esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Baselines in the Literature
### Evaluation Criteria
#### 2.1.5 Proneness to Manipulation  
Proneness to manipulation, also known as integrity, describes the extent with which a baselining methodology is prone to manipulation. Two examples of manipulation are described by [19]. The first example is called user dilemma, which refers to flexibility providers (users) influencing the future baseline by actively delivering flexibility. The future baseline often (only) takes into account recent historical load measurements. These measurements also reflect flexibility delivered in the past, thus affecting future baselines. Secondly, [19] discusses gaming, or baseline cheating, which refers to the baseline being influenced by intentionally increasing consumption (or production) in the days before flexibility is delivered. Gaming possibilities are also identified as a factor by [3; 18].
## 2 Materials and Methods
### Detrended Cross-Correlation and EEG Analysis
#### 2.4.2 Forward Model Computation  
The detrended cross-correlation function was computed between the brain response and the differentiated speech envelope to predict the brain response and investigate brain mechanisms under speech stimuli processing. This study used a detrended cross-correlation function to predict EEG signals in response to sentence speech stimuli. The measured EEG data, denoted as \(r(t,n)\), were acquired at a specific channel \(n\) in response to continuous speech represented by \(s(t)\). Similar to Equation (1), the measured EEG data can be modeled as follows:  
\[r(t,n)\ \propto\ h(t,n)\ *\ s(t)+\epsilon(n)=\sum_{t}h(\tau,n)s(t-\tau)\ + \epsilon(n). \tag{5}\]  
where \(h(t,n)\) represents the TRF of the brain, \(\epsilon(n)\) is the noise process, and \(\tau\) corresponds to the lag between the stimulation and neural response. The TRF of the brain was estimated using the following formula:  
\[h(\tau,n)\propto<\
abla r(\tau,n)\
abla s(t-\tau). \tag{6}\]  
In this study, the trained TRF was computed over a lag range of \(-100\) ms to \(500\) ms, which is relevant to the stimulus onset. The forward model (i.e., the encoding model) was obtained through ridge regression using the mTRF toolbox implemented in MATLAB as the reference model [18]. The optimal alpha value for ridge regression was selected within the range of \(10^{-6}\) to \(10^{6}\). The baseline-normalized TRF was computed as follows:  
\[h(\tau,n)=\frac{h(\tau,n)-\overline{h_{b}(n)}}{\sigma(h_{b}(n))}, \tag{7}\]  
where \(\overline{h_{b}(n)}\) and \(\sigma(h_{b}(n))\) represent the mean and standard deviation of TRF during the baseline period of \(-250\) to \(0\) ms before stimulus onset.  
The univariate model uses a speech envelope as the system input to estimate the TRF of the brain in response to speech stimuli. The multivariate model was computed using the TRFs of 64 frequency bands to approximate the STRF of the brain-to-speech stimuli. The multivariate forward model aims to determine the STRFs for a speech spectrogram ranging from \(250\) to \(6000\) Hz.
# A Rectangular Toroidal Current-Based Approach for Lung Biopsy Needle Tracking
## 3 Results
### System Calibration Error Analysis and Optimization
Among the different calibration position schemes, the 30 mm calibration scheme results in the lowest calibration error. Using this scheme, the system's calibration parameters are obtained in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & **(0, 0, 80)** & **(40, \(-\)40, 80)** & **(\(-\)40, 40, 80)** & **(40, 40, 130)** & **(80, \(-\)80, 80)** & **(\(-\)80, 80, 80)** \\ \hline \(x\left(mm^{2}\right)\) & 4.27 & 1.58 & 0.94 & 4.11 & 2.32 & 7.84 \\ \(y\left(mm^{2}\right)\) & 4.72 & 0.11 & 0.87 & 0.36 & 2.90 & 5.11 \\ \(z\left(mm^{2}\right)\) & 3.19 & 6.81 & 1.65 & 3.48 & 3.56 & 5.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Static sample variance values for different locations. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Spatial Parameter** & **Correction Factor** & **Coil 1** & **Coil 2** & **Coil 3** & **Coil 4** & **Coil 5** & **Coil 6** & **Coil 7** & **Coil 8** \\ \hline
4 \(\times\) 4 & \(r_{i}^{offset}\) & 0.0172 & 0.0231 & 0.0166 & 0.0184 & 0.016 & 0.0141 & 0.0173 & 0.0155 \\
4 \(\times\) 4 & \(k_{i}\) & 17.923 & 20.282 & 20.102 & 23.309 & 23.952 & 24.
###### Abstract  
There are two common ways of coupling first-principles modelling and machine learning. In one case, data are transferred from the machine-learning algorithm to the first-principles model; in the other, from the first-principles model to the machine-learning algorithm. In both cases, the coupling is in series: the two components remain distinct, and data generated by one model are subsequently fed into the other. Several modelling problems, however, require in-parallel coupling, where the first-principle model and the machine-learning algorithm work together at the same time rather than one after the other. This study introduces deep multiphysics; a computational framework that couples first-principles modelling and machine learning in parallel rather than in series. Deep multiphysics works with particle-based first-principles modelling techniques. It is shown that the mathematical algorithms behind several particle methods and artificial neural networks are similar to the point that can be unified under the notion of particle-neuron duality. This study explains in detail the particle-neuron duality and how deep multiphysics works both theoretically and in practice. A case study, the design of a microfluidic device for separating cell populations with different levels of stiffness, is discussed to achieve this aim.  
mathematical modelling; discrete multiphysics; coupling artificial intelligence with first-principle modelling; computer simulations +
Footnote †: journal: Applied Sciences
## 2 Continuous-Time Recurrent Neural Network (CTRNN)
### CTRNN Model  
The CTRNN mathematical model consists of six preliminary components, as shown in Figure 1a and Equation (1). The differential part in Equation (1) makes them intricately intertwined to mimic the behavior of human brain neuron cells [12]:  
\[\boldsymbol{\tau}_{i}\frac{d\boldsymbol{y}_{i}}{dt}=-\boldsymbol{y}_{i}+ \boldsymbol{f}_{i}(\boldsymbol{B}_{i}+\sum_{j=1}^{n}\boldsymbol{w}_{ij} \boldsymbol{y}_{j})+\boldsymbol{I}_{i}(\boldsymbol{t}) \tag{1}\]  
where \(\boldsymbol{\tau}_{i}\) is the time constant of neuron i; \(\boldsymbol{y}_{ii}\) and \(\boldsymbol{f}_{ii}\) are the state and activation function of the _ith_ neuron; and \(\boldsymbol{w}_{ij}\), \(\boldsymbol{B}_{i}\), \(\boldsymbol{I}_{i}(\boldsymbol{t})\) represent the weight vector, bias shift, and external input at time t of the _ith_ neuron. The first part of the neuron is the continuous state of the current neuron, represented by \(\boldsymbol{y}_{i}\) This state changes over time and depends on the previous state and all the other five components. The second part is the activation or nonlinearity function, which is denoted as \(\boldsymbol{f}_{i}\), the Sigmoid function. Rectified Linear Units (ReLUs) and hyperbolic tangent functions (Tanhs) are all examples of widely used non-linear functions. The behavior of a biological neuron inspires the activation function to capture the nonlinearity of a complex pattern. As a group of neurons activate together, signal propagation occurs through them in a specific path. Different paths capture different patterns. The third part is the synaptic weight vector, represented as \(\boldsymbol{w}_{ij}\). It describes the strength of the connection between neurons \(j\) and \(i\). The fourth part is the external input, \(\boldsymbol{I}_{i}(\boldsymbol{t})\), representing the continuous input signal from the sensors or other data sources. The fifth part is the time constant represented by \(\boldsymbol{\tau}_{i}\), which controls how quickly the current cell reacts to any external input; a high \(\tau\) means the neuron needs more time to respond and change its status, while a low time constant means this neuron has a rapid response to any change. The last part is the neuron dynamics, represented by the state's rate of change (differential part), represented by \(\frac{d\boldsymbol{y}_{i}}{dt}\).  
Figure 1: Structure of a Continuous Time Recurrent Neuron (CTRNN): (**a**) differential equation representation; (**b**) using a MEMS dynamic.
## 3 Sampling and Reconstruction
### The S&H Reconstruction by Interpolation  
The S&H tools presented in the previous sub-section have a drawback, since they may lead to a discontinuous signal. The presence of discontinuities transforms the original bandlimited signal into fullband as we can observe in Figure 1. We can avoid this difficulty by implementing some kind of interpolation instead of having an extrapolation. However, this strategy has a price, since it involves the problem of a one sample delay. In many applications this delay has no visible effect. For example, in speech processing, with a sampling interval equal to \(125\,\mu s\), the effect of such delay is negligible. Nevertheless, not all the S&H techniques discussed above can be used. In fact, the S&H-0 leads always to discontinuities. The exponential hold has also that problem, unless we modify the parameter \(\tau\) from sample to sample, which is not easily implementable. This problem does not happen with the proposed general S&H-\(\alpha\), where we only have to make a slight modification.  
**Definition 5**.: _The \(\alpha\)-order (S&H-\(\alpha\)) with delay is expressed as:_  
\[\hat{x}(t)=x(t_{n})+(t-t_{n})^{\alpha}\frac{x(t_{n+1})-x(t_{n})}{\left(t_{n+1 }-t_{n}\right)^{\alpha}},\ \ t_{n}\leq t\leq t_{n+1}. \tag{14}\]  
The impulse response is given by:  
\[\hat{x}(t)=x(t_{n})\left[u(t-t_{n})-u(t-t_{n+1})\right]+(t-t_{n})^{\alpha} \frac{x(t_{n+1})-x(t_{n})}{\left(t_{n+1}-t_{n}\right)^{\alpha}}\left[u(t-t_{n} )-u(t-t_{n+1})\right]. \tag{15}\]  
The S&H-\(\alpha\) with delay (14) leads to a continuous signal, having as consequence that the amplitude spectrum decreases, at least, as fast as \(\omega^{-2}\) when \(\omega\rightarrow\infty\).  
**Example 1**.: _On the left of Figure 2 we depict the extrapolations obtained with the S&H-0, S&H-\(\alpha\) (with \(\alpha=0.3\) and \(\alpha=0.7\)), S&H-1 and S&H-exp (with \(\tau=200\)). On the right, we show the interpolations resulting from the S&H-\(\alpha\) (with \(\alpha=0.3\) and \(\alpha=0.7\)) and the S&H-1._  
_The difference between the two kinds of procedures is clear, as well as their distinct behavior._
# A Numerical Simulation of the Underwater Supersonic Gas Jet Evolution and Its Induced Noise
## 4 Conclusions  
Three-dimensional unsteady numerical simulations of under-expanded and fully expanded underwater supersonic jets were conducted to analyze their complex flow field and noise characteristics. The main conclusions to be drawn are as follows.  
1. During the initial stage of an underwater jet, the supersonic jet forms a continuously expanding gas bubble enveloped in water, and high-frequency noise is generated by the interaction of the shockwaves formed by the supersonic jet. As the jet reaches its maximum length, the impact force gradually weakens, and the gas bubble ruptures and transforms into a conical jet. Consequently, the high-frequency noise transitions into low-frequency noise.
2. When the gas bubble detaches from the nozzle, jet necking occurs at the nozzle outlet, leading to intense jet instability and severe pressure fluctuations. The closer the jet is to full expansion, the more severe the disturbance the jet is subjected to.
3. After the jet transforms into a conical jet, the pressure fluctuations along the center-line of the jet exhibit three peaks. The first two peaks within the core area of the jet correspond to shock interaction in a supersonic jet, which is amplified by the two-phase interaction. The third peak corresponds to the streamwise location where the local Mach number approaches unity, indicating that the jet is most unstable at the sonic point.
4. In cases with equal water depth, the overall trend of the sound pressure and sound pressure level spectrum of the under-expanded and fully expanded jets remains consistent. In particular, the low-frequency noise in both cases is almost identical. However, the high-frequency noise (above 400 Hz) in the under-expanded jet is  
Figure 15: The OASPL curve with angles at the measuring point.  
greater than in the fully expanded jet. Thus, the OASPL of the under-expanded jet is also larger.  
Conceptualization, B.W. and W.Y.; methodology, W.Y. and C.Z.; validation, W.Y. and B.W.; investigation, W.Y. and C.Z.; Writing--original draft preparation, W.Y.; Writing--review & editing, W.Y. and B.W.; supervision, C.Z. and B.W. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by China Ship Scientific Research Center.  
Not applicable.  
All data generated or analyzed during this study are included in this published article (tables and figures).  
The authors declare no conflict of interest.
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 3 Hysteresis Phenomena
### Hysteresis Data Characterization  
The structural materials of McKibben muscle inherently lead to hysteresis phenomena during cyclic contraction and extension. This nonlinearity is addressed as a difficult error source that needs to be handled, especially in a precise control system. The major and minor hysteresis loops can be visibly seen by conducting either an isometric or isotonic tests using vertical load system. As describe in Section 2, the experimental tests were conducted for both quasi-static and quasi-rate dependencies. For quasi-static test, McKibben muscle was vertically attached with different static loads (0-300 N). The change in static load produces different set of hysteresis data. Where, the maximum contracting length reduced with increased of static load. For quasi-rate test, the McKibben muscle was induced with random wave signal (input pressure) and its frequency was varied in between 0.5 and 2.0 Hz. This test was performed because level of hysteresis depends closely on the rate of input excitation. In addition, the change in velocity also causes the friction between the rubber tube and braided mesh sleeve to significantly change, and thus produce a different set of hysteresis data. The desired output from the vertical load system was the contracting length of McKibben muscle. Therefore, a laser displacement sensor was used to measure the contracting length of McKibben muscle during cyclic contraction-expansion. Figure 2 shows two set of hysteresis data obtained from vertical load test. The hysteresis data reflects quasi-static, quasi-rate, and historic behaviors of the [PERSON] muscle.  
Figure 1: Vertical load system.  
In addition, both major and minor hysteresis loops can be visibly seen from the Figure 2. The major hysteresis loop can be seen as an outer layer of the hysteresis data; while the minor hysteresis loop shows the nonlocal memory behavior of [PERSON] muscle. This behavior has historic dependence, where the hysteresis behavior depends on the previous values of force, length, and pressure. The two sets of [PERSON] muscle also produce similar hysteresis data, which clarified the consistency of identical [PERSON] muscle's nonlinear behaviors.
## 4 Experiment Results
### Different Gender and Age  
We also investigate the performance of the ToF-based HR measurements system in terms of the participant's gender and age. Similar to the RGB camera-based system, no significant performance difference is seen among participants of different gender and age. Figure 16 shows the success percentage rate among participants of different gender, we achieve a \(77\%\) success rate in the male group and a \(72\%\) success rate in the female group. The mean HR deviation from reference is \(-5.6\) BPM for the male group (\(18/29\) participants) and \(-7.2\) BPM for the female group (\(11/29\) participants) (Figure 17), respectively. Even though the mean deviation differs slightly, the pattern of deviation data points looks similar. Such differences may be attributed to different skin conditions or even different skincare products used on the skin. Even though participants are asked not to use makeup, there could still be residuals left on the participant's face. Since the blood absorption at  
Figure 14: HR measurements error rate within groups of: lighter skin color (**top**) and darker skin color (**bottom**).  
infrared is already low, any further decrease of signal could affect the success rate of the HR measurements.  
In terms of age, we separated the participants into a younger group (21/29 participants) and an elder group (8/29 participants) at the threshold of 35 years old. The success rate/mean deviation is 84%/ \(-\)7.7 BPM for the elder group and 71%/\(-\)5.89 BPM for the younger group (Figures 18 and 19). The higher success rate among the elder participants  
Figure 16: HR measurements error rate within groups of: male (**top**) and female (**bottom**).  
Figure 15: Bland–Altman plot for HR measurements within groups of: lighter skin color (**left**) and darker skin color (**right**).  
could be caused by elder participants staying more still during the measurements compared to younger participants. In fact, the mean motion score for the younger group is 0.61 cm, compared to 0.47 cm for the elder groups. As we discussed before, the success rate deteriorates as the motion score increases, which could explain the higher success rate among elder participants.  
Figure 17: Bland–Altman plot for HR measurements within groups of: male (**left**) and female (**right**).  
Figure 18: HR measurements error rate within groups of: age > 35 (**top**) and age <= 35 (**bottom**).
## 4 Simulations
### Results
#### 4.3.2 Comparison of Community Detection Algorithms  
In this simulation, we compared the performance of different algorithms from the aspects of community structure detection and message forwarding.  
We used the Infocom 2006 datasets to implement the community structure detection. Figure 6 showed the results that the ECR algorithm was slightly better than the k-clique. In ECR, the community structure was clearer and more accurate, especially in areas of the margin or where the community structure was not obvious. In addition, in the practical application of OppNets, the global attribute of the network (including the topology structure) is difficult to obtain because of the movement of nodes and the lack of continuous end-to-end links. Hence, ECR is more suitable for OppNets.  
In the performance of community detection algorithms, Figure 7 showed the results that ECR had the better forwarding performance, especially compared to CR. In the simulation based on Infocom 2006 datasets, the delivery ratio in ECR increased by 23%, the forwarding delay reduced by 26%, and the number of forwarding decreased by 5%. In the simulation based on synthetic contact trace, the delivery ratio in ECR rose by 4%, the forwarding delay decreased by 5%, and the number of forwarding decreased by 5%. From the simulation results, we can draw the conclusion that our proposed community detection algorithm is effective due to the fact that it has taken advantage of the accurate node relationship and the better method of community construction.  
Figure 5: Influences of ferry nodes on average delay: (**A**) In Infocom 2006; and (**B**) in synthetic trace.  
Figure 6: Comparison of community structure detection: (**A**): K-clique: an efficient algorithm for detecting communities in a network; and (**B**) ECR: our proposed community detection algorithm.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 2 Materials and Methods  
### Data Sources  
The data used in this study are listed in Table 2, with references. For lake surface area, the primary source was the complete time series of Terra and Aqua MODIS 16-day composites (2000-2017), with AVHRR images from 1998-2000, and higher resolution Landsat-5, -7, and -8 images and SPOT-5 images used for validation (Landsat) and threshold selection (SPOT). Water level, mean depth, and lake volume were derived from DEMs from SRTM in 2000 and from stereoscopic ASTER band 3 (NIR) imagery from 2014-2017. ICESat-1 GLAS laser altimetry data (2003-2009) were used for validation of water levels.  
The methods used to analyze these data and derive time-series of lake surface area, water level, and volume are summarized in Figure 4, and are discussed in detail in the following subsections.
## 2 Algorithm Theory and Method
### Pretreatment and Feature Extraction
#### 2.1.3 Selection of Feature Variables  
The extraction of feature variables has a direct impact on the accuracy and reliability of early fault warning. It is not better to select more fault features, and the increase in invalid features may lead to an increase in the complexity of the early warning process and a decrease in the accuracy of the diagnosis results. Therefore, it is necessary to select sensitive features that contain as much fault information as possible as input to the model. In this paper, the _PCA-WLE_ method proposed in the literature [1] is adopted to extract and select sensitive features.
## 2 Dataset Description and Processing
### Data Processing
#### 2.3.5 Data Balancing Layer  
This work follows a minority oversampling technique (SMOTE) [25] with k-nearest neighbors, which is a powerful method used in machine learning and data analysis to solve class imbalance problems. The technique offers several advantages. First, it helps solve the problem of data scarcity or lack of representative samples for the minority class by generating synthetic samples that closely resemble real instances. This improves the model's ability to learn patterns and make accurate predictions for the minority class. Second, SMOTE reduces the bias towards the majority class, ensuring a fair representation of both classes in the dataset. This leads to better model performance and avoids the risk of misclassification or underestimation of the minority class. Overall, by effectively improving the training data, SMOTE significantly improves the performance and reliability of machine learning models.