esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems
## 3 CSI Estimation Error Model
In order to directly calculate the CRLB of the UE position, it is necessary to redefine the unknown parameter vector according to the chain rule, and the equivalent Fisher matrix of the position can be expressed as follows [28]:  
\[\mathbf{J}_{\mathbf{p}_{k}}=\mathbf{b}_{k}^{\text{H}}\mathbf{J}_{\tau_{k} \tau_{k}}\mathbf{b}_{k}, \tag{23}\]  
where the elements of the vector \(\mathbf{b}_{k}\in\mathbb{R}^{M\times 1}\) are expressed as:  
\[[\mathbf{b}_{k}]_{n}=\frac{\mathbf{p}_{k}-\mathbf{a}_{j_{n},k,m_{n}}}{c\Big{\|} \mathbf{p}_{k}-\mathbf{a}_{j_{n},k,m_{n}}\Big{\|}_{2}}. \tag{24}\]  
where \(\mathbf{a}_{j_{n},k,m_{n}}\) denotes the reflection point coordinates of the \(m^{th}\) from the \(j^{th}\) BS for the \(k\)th UE, where \(m_{n}\) and \(j_{n}\) are related to \(n\), respectively, by \(j_{n}=\lfloor n/M\rfloor\), \(m_{n}=n\) mod \(M\). According to the reference point auxiliary location algorithm proposed in the previous section, the location \(\mathbf{\hat{p}}_{k}(t)\) of the UE can be estimated in real time, but in practice, due to the BS coordinate error and measurement error, it is impossible for the estimation algorithm to obtain accurate UE coordinates. The estimation error at this time is expressed as \(\mathbf{e}_{k}(t)=\mathbf{p}_{k}(t)-\mathbf{\hat{p}}_{k}(t)\). Considering the assumption of Gaussian white noise, the least square equivalent and maximum likelihood estimation method can achieve the estimation result under CRLB; that is, the positioning error \(\mathbf{e}_{k}\) obeys the mean value \(\mathbf{0}\), while the variance is \(\mathbf{J}_{\mathbf{p}_{k}}^{-1}\). \[\mathbf{e}_{\mathbf{p}_{k}}(t)\sim\mathcal{CN}(\mathbf{0},\mathbf{J}_{ \mathbf{p}_{k}}^{-1}), \tag{25}\]  
with the location of the \(k\)th UE and the known environmental parameters, the partial channel model \(\mathbf{\hat{\beta}}_{jk,m}(t)\) between the \(k\)th UE and the \(j^{th}\) BS can be constructed, and its elements are expressed as follows:  
\[\Delta\mathbf{\beta}_{jk,m}^{n}(t)\approx\left\{ \begin{array}{ll}\frac{ \lambda}{4\pi}\frac{\left(\mathbf{\hat{p}}_{k}(t)-\mathbf{u}_{j,\mu}\right)^{ \text{T}}\mathbf{e}_{k}(t)}{\left\|\mathbf{\hat{p}}_{k}(t)-\mathbf{u}_{j} \right\|_{2}^{3}},&m=1\\ \frac{\lambda\sqrt{\mathbf{T}}\left(\mathbf{\hat{p}}_{k}(t)-\mathbf{u}_{j,\mu }\right)^{\text{T}}\mathbf{e}_{k}(t)}{4\pi(\left\|\mathbf{\hat{p}}_{k}(t)- \mathbf{u}_{k,\mu}\right\|_{2}+\left\|\mathbf{a}_{k,m}-\mathbf{u}_{j}\right\| _{2})^{3}},&m\geq 2\end{array} \right. \tag{26}\]  
Furthermore, this paper uses the first-order Taylor expansion formula, given by the following equation:  
\[\left\|\mathbf{\hat{p}}_{k}(t)+\mathbf{e}_{k}(t)-\mathbf{u}_{j}\right\|_{2} \approx\left\|\mathbf{\hat{p}}_{k}(t)-\mathbf{u}_{j}\right\|_{2}-\frac{\left( \mathbf{\hat{p}}_{k}(t)-\mathbf{u}_{j}\right)^{\text{T}}}{\left\|\mathbf{\hat {p}}_{k}(t)-\mathbf{u}_{j}\right\|_{2}}\mathbf{e}_{k}(t). \tag{27}\]
## 3 Optimization of the Phase Screen Model
Accordingly, when the subharmonic method is applied alone, a  
Figure 2: Schematic diagram of the geometric relationship in the diffraction process. maximum matching degree of 48.09% can be realized with \(N_{p}\) set to 5. The optimization performance is limited because the number of the subharmonic layers can only change in the integer range. The modeling accuracy can be further improved by adjusting the value of the grid spacing. Figure 4 shows the optimization performance when both the subharmonic method and the strict sampling constraint method are utilized, with \(N_{p}=5\). The PSF curve becomes closer to the theoretical value as the grid spacing \(d\) decreases. The PSF matching degrees with \(d=2\times 10^{-4}\) m, \(1\times 10^{-4}\) m, \(5\times 10^{-5}\) m, and \(3\times 10^{-5}\) m are 1.85%, 9.52%, 48.09%, and 96.19%, respectively. Finally, the phase screen model is effectively optimized by the joint application of the two methods, and is verified to achieve the matching degree of 96.19% with the theoretical statistics. However, it should be noted that the decrease of \(d\) generally means an increase of Monte Carlo simulation workload; thus, it is important to make a trade-off between modeling accuracy and efficiency. Figure 4: The average PSF of the simulation model with different sampling spacing. Figure 3: The average PSF of the simulation model with subharmonic phase screens.
# Aerial Mapping of Coseismic Surface Rupture of 2021 M\({}_{\rm w}\) 7.3 Maduo Earthquake, China
## 3 Materials and Methods  
### D-InSAR  
In this survey, the ascending SAR data from Sentinel-1 satellite were collected in relation to differential interference to show the trace of surface rupture caused by the Maduo earthquake. The pre-seismic data were acquired on 20 May 2021, and post-seismic data were taken on 26 May 2021. The resolution of SAR is 3 \(\times\) 22 m.  
The two-pass differential interferometry method was used to deal with the SAR data. The free single look complex (SLC) and precision orbit data were downloaded from the website of the European Space Agency (ESA, [[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)), accessed on 13 December 2022) and the space shuttle radar terrain mission (SRTM) data from the National Aeronautics and Space Administration (NASA, [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)), accessed on 13 December 2022). The master and slave images were co-registered by the same points on the surface. Then, the interferogram was formed using inputting data, and the coherence graph was generated after Goldstein phase filtering. After the topographic phase removal, the next step was to use the minimum cost flow (MCF) algorithm for phase unwrapping, then orbit refinement and re-flattening. Finally, a displacement map along LOS was generated by the phase to displacement conversion and geocoding (Figure 2) [13, 14, 15, 16].
## 3 Proposed Framework  
A detailed description of the proposed framework is given in this section. This research proposes a specific framework based on an integration of the AHP and Grouping models. The proposed framework improves and enhances the existing approaches in order to overcome the limitations of existing models. The proposed framework comprises four modules: Pre-Processing, Review, Implementation, and Presentation Modules as depicted in Figure 1. The implementation module comprises a Grouping Processing Unit (GPU), which is used to classify the requirements into four groups, and an AHP Processing Unit (APU) responsible for prioritization. The AHP is favored because it is consistent with the reasonable requirements set. The proposed model deals with a sensible set of requirements, such as up to 48 requirements at one time. The APU will work on a FIFO basis if the requirement set is higher than 48. It is an iterative and incremental model.  
In IoT-enabled applications, the requirements for presentation are always irregular. Tasks utilize a specific method from the various prioritization patterns, e.g., BST, AHP, Voting, etc. In the proposed model, the stakeholders' requirements will be negotiated before prioritization. In this study, we assume no mutual dependency among requirements. The assumption of no mutual dependency among requirements simplifies the analytical process within the prioritization framework, which is particularly useful in complex IoT systems where multiple interdependent factors could complicate decision-making. Isolation of requirements allows for a straightforward application of the Analytical Hierarchical Process (AHP) and advanced grouping methods, focusing on individual significance rather than interrelations. This assumption significantly reduces computational complexity, a critical advantage when integrating the extended AHP, in which the number of comparisons increases quadratically with the number of requirements. It also enhances the system's modularity, which is crucial in rapidly evolving IoT environments, by allowing modifications without a full system re-evaluation. The assumption of independence facilitates scalability in handling larger datasets and is practical in the early stages of technology development, aiding in establishing a solid framework foundation. The effectiveness of this approach is corroborated by case studies that show minimal negative impacts from ignoring dependencies, supporting its validity in practical application.  
Two techniques are combined, i.e., AHP and Grouping (numerical assignment). No mutual prioritization exists within the Grouping, while AHP fails when given a vast set of requirements to match. The suggested combined model is developed to deal with the issues of both AHP and Grouping and to obtain the benefits of each procedure. The combined pattern has two phases (the pre-processing phase and the prioritization phase); these phases eliminate the pattern's difficulty and formulate the pattern in comprehensible terms. In every phase, one or more activities are accomplished, an element which will be discussed in the upcoming sections.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach
### Graph Generation
```
1:Input:\(\mathcal{P}\)\(\triangleright\) Preprocessed list of user locations
2:\(dayLevel\leftarrow\) selected day index\(\triangleright\) Selected day index: 1, 2, or 3
3:\(timeInterval\leftarrow\) Selected time interval\(\triangleright\) Selected time interval in hours
4:Output:\(G\)\(\triangleright\) Generated mobility graph
5:Initialize empty sets \(\mathcal{V}\), \(\mathcal{E}\), \(\mathcal{W}\)\(\triangleright\) Initialize sets for vertices, edges, and weights
6:for each \(p\) in \(\mathcal{P}\)do\(\triangleright\) Create vertices
7: Create a vertex \(v\) with \(p\)
8:if\(p.m\) is not empty then\(\triangleright\) Check if location has contextual metadata
9: Assign \(v\gets p.m\)
10:endif
11: Add \(v\) to \(G.\mathcal{V}\)\(\triangleright\) Add the vertex to the set of vertices
12:endfor
13:for each s in \(\mathcal{P}\)do\(\triangleright\) For each source in \(\mathcal{P}\)
14:for each t in \(\mathcal{P}\)do\(\triangleright\) For each target in \(\mathcal{P}\)
15:ifRelationExists(s, t) then
16:\(ts\leftarrow\) ClassifyTimebIndex(\(s\to t.timestamp,timeInterval\))
17:\(td\leftarrow\) ClassifyDayByIndex(\(s\to t.timestamp,dayLevel\))
18:\(fs\leftarrow\) VisitFrequencyGroupbyDayAndTime(\(s,t,ts,td,\mathcal{P}\))
19:\(st\leftarrow\) StayDurationGroupbyDayAndTime(\(s,t,ts,td,\mathcal{P}\))
20:\(dt\leftarrow\) DurationGroupbyDayAndTime(\(s,t,ts,td,\mathcal{P}\))
21:MarkAsProcessed(\(s,t,ts,td,\mathcal{P}\))\(\triangleright\) Mark the data as processed
22: Extract attributes \((ts,td,fs,st,dt)\) for \(s\to t\)
23:if\(edge(s,t)\
ot\in\mathcal{E}\)then\(\triangleright\) Check if the edge already exists
24: Create edge \(e\leftarrow(s,t)\)\(\triangleright\) Create a new edge between source and target
25: Assign \(e\rightarrow\) weight \(\langle ts,td,fs,st,dt\rangle\)
26:if\(e\) has contextual metadata then
27: Associate \(e\) with its contextual metadata using \(f_{m}\)
28:endif
29: Add \(e\) to \(G.\mathcal{E}\)
30:else
31: Update existing edge \(e\) with new attributes \(\langle ts,td,fs,st,dt\rangle\)\(\triangleright\) Handle case where edge already exists
32:endif
33:endif
34:endif
35:endfor
36:return\(G\)\(\triangleright\) Return the generated mobility graph
```  
**Algorithm 2** Mobility Graph Generation.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 4 Results and Analysis
### Interference Path Analysis
Since the simulation only considered the effect of the cable and did not consider specific external devices such as the induction synchronizer, the two anomalous states are not shown in Equation (2). The other states in states 5-17 do not show this phenomenon because there is less interference at the far end of the drive and the transmitter of the excitation signal is less susceptible to interference than the induction synchronizer. Therefore, the scanning mirror does not fail to point but is less accurate than accuracy level 1.
## 3 Results and Discussion
### Effect of Illumination on Antioxidant Capacity  
It has been shown that the germination process promotes antioxidant capacity in different sprouts [4; 7; 10; 54]. In our study, exposure to light generally increased the content of chlorophylls and carotenoids, total polyphenics, and ascorbic acid, which are part of the plant's antioxidative system. Accordingly, it could be expected that the antioxidant capacity in the analyzed chia microgreens exposed to light will also be increased.  
Light treatment caused an increase of total antioxidant capacity in chia microgreens measured by 1, 1-diphenyl-2-picrylhydrazyl scavenging radical (DPPH) assay. A significant difference was found between dark-grown and chia microgreens exposed to light. There were no differences in DPPH scavenging activity between microgreens exposed for 24 and 48 h (Figure 4a).  
In dark-grown chia microgreens, total antioxidative capacity measured by the FRAP assay was the highest (Figure 4b). When different light treatments were compared, similar values were observed in microgreens exposed to light for 24 and 48 h, whilst a significant decrease was observed 24 h after exposure to light in comparison to dark-grown chia microgreens.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dark-Grown** & **24 h** & **48 h** \\ \hline
0.656 a & 0.657 a & 0.317 b \\ \hline (\(\pm\)0.08) & (\(\pm\)0.09) & (\(\pm\)0.02) \\ \hline \end{tabular} Data represent mean values from three experiments with five replicates (n = 15) expressed as ug crystalline cellulose per mg of dry weight. Different letters signify values that are statistically different at \(p\leq\) 0.05 according to [PERSON]’s LSD test. Values in the brackets represent \(\pm\) standard deviation (SD).  
\end{table}
Table 1: The crystalline cellulose content in dark-grown chia microgreens and microgreens 24 and 48 h after exposure to light.  
Figure 4: Antioxidant activity in dark-grown chia microgreens and microgreens after exposure to light for 24 and 48 h evaluated by DPPH scavenging activity (**a**) and FRAP assay (**b**). Data represent mean values from three experiments with five replicates (n = 15). The error bars show standard deviation (SD). Different letters signify values that are statistically different at \(p\leq\) 0.05 according to [PERSON]’s LSD test.  
The FRAP assay exhibited lower values in comparison with the DPPH assay. Other authors have also reported differences between obtained values when the two assays were used [4,55]. For instance, in 7-day-old chia sprouts exposed to 12 h/12 h (light/dark) cycles, DPPH scavenging activity was lower in comparison to the FRAP assay [54]. The reason for such discrepancies might be due to differences in the chemistry and sensitivity of these two assays. In a recently published study by [PERSON] et al. [38], the authors reported that the DPPH assay is more suitable than the FRAP assay for evaluating the antioxidant activity of chia. This suggests that the obtained values of these assays are impacted by the species, germination processes, and growth conditions, as well as temperature and duration of light exposure.
# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 1 Introduction
Benefiting from the inspiration of the Swin Transformer and LIIF, we propose a residual dense Swin Transformer to solve the continuous-scale super-resolution with excellent performance. We propose the residual dense Transformer block (RDTB) structure on the basis of the Swin transformer. By introducing residual connections and dense connections, we realize information interaction between all levels, propose local feature fusion (LFF) to promote feature fusion within the block, and design global feature fusion (GFF) to achieve information flow between blocks. Through the information complementation between the bottom and high levels, the network can pay attention to the low-frequency and high-frequency information of the image at the same time; the Transformer's self-attention mechanism can be used to take into account the local and global information in the image. We combine the patch-embedded characteristic of the Transformer with the implicit local continuous expression of the image, and we better combine feature extraction with the upsampling module to achieve continuous-scale super-resolution reconstruction. In summary, our contributions are as follows:  
1. A high-performance super-resolution network RDST is proposed. The network makes full use of the low- and high-level information in the image and is combined with an LIIF upsampling module to achieve continuous-scale super-resolution reconstruction of a single model. 2. A novel RDTB structure is proposed, which uses LFF to perform local information fusion on features within blocks and uses GFF to perform global information fusion on the features between blocks. At the same time, it combines the shallow information to fully explore the information expression in low-resolution images. 3. Through a comparison experiment with a fixed multiple in the distribution and a continuous-scale super-resolution experiment on the benchmark, it is shown that RDST is equal to the state-of-the-art (SOTA) method in the super-resolution results for the fixed multiple, and the super-resolution results at magnification outside the dataset distribution are greatly improved.
## 5 Experiment
### Model Performance Evaluation Metrics  
The NL2 AOM performance is evaluated in terms of accuracy, precision, recall value, and F-value, which are obtained by manual determination in this paper. The definitions of each formula are as follows.  
Accuracy: indicates the proportion of all parsing processes that correctly parse natural language commands into AOM statements. For example, \"(\"turn on the standelier of the shed\") is converted to an AOM statement as \"(Action_1: \"(\"turn on\"), Object_1: \"(\"(\"shed\"), Member_1: \"(\"handelier\")), is considered to be the correct analysis, other forms are wrong. The formula is shown where \(C\) indicates the number of correctly parsed commands and \(A\) indicates the number of all parsed commands.  
\[\text{Accuracy }=\frac{C}{A} \tag{16}\]  
Precision: indicates the proportion of the correct key information in the generated AOM statement to the actual generated information, consistent with the above example, if the generated AOM statement is {Action_1: \"open\", Object_1: \"shed\", Member_1: \"handelier\"}, then the correct indicator generated is Action_1: \"open\", Object_1: \"shed\", while Member_1: \"Chandelier\" should be \"shed\", then the accuracy rate is calculated as 2/3. The formula is shown where \(P\) denotes the number of all key indicators in the parsed AOM statement, and \(S\) denotes the number of successfully parsed indicators in the AOM statement.  
\[\text{Precision }=\frac{S}{P} \tag{17}\]  
Recall: is the proportion of the correct key information in the generated AOM statement to the actual key information contained in the original command; for example, if the original natural language is \"(\"first turn on the air conditioner of the shed, and then check what the temperature of the shed is\"), the theoretical generated AOM statement should be [{Action_1: \"(\"turn on\"), Object_1: \"(\"shed\"), Member_1: \"(\"air conditioner\")}, {Action_1: \"(\"check\"), Object_1: \"(\"shed\"), Member_1: \"(\"shed\"), Member_1: \"(\"temperature\")}], if only {Action_1: \"(\"turn on\"), Object_1: \"(\"shed\"), Member_1: \"(\"air conditioner\")}; and the recall value is calculated as 3/6. The calculation formula is shown, where \(R\) denotes the original actual number of key messages contained in the natural language command.  
\[\text{Recall }=\frac{S}{R} \tag{18}\]  
\(F-score\): this evaluation index responds to the reconciled average of accuracy and recall, which can reflect the good or bad performance of NL2 AOM in a comprehensive way, calculated as follows.  
\[F-score=\frac{2\times precision\times recall}{precision\times recall} \tag{19}\]
## 3 Results
### FRAP Activity in SF and SSF Extracts in Different Solvents  
The IC\({}_{50}\) of FRAP activity ranged 6-18 ug/uL for SF and 3-10 ug/uL for SSF across the three extraction solvents (Table 2). For SF, the activity was the highest in methanol extract (IC\({}_{50}\) = 6.1 ug/uL), followed by water and ethanol extracts. For SSF, similar levels of activity were observed in ethanol and methanol extracts at IC\({}_{50}\) values of 3.5 ug/uL and IC\({}_{50}\) = 4.2 ug/uL, respectively. In all three extraction solvents, the FRAP activity was higher for SSF than that for SF, indicating that steaming increased the FRAP activity. Notably, the SSF ethanol and methanol extracts showed 4.9-fold and 1.4-fold higher levels of FRAP activity than that of respective SF extracts, whereas their water extracts had a similar activity level (IC\({}_{50}\) = 10.0 ug/uL and 11.5 ug/uL, respectively).  
In agreement with the findings of the present study, [PERSON] et al. [60] showed that the FRAP activity in ethanol extract of _C. abies-marina_ was increased by 2-fold but decreased by 1.7-fold in its water extract after steaming. In addition, the FRAP activity in aqueous and ethanol extracts of _C. abies-marina_ exhibited a similar trend as that of the total phenolic content. Similar to these findings, the SSF methanol extract with a high phenolic content showed a high level of FRAP activity in the present study. [PERSON] et al. [61] measured the FRAP activity for heat-treated (85, 95, 100, 110, and 121 \({}^{\circ}\)C for 15 min) _Laminaria saccharina, Laminaria digitata_, and _Himanthalia longate_ and found that all three marine algae species showed an increase in FRAP activity up to 110 \({}^{\circ}\)C compared to that of the respective fresh samples, followed by a decrease at the higher temperature. The DPPH radical scavenging activity was also shown to increase according to the heat treatment, suggesting that heat treatment increases the antioxidant contents and activities in marine algae. In contrast, [PERSON] et al. [62] evaluated the antioxidant activity in brown algae _Himanthalia longate_ according to the cooking process and found no significant difference between the dried sample and the 40 min steamed sample, and the cooking (steaming) time was hypothesized to have a negative effect on the antioxidant contents. These results suggest that the FRAP activity could vary according to the sample type, cooking time and temperature, and extraction solvent. In this study, the FRAP activities of SF and SSF extracts were investigated after steaming at 95 \({}^{\circ}\)C for 10-15 min; thus, it is considered necessary to observe changes in FRAP activity according to steaming time and temperature.