esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Case Study for DMSA in Dynamic Scheduling
#### 4.2.3 Simulation Results Analysis
Some may be concerned that our trimming method is reducing computational complexity at the cost of losing potential to find a global optimum, but we can see the _CR_ and _PR_ values are both at satisfactory level. Every time 'old' TS stops its iteration and updates its solutions' structure, the 'new' TS searches the observation set again and tries new windows combinations of unscheduled targets. A new revenue check and de-conflict process will be calculated to see whether the scheduled targets can be substituted or deleted due to insertion of new ones, which can exploit new parts of the searching space. Moreover, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target ID** & **Priority** & **Satellite ID** & **Operation** & **Start Time** & **End Time** \\ \hline Target8 & 2 & S4\(\rightarrow\)S1 & reschedule & 17:27:06 & 17:29:43 \\ Target12 & 4 & S3\(\rightarrow\)S1 & reschedule & 11:52:48 & 11:56:00 \\ Target30 & 4 & S2\(\rightarrow\)none & delete & / & / \\ Target31 & 5 & unscheduled & delete & / & / \\ Target32 & 5 & S3 & insert & 15:48:54 & 15:52:55 \\ Target33 & 5 & S4 & insert & 15:11:49 & 15:18:07 \\ Target34 & 5 & S4 & insert & 7:28:31 & 7:30:58 \\ Target35 & 5 & S2 & insert & 16:29:58 & 16:32:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Dynamic scheduling plan. Figure 8: Gantt chart of dynamic scheduling. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Case** & **Satellite** & **Initial** & **Malfunction** & **Newly** & & & & **Run Time** & **Run Time of** & **Evaluation** \\
**No.** & **Number** & **Targets** & **Arrived** & **Targets** & & & & **Plan(s)** & **Plan(s)** & **Plan(s)** & **Plan(s)** \\ \hline No.1 & 3 & 25 & 0 & 5 & 0.967 & 0.969 & 0 & 1 & 4.2 & 1.2 & 0.98 \\ No.2 & 6 & 50 & 5 & 10 & 0.950 & 0.974 & 0.102 & 0.867 & 5.3 & 1.5 & 0.92 \\ No.3 & 8 & 50 & 10 & 20 & 0.971 & 0.981 & 0.22 & 0.967 & 6.1 & 2.4 & 0.92 \\ No.4 & 8 & 100 & 15 & 30 & 0.915 & 0.932 & 0.196 & 0.911 & 18.7 & 3.7 & 0.89 \\ No.5 & 12 & 100 & 15 & 35 & 0.933 & 0.938 & 0.188 & 0.96 & 20.2 & 5.3 & 0.91 \\ No.6 & 16 & 200 & 20 & 40 & 0.979 & 0.981 & 0.12 & 0.983 & 31.4 & 8.1 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Simulation results of dynamic scheduling. the tabu list will memorize the last'moves' and avoid repeated search, which helps to prevent easy convergence to a local optimal.
# Time Shared Optical Network (TSON): A Programmable Network Edge Solution for Multi-Access Support
## 2 TSON, the Past  
The TSON solution has evolved over the course of the TOUCAN project. Its evolution in the past comprised four stages: the TSON baseline solution, the multi-protocol solution in support of cloud radio access network (C-RAN), the extensions required for the support of resilient C-RAN services through network coding (NC) capabilities, and the extensions of TSON to support transmission capabilities for both metro and long-haul networks. This section reviews theses evolution stages.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 6 The Arrowhead Framework
### Service Levels of Arrowhead
#### 6.1.2 Supporting Systems  
These systems maintain general services that are often needed in SoS, so integrators do not have to implement their solutions for such standard services. The Arrowhead framework defines various systems on this level, currently including the Gateway and Gatekeeper Systems for inter-cloud communication (data and control plane, respectively), the Workflow Choreographer and Executor (to trigger the next step in the process execution), the Event Handler (to circulate status and event information), and the Plant Description System (to keep track of SoS- or Plant-related meta-data), amongst others.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Membrane Evaluation Methods
#### 5.1.2 Ion Exchange Capacity, Ionic Conductivity and Area Resistance Measurements  
The ion exchange capacity of the membrane is determined by a titration method described by [PERSON] and [PERSON] [68]. The amount of H\({}^{+}\) and OH is calculated from titration and the ion exchange capacity is calculated as the ratio of amount of H\({}^{+}\) or OH\({}^{-}\) to the weight of the dried membrane. The ionic conductivity of the membranes is determined by impedance spectroscopy and the measurements can be conducted in an area resistance test cell as designed in an experiment done by [PERSON] and [PERSON] [17]. The test cell used, comprised of the membrane secured between two rubber gaskets, certain quantity of electrolyte in each of the half cells and two graphite discs covered with epoxy to act as electrodes. The same test cell can be used for determining the area resistance of the membrane. Resistance of the cell with the membrane (\(r_{1}\)) and resistance of the cell without the membrane (\(r_{2}\)) is measured and substituted into the following equation to calculate the area resistance of the membrane (\(R\)). A represents the area of the membrane.  
\[R=\left(r_{1}-r_{2}\right)\times A \tag{4}\]
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 2 Experimental Method
### Load Application  
For load tests, M1 class weights adhering to the International Organization of Legal Metrology standards were used, specifically weights of 20, 50, 100, 200, and 300 g, as outlined in Table 2. Given the discrepancy between the diameters of the smallest weight (13 mm) and the Flexiforce sensing area (9.53 mm), direct application of the weights could lead to uneven load distribution. To address this, a custom-made puck produced via 3D printing was utilized to distribute the weight evenly across the sensing area (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Flexiforce** & **Interlink Electronics** \\ \hline Model no. & A201-1 & FSR-402 \\ Sensor type & Thru & Shunt \\ Force-sensing range & 0–4.4 N & 0.1–10 N \\ Actuation force & - & 0.1 N \\ Size 1 & 9.53 mm & 18.28 mm \\ Sensing area & 71.29 mm2 & 92.32 mm2 & calculated value \\ Response time & \textless{5} \(\upmu\)s & \\ \hline \hline \end{tabular} \({}^{\ddagger}\) indicates the sensor outer diameter in the case of Interlink and the sensing diameter in the case of Flexiforce.  
\end{table}
Table 1: Test sensor specifications.  
Figure 1: Schematic diagrams and sensing area of the test FSR sensors. (**a**) Flexiforce A201-1 and (**b**) Interlink FSR-402.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 4 Inverse Problem  
The inverse problem entails estimation of the damage parameters by means of a suitable operator, which compares the replica vector to pseudo-experimental or experimental data. A simple and widely used approach to the solution of this inverse problem consists of the minimization of an objective function \(H\), which measures the difference between numerical and measured modal curvatures as a function of the damage parameters \(\mathbf{x}\):  
\[H(\mathbf{x})=\sum_{i=1}^{3}||\hat{\mathbf{x}}_{i}(\mathbf{x})-\mathbf{d}_{i}||. \tag{9}\]  
Here, our aim is to compare the performance of different approaches to the solution of the inverse problem. The approaches include the objective function (9), as well as the Bartlett and the MVDR beamformers. The contributions of the first three modes will be superimposed in order to ensure a unique value of the beamformer:  
\[\begin{split} B_{Bartlett}(\mathbf{x})&=\sum_{i=1 }^{3}B_{Bart,i}(\mathbf{x})\\ B_{MVDR}(\mathbf{x})&=\sum_{i=1}^{3}B_{MVDR,i}( \mathbf{x}).\end{split} \tag{10}\]  
In order to evaluate the performance of the different estimators (9) and (10) in locating and quantifying damage, both numerical and experimental data are used. Notches with different depths and locations, either coinciding with the sensor position or in between two sensors, are considered.
# Coordination in Emergency Response System Design:
## 7 Case Study: Hazmat Transportation in Chengdu City
### Improving Hazmat Emergency Response in Chengdu
As expected, Table 7 also shows that the average response and wait times are monotonically decreasing as the number of new facilities, and hence the number of nodes in the preferred zone, increase. We also carried out computational experiments to demonstrate the value of coordination in designing the emergency response system in Chengdu City. To this end, we compared the results of establishing the ambulance and fire stations independently with that of the simultaneous design approach we propose in this paper. The results are depicted in Table 8, which is split into two parts: The left side corresponds to the independent design approach, whereas the coordinated design approach results are on the right side. Note that currently [NA, NF] = [62, 20]. In Table 7, the column [62, 25] + [67, 20] represents the construction of five new ambulance stations and five new fire stations by solving the [62, 25] and [67, 20] problems separately, and providing hazmat emergency response with the resulting system. Whereas the [67, 25] column on the right side corresponds to building the same number of stations in a coordinated fashion using the model formulation in Section 4. It is evident from Table 8 that the coordinated design approach always performs better than the independent design approach, in terms of the resulting wait times. We also observe that the wait time reduction through coordinated design increases substantially with the problem size. In particular, [62, 45] + [87, 20] has a 25.8% longer average wait time than that of [87, 45].
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 3 Measurement Program Design
### Generation of Square Wave  
The capacitance measurement method proposed in this paper is unaffected by the waveform of the input signal. Whether using square wave excitation signals or trapezoidal signals, the proportional coefficient can be directly fitted to determine the capacitance. The reference voltage reference V_ref of this system is 2.5 V. In order to adapt to the measurement of different capacitance values, the system needs to generate a square wave excitation signal whose square wave excitation times can change over time. For a 12-bit DAC, the digital quantity variation is 0-4095.  
As shown in Figure 4 shows the schematic diagram of the hardware design of the circuit. The DAC generates the square wave signals, and the dual-channel synchronization of ADC acquires the \(V_{i}\) and \(V_{o}\) signals [27]. Taking into account the impact of parasitic capacitance in operational amplifiers, the experimental hardware in this study uniformly employs the GS324 operational amplifier. By consulting the datasheet and conducting actual measurements, the parasitic capacitance of this specific model was determined to be a fixed value of 7 pF. In the software programming for capacitance calculation, this parasitic capacitance is consistently compensated for to eliminate its influence.  
The square wave excitation signal is generated by the DAC module, and the flowchart of square wave excitation signal generation is shown in Figure 5.  
Figure 4: Circuit hardware design schematic.  
Figure 5: Flow chart of square wave excitation signal generation.
## 1 Introduction
Therefore, the vehicle routing problem with time window constraints has become a research hotspot. Currently, many novel intelligent algorithms are applied to solve the vehicle routing problem with time window constraints [37]. [PERSON] solved the vehicle routing problem with time-dependent soft time windows and random travel times by using the tabu search algorithm and the adaptive large neighborhood search algorithm, and showed that the method could be used to solve some other large-scale problems [38]. [PERSON] used artificial bee colonies that simulate the foraging behavior of bees and a hybrid hyper-heuristic algorithm to solve the multi-objective vehicle routing problem with time windows [39]. [PERSON] proposed a method to describe the spatial distribution of solutions using a distribution estimation algorithm using the generalized Mallows distribution as a probabilistic model to solve vehicle routing problems with time windows [40]. This paper starts from an actual scientific engineering optimization problem, takes the artificial electric field algorithm as the research object, and uses the characteristics of the sine cosine algorithm (SCA) so that it is not easy to fall into a local optimum and has a fast convergence speed. The method of combining theoretical analysis, numerical calculation, and engineering application was adopted. The theoretical system of the artificial electric field algorithm for solving intelligent logistics scheduling problems was constructed. The performance of the new algorithm was tested and verified through practical engineering optimization problems to effectively and systematically research the framework, theory, and application of the new optimization algorithm. The work conducted in this paper is the first application of the artificial electric field algorithm to the VRPTW, which is an innovative application. The main problem faced in the solution process of the simulation experiment is how to code and optimize. After many attempts, it was finally decided to use the natural number encoding method, combined with ant colony optimization, to solve the encoding strategy of such problems, and the improved algorithm of this paper was used for loop iteration. The innovations and main contributions of this paper are as follows:* An enhanced artificial electric field algorithm with the sine cosine mechanism, named SC-AEFA, is proposed; An optimization scheduling algorithm for logistics distribution vehicles based on the SC-AEFA is proposed for the first time;
* A map grid model for enterprise logistics distribution vehicle path planning is established;
* Comprehensive experiments were designed and executed to prove the effectiveness of the SC-AEFA through test functions and the case of an actual distribution business, named fresh enterprise A.
## 3 Analysis of Success Probability
### Conditional Success Probability  
The analysis obtained in Section 3.1 is not sufficient when characterizing non-linear metrics such as the average AoI in any wireless network since the probability of successful transmission is a location-dependent random variable, hence the need to obtain the meta distribution [30], i.e., the distribution of the conditional transmission success probability.
## 7 Discussion of Results  
The proposed method was simulated in a heterogenous WSN environment where sensor nodes were randomly distributed with varying. The heterogeneity in node mobility and positioning requires a flexible clustering approach that can adapt to changes in the network topology. The performance of our proposed algorithm was measured in terms of algorithm execution time, error rate, delivery rate, and energy consumption rate when packets of data are transmitted from the sensor nodes to the CHs. The execution time is the total time taken to complete a specific computational task or simulation [32]. The error rate is a measure of the number of packets that are lost or corrupted during transmission as compared to the total number of packets sent. The delivery rate, also known as the packet delivery ratio (PDR), is a measure of the effectiveness of the network in successfully delivering packets from the source to the destination (e.g., from sensor nodes to the sink) [33]. It is the ratio of the number of packets successfully received by the destination, in this case, the base station, to the total number of packets sent from the sensor node. Energy cost is the total energy expense incurred by sensors for generating and transmitting data to their respective CHs [34].  
The Average Energy Cost was computed as the average energy consumed by each sensor when transmitting to its respective cluster head. The average error rate is the average error that occurs across all sensors when transmitting to their respective cluster heads, whilst the average delivery rate is the average of successfully delivered data from all sensors to their respective CHs within the network.  
From Table 3, the proposed K-BCO performs better than H-LEACH, DBCP and ABC-ACO in terms of average error rate (AER), average data delivery rate (ADDR), and average energy consumption (AEC) or energy cost for a data packet to be transmitted from sensors to their respective cluster heads. The K-BCO also shows 20.15%, 24.92% and 5.18% success in data delivery over H-LEACH, DBCP and ABC-ACO, respectively. In terms of energy consumption by sensors, the K-BCO shows lesser energy usage than H-LEACH, DBCP, and ABC-ACO. However, the limitation of the proposed K-BCO is that it did not perform well in terms of execution time (ET) compared to all three state-of-the-art algorithms.
###### Abstract  
Global navigation satellite systems (GNSS) provide a great data source about the ionosphere state. These data can be used for testing ionosphere models. We studied the performance of nine ionospheric models (Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, and GEMTEC) both in the total electron content (TEC) domain--i.e., how precise the models calculate TEC--and in the positioning error domain--i.e., how the models improve single frequency positioning. The whole data set covers 20 years (2000-2020) from 13 GNSS stations, but the main analysis involves data during 2014-2020 when calculations are available from all the models. We used single-frequency positioning without ionospheric correction and with correction via global ionospheric maps (IGSG) data as expected limits for errors. Improvements against noncorrected solution were as follows: GIM ISGC--22.0%, BDGIM--15.3%, NeQuick2--13.8%, GEMTEC, NeQuickG and IRI-2016--13.3%, Klobuchar--13.2%, IRI-2012--11.6%, IRI-Plas--8.0%, GLONASS--7.3%. TEC bias and mean absolute TEC errors for the models are as follows: GEMTEC---0.3 and 2.4 TECU, BDGIM--0.7 and 2.9 TECU, NeQuick2--1.2 and 3.5 TECU, IRI-2012--1.5 and 3.2 TECU, NeQuickG----1.5 and 3.5 TECU, IRI-2016--1.8 and 3.2 TECU, Klobuchar--1.2 and 4.9 TECU, GLONASS--1.9 and 4.8 TECU, and IRI-Plas--3.1 and 4.2 TECU. While TEC and positioning domains differ, new-generation operational models (BDGIM and NeQuickG) could overperform or at least be at the same level as classical empirical models.  
ionosphere; models; global navigation satellite systems; Klobuchar; NeQuick; IRI; GLONASS; GEMTEC; positioning; total electron content +
Footnote †: journal: _Sensors_**2023**, 23, 4773. [[https://doi.org/10.3390/s23104773](https://doi.org/10.3390/s23104773)]([https://doi.org/10.3390/s23104773](https://doi.org/10.3390/s23104773))
## 6 Real-World Application
### BME Parking Area  
We demonstrate the above concept in a real-world experiment at a BME (Budapest University of Technology and Economics) parking area. We set up two LiDAR-camera sensor Ouster (San Francisco, CA, USA) and FLIR (Richmond, BC, Canada) stations and define a reconstruction area, which is shown in red in the satellite image (Figure 14). This is where the _Semantic Mesh Model_ is created corresponding to a vehicle. We drive a variety of vehicles through this area. Then, we use a cheap Dahua IP camera (Hangzhou, China) to accurately localize the vehicle. Please note that this single-frame localization yields training-free 6 DOF pose estimates; therefore, the planarity of the ground or other environmental constraints do not play a role. This cannot be reproduced by other algorithms to the best of our knowledge.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 5 Performed Experiments
### Standard Discretisation Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Nr of**} & \multicolumn{5}{c}{**Inducer**} \\ \cline{3-8}
**Bins** & **Dataset** & **NB** & **BNet** & **J48** & **kNN** & **RndF** \\ \hline
02 & F-writers & **91.18** & 90.63 & **91.18** & 83.89 & 87.64 \\  & M-writers & 75.35 & **76.32** & 73.82 & 68.47 & 75.97 \\ \hline
03 & F-writers & **92.85** & 88.68 & 85.63 & 86.95 & 90.35 \\  & M-writers & 75.70 & **81.11** & 79.72 & 71.25 & 79.31 \\ \hline
04 & F-writers & **93.41** & 92.85 & 86.46 & 88.61 & 91.67 \\  & M-writers & 81.60 & **81.74** & 69.31 & 75.21 & 84.38 \\ \hline
05 & F-writers & 92.85 & 85.63 & 86.18 & 85.07 & **92.92** \\  & M-writers & **81.11** & 75.77 & 78.96 & 73.54 & 82.71 \\ \hline
06 & F-writers & **94.10** & 89.86 & 88.20 & 89.17 & 94.03 \\  & M-writers & 80.35 & **81.11** & 74.79 & 73.47 & 80.35 \\ \hline
07 & F-writers & **95.77** & 89.31 & 81.74 & 85.63 & 95.84 \\  & M-writers & 80.42 & 81.67 & 78.75 & 77.85 & **82.78** \\ \hline
08 & F-writers & 92.29 & 86.25 & 88.20 & 88.47 & **92.78** \\  & M-writers & 80.49 & 78.20 & 77.92 & 75.28 & **81.60** \\ \hline
09 & F-writers & **94.59** & 89.93 & 88.27 & 86.81 & 93.96 \\  & M-writers & 80.42 & 80.49 & 79.79 & 77.57 & **83.96** \\ \hline
10 & F-writers & **93.47** & 91.60 & 87.16 & 86.88 & 92.85 \\  & M-writers & 79.86 & 77.02 & 80.91 & 75.35 & **83.41** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance [%] of inducers for the datasets discretised by the standard unsupervised equal frequency binning.
# Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
## 3 Method: EMD and its Multivariate Extension
Thus, the MEMD algorithm returns (N + 1) matrices \(45^{\circ}\times 1080^{\circ}\), representing the N modes plus the residue in which the signal is decomposed. From each of these N matrices, we selected the 360 central columns and the rows representing the latitudes above \(50^{\circ}\). In this way, (N + 1) rectangular maps of \(40^{\circ}\times 360^{\circ}\) are obtained, represented in polar coordinates, allowing for better visualization of the spatial distributions of the features characterizing each mode. Each multivariate empirical mode is characterized by a distinctive scale, which can be roughly visualized by considering a fixed magnetic latitude and checking how many times the electron density passes from positive to negative values. This happens with a specific periodicity in MLT depending on the mode. We evaluated this average timescale/period by considering the 40 time series of 360 data points, obtainable from each one of the 40 rows of the aforementioned matrix. It is possible to obtain a periodogram from each series that provides the signal spectral density as a function of the frequency. By averaging the 40 periodograms relative to each fixed magnetic latitude, a single mean periodogram characterized by a peak corresponding to the most significant frequency of the mode can be obtained. The inverse of the frequency corresponding to this Fourier PSD peak provides an indication of the longitudinal variability associated with the considered mode. Because we report our results in MLT coordinates, we are able to represent the average periods in units of time (minutes).