esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon-Based Catalysts for Biodiesel Production--A Review
###### Abstract
advantage over the traditional homogeneous base catalysis. The heterogeneous nature of the reaction means that no separation is required and that the catalyst has the potential to be reused multiple times [9; 11; 32]. Similarly in heterogeneous case, the feedstock is required to have a FFA content below 0.5% as the by-products of the saponification reaction poisons the catalyst and prevents re-use [11; 32]. Leaching of the base into the feedstock was also a commonly reported issue [35]. Due to the leaching, homogeneous catalysis was effectively occurring, which was problematic as a separation step was necessary [19; 36]. Heterogeneous acid catalysis is a promising alternative to conventional biodiesel production due to the simplistic and environmentally friendly nature of the process and the simultaneous promotion of both esterification and transesterification [37; 38; 39]. In particular, this study will focus on a branch of solid acid catalysts known as sulphonated carbon-based acid solids (CBASs). CBASs are formed from either carbohydrate or biomass precursors. The mechanism described by [PERSON] et al. [40], with a further stage suggested by [PERSON] (2010) [41], was adapted by the authors in Figure 3. In the first stage (A) of catalyst production, the carbohydrate/biomass is carbonised, which leads to the removal of water from the molecules and (B) the dissociation of the C-O-C bond, forming polycyclic aromatic carbon sheets. The carbon sheets formed are then subjected to sulphonation (C) with sulphuric acid; the acid sulphonates the aromatic rings, introducing SO3H, COOH and OH groups at the edge of the carbon rings, forming the CBAS material that acts as a 3-D structure catalyst. During step (D), the carbonisation temperature is raised to 500 \({}^{\circ}\)C and larger polycyclic aromatic sheets are formed with the layered polycyclic aromatic rings, which reduce the amount of potential locations of sulphuric groups. CBASs show high affinity for both the esterification and the transesterification reaction. They have been shown to have better activities for both the esterification of FFAs and the transesterification of TGs than commercially available solid acid catalysts such as sulphated zirconia and niobic acid [10; 42]. CBASs can tolerate high FFA and water content meaning lower quality unrefined feedstock can be used such as waste cooking oil [43; 44]. Solid acid catalysis demonstrates reliable re-usability, has been shown to be chemically inert and can be used to simultaneously carry out esterification and transesterification [11; 41]. The characteristics that make a CBAS an effective catalyst for the production of biodiesel are strong Bronsted and Lewis acid sites, high acid site density, a hydrophobic surface, high pore volume and unique porosity [10; 11; 45]. This review will focus on the different types of CBASs currently being researched, preparation methods, and classification as catalysts and effectiveness in terms of both esterification and transesterification.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Representation Learning
#### 5.2.3 Graphic-Based Patient Representation  
Each patient can be represented as a compact graph using the graph-based patient representation. The edges between nodes in the graph encode the association between clinical events, while the nodes themselves encode clinical events. For instance, [PERSON] et al. [63], the first to learn graph representation, provided a temporal graph model that may give sufficient information for various analysis tasks. Graph representation has made significant strides in deep learning and evolved into a Graph Neural Network (GNN), despite the fact that the technique in this study is not based on deep learning technology. The use of GNN in EHR improves the model's interpretability and efficiency. [PERSON] et al. [64] introduced the Graph Convolutional Transformer (GCT), which is based on a transformer and data statistics and can narrow the search space to only those areas that exhibit significant attention distributions rather than the full space. [PERSON] et al. [65] also proposed KAME,a knowledge-based attention model that can fully utilize information in medical texts, increase downstream task performance, and obtain both accurate embedding of medical code and knowledge in prior code. They both use a hierarchical ICD-9 knowledge graph as a graph model, which is the same.  
Recently, many efficient open-source graph representation learning platforms have emerged. [PERSON] et al. [41] proposed the CogDL system, which includes graph representation learning and GNN. researchers can quickly build their own experiments through this system. The DIG system proposed by [PERSON] et al. [40] also has interpretable analysis and deep learning of 3D graphs compared with CogDL, which further advances graph representation learning.
## 4 Results and Discussion
### Ablation Experiment on Different Loss Functions Results

Table 7: Ablation experiment on different loss functions for image data. computational complexity of redundant information while ensuring that the generated data are difficult to distinguish, thus balancing privacy protection with model utility. This design allows the projection loss to optimize both privacy protection and data utility, resulting in better classification and detection performance in both time-series and image data tasks.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 5 Swarm Based Solution
### Routing Problem
#### 5.3.2. The Heuristic Information Calculation  
A final environmental impact metric is taken into account to ensure that the routing protocol is not exposed to environmental impact, which helps select cluster heads in suitable  
Figure 5: The proposed routing algorithm flowchart.  
surroundings and bypasses harmful places to a certain extent. Thus, the following heuristic information is calculated by the final environmental impact as follows:  
\[\eta_{xy}^{3}(t)=\frac{EIM_{y}^{i}(t)}{\sum\limits_{l\in FCHN_{x}}EIM_{l}^{i}(t)}. \tag{102}\]  
The candidate cluster head with a higher value of njj1 has the lower adverse environmental effects on the urgent data packet delivery and is more likely to be selected as the next relay.  
To ensure timely delivery of urgent data, the forwarding cluster head with a relaying delay less than the required delay and whose delay is the lowest compared to other candidates should be selected as the next point. Therefore, the relaying delay is considered heuristic information, which is determined by Equation (103) as follows:  
\[\lambda_{xy}^{3}(t)=\frac{Rd_{y}(t)}{\sum\limits_{l\in FCHN_{x}}Rd_{l}(t)}. \tag{103}\]  
The candidate relay that has a higher value of \(\lambda_{xy}^{3}\) offers less delay in the transmission of urgent data and has more chance of being the next relay. Reducing the delivery time will result by selecting the cluster head with the shortest relaying latency at each hop.  
The suggested energy consumption load metric is heuristic information that is taken into consideration since energy consumption balance is a critical difficulty in the design of an energy-efficient routing algorithm for WSNs.  
\[\beta_{xy}^{3}(t)=\frac{ECL_{y}(t)}{\sum\limits_{l\in FCHN_{x}}ECL_{y}(t)} \tag{104}\]  
The cluster head with a higher value of \(\beta_{xy}^{3}\) has a higher residual energy after sending all its traffic load messages, and thus has a more chance of being selected as the next forwarder.  
Some crucial data packets may be lost as a consequence of the lossy links in WSNs, wasting energy and adding to the delay caused by the need to retransmit missing packets. One of the main things that affects end-to-end latency, packet delivery rate, and energy efficiency is packet loss. The lossy feature of wireless connections across it may be described by the \(PRR\). As a result, the following is how the path quality is defined. The connection quality is regarded as heuristic data, which is defined as:  
\[\gamma_{xy}^{3}(t)=\frac{PRR_{xy}}{\sum\limits_{l\in FCHN_{x}}PRR_{xl}}. \tag{105}\]  
The cluster head with a greater value of \(\gamma_{xy}^{3}\) has a better link quality and has more opportunity to be chosen as the relay node.
## 3 Experimental Setup
### Data Evaluation of Molecular Bands  
To determine the heavy particle temperature, two molecular transitions in the visible range, the \(\mathrm{C^{3}\Pi_{u}\to B^{3}\Pi_{g}}\) transition of the excited nitrogen molecule called the second positive system (short SPS) and the \(B^{2}\Sigma_{u}^{+}\to X^{2}\Sigma_{g}^{+}\) transition of the ionized nitrogen molecule, called the first negative system (short FNS), are used. More precisely, when the spectral resolution of the experimental setup is taken into account, the \((0,0)\)-SPS-transition with the band head at 337.13 nm and the \((0,0)\)-FNS-transition with the band head at 391.44 nm are evaluated in the following.  
As can be deduced from Section 2, the fitting procedure of a molecular spectrum is quite complex and cannot be performed manually. Each transition is characterized by a different set of tabulated data and follows different selection rules, which have to be implemented into the fitting procedure. Moreover, several transitions overlap in certain wavelength ranges of a measured spectrum, further complicating the fitting procedure. For these reasons, a commercially available software Specair 3.0 made by SpectralFit S.A.S., Antony, France is used for the parameter evaluation from the measured nitrogen molecular spectra. This software is well established in the scientific world and used by many researchers [41; 52; 63; 64; 65]. Before the parameter evaluation, the measured data are prepared as follows. Similarly to the evaluation of \(H_{\beta}\), an average is calculated from single images (at least 100), then corrected with the radiance calibration measurement and Abel transformed for local intensities. Afterwards, each pixel line of the locally resolved spectra is imported to Specair, where the synthetic spectra are calculated under consideration of the instrumental profile of the setup. An example of the resulting fit is shown in Figure 1. Again, three measurements are taken for each parameter set, evaluated and averaged, subsequently.
## 3 Data and Methodology
### Research Methods
#### 3.2.4 Training Parameters  
Training deep learning models such as deep ANN and CNN require several hyperparameters that need to be configured carefully. In this research, the training parameters are set based on empirical experiments conducted on a subset taken from the whole dataset available to this research. The analyses showed some values to be suboptimal for the proposed classification models and the datasets used in the research. Table 3 summarizes the parameters used to train the ANN and CNN base models. For the ANN, the optimizer Nadam is Adam [36] ([PERSON] et al. 2014) with Nesterov momentum. The learning rate and learning rate decay were set as 0.001 and 0.001/100, respectively. The ANN models were trained for 500 epochs with early stopping criteria set to the patience of 15 epochs monitoring the validation loss. For the CNN, the Adam optimizer was found best with the same learning rate and learning rate decay as used for the ANN. The CNN models were also trained for 500 epochs with early stopping.  
Figure 5: ANN architecture used in integrated OBIA-deep learning frameworks.  
In addition, Table 4 presents the hyperparameters of the ANN and CNN models used in this research. For the ANN, a dropout with a rate of 0.5 was used after the first dense layer to control the overfitting in the network. The hidden layer's activation was set as ReLU (rectified linear unit) as it is the common activation function used in many deep learning models for remote sensing applications. The loss function that was used to train the models with was categorical cross-entropy. The classification layer was a softmax layer. On the other hand, the CNN model had a few additional hyperparameters that needed to be configured to achieve the best classification results. The patch size of 5 \(\times\) 5 was used as a sliding window size to extract image patches. The pool size in the max-pooling layers was set to 2 \(\times\) 2. The size of the convolutional kernels was 3 \(\times\) 3. The other configurations are the same as they were for the ANN models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Optimization** & **Learning Rate** & **Decay** & **Epochs** \\ \hline ANN & Nadam & 0.001 & 0.001/100 & 500 \\ CNN & Adam & 0.001 & 0.001/100 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used to train the base models.  
Figure 6: Typical architectures of the OBIA-CNN integration frameworks identified in this research.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 4 Conclusions
* With respect to the lowest number of features selected, the ranking is MPA > WSA > SOS > GA (T) > GA (R) > PSO > FPA > MRFO > DE, whereas for the mean and standard deviation of the number of features selected, the ranking is derived as SOS > MPA > GA (T) > GA (R) > WSA > PSO > FPA > DE > MRFO. In general, it is possible to conclude from the study that WSA is quite suitable for solving feature selection problems. It is observed that for big-feature datasets, a decrease of as much as 99% in the number of features can be obtained with the WSA algorithm without sacrificing the classification accuracy. The future scope of this research includes extending the algorithm to handle high-dimensional data and exploring its performance on other classification problems. The algorithm can also be hybridized with other optimization techniques to improve its accuracy and efficiency. Finally, the research highlights the need for developing universal algorithms that can address various aspects of data processing. Conceptualization, R.C. and K.K. ; data curation, N.G. and R.S. ; formal analysis, N.G. and R.S. ; methodology, N.G., R.S., R.C., S.C. and K.K. ; software, K.K. ; supervision, R.C. and S.C.; validation, N.G. and R.S. ; visualization, K.K. ; writing--original draft, N.G., R.S. and S.C.; writing--review and editing, R.C., S.C. and K.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available through email upon request to the corresponding author. The authors declare no conflict of interest.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 16 Enhancement Prospects
#### 2.4.2 Simultaneous Multi-User Transmission  
The introduction of new schemes will enable simultaneous multi-user transmissions by using a shared resource in the time and frequency domains, such as Code division multiplexing (CDM), and multi-user multiple inputs multiple outputs (MU-MIMO), without increasing the number of antennae at the UE. In this regard, more dynamic access can also be achieved through enhanced base station receiver for detection of multiple users that are using the same resource unit as cluster and hence be able to schedule them effectively. This is because, for the last releases, NB-IoT UE uses the static or semi-static configuration of more resources for the unexpected application traffic handling. Similarly, the introduction of NB-IoT transmission without grant will cause a collision of data packets so dynamic handling of multiplexing is necessary.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 3 Results
### Summary  
We proposed a prediction technique to predict Zernike coefficients based on GoogLeNet with an interference fringe in a previous paper [25]. After continuous research, we found a novel usage of GAN that can effectively improve the prediction accuracy. RMSE is an  
Figure 10: The Zernike coefficients and predicted values with or without transfer learning.  
Figure 9: The simulated image of interference fringe. (**a**) The phase shift is zero. (**b**) The phase shift is \(\pi/4\). (**c**) The input image of the Generator according to Equation (5).  
evaluation criterion for comparison with the two different methods. The lower RMSE is a better prediction technique than another.  
We use two different methods: the formula (ideal images) and optics simulation (simulated images) to compare the performance of two networks. In the first case, the RMSE using GoogLeNet is about 0.055 \(\pm\) 0.021\(\lambda\), and using GAN, it is about 0.0182 \(\pm\) 0.0035\(\lambda\), as shown in Table 1. In the second case, the RMSE using GoogLeNet is about 0.095 \(\pm\) 0.018\(\lambda\), and using GAN, it is about 0.101 \(\pm\) 0.0263\(\lambda\). After the transfer learning, the RMSE using GAN improved to about 0.0586 \(\pm\) 0.0183\(\lambda\), as shown in Table 2. Therefore, the prediction accuracy of the GAN is better than that of GoogLeNet.  
The time consumption of the prediction technique is another significant issue. After predicting 1000 times, the two averaged time consumptions are 0.0101 s using GoogLeNet [25] and 0.0634 s using GAN, as shown in Table 3, where the Colab service with a GPU provided by Google was used. Although the time consumption of the GAN is longer than that of GoogLeNet, the accuracy of the GAN is relatively better. Moreover, the RMSE can be reduced by 0.0364\(\lambda\).
###### Abstract  
HTS 2G tapes used in Superconducting Fault Current Limiters (SFCLs) have properties that allow for the effective limitation of short-circuit currents; however, due to the specificity of the device operation, they should be characterized by the high stability of the parameters when repeatedly leaving the superconducting state. During the operation of SFCLs, a situation may occur in which the parameters of the HTS tapes used will change several times as a result of the action of short-circuit currents that exceed the critical current \(I_{\mathrm{C}}\) of the superconductor of the tape used. This paper presents the results of microstructural tests of 2G HTS tapes intended for SFCLs, subjected to surge currents corresponding to prospective short-circuit currents with values higher than their critical currents \(I_{\mathrm{C}}\) and for which \(I_{\mathrm{C}}\) changes were observed. The HTS tapes were examined using a JEOL 7600F field emission scanning electron microscope (SEM), and their chemical composition was analyzed using Energy-Dispersive X-ray Spectroscopy (EDS). The test results indicate the possibility of micro-damage in the form of cracks in the superconductor layer, as well as the interruption of the buffer layers and the oxidation of the silver layers. The analysis of the chemical composition of the HTS tape layers may indicate the occurrence of diffusion processes.  
superconducting fault current limiters (SFCLs); HTS tape; the internal structure of HTS tape; microstructural tests of HTS tapes +
Footnote †: journal: Applied Sciences
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Anterior Segment Diseases  
#### 3.2.1 AI and Corneal Ectasia  
In this domain, methods like risk scores, linear models, and AI models have been used to develop a screening protocol.  
For instance, [PERSON] et al. acquired Pentacam HR (Oculus, Wetzlar, Germany) for three patient categories, with firm LASIK (Laser-Assisted in Situ Keratomileusis), post-LASIK ectasia, and clinical keratoconus, coming from three countries to develop a \"Pentacam Random Forest Index\" (PRFI), which was characterized by a sensitivity of 94.2% and a specificity of 98.8% for detecting corneal ectasia with an AUC of 0.992 [33].  
[PERSON] et al. estimated corneal refractive surgery by studying subjects' demographics, corneal tomography, and ophthalmic examinations by means of five different ML methods. They utilized an ensemble of classifiers, confirmed by means of internal and external data, and obtained an AUC of 0.983 and 0.972, respectively [34].
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 2 Existing Methods
### Calibration-Based Method
S-shaped method, polynomial fitting method  
To further improve the accuracy of the correction, the photoelectric response curve can be fitted by an S-shaped nonlinear equation or by a polynomial. This type of correction method is computationally intensive and unsuitable for hardware-based real-time nonuniformity correction. Similarly, this kind of method also needs to face the CIS parameter drift problem, and needs to repeat the calibration periodically. In general, among all calibration-based methods, the two-point method and piecewise linear method are most used in engineering applications, and are very suitable for real-time calibration system, based on field programmable gate array (FPGA). However, these methods need to complete a repeated calibration periodically, which brings great inconvenience to the actual application. Importantly, the correction process is very cumbersome and has strict requirements on the environment and light source.
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 3 Materials and Methods
### Experimental Setup  
To test the efficacy of the FLJM assemblage, we compared its performance to that of a basic convoy controller in a Gazebo simulation environment. The Gazebo world used in the experiment contained a flat ground pane without terrain or obstacles and a three-vehicle convoy of Cleapath Husky robots, a four-wheel skid-steer robotic vehicle. The setup was kept minimal to reduce the variables present when testing convoy performance. For the basic convoy controller, we implemented a leader-follower controller that performed \"delayed following\" with a predecessor-leader following network topology. In a \"delayed following\" approach, a lead vehicle records the path it drove and relays that information to followers to repeat after some time delay [45]. This approach is recognized as a common leader-follower convoying method [46], making it the ideal baseline for comparison.  
The high-level autonomous convoy setup was kept consistent between the basic convoy controller and the FLJM assemblage for an accurate comparison. In both cases, the autonomous convoy was FA and consisted of three vehicles: one convoy leader that drove along preset path plans, and two autonomous followers. The convoy leader precisely followed through given path points, allowing for repeatability in test runs, resulting in better comparisons between the basic convoy controller and the FLJM assemblage. Both unmanned followers would be running either the basic convoy controller or FLJM assemblage to perform autonomous following, depending on which system was being tested at the time. Each vehicle recorded GPS breadcrumbs of its position that it transmitted to its direct follower when wireless network communications were available.  
The two preset path plans created for the test runs were a square loop and a roundabout turn, as seen in Figure 15. These paths were chosen to mimic common road formations and driving maneuvers. The square loop tests basic turns, while the roundabout tests the intersections of the same name. In addition, jamming zones were established along the paths to simulate the effects of denial-of-service attacks on the autonomous convoy. Figure 16 overlays the jamming zones along the paths.  
To comparatively test the performance of the convoy controllers, multiple test runs with the three vehicle convoy were performed for each jammed path shown in Figure 16. Ahead of each test run, the autonomous followers were configured to use either the basic convoy controller or the FLJM assemblage. Five runs were performed under constant jamming, and an additional five runs were performed under random jamming, with tj, and tj set to 10 s and 2 s, respectively. Both the basic convoy controller and the FLJM assemblage were tested in this fashion for each path plan.