esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Chitosan/Graphene Oxide Films for Biomedical Applications
### Chitosan/Graphene Oxide Hydrogels
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ingredients** & \begin{tabular}{c} **Potential** \\ **Applications** \\ \end{tabular} & **Characterization** & **Reference** \\ \hline \hline \multirow{8}{*}{\begin{tabular}{c} Chitosan/GO/ \\ curcumin \\ \end{tabular} } & \multicolumn{2}{c}{Improved Young’s modulus} \\  & \multicolumn{2}{c}{Improved antibacterial activity and} \\  & \multicolumn{2}{c}{in vitro release after addition of} \\  & \multicolumn{2}{c}{curcumin} \\  & \multicolumn{2}{c}{} & Greater proliferation of NIH/3T3 \\  & \multicolumn{2}{c}{fibroblast cells} \\  & \multicolumn{2}{c}{GO/ Curcumin increased hydrophilic} \\  & \multicolumn{2}{c}{properties} \\ \hline \hline \multirow{4}{*}{\begin{tabular}{c} Chitosan/iron \\ oxide/GO \\ \end{tabular} } & \multicolumn{2}{c}{Biomedical} & \multicolumn{2}{c}{Improved thermal and mechanical} \\  & \multicolumn{2}{c}{properties} \\  & \multicolumn{2}{c}{Applications} & Significant antimicrobial activities \\  & \multicolumn{2}{c}{Non-cytotoxic} \\ \hline \hline \begin{tabular}{c} Carboxymethyl \\ chitosan/GO/ \\ polyacrylamide \\ \end{tabular} & \begin{tabular}{c} Bioengineering and \\ drug delivery \\ systems \\ \end{tabular} & \begin{tabular}{c} Excellent mechanical performance \\ Biocompatible \\ Fast recovery \\ \end{tabular} & [48] \\ \hline \hline \begin{tabular}{c} Quaternary \\ chitosan/ \\ cyclodextrin/GO \\ \end{tabular} & \begin{tabular}{c} Wound dressings \\ \end{tabular} & \begin{tabular}{c} Similar conductivity with that of skin \\ and rapid self-healing behavior \\ Superior antibacterial property \\ Accelerated in vivo wound healing \\ Good biocompatibility \\ \end{tabular} & [49] \\ \hline \hline \begin{tabular}{c} Carboxymethyl- \\ hexanoyl \\ chitosan/GO/ \\ cellulose \\ nanocrystals \\ \end{tabular} & \begin{tabular}{c} Excellent biocompatibility \\ Superior antibacterial properties \\ High water absorption capacity and \\ water retention capability \\ GO/cellulose increased hydrophilic \\ GO/cellulose \\ properties \\ \end{tabular} & [50] \\ \hline \hline \begin{tabular}{c} Poly \\ (p-phenylene \\ sulfide)/ \\ chitosan/rGO \\ \end{tabular} & \
\begin{tabular}{c} About Tissue \\ Engineering \\ \end{tabular} &
\begin{tabular}{c} Acceptable biocompatibility and cell \\ attachment \\ Swelling ratio and WVTR * decreased \\ by adding PPS **/rGO \\ Improved mechanical properties \\ \end{tabular} & [51] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chitosan/GO hydrogels and their biomedical applications. Figure 5: Photographs of pH-driven shape memory polymer samples (**a**) CS/5 wt% GO and (**b**) pure CS. The CS strip was coated with methylene blue for better visibility. OS: original sample [45].
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
###### Abstract  
Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.  
condition monitoring; industrial robots; anomaly detection; trend detection 1
# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 3 Materials and Methods
### RDF Generation  
This step converts scientific publications into RDF by using well known bibliographic domain ontologies and vocabularies. Our current method takes as input scientific publications in JATS format, which is an XML file that contains all the information about an article, including metadata (authors, references, identifiers or journal data) and contents. This translation is performed by applying the Biotea RDFization process ([[https://github.com/biotea/biotea-rdfization](https://github.com/biotea/biotea-rdfization)]([https://github.com/biotea/biotea-rdfization](https://github.com/biotea/biotea-rdfization))). Two RDF files are generated for each JATS input file, namely, metadata and sections, which include the bibliographic information, and the structure and contents, respectively.  
The RDF model used for describing the metadata resulting from the conversion is depicted in Figure 2. In this model, the rdf:seeAlso property is used to link articles and journals to their web page in PubMed and the National Library of Medicine (NLM) catalog ([[https://www.ncbi.nlm.nih.gov/nlmcatalog/](https://www.ncbi.nlm.nih.gov/nlmcatalog/)]([https://www.ncbi.nlm.nih.gov/nlmcatalog/](https://www.ncbi.nlm.nih.gov/nlmcatalog/))), respectively. Besides, this model reuses properties and terms from:  
* _Bibliographic Ontology_ (bibo) to represent bibliographic data such as author lists, literature identifiers, cites, references or abstracts;
* _Dublin Core Terms_ (dcterms) [11], to describe predicates like hasPart, title or publisher;
* _Friend Of A Friend Ontology_ (foaf), to represent information about authors;
* _Provenance Ontology_ (prov) [22], using the predicate generatedAtTime in order to save the date in which the RDF was generated;
* _Wikidata_[23], to store the authors ORCID's, which was retrieved from ORCID search web service endpoint ([[https://pub.orcid.org/v2.0/search](https://pub.orcid.org/v2.0/search)]([https://pub.orcid.org/v2.0/search](https://pub.orcid.org/v2.0/search))) from article DOI, author given name, and author family name, when possible;
* _Semanticscience Integrated Ontology_ (sio) [24], to relate an article to a PMC RDF dataset through the predicate is_data_item_in.  
Figure 2: Model used for describing article metadata, including authors, references, identifiers and journal information. Core concepts in the model are marked in blue. Adapted from [10].  
The RDF model for the sections is shown in Figure 3. In this model, the structure elements, namely sections and paragraphs, are described by using _Document Components Ontology_ (doco) [25], bibo and dcterms. In summary, an article has a list of sections, and each section has a a title and a list of paragraphs, and optionally it could have a list of subsections. Moreover, each paragraph has the property rdf:value, which includes its textual content in plain text. Consequently, the structure of the article is represented in RDF, but there is not semantic processing of its content in this step.
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 3 Method  
The ship communication data used in this paper were collected and obtained by means of ship-to-shore communication and automatic collection technology, and stored in the MySQL database. Through further analysis and processing of data, we aim to improve ship management and decision-making capabilities. Specifically, ship communication data can be divided into three categories: ship navigation data, communication basic resource data, and audio-visual image data. Ship navigation data cover important information such as track points, speed, sea area, and weather conditions. The communication basic resource database data include platform basic data, equipment resource basic data, and communication history data (used frequency band and signal strength and other information).  
For multi-source heterogeneous ship communication data, this paper proposes a multi-modal spatio-temporal knowledge graph to integrate ship communication data with multi-modal and spatio-temporal characteristics, which provides outstanding data management support for subsequent data analysis and application tasks, such as the ship communication quality prediction task. As shown in Figure 1, multi-modal spatio-temporal ontology construction is for clear global concepts and semantic relations between concepts of multi-source heterogeneous ship communication data, and to achieve semantic interoperability between data. Based on the multi-modal spatio-temporal ontology, the method automatically maps ship communication data to the ontology by automatic semantic modeling and further achieves the aim of organizing and representing the data into a multi-modal spatio-temporal knowledge graph with hierarchical structure and correlation. Moreover, in order to more naturally organize and represent the relationships between entities, while facilitating more efficient data querying, this article adopts a graph database based on a graphical structure to store a multi-modal spatio-temporal knowledge graph containing complex information such as time and geographic location.
## 1 Introduction
### Review of Empirical Evidence  
Process simulation in empirical evidence has been applied in various scientific disciplines and is still in development [6]. Cases have been reported for modeling and analyzing the behavior of complex systems, understanding their dynamics, and predicting results [7; 8]. Simulations of production lines in a factory Ref. [9] simulate workflow and resource allocation [10] to improve efficiency and productivity [11]. Furthermore, within the simulation of customer service systems, customer flow was analyzed in a service system environment. On the other hand, in the simulation of customer service systems, the flow of customers in a service system has been analyzed [12; 13], as well as staffing strategies [14; 15; 16], waiting times [17; 18], and resource distribution to optimize customer satisfaction [19; 20]. Analyses have also been performed on logistics systems [21; 22]. Another area that has been widely studied with simulation is vehicle flow systems in a transportation network [23; 24; 25], modeling traffic and intersections [26; 27; 28; 29; 30; 31], and modeling public transportation systems [32; 33; 34; 35; 36], as well as health systems [37; 38].  
In the agricultural sector, models such as the Integrated Farm Systems Model Dairy-Mod [39], DyNoFlo [40], SIMS (DAIKY) [41], and the Ruminant Farm Systems Model (RuFaS) [42] have been developed. Other dynamic models found in empirical evidence applied to livestock include Ruminant [43; 44], LiveSim [45], CNCPS [46], Grazplan [47], and GLEAM [48]. Additionally, some agricultural system models reported are IMPACT-HHM [49; 50; 51], APSFARM [52], and FARMSIM [49; 53].  
In agriculture, [PERSON] et al. [54] used Monte Carlo simulation to evaluate the capacity of microbial applications to improve the sustainability of Dutch potato production. In aquaculture, the Energy Plus simulator was used as a building performance simulation (BPS) tool to model a fish farm with a focus on energy efficiency [55]. Another method called computational fluid dynamics (CFD) was used to simulate ammonia distribution on a livestock farm [56]. The finite element numerical approach with COMSOL Multiphysics software (Version 6.2) was used to simulate groundwater flow and salinity in a rice field adjacent to a marine shrimp farm [57].  
To analyze systems with simulation, various software programs have been developed, including the Arena(r) simulation developed by Rockwell Automation. This software allows researchers to create discrete event simulation models to study behavior over time and analyze different scenarios. The flexibility and detailed modeling capabilities of Arena(r) make it suitable for a wide range of applications in engineering and system management [58].
## 5 Conclusions  
In this paper, several RDE cycles with different dynamic characteristics have been performed in an engine test bench. First, an on-road cycle was performed in a vehicle as a baseline; then, a second cycle was defined using transient maneuvers extracted from a WLTP cycle; finally, four additional cycles were defined using a computational tool to generate RDE-compliant conditions from a sub-set of acceleration and deceleration ramps while replicating different driver behavior. This methodology aimed at reducing cost, time, and uncertainties involved in performing a cycle on road, such as climatological conditions, PEMS accuracy, traffic status, and driver behavior. This is particularly relevant for future powertrain systems development since different technologies or calibration strategies can be assessed more reliably and robustly while ensuring RDE representative conditions.  
The results showed how RDE cycles with the same driver behavior produced significant emissions dispersion. For instance, variations of 12.74% in the case of NOx and 7.42% in CO2 were found between different cycles. Moreover, a great improvement was seen from an emissions perspective when the gear shift was adjusted to achieve lower engine speed. Particularly, drawdowns in NOx and CO2 emissions of 34% and 9.2%, respectively, have been noted. Instead, CO and THC emissions were mostly linked to the engine and aftertreatment temperature, so the best results are achieved when the engine load is increased.  
Finally, an attempt to estimate the cycle emissions from steady-state tests was made. The comparison between such estimation and the actual RDE tests emissions showed a good correlation, within 1.6% accuracy for CO2 and better behavior in less dynamic cycles, 4.6% in the case of NOx, and relatively low errors in the case of PM mass. The deviations observed were due to two phenomena, On the one hand, all emissions during the engine warm-up period differ from the estimations since the engine map was performed with the engine previously warmed up. On the other hand, of NOx and PM mass emissions, and their known trade-off, both showed a non-linear behavior as a function of speed and load, inducing an uncertainty linked to the interpolation inside the steady-state map. This uncertainty could be reduced by performing a more complete engine map, which lowers variations of speed and load between adjacent points but at the cost of higher test bench time. In any case, the analysis performed in this study confirms that steady-state maps can be used to provide a meaningful approximation of these emissions during a realistic certification cycle.  
J.M.L.: methodology, conceptualization, writing--original draft, supervision; P.P.: validation, writing--review and editing; J.d.l.M.: formal analysis, writing--review and editing; F.R.: investigation, data curation, visualization, writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by Grant PID2020-114289 RB-I00 funded by MCIN/AEI/10.13039/501100011033.  
The authors declare no conflict of interest.
# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 1 Introduction  
While cyberbullying is a widely recognized problem for adolescents [1], racism, misogyny, homophobia, and other causes of cyberbullying do not just disappear with the granting of a diploma or degree. These attitudes and the large percentage of type-A personalities present in STEM-related businesses [2] lay a fertile ground for cyberbullying of adults. The ever-increasing prevalence and importance of electronic communication mechanisms in organizations today including social networks, along with the ever-expanding global cyber-marketplace, are the foundations necessary to facilitate cyberbullying in the workplace [3]. Cyberbullying in the workplace is typically conducted using social networks and social media, email, and text (SMS) messages, which allow for cyberbullies to often remain anonymous [4]. Additionally, the recent migration of employees to online workplace environments due to the COVID-19 pandemic and consequent reliance on social media networks and the Internet for conducting everyday work may further exacerbate the problem [5, 6].  
Although the subjects of child and adolescent cyberbullying are well-documented in academic research [7, 8, 9, 10, 11, 12], workplace cyberbullying research is still in its infancy and thereis a need for further research on the topic of workplace cyberbullying [3]. There is a need for automatic intelligent detection of cyberbullying in organizational contexts [13].  
Cyberbullying in the workplace is an ongoing and significant problem. Studies conducted in Finland found from 12.6% to 17.4% of employees felt they had experienced cyberbullying on a monthly basis [4]. From 14% to 21% of higher education faculty have been documented to suffer from cyberbullying [14]. Cyberbullying can last for an extended period of time [15]. Various studies have shown how cyberbullying increases employee stress and job dissatisfaction [16; 17]. These effects of workplace cyberbullying in turn may cause a decrease in worker productivity and if not resolved after being reported to human resources (HR), may result in litigation.  
In this paper, a decentralized deep learning approach called Malicious Language Detector (MaLang) is proposed. While cyberbullying may include text, images, photographs, video, and audio content [18], the MaLang system only analyses textual content in user devices. The decentralized methodology facilitates keeping an employee's data private and secure. MaLang is simulated in a workplace environment to demonstrate the applicability of intelligent automated detection of cyberbullying messages for improving workplace safety and well-being. Previous machine learning and deep learning techniques are re-combined in a novel way to improve the overall accuracy of hostile content in an organization's electronic messaging systems. The two-part MaLang method helps to minimize deployed size, improve employee privacy, and increase hostile message detection.
###### Abstract  
One of the most common approaches for enhancing network performance is to retrieve data from nearby data holders that have previously obtained the desired data, not only from the original data source itself. In this case, since a data receiver cannot identify a practical data sender, it is necessary to verify both the received data and the data sender. Moreover, a data sender generally fragments the data into several small segments and sends them. Therefore, if these segments are retrieved from multiple unknown senders, the receiver must verify every segment to safely use the data. MHT (Merkle hash tree) is suitable for efficiently verifying the set of segments shared in the network. NDN (named-data networking) and Bitcoin utilize MHT to verify transmitted data. However, a data authentication scheme based on the MHT has an inefficient factor that repeatedly computes the same node values of the MHT and are repeatedly computed. The larger the size of the MHT is, the greater the number of calculation iterations. Therefore, as a result, the authentication scheme's inefficiency is also more severe. When a sender transmits data consisting of many segments through NDN, the data authentication time may take longer than the data transmission time. Hence, in this paper, the degree of the MHT's inefficiency and the pattern of the iterated operation of the MHT are analyzed first. The proposed improvement is to find repeatedly used node values, store them internally, and use the stored node values without recalculation when required to reuse them. For that process, a rule to select such node values is given. Additionally, when verifying the leaf node value of the MHT, the MHT-based authentication scheme asks a verifier to compute all node values on the path from the leaf node to the root node of the MHT. This paper demonstrates the proposed shortest path selection for verifying the leaf node value. The proposed scheme, using saved node values and the shortest path, reduces the computational overhead of the MHT and improves service latency. It has been proven from performance evaluations that the proposed scheme decreases the computational overhead by more than one-third if the number of segments is more than 1024.  
distributed network; future internet; MEC; data authentication; MHT +
Footnote †: journal: Applied Sciences
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 2 Materials and Methods
### Landslide Susceptibility Modeling
#### 2.5.4 Multi-Layer Perceptron (MLP)  
Multi-layer perceptron (MLP) is one of the most frequently used artificial neural network methods with three layers combining input, hidden and output layers [66]. It can find a non-linear relationship between a lot of independent parameters.  
The associative matrix of processing objects is constructed by joining relations between input layers and hidden layers. The connection between hidden layer and output layer elements forms the decision matrix of the processing object. Through training, the network can form an orderly and stable structure with a decision-making ability. The back-propagation (BP) algorithm is widely used in an MLP model. The BP algorithm consists of forward transmission of information and back propagation of error. Forward transmission. In the process of seeding, the input information is calculated from the input layer to the output layer through the hidden layer, and the state of neurons in each layer only affects the state of the next layer of neurons. If the desired output is not obtained in the output layer, the error variation of the output layer is calculated, and then reverse propagation is carried out.  
Through the network, the error information is reversed back along the original link path to modify the weight of neurons in each layer until the desired goal is reached.
## 1 Introduction
A major advantage of these approaches stems from the fact that they are learned along with the classification task. It has also been shown that features learned for general tasks of image recognition can be re-used for digital pathology problems with remarkable success (e.g., [10; 11]). Autoencoders represent a form of unsupervised learning proposed initially in the context of \"backpropagation without a teacher\" [12] which recently were placed in the spotlight by the introduction of deep learning architectures [13; 14; 15]. Unsurprisingly, there are many successful applications of autoencoders for learning features in histopathology image analysis, from object detection [16; 17], to stain normalization [18] and image registration [19]. Their appeal stems from the ability of capturing underlying structures without the need of (re-)designing \"by hand\" the image features for each application. Here, we propose a novel approach in which a deep autoencoder is trained to disentangle the colour from the structure information. Once the colour information is (almost) confined to a subspace of the learned latent representation, one can use the remaining dimensions for representing (and, if needed, reconstructing) solely the structure. We apply this approach to learn a model for representing histopathology (haematoxylin-eosine (H&E)-stained) and immunohistochemistry (IHC) images (several immune stains) of series of consecutive sections. The models are then used for finding corresponding landmarks across sections (as for image registration) and for building a multi-stain visual codebook for exploring the intra-tumour heterogeneity. We emphasize that these applications, as presented here, serve merely as a device for demonstrating the capacity of the novel architecture to extract and separate the structure from stain information.