esa-sceva/satcom-chunk-collection huggingface dataset:
# Worldwide Coverage Mobile Systems for
## 4 Ku-Band Antennas Design Techniques
### Offset Antenna  
Figure 3 draws the offset antennas are composed of a reflector that is an oval-shaped section of a paraboloid, i.e., the effective antenna surface is not round, it is an ellipse. Additionally, the focal point is not situated at the centre of the plate, it is shifted to one side a certain offset. The main advantage which exhibits this technology exhibits is that the antenna reflector surface is not shadowed by the LNB (from the satellite perspective).  
Figure 3: Offset antenna.  
Figure 2: Primary focus antenna.  
The shape of the reflector and set-up shows and additional deployment advantage: it is not easy that snow or any other substance accumulates on the reflector that would disturb the signal making the antenna gain drop drastically. Due to the reflector shape, it seems the antenna is not oriented appropriately as it is not oriented facing the satellite straight, as in the primary focus antenna. It is shifted about 25\({}^{\circ}\) downwards (in the vertical axis). Nevertheless, the offset plate seems as circular with the horizontal diameter in the plane of view from the satellite. This disposition makes the feeder does not produce a shadow on the reflector. Multibeam, Cassegrain and Gregory are other types of offset antennas, which are often used in satellite communications.
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane
### Transmission Media  
Transmission media convey either data between the forwarding elements or data, control channel information, or management channel information between data plane elements and the control plane or the management plane. There are basically three types of transmission media: wired, optical, and wireless, which are briefly discussed in the following sections.
## 3 Results and Discussion
### Lipid Production of Microalgae Cultured in the Mixed Wastewater
The lipid productivity of microalgae increased considerably with an increase in the mix proportion of FW. A large increase in lipid productivity was observed at the mixing ratio of SW:DFW = 1:19. This reached 49.48 mg L\({}^{-1}\) d\({}^{-1}\), which was 4.9 times greater than that obtained in the wastewater at the mix ratio of SW:DFW = 1:0.5. Compared to photoautotrophic cultivation, algal biomass concentration was markedly enhanced using the mixotrophic culture of microalgae, and this resulted in higher lipid productivity [15; 24; 41; 44]. Therefore,  
Figure 4: Lipid content and productivity of _C. pyrenoidosa_ cultured in mixed wastewater with different mixture ratio of SW to DFW. Data are shown as mean \(\pm\) SD, n = 3. Different letters in the same column represent significant differences (\(p\leq\) 0.05). the combination of SW and FW used to culture microalgae had a distinct advantage in the production of microalgae lipids. Microalgae cultured in a mixture of SW and FW appears to be a sustainable and cost-effective process for simultaneously treating these two types of wastewaters and efficiently producing algal lipids for biofuel production. However, it should be noted that the present study is a preliminary exploration of treating SW and FW using microalgae. There are many technical issues to be solved before commercialization of this method, such as the low efficiencies of nutrient removal, complex wastewater pre-treatment (including sterilization), stability of culture performance at a large scale. The evaluation of microalgae culture in mixed wastewater after pre-treatment without sterilization should be carried out, and algae-bacteria symbiotic systems should be tested by adding additionally microorganism and applied to improve the nutrient removal efficiency. Other work including process optimization, scale up, pilot experiment, special culture system design and demonstration all need to be carried out step by step in the future to move this technology towards the real application.
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 3 Results and Discussion
### Adsorption Thermodynamics
This does not mean that this adsorbent possesses the highest adsorption capacity, but it has the potential to target the solute molecules at lower concentrations. The adsorption isosteric heat ranges from 5.6 to 27.5 kJ/mol, 15 to 53.6 kJ/mol, and 3.5 to 23.8 kJ/mol for the biochar, hydrochar, and activated carbon, respectively. According to \(q_{\rm st}\) at zero coverage, BC700 shows a strong affinity towards ammonium, followed by nitrate and phosphate. For the case of nitrate and phosphate removal, HC180 shows a stronger affinity towards this solute, followed by BC700 and then AC. Another noteworthy observation is that the \(q_{\rm st}\) of BA700 for ammonium and HC180 for nitrate is around 28 and 54 kJ/mol, respectively. These values indicate that adsorption proceeds via chemisorption during the early stage of adsorption. Figure 6: Plot of thermodynamic parameters, \(\Delta G\), \(\Delta H\), and \(\Delta S\) for the (**a**) adsorption of ammonium onto BC700, (**b**) adsorption of ammonium onto HC180, (**c**) adsorption of ammonium onto AC, (**d**) adsorption of nitrate onto BC700, (**e**) adsorption of nitrate onto HC180, (**f**) adsorption of nitrate onto AC, (**g**) adsorption of phosphate onto BC700, (**h**) adsorption of phosphate onto HC180, (**i**) adsorption of phosphate onto AC. A large number of previous studies have indicated that the adsorption of ammonium [51; 52; 53], nitrate [54; 55; 56; 53], and phosphate [57; 58; 59] is typically exothermic, agreeing with our results (note that the \(\Delta G\) values obtained in this work are independent of the temperature). To gain additional insights into Figure 6, we plotted the adsorption capacity of NH\({}_{4}\)\({}^{+}\), NO\({}_{3}\)\({}^{-}\), and PO\({}_{4}\)\({}^{3-}\), which exhibit an upward trend with an increase in the process temperature in Figure 7. Based on the data, it can be inferred that the adsorption of all three ions onto BC700, HC180, and the AC was facilitated by higher adsorption temperatures. This clearly shows no correlation with \(q_{st}\) at zero coverage. This can be expected, as the adsorption at higher concentrations is independent of the binding affinity of the adsorption at zero coverage. Nevertheless, Figure 7 delivers an important message that higher temperatures promote more favourable adsorption, which agrees with some of the earlier works [60; 61].
## 5 Numerical Results
### A Complex Combination Object of Monopole Antenna Arrays and Cuboid  
As shown in Figure 4, a complex combination object of monopole antenna arrays and cuboid is considered. The object is discretized with 30,988 triangular patches to generate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ex.** & **Computing Method** & **CPU Time Cost of \(Z^{max}\)(s)** & \begin{tabular}{c} **CPU Time Cost of the** \\ **Near Part in \(Z^{max}\)(s)** \\ \end{tabular} \\ \hline \multirow{2}{*}{A PEC Cube with four monopole antennas} & Direct RFGG-FG-FFT & 103 & 0.5 \\  & The proposed method & 5 & 0.5 \\ \multirow{2}{*}{A complex combination object of monopole antenna arrays and cuboid} & \begin{tabular}{c} FGG-FG-FFT \\ \end{tabular} & 123 & 0.7 \\  & Direct RFGG-FG-FFT & 2320 & 46 \\ \multirow{2}{*}{A complex combination object of monopole antenna arrays and cuboid} & \
\begin{tabular}{c} The proposed method \\ FGG-FG-FFT \\ \end{tabular} & 84 & 44 \\ \multirow{2}{*}{A complex combination object of monopole antenna arrays and cuboid} &
\begin{tabular}{c} FGG-FG-FFT \\ \end{tabular} & 2718 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CPU time required for the example at 24 GHz.  
Figure 3: Comparisons of the bistatic RCS of PEC Cube with four monopole antennas at 26 GHz.  
Figure 2: Comparisons of the bistatic RCS of PEC Cube with four monopole antennas at 17 GHz.  
\(N=\)46,482 RWG basis functions. The wideband EM scattering over 15 GHz to 30 GHz is analyzed. \(\Delta f=1\) GHz is used as the interval.  
The results are compared with the bistatic RCS at 19 GHz and 28 GHz is shown in Figures 5 and 6. Figure 7 shows the broadband RCS curve with scattering angle \((\theta^{s},\phi^{s})=(130^{\circ},0^{\circ})\) obtained by using the RFGG-FG-FFT with cube polynomial inter/extrapolat- ion method, direct RFGG-FG-FFT and FGG-FG-FFT. The results obtained by three algorithms shows that they have fine consistency. It is obvious that the three sets of results agree well. Table 1 lists the CPU time costs by three methods at 24 GHz. Therefore, it can be found that the proposed method significantly enhances the computing efficiency. The CPU time required for direct RFGG-FG-FFT and the proposed method are 961 and 510 min, respectively.  
Figure 4: A complex combination object of monopole antenna arrays and cuboid.  
Figure 5: The bistatic RCS of complex combination object at 19 GHz.
## 2 Related Work  
In recent years, researchers have tried to overcome these shortcomings, and the iterative regularization method for approximating the solution of the inverse problem has become widely used [12]. This method involves solving linear or non-linear equations iteratively and includes stopping criteria to control the number of iterations. Another method is the double iterative optimization algorithm, which constructs a new regularization matrix based on the delayed-sum beamforming algorithm and cross-spectrum operation [13]. In tandem with the iterative method, the algorithm optimizes the new regularization matrix and beam output. As a result, two iterations with fewer steps effectively improve the accuracy and stability of sound source recognition. [PERSON] et al. introduced a new approach to solving the inverse problem by combining model solving and example learning [14]. They illustrate this approach using compressed sensing nuclear magnetic resonance imaging as an example. The authors demonstrate how to combine the model solving of the compressed sensing model with deep learning based on example learning [15], forming a new method for solving CS-MRI problems. [PERSON] utilized the alternating direction multipliers method in L1 regularization [16], comparing and analyzing the reconstructed image and evaluation parameters of simulated and measured data using the Gauss-Newton (GN) method, back-projection algorithm [17], GREIT algorithm [18], direct sampling method (DSM) [19], and imaging algorithm based on Structure-Aware Sparse Bayesian Learning [20]. The results show that this approach has good anti-interference performance, with minimal impact on reconstruction and the slightest change range of evaluation parameters. The authors in [21; 22] suggest a brand-new approach to model-based image reconstruction that fits well and needs fewer iterations than traditional optimization-based techniques.  
After conducting extensive investigation into algorithms for reconstructing EIT technology, it has come to light that conventional methods such as the back-projection technique and Gauss-Newton (GN) [23] approach are inadequate when it comes to addressing the ill-conditioned and underdetermined issues associated with the inverse problem of EIT [24]. As a result, the resulting image is significantly distorted. Although regularization algorithms partially improve the ill-conditioned nature of the EIT inverse problem [25], the quality of the reconstructed image remains unsatisfactory due to the inherent soft field characteristics of EIT. On the other hand, swarm optimization algorithms used as inverse EIT problem solutions can address ill-posed and underdetermined problems, but they are prone to becoming stuck in local minima, and the solution process often takes a long time [26]. Neural networks [27] and deep learning algorithms [28], with strong non-linear fitting capabilities, offer a more suitable solution. Deep learning techniques have a significant impact on enhancing the resolution of EIT reconstructed images. These algorithms play a vital role in enhancing the contrast of such images and effectively reducing noise interference.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 2 Need of Sensors in Transmission and Distribution Systems
Distributed energy sources could inject or draw large leading or lagging reactive power and affect the offset load current, voltage drop, and power quality of the grid, thereby increasing harmonics, DC injections and flickers [39]. Furthermore, distributed energy sources can have a variety of effects on the short-circuit current leading to improper protective device coordination and unintended islanding in microgrids, causing equipment damage and delay in service restoration [40]. Finally, the increase in penetration of distributed energy sources reduces the number of conventional synchronous generators in the grid, thereby decreasing the grid inertia. This makes the grid very sensitive to disturbances, thereby increasing the grid instability [41]. These factors put a detrimental effect on the ability of the power grid to recover from faults and disturbances and continue power flow services, affecting the overall power system security. Various such characteristics of the current structure of the power grid have been tabulated in Table 1 along with the challenges that they pose. Such challenges call for superior power grid monitoring, control, and management systems that will heavily rely on the real time power system variables that can be gathered by the actuators and sensors. Furthermore, the availability of realistic power system data builds sturdier and better models for classifications and predictions of power system faults, thereby improving power system security. Thus, suitable sensors have been developed that will help address the above issues by continuously monitoring the steady states as well as the transient states of the power grid, leading to its forthcoming transformations and better utilization. Today, compact, cost effective, low powered, and contactless sensors are being used for steady state monitoring of the transmission cables, substations, and distribution networks that were otherwise conducted by bulky, expensive, and invasive instruments e.g., inductive coils, instrument transformers, etc. These sensors enable the monitoring of a wide range of system grid parameters. For example, the lightning currents can be in the range of kiloamps, and the harmonic currents can be in the range of a few amps, whereas the leakage currents might be in the range of milliamps. Since sensors have a wide range of sensitivity and their properties enhance with multilayering fabrication techniques, they can easily accommodate this wide range of measurement. Furthermore, sensors can be used for distributed monitoring [3]. Multiple heterogeneous sensors constituting wireless sensor networks are being installed on multiple nodes of the transmission and distribution system as opposed to the current monitoring devices that are placed only on certain instruments and have a limited range of protection.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
###### Abstract  
With an ever increasing demand for data, better and efficient spectrum operation has become crucial in cellular networks. In this paper, we present a detailed survey of various resource allocation schemes that have been considered for the cellular network's operation in the unlicensed spectrum. The key channel access mechanisms for cellular network's operation in the unlicensed bands are discussed. The various channel selection techniques are explored and their operation explained. The prime issue of fairness between cellular and Wi-Fi networks is discussed, along with suitable resource allocation techniques that help in achieving this fairness. We analyze the coverage, capacity, and impact of coordination in LTE-U systems. Furthermore, we study and discuss the impact and discussed the impact of various traffic type, environments, latency, handover, and scenarios on LTE-U's performance. The new upcoming 5G New Radio and MulteFire is briefly described along with some of the critical aspects of LTE-U which require further research.  
C 2020, 9, 1464; doi:10.3390/electronics901464
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems
### Application and Platform Models  
The application model within the ATMA is a detailed blueprint of the computational tasks that are to be executed on the system. A directed acyclic graph (DAG) describes it, specifying each task's deadlines and worst-case execution times (WCETs). It also outlines the communication patterns, detailing each message, its sender, and its recipient tasks, thereby establishing the precedence constraints that dictate the order in which tasks should be executed and messages sent.  
The platform model serves as a comprehensive inventory of the hardware resources available for executing the tasks defined in the application model. An undirected acyclic graph is used to describe the platform model. It informs the meta scheduler about the specifics of the hardware environment, such as the number and types of processing cores, memory capacities, input/output interfaces, routers, and the pattern of interconnections between routers, which together form the network-on-chip (NoC).  
This model enables the meta scheduler to comprehend the physical constraints and capabilities of the hardware, thereby facilitating the optimal allocation of tasks to cores and the routing of messages through the NoC. By providing a detailed map of the hardware, the platform model enables the meta scheduler to generate schedules that not only meet the temporal and resource requirements of the application model but also adhere to the physical realities of the hardware, ensuring that the computed schedules are feasible for actual implementation on the given system.  
Figure 2(a),b show an example for the application and platform models, respectively.  
Figure 2: Metascheduling with Application, Platform, and Context Models.
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Evaluation Methods
#### 4.3.1 Subjective Evaluation Methods  
Subjective evaluation methods refer to the visual assessment of the fused image's quality through human observation. Observers typically compare aspects such as color preservation, spatial detail, edge retention, brightness, and clarity of the fused image. Since visual evaluation aligns with practical application needs, subjective evaluation methods are highly intuitive. However, in real-world applications, subjective evaluation methods have several limitations: first, since subjective evaluation primarily relies on the observer's visual perception, it is easily influenced by external factors such as environmental conditions and the observer's mental state. Second, for fused images with similar quality levels, the boundary for distinction is often unclear. Third, subjective evaluation entails higher labor costs. As a result, more scientific approaches are required to assess the effects of fusion accurately.
## 3 Results
### Classification Results
#### 3.3.1 HRV Features Only  
There was a total of 40 features for this set. This is due to the measured HRV for each session (10 HRV features \(\times\) 4 sessions). TPOT constructed a pipeline consisting of Recursive Feature Elimination (RFE), Binarizer and Decision Tree Classifier. RFE is a feature selection method eliminates features by ranking them by importance [49; 50]. The binarizer transforms the data to 0 or 1 according to a threshold. The decision tree classifier is a supervised learning algorithm that is used for classification and regression. As with its name, it splits the data into branches until it reaches a threshold value. A decision tree is composed of root nodes, children nodes, and leaf nodes. Using a decision tree classifier has the following advantages: (1) the data does not need to be normalized or scaled, (2) fast and simple, compared to other classifiers, and (3) can handle both categorical and continuous data [51].  
After using LOO cross-validation, an accuracy of 68.75% was achieved. It was also able to achieve a sensitivity of 66.67% and a specificity of 70.83% for MCI. The confusion matrix, with recall and precision rates, is shown in Table 7.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**Game**} & \multicolumn{1}{c}{**NC**} & \multicolumn{1}{c}{**MCI**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \hline \multicolumn{1}{c}{Nostalgic Game} & \multicolumn{1}{c}{23.3333 (1.7110)} & \multicolumn{1}{c}{19.1250 (4.7026)} & \multicolumn{1}{c}{0.000 *} \\ \hline \multicolumn{1}{c}{Whack-a-Mole Game} & & & & \\ \hline \multirow{2}{*}{Level 1} & Hit Ratio & 0.9958 (0.0204) & 0.8318 (0.2476) & 0.001 * \\  & Response Time & 1.2318 (0.2083) & 1.8416 (0.8458) & 0.000 * \\ \hline \multirow{2}{*}{Level 2} & Hit Ratio & 0.9792 (0.0415) & 0.8455 (0.3035) & 0.051 \\  & Response Time & 1.0992 (0.1603) & 1.5788 (0.6251) & 0.000 * \\ \hline \multirow{2}{*}{Level 3} & Hit Ratio & 0.9167 (0.0637) & 0.7591 (0.2889) & 0.024 * \\  & Response Time & 1.0238 (0.1263) & 1.4606 (0.6348) & 0.000 * \\ \hline \multicolumn{1}{c}{Hit-the-Ball Game} & & & & \\ \hline \multirow{2}{*}{Level 1} & Hit Ratio & 0.9500 (0.0780) & 0.9034 (0.1528) & 0.203 \\  & Response Time & 0.3212 (0.2770) & 0.5652 (0.5996) & 0.085 \\ \hline \multirow{2}{*}{Level 2} & Hit Ratio & 0.9958 (0.0204) & 0.8818 (0.2108) & 0.004 * \\  & Response Time & 0.1068 (0.1147) & 0.2490 (0.3424) & 0.090 \\ \hline \multirow{2}{*}{Level 3} & Hit Ratio & 1.0000 (0.0000) & 0.8818 (0.1991) & 0.000 * \\  & Response Time & 0.2005 (0.1073) & 0.4485 (0.3839) & 0.001 * \\ \hline \hline \end{tabular} Data are expressed as mean (standard deviation); \(p<0.05\).  
\end{table}
Table 6: Game Results and Statistical Analysis between the two groups.
# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 2 Materials and Methods
### Background
Additional data manipulation methods (data augmentations) are applied to achieve improved results. These techniques help models generalize better by exposing them to a wider variety of image conditions. The following data augmentation techniques for object detection from satellite images are frequently employed: rotating, flipping, shifting, translating, and clipping [11; 31; 32].
## 3 Novel Approach EKF Tuning
### Mathematical Modeling
#### 3.1.2 INS Mechanization  
For INS mechanization using Equations (1)-(3), the attitude is propagated using quaternions. These equations are a simplification of those presented by [PERSON] [30], where they are detailed in both continuous and discrete forms. In this work, the continuous form of the equations was used, with the fourth-order Adams-Bashforth predictor method applied for numerical integration. A detailed explanation of these equations is beyond the scope of this work.  
Propagation of attitude using quaternions is just one part of the INS process; understanding and accounting for gravitational effects is equally critical. Since accelerometers cannot distinguish the effects of the gravitational field in their acceleration measurements, accurate knowledge of the gravitational field is necessary for INS calculations. The gravity model used in this work is based on the WGS-84 standard, which accounts for the Earth's oblateness. This model estimates gravitational components based on latitude, longitude, and height (lat, long, h) relative to the Earth's center.  
The results of the INS mechanization equations yield positions in terms of latitude, longitude, and height, with velocities expressed in the NED coordinate frame. Additionally, the variances of positions, velocities, and attitude are calculated incrementally. A detailed explanation of this incremental calculation will be provided in the next subsection.