esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Encapsulation Efficiency Determination  
The percentage of _Encapsulation Efficiency_ in the microcapsule slurry was determined via gas chromatographic mass spectrometric analysis. Solid-phase microextraction (SPME) (50/30 \(\upmu\)m DVB/Carboxen/PDMS) was employed.  
Following the procedure outlined in the Formulation into Household Products section, two LFE products were made: _microcapsules in LFE_ and _reference oil in LFE_.  
The _microcapsules in the LFE product_ were made by adding the capsule slurry to LFE, resulting in a perfume weight fraction of 0.3%. The _reference oil in LFE_ was prepared by substituting the microcapsules with 0.3% of the same perfume oil used for the encapsulation. This ensured that the quantity of perfume was equal in both products. Two replicates of each product (_microcapsules in LFE_ and _reference oil in LFE_) were injected into sealed headspace vials and then allowed to equilibrate for 3 h at room temperature before being analyzed by GC-MS. The GC-MS analyses were performed by sampling the headspace of each vial via SPME with a vial penetration of 25 mm and an extraction time of 1 min at room temperature. Ion extraction of the specific mass for each component was obtained. The SPME fiber was subsequently on-line and thermally desorbed into the GC using a ramp from 40 \({}^{\circ}\)C (0.5 min) to 270 \({}^{\circ}\)C (0.25 min) at 17 \({}^{\circ}\)C/min. The perfume raw materials with a molecular weight between 35 and 300 \(m/z\) were analyzed by GC/MS in full scan mode.  
The sum of all the areas under the chromatogram peaks corresponding to the perfume in the microcapsules in LFE and reference oil in LFE were calculated as \(Area_{Microapsules}\) in LFE and \(Area_{Reference\ Oil\ in\ LFE}\), respectively.  
The _Free Oil_ % was then calculated by Equation (2):  
\[Free\ Oil\ \%=\frac{Area_{Microcapsules\ in\ LFE}}{Area_{Reference\ Oil\ in\ LFE}}\cdot 100 \tag{2}\]  
The _Free Oil_ % is defined as the perfume outside the capsule core. Hence, it indicates the amount of perfume that either was not successfully encapsulated or that had already leaked out in the outer slurry formulation at the time of analysis. The _Free Oil_ % values were then calculated in terms of _Encapsulation Efficiency_ (\(EE\)) by Equation (3):  
\[EE\ \%=\frac{100-Free\ Oil\%}{100}\cdot 100 \tag{3}\]  
In synthesis, \(EE\ \%\) represents the amount of perfume present in the core compared to the theoretical perfume concentration added during the capsule synthesis.
## 1 Introduction
This method enhances conventional MPC by introducing dynamic weight adjustment based on real-time traffic urgency, enabling adaptive prioritization among competing control objectives. The proposed DWMPC framework integrates a fuzzy inference system to evaluate an emergency coefficient from relative distance and velocity errors. This coefficient is mapped through a nonlinear exponential function to dynamically tune the velocity tracking weight. Additionally, a four-mode switching mechanism adjusts the acceleration and jerk penalties according to the driving context, facilitating flexible trade-offs between safety, comfort, and energy efficiency. Simulation results under WLTC and stochastic urban driving conditions demonstrate that the proposed approach significantly outperforms fixed-weight MPC and traditional PID control in terms of tracking accuracy, energy efficiency, and emergency responsiveness. Specifically, the core contributions of this study are as follows:  
* A fuzzy inference mechanism to evaluate an emergency coefficient from distance and velocity errors;
* An exponential mapping function to dynamically adjust the velocity tracking weight;
* A four-mode control scheme for adaptive tuning of acceleration and jerk penalties;
* Simulation validation under WLTC and stochastic urban conditions;A unified performance evaluation index (PEI) for multi-objective performance assessment.
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 3 Energy Consumption Model for Sensor Networks  
### Energy Consumption Model for Data Transmission  
We adopt the same radio energy consumption model as in [1], as shown in Figure 3.  
Figure 3: Radio energy consumption model.  
Figure 2: Route of clustering protocol in wireless sensor networks.  
The energy consumption in the process of data transmission consists of the following three parts.  
1. The energy consumption \(E_{T}(d,m)\) for sending \(m\) bit data to the node with a distance of \(d\) meters consists of two parts: sending circuit loss \(E_{\text{elec\_t}}\) and amplifier circuit loss \(E_{\text{amp}}\). \[\begin{array}{l}E_{T}(d,m)=E_{\text{elec\_t}}+E_{\text{amp}}\\ =\left\{\begin{array}{c}m\times E_{\text{elec}}+m\times\varepsilon_{\text{fs }}\times d^{2},(d<d_{0})\\ m\times E_{\text{elec}}+m\times\varepsilon_{\text{amp}}\times d^{4},(d\geq d_{0} )\end{array}\right.\end{array}\] (1) In the formula, \(E_{\text{elec}}\) denotes the data energy consumption of transmitting a unit bit, \(\varepsilon_{\text{fs}}\) denotes the data energy consumption of a unit bit in free space mode, \(\varepsilon_{\text{amp}}\) denotes the data energy consumption of a unit bit in multi-path attenuation mode, and \(d_{0}=\sqrt{\varepsilon_{\text{fs}}/\varepsilon_{\text{amp}}}\) is the critical value for dividing the spatial model.
2. The energy consumption of receiving node receiving m bit data is \(E_{\text{R}}(m)\). \[E_{\text{R}}(m)=m\times E_{\text{elec}},\] (2)
3. The energy consumption of data transfer node fusing m bit data is \(E_{\text{DA}}(m)\). \[E_{\text{DA}}(m)=m\times E_{\text{da}},\] (3) \(E_{\text{da}}\) is the energy required to fuse unit bit data.
## 1 Introduction
Several existing surveys on the cluster-based routing protocols for WSNs can be found in the literature [1; 2; 3; 4; 5; 6; 7; 8; 9]. The articles [1; 2; 3] survey the strengths and weaknesses of limited numbers of existing clustering protocols separately, without providing any classification of them. [PERSON] and [PERSON] in [4] give a taxonomy of the different attributes of clustering algorithms, which are classified and evaluated according to their convergence rate into two groups of variable and constant convergence time. [PERSON] _et al._ in [5] group clustering algorithms into four schemes: heuristic, weighted, hierarchical and grid. The paper also reviews and compares limited numbers of clustering algorithms for each scheme in detail. The authors in [8] provide an insight into routing protocols designed specifically for large-scale WSNs. By focusing on energy efficiency as a problem of great significance in large-scale networks, the article categorizes the algorithms based on the motivation of the methods for improving energy efficiency as control overhead reduction, energy consumption mitigation and energy balance. However, all the aforementioned surveys give a summary of the limited famous routing protocols and compare their attributes without focusing on the limitations that exist in separate phases of a clustering algorithm in homogeneous networks. To the best of our knowledge, the work presented in this paper is the first and the most comprehensive survey, which covers and analyzes a large-number of recent available literatures on cluster-based routing protocols for homogeneous networks according to their contributions in each individual phase of CH selection, cluster formation, data aggregation and data communication. Besides, the paper classifies the schemes based on their main objectives and contribution towards addressing the shortcomings of each phase of clustering process. Furthermore, we believe this paper serves as a useful starting point for the researchers who are interested in conducting research in clustering algorithms. A list of symbols in accordance with the occurrence of the symbols in equations is provided in Table 1. To eliminate the ambiguity, the symbols of common parameters in different equations are unified and presented in the table based on their occurrence in equations. Figure 1: The composition of one round of the clustering process. This paper is organized in the following way: the taxonomy and the state of the research of the setup phase in cluster head selection and cluster formation stages are presented in Section 2 and Section 3, respectively. Section 4 investigates data aggregation process in details in homogeneous cluster-based schemes. In Section 5, different strategies in the data transmission phase are surveyed and analyzed. Finally, Section 6 summarizes the issues and solutions of the attributes and characteristics of clustering
# Application of Mammalian Nudix Enzymes to Capped RNA Analysis
## 1 Introduction  
mRNA-based technology has recently come into focus due to the development and worldwide use of new-generation vaccines against SARS-CoV-2 [1,2]. In the pioneering work, it was shown that in-vitro-synthesized mRNA supports protein synthesis after direct injection into skeletal muscle [3]. Thus, in addition to mRNA-based vaccines against infectious diseases, mRNA appears as a promising platform for other medically relevant applications, such as cancer immunotherapies or protein-replacement therapies [4]. This is highlighted by the 2023 Nobel Prize awarded to [PERSON] and [PERSON] for their research on nucleoside base modifications of mRNA [5] and the long-standing research of other pioneers at the early stages of mRNA characterization [6].  
In addition to the diverse modifications present on the eukaryotic mRNA chain [7], the 5' end of mRNA end bears a specific cap structure, comprising of 7-methylguanosine (m7 Guo) connected via 5'-5' triphosphate bond to the first transcribed nucleotide of mRNA chain (m7 GpppN, or Cap 0) [8] (Figure 1). Due to the biological roles of the 5' cap in mRNA cellular functions (translation, stability, immunogenicity), it is a \"hot-spot\" for designing and synthesising chemically modified cap-structure analogues (e.g., [9,10]). For instance, the modified cap m2/3/0 GpppAmPG is already part of modified mRNAs in designed and distributed vaccine [1].  
These chemically modified and synthesized cap analogues, once incorporated into mRNA, require characterisation in terms of capping efficiency, proper cap orientation, support of efficient cap-dependent translation, induction of cellular immunogenic response, and stability to enzymatic decapping. In this review, we focus on selected mammalian decapping enzymes of the NUDIX protein family, namely Dcp2, Nudt16, Nudt12, Nudt2 and Nudt5. These enzymes have been shown to act on distinct RNA cap structures in vivo and/or in vitro. Dcp2 and Nudt16, due to their initial discovery in the 5\({}^{\prime}\) cap processing of mRNA and snRNA/snoRNA [11; 12; 13; 14; 15], have mostly been used to date for the detailed characterization and validation of mRNA transcripts with chemically modified 5\({}^{\prime}\) caps. Including a wider set of Nudt decapping proteins with different or overlapping specificities towards chemically modified cap structures could be beneficial for a comprehensive understanding of the susceptibility to enzymatic degradation of in vitro designed RNAs. Furthermore, the use of human enzymes provides a more physiologically relevant environment for the analysis of modified caps, which is pivotal for the design, optimisation, and translation of therapeutic (m)RNAs into the clinic.
# A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
## 4 Improved Dyna-Q-Based Smart Defense Communication Solution
The UAVs update their \(Q\) function and value function at time slot \(k\) as follows:  
\[Q\big{(}\mathbf{s}^{(k)},\mathbf{a}^{(k)}\big{)}\leftarrow(1-\alpha)Q\big{(} \mathbf{s}^{(k)},\mathbf{a}^{(k)}\big{)}+\alpha\Big{(}r\big{(}\mathbf{s}^{(k)},\mathbf{a}^{(k)}\big{)}+\gamma V\big{(}\mathbf{s}^{(k+1)}\big{)}\Big{)} \tag{3}\]  
\[V\big{(}\mathbf{s}^{(k)}\big{)}=\max_{\mathbf{a}^{(k)}\in A}Q\big{(}\mathbf{s} ^{(k)},\mathbf{a}^{(k)}\big{)} \tag{4}\]  
where the learning factor \(\alpha\) adjusts the UAV's learning speed and the discount factor \(\gamma\) adjusts the importance of future rewards. In order to balance the exploit and exploration, the \(\varepsilon-\)greedy strategy is often used. \(\varepsilon\in(0,1]\) is a small positive value, which represents the likelihood of choosing the explore strategy, and \(1-\varepsilon\) means the likelihood of choosing the exploit strategy. When all the UAVs send their states \(\mathbf{s}_{u_{i}}^{(k)}=\left[x_{u_{i}}^{(k)},y_{u_{i}}^{(k)},\text{SINR}_{u_{ i}}^{(k)}\right]\) at time slot \(k\) to the SDN controller, the SDN controller can fit a quadratic equation (Equation (5)); that is:  
\[q_{0}+q_{1}x+q_{2}y+q_{3}x^{2}+q_{4}xy+q_{5}y^{2}=v \tag{5}\]  
where \((x,y)\) means the position coordinates of UAV, \(v\) is the SINR at \((x,y)\), and \(q_{0}-q_{5}\) are the parameters of the quadratic equation. Let \(N^{(k)}\) be the number of states received by the SDN controller at time slot \(k\), and let \(\delta\) be the functional error of least squares fitting as follows:  
\[\delta=\sum_{i=1}^{N}\sum_{j=1}^{N}\left[v_{i,j}-\Big{(}q_{0}+q_{1}x_{i,j}+q_{ 2}y_{i,j}+q_{3}x_{i,j}^{2}+q_{4}x_{i,j}y_{i,j}+q_{5}y_{i,j}^{2}\Big{)}\right] \tag{6}\]  
Let the partial derivatives of \(\delta\) to \(q_{0}-q_{5}\) be 0, and the value of \(q_{0}-q_{5}\) can be obtained by solving these equations, which is given by  
\[\left[ \begin{smallmatrix}q_{0}\\ q_{1}\\ q_{2}\\ q_{3}\\ q_{4}\\ q_{5}\end{smallmatrix} \right]=\left[\begin{smallmatrix}\sum\limits_{i=1}^{N} \sum\limits_{j=1}^{N}1&\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}x_{i,j}& \sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}y_{i,j}&\sum\limits_{i=1}^{N}\sum \limits_{j=1}^{N}x_{i,j}^{2}&\sum\limits_{i=1}
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 2 Fragility Analysis  
### Analysis Framework  
In this paper, the seismic fragility of the TTLS is analyzed using the IDA method, which mainly includes four steps [22]: (a) The FEM of the transmission tower-line system considering the joint effect (JM) is established, considering the slippage behavior of bolted joints. (b) The pushover analysis method is used to determine the seismic demand index required for structural fragility analysis. (c) The appropriate seismic waves are selected, and the calculation formula between the ground motion intensity measure and the structural seismic demand index is determined using the IDA method. (d) The seismic fragility of the tower-line system is calculated according to the fragility function, and the fragility curve is drawn. Specifically, the fragility curve of the structure is mainly obtained by two formulas.  
The first calculation formula Is Equation (1), which describes the corresponding relationship between the ground motion intensity measure and the structural seismic demand index.  
\[\ln(SD)=b\ln(IM)+\ln a \tag{1}\]  
where \(SD\) is the structural seismic demand index; \(IM\) is the ground motion intensity measure; \(a\) and \(b\) are the fitting parameters.  
The second calculation formula is the structural fragility function. Regression fitting is performed using the IDA results and seismic intensity measure to determine Equation (1). By substituting Equation (1) into Equations (2) and (3), the exceedance probability of structures under an earthquake can be calculated.  
\[P[SD\geq LS|IM]=\Phi\left(\frac{\ln(SD)-\ln(LS)}{\beta_{SD|IM}}\right) \tag{2}\]  
\[\beta_{SD|IM}=\sqrt{\frac{\sum_{i=1}^{N}\left[\ln\left(SD_{i}\right)-\left(b \ln(IM_{i})+\ln a\right)\right]^{2}}{N-2}} \tag{3}\]  
where \(\Phi\) is the standard normal distribution; \(LS\) is the limit strength index, which is calculated by pushover analysis; \(\beta_{SD|IM}\) is the logarithmic standard deviation of the demand index.
###### Abstract
Ag ions released from nanoparticles can bind to thiol (-SH) groups of proteins and enzymes located on the cell surface, disorganize the membrane and disrupt ATP synthesis. In the cytoplasm, silver nanoparticles interact with protein-containing components, such as ribosomes and DNA [16]. Another mechanism of action is the release of oxygen radicals (singlet oxygen). The silver nanoparticles have been proved to exhibit antimicrobial activity against many Gram-negative (_Acinetobacter baumanni_, _Proteus mirabilis_, _Escherichia coli_, _Klebsiella pneumoniae_, _nitrifying bacteria_, _Pseudomonas aeruginosa_, _Salmonella typhi_, _Vibrio cholerae_) and Gram-positive (_Enterococcus faecalis_, _Listeria monocytogenes_, _Microococcus luteus_, _Staphylococcus epidermidis_) bacteria [17]. This broad antimicrobial spectrum of silver and silver nanoparticles has led to the development of silver-containing dressings. The continuous release of silver ions translates into anti-inflammatory effects, stimulation of blood-vessel development, killing of bacterial cells, and hydrating and softening of the necrotic tissue, with the final result being a clean wound [8; 18; 19; 20; 21]. Silver can be used not only for wound-infection treatment, but also prophylatically, mainly in the case of wounds with an increased risk of infection or infection, such as burns, surgical wounds, pressure ulcers adjacent to the anus, exposed bone wounds, and wounds in immunocompromised patients with impaired circulation, such as unbalanced diabetics [22]. Unfortunately, different mechanisms of silver resistance have been described, including silver sequestration in the periplasm and active efflux, particularly in Gram-negative bacteria isolated from chronic wounds. The resistance mechanisms are encoded by multiple plasmodial genes that could be responsible for the occurrence of multi-drug resistance phenotypes. In this context, the purpose of this paper was to study the phenotypic and genetic profiles of resistance to silver nitrate, a compound currently used in the treatment of leg ulcers, in bacterial strains isolated from Romanian patients with this pathology.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Performance Metrics  
As it was mentioned, the following performance metrics were used to evaluate the results: control message overhead (DIO and DAO), end-to-end delay of successfully received messages in the sink, energy consumed from the sensor nodes of the network, and lost packets. These metrics play a key role to assess the impact of the compared approaches on traffic performance and on the network itself.  
DYNASTI aims to manage the multiple instances to better adapt the requirements of multiple applications by controlling the sent application messages, thus it is important to evaluate the delay and packet lost, mainly for critical applications. The packet lost number is the sum of the number of sent packets by nodes minus the number of received packets by sink (i.e., corrupted or dropped packets in the receiving queue). The end-to-end delay was calculated for critical applications and the application messages successfully received in sink. The delay is calculated separately for each critical application, the messages received by the sink were separated and then compared to the messages sent from the source. It was possible to filter the messages sent by an identifier located in the received messages. To reach this, the times of the received and sent messages were extracted and the difference between the receiving and sending times for each message was established, thus obtaining the delay per message. Equation (1) demonstrates the difference between the times of the message received and the message sent:  
\[\textit{Message Delay}=\textit{Received Message}-\textit{Sent Message} \tag{1}\]  
Then, a sum of the messages delay was calculated, divided by the total number of messages successfully received by the sink (Equation (2)),  
\[\textit{Network Delay}=\frac{\sum\textit{Message Delay}}{\textit{Total Received Messages}} \tag{2}\]  
DYNASTY also manages the control messages, activating or deactivating instances. Thus, the total number of the RPL control messages must be assessed. The total messages sent by all nodes in all active instances of the network, including the sink, is summed to analyze the overhead of the DIO and DAO control messages, obtaining the total control messages in the network.  
Most of the smart devices of IoT LLNs are known to have very reduced battery life; therefore, it is mandatory to evaluate the impact of flexible and dynamic management of multiple instances on energy consumption. We have used the kinetic battery model implemented by [PERSON] et al. [33] to calculate the consumed energy by the sensor nodes. When the simulation ends, the consumed energy from the sensor nodes is calculated, as showed in Equation (3):  
\[\textit{Averaged Energy Consumption}=\frac{\sum\textit{Node Energy Consumption}}{\textit{Number of Nodes}} \tag{3}\]
# Two-Phase Non-Singular Terminal Sliding Mode Control of Nonlinear Systems
## 3 Problem Formulation
In the light of (10), the convergence time of \(V\to 0\) can be expressed as  
\[t_{s,1} = \frac{\arctan(\sqrt{\frac{\phi_{2}}{\phi_{1}}}V(0)^{\frac{1-\alpha_ {2}}{2}})-\arctan(\sqrt{\frac{\phi_{2}}{\phi_{1}}}V_{t}^{\frac{1-\alpha_{2}}{2} })}{\sqrt{\phi_{1}\phi_{2}}(1-\alpha_{2})}+ \tag{16}\] \[\frac{1}{o_{1}(1-\alpha_{2})}\ln(1+V_{t}^{\frac{1-\alpha_{2}}{2}})\] \[\leq \frac{\pi}{2\sqrt{\phi_{1}\phi_{2}}(1-\alpha_{2})}+\frac{V_{t}^{ \frac{1-\alpha_{2}}{2}}}{o_{1}(1-\alpha_{2})}\]  
It is seen that the settling time relies on the initial state, and the width of \([0,t_{s,1}]\) is a finite duration independent of \(V_{0}\) for every \(V(t)\). As the sliding mode surface is reached, it is obtained that  
\[x_{2}=\dot{x}_{1}=\left\{ \begin{array}{ll}-k_{1}x_{1}^{\alpha_{1}}-k_{2}x_{1 }^{2-\alpha_{1}},&|x_{1}|\geq x_{t}\\ -k_{1}x_{1}-k_{1}x_{1}^{\alpha_{1}},&|x_{1}|<x_{t}\end{array} \right. \tag{17}\]  
and the settling time from \(x_{1}\) to \(0\) is formulated as  
\[t_{s,2} = \frac{\arctan(\sqrt{\frac{k_{2}}{k_{1}}}x_{1}(0)^{1-\alpha_{1}}) -\arctan(\sqrt{\frac{k_{2}}{k_{1}}}x_{t}^{1-\alpha_{1}})}{\sqrt{k_{1}k_{2}}(1 -\alpha_{1})}+ \tag{18}\] \[\frac{1}{k_{1}(1-\alpha_{1})}\ln(1+x_{t}^{1-\alpha_{1}})\] \[\leq \frac{\pi}{2\sqrt{k_{1}k_{2}}(1-\alpha_{1})}+\frac{x_{t}^{1- \alpha_{1}}}{k_{1}(1-\alpha_{1})}\]  
Integrating (16) and (18), the upper bound of settling time during the whole process is obtained as \(T\),  
\[t_{s} = t_{s,1}+t_{s,2} \tag{19}\] \[= \frac{\arctan(\sqrt{\frac{\phi_{2}}{\phi_{1}}}V(0)^{\frac{1- \alpha_{2}}{2}})-\arctan(\sqrt{\frac{\phi_{2}}{\phi_{1}}}V_{t}^{\frac{1- \alpha_{2}}{2}})}{\sqrt{\phi_{1}\phi_{2}}(1-\alpha_{2})}+\] \[\frac{\ln(1+V_{t}^{\frac{1-\alpha_{2}}{2}})}{o_{1}(1-\alpha_{2}) }+\frac{\ln(1+x_{t}^{1-\alpha_{1}})}{k_{1}(1-\alpha_{1})}+\] \[\frac{\arctan(\sqrt{\frac{k_{2}}{k_{1}}}x_{1}(0)^{1-\alpha_{1}}) -\arctan(\sqrt{\frac{k_{2}}{k_{1}}}x_{t}^{1-\alpha_{1}})}{\sqrt{k_{1}k_{2}}(1 -\alpha_{1})}\] \[\leq \frac{\pi+2\sqrt{\phi_{2}}V_{t}^{\frac{1-\alpha_{2}}{2}}}{2\sqrt {\phi_{1}\phi_{2}}(1-\alpha_{2})}+\frac{\pi+2\sqrt{k_{2}}x_{t}^{1-\alpha_{1}} }{2\sqrt{k_{1}k_{2}}(1-\alpha_{1})}\]  
However, it should be noted that controller (13) contains the singularity term \(x_{1}^{\alpha_{1}-1}x_{2}\), which will incline to infinity if \(x_{1}=0\) and \(x_{2}\
eq 0\) for the exponential \(0<\alpha_{1}<1\). Therefore, the control input (13) cannot be guaranteed to be bounded in two phases (traveling and reaching phase) of the TSM, and applying a controller (13) has the risk of making the driving system unstable. Therefore, the singularity phenomenon needs to be handled.