esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Comparative Experiments  
Nine benchmarks from [PERSON] et al. [44] are further used to validate the effectiveness of the GRN-based method, as shown in Table 5. Assuming that actual processing times follow the Erlang distribution, these benchmarks are solved by the GRN-based method and the Improved Ant Colony Algorithm (IACO) introduced in [44], respectively. An Intel(r) CoreTM i7-2720 QM CPU @ 2.20 GHz and 8.00 GB RAM based notebook computer (Dell Inc., Xiamen, China) is adopted to test these experiments. Table 6 lists the experimental results. Because both the IACO and the GRN-based method are based on random search procedures, all these results are averaged values over 20 replications. As shown in Table 6, the IACO method achieves better results than the GRN-based methods for the benchmarks \"6 \(\times\) 2\", \"30 \(\times\) 2\" and \"100 \(\times\) 2\". Because these benchmarks are featured with a small-scale solution space, the IACO method is possibly to search out the optimal solutions via its global searching procedure, whereas the GRN-based method might fail to find an optimal one owing to the predetermined rules embedded in its regulation equations. When the problem scale increases in the benchmarks \"6 \(\times\) 4\" to \"100 \(\times\) 8\", the GRN-based method obtains smaller makespans than the IACO method. The IACO can hardly search out optimal solutions, or even near-optimal ones, when the solution space is increased, whereas the GRN-based method ensures a good solution owing to its embedded rules and can further find a better one by optimizing regulatory parameters. Moreover, the GRN-based method optimizes four parameters, rather than all the decision variables in the IACO method. This proposed method can thus save the CPU time and demonstrate better response capability to processing time variations in HFS. Thereupon, the GRN-based method is validated as an effective and efficient method for dynamic scheduling in HFS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Benchmark**} & \multicolumn{3}{c}{**GRN-Based Method**} & \multicolumn{3}{c}{**IACO Method**} \\ \cline{2-7}  & **Makespan** & **Rescheduling** & **CPU Time** & **Makespan** & **Rescheduling** & **CPU Time** \\  & **(s)** & **Times** & **(ms)** & **(s)** & **Times** & **(ms)** \\ \hline \(6\times 2\) & 256.27 & 2.3 & 13.3 & 252.79 & 1.15 & 164.5 \\ \(30\times 2\) & 573.58 & 4.25 & 48.6 & 568.45 & 1.6 & 580.3 \\ \(100\times 2\) & 886.23 & 1.35 & 354.5 & 855.01 & 1.9 & 8130.5 \\ \(6\times 4\) & 1071.56 & 1.2 & 26.0 & 1098.29 & 1.85 & 365.2 \\ \(30\times 4\) & 2249.04 & 2.35 & 67.5 & 2314.21 & 1.2 & 3906.2 \\ \(100\times 4\) & 2532.37 & 3.15 & 498.4 & 2761.85 & 1.35 & 33,538.3 \\ \(6\times 8\) & 3471.06 & 1.3 & 51.7 & 3536.51 & 1.55 & 921.4 \\ \(30\times 8\) & 6859.97 & 1.9 & 97.6 & 6978.14 & 1.9 & 13,641.4 \\ \(100\times 8\) & 7396.41 & 5.1 & 3055.2 & 7646.71 & 1.85 & 21,9613.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the GRN (Genetic Regulatory Network)-based method and IACO (Improved Ant Colony Algorithm) method.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 4 Discussion of Wave-Induced Pile-Seabed Response
### Wave-Height Effect  
Greater water pressure was applied to the seabed surface as wave height increased from 8 cm in T2(f-_H8-D3_p) to 10 cm in T3(f-_H10-D3_p), as shown in Figure 10. The mean pore-pressure amplitudes (\(p_{\text{m}}\)) increased by 12.1%-17.3%, which was the highest value among the experiments with Sample 1 seabed. Wave height seemed to have a major effect on the response of Sample 1 seabed.  
The effect of wave height on residual pore pressure in Sample 2 seabed can be found in Figure 11b. An earlier rapid accumulation process of pore pressure was observed with higher wave height in T9(s-_H10-D3_p) and with an unstable response state containing a descent period. The corresponding oscillating pore pressure values obtained from T8(s-_H8-D3_p) and T9(s-_H10-D3_p) are presented in Figure 16, showing that the oscillating pore pressure at \(z\) = 15 cm was high for \(H\) = 10 cm, including its increments. It is worth noting that the rapid ascending period of \(p_{\text{o}}\) in T9(s-_H10-D3_p) in Figure 16 corresponds to the descending period in Figure 11b, indicating that drastic fluctuations in oscillating pore pressure induced soil particles to form transient drained paths at that time. Moments later, these paths were closed, which resulted in another ascending period of residual pore pressure in T9(s-_H10-D3_p), as shown in Figure 11b.  
Figure 12a also presents the permanent displacement of piles in Sample 1 seabed under different wave loadings (T2(f-_H8-D3_p) and T3(f-_H10-D3_p)). Due to slight residual pore pressure induced by 10 cm high waves (Figure 10), a relatively gentle curve of \(y_{\text{p}}\) was observed in T3(f-_H10-D3_p). The main source of accumulated displacement was the finite plastic strain of surrounding soil induced by the cyclic response of the pile.  
The permanent displacement of piles under 10 cm high waves in Sample 2 seabed is shown in Figure 13a, notated as T9(s-_H10-D3_p), showing a faster development than that subjected to 8 cm high wave action (TS(s-_H8-D3_p)). Apparently, the rise in residual pore pressure accelerated the pile response, which can also be verified by examining the total displacement of the pile presented in Figure 17. When \(p_{\text{r}}\) in T9(s-_H10-D3_p) (Figure 13a) reached a high level, the cyclic displacement gradually magnified and caused a fast progress to pile failure. In conclusion, wave height was the major factor in the responses of piles and seabed according to the experiments; note that the confining pressure around the piles was low when compared to prototype, so the failure process should be partly accelerated.  
Figure 16: Oscillating pore pressure in Sample 2 seabed under different wave actions.  
Figure 17: Pile-head displacement in Sample 2 seabed under different wave actions.
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
###### Abstract  
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts.  
SCADA data; time series; wind turbine; prediction; classification; ensemble Article
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 4 Proposed Method
### Model Architecture
* Dropout Layer: Randomly disables 50% of neurons during training, compelling the model to learn robust features and minimizing overfitting. A dropout rate of 0.5 means that 50% of neurons are dropped. Since emails vary widely in content, dropout aids in generalizing the model to unseen emails. * Output Layer: A single neuron produces a probability ranging from 0 to 1. The sigmoid function compresses the output, making it suitable for binary classification (spam vs. not spam). An output near 1 indicates spam, while an output near 0 signifies not spam. This layer delivers the final prediction: Is this email spam or not? This architecture is effectively designed for spam email detection: Embedding layer for semantic input, LSTM + GRU to capture and enhance temporal patternsand Dense + Dropout to learn and regularize features. Final sigmoid layer for binary classification. The model uses a sequential neural network design to handle sequence data by combining GRU and LSTM components. To start our model processing the input words, a text engineering system turns tens of thousands of words into dense vector components. Our model starts with a 64-unit LSTM layer before moving to an LSTM layer, which returns sequences. Next, the model includes a GRU layer with 32 units. GRU is more lightweight and quicker than LSTM while still efficiently handling sequence data. It introduces an extra time-related layer to improve the context acquired by the LSTM more effectively. We use a dropout layer with a 0.5 ratio to reduce model overfitting. The model includes final dropouts at 50% strength. Our design combines LSTM and GRU modules to process temporal data flows better than regular neural networks with dropout layers for improved model learning and performance.
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 2 Materials and Methods  
After the first lasing of PAL-XFEL in 2016, the XFEL stability in the beam position and timing jitter were steadily improved for various X-ray experiments [14; 15]. In 2017, a single-shot coherent X-ray imaging instrument was successfully commissioned at the nano-crystallography and coherent imaging (NCI) endstation at the hard X-ray beamline of PAL-XFEL. XPCS and CDI, which require highly coherent X-rays, were conducted using this instrument [16; 17].
## 3 Description of the Model and the Accuracy Validation
### Accuracy Validation of Numerical Calculation  
Based on the theory in Section 2, we have established the method to calculate the flow field and sound field numerically. To validate the accuracy of the numerical calculation, we have built up the model according to [PERSON] et al. [27], who have done the experimental test that the air flows a simple support plate. The speed of the air flow is 35.8 m/s. Figure 3 shows the comparison between the experimental test and the numerical calculation.  
In Figure 3, there is a minor difference of sound pressure level in the frequency range between the experimental test and the numerical simulation. The reason is that the microphones are the practical tools with some space, while the points in the numerical simulation are virtual and only picked up according to the position of the receivers, which are indicated in Reference [27]. We have also neglected the flow noise and the acoustic-flow coupling in the simulation. However, we may see that the trend of sound pressure level changing with the frequency in the numerical simulation is very similar to that in the experimental test. The difference of total sound pressure level between them is only 0.6%, which can be neglected. The comparison shows that the results of numerical simulation back up with that of the experimental test. Therefore, the created method of numerical simulation is feasible and can be used to calculate the flow-induced noise from the research model.  
Figure 3: Comparison of results between the experimental test and numerical simulation. The dark line shows the results of the preliminary test, and the red line shows the results of numerical calculation. The horizontal axis denotes the frequency in the range from 0 to 1 kHz, and the longitudinal axis indicates the sound pressure level with a reference of 20 uPa.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
###### Abstract  
As the number of non-geostationary orbit (NGSO) satellites continues to grow, interference with other communication systems, including radio astronomy systems (RASs), is becoming increasingly critical. In this study, an interference simulation framework was developed to analyse the potential impact of NGSO systems on RAS in accordance with the relevant International Telecommunication Union (ITU) regulations and recommendations. In addition to the simulation of interference generated by individual NGSO satellite systems, the framework also supports the analysis of aggregate interference from multiple NGSO satellite systems. By inputting satellite system parameters, including constellation configuration, user distribution and beam scheduling strategy, the framework is able to obtain interference probability distributions for a typical RAS ground station at different latitudes and observation directions. The simulation results provide a reference for the analysis of interference from NGSO satellite systems to RASs, and can also be used to guide the development of strategies to mitigate harmful interference to RASs.  
NGSO; RAS; constellation; interference; aggregate interference Article
# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in the Analysis of the Harmfulness of 3D Printing
These systems are becoming a key element of preventive medicine, allowing for early recognition of micro-injuries or gradual deterioration of motor skills. This approach is used both in the rehabilitation of patients and in monitoring people practicing intensive sports, where the risk of accumulating injuries is high. An important element of therapy optimization is the integration of exoskeletons and orthoses with medical automation and robotics systems [49]. Telemedicine solutions can be used to systematically monitor patients, improving the continuity of diagnostics and the reliability of results. Moreover, the low costs of implementing such systems make them increasingly accessible [50]. Comparing the effectiveness of the remote approach with the traditional and hybrid one indicates the growing potential of telemedicine, especially in the field of rehabilitation [51]. One of the most promising areas of research remains the prediction of the patient's future condition, which is based on the analysis of data collected in real time and archived. The development of ML systems supporting the production of personalized medical devices also allows for more precise adjustment of therapy to the needs of a specific patient. The combination of modern diagnostic methods with ML allows for better prediction of therapy results, rehabilitation, and assessment of the effectiveness of remote medical care (Figure 3) [52]. As more data are collected, these systems will be able to support medical personnel even more effectively in making therapeutic decisions [53]. Figure 3: ML-based preventive medicine system based on 3D-printed medical products (own version).
## 2 System Model and Signal Processing Methods
### Two-Dimensional Direction Observation Model for Spoofing Signal Sources  
The vector of the carrier's Antenna 1 pointing to the signal source \(\mathbf{r}_{\mathrm{s,t}}\) at the same moment can be expressed as \(\mathbf{r}_{\mathrm{s,t}}^{\mathrm{b}}\) in the \(\mathbf{b}\) system, and \(\mathbf{x}_{s,t}^{b}\), \(\mathbf{y}_{s,t}^{b}\), and \(\mathbf{z}_{s,t}^{b}\) are the axial components of \(\mathbf{r}_{\mathrm{s,t}}^{\mathrm{b}}\), as follows:  
\[\begin{bmatrix}\mathbf{x}_{s,t}^{b}\\ \mathbf{y}_{s,t}^{b}\\ \mathbf{z}_{s,t}^{b}\end{bmatrix}=\begin{bmatrix}d_{s,t}^{b}\cos\beta_{s,t}^{b} \cos a_{s,t}^{b}\\ a_{s,t}^{b}\cos\beta_{s,t}^{b}\sin a_{s,t}^{b}\\ d_{s,t}^{b}\sin\beta_{s,t}^{b}\end{bmatrix} \tag{6}\]  
Equation (5) can be reduced to a two-dimensional direction observation equation for signal sources:  
\[\lambda\cdot\left(\Delta\vec{\phi}_{12}^{1}+N_{12}^{\mathrm{s}}\right)= \begin{bmatrix}L&0&0\end{bmatrix}\cdot\begin{bmatrix}\cos\beta_{s}^{b}\cos a _{s}^{b}\\ \cos\beta_{s}^{b}\sin a_{s}^{b}\\ \sin\beta_{s}^{b}\end{bmatrix} \tag{7}\]  
As the scenario defined in this study is that there exists a spoofing risk in satellite navigation, satellite navigation and positioning pose a risk of unreliability. In Equation (5), \(\vec{r}_{1}^{i}\) is obtained by extrapolating the data in ephemeris files, \(\mathbf{r}_{1}\) is obtained by independently extrapolating the data provided by the MIMU, and \(N_{12}^{\mathrm{i}}\) is to be solved.  
Conventional methods for solving inter-station single-difference carrier phases in satellite navigation include multi-epoch filtering techniques such as the least-squares ambiguity decorrelation adjustment (LAMBDA) algorithm and objective function solving techniques such as the AFM algorithm [21; 22; 23; 24; 25]. The former requires multi-epoch filtering of possibly unreliable pseudo-range information, and the latter is disadvantaged by large computational amounts and insufficient reliability. Therefore, this study constructs a novel AFM objective function to rapidly resolve the integer ambiguities of carrier phases in the environment of this study via a single channel. Additionally, in the two-dimensional observation equation for spoofing signal sources, that is, Equation (7), the observed value has one dimension, and the value to be observed has two dimensions. There is a rank loss in single-epoch direction finding for spoofing signal sources. However, due to the non-linearity of the two-dimensional direction observation model, it is more complicated to calculate the matrix of state transfer between two consecutive epochs. Therefore, it is necessary to propound a new multi-epoch observation model and construct a least squares observation equation and an Extended Kalman Filter (EKF) model to determine high-precision directions of spoofing signal sources.
## 4 Simulation and Experimental Results
### Simulations  
In the numerical simulations, a multi-robot system consisting of five nonholonomic mobile robots represented by kinematic points with direction is considered, and their initial poses are generated randomly in a plane. A target remains stationary or in motion state, and its initial position is the point (1.5, \(-1.5\)) in the global frame. Meanwhile, some obstacles
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 2 System Model  
We consider a TWR C-NOMA network over Rician fading channels as depicted in Figure 1, which consists of one relay \(R\) and a near user \(D_{1}\) and a far user \(D_{2}\). It was assumed that there is no direct link between \(D_{1}\) and \(D_{2}\) due to severe shadowing or obstacle. In this case, \(D_{1}\) and \(D_{2}\) can only communicate via a half-duplex (HD) DF relay. We also assumed that the two users and the relay were equipped with a single antenna. In this study, \(h_{RD_{1}}\) and \(h_{RD_{2}}\) are modelled as \(R\) to \(D_{1}\) link and \(R\) to \(D_{2}\) link, respectively.
## 2 Materials and Methods
### Monitoring Materials  
The real-time monitoring systems are shown in Figure 2, and their specifications in Table 3:  
* A TEMTOP M2000C 2 nd CO2 Air Quality Monitor (orange). This includes a Sensair nondispersive infrared CO2 sensor, a sensirition SHT31-ARP sensor for temperature and relative humidity, and an NDIR CO2 sensor.
* A meteorological station, Froggit HP 1000 SE PRO (white), includes a set of sensors to provide a full description of weather data in real time, as well as an indoor hydrothermal sensor able to measure humidity and temperature, and a central console. All of them are connected via radio at 868 MHz.
# A Review of Machine Learning Applications in Land Surface Modeling
## 4 Major Applications of Machine Learning in Land Surface Modeling
### Hybrid Simulation of Land-Surface Variables Other Than ET  
Soil moisture, turbulent momentum, and heat flux are some of the critical variables of land-atmosphere interactions apart from ET. [PERSON] et al. [61] combined Noah LSM and an ML technique to improve the prediction of top-layer soil moisture at nine AmeriFlux tower sites. They were able to obtain a 3-fold decrease in error metric. In particular, they highlighted the potential of learning structural error of a model using a hybrid model at a point-scale. However, they indicated that building a global scale model will need the inclusion of remote sensing data and account for uncertainty propagation through LSMs. In another study, ML was found better than the Monin-Obukhov similarity theory to predict momentum and heat fluxes [62]. The main objective of this paper was to substitute the Monin-Obukhov similarity theory for calculating fluxes in the LSM named 'Veg3d'. This technique of replacing a parameterization with an ML scheme has been extensively used in climate models [63; 64]. [62] also showed that the results of ANN involved LSMs were equivalent, if not superior, to the conventional method. They indicated that implementation of an ML technique might save 5% of a central processing unit (CPU) time of a regional climate model, and this can be further improved by replacing all uncertain components of the climate models with neural network subroutines. This is a further impetus to implement ML methods for efficient prediction.
# Flavour Generation during Lactic Acid Fermentation of Brassica Vegetables--Literature Review
## 5 Flavour of Brassica Vegetables
The sweetness of sugars is expressed as a relative sweetness to sucrose in the following order: glucose (0.64) < sucrose (1.0) < fructose (1.2). In the study focused on the flavour of broccoli, Brussels sprouts, cauliflower, and kohlrabi, it was shown that sweetness and sugar concentration varied between these vegetables; however, in all of them, the sweetness was the most important trait in shaping consumers' desirability based on a sensory analysis [33]. Phenolic compounds are important phytochemicals in plants, including in the _Brassicaceae_ family. They are classified according to their structure into simple phenols, phenolic acids, flavonoids, and hydroxycinnamic acid derivatives. The concentration of these components was determined in many Brassica vegetables, such as in cauliflower, kale, broccoli, white cabbage, turnip, pak choi, leaf rape, leaf mustard, etc. [47] However, only a few studies aimed to correlate their concentration with flavour attributes. [PERSON] et al. [48] concluded that phenolic compounds are likely to contribute to the bitterness of broccoli, cauliflower, Brussels sprouts, and red cabbage, since GLS themselves did not explain the source of the bitter flavour. In contradiction to this, another study focused on the correlation of bitterness with phenolic composition in different cultivars of broccoli, cauliflower, kohlrabi, and Brussels sprouts but did not find any relation between bitterness and phenolic content [33]. Therefore, more data are still necessary if any unambiguous conclusions are to be made in this regard. This is especially the case if the strong impact of different factors, which might influence the composition of phenolic components in these vegetables, such as genetic and environmental factors, but also post-harvest treatment, such as thermal treatment, storage, or fermentation, are taken into consideration [47; 49].
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Absorption Spectrometry (AAS) Techniques  
Atomic absorption techniques rely on the fact that every element absorbs light at a characteristic wavelength, and the amount of light energy absorbed is proportional to the number of analyte atoms in the light path. AAS methods are relatively inexpensive and convenient for uncomplicated elemental analysis in food. Other general advantages include the high precision and accuracy obtained by the calibration curves and the low interelement interferences in absorption signals. Typically, a hollow cathode lamp (HCL) or electrodeless discharge lamp (EDL) is used as the source of light which is characteristic of each target element. Owing to this limitation, in most cases these techniques are limited to single element detection [52; 53]. Another disadvantage is the limited application of AAS techniques, as only about 70 elements, excluding earth metals and non-metals, have been detected by these methods.