esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study proposes a practical method for the early detection of failure signs in a rotating biological contactor (RBC) system that has been in long-term operation at a municipal solid waste landfill. Seventeen years of inspection logs, recorded between 2006 and 2023, were digitized and analyzed with a focus on abnormal noise, electric current values, operational status, and failure history. The analysis revealed that frequent occurrences of abnormal noise and sudden fluctuations in current tend to precede equipment failures. Based on these findings, we developed a scoring model for the predictive maintenance of RBCs. Traditionally, determining the score required professional knowledge such as performing a sensitivity analysis. However, by utilizing AI (ChatGPT o4), we were able to obtain recommended values for these parameters. This means that operators can now build and adjust a scoring model for predictive maintenance of RBCs according to their specific on-site conditions. On the other hand, sudden increases in current and abnormal noises were previously considered strong indicators of failure prediction. These parameters will depend on factors such as the sensitivity of electrical current meters and surrounding noise. Therefore, depending on the specific environmental conditions at the site, the scoring model developed in this study may have limited predictive accuracy.  
waste landfill; leachate treatment facility; rotating biological contacts; preventive maintenance; daily inspection reports; artificial intelligence Article
## 4 Results and Discussion
### Euler Number  
Figure 12 shows the variation of Euler number \(Eu_{\text{o}}\) with Reynolds number \(Re_{\text{o}}\) in double logarithm coordinate. As shown in Figure 12, \(Eu_{\text{o}}\) decreases with the increasing in \(Re_{\text{o}}\) and the decreasing in nanoparticle weight fraction. For a given \(Re_{\text{o}}\), the \(Eu_{\text{o}}\) of nanofluids at weight fraction of 1.0 wt %, 0.5 wt %, and 0.2 wt % increases by 16%, 7.6% and 3.1% in comparison with base fluid, respectively.  
Figure 11: Shell-side temperature distributions of elliptic tube heat exchanger (\(V_{\text{f}}\) = 1.72 m\({}^{3}\cdot\)h\({}^{-1}\), \(Z\) = \(-500\) mm): (**a**) base fluid; (**b**) 0.2 wt % MWCNTs/XG; (**c**) 0.5 wt % MWCNTs/XG; and (**d**) 1.0 wt % MWCNTs/XG.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Non-Dimensional Number** & **Fluid** & **Average Deviation** \\ \hline \multirow{4}{*}{\(N_{\text{Au}_{\text{o}}}\)} & Base fluid & \(5.11\%\) \\  & 0.2 wt \% MWCNTs/XG & \(5.63\%\) \\  & 0.5 wt \% MWCNTs/XG & \(1.17\%\) \\  & 1.0 wt \% MWCNTs/XG & \(-1.14\%\) \\ \hline \multirow{4}{*}{\(f_{\text{o}}\)} & Base fluid & \(0.25\%\) \\  & 0.2 wt \% MWCNTs/XG & \(0.60\%\) \\ \cline{1-1}  & 0.5 wt \% MWCNTs/XG & \(-0.62\%\) \\ \cline{1-1}  & 1.0 wt \% MWCNTs/XG & \(-5.06\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average deviations of \(Nu_{\text{o}}\) and \(f_{\text{o}}\) between experimental and numerical data.
## 5 Discussions
### Spatial Distribution of SSI around Korea
#### 5.2.2 Kma asos SSI  
In order to investigate the difference between in situ SSI and satellite-derived SSI according to spatial and temporal distribution, we derived the KMA ASOS-observed daily SSI by accumulating the hourly SSI for an SZA of less than 80 degrees from 1 January 2020 to 31 December 2021 (Figure 14). The maximum and minimum values of the monthly mean daily KMA ASOS in situ SSI are shown in Table 4. In the case of the minimum value of the monthly daily in situ SSI, like the results of the GK2A/AMI-derived CNN-based SSI, the period from April to June showed higher mean daily SSI values compared with other periods, especially July. In terms of spatial and temporal distribution, like the results of the GK2A/AMI-derived CNN-based SSI, from July to September, the mean daily in situ SSI was lower compared with the period from April to June; it was found that the north-south gradient of the SSI over Korea was reversed in July and August.  
Despite these similar result with the GK2A/AMI-derived SSI, some characteristics were different. Some stations showed different values from neighboring stations. Although stations 131 and 133 are located near to each other, their mean daily SSI measurements were different for specific months, including July, August, and September. This characteristic was also shown in stations 100, 104, 105, 138, and 283. Furthermore, in terms of the maximum value of the mean daily in situ SSI, unlike the results of the GK2A/AMI-derived CNN-based SSI, the period of April to June showed monthly daily SSI values similar to those of July. These differences between the in situ SSI and the GK2A/AMI-derived SSI could be caused by the observation method. When a satellite observes the Earth, the pixel is interpreted as having homogeneous conditions; the GK2A/AMI collects the environmental conditions of the pixels with a spatial resolution of 2 km, assuming homogeneous conditions. However,  
Figure 13: The distribution of annual mean daily SSI, from 1 January 2020 to 31 December 2021, estimated from GK2A/AMI using the CNN approach with respect to administrative districts around Korea in (**a**) 2020 and (**b**) 2021.  
the actual cloud conditions, which directly affect SSI, are often heterogeneous. Furthermore, although the satellite measures the SSI based on two-dimensional observations, the in situ SSI observed from ground-based pyranometers is affected by three-dimensional radiative effects and small-scale cloud conditions [57]. These different observations would be slightly alleviated by using hourly SSI; however, for estimating SSI from satellite data, it is impossible to completely exclude the different methods.
# Thermographic Stall Detection by Model-Inspired Evaluation of the Dynamic Temperature Behaviour
## 3 Experimental Setup
### Wind Tunnel Setup and Experimental Procedures
In order to achieve a near thermodynamic steady state the mean flow velocity is set to 49 m s\({}^{-1}\) and the measurement starts after a compensation time of 10 min. The measurement with a transient input signal is conducted for a flow velocity of 74 m s\({}^{-1}\), the measurement starts directly after the wind tunnel start-up phase. The flow velocities result in the Reynolds numbers \(Re=2\cdot 10^{6}\) and \(Re=3\cdot 10^{6}\), respectively, and emulate realistic flow velocities for rotor blades of wind turbines [27]. With respect to the defined flow velocities, the angle of attack is set to \(AoA=14^{\circ}\) for both experiments, which leads to the existence of laminar, turbulent and separated boundary layer flow regions. Note that the maximum flow velocity of 74 m s\({}^{-1}\) leads to a Mach number of about 0.22. Considering the chosen angle of attack, as well as the resulting surface pressure gradient, weak compressibility effects of the flow are possible, but have only a negligible influence on the evaluated boundary layer properties. Figure 5 shows the averaged profile temperature of the surface over the measurement time for both measurement conditions. Aside from the large scale behaviour depending on the flow velocity, small-scale oscillations are visible. For \(Re=2\cdot 10^{6}\), the wind tunnel heating rate is about 0.01 K min\({}^{-1}\), which leads to a low thermal contrast and short compensation time to achieve the desired near-thermodynamic steady state of the measurement object. For a \(Re=3\cdot 10^{6}\), a transient input signal is realised by a fluid temperature ramp with a heating rate of about 0.1 K min\({}^{-1}\).
# A Scheme for Controlled Cyclic Asymmetric Remote State Preparation in Noisy Environment
## 2 Four-Party Controlled Cyclic Asymmetrical RSP Protocol of Sequentially Increasing Qubits States  
In this section, we aim at a situation where the amount of information transmitted between multiple participants is sequentially increasing. Suppose that there are four participants, employing ten quantum entangled states as quantum channels. [PERSON] prepares any single-qubit state \(|\xi_{A}\rangle=a_{0}e^{i\theta_{0}}|0\rangle+a_{1}e^{i\theta_{1}}|1\rangle\) for [PERSON], [PERSON] prepares any two-qubit state \(|\xi_{B}\rangle=b_{0}e^{i\theta_{0}}|00\rangle+b_{1}e^{i\theta_{1}}|11\rangle\) for [PERSON], [PERSON] prepares any three-qubit state \(|\xi_{C}\rangle=c_{0}e^{i\gamma_{0}}|000\rangle+c_{1}e^{i\gamma_{1}}|111\rangle\) for [PERSON], and [PERSON] is the controller, where \(a_{0},a_{1},b_{0},b_{1},c_{0},c_{1}\in R\)\(a_{0}^{2}+a_{1}^{2}=1\), \(b_{0}^{2}+b_{1}^{2}=1\), \(c_{0}^{2}+c_{1}^{2}=1\), \(\theta_{0},\theta_{1},\phi_{0},\phi_{1},\gamma_{0},\gamma_{1}\in[0,2\pi]\). The scenario of the scheme is shown in Figure 1.  
The quantum channel linking the [PERSON], [PERSON], [PERSON] and [PERSON] has the form  
\[\begin{array}{l}\left|\varphi\right\rangle_{12345678910}\\ =\frac{1}{4}[(0000)+\left|1111\right\rangle)_{1234}(\left|000\right\rangle+ \left|111\right\rangle)_{567}(\left|00\right\rangle+\left|11\right\rangle)_{89 }\left|0\right\rangle_{10}\\ +(\left|0011\right\rangle-\left|1100\right\rangle)_{1234}(\left|011\right\rangle -\left|100\right\rangle)_{567}(\left|01\right\rangle-\left|10\right\rangle)_{89 }\left|1\right\rangle_{10}],\end{array} \tag{1}\]  
where [PERSON] holds qubits (2,3,4,9), [PERSON] holds qubits (5,8), [PERSON] holds qubits (1,6,7), and [PERSON] holds qubit 10.
# An Improved Product Code-Based Data Hiding Scheme
###### Abstract  
This paper explores the data hiding schemes which are based on the principle of matrix embedding. Under the same embedding rate, the efficiency of each data hiding scheme is evaluated by the metric of average embedding efficiency. In the literature, both the row-column embedding and the weight approximation embedding algorithms are sub-optimal solutions for the product code-based data hiding problem. For the former, it is still based on the concept of one-dimensional (1-D) toggle syndrome, and the concept of two-dimensional (2-D) toggle syndrome is directly adopted for the latter one. Data hiding with multiple embedding channels is the practice of hiding messages into hidden media many times. Here, two multi-channel embedding-based data hiding techniques--one is the 1-D toggle syndrome-based embedding scheme (1 DTS-1), and the other is the improved weight approximation-based embedding scheme (2 DTS-1), are presented. In the former, the proposed one-off decision technique is used to determine the locations of the required modification bits, and the amount of modification will be reduced through utilizing the characteristics of the linear code. With the technique of the former, in the latter, the amount of modification bits can be further reduced because that a toggle array with better structure is generated, which is more suitable for being assigned as the initial toggle array while applying the weight approximation approach. The experimental results show our proposed hybrid 1-D/2-D toggle syndrome-based embedding scheme (2 DTS-1) has increased the embedding efficiency by 0.1149 when compared to the weight approximation embedding algorithm. Further, the embedding efficiency of the latter one can be further and significantly enhanced through the Hamming+1 technique.  
matrix embedding; multiple-channel embedding; product code; Hamming+1; embedding efficiency Article
# The Evaluation Method of Particle Clustering Phenomena in RAP
## 4 Results and Discussion
### Clustering Degree Analysis of Each Particle Size  
To further analyze the clustering degree of RAP materials with different particle sizes, the Percentage-Loss (_PL_) rate after extraction was used as the evaluation index. The greater the _PL_, the more serious the clustering degree. Results are shown in Figure 5.  
As is shown in Figure 5, with the increase of particle size, the loss rate of the two kinds of RAP materials showed a general increasing trend. It indicates that the larger the size of the RAP particle, the more severe the clustering degree. Besides, there were two change phases, and 4.75 mm sieving holes can be seen as the cut-off point. The loss rate of the particle size less than 4.75 mm changes smoothly and the clustering degree was smaller; while the particle size which are above 4.75 mm, the loss rate increased rapidly, and the clustering degree was more serious.  
Therefore, the particle size larger than 4.75 mm would seriously affect the variability of RAP and as a result, required further crushing treatment. Furthermore, when compared the RAP1 with RAP2, the clustering degree of RAP1 was greater. To finely crush the RAP materials, it is necessary to reduce the clustering degree of the coarse aggregate (above 4.75 mm), which is of great significance in reducing the variability of the RAP mixtures and to improve the performance of the recycled mixtures.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 2 Background
### Dynamic Scheduling in LTE  
The LTE radio resources are distributed in a time-frequency grid. For every Transmission Time Interval (TTI), the available PRBs for data channels are allocated to the UEs selected by the eNoedB packet scheduler and the downlink/uplink scheduling decisions are communicated to the respective UEs via the PDCCH [29]. For downlink transmissions, the eNodeB sends the grant information while the corresponding UE is monitoring the PDCCH followed by the actual data received by the UE in the designated downlink resources. In case of UE generated traffic, the device needs to send a Scheduling Request (SR) [29] first to the eNodeB in the uplink control channel, and then continuously monitor the PDCCH for possible uplink allocations. Upon reception of the desired grant, the UE makes the transmission in the designated uplink resources attached with a Buffer Status Report (BSR) [30]. If the BSR indicates the requirement of further resource allocation, the UE is again entered in the scheduling queue of the eNodeB and needs to keep monitoring the PDCCH for subsequent grants. The scheduling process is called dynamic because the allocated bandwidth can vary from one allocation to another as well as the timing and Modulation and Coding (MCS) [29] schemes can be adapted based on UE channel conditions and QoS requirements. However, this flexibility comes at a cost of PDCCH signaling overhead and increased power consumption of the UEs for monitoring and processing of control information.
## 2 Materials and Methods
### The Lipid Content and Fatty Acids Analysis  
The lipid content (% d.w.) in powdered biomass determination was performed using the gravimetrical method of [PERSON] [13] with [PERSON] et al.'s modification [14] by taking 600 \(\pm\) 30 mg of sample for lipid extraction. Gas chromatography (YL6100, Young Lin Bldg., Anyang, Hogye-dong, Republic of Korea) with a flame ionization detector was used to determine the fatty acid content in the extracted lipid samples according to the method evaluated by [PERSON] et al. [12]. Measurements were performed in triplicate. The identification of fatty acids was carried out based on the retention time using a lipid standard purchased from Sigma Aldrich (SupelcoTM 37 Component Mix) and quantified as a percentage of the total FAMES content.  
The data from fatty acid composition analysis were used to determine the nutritional quality of the lipid fraction:  
1. Polyunsaturated fatty acid/saturated fatty (\(\Sigma\) PUFA/\(\Sigma\) SFA) acid ratio
2. n-6/n-3 ratio
3. Saturation index (SI) using the formula: \[\text{SI}=(\text{C 14:0 + C 16:0 + C 18:0})/(\text{MUFA cis + PUFA})\]
4. Unsaturation index (UI) using the formula: \[\text{UI}=1\times(\%\text{ monoenoics})+2\times(\%\text{ dienoics})+3\times(\%\text{ trienoics})+4\times(\%\text{ tetraenoics})+5\times(\%\text{ pentaenoics})+6\times(\%\text{ hexaenoics})\]
5. Nutritional value (NV) \[\text{NV}=(\text{C 12:0 + C 14:0 + C 16:0})/(\text{C 18:1 cis9 + C 18:2 n-6})\]
6. Atherogenicity index (IA) [15] \[\text{IA}=[\text{C 12:0 + (4\times C 14:0) + C 16:0}]/\Sigma\text{ UFA}\] where UFA means unsaturated fatty acids
7. Thrombogenicity index (IT) [15] \[\text{IT}=(\text{C14:0 + C16:0 + C18:0})/\] \[\text{[(0.5\times\Sigma\text{ MUFA}) + (0.5\times\Sigma\text{ n-6 PUFA}) + (3\times\Sigma\text{ n-3 PUFA}) + (n-3/n-6)]}\]
8. Fatty acids hypocholesterolic/hypercholesterolic ratio (hH) [16] \[\text{hH}=(\text{C 18:1 cis9 + C 18:2 \times 6 + C 20:4 \times 6 + C 18:3 \times 3+ C 20:5 \times 3+C 22:5 \times 3+C 22:6 \times 3})/(\text{C 14:0 + C 16:0})\]
9. Health-promoting index (HPI) \[\text{HPI}=\Sigma\text{ UFA}/[\text{C 12:0+ (4\times C 14:0) + C 16:0}],\]
10. Linoleic acid/\(\alpha\)-linolenic acid (LA/ALA) ratio
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## Study Area and Data
### Satellite Data and Preprocessing
#### 2.2.1 MODIS Data Products
All Terra and Aqua observations in the 16-day retrieval period were used to retrieve BRDF parameters and then compute NBAR at the mean solar zenith angle. The observations closer to the target NBAR date were weighted higher in the retrieval [27]. The Aqua and Terra combined MODIS BRDF and NBAR products provide higher-quality retrievals than Terra and Aqua retrievals alone [27]. The Collection 6 BRDF and NBAR products are generated at 500 m spatial resolution for all land bands. We will refer to these products as the combined NBAR dataset. To ensure the quality of NBAR data, the Quality Control (QC) layers in the MODIS data products were used. For the in-house daily NBAR, we used both quality layers from the 250 m and 500 m products. For the Collection 6 NBAR products, the QC files (MCD43A2) were also downloaded and used. All daily NBAR data were reprojected from sinusoidal to UTM projection and resampled to 30 m spatial resolution using the bilinear interpolation resample approach in the MODIS Reprojection Tool (MRT). The MODIS and Landsat images were co-registered using an automatic image matching method [15]. Three NBAR datasets were used to fuse with Landsat to generate time-series images at 30 m resolution and were evaluated in Sections 4.1 and 4.2.
## 2 Materials and Methods
### Characterisation of Adsorbent  
Characterisation techniques such as Fourier transform infrared (FTIR) and scanning electron microscopy (SEM) were used on both modified and fluoride-loaded adsorbents to understand the adsorbents as well as the properties that could be responsible for adsorption. The infrared spectrum of the adsorbent before and after defluoridation were captured using Alpha FTIR spectrometer (Bruker Optics, Billerica, MA, USA) in the range of 4000-400 cm\({}^{-1}\). The SEM characterisation was carried out using TESCAN, Vega 3 XMU (TESCAN, Brno, Czech Republic).
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 6 Algorithm Analyses
### Histogram Analysis  
The histogram can reflect the statistical feature of pixel value distribution. For a desirable encryption scheme, the histogram of the encryption image is always uniform [21]. Figure 5a is the histogram of Airfield, and Figure 5b-e are the histograms of Figure 4b-e. The experimental results show that all the histograms of Figure 4b-e are distributed uniformly, which are totally different from Airfield's. Therefore, all these four algorithms can effectively resist the statistical attack.  
Figure 4: Airfield and its corresponding encryption images. (**a**) Airfield, (**b**) encryption image of the proposed algorithm, (**c**) encryption image of [PERSON]’s algorithm, (**d**) encryption image of [PERSON]’s algorithm, and (**e**) encryption image of [PERSON]’s algorithm.
## 3 Results
### The Detection Results of the Different Methods  
To better evaluate the model's performance, three test regions were selected from the orthorectified imagery, and the recognition results of different models in these regions were post-processed for visualization, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ResNet18** & **RGB** & **MS-VIs** & **MSFM** & **HSSFM** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline ✓ & ✓ & & & & 82.7\% & 82.4\% & 82.2\% & 82.3\% \\ ✓ & & ✓ & & & 80.4\% & 77.1\% & 81.0\% & 79.0\% \\ ✓ & ✓ & ✓ & & & 89.4\% & 89.6\% & 89.8\% & 89.7\% \\ ✓ & ✓ & ✓ & ✓ & & 94.1\% & 94.3\% & 94.3\% & 94.3\% \\ ✓ & ✓ & ✓ & & ✓ & 94.2\% & 94.3\% & 94.1\% & 94.2\% \\ ✓ & ✓ & ✓ & ✓ & ✓ & 95.4\% & 95.6\% & 95.6\% & 95.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the ablation experiment. The symbol “✓” indicates the modules included in the ablation experiments.  
Among them, Area 1 primarily consists of stripe rust, Area 2 is dominated by yellow dwarf disease, and Area 3 mainly contains healthy vegetation, with some regions affected by yellow dwarf disease. The results demonstrate that MSFNet achieves high recognition accuracy across all regions, with only a few misclassifications. In contrast, ShuffleNetV2 exhibits significant mutual misclassification between stripe rust and yellow dwarf disease in Area 1, while EfficientNetV2 erroneously classifies roadside trees as stripe rust in the same region. Moreover, MobileNetV3 suffers from both types of misclassification. Additionally, ShuffleNetV2, EfficientNetV2, and MobileNetV3 frequently misidentify wheat field shadows along the edges of Area 2 as disease-affected regions.  
These misclassifications can be primarily attributed to the spatial similarity between different categories. For instance, wheat shadows share similar texture patterns with stripe rust and yellow dwarf disease. However, the comparative models have limited capabilities in capturing spectral variations, making them more susceptible to interference from spatially similar features, leading to misclassification. In contrast, MSFNet effectively integrates spatial and spectral information, enabling it to distinguish regions with similar spatial features but distinct spectral characteristics. This further validates the superiority of MSFNet in wheat disease detection.
###### Abstract  
Probabilistic seismic hazard analysis (PSHA) has been recognized as a reasonable method for quantifying seismic threats. Traditionally, this method ignores the effect of the focal depth, in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to provide a uniform equation, and these equations cannot express the non-linear relationship under geological conditions. Hence, this paper proposed a method to consider the seismic focal depth for the PSHA with the example of California and used a back propagation neural network (BPNN) to predict the peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method were collected separately. Secondly, the unknown PGA data were supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation, PSHA considering the focal depth was completed and compared with the current California seismic zoning results. The results showed that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of the PSHA.  
back propagation neural network; hazard analysis; peak ground motion; probabilistic analysis +
Footnote †: journal: Applied sciences