esa-sceva/satcom-chunk-collection huggingface dataset:
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 3 Case Studies
### Infrastructure Mapping
#### 3.1.1 Incorporating Noisy Training Label Data
In panels (A) and (B) of Figure 8, the x-axis shows the noise from randomly dropped and randomly shifted labels, respectively. Panel (C) combines the effects of noisy deletions and noisy shifts on accuracy in a single graph, showing F1 of the labels on the x-axis and F1 of the prediction on the y-axis. The F1 score of the noisy versus ground truth labels is a function of the pixel-wise errors; this metric has the benefit of measuring the effect of noise on error in a way that is comparable across datasets and object classes. For instance, a noisy shift of 10 in a dataset with large buildings might result in a different proportion of erroneous label pixels than in another dataset with small buildings. From this, panel (C) shows that while some of the shifted datasets have a greater level of noise, the prediction F1 scores are similar between the two series when the noise level is similar. These results present a small step toward determining how much accuracy is sacrificed by using TD from OSM. Preliminary results indicate that accuracy decreases as noise increases, and that the model becomes more conservative as the noise level increases, only predicting central portions of buildings. Furthermore, the noisy shift experiments suggest that the relationship between noise level and accuracy is non-linear. Future work will quantify the functional form of this relationship, and how it varies with the size of the training set. Some preliminary work toward this goal has been described in [PERSON] et al. (1993), which focuses on image classification of Imagenet-style images. One limitation of these results is that the magnitude of error in OSM for most areas is unknown, making it difficult to predict the effect of using OSM labels to train models in a generalized, global sense. Noisy error in OSM can be estimated by measuring the disparity between OSM labels to clean labels, such as the SpaceNet labels used here, providing a local estimate of OSM noise. A more general  
Figure 8: The precision, recall, and F1 scores across different noise levels are shown for the cases in which labels are randomly dropped (**A**) or randomly shifted (**B**). Panel (**C**) compares how prediction quality changes as noise increases for dropped and shifted labels, measured by F1 of the labels and prediction. Figure 7: Predictions of the model trained on different noisy datasets. Each row shows a single scene over different noise levels. The top two rows show noisy drops, while the bottom two rows show noisy shifts. The ground truth is outlined in light blue, and the predictions are filled in orange. but less rigorous approach is to roughly estimate the noise level by visually inspecting the labels in OSM, and comparing to Figure 7, which shows examples of the labels at different noise levels.
## 5 Conclusions  
In this study, numerical simulations are carried out on the composite foundation in sand-overlying-clay deposits under combined \(V\)-\(H\)-\(M\) loadings. Based on the parametric analyses, the bearing capacities and failure mechanisms are found to be closely related to the foundation geometry (\(D_{\rm w}/L\) and \(L/D_{\rm p}\)), the soil properties (\(\tan(\varphi)\gamma^{\prime}sD_{\rm p}/s_{\rm um}\)), the upper sand layer thickness (\(T_{\rm s}/L\)), the lateral loading height (\(e/D_{\rm p}=8\)), and the pre-vertical load (\(V/V_{\rm utl}\)). The following conclusions can be drawn:  
For the bearing capacities in the \(V\)-\(H\)-\(M\) domain, the foundation geometry has a more pronounced influence when the wheel diameter (\(D_{\rm w}/L\)) exceeds 0.5. However, it makes little difference to the shape of the failure envelopes under combined \(V\)-\(H\)-\(M\) loadings. The sand layer thickness (\(T_{\rm s}/L\)) influences the bearing capacity with a nonlinear manner and the trend is determined by the soil properties (\(\tan(\varphi)\gamma^{\prime}sD_{\rm p}/s_{\rm um}\)), which have non-negligible effects on the failure mechanism and the shape of failure envelopes in the \(V\)-\(H\)-\(M\) domain. Due to the \(P\)-\(\Delta\) effect and the \"punch-through\" failure pattern, the vertical load (\(V/V_{\rm utl}\)) influences the lateral bearing capacity with an optimal value when the maximum capacity occurs. Through imposing an additional bending moment onto the composite foundation, the lateral loading height (\(e/D_{\rm p}\)) shows a negative effect on the bearing capacity and the stability of the foundation. In the end, an empirical design approach is proposed with Equations (2)-(4) and Tables 2-4 for the prediction of the composite foundation in sand-overlying clay deposit under the \(V\)-\(H\)-\(M\) combined loadings.  
[PERSON]. (conceptualization, formal analysis, methodology, validation, writing-original draft preparation); [PERSON]. (formal analysis, Funding acquisition, supervision, writing-review & editing); [PERSON]. (writing-review & editing; validation). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51578231 & 52178329).  
Not applicable.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 2 Related Work
### Detection Methods Based on Road Constraints  
Using prior road knowledge to optimize the target detection algorithm is the easiest and most effective way to remove background noise and pseudo-motion target interference. [PERSON] et al. detected moving target regions using temporal differences, and after accumulating the moving regions to generate regions of interest, moving vehicles were detected [6]. [PERSON] et al. used the boundary association of superpixels combined with background subtraction to generate a motion heat map, thereby completing superpixel-based motion region estimation and improving target detection results [7]. However, this method requires a large number of moving vehicles and relies on the accumulation of many video frames, which is a significant waste of video frame resources. [PERSON] et al. used an object-oriented classification approach to extract road masks, which were then used to reject false motions and pseudo-motion targets using the ViBE algorithm [8]. However, this method uses only a single image frame, which can result in significant breaks and missed detections. [PERSON] directly used existing road network data overlaid with satellite video data [9]. When solving problems such as road breaks and missed inspections, the global operating range of video satellites can lead to an overwhelming amount of data on the road network.
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
## 3 Case Study
### Computational Model
#### 3.2.6 Calculation Parameters  
The physical and mechanical parameters of the rock mass in the study area were determined through physical and mechanical experiments of the rock mass; evaluation parameters of the rock mass quality are listed in Table 1. Each formation often contains several rock strata with different lithology. For simplicity, we used the weighted average method to assign a set of parameters to each formation. We first determined the cohesion, internal friction angle, modulus of elasticity, Poisson's ratio, gravity, and other parameters of the main rocks in each rock stratum according to the mechanical experiments, and then determined the parameters of each formation according to the thickness of the rock stratum and the above parameter values using the weighted average method.  
In this paper, \(e_{residual}^{p}\) of the overlying strata is determined by the simplified complete stress-strain curve of the main lithology. Taking the Shanxi formation (P\({}_{1x}\)) as an example, the determination process of \(e_{residual}^{p}\) is briefly described. The Shanxi formation (P\({}_{1x}\)) is mainly composed of sandstone and mudstone. First, the complete stress-strain curves of sandstone and mudstone are obtained by triaxial tests (see Figure 3), which are simplified as ideal curves, as shown in Figure 11, and \(e_{residual}^{p}\) of sandstone and mudstone under various confining pressures are obtained. Second, based on the thicknesses of sandstone and mudstone in the formation, the weighted average value \(e_{residual}^{p}\) of the Shanxi formation under each confining pressure is calculated. Finally, the \(e_{residual}^{p}-\sigma_{3}\) relation curve (Figure 12) can be drawn, and the \(e_{residual}^{p}\) corresponding to arbitrary confining pressure \(\sigma_{3}\) can be obtained on the basis of the curve.  
According to the above method, the \(\varepsilon^{p}_{residual}-\sigma_{3}\) relationship curves of all the overlying strata can be obtained (Figure 12).  
At the study site, the complete stress-strain curve of the yellow clay loam in the Quaternary (the Pingyuan formation) shows weak softening. With the continuous development of deformation, the yellow clay loam has little stress variation, and the strength of the yellow clay loam remains at the residual value. Therefore, it is considered that the yellow clay loam would be in the residual strength stage when \(\varepsilon^{t}\!>\varepsilon^{e}_{peak}(\varepsilon^{p}_{residual}=0)\).  
The \(\varepsilon^{p}_{loosened}\) is determined according to the empirical values in the references, \(\varepsilon^{p}_{loosened}=0.14\) (in tension) or \(\varepsilon^{p}_{loosened}=0.10\) (in shear).
## 3 Experiments and Results  
In order to verify the correctness of the above formulae of the air refractive index based on the etalon, the verification scheme of measurement of the optical path of the air refractive index was constructed as follows (see Figure 3).  
The light from the laser passes through the beam splitter (BS) mirror and divides into two paths. One path passes through the beam bender mirror and the first interferometer to the air refractive index standard (etalon). The returned light passes through the first interferometer and returns to the electronic signal subdivision circuit (SPC). The processed signal data is collected by the computer. The other light path passes through the second interferometer to the cube corner reflector. It is reflected back to the second interferometer and back to the SPC. The processed signal data is collected by the computer. At the same time, the environmental sensors are arranged on a second path in order to calculate the initial value \(n_{0}\) of the refractive index of the air. In particular, these two light paths can be placed close to each other, so it can be thought that the two light paths have the same environmental conditions and the same refractive index of the air.  
In the experiment, a Renishaw RLE10 was selected as the laser source, which has a laser signal with 64 times electronic subdivision. The actual measurement value of the thermal expansion coefficient of the Etalon used in the air refractive index standard tool is 6.1 \(\times\) 10\({}^{-8}\)/K (see Figure 4). The variation of the thermal expansion coefficient of the Etalon in the range of 20 \({}^{\circ}\)C \(\pm\) 5 \({}^{\circ}\)C is not more than 1.0\(\times\) 10\({}^{-8}\)/K, which guarantees the outstanding thermal stability of the length.  
The diagram comparing the variation between the measured values of the air refractive index obtained from the Etalon in the first light path and the calculated value of the air refractive index from the ambient atmosphere sensors in the second path are shown in the following figure (see Figure 5).  
Figure 4: The measurement value of the thermal expansion coefficient of the Etalon.  
Figure 3: The verification scheme for the measurement optical path of the air refractive index. BS: beam splitter, SPC: signal subdivision circuit, PC: personal computer.  
It can be seen from Figure 5 that the change in the air refractive index calculated by the modified Edlen formula is relatively gentle because of the hysteresis of the sensors, which cannot fully reflect the effect of air fluctuation on the actual air refractive index. However, the method proposed in this paper can obtain the real-time changes in the air refractive index easily and quickly, and perform the further modification and compensation in the subsequent length measurement. From Figure 6, it can be seen that the maximum difference between the two measurement methods is up to \(2.5\times 10^{-7}\).
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 4 Results
### Screening Currents
Evidently, the emergence of vortices in practical washer design applications is deemed detrimental. One significant source of 1/f noise is the motion of flux lines (vortices) trapped in the body of the SQUID or in the superconducting input circuit. To model this dependency, we consider the noise power spectral density \(S_{\Phi}(f)\), which is given by  
\[S_{\Phi}(f)=\frac{A}{f^{\alpha}} \tag{23}\]  
where \(A\) is a parameter that depends on the geometry of the washer, and \(\alpha\) is close to 1. Simulation results of magnetic dipole fields clearly indicate that 1/f noise in SQUIDs is significantly influenced by the geometry of the washer. Different geometries, such as square, circular, and octagonal washers, impact the distribution of the current density and the pinning sites for vortices, which, in turn, affect the 1/f noise characteristics. For instance, the octagonal washer geometry tends to distribute the current more evenly compared to the square washer, thereby reducing the concentration of high-current density regions that contribute to vortex movement and, consequently, 1/f noise. Figure 8: Screening currents simulations: (**a**) Current circulation in a square washer. (**b**) Current circulation in a circular washer. (**c**) Current circulation in an octagonal washer. The upper images visualize current circulation patterns, while the lower images detail the current intensity at tangential points. Different colors indicate the simulation results for different tangent positions. Through comprehensive analysis, we have discerned that, in real-world SQUID applications, circular and octagonal washer loops offer distinct advantages over traditional square loops. The configuration of octagonal washers not only significantly reduces detrimental edge effects but also markedly alleviates current crowding at corners and the formation of eddies along the straight edges of the washer. These phenomena can lead to distortions in the magnetic field measurements. Therefore, SQUIDs equipped with circular and octagonal washers demonstrate superior performance, rendering them the optimal choice for optimizing the SQUID sensor design.
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 4 Numerical Results
### System Performance under Simulated UWA Channel
From the figure, we can observe the following: (1) Comparing with OMP channel estimation, the proposed DnCNN-based channel estimation has about 1.1 dB gain at 4 Hz Doppler shift and 3.5 dB gain at 6 Hz Doppler shift, at an MSE of \(0.8\times 10^{-3}\). (2) Comparing the performance of different scales of Doppler shifts for OMP channel estimation, at an MSE of \(0.8\times 10^{-3}\), the SNR required for a 2 Hz Doppler shift is about 1.8 dB, which is 4.8 dB lower than that for 4 Hz and 6 Hz Doppler shift, respectively. Comparing the effect of various Doppler for DnCNN-based channel estimation at the same MSE, the required SNR for a 2 Hz Doppler shift is about 1 dB, which is 1.7 dB lower than that for 4 Hz and 6 Hz. The deterioration of channel sparsity in the Doppler domain  
Figure 3: MSE performance under simulated channel, 4 multipaths. poses a challenge for the OMP algorithm to achieve accurate channel estimation, while our proposed method has better accuracy and robustness under complex UWA channels. It can be seen from Figures 3 and 4 that the channel estimation performance of the system with eight multipaths is worse than that of four multipaths, as the channel structure of eight multipaths is more complex than that of four multipaths. Even in this case, our proposed method shows better performance than the OMP channel estimation. At an MSE of \(0.8\times 10^{-3}\) and Doppler of 6 Hz, our proposed method has a 1.7 dB gain compared with the OMP under the channel of four multipaths. At the same MSE and Doppler, it has a 3.5 dB gain compared with the OMP under the channel of eight multipaths. With the number of multipaths increasing, the SNR gain of the proposed method becomes larger. Even if the channel becomes complex, the cascaded DnCNN in the proposed method can further denoise the preliminary channel estimation of the OMP algorithm and can finally achieve accurate channel estimation results.
## 3 Results and Discussion
### Comparison of EC with eBC  
Figure 3 shows time series of eBC and EC concentrations. Blue line represents eBC concentrations measured every minute and averaged with 8 h for comparison with better display. Red circles represent EC concentrations of samples collected every 8 h as explained earlier. The average of eBC concentrations was 1.23 ug/m\({}^{3}\), and the average of EC concentrations was also 1.23 ug/m\({}^{3}\). The level of EC and eBC concentrations was similar to the average eBC concentration measured by our group [17]. Thus, the EC and eBC concentrations are comparable. EC concentrations are in good agreement with eBC concentrations despite fundamental differences between the thermal-optical technique and the filter-based light attenuation technique. Intermittent breaks in EC measurements stemmed from the failure of the automatic low-volume sampler caused by malfunction of an internal vacuum pump.
## 4 Experiments
### Evaluation Protocols  
We use four evaluation protocols to evaluate our proposed method: (1) accuracy \((Acc)\), (2) sensitivity \((Se)\), (3) positive predictivity \((P_{p})\), and (4) F1 score (F1) [3,5]. These metrics are expressed as follows:  
\[Acc=\frac{XP+XN}{XP+XN+YP+YN} \tag{9}\]  
\[Se=\frac{XP}{XP+YN} \tag{10}\]  
\[P_{p}=\frac{XP}{XP+YP} \tag{11}\]  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Database** & **N** & **V** & **S** & **F** & **Total** & **Records** & **Datasets** \\ \hline MITDB & 89,507 & 6872 & 2764 & 801 & 100,035 & 44 & - \\ \hline INCARTDB & 152,889 & 19,914 & 1952 & 219 & 174,974 & 75 & - \\ \hline SVDB & 161,509 & 9900 & 12,450 & 23 & 183,582 & 78 & - \\ \hline LTDB & 600,167 & 64,080 & 1499 & 2906 & 668,652 & 7 & - \\ \hline MITDB & 45,642 & 3776 & 943 & 413 & 50,774 & 22 & DS1 \\ \hline MITDB & 43,865 & 3196 & 1821 & 388 & 49,261 & 22 & DS2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of preprocessed samples in each database.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Database** & **AMI** & **ASMI** & **IMI** & **ALMI** & **ILMI** & **HC** & **Total** \\ \hline PTB & 6178 & 11,062 & 12,402 & 6456 & 7513 & 10,318 & 53,929 \\ \hline PTB\_X & 850 & 13,898 & 10,371 & 1345 & 3228 & 9678 & 39,370 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of preprocessed samples in PTB and PTB_X databases.  
\[F1=\frac{2\times Se\times Pp}{Se+Pp}. \tag{12}\]  
Here,  
XP = true positive samples  
XN = true negative samples  
YP = false positive samples  
YN = false negative samples
## 3 Overview of Join Processing in WSNs
### Components of Join Implementation
In [20, 22, 23], the statistics are assumed to be given through the executions of a long-running continuous join. **Failure handling. ** In the techniques employing tree routing, failures are handled by restructuring the tree [6, 7, 22, 23]. For this, each node is supposed to maintain information on its neighbor nodes and to send a heartbeat message periodically to its neighbor nodes. The techniques employing the location routing protocol like GPSR [4, 8, 11] can adapt to node failures because such protocols can handle node failures, searching for new routes if a node on a route fails and guaranteeing packet delivery as long as a route exists. Data replication can also be employed to handle node failures [8]. **Duration of join execution. ** It is a component of a join query and therefore relevant to its implementation. Although a continuous query is not necessarily a long-running one, it is often so in many applications (e.g., long-term monitoring). In implementing a long-running continuous join, the opportunities of collecting and maintaining the time-varying statistics for cost-based optimization [11, 20, 21, 22] and of improving the strategy for the subsequent executions [6, 24] should be exploited.
## 4 Classification of MAC Protocols Based on Channel
### Centralized Multi-Channel MAC Protocols
#### 4.5.2 QCH MAC  
The QoS-aware centralized hybrid protocol avoids collision and provides QoS in terms of transmission delays, throughput of the network, and packet delivery. The protocol combines an extended version of EDCA and TDMA. The transmission of safety-based messages is prioritized based on slot scheduling mechanism by RSUs. The access time is divided into two periods: the transmission and reservation periods. The transmission period uses time slots for scheduling, and the reservation period is used only by new vehicles to reserve the slot as soon as it enters the traffic scenario.  
Safety and non-safety messages are treated differently; safety messages are considered higher priority, denoted as CL1, than non-safety messages, denoted as CL2 [88]. The vehicles that enter the transmission initially sense the channel and wait for the medium to become free for transmitting. If the channel is busy, the back-off mechanism is used to prevent collision. Second, after reservation of the slot, the vehicles enter the transmission period and are ready to transmit. The mechanism is depicted in Figure 11.  
The major demerit is that the protocol does not deal with hidden terminal problem and throughput can still be improved using optimized scheduling.  
Figure 11: Access mechanism of QCH MAC [88].  
Figure 10: Network model of ReMAC [87].