esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 4 Results and Discussion
### Visualization  
This paper compares and visualizes the predicted density maps generated by SAFECount and the proposed method, with some results shown in Figures 4 and 5. Figure 4 presents a comparison of the results between the proposed method and SAFECount on the UCSD dataset, while Figure 5 displays the comparison on the TRANCOS dataset. Notably, the original image used in Figure 4 contains pedestrians observed from different angles, whereas the original image in Figure 5 includes vehicles traveling in both forward and backward directions. A detailed comparative analysis of these results reveals that the density maps generated by the proposed method more accurately predict the number of objects under varying angles, demonstrating its effectiveness in addressing the challenges of object feature extraction caused by angle diversity.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**TRANCOS**} & \multicolumn{2}{c}{**UCSD**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline Down-down-right-right & 4.92 & 6.53 & 2.08 & 2.67 \\ Down-up-right-right & 4.89 & 6.44 & 2.06 & 2.67 \\ Up-up-left-left & 5.01 & 6.67 & 2.11 & 2.68 \\ Up-up-right-left & 4.97 & 6.68 & 2.09 & 2.64 \\ Down-up-right-left & **4.78** & **6.33** & **2.02** & **2.54** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study on the necessity of separating H and W directions. For the H direction, the top-to-bottom operation is referred to as down, while the bottom-to-top operation is referred to as up. Similarly, for the W direction, the right-to-left operation is denoted as right, and the left-to-right operation as left. Here, down-down-right-right indicates that the model sequentially performs layer-by-layer convolution on the query feature in the order of down, down, right, and right. Other methods follow a similar interpretation.  
Furthermore, to better illustrate the effectiveness of the similarity maps generated by the SCSAEN, Figure 6 visualizes the intermediate similarity maps for FamFet and SCSAEN. The similarity map in SCSAEN refers to the one processed by the SSA module, whereas the similarity map in FamNet is directly obtained by comparing the raw features of the query image and the support image. As shown in the figure, the intermediate similarity map for SCSAEN has higher accuracy, indicating that it processes object features more effectively in both spatial and channel dimensions, thereby improving the precision of the similarity map and laying a solid foundation for subsequent object counting.  
Figure 4: Comparison of the predicted density maps between SAECount and SCSAEN on the UCSD dataset. From left to right: query image, predicted density map by SAFECount, and our prediction. The numbers below are the counting results.  
Figure 5: Comparison of the predicted density maps between SAEFCount and SCSAEN on the TRANCOS dataset. From left to right: query image, predicted density map by SAFECount, and our prediction. The numbers below are the counting results.
###### Abstract
The anomaly detection process in our framework is a bridge between the knowledge of object types in the source domain and the knowledge of target features in the target domain, which is used to refine the accuracy of the pre-trained model for cross-domain building extraction from the target domain. The effect of this process is equivalent to the training process of the mainstream domain adaptation methods. The reclassification process is designed to refine again the effect of cross-domain building extraction from the predicted non-building data. It should be noted that the framework can be adapted to a variety of mainstream segmentation models. In other words, our framework can be used as a post-processing step for mainstream semantic segmentation models. The main contributions of this paper can be summarized as follows. (1) A novel anomaly detection-based domain adaptation framework is proposed to extract buildings from cross-domain remote sensing images. Compared with the current mainstream domain adaptation methods, our framework does not require a large number of unlabeled samples in the target domain and it retrains in new target domain. (2) Numerous experiments are conducted on three widely used deep learning models, large area data with different region types, and publicly available datasets. Our experimental results indicate that our proposed framework can improve the performance of cross-domain building extraction and can achieve the role of becoming the state-of-the-art similarly to other recent domain adaptation methods. The remainder of this paper is organized as follows. Section 2 provides the related work, including building extraction in the closed datasets, domain adaptation, and its applications in remote sensing. Section 3 describes our anomaly detection-based domain adaptation framework, including source domain-based pre-training, anomaly detection for the target domain, and local region reclassification. Section 4 reports four experiments and their analysis, followed by the conclusions and future work in Section 5.
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
## 2 Methodology of the MPM
### Governing Equations  
In the MPM, the governing equations are typically derived within the updated Lagrangian framework. When thermal effects are not considered, the governing equations consist of the mass conservation equation, the momentum conservation equation, and the energy conservation equation:  
\[\text{Conservation equation for mass:}\frac{d\rho}{dt}+\rho\frac{av_{k}}{ ax_{k}}=0 \tag{1}\]  
\[\text{Conservation equation for momentum:}\frac{\alpha\sigma_{ij}}{ ax_{j}}+\rho b_{i}=\rho\tilde{u}_{i} \tag{2}\]  
\[\text{Conservation equation for energy:}\rho\dot{e}=\dot{\epsilon}_{ij} \sigma_{ij} \tag{3}\]  
where \(\rho\) is the density of the object, \(v_{k}\) is the instantaneous velocity of the object at time \(k\), \(x_{k}\) is the spatial coordinate of the object at time \(k\); \(\sigma_{ij}\) is the Cauchy stress, \(b_{i}\) is the force per unit mass acting on the object, \(\tilde{u}_{i}\) is the acceleration of the material point in the \(i\)-direction; \(\dot{e}\) is the internal energy per unit mass of the object, and \(\dot{\epsilon}_{ij}\) is the strain rate of the object.  
Within the solution domain \(\Omega\), the momentum equation and the equivalent integral weak form (also known as the virtual work equation) of the given surface force boundary condition \(\Gamma_{t}\) are  
\[\int\limits_{\Omega}\rho\tilde{u}_{i}\dot{\varrho}u_{i}dV+\int\limits_{\Omega }\rho\sigma_{ij}^{s}\delta u_{i;j}dV-\int\limits_{\Omega}\rho b_{i}\dot{\varrho }u_{i}dV-\int\limits_{\tilde{\Gamma}_{t}}\rho\tilde{u}_{i}^{s}\delta u_{i}dA= \tag{4}\]  
Figure 1: Flowchart of the MPM computational process.  
where \(\delta u_{i}\) is the virtual displacement, \(\sigma_{ij}^{s}=\sigma_{ij}/\rho\) is the specific stress, \(\overline{t}_{i}^{s}=\overline{t}_{i}/\rho\) is the specific surface traction, \(dV\) and \(dA\) are the volume and area elements, respectively, and \(i\) and \(j\) are the spatial coordinate components.
## 2 Experimental Approach
### Burner Assembly
For non-reacting flows, the axial velocity of the fuel was measured by a hot wire (Dantec, Skovlunde, Denmark) placed at the center of the fuel passage and 1 mm along the axis above the outlet plane, and the precision of these measurements was approximately 2%. A total of 100,000 data points of instantaneous axial velocity (plots in Figure 2b) were obtained at every forcing strength with a sampling frequency of 10 kHz from the continuous hot-wire anemometer. Then, data sequences were analyzed spectrally using the Fast Fourier Transform to determine the fluctuation amplitude of the axial velocity (vertical axis in Figure 2a) by extracting the component at the acoustic forcing frequency. It was found that axial velocity fluctuation was linearly proportional to the loudspeaker voltage for all tested conditions, as shown in Figure 2a. The air velocity was kept constant. Assuming the gas is incompressible, the amplitude of FMRF is the ratio of the axial velocity fluctuation and the mean axial velocity. It should be noted that the tested gas here was air rather than methane. It can be seen that the curves for all tested curves agree well with each other except for the last couple of cases with higher forcing strength. Figure 2b shows the instantaneous velocity response of the fuel flow during three periods for the case with a mean fuel mass flow rate of 0.142 g/s with an FMRF amplitude of 80% under a loudspeaker voltage of 0.6 V, demonstrating an excellent sinusoidal behavior of the fuel mass flow rate to the acoustic forcing. \begin{table}
\begin{tabular}{c c c c c c c c} Voltage on loudspeaker (V) & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1.0 & 1.2 \\ FMRFMRF amplitude (\%) & 0 & 27 & 53 & 80 & 106 & 132 & 159 \\  & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Test conditions at a mean fuel mass flow rate of 0.142 g/s and air velocity of 17.1 m/s.
###### Abstract  
With the increase in the complexity and informatization of power grids, new challenges, such as access to a large number of distributed energy sources and cyber attacks on power grid control systems, are brought to load-frequency control. As load-frequency control methods, both aggregated distributed energy sources (ADES) and artificial intelligence techniques provide flexible solution strategies to mitigate the frequency deviation of power grids. This paper proposes a load-frequency control strategy of ADES-based reinforcement learning under the consideration of reducing the impact of denial of service (DoS) attacks. Reinforcement learning is used to evaluate the pros and cons of the proposed frequency control strategy. The entire evaluation process is realized by the approximation of convex neural networks. Convex neural networks are used to convert the nonlinear optimization problems of reinforcement learning for long-term performance into the corresponding convex optimization problems. Thus, the local optimum is avoided, the optimization process of the strategy utility function is accelerated, and the response ability of controllers is improved. The stability of power grids and the convergence of convex neural networks under the proposed frequency control strategy are studied by constructing Lyapunov functions to obtain the sufficient conditions for the steady states of ADES and the weight convergence of actor-critic networks. The article uses the IEEE14, IEEE57, and IEEE118 bus testing systems to verify the proposed strategy. Our experimental results confirm that the proposed frequency control strategy can effectively reduce the frequency deviation of power grids under DoS attacks.  
aggregate distributed energy sources; power grids; reinforcement learning; convex neural networks; frequency control; stability analysis; DoS attacks
## 3 Results and Discussion  
### Samples Characterization  
#### 3.1.1 TXRF Analysis  
TXRF results of leachate solutions of raw ashes are reported in Table 1, with good agreement with previous studies [36; 37; 38]. The major elements in leachable FA are Cl, Ca and Br. Ca is also identified as the essential constituent of BA but its higher presence in FA is expected due to the addition of Ca(OH)\({}_{2}\) to adsorb acid gases (like SO\({}_{2}\) and HCl), produced during the incineration process. S is another element found in higher amount in FA with respect to BA due to the lower boiling points and higher volatility of the sulphate phases [38]. Other elements are found in trace amounts in all raw ashes. Zn and Pb show significant differences between FA and BA, in agreement with reported literature [13; 38]. These two heavy metals are almost one order of magnitude higher in FA compared to BA and lower in CFA and FGD. As reported in Table 1, these ashes contain mostly Ca and S [14; 39], while other elements are in low concentrations. Literature reports extremely high concentration of Si in CFA [27] but under this experimental conditions, Si could not be quantified. Stabilized sample revealed a noticeable decrease in Pb and Zn leaching after the first month with respect to the raw FA. While, after 2 M, Pb concentration is lower than lower detection limit (LOD) by TXRF spectrometry (see Table 2), which is 0.002 mg/L [40]. In contrast, stabilized sample revealed higher S concentration compared with FA due to FGD residues addition for stabilization. Based on data reported in Table 2, we can conclude that the lower Zn and Pb leachability must be attributed to the carbonation and pozzolanic reactions that occur during stabilization [21]. It is already shown that pH is a key factor that affects heavy metals leaching behaviors. Considering the pH of all raw powders and final stabilized mixtures, it is extremely interesting to note that all ashes have a starting pH higher than 12 (see Table 1), while stabilized sample has a pH lower than 11 (see Table 2). In this way, pH monitoring also allows the following of the carbonation reactions [19]. In particular, the natural aging of stabilized material allows us to sequestrate carbon dioxide, making it possible to take advantage of the carbonation reaction, as a by-product of the stabilization mechanism [19]. Indeed, carbonation produces a pH neutralizing effect (the pH evolution of stabilized material, with time is reported in Table 2, which contributes to decrease the solubility/mobility of heavy metals.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 1 Introduction
Prominent radiosity-based methods include the hemisphere method [5],  
Figure 1: The geometry and variables for calculating view factor between two surfaces \(A_{1}\) and \(A_{2}\). the hemicube method [6] and adaptive integration [7]. Different implementations of the Monte Carlo method have been proposed since the 1960s. One of earliest applications of Monte Carlo method to heat transfer was proposed by [PERSON] in 1969 [8], wherein the possibility of applying this method to calculations with complex surfaces and simultaneous effects was discussed in the context of computing systems. In the subsequent years, several applications and optimizations around this method have been proposed by numerous studies. For example, a triangular subdivision approach to handle occluded surfaces was proposed by [PERSON] and [PERSON] in 2005 [9]. [PERSON] et al. evaluated the impact of discretization of surfaces through meshing to improve the accuracy of simulations [10]. Recent studies also focus on the applicability of Monte Carlo method for specific use-cases, such as the one by [PERSON] and [PERSON] (2011) to calculate the view factor between a 3D strip element to a circular cylinder [11]. [PERSON] et al. (2018) discuss the effectiveness of the Monte Carlo method for calculating view factors for several complex surfaces such as torus, parallel cylinders and radiant tubes in annealing furnaces [12]. Several tools have been developed specifically for the purposes of calculating view factors. Among these View3D [7] and VISRAD [13] employ Radiosity-based approach and Mont3D [14] employs the Monte Carlo method. Presently, none of these tools are under active development and therefore have not been optimized to leverage the features such as multi-processing, cloud-computing and GPU-based calculations that are present in modern computing systems. Additionally, such tools are not particularly suited for calculations with complex shapes that involve multiple obstructions and curvatures [7]. Building simulation tools that perform view factor calculations, usually through the Radiosity approach, do so as a part of their overall workflow, and do not offer specific functionality to calculate view factors alone. This paper introduces the application of RADIANCE, a raytracing tool used primarily for lighting simulations, to calculate view factors. RADIANCE differs from the previously mentioned view factor calculation tools in several aspects. It has been under continuous development for the past three decades and can leverage the advance features offered by modern computing systems. It also supports complex shapes and obstructions. In the following sections we introduce this tool and discuss its view factor calculation methodology.
## 4 Conclusions  
In summary, we proposed and demonstrated a novel denoising technique that uses the weighted singular value decomposition to improve the detection performance of the phase-sensitive OTDR-based distributed vibration sensor. The multiple time traces along the length of FUT, which form the original signal, were arranged to create a 2D image matrix. The application of the proposed WSVD method decomposes the image matrix into the superposition of numbers of independent and interpretable subspaces with unique singular values. By selecting the suitable set of subspaces in the reconstruction, the WSVD method removes the low-frequency polarization-dependent fluctuation and background noise from the original signal. The weight factor along the length of the fiber further eliminates the random noises, thus efficiently denoising the original signal. In the event of applied vibration, the location information and the frequency of vibration are successfully retrieved along a 2.4 km single mode fiber with a 9 m spatial resolution. The SNR of the location information was found to be 10.7 dB for the physical vibration of 102 Hz, and 12.2 dB for an acoustic vibration of 525 Hz. Compared to the conventional method, the SNR of the vibration event obtained after the proposed denoising method exhibited an increase in the range of 6-7 dB, which confirmed the usefulness of the denoising capability of this new approach of WSVD for a truly-distributed vibration sensor based on the phase-sensitive OTDR.  
Conceptualization, K.N.; methodology, K.N.; software, K.N.; validation, B.H.K. and I.-B.K.; formal analysis, K.N.; investigation, K.N.; resources, B.H.K. and I.-B.K.; data curation, K.N.; writing--original draft preparation, K.N.; writing--review and editing, D.-J.Y. and I.-B.K.; visualization, K.N. and D.-J.Y.; supervision, B.H.K. and I.-B.K.; project administration, B.H.K. and I.-B.K.; funding acquisition, B.H.K. and I.-B.K. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by GIST Research Institute (GRI) APRI under a grant funded by GIST in 2021, and by the Korea Ministry of Environment (MOE) as a Service Program for Demand-Responsive Water Supply (127587).  
Not applicable.  
Data Availability Statement:Data is contained within the article.  
Conflicts of Interest:The authors declare no conflict of interest.  
Figure 9: Power spectrum of the denoised temporal data is obtained at the location of the vibration event having a frequency of (**a**) 102 and (**b**) 525 Hz.
## 4 Performance Evaluation
### Results and Analysis  
Two different radio propagation models were considered for the evaluation of DDOR and FPBR. Thus, the performance metrics are presented in two subsections, one for each propagation model. The results obtained when assuming freespace propagation are introduced in Subsection 4.4.1. Additionally, Subsection 4.4.2 presents the metrics obtained when considering the radio propagation model explicitly developed for highway scenarios introduced in [37].
## 3 Results and Discussion
### Effect of the Crosslinker on DSPE Efficiency
From the summarized results in Table 1, it is evident that the type of crosslinker had a significant influence on the loading capacities of the different polymers. The EGDMA crosslinked sorbent exhibited the lowest capacities for all analytes except CinA. Polymers with the crosslinker DVB showed very high loading capacities for all phenolic acids. High capacities for ChlorA, CaffA and DHB were also obtained with the [C\({}_{6}\)-bis-VIM] [Br] crosslinked polymer. It is noteworthy that despite the lower loading capacities of poly(n-VIM/C\({}_{6}\)-bis-VIM) compared to the DVB crosslinked polymer, higher recoveries could be achieved for DHB, ChlorA and CaffA. This circumstance can possibly be explained by the more complete elution when using the [C\({}_{6}\)-bis-VIM] [Br] crosslinked polymer. Furthermore, we investigated the effect of the molar ratio between VIM and [C\({}_{6}\)-bis-VIM] [Br] on the extraction efficiency of phenolic acids. The monomer to crosslinker molar ratio was varied between 1.15 to 1.00 and 13.80 to 1.00. All applied molar ratios and results are summarized in Table S1. It was apparent that analyte recoveries were dependent on the monomer to crosslinker ratios (Figure 4), especially the recoveries of FerA and CinA that strongly increased with increasing monomer content, whereby a maximum was reached at a monomer to crosslinker ratio of 11.50 to 1. ChlorA and CaffA showed a similar behavior; however, the increase in recovery was not as pronounced. The DHB showed a different recovery pattern. With an increasing monomer content, the recoveries steadily decreased. In conclusion, the highest average recovery for the phenolic acids was obtained at a monomer to crosslinker ratio of 11.50 to 1 with the analyte recoveries ranging between 74.5 and 92.7%. Therefore, subsequent DSPE experiments were solely performed with this specific polymer. By optimizing the VIM to crosslinker ratio, all loading capacities could consequently be significantly increased. The optimized [C\({}_{6}\)-bis-VIM] [Br] crosslinked polymer with a molar ratio of 11.50 to 1 exhibited the highest average loading capacity for phenolic acids. Compared to the EGDMA and DVB crosslinked polymers, higher capacities for all analytes except the CinA were achieved with this specific composition. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Applied Crosslinker** & **ChlorA** & **CaffA** & **DHB** & **FerA** & **CinA** \\ \hline DVB & 49.2 \(\pm\) 0.9 & 46.4 \(\pm\) 0.7 & 49.4 \(\pm\) 1.0 & 42.0 \(\pm\) 0.4 & 40.2 \(\pm\) 0.7 \\ EGDMA & 45.5 \(\pm\) 0.7 & 41.9 \(\pm\) 0.8 & 44.6 \(\pm\) 0.5 & 37.1 \(\pm\) 0.7 & 32.5 \(\pm\) 0.5 \\ [C\({}_{6}\)-bis-VIM] [Br] & 47.8 \(\pm\) 0.3 & 45.9 \(\pm\) 0.3 & 48.4 \(\pm\) 0.3 & 38.5 \(\pm\) 0.2 & 29.8 \(\pm\) 0.2 \\ \hline \end{tabular} Loading capacity: mean \(\pm\) SD /mg g\({}^{-1}\) polymer; n = 3.  
\end{table} 
Table 1: Calculated loading capacities obtained from DSPE experiments with three differently crosslinked vinylimidazole polymers.