esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 4 Online Motion Planning
### Dynamic Obstacle  
For each operation, the position of the lifting platform relative to the overhead line changes greatly. During the operation, additional obstacles will be added, such as the drainage line as shown in Figure 17. These obstacles are not considered when constructing the offline tree. However, the full wiring tree in the planning space can help algorithm bypass obstacles and quickly complete the planning. As shown in Figure 18a, blue dynamic obstacles \(p_{dyn\_obs}\) are dynamic obstacles in the environment. When \(p_{dyn\_obs}\) appears on the planning path, the planned path is unavailable and the tree needs to be rewired. Algorithm 5 introduces the method of rewiring and planning the path.  
Figure 16: Tree expanded from the goal and the planned path. (**(left)** The green \(T_{offline}\) and the blue \(T_{goal}\), (**right**) the magenta part in \(T_{offline}\) and the blue part in \(T_{goal}\) are combined to obtain a path from the starting point to the target).  
Figure 15: The beginning and end of motion planning.  
First, section [\(p_{s}\): \(p_{e}\)] of the path that collides with the obstacle is found and is taken as the divider to remove part of the canopy of \(T_{offline}\); the remaining part is \(T_{cut}\). Some branches of \(T_{cut}\) may go through the dynamic obstacle. Detecting these branches consumes many resources, so no detection is performed. The partial path Path (\(p_{e}\): end) is retained on the target side as the new tree (\(T_{dyn}\)). Algorithm 4 is used to expand (\(T_{dyn}\)) and plan the path. It is determined whether the planned path passes through dynamic obstacles, and if so, a new joint node is selected to obtain the path.  
Figure 17: The newly installed drainage wire affects robot movement.  
Figure 18: Replanning for dynamic obstacles. (**(a)** Dynamic obstacles appear on the planned path, **(b)** cut \(T_{offline}\) according to the obstacle position, and (c) expand \(T_{dyn}\) from the left path (black part in (b)), (d) a new path is selected to avoid dynamic obstacles).
## 2 Materials and Methods  
### Experimental Setup  
The setup used in the measurements is schematically depicted in Figure 1a. The laser source was a prototypal four-wave mixing laser (Fianium Ltd, Southampton, UK), providing two separate laser beams (710 and 820 nm) with a temporal duration of about 25 ps full width at half maximum (FWHM) at the repetition rate of 40 MHz. Each beam was properly attenuated by means of the variable optical attenuator (VOA, Edmund Optics Inc., Barrington, NJ, USA) and light was collimated into a 400 \(\upmu\)m fiber by means of a collimator. Collimated beams were then injected into the sample and retro-diffused photons were collected through two 1 mm core fibers (with a NA of 0.37) at a given source-detector distance (5 or 30 mm).  
In the in vivo measurements, it was fundamental to disentangle the contribution of the two wavelengths in order to recover the concentration of both oxygenated and deoxygenated hemoglobin. For this reason, an interferential filter (centered at 710 or 820 nm) was put at the other tip of each collection fiber. Such a solution allowed for the ability to separate the two beams and send each of them to a different fast-gated SPAD (FG-SPAD) module, thus having a parallel acquisition of the two wavelengths. The arrangement of the launching and detection fiber was conceived so as to probe the same area with both source-detector couples.  
The FG-SPAD modules were state-of-the-art time-gated detectors, having an active area of 100 \(\upmu\)m diameter and nearly 200 ps of raising edge for the gate opening. The full description of the FG-SPAD modules can be found in [PERSON] et al. [10].  
Figure 1: (**a**) Schematics of the setup used for the in vivo experiment. (**b**) Instrumental response function recorded for both fast-gated single-photon avalanche diode (SPAD) modules at their operating wavelengths (710 nm for fast-gated SPAD (FG-SPAD) 1—red line; and 820 nm for FG-SPAD 2—blue line). Time bin size: 3.05 ps. IRF = instrument response function, VOA = variable optical attenuator, TCSPC = time-correlated single photon counting. FWM: Four Wave Mixing.  
Concurrently with the optical pulse, the laser provided a synchronism signal, which was split into two paths. The former was fed to the FG-SPAD modules to trigger the enabling of the detector for a given period of time (5 ns). In order to acquire different \"slices\" of the DTOF, the detector was enabled at given delays with respect to the peak of the reflectance curve by means of a home-made delayer. It was a transmission-line based one, and it provided delays from 0 up to 6375 ps at a step of 25 ps.  
On the other side, the second path of the split sync was sent to the time-correlated single photon counting (TCSPC) electronics, feeding the \"stop\" signal for both acquiring boards (SPC-130, Becker&Hickl GmbH, Berlin, Germany). The \"start\" signal for the TCSPC board was provided by each FG-SPAD module. Indeed, each module generated a pulse (timing OUT signal in Figure 1a) when a photon was detected within each SPAD, thus triggering an avalanche.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 3 Experimental Setup
### Dataset Description  
In our experiments, we utilized the Shanghai Telecom base station dataset, which provides internet information for mobile users accessing 3233 base stations. After a previous analysis, 3000 effective base stations were identified, as some were idle or had invalid data. The dataset includes detailed records of the start and end times of base station access for each mobile user. Figure 3 shows the distribution of these 3233 base stations. As a densely populated city, Shanghai is well suited for mobile edge computing networks. Each base station's workload is represented by the number of mobile user requests it handles, which can be estimated using population density or historical access data through linear regression techniques. Specifically, the dataset comprises 4.6 million call records and 7.5 million flow records from approximately 10,000 mobile users over six months, detailing the exact times of base station access.  
Table 1 provides information on 11 randomly selected base stations from the Shanghai Telecom dataset, detailing the workload of each station. The workload is calculated as the total request time based on mobile user activities' start and end times. From the data in Table 1, it is evident that there is a significant workload imbalance among these base stations. If the EDC placement problem focuses solely on communication delay without considering the workload, it could result in an uneven distribution of workload across the EDC.
## 3 Results and Analysis  
The refection coefficient parameter shows the antenna operating band (\(\textless-10\) dB) is from 2.5 to 3 GHz and exhibits bandwidth values of 500 MHz and 4-5 GHz with a bandwidth value of 1000 MHz. Figure 3 shows the Reflection coefficient (dB) in the graph  
The trapezoidal shape helps boost monopole antenna radiation. From the broader base to the smaller top, this form gradually tapers, reducing backlobe radiation and increasing forward gain. For antenna directivity and radiation pattern management applications, like minimising interference or improving coverage in a certain direction, this feature is useful. 4. Mechanical Stability: Lower wireless communication applications may result in antenna vibrations and impacts. Trapezoids are more mechanically stable than vertical monopoles. More structural support from its larger base reduces external pressures from bending or deforming it.  
The simulated results obtained from the FDTD--Finite-Difference Time-Domain analysis match with the measured readings of the prototyped antenna shown in Figure 6. The designed antenna is tested in the anechoic chamber, and the antenna measurement setup and obtained efficient results for the placement of the proposed antenna in practical scenarios in the desired band of applications are shown in Figure 7.
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 3 Simulation Results for Lighting Scenarios
Reduction in battery state of charge level (Battery SOC) starts at 491,000 min, that is, the 341 st of the year, and continues until 515,000 min (the 357 th day of the year). The SOC battery level is 55%. Figure 19:3 Selected results for the 129 th, 130 th, 131 st day of the year. The conclusion of this case is that the most difficult period of the year for an autonomous lighting system is the second and third week of December, when the amount of solar radiation is insufficient to charge the batteries. The operation of the system during this period relies on the proper selection of the energy storage technology, the battery capacity and the way the power luminaire is controlled. During the fourth week of December, the amount of energy increases slightly, allowing the battery charge level to remain constant. The simulation results are in line with the general knowledge that the day lengthens after 21 December, which is evident in the results of this energy analysis. Figure 32 shows the results of the analysis throughout the year. The first waveform (Daily Energy) shows the variation in the energy values consumed by the LED lighting, depending on the current day length. It can be seen from the graph that the energy varies from 0.86 kWh for the longest day of the year to 0.44 kWh for the shortest day of the year. Figure 19:4 Selected results for the period of solar radiation availability between days 129 and 131 of the year. Figure 32 (Battery SOC) shows the waveform of battery charge levels throughout the year. The parameters of the components of the lighting installation are chosen correctly to illuminate the pedestrian crossing in such a way as to guarantee minimum energy performance throughout the year. During the entire year, battery charge level (SOC) does not drop below a level that could result in the unavailability of energy to supply power to the LED luminaires. Therefore, the fifth lighting scenario is possible to achieve.
# An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0
## 1 Introduction
Since the procedure exhibits a novel approach, which enables idle mobile phones to simultaneously receive AEA message and CBS message using the CBS paging, the presented procedure is also a contribution in this paper. Simulation evaluation shows the validity of the proposed emergency alert broadcast approach. The simulation results exhibit that the proposed technique is much superior to the 5G CBS and the ATSC 3.0 AEA methods in terms of latency. This indicates that the presented approach allows more rapid notification in urgent situations. The results also reveal that the proposed method can effectively provide multimedia information as well as text-based information to users in emergency areas. The simulation results also show that various displays of emergency information are available on the receiver screen according to the decoding state. This indicates that the proposed approach is more reliable than the 5G CBS and the ATSC 3.0 AEA methods.
# System Studies on the Fusion-Fission Hybrid Systems and Its Fuel Cycle
## 3 Results and Discussion  
### Neutron Transport and \(k_{\text{eff}}\) Calculations  
The neutron energy spectra in the transmutation area of DEMO-FNS with different coolants are shown in Figure 6:  
* In the case of H\({}_{2}\)O as a coolant, the volume-averaged total neutron flux is \(\overline{\phi}_{n}=2.88\times 10^{14}\) (cm\({}^{2}\)-s)\({}^{-1}\), average neutron energy is \(\overline{E}_{n}=3.52\) MeV and \(k_{\text{eff}}=0.95\).
* In the case of CO\({}_{2}\), \(\overline{\phi}_{n}=3.02\times 10^{14}\) (cm\({}^{2}\)-s)\({}^{-1}\), \(\overline{E}_{n}=3.47\) MeV and \(k_{\text{eff}}=0.91\).  
As follows from the obtained spectra, 99% of neutrons have energy higher than 0.001 MeV, and 40% of them have energy higher than 1 MeV. 1 MeV is the threshold fission energy for most of the actinides that have a threshold type fission cross-section (Figure 2). As follows from this distribution, DEMO-FNS has quite a hard spectrum in the transmutation area, which is a positive feature for MA transmutation. The differences between the two spectra reveal that in the case of CO\({}_{2}\), the spectrum becomes harder, the number of fusion, and especially fission, neutrons rises. It is caused by a decrease in coolant density (from 0.37 g/cm\({}^{3}\) to 0.14 g/cm\({}^{3}\)) and moderating efficiency (from \(1.7\times 10^{4}\) to \(2.1\times 10^{3}\) for 14 MeV neutrons) and also by a decrease in the volume percentage of the fuel in the assemblies.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Data Packet Loss
#### 3.4.2 Data Packet Loss Analysis with Other Approaches  
Let \(\varphi_{p}(^{.})\) represent the total data packet loss for any protocol, which can be calculated as follows [8; 32]:  
\[\varphi_{p}^{(.)}=\lambda_{S}E[S]L_{HO}^{(.)}, \tag{23}\]  
where \(\lambda_{S}\) represents the average session arrival rate at the MN's wireless interface, \(E[S]\) represents the average session length in packets, and \(L_{HO}\) is the handover latency for a specific approach.
## 3 Nonthermal Methods
### High-Pressure Processing (HPP)  
High-pressure processing (HPP) is a nonthermal method that uses high pressures above 100 MPa without using heat to inactivate microorganisms, thus extending the shelf life of food products. The shelf life can vary from a few days to a few weeks, depending on the type of food product [62]. The process causes changes in the structure of the cell membrane, protein structure, cell morphology, and genetic mechanisms, leading to the inactivation of microorganisms. Its efficiency varies depending on the type of microorganism and the composition of the food, which is why optimizing the parameters (pressure, time, temperature) is essential for food safety. For example, this technique is more effective in liquid foods than in solid foods. However, HPP does not always guarantee the complete destruction of microorganisms, and sublethal cells can survive and recover later. Multiple studies have demonstrated the effectiveness of HPP in inactivating _L. monocytogenes_ at pressures of 450-600 MPa for 3-15 min, depending on the food and bacterial strain. However, a treatment of 375 MPa for 15 min was not sufficient for pasteurization of milk and poultry meat [63].  
High-pressure processing (HPP) has minimal effects on food components, preserving natural flavors and a high content of nutrients such as vitamins and antioxidants [63]. Unlike thermal pasteurization, HPP does not affect covalent bonds, which allows the preservation of the sensory and nutritional properties of foods. Treating milk with HPP leads to the breaking of ionic and hydrophobic bonds in proteins without affecting bioactive proteins or other essential nutritional components such as vitamins and amino acids. In addition, HPP causes beneficial changes such as denaturation and aggregation of proteins, influencing the yield of dairy products derived from the treated milk [64]. Recent studies have demonstrated the success of applying HPP in extending the shelf life of products such as cheddar and gorgonzola cheese [65; 66].  
Food composition, type and age of microorganisms, applied pressure, and duration of treatment play a crucial role in the efficiency of microbial inactivation. HPP is effective against vegetative pathogens but has limitations in inactivating spores, which can be overcome by combining HPP with other thermal or nonthermal methods. This synergistic approach preserves the nutritional quality of foods and reduces processing intensity. Although HPP equipment is expensive, technological advances and the increase in the number of HPP units have contributed to the commercialization of this technology, especially in developed countries. Studies have also shown that other nonthermal pasteurization methods, such as ultraviolet radiation, pulsed electric field (PEF), and ultrasound, are less effective than HPP. For example, PEF and ultrasound require moderate temperatures to increase the efficiency of microbial inactivation, and ultraviolet radiation has low penetration in opaque fluids. Membrane filtration also involves high maintenance costs and is less efficient in treating large volumes of liquids [67].
## 2 Background and Preliminary
### Anomaly Types in MTS  
MTS consists of a set of univariate time series(metric), each of which describes different parts or attributes of a complex entity. Therefore, it not only has temporal dependencies within the metrics, manifested as periodic, trend, and other inherent patterns of each metric, but also inter-metrics dependencies within the entity, representing the linear or nonlinear relationships between all metrics of the entity at each time point [2; 21]. Thus, anomalies in MTS can be divided into intra-metric anomalies (temporal anomalies) and inter-metric anomalies. The overall classification framework for anomaly types is shown in Figure 1. Temporal anomalies occur in multivariate, affecting multiple or all dimensions. Temporal anomalies primarily focus on individual data points and subsequences. Point-wise anomalies refer to unexpected events at a single time point, where the anomalous behavior of a time point can be a spike or glitch. Pattern-wise anomalies are anomalous subsequences, usually discordant or disharmonious. Here are the definitions of types of anomalies:
## 1 Introduction
### First-Order plus Dead-Time (FOPDT) Model  
For time-delayed processes, the dynamics of a simple plant similar to (5) is better reflected in the FOPDT model. With the help of the dead time term, we can also model the high-frequency part of its dynamic.  
\[G_{p}(s)=\frac{Ke^{-\theta s}}{1+Ts} \tag{13}\]  
where \(\theta\)--time delay.  
When applying this model to determine PI controller settings, the recommended integral time constant \(T_{i}\) of the designed PI controller remains unchanged, but the calculation of the appropriate proportional gain \(K_{p}\) already encounters an additional difficulty.  
[PERSON] [12] and later [PERSON] [18] adopted the desired transfer function of a closed-loop system as in (8) but only considered the dead time introduced by the plant itself:  
\[G_{d}(s)=\frac{e^{-\theta s}}{\tau_{c}s+1} \tag{14}\]  
Then, according to (2), the desired transfer function of the open-loop system should be equal to  
\[G_{o}(s)=\frac{e^{-\theta s}}{\tau_{c}s+1-e^{-\theta s}} \tag{15}\]  
Thus, the transfer function of the PI controller is as follows:  
\[G_{c}(s)=\frac{Ts+1}{K}\frac{1}{\tau_{c}s+1-e^{-\theta s}} \tag{16}\]  
To make this transfer function realizable, [PERSON] approximated the exponential term by a Taylor series expansion:  
\[\mathrm{e}^{-\theta s}=1-\frac{1}{1!}\theta\,s+\frac{1}{2!}(\theta\,s)^{2}- \frac{1}{3!}(\theta\,s)^{3}+\dots \tag{17}\]  
reaching for only its first two terms  
\[\mathrm{e}^{-\theta s}=1-\theta s \tag{18}\]  
and he obtained a relationship for the controller transfer function:  
\[G_{c}(s)=\frac{T}{K(\tau_{c}+\theta)}\cdot\frac{Ts+1}{Ts} \tag{19}\]  
Comparing it to a PI regulator (6), its settings should be equal to  
\[K_{p}=\frac{T}{K(\tau_{c}+\theta)}\quad T_{i}=T \tag{20}\]  
Although the course of [PERSON] and [PERSON]'s [13] as well as [PERSON] and [PERSON]'s [14] considerations was a little different, as they applied the first-order [PERSON] approximation already at the stage of defining the desired closed-loop transfer function, inserting (18) into (14), which gave  
\[G_{d}(s)=\frac{1-\theta s}{1+T_{c}s} \tag{21}\]  
they obviously obtained the same results (19,20).  
Also, [PERSON], [PERSON] [16], and [PERSON], [PERSON], and [PERSON] [19] approximated the exponential term by [PERSON]'s series expansion only up to the 2 nd order term. In the next step, they obtained the ideal form of the PID controller, applying the Maclaurin series expansion theorem. On the other hand, in relationship (15), [PERSON] [20], [PERSON] and [PERSON] [21], and [PERSON], [PERSON], and [PERSON] [22] replaced the exponential term using the Pade approximation, which according to [PERSON] [23] is more accurate:  
\[e^{-\theta s}=\frac{1-0.5\theta s}{1+0.5\theta s} \tag{22}\]  
Thus, [PERSON] derived the relations for the recommended settings of the PI controller:  
\[K_{p}=\frac{T+0.5\theta}{K(\tau_{c}+\theta)} \tag{23}\]  
\[T_{i}=T+\theta/2 \tag{24}\]
## 1 Introduction
### Literature Review  
In [13], the authors provided an overview of RF and VLC systems, and they compared the basic system components and modulation techniques of RF and VLC technologies. In [15], the channel frequency response (CFR) of mmWave and VLC channels was investigated. The results show that the channel for VLC is frequency-selective and lowpass, while the channel for mmWave is almost flat. The same results can be found in [16], where the path loss (mmWave) and received power (vehicular VLC) are also given. In [17], the authors presented the results of path loss and time dispersion characteristics for mmWave (measurement) and VLC (simulation), respectively, under different configurations, e.g., room size, transceiver deployment, and operating frequency. They find that mmWave and VLC channels share some common characteristics but also some differences in path loss and time dispersion.  
Generally, the existing research of propagation characteristics comparisons between mmWave and VLC bands under the same conditions (conference scenarios and parameters setting) is rare, but it is worth greater attention.
## 3 Preliminaries: Network Model and Problem Definition
### Distributed Offloading Scheduling Rules  
Considering the mobility of users in MEC network and the hidden trouble of nodes and lines, it is necessary to design a set of offloading scheduling rules to deal with a variety of unexpected situations. In an ideal situation, the offloading scheduling rules are shown in Figure 4.  
The description is as follows:  
1. If the user is within the service area, when they generate a task, they first report the summary information of the task to the nearest edge node they are connected to (referred to as the \"decision node\").
2. The decision node makes an offloading decision based on this summary information and network conditions and sends it back to the user.
3. Users perform local calculations or offload tasks to decision nodes based on the received decision results.
4. After receiving task data, the node forwards the task to the offloading target node for calculation.
5. After the calculation is completed, offloading the target node will determine the node closest to the user as the \"return node\", and then send the calculation result to the return node in the shortest path.
6. The final calculation result is sent back to the user by the return node.  
Figure 3: Synchronization timeout due to disconnection (The blue arrow is the direction of information transmission, and the red cross indicates that the sender and receiver are not connected).