esa-sceva/satcom-chunk-collection huggingface dataset:
applied sciences  
Automatizing Automatic Controller Design Process: Designing Robust Automatic Controller under High-Amplitude Disturbances Using Particle Swarm Optimized Neural Network Controller  
**[PERSON]**  
Software Engineering, Afyon Kocatepe University, Afyonkarahisar 03200, Tufkiye; [EMAIL_ADDRESS]; Tel.: +90-505-561-81-72  
**Featured Application: Featured Application: Automatic control of electrical motors and robotic systems actuated with electrical motors are the main featured applications of this study but are not the limit. Any closed-loop automatic control system with known plant dynamics can be a featured application of this study.**  
In this study, a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, the design of an optimal neural network-based controller is performed automatically. Several common reference types are studied including step, square, sine, sawtooth, and trapezoid functions. Expected reference-disturbance pairs are used to train the system for finding optimal neural network controller parameters. A separate test set is used to test the system for unexpected reference-disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. The real DC motor's parameters are estimated using a particle swarm optimization (PSO) algorithm. Initially, a proportional-integral (PI) controller is designed using a PSO algorithm to find the simple controller's parameters optimally and automatically. Starting with the neural network equivalent of the optimal PI controller, the optimal neural network controller is designed using a PSO algorithm for training again. Simulations are conducted with estimated parameters for a diverse set of training and test patterns. The results are compared with the optimal PI controller's performance and reported in the corresponding section. Encouraging results are obtained, suggesting further research in the proposed direction. For low-disturbance scenarios, even simple controllers can have acceptable performance, but the real quality of a proposed controller should be shown under high-amplitude and difficult disturbances, which is the case in this study. The proposed controller shows higher performance, especially under high disturbances, with an 8.6% reduction in error rate on average compared with the optimal PI controller, and under high-amplitude disturbances, the performance difference is of more than 2.5 folds.  
automatic control; artificial intelligence; controller growing; neural network-based controller; disturbance rejection; robust control; DC motor
## 3 Results
### Effects of Various Soil Properties on Displacements
#### 3.1.3 Cohesion  
Figure 7 illustrates the effects of various soil cohesion, which was recorded to have a major impact to tunnel displacements. Cohesionless soil, and soil with cohesion of 10 kPa and 20 kPa were labeled as C0R, C10R, and C20R in reverse faults, and C0N, C10N, and C20N in normal faults. One of the significant changes was the sharp increase in tunnel displacements (from 2 to 22 mm) in C10N, when the vertical component reached 60 mm.  
The results showed that higher soil cohesion can dramatically decrease the tunnel displacements. For instance, 27% of increment was exhibited in tunnel displacements when cohesion changed from C20R to C10R. The results can be discussed in reference to previous studies that showed that a higher cohesion increases the shear strength and the stability of soil [37; 38]. In cohesionless soil, the results showed that the soil became deformed very easily, even with small displacements. Furthermore, in any project, it is important to know that cohesion increases with depth, and each layer has a different cohesion level [24].  
Figure 6: The effect of particle size (D\({}_{50}\)) in reverse and normal faults.  
Figure 7: The effect of cohesion in reverse and normal faults.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.1.3 K-Nearest Neighbors (KNNs) and Regression Trees (RTs)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} **ML** \\ **Technique** \\ \end{tabular} & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{8}{*}{SVMs} & High accuracy and \multicolumn{1}{c}{} & Computationally intensive & Water quality prediction \\ \cline{2-4}  & Works with nonlinear, \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ non-separable data & Sensitive to kernel choice & Wastewater classification \\ \cline{2-4}  & Efficient in \multicolumn{1}{c}{} & Challenging \multicolumn{1}{c}{} & \\ high-dimensional spaces & hyperparameter tuning & MBR fouling modeling \\ \cline{2-4}  & Strong theoretical \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ foundation & Requires expert knowledge & \multicolumn{1}{c}{} & \\ \cline{2-4}  & Robust to outliers & Poor with imbalanced data & \multicolumn{1}{c}{} & \\ \cline{2-4}  & Interpretable (DTs) & \multicolumn{1}{c}{} & \\ \cline{2-4}  & Scalable, real-time, fast \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ (LightGBM) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{2-4}  & Reduced overfitting (RFs) & \multicolumn{1}{c}{} & \\ \cline{2-4}  & High predictive accuracy \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ (XGBoost, LightGBM) & \multicolumn{1}{c}{} & & \\ \hline \multirow{8}{*}{KNNs and RTs} & Non-parametric, flexible \multicolumn{1}{c}{} & Computationally \multicolumn{1}{c}{} & \\ (KNNs) & expensive (KNNs) & Classification/regression \\ \cline{2-4}  & High precision, outlier \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ tolerance (KNNs) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ (KNNs) & \multicolumn{1}{c}{} & & \\ \cline{2-4}  & Interpretable models \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ complex patterns (RTs) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{2-4}  & Robust to overfitting and \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ noise & \multicolumn{1}{c}{} & & \\ \cline{2-4}  & \multicolumn{1}{c}{} & & \\ \end{tabular}
\end{table} 
Table 3: Advantages, disadvantages, and applications of ML models in wastewater treatment optimization processes. Deep Learning Models (DL, Subfield of ML) Applied in the Optimization of Wastewater Treatment Processes
## 3 Results and Discussion
### Quantitative Analysis of Test Results
#### 3.5.1 Elastic Modulus  
As can be seen from Table 4, the elastic modulus of recycled concrete decreased when steel fibre and polyvinyl alcohol fibre were incorporated. The reason for this is that the air-entraining effect of the polyvinyl alcohol fibre increased the porosity of the recycled concrete and therefore reduced the elastic modulus of the recycled concrete, while the larger size of the steel fibre, which requires more cement paste to be wrapped, reduced the compactness of the matrix and created more interfacial weak zones within the concrete, which reduced the elastic modulus.  
Based on the elastic modulus equation presented in [27], a non-linear regression analysis was carried out with the experimental results to obtain the elastic modulus of steel-polyvinyl alcohol fibre recycled concrete as a function of the fibre characteristic values and the uniaxial compressive strength, as shown in Figure 15. In Figure 15, the red dots are the spatial coordinate points corresponding to the elastic modulus, the fibre characteristic value, and the uniaxial compressive strength of the test samples, and the spatial surface was obtained from the regression analysis of the test points. These points were more evenly distributed on the top and bottom sides of the spatial surface. As the fibre characteristic value increased, the elastic modulus of the recycled concrete gradually decreased.  
\[E_{\text{c}}=\frac{10^{5}}{2.2+97.54/f_{\text{c}}}(1-0.365\lambda_{\text{sf}} )\Big{(}1-0.020\lambda_{\text{pf}}\Big{)} \tag{6}\]
# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 3 Methodology
### Cross-Modal Feature Fusion and Vision-Constrained CRF Prediction Layer
#### 3.3.1 Cross-Modal Feature Fusion  
Through a visual signal-guided cross-modal interaction mechanism, we deeply integrate the semantic-enhanced global alignment capability of the SAGC module with the local fine-grained spatial alignment advantages of the MS-SLC module, achieving hierarchical complementarity between text and image features.  
Specifically, we first concatenate the text-visual global alignment features and local alignment features: \(T_{\text{dual}}=[T_{\text{SAGC}},T_{\text{MS-SLC}}]\in\mathbb{R}^{n\times 2d}\). These are then normalized to generate a visual prior mask, compelling the model to focus on high-confidence alignment regions: \(A_{\text{mask}}=\text{Softmax}[S_{g};S_{\text{MS-SLC}}]\in\mathbb{R}^{n\times 2}\). This mask is subsequently input into a cross-modal attention mechanism, where the text acts as the query, and the visual features serve as the key and value, with the attention weights guided by the visual prior mask:  
\[H_{\text{cross}}=\text{CrossModalAttention}(Q=T_{\text{dual}},K=V_{\text{all} },V=V_{\text{all}};A_{\text{mask}}), \tag{12}\]  
where \(V_{\text{all}}\in\mathbb{R}^{m\times d}\) denotes all visual features (\(m=1+k+hw\)).  
Finally, we perform dynamic gated fusion of the cross-modal interaction features \(H_{\text{cross}}\in\mathbb{R}^{n\times d}\) and the original text features \(c_{i}\):  
\[T_{\text{fuse}}=g\cdot H_{\text{cross}}+(1-g)\cdot c_{i},\quad g=\sigma(W_{g} [H_{\text{cross}};c_{i}]), \tag{13}\]  
where \(W_{g}\in\mathbb{R}^{2d\times 1}\) is a learnable parameter matrix, and \(\sigma\) represents the Sigmoid function, producing the gating weight \(g\in[0,1]^{n\times 1}\).  
This gating design mitigates the noise issues arising from fully connected interactions in traditional cross-modal attention; when the visual signal is reliable, \(g\) approaches 1, allowing significant contribution from the cross-modal features. Conversely, when the image is irrelevant to the text or heavily noisy, \(g\) nears 0, causing the fused features to predominantly consist of the text features, with minimal contribution from the cross-modal features.
## 3 RFID Security and Threats
### RFID Security Threats  
Although RFID-based systems are tremendously used currently, several security threats are confronted in their implementation. These can be faced at the physical level or the communication level. This section presents the most common threats that can harm the RFID system and may lead to some serious breaches that reduce user information privacy. Data alteration, ID cloning, communication interruption, and tag tracking are some attacks that need in-depth consideration to address them:  
* **Tracking:** This is known as the act of reading RFID tags without the proper authorization by the use of a considerable number of RFID readers to gather their identifiers, and these identifiers can be personal credit card numbers [10].
* **Counterfeiting:** This attack manipulates the tag, where a smaller amount of information is needed. Here, circumventing the security mechanisms utilized is the main objective of the counterfeiting threat [10].
* **Eavesdropping:** This attack is based on saving the read intercepted communication with the intention to be re-used for analysis and as a baseline for another type of attack such as tag cloning attacks [7].  
* **Tags cloning:** Its major purpose is to duplicate a reliable tag as a copy to be used for unauthorized access to the reader's information with the intention of extracting data to be stored in another tag. Tag cloning leads to several damages such as the manufacturer's reputation and some serious financial losses [7].
* **Physical attacks:** Its main concern is to tamper with the tag physically by damaging one of its components or disrupting its normal performance by glitching the tag's clock or changing the transmitted radio frequencies, and we can mention side channel and timing attacks [10].
* **DoS attack:** The is a denial of service, where the intruder tries to take the tag out of service. Consequently, no information will be leaked or occupied. However, it reduces the RFID system's efficiency and faithfulness. The concept of realizing a DoS attack is to interfere with the signals of the channels used for the tags' radio frequency communications [10].
## 5 Conclusions  
This paper proposes a modified Hammerstein-like (MHL) model which consists of an input time delay, a rate-dependent hysteresis term, and a linear dynamic term in a cascaded structure, effectively describing the dynamic characteristics of FSM systems over a wide frequency range. Furthermore, a composite control approach tailored for the established model is introduced. The presented controller consists of a feedforward compensator and a proportional-integral (PI) controller. The former is the inverse of the rate-dependent term in the MHL model, while the latter is used for closed-loop regulation. Experimental results validate the effectiveness and feasibility of the proposed modeling and composite control methods. This study provides novel and effective methods for modeling and compensating piezoelectric systems, with potential applications in a wider range of fields. In future work, we will explore the application of advanced control algorithms to construct feedback controllers with the goal of achieving greater efficient composite control performance.  
K.C.: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, and Review and editing; Z.L.: Methodology, Investigation, Validation, Data curation, and Writing--original draft preparation; G.H.: Methodology, Conceptualization, and Review and editing; R.L.: Investigation and Methodology; J.Z.: Conceptualization and Investigation; J.M.: Project administration, Funding acquisition, and Supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program under grant 2022 YFC2204203 and in part by National Natural Science Foundation under grant 61503096. The authors declare no conflicts of interest.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 7 Investigation of the Surface Adsorption Energy  
Based on the adsorption energy (\(\varepsilon\)), which is related to the pressures at half saturation (P\({}_{1}\)) and (P\({}_{2}\)) of the substance, we determined the site energy distribution using the formalism of Polanyi potential [38; 39].  
\[\text{P}=\text{P}_{\text{vs}}\text{e}^{\frac{-\varepsilon}{\text{P}_{1}}} \tag{7}\]  
Figure 9: Effect of temperature on the CO\({}_{2}\) adsorption energy (\(-\varepsilon_{1}\) and \(-\varepsilon_{2}\)) of the Na-ZSM-5, Mg-ZSM-5, La-ZSM-5 and Zn-ZSM-5 adsorbents.  
The tested isotherms Q\({}_{a}\)(P) can be described versus \(\epsilon\)and becomes Q\({}_{a}\)(\(\epsilon\)) by incorporating Equation (7) in Equation (3). Afterwards, the site energy distribution F(\(\epsilon\)) is given by identifying the isotherms Q\({}_{a}\) (e) versus \(\epsilon\).  
\[\text{F}(\epsilon)=\frac{\text{d}\text{Q}_{a}}{\text{d}\epsilon} \tag{8}\]  
Figure 10 presents the adsorption energy distributions (AED) determined at the three temperatures for the CO\({}_{2}\) adsorption on M-ZSM-5 (M= Na\({}^{+}\), Mg\({}^{2+}\), Zn\({}^{2+}\), La\({}^{3+}\)). From this figure, we can observe that the majority of (AED) curves present two peaks. Indeed, these peaks define the equilibrium energies of the heterogeneous sites. Despite the identical shape of AED curves at different temperatures, we noticed the difference of energy distribution intensities, corresponding to the maxima peaks. The higher intensity of energy is observed for La-ZSM-5 at T = 273K, confirming the difference structure in the various tested zeolites. The increase in temperature leads to the decrement in energy distribution values explaining that the receptor sites with weak energy become fewer available for CO adsorption. The van der Waals interactions responsible for this process are confirmed by the AED values, which ranged from 10 to 25 KJ/mol for the four tested substances [35; 36; 37]. Consequently, this energy distribution interpretation confirms the type of interactions and shows that the four tested samples are characterized by a heterogeneous energy surfaces.  
Figure 10: Site energy distributions of CO\({}_{2}\) adsorption onto (**a**) Na-ZSM-5, (**b**) Mg-ZSM-5, (**c**) La-ZSM-5 and (**d**) Zn-ZSM-5 at the three tested temperatures.
## 3 Methodology
### Performance Evaluation  
Accuracy, precision, recall, F1-score, and area under the characteristic curve (AUC) metrics have been used to evaluate the classification performance of the pre-trained models. These metrics include the definitions of true positive (_TP_, the number of positive instances correctly predicted as positive), true negative (_TN_, the number of negative instances correctly predicted as negative), false positive (_FP_, the number of negative instances correctly predicted as positive), and false negative (_FN_, the number of positive instances correctly predicted as negative).  
_Accuracy_ is a widely used metric to assess classification performance. It is the ratio of correct predictions to all predictions and is calculated by Equation (1).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
_Precision_ is the ratio of correct positive predictions to all positive predictions. It is formulated as Equation (2) and used to measure the accuracy of positive predictions.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
_Recall_ (True Positive Rate, TPR) is the ratio of correct positive predictions to all positive instances. It measures the ability to correctly expose true positive instances, and its formula is as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\(F1\)_-score_ is the harmonic mean of the _Precision_ and _Recall_ values, as provided in Equation (4). This composite metric is a robust evaluation metric for especially imbalanced datasets.  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
AUC is the area under the ROC (Receiver Operating Characteristic) curve. ROC curve plots the TPR (y-axis) against the _False Positive Rate_ (x-axis). A larger area (close to 1) indicates better classification performance.  
\[False\ Positive\ Rate\ (FPR)=\frac{FP}{FP+TN} \tag{5}\]
## 4 Methodology
### The 9-Parameter Transformation  
Affine 9-parameter transformation consists of three translations and three rotations around three coordinate axes and three scale factors, which are different for all coordinate axes (Figure 7). Because of the non-unique scale factor, this transformation does not belong  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Standard Deviation** \\ \hline \(t_{x}\) (m) & \(512.173\) & \(0.571\) \\ \(t_{y}\) (m) & \(152.010\) & \(0.365\) \\ \(t_{z}\) (m) & \(529.617\) & \(0.901\) \\ \(\mu_{xy}\) (ppm) & \(-1.788\) & \(0.053\) \\ \(\mu_{z}\) (ppm) & \(-12.464\) & \(0.120\) \\ \(r_{x}\) (\({}^{\prime\prime}\)) & \(-5.587\) & \(0.011\) \\ \(r_{y}\) (\({}^{\prime\prime}\)) & \(-3.129\) & \(0.019\) \\ \(r_{z}\) (\({}^{\prime\prime}\)) & \(11.510\) & \(0.010\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of 8-parameter transformation from HDKS to HTRS96.  
to the group of conformal (similarity) transformations. With the change of angles, there is also a change of position, direction, size, and shape of the transformed object.  
General expression for the 9-parameter transformation is [8]  
\[\mathbf{X}_{o}=\mathbf{t}+\mathbf{R}\cdot\mathbf{S}\cdot\mathbf{X}_{i} \tag{64}\]  
or, in matrix form,  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{o}=\begin{bmatrix}t_{x}\\ t_{y}\\ t_{z}\end{bmatrix}+\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}\cdot\begin{bmatrix}u_{x}&0&0\\ 0&\mu_{y}&0\\ 0&0&\mu_{z}\end{bmatrix}\cdot\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{i} \tag{65}\]  
In Equation (65), the abridged form of the rotation matrix is written, and the complete form of the rotation matrix is given in Equation (39).
## 3 Results  
### Article Selection and Characteristics  
The article selection process is summarized in Figure 1. Updating [PERSON] and colleagues' [11] search yielded 335 new results. After initial screening against their eligibility criteria, 11 new articles were added to their original 54 [11], yielding 65 potential articles for review. Application of our refined eligibility criteria relating to RCTs' sample size and year of publication finally resulted in 25 articles being included in the current systematic review: 8 from the updated search and 17 from their original dataset.
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 3 Mobile Node
### Overview  
The UIS is a static WSN. As such, it provides useful information on an area of interest, since it can be deployed with speed and flexibility to adapt to each case requirements. But once the network is deployed, the nodes stay at the same location. Obtaining additional information requires installing supplementary Transmitter nodes, and depending on the distance, also Receiver nodes, due to the limited range of ZigBee communications. Some use-cases require a different approach, such as the study of gas emissions. A significant amount of emissions in urban areas is linked to motor vehicles. Obtaining up-to-date information about the levels of some gases can be very relevant for traffic  
Figure 2: Different types of nodes developed for the Urban Information System.  
managers. But deploying a sensor network large enough to cover a whole city might be impractical. A way of obtaining the relevant data that traffic managers require is by means of sensors directly installed on vehicles. But some other applications require also the capability to measure environmental data in an adaptable fast way, such as in disaster robotics, where the collaboration with humans or dogs can be limited by the level of some gases. For those applications requiring additional flexibility in the acquisition of information, a hybrid version of the UIS network has been developed.  
These features can be achieved by transforming the static WSN into a H-WSN by adding a Mobile node. The next sections describe the Mobile node architecture and implementation, and its integration with the WSN.