esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study proposes a signal\(-\)centric framework to enhance the real-time reliability of nondestructive inspection of structures by correlatively investigating the probability of detection (PoD) of crack formations with the mechanical processes and the passive signal responses. To construct the framework, the detectability of cracking damage in concrete was investigated by two approaches, namely amplitude- and energy-based PoDs. Additionally, critical signal signatures, such as signal-to-noise ratio (SNR) and frequency content, are expected to be pinpointed for their direct influence on the detectability of the crack source. Based on the outcomes obtained, effective parameters were suggested to be used in the framework to determine whether it demonstrates sufficient robustness to achieve the desired quality in inspection technique. Both approaches demonstrated increased detectability with decreasing signal-to-noise ratio and centroid frequency with high regression values. However, the stabilization trend of the energy-based approach after a certain frequency value suggests the use of the amplitude-based approach. In conclusion, a framework was proposed that allows simultaneous reliability assessment during damage detection by amplitude-based PoD calculation, providing the capability to track AE hit rate, SNR, and frequency centroid variations in relation to the technique's reliability, which exhibits clear correlations with these parameters to identify the required configuration for the desired detectability.  
For future works, inspection of the damage processes of a real structure subjected to loading or the samples simulating the structural members in the laboratory environment can be preliminary studies to verify the accuracy of the proposed framework.  
Figure 12: PoD-based signal-centric evaluation to enhance the reliability of the inspection during real-time monitoring by passive NDT.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 7 Use Cases
### Current Implementations Using the DELTA Dataset  
**Refining Image-Based Localization Through Tailored Landmark Segmentation Models:** The importance of landmark segmentation extends beyond mere identification; it significantly enhances image-based localization [49] by providing accurate, context-rich cues that improve the precision and reliability of navigation and localization algorithms. To further refine image-based localization systems, we utilized the high-quality visuals and precise geolocations from the DELTA dataset to develop a custom landmark segmentation model. This model specializes in identifying and segmenting key urban landmarks such as historic sites, unique architecture, and public spaces, which are crucial for navigation in complex urban settings.  
We annotated 27 key landmarks in approximately 800 street-level images using the SAM and trained an instant segmentation model based on the YOLOv8 architecture [43]. This training was conducted over 100 epochs using a Stochastic Gradient Descent (SGD) optimizer without relying on pre-trained models to ensure the model was tailored specifically to our dataset. An early stopping mechanism was included at 50 epochs to prevent overfitting. The model not only recognizes but also provides contextual details about these landmarks, enhancing the precision of localization algorithms. Equipped with detailed knowledge of an area's unique visual markers, autonomous navigation systems can achieve better orientation and accuracy, which are crucial for the safe navigation of autonomous vehicles and devices in urban environments. The effectiveness of our landmark segmentation model is visually represented in Figure 11.  
**Multi-Contextual Georeferenced Dataset for Image-based Localization Retrieval:** For the task of image-based localization retrieval, we developed a multi-contextual georeferenced dataset using the DELTA dataset and the landmark segmentation model. To construct this map, the landmark segmentation model was applied to street-level imagery to identify and segment key urban landmarks. Each landmark was then correlated with its precise geographic location from the DELTA dataset's GNSS coordinates, ensuring accurate mapping to real-world geographical counterparts. We then systematically organized this enriched data by downsampling aerial view images and arranging them as tiles in a sequence based on their path trajectory and timestamps. Each tile contains detailed information including the identified landmarks, their specific geolocations, and a positional index. By overlaying these tiles with segmented landmark data and precise geolocation coordinates, we created a detailed, information-rich map. This map visually represents the urban layout and embeds each image with multi-layered contextual data, enhancing the utility for urban navigation and planning. Figure 12 showcases the multi-contextual georeferenced dataset.  
Figure 11: Examples of our custom landmark segmentation in street-level images captured in Tartu, Estonia.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 2 EDM Interelectrode Gap Model Based on Plate Capacitor
### EDM Interelectrode Gap Modeling  
Based on the EDM interelectrode structure feature (the electrode surface is parallel to the workpiece surface) combined with the interelectrode voltage signal, the EDM breakdown delay stage (regions A) can be used as the plate capacitor. Therefore, the interelectrode dynamic voltage (the voltage of the plate capacitor \(C_{g}\)) can be expressed as  
\[u_{C}(t)=U(1-e^{-\frac{t}{\tau}}) \tag{1}\]  
where \(U\) is the pulse supply voltage and \(\tau\) is the time constant of the first-order \(RC\) loop, which can be expressed as  
\[\tau=R_{t}C_{g} \tag{2}\]  
where \(R_{t}\) is the total resistance of the circuit before discharge and \(C_{g}\) is the capacitance value of the interelectrode plate capacitance before discharge. According to Figure 2, \(R_{t}\) and \(C_{g}\) can be expressed as  
\[R_{t}=R_{P}+R_{E}+R_{W} \tag{3}\]  
\[C_{g}=\frac{\varepsilon S}{4\pi kd} \tag{4}\]  
Combining Equations (2) and (4), the interelectrode gap \(d\) can be expressed as  
\[d=\frac{\varepsilon SR_{t}}{4\pi k\tau} \tag{5}\]  
where \(\varepsilon\) is the dielectric constant of the insulating working fluid medium before discharge, \(S\) is the effective area of the plate capacitance, and \(k\) is the electrostatic force constant. Then,  
\[\frac{\varepsilon SR_{t}}{4\pi k}=\beta \tag{6}\]  
Combining Equations (5) and (6), the expression for the EDM interelectrode gap \(d\) can be obtained:  
\[d=\frac{\beta}{\tau} \tag{7}\]
## 2 Materials and Methods
### Requirements for the Earth-System Spatial Grid  
#### 2.1.1 Definition of the Earth-System Spatiotemporal Grid  
The Earth-System Spatial Grid is a unified global grid constructed for the entire three-dimensional Earth space. It mathematically abstracts Earth's volumetric space into an Earth-system space, studies hierarchical subdivision and encoding of this space, and discretizes it into a series of grid cells following specific rules. Each cell possesses a unique identifier, enabling the description and modeling of entities, processes, spatial relationships, and interactions. This framework supports the integrated organization and storage management of data.
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
###### Abstract  
In contrast to terrestrial networks, the rapid movement of low-earth-orbit (LEO) satellites causes frequent changes in the topology of intersatellite links (ISLs), resulting in dynamic shifts in transmission paths and fluctuations in multi-hop latency. Moreover, limited onboard resources such as buffer capacity and bandwidth competition contribute to the instability of these links. As a result, providing reliable quality of service (QoS) for time-sensitive flows (TSFs) in LEO satellite networks becomes a challenging task. Traditional terrestrial time-sensitive networking methods, which depend on fixed paths and static priority scheduling, are ill-equipped to handle the dynamic nature and resource constraints typical of satellite environments. This often leads to congestion, packet loss, and excessive latency, especially for high-priority TSFs. This study addresses the primary challenges faced by time-sensitive satellite networks and introduces a management framework based on software-defined networking (SDN) tailored for LEO satellites. An advanced queue management and scheduling system, influenced by terrestrial time-sensitive networking approaches, is developed. By incorporating differentiated forwarding strategies and priority-based classification, the proposed method improves the efficiency of transmitting time-sensitive traffic at multiple levels. To assess the scheme's performance, simulations under various workloads are conducted, and the results reveal that it significantly boosts network throughput, reduces packet loss, and maintains low latency, thus optimizing the performance of time-sensitive traffic in LEO satellite networks.  
LEO satellite network; time-sensitive network; software-defined network; queue scheduling 2025254327  
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## 2 Numerical Modeling
### Physical Model Details
#### 2.1.4 Material Properties of Coolants and Channel Structures  
In the present study, n-decane (C10H22), commonly used as a simplified surrogate model for aviation kerosene, was adopted as a coolant. The critical parameters of n-decane have been reported to be 645.5 K and 2.39 MPa [39]. Typically, the operational pressure in the cooling channel exceeds the critical pressure. In this scenario, significant changes occur in its thermo-physical properties near the pseudo-critical temperature. Accurately describing these properties is critical for numerical simulations of the flow and heat transfer. Therefore, in this work, the SUPERTRAPP program developed by the National Institute of Standards and Technology (NIST) was used to determine the thermo-physical properties of n-decane. The retrieved detailed property data can be interpolated at a total of 50 points using the piecewise linear interpolation function within ANSYS Fluent 21.0 software. Figure 10 depicts the temperature-dependent variation in the density, thermal conductivity, isobaric specific heat capacity, and dynamic viscosity of n-decane within a temperature range from 300 K to 1000 K under pressure conditions of 3 MPa. Furthermore, it should be noted that the pyrolysis of n-decane can be safely ignored herein due to the fact that the maximum temperature does not exceed 900 K in all the cases.  
Figure 10: The temperature-dependent variation in n-decane under 3 MPa. (**a**) Density; (**b**) Thermal conductivity; (**c**) Isobaric specific heat capacity; (**d**) Dynamic viscosity.  
A nickel-based, high-temperature alloy was selected as the structural material for the solid channel component, and its thermal and mechanical properties are presented in Table 1. It should be noted that this study did not consider the temperature dependency of the solid material, as the stress results served solely as a reference for selecting the cooling scheme rather than as a decisive factor. It is assumed that the maximum von Mises stresses in the structure should not exceed 600 MPa.
## 3 Results and Discussion
### Phosphorus Removal in Batch Adsorption Experiments
#### 3.2.3 Effect of Coexisting Organic Matter  
Surface water contains substances that can compete with phosphorous for binding sites on the adsorbent surface. In order to simulate the presence of natural organic matter, humic acid (HA) was introduced to the phosphorous solution.  
Addition of HA exhibited a slight decrease in phosphorus removal efficiency (Figure 6). This decrease was associated with an increase in HA concentration, resulting in a reduction of approximately 13% for the highest HA concentration. However, the reduction was relatively low considering the fact that in filtration experiments with surface water similar phosphorous removal efficiency was achieved and also considering the fact that in surface water such high concentrations do not usually occur.  
HAOPs have been reported to be very efficient in removal of NOM in particular HA. [PERSON] et al. [28] reported that when HA is added to the surface water, the UV254 removal efficiency increased to 85-90%. In this case, for the mixture of HA and K2 HPO4 the removal of HA was as follows: HA concentration 3.86 mgC/L--67%, 9.65 mgC/L--69% and for the highest HA concentration of 15.44% the removal was only 33% (Figure 6). In the case of the removal rate for phosphorus the fluctuation in the removal efficiency was: For the concentration of 3.64 mgP/L it was 99.99%, and for the 14.56 mg/L it dropped to 87%.  
Figure 5: Effects of ionic strength on phosphorus removal efficiency (adsorbent dose = 1 g/L, agitation speed = 200 rpm, temperature = 25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C, contact time = 60 min).  
Previous studies evaluated the competition with SO\({}_{4}\)\({}^{2-}\), HCO\({}_{3}\), Ca and Mg. [PERSON] reported that the phosphorus removal by HAOPs with addition of anions such as SO\({}^{4-}\), NO\({}^{3-}\) and Cl\({}^{-}\) or salts did not impact the removal efficiency even in the case of sulfate which is a potential competitor for adsorptive sites on HAOPs [24]. The effects of the concentration of divalent cations were investigated by [PERSON], and addition of Ca\({}^{2+}\) or Mg\({}^{2+}\) had no effect on removal efficiency of HAOPs [30].
## 5 Application to General Examples  
To compare the probability of failure (\(p_{f}\)) and reliability index (\(\beta\)) computed using the four methods (i.e., MSC, subset simulation, KDE, and the combination of KDE and GPD), three general examples (i.e., examples I, II, and III) were investigated. The state functions and associated random variables in examples I, II, and III are listed in Table 1. These examples are based on the state function without considering structural systems. The state functions of the three examples are represented by simple polynomial functions consisting of one or two random variables, as shown in Table 1. The random variables used in these examples are independent and follow a normal distribution with a mean and standard deviation of 0 and 1.0, respectively. The state function values in examples I, II,and III were computed with \(10^{6}\) samples generated using the MCS, and \(p_{f}\) was obtained using Equation (2). Subset simulation with the state function and random variables defined in Table 1 provided \(p_{f}\). KDE and the combination KDE and GPD based on the state function values generated using MCS provided \(p_{f}\) and \(\beta\). In this study, a boundary method was applied for KDE, in which only sample values of the state function less than zero were considered because the purpose of the application was to efficiently compute \(p_{f}\). The upper and lower thresholds were predefined as the top 1% and bottom 1% of the state function values, respectively, to formulate the combination of KDE and GPD. A comparison between the \(p_{f}\) and \(\beta\) computed using the four methods (MSC, subset simulation, KDE, and the combination of KDE and GPD) for the three examples is shown in Table 1. A comparison between the PDFs of the state function values obtained using MCS, KDE, the combination of KDE and GPD, subset simulation and the exact PDF for Example I is shown in Figure 3. The exact PDF shown in Figure 3 is a normal distribution with a mean and standard deviation of 2 and 1, respectively, and the exact \(\beta\) is equal to 2 because the state function g(X) = \(X_{1}+2\), where \(X_{1}\) is normally distributed, as indicated in Table 1. The area under the PDF of \(g(\)X\()\) upper-bounded by the vertical axis located at \(g(\)X\()=0\) indicates \(p_{f}\). A detailed representation of this area (i.e., detail A) is shown in Figure 3b. The exact value of \(p_{f}\) (or \(\beta\)) and its approximate values derived using MCS, subset simulation, KDE, and the combination of KDE and GPD do not differ significantly. Furthermore, the PDF obtained from MCS has many fluctuations because the state function values are estimated with the sample data generated from MCS; the histogram for the state function values can be produced and converted into the PDF by dividing the sample size and bin size into the frequency of the histogram. The degree of fluctuations depends on the bin size of the histogram. An increase in the bin size of the histogram reduces the degree of fluctuations of the PDF but cannot accurately represent the sample data.
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research  
### Design of Modular Buildings  
Modular construction utilises specific design frameworks to ensure strong and efficient structures, notably the Modular Equivalent Frame Method (MEFM) and the Equivalent Shear Wall Method (ESWM). The MEFM focuses on designing two-way slab systems supported by columns, creating two-dimensional frames and adjusting column stiffness to match slab conditions, allowing for separate gravity load analyses [47]. This method preserves wall volume within concrete modules while ensuring the structural integrity of beams and columns.  
Figure 6: Comparison of construction timelines (Adopted from [PERSON] et al. [45]). (The different colours represent the various stages of construction).  
The ESWM designs modular buildings for lateral loads, notably from seismic events, conceptualising the structure as vertical shear walls for stability. It allocates lateral loads based on wall stiffness and evaluates shear forces and bending moments to ensure load capacity, thus protecting the structure and its occupants. While MEFM analyses gravity loads for slabs, ESWM targets lateral loads with an emphasis on seismic resistance, with MEFM breaking modules into frames for analysis and ESWM treating the structure as shear walls for lateral load response [47].  
These frameworks significantly impact the comprehensive design and assembly of modular structures. Crucial aspects, including materials, arrangement of modules, hoisting and installation processes, and connections, are vital to guarantee the effectiveness and sustainability of modular construction practices. Understanding these components is imperative for enhancing the design and execution of modular techniques within construction initiatives.
## 4 Numerical Simulation Study of the Effect of Anchoring on Overburden Rock in Oil Wells
### Analysis of Oil Wells Within a Cluster of Wells with Different Burial Depth Pairs
This phenomenon is attributed to the mechanical support provided by the casing-anchored system, which effectively redistributes stress and constrains vertical displacement in the immediate vicinity of the wellbore. In contrast, overburden at greater distances experiences more pronounced deformation due to the absence of direct structural reinforcement, leading to an increased magnitude of subsidence. These findings underscore the critical role of the wellbore and its anchoring system in stabilizing the surrounding formation and mitigating excessive surface deformation. The presence of oil wells and cementing cement exert a restraining effect on formation subsidence displacement, functioning similarly to anchors embedded within the geological strata. This anchoring mechanism effectively enhances the stability of the overlying rock mass, mitigating excessive deformation and ensuring structural integrity.
## 2 Materials and Methods
### Developing MTVDI  
Here, we used [PERSON] et al. [10]'s method to calculate the MTVDI. First, we calculated _Ts_ (Formula (1) in [PERSON] et al. [10]) based on the AMSR-E 89.0 GHz vertical polarization Tb (\(Tb_{\rm 89V}\)), 23.8 GHz vertical polarization Tb (\(Tb_{\rm 23V}\)) and 18.7 GHz horizontal polarization Tb (\(Tb_{\rm 1 BH}\)) (Formula (1)):  
\[\text{Ts}\ \ =\ 6.134\times 10^{-3}(Tb_{\rm 18H}-278.818)^{2}\ +\ 9.934\ \times 10^{-3}( Tb_{\rm 23V}\ -\ 216.029)^{2}-0.353\ Tb_{\rm 89V}\ +\ 349.582 \tag{1}\]  
where \(Tb_{\rm 18H}\), \(Tb_{\rm 23V}\) and \(Tb_{\rm 89V}\) are 18.7 GHz horizontal polarization Tb, 23.8 GHz vertical polarization Tb, and 89.0 GHz vertical polarization Tb respectively.  
Then, the microwave normalized vegetation index (MNDVI) (Formula (2)) was calculated based on AMSR-E 23.8 GHz microwave polarization difference index (MPDI) (Formula (3)).  
\[\text{MNDVI}\ =\ -0.231\ \times ln(\text{MPDI})-0.578 \tag{2}\]  
where MPDI is the microwave polarization difference index of AMSR-E 23.8 GHz.  
\[\text{MPDI}\ =\ \ \frac{Tb_{V}-Tb_{H}}{Tb_{V}+Tb_{H}} \tag{3}\]  
where \(Tb_{V}\) and \(Tb_{H}\) are the 23.8 GHz vertical polarization Tb and 23.8 GHz horizontal polarization Tb respectively.  
Finally, based on the NDVI-Ts triangle space and definition of the temperature vegetation dryness index (TVDI) [28], we plotted the scatter diagrams between MNDVI and _Ts_ to construct the MTVDI triangle space (see Figure 2). In the MTVDI (Ts-MNDVI) triangle space, the wet edge means that the vegetation growth is not limited by water due to sufficient soil water supply and the surface evapotranspiration is equal to the potential evapotranspiration, while the dry edge represents that the effectiveness of soil moisture is low and the surface evapotranspiration is small. That means wet edge and dry edge represent the thresholds of the wettest and driest condition of vegetation for a given MNDVI. Detailed methods for constructing the MTVDI triangle space were shown in Formulas (4)-(6).  
\[\text{MTVDI}\ \ =\ \ \frac{Ts-Ts_{\it{min}}}{Ts_{\it{max}}-Ts_{\it{min}}} \tag{4}\]  
\[\text{Ts}_{\it{max}}\ =\ a\times\text{MNDVI}+b \tag{5}\]  
\[\text{Ts}_{\it{min}}\ =\ c\times\text{MNDVI}+d \tag{6}\]  
where MNDVI is the Microwave Normalized Difference Vegetation index, \(a\) and \(b\) are the slope and intercept of dry edge; \(c\) and \(d\) are the slope and intercept of wet edge. \(Ts_{\it{max}}\) and \(Ts_{\it{min}}\) are the maximum and minimum surface temperature observation for a given MNDVI.