esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Application of Networked Mobile Sensors
### Surveillance
Surveillance is not only done by groups of ground vehicles, but also is done by air vehicles. The use of networked Unmanned Air Vehicles, in short UAV, has potential benefits since they can cover very large and remote areas and they can identify very small targets via technologically-advanced cameras. Heterogeneous networked unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) are used for searching targets in predetermined large battlefield operation areas as introduced by [PERSON] _et al._[51]. Aerial and ground vehicles have complementary capabilities and characteristics. Networked air vehicle teams can cover large areas much quicker than ground vehicles. However, air vehicles cannot exactly localize targets because of altitude and limitations in the resolution of sensing devices. At that point, ground vehicles help them by using their high resolution sensors for localization process. Feasible path planning for each vehicle in the network and strategy generation for obstacle avoidance are other research issues within surveillance operations which critically affect the efficiency of time and energy of the system performance. Multi-robot path planning, for example, generally causes multiple area coverage creating redundancy of exploration, which can turn into advantages for correcting errors in mapping. However, time and energy have to be consumed for building such accuracy in mapping by error correction based on redundancy. If time and energy become primary issues, multiple area coverage should be prevented when it is detected by the algorithm. Decentralization is the main cause for this need of compensation. Authors have tried to provide centralization to some local problems of networked mobile robotics. [PERSON] and [PERSON] [52] used a novel adaptive genetic algorithm based cooperative path planning algorithm for networked robot systems. Although the proposed algorithm is robust for convergence, it is a centralized method and suffers from the limitations of centralization such as computation burden and single point failure. On the contrary, [PERSON] and [PERSON] [53] gave a decentralized prioritized motion planning method for various types of robot platforms such as flying and wheeled robots or manipulators. A priority is assigned to each robot, then a robot is selected according to the highest priority and a feasible path is planned for that selected robot, considering also dynamic obstacles inside the work environment.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Semi-Automatic Green Tide Extraction Method  
#### 3.2.1 Green Tide Extraction Index  
The optimized semi-automated method for green tide extraction proposed in this paper was based on the _NDVI_, which in turn is based on the principle that vegetation is more reflective in the _NIR_ band: the higher the vegetation cover, the lower the reflection in the _Red_ band and the higher the reflection in the _NIR_ band [7; 24; 25]. The _NDVI_ reinforces the difference in vegetation reflectance between the _Red_ and _NIR_ bands and is defined as in Equation (1), where _Red_ and _NIR_ are the digital number values of the _Red_ and _NIR_ bands, respectively. In addition, before calculating _NDVI_, we performed geometric correction.  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{1}\]  
As the green tide tends to cover water bodies with high aggregation, the water bodies covered by green tide also show high reflectance in the _NIR_ band, so the _NDVI_ can also be used to monitor the scale and distribution of the green tide. The _Red_ and _NIR_ bands, which are mainly used in the _NDVI_, are common to most visible RS satellites, so the use of the _NDVI_ can improve the universality of the method.
# Mesh-Free MLS-Based Error-Recovery Technique for Finite Element Incompressible Elastic Computations
## 3 Finite-Element-Solution Effectivity and Accuracy  
The finite-element-analysis solution errors are the variation in the field variables or gradients in the finite-element=analysis results from those in the field variables or gradients' post-processed results (or exact results). It is standard practice to measure the solution errors in terms of a norm such as energy or L\({}_{2}\) norms, giving the required information in scalar quantity. The error-estimation reliability is found by estimating problem effectivity. The ratio between the projected error and the exact error can be used to define the effectivity (\(\theta\)). The effectivity for an asymptotically exact error estimator converges to one as the mesh size approaches zero [20].  
\[\theta=\frac{\|\textbf{e}\|}{\|\textbf{e}_{\text{ex}}\|}, \tag{9}\]  
where \(\|e\|\) (\(=\left[\int_{\Omega}e_{\sigma}^{*\intercal}D^{-1}e_{\sigma}^{*}d\Omega\right] ^{\frac{1}{2}}\)) represents the evaluated error, and \(\|e_{es}\|\) is the exact error (in energy norms).  
The FEM solution's accuracy (\(\eta\)) is as follows.  
\[\eta=\frac{\left\|\textbf{e}^{*}\right\|}{\left\|\textbf{e}^{*}\right\|}, \tag{10}\]  
\[\text{where}\ \|\sigma^{*}\|^{2}=\left\|\sigma^{h}\right\|^{2}+\left\|e\right\|_{ E}^{2} \tag{11}\]
## 1 Introduction
### Voltage Stress of Switches and Diodes for HG-SIQBC  
The voltage and current stress calculations of the power semiconductor switch are essential for ensuring proper component selection and reduced power loss of the converter [21]. The voltage stress across the power switch can be determined as shown in Figure 8.  
\[V_{sw}-V_{C_{2}}=0. \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Topology** & \begin{tabular}{c} **No. of** \\ **Switches** \\ \end{tabular} & **No. of Diodes** & \begin{tabular}{c} **No. of** \\ **Inductors** \\ \end{tabular} & \
\begin{tabular}{c} **No. of** \\ **Capacitors** \\ \end{tabular} &
\begin{tabular}{c} **Voltage Gain** \\ **(G)** \\ \end{tabular} & **Voltage Stress** \\ \hline \([13]\) & 1 & 3 & 2 & 2 & \(\frac{D(2-D)}{(1-D)^{2}}\) & \(\frac{1}{(1-D)^{2}}\) \\ \([14]\) & 1 & 3 & 2 & 2 & \(\frac{1}{(1-D)^{2}}\) & \(\frac{1}{(1-D)^{2}}\) \\ \([15]\) & 1 & 4 & 2  
Substituting, the value of \(V_{\text{C2}}\) from Equation (15) into Equation (26) yields  
\[V_{\text{sw}}=\frac{V_{in}(D-2)}{2(1-D)^{2}}. \tag{27}\]  
Applying KVL to Figures 5 and 6, the voltage stress across the diodes can be obtained. For example, the voltage stress across the diode \(D_{1}\) can be determined from the equivalent circuit shown in Figure 9.  
\[V_{D_{1}}=V_{L_{1}}=V_{in}+V_{\text{C}_{1}}-V_{\text{C}_{2}} \tag{28}\] \[=V_{in}+\frac{V_{in}D(D-2)}{2(1-D)^{2}}-\frac{V_{in}(D-2)}{2(1-D)^ {2}}\] (29) \[=V_{in}+V_{in}\frac{D(D-2)}{2(1-D)^{2}}[D-1]. \tag{30}\]  
Therefore,  
\[V_{D1}=V_{in}\bigg{[}\frac{3D-4}{2(D-1)}\bigg{]}. \tag{31}\]  
Similarly, the voltage stress across other diodes is determined using the following expressions:  
\[\left\{\begin{array}{l}V_{D_{11}}=\frac{V_{in}}{V_{D_{2}}}\\ V_{D_{2}}=\frac{D(D-2)}{2(1-D)^{2}}\end{array}\right\}V_{in}\\ V_{D_{5}}=\frac{D-2}{2(1-D)}V_{in}\\ V_{D_{6}}=\bigg{(}\frac{(D-2)(D-3)}{2(1-D)^{2}}\end{array}\right.. \tag{32}\]
## 3 Results and Discussion  
### Characterization of the Extract  
#### 3.1.1 HPLC/DAD  
From the HPLC/DAD chromatograms, six compounds were identified (Figure 2) and the corresponding ultraviolet absorption spectra of the most concentrated are provided in Figure 3. The minor compounds were related to the nucleus of flavonoids (compounds **1-4**) and the major were related to chalcones (compounds **5** and **6**).  
These latter components also belong to the flavonoid group of polyphenols; however, the C-ring remained open between the oxygen in position 1 and the carbon in position 2 (for a better understanding, please check the structure in Figure 4). These two compounds were chalcone derivatives that were previously identified by [PERSON] [19].  
Chalcones are polyphenols are structured as \(\alpha\),\(\beta\)-unsaturated aromatic ketones and can be found in various plants. They have been targeted in various studies that provide data about their potential in various therapeutic purposes, attributing bioactivities to them,  
Figure 4: Basic structures of flavone (**a**) and chalcone (**b**).  
Figure 3: Ultraviolet absorption spectra obtained using HPLC/DAD analysis of the flavonoid content. Compounds (**a**,**b**) were chalcone derivatives.  
Figure 2: Example of an HPLC/DAD profile of the early maturation stage flowers from region A obtained with the most active _Acacia danilba_ L. hydroethanolic extract (50% (v/v)). Compounds **1**–**4** were flavonoids and **5**–**6** were chalcone.  
such as helpfulness regarding oncology, infections and even in neurodegenerative diseases, including Alzheimer's [29]. For the latter, the antioxidant and anti-inflammatory effects are also very important, and the data obtained with the present extract was found to be promising for some of these applications, as shown below.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 4 Characterization of ISB
### ISB Characterization Affected by the Receiver Type  
As shown in Table 3, this experiment selects two stations (MAT1, MATE) equipped LEICA GR30 receivers and one station equipped SEPT POLARXSTR receiver. All of them were connected to LEIAR20 antennas. The distance between any two of them was less than 30 m. Therefore, we assumed that the external temperature of three stations was identical. For the reason that the observed BDS satellites were not enough, we only estimated the ISBs using WUM and GFZ products in this experiment due to the CODE precise products exclude the BDS GEO satellites. For the same reason, we skipped the data on DOY 254 and 256, 2018 and displayed the results on DOY 253, 255 and 257, 2018.  
Figure 4 shows the ISB estimated from station MAT1, MAE1 and MAZ1. We see that the ISB time series from stations MAT1 and MATE equipped receivers with same type were consistent. However, see the results from station MATZ, which was equipped with the other type of receiver, the ISB estimates differed from the results from stations MAT1 and MAE1 in some periods. Typically, in the period of hour after 8, there was an offset about 0.1 m between ISBs from two types of receivers when using WUM products. As we can see, the gaps of ISB estimated by different receiver types were similar, it due to the variation of the receiver-code bias was closely related to temperature [10], while the  
Figure 3: GPS/BDS ISB series of CEDU/HOB2/KARR and PNGM stations estimated using WHU, CODE and GFZ precise products on DOY 002, 2018.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **Receiver Type** & **Antenna Type** \\ \hline MAT1 & LEICA GR30 & LEIAR20 \\ MATE & LEICA GR30 & LEIAR20 \\ MATZ & SEPT POLARXSTR & LEIAR20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stations selected for the investigation of ISB characterization affected by the receiver type.  
temperatures were comparable in the adjacent days. In addition, the ISBs based on WUM products were more stable than those based on GFZ products, which was in accordance with what we find in Figure 2.
## 3 Results and Discussion
### Physicochemical Properties of the Samples
#### 3.2.6 XPS Analysis  
To demonstrate the chemical properties of the adsorbent surface, the XPS method was used to explore the atom environment of the samples. As shown in Figure 9, for Co 2p spectra, there were two distinct peaks detected at around 780 and 795 eV, which can be attributed to Co 2p\({}_{3/2}\) and Co 2p\({}_{1/2}\), respectively. Following deconvolution, there were two spin-orbit doublets (D\({}_{1}\) and D\({}_{2}\)) emerging, along with three satellite peaks observed (S\({}_{1}\), S\({}_{2}\), and S\({}_{3}\)). Located at 780.0\(\sim\)780.1 eV and 795.0\(\sim\)795.1 eV, D\({}_{1}\) bands were attributable to Co\({}^{3+}\) species. By comparison, the D\({}_{2}\) bands located at 781.7 and 797.4 eV corresponded to Co\({}^{2+}\) species, suggesting the co-existence between Co\({}^{2+}\) and Co\({}^{3+}\)[2]. A similar conclusion can be drawn for Fe atoms that both Fe\({}^{3+}\) and Fe\({}^{2+}\) were observable in the samples [46]. Upon calculation (Table 2), it can be found out that the addition of Fe\({}_{2}\)O\({}_{3}\) into Co-ZSM-5 increased the ratio Co\({}^{3+}\)/Co\({}^{2+}\) from 0.77 to 1.37 and caused the ratio Fe\({}^{3+}\)/Fe\({}^{2+}\) to drop sharply from 1.62 to 1.29 with the Fe-containing samples as modified by CoO\({}_{x}\), which indicates that the conversion of Co\({}^{2+}\) to Co\({}^{3+}\) can be accelerated by the interaction between Co species and Fe species. This has been proved as highly active in catalytic reactions, thus enhancing the activity of the Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 sample.  
Figure 8: H\({}_{2}\)-TPR patterns of a series of samples.
## 1 Introduction  
Nonsteroidal anti-inflammatory drugs (NSAIDs)-exacerbated respiratory disease (N-ERD) or aspirin-exacerbated respiratory disease (AERD) is a clinical syndrome characterized by chronic rhinosinuisitis with nasal polyps (CRSwNP), asthma, and intolerance to aspirin/NSAIDs. It affects approximately 15% of cases of patients with severe asthma, 10% of those with CRSwNP, and 9% of cases with chronic rhinosinuisitis (CRS) [1, 2]. CRS impacts multiple aspects of health-related quality of life (HRQoL) including mental and physical health, sleep, productivity, cognitive and social functioning, and general health status [3, 4]. HRQoL is further worsened in patients with CRSwNP who have asthma [3]. Moreover, CRSwNP in N-ERD is usually refractory to conventional medical management and surgery and often requires several courses of systemic steroids for nasal polyposis and multiple endoscopic sinus surgeries (ESS) [5]. As a result, patients with N-ERD usually have ongoing and more severe sinonasal symptoms when compared to their non-N-ERD counterparts [6]. Chronic nasal symptoms and poor sense of smell are the major drivers of N-ERD patients' reduced HRQoL. Olfactory dysfunction (OD), in particular, is highly prevalent amongst patients with N-ERD and is reported to affect \(>\)90% of patients [7]. It is well documented that a reduced sense of smell is associated with depression, feelings of isolation, emotional distress, changes in weight, cognitive decline, and neurodegeneration in the brain and it is an independent risk factor for death among older adults [8; 9; 10].  
Aspirin treatment after desensitisation (ATAD) is an effective therapeutic option for CRSwNP in N-ERD and its safe profile and low cost make it an attractive alternative treatment option in case of failure of maximal medical and surgical treatment [6; 11; 12]. The mechanism behind aspirin desensitisation is not completely understood. It has been shown that aspirin desensitisation modulates deregulated immune responses in N-ERD through decreased levels of pro-inflammatory leukotrienes and their receptors, inhibition of Th2 activation, IL-4 production, and mast cell activation [13]. A recent systematic review and meta-analysis [11] has confirmed that oral ATAD improves HRQoL when compared to placebo. However, oral ATAD is associated with adverse outcomes severe enough to cause drug discontinuation, including major gastrointestinal bleeding, gastritis, asthma exacerbation, and severe rash [11; 12]. Intranasal administration of lysine aspirin (LAS) is a faster and safer route for ATAD when compared to oral daily aspirin and has been shown to be equally effective to oral ATAD [14; 15].  
Changes in HRQoL in patients on intranasal LAS have not been documented and in this cross-sectional retrospective study, we aimed to investigate HRQoL in N-ERD patients on or off nasal ATAD. Additionally, a focus on the costs of treatment options currently available for difficult-to-treat CRSwNP in N-ERD patients is also provided.
# Influence of Active Flow Control of Synthetic Jet at
## 7 Conclusions  
1. The total pressure ratio and efficiency of the compressor can be improved by the synthetic jet excitation of the suction surface with reasonable parameters in the range of operating conditions other than the high flow operating point. Compared with the prototype compressor, the peak efficiency of the compressor increased by 0.47% after introducing synthetic jet excitation, and the efficiency near the stall point increased by 0.79%. For the compressor whose instability is triggered by the tip blockage, the synthetic jet of suction surface cannot improve its stability margin due to the limitation of the action position; it will even slightly increase its near-stall flow.
2. After being excited by the synthetic jet on the suction surface, the stability margin of the compressor slightly decreases. This is because the synthetic jet absorbs the separated flow on the suction surface during the suction stage. Although it can reduce the degree of separation on the suction surface, it also causes the radial velocity of the flow in the compressor channel to be higher and more concentrated towards the blade tip, resulting in more severe blade tip blockage than the prototype compressor and a slight decrease in stability margin.
3. The reason for the efficiency improvement of the compressor after being excited by the synthetic jet on the suction surface is that the suction effect of the synthetic jet  
Figure 21: Changes of loss sources.  
significantly reduces the leakage and separation losses at the blade tip, channel losses, and outlet losses. For the subsonic axial flow compressor, the suction effect of the synthetic jet is more favorable for reducing losses than the blowing effect.
4. After the coupled flow control, the stability margin of the compressor can be expanded, which is the contribution of the slot casing treatment. It can suck low-speed flow at the top of the blade and spray from the front section, which alleviates the blockage at the blade top and greatly improves the flow condition at the blade top. The improvement of the compressor efficiency is mainly due to the contribution of the synthetic jet on the suction surface, which reduces the separation loss by suppressing the separated flow on the suction surface at the midspan of the blade.  
Conceptualization, G.W. and Q.W.; methodology, G.W.; software, W.C.; validation, G.W., Q.W. and Y.L.; formal analysis, G.W.; investigation, G.W.; resources, G.W.; data curation, W.C.; writ-ing--original draft preparation, G.W.; writing--review and editing, G.W.; visualization, G.W.; su-pervision, W.C.; project administration, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52366002).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 4 Conclusions  
In this review, we demonstrated recently proposed phased arrays for 5G mobile terminals and access terminals. These state-of-the-art phased arrays are compact in size, low-cost, and have enough beam-scanning and coverage range. The effects of the user's body and human exposure to EM waves by these state-of-the-art phased arrays with 3D coverage designed on single-layer and multilayer substrates were illustrated. In addition, multi-polarized antenna arrays and the space constraints of mobile terminals were presented and discussed. Various beamforming techniques using state-of-the-art mm-wave phased-array architectures to mitigate the co-channel interference and to overcome the path loss were proposed.  
[PERSON] conceived the idea for the study and [PERSON] wrote the manuscript. [PERSON] revised the manuscript and ensured conciseness.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1A4A1023826) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No.2017R1A2B3003856).  
The authors declare no conflict of interest.
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments
### Comparison of Image Recognition Speeds  
To ensure fairness in evaluating the image recognition speeds of different algorithms, all algorithms were run on the same computer. The computer configuration is as follows: CPU--i7-11800, 2.3 GHz; GPU--NVIDIA RTX 3050, 8 GB VRAM; RAM--16 GB. The comparison results of image recognition speeds are shown in Table 4.  
As observed, the Bag-of-Words (BoW)-based methods have the slowest recognition speeds. This is primarily due to the large size of the line-scan images of train carriages, which makes the feature extraction process time-consuming. Additionally, the total number of extracted feature points also impacts the processing time. Considering these factors, the BoW method using SIFT features is the fastest among the BoW methods, with an average recognition time of 2.274 s per carriage line-scan image. Compared to the two-stage BoW methods, multi-class neural network methods demonstrate significantly faster recognition speeds, mainly due to their end-to-end structure. Among these, the ResMLP model has the fastest inference speed, with an average detection time of 31 ms per image. Our proposed method exhibits the fastest image recognition speed among all methods. For the 76-category recognition task, the average recognition time per image is only 24 ms, showcasing a clear advantage over the other two categories of methods. The efficiency of our method can be attributed to the sparse point cloud registration in the template matching process, which inherently involves minimal computation. Additionally, the high efficiency of the C++ programming language contributes to the speed of our algorithm.
## Materials and Methods
### Headspace Volatiles Analysis of _A. sativum_ and _A. cepa Bulbs_  
The HS-SPME volatile analysis was carried out as stated in [31] with slight modifications. Dried finely ground peeled bulbs (100 mg) were placed in solid phase microextraction (SPME) screw cap vials (1.5 mL) and spiked with (_Z_)-3-hexenyl acetate dissolved in water at a final concentration of 2 \(\upmu\)g per vial. The SPME fiber was inserted manually into a vial containing seeds placed in an oven kept at 50 \({}^{\circ}\)C for 30 min. The fiber was subsequently withdrawn into the needle and then injected into the injection port of the gas chromatography-mass spectrometer (GC-MS). GC-Ms analysis was performed on a Schimadzu GC-17A gas chromatogram (Schimadzu, Tokyo, Japan) equipped with DB-5 column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; Supelco) and coupled to Schimadzu QP5050A mass spectrometer. The interface and the injector temperatures were both set at 220 \({}^{\circ}\)C. The following gradient temperature program was used for volatiles analysis. The oven temperature was kept first at 40 \({}^{\circ}\)C for 3 min, then increased to 180 \({}^{\circ}\)C at a rate of 12 \({}^{\circ}\)C min\({}^{-1}\), kept at 180 \({}^{\circ}\)C for 5 min, and finally ramped at a rate of 40 \({}^{\circ}\)C min\({}^{-1}\) to 240 \({}^{\circ}\)C and kept at this temperature for 5 min. The carrier gas helium was used at a total flow rate of 0.9 mL/min. Splitless injection mode was used for analysis considering the lower levels of volatiles in samples. SPME fiber was prepared for the next analysis by placing it in the injection port for 2 min at 220 \({}^{\circ}\)C to ensure complete elution of volatiles. Blank runs were made during sample analyses. The HP quadruple mass spectrometer was operated in EI mode at 70 eV. A scan range was set at \(m/z\) 40-500.  
Volatile components were identified by comparing their retention indices (RI) relative to n-alkanes (C6-C20), mass matching to NIST, WILEY library database and with standards whenever available. Peaks were first deconvoluted using AMDIS software (National Institute of Standards and Technology, Gaithersburg, MD, USA) prior to mass spectral matching.