esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 4 Discussion
As the influence of weather extremes and farming practices on crop yields varied across phenological stages, such capability facilitated more precise and targeted stage-specific management practices (e.g., fertilizer and harvest scheduling), as well as the improved characterization of yield gap or loss under varying weather conditions at the field to sub-field levels.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 4 Modelling and Planning
### Optimization
#### 4.2.3 Charging Service Provider Oriented Planning  
* CS Investment Costs: present in most of the cases.
* Installation Costs: in [52], the authors vary whether the CS is located at an existing gas station. Installation is found to include raw material, an added line to connect CSs to the power grid, etc.
* Operation and Maintenance: this can also include staff salaries, as in [59; 63; 65; 132].
* Land Costs: were recognized as being an important parameter in [51], especially in sizing and locating parking lots for EVs [27; 28].
* Penalty Costs: the cost of applying demand response programs, as in [24].  
The authors of [83] do not provide an AS strategy, but their algorithm can serve as an investment decision tool to avoid stranded assets and an oversized charging system (low average rate of use). However, insufficient infrastructure investments lead to poor voltage quality. The approach draws on a Monte Carlo simulation to estimate EV charging hosting capacity (defined as the number of simultaneous customers who can charge at a power outlet at any given time) for low voltage networks in Sweden.
## 4 Performance Prediction  
Although no assumptions on the homogeneity of the scene are required some theoretical limitations for the proposed technique have to be considered:  
* sufficient SNR: the technique is affected by low SNR (Signal-to-Noise Ratio). Without sufficient signal power from the scene, the antenna pattern cannot be reliably estimated. This is true for all SAR acquisitions
* scene length: to gain a stable and defined range profile, data of several range lines are combined. Depending on the number of samples used, the uncertainty of the estimate will be reduced. Hence, the scene length in azimuth is an important parameter for the antenna pointing estimation.  
Both aspects will be analyzed in the following two sections.
## 2 Forward Model Development
### Remote Sensing Reflectance Formulation  
Nearly all ocean RT algorithm work over the last several decades has used reflectance defined as \(L_{u}/E_{d}\) instead of \(L_{u}/E_{od}\). [PERSON] [28], however, pointed out, as is evident from Equation (1), \(L_{u}/E_{od}\) is most closely aligned with RT theory. The irradiance parameter \(E_{od}\) is also less dependent on solar zenith angle than \(E_{d}\). Furthermore, sensor technology has been available to measure \(E_{od}\), historically from companies Biospherical (www.biospherical.com), Satlantic (www.satalantic.com), and Trios (www.trio.de). Nonetheless, since nearly all field data over the last several decades has focused on \(E_{d}\), any testing and validation of the RT model described here requires modification in terms of \(E_{d}\). Substituting \(E_{d}/E_{od}=\overline{\mu}_{d}\) (the average cosine of downwelling radiance, just below the air-water interface) into Equation (7) gives:  
\[r_{rs}\cong\frac{1}{\overline{\mu}_{d}}\frac{\beta(\psi)}{b_{b}}/\left[\frac{ a}{b_{b}}\left(1-\cos(\theta_{v})\Psi_{K_{La}}\overline{\mu}_{\infty}{}^{-1} \right)+f_{L}\left(1-\widetilde{b}_{b}{}^{-1}\right)+\widetilde{b}_{b}{}^{-1} \right], \tag{12}\]  
where \(r_{rs}\) is the classically known remote sensing reflectance just below the water surface. Full dependencies of variables not shown for clarity.
## 3 Surface Plasmons in Photonic Integrated Circuits
### Plasmonic Nano-Tweezers and Nano-Manipulators
#### 3.2.1 Basis on Optical Tweezers  
Optical tweezers are made of a focused optical beam, which is diffraction limited in the case of a free space beam, which generates two forces on an object (Figure 7, left): the gradient force and the pressure (or diffusion or absorption) force. The former pulls the object towards the highest optical density, usually transversally to the optical beam and up to its center; the latter pushes the object along the beam propagation axis and direction. These forces are expressed as follows:  
\[\text{Gradient force}:\;\;\;F_{g}=\frac{1}{4}\,\alpha_{Re}\
abla|E|^{2}\]  
\[\text{Pressure force}:\;\;F_{s}=-\frac{1}{2}\,\alpha_{Im}\text{Im}\{E.\
abla( E^{*})\}\]  
where \(\alpha\) is the object polarizability: \(\alpha=\alpha_{Re}+j\alpha_{Im}\).  
The gradient force is thus the trapping force. It is proportional to the optical power density (or the electric field square modulus) gradient and to the real part of the object polarizability. The trapping strength is represented by its stiffness and defined by the derivative of the exerted forces. Negative (positive) stiffness corresponds to an attractive (repulsive) force.  
Figure 6: (a) Sketch of a sensor exploiting LSPRs: a chain of gold nanocylinders is integrated on top of an SOI waveguide within a microfluidic circuit. (b) Transmittance calculated for different equivalent thicknesses t of molecules grafted onto the metal pads [95]. Specifically, t represents the thickness of a layer having an index equal to n covering each NP. This result has been validated experimentally for t = 2 nm.  
The trap characteristics can be evaluated as follows:  
* If the trapped object does not modify the local electromagnetic field, the optical density map is calculated first, and then the trap characteristics are deduced from \(\mathbf{F}_{\mathrm{g}}\) and \(\mathbf{F}_{\mathrm{s}}\) calculations for different object positions.
* If the presence of the object induces a non-negligible perturbation, the optical field must be calculated, and the Maxwell stress tensor must be fully integrated for all the possible positions of the object.  
The optimal trap, in terms of temporal and spatial stability, is obtained for object sizes slightly larger than that of the optical beam (or than the optical power density high variations area) to benefit from the highest gradients. Stable trapping criteria (from [PERSON] [96]) state that the potential well must be ten times higher than the thermal energy \(\mathrm{k_{B}T_{0}}\). The total optical power can thus be used to increase the stability as well as the stiffness of a trap.  
Optical beams enable the trapping of objects around 1 \(\upmu\)m in size with a few mW of power, compatible with biological objects whose index is around 1.3 (water). To trap smaller or less dense objects (for air or water quality analysis), more abrupt optical density gradients are required, such as those generated around plasmonic structures.
# Discoloration Mechanisms of Natural Rubber and Its Control
## 6 Methods to Control the Discoloration in Natural Rubber
### Chemical Treatments
#### 6.4.3 Addition of Strong Reducing Agent  
NR latex discoloration can also be prevented by inhibiting either enzymatic activity or eliminating the substrates concerned by chemical treatment, or combining both [45]. Sodium metabisulphite (SMS) is a stronger reducing agent than PPO, an enzyme accelerator for forming color substances in NR. Thus, SMS can inhibit the formation of color substances in NR. In China, the production of standard light-colored rubber usually uses SMS as a reducing agent. However, it has been confirmed that adding SMS only slightly reduced the color of the rubber and showed an insignificant effect on the decoloring [55].  
Discoloration of crepe rubber is normally inhibited or prevented by using chemicals, e.g., oxalic acid, to retard enzymatic discoloration [57] and sulfites for non-enzyme browning [58]. Slim rubber is a by-product of field latex through centrifugal production of concentrated NR latex, and the color is black and brown after sulfuric acid treatment and solidification. Oxalic acid can be used to bleach rubber and make the color of the rubber lighter [59]. The initial plasticity value (P\({}_{0}\)), plasticity retention index (PRI), mechanical properties, and aging properties were improved when the optimum amount of oxalic acid was 2%. Oxalate has a strong reductive property, which can prevent the oxidation and discoloration of PPO and chelate with copper ion, which affects the aging resistance, thus improving the aging resistance of skim rubber. Hence, the clarity of the final product is achieved mainly by fractionation and/or bleaching in the production of crepe rubber from NR latex.
This paper presents a radio link model for underground mines, where the electromagnetic properties of mine walls and other lossy objects have been incorporated together \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specifications** & **DASH7** & **LoRa** & **ZigBee** \\ \hline Frequency & 433.04–434.79 MHz & 866–868 MHz & 2.402–2.482 GHz \\ Discrete channels & 16 & 16 & 16 \\ Modulation & FSK or GFSK & GFSK & QPSK \\ Data Rate & 55.4 Kbps & 200 Kbps & 250 Kbps \\ Max potential data rate & 200 Kbps & 1 Mbps & 500 Kbps \\ Range (0 dBm) & 140 m & 115 m & 75 m \\ Water/Concrete Wall penetration & Good & Fair & Poor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of different wireless technologies. with various underground mine structures. The wave propagation and polarization issues related to optimal node position, coverage range, and antenna orientation have been formulated and addressed in the proposed radio link model. After conducting continuous field experiments over a duration of 3 weeks in the Khewara salt mine, we confirmed that the sensor node can be deployed on either the rooftop or near the side wall of the mine using an Omni directional monopole antenna. A standard transmit power of 12.6 dBm was used in all the experiments. In Section 2, we present the proposed radio propagation model for the tunnel and room-pillar environments of an underground mine. In Section 3, we describe the experimental setup in the salt mine, present the validation of the proposed model, and discuss the results, along with an outline of the theoretical approach used for determining node position and node separation in the mine environment. Section 4 presents an application-based evaluation of the proposed radio link model in a salt mine, and Section 5 concludes the paper.
# Optoelectronic Devices Based on Atomically Thin Transition Metal Dichalcogenides
## 1 Introduction  
The rediscovery of graphene by [PERSON] and [PERSON] [1] has led to renewed interest in layered materials. Beyond graphene, hundreds of other layered materials exist that can be thinned down into monolayers [2]. One important family of two-dimensional (2D) materials is the family of layered transition metal dichalcogenides (TMDs). In their bulk form, TMDs have been studied for decades [3; 4], but the properties of TMD monolayers differ significantly from their bulk characteristics. For example, a layer-dependent indirect-to-direct bandgap transition is commonly observed in TMD semiconductors, which results in bright light emission from monolayers, whereas emission from bulk crystals is practically absent. This and other interesting properties have recently fueled intensive research efforts on TMD-based optoelectronic devices, which have already led to several promising new device concepts.
## 3 Case Study
### Model Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Modal** & **I** & **II** & **III** & **IV** & **Modal** & **I** & **II** & **III** & **IV** \\ \hline
1 & 3.4447 & 3.1309 & 3.0199 & 2.9709 & 11 & 9.3860 & 8.9263 & 7.9325 & 6.6295 \\ \hline
2 & 4.0645 & 3.8043 & 3.6285 & 3.5753 & 12 & 9.5225 & 8.9739 & 8.2783 & 6.9863 \\ \hline
3 & 5.1442 & 4.8549 & 4.6347 & 4.3085 & 13 & 9.7931 & 9.1956 & 9.0917 & 7.4745 \\ \hline
4 & 5.9358 & 5.6521 & 5.6255 & 4.5607 & 14 & 10.448 & 10.102 & 9.1782 & 7.5211 \\ \hline
5 & 6.1389 & 5.7804 & 5.8691 & 5.2231 & 15 & 10.847 & 10.252 & 9.2030 & 7.5768 \\ \hline
6 & 6.7329 & 6.5518 & 5.8905 & 5.3010 & 16 & 11.251 & 10.794 & 9.5273 & 7.8364 \\ \hline
7 & 7.1386 & 6.7042 & 6.6500 & 5.5621 & 17 & 12.170 & 11.418 & 10.063 & 8.0440 \\ \hline
8 & 8.1281 & 7.5553 & 6.7196 & 5.8306 & 18 & 12.233 & 11.460 & 10.385 & 8.5419 \\ \hline
9 & 8.3435 & 7.9089 & 7.6961 & 6.2930 & 19 & 12.726 & 12.194 & 10.762 & 8.6655 \\ \hline
10 & 8.4638 & 8.1444 & 7.7990 & 6.5597 & 20 & 13.198 & 12.584 & 10.816 & 8.6964 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different natural frequencies of the arch dam under different reservoir water models (Hz) (water level 119 m). Figure 6: The first 5 modal order shapes of different reservoir water models: (**a**) model I, (**b**) model II, (**c**) model III, and (**d**) model IV. for the arch dam, it is essential to consider the influence of the compressibility of reservoir water. As is well known, the application of the FSCM in various hydraulic structures has been mature. Many scholars, such as reference [61], have demonstrated that using FSCM is more accurate than using the Westergaard added mass model on dams. At the same time, the dynamic characteristics calculated based on the FSCM in this paper are consistent with the conclusions of references [39; 62].
# Parallel Image Completion with Edge and Color Map
## 3 Materials and Methods
### Image Completion Problem Formulation
\ C=(1-M)\odot C_{gt}, \tag{4}\]  
where \(\mathbb{C}\), \(\mathbb{C}_{comp}\) and \(\mathbb{C}_{gt}\) are the distributions of the corrupted color maps, the completed color maps and the ground truth color maps, \(G_{2}:\mathbb{C}\mapsto\mathbb{C}_{comp}\) is a mapping function from the corrupted color maps to the completed color maps, \(D_{2}(\cdot)\) is the distance between the completed color map and the ground truth color map. Therefore, the image completion problem can be formulated as:  
\[I^{*}_{comp}=\arg\min_{I_{comp}\in\mathbb{I}_{comp}}\mathbb{E}_{ \begin{subarray}{c}I\sim I\\ gt^{*}\to\mathbb{B}gt\end{subarray} }\left[D_{3}(G_{3}(I,B^{*}_{comp},C^{*}_{ comp}),I_{gt})\right],\quad s.t.\ I=(1-M)\odot I_{gt}, \tag{5}\]  
where \(G_{3}:(\mathbb{I};\mathbb{B}_{comp},\mathbb{C}_{comp})\mapsto\mathbb{I}_{comp}\) is the mapping function from the corrupted images to the completed images with the edge and color priors, and \(D_{3}(\cdot)\) is the distance between the completed image and the ground truth image. Thus, the image completion problem can be decoupled into three relatively easy sub-problems that can be modeled using the GANs. The mapping functions of \(G_{1}\), \(G_{2}\) and \(G_{3}\) can be constructed as the generators and the distance measurements of \(D_{1}\), \(D_{2}\) and \(D_{3}\) can be constructed as the discriminators in the GANs. The edge and color priors are learned from massive amounts of training data firstly, and then guide the following up image completion network to generate more realistic results. Inspired by this procedure, we design PIC-EC framework. As the name suggests, the parallel edge and color map networks are responsible for the edge and color map generation, respectively. In addition, the image completion network can convert these edge and color semantic information to a photorealistic image.
## 4 Results
### Defects Detection  
The results presented in Table 5 indicate the performance of different defect detection models in both tests sets, comprising 289 and 619 defects, respectively.  
The results demonstrate the superior performance of YOLO models compared to RetinaNet. RetinaNet achieved mAP@.5 below 30% in both sets, indicative of its difficulties to correctly identify defects. The best performing approaches are the YOLOv5 models, which yielded mAP@.5 scores of around 97% and 56% in test sets 1 and 2, respectively. The two variants of YOLOv8 achieved comparable results, but fell short when compared to alternative YOLOv5 models. It is worth noting that all YOLO models were trained using identical settings and hyperparameters. This difference in performance can potentially be attributed to the initial selection of well-suited anchor boxes using genetic algorithms, a step employed specifically in YOLOv5.  
The discrepancy observed in the results between test set 1 and test set 2 can be attributed to data drift, due to the inclusion of images acquired at different time frames and under slightly varied conditions in the second set. These differences highlight the limited robustness of the model in accommodating minor variations. Additionally, inconsistencies in the ground truth annotations could also contribute to the contrasting outcomes. Therefore, it is evident that the models' performance is impacted by the variations in image acquisition conditions as well as the consistency of the ground truth annotations.  
Overall, considering the computational cost of the models, YOLOv5m appears to be the most effective model, as it delivers the best trade-off between performance and size.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 2 Background Knowledge
### ECDSA/EdDSA Hierarchy  
The hierarchy of the ECDSA/EdDSA combinational design can be illustrated as Figure 2. In this illustration, the operations in the higher position are performed based on the operations in the lower position. Figure 2 also reveals that there are three different layers of abstraction. The first layer is the DSA algorithm, where the processes of key-generation, signature-generation, and verification of a specific DSA are specified. The control flow for a DSA, such as ECDSA or EdDSA, is implemented in this layer. The second layer is the algorithms for the elliptic curve. Depending on the selected curve, the additions and subtractions between two points in this curve are defined. The point multiplication can be used for different curves. The third layer describes the methods of modular operations, such as modular multiplication, inversion, addition, and subtraction. An additional modular reduction may be required for this layer. Finally, the base of all layers is binary addition, subtraction, and comparison. Among these layers, the implementation for point multiplication, modular multiplication, modular inversion, modular addition, modular subtraction, and binary operation can be reused. In Figure 2, the gray boxes illustrate the functions that could be shared among different elliptic curves.
## 4 Localized RCC  
### Diagnosis  
MiRNAs may support the diagnosis of localized RCC and improve the ability to discriminate cancer subtypes from benign lesions, possibly avoiding unnecessary treatment of undefined neoplasms (Table 1). [PERSON] et al. compared miRNA expression profiles between 11 renal tumors and identified 48 miRNAs specific for ccRCC (i.e., miR-27a, miR-221, miR-34a, miR-103, miR143, miR-15a, miR-16, miR-17-5p, and miR-24), 9 specific miRNAs for non-tumor samples, and 33 detected in both groups with different degrees of expression [29]. [PERSON] et al. suggested that miR-210 and miR-221 could be employed to distinguish among clear cell/papillary and oncocytoma/chromophobe subtypes, miR-200c and miR-139-5p to differentiate oncocytoma from chromophore and miR-31/miR-126 to discriminate clear cells from papillary tumors, with a 93% concordance with pathological specimens [30]. [PERSON] et al. found that miR-424 and miR-203 are overexpressed in clear-cell RCC and chromophore, and under-expressed in papillary RCC and oncocytoma, while miR-20, miR-200b, miR-197, miR-320, and miR-186 are consistently under-expressed in oncocytoma and overexpressed in chromophore RCC [31]. [PERSON] et al. isolated 35 miRNAsfrom 28 samples of ccRCC and observed that the downregulation of miR-135a, miR-136, miR-154, miR-337, miR-377 and miR-411 could be used as indirect proof for chromosome deletions linked to ccRCC [32]. Finally, [PERSON] et al. analyzed miRNAs in plasma liquid biopsy from patients with ccRCC before and after surgery, finding that miR-25-3p, miR-126-5p, miR-200c-3p, and miR301a-3p decrease after surgery, while miR-1293 increases, suggesting a role of this miRNA as a biomarker to promptly identify recurrence [11].
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 6 Conclusions  
In this paper, we propose a new centralized clustering algorithm in which the base station does not have energy limitations, as an important component of the network. The main objective is to reduce the energy consumption and enhance the performance of the packet loss rate. To reduce energy consumption, we use a multi-factor decision-making (energy, mobility, distance to the centre, and the length of data queues) and two familiar methods, including the Analytical Hierarchy Process (AHP) and the Cluster Splitting Process (CSP). These two methods perform various tasks, including selecting cluster heads, computing the optimum number of cluster heads, identifying the optimal path between cluster heads, and scheduling for sending data from nodes to the cluster heads. We compare our proposed method with the Base station Controlled Dynamic Clustering Protocol (BCDCP) in terms of increasing the network lifetime. The energy reduction is almost 5% more than the BCDCP method, and the packet loss rate in our proposed method is almost 25% lower than in the BCDCP method. Consequently, the results show that the proposed method in improving the network lifetime has better performance than the BCDCP method.  
All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Ratio Features from PPG Signals  
We used AC/DCs of the PPG signal as features and their ratios (ratios of different wavelength AC/DCs) also as features for effective blood glucose estimation.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 4 Results and Discussion
### Moderator Analysis for VR-Supported Online Learning
#### 4.2.3 Instructional Design in VR Studies  
The descriptive analysis showed that experiential learning was the most frequently used pedagogical approach (32.4%), while inquiry-based learning was the least used (10.8%). We identified that pedagogy had a significant moderating effect on VR-enhanced online learning (Q\({}_{B}\) = 12.830, \(p\) < 0.05). On average, experiential learning was the most effective pedagogy (\(g\) = 1.704), followed by collaborative learning (\(g\) = 1.098), game-based learning (\(g\) = 0.960), direct instruction (\(g\) = 0.370), and inquiry-based learning (\(g\) = \(-\)0.239). These findings emphasize the benefits of experiential learning strategies while offering valuable insights for designing effective online learning activities using VR. This impressive performance is not accidental, but rather, a result of the unique advantages that VR offers. VR can replicate real-world scenes, including classrooms and museums, as well as simulate situations that are difficult to achieve in reality [83], such as complex tasks like controlling river pollution. The use of experiential learning in VR-enabled online learning is thus highly effective.  
We also investigated the moderating effect of learning outcome type. The most frequently studied learning outcome type was the affective outcome (35.1%), followed by knowledge (27%), skills (27%), and behavior (10.8%). The \(Q\) statistics indicated that there were no significant differences in the effect sizes for different types of learning outcome (\(Q_{B}\) = 2.888, \(p\) = 0.409). Interestingly, despite this, it can be seen that behavior yielded the largest effect (\(g\) = 2.348), perhaps because VR can simulate real scenes, which allows learners to practice and improve their behavior patterns in a virtual environment. This can lead to observable changes in behavioral outcomes, such as the ability to perform experiments [31].