esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Retinal Dihedral Analysis
#### 3.6.2 Dihedral Angle Analysis  
The dihedral angle values are also responsible for portraying improved stabilization of bacteriorhodopsin. Figure 5a shows that the C14-C15 = NZ-CE dihedral angle of bR-graphene promptly reaches a range of \(\pm\)120-180\({}^{\circ}\), whereas the control appears to be stable around 60 degrees. These data indicate that 15 = NZ double bond for bR-graphene and control achieve trans (15-anti) and cis (15-syn) configurations, respectively [34]. Although it is curious that the control exhibits characteristics of a cis configuration in the ground state, it is important to highlight that, in contrast, graphene helps bR achieve a stable trans state, which is required in its ground state for a successful photocycle to occur. It is also worth mentioning that before reaching a _trans_ state, the data in Figure 5a originally displays the bR-graphene 15 = NZ bond in a _cis_ state (\(\sim\)60\({}^{\circ}\) angle), but it can be speculated that the adsorption onto graphene quickly corrects for this.  
There is also evidence of stabilization in the bR-graphene C12-C13 = C14-C15 and C13 = C14-C15 = NZ dihedrals, however, C12-C13 = C14-C15, as seen in Figure 5c, is of stronger interest because of its role in the photocycle. More specifically, it is known that the rapid twisting of the 13 = 14 double bond is the mechanism of bR photoisomerization when the protein is exposed to light [35]. In Figure 5c, the bR-graphene is found to have a trend of rapid \(-\)180\({}^{\circ}\) to 180\({}^{\circ}\) dihedral angle switching, but to a lesser degree than the  
Figure 5: Analysis of Retinal Dihedral energies and angles: (**a**) Variation in the dihedral angle C14-C15 = NZ-CE between bR and retinal chromophore over a period of 100 ns; (**b**) Variation in dihedral energy for C12-C13 + C14-C15 dihedral within the retinal chromophore over a period of 100 ns; (**c**) Variation in the dihedral angle C12-C13 = C14-C15 within the retinal chromophore over a period of 100 ns (note the offset of 360 degrees for the bR-graphene plot to avoid overlap between the two plots in the presence and absence of graphene); (**d**) Dihedral energy changes for the dihedral C11 = C12-C13 = C14 over a period of 100 ns. All plots are compared for the presence and absence of graphene.  
bR control. Increased stability provided by graphene can ensure that the protein has proper conformation to achieve photoexcitation, and will immobilize the system as a whole. In summary, these dihedral angle observations along with the dihedral energy for key dihedrals provide an excellent comparison between the control bR and that adsorbed onto graphene in unexcited states. As discussed above, due to the adsorption of the protein onto graphene, not only is there a higher degree of stability, but also higher energy values that suggest a greater photocycle yield. A complete list of angles and dihedrals between the retinal and LYS216 is provided in the supplementary Table S1 and the parameters for the bonds, angles and dihedrals are provided in the supplementary Table S2.
# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 2 Methods  
The proposed 2D CNN with Taguchi parametric optimization to improve the classification performance and the proposed CAD system framework are shown in Figure 1. For the proposed CAD lung cancer detection system, we used three main steps. First, we use the Taguchi method to evaluate optimal parameter combinations for the 2D CNN model (Section 2.1). Next, we extract the input CT images to the 2D CNN model (Section 2.2). Third, we set the optimization parameters of the 2D CNN model to train and achieve malignant and benign tumor classification (Section 2.3).  
Figure 1: The framework of the proposed computer assisted detection (CAD) system.
# Learning Class-Specific Features with Class Regularization for Videos
## 2 Related Work  
Significant advancements have been made in the recognition of actions in videos with the introduction of deep neural approaches that are based on the hierarchical discovery of informative features [5; 6]. These architectures provide the basis to further accommodate temporal information.  
Due to the indirect relationship between temporal and spatial information, one of the first attempts on video recognition with neural models was the use of Two-stream networks [7]. These networks contain two separate models that use still video frames and optical flow as inputs. Class predictions are made after combining the extracted features of the separate networks. Two-stream networks were also used as a base method for works such as Temporal Segment Networks (TSN, [8]) which use scattered snippets from the video and fuse their predictions. This approach sparked research on the selection of informative frames [9; 10]. Other extensions of Two-stream networks include the use of residual connections [11; 12] that share spatiotemporal information across multiple layers.  
An alternative approach to capture temporal information in CNNs is through 3D convolutions [13]. 3D convolutions include an additional time dimension to the two spatial dimensions. 3D convolutions have been shown to outperform standard image-based networks [14] in video classification. A fusion of Two-stream networks and 3D convolutions has been explored with the I3D architecture [15]. The two spatiotemporal models are trained in parallel on frame and optical flow data, with the benefit of also processing temporal-only information. Further structures that have been explored with 3D convolutions include Residual Networks [16].  
Inspired by depthwise and pointwise convolutions performed over image channels [17], researchers have introduced ways of splitting the 3D convolutions in subsequent 2D convolution operations. This can be achieved through either using spatial filters followed by temporal-only filters [18; 19] or convolutions in groups [20; 21]. Alternative implementations include the use of long-sequence and short-sequence kernels [22] and dimension-based iterations of time-width and time-height [23; 24]. Others have worked on the minimization of the computational requirements by shifting activations along time [25]. Previous works that have touched upon regularization were aimed more at the overall temporal consistency of features (e.g., [26]) without the consideration of feature combinations that are most informative for some classes.  
Although these techniques have shown great promise in terms of accuracy and computational performance, there is still a lack of better spatiotemporal representations for intermediate network layers. Currently, there is no standardized way of processing the temporal information. Our proposed method, named _Class Regularization_, fills this void as it can be added to virtually all network architectures with minimum additional computational cost in order to enhance the correlation between specific spatiotemporal features and the action class.
## 3 Materials and Methods
### Method  
Considering the scope of the study area, two satellite images were captured annually. Subsequently, given the features of the remote sensing data, pre-processing was deemed necessary to enhance clarity. Various steps were taken to achieve this. Initially, to enhance the image quality, the layers were re-projected to align with the reference projection system of the study area, specifically the World Geodetic System (WGS)_1984_Complex_UTM_Zone_28N (EPSG:31028). Then, we resampled the images to 50 m, the standard resolution for all images [47]. Geometric correction procedures were executed, including atmospheric correction, gap filling in Landsat 7 ETM, and image mosaicking via ENVI 5.1 software. Following these processes, supervised classification was employed to categorize land use types for this study, involving selecting training samples for each land cover class. Referencing the classification system by [PERSON] et al. [48], we reclassified the land use types into six (6) categories: agricultural land, forested areas, grassland, wetland, artificial surfaces, and bare land (Table 1). After converting raster data into polygons, the ArcGIS 10.6 platform was utilized to conduct statistical analyses related to land use types, focusing on the statistics of forested areas over various periods.  
A precise evaluation is crucial for conducting a land use change analysis and classification. However, the complete precision values derived from the post-classified images generated for 2005, 2010, 2015, and 2020 exhibited yearly variations. Notably, the least accurate year was 2020, registering at 0.91, while the highest accuracy was recorded in 2005, reaching 0.92. However, the overall accuracy for the entire study period remained consistent at 0.91. Furthermore, the kappa coefficient stood at approximately 89.4%, signifying a high consistency and accuracy in the simulation results compared to the actual Land Use and Land Cover (LULC) distribution. This is noteworthy, as the generally accepted standard accuracy for LULC classification is around 85% [49].
## 5 Discussion
### The Effect of Coriolis Force  
It is noted that asymmetrical levees, with the left-hand levees being higher and thicker than the right-hand counterparts, were present in the Pliocene to Quaternary tributary channels C1 and C2 (Figure 4). This phenomenon could be attributed to the influence of the Coriolis force in the Southern Hemisphere, which can deflect the overspilling turbidity currents to the left, thereby resulting in the preferential development of left-hand levees (Figure 8) [4; 7; 8; 65; 66; 67; 68]. Furthermore, we also observed that sediment waves were preferentially developed on the left-hand side of the lower reach of tributary channel C2 during the Pliocene to Quaternary (Figures 4D and 7D-F). This phenomenon could be related to a decrease in channel confinement as the relief of the channel gradually diminishes downstream (Figure 4D). Therefore, relatively high-energy turbidity currents more easily overspill from the lower reach of the channel, resulting in the formation of sediment waves on the left-hand side under the influence of the Coriolis force (Figure 8). This observation aligns with previous findings indicating the preferential development of levees and sediment waves on the left-hand side of the trunk channel of the Bounty Channel [7; 12].  
In addition, the Quaternary tributary channel C1 was characterized by the development of levees on the left-hand side and sediment waves on the right-hand side of the channel (Figures 4B and 7F). The thinning of both levees and sediment waves away from the axis of the channel (Figures 4B and 7F) suggests that they are both parts of the overflow deposits from tributary channel C1. The prevalence of sediment waves on the right-hand side of channel C1 may be attributed to the lower right-hand levees compared to their left-hand counterparts (Figure 4B), affected by the Coriolis force. This results in the overflowing turbidity currents on the right-hand side being thicker, faster, and more concentrated than those on the left-hand side, favoring the development of sediment waves or supercritical turbidity current bedforms on the right-hand side of the channel (Figure 8).
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## Appendix A
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Value** & **Papers** \\ \hline Drift Type & Concept Drift (General) & [31,33,36,50,60,61,98,100,107,108,111,118,128,133,144], \\  & [48] \({}^{\star}\), [38] \({}^{\star}\), [141] \({}^{\star}\), [130] \({}^{\star}\), [104] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Sudden Concept Drift} & [117], [114] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Gradual Concept Drift} & [117], [114] \({}^{\star}\), [127] \\ \cline{2-3}  & \multicolumn{1}{c}{Data Drift} & [59,105] \\ \hline Application Context & \multicolumn{1}{c}{Online Community} & [107,128] \\ \cline{2-3}  & \multicolumn{1}{c}{Cybersecurity} & [31,33,60,117,127] \\ \cline{2-3}  & \multicolumn{1}{c}{Generic Data Stream} & [50,100,108,111,144], [114] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Marine Science} & [114] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Process Mining} & [104] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Energy} & [59,98] \\ \cline{2-3}  & \multicolumn{1}{c}{Education} & [118], [48] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Human-Activity Recognition} & [36] \\ \hline IoT & [61], [38] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Time-Series Forecasting} & [144] \\ \cline{2-3}  & \multicolumn{1}{c}{Retail} & [133], [48] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Healthcare} & [48] \({}^{\star}\), [105] \\ \cline{2-3}  & \multicolumn{1}{c}{Financial} & [48] \({}^{\star}\), [141] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Server Monitoring} & [130] \({}^{\star}\) \\ \hline \multirow{2}{*}{Scope} & \multirow{2}{*}{Local} & [33,36,59,60,61,98,107,111,117,127,128,133], [48] \({}^{\star}\), [38] \({}^{\star}\), [38] \({}^{\star}\), [104] \({}^{\star}\), [141] \({}^{\star}\) \\ \cline{2-3}  & \multicolumn{1}{c}{Global} & [31,50,60,98,100,108,111,118,133,144], [114] \({}^{\star}\), [38] \({}^{\star}\), [48] \({}^{\star}\), [130] \({}^{\star}\) \\ \hline Audience & \multicolumn{1}{c}{End-Users} & [107,118,128] \\ \cline{2-3}  & \multicolumn{1}{c}{Data Scientists} & [31,33,36,50,59,61,98,100,105,108,111,117,127,133,144], \\  & \multicolumn{1}{c}{[114] \({}^{\star}\), [48] \({}^{\star}\), [38] \({}^{\star}\), [141] \({}^{\star}\), [130] \({}^{\star}\), [104] \({}^{\star}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Classification of Feature Attribution and Game Theory literature based on key dimensions.
## 2 Materials and Methods
### Western Blotting, IHC Analysis and Blood Biochemical Parameter Measurement  
CellClytic lysis buffer containing protease (NaVO3, NaS, and Na4P2O7) and phosphatase inhibitors (Leupton, TLCK, TPCK, Aport, and PMSF) was used for lysing liver specimens and cell samples. Western blotting conditions were described previously [31]. Rabbit antibodies against p-Smad2(Ser465/467)/(3(Ser423/425), T-Smad2/3, p-ERK(Thr202/Tyr204), T-ERK, p-MEK(Ser217/221), T-MEK, p-AKT(Ser473), T-AKT, p-p38(Thr180/Tyr182), T-p38, p-JNK(Thr183/Tyr185), T-JNK, p-P70S6K(Thr389), T-P70S6K, p-PDGFR(Tyr751), T-PDGFR, TGF-\(\beta\)R, Caspase-3, Caspase-9, and PARP were purchased from Cell Signaling Technology (Beverly, MA, USA). Rabbit anti-\(\alpha\)-SMA was purchased from Abcam (Cambridge, MA, USA). Mouse anti-\(\alpha\)-tubulin was acquired from Sigma-Aldrich. Hematoxylin and eosin (H&E) staining was obtained for histopathological examination of liver sections. Sirius Red staining (Abcam) and Masson's Trichrome staining (Abcam) of liver sections were conducted, according to the manufacturer's instructions, to qualitatively assess the architecture of collagen and extent of fibrosis. IHC staining of F4/80 (1:200; Cell Signaling, Beverly, MA, USA) was detected by using the EnVision+Dual Link System-HRP (DAB+) kit (DakoCytomation, Carpinteria, CA, USA). Serum levels of alanine aminotransferase (ALT), albumin, and blood urea nitrogen (BUN) were analyzed by a biochemical analyzer (VetTestTM, IDEX, westbrook, ME, USA).
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures
#### 2.3.4 Boundary Conditions  
Because of the boundaries mainly determine the flow pattern, an appropriate selection of boundary conditions is a crucial action in model design [23]. In groundwater flow, the system physical boundaries, including impermeable geologic formations and surface water bodies or hydraulic boundaries that include groundwater, divides, and flow lines, can be used as boundary conditions (Figure 3).  
Figure 3: Boundary conditions of the model domain.  
The employed boundary condition in this study includes specified flow boundaries, head-dependent flow boundaries, river, and drain packages. A general head boundary package was used to simulate groundwater outflow through the outlet of the basin. Specific head boundaries were defined for Lake Gefersa (lake level of 2585 m is used). The catchment boundaries in the western, northern, and eastern parts of the catchment are taken as a no-flow boundary. General Head boundaries usually used at the edge of the model to enable groundwater to flow into or out of the model under a regional gradient. In the General-Head Boundary package, the flux is always proportional to the difference in head. The tributary rivers are considered with a drain package.  
The recharge zone is obtained from the SWAT sub-basin classification, and the sub-basin average values estimated was used in the study area. Using the estimated recharge as an input, a conceptual groundwater model for the area was developed. The recharge zone in the MODFLOW model is the same as the sub-basin of the SWAT model.
## 3 Method
### Detecting Change Dates
#### 3.2.1 Step 1: Detecting Change between Years  
The smoothed time series was divided into segments by calendar year such that each segment contained records acquired in one year. One record for every 16-day interval equals 23 observations for the MODIS NDVI time series. The first and the last years did not have 23 records because the observations in the beginning of 2000 and the end of 2012 were unavailable. Between a pair of neighboring years, we conducted a two-sample Kolmogorov-Smirnov (KS) test to determine if the two time series showed different characteristics. The KS test is a non-parametric test with the null hypothesis that the two samples are from the same continuous distribution. The test is a distribution-free test and commonly used to detect structural change in a time series [24, 25, 26]. The mathematical explanation for the KS test can be found in Massey [27].  
The two-sample KS test compares the first and second years in a time series and then moves on to subsequent years. The null hypothesis is valid only when the two time series represent the same land cover because (1) both non-random noise (cloud cover, snow, device malfunction, _etc._) and random noise (high frequency noise) were already removed in the pre-processing step; and (2) the annual variability of the NDVI time series was likely to be consistent with homogenous land cover. In other words, if the null hypothesis of the KS test is rejected, the time series of the second year represents a different land cover compared to the first year; otherwise, no land cover changed during the second year. The null hypothesis is rejected based on a predefined significance level, denoted by \(\alpha\). If a change is detected in a year, the algorithm flags it for further analysis in the next step. Otherwise, the KS test moves on to the second and third years.
## 5 Conclusions  
In this paper, a Novel FPSO-GAK clustering algorithm has been proposed to effectively solve the problems of difficulty in determining the sensitivity of initializing the clustering center for a K-means clustering algorithm. The Novel FPSO-GAK clustering algorithm has been employed to capture urban hotspots in metropolitan areas in several countries worldwide. After the experimental clustering operation was completed, the SC index, SP index, and SSE index were applied to evaluate the clustering performance. The proposed Novel FPSO-GAK clustering algorithm obtained better clustering results for urban hotspots in metropolitan areas over the GAK, PSOK, PSOCK, PSOWK, PSO-GAK, and K-means++ algorithm. The algorithm presented in this paper can better improve the quality of public service for people in cities. In addition, the Novel FPSO-GAK clustering algorithm proposed in this paper can also be applied to the clustering analysis of remote sensing ground object data, animal migration area analysis, urban subway data analysis, urban heating network optimization analysis, detailed analysis of power supply flow of power grid and other fields.  
There are also some shortcomings of the algorithm proposed in the paper. On the one hand, we only use the GPS data of taxis. The data amount is relatively small, leading to an insufficient resolution in the refined analysis of the distribution relationship of urban hotspots. On the other hand, the Novel FPSO-GAK clustering algorithm does not add urban road constraints, leading to some hotspots falling into particular areas where traffic vehicles cannot be reached, such as large lake areas in cities. These deficiencies will be further improved in future research work.  
Conceptualization, W.D., X.Z., K.G. and Y.L.; methodology, W.D., X.Z. and Y.L.; software, Y.L. and J.G.; validation, Y.L. and J.G.; formal analysis, W.D., X.Z. and Y.L.; data curation, Y.L. and J.G.; writing--original draft preparation, Y.L.; writing--review and editing, W.D., X.Z. and Y.L.; visualization, Y.L. and J.G.; supervision, W.D., X.Z. and K.G. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the Sichuan Science and Technology Program, Grant/Award Numbers: 2019 ZYZF0169, 2020 YFG03007, 2021 YFS0407; the A Ba Achievements Transformation Program, Grant/ Award Number: 19 CGZH0006, R21 CGZH0001; the Chengdu Science and technology planning project, Grant/ Award Number: 2021-YF05-00933-SN.  
Not applicable.  
The authors declare no conflict of interest.
# Development and Assessment of a 4D Printing Technique for Space Applications
## 2 Materials and Methods
### 3D and 4D Printing
Refer to Figure 2 for an illustration of one of Orion's printers used in manufacturing and testing the hinges. The G-code that was maintained after slicing was then utilized by the 3D printer. The printer read the file, interpreting each line as instructions for movement or changes in process parameters, thus gradually printing the hinge. The hinge geometry, designed to be simple yet effective, coupled with the advanced capabilities of Orion's A150 3D printer, laid the foundation for our experiments on 4D printing of foldable, self-opening structures. The emphasis on simplicity in geometry served as a starting point for exploring the complex interplay between design, material properties, and the 4D printing process. The hinge geometry for the second iteration bending device underwent slight modifications. The holes at the end were strategically positioned to avoid any interference with the bending process. Consequently, the overall geometry did not exert any influence on the bending results. The configuration depicted in Figure 3 corresponds to the bending device illustrated in Figure 4, whereas the configuration in Figure 3 corresponds to the device presented in the results section. Figure 3: Technical drawing of the unfolded hinges used for the experiments in millimeters. (**a**) The hinge geometry for the first iteration programming device. (**b**) The adjusted hinge geometry for the second iteration programming device. Figure 2: Orion’s medical grade A150 3D printer, taken from [15]. Outer dimensions: 570 mm \(\times\) 550 mm \(\times\) 830 mm. Build volume: 180 mm diameter \(\times\) 150 mm high.