esa-sceva/satcom-chunk-collection huggingface dataset:
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
###### Abstract  
This study proposes an event-triggered fault-tolerant active disturbance rejection control (ADRC) method for variable-load quadrotors with prescribed performance. The quadrotor, as a nonlinear and underactuated system, faces challenges such as payload variations, actuator faults, and external disturbances, which degrade trajectory tracking accuracy and stability. The proposed approach integrates a cascaded ADRC framework, decoupling the system into position and velocity subsystems, each equipped with extended state observers (ESOs) for real-time disturbance estimation and compensation. To enhance robustness, prescribed performance functions dynamically constrain tracking errors within predefined bounds, while event-triggered mechanisms reduce computational load through condition-based updates of control signals. Additionally, a particle swarm optimization (PSO) algorithm is employed for online parameter tuning, improving adaptability. Theoretical analysis confirms the system stability, and simulation results demonstrate the controller effectiveness in handling actuator faults and variable payloads, ensuring accurate trajectory tracking and reduced resource consumption. The method offers a promising solution for robust and efficient quadrotor control in complex environments.  
active disturbance rejection control; particle swarm optimization algorithm; variable-load quadrotor; trajectory tracking control; event triggered; fault tolerant Article  
29 May 2025  
Revised: 20 June 2025  
Accepted: 20 June 2025  
Published: 22 June 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON] Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance. _Appl. Sci._**2025**, 15,7021. [[https://doi.org/10.3390/app15137021](https://doi.org/10.3390/app15137021)]([https://doi.org/10.3390/app15137021](https://doi.org/10.3390/app15137021))  
Copyright: 62 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods
### Analysis Methods  
#### 2.3.1 Temporal-Spatial Analysis  
Using the dynamic grid analysis method, the GPP and precipitation frequency data were extracted based on annual land cover types; then, the interannual trend and \(p\) value were calculated using the Mann-Kendall trend test in the R language package \"Kendall\". To eliminate anomalies from satellite sensor noise, the rate of temporal variation was calculated by robust regression at a statistical significance level of 0.05. A robust linear regression was built between time (year) and GPP values; slope and \(p\) value were calculated at a 0.05 confidence level in R language with the package \"robust\".
###### Abstract  
District heating and cooling networks based on trigeneration systems and renewable energy technologies are widely acknowledged as an energy efficient and environmentally benign solution. These energy systems generally include back-up units, namely fossil-fuel boilers and electric chillers, to enhance system flexibility and cover peak energy demand. On the other hand, 4 th generation district heating networks are characterized by low-temperature water distribution to improve energy and exergy efficiencies. Moreover, reversible heat pumps are a versatile technology, capable of providing both heating and cooling, alternately. In this paper, the integration of reversible heat pumps as single back-up units in hybrid renewable trigeneration systems serving low-energy micro-district heating and cooling networks is investigated. A detailed modeling of the system is provided, considering part-load and ambient condition effects on the performance of the units. Size and annual operation of the proposed system are optimized in a case study, namely a large office building located in Pisa (Italy), by means of a genetic algorithm-based procedure. A comparison with the conventional trigeneration system is performed in terms of economic and environmental perspectives. Results show that the integration of reversible heat pumps is an economically viable solution capable of reducing by 7% the equivalent annual cost, increasing the installed power of renewables up to 23%, and lowering by 11% carbon dioxide emissions, compared to the energy system with conventional back-up units.  
reversible heat pumps; trigeneration; renewable energy; district heating and cooling; smart energy systems; optimization Article
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination
#### 2.5.5 Fractal Dimension  
The fractal dimension is used to describe a signal's self-similarity and fractal structure, reflecting the signal's complexity and regularity [42].
## 2 The Proposed Long-Distance, Real-Time LED-DCC System
### Lightweight, Back-Forth, Fast Image Processing Algorithm
Appropriately increasing \(l_{init}\) can improve the positioning speed of the LED display area, and reducing \(l_{small}\) can improve the positioning accuracy of the LED display area. In our system, the value of \(l_{init}\) is set as 5 and the value of \(l_{small}\) is set as 1. * LED-DCC data bit extraction. Once the vertical and horizontal boundaries of the LED lighting-emitting areas are detected, the pixel coordinates of the corners of the LED-DCC emitting areas are easily obtained. Thus, the coordinates of the LED-DCC light-emitting area are quickly detected through the relative position offset based on the coordinates of the pixels in the upper left and lower right corners, and the y values of all pixels in the LED-DCC area are integrated. Then, the symbol value of the LED-DCC clustered points is determined by the Sauvola-based adaptive binarization method [15], converting the y value of the LED-DCC clustered points into the bit. Finally, ABRC decoding is conducted to recover the transmitted data.
# The Significance of IL-36 Hyperactivation and IL-36R Targeting in Soriasis
## 4 IL36R Function in Psoriasis Immune Cells  
IL-36R function on inflammatory responses evoked by immune cells is supported by numerous studies [4; 47]. Initially, a high expression of IL-36R mRNA levels was found in human Langerhans cells and dermal CD1a\({}^{+}\) DC, together with CD14+ monocytes and CD11c+ mDC [19; 53]. In these studies, the authors demonstrated that the immune cells strongly respond to IL-36\(\beta\) stimulation by upregulate surface expression of specific activation markers, such as CD83, CD86, and HLA-DR, and by inducing the production of inflammatory cytokines, such as IL-1\(\beta\), IL-12, IL-23, IL-6 and TNF-\(\alpha\), but also chemokines, including CCL1, CXCL1 and GM-CSF in an IL-36R-dependent manner. Additionally, IL-36-matured DC strongly induced T cell proliferation [19; 53] (Table 1).  
Regarding T lymphocytes, in mouse IL-36R is primarily found on the surface of naive CD4\({}^{+}\), but not CD8\({}^{+}\), T cells, suggesting that IL-36\(\gamma\) play a pivotal role in initiating the immune responses [54]. In these cells, IL-36s synergize with IL-12 to drive a potent Th1 response [55]. Unlike mouse cells, human CD4\({}^{+}\) and CD8\({}^{+}\) T cells do not express IL-36R nor respond to IL-36 either, when resting or under CD3/CD28 stimulation [19] (Table 1).  
In addition to DC and T cells, IL-36 cytokines affect the function of macrophages, known to contribute to psoriasis pathogenesis [53]. An interesting characteristic of macrophages is their ability to switch from a pro-inflammatory M1 phenotype to an anti-inflammatory M2 phenotype, and thus participate sequentially in both the induction and the resolution of inflammation [56; 57]. Human dermal macrophages express high levels of IL-36R, and IL-36 cytokines act particularly on the anti-inflammatory M2 macrophages, driving them to a pro-inflammatory M1 phenotype [53; 54] (Table 1). Interestingly, [PERSON]'s group has recently demonstrated that IL-36R activation by IL-36\(\gamma\) induces the production of psoriasis-associated cytokines (i.e., IL-23 and TNF-\(\alpha\)) from human macrophages, and that this response is enhanced in macrophages from psoriasis patients [23]. This effect is specific for IL-36\(\gamma\) and could not be mimicked by other IL-1 family members, such as IL-1\(\alpha\)[23].  
Furthermore, IL-36\(\gamma\)-stimulated psoriatic macrophages potently activated endothelial cells and led to increased adherence of monocytes, which release IL-23, thus promoting polarization of lymphocytes with an increased IL-17/IL-22 expression [23]. Finally, neutrophils do not express IL-36R complex (Table 1). However, IL-36s indirectly contributes to their recruitment and activation in psoriatic skin, by inducing the release of the neutrophil chemoattractant molecules, such as CXCL8, CXCL1, CCL20, and LCN2, by epidermal keratinocytes [13; 50].  
As a whole, IL-36R activation may have a central and pivotal role in both early and chronic phases of psoriasis development, by acting at the interface of innate and adaptive immunity.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 2 Background
### Requirements for the Secure Documentation of Critical Maritime Traffic Situations  
From the above considerations, the following requirements were derived for the development of a system to securely document critical maritime traffic situations for usage in smaller incident investigations and insurance cases:  
1. The system must be able to record arbitrary sources of data.
2. The system must provide evidence that the data was recorded during the incident and was not modified.
3. The system must support decentralized usage such that every involved party can protect the sovereignty of their data.  
* It must be possible for a participant to dynamically join a network with other participants to agree on the common data recording of a potentially critical situation (e.g., berthing of a ship or an evasive maneuver).
* If not all the involved participants are equipped with the system, a fallback mechanism must exist such that data is still being recorded by the remaining parties with a minimal loss of trust.
# Review of Studies on Joint Recovery of Macroalgae and Marine Debris by Hydrothermal Liquefaction
## 3 Prospects for Joint Processing of Macroalgae Biomass and Marine Debris
### Hydrothermal Liquefaction of Polymers and Their Mixtures
This is mainly due to the following: firstly, the higher temperature promoted the gasification reaction; secondly, at higher temperatures, polyethylene will envelop the paper, reducing porosity and preventing heat and mass transfer [45]. A research group from Aarhus University evaluated the prospects of using the HTL process for processing a wide range of polymers [46]. The process was carried out at 350 \({}^{\circ}\)C for 14 min in the presence of a KOH catalyst of 17.2 g/L. The main findings of the study regarding polymers typical of marine debris can be summarized as follows:  
1. PET behaved very different from others, since upon addition of a catalyst the formed insoluble terephthalatic acid transformed into a soluble salt, increasing the COD of the aqueous phase [46]. 2. PVC was the only polymer that exhibited carbonization reactions rather than liquefaction reactions. The addition of alkali did not lead to significant differences in the  
Figure 2: The results of polymer processing by the hydrothermal liquefaction: I—oil yield, %; II—Solid Phase Yield, %; III—Total Organic Carbon in the Aqueous Phase, g/L. Plymers: 1—Polybutylene terephthalate; 2—Polycarbonate; 3—PET; 4—Polylactate; 5—Polymethyl methacrylate; 6—Polyoxymethylene; 7—Polyphenylene oxide; 8—Polyvinyl acetate; 9—Styrene butadiene. *—no data. yields of oil and char; however, a large difference was noted in the volumes of the gas phase. Treatment of PVC in the presence of alkali gives an increased gas yield due to the release of Cl\({}_{2}\) amount. Solid residues of PVC were strongly decchlorinated, which indicates that this fraction can be further used as a carbon source [46]. 3. In general, each type of synthetic polymer exhibits its own depolymerization characteristics at HTL, which creates opportunities and challenges for future applications for net and mixed streams. Short exposure time of subcritical HTL was not efficient for polyolefins and polystyrene, however it could be useful for polymers, which contain heteroatoms. A group of scientists from Pennsylvania State University assessed the effect of the processing time (0.5 h, 1 h) and temperature (350-450 \({}^{\circ}\)C) on the yield, composition, and quality of oil obtained as a result of HTL of polypropylene, polystyrene, polycarbonate and polyethylene terephthalate. The maximum oil yield for each material varied from 16 wt% for PET to 86 wt% for polystyrene. Depolymerization of plastics occurred fastest in supercritical water (T > 400 \({}^{\circ}\)C). Longer reaction times and higher supercritical temperatures reduce the fuel yield but increase its calorific value (HHV) [47].
# The Risk Assessment of the Security of Electronic Health Records Using Risk Matrix
## 4 Background and Context  
There is a need to protect patients' records, as they hold private information such as their date of birth, addresses, phone numbers, financial details, and sensitive medical history. Security of such information is paramount, as such data can lead to catastrophic consequences for patients once leaked. People have been blackmailed using their medical records, while others have lost their money after their credentials were used to access their bank accounts. EHRs need to be secure. Technological advances provide various solutions that can be utilized to protect such data.  
A total of 20 research articles were chosen for the study. A risk matrix was chosen to assess the risks posed in each article, and the level of each risk, and its severity. This matrix helps determine the best security strategy, mainly focusing on high-risk causes.
## 6 Simulation Results  
We evaluate the theoretical results derived in the previous sections by considering a system identification scenario. The unknown MISO system, described by the model of (11), was composed of \(P=5\) impulse responses, each one formed with \(M=7\) independent random values between \(-1\) and one. The input signal was generated by means of a first-order autoregressive model, whose transfer function is \(\sqrt{1-\alpha^{2}}/\ \left(1-\alpha z^{-1}\right)\), with \(\alpha=0.8\), fed with an i.i.d. Gaussian random process. The length of the input signal was \(L=20\),000 samples. An additive i.i.d. noise signal \(v\left[n\right]\) with variance \(\sigma_{v}^{2}=0.01\) (i.e., providing 20 dB of SNR) was added to form the desired signal. We took into account a combined MISO filter to identify the unknown MISO system. We might differentiate the two individual MISO filters of the combined scheme according to the step-size values or to the projection orders.  
The theoretical EMSEs were achieved by considering (24), involving (45) and related approximations for individual EMSEs and (59) and related approximations for the cross-EMSE. The measured steady-state EMSEs were achieved according to:  
\[\mathrm{EMSE}\left[n\right]=\mathrm{E}\Big{\{}\big{(}e\left[n\right]-v\left[ n\right]\big{)}^{2}\Big{\}} \tag{63}\]  
evaluated over 50 independent runs. We also report the measured EMSEs of the two individual MISO filters.
## 2 Critical Infrastructures (CIs)  
Critical infrastructure, as defined by [15], encompasses systems that consist of industries, organizations, and distribution capabilities that provide a continuous flow of essential services vital to the defense and economic security of society. These systems are often referred to as lifeline systems. Critical infrastructure incorporates various systems such as electric power systems, telecommunications, water treatment and supply, natural gas supply, transportation systems, and healthcare systems [16]. These infrastructures do not exist independently; instead, they are interconnected, giving rise to the term \"interdependent critical infrastructures\" (ICI). ICIs encompass all interconnected critical infrastructures utilizing different connection models, such as physical, geographical, cyber, and virtual connections.  
The interconnected nature of ICIs amplifies the need for a comprehensive understanding of how these infrastructures interact with each other. This understanding can help identify potential vulnerabilities and risks associated with their interdependence. In turn, this awareness can lead to the development of more resilient systems that can withstand various threats and maintain their functionality during crises or emergency situations.  
Moreover, as the world moves towards an increasingly digitized and connected environment, the interdependence of critical infrastructures becomes even more pronounced. This shift creates new challenges and opportunities for enhancing the resilience of these interconnected systems. By developing robust and adaptable resilience metrics and models, stakeholders can better manage and mitigate the potential impacts of disruptions on critical infrastructures, thereby ensuring the rapid recovery of essential services and the well-being of society as a whole.
## 5 Multi-Parameter Coupled Coordination Model
### Optimal Value Intervals of Parameters  
The coupling coordinations of the three parameters in Table 6 were highly coordinated; the coupling degree was high, and the coupling type was the coupling coordination period. The main girder parameter was the main influencing parameter among the three parameters, and main girder stiffness was larger when the diagonal cable parameter and length of the cable-free zone in the span were smaller values, which enhanced the overall performance of the structure. The diagonal cable parameter was slightly more important than the length of the cable-free zone in the span; the design calculation should first determine the main girder stiffness and establish the design of the diagonal cable parameter, and finally determine reasonable arrangement of the cable-free zone. Coupling coordination between the main girder and the cable stiffness was highly coordinated, and the range of variations of cable stiffness were not as large as those of the main girder stiffness and there were various values to choose from. The highly coordinated cable stiffness parameters obtained from the current calculation were larger than the original design value, implying that the original design can be further optimized in terms of cable parameters. Coupling coordination between main girder stiffness and cable-free zone in the span was mainly moderate. The coordination degree of coupling between main girder stiffness and cable-free zones in the span was mainly medium coordination. Stiffness of the main girder was larger when the cable-free zone was the original design value, which improved the structural performance. Main girder stiffness can be increased when structural force is optimized. The coordination degree of coupling between diagonal cable stiffness and cable-free zone in the span was medium to high coordination. There were various choices of the length of the cable-free zone when the same diagonal cable stiffness was designed, and the two exhibited synergistic effects.  
Combining Tables 5-10 and research outcomes in Section 3, for the background project, setting the stiffness of the main girder between \(E_{\text{G}}{}^{\prime}I_{\text{G}}/E_{\text{G}}I_{\text{G}}\) = 1-3, stiffness of the stayed cable between \(E_{\text{C}}A_{\text{C}}{}^{\prime}/E_{\text{C}}A_{\text{C}}\) = 0.8-1.4, and the length of the cable-free zone in the span between \(I_{1}/I_{0}\) = 0.08-0.15 improved the span capacity and overall stiffness of the bridge, and cable tower displacement and support reaction forces of the middle pivot point can be kept within a reasonable range. The three parameters studied include the main girder stiffness as the primary parameter, stayed cable stiffness, and length of the cable-free zone in the span.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 2 Materials and Methods
### Quantization Analysis
#### 2.1.2 Problem Brought by Quantization  
The output of the laser IMU is the number of quantized pulses; the quantization process is shown in Figure 1. The measurement \(\mathbf{Z}\) is divided by the equivalent pulse \(\Delta\), which is the numerical value of the angular velocity corresponding to one pulse. The integer part is the measurement output \(Q\), and the remainder part accumulates to the next sampling period. The error between \(Q\times\Delta\) and \(\mathbf{Z}\) is the quantization error. Taking a gyroscope as an example, the pulse equivalency is set to 1\({}^{\prime\prime}\) (4.8 \(\times\) 10\({}^{-6}\) rad) and the sampling period T is 20 ms. When the angular increment in the sampling period is less than one pulse equivalent, the output of the cycle would be 0. The angular increments within the k period would be accumulated to the k + 1 cycle for outputting. The output of pulse equivalency is equal to pulse equivalency 1\({}^{\prime\prime}\)/T = 50\({}^{\circ}\)/h in the sample period. That is to say, after quantization, the instantaneous error is likely to be more than 50 times, which would cause great difficulties in fault detection.  
The quantization causes the problem that quantization errors make it difficult to detect fault correctly. The fault-tolerant air data inertial reference system (FT/ADIRS) developed by Honeywell is the most successful engineering product. The core component is a strapdown inertial reference system consisting of six GG1320 laser gyroscopes and six accelerometers (Hexad system) [23]. We take the hexad system as an example, which is shown in Figure 2. After experiments on quantization, we obtain the data on the inertial sensor, whose gyroscope bias is 0.05\({}^{\circ}\)/h. We add faults of different amplitudes to the 1 st gyroscope in 500 s. The simulation results are shown in Figure 3.  
Figure 1: The quantization process.  
Figure 3: Fault detection results. (**a**) Step fault 5\({}^{\circ}\)/h; (**b**) Step fault 100\({}^{\circ}\)/h.  
Figure 2: The hexad system configuration (2\(\alpha=63^{\circ}26^{\prime}5.8^{\prime\prime}\)).  
Observation matrix:  
\[\mathbf{H}=\left[\begin{array}{cccc}0.52573&0&0.85065\\ -0.52573&0&0.85065\\ 0.85065&0.52573&0\\ 0.85065&-0.52573&0\\ 0&0.85065&0.52573\\ 0&0.85065&-0.52573\\ \end{array}\right] \tag{8}\]  
As Figure 3a shows, the function value does not exceed the threshold value in the whole fault detection phase when the amplitude of fault is \(5^{\circ}/\mathrm{h}\). However, when the amplitude of fault increases to \(100^{\circ}/\mathrm{h}\), the fault can be detected with this method. At this time, the detection performance is essentially the same as the measurement not quantified. That is to say, the quantization error reduces the fault amplitude by about 20 times with the equivalent pulse. This paper develops the three-channel filter to solve the problems brought about by quantization.