esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Nevertheless, they do not address the transferability of their findings to other regions. On the other hand, coarser resolution has convincing advantages for global monitoring systems of the temporal development of global irrigation, such as daily global coverage and low data rates. The existing long time series of medium-resolution LANDSAT data and the new medium spatial and high-temporal-resolution Sentinel-2 data have successfully been used in regional and local studies to determine the extent of irrigated areas with high precision [25; 26; 27]. In principle, they would be the data source of choice for a more complete, global, operational irrigation mapping. Sentinel-2 now allows, in principle, to precisely and operationally resolve, with high spatial resolution, the temporal NDVI-developments on which current approaches to distinguish irrigated areas from non-irrigated areas rely. Improved global irrigation monitoring therefore seems possible but not feasible considering the massive computational resources necessary to analyze frequent time series of large areas with high spatial resolution. This may be one reason why, despite the anticipated added precision, to our knowledge, operational irrigation monitoring on a global scale using Sentinel-2-time series is not available yet. On the other hand, Sentinel-2 time series could potentially be used to augment existing global low-resolution approaches to map irrigated areas, given that the local scaling laws, which govern the change in detected irrigated areas with decreased spatial resolution, are well understood. The hypothesis of our paper, therefore, is that the change in detected irrigated areas with decreasing spatial resolution inherent in the current approaches follows a regional independent scaling relation. We consider the resulting scaling relations as a property of the plot size, the spatial arrangement and the complexity of the shape of the irrigated fields. The complexity of the spatial structure of the irrigated areas can be described by landscape metrics, well known from biodiversity and habitat analysis [28; 29]. The Landscape Shape Index (LSI) was identified to be suitable for explaining the negative changes in the mapping results [30; 31; 32]. A proven correlating functional relation between the differences in the mapping results caused by resolution and the LSI can be used for estimating the accuracy of global low-resolution irrigation monitoring. In order to investigate the scaling properties, we use a proven approach to globally monitor irrigated areasusing NDVI time series, which have been widely used with wide-swath low-resolution sensors such as MERIS and PROBA-V [7]. For the first time, we systematically analyze the impact of spatial resolution from 10 m to 1 km on the pattern and extent of irrigated areas in three selected global regions. We consider different geographical conditions with respect to climate and farming systems by selecting as case studies regions in Sudan, China and the USA.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Theory
#### 2.1.2 PSF-Shape-Based Beamforming Grid  
The shape of the peak of the CCF is related the PSF of the imaging system, i.e., the combination of transducer choice and transmit sequence. Together with the data sampling grid, the PSF strongly influences the appearance of the peak in the CCF. By incorporating knowledge about the PSF dimensions into the data sampling grid, the CCFs can be captured in a standardized way, with an equal number of samples in the axial and lateral direction throughout the peak. These so-called circularly shaped peaks, i.e., peaks with circular isocontours, provide an equal amount of sample points to the subsample estimator in both directions. This ensures the interpolator gives equal weight to the available information in both directions. Using a 2D interpolator, which matches with these circular shaped peaks, allows for joint 2D subsample displacement estimation. In this work, we investigate whether standardization of the shape, or sampling, of the peak combined with 2D cubic interpolation increases the accuracy and precision of multi-step ST-based displacement estimation.  
Circular-shaped CCF peaks were realized by choosing the data sampling grid such that a single period of the system's PSF contained an equal amount of sampling points in axial and lateral direction. Firstly, concerning the RF data (Figure 1a), six samples per wavelength were beamformed in axial direction, in accordance with the all-positive-samples condition [34], resulting in an axial sampling distance of \(\mathrm{d_{ax,rf}=c/(2\times f_{c}\times 6)}\), with \(\mathrm{c}\) the speed of sound and \(\mathrm{f_{c}}\) the center frequency of the emitted waveform. The lateral sampling distance was determined according to \(\mathrm{d_{lat,rf}=d_{ax,rf}\times PSF_{ratio}\times N_{cycles}}\), with \(\mathrm{PSF_{ratio}}\) the average ratio between the lateral and axial PSF dimensions and \(\mathrm{N_{cycles}}\) the number of cycles present in the PSF axially. The envelope data sampling grid (Figure 1b) was generated by down sampling the RF sampling grid in axial direction with the factor \(\mathrm{N_{cycles}}\), resulting in an axial spacing of \(\mathrm{d_{ax,env}=d_{ax,rf}\times N_{cycles}}\). The lateral spacing for the envelope data was set similar to the lateral spacing of the RF data (\(\mathrm{d_{lat,env}=d_{lat,rf}}\)). With these spacing settings, the peak of the CCF will appear circular-shaped when using both envelope and RF data in the multi-step ST algorithm.
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 1 Introduction
In conclusion, the field of OCT image denoising continues to evolve, with adaptive denoising methods based on wavelet transforms emerging as the focal point of contemporary research due to their superior noise suppression capabilities and detail preservation efficacy. This chapter investigates novel algorithmic approaches for OCT image denoising, refining existing methodologies while introducing a synergistic integration of wavelet transforms with DnCNN architectures. The proposed framework decomposes images into low-frequency and high-frequency components via the DWT, processes both the original high-frequency coefficients and those derived from the SWT through DnCNN-based denoising, and finally fuses the outputs using image correlation maximization. The complete denoising pipeline is visualized in Figure 1, demonstrating innovative signal process ing strategies that hold significant promise for advancing the OCT imaging quality in clinical applications.
## 4 Data Analysis and Results
### Measurement Model  
The adequacy of the measurement model was evaluated in terms of reliability, convergent validity, and discriminant validity. Reliability was examined by using composite reliability (CR) values. As listed in Table 2, all CR values were greater than the commonly acceptable level of 0.70, indicating adequate composite reliability. Convergent validity of the scales was evaluated by using the two criteria suggested by [64]: (1) all indicator factor loadings should be significant and exceed 0.70, and (2) average variance extracted (AVE) by each construct should exceed the variance, due to measurement error for that construct (i.e., should exceed 0.50). As shown in Table 2, standardized CFA loadings for all items were significant and exceeded the minimum criterion of 0.7. Furthermore, as seen in Table 3, all AVE values of all constructs exceeded the minimum threshold value of 0.5. As such, the results demonstrate adequate convergent validity. Discriminant validity was evaluated by using the criteria recommended by [64]: the square root of the AVE should exceed the correlation shared between the construct and other constructs in the model. Table 3 lists the correlations among constructs, with the square root of the AVE on the diagonal. The results demonstrate acceptable discriminant validity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Items** & **Frequency** & **Percentage (\%)** \\ \hline Gender & Male & 174 & 65.2 \\  & Female & 93 & 34.8 \\ Age & Below 18 & 36 & 13.5 \\  & 21–25 & 167 & 62.5 \\  & 26–35 & 50 & 18.7 \\  & 36–45 & 10 & 3.7 \\  & Above 45 & 4 & 1.5 \\ Education & High school & 34 & 12.7 \\  & College & 14 & 5.2 \\  & University & 164 & 61.4 \\  & Graduate or above & 55 & 20.6 \\ Favorite social media & Facebook & 162 & 60.7 \\  & BBS & 42 & 15.7 \\  & Line & 61 & 22.8 \\  & Others & 2 & 0.7 \\  & Below 1 & 21 & 7.8 \\ Experience of social & 1–2 & 92 & 34.5 \\ media usage (in year) & 2–3 & 44 & 16.5 \\  & Above 3 & 110 & 41.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic information about the respondents.
## 2 The Steps of Medical Device Development in General  
### Technological Steps  
1. At first, the intended use of the medical device must be defined. The intended use is the primary use purpose of the product, including medical indication and patient group.
2. Next, the functional requirement is formulated. The requirements will form the basis for the development of the system and be used to verify and validate the design changes. These requirements need to be translated to functional requirement specifications, which will become design inputs for manufacturing.
3. The design input is the base of the device planning and engineering--it means that the actual parameters regarding operation (usage, risk, standards) and physical appearance of the device are created. The parameters needed for implementation, such as the characteristics and dimensions of the product, are collected as functional requirement specification (Table 1). The design output consists of drawings, specifications and manufacturing instructions--it describes all parts of the device. These will become part of the design and development documentation of the device and therefore also part of the technical documentation.
4. Prototypes are prepared according to the design output and the approved prototype can go into small quantity production. The produced devices are to be used mainly for process validation. Sterilisation and packaging plans must be finalized, and then these need to be validated as well. The design output is suggested to be patented if the medical device is novel, inventive and industrially applicable.  
\begin{table}
\begin{tabular}{c c} \hline
**Technical Requirements** & **Characteristics and Numerical Specifications (Design Input)** \\ \hline Use & Single use \\ \hline Sterilisation & Ethylene oxide \\ \hline Materials & The device must conform to ISO 10993-1 as per the essential requirement of 93/42/EEC. \\ \hline Volume Ratio & \begin{tabular}{l} 12 mL whole blood can be drawn \\ The ratio of the top and bottom parts must be 40\% top (serum fraction) and 60\% bottom \\ (waste). Note). Note 4.8 mL serum will be in the glass chamber after the centrifuging procedure \\ \hline Mass of device & \
\begin{tabular}{l} Total weight of the system with all parts, except for the plunger part, plus blood included \\ will be 54 g \\ \end{tabular} \\ \hline Dimensions of the Device &
\begin{tabular}{l} The dimensions of the device, including max diameter, length and conical bottom angle are \\ equivalent to a 50 mL conical centrifuge tube \\ \end{tabular} \\ \hline Packaging & Device packaging is to be suitable for ethylene oxide sterilization \\ \hline Users & Nurse, Doctor \\ \hline Medicinal Substance & None \\ \hline External Interfaces & Swing-out rotor centrifuge for 50 mL conical centrifuge vials \\ \hline \end{tabular}
\end{table}
Table 1: Functional requirement specification.
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 3 Mechanical and Dielectric Properties of Nanofibers
### Dielectric Properties  
In this section, the dielectric properties of PAN fibers, carbon nanofillers, and other electrospun fibers and different nanomaterials are discussed. According to classical theory, the dielectric constant (k) is defined as the ratio of the permittivity (E) of a substance to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nanofibers** & **Tensile Strength** & **Tensile Modulus** & **Characteristics** \\ \hline \multirow{2}{*}{PAN CNF} & 5.4 GPa & 287 GPa & Hot drawn and carbonized at 1100 \({}^{\circ}\)C, 400 nm in diameter [139] \\ \cline{2-4}  & 7.3 GPa & 262 GPa & Carbonized at 800 \({}^{\circ}\)C, 108 nm in diameter [104] \\ \hline PCL & 66 MPa & 340 MPa & 400 nm in diameter [102] \\ \hline PVP & 2.30 MPa [156] & - & 300 nm in diameter [156] \\ \cline{2-4}  & 7 MPa [160] & 500 MPa [160] & 800 nm in diameter [160] \\ \hline PEO & - & 0.75 GPa [158] & 200 nm in diameter [158] \\ \cline{2-4}  & 45 MPa [159] & 22 MPa [159] & 700 nm in diameter [159] \\ \hline Nylon 6 & 900 MPa & 304 MPa & 800 nm in diameter [160] \\ \hline \hline \end{tabular} PCL, polycaprolactone; PVP, polyvinyl pyrrolone; PEO, polyethylene oxide.  
\end{table}
Table 2: Mechanical properties of nanofibers.  
Figure 14: (**a**) Tensile strength/modulus vs fiber diameter [103]. (**b**) Crystallinity vs. fiber diameter [103].  
permittivity of free space (E\({}_{0}\)). Values of k are always greater than or equal to 1. For most polymers, k values are in the range of 2 to 10.
## 4 Experimental Evaluation
### Experiment Results  
In almost all settings, ConvProtoNet outperforms all the baselines models with a large margin and a considerable accuracy is achieved even for noisy dataset when only a few samples in each are provided, as shown in Tables 1-4. Suffering from convergence problem, the results of SNAIL in all 20-way settings are not reported. Among the baseline few-shot models, ProtoNet has the closest performance to ConvProtoNet, but its accuracy still falls behind by about 5% on average whereas other models have the accuracy gap over 10% on average. Experiment results on Drebin dataset show that our approach is platform-independent for malware and the minimum performance can be ensured even few classes are provided. ConvProtoNet model trained in meta-learning scheme can perform at a close level to the model trained on other datasets without retraining or fine-tuning. This indicates that our model has strong generalization ability and it can classify malware from different sources after it is trained on only one dataset once for all. The only drawback is that performance degrades with more classes to be classified and the gap is over 10% when increase from 5 classes to 20 classes. Although PE meta-data using kNN often gets high accuracy on many datasets, it will fail to work as long as the PE header of malware is corrupted deliberately or accidentally. Especially for the VirusShare dataset, it is observed that more than 15 classes have invalid PE file header causing extraction failure and the results are not entirely credible despite higher accuracy achieved by PE meta-data using kNN. It means the the approach of PE meta-data with kNN has flaw itself and it can not work on the malware datasets from different platforms like Android which limits its application. On the contrary, ConvProtoNet is platform-independent for malware and robust enough to resist to corrupted data. To precisely present the results ConvProtoNet achieved, we use five assessment measures: accuracy, precision, recall, F1 score and receiver operating characteristic area under curve (ROC-AUC), which is shown in Table 5. This indicates that while achiving high accuracy, ConvProtoNet keeps good balance of precision and recall. We also plot the confusion matrix of first 20 classes for 5-shot 5-way problem on LargePE dataset after 5000 testing epochs, which is shown in Figure 6. Most plotted classes can be properly handled by ConvProtoNet achieving high accuracy whereas only a few classes pull down the average accuracy.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 2 Development Approach: Design Goals, Materials, and Methods  
### Design Goals  
#### 2.1.1 Flowchart of the Rectenna Design, Optimization, Fabrication, and Testing Methodology  
Figure 1 shows the flowchart of the rectenna design, optimization, fabrication, and testing methodology. The process starts with defining the design goals and setting the corresponding electrical (operating frequency, radiation pattern, bandwidth, RF-to-DC power conversion efficiency, etc.) and mechanical (dimensions, flexibility, etc.) requirements. The next essential step involves selecting suitable materials (for the substrate, antenna, rectifier circuit, etc.) and identifying appropriate topologies for the rectenna elements. In the next step, the antenna and the rectifier are designed separately, with each component optimized to meet the specific electrical and mechanical requirements for integration into the rectenna. After that, the rectenna system is optimized by jointly simulating and testing the integration of the antenna and rectifier on the same substrate. Once optimization is complete, the performance of the fabricated rectenna prototype is experimentally evaluated. If the predefined goals are met, the process concludes; if not, the workflow reverts to the material and topology selection stage for further improvement.
## 4 Discussion
Our current results show a large increase in levels of reactive oxygen species after treatment with ferrocenes **1b**, **2a**, and **3**, which indicates their impact on the redox homeostasis in tumor cells. Third, due to the aforementioned increase in ROS production, the effect of the tested compounds on mitochondrial function was also analyzed. Our experiments showed clear changes in mitochondrial membrane potential. These changes are generally considered to be an early step in apoptosis after treatment with different drugs [63; 64]. Depolarization of the mitochondrial membrane leads to the release of cytochrome c from the mitochondria. This process triggers the formation of an apoptosome and the activation of caspases, launching the intrinsic apoptotic pathway [65]. Furthermore, early and late stages of apoptosis were both detected via Annexin V assay and DNA labeling by PI. These findings are in agreement with those of other studies showing that many different ferrocene-containing compounds, e.g., ferrocifens, Pt-ferrocene complexes [66; 67], and ferrocenes combined with retinoids, are able to activate apoptotic cell death in cancer cells [68; 69; 70]. Elevated levels of ROS can also lead to the induction of autophagy as a defense mechanism that allows cell survival during stress conditions. An increase in autophagy-related proteins was observed, especially for cleaved-form LC3B. These changes were most prominent after treatment with ferrocenes **1b** and **2a**. It is well known that autophagy can play a dual role in response to drug treatment. On one hand, upregulation of autophagy could help in the formation of cancer cells resistant to chemotherapy [71]. On the other hand, excessive elevation of autophagy may assist in the induction of programmed cell death [72]. In our study, combination of ferrocenes with irradiation led to a decrease in the amount of LC3B protein. The decrease was most pronounced in response to ferrocene **2a**. It can therefore be assumed that autophagy induction helps cells to overcome exposure to ferrocenes. In line with these findings, combined treatment with ionizing radiation attenuates autophagy induction and results in significantly higher anti-tumor efficacy. This correlates with the CFA results, which showed decreased colony formation rates after combination of ferrocene **1b** and partially **2a** with radiotherapy when compared to radiotherapy alone. To quantify this interplay, the dose-modifying factor for each ferrocene was calculated. The most pronounced synergistic effect was observed for ferrocene **1b** (1.6\(\times\) increased effectiveness of radiotherapy in HeLa and 1.8\(\times\) in SiHa cell lines) and for ferrocene **2a** (1.4\(\times\) for the SiHa cell line).
## 2 System Model
\begin{split}\text{MSE}_{k}&=\mathbb{E}[(\mathbf{ \hat{s}}_{k}-\mathbf{s}_{k})(\mathbf{\hat{s}}_{k}-\mathbf{s}_{k})^{H}]\\ &=(\mathbf{M}_{k}^{H}\mathbf{L}_{k}-\mathbf{I}_{d})(\mathbf{M}_ {k}^{H}\mathbf{L}_{k}-\mathbf{I}_{d})^{H}+\mathbf{M}_{k}^{H}\mathbf{C}_{n_{k} }\mathbf{M}_{k}\\ &+\mathbf{M}_{k}^{H}\mathbf{E}_{n_{k}}\mathbf{M}_{k},\end{split}  \tag{8}\]  
where \(\mathbf{L}_{k}=\mathbf{G}_{k}\mathbf{\Gamma}\mathbf{H}_{k}\) and \(\mathbf{E}_{n_{k}}=\mathbf{G}_{k}\mathbf{\Gamma}\sum_{m=1,m\
eq k}^{K} \mathbf{H}_{m}\mathbf{H}_{m}^{H}\mathbf{\Gamma}^{H}\mathbf{G}_{k}^{H},k=1, \ldots,K\). Optimization is employed to obtain the linear transceiver matrices \(\mathbf{\Theta}_{k}\) and \(\mathbf{\Gamma}\) at the source and relay, and to obtain the linear combiners \(\mathbf{M}_{k}\) at the destinations. The minimization of the sum mean squared error (SMSE) of the received signals at the destinations can be expressed as  
\[\min_{\mathbf{\Theta}_{k},\mathbf{\Gamma},\mathbf{M}_{k}} \text{SMSE} \tag{9a}\] \[s.t. \text{tr}(\mathbf{\Gamma}(\mathbf{y}_{r}\mathbf{y}_{r}^{H}) \mathbf{\Gamma}^{H})\leq P_{r}\] (9b) \[\text{tr}(\mathbf{\Theta}_{k}\mathbf{\Theta}_{k}^{H})\leq P_{ \mathbf{s}_{k}},k=1,\ldots,K, \tag{9c}\]  
where \(\text{SMSE}=\sum_{k=1}^{K}\text{tr}(\text{MSE}_{k})\), and \(P_{\mathbf{s}_{k}}>0\) and \(P_{r}>0\) are the power constraints at the \(k\)th source and relay, respectively.
# Revisiting the High-Performance Reconfigurable Computing for Future Datacenters
## 4 Revisiting the FPGA Virtualization
### Multi-Node Level Virtualization
#### 4.3.2 Frameworks  
Frameworks exploit the conventional server-client architecture, where only the computational part is assigned to one or more FPGAs, but the CPU server manages the rest, including configuration, application related data, and scheduling. The central piece in this architecture is the data management model, and models for CPU are equally extendible to FPGAs. For example, the idea of the MapReduce framework has been extended on FPGAs where mapping and reduction operations are performed by FPGA accelerators [103; 118; 119] in similar way as CPU client-server architecture. These frameworks have an added advantage of bridging the gap between the heterogeneity of datacenters, [120] is one such cluster comprised of FPGAs and GPUs while using MapReduce. Furthermore, [PERSON] and colleagues in [104] extended java virtual machine (JVM) framework using Apache Spark to accommodate the FPGAs, this however comes with a communication overhead and requires precision.  
[PERSON] [77] and his colleagues utilized OpenCL via Xilinx SDAccel framework using an abstract layer to assign the data to multiple FPGAs and maintaining a transparent directory to virtualize the FPGAs at the lower abstraction level. The approach of the FPGA groups [121] suggests that multiple FPGAs can be shared by one group but configured with a matching accelerator. However, this comes with a limitation of occupying a complete FPGA that results in under-utilization but it can be addressed with an automation of the scaling algorithm. A similar concept has been proposed in [122] while using Hadoop YARN with a value-added advantage of ease of programming.  
In the heterogeneous computing environment, the performance is also a function of execution strategy. For the exploration of alternative execution strategies on disaggregated environments, the evaluation platform presented in [123] is useful.