esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Instruments  
In this analysis, we exploit observations of SF events from both the European mid-latitude Digisonde network and corresponding Swarm satellite passes from 2015 to 2017 to investigate the simultaneous bottomside and topside ionospheric irregularities. We analyzed ionograms from European Digisondes to identify SF cases only when the Swarm satellite track (either A-C or B) passes over the Digisonde stations. Out of this dataset, 10 cases met the criteria where SF was recorded across multiple stations with a coinciding Swarm track.  
To consider Swarm and Digisonde SF observations as collocated and coincident, we applied the following conditions:  
* Spatial condition: Collocated observations within an area centred on at least one Digisonde station, extending at \(\pm\)(0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)) in latitude and longitude.  
* Temporal condition: The observations of Swarm Ne profiles should fall within the interval of the Digisonde-recorded SF occurrence. The satellite must enter the designated coincidence area within this time frame.  
Additionally, we employed d-TEC and ROTI maps over Europe from the DRAWING-TEC project \"([[https://aer-nc-web.nict.go.jp/GPS/EUROPE/](https://aer-nc-web.nict.go.jp/GPS/EUROPE/)]([https://aer-nc-web.nict.go.jp/GPS/EUROPE/](https://aer-nc-web.nict.go.jp/GPS/EUROPE/))) (accessed on 20 October 2024)\", with a 10 min resolution, to identify MSTID and ROTI activity coincident with SF. To contextualize our findings, we examined solar activity using diurnal average sunspot numbers (SSNs) obtained from \"[[https://www.sidc.be/SILSO/datafiles](https://www.sidc.be/SILSO/datafiles)]([https://www.sidc.be/SILSO/datafiles](https://www.sidc.be/SILSO/datafiles)) (accessed on 19 October 2024)\" and geomagnetic conditions through hourly Dst index data available from \"[[https://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html](https://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html)]([https://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html](https://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html)) (accessed on 15 October 2024)\".
## 5 Computational Experiments, Results, and Discussion
### Results for Small-Sized Instances
(**a**) Results for total gravitational distances. (**b**) Results for the percentage hypervolume deviations. Statistical results show that the mean value of \(\overline{TGD}_{NSGA-II}-\overline{TGD}_{MOSS}\) was 6.06 with a 95% confidence interval of [3.50,8.62]. Meanwhile, the mean value of \(\overline{\mathcal{H}}_{NSGA-II}-\overline{\mathcal{H}}_{MOSS}\) was 0.908 with a 95% confidence interval of [0.593, 1.223]. These means (labeled as \(\mathfrak{x}\)) and confidence intervals are presented in Figure 9 using blue-colored lines. Based on these results, it can be concluded that, at a 95% confidence level, the metrics of the developed MOS are significantly better than those of NSGA-II. To illustrate detailed results for the small-sized test instances, Figure 10 shows the generated set of non-dominated solutions by both metaheuristics for six selected instances, along with their optimal Pareto front solutions. The Figure 10a-f are ordered in ascending order for the number of optimal Pareto front solutions of the instance, as reported in Table 5. Even though these results were recorded for only one run of each metaheuristic with a computational time limit of 120 s, they reflect the true performance of both metaheuristics that complies with the reported mean results in Table 5 and Figure 9. Based on Figure 10, it is evident that the developed MOS can generate efficient, non-dominated solutions that are very close to the optimal Pareto front with a better spread and better quality compared to the previously developed NSGA-II.
## 4 Discussion and Conclusions
Moreover, the current study simulations are based on a modification of a single climatic variable (air temperature), whereas several other atmospheric variables can also play a key role during a heatwave, such as air humidity, radiation fluxes, wind and pressure, amongst others. Another aspect is related to the occurrence or not of precipitation in the days before the heatwave, as it may also impact the results. Furthermore, there is still some uncertainty related to the effect of high temperatures that are not completely understood [30]. As an example, in some studies with reduced stomatal conductance owed to extreme heat exposure, grapevines recovered within a few days [16; 19; 20]. Furthermore, some varieties are more heat-tolerant than others [55; 56], and several acclimization processes have been identified for extreme temperatures [25; 49; 57]. In the present study, a single variety (cv. Pinot noir) was used in the modelling approach. Nevertheless, in reality, vineyards in Europe are composed of many roostocks, cultivars and botanical clones that eventually determine yields, e.g., see [58]. Given these results, and to the projected increase in heatwave occurrence and intensity under future climate conditions, adequate and timely planning of suitable adaptation measures needs to be adopted by the winemaking sector. One of the main adaptation measures is the selection and cultivation of variety-clone-rootstock combinations more resilient to the projected heat and water stress conditions [45]. Additionally, training systems that promote higher water-use efficiency should be envisioned, such as the Gobelet [59]. In general, all measures that promote higher water-use efficiency may be seen as adequate adaptation measures against the negative impact of heatwaves [60; 61; 62]. The present study may be considered to be a first approach to modelling heatwave impact on grapevines. Although these findings highlight the detrimental impact that heatwaves may bring to the European winegrowers, further research should be envisioned to evaluate and improve these modelling assessments, so as to provide more accurate information regarding the effects of extreme events on viticulture.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 3 Results and Discussion  
### Measurement of Contact Angles  
The CAs measured for all the materials used in this study are shown in Table 2, and example images of the water droplets used in creating Table 2 are presented in Figure 4.  
From Table 2, it can be seen that the natural sand was relatively consistent with the other measured CAs of between 17\({}^{\circ}\) and 27\({}^{\circ}\) for quartz using droplets of a similar size [24; 25]. In addition, the advancing CA of the crushed dacite rock was consistent with the literature, as the measured values were similar to the CAs for quartz [24; 25] and sodium feldspar [26] at 17-27\({}^{\circ}\) and 34\({}^{\circ}\), respectively. The hydrophobizing process of the sand was effective, as the observed advancing CA increased from 28\({}^{\circ}\) to 124\({}^{\circ}\). The table also shows that as the proportion of hydrophobic sand particles increased, the average CA measured for a mixture increased in a non-linear way, as illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Mean Advancing** & **Advancing Angle** & **Mean Receding** & **Receding Angle** \\  & **Contact Angle** & **Standard Deviation** & **Contact Angle** & **Standard Deviation** \\  & **(CA\({}_{\text{mean-ad}}\)) (\({}^{\circ}\))** & **(CA\({}_{\text{SD-ad}}\)) (\({}^{\circ}\))** & **(CA\({}_{\text{mean}}\)) (\({}^{\circ}\))** & **(CA\({}_{\text{SD}}\)) (\({}^{\circ}\))** \\ \hline
100\% natural sand & 28 & 8.7 & Not observed 1. & Not observed 1 \\
75\% natural sand & 84 & 6.5 & 53 & 6.2 \\
50\% natural sand & 101 & 5.6 & 65 & 12.9 \\
25\% natural sand & 120 & 6.1 & 71 & 13.3 \\
100\% hydrophobic sand & 128 & 7.2 & 86 & 7.4 \\ Crushed rock & 28 & 6.8 & 14 & 6.5 \\ Crushed coal & 124 & 7.8 & 90 & 13 \\ \hline \hline \end{tabular}  
* The water was pulled into the pores between particles, and thus no contact angles could be observed.  
\end{table}
Table 2: Mean contact angles and standard deviation for each material (nine or more measurements performed).  
Figure 4: Images of water droplets on a bed of crushed coal (top) and crushed rock (bottom). Both images show droplets used to measure the advancing contact angles, and the top right droplet indicates the angle measured.
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 2 Related Work
### Video Deraining Methods  
[PERSON] [20] proposed a photometric model to detect candidate pixels affected by rain in each frame of a video feed by modeling the intensity of raindrops among frames. The pixels affected by rain were detected and removed by updating the intensity to the mean of the sum with the subsequent frame. However, [PERSON]'s method is unable to detect rain streaks that are severely defocused or against a bright background. [PERSON] [21] analyzed rain streaks in video images in terms of temporal characteristics (i.e., streaks did not affect image pixels throughout the entire video sequence) and chromatic characteristics (i.e., changes in the R, G, and B channels were roughly the same). Neither of these methods perform well under heavy rain conditions. [PERSON] [22] focused on the fact that most rain streaks share similar repeating patterns (e.g., similar direction). [PERSON] [23] proposed a novel algorithm that creates a map of rain streaks via temporal correlation for classification using a support vector machine and removal using the low-rank matrix completion technique.  
[PERSON] et al. [24] used the prior structure of rain streaks in conjunction with a multi-scale convolutional sparse coding model to eliminate streaks based on local patterns and multi-scale features. [PERSON] et al. [25] proposed a hybrid model using a recurrent network and reconstruction network for binary classification, deraining, and background restoration. Using a dynamic rain generator, [PERSON] et al. [26] sought to fit rain layers via statistical modeling with the aim of encoding the physical (spatial) structure and (temporal) variations in rain streaks to facilitate the removal of streaks using a semi-supervised modular approach. Although these methods are effective at removing rain from the video, they depend on the analysis of temporal variations, which makes the model unable to solve the problem of removing rain from a single image. For the solution of the single image derain issues, some experts [3; 4; 5; 6; 27; 28] have begun to consider removing rain streaks from a single image.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 3 Clustering in Stable Instances  
In order to describe the algorithms, it is convenient to think of the input (metric space) as the following (complete) induced weighted graph:  
**Definition 2** (induced graph, spanning forests, clustering).: _Given a metric space \((X,d)\), the induced graph is a complete weighed undirected graph \(G_{X}=(X,E_{X},w)\) where \(E_{X}=\binom{X}{2}\) and for every edge \(e=(x,y)\) we have \(w(e)=d(x,y)\). For any tree \(T\) spanning all nodes \(X\), and for any subset of edges \(F=T\setminus K\), let us denote by \(C^{(F)}\) the connected components (nodes) of the forest \(F\). These connected components is a \(k\)-clustering whenever we remove \(k-1\) edges from a spanning tree \(T\)._  
The known optimal polynomial time algorithms for stable clustering are based on the above tree/forest construction. The simplest of such algorithms is the following one.  
**Single-Linkage (Naive Algorithm):**  
* **Phase 1 (Minimum Spanning Tree):** Compute a tree \(T\) spanning all nodes \(X\) minimizing the overall cost \(d(T)\).
* **Phase 2 (Remove Edges):** To obtain a \(k\)-clustering, remove the subset \(K\) of \(k-1\) edges having a maximum total weight \(d(K)\).  
It is well-known that this naive algorithm does _not_ achieve any (bounded) approximation guarantee even in our \(\gamma\)-pertubation-stable metric instances [2] (see also [15]). The reason is that the criteria for computing the removed edges \(K\) does not directly take into account the cost of the resulting clustering. The following algorithm indeed finds the optimum in several stable clustering problems [1; 2; 3].  
**Single-Linkage++ (Correct Algorithm):**  
* **Phase 1 (Minimum Spanning Tree):** Compute a tree \(T\) spanning all nodes \(X\) minimizing the overall cost \(d(T)\).
* **Phase 2 (Remove Edges++):** To obtain a \(k\)-clustering, remove the subset \(K\) of \(k-1\) edges resulting in minimal cost \(Cost(C^{(T\setminus K)})\).  
**Theorem 1** ([3]).: _The Single-Linkage++ algorithm finds the unique optimal \(k\)-clustering in every \(\gamma\)-pertubation-stable instance, with \(\gamma\geq 2\)._  
Both algorithms above consists of two phases, each of them corresponding to solve _exactly_ a matroid problem. Unfortunately, in order to have a small number of high-cost operations (Lemma 3), we have to content ourself with _approximate_ solutions.
# Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS
###### Abstract
utilized observational data from the Microwave Humidity Sounder on the FY-3B satellite to retrieve global IWP using a deep neural network [11]. However, the application of IWP retrieval still faces several challenges. Due to limitations in the accuracy of IWP retrieval algorithms and the quality and quantity of available data, the precision of IWP obtained using deep learning methods requires further enhancement. Additionally, the lack of long-term continuous observational data hinders existing IWP datasets from providing reliable support for climate change research and long-term weather forecasting. Therefore, improving IWP retrieval algorithms and advancing the research and application of multi-source data fusion technologies are essential. This study will utilize observational data from the Microwave Temperature Sounder-III (MWTS-III) and the Microwave Humidity Sounder-II (MWHS-II) aboard the Fengyun-3F (FY-3F) satellite to explore IWP retrieval algorithms and analyze the resulting data, aiming to achieve high-precision IWP products through physical retrieval techniques. The article is structured as follows: Section 2 introduces the MWTS-III and MWHS-II instruments on FY-3F, the Microwave Humidity Sounder (MHS) and AMSU-A instruments on the Meteorological Operational Satellite-C (METOP-C), and the fifth generation of reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5), providing detailed information on each data source. Section 3 discusses the IWP retrieval algorithms. Section 4 compares and analyzes the IWP results retrieved from FY-3F data with the ERA5 dataset and evaluates the retrieval accuracy. Section 5 summarizes the research and discusses its limitations.
## 2 Lateral-Line System and Artificial Lateral-Line System
### Physical Device of Artificial Lateral-Line System Suitable for the Underwater Robot  
The working environment of underwater robots is complex. Underwater robots must be equipped with sophisticated environmental-awareness systems, to have a good sense of adaptation to the underwater environment and perform underwater operational tasks. Due to the particularity of the water environment, many sensors are difficult to work effectively under water, which restricts the development of the water-environment sensing technology of the underwater robot. At present, underwater robots mainly use  
Figure 2: Brief introduction of a fish’s lateral line [28]. **(a)** Distributions of superficial neuromasts (blue dots) and canal neuromasts (red dots); **(b)** The microstructures of an individual neuromast; (c) A canal neuromast located in a fluid-filled lateral-line canal.  
two kinds of perception technology: underwater acoustic perception and visual perception. Underwater acoustic sensing technology, also known as sonar technology, is a widely used underwater sensing system. However, due to its large volume; high energy consumption; and ease at being affected by propagation attenuation, multipath effects, reverberation interference and ocean noise, a sonar transducer is difficult to use in small underwater robots. Visual perception is the direct acquisition of images by underwater cameras with rich and intuitive information, which has been applied to some small underwater robots. However, due to the influence of weak light and the complex environment underwater, the application is limited. The environment of underwater vehicles and aquatic creatures is very similar. Traditional underwater robots lack systems to sense the water around them, while the artificial lateral-line system can make up for such defects. In order to perceive environmental information of the surrounding water, we use pressure sensors to develop an artificial lateral-line system based on the fish lateral-line system to simulate the function of the fish lateral-line system [29]. The artificial lateral-line system will be an effective sensing system which cannot be replaced by traditional sensors.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 4 Hybrid Simulated Annealing Algorithm
### Local Optimization
#### 4.6.2 Design of the Local Optimization Algorithm
For each of the wireless communication tower locations \(r\), the algorithm calculates all the users covered by \(r\), stores them in \(S\), and identifies all users that are not covered by \(r\), but the distance from the location of \(r\) to the users is less than or equal to \(2d_{max}\), putting these users in \(S^{\prime}\). Note that \(S^{\prime}\) includes all potential users that can be covered by \(r\) by moving \(r\) to a new location without losing any user in \(S\). Then, the algorithm uses the constrained maximum cover circle algorithm to find a new location for \(r\) where it can cover as many users in \(S^{\prime}\) as possible in addition to all users in \(S\). Theorem 3.2: _The time efficiency of Algorithm 3 is \(O(|R||U|^{2})\), where \(|U|\) is the number of users and \(|R|\) is the set of wireless communication towers._  
Proof: For each wireless communication tower \(r\), it takes a time \(c_{7}|U|\) to computer its \(S\) and \(S^{\prime}\) (steps 2-14), and it takes a time \(c_{8}(|S||S^{\prime}|+|S^{\prime}|^{2})\) to find a new location for \(r\) (step 15). Since \(|S|\leq|U|\) and \(|S^{\prime}|\leq|U|\), the total computation time of each iteration is less than or equal to \(c_{7}|U|+2c_{8}|U|^{2}\in O|U|^{2}\). Since there are \(|R|\) wireless communication towers, the while loop iterates \(|R|\) times. Thus, the time efficiency of the local optimization algorithm is \(O(|R||U|^{2})\). Theorem 3.3: _The time efficiency of Algorithm 1 is \(O(l|R||U|^{2})\), where \(|U|\) is the number of users, \(|R|\) is the set of wireless communication towers, and \(l\) is the number of iterations that the algorithm executes._  
Proof: The basic operation in this algorithm is the local optimization procedure in Step 13. In the worst case, the procedure is invoked \(l\) times, where \(l\) is the number of iterations of the while loop. Since the time efficiency of the local optimization procedure is \(O(|R||U|^{2})\), the time efficiency of Algorithm 1 is \(l\)\(O(|R||U|^{2})=O(l|R||U|^{2})\) in the worst case.
# Optimizing Airfoil Aerodynamic Characteristics by Using Proposed CSA-KJ Method
## 4 Aerodynamic Characteristics of CSA-KJ4412 Airfoil  
### Variation of Lift-Drag Ratio with Incoming Wind Speed  
To observe the aerodynamic characteristics of the CSA-KJ4412 airfoil in the range of small angle of attack \(\alpha\), the CL divided by the drag coefficient (CD) is defined as the  
Figure 4: Comparison of numerical calculation values and experimental values.  
Figure 5: Comparison of lift coefficient.  
lift-drag ratio coefficient (CL/CD), and when \(\alpha\) is \(-4^{\circ}\), \(-2^{\circ}\), \(0^{\circ}\), \(2^{\circ}\) and \(4^{\circ}\), respectively. The corresponding variation curves with incoming wind speed are drawn in Figure 6a-e.  
From Figure 6, it is evident that the lift-drag ratio shows a nonlinear trend with increasing incoming wind speed at different angles of attack. When the incoming wind speed is between 0 and 20 m/s, the lift-drag ratio of the airfoil increases rapidly, but the upward trend tends to level off after exceeding 20 m/s. The CL/CD of CSA-KJ4412 is slightly lower than that of NACA4412 when the angle of attack is negative and the incoming wind speed is 2 m/s, but it is higher at other incoming wind speeds. Regardless of the incoming wind speed, the lift-drag ratio of CSA-KJ4412 is significantly improved at non-negative attack angles. When comparing NACA4412 with CSA-KJ4412, the average lift-drag ratio of CSA-KJ4412 increases by 4.86%, 4.20%, 5.62%, 4.38%, and 3.61%, respectively, when the airfoil angle of attack is organized from a small to large algebraic value. This  
Figure 6: Variation of lift-drag ratio coefficient with incoming wind speed under different angles of attack.  
demonstrates that the aerodynamic performance of the airfoil optimized by the CSA-KJ method at the 0\({}^{\circ}\) attack angle shows the most improvement.  
To more easily observe the change in the airfoil lift-drag ratio with a more intuitive angle of attack, the comparison curves under different incoming wind speeds are displayed in Figure 7a-c. It is clear that the CL/CD of CSA-KJ4412 at different angles of attack is higher than that of NACA4412 when the incoming wind speed is constant. In the range of the angle of attack of \(-4^{\circ}\) to \(4^{\circ}\), the CL/CD of the two airfoils initially increases before decreasing with ascending angles of attack. It then reaches a maximum when the angle of attack is \(-2^{\circ}\) or \(0^{\circ}\), and the lift-drag ratio of CSA-KJ4412 at a different angle of attack s is higher than that of NACA4412.