esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antiproliferative Activity of Schiff Bases Complexed with Platinum Group Metals (PGM)
Pt(II) and Ru(II) complexes (**35-38**) containing Schiff bases displayed an in vitro antiproliferative activity against different aggressive cancer cells, viz., human pancreatic carcinoma (MIAPaCa-2), hepatocellular carcinoma (HepG2), triple-negative human metastatic breast adenocarcinoma (MDA-MB-231), human embryonic kidney (HEK-293), and human foreskin fibroblast (HFF-1) compared to the clinical drug oxaliplatin, used as reference (IC\({}_{50}\) = 5.7, 9.8, 19.2, 2.1 and 7.0 \(\upmu\)M, respectively). The most active complex (**37**) is 10-15 times more toxic than oxaliplatin against the MDA-MB-231 cells. Complexes **35-37** initiate the disruption of the microtubule network in MDA-MB-231 cells in a dose-dependent manner within 6 h of incubation, thus affecting the cytoskeleton function, by inhibiting tyrosine phosphorylation of vascular endothelial growth factor receptor 2 (VEGFR2), a key step in angiogenesis. This finally leads to the arrest of the cell cycle in the G2/M phase and apoptosis. [PERSON] et al. (2021) [60] studied mononuclear octahedral Ru(III) complexes **39-41** with Schiff bases. The antiproliferative activities of the complexes on human cervical cancer cells (HeLa) and human breast cancercells (MCF-7) were evaluated by MTT assay. All the complexes showed higher growth inhibition against the HeLa and MCF-7 cell lines, Ru(III) complex (**41**) being the most active, showing an IC\({}_{50}\) against HeLa cells very close to that of cisplatin. Studies of the interaction with the CTDNA supported that these complexes bind DNA as demonstrated by gel electrophoresis studies. Interestingly, it was found that all the Ru(III) complexes cleave the super coiled (SC) pUC19 plasmid DNA efficiently. The study of [PERSON] et al. (2021) [52] has been already described above with regard to the Co(II) complex. The other complex, with Ru(II) [RuCl(\(p\)-cymene)L] (**42**), showed an in vitro higher activity than the Co(II) analogue complex (**22**), showing an IC\({}_{50}\) value of 510 uM against the human colon cancer cell line (Caco-2) and low cytotoxic effects on L-929, as determined by MTT assay. Furthermore, as well this time, Ru(II) complexes increased the cytotoxicity levels by 2.12 times in its combined applications with EP.
## 3 Results and Discussion
### Chemical Oxygen Demand  
#### 3.2.1 Comparison of Input and Output COD in Various Treatments  
The input and output COD of the DAF are compared with the input and output CODs of different membranes in each experiment, as seen in Figure 5. The input and output COD are referred to the feed and permeate/effluent COD respectively. The feed to the DAF and each set of the membranes are the same in part A of Figure 5, but the values are different, since each set of these experiments was started with new feed. The COD limit of 1200 ppm is seen as the dotted line across the two parts of the figure. The DAF discharged to the municipality at 1455 \(\pm\) 504 ppm, which is over the limit at certain times, thus creating the need for a specific treatment to consistently discharge below the limit. There was no significant difference between the output COD of the DAF and the permeate CODs of the 200 kDa and 20 kDa cut-off membranes in both experiments. This can be seen more evidently in Figure 6. However, the DAF output COD was not constantly below the limit. The permeate COD of these two membranes in both experiments was below the limit (small standard deviation), which means that these membranes are consistently discharging below the limit, independent of the input feed COD. This is very important in industries to avoid surcharges. In Figure 5 part B, the output of the DAF is  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Concentration \(\pm\) SD** \\ \hline pH & 11.32 \(\pm\) 0.55 \\ Free Cl & 5 \(\pm\) 1 ppm \\ COD & 8201 \(\pm\) 3010 ppm \\ Protein & 1970 \(\pm\) 359 ppm \\ FOG & 98 \(\pm\) 113 ppm \\ TS & 5857 \(\pm\) 1261 ppm \\ TSS & 2 \(\pm\) 1 ppm \\ \hline \end{tabular}
\end{table}
Table 4: Feed source characterization.  
same as the input to the membranes in this experiment. It is observed that the COD of the permeate reduces with decrease in pore size. This is because as the pore size reduces, less organic material can go through these smaller membrane pores, and thus, the COD decreases. In both A and B, there were no significant differences between the permeate CODs of the various set of membranes (200 and 20 kDa, 8 and 4 kDa, 0.083 kDa and 0.058 kDa). Thus, another parameter is required to select a membrane at a certain pressure.  
If the limit on COD is 1200 ppm, then a 200 kDa or a 20 kDa cut-off membrane would suffice for this purpose. Energy consumption is important in membrane selection. If two different membranes are able to discharge below the limit, then the larger pore sized membrane should be selected, since it could be run at lower pressure/speed. If the limit is lower, then smaller pore sized membranes could be chosen. A 0.058 kDa cut-off membrane provides the lowest permeate COD when compared to all other membranes, and thus, could be used for reclamation purposes.
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 3 ZnO Magnetoelectric Composites
### ZnO Nanocposites
On the other hand, cobalt ferrite (CoFe\({}_{2}\)O\({}_{4}\), CFO)-ZnO nanocomposites exhibited magnetoelectric coupling, which was evaluated through magnetocapacitance measurements at different frequencies and magnetic-field intensities. ME coupling mainly originated from strain effects due to the interaction of CFO and ZnO, inducing electrical polarization in the ZnO crystal via magnetostriction [33].
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 2 Materials and Methods
### Pruning Point Localization Algorithm  
#### 2.9.1 The Basic Principle of Pruning Point Localization Algorithm  
The coordinates of the buds obtained via the target-detection algorithm are processed. (1) First, it is determined whether the pixel coordinates of the buds can be connected by the mask map obtained via the semantic segmentation algorithm, i.e., whether there is a path between the pixel coordinates with a pixel value not 0 on the mask map and if there is, the buds are buds on the same branch are determined and are saved in the set of coordinates corresponding to the branch. (2) For the comparison of the coordinates of the buds on the same branch, the coordinate with the largest vertical coordinate is selected as the key point for determining the clipping point. (3) The longitudinal coordinates of the pixel coordinates of the key point are decreased, the horizontal coordinates from left to right are traversed within a certain range and when the pixel value of the skeleton refinement image at this coordinate is not 0, this coordinate position is marked as the pruning point and its 3D coordinates are determined.
# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 6 Implementation Details  
As stated in Section 3, we have collected datasets from distances of 10 m, 20 m, and 30 m. The implementation consists of three settings: 1. We have meticulously trained our deep learning model using Hyperspectral Imaging (HSI) data captured from a distance of 10 m. Subsequently, to evaluate the model's performance and generalization capabilities, we tested it with HSI data collected from distances of 20 m and 30 m. 2. We have trained HSI data taken from a distance of 20 m and tested it on the images that were taken from distances of 10 m and 30 m. 3. At last, we have trained the model with HSI data collected from a distance of 30 m and tested it on the data that were collected from distances of 10 m and 20 m. The main aim is to make the model robust for all distances, which can help us delve deeper into data generalization. Section 7 describes the reasoning in more detail.  
The model ingests incoming data at the input layer. The input has the following shape: (25, 25, 10, 1), which represents a 3D image with dimensions of (25, 25, 10), and (1), which denotes grayscale mode. The shape of the hyperspectral image was 1332 \(\times\) 854 and the number of bands was reduced to 10 by using PCA. The window size over the image was used in the shape of 25 \(\times\) 25. As every pixel works as a data point, in this case, we can obtain (Height \(-\) Window height) \(\times\) (Width \(-\) Window width) \(\times\) 10 data points for one single hyperspectral image datum. We divided the points into train, test, and validation set with a 7:2:1 ratio. The final testing for detecting the black ice was done on completely unknown images. The test dataset was taken from different distances. The number of total parameters in our model is 6,629,818. Table 1 shows the parameter details according to the layers and the output/input shapes of the incoming data. The hyperspectral image is already high dimensional, capturing spectral and spatial information for all pixels. We have used augmentation and regularization techniques, which caused a huge number of parameters. Additionally, we have used EarlyStopping to halt the training process when it is needed. Our model is made to exhibit robust generalization abilities when it is applied to unseen data. This is achieved by using the data augmentation technique. Additionally, the architecture of the model is optimized to capture both spectral and spatial information. We have trained the model for 100 epochs at a 0.0002 learning rate.
## 4 Results and Analysis  
### The Toronto Urban Scene  
The results of the steps for building detection and classification are shown in Figure 9a-d. The middle-step results include shadow detection, vegetation detection, and seed-block generation, excluding the tree seed-blocks, and the building detection and height-classifications are shown in Figure 9d. We applied object-based random forest (RF) [40] to detect the buildings for comparison. The RF result for the buildings is shown in Figure 9e. Figure 9f shows the buildings after excluding road areas misjudged as buildings.  
Figure 9a,b shows the results of shadow detection and vegetation detection. After shadow detection and vegetation detection, we acquired seed-blocks from the opposite side of the shadow and excluded the tree effect, as shown in Figure 9c. The seed-blocks provided accurate information for buildings. To exclude the tree effect, the seed-blocks that fell in the vegetation areas were deleted. Figure 9d shows the final result for buildings in different height levels. The final result also reveals that the majority of buildings with middle and high height levels are on the right side of the image, and the small, low buildings were located mainly in the center left of the result. Comparing Figure 9d and original image in Figure 1a, the results are in accordance with the buildings in the original image. This illustrates that shadow length is useful information when judging the height level of buildings.  
We also quantitatively estimate the accuracy of our results. For the Toronto scene, the true buildings that we extracted are based on the same segmentation FNEA as our method. As we used multi-scale segmentation, the final result was estimated in pixels. WH shows that the building object is judged at the incorrect height level. The analysis of the precision, recall, omission, and false alarm rates are defined in Ref. [41] and shown in Table 3.  
Figure 8: Process of shadow length measurement: (**a**) shadow rotation to the horizontal direction; (**b**) shadow extraction of the middle height level; (**c**) middle level shadows on the original image; (**d**) shadow extraction of the high height level; (**e**) high level shadows on the original image.  
In Table 3, the precision of the related parameters of buildings and impervious ground were both below 80%. With the help of shadow, we can make a distinction between buildings and impervious ground, to a great extent. According to true building data, the recall of complete building extraction reaches 89.8%, while the false detection and incorrect height judgment were less than 0.5%, with a total precision of about 98%. The omission of building detection was over 10% due to the buildings fell in the shadows. This means that the building detection recall will rise to 94% when ignoring the buildings located in shadows. The accuracy of the height judgment was 97% in visual interpretation; thus, shadow information is reliable when used to roughly estimate the height levels of buildings.
## 2 Materials and Methods
### Data Preprocessing  
Data preprocessing is crucial for ensuring data quality and relevance. The Wisconsin Breast Cancer Dataset consists of 569 cases with 32 attributes. Steps in preprocessing include cleaning the data by identifying and removing missing or irrelevant data points, normalizing and scaling features to ensure equal contributions to model training, and partitioning the data, which involves dividing the dataset into training and testing subsets with varying partitions (from 10:90 to 45:55) to assess robustness.
## 1 Introduction
Two prominent gradients of CUE in ecosystem types and latitude were found worldwide. CUE varied with ecosystem types, being the highest in wetland and lowest in grassland. CUE decreased with latitude, showing the lowest values in tropics, and the highest CUE were found in higher-latitude regions. The above studies were based on annual scales and advanced the knowledge of understanding the global pattern of CUE. However, monthly scale analysis of CUE has rarely been studied. From individual plants to an entire ecosystem, phenology directly or indirectly regulates carbon fluxes (e.g., photosynthesis and respiration) between the land surface and the atmosphere [14] through altering physiological and structural characteristics, including photosynthetic rate, canopy conductance and albedo [14; 15; 16]. Vegetation phenological changes are closely related to spatial-temporal dynamics of carbon cycle [17]. The change in the length of the growing season may have an important impact on vegetation growth, which will cause changes in the GPP and NPP [18]. CUE and its relationships with land surface phenology (LSP) deserve attention. The Songnen Plain (SNP), located in temperate semi-humid to semi-arid transition ecological fragile zone in Northeast China, is highly sensitive to global change. As a key agricultural area and important grain commodity base, the SNP is among the designated ecological red-lines as protected farmland area in China. The natural terrestrial ecosystem acts as an ecological protection barrier for the croplands in the SNP. The productivity and sustainability of terrestrial ecosystems are vital to maintaining regional and national food and ecological security. Due to the combined effects of vulnerable physical conditions and excessive human activities, the SNP suffered from high risk of land degradation during the past century. Concerns for the aggravation of desertification have led to many measures and management actions for ecological and environmental protection. The trend of desertification and exacerbation has gradually slowed down [19]. Previous studies have mostly focused on the land cover/use change and effects of agricultural activities on environment [20]. In addition, most studies only focused on the condition of protected farmland, while ignoring the productivity and sustainability of natural terrestrial ecosystems around it. There is a lack of reports about the spatial-temporal patterns of ecosystem-level CUE and their response to phenology and climate change in the SNP region. This study attempts to fill in the gaps in the knowledge regarding biotic and abiotic impacts on CUE of the SNP region. The objectives of this study are to: (1) estimate CUE of different ecosystems and investigate their monthly and seasonal changes based on MODIS GPP and NPP data from 2001 to 2015; (2) explain how phenology and climatic factors contribute to variations in ecosystem CUE, in order to improve our understanding of the carbon budget in temperate semi-arid and semi-humid transitional zone ecosystems and their driving mechanisms.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 3 MKID Applications in Observational Astronomy
### Observational Astronomy: Near-IR, Optical and UV
As TKIDs have the same multiplexability advantage as MKIDs they promise scalability up to energy resolving, mega-pixel arrays. They are still in an early prototype state but the possibility to achieve high pixel numbers makes them a promising future detector candidate for applications both in astronomy and for synchrotron light sources.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 3 Terminologies Used in WSNs
### SN Residual Energy  
The lifetime of a SN is usually measured by its energy level after a given network data transmission round. The energy level in a SN after each network data transmission phase is referred to as its residual energy. Given that the energy of a SN at the initial deployment is defined as \(E_{in}\), and the energy consumed by the SN after a particular data transmission round is \(E_{r}\), the residual energy of the SN can be defined as:  
\[E_{re}=E_{in}-\sum_{r=0}^{R}E_{r}, \tag{1}\]  
where \(E_{re}=E_{in}\) for \(r=0\), \(r\) is the current round, and \(R\) is the maximum number of rounds. From Equation (1), when \(E_{re}=0\), the SN has completely depleted its energy and cannot participate in any network activity. Thus, the energy level of SNs is an important factor to be considered in order to maintain the functionality of the network for a reasonable time. In fact, as shown in Tables 2-6, the \(E_{re}\) or (RE) is an important decision variable used in the design of most of the studied energy-efficient and energy-balanced routing protocols. The knowledge of the energy level of the SN becomes more important for the design of clustering-based routing protocols [43]. For example, the energy level of the CH node in C3 is zero (\(E_{re}=0\)) as shown in Figure 2, therefore all the SNs in that cluster cannot participate in the next network activity. Hence, in order to avoid untimely network  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Year** & **Survey Paper** & **Contributions** \\ \hline
2014 & [10] & & Provided a general overview of WSNs, stating the areas of application and challenges of WSNs. \\  & & & Reviewed the prime research work and testbeds, standards and platforms, and the techniques and principles of WSNs. \\  & & & Outlined the current happenings in WSN research that considers the possible interaction between WSNs and other technologies. \\ \hline
2016 & [19] & & Compared different multipath routing protocols for WMSNs based on their working operations. \\  & & & Provided the advantages and disadvantages of different multipath routing protocols for WMSNs. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
partitioning, most clustering-based routing protocols consider RE as an important decision variable in the routing algorithms.
## 2 Numerical Simulation Algorithm and Constitutive Model  
### CESE-IBM Coupling Model  
In this study, a high-precision, multidimensional compressible-flow CESE solver based on the spatiotemporal conservation element/solution element method was used [28]. Compared with traditional methods, this solver can handle space and time uniformly, achieving second-order accuracy in space and time. The flow field and its spatial derivatives are simultaneously considered variables for the solution, and high-speed shock waves generated by gas explosions are accurately captured.  
During the coupling solution of chemical models, CESE flow fields, and FEM structure, information exchange is carried out between the chemical solver and CESE solver, which transfer fluid density, energy, temperature, and pressure information to each other, as shown in Figure 1. The structural mesh and fluid should be solved independently. The CESE solver uses a Eulerian mesh, and the structural finite-element solver uses a Lagrangian mesh. The interface between the fluid and the structure is determined by the Lagrange grid element. In each time step, the interface serves as the carrier of information transmission between the CESE solver and the structure solver. The Eulerian grid applies fluid pressure to the structure interface whereas the structure interface feeds back the displacement and velocity information of the structure to the fluid [29; 30; 31]. The moving mesh method (MMM) and immersed boundary method (IBM) are introduced by the CESE solver for fluid structure coupling. The MMM has high calculation accuracy and is suitable for small deformation. However, at each time step, grid information should be updated; thus, the approach is time-consuming. In the IBM, the fluid grid is fixed, and the structural FEM grid moves in the fluid region. This method is suitable for large deformation, and the calculation is relatively stable [32]. The IBM method was adopted considering the stability and calculation cost.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Protocol Operation
#### 3.7.5 Coherent-Based  
Data processing is a major task in WSNs. Hence, different mechanisms are used to reduce the processing costs to conserve energy. In this context, collected information can be processed using a coherent or non-coherent method. In non-coherent protocols, nodes will locally process the sensed data, contrary to the coherent data processing-based protocols where a minimum processing is done locally by the sensor nodes. In [108], the authors have proposed single, and multiple winner algorithms (SWE and MWE) used, respectively, for non-coherent and coherent processing. In the SWE, a single aggregator sensor is selected for complex processing based on its computational capabilities and remaining energy. The MWE is an extension of the SWE algorithm. It limits the number of source nodes that can send data to the central aggregator node in order to limit energy consumption.