esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Fundamental Concepts in Trust Management  
* Network Structure: We consider an IoT network where all nodes can communicate and exchange information with each other. The network primarily consists of two types of nodes: GMN and GLN. GMNs are entities within the network seeking to establish trust, while GLNs play a crucial role in trust management. GLNs collect the computed trust scores for each GMN and decide whether specific GMNs are trustworthy to be a part of the network. In the proposed model, we represent each communication request as a \"task\" in the subsequent text.
* Trustor: This refers to entities within the network that seek to evaluate and establish trust in other network participants, including GMNs and GLNs. In our context, Trustors are nodes or devices within the IoT network that engage in the process of trust calculation for both GMNs and GLNs.  
* Trustee: This represents the entities being evaluated for trustworthiness, including both GMNs and GLNs. Trustees may be individual nodes, devices, or even entire subsystems in the IoT network. Understanding the characteristics and behaviors of Trustees is crucial for effective trust evaluation and management.
* Recommenders: These act as intermediaries that contribute to the trust evaluation process for both GMNs and GLNs. These entities provide valuable insights and recommendations based on their observations of Trustors and Trustees, thereby enhancing the robustness of trust calculations with the network.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 3 STPA-SN Application Example
### Step 2: Construct the System Model
#### 3.2.1 Construct the Hierarchical Control Structure  
The internal block diagram of the train door control system describes the hierarchical control structure of the system, including four levels, which are the Driver level, Door-Controller level, Actuator/Sensor level and PhysicalDoor level, respectively, as shown in Figure 2.  
The commands, i.e., control actions, and feedbacks passed between the components are also shown in Figure 2, and their meanings are described in Table 1.
## 10 Challenges and Open Research Problems
### Integration of Multi-Tier Heterogeneous UAV Networks  
Emerging applications and the heterogeneity of communication devices in contemporary networks with diverse QoS requirements demand the adoption of different communication technologies to satisfy these requirements. As a result, there is a need to address the integration of UAVs into the existing satellite and ground-based BSs, especially in the emerging unified space-air-ground integrated networks paradigm [353], so that the requirement of each device on the network can be satisfied by the most efficient resource available. However, despite the evidence supporting the integration of UAVs into future unified communications paradigms including space-air-ground networks (as evidenced by the large body of published work available), safety concerns and the slow progress of standardization (as highlighted in the preceding Section 10.6) continue to limit practical implementation. There are still issues related to standardization and safety that need to be addressed to ensure seamless integration while guaranteeing safety and privacy preservation. Despite these limitations, continued progress is being made in addressing communication-related issues, such as modeling of UAV-satellite channels [354], as well as addressing backhaul and access resource allocation, trajectory planning [355], etc.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 5 Proposed Solution
### Migration Aspects and Stages  
The main aspects and stages of the migration process are described in Table 5, with an emphasis on clearly outlining the available counterpart in each paradigm for specific elements.  
Migrating from an asynchronous to a reactive model requires a series of changes at the level of code and infrastructure. Although the transition can be complex depending on the size of the project and the technologies employed, the long-term benefits are considerable, as evaluated in this research.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Asynchronous** & **Reactive** \\ \hline Programming model & Imperative & Reactive \\ \hline Java version & Java 21 & Java 21 \\ \hline Framework & Spring Boot 3 & Spring Boot 3 \\ \hline Single result & _CompletableFuture\textless{}T\textgreater{}_ & _Mono\textless{}T\textgreater{}_ \\ \hline Collection/Data stream & _CompletableFuture\textless{}Collection\textless{}T\textgreater{}_ & _Flux\textless{}T\textgreater{}_ \\ \hline Thread pools & _ExecutorService_ & _Schedulers_ \\ \hline API & Standard Java API & Project Reactor, Spring WebFlux \\ \hline Error handling & _try-catch, CompletableFuture, exceptionally()_ & _Mono/Flux, onErrorReturn(), onErrorResume(), onErrorMap()_ \\ \hline Operation retries & Programmatic & Native, _Mono/Flux retry()_ \\ \hline Blocking operations & _CompletableFuture get()_ & _Mono/Flux block()_ \\ \hline Data access & JDBC, Hibernate, JPA & R2 DBC \\ \hline Web server & Tomcat & Netty \\ \hline HTTP communication & Java _HttpClient_ & Spring _WebClient_ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Migration aspects and stages.
# Cyanobacteria as Chassis for Industrial Biotechnology: Progress and Prospects
## 4 Emerging Synthetic Biology Technologies
The _mazF_ gene, encodes an endoribonuclease that cleaves mRNA at the ACA triplet sequence, inhibiting protein synthesis. To create the marker-less mutants, the _nrsB_ promoter controls expression of _mazF_ gene and an antibiotic resistance cassette (in this case _aphIll_ encoding kanamycin resistance was used) is also alongside the _mazF_ gene (under the control of its own promoter). The first selection was carried out on kanamycin, and the second selection on nickel. Attempts were later directed towards reducing the number of cloning steps required to make marker-less transformants using the _sacB/npt1_ cassette [120]. Another marker-less mutant system for _Synechocystis_ 6803 and _Synechococcus elongatus_ PCC7942 was also developed using the FLP/FRT recombination system [122]. This system uses a self-replicable shutter that contains a lipase (FLP) gene from _Saccharomyces cerevisiae_, which is introduced into the strains of _Synechocystis_ 6803 and _Synechococcus_ 7942 where the genome has already been transformed with a kanamycin resistance gene and a lipase recombination target (FRT) flanking either side [122]. Figure 3: Generating marker-less mutants through consecutive insertion and deletion of _sacB_ gene. The colors of the arrows and squares on each end are different to indicate the upstream and downstream regions of the selectable marker (SM) and _sacB_ and that certain boxes match with certain arrows for recombination to occur. A novel system utilised for generating marker-less cyanobacterial mutants is CRISPR/Cas9 [123; 124]. CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/ CRISPR associated protein 9) was discovered in bacteria and archaea as an adaptive immune response to foreign DNA [125]. Initially, invader DNA is incorporated as a spacer, which is transcribed into RNA strands that direct the Cas9 protein to complimentary invader DNA to be cleaved [126]. By engineering the spacer sequence to complement genetic targets, certain genes can be silenced or replaced with desired sequences. The main advantage of using this method for marker-less mutant generation is the ability to target several points for mutagenesis simultaneously [127; 128; 129]. There has been some work aimed at characterizing native CRISPR systems in cyanobacteria [130; 131; 132; 133], yet very limited work on CRISPR/Cas9 application. A recent study has demonstrated that Cas9 is toxic to _Synechococcus elongatus_ UTEX 2973, yet marker-less mutants have been achieved through transient expression of the Cas9 gene [123]. CRISPR interference (CRISPRRi) has also been successful in the suppression of several gene targets in cyanobacteria [124; 134]. Although this system has not been applied on cyanobacterial strains for a long time, its implementation for multiplex genome editing has potential of saving time and effort.
# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 1 Introduction
An advanced part of ML called Deep learning (DL) has been widely investigated for big data analysis with remote sensing [22; 23; 24] and computer vision [25] applications. DL has been attracting a lot of interest recently in the agricultural field, for example in plant disease detection and yield  
Figure 1: Plant water stress assessment methods based on state-of-the-art sensory measurements. prediction; however, it has been noted that DL application in plant water stress assessment is quite new and research has been limited [26]. Several review papers on DL application in plant stress phenotyping have been published [27; 28; 29] showing the high potential of the technique; however, these papers have mainly focused on leaf-based biotic stress detection from the image processing point of view. This investigation aims to comprehensively review almost all the major sub-approaches of plant water stress assessment method connected to DL. Furthermore, an advanced prospect of DL application in the field of plant water stress assessment and its challenges is also presented. The structure of this paper is as follows: the review methodology is presented in Section 2 while Section 3 provides the background of deep learning network architecture. Section 4 presents the implemented approach of recent DL sensor fusion work based on the planning protocol established in Section 2, as well as the performance metrics when available. Section 5 discusses the challenges as well as the future prospects in the application domain, followed by the conclusion in Section 6.
# Architectural Design for Digital Twin Networks
## 6 Tackling Challenges in DTN Implementation
### Interoperability  
Interoperability remains a significant challenge, particularly among systems developed by different companies. This is largely due to the substantial investment organizations make in creating proprietary solutions tailored to their specific needs. Understandably, companies are hesitant to compromise these investments by adopting universal standards. However, achieving interoperability necessitates such standards to ensure seamless communication and integration.  
This issue is especially prominent in the IoT environment, where various groups develop independent standards that often lack compatibility. As a result, systems typically do not function together out of the box, requiring additional effort to bridge these gaps. These fragmented approaches increase complexity, costs, and inefficiencies, hindering scalability and innovation.  
The draft proposal tackles [15] this for the DTN by suggesting a modular approach, dividing systems into smaller, independent components and enabling interfaces to interact in all directions. This flexibility allows developers to incorporate proprietary or open-source solutions with minimal adjustments, significantly mitigating interoperability issues. Although full interoperability may never be fully achieved, this approach fosters greater compatibility and ease of integration.  
However, for this vision to succeed, consensus on the design and implementation of interfaces is essential. This will likely require leadership from either a standardizing body or a pioneering company that introduces a broadly accepted solution. In the future, network management standards, such as YANG models specifying configurable elements and collectable monitoring data, or advancements in network standards like the 3 GPP Network Exposure Function (NEF), could help address these challenges. In the meantime, the best course of action is to remain open-minded and accepting of alternative solutions while actively leveraging open-source tools and frameworks wherever possible. This approach encourages collaboration, reduces redundancy, and accelerates progress toward a more interconnected ecosystem.
## 2 Related Work  
Over the past decades, many researchers have been working on traffic flow prediction, which is one of the main tasks of intelligent transportation systems. Traditional time series statistical theory, which uses mathematical statistics to process traffic historical data, was frequently used for traffic flow forecasting. It assumes that future predicted data have the same characteristics as those in the past. The ARIMA model and its variants were widely used time series models [1; 2; 3; 4; 5]. Most of these investigations are mainly based on small datasets or focus on several road segments, and most of these models are linear models that rely on mathematical equations. In general, traffic predictions based on traditional theories are limited, which focus on capturing temporal information and ignoring spatial information of traffic flow.  
In recent years, as a result of the accumulation of massive data and the improvement of machine computing capabilities, deep learning methods have been widely used in computer vision [6], natural language processing [7], recommendation services [8], and other fields, which have achieved great success. Deep learning performs very well in feature extraction and data modeling [13]. Therefore, some researchers used deep learning methods to predict traffic flow. For instance, [PERSON] et al. use the deep belief network for traffic prediction [14], which works by adding a multi-tasking regression layer on top of the deep belief network. [PERSON] et al. used stack auto-encoders for traffic prediction [15]. [PERSON] et al. compared two deep belief network-based traffic flow prediction models for feature extraction and performance comparisons [16]. [PERSON] et al. proposed a hybrid deep network of unsupervised stacked auto-encoders and a supervised deep neural network to predict passenger flow [17]. However, these deep learning methods cannot capture spatial information of traffic flow well.  
Convolution neural networks have been widely used to solve various spatial correlation problems, such as image classification [6], because of their ability to capture spatial information. Deep residual networks (ResNet) use a shortcut connection that skips two layers to address the degradation problem in the training process, which can make convolution neural networks deep enough to achieve state-of-the-art results in many visual recognition tasks [11]. These have inspired researchers to use convolution neural networks for traffic flow predictions. For instance, [PERSON] et al. used convolutional neural networks to predict citywide crowd flows [9], thereafter, they used deep residual networks to model the crowd flows [10]. [PERSON] et al. [18] proposed a convolutional neural network-based method that learns traffic as images and predicts traffic speed. However, they do not consider the specific scenario patterns of traffic flow. Hence, in this study, we propose a novel method to capture both spatio-temporal correlation and specific scenario patterns of bus traffic flow.
# Costs and Benefits of Autonomous Shipping--A Literature Review
## 3 Results
### Sensitivity Analysis of a Life-Time Cost Assessment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Description** & **Summary of the Analysis** \\ \hline \multirow{3}{*}{Scenario A} & \multirow{3}{*}{Reduced crew} & Expected Present Value of the autonomous ship is 0.5 m USD lower than EPV of the conventional reference vessel \\  & & & Expected Present Value of the autonomous ship is 4.3 m USD lower than EPV of the conventional reference vessel \\ \multirow{3}{*}{Scenario B} & Reduced crew and increased fuel efficiency & Expected Present Value of the autonomous ship is 19.2 m USD higher than EPV of the conventional reference vessel \\  & Reduced crew, increased fuel efficiency, and high-grade fuel & Expected Present Value of the autonomous ship is 19.2 m USD higher than EPV of the conventional reference vessel \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The scenarios prepared for the cost-benefit analyses [21]. Figure 3: Impact of input variables on relative RFR [21]. Compared to a conventional container feeder, her autonomous counterpart is expected to face an internal rate of return at the level of 13.5% (the same as per conventional vessel), as presented by [42]. This is despite the newbuilding price for the autonomous ship being 32% higher than the conventional one (16.5 mUSD excluding the cost of SCC).
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
###### Abstract  
This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures. Special attention is given to the solar-powered aspect, allowing for extended deployment periods without the need for frequent maintenance or battery replacements. To validate the proposed system, a comprehensive field deployment was conducted on an actual bridge structure. The collected data were transmitted through MQTT messages and analyzed by means of OMA. Comparative studies with traditional wired systems underscore the advantages of the solar-powered wireless solution in terms of sustainability, scalability, and ease of deployment. Results from the validation phase demonstrate the system's capability to provide accurate and real-time data needed to assess the health state of the monitored asset. This paper concludes with insights into the practical implications of adopting such a solar-powered WSN, emphasizing its potential to revolutionize bridge health monitoring by offering a cost-effective and energy-efficient solution for long-term infrastructure resilience.  
wireless sensor networks; structural health monitoring; energy autonomous sensors; operational modal analysis +
Footnote †: journal: Future Internet
# Influence of Particulate Matter and Carbon Dioxide on Students' Emotions in a Smart Classroom
## 1 Introduction
### Cognitive Effects  
Numerous studies have shown a clear correlation between poor IAQ and diminished cognitive performance, particularly in tasks that require focus and problem-solving. High CO\({}_{2}\) concentrations, along with insufficient ventilation, have been linked to reduced cognitive function and lower academic scores, particularly in subjects like mathematics [19; 20]. For instance, in classrooms where CO\({}_{2}\) levels reached 2714 ppm--well above the recommended 1500 ppm--students reported discomfort, which negatively impacted their performance, with effectiveness falling between 48% and 62% [20].  
In addition to these findings, there is a moderate correlation between CO\({}_{2}\) concentration and student well-being during the learning process. Although the relationship is not definitive, elevated CO\({}_{2}\) levels and poor ventilation have been shown to affect students' ability to concentrate and perform academically, particularly in problem-solving tasks like mathematics [19; 21].  
Furthermore, studies indicate that higher levels of PM\({}_{2.5}\) and CO\({}_{2}\) are associated with slower response times and lower accuracy in cognitive tasks such as the Stroop test and arithmetic [22]. Reducing CO\({}_{2}\) concentrations from 2100 ppm to 900 ppm, for example, has been shown to improve test performance by 12% in terms of speed and 2% in terms of accuracy, while also increasing daily attendance by 2.5% [23]. These findings underscore the clear benefits of improving ventilation in classrooms to enhance both cognitive performance and attendance.
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 2 Materials and Methods  
The overall framework of the model is shown in Figure 1, which is divided into 2 processes, encoding and decoding, where the input image is extracted with different levels of features through the underlying network in the encoder. Then it arrives at the ConvLSTM module for temporal information extraction, and subsequently enters the decoder section for upsampling and feature fusion through the jump attention module to finally obtain the prediction results. The encoder in the network uses VGG16 [22] as the base network structure, using a 3 \(\times\) 3 convolution, a batch normalization and a relu activation for each downsampling, and, finally, a maximum pooling operation to reduce the feature map size to 1/4 of the original. In this paper, the fully connected layer of VGG16 is removed, which makes the JAUNet encoder network smaller and easier to train than many other recent architectures [23; 24]. After going through four times of downsampling, the number of feature map channels is 1024 and the length and width of the feature map is 1/16 of the input image. In addition, in the upsampling process, the feature map size is scaled up to twice at the time, using transposed convolution, followed by connecting the lower-level features that have been processed with jump attention. After convolutional normalization, the next upsampling is performed, and the image is restored to its original size after four upsamplings, and the number of channels is the predicted number of classes N.  
Figure 1: Network structure of the method in this paper.
## 4 Security Analysis and Simulation Verification
### Applicability of Analytical Methods  
This study employs a formal verification approach combining BAN logic and probabilistic analysis, chosen based on the following technical considerations:  
* **Relevance of BAN Logic**: Addressing the fundamental requirement of mutual authentication, BAN logic rigorously formalizes trust transfer mechanisms between protocol entities through axiomatic rules (e.g., message freshness and jurisdiction rules). Its \"belief-action\" framework effectively models dependency relationships among critical authentication parameters. The logic systematically characterizes how entities establish trust in session keys and ephemeral values through message exchanges, thereby ensuring logical consistency throughout the authentication process. For example, it formalizes the derivation of trust through sequential verification steps during protocol execution.
* **Complementarity of Probabilistic Analysis**: Through formal definitions of adversary success events (Event1-Event7) and computation of their probabilistic bounds, this approach transforms protocol security into mathematically quantifiable propositions. The methodology establishes explicit connections between session-key compromise probabilities and the computational intractability of the elliptic-curve discrete logarithm problem (ECDLP), aligning with modern cryptography's provable security paradigm. This dual perspective enables rigorous security evaluation under practical attack conditions, particularly when temporary session parameters become compromised, while maintaining alignment with computational complexity assumptions.