esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The usage of UAVs (unmanned aerial vehicles) in vessel-based missions has risen significantly due to their ability to provide an effective platform for numerous purposes. With enhancements made to aerial vehicles' technology and sensor miniaturization, UAVs have emerged as significant contributors towards maritime activities. These unmanned planes provide superior situational awareness alongside seamless data collection capabilities, which make them much more affordable than conventional techniques.  
The most common applications of UAV systems are focused chiefly on observation and reconnaissance missions [1]. It is obvious that advanced vision systems and flights at a hundred meters above the ground significantly increase the range of visual observation. This can be proven by drone usage in modern wars, where identifying enemy positions is crucial in a successful strike [2]. Drones and their vision systems are excellent tools for monitoring national borders [3] or forests to protect against fires [4]. Another possibility for UAV observations, which can be a challenge, is to recognize and localize objects on the surface of water that could be dangerous to a vessel's structure [5, 6]. This is especially important if future vessels are unmanned, considering the great number of containers lost at sea. To prevent unexpected collisions, monitoring should be continuous, with a range of view sufficient for the performance of avoidance maneuvers. This could be possible with a UAV that is continuously supplied with power from the deck of the vessel and with a flight altitude of at least 50 m. Therefore, the current paper focuses on such a UAV, i.e., a tethered multicopter whose payload is sufficient for mounting advanced cameras. This paper presents flight tests of the tethered multicopter in maritime conditions during a standard cruise of the \"Wolin\" ferry over the Baltic Sea. The test results, which are a combination of the observations and recorded flight parameters, can be used to identify potential issues of UAV usage in maritime conditions on the deck of a vessel, especially related to wind gusts and turbulence over the helipad. This paper presents the results for the UAV's operation in sea conditions. The UAV was developed based on the conclusions and invaluable experience drawn from tests in quasi-real conditions carried out on a lake, which were presented in [7].  
This article consists of six sections. The first is the introduction, with a subsection containing a review of current advances in tethered UAV systems. The second contains a detailed description of the designed tethered hexacopter system, including separate subsections, sequentially, about the tethered hexacopter, the power cord unwinder, the equipment required for the tracking of the helipad's position, and the ultra-wideband (UWB) positioning system. In the third section, the experimental tests, performed flight missions, and weather conditions during the ferry's cruise over the Baltic Sea are presented. The last two sections are dedicated to a discussion of the results and final conclusions, respectively.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Data Resource Layer  
The data resource layer is the core layer of the IoV system. Therefore, in this layer, it is very important to take the information resources in scientific classification with the standard of the unified data exchange to ensure the reliability of the data. Figure 7 is the architecture of the data resource layer.  
As can be seen in Figure 7, there are several databases, such as the business databases, the basic databases and the theme database groups. With these databases, a variety of theme databases would be developed by classifying the sensor information and the requirements of administration, customers and crews. At the same time, the architecture of the data resource layer needs to obey the standard regulations, including the uniform information coding, uniform database naming and uniform management [39].  
Figure 7: The technical architecture of the Data Resource Layer.  
In order to provide a large amount of data to the upper layer, the data resource should support the present mainstream databases such as the MySQL, SQL server, Oracle, Text, Excel, XML and so on [40]. At the same time, the data resource layer should also have the data interfaces with different facilities. According to the data conversion components, the data resource could solve various complicated problems by the permutation and combination. Now the big data processing platform has realized the massive data storage and mining. The speed of data update could be achieved in milliseconds. With these advanced data resource technologies, the IoV system could store huge amounts of data from different ships, ports and the base stations by the perception facilities.
###### Abstract  
Due to the wide variation in geographic and climatic conditions, the search for high-performance bituminous materials is becoming more and more urgent to increase the useful life of pavements and reduce the enormous cost of road maintenance. Extensive research has been done by testing various bitumen modifiers, although most of them are petroleum-derived additives, such as polymers, rubbers and plastic, which in turn do not prevent oxidative aging of the binder. Thus, as an alternative to the most common polymeric rheological modifiers, selected binder additives falling in the categories of organosilane (P2 KA), polyphosphoric acid (PPA) and food grade phospholipids (LCS) were homogeneously mixed to a base bitumen. The goal was to analyse the micro-morphology of the bitumens (neat and modified) subjected to different cooling rates and to find the corresponding correlations in the mechanical response domain. Therefore, microstructural investigations carried out by Atomic Force Microscopy (AFM) and fundamental rheological tests based on oscillatory dynamic rheology, were used to evaluate the effect of additives on the bitumen structure and compared with pristine binder as a reference. The tested bitumen additives have been shown to elicit different mechanical behaviours by varying the cooling rate. By comparing rheological data, analysed in the framework of the \"weak gel\" model, and AFM images, it was found that both P2 KA and PPA altered the material structure in a different manner whereas LCS revealed superior performances, acting as \"mechanical buffer\" in the whole explored range of cooling rates.  
modified bitumen; Atomic Force Microscopy; Dynamic Oscillatory Rheology; complex modulus; \"weak gel\" model +
Footnote †: journal: Applied Sciences
## 3 System Overview
### TLS Certificate Selection and Generation  
The aim of the proposed tests was to provide a comparative analysis of the performance of RSA and ECDSA in terms of energy efficiency and response time, when securing IoT node communications at different SoC clock frequencies. To provide a fair comparison among the selected algorithms, the tests were driven by the concept of security level. Thus, four different security levels were chosen following the NIST guidelines on TLS implementations, which establish a 112-bit security level as the minimum for public keys. Accordingly, 80-bit (deprecated), 112-bit (minimum), 128-bit (recommended) and 192-bit (future-proof) security levels were selected. Several of the available TLS 1.2 [32] cipher suites that implement RSA and ECDSA signing algorithms were evaluated. To provide valid and applicable results to future standards, the latest TLS standard (TLS 1.3 [53]) was also analyzed. Such a standard establishes the need to implement authenticated encryption algorithms for all the available cipher suites. Thus, two cipher suites that comply with the NIST guidelines and that also provide authenticated encryption (i.e., its block cipher implementation operates in Galois/Counter Mode (GCM)) were selected: ECDHE-RSA-AES256-GCM-SHA384 and ECDHE-ECDSA-AES256-GCM-SHA384. These two cipher suites only differ on their signing algorithm: the former uses RSA and the latter ECDSA. The rest of the algorithms are exactly the same.  
Seven different certificates that provide the previously mentioned security levels were generated. For RSA, three key sizes were used (1024, 2048 and 3072 bits), while, for ECC, the certificates were generated using four curves (prime192v1, secp224r1, secp256r1 and secp384r1). In the case of RSA, no certificate was generated for providing a 192-bit security level, since it would require using a 7680-bit key size, which cannot be handled by the ESP32 crypto engine (it only supports up to 4096-bit RSA operations) and, currently, it would make no sense to use it in IoT applications.
# Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
### Current Trends in IIoT and PdM
Existing studies used fuzzy logic [35], statistical methods [36], and data mining to predict textile equipment failure. Some studies have indicated that industry 4.0 can assistant enterprises understand their maintenance and production activities [37]. Edge computing has been employed in prognostic health management (PHM) from machinery producers' or production management perspective [38; 39]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Year** & **Highlights of Thestudies** \\ \hline
[23] & 2017 & Proposed an intelligent system for maintaining vibration and temperature in an electricity power plant \\ \hline
[24] & 2015 & Predicted electric power transformer failure by monitoring dissolved gases in oil \\ \hline
[25] & 2017 & Analyzed industrial data to predict the remaining life of important components of machining equipment \\ \hline
[26] & 2015 & Proposed a cost evaluation model for optimizing maintenance decision variables \\ \hline
[27] & 2017 & Predicted fault diagnosis and remaining useful life and implemented a maintenance schedule based on the proposed system \\ \hline
[28] & 2017 & Surveyed PdM-related trends and techniques and provided suggestions for implementing factory PdM \\ \hline
[29] & 2016 & Reported that intelligent PdM can satisfy customers’ needs and change global markets in the manufacturing industry \\ \hline
[30] & 2017 & Discussed vehicular IoT and car PdM with connected technologies \\ \hline
[31] & 2018 & Used data analytics to predict airline maintenance scheduling \\ \hline
[32] & 2017 & Reported the evolution of PdM-related solutions in a Big Data environment \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Highly cited PdM-related studies.
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
## 1 Introduction
Issue 3: At present, many congestion control (CC) algorithms are used in multi-path transmission systems, such as Cubic [8; 9], Hybla [10], opportunistic linked-increases algorithm (OLIA) [11], Reno [12], Scalable [13], and Vegas [14]. In practice, it has been found that in order to achieve the best transmission performance in different scenarios of heterogeneous multi-path transmission, the CC algorithm used is also different. How to choose different CC algorithms dynamically, according to the different transmission scenarios, has not been solved. Figure 1: The architecture of MPTCP: (**a**) protocol stack; (**b**) functions. At present, no one has proposed a comprehensive technology to solve the above problems. Therefore, this paper proposes an algorithm to concurrently optimize the performance indicators (such as throughput, buffer size, PM, and CC) of a multi-path transmission system to achieve the overall improvement. This paper is organized as follows: In Section 2, we briefly introduce the PM, CC and buffer configuration. The MPCOA algorithm is proposed in Section 3. Section 4 describes the measurement scenario design, and Section 5 shows the measurement results and detailed data analysis. Finally, Section 6 concludes this paper.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 2 Related Work  
### Federated Learning  
Federated Learning represents a decentralized machine learning framework aimed at safeguarding data privacy. In this paradigm, clients collaboratively train a global model without exposing their private datasets. Unlike conventional centralized training methods, Federated Learning operates in iterative cycles known as rounds. Each round involves four key steps: the central server shares model parameters with participants, participants train the model using their local datasets, the locally trained models are sent back to the server, and the server integrates these models to update the global model. Once a round is completed, the subsequent round of training commences. Figure 1 illustrates the training process.  
1. The server selects the necessary devices for this round of Federated Learning and distributes the global model to them.
2. The local devices receive the global model and use it to train on their data, updating their local models accordingly.
3. All devices then upload their trained local models to the server.
4. The server utilizes an aggregation function to combine the models received from participants and updates the global model accordingly. Upon completing a learning round, the server distributes the updated global model to all participants, enabling the initiation of the next round in the Federated Learning process.  
Figure 1: Federated Learning Framework.  
The classic algorithm FedAvg [17] achieves a superior global model through weighted averaging on local model parameters. Nevertheless, the imbalanced class distribution of local data and the substantial differences in data distribution between clients pose challenges for a simple weighted averaging method to achieve a high-performance global model. Researchers have proposed the FedProx [18] algorithm as a solution to this problem. The incorporation of a regularization term guarantees that updates to local client data are evenly distributed, leading to enhanced performance of the global model. FedOpt [19] efficiently extracts important data from interconnected and hierarchical nodes in the operational space. Subsequently, it evaluates the consumption of resources. SCAFFOLD [20] introduces a scalable and privacy-preserving approach to Federated Learning. This method allows for the creation of accurate and efficient global models, ensuring high-quality performance.
## 3 Act
### Compression
#### 3.3.1 Compression Technique
These compression techniques are usually used in compression of images or audio which are continuous data and have similar data values. We use these compression techniques because the sensed data are continuous and have similar values. DWT, ADPCM, and DWT can drastically reduce the number of packets required by the sensed data. Figure 3 shows an example of compression techniques in ACT. With original data [Figure 3-(A)] which was sensed by sensor node, DWT transforms the data from the time sequence domain to the  
Figure 3: Example of Compression Techniques in ACT. frequency domain [Figure 3-(B)]. With DWT, most of energy in the data is concentrated to the LL sub-band and other sub-bands have less energy than that of LL sub-band. Differential coding is applied to the result values of DWT [Figure 3-(C)]. With differential coding, the values become small and the range of value is reduced. Each sub-band has different rage of values and thus different quantization step size is applied to each sub-band. As a consequence, with adaptive quantization, most of values in high frequency become 0. After quantization, RLC produces a very small amount of bit stream [Figure 3-(D)].
## 2 Theoretical Section
### Measurement of Weight Fraction of Glass Fiber (Gc) of Laminate Structure
#### 2.2.2 Direct Measurement  
One of the simplest approaches for experimentally determining Gc is to measure the dimensions and weight of a section of the laminate structure using Vernier calipers and an electronic scale, respectively. By assuming a perfect cuboid shape (volume = cross - sectional area \(\times\) thickness), the relative density, \(\varrho_{c}\) (g/cm\({}^{3}\)), can be calculated (Equation (3)). The relative density can then be used to calculate Gc based on the theoretical densities of the glass fiber and resin (Equation (4)).  
\[\varrho_{c}=M/(A\times t\times 1000), \tag{3}\]  
\[\text{Gc}=(p\times N\times 0.1)/(\varrho_{c}\times t)\times 100 \tag{4}\]where \(M\) is the weight of the laminate specimen (g), \(A\) is the cross-sectional area (m\({}^{2}\)), and \(t\) is the thickness (mm); \(p\) is the weight of one sheet of reinforcement per unit area (g/m\({}^{2}\)), and \(N\) is the number of sheets in the specimen.  
This method is both theoretically and experimentally simple. However, the volume and weight of the cut specimen must be measured accurately. The specimen density is often only approximated from measurements taken using Vernier calipers and a scale if the laminate specimen has rough or irregular surfaces; therefore, it is more accurate to measure the relative density directly using Archimedes' principle and an instrument such as an immersion electronic densimeter. ASTM D792-13 [20] proposes a procedure for measuring the relative density of plastics, which can be used as a reference. The measured result can then be used in Equation (4).  
For both these methods, it is important that the measurements are accurate, and that the density of the glass fiber and uncured resin are known accurately. ISO 12215, LR, and RINA [9; 10] prescribe the method described in ISO 1172 [21] for density measurements to confirm those reported in the catalog. However, the inner defects in the structure (see Figure 2) cannot be considered with either method. This can lead to an inaccurate measurement of Gc.
###### Abstract  
In order for AGVs to be able to effectively carry out the tasks assigned to them, it is important to accurately determine their position and orientation in the working space. Having data on the location of an AGV is crucial for the navigation process, and the most commonly used odometry method is unreliable due to errors. To correct these errors, additional measuring systems are used. These systems use a variety of sensors. Some of the most widely used types are laser rangefinders. These sensors are also used in the automatic course correction methodology that is developed and presented in this article. The measurements from laser rangefinders are used to determine the shift of the actual trajectory from the set one, and then to guide the AGV to the previously set course. The developed methodology is experimentally verified on the basis of several dozen test drives. The conducted experimental studies prove the correctness of the developed methodology. The proposed course correction algorithm can be implemented in most working conditions, and guarantees correct passage over the given route.  
automated guided vehicle; odometry errors; measurement methods; correction of the course +
Footnote †: journal: Applied sciences
# The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App
## 3 Materials and Methods
### User Evaluation  
#### 3.5.1 Questionnaire  
With the user engagement scale (UES) [44] and system usability scale (SUS) [45], we collected feedback about the use of the PHOENIX solution through the mobile apps with the TUI and ARUI from 24 participants (P1-P24, Group A). We utilized UES and SUS to understand whether the ARUI improved user engagement and perceived usability more than the TUI. For five occupants (P25-P29, Group B), we asked the additional 16 questions regarding the overall experience and impression of the PHOENIX solution that the participants experienced through the TUI for about three months (i.e., March-April, June-August). The 16 questions are listed in Appendix A.  
The UES was utilized to measure the participants' experience regarding the level of engagement on each UI. We used a long version of the questionnaire with 30 questions, proposed by [PERSON] et al. [44], to be answered by the participants with five scales from'strongly disagree (1)' to'strongly agree (5)'. We modified the words in the questions to fit into our context. Through the questions, user engagement was measured in the following four dimensions: \"aesthetic appeal\" (AE), \"focused attention\" (FA), \"perceived usability\" (PU), and \"reward factor\" (RW). Each dimension had from 5 to 10 questions. We randomized the order of questions and hid the dimension label to avoid potential biases that might influence participants' responses [44]. The 30 questions before randomization are presented in Appendix B.  
We also asked about the participants' perceived usability of each UI. In total, 10 questions from SUS, proposed by [PERSON] [45], were used (Appendix C), and the participants were asked to select one option that reflected their feelings between strongly disagree (1) and strongly agree (5).  
Both questionnaires were prepared in English and Swedish to reduce confusion among participants. Due to the sample size (N \(\leq\) 30), the collected questionnaire answers were analyzed with a paired sample \(t\)-test to determine whether a statistically significant difference exists and descriptive statistics to check the central tendency and dispersion of the data. Apart from the questionnaire answers, we coded user feedback that was spoken by participants during the user evaluations to gain insight into the user perspective.