esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Enzyme Pretreatment on Total Curcuminoids Recovery of Fresh Turmeric  
Enzyme pretreatment was considered an environmental and non-thermal treatment, increasing selective extraction without the side effect of temperature with bioactive compounds [43]. The effect of EP on total curcuminoid recovery was shown in Figure 2.  
In the present study, enzyme soaking time was 1 h, 3 h and 5 h. EP-1 showed TC 53% higher than the untreated sample, and there was no significant increase in the TC observed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pretreatment**} & \multirow{2}{*}{\begin{tabular}{c} **Pretreatment** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Specific Energy** \\ **Input [kJ/kg]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Start** \\ **Temperature** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **End** \\ **Temperature** \\ \end{tabular} } \\  & & & [\({}^{\circ}\)C] & [\({}^{\circ}\)C] \\ \hline UP-40 & 1 min & 40 & 21.9 \(\pm\) 0.2 & 26.1 \(\pm\) 0.1 \\ \hline UP-80 & 1 min & 80 & 22.5 \(\pm\) 0.2 & 37.6 \(\pm\) 0.2 \\ \hline UP-120 & 1 min & 120 & 22.8 \(\pm\) 0.1 & 44.3 \(\pm\) 0.3 \\ \hline PEF-3.5 & 0.54 ms & 3.5 & 22.8 \(\pm\) 0.2 & 22.8 \(\pm\) 0.2 \\ \hline PEF-7 & 1.03 ms & 7 & 24.2 \(\pm\) 0.3 & 25.8 \(\pm\) 0.2 \\ \hline PEF-14 & 2.05 ms & 14 & 24.1 \(\pm\) 0.2 & 26.2 \(\pm\) 0.2 \\ \hline TP-105 & 5 min & 105 & 22.6 \(\pm\) 0.1 & 34.5 \(\pm\) 0.2 \\ \hline TP-188 & 5 min & 188 & 23.5 \(\pm\) 0.2 & 52.7 \(\pm\) 0.2 \\ \hline TP-272 & 5 min & 272 & 24.3 \(\pm\) 0.2 & 67.4 \(\pm\) 0.3 \\ \hline EP-1 & 60 min & 105 \({}^{\text{1}}\) & 20.2 \(\pm\) 0.3 & 51.3 \(\pm\) 0.3 \\ \hline EP-3 & 180 min & 105 \({}^{\text{1}}\) & 20.3 \(\pm\) 0.3 & 51.3 \(\pm\) 0.2 \\ \hline EP-5 & 300 min & 105 \({}^{\text{1}}\) & 20.2 \(\pm\) 0.2 & 51.2 \(\pm\) 0.2 \\ \hline \hline \end{tabular} \({}^{\dagger}\) The heat loss to the environment during enzymatic treatment was ignored.  
\end{table}
Table 1: Treatment time, specific energy input [k]/kg], start and end temperature [\({}^{\circ}\)C] of different pretreatment on fresh turmeric. Ultrasound pretreatment (UP), Pulsed electric field pretreatment (PEF), Thermal pretreatment (TP), Enzyme pretreatment (EP).  
beyond 1 h of soaking (Figure 1). This phenomenon can be explained by the watery contacting time of EP is considerably higher than that of UP. Even more, curcuminoids were also reported to be unstable in water [38], and they could be decomposed into smaller molecules. The degradation products found were vanillin, p-hydroxybenzaldehyde, ferulic aldehyde, p-hydroxybenzoic acid, ferulic acid and vanillic acid. [PERSON], [PERSON], [PERSON] and [PERSON] [43] reported that extraction time, however, may become a hindering factor as prolonged durations frequently cause bioactive compound disintegration. Besides, extended exposure at elevated temperatures or oxidation processes. In this case, 1 h of soaking with pectinase was the suitable time.
## Appendix A  
For a transversely isotropic cylinder, the three stresses are given as  
\[ \begin{array}{l}\sigma_{rrf}=c_{11}\Big{(}\frac{\partial u_{rf}}{\partial\xi} \Big{)}+c_{12}\Big{(}\frac{u_{rf}}{\xi}+\frac{1}{\xi}\frac{\partial u_{bf}}{ \partial\theta}\Big{)}+c_{13}\Big{(}\frac{\partial u_{if}}{\partial\eta}\Big{)} \\ =P_{f}\Big{(}a_{11f}A_{mf}+a_{12f}B_{mf}+a_{13f}C_{mf}+b_{1f}\Big{)}e^{-i \omega t},\\ \sigma_{r\theta f}=\frac{(c_{11}-c_{12})}{2}\Big{(}\frac{\partial u_{bf}}{ \partial\xi}-\frac{u_{bf}}{\xi}+\frac{1}{\xi}\frac{\partial u_{if}}{\partial \theta}\Big{)}\\ =P_{f}\Big{(}a_{21f}A_{mf}+a_{22f}B_{mf}+a_{23f}C_{mf}+b_{2f}\Big{)}e^{-i \omega t},\\ \sigma_{rzf}=c_{44}\Big{(}\frac{\partial u_{rf}}{\partial\xi}+\frac{\partial u _{if}}{\partial\eta}\Big{)}\\ =P_{f}\Big{(}a_{31f}A_{mf}+a_{32f}B_{mf}+a_{33f}C_{mf}+b_{3f}\Big{)}e^{-i \omega t},\\ \end{array}  \tag{18}\]  
where \(f=z\) or \(\theta\). Substituting Equation (32) into Equations (18)-(19) gives the elements \(a_{ijf}\) and \(b_{if}\) (\(i,j=1\)-3)  
\[a_{1 jf}=c_{11}\frac{\partial F_{rf}^{j}}{\partial\xi}+c_{12}\frac{F_{rf}^{j}} {\xi}+c_{12}\frac{1}{\xi}\frac{\partial F_{gf}^{j}}{\partial\theta}+c_{13} \Bigg{(}\frac{\partial F_{zf}^{j}}{\partial\eta}\Bigg{)}, \tag{20}\]  
\[a_{2 jf}=\frac{(c_{11}-c_{12})}{2}\Bigg{(}\frac{\partial F_{\theta f}^{j}}{ \partial\xi}-\frac{F_{\theta f}^{j}}{\xi}+\frac{1}{\xi}\frac{\partial F_{rf}^ {j}}{\partial\theta}\Bigg{)}, \tag{21}\]  
\[a_{3 jz}=c_{44}\Bigg{(}\frac{\partial F_{zz}^{j}}{\partial\xi}+\frac{\partial F _{rz}^{j}}{\partial\eta}\Bigg{)}, \tag{22}\]  
\[b_{1f}=c_{11}\frac{\partial F_{rf}^{4}}{\partial\xi}+c_{12}\frac{F_{rf}^{4}} {\xi}+c_{13}\frac{\partial F_{zf}^{4}}{\partial\eta}, \tag{23}\]  
\[b_{2f}=\frac{(c_{11}-c_{12})}{2}\Bigg{(}\frac{\partial F_{\theta f}^{4}}{ \partial\xi}-\frac{F_{\theta f}^{4}}{\xi}+\frac{1}{\xi}\frac{\partial F_{rf}^ {4}}{\partial\theta}\Bigg{)}, \tag{24}\]  
\[b_{3f}=c_{44}\Bigg{(}\frac{\partial F_{zf}^{4}}{\partial\xi}+\frac{\partial F _{rf}^{4}}{\partial\eta}\Bigg{)}. \tag{25}\]  
For the \(P_{z}\) CF,  
\[ \begin{array}{l}a_{11z}=\big{(}ik_{\eta}\big{)}\Big{[}\Big{(}c_{12}\frac{m^{ 2}}{\xi^{2}}+c_{13}k_{\eta}^{2}\Big{)}J_{m}(\alpha\xi)-c_{12}\frac{1}{\xi} \frac{\partial J_{m}(\alpha\xi)}{\partial\xi}-c_{11}\frac{\partial^{2}J_{m}( \alpha\xi)}{\partial\xi^{2}}\Big{]}\\ \times\cos(m\theta)e^{-ik_{\eta}\eta},\\ \end{array}  \tag{26}\]  
(Note: this corrects the erratum in Equation (77) for \(a_{11z}\) in Ref. [18].)
# Genetic Programming to Remove Impulse Noise in Color Images
## 2 Related Works
While the first model identifies the majority of noisy pixels located within homogeneous regions of the image, the second is trained to specialize in identifying complex cases of noisy pixels (e.g., those with amplitudes close to their local neighborhood). The classification models were trained using test images contaminated with a mixture of two impulse noise models: salt and pepper and uniform. Once the noisy pixels are detected, they are corrected using a conventional \(\alpha\)-trimmed mean method as part of a two-stage filter. On the other hand, [PERSON] et al. [39] used genetic programming to estimate the optimal value of every noisy pixel in a grayscale image. The estimation combines the useful information of local clean pixels within a small neighborhood with arithmetic operators. In their approach, [PERSON] et al. used the directional derivative to detect pixels perturbed by salt-and-pepper and uniform noise. Their filter was tested using standard images perturbed with a noise density from 10 to 90%. Their experimental results showed that, compared to other approaches, their filter can restore noisy images while preserving edges and fine details, especially in the presence of high impulse noise density. [PERSON] et al. [40] presented a filter based on a two-step switching scheme to remove both salt-and-pepper and additive white Gaussian noises. Their filter uses a patch-based approach, which decomposes a noisy image into a group of patches and then applies a clustering process to gather all patches with the same features and textures. They use genetic programming to generate an adaptive local filter to denoise each singular class of patches with different textural forms. The restoration process of these filters is performed by applying second-generation wavelet thresholding to the clustered patches. Other applications of genetic programming in image processing can be found in [41]. It is important to emphasize that although many of the techniques developed for monochrome images can be directly applied to color image denoising, the independent processing of color image channels is commonly inappropriate, leading to the generation of strong artifacts. To address this challenge, developing methodologies that effectively exploit the inherent correlation among color channels becomes imperative, ensuring more accurate and artifact-free denoising results in the realm of color image processing.
###### Abstract  
With the rapid increase of network users and services, the breadth and depth of Internet have greatly changed. The mismatch between current network requirements and original network architecture design has spurred the evolution or revolution of Internet to remedy this gap. Lots of research projects on future network architecture have been launched, in which Universal Identifier Network (UIN) architecture that is based on the identifier/location separation, access/core separation and control/forwarding separation can provide better mobility, security and reliability. On the other hand, the demand of group communication has increased due to the fine-grained network services and successive booming of new applications such as IoT (Internet of Things). Most of current multicast schemes are based on the open group model with open group membership (multicast only care the multicast group state, not the group member) and open access to send/receive multicast data, which are beneficial to multicast routing for its simplification. However, the open group membership makes the group member management difficult to be realized, and open access may result in lots of security vulnerabilities such as Denial of service (DoS), eavesdropping and masquerading, which make deployment more difficult. Therefore, in this paper we propose a Central-Controllable and Secure Multicast (CCSM) system based on the UIN architecture, and redesign the multicast service procedures including registration, join/leave, multicast routing construction and update with objective to achieve better mobility support, security, scalability and controllable. More specifically, we design a new group management scheme to perform the multicast members join/leave with authentication and a central-controllable multicast routing scheme to provide a secure way to set up multicast entries on routers. The CCSM inherits the characteristics of UIN in terms of mobility and security, and it can provide the centralized multicast routing computation and distributes the multicast routing into forwarders. We compare CCSM with Protocol Independent Multicast-Sparse Mode (PIM-SM), and the results show that CCSM reduces the multicast join delay, and performs better than PIM-SM in term of reconstruction cost under low multicast density.  
multicast, universal identifier network, SPT, central, controllable, secure +
Footnote †: journal:
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Feature Extraction
#### 2.4.2 Index Features  
Vegetation indices are widely and easily used to express vegetation growth situations in the field of remote sensing. In view of the obvious differences in the LAI, chlorophyll content, and canopy density of rubber trees between the defoliation period and foliation period, several indices confirmed to be sensitive to the chlorophyll content and the LAI were selected to estimate rubber plantation characteristics in our work: the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), difference vegetation index (DVI), green difference vegetation index (GDVI), green normalized difference vegetation index (GNDVI), modified simple ratio (MSR), chlorophyll index (CI), ratio vegetation index (RVI), triangular vegetation index (TVI), soil adjusted vegetation index (SAVI), and optimized soil adjusted vegetation index (OSAVI). Furthermore, the difference in each index between the rubber defoliation period and foliation period was considered, as this difference can be used to effectively delineate changes in the rubber tree cycle. The difference in each index was calculated as follows. Using the difference in NDVI (dNDVI) as an example, dNDVI was calculated by obtaining the NDVI value from the PlanetScope image on 8 June 2019, and subtracting that obtained on 27 February 2019. The calculation methods for the DEVI, dDVI, dGDVI, dGNDVI, dMSR, dCI, dRVI, dTVI, dSAVI, and dOSAVI were the same as those for the dNDVI. We extracted 11 vegetation indices for each PlanetScope image and the corresponding difference between the two periods, eventually acquiring a total of 33 features for each classification method. These indices were also calculated based on pixel information and object information. Detailed information on the vegetation indices selected in this study is shown in Table 3.
## Abbreviations  
\begin{tabular}{l l} List of Abbreviations & **Definition** \\ A-SEP & Advance Stable Election Protocol \\ AMQP & Advanced Message Queuing Protocol \\ CoAP & Constrained Application Protocol \\ CFS & Cyber-Physical System \\ DCPS & Data-Centric Publish-Subscribe \\ DDS & Data Distribution Service \\ DoS & Denial of Service \\ DLRL & Data Local Reconstruction Layer \\ DTLS & Datagram Transport Layer Security \\ GIS & Geographic Information System \\ HetNet & Heterogeneous Networks \\ ICT & Information and Communication Technology \\ IPv6 & Internet Protocol version 6 \\ IETF & Internet Engineering Task Force \\ LSH & Locality-Sensitive Hashing \\ LEACH & Low-Energy Adaptive Clustering Hierarchy \\ LLN & Low-Power and Lossy Network \\ MAC & Medium Access Control \\ MEN & Mobile Edge Nodes \\ MQTT & Message Queue Telemetry Transport \\ OT & Operative Technology \\ PIR & Proximity infra-red \\ RPL & Routing Protocol for Low Power and Lossy Network \\ RFID & Radio Frequency Identification \\ SEP & Stable Election Protocol \\ SSN & System Security Networking \\ V2V & Vehicle-To-Vehicle \\ \end{tabular}
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
## 4 Evolution Algorithms  
To get the synthetic radiation pattern, the best combination of feed line lengths needs to be found. This can be done by changing the length of signal feeding line of the element in the circular array. Then, the efficiency of energy harvesting can be maximized for multi-users. We search for the best solution of the radiation pattern using the evolution method that converts the pattern problem into an optimization problem. The evolution method defines the fitness function with the constraint in Equation (23):  
\[\text{Objective Function}=\frac{1}{\eta_{eff}(w)} \tag{24}\]  
If the particle does not satisfy the constraint condition, then the particle will be discarded. However, for increasing the convergence speed, we can add some penalty weighting value to the objective function. For instance, the penalty weighting value is set as 2. In other words, the objective value in Equation (24) will multiply by 2 if the constraint is violated. We can check the final objective value to know whether the constraint is satisfied or not. The numerical results show that the final particle satisfies the constraint.
# Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer
## 5 Challenges and Opportunities
### Ethical Considerations  
The use of AI in drug discovery raises ethical considerations regarding data privacy, algorithmic biases, and equitable access to AI-driven therapies. Biases in training datasets can lead to inequitable treatment predictions, disproportionately affecting under-represented populations. For instance, in drug repurposing, sex-specific differences in genetics, hormones, and metabolism often result in male- or female-biased drug responses and adverse events. Computational approaches are being developed to identify sex-inclusive drug candidates, improving therapeutic options and reducing adverse effects for all patients [55]. Addressing these concerns, an emerging number of members of the scientific community are applying the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This approach reduces bias in biomedicine by promoting ethical, transparent, and interoperable data management. Biomedical researchers can access diverse, FAIRified datasets, minimizing bias from localized data [56; 57]. In 2021, the FDA received 132 regulatory submissions for drugs that included the use of AI in their discovery and development, resulting in a 10-fold increase over the previous year. This federal agency recognizes the transformative potential of AI in drug development and emphasizes rigorous evaluation through well-controlled clinical studies to ensure that the benefits outweigh the risks. Regulatory oversight requires deep technical expertise to review AI-assisted applications, such as target selection or intervention strategies. Maintaining a workforce with technical expertise is vital to provide timely and effective guidance and support innovation in this evolvingfield [58]. The European Medicines Agency (EMA) has developed an artificial intelligence (AI) workplan for the European medicines regulatory network to guide the use of AI in medicine regulation in Europe up to 2028 [59]. Regulatory frameworks must evolve to include guidelines for AI in healthcare, ensuring that ethical standards are maintained [60]. By prioritizing ethical considerations, the industry can foster trust and ensure that AI technologies benefit all patients equitably.
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
## 5 Conclusions  
We mainly studied the statistical distribution models of multipath characteristics based on the experimental data in an urban canyon environment. The characteristics contained the multipath time delay, multipath power attenuation, and multipath fading frequency. In addition, the relationship between the statistical distribution and elevation or orbits was analyzed. Several main conclusions can be drawn:  
* The probability distribution of multipath time delay followed a gamma distribution, and the mean time delay is inversely proportional to the elevation angle.
* The mean multipath power attenuation linearly decreases as the multipath time delay increases, and the multipath attenuation is normally less than \(-17\) dB.
* The probability distribution of the multipath fading frequency is axisymmetric around 0 Hz, and it follows a double-sided exponential distribution.
* For different elevations and different satellites orbits, the type of distribution function to characterize each core parameters is the same, but the feature parameters are different.  
The contribution of this work provides some complements to the existing multipath models. The novel probability functions of multipath time delay and fading frequency are proposed. The results can be used in some research fields, such as multipath simulator, multipath mitigation, and spoofing detection. For example, the statistical model can help us to design the initial parameters for algorithms based on filter theory or estimation theory. The distribution characteristics can also be used to distinguish between a spoofing signal and multipath. In the future, we will continue this research in different types of environments.  
**Acknowledgments:** The research work was funded by the Science and Technology Commission of Shanghai Municipality (grand No. 16 DZ0504600, 16 DZ1100402).  
**Author Contributions:** [PERSON] wrote the paper and performed the experiments. [PERSON] contributed the analysis tools, analyzed the data and refined the full paper. [PERSON] contributed the signal collection device.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Limitations and Future Trajectories  
Notably, the model exhibits limitations in Class Imbalance Handling: Performance on rare disaster categories (e.g., Hazardous_materials_release) could be improved, as data augmentation alone provides partial mitigation (1.42% accuracy drop in ablation).  
Fine-Grained Modality Alignment: Current BCA operates at the global feature level, potentially missing spatial-temporal associations (e.g., aligning \"earthquake epicenter\" text with map coordinates in images). Future work will focus on (1) integrating spatial-aware attention for geo-coded crisis data; (2) developing category-specific dynamic augmentation strategies; (3) extending the model to video-text fusion for real-time disaster scene analysis.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 4 Results
### Discussion on Computational Cost  
We conducted a statistical analysis of the computational cost for the backbone model OneRel and the proposed model. The FLOPs of OneRel are 63.80 GFLOPs, and the number of parameters is 91.18 M. The FLOPs of the proposed model are 134.62 GFLOPs, and the number of parameters is 98.27 M. Compared to the baseline model OneRel, the model proposed in this paper increased both the computational complexity and parameter count. This increase primarily arises from the introduction of dependency syntax information through the GNN and the mixture-of-experts architecture. These modules increase both computational and storage demands. The graph neural network processes dependency relations through multiple layers of graph convolutions, not only making each word's representation dependent on its contextual information but also integrating syntactic dependency structures. Furthermore, the mixture-of-experts architecture introduces multiple expert networks and gating mechanisms, requiring more parameters to dynamically select  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **P/\%** & **R/\%** & **F1/\%** \\ \hline OneRel & 92.25 & 90.62 & 91.42 \\ Ours-1 & 93.33 & 90.68 & 92.00 \\ Ours-m & 92.74 & **92.54** & 92.64 \\ Ours & **93.62** & 92.05 & **92.83** \\ \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results of the model on NYT.  
experts during the learning process. Although the computational cost increased, the accuracy and expressiveness of the model improved, enabling it to better handle complex language structures and diverse relationship types, thus offering greater practical value and applicational potential.
## 2 Kirchhoff Pre-Stack Depth Migration Imaging Principle  
### Method Principle  
The theoretical basis of Kirchhoff integral pre-stack depth migration is the solution of the Green's function of the wave equation. Green's function represents the impulse response between a secondary source and a calculation point. By establishing the integral relationship between the underground wavefield and the reflected waves using the Green's function, seismic wave migration and imaging can be realized based on imaging constraints. The implementation process can be divided into two steps: model building and migration imaging. The formula for Kirchhoff integral pre-stack depth migration is given in [28; 29; 30; 31].  
\[R(x,x_{S})=\int_{\sum}n\
abla_{\tau R}(x_{R},x)\zeta(x_{R},x,x_{S})\frac{ \partial u[x_{R},\tau_{S}(x,x_{S})+\tau_{R}(x_{R},x),x_{S}]}{\partial t}{\rm d }x_{R} \tag{1}\]  
In Equation (1), \(R(x,x_{S})\) is the reflection coefficient; \(n\) is the outward normal vector of the reflector \(\sum\); \(x_{S}\), \(x_{R}\), and \(x\) represent the source point, receiver point, and imaging point, respectively; \(\tau_{S}\) and \(\tau_{R}\) are the travel times from the source point to the imaging point and from the imaging point to the receiver point, respectively; and \(\
abla_{\tau R}(x_{R},x)\) describes the local propagation direction of the wave from the imaging point \(x\) to the receiving point \(x_{R}\). Its direction is tangent to the propagation path of the wave, and its size is related to the reciprocal of the propagation velocity. \(\zeta(x_{R},x,x_{S})\) is the wavefield spreading factor, and \(\partial u[x_{R},\tau_{S}(x,x_{S})+\tau_{R}(x_{R},x),x_{S}]\) is the displacement of the seismic wave propagating in the medium.
## 3 Results
### Parameter Sensitivity  
As shown in Table 6, the simulation results are most sensitive to changes in the \(Z_{\rm{0m}}\). For the cropland, a 20% increase in the \(Z_{\rm{0m}}\) decreased ST and NEE by 26.68% and 18.25%, respectively, and increased SW, ET, and Re by 86.43%, 42.71%, and 38.07%, respectively. Therefore, the accuracy of the input parameters plays an important role in the simulation results. While the +20% change in the SLA value resulted in a moderate NPP increase of 5-8%, the sensitivity of NEE to SLA is much higher than that of NPP.