esa-sceva/satcom-chunk-collection huggingface dataset:
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 7 Conclusions  
Thanks to numerical channel measurements and successful demos, mm-wave communication has been considered as a feasible and potential technology for the outdoor 5G system, which could provide much larger transmission rates than conventional lower frequency communications. This article has demonstrated a backhaul system that adopts mm-wave links to replace most of the fibre links in a conventional backhaul network. A design framework has been proposed by exploiting several newly-developed physical layer technologies of mm-wave communication. By modelling the routing and scheduling together and developing optimization techniques to jointly examine them, the proposed framework significantly enhances the total capacity of the mm-wave backhaul network. Extensive simulation results have confirmed the achievable benefits of the proposed design framework. Compared to most proposals for 5G backhaul, which still considers conventional physical layer techniques, such as point-to-point beamforming, the proposed routing and scheduling scheme, which integrates new mm-wave physical layer techniques, achieves much larger throughput, a shorter delay and a smaller packet loss rate. This article thus may help open a new research direction for designing the next-generation wireless backhaul. In addition, the standardization support, e.g., hybrid beamforming, has been incorporated as one of the essential techniques in the next generation of the mm-wave system in the IEEE 802.11 ay standard, as well as cost reduction due to progress in mm-wave equipment, which may both help facilitate the practical implementation of our proposed framework in the 5G network. **Acknowledgments:** This work was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61201189 and 61132002, the National High Tech (863) Projects under Grant No. 2011 AA010202, the Research Fund of Tsinghua University under No. 2011Z05117 and 20121087985 and the Shenzhen Strategic Emerging Industry Development Special Funds under No. CXZZ20120616141708264. **Author Contributions:** [PERSON] contributed to the preparation of this paper; [PERSON] did theoretical analysis and performed simulation experiments; [PERSON] and [PERSON] analysed the simulation results; [PERSON] wrote the paper; [PERSON] and [PERSON] polished the paper. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Physicochemical Composition
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **S1** & **S2** & **S3** & **S4** \\ \hline \multirow{4}{*}{Chemical Properties} & Moisture & 74.25 \(\pm\) 0.52 \({}^{\text{c}}\) & 8.25 \(\pm\) 0.07 \({}^{\text{a}}\) & 11.85 \(\pm\) 0.13 \({}^{\text{a}}\) & 41.35 \(\pm\) 0.47 \({}^{\text{b}}\) \\  & Dry matter & 97.51 \(\pm\) 4.55 \({}^{\text{c}}\) & 94.75\(\pm\)3.77 \({}^{\text{b}}\) & 93.63\(\pm\)3.33 \({}^{\text{a}}\) & 92.65 \(\pm\) 2.52 \({}^{\text{a}}\) \\  & Protein & 7.68 \(\pm\) 0.22 \({}^{\text{a}}\) & 69.54 \(\pm\) 3.44 \({}^{\text{d}}\) & 54.52 \(\pm\) 3.01 \({}^{\text{c}}\) & 37.42 \(\pm\) 2.68 \({}^{\text{b}}\) \\  & Lipid & Nd & 7.18 \(\pm\) 0.42 \({}^{\text{c}}\) & 5.15 \(\pm\) 0.28 \({}^{\text{b}}\) & 2.75 \(\pm\) 0.19 \({}^{\text{a}}\) \\  & Ash & 5.02 \(\pm\) 0.51 \({}^{\text{a}}\) & 10.69 \(\pm\) 0.32 \({}^{\text{c}}\) & 8.07 \(\pm\) 0.49 \({}^{\text{b}}\) & 7.54 \(\pm\) 0.23 \({}^{\text{b}}\) \\ \hline \multirow{4}{*}{P phytochemicalentical Content} & TPC & 131.14 \(\pm\) 5.25 \({}^{\text{d}}\) & 4.59 \(\pm\) 0.21 \({}^{\text{a}}\) & 34.5 \(\pm\) 1.72 \({}^{\text{b}}\) & 66.75 \(\pm\) 3.25 \({}^{\text{c}}\) \\  & TFC & 6.75 \(\pm\) 0.25 \({}^{\text{d}}\) & 2.75 \(\pm\) 0.05 \({}^{\text{a}}\) & 3.41 \(\pm\) 0.12 \({}^{\text{c}}\) & 4.27 \(\pm\) 0.18 \({}^{\text{b}}\) \\  & TAC & 24.51 \(\pm\) 1.15 \({}^{\text{d}}\) & 0.51 \(\pm\) 0.01 \({}^{\text{a}}\) & 5.87 \(\pm\) 0.05 \({}^{\text{b}}\) & 12.02 \(\pm\) 0.65 \({}^{\text{c}}\) \\ \hline \multicolumn{6}{c}{S1 (pomerangate peel), S2 (_Spirulina_), S3 (pomeranate peel–_Spirulina_: 25/75%) and S4 (pomerangate peel–_Spirulina_: 50/50%). \(\pm\): standard deviation of the mean; values with a different letter (a–d) of the same chemical properties or phytochemicalentical content are significantly different (\(p<0.05\)).} \\ \end{tabular}
\end{table} 
Table 1: Physicochemical composition (moisture (%), dry matter (%), protein (%), lipid (%) and ash (%)) and phytochemical content total polyphenols content: TPC (mg GAE/g), total flavonoids content: TFC (mg QE/g)) and total anthocyanins content: TAC (mg cy-3-glu/100 g) of the extracts from _Spirulina platensis_ and its combinations with pomeranate peel.
## 3 Current Research and Applications
### Road Safety  
One of the widely explored applications of UAVs in the transportation domain is road safety. The road safety-based applications of UAVs include accident investigation, risk assessment, and monitoring of the overall road network. To investigate a specific traffic incident, images collected by UAVs have been used to generate an incident scene. With the advancements in UAV technology, the current research focuses on the software capabilities related to image processing and scene reconstruction. [PERSON] et al. [84] proposed a Structure-from-Motion (SFM) algorithm that uses UAV photogrammetry to reconstruct a 3D traffic accident scene. [PERSON] [85] used 3D and orthophoto computer models for the same purpose. Moreover, research efforts have been made for UAV flight planning and control [86], capturing accident-related video data by varying heights and angles, data transferring methods, generating 3D models, and accuracy analysis [87].
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
###### Abstract  
This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.  
energy management; data analytics; LNG satellite plants; predicting LNG consumption; fault detection +
Footnote †: journal: Applied system
# A Two-Dimensional Non-Destructive Beam Monitoring Detector for Ion Beams
## 2 Materials and Methods
This means that if the film is irradiated below this level, a heatmap of the change in luminance will directly visualise the beam profile shape. The UniBeaM detector measures the horizontal and vertical beam profiles separately by means of two scintillating fibres. The fibre measuring the horizontal profile is oriented vertically and performs a continuous horizontal sweep of the sensitive area. Analogously, the fibre measuring the vertical profile is oriented horizontally and performs a vertical sweep. The scintillating fibres are coupled to optical fibre cables to transmit the scintillating light to a dedicated readout unit. The next section provides a comprehensive overview of the experiments conducted to evaluate the performance of the \(\pi^{2}\) detector. The following tests were performed with the aim of characterising its capabilities and assessing its suitability for proton beam profiling applications:  
1. The first experiment aimed at examining the response function of the apparatus with varying proton flux, and at establishing a calibration for displaying the beam footprint in terms of proton flux. 2. The second experiment aimed at validating the beam profile measurements by the \(\pi^{2}\) and involved a comparison between the measured beam profile and those obtained from radiochromic films and the UniBeaM detector. The comparison was carried out for parallel beams and focused beams, respectively. 3. Beam Scattering Analysis: The third experiment focused on characterising the amount of beam scattering introduced by the \(\pi^{2}\) foil. The goal was to evaluate the impact of the foil on the beam profile downstream of the detector. 4. Radiation Tolerance Study: The final experiment aimed at investigating the radiation tolerance of the scintillator material used in the \(\pi^{2}\) detector. The goal was to determine if the light yield of the scintillator is affected by exposure to radiation and to assess the long-term stability of the detector. The experimental setups for each of these tests are depicted in Figure 4.  
Figure 3: Raw (**left**) and corrected image (**right**) of a beam collimated to a \((2.5\times 2.5)\) cm\({}^{2}\) square. The raw image is used to determine a transformation matrix to correct for the perspective distortion.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 4 Solution Method
### Evaluation of Penalties
#### 4.2.1 Forward Functions  
We introduce a set of variables for propagating from node \(i\) to node \(j\). In particular, variable \(a_{j}^{min}\) (resp. \(a_{j}^{max}\)) denotes the earliest allowed arrival time adding the minimum required (resp. the maximum possible) charging time at previous CSs. Variables \(\tilde{a}_{j}^{min}\) and \(\tilde{a}_{j}^{max}\) replace \(a_{j}^{min}\) and \(a_{j}^{max}\), respectively, when penalties occur in order to avoid over-penalization. In addition, slack time due to the waiting time before node \(j\) is served results in the available time for charging, denoted by \(a_{j}^{sl}\). Variable \(a_{j}^{rt}\) tracks the inverse residual battery capacity at node \(j\). Finally, variable \(a_{ij}^{add}\) determines the minimum charging time in order to reach from node \(i\) to node \(j\). Forward equations derived from [52] are shown as follows. To shorten forward and backward functions, values \(t_{ij}\) also involve service time at node \(i\).  
\[a_{j}^{min}=\max\{e_{j},\tilde{a}_{i}^{min}+t_{ij}\}+a_{ij}^{add} \tag{22}\]  
\[a_{j}^{max}=\begin{cases}\max\{e_{j},\tilde{a}_{i}^{min}+\max\{0,a_{i}^{rt}-s_ {i}\}+t_{ij}\}&\text{if $i\in F$}\\ \max\{e_{j},\tilde{a}_{i}^{max}+t_{ij}\}&\text{else}\end{cases} \tag{23}\]\[\hat{a}_{j}^{min}=\min\{a_{j}^{min},a_{j}^{max},l_{j}\} \tag{24}\]  
\[\hat{a}_{j}^{max}=\min\{l_{j},\hat{a}_{j}^{min}+\max\{a_{j}^{max}-a_{j}^{min},0\}\} \tag{25}\]  
\[a_{ij}^{sl}=\max\{0,e_{j}-\hat{a}_{i}^{min}-t_{ij}\} \tag{26}\]  
\[a_{j}^{rt}=\begin{cases}\min\{H,\max\{0,a_{i}^{rt}-s_{i}-a_{j}^{sl}\}+h_{ij}\}& \text{if $i\in F$}\\ \min\{H,\max\{0,a_{i}^{rt}-\min\{a_{j}^{sl},a_{i}^{max}-\hat{a}_{i}^{min}\}\}+ h_{ij}\}&\text{else}\end{cases} \tag{27}\]  
\[a_{ij}^{add}=\begin{cases}\max\{0,\max\{0,a_{i}^{rt}-s_{i}-a_{j}^{sl}\}+h_{ij }-H\}&\text{if $i\in F$}\\ \max\{0,\max\{0,a_{i}^{rt}-\min\{a_{j}^{sl},\hat{a}_{i}^{max},\hat{a}_{i}^{ min}\}\}+h_{ij}-H\}&\text{else}\end{cases} \tag{28}\]  
At the starting depot 0, all values (i.e., \(a_{0}^{min}\), \(a_{0}^{max}\), \(a_{0}^{sl}\), \(a_{0}^{rt}\), and \(a_{0}^{add}\)) are initialized as 0. Based on the above-mentioned variables, forward battery \(\overrightarrow{\lambda}_{B}\) and time window \(\overrightarrow{\lambda}_{T}\) penalties for each route \(r\) are computed as follows.  
\[\overrightarrow{\lambda}_{B}(V)=\sum_{j\in r}\max\{a_{j}^{min}-a_{j}^{max},0\} \tag{29}\]  
\[\overrightarrow{\lambda}_{T}(V)=\sum_{j\in r}\max\{\min\{a_{j}^{min},a_{j}^{ max}\}-l_{j},0\} \tag{30}\]
## 4 Discussion
### Fundamental Phonatory Parameters  
In comparison with the mean values of [PERSON] et al. (2016), it is noticeable that the fundamental frequency \(\text{f}_{0}\) shows higher mean values (+8%) as do the minimum (+9%) and maximum values (+4%); see Table 2. This may be an indication for pathological voice, since [PERSON] et al. (2016) found a relationship between incomplete glottis closure and an increase of the fundamental frequency, while investigating data of patients with vocal fold paralysis. Also, similar findings were reported by [PERSON] et al. (2016) who found a correlation between jitter and the degree of dysphonia by considering the mean fundamental frequency. Hence, an increased fundamental frequency may be seen as an indicator of a pathological or disordered voice ([PERSON] et al., 2017). Air flow strength here (120 mL/s) is close to the value of [PERSON] et al. (2016) of 117 mL/s, which was to be expected since the data were collected during the same series of experiments.  
Figure 7 shows the fundamental frequencies (GAW, audio and subglottal). This figure shows that the frequency of the GAW is the lowest and that of the subglottal and audio measurements tend to be higher. However, due to the aperiodicity of the signals, this tendency is not necessarily a causal interrelation. Hence, an interpretation of this effect is difficult.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Mean \(\pm\) standard deviation**} & \multirow{2}{*}{**Tendency for Group\({}_{\text{S,A}}\)**} \\ \cline{2-3}  & **Groups** & & \\ \hline \multicolumn{3}{c}{**(A) GAW measures**} \\ \hline
**ALR (a.u.)** & 18.0 \(\pm\) 5.5 & 8.1 \(\pm\) 4.4 & decrease \\
**STIFNFNESS (frames\({}^{-1}\))** & 0.30 \(\pm\) 0.06 & 0.28 \(\pm\) 0.05 & decrease \\
**ASQ (a.u.)** & 0.50 \(\pm\) 0.14 & 0.59 \(\pm\) 0.14 & increase \\
**CQ (a.u.)** & 0.49 \(\pm\) 0.15 & 0.35 \(\pm\) 0.11 & decrease \\
**OQ (a.u.)** & 0.96 \(\pm\) 0.06 & 0.93 \(\pm\) 0.15 & decrease \\
**SQ (a.u.)** & 1.46 \(\pm\) 1.11 & 2.18 \(\pm\) 2.40 & increase \\
**ASI (a.u.)** & 0.81 \(\pm\) 0.13 & 0.79 \(\pm\) 0.11 & decrease \\
**PAI (a.u.)** & 0.11 \(\pm\) 0.13 & 0.13 \(\pm\) 0.05 & increase \\ \hline \multicolumn{3}{c}{**(B) Aerodynamic parameters**} \\ \hline \(\text{R}_{\text{B}}\) (\(\text{Pa s}^{-1}\)) & 11001 \(\pm\) 5846 & 12363 \(\pm\) 5387 & increase \\
**SPL (dB)** & 78.1 \(\pm\) 6.8 & 65.9 \(\pm\) 7.3 & decrease \\
**P\({}_{\text{S}}\) (Pa)** & 1370 \(\pm\) 824 & 1223 \(\pm\) 781 & decrease \\ \hline \multicolumn{3}{c}{**(C) Harmonic measures**} \\ \hline \(\text{CPP}_{\text{A}}\) (dB) & 16.1 \(\pm\) 6.2 & 15.4 \(\pm\) 5.3 & decrease \\
**CPP\({}_{\text{P}}\) (dB)** & 17.9 \(\pm\) 6.0 & 16.0 \(\pm\) 4.5 & decrease \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean values and standard deviations for the GAW, aerodynamic and harmonic parameters of the two different vibrational characteristics with tendencies.
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 4 Materials and Methods
### Squalene Quantification by GC-MS  
The separation and quantification of squalene were conducted on a DB-5 ms capillary column (30 m length, 250 \(\upmu\)m internal diameter, and 0.25 \(\upmu\)m film thickness: Agilent Technologies J&W Scientific, Santa Clara, CA, USA, part number 122-5532G) using an Agilent Technologies 6890 Series gas chromatograph (GC) system. The GC system was coupled to a Leap Technologies COMBIPAL (CTC Analytics, Zwingen, Switzerland) autosampler. A sample injection volume of 1 \(\upmu\)L was used throughout, and the sample was injected in the splitless mode. Helium was used as the carrier gas at a constant flow rate of 1.3 mL/min. For the GC temperature gradient program, initial isothermal heating of 210 \({}^{\circ}\)C was applied for 1 min; the temperature was then increased to 280 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min for 3.5 min, and held at 280 \({}^{\circ}\)C for 3 min, increased to 300 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min for 1 min, and further held at 300 \({}^{\circ}\)C for 3 min. The total fun time was 11.5 min. The Agilent Technologies 6890 Series GC system was coupled to an Agilent 5973 network mass selective detector. The temperature of the inlet, transfer interface, and ion source was set to 250, 250, and 230 \({}^{\circ}\)C, respectively. A solvent delay of 2 min was implemented throughout the course of the experiment. Electron impact ionization (70 eV) was used, and an acquisition rate of 0.98 cycles/s was employed. The fragment ions used for selected ion monitoring experiments were 69, 81, 136, 137, and 410 \(m/z\) (the parent ion). The identification of squalene was based on the retention time and the abundance of the fragment ions. The fragment ion at 69 \(m/z\) was used for quantification since it was the most abundant ion in the mass spectrum of squalene. Data acquisition and analysis were performed by the Chemstation software package (from Agilent Technologies). Squalene from the guayule extract was quantified via a seven-point calibration curve, ranging from 1.5625 to 100 \(\upmu\)M. The R\({}^{2}\) coefficients for the calibration curves used in this study were >0.99 (with a 0-intercept setting).
## Appendix A Basic Concept of Allan Variance
### Representation of Noise Terms in Allan Variance  
Some sources of the noise will be subsequently introduced in the subsections. They are expected to exist in the clock drift in a typical crystal oscillator of the GNSS receiver which are suspected to have some effects on the performance of the navigation solutions. The Allan variance is frequently applied to identify and quantify the noise terms of the data outputs for the inertial sensors [64]. Similarly, the idea that the Allan variance is used for inertial sensor error modelling can be taken to quantifyand identify the noise source in the clock drift of the GNSS receiver. Some examples will be provided referring to the previous descriptions for the representation of noise terms in Allan variance [64].
# 3D Shape Generation via Variational Autoencoder with
## 3 Proposed 3D-VAE-SDFRaGAN
### 3D-VAE-SDFRaGAN Framework
#### 3.3.1 2D-Image Encoder Network \((E)\)  
The latent representation of a 2D image is learned by the encoder network in the proposed 3D-VAE-SDFRaGAN framework. This network is composed of five convolutional layers with channels of \([64,128,256,512,400]\), kernel sizes of \(\{11,5,5,5,8\}\), and strides of \(\{4,2,2,2,1\}\). To ensure the output of the convolutional layers is free of negative values and has consistent mean and variance, ReLU activation functions and batch normalization layers  
Figure 2: The overall architecture of the proposed 3D-VAE-SDFRaGAN network. (**a**) the encoder network, which converts a 2D image to latent spaces; (**b**) the generator network, which produces an SDF from a 2D image latent vector; and (**c**) the relativistic discriminator network, which evaluates the likelihood of an actual SDF being more authentic than a randomly selected generated SDF from the generator.  
are included between the convolutional layers. A 400-dimensional latent vector is outputted by the last layer of the encoder network, which is split into a 200-dimensional mean latent vector and a 200-dimensional variance latent vector. Furthermore, a 200-dimensional latent vector is sampled from the Gaussian distribution by the encoder network's sampling layer. The loss function of the encoder network (\(L_{E}\)) is composed of two parts: the KL divergence loss (\(L_{KL}\)) and the reconstruction loss (\(L_{R}\)) defined as  
\[L_{KL}=D_{KL}(q(z_{img}|img)\|p(z)) \tag{5}\]  
\[L_{R}=\|G(\mathbb{E}(img))-sdf_{real}\|_{2} \tag{6}\]  
where \(L_{KL}\) is divergence loss between the learned latent distribution (\(z_{img}\)) and the prior distribution \(p(z)\) from a uniform distribution over \([-1,1]\), \(sdf_{real}\) is an SDF of a 3D shape from the training set, \(img\) is the corresponding 2D image, \(q(z_{img}|img)\) denotes the variational distribution of the latent representation.  
The KL divergence loss measures the difference between the learned latent distribution (\(q(z_{img}|img)\)) and the prior distribution \(p(z)\). The reconstruction loss measures the difference between the output of the SDF-generator network when fed with the mean latent vector (\(G(\mathbb{E}(img))\)) and the ground truth SDF of the 3D shape from the training set (\(sdf_{real}\)). The overall loss function encourages the encoder network to generate a latent representation that both follows the prior distribution and accurately reconstructs the SDF of the 3D shape. To allow the SDF-generator network to sample \(z_{img}\) from the same distribution as \(p(z)\), KL divergence is used to limit \(q(z_{img}|img)\) to be as similar to \(p(z)\) as possible.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 4 Results and Discussion
### Spatial and Temporal Distribution of Chl-a Concentration in the Study Area  
Through the comparison of the above experiments, the OGolden-DBO-XGBoost model proposed in this paper has the best effect on the Chl-a concentration inversion in Wuliangsu Lake. Based on this model, the Chl-a concentration inversion results of Wuliangsu Lake were obtained, and the Chl-a concentration distribution map of Wuliangsu Lake was plotted, as shown in Figure 9. From the figure, it can be seen that the Chl-a concentration in Wuliangsu Lake has obvious temporal characteristics. In June and July, due to the low water temperature in the north, which is not suitable for the growth of algae and other plankton, the overall Chl-a concentration in June 2016, and June and July 2017 was low, mainly concentrated in the range 1-11 \(\upmu\)g/L. Meanwhile, in August and September, Wuliangsu Lake needs to undertake the drainage of farmland in the river-loop irrigation area, and a large amount of phosphorus and other nutrients were discharged into the lake, which provided the growth of algae organisms with nutrients, leading to a sharp increase in Chl-a concentration. As can be seen from Figure 9, the Chl-a concentration was higher in September 2015 and August and September 2017; it was generally higher than 10 \(\upmu\)g/L, reaching a peak in August, with some areas exceeding 30 \(\upmu\)g/L. Meanwhile, it can be clearly seen that the Chl-a concentration in the northern region of Wuliangsu Lake was generally higher than that in the southern region. In the spatial and temporal distribution map of Chl-a concentration in August and September 2017, the central region had the highest Chl-a concentration. This was due to the fact that the ditches for discharging agricultural irrigation wastewater were located in the central part of Wuliangsu Lake, a large number of reeds were distributed in the northern region [34], and pollutants were aggregated to the northern region, which resulted in higher Chl-a concentrations and higher eutrophication in the northern region than in the southern region. These findings are highly consistent with those of other scholars [35; 36].  
Figure 9: Distribution of Chl-a concentration in Wuliangsu Lake.