esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing QoS in Opportunistic Networks Through Direct Communication for Dynamic Routing Challenges
## 5 Conclusions  
Existing routing protocols in OppNets, such as HBRP and AEProplet, face significant challenges in selecting the optimal next-hop node during message forwarding. This difficulty arises due to various factors, including node mobility, energy constraints, geographic location, and the perpendicular distance from the source-to-destination trajectory. These dynamics make efficient next-node selection a complex task. However, the opportunistic nature of message forwarding helps maintain network connectivity by preventing nodes from becoming inactive or \"dead.\" In this study, we propose a method that leverages line-of-sight (LoS) communication when traditional next-hop selection becomes inefficient, as observed in existing schemes. By introducing a threshold-based LoS mechanism, the proposed approach enhances the message delivery ratio, reduces communication overhead,  
Figure 8: Dead nodes vs. message size.  
and conserves node energy. Collectively, these improvements contribute to overall network performance and reliability. We selected HBRP and AEProphet for evaluation due to their context- and history-aware characteristics, which closely align with the operational context of the proposed scheme. In future work, we intend to extend the evaluation by incorporating additional protocols such as epidemic routing (ER) and Spray-and-Wait. Future work will address urban and obstacle-rich environments.  
Conceptualization, A.M. and M.N.A.; methodology, A.M., M.N.A., A.I., and B.-S.K.; validation, M.N.A., A.M., and B.-S.K.; formal analysis, A.I. and A.M.; investigation, M.N.A., A.M., and B.-S.K.; resources, B.-S.K.; writing--original draft preparation, A.M. and M.N.A.; writing--review editing, M.N.A. and B.-S.K.; supervision, B.-S.K.; funding acquisition, B.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study includes all the data used in this work.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2022-NR069069).  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this study, we examined the capability of the PRI and SIF to track plant photosynthetic function in order to estimate diurnal variations of GPP in a cornfield over multiple growing seasons. Our results showed that the PRI and the SIF successfully captured the diurnal and seasonal dynamics in plant physiological status, in response to environmental and phenological conditions. Similar diurnal and seasonal patterns were observed for PRI and tower-based LUE as well as SIF and GPP. Although the PRI delivered the best performance (\(\text{r}^{2}=0.54\); \(\text{RMSE}=0.000322\) mg CO\({}_{2}\)/umol PAR) when using a single optical variable for modeling canopy LUE, it nevertheless, was inconsistent when applied among multiple years. The most significant result was obtained by integrating both the PRI and the SIF, especially the SIF (red), in an empirical model to monitor diurnal variations in GPP for four growing seasons (\(\text{r}^{2}=0.80\); \(\text{RMSE}=0.1894\) mg CO\({}_{2}\)/m\({}^{2}\)/s), which showed a minimum of 26% performance improvement than using either PRI or SIF alone. The robustness and consistency of which was demonstrated by cross-validation approaches (\(\text{r}^{2}_{\text{cv}}=0.79\); \(\text{RMSE}_{\text{cv}}=0.188\) mg CO\({}_{2}\)/m\({}^{2}\)/s). These results demonstrate that the PRI and SIF, utilized together, provide a powerful tool for carbon monitoring. This concept must be pursued over various vegetation types, given the success we report here for a cornfield, but we recommend more emphasis on SIF (red) retrievals in the future.
## 1 Introduction
### Significance  
In this research, two NDT techniques (UGWL and HCP) have been used and compared for the detection of corrosion from the onset and through mild corrosion build-up. The team's previous work on the UGWL method showcased that it can detect changes in the condition of an RC element sooner than any other method currently available. Early detection of corrosion activity with an established statistical certainty can reduce significant issues with bridge decks and reduce maintenance and repair costs for the departments of transportation (DOTs). This paper builds upon the prior work on the development of the UGWL method. The specific contributions presented here are as follows: (1) providing a comparison to the HCP method in a variety of ways (e.g., time of detection, limitations in application, etc.), and (2) quantifying the level of corrosion in terms of changes in the UGWL amplitudes with respect to measured chloride levels.  
It is estimated that more than half of the bridges in the United States are made up of reinforced concrete [4]. Three major causes that lead to the deterioration of RC bridge decks are chloride-induced corrosion, freeze-thaw cycles, and poor construction practices [5]. Corrosion is a major problem with RC bridge decks, especially from chloride products and carbon dioxide [6]. Approximately 15 percent of deterioration in RC bridge decks is caused by corrosion; more than any other degradation mechanism [7]. Corrosion in RC structures leads to significant repair costs, sometimes exceeding the initial cost of construction or even resulting in structural collapse [8]. The annual repair cost caused by corrosion in highway bridges is around USD 8.3 billion, of which approximately USD 2 billion is for bridge decks [9]. Thus, early corrosion detection in RC bridge desks should provide long term cost savings and improve the infrastructure's function, reliability, and lifespan.  
The motivation for comparing the UGWL and HCP methods is also two-fold: First, because UGWL is a newly developed non-destructive evaluation technique for the detection of corrosion in RC bridge decks, it is important to benchmark it with a monitoring method traditionally used by DOTs, such as HCP, to highlight its advantages over the existing approach. Second, detecting corrosion activity at earlier stages than the previously established methods (i.e., HCP) can help better prevent progression of the corrosion into irreversible damage, such as cracking, spalling, and delamination in the bridge deck. Detecting these problems earlier can significantly reduce the costs associated with repairs.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
###### Abstract  
Advancements in remote sensing technology have remarkably enhanced the 3D Earth surface reconstruction, which is pivotal for applications such as disaster relief, emergency management, and urban planning, etc. Although satellite imagery offers a cost-effective and extensive coverage solution for 3D reconstruction, the quality of the resulted digital surface model (DSM) heavily relies on the choice of stereo image pairs. However, current approaches of stereo Earth observation still employ a post-acquisition manner without sophisticated planning in advance, causing inefficiencies and low reconstruction quality. This paper introduces a novel quality-driven planning method for satellite stereo imaging, aiming at optimizing the search of stereo pairs to achieve high-quality 3D reconstruction. Moreover, a regression model is customized and incorporated to estimate the reconstructed point cloud geopositioning quality, based on the enhanced features of possible Earth-imaging opportunities. Experiments conducted on both real satellite images and simulated constellation data demonstrate the efficacy of the proposed method in estimating reconstruction quality beforehand and searching for optimal stereo pair combinations as the final satellite imaging schedule, which can improve the stereo quality significantly.  
task planning; stereo surface reconstruction; reconstruction quality; metaheuristic algorithm; regression-based estimation
## 5 Conclusions  
(1) Using the sliding window model and the bin method to process the data, a polynomial fitting modeling method for wind turbine operation state based on SCADA data relation was proposed.  
(2) Based on the Euclidean distance of the data relation curve, a dimensionless health index for wind turbine operation and its calculation were proposed. The proposed health index showed good stability and sensitivity.  
(3) The width of the data window in the sliding window model must cover all working conditions of the wind turbine to ensure that the health index depicts the running state of the wind turbine.  
(4) The data window width, window increment, and data fitting modeling affect the health indicators. The selection of sliding window model parameters and data relationship modeling methods should comprehensively consider the accuracy and real-time performance of health indicators. Considering the SCADA data of the two wind turbine sets of the same model on a given wind farm as an example, the analysis showed that the data acquisition cycle had no effect on the health indicators. Once the basic characteristics of data relations were known, direct data fitting modeling was more efficient than bin preprocessing modeling.  
The research presented in this paper was a collaborative effort among all authors. Conceptualization, F.Z. and Z.W.; methodology, F.Z. and Z.W.; visualization, J.J. and H.W.; writing, original draft, F.Z.; writing, review and editing, D.L. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (Grant Number 2016 YFF0203400), the Scientific Research Fund of Hunan Provincial Education Department (Grant Number 17A069), and the National Natural Science Foundation of People's Republic of China (Grant Number 51475160).  
The authors declare no conflict of interest.
## 6 Conclusions  
The conclusions taken from the various stages of this work are:  
* The data measurement uncertainty given by the sensors manufacturers was higher than the obtained by the calibrations, so all MEMS must be calibrated before installed in the setup.
* The iteration number for simulation 1 and simulation 2 was negligible, because the maximum output errors were obtained when the input values and respective errors took the limit values of their range.
* In simulation 1, it was observed that both the actual volumetric flowrate and standard volumetric flowrate largely exceeded the accuracy requirement (\(\pm 1\%\) full scale). So, in coherence with the data measurement uncertainty given by the manufacturer this simulation was very conservative.
* In simulation 2 the actual volumetric flowrates maximum error was lower than the accuracy requirement established by the technical documentation delivered by the test bench manufacturer (\(\pm 1\%\) full scale).
* The maximum error of the standard volumetric flowrate, obtained by simulation 2, slightly exceeded the accuracy requirement established by the test bench technical manual.
* The automated flowmeter calibration performed according to the Guide to the Expression of Uncertainty in Measurement shows the lowest uncertainty values.
* The measurement uncertainty given by the calibration was lower than the values obtained by both simulations.
* According to the automated flowmeter calibration, the uncertainty obtained (\(\pm 0.45\%\) fs) was accepted by the requirement and therefore recommended for this application.  
By analyzing all the results, this automated flowmeter can be used in this test bench, but the authors only recommended its use for actual volumetric flowrate measurements.  
All the authors provide critical feedback and helped shape the research and analysis. [PERSON] performed the experiments. [PERSON] and [PERSON] contributed to the experiments by scientific supervision. [PERSON] and [PERSON] wrote the paper first version. [PERSON] wrote, reviewed and edited. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Simulation 1** & **Simulation 2** & **Calibration** \\ \hline Actual Volumetric Flowrate & \(\pm 3.56\%\) fs & \(\pm 0.64\%\) fs & \(\pm 0.45\%\) fs \\ Standard Volumetric Flowrate & \(\pm 4.27\%\) fs & \(\pm 1.11\%\) fs & \(\pm 0.45\%\) fs \\ \hline \hline \end{tabular}
\end{table}
Table 13: Uncertainty of the actual and standard volumetric flowrate.
## 2 Scenario and Ray-Tracing Simulation Setup  
In this section, we introduce a realistic industrial scenario and describe how we conducted a channel simulation at multiple electronic wave bands and VLC bands. Firstly, detailed information on the industrial scenarios and internal reflectors, including their position, size, and materials, is presented. Secondly, the channel modeling methodology for the electronic wave band is introduced, and the corresponding setups are provided. Thirdly, the channel modeling methodology and the corresponding setups for the VLC band are introduced.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Informal Security Analysis
#### 5.5.1 Sybil Attack  
In a Sybil attack, an adversary spawns multiple identities to flood the trust system. The purpose is to artificially inflate its own trust score. IOTA-based trust metrics prevent the nodes from gaining high trust maliciously. Each node must issue verifiable transactions with confirmation. Repeated transaction failures and reattachment attempts by fake identities lower RF scores.
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 3 System Model and Mathematical Background
### Objective Function and Constraints  
Effective network slicing maximizes the physical network resource utilization while minimizing the slice provisioning costs. The provided security level, CPU capacity, and bandwidth are used to measure the slice provisioning cost. Accordingly, the cost of provisioning slices is minimized using the integer linear programming model with the constraints shown below.  
Minimize,  
\[\sum_{k\in N^{NR}}\sum_{i\in N^{PI}}x_{i}^{k}(1+SL_{i}^{PI})(CPU_{k}^{NR})+\sum _{m\in E^{NR}}\sum_{n\in E^{PI}}a_{n}^{m}BW_{m}^{NR} \tag{9}\]  
subject to,  
\[\sum_{N_{i}^{NR}}x_{i}^{k}=1;\ \forall N_{i}^{NR}\in N^{NR} \tag{10}\]  
\[\sum_{N_{k}^{PI}}x_{i}^{k}\leq 1;\ \forall N_{k}^{PI}\in N^{PI} \tag{11}\]  
\[x_{i}^{k}CPU_{k}^{NR}\leq CPU_{i}^{PI};\forall N_{i}^{PI}\in N^{PI};\forall N_{ k}^{NR}\in N^{NR} \tag{12}\]  
\[x_{i}^{k}SR_{k}^{NR}\leq SL_{i}^{PI};\forall N_{i}^{PI}\in N^{PI};\forall N_{k}^ {NR}\in N^{NR} \tag{13}\]  
\[\sum_{N_{ij}^{PI}}(a_{ij}^{pl}-a_{ji}^{pl})=x_{i}^{k}-x_{i}^{l};\forall N_{i}^ {PI}\in N^{PI};\forall E_{kl}^{NR}\in E^{NR} \tag{14}\]  
\[\sum_{E_{kl}^{NR}}a_{ij}^{pl}BW(E_{kl}^{NR})\leq BW(E_{ij}^{PI});\forall E_{ij }^{PI}\in E^{PI} \tag{15}\]  
\(x_{i}^{k}\in(0,1)\) is a binary variable, where \(x_{i}^{k}=1\) indicates that \(N^{NR}\) is served onto \(N^{PI}\), otherwise 0. \(a_{n}^{m}\) indicates whether the link '\(n\)' of \(E^{PI}\) hosts the request link '\(m\)' of \(E^{NR}\). If a link presents then the \(a_{n}^{m}\) is 1, otherwise 0. Network providers use extra resources to ensure node security. Constraint (10) ensures that each request node is assigned to a physical node. Constraint (11) limits the number of nodes per physical node. The constraint on CPU capacity (12) ensures that the requested CPU does not exceed the available CPU capacity. Constraint (13) ensures each node's security. Constraint (14) ensures that each slice link is assigned to a physical path, and all nodes along the path, with the exception of the beginning and ending nodes, see zero throughput. The constraint (15) ensures that the requested bandwidth does not exceed the available bandwidth.
## 4 Future Research Challenges
### M-MIMO  
In mmWave frequency bands, the blockage and path loss phenomena are considerably high. Nonetheless, it can be (partially) surmounted by keeping the structure of antenna array on the basic physical size as it is used in lower frequencies; this can be accomplished by M-MIMO [282]. However, M-MIMO technologies are constructed, implemented, and utilized differently [283]. The main requirements regarding stability, flexibility, and coverage must be investigated for different frequency bands, antenna geometries, and propagation environments [284]. Besides, various current precoding schemes have high-computational complexity and fail to maximize spatial information [224]. Conventional digital beamforming involves the complexity of large antenna arrays in addition to the increased cost of the system, whereas analog beamforming can handle only a single data signal at a time. Therefore, various low-cost and less complex hybrid precoding methods are required to model efficient transmitters and testbeds to mitigate jamming for MIMO-based mmWave systems [285; 286; 287; 288; 289]. This goal can be achieved by designing architecture with a combination of analog and digital processing that can be utilized to enable beamforming and spatial multiplexing with minimum complexity in achieving optimal performance [290; 291]. An ML algorithm can be utilized to predict various channel characteristics and create a beamforming M-MIMO dataset framework [292; 293]. The AI-and M-MIMO-based systems can deliver good QoS performance for high altitude users [294]. Nonetheless, the explainable AI-controlled based architecture would be useful for several current limitations while performing resource allocation, energy optimization, and minimizing interferences [295].
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 2 Materials and Methods
### Model Optimization
#### 2.4.2 Introduce Accelerated FusedMBConv Structure  
For lightweight networks, most researchers care about model parameters and accuracy. However, the small number of parameters does not mean faster inference speed. Efficient-Netv2 [28] focuses on the training speed and inference speed of the model. Compared with deep convolution, ordinary convolution has more parameters and FLOPs, but it can usually take advantage of accelerators and have faster speeds in the shallow layers of the network. To improve the training speed and computational efficiency, EfficientNet introduces the structure of Fused-MBConv. Fused-MBConv replaces the depthwise conv3\(\times\)3 and expansion conv1\(\times\)1 in the original MBConv structure with an ordinary conv3\(\times\)3, as shown in Figure 9. Heavy use of Fused-MBConv in the first few layers of the model speeds up training by a factor of 2.2. Also, Fused-MBConv enhances the expressiveness of the model because it can increase the number of channels without increasing the parameters.  
Figure 8: The CS-Ghost Module.  
Figure 9: Structure of MBConv and Fused-MBConv.  
2.4.3. Replace with a More Efficient Attention Module  
The attention mechanism is a method to let the deep learning model only focus on the important parts when processing massive amounts of information. It improves the performance and efficiency of the model and also allows the model to better understand the relationship between features and contextual information. There are many forms of attention mechanisms, which are capable of participating in different dimensions and tasks, such as channel, space, self-attention, etc., as well as image recognition, machine translation, picture description, etc.  
However, after experiments, it was found that SE [29] in the original model did not pay much attention to the characteristics of wheat yellow rust disease, so we explored an attention mechanism that is more interested in the features of yellow spot disease. ECA [30] (as shown in Figure 10) is an efficient channel attention module, which is characterized by only using one-dimensional convolution to achieve local cross-channel interaction, avoiding dimensionality reduction and global pooling operations, and reducing parameters and calculations. It can adaptively select the size of the convolution kernel, determine the coverage of the interaction, and adapt to feature maps of different scales. It can improve the performance of deep convolutional neural networks while reducing the complexity of the model and has achieved good results in tasks such as image classification, target detection, and instance segmentation. Compared with other channel attention modules, such as SENet, ECA is simpler and more efficient, involving only a small number of parameters and calculations.
## 2 Materials and Methods
### Chemicals  
6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox, 97%) and n-alkane standard solution C\({}_{7}\)-C\({}_{30}\) (1000 ug/mL each component in hexane) were purchased from Sigma-Aldrich Chemie (Taufikichen, Germany); 2-(3-hydroxy-6-oxo-xanthen-9-yl)benzoic acid, fluororescein (FL) and 2,2\({}^{\prime}\)-azobis-2-methyl-propanimidazole dihydrochloride (AAPH) were from Fluka Analytical (Bornen, Belgium); NaCl, KCl, KH\({}_{2}\)PO\({}_{4}\) and K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) were from Lach-Ner (Brno, Czech Republic); Na\({}_{2}\)HPO\({}_{4}\) was from Merck KGaA (Darmstadt,Germany); carbon dioxide and nitrogen gases (99.9%) were from Gaschema (Jonava, Lithuania). International Calibration Extract 4 (ICE-4), containing \(\alpha\)- and \(\beta\)-acids (10.98% of cohumulone; 31.60% of humulone+adhumulone; 13.02% coplupulone; 13.52% lupulone+adlupulone), was obtained from Labor Veritas AG (Zurich, Switzerland). Divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibers and 20 mL SPME vials were purchased from Supelco (Bellefonte, PA, USA). All solvents were of analytical and HPLC-grade.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 2 Related Works
### Blockchain-Based Identity Authentication Protocol  
Blockchain offers unique advantages and is widely applied across various fields. It enables cross-domain authentication and key management without relying on a central Trusted Authority, simplifying the authentication process. As a result, numerous blockchain-based identity authentication protocols have been proposed to ensure secure and reliable access. [PERSON] et al. [18] proposed an authentication scheme combining blockchain and SDN to tackle repeated authentication issues during frequent handovers in 5G heterogeneous cells, enabling fast and privacy-aware switching within 1 millisecond. However, this scheme causes high communication overhead between the SDN controller and blockchain center and focuses only on data privacy, overlooking identity and location privacy. To address identity privacy concerns from [17], [PERSON] et al. [19] developed RUSH, a robust universal seamless handover authentication protocol for 5G heterogeneous networks. RUSH offers features like perfect forward secrecy and strong traceability to enhance handover security and reliability. It protects user anonymity by storing a chameleon hash of the user's identity on the blockchain instead of the actual identity. [PERSON] et al. [20] introduced BASA, a blockchain-assisted authentication mechanism for cross-domain industrial IoT, which uses identity-based signatures and anonymous identity management to protect device privacy. The parties also negotiate session keys to secure future communications. [PERSON] et al. [21] proposed a lightweight blockchain-based handover authentication protocol for VANETs to improve network efficiency during handovers. However, vehicles and RSUs in this scheme still undergo time-consuming authentication processes and it does not effectively protect against RSU capture attacks. In a similar vein, [PERSON] et al. [22] developed a handover authentication protocol using blockchain smart contracts that streamlines mutual authentication and key negotiation between 5G vehicles and base stations, lowering authentication costs. Despite these improvements, the protocol lacks guarantees of