esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Message Forwarding  
This section discusses the message-forwarding policies in MPMF. Section 5.1 presents the procedural description of the MPMF. Section 5.2 gives the time complexity of the MPMF based on Algorithm 1.  
```
Input :\(N_{i}\), \(N_{i,j}\), \(\text{TTL}_{m_{i}}\), \(\epsilon,W_{0}\), and \(r\)
1\(N_{i}\leftarrow\) all one-hop neighbor in \(r\) of \(s\);
2\(N_{i,j}\leftarrow\) all one-hop neighbor across \(n_{i}\in N_{i}\);
3\(m_{i}\gets s\) intends to send this message to \(n_{i}\in N_{i}\);
4\(L_{rem}\leftarrow\) remaining message copies for \(m_{i}\), assuming \(|L_{rem}|>1\);
5 if\((|N_{i}|>1)\)then
6 for\((\forall n_{i}\in N_{i})\)do
7 if\((\theta_{i}\leq\frac{\pi}{2})\)then
8 for\((n_{i,j}\in N_{i,j})\)do
9 if\((\theta_{i,j}\leq\frac{\pi}{2})\)then
10 /* Refer to (7)*/
11 if\((W_{p}>W_{0}\wedge\theta_{m}\leq\epsilon)\)then
12\(s\) forwards \(m_{i}\) to \(n_{i}\) and \(n_{i}\) forwards this message to \(n_{i,j}\);
13 update \(L_{rem}=L_{rem}-2\) for \(m_{i}\) in \(s\);
14 update \(L_{rem}=1\) for \(m_{i}\) in \(n_{i}\) and \(n_{i,j}\), and break;
15
16 end if
17
18 end if
19
20 end if
21
22 else
23 if\(((\theta_{i}\wedge\theta_{d}\leq\frac{\pi}{2})\wedge\frac{\pi\log\pi}{2})<0)\)then
24\(s\) forwards \(m_{i}\) to \(n_{i}\);
25 update \(L_{rem}=L_{rem}/2\) for \(m_{i}\) in both \(s\) and \(n_{i}\);
26
27 else
28 /* Refer to (9): replace \(t\) with \(\text{TTL}_{m_{i}}\) and obtain \(C_{s}\) of \(s\) and \(C_{i}\) of \(n_{i}\)*/
29 if\((C_{s}<C_{i})\)then
20 \(s\) forwards \(m_{i}\) to \(n_{i}\);
21 update \(L_{rem}=L_{rem}/2\) for \(m_{i}\) in both \(s\) and \(n_{i}\);
22
23 end if
24
25 end if
```  
**Algorithm 1**Message Forwarding Policy.
# Cost-Benefit Analysis of Small-Scale Rooftop PV Systems: The Case of Dragotin, Croatia
## 2 Materials and Methods  
### Meteorological Data for the Selected Location  
The analyzed photovoltaic system was installed on the sloping roof of a residential building in Dragotin, Croatia. The PV modules are facing south and there is no shading of the modules. The PV modules are mounted on brackets at a roof angle of 35\({}^{\circ}\), thus enabling rear ventilation. The PV system is directly connected to the low voltage network via an inverter. The paper does not consider the storage of electricity using a battery system. Meteorological data were taken using the Solargis Prospect application [24] and are shown in Table 1 and Figures 1-3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Value** & **Unit** \\ \hline Direct normal irradiation & 1237.6 & kWh/m2 \\ Global horizontal irradiation & 1332.7 & kWh/m2 \\ Diffuse horizontal irradiation & 620.4 & kWh/m2 \\ Global tiled irradiation at the optimum angle & 1549.1 & kWh/m2 \\ Annual average air temperature at height of 2 m & 12.1 & \({}^{\circ}\)C \\ The optimum tilt of PV modules & 35 & \({}^{\circ}\) \\ Terrain elevation & 136 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Long-term annual average data for city Dragotin [24].
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 5 Experimental Analysis  
In order to verify the performance of the ACNA algorithm, we try to compare it with the traditional extended Kalman filter method (EKF) [30] and the reduced IMU and odometer algorithm (RIO) [24]. The EKF algorithm uses the traditional EKF algorithm to fuse GPS and DDE/INS data with no GNSS verification process; that is, the updated measurement is the direct input of the Kalman filter for state update and position prediction. The RIO algorithm has distance constraints and can detect GNSS jump attacks. In the following passage, we compare the three algorithms using data set \(\widetilde{\Psi}_{2}\), \(\widetilde{\Psi}_{3}\) and \(\widetilde{\Psi}_{4}\).
## 6 Discussion  
Adoption of the MOMI tuning method for integrating processes (IP) was presented in this paper. The closed-loop responses on several different IP models showed that the proposed method resulted in relatively fast closed-loop responses while retaining system stability. Furthermore, the proposed method was also compared to existing tuning methods that are suitable for IP. By using simulated examples, the two distinctive advantages of the proposed method over existing approaches were shown, namely, the closed-loop responses were evaluated by 5% settling time and the ITSE criterion. The comparison with other methods revealed that the proposed method was very good.  
Another advantage is that the MOMI method does not require an explicit process model. Besides the calculation of the controller parameters from the general transfer function with pure time-delay, a process open- or closed-loop time response can also be used to calculate the controller parameters. Moreover, both approaches were equivalent, and the latter did not introduce any error in the calculation of controller parameters. Since the computation of the controller parameters is based on relatively simple analytical equations (in the frequency- or time-domain), the proposed tuning method for IP is useful in practice, especially for less-demanding hardware like simpler PLC controllers. Furthermore, reference weighting factor \(b\) allows users to emphasise disturbance rejection, reference following, or something in between.  
The method was tested on a control system for charge-amplifier drift compensation, laboratory plant, industrial autoclave, and solid-oxide fuel cell. The responses on all processes were relatively fast and highly damped.  
In future work, we will concentrate on additionally improving the tracking response while retaining the best disturbance-rejection performance. As already mentioned in Section 2, this can be done by implementing the reference pre-filter instead of reference weighting factor \(b\). Although a pre-filter solution (realisation and parameter computation) is much more complex than reference weighting factor \(b\), it could be beneficial to obtain the overall optimal response if the process model is well defined.  
Conceptualisation, T.K. and D.V.; software, T.K. and D.V.; validation, T.K. and D.V.; formal analysis, T.K. and D.V.; investigation, data curation, and writing, all authors; supervision, D.V. All authors have read and agreed to the published version of the manuscript.  
This work was carried out within research programs P2-0001 and PR-07603, financed by the Slovenian Research Agency.  
The authors would like to thank [PERSON] for the helpful discussions.  
The authors declare no conflict of interest.
## 3 Simulation Results and Analysis  
The design method and theoretical development in the previous sections should be validated by the simulation and test analysis. A time-stepping finite element (FE) simulation analysis is used to model the IPMSM with the original and optimized structures. The motor performance under different speeds and operating conditions is simulated.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 4 Galloping Energy Harvesting
### Galloping EM-FBEHs  
Converting ambient and aeroelastic vibrations to useable electrical energy with galloping EM-FBEHs has also been in use for operating low-power electronic devices. In [92], a theoretical model was generated and experimentally verified by utilizing three different cross-sections (square, triangular and D-shaped) of the bluff bodies. A was magnet attached to a beam vibrating with respect to a stationary coil. It was observed that maximum power of 37 uW was obtained at a wind speed of 3.25 m/s and load resistance of 300 \(\Omega\) when using the D-shaped bluff body. [PERSON] et al. [93] developed a Y-shaped bluff body as shown in Figure 7a. The bluff body's aeroelastic response caused the coil to cut the magnetic induction lines. An average power of 2.5 mW was measured at 4 m/s wind speed with the Halbach effect, which is superior to other aeroelastic energy harvesters reported in the literature. Moreover, it is also more environmentally adaptable.  
Recently, [PERSON] et al. [94] proposed a spring-coupling (Figure 7b) galloping EM-FBEH to enhance the output characteristics in a high-speed flow field. The coupling spring's stiffness and the initial gap between it and the bluff body will have an impact on the output characteristics. The output power was up by 92.7% when compared with the energy harvester without a coupling spring.  
Most of the research on energy harvesters concentrates on transverse galloping. The term \"torsional galloping\", which refers to a back-and-forth rotation of the bluff body, is effective in confined places and can also be integrated with the energy harvesters. Additionally, adding extensions to it gives a boost to torsional galloping-based EHs. The study in [95] focused on torsional galloping EM-FBEH. Figure 7c (i) shows the schematic of the initial prototype, featuring a bluff body connected to the DC generator. Figure 7c (ii) illustrates the modified design of the torsional EH, which integrates three beams and linear springs. The modified design helps bluff body to exhibit a combinations of transverse and twisting modes, which marks its suitability for the development of self-sustained environmental sensing systems.  
[PERSON] et al. [96] developed a novel EM-FBEH that uses parallel elastic strips suspended in a hollow square tube with magnets, as shown in Figure 7d. When the wind velocity increased to 4 m/s, the average power was 7.8 mW, with an RMS voltage of 3.2 V produced at an optimum resistance of 1.2 k\(\Omega\).  
Figure 6: (**a**) Typical trend of a galloping flow energy harvester; galloping-based PE-FBEHs with different shaped bluff bodies: (**b**) bulb-like cross-sectional reproduced with permission from [77] Elsevier, 2019; (**c**) fork-shaped reproduced with permission from [75] Elsevier, 2019; (**d**) cut-corner prism reproduced with permission from [84] Elsevier, 2021; (**e**) hyper-structure corrugated [86]; (**f**) double-airfoil reproduced with permission from [88] Elsevier, 2023; (**g**) tri-section beam and square body with splitters [91].
## 1 Introduction
Despite its proven effectiveness, it has encountered practical bottlenecks in real-world applications. Specifically, the tapping operation needs to be carried out directly or in close proximity to the bolt, which is difficult realize in spatially restricted or difficult scenarios. Secondly, there are also limitations in feature extraction. Most studies have focused on MFCC or power spectrum features, which are relatively homogeneous and poor at comprehensively and accurately capturing the complex signal characteristics generated when a bolt is loosened, which in turn may affect the accuracy of the detection results. Furthermore, the analysis models are also insufficient. Existing models either rely on traditional machine learning algorithms such as Bayes or KNN, simple but with limited recognition accuracy, or use deep neural networks that can improve recognition accuracy, but are accompanied by high data requirements, computational resource consumption, tuning difficulty, training time, and deployment complexity. Of particular concern is that there is a gap in current research and a lack of targeted solutions for the identification of loose bolts in large steel truss structures, such as transmission towers and cableway towers. In view of the above status quo and deficiencies, this paper focuses on the damage detection of bolted connections in steel truss structures, and innovatively proposes a recognition method based on acoustic signals. The method integrates MFCC-WPES feature extraction and Random Forest algorithm, aiming to overcome the limitations of existing techniques. In order to verify the effectiveness and practicality of the method, this paper also designs and implements an experiment on the loosening of bolts in steel joint structures, aiming to contribute to the technological advancement in this field.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Comparative Analysis of Selected Machine Learning Methods  
#### 3.4.1 Random Forest  
Random Forest [41; 42], as a decision-tree-based method, is able to model complex, nonlinear relationships between variables. This is an important advantage in anomaly detection, where anomalous patterns often result from interactions between multiple features. The effectiveness of RF comes from the fact that multiple decision trees are created, each built on a random subsample of the training data. This makes the model more resistant to overlearning and generalizes better to new data. Once all trees are trained, each new observation is passed through each tree. The class that the majority of the trees indicate is assigned to that observation. In the case of anomaly detection, observations that are consistently classified as belonging to a different class by the majority of the trees are considered anomalies. Although RF is a complex model, there are methods to interpret its performance. One of them is the variable importance analysis.  
Figure 2 illustrates how RF can be used to detect anomalies in two-dimensional data. Areas of different colors represent different classes, and data points are labeled as normal or anomalous. We can see that the model effectively separates most data points into two classes, with anomalies being found in areas that are less densely populated by data points.  
It is also worth noting that RF can be combined with other anomaly detection methods. It can be used to reduce the dimensionality of the data or extract features that are then used by other algorithms. Combining RF with other techniques can increase the efficiency and optimize the anomaly detection process.
## 3 Blind Adaptive Beamforming Based on Direction Lock Loop  
### Interference Suppression  
Since the power of the interference signal is much higher than that of the navigation signal, the covariance matrix of the array signal is determined mainly by the interference and noise terms.  
\[\mathbf{R}=E\Big{[}\mathbf{x}\mathbf{x}^{H}\Big{]}=\sum_{n=1}^{N}\lambda_{n} \mathbf{u}_{n}\mathbf{u}_{n}^{H}=\sum_{n=1}^{Q}(\lambda_{n}-\sigma_{\epsilon} ^{2})\mathbf{u}_{n}\mathbf{u}_{n}^{H}+\sigma_{\epsilon}^{2}\mathbf{I}_{N \times N} \tag{5}\]  
Figure 1: Array configuration of the ULA.  
where \(\lambda_{n}\) and \(\mathbf{u}_{n}(n=1,2,\cdots,N)\) denote the eigenvalues and the corresponding eigenvectors of \(\mathbf{R}\). \(\sigma_{e}^{2}\) represents the power of the noise. The eigenvalues are arranged in descending order:  
\[\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{Q}\gg\lambda_{Q+1}=\cdots= \lambda_{N}=\sigma_{e}^{2} \tag{6}\]  
The inverse matrix \(\mathbf{R}^{-1}\) can be calculated as  
\[\begin{array}{ll}\mathbf{R}^{-1}&=\sum\limits_{n=1}^{N}\frac{1}{\lambda_{n} }\mathbf{u}_{n}\mathbf{u}_{n}^{H}\\ &=\sum\limits_{n=1}^{Q}\frac{1}{\lambda_{n}}\mathbf{u}_{n}\mathbf{u}_{n}^{H} \,+\frac{1}{\sigma_{e}^{2}}\sum\limits_{n=Q+1}^{N}\mathbf{u}_{n}\mathbf{u}_{n }^{H}\\ &=\frac{1}{\sigma_{e}^{2}}\Bigg{(}\mathbf{I}_{N\times N}-\sum\limits_{n=1}^{Q }\frac{\lambda_{n}-\sigma_{e}^{2}}{\lambda_{n}}\mathbf{u}_{n}\mathbf{u}_{n}^{H }\Bigg{)}\end{array} \tag{7}\]  
Assuming the interference power is much larger than the noise power, the orthogonal complementary space \(\mathbf{U}_{I}^{\perp}\) can be approximated by the inverse of the covariance matrix \(\mathbf{R}\)[26], i.e.,  
\[\mathbf{R}^{-1}\approx\frac{1}{\sigma_{e}^{2}}\mathbf{U}_{I}^{\perp} \tag{8}\]  
In Equation (8), the coefficient \(1/\sigma_{e}^{2}\) is a constant related to the noise power, which can be omitted. There is no need to perform eigenvalue decomposition, which can significantly reduce the amount of computation. By projecting the array signal into the orthogonal complementary space, the interference can be mitigated. The array signal after the interference suppression can be expressed as  
\[\hat{\mathbf{x}}(t)\approx\mathbf{R}^{-1}\mathbf{x} \tag{9}\]
## 5 Discussion  
In this paper, a moving target detection model under 50 Hz background is proposed, in which a ferromagnetic target with a CPA distance of 5 m is deployed. The intended speed is 5 Km/h. Its design, size, and material are comparable to those of a tiny passenger car. The simulation results illustrate the viability of power frequency magnetic detection technology and the usefulness of synchrosqueezed wavelet transform in information extraction, specifically in the following ways:  
1. In the time-frequency picture created by the SSWT technique, the target motion causes the intensity change of 50 Hz power frequency background field at the observation point, which is 5 m distant;
2. In the case of SNR = 0 db, compared with continuous wavelet transform and short-time Fourier transform, the Rayleigh entropy of synchrosqueezed wavelet transform falls by 4.1886 and 4.3623 correspondingly.  
We also took actual field trials and reproduced the simulated circumstances. The car passed the sensor at a speed of 5 km/h, and the CPA distance between the vehicle and the sensor was 5 m. Through trials, we proved that the approach can successfully identify the power frequency magnetic anomaly disturbance signal induced by the presence of the target. The phenomenon is that the extracted wavelet energy ridge anomaly is the same as the occurrence time of the target calibrated by the geomagnetic sensor, or in other words, the fluxgate sensor receiving the geomagnetic signal is deployed in the same position as the inductive magnetic sensor receiving the alternating magnetic signal.  
Simulation and experimental results demonstrated the following findings:  
1. A magnetic field with a high power frequency can be observed.
2. The target's mobility can impact the power frequency magnetic field and be detected.
3. The approach described in this research is more effective than the STFT and CWT methods in detecting the power frequency magnetic field.
4. The Bump mother wavelet is more sensitive to magnetic anomalies than the Gauss, Cmhat, and Morlet mother waves.  
The technique is projected to play its part in non-destructive testing, indoor positioning, target recognition, and other domains.  
The technique still requires more air and underwater experimental verification and lacks further verification trials under extreme interference circumstances. This approach may also be integrated with the magnetic gradient anomaly model and target analysis method discussed in the introduction to produce a more effective target location and target recognition algorithm. However, as a fresh notion, it has a certain importance.  
Figure 12: Schematic diagram of engineering application.
## 3 Controller Design
### Stability Analysis  
We define a candidate Lyapunov function \(V_{DM2}\) (i.e., a positive definite function) as follows:  
\[V_{DM2}=\frac{1}{2}s^{T}s+\frac{1}{2}tr\Big{(}\widetilde{W}^{T}\Lambda^{-1} \widetilde{W}\Big{)} \tag{46}\]  
where \(\Lambda\in R^{nk\times nk}\) is a positive definite matrix. Differentiating Equation (46) with respect to time, we obtain  
\[\dot{V}_{DM2}=s^{T}\hat{s}+tr\bigg{(}\widetilde{W}^{T}\Lambda^{-1}\dot{ \widetilde{W}}\bigg{)} \tag{47}\]  
According to Equation (30), the first term in Equation (47) can be rewritten as follows:  
\[\begin{split} s^{T}\hat{s}&=s^{T}\big{(}\dot{e}+O_ {1}\otimes\dot{e}+O_{2}\otimes\ddot{e}\big{)}\\ &=s^{T}\bigg{(}\dot{e}+O_{1}\otimes\dot{e}+O_{2}\otimes\begin{pmatrix} M_{0}^{-1}U_{DM2}+F+\mathcal{N}\\ +\Delta\big{(}q,\dot{q},U,t\big{)}-\tilde{q}_{d}\end{pmatrix}\bigg{)}\end{split} \tag{48}\]  
Substituting Equation (45) into Equation (48), we obtain  
\[s^{T}\hat{s}=s^{T}O_{2}\otimes\bigg{(}\widetilde{\mathcal{N}}-\varpi s+ \varepsilon-\big{(}(\delta+\varphi+\varrho)\otimes\text{sign}(s)\big{)}\big{)} \tag{49}\]  
Then, substituting Equation (49) into Equation (47), we obtain  
\[\begin{split}\dot{V}_{DM2}&=s^{T}O_{2}\otimes\begin{pmatrix} \widetilde{\mathcal{N}}-\varpi s+\varepsilon\\ -((\delta+\varphi+\varrho)\otimes\text{sign}(s))\end{pmatrix}+tr\bigg{(} \widetilde{W}^{T}\Lambda^{-1}\dot{\widetilde{W}}\bigg{)}\\ &=s^{T}O_{2}\otimes\begin{pmatrix}-\widetilde{W}^{T}\mu+\Delta N- \varpi s+\varepsilon\\ -((\delta+\varphi+\varrho)\otimes\text{sign}(s))\end{pmatrix}+tr\bigg{(} \widetilde{W}^{T}\Lambda^{-1}\dot{\widetilde{W}}\bigg{)}\end{split} \tag{50}\]Based on Equation (37), we know that \(\dot{\tilde{W}}=\dot{\tilde{W}}-\dot{W}^{*}=\dot{\tilde{W}}\), such that we obtain the following:  
\[\dot{V}_{DM2}=s^{T}O_{2}\begin{pmatrix}-\tilde{W}^{T}\mu+\Delta N-\varpi s+ \varepsilon\\ -((\delta+\varphi+\varrho)\otimes\text{sign}(s))\end{pmatrix}+tr\bigg{(} \tilde{W}^{T}\Lambda^{-1}\dot{\tilde{W}}\bigg{)} \tag{51}\]  
The adaptive law used to adjust the RBNN weights is as follows:  
\[\dot{\tilde{W}}=\Lambda\Big{(}\mu(O_{2}\otimes s)^{T}-\phi\tilde{W}\Big{)} \tag{52}\]  
where \(\phi\in R^{nk\times nk}\) is a positive definite matrix. Substituting Equation (52) into Equation (51) yields  
\[\dot{V}_{DM2}=-s^{T}(O_{2}\otimes\varpi s)-\Psi-tr\Big{(}\tilde{W}^{T}\phi\tilde {W}\Big{)} \tag{53}\]  
where \(\Psi=s^{T}(O_{2}\otimes((\delta+\varphi+\varepsilon)\otimes\text{sign}(s)- \Delta\mathcal{N}-\varepsilon))\).  
Thus, we obtain the following:  
\[-\Big{(}\tilde{W}^{T}\phi\tilde{W}\Big{)}\leq\frac{1}{2}\Big{(}W^{*T}\phi W^{* }-W^{T}\phi\tilde{W}\Big{)} \tag{54}\]  
Accordingly, we can conclude that  
\[\dot{V}_{DM2}\leq-s^{T}O_{2}\varpi s-\Psi+\frac{1}{2}tr\Big{(}W^{*T}\phi W^{*} -\tilde{W}^{T}\phi\tilde{W}\Big{)}\leq-\rho V_{DM2}+\frac{1}{2}tr\Big{(}W^{*T }\phi W^{*}\Big{)} \tag{55}\]  
where \(\rho=\min(\lambda_{min}(O_{2}\varpi),\ \lambda_{min}(\phi\Lambda))\). Based on Equation (54), we can conclude that the error will eventually be bounded.