esa-sceva/satcom-chunk-collection huggingface dataset:
# Elastic-Plastic Threshold and Rational Unloading Level of Rocks
## 1 Introduction  
Rock mechanics studies have been extensively conducted in China and abroad, which cover a broad range, from uniaxial to triaxial compression, simple to multiple conditions, and single to complex paths [1; 2; 3; 4; 5; 6]. In triaxial tests, the initial stress level mainly includes the initial confining pressure and the initial axial pressure. Apart from the properties of rocks, the axial pressure is mainly affected by the confining pressure and the intrinsic attributes of rocks. During a triaxial unloading test, the initial axial pressure (i.e., unloading axial pressure or unloading level) is typically lower than the peak strength under the corresponding confining pressure. According to the results obtained from conventional triaxial compression tests, [PERSON] [7] and [PERSON] [8] determined that 85-95% of the peak strength is the unloading point under the corresponding confining pressure. [PERSON] et al. [9] selected different levels of initial axial pressure (70%, 80%, and 90%) for unloading tests on the sandstone. The research revealed that decreasing the initial axial pressure could cause lower ultimate strength and axial strain of rocks during unloading accompanied with more pronounced brittleness and expansion characteristics. [PERSON] et al. [10] investigated the relationships between elastic strain energy and axial strain while the unloading stresses were set at levels of 60% of the peak stress and 80% of the peak stress. The results indicated that the energy change curves at both levels are parallel after unloading the confining pressureand the unloading level has no significant effect on the energy evolution process. [PERSON] [11] and [PERSON] [12] considered that the unloading stress level mitigated possible damage within rocks during the unloading damage stage. Regarding the initial axial pressure of unloading, [PERSON] et al. [13] selected 50% of the conventional triaxial compressive strength and the corresponding confining pressure, whereas other researchers often adopted 60% and 80% [14; 15; 16], 70% [17; 18; 19; 20; 80; 81; 82; 83; 84; 85; 86; 87; 88  
The stress path and loading method used in the tests are shown in Figure 3. The specific procedures was described as follows: (1) alternating stress application, which refers to alternately apply confining pressures (\(\sigma_{2}=\sigma_{3}\)) and axial pressures (\(\sigma_{1}\)) at a loading rate of 0.05 MPa/s to reach a preset confining pressure; (2) fixed stress application, which means a fixed confining pressure and an axial pressure were maintained at a loading rate of 0.25 MPa/s until failure of rock specimens; and (3) displacement pressure application, which means that the loading forces continued to apply after the peak to obtain the entire stress-strain curve.
# Insulators and Defect Detection Based on the Improved Focal Loss Function
## 5 Conclusions  
In order to better detect the defects of transmission line insulators, this paper proposes an improved YOLOv5 model based on Focal loss. First, we use the CPLD data set and the National Grid Power's inspection data to form a mixed data set, and then expand the data set through a variety of data enhancement methods. Secondly, transfer learning is performed with pre-training weights trained on large-scale data sets, which reduces the training time and improves the convergence speed of the model. Finally, the Focal loss function is introduced into the loss function and the weight factor is redesigned. The weight factor is dynamically updated according to the ratio of positive and negative samples in the feature diagram and the difference between the actual value and the predicted value in each training. By adjusting the loss function, the new algorithm increases the overall contribution of positive samples and difficult samples, makes the model more focused on insulator defects, and improves the model's ability to detect insulator defects. From the experimental results, it can be concluded that the average accuracy of the algorithm in this paper can reach 98.3%, which is 5.7 percentage points higher than that of the original model, especially since the accuracy of defective insulators has been greatly improved.  
Conceptualization, Y.L. and G.Z.; methodology, Y.L.; software, Y.L.; validation, Y.L., G.Z. and S.A.; formal analysis, S.A.; investigation, G.Z.; resources, H.Z.; data curation, C.Z.; writing--original draft preparation, Y.L.; writing--review and editing, G.Z.; visualization, S.A.; supervision, C.Z.; project administration, G.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 52077203) and the Fundamental Research Funds for the Provincial Universities of Zhejiang (No. 2021 YW06).  
Figure 7: Comparison chart of detection effects. (**a**) Use the cross-entropy loss function model to detect the effect. (**b**) Use the dynamically changing Focal loss function model to detect the effect.
## 3 Problem Description
### Permutation Invariance Of Input Space  
As mentioned earlier, the simulator splits wells into two groups, injectors and producers, managing them collectively under group control policies. Targets and constraints are set for each group, with an embedded optimizer in the simulator responsible for adjusting schedule parameters for individual wells within each group. Essentially, a group comprises wells of the same type, distinguished solely by their location on the grid. On the other hand, the wells' locations serve as input variables in the BO framework, ordered in a vector. Therefore, the simulator handles the wells' positions as two sets of unordered objects, partitioned on the respective group, while BO treats them as a vector. Consequently, for the simulator, the input space is invariant under permutations for well positions within a group, whereas for BO, it is not.  
Let us consider a scenario following the mapping described in Algorithm 1. If variables \(m\) and \(n\) represent the positions of injectors and producers, respectively, the input space's invariance implies that a sampled input exerts the same effect on the simulator, thus also on the objective function, across \(m!\cdot n!\) vectors, generated by all of the permutations of the inputs within the same group.  
To illustrate this concept, let us consider a simple case with two wells of the same type placed on a \(3\times 3\) Cartesian grid (refer to Figure 5). In this scenario, the top-left cell is denoted as [1,1]. Moving right from [1,1] brings us to [1,2], while moving downwards from [1,1] leads to [2,1]. This structure mirrors the conventions of matrix elements, facilitating a straightforward understanding of the grid's layout. The coordinates for the left sample are \(\mathbf{x}=(f([2,1]),f([3,2]))\), and for the right sample, \(\mathbf{x}=(f([3,2]),f([2,1]))\), where \(f\) is the mapping defined by Algorithm 1. Despite the differing configurations, the simulator produces identical outputs for both samples, since they belong to the same group, and the simulator handles them as a set, regardless of their order. Each sample has its counterpart, or \"doppelganger\". As the number of wells increases, the number of potential configurations grows exponentially, since for \(N\) wells, each unordered configuration can result from \(N!\) different orderings. This example highlights the fundamental principle of group control and the resultant invariance in the input space, offering insights into the system's behavior regardless of the specific arrangement of wells.  
Figure 4: Mapped value of each cell.
## 3 Results and Discussion
### Coral Physiological Data
The biplot rays in (**a**) are as follows: colony length (1), Symbio-dinaceae (Sym) _rbcl_ (2), Sym _ziflll_ (3), host coral _lectin_ (4), host coral _ca_ (5), host coral _gfp_-_cp_ (6), RNA/DNA ratio (7), mean coral color score (8), Sym _hsp0_ (9), host coral _cu-ztn-sod_ (10), Sym _ubiqu-lig_ (11), and Sym genome copy proportion (GCP; 12). The legend in (**a**) extends to (**b**). In the predictor screen of CHI (**c**), a bootstrap forest model with 100 random trees was used to estimate the contribution of each of the 14 environmental parameters to the CHI. Upon adding in the 50 ecological parameters, R\({}^{2}\) increased to 0.92 for the best neural network (Table 3 and Figure S2a). The 12 most influential model terms from a dependent resampled inputs algorithm are shown in Figure S2b, and the associated desirability analysis projected that a fore reef in the Reef Islands without the following coral genera would be associated with pocillopoids with the highest CHI: _Symphyllia_, _Montastrea_, _Leptastrea_, and _Stylophora_. In the case of the latter genus, this is unsurprising since _Pocillopora_ spp. and _Stylophora_ spp. are closely related and known to compete with one another [40]. However, three of the environmental parameters would likely not be of use to managers and future researchers: date (since future surveys would inherently occur on different dates), site (since the goal is to use the model to find other locations with resilient or highly stress-prone corals), and time of day (important for coral molecular physiology but the timescale is not commensurate with a resilience assessment). This is why the two aforementioned neural networks have been shown in Supplementary File S1 only. We therefore re-ran the ML GUI with the resulting 11 environmental parameters as predictors, and, for those characterized by validation R\({}^{2}\) > 0.8, we took the mean value of 9-20 additional simulations. Although several with R\({}^{2}\) > 0.8 were uncovered, the means of the simulated runs fell below this value (Table S2). We then combined these 11 environmental parameters with the 50 benthic categories as predictors and re-ran the ML GUI with all 61 predictors. The mean of 20 simulations with a neural network featuring 10% holdback validation was associated with a validation R\({}^{2}\) of 0.83 \(\pm\) 0.13 (Table 3 and Figure 5). Note that the maximum R\({}^{2}\) obtained across all simulations (0.98) is instead shown in Table 3. Of the 12 most influential model terms derived from the dependent resampled inputs analysis, several are the same as those of the environmental parameter-only neural network of Figure S1 and the environmental parameter (\(n\) = 14) + benthic data neural network of Figure S2. All predict that fore reefs in the eastern region of the country will be characterized by corals with the highest CHI, and both the models of Figure S2 and Figure 5 predict that pocillopoid corals with the highest theoretical CHI will be found in
## 2 Related Work
### Summary  
In summarizing, we note that the accuracy of empirical models proposed so far for UAV LoS blockage analysis heavily depends on the similarities of the analyzed deployment and the one where measurements have been taken. Specifically, measurement-basedmodels require large-scale measurement campaigns for each specific environment. Purely analytical models are either too simple to account for critical details or do not provide the solution in closed-form.  
In this paper, we will fill the abovementioned gap by proposing an accurate analytical model accounting for all the major specifics of the environment. The distinguishing feature of the proposed model is that, as opposed to other models, it provides the result in closed-form and is capable of capturing the specifics of both ground- and rooftop-mounted BS simultaneously.
## Appendix A Additional Error Metrics  
An additional analysis of error metrics has been made for the week of 15-22 May 2019, 15-22 June 2019, and 15-22 July 2019. For curtailment option 1, all loads higher than the curtailment limit (i.e., 578 \(W\)) is curtailed to the limit. For curtailment option 2 (a and b)loads exceeding 578 W are curtailed to 578 W on 15-19 May/June/July between 12:00-16:00. Additionally, for curtailment option 2b, loads exceeding 578 W are curtailed to 578 W on 13-14 May/June/July. The error metrics for these weeks can be found in Table 11.  
As discussed in Section 4.5, the performance of method III strongly depends on historical measurements. In periods of stable weather, method III generally outperforms method II, while method II and III perform relatively similar in case the historical irradiance is less stable. This can also be seen in Table 11.  
Similar to the results from Table 3, Table 11 shows method IV overall performs best, compared to methods I, II and III. The error of method IV is in all simulated weeks improved compared to the traditional baselining methods.
###### Abstract  
Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsula Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R\({}^{2}\)) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting.  
short-term load forecasting; least square support vector machine (LSSVM); improved bacterial foraging optimization algorithm (IBFOA); hybrid model; machine learning (ML) +
Footnote †: journal: _Algorithms_
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 4 UAV Path Optimization
Step 3: The feasible region of non-linear Equations (53)-(56) in the constraints can be defined as:  
\[S=\big{\{}\textbf{u}_{k}\big{|}g_{1 ij}(\textbf{u}_{k})\geq 0,g_{2 ij}(\textbf{u}_{k} )\geq 0,g_{3 ij}(\textbf{u}_{k})\geq 0,g_{4 ij}(\textbf{u}_{k})\geq 0\,\big{\}}. \tag{57}\]  
The logarithmic barrier function can be obtained as:  
\[G(\textbf{u}_{k},\gamma)=f(\textbf{u}_{k})+\gamma B(\textbf{u}_{k}),\gamma>0, \tag{58}\]  
where \(\gamma\) is a logarithmic barrier function, \(\gamma\to 0\),  
\[B(\textbf{u}_{k})=-\sum_{i=1}^{M}\ln(g_{1 ij}(\textbf{u}_{k}))-\sum_{i=1}^{M} \ln(g_{2 ij}(\textbf{u}_{k}))-\sum_{i=1}^{M-1}\sum_{j=i+1}^{M}\ln(g_{3 ij}( \textbf{u}_{k}))-\sum_{i=1}^{M-1}\sum_{j=i+1}^{M}\ln(g_{4 ij}(\textbf{u}_{k})). \tag{59}\]  
Therefore, the non-linear constraint is converted to an unconstrained problem. The minimum value of \(G(\textbf{u}_{k},\gamma)\) can be solved by setting the initial internal point. Step 4: As for linear Equation (52), for the convenience of calculation, the \(\textbf{u}_{k}\) solved in Step 3 can be substituted into the constrained inequality. When the constraint conditions are satisfied, it is the final output result; otherwise, the boundary value \(u_{\max}\) is selected. Step 5: The control amount \(\textbf{u}_{k}\) of the UAV for the next waypoint. Figure 3 demonstrates the steps of the algorithm for UAV path planning based on CRLB. In this figure, \(\textbf{u}_{k}\) is the output of the algorithm at time step \(k\). When UAVs arrive at new waypoints, new measurements are collected and new estimations are acquired.
## 3 Results  
### Multispectral Data  
Both Sentinel-2 and LS8 optical satellite images faintly show the Rimaal structure consists of a single circular wrinkle ridge surrounding a basin that is completely infilled by smooth sandy material (Figure 3). The area appears as a featureless desert surface, except for segments of ancient rivers that are most probably of Tertiary age [13]. The images barely reveal the structure as a light tonal circular feature coated by Aeolian sand. The topography of the area in Sentinel-2 and LS8 is relatively subdued with no obvious expression of a topographic rim to the Rimaal structure. In the visible part of the spectrum, the albedo of the filling sand is close to the albedo of the surrounding country rocks, leading to the subdued expression of the surface features [9]. In commonly used moderate to high spatial resolution multispectral data, such as LS, Sentinel-2, and ASTER (30 m and 10 m in the VNIR), sandy surfaces in areas that are made up of relatively low contrasting bedrocks show similar spectral responses that hinder detailed mapping or classification of the area. This issue could likely be improved when using very high spatial resolution multispectral data with spectral channels in the mid-infrared region of the spectrum (e.g., WorldView-3) [27].
## 4 Discussion
### TGFB Signaling, Its Antiproliferative Effect, and GPP  
TGF\(\beta\) signaling is known for its anti-proliferative and immunosuppressive effects [8]. Thus, the hyperproliferation of psoriatic epidermis seen in patients 1 and 3 is consistent with disrupted TGF\(\beta\) signaling as a result of a heterozygous loss of function _TGFBR2_ mutation. In psoriatic epidermis, aberrant TGF\(\beta\) signaling has been shown to lead to downregulation of the microRNA, miR-486-3p, which in turn leads to overexpression of KRT17, a hallmark of psoriatic lesions. Concomitant with the overexpression of KRT17 is hyperproliferation of the keratinocytes and subsequent neutrophil infiltration [17] (Figure 6). Overexpression of KRT17 found in patients 1 and 3 is therefore also consistent with a role for reduced TGFBR2 in GPP and AOID in patients 1 and 3, respectively (Figure 6). This neutrophil infiltration results in overproduction of pro-inflammatory cytokines, including IL17A, IL22, IFN-\(\gamma\), IL6, and TNF\(\kappa\) from Th1 and Th17 cells, which can result in subsequent tissue destruction. This is supported by the increased expression of IL1B, IL6, IL17, IL23, and IFNG in the lesional skin of patients 1 and 3 (Figure 6). Aberrant TGF\(\beta\) signaling might stimulate keratinocytes to produce IL23 and induce inflammatory dendritic cells into evolving psoriatic lesions [18]. Interestingly, we did not find changes in staining for the inflammatory cytokine, TNFA (Figure 7).
###### Abstract  
Phase wrapping is a common phenomenon in optical full-field imaging or measurement systems. It arises from large phase retardations and results in wrapped-phase maps that contain essential information about surface roughness and topology. However, these maps are often degraded by noise, such as speckle and Gaussian, which reduces the measurement accuracy and complicates phase reconstruction. Denoising such data is a fundamental problem in computer vision and plays a critical role in biomedical imaging modalities like Full-Field Optical Interferometry. In this paper, we propose WPD-Net (Wrapped-Phase Denoising Network), a lightweight deep learning-based neural network specifically designed to restore phase images corrupted by high noise levels. The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. The RDABs incorporate attention mechanisms that help the network focus on critical features and suppress irrelevant noise, especially in high-frequency or complex regions. Additionally, WPD-Net employs a growth-rate-based feature expansion strategy to enhance multi-scale feature representation and improve phase continuity. We evaluate the model's performance on both synthetic and experimentally acquired datasets and compare it with other state-of-the-art deep learning-based denoising methods. The results demonstrate that WPD-Net achieves superior noise suppression while preserving fine structural details even with mixed speckle and Gaussian noises. The proposed method is expected to enable fast image processing, allowing unwrapped biomedical images to be retrieved in real time.  
deep learning; denoising; dense feature fusion; interferometry; residual dense attention blocks; wrapped phase +
Footnote †: journal: Applied sciences
## 4 Numerical Examples and Validation of the Software
### Simulation  
In this section, the simulations according to the model above will be discussed. Assuming concentrations of \(c_{S}^{b}=0.5\ \frac{\text{mol}}{\text{m}^{3}}\), for \(S\in\{\text{Cu},\beta-\text{ala},\text{Cu}-\beta-\text{ala}\}\) in the bath and a current density of \(j=0.6\ \frac{A}{m^{2}}\) on the cathode, one obtains the concentration profile shown in Figure 24 by evaluating the classical model 3, with the numerical scheme described for the treatment of (5) under the reduction described in Remark 1. As seen in the figure, one obtains a concentration of \(-0.4\frac{\text{mol}}{\ell}\), which can be considered to be unphysical.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & \(D\ \mathbf{in}\ \frac{\mathbf{m}^{2}}{\mathbf{s}}\) & \(k_{1}\ \mathbf{in}\ \frac{1}{\mathbf{s}}\) & \(k_{2}\ \mathbf{in}\ \frac{\mathbf{m}^{3}}{\text{mol}\,\mathbf{s}}\) & \(\gamma\mathbf{s}\) \\ \hline Cu & \(7.14\times 10^{-10}\) & - & - & 0 \\ \(L\) & \(9.29\times 10^{-10}\) & - & - & 0 \\ Cu\(L\) & \(9.29\times 10^{-10}\) & \(2\times 10^{5}\) & \(11\)When applying the augmented Lagrangian method with adaptive meshrefinement on (4), cf. Figure 25, one obtains that 75 iterations of the augmented Lagrangian algorithm were needed to evaluate non-negative concentrations. As seen in the corresponding figures, not only the behavior of \(c_{\text{Cu}^{2+}}\), but, as directly seen, the behavior of \(c_{\beta\text{-}\text{alal}}\), in the close area of the cathode the concentration of \(c_{\beta\text{-}\text{alal}}\) increases. Furthermore, it can be seen that the augmented Lagrangian algorithm needs 75 steps for the simulation of non-negative species concentrations.  
**Remark 9**.: _During the application of the numerical scheme that is described in Section 3, one observes that the adaptive scheme converges relative fast for the respective model 4 given through the parameters that are defined in Table 7 and the given boundary conditions, while the quasi-Newtonian part of the numerical scheme based on the uniform refinement does not converge, unless a high number of homotopy steps \(>10^{6}\) is used._  
In summary, the numerical methodology that is derived in this article is applicable to practical relevant settings, and the adaptive scheme has a major part on the efficiency of the described.  
Figure 24: Concentration profiles \(c_{\text{Cu}^{2+}}\), \(c_{\beta\text{-}\text{alal}}\)\(c_{\text{Cu}(\beta\text{-}\text{alal})}\) with negative concentrations w.r.t. the model parameters discussed in Table 7.  
Figure 25: Concentration profiles \(c_{\text{Cu}^{2+}}\), \(c_{\beta\text{-}\text{alal}}\)\(c_{\text{Cu}(\beta\text{-}\text{alal})}\) (**a,c,e**) with corresponding Lagrangian multipliers (**b,d,f**) in the 1 st, 38 th, and 75 th iteration of the augmented Lagrangian algorithm.