esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 EVs Performing Multiple Functions in Cancer Formation
### EVs in Promoting Metastasis Initiation and Progression  
The metastatic process involves invasion and proliferation of tumor cells into nearby and distant tissues via the circulatory and lymphatic systems. Metastatic cells establish a microenvironment which favors angiogenic and proliferative processes, promoting cancer metastasis. EVs from initial tumor cells promote EMT by targeting EMT-related factors, such as transforming growth factor beta (TGF\(\beta\)), hypoxia-inducible factor 1 alpha (HIF-1\(\alpha\)), thus initiating metastasis. For example, EVs released by prostate cancer cells are enriched with lysosomal hyalurones, which promotes the mobility of stromal cells, aiding in metastasis [25]. Moreover, EVs secreted by highly metastatic cells can increase cell ability to migrate and invade low-metastatic cells by promoting an EMT process via MAPK/ERK signaling [26]. The hypoxia that characterizes the TME also influences tumor cells to release EVs enriched in matrix metalloproteinase-13 (MMP-13), with a consequent increase in vimentin and decrease in E-cadherin in normoxic cells, thus improving the metastases that occur via EMT [27].  
Interestingly, [PERSON] et al. demonstrated that cancer-derived EVs express a series of integrins that regulate adhesion to specific cancer cells and the extracellular matrix (ECM) molecules expressed in some organs [28]. They revealed that EVs containing ITG\(\alpha\)6\(\beta\)4 and ITG\(\alpha\)6\(\beta\)1 are associated with lung metastases through their ability to bind to lung-resident fibroblasts and epithelial cells. Similarly, integrin-expressing EVs containing \(\alpha\)v\(\beta\)5 are linked to liver metastases because of their ability to bind to Kupffer cells [28]. Macrophage migration inhibitory factor in EVs from pancreatic cancer can alter growth factor \(\beta\) production in Kupffer cells and increase fibronectin secretion of hepatic stellate cells [28]. Gastric tumor cell-derived EVs mediate the formation of a liver-like microenvironment through EGFR-mediated hepatocyte growth factor upregulation, ameliorating metastasis [29]. Many studies have also reported brain-derived secretory proteins, which can alter the brain microenvironment to promote the colonization of cancer cells, leading to brain metastasis [29]. In this respect, proteins present within EVs derived from the brain can increase the adhesive and invasive capacity of non-brain metastatic cells [30].  
These results indicate that cancer-derived EVs released into the body have the potential to remodel cancerous and non-cancerous cells, leading to pre-metastatic niche formation and metastatic organotropism.
# A Palette of Efficient and Stable Far-Red and NIR Dye Lasers
## 1 Introduction
Moreover, according to this last procedure, two (bis-BODIPYs) [36] or several BODIPY units (oligomers) [37] can be linked through spacers allowing electronic coupling or directly fused through aromatic rings (fused bis-BODIPYs) [38; 39; 40]. In this way, NIR absorbers are achieved. However, the electronic  
Figure 1: Molecular structures of the most commonly tested red and near-infrared (NIR) dyes based on cyanine backbones. rearrangement upon resonant interactions between the electronic clouds of the BODIPYs usually leads to poorly emissive compounds. 3. The replacement of the _meso_ carbon by an aza group (aza-BODIPY) [41; 42; 43]. Such simple modification induces pronounced bathocromic shifts [44], which can be enlarged spanning the delocalize \(\pi\)-system, as in the preceding point. Indeed, the aza-BODIPY dye family stands out as a promising alternative to design brighter, more stable, and compact NIR emitters than the classic phthalocyanines. Against this background, in the last few years, we intensively explored the rich chemistry of BODIPY dyes. As a result, we designed, synthesized, and characterized a library of BODIPY-based far-red and NIR emitting dyes, covering the spectral region ranging from 650 to 750 nm, and applied them as lasers. The benefits of these kinds of coherent and powerful radiation holds are well known. As a matter of fact, many laser-based devices and technologies are applied intensively and successfully in industry, communications, surgery, and daily life. Among the different kinds of available lasers, those based on organic molecules as active media are in the spotlight [45; 46]. The main advantage of dye lasers or organic lasers is the chemical versatility afforded by synthetic tools. Thus, there are a wide chart of commercially available laser dyes across the ultraviolet, visible, and NIR spectral regions. Moreover, they display a broad band enabling a certain sintonization range, not usually available in those lasers based on inorganic photoactive media, along with the generation of ultrashort pulses (in the femtosecond scale). This last property is appealing to study fast molecular dynamics in the excited state by advanced spectroscopic techniques. On the other hand, the main drawback of organic lasers is their limited photostability. To circumvent such restriction, complex and bulky experimental set-ups are required. Therefore, to offset this deficiency, we developed red and NIR dye lasers with improved photonic performance, particularly focused on enhancing the photostability, i.e., benzofuran-fused BODIPYs (Section 2) as laser dyes emitting in the far-red region of the visible spectral region [47] and polyarylated aza-BODIPYs (Section 3) for the NIR spectral region (Figure 2) [48]. Hereafter, we overview the photophysical and laser properties of these novel BODIPYs designed in our laboratories and compare their photonic performance with commercially available laser dyes working in the same spectral region.
## 2 Materials and Methods
### Performance Comparison with Existing Methods  
This study used state-of-the-art classification models such as iEnhancer-EL [15], iEnhancer-2L [3], EnhancerPred [27], iEnhancer-XG [33], iEnhancer-DSNet [35], tan2019 ensemble [36], and iEnhancer-CNN [26]. These prediction methodologies were compared to that of this study for better evaluation. These studies employed a similar dataset for the functional evaluation and analysis of the benchmark dataset. The comparison of the accuracy of our model to other models demonstrated a significant increase for both layers. However, a detailed comparison using cross-validation tests of performance on the state-of-the-art predictors and our predictor in the first stage is shown in Table 5, and a similar comparison with state-of-the-art predictors for the second stage is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stages** & **Sensitivity** & **Specificity** & **Accuracy** & **MCC** \\ \hline Stage 1 & 86.99 & 88.54 & 87.77 & 0.75 \\ Stage 2 & 83.57 & 78.16 & 80.86 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the proposed model iEnhancer-Deep in both stages using cross-validation.  
Figure 4: Radar plot for the performance of the proposed model in both stages.  
shown in Table 6. The most noteworthy values for each class of metrics are highlighted. Furthermore, the performance of enhancer identification in the first stage was amended by 8.9%, 7.14%, and 0.06% for Sn, ACC, and MCC, respectively, as illustrated in Figure 5. Similarly, there was a significant improvement in the second stage by 3.92%, 1.36%, 4.43%, and 0.17 for Sn, Sp, ACC, and MCC, as shown in Figure 6. A detailed comparative analysis of the performance of the proposed model illustrates that our study achieved a significant improvement in model execution based on the performance assessment metrics. In both stages 1 and 2, the suggested predictor resulted in a significant enhancement of the perceived parameters. The considerable increase in MCC values demonstrates that our study increases the stability of the predictor and the overall performance of the model in comparison with the state-of-the-art methodologies that generally have smaller MCC values. This advancement is key to verifying the consistency of binary classification problems. MCC values are considered comparatively more instructive than ACC values because it considers the extent of all four parameters (TN, TP, FP, and FN) of the confusion matrix to show a balanced assessment during model appraisal [37]. In general, iEnhancer-Deep produced comparable performance with other previously proposed strategies.
## 3 Results
### Regional Differences in the Percentage Signal Change
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unadjusted**} & \multicolumn{2}{c}{**Adjusted**} & \multicolumn{2}{c}{**Unadjusted**} & \multicolumn{2}{c}{**Adjusted**} \\ \hline \hline  & \multicolumn{2}{c}{**Signal**} & \multicolumn{2}{c}{**Signal**} & \multicolumn{2}{c}{**Signal**} & \multicolumn{2}{c}{**Time to Peak**} & \multicolumn{2}{c}{**Time to Peak**} \\  & \multicolumn{2}{c}{**Change [\%]**} & \multicolumn{2}{c}{**CI**} & \multicolumn{2}{c}{**Change [\%]**} & \multicolumn{2}{c}{**CI**} & \multicolumn{2}{c}{**[\%] Mean \(\pm\)**} & \multicolumn{2}{c}{**CI**} & \multicolumn{2}{c}{**[\%] Mean \(\pm\)**} & \multicolumn{2}{c}{**CI**} \\  & \multicolumn{2}{c}{**(Mean \(\pm\) SD)**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**SD**} \\ \hline  & PCS\({}_{\text{mc}}\)1  & 0.83 \(\pm\) 0.32 & 0.63–1.03 & 0.78 \(\pm\) 0.11 & 0.60–1.01 & 27.20 \(\pm\) 3.19 & 25.12–29.12 & 26.41 \(\pm\) 1.39 & 24.91–29.70 \\ \cline{2-10}
**Global** & PCS\({}_{\text{mc}}\)2  & 0.84 \(\pm\) 0.22 & 0.71–1.01 & 0.84 \(\pm\) 0.10 & 0.62–1.06 & 26.10 \(\pm\) 2.51 & 24.40–27.86 & 26.32 \(\pm\) 1.36 & 23.10–28.70 \\ \cline{2-10}  & \begin{tabular}{c} Control \\ group \\ \end{tabular}  & 0.92 \(\pm\) 0.25 & 0.82–1.04 & 0.87 \(\pm\) 0.07 & 0.75–1.02 & 29.08 \(\pm\) 3.82 & 27.41–30.97 & 29.52 \(\pm\) 0.93 & 26.79–31.85 \\ \hline  & PCS\({}_{\text{mc}}\)1  & 0.77 \(\pm\) 0.45 & 0.46–1.03 & 0.77 \(\pm\) 0.13 & 0.53–0.99 & 26.97 \(\pm\) 3.25 & 25.12–29.12 & 26.16 \(\pm\) 1.71 & 24.46–29.61 \\ \cline{2-10}
**Frontal lobe** & PCS\({}_{\text{mc}}\)2  & 0.93 \(\pm\) 0.17 & 0.81–1.05 & 0.91 \(\pm\) 0.12 & 0.70–1.11 & 26.06 \(\pm\) 2.52 & 24.40–27.86 & 25.87 \(\pm\) 1.67 & 22.31–28.55 \\ \cline{2-10}  & \begin{tabular}{c} Control \\ group \\ \end{tabular}  & 0.97 \(\pm\) 0.23 & 0.86–1.09 & 0.92 \(\pm\) 0.08 & 0.77–1.14 & 27.69 \(\pm\) 5.53 & 25.00–30.18 & 27.68 \(\pm\) 1.17 & 23.84–31.30 \\ \hline  & PCS\({}_{\text{mc}}\)1  & 0.89 \(\pm\) 0.38 & 0.65–1.13 & 0.80 \(\pm\) 0.12 & 0.63–1.09 & 27.45 \(\pm\) 3.06 & 25.58–29.70 & 27.32 \(\pm\) 1.85 & 24.80–30.14 \\ \cline{2-10}
**Occipital lobe** & PCS\({}_{\text{mc}}\)2  & 0.87 \(\pm\) 0.20 & 0.73–1.01 & 0.87 \(\pm\) 0.12 & 0.63–1.07 & 27.78 \(\pm\) 3.31 & 25.60–30.26 & 27.37 \(\pm\) 1.81 & 22.91–30.55 \\ \cline{2-10}  & \begin{tabular}{c} Control \\ group \\ \end{tabular}  & 0.99 \(\pm\) 0.30 & 0.86–1.14 & 0.92 \(\pm\) 0.08 & 0.80–1.08 & 28.46 \(\pm\) 5.75 & 25.64–30.87 & 28.66 \(\pm\) 1.23 & 24.32–31.99 \\ \hline  & PCS\({}_{\text{mc}}\)1  & 1.01 \(\pm\) 0.27 & 0.84–1.19 & 0.94 \(\pm\) 0.10 & 0.82–1.21 & 27.57 \(\pm\) 2.67 & 25.89–29.37 & 27.48 \(\pm\) 1.70 & 25.36–30.14 \\ \cline{2-10}
**Farietal lobe** & PCS\({}_{\text{mc}}\)2  & 0.94 \(\pm\) 0.15 & 0.83–1.04 & 0.91 \(\pm\) 0.02 & 0.63–1.06 & 26.51 \(\pm\) 2.44 & 24.90–28.37 & 26.50 \(\pm\) 1.67 & 22.75–29.29 \\ \cline{2-10}  & \begin{tabular}{c} Control \\ group \\ \end{tabular}  & 1.02 \(\pm\) 0.34 & 0.85–1.18 & 0.95 \(\pm\) 0.07 & 0.78–1.12 & 27.71 \(\pm\) 5.50 & 24.76–30.31 & 27.74\(\pm\) 1.14 & 2
## 2 Related Work
### Supervised Feature Selection Methods on Incomplete Data Sets  
In recent years, some researchers have considered the use of SFS on incomplete data sets without preprocessing missing values. Similarly to the use of UFS methods on incomplete data sets, SFS would also require some imputation methods to be implemented prior to the application of existing SFS methods, such as Simba [32] and Relief [33]. In [34], a method for modeling multivariate spatio-temporal data was presented, in which the missing values are estimated first, and then the feature selection procedure is applied. [PERSON] proposed the SID (margin-based feature selection in incomplete data) feature selection algorithm [13]. Due to the uncertainty of the neighbor relationship caused by the missing data, the SID algorithm does not aim to determine the neighbors but to calculate the probability that all samples are the neighbors of a specific sample and replaces the class margin with the expectation of the class margin. Through experiments, it was shown that the SID algorithm could filter out more irrelevant features compared with feature selection based on standard preprocessing imputation methods.  
However, the SID algorithm does not solve the problem that the distance between samples cannot be calculated due to missing data. On the other hand, when calculating the class margin for a sample, SID only considers the samples of which the observable features include the observable features of the sample. This, in turn, can cause the actual nearest neighbor samples to be ignored, resulting in inaccurate class margin calculations.  
Recently, several classification methods have been proposed to deal with incomplete data sets directly, without estimating the missing values in advance. Samples are treated as sets of pairs in order to naturally process the incomplete data set [35]. In [36], a generalization of the RBF (radial basis function) kernel approach to the case of missing data was proposed. The authors in [37] adapted a CNN architecture to incomplete images, taking the uncertainty contained in missing pixels into account. However, these are all classification approaches, rather than SFS approaches. They are not suitable for high-dimensional data with a large number of irrelevant features. In contrast, in our proposed approach we directly integrate feature selection with unobserved information, instead of estimating missing values.
# Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation
## 4 Experimental Results and Analysis
### Stationarity Test  
There are other ways to verify the stationarity of a time series. The autocovariance may quantify the degree of association between two points separated by a lag. Low values of autocovariance indicate no similarity with a delayed version of itself [28].  
Figure 23 presents the autocovariance of the measured signal for all scenarios (without audio source). Thus, it seems reasonable to consider that the outdoor scenario represents non-stationary behavior due to the uncorrelated signal with itself. On the other hand, the indoor scenario has high values of autocovariance, representing a stationary behavior.  
Now, we use a stationarity test, named Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test for a unit root in the univariate time series [29]. In this statistic test, \(\mathcal{H}_{1}\) indicates rejection of the stationary null in favor of the unit root alternative, and \(\mathcal{H}_{0}\) indicates a failure to reject the stationary null. We apply the test for all scenarios and evaluated if the signal is a unit root process, i.e., stationary, against the alternative that there is no unit root.  
Table 3 shows the KPSS for our measured data. The indoor and hall environments reject the stationary null in favor of the unit root alternative with low standard error. However, the outdoor environment test fails to reject the null hypothesis that the signal is stationary, as expected. The test statistic \(p\)-value reaches the maximum value of 0.10, and the test statistics, computed by ordinary least squares regression, is lower than the other scenarios.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 2 Materials and Methods
### Determination of Optimal Parameters for Enzyme Production
#### 2.4.3 Effect of Agitation Speed on Cellulase and Xylanase Production  
In determining the optimal agitation speed for enzyme production, the inoculated and control fermentation medium were incubated under static condition, and at varying agitation speed of 50-200 rpm with magnitude interval of 50 rpm.
## 5 Results and Discussion
### Comparing the Results with the Literature
While the best MAPE values for XGBoost-MLFS in this study were slightly higher, ranging between 4.42% and 5.87%, the inclusion of additional variables such as macroeconomic indicators and feature selection techniques enhanced the model's robustness under varying market conditions. This study demonstrated greater adaptability across regular, volatile, and holiday scenarios, making it more versatile for practical applications in real-world settings. However, since [PERSON] et al. 's dataset was from a different region with its own unique market dynamics, the findings may not be directly comparable. One of the distinguishing features of this study is the use of feature selection techniques like mRMR and MLFS, which contributed to substantial improvements in model performance. While several previous studies did not employ such techniques or used simpler selection methods, the application of MLFS in this study significantly reduced the dimensionality of the input space and allowed the models to focus on the most relevant variables, thereby enhancing interpretability and predictive accuracy. This impact is particularly evident when comparing the performance of XGBoost-MLFS to models like SVM and ANN in studies by [PERSON] et al. (2017) [11] and [PERSON] et al. (2023) [15]. Overall, the results of this study reaffirm findings from the literature that traditional statistical models such as ARIMA and SARIMA exhibit limited performance in environments characterized by non-linear dependencies and sudden shifts in market dynamics. In this study, ARIMA and SARIMA consistently showed higher MAPE values compared to machine learning models, particularly on dates with irregular market behavior. This outcome is consistent with the observations made in the studies by [PERSON] et al. (2017) [10] and [PERSON] and [PERSON] (2019) [13]. Conversely, machine learning models like XGBoost and ELM demonstrated resilience and adaptability, achieving superior predictive accuracy even under challenging market conditions.
## 4 Results
### Overall Adaptability Analysis  
In order to more clearly compare the detection capabilities of the two data-gathering systems, we summarized their indicators and detection results, as shown in Table 4 and Figure 12. In general, although Sentinel\(-\)1A has a short revisit cycle and can obtain more data, the proportions of data and interference pairs available in data processing are 5.79% and 13.94% lower, respectively, than the proportions available from PALSAR\(-\)2, (Table 4). The discrete error point in the deformation rate is 2% higher than that of PALSAR\(-\)2, and the effective monitoring area of the deformation rate is 15.82% lower. In terms of accuracy in detecting the deformation rate, PALSAR\(-\)2 is 2.78 times more accurate than Sentinel\(-\)1A. In summary, Sentinel\(-\)1A is far less well-suited than PALSAR\(-\)2 to detecting landslides in complex and dangerous areas. Therefore, its total detection rate is 47.7% lower than that of PALSAR\(-\)2. Because of the low detection rate, we combined the characteristics of Qinling-Daba Mountains landslides and the characteristics of two data sources to analyze our results.  
\begin{table}
\begin{tabular}{c c c} \hline
**Indictors** & **Sentinel\(-\)1A** & **PALSAR\(-\)2** \\ \hline Proportion of reliable data & 84.21\% & 90\% \\ Proportion of available interference pairs & 61.81\% & 75.75\% \\ Proportion of effective deformation area of deformation rate & 72.58\% & 88.40\% \\ Proportion of discrete error of deformation rate & 5\% & 3\% \\ Deformation accuracy of deformation rate & 26.8 m & 9.65 m \\ Mean value of deformation rate & \(-\)5.63 mm/year & \(-\)6.26 mm/year \\ Number of detected disaster points & 38 & 80 \\ \hline \end{tabular}
\end{table}
Table 4: Indicators of Sentinel\(-\)1A and PALSAR\(-\)2 data.  
Figure 11: Sankingwan landslide. (**a,b**) show annual deformation rate results of ALOS/PALSAR\(-\)2 and Sentinel\(-\)1A, respectively. (**c**) is the optical image on Google Earth. The P1 and P2 points marked on the figure are the points for time series deformation analysis in Chapter 5.4.3
## 3 Results  
### Multi-Parameter Ionospheric Anomalies  
#### 3.1.1 GIM-TEC Anomalies  
The GIM-TEC data provide vital insights into the spatiotemporal distributions of ionospheric disturbances on a large scale. The detected abnormalities of GIM data were processed through the SIQR technique and are shown in Figures 4 and 5 for both cases. After excluding the solar-geomagnetically disturbed days, the possible seismic ionosphere anomalies were as follows: the disturbances as positive and negative anomalies started appearing on 18, 19, 22, 24, 28, 29, and 30 September and 17, 18, 19, 20, 24, and 25 of October.  
Figure 3: Solar–geomagnetic weather conditions (**a**) prior to 26 October 2015 earthquake, where 20 and 21 September and 4–10 and 14 October were excluded due to active solar and geomagnetic events, and (**b**) prior to 7–15 October 2023 earthquake, where 2, 13, 19, and 26 September were excluded. The dark blue curved lines and blue shaded bars represents the daily variation of Dst index, Kp*10 (10 multiplied by Kp) and F10.7 indices respectively, while the dashed vertical line represents the day of earthquakes.  
Figure 4: GIM anomalies prior to 26 October 2015 earthquake; magnetic equator (red line), earthquake epicenter (green circle enclosed black star), positive anomalies over epicenter (red cloud), and negative anomalies (blue cloud) are shown.  
Figure 5: The GIM anomalies prior to October 2023 earthquakes are shown, with the magnetic equator (red line), earthquake epicenter (green circle enclosed black star), positive anomalies over the epicenter (red cloud), and negative anomalies (blue cloud).  
Exploring the days just before the earthquake, we observed minor negative disturbances around the northeast of the epicenter on 24 October 2015, while slight positive disturbances appeared on 25 October 2015. These disturbances serve as indications of pre-seismic activity. However, on 26 October 2015, no notable disturbances were observed. The spatiotemporal details can be seen in Figure 4.  
Following the mitigation of anomalies arising from solar and geomagnetic disturbances, the residual irregularities within the dataset for the 2023 October earthquake are indicative of potential seismic ionospheric disruptions (see Table 1). Notably, pre-earthquake anomalies manifested on multiple dates, starting from 9, 12, 14, 21, 24, and 30 September, as well as 1, 2, 3, 6, 7, 9, 10, 11, 13, 14, and 15 October 2023.  
The anomalies started appearing 28 days before the earthquake; the nearest anomaly witnessed before the earthquake occurred on 6 October 2023 at 14:25 UTC, which was only 7-5 h before, and that for the first earthquake that occurred on 7 October 2023 at 07:12 UTC was seen 5-3 h before the impending earthquake, as shown in Figure 5. The 24 h anomaly appearances for all mentioned days are provided in Supplementary Materials Figures S1 and S2 for both cases.