esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fully Automated SHS Detection and Quantification
True negatives (TN) were not informative, because any non-SHS features can be regarded as TN. SHS boundary segmentation accuracy was assessed using the Dice coefficient, defined as the intersection over the union between the automatic and manual segmentation (2 \(\times\) (Auto \(\cap\) Manual)/(Auto + Manual)), which is in the range of 0-1 (Figure 2A). Figure 1: Fully automated algorithm for subsurface hyposcattering structure (SHS) detection and quantification. Inset: zoomed-in view. Scale bars: 1 mm.
###### Abstract  
This article presents the design and evaluation of a compact-sized antenna targeting heterogenous applications working in the C-band, 5G-sub-6 GHz, and the ISM band. The antenna offers frequency reconfigurability along with multi-operational modes ranging from wideband to dual-band and tri-band. A compact-sized antenna is designed initially to cover a broad bandwidth that ranges from 4 GHz to 7 GHz. Afterwards, various multiband antennas are formed by loading various stubs. Finally, the wideband antenna along with multi-stub loaded antennas are combined to form a single antenna. Furthermore, PIN diodes are loaded between the main radiator and stubs to activate the stubs on demand, which consequently generates various operational modes. The last stage of the design is optimization, which helps in achieving the desired bandwidths. The optimized antenna works in the wideband mode covering the C-band, Wi-Fi 6E, and the ISM band. Meanwhile, the multiband modes offer the additional coverage of the LTE, LTE 4G, ISM lower band, and GSM band. The various performance parameters are studied and compared with measured results to show the performance stability of the proposed reconfigurable antenna. In addition, an in-depth literature review along with comparison with proposed antenna is performed to show its potential for targeted applications. The utilization of FR4 as a substrate of the antenna along with its compact size of 15 mm \(\times\) 20 mm while having multiband and multi-mode frequency reconfigurability makes it a strong candidate for present as well as for future smart devices and electronics.  
Keywords:compact antenna; frequency reconfigurability; 5G-sub-6 GHz; ISM band; smart electronics +
Footnote †: journal: _micromachines_
## 2 Results and Discussion
### Preparation of Protein Hydrolysate from Hairtail (T. japonicus) Muscle (HTP)
#### 2.2.1 Fractionation of HTP Using Membrane Ultrafiltration  
Membrane ultrafiltration is usually used to enrich the particle sizes of functional molecules and is widely applied in food and beverage processing, biotechnological applications, and pharmaceutical industry [6; 7]. Consequently, HTP was divided into four fractions including HTP-I (<1 kDa), HTP-II (1-3 kDa), HTP-III (3-5 kDa), and HTP-IV (>5 kDa) by ultrafiltration with a molecular weight (MW) Cut Off (MWCO) membrane of 1, 3, and 5 kDa. At the concentration of 6.0 mg protein/mL, DPPH\(\bullet\) and HO\(\bullet\) scavenging activities of HTP-I were 62.13 \(\pm\) 1.97% and 78.6 \(\pm\) 1.74%, respectively, which were significantly stronger than those of HTP, HTP-II, HTP-III, and HTP-IV (\(p\) < 0.05) (Figure 3). The activities  
Figure 1: DPPH\(\bullet\) (**A**) and HO\(\bullet\) (**B**) scavenging activities of different enzymatic hydrolysates from hairtail (_T. japonicus_) muscle at the concentration of 6.0 mg protein/mL. All data are presented as the mean \(\pm\) SD of triplicate results. \({}^{a-d}\) Values with same letters indicate no significant difference of different sample at same concentrations (\(p\) > 0.05).  
Figure 2: Effect of Papain, Alcalase, and Papain + Alcalase on DPPH\(\bullet\) and HO\(\bullet\) scavenging activities of protein hydrolysates from hairtail (_T. japonicus_) muscle at the concentration of 6.0 mg protein/mL. All data are presented as the mean \(\pm\) SD of triplicate results. \({}^{a-c}\) Values with same letters indicate no significant difference of different sample at same concentrations (\(p\) > 0.05).  
of protein hydrolysates and their fractions were affected by the multiple peptides with different chain length and amino acid composition. [PERSON] et al. [7] and [PERSON] et al. [29] reported that MWs of hydrolysate play an important factor in their bioactivities, and hydrolysate fractions with smaller MW showed stronger antioxidant activity than those of larger MW hydrolysates. In the report, HTP-I with short chain peptides showed stronger radical scavenging activity, and this finding was in line with previous reports that the antioxidant abilities of protein hydrolysates were negatively correlated with their average MW [29; 30]. Therefore, HTP-I was selected for the subsequent chromatographic separation.
## 2 Related Work  
### Handcrafted Feature Extraction Methods  
The [PERSON] algorithm (1988) [8] is based on [PERSON]'s corner detection and considers the direction of the intensity change making the distinguishing between corners and edges more accurate. While the [PERSON] method was rotation-invariant, it was not scale-invariant. The Scale-Invariant Feature Transform (SIFT, 2004) [9] has been scaling tolerant by changing the window size depending on the scaling in the image. In 2005 the Features from Accelerated Segment Test (FAST) [10] was proposed to deal with real-time applications by applying a fast feature detection test. In this test, a feature is detected at a pixel \(p\) if the pixels at the cardinal points of a 16-pixel radius circle with center the pixel\(p\) have all intensities above or below the intensity of \(p\). The Speeded-Up Robust Feature (SURF, 2006) [11], as its name implies, is the speeded-up (fast) version of SIFT. Here the Laplacian of Gaussian filters is approximated with BisFilters, which can be calculated for different scales simultaneously. SIFT and SURF have the disadvantage of large feature vectors, which are detrimental in terms of memory. This problem is solved by the Binary Robust Independent Elementary Features (BRIEF, 2010) [12] method, which gives binary strings by comparing the intensities of pixel pairs. This method does not detect the features but needs to be preceded by a detection algorithm. Oriented Fast and Rotated Binary Robust Independent Elementary Features (ORB, 2011) [13] is a combination of the FAST, SIFT, and SURF algorithms, freely available from the OpenCV Labs. The KAZE features (2012) [14] confront Gaussian blurring by detecting and describing two-dimensional features in nonlinear scale space. Additive operator splitting techniques result in noise reduction while simultaneously maintaining object boundaries. In addition, Local Binary Patterns (LBP, 1996) [15] and Histogram of Oriented Gradients (HOG, 2005) [16] are two practical and widely used algorithms that we also employ in this research and analyze extensively in next the section. Studies comparing the above methods have also been performed [17].
# Aerodynamic Study of the NASA's X-43A Hypersonic Aircraft
## 4 Results
### Comparison Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & \(AoA\) (\({}^{\circ}\)) & \(C_{L}\) & \(C_{D}\) & \(E\) & \(C_{MC_{CG}}\) \\ \hline \(C_{M_{CG}}=0\) & \(-3.89\) & \(-0.0471\) & \(0.0152\) & \(-3.1\) & \(0\) \\ VFB & \(5.21\) & \(0.0899\) & \(0.0271\) & \(3.32\) & \(0.026\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Aerodynamic parameters for the zero pitching moment (\(C_{M_{CG}}=0\)) and Vertical Force Balance (VFB) conditions of the CFD case with the scramjet neglected. In order to further validate the CFD model, the results of the CFD case 1 presented in Section 4.5 were compared by those presented in [2,3,8], where full three-dimensional CFD models and experimental tests at Mach number 6 were analysed. The parameters involved in the comparison study were the lift and drag coefficients for \(AoA=2^{\circ}\), \(AoA=4^{\circ}\), \(AoA=8^{\circ}\) and \(AoA=10^{\circ}\). It is particularly remarkable to see that the experimental data presented in [2,8] is almost identical. In each of these two studies, there is also a good agreement between the experimental and CFD results. In addition to that, it is seen that the drag coefficients presented in the current paper are slightly smaller than those presented in the other studies. This fact is understandable since the wings, stabilizers and the lateral side of the aircraft are not considered in the present approach. It is also remarkable to see that the lift coefficients of the present paper are very close to the experimental and simulated ones obtained by [2,8]. For the drag coefficient, a maximum deviation of 37% between the experimental results from [2] and the results from the 2D CFD case 1 is obtained for an angle of attack of \(2^{\circ}\). For the rest of the angles of attack evaluated, the \(C_{D}\) average deviation is 17%. The maximum deviation for the lift coefficient is observed to be 20% for an angle of attack of \(2^{\circ}\). The average \(C_{L}\) deviation for the other angles of attack is 10%. Perhaps one positive conclusion extracted from the comparisons presented is that simple 2D simulations are capable of showing a trend in the same direction as full 3D experimental tests and CFD approaches. Furthermore, they show that the main body of the aircraft is responsible for most of the lift and drag exerted on the body.
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 2 Related Work
[PERSON] and [PERSON] [20] discussed the challenging task of multi-class weather classification from a single image in outdoor computer vision applications. [PERSON] et al. [21] achieved quite precise weather classification using only multi-echo LiDAR sensors. The point cloud is first converted into a grid matrix, and the presence of rain or fog can be easily observed through the appearance of secondary echoes on objects. [PERSON] et al. [22] estimated road friction coefficients by constructing a vision-based Deep Neural Network (DNN), as surfaces withdifferent frictional force reductions, such as dry, slippery, muddy, and icy, can essentially be identified as clear, rainy, snowy, and icy weather, respectively. Their algorithm not only detects wet conditions but also classifies combinations of wet conditions and road types. [PERSON] and [PERSON] [23] proposed a fast and robust multi-resolution scan-matching algorithm for local vehicle positioning in autonomous driving. The algorithm solves the weather problem of vehicle positioning in autonomous driving by introducing Gaussian mixture maps, multi-resolution inference, and global optimization search, improving robustness under adverse conditions. Almost all of the aforementioned methods fundamentally rely on specific types of sensors, such as cameras, millimeter-wave radars, and LiDAR. These sensors primarily collect high-dimensional measurement data in image and video formats, whose processing is not only cumbersome but also significantly energy-consuming. Moreover, meteorological conditions have a direct impact on the environmental state, thereby weakening the perception capabilities of sensors in autonomous driving systems and significantly increasing the difficulty of completing key perception tasks such as target detection and identification. To overcome the limitations of traditional sensors in adverse weather conditions, a novel deep learning-based method for rainy environment identification is proposed in this paper. This method is designed for autonomous vehicles and allows for environmental perception without relying on specific sensors by utilizing the wireless channel characteristics shared among vehicles in the vehicular network. The core of this method is the Channel State Information (CSI), which is the most accurate indicator of wireless channel characteristics [24]. We use the CSI values estimated from CAM exchanged in Vehicle to Vehicle (V2V) communication as input features for the proposed convolutional neural network model. After training, the model can reliably identify the surrounding environment based on the channel characteristics of different rainy environments. Thus, intelligent vehicles can adjust their driving parameters according to the identified environmental information to adapt to rainy conditions, thereby ensuring driving safety.
## 5 Discussion
Similarly, OMP (orthogonal matching pursuit) achieved 73.2% accuracy with 100 features, indicating its ability to select an efficient subset of features. However, the group LASSO method, which selects groups of correlated features, had a lower classification rate of 69% for 142 features. The random projections method performed the worst, with only 59.5% accuracy, confirming that a random feature selection approach can drastically reduce model performance.
## 3 Materials and Methods
### Data Source and Processing  
The experimental data involved in this study primarily focus on two components: HF and HQ. For HF, the study used data on land use and land cover change, population density (POP), nighttime light (NL), and GDP density (GDP). The land use and land cover change data came from the Resource and Environment Science and Data Center (www.resdc.cn, accessed on 22 January 2025). POP data were obtained from the WorldPop website ([[https://www.worldpop.org/](https://www.worldpop.org/)]([https://www.worldpop.org/](https://www.worldpop.org/)), accessed on 22 January 2025). NL data were acquired from the NOAA National Centers for Environmental Information ([[https://www.ncei.noaa.gov/](https://www.ncei.noaa.gov/)]([https://www.ncei.noaa.gov/](https://www.ncei.noaa.gov/)), accessed on 22 January 2025). GDP data originated from the study by [PERSON] [28].  
For HQ, the study used three Moderate Resolution Imaging Spectroradiometer (MODIS) datasets to calculate the RSEI. MODIS, developed by the National Aeronautics and Space Administration, is a spaceborne remote sensing sensor ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)), accessed on 26 January 2025) that supports large-scale ecological monitoring [29]. Specifically, MOD13A1 calculated the normalized difference vegetation index (NDVI), MOD11A2 estimated land surface temperature (LST), and MOD09A1 derived wetness (WET) and the normalized difference bare soil index (NDBSI). To ensure comparability and temporal consistency, high-quality MODIS images in 2000, 2010, and 2020 were selected. To eliminate interference from large water bodies in the wetness component, the study applied a normalized water index for masking. During data preprocessing, all data were standardized into a 1 km grid using the Krasovsky_1940_Albers projection system.
## 3 Results and Discussions
Specifically, 21 features were identified both for PET, and PET co-registered with FLAIR while 19 for PET co-registered with T1. Although the number of uncorrelated features is similar (in the case of PET and PET/T1 is identical), the same features were not selected. In particular, nine features were different between PET and PET co-registered with FLAIR, while eight were different between PET and PET co-registered with T1. Approximately 40% of the uncorrelated features were different within the three image datasets. The different features from each other group are detailed in Tables 2 and 3. \begin{table}
\begin{tabular}{l l} \hline \hline
**PET** & **PET FLAIR Co-Registration** \\ \hline shape-MinorAxisLength & firstorder-90 Percentile \\ \hline shape-Sphericity & glcm-Autocorrelation \\ \hline firstorder-10 Percentile & glcm-ClusterShade \\ \hline firstorder-InterquartileRange & glcm-ClusterTendency \\ \hline firstorder-Skewness & glcm-Correlation \\ \hline glrlm-LongRunHighGrayLevelEmphasis & glrlm-LowGrayLevelRunEmphasis \\ \hline glszm-GrayLevelNonUniformity & glrlm-RunVariance \\ \hline glszm-GrayLevelNonUniformityNormalized & glszm-SmallAreaEmphasis \\ \hline gldm-SmallDependenceLowGrayLevelEmphasis & glszm-SmallAreaEmphasis \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The different features selected in PET and PET co-registered with FLAIR datasets. Figure 5: Correlation matrix for the 107 radiomics features. These results show that the co-registration process not only modifies the value of the features, as shown in the previous analysis based on the difference percentage value, but that the changes are severe enough that the selection process identifies about 40% of different features in the three image datasets. Arguably, resizing voxels can be the parameter with the greatest impact on feature robustness when co-registration is performed. However, we cannot say for sure which parameter harmed the process the most. In general, it can be argued that feature robustness can be improved if an effort is made to harmonize image acquisition and processing as defined by EARL accreditations [35]. Further analyses will be needed to provide more detailed information on this issue.
## 5 Application  
The tension control strategy developed in this paper was applied to a CRPM with hydraulic tension, which is shown in Figure 16. This mill can be used for research into the cold-rolling process and new product development of metal materials such as steel, pure titanium, titanium alloy, and other alloys. The main parameters of the equipment are shown in Table 1, where ESR is the abbreviation for electroslag redissolution; HSD stands for Shore hardness; HGC is the abbreviation for hydraulic gauge control.  
Figure 15: The simulation results for tension control with the proposed method when \(S_{\rm h}=0.04\) and \(S_{\rm H}=0.4\). (**a**) The control effect of forward tension; (**b**) the control effect of backward tension; (**c**) the flow curves on the exit side; (**d**) the flow curves on the entry side.  
As can be seen, after five rolling passes, the specimen was rolled from 3.6 mm to 1.017 mm, with a total reduction of 71.75%. As the length of the specimen increased, the maximum rolling speed increased from 0.15 m/s to 0.4 m/s. During the multipass rolling process, the tension control error remained within \(\pm\) 0.2 kN, which is better than in the simulation result because the friction force set in the simulation is very strict. Firstly, the friction force value of 0.2 kN is higher than the maximum friction force in the actual process. Moreover, the set friction force has a step change process from 0 to the maximum value and from the maximum value to 0, which will cause significant errors in tension. From the single pass tension control curve, the actual tension trend of the rolling process was consistent with the simulation process. When the rolling started or stopped, the forward tension showed a decreasing trend, and the backward tension showed an overshooting trend. When switching from the acceleration section to the uniform speed section or from the uniform speed section to the deceleration section, the forward tension showed an overshooting trend, while the backward tension showed a decreasing trend.
## 4 Results and Discussion  
For validation purposes, the waveforms of the output voltage and voltage across the various switching devices (transistors and diodes) are obtained by modeling the proposed circuit in a Simulink-based platform. Then, these plots are also compared and validated  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparing Parameters** & **Proposed Circuit** & **Circuit Reported in [20]** \\ \hline Used transistors & 4 & 6 \\ \hline Used diodes & 6 & 8 \\ \hline Gate-controlling circuits & 4 & 6 \\ \hline Isolated DC sources & 4 & 6 \\ \hline Bipolar voltage gain & symmetric \(\left(\frac{\pm 1}{1-k}\right)\) & symmetric \(\left(\frac{\pm 1}{1-k}\right)\) \\ \hline Peak reverse voltage of the switching devices & \(V_{o(p)}\) & \(V_{o(p)}\) \\ \hline Conducting transistors & 2 & 2 \\ \hline Conducting diodes & 3 & 3 \\ \hline Average conduction losses & \(\frac{6V_{d}I_{o(p)}}{\pi(1-k)}+\frac{6I_{o(p)}^{2}}{4(1-k)^{2}}R_{d}\) & \(\frac{6V_{d}I_{o(p)}}{\pi(1-k)}+\frac{6I_{o(p)}^{2}}{4(1-k)^{2}}R_{d}\) \\  & \(+\frac{I_{o(p)}^{2}}{(1-k)^{2}}R_{m}\) & \(+\frac{I_{o(p)}^{2}}{(1-k)^{2}}R_{m}\) \\ \hline Average switching losses & \(\frac{V_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}}\) & \(\frac{V_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}I_{o(p)}}\) \\  & \(+\frac{4Q_{RR}V_{o(p)}I_{o}}{\pi}\) & \(+\frac{4Q_{RR}V_{o(p)}I_{o}}{\pi}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation through comparison.  
with the practically obtained result as well. The details of the results obtained through the simulation-based platform and practical setup are detailed here.
## 3 Results
### Optimizing the FD-FLIM Detection Band for Enhanced Delineation of PpIX from Tissue Autofluorescence
For each of the groups, we further distinguished between specimens where no 5-ALA was administered prior to surgery, specimens with 5-ALA administration but no visible fluorescence during surgery and samples with visible fluorescence during surgery. Median as well as 0.25 and 0.75 quartiles were visualized with box plots for all subgroups. As expected, the 615 nm long-pass filter led to an increase of the measured lifetime for most subgroups with 5-ALA administration. Median lifetimes in LGG specimens were 5.4 ns and 4.7 ns for the 615 nm long-pass and the 590 nm to 740 nm bandpass filters, respectively. However, no significant differences were found between the two emission filters (_p = 0.17_). Similarly, median lifetimes were increased for HGG without (3.3 ns, 2.9 ns, _p = 0.18_) and with (14.1 ns, 13.4 ns, _p = 0.04_) visible fluorescence. This was also observed in MET without visible fluorescence (4.9 ns, 4.3 ns, _p = 0.03_) and visible fluorescence (9.8 ns, 8.4 ns, _p = 0.11_). Only in the MNG group did specimens lacking visible fluorescence have lower lifetimes for the 615 nm longpass filter (3.8 ns, 4.2 ns, _p = 0.67_), which will be discussed later on. Lifetimes in specimens from patients without 5-ALA administration were barely affected by the filter change in MET (2.5 ns, 2.5 ns, _p = 0.47_) and LGG (2.4 ns, 2.3 ns, _p = 0.44_), and even decreased in MNG (2.2 ns, 2.4 ns, _p = 0.90_). Please refer to Table 1 for complete data of all subgroups. Lifetimes measured in non-pathological brain from our previous work [15] are indicated with gray lines (solid: median, dashed: first and third quartiles). Specimens lacking visible fluorescence during surgery could be distinguished from this autofluorescence baseline with both filters. Yet, the optimized filter increased the measured lifetimes in most subgroups, which benefits the detection of weak PpIX accumulations.