esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments
### Datasets  
* ETT [4]: It consists of oil temperature readings of electrical transformers and six other factors affecting the temperature; collected from July 2016 and July 2018.
* Exchange [57]: It includes daily exchange rates of eight different currencies collected from 1990 to 2016.
* Electricity ([[https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams2011201](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams2011201)]([https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams2011201](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams2011201)) 4, accessed on 22 September 2021): This dataset contains electricity consumption recordings of 321 customers from 2012 to 2014.
* Weather ([[https://www.bgc-jena.mpg.de/wetter/](https://www.bgc-jena.mpg.de/wetter/)]([https://www.bgc-jena.mpg.de/wetter/](https://www.bgc-jena.mpg.de/wetter/)), accessed on 22 September 2021): A collection of measurements of 21 different meteorological indicators, such as air temperature and humidity, collected every 10 min throughout 2020.
* Traffic ([[https://pems.dot.ca.gov/](https://pems.dot.ca.gov/)]([https://pems.dot.ca.gov/](https://pems.dot.ca.gov/)), accessed on 22 September 2021): Records of readings collected hourly from sensors on San Francisco Bay area freeways, indicating the occupancy rate of roads; provided by the California Department of Transportation.
* ILI ([[https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html](https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html)]([https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html](https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html)), accessed on 22 September 2021): This dataset was collected by the Center for Disease Control and Prevention of the United States; it consists of the weekly counts of patients displaying influenza-like illness symptoms between 2002 and 2021.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 3 Research Progress in Flyer Velocity  
### Investigation of Flyer Velocity  
Generally, flyer velocity measurement technology can be divided into continuous methods and discrete methods. Continuous methods include displacement interferometers, velocity interferometers, and microwave interferometers. Photon Doppler velocimetry (PDV) and the laser displacement interferometer system for any reflector (DISAR) belong to the displacement interferometer category. The velocity interferometer system for any reflector (VISAR) and Fabry-Perot interferometer (FPI) are classified as velocity interferometers. The discrete method mainly involves optical beam blocking, magnetic velocity induction systems (MAVIS), sensor techniques, high-speed photography, and electrical probe methods.
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
###### Abstract  
Despite the efforts for minimizing the usage of heavy metals, anthropogenic activities still generate high amounts of wastewater containing these contaminants that cause significant health and environmental problems. Given the drawbacks of the conventional physical and chemical methods currently used, natural biosorbents (microbial cells or their products) arise as promising environmentally friendly alternatives. In this study, the binding efficiency of the polysaccharide secreted by _Enterobacter_ A47, FucoPol, towards lead (Pb\({}^{2+}\)), cobalt (Co\({}^{2+}\)), copper (Cu\({}^{2+}\)) and zinc (Zn\({}^{2+}\)) cations was demonstrated. FucoPol revealed a higher performance for the biosorption of Pb\({}^{2+}\), with a maximum overall metal removal of 93.9 \(\pm\) 5.3% and a specific metal uptake of 41.1 \(\pm\) 2.3 mg/gEPS, from a Pb\({}^{2+}\) solution with an initial concentration of 10 mg/L, by a 5 g/L FucoPol solution. The overall metal removal decreased considerably (\(\leq\)31.3 \(\pm\) 1.6%) for higher Pb\({}^{2+}\) concentrations (48 and 100 mg/L) probably due to the saturation of FucoPol's binding sites. Pb\({}^{2+}\) removal was also less efficient (66.0 \(\pm\) 8.2%) when a higher FucoPol concentration (10 g/L) was tested. Pb\({}^{2+}\) removal efficiency of FucoPol was maximized at pH 4.3, however, it was affected by lower pH values (2.5-3.3). Moreover, the FucoPol's sorption performance was unaffected (overall metal removal: 91.6-93.9%) in the temperature range of 5-40 \({}^{\circ}\)C. These findings demonstrate FucoPol's great potential for utilization as a biodegradable and safe biosorbent for treating waters and wastewaters contaminated with Pb\({}^{2+}\).  
biosorption; biosorbent; heavy metals; _Enterobacter_ A47; water/wastewater; exopolysaccharideaccharide; FucoPol +
Footnote †: journal: Applied Physics Letters
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## 4 Discussion
For instance, ALIA could not be estimated despite its high sensitivity throughout the covered spectral range [55]. Early in the development of PROSAIL it was stated that ALIA and LAI are highly correlated and therefore can hardly be separated in the inversion [56]. In fact, if LAI is inverted with an accuracy of 20%, there is no autocorrelation (R2 < 0.01) of the ALIA residuals, although the parameter itself could not be retrieved in any acceptable way. For an improvement of leaf pigment estimations, the new version of PROSPECT (Prospect-D [57]) is eagerly awaited for a more detailed representation of the leaf-biochemistry, namely the consideration of anthocyanins.
## 3 Test Results and Analysis
### Analysis of Measured Curves  
#### 3.3.1 Stress-Strain Curves of RAC Prism Specimens  
According to the load-displacement curve measured by the test, the stress-strain curve of the RAC prism specimens subjected to high temperatures can be obtained, as shown in Figure 8. It can be seen from the figure that the shape of the stress-strain curves of the RAC subjected to elevated temperature is similar to that of ordinary concrete. As the temperature increases, the peak stress of the curves gradually decreases, the peak strain gradually increases, the descending section of the curves becomes gentle, and the whole curve tends to be flat. As the replacement percentage increases, the effect of temperature on peak strain becomes more and more significant: at room temperature, the peak strain of ordinary concrete and RAC is between \(4\times 10^{-3}\) and \(5\times 10^{-3}\). At 800 \({}^{\circ}\)C, the peak strain of ordinary concrete is \(7.39\times 10^{-3}\), the replacement percentage is \(30\%\), \(50\%\), \(70\%\) of RAC is \(8.78\times 10^{-3}\), \(9.21\times 10^{-3}\) and \(10.74\times 10^{-3}\) respectively, while the peak strain of RAC with the replacement percentage of \(100\%\) is \(11.55\times 10^{-3}\).  
Figure 8: Stress-strain curves of recycled aggregate concrete (RAC) prism specimens. (**a**) r = 0%; (**b**) r = 30%; (**c**) r = 50%; (**d**) r = 70%; (**e**) r = 100%.
# Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
## 2 Supervised Dynamic Correlated Topic Model
### Parameter Estimation  
In this section, we present a method for parameter estimation. In particular, given the corpus \(\mathcal{C}\) with \(M\) documents and their associated responses denoted by \((\mathcal{D},\mathbf{y})_{1:M}\), we wish to estimate the model parameters \(\mathbf{\Phi}^{J\times J},\mathbf{\Sigma}^{J\times J},\mathbf{\beta}^{J\times V}\), and \(\mathbf{\eta}^{J\times C}\) in order to maximize the log likelihood of the data given by  
\[l(\mathbf{\Phi},\mathbf{\Sigma},\mathbf{\beta},\mathbf{\eta}\mid(\mathcal{D},\mathbf{y})_{1:M})= \sum_{d=1}^{M}\log p(\mathcal{D}_{d},\mathbf{y}_{d}|\mathbf{\Phi},\mathbf{\Sigma},\mathbf{\beta },\mathbf{\eta}). \tag{8}\]  
Because the log likelihood is analytically intractable, we consider a tractable lower bound on the log likelihood by summing the ELBO for \(\log p(\mathcal{D}_{d},\mathbf{y}_{d}|\mathbf{\Phi},\mathbf{\Sigma},\mathbf{\beta},\mathbf{\eta})\), defined in Section 2.1 over \(M\) documents. We estimate the model parameters via an alternating variational EM (VEM) procedure [26] described below:  
**E-Step**: For each document, we find the optimizing values of the variational parameters described in Section 2.1.  
**M-Step**: Maximize the resulting lower bound on the log likelihood with respect to the model parameters.  
In Appendix A, we derive the lower bound on the log likelihood defined in Equation (8) and provide the updated equations in order to estimate the model parameters. We estimate model parameter \(\mathbf{\beta}\) using a fixed-point update. The model parameters \(\mathbf{\Phi}^{J\times J}\) (with the stationarity condition), \(\mathbf{\Sigma}\) (with the positive definite condition), and \(\mathbf{\eta}\) are estimated using a randomized search optimization technique, simulated annealing, which we describe in the next section.  
For parameter estimation, we use frequentist methods to estimate the unknown parameters of the sDCTM model. As an alternative, one can set up a Bayesian model for parameters estimation in the sDCTM model by employing the Minnesota prior on \(\mathbf{\Phi}\)[27], inverse Wishart or Lewandowski-Kurowicka-Joe (LKJ) prior on \(\mathbf{\Sigma}\), Dirichlet prior on \(\mathbf{\beta}\), and multivariate normal prior on \(\mathbf{\eta}\).
## 2 Materials and Methods
### Analysis of Relative Contribution of Urban ERS  
This study analyzed the relative contribution of each ERS by pairwise comparison in order to understand the interrelationship between them in detail. When there are multiple criteria in decision-making, pairwise comparison analysis is one of the leading methods used when estimating the significance (or importance) of several factors and details [67,68]. This method has an advantage to judge relative importance (contribution) of ERS in this study accurately [69]. A pair of two detailed elements is built to determine the relative importance, and such pairwise comparison is repeated to form a matrix of \(n\) assessment elements. The eigenvectors for each assessment element are calculated based on the constructed matrix, and the sum of the eigenvectors is 1. In other words, in a situation wherein the sum of all the elements of the ERS is assumed to be 1, the contribution of each element is determined based on the interrelationship. Based on this pairwise comparison analysis method, the relative contribution to the ERS was analyzed, as shown in Figure 3. The assessment results of the selected ERS need to be normalized because they have different units and scales. In this study, all the analysis results were normalized by 0-1 using the Min-Max method, as shown in Equation (1).  
\[N=\frac{X-\text{min}(X)}{\text{max}(X)-\text{min}(x)} \tag{1}\]  
\[N:\text{Normalized value, }X:\text{Original value, }\min(X):\text{Min value, }\max(X):\text{Max value}\]  
Matrix A was then constructed by deriving slopes through linear regression slope analysis between ERS. In this study, a 4 \(\times\) 4 matrix is formed because four regulating services have been selected. Next, the relative contribution was calculated by calculating the eigenvectors of the ERS elements based on the set matrix. In addition, the reliability of the analysis result was verified by checking the CI (Consistency Index) and CR (Consistency Ratio) values to diagnose the consistency of the calculated contribution.  
The relative contribution of the ERS was calculated by dividing the entire study area by type of urban space, and the balance of the ERS was observed based on the analysis result. In other words, if the ERS selected in this study were ideally balanced, the eigenvector value would be equally calculated as 0.25. The balance of ERS by spatial type was diagnosed using the standard deviation of contribution.
## 1 Introduction
This allows for the dynamic control of the radiation pattern without requiring mechanical movement. However, traditional phased arrays have been constrained by factors such as limited impedance bandwidth, reduced scan volume, and significant size and weight requirements, which have historically impeded their widespread deployment [17]. Recent advances in fabrication technologies, notably the integration of impedance matching and phase control within lithographically defined microstrip feed networks, have helped mitigate these limitations. This approach enables the realization of higher-gain arrays without substantial increases in manufacturing complexity or cost, as feed network optimization is performed concurrently with the fabrication of the radiating elements. This paper presents the design, simulation, prototyping and testing of an innovative receiving antenna array intended for use in a bistatic radar system. The array is designed to operate at a center frequency of 412 MHz, with an operational bandwidth of approximately 10 MHz, spanning from 407 MHz to 417 MHz. The proposed architecture consists of 19 radiating elements arranged in a hexagonal lattice, with a maximum array dimension of approximately 3 m. The adoption of a hexagonal configuration alters the angular periodicity of the element distribution, effectively suppressing the formation of grating lobes and enhancing the beam-steering performance of the receiving antenna [18]. Thissetup effectively reduces the Side Lobe Level (SLL) to \(-17\) dB, underscoring the importance of suppressing unwanted radiation while achieving the targeted array gain of 17 dBi. Each radiating element within the array system is implemented as a circular slotted-patch antenna integrated with a cavity-based metallic superstrate. It features dual feed points, enabling the reception of signals with both Right-Hand Circular Polarization (RHCP) and Left-Hand Circular Polarization (LHCP) signals. The array exhibits radiation characteristics comparable to those of an equivalently sized parabolic reflector, while offering superior beam-steering capabilities. Moreover, it ensures dual circular polarization reception, which is essential for space applications such as the one considered in this work. A prototype of the novel design has been fabricated and tested, confirming that the measured results are in excellent agreement with full-wave simulation data, thereby validating both the electromagnetic model and the practical feasibility of the proposed solution. The paper is organized as follows: Section 2 outlines the characteristics and performance of selected European radar sensors currently active in space surveillance and tracking. Section 3 focuses on presenting the design and analysis of the array system, detailing its individual elements and the associated feeding network. Sections 4 and 5 present the simulated performance of the dual-CP array and the experimental results obtained from the prototype measurements, respectively. Finally, Section 6 summarizes the main contributions of this work and outlines potential directions for future research.
## 4 Enabling Technologies for FSO Communication
### Modulation Schemes
#### 4.3.2 Advanced Modulation Techniques
Additionally, SIM systems face the challenge of requiring a time-varying DC bias to restrain the transmitted signal from going negative [83; 167; 184; 191]. MIMO spatial multiplexing (SM) techniques are also taken into account when designing spectrally efficient FSO systems. However, these systems face several challenges, including inter-channel interference (ICI) from simultaneous transmissions, increased complexity in transceiver design to mitigate ICI, high computational complexity, the need for inter-antenna synchronization, and the need for numerous components to implement MIMO transceivers [152; 167; 184]. SM, initially developed for antenna-based systems, marked a shift towards free inter-antenna synchronization (IAS) [197; 198]. In this approach, only one antenna is activated during each time slot, keeping the other transmission antennas inactive. This means SM requires just a single RF chain, making it a more cost-effective and less complex MIMO transmission structure that is resilient to IAS and ICI [158].
# Future Perspectives for Gamma-ray Burst Detection from Space
## 12 Theseus  
### Mission Overview  
The Transient High-Energy Sky and Early Universe Surveyor ([[https://www.isdc.unige.ch/theseus/](https://www.isdc.unige.ch/theseus/)]([https://www.isdc.unige.ch/theseus/](https://www.isdc.unige.ch/theseus/))) (THESEUS) mission concept aims to fully exploit GRBs for investigating the early universe, around the epoch of re-ionization, and substantially advancing multi-messenger astrophysics (see Figure 20). THESEUS is planned to also simultaneously increase the discovery space of high-energy transient phenomena and allow tests of fundamental physics. The core science goals of THESEUS are summarized as follows:  
* Investigating the first billion years of the universe through high-redshift GRBs, thus shedding light on the main open issues in modern cosmology, like (i) the population of primordial low-mass and -luminosity galaxies; (ii) the drivers and evolution of cosmic re-ionization; and (iii) the star-formation rate (SFR) and metallicity evolution up to the \"cosmic dawn\" and across Pop-III stars.
* Providing a substantial advancement of multi-messenger and time domain astrophysics by enabling the identification, accurate localization, and study of (i) electromagnetic counterparts to sources of gravitational waves and neutrinos, which will be routinely detected in the mid-2030s by the second- and third-generation gravitational wave (GW) interferometers and future neutrino detectors and (ii) all kinds of GRBs and most classes of other X-/gamma-ray transient sources.  
The achievement of these scientific objectives will be possible by a mission concept including: (a) a set of innovative wide-field monitors with an unprecedented combination of a broad energy range, sensitivity, FoV, and localization accuracy and (b) an on-board autonomous fast follow-up in the optical/NIR band, arcsec location, and redshift measurement of detected GRB/transients.  
THESEUS has been selected twice by the European Space Agency (ESA) for a phase A study (in 2018 and 2022), aiming at demonstrating its technological and programmatic feasibility within the boundaries of a medium-sized mission. THESEUS is currently undergoing  
Figure 20: Examples of THESEUS’s capabilities for multi-messenger and time domain astrophysics.  
its second phase A study, with a further selection step expected in 2026 and eventually a launch planned in 2037. Nominal scientific operations are being planned for four years, but the lack of on-board consumables makes it feasible for the mission to extend operations in space well beyond the nominal lifetime.
## 4 Results and Evaluation
### Recommendation System for HVAC Systems  
For the second use case, the evaluation was much simpler compared to the first use case, as the target variable of this RS is a numerical value (functionality level of a smart-ready service). After running the code, the following results were obtained, which are presented in Table 8.  
The resulting MAE is 0.33 and the RMSE is 0.57. In this case, it is important to note that the MAE and RMSE values do not fully depict the accuracy of the produced model, since there are three instances in the test set and the algorithm predicts correctly two times for the instances in the test set. To make a sound decision on this, and to find out if the above-mentioned results are acceptable or not, a DT algorithm was also used, yielding the results presented in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Wall U Value** & **Roof U Value** & **Wall Insulation** & **Roof Insulation** & **Window U Value** \\ \hline Random Forest & MAE = 0.0326 & MAE = 0.0349 & MAE = 0.0263 & MAE = 0.0238 & MAE = 0.0471 \\  & RMSE = 0.0423 & RMSE = 0.0430 & RMSE = 0.0329 & RMSE = 0.0316 & RMSE = 0.0647 \\ Linear Regression & MAE = 0.0370 & MAE = 0.0331 & MAE = 0.0251 & MAE = 0.0305 & MAE = 0.0459 \\  & RMSE = 0.0449 & RMSE = 0.0387 & RMSE = 0.0300 & RMSE = 0.0455 & RMSE = 0.0554 \\ K-Nearest & MAE = 0.0374 & MAE = 0.0349 & MAE = 0.0234 & MAE = 0.0291 & MAE = 0.0512 \\ Neighbor & RMSE = 0.0464 & RMSE = 0.0436 & RMSE = 0.0294 & RMSE = 0.0393 & RMSE = 0.0723 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical evaluation results for different algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Wall Insulation** & **Roof Insulation** \\ \hline Linear Regression & 30.3 & 40.4 \\ Logistic Regression & 16.67 & 13.3 \\ Decision Tree Classifier & 60 & 57 \\ K-Nearest Neighbor & 11 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Categorical evaluation results for different algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Actual Functionality Level** & **Predicted Functionality Level** \\ \hline
3 & 2 \\
2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: KNN algorithm performance.  
Based on the presented results, it becomes apparent that the DT algorithm exhibits limitations in providing accurate predictions. Furthermore, it appears to predict values dynamically. Something like that may not be desirable, as the target variable represents the functional level of a smart service and cannot take on decimal values. Therefore, it is much more suitable to use the KNN algorithm, as it predicts values based on similarity rather than dynamically calculating them.
## 4 Algorithms to Determine the Diameter of the DG Tree and Minimum Delay for Data Aggregation
### Algorithm to Determine the Minimum Aggregation Delay of a Data Gathering Tree
```
Input: DG Tree \(T_{\textit{Max}}^{\textit{BNF}}(V,E_{\textit{T}})\), Leader Node \(s\) Output: Diameter, Nodes-At-Levels, Child-Nodes-List Auxiliary Variables: Nodes-Explored; Candidate-Nodes-List, Child-Nodes-List; Neighbor-List, Node-Level Initialization:\(\forall i\in V:\textit{Neighbor-List}[i]=\phi\), Node-Level\([i]=0\); Child-Nodes-List\([i]=\phi\) Nodes-At-Levels\(=\phi\); Diameter \(=0\); Nodes-Explored \(=\phi\); Candidate-Nodes-List \(=\phi\)
1
```
**Begin**_Algorithm Diameter-DG Tree_
```
1 for(\(\mathrm{edge}\ (u,\ v)\in E_{\textit{T}}\))do
2 Neighbor-List\([u]=\textit{Neighbor-List}[u]\ \cup\ \{v\}\)
3 Neighbor-List\([v]=\textit{Neighbor-List}[v]\ \cup\ \{u\}\)
4 endfor
5 Candidate-Nodes-List \(=\{s\}\)
6 Node-Level\([s]=0\) Nodes-At-Levels\([0]\).add\((\{s\})\) // adds the leader node \(s\) to the list of nodes at level 0
7 Nodes-Explored \(=\textit{Nodes-Explored}\ \cup\ \{s\}\) while(Candidate-Nodes-List \(\
eq\phi\))do
8 Node \(u=\mathrm{Dequeue}\)(Candidate-Nodes-List) // removes \(u\) from Candidate-Nodes-List for( Node \(v\in\textit{Neighbor-List}(u)\) AND \(v\
ot\in\textit{Nodes-Explored}\)) do
9 Node-Level\([v]=\textit{Node-Level}[u]+1\) Nodes-At-Levels\([\textit{Node-Level}[v]]\).add\((\{v\})\) Child-Nodes-List\((u)=\textit{Child-Nodes-List}(u)\ \cup\ \{v\}\) Nodes-Explored \(=\textit{Nodes-Explored}\ \cup\ \{v\}\) Candidate-Nodes-List \(=\textit{Candidate-Nodes-List}\ \cup\ \{v\}\)
10 if(Diameter \(<\textit{Node-Level}[v]\))then
11 Diameter \(=\textit{Node-Level}[v]\)
12 endif
13 endfor
14 endwhile
15 returnDiameter, Nodes-At-Levels, Child-Nodes-List
```
**End**_Algorithm Diameter-DG Tree_
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 3 Fast Computation Strategies  
Numerous FSMPC proposals for MPS address the issue of fast computation. Some of these approaches combine multiple strategies. For example, in the context of Virtual Voltage Vectors, techniques such as control set reduction, elimination of weighting factors, and cost function simplification can be found. To provide a meaningful classification, the following fundamental techniques are considered.