esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Ossite Welding Results
#### 4.2.2 Subjective Measures  
The NASA task load index (NASA-TLX) assessed the cognitive workload. On a scale of 0 to 100, with 100 being the most difficult, the participants rated their qualitative experience of mental demand, physical demand, temporal demand, performance, effort, and frustration after completing each task. Figure 11 shows all average NASA-TLX scores were lower for the MR-integrated visual and haptic HRI module (MRVF) compared to the baseline and MRnoVF cases. The MRVF visual-haptic mapping module significantly reduced the mental and physical demands and effort of participants. In particular, the mental workload was reduced from baseline (M = 80.31) compared to the MRnoVF module (M = 75.31) and the MRVF module (M = 45.00). The physical workload was reduced from baseline (M = 77.94) compared to the MRnoVF module (M = 74.44) and the MRVF module (M = 32.25). In addition, the average effort score in the NASA-TLX decreased significantly by 56% and 19% in comparison to the baseline and MRnoVF, respectively (F (1.590, 23.852) = 27.782, \(p<0.001\), Partial = 0.65), when the visual and haptic feedback were incorporated in the MRVF.  
The technology acceptance model (TAM) evaluated the system functionality, usability, and user's acceptance and perception of the three tele-welding modules. Each scale consisted of three items measured on a seven-point scale (1 = strongly disagree; 7 = strongly agree). The MRVF visual/haptic HRI method (M = 4.19) was reported to be more acceptable than the MRnoVF module (M = 2.69) and baseline case (M = 2.25) in terms of perceived usefulness, as shown in Figure 12. The TAM results indicate there was an overall significant difference between the means of the users' appeal with the three different HRI modules. The participants found the MR-integrated visual/haptic tele-welding framework (MRVF) (M = 5.50) to be significantly easier to use compared to the 2D baseline (M = 2.63), and marginally easier to use than the MRnoVF module (M = 3.13). The subjective measures analysis proved the MRVF vision/force mapping approach for tele-welding outperformed the MRnoVF and 2D baseline modules in task workload and user perception.  
Figure 10: (**a**) The average number of collisions for each of the three conditions (baseline, MRnoVF, and MRVF) in the flat position tele-welding tasks. The prevention effect in MRVF eliminated all unintentional contacts. (**b**) The amount of time participants spent aligning the torch tip across all conditions. The alignment time serves as a major component of task completion time.
## 3 Scenario and Problem Formulation
### RIS-Assisted Scenario  
Consider a manufacturing block of ABC Ltd. as shown in Figure 4, one BS has \(\mathbb{N}\) antennas. RIS connects BS, and the plane of BS is YZ, whereas the plane of RIS is XZ. There are \(\mathbb{K}\) reflecting elements of RIS. This surface serves \(\mathbb{X}\) users. There is no line of sight (LOS) between the user (\(U_{E}\)) and the BS due to hurdles. \(\mathbb{C}_{1}\) is the channel between BS and RIS, and \(\mathbb{C}_{2}\) is the channel between the RIS and the \(i\)th \(U_{E}\). \(\Theta\) is the phase shift matrix of the RIS.
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Anomaly Detection
#### 4.2.5 Phase 2--Finding the Nearest Base Station for Each Anomalous User  
After the detection of the anomalous users in the given test KPIs, phase 2 was implemented, as shown in the right block in Figure 3. The second phase consisted of finding the nearest base station for each anomalous user by using the Euclidean distance. Another approach used for identification is cell outage, which is based on z-score computation, as described in [69]. A similar approach can be implemented in the proposed framework, as shown in the Figure 3. Finally, the base station with the highest number of anomalies can be identified as the broken base station, or the z-score computed for the test set anomalies can be compared to the training set or reference z-score values for the identification of broken stations.  
The result from the autoencoder is represented in Figure 4, which shows the ROC curves for the test set predictions. We can also observe the confusion matrix for the results. In this case, we have the number of true positives, the number of false positives, and the number of false negatives. In the next step, we identified the outage cells and their nearest base stations. This was then used to find the broken base-station. The maximum number of anomalies in this case was associated to the fifth base station. The reference z-score was also used to find the broken base station.  
The results of the autoencoder were compared with those from nearest-neighbor approaches such as the k-nearest neighbor (k-NN), the local outlier factor (LOF) analysis, and the LOF with the average density, as shown in the Figure 4. The nearest neighbor approaches require the dimensionality of the input data to be reduced, because distance-based approaches are not suit  
Figure 4: The receiver operating curves (ROC) and AUC values.  
Figure 3: The cell outage detection framework based on the autoencoder model.  
data. In this case, we used the principal component analysis (PCA) to reduce the dimensionality of the data, and we observed that 80% of the variance in the data was captured by five PCA components. Therefore, when training the nearest neighbor approaches, we used the five-dimensional PCA reduced data. Figure 5 shows the normalized singular values compared to the number of PCA components.  
From the results, we can observe that the autoencoder approach performs the best in terms of accuracy.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 2 Related Work
### QoS Routing in Wireless Sensor Networks
Additionally, objective functions can be delineated to identify optimal paths. In [10], the authors distinguished between primary and composite metrics. By combining several primary routing metrics, a composite metric can be formulated, optimizing multiple performance aspects. The proposed combinations may either take the form of a weighted sum or a lexical combination of the primary metrics, the latter necessitating an explicit prioritization of the metrics under consideration according to a strict hierarchical order. The foundational routing metrics and their combinations to generate composite routing metrics were outlined in [43]. Utilizing a routing algebra, the authors demonstrated that the routing protocol converges to optimal routes and can be reliably employed within the RPL. The concept of Software-Defined Networking (SDN) facilitates the realization of advanced software-supported mechanisms. According to [40], SDN is characterized by its centralized nature, where controllers maintain a comprehensive view of the network, programmability, separation of data and control planes, and optimization of flow management and resources. QoS provisioning is rendered more straightforward and practical for network administrators by implementing the OpenFlow standard. In Software-Defined Wireless Sensor Networks (SD-WSNs), the SDN paradigm enables the remote configuration of forwarding elements with forwarding rules for data packets across various flows. The computation is centralized in the control plane, which can enhance the operational efficiency of WSNs [39]. A comparative analysis of the performance of conventional WSN networks using the RPL versus SD-WSNs was presented in [38]. Figure 1: A classification of QoS routing protocols in Wireless Sensor Networks (WSNs). This categorization is not exhaustive; other classifications exist based on criteria such as mobility support, fault tolerance, security, and energy efficiency [26; 27; 28; 29]. The experimental findings confirm the anticipated overhead associated with SD-WSNs and reveal design flaws inherent in SDN architectures. A review of Traffic Engineering tools in traditional and Software-Defined Networks can be referenced in [44]. Notably, given that sensors possess limited capacities and energy resources, resource-intensive solutions of Traffic Engineering cannot be applied within WSNs.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 2 Materials and Method  
### GPS L1C Signal  
Consider the GPS L1C received signal \(r(t)\) in Equations (1) and (2) [21]:  
\[r(t)=S_{L1C}(t)\cos(2\pi(f_{IF}+f_{D})t) \tag{1}\]  
\[\text{and}\ S_{L1C}\ (t)=\sqrt{\frac{3}{2}}c\cdot C_{OL}(t)C_{P}(t)SC_{P}(t)+ \sqrt{\frac{c}{2}}\cdot D(t)C_{D}(t)SC_{D}(t) \tag{2}\]  
where, the total signal power is denoted as \(c\) (Watts) which includes any antenna gain and receiver losses, \(f_{IF}\) is the intermediate frequency, \(f_{D}\) is the Doppler frequency, \(C_{OL}(t)\) is the overlay code of the pilot channel, \(D(t)\) is the data, \(C_{P}(t)\) and \(C_{D}(t)\) are ranging codes of the pilot and data channel, respectively, and \(SC_{P}(t)\) and \(SC_{D}(t)\) are subcarriers of the pilot and data channel, respectively. The chip rate of the ranging codes, \(C_{D}(t)\) and \(C_{P}(t)\), is \(f_{0}=1.023\) MHz, and the length of \(C_{P}(t)\) and \(C_{D}(t)\) is 10,230 chips (10 ms). The duration of each data bit and each chip of the overlay code is also 10 ms.  
The overlay code \(C_{OL}(t)\) is 18 s in length at a rate of 100 bps, which results in the total length of 1800 bits.  
Figure 1 shows a generic view of the L1C signal generation, where the subscript \(i\) denotes the number of a satellite, but it is not used in the equations for the brevity of notation.  
Figure 1 shows the generation procedure of \(C_{P}(t)\) and \(C_{D}(t)\), while [6] gives the details. Equation (3) expresses the subcarriers \(SC_{D}(t)\) and \(SC_{P}(t)\), while Figures 2 and 3 show the structure of \(BOC_{(1,1)}\), \(BOC_{(6,1)}\), and \(TMBOC\). The \(TMBOC\) uses a time multiplexed combination of \(BOC_{(1,1)}\) and \(BOC_{(6,1)}\) as in Figure 3, where \(BOC_{(6,1)}\) is placed at the 0 th, 4 th, 6 th, and 29 th chip, and \(BOC_{(1,1)}\) is placed at the other chips among 33- chips. This pattern repeats 310 times, which makes 10,230 chips, and 10 ms in time:  
\[\text{SC}_{\text{D}}(t)=\text{sin}BOC_{(1,1)}(t)\text{,SC}_{\text{P}}(t)=TMBOC _{(6,1,\frac{4}{33})}(t) \tag{3}\]  
where, \(BOC_{(\text{m},1)}(t)=\text{sin}BOC_{(\text{m},1)}(t)=\text{sgn}(\text{sin}(2 \pi mf_{0}t))\).  
Figure 1: GPS L1C signal structure.  
Figure 2: BOC modulation; (**a**) \(BOC_{(1,1)}\); (**b**) \(BOC_{(6,1)}\).  
In the notation of \(TMBOC_{(6,1,\frac{4}{33})}\), \((6,1)\) stands for \(BOC_{(6,1)}\) and \(\frac{4}{33}\) means that \(BOC_{(6,1)}\) is placed at 4 pre-designated chips in the 33-chip period as in Figure 3.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 3 Service Composition
### Service Composition's Research Landscape
#### 3.1.1 The Description of Services  
It is clear that the service description plays an important role in the composition process. A well-described service increases the relevance of its selection as well as the consistency (correctness) of the resulting composition pattern. Indeed, a service is represented by its description, which corresponds to the functional and non-functional priorities.  
The functional properties, as their name suggests, refer to the functionality delivered by the service. It includes descriptions of the input/output parameters and logic function (business) that the service performs. For example, a service whose logic function is sending an SMS has as input of two strings: the number of the recipient and the message body. The description of non-functional properties is an important aspect of the process of composition. Indeed, this part of the description indicates, for instance, the availability of the service, response time, or even its business model (for example, the rates per hour). For instance, for the SMS service, non-functional properties could be the business model (prices, promotions,...) and quality of service (the maximum delay of delivering a message,...) [17].  
The functional and nonfunctional descriptions generate non-insignificant complexity in the composition process. Thus, several protocols have been proposed where the functional aspect is predominant compared to the non-functional aspect. For example, WSDL (the current reference) is used to express the operations provided by the service. Web Ontology Language for Service (OWL-S) and Web Service Modeling Ontology (WSMO) add a layer of semantic description based on either domain-specific or general ontologies to assist service discovery (see Section 4.1.3).  
In addition, all proposed protocols and languages (described later) were designed from the perspective of information systems and are intended for experienced users. The semantic description, especially tagging techniques, contributes not only to a better interpretation by machines through reasoning but to bridging the gap between service description technologies and end-users as well.
# OTFS-Based Handover Triggering in UAV Networks
## 4 OTFS Modulation/Demodulation
### OTFS Demodulation
To obtain the DD-received signal \(y_{DD}[l,k]\), SFFT is applied to \(y_{FT}[m,n]\) in (17) as follows:  
\[y_{DD}[l,k]=\frac{1}{MN}\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}y_{FT}[m,n]e^{-2\pi( \frac{M}{N}-\frac{m}{M})}, \tag{20}\]  
In the matrix notation:  
\[\mathbf{Y}_{DD}=\frac{1}{MN}\mathbf{F}_{M}^{\dagger}\mathbf{Y}_{FT}\mathbf{F}_ {N},\stackrel{{\eqref{eq:y_DD}}}{{\Rightarrow}}\frac{1}{MN} \mathbf{F}_{M}^{\dagger}(\mathbf{F}_{M}\mathbf{B}_{Rx}\mathbf{R}_{DT})\mathbf{F }_{N}=\frac{1}{MN}\mathbf{B}_{Rx}\mathbf{R}_{DT}\mathbf{F}_{N} \tag{21}\]If rectangular pulses are used at the Rx side, i.e., \(\mathbf{B}_{Rx}=\mathbf{I}_{M}\), then \(\mathbf{Y}_{DD}=\frac{1}{N}\mathbf{R}_{DT}\mathbf{F}_{N}\), which is called ZT [13, 14]. Thus, the OTFS demodulation including the Wigner transform followed by SFFT is equivalent to just performing ZT on the received DT data matrix [13, 14]. The input-output relationship in the DD domain between \(s_{DD}[l,k]\) and \(y_{DD}[l,k]\) can be obtained by substituting (5) and (18) into (17) then in (20) while assuming bi-orthogonal pulses, as follows [13]:  
\[y_{DD}[l,k]=\frac{1}{MN}\Big{[}\sum_{l^{\prime}=0}^{M-1}\sum_{k^{\prime}=0}^{N- 1}s_{DD}\big{[}l^{\prime},k^{\prime}\big{]}h_{DD}\big{[}l-l^{\prime},k-k^{ \prime}\big{]}+G_{DD}[l,k]\Big{]}, \tag{22}\]  
where \(G_{DD}[l,k]\) is the AWGN in the DD domain, and \(\mathbf{G}_{DD}=\frac{1}{N}\mathbf{G}_{DT}\mathbf{F}_{N}\in\mathbb{C}^{M\times N}\). Alternatively, in matrix-vector notation, the input-output relation between \(\mathbf{y}_{DD}\in\mathbb{C}^{MN\times 1}\) and \(\boldsymbol{s}_{DD}\) in DD domain can be obtained by vectorizing (22), i.e., \(\mathbf{y}_{DD}=\text{vec}(\mathbf{Y}_{DD})\), and then substitute (12) followed by (9) as follows:  
\[ \begin{array}{rl}\mathbf{y}_{DD}&=\frac{1}{N}(\mathbf{F}_{N}\bigotimes \mathbf{B}_{Rx})\mathbf{r}_{DT},\\ \stackrel{{(\ref{eq:12})}}{{\Rightarrow}}&\frac{1}{N}((\mathbf{F} _{N}\bigotimes\mathbf{B}_{Rx})\mathbf{H}_{DD}\mathbf{x}_{DT}+(\mathbf{F}_{N} \bigotimes\mathbf{B}_{Rx})\boldsymbol{s}_{DT}),\\ &\stackrel{{(\ref{eq:12})}}{{\Rightarrow}}&\frac{1}{N}(( \mathbf{F}_{N}\bigotimes\mathbf{B}_{Rx})\mathbf{H}_{DD}\big{(}\mathbf{F}_{N} ^{\dagger}\bigotimes\mathbf{B}_{Tx})\boldsymbol{s}_{DD}+(\mathbf{F}_{N} \bigotimes\mathbf{B}_{Rx})\boldsymbol{s}_{DT}).\end{array}  \tag{23}\]
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 5 Conclusions  
We have experimentally and theoretically proven that a vertically mounted monopole antenna enables a high-voltage surface power to be wirelessly transmitted along the rubber/seawater interface to multiple loads. This interface can be flat, straight, curved, or heavily bent. On the other hand, our experimental results suggest that it is not possible to deliver a wireless power to the loads if the high-voltage surface power was replaced with a low-voltage electromagnetic wave, or if the load was disconnected from the vertically mounted monopole antenna. The outcome of our experiment also suggests that surface power cannot be wirelessly transmitted on the interface between air and a non-polar substance. The overall transmission efficiency was found to positively correlate with the water's salinity levels. The antenna height was found to vary as the square root of the maximum transmission distance, consistent with [PERSON]'s law of transmission. The experimental outcome of this work can be used not only for wireless power transfer, but also for detection if a substance is a polar or non-polar.  
Conceptualization, L.W.Y.L.; Investigation, L.W.Y.L.; Methodology, L.W.Y.L.; Project administration, L.W.Y.L.; Resources, S.N.T.; Writing--original draft, L.W.Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Accuracy Comparison of MTL-AMWO, MTL-WO and M-FNAS
The accuracies are 0.769, 0.778, 0.818, 0.750, and 0.800, respectively. As shown in Figure 8 and Tables 2-5, the accuracy of MTL-AMWO is the highest. Meanwhile, the accuracy of MTL-WO is higher than M-FNAS; the reason may be that the transfer strategy in MTL-WO can improve the population's search speed and efficiency in the search process by transferring the optimal solution set of similar historical problems in the source domain to the target problem. The accuracy of MTL-AMWO is higher than MTL-WO. There are two possible reasons. Firstly, the AMWO algorithm in MTL-AMWO can bring the population closer to the optimal solution by adaptively migrating walnurses based on changes in the population's average fitness value over multiple iterations. Secondly, the BRDPC algorithm in MTL-AMWO can further improve the effectiveness of the transfer by clustering the samples in the source domain and the target domain into several clusters \begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **M-FNAS** & **MTL-WO** & **MTL-AMWO** \\ \hline D\({}_{1}\) & 0.799 \(\pm\) 0.024 & 0.866 \(\pm\) 0.036 & **0.932 \(\pm\) 0.067** \\ D\({}_{2}\) & 0.750 \(\pm\) 0.028 & 0.833 \(\pm\) 0.026 & **0.916 \(\pm\) 0.029** \\ D\({}_{3}\) & 0.769 \(\pm\) 0.037 & 0.846 \(\pm\) 0.038 & **0.923 \(\pm\) 0.049** \\ D\({}_{4}\) & 0.727 \(\pm\) 0.055 & 0.818 \(\pm\) 0.029 & **0.909 \(\pm\) 0.061** \\ D\({}_{5}\) & 0.700 \(\pm\) 0.051 & 0.800 \(\pm\) 0.076 & **0.900 \(\pm\) 0.083** \\ D\({}_{6}\) & 0.823 \(\pm\) 0.071 & 0.884 \(\pm\) 0.043 & **0.941 \(\pm\) 0.056** \\ \hline \end{tabular}
\end{table} 
Table 4: Experiment results of M-FNAS, MTL-WO, and MTL-AMWO in senaries. \begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **M-FNAS** & **MTL-WO** & **MTL-AMWO** \\ \hline D\({}_{1}\) & 0.692 \(\pm\) 0.031 & 0.769 \(\pm\) 0.027 & **0.846 \(\pm\) 0.026** \\ D\({}_{2}\) & 0.667 \(\pm\) 0.045 & 0.778 \(\pm\) 0.024 & **0.889 \(\pm\) 0.067** \\ D\({}_{3}\) & 0.727 \(\pm\) 0.062 & 0.818 \(\pm\) 0.071 & **0.909 \(\pm\) 0.090** \\ D\({}_{4}\) & 0.625 \(\pm\) 0.074 & 0.750 \(\pm\) 0.079 & **0.875 \(\pm\) 0.083** \\ D\({}_{5}\) & 0.700 \(\pm\) 0.085 & 0.800 \(\pm\) 0.063 & **0.900 \(\pm\) 0.072** \\ D\({}_{6}\) & 0.700 \(\pm\) 0.049 & 0.700 \(\pm\) 0.037 & **0.900 \(\pm\) 0.066** \\ \hline \end{tabular}
\end{table} 
Table 5: Experiment results of M-FNAS, MTL-WO, and MTL-AMWO in septenaries. with similar distributions, respectively. Therefore, the accuracy of MTL-AMWO is higher than MTL-WO and M-FNAS.
## 2 Modelling
### The Calculation of the Power Dissipation  
With the electromechanical coupling model of an Akiyama probe, the power dissipation caused by damping effects in the sample under test is:  
\[P_{\rm{dis}}=\frac{1}{2}b_{\rm{sa}}D_{tip}^{2}\omega^{2}=\frac{1}{2}b_{\rm{sa} }v_{\rm{tip}}^{2}=\frac{1}{2}b_{\rm{sa}}(\frac{1}{2}pni)^{2}=\frac{1}{2}(\frac{ 1}{4}p^{2}n^{2}b_{\rm{sa}})^{2}=\frac{1}{2}i^{2}R_{\rm{sample}} \tag{26}\]  
Given the definition in Equation (13), with parasitic capacitance (\(C_{\rm{p}}\)) compensation, the impedance of the circuit in Figure 5 is:  
\[Z(\omega)=R_{\rm{probe}}+j\omega L_{\rm{probe}}+\frac{1}{j\omega C_{\rm{probe} }}=\frac{j\omega R_{\rm{probe}}C_{\rm{probe}}+[1-(\frac{\omega}{\omega_{0}})^{ 2}]}{j\omega C_{\rm{probe}}} \tag{27}\]  
When the circuit is resonance oscillating, \(\omega=\omega_{0}\), the non-contact impedance is:  
\[Z(\omega_{0})=R_{\rm{probe}}=\frac{p^{2}}{2}(b_{\rm{tf}}+b_{\rm{cl}}) \tag{28}\]  
The contact impedance is:  
\[Z^{\prime}(\omega_{0}\,^{\prime})=R_{\rm{probe}}+R_{\rm{sample}}=\frac{p^{2}}{ 2}(b_{\rm{tf}}+b_{\rm{cl}}+\frac{1}{2}n^{2}b_{\rm{sa}}) \tag{29}\]  
As mentioned above, the resonant frequency is not changed, so \(\omega_{0}=\omega_{0}\,^{\prime}\), and the change in current is given by:  
\[\Delta I=\frac{U}{Z(\omega_{0})}-\frac{U}{Z^{\prime}(\omega_{0}\,^{\prime})}= U\frac{R_{\rm{sample}}}{R_{\rm{probe}}+(R_{\rm{probe}}+R_{\rm{sample}})}=I^{ \prime}\frac{R_{\rm{sample}}}{R_{\rm{probe}}} \tag{30}\]  
where \(U\) is the input voltage, \(I^{\prime}\) is the current after contact. With Equations (26) and (30), the power dissipation is given by:  
\[P_{\rm{diss}}=\frac{1}{2}I^{\prime 2}R_{\rm{sample}}=\frac{1}{2}I^{\prime 2} \frac{\Delta I}{I^{\prime}}R_{\rm{probe}}=\frac{1}{2}I^{\prime}\Delta IR_{\rm{ probe}}=\frac{1}{2}\frac{UI^{\prime}(I-I^{\prime})}{I} \tag{31}\]  
Figure 14: The division of the amplitude change in AM mode. \begin{table}
\begin{tabular}{c c c c c} \hline \(k_{\rm{eff}}\) (N/m) & Resonance Frequency/Hz & Charge/C & Tuning Fork Amplitude/m & Cantilever Amplitude/m \\ \hline
0 & 206,153 & \(2.07\times 10^{-13}\) & \(2.93\times 10^{-8}\) & \(1.03\times 10^{-9}\) \\
0.0001 & 206,153 & \(2.07\times 10^{-13}\) & \(2.93\times 10^{-8}\) & \(1.03\times 10^{-9}\) \\
0.001 & 206,153 & \(2.07\times 10^{-13}\) & \(2.93\times 10^{-8}\) &where \(I\) is the current without contact. The classical equation using phase to calculate the power dissipation is [14]:  
\[P_{\text{diss}}=\frac{1}{2}\frac{k\omega}{Q}(A_{0}A\sin\varphi-A^{2}\frac{\omega} {\omega_{0}}) \tag{32}\]  
where \(k\) is the spring constant of the probe, \(Q\) is the quality factor, \(A_{0}\) and \(A\) are the amplitude with or without contact, \(\omega\) is the exciting frequency, \(\omega_{0}\) is the resonance frequency, \(\varphi\) is the phase.