esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Possible Change in Inclination  
Further analyses concerned performing orbital manoeuvres by nanosatellites in order to maximise the duration of the satellite's flight over the area of interest. The aim of the third analysis was to find an orbit with the inclination that will be optimised in terms of fuel consumption while performing the manoeuvre of changing the inclination of the orbit and the duration of flight over the given area. The analysis took into consideration two cases: (1) The weight of fuel is constant and is 0.5 kg, but the weight of the satellite, however, takes different values. Depending on the quality of the telescope, it may range from 8 to 18 kg. (2) The initial weight of the satellite is 15 kg, and the fuel weight ranges from 0.3 to 0.7 kg, at intervals of 0.05 kg.  
Table 9 presents the results of the analysis of the influence of the initial weight of the satellite and variable fuel weight on the possible change in the inclination of the orbit. With the increase in the effective exhaust velocity and growing increase in velocity, a gradual increase in the possible change in inclination is observed. Detailed results reveal that higher fuel weight and specific impulse enable a larger change in inclination, while at the same time prolonging the duration of the manoeuvre.  
As illustrated in Figure 6, the simulation of the inclination change manoeuvre demonstrates the orbital geometry before and after the adjustment. The comparison of the results of analytical calculations and computer simulation in the STK 12.7.1 software for the inclination change manoeuvre for a nanosatellite of the weight of 8 kg, a difference in the increase in velocity was noted, of 98.63 \(\frac{\text{m}}{\text{s}}\) in the analytical calculations and 95.84 \(\frac{\text{m}}{\text{s}}\) in STK 12.7.1, which means a difference of approx. 2.79 \(\frac{\text{m}}{\text{s}}\).  
This difference is caused mainly by the higher accuracy of the STK 12.7.1 numerical model, which takes into account additional factors, such as propulsion characteristics and potential environmental influences that are not taken into account by simplified analytical assumptions. In spite of that, both approaches are consistent, and the results of STK 12.7.1 simulations are more realistic in the context of the actual conditions. More detailed conclusions are provided in Section 4--Discussion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **End Weight** \\ **of the** \\ **Satellite** \\ **m\({}_{\text{k}}\) [kg]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Fuel** \\ **Specific** \\ **Weight** \\ **m\({}_{\text{f}}\) [kg]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Specific** \\ **Impulse** \\ **I\({}_{\text{sp}}\) [s]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Effective** \\ **Exhaust** \\ **Velocity** \\ **V\({}_{\text{v}}\) [\(\frac{\text{m}}{\text{s}}\)]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Weight of Fuel** \\ **Spent to** \\ **Perform the** \\ **Maneovre** \\ **m\({}_{\text{f}}\)
# Understanding Microgrid Sustainability: A Systemic and Comprehensive Review
## 3 Energy Field
### Energy Elements  
In this work, the energy component is composed of four types of elements: sources, storage, loads, and power electronics converters [99].  
There is a wide variety of sources that can be connected to microgrids [100], which are mainly separated into two main types: conventional generators (diesel generator, gas turbine) and non-conventional generators (fuel cells, photovoltaic, hydro turbine biomass, wind, geothermal, and solar thermal) [101]. Most microgrids work with renewable energy-based sources such as photovoltaics and wind turbines usually associated with diesel generators. Diesel generators are top-rated in microgrid applications due to their flexibility, low-cost and ease of implementation, despite their known stability and low inertia issues [102].  
Figure 4: Structure of the review.  
Storage systems provide stability, allowing them to face energy production intermittency and downtime, both typical of distributed generation [103]. Storage systems can be divided into electrical storage (magnetic storage, supercapacitor), mechanical storage (flywheel, potential storage), electrochemical batteries, and fuel cells [104].  
All of the energy produced and stored is to be consumed by the users through loads that are commonly categorized into two types: fixed and variable loads [105]. A fixed load requires a constant flow of energy, while a variable load turns on and off according to a random control signal. Taken together, these types of loads make up a load profile used to design and manage modern microgrids [106].  
Power electronics converters are used as an interface between sources, storage, and loads [107]. Power converters have two main functions: a power flow management function that controls the current and/or the voltage ratings between two elements and a passive filter function that eliminates harmonic contents created by the active fields whose switching is responsible for the power flow management [108].
## 4 Experiments and Discussion
### Methods in Comparison Study
#### 4.3.4 Svm  
In a bus arrival time prediction task, SVM [4] is used as a regression method. Reference [4] divides the entire bus line into road segments and then predicts the travel time on each segment separately. The optimization of this benchmark is referenced to in the literature [24; 25].
## 2 Effects of the Imbalanced Data Problem on TSSP
### The Effect on the Classification Performance  
Generally, the classification performance of TSSP can be characterized by the confusion matrix shown in Table 1.  
The true stable class rate (TSR) and the true unstable class rate (TUR), utilized as the classification performance indexes of TSSP, are defined respectively as follows:  
\[TSR=\frac{TS}{TS+FU}. \tag{4}\]  
\[TUR=\frac{TU}{TU+FS}. \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Real Status**} & \multicolumn{2}{c}{**Prediction Status**} \\ \cline{2-3}  & **Stable** & **Unstable** \\ \hline Stable & _TS_ & _FU1_ \\ Unstable & _FS_ & _TU_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of TSSP.  
Figure 2: Decision boundaries under different imbalanced ratios (IRs).  
In addition to using the TSR and TUR as the performance indexes for stable class and unstable class, respectively, the geometric mean (GM) of TSR and TUR is also employed to measure the overall classification performance of TSSP [20] and the expression of GM is shown as follows:  
\[GM=\sqrt{TSR\times TUR}. \tag{6}\]  
The value of GM is not affected by the IR, so it is more suitable than the overall classification accuracy when dealing with the imbalanced data problem. The value of GM ranges from 0 to 1. The higher the value, the better the overall classification performance of TSSP.  
The classification performance of TSSP is analyzed and compared under different IRs, shown in Table 2.  
From Table 2, when IR = 1, the values of TSR and TUR are almost the same. With the increase of IR, the TSR gradually increases and then remains unchanged, while the TUR continues to decrease. When IR = 10, the TSR increases to 98.40% and the TUR decreases to 33.00%. In addition, as IR increases, the GM value first increases and then decreases. When IR = 2, the GM reaches its highest value, which is 78.66%. The results clearly validate that the imbalanced data problem dramatically deteriorates the classification accuracy of the unstable class, which seriously hinders the application of machine learning methods for TSSP.
_applied_  
_sciences_  
_Article_  
**Positivity-Preserving \(H_{\infty}\) Model Reduction for Discrete-Time Positive Systems via a Successive Convex Optimization Algorithm**  
[PERSON] \({}^{1,2}\), [PERSON] \({}^{3,*}\), [PERSON] \({}^{1,4}\) and [PERSON] \({}^{1,4,*}\)  
1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China  
2 Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China  
3 Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China  
4 Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, Beijing 100083, China  
\({}^{*}\)Correspondence: [EMAIL_ADDRESS] (Y.X.); [EMAIL_ADDRESS] (D.-W.D.)  
**Abstract: This paper considers the positivity-preserving model reduction for discrete-time positive systems. Given a stable high-order positive system, we aim to find a reduced-order model such that the approximation error is minimized within a prescribed \(H_{\infty}\) performance and positivity is preserved. Regarding the bounded real lemma, the sufficient and necessary condition for the existence of a reduced-order model is established in terms of bilinear matrix inequality and convex semi-definite constraint, which ensures that the reduced-order system is positive and the resulted error system is stable and has an \(H_{\infty}\) performance level. Based on the inner-approximation strategy, we approximate the bilinear constraints with convex ones, under which an iterative procedure is provided to calculate the desired reduced-order model. Finally, an example is provided to demonstrate the effectiveness and potential benefits of the presented results.**  
**Keywords: positive systems; \(H_{\infty}\) model reduction; bilinear matrix inequalities; successive convex optimization algorithm**
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 5 Results and Discussion
### Proposing a Weighted Average for Model Aggregation  
The preceding results demonstrated the need to use an aggregation method that benefited those clients that contributed the most to the global model. This could be achieved by giving more weight to the contributions of clients with a more complete dataset, i.e., clients that predictably contributed more to a higher accuracy of the model.  
Therefore, a new aggregation model was tested to meet this premise. Instead of an arithmetic average with equal weights for each client, we introduced a weighted average where the weights were computed as a fraction of the number of images each client had locally compared to the total number of images used for training.  
Equation (3) represents the weighted average used hereafter.  
\[w\leftarrow\sum_{k=1}^{K}\frac{n_{k}}{n}w^{k}, \tag{3}\]  
where \(n\) represents the total number of images considered for training in all clients in a round and \(n_{k}\) represents the number of images in each client \(k\). With this aggregation method, little importance is given to clients with a small number of images.  
Once again, we performed tests with a nonuniform distribution of the dataset with four clients: one client with 15%, one with 25%, one with 60% of the CIFAR10 images, and one client with only 10 images; and with eight clients: two with 7.5%, two with 12.5%, two with 30% of the dataset, and two clients with 10 images for each class of CIFAR10 (see Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**\# Devices**} \\ \hline
**Elapsed Rounds** & **2** & **4** & **8** \\ \hline
10 & 27\% & 22\% & 12\% \\ \hline
30 & 34\% & 43\% & 26\% \\ \hline
50 & 42\% & 78\% & 44\% \\ \hline
100 & 48\% & 90\% & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average accuracy for nonuniformly distributed datasets with 2 devices (one with 50% of CIFAR10 images and another with 0.0002%), 4 devices (15%, 25%, 60%, and 0.0002% of CIFAR10 images) and 8 devices (two clients with 7.5%, two with 12.5%, two with 30%, and two with 0.0002% of CIFAR10 images).  
Figure 6 shows that using a weighted average in the aggregation of the global model improved the accuracy of the training in the first rounds compared to an aggregation method using an arithmetic average with both four and eight clients. However, given a sufficient number of rounds of training, nearly identical accuracy was achieved at the end of training in both cases. As expected, training accuracy converged faster in both cases when a weighted average was used, as clients with large assigned datasets were favored over clients with only a few images that had almost no effect on model aggregation.  
To further evaluate the performance of our solution for as many clients as possible, only the tests with eight clients were performed in the following sections.
## 2 Scope and Aim of the Research  
As mentioned before, BIM and IoT- based data sources is a relatively new field. One can consider BIM and IoT data as two complementary entities, where one covers the lack of the other. Researchers have addressed different aspects of BIM, IoT and their use in an integrated way: sustainability, risks, safety and so forth.  
In this article, integration is addressed more from the point of view of the information collected/transmitted by sensors and actuators (and used for a specific purpose) than from that of the software or platform used. Hence, studies and research published on BIM and IoT data integration are analysed in this paper. The content is structured as a bibliographic investigation through which an analysis of these technologies' current use is carried out. The aims of this research are:  
1. To provide a detailed bibliographic analysis of the research efforts on BIM-IoT integration for FM processes improvements
2. To identify limitations in the research and introduce a roadmap for further research on FM improvements through new technologies.
## 3 Multi-GNSS PPP Mathematical Model
### C/N\({}_{0}\)-Dependent Stochastic Model  
In high-precision positioning using a geodetic receiver, the stochastic model depending on elevation is commonly used, such as:  
\[\sigma_{S}^{2}=\sigma_{0}^{2}\csc(E) \tag{11}\]  
where \(\sigma_{0}^{2}\) is the precision of the observation at zenith and \(E\) is the elevation angle. However, according to the experiments of GNSS signal characteristics of smartphones, it is found that the C/N\({}_{0}\)-dependent weighting stochastic model is more suitable for GNSS positioning using Android smartphones [8; 17]. [PERSON] et al. [24] also indicated that carrier-to-noise weighting should replace elevation-dependent weighting, and proposed a measurement weighting based on carrier-to-noise ratio values suitable for processing of low-cost GNSS receiver data. [PERSON] [25] derived a formula that expresses the phase variance \(\sigma_{S}^{2}\) in mm\({}^{2}\) as a function of the measured C/N\({}_{0}\) values:  
\[\sigma_{S}^{2}=C_{S}\cdot 10^{\frac{-(C/N_{0}-measurement)}{10}} \tag{12}\]\[C_{S}=B_{S}\left[\frac{\lambda}{2\pi}\right]^{2} \tag{13}\]  
where \(B_{S}\) is the carrier tracking loop bandwidth (Hz), and the effect of the oscillator stability on the phase variances is considered negligible. Equation (12) can be used to estimate variances of the raw phase observations at one station to a satellite, i.e., undifferenced [26; 27]. However, the constant value obtained by the fitting with the phase variances and \(C\)/\(N_{0}\) variances is usually used as the parameter \(C_{S}\) due to the lack of bandwidth information. Meanwhile, taking into account the GNSS signal characteristics of smartphones, the method of segmental weighting is adopted by referring to the elevation-dependent weighting [28]. The step function variance can be given as:  
\[\sigma_{S}^{2}(C/N_{0})=\left\{\begin{array}{cc}C_{S}\cdot 10^{\frac{-(C/N_{0} \text{unsaturated})}{10}}&C/N_{0}>\alpha\\ C_{S}\cdot 10^{\frac{-(C/N_{0}\text{unsaturated})}{10}}&C/N_{0}\leq\alpha,\kappa\in(0,1)\end{array}\right. \tag{14}\]  
where \(\alpha\) is the threshold value of \(C\)/\(N_{0}\), \(\kappa\) is the coefficient of \(C\)/\(N_{0}\); the specific value can be determined by the gross measurement noise of satellites below the threshold value. Although some studies suggest that the cutoff value of \(C\)/\(N_{0}\) should be directly set, [PERSON] et al. [17] suggested that the pseudorange observations whose \(C\)/\(N_{0}\) are below 30 dB-Hz should be excluded in the Android GNSS positioning process, and [PERSON] et al. [29] indicated that GNSS observations with \(C\)/\(N_{0}\) less than 30 dB-Hz should be rejected. However, for some early smartphones, such as the Huawei Mate9, about 44.17% of the GNSS observations with the \(C\)/\(N_{0}\) below 30 dB-Hz in one hour, and the number of visible satellites that smartphones receive in urban environments is limited. Therefore, the cutoff \(C\)/\(N_{0}\) value is not directly set in the PPP mathematical model.
## 2 Results and Discussion
### Effect of Stirring Rate and Ionic Strength of the Sample Solution  
Exposing the sample matrix to mechanical shaking, stirring, agitation, ultrasonic waves or microwave irradiation is essential to inducing stress in the sample tissue and improving the mass transfer to reduce the equilibration time. Therefore, different experiments were conducted using varying stirring rates (250-750 rpm). For both the DI- and EE-DI-SPME procedures, the extraction efficiency was increased up to 500 rpm and then remained constant (Figure 6). Thus, 500 rpm was selected as the optimum stirring rate.  
To evaluate the effect of ionic strength, different amounts of NaNO\({}_{3}\) (0-10% \(w/v\)) were added to the sample solution, and the EE-DI-SPME-GC-FID procedure was conducted. The results showed that increasing the ionic strength caused a gradual decrease in the extraction efficiency. A plausible explanation for this effect is the interference effect of the electrolyte ions with the electrophoretic migration of the target analyte to the fiber surface. Accordingly, for further studies, no salt was added to the sample solution.
## 5 Interference Distribution Characteristics  
Based on the proposed calculation model, the interference distribution characteristics were analyzed under different parameters. These parameters include the electrode currents, soil resistivity, and the distance between the electrode and pipelines. Considering the following typical interference model, the length of the pipeline is \(L\), and the distance between the electrode and pipeline is \(d\). The soil is considered as a homogeneous medium and the soil resistivity is \(r\), pH = 8. The pipe material was X80 steel. A schematic diagram of the pipeline and HVDC grounding electrode arrangement is shown in Figure 9.
## 3 Experiments and Results
### Numerical Simulation and Criterion  
In simulation experiments, we assume that the object with the optically rough surface is illuminated with the laser. The amplitude of the object field is simulated by taking the square root of the test images. A simple way of modeling the surface roughness is to apply a random phase term to the object. The optical field is then propagated to the far field, and the square magnitude is taken to get the object speckle pattern. Independent speckle pattern realizations are obtained by computing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Images**} & \multicolumn{2}{c}{**Random**} & \multicolumn{2}{c}{**Spectral**} & \multicolumn{2}{c}{**HIO 2 Results**} \\ \cline{2-7}  & **SSIM** & **Iteration** & **SSIM** & **Iteration** & **SSIM** & **Iteration** \\ \hline ’WORLD’ & 0.730 & 180 & 0.404 & 200 & **0.742** & **60** \\ ’258’ & **0.748** & 250 & 0.492 & 500 & 0.743 & **40** \\ ’Smile’ & 0.670 & 450 & 0.749 & 250 & **0.792** & **50** \\ ’Star’ & 0.672 & 230 & 0.624 & 150 & **0.694** & **35** \\ \hline \hline \end{tabular}  
* SSIM is the structural similarity index. 2 HIO is the hybrid input-output algorithm.  
\end{table}
Table 1: SSIM 1 and iterations of the proposed algorithm with different initialization methods.  
Figure 6: Four test images, including letters, numbers, and simple geometric figures. (**a**) WORLD. (**b**) 258. (**c**) Smile. (**d**) Star.  
the optical phase with different random numbers. Toward the ptychographic imaging methods, an aperture function is introduced to select a subsection of the object, which has been described previously. Similar to conventional imaging correlography, each section of the object obtains its corresponding speckle patterns. By using Equation (6), one can obtain the autocorrelation function of the subobject. The subobject's Fourier magnitude is directly estimated according to Equation (7). People can then reconstruct the image of the object from the Fourier modulus by using the proposed ptychographic reconstruction algorithm. Intuitively, reconstruction quality is improved as the amount of overlap increases. The previous research indicates that a relative overlap of 60% yields high-quality reconstruction of the scanned object area [43]. The support size for conventional correlography is set to be consistent with the scanning range of the ptychographic measurements. The measured speckle frames for conventional imaging correlography are 1200, and the measured speckle frames for the proposed method are 150 at each position. The data processing steps used for the proposed ptychographic imaging correlography are presented in the flow chart in Figure 7.  
In addition to the visual results, the structural similarity index (SSIM) [44] is used as a quantitative criterion to assess the quality of reconstruction appropriately. Instead of using a straightforward distance metric, the SSIM compares the restored image with the ground truth in terms of structure, contrast, and luminance.
## 3 Mobile RAN Entity Integrated into 5G and Beyond  
In contrast to its predecessors, 5G has been designed to cater to the diverse requirements stemming from various industries, including the automotive, railway, public safety, broadcasting, and maritime sectors. Furthermore, continuous efforts are devoted to advancing these technologies, facilitating seamless communication across diverse deployment scenarios that are indispensable for the mobilization and automation of various industries. It is crucial to delve into the cutting-edge advancements in 5G technologies related to the efficient utilization of multiple RANs, which assume a pivotal role in providing ubiquitous connectivity to autonomous ships within the maritime sector considering the increase in the heterogeneity in RANs, spanning the 5G and beyond systems.  
Traditionally, RAN entities based on 3 GPP standards were perceived as stationary and lacking mobility, while User Equipment (UE) was recognized as a mobile entity, transitioning between different spatial points. This dichotomy necessitated solutions such as handovers for UE in connected mode or cell reselection for UE in idle mode to ensure uninterrupted communication as the UE moves. However, with the advent of the 5G and beyond era, mobile entities like satellites and UAVs are poised to be integrated into RAN equipment, despite the myriad challenges arising from their departure from conventional assumptions concerning the mobility capabilities of both RANs and UE. This section provides a comprehensive exploration of the integration of satellites and UAVs into the 5G and beyond system, formulated within the framework of 3 GPP standardization.