esa-sceva/satcom-chunk-collection huggingface dataset:
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 3 Analytical Solutions to Peristatic Problems  
### Simply Supported Beam  
Consider a simply supported beam subjected to a transverse load \(p(x)\), as shown in Figure 4. In this case, the PD boundary conditions can be given as follows:  
\[w(-\xi)=-w(\xi),\quad w(L+\xi)=-w(L-\xi),\quad(0<\xi\leq 2\delta). \tag{23}\]  
Figure 4: Simply supported beam.  
Figure 3: Roller clamped edge with a fictitious region.  
Figure 2: Simply supported edge with a fictitious region.  
With the BCs (23), the deflection field can be extended periodically with the period of \(2L\), as shown in Figure 5.  
Therefore, the deflection function can be expanded by applying the following Fourier series:  
\[w(x)=\sum\limits_{n=1}^{\infty}b_{n}\sin\frac{n\pi x}{L}. \tag{24}\]  
Substituting Equation (24) into (17) provides, after some simplifications,  
\[\frac{EI}{\delta^{2}}\sum\limits_{n=1}^{\infty}b_{n}\sin\frac{n\pi x}{L}\left[ \int\limits_{-\delta}^{\delta}\frac{1}{\xi^{2}}\bigg{(}1-\cos\frac{n\pi\xi}{L} \bigg{)}\mathrm{d}\xi,\right]^{2}=p(x) \tag{25}\]  
where the unknown coefficients can be determined by invoking the orthogonality of trigonometric functions:  
\[b_{n}=\frac{2\delta^{2}}{LEI}\int\limits_{0}^{L}p(x)\sin\frac{n\pi x}{L}dx \left[\int\limits_{-\delta}^{\delta}\frac{1}{\xi^{2}}\bigg{(}1-\cos\frac{n\pi \xi}{L}\bigg{)}\mathrm{d}\xi\right]^{-2}. \tag{26}\]  
Plugging Equation (26) back into (24) gives the following explicit PD solution for the simply supported beam:  
\[w(x)=\frac{2\delta^{2}}{LEI}\sum\limits_{n=1}^{\infty}\int\limits_{0}^{L}p(x) \sin\frac{n\pi x}{L}\mathrm{d}x\left[\int\limits_{-\delta}^{\delta}\frac{1}{ \xi^{2}}\bigg{(}1-\cos\frac{n\pi\xi}{L}\bigg{)}\mathrm{d}\xi\right]^{-2}\sin \frac{n\pi x}{L}. \tag{27}\]
# Antioxidant Properties of _Jatropha curcas_ L. Seed Shell and Kernel Extracts
## 4 Conclusions  
The extraction yield and antioxidant capacity of extracts depend on the plant part from which the extract is derived and the polarity of the extraction solution. In the case of _J. curcas_ seeds, the extract yield of the seed shell was lower than that of the seed kernel. However, the seed shell extract had higher antioxidant capacity, including DPPH and ABTS radical scavenging activities, reducing power, and total phenolic content, than the seed kernel extract. The DPPH and ABTS radical scavenging activities of the seed shell extract obtained using 95% ethanol solution were close to those of ascorbic acid. The in vitro toxicity was conducted to determine the concentration of the seed shell extract that should be used. Further research and development of _J. curcas_ seeds can considerably benefit domains such as health care and cosmetics.  
Conceptualization and supervision, Y.-S.L.; data curation, X.-Y.Z. and C.-T.L.; formal analysis, S.-L.H., W.-H.W., W.-S.L., and M.-Y.C.; writing--original draft preparation, X.-Y.Z. and C.-T.L.; writing--review and editing, Y.-S.L. All authors have read and agreed to the published version of the manuscript. This research was funded by National United University (108-NUUISP003) and the Ministry of Science and Technology, Taiwan.  
The authors declare that they have no conflicts of interest. The funding sponsor played no role in the design of the study; the collection, analyses, or interpretation of data; the writing of the manuscript; nor in the decision to publish the results.  
Figure 6: L929 cell viability after 24-h treatment with shell extracts of different concentrations. Mean followed by the same letter(s) are not significantly different at the 5% level by Tukey test.
## 3 Proposed Max-SNR-FP Scheme  
In this section, to maximize the SNR, we present a Max-SNR-FP approach to jointly optimize beamforming vector \(\mathbf{v}\), passive IRS PSM \(\mathbf{\Phi}\), and active IRS PSM \(\mathbf{\Psi}\). In what follows, we alternately solve for \(\mathbf{v}\), \(\mathbf{\Phi}\), and \(\mathbf{\Psi}\).
## 4 Conclusions  
In this work, the load-carrying capacity, deformation characteristic, and ductility performance of the BFRPC composite column under cyclic lateral loading were investigated through comparing with PHC and PRC tube columns. The failure features, hysteretic curve, and skeleton curve for the three columns were compared and analyzed through the load-displacement hysteretic curve. The main conclusions are as follows:  
1. The forward/reverse extreme loads are 118.77 kN/107.44 kN for the PHC tube column specimen, 169.59 kN/156.93 kN for the PRC tube column specimen, and 592.07 kN/553.72 kN for the BFRPC composite column specimen. The forward/reverse extreme displacements are 23.21 mm/22.21 mm for the PHC tube column specimen, 81.76 mm/78.74 mm for the PRC tube column specimen, and 140 mm/140 mm for the BFRPC composite column specimen.
2. The BFRPC composite column is about 5 times and 6 times than PHC tube column and is about 3.5 times and 2 times than PRC tube column for the horizontal bearing capacity and extreme displacement, respectively.
3. The failure of the BFRPC composite column belongs to ductile failure without yield point, and its ductility is significantly less than that of PRC tube column. The PHC tube column presents a certain brittle failure behavior.
4. For the BFRPC composite column the installation of FRP tube is equivalent to an increase in the reinforcement ratio of the concrete column, which can significantly improve its ultimate load carrying capacity.
5. The functional form for the skeleton curve of BFRPC composite column can be approximately represented by the inverse hyperbolic sine function.  
All authors conceived and designed the study. Conceptualization, methodology, and writing--review and editing: [PERSON], H.L. and G.D.; writing--original draft preparation and visualization: [PERSON] and Q.G.; investigation and resources: [PERSON] and P.X.; supervision: [PERSON]; project administration, [PERSON], J.Z. and P.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research and Development Project of CSCEC (No. CSEC-2020-Z-21), and the National Natural Science Foundation of China (NSFC Grant Nos. 52078128, 51878160).  
Not applicable.  
Figure 12: The fitting result of the skeleton curve for the BFRPC composite column.
### Literature Review on Available Solutions
Other important and innovative examples of ICT solutions for emergency management refer to: (a) smartphone-based information systems [34]; (b) mobile post-disaster management systems based on free and open source technologies [35]; (c) satellite remote sensing for disaster management [36]; (d) exploiting data from social media [37]; (e) using deep learning to identify survivors in debris from images gathered by smart infrastructures [38]; (f) using an IoT ecosystem (wellbeing-wearable and home automation system sensors with varying communication protocols) to empower the elderly population to self-manage their own health and stay active, healthy, and independent as long as possible within a smart and secured living environment [39]. Recommender systems technology aims at reducing the consumer over-choice due to the huge amount of information available on the web [40]. These systems use information (e.g., location, preferences, past interaction, item features, etc. ), gathered through few interactions with its user, to create a customized selection of items (e.g., recommendation list) that can interest the user and can facilitate a better user experience [40; 41]. Recommender systems could be further improved if Machine Learning (ML; users extract the features from raw data to feed the algorithm) and Deep Learning (DL; the algorithm automatically extracts features from raw data) algorithms were used for information retrieval [40]. Noteworthy applications of recommender systems in smart cities include: 1) a mobile IoT recommender system for users that need to find Park-and-Ride infrastructures to switch from a private to a public transportation mode [42]; 2) an autonomous situation-aware evacuation route recommender
## 4 Experimental Results
### Ablation Experiment
#### 4.2.1 Ablation Experiment for Weighted Module of Segmentation Network  
To quantify the contribution of the contribution weights to the recognition of spoof samples, this experiment tests the anti-spoofing performance of the fusion module with the introduced contribution weights \(w_{i}\), the fusion module without weights, the single-path fusion module, and the single-reconstruction branch. Among them, the single-path fusion module refers to placing the segmentation results of different levels of the input bona fide samples on the same segmentation label for training. The fusion module based on the contribution weights \(w_{i}\) proposed in this paper uses three segmentation networks to segment different tissue structures of the samples, respectively. In this experiment, the SpoofScore\({}_{\mu-\sigma}\) is used as the anomaly detection score.  
The experiment shows the experimental results of the weighted fusion in Table 4. From the results, it can be found that when the dual-branch network is used, since the segmentation task can prompt the reconstruction task to learn the data distribution of the training samples, the error rate of the model is reduced. Separately training the labels can reduce the interference factors brought by other regions, enabling the reconstruction branch to learn features more robustly. Research shows that fusing the contribution weights into the model can play the role of an attention prior, and introducing this prior condition helps the model to better judge spoof samples. In the single-branch segmentation experiment, an additional semantic segmentation branch was introduced to segment the OCT images into three target tissue regions. The segmentation results in the testing phase were used as spatial masks to extract region-specific representations, which were then passed to the unsupervised reconstruction branch for spoof score calculation. However, when the segmentation quality during testing is suboptimal, the generated spatial masks may fail to accurately localize tissue-specific areas. This results in the feature weighting module amplifying features unrelated to the target tissues. Consequently, it may distort the representations of bona fide samples and misclassify them as spoof samples, leading to a higher BPCER value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AUC** & **EER** & \(\mathbf{BPCER_{10}}\) \\ \hline AE [49] & 0.8431 (0.0140) & 0.2172 (0.0129) & 0.3342 (0.0304) \\ VAE [50] & 0.8674 (0.0189) & 0.2158 (0.0153) & 0.3253 (0.0206) \\ MemAE [51] & 0.8981 (0.0135) & 0.1429 (0.0138) & 0.1590 (0.0200) \\ OCPAD [9] & 0.9181 (0.0114) & 0.1224 (0.0072) & 0.1368 (0.0179) \\ PPAD [21] & 0.9574 (0.0044) & 0.0705 (0.0106) & 0.0672 (0.0131) \\ U\_OCFR [22] & 0.9681 (0.0073) & 0.0777 (0.0121) & 0.0678 (0.0175) \\ Ours & **0.9688 (0.0610)** & **0.0668 (0.0112)** & **0.0592 (0.0074)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with anti-spoofing methods (data in brackets are standard deviation values).
## 4 Discussion of CPMA Application  
As the proposed monitoring system has two main functions, namely data collection and data prediction, CPMA implementation can also be shown in two applications. The first application involves redisplay of the data collected from the sensor, and the second application is to redisplay the model prediction result from the main application. Furthermore, as previously mentioned, the application was applied to various machines to redisplay the monitoring results from two different machines.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 3 Experiments
### Results  
The proposed method is able to generate a grasp position for each object in 0.1 s. Figure 17 shows an example of the results of a typical experiment for an object. In this case, the object is a Swiss Army knife. The figure shows how the position and orientation of the object are varied across all grasp attempts. Where possible, the shape of the object is also manipulated across attempts. In this example, the various tools of the Swiss Army knife are opened in different combinations.  
Figure 16: (**a**) Similar object set used for GPP testing [18]. (**b**) Similar object set used for DexNet 2.0 testing [19]. (**c**) Similar object set used for GG-CNN testing [20]. (**d**) Object set designed by this study for algorithm testing.  
Figure 19: Examples of grasp poses for each of the 40 objects across all test sets generated by the algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Training Size** & **Test Set** & **Attempts** &
\begin{tabular}{c} **Success Rate (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Proposed method} & \multirow{2}{*}{400} & similar to GPP & 80 & 97.50 \\ \cline{3-5}  & & similar to DexNet2.0 & 90 & 97.78 \\ \cline{3-5}  & & similar to GG-CNN & 120 & 99.17 \\ \cline{3-5}  & & Self-designed & 180 & 98.33 \\ \cline{3-5}  & & All objects & 400 & 98.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grasp success rates of proposed method on various object test sets.
## 4 Summary  
This paper presented a methodology for the extraction of spatial objects from terrestrial laser scanning data. The methodology is based on the fusion of individual channels, including range, intensity and color. In order to make the framework as general as possible, use of a set of low level features, such as cornerness, derivatives, and colorimetric measures, have been used. In contrast with existing model driven approaches, the proposed method is characterized by minimal reliance on domain knowledge. For the extraction, the channels are segmented first and then a set of features is extracted per segment. Each of the features is computed per segment and is then averaged. Classification of the segments is then carried out in a supervised manner using the non-parametric kNN classifier. To eliminate potential bias in the classification, the training data have been first normalized using the whitening transform. Following the segment-level classification, a pixel-level refinement phase has been applied using graph-cuts.  
Results demonstrate that detection of objects with high level of accuracy can be reached by learning object characteristics from a small set of features and a limited number of samples. The detection scheme has managed to identify trees both at different depths (scales) and partially occluded ones. Pixel classification accuracy was on the order of 99%. The small number of false alarm detections indicates the appropriateness of the selected features for the recognition.  
Figure 21: Results of the tree detection scheme applied on scans 3, 5, 23. The denser tree crowns against the sky background (contrasting those against buildings) are due to the last return operation mode of the laser scanner.  
Extensions of the proposed method would see avenues in the utilizing additional data channels, e.g., spectral data. It is noted that such channel addition is native to the proposed model but requires incorporation of an additional set of low-level features. Furthermore, the proposed approach can be extended to detect other objects e.g., buildings, and cars. Color saturation, size, surface smoothness, and other features which correspond to the channels to be used would be candidates for the application of such recognition.  
Funding for this research was provided in part by the Volkswagen-Niedersachsen Foundation.
# MRI and CT Fusion in Stereotactic Electroencephalography: A Literature Review
###### Abstract  
Epilepsy is a common neurological disease characterized by spontaneous recurrent seizures. Resection of the epileptogenic tissue may be needed in approximately 25% of all cases due to ineffective treatment with anti-epileptic drugs. The surgical intervention depends on the correct detection of epileptogenic zones. The detection relies on invasive diagnostic techniques such as Stereotactic Electroencephalography (SEEG), which uses multi-modal fusion to aid localizing electrodes, using pre-surgical magnetic resonance and intra-surgical computer tomography as the input images. Moreover, it is essential to know how to measure the performance of fusion methods in the presence of external objects, such as electrodes. In this paper, a literature review is presented, applying the methodology proposed by [PERSON] to determine the main techniques of multi-modal brain image fusion, the most relevant performance metrics, and the main fusion tools. The search was conducted using the databases and search engines of Scopus, IEEE, PubMed, Springer, and Google Scholar, resulting in 15 primary source articles. The literature review found that rigid registration was the most used technique when electrode localization in SEEG is required, which was the proposed method in nine of the found articles. However, there is a lack of standard validation metrics, which makes the performance measurement difficult when external objects are presented, caused primarily by the absence of a gold-standard dataset for comparison.  
image fusion; stereotactic electroencephalography; computer tomography; magnetic resonance imaging; image registration +
Footnote †: journal:  
The image fusion (Figure 1) maps images into the same coordinate system and then blends the aligned result into an output image. Among the methods for image fusion, characteristics such as imaging modalities determine the performance of a procedure. For example, methods that use the Mean-Squared Difference (MSD) as the optimization metric perform better in single-modality fusion [10; 11]. Thus, it is essential to know the performance of these techniques, especially in applications such as SEEG that involve external objects.  
This paper aims to present an overview of the main methods, tools, metrics, and databases used in multi-modal image fusion. This review contributes to the literature by summarizing the main techniques of image fusion, distinguishing between the two main steps of registration and merging.
# Experimental Study of Erosion Prevention Model by Bio-Cement Sand
## 3 Results and Analysis
### Penetration Test Results and Analysis
The non-uniform arrangement of sand particles during model fabrication results in finer particles migrating downward with water flow around the model post slurry application. The close packing among these fine particles diminishes pore size and subsequently enhances sample strength. Figure 8 delineates the correlation between cementation depth and penetration resistance across eight test points, alongside the calcium carbonate content under diverse grouting conditions. The cementation depth of microbially reinforced sandy slopes exhibits a positive trend with increasing calcium carbonate content. Assuming a linear relationship between these two parameters, the fitting result is \(h_{0}=1.14868\times B_{ca}-1.72889\). As the frequency of grouting reinforcement cycles escalates, there is a gradual increase in calcium carbonate content while variations in cementation depth remain negligible. The typical cementation depth observed for sandy slopes reinforced via microbial spraying is approximately 10 mm; this observation can be attributed to the establishment of a dense cemented layer at the surface that impedes further infiltration of grouting fluid into deeper sand strata over time. Furthermore, penetration resistance intensifies concomitantly with rising calcium carbonate levels. Assuming an exponential relationship between these variables yields a fitting result of \(P_{s}=2.51412\times B_{ca}^{2.39453}\). Although an incremental rise in calcium carbonate content occurs with additional grouting cycles, penetration resistance demonstrates rapid enhancement due to the formation of a robust surface layer resulting from microbial spraying activities. Despite challenges encountered by late-stage grouting fluids attempting to penetrate deeper into sand layers, continuous precipitation and accumulation of calcium carbonate on the surface significantly contribute to developing a solidified layer that markedly improves sand strength. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Station Number** & **A-1** & **A-2** & **A-3** & **B-1** & **B-2** & **C-1** & **C-2** & **C-3** \\ \hline \multirow{4}{*}{Unreinforced} & Penetration resistance (kPa) & 140.01 & 153.34 & 143.34 & 176.68 & 170.01 & 143.34 & 138.67 & 96.67 \\  & Carbonate content (\%) & / & / & / & / & / & / & / \\  & Depth (mm) & / & / & / & / & / & / & / \\  & Penetration resistance (kPa) & 497.77 & 448.88 & 515.55 & 382.22 & 284.44 & 435.55 & 328.89 & 395.55 \\
3 times & Carbonate content (\%) & 8.24 & 8.89 & 9.16 & 7.31 & 6.56 & 8.64 & 6.87 & 8.36 \\  & Depth (mm) & 8.87 & 8.63 & 9.18 & 7.59 & 5.42 & 8.32 & 5.65 & 7.64 \\  & Penetration resistance (kPa) & 555.55 & 537.77 & 1106.66 & 448.88 & 582.22 & 871.10 & 426.66 & 879.99 \\
6 times & Carbonate content (\%) & 10.29 & 10.09 & 12.24 & 9.33 & 9.56 & 11.34 & 9.28 & 11.49 \\  & Depth (mm) & 9.33 & 9.16 & 13.23 & 8.43 & 9.84 & 11.27 & 8.24 & 11.14 \\ \hline \end{tabular}
\end{table} 
Table 1: Results of microbially reinforced sand penetration test.