esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The PRISMA Strategy Article Selection  
The search is narrowed down to the basic concepts relevant to the scope of this review. Various published studies are likely outside this review article's scope due to the substantial applications in ML. Figure 1 depicts the article selection process following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) strategies [46]. Google Scholar, Scopus, Web of Sciences, IEEE Explore, Science Direct, SpringerLink, and Taylor Francis were the databases used to find the relevant articles. Searching for the most basic information was performed by an automated search engine. We consulted several reputable websites in order to obtain specific information. The scope of this review is primarily determined by two keywords: (\"machine learning\" OR \"deep learning\") AND \"oil palm\". As a result, in the majority of the searches, we used these two keywords in conjunction with other keywords such as \"crop yield\", \"palm oil\", \"genomic prediction\", \"yield forecasting\", \"yield estimation\", \"prediction model\", \"_Elais guinensis_\", \"machine learning\", \"deep learning\", \"artificial intelligence\". Among the documents we have collected are original articles, review articles, book chapters, conference papers, lecture notes, and reports.  
We downloaded, read the title, abstract, conclusion, keywords datasets, models used, techniques, and metric evaluations in the first round of review and selected only the relevant articles manually and then entered them into excel. Articles that have been included in excel will be refiltered to be analyzed with data that may be important in this study to facilitate information retrieval to improve contextual understanding. This selection is due to constraints such as the difficulty to find palm oil study factors, especially in phenotypic data, because they are rarely studied because they have a long plant life span to bear fruit. After removing duplicates, the remaining articles are classified according to their application. During the first round of screening, machine learning-based palm oil and crop prediction articles written in English are used as selection criteria.  
The selected manuscripts are scrutinised in the second round of screening. The detailed methodology was the final article selection criteria in this case. After following the above procedures, only \"163\" articles are chosen for this review.  
The following information was meticulously extracted from each article: publication year, dataset information, detailed information about features, prediction algorithms, and system performance. The number of selected documents is divided into four categories: journal articles (82%), conference papers (11%), book chapters (5%), and websites (2%), as shown in Figure 2. In terms of the annual distribution of published work, trends show that a greater number of research articles were published in 2017, 2019, and 2021, while the number of articles dropped too low in 2015. Figure 3 depicts the annual distribution of research publications.  
Figure 1: The PRISMA flow diagram.  
Figure 2: Type of Articles.
## 7 Conclusions  
This study has reviewed the state of the art in EO sensors and platforms and has presented a methodology to select the best instruments' technologies and platforms required to complement the Copernicus system in the time frame 2020-2030. Suitable instruments for small platforms have been analyzed using several attributes, and they have been ranked using a quantitative scoring method. Results show that the most relevant payloads capable of filling the measurements gaps are: GNSS-R at 10 km spatial resolution, X-band imaging SAR at 1 km spatial resolution, and multispectral Optical instrument with bands in the VIS (10 m of spatial resolution), NIR (10 m), MWIR (1 km), and TIR (1 km).  
The high temporal resolution of one hour required can only be achieved if a sufficiently large number of spacecrafts are used; then, the architecture selection could be analyzed and optimized [31, 71]. A distributed or Federated Satellite System (FSS) will help to reduce the temporal gaps. The possibility to create strategic alliances to establish distributed or federated architectures between different missions and agencies must be carefully evaluated to safe costs. Federated Satellite System (FSS) concepts could also be applied to future instrument technologies to cover the gaps, taking into account different satellites program and space agencies.  
[PERSON] performed the survey of the instruments and commercial platforms; [PERSON], [PERSON], H.P., P.R., S.T., J.C. and S.P. identified the potential instruments to cover the measurements gaps; [PERSON] developed the quantitative method; [PERSON] revised the quantitative method; [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] revised it.  
This project has been funded by the EU H2020 ONION project, under grant agreement 687490. It has also received support from projects AGORA (ESP2015-70014-C2-1-R) of the Spanish Ministry of Economy and Competitiveness, an ICREA Academia Award from the Catalan Government.  
The authors declare no conflicts of interest.
## 3 Results  
### Patient Characteristics  
Of the 37 patients, two had to be excluded because the acquired i3D US did not capture the whole lesion due to artifacts. Of the remaining 35, 18 were females and 17 males. Mean patient age was 62.7 \(\pm\) 12.1 (mean \(\pm\) SD) years, ranging from 28.6 to 79.1 years. Thirty-one patients suffered from single brain metastasis and four were diagnosed with more than one lesion. The most common tumor location was in the frontal lobe (15), followed by the parietal (five, of which each one was temporo-parietal or parieto-occipital), occipital (five), and temporal (two). In six cases, the tumor was located cerebellar. One lesion was located at the head of the caudate nucleus, and one lesion was insular. Histopathological work-up revealed metastasis of an adenocarcinoma of the lung in eleven cases and neuroendocrine carcinoma of the lung in three cases. In seven patients, a melanoma was found as the primary site, three patients suffered from breast cancer, four were diagnosed with renal cell carcinoma, six with gastrointestinal adenocarcinoma encompassing colon (three), gall bladder (one), esophagus (one), and one which was not specified further. One lesion was defined as a carcinoma of unknown primary site. Table 1 summarizes patient characteristics.
###### Abstract  
In both ecological research and engineering, the selection of indicator species is crucial. Biodiversity has always been an important policy objective for ecologists and environmental managers. Based on this target requirement, we developed a method that reveals the individual contributions of species to biodiversity to quantitatively identify indicator species for selection during environmental monitoring. The Siangshan Weltland in Hsinchu, Taiwan, was selected as an application case. The spread of mangroves not only changed the original habitat composition and function of benthic organisms in wetlands, but also led to problems such as estuary filling, flooding, and black mosquito breeding. Therefore, a large-scale mangrove removal project was undertaken by the Hsinchu City Government from October 2015 to March 2016. In this study, the biological effects of mangrove removal on benthic organisms and adjacent habitats were investigated from October 2015 to September 2016. According to biodiversity contribution algorithms, we identified five indicator species, namely, _Mictyris breidactylus_, _Macrophthalmus bunzai_, _Uca arcuata_, _U. lactal_, and _U. borealis_. These indicator species had the most prominent biodiversity contribution, and they provided direct evidence of the beneficial effect of mangrove removal for wetland restoration. After mangrove deforestation, tidal flat species returned to their original habitats, and their related densities increased significantly in mangrove removal areas. Improving our understanding of the relationships between biodiversity and indicator species is crucial for the development of coastal management processes. Mangrove removal can be confirmed as an appropriate habitat rehabilitation strategy for benthic organisms. Consequently, these indicator species and the results obtained can provide valuable ecological information for those involved in coastal management or other officials seeking to control the spread of mangroves.  
environmental monitoring; restoration; Siangshan Weltland; mangrove removal; benthic organisms +
Footnote †: journal: Applied Sciences
## 3 MALDI Non-Imaging MS Analysis of GSLs  
### Fragmentation Map of GSLs  
Fragmentation can occur at many levels in the GSL molecule, and, because of the complexity and the diversity of the products formed, Domon and Costello proposed a nomenclature [110]: the labels Ai, Bi, and Ci are employed to indicate segments containing a sugar unit at the terminal (non-reducing) end, while Xj, Yj, and Zj denote ions that still retain the aglycone (or the reducing sugar unit). Subscripts convey the position relative to the ends, similar to the peptide nomenclature, and superscripts signify cleavages within the carbohydrate rings. When the charge is preserved on the carbohydrate segment, the fragments are denoted as Ai, Bi, and Ci. Here, 'i' signifies the sequential number of the glycosidic bond cleaved, starting from the non-reducing end. Conversely, ions encompassing the aglycone (or the reducing sugar unit in the context of oligosaccharides) are identified as Xj, Yj, and Zj. In this scenario, 'j' indicates the number of the interglycosidic bond counted from the aglycone (starting from the reducing end of carbohydrates). The glycosidic bond connected to the aglycone is labeled as 0 (Figure 1a).  
For the designation of the ions obtained after fragmentation of the ceramide moiety occurs some established nomenclatures are used [111; 112; 113], the most complete being the one introduced by Ann and Adams [111]. According to this nomenclature, sphingoid-base-containing ions are designated as O and P, N-acyl-chain-containing ones are designated as S, T, U, and V, the ones that result from the fragmentation of the sphingoid base are designated as G and H, and after fragmentation of both the sphingoid base and the N-acyl chain, the E ion results (Figure 1b).
## 2 Train Dynamical Model and Problem Statement
### Description of Train Dynamical Model with Actuator Faults and Actuator Saturation  
#### 2.2.1 Actuator Faults  
When the train is running on the line, due to the frequent execution of tasks, unanticipated actuator faults such as faults caused by overheating or turn-to-turn short circuits of motor, faults from overcurrent in the traction converter, faults from wear of mechanical drivers and so on, are inescapable. These failures can be classified into the following categories: lock-in-place fault, hard-over fault, float type fault, loss of actuator effectiveness, and so on [27], among which loss of actuator effectiveness is more severe and has higher probability of occurrence than other component faults [28]. Hence, loss of actuator effectiveness is considered in this paper.  
For single-point-mass train dynamical model, the loss of actuator effectiveness faults, which means that the actuator partially loses its actuating power, can be considered as a whole [22]. Therefore, the real output of actuator subject to loss of actuator effectiveness faults can be described as follows:  
\[F^{\prime}=k_{c}F, \tag{4}\]  
where \(F^{\prime}\) is the actual actuator output and \(F\) represents output of the controller without actuator faults. \(k_{c}\) stands for actuator's effectiveness and satisfies \(0\leq k_{c}\leq 1\). \(k_{c}=0\) indicates the actuator totally failed; \(k_{c}=1\) indicates the actuator is healthy and \(0<k_{c}<1\) indicates the actuator occurs partial loss of effectiveness fault.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 1 Introduction
### Machine Learning and Deep Learning in Acute Aortic Syndrome
#### 1.1.1 Medical Imaging Interpretation  
* Computed Tomography (CT) Scans: Algorithms analyze CT scans to detect subtle patterns of AAS, learning from extensive datasets to improve accuracy [11].
* Magnetic Resonance Imaging (MRI): Similar to CT scans, algorithms analyze MRI images to recognize AAS features, enhancing subtype identification [12].
## 3 Results
### HE Promotes \(\beta\)-Catenin Signaling Pathway in HDP Cells  
As mentioned prior, \(\beta\)-catenin signaling plays an essential role in the hair follicle growth initiation [50; 51]. Thus, we first examined the phosphorylation level of \(\beta\)-catenin in DMSO or HE-treated HDP cells. As shown in Figure 3a, HE increased the protein level of \(\beta\)-catenin and inhibited the phosphorylation level of \(\beta\)-catenin in a dose-dependent manner. The protein expression level of \(\beta\)-catenin was highest and phosphorylation levels of \(\beta\)-catenin were lowest in HE-treated HDP cells for 24 h at a dose of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. After this, to determine the activity of \(\beta\)-catenin, which is a transcription factor, we performed a cell  
Figure 2: Characterization of _S. deserticola_ JD052 extract and effect of HE on cell viability and hair-inductive properties compared to PE. **(a)** HDP cells were treated with HE at various concentrations (0 to 500 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) for 48 h. Cell viability was assessed using a WST-1 assay. **(b)** HDP cells were treated with 10, 50, and 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of HE for 48 h, using a 96-well ultra-low-attachment microplate to measure the diameter of spheroids. Images of HDP spheroids were captured by phase-contrast microscopy. **(c)** 293T cells were treated with 50 and 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of _S. deserticola_ JD052 extracts for 24 h. The TCF/LEF luciferase reporter assay was assessed using luminescence and quantitated by \(\beta\)-galactosidase activity. The black bar group is DMSO-treated control group. Each bar represents the mean \(\pm\) SD of 3 independent experiments. Significance was determined using a one-way ANOVA followed by Tukey’s test. * \(p\) < 0.05, **_p_ < 0.01, and *** \(p\) < 0.001 compared with the vehicle-treated group. n.s. means non-significant. PE, photoautotrophic-cultured _S. deserticola_ JD052 extract; HE, heterotrophic-cultured _S. deserticola_ JD052 extract; HDP, human dermal papilla.  
fractionation assay [52; 53; 54]. Cell fractionation showed that \(\beta\)-catenin trans-localization into the nucleus was observed in HE-treated HDP cells compared to DMSO-treated controls (Figure 3c). These results showed that the possible effects on the hair-inductive properties of HE were regulated by increasing \(\beta\)-catenin signaling. \(\beta\)-catenin signaling is activated by the phosphorylation of GSK3\(\beta\)[55; 56; 57]. Furthermore, we evaluated whether HE phosphorylates GSK3\(\beta\) and its upstream modulators AKT and MAPK.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 7 Conclusion  
With the widespread use of JavaScript on the Internet, a large number of JavaScript-based attacks have been developed, such as XSS and drive-by-download attack. This poses a serious threat to Internet users. At present, the detection method based on expert knowledge cannot alleviate the growth rate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BLSTM & **92.01\%** & **94.23\%** & **93.03\%** & **93.63\%** \\ \hline Naive Bayes & 62.73\% & 65.32\% & 93.16\% & 76.79\% \\ \hline SVM & 80.22\% & 90.82\% & 71.48\% & 80.00\% \\ \hline Random forest & 86.32\% & 89.12\% & 87.20\% & 88.15\% \\ \hline JaSt & 92.14\% & 95.91\% & 92.45\% & 94.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: The performance of the models on DB_Ob.  
of malicious code on the Internet. Fortunately, the latest studies demonstrate that the deep learning model shows encouraging results in detecting malicious scripts.  
Based on the existing research, we proposed a novel malicious JavaScript detection approach based on BSLTM neural network. In order to transform JavaScript programs to vectors, we proposed the concept of _semantic slices_, which preserve rich semantic information and are easy to transform into vectors. Then we trained the BLSTM neural network to classify JavaScript samples. Experimental results show that our model achieves the best performance compared with other four machine learning-based models and a traditional antivirus software, with 97.71% accuracy and 98.29% F1-score. At the same time, we preliminarily studied the influence of obfuscation on the performance of the learning-based detector, and corrected the relevant assumptions of the existing research.  
Future work will be carried out from two aspects. On the one hand, static analysis cannot detect the malicious JavaScript code generated dynamically at present. We hope to alleviate the negative impact of JavaScript dynamics through more sophisticated string processing. On the other hand, researchers have proposed many new models, such as tree structure neural networks and graph structure neural networks. We hope to study the effectiveness of more types of neural networks in malicious JavaScript detection.  
Conceptualization, X.S. and C.C.; Methodology, X.S.; Software, X.S.; Validation, X.S., C.C. and B.C.; Data Curation, C.C.; Writing.--Original Draft Preparation, X.S.; Writing.--Review and Editing, J.F.; Visualization, J.F.; Supervision, B.C.; Project Administration, B.C.; Funding Acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Beijing Natural Science Foundation(4204107), the Fundamental Research Funds for the Central Universities (No. 500419810), Funds of \"YinLing\" and in part by the China Postdoctoral Science Foundation under Grant 2019M650020.  
The authors declare no conflict of interest.
## 2 Literature Review
### Receivers
Moreover, the use of Fresnel lenses may result in the loss of initial directional information due to light convergence. [PERSON] et al. developed a quasi-omnidirectional smart transceiver utilizing a 3D spherical arrangement of lenses to focus light onto a planar array of photodiodes [18]. This transceiver exhibits a quasi-omnidirectional FOV and the ability to estimate angle-of-arrival using a pattern-matching algorithm accurate to \(\pm 20^{\circ}\) at the best resolution, demonstrating electronically switched beam steering. They use a static design for the receiver array; however, it is mounted on servo motors in a \"pan-and-rotate\" system. Likewise in 2012, [PERSON] and [PERSON] tested a fixed, circular photodiode array for integrated communication and localization [19]. They controlled a remotely operated vehicle using remotely transmitted thrust commands. In addition, Table 1 further summarizes a survey of the existing commercial transceivers by their port shapes and performance (information on the receiver type used, PMT, is only publicly available for Sonardyne devices). Despite extensive research on transmitters in underwater optical wireless communication (UOWC), there appears to be a lack of coherent and goal-oriented research regarding receivers. From an engineering perspective, an ideal mobile UOWC receiver should fulfill three main tasks: ensuring spatially reliable communication, achieving strong link quality, and enabling localization [20]. This discrepancy prompts questions about whether the lack of coherence is due to a lack of preliminary analyses conducted on the performance of photodetector array-based receivers in UOWC. To our knowledge, analyses that factor in the numerous determining parameters which include water quality, geometric losses, transmission factors, and photodetector characteristics have not been conducted. We conduct our analysis in hopes of bridging this gap and providing insights towards more goal-oriented UOWC arrayed-receiver designs.
## 4 Experimental Results and Discussion  
Qualitative and quantitative evaluations from various experiments were performed to evaluate the invisibility and robustness of the proposed digital watermarking scheme. First, the dataset used and the environment set for implementation are described, and the measurement method for quantitative evaluation is described. The invisibility, robustness, WM adaptability, host image adaptability, and controllability of the invisibility and robustness are then verified. Finally, the results are compared with the state-of-the-art methods.
## 3 Overview of the Proposed Method
### License Plate Based Vehicle Re-Identification  
License plate recognition (LPR) systems read license plate characters to match between the query vehicle and the gallery images to Re-Id the vehicle. However, for this purpose, it is important to have a consistent environment because it requires auxiliary infrastructure, such as flashlights and sensors. In fact, license plates are often available in inconsistent environments, which makes it difficult because of lighting, viewpoint angle, and occlusion. Hence, we are here verifying vehicle license plates instead of recognizing if two license plates are the same or not, using the siamese neural network (SNN). SNN was proposed by [PERSON] et al. [36] and was designed for person face verification. SNN is suitable for problems where a number of categories are large but instances from each category are limited. SNN is designed with two identical parallel convolutional neural networks to extract the robust discriminative feature vectors from license plate pairs. Convolutional neural networks are trained in an end-to-end way to map pixel images to outputs. The key benefit of using the CNN in our approach is that we can learn optimal shift-invariant local feature detectors and generate features that are robust to geometric distortions of the query image and then calculate the Euclidean distance between the feature vectors. In a license plate verification system, distance should be the minimum between a pair of license plates from the same vehicle, and large from pairs of license plates from different vehicles.  
Suppose A\({}_{1}\) and A\({}_{2}\) are input pairs of license plates, B is a binary label of the pair. The pair = 0 if the images A\({}_{1}\) and A\({}_{2}\) belong to the same vehicle and B = 1 otherwise, W the weights of the convolutional neural network, and C\({}_{w}\)(A\({}_{1}\)) and C\({}_{w}\)(A\({}_{2}\)) are features extracted from A\({}_{1}\) and A\({}_{2}\) images of the vehicle license plates, respectively. Then our system calculates the Euclidean distance between them as:  
\[\text{E}_{w}(\text{A}_{1},\text{A}_{2})=\ |\ |\text{C}_{w}(\text{A}_{1})-\text{C} \text{w}(\text{A}_{2})\ |\ |\.\]  
With \(\text{E}\text{w}(\text{A}_{1},\text{A}_{2})\), the contrastive loss is defined as:  
\[\text{L}(\text{W},(\text{A}_{1},\text{A}_{2},\text{B})^{\text{i}})=(1-\text{B })\cdot\text{L}c(\text{E}_{w}\ (\text{A}_{1},\text{A}_{2})^{\text{i}})+\text{BL}_{1}\ (\text{E}_{w}\ (\text{A}_{1},\text{A}_{2})^{\text{i}})\]  
where (B, A\({}_{1}\), A\({}_{2}\))\({}^{\text{i}}\) is the i-th tuple of two training vehicle license plates. L\({}_{\text{c}}\) is the partial loss function for the same license plate and L\({}_{\text{i}}\) is the loss function for different license plates.  
Figure 8: Vehicles that are the same in global appearance but differentiated by local regions, as marked with red circles.