esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Synthesized Graphene-Based Photonics and Optoelectronics Devices
## 2 Growth Mechanism of Graphene
### Interfacial Growth
The average grain size was found to be approximately 5-20 \(\upmu\)m for the Ni/SiO\({}_{2}\) arrangement and 40-50 nm for the Ni on plastic and glass substrates. Furthermore, the estimated carrier (hole) mobility of the graphene-enabled field-effect transistor was found to be 667 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) at room temperature. Transfer-free growth of multilayer graphene using the self-assembled monolayers (SAM) method was reported in [78], which is efficient and promising because the amount of carbon can be easily controlled [79; 80; 81]. In this process, SiO\({}_{2}\)/Si, quartz, GaN, and textured Si were used as substrates. First, the targeted substrates were cleaned thoroughly with acetone/isopropyl alcohol and piranha solutions (1:3 mixture of hydrogen peroxide and sulfuric acid). Later, the substrates were immersed in 0.1 M trimethoxyphenylsilane (PhSi(OMe)\({}_{3}\), Sigma-Aldrich solution in hexane overnight followed by thermal annealing at 120 \({}^{\circ}\)C for 20 min to prepare the phenyl-SAM/substrate structure. Then an electron-beam evaporator was used on top of the SAM for Cu (200 nm) or Ni films (300 nm) deposition. To form a Cu/graphene/substrate structure, a low-vacuum furnace was loaded with the prepared samples at 600\(-\)1000 \({}^{\circ}\)C. Subsequently, 1 wt. % ammonium persulfate was used to wet-etch the Cu films. High-quality graphene was produced on SiO\({}_{2}\)/Si and quartz substrates, although further optimization of growth conditions is required for GaN and textured Si. The transfer-free growth method for graphene proposed in this study uncovers new possibilities for industrial graphene-based device applications. Raman spectroscopy further shows the effect of flow rates of Ar and H\({}_{2}\) on the quality of the synthesized graphene, as shown in Figure 2f. Raman analysis revealed that a high flow rate of Ar is required for the optimal growth of graphene under certain conditions.
###### Abstract  
In this study, we maximized the reactivity of phospholipids hydrolysis with immobilized industrial-class phospholipase A\({}_{1}\) (PLA\({}_{1}\)) at the desired water content in the water-in-oil (W/O) microemulsion phase. The optimal hydrophobic-hydrophilic condition of the reaction media in a hydrophobic enzyme reaction is critical to realize the maximum yields of enzyme activity of phospholipase A\({}_{1}\). It was attributed to enzymes disliking hydrophobic surroundings as a special molecular structure for reactivity. Immobilization of PLA\({}_{1}\) was successfully achieved with the aid of a hydrophobic carrier (Accurel MP100) combination with the treatment using glutaraldehyde. The immobilized yield was over 90% based on simple adsorption. The hydrolysis reaction was kinetically investigated through the effect of glutaraldehyde treatment of carrier and water content in the W/O microemulsion phase. The initial reaction rate increased linearly with an increasing glutaraldehyde concentration and then leveled off over a 6% glutaraldehyde concentration. The initial reaction rate, which was predominantly driven by the water content in the organic phase, changed according to a typical bell-shaped curve with respect to the molar ratio of water to phospholipid. It behaved in a similar way with different glutaraldehyde concentrations. After 10 cycles of repeated use, the reactivity was well sustained at 40% of the initial reaction rate and the creation of the final product. Accumulated yield after 10 times repetition was sufficient for industrial applications. Immobilized PLA\({}_{1}\) has demonstrated potential as a biocatalyst for the production of phospholipid biochemicals.  
immobilization; phospholipase A\({}_{1}\); hydrolysis; Accurel MP100; water content +
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## 3 Materials and Methods  
### Materials  
#### 3.1.1 Datasets  
For the experimental evaluation of the proposed DCAT model, we employ Tamil versions of three benchmark datasets: MSCOCO [30], Flickr8k [15], and Flickr30k [28]. These datasets are renowned in the field of image captioning, and each offers a distinct set of challenges and complexities. For pre-training we have used the Linguistic Data Consortium for Indian Languages (LDC-IL) Tamil Text Corpus [31].  
LDC-IL Tamil Text Corpus for Pre-Training  
The Linguistic Data Consortium for Indian Languages (LDC-IL) provides a rich and diverse Tamil Text Corpus that has been leveraged for the pre-training stage of our model. This corpus is meticulously designed to cover a wide array of domains, thereby ensuring a balanced and comprehensive linguistic foundation for the model. The corpus is composed of literary and non-literary texts sourced from books, magazines, and newspapers. It is encoded in Unicode and stored in an XML format, making the data machine-readable and easily retrievable. Table 1 shows the details of the tamil text corpus.  
The corpus offers a holistic exposure to various facets of the Tamil language, from formal to informal styles and from general to specialized jargon. The broad scope of the corpus, covering fields such as Aesthetics, Commerce, and Science, ensures that the model is versatile and robust, capable of understanding the nuanced structures and vocabulary inherent to each domain.  
Flickr8k is one of the most widely used datasets for image captioning. It consists of 8000 images, each of which is accompanied by five unique captions that describe the content of the image. This dataset is particularly useful for capturing everyday scenes and activities.  
Flickr30k is an expanded version of the Flickr8k dataset and includes 31,783 images. Similar to Flickr8k, each image in this dataset is also accompanied by five unique captions. The dataset is more diverse and covers a broader range of scenarios compared to Flickr8k.  
Microsoft Common Objects in Context (MSCOCO) Dataset  
The MSCOCO dataset is another commonly used benchmark dataset, containing approximately 328,000 images. Each image is annotated with five captions, and the dataset includes around 1.5 million object instances. MSCOCO is known for its complexity and diversity, depicting various daily activities and scenarios. The statistics summary of all these 3 datasets which used to evaluate the proposed work is presented in Table 2.
## 2 Modeling and Parking Path Planning
### Parallel Parking Path Planning  
According to the analysis of space constraints, this section uses M language to simulate the parallel parking path of vehicles in MATLAB, as shown in Figure 4. Refer to Table 1 for vehicle parameters.  
The content of the program is as follows:  
1. Set parameters such as road color and indicator signs, set parameters of parking scene, slot length is set at 6.5 m, slot width is set as 2.5 m, and side distance is set as 1.2 m,
2. Define the initial position of the vehicle and the location of the target parking space;
3. As for the turning radius and travel distance of the vehicle in a circular motion, the radius of the circle is defined as the minimum turning radius of the vehicles, that is, 5.8 m;
4. The driving distance of the vehicle in a straight line is about 4.5 m.  
The relevant parameters of the vehicle are shown in Table 1.  
As shown in Figure 5, the parking path meets the requirements of the parallel parking space constraints, that is, no collision with surrounding obstacles, and can maintain a certain safe distance, which verifies the feasibility of the planned path.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Parameter** & **Symbols** & **Data/Unit** \\ \hline Width & \(W\) & 1.90 m \\ Wheelbase & \(L\) & 2.80 m \\ Front Suspension Length & \(L_{f}\) & 0.96 m \\ Rear Suspension Length & \(L_{r}\) & 0.94 m \\ Minimum Turning Radius & \(R_{min}\) & 5.80 m \\ Steering Ratio & \(i\) & 14.6 \\ Unit Pulse Value & & 0.004438 m \\ Distance Front to Rear Radar & & 3.96 m \\ Diatance Front Radar to Rear Axle & & 3.28 m \\ Diatance Rear Radar to Rear Axle & & 0.44 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle Parameters.  
Figure 4: Parallel Parking Path Simulation with M Language.  
However, the path is composed of two segments, i.e., the arc with constant curvature and a segment of straight line between two arcs. The curvature of the connecting point of arc and line in the path is discontinuous. In the next section, the preview theory is introduced to modify the curvature outlier of the path to optimize the parking path.
## 3 Sky vs. Earth Observations with Antenna Arrays
### The Remote Sensing Point of View  
Unlike interferometers observing the sky in radio astronomy, which fall into the category of non-regular antenna arrays requiring a gridding preliminary step prior to the inversion of the visibilities, the antenna arrays observing Earth from a low-elevation orbit (LEO) belong, or will belong, for space accommodation reasons to the family of regular antenna arrays leading to a regular sampling of the experimental frequency coverage. As a consequence, in Earth remote sensing, the overall angular extent of the field of view of an interferometer is determined by the shortest spacing \(d\) between the antennas and by the observing wavelength \(\lambda\):  
\[\sin\theta_{FOV}=\frac{1}{d/\lambda}. \tag{5}\]  
When the instrument points into the nadir direction, as illustrated in Figure 1, Earth is a disk with a radius \(\sin\theta_{\oplus}=R_{\oplus}/(R_{\oplus}+h)\) in the direction cosines domain of the reference frame attached to the antenna array, where \(R_{\oplus}\) is the radius of Earth and \(h\) is the elevation of the platform. To fix the ideas, from the elevation of SMOS, \(h=755\) km, Earth is seen from an angle \(2\theta_{\oplus}\simeq 126.8^{\circ}\) and it subtends a solid angle \(\Omega_{\oplus}=2\pi(1-\cos\theta_{\oplus})\simeq 1.1\pi\) sr. Due to the Y-shaped geometry of the interferometric array and to the spacing between the antennas, \(d=0.875\lambda\) with \(\lambda=21.2\) cm, the synthesized field of view of SMOS is a hexagon with outer radius \(2\lambda/3d\simeq 0.76\)[26] in the direction cosines domain of the same reference frame, that is to say, \(49.6^{\circ}\). Such a hexagonal field of view subtends a solid angle \(\Omega_{FOV}\simeq 0.56\pi\) sr. This value is very far from those encountered in radio astronomy as it is an important fraction of the \(2\pi\) sr space in front of the instrument. With regards to the voltage patterns of the elementary antennas, it turns out that the averaged FWHM is about \(98^{\circ}\), which is comparable to the overall angular extent of the hexagonal field of view \(\theta_{FOV}\simeq 99.2^{\circ}\). Finally, with three arms as long as 3.9 m, the best angular resolution of SMOS is about \(2.1^{\circ}\) with a Blackman apodization window [30]. As the number of pixels \(N^{2}\) sampling the hexagonal field of view is set to \(128^{2}\), the size of the hexagonal pixels is about \(0.6^{\circ}\) in the boresight direction. As a consequence, the synthesized beam is more than three pixels wide.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.5 Comparison with Traditional Iris Positioning Algorithm  
In order to further verify the accuracy of the algorithm proposed for multi-source heterogeneous iris segmentation, a comparative experiment was carried out with the widely used method OSIRIS V4.1 [32]. The contour detection method was performed on the mask images of the four heterogeneous iris test sets and positioning images of the iris area were obtained. The number of images with wrong position area was calculated. The original iris images of four heterogeneous iris test sets were input into OSIRIS V4.1, respectively, and the number of images with wrong position area was calculated. In this experiment, the three iris datasets, MICHE-I, UBIRIS.V2, and Mmu2, were not used because OSIRIS V4.1 is not applicable to iris images containing lots of realistic noises and taken from non-Asians. As the positioning effect is very poor and there is no comparability. The experimental results are shown in Table 8. The comparison of positioning results of PFSeglris and OSIRIS V4.1 for four heterogeneous iris test sets is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Method** & **Error Number** & **Error Rate** \\ \hline JLU-6.0 & \begin{tabular}{c} **PFSeglris** \\ OSIRIS V4.1 \\ \end{tabular} & \begin{tabular}{c} **23** \\ 285 \\ \end{tabular} & \begin{tabular}{c} **0.023** \\ 0.285 \\ \end{tabular} \\ \hline JLU-7.0 & \begin{tabular}{c} **PFSeglris** \\ OSIRIS V4.1 \\ \end{tabular} & \begin{tabular}{c} 33 \\ 382 \\ \end{tabular} & \begin{tabular}{c} **0.033** \\ 0.387 \\ \end{tabular} \\ \hline CASIA-Interval & \begin{tabular}{c} **PFSeglris** \\ OSIRIS V4.1 \\ \end{tabular} & \begin{tabular}{c} **16** \\ 127 \\ \end{tabular} & \begin{tabular}{c} **0.017** \\ 0.132 \\ \end{tabular} \\ \hline CASIA-Lamp & \begin{tabular}{c} **PFSeglris** \\ OSIRIS V4.1 \\ \end{tabular} & \
\begin{tabular}{c} **29** \\ 368 \\ \end{tabular} &
\begin{tabular}{c} **0.030** \\ 0.383 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of experimental results.  
Figure 12: The segmentation results predicted by baseline and PFSeglris on four iris datasets: (**a**) original images; (**b**) ground truth; (**c**) segmentation results of baseline; (**d**) segmentation results of PFSeglris.  
We can see from Table 8 and Figure 13 that the proposed PFSegIris can divide the iris regions more accurately than the traditional positioning method. This is because the segmentation algorithm in this paper can avoid the problems of noise interference caused by eyelashes, eyelids, and lighting in the traditional method.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 7 Conclusions and Future Research Directions
In addition, the update methods could be tested in the context of IoT projects based on other communication protocols such as NBIoT [37] and Zigbee [38], or even proprietary ones such as Enhanced ShockBurst (ESB) running in nRF24L Series chips [39]. Finally, other KPIs apart from the update time could be estimated and compared, such as the energy cost incurred by each method. Although energy is not a problem in our specific use case, it can be relevant in other situations. Conceptualization, V.M., [PERSON] and J.S. ; methodology, V.M., G.B. and J.S. ; software, V.M. ; validation, V.M., G.B., J.S. and J.M. ; formal analysis, V.M., G.B. and J.S. ; investigation, V.M., G.B., J.S. and J.M. ; resources, V.M., G.B., J.S. and J.M. ; data curation, V.M., G.B., J.S. and J.M. ; writing--original draft preparation, V.M., G.B. and J.S. ; writing--review and editing, V.M., G.B., [PERSON] and J.M. ; visualization, V.M. ; supervision, J.S. ; project administration, [PERSON] and J.M. ; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the project \"Arquitectura inteligente para control de luminarias\" (00137209/IDI-20211320), supported by FONDO EUROPEO DE DESARROLLO REGIONAL (FEDER) and Ministerio de Ciencia e Innovacion and CDTI; and by the program \"I + D + I, Actuacion AP01 Fomento de Inversion de I + D + I en el teijdo productivo de Aragon,\" (PAIP-23, line TDI-Feder 0313/2023/TDI) funded by FEDER, Government of Spain and Government of Aragon, 2023. Not applicable. Data Availability Statement:Data are contained within the article. The authors want to acknowledge the support of [PERSON], who was in charge of making the photographs of the node and the gateway. And the support of [PERSON] and [PERSON] in the tests. Author [PERSON] was employed by the company Airfal International. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction model based on sparse representation, optimized using Stokes vector images. This model forms the basis for our method aimed at achieving super-resolution reconstruction of infrared polarization images. In this method, we utilize the proposed model to initially reconstruct low-resolution images in blocks. Subsequently, we perform a division by weight, followed by iterative back projection to enhance details and achieve high-resolution reconstruction results. As a supplement, we establish a near-real-time short-wave infrared time-sharing polarization system for data collection. The dataset was acquired to gather prior knowledge of the over-complete basis set and to generate a series of simulated focal plane images. Simulation experimental results demonstrate the superiority of our method over several advanced methods in objective evaluation indexes, exhibiting strong noise robustness in quantitative experiments. Finally, to validate the practical application of our method, we establish a split-focal plane polarization short-wave infrared system for scene testing. Experimental results confirm the effective processing of actual captured data by our method.  
infrared polarization; super-resolution reconstruction; sparse representation Article  
A Super-Resolution Reconstruction Method for Infrared Polarization Images with Sparse Representation of Over-Complete Basis Sets  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], F. A Super-Resolution Reconstruction Method for Infrared Polarization Images with Sparse Representation of Over-Complete Basis Sets. _Appl. Sci._**2024**, _14_, 825. [[https://doi.org/10.3390/app14020825](https://doi.org/10.3390/app14020825)]([https://doi.org/10.3390/app14020825](https://doi.org/10.3390/app14020825))
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Dataset Validation
#### 4.2.4 Performance Evaluation Metrics  
For this study, evaluation metrics used to measure the performance of our models included the confusion matrix, accuracy, precision, recall, and F1-scores.  
* **Accuracy**: Accuracy measures the overall correctness of the model by calculating the ratio of correctly predicted instances (both true positives and true negatives) to the total number of predictions.  
Figure 8: The result of the outlier removal using IQR.  
Figure 7: The skewness level of Box-Cox transformed data vs. original dataset.  
\[Accuracy=\frac{TF+TN}{TP+TP+FP+FN} \tag{1}\]  
where TP (true positive): the model correctly predicted the positive class, TN (true negative): the model correctly predicted the negative class, FP (false positive): the model incorrectly predicted the positive class, and FN (false negative): the model incorrectly predicted the negative class.
* **Precision:** Precision measures the accuracy of the positive predictions. It indicates how many of the predicted positive results were correct. High precision means that when the model predicts a positive, it is usually correct. \[Precision=\frac{TP}{TP+FP}\] (2)
* **Recall:** Recall measures the ability of the model to identify all relevant instances (positives). It indicates how many actual positive cases were correctly predicted. High recall means the model captures most of the actual positives. \[Recall=\frac{TP}{TP+FN}\] (3)
* **F1-score:** The F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both precision and recall, which is especially useful when the class distribution is imbalanced. A high F1-score indicates a good balance between precision and recall. \[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall}\] (4)  
Moreover, the Matthews correlation coefficient (MCC), which provides a comprehensive measure of classification performance considering all categories in the confusion matrix, and the area under the ROC curve (AUC-ROC), summarizing model performance across various thresholds also used. Each of these metrics plays a vital role in evaluating and interpreting the effectiveness of classification.
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 4 Experimental Results
### K-Shortest Paths and Eclipse-Aware Routing Results  
Table 2 compares the standalone KSP results with those obtained after EAR is applied. When routing from node 38 to node 98, KSP alone generates three candidate paths and selects path 1, whereas adding EAR changes the selection. EAR first identifies satellites that are in eclipse and computes a node cost for each one based on energy use, remaining eclipse time, and DoD. For each candidate path, the largest node cost is taken as the path metric, and the path with the smallest metric becomes the final choice. In this case, path 3 is selected after EAR is applied. In Table 2 nodes in eclipse are shown in red, each node's cost appears in parentheses, and the path metric for each route is highlighted in blue. This procedure optimizes the energy balance of the LEO constellation and mitigates battery degradation.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 3 The Sentinel-3 Tandem for Climate Study  
### Study Objectives  
Measurements acquired during the tandem phase can be used for several purposes:  
* Supporting commissioning of Sentinel-3B operations, by allowing straightforward comparisons with a similar operational satellite
* Understanding the impact of different instrument operations modes: this has been especially important for the altimetry mission
* Understanding inter-satellite differences, assessing harmonization needs and requirements; this point being the core objective of the tandem phase
* Validating uncertainties
* Exploiting differences in the geophysical signal, due either to the time lag during the tandem phase or the differences in geometry during the drift phase  
The Sentinel-3 tandem for climate study was commissioned by ESA to exploit the tandem data and improve the long-term usability of data from the Sentinel-3 constellation for climate data records. The study looked at all Sentinel-3 instruments and covered a large range of the topics listed above.