esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Effect of Process Parameters on the Characteristics of Microcapsules  
1.1 Effect of Oil-to-Water Ratio, Emulsifier Dosage, and Emulsification Speed on the Particle Size of Microcapsules  
The particle size of microcapsules is crucial to the performance of the coating. A particle size that is too large may lead to uneven dispersion of the microcapsules in the coating, resulting in a rough coating appearance and reduced adhesion to the substrate. On the other hand, a particle size that is too small may result in insufficient release of the healing agent, compromising the self-healing effect. Therefore, in this study, the optimal particle size for the microcapsules is set at 35 \(\upmu\)m, with a permissible range of 25 \(\upmu\)m to 45 \(\upmu\)m, to ensure excellent dispersion and functionality of the coating. To successfully prepare microcapsules that meet the desired particle size, it is necessary to investigate the various factors that influence microcapsule size.  
In the preparation of microcapsules via interfacial polymerization, factors such as the oil-to-water ratio, emulsifier dosage, and emulsification speed control the particle size of the microcapsules. The oil-to-water ratio determines the dispersion degree of the oil phase in the water phase. If the oil-to-water ratio is too high, the small oil droplets in the emulsion become densely packed, leading to aggregation and rupture. If the oil-to-water ratio is too low, the oil droplets are sparsely distributed, resulting in wasted experimental resources. The particle size distribution of small oil droplets at the optimal oil-to-water ratio is shown in Figure 3a, and the emulsifier dosage determines the stability of the emulsion. An excessive amount of emulsifier can overly stabilize the emulsion, leading to the aggregation or settling of the small oil droplets, while an insufficient amount of emulsifier will result in an unstable emulsion system and difficulty in forming small oil droplets. The particle size distribution of small oil droplets at the optimal emulsifier dosage is shown in Figure 3b; the emulsification speed determines the particle size and uniformity of the small oil droplets. Generally, at low emulsification speeds, the resulting small oil droplets have larger and more uneven particle sizes. As the emulsification speed increases, the cutting frequency within the same time frame increases, causing the particle size of the small oil droplets to gradually decrease and the distribution to become more uniform. However, excessively high emulsification speeds can lead to the formation of excessively small oil droplets. The particle size distribution of the small oil droplets at the optimal emulsification speed is shown in Figure 3c.
## 4 Implementation and Result  
The neural network modules used in the system were developed and operated using the Python language and the PyTorch deep learning framework and a library providing a high-level interface to PyTorch--PyTorch Lightning. The Docker environment was used to compile the module and the Flask environment, written in Python, was used to develop the API for the neural network module. The mobile application for the Android environment was written in the Kotlin language, using the Android SDK, based on the MVVM architecture pattern (Model-View-Viewmodel).  
The Labeled Faces in the Wild (LFW) [54] dataset was used for basic training of the neural network responsible for face embedding. It is a public set of data used to train verification and facial recognition algorithms made up of over 13,000 images collected from the Internet. The database includes 1680 famous people, labelled by name, and at least two different photos are collected for each person. The dataset is characterized by the following constraints:  
* small number of photographs of children;
* no photographs of infants;
* small percentage of women;
* imbalance of data in terms of representation of different ethnic groups.  
Due to the aforementioned restriction, the basic model was retrained with a transfer learning technique [55] using a dataset of approximately 400 images corresponding to postmortem and antimetorn data for each individual. The described dataset is not subject to further distribution due to project constraints governed by contractual provisions.  
The following hyperparameters were assumed during training:  
* number of epochs--300 (for both main and fine-tuned model),
* optimizer--_Adam_ with _lr_ = 0.0001,
* batch size--train batch size = 16, validation batch size = 6,
* sampler--MPerClassSampler with number of elements per class = 2.
# Energy Consumption and Financial Development in NAFTA Countries, 1971-2015
###### Abstract  
To satisfy human needs and desires, it is necessary to produce goods and services that require the use of some production factors, such as labor, capital, and energy, among others. Nowadays, energy is a key production factor for economic activity in all countries. The main objective of this paper is to analyze the relationship between energy, economic growth, urbanization, and financial development in the country-members of the North American Free Trade Agreement (NAFTA) during the period of 1971-2015. Panel data Econometric methods are applied in this research, namely cross-section dependence (Pesaran test), unit root (Cross-sectional Augmented Dickey Fuller and Cross-sectional Im, Pesaran, and Shin tests), cointegration ([PERSON] and [PERSON] tests), and heterogeneous causality ([PERSON] and [PERSON] test). The results achieved in this research demonstrate that the variables of this model are characterized by a cross-section dependence, and they are integrated in order one. An equilibrium or long-term relationship between them exists. By means of the Fully Modified OLS and Dynamic OLS estimators it this demonstrated that there is a positive relationship between GDP and EC, while there is a negative relationship between FD, CPI, URB, and TO and EC. The economic policy recommendations drawn from this investigation are that financial development promotion, urbanization, and trade openness may contribute to reducing energy consumption in these countries.  
financial development; energy consumption; unit root; cointegration; causality Article
# Novel Approaches to Combat Medical Device-Associated BioFilms
## 2 Medical Device-Related Biofilm Infection and the Challenges of Treatment
Approximately 2 million healthcare-related infections occur in the United States each year, resulting in more than $5 billion in additional medical expenses each year. Among them, 50%-70% of nosocomial infections are related to indwelling medical devices [17; 18; 19]. Additionally, the mortality from nosocomial infections associated with medical devices is largely dependent on the type of devices, ranging from <5% for dental implants and foley catheters to >25% for mechanical heart valves [108]. In the intensive care unit, the incidence of catheter-related bloodstream infections is as high as 2.5-6.9/1000 catheter days, depending on the type of catheter and the site of catheter insertion [17; 109]. The additional economic cost of treatment for each catheter-related infection may be as high as $30,000, with the potential for increased mechanical ventilation, an additional 1-week stay in the ICU, and an additional 2-3-week hospital stay [110; 111]. Therefore, the constant improvement of prevention strategies and the development of novel therapeutic approaches are needed to overcome these weaknesses. Currently, we still need to emphasize the basic role of antimicrobial therapy in combatting medical device-associated biofilms, while the combination of novel promising approaches and antimicrobial therapy may help us eradicate existing biofilms.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 3 SatSINS
### Module 1: Landfast Ice Thickness Estimation and Large-Scale Sea Ice Analysis
#### 3.1.2 Large-Scale Sea Ice Analysis  
This analysis was conducted during the first two weeks of the voyage. The data in Module 1 (Figure 3) includes georeferenced SIC maps from SSMIS/AMSR-2 data and MODIS true-color images. The large-scale sea ice characteristics are tracked to delineate the ice edge and broadly identify distinct ice features of the pack ice in the Prydz Bay, with a view to detecting possible navigation paths with benign ice conditions. It facilitates a later decision on whether finer resolution satellite data will be needed and may therefore be ordered. The analysis involved examination of daily SIC maps to identify any large-scale ice features. This was communicated daily to the vessel by the team at BNU. Cloud-free MODIS data were also used to verify the SIC derived from the passive microwave data. The ice-edge demarcation and the overall ice characteristics in the marginal ice zone and the pack ice in the Prydz Bay had to be verified. This process usually takes an hour after downloading the data.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection
### Data Preprocessing and Training of Signal Detection
#### 3.1.2 Offline Training  
In the offline training stage, MIMO signals are pre-detected by ZF detection using MIMO-OFDM signal samples based on FrFT. After ZF pre-detection, the signal is preprocessed by Formula (4). The preprocessed data are used as the input of the neural network model to train the model so that it can learn the channel state features better. The parameters used in training the IM-LSTMNet model are shown in Table 1.  
When the IM-LSTMNet model is trained under different signal noise ratio (SNR) values, the accuracy and loss of IM-LSTMNET model are different, which will affect the BER of online signal detection. In order to maximize the performance improvement of the IM-LSTMNet model on MIMO-OFDM signal detection based on FrFT, we discuss the accuracy and loss of the IM-LSTMNet model under different SNR values, as shown in Table 2.  
As can be seen from Table 2, when the training SNR is 10 dB, the model accuracy is higher and the loss is lower. When the training SNR is less than 10 dB, the model accuracy is lower and the loss is higher. Training SNR greater than 10 dB is prone to overfitting.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Antenna number & 2\(\times\)2 \\ Channel & Gaussian \\ Cyclic prefix & 16 \\ Modulation system & QPSK \\ Subcarrier number & 64 \\ FrFT order & 1 \\ Signal noise ratio & 10 dB \\ Optimization algorithm & Adam \\ Max epochs & 10 \\ Mini batch size & 10 \\ Learning rate & 0.001 \\ Learning rate decline factor & 0.3 \\ Simulation tool & MATLAB (2022a) Deep learning toolbox \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training neural network parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SNR** & **4** & **6** & **8** & **10** & **12** \\ \hline Accuracy (\%) & 95.69 & 96.75 & 98.16 & 99.66 & 100 \\ Loss & 0.2149 & 0.1705 & 0.1095 & 0.0605 & 0.0315 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy and loss of IM-LSTMNet model.  
As can be seen in Figure 9, the accuracy rate reached 99.66% in the first round of training, the accuracy range of small batches before the fifth round fluctuated greatly, and the tenth round basically converged to 99.66%.  
As can be seen from Figure 10, in the first round of training, the loss dropped below 0.5, the loss range of small batches before the fifth round fluctuated greatly, and the tenth round basically converged to 0.0605.  
When the training SNR is 10 dB, the model accuracy is higher, the loss is smaller, and the model performance is better. Therefore, in the subsequent signal detection process, the training model with SNR = 10 dB is used for simulation.
# Routing Protocols for Delay Tolerant Networks:
###### Abstract  
In this paper, we propose a reference architecture for Delay-Tolerant Networking (DTN) routing protocols and a thorough quantitative evaluation of many protocols proposed in the literature. We categorize DTN protocols according to their use of the three techniques that are the key elements of our reference architecture: _queue management_, _forwarding_and _replication_. Queue management orders and manages the messages in the node's buffer; forwarding selects the messages to be delivered when there is a contact; and finally, replication bounds the number of replicas in the network. Contrary to most previous papers, where either only qualitative comparisons have been presented or only a single category of protocols has been analyzed, in our work, we discuss the results of our experimental activity on many of the DTN protocols in the literature. Our results, which have been obtained both using synthetic and real mobility traces, show that an effective combination of the proposed techniques can significantly improve the performance of the protocols in terms of delivery ratio, overhead and delay.  
delay-tolerant networks; routing protocols; performance evaluation; experimentation 2016
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 2 Related Work  
Recently, signal recognition via deep learning has achieved a series of successes. The convolutional radio modulation recognition network was proposed in [28], which can adapt itself to the complex temporal radio signal domain and also works well at low signal-to-noise ratios (SNRs). The residual neural network was employed in [2] to perform the signal recognition tasks across a range of configurations and channel impairments, offering referable statistics. Two convolutional neural networks, AlexNet and GoogLeNet, were used in [29] to address the modulation classification tasks by demonstrating the significant advantage of the deep learning-based approach in this field. Another deep learning-based big data processing architecture for the end-to-end signal processing task was proposed in [30] to obtain important information from radio signals. The adversarial evasion attacks, which lead to misclassification in the context of wireless communications, were evaluated in [31]. Further, an automatic multiple multicarrier waveform classification was introduced in [32], which can use the principal component analysis to suppress the additive white Gaussian noise and reduce the input dimensions of CNNs. Some other CNN-based signal recognition algorithms were also proposed to demonstrate the efficient recognition ability [33; 34; 35].  
In summary, deep learning-based open-set recognition methods can be categorized into two groups: discriminative model-based methods and generative model-based methods. The discriminative model-based methods calibrate the classification logistics to detect UUCs. The typical OpenMax [19] employed an OpenMax layer and output probabilities with Weibull distributions. On the basis of OpenMax, CROSR [23] performed more encoding processing on the original sample x by rebuilding the sample to improve the performance of the method in the open set scenarios. In addition, there are some methods to improve on OpenMax, such as DOC [23], COOL [36], C2 AE [37], etc. The generative model-based methods, on the other hand, learn distributions of known classes. G-OpenMax [38] introduced a conditional GAN to synthesize the mixtures of unknown classes and predict explicit probability estimations over unknown classes. OSRCI [37] does not generate unknown class samples through the confrontation generation network but generates unknown class samples that are close to the known class to improve the performance of the method. ASG [39] can generate not only data samples of unknown classes but also data samples of known classes. After generating the samples, we can distinguish between known and unknown classes by the supervised learning proposed in GCM-CF [40], which can disentangle sample attributes and class attributes. However, none of these methods focused on designing new networks for open-set problems but used some existing popular networks for closed-set problems.
## 3 Results and Discussion
### Grade of recommendation GPP, (88%) Consensus
#### Comment--Improved quality of life:  
Improvements have been observed in the Chronic Respiratory Disease Questionnaire (CRQ) (which measures the presence of dyspnoea, fatigue, emotion, and mastery) and quality of life as measured by the St George's Respiratory Questionnaire in underunwished patients with COPD, as well as anxiety and depression scores by HADS and the EQ-5D, in patients with nutritional supplementation [67; 69; 92; 107; 108; 109; 110; 111; 112; 113; 114; 115].
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Test Turbine 2
#### 4.2.1 Comparison of Radial Positions  
A comparison of sensors deployed at different radial positions along the blades is displayed in Figure 14 (left). Fingerprints of four sensors are visualised including two sensors mounted at the blade roots (\(S13\) and \(S27\)) and two sensors mounted at the blade tips (\(S15\) and \(S29\)) of Test Turbine 2. All sensors were mounted at the leading edge. Prominent Type 2 vibrations are reflected in the fingerprints of both root and tip sensors and correspond to global vibrations of the blade (see Vibrations 1, 3, 5 and 6 from the top). In addition, few vibrations were exclusively detected by Sensors S13 and S27 mounted the blade root (see Vibrations 2 and 4 from the top). Hence, fingerprints vary with the radial position of sensors, which corresponds to the extent of blade vibrations varying across radial positions due to the varying stiffness of the blade.  
Figure 14: (**Left**) Fingerprint comparison for Sensors S13, S27, \(S29\) and \(S15\) with varying radial positions on Turbine 2. (**Right**) Fingerprint comparison for sensors mounted on the blade tips of all three blades of Turbine 2 (S15, S10 and \(S29\)). Solid lines represent vibrations only occurring in the fingerprint of one sensor, dotted multi-coloured lines represent vibrations occurring jointly in multiple fingerprints. Error bars show locations of associated peaks with their length representing peak prominence.  
Figure 13: Fingerprint results for Sensors \(S15\) (**top**) and \(S27\) (**bottom**) mounted on tip and root of the same blade on Turbine 2. (**Left**) Linear-scale spectrogram with colour corresponding to logarithmic amplitude. (**Middle**) Extracted fingerprints, both Type 1 and Type 2 vibrations displayed. (**Right**) Sensor positions on the blade.  
In addition, joint vibrations were detected in the lower part of spectrograms, i.e., \(f\leq 25\) Hz at \(f_{rot}=0.1\) Hz. However, regular block-wise gaps in data transmission increased noise especially in the lower frequency range. Therefore, assessing those vibrations is challenging and similarities or differences in fingerprints may be caused by data impurities and not by physical principles.
# IoT Technologies during and Beyond COVID-19: A Comprehensive Review
## 3 Methodology  
In order to answer the usage and impact of IoT Technologies during and beyond COVID-19 (i.e., the research question mentioned in Section 1), the below research objectives are drafted:  
1. Identification of key IoT Technologies, which had an impact on managing the COVID-19 pandemic via a comprehensive literature review
2. Review of social-economic impact, maturity of the technology, and timeline for wider deployment
3. Analysis and discussion of policy, privacy, and security challenges, and finally identification and analysis of best practice and code of practices for IoT technologies  
In order to achieve the above research objectives, the following research methodology is followed in this study. The review was carried out using publicly available, secondary data sources, which discuss different aspects of IoT technologies in diverse sectors. The main data sources used in this review are SCOPUS library, Web of Science citation database, ACM library, IEEE Xplorer, Google Scholar, etc. A number of keyword searches were used to find relevant studies and reviews necessary to answer the research questions of our study. The main keywords combinations included \"COVID-19\", Coronavirus, IoT, IoE, IIoT, IoMT, and other relevant key words. An exclusion criterion was not used.  
As for the review protocol, key IoT areas for the review was selected based on the number of search results that appeared for all keyword combinations. In addition to the above keyword search by the authors, recommendations by previously published research, tutorials, surveys, and reviews were used to select the sectors to focus on this review. The details of each IoT application was analyzed, categorized, and summarized. The analyzed IoT applications were categorized based on the sector and the industry. Then a deep dive into individual IoT technology under each sector was performed. Each key application was also ranked based on the potential economic and social impact it would make. These decisions were made based on the insights from the data collected about each application. In addition, the timeline for wider adaption of the each IoT technology is predicted based on the collected data about the application area. Finally, Technology Readiness Level of each IoT application is depicted based on the current maturity level. In order to determine the TRL levels, the European Union's scale of TRLs with nine categories were used [24].  
The privacy and security concerns as well as best code of practices for IoT were also derived from the data collected from the relevant literature. A deep analysis is performed to identify the key challenges and opportunities for each application area under respective sector.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 3 Problem Definition
### Assumptions  
This work focus on monitoring-type applications, in which all sensor nodes periodically gather information by monitoring an geographic area, denoted region of interest. The nodes can be heterogeneous, i.e., the nodes do not need to have the same set of sensors. The nodes also cannot change its positions after deployment (static nodes) [45]. It is considered that the provisioning task is performed by the middleware of the sensor cloud. The middleware has a local database that stores information about all the sensor nodes in the Sensor Cloud. This local database is updated whenever a new \(WSN\) is deployed in the Cloud or when a sensor node is disabled or added to an existing \(WSN\). The middleware does not need to be aware of the routing protocol used by each \(WSN\) since only the sink node (base station) of each \(WSN\) has the responsibility of delivering the messages required to the middleware. Thus, whenever the middleware needs to create a virtual sensor, it must inform the selected sensor nodes, using the sinks as a proxy for the \(WSN\). Once the nodes are selected, they start sending their measurements according to the received parameters.