esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Crocetin Esters by HPLC-PDA
#### 2.8.2 HPLC-PDA  
The identification and quantification of crocetin esters were carried out by high-performance liquid chromatography (HPLC) using HPLC AltusTM 10 PerkinElmer (Waltham, MA, USA) equipped with a C18 KromaPhase column (150 \(\times\) 4.6 mm inner diameter, 3.5 \(\mu\)m) (Scharlab, Barcelona, Spain) that was equilibrated at 30 \({}^{\circ}\)C. The analysis were carried out as described by [PERSON] et al. [17] with some modifications. The eluents used were acetonitrile (A) and water (B), with a proportion of 20% A and 80% B. The flow rate was 0.8 mL/min, and the injection volume of saffron extracts was 25 \(\mu\)L. Crocetin esters were measured at 440 nm in UV-VIS (UV-6300 PC double beam spectrophotometer) with the Photodiode Array (PDA) Detector (PerkinElmer, Waltham, MA, USA).  
The identifications of crocetin esters, such as _trans_-crocetin di(\(\beta\)-D-gentibiosyl) ester (_trans_-4-GG), and _trans_-crocetin (\(\beta\)-D-glucosyl)-(\(\beta\)-D-gentibiosyl) ester (_trans_-3-Gg), were determined using the UV\(-\)VIS spectrum, and the retention time was carried out by the HPLC\(-\)PDA method at 440 nm. Their quantification was performed through calibration curves with the standards, y = 0.0075x \(-\) 0.0080 (R value = 0.99) for _trans_-4-GG in the range from 0.8 to 50 mg/L, and y = 0.0071x\(-\) 0.0047 (R value = 0.99) for _trans_-3-Gg concentration in the range from 0.8 to 25 mg/L.
## 2 Distributed Generation Technology
### Distributed Generation Types and Capacity  
There are two possibilities of DG; Grid connected and autonomous DG [40]. These possibilities can be split up into two groups depending on the availability: firm and intermittent power. The firm power generation which gives the ability to control the power of DG as a function of the demand. \"Firm DG plants\" are often classified as supporters; in periods of high consumption. The intermittent power generations cannot be depended on for fulfilling all load requirements, having such a generation character. For this type of power generation technology there is wind power technology or solar power technology that is capable of energy production only when the wind is blowing and the sun is shining [8; 9; 10].  
Subsequently, DG is able to support traditional generation, for example, gas turbines, and internal combustion engines and, in huge institution, combined-cycle turbines steam turbines. Different types such as, Stirling engines micro turbines, renewable energies or fuel cells, including solar energy, geothermal energy, or wind energy can be used [8; 9; 10].  
As earlier mentioned, the effect of DG units strongly depends on the type of DG unit. Generally, four major types of DG are classified, based on their real and reactive power delivering the capability [36], as shown in Table 1. Considering the single line diagram shown in Figure 1, assuming the real power output of DG connected to bus \(n\) is \(P_{DGn}\), the DG reactive power output is \(Q_{DGn}\), the DG power factor is \(PF_{DGn}\), the real power of the load connected to bus \(n\) is \(P_{Ln}\), the reactive power of the load connected to bus \(n\) is \(Q_{Ln}\). Then the active and reactive power injected at bus \(n\), where the DG is located, are given by [39]:  
\[P_{n}=P_{DGn}-P_{Ln} \tag{1}\]  
\[Q_{n}=Q_{DGn}-Q_{Ln} \tag{2}\]  
in which  
\[Q_{DGn}=cP_{DGn} \tag{3}\]  
where  
\[c=(sign)\tan\Bigl{(}\cos^{-1}(PF_{DGn})\Bigr{)} \tag{4}\]  
The sign function is defined as:  
\[sign=\left\{\begin{array}{ll}+1;&\quad DG\;\text{\emph{injecting reactive power}}\\ -1;&\quad DG\;\text{\emph{consuming reactive power}}\end{array}\right\} \tag{5}\]  
The characteristics of the four DG types are listed in Table 2.  
Figure 1: Part of a distribution system with distributed generation (DG).  
In order to study the impact of DGs capacity on the network operation characteristics, the Penetration Level of DGs can be defined as the ratio of the total complex power generated from DGs (\(\sum S_{\text{DG}}\)) to the total complex power peak load demand (\(\sum S_{\text{Peak}}\)) and it can be calculated as [3; 4; 5; 6; 7]:  
\[\text{Penetration Level}=\frac{\sum S_{\text{DG}}}{\sum S_{\text{Peak}}}*100\% \tag{6}\]
# A Robust Localization System Fusion Vision-CNN
## 7 Conclusions  
In this paper, based on the original posenetLSTM convolutional neural network, laser information is added as a geometric constraint and the rotation loss function is redesigned. Both simulation and experiment prove that the accuracy of visual relocalization can be greatly improved after such modification. Taking visual pose estimation as coarse estimation and using the correlation scanning method for particle sampling in combination with the branch and bound method to quickly search for the optimal solution, the rapid, high-precision relocation of the system is realized. The system can complete accurate relocalization within 0.4 s without manual assistance, which greatly enhances the robustness of the system in the case of an unknown initial pose and the loss of pose tracking. In the pose tracking stage, we use a method combining correlation scanning and nonlinear optimization; compared with the AMCL algorithm, it improves accuracy and greatly improves real-time performance. In the experiment, compared with the AMCL method, the localization accuracy of the system in \(x\) and in \(y\) directions can be improved by 10% and 12%, respectively, and the calculation time can be reduced by 83.87% and 83.92%. In the future, we will study how to remove the measurement of dynamic objects (for example, according to the velocity estimation of dynamic objects) to improve the positioning accuracy of the system. At the same time, the neural network used in this paper can only perform relocation. Next, we will combine information such as semantic segmentation and odometry to improve the positioning accuracy of the system and the ability to complete multiple tasks.  
Conceptualization, Y.L. and C.Z.; methodology, C.Z.; software, Y.L.; validation, Y.L., C.Z. and Y.W.; formal analysis, C.Z.; investigation, C.Z.; resources, Y.L.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, Y.W.; visualization, C.Z.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Robotics and System, grant number SKLRS201813B.  
Not applicable.  
All the data generated during the experiments are presented in the article.  
The authors declare no conflict of interest.
## 4 Experimental Results and Analysis
### Wind Speed Decomposition  
For the prediction of wind speed data, the nonlinear characteristics of the original sequence may lead to a limited applicability of the prediction model. CEEMDAN is used to decompose the original wind speed series into different modal functions, and each IMF has a clear frequency characteristic, which effectively eliminates the nonlinear characteristics in the original sequence.  
In this study, the CEEMDAN algorithm was used to preprocess the original wind speed series. The noise weight was set to 0.2, and the number of noise additions was 100. The decomposition results of the CEEMDAN are shown in Figure 7. From the figure, the results show that the CEEMDAN effectively decomposed the original sequence into components with significant frequency characteristics, and there was no obvious oscillation of the original signal at different time scales. Starting from IMF4, the periodic variation in the wind speed series was captured.  
Figure 6: Raw wind speed series.  
The wind speed series obtained after reconstructing the IMF component of the original signal is shown in Figure 8. The reconstructed signal is smoother and more stable and is highly consistent with the original signal, verifying the effectiveness of the signal reconstruction. By using the CEEMDAN algorithm for decomposition, key periodic features in the signal are successfully retained, while fluctuations are reduced, and harmonic interference is removed. This noise reduction treatment significantly improves the prediction accuracy and overall stability of the model.  
Figure 8: Comparison of the original and reconstructed signals.  
Figure 7: Results of CEEMDAN of wind speed series.
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 1 Introduction
Information measured through EC allows for the better comprehension of biogeochemical cycles, energy fluxes and other atmospheric controls. It also provides clues to other pivotal questions regarding ecosystem (in this study, tropical ecosystems) controls over climate. However, due to the lack of an extensive EC measurement network throughout Brazil, other data sources are required, such as satellite-derived data. Our hypothesis is that both gross primary production (GPP) and water-use efficiency (WUE) data respond differently to varied water availability conditions on the main tropical ecosystems in Brazil. Thus, our objective is to analyze the dynamics of GPP and WUE in the Amazon, Cerrado (savanna), Pantanal and Caatinga biomes through data collected through EC systems and to assess the performance of data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for the quantification of CO\({}_{2}\) balance components over these environments.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 2 Related Work
### Extended Variational (Graph) Autoencoders  
The variational (graph) autoencoder assumes that the latent variables' prior is a standard normal distribution, which easily causes the posterior to collapse to the prior.  
Recently, many methods have been proposed to study the problem of posterior collapse. Some methods solve the problem by adding additional loss. For example, [PERSON] et al. [9] and [PERSON] et al. [10] analyzed the activity of the hidden layer units and divided these units into multiple groups. In addition, they introduced a hidden variable to specify the group corresponding to each sample. [PERSON] et al. [11] introduced mutual information between a sample and a hidden variable in the loss function. Some others employ a more complex distribution to construct the prior of variational (graph) autoencoder. For instance, [PERSON] and [PERSON] [13] replaced the prior and posterior of latent variables with the multivariate Student's t-distribution, which was regarded as weak information. [PERSON] et al. [14] and [PERSON] et al. [15] applied a Dirichlet distribution as the prior. [PERSON] and [PERSON] [16] utilized pseudo-input to learn an approximate mixed posterior and utilize the posterior to construct a more flexible prior. [PERSON] et al. [17] utilized Gaussian mixture distribution to construct a more complex prior and posterior distribution, for the reason that they were convinced the latent space might be multi-peak.  
There are also some works with the idea of the noninformative prior distribution. [PERSON] et al. [21] were the first to apply the noninformative prior to neural networks and analyzed the advantage of this prior. [PERSON] et al. [22] expanded [23] and proposed a variational graph autoencoder based on the community discovery task. They generalized the generation process of VGAE to the mixed Gaussian distribution by introducing a community assignment parameter. Moreover, they assumed that the noninformative prior of the community assignment parameter was a uniform distribution, i.e., the probability that a node belonged to each community was the same initially. Although the model improved community division performance, it could be utilized only for the community division task.
### Case Study No. 3: OpenPLC Linked with Node-RED Acting as a Software IIoT Gateway
#### 6.3.5 Communication from Factory I/O to OpenPLC  
In this subsection, we will show how the communication from Factory I/O towards the OpenPLC runtime takes place. Factory I/O needs to send values to the OpenPLC runtime from inputs, i.e., from sensors that detect the presence of a product. For this we need the variables **fio_Sensor_1_Q1-0** and **fio_Sensor_2_Q1-1**, which we will send to OpenPLC. In Figure 29 we see on the left two nodes of type _Inject_ named after specific variables. These nodes are queried every 500 ms for the values of these variables by the _OPC UA client_ node. This node then returns the value of the first or second sensor, and this is detected using the custom functions that follow in the flow (functions starting with the word _if_). Depending on the type of value, the value is written using _Modbus Write_ to address %QX1.0 (address 8 in Modbus) or %QX1.1 (address 9 in Modbus) in OpenPLC runtime.  
In a similar way, a program in Node-RED could be extended to control an entire production line.
## 3 Experimental Section
### Validation of the Quantitative Method  
#### 3.5.1 Linearity of Calibration Curve in Different Wine Matrix  
All 15 chiral monoterenes were quantified using six point calibration curves. Two de-aromatized wines were prepared as the base wine: one was a 2012 Pinot Grigio, alcohol content 12.3%, residual sugar 3.7 g/L, defined as the Low RS (L) wine base; another one was a 2012 late harvest Riesling, alcohol content 9.6%, residual sugar 64.1 g/L, defined as the High RS (H) wine base. Wine (100 mL) was de-aromatized using a rotary evaporator (Buchi heating bath B-490, Newcastle, DE, USA) at 35 \({}^{\circ}\)C water bath and 135 rpm rotation for 2 h under 63 cmHg vacuum. The dearomatized wine was adjusted with ethanol solution to its original alcohol content and dH\({}_{2}\)O was added to bring the volume up to the original 100 mL, ensuring the same nonvolatile matrix. The dearomatized wine contained only trace amounts of linalool oxide and \(\alpha\)-terpineol isomers. These concentrations were subtracted to from the calibration curves to ensure accurate measurements.  
Six point calibration curves for all compounds were plotted. Compounds were added to de-aromatized wine base at specific concentrations (Table 6) in 20 mL amber glass, screw cap vials, 22.5 \(\times\) 75.5 mm, followed by the composite isotopically-labelled internal standard solution (40 \(\mu\)L), to make a final volume of 9.0 mL.  
Over the lifetime of the SPME fiber, the partition coefficients of volatile compounds with the SPME fiber may have varied thus resulting in poor fiber repeatability. Calibration curves were divided into three parts at the beginning of a new fiber, middle use of fiber and at the last life of fiber. In total six kinds of calibration curves were plotted in terms of wine matrix and fiber over time. Each calibration curve was generated in triplicate (Table 6).  
5.2 Limit of Detection (LOD), Limit of Quantitation (LOQ), Wine Reproducibility and Internal Standards Stability  
LOD and LOQ (Table 2) were calculated as in [56] and [57]. Two wine samples (2011 dry Riesling, ALC of 10.8%, RS of 0.58 g/L; 2013 Pinot Gris, ALC of 11.8%, RS of 16.43 g/L) were measured every 2 days over the course of the analysis period to determine stability and reproducibility. The wine sampleswere all kept frozen (at -18 \({}^{\circ}\)C) prior to analysis. The Standard 4 point of the calibration curve was made fresh and measured every 2 days over the course of the analysis period to measure internal standard stability (Table 2).
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN
### IGWO-Based BPNN Parameter Optimization
#### 4.2.3 IGWO-Based BPNN Parameter Optimization  
In IGWO-BPNN, the hyperparameters of the BPNN are optimized as the position of the grey wolves. By updating the position, the hyperparameters of the BPNN are dynamically optimized, thus obtaining the global optimal solution. The parameter optimization process of the BPNN based on IGWO is shown in Figure 7.  
Figure 6: Opposition-based learning strategy. B, C and D denote the optimal solutions; the reverse solutions (B\({}^{1}\), C\({}^{1}\) and D\({}^{1}\)) are obtained through OBL strategy.  
The steps to optimize the hyperparameters of the BPNN through IGWO are as follows:  
**Step 1**: Initializing parameters of IGWO and BPNN. The initial position of the grey wolves is determined according to Equations (17) and (18). The population size is set to \(M\). The maximum number of iterations is \(T_{max}\). The upper and lower boundaries of the \(j\)-th dimension are set to \(\overline{X}_{j}\underline{X}_{j}\), respectively. The connection weight of each layer of the neural network is \(\omega\). The error threshold is \(\varepsilon\), and the number of neurons in the hidden layer is \(n\).  
**Step 2**: Data preprocessing. In order to eliminate the influence of the residual dimension, the residuals are normalized to \([-1,1]\) according to Equation (22) and divided into a training dataset and a testing dataset.  
\[x_{i}=\frac{2(x^{\prime}_{i}-x_{\min})}{x_{\max}-x_{\min}}-1 \tag{23}\]  
where \(x_{i}\) denotes the normalized data, \(x^{\prime}_{i}\) is the \(i\)-th raw data and \(x_{\max}\) and \(x_{\min}\) represent the maximum and minimum of the corresponding data, respectively.  
**Step 3**: The termination condition (i.e., the current solution is the minimum) is determined as follows: If a value remains constant in continuous iterations, it is considered to be the minimum value. The selected sample performs **Step 4-Step 7** below until the termination condition is met and then exits.  
**Step 4**: The position of each individual contains the hyperparameters of the BPNN, which are trained on the training dataset according to Equation (16) to Equation (18) to constructing the corresponding BPNN model.  
**Step 5**: The performance of the BPNN was evaluated with the testing dataset and mean square error (MSE) was utilized to calculate the fitness \(fit_{i}\).  
**Step 6**: The grey wolf population was graded according to \(fit_{i}\). The best adapted individual was retained, and the remaining individuals were updated according to Equation (16) to Equation (18).  
Figure 7: Flow of IGWO-based BPNN parameter optimization.  
**Step 7**: If the number of iterations reaches \(T_{\text{max}}\), the hyperparameter optimization will end and the loop will jump out. The output of the individual position \((\varepsilon_{i},n_{i})\) with the best \(fit_{i}\) is selected as the optimal hyperparameters of the BPNN to construct the prediction model.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies  
This study evaluates the performance of the protocols for V2X communications. To this end, the metrics of the code rate, transmission latency, and data rate are computed and analyzed. The mathematical models for calculating the metrics are based on research carried out in [22; 29; 30]. These metrics are important for assessing the performance of a \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **LTE** & **5G** \\ \hline Carrier aggregation & Up to 32 & Up to 16 \\ \hline Digital beamforming & Up to 8 layers & Up to 12 layers \\ \hline Analog beamforming & Not Supported & Supported \\ \hline \multirow{2}{*}{Channel coding} & Data: turbo coding & Data: LDPC coding \\  & Control: convolutional coding & Control: polar coding \\ \hline \hline Spectrum occupancy & 90\% of channel BW & Up to 98\% of channel BW \\ \hline Subcarrier spacing & 15 kHz (fixed) & 15, 30, 60, 120, 240 kHz \\ \hline Max number of subcarriers & 1200 & 3276 \\ \hline Subframe & 1 ms (moving to 0.5 ms) & 1 ms \\ \hline Latency (air interface) & 10 ms (moving to 5 ms) & 1 ms \\ \hline Slot length & 7 symbols in 500 ms & 14 symbols (duration depends on SC spacing) \\  & 2, 4, and 7 symbols for mini slots \\ \hline Duplexing & FDD, static TDD & FDD, Static TDD, Dynamic TDD \\ \hline Modulation & Up to 256-QAM DL & Up to 256-QAM UL and DL \\ \hline \end{tabular} Based on some comparison parameters between the protocols analyzed in this work, namely 802.11p, 802.11 bd, LTE-V2X and NR-V2X, Table 3 was drawn up.  
\begin{tabular}{c c c c} \hline
**Parameter** & **802.11p** & **802.11 bd** & **LTE-V2X** & **NR-V2X** \\ \hline \multirow{3}{*}{Frequency band} & \multirow{3}{*}{5.9 GHz} & \multirow{3}{*}{5.9 GHz, 60 GHz} & \multirow{3}{*}{5.9 GHz} & \multicolumn{2}{c}{FR1 (Sub-6 Ghz):} \\  & & & & 410 MHz to 7.125 GHz \\ \cline{3-5}  & & & & FR2 (mm wave): \\ \cline{3-5}  & & & & 24.25 GHz to 52.6 GHz) \\ \hline Subcarrier spacing & \multirow{3}{*}{156.25 kHz} & \multirow{3}{*}{156.25 kHz} & \multirow{3}{*}{15 kHz} & FR1 (Sub-6 GHz):} \\  & & & & 15, 30, 60 kHz \\ \cline{3-5}  & & & & FR2 (mm wave): \\ \cline{3-5}  & & & & 60, 120 kHz \\ \hline Base technology & IEEE 802.11a/n & IEEE 802.11n/ac & 4G/LTE & 5 GNR \\ \hline PHY layer & N/A & OFDM & SC-FDMA & SC-FDMA, OFDM \\ \hline mm wave support & N/A & Yes & N/A & Yes \\ \hline Latency & \textless{}100 ms (average) & 0.5–10 ms (300 m range) & \multirow{3}{*}{50 ms (average)} & 0.5–10 ms (300 m range) \\  & & 10–100 ms & & 10–100 ms \\  & & (300 m–2 Km range) & & (500 m–2 Km range) \\ \hline Range & 1 Km & 2 Km & 1 Km & 2 Km \\ \hline Modes & Broadcast & Broadcast, groupcast & Broadcast, groupcast, \\  & & & unicast \\ \hline Retransmission & none & Congestion-dependent & Blind & HARQ-based \\ \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)
# Numerical Study on the Mechanical Behavior of Sand-Rubber Mixtures under True Triaxial Tests
## 3 Results  
### Macroscopic Behavior  
#### 3.1.1 Stress-Strain Relationship  
Figure 5 shows the stress-strain relationship curves of samples with different RCs under a confining stress of \(\sigma_{3}\) = 100 kPa. It is demonstrated in Figure 5a that when RC = 0%, as the value of \(b\) increases, the curve becomes steeper and steeper and the tangential modulus and peak strength continue to increase. The samples show obvious strain softening during the shearing process, and the softening characteristics are more obvious as b increases. Figure 5b,c illustrate that when RC = 10% and RC = 30%, \(0\leq b\leq 0.4\), and the sample does not show obvious strain softening; and when \(0.6\leq b\leq 1.0\) in the test, strain softening appears, but the phenomenon of strain softening is not very significant. With the addition of rubber particles, the increase in \(b\) values leads to an increase in the peak deviatoric stress, and the sample gradually transitions from strain hardening to strain softening. This phenomenon may occur because rubber particles are less rigid than sand particles, and the material itself has high compression-rebound characteristics, so the deviator stress gradually increases with an increase in the medium principal stress. The addition of large rubber particles can also cause the samples to initially be dominated by large pores. With the loading and redistribution of small particles, the large pores are filled with sand particles, and the porosity ratio of the sample decreases and the strength increases.  
Figure 5: \(Cont.\)