esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Signal-to-Noise Trends  
The SNR metric is used to differentiate the two regions of interest (ROI). The quantification of a defect is based on the information of two areas: the defective area and the reference or sound area. For the quantitative evaluation of each algorithm performance, the SNR of each defect for all the chosen modulation frequencies was calculated and compared. Two ROIs of \(5\times 5\) pixels were selected, one at the center of defective area and another in the adjacent sound area. ROI in the defective area will be considered as \"signal\" (DROI), and ROI in the sound area will be considered as \"noise\" (SROI). The SNR metric for a defect can be expressed by Equation (12) [45]:  
\[\mathrm{SNR}=20\log_{10}\left(\frac{|\mathrm{DROI}_{\mathrm{mean}}-\mathrm{ SROI}_{\mathrm{mean}}|}{\sigma}\right) \tag{12}\]  
where \(\mathrm{SROI}_{\mathrm{mean}}\) is the arithmetic mean of the sound area; \(\mathrm{DROI}_{\mathrm{mean}}\) is the arithmetic mean of the defective area; and \(\sigma\) is the standard deviation of the defective area.  
To analyze the effect of the defect on the SNR of each parameter, the SNR values for all modulation frequencies were calculated by Equation (12). The SNR values for defects by each sample group were shown from Figures 15-17. Figure 15 shows the SNR values of group 2 in which the width and depth for defect are constant and the defect length is considered differently. As shown in Figure 15, the HA showed better SNR than FFT. The longer the defect length, the better the SNR value. In the case of a defect length of 100 mm at  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Geometric Information**} & \multicolumn{10}{c}{**Phase Contrast \(\mid\)AC\(\mid\)(Radian)**} \\ \cline{3-14}
**Defect** & \multicolumn{3}{c}{**(mm)**} & \multicolumn{3}{c}{**HA**} & \multicolumn{3}{c}{**PPT**} & & & & & & & & & & \\
**Id.** & **Length** & **Width** & **Depth** & **1 Hz** & **0.2** & **0.1** & **0.05** & **0.03** & **0.01** & **1 Hz** & **0.2** & **0.1** & **0.05** & **0.03** & **0.01** \\  & & & & **Hz**
###### Abstract  
In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.  
Keywords: intrusion detection system; wireless sensor network; selective routing attack; two-hop monitoring; relaxed flow conservation constraint +
Footnote †: journal: Sensors
## 2 Real-Time Monitoring of Power Distribution Systems
### State Estimation  
WLS State Estimator  
The state estimation problem for a power system with \(m\) measurements and \(n\) state variables is usually formulated using the following measurement model [9]:  
\[z=h(x)+e, \tag{1}\]  
where \(z\) is the measurement vector (\(m\times 1\)), \(x\) is the state variables vector (\(n\times 1\)), \(h(.)\) is the nonlinear state estimation function (\(m\times 1\)) that relates the measurements to the state variables, and \(e\) is the vector of measurement errors (\(m\times 1\)), usually considered as independent random Gaussian variables with zero mean and diagonal covariance matrix \(R_{z}\) (\(R_{z}=diag\)\(\{\sigma_{z1}^{2},\sigma_{z2}^{2},\...,\ \sigma_{zm}^{2}\}\), where \(\sigma_{zi}\) is the standard deviation of measurement \(z_{i}\)).  
Through the classical WLS SE, the state estimate vector \(\hat{x}\) is obtained by minimizing the index \(J(x)\) given by [9]:  
\[J(x)=[z-h(x)]^{T}W[z-h(x)], \tag{2}\]  
where \(W=R_{z}^{-1}\) (the weighting matrix). An iterative process is used to obtain a correction \(\triangle x\) of the state vector. At each iteration the following linear system is solved:  
\[\triangle x=[G(x)]^{-1}H(x)^{T}W\triangle z(x). \tag{3}\]  
Equation (3) is known as the Normal equation and the coefficient matrix \(G(x)\) is called the Gain Matrix (\(G(x)=H(x)^{T}WH(x)\)).
## 3 Results and Comparison
### Effects of 2D and 3D Models  
Figure 6a-c presents the vorticities for Risers 1, 2 and 3 with 2D models while Figure 6d-f illustrates the vorticities for Risers 1, 2 and 3 with 3D models. As can be seen in Figure 6, the vortex shedding modes for all three risers are 2S (two single vortices per cycle). However, the vorticities from the 2D model are more stable as in the ideal condition.  
Figure 7a-c presents the \(C_{L}\) for Risers 1, 2 and 3 with 2D and 3D models, while Figure 7d-f illustrates the \(C_{D}\) for Risers 1, 2 and 3 with 2D and 3D models.  
In Figure 7, the value and stability of \(C_{L}\) and \(C_{D}\) with 2D and 3D models are quite different, although the trend for \(C_{L}\) and \(C_{D}\) with 2D and 3D simulation are similar. The curves of \(C_{L}\) and \(C_{D}\) for all the risers with 2D model are more stable and the values are significantly larger than those with 3D models. It also can be seen that the \"beat\" phenomenon of \(C_{L}\) occurs in the 3D simulation, i.e., \(C_{L}\) increases and decreases periodically.  
In the 2D simulation, the size in depth direction (\(Z\)-direction) is omitted; therefore, the effects of depth of the flow field on the VIV are not considered in 2D simulation. However, in any real flow, 3D effects are included and the 3D vorticity profiles of Risers 1, 2 and 3 are presented in Figure 8a-c, respectively. It can be seen in Figure 8 that the VIV of the risers have obvious 3D feature.  
Figure 6: Vorticities of the risers for 2D and 3D models. (**a-c**) Presents the vorticities for Risers 1, 2 and 3 with 2D models; (**d-f**) illustrates the vorticities for Risers 1, 2 and 3 with 3D models.  
Figure 7: _Cont._Figure 8: Profile of vorticity in 3D models for: (**a**) Riser 1; (**b**) Riser 2; and (**c**) Riser 3.  
Figure 7: \(C_{L}\) and \(C_{D}\) of the risers for 2D and 3D models. (**a–c**) Presents the \(C_{L}\) for Risers 1, 2 and 3 with 2D and 3D models; (**d–f**) illustrates the \(C_{D}\) for Risers 1, 2 and 3 with 2D and 3D models.
## 4 Sensitivity Analyses
### Mechanical Responses of the Rock Bolts
#### 4.1.1 Elongation  
As shown in Figure 7, numerical models with different in situ stresses and rock bolt lengths were analyzed and calculated. Rock bolt elongation, \(\delta\), was used to investigate the behavior of the bolts themselves. Rock bolt elongation is defined as the ratio between the stretched length (\(\Delta L\))of the rock bolt and the original length of the rock bolt (\(L\)) as follows:  
\[\delta=\frac{\Delta L}{L}. \tag{35}\]  
Rock bolt elongation curves with varying bolt lengths and collected under different in situ stress conditions are shown in Figure 7. With the increase of in situ stress, the elongation gradually increased. However, under different in situ stress conditions, the elongation \(\delta\) of the bolts with different lengths varied with time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classes** & **A** & **B** & **C** & **D** & **E** \\ \hline Degree of squeezing problems & Few & Minor & Severe & Very severe & Extreme \\ Strength-to-stress ratios \(\beta\) & \(\beta>0.45\) & \(0.28<\beta<0.45\) & \(0.20<\beta<0.28\) & \(0.24<\beta<0.20\) & \(\beta<0.14\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classes of difficulty associated with tunneling through squeezing rock based on Hoek [38].  
Figure 7: Elongation at different times after the tunnel excavation: (**a**) \(\beta=0.8\) (Class A); (**b**) \(\beta=0.4\) (Class B); (**c**) \(\beta=0.26\) (Class C); (**d**) \(\beta=0.16\) (Class D).  
When there is Class A large extrusion deformation, the rock bolt elongates quickly during the early stage and presents a small time effect. The rock bolt length does not significantly affect the elongation strain at a low in situ stress level. Therefore, when the stress is low, the design length of the rock bolt is not critical.  
When there is Class B large extrusion deformation, as time increases, the final increase value of \(\delta\) is larger for shorter rock bolts. When there is Class C or D large extrusion deformation, a 1 m rock bolt can elongate by 11% after 1000 h at Class D, whereas a 9 m rock bolt only elongates by 0.45%. For the short rock bolts, the extension first reaches the peak value and then decreases to be stable. The nonlinear curve indicates that the short bolts can experience stress relaxation under high ground stress conditions.  
It can be seen from Figure 7 that under the same in situ stress conditions, the longer the rock bolt length, the smaller the elongation. Hence, elongation is more sensitive to short rock bolts. In terms of the construction economy, there is an optimal value for the rock bolt length. Namely, when the length of the bolt exceeds the threshold, its elongation is no longer significantly reduced, and the influence of the length of the bolt is gradually reduced.
## 4 Discussion
### Limitations and Future Recommendations  
Although our study provides valuable insights, it is not without its limitations, which must be considered when interpreting the results. Firstly, the cross-sectional design adopted does not allow causal relationships to be established between the variables analyzed. This type of design limits the ability to determine whether the associations observed are a direct result of the variables studied or whether they are influenced by other uncontrolled factors. For example, although we have identified correlations between muscle mass, peak torque and quality of life, it is not possible to say with certainty whether the increase in muscle mass is the direct cause of improvements in quality of life or whether other intervening factors are masking the results. Furthermore, the size of the sample used in the study may limit the generalizability of the results. A larger and more diverse sample, including individuals from different geographical, socio-economic and clinical backgrounds, would be necessary to ensure that the results can be extrapolated to other populations or to individuals with different degrees of IDD if there is a possibility of examining in detail the relationship between physical fitness and the subdomains of quality of life. Our results only characterize the IDD population of the institution recruited. Another relevant limitation is the possible influence of external variables that were not controlled in the study, such as environmental, social and family factors and physical activity, which can impact on the participants' quality of life. Similarly, social and family factors, such as the support received, the presence of support networks and the quality of social interactions, can play a crucial role in the perceived quality of life of individuals with IDD. Finally, it is important to emphasize that the results of our study suggest promising associations between increased muscle mass, peak torque and quality of life, but future longitudinal studies are needed to confirm these relationships over time. Longitudinal studies would make it possible to assess whether the improvements observed are sustainable and translate into lasting benefits for the participants' quality of life. In addition, it would be interesting to explore the impact of specific interventions, such as personalized strength training programs, on maintaining these gains and promoting healthy aging in individuals with IDD. The inclusion of subjective and objective measures of quality of life, as well as the assessment of biological and psychosocial markers, could enrich future research and provide a more comprehensive understanding of the factors that contribute to well-being in this population.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 4 Experimental Setup
### Transmission Rates of AMR-WB Codec  
This work also aims to test the impact of the previously mentioned routing protocols in a real communication service, to this end, VoIP communication scenario is used as a case study. Thus, a traffic from node C to E is simulated with different bit-rates defined according to the AMR-WB codec. In addition, we used UDP communication and a packet time-length of 20 ms.  
Speech signal transmitted on an IP network is compressed by a speech codec, and them this payload must be packaged. For this, Real Time Protocol (RTP), the UDP and IP headers are inserted. The bit-rates presented in Table 2 only refer to the payload, then, it is necessary to add the number of bits regarding the RTP (12 bytes), UDP (8 bytes) and IP (20 bytes) headers to obtain the transmission rate. For example, AMR-WB-Mode 2 (12.65 kbps) contains 253 bits that are sent every 20 ms, then if the 320 bits of headers are added, a total of 573 bits are sent in this same period of time, that represents a transmission rate of 28.65 kbps. Table 7 shows the transmission rates used in the test scenarios.
## Materials and Methods
### CO\({}_{2}\) Adsorption  
The Micromeritics ASAP 2020 was also used to measure the adsorption isotherms of CO\({}_{2}\) gas on the adsorbents. To eliminate the humidity and some trace impurities adsorbed on the surface, the sample was outgassed at 350 \({}^{\circ}\)C for 24 h prior to the measurement. A circulating water bath was used to control the water temperature in the adsorption dewar, where the temperature was set at 25, 40, or 55 \({}^{\circ}\)C. The equilibration interval for each pressure point was set as 45 s. The experimental equilibrium data were fitted with the Freundlich equation (Equation (1)), which is a well-known empirical equation for describing adsorption phenomena taking place on a heterogeneous surface with a diversity of adsorption sites:  
\[\mathrm{q_{e}=K_{F}P^{1/n}} \tag{1}\]  
where \(q_{e}\) (mmol/g) is the equilibrium adsorption capacity at each pressure point, \(K_{F}\) [(mmol/g)(1/kPa)\({}^{1/n}\)] is the Freundlich adsorption coefficient, \(p\) (kPa) is the gas pressure, and \(n\) is a constant associated with the curvature of the isotherm. The goodness of fit of the above isotherm model to the experimental data was quantified using two different error functions: the nonlinear regression coefficient (R\({}^{2}\)) and the average relative errors (ARE). The value of R\({}^{2}\) indicates the percentage of deviation that can be explained by this fitting equation. On the other hand, the deviation between the experimental adsorption data and the fitting values could be calculated by ARE according to Equation (2) [26], where \(q_{j,exp}\) and \(q_{j,cal}\) (mmol/g) are the experimental and calculated adsorption capacity, and N is the number of the measured data.  
\[\mathrm{ARE\ (\%)=\sum_{j=1}^{N}\left[\frac{\left|\mathrm{q_{j,\ exp}-q_{j,\ cal}} \right|}{\mathrm{q_{j,\ exp}}}\right]\times\frac{100}{N}} \tag{2}\]  
The adsorption-desorption cycle tests of the adsorbents were also measured using ASAP2020. The same adsorption processes were conducted repeatedly for ten cyclic runs to evaluate the feasibility of their practical application for CO\({}_{2}\) adsorption. The isosteric heat of adsorption (\(Q_{\mathrm{st}}\), kJ/mol) was determined according to the Clausius-Clapeyron equation (Equation (3)), where \(A\) is the coefficient, \(R\) (=8.314 J/mol/K) is the universal gas constant, and \(T\) (K) is the adsorption temperature.  
\[\mathrm{ln\,P=A-\frac{Q_{st}}{R}\left(\frac{1}{T}\right)} \tag{3}\]
## 3 Proposed Circuit Implementation
### Notch Filter  
In order to reduce the output noise, on the one hand, the chopper-stabilized circuit will transfer the output noise PSD from low frequency to high frequency. The chopping frequency of the op amp should be higher than the noise corner frequency. On the other hand, it is necessary to add an LPF in the post-stage of the chopper-stabilized op amp to weaken the noise at high frequency and the higher harmonics caused by chopping.  
As shown in Figure 8, the circuit consists of two sampling circuits and an adder [42]. During the \(\varphi_{1}\) period, the capacitor \(C_{\text{1B}}\) samples the input signal \(V_{\text{in}}\) and transfers the charge from \(C_{\text{1A}}\) to \(C_{\text{2}}\); during the \(\varphi_{2}\) period, the capacitor \(C_{\text{1A}}\) samples the input signal \(V_{\text{in}}\) and transfers the charge from \(C_{\text{1B}}\) to \(C_{\text{2}}\). The charge after such a cycle is the sum of the two transferred charges. The overall circuit is actually equivalent to a sample-and-hold circuit, so the frequency response of the notch filter is a SINC function, and the gain is close to 0 when the input frequency is an even multiple of the chopping frequency. The spectrum of the notch filter is shown in Figure 9. In order to filter the periodic spike signals caused by the front-end chopper switch, the notch filter's stop frequency needs to be the same as the chopper frequency, and the clock of the switch in the notch filter needs to be delayed by T/4 cycles compared to the clock of the chopper switch to avoid the influence of spike pulses.  
Figure 7: Input-stage operational amplifier circuit.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 3 Measurement System  
The architecture of the measurement system is shown in Figure 5. The beacons generate infrared light, blinking with unique frequencies. The beacons contain a PIC12F1572 microcontroller, a driver stage and a high power infrared LED. The illustration of Figure 5 contains \(K=4\) beacons.  
The current implementation of the PCPA consists of \(N=16\) photodiodes. These SFH205F types have a cylindrical front side [14]. Each of them is followed by a two-stage transimpedance amplifier, providing fourth order lowpass filtering and 130 dBohm gain. After multiplexing, the amplified and filtered signals are sampled and converted by the internal analog-digital converter of the PIC32 MZ2048 EFM064 microcontroller with 40 kHz/channel. The spectra of the 16 signals are calculated by the microcontroller with fast Fourier transform using a flat top window and a transform size of 1024. For each beacon, the amplitude, corresponding to the beacon frequency, is measured on each channel, thus the PCPA provides \(16K\) amplitude values with an update frequency of 39 Hz.  
The second processing step is currently performed on a PC, where the bearing estimates are computed using either the LS or the FD estimate, as described in Sections 2.3 and 2.4, respectively. The current LS estimator can provide bearing values with 6 Hz on a PC, while the FD estimator's update frequency is higher than 1000 Hz. The execution time of the FD estimator, implemented and optimized on the microcontroller, is approx.. 100 \(\upmu\)s.  
The bearing measurements are then used to estimate the position of the sensor. Using the known beacon locations and the measured bearings the position estimation can be computed by any AoA/ADoA method, e.g., exhaustive search [4], consensus functions [5], geometric methods [6], or LS methods [4] may be utilized The location estimates on the PC can be calculated to provide an update for each bearing measurement (i.e., with 39 Hz).  
The photo of the PCPA is shown in Figure 6. The PCPA sensor (photodiodes) is a cylinder with diameter of 38 mm and height of 20 mm. In the current implementation the analog and digital electronic circuits are located on two circular PCBs with diameter of 100 mm (no attempt to optimize the size was made). The photo also shows the turntable, used in the measurements.  
Both the beacons and the sensor are of low cost: the bill of material (BOM) cost is USD 14 per beacon (including main power supply), and USD 30 per receiver.  
Figure 5: The measurement system containing the beacons, the 16-channel PCPA with the digital signal processing unit, the bearing estimation and the final position estimation units.
## 3 Classification Algorithm Design
### Deep Belief Network  
Deep belief networks (DBNs) include multiple Restricted Boltzmann Machines (RBMs) used for unsupervised feature learning and generation tasks [77; 78; 79]. By sequentially training a DBN, the inferred representation of the hidden layer from the data vector in each step is utilized as the data vector input for the next layer in the network. The RBM is composed of observable neurons and latent neurons forming multiple layers of neurons, where visible neurons receive inputs and hidden neurons extract features.  
The RBM incorporates weights denoted as w to signify the connection strength between interconnected neurons. Additionally, each neuron is assigned a bias coefficient to indicate its individual weight. Consequently, the energy of an RBM is formally articulated through the ensuing formula:  
\[E(v,h)=-\sum_{i=1}^{N_{v}}b_{i}v_{i}-\sum_{j=1}^{N_{h}}c_{j}h_{j}-\sum_{i,j=1} ^{N_{v},N_{h}}W_{ij}v_{i}h_{j} \tag{8}\]  
The likelihood of hidden layer neuron activation in an RBM is as follows:  
\[P\big{(}h_{j}\big{|}v\big{)}=\sigma\Big{(}b_{j}+\sum_{i}W_{ij}x_{i}\Big{)} \tag{9}\]  
Due to bidirectional connections, the neurons within the latent layer have the capability to stimulate neurons found in the visible layer:  
\[P(v_{i}\big{|}h)=\sigma\Big{(}c_{i}+\sum_{j}W_{ij}h_{j}\Big{)} \tag{10}\]  
The independence of neurons within a given layer leads to their probability densities satisfying independence as well, thereby yielding the subsequent equation:  
\[P(h\big{|}v)=\prod_{j=1}^{N_{h}}P\big{(}h_{j}\big{|}v\big{)} \tag{11}\]  
\[P(v\big{|}h)=\prod_{i=1}^{N_{v}}P(v_{i}\big{|}h) \tag{12}\]  
The preceding depiction shows the fundamental architecture of an RBM.  
DBN can be viewed as a probabilistically derived model, establishing the distribution function from data to labels, allowing the neural network to train on the dataset with maximum probability, widely used in the data classification and feature recognition fields. In this study, DBN is utilized for the classification of partial discharge and corona discharge signals in cable termination. The schematic representation of the model can be observed in Figure 7.