esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Generation of Converter Transformer Inrush Current and Its Time-Frequency Characteristics
### Time-Frequency Characteristic Analysis of Magnetizing Inrush Current
#### 2.2.2 Breaking Angle Characteristics  
In a cycle, when entering the saturation zone, the instantaneous value of the inrush current will rise sharply. The inrush current is between \(\theta_{1}\) and \(\theta_{2}\) on the way. At this time, the expression of the disconnection angle \(\theta\) is  
\[\theta_{d}=2\pi-(\theta_{2}-\theta_{1}) \tag{7}\]  
In the unsaturated zone, only the excitation current with a small amplitude can be ignored, that is, the excitation current generated by the converter transformer in the unsaturated state \(i_{\rm s}\approx 0\). Substituting the instantaneous value of the current to 0, we can get  
\[\cos(\omega{\rm t}+\theta)=cos\theta+\frac{\phi_{r}-\phi_{\rm s}}{\phi_{m}} \tag{8}\]  
By solving the above equation, we can obtain  
\[\theta_{1}=\arccos(\cos\theta+\frac{\phi_{r}-\phi_{\rm s}}{\phi_{m}})-\theta \tag{9}\]  
\[\theta_{2}=2\pi-(\arccos(\cos\theta+\frac{\phi_{r}-\phi_{\rm s}}{\phi_{m}}))-\theta \tag{10}\]  
Substituting Formulas (9) and (10) into (8), the expression of the breaking angle can be obtained:  
\[\theta_{d}=2\arccos(\cos\theta+\frac{\phi_{r}-\phi_{\rm s}}{\phi_{m}}) \tag{11}\]  
The main cause affecting the inrush current breaking angle is the size of the closing angle. When cos\(\theta\) is larger, the breaking angle is smaller. In addition to the closing angle, the core remanence also has a certain effect, but its influence is small.
## 5 Discussion
### Uncertainties in SIF Retrieval Caused by Time Mismatch Between Measurements  
The light path-switching approach was employed in this SIFSpec system using a single spectrometer to sequentially measure the up-welling and down-welling radiation fluxes. Making use of a single spectrometer has several advantages compared with the use of two spectrometers. It will result in a lower-cost, lighter system and there will also be no need for regular inter-calibration of the two spectrometers. However, there are also several unavoidable disadvantages. The problem of non-simultaneous up-welling and down-welling measurements is the biggest issue.  
The mismatch between the time of the upward radiance and downward irradiance measurements is a major problem in long-term in situ SIF observations [27]. The widely used ground-based SIF retrieval method, 3 FLD, suffers from the problem that the SIF values cannot be accurately retrieved if the weather conditions vary quickly when the upward radiance and downward irradiance are being measured. In contrast, the data-driven method [58; 59] is an alternative method that can be used for long-term in situ SIF observations to avoid the need for downward irradiance measurements. This is based on the idea that irradiance spectra can be reconstructed using a linear combination of several principal components (PCs) using the SIF-free spectra. However, the SIF retrievals based on the data-driven method are also sensitive to the training dataset selected to produce the PCs.
## 2 Material and Methods  
### EEG Databases  
This work considers two publicly available EEG datasets for testing the proposed DMOSM-GP methodology. The two datasets are widely used in the development of neuroscience techniques as they hold challenging cognitive conditions. Contained EEG data allows testing the quantification of channel dependencies at the frequency level by the DMOSM-GP for binary classification experiments.  
**DEAP dataset**: \"A Database for Emotional Analysis using Physiological data\" (DEAP) contains EEG data from 32 subjects acquired under 40 emotion elicitation experiments, with one-minute recordings at 128 Hz and 32 channels distributed over the scalp [25]. Each participant rated the emotional stimulus at the end of a video, following two dimensions: arousal and valence. Other scores, such as dominance and liking, were also reported, although arousal and valence are the most prominent dimensions for affective computing works. These dimensions characterize a more extensive range of emotions than just the classical categorical description in six basic emotions. For our experiments, we consider the classification valence dimension as either low (ranging from one to five) or high (from five to nine) valence.
**MI dataset**: The \"Brain Computer Interface (BCI) competition 2008-Graz data set A\" contains motor imagery experiments from nine subjects performing four specific tasks involving movements of hands and feet [26] under the motor imagery (MI) paradigm. The set of 22 EEG channels was band-pass filtered between 0.5 Hz and 100 Hz, and further down-sampled at 128 Hz. Each subject performed two experiment sessions, consisting of six runs and 48 six second-long trials per run and task. From the BCI dataset, we select the left- and right-hand movement imagination tasks for evaluating the proposed discriminative framework in a subject-wise scheme.
## 4 Numerical Simulation Results  
The numerical simulations were run for two excitation frequencies, namely 1 kHz and 2 kHz. Figure 7 shows the initial stages of the simulation with a traveling pressure wave between the scala vestibuli and scala tympani. This simple linear model provides reliable simulation results, although it does not take into account the connections between oscillators. The proposed system is made of separate oscillators, where mass is connected by springs and dampers. Each of these systems oscillates independently because they are not connected to each other.  
Based on the obtained results, the RWM responses to the applied pressure were calculated and the results are shown in Figure 3 as black dots. It is interesting that despite the use of a linear model and simplifying the coupling between domains, the consistency of the numerical simulations with the experiment is satisfactory. This was achieved for the two tested frequencies, corresponding to the limits of the high amplification zone (1 and 2 kHz). Nevertheless, the proposed model of the cochlea needs further tests, and will be developed in future studies.
# Can Anaerobically Digested Food Effluent Support _Arthrospira platensis_ Cultivation in Open Ponds?
## 2 Materials and Methods
### Experimental Setup and Cultivation Conditions  
The overall experimental design is summarized in Figure 1, showing the different ADFE concentrations used for _Arthrospira_ cultivation.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Ammonia (mg N-NH\({}_{3}\) L\({}^{-1}\)) & 2800 \\ Nitrate (mg N-NO\({}^{3-}\) L\({}^{-1}\)) & \textless{}1 \\ NO\({}_{\text{x}}\)-N (mg L\({}^{-1}\)) & \textless{}1 \\ Total nitrogen (mg L\({}^{-1}\)) & 5300 \\ Total phosphorus (mg L\({}^{-1}\)) & 520 \\ Total iron (mg L\({}^{-1}\)) & 16 \\ Total calcium (mg L\({}^{-1}\)) & 170 \\ Magnesium (mg L\({}^{-1}\)) & 24 \\ Chemical oxygen demand, COD (mg L\({}^{-1}\)) & 50,000 \\ Potassium (mg L\({}^{-1}\)) & 120 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of raw ADFE used as a medium for microalga consortium.  
Figure 1: Experimental design for Spirulina cultivation in Zarrouk medium with varying ADFE concentrations (37.5%, 50%, and 70%).  
Four treatments were established as shown in Table 2: (1) Zarrouk medium (control, no ADFE), (2) 37.5% ADFE, (3) 50% ADFE, and (4) 70% ADFE. Nutrient concentrations in all media were adjusted to achieve a total nitrogen level of 415 mg L\({}^{-1}\), equivalent to Zarrouk medium, with nitrogen supplied from both Zarrouk and ADFE sources (Table 3).  
Our preliminary studies revealed that replacing all nutrients with ADFE at 50% and 70% significantly inhibited Spirulina growth, likely due to potential toxicity. To mitigate this issue, ADFE for the 50% and 70% media was added incrementally in five steps on days 0, 1, 5, 8, and 13. These specific days were chosen based on the N-NH\({}_{4}\)+ concentration in the culture medium, ensuring that nitrogen levels were replenished before reaching depletion. In contrast, for the 37.5% ADFE medium, 37.5% of all nutrients were supplied exclusively by ADFE as a single dose at the start of the experiment.  
The experiment was conducted in batch mode over 22 days, beginning on 4 April 2022, with each treatment tested in triplicate. The working volume for all cultures was maintained at 20 L and daily evaporation loss was compensated by adding tap water.
# Time Series Feature Selection Method Based on Mutual Information
## 3 Experiments
### Data Normalization  
Due to the different physical quantities measured by different sensors, the data need to be normalized to make the variable range consistent, so as to achieve better classification and prediction effects. This paper uses Z-score standardization to normalize the input data, which can be expressed as:  
\[X^{\prime}=\frac{X-\overline{X}}{\sigma} \tag{15}\]  
where \(X\) is the time series data, \(\overline{X}\) and \(\sigma\) are the average and variance of \(X\), respectively, and \(X^{\prime}\) is the normalized monitoring data. Figure 4 shows the trend in nine variables after normalization.  
Figure 3: Mutual information matrix among variables (figure drawn by seaborn [46]).
## 8 Issues and Recommendations for Future Research
### Smart Naming and Discovery  
As IoV networks are distributed, intelligent IoV applications will be available in multiple vehicles and edge devices. Vehicles and edge nodes may run different IoV services and generate content. Content- and service-naming enable addressing and identification of content and services. Thus, it is critical to design efficient service- and content-naming schemes and also to service discovery protocols so that consumers can identify and locate the relevant content/service in a timely manner. However, naming in a dynamic environment is challenging due to intermittent connectivity caused by rapid mobility and topology changes. Solutions such as intelligent attribute-value-based naming can help. Anadaptive encoding technique would be a viable option to tackle the issue of associating names. However, much more research is needed on this topic.
## 2 Experimental Section
### Data
#### 2.2.3 RapidEye  
A time series of 46 images (September 2009 to October 2011) were used with a top of atmosphere, dark object subtraction (TOA-DOS) correction applied by RapidEye, Inc. (now BlackBridge Ltd. of Berlin, Germany). The RapidEye data were reasonably registered on delivery, but to minimize variability in the spectral signature due to pixel shifts between scenes, we manually co-registered each image to a master image (September 2009) using 50 ground control points and a second order polynomial transformation. A total of 15-20 check points per image resulted in each scene being co-registered to a root mean square error (RMSE) <2.5 m (0.5 pixels). Eighteen images were excluded from the time series due to snow or cloud cover. Existing site boundary shapefiles were used to clip the images to the extent of both the control and girdled sites, coincident with the pixels extracted for the Landsat ETM+ analysis (Python Software Foundation. Python Language Reference, version 2.7). Figure 1 illustrates the distribution of RapidEye images throughout the duration of this experiment for the girdle and control site.
## 4 Results and Discussion
### Compressive Tests on IR- Samples  
The compressive test results of the IR- samples are shown in Figure 12a. The inserted staples varied the compressive strength from 16.8 (IR-6) to 22.9 MPa (IR-2) with an average improvement of 5% and maximum improvement of 25%, based on the 18.3 MPa of IR-0. The improvement had an apparent relationship with the staple density (volume fraction), as plotted in Figure 12b. The density of 0.25 staples per cubic centimeter achieved the highest compressive strength of 21.5 MPa, and other densities had no noticeable improvement. This finding indicates that the staple locked the composite under compressive loading and thus increased the compressive strength, which could be referred to as the inner locking effect.  
Typically, printed structures have three dimensions of compressive strength anisotropy [48; 49]. As in this study, the Z-axis gave the lowest value because the sandwich-like layers were orthogonal to the loading direction. Thus, the testing sample resulted in many cracks and broke into pieces under compressive load. Random cracks still occurred when the IR-samples were reinforced with staples, as proved by the accidental drops in the stress curves, as shown in Figure 13. The central part of the specimen was locked by the staples and remained together as the failure modes shown in Figure 14a-c. The staples contributed to the compressive strength by holding the composite from horizontal extension when under vertical load. To conclude, the inner locking staple could improve the compressive strength along the Z-axis by the inner locking effect.  
Figure 12: Compressive strengths of the IR- samples. (**a**) Strengths vs. sample type and (**b**) strengths vs. staple density/volume fraction.  
Figure 13: Typical compressive stresses of the IR- samples.  
Few researchers have investigated the compressive strength improvement among the studies on the inner reinforcement and across the interfaces. [PERSON] and [PERSON] [39] conducted compressive experiments vertically to the printing direction, as shown by the \(Y\)-axis in Figure 8e, and found that the printed sample was 33% lower than the cast samples in the bar-penetrating reinforcement.  
For the studies with the same loading direction described herein [Figure 8f], [PERSON] and [PERSON] [13] carefully designed the printing paths to spiral-spread the embedded-cables and increased the compressive strength by 50%; [PERSON] and [PERSON] [32] placed wire meshes between the layers to improve the compressive strength by 75%. The spiraled-cables and placed-meshes enhanced the compressive strength by limiting the material extension. This study achieved a maximum compressive improvement of 18.3% at a volume fraction of 0.48%, as shown in Figure 12a,b. It was lower than the cables and meshes because the short staples functioned separately without overlap, as discussed for flexural results in Section 4.1. Furthermore, poor contact between the staples and composite was the primary defect. The higher the staple density, the more voids, and the mechanical properties declined.
## 3 Results
### Chord Methods & Optimization  
The absolute error of the acoustic roughness for the four chord methods integrated in the measurement setup are compared in Figure 10. In addition, the course of the optimized acoustic roughness is provided. It can be seen that the curve of the optimized acoustic roughness over the entire spectrum is closer to the reference compared to the individual methods. For a wavelength of 16 mm, for example, the deviation of the optimized acoustic roughness is 2.13 dB, while method 3 AP3 deviates by 5.66 dB.  
Figure 10: Absolute error of acoustic roughness for an undisturbed measurement and comparison of different chord lengths with the optimization approach.  
The average absolute error over all one-third octave bands assumed the lowest value of 2.11 dB for the optimized acoustic roughness method. For the individual chord methods, the minimum was 2.43 dB (3 AP1) and the maximum 3.19 dB (3 AP3). The median (calculated for the respective spectrum) of the absolute error shows the identical trend. The optimized acoustic roughness method has a median value of 1.99 dB, while the individual methods varies between 2.5 dB and 3.6 dB.
## 5 Experimental Verification
Taking the control input \(u_{1}^{V}\) as an example, for \(t\in[2.1\ s,\ 2.2\ s]\), \(u_{1}^{V}\) is not affected by the attack since there is no triggering time instant during this jamming interval. For \(t\in[2.4\ s,\ 2.6\ s]\), although the attacker starts to block the communication link at \(t=2.4\ s\), \(u_{1}^{V}\) changes to 0 at the triggering time instant \(t=2.46\ s\) due to the unsuccessful communication attempt between DG1 and DG2. Then, the ACK-based mechanism is activated to check whether the attack shifts to a sleep period. When the attack is cleared at \(t=2.6\ s\), the ACK-based mechanism successfully detects that the communication link returns to normal. Then, DG1 receives the information from DG2 and \(u_{1}^{V}\) updates according to the relative state difference between DG1 and DG2 immediately. It can be concluded that the proposed control scheme ensures the immediate update of the control signals when the attack shifts to a sleep period. Thus, the control objectives of voltage restoration and reactive power sharing can still be achieved even under DoS attacks.
## 3 Related Work
### V2G Systems Optimization under Uncertainty  
As mentioned in Section 2, a variety of uncertain features are present in real-life problems. Considering these features enhances the realism of a problem while increasing its complexity. In the last years, researchers have focused on providing methods to solve problems where uncertainty is present in diverse ways. In the study conducted by [PERSON] et al. [23], a flexible multi-objective optimization approach is introduced for V2G and grid-to-vehicle (G2V) technologies, taking into account environmental and economic stochastic considerations. In order to control this stochastic parameters, a Latin hypercube sampling strategy is employed. A firefly algorithm is employed to address the problem under uncertainties, resulting in significant reductions in both operating costs and COemissions, along with an improvement in the network's stability. Additionally, the performance of the algorithm is compared to that of a PSO algorithm, revealing that the proposed algorithm obtains slightly better solutions within a shorter computation time. Another method to deal with uncertainties is provided by [PERSON] et al. (2018), who applied a probabilistic optimal power flow approach to a hybrid power system. In this system, photovoltaic, plug-in electric vehicles and wind energy sources are considered. Combining Monte Carlo Simulation with the antithetic variates method, uncertainties are accurately modeled. Subsequently, a multi-objective GA is utilized to solve the problem, and computational tests validate the effectiveness of the proposed method when compared to alternative optimization algorithms. Another way of providing high-quality solutions to problems with uncertain features is the use of fuzzy logic-based techniques. In the paper presented by [PERSON] et al. (2018), a V2G system is implemented in a grid, where the energy is provided by a photovoltaic system, which provides uncertain intermittent energy amounts. The objective of this system is to maximize energy extraction while protecting the grid from energy oscillations and protecting the batteries from overcharging. A fuzzy logic-based charge management system is implemented to provide intelligent management of battery charge based on well-defined conditions, and a combination of an artificial neural network and a PSO algorithm is utilized to improve the performance of a V2G system. The simulation results of the proposed system in different scenarios show maximum power extraction without oscillation or overshoot, while ensuring stable energy efficiency. However, the energy efficiency value decreases when EVs do not participate in energy management, highlighting the importance of V2G operations.
## 6 Conclusions  
This paper presented the DCF algorithm, a forecasting algorithm that accurately predicts solar irradiance. Unlike other state-of-the-art models, the forecast accuracy was validated for short- and long-term predictions in three cities. The DCF algorithm had two main parts. Initially, it utilised the most accurate data-driven (ML) algorithms and then optimised their performance using contextual information. SVM and FBP were used as the data-driven models. SVM has been used for solar forecasting for over a decade. FBP, in contrast, is a novel algorithm that has rarely been used in the field of solar prediction. Nevertheless, its design characteristics seemed inherently promising for solar prediction.  
Firstly, a basic model was constructed for both algorithms with only hourly solar irradiance as input. The data were taken from the National Solar Radiation Database (NSRDB). Adding extraterrestrial radiation led to the largest improvement in R\({}^{2}\), MAE, and RMSE, for both SVM and FBP models. For the SVM model, the regularisation parameter \(C\) was tuned using grid search cross validation. This did not have a significant impact on the performance of the model. After training the model with the additional input features and the tuned hyperparameters, solar irradiance was predicted. The prediction was subject to several adjustments. All negative values and all values between sunset and sunrise were set to zero. This had a greater impact on FBP than on SVM, as FBP would generate larger non-zero predictions at night. Furthermore, a seasonal adaptation was applied. This  
Figure 13: DCF accuracy, evaluated in three cities using all evaluation metrics. (**a**) Coefficient of determination (R\({}^{2}\)) (**b**) Mean absolute error (**c**) Root-mean-squared error.  
increased or decreased every hour of the day for each month if it was above or below the average of the last years. It led to a significant improvement, as shown in Table 6.  
For the 1-hour short-term prediction, the final SVM model outperformed FBP and, thus, was utilised for the DCF algorithm. As shown in Table 7, it achieved an R\({}^{2}\) value of 87.6% for Denver, 90.6% for Seattle, and 91.2% for Boston. An MAE value of 36 W/m\({}^{2}\) was attained for Boston and similar values for Seattle and Denver. RMSE varied from 75 W/m\({}^{2}\) (Seattle) and 77 W/m\({}^{2}\) (Boston) to 107 W/m\({}^{2}\) (Denver). For the 2-hour prediction, SVM mostly outperformed FBP. On occasions in which this was not the case, the results were very similar. However, the SVM model displayed a strong decrease in forecasting accuracy with the increase in the forecast horizon. Therefore, for the 3-hour prediction, the FBP model yielded better results and thus was used beyond the 3-hour forecast in the DCF algorithm. The FBP performance only decreased very slightly over time, compared to the SVM. The reason for its sustained performance is its specific design for time-series predictions. The FBP model performed better for the long-term forecast than the SVM model. This was true for all cities and thus validated the use of the suggested model.