esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 2 Design of Proposed PIDD\({}^{\bullet}\) Controller
### FOPID Controller  
The FOPID controller utilises fractional calculus principles in its design and implementation to provide a more flexible and robust control method than traditional PID controllers. It combines non-integer orders of differentiation and integration. To comprehend frac  
Figure 2: Block diagram of PIDD2 controller.  
tional calculus, it is essential to establish the fundamental principles and approximation techniques for resolving fractional differential equations. The differential operator, which combines fractional differentiator and integrator, is widely utilised. The equation provided below defines this operator.  
\[\mathcal{D}_{t}^{q}f(t)=\begin{cases}\dfrac{d^{q}}{dt^{q}}f(t),&q>0,\\ f(t),&q=0,\\ \int_{0}^{t}f(\tau)d\tau,&q<0,\end{cases} \tag{6}\]  
where \(q\) is the fractional order.  
The Riemann-Liouville definition is popular among the definitions in the literature [19; 42; 43; 44]. The fractional order of order \(q\), denoted by \(D^{q}\), is given by  
\[D^{q}[f(x)]=\dfrac{1}{\Gamma(n-q)}\dfrac{d^{n}}{dx^{n}}\int_{a}^{x}\dfrac{f(t) }{(x-t)^{q+1-n}}dt, \tag{7}\]  
where \(f(x)\) is a sufficiently well-behaved function defined on the interval \([a,b]\), \(n\) is the smallest integer greater than \(q\), \(\Gamma\) is the gamma function, and \(\dfrac{d^{n}}{dx^{n}}\) represents the \(n\)th derivative of the function with respect to \(x\).  
The Riemann-Liouville fractional derivative extends the classical derivative to non-integer orders. The integral in the definition represents the memory effect of the function \(f(x)\) on its fractional derivative, where the order \(q\) determines the degree of memory.  
The FOPID controller comprises proportional, integral, and derivative components that require a corresponding gain value to function effectively. The controller employs fractional-order parameters, \(\beta\) and \(\mu\), to adjust the integral and derivative terms, enhancing the system's stability, transient response characteristics, and robustness. The fractional integral represents the curve's projection onto a plane, with the fractional integration order acting as a weight that influences the system's responsiveness and steady-state errors [19].  
\[C(s)=\dfrac{U(s)}{E(s)}=\dfrac{U(s)}{R(s)-Y(s)}=K_{p}+\dfrac{K_{i}}{s^{\beta}}+ K_{d}s^{\mu}. \tag{8}\]  
In the above equation, \(K_{p}\), \(K_{i}\), and \(K_{d1}\) are the proportional, integral, and derivative constants. The parameters \(\beta\) and \(\mu\) represent the degree of non-integer integration and differentiation used in the controller, respectively, where \(\beta>0\) and \(\mu<2\)[43]. Figure 3 shows the block diagram for the FOPID controller where all five parameters can be tuned. Notice that a regular PID controller is achieved by setting both \(\beta\) and \(\mu\) to be 1. By utilising non-integer values for the integral and derivative terms, the controller can simulate more complex and non-linear systems in control systems.  
Figure 3: Block diagram of FOPID controller.
# Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
## 2 Background
### Maritime SOC Adoption
While it has been noted to be improving [43], offshore connectivity remains a challenge compared to shoreside connectivity. M-SOCs therefore have to prepare for both potential bandwidth limitations and technical integration challenges that may impede their ability to conduct real-time vessel monitoring. The vessel itself is, at any one time, a fusion of both static and dynamic attributes. The static attributes are most notably represented by the multiple vessel types that exist in operation, each of which is unique in its design [44]. M-SOCs would also have to deal with the fact that vessels are constantly in motion, meaning that contextual information plays a big role both in the type of alerts seen and the volume. The use of contextual information in the incident response process is not limited to M-SOCs but is rather a common routine in SOC operations and has been applied by SOCs in other domains. A 2023 SANS SOC survey [45] highlighted the lack of context as one of the greatest barriers to the full utilization of SOC capabilities. Contextual information in the maritime domain plays a critical role during the incident response process, mainly because the asset being monitored is in constant motion, which exposes it to a greater scope of threats. For example, one of the contextual information categories that could be utilized is the vessel's geographical location, a dynamic attribute that could be defined as either open ocean, coastal port, harbor, restricted waterways, or inland waterways [46]. Connectivity challenges and dynamic vessel states are some of the key attributes that make M-SOC operations a unique undertaking. Therefore, to fully understand M-SOCs, these maritime-specific attributes cannot be ignored.
## 3 Frame Optimization Design
### Topology Optimization of Frame  
#### 3.2.1 Topology Optimization Settings  
1. Design variable: Unit density of the frame space;
2. Establish responses: Create responses such as weighted comp and mass, respectively;
3. Constraint condition: The mass of the frame design space is limited to 0.3-0.5, which means that the solved material mass accounts for 30-50% of the original mass (placement penalty factor);
4. Establish objective function: The optimization objective is flexibility, which is equivalent to the displacement formed by a unit force at that point in data, while stiffness is equivalent to the force required to cause a unit displacement at that point in data. Therefore, the objective function sets the minimum flexibility as the maximum stiffness;
5. Minimum and maximum member sizes: The minimum size needs to be greater than three times the size of the grid unit, and the maximum member size needs to be greater than two times the minimum member size. The measured unit distance is 13.15 mm, so the minimum size min-dim is set to 50 mm and the maximum size max-dim is set to 100 mm;
6. Symmetric constraint: The frame structure is symmetrical about the longitudinal plane. Since only the degrees of freedom of the left front suspension are fixed in the load step, a symmetric constraint about the xoz plane is applied in the design variables in order to simultaneously consider the right front suspension.  
Based on the above settings, establish a topology model, as shown in Figure 9.
## 5 Future Directions
#### Towards Implementation: A Proposed Protocol for AR Integration in Medical Education
Particular attention will be paid to usability, AR experience quality, and overall perceived value in clinical education. Based on the feedback, the course content and AR interface will be iteratively improved. This may include technical enhancements (e.g., better control mechanisms,smoother animations), pedagogical adjustments (e.g., pacing, case complexity), or additional learning tools (e.g., voice commands, interactive quizzes). Phase 4: Curriculum Integration and Scaling  
Following successful pilot implementation, the course will be formally integrated into the medical curriculum. Depending on institutional preferences and logistical factors, it may be offered as an elective module, an embedded component of the cardiology course, or as a stand-alone session within the clinical skills lab. Long-term goals include expanding the AR-based teaching approach to other medical disciplines, such as pulmonology, nephrology, or emergency medicine. Interprofessional learning opportunities, involving students from nursing or medical technology programs, may also be developed. Scientific evaluation and publication of outcomes are planned to share findings with the broader medical education community. Presenting results at academic conferences and applying for funding through educational foundations or innovation programs will help support further development, scalability, and technical advancement of the project. The introduction of an AR-supported teaching module for cardiovascular education represents an innovative advancement in medical training. By combining patient-specific 3D heart models with interactive case-based learning, this approach enhances anatomical understanding and strengthens clinical reasoning. With thoughtful implementation and rigorous evaluation, the project has the potential to become a scalable and transferable model for AR integration across multiple areas of medical education.
## 2 Materials and Methods
### Bioinformatics for Bisulfite Amplicon Sequencing  
Raw bisulfite sequencing data firstly underwent quality assessment with FastQC version 0.11.7 [31] and was trimmed with FastP version 0.19.6 [32] to remove adapter sequences, contamination and low-quality fragments. Mapping was carried out with Bismark version 0.7.0 [33] to a customized genome built with GRCh38 and spiked-in Lambda DNA sequences. Alignments were performed with Bowtie2 version 2.3.4 aligner [34] with default parameters allowing 0 mismatch in a 20 bp seed.  
The bisulfite conversion efficiency (%) was calculated as 100 - non-conversion ratio (%). The non-conversion ratio was calculated by dividing the number of reads corresponding to non-converted methylated cytosine residues by the total reads (non-converted methylated + converted unmethylated cytosine) for each cytosine residue on Lambda DNA, as follows:  
Rnc (%) = Nmc/(Nmc + Nc) \(\times\) 100  
Rnc; non-conversion ratio; Nmc; number of reads corresponding to methylated cytosine; Nc, number of reads corresponding to unmethylated cytosine.  
We further investigated symmetric methylation (i.e., on both DNA strands) considering only methylated cytosines in the CpG context by calculating reads supporting methylation and the total read coverage on that cytosine. The fraction methylation for individual CpG sites was then determined from the ratio of total methylated cytosine (in a CpG context) reads to total reads covering that cytosine as follows:Fraction methylated cytosines = Total methylated cytosine sequencing reads/Total sequencing reads (forward plus reverse) covering that cytosine  
'BedSort' and 'bedGraphToBioWig' from UCSC genome browser 'kent' bioinformatics utility version 376 [35] were used to generate BigWig format data group files for visualization and analysis.
## 2 Error Sources Analysis of Antenna Holographic Measurement
### Holographic Measurement Theory  
As shown in Figure 1, it is a schematic diagram of the geometric relationship of the reflector antenna. According to the theoretical foundation of microwave holographic measurement, the far-field radiation pattern of the antenna can be expressed as [21]:  
\[T(\theta,\phi)=\iint\limits_{S}A\big{(}r,\phi^{\prime}\big{)}e^{j\delta(r,\phi^{ \prime})}e^{j\delta r\sin\theta\cos\left(\phi-\phi^{\prime}\right)}rdrd\phi^{\prime} \tag{1}\]  
where \(A(r,\phi^{\prime})\) is the aperture illumination function. \(r\), \(\phi^{\prime}\) is the polar coordinate \((r,\phi^{\prime})\) on the aperture surface, \(\delta(r,\phi^{\prime})\) is the phase error distribution function, \(k=2\pi/\lambda\), \(\lambda\) is the wavelength. In Figure 1, S represents the aperture area, \(p\) is the far-field observation point, and \(dS^{\prime}\) is the surface element.  
Transform Equation (1) into a Cartesian coordinate system [22]:  
\[T(u,v)=e^{-j2 kf}\iint\limits_{S}A(x,y)e^{j\delta(x,y)}e^{j\delta(ux+vy)}dxdy \tag{2}\]  
where \(u=\sin\theta\cos\phi\), \(v=\sin\theta\sin\phi\).  
The far-field pattern is related to the distribution of the surface shape error by the equation [23]:  
\[\varepsilon(x,y)=\frac{\lambda}{4\pi}\sqrt{1+\frac{x^{2}+y^{2}}{4f^{2}}}\times phase \Big{\{}e^{j2 kf}FFT^{-1}[T(u,v)]\Big{\}} \tag{3}\]  
where \(f\) is the focal length and \(FFT^{-1}[]\) is the two-dimensional inverse Fourier transform.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 5 Proposed Contention Model for Defence Against Wi-Fi Sensing  
The previous section showed empirically that the efficacy of WiFi-based keystroke recognition attacks is predicated on the underlying CSI data rate. In this section, we theorise how the CSI data rate can be reduced by intentionally injecting contention into the Wi-Fi channel. In this way, we formulate a contention-based model for preventing undesirable Wi-Fi sensing applications and safeguarding our privacy.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 6 Discussion
### Calibration  
Light-intensity distribution may vary at different locations within a room. The natural changes in the ambient light level within limits should be managed by the amplitude normalization step. For optimized performance, a calibration should be made if the system is moved to a new location where the ambient light range may be different. The calibration routine which could quickly cycle through parameters such as Tx LED amplitude, equalizer coefficients, Tx-amp bias, and Rx TIA tuning to find optimized values. Alternatively, a look-up table can supply the coefficients based on the location, time of day, and season. Aging of components and heating may also result in drift, which can be resolved by a relatively in-frequent calibration once a week. The calibration routine could also be executed automatically once the system is first switched on.
## 4 Discussion
### Utility of Including Sentinel-2 Spectral Indices  
The higher variability, in some S2-indices than others (Figure 5), supports the idea that these indices can distinguish soil surface conditions. For example, the NDVI and Maccioni indices, both developed to detect green vegetation [70; 82], showed different variation ranges. Since we selected only bare soils, we expected the Maccioni index to vary less than the NDVI, but this was not the case. This supports the assumption that the spectral regions of vegetation indices are sensitive to SOC content [33; 117]. Similarly, for NBR and NBR2, which were developed to detect burn severity, NBR varied more than NBR2. [PERSON] et al. [118] and [PERSON] et al. [119] found that NBR2 was correlated with the presence of crop residues, so they used it with NDVI to select bare soil to predict SOC [18; 30; 38]. In the present study, performance increased significantly when including S2-indices during calibration but not validation of DNN models. This may have been due to potential variation in correlation between Sentinel-2 bands and their derived S2-indices or the SOC content among dates depending on soil surface conditions. For example, [PERSON] et al. [33] observed that the correlation between Sentinel-2 spectral information and SOC content or soil texture varied among study areas.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 3 IoT Architectures for EW Systems
### Reference Architecture
#### 3.1.2 Communication Layer
Disasters can have a large impact on infrastructures and networks facilities, for example cutting off the affected region in case of antennas, optical fiber links, or overhead cables failures [27]; as such, redundant communication channels should be considered to ensure that working communication links are always available.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
###### Abstract
Given the above problems, this paper proposes a fault diagnosis model of PMSM inter-turn short circuits based on multi-scale space. The original data is denoised by VMD. The maximum product of correlation and kurtosis is taken as the objective function, and the grey wolf (GWO) algorithm [15] is used to optimize the modal decomposition layer and penalty factor of VMD. The decomposition signal is reconstructed by kurtosis, and the noise in the signal is filtered out. The PCA [16] is adopted to select the main features to remove redundant features and reduce dimensionality. The improved coarse-grained multi-scale feature extraction is employed to reduce the instability of the time-frequency domain feature results caused by the mutation at the breakpoint. Moreover, by exploiting the advantages of the whale algorithm (WOA) [17] in optimization, the number of hidden-layer nodes and the learning rate of the Bi-LSTM network are optimized, which effectively reduces the influence of the model parameters on the diagnostic accuracy and reduces the diagnostic error. In practical engineering applications, a new fault diagnosis method is proposed to make fault diagnosis more intelligent. The diagnostic process of this method is shown in Figure 1.
# Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
###### Abstract  
Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model's calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.  
supred-resolution reconstruction; generative adversarial networks; dense convolutional networks; texture loss; WGAN-GP 2020; 10.1729; doi:10.3390/app10051729
## 1 Introduction
designed a haptic cue vehicle accelerator pedal device by MRB, and a tactile cue algorithm to obtain the best gear shift was proposed [21]. In addition, different control strategies focus on improving the braking performance. [PERSON] et al. proposed an IFOA, and the simulation and experiment results showed that IFOA is superior to the conventional PID and FOA PID controller [22]. Basing on integration of FOA and PSOA, [PERSON] et al. proposed a combined braking approach of MRB [23]. Although many promising studies have been carried out on MRB, many problems are far from being solved. Firstly, the MRB still has some challenges in braking with large torque. Secondly, due to the influence of nonlinear factors, such as magnetic powder wear, hysteresis characteristics and system friction, the control of the braking system is not accurate. In this work, a novel MRB was proposed with EGWOA-based PID controller. Initially, a disc MRB was designed, and the structure of magnetic field was simulated, which proved that the designed structure could meet the requirements of transmission. Additionally, the dynamic model and transfer function of the MRB system were derived. Moreover, an enhanced grey wolf optimization algorithm (EGWOA) was proposed to adjust PID parameters. Compared with the original algorithm, the algorithm could find the optimal solution faster and with higher accuracy. Finally, the system was simulated by using the unit step signal as the excitation source in MATLAB. The experimental results indicated that the proposed controller has the merits of small overshoot and a quick response compared with other controllers. The remainder of this paper is organized as follows. In Section 2, the MRB is designed, and the transfer function of the system is obtained. In Section 3, the EGWOA is introduced and its feasibility is verified. In Section 4, the simulation example and experiments are carried out to verify efficiency of proposed controller. Conclusions and future works are summarized in Section 5.
## 3 Results and Discussion
### Fabricated Prototype and Measured Results  
The front and rear views of the fabricated prototypes of a proposed monopole SWB antenna printed using a DMP-2800 Dimitax Fujifilm conductive material printer on Rogers RT/Duroid 5880 TM material are displayed in Figure 11. The simulated and measured reflection coefficients, VSWR properties, and their enlarged views are shown in Figures 12 and 13, respectively. The performance of fabricated antennas is measured using an Agilent E5071B vector network analyzer (VNA), and Figures 12 and 13 demonstrate a strong similarity between the simulation and the experimental outcomes.  
Figure 11: Fabricated prototype: (**a**) front view, (**b**) rear view.  
Figure 10: Efficiency characteristics of the suggested antenna.  
Figure 9: Gain–characteristics of the suggested antenna.