esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 2: Postprocessing Filters
#### 3.2.4 Hole-Filling Filter  
In some areas of the scene, the camera was not confident about the depth of the pixel based on the confidence score, hence an invalid point was created which resulted in a depth reading of 0. To recover the depth from patches of invalid pixels, the hole-filling filter obtained depth pixels from neighbouring pixels and rectified it. The route chosen in this scenario was to take the depth data of the pixels closest to the sensor, instead of the farthest. The invalid patches shifted in position depending on the combination of water level and daylight; hence it helped to keep the depth readings valid for the water level extraction pipeline.
###### Abstract  
Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least \(N+1\) sensors in \(N\)-dimensional space to obtain more than \(N\) pairs of TDOAs and FDOAs, thereby establishing more than \(2N\) equations to solve for \(2N\) unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss-Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramer-Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than \(N+1\), thereby corroborating the theoretical analysis.  
moving target localization; Time Difference of Arrival (TDOA); Frequency Difference of Arrival (FDOA); minimum sensors Article
## 3 Results
### Pharmacological In Vitro Cytotoxicity and Antiproliferative Results
#### 3.2.2 In Vitro Antiproliferative MTS Test Results at 48 h  
* Antiproliferative activity tests were done on Slae26, on the five polyphenols compounds, and on Slae26 combinations, 1:1 quantitative ratio (_w/w_), with the five polyphenols selected. Figure 4 presents antiproliferative activity of the five polyphenols compounds (ref.) on Caco-2 cells in comparison with the solvent sample control series (70% ethanol), 48 h after the treatment. Figure 5 presents antiproliferative activity of the test vegetal extract Slae26 on human colon cancer cell line Caco-2, and on human breast cancer cell line BT20 [33] and murine melanoma cell line B16 [34], in comparison with the solvent sample control series (40% ethanol), 48 h after the treatment. Figure 6 presents antiproliferative activity of the combinations of Slae26 with the five polyphenols compounds compounds on human colon cancer cell line Caco-2, in comparison with  
Figure 3: Caco-2 cell viability treated with the five reference compounds (phenolics), at 48 h.  
Figure 2: Caco-2 cell viability treated with the five reference compounds (phenolics), at 24 h.  
certain inhibitory effects upon Caco-2 cells viability, therefore an antiproliferative activity against human tumor cancer cells Caco-2.
* Slae26 test sample in comparison with 40% ethanol sample control series (Figure 5) indicated certain inhibitory activity upon the viability of human tumor cancer cell Caco-2 (IC\({}_{50}\) = 36 \(\upmu\)g GAE/mL extract), similar to that previously found for human tumor breast cell BT20 (IC\({}_{50}\) = 42 \(\upmu\)g GAE/mL extract) [33] and murine melanoma cell line B16 (IC\({}_{50}\) = 39 \(\upmu\)g GAE/mL extract) [34].
* The antiproliferative MTS tests made on the combinations of Slae26 with the five reference compounds (Figure 6), and also in comparison with the control ethanol sample series (represented by 1:1 mixture between 40% ethanol from Slae26 sample and 70% ethanol from reference compounds samples) indicated their massive inhibitory effects upon the viability of Caco-2 cells and an IC\({}_{50}\) value towards 5 \(\upmu\)g active compounds per 1 mL sample. The results on Slae26 combinations with the five plant phenolics support the possibility of boosting the antiproliferative activity of plant-derived products with IC\({}_{50}\) values < 50 \(\upmu\)g active compounds/mL sample.
# Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images
## 2 Materials and Methods
### Training the Neural Network  
This study focused on the 6 most common classes in the WSI fragments: normal glands (NG), adenocarcinoma G1 (AKG1), adenocarcinoma G2 (AKG2), adenocarcinoma G3 (AKG3), tubular adenoma (TA), and villous adenoma (VA). The \"tubular\" and \"villous\" classes only encompassed low- and high-grade adenomas and did not contain adenocarcinomaomas (as these classes could overlap). We selected these labels to make our work comparable with existing AI applications, namely the web platform cancercenter.ai, based on ICD-O 3 morphological codes.  
Two strategies were used to solve the classification problem:  
1. Training a single neural network to classify fragments into six target classes.
2. Training six independent neural networks to solve the one-vs-rest binary classification problems.  
The neural networks for both classification problems were based on a common principle. They relied on a convolutional neural network for feature extraction. Three fully connected layers with the ReLU activation function that performed the classifier task were added to this CNN. A sigmoid activation function was applied to the network output.  
For the task of 6-class classification, the outputs of the neural network did not readily correspond to the probabilities of the corresponding classes. A procedure was implemented to convert the output of a neural network to values characterizing the probabilities.
## 2 Background
### Real-Life Applications of IoT and M2M Communication  
The connectivity of devices aims to improve human life. Let us look at a few real-life applications of the connected world. In terms of smart parking and intelligent parking reservation [19], the developed algorithm helps users to find free parking slots and estimate the distance to free parking spots. Smart CCTV monitoring systems, equipped with artificial intelligence, upload video footage to remote servers (data usage, throughput, and latency requirements). Smart power meters used daily regulate energy usage in houses; with the prepaid electricity model, the power meters register our daily usage and send the data usage to a remote server for monitoring [20]. The usage of smart heating systems to control and monitor appliance temperatures in homes, saving energy and efficiently managing costs [21], is another important application of IoT technology.  
Smart refrigerators, smart lighting systems, health monitoring gadgets, and smart cars are all real-life illustrations of currently popular IoT applications. The devices linked to the mentioned applications upload a massive amount of data to remote central servers through communication networks.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 2 Analysis of Storage and Exchange Efficiency for SAR Data  
### Analysis of Chirp Scaling (CS) Algorithm  
In the realm of synthetic aperture radar (SAR) imaging processing, the range Doppler (RD) algorithm and the Chirp scaling (CS) algorithm represent the two most prevalent methodologies. The RD algorithm, characterized by its simplicity and straightforward hardware implementation, is infrequently employed due to its lower processing precision. In contrast, the CS algorithm is primarily composed of fast fourier transform (FFT), inverse fast fourier transform (IFFT), and complex multiplication operations. These can be implemented on hardware platforms such as field-programmable gate arrays (FPGAs), conferring a high processing precision that satisfies the requirements of general SAR imaging accuracy. As such, the CS algorithm has ascended to become a popular choice for the implementation of hardware platforms in spaceborne SAR imaging processing [36; 37]. The CS algorithm comprises three stages, detailed as follows:  
Step 1: Chirp scaling operation. First, the Doppler center frequency (FDC) is estimated. Then, the raw two-dimensional time domain data of SAR are transformed into the range-Doppler domain by fast fourier transform (FFT) method in the azimuth direction, and multiplied with the first phase function in the range-Doppler domain. Due to the spatial shift characteristics of the range migration curve, the range migration curves of point targets in different range directions have different bending degrees. Thus, the chirp scaling operation will keep the bent degrees of the range migration curves the same for all range directions [38; 39].  
Step 2: Range cell migration correction (RCMC), range compression, and secondary range compression (SRC). After processing in Step 1, the two-dimensional matrix in the range-Doppler domain is converted to the two-dimensional frequency domain by the fast fourier transform (FFT) method in the range direction, and is multiplied with the second phase function to complete the range cell migration correction (RCMC) and range compression operations. Then, the data matrix in the two-dimensional frequency domain is transformed into the range-Doppler domain by performing an inverse fast fourier transform (IFFT) operation in the range direction.  
Step 3: Azimuth compression. First, the Doppler frequency rate (FDR) is estimated. Then, the two-dimensional matrix in the range-Doppler domain processed in the Step 2 stage is multiplied by a complex number with the third phase function in the azimuth direction to complete the azimuth compression. Finally, the data domain is converted from the range-Doppler domain to the two-dimensional time domain, and this conversion is obtained by inverse fast fourier transform (IFFT) operation in the azimuth direction. After the azimuth compression is completed, the two-dimensional time domain data are quantized to obtain the SAR result image. The CS algorithm is executed. Figure 1 shows a schematic flowchart of CS algorithm processing.
## 10 Future and Perspectives
### Sustainability-Driven Optimization  
With sustainability emerging as a core priority across industries, optimization frameworks in the food sector will increasingly integrate sustainable development principles. Future strategies will emphasize resource efficiency, waste reduction, and lower carbon footprints, aligning with the objectives of the United Nations' Sustainable Development Goals (SDGs) and the 2030 Agenda. Optimization approaches will incorporate life cycle assessment (LCA), energy efficiency metrics, and circular economy principles, enabling decision-makers to balance environmental, social, and economic considerations effectively [87]. The development of sustainability-focused optimization algorithms, capable of quantifying trade-offs and providing actionable insights, will be crucial for addressing the global challenges of climate change and resource scarcity.
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
###### Abstract  
The growing amount of space debris in geocentricorbit poses a significant threat to the future of space operations. To mitigate this problem, current international guidelines state that a satellite should be able to deorbit or insert into a graveyard orbit within 25 years from the end of its operational life. In this context, drag-enhancing devices such as drag sails are currently an active field of research and development because of their ability to make a spacecraft decay from low Earth orbit without the need for any on-board propellant. Drag sails, conceptually similar to solar sails, are thin membranes deployed by a spacecraft at the end of its operational life to increase the area-to-mass ratio and, consequently, atmospheric drag. To be effectively exploited, a drag sail should maximize the surface area exposed to atmospheric particle flow. However, this would require a fully functional three-axis stabilization system, which may either be unavailable or non-functional on an orbiting satellite after years of space operations. To simplify the deorbiting phase, in this paper we propose to use a spin-deployed and spin-stabilized drag sail, which represents a reasonable compromise between simplicity of implementation and deorbiting performance in terms of total decay time. In fact, a spinning drag sail could take advantage of centrifugal force to unfold and of gyroscopic stiffness to maintain an inertially fixed axis of rotation. Numerical simulations accounting for the main perturbation effects quantify the effectiveness of the proposed device compared with an optimal configuration (i.e., a three-axis stabilized drag sail) and a tumbling drag sail.  
drag sail; spacecraft deorbiting; drag-augmentation device; orbital debris; spin-stabilization +
Footnote †: journal: Applied
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 4 Dose Calculation Algorithms for Hadrontherapy
### Tissue Stopping Power and Beam Range  
Beam range is of the utmost importance in particle therapy due to the sharp dose gradients in the Bragg peak. Stopping the power estimation of the various tissues in the human body is therefore an integral part of dose calculation. In a good approximation, tissue can be transformed into a water-equivalent space, using ray tracing along the beam axis and a lookup table [57] to convert Hounsfield units (HU) into water-equivalent thickness (WET) values. There is no unique relationship of stopping power and HU in single-energy CT, contributing to considerable range uncertainty [58]. Dual-energy CT has been employed recently to improve stopping power predictions [59], thus permitting a significant reduction in range margins.  
This strategy enables dose calculation in a water-equivalent space, which allows for significant simplifications. Errors occur in tissue, which is greatly different from water in terms of either the elements contained or the density, such as cortical bone or lung tissue. Low density tissue, such as lung tissue, air cavities or distances between nozzle elements and the patient, can lead to an underestimation of the effect of scattering and beam divergence, as the geometrical distance is compressed in calculation of WET values. This is especially important for protons.  
It should be noted that the conversion of the CT to WET is beam-angle-dependent. Consequently, margins against range uncertainty should ideally be anisotropic, extending only in the beam direction. This is an issue in intensity-modulated particle therapy, in which multiple beams are optimized simultaneously.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 4 The Rainfall Rate Estimation Model
### Stratiform-Convective Discrimination Method  
A key step of the model consists in classifying rain events as stratiform or as convective, hence in deciding whether (8) or (10) should be used to calculate the rain height. In temperate regions (like in Italy), there is a clear prevalence of stratiform events during cold months, and of convective ones during warm/hot months. This allows for adopting a monthly-based approach (rather than focusing on single events) to classify rain events and to, consequently, determine the values of \(\tau(m)\). To this purpose, as \(\tau(m)\) is expected to be tightly linked to the connectivity level of each month, we have investigated the relationship between \(\tau(m)\) and \(\beta(m)\), with the latter being defined as the monthly ratio between the local convective and total accumulated rain amounts, \(R_{T,env}\) (mm) and \(R_{T}\) (mm), respectively:  
\[\beta(m)=\frac{R_{T,env}(m)}{R_{T}(m)} \tag{11}\]  
Figure 8 shows the yearly trend of \(\beta(m)\) for Milan, as derived from the ERA15 database that was produced by the ECMWF. The figure also reports the threshold on \(\beta(m)\), set to distinguishing between stratiform and convective months (black dashed line): we have chosen 0.1 based on the observation ofan extensive database of rain events recorded in Milan. Using that threshold, six months of the year are classified as stratiform, while the remaining ones are labelled as convective. Therefore:  
\[h_{\text{R}}(t)\ [\text{km}]=\left\{\begin{array}{l}\tau(m)h_{0}(t),\text{ if }4 \leq m<9\\ h_{0}(t)+h_{\text{BB}}(f),\text{ otherwise.}\end{array}\right. \tag{12}\]
# Design and Application of a Distribution Network Phasor Data Concentrator
## 1 Introduction
Reference [16] introduced an FNET/GridEye example of WAMS deployed in the distribution network. According to reports, the frequency monitoring network FNET/GridEye was originally developed in 2003 and was the first WAMS designed for distribution networks. Its sensors have the advantages of low cost and easy installation. Since FNET/GridEye relies on public networks for communication, its latency and other communication problems are more serious than WAMS for transmission networks. The existing PDC solution is mainly for connecting to the PMUs in the transmission network WAMS. Although there are some discussions on PDCs suitable for distribution networks, there is a lack of consideration of the working environment and communication environment of \(\upmu\)MPMU and DPDC in distribution networks and the lack of analysis of DPDC application requirements. Various software and hardware PDCs have been introduced in the above solutions but they may face problems such as fewer application scenarios, single functions, higher costs, and limited equipment platform resources. To better implement the promotion of \(\upmu\)MPMU in the distribution network, discussions on DPDC design and testing. The main contributions of this paper are:  
* Explain the \(\upmu\)MPMU-DPDC networking structure, analyze the application requirements of DPDC, and give the key performance indicators of DPDC;
* A hardware DPDC design method is proposed. Both software design and hardware selection take into account the need to meet the key performance indicators of DPDC;
* Proposed a DPDC key performance indicators test method and verified the performance of the designed DPDC through testing. The structure of this paper is as follows: Section 2 introduces the networking structure of \(\upmu\)MPMU-DPDC, analyzes the application requirements of DPDC, and provides key performance indicators of DPDC; Section 3 describes the design of DPDC, including software structure and hardware selection, and discloses software implementation details; Section 4 introduces the DPDC test environment and test tools, evaluates the DPDC's key performance indicators; Section 5 describes the DPDC's field installation and operation; Section 6 summarizes the article.
## 3 Results
### Seasonal Variation in Daily NEE  
The daily average NEE over the crop season varied from+0.67 to \(-\)8.83 g C m\({}^{-2}\) d\({}^{-1}\) in rice (Figure 4A) and from +01.07 to \(-\)11.96 g C m\({}^{-2}\) d\({}^{-1}\) in wheat (Figure 4B). The daily average NEE increased with the rice growth stage and peaked during the reproductive stage. During the reproductive stage of heading-flowering, the NEE was \(-\)8.83 g C m\({}^{-2}\) d\({}^{-1}\) in rice and \(-\)11.96 g C m\({}^{-2}\) d\({}^{-1}\) in wheat. The average daily NEE for the crop season was 25% lower in rice at \(-\)3.74 g C m\({}^{-2}\) d\({}^{-1}\), as compared to \(-\)5.01 g C m\({}^{-2}\) d\({}^{-1}\) in wheat (Table 1). Wheat had a cumulative NEE of \(-\)576 g C m\({}^{-2}\), as compared to rice at \(-\)368 g C m\({}^{-2}\) (Table 1). The fallow period duration, after rice and wheat, was 61 and 91 days long, respectively. The NEE at the harvest of rice and wheat were 0.35 g C m\({}^{-2}\) d\({}^{-1}\) and 0.46 g C m\({}^{-2}\) d\({}^{-1}\), respectively (Table 1). The cumulative NEE was higher in the fallow period after wheat at 32.76 g C m\({}^{-2}\), as compared to after rice at 17.08 g C m\({}^{-2}\).  
Figure 4: Daily mean net ecosystem exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (RE) at different growth stages of rice (**A**) and wheat (**B**) in northwestern Indo-Gangetic Plains (IGP).
## 3 Numerical Experiments  
The numerical experiments conducted in this work involve two datasets: the Marmousi2 synthetic dataset and a field dataset. For a fair comparison, the FCRN [24], multi-task FCRN (MFCRN), multi-task FCRN and GRU hybrid network (MFCRGRU), and M2S-MFCRGRU are trained using the same dataset for inverting Vp and density. As FCRN is a single-task network, it is used to independently invert Vp and density. It should be noted that the inputs for FCRN, MFCRN, and MFCRGRU are single-trace seismic data, whereas the inputs for M2S-MFCRGRU are multi-trace seismic data. Additionally, all these methods are trained under identical environmental conditions.
# Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition
## 4 Simulation Results
### IWF-TOT-CG versus WF-CG
Moreover, we can notice that increasing the value of \(P\) beyond a certain value does not improve the overall performance, which also certifies the low-rank approach. Finally, the last experiment from this first set concerns the influence of different conditions on the performance of the proposed IWF-TOT-CG, as compared to the WF-CG. To this purpose, in Figure 7, several scenarios are considered using different amounts of data for estimating the statistics and lower SNRs. The IWF-TOT-CG uses the same CG parameters as in the previous simulation, while \(P=1\). This represents a very advantageous setup as compared to the WF-CG counterpart. While the benchmark algorithm involves a single filter of length \(L=512\), the proposed version combines the estimates provided by three filters of lengths \(PL_{11}L_{2}\), \(PL_{12}L_{2}\), and \(L_{2}^{2}\), which have 32, 32, and 4 coefficients, respectively. Consequently, there is an important reduction in terms of the parameter space, using only 68 coefficients instead of 512. As a result, due to the significantly smaller data structures used within the IWF-TOT-CG, the proposed algorithm is much more robust in harsh conditions, as compared to the WF-CG. This is supported in Figure 7, where the  
Figure 5: Normalized misalignment of the WF-CG (after \(K=50\) CG updates) and IWF-TOT-CG with \(P=2\), \(K_{1}=10\), and using different values of \(K_{2}\). The required statistics are obtained by averaging across \(N=5L\) data samples, \(L=512\), and \(\text{SNR}=20\) dB. IWF-TOT-CG outperforms the WF-CG algorithm, especially when using a low amount of data for estimating the statistics (e.g., \(N=L\) in Figure 7b) and/or in noisy environments (e.g., \(\text{SNR}=10\) or \(0\) dB in Figure 7c and d, respectively). Figure 6: Normalized misalignment of the WF-CG (after \(K=50\) CG updates) and IWF-TOT-CG with \(K_{1}=12\), \(K_{2}=1\), and using different values of \(P\). The required statistics are obtained by averaging across \(N=5L\) data samples, \(L=512\), and \(\text{SNR}=20\) dB. Figure 7: Normalized misalignment of the WF-CG (after \(K=50\) CG updates) and IWF-TOT-CG with \(K_{1}=12\), \(K_{2}=1\), \(P=1\), and using different amounts of data (\(N=ML\), with \(L=512\)) to estimate the required statistics, under different SNR conditions. (**a**) \(M=2\) and \(\text{SNR}=20\) dB; (**b**) \(M=1\) and \(\text{SNR}=20\) dB; (**c**) \(M=2\) and \(\text{SNR}=10\) dB and (**d**) \(M=5\) and \(\text{SNR}=0\) dB.