esa-sceva/satcom-chunk-collection huggingface dataset:
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## 5 Conclusions  
To investigate the applicability of bio-soil treatment on Ni\({}^{2+}\) removal, a series of Ni\({}^{2+}\) adsorption tests were conducted on GG-treated jumunijn sand using a pressurized upward flow system. Additionally, adsorption isotherm and kinetic studies were performed to determine the possible mechanisms for Ni\({}^{2+}\) adsorption. The main findings of this study are as follows:  
* GG can improve the Ni\({}^{2+}\) adsorption of sand by 150% or greater with the usage of 1% GG content or higher. The Langmuir model is the best fitting model for adsorption data of GG and sand. The potassium content within the GG mainly works on the cation exchange mechanism along with functional groups to interact with Ni\({}^{2+}\).
* GG enhances the Ni\({}^{2+}\) removal efficiency of the sand column, controlled by GG content, and the flow rate via the interactions of water-GG, Ni\({}^{2+}\)-GG, and Ni\({}^{2+}\)-sand.
* The water absorption of GG causes the pore-clogging effect within the GG-sand system and, in turn, improves the Ni\({}^{2+}\) removal efficiency. The pore-clogging effect was observed earlier in the process with an increase in the GG concentration.
* In general, GG shows a good performance for Ni\({}^{2+}\) adsorption and pore-clogging effect, which controls the movement of flow rate and the amount of Ni\({}^{2+}\) ion in the flow. Therefore, GG poses a promising alternative for hydraulic barriers.  
Conceptualization, T.P.A.T., H.C. and I.C.; experiment and analysis, T.P.A.T. and H.C.; writing--review and editing, T.P.A.T., H.C., G.-C.C., J.-H. and I.C. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Ministry of Land, Infrastructure, and Transport (MOLIT) of the Korean Government, grant number 21 AWMP-B114119-06.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
This study was financially supported by the Water Management Research Program funded by the Ministry of Land, Infrastructure, and Transport (MOLIT) of the Korean Government (21 AWMP-B114119-06) and the New Faculty Research Fund of Ajou University.  
The authors declare no conflict of interest.
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
###### Abstract  
In modern life, a large amount of information is transmitted through satellites. The anti-jamming ability of satellite communication earth stations is the basis for ensuring the smooth flow of information. In the case that multiple small earth stations cannot communicate due to jamming, the minimum variance distortionless response (MVDR) algorithm based on reflector antenna array is proposed. Firstly, the gain expression of satellite communication earth station antenna is derived, and the mathematical model of the received signal of the reflector antenna array is established. Then, the MVDR beamforming algorithm based on the reflector antenna is proposed and applied to the satellite communication earth station reflector antenna array, and the anti-jamming capability of the reflector antenna array using this algorithm is analyzed through simulation. The simulation results show that the earth station reflector antenna array using MVDR algorithm can effectively achieve array gain of the direction of the satellite signal, suppress the jamming signals, increase the output signal to interference plus noise ratio (SINR) of the reflector antenna array, and increase the output signal to jamming plus noise ratio with the increase in the number of snapshots.  
satellite communication; jamming suppression; antenna array; minimum variance distortionless response (MVDR) +
Footnote †: journal: Applied sciences  
1
Footnote 1: institutetext: School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China; [EMAIL_ADDRESS] (J.L.): [EMAIL_ADDRESS] (W.Z.)  
2
Footnote 2: institutetext: Correspondence: [EMAIL_ADDRESS]
## 4 Results
### Satellite and the Acquisition of Data
As the calculation efficiency of GNSS-SDR is relatively low, we conduct data calculation at an interval of 50 ms, and 19 waveform data will be obtained in a single channel within 1 s. After the code signal path differences calculation of each of the 19 waveform data, the median value is taken as the code signal path differences value in this second. The standard deviation of 19 groups of data within 1 s can also be calculated accordingly, which reflects the dispersion degree of internal code signal path differences in 1 s, and its satellite elevation-varying figures can also be given. By arranging the results of each second in the processing period, we can obtain the corresponding time sequence change diagram.
# Switch-Off Policies in Job Shop Controlled by Workload Control Concept
## 6 Numerical Results
Figure 4: Number of on/off activities. Figure 5: Parts delayed. Figure 6: Total time delay. The throughput of the systems does not change with the switch-off policies. From the analysis of the results, the following points can be summarized:  
* Policy 1, proposed in the literature, leads to a greater reduction in energy consumption; the main limitations of this policy are greater variability in performance and an extremely high number of machine on/off activities that can reduce the reliability of the machines. Moreover, the production performance measures are worst in these cases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Variance** & **Sum of Square** & **Degree of Freedom** & **Mean of Square** & **F-Ratio** & \(p\)**-Value** \\ \hline \multicolumn{5}{c}{**Policy 1**} \\ \hline
**Idle power** & 0.023625 & 2 & 0.011813 & 911.52 & 0.000 \\
**Warm-up power** & 0.001617 & 2 & 0.000808 & 62.38 & 0.000 \\
**Warm-up time** & 0.004072 & 2 & 0.002036 & 157.12 & 0.000 \\
**Residual** & 0.000259 & 20 & 0.000013 & & \\ \hline \multicolumn{5}{c}{**Policy 2**} \\ \hline
**Idle power** & 0.003696 & 2 & 0.001848 & 1180.47 & 0.000 \\
**Warm-up power** & 0.000163 & 2 & 0.000082 & 52.07 & 0.000 \\
**Warm-up time** & 0.000299 & 2 & 0.000149 & 95.40 & 0.000 \\
**Residual** & 0.000031 & 20 & 0.000002 & & \\ \hline \multicolumn{5}{c}{**Policy 3**} \\ \hline
**Idle power** & 0.003892 & 2 & 0.001946 & 1542.30 & 0.000 \\
**Warm-up power** & 0.000184 & 2 & 0.000092 & 72.79 & 0.000 \\ Warm-up time & 0.000320 & 2 & 0.000160 & 126.80 & 0.000 \\
**Residual** & 0.000025 & 20 & 0.000001 & & \\ \hline \multicolumn{5}{c}{**Policy 4**} \\ \hline
**Idle power** & 0.005817 & 2 & 0.002909 & 505.34 & 0.000 \\
**Warm-up power** & 0.000280 & 2 & 0.000140 & 24.35 & 0.000 \\ Warm-up time & 0.000338 & 2 & 0.000169 & 29.33 & 0.000 \\ Residual & 0.000115 & 20 & 0.000006 & & \\ \hline \multicolumn{5}{c}{**Policy 5**} \\ \hline
**Idle power** & 0.003873 & 2 & 0.001937 & 20.23 & 0.000 \\ Warm-up power & 0.000573 & 2 & 0.000286 & 2.99 & 0.073 \\ Warm-up time & 0.000125 & 2 & 0.000062 & 0.65 & 0.532 \\ Residual & 0.001914 & 20 & 0.000096 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA analysis. Figure 7: Time delay for unit of product. Among the policies proposed, policy 4 (based on the modified workload computation) leads to the best compromise of energy reduction, production performance measures, and number of machine-on/off activities. * The ANOVA highlights how policy 5 is the more robust to the change in the parameters studied. * The simulation model is a crucial method used to estimate the performance of a switch-off policy from several points of view.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 5 Discussion
For a fair comparison, the same dataset was used, and the STFT-CNN achieved an accuracy of 93.05%. The lower accuracy is primarily due to information loss caused by the windowing effect in STFT, which reduces its ability to capture transient signal variations accurately. This limitation affects the TFD resolution, resulting in decreased fault classification performance compared to the proposed model. In comparison to the methods previously stated, the results obtained using the proposed method show a higher classification accuracy. Figures 10 and 11 show the confusion \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **F1 Score** & **Recall** \\ \hline Proposed & 99.69 & 99.71 & 99.82 & 99.75 \\ \hline [PERSON] et al. [39] & 85.18 & 90.72 & 70.08 & 72.15 \\ \hline [PERSON] et al. [40] & 92.05 & 97.41 & 78.91 & 88.35 \\ \hline STFT-CNN & 93.05 & 98.01 & 79.58 & 89.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison metrics of the proposed model with the reference models.
# Analysis on the Exhaust Air Recirculation of the Ventilation System in Multi-Story Building
## 2 Materials and Methods
### Method of Evaluating the Recirculation Rate of Exhaust Air  
A mass fraction boundary condition was applied to the CFD analysis. Thus, it is necessary to calculate the amount of air from each floor exhaust port to determine the recirculation rate of the exhaust air. Each component gas was given an ID, and the partial differential transport equations of each gas that passed through the control volume (CV) of the 3D space are as shown in Equation (1) [23].  
\[\frac{\partial Y_{i}}{\partial t}+\
abla\cdot(\varrho Y_{i}u)=-\
abla\cdot j_{i} \tag{1}\]  
where \(Y_{i}\) is the mass fraction of the \(i\)-th air constituent. Due to the reasonably low thermal parameters (pressure and temperature) of air, it can be treated as a rarified mixture.  
Figure 3: The geometry and mesh of target space for CFD analysis: (**a**) geometry; (**b**) mesh.  
Figure 2: The interior and exterior appearance according to installation of the ventilation system: (**a**) the ventilation system installed indoor; (**b**) ventilation cap and guide panel to prevent recirculation (Outdoor); (**c**) modeling of ventilation cap and guide panel.  
In the experimental method, carbon dioxide (CO\({}_{2}\)) or sulfur hexafluoride (SF6) is used as a tracer gas, and the concentration is monitored to analyze the recirculation rate of exhaust air [24]. It has the advantage of being able to easily calculate the recirculation rate through the concentration detected by the measuring device. However, it is difficult to separately analyze the sources that affect the recirculation rate when several sources exist. Additionally, its molar mass is heavier than air. Therefore, the sum of the mass fraction of air at the intake port of each floor when calculating the exhaust recirculation rate is always one. The mass fraction ID gas value through an outdoor air channel can be subtracted from one and expressed as a percentage, and the recirculation rate of exhaust air can be calculated.  
\[\text{Recirculation rate}_{j}=\left(1-q_{jo}\right)\times 100\% \tag{2}\]  
where \(j\) is the floor, and \(q_{jo}\) is the mass fraction of outdoor air at \(j\).
## 3 Results and Discussion
### Effect of CNT Functionalization
Thus, the polarity and functionalized forms of support material are important in the matter of catalytic activity. In the case of toluene as solvent, the results were the opposite. The np-MWCNT-Pd showed significantly higher catalytic activity than p-MWCNT-Pd, which was better than Pd/C. Compared to Pd/C, the enhancement for np-MWCNT-Pd and p-MWCNT-Pd were 60% and 32% respectively. Therefore, the enhancement nearly doubled for the nonpolar np-MWCNT-Pd. In this case, the catalytic activity of p-MWCNT-Pd with larger particle size was lower than np-MWCNT-Pd with smaller particle size. The steric hindrance from the octadecyl group did not reduce yield. Therefore, the higher yield of np-MWCNT-Pd was attributed to the higher interactions of the nonpolar np-MWCNT-Pd with nonpolar toluene. Figure 6a,b show yield as a function of time in DMF and toluene, respectively. As already mentioned, DMF was the better solvent with these catalysts, with yield reaching as high as 99.78% in 60 min. Yields were relatively lower and reaction times were longer in toluene, which was in line with what has been reported before [34; 35]. In the case of DMF, the reactions were completed in 60 min with the nanohybrids and it took 90 min with Pd/C. On the other hand, when toluene was the solvent, the time taken for the same reactions were 90 min with the nanohybrids, and 105 min with Pd/C. Yield enhancement of CNT-NM hybrids with respect to Pd/C for the Heck reaction at 60 min reaction time in both DMF and toluene are shown in Figure 6c,d. In DMF, the enhancements were 79% and 5% for p-MWCN-Pd and np-MWCN-Pd, respectively, while the corresponding enhancements in toluene were 32% and 60% in toluene. It is evident that the polarities of the functionalized form of catalysts and solvents is the important factor for catalytic activity. DMF is a better solvent than toluene for Heck reaction due to the greater dispersibility of the catalyst in DMF and high boiling point. High boiling point of the solvent enables the reaction to be carried out at a relatively higher temperature. Also, for catalysts containing polar groups (-COOH in this case), a polar aprotic solvent like DMF is a suitable solvent for Heck reaction. The reason for higher yields in DMF compared to the toluene (Boiling Point (B.P); 110.6 \({}^{\circ}\)C) is due to the better dispersibility of catalyst in addition to the higher boiling point of DMF (B.P; 153 \({}^{\circ}\)C). Nevertheless, when toluene was used as the solvent, np-MWCNT-Pd showed significantly higher catalytic activity than p-MWCNT-Pd, which was better than Pd/C. These were also attributed to dispersibility of catalyst (refer to Figure 6). Hence, it is evident that disparities in the polarity play an important role.
## 4 Experiment  
### Data Source  
In this study, we used the road traffic congestion data in the form of a roadmap, which was obtained from our previous work [47; 48], as shown in Figure 1. The data was collected by taking snapshots of traffic congestion maps from the open-source online web service, Seoul Transportation Operation and Information Service (TOPIS). This dataset includes information on the congestion levels (Jam, Slow and Free) of each road, represented by the colors red, yellow, and green, respectively. The dataset covers the traffic information of central Seoul, South Korea, with each snapshot image sized at \(192\times 448\), covering a geographical area of 7.5 km \(\times\) 17 km (with a scale of 1 cm = 1.3 km).  
According to our previous research [48], the stochastic congestion maps of Central Seoul indicate a high likelihood of congestion between 09:00 to 21:00 h and a low likelihood in other hours. Additionally, all road networks experience congestion between 18:00 to 21:00 h, and forecasting alternate routes is not an effective method for traffic management during that period. Instead, congestion pricing, traffic redirect, or no-entry to heavy vehicles can be utilized. In contrast, from 09:00 to 12:00 h, the road networks are moderately congested, and the traffic forecasting method could be a more effective method. Thus, in this study, we focused on predicting traffic congestion levels during the morning peak hours from 07:00 to 12:00 on weekdays. The data was collected every 5 min from 19 September 2019 to 31 December 2019, resulting in a total of 104 days. However, However, for 26 days, either data was missing or only partially collected, and therefore these days were removed from the database. The training set consists of data from 19 September to 25 November, the validation set consists of data from 25 November to 30 November, and the remaining data from 1 December to 31 December are used for testing the trained model. Further details on data source, acquisition and preprocessing can be found in our previous work [47; 48].  
Figure 3: ConvLSTM-based decoder module. The output from all four levels of the backbone is fed to the decoder block where two ConvLSTM are performed. After that, at first, low-resolution feature maps are upsampled to match with the L\({}_{1}\) feature map and then concatenated to obtain 480 channels. The channel size is then reduced to 32 using 1 \(\times\) 1 convolution operation and concatenated with L\({}_{1}\) feature map from end of backbone. This is followed by a 3 \(\times\) 3 convolution and two transpose convolutions to obtain the original input image resolution with four channels, one for each class (Jam, Slow, Free, and background).
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 3. Configure the Platform for the Leader-Follower Formation
### Leader-Follower Formation Control  
As the typical representation of multi-robot formation, leader-follower formation control is to maintain the desired separation (\(l\) and the desired orientation (\(\varphi\) ), between the leader and the follower, which is called \(l-\varphi\) control strategy described in Reference [31].  
Considering a group of \(n\) non-holonomic wheeled mobile robots, we denote \(l_{iL}\in R\) as the actual distance between the follower robot \(F_{i}\) and the leader robot \(L\). \(l_{iL}\in R\) is the desired distance, \(\varphi_{iL}\in\left[\begin{array}{cc}-\pi&\pi\end{array}\right]\) is the actual orientation, which is the angle from the follower robot to the \(X\)-axis connecting, and \(\varphi_{iL}\in\left[\begin{array}{cc}-\pi&\pi\end{array}\right]\) is the desired orientation. The follower robot's desired pose \(p_{i}^{d}=\left(x_{i}^{d},y_{i}^{d},{\theta_{i}^{d}}\right)^{T}\) with respect to the leader can be obtained using  
\[\left\{\begin{array}{l}x_{i}^{d}=x_{L}-l_{iL}^{d}\cos(\theta_{L}+\varphi_{iL }^{d})\\ y_{i}^{d}=y_{L}-l_{iL}^{d}\sin(\theta_{L}+\varphi_{iL}^{d})\\ \theta_{i}^{d}=\theta_{L}\end{array}\right., \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Module** & **Item** & **Description** \\ \hline - & Dimension & - & L*W*H: 21 cm \(\times\) 18 cm \(\times\) 8 cm \\ 1 & Microcontroller & STM32F407 ZEG6 & Microcontroller unit \\ 2 & Stepper motor & MG42S1 & Differential motors \\ 3 & Communication & ESP8266 & Wireless communication unit \\ 4 & Mobile beacon & Marvelmind HW v4.9 & Providing the position of robots \\ 5 & Orientation & MPU-6050 & Providing the orientation of robots \\ 6 & Buck module & DC-DC Buck module & Voltage converter 12 V–5 V \\ 7 & Oled Display & 0.96 OLED & Displaying \\ 8 & Switch & - & Power switch \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobile robot specification.  
where \(\left(x_{L},y_{L},\theta_{L}\right)^{T}\) is the pose of the leader robot.  
Comparing the follower robot's desired pose \(p_{i}^{d}=\left(x_{i}^{d},y_{i}^{d},\theta_{i}^{d}\right)^{T}\) with the follower robot's current pose \(p_{i}=\left(x_{i},y_{i},\theta_{i}\right)^{T}\), the tracking error in a coordinate system can be described as:  
\[\left(x_{e},y_{e},\theta_{e}\right)^{T}=\left(x_{i}-x_{i}^{d},y_{i}-y_{i}^{d}, \theta_{i}-\theta_{i}^{d}\right)^{T}, \tag{2}\]  
and the controller for follower robot is selected from Reference [33]:  
\[\left[\begin{array}{c}v_{i}\\ \omega_{i}\end{array}\right]=\left[\begin{array}{c}v_{i}^{d}\cos\theta_{e}+ k_{1}x_{e}\\ \omega_{i}^{d}+k_{2}v_{i}^{d}y_{e}+k_{3}v_{i}^{d}\sin\theta_{e}\end{array} \right], \tag{3}\]  
where \(\left(v_{i},\omega_{i}\right)^{T}\) are the control inputs, and \(\left(v_{i}^{d},\omega_{i}^{d}\right)^{T}\) is the desired velocity of followers.
## Case Study
### Network Determination and Training
#### 4.2.2 Network Structures
Maintaining a constant number of LSTM network layers, increasing the number of neurons gradually enhances acceleration prediction accuracy. However, the trend of APVE changes is not consistently apparent. Specifically, as the number of neurons increases from 128 to 256, certain metrics exhibit a downward trend, indicating a decrease in prediction accuracy. Figure 8: The influence of LSTM network structure on the prediction accuracy of bridge displacement. Figure 7: Train load identification results of a railway bridge. In summary, the \"2-128\" model demonstrates excellent predictive performance. For displacement prediction, it achieves a Pearson correlation coefficient of 0.99985 and an APVE of 0.64%, while, for acceleration prediction, it yields a Pearson correlation coefficient of 0.89029 and an APVE of 15.26%. Figures 10 and 11 illustrate the theoretical and predicted values of displacement and acceleration under this network structure. It is evident that displacement prediction accuracy notably surpasses that of acceleration. Although the peak values of predicted acceleration are slightly lower than the theoretical values, overall, the predicted results exhibit a strong correlation with the theoretical values. To delve deeper into the prediction results, Figure 12 offers a comparison of the power spectral density of displacement and acceleration. The findings indicate that the principal frequency components of the predicted response in the frequency domain align closely with the theoretical values, particularly in proximity to the quasi-static component near 0 Hz and the first-order resonance frequency, that is, 5.12 Hz. Figure 10: Predicted vehicle-induced dynamic response using neural network structure ‘2-128’. Figure 9: The influence of different neural network structures on the accuracy of acceleration prediction.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
###### Abstract  
When they were first conceived, solar cooling systems were designed to be cost-effective and environmentally safe alternatives for the majority of the developing nations that are characterised by their hot climates in contrast with the traditional air conditioning systems powered by electricity that is produced from fossil fuel resources. Nevertheless, developments in photovoltaic (PV) and air-conditioning technologies have impacted on the prospects of solar cooling systems. This study examined two different options: a coupled PV and air conditioner system and a solar cooling system (absorption chillers where thermal energy is provided by solar collectors) for a specific developing country located in the Eastern Mediterranean region whose climate is hot and dry (Jordan). The cooling system comprised a pair of cooled multistage compression, both of which were 700 kW, while the PV system's size was 2.1 MWp, the utility grid connection was a 0.4 kV 50 Hz net meter (2 m) and it was anticipated that 3300 MWh/year would be generated. The solar cooling system operated at a maximum coefficient of performance (COP) of 0.79 and had an actual recorded COP of 0.32 on the site; when the electricity tariff of $0.1/kWh was considered, the respective levelised cost of energy (LCOE) values were $0.9/kWh and $2.35/kWh respectively. The findings indicate that the initial costs for the solar thermal cooling system and the PV system were approximately $3.150M and $3M, respectively. The current value of future cash payments when discounts of 6% per year were applied to the payments for the combination of PV and air conditioning was about $9,745,000, whereas the solar thermal cooling system will not reach the breakeven point at negative $1,730,000. It is clear the absorption chiller did not display economic feasibility, whereas the value for the coupled PV and air-conditioning systems was under $0.05/kWh. In addition to the extensive maintenance needs, the reduced COP and the practicality and feasibility of the solar thermal cooling systems mean these kinds of technologies are under significant pressure to remain competitive when faced with the development of new air conditioning and PV technologies.  
coupled PV and air conditioner; solar cooling system; cooling of building; emerging markets 18 December 2020  
14 January 2021