esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of Seismic Pounding between Adjacent Buildings Accounting for SSI
## 2 Theoretical Model Framework
### Equation of Motion for SSI System  
Based on Figure 1, the equation of motion for the entire foundation system for Building A can be derived from Equation (11) for the translation along the \(x\) and \(y\) axes, rotation around the \(z\) axis, and rocking along the \(x\) and \(y\) axes.  
\[\begin{split} m^{a}_{o}\Big{(}\ddot{x}_{g}+\ddot{x}^{a}_{o} \Big{)}+\{1\}^{T}[M_{a}]\Big{\{}\ddot{x}^{t}_{ic}\Big{\}}+P_{xa}(t)=0\\ m^{a}_{o}\Big{(}\ddot{y}_{g}+\ddot{y}^{a}_{o}\Big{)}+\{1\}^{T}[M_{a}] \Big{\{}\ddot{y}^{t}_{ic}\Big{\}}+P_{yu}(t)=0\\ r^{2}_{a}m^{a}_{o}\ddot{\theta}^{a}_{o}+r^{2}_{a}\{1\}^{T}[M_{a}] \Big{\{}\ddot{\theta}^{t}_{i}\Big{\}}+T_{a}(t)=0\\ \sum_{i=0}^{N}I_{xi}\ddot{\psi}^{a}_{o}+\{h_{i}\}^{T}[M_{a}] \Big{\{}\ddot{y}^{t}_{ic}\Big{\}}+Q_{xa}(t)=0\\ \sum_{i=0}^{N}I_{yi}\ddot{\psi}^{a}_{o}+\{h_{i}\}^{T}[M_{a}] \Big{\{}\ddot{x}^{t}_{ic}\Big{\}}+Q_{yu}(t)=0\end{split} \tag{11}\]  
The soil-structure interface has been modelled using a parallel set of frequency-independent springs and dashpots considered in the study of [PERSON] et al. [30]. \(P_{xa}\), \(P_{ya}\)\(T_{a}\), \(Q_{xa}\), and \(Q_{xa}\) in Equation (11) are the interaction forces of Building \(A\) as given in Equation (12).  
\[\begin{split} P_{xa}(t)&=C_{T}\dot{x}_{o}^{a}+K_{T}x_{o }^{a}\\ P_{ya}(t)&=C_{T}\dot{y}_{o}^{a}+K_{T}y_{o}^{a}\\ T_{a}(t)&=C_{\phi}\dot{\theta}_{o}^{a}+K_{\theta} \theta_{o}^{a}\\ Q_{xa}(t)&=C_{\phi}\dot{\psi}_{o}^{a}+K_{\phi}\psi_{o }^{a}\\ Q_{yu}(t)&=C_{\phi}\dot{\psi}_{o}^{a}+K_{\phi}\phi_{o }^{a}\end{split} \tag{12}\]  
where \(K_{T,\theta,\psi,\varnothing}\) and \(C_{T,\theta,\psi,\varnothing}\) are the spring and dashpot coefficients of translations along both x and y axes, the torsion and rocking movements along both \(x\) and \(y\) axes, respectively. These constants of the static impedance functions are shown in Table 1 with many subscripts, as seen in Equation (12) [30].  
Where \(M_{T}\), \(I_{\theta}\), and \(I_{\psi,\phi}\) are the total mass, polar moment of inertia, and moment of inertia of the rigid body for rocking, respectively. \(G,\rho,\upsilon\), and \(\upsilon_{s}\) are the shear modulus, mass density of half-space, Poisson's ratio, and shear velocity of the elastic medium, respectively. \(r_{o}\) is the radius of the massless disc on the surface of an elastic homogeneous half-space.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 3 State of the Art Methods for Laser Line Segmentation
### Performance Evaluation  
Figure 2a shows a typical image captured by the structured light monitoring system with the following welding parameters. The wire feed speed is 84.67 mm/s, the welding speed is 3.33 mm/s, the peak current is 230 A and the base current is 70 A. As can be seen, these parameters are significantly higher than those used in References [19; 20]. With these parameters, the resultant weld pool surface changes more rapidly and irregularly. Hence, the quality of the captured image in this study is much reduced. The results by top hat method, FFT method, difference method, GLCM method and combination method are shown in Figure 2b-f respectively. Compared to the segmentation result by combination method, the segmentation results by other state of the art methods appear to be very inaccurate. Although the difference method achieves the second best result, there is one big segmented line on the top caused by the edge of the imaging plane. It costs additional effort for the subsequent unsupervised clustering. In addition, there are noise blobs that are hard to be distinguished from the laser line parts.
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 2 Array Signal Reception and Anti-Jamming Model
Equivalizing interference by noise, the equivalent \(CNR\) was developed to help analyze the impact of residual interference. It can be calculated as  
\[CNR_{\varphi,dB}=-10\mathrm{lg}(\frac{p_{n}B_{w}}{p_{sout}}+\frac{p_{inres}/p _{sout}}{QR_{c}}) \tag{6}\]  
where \(B_{w}\) is the receiver bandwidth, \(p_{sout}\) is the signal power after the anti-jamming process, \(p_{inres}\) is the residual interference power, \(R_{c}\) is the chip rate of the navigation signal, and \(Q\) is the quality factor of interference. The increase of \(p_{inres}\) leads to the decrease of \(CNR_{\varphi,dB}\). If \(CNR_{\varphi,dB}\) is smaller than the acquisition threshold, the acquisition fails and the receiver is unavailable. The covariance matrix of the array signal is  
\[\mathbf{R_{xx}}=E\Big{\{}\mathbf{x}(t)\mathbf{x}^{\mathrm{H}}(t)\Big{\}} \tag{7}\]where \(E\{\cdot\}\) denotes expectation. Suppose the navigation signals, interferences, and noise are independent from each other, the interferences are stationary, and the interference intensities are much greater than the navigation signal. The covariance matrix of the received signal can be written as follows, where \(\mathbf{R_{jj}}\) and \(\mathbf{R_{nn}}\) are the interference covariance matrix and noise covariance matrix, respectively, and \(\mathbf{I_{N}}\) is \(N\)-dimensional unit matrix. \[\mathbf{R_{xx}}\approx\sum_{k=1}^{K}p_{jk}\mathbf{a_{jk}}\mathbf{a_{jk}^{H}}+p _{n}\mathbf{I_{N}}=\mathbf{R_{jj}}+\mathbf{R_{nn}} \tag{8}\]  
The covariance matrix can be constructed if the interference DOA, power, and noise power are known.
## 2 Method
After accumulation, [PERSON] calculates the normalized detection probabilities \(p_{i}\) as [53; 54] \[p_{i}=\left(\frac{N}{2}\right)\times C_{i}/\Sigma C_{i},\] (2)  
Figure 1: (**a**) Transfer functions of the Mach–Zehnder modulator (MZM) with and without a transmission distortion owing to the bias drift \(\theta_{drift}\); (**b**) Block diagram of the proposed method applied to a decoy-state BB84 quantum key distribution (QKD) system; (**c**) Calculated \(\mathrm{Err}\left(\theta_{drift}^{T}\right)\) according to the theoretical phase drift \(\theta_{drift}^{T}\). The error is minimized when the \(\theta_{drift}^{T}\) maximally matches the practical value \(\theta_{drift}\). Thus, [PERSON] can estimate \(\theta_{drift}=70^{\circ}\) from the minimum error at \(\theta_{drift}^{T}=70^{\circ}\). where \(C_{i}\) is the count for the \(i\)-th diagnostic pulse. \(C_{i}\) can be easily obtained from [PERSON]'s announcement. Subsequently, [PERSON] builds the following error model based on the least-squares method [53, 54]:  
\[\text{Err}\left(\theta_{drift}^{T}\right)=\Sigma\left[p_{i}-p_{Ti}\left(\theta_ {drift}^{T}\right)\right]^{2}, \tag{3}\]  
where \(p_{Ti}\left(\theta_{drift}^{T}\right)=\cos^{2}\left(\frac{\theta_{mod}^{i}+ \theta_{drift}^{T}}{2}\right)\) is the theoretical detection probability for the \(i\)-th diagnostic pulse with corresponding phase value \(\theta_{mod}^{i}\) and theoretical phase drift \(\theta_{drift}^{T}=\left[0^{\circ},\ 360^{\circ}\right)\). [PERSON] may calculate the theoretical values each time the probability is examined or may use a look-up table that has been created in advance. 5. [PERSON] finds \(\theta_{drift}^{T}\) minimizing \(\text{Err}\left(\theta_{drift}^{T}\right)\) by adjusting \(\theta_{drift}^{T}\) from 0-360\({}^{\circ}\). Subsequently, the found value is estimated as \(\theta_{drift}\) because the error is minimized when \(\theta_{drift}^{T}\) maximally matches the practical value \(\theta_{drift}\). For example, [PERSON] can assume \(\theta_{drift}=70^{\circ}\) with the smallest \(\text{Err}\left(\theta_{drift}^{T}\right)\) at \(\theta_{drift}^{T}=70^{\circ}\), as shown in Figure 1c. The estimation accuracy depends on the adjustment interval of \(\theta_{drift}^{T}\). As the interval becomes smaller, the prediction accuracy becomes better. However, more computational power and time are required. 6. Finally, [PERSON] compensates for the estimated phase drift by applying \(\theta_{mod}=\theta_{mod}-\theta_{drift}\), therefore, the MZM bias point is maintained at the desired point. Accordingly, as the \(\theta_{drift}\) term of the MZM output intensity is erased, Equation (1) becomes \[ \begin{split} I_{out}&=L_{in}I_{in}\cos^{2}\left( \frac{\theta_{mod}-\theta_{drift}+\theta_{drift}}{2}\right),\\ &=L_{in}I_{in}\cos^{2}\left(\frac{\theta_{mod}}{2}\right).\end{split} \] (4)  
[PERSON] repeats the above calibration steps at specific intervals or when a phase drift higher than a predefined threshold is detected.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
###### Abstract  
The stress wave reflection method is widely used in the detection of structure size and integrity due to its advantages of low environmental impact and convenience. The detection accuracy depends on the accurate extraction of the stress wave reflection period. The traditional peak-peak method (PPM) measures the time interval between the first two peaks of the reflected waves to extract the reflection period. However, human interpretation is not avoidable for identifying the weak peak due to signal energy leaks into the surrounding environment. This paper proposes an algorithm for automatic extraction of the stress wave reflection period based on image processing to avoid human interference. The image is the short-time Fourier transform (STFT) spectrogram of the reflected wave signal after applying wavelet denoising and quadratic self-correlation operations. The edge detection method of image processing is used to extract the periodically occurring trough in the image. Graying and filtering are performed to eliminate interference. The frequency of the trough distribution is calculated by using the fast Fourier transform (FFT), and then the reflection period of the stress wave is obtained. The effectiveness and accuracy of the proposed method are validated by measuring the different lengths of two buried metal piles in soil. Comparing with the existing method of extracting the stress wave reflection period, this new algorithm comprehensively utilizes the time-frequency domain information of the stress wave reflection signal.  
n pacs: 1
## 3 Results
### \(H_{2}O_{2}\)-Treated PDiff Caco-2 Cells  
#### 3.3.1 Differential Gene Expression  
We identified 5639 DEGs (adjusted \(p<0.05\)) between untreated and H\({}_{2}\)O\({}_{2}\)-treated PDiff Caco-2 cells. Of these, 2743 genes were upregulated and 2896 were downregulated upon treatment of cells with H\({}_{2}\)O\({}_{2}\) (Table S11). Similar to 13-HPODE-treated cells, we observed downregulation of _PIM1_, _CCN1/N2_, and _CYTOR_ in H\({}_{2}\)O\({}_{2}\)-treated cells suggesting reduced cellular proliferation. _SLC38A2_, an amino acid transporter, _SLC20A1_, a phosphate transporter, and _EPHA2_ (ECK), which has a role in intestinal homeostasis and barrier function [51], were suppressed which might suggest impairment of molecule transport and intestinal barrier upon treating the cells with H\({}_{2}\)O\({}_{2}\). In addition, _DKK1_, a Wnt signaling inhibitor, _DUSP6/P4_, which dephosphorylate and inactivate MAP/ERK kinase, _FAT1_, which has a role in cell polarization, and _JAG1_, which is involved in notch signaling, were downregulated, which may also suggest disrupted intestinal homeostasis. Tumor suppressor genes such as _KLF6_ and _MAFF_ which also has a role in stress response [52], were downregulated. H\({}_{2}\)O\({}_{2}\) may affect lipid metabolism as it induced the expression of _HMGCS2_, _AICF_ and _HSDL2_, which may have a role in fatty acid metabolism [53], while it suppressed other genes such as _PTGS2_ and _OLRI_. H\({}_{2}\)O\({}_{2}\) treatment caused upregulation of antioxidant genes such as _BPNT1_, which has a detoxifying effect by preventing accumulation of phosphoadenosine phosphate (PAP) [54], _CAT_, which has a protective effect against H\({}_{2}\)O\({}_{2}\), and _IDH1_, which is involved in NADPH production and reduces the production of ROS [55]. The top 50 DEGs upon treating PDiff cells with H\({}_{2}\)O\({}_{2}\) are shown in Figure 5.
## 3 Results
### Experiments with Dimensionality and Sample Size
#### 3.3.2 Conditional GAN Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Original Data**} & \multicolumn{2}{c}{**CGAN**} & \multicolumn{2}{c}{**GAN**} \\ \cline{3-8}  & & **PC1** & **PC2** & **PC1** & **PC2** & **PC1** & **PC2** \\ \hline \multirow{8}{*}{Sample 1} & Shapiro \(w\) & 0.98 & 0.97 & 0.97 & 0.97 & **0.98** & **0.98** \\  & Shapiro \(p\) & 0.75 & 0.59 & 0.25 & 0.25 & **0.31** & **0.31** \\  & Central Tendency \(\ddagger\) & \(-0.03\) & \(-0.33\) & \(-0.14\) & \(-0.26\) & **0.003** & \(-0.21\) \\  & Deviation \(\lx@sectionsign\) & 0.43 & 0.37 & 0.41 & 0.42 & **0.47** & **0.34** \\  & Minimum Value & \(-0.87\) & \(-1.00\) & \(-0.89\) & \(-1.00\) & \(-0.87\) & \(-1.00\) \\  & IR 0.05 Limit & \(-0.79\) & \(-0.99\) & \(-0.81\) & \(-0.98\) & \(-0.79\) & \(-0.98\) \\  & IR 0.95 Limit & 0.64 & 0.13 & 0.47 & 0.52 & **0.69** & **0.22** \\  & Maximum Value & 1.00 & 0.52 & 1.00 & 0.66 & **1.00** & **0.62** \\  & Kurtosis & \(-0.44\) & \(-0.49\) & \(-0.49\) & \(-0.53\) & \(-0.97\) & **0.20** \\  & Skewness & 0.06 & \(-0.09\) & 0.12 & 0.34 & \(-0.02\) & \(-0.20\) \\ \hline \multirow{8}{*}{Sample 2} & Shapiro \(w\) & 0.98 & 0.97 & 0.98 & 0.97 & **0.97** & **0.98** \\  & Shapiro \(p\) & 0.70 & 0.43 & 0.61 & 0.13 & **0.20** & **0.28** \\  & Central Tendency \(\ddagger\) & \(-0.17\) & 0.01 & \(-0.08\) & 0.19 & \(-0.14\) & **0.03** \\  & Deviation \(\lx@sectionsign\) & 0.38 & 0.44 & 0.43 & 0.46 & **0.50** & **0.40** \\  & Minimum Value & \(-1.00\) & \(-0.85\) & \(-1.00\) & \(-0.85\) & \(-1.00\) & \(-0.85\) \\  & IR 0.05 Limit & \(-0.81\) & \(-0.59\) & \(-0.78\) & \(-0.59\) & \(-0.85\) & \(-0.55\) \\  & IR 0.95 Limit & 0.43 & 0.85 & 0.51 & 0.85 & **0.75** & **0.81** \\  & Maximum Value & 0.47 & 1.00 & 0.92 & 1.00 & **0.97** & **1.00** \\  & Kurtosis & \(-0.78\) & \(-0.23\) & \(-0.58\) & \(-1.00\) & \(-0.79\) & \(-0.46\) \\  & Skewness & \(-0.28\) & 0.42 & 0.18 & \(-0.14\) & **0.31** & **0.39** \\ \hline \hline \end{tabular} \(\ddagger\) Metric used = mean; \(\lx@sectionsign\) Metric used = standard deviation.  
\end{table} 
Table 9: Descriptive statistics for augmented DS3 targeting label values specifically. Numbers marked in bold indicate the synthetic data that obtained the most significant rTOST equivalency \(p\)-values. Both CGAN and GAN were trained using Wasserstein Gradient Penalty loss. Figure 9: PCA scatter plot presenting data augmentation techniques on DS3. Ellipses mark 95% confidence intervals. Both CGAN and GAN were trained using Wasserstein Gradient Penalty loss. from the target central tendency (Table 9) and appears to shift the general skew of the distribution (Figure 9). When using GANs to augment each of the samples separately, however, the generated data is arguably truer to the original domain. This is not to say, however, that CGANs are unable to augment feature spaces successfully. With the right configuration, CGANs are likely to reach similar results to traditional GANs. This, however, goes beyond the scope of the present study.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 5 Design of La-CTP
### Depressing Loops
The C3 conditional expression of CTP usually fails in choosing another proper parent when the current parent does not work. The essential reason behind is that CTP neglects the following fact. For a node \(u\) and its parent \(v\), the deterioration or the disappearance of link \(l_{uv}\) will increase the link ETX value of \(l_{uv}\) continually and dramatically, which further increases the current local ETX of \(u\), easily making it greater than that of the upstreaming nodes of \(u\); therefore, conditional expression C3 will be effortlessly satisfied, followed by the unwise parent updating. To restrain the loop formation as much as possible, La-CTP uses a new condition expression as follows, denoted by C3\({}^{\prime}\), to take over the C3 of CTP. \[\text{C3${}^{\prime}$:}\texttt{minEtx}+\texttt{PARENT\_SWITCH\_THRESHOLD}< \text{original currentEtx}\]  
In the conditional expression C3\({}^{\prime}\), La-CTP does not compare the minEtx (provided by the best neighbor) with the latest currentEtx; instead, it compares the minEtx with the original currentEtx, which represents the path ETX value before updating the local routing table. With doing so, C3\({}^{\prime}\) cannot easily hold true, even when the current parent is invalid; and then La-CTP cannot easily enter the procedure of updating parent--suppressing the loop formation with descendant nodes involved. It is worth noticing that only the C3\({}^{\prime}\) is insufficient to solve the loop problem: it can avoid loop formation as much as possible, but it alone cannot find feasible route for the parent-losing node. In Figure 6, for instance, C3\({}^{\prime}\) is false at time \(t_{3}\) (C1 and C2 are also false here) and therefore, node \(X\) will not carry out the parent updating, i.e., \(X\) will not choose \(B\) as its new parent. In such a case, however, \(X\) does not have an idea about how to find another feasible route to deliver its passing-by or locally-created packets. Next we will introduce the beaconing scheme of La-CTP that can recover the routes as long as the network is not segmented permanently.
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 3 Clinical Brain Monitoring with TD-NIRS
### Others  
[PERSON] and colleagues [116] reported the haemodynamic and neuronal time course to unilateral hand movement in 10 patients with cortical myoclonus, a pathology that triggers sudden, brief, involuntary muscle jerks, compared with 12 healthy volunteers. This particular study was highly multimodal with simultaneous EEG-fMRI and EEG-TD-NIRS measurements. An in-house developed multimodal TD-NIRS system (ID: 9 in Table 2) was used together with the time-gated approach in order to enhance that brain sensitivity. The authors reported a good agreement between the TD-NIRS and fMRI findings, both for controls and patients, and showed reduced hemodynamic changes in patients compared to controls.  
Finally, it is known that NIRS can be used within a brain computer interface framework to allow the use of brain activity to control computers or other external devices [117]. In a clinical context, this can be used to communicate with patients with locked-in syndrome. [PERSON] and colleagues [107] reported the ability to successfully communicate with a functionally locked-in patient suffering from Guillain-Barre syndrome. At the time of the study, the patient had regained limited eye movement, which could be used to confirm the TD-NIRS findings. The hemodynamic response to a motor imagery (MI) activity was measured using an in-house developed multichannel TD-NIRS systems (ID: 18 in Table 2) and using the methods of moments to quantify the changes in \(\mu_{a}\) to extract the changes in [HHb] and [HbO\({}_{2}\)]. The subject was asked to answer three questions: (1) confirming his last name, (2) whether he was in pain, and (3) if he felt safe. He was instructed to stay relaxed if he wanted to answer \"no\" or to perform a tennis imaginary task if the answer was \"yes\". The brain activation pattern successfully predicted the correct answers, evaluated by eye movement, to the three questions (see Figure 5d). Moreover, the metrics presented in this paper were in accordance with a previous study, using the same system, on healthy volunteers [118].
## 4 Results and Discussion
### Recycled Aggregates

Table 3: Results of the experimental tests carried out on fine and coarse recycled aggregates. The comparison shows that the particle size distribution of the fine recycled aggregate is very similar to that of the fine natural aggregate. It has only a quantity of dust (diameter less than 0.250 mm and 0.125 mm) slightly lower than the natural aggregate equal to 4% and 2%, respectively. On the other hand, the coarse recycled aggregate shows a finer distribution up to 14 mm and a coarser one from 14 mm to 31.5 mm. In fact, the natural aggregate passing at a sieve size of 14 mm is 10.2%, while the recycled aggregate is 44.5%. Referring to the environmental behavior, the leaching test carried out on both fine and coarse aggregates shows pollutant release levels below the limits imposed by the M.D. 186/2006, as shown in Table 4.  
The pH of both aggregates is found to be alkaline in the range 10-12. In particular, the pH value of the fine aggregate is equal to the maximum permissible regulatory limit of 12.
## 6 Conclusions  
This paper discusses the performance degradation caused by CP length violation and the STO in data communication systems that rely on transmitting blocks of symbols over narrowband PLC channels corrupted by white or colored Gaussian random processes. Additionally, it introduces a unified formulation from which HS-OFDM, OCDM, SCCP, and OTFDM schemes are derived, providing the opportunity to design future multi-scheme transceivers capable of dealing with distinct sets of constraints faced by data communication systems. The paper also deduces closed-form expressions for estimated signal, SINR, and upper bound SINR for OCDM, SCCP, and OTFDM, allowing comparisons between them and HS-OFDM when the CP length violation and the STO occur.  
Numerical results show that the closed-form expressions correctly characterize the performance of these data communication schemes. They also demonstrate that frequency domain equalization with different capacities presents similar behavior in all data communication schemes. Moreover, they show that STO can benefit the performance of data communication systems when the CP length violation occurs. The analysis of achievable data rates demonstrates that HS-OFDM and OTFDM typically provide the highest data rates in various scenarios, with or without interference. However, according to the BEP analysis, OCDM yields the most favorable outcomes for both types of noise, followed closely by SCCP and OTFDM without interference. In the presence of interference, HS-OFDM is the second-best alternative.  
Conceptualization, T.F.M.; data curation, T.F.M. and A.C.; formal analysis, T.F.M., A.C., S.B. and M.V.R.; investigation, T.F.M. and A.C.; methodology, T.F.M., A.C., S.B. and M.V.R.; supervision, M.V.R.; validation, A.C., S.B. and M.V.R.; visualization, T.F.M. and A.C.; writing--original draft, T.F.M. and A.C.; writing--review and editing, A.C., S.B. and M.V.R. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) under Grant 001, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) under grants 404068/2020-0 and 314741/2020-8, Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) under grants APQ-03609-17 and TEC-PPM 00787-18, and Instituto Nacional de Energia Eletrica (INERGE).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.