esa-sceva/satcom-chunk-collection huggingface dataset:
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 3 Dataset
### Images of Culvert Openings and Blockage (ICOB)  
This dataset consisted of real images of culverts and referred to as \"Images of Culvert Openings and Blockage (ICOB)\". Primary sources of images included WCC historical records, online records, and custom captured local culvert images. WCC records were scrutinized using a Microsoft ACCESS based application for filtering the culvert images with visible openings. The dataset contained images with a high level of variation from each other (intra-class variation) in terms of culvert types, blockage accumulation, presence of debris materials, illumination conditions, culvert viewpoint variations, scale variations, resolution, and backgrounds. This high level of diversity within a relatively small dataset makes it a challenging dataset for visual analysis, even for a binary classification problem. In total, there were 929 images in ICOB with 487 images in the \"clear\" class and 442 images in the \"blocked\" class. Figure 1 shows the sample instances from each class of ICOB.  
Figure 1: Sample instances of clear (First row) and blocked (Second row) culverts from ICOB.
## 5 Solar and Geomagnetic Conditions of the Storm  
In Figure 2, the solar wind velocity, _F10.7_ solar flux index, the interplanetary magnetic field (IMF) Bz, By, and geomagnetic parameters Dst and Kp are used to reflect the variation of geomagnetic condition during 23-29 August 2018. It can be seen that the indices IMF Bz, By, Dst and Kp all show a regular variation during 23-24 August. However, the _F10.7_ index shows a sudden increase to the peak value of 74 and the solar wind velocity presents a slight decrease on 24 August. The Dst index suddenly changes at 06:00 UT and then rises to 19 nT at 08:00 UT on 25 August 2018, which remains at a stable variation of around 5 to 20 nT until 16:00 UT. The geomagnetic storm sudden commencement (SSC) starts at 06:00 and lasts for 10 h. From the beginning of 17:00 UT on 25 August 2018, the Dst index decreases rapidly and reaches the minimum of \(-\)174 nT at 07:00 UT on 26 August 2018, while the Kp index reaches the maximum of nearly 7. This indicates that a strong geomagnetic storm has occurred. The main phase of the storm lasts for 14 h from 17:00 UT on 25 August to 07:00 UT on 26 August. During the main phase, the IMF Bz and By both show a disturbance  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **Geographic Coordinates** & **Geomagnetic Coordinates** \\ \hline CIBG & 6.4°S, 106.8°E & 15.7°S, 179.5°E \\ CPNM & 10.7°N, 99.3°E & 1.3°N, 172.1°E \\ DARW & 12.8°S, 131.1°E & 21.3°S, 155.0°W \\ DJIG & 11.5°N, 42.8°E & 7.2°N, 116.9°E \\ HKSL & 22.4°N, 113.9°E & 12.9°N, 173.7°W \\ IISC & 13.0°N, 77.6°E & 4.7°N, 151.0°E \\ KAT1 & 14.4°S, 132.2°E & 22.8°S, 153.7°W \\ LAUT & 17.6°S, 177.4°E & 20.5°S, 106.8°W \\ MAYG & 12.8°S, 45.3°E & 16.9°S, 115.7°E \\ NKLG & 0.4°N, 9.7°E & 1.6°N, 82.5°E \\ POHN & 6.9°N, 158.2°E & 0.8°N, 129.6°W \\ PTGG & 14.5°N, 121.0°E & 5.2°N, 166.7°W \\ SEYG & 4.7°S, 55.5°E & 10.4°S, 127.1°E \\ SOLO & 9.4°S, 159.9°E & 14.9°S, 125.7°W \\ XMIS & 10.5°S, 105.7°E & 19.8°S, 178.3°E \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of geographic and geomagnetic coordinates of multi-GNSS experiment (MGEX) stations.  
Figure 1: Blue triangles represent locations of MGEX stations, and the green squares represent ionosonde stations. The red stars represent projections of BeiDou Navigation Satellite System Geostationary Earth Orbit (BDS-GEO) satellites on the ground.  
that the magnitude of Bz drops from the maximum of 9 nT to the minimum of \(-\)16.8 nT, and By index increases to the peak value of nearly 15 nT. After 07:00 UT on 26 August 2018, the recovery phase of the storm lasts for about three days, shown in Figure 2. During the recovery phase, the Dst index increases gradually and returns to normal state. However, the solar wind velocity unexpectedly increases from 372 km/s to 580 km/s, as well as the IMF Bz, which is an unusual phenomenon compared with previous geomagnetic storms. The Kp index shows a peak value of 5 and then gradually decreased to quiet state during the recovery phase. During 28-29 August, all indices return to regular variation, except the solar wind velocity shows a decrease.
## 4 Conclusions  
Thorough theoretical derivation the excellent transmission characteristics of SPPs were analyzed and verified effectively in this paper. The dependence of the transmission characteristics on frequency for different filling ratios was obtained in this paper. Moreover, the possibilities to increase the frequency ranges where DSW can exist have been demonstrated. It has been concluded that with the increase of frequency, the imaginary part of \(\beta\) decreases at certain intervals, and the transmission distance increases. The former reveals the reduction of transmission loss with increasing frequency. The filling ratio has a dramatic impact on the transmission characteristics of SPPs. From this relationship, the  
Figure 4: Dependence of transmission characteristics versus frequency for different filling factors: (**a,c**) the imaginary part of \(\beta\); (**b,d**) the propagation length \(L_{p}\). The case \(\varepsilon_{n}=2.25\), \(\varepsilon_{d}=11.8\).is presented in (**a,b**); \(\varepsilon_{n}=11.8\), \(\varepsilon_{d}=2.25\)—(**c,d**). All of the presented results were obtained for the AZO inclusions.  
realization of dimension tunable SPPs is enabled. This work is of great significance to the further research of optical devices based on SPPs.  
Conceptualization, T.I. and T.G.; methodology, T.I.; software, T.G.; validation, T.G. and E.R.; formal analysis, E.R.; investigation, T.I.; resources, E.R.; data curation, T.G.; writing--original draft preparation, T.I.; writing--review and editing, T.G. and E.R.; visualization, T.I.; supervision, T.G.; project administration, E.R.; funding acquisition, T.G. and E.R. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant agreement No 713694 and from Engineering and Physical Sciences Research Council (EPSRC) (Grant No. EP/R024898/1). The work of [PERSON] was partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 The Proposed Model
### Bi-LSTM Attention Layer  
The main classification component of our model was built on an attention-based Bi-LSTM. Although the CNN shrinks the input features to be used for prediction, the correlation between each word and final classification is not the same for all input words. In this study, we want to leverage both advantages of CNN and Bi-LSTM. The Bi-LSTM is used to effectively encode long-distance word dependencies.  
The Bi-LSTM process is depicted in Figure 4. It receives the features generated by the CNN stage and generates features by extracting the last hidden layer. The Bi-LSTM has access to both the preceding and subsequent contextual information, and the information obtained by Bi-LSTM can be regarded as two different textual representations. The features obtained from the CNN S\({}_{ij}\) are fed to a Bi-LSTM model that produces a representation of the sequence. This final feature representation is fed to an attention layer that chooses which features are highly correlated for final classification. For a sentence such as \"Though he is sick he is happy\" to be classified as having positive sentiment, the attention mechanism gives more weight to \"happy\" and less weight to \"sick\" To show that the sickness (though negative occurrence) is not affecting the \"happy\" sentiment. With that process the attention mechanism increases the prediction accuracy considerably and reduces the number of learnable weights needed for the prediction. The proposed model's attention mechanism uses the Bahdanau attention with attention scores as follows:  
\[\textit{score}(\textit{query},\textit{key})\ =V^{T}\textit{tanh}\Big{(}W_{ \textit{1 key}}+W_{\textit{2 query}}\Big{)} \tag{6}\]
## 3 Results and Discussion
In this case, even if the position of the defects is exactly at the focal point, part of the incident THz waves will pass through the surrounding area instead of the defect area. This effect manifests in the form of signal offsets of the transmission amplitudes. In addition, the THz wave incident on the artificial void defect, which can be classified as a subwavelength, cylindrical dielectric waveguide, experiences a different phase shift at each position. This may eliminate the phenomenon in which the measurement signal is not distinguished because of the blind spots. This means that general types of defects can be readily detected using the coherent CW-THz imaging system.
## 4 Experimental Validation
### Case 1: Public Bearing Dataset of Paderborn University (PU)
#### 4.1.3 Evaluation and Analysis of Case 1
Significantly, the current signal processing methods adopted are all based on current frequency, such as the fast Fourier transform (FFT), power spectral density (PSD), and wavelet packet decomposition (WPD). Then, diagnosis was performed through machine learning algorithms. The highest accuracy is 93.3%, slightly lower than the accuracy of 95.40% of our proposed method. Table 6 shows the comparison between the four latest mechanical fault diagnosis methods based on current signals and the proposed methods on their respective datasets. The results indicate that the proposed method can effectively perform bearing fault diagnosis based on motor current signals. Meanwhile, the accuracy of the latest method [53; 54; 55] is slightly  
Figure 7: Accuracy curves for different methods on the public dataset PU: (**a**) N15_M01_F10; (**b**) N15_M07_F10. higher than that of the proposed method, which is because of the smaller number of fault categories. In addition, the label of composed faults and varying degrees of faults were also considered in this paper, as shown in Table 4. M&E refers to mechanical and electrical faults in Table 6. Generally speaking, mechanical fault diagnosis using MCS is more difficult than electrical faults. This is because electrical faults can cause more significant changes in the MCS. Compared to [56] with the same number of labels, the accuracy of the proposed method has significantly improved. Based on the comparison results, the proposed method has certain competitiveness in bearing fault diagnosis based on MCS.
## 6 Simulation Evaluation
### Comparison and Analysis of Simulation Results  
#### 6.2.1 Network Coverage Rate  
Figure 9 shows the comparison of the network coverage rate that varies with the number of nodes and the sensing radius on the NDACS and VBDA, respectively. Figure 9a shows the comparison of the network coverage rate varying with the number of nodes on the NDACS and VBDA, where the default sensing radius is 50 m in the simulation scenario. Figure 9b shows the comparison of the network coverage rate that varies with the sensing radius of the NDACS and VBDA, where the default number of nodes is 200 in the simulation scenario. As shown in Figure 9a, compared with the VBDA, the NDACS algorithm can maintain a higher network coverage rate consistently under the same sensing radius because VBDA can only guarantee the upper layer network with high network coverage but does not consider the coverage overlaps between its neighbor interlayer, resulting in the failure of deeper waters to achieve better coverage. Figure 9b shows that with the increase sensing radius, the NDACS algorithm for network coverage rate increases steadily and consistently outperforms VBDA. This is mainly because the NDACS algorithm, after completing the 2D convex hull activation process, executes depth-adjustment strategy of the time marker to greatly reduce the network coverage overlaps, and the set _NeigInteri\({}^{j+1}\)_ is established to provide more adjustment candidates on deeper location network deployment. Therefore, the NDACS algorithm can greatly improve the network coverage rate.
## 3 Results and Discussion  
### Experimental Details  
To evaluate the performance of different models on the soil pore segmentation task, we evaluated models on a soil CT image dataset containing 3570 soil CT images. The image data were divided into a training set and a test set, at a ratio of 7:3. The training set was used to train all network models, while the test set was used to validate the results of the models. Our model was implemented using the PyTorch framework, and model training was performed using an NVIDIA RTX 2080 Ti GPU for acceleration. The model was not spliced into separate components, and an end-to-end training strategy was adopted.  
During the training process, the parameters were randomly initialized, the loss function was the weighted cross-entropy loss function, the parameter optimizer was the Adam optimizer, the learning rate was always \(lr=1e-5\), and the batch size for batch training was four. The features of adjacent images were added to the LSTM network to produce a beneficial effect for classification, and the number of sequence images affected the model accuracy. For the CLSTM, U-Net-LSTM, and BDULSTM models, we tested inputting three, five, seven, and nine sequential images for training. The soil pore segmentation accuracy, precision, recall, and F\({}_{1}\)-score were measured to evaluate the performance of different network models.
## 3 Results and Discussions  
### Life Cycle Cost of Intelligent Scheme at Vehicle-Side  
Based on the vehicle-road intelligent schemes for different levels of autonomous driving and combining the cost of intelligent devices and energy consumption with vehicle usage characteristic data, the lifecycle cost and per-kilometer allocation cost of the vehicle intelligence scheme can be calculated, as shown in Figure 12. In the \"vehicle intelligence\" scenario, the improvement of autonomous driving level means more sensors of various types and greater computing power, which significantly increases the cost on the vehicle side. At this time, the lifecycle cost of the intelligent scheme on the vehicle side for intermediate and advanced autonomous driving will reach \(\mathbb{Y}\)65,301 and \(\mathbb{Y}\)126,938, respectively. In the \"collaborative perception\" scenario, roadside primary perception can serve primary ICVs to achieve perception capabilities for intermediate autonomous driving. Combining vehicle-side intermediate-level planning and control neural networks matched with appropriate computational power, the primary ICVs can realize intermediate autonomous driving. At this time, the lifecycle cost of the intelligent scheme on the vehicle side to achieve intermediate autonomous driving will be reduced to \(\mathbb{Y}\)37,703. Roadside intermediate perception and advanced perception serve ICVs to achieve advanced autonomous driving and significantly reduce the cost of achieving advanced autonomous driving on the vehicle side. When the road side completes advanced perception coverage, primary ICVs can achieve perception capabilities for advanced autonomous driving. Combining vehicle-side advanced-level planning and control neural networks matched with appropriate computational power, the primary ICVs can realize advanced autonomous driving. The lifecycle cost of the vehicle side to achieve advanced autonomous driving will be reduced to \(\mathbb{Y}\)42,180. In the \"collaborative perception and collaborative decision-making\" scenario, through the collaborative planning and decision-making of the cloud control platform at the road network level, ICVs do not need to make complex decisions for multi-vehicle autonomous driving games, and the complexity of the vehicle-side planning and control neural network is also correspondingly reduced, thereby reducing the requirements for on-vehicle computing power and the purchase, maintenance, and power consumption costs related to computing power. In the \"collaborative perception and collaborative decision-making\" scenario, compared to the \"collaborative perception\" scenario, the per-kilometer allocation cost for advanced autonomous driving on the vehicle side will be reduced from \(\mathbb{Y}\)0.205/km to \(\mathbb{Y}\)0.197/km. For society, the reduction in per-kilometer allocation costs also means that the application of large-scale urban fleets can lead to lower overall energy consumption and carbon emissions.
## 5 Optimization of Scan Parameters  
During the acquisition phase of the APT process to establish the inter-satellite laser link, in the presence of satellite platform vibrations, the scan parameters that can be varied are the overlap factor \(a\), the beam divergence \(\theta_{b}\), and the uncertainty area \(\theta_{\text{area}}\)[40]. The primary objective is to reduce the scan time while increasing the acquisition probability in the presence of jitter.
# Arsenic Removal from Water by Adsorption onto Iron Oxide/Nano-Porous Carbon Magnetic Composite
###### Abstract  
This study aimed to develop magnetic Fe\({}_{3}\)O\({}_{4}\)/sugarcane bagasse activated carbon composite for the adsorption of arsenic (III) from aqueous solutions. Activated carbon (AC) was prepared from sugarcane bagasse by chemical activation using H\({}_{3}\)PO\({}_{4}\) as an activating agent at 400 \({}^{\circ}\)C. To enhance adsorption capacity for arsenic, the resultant AC was composited with Fe\({}_{3}\)O\({}_{4}\) particles by facile one-pot hydrothermal treatment. This method involves mixing the AC with aqueous solution of iron (II) chloride tetrahydrate, polyvinyl pyrrolidone (PVP), and ethanol. Batch adsorption experiments were conducted for the adsorption of As (III) onto the composite. The effects of pH, adsorbent dosage, and contact time on the arsenic adsorption were studied. The result showed that the composite could remove the arsenic from the water far more effectively than the plain AC. The highest percentage of arsenic removal was found at pH at 8, adsorbent dose of 1.8 g/L, and contact time of 60 min. [PERSON] and Freundlich adsorption isotherm was used to analyze the equilibrium experimental data. Langmuir model showed the best fit compared to the Freundlich model with a maximal capacity of 6.69 mg/g. These findings indicated that magnetic Fe\({}_{3}\)O\({}_{4}\)/sugarcane bagasse AC composite could be potentially applied for adsorptive removal of arsenic (III) from aqueous solutions.  
Keywords: sugarcane bagasse; activated carbon; magnetic composite; arsenic adsorption +
Footnote †: journal: Applied Sciences
## 3 SMO Synthesis
### SMO Conception  
The method is based on substituting the linear correction terms with a discontinuous function as in the case of standard full order observer [50]. Then, by adding additional terms to the nonlinear auxiliary inputs of the system (8), this results in the following relationships,  
\[\left\{\begin{array}{l}\dot{\hat{x}}=f(\hat{x},z,\zeta,u)+G_{x}I_{smo}\\ \dot{\zeta}\dot{\hat{z}}=g(\hat{x},z,\zeta,u)+G_{z}I_{smo}\end{array}\right. \tag{9}\]  
where the switching function \(I_{smo}\) is given by \(I_{smo}=sign\ (S(y,\hat{y}))\) and \(G_{x}\) and \(G_{z}\) represent the observer gains to be calculated with dimensions of \((n\times m)\) and \((m\times m)\), correspondingly. In addition, the error \(y-\hat{y}\) is selected as a linear function which outlines the dynamics of the sliding surface \(S\) by  
\[(y,\hat{y})=\Lambda(y-\hat{y}) \tag{10}\]  
where \(\Lambda\) is an invariant matrix of dimension \((m\times m)\) to be determined, and  
\[(y-\hat{y})^{T}=\left(\begin{array}{cc}(y_{1}-\hat{y}_{1})&(y_{2}-\hat{y}_{2 })&\ldots\ldots\ldots&(y_{m}-\hat{y}_{m})\end{array}\right) \tag{11}\]  
Subtracting Equation (9) from (8), the error dynamics are given by  
\[\left\{\begin{array}{l}\dot{e}_{x}=f(x,z,\zeta,u)-f(\hat{x},z,\zeta,u)-G_{x} I_{smo}\\ \dot{\zeta}\dot{e}_{z}=g(x,z,\zeta,u)-G(\hat{x},z,\zeta,u)-G_{z}I_{smo}\end{array}\right. \tag{12}\]  
The expressions (12) can be also rewritten as  
\[\left\{\begin{array}{l}\dot{e}_{x}=\Delta f-G_{x}I_{smo}\\ \dot{\zeta}\dot{e}_{z}=\Delta g-G_{z}I_{smo}\end{array}\right. \tag{13}\]  
where  
\[\left\{\begin{array}{l}\Delta f=f(x,z,\zeta,u)-f(\hat{x},z,\zeta,u)\\ \Delta g=g(x,z,\zeta,u)-g(\hat{x},z,\zeta,u)\\ e_{x}=x-\hat{x}\\ e_{z}=z-\hat{z}\end{array}\right. \tag{14}\]  
Now, by applying the singular perturbation methodology and through utilizing the resulted subsystems of (14) and applying them sequentially, the observer's gains can be computed precisely. This computation procedure starts with studying the tracking of fast variables using what is called hit condition (which depends on the measured state variables). Then, the asymptotic-convergent slow variables (for the unmeasured state variables) are analyzed.
## 4 Payload and Subsystem Architectures and Design
### Electrical Power Subsystem  
In the proposed nanosatellites' EPS, shown in Figure 16a, the solar panels are body -mounted and their opposing faces are coupled to the same power converter (MPPT1 for the \(-\)X array and +X array, MPPT2 for the \(-\)Y and +Y array, and MPPT3 for the \(-\)Z and +Z array). The power converters can be connected in parallel, and each converter has a Maximum Power Point Tracker-implemented method (MPPT) and a Battery Charge Regulator (BCR). The module of the power distribution and protection circuits based on LCL is presented in Figure 16b.  
Using the above topology also functions as a built-in redundancy: the loss of a single boost converter or damage to a solar panel will not deactivate the whole EPS. There is  
Figure 16: Proposed EPS configuration for CubeSat with open solar panels structure: (**a**) power regulation and control unit, and (**b**) power distribution unit.  
no requirement to regulate the main bus, as the subsystems themselves will separately regulate their specific supply [47]. A similar topology is used in commercial and developed CubeSat EPS, as presented in [48; 49; 50; 51; 52; 53].