esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Hybrid Photovoltaic Solar Assisted Loop Heat Pipe/Heat Pump System
## 4 Simulation Results and Discussion
### Factors Affecting the Switchover from LHP Mode to HP Mode  
Solar irradiation and ambient temperature were the two main external factors influencing the operating performances of the PV-SALHP/HP system. In order to contrast the variations of operating performances with the two main factors, we assumed heating time to be the same under different conditions. Therefore, water was assumed to be heated within 360 min from 15 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. When using LHP/HP mode to make hot water within this limited time, it was necessary to choose a rational moment for switching LHP mode to HP mode based on the consideration of optimal energy savings.  
In this model, the operating assumption was that the LHP/HP mode took 360 min in total to heat 150 L of water from 15 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. The water temperature when switching LHP mode to HP mode was considered to be a key parameter, representing the moment of switchover under the consideration of optimal energy savings. The simulated operation performances of LHP/HP mode under different solar irradiation and ambient temperature are shown in Figures 8 and 9. It can be observed that with the increase of solar irradiation and ambient temperature, LHP mode could be utilized for longer heating times because the temperature of the working medium in the PV/T evaporator was much higher than that in the condenser. Furthermore, the switching water temperature was higher and HP mode was utilized for a shorter heating time. Therefore, the compressor of HP mode consumed less power, as shown in Figure 9. In addition, COP of HP mode slightly increased with the increase of solar irradiation and ambient temperature. This result was owing to the fact that although the heat gain of evaporator increased with the increases of solar irradiation and ambient air temperature, the compressor power of HP mode also increased with the rising water temperature.  
Figure 8: Variations of switching water temperature with weather conditions for LHP/HP mode.  
Figure 7: Variations of compressor work and overall efficiency with heating time for HP mode.  
Therefore, with the increase in solar irradiation and ambient temperature, LHP mode should be prioritized as much as possible to save energy when solar irradiation and ambient temperature are high.
## 4 Evaluation of Quality and Efficiency
### Quality and Efficiency Criteria
Two load-time histories are used in this paper to compare the service lives:  
* A load sequence with a constant amplitude;
* A normally distributed load sequence with irregularity factor I = 0.7 [76]. Figure 12A shows the load sequence with a constant amplitude, and Figure 12B shows a section of 0.01% of the reversal points of the normally distributed load sequence [76], which contains the maximum load. By using a constant amplitude loading and a variable amplitude loading, the two most relevant load cases are considered in the quality rating. From the load-notch-strain curve and the load-time history, the local stress-strain paths are simulated using the principles described in Section 2.2, and the closed hystereses are determined. For each closed hysteresis, a load parameter PRA  
Figure 11: Procedure used for service life estimation based on the FKM guideline nonlinear [25]. Figure 12: Load-time history: (**A**) constant amplitude loading; (**B**) 0.01% of the normally distributed load sequence with I = 0.7 [76]. culated, and a load parameter spectrum is created. The second possible damage parameter \(\mathrm{P_{RAJ}}\)[25] (see Equation (2)) is not considered for carrying out the quality assessment. Since this is a comparison of two calculated service lives that differ only in the load-notch-strain curve used, no qualitatively different results are expected if \(\mathrm{P_{RAJ}}\) is used. Using linear damage accumulation, a service life is calculated from the load parameter spectrum and the load parameter Wohler curve. For the quality assessment, this procedure is carried out once with a reference load-notch-strain curve and once with load-notch-strain curve using the methods described in Section 3. Afterwards, the resulting service lives are put into relation. The boundary conditions used for determining the reference load-notch-strain curve and the load-notch-strain curve with the methods from Section 3 are given in each section of the quality assessment. The efficiency criterion is the comparison of the sum of all computation times on all computation kernels used to determine the reference load-notch-strain curve or the load-notch-strain curve with the methods given in Section 3. The sum of all the computing times is referred to as CP-Time (computing time).
# An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart
## 1 Introduction
Although there is a certain number of works on control charts coupling attribute and variable inspections in the literature, research on monitoring the variability of univariate processes through variance, i.e., B-ATTRIVAR SS \(S^{2}\), is still lacking. The novelty of this study lies in proposing an interface capable of receiving input data from a user thereof, to obtain control limits through simulations and generate a (B-ATTRIVAR SS \(S^{2}\)) chart so as to monitor the variability of a univariate process. The control chart strategy proposed herein is similar, particularly to those from: Ref. [9], as both make use of attribute inspections to monitor the process variance, Ref. [10], given that the attribute and variable data were also used to monitor process variance, and [15], who proposed the most similar strategy but used it to monitor process mean using three attributes in the first monitoring stage (instead of two as in this study). Even though in-control process parameters are usually unknown in practice and require estimates using historical data, this paper focuses on monitoring processes where these parameters are known [16]. In addition to advances in statistical process control strategies, an integration of computer interfaces is also crucial to increase the manufacturing system's efficiency. As discussed by [17], the convergence between digital models and physical industrial environments using data is of paramount relevance to smart manufacturing. This is due to the fact that it can provide interaction and information exchange between software, the computational interfaces, and the physical systems of manufacturing processes. In this context, the application of computational interfaces offers new possibilities for monitoring and controlling industrial processes in real time. Although this work mainly focuses on introducing an interface developed using R and Shiny to monitor process variability, it is also worth mentioning the potential of integrating it with industrial digital systems, such as manufacturing execution systems (MES). Despite not being directly discussed in this study, the role of MES in facilitating quality monitoring and process optimization is also worth being mentioned. The authors of [18] explain that MES can provide a useful platform in quality monitoring, as it allows direct data acquisition and the monitoring of quality parameters' variability. Ref. [19], for instance, explores the possibilities of using control charts to analyze the data stored in MES and provide feedback to the systems aiming to optimize production parameters. This manuscript is organized as follows: Section 2 describes the B-ATTRIVAR SS \(S^{2}\) chart and draws a comparison with more usual charts; Section 3 explains how its application was conceived and the manner in which the results have been achieved; Section 4 presents its interface and operation, as well as a validation of its results; and Section 5 draws its conclusion and proposes suggestions for future works.
## 10 Power Consumption Comparison  
The power consumption of the WAS using the sleep/wake scheme and the _SWORD_ algorithm is compared with the power consumption of other schemes presented in existing studies on agriculture monitoring systems (Figure 18) to verify the proposed system.  
The performance of this work in terms of current consumption is achieved based on the methodology designed in Sections 6-8 and is validated by the actual hardware (prototype) implementation (Section 4) and on-site measurement (Section 9). The proposed algorithms are compared with those presented in related studies based on the values recorded during measurements in the previous literature. The performance in terms of average current consumption of previous methods is presented in detail in Section 2.  
Although the previous works are based on energy efficient approaches, however, it is important to acknowledge that the measurement taken from this study might differ from the previous works in the literature, taken into account different factors, which include: (i) the sensors' power requirements, (ii) techniques/algorithms introduced in the previous works and (iii) variation of the agriculture conditions (such as humidity, temperature and other environmental conditions) where the previous works have been deployed.  
The sensor node current consumption of the proposed WAS is reduced based on two approaches: the first is sleep/wake scheme adopting DC and the second is _SWORD_ algorithm. The current consumption of the sensor node (0.731 mA), router node (0.967 mA), and main router node (1.86 mA) are achieved based on sleep/wake scheme using low DC. We observe that the current consumption of the proposed WAS using the _SWORD_ algorithm is approximately similar to the performance of the approaches in [35; 36; 37], which have achieved a current consumption of 0.1 (based on sleep/wake), 0.118 (based on sleep/wake), and 0.227 mA (based on DC), respectively. The average current  
Figure 18: Current consumption of the proposed algorithms, together with the other energy efficient schemes used specifically in precision agriculture application.  
consumption of our proposed WAS is 0.1 mA and 0.731 mA for the _SWORD_ algorithm and the sleep/wake scheme, respectively, as shown in Figure 18.
## 2 Materials and Methods
### Investigated Datasets
#### 2.1.2 Earthquake Catalog Acquisition and Pre-Processing  
We retrieved the M5+ global earthquakes (EQs) that occurred in the same analyzed time interval from the USGS global catalog, which provides an estimation of the hypocenter, origin time, and magnitude (generally, the moment magnitude Mw calculated from the centroid moment tensor inversion, or, in alternative, the short-period body wave magnitude mb or other values). We then applied a declustering technique using [PERSON]'s [35] method to remove aftershocks and foreshocks. With respect to the previous work of [PERSON] et al. [29], here, a slightly larger time window is utilized--10 days before and 20 days after the events--to search for other possible related events of the same cluster, while the other parameters were unchanged (i.e., a radius of 10 km, magnitude cut-off of 5.0, confidence level of 95%, hypocenter uncertainty according to the catalog). For further details on the declustering of the catalog and how this impacts this work, the reader is invited to see Appendix A. In the declustered catalog, only the shallow (depth \(\leq\) 50 km) EQs with a magnitude greater than or equal to 5.5 are selected as the seismic dataset for further processing. In the first 8 years of _Swarm_, slightly fewer than 2200 shallow M5.5+ earthquakes were finally selected.
## 4 Verification of Algorithm Feasibility
The results of the datasets after network training and cross-validation are listed in Table 1: the accuracy rate of recognizing effective signals is 97%, the accuracy rate of recognizing noise signals is 98.5%, and the accuracy rate of identifying the signals in the whole dataset is 98%. Figure 4: The signal-to-noise ratios of typical signals before and after processing by the algorithm. Figure 5: The root-mean-square errors of typical signals before and after processing by the algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Signal Type**} & **Data Quantity** & **Number of Correctly Identified Components** & **Accuracy Rate of** & **Accuracy Rate of** \\  & **Quantity** & **Identified Components** & **Component Identification** & **Signal Identification** \\ \hline Effective components of effective signals & 200 & 196 & 98\% & 97\% \\ Ineffective components of effective signals & 400 & 387 & 96.75\% & 97\% \\ Components of noise & 1200 & 1174 & 97.83\% & 98.5\% \\ Total & 1800 & 1757 & 97.61\% & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of dataset network training and cross-validation.
## 3 Methodology  
We present a methodology that was adapted to quantify the propagated uncertainty in QoE due to statistical errors in QoS parameter measurements. The QoS parameters considered in this paper are PLR and jitter. In this work, we considered PLR as purely random Bernoulli process, and jitter is defined as the standard deviation of the delay distribution.
## 2 Methodology
The excitation in time was a Hanning-windowed tone burst with the centre frequency of the desired mode. Data were evaluated using 4096 points over a 500 mm distance resulting in a 0.12 mm step. Modelling was performed in the frequency domain allowing the use of infinite domains. For GW to be accepted for COPVs' health monitoring, damage must be detected in any part of the structure, be it the aluminium liner or the CFRP overwrap. To allow for damage detection in both parts, modes showing characteristic interaction with damage, therefore, need to be identified. For this, damage in the aluminium part was modelled by disconnecting two SBFEM domains, representing a crack of 1 mm in depth and zero width, respectively. Damage in the CFRP part was modelled by disconnecting two SBFEM boundaries, representing a delamination of 10 mm in length and zero width between two CFRP-plies. The delamination was modelled at two different positions in two separate simulation runs. The positions were between the second ply and the third ply (Position (1)), and the third ply and the fourth ply (Position (2)) (counted from top to bottom). Figure 3 schematically shows \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\rho\) (kg/m3) & \(E_{1}\) (GPa) & \(E_{2},E_{3}\) (GPa) & \(G_{12},G_{13}\) (GPa) & \(\
u_{12},\
u_{13}\) & \(\
u_{23}\) \\ \hline
1490 & 121 & 8.6 & 4.7 & 0.27 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material properties of the epoxy carbon ply used (\(E_{1}\) is in the fibre direction). \begin{table}
\begin{tabular}{c c c} \hline \hline \(\rho\) (kg/m3) & \(E\) (GPa) & \(\
u\) \\ \hline
2770 & 70 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties of aluminium used. Figure 3: Schematic of the numerical model used in the SBFEM. the different locations of the damages. It further shows that the excitation has been chosen to act along the thickness while the recordings have been made on the top and bottom of the hybrid composite as well as between the CFRP and aluminium part respectively being denoted as \"evaluation points\".
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Other Mobility Issues  
Additional crucial parameters that determine network performance are QoS, bandwidth, power levels, coverage, use of high frequency bands, and latency [67]. Due to the high mobility of drones, drone networks are more vulnerable to frequent handovers. Conventional handover mechanisms will be ineffective. New techniques must consider the potential challenges that drone networks will face.
# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
## 2 Materials and Methods
### TCN-RVM Model
#### 2.2.1 Temporal Convolutional Network  
TCN is a convolutional neural network architecture specifically designed for processing complex time series data. It combines the efficiency of traditional convolutional neural networks with the sequence modeling capabilities of recurrent neural networks (RNNs), while avoiding common issues associated with RNNs, such as vanishing gradients and low computational efficiency. The core idea of TCN lies in the use of causal convolution and dilated convolution to capture long-term dependencies in time series data. As a result, TCN exhibits characteristics such as causality, dilation, and parallelism.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 6 Discussion
### Research Questions
#### 6.1.3 RQ3--Reward Design  
The reward design heavily influences whether the agent will behave as expected. It will not only guide the agent throughout the optimization, but it will also influence the speed at which it converges to the desired behavior. There is no clear preference regarding mixing penalties and rewards or simply using one of them. However, a careful balance between the magnitude of all rewards returned during an episode seems to be useful.  
Interestingly, the reward design can be seen as the most modular approach of the three agent-environment interactions. There are many approaches to the design of the reward function, yet these can be broken down into a relatively small set of concepts, such as mirroring the objective function as the reward. Reward functions can be highly customizable by simply picking and choosing a number of options from this set.  
Most approaches tend to mirror the objective function change between different episode iterations. Moreover, it is very common to have a conditional reward function. In either case, the studied approaches often include extra penalties based on lateness and formulation constrains. Similarly, rewards can be associated with successful entity or resource events.
## 2 Optical Data Information Encoding
### Data Encoding Based on Structure-Specific Slope Angle  
Figure 3 shows the spectrum under _TE_/_TM_ polarizations for structures having increasing slope angles. Twelve reflectance spectra were obtained when the azimuth angle of the structure was fixed at 0\({}^{\circ}\) and the slope angle was adjusted from 25\({}^{\circ}\) to 66.25\({}^{\circ}\) at an interval of 3.75\({}^{\circ}\).  
Figure 2: Description of the encoding method: The “\(\alpha\) v\({}^{\prime\prime}\) encoding form is used, with \(\alpha\) indicating the slope angle and \(\psi\) indicating the azimuth of the overall structure; “1 0” indicates data information encoded for a structure with a slope angle of 25\({}^{\circ}\) and an azimuth of 0\({}^{\circ}\). (**a**) for each 3.75\({}^{\circ}\) increase in slope angle, \(\alpha\) increases by one; (**b**) for each 10\({}^{\circ}\) increase in azimuth angle, \(\psi\) increases by one.
## 3 Results and Discussions
### Loss of Heat Sink Accident
#### 3.3.2 Protected Loss of Heat Sink  
The simulation results of ADS and CNES for the protected loss of heat sink are shown in Figure 13, where all secondary pumps stop at 5 s, and the shutdown signal occurs at 12 s. As the secondary circuit pumps all stop running, the mass flow rate of the secondary side in the heat exchanger (IHX) drops rapidly, and the outlet temperature of the primary side in the IHX rises, which means that the coolant inlet temperature of the core increases. With the rapid decrease of the reactor power due to the activation of the shutdown protection system, the temperatures of the fuel center and cladding surface also drop rapidly, and then the coolant inlet temperature gradually declines as well. These simulation results indicate that both the ADS and CNES systems have the inherent safety characteristics to ensure reactor a shutdown against loss of heat sink accident.  
Figure 12: Transient responses of unprotected loss of heat sink between the ADS core and the CNES core.
## 3 Preliminaries
### Ad Hoc Routing Protocols  
In ad hoc networks, we have mainly proactive and reactive routing protocols. Proactive routing requires high bandwidth space as, it maintains paths between source and destination even if they are not interacting. In AODV reactive routing paths are made on demand, so highly popular with ad hoc networks [23]. Trust model based on demand reactive routing is suitable for the distributed and pure ad hoc network [24].  
Ad-Hoc on Demand Distance Vector Routing (AODV)  
AODV is the most efficient reactive routing protocol for the ad hoc networks as paths are made on fly that is why called ad hoc on demand distance vector. On-demand is a key feature in AODV routing which means that paths are made on fly and maintained till they require each other services. In AODV each route request packet contains broadcast id, source sequence number, destination Ip address, hop counts and control flags. The sequence number identify the freshness of the routing packet and hop count contains the distance between the source node and the current node.  
When source node S broadcast RREQ packet in search of destination node, each recipient of the RREQ packet looks up in its routing table. If receiving node doesn't contain any information about the destination. It will create a backward path towards RREQ packet initiator and rebroadcast the routing request. Intermediate node receiving this RREQ and will generate a RREP message either if it has fresh route request information to satisfy or itself a destination. After that, this intermediate node will generate RREP packet and will forward it to the next hop towards RREQ initiator intermediary node, as indicated by source node routing table entry. when a node receives RREP packet, it update some fields in the routing table of RREP packet, and then forward it to the next hop and towards the originator (source node). After that a bidirectional path is setup and maintained as long as they required each other services [25].  
Route maintenance is performed by either sending hello messages which acknowledged about the positive connectivity about the nodes and sender can listen these hello messages. Another way is to maintain local connectivity by some link or network layer intrusion detection mechanism [26]. Route maintenance can also be achieved using packet acknowledge in which nodes are in promiscuous mode and can overhear the packet transmission and easily detect malicious attacks [27].