, two groups of simulations are conducted and the results are compared. The output current waveforms of the FCS-MPC method and proposed method are shown in Figures 6 and 7 respectively. As seen in the figures, the total harmonic distortion  
Since the A-MPC method removes the process of evaluating all the optional switching states, the computation is reduced. During above simulations, the processing time of each method is shown in Figure 8. The total recorded time of simulation under control of the proposal (265.62 s) is less than that of FCS-MPC based method (338.16 s). The listed total recorded time includes both the time cost by the control method and by other parts of the simulation model. Since there is no difference in the inverter models of each simulation, all of the reduced time (72.54 s) is caused by the simplification of control method. Therefore, the computation reduction in control method is significant.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 4 Challenges and Solutions
### Real-World Building  
In light of the recent development of a digital replica of the physical world, users are afforded the opportunity to create a virtual representation of themselves, incorporating personal information such as interests, friendships, and hobbies. However, this poses a significant risk to user privacy, as extensive profiling could potentially result in the unauthorized disclosure of sensitive information. The creation of such digital profiles opens the door to exploitation by malicious actors, as such profiles can be used to target users with tailored advertising, phishing attacks, and other forms of cybercrime. Furthermore, the sheer volume of data generated by such profiles means that the potential for data breaches and leaks is substantial.  
The \"Graph-based privacy-preserving data publication\" essay [42] proposes a framework for securely sharing data while protecting individual privacy. This approach utilizes graph theory, where individuals, devices, or locations are represented as nodes (vertices) in a network, and their relationships (e.g., friendships, connections, interactions) are represented as edges. By leveraging this graph structure, the framework can effectively anonymize sensitive information, such as personal identities or locations, while still allowing for valuable data analysis. This is achieved through techniques such as node degree modification, edge perturbation, or differential privacy. In the context of edge computing, where data processing occurs closer to the data source, this graph-based approach can be particularly advantageous for enabling privacy-preserving data sharing in decentralized and resource-constrained environments.