esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reliability-Based Optimum Design of Preform
### Screening of Design Variables  
Design variable screening is a process designed to identify critical variables that enhance the efficiency of optimal design while eliminating unnecessary ones to improve the accuracy of optimization outcomes. This process can also reduce the number of experimental points required for constructing an approximation model. The design variables and their corresponding ranges for screening are presented in Table 3, which includes six major design variables related to the shapes of the preforms. A full factorial design (FFD) with six factors and two levels was applied, resulting in a total of 64 experimental points, which have been condensed and are presented in Table 4. The significance of the design variables and the method for selecting suitable ones were determined through a screening process that employed an analysis of variance (ANOVA). No outliers were found among the 64 experimental points, and the resulting classifications from the screening are illustrated in Figure 12. Among the design variables, H2 exhibited the highest sensitivity regarding the defect count and forming load, while V1 demonstrated the highest sensitivity for the effective strain. We aimed to eliminate design variables with less than 5% sensitivity to the performance indicators; however, because no design variables fell below this threshold across all performance indicators, we decided to proceed with the additional design phase to develop the approximation model without removing any variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & \(\mathbf{\theta_{A}}\) **(deg.)** & \(\mathbf{L_{V1}}\) **(mm)** & \(\mathbf{L_{H1}}\) **(mm)** & \(\mathbf{L_{H2}}\) **(mm)** & \(\mathbf{L_{H3}}\) **(mm)** & \(\mathbf{L_{R}}\) **(mm)** \\ \hline
1 & 10 & 31.65 & 24.74 & 37.62 & 21.56 & 7 \\ \hline
2 & 10 & 31.65 & 24.74 & 37.62 & 21.56 & 17 \\ \hline  & & & & & \\ \hline
63 & 20 & 31.97 & 26.27 & 41.58 & 22.44 & 7 \\ \hline
64 & 20 & 31.97 & 26.27 & 41.58 & 22.44 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Range of design variables for analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **DDO** & **RBDO** \\ \hline Objective & \multicolumn{2}{c}{Minimizemeanof\((x_{i})\)} \\ \hline Constraint & \(g\leq 0.01\) & \(\mathrm{P}\big{[}P_{g}\;(x_{i})\leq 0.01\big{]}\geq 0.99\) \\ \(F\geq 2\)kton & \(\mathrm{P}[P_{F}(x_{i})\geq 2\;\text{kton}]\geq 0.99\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Problem formulation for DDO and RBDO.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variables** & **Minimum** & **Maximum** \\ \hline \(\theta_{A}\) (deg.) & 10 & 20 \\ \hline \(L_{V1}\) (mm) & 31.65 & 31.97 \\ \hline \(L_{H1}\) (mm) & 24.74 & 26.27 \\ \hline \(L_{H2}\) (mm) & 37.62 & 41.58 \\ \hline \(L_{H3}\) (mm) & 21.56 & 22.44 \\ \hline \(L_{R}\) (mm) & 7 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Range of design variables for analysis.
# Full-Parallax Multiview Generation with High-Speed Wide-Angle Dual-Axis Scanning Optics
## 6 Discussion  
We proposed the full-parallax multiview display, and our system is a kind of volumetric display because it constructs view volume in the ball lens. So, we selected four remarkable systems for comparison with our proposed system and they are listed in Table 3. Four remarkable systems use multiplexing methods, but multiplexing has a limit to express 3D content with wide viewing angle. System 1 and system 2 are based on the spatial multiplexing method. They require a large number of SLMs. Systems 3 and 4 are based on temporal multiplexing. They require a high-speed driving SLM and scanner, and there is a limit to increasing the number of views with sufficient frame rate and brightness. Systems 1-4 cover the 360-degree horizontal viewing angle, and they need a lot of horizontal views. In other words, they only generate horizontal parallax because of the limitation of their methods, and it appears as distortion according to viewing distance. By contrast, the proposed system allocates horizontal and vertical view for the full-parallax system. It offers better performance than the horizontal parallax-only multiview display because the latter has an intrinsic problem of image distortion in the vertical direction when the viewing distance of the observer changes. To examine this phenomenon, we analyzed the observation characteristics within the VZ using WDF. For the experiment, we changed two scanners to manually controllable mirrors. Moreover we captured the SVZ with 81 SVZs, nine each in horizontal and vertical directions by manually controlling the reflecting angle to analyze IVZ. Finally, we showed the content without distortion in Section 3, unlike horizontal parallax-only systems. The WDF analysis and IVZ experimental results demonstrate the applicability of the full parallax multi-view display.  
Figure 13: Experimental results. (**a**) Target images and (**b**) images captured according to viewing angles at \(-20\) degrees left, \(+20\) degrees right, \(20\) degrees top, and \(-20\) degrees bottom.
## 3 Results and Discussion
### Development of MW-SPM
#### 3.1.3 Optimization of Reaction Conditions  
Reaction conditions for the accomplishment of the reactions in the 96-microwell plate were adjusted by changing each variable in a turn while the other variables remain constant. All the readings were taken using a plate reader at 450 nm (the nearest filter to the \(\lambda\)max of the CT complexes of DUV and SEL with DDQ). As demonstrated in Figure 5A, the results of several concentrations of DDQ and times of reaction at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) showed that the ideal DDQ concentration was 0.5% (\(w/v\)). Investigations were conducted to refine the reaction time, and it was revealed that the reaction required 5 min to complete in the case of DUV; however, it was instantaneous in the case of SEL (Figure 5B). In both DUV and SEL, better reading precisions were achieved when measurements were performed after 5 min from the reaction start time. Furthermore, the readings remained stable for 30 min in the case of SEL; however, it remained stable for 15 min in the case of DUV. The reactions in all the further experiments were left to proceed for min before taking the readings. The effect of temperature on the reactions of both DUV and SEL with DDQ was studied at variant temperatures (25-50 \({}^{\circ}\)C), and the findings revealed that the reactions were independent of the temperature in the studied range. Therefore, the reactions in the subsequent experiments were performed at ambient temperature (25 \(\pm\) 2 \({}^{\circ}\)C).  
Figure 4: Tauc plots of energy (eV) against (\(\alpha\)h\(\upsilon\))\({}^{2}\) for the CTC of DUV and SEL with DDQ in methanol.  
For choosing the most appropriate solvent for the ideal reaction and color development, the reactions of DUV and SEL with DDQ were performed in different solvents of varying polarities (acetonitrile, methanol, propanol, diethyl ether, dichloroethane, and chloroform) and the results are shown in Figure 6. It was observed that the reactions in polar solvents (e.g., acetonitrile and methanol) offered greater readings compared to those acquired in solvents of low polarity (e.g., diethyl ether and dichloroethane). This outcome was believed to be due to the full electron transfer from DUV and SEL molecules (electron donors) to DDQ (electron acceptor) that occurs in polar solvents. The subsequent experiments were carried out in methanol.  
Figure 5: Effect of DDQ concentrations (**A**) and reaction time (**B**) on its reaction with DUV (\(\blacktriangle\)) and SEL (\(\bullet\)). Concentrations of DUV and SEL in their mixtures were 150 \(\mathrm{\SIUnitSymbolMicro g}\)/well (100 \(\mathrm{\SIUnitSymbolMicro L}\)). All solutions were in methanol.  
Table 1 gives an overview of the examined conditions and the optimum value that was chosen for the construction of the MW-SPM for DUV and SEL.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 1 Introduction
### Problem Statement and Motivation  
Cyber threat intelligence (CTI) sharing entails the exchange of sensitive and contextual security information among organizations that often operate in mutually distrusting environments. While centralized CTI sharing architectures are widely used, they exhibit fundamental limitations related to trust, auditability, compliance, and resilience:  
* **Trust and Autonomy:** Centralized systems require participants to trust a single managing authority. This is problematic in cross-sector scenarios (e.g., finance and healthcare), where entities prioritize operational independence and data sovereignty.
* **Auditability and Tamper Resistance:** Traditional platforms frequently lack verifiable audit trails and tamper-resistant logs. In security-sensitive domains, such capabilities are essential for traceability and forensic analysis.
* **Regulatory Compliance:** Legal frameworks such as the General Data Protection Regulation (GDPR) impose requirements including the right to be forgotten (RtbF), fine-grained access control, and revocation capabilities. Centralized solutions often struggle to provide transparency and enforcement guarantees in this context.
* **Availability and Resilience:** Single points of failure in centralized systems expose them to outages and targeted attacks. Decentralized alternatives offer improved fault tolerance and continuous availability.  
While private blockchains offer control and efficiency, they sacrifice decentralization, introduce governance and insider risks, and face challenges like limited transparency and vendor dependency. These limitations highlight the need for a decentralized architecture that supports secure, policy-compliant CTI sharing.  
In this paper, we explore the design of such an architecture guided by the following key questions:  
* How can CTI be shared securely in a decentralized trustless environment while ensuring regulatory compliance (e.g., the GDPR)?
* What cryptographic access control mechanisms are best suited for attribute-based, revocable, and selective data disclosure?
* Can decentralized platforms based on Hyperledger Fabric and the IPFS meet the latency, scalability, and interoperability demands of operational CTI workflows?  
Figure 1: TrustShare workflow for secure threat intelligence sharing.  
To address these challenges, we propose **TrustShare**--a decentralized CTI sharing framework built on permissioned blockchain (Hyperledger Fabric) and equipped with the following capabilities:  
* Fine-grained access control using Ciphertext-Policy Attribute-Based Encryption (CP-ABE).
* Encrypted off-chain data storage via the Interplanetary File System (IPFS).
* Smart contract-based policy enforcement.
* Regulatory compliance features supporting the RtbF and attribute-based revocation.  
While decentralized solutions may introduce performance overheads compared to their centralized counterparts, the resulting gains in transparency, trust, compliance, and operational resilience justify their adoption in inter-organizational CTI sharing ecosystems.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 6 Simulation Platforms for AV Traffic  
Simulation platforms are vital for the advancement of autonomous vehicle (AV) technologies, offering a safe and controlled environment for testing and development. This section presents an overview of key simulation platforms used in AV traffic research, including SUMO (Simulation of Urban Mobility), MATSim (Multi-Agent Transport Simulation), VISSIM (visual simulation system), and AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-Urban). Each platform offers unique features and capabilities that contribute to the refinement and validation of AV systems.
# Yielding Multi-Fold Training Strategy for Image Classification of Imbalanced Weeds
## 5 Experiments
### Results  
#### 5.4.1. CNU Weeds Dataset  
Figure 11 shows the learning curves of the 3 models trained by the conventional and YMufT strategies. In contrast to the smooth training curve of the conventional method, the training curve of YMufT had a sawtooth appearance, which reflected the transitions between folds. When the model began to converge, sawtooth marks appeared when it learned features from samples in one fold on a few training loops but then changed to a new fold with new samples. In this case, the model first showed a drop in performance but then began generalizing on the next training loop. Notice that later folds contained many species that already appeared on previous folds, which helped the model not decrease in performance to the same extent and made it easier to converge. After a few periods, the amplitude of each sawtooth was reduced and the model started to converge throughout many folds.  
The model converged at the 10 th validation in the conventional strategy; this was faster than in the YMufT strategy, which required 60 validations to converge. However, as illustrated by the overall performance scores in Figure 12, use of YMufT led to a higher F1 score, while the accuracy was approximately the same as that seen with conventional training. Figure 13 shows a comparison of the precision, recall, and F1 scores by species between the YMufT and conventional training strategies. In this figure, the species are arranged in ascending order, based on the ratio of the number of samples in a species to the maximum number of samples.  
On Mobilenet, although YMufT did not have a clear advantage in terms of precision, it showed improved recall on minority species over the conventional training strategy while maintaining recall on majority species. Thus, the F1 score slightly increased on minority species. In Resnet, YMufT showed an advantage in terms of precision over the conventional training strategy on minority species but remained imprecise in terms of recall. Still, the F1 score of minority species was slightly better than that of the conventional training method. In NASNet, although the overall F1 score using YMufT was marginally higher than that of the conventional strategy, recall on minority species did not improve. Only precision showed a clear advantage of YMufT on minority species, which resulted in a slight improvement in F1 score on minority species.  
Figure 11: Performance of models trained on the CNU Weeds dataset using the conventional training strategy (upper row) and the YMufT strategy (lower rows).
## 2 Methods
### Case 2: Low-Latitude Region
#### 3.2.4 Validation of Ionospheric Correction  
Figure 12 presents the interferometric phase at the profile B-B' in Figure 11a,c,d. It is observed that the integration-corrected interferometric phase is close to zero, while the other two show the obvious phase error. The statistics shows that the standard deviations have reduced nearly a factor of 28 times for integration-corrected phase (0.46 rad) versus original phase (12.83 rad) in this case. Both results demonstrate the validation of our correction.  
Figure 11: HH-polarized original and corrected interferogram. (**a**) Original interferogram, (**b**) residual-ionosphere-corrected interferogram by subtracting the fitted residual ionospheric phase from Figure 11a, (**c**) ionosphere-corrected interferogram by subtracting ionospheric phase from Figure 11a, (**d**) integration-corrected interferogram by subtracting the fitted phase in (11) from Figure 11a.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
###### Abstract  
While global navigation satellite system (GNSS) technologies have always been the go-to solution for localization problems, they may not be the best choice for some Internet-of-Things (IoT) applications due to the incurred power consumption and cost. In this paper, we present an alternative satellite-based localization method exploiting the signature of Doppler shifts and angle-of-arrival measurements as seen by a low-Earth-orbit (LEO) satellite. We first derive the joint likelihood function of the measurements, which is represented as a combination of three Gaussian distributions. Then, we show that the maximum likelihood problem reduces to a more-efficient mean squared error minimization in the Gaussian case as inferred from real measurements we collected from low-Earth-orbit satellite using a tracking ground station. Thus, we propose utilizing a stochastic optimizer to search for the global minimum of the mean squared error, which represents the location of the ground IoT device as estimated by the satellite platform. The emulated results show that the IoT device localization, in such a realistic model, can be performed with sufficient accuracy for IoT applications.  
localization; satellite; Internet of Things; AoA; Doppler; likelihood; low Earth orbit +
Footnote †: journal: Remote Sensing  
0203
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 3 Description of the UWSO-TDMA Protocol
### Network Model  
The network can be modeled as a directed graph \(G=(V,E)\), in which \(V\) is the set of nodes in the network and \(E\) the set of edges. If two nodes \(u\) and \(v\) are within transmission range, there is an edge connecting them, \(e=(u,v)\). The slot assignment problem is an extension of the graph coloring problem [21], where the goal is to color each node of the graph with the minimum number of colors, such that no adjacent nodes and two-hops neighbors have the same color. The authors avoid the use of sub-frames by considering a slot allocation based on a previously-computed template that considers the two hop distance. However, this approach does not consider the transmission delay in the assignment that makes the problem more complex, as there may be collisions even if nodes used different slots. The problem is similar to the L(2,1) labeling on graphs and the frequency assignment [22].  
Allocating nodes to slots in a frame is a classical optimization problem that may be solved by integer linear programming or some custom heuristics. However, this approach assumes a complete knowledge of the network, a centralized allocation procedure that is difficult to adapt to changes in the nodes. The latency in the frame is minimum, as the frame length is also minimum. In this work, we proposed the use of a partially-distributed slot assignment that is based on local information and is performed in a hierarchical way by each synchronizing node, avoiding the problems that come from the transmission delay in acoustic signals. We introduce the use of sub-frames for the different layers for simplifying the allocation protocol. However, these sub-frames increment the latency of TDMA as the frame becomes larger in comparison with the optimal one. However, we prefer this structure, as the slot allocation is made in a distributed way, and the transmission order is reshuffled, periodically updating the active nodes list.  
During the initialization phase, in which the nodes synchronize their clocks and follow the slot selection process, the graph representing the network topology is built. Moreover, each node is aware of its neighbors, the path to the next hop and, eventually, of the alternative paths for the case of link or node failures.
## 2 Calculation Model of Drainage Radius of Single-Layer Gas Well
### Method Validation  
In order to validate the accuracy of the above flowing material balance method, a circular and enclosed gas reservoir model with a central vertical production well is established using Eclipse (Version 2010) numerical simulation software. The physical properties of the gas reservoir model are listed in Table 1 and this model is used to simulate the entire production process of the gas reservoir with a single layer. The gas well is operated under a constant gas rate, and the numerical simulation results during the pseudo-steady stage are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline \(K\) (mD) & 1 \\ \(h\) (m) & 10 \\ \(\phi\) (\(\%\)) & 7 \\ \(S_{\rm gi}\) & 0.25 \\ \(P_{\rm i}\) (MPa) & 20 \\ \(T_{\rm i}\) (\({}^{\circ}\)C) & 88.66 \\ \(P_{\rm i}\) (MPa) & 20 \\ \(r_{\rm w}\) (m) & 0.1 \\ \(q_{\rm sc}\) (m\({}^{3}\)/d) & 30,000 \\ \(r_{\rm e}\) (m) & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of the gas reservoir model.  
Figure 2: Schematic diagram of the determination of original gas in place using modified flowing material balance method.  
From Figure 3, it can be seen that \(|\partial(P_{\text{R}}/Z_{\text{R}})/\partial G_{\text{P}}|<|\partial(P_{\text{ wf}}/Z_{\text{wf}})/\partial G_{\text{P}}|\) and the slope of \(p_{\text{wf}}/Z_{\text{wf}}\sim G_{\text{P}}\) (i.e., \(m\)) is \(-39.298\). Also, the value of \(p_{\text{i}}/Z_{\text{i}}\) can be determined as \(22.691\) by the mathematical formula of the regression trend line using the red points. The value of \(\lambda\) can be obtained using Equation (6) and is approximately \(0.78\). And then, using Equation (7), when \(P/Z=0\), the original gas in place can be calculated as \(G_{\text{i}}=(P_{\text{i}}/Z_{\text{i}})/(\lambda m)=22.691/(0.78\times 39.298)=0.7403 \times 10^{8}\) m\({}^{3}\). Using this value and Equation (8), we can obtain the value of the drainage radius \(r_{\text{e}}\) as \(494.1\) m.  
Table 2 compares the calculation results of different methods. It can be seen that the calculation error of the drainage radius using flowing material balance method is very large (\(12.72\%\)), while that of the modified flowing material balance method is only \(1.18\%\), suggesting that our proposed modified flowing material balance method is more accurate.
## 4 Experimental Results and Discussion
### Correspondence of the Obtained Experimental Results to the Available Data  
Eight cylindrical NSC specimens with a height-to-diameter ratio equal to 2 were tested under uniaxial loading [7]. Diameters of the specimens differed from 10 to 80 cm. The presented data report crack development in the vertical direction and the formation of an unbroken circular inner core that corresponds to the failure pattern described in this paper (see Figure 2: 20-40-02 and 40-80-02 [7]). Additionally, all of the specimens demonstrate elastic behavior in the longitudinal direction practically until failure, regardless of the size difference (see Figure 3 [7]).  
Thirty-three 15 \(\times\) 30 cm fibered-concrete cylindrical specimens with a minimum 28-day concrete compressive strength of 27.6 MPa were uniaxially loaded. The typical failure pattern with vertical longitudinal cracks is presented in Figure 8 [8]. The obtained stress-longitudinal strain relationships for compressed concrete specimens are linear, al  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen** & **(A+B), cm\({}^{2}\)** & **A, cm\({}^{2}\)** & **A+B** \\ \hline Sp1.1 & 176.5 & 91.6 & 1.9 \\ Sp1.2 & 176.4 & 75.1 & 2.3 \\ Sp1.3 & 176.4 & 74.9 & 2.4 \\ Sp1.4 & 177.1 & 99.4 & 1.8 \\ Average & 176.6 & 85.3 & 2.1 \\ \hline \end{tabular}
\end{table}
Table 6: Measured values of \((\mathrm{A+B})\) and A and their ratio.  
Figure 11: Failure pattern of the upper surface of the specimens.  
Figure 12: Typical failure pattern of compressed concrete specimen.  
most until peak load [8]. Thus, it should be mentioned that the results obtained for concrete mixes with different amounts and types of fiber correspond to the results obtained in this paper for NSC specimens.  
Six cylindrical NSC specimens with different end conditions were subjected to uniaxial compression [9]. In this case, transverse deformations were additionally measured. The obtained data show a good convergence with the present research results. The stress-longitudinal strain relationships are linear until failure when the stress-transverse strain relationships have well pronounced non-elastic behavior until a peak load (see Figures 2 and 3 [9]). The earlier described failure pattern is obtained for specimens with reduced friction conditions (see Figure 5 [9]).  
Thus, the results presented for 47 specimens [7; 8; 9] correspond to the data obtained by testing 7 specimens within the framework of the present research. This, in turn, significantly increases the reliability of the obtained results.