esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
### Practical Implications, Policy Recommendations and Future Research
(2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Mapping Cropland Extent of Southeast and Northeast Asia Using Multi-Year Time-Series Landsat 30-m Data Using a Random Forest Classifier on the Google Earth Engine Cloud. _Int. J. Appl. Earth Obs. Geinf._**2019**, _81_, 110-124. [CrossRef]
* [PERSON] et al. (2022) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Phenotyping a Diversity Panel of Quino Using UAV-Retrieved Leaf Area Index, SPAD-Based Chlorophyll and a Random Forest Approach. _Precix. Agric._**2022**, _23_, 961-983. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning-Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction. _Remote Sens._**2022**, _14_, 1707. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Integrating Climate and Satellite Remote Sensing Data for Predicting County-Level Wheat Yield in China Using Machine Learning Methods. _Int. J. Appl. Earth Obs. Geinf._**2022**, _111_, 102861. [CrossRef]* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites. _Remote Sens._**2021**, _13_, 3141. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales. _Front. Artif. Intell._**2022**, \(4\), 744863. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data. _Remote Sens._**2021**, _13_, 1769. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] 10 m Crop Type Mapping Using Sentinel-2 Reflectance and 30 m Coropland Data Layer Product. _Int. J. Appl. Earth Obs. Geinf._**2022**, _107_, 102692. [CrossRef]
* [PERSON] (2021) [PERSON] Tomato Detection Based on Modified YOLOv3 Framework. _Sci. Rep._**2021**, _11_, 1447. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] Real-Time Growth Stage Detection Model for High Degree of Occultation Using DenseNet-Fused YOLOv4. _Comput. Electron. Agric._**2022**, _193_, 106694. [CrossRef]
## 5 Main Functionalities (Tested and Validated)
From a technical point of view, the most important achievements during the project design and implementation were:  
1. Using a pure mathematical translation, it was necessary to be able to instantaneously convert the WGS84-based coordinates into the Stereo70 system coordinates format and vice versa. Usually, these transformations are available only in expensive commercial products. We used only open source applications; thus, human intervention or calculations are no longer required, and it is not necessary to use other proprietary software applications. The user graphical interface allows coordinates to be entered in the WGS84 system (Figure 20) or Stereo70 system (Figure 21). To ensure the user does not enter data in the wrong format, examples are provided for each coordinate format. Figure 18: System warning issued in the case of a comparison of overlapping areas with validated archeological sites (purple). 2. Due to their work specificity, archeologists may be required to access the computer application from remote sites having poor data coverage, although most private users use good-quality fixed or mobile Internet connections. Thus, we designed the computer application in such manner to minimize the necessary amount of data to be sent and received between the users' terminal equipment and central servers. Figure 19: Distance measurement between any map points. In this case between two validated archeological sites (purple). Figure 20: User graphical interface to input coordinates in WGS84 format and map representation. The validated archeological site represented in purple color. The central servers host the application, the graphical user interface, and the databases. The terminal equipment used by archaeologists in the field or by regular users includes tablets, GPS devices, smartphones, and performance laptops. 3. The access devices used by archeologists and private users differ not only in terms of resolution and screen size, but also in terms of software applications used to collect the GIS data and to access the project graphical user interface. This diversity had to be addressed by the technical team to maintain the same user satisfaction level and compatibility of wide-area devices.
###### Abstract  
This paper describes the total antioxidant capacity (TAC) of sweet lupin (_Lupinus luteus_ cv. [PERSON]--LLL) and narrow-leaved bitter lupin (_Lupinus angustifolius_ cv. [PERSON]--LAM) sprouts fermented by yeast, determined by deploying the updated analytical strategy based on three assays. The procedures covered electrochemical, spectrophotometric, and photochemiluminescence methods. Cyclic voltammetry (CV), the scavenging of 2,2-diphenyl-1-picrylhydrazyl radicals (DPPH*), and photochemiluminescence (PCL) assays against superoxide anion radicals were applied to hydrophilic (ACW) and lipophilic (ACL) fractions, and the data obtained were used to calculate the TAC after sequence extraction of the samples with 80% methanol followed by methanol/hexane (4:1; \(\
u/\
u\)). The total polyphenol content (TPC) in the hydrophilic fractions was measured using Folin-Ciocalteu reagent. The fermentation of the LLL and LAM seeds had no impact on the antioxidant capacity of their H fractions, whereas it increased the content of their L fractions up to 56%. The germinated lupin seeds of both cultivars showed slightly increased TAC than the fermented ones. The TPC in the yeast-fermented sprouts was slightly higher compared to the LLLL sprouts, about twofold higher than in the fermented LLL seeds, and finally almost fivefold higher compared to the LLL seeds. A beneficial effect of fermentation was found with respect to LAM materials. The TAC of the fermented LLL and LAM sprouts measured via CV and PCL assays was almost twofold higher and eight and six times higher, respectively, compared to the seeds. These findings clearly suggest that the seeds of yellow sweet lupin (_Lupinus luteus_ cv. [PERSON]) are the most suitable for producing fermented sprouts with a high content of electroactive polar compounds able to scavenge multiple free radicals of biological and non-biological origin. The updated analytical strategy for the determination of the total antioxidant capacity proved to be a viable tool for screening processed lupin seeds.  
lupin seeds; sprouts; fermentation; total polyphenols; antioxidant capacity; cyclic voltammetry; DPPH assay; photochemiluminescence
# Percolation Threshold of Red-Bed Soft Rock during Damage and Destruction
## 2 Research Contents and Methods
### Percolation Test for Compression Damage of Red-Bed Soft Rock
#### 2.3.2 Determination of Percolation Threshold in Uniaxial Compression Test of Red-Bed Soft Rock  
Using AE data and the derivation outlined in Section 2.2, the stress-strain curve of the rock sample was obtained according to the percolation damage constitutive model. The percolation threshold of the rock sample was then determined. The specific steps are as follows:  
Figure 5: Rock samples and their loading conditions: (**a**) silty mudstone samples, (**b**) damaged rock samples after uniaxial compression test, (**c**) instrument and loading position.  
(1) record the cumulative number of AE events to determine when AE events occur most frequently. Namely, record the cumulative value of the ringing count \(\Omega_{0}\) at the time when the ringing count is at its maximum during a burst period; (2) establish the damage model according to Equation (17); (3) ascertain the amount of damage during the percolation process (\(D_{b}\)) and adjust \(D_{b}\) so that the two compressive strengths are equal and the damage model is as close as possible to the test curve; (4) add the initial amount of damage (\(D_{a}\)) and the amount damaged in the process (\(D_{b}\)) to obtain total damage during percolation; that is, the percolation threshold \(D_{p}\). When the percolation threshold of red-bed soft rock under the failure conditions for loads is \(D_{p}\), the inner structural unit of the red-bed soft rock is destroyed by \(D_{p}\times 100\%\), the perforation failure occurs.
## 3 Results and Discussion
### Hardened State Properties
#### 3.4.7 Water Absorption by Immersion  
Figure 11 shows that, contrary to expectations, negligible changes were observed in the 28-day water absorption by immersion; the CC, C10 FA20 BA, C20 FA10 BA, C20 FA40 BA and C40 FA20 BA exhibited values of 13.5%, 13.9%, 13.0%, 14.8% and 14.3%, respectively. A significantly higher porosity increase of mixes with MIBA would be expected due to the corrosion of Al. It is possible that a notable portion of these pores formed by H\({}_{2}\) gas may have been internal and inaccessible. A more expected difference was observed after 91 days, wherein an increase in the water absorption by immersion was noted with the increase in \(\mathrm{f_{ad}}\) and MIBA content, whereby C20 FA40 BA showed the highest values; the CC, C10 FA20 BA, C20 FA10 BA, C20 FA40 BA and C40 FA20 BA exhibited values of 10.1%, 13.8%, 13.3%, 17.2% and 13.9%. This result is consistent with various investigations, in which there was the production of H\({}_{2}\) from the reaction between metallic Al, present in MIBA, and the OH\({}^{-}\) ion, present in water, thus generating porosity in mortars and concretes in the hardened state [10].  
Figure 11: Water absorption by immersion at 28 and 91 days.  
Figure 10: Carbonation depth of samples with MIBA.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks  
[PERSON]  
1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [EMAIL_ADDRESS] (Y.Z.)1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; zhouyuke@bupt.  
network, some load-balancing routing schemes have been proposed to provide reliable ISL-based relaying [15; 16; 17]. However, only some works take feeder links into account and provide satisfactory handover schemes for feeder links. Although the feeder link has more transmission capacity than the ISL, there may still be a backhaul bottleneck in the feeder link due to rain attenuation and atmospheric fading [18]. In addition, limited by geographical and political factors, GSs are generally deployed in a specific domain. Since the number of UEs far exceeds GSs, the global-distributed traffic converges to the GSs, resulting in severe congestion and heavy network load around the GSs [19]. Therefore, considering that the feeder link selection determines the last-hop satellite and the backhaul capacity, the feeder link handover is a critical issue that directly affects the performance of the satellite-based backhaul.  
To solve the above issues, we propose a maximum backhaul capacity handover strategy (MBCHS) to improve the backhaul capacity by handover decision making and jointly consider the feeder links and inter-satellite network. The main contributions are summarized as follows:  
* We present the LEO network scenario and formulate the feeder link handover problem to maximize the available backhaul capacity while optimizing the handover frequency.
* To solve the optimization problem, we design an available backhaul capacity ratio to estimate the available capacity of satellites for handover decisions. Then, we introduce a service time factor and handover control factor to reduce the handover frequency.
* Based on the three handover factors, we present a handover utility function for handover decisions. Then, we propose a maximum backhaul capacity handover strategy (MBCHS) based on a greedy strategy and maximum flow algorithm.
* The performance of the proposed strategy is evaluated using simulations. The results show that MBCHS outperforms the benchmarks based on maximum service time and maximum feeder link quality.  
The rest of this article is organized as follows: In Section 2, we introduce the recent advances of related works. In Section 3, we formulate the network model and define the optimization problem. Then, we propose a maximum backhaul capacity handover strategy in detail in Section 4. In Section 5, simulation results are provided. Finally, we conclude the article in Section 6.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 3 Methods
### Deformable Dilated Transformer  
Inspired by [17; 35], we combine the merits of them to construct a Deformable Dilated Transformer as the backbone. The key characteristic of the regular transformer is that it can explore all locations in an image for long-range spatial modeling. To mitigate the problems of spatial resolutions and convergence, as shown in Figure 3, we leverage the deformable attention based on [17] to learn a small set of vital sampling points around a reference point, without considering the resolution of feature maps. Given the original input \(\mathbf{x}^{C\times H\times W}\), the deformable attention is calculated as Equation (6):  
\[DefAtt(\mathbf{z},\mathbf{p}_{q},\mathbf{x})=\sum_{n=1}^{N}\mathbf{W}_{n}[\sum _{k=1}^{K}A_{n\alpha k}\cdot\mathbf{x}(\mathbf{p}_{q}+\Delta\mathbf{P}_{n \alpha k})\mathbf{W}_{n}^{\prime}] \tag{6}\]  
where \(\mathbf{W}_{n}\) and \(\mathbf{W}_{n}^{\prime}\) are the learnable weight matrix, \(q\) is the index of a query element with content feature \(\mathbf{z}\), and a 2D reference point \(\mathbf{P}_{q}\). In addition, \(n\) and \(k\) index the attention head and sampled keys, respectively. \(K\) is the total sampled key that is much smaller than \(H\times W\). \(\Delta\mathbf{P}_{n\alpha k}\) denotes the sample offset, \(A_{n\alpha k}\) is the attention matrix of \(k^{th}\) sampling point in the \(n^{th}\) attention head. \(\Delta\mathbf{P}_{n\alpha k}\) and \(A_{n\alpha k}\) are obtained by the linear transformation on the content feature \(\mathbf{z}\). \(\Delta\mathbf{P}_{n\alpha k}\) are \(2^{d}\) real numbers without constrained range and the attention \(\sum_{k=1}^{K}A_{n\alpha k}=1\). We implement the above principle as [17], with the aim to reduce computational cost and avoid overfitting risk. Moreover, we replace the feedforward layer (i.e., regular 2D convolution) with two dilated convolutional layers to further enlarge spatial receptive fields. Let \(m\) be the number of stacked \(k\times k\) dilated convolution, where \(k\) is the filter size. We denote the dilation rate as \(r\) and then the kernel size \(k\) after dilation is calculated as Equation (7):  
\[k^{\prime}=(k-1)*r+1 \tag{7}\]  
Let \(R_{m}\) be the effective receptive field of layer \(m\), which is defined as Equation (8):  
\[R_{m}=R_{m-1}+(k^{\prime}-1)\times\prod_{i=1}^{m}s_{i} \tag{8}\]  
where \(R_{m-1}\) is the receptive field of layer \(m-1\), and \(s_{i}\) is the stride of layer \(i\). From the above equations, we can know that the dilated layer can increase the receptive field without introducing additional parameters. By combining the deformable attention and dilated convolutional layers (Figure 3), we can obtain a generalized high-level global-local representation.
## 3 Digital Beamforming General Considerations  
DBF is a signal processing technique used in sensor arrays to steer the direction of the received signal. The technique is well known and widely used in communications systems and the remote sensing framework, even though to the authors' knowledge this is the first time it is used in microwave radiometry. However, some studies and operative instruments using the related idea of phased array have been carried out [22], especially in the radio-astronomy field. Both concepts, digital beamforming and phased array, are similar, but some substantial differences exist. The phased array is based on the variation of the phase for each individual antenna in such a way the effective radiation pattern of the array is steered to the desired angle. In addition, the phase shifters typically used in phased arrays, introduce losses that degrade the noise figure, and therefore the radiometric resolution. On the other hand, DBF is the result of digital signal processing, which implies absolute reconfigurability. Furthermore, since each antenna signal is digitized, it is possible to obtain more than one beam simultaneously. In theory, a continuous scan can be performed obtaining an infinite number of beams simultaneously, only limited by the hardware requirements. These differences make the DBF more suitable for passive remote sensing applications.  
A DBF is a spatial filter. Each antenna's collected signals are properly weighted and added to get the system output. The system controls digitally the complex weight (phase and amplitude) of each antenna in order to attenuate some directions and reinforce some others, so it creates an interference pattern in the wavefront to steer the beam. The DBF implementation (Equation 1) is straightforward: each antenna's signal is amplified, down-converted, sampled, weighted with a complex coefficient and, finally, added with the other antenna signals to get the DBF signal:  
\[S_{DBF}^{p}(n,\varphi_{0},\theta_{0})=\!\!\!\sum_{m=1}^{M}\!w_{m}\ S_{a_{m}}^{p}(n)\,, \tag{1}\]  
where:  
* \(S_{DBF}^{p}(n,\varphi_{0},\theta_{0})\) is the resulting signal of the digital beamforming steered to (\(\varphi_{0},\theta_{0}\)) at the \(n\) instant for the \(p\) polarization;
* (\(\varphi,\theta\)) are the azimuth and zenith angles of the system;
* \(M\) is the total number of antennas distributed over a plane in a linear or planar array distribution;
* \(w_{m}\) is the complex weight applied to the \(m^{\text{th}}\) antenna to steer the beam to (\(\varphi_{0},\theta_{0}\)) ; and
* \(S_{a_{m}}^{p}(n)\) is the collected signal of the \(m^{\text{th}}\) antenna at the \(n\) instant and for the \(p\) polarization.
## 3 Results and Discussion
### Key Odorants in RZT Identified by GC-O Analysis  
GC-O, which uses the human olfactory system rather than an electronic nose, offers heightened sensitivity and is widely applied in contemporary research to detect a multitude of significant aroma compounds [48; 49; 50]. Compared to OAV analysis, GC-O is capable of evaluating the individual contributions of volatiles to the overall aroma, facilitating the identification of aroma-active compounds within complex mixtures [51].  
By applying GC-O analysis, the sensory panel detected 37 key odorants from RZT samples, as indicated in Table 2. These odorants were categorized into seven groups (Figure 4a) based on their aroma types: floral and sweet (14 volatiles), fruity, citrus, and tropical (7 volatiles), green and minty (7 volatiles), aldehyde (4 volatiles), herbal (3 volatiles), cheesy (1 volatile), and earthy (1 volatile). Figure 4b shows that the floral and sweet category was the most prevalent in RZT, both in terms of the number of compounds and overall aroma intensities, with linalool exhibiting the highest aroma intensity (AI = 3.67). Followed by the green and minty aroma type, heptanal had the highest aroma intensity value (AI = 2.80). In the fruity, citrus, and tropical category, 2-pentylfuran had the highest aroma intensity (AI = 3.00). Among the aldehyde types, octanal and nonanal were found to have the most pronounced aroma intensity (AI = 3.00). Additionally, two unidentified chemicals (unknown 1 and unknown 2) with high aroma intensities that appeared as coconutty and floral and sweet were included in the samples. However, it is presumed that their concentrations in the tea were too low for structural identification. Therefore, the use of more sensitive instruments with lower detection limits, such as comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS), is necessary for further systematic identification analysis.  
Figure 4: The results of GC-O analysis. (**a**) Circle packing plots of the odorants. (**b**) Radar map of the odorants. The \(X\)-axis represents the aroma type and number of corresponding compounds in that type, while the \(Y\)-axis represents the cumulative aroma intensity of different types of odorants.
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 3 Framework Architectures
### Distributed Implementation  
To achieve fully distributed operation, LEAD-RP leverages localized computations for critical parameters:  
Local Density Estimation (\(\rho_{i}\)): Each node \(i\) estimates local density via neighbor discovery beacons:  
\[\rho_{i}=\frac{|\mathcal{N}_{i}|}{\pi R_{\text{init}}^{2}},\quad\mathcal{N}_{ i}=\{j:\|pos_{i}-pos_{j}\|\leq R_{\text{init}}\} \tag{25}\]  
where \(R_{\text{init}}=\sqrt{M^{2}/N}\) is the initial discovery radius.  
Distributed \(R_{i}\) Calculation: Equation (10) is reformulated using local metrics:  
\[R_{i}=\sqrt[4]{\frac{2(\epsilon d_{i}^{2}+E_{PX}-E_{RX})}{\epsilon\rho_{i}\pi}} \tag{26}\]  
where \(\hat{d}_{i}\) is the 1-hop-averaged distance to BS.  
Distributed \(E_{\text{local}}(i)\): It replaces global averaging with neighborhood consensus:  
\[E_{\text{local}}(i)^{(k+1)}=\frac{\sum_{j\in\mathcal{N}_{i}}E_{\text{local}}(j )^{(k)}}{|\mathcal{N}_{i}|} \tag{27}\]  
which was updated via periodic 2-byte messages (converges in 3 iterations, as verified in [13]).