esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 5 Result
In the lake system, recurrent neural networks have shown significant results. These two kinds of neural networks have applications in reservoirs. In contrast, feed-forward neural network can predict water quality with relatively little data. In addition to being able to perform prediction tasks, GRNN is also suitable for small data sets (28, 32, 61, 151, 159, 265 samples) compared with other types of ANNs [24, 39, 40, 41, 42, 43], so researchers should pay some attention to it. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Quality** & **Categories** & **Unit** &
\begin{tabular}{c} **Major** \\ **Sensors** \\ \end{tabular} & \multicolumn{1}{c}{**Research Scenarios**} \\ \hline DO & chemical & mg/L & ✓ & river, lake, reservoir, WWTP, ponds, coastal waters, \\  & chemical & mg/L & - & river, lake, WWTP, mine water experimental system \\  & chemical & mg/L & - & river, lake, reservoir, WWTP, groundwater, mine water \\  & physical & \({}^{\circ}\)C & ✓ & river, lake, ponds, catchment, stream, coastal waters \\ Chl-a & biological & \(\mu\)g/L & ✓ & lake, reservoir, surface water, coastal waters \\ pH & physical & none & ✓ & river, lake, WWTP, stream, coastal waters \\ SS & physical & mg/L & - & river, stream, coastal waters, Creek, catchment \\ EC & physical & us cm\({}^{-1}\) & ✓ & river, lake, reservoir, groundwater, stream \\ TP & physical & \(\mu\)g/L & - & river, lake, WWTP \\ NH\({}_{3}\)-N & chemical & mg/L & ✓ & river, lake, reservoir, groundwater experimental system \\ Tur & physical & FNU & ✓ & river, stream \\ NO\({}_{3}\) & chemical & mg/L & - & river, groundwater, catchment, wells, aquifer \\  & physical & mg/L & - & experimental system \\ TDS & physical & mg/L & - & river, groundwater, drain \\ S & physical & psu & ✓ & groundwater, coastal waters \\ TN & chemical & mg/L & - & lake, WWTP, coastal waters \\ B & physical & mg/L & - & river \\ TH & physical & mg/L & - & river \\ TOC & chemical & mg/L & - & river \\ TSS & physical & mg/L & - & river \\ COD\({}_{\text{Mn}}\) & chemical & mg/L & - & river \\ NO\({}_{2}\) & chemical & mg/L & - & groundwater \\ P & physical & mg/L & - & experimental system \\ SD & physical & cm & - & lake \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic information of water quality variables.
## 4 Experimental Results
### Lung Nodule Segmentation Result  
To examine whether the random walker method help to improve the segmentation performance, the boundaries of the pulmonary nodules are segmented and extracted using the random walker method. The pre-processed lung nodule CT images are fed into the segmentation block to extract the boundaries of the nodules. In this proposed research, the random walker method is used. The original CT image, the ground truth standard, and the result of segmentation is illustrated in Figure 4. It can be seen that the random walker method segments the small and micro nodules accurately for further processing. The masked portion indicates the nodule under consideration for analysis.  
The accuracy of the proposed segmentation method is validated using the DSC and JSC, and it is obtained as 0.979 \(\pm\) 0.011 and 0.877 \(\pm\) 0.008, respectively. From this result, is it can be seen that the proposed method provides better segmentation to extract the lung nodules. The various samples of benign class and malignant class are fed into the proposed model, and the boundaries are segmented. The segmented boundaries for various classes using the random walker method are illustrated in Figure 5.  
Figure 4: Lung nodule segmentation result using the random walker method.  
Figure 3: (**a**) Input CT image, (**b**) Preprocessed image using anisotropic nonlinear diffusion filter, (**c**) Texture features.
## 3 Experimental Results and Discussion
### Failure Mode and Damage Processes
Through the tests and a large number of finite element analyses, it can be known that, under cyclic loading, the deformation of the specimens is concentrated on the replaceable links, and most of the stress reaches the ultimate strength of the materials. However, the rest of the structure (frame column, frame beam, brace) remains elastic. The residual deformation of the frame is very small. According to the follow-up test data we did (Table 3), after plastic damage has occurred on the link, the residual deformation of the frame is very small. The maximum stress on frame beam, column, and brace is very small, which is far less than the yield stress of the material. The above analysis shows that the frame beams columns and brace are still in the elastic stage after several energy dissipation links replacement. The link has good replaceable properties. The high-strength bolt is removed from the original frame by an electric wrench when the link is broken. It is feasible to replace the link after adjusting to the original frame position with the jack, as shown in Figure 9.  
From the above-mentioned damage processes, eight specimens showed primarily two types of failure modes. The failure mode of Specimens L2, L3, and L6 was shear failure, the flange stress was mainly distributed on the flange between the first row and the third row bolts. The deformation was mainly concentrated on the web and flange near the first row of bolt holes that were connected with the frame beam. The bolt holes on the web were shear deformed and the flange was bent. Specimens L1, L4, and L5, L7, L8 failed due to local buckling of the flange after shear yield of the web, which was likely to be caused by the stress at the web point when it reached the limit stress yield before that at the flange point. In addition, the local buckling load of web and the stability of the connection can be improved by setting transverse stiffener. In this test, a total of eight groups of replacement links with web connection types have been tested, and a total of eight links have been replaced. According to the results of the experiment, the link of the web plate connection type is convenient for the post-earthquake repair and replacement, which can achieve the purpose of post-earthquake repair and substitute and reduce the overall cost of the project at the same time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**specimen** & **RSL-1s** & **RSL-2s** & **RSL-3s** & **RSL-4s** \\
**plastic corner** & 0.230 & 0.170 & 0.140 & 0.080 \\
**specimen** & **RSL-5s** & **RSL-6s** & **RSL-7s** & **RSL-8s** \\
**plastic corner** & 0.100 & 0.150 & 0.060 & 0.050 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The specimen plastic corner. Figure 9: Replacement of energy-dissipating links. (**a**) replacement of connection; (**b**) replacement of connection.
## 5 Field Tests and Discussion on Results
### Field Test of NB-PLC-Based SACs on Conventional and Inverter Based Air Conditioners
#### 5.2.1 Response of Typical Air Conditioners  
The DSM control technique is applied to typical SACs by setting the thermostats at temperature values 25 \({}^{\circ}\)C and then 27 \({}^{\circ}\)C. The measured results of hourly power consumption profile are shown in Figure 10a,b at 25 \({}^{\circ}\)C and 27 \({}^{\circ}\)C, respectively. The displayed results show that when the temperature was 25 \({}^{\circ}\)C then the average power consumption was 1415 watts, but when the setting of temperature increases to 27 \({}^{\circ}\)C then average power consumption reduces to 935 watts with 33.92% power saving.  
Figure 10: Hourly power consumption profiles. (**a**) Typical air conditioner at 25 \({}^{\circ}\)C. (**b**) Typical air conditioner at 27 \({}^{\circ}\)C. (**c**) Variable frequency air conditioner at 25 \({}^{\circ}\)C. (**d**) Variable frequency air conditioner at 27 \({}^{\circ}\)C.  
Figure 9: SNR profiles of NB-PLC. (**a**) Communication over the transformer bridge for residential consumers. (**b**) Communication over the transformer bridge for commercial consumers. (**c**) Communication over the transformer bridge for industrial consumers.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Proposed Novel PSO  
In this subsection, we propose a novel PSO algorithm to obtain \(\omega^{*}\) and \(\gamma^{*}\) values for the OAOR equation. The PSO was first presented in 1995 [50]. It has been in use for a long time since its presentation in various academic and technological domains because of its easier implementation, as it has few parametric requirements. The primary concern with PSO is its likelihood of being confined to local optimum values, which can lead to slow convergence and inaccurate results. Various modifications have been proposed in the literature to enhance the PSO convergence properties [44; 45; 51]. However, these approaches tend to improve PSO convergence without ensuring the optimization of parameters like \(\omega^{*}\) and \(\gamma^{*}\) in the context of massive MIMO signal detection. As we know, these parameters are crucial in our proposed method for improving performance in signal detection to achieve faster convergence while ensuring high accuracy and low complexity. So, we needed to modify it for our specific use case. Our proposed modifications are necessary because they provide a targeted strategy for systematically tuning \(\omega^{*}\) and \(\gamma^{*}\) in OAOR. This focus enables us to enhance the effectiveness of the OAOR method in addressing the unique challenges posed by massive MIMO systems, such as high dimensionality and varying channel conditions. By aligning the parameter optimization process with the specific needs of our signal detection framework, we ensure that the advantages of PSO are fully leveraged, leading to improved convergence rates and overall performance. Before moving towards the proposed algorithm, we first understand the workings of the basic version of PSO.
## 2 Results  
### HS-SPME/GC-MS Analysis  
#### 2.1.1 DVB/CAR/PDMS Fiber  
VOCs of four populations of _Sideritis romana_ and two populations of _S. montana_ were extracted by HS-SPME, using DVB/CAR/PDMS fiber and their composition was analyzed by GC-MS. In total, 69 VOCs were identified, accounting for 86.18-90.29% of the total VOCs content (Table 1). The VOCs compositions of investigated populations of both investigated species were characterized by sesquiterpene hydrocarbons, followed by monoterypene hydrocarbons, except for that of _S. romana_ from Morinje Bay (_S. romana_ M), in which monoterypene hydrocarbons and other compounds were more abundant than sesquiterpene hydrocarbons. Additionally, for the latter, oxygenated sesquiterpenes were more represented than in other investigated samples (Figure 1). The main individual compounds that were identified for _S. romana_ were bicyclogermacrene (15.16-23.04%) (except _S. romana_ M), _trans_-caryophyllene (3.60-23.88%) (except _S. romana_ from Kamenjak (_S. romana_ K) and Blato (_S. romana_ B)), _trans_-\(\beta\)-farnesene (1.00-12.79%), limonene (1.68-7.58%), allaoramodendrene (2.35-7.32%), \(\beta\)-pinene (2.61-8.01%), and isocaryophyllene (2.39-4.70%) (except _S. romana_ M). Exceptionally, for _S. romana_ M, the main identified compounds were benzyl alcohol (13.28%), viridiflorol (10.29%), ledene (8.20%), limonene (7.10%), \(\beta\)-pinene (6.67%), and \(p\)-cymene (5.49%). For _S. montana_, the main identified individual compounds were germacrene D (17.04-23.23%), _trans_-caryophyllene (6.56-11.89%), \(\delta\)-cadinene (7.57-8.85%), bicyclogermacrene (4.19-11.76%), limonene (6.31-8.14%), and _trans_-\(\beta\)-farnesene (3.56-4.83%) (Table 1).
## 3 Cross Deployment Networking Method  
In this section we are going to improve the normal body-centered cubic lattice (NBCL) [6] to cross body-centered cubic lattice (CBCL) for the deployment of UWSNs, and propose a Cross Deployment Networking Method (CDNM) for UWSNs suitable for the underwater environment.  
First, building an UWSN requires deploying the sensor nodes to the corresponding positions and organizing them into a network. Hence, the formation of an UWSN includes the network node deployment and networking. Node deployment is to place a certain number of sensor nodes in the designated area, to set up the cruising path of UUVs, and so forth. Networking mainly organizes the already deployed nodes and establishes the network topology, determines the routing policy and data transmission strategy of the network, and makes the network available to users. Therefore, the systematic performance of an UWSN depend not only on the physical performance of its nodes, but also on how these nodes are organized.
## 3 Results and Discussion  
### Model Verification  
Through a comparison of the dimensionless temperature, or \(T_{\text{D}}\), between the works in this study and research in the literature [32], the suggested numerical simulation model is validated in this part. \(T_{\text{D}}\) is defined as \(T_{\text{D}}=(T_{\text{pro}}-T_{\text{inj}})/(T_{0}-T_{\text{inj}})\). Figure 5 demonstrates \(T_{\text{D}}\) of a 1D numerical model [32], 2D numerical model in this work. The evolution tendency of \(T_{\text{D}}\) in various stimulation models is essentially the same, as illustrated in Figure 5. The early stage's temperature is steady or increasing throughout the first year. However, the progress of \(T_{\text{D}}\) has steadily stagnated as mining time has increased. During the course of 20 years, \(T_{\text{D}}\) of the 2D numerical model decreases by about 42%, \(T_{\text{D}}\) of the 1D numerical model decreases by about 43.3%, and \(T_{\text{D}}\) of the numerical model in this work has a 45.6% drop. The comparison result shown in Figure 5 verify the rationality and effectiveness of the numerical model proposed in this paper.
## 4 Conclusions  
Gaofen satellites enjoy the advantages of high temporal and comparably high spatial resolution, where both optical and radar sensors are deployed. The GF-2 multispectral images and GF-3 SAR images were used in this investigation to analyze the characteristics of land cover in Yuli County, Xinjiang, China, by means of the CART algorithm of a decision tree. The overall classification accuracy of 83.15% with a kappa coefficient of 0.803 was observed for the study area. The vegetation, water, land with plastic film, bare soil, desert and buildings with metal or cement rooftops were classified accordingly. The DT-based classification for Gaofen satellite images proposed in this paper can be used in the reconnaissance and analysis of human activities in the sparsely populated zones of Western China to promote the effectiveness and accuracy of a routine administrative and supervised workflow.  
Conceptualization, R.C. and C.L.; methodology, C.L. and W.T.; image processing, J.Y. and X.M.; validation, C.L., W.T. and J.Y.; writing--original draft preparation, C.L. and W.T.; writing--review and editing, R.C. and C.L.; visualization, C.L. and W.T.; supervision, R.C.; funding acquisition, C.L. and W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China High-Resolution Earth Observation System (grant no. 01\(-\)Y30F05\(-\)9001\(-\)20/22), the National Key R&D Program of China (grant no. 2017 YFC0822004) and the Civil Aerospace Pre-research Project of China (Key technologies for emergency observation and information support of domestic satellites, grant no. B0302).  
Not applicable.  
The Gaofen series satellite images can be found on the website of China Centre for Resources Satellite Data and Application ([[https://data.cresda.cn/#/home](https://data.cresda.cn/#/home)]([https://data.cresda.cn/#/home](https://data.cresda.cn/#/home)), accessed on 23 January 2023).  
The authors thank the Remote Sensing Center of Public Security, People's Public Security University of China, for providing the Gaofen satellite images used in this investigation. The authors also thank three anonymous reviewers for their helpful comments and suggestions.  
The authors declare no conflict of interest.
# Numerical Study of Partial Dam-Break Flow with Arbitrary Dam Gate Location Using VOF Method
## 3 Results and Discussions
### Effect of Initial Water Stage, Breach Size and Position of Dam Gate on Hydraulic Characteristics
#### 3.2.1 Dynamics and Structure of Dam-Break Wave Inside Reservoir
The quantity \"\(q\)- criterion\" represents the local balance between shear strain rate and vorticity magnitude, defining vortices as areas where the vorticity magnitude is greater than the magnitude of rate of strain, if \(q\) is greater than 0. The expression of \(q\)-criterion is [33]:  
\[q_{criterion}=w_{x}^{2}+w_{y}^{2}+w_{z}^{2}-\frac{1}{2}e_{i,j}e_{i,j} \tag{6}\]  
where:\(e_{i,j}=\frac{1}{2}\Big{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\Big{)}\)  
As can be seen from Figure 13, the value of near-bed \(q\)-criterion at the center of the dam gate is counterclockwise in all cases 7A, 7B, 8A, 8B, 9A, 9B, in all computational times. The larger the opening width is, the greater the \(q\)-criterion gets. Regarding the largest breach width, the \(q\) value is clockwise, since \(t>0.6\) occurs in both positions of the dam gate. This means that light swirling flow occurs at the dam mouth at an early stage of dam-break flow. In addition, vortex flow in case 9A appears more clearly in time than that of 9B, which seems to grow fast at the end of the first 5 s of dam-break flow. The \(q\) value is 9.11 1/s\({}^{2}\) and 1.62 1/s\({}^{2}\) for former and latter, respectively. On the other hand, Figure 14 indicates minor differences in \(q\)-criterion contour maps between models 8A and 8B. The largest area of negative value appears at two sizes of dam gate, which means swirling flow occurs strongly at these positions. However, when water depth increases, these areas reduce and nearly vanish near free surface (Figure 15). So, vorticity occurs more strongly at the near-bed of the dam gate's position than at near surface. Figure 12: \(f_{x}\)–vortex core strength at early stage of dam breach at \(t=1.5\) s. (**a**) 7A, (**b**) 7B, (**c**) 8A, (**d**) 8B, (**e**) 9A, (**f**) 9B. Figure 13: Near–bed \(q\)–criterion at the center of dam gate. Solid line–asymmetrical dam gate; dot line–symmetrical one.
# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
## 1 Introduction
Secondly, an effective method is raised to produce higher-resolution feature maps, simultaneously utilizing low-level features and high level features, which is very critical to enable us to detect small remote sensing objects. Thirdly, we leverage the context information enclosing an object proposal during the training process to further boost the small object detection performance. Finally, we present a simple yet effective approach, called 'random rotation', to augment our available optical remote sensing data while applying a sampling strategy to solve the problem of non-uniform class distribution during training.
## 4 Discussion  
The experimental results show the proposed low-cost HIL architecture effectively validating nonlinear control strategies for mobile robotics. It uses a Lyapunov-based Controller on an ECU-RBP integrated with a DT-RBP and the physical P3-DX robot via a wireless network. The control system consistently performs across DT and physical platforms, as evidenced in Figures 8a and 12a. Both platforms accurately track the trajectories even with velocity disturbance. Velocity profiles in Figures 8b and 12b confirm the DT's dynamic behavior mirrors the physical robot, validating the DT model's fidelity and the controller's adaptability.  
Figure 12: (**a**). Comparison of reference and followed trajectories, by DT-RBP (red line) and P3-DX (yellow line) (**b**). Output linear and angular velocities obtained from the real robot and its DT.  
Figure 13: Comparison of pose (position and orientation) errors once the control signals are applied from the ECU-RBP to the DT-RBP (blue line) and to the P3-DX (red line). S-form trajectory experiment.  
The implemented control architecture integrates both controller and plant (real and DT) through a wireless network, addressing not only theoretical challenges but also practical considerations that affect overall system performance. Firstly, although the plant model derived through a system identification process (as shown in Figure 3) includes inherent delays due to the wireless communication channel, these delays are not embedded in the DT-RBP model. This omission is intentional, as delays are already present in the current communication between the Electronic Control Unit (ECU-RBP) and the Robot-Based Platform (DT-RBP) and duplicating them would result in an unrealistic control response within the HIL context. Secondly, the wireless communication channel used during the experimental validation exhibited non-deterministic behavior. The observed variability in transmission delay led to fluctuations in the system response, highlighting the importance of accounting for such non-idealities when transitioning from simulation to physical implementation. Thirdly, minor adjustments of control gains were required for approaching control errors, from simulation to emulation with DT, and finally to implementation on the physical robot. As it is known, the behaviour of the process under control is slightly time variant due to changes in physical factors such as battery charge level, wheel pressure, or mechanical backlash, among others, as well as environmental conditions like floor surface and wireless signal quality. These practical aspects contribute to the slight discrepancies observed between the responses shown in Figures 8 and 9. Despite these differences, the overall performance remains consistent and validates the proposed control strategy.
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 4 Experiments
### Analysis of Results
#### 4.3.4 Different Prototype Set Selection Methods  
In this part, we compare the effects of several different prototype set selection methods on the experimental results.  
Figure 5 compares three different methods of prototype set selection, namely random algorithm, the K-means algorithm, and the DPC method for DM-CPDP. For the random method, researchers generally select 3%-10% of the dataset [27]. When using the random method to select the prototype set, we select \(r\) samples as the prototype set, \(r=\log I\) (\(I\) is the number of the instance), and repeat the results 10 times to take the mean. When using the K-means method, we also select \(r\) samples as the initial cluster center for clustering and select the output cluster center as the prototype set.  
It can be seen from the box plot that the density-based prototype selection method used in this paper is better than the random selection method and the K-means clustering method. In Figure 5, the density-based prototype selection method used by the DM-CPDP has higher value of median, quartile, maximum, and minimum on the F-measure and AUC than the other two methods. These cost-effectiveness values for density-based prototype selection method are lower than other methods. However, the K-means clustering method performs the worst, so this method is not suitable for prototype selection.  
The reason for this problem is that the K-means algorithm is not suitable for solving non-spherical clusters and is greatly affected by outliers. Although the average effect of random selection is ideal, the prediction results are unstable due to the randomness of the instance selection. Therefore, it is reasonable to use the DPC method for prototype selection.