esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
We use balanced accuracy and F1 score performance metrics to compare the different methods. Balanced accuracy is a raw accuracy, where each sample is weighted according to its actual class's inverse prevalence. It helps us deal with imbalanced datasets by avoiding inflated performance estimates in our datasets. If the classifier performs equally well oneither class, this term reduces to the standard accuracy. In contrast, if the classical accuracy is above chance only because the classifier takes advantage of an imbalanced test set, then the balanced accuracy, as appropriate, will drop to \(\frac{1}{n\_classes}\).  
\[F1\ Score=\frac{2*(precision*recall)}{precision+recall} \tag{10}\]  
\[Balanced\ Accuracy=\frac{specificity+sensitivity}{2} \tag{11}\]  
where \(Precision=\frac{TP}{TP+FP}\), \(Specificity=\frac{TN}{TN+FP}\), and \(Recall(Sensitivity)=\frac{TP}{TP+FN}\). The experiments have been implemented using PyTorch framework and the labeled datasets used for training, validation, and testing with 50%, 20%, and 30%, respectively. We run the training twenty times to obtain the average results with a learning rate of 0.0001, a batch size of 64, and 1300 epochs.  
Furthermore, we conducted five different experiments so that each experiment was designed with specific goals, including comparison with existing fall-detection methods, finding the optimal sensor position for fall detection, and comparison of different techniques to balance the fall datasets. The experiment and results obtained are discussed below.
# Vibration Responses of a Coaxial Dual-Rotor System with Supporting Misalignment
## 4 Experimental Study of Bearing Supporting Misalignment
### Supporting Misalignment at Bearing 3  
When Bearing 1 is intentionally elevated, the orbit and frequency spectra of the rotor system are shown in Figures 22-24. The figures indicate that the spectral characteristics of the misalignment at Bearings 3 and 1 are similar. It confirms that the supporting misalignment of Bearing 3 will also affect the rotor system. However, the dynamic behavior caused by the misalignment of the two bearings is also somewhat different. One difference is that the orbit is no longer 8-shaped, but a \"banana shape\". Another difference is that the overall amplitude caused by Bearing 3 misalignment is smaller than that of Bearing 1 misalignment. The amplitude of 2\(\times\) also decreases slightly. All in all, these experimental results are basically consistent with the simulation results, which identify the correctness of the supporting misalignment model proposed in this study.  
Figure 19: The spectrum of horizontal and vertical direction of Bearing 1.  
Figure 20: The spectrum and the orbit of Bearing 3.  
However, in addition to the frequency components predicted by simulation such as \(1\times\), \(1.6\times\), and \(2\times\), there are other frequency components in the measured results. These frequency components may be caused by nonlinear factors of the system, such as nonlinear vibration caused by bolt looseness and bearing clearance.
## 2 Design Concept of Smart Transfer Planer
### Antenna Array  
Microstrip antennas are renowned for their lightweight, thin and low profile characteristics, and their planer nature enables them to be utilized in multi-layer board designs [2; 3; 4; 5], thus avoiding the issue of excessive overall volume. Additionally, microstrip antennas can achieve high gain and phase-shifted radiation patterns through array theory. Consequently, microstrip antennas find widespread application in various wireless communication fields. However, while individual unit antennas also possess gain and directivity, if it is necessary to enhance gain or achieve higher directivity to meet different design requirements, or to change the beam direction for wider beam scanning, designing array antennas is the most commonly used approach. In addition, antenna arrays are designed with various feeding methods to accommodate different needs and limitations. One common feeding method is parallel feed networks, which exhibit symmetry, aiding in balancing mutual coupling effects, and have the advantage of a wide bandwidth. However, due to the nature of the feed lines, this design is more challenging to arrange compactly. In contrast, the series feed [3], as depicted in Figure 3a, offers the advantages of a simple and compact structure and is commonly used to design antenna arrays with narrow beams and high gain. This design approach is adopted in this paper. Microstrip antennas are used as design units in the antenna array. The antenna design is conducted using the high-frequency structure simulator (Ansys HFSS).  
The serial antenna design is implemented for broadband performance, with a center frequency of 13.375 GHz selected to cover both uplink and downlink frequencies. A unit microstrip patch antenna with dimensions of 5.3 \(\times\) 7.6 mm\({}^{2}\) forms a 1 \(\times\) 8 series sub-array antenna, with a distance of 1.6 mm between the antenna and the ground to achieve broadband performance. After designing the 1 \(\times\) 8 series sub-array antenna, it is arranged into an 8 \(\times\) 8 series antenna array, forming a balanced layout. This manufacturing process helps avoid board bending and reduce overall thickness, albeit with additional design constraints. Figure 3b illustrates the 2D radiation pattern of the 8 \(\times\) 8 microstrip array antenna. When Theta equals 0\({}^{\circ}\), the maximum Gain value is 23.87 dBi. By changing the feeding phase, beamforming effects can be achieved, with a gain of 21.2 dBi at 60\({}^{\circ}\) and  
Figure 2: STP functional block diagram.  
\(-60^{\circ}\). The reflection coefficient of the microstrip array antenna is shown in Figure 3c. The reflection coefficient across the operating frequency range from 10.3 GHz to 15 GHz remains below \(-10\) dB.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### Modified DM Acquisition for GPS LIC Signal Using Combined L1/L1C Signal Acquisition  
In Section 3.1.1 the optimal delay is chosen as \(\sigma_{0}=33T_{0}\) for the DM signal and the magnitude of the received DM signal \(s_{DM,\sigma_{0}}\) (\(t\)) is given as \(d_{0}=\frac{\pm}{4}\cos(2\pi f_{D}\sigma_{0})\) in Equations (10) and (13). This optimal delay \(\sigma_{0}=33T_{0}\) is so long that the Doppler frequency effect cannot be ignored in \(d_{0}=\pm\frac{\varepsilon}{4}\cos(2\pi f_{D}\sigma_{0})\) as discussed in Section 3.1.1, and thus the proposed DM methods in Section 3.1.2 can be used only for low speed up to approximately 200 km/h.  
In this section we propose a modified DM acquisition method which can be used even for high speed and high dynamics. Suppose that we obtain the Doppler frequency in advance by using the L1 signal acquisition.  
Define the generated signal \(g_{D}(t)=\cos\left(2\pi(f_{IF}+\hat{f}_{D})t\right)\), where \(\hat{f}_{D}\) is the Doppler frequency obtained from the L1 signal acquisition, and \(r_{g}(t)\) as follows:  
\[r_{g}(t)=r(t)g_{D}(t) \tag{25}\]  
\[=S_{L1C}(t)\cos\left(2\pi(f_{IF}+f_{D})t\right)\cos\left(2\pi(f_{IF}+\hat{f}_{ D})t\right)\approx\frac{1}{2}S_{L1C}(t)\cos\left(2\pi(f_{D}-\hat{f}_{D})t\right)\]  
where the high frequency term is considered as removed during the correlation process.  
The DM signal of \(r_{g}(t)\) with the optimal delay is given as follows:  
\[s_{gDM,\sigma_{0}}\left(t\right)=r_{g}(t)r_{g}(t-\sigma_{0})\approx S_{L1C}(t )S_{L1C}(t-\sigma_{0})\times\frac{1}{4}\cos\left(2\pi(f_{D}-\hat{f}_{D})\sigma _{0}\right).\]  
If the Doppler frequency error is 100 Hz, then \(\cos\left(2\pi(f_{D}-\hat{f}_{D})\sigma_{0}\right)=0.998\), and thus the magnitude of the received DM signal \(s_{gDM,\sigma_{0}}\left(t\right)\) can be considered as constant regardless of theDoppler frequency. We can substitute the \(r_{g}(t)\) in Equation (25) for \(r(t)\) Figures 5a and 6a as a modified DM acquisition method.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Packet Delivery Ratio and Energy Consumption Versus the Number of Nodes  
In this section, we demonstrate the effect of the number of nodes on the packet delivery ratio and energy consumption when the link reliability is 90%. Figure 12 shows the packet delivery ratio versus  
Figure 11: Average energy consumption vs. the number of source nodes with 90% of the link reliability.  
Figure 10: Packet delivery ratio vs. the number of source nodes with 90% of the link reliability.  
the number of nodes. To investigate the variation in the packet delivery ratio, the failed packets do not retransmit. The packet delivery ratio of the protocols is degraded when the number of nodes decreases because the decrease of node density disturbs multipath construction. Thus, the proposed scheme and RGMR, which construct a number of paths, are the most affected. On the other hands, as REER constructs only two paths, it maintains a similar packet delivery ratio with little effect by on the node's density. In the case of RRDLA, when the node density is increased, the probability of obtaining a path consisting of nodes with high link reliability is increased. Thus, the packet delivery ratio of RRDLA slightly increases when the number of node is increased.  
Figure 13 shows the energy consumption versus the number of nodes. To investigate the energy consumption when achieving the same packet delivery ratio, the failed packets are retransmitted. When the number of nodes decreases, the energy consumption is increased. Especially, the energy consumption of RGMR which constructs the number of paths is sharply increased. It is because RGMR requires a number of nodes to construct multipath; however, it cannot construct sufficient multipaths. Thus, RGMR continuously tries to construct multipaths and a large number of failed packets by path construction failure are retransmitted, increasing energy consumption rapidly. Similarly, the energy consumption of the proposed scheme is increased under the low-density network because it cannot secure enough number of nodes to exploit an advantage of the opportunistic routing. In the case of REER and RRDLA, there is a slight increase in energy consumption. The reason why they have not suffered from significant energy drain compared to RGMR and the proposed scheme is because when they perform packet retransmission to satisfy the required packet delivery ratio, the energy consumption of the retransmission is less than the energy consumption for path recovery.  
Figure 12: Packet delivery ratio vs. the number of nodes with 90% of the link reliability.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Control Overhead
#### 4.2.1 Under MP-AODV  
During the main route discovery, the RREQ is transmitted by the source and re-transmitted by all the other nodes when they receive the RREQ for the first time, except the destination.  
The number of RREQ copies transmitted and re-transmitted during the main route discovery, denoted by \(numRREQ_{1}^{\text{MP-}AODV}\), is calculated as follows:  
\[numRREQ_{1}^{\text{MP-}AODV}=N-1, \tag{3}\]  
where \(N\) is the number of nodes in the network.  
The number of RREP copies transmitted and re-transmitted during the main route discovery, denoted by \(numRREP_{1}^{\text{MP-}AODV}\), is determined as follows:  
\[numRREP_{1}^{\text{MP-}AODV}=n_{1}^{\text{MP-}AODV}, \tag{4}\]  
where \(n_{1}^{\text{MP-}AODV}\) is the hop count of the first path.  
During the backup route discovery, the RREQ_2 is transmitted by the source and re-transmitted by intermediate nodes, except for the destination and intermediate nodes belonging to the first path.  
The number of RREQ_2 copies transmitted and re-transmitted during the backup route discovery, denoted by \(numRREQ_{2}^{\text{MP-}AODV}\), is calculated thusly:  
\[numRREQ_{2}^{\text{MP-}AODV}=N-n_{1}^{\text{MP-}AODV}. \tag{5}\]  
The number of RREP_2 copies transmitted and re-transmitted during the backup route discovery, denoted by \(numRREP_{2}^{\text{MP-}AODV}\), is determined as follows:  
\[numRREP_{2}^{\text{MP-}AODV}=n_{2}^{\text{MP-}AODV}, \tag{6}\]  
where \(n_{2}^{\text{MP-}AODV}\) is the hop count of the second path.  
The total numbers of request and reply messages transmitted and re-transmitted for discovering two node-disjoint paths, denoted by \(numRREQ^{\text{MP-}AODV}\) and \(numRREP^{\text{MP-}AODV}\), respectively, are as follows:  
\[numRREQ^{\text{MP-}AODV}=2N-n_{1}^{\text{MP-}AODV}-1, \tag{7}\]  
\[numRREP^{\text{MP-}AODV}=n_{1}^{\text{MP-}AODV}+n_{2}^{\text{MP-}AODV}. \tag{8}\]  
For the sample network in Figure 5, the number of RREQs and RREPs are as follows.  
* Case 1: Source node 0 communicates with destination node 22: \[numRREQ^{\text{MP-}AODV}=55\] \[numRREP^{\text{MP-}AODV}=8\]
* Case 2: Source node 0 communicates with destination node 24: \[numRREQ^{\text{MP-}AODV}=55\] \[numRREP^{\text{MP-}AODV}=8\]
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 1 Introduction
### Contributions of This Work  
The main novelty and contributions of the presented work can be summarized as follows:  
* Demonstration of quantum circuit width reduction transformations based on Circuit Parameterisation and Qubit Specialisation for quantum algorithms performing arithmetic operations as part of the computational work;
* Demonstration of the derivation steps used in creating parameterised quantum circuits for quantum arithmetic. The formulation in parameterised form for a quantum comparator and a quantum subtractor are detailed. To the best of the authors' knowledge, these parameterised circuits have not been considered in the literature before. The derivations detailed here also show how similar parameterisation can be applied to a wider range of arithmetic circuits;
* Analysis of the quantum circuit design of integer dividers in terms of suitability for the proposed quantum circuit width transformations;
* Demonstration how for the quantum divider exemplar the pre-computed and verified comparator and subtractor circuits in parameterised form can be imported into the complete quantum circuit implementation, followed by the automated selection of qubits suitable for specialisation and the automated specialisation for different user-defined inputs;
* Analysis in terms of circuit complexity of specialised quantum integer divider circuits as obtained from the transformation techniques introduced. The correctness of the circuits is verified using a quantum circuit simulator.  
The rest of this manuscript is structured as follows. Section 2 presents the two main types of data encoding techniques used in quantum information processing. Section 3 discusses key aspects of simulation of quantum circuits on a classical computer. The key concepts introduced in this work are outlined in Section 4. Section 5 describes the derivation of the parameterised comparator and its specialised forms. Section 6 then details the parameterised subtractor circuits. Section 7 describes the design and quantum circuit implementation of quantum integer dividers. The quantum circuit toolchain employed in this work is described in Section 8. Section 9 demonstrates the implementation of the divider in the presented toolchain and describes its specialisation and verification. The results of using the toolchain to reduce the divider are presented. Finally, Section 10 presents the conclusions and suggestions for future work.
# Molecularly-Imprinted SERS: A Potential Method for Bioanalysis
## 2 Molecularly Imprinted Polymer (MIP)  
MIPs (molecularly imprinted polymers) are synthetically generated high-affinity recognition materials. They are commonly utilized as a solid-phase extraction (SPE) sorbent because they efficiently separate and enrich analytes, particularly in complex matrix. Because of its recognition ability, chemical and thermal durability, easy synthesis, and low manufacturing cost, MIPs appear more promising than antibodies and aptamers [27; 29; 30; 31].  
Components needed for MIP synthesis are functional monomer, crosslinkers, porogen solvent, initiator, and template [30]. The molecularly imprinted polymer can be synthesized through covalent [32; 33; 24; 25] and noncovalent methods [35; 36; 37; 38; 39; 40; 41]. Covalent imprinting procedures used reverse condensation processes such as Schiffs base, boronate ester, ketal, and acetal. Covalently prepared MIP's use covalent bonds to bind the target molecule to the functional monomer before polymerization, and the bond must be cleaved before the use of the MIP. Noncovalent imprinting techniques include hydrogen bonds, ion-pairs, dipole-dipole interactions, and van der Waals interactions. Nonscovalent polymer allows for easy removal of the target molecule and reversible binding during later use of MIP [42; 43]. The methodologies for synthesis rely on the copolymerization of functional monomers and crosslinkers in the presence of a template or target molecule. The orientations and locations of the functional residue monomers are trapped in the polymer after polymerization, simulating a lock and key between a polymer and a target molecule [28].
## 2 The Finite Element Method Analysis for the SRM  
The SRM only has stator windings, no rotor windings or magnets. The whole or part of each phase is comprised of two stator windings on diametrically opposite sides of the SRM. A three-phase 6/4 SRM with 7.5 kW, 3000 rpm, and 270 V DC-link voltage is designed (seen Table A1 in Appendix A). Figure 1a-d shows the analysis model of the studied motor using the finite element method (FEM) with the magnetic flux at rotor positions of \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\) respectively. The anti-clockwise is the positive direction of the rotor angular position \(\theta\) and the alignment position of the stator and rotor poles is defined as \(0^{\circ}\).  
In practical SRM analysis, the non-linear characteristics and the electromagnetic analysis of the SRM are important. By using ANSOFT/Maxwell software (ANSOFT/Maxwell 14.0, Ansys Inc., Canonsburg, PA, USA), the flux linkage, the phase inductance profiles and the torque characteristics of the studied SRM were analyzed. And the results are shown in Figure 2a-c. Due to the saturation effect, the maximum value of the phase inductance decreases with the increase of the current. In Figure 2b, the flux linkage characteristics demonstrate the saturation effects. The lowest curve corresponds to a 0 A phase current and the top of the curve to an 8 A phase current. The torque generated by a constant current and a \(\pm 45^{\circ}\) in the phase-A winding is shown in Figure 2c and is the same as the other phases. The phase torque has nonlinear properties which strongly depends on the phase current values and the rotor position. It is significant that the phase torque decreases when the rotor position is near the alignment position of the stator and rotor poles. This is caused by the saturation effect, which leads to a decrease in the derivative of the co-energy to the rotor position. This, in turn, causes the large torque ripple of the SRM, which requires a complex control strategy to reduce. As shown in Figure 3a,b, the linear interpolation resulted in a surface fitting and this linear interpolation approach can be used for other non-test states to obtain the necessary data for the flux linkage and the torque for any given value (\(i\), \(\theta\)).  
Figure 1: The finite element method (FEM) analysis model of the switched reluctance motor (SRM) with the magnetic flux (I = 8 A): (**a**) \(\theta=0^{\circ}\); (**b**) \(\theta=15^{\circ}\); (**c**) \(\theta=30^{\circ}\); (**d**) \(\theta=45^{\circ}\).
## 5 Governmental Environmental Regulatory Efforts
### European Union Regulatory Efforts
The original recommendation was updated in 2016, and although there are currently no legally binding limits for PFAS in drinking water, the Swedish National Food Administration SLVFS legislation 2001: 30 S 7 states that drinking water should not contain substances in levels high enough that they could pose a risk to human health [135]. There is a proposed 530 ng/L limit of PFOS in drinking water in Norway, and the advised maximum acceptable amount of PFOS in drinking water is 300 ng/L [133; 136]. The United Kingdom Health Protection Agency established the 300 ng/L risk limit of PFOS in drinking water, and also established a 10 ug/L risk limit for PFOA in drinking water [133]. This report, originally published in August of 2007, also outlines different tiers with maximum concentrations [133]. Within the lowest tier (Tier 1) is when the concentration is above 300 ng/L; there should be increased monitoring and consultation of local health professionals [133]. This also applies to the highest tier where concentrations of PFOS are above 90 ug/L and people should contact local health professionals immediately as well as refrain from drinking the water for 7 days [133]. The Drinking Water Commission of the German Ministry of Health established a value for safe lifelong exposure of PFOS and PFOA to be 300 ng/L in drinking water [132]. Some countries, such as Italy, have established legally binding limits to PFAS after detecting extremely high levels [134]. The EU established a project called Perfluorinated Organic Compounds in the European Environment (PERFORCE) which had the goal of sampling and measuring PFCs across Europe, primarily in surface water, sewage treatment plants, and sediments [137]. This study showed that the highest concentrations of PFHpA, PFOA, and perfluorononanoic acid (PFNA) across all cites in Europe were found in the Po River in Italy [137]. This triggered an investigation by the Italian government into other sources of water in Italy in the early 2010s, with the highest concentration of PFAS being 1,475 ng/L [134]. The government installed activated carbon filters and was able to lower the concentration to 386 ng/L [134]. Threshold levels were established in January of 2014 in Italy with a Ministerial Decree to be 500 ng/L for PFOA, 30 ng/L for PFOS, and 500 ng/L for all other PFASs in drinking water [134]. Norway has established regulations for the amount of PFOS and PFOA in soil based on a study on the reproductive effects of these chemicals on earthworms [138; 139]. The Netherlands has established similar risk limits for both soil and groundwater, but these limits are based on human exposure through consuming vegetables or animals that were in contact with these substances [140]. The limit for PFOS in soil is 2.3 ug/kg, and the risk limit for PFOS in groundwater is 23 mg/L; the Dutch National Institute for Public Health and the Environment stated that there were not enough data to derive risk limits for vegetables, milk, or meat [140]. A summary of United States and European regulations regarding PFOS/PFOA limits is given in Table 2.