esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Admm  
Researchers have paid a significant amount of attention to ADMM because of its capability of dealing with objective functions independently and simultaneously. Second, ADMM has proved to be a genuine fit in the field of large-scale data-distributed optimization. However, ADMM is not a new algorithm; it was first introduced by [PERSON] [20; 21] in the mid-1970s, with roots as far back as the mid-1950s. In addition, ADMM originated from an augmented method with a Lagrangian multiplier [22]. It became more popular when [PERSON] et al. [9] published papers about ADMM. The classic ADMM algorithm applies to the following \"ADMM-ready\" form of problems.  
\[\begin{cases}minimize&f(x)+g(z)\\ s.t.&Ax+Bz=c\end{cases} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Regularizers** & **Promoting** & **Convex** & **Smooth** & **Adaptive** & **Tractable** & **Correlation** & **Stable** \\ \hline \(\ell_{0}\)[10; 11] & Sparsity & No & No & No & No & No & No \\ \(\ell_{1}\)[12] & Sparsity & Yes & No & No & Yes & No & No \\ The Ordered \(\ell_{1}\)[5] & Sparsity & Yes & No & Yes & Yes & No & No \\ \(\ell_{2}\)[13] & Grouping & Yes & Yes & No & Yes & Yes & Yes \\ The Ordered \(\ell_{2}\) & Grouping & Yes & Yes & Yes & Yes & Yes & Yes \\ Partial Sorted \(\ell_{p}\)[6] & Sparsity & No & No & Yes & No & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the different regularizers: Correlation is a method that shows how strong the relationship between explanatory variables is. In the correlation column, “Yes” means a strong correlation between variables and “No” means weaker (or no) correlation between variables. Stable means that estimates of the \(\ell_{2}\)-based methods are more stable when the explanatory variables are strongly correlated.  
The wide range of applications have also inspired the study of the convergence properties of ADMM. Under mild assumptions, ADMM can converge for all choices of the step size. [PERSON] et al. [23] provided some advice on tuning over-relaxed ADMM in the quadratic problems. [PERSON] and [PERSON] [24] have also suggested linear convergence results under the consideration of only a single strongly convex term, given that linear operator's A and B are full-rank matrices. These convergence results bound error as measured by an approximation to the primal-dual gap. [PERSON] et al. [25] created an accelerated version of ADMM that converges more quickly than traditional ADMM under an assumption that both objective functions are strongly convex. [PERSON] and [PERSON] [26] explained, in detail, the different kinds of convergence properties of ADMM and their prerequisite rules for converging. For further studies on ADMM, see Reference [9].
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 3 Example Application  
### Geological Setting of Rock Slope  
The rock slope is located in Tai'an City, China, and the lithology is mainly metamorphic rocks. Under the influence of tectonic movement and intense weathering, many discrete fractures form in the slope. Moreover, the stability state of the rock slope is mainly controlled by the combination of varying fractures.  
A total of 115 fractures were collected in this slope through three scanlines. The results of the strike rose diagram and pole isodensity diagram show that the slope mainly develops two sets of dominant fractures (Figure 2). One set of fractures is nearly horizontal (called horizontal fractures), and the other set of fractures is approximately parallel to the slope (denoted as inclined fractures). The results of probability statistics show that the fracture dip direction and dip have a normal distribution, while the fracture trace length and spacing have a lognormal distribution and negative exponent distribution, respectively. Table 1 lists the statistics of geometric characteristics for two sets of fractures. The density values  
Figure 1: Flowchart of the DFN model employed in 3 DEC software.  
per unit volume for horizontal and inclined fractures are 0.0047 and 0.0819, respectively (Table 1). Note that the spatial correlation of fracture characteristics is not considered.
###### Abstract  
Mathematical models, such as the logistic curve, have been extensively used to model the temporal evolution of biological processes, though other similarly shaped functions could be (and sometimes have been) used for this purpose. Most previous studies focused on agricultural regions in the Northern Hemisphere and were based on the Normalized Difference Vegetation Index (NDVI). This paper compares the capacity of four parametric double S-shaped models (Gaussian, Hyperbolic Tangent, Logistic, and Sine) to represent the seasonal phenology of an unmanaged, protected savanna biome in South Africa's Lowveld, using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) generated by the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) processing system on the basis of data originally collected by National Aeronautics and Space Administration (NASA)'s Multi-angle Imaging SpectroRadiometer (MISR) instrument since 24 February 2000. FAPAR time series are automatically split into successive vegetative seasons, and the models are inverted against those irregularly spaced data to provide a description of the seasonal fluctuations despite the presence of noise and missing values. The performance of these models is assessed by quantifying their ability to account for the variability of remote sensing data and to evaluate the Gross Primary Productivity (GPP) of vegetation, as well as by evaluating their numerical efficiency. Simulated results retrieved from remote sensing are compared to GPP estimates derived from field measurements acquired at Skukuza's flux tower in the Kruger National Park, which has also been operational since 2000. Preliminary results indicate that (1) all four models considered can be adjusted to fit an FAPAR time series when the temporal distribution of the data is sufficiently dense in both the growing and the senescence phases of the vegetative season, (2) the Gaussian and especially the Sine models are more sensitive than the Hyperbolic Tangent and Logistic to the temporal distribution of FAPAR values during the vegetative season, and, in particular, to the presence of long temporal gaps in the observational data, and (3) the performance of these models to simulate the phenology of plants is generally quite sensitive to the presence of unexpectedly low FAPAR values during the peak period of activity and to the presence of long gaps in the observational data. Consequently, efforts to screen out outliers and to minimize those gaps, especially during the rainy season (vegetation's growth phase), would go a long way to improve the capacity of the models to adequately account for the evolution of the canopy cover and to better assess the relation between FAPAR and GPP.  
FAPAR; Lowveld; MISR; MISR-HR; modeling; phenology; savanna; seasonality 2020, 12, 3927; doi:10.3390/rs12233927
# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
## 3 Results
### Evaluation Metric and Comparison  
We utilized two commonly used evaluation metric to compare our proposed method with some state-of-the-art methods.  
2D Projection metricThis metric computes the mean distance in the 2D image between the projections of the 3D mesh model from the estimated pose and the ground truth pose. A pose is considered correct if the distance is less than \(\sigma\) pixels.  
ADD metricThis metric [32] computes the mean distance between two transformed model points using the estimated pose and the ground-truth pose through  
\[ADD=\frac{1}{m}\sum_{x\in M}\parallel(Rx+\mathbf{t})-(\tilde{R}x+\mathbf{\hat {t}})\parallel \tag{11}\]  
When the distance is less than a certain percentage of the model diameter, it is claimed that the estimated pose is correct.  
Figure 4: Examples of the rendered images. (**a**–**d**) are examples that random selected from the synthetic dataset with different view angles.  
We compare our method with PSGMN [26], DPOD [25], and HybridPose [23]. As all of the three methods are one stage pose estimation schemes that are not able to detect multiple instances in one frame, we used the EfficientDet as the backbone for all the methods and tested these methods with the fixed size image that only contains one object per image. The results in terms of 2D Projection metric are shown in Table 1. It can be seen that our proposed method achieves better performance than the other methods, especially when the metric is stricter.  
The results of comparison in terms of ADD metric are shown in Table 2. Our method also outperforms the other method with a large margin. As this metric focus on the measurement of the distances between the 2D-3D correspondences, our method takes advantages of the dense matching loss and shows a great improvement in the scores.  
Some qualitative examples of our proposed method are shown in Figure 5. It is shown that our proposed method can handle the multi-instance situation well and successfully deal with partial occlusion and light changing conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **HybridPose** & **DPOD** & **PSGMN** & **Proposed Method** \\ \hline \(\sigma=5\) & 89.3 & 86.2 & 93.9 & 96.5 \\ \(\sigma=4\) & 83.2 & 85.3 & 88.5 & 92.0 \\ \(\sigma=3\) & 76.5 & 77.6 & 81.0 & 87.3 \\ \(\sigma=2\) & 64.1 & 69.8 & 74.5 & 82.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed method with the other methods in terms of 2D Projection metric.  
Figure 5: _Cont_.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **HybridPose** & **DPOD** & **PSGMN** & **Proposed Method** \\ \hline
0.1-ADD & 72.2 & 71.4 & 76.5 & 84.3 \\
0.08-ADD & 64.3 & 65.2 & 72.3 & 79.4 \\
0.05-ADD & 51.1 & 53.3 & 57.9 & 74.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed method with the other methods in terms of ADD metric.
## 4 Discussion  
### Computational Efficiency  
In terms of the size of our numerical experiment (20 shots, 151 receivers, 1001 time grids per signal), the calculation time needed to implement each imaging method ranged from several seconds to minutes, as summarized in Table 4. DAS beamforming was the most efficient method, with a fast imaging time of 3.9 s, followed by MVSS beamforming at 201.1 s, and Kirchhoff migration took the longest at 520.1 s. If the time delay was calculated according to the background velocity and stored prior to imaging, the calculation times of DAS and MVSS beamforming were further shortened. In the Kirchhoff migration adopted in this paper, the travel times from source to scatter point and from scatter point to receiver were approximated by a Dix equation using the RMS velocity. On the other hand, the travel times were calculated based on the geometry relationship between the source, scatter point, and receiver. To some extent, DAS beamforming is a simplified option of Kirchhoff migration, which results in the computational efficiency difference. Conceivably, if the condition of the medium at each point is known, the Kirchhoff migration method should theoretically have the best imaging results. In this experiment, the geological information was known approximately (background velocity), so the accuracy advantage of Kirchhoff migration was almost negligible, but the efficiency advantage of beamforming persisted. Beamforming methods assume that the wave propagates in a straight line and that the seismic velocity in the area is the same. This assumption is also available for Kirchhoff migration. However, in the adopted algorithm it still took time to estimate the travel time by a Dix equation using the RMS velocity desired from a homogeneous background model. This meant that the travel time was almost same as that in DAS beamforming, in which the travel time was simply calculated according to geometry and background velocity, but the calculation time was quite different. Therefore, the efficiency of DAS beamforming was as high as 130 times that of Kirchhoff migration. For this reason, we would like to apply beamforming imaging to detect underground anomalies in a scene with a single background velocity. The computational efficiency and accuracy of an algorithm have always been contradictory, but in near-surface exploration, the prior information is simple, which is suitable for beamforming methods to improve efficiency.  
Figure 26: Imaging after applying a time correction in model B-2: (**a**) true velocity model; (**b**) no processing; (**c**) 1/4-cycle advance; (**d**) 1/2-cycle advance; (**e**) 3/4-cycle advance.
## 4 Materials and Methods  
### Two-Way Range (TWR) Protocol  
The TWR protocol is a widely adopted method in IoT networks, particularly in scenarios where time synchronization is unnecessary. Ultra-wideband (UWB) is a radio technology, standardized as IEEE 802.15.4a, which enables the estimation of distance between an AP and a target node [34]. This estimate is obtained by measuring the time it takes for radio frequency (RF) signals to travel between them--known as time-of-flight (ToF)--and subsequently multiplying that time by the speed of light (c) [35]. To estimate the distance between the \(AP\) and the target node for localization purposes, the TWR protocol employs the following procedure, as depicted in Figure 4. The AP initiates the process by sending a request packet to the target node at the starting time \((t_{trans})\). After a specific delay \((t_{delay})\), accounting for processing time and other hardware-related delays (which is assumed to be known by both the AP and the target node), the target node responds to the request message. Subsequently, the AP receives the response message and computes the round trip time \((t_{round})\) to the target node using the following formula [36; 37]:  
\[t_{round}=2*(t_{prop}*t_{trans})+t_{delay}. \tag{11}\]  
At the \(AP\), \(t_{round}\) is measured by computing the difference between \(t_{rec}\) and \(t_{trans}\), which is expressed as \(t_{round}=t_{rec}-t_{trans}\). Based on \(t_{round}\), the \(AP\) can compute the propagation time \((t_{prop})\) and estimate the distance to the target node using the following equations:  
\[t_{prop}=\frac{t_{round}-t_{delay}}{2} \tag{12}\]  
\[d=t_{prop}*c \tag{13}\]  
In the context of estimating the distance between the AP and the target node, let \(d\) represent the distance and \(c\) denote the constant speed of light, which is equal to \(3\times 10^{8}\)m/s.  
Figure 3: System model.
# Prediction of Thermal Cracking During Construction of Massive Monolithic Structures
## 2 Materials and Methods
\begin{bmatrix}\frac{\partial N_{1}}{\partial x}&0&0&\frac{\partial N_{2}}{ \partial x}&0&0&\dots&\frac{\partial N_{k}}{\partial x}&0&0\\ 0&\frac{\partial N_{1}}{\partial y}&0&0&\frac{\partial N_{2}}{\partial y}&0& \dots&0&\frac{\partial N_{k}}{\partial y}&0\\ 0&0&\frac{\partial N_{1}}{\partial x}&0&0&\frac{\partial N_{2}}{\partial x}& \dots&0&0&\frac{\partial N_{k}}{\partial x}\\ \frac{\partial N_{1}}{\partial y}&\frac{\partial N_{1}}{\partial x}&0&\frac{ \partial N_{2}}{\partial y}&\frac{\partial N_{2}}{\partial x}&0&\dots&\frac{ \partial N_{k}}{\partial y}&\frac{\partial N_{k}}{\partial x}&0\\ \frac{\partial N_{1}}{\partial x}&0&\frac{\partial N_{1}}{\partial x}&\frac{ \partial N_{2}}{\partial x}&0&\frac{\partial N_{2}}{\partial x}&\dots&\frac{ \partial N_{k}}{\partial x}&0&\frac{\partial N_{k}}{\partial x}\\ 0&\frac{\partial N_{1}}{\partial x}&\frac{\partial N_{1}}{\partial y}&0&\frac {\partial N_{2}}{\partial x}&\frac{\partial N_{2}}{\partial y}&\dots&0&\frac {\partial N_{k}}{\partial x}&\frac{\partial N_{k}}{\partial y}\end{bmatrix} . \tag{20}\]  
The vector \(\{\Delta F\}\) is determined by the following formula:  
\[\{\Delta F\}=\int\limits_{V}{[B]^{T}[D]\Big{\{}\Delta\varepsilon_{f}\Big{\}}}, \tag{21}\]  
where \(\Big{\{}\Delta\varepsilon_{f}\Big{\}}\) is the vector of forced deformations increments, which, in addition to temperature deformations, may also include shrinkage deformations \(\varepsilon_{sh}\):  
\[\Big{\{}\Delta\varepsilon_{f}\Big{\}}=(\alpha\Delta T+\Delta\varepsilon_{sh }) \begin{bmatrix}1\\ 1\\ 1\\ 0\\ 0\end{bmatrix} . \tag{22}\]  
After determining the displacement increments from Formula (18), the stress increments are calculated using the following formula:  
\[\{\Delta\sigma\}= \begin{cases}\Delta\sigma_{x}\\ \Delta\sigma_{y}\\ \Delta\sigma_{z}\\ \Delta\tau_{xy}\\ \Delta\tau_{xz}\\ \Delta\tau_{yz}\end{cases} =[D]\cdot\Big{(}[B]\cdot\{\Delta U\}-\Big{\{} \Delta\varepsilon_{f}\Big{\}}\Big{)}. \tag{23}\]  
The heat release function of concrete was taken as [30]:  
\[Q(t)=Q_{28}\cdot\exp\Big{[}a\cdot\Big{(}1-\Big{(}\frac{28}{t}\Big{)}^{x}\Big{)} \Big{]}, \tag{24}\]  
where \(Q_{28}\) is the amount of heat released during the first 28 days of hardening for 1 m\({}^{3}\) of concrete, \(t\) is the time in days, the coefficients \(a\) and \(x\) determine the kinetics of the heat release of concrete. The calculation was performed at \(Q_{28}=\)79.4 MJ/m\({}^{3}\), \(a=0.002042\), \(x=1.564\).
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.5 Fully Convolutional Network (FCN) and Conditional GAN  
In 2019, [PERSON] et al. developed a system to generate virtual positron emission tomography (PET) images from CT scans utilizing FCN and conditional GAN technologies. This system was designed to simulate PET data from CT scans, which could potentially reduce false positives in lesion detection. Clinically, the technology offers a significant advantage by enabling lesion detection and drug treatment assessments in a CT-only setting, reducing the dependency on more costly and radiation-heavy PET/CT scans. The research utilized 60 PET/CT scans from Sheba Medical Center, with 23 for training and 37 for testing. This incorporated the synthetic PET data as a mechanism to decrease false positives in the identification of malignant liver lesions. The initial outcomes are encouraging, with a 28% reduction in the average number of false positives per case, decreasing from 2.9 to 2.1 [16].
## 4 Wind Power Forecast
### Prediction Experiment and Result Analysis
In this paper, we used the same dataset to evaluate the performance of time series forecasting by different approaches, and the results of multivariate and univariate prediction are shown in Tables 8 and 9. As shown in Table 8, in multivariate prediction, the prediction effects of Transformer-based methods other than Reformer [56], such as LogTrans [52] and Informer [55], outperformed the RNN-based methods, such as LSTMa [5]; the performance of TCN [22] further outperformed Transformer-based methods; compared with these methods, SCINet model achieved better performance, because the downsample-convolve-interact architecture enabled multi-resolution analysis, which facilitated extracting temporal relation features with enhanced predictability. Overall, in this paper, as shown in all subtasks with ETT data, the prediction performance using SFINet was all better than that using SCINet--see the relative performance improvement given by RIP as shown in green color. By comparison with the multivariate prediction, the performance of these methods in discussion for univariate prediction was gradually improved. N-Beats [57] outperforms the above methods, and it is observed that SCINet is superior to other baseline methods. However, the performance of SFINet in time series forecasting is even better than that of SCINet. Specifically, for the two different tasks using the ETTh1 data set, the evaluation criterion of MAE was improved by 6.33% and 0.76% or more, respectively, while using the ETTh2 and ETTm1 data sets, the prediction performance was improved by 6.94% and 1.76%, and 3.06% and 1.14% or more, respectively. When increasing the horizon, the improvement in MAE showed an increasing trend. The results further confirmed the effectiveness and universality of the algorithm proposed in this paper. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**WPm1**} & \multicolumn{4}{c}{**WPm2**} \\  & & \multicolumn{4}{c}{**Horizon**} & \multicolumn{4}{c}{**Horizon**} \\ \cline{3-10}  & & **6** & **12** & **24** & **48** & **6** & **12** & **24** & **48** \\ \hline \multirow{4}{*}{Multivariate} & SCINet & 31.24\% & 0.3814 & 49.95\% & 55.38\% & 30.06\% & 39.69\% & 53.52\% & 65.83\% \\  & SFINet & 30.69\% & 36.44\% & 44.16\% & 50.47\% & 29.08\% & 37.06\% & 48.91\% & 60.97\% \\  & AIP & 0.55\% & 1.70\% & 5.79\% & 4.91\% & 0.98\% & 2.63\% & 4.61\% & 4.86\% \\  & SCINet & 29.94\% & 38.88\% & 52.74\% & 66.37\% & 30.79\% & 39.16\% & 52.73\% & 65.32\% \\ \cline{1-1}  & SFINet & 28.84\% & 37.32\% & 49.58\% & 61.44\% & 29.08\% & 37.06\% & 48.91\% & 60.97\% \\ \cline{1-1}  & AIP & 1.10\% & 1.56\% & 3.16\% & 4.93\% & 1.71\% & 2.10\% & 3.82\% & 4.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Forecasting results evaluated in WMAPE on wind power data sets. Figure 5: The prediction comparison between SFINet and SCINet for different datasets.
# Numerical and Experimental Analysis of Labyrinth Seals with Rhomboidal Cells
## 1 Introduction
Subsequently, in order to verify the flow split obtained using the CFD model and to predict the thermal behavior of the new seal, in a more realistic environment, the selected configuration was introduced in the numerical models reproducing the fluid and thermal behavior of the available testing facility (i.e., Thermalcase rig) [18]. Finally, the new type of seal, made using the Direct Metal Laser Sintering (DMLS) technique, was mounted in the Test Article (TA) of the experimental rig. The experimental results allowed validating the numerical predictions about the onset in rhomboidal cells of more dissipative vortices than those generated using the honeycomb for sealing. Thanks to these vortices, the rhomboidal pattern behaves better than the honeycomb one from the fluid-dynamic point of view, while it does not significantly affect the thermal distribution compared to the honeycomb.
# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 2 Materials and Methods
### Model Validation  
The model presented in the previous sections is a scaled version of the Irish county agent-based model [20]. Although it is based off a previously validated model, we made fundamental changes to the society component of the model with the introduction of the scaling factor and altered the level of detail in the environmental component. Thus, it is necessary to validate the new scaled-up version of the model.  
To validate the model, we follow the framework laid out in [33]. Agent-based model validation has three main steps: cross validation, where we compare the model output to a previously validated model, sensitivity analysis, and comparing model output to real data.