esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Potential Significance  
Potato cultivation, at present, has a great relevance worldwide [6; 23; 24]. It is the fourth most important crop, after rice, wheat, and corn [25]. Potatoes are second to none in the diet and livelihoods of millions of people around the world. World potato production has increased considerably in recent years, mainly due to increased cultivation in developing countries. The improvement of the varieties, the seeds used, and the methods of cultivation has led to higher yields. Furthermore, demand has been driven by changes in eating habits in several countries, which has led to increased consumption of potato-based products with a higher degree of industrial processing [24]. The area sown and production in developing countries in 2005 exceeded those of industrialized countries for the first time. Currently, the main producer is China, with 71 million tonnes, which represent more than 20% of world production [6].  
Potato cultivation is an important source of income for many farmers. In the Andean region in South America it is one of the few commercial crops produced by small farmers. In the tropical lands of Bangladesh and India it is cultivated commercially, mainly under irrigation, as a winter crop. The potato is especially popular with farmers in the mountainous areas of Vietnam, who benefit from favorable prices on the local market. They produce potato as a catch crop, rotating it with rice and corn. There, the income generated by potatoes is equal to that of rice and is double that of corn and sweet potato.  
In potato cultivation, humidity management, the subject of this article, is of great importance for productivity at harvest and for determining the best times of the year for planting. An increase in temperature leads to greater plant transpiration, which increases the water demand of the plants. This will cause water stress in many of the drier producing areas, causing a decrease in yields. This effect will worsen with greater intensity because of changes in the distribution of rainfall. Yields will decline further where irrigation is not available, to the point where potato cultivation becomes nearly impossible. The expected decrease in yields in several countries, particularly in tropical and subtropical regions, will reach 20-30%. The night temperature has a crucial influence on the formation of starch in the tubers, with the ideal being 15 to 18 \({}^{\circ}\)C. When this temperature exceeds 22 \({}^{\circ}\)C, the humidity in the soil decreases and the development of the tubers is severely affected. However, climate change is expected to have a favorable effect on yields in growing areas located at higher altitudes. Climatic conditions for potato cultivation are improving because of rising temperatures in mountainous regions. This favor yields and leads to an expansion of production toward higher and higher latitude areas. In some regions, potatoes may be grown as a winter crop.
## 6 Testing and Results
### Testing of HSFW2  
HSFW2 was tested in a laboratory pool. The experiments were carried out on a thin slick of various types of marine fuel. To test the underwater channel of the LIF spectrometer, a pool with a capacity of 50 L was filled with marine fuel solutions of various concentrations. In experiments with slick, the drone with a spectrometer hovered over the surface at a height of 1.2-1.5 m. This height was optimal in order to ensure the maximum signal ratio from the LIF to the daytime background in sunny conditions, on the one hand, and to ensure the minimum impact of the drone propellers on the slick, on the other hand. The experiments were carried out under conditions excluding the ingress of sun glare into the receiving channel of the spectrometer when studying the slick. In experiments with solutions, the drone was in a flooded state, such that the receiving lens was under the surface at a depth of about 15 cm.  
The operation of the fuel type identification element of HSFW2 in an aqueous solution and a slick state was determined using the example of the DMA identification. In Figure 11, the spectrum from a DMA solution with a concentration of 12 ppm and the LIF spectrum from a DMA slick with a thickness of about 100 \(\upmu\)m are presented. The vertical axis is the value of the fluorescence intensity in relative units, while the horizontal axis is the wavelength in nm.  
The LIF spectra obtained during testing were analyzed by the HSFW2 recognition element according to the method described in Section 3. The result of determining the fuel type is shown in Figure 12. The horizontal axis shows eight types of oil products, the LIF spectra of which were placed in the dataset. The vertical axis represents a probability of about 0.8, which means that the measured LIF spectrum likely represented the spectrum of the DMA marine fuel.  
The ability to measure the concentration of the dissolved oil product in water-flooded mode allowed us to assess the concentration of the solution under the slick. Thus, the concentration of dissolved DMA in the surface layer (at a depth of 15 cm from the surface) under a slick with a thickness of about 100 \(\upmu\)m, 2 days after its  
Figure 11: LIF spectra of DMA marine fuel of solution (**blue**) and slick (**orange**).
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 3 Experiments
### Experimental Setting  
In order to train and evaluate the proposed deep learning framework, we introduce the experimental setting.  
For the geoeffectiveness prediction of CMEs, 2400 CME events are randomly divided into the training set and the testing at approximately 4:1 ratio, and the proportion of geoeffective and non-geoeffective samples in the training set is basically the same as that in the testing set. Specifically, the training set has 1725 non-geoeffective samples and 197 geoeffective samples, and the testing set has 429 non-geoeffective samples and 49 geoeffective samples. The training procedure is performed in the PyTorch framework. In the training phase, we use Adam [52] as the default optimizer to update the parametersof all deep learning methods in which the learning rate is 0.001, the related parameters \(\beta 1\) and \(\beta 2\) are set to 0.9 and 0.999. One hundred epochs are used for training, and the batch size is 1. The training of all methods adopts a down-sampling strategy due to the imbalanced geoeffective and non-geoeffective samples. Specifically, one eighth of the non-geoeffective samples are combined with the geoeffective samples as the training samples in each epoch. The loss function adopts binary cross-entropy, the formula is as follows:  
\[Loss=\frac{1}{N}\sum_{i=1}^{N}-[y_{i}logp_{i}+(1-y_{i})log(1-p_{i})], \tag{6}\]  
where \(y_{i}\) represents the actual label of the \(i\)th sample, when the current sample is geoeffective, the corresponding value of \(y_{i}\) is 1, otherwise it is 0, \(p_{i}\) represents the probability that the \(i\)th sample is predicted to be a geoeffective, and \(N\) represents the number of samples.  
For the arrival time prediction of geoeffective CMEs, 246 geoeffective CME events are randomly divided into the training set and the testing set at approximately 8:1 ratio. Specifically, the training set contains 218 CME events and corresponding 3042 observation images while the testing set contains 28 CME events and corresponding 418 observation images. The training procedure is also performed in the PyTorch framework. In the training phase, we use AdamW as the default optimizer to update the parameters and avoid the network overfitting, the related parameters \(\beta 1\) and \(\beta 2\) are also set to 0.9 and 0.999, the weight decay coefficient is set to 0.01. We train with a total number of 100 epochs and the default batch size is 32. The learning rate is set to 0.01 initially, and is multiplied by 0.6 at epoch 40, 60 and 80. The comparing traditional convolutional neural networks all adopt L2 Loss and data expansion based on sample split in the training phase.  
Figure 5: The distribution of different formats in CME events.  
Figure 6: The distribution of the number of observation images in CME events.
# Wind Turbines Optimal Operation at Time Variable Wind Speeds
## 4 PMSG's Optimal Power Control Based on the PI-type Regulator  
The time-varying curve of the PMSG's optimal power was previously determined based on the kinetic motion equation, and it represents the ideal (theoretical) curve to be followed by the wind turbine in order to operate at the maximum power point. This means that the wind turbine should be controlled in such a way as to closely follow the ideal curve.  
Thus, the main objective is to control the PMSG's power curve, following the PMSG's optimal power curve (\(P_{PMSG}(t)\to P_{PMSG\_{optim}}(t)\)), or, as otherwise stated, the PMSG's angular speed becomes equal to its optimal speed (\(\omega(t)\rightarrow\omega_{optim}(t)\)). One way of doing this is by implementing a control based on a PI-type regulator.  
The equation of the PI-type regulator is below described:  
\[P_{PMSG\_PI}(t)-P_{PMSG\_{optim}}(t)=k_{p}\Big{[}\omega(t)-\omega_{optim}(t) \Big{]}+k_{i}\int\Big{[}\big{(}\omega(t)-\omega_{optim}(t)\big{)}\Big{]}dt \tag{23}\]  
where \(P_{PMSG\_PI}\) is the reference value of the PMSG's power for the case of a PI-type based control, \(P_{PMSG\_{optim}}\) is the computed/measured value of the PMSG's power, \(k_{p}\), is the proportional constant and \(k_{i}\) is the integral constant of the PI regulator.  
By derivation, Equation (23) becomes  
\[\frac{dP_{PMSG\_PI}(t)}{dt}=k_{p}\frac{d\omega(t)}{dt}-k_{p}\frac{d\omega_{ optim}(t)}{dt}+k_{i}\Big{(}\omega(t)-\omega_{optim}(t)\Big{)} \tag{24}\]  
Then, by replacing Equation (3) in Equation (24), it results in  
\[\frac{dP_{PMSG\_PI}(t)}{dt}=k_{p}\frac{d\omega(t)}{dt}-k_{1}k_{p}\frac{dS_{w} (t)}{dt}+k_{i}(\omega(t)-k_{1}S_{w}(t)) \tag{25}\]  
The PMSG's power in Equation (25), \(P_{PMSG\_PI}(t)\), can be determined from the kinetic motion equation from Equation (11).  
Replacing Equation (1) in Equation (11), the below kinetic motion equation is obtained as  
\[J\frac{d\omega(t)}{dt}\omega(t)=a\bigg{(}\frac{S_{w}(t)}{\omega(t)}-b\bigg{)} e^{-c\frac{S_{w}(t)}{\omega(t)}}S_{w}^{3}(t)-P_{PMSG\_PI}(t) \tag{26}\]  
The kinetic motion Equation (26), together with the PI-type regulator Equation (25), defines the below system of differential equations used in the simulation environment for determining the PMSG's power curve and the PMSG's angular speed:  
\[\left\{\begin{array}{l}\frac{d\omega(t)}{dt}\omega(t)=a\bigg{(}\frac{S_{w}( t)}{\omega(t)}-b\bigg{)}e^{-c\frac{S_{w}(t)}{\omega(t)}}S_{w}^{3}(t)-P_{PMSG\_PI}(t)\\ \frac{dP_{PMSG\_PI}(t)}{dt}=k_{p}\frac{d\omega(t)}{dt}-k_{1}k_{p}\frac{dS_{w} (t)}{dt}+k_{i}(\omega(t)-k_{1}S_{w}(t))\end{array}\right. \tag{27}\]
## 2 Datasets and Methods  
### Datasets  
The NDVI dataset used in this study is the third-generation NDVI (NDVI3g), the newest dataset derived from NOAA/AVHRR produced by the Global Inventory Monitoring and Modeling Studies (GIMMS), at a spatial resolution of \(1/12\) degree (\(0.083^{\circ}\)) and 15-day interval, spanning from January 1982 to December 2011. The GIMMS NDVI3g data sets have been corrected to minimize the effects of stratospheric aerosol loadings from volcano eruptions, solar angle and sensor errors, thus are suitable to evaluate the long-term trends in vegetation activity [17, 39].  
The climate datasets include temperature at 2 m above ground level, precipitation and incoming shortwave solar radiation from1982 to 2010. The air temperature and solar radiation datasets with a high spatial resolution of \(0.05^{\circ}\times 0.05^{\circ}\) and 3-hour interval came from the Land-Atmosphere Interaction Research Group at Beijing Normal University [40], generated according to observation data from 700 stations within the mainland of China and handled with interpolation, correction and validation. The daily precipitation data product with resolution of \(0.25^{\circ}\times 0.25^{\circ}\), coming from National Meteorological Information Center (NMIC) [41], was generated according to the meteorological observation data of 2419 stations across the country, which has a higher spatial resolution than 680 stations in the study by [PERSON] _et al._[36] and 728 stations by [PERSON] _et al._[32]. To uniform the spatial and temporal resolution, all of the climate and NDVI data sets were averaged to half-monthly values and resampled linearly to \(0.25^{\circ}\times 0.25^{\circ}\) grids.  
The land cover data in 2000 was obtained from the 1-km product SYNMAP [42]. As in previous studies [19,32], dynamic changes of land cover are not considered in this study, which may bring in uncertainties due to change of vegetation type, urbanization, land reclamation, reforestation and _etc_. The main land cover types in China include evergreen needle forest (ENF), evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), mixed forest (MF), shrubs, grasses, crops and barren (Figure 1). Among these land cover types, crops are greatly affected by agricultural management practices and barren shows little vegetation cover, therefore crops, barren and urban area are masked out and only natural vegetation are considered in this study. Human activities affecting natural vegetation were not considered in the study, which might bring in uncertainties to the following analysis. The land cover data were aggregated to grid cells of \(0.05^{\circ}\times 0.05^{\circ}\) via majority method in ArcGIS [43] to be consistent with climate and NDVI datasets.
## 3 Results and Discussion
### Correlation between E-Nose Values and Key Flavour Compounds  
Correlation analysis was further conducted between the changes in response values of E-nose and 22 key flavour compounds (Figure 5D). W5S, W6S, W1S, W2S, W2W and W3S values were also found to be positively correlated with 10 key flavour compounds, including 1-nonanol (OAV 152.8), 3-methyl-1-Butanol acetate (OAV 39.3), hexanoic acid ethyl ester (OAV 670.0), heptanoic acid ethyl ester (OAV 1.6), octanoic acid ethyl ester (OAV 16,466.7), decanoic acid ethyl ester (OAV 61.7), dodecanoic acid ethyl ester (OAV 3.4), benzenepropanoic acid ethyl ester (OAV 488.9), furufural (OAV 4.4) and 1-(2,6,6-trimethyl-1,3-cyclohexadien-1-yl)-2-buten-1-one (OAV 132,666.7). W1C, W3C and W5C values were negatively correlated with the above 10 VOCs, indicating that these sensors reflected their downward trends. The above 10 VOCs all contributed to the overall flavour of sea buckthorn distilled liquor, particularly octanoic acid ethyl ester (OAV 16,466.7) and 1-(2,6,6-trimethyl-1,3-cyclohexadien-1-yl)-2-buten-1-one (OAV 1132,666.7), indicating that the seven sensors (W5S, W6S, W1S, W1W, W2S, W2W and W3S) of E-nose can effectively reflect the strong aroma of sea buckthorn wine and distilled liquor in production. These  
Figure 5: Comparison of flavour files in SJ, SW and DL by E-nose (\(n=3\)). (**A**) PCA analysis, (**B**) load diagram of the sensors, (**C**) bar chart for response values of sensors, (**D**) correlation of sensors and key VOCs.  
results provide guidance for the rapid detection of the trends in key flavour compounds in SW and DL during production.
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Observation
#### 4.3.4 Time  
Time represents the instant of observation (i.e., timestamp) [79]. Information about time and location are critical features of some applications (called spatial-temporal-aware applications) that require tasks to make observations at a specific location during a defined period [120]. In [24], latency relates to the time an IoE system needs to answer to a stimulus. Interaction between IoE smart devices can influence the service's response time to end-users. The time interval between the initiation and the conclusion of the task is the response time [49; 107; 109].  
The time dimension depends on how sensors are requested or provide data to the system in specific periods or on an ad-hoc basis (as software system makes a request), which is characterized as the following two distinct methods that were proposed in [107]:  
* Pull method: The software component in the control of obtaining sensor data from sensors makes a requisition periodically (after specific intervals) or instantly obtains sensed data [107].
* Push method: The physical or virtual sensor pushes data to the software component in the control of obtaining sensor data periodically [36; 107]. In many cases, a sensor observation can be the result of a local sensor data fusion [68].  
Real-time applications monitor the state of the environment and react to changes accordingly and in a timely manner.  
The deployment of IoE applications in real-world scenarios creates a massive amount of data from real-time interactions, usually at high data rates. It faces challenges as temporal data consistency related to the coherency between the value of the data in the system and its environment state [107]; and high latency during interactions [39], when inferred contexts evolve with time [91; 94], and the exchanged data may not be accurate.  
Hard real-time data cannot accept any delay; in contrast, soft real-time data can accept various bounded delays. Delay-tolerant applications can be categorized as nonreal time [60; 107]. In offline circumstances, the delayed transmission may be crucial to address quality and security constraints [37; 65].  
In real-time situations, timeliness [69; 107] describes data processing in a specific deadline, which is real time, near real time, or batch processing [113].  
* Real time: refers to the immediate data processing to provide instant results for a time-sensitive application.
* Near real time: refers to situations when the delay time is still relevant for the application, but the computation process is not as immediate as real time.
* Batch-processing: refers to situations when data are first collected and processed at a predetermined interval or when a specified volume of data is available [37].
# Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation
## 4 Simulations  
In this section, we present the simulation results to validate the Bussgang decomposition-based MPA. Without sacrificing generality, a simplistic codebook from [23] is considered in our simulations. We set \(p=1\) and \(x_{sat}\) to be equal to the maximum dynamic range of \(\mathbf{x}\). Furthermore, the BER simulations are performed over \(10^{7}\) bits, and 15 MPA iterations are used. The simulation results for a Rayleigh channel are depicted in Figure 4. The simulation parameters are summarized in Table 1.  
In Figure 4, saturation is observed in Bussgang-based MPA's BER performance. In addition, we observe no significant change in Bussgang-based MPA's BER floor when the number of pilots is increased from 137 to 880. However, for the RFF-based MPA detection in [12], its BER performance is found to improve as the number of pilots increases, and the saturation due to the BER floor is completely invisible at 880 pilots. Furthermore, the analytical expression for the BER of the Bussgang-based detector derived in (12) is validated in Figure 5, which illustrates close agreement between the analytical BER (denoted by [A]) and the simulated BER (denoted by [S]). Figure 6 shows a similar validation of the analytical result derived in (12) assuming a Nakagami-\(m\) distributed \(\mathbf{h}\), with \(m=0.5\). Since the mode of the Nakagami-\(m\) distribution (with \(m=0.5\)) is zero, we observe degraded BER performance for Nakagami-\(m\) fading as compared to the BER performance for the Rayleigh channel presented in Figure 5. However, due to the distribution-independent quantification of the performance gap presented in (12), a close match is observed between the simulated BER and the analytical BER for the Bussgang-based detector in Figure 6. This quantification of the BER floor helps when predetermining the viability of using a lightweight Bussgang-based MPA (which has a complexity of \(\mathcal{O}(TKM^{d_{f}})\), where \(d_{f}\) denotes the free distance) over a complex RFF-based detector (which has a complexity of \(\mathcal{O}(TKM^{d_{f}}+n_{G}^{2})\), where \(n_{G}\) denotes the number of RFFs) subject to achieving a BER-based level of QoS.  
\begin{table}
\begin{tabular}{l l} \hline \hline Codebook & Section ILA [23] \\ \hline Modulation & OOK \\ \hline Value of p & 1 \\ \hline Kernel-width assignment & Silverman’s rule [24] \\ \hline Number of MPA iterations & 15 \\ \hline Number of transmitted bits & \(10^{7}\) \\ \hline Parameter values for Rayleigh distribution & \(\sigma_{h}^{2}=1\) \\ \hline Parameter values for the Nakagami-\(m\) distribution & Shape: \(m=0.5\), \\  & Spread parameter: 1 \\ \hline \(n_{G}\) & 110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 4: BER vs. SNR comparison of the Bussgang-based detector with RFF-based detector for a Rayleigh Channel by varying the number of pilots.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 1 Introduction
### Reluctance Coil Guns: A Ball Launcher That Can Be Optimized  
Even if reluctance coil guns are a relevant solution for kicking soccer balls efficiently, it is important to note that they are not very efficient in terms of energy conversion. In [11], electrical energy for the coil gun is stored in a capacitor having a capacitance value \(C=4700\)\(\mu\)F under 425 V. Stored electrical energy is equal to:  
\[E_{C}=\frac{1}{2}CU_{C}^{2}=424\,\mathrm{J}\]  
Consequently, the ratio of ball kinetic energy to the input electrical one is only 7%, and the ratio of the overall mechanical transmitted energy (including the kinetic energies of the iron rod, the lever and the ball as explained later) to the input electrical one is 12%. However, the energy necessary for kicking like human soccer players is already stored in the capacitor. This means that if a robot's kick is 10 times less powerful than a human one, it is not an issue related to available energy, but it is a problem of inefficiency of energy transfer in reluctance coil guns.  
Optimizing this energy transfer can be done in two main ways without changing the size and the weight of the launcher. The first one is to adjust the initial position of the plunger, and the length of its non-magnetic extension. [12] shows that energy transmission can be increased by 70% using this technique compared to the reference case presented in [11]. This optimization is interesting because nothing is changed on the coil gun structure and size; it is only an optimization of initial conditions and a plunger parameter adjustment.  
A second way of improving the energy transfer of a coil gun is to modify its inner structure by splitting the coil and the energy storage capacitor into several ones [7; 13], without changing the overall quantity of coil copper and the overall capacitance value. Instead of sending an energy pulse to a single coil, a sequence of smaller energy pulses will be sent to the different coils propagating the magnetic force along the coil as the plunger enters it. The number of coils and the triggering sequence are the parameters to be optimized.  
This paper focuses on this second method for optimizing the energy transfer in a reluctance coil gun. It is divided into three sections:  
* Section 2 recalls the principles of coil guns.
* Section 3 describes 4 melatronic coupled models of reluctance coil guns. All these coil guns use the same coil copper quantity and have the same overall electrical energy storage capacitance, but they have, respectively, one, two, three and four coils. The electromagnetic part of each model has been implemented using _FEMM 4.1_, a finite element electromagnetic simulation tool, and Matlab Simulink is used for modelling the electrical and mechanical parts.
* Section 4 presents results, which are discussed in order to conclude on the most relevant coil structure for maximizing the ball speed and the energy transfer of the reluctance coil gun, while maintaining a high level of robustness.