esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Priority-Based Layered QC-LDPC Decoder with Double Update Queues  
### Priority-Based Layered Decoding Schedule  
In the layered schedule, the LLR in a layer has not been updated yet while the next layer needs this updated LLR. This is called pipeline conflict. When a pipeline conflict occurs, the practical method is to ignore the update of LLR. However, a small percentage of ignored updates will lead to significant performance degradation [19]. For this reason, we propose a priority-based layered schedule.  
In the layered schedule, the update of LLR can be equivalent to the sum of LLR and difference between the newly calculated check-to-variable messages in the current iteration and the one in the previous iteration [15]. This difference can be understood as a gain that helps the decoding. The update of LLR can be expressed as (7)  
\[\begin{split} LLR_{v}^{L_{i},it}&=LLR_{v}^{L_{i} -it}+R^{L_{i},it}-R^{L_{i},it-1}\\ &=LLR_{v}^{L_{i}-it}+G_{v}^{L_{i},it},\end{split} \tag{7}\]  
where \(LLR_{v}^{L_{i},it}\) represents the LLR for the variable node \(v\) at the \(it\)-th iteration in the \(L_{i}\) layer, \(R^{L_{i},it}\) represents the check-to-variable messages at the \(it\)-th iteration in the \(L_{i}\) layer and \(G_{v}^{L_{i},it}\) represents the gain for the variable node \(v\) at the \(it\)-th iteration in the \(L_{i}\) layer.  
In the priority-based layered schedule, when a pipeline conflict happens to two check nodes between two adjacent layers, \(L_{i}\) and \(L_{i+1}\), new LLR will be updated in the layer \(L_{i}\). The gain \(G^{L_{i+1},it}\) will be calculated in the layer \(L_{i+1}\). The gain \(G^{L_{i+1},it}\) then will be added to the newly updated LLR later. In this way, updates of LLRs can be guaranteed no matter how the base graph matrix is dense.  
Suppose there are three check nodes in layers \(L_{i}\), \(L_{j}\) and \(L_{k}\) connected to the same variable nodes. During decoding, pipeline conflicts happen between \(L_{i}\) and \(L_{j}\), \(L_{j}\) and \(L_{k}\).  
The priority-based layered schedule works as follows. The LLR in layer \(L_{i}\) can update in priority and get \(LLR_{v}^{L_{i},it}\). Due to pipeline conflicts, the layer \(L_{j}\) reuses the old LLR value \(LLR_{v}^{L_{i},it}\) to calculate the gain \(G_{v}^{L_{i},it}\). If the update of LLR in layer \(L_{i}\) can be done before decoding the layer \(L_{k}\), then the layer \(L_{k}\) can update the value based on the result of \(LLR_{v}^{L_{i},it}\) and the gain \(G_{v}^{L_{i},it}\) can also be added to the updated LLR in layer \(L_{k}\). As shown in Figure 1, the update can be expressed as (8) and (9)  
\[LLR_{v}^{L_{i},it}=V^{L_{i},it}+R^{L_{i},it} \tag{8}\]  
\[LLR_{v}^{L_{i},it}=V^{L_{k},it}+R^{L_{k},it}+G_{v}^{L_{i},it} \tag{9}\]  
where the variable-to-check message at the \(it\)-th iteration in the layer \(L_{k}\) is denoted as \(V^{L_{k},it}\).
# Chemical Composition and Antimicrobial Properties of Honey Bee Venom
## 2 Results and Discussion
### Chemical Composition of Volatile Compounds
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Groups of Compounds**} & \multicolumn{2}{c}{**Dried Venom**} & \multicolumn{2}{c}{**Fresh Venom**} \\ \cline{2-5}  & **Dv-1** & **Dv-2** & **Fv-1** & **Fv-2** \\ \hline \multicolumn{5}{c}{Aliphatic carbonyls, including:} & 26.67 & 8.14 & 5.51 & 19.13 \\ \hline - acetone & 0.59 & 0.50 & 0.15 & 2.30 \\ \hline - 2-butanone & trace * & 0.18 & 1.18 & 1.56 \\ \hline - 2-pentanone & 1.37 & trace & 0.40 & 0.73 \\ \hline - 2-heptanone & 16.08 & 0.49 & 0.76 & 1.53 \\ \hline - 2-nonanone & 1.99 & 1.57 & 1.19 & 10.94 \\ \hline - 2-undecanone & - ** & - & 0.57 & 0.76 \\ \hline - isobutanal & 1.88 & 0.02 & - & - \\ \hline - isopentanal & 0.89 & 0.71 & - & - \\ \hline - hexanal & 1.73 & 0.01 & - & - \\ \hline - nonanal & 0.71 & trace & - & - \\ \hline - (\(2E\))-decenal & - & - & 0.30 & 0.23 \\ \hline \multicolumn{5}{c}{Aliphatic alcohols, including:} & 1.85 & 50.37 & 15.66 & 20.11 \\ \hline - ethanol & 1.46 & trace & 0.33 & 0.26 \\ \hline - isopentanol & 0.40 & 28.90 & 3.56 & 5.10 \\ \hline - (\(32\))-hexen-1-ol & - & 0.73 & 0.34 & 0.23 \\ \hline - 2-heptanol & trace & 0.21 & 0.15 & 0.40 \\ \hline - 2-nonanol & trace & 0.57 & 5.79 & 8.46 \\ \hline - 2-ethyl-1-hexanol & - & - & 1.84 & 1.38 \\ \hline - 2-undecanol & - & - & 0.42 & 0.40 \\ \hline - (\(2E\))-decen-1-ol & - & - & 0.55 & 0.79 \\ \hline \multicolumn{5}{c}{Aliphatic acids, including:} & 32.70 & 4.53 & 12.50 & 9.24 \\ \hline - formic acid & 2.37 & trace & - & - \\ \hline - acetic acid & 6.60 & 3.00 & 7.97 & 7.83 \\ \hline - isobutyric acid & - & - & 0.60 & 0.91 \\ \hline - butyric acid & trace & trace & 0.59 & 0.50 \\ \hline - hexanoic acid & 1.43 & 0.30 & 0.76 & - \\ \hline - octanoic acid & 20.72 & 0.05 & 0.93 & 0.50 \\ \hline - 2-octenoic acid & 0.50 & trace & 0.50 & 0.15 \\ \hline \multicolumn{5}{c}{Aliphatic esters, including:} & 12.49 & 14.43 & 9.13 & 9.80 \\ \hline - ethyl acetate & 0.95 & trace & 0.54 & trace \\ \hline - isoamyl acetate & trace & 0.13 & 1.45 & 4.92 \\ \hline - isoamyl isobutanoate & trace & 0.76 & 0.25 & 0.38 \\ \hline - ethyl 2-methylvalerate (manzanate) & - & - & 1.23 & 1.51 \\ \hline - isoamyl pentanoate & - & - & 0.45 & 0.83 \\ \hline - isoamyl 3-methyl-2-butenoate & - & 2.67 & 1.15 & 0.14 \\ \hline - isoamyl octanoate & trace & trace & 0.08 & 0.08 \\ \hline - isoamyl benzoate & - & 0.23 & 0.16 & 0.29 \\ \hline - isopropyl tetradecanoate & - & - & 0.60 & 1.09 \\ \hline - ethyl octanoate & 10.59 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Group composition (% of TIC) of VOCs from dry and fresh venom of honey bees.
## 3 Results  
### Ocean Wind Speed  
Figure 1 shows example DDMs taken over the open ocean from signals originating from GPS, Galileo, and BeiDou-3. The Galileo and GPS DDMs were collected simultaneously on 18 October 2018. It is clear that all three DDMs show the same general shape. However there are some differences in SNR levels that will originate from differences in signal geometry, surface conditions, and the transmitted signals. It is notable that the GPS DDM delivers the highest SNR (~1.5 dB greater than BeiDou-3 and Galileo), despite this surface having the highest wind speed conditions.  
Figure 1: Delay Doppler maps (DDMs) retrieved over the open ocean from (**a**) global positioning system (GPS) (**b**) Galileo, and (**c**) BeiDou-3. All DDMs show a similar shape with a typical DDM “horseshoe”, albeit with different SNR responses, dependent on different surface conditions, bistatic geometry, and signal characteristics. Note that these DDMs were collected over the open ocean with different surface parameters, including wind speed. GPS and Galileo DDMs were collected simultaneously on parallel tracks on 18 October 2018 but the specular points had an average spatial separation of ~214 km.  
An example Galileo track is shown in Figure 2 collected on 11 November 2015, over the ocean near the East Coast of North America. Figure 2a shows the location of the track within the ERA-5 wind speed field at the nearest hour, whilst Figure 2b shows the SNR of the track relative to the ERA-5 wind speed at the nearest point in space and time. Results indicate a well-defined inverse relationship between Galileo SNR and wind speed, similar to what has been found in previous work based on reflected GPS observations. It is also noticeable that sensitivity is reduced at higher wind speeds, where signal power is lower and the effect of noise variance becomes more visible. This Galileo track is of particularly low SNR which translates to increased dispersion of the observable already at moderate wind speeds. This track shows for the first time a clear geophysical sensitivity of spaceborne Galileo returns to ocean wind speed conditions.  
Figure 3 shows results based on all TDS-1 Galileo raw collections available at present (2015-2019), clearly showing that SNR generally tends to decrease as surface wind speed increases. The line of best fit follows a simple exponential similar to the geophysical model function (GMF) derived from GPS reflected data, and adopted in the National Oceanography Centre (NOC) calibrated bistatic radar equation (C-BRE) ocean wind speed algorithm [4]. Specifically, the line of best fit follows:  
\[\text{SNR}=\text{A}\ \text{e}^{\text{B-U10}}+\text{C} \tag{1}\]  
where A = 1.2, B = -0.5, C = 0.3, and U10 is the wind speed at a height of 10 m above ocean surface.  
Figure 2: (**a**) Shows the location of one Galileo track on 11 November 2015 (descending pass), transmitted by E19, overlaid on the ERA-5 wind speed field, (**b**) shows that Galileo SNR and wind speed present a clear along-track inverse relationship. ERA-5 U10 indicates the wind speed at 10 m above the ocean surface.
## 3 Results
### Prediction and Validation of Temperature Profile and Part Distortion  
#### 3.2.1 Validation of Temperature Profile  
Accurate prediction of distortion and residual stress relies on the transient temperature profile. Four different experimental settings (DB-1, DB-2, DB-4, and DB-5) were explored to investigate the effect of interlayer cooling time on temperature profile and the resulting distortion. Figures 9 and 10 show a comparison of the predicted temperature profile with the measured one, for the full clamp condition (Figure 9) and the center clamp condition (Figure 10). The temperatures were extracted from the model time histories at the same locations with thermo-couples of TC-13 (=Right), -15(=Left) and -17(=Center), as shown in Figure 4.  
Figure 8: Influence of build conditions on the plate distortion. The distortion increases with interlayer cooling time. Larger distortion is observed at the center clamp compared to the full clamp conditions.  
Figure 7: Tool path strategies used in this study, (**a**) unidirection, (**b**) bidirection and (**c**) bidirection with 180 degree rotation at every layer.  
The temperature profiles agree reasonably well with the thermo-couple measurements of TC-13, -15 and -17 in both the full clamp and center clamp conditions. Temperature mismatches were observed in the temperature profile, for instance, at the right region in DB-5. This is likely due to the gap created between the Ti and steel plates, which strongly influence the heat transfer through the plates during the heating and cooling processes. In our model, the gap heat transfer coefficient values were used as calibration parameters. To improve the model accuracy, a detailed measurement of the heat transfer coefficient between Ti and steel base plate as a function of contact configuration are required and are a part of our ongoing research. The calculated temperature profiles were used for mechanical simulation.
# Investigation on Unsteady Flow Characteristics of a SCO\({}_{2}\) Centrifugal Compressor
## 4 Results and Discussion
### Unsteady Forces on a Rotor Blade  
The periodic flow exciting force on a rotor blade caused by the unsteady working condition is vital when studying the vibration of the impeller and the reliability of the entire rotor.  
The axial and circumferential forces on a single rotor blade in one rotation period are presented in Figure 12. In the centrifugal compressor, the rotor blade is exposed to the violent unsteady loading due to the periodically high turbulence and energy dissipation flow generating in the cascade of the diffuser's leading edge. Figure 12 reveals obvious periodic characteristic in each of the 20 time steps (i.e., every time a rotor blade passes a diffuser flow passage).  
The Fast Fourier Transform (FFT) is usually used to investigate frequency components of exciting force. The spectral function is defined as below:  
\[p_{k}=\frac{1}{2\pi}\int_{0}^{2\pi}x(t)e^{-kt}dt \tag{13}\]  
where \(k\) is the order of exciting force.  
To study the distribution of axial and circumferential forces in time domain, FFT is employed in this paper. The spectrum analyses of the circumferential and the axial forces are conducted.  
Hence, the spectrum analyses of circumferential and axial exciting force on a rotor blade are shown in Figure 13, in which the horizontal axis represents the multiple frequencies of the sampled signal and the vertical axis is the corresponding amplitude.  
Figure 12: Unsteady forces on a rotor blade.  
Figure 13: Spectrum analyses of circumferential and axial exciting force: (**a**) circumferential; (**b**) axial.  
The largest amplitude of exciting forces lies in the frequency of 3750 Hz. Meanwhile, the exact value of circumferential force and axial force are respectively 160.2 N and 59.3 N. The exciting-vibration force factor is defined as follows:  
\[a=\frac{F_{\text{max}}}{\overline{F}} \tag{14}\]  
where \(F_{\text{max}}\) is the maximum exciting force and the denominator represents the average exciting force.  
The maximum exciting-vibration force factors are respectively 0.06 for circumferential force and 0.08 for axial force, which can guarantee the safety of the compressor operation. The exciting force of low frequency is derived from the interference between stator and rotor. The blocking effect of the diffusers results in the largest exciting force. Additionally, other frequencies of large amplitude correspond to a certain multiple of rotational frequency. For instance, the second largest amplitude 47.6 N and 6.1 N (respectively circumferential and axial) correspond to 7500 Hz, which results from the nonuniformity at the TE of rotor blades. In addition, in the spectrum figure, the exciting force magnitude of high frequency is much smaller than that of low frequency, which suggests that the low frequency should be considered in a vibration analysis.
# Morphological Changes in H1299 Human Lung Cancer Cells Following W-Band Millimeter-Wave Irradiation
## 2 Materials and Methods
### Penetration of MMW through Petri Dish and Cell Growth Medium  
The responses of five empty Petri dishes from different manufacturers to low-intensity MMW irradiation were recorded and compared. Petri dishes from Nunc(tm) (Thermo Fisher Scientific(r)) showed the best results in terms of uniformity and good transparency, hence all experiments were performed using these Petri dishes. An empty dish was placed over the lower antenna and the reflected and transmitted signals were measured in the sweeping regime from 75 to 110 GHz. Then, using a micropipette with an accuracy of 1 \(\mu\)L several doses of 0.25 mL RPMI 1640 medium were successively added to obtain volumes of 0.5, 0.75, 1.00, 1.25, 1.50, 1.75, and 2.00 mL. These volumes correspond to an RPMI 1640 medium layer thickness \(d=\) 0.52, 0.80, 1.03, 1.30, 1.56, 1.82, and 2.08 mm in the Petri dish. For each new volume, a new sweeping run was executed. From the obtained measurements in dB presented by Equation (1), the normalized reflected and transmitted powers were calculated by:  
\[\frac{P}{P_{o}}=10^{s/10}, \tag{2}\]  
where \(P\) is either the reflected or transmitted power and \(P_{o}\) the incident power, respectively.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 5 Landing Buffering and Resetting Mechanisms in Animals and Robots
### Gliding Wings Help Landing  
\"Glumper\", the jumping and gliding robot proposed by the University of Bath [10], represents the first combination of jumping and gliding. Due to the increased air resistance, added body weight, and friction caused by foldable mechanism, jumping height and distance are severely decreased [285]. Inspired by the wing structure of insects, [PERSON] et al. [92; 286; 287; 288] designed three different wing foldable mechanisms inspired by bats, butterflies, and locusts, based on their previous gliding robots. They compared the jumping performances and flight trajectories of robots: without wings, with fixed wings, and locust-inspired foldable wings. They made robots jump from a 2 m high platform. Results demonstrated that, by adding fixed wings, it is in most cases possible to increase flight distance and decrease 54% the landing impact [92]. Jumping height, instead, decreased from 1.4 m to 12 cm because of the increased body mass.  
Experimental results confirmed that the combination of gliding and jumping can greatly increase the flight distance and reduce landing shock. [PERSON] et al. proposed a jumping and gliding robot, studied the effect of gliding wings on extending the flight distance [289], and found that the foldable wings have a significant effect on reducing the resistance during the ascending phase.  
[PERSON] et al. [98] added foldable gliding wings and tails to their developed locust-inspired jumping robot, in order to help it achieve aerial righting and reduce landing shock. Experimental results show that the foldable wing, compared to the fixed wing, works better both in increasing the jumping height and in extending flight distance by decreasing gliding angle. After adding the foldable wing, their jumping robot can jump 1.7 m, which gives enough flight time for the foldable wing to unfold at the peak point of trajectory. Gliding/flapping is a common locomotion mode adopted by animals, and their actuation mechanisms is more flexible, simple, and light. In contrast, the wing flapping and unfolding wing mechanisms used in robots are much more complex both in mechanical design and control.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 6 Conclusions  
In this paper, a downlink multiuser OBON system is proposed, where multiple users are non-orthogonally multiplexed on different subcarriers with an OBF method. Compared to the conventional multiple-access systems, the spectrum efficiency of OBON is higher, due to the multiuser diversity and multiplexing gains. Then, we formulate an optimization problem to maximize the spectrum efficiency with the QoS constrains of the users. However, the optimization problem is a non-convex problem, which is difficult to solve. To obtain the solution, the proposed optimization problem is divided into two suboptimal problems, and an iterative algorithm is applied to jointly consider both the suboptimal problem. Finally, numerical results are presented. Compared to other beamforming schemes with low feedback information, it is found that the proposed system can obtain higher spectrum efficiency with lower computation complexity. Moreover, there are still many interesting topics left in OBON systems, i.e., peak to average power ratio (PAPR) problem, multiplexing more users on each subcarrier and the performance loss caused by correlated channels.  
Conceptualization, W.-B.S.; Data curation, M.-L.T.; Formal analysis, R.-Z.Z.; Project administration, L.W.; Validation, Z.-X.Y.; Investigation, X.Y.; Writing-original draft, W.-B.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China under Grant No. 61771404 and No. 61901390.  
Not applicable.  
The authors declare no conflict of interest.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods
### Analytical Methods
#### 2.3.4. Hurst Exponent (Sustainability Trend)  
The Theil-Sen trend, Mann-Kendall test, and VC are used to determine the variation trend in the past. In addition of past variation trends, it is also meaningful to address the future variation trends. Here, we use the Hurst exponent to predict whether the variation trends of the past will continue (sustainability) or not. To predict the future variation trend, the Hurst exponent was adopted in this study. The major procedure used to calculate the Hurst exponent in this study was as follows:  
1. Obtain the annual average WUE time series, \[\Big{\{}\text{WUE}_{(t)}\Big{\}},t=1,2,\cdots,n,\] (8)
2. Calculate the average WUE over the time range, \[\overline{\text{WUE}}_{(\tau)}=\frac{1}{\tau}\sum_{t=1}^{\tau}\text{WUE}_{(t )}\ \ \tau=1,2,\cdots,n,\] (9)
3. Calculate the accumulated deviation of WUE over the time range, \[X_{(t,\tau)}=\sum_{t=1}^{t}\Big{(}\text{WUE}_{(t)}-\overline{\text{WUE}}_{( \tau)}\Big{)}1\leq t\leq\tau,\] (10)
4. Create the range sequence \(R_{(\tau)}\), \[R_{(\tau)}=\max_{1\leq t\leq\tau}X_{(t,\tau)}-\min_{1\leq t\leq\tau}X_{(t, \tau)}\ \ \ \tau=1,2,\cdots,n,\] (11)
5. Create the standard deviation sequence \(S_{(\tau)}\), \[S_{(\tau)}=\left[\frac{1}{\tau}\sum_{t=1}^{\tau}\Big{(}\text{WUE}_{(t)}- \overline{\text{WUE}}_{(\tau)}\Big{)}^{2}\right]^{\frac{1}{2}}\ \ \ \tau=1,2,\cdots,n,\] (12)
6. Calculate the Hurst exponent, \[\frac{R_{(\tau)}}{S_{(\tau)}}=(c\tau)^{H},\] (13)  
where \(c\) is a constant, and the Hurst exponent \(H\) can be calculated by fitting the equation \(\log\Big{(}\frac{R}{5}\Big{)}=H\log\tau+H\log a\) using the least squares method. The range of Hurst exponent is 0-1. When \(H\) < 0.5, the time series has a trend of anticontinuity in the future, while when \(H\) > 0.5, the time series has positive sustainability. When \(H\) = 0.5, the time series is a random sequence.
## 3 Results and Discussions
### Durability  
#### 3.6.1 Sorptivity and Permeable Porosity  
Figure 10 shows the sorptivity coefficient and permeable porosity results of the blended cement mixes prepared with varying content of calcined clay and silica fume. The sorptivity coefficient was reduced by 45% and 75.8% with the addition of 30% calcined clay and 30% silica fume, respectively. It is, therefore, clear that silica fume is superior to calcined clay in resisting water absorption. The sorptivity values recorded for 20 CC10 SF, 10 CC20 SF and 15 CC15 SF were 0.028 mm/s \({}^{0.5}\), 0.019 mm/s \({}^{0.5}\) and 0.026 mm/s \({}^{0.5}\), respectively. The higher the silica fume content, the lower the sorptivity coefficient. Permeable porosity results followed a similar trend. The sole inclusion of 30% calcined clay and silica fume caused reductions in permeable porosity by 73.4% and 87.1%, respectively. The control sample showed the least sorptivity coefficient and permeable porosity values. The presence of low-grade calcined clay in the mix could not significantly impact the sorptivity and porosity of the blended cement mixes. It appears that the properties of the silica fume overshadowed and affected the performance of calcined clay in the blended mixes. The results obtained are due to the particle size distribution of the silica fume which fill the voids within the cementitious matrix, refining the pores and leading to a decrease in sorptivity and porosity. [PERSON] et al. [51] studied the influence of ternary blended silica fume and metakaolin on porosity and sorptivity and reported similar results.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Benchmark Algorithms  
To better compare the behavior and performance of the proposed algorithms, we also used several well-known opportunistic routing algorithms found in the literature. Next, a brief description of each of them is provided:  
* **Direct Delivery Routing (DDR)**[37]. This algorithm sends a message only when there is a direct communication between nodes. No messages are relayed or copied, and a direct path between source and destination must exist.
* **Epidemic Routing (ER)**[24]. In this routing algorithm, a copy of the message is sent to each neighbor. It is assumed that each node has unlimited storage space (i.e., buffer) and unlimited bandwidth.
* **MaxProp Routing (MPR)**[38]. This algorithm computes the shortest path to each destination on the basis of contact history and the number of hops. Copies are deleted when a message reaches its destination.
* **ProPHET Routing (PR)**[39]. The aim of this algorithm is to improve the routing performance by adopting the probabilistic scheme that reflects the contact observation of the nodes.
* **Spray and Wait Routing (SWR)**[40]. In this algorithm, the source node creates \(N\) copies of the message to spread it to relaying nodes. After that, the latter ones perform direct message transmission such in the case of DDR.
* **Geographic-Based Spray-and-Relay (GSaR)**[28]. The idea behind this algorithm is to exploit historical geographic information and the trajectory of nodes for making routing decisions depending on the probabilities of encounters.  
Figure 7: Mall scenario.