esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
### Enzymatic Biosensors
#### 4.1.3 Other Analytes Detected Enzymatically  
Uric acid (UA) is the end-product of purine metabolism in human body. The basal concentrations of uric acid in physiological samples vary from 10\({}^{-7}\) to 10\({}^{-3}\) M depending on the matrix [134].  
[PERSON] et al., in the same article where they reported glucose sensing [99], also reported uric acid sensing, using the enzyme uricase immobilized on the gate electrode. Through glutaraldehyde cross-linking, a limit of detection of 30 nM was measured, which was lowered to 10 nM using an uricasegraphene oxide mixture. The OECT showed good linearity in a very wide range of UA concentrations, between 100 nM and 500 \(\upmu\)M. The authors reported no interference caused by dopamine and ascorbic acid.  
Tyrosine (4-hydroxyphenylalanine, Tyr) is a natural phenolic amino acid and plays a key role in various biological processes as a precursor in biosynthetic pathways of neurotransmitters, hormones, alkaloids, natural phenols and pigments [135; 136].  
A textile-based wearable biosensor for direct detection of tyrosine, without the use of any electron mediator, was described by [PERSON] et al. [122]. Laccase oxidase was non-covalently immobilized onto PEDOT:PSS-coated cotton yarn and the authors claimed direct electron transfer from the enzyme to PEDOT; the analytical performances were relatively low, however, with a detection limit around 10\({}^{-6}\) M tyrosine. Another more sensitive tyrosine OECT sensor was fabricated by [PERSON] et al. [137]. The Au gate electrode was modified by a nanocomposite consisting of gold nanoparticles (AuNPs), poly(diallydimethylammonium chloride) (PDDA) and multi-walled carbon nanotubes. This OECT displayed a linear response to Tyr over a wide concentration range of 0.3 uM to 10 uM, with a detection limit of 10 nM (in PBS).
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 5 Model Results
The observation that validation performance appears better than training performance can be attributed to the factors of regularization techniques, batching the normalization and adaptive learning process, the early stopping mechanism, drop in training accuracy due to regularization, and mini-batch training variability. The proposed spam detection system using LSTM-GRU offers impressive outcomes, but it operates against certain limitations. Systematic constraints in deep learning models  
Figure 13: AUC. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Accuracy** & **Reference** \\ \hline Naive Bayes & 85\% & [PERSON] et al., 2018 [20] \\ k-Nearest Neighbors (k-NN) & 83\% & [PERSON] et al., 2021 [18] \\ Support Vector Machine (SVM) & 88–90\% & [PERSON] et al., 2020 [8] \\ Logistic Regression & 89\% & [PERSON] et al., 2023 [25] \\ Random Forest & 97\% & [PERSON] et al., 2023 [26] \\ GRU & 89\% & [PERSON] et al., 2023 [3] \\ LSTM & 89\% & [PERSON] et al., 2022 [22] \\ LSTM-GRU (Proposed) & 90\% & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative performance of various models used in spam email detection. require both abundant training data and comprehensive computational resources that many organizations both big and small together with time-critical systems cannot manage. The training process for the model demands additional time compared to the rapid decision tree classifiers and logistic regression approaches. The performance of this model decreases when detecting new spam patterns that have distinctive structural characteristics due to its dropout and regularization implementations. The inability to determine model interpretations limits the usefulness of deep learning systems because systems requiring transparent operations or regulatory compliance may not find them suitable. The present model opens new research avenues for AI system compression methods and interpretable AI systems and adaptive learning algorithm techniques.
## 1 Introduction
[10] developed the equation for the reverse torque, which was deduced from the pressure of water and soil, the weight of the shield and segments, the friction force between the support boots and the segments, and the friction force between the shield tail seal brush and the segments based on the working principle of the EPB shield. [PERSON] et al. [11] analyzed the interaction between mixed soil-rock TBMs and RSI condition and established a total load prediction model that focuses on the influence of variable rock and soil properties. [PERSON] et al. [12] investigated the main factors influencing the excavation process, developed an approximate prediction model for the power of a hard rock TBM, and then used engineering data to identify the pending coefficients of other factors affecting the power of the equipment using regression methods. It is necessary to propose a power calculation approach for TBM in mixed-face conditions. In the past two decades, many scholars have successively proposed performance prediction indexes for TBMs under different geological conditions. The penetration rate per revolution (PRev) and the penetration rate (PR) were presented by [PERSON] et al. [13] and [PERSON] et al. [14] to directly forecast hard rock TBM tunneling performance. [PERSON] et al. [15] discovered a strong link between geological characteristics (UCS, joint spacing, and RDQ) and hard rock TBM performance parameters, particularly the field penetration index (FPI). Based on the model of [PERSON] et al. [15], [PERSON] et al. [16] investigated the link between the FPI and the volumetric joint count (Jv) and the intact rock uniaxial compressive strength (UCS). Based on the Norwegian University of Science and Technology in Trondheim (NTNU) TBM performance prediction model, [PERSON] et al. [17] introduced PRev, a performance index for TBM in composite grounds. Subsequently, [PERSON] et al. [18] defined and categorized mixed-face ground and analyzed that the PR decreases when the proportion of the hard rock on the excavation surface increases. By incorporating the total thrust and torque into the penetration rate calculation, [PERSON] et al. [19] and [PERSON] et al. [2] proposed new TBM performance prediction indexes in the mixed-face ground, the mixed-face penetration index (MFPI), and mixed torque penetration index (MTPI). [PERSON] et al. [20] proposed the relative variation coefficient (CRV),an evaluation index of thrust suitable for sudden loads. The results show that the optimized nonuniform propulsion system has a small CRV value and a good stratum adaptability. In this paper, the calculation model of TBM thrust, torque, and power in the rock-soil interface (RSI) of mixed ground is established. A new model is proposed, which can not only alter each tool's position on the TBM cutterhead but also alter the percentage of soft and hard strata on the excavation face according to the actual project. Then, a new index for evaluating TBM excavation performance in mixed-face ground is developed, and the reliability of the model and the index are validated by specific project.
## 4 Discussion  
We have shown that, even with a very coarse measuring grid, it is possible to detect flaws. By using the interpolation on the 3D data set of the C-scan, it is possible to refine a coarse measuring grid to a fine measuring grid. Thus, more terms can be considered in the numerical evaluation of the Rayleigh-Sommerfeld diffraction integral. Because the local affiliation of the terms remains considered by the derivation of the Green's function, a result image is generated which has a higher detectability of flaws than if the same measurement window would work without interpolated measurement points. We have shown that up to 98% of the experiment time can be saved in this way. With the different interpolation methods, it has been shown that, in the example presented here, the \"spline\" method can be used for the lowest error in the reproduction. The \"nearest\" interpolation method with its reproduction of the image is only about 4% worse than the \"spline\" method and takes less time to evaluate. If you think of the arithmetic operations behind it, we suggest the interpolation method \"nearest\". This method only describes the copying of the data to surrounding points. If one wanted to automate these methods, then \"nearest\" is certainly the most favorable method. In relation to \"spline\" interpolation, we have shown the dependence of the measuring grid position on the position of the flaw, thereby determining the coarsest measuring grids for which a clear identification of the flaws is still possible. We have shown that, for flaws with sub wavelength sizes, a measuring grid point distance of \(>\)\(\frac{\lambda}{3}\) can be used. On the basis of this, Equations (7) and (8) and Figure 17 give the possibility of adjusting the measuring grid to the desired flaw size prior to the actual measurement. We are convinced that, on further developments of the proposed method in future investigations, it will be possible for residual stresses/locational effects of the identified flaw areas to be quantified into strength, lifetime predictions. The proposed method is not only time efficient but reduces resources and energy expenditure, as well.  
Conceptualization, A.S.S. and J.T.; methodology, A.S.S and T.M.; software, A.S.S.; validation, A.S.S.; normalisation, A.S.S.; data curation, A.S.S.; writing--original draft preparation, A.S.S.; funding acquisition J.T. and J.H.; writing--review and editing, A.S.S., J.T., T.M. and J.H.; visualization, A.S.S.; supervision, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the AiF Projekt GmbH. With the project number ZF4176504 WM7. The publication of this article was funded by the Open Access fund of Leibniz Universitat Hannover.  
This research was supported by [PERSON].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NDT & Non destructive testing \\ ACU & Air-coupled Ultrasound \\ MDF & Medium-density fiberboard \\ CFRP & Carbon fiber reinforced polymer \\ GFRP & Glass fiber reinforced polymer \\ NI & National Instruments \\ \end{tabular}
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 6 Results and Discussion
### Analysis of the Results of the Mobile Device Edge Node Placement Algorithm
Enforcing a strict requirement that the response time always remains within a predefined maximum value may lead to overly conservative node selection, potentially hindering the optimal allocation of tasks. For Assumption 4, the cost of deploying an edge node encompasses not only the initial hardware purchase but also long-term expenses, such as operation and maintenance, energy consumption, and bandwidth usage. Strictly limiting the deployment cost of edge nodes may result in excessive resource optimization, potentially compromising system performance or scalability. These assumptions may make the model over-idealized and ignore the complexities of practical applications. Meanwhile, in underground mines or remote areas with limited network connectivity and device availability, the deployment methodology must account for factors such as energy consumption, communication latency, and redundancy design to ensure system stability and efficiency. The types of equipment and tasks in different mining environments vary significantly, necessitating deployment methods that dynamically adjust node placement and load distribution based on the processing capacity of the equipment, the computational intensity of the tasks, and the data transmission requirements. Therefore, future research should focus on relaxing these assumptions, customizing them according to the environmental characteristics, equipment requirements, and economic considerations of different mining operations. This would enable the development of a more realistic edge node deployment model, thereby enhancing the system's flexibility and adaptability.
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Proposed Model (VMD-LSTM-RF)  
Combining the above-mentioned theories, this section proposes a hybrid model based on multi-scale prediction, and its process is shown in Figure 4. The implementation of the hybrid model is mainly divided into the following steps:  
Step 1: Decomposition. Decomposing the original displacement sequence of the dam into several components such as low frequency, high frequency and residual by VMD method.  
Step 2: Components Prediction. Using LSTM to predict high-frequency components and residuals (ER). The low-frequency components are predicted by RF. The optimal parameters of the model are determined by grid search and five-fold cross-validation.  
Step 3: Refactor. Adding and reconstructing the components obtained by the above prediction model to obtain the final dam deformation prediction value.  
Step 4: Evaluation. In order to verify the accuracy of the model prediction, the average absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) are used for evaluation and compared with the basic model.
## 4 Results and Findings
### Analysis of Load-Balancing Algorithms  
This scenario is created to analyze PSO, RR, ESCE, and throttled algorithms' performance parameters based on the CDC, optimize response time, and reconfigure dynamically with load service broker policies, and the goal of this scenario is to figure out overall response time, DCPT, and cost parameters (see Figures 11-14). Furthermore, the simulation environment consists of six user bases and three data centers, having five virtual machines in Data Center 1 and Data Center 2 and fifty virtual machines in Data Center 3, and each simulation is performed for 60 minutes with the average peak users being 500, 1500, 1000, 35,000, and 5000 and average off-peak users being 50, 100, 150, 350, and 500 in user bases.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Ionospheric Pierce Point (IPP)  
As this study focuses on ionospheric effects, the results are analyzed at the ionospheric boundary, where the IPP coordinates are calculated. Prior to that, an intermediate quantity, which is the intersection angle of receiver and the IPP to the center of Earth, is calculated using Equation (13):  
\[p=\frac{\pi}{2}-ele-\sin^{-1}\bigg{(}\frac{Rcos(ele)}{R+H}\bigg{)} \tag{13}\]  
where \(ele\) is the elevation angle of the satellite from the receiver. The coordinates of the IPP can then be calculated in latitude and longitude using Equations (14) and (15):  
\[\phi_{IPP}=\sin^{-1}(\sin(\phi)\cos(p)+\cos(\phi)\sin(p)\cos(azi)) \tag{14}\]  
\[\lambda_{IPP}=\lambda+\sin^{-1}(\sin(p)\sin(azi)/\cos(\phi)) \tag{15}\]  
where \(\phi\) and \(\lambda\) are respectively the latitude and longitude of the receiver position; \(azi\) is the azimuth angle of the satellite from the receiver.
## 5 Analysis of Response-Influencing Factors  
### Effect of Seismic Wave Amplitude on Dam Stability  
The kinematic parameters of monitoring point C4 were observed at various amplitudes. Figure 10 illustrates that the displacement value at this monitoring point increases with amplitude, demonstrating a nearly linear relationship. Figure 11 reveals that the peak ground acceleration ratio (PGAR) does not increase linearly with elevation; instead, it exhibits a pattern of increasing, followed by decreasing, and then increasing again. This nonlinear behavior is attributed to the accumulation of shear strain in the tailings sand under seismic action, which reduces the stiffness and increases damping, thereby enhancing the filtering effects. Additionally, it was observed that the PGAR decreases as the seismic wave amplitude increases. This is because the filtering effect of the tailings sand becomes more pronounced at higher seismic wave amplitudes, leading to a reduction in the PGAR.  
Figure 11: PGAR with elevation for different seismic wave amplitudes.  
Figure 10: Time course of C4 displacement for different seismic wave amplitudes.
## 6 Repair of Resorbed Cementum by Cementoblasts  
When no more hyaline tissue presents and/or the force level diminishes, the resorption process stops and the repair of cementum starts [33; 34]. Initially, the cementoclasts/odontoclasts detached from the resorbed lacunae [35]; they die due to apoptosis [36]. After detachment of cementoclasts/odontoclasts, early cementum repair starts with fibroblast-like cells [33; 34]. These fibroblast-like cementoblasts secrete noncollagenous matrix proteins, such as osteopontin and bone sialoprotein, filling the spaces in the residual collagen fibril structure [37]. Subsequently, the cementoblasts secrete collagen fibrils, including a thin cementoid repair matrix [37]. Mineralization ensues with hydroxyapatite crystals development and growth between the collagen fibrils [38], forming reparative cementum of the cellular intrinsic fiber type [39; 40].
## 5 Blockchain-Based Supply Chain Security Solutions and Integration Models
### Data Privacy with Zero-Knowledge Proof (ZKP) and Confidential Transactions
#### 5.3.1 Comparative Evaluation of Cross-Chain Interoperability  
In blockchain-based supply chains, cross-chain protocols do not merely serve as communication bridges between networks: they fundamentally shape how data security, transparency, and operational integrity are maintained across enterprise boundaries. To this end, Table 9 offers a multi-dimensional analysis that connects technical properties with real-world supply chain requirements, particularly through the lens of the STI (security, traceability, and integrity) framework.  
Figure 7: Classification of security risks in cross-chain transactions.  
Chainlink-based bridges, on the other hand, offer flexibility and existing adoption advantages but suffer from centralized oracle risks and limited throughput, reducing their alignment with data integrity (I) and real-time traceability. As a result, they are rated Moderate, better suited to non-critical or loosely coupled supply chain components where interoperability is secondary to availability [102]. Polkadot XCMP and Cosmos IBC are rated as highly suitable due to their robust decentralized architectures, low latency, and support for parallel validation. These properties make them ideal for real-time traceability (T), data consistency (I), and low-risk authentication (S), all of which are fundamental in large-scale logistics, perishable goods tracking, or international trade compliance [103,104]. ZKP-based custom bridges, while slightly limited in throughput and latency, offer exceptionally strong privacy guarantees and cryptographic security. These make them ideal in sensitive SCM scenarios involving proprietary data or regulatory requirements for confidential auditing, such as pharmaceutical or defense supply chains. Their Moderate-High rating reflects this domain-specific strength, despite lower general scalability [105,106]. Importantly, the SCM suitability column in Table 9 is not a replication of STI alignment, but a practical synthesis, incorporating protocol maturity, real-world integration feasibility, and responsiveness to SCM-specific demands. This separation allows researchers and practitioners to distinguish between theoretical alignment and operational fitness.
## 2 Materials and Methods
### Image Restoration  
In the generator module, effective estimation of the transmission map \(t(x)\) and atmospheric light value \(A\) is achieved through the encoder-decoder structure, and the dehazed image can be obtained based on Equation (2).
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Feature Extraction
#### 2.4.1 Spectral Features  
The reflectance features of the blue, green, red, and near-infrared bands of the PlanetScope images obtained on 8 June 2019 (t1), and 27 February 2019 (t2), were applied in this study. Therefore, a total of 8 spectral features were applied in the pixel-based and object-based classification approaches: BLUEt1, GREENt1, REDt1, NIRt1, BLUEt2, GREENt2, REDT2, and NIRt2.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 1 Introduction
* A method for data clustering based on the MFO is presented;
* The quality of the solutions provided by the suggested technique is compared to three well-known algorithms to determine which is superior;
* A total of five statistical tests have been conducted using different grid sizes to evaluate the proposed approach's statistical quality;
* The use of k-means density and the MRCQ approach for data compression has been employed to improve the CH selection process;* Experimental and statistical graphs demonstrate the effectiveness of the suggested technique. The remainder of the paper is arranged as follows. In Section 2, the background information on FANETS and Moth flame optimization algorithms can be seen. In Section 3, a description and working details of our proposed EECP-MFO are present. While our result and discussion is presented in Sections 4 and 5, we end the paper with a conclusion in Section 6.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 3 Integrated System and Algorithm Design
### Design Goals  
Currently, the congestion control methods used in NDN face significant challenges when applied to LEO satellite networks. Issues such as imprecise traffic control, low network throughput, high flow completion times, delayed congestion responses, and frequent packet loss collectively prevent existing approaches from achieving optimal performance in these environments. Additionally, current congestion control methods in NDN do not support the prioritized forwarding of critical data, which results in an inability to meet the diverse Quality of Service (QoS) requirements.  
Figure 2: Packet loss statistics under PCON mechanism.  
To address these issues, we propose a Congestion Control method with Lossless Forwarding (CCLF). In this paper, multiple Data packets sharing the same prefix in their names are considered to form a data flow. The objectives of the proposed work are as follows:  
Lossless forwarding: We aim to achieve congestion-free forwarding of Data packets, ensuring zero packet loss at the network layer of NDN.  
High throughput: We design the router to directly manage the forwarding of Data packets, thereby maintaining high throughput and full bandwidth utilization.  
QoS guarantee: We introduce a scheduling scheme for Data packets based on the priority of data flows, facilitating low-delay forwarding of time-sensitive data packets.