esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since the ANN trains the relationship between strain (= input) and displacement (= output), the number and locations of strain-measurement points used as input have a significant effect on the training results. In general, the larger the number of strains used as input, the higher the accuracy of displacement prediction. Also, advances in sensor technology make it possible to install large numbers of sensors in structures [19; 20]. However, this can cause some economic problems such as cost of installations, data acquisition and analysis, etc. [20; 21]. In addition, even when the same number of strains is used, the displacement prediction accuracy varies greatly depending on the location of the strains. In the field, the number and locations of sensor installations may be determined by one's intuition or empirical knowledge [22]. However, this reduces the usefulness of the measured data, resulting in economic and time loss. Since the number and locations of sensors have a large impact on data utilization, considerable research has been conducted for sensor optimization [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. In those researches, many intelligent algorithms such as GAs, particle swarm, glowworm swarm, artificial bee colony, monkey, ant colony, etc., have been utilized for the optimization. As mentioned, various algorithms have been used for optimization, but methods to optimize the number and locations of strains required for ANNs to predict displacements have not been studied. Hence, in this study, the number and locations of strain-measurement points are optimized using a genetic algorithm. The locations of strain-measurement points as optimization variables and mean squared error of the trained ANN as the objective function are used for the optimization. To verify the proposed method, two cases, a simple beam and bridge under dynamic loading, were carried out by utilizing the numerical models. Also, a laboratory experiment was carried out with the steel cantilever beam. Using the commercial program ABAQUS, the two numerical models are made and analyzed for obtaining ANN training and test data. The neural network toolbox in MATLAB is utilized for ANN training and tests. Also, the global optimization toolbox in MATLAB is used for optimizing the number and locations of strain-measurement points based on a GA.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 6 Discussion  
As presented in the paper, the digital twin of a ship, an SDT, is an evolving concept that can be a useful supplement in the control of the individual ship processes, or can improve the overall control strategy and ship management. As such, an SDT is becoming an important part in both shipbuilding and the ship's exploitation. However, it also presents challenges in all aspects, from data acquisition and communication, modelling and simulation, to the implementation of the concept and standards establishment.  
It can be noted how SDTs should stream towards flexible structures that could be extended and upgraded over time, and how further research should be performed in each direction of the SDT formulation process in order to improve the concept, explore the methods, and expand the application area.  
Further research planed by the authors is oriented towards modelling and prediction of ship motion and hull stress induced by waves. The authors of the paper intend to develop a digital twin of a ship for ship hull condition monitoring and fatigue prediction based on full-scale measurements and also to model the relationship between waves and ship motion. The measurements have already been performed in both case studies.
# Study of System Integral Energy Efficiency of a Hybrid Pneumatic Power System
## 2 Materials and Methods
### Efficiency
#### 2.3.3 Air Motor Efficiency  
The inlet and outlet pressures are fixed for an adiabatic turbine in a steady flow process. Therefore, the turbine is an isentropic process between the inlet and outlet pressures under an idealized process. Hence, the ratio of the actual work outlet of the turbine to the work output of the turbine can be defined as the isentropic efficiency, if the turbine is under an isentropic process.  
\[\eta=\frac{\text{Actual turbine work}}{\text{Isentropic turbine work}}=\frac{W_{at}}{W_{st}} \tag{8}\]  
The value of \(W_{at}\) and \(W_{st}\) can be acquire form the energy balance of the turbine. Otherwise, the efficiency of the turbine is calculated based on the following equation [24]:  
\[\eta=\frac{\omega T}{m\Delta P} \tag{9}\]  
where \(\eta\) is the theoretical efficiency (%); \(\omega\) is angular velocity (rad/s); \(T\) is the torque (Nm); \(\Delta P\) is the pressure difference across the air turbine (bar); and \(m\) is the inlet flow rate of the air turbine.
## 3 Results & Discussion
### Validating the Trained Agents
The SAC agent was employed for stator winding temperature prediction with and without the inclusion of airflow information. The prediction of stator temperature in the first figure, which includes airflow, displays more precise predictions of the operating conditions, whereas the 2 nd figure represents the predicted temperatures without considering the airflow information. The absence of airflow data in the thermal model results in less accurate predictions, in which the agent is unable to capture the immediate effects of varying stator temperature. Adding airflow information to the proposed thermal models matters because it affects the prediction of stator winding temperature. Figure 7: Stator and rotor temperature predictions of driving cycles (DC): DC1, DC2, DC3 using the TD3 agent, DC4 using DDPG, and DC5 using SAC.
## Instrument Description  
### Architecture  
A block diagram of the SPIR instrument is shown in Figure 1. It features two arrays, up- and down-looking, of eight elements each. The polarization of the antenna elements is RHCP (Right Hand Circular Polarization) and LHCP (Left Hand Circular Polarization) for the up and down-looking arrays, respectively. The signals delivered by these antenna elements are fed into 16 identical radio-frequency (RF) receivers that down-convert the signals to an intermediate frequency (IF). The in-phase and quadrature components are digitized with one bit per sample and are sampled at a rate of 80 Msamples/s.  
A clock synthesizer board generates the system reference frequency, from which the sampling clock, the local oscillator and the calibration signals are derived. Sixteen replicas of the local oscillator are delivered to all RF front-ends by using a 1:16 passive splitter. An identical splitter is used to deliver the calibration signal to all receivers.  
The quantized and sampled signals (16\(\times\)21/Q bits) are then injected into the control and recording system, which consists of an industrial computer with a Field Programmable Gate Array (FPGA) board connected to one of its Peripheral Component Interconnect Express (PCIe) slots. This FPGA collects and buffers the sampled signals to write them to an array of disks. In addition to the raw data, it also records ancillary data and the instrument configuration status (reference frequency, local oscillator, selected band, calibration parameters, and time-tagging information). The control and recording subsystem also retrieves GPS time and position information from a standard GPS receiver, as well as a one pulse per second (PPS) signal that is used to synchronize the recording of the data to UTC time.  
The instrument is configured through a program running on the computer, which is also used to start and stop the recording. Once the data have been recorded, they are processed by a dedicated software processor (see Section 2.6).
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 3 Signal Measurement Techniques
### Time-Based Methods
#### 3.1.3 Round Trip Time (RTT)  
The RTT represents the time between sending a signal plus the time required to confirm that signal [27]. In some of its implementations, it is a technology that utilizes the fine timing measurement (FTM) protocol to measure time with picosecond resolution. It allows a mobile device to determine its distance from an access point (AP) by measuring the duration of a time interval of transmission of radio waves traveling back and forth between the transmitter and receiver, which are generally called the initiator and responder. With it, the distance of the target node (R) is obtained with the following equation:  
\[\mathrm{R}=\frac{(t_{RT}-t)c}{2}, \tag{5}\]  
where \(t_{RT}\) represents the time of the signal to travel from a node and vice versa, \(t\) represents the hardware's default delay time, and \(c\) is the transmitted signal's speed. This method solves the synchronization problem. Instead of using two local clocks in both nodes to calculate the delay, a single node is used to record the transmission and arrival times. Errors in estimating RTT distance and user location due to factors such as multipath fading occur most often in an indoor environment. The following factors that may be detrimental are related to the topology of the network infrastructure used for the connection between transmitter and receiver:  
* Propagation delay, which depends on the distance between the transmitter and the receiver;
* Processing delay that depends on the number of nodes on the network. A node can also experience congestion by slowing the connection and increasing the RTT.
# Hybrid Harmonic Suppression Method at DC Link of Series-Connected 18-Pulse Rectifier
## 3 Injection Transformer Turned Ratio Calculation
\begin{array}{l}-4.266N_{8}+0.373N_{8}\cos\frac{\pi\pi}{18}+0.167N_{8}\cos \frac{\pi\pi}{54}+0.132N_{8}\sin\frac{\pi\pi}{9}\\ +6.875N_{7}-2.776N_{7}\cos\frac{\pi\pi}{18}-8.549N_{7}\cos\frac{5\pi\pi}{54}- 1.213\cos\frac{7\pi\pi}{54}\\ -0.54\cos\frac{\pi\pi}{27}-2.162\sin\frac{2\pi\pi}{27}+1.081\sin\frac{\pi\pi}{ 9}+6.222N_{7}\cos\frac{5\pi}{54}\\ -0.156\cos\frac{11\pi\pi}{54}-0.624\cos\frac{13\pi\pi}{54}+0.624\sin\frac{4\pi \pi}{27}-0.653N_{8}\\ -1.508N_{7}+11.793N_{7}\cos\frac{7\pi\pi}{54}+1.508N_{7}\sin\frac{\pi\pi}{27}+ 6.031N_{7}\sin\frac{2\pi\pi}{27}\\ -7.706N_{7}\sin\frac{\pi\pi}{9}-3.479N_{7}\cos\frac{11\pi\pi}{54}-13.916N_{7} \cos\frac{13\pi\pi}{54}\\ +5.05N_{7}\sin\frac{2\pi\pi}{9}-3.817N_{8}\sin\frac{2\pi\pi}{9}+4.06N_{7}\sin \frac{5\pi\pi}{27}\\ \end{array} \right\}\sin nx \tag{22}\]  
From Equation (22), after using the proposed suppression method, the (18\(k\pm\) 1)th and (36\(k\pm\) 1)th harmonics of the input voltages are reduced.
# Decision Support Model for Allocating Maintenance Budgets for Bridges
## 4 Proposed Model
### Computation Process Workflow
#### 4.4.2 Model Objective Function  
The objective function is set for maximizing the bridge priority to be maintained from a set of bridges defined by the maintenance managers. It can be mathematically expressed as Equation (3).  
\[Max\ Priority=\sum_{B=1}^{B=N}\sum_{C=1}^{C=6}(BR_{B}\times CW_{C}\times UCA_{ BC}\times MSW_{BC}) \tag{3}\]  
where:  
\(B\) is the Bridge Number among the bridges prioritized this year for maintenance (1, 2, \(\ldots\), N), \(C\) is the Structural Element Number (6 Structural Elements defined in this study) (\(C\) = 1, 2, 3, 4, 5, 6), \(BR_{B}\) is the Bridge B Rank, \(CW_{C}\) is the Weight of Structural Element \(C\), \(UCA_{BC}\) is Utility Value of Condition Assessment which corresponds to Structural Element \(C\) in Bridge \(B\), and \(MSW_{BC}\) is the Maintenance Strategy Weight that corresponds to Structural Element \(C\) in Bridge \(B\).  
The model is searching for the greatest sum of priorities resulting from having more structural elements included in the maintenance plan for this year while taking into consideration the budget constraint. The model in each iteration records the sum of the priority of the structural elements and their maintenance cost. As the model starts with the first structural element it sums the priority and then takes the corresponding cost and sums it. The second iteration takes the first and second structural elements and sums their priorities and the corresponding costs, and so on. The model records all the iterations and finds the maximum sum of priority and compares the corresponding cost with the budget. If the budget is less, then the model looks for the next maximum sum and compares it with the budget. The process keeps going until it reaches a certain priority sum which corresponds to a total cost less than the budget. The priorities forming that sum are identified by the model as the structural elements requiring maintenance this year.
## 3 Dynamic Modelling
### System Motion Equation  
The wind system motion in the dynamic regime is described by a second-order differential equation represented as a function of the independent input motion of the wind rotor \(R\), i.e., the angular speed \(\omega_{R}=\varphi_{R}\) and angular acceleration \(\varepsilon_{R}=\bar{\varphi}_{R}\), where \(\varphi_{R}\) is the angular displacement of the wind rotor, \(\dot{\varphi}_{R}=d\varphi_{R}/dt\) and \(\ddot{\varphi}_{R}=d^{2}\varphi_{R}/dt^{2}\).  
According to Equations (10) and (18), the torques \(T_{1}\) and \(T_{2}\) are obtained:  
\[T_{1}=T_{\rm G}-J_{1}\varepsilon_{1} \tag{31}\]  
\[T_{2}=\frac{s_{3}J_{3}\varepsilon_{3}-T_{1}i_{1,3}\eta_{3,1}^{-1}}{i_{2,3} \eta_{2,3}} \tag{32}\]  
Figure 5: The influence of wind speed on the mechanical characteristic of a wind rotor: (**a**) mechanical characteristic zones; (**b**) linearized active sector of mechanical characteristics.  
and considering Equations (11), (12), and (31), the angular acceleration is obtained:  
\[\varepsilon_{R}=\frac{T_{R}+T_{G}\Big{(}\frac{i_{1,2}}{n_{2,1}}-1\Big{)}}{J_{1} \frac{i_{1,2}^{2}}{n_{2,1}}+J_{2}+s_{3}J_{3}\frac{i_{3,2}^{2}}{n_{2,3}}} \tag{33}\]  
in which the following obvious replacements are considered:  
\[\varepsilon_{1}=i_{1\_2}\varepsilon_{2};\ \varepsilon_{3}=i_{3\_2}\varepsilon_{2};\ T_{ GS}=-T_{G} \tag{34}\]  
Equation (35) is obtained from Equations (23), (5), and (11):  
\[T_{G}=-a_{G}(i_{1\_2}-1)\omega_{R}+b_{G} \tag{35}\]  
The motion equation of the wind system is obtained replacing Equations (22) and (35) in (33):  
\[\varepsilon_{R}=\frac{-\Big{[}a_{R}+a_{G}(i_{1\_2}-1)\Big{(}\frac{i_{1,2}}{n_{ 2,1}}-1\Big{)}\Big{]}\omega_{R}+\Big{[}b_{R}+b_{G}\Big{(}\frac{i_{1,2}}{n_{2,1} }-1\Big{)}\Big{]}}{J_{1}\frac{i_{1,2}^{2}}{n_{2,1}}+J_{2}+s_{3}J_{3}\frac{i_{3,2}^{2}}{n_{2,3}}} \tag{36}\]  
The following a priori known constant parameters interfere in the differential Equation (36):  
\[a_{R},\ b_{R},\ a_{G},\ b_{G},\ J_{1},\ J_{2},\ j_{3},i_{1,2},\ i_{3\_2},\!\eta_{ 2\_1},\!\eta_{2\_3} \tag{37}\]  
in which the parameters \(a_{R}\) and \(b_{R}\) are dependent on the wind speed, according to linear trend lines in Figure 5b.  
The time variation of the independent parameters (\(\varphi_{R},\ \dot{\varphi}_{R},\ \ddot{\varphi}_{R}\)) is obtained solving Equation (36) under given initial conditions, which describes the evolution in time of the kinematic (displacements, angular speeds, and accelerations) and dynamic behaviour (torques, powers, and efficiency) of the wind system and its components. Equation (36) is numerically solved using Simulink-MATLAB by MathWorks, aspects of which are presented in Section 4.
## 2 The Theoretical Background
### The Physical Internet (PI) Paradigm
They introduced the PI-BGP protocol, a routing approach based on the Border Gateway Protocol (BGP) used in digital networks, to control the transit of PI-containers over interconnected logistics networks. The PI-BGP protocol sought to eliminate delays and increase the container flow throughout a large logistics system by optimising the routing decisions. The simulation-based results showed significant improvements in routing efficiency, reduced delivery times, and better utilisation of transport assets, indicating that effective routing protocols for PI-containers are essential for achieving the PI framework's sustainability goals [34]. [PERSON] et al. [35] proposed a PI-based selective vehicle routing problem (PI-SVRP) to optimise inland container transportation by utilising idle repositioning activities in relay mode, enhancing the profitability and resource efficiency. They developed a mathematical model and efficient heuristics, offering practical insights for reducing idle runs and improving the operational effectiveness.
## 2 Literature Review
[21], [PERSON] et al. [22] and [PERSON] et al. [23]), there has been an increasing number of researches aiming towards a vehicle routing problem with time-dependent traffic information (TD-VRP). Variation of vehicle speeds under different traffic conditions throughout the course of the day, especially in the urban area, reflects real-life complexities of a traffic congestion. In the early TD-VRP research, the problem concerned a step function travel time [24], a probability function [25] or a time-dependent matrix [26]. These time-dependent properties do not satisfy the _first-in-first-out_ (FIFO) property--if a vehicle leaves a node \(i\) for a node \(j\) at a given time, any identical vehicle leaving node \(i\) for node \(j\) at a later time will arrive later at node \(j\)[27; 28]. In order to preserve the FIFO property, [PERSON] et al. [27] proposed a time-dependent model in terms of a vehicle speed in which the speed of a vehicle changes whenever the boundary between two consecutive time periods is crossed. In 2012, [PERSON] et al. [29] adopted the travel speed model and improved the time-dependent matrix by floating car data information which contains travel time information during a 15-minute time slot from GPS probe. To the best of the authors knowledge, so far no study has been carried out in the context of logistics planning with driver experience and time-dependent traffic information. In addition, there are only a few studies that address the practicality of using online time-dependent travel time for a large-scale problem. For example, [PERSON] et al. [30] applied an exact algorithm to solve instances up to 40 locations, while [PERSON] et al. [29] applied a heuristic algorithm to [PERSON]'s instances with 100 locations. In the present study, we deal with two large-scale instances of problem size 609 and 978 customer locations, respectively. In addition, [PERSON] et al. [31] discussed the issue of the time-dependent logistics planning which relies on the next-generation web-based travel information technologies to provide data in milliseconds or microseconds. With today's online mapping technologies, the map API requesting time to obtain a full time-dependent matrix itself for problems considered in this work already exceeds an hour computational time limit. Therefore, searches for the global optimal solution to a large-scale problem are not an option with existing technologies in the present study.
## 2 Results
### Cis-Acting Regulatory Elements Analysis  
Promotor analyses were carried out in order to predict potential transcription factor binding sites (TFBS) involved in the regulation of AsA, by using the software MatInspector (Genomatix software suite version 3.7, Munich, Germany). The MatInspector library of weight matrices for plant TFBS (version 9.4) was screened using 600 bp sequences upstream of the translation start of each of the 23 genes studied. The results were filtered for \(p\)-value \(\leq\) 0.05, core similarity set to 0.75 and the matrix similarity optimized (Table S3).  
Two hundred and sixty-seven matches were found in the sequences analyzed, which were distributed as follows: approximately 50% of the matches were found in the positive strand and 50% in the negative strand. Ten different putative TFBS were identified in the promotors of at least one of the genes analyzed: ABA response elements (ABRE); AS1/AS2 repressor complex (ASRC); ATAF-like NAC domain containing proteins (ATAF); floral homeotic protein APETALA 2 (FLO2); GT-box elements (GTBX); high mobility group factors (HMGF); ID domain factors (IDDF); soybean embryo factor 4 (SEF4); storekeeper motif (STKM); sugar response elements and SURE and SURE-like sequences in plant promoters (SURE, Figure 9; Tables S3 and S4).  
The GTBX putative binding sites were present in all the sequences studied and the number of matches was the highest (112), followed by the ASRC found in 17 genes and, ABRE and the IDDF that were both present in 16 sequences (Table S3). _GalUR12_ and _MDHAR1_ were the genes with more predicted TFBS, 22 and 21, respectively. In contrast, _GGP_, _MDHAR2_, _GalUR10_, _GalUR17_, _DHAR2_, _AO_, _APX1_, and _APX2_ had less than 10 TFBS. The genes _AO_, _DHAR2_, _GalUR10_, and _GGP_ contained the lowest number of different DNA binding motifs in the analyzed upstream sequences. The sugar response elements (SURE) were only found in _GalUR10_, while the storekeeper motif (STK), that has sucrose-inducible gene expression, was identified in 12 genes (_GMP_, _GPP_, _GallDH_, _GalLDH_, _GalUR8_, _GalUR14_, _MDHAR1_, _MDHAR3_, _DHAR1_, _AO_, and _APX3_ (Figure 9, Tables S3 and S4).
## 4 Residual Error Optimization Strategy Based on Root-Mean-Square and Peak-to-Valley Maps  
Based on results of the simulation experiment in Section 3, it can be seen that the RMS and PV values of the residual error after deterministic polishing are influenced by both the bulk material removal depth and the path spacing. This section aims to establish their relationship models with the path spacing and bulk material removal depth, respectively. To achieve this goal, this paper introduces the concept of RMS and PV maps for the first time and proposes a path spacing optimization model based on the above two maps in order to optimize the corresponding RMS and PV values of the residual error. The detailed flow for variable pitch spiral path planning in residual error optimization strategy is also given.
## 3 Results and Discussion
### Molecular Weight-Based PAH Concentration at Different Agricultural Soils  
Figure 4 indicates the three classifications of PAHs based on the molecular weight and their distribution in the different land categories. The results revealed that the low-molecular-weight (LMW), intermediate-molecular-weight (IMW), and high-molecular-weight (HMW) PAHs were in the ranges of 46.06-83.18%, 16.47-48.68, and 0.23-6.51%, respectively. The molecular weight-based concentration followed the sequence as LMW > IMW > HMW. The result noted that a similar pattern was reported for the rural and research area, where the maximum concentration was found from the low molecular weight. This is due to the limited sources of anthropogenic sources of PAHs. Similarly, PAH concentration according to molecular weight followed a similar pattern. But in the industrial areas, IMW PAHs recorded higher concentrations. As in the industrial areas, the  
Figure 2: Depth-wise PAH concentration at different agricultural soils [mean values represented on bars with different letters are significantly different (\(p\) < 0.05)].  
sources of the PAHs were unlocked from the anthropogenic sources along with agricultural sources. The similar finding of increasing medium-molecular-weight PAHs was recorded from industrial areas of Dhaka city, Bangladesh [27]. The industrial area the different mega thermal reactions like heavy machinery, burning of fossil fuel from the machineries used in different industries, and vehicular movement for transportation might cause the highest concentration of IMW PAHs over other groups. The LMW PAHs release from low-temperature processes (biomass burning, smoking), whereas the HMW PAHs originate during high thermal processes (burning fuel, industrial sources) [27]. A similar finding was reported by [PERSON] et al. [31] and [PERSON] et al., [18] who mentioned that anthropogenic sources affect the PAH concentration.  
Figure 4: Molecular weight-based PAH concentration at different agricultural soils [LMW: low molecular weight, IMW: intermediate molecular weight, HMW: high molecular weight].  
Figure 3: \(\sum\)16 PAHs concentration at different agricultural soils [mean values represented on bars with different letters are significantly different (\(p<0.05\))].