esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
## 4 Pipeline Crack Location Algorithm and Quantitative Analysis
### Quantitative Analysis of Welds
#### 4.2.2 Radial Defects  
The axial width of the pipeline defect is set to 5 mm, and the radial depth is 1 mm. The radial depth of the pipeline circumferential weld defect is changed, the time step is 0.5 mm, and the defect is quantitatively detected and analyzed. The reflection coefficients of circumferential weld defects with different radial depths are calculated as shown in Table 3, and the relationship between the reflection coefficient and the radial depth of pipeline defects is observed. Some data on the signals received by the pipeline with different radial depth defects are shown in Figure 13a-c.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Radial Depth (mm)**} & **Maximum Displacement of** & **Maximum Echo** & \\  & **Initial Wave (mm)** & **Displacement (mm)** & **Reflectance** \\ \hline
1 & 0.3889 & 0.0276 & 0.07093 \\
1.5 & 0.3888 & 0.0567 & 0.14596 \\
2 & 0.3890 & 0.1023 & 0.26321 \\
2.5 & 0.3890 & 0.1112 & 0.39586 \\
3 & 0.3899 & 0.1539 & 0.51239 \\
3.5 & 0.3897 & 0.2415 & 0.61979 \\
4 & 0.3897 & 0.2743 & 0.70392 \\
4.5 & 0.3901 & 0.3151 & 0.80782 \\
5 & 0.3900 & 0.3551 & 0.91051 \\
5.5 & 0.3952 & 0.3759 & 0.95133 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relationship between reflection coefficient of defects with different radial dimensions and pipeline defects.  
Figure 13: Quantitative analysis of pipeline defects with different radial depths: (**a**) 2 mm; (**b**) 2.5 mm; (**c**) 3 mm; (**d**) reflection coefficient relation–radial dimension curve.  
With the increase in crack radial length, the amplitude of the defect echo increases obviously. According to the data on the echo and initial wave at the defect, the fitting curve of the relationship between the reflection coefficient and the radial size of the defect is drawn. As shown in Figure 13d, the radial depth of the pipeline defect is not linear with the reflection coefficient of the echo at the defect. With the increase in the radial depth of the defect, the reflection coefficient of the defect not only increases but also increases faster and faster.  
Compared with the quantitative analysis results of circumferential length change in Section 4.2.1 and the change in reflection coefficient, the change law of the radial depth of defects is quite different.
## 3 The Proposed EGR Approach for Highway Traffic Environment
### The Route Construction Algorithm  
The proposed EGR routing algorithm is a temporal location-based geocast routing technique in which each sending vehicle \(N_{i}\) discovers the temporal locations of its neighboring vehicles utilizing the mathematical formulation given in Equation (1). This is also shown in step 1 of the proposed Algorithm 1  
* _Neighbor Discovery:_ Every vehicle \(N_{i}\) sends HELLO packets in its FTA including its coordinates. When a vehicle receives a HELLO packet, it responds with an \"ECHO\" packet containing positional data. Every vehicle creates a list of its neighbors after receiving these ECHO packets. Only those vehicles that are included in the relevant FTA will be included in the list.
* _Residual Link Lifetime Calculation_: The sender \(N_{i}\) estimates the residual link lifetime and temporal transmission probability that will be used to find the best forwarding vehicle \(N_{j}\) among the discovered neighboring vehicles, as shown in step 2.  
Figure 6: A sending vehicle deviation.  
* _Route Computation_: In step 3, the EGR searches for forwarding vehicle \(N_{j}\) using residual link lifetime and temporal transmission probability.  
```
Input:\((x_{i},y_{i})\), \(\left(x_{j},y_{j}\right)\), \(v_{i}\), \(v_{j}\), \(\theta_{i}\) and \(\theta_{j}\) Output: forwarding vehicle \(N_{j}\)
1. Neighbor discovery: Each vehicle \(N_{i}\in N\) broadcasts HELLO packet to learn about its each neighboring vehicle \(N_{j}\) vehicle \(N_{i}\) computes its temporal location \(\left(x^{\prime}_{i},y^{\prime}_{j}\right)\) and \(N_{j}\) computes its temporal location \(\left(x^{\prime}_{j},y^{\prime}_{j}\right)\) using Equation (1) at time \(t^{\prime}\)
2. Residual link lifetime calculation: for each vehicle \(N_{j}\)do\(N_{i}\) computes temporal distance \(d(t^{\prime})\) using Equation (2) \(N_{i}\) computes link quality \(\mathcal{P}_{ij}(d(t^{\prime}))\) using Equation (8) \(N_{i}\) estimate residual link lifetime \(T_{II}\) using Equation (9) endfor
3. Route construction: if at least one neighbor exists in \(N_{i}\)'s forward region of forward lane and no deviation then\(N_{j}\)\(=\) argmax\(\left(d(t^{\prime})\right)\), \(st\). \(T_{II}-t_{tt}>0\) Vehicle \(N_{i}\) sends packet to the vehicle \(N_{j}\)elseif at least one neighbor exists in \(N_{i}\)'s forward region of opposite lane and no deviation then choose \(N_{j}\) using Equation (12) Vehicle \(N_{i}\) sends packet to the vehicle \(N_{j}\)elseif no vehicles present in forward lane and opposite lane and no deviation then Vehicle \(N_{i}\) caches the packet until a neighboring vehicle found its forward region elseif (\(N_{i}\) wishes to deviate) \(N_{j}=argmax\)\(\left(MIN(d(t^{\prime}))\times\mathcal{P}_{i,j}(d(t^{\prime}))\right)\) Vehicle \(N_{i}\) sends packet to the vehicle \(N_{j}\)endif
```  
**Algorithm 1** EGR Algorithm  
A flowchart (see Figure 7) of the proposed EGR for a highway traffic environment when shadowing fading is present is provided for better understanding the logic of it. The flowchart shows the main steps of EGR under fading channel conditions.  
Figure 7: Flowchart of EGR.
## 6 Analysis of Schemes
### Proof of Correctness
#### 6.1.1 Verification of Message Source  
To ensure the authenticity of the message source, the receiving CN, labelled \(CN_{r}\), initiates a verification process. First, \(CN_{r}\) selects a random number \(b\in\mathbb{Z}_{p}\) and retrieves the group public key \(gpk_{r}\), which is generated during the System Setup phase. Then, \(CN_{r}\) computes the value \(B=\left(gpk_{t}\right)^{b}=\left(g^{u_{t}}\right)^{b}=g^{bu_{t}}\). Next, \(CN_{r}\) transmits \(B\) to \(AA_{t}\).  
Upon receiving \(B\), \(AA_{t}\) verifies whether a message has been sent to \(CN_{r}\). If so, it computes \(C^{\prime}=B^{1/u_{t}}=\left(g^{bu_{t}}\right)^{1/u_{t}}=g^{b}\). This operation effectively reverses the exponentiation by \(u_{t}\), thus isolating the original random value \(g^{b}\).  
Finally, \(CN_{r}\) compares \(C\dot{=}\,C^{\prime}\). If the values match, then the message is confirmed as being sent by \(AA_{t}\); otherwise, it is deemed unverified, and further actions are halted.
# Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation
## 2 Materials and Methods
### Behavioral Estimates  
The participants were instructed to press either the left or right key in response to the left or right stimulus orientation, respectively. For each stimulus, we registered presentation time (PT)--the time between the beginning of the experiment and the moment when the current stimulus appeared on the screen. The behavioral response to each stimulus was assessed by measuring the decision time (DT), which corresponded to the time passed from the stimulus presentation to the button pressing (Figure 1C). We also monitored the correctness using error rate (ER) by comparing the actual stimulus orientation with the  
Figure 1: Experimental paradigm: (**A**) An example of the Necker cube image with the labeled inner edges. (**B**) Visual stimuli (Necker cubes) with different values of the contrast parameter \(a\), which determines orientation and ambiguity. (**C**) Experimental protocol including 150-s resting state recordings and presentation of 400 stimuli alternating with the pauses. Colored horizontal stripes indicate 575-s time intervals. These intervals equally divide the stimuli presentation session. Each interval includes 100 stimuli. (**D**) Detailed illustration of a single stimulus presentation and abstract image. The cube presentation starts at the presentation time PT and lasts \(\tau_{i}\in[1,\,1.5]\) s. The decision time (DT) is determined by the interval between PT and button pressing. The pause time \(\gamma_{i}\) varies from 3 to 5 s. The time interval of interest (TOII) is the 1.5-s pre-stimulus segment time-locked to the PT.  
subject's response. The actual orientation of the Necker cube was defined by the contrast of the inner edges. Thus, \(a=\{0.15,0.25,0.4,0.45\}\) defined the left-oriented cubes, while \(a=\{0.55,0.6,0.75,0.85\}\) stood for the right-oriented ones. To define the correctness, we checked whether the subject pressed the left button for \(a=\{0.15,0.25,0.4,0.45\}\), or the right button for \(a=\{0.55,0.6,0.75,0.85\}\). Otherwise, their response was considered as incorrect. We excluded two subjects with \(ER>20\%\), as they exceeded the 90 th percentile of ER distribution in the group.
## 5 Numerical Results and Discussion
### Performance Analysis  
We describe the performance of PU1, PU2, and SU packets in different network environments. Table 1 shows the parameters with fixed values.
## 4 Simulation and Results
### Results for the Best Model with the TMR Effect  
In this section, we consider the ability of the proposed model to resist the TMR effect. Because the best model achieves the best performance, we used the best model for comparison with previous studies. In practice, we do not know the extent of the TMR effect. Therefore, we assumed that 10% is the level of the TMR effect endured by the proposed model. The results are shown in Figure 10. In addition, for simplicity, we assumed that the channel is affected by the TMR with the parameter \(-\Delta_{off}\) in (21).  
In the next simulation, we experimented with the BER performance while varying the level of the TMR from 10% to 30% at SNR = 15 dB. The results are shown in Figure 11.  
Figure 10: BER performance of the proposed model with 10% TMR.  
Figure 9: BER performance of the proposed model and models from previous works.  
As shown in Figures 10 and 11, our proposed model performs better than those of previous studies. In particular, in Figure 11, the detection of the 8-way GPR target shows high resistance to the TMR effect.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 2 System Model
### Memoryless Nonlinear PA Models
#### 2.1.1 Ideal Linear PA Model  
For comparison purposes, we first introduce an ideal linear PA model. In such a model, the amplifier has no phase distortion, i.e., we have \(f(r)=0\) for any \(r\). The AM-AM characteristic of the linear PA model can be expressed as [46]  
\[g(r)=\begin{cases}r_{o,\max}\bigg{(}\dfrac{r}{r_{\max}}\bigg{)},&r<r_{\max}, \\ r_{o,\max},&r\geq r_{\max},\end{cases} \tag{4}\]  
where \(r_{o,\max}\) is the maximum output signal envelope and \(r_{\max}\) is the maximum input signal envelope for linear amplification input. These two variables are related by  
\[r_{o,\max}=Ar_{\max} \tag{5}\]  
Figure 3: System model.  
where \(A\) represents the constant amplitude (envelope) gain of the idealized PA. The characteristics of this ideal model can be used as a reference benchmark for other nonlinear PA models [53].
# Possibility of Bridge Inspection through Drive-By Vehicles
## 3 2D VBI Model  
In this study, the vehicle model was simulated as a mass point, similarly as conducted by [PERSON] et al. [22]. The bridge was simulated as a 2D beam. To set the moment of inertia to 2.9 m\({}^{4}\) as the literature model, the cross section's profile was selected as a 4.35 m \(\times\) 2 m rectangle. The bridge mass per length is 2303 kg/m, and the linear density of the member can be calculated as 264.71 kg/m\({}^{3}\). [PERSON]'s modulus of the material is 2.87 \(\times\) 10\({}^{9}\) Pa, and [PERSON]'s ratio is 0.3. The length of the bridge was 25 m. The element type was a beam and its mesh size was set as 0.05 m to ensure the modeling accuracy.  
To verify the numerical convergence, the displacements of the bridge mid-span under vehicle load were compared when the mesh size was 1, 0.5, 0.25, 0.1, 0.05, and 0.02 m, respectively. Figure 1 shows that when the mesh size is below 0.25 m, the displacement converges to 0.00223 m with negligible differences.  
Figure 1: The convergence of mesh size on the mid-span bridge vertical displacement.  
The vehicle was modeled as a 1 m block with a rectangular cross-section of 1 \(\times\) 1 m. The density of the material is 5707 kg/m\({}^{3}\), which leads to the mass of the vehicle equal to 5707 kg. The [PERSON]'s modulus is 2.87 \(\times\) 10\({}^{11}\) Pa. Since the vehicle was modeled as a mass-sprung system, the vehicle block was kept short to avoid its vibration modes.  
The vehicle model tire was simulated as a point mass, which is a rigid body with an assigned mass of 1 kg. The vehicle mass part was set 1 m above the bridge's left end to assemble the model. The tire point has the same coordinate as that of the left end point of the bridge. A spring that has a stiffness of k = 0.5 \(\times\) 10\({}^{7}\) N/m was connected between the mid-point of the vehicle mass and the tire. To correctly model the vehicle-bridge interaction, the bridge part and the tire have a hard and surface-to-point contact. The bridge's boundary conditions are set like this: the left end was pin supported and the right end was roller supported, with a restraint on the vertical displacement. All these settings were set in the initial step of the ABAQUS model [23].  
In the second step, a gravity load was added onto the vehicle mass part. Static analysis was conducted thereafter, which simulates the vehicle gravity load on the model.  
In the third step, the vehicle mass and tire parts were applied with a 27.7 m/s horizontal velocity. A dynamic, implicit analysis was performed to simulate the vehicle-bridge interaction along the whole bridge.  
After completing the analysis, the mid-point vertical displacement of the bridge was extracted and compared with the literature results [24]. The comparison is shown in Figure 2.  
Figure 2 shows that the ABAQUS modeling results are almost the same as the displacement result of the reference [24], with a maximum difference of 5.1%. The small difference may be caused by the ABAQUS contact model, which is different from the semi-analytical contact model adopted in [PERSON] et al. [24].
## 3 Materials and Methods
### The NeuroPredict Platform  
The NeuroPredict platform [64] aims to construct sophisticated predictive models designed for the early detection and ongoing management of neurodegenerative disorders. These models are intricately connected to a comprehensive spectrum of medical data and information, collected through non-invasive intelligent monitoring technologies and clinical data repositories.  
The NeuroPredict platform's foundation is established upon the integration of diverse data streams, including high-dimensional health parameters, behavioral metrics, and environmental data, facilitated by advanced IoT devices. This data acquisition encompasses a wide array of health indicators such as heart rate, electrocardiogram (ECG), EEG, blood pressure, and bioelectrical impedance analysis, alongside environmental and motion-sensing data. The platform's architecture also leverages open data repositories specific to neurodegenerative or neuropsychiatric conditions and integrates cognitive assessment results from different tools in order to manage the mental health status of patients. Employing advanced AI algorithms empowers the platform to identify patterns relevant to cognitive health, facilitating accurate prognostication of disease progression. This AI-driven analysis is designed to inform and enhance the longitudinal monitoring of patients, with the aim of providing a proactive approach to disease management.  
A notable aspect of the platform is its capacity to develop and refine multivariate AI-driven predictive models. These models are instrumental in discerning, evaluating, and tracking neurological conditions, incorporating an extensive range of data points to ensure precision and relevance. The platform's scalability and adaptive architecture are key features, allowing for the incorporation of emerging smart devices and the expansion of its functional capabilities in response to advancements in IoMT and intelligent health monitoring technologies.  
The NeuroPredict platform represents a significant advancement in the management of schizophrenia, offering innovative approaches through its integration of advanced AI solutions and personalized care strategies in order to be more proactive, adaptive, and patient centric. Real-time data analysis from wearable devices, such as EEG headbands, and integrating multiple health metrics, enables the platform to early detect cognitive or behavioral deterioration, allowing for timely interventions tailored to individual patient profiles. This dynamic system not only enhances the precision of diagnosis and treatment plans but also promotes long-term mental health stability by offering continuous monitoring and personalized care, ultimately improving outcomes and quality of life for individuals with schizophrenia.
## 2 Background Theory
### Signal Detection and Demodulation  
The estimate of the timing, \(t_{0}\), is then utilized for signal demodulation using either non-coherent detectors, such as energy detector (ED) and peak detector (PD) or a coherent detector (CD). For low system complexity and power consumption, we focus on the noncoherent reception of PPM signals [32], which is a generalized likelihood detection. The symbol decision is based on finding the pulse position that contains the maximum energy. The symbol-by-symbol detection process does not require the estimation of the channel parameters. The energy of the multipath components is collected to increase the detection probability of the actual transmitted pulse. For binary PPM, we first calculate two energy quantities:  
\[E_{0}\triangleq\int_{-\frac{T}{2}}^{\frac{T}{2}}r^{2}(t-t_{0})dt,\ \ E_{1} \triangleq\int_{-\frac{T}{2}}^{\frac{T}{2}}r^{2}(t-t_{0}-\Delta)dt \tag{27}\]  
where \(r(t)\) is the the received focused pulse for demodulation. \(T\) is the time window for energy integration. The choice of \(T\) depends on the time support, \(T_{s}\), of the initial probe pulse, \(p(t)\). In practice, one can choose \(T=T_{s}\) or a faction of \(T_{s}\), such that the majority (e.g., \(99\%\)) of the signal energy is retained, (_i.e._, \(\int_{-T/2}^{T/2}p^{2}(t)dt/\int_{-T_{s}/2}^{T_{s}/2}p^{2}(t)dt=0.99\)). Thus, the data symbols can be detected using the energy detector by:  
\[\left\{\begin{array}{ll}\mathrm{Symbol}\ ^{\prime}0^{\prime}\ \mathrm{detected}& \mathrm{if}\ E_{0}>E_{1}\\ \mathrm{Symbol}\ ^{\prime}1^{\prime}\ \mathrm{detected}&\mathrm{if}\ E_{0}<E_{1} \end{array}\right. \tag{28}\]  
Assuming additive white Gaussian noise with power spectrum density, \(N_{0}\), the bit error rate (BER) can be calculated by [32]:  
\[\mathrm{BER}_{\mathrm{ED}}=\frac{1}{2}\mathrm{erfc}\left(\frac{E_{p}}{2(N_{0}+ N_{I})}\right) \tag{29}\]where the complementary error function, \(\mathrm{erfc}(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-t^{2}}dt\). The signal energy and the inter-symbol interference (ISI) are given by:  
\[E_{p} = \int_{-\frac{T}{2}}^{\frac{T}{2}}r^{2}(t-t_{0})dt \tag{30}\] \[N_{I} = \int_{-\frac{T}{2}}^{\frac{T}{2}}\left(\sum_{k=-K,k\
eq 0}^{k=K}r(t -kT-t_{0})\right)^{2}dt \tag{31}\]  
respectively, where \(K\) is the maximum number of the modulated pulses whose tails overlap the focusing pulse. Note that if we choose \(T=\epsilon\), where \(\epsilon\) stands for a very small time interval, the energy detector approximates a peak detector (PD). The peak detector searches for the focusing peak of the received signals in a time window of duration, \(T\), once the starting time, \(t_{0}\), is obtained. Next, the corresponding time, \(t_{\mathrm{PD}}\), when the focusing peak occurs is then identified and is used to demodulate the PPM signals.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 3 Convolutional Neural Network Architecture
### CNN Model Salient Features
#### 3.1.3 Regularization  
CNN with high learning parameters and sparse or noisy training data are prone to overfitting problems. Overfitting occurs when a model improves its performance on training data but fails to classify fresh test instances that are part of the same domain problem. We employed an L2 regularization approach to avoid this problem.  
In this method, a regularization term is taken into account when updating the cost function. This regularization term reduces the value of the weight matrices by resulting in a simpler model, minimizing the problem of overfitting greatly.  
In L2, we have:  
\[Loss=error+\lambda\sum_{i=1}^{N}\left|w_{i}^{2}\right| \tag{3}\]  
where \(w\) is weight and \(\lambda\) is the regularization parameter (which is adjusted for more exact results). The L2 method is known as a decay approach for updating weights because it forces the weights to decay towards zero, though they never reach zero exactly.  
We have developed a foundation of all components of a CNN, the DPD CNN, which was used to distort the RoF system, and the replicated RoF CNN, which is needed for training this DPD CNN, based on the conversation so far. The CNN used here is a feed-forward fully connected network with \(N\) hidden layers and \(K\) neurons per hidden layer. Figure 3 depicts the symbolic structure of the employed CNN. Due to the complex nature of baseband signals, which represent both the real and imaginary components of the signal, the CNN has two inputs and two outputs. At least one of the several hidden levels has been activated using the ReLu function (owing to its lower complexity).  
The O/P for the primary layer (hidden) is represented:  
\[l_{1}(n)=f\bigg{(}W_{1}\bigg{[}\begin{array}{c}\mathbb{R}(x)\\ \mathbb{I}(x)\end{array}\bigg{]}+b_{1}\bigg{)} \tag{4}\]  
where, \(l_{1}\) represents the primary hidden output layer, \(f\) is the nonlinear function of activation and \(W_{1}\) is the weight and \(b_{1}\) is the bias for the first output layer in the network.  
The general output for the i\({}^{th}\) layer is represented as:  
\[l_{i}(n)=f(W_{i}h_{i-1}(n)+b_{i}) \tag{5}\]  
where, \(i\in\mathbb{N}\ :2\leq i\leq N\).  
After \(N\) hidden layers, the ultimate result will be:  
\[\hat{x}(n)=W_{N+1}h_{N}+b_{N+1} \tag{6}\]