esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
#### In-Depth Analysis of Antioxidant Activity  
All tested samples showed strong antiradical activity. Taking into account the ability to scavenge ABTS free radicals, samples can be ordered as follows: _[PERSON]. [PERSON] >_J. sibrica >_[PERSON]. [PERSON] >_[PERSON]. [PERSON] \(\approx\)_[PERSON]. [PERSON] >_[PERSON]. [PERSON]_. The activity (expressed as EC\({}_{50}\)) ranged from 331.64 \(\pm\) 5 ug DE/mL (J. _ahei_) to 798.71 \(\pm\) 10 ug DE/mL (_J. ashua_) (Figure 2A). All tested extracts were also able to chelate metal ions. The higher activity was observed in the case of _J. sibrica_ (0.83 mg DE/mL) whereas the lowest was observed for _J. communuis_ (3.2 mg DE/mL). In terms of this activity, samples can be ordered as follows: _J. sibrica >_[PERSON]. fornozama >_[PERSON]. [PERSON] >_[PERSON]. pinchtii >_[PERSON]. [PERSON] >_[PERSON]. [PERSON] >_J. communuis_ (Figure 2B). All extracts were also able to inhibit LOX. In the case of this activity, the smallest differences between extracts were observed. EC\({}_{50}\) values ranged from 1.77 mg DE/mL to 2.44 mg DE/mL. The most effective inhibitors were extracts from _[PERSON]. [PERSON]_ and _[PERSON]. [PERSON]_, whereas the lowest activity was observed in the case of _[PERSON]. communuis_ extract (Figure 2C).  
Figure 2: Antioxidant activity of extracts from leaves of previously selected juniper representatives. (**A**)—antiradical activity against 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzthiaazoline-6-sulphonic acid (ABTS), (**B**)—chelating power, (**C**)—ability to lipoxygenase inhibition. Standard deviation (SD) is shown as error bars. Values (\(\pm\)SD) with different letters (a–g) are significantly different at \(p<0.05\).  
Interestingly, despite the similar inhibitory activity, the extracts have different modes of action. The most effective samples acted as uncopettitive LOX inhibitors (Figure 3). Extracts from _J. pinchotti_ and _J. excelsa_ demonstrated a non-competitive mode of action, whereas the weakest inhibitor--extract from _J. communis_--acted as a competitive LOX inhibitor (Figure 3D,F).  
Figure 3: Mode of lipoxygenase (LOX) inhibition by extracts from leaves of previously selected juniper representatives. (**A**) _J. sabina_, (**B**) _J. sibirica_, (**C**) _J.ashei_, (**D**) _J. communis_, (**E**) _J. fornozana_, (**F**) _J. pinchotti_, (**G**) _J.exelsa_. —–rate of enzymatic reaction expressed as the absorbance unit (see methodology), S—the substrate concentration (see Methodology).
## 3 Results and Discussion
### Retrogradation Tendency of SDexF  
Starch retrogradation refers to the process where gelatinised starch, which has been heated and swelled in water, undergoes structural changes upon cooling. This cooling leads to the rearrangement and reassociation of amylose and amylopectin molecules within the starch granules, resulting in the formation of a more ordered and crystalline structure [64]. Foods containing retrogradated starches may lose their desired taste, moistness, and freshness due to the changes in texture and structure over time, affecting consumer acceptance. Additionally, this process can change the functional properties of the product and cause deterioration of the overall quality [65]. Therefore, it is important that starch modification can inhibit the retrogradation process. To compare the retrogradation ability of potato starch and SDexF, a turbidimetric method was used. In the beginning, on the day of preparing slurries, both SDexF and native starch exhibited low initial turbidity values (Figure 5).  
The turbidity of SDexF slightly increased throughout the entire storage period (by 0.021), while native potato starch showed a significant increase in turbidity, especially after the third day of storage. On the last day of storage (21 st day), SDexF still had a lower turbidity than native potato starch on the day of preparation. Additionally, an approximately 15-fold increase in the turbidity of native potato starch and an approximately 1.3-fold increase in turbidity of SDexF were observed throughout the study period. Compared to SDexF obtained on a laboratory scale [22], the turbidity values for SDexF were slightly lower. With reference to pasting properties of SDexF, setback viscosity indicates the tendency of starch to retrogradation [66] and in this study, low SB values are associated with a low tendency of retrogradation of SDexF.  
Figure 4: The pasting profile of native potato starch (green; 5% concentration) and SDexF (blue; 20% concentration) and the temperature profile used (red).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **PV** & **HPV** & **BD = PV – HPV** & **FV** & **SB = FV – HPV** \\ \hline Native potato starch & 1704 \(\pm\) 344 & 1117 \(\pm\) 52 & 587 \(\pm\) 392 & 1986 \(\pm\) 114 & 869 \(\pm\) 62 \\ SDexF & 28 \(\pm\) 4 & 13 \(\pm\) 3 & 16 \(\pm\) 2 & 21 \(\pm\) 2 & 7 \(\pm\) 1 \\ \hline \end{tabular} PV—peak viscosity, HPV—hot paste viscosity, BD—breakdown, FV—final viscosity, SB—setback.  
\end{table}
Table 8: Pasting property parameters [mPas] of native potato starch (5% concentration) and SDexF (20% concentration).
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### Basic Clustering Concept  
Clustering in WMASNs can be considered as the virtual partitioning of dynamic nodes in the flat structure or distributed network structure into several clusters [30]. Clusters of the nodes in the flat structure or distributed network structure are made with respect to their nearness to each other. Such nodes are considered neighbors when all neighboring nodes are located within their transmission range and set up a bidirectional link between them. Typical algorithms for clustering in the flat structure or distributed network structure are known as one-hop clustering and multi-hop (d-hop) clustering algorithms [30]. In the one-hop clustering, every member node is at most 1-hop distance away from a central node that is called the cluster head. Thus, all member nodes remain at most two hops distance away from each other within a cluster category. On the contrary, in multi-hop clustering [21,30], the management of neighboring nodes to the cluster head is performed by allowing the nodes to be presented at most d-hop distance away from each other to form a cluster. A typical WMASN structure consists of flat and hierarchical structures as shown in Figure 1(a,b).  
The small circle in the figure represents the nodes in WMASNs. The lines joining the circles denote connectivity among the nodes. Every node is identified with an ID number (_i.e._, 1-14) along with a number within parenthesis. The numbers in the parenthesis are the weights of the nodes. These weights are measured with respect to various node parameters and apply the selection of clusterheads. Every node in the flat structure shares equal responsibility to act as a router to route the packets to every other node. However, to achieve better routing efficiency, this structure requires an amount of message flooding. Occasionally, such message flooding has the merit of reducing overhead of the MAC layer. On the other hand, nodes in the hierarchical structure are assigned with different functionalities while acting as a clusterhead, gateway, or a cluster member as shown in Figure 1(b). The clusterhead in the hierarchical structure plays an important role in inter-cluster and intra-cluster communication. Thus, the clusterhead works as the local coordinator for its member nodes and manages the cluster members. A gateway node is a node that connects the bridge between the inter-cluster and intra-cluster communication. A gateway works as the common or distributed access point for two cluster heads. Both of the distributed gateways provide the path for inter-cluster communication. The ordinary nodes of the cluster are the immediate neighbors of the cluster heads. They have the capability of serving as either a head or a gateway whenever selected to do so.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 3 Methods
### Simulation of High-Resolution Regional ET  
ET is the critical link of the hydrological cycle and an essential part of water balance and energy balance [43]. Therefore, the accurate estimation of ET is very important for estimating the TWSC based on the water balance equation in this study.  
In order to obtain high-precision ET in the GRB, we used the Penman-Monteith-Leuning Version 2 (PML_V2) model to simulate ET. The PML_V2 has the following features: (i) a relatively simple structure to estimate the coupling of ET and GPP while retaining reasonable biophysical meaning; (ii) includes the effects of CO2 concentration changes on carbon assimilation and canopy conductance; and (iii) can be applied on regional and large scales easily because it only requires daily meteorological and remote sensing forcing data [44]. Furthermore, by comparing with the ground observation data of 95 global flux stations, this product has higher accuracy and is better than most available ET products [45; 46; 47].  
Although PML_V2 performs well on a global scale, there is still room for improvement at regional scales. The MODIS and GLDAS data were used to produce PML_V2 ET data, but GLDAS data used less ground observation data in China [48; 49]. Thus, in order to further improve the accuracy of the modeled ET in China, the input data were changed from GLDAS to CMFD in this study. CMFD has advantages of high precision and wide coverage, and it has been widely used in hydrological research [30; 31]. Therefore, we used the PML_V2 model and CMFD dataset to estimate the regional ET for the GRB.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 6 Conclusions  
In this paper, we investigate the link connectivity and the probability of coverage of secondary users in underwater cognitive acoustic networks (UCANs). In particular, we propose an analytical model to analyze the above QoS metrics of SUs in UCANs. This model takes both topological connectivity and spectrum availability into account. The extensive simulations validate the accuracy of our model. From the results, we observe that both the link connectivity and the probability of coverage of SUs in UCANs depend on frequency \(f\), spreading factor \(k\), and wind speed \(w\). Meanwhile, both the link connectivity and the probability of coverage of SUs in UCANs are also significantly influenced by the activities of PUs (in terms of the intensity of PUs and transmission power of PUs).  
The work described in this paper was partially supported by Macao Science and Technology Development Fund under Grant No. 096/2013/A3, the National Natural Science Foundation of China under Grant No. 61672170 and the Science and Technology Planning Project of Guangdong Province under Grant No. 2017A050501035. The authors would like to express their appreciation for [PERSON] for his thoughtful discussions. The authors would also like to thank the anonymous reviewers for their constructive comments.  
[PERSON] proposed the idea, derived the results and wrote the paper. [PERSON] supervised the work and revised versions. [PERSON] contributed to the initial idea and proofreading the article. [PERSON] gave valuable suggestions on the motivation of conducting analysis on link connectivity of underwater cognitive acoustic networks and assisted in revising the paper.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Expectation Survey  
The aim of this survey is to find out whether students and professors have had previous remote experiences and their opinion on the use of these new technologies. As can be seen in Tables 4 and 5, few students and professors have previously had the opportunity to use remote laboratories in the teaching-learning method. Similarly, students and professors have heard about this or seen videos about remote laboratories, but they have not actively participated. Both students and professors think that the use of remote laboratories enhances the learning method. Finally, a large majority of them think that remote labs should be included within the curricula plans.  
As we can see from the data collected in Table 4, 80% of participants did not have any previous experience with remote laboratories. Nevertheless, 90% of students consider remote laboratories useful or very useful, and 72% of participants recommend their inclusion in the curricula plans.  
From Table 5, 73% of professors did not have any previous experience with remote laboratories. Similar to students, despite not having previous experience, a large percentage, 85%, of professors consider remote laboratories useful or very useful. Nevertheless, professors are not yet convinced that remote labs should be included in the curricula.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Q1** & \multicolumn{2}{c}{**Yes (\%)**} & \multicolumn{2}{c}{**No (\%)**} \\ \cline{2-4}
**Have you previously had the opportunity to gain experience with remote laboratories?** & & 20 & 80 \\ \hline Q2 & I have heard about this but but have not actively participated (\%) & I have seen a video on internet but have not actively participated (\%) & I had one remote laboratory (\%) & I have had several such remote laboratories in my education (\%) \\ \hline Q3 & It is not at all useful (\%) & Little useful (\%) & **Partially useful (\%)** & **Useful (\%)** & **Very useful (\%)** \\ \hline Q3 & It is not at all useful (\%) & Little useful (\%) & **Partially useful (\%)** & **Useful (\%)** & **Very useful (\%)** \\ \hline Q4 & Yes (\%) & No (\%) & & **Unsure (\%)** \\ \hline
**Do you think that the remote laboratories should be included more in the curricula plans?** & & 72 & 8 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Students’ responses to expectation survey.
## 2 Materials and Methods
### Characterization  
The photocatalytic experimental device was a high-pressure mercury lamp (GGZ300, Shanghai Jiguang, Shanghai, China) with a light intensity of 23 mW/cm\({}^{2}\). The concentrations of MO dye were quantitatively analyzed using a TU-1950 Beijing Puxi UV-VIS spectrophotometer from Beijing, China, specifically at a peak absorption wavelength of 508 nm, both before and after conducting the photocatalytic tests. Morphological examinations of the samples were conducted using a JSM-7800 field-emission scanning electron microscope from Tokyo, Japan, operating at 3 kV. Additionally, the crystal structures of the samples were detailed through X-ray diffraction analysis using an Empyrean device from Panaco, the Netherlands, employing Cu-K\(\alpha\) radiation, with the 20 scanning ranging from 10 to 80 degrees, a step length of 0.06, and a scanning speed of 4 degrees per minute. Diffuse reflectance spectra for the samples were recorded on a Shimadzu Solid Spec-3700 UV-VIS-NIR spectrophotometer from Kyoto, Japan, which was coupled with an integrating sphere and operated in a wavelength range of 200-800 nm at ambient temperature. BaSO4 served as the reference standard. XPS measurements were calibrated against the binding energy standard of C 1s at 284.8 eV using a Thermo Scientific ESCALAB 250 Xi from Waltham, MA, USA. The total organic carbon (TOC) was measured in a Shimadzu TOC-L from Kyoto, Japan analyzer coupled with an autosampler.
## 4 WOA's for Phase Modulation of GTF and Its Phase-Modulated Spectra
To examine if the WOA sets found by our angle-finding method can embody the predicted continuous phase modulation of \(t_{GTF2}\), the transmission spectra of \(t_{GTF2}\) were calculated at eight WOA sets, which were chosen from the WOA sets found above for the phase modulation of \(t_{GTF2}\), with \(\theta_{P1}\) and \(\theta_{P2}\) set at 0\({}^{\circ}\) and 22.5\({}^{\circ}\), respectively. The chosen WOA sets, denoted by Sets IGTF2, IIGTF2, IIIGTF2, IVGTF2, VGTF2, VIGT2, VIIGT2, and VIIIGT2, enable \(\phi\)'s to be chosen as 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), and 315\({}^{\circ}\), respectively. The transmission spectra of the GTF \(t_{GTF2}\), shown in Figure 5, were calculated at the eight WOA sets (Sets IGTF2-VIIIGT2) in a wavelength range of 1548-1552 nm. For the calculation of the transmission spectrum, the length \(L\) and birefringence \(B\) of PMF (PMF 1 or PMF 2) were set as 7.2 m and 4.166 \(\times\) 10\({}^{-4}\), respectively, so that \(\Delta\lambda\) became \(\sim\)0.8 nm at 1550 nm. It can be seen from the figure that the first-order comb spectrum of deformed narrow passbands makes a redshift as \(\phi\) is modulated as 0\({}^{\circ}\) to 315\({}^{\circ}\) (increment: 45\({}^{\circ}\)). If the peak wavelength of one passband is designated as \(\lambda_{peak,GTF}\) at Set IGTF2 (\(\phi\) = 0\({}^{\circ}\)), one step transition in the WOA set (e.g., from Set IIGTF2 to Set IIIGTF2) leads to an increase of 45\({}^{\circ}\) in \(\phi\) and of 0.1 nm in \(\lambda_{peak,GTF}\). Linear movement of \(\lambda_{peak,GTF}\) with respect to \(\phi\), displayed as red-dotted arrows, directly tells us that the continuous or the desired arbitrary phase modulation of \(t_{GTF2}\) can be realized. These calculation results clearly manifest that \(t_{GTF2}\) can be continuously wavelength-shifted within \(\Delta\lambda\) using the WOA sets shown in Figure 4, implying the arbitrary phase modulation capability of \(t_{GTF2}\) within 360\({}^{\circ}\).
# Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning
## 3 Proposed Anomaly Detection Framework
### Treshold Setting  
As mentioned earlier, Tri-CAD is a semi-supervised anomaly detection framework and thus does not rely on labels to train models for detecting anomalies. Likewise, Tri-CAD  
Figure 6: The AE neural network structure.  
does not depend on labels to set the threshold of detecting anomalies. Tri-CAD actually assumes that the first \(D\) data are normal, and derives the mean \(\mu\) and the standard deviation \(\sigma\) of certain features of the \(D\) data. It then checks if the standard anomaly score \(\lambda_{x}\) of a test data point \(x\) exceeds a certain deviation threshold \(\tau\) (i.e., if \(\lambda_{x}\succ\tau\times\sigma\)). If so, the data point \(x\) is assumed to be anomalous; otherwise, \(x\) is assumed to be normal.  
The deviation threshold \(\tau\) plays the role of making the anomaly detection more sensitive or less sensitive. We can thus set different levels of sensitivity based on the deviation threshold \(\tau\). For example, setting \(\tau\) to be 2.575829, 3.290527, 3.890592, 4.417173, 4.891638, 5.326724, 5.730729, and 6.109410 can make the framework have eight sensitivity levels of anomaly detection, so that 1%, 0.1%, 0.01%, 0.001%, 0.0001%, 0.00001%, 0.000001%, and 0.0000001% of data are detected as anomalies. Certainly, if the framework can obtain some data labels in advance, then the deviation threshold \(\tau\) can be set as an optimized value to make the anomaly detection have the best performance.
## 4 Materials and Methods
### Genotyping  
The present study examined the rs1051931 SNP of the _PLA2G7_ gene. Genomic DNA was extracted from whole-blood samples using standard procedures. The extracted DNA was dissolved in TE buffer (10 mM Tris-HCl and 1 mM ethylenediaminetetraacetic acid, pH 8.0), as described previously [57; 58]. Briefly, whole-genome genotyping was performed using Infinium Assay II and the iScan System (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Five kinds of BeadChips (HumanHap300, total markers: 317,503; HumanHap300-Duo, total markers: 318,237; Human610-Quad v1, total markers: 620,901; Human1M v1.0, total markers: 1,072,820; Human 1M-Duo v3, total markers: 1,199,187) were used for genotyping 40, 67, 6, 120, and 128 samples, respectively, in the TD group. The Infinium Asian Screening Array-24 v. 1.0 BeadChip (total markers: 659,184) was used for genotyping 333 patients' samples in the JUH group that remained after the filtration process based on the exclusion criteria of the clinical data. Some BeadChips included a few probes that were specific to copy number variation markers, but most were for SNP markers on the human autosome or sex chromosome. Approximately 300,000 SNP markers were commonly included in all five kinds of BeadChips that were used for genotyping in the TD group. Genome Studio with the Genotyping v. 3.3.7 or v. 2.0.4 module (Illumina) was used to examine data for samples that had their entire genomes genotyped to assess quality of the findings, and quality control was performed as described in our previous report [57]. Because the genotype data for the _PLA2G7_ rs1051931 SNP were included in the aforementioned BeadChips for whole-genome genotyping, the data were extracted from the results of whole-genome genotyping and used for the association study.
## 5 Method
### Problem Posing  
In order to further optimize the performance of the unmanned emergency support system and make full use of the existing computational and storage resources in the system, this paper takes the rescue mission requirements as the traction and aims at reducing the delay and energy consumption, based on the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) joint optimization of the rescue task diversion strategy, system communication, and computational resource allocation problems. The weighted sum of delay and energy consumption is expressed in terms of the offloading overhead; therefore, from Equation (10), in order to reduce the total offloading overhead of the target system, problem Q can be obtained:  
\[\text{Q}\min_{\alpha,f_{mec},\alpha_{i}}F_{system} \tag{12}\]  
\[\text{s.t. }\alpha_{i}\in\{0,1\},\forall i\in n \tag{13}\]  
\[\sum_{i=1}^{n}{f_{i,mec}}\leq f_{mec,total} \tag{14}\]  
\[0\leq f_{i,mec}\leq f_{mec,total} \tag{15}\]  
\[\sum_{i=1}^{n}{w_{i}}\leq W \tag{16}\]  
\[0\leq w_{i}\leq W \tag{17}\]  
In the above model, \(\alpha_{i}=\{\alpha_{1},\alpha_{2},\alpha_{3},...,\alpha_{n}\}\) represents the set of offloading decisions at the task edge layer. The constraints in Equations (13)-(17)are modeling the channel resource planning, representing the channel resources acquired by the UAV when it shunts the collected data to the superior node, which indicates that communication channels are allocated to the terminal devices at the time of offloading and that the sum of the sub-bandwidths does not exceed the total bandwidth; (14) and (15) are modeling the computational power planning process in the target system, which indicates that the tasks diverted to the edge node are allocated to the reasonable computational resources; and (13) corresponds to the task diversion policy, indicating that there is a total of \(2^{n}\) implementation per layer when offloading is performed at the terminal and edge layers.  
The implementation of a data triage is a nonlinearly constrained 0-1 planning problem, whereas in the two subproblems of communication and arithmetic decomposed above, the vast majority of the variables are linearly related to the emergency rescue mission and the data triage; communication and arithmetic resource planning problems are MINP problems [25]. However, the emergency rescue task, as well as the distributed deployment of the large model in the system, is a systematic problem that should not be separated for optimization and analysis, and thus the original modeling problem needs to be decoupled and split.