esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model of the PMSM  
Assuming that the permanent magnet synchronous motor is an ideal motor, under ideal conditions, the stator voltage equations and stator flux linkage equations in the d-q coordinate system can be expressed as follows [25]:  
\[\left[\begin{array}{c}u_{d}\\ u_{q}\end{array}\right]=\begin{bmatrix}R_{s}&-\omega_{e}L_{q}\\ \omega_{e}L_{d}&R_{s}\end{bmatrix}\left[\begin{array}{c}i_{d}\\ i_{q}\end{array}\right]+\frac{\mathrm{d}}{\mathrm{dt}}\left[\begin{array}{c} u_{d}\\ u_{q}\end{array}\right]+\begin{bmatrix}0\\ \omega_{e}\Psi_{f}\end{bmatrix} \tag{1}\]  
where \(u_{d}\) and \(u_{q}\) are the \(d\)-axis and \(q\)-axis stator voltage components; \(i_{d}\) and \(i_{q}\) are the \(d\)-axis and \(q\)-axis stator current components; \(L_{d}\) and \(L_{q}\) are the \(d\)-axis and \(q\)-axis inductance components; \(\psi_{f}\) is the rotor permanent magnet flux linkage; and \(\omega_{e}\) is the electrical angular velocity of the motor.  
The flux linkage equation in the d-q coordinate system is as follows:  
\[\left[\begin{array}{c}\Psi_{d}\\ \Psi_{q}\end{array}\right]=\begin{bmatrix}L_{d}&0\\ 0&L_{q}\end{bmatrix}\left[\begin{array}{c}i_{d}\\ i_{q}\end{array}\right]+\begin{bmatrix}\Psi_{f}\\ 0\end{bmatrix} \tag{2}\]  
where \(\psi_{d}\) and \(\psi_{q}\) are the \(d\)-axis and \(q\)-axis stator flux linkage components.  
Considering the surface-mounted PMSM, the \(d\)-axis and \(q\)-axis stator inductances satisfy \(L_{d}=L_{q}=L_{s}\). In the d-q coordinate system, the electromagnetic torque equation can be expressed as follows:  
\[T_{e}=\frac{3}{2}p\Psi_{f}i_{q} \tag{3}\]  
where \(T_{e}\) is the electromagnetic torque of the motor and \(p\) is the number of pole pairs of the motor.  
Under mechanical load, the dynamic motion equation of the PMSM can be written as follows:  
\[T_{e}-T_{L}-k_{f}w=J\frac{d\omega}{dt} \tag{4}\]  
where \(T_{L}\) represents the motor load torque, \(J\) is the total moment of inertia of the powertrain, \(\omega\) is the mechanical angular velocity, and \(k_{f}\) is the damping coefficient.  
We define the following:  
\[\begin{array}{l}a=\frac{3p}{2f}\psi_{f},\\ u_{i}=i_{i_{0}}\\ b=\frac{k_{f}}{T}\\ d_{i}=\frac{T_{i}}{I}\end{array} \tag{5}\]  
Equation (4) can be simplified as follows:  
\[\dot{\omega}_{i}=au_{i}-bv_{i}-d_{i} \tag{6}\]
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### ET Estimation  
Satellite Remote Sensing retrieval of evapotranspiration has become a popular tool and a focus of study in the past two to three decades. High-resolution daily evapotranspiration (ET) maps would greatly improve irrigation crop management [116]. Remote Sensing based ET estimation roughly started in the 1980s and has evolved into a variety of approaches and models as reported in an exhaustive paper by [PERSON] et al. [117]. In addition, [PERSON] and [PERSON] [118] reported the estimation of ET with RS and various surface-energy-balance algorithms (SEBAL). The authors reviewed these different satellites based approaches and the main physical bases and assumptions of these algorithms. It is difficult to categorize these methods, since they try to compensate for the empirical modules and physically based modules used. Table 4 lists a classification by [PERSON] et al. [119]  
Remote sensing techniques for estimating ET from satellites [120] have been developed from energy balances, thus generating estimates of actual evapotranspiration [121; 122]. The University of Idaho (USA) has developed a model to estimate ET called Mapping Evapo Transpiration with high Resolution and Internalized Calibration (METRIC) [122], based on the Surface Energy Balance Algorithms from the Land (SEBAL) model of [PERSON] et al. [121]. In METRIC, the evapotranspiration is calculated from Landsat imagery calculating the available energy using the earth surface temperature (Ts) derived from satellite imagery thermal bands, to constrain the heat flux for one or more layers (canopy and soil), and then computing the latent heat as a residual to the surface energy balance [123]. In other words, the energy utilized by the ET process is considered as a residual of the SEBAL equation. The ratio between actual evapotranspiration (Et\({}_{\rm crop}\)) and reference evapotranspiration (Et\({}_{\rm o}\)) can be calculated only when ET is measured; this ratio is the K\({}_{\rm \begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline
1 & Empirically direct methods (remote sensing data are introduced directly in semi-empirical models to estimate ET) \\ \hline
2 & Residual methods of the energy budget (combining some empirical relationships and physical modules in models such as SEBAL) \\ \hline
3 & Deterministic methods (more complex models such as Soil-Vegetation–Atmosphere Transfer models (SVAT), which compute the different components of energy budget (ISBA, Meso-NH) \\ \hline
4 & Vegetation-index methods (remote sensing to compute Kc or Priestley Taylor-alpha parameters). \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evapotranspiration (ET) estimation model categories. From [PERSON] et al. [119]
## 2 Related Work  
This section presents the current state-of-the-art image-based tomato detection and ripeness classification based on colour feature analysis (Table 1) and DL approaches (Table 2), presenting the respective authors and results achieved. Colour is perhaps the most widely used feature in image segmentation, especially to distinguish a ripe fruit from the complex natural background, as it has significant and stable visual characteristics that are less dependent on the size of the image. However, colour segmentation may not be the most effective method given its sensitivity to problems such as different illumination conditions or occlusions. To alleviate these problems, besides the usual RGB (Red, Green, Blue), different colour spaces are used, such as HSV (Hue, Saturation, Value), HIS (Hue, Intensity, Saturation), L*a*b (CIELAB), among others, to extract colour information from the object to be detected, in this case the fruit. Either one or more colour spaces can be used, as in the case of [PERSON] et al. [29] who developed a riped tomato harvesting robot for a greenhouse, whose identification and location of fruits consist of transforming RGB colour space images into a HIS colour model or [PERSON] et al. [30]that proposed an algorithm for recognising riped tomatoes through a combination of RGB, HSI and YIQ colour spaces and morphological characteristics of the image. To segment and extract colour information from the fruit, different thresholding algorithms and mathematical morphology approaches are used, especially to overcome occlusion and overlap situations. [PERSON] et al. (2015) presented a riped tomato detection algorithms based on HSV colour space and a Watershed segmentation method was used to \"separate\" the clustered fruits. [PERSON] et al. (2014) multiplied the Cb and V channels, from the YCbCr and YUV colour spaces, respectively, through the Otsu segmentation algorithm, when classifying tomatoes into 6 different classes. [PERSON] et al. (2011) segmented riped tomatoes through K-means clustering using the colour space L*a*b* and [PERSON] et al. (2019) combined the HSV colour space with Gray Level Co-occurrence Matrix and K-Nearest Neighbour to differentiate tomatoes into 5 different classes. [PERSON] et al. (2011) used the L*a*b* colour space to segment and localise riped tomatoes in a greenhouse and bi-level partition fuzzy logic entropy to discriminate the fruits from the background and [PERSON] and [PERSON] (2018) applied Fuzzy Rule-Based classification through the RGB colour space. To improve detection and achieve better segmentation, some studies make use of edge detection operations like the [PERSON] (2018) or the Sobel (2018) operators as proposed in the [PERSON] (2018) and [PERSON] et al. (2018) studies, respectively. Deep Learning (DL) ([PERSON] et al., 2018) is one of the Machine Learning (ML) based methods most used nowadays, having more recently entered the agricultural domain as a new solution to image analysis. Its success is based on the fact that they have high levels of abstraction and the ability to automatically learn complex features present in images ([PERSON] et al., 2018). The main
# A Field Investigation on Vortex-Induced Vibrations of Stay Cables in a Cable-Stayed Bridge
## 4 Correlation between Cable Response and Wind Characteristics
### Comparison of the Estimated Participative Modes with the Measured Vibration Modes of VIVs  
The data from April 2010 were first employed to estimate the participative modes of VIVs, and the estimated participative modes were subsequently compared with the measured vibration modes of VIVs. The preprocessing of the data was performed in advance, and the data with the acceleration amplitude of less than 2 m/s\({}^{2}\) were deleted first. The time history of the measured vibration modes was shown in the upper sub-figure of Figure 16. The colors indicate the participative mode numbers. The lower sub-figure of Figure 16a is the time history of the mean wind speed measured by only one anemometer at the bridge deck level, because the mean wind speeds are close to each other for two anemometers, as shown in Figure 8a.  
Figure 16a shows that the mean wind speeds corresponding to the acceleration amplitudes less than 2 m/s\({}^{2}\) are notably small. These parts of the mean wind speeds are subsequently deleted, as shown in Figure 16b. It is determined that the shapes of the time histories of the measured vibration modes and the mean wind speeds are similar to each other. The magnitude of the vibration mode increases or decreases with the mean wind speed.  
Figure 16: Change of measured vibration modes and mean velosities with time. (**a**) Change of measured vibration modes versus mean velocities; (**b**) Change of measured vibration modes versus mean velocities with the small components being filtered.  
According the present estimation method for the participative modes, the lower and upper ends of the velocities were first obtained according to Equation (2) and the lower sub-figure of Figure 16b. Then, the vortex-shedding-frequency range, [\(f_{\text{sL}}\), \(f_{\text{sU}}\)], was calculated by using Equation (6), as shown in Figure 17. Similar to the results in Figure 16, the vortex-shedding-frequency range changing with time has a similar trend to that of the measured vibration modes, varying with time.  
After they were combined with the fundamental natural frequency of the cable CAC20, the estimated participative vibration modes were then calculated and compared with the measured vibration modes in April, as shown in Figure 18. The results indicate that the estimated vibrations are close to the measured vibration modes. Simultaneously, the presented method was validated by the data in July and August, and the same conclusion can be obtained from the comparison shown in Figure 19.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 2 Methodology
### Data Flow, Extraction, and Processing  
Raw data are recorded by Elspec Power Quality Analyzers (PQAs), [[https://www.elspec-ltd.com/metering-protection/](https://www.elspec-ltd.com/metering-protection/)]([https://www.elspec-ltd.com/metering-protection/](https://www.elspec-ltd.com/metering-protection/)) (accessed on 26 April 2022) compressed, and then forwarded to a PQSCADA, [[https://www.elspec-ltd.com/power-quality-software-pqscada-software/](https://www.elspec-ltd.com/power-quality-software-pqscada-software/)]([https://www.elspec-ltd.com/power-quality-software-pqscada-software/](https://www.elspec-ltd.com/power-quality-software-pqscada-software/)) (accessed on 26 April 2022) database for permanent storage. The Elspec PQAs  
Figure 2: Dataflow (left to right) from source to analysis. Boxes indicate processing steps and text on arrows indicates the file format used. Grey shading indicates proprietary technology. Unshaded steps are our own scripts (based on the Python). The ETL (Extract-Transform-Load) steps interact with Elspec’s proprietary PQSCADA system (using our own _Dynamic Data Grabber_ package) to extract voltage and harmonics data as dataframes into Parquet files. Dataframes are aggregated by month and then consumed by various analysis scripts. These output data in HDF5 format for use by plotting scripts.  
sample voltage, current, power waveforms at up to 50 kHz, but employ lossy compression (at the edge) to reduce the data volume and velocity. Due to their proprietary nature, the details of the compression are not well documented. The PQSCADA database can be queried for a wide range of performance parameters, including--but not limited to--aggregated voltage and harmonics data. We extract data from this database through a small stack of Python [66] scripts that abstract away various data engineering complications. These depend heavily on Numpy [67] and Pandas [68].  
We extract data from sites at three different grid voltage levels. For each level, there are two data packages. The first covers four years, six voltage (three phase-to-ground, three phase-to-phase) channels, and eight harmonics. The second covers a month, three voltage channels (phase-to-ground), and 96 harmonics. Data are aggregated by calculating mean values in intervals of 1/50 Hz. Each site in the first (second) package contains \(3.6\times 10^{11}\) (\(3.9\times 10^{10}\)) samples. See also Table 1.  
Uncompressed sizes for the data packages are \(\sim\)2.8 TB and 240 GB, respectively. To deal with this amount of data efficiently, we use column storage with lossless compression (Parquet ([[https://parquet.apache.org](https://parquet.apache.org)]([https://parquet.apache.org](https://parquet.apache.org)) (accessed on 26 April 2022))) and slice data into subsets for processing.
## 4 Conclusions  
A pure Zn-Fe LDH having extraordinary characteristics of light absorption was successfully synthesized by a simple co-precipitation method. The synthesized material was then characterized by XRD, SEM, FTIR, and UV-Visible DRS spectroscopy for the investigation of crystallinity, surface morphology, surface functionality, and optical absorbance, respectively. Since Zn-Fe LDH possesses a narrow bandgap (2.53 eV), it is presumed that photogenerated charges on the surface of the photocatalyst will improve photocatalytic reactions. The Zn-Fe LDH photocatalyst was employed successfully for biomass conversion/value addition using a rice husk extract. The results were confirmed by qualitative and quantitative analysis of the obtained products via GC-MS. Before the photocatalytic reaction, the rice husk extract contained 41.33% of carboxylic acids, 30.32% of phenols, 10.12% of alcohols, 5.06% of long-chain alkanes, 3.2% of aldehydes, 1.93% of ketones, and 4.04% of other compounds. In contrast, after the photocatalytic reaction, GC-MS detected 7.83% of alcohols, 82.62% of long-chain alkanes, and 9.17% of some other compounds in the rice husk extract. The reaction chemistry seems to be complicated, as many reactions such as condensation, polymerization, oxidation, and reduction may have occurred during the photocatalytic process. Because of the inexpensive methodology utilized, this work delivers an alluring approach to effectively utilize rice husk and its extract for the production of useful chemicals and value-added products, as an alternative to burning it for energy purposes, a process that produces CO\({}_{2}\) gas.  
Conceptualization, M.S., S.A.K. and A.R.; Investigation, M.S., A.R. and Z.K.; Data curation, M.Z.; Visualization, M.S. and S.A.K.; Writing--original draft preparation, M.S., M.Z. and A.R.; Writing--review and editing, Z.K. and W.Y.K.; Supervision, A.R. and Z.K.; Methodology; A.R., M.Z. and W.Y.K.; Funding acquisition, A.R. and W.Y.K. All authors have read and agreed to the published version of the manuscript. This research was supported by a National University Development Project funded by the Ministry of Education (Korea) and the National Research Foundation of Korea (2021). The authors declare no conflict of interest.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 1 Introduction  
The space industry has been transformed in recent decades, driven largely by the adoption of the CubeSat standard [1], which has led to an exponential increase in the number of satellites being launched and planned [2, 3]. This trend can be attributed to the emergence of a novel philosophy known as \"New Space\" in the late 1990s [4]. This concept emphasizes the maintenance of a balanced budget and the acceleration of the development cycles by the use of commercial off-the-shelf (COTS) components, despite recent advances in new technologies for integrated circuits [5, 6, 7]. Consequently, this has reduced the entry barriers, which has garnered the interest of numerous non-space sectors seeking to undertake the launch of their own microsatellites, who have enabled technological transfer from their areas.  
The emergence of the \"New Space\" industry has also prompted the adoption of commercial 18650 Li-ion battery cells for space applications [8, 9]. Despite the adoption of this energy storage option, it should be noted that a group of cells of the same model usually exhibit discrepancies in characteristics such as capacity, internal resistance, or self-discharge [10]. Although a screening process is typically implemented to create matched sets, differential aging can exacerbate these inconsistencies, leading to increased voltage divergence. Thus, the inclusion of a cell equalization circuit in satellites is crucial to ensure voltage balance and promote the safer, more reliable operation of the battery pack.  
The objective of this paper is to design a CubeSat battery equalizer circuit for a deep space mission under the New Space philosophy. High reliability was the main aspect of the design, and an analog control circuit was implemented. Moreover, multiple balancingtopologies were studied, culminating in the implementation and comparison of the most viable candidates.  
The primary contribution of this paper is the implementation of two prototypes of battery equalizers in order to compare them. Furthermore, while it is true that other studies of this type exist, they have all been conducted in the context of electric vehicles. This paper focuses on space applications, where the environment is different, and other challenges need to be considered [11].  
The key contributions of this article are outlined below:  
* Study of the state of the art: this work included a study of the existing balancing cell methods.
* Implementation of two potential equalizers for space applications. these topologies were implemented with COTS components in order to assure the scalability and the ease of implementation.
* Comparative analysis: a thorough comparative analysis of the chosen equalizer designs was performed.
## 3 Results
### Mass Balance of Glucose and Xmg through LAH Fractionation  
Changes in the composition of the solid residues and liquid hydrolyzate after LAH fractionation of pine wood under optimal conditions obtained in the previous experiments: 190 \({}^{\circ}\)C, 0.5% (\(w/v\)) H\({}_{2}\)SO\({}_{4}\), and 10 min., presented in Table 2. After the fractionation process, the remaining fractionated solids were 64.8% from raw pine wood. The glucan content in fractionated pine wood increased from 41.1% to 54.4%, which corresponded to 35.3% on the basis of the raw pine wood. That is, 14.1% of glucan was hydrolyzed during the LAH fractionation process, but only glucose corresponding to 3.5% of glucan was present in the fractionated hydrolysate, indicating that about 5.6% of glucan was over-decomposed into 5-HMF, levulinic acid, or an unknown substance. Meanwhile, the XMG contents in the solid residues decreased from 17.2% to 6.3%, which corresponded to 4.1% on the basis of the raw pine wood. That is, 73.8% of XMG was hydrolyzed to xmg, and 14.4% of xmg, which corresponds to 12.7% of XMG, was present in the fractionated hydrolysate, indicating that about 2.4% of xmg decomposed into products, such as furfural and formic acid. Glucan and XMG were retained in the fractionated pine wood at levels of 85.9% and 23.8%, respectively. Consequently, a xylose-rich hydrolysate was obtained. Consequently, a xylose-rich hydrolysate was obtained, and also residual solid rich in glucan and lignin (data are not shown), which can be used as an excellent biomass fuel, was obtained through LAH fractionation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Biomass**} & \multirow{2}{*}{**Fratctionation**} & \multirow{2}{*}{**Solid**} & \multicolumn{3}{c}{**Solid (\%)**} & \multicolumn{3}{c}{**Liquid (\%)**} & \multicolumn{3}{c}{**EMB \({}^{\text{b}}\) (\%)**} \\ \cline{3-8}  & & **Remaining (\%)** & **Glucan** & **XMG** & **AIL** & **Gluose** & **xmg** & **Glucan** & **XMG** \\ \hline \multirow{4}{*}{Pine wood a} & Untreated & 100 & 41.1 & 17.2 & 35.4 & - & - & 94.4 & 97.6 \\  & Fractionated c & 54.4 & 6.3 & 33.7 & 3.5 & 12.7 & (\(-\)5.6) & (\(-\)2.4) \\ \cline{1-1}  & \multirow{2}{*}{
\begin{tabular}{c} Component Retention (\%) \\ \end{tabular} } & 85.9 & 23.8 & 61.5 & & & \\ \hline \end{tabular}  
* Pine wood was fractionated at the conditions; 190 \({}^{\circ}\)C of temperature 0.5% (\(w/v\)) of H\({}_{2}\)SO\({}_{4}\), and 10 min. of reaction time. b Extraction mass balance (EMB) = \(\frac{\Sigma C_{ij}+\Sigma C_{ij}}{\Sigma C_{ij}}\); where \(C_{i}\) is the mass of each sugar component as \(C_{ij}\) determined through HPLC chromatography, the subscripts \(L\), \(S\), and \(R\) refer to the extracted liquid, fractionated solids, and raw straw fractions, respectively. c Analysis data is based on the oven dry untreated biomass.  
\end{table}
Table 2: Remaining solid and changes in sugar contents in the fractionated solid residue and liquid hydrolysate and mass closure for batch fractionation of pine wood under optimized conditions.