esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wind Tunnel Apparatus, Equipment, and Test Conditions
### Test Conditions  
One of the governing parameters was considered to be the freestream velocity (\(U\) velocity) since it is important to determine the wind tunnel freestream velocity accurately so that the boundary layer has already transited from laminar to turbulent and is marginally in the turbulent state at measurement points. Moreover, it is desirable to be in the range where the skin friction-reducing effect is likely to appear. In this sense, the test condition was determined by one of the most effective parameters for evaluating the skin friction drag: The nondimensional width of the riblet, \(s^{+}\), which is expressed by Equation (4):  
\[s^{+}=\frac{s\cdot u_{\tau}}{v}, \tag{4}\]  
\[u_{\tau}=\sqrt{\frac{\tau_{0}}{\rho}}, \tag{5}\]  
\[\tau_{0}=c_{f}\Big{(}\frac{1}{2}\rho U_{\infty}^{2}\Big{)}, \tag{6}\]  
\[c_{f}=0.059\cdot Re_{x}^{-\frac{1}{5}}, \tag{7}\]  
where \(v\) is the kinematic viscosity, the value of which at the test condition was found to be \(1.57\times 10^{-5}\) m\({}^{2}\)/s assuming that the total and static temperatures of the freestream are the same because the flow was incompressible, and that the measured total temperature of the freestream was 30 \({}^{\circ}\)C. The value of \(s^{+}\) under these conditions was 17, where the riblet's effect would be dominant if the boundary layer had become turbulent [26]. Other variables used to calculate for \(s^{+}\) are the friction velocity (\(u_{\tau}\)), Reynolds stress (\(\tau_{0}\)), and local skin friction coefficient (\(c_{f}\)) expressed in Equations (5)-(7) [27], respectively. Note that the Reynolds stress (\(\tau_{0}\)) is physically equivalent to the skin friction drag. The actual freestream velocity (\(\overline{U}\)) was in the range of 40 to 75 m/s, determined from a pitot-tube measurement carried out separately. In this study, the freestream velocity was fixed at 41.7 m/s or Mach 0.12. The freestream Reynolds number at \(x=600\) mm (\(x\)/\(x_{chont}=2/3\)), where the hot-wire probe and the pitot-rake would be placed, was \(1.6\times 10^{6}\). Accounting for the Reynolds number value and the observation from Figure 3, the boundary layer was considered to be fully turbulent at this measurement location. The nondimensional height of the riblet ranges between 3.40 and 4.25, which were calculated by Equation (4) with the riblet heights described in the previous section.
### Colour Perception and Colour Constancy  
Recent studies have explored how colour influences the perception of real objects, such as translucent materials [7; 8], and have highlighted the nuanced interplay between light, material properties, and colour rendering. Research into the role of colour in the visual-tactile discrimination of materials [9] adds another layer and shows how sensory perception is closely linked to colour. In addition, studies on the effects of LED lighting on colour judgement, especially concerning printing materials [10], are crucial to understanding how artificial lighting, a common element in modern photography, alters colour perception in different environments.  
Colour constancy continues to be a key research topic, especially to understand how human perception adapts to different lighting conditions [11; 12; 13]. This concept is particularly relevant for photographic applications where maintaining consistent colour perception is crucial, such as object recognition, scene understanding, and image reproduction [14; 15].
# Slotted E-Shaped Meta-Material Decoupling Slab for Densely Packed MIMO Antenna Arrays
## 4 Results and Discussion  
The proposed algorithm is compared with previously published research in designing and implementing the array of antennas in the same frequency band. Table 3 shows the measurements results of three parameters: maximum isolation, patch separation, operating bandwidth reduction, design complexity, and cost for the three isolating techniques discussed in this work. A merit was adjusted for each parameter where the best value takes 100% and the worst value take (worst/best) \(\times\) 100. The average percentage was calculated for each technique. The average percentage for the complementary split-ring resonators [16] is the best (88%); however, the design complexity and the cost are high, which makes it an unprovable solution for the design and implementation problem. The second in the rank is both: the complementary split-ring resonator [17] and the metamaterial decoupling slab [18]. In [17], the maximum isolation was low compared to the slotted MTM-DS, and in [15], although the maximum isolation was better than slotted MTM-DS, the average isolation was worse than it. Additionally, the separation between  
Figure 12: Simulated polar plots of the antenna array without DS, with MTM-DS, and with slotted MTM-DS (**a**) Antenna element #1 (**b**) Antenna element #2.  
the patches is 0.66 \(\lambda\)0, which is worse than it in the slotted MTM-DS which is 0.57 \(\lambda\)0. Although the presented technique in this research (slotted MTM-DS) has the third rank in the average percentage merit, it outperforms the other three techniques in [16, 17], and [18] in the average isolation and the parameter value, design complexity, and cost forms a wonderful assembly to design and implement easily.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Constant Bandwidth
#### 5.1.4 Verification of Zero Packet Loss  
We evaluate the packet loss count under burst flow conditions. We use a scenario similar to the previous experiment. The moment when consumers start sending Interests is set to 0 s, 1 s, 3 s, and 5 s. In this experiment, Flows 3 and 4 are burst flows that cause a significant increase in forwarded packets, leading to potential packet loss. We evaluate the packet loss performance of the CCLF and PCON methods on the congested link. The statistics of packet loss count are presented in Figure 13.  
Figure 13 indicates that, in the presence of sudden traffic surges, the use of the PCON method results in the loss of 57 packets within 8 s. In contrast, the CCLF method achieves zero packet loss in network-layer transmission, enabling lossless forwarding.  
This occurs because PCON requires consumers to reduce the sending rate of Interests when detecting congestion. Consumers implement multiple rate adjustments before the relief of congestion, during which packet loss is inevitable. In contrast, the CCLF method instructs routers to use CS storage to buffer burst data flows. Data packets are scheduled according to available bandwidth, ensuring zero packet loss.
## 6 Conclusions  
Timely extraction of meaningful data from a large number of high-resolution images is currently the bottleneck in high-throughput field phenotyping, so developing more rapid image-analysis pipelines is imperative. In the present study, we use an improved RF-based segmentation method to estimate maize leaf coverage in the seedling stage. First, a custom training and validation image dataset captured by using a UAV remote-sensing platform was preprocessed through a standardization procedure. Features based on color and texture were then input into an improved RF classifier as the training target, which we used to generate a set of binary masks for leaf coverage in the images. A comparison with four conventional color-based or DL-based methods shows that the proposed method produces more accurate image-segmentation results (an improvement of 15%-30%) as per the established evaluation system. Two main conclusions are warranted, based on these experimental  
Figure 7: Results of image-segmentation methods for input data with different noise added. The color points in each box represent how the indices vary as different types of noise are added. Panels 1–6 show the results for the different types of noise: (1) Gaussian, (2) Poisson, (3) salt and pepper, (4) Gaussian noise removed by a median filter, (5) Poisson noise removed by a median filter, (6) salt and pepper noise removed by a median filter.  
results: (i) The dataset size is critical for DL methods, and (ii) preprocessing the data to ensure the correct color space improves the results for all methods. Based on the characteristics of ML itself, more abundant training data are crucial to improving the accuracy of the results. In future research, the use of an alternative augmentation method would be the easiest to test without requiring more data collection. In addition, we should add multiple precomputed features to the input data in order to increase the performance of the model.  
[PERSON] and [PERSON] analyzed the data and drafted the article; [PERSON] and [PERSON] designed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] provided the data and figures for field-based phenotyping. All authors gave final approval for publication.  
This study was supported by the National Key Research and Development Program of China(2016 YFD0700303), the Beijing Natural Science Foundation (6182011), the Natural Science Foundation of China (61661136003,41471351).  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Advancing in RGB-D Salient Object Detection: A Survey
## 2 Classification and Comparison of Cross-Modal Fusion Strategies
### Classification of Fusion Strategies  
Cross-modal fusion strategies in RGB-D SOD are crucial for maximizing the complementary advantages of RGB and depth modalities. These strategies can be categorized based on the timing and methods of fusion.  
**Early Fusion**. Early fusion methods [19; 20] integrate features at the input level. They first combine RGB and depth data before or at the early stages of feature extraction, and they then input them into the detection model, as shown in Figure 1a. This approach allows the model to take advantage of the spatial consistency between modalities from the very beginning of the processing pipeline. Early fusion is usually achieved by concatenating feature maps or channels to ensure that all subsequent layers in the network process the combined data. In the initial stage, the information from multiple data sources is utilized to maximize the complementary advantages of each modality.  
**Middle Fusion**[14]. The integration in this strategy typically occurs in the middle stages of the network, usually after the initial feature extraction. This combination takes place in the intermediate layers of the neural network, as shown in Figure 1b. This strategy allows the network to initially process the RGB and depth data independently, utilizing dedicated networks customized for each modality before merging their features. At this stage, some strategies such as feature concatenation, weighted sums, and even more sophisticated methods are used to integrate features [21].  
**Late Fusion**[22]. The RGB and depth features in this strategy undergo complete independent feature extraction and processing and are combined in the final stage, as shown in Figure 1c, For example, as discussed in [PERSON] et al. [23], features of the different stages of the network are integrated so as to efficiently utilize both low-level and high-level semantic information. As seen in the work of [PERSON] et al. [24], late fusion allows each network to independently form an opinion before a final decision being made, thus potentially improving robustness to noise in any single modality.  
**Hybrid Fusion**[25]. This approach combines two or more of the above strategies to fully utilize the advantages of each strategy at different processing stages, integrating features from different stages of the network to efficiently utilize different semantic information and dynamically weighting the importance of each modal feature based on context. As in the work of [PERSON] et al. [26], models may use early fusion to quickly integrate basic color and depth information and then use intermediate attention-guided fusion to refine feature integration based on context-specific cues.  
Figure 1: Typical schemes for RGB-D SOD.
## 1 Introduction  
Amphiphilic composites can be potentially applied in several important processes such as adsorption, emulsification and demulsification of oil-water systems [1; 2; 3] present in different industrial and environmental areas such as pesticides [4], essential oils and flavors [5], pharmaceutical [6], laundries and effluents treatment [7].  
We have recently reported a new amphiphilic magnetic nanostructured composite, based on carbon nanotubes and nanofibers supported on hydrophilic materials, e.g., Al and Si oxides, containing carbon coated magnetic Fe particles [8; 9; 10; 11; 12; 13]. These composites showed remarkable capacity to break emulsions under magnetic field. Microscopic investigation showed that, due to the amphiphilic properties the composite particles interact with oil droplets in emulsions and under a magnetic field, the droplets are dragged and by collision collapse lead to a phase separation.  
In this work, we produced novel magnetic amphiphilic composites by combining two different types of fibers, _i.e._, the inorganic chrysotile and carbon fibers. The chrysotile fibers are impregnated with Fe catalyst to grow carbon nanotubes and nanofibers by CVD (Chemical Vapour Deposition) [10]. The amphiphilic character of these composites is due to the hydrophobic nature of carbon nanotubes and nanofibers combined with the hydrophilic surface of the Mg silicate present in the chrysotile fibers. Chrysotile is natural lamellar phyllosilicate mineral with a fibrous structure [14], with nano to micrometric dimensions [15].The ideal chemical formula of chrysotile is Mg\({}_{3}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\) with tetrahedral layer of SiO\({}_{4}\)[2] and a octahedral layer of Mg(OH)\({}_{2}\). These composites were used to separate oil contamination in emulsified wastewaters from biodiesel production. In general, for each liter of biodiesel approximately three liters of emulsified wastewater are produced and must be treated [16]. These emulsified wastewaters (oil/water) generated during the process of purification and washing of biodiesel are characterized by high concentration of suspended solids and high levels of organic matter, _i.e._, chemical oxygen demand (COD), oils and greases. Therefore, there is a considerable interest to develop efficient processes to separate oil from emulsified biodiesel wastewaters.
# Wind Turbines Optimal Operation at Time Variable Wind Speeds
## 6 Case Study
### PMSG's Optimal Power Determination
Within the time interval between 230 (s) and 570 (s), the wind speed is slowing down while the value of the PMSG's optimal power is getting larger, due to the increasing value of the inertial power, \(P_{inertial}\). The wind turbine's power is lower than the PMSG's power, and the difference between them is represented by the inertial power, which is negative due to the decrease in the PMSG's angular speed.
## 2 Results
### The Self-Assembly Approach
#### 2.1.4 Physical Characterisation  
Scanning electron microscopy was used to examine the surface morphology of both MIPs and NIPs (non-imprinted polymer). No significant difference in surface morphology was observed between MIPs and their corresponding NIPs. MIP surface morphology was unaffected by variations in the FM or T:FM ratio, but differences based on XL were apparent (Supplementary Figures S16 and S17). The TRIM-based MIPs appeared to be more compact, more dense and less porous than the surface of the EGDMA crosslinked polymers (Supplementary Figure S18). This was consistent with previous reports, which indicate that the nature of the crosslinker can affect polymer surface morphology [50]. It is also clear from Figure 3 that using CH\({}_{3}\)CN as the porogen produced a macroporous surface morphology, while CHCl\({}_{3}\) produced smoother, more dense surfaces with fewer visible pores. This most likely resulted from a delayed phase separation from CHCl\({}_{3}\); that is, the growing polymer was more soluble in CHCl\({}_{3}\) than in CH\({}_{3}\)CN, a product of solvent-polymer polarity mismatch [51]. The porogen effect on morphology was evident in swelling experiments, wherein all CHCl\({}_{3}\) polymers displayed an enhanced swelling capacity relative to the equivalent CH\({}_{3}\)CN polymers.
# A Study on the Design of Fog Computing Architecture Using Sensor Networks
## 3 Proposed System
### Communication Control by Power Consumption
On the other hand, for the cycle of 10 rounds, as shown in the bottom of Figure 10, an energy level is transmitted from all devices, and the average power consumption of a small unit of proxy server and a normal sensor device are calculated at the first and the second round. The calculated power consumption is used to estimate an energy level, without making communication at rounds 3, 4, 5, 6, 7, 8 and 9. Then, an energy level is transmitted for synchronization at rounds 10 and 11 and the power consumption is calculated. With the result, estimation is conducted in rounds 12, 13, 14, 15, 16, 17, 18 and 19. In other words, this could save energy accounting for power consumption for eight rounds in the complete cycle of 10 rounds. However, it cannot be said that the cycle of 10 rounds is more effective than the cycle of five rounds in terms of energy consumption because the cycle of 10 rounds could have bigger error tolerance than  
Figure 10: Round-by-Prediction Cycle. Figure 9: Model of Energy Level Estimation. the cycle of five rounds. As the overall network life-time is more important than communication of an individual device in a sensor network, it is important to find the optimized estimation cycle according to the errors. According to the experiments, the cycle of eight rounds shows the best performance.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 2 Related Work  
### Single Indexing Structure  
With the widely utilized of 3D sensors, the volume of Point Cloud data is increasing dramatically. Point Cloud data organization and management have been attracting more and more attention, whose core technology are the spatial index methods. Among these methods, R-tree is a highly balanced tree structure in theory. However, the query efficiency is expected to improve because the overlaps between nodes are ignored, which affect the query efficiency greatly. R+-tree [13] intends to decrease the overlaps and has improved the R-tree to some extent. R*-tree [12] is the best-improved version of R-tree so far, which decreases the number of nodes and the area of overlaps between nodes. Hilbert R-tree [14] utilizes Hilbert-curve to sort R-tree nodes, and improves the storage utilization. Among these spatial index methods, octree and KD-tree are the most frequently used in 3D Point Cloud data organization [15]. In fact, octree performs badly if the data distributes non-uniformly, and KD-Tree would be very deep when data is huge. The single indexing method is no longer satisfies the real-time requirement, and the hybrid indexing method comes into being.
# A Patch/Dipole Hybrid-Mode Antenna for Sub-6 GHz Communication
## 4 Conclusions  
An elliptical patch/dipole antenna operating in a hybrid mode has been designed and investigated to simultaneously enhance the gain and bandwidth in a low profile. The bandwidth is about 67.50%, covering the frequency from 2.75 GHz to 5.45 GHz. An average gain of 8.40 dBi in the operating band of 3.0-5.0 GHz is achieved with a projection size of 0.90 \(\lambda_{0}\)\(\times\) 0.78 \(\lambda_{0}\) and a profile of 0.13 \(\lambda_{0}\). The proposed antenna has a simple structure and low fabrication cost. The antenna may find its applications in the micro base station for Sub-6 GHz communications. The distortion of the radiation pattern resulting from the spurious radiation of the balun [17] will be addressed in the future.  
**Author Contributions:** Formal Analysis, B.H. and J.Z.; Investigation, W.L. and J.H.; Methodology, B.H. and J.Z.; Writing-Original Draft Preparation, B.H. and J.Z.; Writing-Review & Editing, G.Z and F.W.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) under Grant Numbers 61701120 and 61574049.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 2 System Model
Assuming that the channel is invariant over the duration of the OFDM symbol and is in perfect synchronization, the received signal \(\mathbf{y}_{\mathrm{cp}}\) is written as  
\[\mathbf{y}_{\mathrm{cp}}=\mathbf{x}_{\mathrm{cp}}\otimes\mathbf{h}+\mathbf{n} _{\mathrm{cp}}, \tag{2}\]  
where \(\mathbf{h}=[h_{0},h_{1},\cdots,h_{L-1}]^{T}\) is an \(L\times 1\) CIR vector generated by Gaussian random variables, and \(\mathbf{n}_{\mathrm{cp}}\) is a \((N_{\mathrm{fft}}+N_{\mathrm{cp}})\times 1\) vector, which is Additive White Gaussian Noise (AWGN) with zero mean and variance \(\sigma_{n}^{2}\). When sampling for the received signal is completed, \(N_{\mathrm{fft}}\)-point FFT is performed after removing the CP for demodulation. Therefore, the FFT output for the received signal is expressed as  
\[\mathbf{Y}=\mathbf{X}\mathbf{H}+\mathbf{w}, \tag{3}\]  
Figure 1: Comb-type pilot arrangement for OFDM (\(K_{TC}=4\)). Figure 2: Block diagram in OFDM for channel estimation. where \(\mathbf{X}\) is an \(N_{\text{sc}}\times N_{\text{sc}}\) diagonal matrix containing the transmitted signal. \(\mathbf{H}\) is an \(N_{\text{sc}}\times 1\) CFR vector and \(\mathbf{w}\) is an \(N_{\text{sc}}\times 1\) i.i.d. complex Gaussian random vector with zero mean and variance \(\sigma_{n}^{2}\). The \(n\)-th component of CFR \(H_{n}\) is expressed as [23]  
\[ \begin{split} H_{n}=FFT\{\mathbf{h}\}&=\sum_{l=0}^{ L-1}h_{l}e^{-2\pi ln/N_{\text{th}}},\\ 0\leq n&\leq N_{\text{fft}}-1.\end{split}  \tag{4}\]  
Since the transmission signal is concentrated on the pilot symbol-based method through the reference signal, it is written with the received signal and CFR for the subcarrier containing the pilot as  
\[\mathbf{Z}=\mathbf{SG}+\bar{\mathbf{w}}, \tag{5}\]  
where \(\mathbf{Z}\) and \(\mathbf{G}\) are \(N\times 1\) vectors that are CFR and received signals for pilot subcarriers, respectively. \(\mathbf{S}\) is the \(N\times N\) diagonal matrix of the reference signal according to \(\mathbf{Z}\), and \(\bar{\mathbf{w}}\) represents \(N\times 1\) i.i.d. complex Gaussian random vectors with zero mean and variance \(\sigma_{n}^{2}\). Consequently, the channel estimation method is performed through Equation (5).