esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Statistical Analysis  
Analyses were performed using the \(R\) Statistical language (version 4.3.1; R Core Team, 2023) on Windows 10 pro 64 bit (build 19045), using the packages _sjPlot_ (version 2.8.15; [PERSON], 2023), _report_ (version 0.5.7; [PERSON] et al., 2023), _ggstatsplot_ (version 0.12.1; [PERSON], 2021), _[PERSON]_ (version 1.7.2; [PERSON] et al., 2021), _MASS_ (version 7.3.60; [PERSON], [PERSON], 2002) and _dplyr_ (version 1.1.3; [PERSON] et al., 2023).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multirow{2}{*}{\(N\) (\%)} & \multicolumn{3}{c}{**Age Group**} & [PERSON]multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-5}  & & **Adolescents,** & & **Adults,** & \\ \hline female & 60 (63.16\%) & 25 (56.82\%) & 35 (68.63\%) & 0.234 \\ male & 35 (36.84\%) & 19 (43.18\%) & 16 (31.37\%) & ([PERSON]’s chi-square test) \\ total & 95 (100\%) & 44 (46.32\%) & 51 (53.68\%) & \\ \hline age, years. & 18.34 & 15.77 & 25.23 & **<0.001** \\ Mdn (Q1, Q3): & (16.08, 25.61) & (13.26, 16.60) & (21.51, 29.03) & (Mann–Whitney U test) \\ \hline skeletal class: & & & & \\ Class I & 9 (9.47\%) & 6 (13.64\%) & 3 (5.88\%) & **0.222** \\ Class II & 18 (18.95\%) & 8 (18.18\%) & 10 (19.61\%) & **([PERSON]’s exact test)** \\ Class III & 68 (71.58\%) & 30 (68.18\%) & 38 (74.5\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the study group according to age, sex and type of skeletal sagittal morphology.  
Figure 4: Questionnaire for the patients.  
The threshold for significance was set at 0.05. The Shapiro-Wilk test was used to assess the distribution of parametric variables. The median and first and third quartiles were presented for variables not conforming to a normal distribution. For non-parametric variables, frequencies and percentages were reported.  
For the analysis of two independent groups with parametric data that did not adhere to a normal distribution, the Mann-Whitney U test was employed. The association between two categorical variables was examined using [PERSON]'s chi-square test or [PERSON]'s exact test, depending on the cell frequencies expected in the contingency tables.  
When analyzing differences among more than two groups with non-normally distributed numerical data, the Kruskal-Wallis test was applied, followed by [PERSON]'s test for post hoc analysis, adjusting the \(p\)-values using [PERSON]'s method to control for the type I error rate.  
Differences between two proportions from a dichotomous variable were analyzed using [PERSON]'s goodness-of-fit test. For multivariate analyses with a numeric outcome, a robust linear regression model was employed, while for binary outcomes, we used a generalized linear model with a logit link function.
## 3 Results
### Landsat-8 (Synthetic)  
We applied nine existing algorithms in the form of 17 variants to the 30-m, synthetic Landsat-8 imagery. Landsat-8 lacks a narrow band capable of measuring the 620 nm phycocyanin absorption feature. All of the algorithms applied here were indirect Chl-a proxy algorithms [6] accordingly. Moreover, the widths and positions of Landsat-8 bands also make the application of shape metrics for the Chl-a NIR reflectance peak feature infeasible and the application of some of the simple band ratio algorithms challenging (Table 2 and Table S1, Figure 10). The best performing algorithm was the green peak FLH Violet algorithm that incorporated the new ultra blue (\"violet\") coastal band for this sensor in this experiment [6].  
Figure 10: _Cont_.  
Figure 9: Results of S2 Be162 Bsub algorithm (this paper) converted to BGA values as applied to synthetic Sentinel-2 imagery with brighter pixels in the reservoir indicating higher BGA values (**a**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. predicted (calculated) raw index value (S2 Be162 Bsub) with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.704, \(p\) < 0.001, \(n\) = 29 to avoid shorelines) (**b**). Evaluation via observed (\(Y\) axis = BGA_PC_RFU) vs. normalized predicted (calculated) BGA values (S2 Be162 BsubPHY) with [PERSON]’s r\({}^{2}\) (r\({}^{2}\) = 0.704, \(p\) value < 0.001, \(n\) = 29 to avoid shorelines) (**c**). The CASI data allowed the synthesis of Sentinel-2 bands 2 through 9 only. Details of the synthetic bands and band math are available in Tables 1 and 2, respectively.  
None of the simple spectrally-oriented semi-analytical algorithms (based at least in part on known spectral features associated with known water quality parameters) considered here had acceptable performance ([PERSON]'s r\({}^{2}\) > 0.6; \(p\) < 0.001) with regard to BGA estimation with Landsat-8 in this exercise due to its design for land vegetation (Chl-a) rather than BGA/PC in water. Index imagery with raw and normalized index Type 1 (Pearson's r) linear regressions are shown for the best performing algorithm (L8 Be15 Flhviolet) in Figure 10. Local empirical algorithms based on other water quality parameters such as Chl-a and turbidity with local calibration and validation are required for use with Landsat-8 for reliable BGA estimation because Landsat-8 cannot sense the 620 nm phycocyanin absorption feature [22].
## 2 Materials and Methods
### The Proposed SD-CEEMDAN-BiLSTM Prediction Model  
In the BiLSTM model, the rectified linear unit (ReLU) function is used as an activation function of the stack of a fully connected layer and the mean square error (MSE) is used as a loss function.  
\[Loss=MSE=1/N\sum_{n=1}^{N}(d_{n}-y_{n})^{2} \tag{22}\]  
Figure 2: The architecture of the GRU cell. Numbered values correspond to the equation numbers below.  
where \(N\) is the sum of estimated number of days, \(y_{n}\) is the estimated value, and \(d_{n}\) is the actual value.  
The proposed model generally comprises the following steps, which are shown in Figure 3.  
1. The feature weight is calculated by the XGB method and is merged with the k-means algorithm to establish the SD cluster. II. The CEEMDAN method is utilized to decompose the charging load into several IMF sequences \(C_{i}(t)\) (\(i=1,2,\cdots,M\)) and one residue \(R_{M}(t)\). III. Each IMF and the residue item are normalized and used as the input to the BiLSTM model for training and obtaining the predicted values, respectively. The results of the test set predictions are \(C_{i}(t)\) (\(i=1,2,\cdots,M\)) and \(R_{M}(t)\). IV. Then, the final forecast results are adjusted using the below formula. \[S^{i}(t)=C_{i}(t)+R_{M}(t)\ (t=1,\ 2,\ \cdots,\ L)\] (23) where \(L\) is the test series length, and \(S^{i}(t)\) is the final predictive series of the test set.  
The architecture of the BiLSTM was derived by testing different configurations of units in each layer and calculating the MAPE on training and testing datasets. Table 1 shows the MAPE for a different number of layers and units for data with one-day-ahead prediction. Increasing the capacity of the neural network can be seen by increasing the number of layers and units, which improves error on the training and testing dataset. However, the  
Figure 3: Hybrid forecast model based on SD-CEEMDAN-BiLSTM.  
proposed model executes well on the training data by a 2-layer network with 100 units in layer 2 and 30 units in layer 1.
# Misconfiguration in Firewalls and Network Access Controls: Literature Review
## 4 Challenges and Research Trends
### Algorithms vs. Model Based Approaches  
As mentioned earlier, modern papers not using Openflow focus on particular algorithms. The reason for this is that openflow requires new implementation to these tools since the very structure of SDNs is different. Papers not discussing SDN on the other hand are focused on unique algorithms with the main focus being the efficiency of the algorithm. This trend is rather recent as many of the earlier papers do not even discuss the topic of efficiency, or if they do, it is a one-off statement of how long the program took to run.  
Those early projects such as [12; 17] gave very little if any attention into the efficiency of their approaches. These earlier approaches focused on completing the task of finding misconfigurations. The papers after [PERSON]'s \"Firewall Policy Advisor\" focused on adding extra features such as visualization to their implementation of misconfiguration checking. They modeled their algorithms after the process that humans would take when auditing rules. Given a list of rules, the algorithm looks at one rule and compares it against the rest of the rules. This process is repeated with every rule until a full list of misconfigurations between any two rules is created. During this time period, the test rule sets were fairly short with maybe a couple dozen rules. These rule sets for the most part were a proof of concept to show that the tool functions properly, rather than to be representative of a real rule set. This initial approach was replicated with little to no changes for years. Even [PERSON] himself continued with his project in [26], which kept the same model while adding stateful firewall support to his existing \"Firewall Policy Advisor\".  
In the mid 2010s, we can see that this approach became less normal. Over time, rule lists began getting longer with more complex features as smarter firewalls were capable of looking for more specific types of attacks on the network. Modern papers eventually stop adding new features. Instead, they focus on making the misconfiguration detection algorithms more efficient. We can see that recent papers such as [13; 27; 32; 35; 64; 65] all take efficiency of their detection algorithm seriously, whereas before it was an afterthought. Since networks are much larger and have many more devices and services than in the past, a firewall rule list is much larger on average. There is also now the consideration that there are several firewalls on a network with anomaly detection needing to be checked between different components. The initial anomaly detection programs test with at most, a few dozen rules and maybe gave the number of seconds the program took to run. New research in the topic is more likely to heavily focus on this. Newer papers have large artificial rule sets that can reach tens of thousands. Many of them give a Big O notation alongside the time it takes for the algorithm to run for each of their test rule sets.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 5 Performance Evaluation  
After establishing a global population density-based call model and configuring simulation parameters for the giant constellation network, we can derive three key network performance metrics--average call distance of transmitted packets, number of packet-forwarding hops, and total delay across 66 time slices. These data points will be analyzed to assess the performance of the designed topology.
# In Search of the Densest Subgraph
## 4 Some Related Results and Open Problems  
### Conditions that Force the Existence of a Dense Subgraph  
Given that some of the dense subgraph types are hard to find or even to approximate in large graphs, it can be useful to have results that _guarantee_ the existence of such subgraphs. The typical form of such theorems is this: if an \(n\)-vertex graph has at least a certain number of edges, then it is guaranteed to have a certain type of subgraph. Let us briefly review some examples below.  
A famous and fundamental early result is [PERSON]'s theorem ([PERSON], 1976):  
**Theorem 9**: _Let \(G\) be an \(n\)-vertex graph with \(m\) edges. If \(m>\frac{n^{2}}{2}\frac{k-2}{k-1}\), then \(G\) contains a \(k\)-clique._  
A generalization of [PERSON]'s theorem was proved by [PERSON] (1976):  
**Theorem 10**: _Let \(G\) be an \(n\)-vertex graph with \(m\) edges. Let \(k,r\) be integers with \(n\geq k+r\) and \(0\leq r\leq k+2\). If \(m>\frac{n^{2}}{2}\frac{k-2}{k-1}\), then \(G\) contains a subgraph on \(k+r\) vertices, such that its average degree is \(k+r-1-\frac{r}{k+r}\)._  
It is worth looking at some special cases of [PERSON]'s theorem. If \(r=0\), then the conclusion is that there is a \(k\)-vertex subgraph with average degree \(k-1\), which is exactly a \(k\)-clique. Therefore, [PERSON]'s theorem is included as a special case. If, for example, \(k=r\leq n/2\), then we obtain the conclusion that there is a subgraph on \(2k\) nodes with average degree \(2k-3/2\). This is not a clique, but it is still quite close to that, since a \(2k\)-vertex graph with average degree \(2k-1\) would be a \(2k\)-clique. Thus, by relaxing the density slightly to \(2k-3/2\), we can double the size of the guaranteed dense subgraph.  
Another generalization of [PERSON]'s theorem is a powerful result of [PERSON] and [PERSON] [38]. It provides a sufficient condition, similar to [PERSON]'s theorem, to contain an _arbitrary_ given subgraph \(H\), rather than a \(k\)-clique. The role of \(k\) is taken over here by the _chromatic number_\(\chi(H)\) of \(H\), which is the minimum number of colors needed to assign a color to each vertex of \(H\), such that neighboring nodes have different colors.  
Theorem 4.1: _Let \(G\) be an \(n\)-vertex graph with \(m\) edges. Let \(H\) be an arbitrary graph with chromatic number \(\chi(H)=k\). There is a sequence \(\theta_{n}\) with \(\lim_{n\to\infty}\theta_{n}=0\) such that for each \(n\)-vertex graph \(G\) with \(m\) edges, if \(m>\frac{n^{2}}{2}\left(\frac{k-2}{k-1}+\theta_{n}\right)\), then \(G\) contains a subgraph isomorphic to \(H\)._  
[PERSON]'s theorem and the Erdos-Stone theorem, both originated in the 1940s, became cornerstones of extremal graph theory.  
There is also a result of similar spirit, due to [PERSON] and [PERSON] [8], that provides sufficient condition for the existence of a \(k\)-core (for definition, see Section 3.1.2):  
Theorem 4.2: _Let \(G\) be an \(n\)-vertex graph with \(m\) edges, and let \(k\) be an integer with \(1\leq k\leq n-1\). If \(m>(k-1)n-\frac{k(k-1)}{2}+1\), then \(G\) has a non-empty \(k\)-core._
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 2 Materials and Methods
### D-DRUNet Fusion Semantic Segmentation Network
#### 2.1.2 Layered Fusion Mechanism Design  
We proposed a layered feature fusion mechanism for multimodal disparity images based on the encoder-decoder structure. This mechanism enhances the feature expression capability and regional change localization capability of the disparity maps. The proposed  
Figure 3: D-DRUNet Fusion Semantic Segmentation Network: Rational Block Diagram.  
method employs different fusion strategies at different stages of image encoding and decoding. The layered feature fusion is realized by two stages. In the first stage, the depth features extracted from the two difference images are fused in the bottom layer in a splicing way to achieve semantic fusion before up-sampling and decoding. In the second stage, the multimodal information of the corresponding resolution is supplemented by the two-way jump connection during the decoding up-sampling to achieve pixel fusion. In the decoding pixel-level fusion structure marked in the decoding side of Figure 3 network, the splicing features at the same level contain three parts: the logarithmic modal coding features and the mean modal coding features at the encoding side, and the up-sampling fusion features at the decoding side. In order to improve the fusion efficiency and reduce the computational effort, both semantic-level fusion and pixel-level fusion of features are used in the concatenation method.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## Appendix A
### XGBoost-Based Reconstruction Framework for Wyrrki Jet Zonal Currents  
During the development of machine learning models, datasets are typically divided into three independent subsets: training data, validation data, and test data. The training data are used to optimize model parameters, while the validation data evaluate model performance during training, guide hyperparameter tuning, and control overfitting risks. The test data, functioning as an independent evaluation set, are primarily utilized for an unbiased assessment of the generalization capability of the model on unseen data, thereby rigorously validating the reliability of the model in practical applications. In this study, all data from January 1993 to December 2022 were randomly divided into three parts: training, validation, and test sets. We used 348 months of data for training, with 80% randomly selected for training and 20% for validation. An additional 12 months of data were used for testing to evaluate the final model performance. The proposed XGBoost model flowchart is shown in Figure 10. The application process involves two main steps: (1) model training and validation; and (2) using the trained model to estimate WJ zonal current speed. Prior to applying the models to reconstruct the WJ zonal current, we trained and validated it using satellite sea surface data and OSCAR labeled data. Based on prior research, we selected SLA, Tx, Ty, Curlz, MLD, D20, SSS, SST, and geographical information (longitude and latitude) as independent input variables for the machine learning model to estimate the zonal speed of the WJ in the equatorial Indian Ocean. Furthermore, we used a separate XGBoost model to estimate WJ zonal speed based on sea surface data.
## 4 Antiproliferative Activity of Schiff Base Complexed with Lanthanides  
Lanthanide-based complexes with Schiff bases are depicted in Table 3. [PERSON] et al. (2018) [61] studied a series of lanthanides (i.e., praseodymium (Pr), erbium (Er), and ytterbium (Yb)) based with a Schiff base ligand (SBL), the \(N^{2}\),\(N^{3}\)-bis(anthracen-9-ylmethylene) pyridine-2,3-diamine and studied their cytotoxicity activity against Vero, human breast cancer (MCF7), and cervical (HeLa) anticancer cell lines by MTT assay. The SBLPr (**43**) and SBLEr (**44**) complexes exhibited anticancer activity against the above-mentioned cancer cell lines, reducing their viability by about 50% at a concentration of 25 ug/mL). Both the complexes were shown to induce the apoptosis of MCF7 and HeLa cells as observed by the arcidine orange (AO)/ethidium bromide (EB) staining assay. a further confirmation was obtained by the propidium iodide (PI) staining assay that showed that both the complexes induced DNA fragmentation. [PERSON] et al. (2020) [62] described a series of lanthanum(III) complexes with a Schiff base, particularly praseodymium (Pr), samarium (Sm), and ytterbium (Yb) (**45**, **46** and **47**, respectively). In vitro antiproliferative activity studies by MTT assay showed that the compounds were active against HeLa tumor cells, the compound **46** being the most active with an IC\({}_{50}\) equal to 34 ug/mL. Hoechst 33,258 staining and AO/EB dual staining suggested that the cells underwent the apoptosis mechanism in a dose-dependent manner and the flow cytometric analysis evidenced a block at the G1 phase, where there was no cell replication. Moreover, the docking studies suggested that the anti-cancer effect of complex **46** may occur via the caspase 3-specific substrate activation (polyADP-ribose polymerase, PARP, cleavage). The complex **46** binds DNA via intercalation producing an arrest at the S-phase of HeLa cells. [PERSON] et al. (2021) [63] studied a hexanuclear Nd(III) complex [[Nd\({}_{6}\)(HL)\({}_{2}\)L\({}_{2,2}\)(NO\({}_{3}\))\({}_{8}\)(EtOH)\({}_{6}\)](EtOH)(H\({}_{2}\)O)\({}_{3}\)] (**48**) (given the complexity of the structure it has not been reported herein and only the H\({}_{2}\)L and H\({}_{2}\)X ligands are depicted in Table 3) and studied its antitumor activity against the human hepatocellular carcinoma SMMC-7721 cells by MTT assay. It showed a higher activity than the reference compound (IC\({}_{50}\) = 4.42 uM versus 10.11 uM of gemcitabine).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Structure** & **Compd, Activity** & **Ref** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antiproliferative activity of lanthanide-based complexes with Schiff bases.
## 3 Results
### Ultrasound-Assisted Maceration (Ulam)
Olive oil that has been fortified with pomegranate peel showed the highest antioxidant capacity, followed by orange peel (1.24 \(\pm\) 0.24 mmol Fe\({}^{2+}\)/L), with a non-significant difference (\(p>\) 0.05). The values for basil, St. John's wort, and pomace are considered lower, with mean values of 0.42 \(\pm\) 0.03 mmol Fe\({}^{2+}\)/L, 0.32 \(\pm\) 0.08 mmol Fe\({}^{2+}\)/L, and 0.37 \(\pm\) 0.05 mmol Fe\({}^{2+}\)/L, respectively. The total antioxidant capacity values range from 0.06 to 0.11 mmol Fe\({}^{2+}\)/L after digestion. Regarding the predicted bioavailability of total antioxidant capacity of the selected extracts, the refined olive oil enriched with pomace has the highest bioavailability (29.7%), followed by St. John's wort (21.9%) and basil (21.4%), while those with the lowest bioavailability were orange peel  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Total Antioxidant Capacity (mmol Fe\({}^{2+}\)/L)**} \\ \cline{2-10}  & \multicolumn{2}{c}{**30 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**40 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**30 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**40 \({}^{\circ}\)C**} & & & \\ \cline{2-10}
**Food Sample** & \multicolumn{2}{c}{**UAM (0 min)**} & \multicolumn{2}{c}{**UAM (0 min)**} & \multicolumn{2}{c}{**UAM (0 min)**} & \multicolumn{2}{c}{**UAM (0 min)**} & \multicolumn{2}{c}{**P1**} & \multicolumn{2}{c}{**P2**} & \multicolumn{2}{c}{**P3**} \\ \cline{2-10}  & **1.5 g** & **3.6** & **1.5 g** & **3.6** & **1.5 g** & **3.6** & **3.6** & **1.5 g** & **3.6** & & & \\ \hline  & &  
and pomegranate peel, with 4.8% and 6.4%, respectively. The mean values of total phenolic content before digestion varied significantly (\(p<\) 0.05) from 17.38 to 64.35 mg GA/L. Orange peel has the highest phenolic content, followed by basil, with a non-significant difference. The values for pomegranate, pomace, and St. John's wort were observed to be lower, with 42.31 \(\pm\) 4.77, 20.27 \(\pm\) 4.86 and 17.38 \(\pm\) 8.59 mg GAE/L, respectively. After in vitro digestion process, the total phenolic content ranged from 9.23 \(\pm\) 8.47 to 20.95 \(\pm\) 13.93 mg GAE/L. St. John's wort displayed the highest predicted bioavailability (53.1%), followed by pomegranate peel (49.5%) and pomace (48.6%), while orange peel (20.6%) and basil (19.4%) presented the lowest predicted bioavailability. Regarding correlations between the samples before and after in vitro digestion experimental process, four samples (refined olive oils fortified with orange peel, pomegranate peel, pomace, and basil) suggest a significant correlation (\(p<\) 0.05), while Saint John's wort illustrates significant correlation at the 0.001 level. As for the phenolic concentration, pomace displayed a significant correlation (\(p<\) 0.05), while pomegranate peel and basil presented a significant correlation at 0.01 level.
## 4 Discussion  
This study combined UAV-based hyperspectral and thermal imagery with ground measurements and physical models to provide a comprehensive assessment of relative changes in upland rice growth, soil water availability, and WUE after biochar application. While our campaign was relatively short, it allowed monitoring the integrated effects of biochar during most of the rice growing season, from sowing to early senescence. During the four-day campaign, we found that in the two biochar groups, rice GPP, NDVI, chlorophyll content, soil moisture content, and WUE were significantly higher than the control group. The increments were larger in the BC1 group that also had higher leaf nitrogen concentration. In contrast, we found opposite behaviors between the two biochar treatments for soil matric potential, leaf area index, and fractional vegetation cover, which were higher in BC1, but lower in BC2, compared to the control (Table 1). Looking at surface energy fluxes, the net radiation, latent heat flux (evaptranspiration), and ground heat flux significantly increased in BC2, but not in BC1, which translated to a significant increase in the evaporative fraction in BC1, but no change in BC2 compared to the control plots.