esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Image Collection and 3D Point Cloud Generation  
#### 3.2.1 Data Acquisition Device  
In the AEC industry, many devices are used for image acquisition, including camera (monocular/binocular/camera array), smart devices (mobile phone/tablet/personal laptop), monitor, UAV with camera, laser scanner, depth camera (Kinect), satellite, etc. Main performance indexes of these devices are shown in Table 2.  
The data generated by these devices can be divided into two categories: image and point cloud. The laser scanner for generating point cloud has the characteristics of high equipment cost, high technical requirements, limited texture information, etc., so that it is not accepted by most construction companies. Therefore, the image (including photograph and video) becomes an alternative way. Images can be collected by a variety of devices, and most of these devices have the characteristics of low cost, low technical requirements, portable, high-resolution, rich-texture information. This makes image-based 3D reconstruction a key technology for automated construction progress monitoring.
## Appendix A Implementation of Our Proposed CL-BS Scheme in AVISPA  
The proposed scheme has been implemented for blind signer and verifier in HLPSL, as illustrated in Algorithms A1 and A2. The experiment was performed on a computer workstation having the specifications as follows: Haier Win8.1 PC; Intel Core i3-4010U CPU @ 1.70 GHz; 64-bit operating system and x64-based processor. The software platforms consulted were Oracle VM Virtual Box (version: 5.2.0.118431) and SPAN (version: SPAN-Ubuntu-10.10-light_1). As with any security protocol, to be analyzed in AVISPA, the roles for session, goal, and environment were executed as shown in Algorithms A3 and A4. In order to gauge the probability of attacks on the proposed scheme, the widely-used OFMC and CL-AtSe backends were selected for the execution test. Because other backends such as SATMC and TA4 SP are not compatible with bitwise XOR operations, the simulation results of SATMC and TA4 SP were not included in the research work. Here, it is imperative to ascertain the execution of specified protocol in terms of whether the authentic agents can execute the specified protocol or not. To do so, the back-ends perform check operations. Then, the information is provided to the intruder about a few normal sessions between authentic agents. Secondly, the susceptibility of the system to man-in-the-middle attack is also estimated by the back-ends. This is done to verify the Dolev-Yao (DY) model [54]. The scheme is also simulated under SPAN (specific protocol animator for AVISPA) web-tool. The results for OFMC and AtSe are shown in Figures A1 and A2, respectively. It is evident that the scheme is safe against replay and man-in-the-middle attack. ```
role_Blindsigner(Blindsigner:agent,Verifier:agent,Xs:public_key,Xv:public_key,SND,RCV:channel(dy)) played_by Blindsigner def= local State:nat,Ns:text,Sub:hash_func,Z:text,T:text init State: = 0 transition 1. State=0 / RCV (start) = |> State': = 1 / SND (Blindsigner.Verifier) 2. State=1 / RCV (Verifier. {Ns'} _Xv) = |> State': = 2 / T:=new() / Z:=new() / SND(Blindsigner. [Sub(Z:T)]_inv(Xs)) end role
```  
**Algorithm 1** High-level protocol specification language (HLPSL) code for Signer role ```
**Algorithm A2** High-level protocol specification language (HLPSL) code for Verifier role
``` role \(\_\)Verifier(Blindsigner:agent,Verifier:agent,Xs:public_key,Xv:public_key,SND,RCV:channel(dy)) played_by Verifier def= local \(\_\) State:nat,Ns:text,Sub:hash_func,Z:text,T:text

```
init \(\_\) State: = 0 \(\_\) transition 1. State=0 \(\land\) RCV(Blindsigner.Verifier) =|> State'=1 \(\land\) Ns':=new() \(\land\) SND(Verifier. [Ns']_Xv) 2. State=1 \(\land\) RCV (Blindsigner. [Sub (Z'.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 5 Evaluation
### Read Performance Test  
#### 5.2.1 The Performance in Sequential Reading  
The result of the IOPS in sequential reading is shown in Figure 9. The value of the IOPS decreased as the size of the data block increased, since the IOPS of the system was inversely proportional to the time reading each block, which increased as the block size increased. When the block was 4 K, the value in the NewLib code was about 25% lower than that in the triple replica.  
The throughput value in the sequential reading changed with the block size and backup mode, which is shown in Figure 10. We can see that as the data block increased, the value of the throughput also increased. This is because after the data block became larger, each read could read more data while minimizing the metadata overhead and disk movement overhead. Reading the same quantity of data, the smaller the data block, the greater the metadata processing overhead required. For a fixed block size, we can see that the copy mode still had some advantages over the EC mode when reading data.  
From Figures 9 and 10, we can see that as the data block increased, the IOPS decreased steadily, but after the throughput increased to a certain extent, the value remained basically stable. For the data blocks of 512 K and 1024 K, whether in copy mode or in erasure code mode, the throughput values were basically equal. This is because once the data block was large enough, its size had little impact on the overhead of the metadata.
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 2 Explicit Formulas to Describe Nonlinear-Hysteretic Response
### Damping Ratio  
The damping ratio of a granular material is the summation of the linear damping ratio in the limit of small strain (\(\zeta^{\rm L}\)) that represents the inherent viscoelastic absorption and a nonlinear damping ratio due to the hysteresis that increases with the strain (\(\zeta^{\rm NL}(\gamma)\)), i.e.,  
\[\zeta(\gamma)=\zeta^{\rm L}+\zeta^{\rm NL}(\gamma) \tag{7}\]  
The nonlinear damping ratio is defined [1; 26]:  
\[\zeta^{\rm NL}(\gamma)=\frac{W_{D}}{4\pi W_{S}} \tag{8}\]  
where \(W_{S}\) is the maximum strain energy stored during the cycle (= G\({}_{\rm sec}\cdot\Delta\gamma^{2}\)/2), and \(W_{D}\) is the area closed by the hysteresis loop in the stress-strain curve, which represents the dissipated energy per cycle. The energy dissipation per cycle can be obtained by integrating Equation (1):  
\[\begin{array}{ll}W=&\tau d\gamma=G\Big{[}\gamma-\frac{\alpha}{2}\big{[}2 \Delta\gamma\cdot\gamma-((\Delta\gamma)-\gamma)\big{]}\Big{]}d\gamma\\ &+G\Big{[}\gamma-\frac{\alpha}{2}\big{[}2\Delta\gamma\cdot\gamma+((\Delta \gamma)-\gamma)\big{]}\Big{]}d\gamma=\frac{\alpha}{3}\alpha(\Delta\gamma) \end{array} \tag{9}\]  
The dissipated energy depends on the strain amplitude, hysteretic nonlinearity parameter, and shear modulus in the limit of infinitesimal strain. Thus, the nonlinear damping ratio can be obtained from Equation (8):  
\[\zeta^{\rm NL}(\gamma)=\frac{2}{3\pi}\frac{G_{\rm max}}{G_{\rm sec}}\alpha \gamma=\frac{2}{3\pi}\frac{\alpha\gamma}{1-\alpha\gamma}\stackrel{{ \alpha\gamma\ll 1}}{{\cong}}\frac{2}{3\pi}\alpha\gamma \tag{10}\]The strain-dependent damping ratio response cannot be described with the Kelvin-Voigt model that consists of a linear spring element and a linear dashpot element in parallel system:  
\[\tau=G\gamma+\eta\dot{\gamma}=G\gamma\pm\eta\alpha\sqrt{\left(\Delta\gamma\right) ^{2}-\gamma^{2}} \tag{11}\]  
where \(\eta\) is the viscosity and \(\gamma\) is the harmonically varying strain as \(\Delta\gamma\text{sin}(\omega\text{t})\). The damping ratio can be expressed:  
\[\zeta_{KV}^{L}=\frac{\eta\omega}{2G} \tag{12}\]  
There is no strain-dependence in the shear modulus and the damping ratio. These correspond to the constant damping ratio and resonant frequency monitored at low shear strains.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 3 Results
### Initial System Configuration  
To initially set up the SIBERIA environment, it is necessary to register the DID and DID document of the TAO, the trusted issuer of SIBERIA. Through the SIBERIA API, the TAO's DID is created along with its corresponding public and private keys. Subsequently, this identity is registered in the DID Registry, thereby establishing the TAO as a recognized entity within the system.  
Next, the general schema called SIBERIA Verifiable Attestation is defined and registered. This schema forms the basis for all credentials issued by the various issuers and incorporates the minimum required elements for credentials, following recommendations from EBSI and W3C. These elements include the following:  
* @context: Semantic context defining the terms used within the document.
* id: Globally unique identifier for the verifiable credential.
* type: Declaration of the credential type.
* issuer: Entity issuing the credential.
* issuanceDate: Official issuance date.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **F1-Score** & **Accuracy (Std)** & **F1-Score (Std)** \\ \hline
0.8592 & 0.9132 & 0.1795 & 0.1217 \\ \hline \hline \end{tabular}
\end{table}
Table 7: HMOG’s results for the validation of 1 user on all other users of the dataset.  
* issued: Similar to issuanceDate, indicating the issuance date.
* validFrom: Date from which the credential is valid.
* credentialSubject: Information and claims related to the subject of the credential.
* credentialSchema: Reference to the structure that the credential adheres to.  
Additionally, other fundamental schemas are registered for the system's operation, including the following:  
* The \"SIBERIA Verifiable Presentation\" schema, defining the structure of VPs created by holders when sharing their credentials with verifiers.
* The \"SIBERIA StatusList2021 Credential\" schema, representing status lists indicating the validity or revocation of other credentials; its use is optional if the credential's validity is specified directly in the issued credential.
* The \"SIBERIA Voice ID\" schema, used for credentials issued through the secure SASV module.  
Finally, the TAO is registered as a TI in the TIR, authorizing it to issue onboarding credentials to other issuers and to issue credentials conforming to the SIBERIA Verifiable Attestation schema. These steps establish the initial deployment of the SIBERIA environment, enabling secure and controlled participation of SIBERIA stakeholders: regular users (holders), owners of the service to be protected (verifiers), and SASV module owners (issuers).
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 3 Semiconductor Stresses and AC Analysis of the Proposed SEPIC-Based Converter
### Comparative Study to Other Step-Down/Step-Up Topologies  
Examining Equation (8), the duty cycle can be expressed in terms of the static conversion ratio and transformer ratio \(n\) as a solution of a second-degree equation:  
\[D=\frac{-n\times(1+M)\times\left(\sqrt{n^{2}\times\left(1+M\right)^{2}+4\times n \times M}\right)}{2} \tag{29}\]  
The dependency of the static conversion ratio against the duty cycle for the classical isolated SEPIC [61], the hybrid SEPIC from [24] and the proposed hybrid SEPIC-based converter is presented in Figure 7. For a better representation, a detail for a duty cycle between [0-0.5] is provided. At a first glance, the step-down and step-up allure of the proposed converter can be observed. From (29) and Figure 7, it can be observed that the proposed SEPIC converter starts to step up at a lower duty cycle value, compared to the isolated and hybrid SEPIC, or, in other words, at the same turns ratio, a higher static conversion ratio is achieved for the proposed converter at the same duty cycle, compared to the same converters. Figure 7 also reveals the fact that the lower the value of \(n\), the more step-up is achieved, thus recommending the converter for step-up applications where a big difference between the input and output voltage is needed. Another comparison, where the comparative parameters are the number of active and passive semiconductors, the number of cores/windings, the total number of components, the system order, the expression of the static conversion ratio \(M\), the expression of the duty cycle \(D\), and the current and voltage semiconductor stresses, is realized in Table 1. The comparison is made between the classical Buck-Boost, classical non-isolated SEPIC, hybrid SEPIC and proposed hybrid SEPIC converter. For a fair comparison, the converters are  
Figure 7: Static conversion ratio against the duty cycle for the classical isolated SEPIC, hybrid SEPIC and proposed converter with the turns ratio as a parameter. supposed to be supplied by the same input voltage \(V_{g}\), and to deliver the same output voltage \(V_{o}\) on the same load \(R\). Consequently, the conversion ratio \(M\) and the output power \(P_{o}\) are the same. Analyzing the results from Table 1, it can be remarked that each topology has only one transistor. The proposed SEPIC-based converter has two additional diodes, in comparison to the classical Buck-Boost or SEPIC converter, and one diode less if it is compared to the hybrid SEPIC from [24]. The lowest system order is the classical Buck-Boost, followed by the classical SEPIC and the proposed SEPIC. From Table 1, it can be seen that the static conversion ratio for the proposed SEPIC topology is \((1+D/n)\) times higher than the
## 7 Challenges, Opportunities, and Future Trends
### Challenges Faced by EH-WSNs  
Table 2 presents the challenges faced by EH-WSNs, providing a detailed and structured summary of both the achievements and unresolved issues in this domain. Significant progress has been made in addressing key areas such as energy efficiency [106; 107], where advancements in energy harvesting and management techniques have enabled more reliable data transmission and network functionality despite limited energy resources. Additionally, efforts to enhance compatibility and interoperability with existing technologies [108] have paved the way for the smoother integration of EH-WSNs into established systems, broadening their applicability across diverse scenarios [109; 110]. However, several challenges remain unresolved, demanding further investigation. Managing energy efficiency is still a critical issue, especially in scenarios with fluctuating energy availability and varying network demands [111; 112]. Network scalability poses another challenge as the performance and lifetime of EH-WSNs are adversely impacted when the network grows in size [113], requiring innovative resource allocation and optimization techniques. Data security, particularly in energy-constrained environments, continues to be a pressing concern, as ensuring secure transmission without overburdening the system remains a difficult balance to strike [114; 115]. Similarly, the dynamic topologies characteristic of EH-WSNs [22; 102] arising from changes in node availability and shifting network structures introduce complexities in maintaining consistent performance and reliability. By explicitly distinguishing the progress made and the gaps that persist, Table 2 not only highlights the evolving state of EH-WSNs but also identifies critical avenues for future research.  
Addressing these challenges, such as developing adaptive energy management strategies, designing lightweight security protocols, and enhancing scalability, will be essential for realizing the full potential of EH-WSNs in practical, large-scale deployments.
## 3 Numerical Model and Calculation Scheme
### Establishment of Numerical Calculation Model  
#### 3.2.1 Modelling Principles  
It is particularly important to establish a reasonable and accurate numerical model in order to correctly reveal the instability and failure law of load transfer evolution of deep high-stress mining overburden. Therefore, the establishment of the model needs to follow the following principles:  
1. The movement and stress change of deep high-stress overlying strata are affected by geological factors and mining technology; the actual situation is more complex. When establishing the numerical calculation model, it is necessary to simplify the model as much as possible, remove the secondary influencing factors, and establish the model mainly considering the main influencing factors.
2. Consider boundary conditions. Compared with the actual situation, the numerical simulation calculation belongs to the finite element structural analysis. In order to avoid the influence of the boundary effect on the simulation results, it is necessary to reserve space at the boundary to set up protective coal pillars.
3. The establishment of the model needs to restore the actual situation as much as possible. The parameters used in the model correspond to the actual situation one by one. Considering the lithological characteristics, it conforms to the applied mechanical properties.
## 4 Simulation and Experiment  
### Large-Signal Stability Simulations of a Single-Node CSO Undersea DC Power System  
In this paper, the large-signal stability of a single-node CSO undersea DC power system is simulated by using Saber. The simulation file is shown in Figure 7. The shore station PFE is modeled by an ideal voltage source. The submarine cable is modeled by cascaded lumped parameter equivalent circuits. The undersea station is modeled by a CPL. The total length of the submarine cable is assumed to be 360 km. Every cable segment of 10 km is represented by a \(\pi\)-type lumped parameter equivalent circuit.  
In order to study the effects of the output voltage of the shore station PFE, the power consumption of the undersea station and the submarine cable parameters on the large-signal stability of a single-node CSO undersea DC power system, five simulation sets are needed. The parameter sets for Saber simulation are shown in Table 4. \(U_{S}\) represents the output voltage of the source, i.e., the shore station PFE. \(R_{0}\), \(L_{0}\), and \(C_{0}\) represent the resistance, inductance and capacitance of a cable segment, respectively. \(P_{L}\) represents the power consumption of the CPL, i.e., the undersea station with external science payloads.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & \(\mathbf{U_{S}/V}\) & \(P_{L}/\mathbf{W}\) & \(\mathbf{R_{0}/\Omega}\) & \(\mathbf{L_{0}/mH}\) & \(\mathbf{C_{0}/\mu  
Initially, \(U_{S}\) in the system is 0 V. At 0.1 s, according to experimental needs, the voltage rises from 0 V to 400 V or 350 V, and the voltage and current transient processes are generated. The simulation results are shown in Figure 8. The blue line indicates the output voltage of the shore station PFE. The green line indicates the voltage in the middle section of the submarine cable. The orange line indicates the input voltage of the undersea station. The red line indicates the output current of the shore station PFE.  
Figure 8: _Cont._  
Figure 7: The Saber file for the large-signal stability simulation of a single-node CSO undersea DC power system.  
Figure 8: _Cont._  
In Figure 8, the parameter set #1 satisfies the criterion of the inequality Equation (29), so that its power system is stable in the case of power-on. The parameter sets #2, #3, #4 and #5 do not satisfy the criterion of the inequality Equation (29), so that their power systems are unstable in the case of power-on. It proves that the criterion of the inequality Equation (29) is valid. Moreover, the simulation results also show that the large-signal stability of a single-node CSO undersea DC power system is positively correlated with the output voltage of the shore station PFE and the capacitance of per unit length submarine cable, and negatively correlated with the power consumption of the undersea station and the inductance of per unit length submarine cable.
## 4 Examples of Seismic Analysis of the SFSI System  
In this study, the dynamic SFSI system is analyzed under seismic excitation. In the analysis, the north-south acceleration component of the 1940 El Centro earthquake, with a magnitude of Mw = 6.9, is used (see Figure 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mass (\(m\))** & **Natural Period** & **Damping Ratio** & **Angular Frequency (\(\omega_{m}\))** & **Damped Angular Frequency (\(\omega_{D}\))** & **Stiffness Coefficient (\(k\))** & **Damping Coefficient (\(c\))** \\
**(kg)** & **(\(T\))** & **(\(\Delta\))** & \(\left(\frac{\text{nd}}{\text{s}}\right)\) & \(\left(\frac{\text{nd}}{\text{s}}\right)\) & \(\left(\frac{\text{kg}}{\text{s}^{2}}\right)\) & \(\left(\frac{\text{kg}}{\text{s}}\right)\) \\ \hline
2 & 1 & 0.05 & 6.283 & 6.275 & 78.957 & 1.257 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of the damped one-degree-of-freedom system.  
Figure 4: Comparison of results obtained using piecewise linear interpolation and the central FD method for \(\Delta t\) = 0.1 s.  
Figure 5: Comparison of results obtained using piecewise linear interpolation and the central FD methods for \(\Delta t\) = 0.01 s.  
The Turkish Building Seismic Code (TBSC 2018) classifies soil into six groups based on geological subsurface conditions [28]: ZA, ZB, ZC, ZD, ZE, and ZF. These categories are defined as follows: ZA (solid hard rocks), ZB (strong rocks), ZC (weak rocks), ZD (solid clay), ZE (soft clay), and ZF (highly sensitive clays). This study performs a dynamic analysis based on four different soil conditions, classified as ZB, ZC, ZD, and ZE, each with different mass densities \(\rho\). Based on the approximate values of Poisson's ratios for various soils, a Poisson's ratio of \(v=1/3\) is assumed for all soil types.  
The soil properties, including the unit mass density and the propagation velocities of shear and dilatational waves for the four different soil types, are provided in Table 2. The constrained elastic modulus (\(E_{c}\)) and shear modulus (\(G\)) values are calculated using the propagation velocities of dilatational waves (\(c_{p}\)) and shear waves (\(c_{s}\)) for each local soil class.