esa-sceva/satcom-chunk-collection huggingface dataset:
# New Nonlinear Second-Order Phase-Locked Loop with Adaptive Bandwidth Regulation
## 2 Materials and Methods
### New PLL Coupled with a Nonlinear Element
The state equations of the new nonlinear second-order PLL are constructed in accordance with Figure 2.  
\[U_{d}(t)=K_{d}[\sin(\omega_{i}t+\phi_{i}(t))+n_{i}(t)]\times\cos(\omega_{o}t+K _{o}Z(t)+\phi_{n}(t)) \tag{7}\]  
\[\overset{\bullet}{x}(t)=u_{d}(t) \tag{8}\]  
\[U_{c}(t)=(\frac{1}{\tau_{1}})x(t)+(\frac{\tau_{2}}{\tau_{1}})\overset{\bullet }{x}(t) \tag{9}\]  
\[\overset{\bullet}{\zeta}(t)=-a\zeta(t)+aN(U_{c}(t)) \tag{10}\]  
\[\overset{\bullet}{z}(t)=\zeta(t) \tag{11}\]  
\[\phi_{0}(t)=K_{o}z(t) \tag{12}\]  
It is widely known that the traditional second-order PLL is unconditionally stable which is not related to the loop gain. The addition of the nonlinear element realizes the adaptive adjustment of the loop gain. Then, the nonlinear element will not affect the stability of the entire loop. And, the loop gain, namely K, is adaptively adjusted with the frequency-phase error due to the addition of the nonlinear element. Thus, the corresponding transfer function of the small signal model of the proposed PLL cannot be represented by a uniform expression. However, the input-output phase error of the new PLL can be calculated by solving the above-mentioned state equations (numerical method). The results can be used to analyze the PLL performance.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### Artificial Neural Network (ANN) Based FC
#### 4.1.4 Chebyshev Neural Network (ChNN)  
[PERSON] et al. used a Chebyshev neural network (ChNN) for fault classification in TLs [73]. In ChNN polynomials, functional expansion is used to map original input into higher-dimensional space; the hidden layer is interchanged, leaving only one layer in the network as shown in Figure 5[74]. Thus only one parameter needs to be tuned in ChNN because of its single-layer structure, making it easy to implement than other ANN models with efficient fault classification results.
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 5 Experimental Setup and Measurement
### Description of the Proposed Approach and the Compared Works  
We compare our proposed approach depicted in Figure 7 to the technique described in Figure 8. In this approach, called Step-by-Step (SBS), after receiving the REQ from the sink, the Fog Node sequentially wakes up each sensor node used as a relay and the destination last. Following the forwarding of the REQ from the Fog Node to the destination node, the destination starts sending data using the woken up relays. After sending the data, the sensor nodes all go back into sleep mode.  
The second strategy that we used in the comparison, called Node-to-Node (NTN), is described in Figure 9. Here, after receiving the REQ from the sink, the Fog Node sends a WuPt to the destination followed by the forwarding of the REQ. Then, the destination node sends a WuPt to the next relay followed by a data packet. After waking up and receiving  
Figure 8: Sequence diagram of the SBS approach.  
Figure 7: Sequence diagram of the proposed approach CWM using multicast WuPt transmission.  
the data packet, the relay sensor node sends another WuPt and transmits the data packet to the next relay. The next relay wakes up and receives the data packet, which it sends on. After transmitting the data, all the sensor nodes return to sleep mode.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Physical Methods
#### 2.1.3 Laser Evaporation  
Laser evaporation, also referred to as laser ablation or vapor deposition, is an additional physical technique that follows a bottom-up strategy. This method involves the formation of nanoparticles by the condensation process from either a liquid or gaseous phase. High-energy lasers are utilized to vaporize raw materials when concentrated by a laser beam. The vaporous substance is subsequently cooled in a gaseous state, causing fast condensation and nucleation, ultimately leading to the creation of nanoparticles. This pro  
Figure 1: A brief representation of MNP synthesis methods. Created with BioRender.com, accessed on 7 February 2024.  
cess is cost-effective, efficient, and does not generate any hazardous waste, unlike certain wet chemistry procedures [7; 8; 9; 10; 11].
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
###### Abstract  
With recent adoption of Wireless Sensor-Actuator Networks (WSANs) in industrial automation, wireless control systems have emerged as a frontier of industrial networks. Hence, it has been shown that existing standards and researches concentrate on the reliability and real-time performance of WSANs. The multipath retransmission scheme with multiple channels is a key approach to guarantee the deterministic wireless communication. However, the efficiency of resource scheduling is seldom considered in applications with diverse data sampling rates. In this paper, we propose an efficient resources scheduling algorithm for multipath retransmission in WSANs. The objective of our algorithm is to improve efficiency and schedulability for the use of slot and channel resources. In detail, the proposed algorithm uses the approaches of CCA (clear channel assessment)-Embedded slot and Multiple sinks with Rate Monotonic scheme (CEM-RM) to decrease the number of collisions. We have simulated and implemented our algorithm in hardware and verified its performance in a real industrial environment. The achieved results show that the proposed algorithm significantly improves the schedulability without trading off reliability and real-time performance.  
Keywords:Wireless Sensor and Actuator Networks (WSANs); multipath retransmission; resource scheduling; realtime wireless communication; monitoring and control system +
Footnote †: journal: Sensors
## 1 Introduction
Laser crystals usually consist of three or more chemical elements. Usually, they also are doped with additional elements to achieve necessary optical or electro-optical properties. Ion impacts on compound materials induce undesirable stoichiometric changes within their surface/thin subsurface layer due to preferential sputtering [21]. For example, it was demonstrated, that the bombardment of a single spinel (MgAl\({}_{2}\)O\({}_{4}\)) crystal with 1.2 keV Ar+ ions resulted in a Mg-deficient subsurface layer of \(\sim\)5 nm thickness and a Mg/Al ratio equal to 0.25 or 0.4, depending on the process temperature [21]. Therefore, it is difficult to predict the final influence of the ion- or plasma-based treatment of crystalline substrates on their properties. Experimental studies are efficient for the evaluation of the possible process applicability for the efficient removal of damaged and contaminated subsurfaces. In this research, we present a low-energy oxygen plasma etching of commercially polished Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystals using various etching depths, and analyze their spectral, surface roughness, subsurface structure and finally, their LIDT properties for 1540 nm nanosecond laser pulses.
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## Mathematical Model
### Governing Equations
#### 2.1.3 Conservation of Platelets Concentration  
In this paper, we do not consider blood to be a three-component fluid (plasma, RBCs and platelets), and we assume that the platelets are influenced by the motion of the plasma and RBCs, but they do not affect the motion of the plasma and the RBCs. This is an assumption and as a result we model the transport of the platelets using a convection-diffusion equation due to the motion of the mixture, composed of plasma and RBCs [24; 25; 26]:  
\[\frac{\partial C}{\partial t}+div(\mathbf{v}_{m}C)=div(\mathbf{Q}) \tag{5}\]  
where \(C\) is the concentration of platelets, \(\mathbf{v}_{m}=\phi\mathbf{v}_{s}+(1-\phi)\mathbf{v}_{f}\) is the velocity of the whole blood flow, and \(\mathbf{Q}\) refers to the platelets diffusion flux of platelets in blood [27]. In the next section, we discuss the constitutive relations used in this paper.
###### Abstract  
This paper addresses trajectory tracking of an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration. The omni-directional wheeled robot outperforms the non-holonomic wheeled robot due to its ability to rotate and translate independently and simultaneously. A kinematics model of the OMR is established and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking. Simulation results validate the accuracy of the established kinematics model and the effectiveness of the proposed MPC controller.  
omni-directional mobile robot (OMR); kinematics model; model predictive control; point stabilization; trajectory tracking 1
# Indoor Positioning Systems Can Revolutionise Digital Lean
## 3 IPS-Driven Development Framework for Lean 4.0  
The previous section presented that the application of IPS in Lean 4.0 could open new possibilities if the positional data could be transformed into real-time contextualised and actionable information. This section proposes a framework that we developed for this purpose.  
The proposed framework that also utilizes information sources of a typical Industry 4.0 manufacturing system is presented in Figure 1. In addition to location data, acquired data from existing technologies such as bar-code scanners, machines and event logs can be incorporated to provide a comprehensive view of the current system state. The facility information, such as the overall layout and designated area map, can provide supportive data to clarify the facility context. The information of the overall layout is added, as well as some designated areas for buffer inventory, waiting for queues, and maintenance preparation. After data processing and computation of LM KPIs, different system monitoring techniques can be applied, such as optimal material route, optimal labour assignment and JIT preparation. Process- and data mining are performed on the collected and contextualised data to explore frequent patterns of material flow and states of the production process.  
We applied Gantt diagrams to analyze the periods of operations. As it will be presented in the application example there are many missing time periods in the Gantt diagram when the states of the material flow and the resources are not monitored in the MES. Based on the analysis of positional data, additional states of the manufacturing process can be defined and assigned to the product and material flows and states of the resources (e.g., temporal inventories can be defined). The explored states and additional time-stamps provided by the IPS process mining algorithms can be utilised to update VSMs. Thanks to the real-time position of the process flows, motion-based anomalies can be detected.  
Figure 1: The proposed framework of Lean 4.0 data-driven development.  
Figure 2 shows that proposed methodology follows the PDCA (Plan, Do, Check, Act) cycle of continuous improvement. The core element of the method is the process model (represented as a VSM in the figure) that contains all the essential information about the manufacturing process. The proposed improvement cycle continuously updates the model with the help of IPS data. The motivation is to continuously and automatically monitor the production. The developed framework can discover the real process model continuously based on the IPS data with the toolset of process mining. The resulted models are used to update the VSMs, evaluate the performance of the process by calculating the Lean KPIs. The most apparent benefit of Lean 4.0 KPIs are the readiness of decision making and optimisation based on real-time information about the manufacturing system.
# Asynchronous Gathering in a Dangerous Ring +
## 3 Gathering When \(k\) Is Known and \(n\) Is Unknown
### Unoriented Rings
#### 3.2.1 \(k\) Odd
The rules for the _right-collector_ are exactly those for the _left-collector_, where \"left\" and \"right\" are switched and so are \"blue\" and \"red\". 5. A _notified_ agent moves in the direction of the collection point. 6. If an agent is at the collection point, and there are both the left-collector and the right-collector, it enters a terminal state. Notice that, when an agent writes on the whiteboard, it provides all information necessary to the others to complete the required computations (e.g., if the _left-collector_ already left, etc.). **Theorem 6**. : _Within a finite amount of time, a single collection point is formed, and at least \(k-2\) agents will gather in the collection point._  
Proof. : Since \(k\) is odd, one of the two groups is bigger than \(\frac{k-1}{2}\). In addition, since all robots in a group move in the same direction, it follows that exactly one collection point is formed. Moreover, by construction, at any point in time there is at most one right-collector and at most one left-collector, moving in opposite directions. Within a finite amount of time, there will be exactly one right-collector and exactly one left-collector. After this time, and within a finite amount of time, all agents not at the collection point and not at (or moving to) the black hole, are notified by the two collectors. After receiving the notification, an agent starts safely moving to the collection point, reaching it within a finite amount of time. Because of the use of cautious walk, at most two agents can enter the black hole, hence at least \(k-2\) agents gather at the collection point, thus the theorem follows. Let us now examine the time costs of algorithm Gr-Odd. **Theorem 7**. : _Algorithm Gr-Odd terminates in a time of at most \(5(n-2)\), using at most \(3 nk/2-k^{2}/4+O(n)+o(nk)\) moves._  
Proof. : Let \(t_{1}\) be the time when the collection point was formed and let \(s\) be the number of edges explored at time \(t_{1}\). Since traversing one edge using cautious walk takes at most 3 time steps, we obtain \(t_{1}\leq 3s\). Let \(t_{2}\) be the time by which every edge non-incident to the black hole has been traversed by a collector. We obtain \(t_{2}\leq t_{1}+3(n-2-s)+s\), since traversing each of the \(s\) already explored edges takes at most one time step. Finally, let \(t_{3}\) be the time when all agents informed by the collectors arrive to the collection point. Clearly, \(t_{3}\leq t_{2}+n-2\leq 3(n-2)+s+n-2\). Since \(s\leq n-2\), we obtain \(t_{3}\leq 5(n-2)\). Let us now consider the number of moves. By Theorem 5 the \((k-1)/2\) followers that triggered the creation of the collection point have spent \(nk/2-k^{2}/8+o(nk)\) moves. The collectors spend at most \(2n\) moves. The remaining \((k-1)/2\) agents might have collected at the opposite end (spending \(nk/2-k^{2}/8+o(nk)\) moves). Adding the \((n-2)(k-1)/2\) moves sufficient to bring them to the collection point yields the theorem.
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 5 Simulation Studies
### Data Rule for Low Complexity Sparse WSN (\(N=7\))
#### 5.1.1 \(M=2\) MoEls Case  
In the first simulation case, \(M=2\) MoEls are employed, and the first five iterations of the MES scheme and the extracted routes are presented in Figure 2. In Table 1, the estimated values for the optimization problem variables for the first 15 iterations are stored. The first column of this table notes the iteration steps, whereas the extracted routes for each one of the two MoEls are presented in the second column. Each route duration is shown in the third column, and it is actually bounded by the time budget \(B\) that was set as upper limit for the route duration in equality (11). The timestamp of the route by which the MoEI visits each static node is noted in the next column (_depot2 node_duration_). This timestamp vector in the fourth column has as many elements as the number of static nodes, so as each element index is the visit timestamp for the corresponding node in order. A zero value in this vector means that this node was not visited during the current route. Furthermore, the first element of each vector in this column coincides with the corresponding route duration, since the MoEls return always to the depot node after visiting the assigned static nodes.  
The fifth and sixth columns include the priority nodes and the expiration time vector as calculated after the extraction of the \(M\) routes for the current iteration. These two variables function as input for the next iteration and contribute to the formulation of the priority-related constraints of the MILP problem. Thus, the _priority nodes_ column shows the nodes that have to be visited in the next route by an MoEl; otherwise, the stored data of such a node will be taken as data loss. These priority nodes are extracted based on the values of the expiration time vector \(e\) given the condition that is checked in lines 8-12 of Algorithm 3. Therefore, nodes are indicated as prioritized only those for which this condition holds. Otherwise, each node has its order in the priority list, but it is not critical to visit them in the successor route. Vector \(e\) imposes the upper bound of the visit timestamp  
Figure 2: Mobile Element Scheduling Routes with 2 MoEls and 7 Nodes.  
for each static node in the next route. The values of \(e\) that are over the aforementioned condition bound denote exactly these non-critical to visit in the upcoming route nodes. In the case of no visit to a node within the time period imposed by the vector \(e\), this node's stored data are counted as data loss. The last column of this table presents the nodes whose data are finally regarded as lost in the current \(M\) routes. Thus, the expiration time of such a node is re-initialized for the next iteration of the algorithm.
## 4 Simulation Environment
### Generation of Radio Channel Coefficients  
The simulation loop starts with the generation of the radio channel coefficients. There are two radio channel models implemented within the simulation environment. Both rely on the data from the Wireless InSite 3D ray-tracing software. The ray-tracing software can be controlled by setting different numbers of reflections and diffractions. The first option is to directly obtain the realistic radio channel coefficients from the 3D ray-tracer  
Figure 2: A block diagram of the main simulation loop.  
output. This radio channel model closely reflects the reality of M-MIMO systems because it takes into account the spatial correlations between the radio channels of various users. The second option is to utilize the uncorrelated Rayleigh channel, commonly used in the literature. When this option is used, the radio channel coefficients are generated as uncorrelated complex Gaussian noise with a mean of zero and a variance equal to the average channel gain (including large-scale fading such as path loss) from the 3D ray-tracer output. It should be noted that the Rayleigh channel replaces the small-scale fading from the 3D ray-tracer output. The large-scale fading in both cases is a result of the ray-tracing simulations. This approach enables achieving comparable results between the two considered radio channel models. As such, the computational cost is nearly the same for both radio channels. However, as mentioned above, the channel is precomputed, thereby not increasing the computation time for a single simulator run. Potentially, by replacing the ray-tracing software from the simulation environment with a much simpler empirical large-scale fading model combined with the Rayleigh channel, the simulation time would be reduced at the cost of losing some of the close-to-reality properties of the considered radio environment.
# Current Uncertainties and Future Challenges Regarding NAD+ Boosting Strategies
## 14 Which Precursor Is Better?  
All NAD+ precursors: NMN, NR, nicotinic acid, and tryptophan increase the availability of NAD+ [6; 7; 8; 9]. As noted above, the precursors differ in their ability to enhance NAD+ synthesis via their position in the NAD+ assembly pathway and differ in the number of steps required to form NAD+ in the biochemical pathways. There is a need for more data on precursor differences in absorption, bioavailability, distribution, metabolism, excretion, tissue specificity, and efficacy in increasing NAD+ levels in humans. Currently, there are no studies that directly compare the efficiency of the different precursors to conclude which of the NAD+ boosters has the best therapeutic prospects. Intravenous administration of NR and NMN to compare their efficiency to convert to NAD+ was only investigated in peripheral rodent tissues [59].
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
###### Abstract  
A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links.  
adaptive modulation; hybrid FSO/RF link; link budget; machine learning Article