esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antibiotics Affecting tRNA Biogenesis and Modification
### Antibiotics Affecting tRNA Modification  
tRNAs contain a large number of modified bases that are involved in a variety of roles from structural stability, recognition elements required for discrimination by aaRSs, to hyper modified bases on the anticodon loop of the tRNA allowing a single tRNA isoacceptor to read multiple codons and prevent frameshifting on the mRNA [32]. A number of the tRNA modifications, and their related biosynthetic enzymes, are found in all domains of life while others are conserved amongst bacteria. Despite their ubiquitous nature, very few of these modifications are actually essential for bacteria [33] and so there is a restricted range of candidate biosynthetic enzymes that can be targeted by antibiotics. One noticeable exception is TrmD, a tRNA methyl transferase [34] found to be essential in _Streptococcus pneumoniae_[35], _Bacillus subtilis_[36] and _E. coli_[37]. TrmD methylates the guanine base at position 37 on the anticodon loop to form M\({}^{1}\)G37 on a number of tRNA species, which prevents frameshifting on the mRNA bound at the A-site of the ribosome [38]. This essential enzyme is now under investigation as a promising target for developing antibiotics [39]. Another tRNA modification, which is a potential target for antimicrobials is threnoylcarbamoyl adenosine (t\({}^{6}\)A), which is essential and universally conserved in all three domains of life and found on the anticodon loop on tRNAs that decode the ANN codon [40,41]. Recent exciting discoveries have identified the genes responsible for biosynthesizing this hyper modified base opening up the possibility of developing new antimicrobials that inhibit t\({}^{6}\)A's biosynthesis [42,43]. Although,genes that are essential is an important feature of targeting novel broad spectrum antibiotics, this does not exclude the possibility that some tRNA modifications maybe required to support pathogenic lifestyles of certain bacteria and could be targets for more selective antimicrobials. Indeed, such pathogenic specific antimicrobials maybe beneficial as they would not kill commensal bacterial, which can restrict the growth of newly antibiotic resistant forms of the pathogen taking over the environment. One such example is the 5-methylaminomethyl-2-thiouridine (mnm5s2U34) modification synthesized by the GidA and MnmE enzymes, which form a binary biosynthetic complex [44]. Deletion of these genes that encode these enzymes was reported to attenuate bacterial virulence in a number of important pathogens.
## 3 Fault Classification
### Back-Propagation Neural Network: BPNN  
The back-propagation algorithm process involves calculating the gradient of the loss function with respect to each weight by the chain rule. Then, the gradient is calculated one layer at a time, and backward calculations are performed from the last layer to avoid over-calculations of intermediate terms in the chain rule. BPNN consists of four layers (an input layer, two hidden layers, and an output layer). In all hidden layers, hyperbolic tangent sigmoid functions are used, while linear functions are used in the output layer. The training process starts with a random weight and bias to the input layer. One round of training is divided into three steps. The weight and bias from the input layer  
Figure 5: Training process of the probabilistic neural network (PNN).  
are used to calculate the results of the neural networks. Then, value back-propagation of the error is performed between the output of the neural network and the target output. Next, the weight and bias are adjusted, and 20,000 iterations are performed to compute the best value of the mean absolute percentage error (MAPE). The number of neurons in hidden layers 1 and 2 is increased by one step until the MAPE value is below 0.5%. The MAPE index indicates the efficiency of BPNNs. MAPE can be computed using Equation (1). The training process of BPNN is shown in Figure 6. The training time for the BPNN is approximately 193 s. The BPNN training process consists of nine neurons on hidden layer 1 and eight neurons on hidden layer 2.  
\[MAPE=\frac{1}{n}\times\sum_{t=1}^{n}\left|\frac{p_{ANNi}-p_{TARGETi}}{p_{TARGETi}} \right|\times 100\% \tag{1}\]
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 3 Multiview Temporal Alignment
A block diagram of the proposed algorithm for temporal alignment of time series is shown in Figure 2. First, the feature vectors of both time series (denoted as \(x_{i};i=1,\ldots,T_{x}\) and \(y_{j};j=1,\ldots,T_{y}\) in the figure) are projected to a common latent space where the resulting embedding vectors (\(z_{i}^{x}\) and \(z_{j}^{y}\)) are maximally similar. To this end, two DNNs are employed, one for each sequence. Next, both sequences of embedding vectors are aligned using the DTW algorithm, and the resulting warping paths \(\langle\phi^{x},\phi^{y}\rangle\) are employed to align the original sequences \(\mathbf{X}=(\mathbf{x}_{1},\dots,\mathbf{x}_{T_{x}})\) and \(\mathbf{Y}=(\mathbf{y}_{1},\dots,\mathbf{y}_{T_{y}})\). During the training stage, the DNN weights are optimized using a combination of different loss functions, as shown in Figure 2. These losses are described in detail in the following subsections.
## 3 Results
### Cyclic Voltammetry
Let us denote as \(\Gamma^{0}\) the initial surface concentration of E\({}_{\text{Ox}}\) species and assume a Butler-Volmer law for the rate constants  
\[k_{\text{c}} =k^{0}e^{-\frac{dE}{RT}(E-E^{0\prime})} \tag{2}\] \[k_{\text{a}} =k^{0}e^{\frac{(1-\mu)E}{RT}(E-E^{0\prime})} \tag{3}\]  
Thus, the current density expression of the Faradaic current during the redox reaction of the immobilised species can be given from Equation (4) as a funtion of the surface coverage of the Ox species \(\theta=\frac{\Gamma_{\text{Ox}}}{\Gamma^{0}}\), with \(\Gamma_{\text{Ox}}\) the surface coverage of the E\({}_{\text{Ox}}\) species  
\[i_{\text{F}}=\Gamma^{0}Fk^{0}[(1-\theta)e^{\frac{(1-\mu)E}{RT}(E-E^{0\prime}) }-\theta e^{-\frac{dE}{RT}(E-E^{0\prime})}] \tag{4}\]  
where \(F\) is the Faraday constant and the coverage is found by solving the following differential equation,  
\[\frac{d\theta}{dt}=\frac{i_{\text{F}}}{F} \tag{5}\]  
Assuming a cyclic voltammetry experiment where the potential starts from an initial value \(E_{\text{I}}\) and is scanned linearly towards more cathodic potentials against time at a certain scan rate \(v\), and the scan is reversed at a time \(t_{\text{R}}\), the applied potential can be expressed as in Equation (6)  
\[E=E_{\text{I}}-vt_{\text{R}}+v|t-t_{\text{R}}| \tag{6}\]  
In order to also include capacitance in the problem, we shall assume a contribution to the current, which can be expressed as follows  
\[i_{\text{C}}=C_{\text{dl}}\frac{\text{d}E}{\text{d}t} \tag{7}\]  
Figure 1: Experimental results: Cyclic voltammetry of \(M\)/LPMO9H immobilised on a Co-f-MWCNTs modified GC electrode (black) and Co-f-MWCNTs modified GC electrode without enzyme (red) at 25 \({}^{\circ}\)C. Experimental conditions: acetate buffer 500 mM (pH 6) and \(v=0.1\) V s\({}^{-1}\). where \(C_{\rm dl}\) is the specific capacitance of the double layer. The total current density, \(i\), shall be expressed as a sum of the Faradaic and the capacitance contributions,  
\[i(t)=i_{\rm F}(t)+C_{\rm dl}\Big{(}v\frac{t-t_{\rm R}}{\mid t-t_{\rm R}\mid}\Big{)} \tag{8}\]  
By solving the problem numerically, the cyclic voltammograms are presented in Figure 2, for different \(k^{0}\) values. The other parameteres used for this example are \(\Gamma^{0}=5.1\times 10^{-6}\) mol m\({}^{-2}\), \(C_{\rm dl}=1\) F m\({}^{-2}\), \(E^{0\prime}=0\) V, \(v=0.1\) V s\({}^{-1}\). For the different \(k^{0}\) values, the peak separation values are presented in Table 1. From these values, one would expect that based on the peak separation of the experimental results at 0.1 V s\({}^{-1}\) at 0.029 V, a rough estimation of the \(k^{0}\) value should be around 5 s\({}^{-1}\).
## 4 Self-Healing Approaches
### Machine-Learning Algorithms  
Machine-learning (ML) algorithms can be used individually or in combination with each other to create more accurate and comprehensive models of system behaviours. By analysing system data in real-time, ML algorithms can enable self-healing functionality in cyber-physical systems to detect, diagnose, and potentially correct faults or failures before they cause significant damage or downtime. Table 3 lists the state-of-the-art algorithms used in self-healing systems, utilising sensing, mining, and prediction.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Usage** & **Algorithms** \\ \hline \multirow{3}{*}{Sensing} & \(\bullet\) Support Vector Machine (SVM) \\  & \(\bullet\) Genetic Algorithm \\  & \(\bullet\) Dynamic Detection Algorithm \\ \hline \multirow{2}{*}{Mining} & \(\bullet\) Supervised Knowledge-Based Algorithm \\  & \(\bullet\) DBSCAN Algorithm \\ \hline \multirow{3}{*}{Prediction} & \(\bullet\) Auto-Regressive Moving Average (ARMA) \\  & \(\bullet\) Long Short-Term Memory (LSTM) \\ \cline{1-1}  & \(\bullet\) Multi-Layer Perceptron (MLP) \\ \cline{1-1}  & \(\bullet\) Naive Bayes \\ \hline \multirow{2}{*}{Decision} & \(\bullet\) Monte Carlo Tree Search \\  & \(\bullet\) Random Forest \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of the existing self-healing algorithms.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices
### RF Energy Harvesting for Wearable Medical Devices
#### 3.1.1 Wearable Inkjet-Printed RF Energy Harvester
At the input power of 0 dBm, the RF energy harvester achieved a DC output voltage of 1.2 V. Furthermore, 3D printing can be a good way of solving huge parasitic problemsfor traditional package structures that have a major impact on the performance of the system [133; 134]. An embedded-on-package 5G RF energy harvester that operates at 26 GHz was presented by [135]; this system performs within the 3D printed multilayer flexible packaging. The entire system is made using low-cost, quick-to-prototype additive manufacturing processes such as 3D printing and inkjet printing. In this system, the use of the isotopically radiated power EIRP that is permitted for 5G communication (which is 75 dBm) [136] rules out the issue of low power density for RF energy [137]. At a 20 cm gap from the source, an energy output voltage of 0.9 V was harvested with a transmitted equivalent (EIRP) of 59 dBm, whereas a >1 m range is anticipated when applying the full 75 dBm EIRP for 5G communication. Another work by [138] used a fabrication process that relies on a masking process that is inkjet-printed and then etched. The work focused on improving the capability of typical shunt Schottky diode topologies that are deployed for energy harvesting in rectenna systems. This was accomplished by providing both flat and high-power conversion capabilities across the whole frequency range of interest. The system is a dual-tapered transmission-line-based matching network for the improvement of the rectification capability of the embedded Schottky diode. The rectenna is made up of a rectifier and a tiny monopole antenna that is optimized for a liquid crystal polymer (LCP) substrate. At an input power of 0 dBm, the flexible rectenna achieved a maximum PCE of 40%.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
###### Abstract  
This contribution analyzes the fundamental performance limits of traditional two-step Global Navigation Satellite System (GNSS) receiver architectures, which are directly linked to the achievable time-delay estimation performance. In turn, this is related to the GNSS baseband signal resolution, i.e., bandwidth, modulation, autocorrelation function, and the receiver sampling rate. To provide a comprehensive analysis of standard point positioning techniques, we consider the different GPS and Galileo signals available, as well as the signal combinations arising in the so-called GNSS meta-signal paradigm. The goal is to determine: (i) the ultimate achievable performance of GNSS code-based positioning systems; and (ii) whether we can obtain a GNSS code-only precise positioning solution and under which conditions. In this article, we provide clear answers to such fundamental questions, leveraging on the analysis of the Cramer-Rao bound (CRB) and the corresponding Maximum Likelihood Estimator (MLE). To determine such performance limits, we assume no external ionospheric, tropospheric, orbital, clock, or multipath-induced errors. The time-delay CRB and the corresponding MLE are obtained for the GPS L1 C/A, L1C, and L5 signals; the Galileo E1 OS, E6B, E5b-I, and E5 signals; and the Galileo E5b-E6 and E5a-E6 meta-signals. The results show that AltBOC-type signals (Galileo E5 and meta-signals) can be used for code-based precise positioning, being a promising real-time alternative to carrier phase-based techniques.  
GNSS; Cramer-Rao bound; time-delay estimation; maximum likelihood estimation; code-based positioning; precise positioning; GPS/Galileo signals; Galileo meta-signals +
Footnote †: journal: Sensors
## 5 Conclusions  
The findings reveal several key insights: (i) ZnO nanoparticles were successfully synthesized using zinc chloride and sodium hydroxide as starting materials by varying the precursor concentration and reaction time. Characterization through XRD, SEM, TEM, and BET analysis confirmed the exclusive formation of hexagonal wurtzite with high crystallinity, predominantly plate-like and rod-like morphologies and significant surface area variations in the synthesized ZnO nanoparticles (reduced reaction times and precursor concentrations yielded smaller particle sizes with increased specific surface areas); (ii) the antimicrobial activity results showed a clear dependency on particle size and synthesis parameters. The study identified that higher precursor concentrations and prolonged reaction times in microwave-assisted hydrothermal synthesis led to larger particle sizes and reduced antimicrobial efficiency. Conversely, shorter reaction times and lower precursor concentrations resulted in smaller nanoparticles with enhanced antimicrobial properties. The antimicrobial activity was most pronounced against _S. aureus_, suggesting a significant role of ZnO nanoparticle morphology and size in bactericidal mechanisms.  
Conceptualization, I.E.D., I.A.N., V.L.E. and E.A.; Data curation, A.C.B. and B.S.V.; Formal analysis, M.D.P.; Funding acquisition, E.A.; Investigation, V.L.E., A.C.B. and B.S.V.; Methodology, I.E.D. and M.D.P.; Project administration, I.A.N.; Supervision, V.L.E. and E.A.; Writing--original draft, I.E.D., M.D.P. and I.A.N.; Writing--review and editing, I.A.N., V.L.E. and E.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UEFISCDI PN-III-P2-2-1-PED-2019-1375, Project No. 331 PED/ 2020--\"Development of novel antiseptic based on zinc oxide for clinical wound management\".  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
The above feature could enhance the capability of PPARs to bind diverse synthetic and naturally occurring molecules that possess increased lipophilicity and an acidic moiety [11; 34; 35]. The PPAR-\(\beta\)/\(\delta\) LBD is considerably smaller compared to those of PPAR-\(\alpha\) and PPAR-\(\gamma\), which are both similar concerning their shape and size. The above distinction could explain the fact that PPAR-\(\beta\)/\(\delta\) agonists/antagonists are currently scarce compared to the other two isotypes, demonstrating that these structural features of PPAR-\(\beta\)/\(\delta\) LBD influence its ligand-binding specificity. On the other hand, the PPAR-\(\alpha\) LBD is characterized by higher lipophilicity, which may explain the fact that certain strong PPAR-\(\gamma\) ligands cannot bind to PPAR-\(\alpha\), also justifying the fact that PPAR-\(\alpha\) is capable of binding to saturated fatty acids that possess higher lipophilicity. Importantly, PPARs have different ligand isotype selectivity from each other, which seems to be ascribed to single amino acid variations in their LBDs [11; 34; 35]. Fatty acids and their analogs constitute the most frequent in vivo PPAR ligands [36], while the main drugs used to treat metabolic syndrome are their synthetic ligands [37]. PPAR-\(\alpha\) ligands such as fibrates and PPAR-\(\gamma\) ligands like thiazolinedones are currently utilized for the therapy and management of hypertriglyceridemia and diabetes mellitus type 2, respectively [38; 39]. PPAR-\(\beta\)/\(\delta\) activation can enhance the functions of mitochondria and energy metabolism; however, PPAR-\(\delta\)-related drugs remain extremely scarce [38; 39]. PPAR-\(\alpha\)/\(\gamma\) dual agonists have also been considered for the treatment of diabetic dyslipidemia. However, dual PPAR-\(\alpha\)/\(c\) agonists such as naveglitazar, tesaglitazar, aleglitzazar, raagglitazar, muraglitazar, and tesaglitazar have demonstrated severe side effects, and their development was discontinued at diverse steps of the drug design procedure [40]. In recent years, novel compounds such as saroglitazar have been shown in early-stage and preclinical studies to be dual PPAR-\(\alpha\)/\(\gamma\) agonists with significantly improved therapeutic activity and decreased side-effect profiles [41]. In addition, pan-PPAR ligands, which refer to single compounds that simultaneously activate all the PPAR subtypes (\(\alpha\), \(\gamma\), and \(\delta\)) may very well provide the rationale for a new category of compounds that can target all three PPAR receptors, leading to generally better outcomes than single-receptor or dual-combined (\(\alpha\)/\(\gamma\)) ligands [42; 43]. The present review aims to analyze and scrutinize the natural compounds that behave as agonists for the PPARs system, aiming to demonstrate whether the remarkable biochemical diversity of the natural compounds can yield new insights into this \"hotspot\" of the scientific field.
## 2 Two-Layer Optimization Framework for VPP
### Operation Tasks of the Two-Layer Optimization Framework  
The operation task of each layer in the proposed framework is described as follows:  
1. Upper layer: Coordinate the scheduling and benefit allocation among stakeholders  
The mechanism for energy and information flow within the upper layer framework is designed as follows: the grid and the VPP aggregator sign contracts, with the VPP aggregator coordinating and managing household users' energy consumption. The EST ensures its interests by complying with the VPP aggregator's management. Each household user reports load adjustment space and satisfaction cost coefficients, which are aggregated into the household collection model as predicted power demands. The focus of the upper-layer framework research lies in the game of multiple stakeholders, which involves the power allocation across each stakeholder.  
Figure 1: Two-layer multi-agent game framework for VPP. (EPS: electric power strip. REF: refrigerator. AC: air conditioner. CP: charging post. WM: washing machine. WH: water heater).  
Lower layer: Execute intelligent decision-making for users  
The mechanism for energy and information flow within the lower layer is designed as follows: household users receive the outcomes of the game from the upper layer and adjust household electricity loads by training their HEMS. Users' appliances are categorized into three types based on their electricity usage: fixed appliances, curtailable appliances, and shiftable appliances, and are assisted by reinforcement learning theory for decision-making. The detailed classification of the devices is provided in Section 3. The focus of research in the lower-layer framework is to devise load adjustment strategies for users considering their energy consumption preferences.
# An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
## 5 Results
### Preliminaries  
During all the experiments, the robot's end effector must be able to achieve a given position and orientation inside the workspace thus ensuring a precise contact with the surface that allows us to complete a hypothetical surface treatment task (polishing, deburring etc.). The details of the two different robots are given next.  
* A simulated 4 DoF planar robot (shown in Figure 1 in the three simulated workspaces first considered): This robot is composed of four rotational joints and four links of lengths \(l_{1}=l_{2}=l_{3}=1\) m and \(l_{4}=0.3\) m. The robot is used to track a line defined in the workspace. The robot end effector must thus reach a set of goal points in the workspace \(\mathbf{x}_{i}=(x_{i},y_{i},\phi_{i})\) with \(i=1,\ldots,N\) representing the different task goals \((x_{i},y_{i})\), and a given orientation \(\phi_{i}\) set to remain always perpendicular to the 1D surfaces (lines) to be traced. Since the robot has 4 DoF, there exists a free degree of freedom. At each step \(i\), our proposed algorithm will compute the robot's path \(q\) exploiting the null space of the previous task point as described in Section 4.
* A real 7 DoF manipulator: The [PERSON] robot, by Rethink Robotics, is composed of seven rotational joints. The manipulator is made to follow a virtual straight path that lies exactly on a surface. Thus, the robot end effector must be capable of visiting a set of positions defined by \(\mathbf{x}_{i}\) with \(i=1,\ldots N\) that sit on the surface, and do so with an orientation of the end effector perpendicular to the surface. Since the robot has 7 DoF, there exists a free degree of freedom. Again, at each time step \(i\), our proposed algorithm will compute the robot's path \(\mathbf{q}_{i}\), exploiting the null space of the previous task point just visited.  
During the experiments, our proposed algorithm will be used to find a trajectory that is able to complete the required task while, at the same time, optimising the manipulability at each time step and avoiding collisions.  
Figure 1: Workspaces for simulation experiments. Experiment I **(a)**, Experiment II **(b)** and Experiment III **(c)**. The manipulator fixed base is located at the \((0,0)\) coordinates. The dark grey sections define the obstacle walls, with its surface identifying the contact line to be tracked by the manipulator end effector.