esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 3 Results
### Overall Classification Accuracies  
The computation time of the evaluated object-based DTW classifications was approximately 1 hour for TA1 and 15 minutes for TA2. For both single-band and multi-band DTW, the pattern of OAs values followed the same asymmetrical concave downward form, with peaks reached around 30 and 45 days of constraint (Table 5). For TA1, both single-band and multi-band DTW achieved highest OAs for time-constraints of 30 and 45 days, with 79.5% and 85.6% overall accuracy, respectively. Lowest OAs were achieved for DTW0 and DTWFull, of 75.2% and 72.3% for single-band approach, and for DTW0, DTW15, and DTWFull, of 76.1%, 80.3%, and 82.3% for multi-band approach (Table 5). For TA2, the highest OAs were achieved for 45 days constraints for single-band approach (89.1%) and for 30 days constraints for multi-band DTW (87.6%). The lowest OAs for TA2 were for DTW0, DTW15, and DTWFull for single-band approach (86.6%, 86.7%, and 82.3%) and DTW60 and DTWFull for multi-band approach (85.5% and 83.3%) (Table 5). The OAs pattern highlighted the importance of time-constraints in DTW for crop classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Area** & **SP** & **Objects** & **OS** & **US** & **AFI** & **D** & **QR** \\ \hline TA1 & 241 & 3492 & 0.05 & 0.04 & 0.01 & 0.05 & 0.91 \\ TA2 & 307 & 1585 & 0.16 & 0.02 & 0.14 & 0.11 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Segmentation accuracy results depicting the scale parameter (SP), number of objects, over-segmentation (OS), under-segmentation (US), area fit index (AFI), root mean square (D), and quality rate (QR) metrics, as detailed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Area**} & **DTW** & **DTW0 (\%)** & **DTW15** & **DTW30** & **DTW45** & **DTW60** & **DTWFull** \\  & **Type** & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline TA1 & single-band & 75.2 & 77.7 & 79.5 & 79.5 & 76.6 & 72.3 \\  & multi-band & 76.1 & 80.3 & 85.6 & 85.6 & 84.3 & 82.3 \\ TA2 & single-band & 86.8 & 86.7 & 87.8 & 89.1 & 88.6 & 82.3 \\  & multi-band & 86.1 & 86.1 & 87.6 & 86.0 & 85.5 & 83.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracies for single-band and multi-band object-based time-constrained dynamic time warping (DTW) using 15, 30, 45, and 60 days. For comparison, the accuracies obtained using no time constraint (DTWFull) and using Euclidean distance (DTW0) are shown. With bold fonts are shown the DTW classifications with the highest overall accuracies.
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 4 Seismic Interpretation  
In order to compare the results obtained before and after the depth processing using the MVA technique was applied, we interpreted a reflector, which was strong amplitude (Figure 11). We picked the same reflector from the results obtained both before and after depth processing was applied, and the reflectors are represented by contour maps in Figure 11a,b. We confirmed the boundary using the seismic attribute of RMS analysis, indicating an amplitude anomaly (Figure 11c,d). The RMS seismic attribute is a post-stack analysis technique and computes the square root of the sum of squared amplitudes within the specified window used.  
The strong reflector from time processing (upper image in Figure 12a) vertically ranged from 910 to 980 m, while the reflector from both time and depth processing (lower image in Figure 12a) was mainly positioned below, ranging from approximately 970 to 1020 m. The reflector was interpreted as the top of the volcanic mound. The volcanic body showed high acoustic impedance compared with the surrounding clastic rocks, and products derived from igneous rocks could be easily detected in sedimentary basins. The reflector constrained to the RMS seismic attribute showed a circle-shaped geometry and the volcanic body was located high in the center, indicating a volcanic mound (Figure 11a,c). Compared with mound shape, in both time and depth processing (Figure 11b,d), lateral changes were presented in greater detail, although the mound subjected to only time processing (Figure 11a,c) was smoother. As a result of the interpretation, the results (Figure 12a) from both time and depth processing using MVA show that the volcanic mound vertically shifted  
Figure 10: 3D depth-domain stack sections after applying MVA at the 3 rd iteration. The green and blue lines represent the top of the basalt before and after applying MVA, respectively.  
to a deeper position. The difference (Figure 12b) in the mound before and after depth processing was applied was varied, ranging from 32.5 to 60 m. We suggest that geological geometry or objects derived from both time and depth processing using MVA represent vertical differences and show lateral variations (Figure 12b).  
Figure 11: Contour maps of the strong reflector and RMS attributes. Geologically interpreted tops of the volcanic mounds (**a**) before and (**b**) after depth processing. RMS amplitude attributes (**c**) before and (**d**) after depth processing showing volcanic mounds’ anomalous region.
## 2 Methodology
### Bayes Estimation of Model Parameters  
According to the Bayesian inference theorem, the joint posterior distribution of the parameters of \(m\), \(\eta\) and \(\beta_{i}\) to be determined using the observed censoring samples \(D\) for the improved Weibull model can be obtained as:  
\[f(m,\eta,\beta_{i}|D)=\frac{L(D|m,\eta,\beta_{i})f(m,\eta,\beta_{i})}{\int\cdots \int\limits_{(m,\eta,\beta_{i})}L(D|m,\eta,\beta_{i})f(m,\eta,\beta_{i})d(m, \eta,\beta_{i})} \tag{11}\]  
where:  
\[L(D|m,\eta,\beta_{i})=\prod\limits_{j=1}^{n}f(t_{j},X) \tag{12}\]  
where \(L(D|m,\eta,\beta_{i})\) is the likelihood of Weibull model parameters and \(f(m,\eta,\beta_{i})\) is the joint prior distribution of the parameters.  
Given that there are no revealed function form references for the multi-dimensional probability density function \(f(m,\eta,\beta_{i})\), we need to designate the initial distributions the parameters of \(m\), \(\eta\) and \(\beta_{i}\) assuming they are independently distributed. Considering the ease of computation resulting from conjugate prior distributions and the experiences and knowledges, we assume that the unknown parameters \(m\) and \(\eta\) follow independent Gamma priors such that \(m\sim Gamma\) (\(a_{1}\), \(b_{1}\)), \(\eta\sim Gamma\) (\(a_{2}\), \(b_{2}\)), and the prior of parameter \(\beta_{i}\) follows normal distribution as \(\beta_{i}\sim N\) (\(\mu_{i},\sigma_{i}^{2}\)), where hyper parameters \(a_{1}\), \(b_{1}\), \(a_{2}\), \(b_{2}\), \(\mu_{i}\) and \(\sigma_{i}\) are assumed as non-negative and known. In this manner, the prior distributions of the unknown Weibull parameters can be expressed as:  
\[f(m)=\frac{b^{\alpha_{1}}}{\Gamma(a_{1})}m^{\alpha_{1}-1}\exp(-b_{1}m),m\in[0, +\infty] \tag{13}\]  
\[f(\eta)=\frac{b^{\alpha_{2}}}{\Gamma(a_{2})}\eta^{\alpha_{2}-1}\exp(-b_{2}\eta ),\eta\in[0,+\infty] \tag{14}\]  
\[f(\beta_{i})=\frac{1}{\sqrt{2\pi}\sigma_{i}}\exp\left[-\frac{(\beta_{i}-\mu_{ i})^{2}}{2{\sigma_{i}}^{2}}\right],\beta\in[-\infty,+\infty] \tag{15}\]  
Consequently, the joint prior distribution \(f(m,\eta,\beta_{i})\) is:  
\[f(m,\eta,\beta_{i})=f(m)f(\eta)\prod\limits_{i=1}^{k}f(\beta_{i}) \tag{16}\]  
By substituting Equations (13) and (16) into Equation (11), the joint posterior distribution function of Weibull parameters on observed data samples can be derived. Subsequently, the Bayes estimate of each individual parameter can be obtained as the mean of the posterior function by the following equation, taking the parameter \(m\) as an example:  
\[\hat{m}=\int\cdots\int\limits_{(\eta,\beta_{i})}m\cdot f(m,\eta,\beta_{i}|D)d (m,\eta,\beta_{i}) \tag{17}\]  
However, it is rather challenging to solve the multi-dimensional integral equations such as Equations (11) and (17) with an explicit expression for Weibull parameters. To tackle this challenge, the Hamiltonian Monte Carlo (HMC) method will be employed to approximate the Bayes estimation.
## 2 Methodology
### Weibull and Rayleigh Distribution Functions
#### 2.2.1 Weibull Distribution
The calculated parameters of shape (\(k\)), scale (\(c\)), the mean wind speed (\(v_{m}\)), and the standard deviation are listed by month in \[\sigma=\frac{1}{N-1}\left[\sum_{i=1}^{N}(v_{i}-v_{m})^{2}\right]^{1/2} \tag{2}\]1 The scale factor, \(c\), ranges from 2.45 to 4.04 m/s, according to the Weibull parameters outlined in Table 1, while the shape factor, \(k\), is between 0.95 and 2.12. October and November have the lowest values of the scale parameter, indicating that they are the least windy months of the year. The value of the scale parameter is lowest in January and February, which means that these two months are the windiest months of 2019 year in Canakkale Province.
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 1 Introduction
Although these methods reduce the influence of noise to a certain extent, some limitations cannot be ignored. The threshold method is commonly used to reduce the effect of uniform noise, but it has the problem of manual parameter selection. The matching window method can eliminate the influence outside the matching window, but cannot reduce the noise inside the matching window. Processing with the image interpolation method can improve the signal-to-noise ratio of the image to a certain extent, but the measurement algorithm consumes more and more time and cannot meet the requirements of real-time detection as the interpolation accuracy increases [19; 20; 21]. An iterative double-area shrinkage method based on the baseline method, which can determine the exact location of the laser spot center, is presented in this paper. The proposed method eliminates the effect of noise by calculating the background noise baseline from multiple background images. Subsequently, the real spot area is located using double-area shrinkage iteration to calculate the spot center. Section 2 describes the traditional center positioning method. Section 3 introduces the realization principle of this approach. Section 4 conducts simulations, experiments, and comparisons with typical algorithms to verify the effectiveness of the proposed method. Lastly, Section 5 summarizes the conclusion.
## 2 Problem Formulation
#### 3.2.2 Ternary Event-Triggering (TET)  
The counterpart to using traditional binary decision making is to use the triggering approach called the ternary event triggering (TET) mechanism. The TET mechanism is a deterministic and ternary scheduler which proposes three decision levels instead of the conventional binary decision making. The sensor in the ternary mechanism initially determines the discrepancy between its current and previously communicated measurements [14]. Later on, the TET method uses this difference to decide whether or not to send the data. The TET scheduling mechanism is specified as:  
\[\gamma_{k}=\left\{\begin{array}{ll}0,&\text{if }|z_{k}-z_{\tau_{k}}|< \Delta_{1}\\ 1,&\text{if }\Delta_{1}\leq|z_{k}-z_{\tau_{k}}|<\Delta_{2}\\ 2,&\text{if }|z_{k}-z_{\tau_{k}}|\geq\Delta_{2}\end{array}\right., \tag{3}\]  
where \(\tau_{k}\) indicates the time of the previous transmission from the sensor to the estimator, and \(\Delta_{1}\) and \(\Delta_{2}\) are two triggering thresholds characterizing the ternary levels [14,19]. Inclusion of the ternary framework leads to a hybrid observation vector \(\mathbf{Y}_{k}=\left\{\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{k}\right\}\) where  
\[\mathbf{y}_{i}=\left\{\begin{array}{ll}\{z_{i}:z_{i}\in(z_{\tau_{i}}-\Delta_ {1}\,\ z_{\tau_{i}}+\Delta_{1})\}&\text{if }\gamma_{i}=0\\ z_{i}^{(Q)}\wedge\{z_{i}:z_{i}\in(z_{\tau_{i}}+\Delta_{1}\,\ z_{\tau_{i}}+\Delta_{2}) \lor z_{i}\in(z_{\tau_{i}}-\Delta_{2}\,\ z_{\tau_{i}}-\Delta_{1})\}&\text{if }\gamma_{i}=1\\ z_{i}&\text{if }\gamma_{i}=2\end{array}\right. \tag{4}\]  
The immediate consequence of utilizing the TET framework is the accessibility of the below hybrid measurements at the fusion center:  
1. _Set-Valued Data_: When \(\gamma_{k}=0\), the circumstances of the ternary mechanism are not satisfied; hence the sensor does not transfer any measurement to the estimator. In this scenario, fusion center does not have the specific value of the existing sensor's observations. However, based on secondary information (i.e., the observation \(z_{k}\) associated with the following set \((z_{\tau_{k}}-\Delta_{1}\,\ z_{\tau_{k}}+\Delta_{1})\)), the FC knows which observations belong to which set.  
Set and Quantized-Valued DataWhen \(\gamma_{k}=1\), the estimator lacks the exact value of the current measurement (\(z_{k}\)), though it has access to the quantized version of the current measurement, which is transferred to the FC. Meanwhile, the FC has access to the set-valued data (i.e., the observation \(z_{k}\) belongs to either of the following two sets \((z_{\tau_{k}}+\Delta_{1}\), \(z_{\tau_{k}}+\Delta_{2})\) or \((z_{\tau_{k}}-\Delta_{2}\), \(z_{\tau_{k}}-\Delta_{1})\)). This additional secondary information contributes to the enhancement of the approximation efficiency through quantized measurements [14; 19].
3. _Point-Valued DataWhen \(\gamma_{k}=2\), the TET mechanism conveys the specific measurement of the sensor \(z_{k}\) to the FC._
# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
## 1 Introduction
However, most researchers used some methods, such as potentiodynamic polarization, potentiostatic polarization, or heating, to accelerate the pitting corrosion of metals in a short time, thus not realistically simulating the corrosion process of materials under natural conditions. It is very important that the corrosion behavior of steel through in-situ monitoring of the pitting corrosion process under natural conditions is understood to implement structural integrity management for steel construction. The processing and analyzing of the AE signal is the most important step in AE studies. In many studies, the acoustic emission signals are processed and classified by various methods, like K-means, random forest, wavelet analysis, or some other parameter analysis methods [26; 27; 28; 29; 30; 31; 32; 33]. The application of some methods, for instance, the K-means and random forest, only utilize some parameters of the AE waveform (raw or pre-treated). The single use of a wavelet can only optimize AE waveforms. Therefore, there remains much work to do for the development of an effective and integrative method to identify the corrosion type based on raw data from in-situ AE monitoring. In this paper, the pitting corrosion of carbon steel was monitored by the AE technique in NaHCO\({}_{3}\) + NaCl solutions, in which the pitting initiation--growth--re-passivation process was present [5]. Open circuit potential experiments were carried out simultaneously to probe the relationship between the AE and electrochemical behavior. In addition, an optical microscope was used to obtain in-situ recordings of the surface morphology of carbon steel under corrosion attack in different conditions and SEM was used to observe the surface micromorphology after corrosion. Finally, Matlab was involved to establish the methods for AE data processing [33] and analyzing.
# TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts
## 3 Method
### Task Flow of TDLearning  
As shown in Figure 3, the collaborative-learning-task process is divided into a task-information-initialisation phase and a task-execution phase from the perspective of each node participating in the collaborative training of the distributed model. The core steps of the task-information-initialisation phase are as follows.  
1. _Su_ deploys the relevant smart contracts and assigns a set of _Mv_ to initialise their credibility ratings.
2. Each collaborating node registers identity information through the AMC, and the _Su_ assigns the nodes corresponding to role authority based on the identity request information.
3. _Tp_ registers the local data summary via the DMC. The DMC invokes the DRC and DEC to index the newly registered data and initialise its quality evaluation.
4. _To_ registers and initialises collaborative tasks via the TMC, which specifies the task overview, pretrained model structure, standard test sets and _Mv_ credibility threshold, and pledges task incentives to the TMC. _To_ uses the smart contract to perform secure on-chain searches and queries the quality ratings of relevant datasets to find high-value trusted data.
5. _To_ specifies the data nodes involved in the task, i.e., the task participants, while the supervisor specifies the model verifiers for the task based on the _Mv_ credibility threshold requirement. After the above steps are completed, if the TMC determines that sufficient collaboration incentives have been deposited, the collaboration task is formally published.  
The core steps of the task-execution phase are as follows.  
1. _Tp_s develop a pretrained model locally by using private data in the specified model structure. Once the pretrained model is developed, _Tp_s send it to the specified _Mv_s as an on-chain encrypted transmission.
2. _Mv_s complete pretrained model verification locally with a standard test set specified by _To_ and upload the verification results encrypted to the TMC.
3. The TMC determines whether a consensus on pretrained model verification is reached based on the verification results of _Mv_s. When the consensus is reached, _Mv_s send the pretrained model to _To_ via on-chain encryption. At the same time, the TMC creates the TIC, and the TIC completes the collaboration-incentive allocation, and both _Tp_s and _Mv_s involved in the collaboration will be automatically incentivised by the smart contract.
4. _To_ invokes the DEC to update the collaborative data-quality evaluation. At the same time, _To_ invokes the AMC to update the credibility evaluation of _Mv_s. If a collaborative node believes that _Mv_ has suspicious malicious behaviour, the collaborative node can request to initiate a trust arbitration vote on the _Mv_, and the AMC updates the _Mv_ credibility evaluation based on the vote arbitration result.  
Figure 2: Model-fusion method based on feature fusion.  
5. _To_ completes pretrained model fusion to develop the global model, and the collaborative task ends.
## 5 Discussion  
The aim of this study was to enable traffic flow in cases where PT is not physically separated from individual traffic and uses a common traffic lane. VISSIM, with its EPICS module, was used as a simulation tool. EPICS enables the assignment of weighting coefficients to individual signal groups, and direct implementation at real intersections is possible.  
The basic problem addressed in this study was determination of the weight coefficients of the signal groups used by PT vehicles. The methodology was based on the counting of passengers in each of PT and individual vehicles. In further research, it is proposed to automate passenger counting and estimation. In the case of individual vehicles, cameras can be used at the intersections and, using video processing, the number of passengers in individual vehicles can be determined. For PT vehicles, video processing of recordings from cameras located in the vehicles themselves is proposed. Cameras placed at intersections can be also used to determine trapezoidal probability density function of PT arrival time to stop line.  
Even though the EPICS module is user friendly, it should enable changing of several parameters and be more easily adapted to local conditions. In particular, it is especially necessary to work on its functioning at complex intersections where several signal groups contain PT lines. In addition, the methodology proposed in this paper addresses the gap that existed in the definition of the weight coefficient of signal groups.  
In further research, it will be necessary to develop a methodology for the dynamic change in the weight factors, depending on the current ratio of the number of passengers at intersections.  
In addition, the further development of EPICS should strive to allow it to work at a network level, rather than the local level in microsimulation tool. Even though BALANCE is intended for network optimization of signal plans, its development is extremely complex and first requires defining a macrosimulation model with origin-destination matrices in VISUM prior to exporting to VISSIM.
## 3 Case Study and Simulation
### Residual Errors  
The simulated synchronization performance previously described is expected to exceed the performance of an actual deployed system constructed with hardware & software owing to the degrading effects of the residuals. However, the residuals cannot be simulated unless that the specified hardware, frequencies and space scenario is confirmed. Therefore, in Figure 11, clock instability is analyzed for different residual errors, which are uniform randomly distributed in the range 0.05 nanoseconds, [-0.05, 0.05] nanoseconds, [-0.1, 0.1] nanoseconds, [-0.2, 0.2] nanoseconds and constant 0.05 nanoseconds, respectively. The bandwidth of the control loop is 0.5 Hz. It can be seen that the residual error has a great effect on the short-term stability of the slave clock when it is not a fixed value, which could be explained by that the PLL cannot get the absolute ideal control signal with the increasing instability of residual error.