esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 2 Small Molecules
### Vitamin D Analogs  
#### 2.4.1 Hydrogel  
Vitamin D and its analogs participate in regulating cell differentiation and proliferation. They also modulate apoptosis and exhibit immunomodulatory properties. Over the years, the potential of vitamin D analogs for topical psoriasis treatment has been developed well [83]. Recent advancements in nanocarrier-based delivery systems have enhanced the bioavailability of these analogs [84]. [PERSON] et al. created and optimized a calcipotriol-loaded nanostructured lipid carrier (CPT-loaded NLC) through the Box-Behnken design. The NLCs featured a 123.60 \(\pm\) 1.21 nm average particle size, 85.31 \(\pm\) 1.18% encapsulation efficiency, and \(-\)36.8 \(\pm\) 8.85 mV Zeta potential and spherical structure. The release profile indicated that the sustained release aligned well with the Higuchi model. Additionally, the penetration experiments proved that the amount of drug entering the systemic circulation was minimal and considered negligible. Compared with pure CPT gel, the retention of CPT delivered by NLCs in the stratum corneum and viable layers was enhanced by 1.57- and 3.67-fold [85].  
Nanofibers (NFs) can enhance skin hydration and increase drug bioavailability. Mimicking the extracellular matrix, nanofibers possess regenerative properties, support cell adhesion, and hold drug release in a controlled way at psoriatic sites compared with conventional treatments. Researchers have developed and tested nanofibers loaded with taxarotene (TZT) and calcipotriol (CPT) combined with carbohydrate-based hydrogel membranes. Nanofibers were prepared from a polyvinyl alcohol/polyvinylpyrrolidone (PVA/PVP) K-90 blend polymer using the electrospinning method, and then the nanofibers were mixed into the CarbopoL group to form a hydrogel film. These formulations were fully biodegradable within 2 weeks of application, demonstrating good biocompatibility. After 72 h, the drug release rate of TZT-CPT-NFs was 95.68 \(\pm\) 0.03%, fitting the Higuchi model. TZT-CPT-NF gel membranes showed anti-sporiasis activity in psoriasis Wistar rats, decreasing the relative epidermal thickness percentage to 58.23% [86].
## 2 Data Quality Analysis
### DQA3: Ephemeris Applicable Period  
According to [2; 11], the GPS satellite updates its ephemeris message every two hours and the BDS satellite updates it every hour. If a receiver does not receive the regular ephemeris update, then the original received ephemeris should be removed after a certain time, because the original received ephemeris is invalid to estimate satellite location and clock. In DQA3, this research selects one specific ephemeris message and uses that for six hours to analyze the satellite location in comparison with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & & **BDS** & \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\
**Max** & 2.01 & 3.41 & 27.78 & 54.28 \\
**Mean** & 0.50 & 0.39 & 0.41 & 3.28 \\
**Std** & 0.30 & 0.26 & 0.77 & 5.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DQA1 maximum (Max), mean value (Mean), and standard deviation (Std) results of satellite position difference between using original and new ephemeris (unit: m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & & **BDS** & \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\ \hline
**Max** & 0.35 & 1.70 & 1.88 & 2.85 \\
**Mean** & \(-\)0.001 & 0.005 & \(-\)0.01 & 0.10 \\
**Std** & 0.12 & 0.20 & 0.32 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: DQA2 statistical results of satellite clock bias in pseudorange domain (unit: m).  
that of the updated ephemeris. As shown in [19], most of the GPS satellite position errors increase quickly after using the same ephemeris for more than four hours, and on the other hand, most of the BDS satellite position errors rise rapidly after applying the same ephemeris for three hours. Therefore, the applicable period of ephemeris is four hours for GPS and three hours for BDS.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 4 Methodology
### Minimum Spanning Tree Algorithm Based on Fuzzy Cut-Set Optimization
```
Input:\(G(V,E)\), \(E=\infty\), \(E_{new}=\varnothing\)//initialization graph \(G\) and arc Output \(MST(G)\)
1: Procedure FCO-MST()
2:\(V_{new}\leftarrow\left\{V_{0}|V_{0}\in N\right\}\); //initialize the root node
3:\(T\leftarrow(V_{0},\varnothing)\); //initialize the spanning tree
4:for each node \(V_{i}\in V_{new}\) and \(V_{j}\in V-V_{new}\)
5:\(\widetilde{A}_{ij}=Residual\left(\widetilde{V}\right)\); //residual energy of node
6:\(\widetilde{B}_{ij}=Resourc\left(\widetilde{V}_{j}\right)\); //resource occupancy of node
7:\(\widetilde{R}_{ij}=\widetilde{A}_{ij}\times\widetilde{B}_{ij}\); //fuzzy inference-based arc weights
8:\(\widetilde{W}_{V_{i}\to V_{j}}\leftarrow\max\left\{\overset{\overset{\overset{ \overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{ \overset{\overset{\overset{\overset}}}}}}}}}}}}}\sqrt\prod \prod_{j=1}^{\overline{\overset{\overset{\overset{\overset{\overset{ \overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{\overset{ }}}}}}}}}}}}}}\mu\left(\widetilde{R}_{ij}\right)\right\}\);
9:\(W_{V_{i}\to V_{j}}\leftarrow\min\left(\widetilde{W}_{V_{i}\to V_{j}}\right)\);
10:if (\(V_{new}=V\))
11:\(T=(V_{new},E_{new})\);
12:\(MST(G)\gets T\);
13:elseif (\(V_{new},V-V_{new})=\varnothing\)
14:\(MST(G)=\varnothing\); //unable to construct an MST tree
15:else
16:\(V_{new}\leftarrow\left\{V_{i}|\,V_{i}\in N\right\}\); //add the node \(V_{i}\) to \(V_{new}\)
17:\(V_{new}=V_{new}\cup\left\{V-V_{new}\right\}\); //update \(V_{new}\)
18:\(E_{new}\gets E_{V_{i}\to V_{j}}\); //add the arc \(E_{V_{i}\to V_{j}}\) to \(E_{new}\)
19:\(E_{new}=E_{new}\cup\left\{E-E_{new}\right\}\); //update \(E_{new}\)
20:\(T=T\cup(V_{new},E_{new})\); //update \(T\)
21:\(MST(G)\leftarrow(V_{new},E_{new})\); //construct \(T\)
22:endif
23: Continue;
24:End if
25:End for
```  
**Algorithm 1** The FCO-MST algorithm
## 4 Experiment I--DSM Quality Evaluation
### Profile Comparison  
To visually evaluate the quality of the DSMs, pixel-wise profiles were generated for two groups (Figure 4). The first group relates to the moderately-high resolution, space-borne data sets, which include ALOS/PRISM, Cartosat-1, RapidEye and TanDEM-X. The LiDAR data from 2010 is plotted as the height reference. The second group comprises the two very-high resolution airborne DSMs and the WorldView-2 DSM. For comparison, the two LiDAR DSMs are plotted in the same figure.  
In Figure 4, the upper white line passes through three types of land cover, namely high-dense forest region, low-dense forest region and ground area (agricultural land or meadows). Except for RapidEye2009, all DSMs can clearly identify the forest boundary (between low-dense forest and ground), although the boundaries from ALOS2008 and TanDEM-X2013 are less sharp compared to the other investigated DSMs.  
In the ground and high-dense forest areas, the difference between the DSMs derived from the high and low density LiDAR point clouds (LiDAR2010 and LiDAR2001, respectively) is not obvious. Using the LiDAR2010 as reference, the first profile plot displays the quality difference among the moderately high resolution DSMs. The profiles show that Cartosat2008 and TanDEM-X2013 match the reference data better than ALOS2008 and RapidEye 2009, because the canopy heights are smooth. Nevertheless, none of the mentioned DSMs is able to capture small gaps in the high-dense forest region, as the reference LiDAR DSM does. On the other hand, Cartosat2008 displays more details than TanDEM-X2013 showing some of the gaps in the low-dense forest region correctly, while RapidEye2009 can only show a rough height change along the profile line.  
For both ground and high-dense forest areas, there is no clear difference between the LiDAR DSM and the very high resolution photogrammetrically generated DSMs. However, in the low-dense forest region, the DSMs vary in detail. As shown in the second profile plot in Figure 4, in the low-dense forest area the LiDAR DSMs, even the lower quality LiDAR2001 DSM, show many gaps in the canopy, which can only be partly seen in the HRSC2003, Aerial2012 and WorldView2012 DSMs. However, the upper layers of the canopy from HRSC2003, Aerial2012 and WorldView2012 are well matched with both LiDAR DSMs. A mismatch among the LiDAR2001, HRSC2003 and other DSMs can be identified in the second low-dense forest region (profile pixels 170-230). This can be explained by the storm event in 2007, which caused this region to change from high-dense to low-dense forest.
## 6 Steady State Performance Analysis  
This section presents theoretical performance analyses of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter using the proposed design strategy. We compare the RMS index calculated using Equations (20), (26) and (28) for the following filters:  
* Proposed filter: the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter with the proposed strategy.
* RA filter: the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter with the RA model using optimal \(q\) (from Equation (16)) with respect to the RMS index.
* Best \(\alpha\)-\(\beta\) filter: the conventional \(\alpha\)-\(\beta\) filter obtained with the proposed strategy, assuming \(\eta=\theta=0\).  
Comparison with the RA filter indicates the effectiveness of the proposed strategy (i.e., considering \(\mathbf{Q}_{\text{gen}}\)), and comparison with the best \(\alpha\)-\(\beta\) filter illustrates the effectiveness of the velocity measurements. Note that the analysis in this section investigates the steady state performance, and this also corresponds to the steady state Kalman filter analysis. We assume that \(B_{\text{x}}\) and \(T\) are normalized to one.
## 5 Future Work and Limitations  
In future work, the proposed framework will be extended to cover a broader range of energy management domains, including electric power usage, renewable energy generation, and multi-source energy integration. While our current experiments are limited to LPG consumption data due to data availability and reliability, the proposed framework is designed to be domain-agnostic. Since it operates on statistical meta-features extracted from time series, the same methodology can be extended to other energy domains, such as electricity, heat, and solar generation. Future validations will be conducted across these domains to assess the generalizability under diverse operational conditions. These domains exhibit diverse time-series patterns and operational constraints, which will require the development of more generalized meta-feature extraction and model selection strategies that can accommodate heterogeneous energy profiles.  
Furthermore, to ensure applicability in real-time embedded or edge environments, future research will focus on optimizing the runtime efficiency and adaptability of the framework. This includes developing lightweight meta-models suitable for deployment on constrained devices, incorporating reinforcement learning for dynamic prioritization under varying system loads, and enabling online updating of the meta-learner to reflect temporal drift and changes in forecasting context.  
Together, these extensions aim to position the framework as a core component of next-generation intelligent energy control systems that operate autonomously, efficiently, and reliably under practical, real-world conditions.  
The current framework is constrained by (i) training data that focus almost exclusively on LPG usage, (ii) unreliable recommendations for cold-start sensors that lack historical data, and (iii) an assumption that meta-features remain stationary within each retraining cycle.
## Materials and Methods
### Characteristics of the Liver Nodules to Be Quantified in a CT: The Main Features
#### 2.3.3 Density  
Density is examined in all four phases as it changes with time. As blood flows through the vessels, the tissue changes its capability for X-ray absorption, so the attenuation quotient differs, and this is reflected as a different number of Hounsfield units. In non-enhanced scans, most of the lesions are iso- or hypodense. If the lesion is hyperdense in the non-enhanced phase, this means that it contains blood (50-70 HU) or calcification (over 120 HU). In contrast-enhanced phase, the blood supply of the lesion is examined. Malignant primary lesions have a massive arterial supply, which makes them hyperdense in arterial phase images. Hyperdensity is not necessarily homogeneous; usually small lesions are homogenous, and larger ones have more or less heterogeneous enhancement. Washout is a sign indicating mostly arterial blood flow, which is typical for a malignant lesion. In CT images, such nodules show a bright enhancement in the arterial phase and a relative hypovascularity in comparison with the normal tissues the in portal and late phases. Benign lesions such as FNH and adenoma show bright arterial enhancement with an iso- or slightly hyperdense appearance on the portal and delayed phase. Lesions which are isodense in the portal phase are most probably non-malignant.
## 6 Experimental Results
### Experimental Results

Table 11: Information about known threats for the second test instance. First, the performance of each algorithm is tested in different \(D\). The mean results, the best fitness value and the worst fitness value between the algorithms of 50 independent runs are shown in Table 12. In the following table, bold results indicate that the algorithm performed the best. According to Table 12, we can see that the mean normalized optimization results of QPSO algorithm on UCAV path planning problem is the best in \(D=5\) and \(D=10\). However, in the rest of the cases, QWDO algorithm performed best. We can see that the best-normalized optimization results of QWDO algorithm on UCAV path planning problem are all the best. What is more, the worst normalized optimization results of QWDO algorithm are all the best. As we can see from Table 12, in all cases, the presented global optimization algorithm QWDO algorithm is better than the original WDO algorithm. It shows that the QWDO algorithm is effective in improving the WDO algorithm. When the maximum number of iterations is different, the results will also be different. Second, the performance of each algorithm is tested in different _Maxgen_, and the results of the simulation experiment are shown in Table 13. In the following table, bold results indicate that the algorithm performed the best. From Table 13, we can see that the mean normalized optimization results of QWDO algorithm on the UCAV path-planning problem is always the best. In all different _Maxgen_ cases, the best-normalized optimization results of QWDO algorithm on the UCAV path-planning problem are also the best. As can be seen in Table 13, the worst normalized optimization results of QWDO algorithm also performed best. Through the above data we can find QWDO algorithm is better than other intelligent algorithms in global search and local search. QWDO algorithm is very efficient in solving the UCAV path-planning problem. Figures 13-17 show the UCAV flight path obtained by the QWDO algorithm testing the second test instance on different _Maxgen_. For all cases in the second test instance, the QWDO algorithm can find the flight path that avoids the threat areas with the smallest threat cost. Figures 18-22 show the evolutionary process of fitness value on different _Maxgen_. As can be seen in Figures 18-22, QWDO algorithm has the fastest convergence speed and the highest convergence precision in all of these tests. It shows the QWDO algorithm has a strong ability to find the optimal solutions. Figure 13: Result of the second instance for \(\emph{Maxgen}=50\).
## 2 Research Area and Data Sources  
### Research Area  
In HMA, mountain glaciers can also be divided into continental glaciers and maritime glaciers. Continental glaciers are mainly developed under continental climatic conditions, and they mainly depend on the cold storage conditions provided by the cold and severe climate to survive, with a low glacier movement rate and a high elevation of terminus. Continental glaciers are one of the main types of glaciers in China, constituting approximately 78% of the country's glaciers [12]. Maritime glaciers (also known as monsoonal maritime glaciers) are mainly developed in climatic zones influenced by a monsoonal climate and are one of the main types of glaciers in China [12]. They are mainly characterizedby high accumulation, high ablation, rapid movement, and a very sensitive response to climate change.  
Eastern Pamir [11; 56] (Figure 1) mainly comprises mountains and valleys in the northwest-southeast direction. The terrain is complex, with an average elevation of around 5500 m. The highest peak, Kongur Tagh Mountain, is approximately 7649 m above sea level [57]. The large-scale glaciers of the Eastern Pamir are mainly concentrated in the two areas centered on the Muztagh Ata-Kongur Tagh in the southeast. The other mountains host primarily small-scale glaciers, which are continental glaciers.  
The glaciers in the western section of the Nyainqentanglha [58; 59] Mountains (Figure 1) are continental glaciers. The eastern part of the mountain range is significantly affected by the southwestern Indian Ocean monsoon, with a great deal of precipitation, low snow line, and concentrated glaciers. It is the largest maritime glacier concentration area in China and one of the strongest centers of glacial action in the middle and low latitudes of the earth.
## 3 Findings for Each of the Three Layers
### Market Layer
#### 3.2.1 Pricing of Electricity and the EU Electricity Market  
Concerning the pricing of electricity, in nodal pricing (or locational marginal pricing), which is incorporated in the electricity system of the USA, prices are set at different nodes in the system (places where supply and demand meet). In zonal pricing, as used in the EU electricity markets, prices are the same across the entire zone, not taking transmission limits into account. Therefore, the criticism of zonal pricing is that it does not stimulate the optimal placement of variable renewable electricity production [47; 48]. For example, in Germany, much wind electricity production is located in the north, but the transmission line does not have the capacity to transfer this electricity to the south, resulting in congestion losses [47; 49]. In 2015, 566 TWh was traded on the European Power Exchange (EPEX, including Germany, Austria, Luxembourg, France, the UK, the Netherlands, Belgium, Switzerland), while 59 TWh was traded on the intraday market, although the intraday market grew faster (26% versus 20%) [50].  
There may be concerns about price and power fluctuations, regarding the market and technologies layers; however, flexible market scenarios, which control unwanted fluctuations, have recently been reported [51; 52]. In addition, moderate fluctuations may potentially accelerate DER deployment, the design of various smart energy products and services, widen the international electricity networks' power transfer capacities and agreements as part of EPEX, meaning that one aspect viewed as unbeneficial for one layer, may come to be potentially beneficial for the future of smart grids. For such perspectives, the three-layer approach facilitates the identification of interactions between layers, and therefore permits more global and multidisciplinary approaches on smart grids and energy transitions.
## 3 Simplified Swarm Optimization-Based Detection
### Mathematics Model  
#### 3.3.1 Model Establishment  
Assume the initial particle swarm size \(n\), the problem space dimensions \(m\), the location \(\mathrm{X}_{i}^{t}=\left(x_{i1}^{t},x_{12}^{t},\cdots,x_{im}^{t}\right)\), where \(i=1,\,2,3,\ldots\,\,n\), \(x_{im}^{t}\) is the value of \(i\)-th particle with respect to \(m\)-th dimension of feature space at time \(t\); the particles in the search process reach the optimal location and are marked as \(p^{t}i\); the optimal location for the group is \(g^{t}i\). Therefore, the location of particle \(i\) in \(j\) dimension at time \(t\) is described in the following formulas:  
\[x_{ij}^{t}=\left\{\begin{array}{ll}X_{ij}^{t},&if\,\,random\in[0,C_{w});\\ p_{ij}^{t},&if\,\,random\in[C_{w},C_{p});\\ g_{ij}^{t},&if\,\,random\in[C_{p},C_{g});\\ X,&if\,\,random\in[C_{g},1).\end{array}\right. \tag{3}\]  
In Equation (3), \(X\) represents the new value of the particle in every dimension randomly generated; the random number is between (0, 1); \(C_{w}\), \(C_{p}\), and \(C_{g}\) are the three predetermined positive constants with \(C_{w}<C_{p}<C_{g}\).  
In this study, we use the topological structure of a PPIN [29] as the basis, with the individual proteins as nodes and the interactions between proteins as lines, to construct a PPIN model (with interaction model shown in Figure 2, and adjacency matrix in Table 1). The interaction between the proteins is denoted as 1, whereas no relationship between proteins is denoted as 0.  
Therefore, the distance between proteins \(d_{ij}\) (difference between two proteins), can be calculated according to Equation (4):  
\[d_{ij}=\frac{|Int(i)\cup Int(j)|-|Int(i)\cap Int(j)|}{|Int(i)\cup Int(j)|+|Int( i)\cap Int(j)|} \tag{4}\]  
where \(i\) and \(j\) express the two proteins. Normally, the value of \(d_{ij}\) is greater than 0; in some special cases, when proteins \(i\) and \(j\) are in completely different function modules, \(d_{ij}\) achieves the highest value 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline
**A** & 0 & 1 & 1 & 1 & 0 & 0 \\
**B** & 1 & 0 & 0 & 0 & 1 & 0 \\
**C** & 1 & 0 & 0 & 0 & 1 & 1 \\
**D** & 1 & 0 & 0 & 0 & 0 & 1 \\
**E** & 0 & 1 & 1 & 0 & 0 & 1 \\
**F** & 0 & 0 & 1 & 1 & 1 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Adjacency matrix.  
Figure 2: Protein interaction network.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Flood Map Validation and Statistics
* The area flooded by Tanaro near Alessandria in 2016 (Figure 13B). Here, there are high values of FR for near co-flood data of MNDWI based on MODIS-Aqua (FR \(\approx\) 90%) and Proba-V (FR \(\approx\) 82%). Figure 13: Temporal evolution of flood Ratio (FR) on the Y-axis and not Flood Ratio (NFR) inside parenthesis compared with rivers’ stage on the secondary Y-axis. (**A**) Ebro River at Zaragoza gauge from 23 February 2015 to 8 April 2015, (**B**) Tanaro River at Alessandria gauge, and (**C**) Po River at Carignano gauge. For a post-flood image, the relationship \"time of flood peak vs. time of satellite pass\" is more important than spatial resolution; 250 m NDVI based on MODIS data of 30 November 2016 show a better result (FR \(\approx\) 80%) than do 10 m NDVI based on Sentinel-2 of 8 December 2016 (FR \(\approx\) 60%). SAR data based on 28 November imagery from Sentinel-1 show low values of FR (\(\approx\)20%), making detecting the entire flooded area more complicated. Another critical factor influencing flood detection with post-flood data is the local conditions. In particular, the presence of a thin layer of silt deposits on crop fields helped the identification of the inundated area. Two weeks after the flood, the VIS\({}_{\text{var}}\) based on Sentinel-2 data showed an FR of 70% and NFR \(\approx\) 84%
* The area flooded by the Po at the south of Turin, compared with Carignano gauge (Figure 13C). Here, the flood detection presented results similar to those of the Alessandria area. In particular, Sentinel-2 data acquired on 1 December 2016 show better results for MNDWI\({}_{\text{var}}\) (\(\approx\)50% FR) with respect to NDVI\({}_{\text{var}}\). (\(\approx\)35% FR). It was not possible to the map the flooded area with \(\Delta\)\(\sigma\)\(\text{o}\) of Sentinel-1 because three days after the flood peak, only 4% of the inundated area was detected. For this area, the accuracy in flood detection is also unrelated to spatial resolution; the best performance comes from MNDWI based on the MODIS image of 26 November 2016 (FR \(\approx\) 80%).