esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This approach was also tested on a Chinese offshore wind farm and provided a more accurate forecast compared with the other variants. Processing outliers in the wind power datasets is essential for power curves' estimation [19; 31; 32]. Outliers can stem from various sources, including operational factors such as output curtailment, turbine failures, and maintenance activities, as well as measurement errors [31]. The detectability of these outliers is influenced by the time resolution of the data; higher resolutions (ideally, under 10 min [19]) make outlier detection more straightforward. However, accessing a sufficient quantity of high-resolution data can be challenging; as a rule, the existing articles consider 1 h intervals. The study [19] identifies three areas related to outliers (as shown schematically in Figure 1). The first group involves power output values close to zero despite significant wind speeds (\"1\" in the Figure 1). The second group comprises output values substantially exceeding calculated estimates, likely due to measurement errors [31], as power generation is unlikely to surpass predicted levels at given wind speeds (\"2\" in the Figure 1). The third group pertains to situations where output is significantly lower than calculated, excluding cases from the first group (\"3\" in the Figure 1). Excluding zero-power output values due to measurement and data recording system failures, as well as the second group of outliers, the remaining cases that appear as outliers could indeed be true values, especially at 10 min intervals. Therefore, constructing a wind power curve that disregards such values may reduce the actual accuracy of wind farm power output calculations. In power generation forecasting tasks for grid management, this may be insignificant. However, in a small, isolated power system based on wind energy,maximum forecasting accuracy is necessary for managing electrical power flows within the system. Thus, research on constructing models to estimate wind turbine power based on various factors over short-term intervals remains relevant. The main contributions of this research to the field of forecasting wind power plant generation and modeling a power curve of a wind turbine are as follows:  
* Analysis of the impact of different features beyond wind speed on the accuracy of wind turbine output power estimation using real SCADA system open data with 10 min measurement intervals;
* Application of a hybrid approach based on the theoretical power curve and machine learning models to more accurately assess the actual wind turbine output power;
* Investigation of the hypothesis of using power values from the previous time step to enhance accuracy by considering the inertial properties of the wind turbine.
## 3 Numerical Experiments on a Turbine Blade  
To verify the feasibility and performance of the proposed ice-coupled ultrasonic FWI tomography to evaluate the defects in complex-shaped parts, a series of numerical experiments were conducted. The turbine blade is a typical safety-critical component in the aerospace industry with a complex geometry, as shown in Figure 2a. In conventional ultrasonic testing, phased array ultrasonic transducers can only be placed to directly contact smooth surfaces at the upper part. It makes the defects at the lower part difficult to inspect, such as the red circles in Figure 2a. To inspect the defects at different positions, the transducer should be moved to different positions, shown as the red line. This complex object was applied in this study. To save computational costs and storage space, only 2D tomography was carried out for a typical cross section of the turbine blade in this study, as shown in Figure 2b. To facilitate the fast acquisition of a full ultrasonic wavefield for FWI tomography, an acoustic model rather than an elastic model was adopted here to simulate the process of ice-coupled ultrasonic testing because the acoustic model is commonly employed in ultrasonic NDT research [21; 29]. All of the wave phenomena including multiple reflections from the ice-steel interface and the wave-defect interaction can be revealed in such a model. It is necessary to note that in a real case shear waves could exist in a solid due to mode conversion at the interface and defects. Numerical experiments were carried out to optimize the key parameters such as the frequency selection and iteration number to quantitatively detect different types and numbers of defects in a turbine blade.  
Figure 1: Workflow of the ultrasonic full waveform inversion algorithm.
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 1 Introduction
Assignment of certain values to the input data is possible provided that the latter is reliable, available and sufficiently detailed. Application of the fuzzy logic method can help improve the evaluation result where certain data are missing or unreliable. Application of fuzzy logic and the MCDA can boost effectiveness of vessel selection for an SAR operation. The proposed method is aimed to streamline planning of SAR operations, with a mathematical solution supporting the SAR coordinator in the decision-making process.
## 3 Results and Discussion
### OAV Analysis of the Differential Aroma Substances
Notably, for combined VIP > 1, \(p\) < 0.05, and fold change \(\geq\)2 or \(\leq\)0.5, (_E_)-linal oxide, (_Z_)-jasmone, and \(\delta\)-cadinene were identified in different seasons in two cultivars. In summary, the above-mentioned representative volatile compounds, contributing fragrance and fruity flavor characteristics, played an essential role in distinguishing the harvest seasons of two cultivars of white teas in southwestern China. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Index** & **Odor Description a** & **Threshold** & \multicolumn{4}{c}{**OAV**} \\ \cline{4-7}  & & & \(\mu\)g/L & **SH-S** & **SH-A** & **FD-S** & **FD-A** \\ \hline
1 & Dimethyl sulfide & green, fresh & 1.1 [61] & 2.587 ab & 2.408 ab & 3.039 a & 1.850 b \\ \hline
2 & Hexanal & fresh, green, grass, fruity, apple & 4.5 [62] & 0.624 a & 1.310 a & 0.583 a & 1.292 a \\ \hline
3 & Nonanal & fresh, fruity, floral, & 1 [62] & 3.747 a & 7.05 a & 2.937 a & 4.789 a \\ \hline
4 & (E)-2-Nonneal & cucumber flavor & 0.4 [62] & 3.509 a & 3.645 a & 3.806 a & 3.613 a \\ \hline
5 & Decanal & soap, orange peel, tallow & 0.1 [62] & 16.894 a & 18.663 a & 16.601 a & 19.326 a \\ \hline
6 & (E,E)-2,4-Nonadien & fresh, cucumber-like & 0.062 [62] & 0.000 b & 14.396 a & 0.000 b & 14.836 a \\ \hline
7 & (E,E)-2,4-Heptadien & floral & 0.03 [62] & 85.000 a & 50.000 b & 62.500 ab & 63.130 ab \\ \hline
8 & (E)-2-Decenal & fat & 0.3 [62] & 5.412 a & 0.000 b & 6.732 a & 0.000 b \\ \hline
9 & 2-Undecen-1-al & green, fresh & 0.78 [63] & 1.498 a & 0.000 b & 1.977 a & 0.000 b \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key compounds of four white teas (OAV > 1).
## 3 IoT Architectures
### Blockchain-Based Smart City Architecture  
Blockchains are decentralised distributed ledgers, a game-changing technology that may help solve IoT security issues. A blockchain-based approach to IoT networks might address many of the issues with the existing paradigm while also increasing security. The use of blockchains in IoT security allows for direct information exchange between connected devices rather than communicating through a centralised network, reducing the IoT's vulnerability to cyberattacks [76]. According to a Gartner report, healthcare, transportation, and energy have the greatest rates of blockchain use among IoT-enabled firms in the United States.  
When it comes to encryption, the technology employed in digital ledger systems is rather advanced. Because a blockchain is an encrypted database that is distributed, decentralized, and impenetrable, it has sparked the minds of today's youth. Since its inception, it has been used to reshape the way business is done in nearly every area, spanning government, finance, healthcare, and smart cities. Figure 8 depicts a blockchain-based smart city architecture that includes different IoT devices and infrastructure applications. IoT device integration involves cloud systems, edge computing, gateways, and many types of IoT devices, ranging from basic sensors that can only communicate with adjacent gateways to devices with computational and processing capabilities. It is amazing to see how blockchain and edge computing are linked. Blockchain nodes can use edge computing or distributed computation architectures to store and validate transactions. On the other hand, blockchains can create a completely open and distributed cloud marketplace.  
Figure 8: Blockchain-Based Smart City Architecture.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 2 Energy Constraints--A Persistent Problem  
The need for high data rates with high frequency spectrum in 5G cellular networks [14; 15] and the objective of uninterrupted internet connectivity in the massive IoT [10] motivate us to explore the feasibility of battery-free devices in emerging wireless communications [8]. Battery-free devices can have the benefit of continuous communications utilizing the same bandwidth. The advantage of using passive devices in wireless communications lies in information exchange without any interruption. Moreover, reliable communication with limited battery life is an important research area. Various steps have been taken to solve the limited battery life problem, and these steps include the designing of energy-efficient network architectures using mm-waves and energy harvesting in mobile networks. We will first discuss the power issues in the cellular, D2D, and IoT perspectives, and we will then review various attempts taken to resolve the energy constraints.
## 2 Green Routing Algorithms in WSNs
### Setting Special Node-Based Routing Algorithms  
In WSNs, each sensor node not only transmits sensing data but also forwards them to other sensor nodes. Additionally, nodes closer to the sink will be saddled with more tasks around relaying packets than nodes farther, which causes an imbalance of network energy and leads to a short WSN lifetime. By setting special nodes, these issues can be relieved or even solved. Special node-based routing algorithms are classified into two categories: hierarchical node-based routing algorithms [51; 52] and special function node-based routing algorithms [53; 54].
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 2 Synthesis Methods for Copper- and Iron-Based Nanoflowers
### Biological Method/Green Synthesis  
The green synthesis of metal nanoparticles (Figure 9) has received notable attention, especially in medicine [99].  
Green synthesis is considered a better approach because traditional methods often rely on hazardous chemicals and require significant energy inputs, both of which have negative environmental impacts. In contrast, green synthesis employs natural sources such as enzymes, plant extracts, microbes, or biocompatible agents for the synthesis process. Using biomolecules and plant extracts to synthesize nanoflowers ensures environmentally friendly practices while also introducing biocompatibility, which is crucial for biomedical applications [32]. The utilization of plant extracts for producing metal nanoparticles is favored for its simplicity, scalability, and non-toxicity [100]. However, the application of this approach for synthesizing NFs is still in its early phase, with only a few works exploring this specific method.  
Figure 9: Biological employed techniques for synthesizing inorganic nanoflowers.  
A green synthesis process has been proposed for creating HNFs using Cu(II) and Co(II) metal ions as inorganic components, along with quercetin as the organic component. When evaluating the anticancer activities of HNFs, it was found that CuHNFs exhibited more effective anticancer properties compared to quercetin and CoHNFs, depending on concentration levels. Specifically, CuHNFs led to 80% cell death, while ZnhNFs resulted in 30% cell death, and quercetin caused 40% cell death. These observations clearly demonstrate that cell death is concentration-dependent [101].  
A green synthesis mechanism was developed using _Tribulus terrestris_ L. extract as the organic component along with copper, zinc, and cobalt metal ions. Cytotoxic evaluations indicated that cobalt nanoflowers may be a safer therapeutic option compared to other plant extracts and nanoflowers. To synthesize _T. terrestris_-Cu nanoflowers (CuHNFs), a stock solution of CuSO4 5H2O at 120 mM was prepared and mixed with 8 mL of 10 mM phosphate-buffered saline (pH 7.4). The plant extract was then added at a concentration of 0.02 mg/mL. The mixtures were incubated at +4 \({}^{\circ}\)C for 72 h, followed by centrifugation at 10,000 rpm for 20 min. The hybrid nanoflowers were finally dried at room temperature (Figure 10) [102].  
The zinc and copper hybrids had a notable impact on cell lines up to a specific concentration, beyond which they increased cell viability. Furthermore, all hybrids demonstrated improved anticancer activity compared to the extract. These results could contribute to the development of effective new-generation formulations for treating A549 lung cancer [102].
# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 4 Experiment
### Experimental Results  
We conducted a comprehensive comparison of various resampling algorithms applied to ten datasets, evaluating their performance with logistic regression, random forest, and XGBoost as classifiers. The results using logistic regression are presented in Table 2. For the performance comparison results using random forest and XGBoost classifiers, please refer to Tables 10 and 11 in Appendix A. The hyperparameters for the resampling approaches and classifiers employed in this study are shown in Table 12 in Appendix B. The experimental results highlight the superior performance of Ad-RuLer in most cases, compared to other resampling algorithms. For instance, when using logistic regression as the classifier, Ad-RuLer outperformed other resampling algorithms on all four evaluation metrics in the Breast Cancer and Car-good datasets. In the Heart Disease, Vehicle0, and Abalone9-18 datasets, apart from the Precision, the other three metrics all exceeded those of other resampling algorithms. When adopting random forest as the classifier, Ad-RuLer outperformed other resampling algorithms on all four metrics in the Heart Disease dataset. In the Vehicle0, Yeast3, Yeast-0-2-5-7-9 vs3-6-8, Abalone9-18, and Car-good datasets, at least two metrics exceeded those of other resampling algorithms. In Figure 2, the synthesized minority class data samples obtained via Ad-RuLer for the Yeast3 and Heart Disease datasets were visualized together with the original data samples using the t-SNE technique [47], to give an idea of the distribution of the new data generated via Ad-RuLer. Figure 2: The synthetic data generated via Ad-RuLer for the Yeast3 dataset (**a**) and Heart Disease dataset (**b**).
# CMAG: A Mission to Study and Monitor the Inner Corona Magnetic Field
## 2 Mission Concept
### CMAG Data Products  
The instrument will observe the full Stokes vector in several wavelength samples across the aforementioned coronal lines. To transform the observations into scientific data products, most of them will be processed in the DPU, including dark and flat-fielding corrections, demodulation, normalization, and subsequent transformation to scientific products, minimizing telemetry usage. The expected data products are maps of polarization brightness (K-corona), the line-of-sight plasma velocity component, line Doppler widths, the total line polarization, the (non-disambiguated) orientation of the magnetic field vector, and the amplitude and sign of Stokes V. The latter two products can readily be calculated from the linear polarization signals and from the circular polarization (using the weak field approximation) since the lines lie in the strong limit of the Hanle effect [(33; 49)] in optically thin coronal plasma at locations where circular polarization is above the noise level of \(10^{-3}\). To increase the polarimetric sensitivity, longer integration times (by adding datasets, reducing spatial resolution, or increasing exposure time) are foreseen in well-defined cases. For instance, when moving away from the solar limb and in the absence of solar structures  
Figure 5: CMAG coronagraph instrument concept.  
such as coronal loops, the coronal density drops considerably with distance. Therefore, meeting the signal-to-noise requirement necessitates longer exposure times. In order to infer the full magnetic field vector, downloading raw or preprocessed data is necessary. This is because, to fully interpret optically thin forbidden coronal lines, including collisional excitation and resonance scattering, one needs high computing resources. Other products will be determined on the ground, e.g., coronal density maps can be inferred from the polarization brightness. Intermediate products are also foreseen. For instance, brightness temperatures can also be inferred provided the spectral line profile is available. CMAG will also provide forward modeling tools for the interpretation of the products on the ground and for aiding field extrapolations and modeling of the solar corona.
## 2 Materials and Methods  
### Design of Experiments (DOE) and Sample Preparation  
Magnesium powder (99% purity, 0.06-0.3 mm diameter supplied from Sigma Aldrich Ireland Limited, Wicklow, Ireland) was ablated at various laser fluences, ablation times and scan speeds as shown in Table 1. Table 1 shows a 3 \(\times\) 3 full factorial design of experiments (DOE). Three repeat experiments (\(n\) = 3) were conducted for each sample set, making it a total of 81 samples (27 \(\times\) 3). Ablations were performed by an Nd:YAG laser system (WEDGE HF 1064, Bright Solutions) with pulses centred at 1064 nm. Three ablation durations were investigated (2, 5 and 25 min). Three laser fluences were investigated (1.83, 1.88, 1.91 J/cm\({}^{2}\)) and three laser scan speeds were investigated (3000, 3250 and 3500 mm/s). All experiments were conducted at a repetition rate of 10 kHz and a pulse width of 600 ps. These parameters were chosen after preliminary experiments which showed that the magnesium nanoparticle yield was high within these parameters. DI water is the most commonly used liquid for PLAL; however, water reacts with magnesium which affects the ablation process especially when powders are used (foam and bubbles are produced when water reacts with Mg powder). Therefore, a simple organic solvent called isopropyl alcohol (IPA) was used. Ethanol was also investigated; however, IPA was more compatible with the powder (the powder settled down the bottom of the liquid much faster even after vigorous mixing and showed no mixing/reactivity with the powder). A thin powder layer (2 mm thickness) was carefully placed and evenly spread on the bottom of a glass beaker (60 mm diameter). A 3 mm height of liquid (IPA) above the powder layer was ensured for each sample. Fresh powder was used for each ablation experiment. The laser scanning pattern was in a spiral shape (outer diameter of 55 mm) with a hatch spacing of 50 \(\upmu\)m. Each PLAL experiment was carefully timed using a stopwatch and the resulting nanocolloid was immediately collected by carefully pipetting some of the colloid (5 mL) on top of the remaining powder using a standard plastic pipette, ensuring that no powder particles (large particles) were collected. The as-collected colloid was used in characterisation processes without any filtration process to demonstrate the simplistic nature of PLAL and also to observe the effects of processing parameters alone without additional processing steps.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Classification Algorithm  
Prior to undertaking the classification procedure, all the features were standardized, yielding the column vectors with zero mean and unit variance. Due to its robustness against a multicollinearity effect [57], a least absolute shrinkage and selection operator (LASSO) was employed as a classification algorithm. Initial exploration of the data revealed that approximately 60% of the metrics were highly correlated (\(r>0.8\)).  
The second reason for using the LASSO algorithm was related to its inherent property of feature selection. Since this method exploited an L1 norm as a penalty criterion [58], it forced some of the regression coefficients to be exactly zero, hence \"removing\" the unnecessary metrics. Since the databaseused in the present study contained a large number of features (1376), the above property of the classification algorithm could, in theory, improve the generalizability of the model by reducing its number of degrees-of-freedom (parameters).  
The hyper-parameters of the LASSO algorithm (alpha and lambda) were \"tuned\" using the standard 10-fold cross-validation procedure repeated 10 times. The selected values varied across the models (experiments), and, therefore, they will be provided in the next section.  
The classification algorithm was implemented in R system using the caret package [59]. The code is openly available at Zenodo repository (10.5281/zenodo.2639058).
## 3 Simulation Results
### Reliability of the Proposed Method against Code Rate Change  
In 5G systems, the code rate parameter can also be changed depending on the requirements. A code rate of 490/1024 is considered for the base scenario. In this section, code rates of 245/1024 and 980/1024 are also considered.  
For the aforementioned code rate use cases, the peaks of EVM vs. RB are calculated for each SJR, and the results are shown in Figure 8a. The proposed method provides stable results without being affected by the code rate parameter; therefore, it can be safely used for jamming detection in system scenarios in which the code rate changes.  
However, Figure 8b shows that different throughput results are obtained for different code rate conditions for a fixed SJR case. For example, when the SJR is 0 dB and a code rate of 245/1024 is used, the throughput approaches 100%. Therefore, the jamming effect cannot be clearly observed. If the system relies on this and decides to increase the code rate to 490/1024 or 980/1024, the throughput decreases significantly. Meanwhile, the proposed measurement can prevent such incorrect decisions, because it detects jamming threats without being affected by code rate changes.  
Figure 8: Effect of the Code Rate Change, Tone Jammer. (**a**) Peak-EVM vs. SJR, and (**b**) Throughput vs. SJR.  
Figure 7: Effect of the Modulation Type Change, Tone Jammer. (**a**) Peak-EVM vs. SJR, and (**b**) throughput vs. SJR.  
This achievement of the proposed method for the tone-jamming scenario is also valid under chirp-jamming, as shown in Figure 14.