esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 5 Conclusions  
In this paper, focusing on the long-standing switching time sensitivity of the optimal aerocapture bang-bang control structure, a new numerical predictor-corrector aerocapture guidance scheme based on the profile of the saturation function is proposed. By replacing the previous instantaneous switching segmented guidance structure with saturation function, the method performance's dependence on the switching time and other related parameters is reduced. Furthermore, the whole aerocapture guidance process is more unified without losing more optimality. Taking into account parameter uncertainties, disturbances, and operational constraints, the corresponding strategies are implemented. A large number of Monte Carlo simulations in Earth and Mars aerocapture scenarios show that the guidance method proposed in this paper is robust and accurate, which effectively alleviates the problem that a small error in switching time will lead to a significant error in exit state. Compared to the most advanced numerical predictor-corrector guidance scheme, it provides near-optimal performance and improved robustness. At the same time, the performance index's dispersion is smaller under various disturbances. The parameter impact analysis shows that the proposed guidance method can adapt well to large-scale variations in the angle of the entry flight path and the lift-to-drag ratio. Based on these analyses, this new numerical predictor-corrector algorithm should be a feasible guide choice for future aerocapture missions.  
Conceptualization, J.C. and H.H.; methodology, J.C. and T.Q.; software, J.C. and R.T.; validation, J.C. and T.Q.; formal analysis, J.C.; investigation, R.T. and T.Q.; resources, H.H. and T.Q.; data curation, J.C. and R.T.; writing--original draft preparation, J.C.; writing--review and editing, H.H., R.T. and T.Q.; visualization, J.C. and R.T.; supervision, H.H. and T.Q.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, under Grant No. U20B2001 and Beijing Institute of Technology Research Fund Program for Young Scholars (XSQD-2 2060303).  
Not applicable.  
The authors thank the colleagues for their constructive suggestions and research assistance throughout this study.  
Figure 13: The dispersion of the velocity increases with different lift-to-drag ratios for the Mars aerocapture.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 4 Proposed Framework
At this point, as indicated in Figure 4, the baseline models are used as feedback for the dataset creator and included as input groups (the baseline and external baseline) in some of the datasets (see Table 2). The trained models are then evaluated and ordered according to their performance metrics. The best models and their characteristics are stored in a database and are used by the daily forecasting block, which executes the best available model once a day (obtaining the day-ahead forecast). Notably, in this process, the type of information needed to execute each of the models must be specified, as the system must check the availability of the information before deciding which model to use. For example, if the temperature information is missing for a specific day (because of a failure in data reception from the provider), the forecasting models that require temperature are discarded for that day, and another model (whose required input information is available) is applied. Finally, the obtained forecast (for all the power variables that are included in the system) is taken by the application block, whose characteristics depend on the locations  
Figure 4: Proposed power forecasting framework. of the resources; the type of power information utilized; and the objective of the operator, aggregator or customer who owns the system. As previously specified, the framework does not necessarily require online training. Therefore, the model updating (the execution of the model training/execution block in Figure 4) procedure can be done every few days or even every few weeks, when the inclusion of newly available historical data is desired to retrain the models. This characteristic may maintain the bounded daily computational cost of the proposed framework, as it does not need continuous updates.
## 2 Related Work
In [37], a thorough study of downlink NOMA is given while taking into account networks with two to k users per cell using orthogonal multiple access (OMA) approaches. It goes through NOMA's key characteristics, which raise the system's spectrum efficiency. The discussion goes into great length on several aspects and difficulties in the NOMA system, such as SIC, inter-channel channel interference, channel state information (CSI), and power allocation factor. Additionally, the effectiveness of DL and machine learning (ML) for the next-generation NOMA system is examined. A review of the NOMA-DL potentials may be found in [38]. The study presents a thorough analysis of the difficulties in the wireless system and mentions the potential of DL. It is divided into sections on resource distribution, signal decoding, and the design of signal constellations. The scope and difficulties of DL-aided NOMA in wireless systems are discussed in that review paper. In-depth reviews of recent cellular and IoT network studies are provided in [39]. The authors talk about the difficulties with network coverage expansion, user clustering, interference control, and channel access for highly linked devices. In addition, the basics of DL approaches and the DL algorithm's application to resource management for IoT-enabled devices are examined. The different types of DNNs and their most recent developments to the issues with wireless systems are thoroughly examined. The examination advances knowledge in devices-to-device communication, heterogeneous networks, NOMA, and MIMO, which are part of the next generation of this technology. Table 1 summarizes the recent reviews on DL-based NOMA systems.
# Designing a Novel High-Performance FBG-OADM Based on Finite Element and Eigenmode Expansion Methods
## 1 Introduction
Therefore, in this study, the acceptable maximum error value was set at \(-40\) dB as the review standard. All subsequently employed mesh-cutting resolutions must, therefore, generate modes with an orthogonal value of less than \(-40\) dB. For mesh-cutting resolutions that failed to meet this standard, the resolution was increased; subsequently the solutions to the modes were recalculated and the orthogonal values were reevaluated [12]. Section 4 introduces the roles and functions of EEM in the numerical simulations and expounds why the combined FEM-EEM approach is superior to traditional numerical simulation methods for designing and analyzing large optical components containing periodic structures. Mathematically, EEM embodies an operation concept similar to that of the Fourier series expansion method; that is, if too few expansion functions are used, discrepancies will occur between the expansion and expanded functions. In numerical operations, it is impossible to include all modes; consequently, errors are unavoidable. To overcome this problem, similar to the FEM approach adopted in this study, the acceptable maximum error (i.e., power loss) for modal expansions was set to \(-40\) dB. Section 5 shows how the FEM and EEM were combined to research and design the proposed FBG-OADM. The design process involved the following procedure: (1) solve the FBG-OADM modes, (2) plot 2D power distribution maps of the modes, (3) review the orthogonal values of the modes, (4) identify the optimal design parameters, (5) inspect the overall power loss, (6) plot the spectrum for the drop port, (7) examine heterodyne and homodyne crosstalk, and (8) calculate the \(-3\) dB bandwidth. Finally, Section 6 summarizes the study findings. This study developed a novel, high-performance FBG-OADM that was designed through the combined FEM-EEM approach. To minimize the number of errors, a PML and PRB were simulated through various numerical methods (i.e., the OMM and BMM) and a review standard was formulated to assess the orthogonal values and power loss. The numerical simulations showed that the proposed FBG-OADM overcomes the disadvantages of current OADMs. Moreover, the proposed FBG-OADM possesses nearly zero heterodyne and homodyne crosstalk and meets the full width at half maximum (FWHM) requirement for dense wavelength-division multiplexers (DWDMs) according to the International Telecommunication Union (ITU; FWHM \(<\) 0.4 nm).
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
###### Abstract  
With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle-ground communication system has been unable to meet the future vehicle-ground communication requirements. To improve the performance of vehicle-ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle-ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV.  
ad hoc; urban rail transit; multipath routing; routing maintenance
## 4 Discussion
### Discretisation Approach  
The relative performance of the uniform and defect-centric discretisation approaches varied by voxel size and class-balancing approach but showed similar trends between classifiers. In many cases, the defect-centric approach performed better on validation data but worse on test data. One explanation is that relevant test data could only be generated using the uniform discretisation approach since the defect-centric approach requires prior knowledge of the locations of pores gathered from the post-manufacturing CT scans that would not be available in real-world applications. On the other hand, the uniform discretisation approach creates training and validation data in the same way that test data are created or that in-service data are collected.  
However, at 0.5 mm voxel size, the defect-centric approach performed relatively well (Figure 13), achieving higher accuracy on test data and smaller differences between test and validation accuracy. Typically, a bigger difference between validation and test accuracy suggests more overfitting of the model to the training data and therefore a reduction in the generalisation ability of the model.  
At 2.0 mm voxel size, the uniform discretisation approach appears better (Figures 15 and 18), with higher test accuracy and lower test/validation difference.
# Demonstration of Diode-Pumped Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) Lasers
## 2 Spectral Properties of Materials  
The growth method of the Yb:LaF\({}_{3}\) crystals (Pujing Company, Suzhou, Jiangsu, China) was the Bridgman method which has the advantage of inhibiting the volatilization of fluoride. The Yb:LaF\({}_{3}\) crystals with dimensions 3 \(\times\) 3 \(\times\) 6 mm\({}^{3}\) had a Yb doping concentration of 1%. As seen from Figure 1a, the crystals had a relatively high absorption coefficient over a wide wavelength range. The maximum absorption coefficient was 0.25 cm\({}^{-1}\) at 973 nm. Figure 1b shows the broad emission band whose full width at half-maximum (FWHM) was 52.4 nm with Gauss fitting (the red curve), revealing Yb:LaF\({}_{3}\) crystals to be a candidate for wavelength-tunable lasers.  
The Tm,Ho:LaF\({}_{3}\) materials were also prepared using the Bridgman growth method. The doping concentration was 5 atom % of Tm and 0.5 atom % of Ho ions, and the dimensions were  
Figure 1: (**a**) Absorption spectrum of Yb:LaF\({}_{3}\) (1 atom % of Yb); (**b**) emission spectrum with an 896 nm excitation source.  
\(3\times 3\times 10\) mm\({}^{3}\). The absorption and emission spectrum information at room temperature are shown in Figure 2. It can be seen that the Tm,Ho:LaF\({}_{3}\) materials had several peaks in the absorption spectrum. Among them, a special absorption peak is presented deliberately in the inset of Figure 2a. This peak had a center wavelength of 792.5 nm, which illustrated that the Tm,Ho:LaF\({}_{3}\) materials could be stimulated with a commercial LD. In general, LDs have the disadvantage of wavelength drift caused by changes in the LD output power and temperature. The Tm,Ho:LaF\({}_{3}\) crystals had a FWHM of 34.8 nm, so they could avoid absorption instability caused by the wavelength drift of LDs. The emission spectrum had a broad fluorescence range mainly centered at 1925-2075 nm.
# Heart Attack Detection in Colour Images Using Convolutional Neural Networks
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Model** & **Activity Identified** & **Accuracy** & **Data Source** & **Data Set** \\ \hline
[24] & CNN & Fall & 74\% & \
\begin{tabular}{c} RGB-D input, \\ background \\ subtraction \\ \end{tabular} &
\begin{tabular}{c} Own, captured \\ with Kinect \\ \end{tabular} \\ \hline
[34] & \begin{tabular}{c} Hierarchical \\ maximum entropy \\ Markov model \\ \end{tabular} & \begin{tabular}{c} talking on the \\ phone, drinking \\ water, talking, \\ relaxing, writing \\ \end{tabular} & 84.7\% & \
\begin{tabular}{c} RGB-D input, \\ Skeleton \\ \end{tabular} &
\begin{tabular}{c} Own, captured \\ with Kinect \\ \end{tabular} \\ \hline
[27] &
\begin{tabular}{c} CNN and \\ RNN/LSTM \\ \end{tabular} & Fall & 88.9\% & RGB input & Not reported \\ \hline
[25] & \begin{tabular}{c} CNN and \\ RNN/LSTM \\ \end{tabular} & Fall & 60.76\% & \
\begin{tabular}{c} RGB-D input, \\ background \\ subtraction \\ \end{tabular} &
\begin{tabular}{c} Own, captured \\ with Kinect \\ \end{tabular} \\ \hline
[26] & CNN & Fall & 65.0\% & RGB input & \begin{tabular}{c} YouTube Fall data \\ (YTFD) set \\ \end{tabular} \\ \hline Ours & CNN & Infract & 91.7\% & \
\begin{tabular}{c} RGB input, \\ background \\ subtraction \\ \end{tabular} &
\begin{tabular}{c} Own, Internet \\ images \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between similar approaches. network/long short-term memory)network using skeletons generated with 14 joints to detect a fall. They communicate an 88.9% accuracy, close to our proposal. Another proposal [25] makes use of several of the aforementioned strategies. On the one hand, it uses the Kinect device to generate binary images, leaving the background in black and the silhouette of the person in white. Subsequently, the generated images are analysed by a network that starts with convolution layers followed by LSTM layers. This is the most striking aspect of the proposal, its accuracy being the lowest of all the compared papers. Finally, a work that starts with the creation of a fall data set constructed with YouTube videos has been presented [26]. The novel element of this proposal with respect to other works is the creation of dynamic images. These types of images are an amalgam of several frames of a video in a single image, including the changes occurring in a window of time. This generates images with shadows or strokes coming from the subtraction of pixels. After generating the images, these are passed through the pretrained Oxford's Visual Geometry Group VGG-16 model [42], which helps to reduce the processing needs due to the learning transfer strategy. However, the results are not very good, which may be due to insufficient data in the training data set or because the type of images used for training does not present any advantage in comparison to traditional images.
# Entropy-Based Dynamic Rescoring with Language Model in E2E ASR Systems
## 4 Our Proposal: Dynamic Rescoring Method  
For simplicity, \(\beta\text{penalty}(|y|_{c})\) in Equation (5) can be included in \(\log(P_{E2E}(y|x))\) or \(\alpha\log(P_{LM}(y))\). Then, this rescoring equation becomes a simple linear combination in Equation (6). Here, \(\lambda\) is a hyperparameter, and score \(s(y|x)\) is a linear combination of the likelihood of the E2E model and that of the LM. In this approach, \(\lambda\) determines the importance of both models statically, which may be obtained by greedy search using development datasets.  
However, it is easily assumed that the optimal value of \(\lambda\) depends on context and \(\lambda\) should be controlled dynamically and adaptively. By taking into account that any language model can be assessed by the entropy of the predicted tokens of the model, we propose a method of dynamic control of \(\lambda\) based on the entropies of \(P_{E2E}(y|x)\) and \(P_{LM}(y)\).  
As a model is better with smaller entropy, in Equation (7), \(\lambda\) is defined to quantify goodness or importance of the LM, which vary dynamically depending on \(x\). Finaly, we summarize this method in the Algorithm 1.  
\[\begin{split} s(y|x)=&(1-\lambda)\log(P_{E2E}(y|x ))+\lambda\log(P_{LM}(y))\\ \lambda=& 1-\frac{H_{LM}(Y)}{H_{E2E}(Y|X=x)+H_{LM}(Y)} \end{split} \tag{6}\]  
```
Data:\(\textit{speech}\),\(\textit{previous\_tokens}\) Result:\(\textit{next\_token}\) \(\textit{s2s\_next\_token}=\textit{s2s\_model}(\textit{speech},\textit{previous\_tokens})\) \(\textit{lm\_next\_token}=LM(\textit{previous\_tokens})\) \(\textit{entropy\_s2s}=\textit{entropy}(\textit{s2s\_next\_token})\) \(\textit{entropy\_lm}=\textit{entropy}(\textit{lm\_next\_token})\) \(\textit{lm\_weight}=1.0-\textit{entropy\_lm}/(\textit{entropy\_s2s}+\textit{entropy\_lm})\) \(\textit{next\_token}=(1.0-\textit{lm\_weight})*\log(\textit{s2s\_next\_token})+ \textit{lm\_weight}*\log(\textit{lm\_next\_token})\)
```  
**Algorithm 1**Calculate output token logits.
# Enhancing Brioche Bread with Emulsified Seed and Nut Oils: Nutritional and Sustainable Benefits
## 3 Results and Discussion
### Oil Quality
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameters** & **Poppy** & **Chia** & **Pumpkin** & **Melon** & **Almond** & **Pistachio** & **Walnut** \\ \hline Oil yield (\%) & 23.98 \(\pm\) 1.08 \({}^{\circ}\) & 20.07 \(\pm\) 1.19 \({}^{\circ}\) & 7.94 \(\pm\) 0.33 \({}^{\circ}\) & 16.07 \(\pm\) 0.92 \({}^{\d}\) & 32.65 \(\pm\) 1.08 \({}^{\rm b}\) & 28.84 \(\pm\) 1.19 \({}^{\rm b}\) & 58.02 \(\pm\) 0.33 \({}^{\rm a}\) \\ \hline Oil quality & - & - & - & - & - & - & - \\ \hline Oxidative stability (h) & 8.78 \(\pm\) 0.97 \({}^{\rm b}\) & 2.39 \(\pm\) 0.13 \({}^{\circ}\) & 22.57 \(\pm\) 1.05 \({}^{\rm a}\) & 9.61 \(\pm\) 0.93 & 25.94 \(\pm\) 1.90 \({}^{\rm a}\) & 28.65 \(\pm\) 1.31 \({}^{\rm a}\) & 7.32\(\pm\) 1.10 \({}^{\rm b}\) \\ \hline Viscosity (Cp) & 57.12 \(\pm\) 0.43 \({}^{\rm b}\) & 43.27 \(\pm\) 0.35 \({}^{\rm c}\) & 71.69 \(\pm\) 1.10 \({}^{\rm a}\) & 63.9 \(\pm\) 1.02 \({}^{\rm b}\) & 46.71 \(\pm\) 0.43 \({}^{\rm c}\) & 45.84 \(\pm\) 0.35 \({}^{\rm c}\) & 45.84 \(\pm\) 1.10 \({}^{\rm c}\) \\ \hline Color & - & - & - & - & - & - & - \\ \hline L\({}^{\star}\) & 92.87 \(\pm\) 1.23 \({}^{\rm a}\) & 95.49 \(\pm\) 0.41 \({}^{\rm a}\) & 45.32 \(\pm\) 0.37 \({}^{\rm c}\) & 89.06 \({}^{\rm b}\)\(\pm\) 2.58 & 92.87\(\pm\) 1.23 \({}^{\rm a}\) & 65.01 \(\pm\) 0.41 \({}^{\rm b}\) & 93.32 \(\pm\) 0.37 \({}^{\rm a}\) \\ \hline a\({}^{\star}\) & \(-\)4.66 \(\pm\) 0.08 \({}^{\rm d}\) & \(-\)7.10 \(\pm\) 0.11 \({}^{\rm e}\) & 41.93 \(\pm\) 0.45 \({}^{\rm a}\) & \(-\)5.55 \(\pm\) 0.04 & \(-\)3.33 \(\pm\) 0.46 \({}^{\rm c}\) & 3.00 \(\pm\) 0.11 \({}^{\rm b}\) & \(-\)2.88 \(\pm\) 0.86 \({}^{\rm c}\) \\ \hline b\({}^{\star}\) & 23.69 \(\pm\) 0.54 \({}^{\rm c}\) & 27.51 \(\pm\) 0.16 \({}^{\rm c}\) & 76.28 \(\pm\) 1.17 \({}^{\rm a}\) & 69.24 \(\pm\) 1.10 \({}^{\rm a}\) & 17.41 \(\pm\) 0.80 \({}^{\rm d}\) & 49.98 \(\pm\) 0.16 \({}^{\rm b}\) & 22.03 \(\pm\) 1.20 \({}^{\rm c}\) \\ \hline \end{tabular} The numbers represent the means \(\pm\) standard deviations. Different letters in the same row for each attribute indicate significant differences (\(p<\) 0.05).  
\end{table} 
Table 1: The quality parameters of the oils used to replace butter in the bricoche breads. such as supercritical fluids or solvent extraction should be considered. Pumpkin seed oil showed the highest stability, while chia seed oil, due to its high polyunsaturated fatty acid content, had the lowest stability. Nut oils (almond and pistachio) showed higher stability values than seed oils. Pumpkin seed oil was the most viscous, while chia seed and nut oils had similar lower values. All of these characteristics may influence their applications in food products.
# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 3 Results
Producer’s (PA), user’s (UA), and overall (OA) accuracies (as percentages), as well as the Cohen’s kappa coefficient (K) are reported. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**K = 0.59**} & \multicolumn{1}{c}{**Classification**} & \multicolumn{1}{c}{**\#C**} & \multicolumn{1}{c}{**\#C**} & \multicolumn{1}{c}{**\#C**} & \multicolumn{1}{c}{**\#C**} & \multicolumn{1}{c}{**\#C**} & \multicolumn{1}{c}{**\#C**} & \multicolumn{1}{c}{**\#C**} \\ \cline{2-11}
**Reference Map** & **\#C** & **\#C** & **\#C** & **\#C** & **\#C** & **\#C** & **\#C** & **\#C** & **\#C** \\ \hline
**Winter cereals** & **9759** & 2510 & 0 & 0 & 0 & 62 & 40 & 0 & 12,371 & 78.9 \\
**Clover and Alfafla** & 2170 & **7743** & 0 & 0 & 0 & 13 & 146 & 0 & 10,072 & 76.9 \\
**Maize** & 0 & 0 & **19** & 5 & 4 & 0 & 2 & 1 & 31 & 61.3 \\
**Sorghum** & 0 & 0 & 4 & **20** & 1 & 0 & 2 & 0 & 27 & 74.1 \\
**Sunflower** & 0 & 0 & 3 & 0 & **12** & 0 & 0 & 2 & 17 & 70.6 \\
**Rape** & 13 & 0 & 0 & 0 & 0 & **9** & 0 & 0 & 22 & 40.9 \\
**Horticultural crops** & 5 & 2 & 0 & 5 & 0 & 0 & **428** & 1 & 441 & 97.1 \\
**Soy** & 0 & 0 & 0 & 1 & 1 & 0 & 0 & **3** & 5 & 60.0 \\
**Total** & 11,947 & 10,255 & 26 & 31 & 18 & 84 & 618 & 7 & **22,986** & **OA\%** \\ \hline
**UA \%** & 81.7 & 75.5 & 73.1 & 64.5 & 66.7 & 10.7 & 69.3 & 42.9 & **OA \%** & 78.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of the RF results from the LAI time series analysis. Producer’s (PA), user’s (UA), and overall (OA) accuracies (as percentages), as well as the Cohen’s kappa coefficient (K) are reported.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results  
In this section, we conduct numerical simulations using the MATLAB 2017b software package for performance evaluation of AmBC systems. We first provide a brief comparison of the TD-AmBC and the MSC-AmBC, and then describe practical simulation environments reflecting massive IoT networks with OFDM carriers. The effect of various system parameters and some interesting remarks in AmBC are also described in the section.