esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 4 Discussion  
Firstly, a novel portable radar device has been developed to effectively improve Shoalhaven Water's WCN management procedures. A prototype device comprising of an 80 GHz short range radar sensor a low power, microcontroller and Bluetooth enabled device has been paired with a mobile application to offer an innovative pump testing method.  
This paper offers detailed results and analysis of testing the prototype solution at specifically selected sites within Shoalhaven Water's SPS network. This data yielded that this device is a much more accurate solution to the current manual drawdown method in all three initially proposed cases:  
(1) A standard DOL pump SPS,  
Figure 23: Daily signal statistics—Site B.  
Figure 22: Daily signal characteristics—Site B.  
A SPS with soft-starters to control the power ramp-up/down, and  
A SPS with VSDs to control the power ramp-up/down of each pump.  
Additionally, a single SPS pump fitted with a flush valve was tested, with supplementary data gathered in this pilot study propounding the view that this device is an innovative solution, currently rolled out into the field with relevant staff to undertake true Quality Assurance (QA) and Quality Control (QC) testing. SPUDD has proactively identified a number of performance issues within operational SPS before they have presented as severe problems. These include rising main blockages (chokes), damaged pump impellers and partially closed reflux valves.  
A low-cost method to monitor the wastewater levels within SPS collection wells and, indirectly, accurately estimate the corresponding pump flows, this device offers a practical engineering solution to not only effectively monitor SPS pump performance, but also act as a portable initial solution to monitor the gravity sewer system for extended periods.  
Further, a LoRaWAN network has been configured and deployed. Comprising of a single gateway and a number of prototype wireless sensor nodes, this paper has proven the effectiveness of LoRaWAN as a low power, wide area network for utilities in an urban environment.  
Covering a large portion of the Shoalhaven region with a single base station, a comprehensive radio channel ranging survey was undertaken to measure both signal strength and reception characteristics. Furthermore, a prototype end device utilising these LoRa modulation and LoRaWAN protocol stack has been installed within Shoalhaven Water's WCN to effectively monitor sewer levels and flows; offering a long range, low cost and low power alternative solution to that of a standalone sensor with 3/4G connectivity. This paper presents a real-world case study on the practical implementation of a LoRaWAN network to establish communications from remote sensors spread throughout a WCN, maintaining continuous monitoring. Whilst offering a number of utility-focused solutions in this paper, there is significant research being published around the world. With IoT and wireless sensor networks a current hot topic, a select sample of these works is reviewed in Section 5 below.
## 4 Results and Analyses
### LSM of SVM  
Software LibSVM3.22, which is a widely used SVM software library, was employed in this study [45]. Currently, there is a lack of information in most related studies concerning the SVM learning parameter estimation of C and Gaussian kernel width \(\gamma\)[46]. In this study, the optimization of parameters was conducted by the cross validation algorithm of software LibSVM3.22 using matlab2012b [45].  
The maps of ten influence factors with a grid cell size of \(30\times 30\) m were adopted to produce the landslide susceptibility maps. The training dataset included 9364 grid cells and each grid consisted of ten layers representing the landslide-influencing factors. As the prediction accuracy of the SVM depends on sample size, a model test was carried out by varying the sample size. Using the RBF kernel parameters, the best C and \(\gamma\) were determined as 2 and 0.5, respectively. The SVM model was constructed. Then the landslide susceptibility map (Figure 13a) for the study area was created, the closer the value is to 0, the smaller the possibility of landslide occurrence is; and the closer the value is to 1, the greater the possibility of landslide occurrence is. The landslide susceptibility index was ranked into five classes: (1) Very low (0 to 0.2), (2) Low (0.2 to 0.4), (3) Moderate (0.4 to 0.6), (4) High (0.6 to 0.8), and (5) Very high (0.8 to 1) (Figure 13b).  
Figure 14 and Table 4 are the statistical results of SVM, the number of landslides, landslide area density (LAD) and the landslide number density (LND). In the SVM model, the area of middle susceptibility class is 133.98 km\({}^{2}\), which accounts for 22.78% of the entire study area. The actual number of landslides is 587, accounting for 8.78% of the entire landslides, and the LAD and LND are 0.12% and 0.30 km\({}^{2}\), respectively. The area of low susceptibility class is 332.96 km\({}^{2}\), which accounts for 56.61% of the entire study area. The number of landslides is 103, accounting for 1.54% of the entire number of landslides, and the LAD and LND are 1.19% and 4.28/km\({}^{2}\), respectively.  
Figure 12: Histogram showing relative distribution and landslide occurrence of various susceptibility classifications of LR. LND is landslide number density. LAD is landslide area density.
## 2 Kirchhoff Pre-Stack Depth Migration Imaging Principle
### Applications of Model-Constrained Kirchhoff Pre-Stack Depth Migration  
The proposed model-constrained Kirchhoff pre-stack depth migration method is versatile and can be applied to a wide range of subsurface investigations--including groundwater flow modeling, hydrocarbon reservoir characterization, contaminant plume delineation, geothermal exploration, carbon capture and storage monitoring, engineering site assessment, and archeological prospection--where its ability to preserve true-amplitude responses and deliver high-resolution images of stratigraphic interfaces, fault networks, and small-scale heterogeneities leads to more reliable predictive modeling, resource quantification, and risk evaluation.
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 5 Test performance of the Optimal MR Damper
### Experimental Analysis of Damping Performance
Therefore, the simulation can effectively predict the damping characteristics of the optimal MR damper. Figure 22 shows that the dynamic range of the experiment and simulation of the optimal MR damper is in good agreement, but the experimental dynamic range of the initial damper is much larger than that of the simulation. This is mainly due to the inevitable error caused during the manufacturing and experiment. For instance, if a MR fluid leakage occurred during the test process, the experimental results would be lower than the simulation results at the field-off, while a negative correlation was observed between the dynamic range and field-off damping force under certain conditions. Figure 23 shows the comparison of the experimental damping force before and after the optimization. The damping force of the optimal MR damper is significantly better than that of the initial MR damper. The maximum output damping force of the optimal damper is 7247 N, while that of the initial damper is 4346 N, which was increased by 66.7%. Figure 21: Comparison of damping force between simulation and experiment: (**a**) initial; (**b**) optimal. Figure 22: Comparison of dynamic between simulation and experiment.
## 3 Results
### Outlier Elimination  
Regardless of the method used for modeling, data preprocessing co-operates to improve model performances [39]. Initially, faulty measurements characterized by the value \"\(-999.25\)\" were removed and, posteriorly, [PERSON]'s box plot method was applied to identify the outliers in the WOB, RPM, TOR, FLOW, and ROP datasets. The results are shown in Figure 2.  
As shown in Figure 2, only the WOB, RPM, and TOR variables presented outliers. The WOB's nonoutlier range varies between 1090.98 lb (minimum value) and 43.31 klb (maximum value) (Figure 2a). The nonoutlier RPM range varies between 109.36 rev/min (minimum value) and 160.68 rev/min (maximum value) (Figure 2b), while the nonoutlier range of TOR varies between 7.69 lb-ft (minimum value) and 19.00 klb-ft (maximum value) (Figure 2c). The dataset contained a total of 546 raw samples and, after processing, only 457 samples remained. Outliers were detected and omitted in order to improve data quality and to achieve a highly accurate model. Consequently, this led to an increase in the calculated correlation coefficients.  
Figure 2: Tukey’s box plot method: (**a**) WOB; (**b**) RPM; (**c**) TOR; (**d**) FLOW; (**e**) ROP.
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 1 Introduction
Therefore, the establishment of systematic and standardized data collection methodologies is urgently required to enhance the reliability and utility of UAV photogrammetry. This study aims to address the lack of standardized guidelines in the process of collecting image data using UAVs and applying SfM for diagnosing building exteriors. It systematically experiments with and quantitatively verifies the effects of various UAV shooting patterns (Circular, Close-up, Aerial, etc. ), key variables (Distance, Overlap Rate, Camera Angle, etc. ), and their combinations on the quality and processing efficiency of SfM results. Specifically, image data of actual buildings were collected for various shooting scenarios according to the designed experimental framework, and all collected data were processed using a specified and consistent SfM pipeline (e.g., utilizing the SIFT algorithm). Quantitative indicators such as reprojection error, final point count, computation time, and reconstruction completeness were calculated from the processing results, and the obtained data were analyzed using multiple regression analysis, nonparametric statistical tests, variable importance analysis, dimension reduction, clustering, response surface analysis, multi-criteria decision-making, and Pareto optimization. While this study focuses on common facade types under typical daylight conditions using standard commercial UAVs, its findings require careful consideration for highly reflective surfaces or extreme weather. The optimal data acquisition strategy derived from this study will provide concrete and reliable quantitative evidence that supports rational decision-making by balancing the quality of results required in practice with practical constraints (time, budget, etc. ), surpassing the limitations of existing empirical methods.
## 1 Introduction  
### Research Background and Motivation  
Blind source separation (BSS), defined as the task of separating the source signals from the observed signals when the source signals and the mixing mode are not known exactly, enables the separation of the mixed signals and the extraction of the source signals without a priori knowledge. As a kind of front-end technology in the field of signal processing, the BSS is of great importance in communication anti-jamming and improving spectrum utilization [1]. Unfortunately, the classical independent component analysis (ICA) and many extended algorithms [2; 3; 4] are based on the main assumption that the number of observed signals is greater than or equal to the number of source signals, which cannot realize the blind signal separation in underdetermined cases [5]. In practical communication scenarios, the number of observed signals is usually less than the number of source signals on account of system cost or environmental constraints, thus the study of underdetermined blind source separation (UBSS) is of more practical significance. At present, the main approach to tackle the issue of UBSS is based on sparse component analysis (SCA) [6]. The main strategy of the SCA method is solving the UBSS by exploiting the sparsity of the source signal or sparsity in the transform domain. However, with the dramatic increase in RF devices, it is almost inevitable that signals will be overlapped in the time and frequency domains. For communication signals that are not completely sparse in the time and frequency domains, it has been a challenge to solve the UBSS problem using the SCA method. It is of practical importance to separate the TF overlapped signals in underdetermined cases. This paper aims to improve the SCA algorithm for the effective separation of TF overlapped wireless communication signals with unknown source numbers.
# Effects of the Hybrid CRITIC-VIKOR Method on Product Aspect Ranking in Customer Reviews
## 2 Literature Review and Theory
### VIKOR Method  
The VIKOR method was originally developed by [39] as an MCDM approach for finding a compromise solution in the existence of multiple criteria. It has been utilized in many domains for ranking several alternatives based on a variety of decision-making criteria. For instance, the fuzzy-VIKOR multicriteria decision-making approach was utilized in the study of [40] to evaluate, select, and rank the best wheat suppliers in Jordan. In the study of [41], a novel methodology was introduced for the risk management of subsea pipelines by integrating CRITIC with VIKOR. The two MCDM methods, VIKOR and WASPAS, are used in the study of [42] on a silent diesel generator according to several criteria. Overall, the main concept of VIKOR is to use a multicriteria ranking index to select the compromise solution based on its closeness to the ideal solution [43]. The ranking index in VIKOR is derived by considering both the maximum group utility and the minimum individual regret of the opponent [43]. In this manner, the closeness of each alternative to the feasible solution can be determined.  
VIKOR Procedure
* Determine the best \(r_{j}^{+}\) and the worst \(r_{j}^{-}\) value for each evaluation criterion j, where \(r_{j}^{+}=max(r_{ij})\) and \(r_{j}^{-}=min(r_{ij})\).
* Compute \(S_{i}\), which represents the distance of the aspect to the positive ideal solution, and the value of \(R_{i}\), which expresses the distance of the \(i\) aspect from the negative ideal solution, as shown in Equations (7) and (8), respectively [44]. \[S_{i}=\sum_{j=1}^{n}w_{j}(r_{j}^{+}-r_{ij})/(r_{j}^{+}-r_{j}^{-})\] (7) \[R_{i}=max\ [w_{j}(r_{j}^{+}-r_{ij})/(r_{j}^{+}-r_{j}^{-})]\] (8)
* Compute the value of \(Q_{i}\), which represents the rating value for each alternative. Its formulation is given by Equation (9): \[Q_{i}=\frac{v(S_{i}-S^{-})}{S^{+}-S^{-}}+\frac{(1-v)(R_{i}-R^{-})}{R^{+}-R^{-}},\] (9) where \(S^{-}=min\ (S_{i})\), \(S^{+}=max\ (S_{i})\), \(R^{-}=min\ (R_{i})\), and \(R^{+}=max\ (R_{i})\). Furthermore, \(v\) is the weight of the maximum group utility, and \((1-v)\) is the weight of the minimum individual regret. The value of \(v\) is usually set to 0.5 [45].
* Finally, rank the alternatives based on \(Q_{i}\). The lower the value of \(Q_{i}\), the better the alternative.
## 3 Main Results
### Observer-Based Output Feedback Control with Event-Triggered Sampling
The analysis approach adopted in this study draws inspiration from the continuous-time high-gain observer analysis approach on the separation principle [46]. With bounded interference introduced, the observer does not impose any additional changes to the calculation of control feedback gain for the system. Furthermore, incorporating an event-triggered mechanism does not introduce any extra design burden to the system. **Theorem 2**. : _Let us consider that plant (1) observer-based output feedback control law (16), states \(r(t)\), and \(\dot{r}(t)\) will be practically convergent; this means that the states of (1) meet a certain limit bound by the controller (16)._  
Theorem 2 proposes the performance of the output-feedback control law, and the proof is presented in the appendix. **Remark 3**. : _The determination of a convergence interval is an inherent outcome in the design of output feedback control under bounded disturbances, and this outcome remains valid even with the incorporation of event-triggered estimation design._  
**Remark 4**. : _It is obvious that \(\limsup_{\ell_{i}\rightarrow\infty}\left\|e^{-\frac{\alpha\dot{\ell}_{i}}{2}( t-t_{0})}\right\|=\limsup_{\ell_{i}\rightarrow\infty}\left\|e^{-\frac{\lambda(1-r) \ell_{i}}{2\delta}(t-t_{0})}\right\|=0\) holds on, since that we get \(\limsup_{\ell_{i}\rightarrow\infty}\lVert W_{i}(t)\rVert=0\) from (A19). It means that we obtain a ultimate boundedness:_  
\[\limsup_{\ell_{i}\rightarrow\infty}\lVert\Theta_{i}(t)\rVert=0 \tag{23}\]  
**Remark 5**. : _Here, we propose a general condition for designing output feedback control based on the aforementioned event-triggered extended observer that can be applied to the system. Specifically, after extracting disturbances and event-driven effects, the estimated state information is utilized to design output feedback control, satisfying conditions (20)-(22), thereby ensuring the practicality and convergence of the system. The employed output feedback control method can be a commonly used design approach, and in subsequent design examples, we will also present a simplified design criterion._  
**Remark 6**. : _Decoupling the system is a commonly employed technique in spacecraft control system design. In this study, we propose an observer-based approach to address this issue, which effectively mitigates decoupling-induced errors by estimating and compensating for them using the extended state. The design parameters of each axis can be adjusted based on the specific control requirements of that axis. This article presents a general conclusion that convergence can be achieved by satisfying these requirements._
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## 4 Discussion
This shows the applicability of the proposed methodology for a broad field of research: it allows to study the dynamic behavior of multiple tissue types, such as the carotid artery, the heart and breast tissue, where very small displacements need to be captured, but also very large displacements and deformations can be present simultaneously. In this work, the proposed beamforming strategy was combined with cross-correlation-based ST. However, this strategy is also applicable to other pattern matching functions, for example the sum of absolute squared differences. Furthermore, while this work presents a methodology in 2D, it could be easily adapted to 3D. The system's PSF needs to be characterized in 3 dimensions and interpolation of the 3D CCF in 3 directions should be performed simultaneously. Zero-degree plane waves were used to acquire data in this work. The estimation of vessel wall displacements and blood flow velocities could be further improved by performing displacement compounding [40; 49; 62], a method introduced by [PERSON] et al. [63] and adapted for vascular strain estimation [64] and blood flow imaging [52]. Angled plane waves are used to derive the displacement or velocity vector, using solely axial displacement estimates. However, to improve the accuracy and precision of the estimates, often 2D ST is performed to acquire these axial displacements [62; 64; 65], meaning the accuracy of both the lateral and axial displacement estimates influence the performance of the compounding method. Therefore, displacement compounding would also strongly benefit from the methodology as presented in this work.
## 6 Experiments
### Ablation Experiments (Rq2)
#### 6.6.1 Analysis of our Diverse Model Stacking Ensemble in a High-Resource Scenario  
We developed a stacking ensemble of the aforementioned six base classifiers. All of these models were trained using the complete manually labeled training data of the benchmark datasets. The performance of the stacking ensemble model and that of the individual base classifiers on the AETD and ArPanEmo datasets is presented in Tables 8 and 9, respectively.  
The results obtained from both the AETD and ArPanEmo datasets clearly indicate that the stacking ensemble model effectively harnesses the strengths of all the base models when evaluated separately from self-training using the complete datasets of manually labeled data. When using transfer learning alone by fine-tuning the BERT-based model, a weighted average F1-score of 78.85% was achieved on the AETD dataset. On the other hand, training an emotion recognizer using the Naive Bayes algorithm and the AETD dataset resulted in a weighted average F1-score of 65.41%. However, the stacking ensemble of diverse models outperformed these individual models, achieving a weighted average F1-score of 80.77% on the AETD dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Weighted**} & \multirow{2}{*}{**Training Data (Size)**} \\ \cline{3-3} \cline{5-6}  & & **F1** & **P** & **R** \\ \hline Zero-Shot Classification & 32.08 & 33.60 & 38.45 & 34.92 & No-training \\ \hline Naive Bayes & 66.95 & 65.41 & 61.02 & 70.48 & \\ Logistic Regression & 65.62 & 63.51 & 58.42 & 69.57 & Entire AETD \\ Data Augmentation & 65.18 & 62.86 & 57.55 & 69.25 & dataset: \\ Embedding Lookup & 61.41 & 60.43 & 57.55 & 63.62 & 10,065 manually \\ BERT & 78.49 & 78.86 & 78.34 & 79.38 & labeled examples \\ \hline Stacking Ensemble & **80.26** & **80.77** & **80.94** & **80.59** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of the stacking ensemble model and base classifiers on the AETD dataset.  
The overall performance of the stacking ensemble model on ArPanEmo, as shown in Table 9, does not differ from that when using AETD dataset. The stacking ensemble model outperformed all six base classifiers, achieving a weighted average F1-score equal to 69.07%. This indicates that the meta-learner, which resulted from learning and combining the predictions of multiple base classifiers, yields superior results and improves predictive performance by leveraging the strengths of these base classifiers.