esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The Intergovernmental Panel on Climate Change (IPCC) study from 2014 showed that 14% of global greenhouse gas emissions are due to transportation [1]. Since 65% of greenhouse gas emissions are related to CO\({}_{2}\), it has become crucial to decrease their global warming impact. Taking well-to-wheel emissions into consideration, electric vehicles reach 180 g CO\({}_{2}\)eq/km (because of a global 68% electricity production still coming from oil, gas, and coal) whereas fuel cell vehicles (FCVs) reach 127 g CO\({}_{2}\) eq/km [2,3]. Even if current regulations only take into account tank-to-wheel emissions, which are null for both of these types of vehicle, some car manufacturers such as Toyota (Mirai), Honda (Clarity Fuel Cell), or Daimler Group (GLC F-cell) are investing in fuel cell technology to prepare for an uncertain future. To become a viable solution for transportation, fuel cell vehicles must deal with power density challenges. For instance, the Hyundai Tucson Fuel Cell edition is 300 kg heavier than the gasoline version for the same output power. To decrease the weight of the fuel cell vehicle means less fuel consumption, and thus a higher driving range.  
There is a large potential for increasing the power density by using a boosting system for the air supply. A higher pressure of air means a higher output power and efficiency. As seen in Figure 1, a recent paper from Honda underlined that increasing the pressure ratio from 1.0 to 1.7 provided 10% more output power [4]. As a consequence, it is possible to reduce the number of cells and thus the weight of the fuel cell stack for the same output power. Given that the requirements differ from those of an internal combustion engine (ICE), the choice of compressor type must be adapted to fuel cell vehicle application.  
In order to determine which type of compressor to use, a literary survey was conducted to identify which types of fuel cell are relevant to transportation application. This was followed by the development of fuel cell vehicle powertrain models, employing co-simulation between GT-SUITE and MATLAB/SIMULINK. Finally, simulations have included driving cycle simulations to analyse the impact of the air supply system on vehicle performances.
## 3 Experimental Results
### Load Capacities  
The test results in Table 4 show the test bearing capacity \(N_{e}\), value of bearing capacity \(N_{c}\) estimated by GB 50017-2017 [32] for steel pipes, GB 51367-2019 [12], and GB 50936-2014 [28] for specimens with and without initial stress, respectively, and the percentage of the added bearing capacity of the members strengthened by RPC compared with the pure steel tube, which is defined as the reinforcement material contribution RP (Reinforced Percentage) as follows:  
\[RP=\frac{N_{sc}-N_{s}}{N_{s}}\times 100\% \tag{1}\]  
where \(N_{sc}\) and \(N_{s}\) are the test bearing capacity of concrete-filled steel tubes and steel pipes, respectively.  
For steel pipes, the value of \(N_{c}\) was predominantly consistent with \(N_{e}\), with errors less than 10%. However, there was a relatively substantial difference between the values of \(N_{e}\) and \(N_{c}\). It is worth noting that the value of \(N_{e}\) for RS-ISS-60 was lower than that of \(N_{c}\). The  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & \(N_{e}\) & \(N_{c}\) & \
\begin{tabular}{c} **Error** \\ **(\%)** \\ \end{tabular} & \(RP\) **(\%)** & **Specimen** & \(N_{e}\) & \(N_{c}\) &
\begin{tabular}{c} **Error** \\ **(\%)** \\ \end{tabular} & \(RP\) **(\%)** \\ \hline S-IS0-60 & 196.59 & 211 & \(-\)6.8 & & S-IS0-75.5 & 270.39 & 292 & \(-\)7.4 & \\ RS-IS0-60 & 345.54 & 444 & 28.4 & 75.77 & RS-IS0-75.5 & 604.22 & 735 & 21.7\% & 123.46 \\ RS-IS4-60 & 276.31 & 355 & 28.5 & 40.55 & RS-IS4-75.5 & 494.64 & 588 & 18.9\% & 82.94 \\ RS-IS6-60 & 249.77 & 333 & 33.2 & 27.05 & RS-IS6-75.5 & 411.36 & 551 & 34.1\% & 52.14 \\ RS-ISS6-60 & 204.48 & 311 & 51.9 & 4.01 & RS-ISS-75.5 & 383.53 & 515 & 34.2\% & 41.84 \\ \hline S-IS0-88.5 & 329.50 & 359 & \(-\)8.2 & & S-IS0-114 & 408.19 & 426 & \(-\)4.2 & \\ RS-IS0-88.5 & 774.38 & 985 & 27.2\% & 135.02 & RS-IS0-114 & 1299.43 & 1445 & 11.2\% & 218.34 \\ RS-IS4-88.5 & 680.91 & 788 & 15.7\% & 106.65 & RS-IS4-114 & 1208.26 & 1156 & \(-\)4.3\% & 196.00 \\ RS-ISS6-88.5 & 666.67 & 739 & 12.5\% & 99.29 & RS-ISS6-114 & 1052.02 & 1084 & 3.0\% & 157.73 \\ RS-ISS8-88.5 & 606.11 & 690 & 13.8\% & 83.95 & RS-ISS-114 & 905.02 & 1011 & 11.8\% & 121.72 \\ \hline S-IS0-159 & 1093.70 & 1149 & \(-\)4.8 & & & & & \\ RS-IS0-159 & 2503.70 & 3501 & 39.6\% & 128.92 & & & & \\ RS-IS4-159 & 2377.63 & 2801 & 17.8\% & 117.39 & & & & \\ RS-IS6-159 & 2344.13 & 2626 & 12.0\% & 114.33 & & & & \\ RS-ISS8-159 & 2079.69 & 2451 & 17.8\% & 90.15 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of ultimate bearing capacity (kN).  
Figure 11: Typical failure mode in descending branch of load-displacement curves.  
current specifications in China cannot precisely estimate the bearing capacity of RPC-filled steel tubes with regular dimensions in grid structures, which is not conducive to applying compression members reinforced with RPC.
## 5 Results and Discussion
### Effect of Fuel Utilization Factor  
The effect of fuel utilization factors on system performance is investigated. This factor represents the ratio of the hydrogen consumed by the fuel cell to the amount of hydrogen fed. In this study, U\({}_{\text{f}}\) is varied from 0.8 to 1. From Figure 8a, increasing U\({}_{\text{f}}\) increases fuel cell power output. This is because more hydrogen is consumed, and hence more current and power is produced, while the fuel cell temperature is not significantly affected, as shown in Figure 8b. However, the TIT (Figure 8b) and turbine output power (Figure 8a) decrease with increase in U\({}_{\text{f}}\) due to a decrease in the amount of unreacted fuels entering the CC. Overall, the efficiency of this system slightly increases with increase in U\({}_{\text{f}}\) due  
Figure 7: Effect of air flow rate on (**a**) power consumption of air compressor, power output of PCFC and GT, (**b**) PCFC operating temperature and mole fraction of H\({}_{2}\), and (**c**) energy efficiency, exergy efficiency, and total exergy destruction.  
to high fuel conversion and reduced total exergy destruction, as shown in Figure 8c. A maximum difference of 3% is observed in the total power output of the hybrid system operating between U\({}_{\text{f}}\) of 0.8 and U\({}_{\text{f}}\) of 1. Operating at moderate U\({}_{\text{f}}\) helps in the long-term stability of the PCFC. Therefore, a common U\({}_{\text{f}}\) of 0.85 is chosen for further study.
## 4 Discussion
### General View of Classification Performance  
Table 3 displays the differences in the classification quality of the gradient boosting classifiers, depending on the provided independent variables. The order of quality of the obtained MCC and F1 results mimics the historical development of the feature extraction methods that were described briefly in Section 2.3, i.e., simple Term Frequency and Lexicon-based methods provide the lowest quality, whereas fine-tuning of the recent transformer model RoBERTa large provides the highest quality. In general, from Table 3, we conclude that fine-tuning the recent transformer models, including the tiny Albert model, allowed us to obtain significantly higher MCC scores than those with the other feature extraction approaches. In our study, the exception to this rule was apparent when methods using LSTMs were considered. The quality of the feature extraction models that utilize bidirectional LSTMs to produce tweet-level embeddings strongly depends on the token-level embedding quality. If simpler fasttext embeddings are used, then the trained fasttext+LSTM embedding method can be surpassed by a pre-trained but more recent token embedding (roberta-large-nli-stsb-mean-tokenpooled). In addition, if a bidirectional LSTM is provided with the highest quality embeddings from the RoBERTa large model, then the resulting prediction quality can even surpass that of some of the fine-tuned transformer models.
# XOR Multiplexing Technique for Nanocomputers
## 4 Fault-Tolerant Ability Analysis
If the value of one of the inputs is in this interval, then the output will always be non-stimulated for XOR multiplexing. In order to demonstrate the tolerant ability of the input signal error probability of the system more intuitively, we extracted several fixed \(Y\) and the corresponding \(x_{0}\) from Figure 10. These lead to Figure 9.0 Let us take \(Y=0.7\) as an example; when input \(Y\) has a probability of 70% of being stimulated (means 30% error probability), any stimulated probability smaller than 23.1% of the other input \(X\) can be accepted. That is to say, the system can tolerate error probabilities of 30% and 23.1% for the inputs \(Y\) and \(X\). Other situations are similar, so we omit them here. It also can be obtained that the maximum input signal error probability that the system can tolerant is 0.3924 (39.24%); namely, the input signal error threshold is 0.3924. \begin{table}
\begin{tabular}{c c} \hline \hline
**Fixed \(Y\)** & **Critical Points** \\ \hline \(Y=0\) & 0.60760 \\ \(Y=0.1\) & 0.63450 \\ \(Y=0.2\) & 0.67933 \\ \(Y=0.3\) & 0.76900 \\ \(Y=0.7\) & 0.23100 \\ \(Y=0.8\) & 0.32067 \\ \(Y=0.9\) & 0.36550 \\ \(Y=1\) & 0.39240 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Critical points for several fixed \(Y\). Figure 10: Critical points against each \(Y\).
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Signal Reconstruction Experiment  
In the signal reconstruction experiment, four sparse unidimensional signals (\(S_{1}\), \(S_{2}\), \(S_{3}\) and \(S_{4}\)) are selected. And the ABA performance is tested by comparing the error values of five observation matrices and the curve fluctuation amplitude with different compression ratios.
# Overview of the Development of 3D-Printing Concrete: A Review
## 3 Performance Requirements of 3D-printing Concrete Materials
### Printability of 3D-printing Concrete Materials
#### 3.1.1 Fluidity  
Fluidity refers to the ability of concrete materials to be easily pumped, transported, and smoothly extruded from the discharge port of the print head. It is an important parameter for evaluating printability. If the fluidity is small, it is likely to cause a high mechanical wear rate and equipment blockage. If the fluidity is large, the printed components are easy to collapse. Therefore, it is necessary to make reasonable adjustments to fluidity to meet printing needs.  
The most important factor affecting fluidity is water content. If the water content is too small, the mixture will become dry and hard and cannot pass the conveying pipeline smoothly. If the water content is too large, it will cause a large number of harmful pores in the printed sample, which will affect the later strength. The water content of cement slurry can be effectively controlled by adding high-performance water-reducing agent to improve the fluidity of cement slurry. [PERSON] et al. [15] obtained a mixture that satisfies 3D-printing which contains water-cement ratio of 0.41 and polycarboxylate polymer powder of 0.3% cement mass. Results showed the best construction speed was 1.1m/h. [PERSON] et al. [16] obtained 3D printed mixture could be smoothly extruded through the nozzle and achieved the construction of a specimen with the highest 61 layers. The gelling material is a water-to-binder ratio of 0.26 and a water reducing agent of 1%. The fluidity can also be improved by adding mineral admixtures to optimize the particle size gradation. The more continuous the particle gradation, the more conducive to the formation of a densely packed state of the mixture, resulting in better fluidity. [PERSON] et al. [17] replaced the mixture produced by Portland cement with 50% fly ash, and the flow time reduced by 43.2%. [PERSON] et al. [18] studied the relationship between fluidity and aggregate content and aggregate fineness, and the results showed that there is a linear relationship between fluidity and aggregate, as  
Figure 1: Development history of 3D-printing construction technology. Reprinted with permission from ref. [14]. Copyright 1969 Elsevier.  
shown in Figure 2. [PERSON] [19] and others used recycled glass as fine aggregate and added it to concrete, which significantly improved the fluidity of concrete.  
The fluidity test is generally determined by the jumping table test, and its expanded diameter is used as the fluidity index of the material, as shown in Figure 3. [PERSON] [20] and [PERSON] [21] etc. used the jumping table test to measure the fluidity range of 170-226 mm and 174-210 mm, respectively. [PERSON] et al. [22] used slump test and slump flow test to study the 3D printable range of concrete. The results show that a mixture with a slump of 4-8 mm and a fluidity of 150-190 mm can have a better printing characteristic.
# An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries
###### Abstract  
In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases. However, real-life situations also contain small-sized instances that deserve a researcher's attention. For example, within a web development context, a developer might face small combinatorial optimization cases that fall in the following situations to solve them: (1) the development of an ad hoc specialized strategy is not justified; (2) the developer could lack the time, or skills, to create the solution; (3) the efficiency of naive brute force strategies might be compromised due to the programming paradigm use. Similar situations in this context, combined with a recent increasing interest in optimization information from databases, open a research area to develop easy-to-implement strategies that compete with those naive approaches and do not require specialized knowledge. Therefore, this work revises Structured Query Language (SQL) approaches and proposes new methods to tackle combinatorial optimization problems such as the Portfolio Selection Problem, Maximum Clique Problem, and Graph Coloring Problem. The performance of the resulting queries is compared against naive approaches; its potential to extend to other optimization problems is studied. The presented examples demonstrate the simplicity and versatility of using a SQL approach to solve small optimization problem instances.  
decision support system; combinatorial optimization; Three-Coloring Graph Problem; Portfolio Selection Problem; Maximum Clique Problem; SQL queries +
Footnote †: journal: Applied Sciences
###### Abstract  
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis. Therefore, data imputation needs to be performed first. Generally, data imputation methods include statistical imputation, regression imputation, multiple imputation, and imputation based on machine learning methods. However, these methods currently have problems such as insufficient utilization of time characteristics, low imputation efficiency, and poor performance under high missing rates. In response to these problems, we propose the informer-WGAN, a network model based on adversarial training and a self-attention mechanism. With the help of the discriminator network and the random missing rate training method, the informer-WGAN can efficiently solve the problem of multidimensional time series imputation. According to the experimental results under different missing rates, the informer-WGAN model achieves better imputation results than the original informer on two datasets. Our model also shows excellent performance on time series imputation of the key parameters of a spacecraft control moment gyroscope (CMG).  
Keywords:informer; GAN; high missing rate; time series imputation +
Footnote †: journal: _Algorithms_
## 3 Proposed Configuration and Beamforming Method Eliminating CSI in the Downlink
### Effectiveness of Proposed Configuration and Method Through Computer Simulations
Figure 4: Cumulative density function (CDF) versus signal-to-interference-plus-noise power ratio (SINR) by analog beam patterns at base station (BS). Figure 5: CDF versus SINR by analog beam patterns at BS and applying CMA at UTs. pattern is regarded as the omni-directional. However, because the power of the interference signals also increases, interference cancellation at the UTs is essential. Figure 6 shows the SINR versus AoA at the UTs when the number of beams is one or three. The amount of increase in the desired power and the interference power due to the increase in the number of beams are different depending on the AoAs between two UTs. In the case of relatively close AoAs between two UTs, because the influence of the sidelobes of the beams facing the other UTs is large, the interference power is increased. On the other hand, in the case of relatively large AoAs between two UTs, because the influence of the sidelobes of the beams facing the other UTs is small, the SINR is improved. Moreover, by applying CMA at the UTs, the SINR is improved even when the AoA between two UTs is close. Incidentally, it has been confirmed that the SINR does not improve any further even when the beam number is increased from three. Finally, the effectiveness of the proposed configuration and method, which does not need CSI in the downlink, is verified based on the bit rate. Figure 7 shows the average bit rate versus the AoA at the UTs when the number of UTs is two, three and four. In Figure 7, the modulation scheme with maximum bit rate is selected when the BER is less than \(10^{-3}\). We compare the case of transmitting with one beam without CMA (Prop. 1) in Figure 7 and the case of applying CMA at the UTs after transmitting with the optimum number of beams (Prop. 2) in Figure 7. The bit rate is decreased because the total interference power is increased when the number of UTs is increased. However, owing to the CMA at the UT, the average bit rate is improved even if the number of users is increased. The result indicates that the performance is improved greatly by the proposed configuration and CMA at UTs. Figure 6: SINR versus angle of arrival (AoA) at UTs. Figure 7: Average bit rate versus AoA at UTs.
## 7 Results and Discussion
### Validation of Visualized Ground Control Station  
For the validation and the stability check, we conducted a vertical takeoff and landing test of the quadcopter using visualized GCS. The latitude, longitude, altitude, heading, roll angle, and pitch angle of the quadcopter during the vertical takeoff and landing is illustrated in Figures 28-33, respectively.  
From Figures 28 and 29, we can see that there is only a marginal difference in latitude and longitude position of the quadcopter from the initial stage to the final stage, during the vertical takeoff and landing. The heading changes that occurred in the quadcopter are also small (see Figure 31) and, both roll and pitch angle changes are between \(-4\) to \(4\) degrees (see Figures 32 and 33). Note that the soft landing of any quadcopter flight is truly a difficult task, which we overcame in this study. Figure 30 shows that we landed our quadcopter safely using the visualized GCS. For the safe and soft landing, we took almost \(22\) s from around a \(9\) m height.  
Figures 28-33 show that the performance of the quadcopter during the vertical takeoff and landing was stable using the visualized GCS. Figure 34 illustrates the comparison of the pictures of the real and visualization part during the vertical takeoff and landing of the quadcopter. Due to the distance limitation of the Wi-Fi network connectivity, the distance between the visualized GCS and the quadcopter was restricted herein to \(20\) m. If we use an interconnecting network system, we can use the developed visualized GCS for long-distance missions. The total time delay of the system, including the visualization part was approximately \(420\) ms.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 3 Result and Discussion
### Energy Consumption  
The energy consumption in one node for LoRa in the system, using the battery voltage of 3.3 volts, battery capacity of 10,050 mAh and the duty cycle based on LoRa characteristics (process, receiver, transmit) is 5970 ms with interrogations mode three times per day. The microcontroller consumed 16 mA, and the sensors consumed not more than 0.024 mA. LoRa itself consumed 10.3 mA. It takes 25 mA, \(-\)16 dBm if the GPS was working in the tracking process. In sleep mode, GPS consumes about 55 \(\mu\)A. LoRa consumed less than 60 \(\mu\)A, and the sensors less than 1 \(\mu\)A.  
The estimation of battery life is solved by a power energy formula for electricity. The estimation of battery life for the LoRa performance without GPS takes 195.9 h. In contrast, the estimation of battery life for the system while using the GPS in the tracking process takes 140.95 h. The energy runs out quickly if the GPS is working in the tracking process.
## 4 Experimentation, Performance Evaluation, and Discussion
### System Configuration  
Experiments were carried out using a 2.80 GHz Intel(r) Core(tm) i7-1165 processor with 8 GB RAM on the Windows 11 OS Version 21H2. The proposed algorithm was programmed in MATLAB R2020b and used the IBM SPSS statistics Version 25 to validate the experimental results' statistically significant differences.