esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Air Analysis
#### 3.2.3 Analysis of the Effects of Emission Sources for Selection Cases (SCs)
Above all, the increasing ratio of carbon components (OC and EC) in PM2.5 at around 09:00 (i.e., the morning rush hour) indicates that there is an impact on air quality in the short time between emissions and measurements. * Impacts of emissions from small businesses. The results of the time-series analysis of the concentrations of heavy metals in PM2.5, including Pb, As, and Zn (Figure 12), show that distinct temporal changes did not occur in ALL; however, in SCs, they simultaneously increased at 08:00-09:00 when the daily work commenced, reaching the peak at 09:00. These temporal changes did not appear during holidays (including weekends). Thus, they are presumed to have occurred during the weekday operation of production facilities in the industrial complexes. However, considering that working hours are 09:00-18:00, the emission source for the peak concentrations of the three heavy metals appears to be in an area close to the monitoring station. In other words, we assume that the emission sources are small businesses with no emission reduction facilities, rather than the two large industrial complexes. * Impacts of emissions from the Onsan Industrial Complex. The changes in the concentrations of Mn and Fe over time in ALL and SCs were analyzed (Figure 13). The time-series analysis results show that the concentrations begin to increase at 10:00 and peak at 12:00 and 17:00. They tend to decrease at 14:00, which is around lunchtime, and at night. The decrease in Mn/Fe concentration at 14:00 was due to reduced production activities during lunchtime. Considering that lunchtime was usually 12:00, the change was observed two hours after emission. These temporal changes seem to originate in an area that is at least two hours away (12.17-16.4 km) at a wind speed of 1.69-2.28 m/s, with the dominant wind direction of 90-180\({}^{\circ}\) in SCs (D in Figure 1, Onsan National Industrial Complex). Based on this estimation, it is presumed that the emissions were generated during work hours, and that heavy metals in PM2.5 were transported by the wind and were observed at the monitoring station. The concentrations increased at 11:00 and decreased at 14:00, which could also be related to the commencement of work and lunch break. Figure 12: Diurnal changes in zinc (Zn), lead (Pb), and arsenic (As) concentrations (**left**: diurnal changes of ALL and SCs) (**right**: diurnal changes of weekdays and holidays (including weekends) in SCs). When we compare the Mn and Fe emissions from similar emission sources with the results in Figure 7 and temporal changes, it can be assumed that there is an industrial company in the Onsan Industrial Complex that emits Mn and Fe at a ratio of 3.8.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 2 Multistatic Architecture
### Baseline Configuration  
This work marks the initial phase in the development of a prototype, which is currently undergoing construction. As a result, design constraints have been defined by considering the available hardware and its specific characteristics. The system's architecture relies on multiple transmitters (\(\mathrm{t}\mathrm{x}_{m}\)) and receivers (\(\mathrm{r}\mathrm{x}_{n}\)), positioned at \(\mathrm{t}_{m}\) and \(\mathrm{r}_{n}\), respectively. This work focuses on analyzing a specific architecture consisting of a 2D sparse array with 64 transmitting antennas (NTX) and 49 receiving antennas (NRX) positioned in front of an object under test (OUT).  
The transmitting antennas are equidistantly spaced at a distance of \(\mathrm{d}_{tx}\) cm, while the receiving antennas are also equidistantly spaced at a distance of \(\mathrm{d}_{rx}\) cm. To ensure symmetrical responses with respect to both the X = 0 and \(Z\) = 0 axes, this equidistance is maintained in both the horizontal and vertical directions. The system operates in the frequency range of 120 to 150 GHz using only 12 different frequencies, which corresponds to a minimum working wavelength (\(\lambda_{min}\)) of 2 mm.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 3 Rural Microgrid Modeling  
### Distributed Generation Unit Modeling  
The distributed generation unit includes photovoltaic (\(PV\)) and wind turbines. Both rural residents and greenhouses are equipped with photovoltaic equipment for power generation. Wind turbines need to be placed in relatively wide areas, such as loosely spaced residential areas and open planting areas.  
The output power of \(PV\) is mainly affected by solar radiation intensity and ambient temperature, and the power generation is as follows:  
\[P_{pv}=\ P_{STC}\frac{G_{c}}{G_{STC}}[1+k(T_{C}-T_{STC})], \tag{1}\]  
where, \(STC\) refers to the test data under standard conditions, in which the light intensity \(G_{STC}\) is 1 kW/m\({}^{2}\) and the temperature \(T_{STC}\) is 25 \({}^{\circ}\)C. \(P_{STC}\) is the rated output power of \(PV\) under the standard conditions, which is generally given by manufacturers.  
The output power of wind turbines can be obtained according to the wind speed. The segmented function of the wind power-generation model is as follows:  
Figure 1: Rural microgrid architecture based on cloud energy storage (CES).  
\[P_{wt}(v)\ =\left\{\begin{array}{ll}p_{r}(v-v_{ci})/(v_{r}-v_{ci}),&v_{ci}\leq v \leq v_{r}\\ p_{r},&v_{r}\leq v\leq v_{co}\\ 0,&v\leq v_{ci}\;or\;v>v_{co}\end{array}\right., \tag{2}\]  
Combined with the wind speed data of the system statistics, the amount of power generation at each moment can be calculated by the function of the above wind power generation model.
## 5 Conclusions  
In this paper, the precision measurement method of the twist of the aeroengine blade is studied, and a comprehensive measuring method of the blade twist based on evaluations of twist angular position and twist angle is proposed. Three key problems for the twist metrology of a blade are solved based on technologies of calibration, a priori planning and geometric analysis: aeroengine axis matching, high-precision coordinate acquisition of the leading and trailing edges, and extraction of twist angular position of the profile. The contents and conclusions of the innovative research are as follows:  
(1) A simple and efficient method for registration of the engine axis based on the special fixture and least squares algorithm is studied, which achieves the fast establishment of the measurement benchmark of blade twist and improves the datum positioning accuracy significantly. (2) A high-precision method for the measurement of the leading and trailing edges based on a priori planning is proposed in this study, and the technical difficulties of blade edge measurement are solved. By optimizing the sampling strategy automatically, the measurement accuracy is improved significantly and the edge features of blade are extracted effectively, which provides authentic measurement data for the twist evaluation. (3) A high-precision extraction method for blade edges based on sampling optimization and least squares fitting is presented, which can overcome the fluctuation error of measured data and extract the parameters of blade edges exactly. The statistical uncertainty of the method is less than 3 \(\upmu\)m.  
(4) An algorithm for parameters calculation of the blade twist based on geometric analysis is analyzed in this study, which solves the problem of characterization and evaluation of aeroengine blade twist. (5) Taking an aeroengine blade as the test object, the measuring method is studied experimentally. The results show that the metrological method of blade twist presented in this study is effective and its measurement uncertainty is less than 0.01\({}^{\circ}\). This method is form-free, efficient, accurate, and can solve the problems of high-precision measurement and evaluation for the twist of aeroengine blade primely. **Author Contributions:** X.L. and Z.S. ; methodology, X.L. ; software, X.L. ; validation, X.L. ; formal analysis, X.L. ; investigation, X.L. ; resources, Z.S. ; data curation, Z.S. ; writing--original draft preparation, X.L. ; writing--review and editing, X.L. ; visualization, X.L. ; supervision, Z.S. ; project administration, Z.S. ; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Key Project of National Natural Science Foundation of China, grant number 51635001. The APC was funded by 51635001. **Acknowledgments:** This work was supported by the Key Project of National Natural Science Foundation of China (Grant No. 51635001) and the Fundamental Research Funds for the Central Universities of China (Grant No. 3142018054 and No. 3142019055). **Conflicts of Interest:** The authors declare no conflict of interest.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Environment Perception
#### 3.2.2 Data Fusion Technology
According to the sensor information fusion processing hierarchy, the technique is divided into three levels, namely the data layer, the feature layer, and the decision layer [61]. Their specific scopes, characteristics, and fusion algorithms are shown in Table 2.  
From Table 2, it can be seen that information fusion in agricultural multi-robots is focused on multi-sensor information fusion of single robots, and there is almost no research on sensor information fusion between homogeneous or heterogeneous agricultural multi-robots. To obtain more information, the information collected by different types of sensors on a single robot is mostly different (e.g., GPS collects position information, IMUcollects robot heading, odometer, etc. ), but some information has a mutual transformation relationship (e.g., the integration of velocity from odometer can give distance), which can be regarded as the same kind of sensors at this time. The fusion algorithms are mainly based on classical EKF and particle filtering.
## 4 Experiments
### Outdoor Environment  
The outdoor environment includes different types of wireless signals in each frequency band. The oscilloscope can collect signals in the whole frequency range, and the long-distance UAVs signal strength is weak, so the wireless signal acquisition combined with the oscilloscope and the antenna is no longer sufficient. According to the frequency characteristics of the UAVs signal, the USRP B210 is suitable for collecting signals, which has a radio frequency range from 70 MHz to 6 GHz, supports a maximum real-time bandwidth of 56 MHz and has a reference sampling rate of 61.44 MS/s [45]. After collecting long strings of wireless signals through USRP, the signals were processed through IQ combination and start-point detection, and the useful signals were intercepted. The different kinds of signals in the outdoor environment are shown in Figure 9. Figure 8(a) is the signal acquisition equipment: USRP B210. Figure 8(b),\(c\) shows the original and the amplitude envelope of UAVs wireless signals. Figure 8(d) shows four types of different wireless signals, including UAVs signal, unknown signal, and unexpected signal.  
Figure 8: The classification of indoor environment.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Phantom3** & **Hubsan** & **Mi** & **Wifi** & **Xiro** \\ \hline Phantom3 & 86.0\% & 0.0\% & 0.0\% & 0.0\% & 0.0\% \\ Hubsan & 0.5\% & 99.5\% & 0.0\% & 0.0\% & 0.0\% \\ Mi & 0.0\% & 0.0\% & 99.5\% & 0.0\% & 0.0\% \\ Wifi & 0.0\% & 0.0\% & 0.0\% & 100.0\% & 0.0\% \\ Xiro & 1.5\% & 0.0\% & 0.0\% & 0.0\% & 99.5\% \\ Unknown & 12.0\% & 0.5\% & 0.5\% & 0.0\% & 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of different signals at 5 dB: confusion matrix.  
After setting the parameters of the USRP acquisition signal, the wireless signals of the flying UAVs could be collected. After pre-processing, various signals were separated, including various UAVs signals, unknown signals, and other unexpected signals. To train the AC-WGANs classification model, unsupervised learning such as K-means could be used to separate the signals, as shown in Figure 10. In the outdoor environment, the UAV signals of distances in the range of 10 m to 400 m were collected. After training the classification model, the USRP B210 collected the wireless signal in real time and performed pre-processing. The processed signal was input into the discriminative model \(D\) of AC-WGANs to classify and detect the wireless signals. The recognition results are shown in Figure 11. Figure 10(a) is the real-time testing in the outdoor environment, and Figure 10(b) is the classification of different distances.  
Figure 10: The real-time classification system of the UAV signal.  
Figure 9: Different kinds of signals in the outdoor environment.
## 2 Hydrogen Production Based on a Two-Step Chemical Regenerative Coal Gasification  
In a traditional coal to hydrogen plant, GE or Shell gasification is typically employed. Figure 1a shows the most popular process based on GE gasification, wherein coal is mixed with water to produce slurry. The slurry reacts with oxygen from an air separation unit (ASU) to produce syngas containing CO and H\({}_{2}\). Water is used to quench the syngas and then a waste heat boiler (WHB) is used to recover heat. Afterward, syngas is sent to a water gas shift (WGS) where sulfur removal and a pressure swing adsorption (PSA) unit are used to produce high purity H\({}_{2}\). Limited by the low cold gas efficiency of gasification, it is typically lower than 80% [10; 11]. Considering a huge power consumption for ASU, the efficiency of coal to hydrogen in such a traditional plant is lower than 60% (LHV based) [12; 13].  
In this novel process, a two-step chemical regenerative coal gasification is proposed for H\({}_{2}\) production (Figure 1b). Coal gasification is divided into two steps: coke making and char gasification. The coke making process can drive volatile out of coal and can get char and high H\({}_{2}\) concentration  
Figure 1: (**a**) Traditional hydrogen production process from coal. (**b**) Proposed hydrogen production process based on a two-step chemical regenerative coal gasification.  
gas, i.e., coke oven gas (COG). For char-steam gasification, steam is used to recover syngas sensible heat, providing part of the heat requirement of the char-steam reaction. Through such a chemical regenerative pathway, the sensible heat of the syngas can be converted into the chemical energy of the syngas, wherein cold gas efficiency of gasification is expected to be lifted. To produce H\({}_{2}\), COG and syngas will be sent to the WGS unit, sulfur unit, and PSA unit. Moreover, the purge gas from the PSA unit mainly contains CH\({}_{4}\), H\({}_{2}\), CO, and CO\({}_{2}\) and can be recovered to combust together with coal, which provides heat for coke making and char-steam gasification. Furthermore, no oxygen is needed for coal gasification and consequently ASU is eliminated.
## 6 Time Frequency Transforms
### Wiegner-Ville Distribution  
The Wigner-Ville Distribution (WVD) function has been chosen in this study because it provides the highest possible temporal vs frequency resolution, which is mathematically possible within the limitations of the uncertainty principle. This characteristic is useful in the application of CNN because the high resolution can be exploited by the neural network to extract the discriminating elements of the fading channels. The downside of the WVD is the introduction of large cross terms between every pair of signal components and between positive and negative frequencies, which may confuse the CNN, especially in the presence of noise [26]. There are different formulations in the literature of the WVD. In this paper, we adopt the following definition for a continuous signal x(t):  
\[WVD(x,f)=\int_{-\infty}^{\infty}x(t+\tau/2)x^{*}(t-\tau/2)e^{-j2\pi f\tau}d\tau \tag{7}\]  
A smoothed version of the WVD (SWVD) is also used in this study, where independent windows are introduced to smooth in time and frequency:  
\[SWVD(x,f)=\int_{-\infty}^{\infty}g(t)H(f)x(t+\tau/2)x^{*}(t-\tau/2)e^{-j2\pi/ \tau}d\tau \tag{8}\]  
The smoothed version SWVD was chosen because it may be more robust to the presence of noise. The word'may' is used because the smoothing effect could also obfuscate the same fading channel discriminating characteristics this study tries to classify. In literature, the WVD and SWVD were not used to the knowledge of the authors together with CNN for channel identification, but it was used to automatically recognize the waveform typesof wireless signals in coexistence radar-communication systems in [27], which prompted the authors of this study to investigate the application of WVD and SWVD to the channel identification as well.
# Differences in Performance of Models for Heterogeneous Cores during Pulse Decay Tests
## 3 Dual-Porosity Model with Different Matrix Shape
### Effect of Interporosity Flow Coefficient  
The numerical modeling results of pulse decay tests for dual-porosity cores with different interporosity flow coefficients are shown in Figure 5. At the early stage (\(t_{\rm D}<1\)) and the equilibrium stage (for \(\lambda=10^{-4}\), \(t_{\rm D}>10^{5}\)), the pressure and pressure derivative curves of the sphere and slab matrix coincide, except for a slight difference in the interporosity flow stage (for \(\lambda=10^{-4}\), \(1<t_{\rm D}<10^{5}\)). The pressure derivatives of the two in the interporosity flow stage are close in shape and value and cannot be distinguished. At the same time, the interporosity flow coefficient has little effect on the difference between the two.  
Figure 4: Effect of storativity ratio on pulse decay tests for dual-porosity cores. (**a**) Pressure history. (**b**) Pressure derivative.
## 2 Materials and Methods
### Transformer Neural Networks
#### 2.4.2 Universal Sentence Encoder (USE)  
We also tested a different approach to using pre-trained neural networks. In this method, the fine-tuned part of the model (adapted to the new task) is separated from the constant, fixed part that computes utterance representations. In other words, text representations, also called text embedding vectors, are computed by a universally applicable neural network that does not adapt to the new task. It is another model (and not necessarily a neural network trained using the gradient descent method) that learns to classify these representations as false or true utterances.  
The model we used to obtain text representations was the Universal Sentence Encoder (USE) [20]. We used version 3 of the multilingual USE, which is based on transformer architecture. This deep neural network text encoder supports 16 languages, including Polish. The input to the network is variable-length text in any of the 16 supported languages, and the output is a 512-dimensional vector. The model is intended to be used to compute universal representations of any text, suitable for tasks such as clustering, search, or information retrieval in many languages. It serves as an accurate but general-purpose vector representation of texts.  
The multitask training setup of USE was based on the method described in [21]. In this technique, representations are obtained using a dual-encoder based model trained to maximize the representational similarity between sentence pairs drawn from parallel data. The representations are enhanced using multitask training and unsupervised monolingual corpora.  
We used the USE text representations in radial kernel support vector machine models. The usage of this specific pairing was motivated by many papers reporting top results for moderate and small size datasets, as, for example, [22] and multiple other SemEval competitors in several tasks.  
In this scenario, we used the USE network only to compute utterance embedding vectors--we did not re-train, fine-tune, or modify it for our task. We only trained an SVM model using these vectors as input. We used the NuSVC implementation from the scikit-learn package [23]. Except for the use of the radial kernel, all hyperparameters were set to their default values (not optimized in any way). The results were obtained in a 10-fold cross-validation.
## 3 Flexible Merging Decision Methods
### Motion Decision Based on DRL
Since the value learning-based DRL has the advantage of being easy to learn, the Deep Q Network (DQN) algorithm is used in this study as the base algorithm for decision-making. However, the DQN algorithm has a large theoretical error due to the maximum estimation method. Therefore, an improved algorithm of DQN--DDQN with Prioritized Experience Replay (PER)--is used in this study to enhance the effectiveness and efficiency of learning. The improvement of the DDQN method over the traditional DQN method embodies the use of two sets of neural networks with the same structure but different parameters to temporarily freeze the correlation, which in turn reduces the estimation error on the value of the optimal action for future states' estimation errors [42]. Meanwhile, the literature [43] states that when updating the decision network, the use of experiences with large errors is more helpful for fast learning, i.e., state transfer information with large errors is learnt preferentially. The PER technique works by calculating the error of the state transfer information before updating the parameters of the neural network and giving the experience, with large errors its higher priority. For more details about the DDQN and PERalgorithms, please refer to related studies [42; 43]. In summary, the decision of DDQN with PER is shown in Equation (15), and the loss function is shown in Equations (16) and (18):  
\[a_{t}=\underset{a}{\mathrm{argmax}}Q(o_{t},a;\theta) \tag{15}\]  
\[L(\theta)=\frac{1}{k}\sum_{j=1}^{k}w_{j}\big{[}r+\gamma Q\big{(}o_{t+1},a_{t+1}; \overline{\theta}\big{)}-Q(o_{t},a_{t};\theta)\big{]}^{2} \tag{16}\]  
\[a_{t+1}=\underset{a_{t+1}}{\mathrm{argmax}}Q(o_{t+1},a_{t+1};\theta) \tag{17}\]  
\[\theta_{t+1}=\theta_{t}-\omega.\frac{\partial L(\theta)}{\partial\theta} \tag{18}\]  
where \(\gamma\) is the reward decay factor, \(r\) is the reward agent obtained from the environment, \(k\) is the minibatch size, \(w_{j}\) is the weight of experience \(j\) in the minibatch, \(\omega\) is the learning rate, and \(\theta\) and \(\overline{\theta}\) are parameters of the decision and evaluation networks, respectively.