esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Probabilistic Approach
#### 2.2.1 Ground Motion Variations  
The seismic ground motion input is composed of artificially generated spectrum-compatible sets of 3 accelerograms, one for each direction (X, Y and Z).  
ASCE 4-16 recommends the following two methods for the generation of the seismic input:  
* Method 1 generates spectra with a similar shape, which seem scaled by varying coefficients. The disadvantage of this method is that the frequency content of the resulting spectra and the general shape of the seismic input remains unchanged and only the seismic hazard levels vary. Additionally, a poor variance in spectra peaks is obtained.
* Method 2 generates spectra with a random shape, which do not look like the initial spectra used for the generation. This is a true \"probabilistic\" approach, as all the generated seismic inputs have large variations in the frequency content, while their mean values still show good compliance with the initial spectra. When generating the artificial accelerograms, a statistical correlation between the spectral amplitudes at different frequencies is considered.  
The differences described can be observed in Figure 11, which presents an example for 5 time-histories, using the two methods of generation [49].  
For the current study, Method 2 was used, as it produces much more realistic accelerograms. The parameters used for the generation of the time histories are as follows:  
* Amplitude coefficient of variation: 0.25 (ASCE 4 recommended interval is 0.2-0.3);
* Frequency-independent constant correlation length of 0.70.  
Figure 12 shows the spectra for the generated time histories, for each of the following three main directions: X, Y and Z [49].
# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 6 Comparison of Cognitive Control and Similar Control Methods
### Cognitive Control, Robust Control and Adaptive Control  
With the popularization of Industry 4.0, the CPS has been widely used in engineering systems, and the uncertainties become non-negligible influences. The common uncertainties include structured uncertainties, parameter uncertainties, network uncertainties such as packet drop and time delay, and other unexpected uncertainties such as malicious cyber-attacks. In robust control, the uncertainties are described by a perturbation that is considered limited, and as such a set of objects is established. For every object in the set, the controller could ensure the feedback system is internally stable; this controller is considered to have robust stability. In adaptive control, the control problem is formulated in a way that the controller has some adjustable parameters. The controller is also equipped with an adaptation mechanism for updating the parameters according to variations in dynamics of the system with which it interacts as well as the nature of disturbances. Typically, these controllers function well under the prespecified conditions for which they are designed. However, they will not function properly if the system of interest confronts highly uncertain environments and so the presence of a human operator in the control loop is indispensable. In such environments, the controller may reach points of surprise for which it has not been programmed. This issue normally arises because the controller is unable to collect the sufficient information it requires to achieve its goals in a self-organized manner. Cognitive control systems equipped with cognitive abilities are capable of self-learning, self-diagnosis, self-adjustment, and self-optimization based on less prior knowledge; they could perform better when operating in unknown environments and/or confronted with unexpected uncertainties. In [107], it is considered that the lack of memory is the main difference between cognitive control, robust control, and adaptive control; memories, including long-term memory and short-term memory, are critical for self-x abilities and they play important roles in behaviors selection and adjustment, which is significant for the continual progress of the control system in unknown environments and for realizing optimal or suboptimal control. However, equipping the control system with memories will increase the complexity of the system; therefore, the enhancement of the robustness and adaptiveness of the cognitive control system is at the cost of increasing the complexity of the system architecture, which in turn increases the security risks of the system. A comparison between cognitive control, robust control, and adaptive control is presented in Table 6.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
###### Abstract  
In the rapidly evolving domain of mobile edge-cloud computing (MECC), the proliferation of Internet of Things (IoT) devices and mobile applications poses significant challenges, particularly in dynamically managing computational demands and user mobility. Current research has partially addressed aspects of service migration and resource allocation, yet it often falls short in thoroughly examining the nuanced interdependencies between migration strategies and resource allocation, the consequential impacts of migration delays, and the intricacies of handling incomplete tasks during migration. This study advances the discourse by introducing a sophisticated framework optimized through a deep reinforcement learning (DRL) strategy, underpinned by a Markov decision process (MDP) that dynamically adapts service migration and resource allocation strategies. This refined approach facilitates continuous system monitoring, adept decision making, and iterative policy refinement, significantly enhancing operational efficiency and reducing response times in MECC environments. By meticulously addressing these previously overlooked complexities, our research not only fills critical gaps in the literature but also enhances the practical deployment of edge computing technologies, contributing profoundly to both theoretical insights and practical implementations in contemporary digital ecosystems.  
Advantage Actor-Critic; deep reinforcement learning; mobile edge-cloud computing; resource allocation; service migration +
Footnote †: journal: _Algorithms_
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 3 Biochar Adsorption of Heavy Metals
### Mercury  
Until now, few studies have reported Hg adsorption by biochar. Two biochars from bagasse and hickory chips were synthesized by [PERSON] et al. [56], revealing that surface complexation and cation-\(\pi\) bonding on biochar are the most important mechanisms in the Hg adsorption (Figure 3). [PERSON] et al. [57] produced thirty-six biochars from wood, agricultural residue, and manure feedstocks at 300 and 600 \({}^{\circ}\)C. They concluded the preference of Hg to bind to sulfur atoms when its level is high in the biochar, while at a low level, Hg is bound to oxygen and chlorine functional groups. Thus, sulfurized biochar enhances Hg removal by forming a Hg-S bond [58]. In this context, biochars produced from tobacco, rice and wheat were treated with H2S, showing the play role of C-S functional group in the adsorption process that is increased more than seven-fold compared to the level achieved in unmodified biochar [59]. Similarly, biochars produced by pyrolysis (range temperature 300-900 \({}^{\circ}\)C) from poplar wood were able to remove Hg from water, although their modification with FeCl3 and FeSO4 increased the Hg removal due to the contribution of adsorbing sites of Fe\({}^{0}\), Fe\({}_{1\text{-}}\)S, and Fe\({}_{2}\)O\({}_{3}\)[60].  
Another new alternative is the application of microwave activation to increase the porosity of the adsorbent. In this framework, [PERSON] et al. [61] prepared biochar from cotton straw by \"microwave activation and Mn-Fe mixed oxides impregnation assisted by ultrasound treatment\". The combination of these techniques allows for increasing its porosity, and ultrasound favors Mn and Fe distribution in the biochar composite.  
Recently, biochar produced by pyrolysis of high-salinity Spirulina residue in a temperature range from 350 to 700 \({}^{\circ}\)C for 90 min (heating rate of 15 \({}^{\circ}\)C/min) shows excellent sorption of Hg (uptake ranged from 107.1 to 196.1 mg/g), which is mainly due to the high content of nitrogen, sulphur-heterocycles and chlorine-containing minerals formed in the biochar production. The results are in agreement with previous studies, in which by the use of a nitrogen-doped microalgae biochar, Hg sorption was enhanced [62].
## 4 Proposed Robust and Accurate ET Anomaly Detector
### The Proposed Detector's Architecture  
The proposed anomaly detector consists of four components: clustering, XAI, DAE, and OCSVM, as illustrated in Figure 2. The first component is a clustering algorithm that divides electric utility consumers into groups based on their associated metadata, which directly influences their electricity consumption, such as maximum contracted power, house size, occupancy, etc. [32]. Here, we utilize the K-means clustering algorithm to divide consumers into groups based on their consumption levels due to the absence of metadata in the dataset. The second component is the XAI SHAP method that extracts SHAP explanations from the consumption readings. Here, we extract the SHAP explanations for the consumption reading samples. The SHAP algorithm [30] computes Shapley values for each feature (i.e., consumption reading) in each sample across all data samples. These values are determined based on the marginal contribution calculated using a reference subset through iterative calculations. Figure 3 shows an example of the SHAP values for the top-20 features (readings). Practically, we train using all the features (i.e., explanations for all 48 readings). The third component is a DAE that generates latent space representations for the SHAP explanations. The fourth component is an OCSVM that uses the latent space representations to detect ET. Figure 2 depicts both phases of the proposed detector, including training and inference.  
Figure 2: The proposed XAI-based robust and accurate ET anomaly detector.
# Neutrosophic Linear Equations and Application in Traffic Flow Problems
## 3 Neutrosophic Linear Equations and Neutrosophic Matrices  
### Neutrosophic Linear Equations  
**Definition 1**.: _A neutrosophic linear equation in \(n\) variables (unknowns) \(x_{1}\), \(x_{2}\),..., \(x_{n}\) is defined as the following form:_  
\[z_{1}x_{1}+z_{2}x_{2}+\ldots+z_{n}x_{n}=z \tag{1}\]  
_where \(z\), \(z_{j}\in Z\) (\(j=1,2,\ldots,n\)) are the given real NNs and \(Z\) are all NNs._  
For example, if the neutrosophic linear equation is \(2 lx_{1}+3x_{2}=6\) for \(I\in[1,2]\), then it indicates the indeterminate area between two lines \(2x_{1}+3x_{2}=6\) and \(4x_{1}+3x_{2}=6\), which can be represented as one neutrosophic function [2; 4]\(x_{1}+3x_{2}=6\), and then if the indeterminacy \(I\in[2,4]\) is considered as a possible interval range, then the neutrosophic linear equation can be also described as the indeterminate area between two lines \(4x_{1}+3x_{2}=6\) and \(8x_{1}+3x_{2}=6\) and be represented as another neutrosophic function [4; 8]\(x_{1}+3x_{2}=6\).  
**Definition 2**.: _A system of \(m\) neutrosophic linear equations in \(n\) variables (unknowns) \(x_{1}\), \(x_{2}\),..., \(x_{n}\) is defined as a family of neutrosophic linear equations:_  
\[\begin{array}{l}z_{11}x_{1}+z_{12}x_{2}+\ldots+z_{1n}x_{n}=z_{1}\\ z_{21}x_{1}+z_{22}x_{2}+\ldots+z_{2n}x_{n}=z_{2}\\ \vdots\\ z_{m1}x_{1}+z_{m2}x_{2}+\ldots+z_{mn}x_{n}=z_{m}\end{array} \tag{2}\]  
For convenient expression, the above system can be written concisely as  
\[\sum_{j=1}^{n}z_{ij}x_{j}=z_{i}\,\text{for}\,i=\,1,\,2,\,\ldots,m.\]  
**Definition 3**.: _Let_  
\[\left[\begin{array}{cccc}z_{11}&z_{12}&\cdots&z_{1n}\\ z_{21}&z_{22}&\cdots&z_{2n}\\ \vdots&&\vdots\\ z_{m1}&z_{m2}&\cdots&z_{mn}\end{array}\right],\]  
_which is called the neutrosophic coefficient matrix of the system, while_  
\[\left[\begin{array}{cccc}z_{11}&z_{12}&\cdots&z_{1n}&z_{1}\\ z_{21}&z_{22}&\cdots&z_{2n}&z_{2}\\ \vdots&&&\vdots\\ z_{mn}&z_{mn}&\cdots&z_{mn}&z_{m}\end{array}\right]\]  
_is called the neutrosophic augmented matrix of the system._Let us write  
\[A=\left[\begin{array}{cccc}z_{11}&z_{12}&\cdots&z_{1n}\\ z_{21}&z_{22}&\cdots&z_{2n}\\ \vdots&&&\vdots\\ z_{m1}&z_{m2}&\cdots&z_{mn}\end{array}\right],\;X=\left[\begin{array}{c}x_{1} \\ x_{2}\\ \vdots\\ x_{n}\end{array}\right],\;\text{and}\;B=\left[\begin{array}{c}z_{1}\\ z_{2}\\ \vdots\\ z_{m}\end{array}\right].\]  
Then, the system of neutrosophic linear equations for Equation (2) can be expressed by the following form of the neutrosophic matrix equation:  
\[AX=B. \tag{3}\]
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 8 Results
### Minimum Rank with Hysteresis Routing Protocol  
Our next experiment utilized the Minimum Rank with Hysteresis Objective Function (MRHOF) routing protocol. We used the same parameters as for OF0 and comparing the results with the default MSF. Our proposed clustering method directly allocates cells to each slot frame, facilitating packet retransmission to the destination node, especially in high-density clusters. This method achieved slightly better PDR percentage results than other clustering techniques; unlike OF0, it also supports multi-path routing. Clustering improves PDR by allocating cells directly. The clustering result improves the PDR value by directly determining the cells to be used. The highest PDR, observed with 100 nodes, reached 98.15%. This decreased to 93.35% with 300 nodes, reflecting the increased traffic load on network resources. The end-to-end latency performance metric remained stable despite a slight increase compared to grid-based K-means, reaching 112.91 s for 300 nodes due to fewer retransmissions.  
The joining time was consistent with other K-means clustering methods, achieving 55,219 slots compared to 55,394 slots for ECRP clustering. Congestion at the cluster head delays the transmission queue when transmitting to other slot frames, as depicted in Figure 8. Increased data traffic or competition for cluster resources likely caused congestion between cluster heads, delaying the transmission queue. Figure 10 also shows the throughput values for the data transmission speed, which differ from the OF0 RPL. Throughput, which is closely related to PDR, decreased from 17.12 kbps to 9 kbps as the number of nodes increased from 100 to 300, likely due to higher message transmission during the 6p transaction in grid-based K-means; the throughput decreases linearly as the number of nodes increases. Figure 10 highlights an anomaly in which fewer nodes encountered difficulties due to clustering on low nodes in top-6 protocol cell allocation, resulting in performance similar to MSF without clustering. Compared to MSF, our method showed a 12.34% increase in average PDR, a 21.06% reduction in latency, a 12.68% decrease in joining time, and a 25.97% increase in throughput speed.  
Figure 10: Graph results of the Minimum Rank with Hysteresis (MRHOF) routing protocol: (**a**) Packet Delivery Ratio (PDR), (**b**) end-to-end Latency, (**c**) joining time, and (**d**) throughput.
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
###### Abstract  
The F10.7 solar flux index is a measure of microwave solar emissions at a wavelength of 10.7 cm or 2800 MHz. It is widely used in thermosphere and ionosphere models as an indicator of solar activity and is recorded at only one terrestrial observatory in Penticton, Canada during daylight hours. The lack of geographical and temporal coverage of F10.7 measurements and no external redundancy to the existing system has led to the development of the RMCSat mission, which seeks to demonstrate the feasibility of collecting microwave solar flux emissions from a space-based platform. RMCSat is the first CubeSat mission by the Royal Military College of Canada. It offers a training environment for personnel in space mission analysis and design, satellite assembly, integration and testing, and satellite operations. This paper introduces the mission concept and preliminary design of a space-based solution that captures solar density flux measurements during each orbit as the Sun passes through the boresight of the primary payload antenna. In addition to two channels recording the 2800 MHz frequency (2785 MHz and 2815 MHz), a third channel records 2695 MHz using the same calibration standard to determine if the United States Radio Solar Telescope Network (RSTN) could be leveraged to supplement the existing F10.7 solar flux measurements and improve solar flux approximations. The RMCSat mission, satellite design, and system budgets are demonstrated here as being viable. Future design considerations pertain to the payload antennas and achievable launch orbits.  
F10.7 solar flux index; RMC CubeSat mission; 2800 MHz microwave emissions; DRAO observations 2021
## 6 Experimental Results
### Results

Table 1: Experimental results obtained using \(NowDeepN\). 95% CI are depicted. longitude and latitude values, the OY axis runs along the latitudes and OX axis runs along the longitudes (the (0,0) origin of the images being the most north-western point of the geographical area the image represents). In Table 1 an average NRMSE of less than 4% is reported for the \(R\) products, which would entail a close resemblance between the predicted data and the real data, resemblance which can be observed in the comparison between the Figures 9 and 10. It can be observed  
Figure 10: Predicted data for product \(R01\) (reflectivity at the lowest elevation angle, measured in dBZ with values ranging form 0 to 70) at 14:37:22 UTC. Figure 9: Real data for product \(R01\) (reflectivity at the lowest elevation angle, measured in dBZ with values ranging form 0 to 70) at 14:37:22 UTC. that the predicted pattern closely follows the real pattern, closely matching the shape and intensity of R01 in the studied area. There are, however, visible limitations of the prediction. There is a clear smoothing effect present, very clearly seen around areas where there are scattered points with non-zero values, the prediction tends to smooth out the area between them, resulting in a much larger areas of close to 0 non-zero values (colored in dark green) than in the real data. This effect can be also seen on areas with points with higher values, for example, around the point at roughly (170, 230), where in the real data present some ragged shape of higher intensity points, but the predicted data would present the area as a smooth shape, much less ragged, the space between the higher intensity points being predicted with a similar intensity. Another example can be seen at (110, 120), where, in the real data, there is a small shape with extremely high values; but in the predicted data, while the real shape is largely retained, the intensity is greatly diminished. Still, these kinds of effects are on a small scale relative to the entire area represented by the figure. Smoothing may be responsible for the higher NRMSE obtained for non-zero values, as areas with higher values are more affected by smoothing than areas with zero values.
# The State of the Art of the Control Strategies for Single-Phase Electric Springs
## 3 Control Strategies of ES-2
### Power Control Strategy  
Maximum power point tracking (MPPT) technology is commonly used in wind or solar power plants, and the tracked active power is consumed by the electrical load at domestic home, which can be classified into CLs and NCLs. For ES installed in the same position as the wind power plant, even if the equipment changes rapidly, the active and reactive power generated can be measured by the ES. To control the input active power at a fixed value, the aforementioned control methods cannot be realized except in the phase-control method. The shortcomings of the phase control are obvious, almost all circuit parameters are required, which is not applicable when the power side and the power generation side are far apart. Therefore, a simple power control strategy has been proposed by [PERSON] et al. [16], which enables simultaneous control of input active power and reactive power.  
From the PCC to the right, in Figure 5, which has been adopted from [PERSON] et al. [16], active power, reactive power, and apparent power can be represented by Equations (1)-(3), where \(P_{in}\) and \(Q_{in}\) represent the total active power and reactive power of the ES system, respectively. \(\widehat{S}\) denotes the apparent power.  
\[\begin{array}{l}\widetilde{S}=\overset{\rightarrow}{V}_{S}\cdot\overset{ \rightarrow}{I}\\ =V_{S}I_{1}[\cos(\phi-\psi)+j\sin(\phi-\psi)]\\ =P_{in}+jQ_{in}\end{array} \tag{1}\]  
where  
\[P_{in}=V_{S}I_{1}\cos(\phi-\psi) \tag{2}\]  
\[Q_{in}=V_{S}I_{1}\sin(\phi-\psi) \tag{3}\]  
According to [17], the active and reactive powers can be represented in terms of the variables on a rotating frame as follows.  
\[P_{in}=v_{d}i_{d}+v_{q}i_{q} \tag{4}\]  
\[Q_{in}=v_{q}i_{d}-v_{d}i_{q} \tag{5}\]  
where \(v_{d}\) and \(v_{q}\) are the components of \(v_{S}\) in the \(dq\) rotating frame, and the \(i_{d}\) and \(i_{q}\) are the components of \(i_{1}\) in the \(dq\) rotating frame.  
\[P_{in}=v_{d}i_{d} \tag{6}\]  
\[Q_{in}=-v_{d}i_{q} \tag{7}\]  
Figure 5: Power control scheme of ES-2 [16].  
At this step, the active power and the reactive power are decoupled. In actual control, the \(d\)-axis controller controls the \(P_{in}\) and the \(q\)-axis controller controls the \(Q_{in}\). If the control target is to regulate CL voltage instead of \(Q_{in}\), then the loop outside the \(q\)-axis will be added to the PI controller and its output will represent the reference to \(Q_{in}\), designated as \(Q_{in}\).  
The control strategy is simple, which has little dependence on the circuit, and can realize any two control targets among the active power, reactive power, and the critical load voltage. However, it is not decoupled completely.