esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamics and Stability on a Family of Optimal Fourth-Order Iterative Methods
## 2 The Proposed Family  
In this section, we present particular cases of optimal fourth-order iterative methods for solving the nonlinear equation \(f(x)=0\), \(f:D\subseteq\mathbb{R}\rightarrow\mathbb{R}\), which was proposed in [9] by using the weight function procedure, the iterative expression of which is:  
\[\begin{cases}y_{n}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})}\\ x_{n+1}=x_{n}-\dfrac{f(x_{n})}{f^{\prime}(x_{n})}H(u(x_{n})),\end{cases} \tag{1}\]  
where \(u(x_{n})=\dfrac{f(y_{n})}{f(x_{n})}\) and \(H(u)\) is a real-valued weight function that achieves an optimal fourth-order convergence by using three functional evaluations. The following result indicates the necessary conditions that the weight function in question (1) has to fulfil so that the order of convergence reached is four [9].  
**Theorem 1**.: _Let \(f\) be a sufficiently differentiable function \(f:D\subseteq\mathbb{R}\rightarrow\mathbb{R}\) with a single-zero \(\xi\) on the open interval \(D\). Let \(H(u)\) be a real-valued differentiable function. If an initial approximation \(x_{0}\) is sufficiently close to the required root \(\xi\) of equation \(f(x)=0\), then the order of convergence of the scheme (1) is four when \(H(u)\) satisfies the following conditions:_  
\[H(0)=1,\quad H^{\prime}(0)=1,\quad H^{\prime\prime}(0)=4\quad\text{and}\quad \left|H^{\prime\prime\prime}(0)\right|<+\infty. \tag{2}\]  
_In this case, the error equation is_  
\[e_{n+1}=\left[\left(5-\dfrac{1}{6}H^{\prime\prime\prime}(0)\right)c_{2}^{3}-c _{2}c_{3}\right]e_{n}^{4}+\mathcal{O}\Big{(}e_{n}^{5}\Big{)}, \tag{3}\]  
_where_  
\[e_{n}=x_{n}-\xi\quad\text{ and }\quad c_{k}=\dfrac{1}{k!}\dfrac{f^{(k)}(\xi)}{ f^{\prime}(\xi)},k=2,3,4,\ldots\]
###### Abstract  
This paper investigates the issues of iterative learning algorithm-based robust thruster fault reconstruction and reconfigurable fault-tolerant control for spacecraft formation flying systems subject to space perturbations. Motivated by sliding mode methodology, a novel iterative learning observer (ILO) was developed to robustly reconstruct the thruster faults. Based on the fault signals obtained from the ILO, a learning output-feedback fault-tolerant control (LOF\({}^{2}\)TC) approach was explored such that the closed-loop spacecraft formation configuration was accurately maintained in the presence of space perturbations and thruster faults. Numerical simulations were employed to demonstrate the effectiveness and superiority of the proposed ILO-based fault-reconstructing approach and LOF\({}^{2}\)TC-based configuration maintenance approach for spacecraft formation flying systems.  
spacecraft formation; fault reconstruction; iterative learning observer; fault-tolerant control; learning output-feedback control Article
## 3 Standardization in Thermographic Research
In medical applications, thermal analysis mainly refers to the local skin temperatures, measured at fixed points/areas on the surface of the body, as well as to the mean skin temperature over the entire body surface, which can be estimated by calculating a weighted average of a series of local skin temperatures related to the surfaces that characterize it [32]. Thermovision is used in a dozen or more fields of medicine. Those most frequently described in the literature are vascular disorders, surgeries, rheumatology, neurological disorders, gynecology, traumatology, dermatology, ophthalmology, laryngology, endocranial disorders, and musculoskeletal disorders, including, in particular, the assessment of skin temperature changes for the diagnosis, localization, and assessment of the extent and intensity of inflammation in the examined tissue, based on the phenomenon of high thermo-emission as a result of local increased blood supply and tissue metabolism. Thermal imaging can detect joint inflammation in patients with arthritis. Arthritis has been diagnosed for centuries with the underlying symptoms of inflammation (pain, swelling, redness, and loss of function). An increase in temperature around the affected joint indicates exacerbation of inflammation. It is now known that the results of thermographic analysis strongly correlate with both the severity of the inflammation in the joints and the results of scintigraphy, which was confirmed both in human studies and in an animal model. It was found that infrared imaging can be used as a complementary method, especially in the early stages of the disease, as it can provide information on the dynamics of the pathophysiological processes in the joints [33; 34]. The correlation between thermographic images and the severity of the degenerative changes has also been documented [35]. The diagnostic potential of thermography is also indicated in spine pain of various origins including root pain [36], complex regional pain syndrome [37; 38], and fibromyalgia [39]. In non-specific MSD, a relationship between the Tsk changes and pain intensity was found [40]. [PERSON] and [PERSON] assessed the usefulness of thermography in muscle injuries in athletes. They found that thermal images are helpful in assessing the severity of an injury, especially in the acute phase, in monitoring its evolution, and in prognosticating healing and recovery [41]. Correlation was confirmed between the clinical, intraoperative, and thermal images in the case of supraspinatus and Achilles tendon [42]. It has been proved that in the case of scoliosis there is thermal asymmetry, mainly in the areas of the upper back, thigh, and back shank, and the high positive correlations of the angle of trunk rotation with the size of the thermal asymmetry occurs [43]. A clear and relatively wide area of interest of researchers related to rehabilitation and the use of objective tools for the assessment of therapeutic effects is the use of IRT in the assessment of the effectiveness of physiotherapeutic treatments.
###### Abstract  
The use of hydrocarbon fuels increases with population growth and rising standards of living, and so does natural gas flaring. Natural gas flaring is both a waste of natural resources and a violation of Nigeria's energy policy for sustainable development through natural gas conservation. However, it remains the most cost-efficient and effective associated natural gas (ANG) management option in developing countries such as Nigeria. The World Bank's initiative to eliminate routine gas flaring by 2030 has increased the need to limit or eliminate routine gas flaring. Often, studies on natural gas utilisation techniques fail to consider the lack of practical tools that integrate economic, technical, and regulatory factors into a gas flaring management framework, and the intricacies of existing tools, which often come at the expense of simplicity to achieve real-time information output. This paper aims to establish a framework and ANG management tool to reduce regular gas flaring in Nigeria. This research established a management framework (using a flowchart decision tree) and models to provide a user-friendly ANG flaring tool (using a MATLAB graphical front end user interface with back-end ASPEN HYSYS thermodynamic models). This was combined with techno-economic models for liquefied natural gas, gas-to-methanol, and gas-to-wire ANG utilisation options. The tool was then tested with data obtained from Fields Y and X in the Niger Delta region of Nigeria. The results, considering both economic and technical factors, showed that the choice of liquefied natural gas for Field Y was best due to its proximity to the pipeline infrastructure and its cost-effectiveness, and the availability of a high-demand LNC market for that area. For Field X, gas-to-wire was best due to its proximity to the electrical grid and high electricity requirements for that area. Additional geographical profiles in West Africa and ANG utilisation alternatives were recommended for further investigation. This paper developed and validated a one-of-a-kind ANG flaring management tool that incorporates techno-economic analysis of selected ANG utilisation options to assist operators and investors in making more profitable investment decisions.  
associated natural gas (ANG); gas flaring; gas flaring reduction; ANG utilisation options; ANG flaring management
## 5 Results and Discussion
### C-1: Construction of the FBN Using CPr Plus FPr  
To develop the FBN, a cause-and-effect relation was established, and CPr and FPr were fed to the GiNIe software 5.0 [53], as shown in Figure 7. Using this innovative approach, the model delineates accident scenario modeling based on CPr from RTO data and FPr calculated by experts. All twenty-six BEs lead to TEs through IEs, showing the likelihood of a power grid shutdown as a TE. Here, it is evident that the significant factors leading to power grid shutdown are external factors, which include the tripping of transmission lines with a 30% failure probability, faults due to circuit breakers with a 19% failure probability, and generators with a 7% failure probability. Weather conditions and wind speeds are also disruptions caused by prior probabilities of 6% and 4%, respectively. Internal factors, i.e., cybersecurity and operational parameters, significantly lead to TE. Operating parameters such as frequency fluctuation and current flow may lead to disruptions in the power grid system. In addition, cybersecurity vulnerabilities, such as unauthorized access, increase the likelihood of TE occurrence. Although prior probabilities of transmission line tripping  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Intermediate Event** & **Basic Event** & **FPs** & **K** & **FPr** \\ \hline \multirow{6}{*}{Operational parameters} & Power factor & 0.1582 & 4.0174 & 0.0265 \\ \cline{2-5}  & Voltage levels & 0.1467 & 4.1385 & 0.0201 \\ \cline{2-5}  & Frequency & 0.1970 & 3.6760 & 0.0582 \\ \cline{2-5}  & Current flow & 0.1827 & 3.7915 & 0.0446 \\ \cline{2-5}  & Maintenance & 0.1621 & 3.9784 & 0.0290 \\ \cline{2-5}  & Disconnect & 0.2004 & 3.6498 & 0.0618 \\ \hline \multirow{6}{*}{Cybersecurity} & SCADA (supervisory control and data acquisition) systems & & & \\ \cline{2-5}  & Unauthorized access & 0.1760 & 3.8496 & 0.0390 \\ \cline{1-1} \cline{2-5}  & Cyberattacks & 0.1508 & 4.0936 & 0.0223 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Data** & **Basic Events** \\ \hline C-1 & Historical + Expert\({}^{\prime}\)s elicitation & Sixteen BEs (CPr) \\  & Ten BEs (FPr) \\ \hline C-2 & Expert’s elicitation & Twenty-six BEs (FPr) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the FBN using CPr and FPr.  
and malfunctioning circuit breakers are the major contributors, the simulation results show that the chances of TE occurrence are very low, with a failure probability of only 3%. However, the likelihood of equipment conditions being affected is 36%, and the likelihood of environmental factors being affected is 11%, much greater than that of BEs. The lower likelihood of a TE of 3% can be attributed to good safety and maintenance practices that maintain a smooth electricity supply to users. External risk factors must be considered and mitigated to maintain a resilient and robust power grid system.
## 1 Introduction  
With the rapid development of the semiconductor industry in the past decades, aluminium wire materials have gradually been replaced with Cu in integrated circuit interconnects because of its good electrical conductivity and electromigration resistance [1]. However, Cu has a high diffusivity, which results in the easy diffusion between Cu and Si/SiO\({}_{2}\) layers [2], causing defects and affecting the service life of carriers as well as deteriorating the performance of the electronic components [3; 4; 5]. In order to solve the problems associated with copper diffusion and to enhance the bonding force between the copper and dielectric components, a diffusion barrier layer was introduced between the Cu and Si/SiO\({}_{2}\) layers [6; 7; 8; 9; 10]. The earlier diffusion barriers mainly included transition metals (Ti [11], Ta [12], W [13]) along with related binary (TaN [12], ZrSi [14], TiN [15]) and ternary (Zr-Ti-Ni [16], W-B-N [17]) compounds. Meanwhile, the above-mentioned diffusion barriers cannot completely cover the surface of the device in the groove with a large depth ratio, which decreases the continuity of the copper film. In addition, most barriers are mainly prepared via magnetron sputtering, which imposes limits on their minimum thickness to 5 nm. Thus, the fabrication of diffusion barriers with good thermal stability, perfect coverage, and ultra-small thickness for copper interconnections and their large-scale application in the new generation of Si integration technology are urgent tasks.  
In recent years, self-assembly technology has aroused considerable attention among researchers [18] as a promising method for preparing 3-aminopropyltrimethoxysilane (APTMS) diffusion barriers for Cu metallization systems [19; 20; 21; 22; 23; 24; 25; 26; 27]. The organosilane is first hydrolyzed and adsorbed on the treated silicon substrate and then dehydrated to form a Si-O-Si bond, whereas the other end of -NH\({}_{2}\) can be well bonded to copper. Compared with -NH\({}_{2}\), the -COOH groups can better fix copper and prevent copper diffusion [28].  
In view of the above, a chemically modified APTMS is proposed in this study to enhance the diffusion barrier properties and the adhesion between the APTMS and the copper film. Trimesic acid is chosen as a modifying molecule for the APTMS. According to the results, the presence of benzene rings in trimesic acid enables one to enhance the properties of the diffusion barrier. In addition, two active sites in the -COOH groups ensure better bonding between the barrier and the copper film.
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 3 Proposed Method
### Pre-Processing  
#### 3.2.1 Short-Time Fourier Transform  
When a target moves, the received and transmitted radar signals have different frequencies, which is known as the Doppler effect. The difference between the two frequencies is referred to as the Doppler frequency. If vibration or rotational movement is added to a moving object, an additional Doppler effect, called the micro-Doppler effect, occurs. FFT is a signal processing method that converts a signal from the time domain to the frequency domain. Because FFT is applied to the entire signal, it cannot detect the micro-Doppler signature, which is a subtle signal over short periods of time. Therefore, STFT, which divides the entire signal into intervals and performs FFT operations, was used. The calculation process is shown in Equation (1). Since STFT performs FFT on segmented signals, it  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Action** & **No. of Data** \\ \hline \multirow{4}{*}{Fall} & (a) standing and then falling forward & 153 \\  & (b) standing and then falling to the left/right & 306 \\  & (c) standing and then falling backward & 153 \\  & (d) sitting and then falling forward & 135 \\  & (e) sitting and then falling to the left/right & 270 \\ \hline \multirow{4}{*}{Non-Fall} & (f) walking slowly without moving arms & 145 \\  & (g) walking quickly while swinging arms & 145 \\ \cline{1-1}  & (h) squatting & 265 \\ \cline{1-1}  & (i) sitting on a chair & 190 \\ \cline{1-1}  & (j) standing up from sitting on a chair & 190 \\ \cline{1-1}  & (k) lying down and then lifting the upper body & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of data.  
Figure 3: Architecture of the proposed method.  
provides time information and allows the signal to be represented as a spectrogram in the time-frequency domain. To perform STFT, three parameters must be determined: The first is the window size, which is the length of the signal on which the FFT is to be performed. The second is the window overlap ratio, which determines how much the windows overlap to perform the STFT. The third is the FFT point. The STFT parameters used in the proposed method were a window size of 128, window overlap ratio of 50%, and 128 FFT points. Examples of the spectrograms for actions (a)-(k) are shown in Figure 4.  
\[X(m,w)=\sum_{n=-\infty}^{\infty}x[n]w[n-m]e^{-iwn} \tag{1}\]
## 2 State of Knowledge
### Research Gap  
While port digitalization and AI implementation have advanced, there are still limitations in applying multi-agent models to optimize vehicle movements in Ro-Ro terminals [13]. Most studies focus on optimizing specific processes, such as space allocation or route planning, but few have integrated these elements into a coordinated autonomous system [23; 24].  
Additionally, many terminals continue to use traditional methodologies or optimization systems that lack real-time adaptability [25]. Current models struggle to respond efficiently to dynamic port conditions, such as fluctuating boarding demand or unexpected congestion. Furthermore, inadequate communication between different port systems hinders overall vehicle flow optimization.  
This study aims to bridge these gaps by developing a multi-agent system for Ro-Ro terminal management, enabling decentralized decision-making, optimized space and route allocation, and improved coordination among operational agents. By integrating these components into a single autonomous system, the research seeks to enhance port efficiency, aligning Ro-Ro terminal management with Industry 4.0 principles.
## 4 Discussion
### Present and Future: Traffic Management and Environmental Sustainability  
Apart from being considerably favourable, the valuation of new technologies applied to mobility, ITSs, and mobile apps has also been significantly associated with having a greater concern about environmental issues [65]. In other words, the people considering environmental issues as relevant problems for the country were the ones who best perceive the benefits that digital tools can bring to the traffic environment.  
Indeed, the reality is that the Dominican Republic has concerningly high rates of pollution, and this can be an attractive opportunity to change this challenging state of affairs [66]. In recent years, the renovation of vehicles to reduce emissions has been boosted. However, currently, more than 52% of the country's vehicle fleet is composed of vehicles older than 10 years, which produce excess polluting gases [67]. This situation, together with the dynamics of Dominican commuting, does not favour the reduction of emissions produced by transportation. A large part of the population travels by private vehicle, with very few people opting for bicycles or walking for their regular trips [38; 50; 51]. Therefore, ITS systems that manage and regulate traffic and public transport could be very beneficial for the environment. Their implementation would reduce traffic jams in urban areas and minimize the time spent commuting, with a direct impact on the country's pollution rates [68]. To complement this measure, it would be highly recommended to improve the conditions of sidewalks and pedestrian areas to encourage travel by non-polluting means of transport.  
Transport dynamics can also explain the relationships between other variables. Thus, the degree of public transport use is related to the perception of how much a traffic application can contribute to mobility. At the same time, the greater use of private transport is also related to the prioritization of ITS systems. Thus, frequent drivers consider the implementation of certain ITS services to be more urgent.
## 3 Methods
### Approximator Learning
It expresses how often values from \(\overline{M}(N(x))\) are the same as \(d_{x}\). * Prediction consistency with targets in the neighborhood--measures the consistency of the prediction of the diagnosed instance with the targets from the neighborhood of this instance. It expresses how often values from \(d_{N(x)}\) are the same as \(M(x)\). * Target consistency with targets in the neighborhood--measures the consistency of the target of the diagnosed instance with the targets from the neighborhood of this instance. It expresses how often values from \(d_{N(x)}\) agree with the class of \(x\), i.e., \(d_{x}\). * Targets and approximations inconsistency in the neighborhood--measures the inconsistency of targets and approximations in the neighborhood of the diagnosed instance. This attribute is calculated as \(\frac{1}{|N(x)|}\sum_{x^{\prime}\in N(x)}\left[1-\overline{q_{d_{x^{\prime}}} }(x^{\prime})\right]\), where \(d_{x^{\prime}}\) is the ground truth target class of \(x^{\prime}\), and \(\overline{q_{d_{x^{\prime}}}}\) is the approximation of its probability. * Targets diversity in the neighborhood--measures the diversity of targets in the neighborhood of the diagnosed instance in comparison to the diversity of targets calculated on the whole diagnosed data set. It is calculated as \(h(p,p(N(x)))\), where \(p\) is the prior probability distribution of decision classes and \(p(N(x))\) is the distribution of decision classes in the neighborhood of \(x\). * Approximations diversity in the neighborhood--measures the diversity of approximations in the neighborhood of the diagnosed instance in comparison to the diversity of approximations calculated on the whole diagnosed data set. It refers to \(h(p,\overline{q}(N(x)))\), where \(\overline{q}(N(x))\) is the distribution of the approximated predictions in \(N(x)\). * Uncertainty--we define a normalized entropy of a classification distribution \(q(x)\) as \(H_{norm}(q(x))=-\frac{\sum_{j=1}^{l}q_{j}(x)\log(q_{j}(x))}{log(l)}\), where \(l\) is a number of classes. In a case when the prior is not uniform, we need to transform it by scaling the simplex space. Let us take the distribution norm of \(q(x)\) with respect to \(p\) as \(\left|q(x)\right|_{p}=\sum\frac{q_{j}(x)}{p_{j}}\). It measures the distribution \(q(x)\) with the units of the prior distribution. Let us define a \(p\)-simplex off-centering as \([OC_{p}(q_{i}(x))]_{i}=\frac{q_{i}(x)}{p_{i}\left|q(x)\right|_{p}}\). Then we obtain the prior-off-centered normalized entropy \(OCE_{p}(q(x))=H(OC_{p}(q(x)))\), which, for the sake of simplicity, we will denote as \(Unc(x)\), which is the prediction uncertainty of model \(M\) calculated for a diagnosed instance \(x\). * Neighborhood size--the number of instances in the neighborhood of the diagnosed instance. Diagnostic attributes are a standardized format of comparing and diagnosing diverse data sets regardless of the number of classes or attributes.
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Optimization of the RB and FL EMSs  
#### 3.5.1 RB EMS Optimization  
Ten optimization processes of the RB EMS have been computed using 90 chromosomes and 10,000 iterations. The obtained optimized parameters values for the best case were as follows: \(P_{BESSmax}=150\) kW, \(P_{BESSstep\_max}=20\) kW, \(SOC_{BESSmin}=35\%\), \(SOC_{BESSmax}=85\%\), and \(\mathbf{\upxi}_{Giltr}\)= 0.2 \(\mathbf{\upxi}\). Regarding the obtained best operational cost, it is 14.3% better than that of the original charging.