esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Regional Geological Framework  
The investigated areas are located in the upper Susa Valley of the Western Alps (Figure 1), which are part of the alpine orogen developed since the Cretaceous as a result of the convergence between the Europe and Adria plates, separated by the Liguria-Piemonte ocean [34]. As reported in the official geological map of Italy at a 1:50.000 scale [32; 33], these areas include metamorphic units belonging to the oceanic Liguria-Piemonte and continental margin domains (Brianconnais, including Pre-Piedmont Auct.) of the precollisional Jurassic paleogeographic scenario. The Liguria-Piemonte oceanic units, in their general traits, consist of sequences of Cretaceous carbonate metasediments (calcschists Auct.) with intercalation of black phylladic schist in the upper stratigraphic levels, levels of continental and ophiolitic detritus, and scattered blocks of ophiolites that are locally overlain by radiolarian cherts (Oxfordian-Kimmeridgian) and meta limestones (Late Jurassic-earliest Cretaceous). At the highest structural levels of the Liguria-Piemonte units, the Chenaillet unit is a klippe of oceanic lithosphere with no post-rift sediments. The oceanic units recorded an alpine metamorphic peak under either lawsonite or epidote-blueschist facies conditions [35; 36]. Among the continental units, the Ambin Massif Auct. and the Chaberton-Grand Hoche-Grand Argentier unit are of interest in this study. The Ambin Massif comprises two different prealpine tectonic basement units, i.e., the Clarea and Ambin units, resting at the lower and upper structural levels, respectively, and sharing a similar Alpine tectono-metamorphic evolution from blueschist to greenschist facies conditions [37]. The Clarea unit is composed of quartz-bearing micaschist, containing bodies of metabasites and masses of gneiss. The Ambin unit consists of micaschist and gneiss embedding massesof metabasites and is intruded by a Cambrian aplitic greiss. These rocks are unconformably overlain by a Mesozoic cover consisting of Upper Permian (-Lower Triassic) quartzite, quarzitic microcongglomerate and schists, and Lower Triassic massive quartzite passing up through the section to Middle Triassic to Cretaceous dolomitic marble and calcareous schist with a local intercalation of carbonate breccias.  
The Chaberton-Grand Hoche-Grand Argentier unit (belongingd to the Pre-Piedmont zone Auct) is composed of a thick Norian dolomitic succession, Rhaetian-Hettangian calcchists, and Jurassic calcareous schist and phyllite with discontinuous levels of breccia [38].  
At the regional scale, the framework of post-metamorphic faults is dominated by NE-SW, N-S, and NW-SE trends.
## 1 Introduction
Overall, very few studies can be found focusing on the fatigue evaluation of fatigue-prone details in the steel railway bridges with the coupled dynamic effects of the train-track-bridge (TTB) system included. The contribution of this paper lies in proposing a framework for fatigue evaluation of steel bridge details based on multi-scale dynamic analysis of the coupled TTB system and linear elastic fracture mechanics (LEFM). In this framework, multi-scale vibration analysis of the TTB system leads to accurate and efficient fatigue stress computation of structural details and the integration of LEFM allows enhanced fatigue analysis with insights into the fatigue crack propagation of critical details. Specifically, a multi-scale bridge FE model including the model of the track system is established and incorporated into a vehicle-bridge interaction model to achieve coupled dynamic analysis of the TTB system. Accurate stress calculation of both the primary structural members and fatigue-critical structural details considering the TTB interactions is realized simultaneously with reasonable computation costs. Stress responses of a real steel-truss railway bridge obtained from the multi-scale coupled dynamic analysis are compared with the field data measured in a bridge inspection. Fatigue crack propagation history of the stringer-to-floor-beam connection bracket in the bridge deck system is subsequently predicted using the theory of LEFM. The influential factor of fatigue crack initiation and propagation is discussed. Additionally, effects of track irregularity and operating train speed on the fatigue crack propagation life of the critical detail under consideration are investigated.
## 4 Experimental Verification
### Result in High-Speed Tactile and Acceleration Sensor Network System  
In this section, we constructed the high-speed tactile and acceleration sensor network system, as shown in Figure 7. Further, we verified the effectiveness of the proposed method in a sensor network system using a single tactile sensor node and three acceleration sensor nodes. In addition, even if the sampling times (1 ms, 0.5 ms and 0.2 ms) are shortened, we confirmed that the system operates  
Figure 5: Time error for data-acquisition in Experiment 1: (a) shows overall result and (b) shows enlarged result. Adapted from [14].  
Figure 6: Result of Experiment 2: (a) shows tracking data and force data, and (b) shows enlarged result.  
similarly as the network system with two sensor nodes. In the experiment, a human impacts the board attached to the tactile sensor and the acceleration sensors by a hammer.  
The outputs of the tactile sensor node and the acceleration sensor nodes can be obtained by the same method as described in Section 4.2. In the acceleration sensor nodes, when the force at which the hammer was impacted against the table, the output of the acceleration sensor according to the impact was generated. Subsequently, the AD board converted the output to digital values such that the acceleration data could also be handled on a PC.  
Figure 8a-c shows experimental results with sampling times of 1 ms, 0.5 ms, and 0.2 ms, respectively. In this experiment, one tactile sensor's data and three acceleration sensors' data can be obtained. These sampling times mean the sampling rates of 1, 2, and 5 kHz, respectively. We consider that these sampling rates are high and can be applied to the real-time feedback control systems. It is noteworthy that the validity of the output value is not important in this experiment, but the synchronization of the timing of the data acquisition from various sensors is important in this research. The results show that the data-acquisition synchronization is successful even with a sampling time of 0.2 ms. Furthermore, it has been demonstrated that it can also be applied to a sensor network system using various sensors. Therefore, it is considered that the same result can be obtained for sensor network systems other than this combination. Additionally, it is considered that the performance of the system can be applied to high-speed robots and precise machines.  
Moreover, we confirmed from the experimental results that the error of the time stamp was approximately 0.06 \(\upmu\)s on average and the standard deviation was approximately 0.3 \(\upmu\)s.  
Figure 7: Experimental set-up using various sensors. Adapted from [15].
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 5 Stable Throughput of Economic System Model
### Simultaneous Multipacket Transmission Mode  
The success rate of \(n\) packets \(v_{j,n,0}\) and \(v_{j,n,1}\) can be obtained from Equations (A5), (A6) and (A9) and \(v_{\texttt{R0},2,0}=v_{\texttt{R0},2,1}=0\), \(v_{\texttt{HY},3,0}=v_{\texttt{HY},3,1}=0\), since \(s_{f_{2},j}=s^{I}_{f_{2},j}=0\). The average service rate of \(\mu_{j,1}\) is given in (8).  
In the first dominant system, the service rates for \(Q_{\texttt{HY}}\) and \(Q_{\texttt{R0}}\) are given (29) and (30) with \(s_{f_{2},j}=s^{I}_{f_{2},j}=0\). The stable region for the first dominant system \(R^{(1)}_{ECO,\text{SMT}}\) thus can be expressed by  
\[R^{(1)}_{\text{ECO},\text{SMT}}=\Big{\{}\lambda^{(1)}_{\texttt{HY}},\lambda^{( 1)}_{\texttt{R0}}|\lambda^{(1)}_{\texttt{HY}}<\mu_{\texttt{HY},1}+\mu_{ \texttt{HY},2}+\psi^{(1)},\lambda^{(1)}_{\texttt{R0}}<\mu^{(1)}_{\texttt{R0}} \Big{\}}. \tag{43}\]  
In the second dominant system, the service rates for \(Q_{\texttt{HY}}\) and \(Q_{\texttt{R0}}\) are given (32) and (33) with \(s_{f_{2},j}=s^{I}_{f_{2},j}=0\). The stable region for the second dominant system \(R^{(2)}_{ECO,\text{SMT}}\) thus can be expressed by  
\[R^{(2)}_{\text{ECO},\text{SMT}}=\Big{\{}\lambda^{(2)}_{\texttt{HY}},\lambda^{( 2)}_{\texttt{R0}}|\lambda^{(2)}_{\texttt{HY}}<\mu^{(2)}_{\texttt{HY},1}+\mu^ {(2)}_{\texttt{HY},2}+\psi^{(2)},\lambda^{(1)}_{\texttt{R0}}<\mu^{(1)}_{ \texttt{R0}}\Big{\}}. \tag{44}\]  
Finally, the stable region for the economic system with SMT is given by  
\[R_{ECO,\text{SMT}}=\{R^{(1)}_{ECO,\text{SMT}}\cup R^{(2)}_{ECO,\text{SMT}}\}. \tag{45}\]
## 2 Materials and Methods
### Evaluation  
The performance of our method was quantitatively evaluated on the basis of landmarks provided by the challenge organizers. Specifically, the quantitative performance evaluation framework reported here is consistent with the one used during the ANHIR 2019 challenge (anhir.grand-challenge.org/Performance_Metrics/, last accessed: 13 May 2020) and it was based on the metrics of (a) the average of the median relative target registration error (\(rTRE\)) and (b) the robustness (\(R\)) criterion. Notably, the benchmarking framework to calculate these metrics is available in borda.github.io/BIRL, as provided by the ANHIR challenge organizers [6]. Since the challenge participants did not have access to neighboring slices, the challenge organizers had asked for pairwise registration and not the complete 3D reconstruction of the tissue to generate the aforementioned metrics.  
\(rTRE\) represents the geometric accuracy between the target and warped landmarks in the target image frame. The motivation for using the median is to avoid penalization of few inaccurate landmarks, especially when the others are well-registered. Since only the challenge organizers had access to the testing dataset of the challenge for obvious reasons, in this study, results based on the \(rTRE\) achieved in the public data of the ANHIR challenge are reported. Specifically, \(TRE\) is defined as  
\[TRE=d_{e}\Big{(}x_{l}^{T},x_{l}^{W}\Big{)} \tag{11}\]  
where \(x_{l}^{T}\) and \(x_{l}^{W}\) are the coordinates of the landmarks \"\(l\)\" in the target and warped image, and \(d_{e}(.)\) defines the Euclidean distance. All \(TRE\) are then normalized by the diagonal of the image to define the \(rTRE\):  
\[rTRE=\frac{TRE}{\sqrt{w^{2}+h^{2}}} \tag{12}\]  
where \(w\) and \(h\) denote the image's width and height, respectively.  
The proposed approach was also evaluated according to the metric of robustness (\(R\)), which takes values in the range of 0 and 1. When \(R\) is equal to 1, the average distance of all the landmarks in the moving and fixed images is reduced after registration (defining the absolute algorithmic robustness), and 0 means that none of the distances are reduced. The mathematical formulation of \(R\) for the \(i^{\text{th}}\) image of the dataset marked with \(L_{i}\) landmarks is a defined as  
\[R_{i}=\frac{1}{L_{i}}\sum_{j\in L_{i}}(rTRE_{j}^{regist}<rTRE_{j}^{init}) \tag{13}\]  
where \(rTRE_{j}^{init}\) is the \(rTRE\) of the \(j\)th landmarks initially and \(rTRE_{j}^{regist}\) is the \(rTRE\) after registration. \(R\) is therefore a relative value of how many landmarks have an improved \(rTRE\) after registration.  
It is worth noting that the ranking of the ANHIR challenge was not based on the absolute \(rTRE\) and \(R\) metrics, but on the relative performance considering all participatingteams. This was obtained by averaging the ranked \(rTRE\) scores (unavailable for participants) across each pair of images.
# Time and Cost Schedule Dynamic-Hidden Trojan Horses
## 6 Case Study-Extension  
The characteristics in Figure 6 have been constructed for 100 simulation runs of the illustrative example. The time series {Q\({}_{i}\)\({}^{\prime}\)} of the productivities (production rates) of the \(\mathrm{TAB}_{(\bullet)}\) project is a data source for evaluating the project properties. It characterizes how the project was designed from a technical, technological, and organizational point of view. An answer to the following question is expected: to what extent does the method of resource distribution in individual \(\mathrm{TAB}_{(Lin)}\) or \(\mathrm{TAB}_{(Log)}\) activities regulate the properties of the project as a whole? In this case, there is a comparison of the variants \(\mathrm{TAB}_{(Lin)}\) and \(\mathrm{TAB}_{(Log)}\):  
* Variant 1-\(\mathrm{TAB}_{(Lin)}\)-linear distribution of resources of individual activities A provides a time series {Q\({}_{i}\)\({}^{\prime}\)}\({}_{Lin}\); see Figure 3.
* Variant 2-\(\mathrm{TAB}_{(Log)}\) -logistical distribution of resources of individual activities A provides a time series {Q\({}_{i}\)\({}^{\prime}\)}\({}_{Log}\), solved under the same conditions; see Figure 4. The Hurst (H) exponent was chosen as the basic indicator for the time series evaluation. It is considered an indicator of dependence, an indicator of long-range dependence, or a long-term memory of a time series. It belongs among the initial indicators of the possible presence of chaos in the investigated time series. It ranges between zero and one.
* Values approaching the extremes of 0 and 1 point to the significant deterministic nature of the process.
* If the Hurst exponent values lie between 0 and 0.5 or 0.5 and 1, this indicates the presence of deterministic chaos: \(\Circle\) & H \(<\) 0.5 anti-persistent, (temporary, non-recurrent), \(\Circle\) & H \(>\) 0.5 persistent and has long-term memory (permanent), \(\Circle\) & H = 0.5 stochastic. The calculation of H exponents for cash-flow, {Q\({}_{i}\)\({}^{\prime}\)}\({}_{\mathrm{Lin}}\) and {Q\({}_{i}\)\({}^{\prime}\)}\({}_{\mathrm{Log}}\), is based on the data of Figures 3 and 4. It indicates that the implementation of the processes in \(\mathrm{TAB}_{(Lin)}\) and \(\mathrm{TAB}_{(Log)}\) have a significant deterministic disposition, and a long-term positive autocorrelation (high productivity values in a time series will tend to continue with high values in the future); the time series data are significantly persistent with a long-term memory and with an indication of the possible presence of chaos.
# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 4 Findings
### IT and Data Governance  
Our results on IT and data governance mirror the findings of those on the roles and services. There is a continuum of options between a strong cooperative and a more decentral handling of governance-related questions. This particularly applies to the IT strategy definition (CO2, CO5, CO9, CO10, CO11), responsibility for the process and product development (CO2, CO3, CO4, CO5, CO6, CO7, CO8, CO10, CO11, CO12, CO13), formulation of a binding and formal code of conduct (CO10, CO7, CO6, CO2, CO3, CO8, CO12, CO13, CO4, CO14), centrality of defining data access rights (DS1, DS4, DS8), and standardization of data quality requirements (DS8). A prominent factor that seems to influence the degree of centrality seems to be the competition between the ecosystem members. The more competition there is, the more it is deemed necessary to have strong and formal governance that is handled by the cooperative (CO2, CO8, CO13, DS7).  
_Finding 6: Competition between ecosystem members demands a central and formally defined IT and data governance._  
Furthermore, consistent with the coordinative role of a cooperative, the cooperative is seen as an interface of the ecosystem to external IT or analytics providers (CO2, CO3, CO4, CO6, CO7, CO8, CO10, CO11, CO12).  
_Finding 7: The data cooperative can act as an interface between the business ecosystem and external IT or analytics providers._  
We identify the definition of verifiable data integrity requirements (DS2), setting the visibility of the data for the different members (CO5, CO11, DS1), automation of data quality tests (DS7), and a classification of data according to relevance and confidentiality (DS7) as data governance measures that seem to be advisable in a data sharing cooperative.  
_Finding 8: The data cooperative should be responsible for a variety of central data management tasks._
# Investigation of 2D-WH/TS OCDMA Code Stability in Systems with SOA-Based Device
## 3 Impact of SOA High Bias Current/Gain on 2D-WH/TS OCDMA Prime Code Fidelity
The results obtained are shown in Figure 8. Solid lines show the probability of error of two different 2D-WH/TS OCDMA systems: one that has four wavelength code carriers (\(w=4\)) and \(N=53\) chips, denoted by (\(w\), N) = (4, 53), and the second, having \(w=8\) and \(N=53\), denoted by (8, 53). Dashed lines represent calculations of the respective degraded _Pe_ due to the code carrier redshift, which causes autocorrelation peak reduction from \(w\) to \(w-1\) (i.e., from 4 to 3 and from 8 to 7, respectively). Figure 7: Impact of SOA on 2D-WH/TS code based on four-wavelength code carriers as recorded by an optical spectrum analyser: (**a**) without and (**b**) with the SOA present in the chromatic-dispersion (CD) compensated transmission link.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 1 Introduction
Through immersive experiences, students feel as if they are in a real classroom or simulated scenario, which greatly encourages active and reflective learning. By contrast, VC platforms allow for real-time communication without the need for complex equipment or high costs: effective online learning can be achieved with just a mobile phone or laptop. VC platforms have become the preferred choice for many online courses due to their ease of use and widespread availability. A comparison of these two technologies would thus be valuable, given their differences. Although VR and VC platforms are increasingly used in online learning, previous research often failed to assess and compare their effects comprehensively. The lack of an overall assessment of their effectiveness hinders our understanding of how these technologies impact students' learning outcomes in terms of research context, technological features, and instructional design. Moreover, the absence of systematic comparisons between the two technologies poses challenges for educators in selecting the most suitable technology for specific teaching needs and contexts. Based on these research limitations, this study conducted meta-analyses of relevant experimental studies in the literature on VR and VC platforms, and systematically compared their respective advantages and limitations. The aim of this study is to deepen our understanding of the educational potential of both technologies and provide practical implications for future technology selection. The following questions guided this study:  
**RQ1:**: What is the overall effect of VR and VC platforms in enhancing online learning? **RQ2:**: How do research context, technological features, and instructional design affect the learning effect?
# Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision
## 3 Results
### Offshore Test Verification
Figure 11: Illustration: (**a**) at 14:00 with stereo vision, variation in coordinate (0, 0) of sea surface displacement with time; (**b**) at 15:00 with stereo vision, variation in coordinate (0, 0) of sea surface displacement with time; (**c**) at 16:00 with stereo vision, variation in coordinate (\(-320\), 0) of sea surface displacement with time; (**d**) at 16:00 with stereo vision, variation in coordinate (0, 0) of sea surface displacement with time; (**e**) at 14:00 with RBR wave high meter of sea surface displacement with time; (**g**) at 16:00 with RBR wave high meter of sea surface displacement with time; (**g**) at 16:00 with RBR wave high meter of sea surface displacement with time. As shown in Figure 12, the wave direction of the normal vector was basically consistent with the actual movement direction of the wave. The wave direction measurement error is \(\pm 10\) degrees and the resolution is 1 degree. These findings imply that the measurement results are accurate. The calculation results are shown in Table 8: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Parameter** & **Coordinate (\(-\)320, 0)** & **Coordinate (0, 0)** & **Error** \\ \hline \multirow{6}{*}{14:00} & \(1/3\) wave height (m) & 0.942 & 0.939 & 0.3\% \\  & \(1/10\) wave height (m) & 1.220 & 1.218 & 1.4\% \\ \cline{1-1}  & \(1/100\) wave height (m) & 1.594 & 1.602 & 8.0\% \\ \cline{1-1}  & Average wave height (m) & 0.622 & 0.625 & 0.1\% \\ \cline{1-1}  & Wave period (s) & 7.054 & 7.253 & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Data comparison for different measured coordinates. Figure 12: Illustration: (**a**) wave direction diagram of the first image frame; (**b**) wave direction diagram of the second image frame; (**c**) wave direction diagram of the third image frame; (**d**) wave direction diagram of the thirteenth image frame. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Parameter** & **RBR** & **Stereo Vision** & **Error** \\ \hline
14:00 & Wave direction (degrees) & 170 & 175 & 5 \\ \hline
15:00 & Wave direction (degrees) & 171 & 177 & 6 \\ \hline
16:00 & Wave direction (degrees) & 170 & 172 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Measurement accuracy verification of wave direction. As is evident from Figure 12, the proposed method can effectively realize the three-dimensional reconstruction of an ocean wave field. The wave height value of the 3D inversion is basically the same as the measurement result of the RBR wave height meter.
## 2 Materials and Methods  
Images with haze are characterized by uneven illumination, low visibility, and low contrast. The atmospheric scattering model describes the degradation process of foggy images [33] and is expressed as:  
\[I(x)=J(x)t(x)+A(1-t(x)) \tag{1}\]  
where \(I(x)\) represents the original image, and \(x\) represents the pixels. \(J(x)\) is the clear image restored by dehazing. \(A\) is the atmospheric luminance intensity, and \(t(x)\) is the transmittance.  
The unknowns \(A\) and \(t(x)\), the keys to obtaining a clear image \(J(x)\), are generally estimated in algorithms such as DCP.  
DCP is designed based on a basic assumption, which is that, in most non-sky local areas, there are some pixels with very low values in at least one color channel, approaching 0. Therefore, the relevant parameters are estimated as follows.  
The atmospheric luminance intensity \(A\) is normally estimated from the pixels with the highest fog concentration and highest brightness in the image [34]. The calculation of the transmittance \(t(x)\) is as below [20]:  
\[t(x)=1-\omega\min_{c\in\{R,G,B\}}\left(\min_{y\in\Omega(x)}\left(\frac{I^{c}( y)}{A}\right)\right) \tag{2}\]  
where \(\Omega(x)\) is a local patch centered at \(x\), \(I^{c}\) is a color channel of \(I\), and \(\omega\) = 0.95, which is a constant to ensure the true image perception. It represents retaining a small amount of fog in the resulting image to pursue the authenticity of the image, especially for distant objects. In fact, the dual min function obtains the dark channel of the normalized haze image. It directly provides the estimation of the transmission.  
DCP has good defogging effects for landscape photos. However, the disadvantage of DCP is color distortion in bright areas or areas with massive gray and white colors, which results in dark dehazed images [35].  
In order to solve the above problems, this paper proposes a hybrid dark channel prior (HDCP) algorithm.  
In Figure 1, Retinex is used in the preprocessing to remove the interference of the illumination component. In the modified DCP, the atmospheric light intensity \(A_{i}\) of each of the RGB channels is estimated iteratively after the dark channel is determined. Then, the transmittance \(t(x)\) is optimized by guided filtering. The grayscale image of \(R(x)\) enhanced by the variant genetic algorithm (VGA) is the guided image in the filter. Finally, the fog-free image \(J(x)\) is obtained according to the atmospheric scattering model.