esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bundle Adjustment for Motion Estimation Using HFR Cameras
### Jacobian Matrix for HFR Cameras with Frame-Rate Multiplication  
For HFR cameras, the captured frame-rate is highly reliant on the resolution of the frame, the exposure time, the bandwidth of the hardware, etc. In general, the resolution affects the frame-rate the most. the resolution influences the monitoring field of view (FOV). For fragments produced by the explosion, we want to use high frame-rate (over 500 FPS) to capture the moment of the explosion, and we also want to record the scattered fragments as long as possible.  
To solve this problem, we use two HFR cameras with frame-rate multiplication, e.g., one HFR camera under 540 FPS, and another HFR camera under 1080 FPS. The frame-rate of the second HFR camera is twice that of the first camera. This is the same as Figure 2 for the first 37 ms, i.e., 20 frames for the first camera and 40 frames for the second camera. The Jacobian matrix of these two HFR cameras are presented in Figure 3. Each 6 \(\times\) 18 matrix block of yellow and purple presents the Jacobian matrix of both cameras at time stamp \(j\) and \(j+1\), with \(4\times 18\) for both cameras at time \(j\), \(2\times 18\) for camera two at time \(j+1\). The matrix block in yellow is the \(\mathcal{J}_{e}^{i,j}\). The matrix block in light blue is \(\left[\mathcal{J}_{pw}^{1j},\mathcal{J}_{pw}^{2j}\right]^{T}\) at time stamp \(j\) and \(\mathcal{J}_{pw}^{2,j}\) for time \(j+1\). The Jacobian matrix and Hessian matrix are also the sparse matrices, and with Equations (11) and (12), the iteration step can be solved efficiently.  
The interval between captured frames and the synchronization of our HFR cameras is strictly controlled by hardware instruments. This gives us the benefits that: (1) the Jacobian matrix is formatted strictly along the time axis with the same interval; (2) the Jacobian matrix presented in this paper is blocked diagonally, which reduces the complexity of the iteration. These are clearly demonstrated in the examples of Figures 2 and 3.  
To improve the accuracy and reduce the iteration step, the initialization of \(i\) is important. For synchronization stereo HFR cameras, we can initial \(i\) by a stereo calculation at each time stamp. In this section, for a certain time stamp, there is only one camera observing the fragment. To solve this problem, we use pose averaging to initialize the position which is observed by only one camera, i.e.,  
\[PW_{j}=\frac{1}{2}\Big{(}PW_{j-k}+PW_{j+k^{\prime}}\Big{)} \tag{13}\]  
Figure 3: (**a**) Jacobian matrix for HFR cameras with frame-rate multiplication; (**b**) related Hessian matrix of these two HFR cameras.  
where \(k\) is the time interval to the last time stamp where at least two cameras observed, and \(k^{\prime}\) is the time interval to the follow up time stamp where two cameras captured the fragment. Based on our initialization strategy, the iteration step can be reduced to less than 300 steps for both frame synchronization stereo cameras and the frame-rate multiplication where we set \(\epsilon_{1}=10^{-6}\), and the norm of all reprojection errors is less than \(10^{-2}\) pixels.
# Comparison of Damage Indexes for Assessing Seismic Fragility of Bearings in an Offshore Bridge
## 2 Methodology
### Fragility Analysis Method for Bearing
In the subsequent analysis, the earthquake resistance for the bridge will be evaluated using theoretical fragility as indicated by Equation (18). \[P_{f}[D\geq C|IM]=\Phi[(\frac{-\ln(\mu_{C}/\mu_{D})}{\sqrt{\beta}})]=\Phi[( \frac{\ln(\mu_{D}/\mu_{C})}{\sqrt{{\beta_{D}}^{2}+{\beta_{C}}^{2}}})] \tag{18}\]  
where \(\Phi\) (\(\cdot\)) refers to the standard normal cumulative distribution function, \(\mu_{C}\) and \(\beta_{C}\) represent the mean and logarithmic standard deviation of the seismic capacity of the structure, respectively, while \(\mu_{D}\) and \(\beta_{D}\) represent the mean and logarithmic standard deviation of the seismic demand placed on the structure, respectively [23]. Based on the properties of regression, \(\ln\) (\(\mu_{D}/\mu_{C}\)) is assumed to follow a normal distribution of the peak ground acceleration [2; 6; 24; 25]. Additionally, according to 100 example bridges and 100 ground motion records, [PERSON], [PERSON] [2] found that \(D\) and \(IM\) were linearly correlated on a logarithmic scale representation. See Equation (19) for details. \[\ln(\mu_{D})=\ln a+b\cdot\ln(IM) \tag{19}\]  
where the coefficients \(a\) and \(b\) are determined through regression analysis. The failure probability can be updated to the following equation:  
\[P_{f}[D\geq C|IM]=\Phi[(\frac{-\ln(\mu_{C}/\mu_{D})}{\sqrt{{\beta_{D}}^{2}+{ \beta_{C}}^{2}}})]=\Phi[(\frac{\ln(a\cdot IM^{b}/\mu_{C})}{\sqrt{{\beta_{D}}^{2 }+{\beta_{C}}^{2}}})] \tag{20}\]  
where \(\mu_{C}\) can be represented by the damage index value of the component under different damage states. Following the recommendations in HAZUS99, the value of the parameter \"\(\sqrt{{\beta_{D}}^{2}+{\beta_{C}}^{2}}\)\" is set at 0.4 for SA and at 0.5 for PGA.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 7 Materials  
The ESA _Swarm_ mission data access and data policy is publicly available via the web page earth.esa.int/web/guest/swarm/data-access (accessed on 19 March 2021), L1b-data are used, in particular the magnetic field high-res 50 Hz files with their meta-data (stored in a single file per day per satellite A/B/C) under the \"latest baseline folder\". The CSES data access and data policy is by the link www.leos.ac.cn (accessed on 19 March 2021), a registration and completion of a request form is necessary, HPM L2-data in Hierarchical Data Format version 5 (HDF5) are mainly used; a second source are data (calibrated according the nominal standards, i.e., L2 pipeline) from intercalibration campaigns of the CSES HPM instrument suite (checked for spurious satellite disturbances). In both cases ordinary (ftp-)clients and unzip-software are sufficient to get the requested data products. The _Swarm_ datasets are in the Common Data Format (CDF), ordinary software is used to read the data. The earthquake catalogue is from the United States Geological Survey (USGS) via the application programming interface earthquake.usgs.gov (accessed on 15 March 2021), a script handles the on-demand access (query string and result form). Information and activity status about volcanos are from VolcanoDiscovery web.volcanodiscovery.com (accessed on 22 March 2021) and from the Global Volcanism Program of the Smithsonian Institution volcano.si.edu (accessed on 22 March 2021). In both cases textual parts and descriptions are manually translated into machine readable sequences (mainly about eruption type and timing aspects). The events presented in this paper were checked with respect to geomagnetic disturbances. The calculated geomagnetic indices were made available by International Service of Geomagnetic Indices (ISGI) isgi.unistra.fr (accessed on 29 March 2021) collaborating institutes from data collected at magnetic observatories. The map is generated with the software GMT 5 ([PERSON], 2019). Programmes (scripts and functions) are written with the software Matlab (version R2019a; \"The MathWorks, Inc.; Natick, MA, USA\") under Windows 10 (\"Microsoft Corporation, Redmond, USA\") operating system.
## 3 A Short Overview of Selected Examples of Measuring and Monitoring Systems on Sea Ships
### Temperature Measurements in the Ship's Hazardous Areas
#### 3.3.2 Real Measurement Line with Transmitter as First-Order Inertial Element  
For transmitters of first-order inertial type the transconductance y\({}_{\text{m}}\)  
\[\text{y}_{\text{m}}=\text{g}_{\text{m}}\frac{\omega_{2}}{\text{s}+\omega_{2}} \tag{3}\]  
where g\({}_{\text{m}}\)--transconductance depends on measured range of temperature, \(\omega_{2}\)--3 dB frequency.  
An analysis of stability of the circuit presented in Figure 7 was done for the set of real parameters given in Table 1[1].  
While performing computer simulations for the parameter values presented in Table 1, it was shown that the temperature measurement line with first-order inertial transmitter in special condition can work in an unstable manner and as a result an alternating current can occur.  
The tendency to unstable working rises when the range is relatively small. That case is theoretical rather than practical because oscillating condition is fulfilled only for boundary conditions.
## 3 Sensitivity Analysis Method  
### Design of Experiments  
Design of experiments (DOE) was conducted to investigate the effect of the variation of the elliptic aspect ratios of the blade leading and trailing edge (see Figure 4) on the performance of the hydro turbine. The DOE is a method of logically analyzing the results, using statistical theory by efficiently placing experimental conditions, rather than performing experiments on all design variables within the range of the experimental area of interest. Unlike physical experiments, there is no random error in computational experiments; therefore, it is known that sampling methods which uniformly fill the experimental areas, the so-called \"space-filling\" concept, are appropriate [41]. Latin hypercube sampling (LHS) is a method of dividing each of the dimensions in the experimental design into regions with equal levels and extracting one sample from each region [42]. It is known to be a suitable sampling method for computational experiments, because it satisfies the concept of space-filling. One disadvantage of LHS is that it places samples randomly in the experimental area, hence it is likely that the distributed samples will be uneven. Optimal space-filling (OSF) is a sampling method based on LHS, but by maximizing the minimum distance between the points, it provides a more uniform distribution within the design space [43]. In this study, the design variables were sampled using OSF, and Figure 5b shows the distribution of extracted design variables within the experimental area. However, OSF  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Power [W]** & **Head [m]** & **Efficiency [\%]** \\ \hline Steady & 9.423 & 0.350 & 62.13 \\ Unsteady & 9.346 & 0.348 & 61.95 \\ Reference & 9.393 & 0.34 & 63.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the steady state, unsteady state and reference experimental results.  
does not include endpoints in the experimental area, which has the disadvantage of being less accurate at endpoints. Therefore, the samples for DOE added endpoints to the samples from OSF as shown in Figure 5c. The ranges of the elliptic aspect ratios of the blade leading and trailing edge were set to 2-16.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 4 Mixed-Integer Second-Order Cone Programming (MISOCP) Model Conversion
### Planning Results  
#### 5.1.1 Economic Benefits Improvement of ADN  
The planning results are shown in Tables 4-6. It can be seen that different types of DES have different performances. A lead-acid battery and Li-ion battery are both helpful but with different economic benefits in this case; a VRB is not selected to install because of the negative profit. In coordinated planning, the lead-acid battery and Li-ion battery are selected to allocate at the nodes 31 and 32 simultaneously. From Table 6, when only the Li-ion battery is allocated, the operation cost of ADN is lowest. However, the annual comprehensive cost is lowest in the coordinated planning, reducing by $10,378; the annual operation cost of the ADN is reduced by $67,881, a decrease of 5.21%. The price of lead-acid batteries is relatively low, but the cycle efficiency and the life cycle of the Li-ion battery are high. The two DESs have complementary advantages, and the economics of system operation is improved by coordinated planning. Although VRB has the highest cycle life compared to the other two DESs, VRB energy storage is expensive and has not been selected to be allocated in ADNs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Location** & **Power Capacity (kVA)** & **Energy Capacity (kWh)** \\ \hline \multirow{4}{*}{Only lead-acid battery} & 14 & 120 & 610 \\  & 17 & 100 & 450 \\  & 30 & 390 & 1750 \\  & 32 & 210 & 930 \\ \hline \multirow{4}{*}{Only Li-ion battery} & 17 & 120 & 990 \\  & 30 & 160 & 1420 \\ \cline{1-1}  & 31 & 80 & 690 \\ \cline{1-1}  & 33 & 100 & 900 \\ \hline \multicolumn{2}{c}{Vanadium redox flow} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ battery (VRB) & — & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 4: Planning results of economic benefits improvement in IEEE 33-node system.  
Figure 4: Annual power output of photovoltaic generators (PVs).
# The Blockchain Oracle Problem in Decentralized Finance--A Multivocal Approach
## 4 Oracles in Decentralized Finance
### Stablecoins
Second, it counts on a broader source of price feeds [90]. Unlike V1, in this new version, the identities of price oracles are disclosed (0x, dYdX, and Gnosis), and the contract also introduces a novel _medianizer_ mechanism. The new protocol requires the presence of an Oracle Security Module (OSM) for each collateral type. In addition to the V1 functions, a poke function is introduced, which excludes feeds that lack three critical requirements [91]. First, feeds should be provided by a minimum number of sources. Second, the values should be all positive and presented in ascending order. Finally, signatures must be verified and belong to all different whitelisted feeds. _Non-custodial algorithmic stable-coins_ constitute a complex experiment in which the pegged value is not ensured by collateral but relies only on a system of algorithms and smart contracts [51]. Those projects employ a model in which the token holder receives new coins when demand increases. On the other hand, if the demand decreases, the amount is automatically deducted from the market to limit the loss of value. Although simple in principle, algorithmic stable coins are challenging projects to realize, and some, such as Basis, already shut down due to regulatory hurdles [92]. Ampleforth is a project which is still active and employs the algorithmic principle; however, the stability of its value still represents a challenge [93]. Ampleforth utilizes a system of oracles trusted by the platform that reports price feeds cyclically. The platform administrator sets min, delay, and expire parameters, where min is the minimum number of reports. Delay indicates the time from which the reports can be used and expire the time in which the report becomes unreliable. CeloUSD is another algorithmic stable coin that implements a quite complex oracle type [94]. Celo protocol has a smart contract called SortedOracles that recognizes only four trusted price sources (Binance, Bittrex, Coinbase, and OKCoin). The Celo Oracle data aggregator, other than deploying the mean value, also checks if a minimum number of exchanges were queried [95]. The reporter then transfers the feed from the aggregator to the SortedOracle contracts, ideally on a stable time basis. For example, if the maximum age of a report is 5 min and there are ten participating oracles, then, every 30 s, an oracle should send a report. Celo also employs a \"MetricCollector\" that checks on the performance of oracles to detect anomalies in their behavior. The most attractive feature of Celo oracle is the so-called \"Circuit Breaker\". The circuit breaker basically shut down the oracle service in case of high volatility. Once shut down, the system should be restarted manually and revised by the platform expert before being operative again. During the shutdown, a trusted provider will adjust the price dynamically until the oracle restarts [96].
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 3 Experiments  
In this section, we apply the proposed method on one set of synthetic data and one field data for numerical experiments to verify the effectiveness of our method. The set of synthetic data is obtained by forward modeling on the Pluto model (Figure 3) to obtain clean P-wave signal data, and a total of 94 receivers and one seismic source are deployed with a recording time of 7 s and a sampling interval of 2 ms. The P-wave signal data are regarded as clean Z-component data. Then, we add horizontal component data (Figure 4b,c) from the field data in various proportions to obtain a set of noisy Z-component data. Such an approach mimics the actual situation where noise behaves as coherent noise in CRG, and the proportions and experimental details are described in Section 3.1. Field OBN data used in this paper were collected in offshore China. We select part of the data from one of the CRGs for testing. Each component of the geophone data contains 94 traces of seismic data, and there are 3501 time samples with a time interval of 2 ms in each trace. The size of synthetic data and field data for experiments are both 3501 \(\times\) 94. Before feeding the data into the network, we pad zeros around the data according to the structure of the U-Net (Figure 2) to ensure that the size of the data remains consistent after the convolution operation and then normalize the data between 0 and 1. The processed data are input into the network for training, utilizing the Adam optimization algorithm. In addition, all experiments are implemented on the Pytorch 1.10.2 framework with CPU and GPU calculations.  
Figure 3: Pluto velocity model used for synthetic data generation. The red asterisk represents the location of the source, while the green triangles indicate the position of the receivers.
# Raman Spectra of ZrS\({}_{2}\) and ZrSe\({}_{2}\) from Bulk to Atomically Thin Layers
## Appendix C Raman Spectra
In the case of the Raman allowed modes, the mass (M) that is entered in this expression is the reduced mass of the two S atoms vibrating one against the other. However, this is not the case with the IR modes since, in that case, the Zr atom moves against two S atoms and creates a dipole. In that case the reduced mass, \(\mu\), must be calculated as:  
\[\mu^{-1}\,=\,(2\mathrm{M_{S}})^{-1}\,+\,(\mathrm{M_{Zr}})^{-1}. \tag{3}\]  
Equation (3) gives a much smaller value for the mass, which means a much higher frequency. Actually, the IR spectra of several transition metal sulfides are in agreement with the previous tendency. In the case of ZrS\({}_{2}\), it is around 500 cm\({}^{-1}\)[34]. Thus, the high broadening of the A\({}_{1\mathrm{g}}\) peak in ZrS\({}_{2}\) seems to be unrelated to the appearance of IR modes (all polar modes have TO and LO). The clearest case is the comparison with its parent compound, ZrSe\({}_{2}\). The A\({}_{1\mathrm{g}}\) mode in that case is a sharp peak. On the contrary, there are several factors contributing to the phonon line width or broadening. The most important one is the anharmonicity, the second is the isotopic disorder, and the third is the electron-phonon interaction. At room temperature, anharmonicity is usually the most important one. Ab initio calculations in the lattice dynamics of ZrSe\({}_{2}\) and ZrS\({}_{2}\) reveal that there is no LO-TO splitting. There is a huge density of states (a very flat dispersion, covering the entire Brillouin zone) around the A\({}_{1\mathrm{g}}\) mode in ZrS\({}_{2}\), while in the case of ZrSe\({}_{2}\) the dispersion is not so flat [35]. Thus, the reason for the broadening of the A\({}_{1\mathrm{g}}\) peak can be attributed to anharmonicity. The phonon can emit acoustic phonons (with practically no energy), giving rise to this huge broadening.
# Review of Works Combining GNSS and InSAR in Europe
## 4 Temporal and Spatial Distribution in Italy
[142] looked for the source parameters and triggering link between the earthquake sequences of 2009 and 2016 recorded in central Italy. In addition, 4 works combining InSAR and GNSS data were developed for determining the source parameter of the three main shocks recorded during the 2016 Central Italy earthquake sequence [50; 140], for a modern observation of the spatio-temporal evolution of the seismic sequence [143] and for a novel record of near-field co-seismic fault slip measurement [21]. The spatial distribution of 5 volcanic scientific contributions, including 4 journal articles and one symposium proceeding, is strictly related to the location of volcanic cones in Italy. In fact, 3 publications focus on the analysis of nonlinear deformation [51], the evaluation of fault reactivation [203] and the investigation of magma injection [204] of the Campi Flegrei, Campania Region, and two on the ground deformation pattern of the Mt. Etna volcano in Sicily [160; 161]. The Mt. Etna volcano is also the subject of two peer-reviewed articles investigating the varied tropospheric compensation expectations between the GPS and SAR interferograms [38] and the dynamic models of atmospheric movement for calculating the delays affecting radar processing [17]. The other area where the atmospheric delay analysis was undertaken was centered over the Como province in Lombardia Region, North Italy,presenting the results in an international scientific journal [18] and in an international symposium [170]. In conclusion, the Italian territory has been a relevant case study since 1999 [42], and it was continuously under investigation by the scientific community until 2019 that analyzed both GNSS and SAR data for different purposes (Figure 7).
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 4 Experimental Results and Analyses
### Experimental Datasets  
The datasets utilized in the trials are split into a synthetic scene dataset and an actual scene dataset in order to address the challenges of low levels of data with annotations and high costs associated with manual annotation in natural settings, as well as to confirm the model's generalization.  
(1) Synthetic scene dataset  
Preprocessing yields the synthetic scene dataset text-image, which has 180,000 text images and tagged pictures for each scene and is separated into training, validation, and test sets in the ratio 14:1:1, designated as text-train, text-valid, and text-test, respectively.  
The MJSynth (MJ) synthetic scene dataset [58] is intended for scene text recognition applications. It comprises 9 million synthetic pictures that span 90,000 English words, with the first 1.5 million photos serving as the experiment's training set.  
(2) Real scene dataset  
The IIIT5K dataset [59] comprises 5000 actual scene word pictures. The training set consists of 2000 photos, whereas the test set consists of 3000 images. The experiment solely uses the test set.  
The ICDAR 2015 (IC15) dataset [60] comprises 1500 real scene word pictures. There are 500 test photos and 1000 training images.  
The ICDAR 2003 (IC03) dataset [61] consists of two datasets: character recognition (Char) and word recognition. The character recognition dataset has 6185 frames for the training set and 5430 frames for the test set, whereas the word recognition dataset contains 1157 frames for the training set and 1111 frames for the test set, with just two test sets utilized in the experiment.  
The CUTE80 dataset [62] comprises 80 high-quality photos of real-world settings, as well as 288 cropped text areas used as test samples.  
As demonstrated in Figure 9, the backdrop of real-world photographs is more complicated, with great contrast between light and dark, huge changes in the background environment, and partial occlusion and perspective effects. To produce a large number of labeled datasets for training the models in this study, we mix the real scene datasets with the Text Recognition Data Generator tool. These complicated scene photos can help the model improve its generalization abilities and tackle the text recognition challenge in difficult scenarios. Figure 9,b depicts the synthetic and actual datasets, respectively.  
Figure 9: Comparison between synthetic datasets and real datasets.