esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this article, a novel combination structure of the LQR and fractional integral controller (LQR-FIC) was developed to compensate for the non-minimum phase dynamics of Boeing 747 aircraft. A fractional-order integrator was employed as a pre-compensator to convert the longitudinal dynamics of the aircraft from the non-minimum phase dynamics to the minimum phase. Then, the LQR was implemented to find an optimal control law for the augmented dynamics. Nevertheless, the LQR optimal feedback gains and fractional integral parameters are simultaneously adjusted employing the proposed PSO optimization algorithm to create a more flexible combination of controllers with fractional order pre-compensators. Finally, the proposed controller was evaluated in the cases of fault-free flight conditions, internal stability problems, phase and gain margins, and for 50% and 80% losses of actuator effectiveness faults.  
In the current study, it is observed that in all implementations with fractional order compensators (FIC and FDC) and full state feedback (pole placement) control systems, the results proved all configurations under examination could ward off the adverse effects of non\(-\)minimum phase dynamics when analyzed in the time domain. However, as matter of fact, the frequency domain analysis revealed that a fractional-order integral pre-compensator represents more robust results with stabilized internal states compared with the fractional-order derivative pre-compensator and pole placement methods. Although the LQR-FDC (adaptive fractional\(-\)order derivative) and pole placement controllers produced promising results in the cases of faults, the open\(-\)loop dynamics exhibited non\(-\)minimum phase characteristics, which may lead to internal instability. The proposed control structure, namely adaptive fractional-order integral controller, LQR-FIC showed superior results in the time and frequency response analyses.  
Methodology, software implementation, A.S.E.; conceptualization, formal analysis, investigation, A.S.E. and S.N.E.; writing--original draft preparation, A.S.E.; writing--review and editing, S.N.E., A.A.P., M.M.R. and S.N.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work is produced out of the studies of [PERSON] under the advisory of [PERSON]. This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (D-145-135-1443). The authors, therefore, gratefully acknowledge DSR technical and financial support.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 3 Datasets  
### ACDC Dataset  
The ACDC challenge [4] provides a total of 150 short-axis cine-MRI volumes from both diastolic and systolic phases, of which 100 are available for training and validating, 50 being reserved for testing. In this dataset, segmentation masks are provided for the following heart substructures: right ventricle (RV), myocardium (Myo), and left ventricle (LV).
# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 2 Literature Review
They then applied the ANN for each criterion test. The results showed that a very high correlation coefficient of the neural network was able to identify suitable regions. In selecting the right region for a wind farm, [PERSON] [7] utilized the ANN by taking into account the criteria related to the profitability of the power plant and the assumption of public interest. Studies have shown that a properly trained neural network can process an automation area classification in terms of placement on wind turbines. Moreover, based on an assessment of the relationship between the environmental attributes of existing retail stores and their financial rankings, [PERSON] and [PERSON] [10] combined Google Places API and ANN to resolve the selection of the locations of retail stores within a given radius. They then applied this method to the location selection of a Turkish retail chain. They found that this method was highly applicable in similar situations. Although the above work, using the location selection method established using ANN technology, is valuable, it discerned the selected location for a region rather than a specific site selection. This selection needs to consider the situation of the trading area, including the site area's target market income, competition, and people flow, as well as whether the values of these metrics satisfy the expected performance after opening the store. It is notable that the factors considered in site selection entail a comprehensive analysis, but the information obtained in the aforementioned literature fails to provide such comprehensiveness. This situation may well explain why site selection continues to be predicated on experienced mangers' evaluation efforts. Current site selection processes thus need enhancement and marked improvement. Therefore, in this paper, we develop an objective site selection prediction model store expansion. The data from a chain of CVSs are used to verify the accuracy rate by using the established prediction model.
# PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach
## 5 Materials and Methods
### PK-Judge: Error-Corrected Cryptographic Ownership Verification  
PK-Judge integrates digital signatures, error-correcting codes, and a challenge-response protocol to establish cryptographically secure ownership of neural networks. As shown in Figure 3, a digital signature is encoded and embedded in the network parameters, preserving fidelity while ensuring that any bit errors introduced by fine-tuning, pruning, or other transformations are correctable.  
Figure 4 illustrates the verification workflow between the claimant owner and a judge evaluating an ownership claim over a watermarked model. First, the claimant reveals the extraction parameters so the judge can extract and ECC-decode the embedded digital signature from the model. If the extracted signature is valid under the public key, the judge proceeds by issuing a challenge message, which the claimant signs with their secret key. Finally, the judge verifies that challenge signature to conclusively confirm (or reject) the claimant's ownership of the model. Algorithm 1 outlines the entire process,  
```
0:\(m\): A designated message for ownership \(sk\): Owner's private signing key, \(pk\): Owner's public key \(t\): Error correction code parameters \(Cert\): Certificate associating the owner's identity with \(pk\) \(\kappa\): Embedding and extraction hyperparameters Param: Parameters for challenge-response verification
```
0: Embedding Phase:
1:\(M\leftarrow\text{hash}(m)\)
2:\(\sigma\leftarrow\text{Sign}(M,sk)\)
3:\(\psi\leftarrow\text{ECC\_Encode}(\sigma,\,t)\)
4:\(\theta_{w}\leftarrow\text{Embed}(\theta,\psi,\kappa)\) Extraction Phase:
1:\(flag\leftarrow\text{VerifyPublickKey}(Cert,pk)\)
2:if not\(flag\)then
3:return False, \"Invalid Certificate\"
4:endif
5:\(b^{\prime}\leftarrow\text{Extract}(\theta_{w}^{*},\kappa)\)
6:\(\sigma^{\prime}\leftarrow\text{ECC\_Decode}(b^{\prime},t)\) Ownership Verification Phase:
7:if VerifySignature\((\sigma^{\prime},pk)\)then
8:if\(\text{ChallRSPVerify}(\textsc{Param},sk,pk)\)then
9:return True, \"Verification Successful\"
10:else
11:return False, \"Additional Verification Failed\"
12:endif
13:else
14:return False, \"Signature Verification Failed\"
15:endif ```  
**Algorithm 1** PK-Judge: Error-Corrected Digital Signatures for Verifiable Ownership
## 3 Operational Requirements
### Technical Specifications
#### 3.4.10 Different Types of Soil  
For a fruit-harvesting robot to adapt to different types of soil, several key requirements must be considered to ensure optimal performance under various conditions:  
The robot needs a traction system that can adjust to different soil types, such as sand, clay, mud, or rocky terrain. This might involve using adjustable wheels, tracks, or suspension systems to provide adequate traction and prevent the robot from getting stuck or bogged down.  
To adapt its operation to different soil types, the robot should be equipped with sensors that can assess terrain conditions in real-time. Sensors like lidar, radar, or stereo cameras can help detect irregularities in the ground and adjust the robot's behavior accordingly.  
On uneven or unstable soils, the robot may require an advanced stabilization system to maintain balance and prevent tipping. This could include gyroscopes and motion control systems that automatically adjust the robot's position to keep it stable.  
The harvesting arm must be capable of operating effectively at different heights and angles depending on the soil type. This may require adjustments in the arm's height and angle, as well as grip strength, to handle fruit at various positions and elevations.  
The robot's navigation algorithms should be able to adapt to terrain variations. This might involve integrating advanced path-planning techniques and dynamic adjustments to ensure the robot can move efficiently across different types of soil.  
The robot's components, especially wheels, tracks, and traction mechanisms, must be designed to withstand wear and impacts associated with operation on hard, rocky, or wet soils. This ensures long-term durability and reliable performance in diverse conditions.  
The robot should have the ability to correct its trajectory and position in case of slippage or unforeseen issues caused by the soil type. Adaptive control systems and feedback mechanisms are crucial for continuously adjusting the robot's operation.  
These requirements ensure that the fruit-harvesting robot can adapt to various soil conditions, enhancing its versatility and effectiveness in the field.
## 3 Results  
### Risk Perception Groups and Socio Demographic Patterns  
The grouping of the respondents into the three aforementioned clusters showed the following distribution. Only about one-third (228) of respondents who claimed that they are concerned are enduringly concerned, i.e., report that they think, or think and talk about RF EMF risks in their everyday lives, while 548 are not enduringly concerned, and 931 are not and less concerned. The resulting distribution tree is displayed in Figure 1. A total of 102 respondents were excluded due to being not and less concerned yet still thinking about the topic (\(n=37\)), as well as the interviewees with missing answers in one of the three questions responsible for grouping or with contradictory responses (talking about EMF topic but not thinking about it) (\(n=65\)).  
First, the three risk perception groups were compared with regard to differences in socio-demographic patterns using age, gender, education and living area as independent variables in a [PERSON]'s ANOVA. Significant differences appeared only with respect to gender ([PERSON]'s F(2, 590.12) = 7.44, \(p=0.001\)). A subsequent Games-Howell post hoc test revealed differences comparing not and less concerned subjects with not enduringly concerned subjects (\(p=0.001\)) and comparing not and less concerned and enduringly concerned respondents (\(p=0.033\)). The proportion of men was significantly higher in the not and less concerned group.
## 2 Methodology
### Lightweight Super-Resolution Network Optimization Based on Channel Attention Mechanism
#### 2.4.2 Channel Attention Layer (CALayer)  
The channel attention mechanism is introduced to improve the network's capacity to recognize the features of important channels. This approach improves the relevance and efficacy of detail restoration by assisting the network in concentrating on the important information channels associated with artifacts.  
In the residual module, the channel attention mechanism extracts the global responses of each channel in the feature map through the global average pooling operation and then learns a set of normalized channel weights through a two-layer fully connected network. These weights reflect the ability of different channels to express the periodic information of the lattice structure. The attention mechanism enables the network to dynamically enhance the focus on key feature channels such as periodic stripes and structural intersection areas and suppress the response to fuzzy areas or redundant background information.  
In the sinogram of the lattice structure, the low-frequency channels often carry periodic skeleton information, while the high-frequency channels are more sensitive to boundary details and texture disturbances. The channel attention mechanism can adaptively assign weights according to the image content to enhance the expressive ability of effective features.  
Ultimately, the reconstruction results enhance the edge contour and local details while maintaining the integrity of the global periodic structure, achieving high-quality super-resolution reconstruction of the lattice structure sinogram. The following are the precise stages involved in implementation.  
Feature pooling: We enter the feature map \(F\in\mathbb{R}^{C\times H\times W}\). Global maximum pooling (GMP) and global average pooling (GAP) will yield two channel descriptors, respectively:  
\[f_{avg}=GAP(F)=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}F_{c,i,j} \tag{8}\]  
\[f_{max}=GMP(F)=\max_{i,j}F_{c,i,j} \tag{9}\]  
where, \(c=1,2,\dots,C\) represents the channel index.  
Shared multi-layer perceptron (MLP) transformation: Two descriptors, respectively, travel through MLP with shared parameters. MLP consists of two fully connected layers, and the number of nodes in the hidden layer is \(\frac{C}{r}\), where \(r\) is the reduction rate, generally given as 16:  
\[MLP(f)=W_{2}\cdot ReLU(W_{1}\cdot f) \tag{10}\]  
Co-fusion weight calculation: To create the final channel weights, we combine the two outputs and turn them on using Sigmoid:  
\[M(c)=\sigma(MPL(f_{avg})+MPL(f_{max}) \tag{11}\]  
Channel recalibration: To perform channel weighting, we multiply the weight \(M_{c}\) back to the original feature map:  
\[F^{\prime}=M(c)\odot F \tag{12}\]
## 3 Methodology
### Evaluation Indicators  
The quality of precipitation data is evaluated using two types of indices: quantitative and categorical. The quantitative evaluation index assesses factors such as rainfall magnitude and temporal distribution. It employs several measures, including relative deviation (_BIAS_), root mean square error (_RSME_), correlation coefficient (_CC_), and rainfall ratio (_RATIO_). Meanwhile, the categorical evaluation index focuses on the spatial distribution of  
Figure 4: Structure of the three-layer ConvLSTM.  
precipitation and uses probability of detection (_POD_), false alarm rate (_FAR_), threat score (_TS_), and missed alarm rate (_MAR_).  
\[BIAS=\frac{\sum\limits_{i=1}^{n}SAT_{i}-\sum\limits_{i=1}^{n}OBS_{i}}{\sum \limits_{i=1}^{n}OBS_{i}} \tag{10}\]  
\[RSME=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}\left(SAT_{i}-OBS_{i}\right)^{2}}} \tag{11}\]  
\[CC=\frac{\sum\limits_{i=1}^{n}\left(SAT_{i}-\overline{SAT}\right)\left(OBS_{i }-\overline{OBS}\right)}{\sqrt{\sum\limits_{i=1}^{n}\left(SAT_{i}-\overline{ SAT}\right)^{2}\left(OBS_{i}-\overline{OBS}\right)^{2}}} \tag{12}\]  
\[RATIO=\frac{\sum\limits_{i=1}^{n}SAT_{i}}{\sum\limits_{i=1}^{n}OBS_{i}} \tag{13}\]  
\[POD=\frac{TP}{TP+FN} \tag{14}\]  
\[FAR=\frac{FP}{TP+FP} \tag{15}\]  
\[TS=\frac{TP}{TP+FN+FP} \tag{16}\]  
\[MAR=\frac{FN}{FN+TP} \tag{17}\]  
where \(n\) denotes the number of observation data; _SAT_ and _OBS_ refer to GPM observation and rain gauge observation, respectively; _TP_ represents the number of accurately forecasted precipitation data; _FN_ is the number of missed reports; and _FP_ denotes the number of false reports. These variables are presented in Table 2. Specifically, _TP_ indicates the number of precipitation data observed by both the satellite and the rain gauge, whereas _FP_ refers to the number of precipitation data observed by the satellite but not by the rain gauge. On the other hand, _FN_ represents the number of rainfall data observed by the rain gauge but not by the satellite.
###### Abstract  
Mahalanobis distance (MD) is a well-known metric in multivariate analysis to separate groups or populations. In the context of the Mahalanobis-Taguchi system (MTS), a set of normal observations are used to obtain their MD values and construct a reference Mahalanobis distance space, for which a suitable classification threshold can then be introduced to classify new observations as normal/abnormal. Aiming at enhancing the performance of feature screening and threshold determination in MTS, the authors have recently proposed an integrated Mahalanobis classification system (IMCS) algorithm with robust classification performance. However, the reference MD space considered in either MTS or IMCS is only based on normal samples. In this paper, an investigation on the influence of the reference MD space based on a set of (i) normal samples, (ii) abnormal samples, and (iii) both normal and abnormal samples for classification is performed. The potential of using an alternative MD space is evaluated for sorting complex metallic parts, i.e., good/bad structural quality, based on their broadband vibrational spectra. Results are discussed for a sparse and imbalanced experimental case study of complex-shaped metallic turbine blades with various damage types; a rich and balanced numerical case study of dogbone-cylinders is also considered.  
Mahalanobis-Taguchi system (MTS); integrated Mahalanobis classification system (IMCS); Mahalanobis distance space; feature selection; classification; binary particle swarm optimization; nondestructive testing; vibrations
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 1 Introduction
Since the SAR signal obtained at plot scale could be also affected by the vegetation contribution and the surface roughness, several filters considering these effects were introduced in the proposed tree-based approach. The method was tested over irrigated plots in Catalonia, Spain and in Tarbes, South-west France.
# Therapeutic Hydrogel Lenses and the Antibacterial and Antibiotic Drugs Release
## 2 Materials and Methods
### HPLC Gradient Analysis  
The dexamethasone, betamethasone, bromophenacyl bromide and chloramphenicol-col contained in the pharmacological eye drops were quantified in the liquid phase upstream and downstream, and their interaction with the lenses was evaluated with high-performance liquid chromatography (HPLC) gradient analysis. The separation was carried out using an HPLC system consisting of a vacuum degasser, an autosampler, a capillary pump and a thermostatically controlled column compartment (Agilent series 1200, Agilent Technologies, Palo Alto, CA, USA). Briefly, five microliters of sample were injected onto a 1.0 \(\times\) 150 mm, 3.5-\(\mu\)m particle size Symmetry300 TM-C18 column (Waters). Mobile phase A was 0.1% formic acid in water; mobile phase B was 0.1% formic acid in acetonitrile. The flow rate was 20 \(\mu\)L/min, and the elution was performed in the following sequence:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Contact Lens** & **Characteristics** \\ \hline Contaflex 75\% (C75) & Noionic hydrogel lenses with 75\% water \\ \hline G72-HW & Ionic hydrogel lenses with 72\% water \\ \hline Balafilcon A & Silicone hydrogel lenses with 36\% water. \\ \hline
**Pharmaceutical Agents** \\ \hline Dexamethasone 0.1\% & Anti-inflammatory eye drops \\ \hline
0.2\% betamethasone 0.9 mg/mL & Anti-inflammatory eye drops \\ bromophenacyl bromide & Antibiotic eye drops \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the contact lenses and pharmaceutical agents.  
isocratic 90% A for 10 min, a linear gradient over the course of 50 min to 100% B, maintenance at 100% B for 15 min and, finally, a linear gradient to 90% A over the course of 5 min. The re-equilibration time in 90% A was 20 min. The eluent flow was directly sent to the electrospray ionization (ESI) ion source of a 6210 time-of-flight mass spectrometer (Agilent Technology) to characterize the HPLC peaks. The following operation parameters were applied: capillary voltage, 4300 V; nebulizer pressure, 20 psi; drying gas, 5 L/min; gas temperature, 300 \({}^{\circ}\)C; fragmented voltage, 250 V; skimmer voltage, 80 V; octopole RF, 250 V. The full-scan data, recorded using Agilent's Mass Hunter software, were processed with Mass Hunter Qualitative Analysis (Agilent Technology).  
We tested the 3 types of lenses by incubating the pharmacological compounds together for different times (0.5, 1, 2 and 24 h). We stratified them over the lens, which acted as a filter, with 100 microliters of eyedrops applied to the upstream side and 50 microliters of physiological solution applied to the downstream side. These volumes guarantee that solution did not dry, did not fix to vial walls and avoided exceeding dilutions hampering drug detection. The areas of the peaks of the compounds, normalized by volume, were measured.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 2 FRA, Frequency Bands, and Their Impact on Fault Detection
### Summary of Recent and Related Studies  
Table 1 illustrates the advancements in transformer fault diagnostics are presented by addressing gaps identified in previous studies. Earlier work lacked advanced AI techniques for accurately classifying partial discharges in noisy environments, did not utilize ML or deep learning for automated fault detection, and focused on limited fault types, such as forced buckling or disk space variation.  
The review of recent studies highlights several critical gaps in transformer fault diagnostics that this work seeks to address. Earlier research lacked the application of advanced AI techniques capable of accurately classifying faults like partial discharges in noisy environments, limiting the robustness of predictive maintenance strategies. Additionally, traditional methodologies often failed to utilize machine learning or deep learning models, which are better suited for interpreting complex patterns in FRA signatures. Previous studies primarily focused on specific fault types, such as forced and free buckling, neglecting critical faults like axial displacement, inter-turn faults, and contamination. Furthermore, data scarcity and limited test setups hindered the development of ML models tailored for interpreting FRA data. This study bridges these gaps by employing advanced ML models like Decision Trees and Subspace KNN, expanding fault classification to diversefault types, and creating a comprehensive annotated FRA dataset. These contributions enhance fault detection accuracy, address data limitations, and integrate ML directly into FRA diagnostics.