esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cleaning, shaping, and three-dimensional obturation of the root canal system represent the goals of modern endodontics [1, 2]. The mechanical action of endodontic instruments on the canal walls and the chemical action of the irrigating solutions provide the necessary cleaning for the elimination of pulp tissue, microorganisms, and their metabolic products [3, 4, 5, 6]. The essential role of shaping is to create the conditions for effective penetration of the irrigants, achieved through activation techniques, to remove organic and inorganic content and break down the bacterial load, even in the anatomical complexities of the endodontic system [7, 8, 9, 10]. Microorganisms remaining in the endodontic space at the end of the treatment could potentially recololize the root canal system by exploiting any residual pulp tissue as a source of nonurishment. In fact, the presence of infected necrotic tissue within the canals after treatment represents the main cause of endodontic failure [11, 12, 13].  
According to the literature, approximately half of the endodontic system is untouched by instruments, emphasizing the crucial role of chemical cleaning [3, 14, 15, 16]. Irrigants need to come into direct contact with the entire volume of the endodontic system to ensure effective action [1; 9; 15]. Recently, concerns have been raised regarding the ability of irrigants to reach the apical portion of the canal system due to the possible formation of gas bubbles known as vapor lock [1; 14; 17; 18; 19]. The root canal system is normally a closed-end canal, as the roots are embedded in periodontal tissues and the alveolar bone, although pathways of reduced resistance towards soft tissue can be present, such as canal communications with the maxillary sinus, root perforations, or periapical lesions [20; 21; 22]. This particular anatomical condition can contribute to the trapping of gas at the apical end of the canals, leading to the occurrence of the vapor lock effect during irrigation [22; 23; 24; 25; 26; 27; 28]. Vapor lock is caused not only by physical factors but also by chemical factors, such as the release of CO\({}_{2}\) from necrotic pulp tissue dissolved by NaOCl [5].  
Small bubbles that are completely surrounded by an irrigant or only partially in contact with the root canal walls are mobile and cannot block the penetration of the irrigant into any part of the root canal [29; 30]. However, these bubbles may coalesce, forming a column of gas into which further fluid penetration is impossible. Passing endodontic instruments through this bubble does not result in its removal or reduction [15; 18; 19]. The presence of bubbles could prevent the deep penetration of any irrigant, thereby affecting the effectiveness of debris removal and cleaning [17; 18]. Failure to clean and debride the apical portion due to vapor lock can result in the failure of endodontic therapy [5].  
The objective of this research is to evaluate the presence of vapor lock during endodontic treatment in vitro and to quantify its amount.
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### FTA Qualitative Analysis of Repairable Systems  
#### 2.6.1 Minimum Cut Set Algorithm for RDFTA  
The minimum cut set algorithm for RDFTA aims to find the sets of all failure event combinations in the RDFTA, including the static and dynamic transformation method, the uplink and downlink method, the binary decision diagram transformation method, the sequence operator method, and the topological sorting method [45]. The static and dynamic transformation method is usually selected for calculation using the following formula:  
\[\phi(x)=\prod\limits_{j=1}^{l}\prod\limits_{i\in C_{j}}x_{i}, \tag{14}\]  
where \(i\) is the number of vectors of the basic event \(x_{i},j\) is the number of minimum cut sets, \(C_{j}\) is the minimum cut set of \(x_{i}\), and \(\phi(x)\) is the structure function of the RDFTA.
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 3 Control Design
### Controller Design Based on an Extended State Observer
#### 3.2.2 Adaptive Nonsingular Terminal Sliding Mode Controller Design and Stability Analysis  
The tracking error of the system is defined as follows:  
\[e_{\theta}=\theta-\theta_{d}, \tag{32}\]  
where \(\theta_{d}\) is the reference trajectory.  
Firstly, a nonsingular terminal sliding mode variable is introduced as follows:  
\[s=e_{\theta}+\lambda\big{|}\dot{e}_{\theta}\big{|}^{\gamma}sign\big{(}\dot{e}_ {\theta}\big{)}, \tag{33}\]  
where the parameters \(\lambda>0,1<\gamma<2\) need to be designed.  
Supposing \(\dot{s}=0\), the following equivalent control input is generated:  
\[v_{0}=-\hat{b}^{-1}\Big{(}(\lambda\alpha)^{-1}\big{|}\dot{e}_{\theta}\big{|}^{ 2-\gamma}sign\big{(}\dot{e}_{\theta}\big{)}+\hat{m}\hat{x}_{2}+\hat{n}\alpha+ \hat{f}-\ddot{\theta}_{1d}\Big{)}. \tag{34}\]  
In addition, a reaching control input is introduced:  
\[v_{1}=-\hat{b}^{-1}\Big{(}k_{1}s-k_{2}\alpha+\hat{k}_{s}sign(s)\Big{)}, \tag{35}\]  
where \(k_{1}>0\), \(k_{2}>0\) and \(\hat{k}_{s}\) represent an adaptive switching gain.  
Therefore, the controller is designed to be as follows:  
\[v=-\hat{b}^{-1}\Big{(}(\lambda\gamma)^{-1}\big{|}\dot{e}_{\theta}\big{|}^{2- \gamma}sign\big{(}\dot{e}_{\theta}\big{)}+\hat{m}\hat{x}_{2}+\hat{n}\alpha+ \hat{f}-\ddot{\theta}_{1d}+k_{1}s-k_{2}\alpha+\hat{k}_{s}sign(s)\Big{)}. \tag{36}\]  
The adaptive law of switching gain s is proposed:  
\[\dot{\hat{k}}_{s}=(\lambda\gamma)^{-1}\big{|}\dot{e}_{\theta}\big{|}^{\gamma- 1}\big{|}s\big{|}-k_{3}\hat{k}_{s}, \tag{37}\]  
where \(k_{3}>0\), with the initial integral values satisfying \(\hat{k}_{s}(0)>0\).  
**Remark 5**.: _In the joint space, the control of the FLM system will be affected by the flexible deformation and vibration of the link. The vibration of the FLM can be considered as a disturbance, and the ESO can be used for real-time estimation and compensation. Since the flexible vibration is a high-frequency disturbance, the observer needs to use high-bandwidth parameters, which will bring high-frequency noise into the system. Therefore, in the proposed controller (36), the deformation dynamic term is introduced to reduce the high bandwidth of the observer and effectively suppress the vibration._  
The stability analysis of the ANTSM controller based on the ESO is shown below.  
**Theorem 3**.: _Considering the system (19), if the ANTSM controller is designed by (36) with sliding mode variable (33), the tracking error and the first derivative of the tracking error converge to a small neighborhood of zero in finite-time._  
**Proof.** A Lyapunov function \(V_{1}\) is chosen as follows:  
\[V_{\theta}=\frac{1}{2}s^{2}+\frac{1}{2}\widetilde{k}_{s}^{2}, \tag{38}\]  
where \(\widetilde{k}_{s}=k_{s}-\hat{k}_{s}\).
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 2 Method
### Monte Carlo Simulation
#### 2.1.3 Photon Scattering Direction  
After the interaction between the photons and the medium, the new direction of photons is randomly determined based on the scattering phase function \(\widetilde{\beta}\). Therefore, the probability density of scattering into the unit solid angle \(\mathrm{d}\Omega(\widetilde{\xi}))\) centered on direction \(\widetilde{\xi}\) is  
\[\widetilde{\beta}\bigg{(}\widetilde{\xi^{\prime}}\rightarrow\widetilde{\xi} \bigg{)}\mathrm{d}\Omega(\xi)=\widetilde{\beta}(\theta,\varphi)sin\theta \mathrm{d}\theta d\varphi \tag{9}\]  
where \(\theta\) and \(\varphi\) are the scattering angle and azimuth angle of photons in the coordinate system centered on the incident direction \(\widetilde{\xi^{\prime}}\), respectively.  
In natural water bodies, \(\widetilde{\beta}\) depends only on the scattering angle, \(\theta\) and \(\varphi\) are independent random quantities that can be sampled separately using two random numbers. Therefore, the probability density function of the photon scattering direction could be written as  
\[\widetilde{\beta}(\theta,\varphi)sin\theta\mathrm{d}\theta d\varphi=p_{ \theta}(\theta)\mathrm{d}\theta\cdot p_{\varphi}(\varphi)\mathrm{d}\varphi \tag{10}\]  
The azimuth angle \(\varphi\) is uniformly distributed within the range of 0-2\(\pi\), the probability density function corresponding to \(\varphi\) is  
\[p_{\varphi}(\varphi)\mathrm{d}\varphi=(1/2\pi)\mathrm{d}\varphi \tag{11}\]  
Then, the random sampling of \(\varphi\) is  
\[\varphi=2\pi\zeta \tag{12}\]  
where \(\zeta\) is another random variable uniformly distributed between 0 and 1.  
The probability density function corresponding to \(\theta\) is  
\[p_{\theta}(\theta)=2\pi\overset{\sim}{\beta}(\theta)sin\theta \tag{13}\]  
To determine the scattering angle \(\theta\), a random number \(\zeta\) uniformly distributed between 0 and 1 was generated, and the cumulative distribution function \(\Psi_{\theta}(\theta)\) is  
\[\zeta=\Psi_{\theta}(\theta)=\int_{0}^{\theta}p_{\theta}\big{(}\theta^{\prime} \big{)}\mathrm{d}\theta^{\prime}=2\pi\int_{0}^{\theta}\overset{\sim}{\beta} (\theta^{\prime})sin\theta^{\prime}\mathrm{d}\theta^{\prime} \tag{14}\]  
Due to the complex shape of \(\overset{\sim}{\beta}(\theta)\), it is usually necessary to numerically solve Equation (14) to determine the \(\theta\) corresponding to \(\zeta\).  
Substituted the cosine of the scattering angle \(\mu=\cos\theta\) into Equation (14) could have  
\[\zeta=\Psi_{\mu}(\mu)=\int_{\mu}^{1}p_{\mu}\big{(}\mu^{\prime}\big{)}\mathrm{d }\mu^{\prime}=2\pi\int_{\mu}^{1}\overset{\sim}{\beta}\big{(}\mu^{\prime}\big{)} \mathrm{d}\mu^{\prime} \tag{15}\]  
where \(\zeta\) is a random number evenly distributed between 0 and 1.
# Overview of Methods for Large-Scale RNA Synthesis
## 2 Methods of RNA Synthesis
### Ribozymes for RNA Production with Homogeneous Ends
#### 2.3.2 Hairpin Ribozyme  
The hairpin ribozyme was identified in the minus strand of the tobacco ringspot virus satellite RNA (TRSV). It possesses four helical regions and two internal bulges (Figure 6B) [120]. The secondary structure of the hairpin ribozyme resembles the shape of a paperclip, but in three-dimensional space, the helices are coaxially arranged, representing two hairpin structures linked by a four-way junction [121]. Similar to the hammerhead ribozyme, helical regions of hairpin ribozyme can be engineered with different lengths and sequences without influencing the cleavage efficiency, as long as the integrity of the helices is maintained. The cleavage site is located in one of the conserved bulge regions. The optimal cleavage sequence is RYN\(\downarrow\)GUC (R is a purine, Y is a pyrimidine, and N is any nucleotide) and occurs between N and G residues [100].  
Due to different sequence constraints, the hairpin ribozyme has been proposed as an alternative to the hammerhead ribozyme. Although the cleavage efficiency has been observed to be high, the presence of the hairpin ribozyme leads to the generation of nonspecific products. This activity was shown to be reduced by a higher concentration (30 mM) of Mg\({}^{2+}\) ions [61].
## 4 Method Analysis and Control Strategy Development
### Motor Rotary Velocity Regulation Control Algorithm
#### 4.1.2 PID-Bang-Bang Compound Algorithm  
The bang-bang control algorithm is also known as the minimum-time control algorithm. Its primary control principle is to make the system switch from one state to another at the fastest speed in the control domain. Considering the purpose of rapidly following the target rotary velocity, the bang-bang control algorithm is a control algorithm that can be considered. In this study, the PID control algorithm and bang-bang control algorithm were coupled to form a PID-bang-bang compound control algorithm, and a schematic of the principle is shown in Figure 7. Here, \(\varepsilon\) is the difference between the target rotary velocity and the actual rotary velocity corresponding to the mode switching point, that is, the algorithm switching threshold of the bang-bang control mode and the PID control mode.  
The PID-bang-bang compound controller was used to control the rotary velocity of the motor in the shifting process, where the control mode was selected online using a mode selection switch. When the difference between the target rotary velocity and the actual rotary velocity was large, the bang-bang control algorithm was used to ensure that the motor rotary velocity reached the target rotary velocity as soon as possible. When the difference was small, the PID control algorithm was used to improve the robustness and control accuracy of the control system. The influence of the switching timing of the two algorithms on the motor rotary velocity regulation effect will be further explained in Section 5.1.3.  
Figure 6: Schematic of control principle, based on PID algorithm.  
Figure 7: Schematic of control principle based on PID–bang-bang compound algorithm.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 2 Literature Review
### Summary  
In free space-based sensors, the precision of ultrasonic sensors is highly dependent on the positioning of adjacent vehicles, and ultrasonic sensors cannot function without reference vehicles. The high cost of laser scanners hinders their commercialization as an automobile accessory. Depth cameras are difficult to use in real-time settings because generating a depth map requires an enormous amount of computing power for embedded systems. In parking space marking-based sensors, monocular cameras were gradually replaced because their field of vision is too narrow. In parking space detection technology, detection methods based on line and corner features perform equally well. The two methods differ by the type of technology used for space occupancy classification. In space occupancy classification technology, ultrasonic sensor-based methods have broad applications in the current market. However, these methods still rely on the existence and positioning of adjacent cars. Image analysis-based methods that analyze the edge ratios within parking spaces perform well; however, basing occupancy classification on a single feature seems somewhat inadequate. Based on these issues, we chose the AVM system as a sensor for the surrounding parking environment. This system was paired with corner feature detection to detect parking spaces in the system's field of vision. Finally, in space occupancy classification, the proposed method simultaneously analyzes both road and obstacle features to improve the robustness of the algorithm. The proposed method is introduced in the next chapter.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 5 Experimental Results and Discussion
### Security & Efficiency Analysis  
In this section, the security and efficiency of the work are discussed for the proposed as well as previous works.  
By increasing the strength of security, the proposed work is more secure against different attackers. Apart from the CVD, the proposed model is capable of ensuring high _QoS_, security and energy efficiency for many disease diagnosis applications. In the following, we discuss the attacks mitigated in this system.  
* _BAN Impersonation Attack_: In this attack, unauthorized patients attempt to submit and access data from the cloud. However, the sensors from the body and environment are compromised by the attackers. The authentication to the blockchain cannot be compromised by an attacker and cannot be passed until the correct credentials are submitted.
* _Flooding Attack_: In this attack, more illegitimate requests are forwarded by attackers. Hence, sink nodes and any other communication devices cannot tolerate a high  
Figure 19: Success rate vs. simulation time (periodic packets).  
Figure 18: Success rate vs. simulation time (emergency packets).  
number of requests. For each BAN, the PBA's role is to monitor and audit the abnormal packets and notify this information to the blockchain and remote server.
* _Path-based DoS Attack_: DoS is a denial of service attack in which attackers' behavior is to exhaust the resources for sink nodes and other devices and creates a busy route. This makes the huge delay in processing legitimate packets, and hence, the emergency packet delay is higher and leads to more packet losses.  
The efficiency of the work is analyzed using two parameters: fault data elimination check and reliability. The comparison analysis of these two metrics is represented in the following.
## 5 Practical Applications in Cannabis Cultivation  
### Controlled Environment Agriculture  
Studies have shown that the exogenous application of MeJA at concentrations ranging from 0.1 mM to 15 mM can significantly enhance cannabinoid levels, often by 15-100% compared to untreated controls. Although this range underscores MeJA's potency as an elicitor, it is essential to note that the optimal dose varies depending on the cultivar, the plant's developmental phase, and the specific growing conditions [20]. Generally, moderate doses of MeJA, in the range of 0.05-0.1 mM, tend to maximize trichome density and boost cannabi-noid yield without significantly restricting vegetative growth. This moderate approach is often accomplished through foliar sprays administered one or two times at key moments in flower development--usually early-to-mid-flowering--so that the plant dedicates ampleresources to resin production. Some growers have also experimented with atomizing or vaporizing MeJA in indoor growing chambers to achieve uniform distribution; however, the efficiency of this method depends on various factors, including airflow, temperature, plant morphology, and growing practices. At higher concentrations, around 15 mM or more, MeJA can produce more pronounced increases in total cannabinoid accumulation, sometimes approaching two-fold gains. However, these stronger treatments often induce substantial dwarfing, reduced biomass, and occasional leaf stress symptoms [40]. While reduced vegetative growth can be advantageous in vertical or space-limited indoor farms, the potential drawbacks include lower overall yields of harvestable material if the primary goal is biomass production [61]. In some cases, increased biomass may lead to cannabinoid dilution, as the concentration of secondary metabolites becomes lower relative to the overall dry weight compared to more compact, denser plants [62].  
Consequently, cultivators who prefer to grow shorter plants for space optimization may find that MeJA-induced dwarfing in conjunction with increased trichome formation strikes a desirable compromise. When applying MeJA in controlled environments, timing also plays a key role.  
Many growers opt to introduce MeJA applications during the early-to-mid-stages of flowering, once or twice, so that plants have time to form additional glandular trichomes before harvest (Table 1). Repeated treatments require careful scheduling to prevent overwhelming the plants with phytohormones, an approach that can sometimes lead to leaf chlorosis or developmental abnormalities. Some growers have even explored pairing MeJA treatment with specific nutrient regimens or specialized lighting conditions, such as enriched blue or UV wavelengths, to create synergistic effects that further stimulate secondary metabolism. Although MeJA costs more than conventional fertilizers, its ability to considerably raise cannabinoid potency can offer a strong return on investment for producers catering to the medical and high-end markets.
# Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme
## 4 Simulation Results
### Impact on Changing the Size of the Minimum Unit of the Quad-Tree  
According to the previous simulation results, it is found that the Quad-Tree takes more time to partition large-size maps. During the Quad-Tree partitioning process, the minimum unit will not be further divided. Therefore, increasing the size of the minimum unit of Quad-Tree and ending Quad-Tree division earlier should reduce the time required for Quad-Tree division and also reduce the number of positions that need to be considered during subsequent MSC runs, thus further reducing the algorithm running time. However, due to insufficient partitioning, the optimal pad placement may have been overlooked,  
Figure 14: Comparisons of (**a**) the required number of pads and (**b**) the average execution time of related methods on the fourth group of special test maps.  
Figure 13: Comparisons of (**a**) the required number of pads and (**b**) the average execution time of related methods on the third group of special test maps.  
increasing the number of pads used in the resulting pad configuration. The following simulation uses QT&MSC-n to represent the performance of the QT&MSC algorithm when the minimum unit is set to \(n\) meters by \(n\) meters in size. The original QT&MSC is used as the comparison baseline, and its minimum division unit is 1 m by 1 m.  
Figure 15 illustrates the performance of QT&MSC with varying minimum Quad-Tree units in a 4096 \(\times\) 4096 network environment. The data indicate that as the minimum Quad-Tree unit size increases, the number of pads required for QT&MSC also increases, ranging from 1.63% to 27.25%. Simultaneously, the reduction in execution time also rises from 7.38% to 61.00%, aligning with the anticipated impact of larger Quad-Tree units.  
In a network environment of 6144 \(\times  
Overall, across the three different map sizes, QT&MSC-8 consistently reduces computation time by approximately 4% to 7% while increasing the pad count by around 1%. On the other hand, QT&MSC-512 saves over 50% of execution time but increases the pad count by more than 20%. Further exploration and analysis on how to select the optimal size of Quad-Tree minimum units will be one of our future research directions.
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 4 Noble Metal-Based Heterogeneous Catalysts
### Alloying Strategy
#### 4.1.2 Pd-Based Alloy  
Palladium (Pd) is widely found in the Earth's crust and is about one-fifth of the price of Pt. Due to its similar atomic size and lattice mismatch of 0.77%, Pd is a potential alternative material for Pt [69]. In the latest research progress of Pd-based alloys, [PERSON] et al. [70] introduced Ru to improve the HER catalytic activity in an alkaline electrolyte and prepared Pd\({}_{3}\)Ru alloy nanoparticles enriched with Ru on the surface. The Ru atoms/clusters on the catalyst surface weakened the hydrogen bonding energy and promoted the adsorption of OH*, thus reducing the reaction barrier of the HER. The overpotential \(\eta_{10}\) of Pd\({}_{3}\)Ru alloy in a 1 M KOH solution is 104 mV and 6 mV lower than Pd and Pt, respectively. In addition, non-noble metals can also form alloys with Pd, such as Co, Bi, Ni and Cu [71]. Due to the synergistic effect between the PdNi alloy and carbon materials, the dispersion of the Pd-Ni alloy on carbon materials can improve the HER activity [72]. Alloying with different metals can move the d-band center of Pd downshift (i.e., away from the Fermi level). Alloying can adjust the single element to keep the appropriate hydrogen binding energy of the catalyst and make the intermediate state have the optimal Gibbs free energy, thus improving the HER activity.