esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
## 4 Conclusions  
High brightness, low energy spread and good current stability are critical for CFE electron sources for electron microscopy applications. Although considerable research has been carried out into developing single crystal tungsten and other 1D nanostructure-based field emitters, they have as yet, faced many practical technological difficulties, such as stringent UHV requirements, poor current stability and fast emission decay. Therefore, other ways to make CFE electron sources need to be investigated. With the fast development of 2D graphene in recent years, graphene has been proven to have ultra-high aspect ratios, good electric conductivity, chemical inertness and mechanical hardness, and these properties make graphene-based field emitters an attractive possible alternative. Three types of graphene-based field emitters have been highlighted in this review: graphene film emitters, graphene point/edge-cathode emitters, and graphene ring-cathode emitters. Most graphene field emitters proposed and studied so however, involve field emission from multiple emission sites, and are therefore not suitable for electron microscopy applications.  
This paper has highlighted some recent promising developments in the category of single-point and ring-shaped graphene field emitters for electron microscopy applications, however, the investigation of graphene field emitters is still in an early stage of development, and there is still much room for further improvement. More emission characterization tests need to be performed, ones that can measure the source virtual source size, transverse coherent length, emission under a variety of different vacuum conditions, and emitter lifetime need to be made. An electron gun unit that can accommodate promising graphene emitters, which has its optical axis well aligned to the central axis of the cathode still needs to be developed. Beyond that, electron guns based upon the new class of graphene-based field emitters need to be fitted on to electron microscopes, and their performance critically compared to convention systems, in terms of parameters such as image resolution and signal-to-noise ratio.  
This research was funded by National Research Foundation Singapore (NRF-CRP13-2014-04).  
We acknowledge many discussions with [PERSON] in the Department of Electrical and Computer Engineering, National University of Singapore. This work is funded by National Research Foundation Singapore (NRF-CRP13-2014-04).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Crucial Factors Summarization and Experimental Methodology
In summary, failed transmissions are separated in the following four groups:  
* Sensitivity: Related to distance and obstacles and is depended mostly on TX power and distance;
* Interference: Related to radio signal interference between simultaneous transmission of the network or even from other networks on the same radio frequencies spectrum. Interference within the same network causing collisions that are indirectly caused from configurations such as data rate, EDs number, TX power, ACKs, retries or generally by ToA (transmission interval and payload size);
* GW channel availability: Occurs when all concentrators of a GW that are listening to a channel are occupied for an active transmission. This is directly related to the number of EDs, the retransmissions and ToA;
* GW in TX state: Occurs when GW is busy by transceiving a downlink. This depended mostly on ACKs but also in application logic that handles downlink processes. Throughout our literature review we have identified a vast number of cross-relations between factors that we need to experimentally assess. Thus, our experimentation was planned based on these relations that we knew beforehand. Firstly, in all radio communications, PDR is clearly related to the range. In LoRaWAN specifically, according to our survey, which we have analysed in the background chapter, PDR is also decreased proportional to the amount of EDs that are communicating with a GW--while simultaneously SFs or ACKs or retransmissions or payload size also increased. It is important to mention that, based on previous studies, deploying more GWs can overcome these causalities [24] but also cause some scalability issues when more than one GW is accessible by an ED. Furthermore, if the ToA of a transmission between an ED and a GW increases, then the power consumption will also increase. Apart from ToA, power consumption of EDs is related inversely to the interval between transmissions and to the DR, and is directly affected by the TX parameter  
Figure 3: Simulation scenario with multiple rings end node spatial distribution. of the ED which controls the transmission power. If TX is decreased, the range of the communication is also decreased, which is also happening if the SF is decreased. Finally, capacity is uniformly increased if the channels to be used for the communication also increase, which indirectly leads to more capacity on the long ED clusters, which implies better coverage.
# Automatic Extraction of Flooding Control Knowledge from Rich Literature Texts Using Deep Learning
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Type** & **Research Methods** & **References** & **Methods Evaluation** \\ \hline \multirow{4}{*}{Rule-based methods} & Heuristic rules & [PERSON] et al. [8] & The rule-based method has a high \\  & TCL rule base & [PERSON] et al. [9] & accuracy rate, but a low recall rate, \\  & Rules for the detection and classification of & & and needs to formulate matching rules \\  & emotion signals & [PERSON] et al. [10] & an advance. \\  & Sentiment composition rules & [PERSON] et al. [11] & in advance. \\ \hline \multirow{4}{*}{Machine learning methods} & [PERSON], complement [PERSON] and & [PERSON] et al. [12] & \\  & Decision Tree & [PERSON] et al. [13] & Machine learning methods require \\  & logistic regression model & [PERSON] et al. [13] & more time for feature extraction, and \\  & SVM and CRF & [PERSON] et al. [14] & the quality of the feature selection also \\  & [PERSON] & [PERSON] et al. [14] & affects model performance. \\  & SVMs and NB & [PERSON] et al. [15] & better performance. \\ \hline \multirow{4}{*}{Artificial neural network-based deep learning methods} & LSTM and GRU & [PERSON] et al. [16] & \\  & BERT model & [PERSON] et al. [20] & Artificial neural network-based deep \\  & BERT-NLSTM & [PERSON] et al. [21] & learning methods have addressed the \\  & BERT, CNN, LSTM & [PERSON] et al. [22] & feature selection problem [17; 18; 19]. It \\ \cline{1-1}  & BERT-CNN model, char-CNN, fast Text, DRNN & [PERSON] et al. [23] & can automatically obtain feature \\ \cline{1-1}  & deep, LSTM, NB, KNN, SVM, CNN, RNN, FastText, BERT & \\ \cline{1-1}  & Word2 vec, ELMo and [PERSON] & [PERSON] et al. [25] & expansion, replace the artificial \\ \cline{1-1}  & CNN, BiLSTM, RNN, BGRU, BERT, ALBERT & [PERSON] et al. [24] & feature engineering in machine \\ \cline{1-1}  & [PERSON], XLNet, RoBERTa, and ERNIE & [PERSON] et al. [25] & learning methods, and it also has \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research status of text classification methods. of vocabulary. Artificial neural network-based deep learning methods avoid the manual design of rules and features and automatically provide the semantic meaning representation of text mining, becoming the current mainstream method. However, the applicability of different deep learning methods remains unclear. Based on previous research, the purpose of this study was to mine valuable knowledge about flood control from long-tail data found in the academic literature. We used machine learning tool to parse academic literature documents. Various machine learning and artificial neural network-based deep learning methods have been used for flood control knowledge extraction at the sentence level. The advantages, disadvantages, and applicability of various methods were compared from the perspectives of performance and principles. This study enriches literature-based knowledge extraction research at the sentence level.
## 3 Detection Approaches Review
### Visual Sentiment Analysis  
In general, there are three main state-of-art approaches to visual sentiment analysis [33], which are mid-level sentiment ontology, deep sentiment prediction, and multi-modal sentiment prediction.  
Mid-level sentiment ontology provides a set of mid-level features for visual sentiment analysis. [PERSON] and [PERSON] [17] proposed a mid-level feature named adjective-noun pairs (ANPs), which are related to sentiments and a visual sentiment ontology (VSO). These ANPs with sentiment are extracted from a set of annotated images, then they are used to train VSO detectors, and a set of detectors are used to construct SentiBank, which is finally used for sentiment prediction on images. Deep sentiment prediction uses deep learning models to predict sentiment on images. [PERSON] et al. [18] proposed a Progressive Convolutional Neural Network (CNN) that used a selection of images from the training set, according to the output of the trained model on them to fine-tune the training, and also address domain transfer by using a set of manually labeled Twitter images to fine-tune a previously trained CNN model. Finally, multi-modal sentiment prediction refers to creating classifiers that use different kinds of data to predict a sentiment, such as text and images. A multi-modal correlation model based on Markov Random Fields (MRFs) was proposed in Reference [34]. Multi-modal features are extracted, with ANPs as image features, words as text features, and symbols as emoticons features. A MRF model is created and decomposed in 4 subgraphs (three single graphs and one correlation graph). The single graph denotes the contribution of each modality to the sentiment, and the correlation graph denotes the correlation contribution. According to the correlation of each modality, the final model is graphed and then learned. Following the inclusion/exclusion criteria, in this section the three different approaches used in the literature for visual sentiment analysis are presented in three different works. The three different strategies or techniques have clear differences, either use mid-level features that help predict an emotion from images, such as ANP that are text concepts related to the image (adjective-noun pairs), deep learning models, or a multi-modal model that analyzes both text and images to predict emotion. Deep models reported to have reached a better accuracy overall but slightly less maximum accuracy than mid-level sentiment ontology prediction in the reviewed works.
## 4 Mission S-Net
### Mission Overview  
With increasing numbers of communication entities participating in the network, the network protocol and hardware can be tested in more complex scenarios. For a demonstration mission, four satellites present the best cost-benefit ratio. Four entities enable six independent communication links, while three satellites would only make three links possible. Furthermore, multi-hop communication can only be realized with at least four nodes if they are in a line topology (as in Figure 6(c)). Additionally, four satellites allow for redundancy on the satellite level, as ISL can also be performed, with some restrictions, with only three satellites.  
The S-NET satellites were inserted into a 580 km SSO by a single upper stage and ejected from dispensers with a short time separation. To keep the space segment simple, a propulsion system was intentionally omitted and, therefore, the relative formation is mainly controlled by the separation parameters from the launcher upper stage. The goal is to keep the satellites in a range to enable a stable ISL for at least four months. Therefore, the separation direction, angle, and sequence are the initial values for the formation drift. A summary of the mission parameters is provided in Table 8.
## 4 Experimental Setup and Results
### Main Results Analysis
These NLP features, extracted from news articles, were instrumental in predicting and comparing accuracy across different instances of the ProText dataset, including data sourced from Facebook and Twitter, as illustrated in Figure 10.  
Figure 8: The performance of the proposed framework when evaluated with different models on the development set (**a**), training set (**b**), and testing set (**c**). The results highlight the effectiveness of the proposed framework across different datasets and demonstrate its robustness and versatility in various scenarios. Transformer architectures, particularly those based on autoregressive layers, proved to be the optimal choice for analyzing ProText data due to their ability to predict future behavior based on past data. Our proposed ML-MC model achieved an accuracy rate of 82% on ProText test data. In summary, the fine-tuned RoBERTa model, as presented in this article, has been seamlessly integrated with NLP modules, leveraging extensive public participation in social media discussions. Moreover, the Kaplan-Meier curve is estimated for the time-to-event distribution and is disaggregated by high-low theme scores, as shown in Figure 11.  
Figure 10: Evaluation performance (Acc) compared to the traditional model on multiple data, with NLP features network model. Figure 9: Evaluation performance accuracy of each propaganda theme: (**a**) suspected, (**b**) conform, (**c**) death, (**d**) recover, (**e**) vaccinated, (**f**) unvaccinated on propaganda classes using RoBERTa in ProText data. The ML-MC-based NLP model constructed these curves algorithmically, extracting progression events. Consequently, higher theme scores correlate with a shorter tail of the time-to-event distribution, indicating that discussions related to various topics (such as vaccination status, suspected cases, confirmed cases, deaths, and recoveries) occur more rapidly for samples with high theme scores. \(p\)-values, determined through a log-rank test, were used for statistical significance assessment. All models underwent rigorous examination, and each model led to consistent conclusions.
# Analyzing Demand with Respect to Offer of Mobility
## 3 DORAM Architecture  
The DORAM solution aimed at satisfying the above requirements, and it is based on the architecture reported in Figure 1, with components:  
* **Scenario Production**. A new scenario is created by aggregating different kind of data. The Scenario model is formalized in a knowledge base NoSQL RDF Storage with all the city relationships and services are modeled. In particular: (i) to load/change/create a transport offer in the so called static GTFS manager (GTFS) has been used to edit an existing GTFS data feed, (ii) Snap4 City OSM2 SM tool allowed to retrieve the residential building data from OpenStreetMap (OSM) [40] database and save them in a PostgreSQL database, (iii) census data, which includes different categories (e.g., daily resident flow from each locality to another for work or study, the daily tourist flow), retrieved from various sources (e.g., [41; 42]), (iv) Points Of Interest (POI) data, retrieved using ServiceMap of Snap4 City [43].
* **DORAM Modelling** takes the scenario information to define the simulation and relating the data, associated with service offer and the mobility demand, and the simulation output, as described in Section 4.
* **DORAM Analyzer** performs the analysis of the public transportation system by comparing the mobility demand and service offer. In order to do that, the scenario data are retrieved using ServiceMap APIs calls and SPARQL queries performed on Snap4 City/Km4 City knowledge base [44] provided for each scenario in a separated Docker (e.g., [41; 42]).
* **DORAM Front-End** allows to (i) select the scenario to be analyzed, (ii) browse the scenario by navigating in the transport offer (bus stops, lines, time schedule,start stops, etc.), (iii) browse the city areas to see the services and point of interests, (iv) analyze the results of each scenario.
## 4 Measurement Campaign & Equipment  
In order to validate the suitability of our solution for providing wireless connectivity at a local area level, we planned and executed a measurement campaign of the received signal strength and data rate for the WiFi and LoRaWAN networks. To this aim, we developed a portable logging system presented in Figure 5a. It includes two different radios, the RFM95 CW [29] LoRa transceiver module and the Intel Wi-Fi 6 AX200 adapter [30], integrated into a single system by the OnLogic CL200G-10-RS [31] single board computer (SBC). The Wi-Fi adapter is placed at the waist at a height of 1 m from the ground and the LoRa module 20 cm lower at 0.8 m from the ground. These positions are indicative of the equipment attached to the belt of the FR's uniform, while the height of the Wi-Fi adapter from the ground is close to the height that the devices placed on top of tables would have been, covering both cases. Additionally, there is a touch screen for initiating the logging process and presenting the results so far to give the operator a notion of the area covered, as can be seen in Figure 5b. The position of each signal measurement is captured by an RTK GNSS receiver installed on top of the helmet worn by the operator.  
Figure 4: (**a**) View of the UI for monitoring the state of the various system subcomponents and providing key information. (**b**) Graph of the network traffic of selected interfaces.  
Figure 5: (**a**) The human operator wearing the measuring equipment. (**b**) The traversed path as it is displayed on the operator’s screen, depicting the covered area and RSSI. A darker color means lower signal strength.  
The location selected for the measurement campaign can be seen in Figure 6. This is a dirt football field surrounded by trees within the university campus. At a greater distance, there are several buildings. This open space, which resembles a park, is a good example of a location that would have been selected by the FRs to deploy their equipment, local command center, and logistic infrastructure.  
For the Wifi, we have selected a configuration optimized for a maximum communication range. This means selecting the longer possible guard interval for every standard to safeguard against intersymbol interference and a lower bandwidth to reduce the noise power ensuring the highest possible signal-to-noise ratio (SNR). On the other hand, LoRaWAN's parameters favor transmission speed and, as a result, data volume over range. For both WiFi and LoRaWAN, the samples taken relate to actual data transfer and not just RSSI or speed measurement. Wireless adapters calculate new channel-related metrics when they receive new traffic, making data exchange between the station/node and AP/concentrator a necessity. Due to this, sample points mark locations where communication is possible and can provide a rough estimation of the validated coverage area. Table 1 summarizes the parameters used by every technology and the number of samples taken during the measurement campaign.
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
## 3 Numerical and Experimental Study Results
### Effects of Constraint Gap  
In this subsection, the effects of the constraint gap on the resonant-impact DEA system are investigated. In our nondimensional numerical simulation the nondimensional constraint gap \(g\) is varied from 0.02 to 0.2 in 80 steps, with the frequency response results plotted in Figure 11. It can be noted that the frequency band at which impacts are triggered is reduced as \(g\) increases, while the oscillation amplitude peak increases. The reason for this is that as \(g\) increases (i.e., the constraint separates further from the DEA), the DEA is able to travel further, which increases its oscillation amplitude near its resonance. The relationships between \(g\) and the peak frequency \(\omega_{p}\) and peak impact force \(\mid f_{max}\mid\) are plotted in Figure 11. Note that increasing the constraint gap \(g\) causes a continuous reduction in \(\omega_{p}\). This results in a sharp drop in \(\mid f_{max}\mid\) from \(g=0.02\) to 0.04; however, as the \(g\) value increases further, \(\mid f_{max}\mid\) begins to fluctuate around \(\sim\)5.2.  
In the experimental study, the constraint gap \(G\) is varied from 0.2 to 1.6 mm with a step of 0.2 mm, and the results are plotted in Figure 12. It can be seen from Figure 12 that increasing \(G\) results in a gradual reduction in peak frequency \(\Omega_{p}\). However, the peak amplitude \(\mid Amp.\mid\) increases approximately linearly with increasing \(G\). These findings agree well with the nondimensional studies conducted in Figure 11. It should be noted that the peak impact force \(\mid F_{max}\mid\) in this experimental study shows a slight fluctuation against the varying value of \(G\). No sharp drop in \(\mid F_{max}\mid\) is observed in Figure 12b in comparison with the nondimensional simulation results in Figure 11b, which is due to the lowest value of \(G\) being set at 0.2 mm in our experiments. In addition, note that the model's predicted impact force values in Figure 12b are slightly higher than the measured data, which could be due to the nonideal impact condition in our experiments.  
Figure 11: Effects of constraint gap on (**a**) the frequency responses of the resonant-impact DEA system and (**b**) the peak frequency and peak impact force.  
Figure 10: Experimental and modelling results on the effects of number of DEA layers on (**a**) peak frequency and oscillation amplitude and (**b**) peak impact force. The solid bars are the experimental data and the dashed lines with markers are the modelling results.
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 5 A Smart City Testbed: The Rigers Project  
The RIGERS project is aimed at monitoring residential and public buildings in two districts of Bologna, called Saragozza and Navile, for a time period of one year. Different rooms of each monitored building will be equipped with a multi-sensor platform using a LoRa transceiver to communicate the measured data to the control center. In particular, humidity, temperature, lighting, CO\({}_{2}\) sensors and a globe thermometer will be installed in each platform. Each of these sensors will periodically (every five minutes) take a measurement from the environment and every hour the average of the measured values will be sent to the LoRa gateway via single hop (star topology). The gateway will then forward the received data to the control center via a 3G network.  
The RIGERS project is still ongoing and the final deployment has not been carried out yet. As a result, in this section we report: (1) the experimental derivation of the path-loss model in the two districts of Bologna cited above; (2) the assessment of the network-level performance through a simulator implementing the real final scenarios and incorporating the derived path-loss models.