esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of ALG-Ca Fibers  
The preparation process of the ALG-Ca fibers is schematically shown in Figure 2A. First, 7.4 wt% of ALG solution was added to a syringe (2.5 mL, TERMO), and the syringe was equipped with a syringe pump (Legato 110, KDS). An amount of 3.0 wt% of CaCl\({}_{2}\) aqueous solution (300 mL) was added to a graduated cylinder as a coagulation bath. The reason for using a graduated cylinder was to prevent fibers from touching each other at the bottom of the container before being solidified. The tube connected to the needle tip of the syringe was then immersed in the bath. The ALG solution was added to the bath at a constant rate (1000 mL/min) for spinning fibers. Afterward, the obtained fibers were dried at room temperature to obtain ALG-Ca fibers. For comparison, the fibers made of the PICs of CS and CHI (PIC fiber) were also prepared using the interface-spinning method [47; 48] with several modifications. In order to form CS/CHI PIC interfaces, 2.0 wt% of CS solution (5.0 mL) was placed onto a predetermined concentration of CHI solution (5 mL) gently. The formed film at the interface was withdrawn, passed through an ethanol bath, and rolled up to give water-insoluble PICs fibers. Afterward, the obtained fibers were dried at room temperature.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 3 Measurement Monitoring Techniques for Multipath Mitigation  
In this section, the CMC-based multipath error correction, \(\mathrm{C/N_{0}}\)-based stochastic weighting and detection of affected measurements are discussed in details. While the two first approaches have been previously investigated in the literature [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24], the focus will be on detection techniques where a new Geometric-Free (GF)-based multipath detection is presented. The detection results are then used in the following sections to exclude or de-weight the affected measurements.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 5 Applications  
Rolling bearings are liable to be damaged during operation. It is necessary to collect bearing vibration data in real time with appropriate sensors to monitor the bearing state so as to avoid equipment failure. Two groups of experimental data are used to verify the method mentioned in this paper. The first group is the fault data of bearing outer ring, which verifies that this method is more accurate, rapid, and convenient than EWT when dealing with the fault. The second group is the failure data of bearing inner ring, which verifies the application of this method in fast kurtogram (FK) and the processing result is better.
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 2 Methodology  
This study employs three commonly used ML models for DO modeling: support vector regression (SVR), regression tree (RT), and boosting ensemble. These models are coupled with Bayesian optimization (BO) to achieve automated optimization and enhance model performance. This section provides an overview of the theoretical background of these methods.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
###### Abstract  
Full waveform inversion is a widely used technique to estimate the subsurface parameters with the help of seismic measurements on the surface. Due to the amount of data, model size and non-linear iterative procedures, the numerical computation of Full Waveform Inversion are computationally intensive and time-consuming. This paper addresses the parallel computation of seismic full waveform inversion with Graphical Processing Units. Seismic full-waveform inversion of in-plane wave propagation in the finite difference method is presented here. The stress velocity formulation of the wave equation in the time domain is used. A four nodded staggered grid finite-difference method is applied to solve the equation, and the perfectly matched layers are considered to satisfy Sommerfeld's radiation condition at infinity. The gradient descent method with conjugate gradient method is used for adjoined modelling in full-waveform inversion. The host code is written in C++, and parallel computation codes are written in CUDA C. The computational time and performance gained from CUDA C and OpenMP parallel computation in different hardware are compared to the serial code. The performance improvement is enhanced with increased model dimensions and remains almost constant after a certain threshold. A GPU performance gain of up to 90 times is obtained compared to the serial code.  
Keywords:seismic wave propagation; full waveform inversion; CUDA; GPU parallelisation; finite difference method +
Footnote †: journal: Applied
# Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses
## 2 Methods
### Model Predictive Control (MPC)  
Model predictive control (MPC) solves an open-loop dynamic optimization problem over a time interval \(\left[t_{k},t_{k+H_{p}}\right]\), with \(H_{p}\) as the prediction horizon to optimize the future of a process. The novel obtained measurements and state and parameter estimates at \(t_{k}\) are used as initial conditions for the open-loop dynamic optimization problem that is solved at every iteration. This principle is illustrated in Figure 2: based on the state estimate or measurement at \(t_{k}\) a dynamic optimization problem is solved over the time interval \(\left[t_{k},t_{k+H_{p}}\right]\) (a). The solution is applied to the process until a new measurement at \(t_{k+1}\) is obtained and then a new dynamic optimization problem is solved over \(\left[t_{k+1},t_{k+H_{p}+1}\right]\) (b).  
MPC has become a widespread control strategy and numerous successful applications to industrial problems have been reported, e.g., [18; 19]. MPC typically refers to linear MPC in which a quadratic objective function is minimized using a linear process model and subject to linear constraints. In the last decades, nonlinear MPC (NMPC) approaches using nonlinear process models, constraints, and objective functions have become more popular.  
In this article the following (N)MPC formulation for \(t\in\left[t_{k^{\prime}},t_{k+H_{p}}\right]\) is used, based on [20]:  
\[\min_{\mathbf{x}(\cdot),\ \mathbf{u}(\cdot)}\int_{t_{k}}^{t_{k}+H_{p}}L( \mathbf{x}(\tau),\mathbf{u}(\tau),)d\tau+M(t_{k}+H_{p}) \tag{12}\]  
Figure 2: Illustration of two consecutive MPC iterations.  
subject to:  
\[\dot{\mathbf{x}}(t) =\mathbf{S}_{\mathbf{e}}\cdot\mathbf{K}\cdot\mathbf{u}(\mathbf{x}, \mathbf{p})\cdot\mathbf{q}_{\text{bio}}^{\top}\cdot\mathbf{x}(t)+(\mathbf{X}_{ \text{in}}\cdot\mathbf{r}-\mathbf{x}(t))\cdot D \tag{13}\] \[\dot{\mathbf{p}}(t) =\mathbf{0}\] (14) \[\mathbf{x}(t_{k}) =\hat{\mathbf{x}}_{k}\] (15) \[\mathbf{p}(t_{k}) =\hat{\mathbf{p}}_{k}\] (16) \[\mathbf{0} =\mathbf{z}(t)-\mathbf{I}_{\text{irr}}\cdot\mathbf{K}\cdot \mathbf{u}(\mathbf{x},\mathbf{p})\] (17) \[\mathbf{y}(t) =\mathbf{x}(t)\] (18) \[\mathbf{x}(t) \geq\mathbf{0},\ \mathbf{z}(t)\geq\mathbf{0}\] (19) \[\mathbf{0} \geq\mathbf{c}(\mathbf{x},\mathbf{r},\mathbf{p},t). \tag{20}\]  
with \(\hat{\mathbf{x}}(t_{k})\) being the measured or estimated state values at \(t_{i}\), \(\mathbf{c}(\mathbf{x},\mathbf{r},\mathbf{p},\tau)\), constraint functions on the systemare not comprised by the previous constraints, \(\tau\) is the time used in solving the dynamic optimization problems in the NMPC routine, and \(\mathbf{x}\) and \(\mathbf{r}\) the states and controls computed by solving the dynamic optimization problem on the interval \(\left[t_{k},t_{k}+H_{p}\right]\). Note that for the other symbols the same notation is used as in the MHE problem (Equations (4)-(11)). As MPC requires that the entire state vector is known, the states and parameters estimates at \(t_{k}\) are denoted by \(\hat{\mathbf{x}}_{k}\) and \(\hat{\mathbf{p}}_{k}\).
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
## 5 Results
The results of the proposed multipliers were compared with state-of-the-art decimal multipliers implemented in FPGA [41; 42; 43; 44], (see Table 13). All designs consider registered inputs and outputs. The multiplier in [41] uses the final flip-flops to implement the final conversion from the internal representation to BCD. To register the outputs an extra level of LUTs is required. In our circuit, the extra level of LUTs implements both the converter and the register. In this reference, the decimal digit adder occupies five LUT, while our decimal adder occupies only four. For a fair comparison, the multiplier from [41] was redesigned and implemented with our decimal adder. The decimal multiplier from [42] uses the Karatsuba-Offman algorithm to reduce the area at the cost of delay. The area reduction impact is higher for larger operands. However, even for the 16 \(\times\) 16 decimal multiplier, the proposed multiplier 1 achieves a similar area with a reduction of 34% in the delay. Multiplier 2 reduces the area by 15% for the 16 \(\times\) 16  
Figure 8: Area variation of both multipliers. Figure 9: Delay variation of both multipliers. multiplier with with a reduction of 28% in the delay. The improvements obtained with the proposed decimal multipliers are higher for smaller operands since the relative overhead of the Karatsuba-Offman algorithm is higher for smaller operands. The proposed multiplier 1 is smaller (up to 14%) and faster than the previous best decimal multiplier from [44]. This is due to the reduction in the area of the partial product generator. The proposed multiplier 2 further improves the area of multiplier from [44] (up to 35%) and also the performance for the multipliers with operands of 16 and 32 digits. The area of multiplier 2 was compared with the area of a binary multiplier of equivalent input operands generated for best speed with Xilinx Core Generator (see results in Table 14)  
Small decimal multipliers are relatively expensive compared to the binary multiplier, but the proposed \(16\times 16\) decimal multiplier 2 has an area only 4% higher than the area of the binary multiplier and the proposed \(32\times 32\) decimal multiplier 2 is smaller than the binary multiplier. The area ratio reduction has to do with the overhead associated with the generation of the multiples which are amortized as the multiplier size increases. The delay of the decimal multiplier is always worse but the relative difference also decreases with the operand size for the same reasons.
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### 5G Non-Public Network on the Industrial Shopfloor
#### 3.1.3 5G URLLC Test System  
The 5G URLLC (ultra-reliable low latency communication) system used in our experimental evaluation on the shopfloor is a standard compliant pre-commercial standalone test system. The 5G URLLC test system is configured to operate in 28 GHz (n261 5G band) with 200 MHz of system bandwidth. The 5G URLLC test system leverages various 3 GPP Rel. 15/16/17 specifications on reliability enhancements and low latency [25]. Moreover, the 5G URLLC test system supports features such as Ethernet PDU sessions, Ethernet header compression, over-the-air (OTA) time synchronization, traffic classification, prioritization, and QoS mapping to allow easy integration with TSN protocols as specified by 3 GPP [26; 27]. We used a test 5G URLLC UE for our experiments on the shop floor.  
The choice of 5G system operating frequencies in the sub-6 GHz and the mmW ranges is mainly attributed to the availability of frequency spectrum for industrial use and the industrial use-case deployment conditions. In Germany, the 3.7-3.8 GHz frequency range has been allocated by the Federal Spectrum Regulatory Authority (BNetzA) for local industrial use. Similarly, the 26 GHz range is available for non-public network deployments. The two frequency ranges have significantly different propagation characteristics, and their use depends upon the specific requirements of the industrial application and its deployment conditions. Most notably, the sub-6 GHz range shows, in general, better propagation characteristics compared to the mmW frequencies and is not as prone to blockage and penetration loss. mmW frequency range, on the other hand, has a significantly large available bandwidth that is well-suited for data-intensive applications. However, due to beamforming, these may require additional measures for non-LOS and beyond LOS propagation [28] and may not be well-suited to applications involving high mobility.
## 4 Modified Weight Update Method by LS-CMA and SD-CMA  
### Issue on LS-CMA Snd Switching Algorithm of CMA  
In Section 3, the ideal performance of the proposed method, where the sufficient iteration and data smoothing number by CMA is given, was verified. Next, assuming that the CMA operates in an actual system, we focus on evaluating the performance for realistic data sizes, because, it is not possible to secure the large number of symbols required for controlling the weights. Although CMA focuses on modulation schemes with constant modulus, such as the Gaussian minimum shift keying (GMSK) or quadrature phase shift keying (QPSK) [17], it has been reported that QAM signals can also be used [18; 26; 27].  
Figure 8 shows the SINR by the weight update using LS-CMA and SD-CMA versus the iteration, when the AoA between two UTs is 60 degree and the smoothing size is 100 symbols for both. In the case of the LS-CMA method, convergence is rapid because the fluctuation in weight is large with one update. However, as can be seen in Figure 8, the weight does not stabilize after convergence. This issue can be solved by using data smoothing wherein the weights of the CMA are averaged for multiple samples. However, the number of symbols used increases according to the increase in the smoothing sizes. On the other hand, in the case of the SD-CMA method, the weight stabilizes after convergence because the fluctuation in weight is small with one update. Therefore, we propose a method that combines the LS-CMA and the SD-CMA approaches.  
When the total number of weight updates is \(n\), the weight is updated \((1,2,\cdot\cdot\cdot,k)\) times with the (3), which is the expression for the LS-CMA method. Next, using that weight, the weight is updated \((k+1,k+2,\cdot\cdot\cdot,n)\) times with the (2) which is the expression for the SD-CMA method. Hence, speed improvement and stabilization of weight convergence can be realized. In this paper, this proposed method is called the LS-to-SD-CMA.
## 6 Verification of the Theory  
The experiments with the 320-330 GHz FMCW radar were carried out without fog, as a reference background case, and with fog where the visibility was 0.5 m. The output signal at IF was recorded in the form of power spectral density in the spectrum analyzer. The signal obtained contained relatively large spectral fluctuations, mainly due to multi reflections from different objects along the corridor, as can be seen in Figure 7a. Knowing the physical distance of the target, we perform numerical smoothing algorithm. The detected spectrum measured in a clear corridor (without fog) and that obtained in the presence of fog are both shown in Figure 7b.  
When the fog is introduced, 3 dB attenuation and 9 Hz frequency up-shift are revealed in the IF signal in respect with the reference (no fog) measurement. These results correspond to an attenuation of 80 dB/km and a time delay of 230 ps/km. Using the theoretical model, we evaluated these values as summarized in Table 3.  
Simulations were carried out for reference conditions as prevailed during the experiment; ambient temperature 16 \({}^{\circ}\)C, air pressure 101 kPa and relative humidity \(RH\) = 70%. Some small differences between simulation results and experimental measurements were noted. In order to demonstrate the effects pointed out theoretically, in a limited experimental space (a corridor with a length of about 20 m), we generated a heavy fog and brought the visibility to be very low. This may  
Figure 6: (**a**) The process of filling the corridor with fog, the visibility of about 1.5 m; (**b**) The corridor is filled with fog until the visibility was less than a meter.  
Figure 7: (**a**) The detected intermediate frequency (IF) signal spectrum when the target is at a physical distance of 18.8 m in both cases with and without fog; (**b**) after filtering and smoothing.  
lead to some uncertainties in the visibility estimation, mainly due to extremely low, hard-to-measure visibility of the artificial fog along the whole corridor. However, the typical droplets dimension generated by the 'fog machine' were small enough to be well in the Rayleigh scattering regime even if the temperature changes during the experiment. This enables neutralization of temperature fluctuation and its effect on the diameter of the droplets on the scattering phenomena even in sub-millimeter wavelengths.  
According to expression (21), a frequency shift of \(\Delta f_{IF}=9\) Hz corresponds to an incremental group delay of \(\Delta\tau_{d}=4.32\) ps. This means that the fog introduces additional delay in the received signal with respect to the physical distance measures at clear conditions. The resulted time delay enables calculation of the index of refraction of the fog at 320 GHz. Using relations (15), we estimated the real part of refractivity in the presence of the fog to be \(N_{0}+N^{\prime}(f)=68\) ppm. From the measured attenuation of 3 dB, we also estimated the imaginary part of the refractivity to be \(N^{\prime\prime}(f)=50\) ppm.
# Digital Twins for Additive Manufacturing: A State-of-the-Art Review
## 3 Major Issues and Research Needs
### Prediction of the Additive Manufacturing (AM) Results  
By now, additive manufacturing is still used in a trial-and-error fashion. Lots of experiments should be done and lots of specimens will be destructively tested to check the microstructures and mechanical properties. If a site-specific part is expected, prediction of the AM results is rather important.  
Figure 4: Schematics of main additive manufacturing processes.  
Through the concept of the digital twin, it will be possible to predict the results (geometry, microstructures, and mechanical properties) according to accurate heat transfer models and mechanical models as shown in Figure 5[15], before actual additive manufacturing, which will provide great help in finding the optimal combination quickly without wasting too much time and material. Currently, the prediction for the AM parts is more likely focused on the distortion and temperature distribution, based on the basic ideas of dividing the part into different unit of grids, and by computing the properties of the grids to obtain the properties of the integral part.  
Some simulations of the additive manufacturing process are similar to those of welding [4; 27], which has been researched in more depth and more well-published knowledge could be leveraged, including prediction of weld metal solidification [28; 29] solid-state transformation under single and multi-pass conditions [30; 31], as well as static and dynamic mechanical properties [32; 33; 34; 35; 36]. [PERSON] et al. [9] developed a robust numerical model to simulate convective fluid flow and balancing of surface tension forces at the air-fluid interface. An arbitrary Lagrangian-Eulerian (ALE) moving mesh approach was used to calculate the free surface physical interface. Finally, they successfully predicted the thermal gradient directions and solidified clad dimension. The experimental validation of clad with height and melt-pool depth in various processing parameters were all lower than 10%.  
Predicting the thermal history, parts' distortion, microstructure, and mechanical properties are vital. It is also one of the main significant elements of a digital twin for additive manufacturing. Only through prediction of the aforementioned information could it be possible to optimize the parameters and control the process, monitor the conditions, and get the expected results. However, actual tailoring of the final component's properties based on the predictive model is still a long way off [18].
## 4 Application
### Clinical Application
#### 4.1.3 Critical Patient Monitoring  
[PERSON] et al. [126] used the iPPG technique in patients after heart surgery using a mobile camera under critical care conditions. In their proposed method, ROI was selected manually, and optical flow techniques were used to detect motion artefacts. Moreover, high pass filter and Fourier spectra were employed to calculate HR. Nevertheless, their method is affected by patient movements and illumination variations.
## 3 Result and Discussion
### Variation in Flammable Gas Reduction Rate  
In this study, the onset of the explosion was defined as the time at which the flammable gas mixture attained a pressure of 0.01 bar. The flammable gas reduction rate was quantified based on the time elapsed from ignition to complete depletion of the gas. This metric was used to evaluate the influence of the ignition position on the combustion propagation and explosion characteristics. Figure 9 illustrates the variations in the flammable gas reduction rate and gas volume over time for ignition points located along the X-, Y-, and Z-axes. For the X-axis, ignition at the centre (6.1 m) resulted in the highest reduction rate of 456.64 m\({}^{3}\)/s. In contrast, ignition near the port (0.1 m) and starboard (9.9 m) walls yielded significantly lower rates of 363.37 m\({}^{3}\)/s and 357.79 m\({}^{3}\)/s, respectively. These results indicate that central ignition facilitates symmetric and rapid combustion, resulting in a faster depletion of the flammable gas. A similar trend was observed along the Y-axis. Here, ignition at the centre (5.5 m) produced the maximum reduction rate of 454.45 m\({}^{3}\)/s, whereas the lowest rate of 313.89 m\({}^{3}\)/s was recorded near the forward wall (14.9 m). The reduction rate decreased consistently as the ignition point shifted toward the FWD direction. This indicates that there is a less favourable combustion environment near the wall. For the Z-axis, ignition near the floor (1.1 m) generated the highest gas reduction rate of 409.55 m\({}^{3}\)/s, whereas the lowest rate of 370.59 m\({}^{3}\)/s occurred near the ceiling (4.1 m). This result implies that ignition in the lower region may create favourable conditions for vertical flame propagation and enhanced combustion efficiency. These observations demonstrate that the ignition location has a direct impact on the combustion rate and flame development pathways. This, in turn, influenced the rate of flammable gas depletion. The spatial distribution of the gas reduction rates showed a strong correlation with the previously analysed MPRR. This reinforces the assertion that rapid gas consumption is the primary factor that drives the pressure escalation during an explosion. Therefore, the flammable gas reduction rate can serve as a critical predictive parameter for assessing the explosion severity and risk potential. Optimization of the ignition position based on this metric could contribute significantly to the design of safer fuel preparation systems.