esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Bibliometric Analysis
#### 3.1.1 General Overview
The evolution of keywords (Figure 3) reveals a clear progression in research focus and priorities. In the early stages, from around 2012, \"Carbon dioxide emissions\" was a primary concern. Studies on carbon dioxide and its geological storage reflected a more sophisticated approach to studying hydrogen storage and a link to broader carbon capture and storage efforts, including blue hydrogen production. In 2013-2019, the inclusion of \"electricity\", \"energy modelling\" and \"CO\({}_{2}\) emissions\" signaled a growing emphasis on hydrogen production and its storage viability. The year 2017 saw the appearance of keywords like \"leakage\" and \"methanogenesis\", indicating a broader exploration of energy-related applications and potential synergies between hydrogen storage and other subsurface energy processes. In 2020, 2021, and 2022, the use of the keywords \"energy storage\", \"depleted reservoir\", \"salt cavern\", \"saline aquifer\" and \"numerical modelling\" showcases a real increase in the research of this type of energy storage. Finally, in 2023, \"cushion gas\", \"molecular simulation\", \"energy transition\" and \"wettability\" point to a deeper examination of surface interactions for optimizing hydrogen storage with more specific focus on H\({}_{2}\) production solutions within geological structures. Overall, this trend suggests a maturation of research efforts, progressing from fundamental understanding and safety considerations to broader energy applications and practical storage solutions. It reflects the increasing recognition of hydrogen's role as an essential component of future energy systems and the need for advanced research to make H\({}_{2}\) storage in rock formations a viable and sustainable option.
## 1 Introduction
### Background  
Broadly, entropy is a measure of the state of disorder, randomness, or uncertainty in a system. Definitions span multiple scientific fields, and the concept of 'order' can be somewhat subjective. However, for the purpose of this work, we are concerned with information entropy. In terms of physics, for example, the configuration of the primordial universe has the lowest overall entropy since it is the most ordered and least likely state over a longer timeframe. We can consider entropy as the number of configurations of a system. It may also be viewed as a measure of the lack of information in a system. In the context of information theory, entropy was first described by [PERSON] in his seminal 1948 paper [PERSON] (1948), which provides a mathematical framework to understand, measure, and optimise the transmission of information. [PERSON] formalised the concept of information entropy as a measure of the uncertainty associated with a set of possible outcomes: the higher the entropy, the more uncertain the outcomes.  
Practically, if we consider entropy in data, we are interested in the frequency distribution of symbols, taken from a finite symbol set. The higher the entropy, the greater the diversity in symbols. Maximum entropy occurs when the symbolic content of data is unpredictable, so for example, if a file or network byte stream has high entropy, it follows that any symbol (here, we typically equate a byte to a symbol) is almost equally likely to appear next (i.e., the data sequence is unpredictable, close to random); see Figure 1.  
[PERSON]'s entropy [PERSON] (1948)--sometimes referred to as information density, is a measurement of the optimal average length of a message, given a finite symbol set (an alphabet). The use of entropy to measure uncertainty in a series of events or data is a widely accepted statistical practice in information theory [PERSON] (1948); [PERSON] (1948).  
We can compute the entropy of a discrete random event \(x\) using the following formula:  
\[H(X)=-\sum_{i-1}^{n}p(x_{i}\ )\log_{2}(p(x_{i}))\]  
where \(H(X)\) is the information entropy of a random variable \(X\), with a finite set of n symbols. This formula is effectively the weighted average uncertainty for each outcome \(xi_{i}\), where each weight is the probability \(p(x_{i})\). \(p(x_{i})\) is the probability [0,1] of the occurrence of the ith outcome. Log base 2 is convenient to use since we are measuring entropy in bits (i.e., \(x\in\) [0,1]). The negative sign ensures non-negative entropy. In Section 3, we describe how we normalise entropy values to lie within the range of 0 to 8 for the purpose of our packet analysis.
## 1 Introduction
* The hardware platform is compatible with the Contiki OS and openWSN, allowing reuse of existing IPv6 IoT protocol stacks for UWB system designs, as well. The Time-Annotated Instruction Set Computer (TAISC) framework [8] provides an efficient way to implement the  
Figure 1: Ultra-wideband (UWB) link with the main causes of pulse distortion in a UWB localization system. network stack and gives the opportunity to research different MAC designs and easily adjust the MAC layer to different use cases. * The hardware devices can be configured as both anchor and tag node to build a full localization system. The main contributions of this paper are as follows:
* The hardware platform is provided as open-source code allowing easy integration in other projects. The source-code of the UWB hardware and MAC protocol software is provided as open-source contributions [9]. * The hardware platform is thoroughly evaluated demonstrating superior range and accuracy with an extremely low power consumption. * Guidelines in addition to the DW1000 application notes are given, describing design optimizations which allow other designers to optimize their UWB hardware solutions. The remainder of this paper is organized as follows: Section 2 discusses relevant related work and alternative hardware boards. Section 3 provides an overview of the most important design choices and the technical specifications of the hardware platform, along with an introduction on the integration of Contiki-NG OS and the TAISC [8] software platforms. Section 4 presents and discusses experimental results that were achieved with the Wi-PoShardware platform. Section 5 summarizes the work and presents the concluding remarks.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 4 Closing the Technological Gap: The ConTAD Approach
### Obtaining Information on Ships  
If the loss of containers can be automatically detected, it is mandatory to inform the ship losing cargo about the accidents. Therefore, a ConTAD tracking unit needs a method to communicate with the ship, preferably on already existing means of communication. Since every commercial vessel is equipped with VHF and AIS, these are possible options to be considered. The advantage of these systems is that also other ships in the area can be warned and avoid collisions with floating containers.
# A Novel Framework for Cross-Cluster Scaling in Cloud-Native 5G NextGen Core
## 3 Comparison between ETSI MANO and Kubernetes
### Horizontal vs. Vertical Cloud Scaling  
In cloud environments, vertical scaling makes use of the existing infrastructure in terms of adding more computational power, such as RAM, CPU, etc., whereas horizontal scaling, also called \"scaling out,\" relies on the deployment of new infrastructure by adding more instances of the same kind.  
Horizontal scaling serves the purpose of organizations that need high availability and near-zero downtime or other disruptions, and it is faster and easier compared to vertical scaling. At a basic level, scaling out can mean adding new computing nodes or machines to enhance the data processing and storage capabilities. On the other hand, when we refer to vertical scaling, we address the optimization of data processing and multi-threading for one instance. Both horizontal and vertical cloud scaling aim to leverage processing capabilities and storage, increase flexibility, and reduce costs [63].  
In Kubernetes, horizontal scaling means to expand the number of pods as a response to the increased workload. The HorizontalPodAutoscaler (HPA) feature automatically updates a workload resource (such as Deployment or StatefulSet), with the aim of automatically scaling the workload to match capacity demands [64].  
In terms of vertical scaling, the Kubernetes scheduler assigns more resources (i.e., memory or CPU) to the pods that are already running as part of the workload. The key role of the vertical pod autoscaler (VPA) [65] is to automatically set the requests for the containers based on pod usage and utilize the optimal scheduling mechanism to allocate on-demand the necessary computational resources for each pod.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 4 Adaptive Channel Allocation Algorithm  
Throughout this section, we introduce a method to solve the channel allocation problem dynamically but with adaptive control. The adaptive channel allocation algorithm is separated into two parts, the initial channel allocation (installation phase), and the adaptive channel allocation (running phase).  
The initial channel allocation draws out the number of NICs installed per AP in the network and determines the channel allocation as a start up phase. According to this part, the traffic of each AP is assumed to be the maximum associated load.  
For the adaptive channel allocation phase, we introduce a decision function to determine whether the network should re-allocate the channel or continue with the last allocation.
## 3 Results
### Aviation Health Management Framework
Communication and Coordination A centralized communication platform ensures that all relevant stakeholders, including pilots, maintenance crews, and operations managers, have access to up-to-date information and recommendations. The system facilitates seamless coordination with maintenance, repair, and overhaul (MRO) organizations and Original Equipment Manufacturers (OEMs) for parts availability, maintenance scheduling, and addressing systemic issues. 6. Regulatory Compliance and Safety Management The HM system monitors compliance with aviation regulations and safety standards, automatically documenting maintenance actions and system checks for audit purposes. Safety management components analyze data for safety trends, contributing to industry-wide safety improvements and helping to prevent incidents before they occur. 7. Continuous Improvement and Learning  
Figure 1: Aviation health management framework. The system incorporates feedback mechanisms to continuously update and refine predictive models, ensuring they remain accurate over time. Insights and best practices derived from the AHM system are shared across the organization and with industry partners to drive continuous improvement in maintenance practices and aircraft design. By leveraging advanced technologies and integrating data across the aircraft lifecycle, HMGT enhances operational efficiency, improves safety, and reduces maintenance costs, offering a comprehensive strategy for managing the health of aviation assets.
## 4 Multi-Objective Modeling and Solving
### Optimization Model
#### 4.2.2 Compromise Solution Based on TOPSIS  
After obtaining the Pareto optimal set, evaluating the solution in the Pareto optimal set and selecting the compromise solution according to the preference of the DM are necessary. To date, many methods can be used to obtain a compromise solution from the Pareto optimal set. We used the classical TOPSIS method proposed by [PERSON] and [PERSON] and [PERSON] (2000) to obtain the compromise solution. TOPSIS essentially compares the proximity between different schemes and the ideal scheme. The main steps are as follows:  
Step 1: Establish a decision matrix. Establish and normalize the decision matrix \(A=\left(a_{ij}\right)_{p\times 2}\). \(p\) is the number of solutions, and 2 is the number of evaluation criteria, which, in this paper, are the threat gap and weapons development risk.  
\[x_{ij}=a_{ij}/\sqrt{\sum_{i=1}^{p}{a_{ij}}^{2}i}=1,2,\ldots,p;\,j=1,2 \tag{27}\]  
Step 2: Standardization the Decision matrix. Establish the weighted standardized decision matrix \(R=\left(r_{ij}\right)_{p\times 2},r_{ij}=x_{ij}\times q_{j}\), where \(q_{j}\) is the weight value of the \(j\)th criteria.  
Step 3: Define the ideal solution. Define the positive ideal solution \(f_{j}^{+}\) and the negative ideal solution \(f_{j}^{-}\). Because the threat gap and weapons development risk are both cost-type indexes:  
\[\begin{array}{ll}f_{j}^{+}=\min_{i}r_{ij},&i=1,2,\ldots,p,\\ f_{j}^{-}=\max_{i}r_{ij},&i=1,2,\ldots,p,\end{array} \tag{28}\]  
Step 4: Calculate the Euclidean distance between each scheme and the positive and negative ideal solutions.  
\[\begin{array}{ll}D_{i}^{+}=\sqrt{\sum\limits_{j=1}^{2}{\left(r_{ij}-f_{j}^{+ }\right)}^{2}}\\ D_{i}^{-}=\sqrt{\sum\limits_{j=1}^{2}{\left(r_{ij}-f_{j}^{-}\right)}^{2}}\end{array} \tag{29}\]  
Step 5: Sort all solutions. Calculate the relative approach degree \(C_{i}\) between each scheme and the ideal point.  
\[C_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}},\,\,i=1,2,\ldots,p \tag{30}\]  
Finally, sort \(C_{i}\) in descending order. The solution with the largest \(C_{i}\) value is the optimal solution in the Pareto solution set.
## 3 Results
### Pilot Test  
The pre-test using a reduced and similar sample of subjects, following [50], is a phase that contributes to reinforce the validity of a designed measurement instrument and therefore it was chosen to be conducted. In this line, [51] added that it is convenient to carry out a pilot test, because it contributes to identify incorrectness in the instrument, estimate the time needed to complete it, review the clarity of the items, as well as estimate the reliability.  
After submitting the questionnaire to expert judgment, the version obtained was transferred to Google Forms and a pilot test was carried out with a sample of students during class hours. As this was a nationwide study covering the different Autonomous Communities, the pilot was carried out with 134 students chosen for convenience at two universities in different communities, specifically at the University of Alicante and the University of Barcelona. Of these, 14.9% (\(n=20\)) were men and 85.1% (\(n=114\)) were women, with a mean age of 21.1 years (SD = 4.1). Likewise, 42.6% (\(n=57\)) of the students were in the Pre-School Education Teacher's Degree and 57.4% (\(n=77\)) in the Primary Education Teacher's Degree. After completing the questionnaire, the time needed to complete it was recorded and the participating students were asked to express their opinion about the design of the questionnaire, the writing, the understanding of the items and any other aspect they considered could be improved, and this was collected in writing.  
Firstly, it was found that the time of completion of the questionnaire was reduced, BY approximately five minutes, and that there was no fatigue in the students. Secondly, the opinions collected were favorable to the questionnaire, although some aspects that could be improved were identified. In this sense, in items 17 and 25, examples were added to facilitate understanding. Furthermore, additional information was included for completion and a more visually attractive design was promoted by adding a header image and changing the colors. The improvements made resulted in the third version of the PECID questionnaire, ready to be passed on to the final sample.
## 4 Trust Evaluation Mechanism  
Trust evaluation is used to evaluate the reliability of nodes in networks based on their behavior and has been widely investigated in networks such as WSNs [20; 21]. In WSNs, the behavior of sensor nodes includes HELLO packet forwarding, DATA packet forwarding, etc. Evaluating the behavior of nodes to identify MNs in the network provides a strong guarantee for the secure and reliable communication of WSNs.  
Compared with nodes in WSNs, underwater nodes are more likely to be captured, which makes UAN communication vulnerable to MN attacks. Therefore, it is essential to establish a trust evaluation mechanism for the nodes to identify MNs and ensure the reliable communication of the UANs. In this paper, a fuzzy logic-based trust evaluation mechanism (FLTEM) is presented. In FLTEM, nodes are evaluated according to their residual energy and forwarding behavior. The DTV of the nodes is calculated by using a fuzzy comprehensive evaluation decision model, and the average of the DTVs from multiple common neighbor nodes is taken as the ITV of the node [22]. Furthermore, the reliability of a node is denoted by the CTV, which is determined according to the DTV and ITV. The framework of FLTEM is shown in Figure 2, where HPFBTF represents the trust factor of HELLO packet forwarding behavior, DPFBTF is the trust factor of DATA packet forwarding behavior, and RERTF is the trust factor of residual energy. DTV, ITV, and CTV will be described in detail later in this section.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 2 HARQ Transmissions with Rateless Codes
### HARQ with Rateless Codes  
An HARQ scheme is a combination of an FEC scheme with a classical ARQ scheme. HARQ schemes are classified into three types. In a type-I scheme, the same codeword is retransmitted whenever NACK is received. On the other hand, in a type-II or type-III scheme, the very first transmission packet contains only systematic bits or systematic bits with a few parity bits. As the retransmission steps increase, the type-II scheme transmits newly-generated incremental redundancy bits, while the type-III scheme transmits the previously-transmitted bits along with newly-generated incremental redundancies.  
Therefore, an FEC scheme used in a type-II or type-III HARQ scheme should have rate-compatibility, i.e., codes with various code rates can be generated with a mother code, so that a single encoder and decoder can be utilized. For this reason, codes based on convolutional codes which have inherent rate compatible characteristics are usually adopted for application in HARQ schemes. Because a rateless code can continuously generate parity bits, it can provide rate compatible characteristics and thus can be used in type-II and type-III HARQ schemes.  
Figure 1: Conceptual diagram of rateless code transmissions.
# Edge and Cloud Computing in Smart Cities
## 2 Architectural Models for Edge and Cloud Computing in Smart Cities  
The architectural models governing edge and cloud computing integration in smart cities define the distribution of computational tasks, data flow, and communication among various entities. These architectures address the trade-offs between latency, computational power, bandwidth consumption, and energy efficiency. A well-structured architectural framework is important to achieving optimal service delivery in applications requiring real-time decision-making and large-scale data analytics.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Mode Selection (MS)
#### 4.3.1 Static
The optimization problem is then solved using a search plus concave-convex procedure algorithm for both full and half duplex overlay modes. Lastly, in half-duplex underlay mode, all that remains is to locate the best solution. The MS can be chosen according to the highest SE among the four modes. The simulation results illustrated the effects of interference cancellation capabilities and channel gain on four transmission modes with maximum SE and MS. Moreover, the full-duplex mode is preferred when interference cancellation is satisfactory, while the underlay mode is preferred when the interference connection is poor. Nevertheless, the EE and imperfect channel state information are not considered. In [118], the authors proposed an MS scheme that uses a distance-based mechanism to select between different communication modes and a non-orthogonal scheme for sharing resources. The proposed mechanism derives the formulas of throughput and the policy for utilizing resource blocks, two crucial metrics for evaluating the efficiency of the network. A user's equipment can select its communication mode based on the MS condition, such as the distance and threshold SINR between machine-to-machine (M2M) users. The MS prior to data rate has potentially improved the network's performance. The traffic load on the BS can be reduced through the strategic design of distance-assisted suggested resource block utilization policies. The system's performance is evaluated in relation to a number of different parameters that can be altered, including throughput, MS threshold SINR, threshold distance, and resource blocks. The proposed mechanism also improved network performance and decreased the proposed network traffic load. Nevertheless, EE needs more attention and investigation.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Evaluation
#### 6.4.3 Statistical Tests  
Statistical tests can be used to assess the significance of classifier performance and serve as a comparison of different classifiers. Generally, the test can be differentiated in parametric and non-parametric tests. In parametric statistical tests, the sample data are assumed to be derived from a population that can be adequately modeled by a probability distribution with a fixed set of parameters. In a non-parametric model, no explicit mathematical form is assumed for the distribution when modeling the data, but assumptions are made about that distribution, such as continuity or symmetry.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Actually Positive** & **Actually Negative** & \\ \hline Predicted positive & True positive (\(TP\)) & False positive (\(FP\)) & \(TP+FP\) \\ Predicted negative & False negative (\(FN\)) & True negative (\(TN\)) & \(FN+TN\) \\ \hline  & \(TP+FN\) & \(FP+TN\) & \(TP+FP+FN+TN\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Schematic overview of a binary confusion matrix.  
The McNemar test is non-parametric and evaluates whether two given classifiers perform equally well [173]. Specifically, it compares whether the marginal frequencies, i.e., the total row and column values of the confusion matrix, of the predictions of the two classifiers are equal. Similarly, the sign test compares the number of data instances misclassified by the first classifier and correctly classified by the second classifier. A one-sided test is performed to determine which classifier has fewer misclassified data instances based on these differences. Next to these two tests, a Wilcoxon signed-rank test can be used [174]. The difference between the classifier results is calculated and increasingly ordered to build ranks for the test statistic. One classifier outperforms the other if the population ranks differ substantially, since, in this case, the corresponding test statistic exceeds the critical value [36]. The Nemenyi parametric test can be used to compare pairwise performances [175]. The average rank of all classifiers over multiple datasets is calculated and compared to determine the best classifier.  
Although these tests are discussed in several stream classification surveys, they are often criticized, since they are inherently static and do not represent the temporal evolution of stream classifier performance [36, 84, 169]. In consequence, since there has been no advancement in replacing them with more appropriate statistical tests, statistical tests, in general, are not discussed in more recent studies [7, 88]. Additionally, no comparative studies exist that evaluate the usefulness and suitability of different tests.