esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Emission Characteristics
#### 3.3.2 HC Emissions  
Figure 9 demonstrates the variations of HC emission for four test fuels according to various engine speeds. As shown in Figure 9, the HC emissions of all test fuels show a downward trend with increased engine speed. The reason for the decrease in HC emissions may be attributed to the increase in engine speed, which increases the temperature in the cylinder, especially the cylinder wall temperature, and reduces the misfire phenomenon of the mixture contacting the cold wall at idling conditions. In addition, the increase in engine speed ensures better mixing of air and fuel [34; 35]. The running state of the engine and the fuel properties directly affect the HC emission. Generally speaking, HC emission is one of the main emission pollutants of the gasoline engine. However, under idle conditions, various conditions such as fuel injection pressure, temperature and pressure in the cylinder, turbulence intensity, and so on are not optimal, which will lead to a large amount of HC emission from diesel engines. Therefore, HC emission has become a serious problem for diesel engines under idling conditions [17]. On the other hand, adding palm oil to diesel reduces the HC emission of diesel engines, which may be related to the oxygen contained in palm oil, and promotes the oxidation of HC.  
Figure 8: CO emissions of all test fuels according to various engine speeds.
## 5 Conclusions  
Many process-based models for carbon flux predictions have faced a wide range of uncertainty issues in the model result. The complex interactions between atmosphere and forest ecosystems are the main reason for the uncertainty. However, the setting of too many initial parameters might lead to disturbing accuracy improvement. On the other hand, artificial intelligence techniques, which are novel methods to resolve complex and nonlinear problems, have shown a possibility for forest ecological applications. This study is the first step to present an objective comparison between multiple AI models for the daily forest GPP prediction using satellite remote sensing data. In particular, the DNN model outperformed the other three models through an intensive hyperparameter optimization in a deep learning network. We showed that the DNN model also performed well under conditions of cold waves, heavy rain, and an autumnal heatwave. In the experiment, our DNN model showed better accuracy than the operative MODIS GPP product, presumably because of the localized optimization for the forests in South Korea. A spatially continuous retrieval of forest GPP can be achieved after the integration of more improved DNN models and higher-quality datasets from satellite remote sensing and meteorological reanalysis. As future work, a comprehensive comparison with the result of process-based models will be necessary using a more extensive EC database from various forest ecosystems. Such comparisons can produce a synergy between the process-based models that have advantages for the understanding of the mechanism and the AI models that have the ability to solve nonlinear problems. In this sense, more efforts for the AI-based modeling of forest GPP will also be required.  
**Author Contributions:** Conceptualization, B.L. and Y.L.; Data curation, B.L., N.K., K.J., and M.K.; Formal analysis, B.L. and N.K.; Methodology, B.L. and Y.L.; Resources, E.-S.K. and J.-H.L.; Writing--Original draft, B.L. and Y.L.; Writing--Review & editing, N.K., E.-S.K., K.J., M.K., J.-H.L., J.C., and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The present study was conducted as a part of a research project of the National Institute of Forest Science (Project No. FE0100-2018-05). Additionally, this work was funded by the Korea Meteorological Administration Research and Development Program under the Grant KMI2018-05510.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Design of ADRC and ST-SMC Hybrid Control
#### 2.3.3 ADRC and ST-SMC Hybrid Control
The two components of the super-twisting sliding mode control are \(-k_{1}|S\,{}^{1/2}\text{sat}(S)\) and \(-k_{2}S\):  
For \(-k_{1}|S\,{}^{1/2}\text{sat}(S)\), we have:  
\[\dot{z}_{2}=z_{3}-\beta_{2}(z_{1}-y)+g\cdot u \tag{35}\]8  
Since \(sat(S)\) is \(|S|>\delta\) when \(\text{sign}(S)\), and \(|S|\leq\delta\) when \(\frac{S}{\delta}\), we have:  
\[S\cdot sat(S)= \begin{cases}|S|&\text{if }|S|>\delta\\ \frac{S^{2}}{\delta}&\text{if }|S|\leq\delta\end{cases}  \tag{53}\]  
\[S\Big{(}-k_{1}|S\,{}^{1/2}sat(S)\Big{)}= \begin{cases}-k_{1}|S\,{}^{3/2}&\text {if }|S|>\delta\\ -k_{1}|S\,{}^{1/2}\frac{S^{2}}{\delta}&\text{if }|S|\leq\delta\end{cases}  \tag{54}\]  
For \(-k_{2}S\), we have:  
\[S(-k_{2}S)=-k_{2}S^{2} \tag{55}\]  
By combining the disturbance terms and the sliding mode control terms, we obtain:  
\[\dot{V}(S)\leq|S|\Big{(}\epsilon_{f}+\epsilon_{d}\Big{)}-k_{1}|S\,{}^{1/2}S \cdot sat(S)-k_{2}S^{2} \tag{56}\]  
According to the different cases of \(sat(S)\), we can discuss the following two situations:  
Case 1: When \(|S|\ >\delta\), at this time, \(sat(S)=\text{sign}(S)\); therefore:  
\[\dot{V}(S)\leq\ |S|\Big{(}\epsilon_{f}+\epsilon_{d}\Big{)}-k_{1}|S\,{}^{3/2}-k_{ 2}S^{2} \tag{57}\]  
Figure 5: Control algorithm block diagram. To ensure \(\dot{V}(S)\leq 0\), it is necessary to satisfy:  
\[k_{1}|S|^{\,1/2}+\ k_{2}|S|\ \geq\frac{\epsilon_{f}+\epsilon_{d}}{|S|} \tag{58}\]  
Case 2: When \(|S|\ \leq\delta\), at this time, \(sat(S)=\frac{S}{\delta}\); therefore:  
\[\dot{V}(S)\leq\ |S|\Big{(}\epsilon_{f}+\epsilon_{d}\Big{)}-k_{1}|S|^{\,1/2} \frac{S^{2}}{\delta}-k_{2}S^{2} \tag{59}\]  
To ensure \(\dot{V}(S)\leq 0\), it is necessary to satisfy:  
\[k_{1}|S|^{\,1/2}\frac{S^{2}}{\delta}+k_{2}S^{2}\geq\ |S|\Big{(}\epsilon_{f}+ \epsilon_{d}\Big{)} \tag{60}\]  
Finally, to ensure the asymptotic stability of the system in the Lyapunov sense, the sliding mode gains must satisfy the following conditions:  
\(1\). When \(|S|\ >\delta\), we have:  
\[k_{1}>\max_{|S|}\frac{\epsilon_{f}+\epsilon_{d}}{|S|^{\,1/2}},\ k_{2}>\max_{|S| }\frac{\epsilon_{f}+\epsilon_{d}}{|S|} \tag{61}\]  
2. When \(|S|\ \leq\delta\), we have:  
\[k_{1}>\max_{|S|}\frac{\epsilon_{f}+\epsilon_{d}}{|S|^{\,1/2}\frac{\delta}{S^{ 2}}},\ k_{2}>\max_{|S|}\frac{\epsilon_{f}+\epsilon_{d}}{S^{2}} \tag{62}\]  
Therefore, when \(k_{1}\) and \(k_{2}\) satisfy the above conditions, the system is asymptotically stable in the Lyapunov sense.
## 1 Introduction
### Review of Curtailment Methods
This method, as shown in Figure 2b, has the advantage of maintaining fairness by considering the maximum potential capacity of each DER rather than their real-time output. However, while this approach may appear fairer, it does not always optimize grid efficiency or resource utilization, as it treats all contributors uniformly regardless of their actual grid impact. To address these challenges, it is imperative for South Korea to develop a more robust and flexible curtailment framework. The LIFO approach prioritizes curtailing the newest additions to the grid, thus protecting the operational stability of older, more entrenched generators [21]. However, this method does not account for the real-time contribution of individual generators to grid stability, potentially leading to inefficiencies. The Pro-Rata method, on the other hand, distributes curtailment proportionally across all generators, based on either their current active output or maximum capacity [21]. While this can be seen as fairer, it may not always optimize grid efficiency or resource utilization, as it treats all contributors uniformly irrespective of their actual grid impact. To address these challenges, it is imperative for South Korea to develop a more robust and flexible curtailment framework. Therefore, in this paper, we have developed a sensitivity-based control algorithm aimed at minimizing curtailment and control within the VPP. This algorithm seeks to effectively utilize the electrical characteristics of VPP resources to address issues inherent in traditional curtailment and control methods. We validated the proposed algorithm by comparing it with existing methods from a voltage perspective to ensure consistency. The algorithm was implemented using actual power system data from Jeju Island, based on an integrated OpenDSS-MATLAB integrated system.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 3 Proposed Method
### ADS-B Signal Dataset  
Currently, it is difficult to obtain a large number of ADS-B overlapped signals in practice. Thus, we created a semi-real ADS-B dataset (SR-ADSB) with two signals overlapping in this  
Figure 4: The temporal convolutional network.  
Figure 3: The multi-scale convolutional separation network.  
paper. Firstly, the ADS-B data of Tianjin Airport from 21 April to 21 May 2021 were collected. The abnormal data and duplicate information of the longitude, latitude and altitude were removed. A total of 171,850 ADS-B messages of 2315 aircraft were obtained. Among them, 600 ADS-B messages with different aircraft number (AA), longitude, latitude and altitude were randomly selected as the initial samples.  
The overlapping signals parameters of the dataset are set according to the single antenna ADS-B overlapped signal model in Section 2. The range of SNR is 5-25 dB, and the interval step is 5 dB. The power difference between the signals is set to 0 dB, 1 dB and 2 dB. The carrier frequency of the signals is set as the intermediate frequency 9-11 MHz, the interval step is 0.5 MHz, and the carrier frequency difference between signals is set as 0 Hz, 500 Hz, 1000 Hz, and 1500 Hz. For the relative time delay between signals, 0 us, 5 us, 10 us, 20.3 us are used. It can generate 1200 kinds of overlapping signals with different SNR, carrier frequency, relative delay for a pair-wise signal. From the initial sample, 400 ADS-B signals are selected and two signals are used as a group. Hence, 240,000 samples were obtained as the training data for the SR-ADSB. The remaining 200 ADS-B signals are used to generate 120,000 test samples.
## 3 Results and Discussion
### Characterization of the Photocatalytic Nanocomposites
#### 3.1.3 Morphological Characteristics and BET Analysis  
The SEM micrographs for the TC-50, TC-30, TC-20, and TC-10 nanocomposites are shown in Figure 3. An irregular morphology can be observed in Figure 3a,b, which was similar to that reported by [PERSON] et al. for multiwall carbon nanotube-TiO\({}_{2}\) composites [22]. Contrary to this, Figure 3c,d showed quasi-spherical agglomerates consistent with the OLNCs morphology [23].  
The TEM micrographs of all of the nanocomposites are shown in Figure 4. The images show that the material is quasi-spherical and multi-layered, and resembles that of an onion, as seen from all the nanocomposites. This is consistent with the morphology reported by [PERSON] et al. [23]. It can be seen that as the mass of OLNCs increased, the shape of the TiO\({}_{2}\) became more irregular. The TC-50 material shows lumps of irregular spheres (Figure 4a). When the mass of OLNCs is decreased to 30 mg (TC-30), the morphology of the TiO\({}_{2}\) consists of irregular spheres with a spider web connection (Figure 4b). The TiO\({}_{2}\) transforms into a tube-like nanostructure when the mass of the OLNCs is decreased to 20 mg (TC-20) (Figure 4c). Similar morphology is seen when the mass of the OLNCs is decreased to 10 mg (TC-10) (Figure 4d). Such a morphology of the TiO\({}_{2}\) is familiar to the titania made in alkaline media, e.g., in synthesized TiO\({}_{2}\) (titanate) nanotubes [24]. [PERSON] et al. reported that the conversion of anatase to titanate could be hindered by the presence of a carbon layer [25]. This may be the case in this study, as the occurrence of nanotubes disappears in the material with higher carbon mass, as seen in the TC-30 and TC-50 materials.  
Figure 2: TGA (**a**) and DTA (**b**) thermograms for the TC-50, TC-30, TC-20, and TC-10 nanocomposites.  
Figure 4: TEM micrographs for the TC-50 **(a)**, TC-30 **(b)**, TC-20 **(c)**, and TC-10 **(d)** nanocomposites.  
Figure 3: SEM micrographs for the TC-10 **(a)**, TC-20 **(b)**, TC-30 **(c)**, and TC-50 **(d)** nanocomposites.  
The surface area of the nanocomposites was considerably higher than that of pure TiO\({}_{2}\), in the order TiO\({}_{2}\) (7 m\({}^{2}\)/g) \(<\) TC-10 (56 m\({}^{2}\)/g) \(<\) TC-20 (69 m\({}^{2}\)/g) \(<\) TC-30 (78 m\({}^{2}\)/g) \(<\) TC-50 (120 m\({}^{2}\)/g). This suggests that the addition of the OLNCs contributed to the increased surface area, since OLNCs alone (80.89 m\({}^{2}\)/g) [14] have a higher surface area compared to the as-prepared TiO\({}_{2}\) (Table 1). The pore volume and average pore size were also increased by the addition of the OLNCs, but did not show any particular trend.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 3 Evolution of Industrial Maintenance
### Predictive Maintenance Tools and Materials
#### 3.2.7 Machine Learning and Deep Learning  
Machine Learning (ML) is a subfield of AI that has evolved from pattern recognition to analyzing data structures and integrating them into models that users can understand and regenerate [46]. Figure 7 identifies all ML categories, methods, and models applicable to maintenance-related projects [47]. Furthermore, ML is classified into four categories, namely supervised learning, unsupervised learning, reinforcement learning, and deep learning. The types of supervised and unsupervised learning intended to predict or describe existing relationships in a dataset are said to be supervised when the dependent variable is available and unsupervised when they are not, while reinforcement learning is a computational approach that learns from the interaction with the environment, which means determining how the actors in a system can perform actions in their environment to maximize the cumulative rewards.  
Deep learning (DL) belongs to artificial neural networks (ANNs). It is a large group of techniques in many fields, which can be applied to both supervised and unsupervised learning. ANNs are inspired by brain function, with the primary goal of learning from unstructured or unlabeled data, using one or more layers to extract higher-level features step by step from raw input. Deep learning techniques can be applied to industrial equipment in different situations such as fault detection, failure prediction, etc.  
Figure 7: Classification of data-driven techniques and machine learning categories [47].
## 2 Materials and Methods
### Surface Electromyography (sEMG) Acquisition  
sEMG signals were recorded from the _biceps femoris_ (BF), _semitentinosus_ (ST), _rectus femoris_ (RF), _vastus medialis_ (VM), _vastus lateralis_ (VL), _lateral gastrocnemius_ (LG), _soleus_ (SO), and _tibialis anterior_ (TA) muscles using a 16-channel Bagnoli surface electromyograph (Delsys Inc., Boston, MA, USA). Before electrode attachment, the skin was prepared by shaving and cleaning with alcohol. Bipolar electrodes (Ag/AgCl) were placed on the dominant leg following the guidelines outlined by SENIAM [24]. The measurements were taken as follows: BF, 50% of the line between the ischial tuberosity and lateral femoral epicondyle; ST, 50% of the line connecting the medial tibial epicondyle and ischial tuberosity; RF, 50% of the line between the ASIS and superior patella; VM, 4 cm medial and superior to the patella's medial border; VL, 2/3 of the line between the ASIS and lateral patella; LG, 1/3 of the line between the fibular head and Achilles tendon on the lateral belly; SO, midway between the fibular head and Achilles tendon, medial to the leg; and TA, 1/3 of the line between the tibial tuberosity and medial malleolus lateral to the tibia (Figure 1). Signal acquisition was performed using the EMGworks software version 4.0.13 (Delsys Inc., Boston, USA) at a sampling frequency of 4000 Hz and a gain of 1000. The sEMG assessment involved performing the maximum voluntary isometric contraction (MVIC), as recommended by SENIAM, with 5 s of isometric contraction, three repetitions, and 30 s of rest.  
Figure 1: EMG assessment during a series of continuous kicks. BF: _biceps femoris_; ST: _semitentinosus_; RF: _rectus femoris_; VM: _vastus medialis_; VL: _vastus lateralis_; LG: _lateral gastrocnemius_; SO: _soleus_; TA: _tibialis anterior_.
# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
###### Abstract  
Radar rainfall forecasting is of major importance to predict flows in the sewer system to enhance early flood warning systems in urban areas. In this context, reducing radar rainfall estimation uncertainties can improve rainfall forecasts. This study utilises an ensemble generator that assesses radar rainfall uncertainties based on historical rain gauge data as ground truth. The ensemble generator is used to produce probabilistic radar rainfall forecasts (radar ensembles). The radar rainfall forecast ensembles are compared against a stochastic ensemble generator. The rainfall forecasts are used to predict sewer flows in a small urban area in the north of England using an Infoworks CS model. Uncertainties in radar rainfall forecasts are assessed using relative operating characteristic (ROC) curves, and the results showed that the radar ensembles overperform the stochastic ensemble generator in the first hour of the forecasts. The forecast predictability is however rapidly lost after 30 min lead-time. This implies that knowledge of the statistical properties of the radar rainfall errors can help to produce more meaningful radar rainfall forecast ensembles.  
Keywords:nowcasting; flow forecast; radar ensembles; probabilistic forecasts +
Footnote †: journal: Geosciences
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 5 Conclusions  
This work discussed the feasibility of using GNSS-RTK technology for the computation of the attitude of static and dynamic solid bodies. The accuracy and reliability of the measurements in the tests carried out confirm that the application of this technology is justified, presenting the following advantages:  
* No calibrations are required.
* It is an autonomous system, which does not need external corrections.
* Very low cost for the accuracy reported compared to other technologies. The price of the equipment used (excluding the hours of development) is around 700 EUR, and this technology is expected to reduce its costs as it becomes more popular.
* Technology without export restrictions.
* The accuracy of the system can be improved by increasing the distance between antennas. However, for more than 10 m of cable between antenna and receiver, it is necessary to install signal amplifiers or special cables.
* HDOP-type variables, the number of satellites used in position calculation, or the \"Fix\" type can be used to validate or discard system measurements.
* The vehicle dynamics (<4G) do not affect measurements.  
Nevertheless, some certain limitations must be pointed out:
* Low data frequency compared to other technologies. There are GNSS-RTK receivers that operate up to 100 Hz, but their price is higher.
* The system does need to operate outdoors with good visibility of the sky.
* It is not valid for lightweight and small-size systems because the antennas need a minimum distance.  
In short, this system is very convenient to be installed on aircraft, ships, or transport systems where it is possible to take advantage of the installation of the antennas as separately as possible.  
Further lines of work are:  
Figure 17: Pitch and roll angles and height during the dynamic testing.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Pitch** & **Roll** & **Yaw** \\ \hline RMS [deg] & 0.295 & 0.459 & 0.338 \\ \hline \end{tabular}
\end{table}
Table 3: Standard deviation of Euler angles in the dynamic configuration.  
* Analysis and compensation of the multipath error in environments with limited sky visibility, such as urban scenarios.
* System validation in a dynamic configuration with high-precision IMUs.
* Validation of the system and its degradation under high dynamic conditions (>4G).  
**Author Contributions:** All authors have contributed to the preparation of the paper, the discussion of the results, and the reviewing of the content of this article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This article was partially supported by the Spanish Ministry of Economy and Competitiveness under contract DPI2017-84259-C2-2-R.  
**Acknowledgments:** The materials, tools, and facilities used for the manufacturing of the prototype have been provided by Ardusimple--EPS Works Consulting SLU.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 3 Proposed Indoor Positioning Method
### Adaptive Location Estimation
When the AP has the first or last number, the same estimation method cannot be applied. The estimated location will be outside the intended region if the AP location is determined vertically. The position is determined for the AP with the first or last number by moving a distance of \(r\) toward the incenter of an equilateral triangle formed by three neighboring APs, including the identified nearby AP. The position moved a distance \(r\) towards the incenter from an identified nearby AP can be calculated by the trigonometric ratio and the properties of triangles that the incenter of a triangle is the intersection point of its angle bisectors. As shown in Figure 8, a line toward the incenter can be formed by drawing an angle bisector of length \(r\) from AP1. Additionally, we can obtain a virtual right triangle with this bisector as the hypotenuse. With the knowledge of all angles and the hypotenuse length of this right-angled triangle, the \(x\)- and \(y\)-coordinates of the estimated position can be calculated using trigonometric ratios. For the imaginary right triangle, cos \(30^{\circ}\) for the AP1 vertex is equal to the \(x\)-coordinate to be estimated divided by the hypotenuse length \(r\). This expression is then rearranged to formulate the \(x\)-coordinate, resulting in the computation of \(x\) as \(r\)-cos \(30^{\circ}\). For the imaginary right-angled triangle, where the vertex representing the estimated position has an angle of \(60^{\circ}\), the cos \(60^{\circ}\) at this vertex is equivalent to the \(y\)-coordinate divided by the hypotenuse length \(r\). This relationship can be rearranged to derive a formula for the \(y\)-coordinate, resulting in the calculation of \(y\) as \(r\)-cos \(60^{\circ}\). Conversely, if the identified AP is the last number, \(x\) is subtracted from \(r\)-cos \(30^{\circ}\) and \(y\) is subtracted from \(r\)-cos \(60^{\circ}\) using the coordinates of the last AP location, and the coordinates are used as the estimated location. Figure 6: An environment example of modified proximity positioning. \[r_{1}^{2} =(x-x_{1})^{2}+(y-y_{1})^{2} \tag{8}\]0 An example of modified proximity positioning in the vicinity of AP3.