esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 TFR Techniques
### Time-Frequency Analysis Using TFR  
TFR techniques offer another approach to sharpening the time-frequency representation while keeping the temporal localization and can extract improved frequency resolution [39; 40]. Based on the STFT frame, the implementation process is as follows.  
First, the spectrogram \(P_{s}^{S}(t,\omega)\) of signals can also be expressed as  
\[P_{s}^{S}(t,\omega)=\iint\limits_{R^{2}}W_{s}(\tau,v)W_{g}(\tau-t,v-\omega) \frac{\mathrm{d}\tau\mathrm{d}v}{2\pi}, \tag{5}\]  
where \(W_{s}\) and \(W_{g}\) are the WVD of signal \(s(t)\) and window function \(g(t)\), defined as the FT of instantaneous correlation function:  
\[W_{s}(t,\omega)=\int_{R}s(t+\frac{\pi}{2})s^{*}(t-\frac{\pi}{2})e^{-i\omega \tau}\mathrm{d}\tau, \tag{6}\]  
WVD can reflect the instantaneous time-frequency relationship of the signal, especially for a single component signal. However, the quadratic representations may produce cross-term interference for multi-component signals. Therefore, the spectrogram function is thus the two-dimensional smoothing function to reduce adverse effects. Then, the reassignment method is used to redefine the time-frequency spectrogram \(P_{s}^{S}(t,\omega)\) based on the principle of centroid distribution:  
\[\overline{P}_{s}^{S}(t,\omega)=\iint\limits_{\mathbb{R}^{2}}P_{s}^{S}(t, \omega)\delta(\omega-\omega_{s}(\tau,v))\delta(t-t_{s}(\tau,v))\mathrm{d}\tau \mathrm{d}v, \tag{7}\]  
where \(\overline{P}_{s}^{S}(t,\omega)\) is the reassignment spectrogram of a signal \(s(t)\). \(\omega_{s}(\tau,v)\) and \(t_{s}(\tau,v)\) are calculated by  
\[\omega_{s}(\tau,v)=\omega-\mathrm{Im}\left[\frac{F_{s}^{g^{\prime}}(t,\omega)} {F_{s}^{S}(t,\omega)}\right],t_{s}(\tau,v)=t+\mathrm{Re}\left[\frac{F_{s}^{tg} (t,\omega)}{F_{s}^{S}(t,\omega)}\right], \tag{8}\]  
where \(g^{\prime}\) is the derivative of the window function \(g(t)\) and \(tg\) represents the factor \(t\cdot g(t)\).  
Based on Equations (7) and (8), the energy is reassigned in the time-frequency plane. Thus, the local time-frequency characteristic is easy to follow from the reassignment spectrogram \(\overline{P}_{s}^{S}(t,\omega)\). The TFR techniques provide a new approach to identifying the time-varying frequency, particularly well adapted to multi-component signals.
## 4 LQG  
The name LQG arises from the use of an integral cost function, a linear model, and Gaussian white noise processes to noise signals and model disturbances. The Kalman filter and optimal state feedback gain \"k\" are the two main parts of the LQG controller. The optimal feedback gain is calculated such that the feedback control law \(u=-\)kx minimizes the performance index:  
\[H=\int_{0}^{\infty}(X^{T}QX+u^{T}Ru)dt\]  
Where \(R\) and \(Q\) are positive definite or semi-definite real symmetric or Hermitian matrices [15]. The optimal state feedback \(u=-\)kx needs full state measurement. In our case, the states are chosen to be the frequency deviation \(\Delta f\), mechanical power change \(\Delta\)P\({}_{mi}\), the governor output change \(\Delta\)P\({}_{gi}\), and the area tie-line power change \(\Delta\)P\({}_{tie,i}\). The frequency deviation \(\Delta\)f\({}_{i}\), the area tie-line power change \(\Delta\)P\({}_{tie,i}\) and the supplementary control action \(\Delta\)P\({}_{ci}\) are chosen to be the measured signals which are fed to the Kalman estimator. The Kalman filter estimator is used to drive the state estimation:  
\[\hat{x}_{i}\ =\ [\Delta\hat{f}_{i}\Delta\hat{p}_{mi}\quad\Delta\hat{p}_{gi} \quad\Delta\hat{p}_{tie,i}]\]  
Such that \(u=-\)kx remains optimal for the output feedback problem. The state estimation is generated from  
\[\left(\hat{x}\right)=\ (A-Bk-LC)\hat{x}+Ly\]  
Where \(L\) is the Kalman gain and can be calculated by knowing the measurement covariance and system noise \(R_{n}\) and \(Q_{n}\). The Kalman filter's parameters and the LQG gains have been calculated off-line.
## 4 RAS Laboratory Mixing Methods
### Wet Mixing Method  
A schematic view of the wet laboratory mixing method used to add RAS to asphalt binders is given in Figure 3, where it can be seen that an RAS powder (less than 0.075 mm) or small particles are mixed with a virgin binder with the help of a standard or high-shear mixer to achieve an RAS-modified asphalt binder.  
Then, such a binder is blended with aggregates to prepare RAS-modified asphalt mixtures. In this method, relatively high effort (time, temperature, shear stress, etc.) is required to achieve the homogenous mixing of the RAS powder/particles in the binder.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 6 Results
### Non-Parametric Tests
#### 6.2.2 Mann-Whitney U Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Req.** & **Groups Compared** & **Median Group A** & **Median Group B** & **U** & **Z** & \(p\)**-Value** \\ \hline \multirow{2}{*}{MGV\_001} & Academic vs. End-User & 4 & 3 & 4 & \(-\)2.214 & 0.045 \\ \cline{2-7}  & End-User vs. Stakeholder & 3 & 5 & 0 & \(-\)2.16 & 0.048 \\ \hline \multirow{3}{*}{CCWA\_007} & Academic vs. End-User & 4 & 3 & 4.5 & \(-\)2.383 & 0.024 \\ \cline{2-7}  & Disaster Management Professional & \multirow{3}{*}{5} & \multirow{3}{*}{3} & \multirow{3}{*}{0} & \multirow{3}{*}{\(-\)2.029} & \multirow{3}{*}{0.048} \\  & vs. End-User & & & & & \\ \hline \multirow{3}{*}{CCWA\_014} & Academic vs. End-User & 4.5 & 3 & 2.5 & \(-\)2.754 & 0.007 \\ \cline{2-7}  & Disaster Management Professional & \multirow{3}{*}{5} & \multirow{3}{*}{3} & \multirow{3}{*}{0} & \multirow{3}{*}{\(-\)2.449} & \multirow{3}{*}{0.048} \\  & vs. End-User & & & & & \\ \hline FRMA\_008 & Academic vs. End-User & 4 & 3 & 4.5 & \(-\)2.531 & 0.013 \\ \hline ARECH\_002 & Academic vs. End-User & 4 & 5 & 4.5 & \(-\)2.249 & 0.034 \\ \hline ARECH\_003 & Academic vs. End-User & 4 & 5 & 2.5 & \(-\)2.785 & 0.005 \\ \hline SOCI\_001 & Academic vs. End-User & 4 & 5 & 7.5 & \(-\)2.277 & 0.031 \\ \hline SOCI\_002 & Academic vs. End-User & 4 & 5 & 6 & \(-\)2.411 & 0.035 \\ \hline \multirow{3}{*}{RS\_001} & Academic vs. End-User & 5 & 3 & 8.5 & \(-\)1.993 & 0.036 \\ \cline{2-7}  & Disaster Management Professional & \multirow{3}{*}{5} & \multirow{3}{*}{3} & \multirow{3}{*}{0} & \multirow{3}{*}{\(-\)2.160} & \multirow{3}{*}{0.048} \\  & vs. End-User & & & & & \\ \hline REL\_001 & Academic vs. End-User & 4 & 5 & 2.5 & \(-\)2.559 & 0.015 \\ \hline PRE\_002 & Academic vs. End-User & 4 & 5 & 5 & \(-\)2.56 & 0.015 \\ \hline \multirow{3}{*}{SAM\_004} & Academic vs. End-User & 5 & 3 & 0 & \(-\)3.071 & 0.003 \\ \cline{2-7}  & Disaster Management Professional & \multirow{3}{*}{4.5} & \multirow{3}{*}{3} & \multirow{3}{*}{0} & \multirow{3}{*}{\(-\)2.415} & \multirow{3}{*}{0.048} \\  & vs. End-User & & & & & \\ \cline{2-7}  & End-User vs. Stakeholder & 3 & 5 & 0 & \(-\)2.449 & 0.048 \\ \hline \multirow{3}{*}{TF\_005} & Academic vs. Disaster & \multirow{3}{*}{4} & \multirow{3}{*}{5} & \multirow{3}{*}{0} & \multirow{3}{*}{\(-\)2.582} & \multirow{3}{*}{0.022} \\  & Management Professional & & & & \\ \hline \multirow{3}{*}{INT\_017} & Academic vs. Disaster & \multirow{3}{*}{3} & \multirow{3}{*}{5} & \multirow{3}{*}{0} & \multirow{3}{*}{\(-\)2.049} & \multirow{3}{*}{0.048} \\  & Management Professional & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Significant differences identified by the Mann–Whitney U tests. Taking into consideration the results above, the Mann-Whitney U tests showed significant differences among participant groups in 14 out of the 25 requirements pinpointed by the Kruskal-Wallis H test, and the null hypothesis was rejected for 20 out of the 525 Mann-Whitney U tests run in total.
# Rapid Molecular Identification of Scolytinae (_Coleopterara: Curculionidae_)
## 1 Introduction  
Diseases associated with wood-boring ambrosia and bark beetles (Curculionidae: Scolytinae and Platypodinae) are a rapidly increasing threat to forests and fruit production around the world [1, 2]. The accumulation of new introductions has been exponential, which is reflected in published literature [3, 4]. Over half of all journal articles on this taxonomic group have been published within the past ten years [1]. Some of these pathosystems, such as Dutch elm disease [5], have been the focus of research for many years, but others such as laurel wilt vectored by the redbay ambrosia beetle (_Xyleborus glabatus_) [6, 7], thousand markers disease caused by the walnut twig beetle (_Pitypothorus juglandis_) [8], and _Fusarium_ dieback spread by shot hole borers in the _Euwallacea fornicatus_ species complex [9, 10] are the result of pest introductions that have prompted more recent investigations.  
Although Scolytinae represent over half of all insects intercepted at U.S. ports of entry [11] and 93% of insects on solid wood packing materials [12, 13], only 40% of these were identified to the specieslevel [14]. A molecular identification approach that could be performed using basic molecular biology skills and equipment could address this knowledge gap.  
Although genetic sequences of these insects have been generated from diversity analyses and entomological studies [15; 16], molecular identification of ambrosia and bark beetle species is not used on a wide scale. In addition, this is the only way to make reliable identifications from structurally damaged samples where key morphological characteristics are absent or compromised, or to distinguish among members of a cryptic species complex (e.g., _Euwallacea_ nr. _forniactus_[17]).  
To develop an efficient introductory approach for molecular identification of ambrosia and bark beetles, this study examined three DNA extraction protocols, determined the minimum amount of DNA that could yield an informative sequence, and compared the usefulness of five primer pairs in distinguishing several genera. These methods can be used by plant pathologists, horticulturalists, and agricultural inspectors lacking specialized knowledge of insect taxonomy.
## 2 Materials and Methods
### Identification of Volatile Compounds by GC-MS  
Three methods were employed to obtain the volatile compounds from the orange juice. The first method included dynamic headspace sampling using a simple pull system (HS-P). This system consisted of an air pump, a collection tube (capped with glass wool and filled with Super Q, CNW(r) technologies, Dusseldorf, Germany), an odor source bottle, a filter tower (containing a molecular sieve and activated charcoal), and a flowmeter. The air stream was controlled at 1.2 L/min, and the collection lasted 24 h. After removing the collection tube, it was rinsed once with 1.5 mL of n-hexane, and the sample was concentrated using a Termovap sample concentrator (Yooning Instrument(r), Hangzhou, China) and stored at \(-20\)\({}^{\circ}\)C. The second method involved static headspace sampling with a solid phase microextraction (HS-SPME) device. For this, 1 mL of orange juice was placed in a 20 mL glass container. SPME Fiber (50/30 \(\upmu\)m DVB/CAR/PDMS, Supelco(r)) was employed for the solid-phase microextraction, inserted 4 cm above the orange juice surface and allowed to extract for 20 min. The samples were injected following the extraction. The third method involved the direct injection of headspace (HS-I). In this method, 1 mL of orange juice was directly put into 20 mL headspace vials for direct injection.  
The volatile compounds harvested from the orange juice were identified using gas chromatography-mass spectrometry (GC-MS, Thermo, TSQ9000 + trace1310 Agilent, CA, USA) on a DB-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness Agilent, CA, USA). The temperature program was as follows: the initial column temperature was 50 \({}^{\circ}\)C for 2 min and then heated at 5 \({}^{\circ}\)C/min to 290 \({}^{\circ}\)C, before being retained for 3 min. The inlet temperature was 250 \({}^{\circ}\)C, and the sample size was 1 \(\upmu\)L. Helium was employed as the carrier gas at a flow rate of 1.2 mL/min. The mass spectrum analysis conditions were as follows: the ion source was set at EI, and the ion source temperature was set at 300 \({}^{\circ}\)C; the electron energy was set at 70 eV, the electron multiplier voltage was set at 350 V, and the quality range was scanned from 50 to 500 \(m/z\). The chemical compounds in the orange juice were initially identified using computer-standard mass spectrometry (NIST) and manual identification. Subsequently, according to the preliminary identification results, corresponding standards were prepared for re-sampling and verification. The results that were consistent with the standard were considered the final identification results.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 6 Experiments and Numerical Results
### Effect of Flow-Based Cost Function on the Hub Links  
In this work, we also consider a flow-based transportation discount factor, in contrast to most of the research in the body of the literature that assumes a fixed value for that. In this section, we run numerical experiments with a flow-based cost function to study how this would make our formulation more realistic. The results are reported in Table 5. The results show that the flow-based cost function results in equal or better total costs compared to the case with \(\alpha=0.5\), which is intuitive. In the same way, we can observe that the results of variable cost coefficient are no better than \(\alpha=0.2\), which has the lowest total cost. It can also be learnt from the results that considering a flow-based alpha can affect the number of hub facilities/hub links, e.g., for the network with 40 nodes, the number of hub links increases from 70 (\(\alpha=0.5\)) to 72 (for flow-based \(\alpha\) from 0.2 to 0.5). This indicates the higher preference of the model to use more hub facilities/hub links to further benefit from the economies of scale discount. It also depicts that the run time is longer for the flow-based alpha, since the model performs several different tradeoffs, within a larger solution space, and also a set of additional variables are introduced to the model \(\alpha_{kl}^{i},f_{kl}^{i},Z_{kl}^{i}\).
# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
## 5 Conclusions  
In this paper, on the basis of the original YOLOv3 model, the CSYOLOv3 algorithm is proposed to address the problems of SHW detection under different scenarios of occlusion, dense crowd, small-scale target and so on. Firstly, so as to reduce the computing consumption of the detection model and ameliorate the training speed, CSPNetwork (Cross Stage Partial Network) is introduced to construct CSPDarknet53, which is the improvement to Darknet53. Then, the improved spatial pyramid pooling (SPP) structure is introduced, and the multi-scale prediction network is strengthened through the top-down and bottom-up feature fusion strategies, in order to realize the feature enhancement. Finally, training and testing are carried out on the newly labeled dataset. The experimental data and contrast curves reveal that the presented method can productively strengthen the accuracy and the speed of SHW detection in complex construction site scenes, and the average accuracy value reaches above 90%, and FPS reaches above 20. Since the dataset in this paper are collected under ideal illumination conditions, the primary work in the future is to further expand the dataset on this basis. Considering the illumination factor, some optimization and improvement of the network structure can be done to construct a better lightweight model for real-time target detection in a construction site; thus, the accuracy and real-time performance of the model can be improved to a greater extent.  
Conceptualization, H.W.; Data curation, Z.H.; Formal analysis, F.Z.; Investigation, Y.G. and Z.Y.; Methodology, P.X., H.W., Z.H., Z.Y. and F.Z.; Resources, H.W., P.X. and Z.Y.; Software, H.W. and F.Z.; Validation, Y.G. and F.Z.; Visualization, Z.H.; Writing--original draft, H.W., Z.H. and Y.G.; Writing--review & editing, H.W., Z.H., P.X., Y.G. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Science Foundation of China (52077213, 62003332, 61473182, 51607177, 61433012, U1435215), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 16010500300, Shenzhen Science and Technology Innovation Commission application demonstration project (No. KJYY20160608154421217), Natural Science Foundation of Guangdong Province (2018A030310671); Outstanding Young Researcher Innovation Fund of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (201822).  
The authors declare that there is no conflict of interest regarding the publication of this paper.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Situation** & **Normal SHW** & **Occlusion SHW** & **Dense SHW** & **Small Size SHW** \\ \hline YOLOv3 & 0.0526 s & 0.0526 s & 0.0528 s & 0.0523 s \\ CSYOLOv3 & 0.0400 s & 0.0397 s & 0.0405 s & 0.0396 s \\ tiny-YOLOv3 & 0.0371 s & 0.0371 s & 0.0374 s & 0.0369 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time of per image processing for four SHW situation.
## 3 Results and Discussion
### Effect of Wake on the Integrated LPT-GV
However, when \(d/s\) was large, the total pressure coefficient was still smaller than that in the case of a small \(d/s\), which indicated that the aerodynamic benefit brought by increasing the intensity of wakes was still insufficient to offset the total pressure loss caused by its dissipation near the shroud. Given this situation, to improve the aerodynamic performance of AITD by using the sweeping wake, the authors believed that the following methods could be adopted: (1) Reduce the blade loading of the LPT-GV by increasing the number of blades, thereby suppressing the size of the passage vortex near the hub and separation bubble in the middle of the blade section, reducing the loss of the LPT-GV's middle and lower sections; then, use the HPT wakes with a small trailing-edge radius to control the separation bubble on the blades' middle section to suppress the loss of the separation bubble when reducing the loss caused by the wake dissipation. (2) Further shorten the axial distance between the HPT and LPT-GV so that the wakes of the HPT can reach the LPT-GV before being completely dissipated to make full use of the HPT wake with a small trailing-edge radius to suppress flow separation and passage vortices, reduce the wake dissipation loss and improve the aerodynamic gain brought by the flow control using HPT wakes. Figure 18 shows the total pressure loss of the integrated AITD and its components in all cases. The total pressure loss of AITD increased with increasing \(d/s\), and the loss of the integrated LPT-GV was the main loss source, accounting for more than 82.9% (@\(d/s\) = 0.144). If the design condition with \(d/s\) = 0.10 was used as a benchmark, then reducing the \(d/s\)  
Figure 10:6 Radial distribution of the pitchwise averaged total pressure coefficient at the outlet of the AITD. value of the sweeping rod helped to decrease the total pressure loss of the integrated AITD, which indicated that the radius of the HPT trailing edge should be minimized to reduce the additional loss caused by the wake dissipation within the scope of this paper. When \(d/s\leq 0.078\), the loss of the integrated LPT-GV remained basically unchanged, which indicated that the aerodynamic gain of wakes could offset their dissipation. Whether a further reduction of the \(d/s\) value can lead to the reverse overshoot of aerodynamic gain needs to be further studied. When \(d/s>0.078\), the aerodynamic gain of the sweeping wake was obviously insufficient to compensate for its dissipation, resulting in increased loss of the integrated LPT-GV with increasing \(d/s\). For the AITD upstream of the LPT-GV, the loss mainly included the wall friction loss and the wake dissipation loss and increased with increasing \(d/s\).
## 3 System Concept
### RF Concept and Design
#### 3.1.3 Link Budget  
Knowing the characteristic parameters of the antenna, the link budget of the system can be estimated to verify the system concept and to determine the required gain of the receiver. The signal generated by the VCO has an adjustable output power of maximum 11 dBm [38], which is attenuated by 3 dB due to the losses on the printed circuit board (PCB), connectors and cable. The effective signal strength is then enhanced by the antenna gain (17.7 dBi). To make full use of the limit of 20 dBm equivalent isotropically radiated power (EIRP) given by the regulations of the industrial, scientific and medical (ISM) band, the VCO output power was adjusted accordingly. The free space attenuation at the given distance of 40 cm can be calculated to be 58 dB including the return path and assuming an ideally reflective target. From simulations with BioModels from CST Microwave Studio, a value of 10 dB was obtained as additional attenuation due to the energy that is absorbed by the human body and the energy radiated in other directions than the receiving antenna, which could be confirmed by measurement. After the LNA with a maximum gain of 15 dBm, an Rx power level of \(P_{2}=-17,3\) dBm  
Figure 4: A flowchart showing the overall signal processing steps.  
is incident on the input of the six-port receiver. The attenuation of the reference signal \(P_{1}\) is adjusted such that the maximum power at ports 3-6 equals about \(-10\,\mathrm{d}\mathrm{B}\mathrm{m}\), which is well in the linear region of the detector transfer curve.