esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
[PERSON] and [PERSON] [34] proposed an algorithm for finding an optimal allocation of operators to machines that minimizes the cycle time for the minimum number of operators. [PERSON] [35] proposed a mathematical model that takes walking time into account, with operators following circular paths and walking around other operators. A heuristic algorithm was developed to optimize the allocation of operators. Although [PERSON] and [PERSON] [33], [PERSON] and [PERSON] [34] and [PERSON] [35] take walking times into consideration, the sequence of tasks is fixed and unchangeable. [PERSON] et al. [36] proposed an integer programming formulation which takes both precedence relationships of tasks and walking times between locations into consideration. The sequence of tasks and allocation of operators are optimized simultaneously. The four studies that consider walking times, described above, assume that the locations of items are fixed, and a conveyor is not used to move the assembled products. If operators have to execute another consecutive assembly task, they have to walk to the next location. However, as shown in the example illustrated in Figure 1, the present study aims to deal with a U-shaped conveyor assembly line balancing problem in which items are moved on when assembled. When operators have to execute another consecutive assembly task, they are already located at the start point of the next assembly task, and no additional walking is required. This research aims to optimize a Type 2 U-shaped conveyor assembly line balancing problem with walking time between assembly tasks. Although very few research studies deal with walking times, such studies assume that items are assembled in fixed locations. Therefore, the problem that the present study addresses cannot be found in the literature. Since walking time is an important component of the cycle time, the present study is novel and contributes to the related literature on industrial processes. Based on the proposed problem, this research proposes integer programming to minimize cycle times with a given number of operators. An SA algorithm is also proposed to solve the problem efficiently. The resulting process may be of benefit in many related industries.
## Appendix A Comparison of the Retrieved Dust Particle Size to SEM Measurements  
This section describes the results of an experiment in which dust particles are grown and collected on a substrate. Subsequently, the particles on the substrate have been analyzed using a scanning electron microscope (SEM). The main goal of this section is to compare the dust particle size retrieved from the fitting procedure with that measured by the SEM.  
The plasma power was similar to the experiments described in the main text (\(P=20\,\mathrm{W}\)), but the pressure and mixing ratio are slightly different: \(p_{\mathrm{Ar}}=5.4\,\mathrm{Pa}\) and \(p_{\mathrm{HMDSO}}=1.6\,\mathrm{Pa}\). After 60 s of dust growth, the HMDSO flow was terminated, the plasma was switched off and some of the dust particles were collected on the SEM substrate. Figure 11 shows an SEM micrograph with 15 dust particles, labeled and marked by red circles. Using an edge detection filter, the size of the particles is measured under the assumption that the particles are spherical. The results of the size determination of the individual particles are shown in Table 11, from which the average dust particle size over the sampled particles is found to be \(a=146.2\pm 6.8\,\mathrm{nm}\).  
Similar to the results shown in Figure 5, the fitting procedure is started as soon as the transmittance took a value of \(T\leq 0.99\). Figure 12 shows the results of the dust particle size inferred by the fitting procedure as a function of time. The red line indicates the mean of the dust particle size of the SEM image, with the \(1\sigma\) and \(2\sigma\) regions indicated by the red regions. At the time of switching off the plasma \(t=60\,\mathrm{s}\), the dust particle size is expected to be \(a_{\mathrm{fit}}=155.9\,\mathrm{nm}\), which is in the \(2\sigma\)-region of the dust particle size determined by the SEM image. Even though the difference between the measured and fitted dust particle size is quite small, several effects could explain the discrepancy. For example, the number of particles on the SEM image is relatively low and may therefore be an inaccurate measure of the dust particle size. On top of that, the dust particle size seems to be quite sensitive to the refractive index of the particles, via the extinction coefficient \(Q_{\text{ext}}\). Since the refractive index data has been taken from a sample with different processing conditions, its value might explain the overestimation. Nevertheless, the fact that the dust particle sizes agree relatively well gives us some confidence in the developed fitting method and its underlying assumptions.
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
###### Abstract  
The objective of this research was to investigate silver adsorption on biochars prepared by the carbonization of deslicionized rice husks and cotton fabric, and to elucidate the adsorption mechanism. The biochars were characterized using various instrumental techniques, including thermogravimetric analysis (TGA), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and low-temperature nitrogen adsorption. The adsorption process was studied under multiple parameters affecting silver adsorption and was best described by the pseudo-second-order kinetic model. Both the Langmuir and Freundlich isotherm models provided good correlations for the adsorption of silver ions onto the biochars. Thermodynamic analysis indicated that the adsorption of Ag\({}^{+}\) ions was spontaneous and exothermic. XPS confirmed the presence of metallic silver (Ag\({}^{0}\)) in the biochars RHB and ARHB, suggesting that a reduction process occurred during silver adsorption. Additionally, in vitro studies were conducted to evaluate the antimicrobial activity of silver-loaded activated rice husk biochar against _E. coli_ strains.  
biochar; adsorption; silver ions; antimicrobial activity +
Footnote †: journal: Applied sciences
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 3 Design, Fabrication, and Validation of Graded Micro-Porous Material System
### Fabrication of Graded \(\upmu\)PHB Composite Material  
The processing and fabrication of \(\upmu\)PHB composite material of three different particle-size groups were extended to the fabrication of a graded pore-size distribution (large to small) \(\upmu\)PHB composite material. The pre-processed size group of hollow microbubbles and their respective binder percentages, as listed in Table 3, were used. During the processing, each layer was molded with small (applied safe molding pressure [25] and compacted), medium, and large-size microbubbles, respectively, of the required thickness of each layer to build a three-layer gradation. The molded specimens were placed inside the oven @ 60 \({}^{\circ}\)C for 15 min to take out Iso Propyl Alcohol (IPA) (used for resin dilution) (the boiling point of IPA is 82.5 \({}^{\circ}\)C [51]) completely; then, with a ramp rate of 5 \({}^{\circ}\)C/min, the oven temperature was raised to 125 \({}^{\circ}\)C and held for 10 min. Then, the temperature was raised to 135 \({}^{\circ}\)C and held for 10 min, and finally, the oven temperature was raised to 150 \({}^{\circ}\)C and held  
Figure 12: (**a**) Graded pore-size distribution, (**b**) two different selected combinations of graded-distribution \(\upmu\)PHB composite materials.  
for 2 h (the curing conditions for Durite SC1008). At the curing temperature, the binder softens, limits the flow because of the limited resin used, creates a strong bond between the particles, and then is cured. Since this is a graded material system with three different size groups of hollow microbubbles built as a continuous system, we followed a slow temperature rise procedure such that the softened binder would not flow and result in the closure of pores. Figure 13 shows the graded \(\mu\)PHB composite material of two different selected combinations, L50, M25, S25, and L37.5, M37.5, S25.
## 2 UAC Impulse Response Measurement  
The UAC channel impulse response measurements are usually performed by the correlation method with the use of probe signal having an impulse-like autocorrelation function [15]. One of these signals is a pseudo-random binary sequence (PRBS) being a repetition of a bipolar m-sequence \(c_{m}\in\{-1;1\}\), modulated onto a binary phase-shift keyed waveform \(s(t)=\text{Re}[x(t)e^{j2\pi f_{0}t}]\), and \(x(t)\) can be denoted as:  
\[x(t)=\sum_{m=0}^{M-1}c_{m}rect(t-\frac{m}{M}T) \tag{1}\]  
where \(T=1/B\), \(B\) is the signal bandwidth, \(f_{c}\) is the carrier frequency, and the sequence length \(M=2^{L}-1\) is determined by its order \(L\). The \(rect(t)\) denotes the rectangular function. The probe signal is constructed of \(K\) subsequent sequences \(s(t)\), each of duration \(T_{s}\), without any pause in between. To produce signal \(s(t)\), m-sequence \(c_{m}\) is upsampled by a factor \(R=f_{s}/B\). Next, the signal is passed through a binary phase shift keying (BPSK) block and it modulates the carrier frequency \(f_{c}\).  
At the receiver the analog signal from the ultrasonic transducer is digitised by an analog-to-digital converter. After demodulation, the signal is passed through a matched filter with coefficients corresponding to the upsampled m-sequence used in the transmitter. The matched filtration algorithm is usually implemented in a frequency domain, using a discrete Fourier transform (DFT) [2 ]. As a result, at each observation time \(t\), a single realisation of the time-varying impulse response \(h(t,\tau)\) is obtained.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### LEO Constellation Patterns  
To design the LEO satellite constellation for a spacecraft mission, two walker constellation patterns, walker delta and walker star constellation, are generally chosen [29; 30]. Two types of lattice flower constellations, 2D-LFC and 3D-LFC, can also be considered as walker constellations [31; 32]. Here the LEO constellation patterns of 2D-LFC and 3D-LFC are taken into consideration. Considering that the uniformity and the symmetry of the constellation can be affected by elliptical orbits in 2D-LFC [26], only circular orbits are considered for optimizations based on this pattern, while 3D-LFC is applied for constellation pattern of elliptical orbits.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 4 DPTracer Logger
### Interval Proof
#### 4.3.2 Verifying an Interval Proof  
From an interval proof, a node can confirm that the logger holds identical data to the target log entries and maintains consistent log records between versions. The node verifies an interval proof by reconstructing the root hash and comparing it with the received commitment. The verification process is as follows:  
1. **Initialize leaf hashes**: use the received log entry data to compute the hash values of the target leaf elements.
2. **Reconstruct tree paths**: using the hash values from the interval proof, rebuild the hash values of the interior elements along the paths to the root.
3. **Compute root hash**: combine the reconstructed paths to compute the root hash corresponding to the anchor version \(v_{\max}\).
4. **Verify commitment**: check if the computed root hash matches the received commitment \(C_{v_{\max}}\). If they match, the proof is valid.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 3 Fog Generator Model
### Model Architecture
#### 3.2.3 Fog Parameter Layers  
The atmospheric light \(A\) and fog strength are modeled as learnable parameters. The base fog color is initialized as \(\mathbf{A_{base}}=[0.8,0.8,0.9]\) with a variance \(\mathbf{A_{var}}=[0.1,0.1,0.1]\), adjusted via a tanh function during training. The fog intensity is controlled by a scalar parameter \(\beta\), initialized at 0.5 and fine-tuned to match target fog levels (e.g., 0.2 in our experiments). These parameters allow the model to adaptively synthesize fog with varying characteristics.
# Smooth Information Criterion for Regularized Estimation of Item Response Models
###### Abstract  
Item response theory (IRT) models are frequently used to analyze multivariate categorical data from questionnaires or cognitive test data. In order to reduce the model complexity in item response models, regularized estimation is now widely applied, adding a nondifferentiable penalty function like the LASSO or the SCAD penalty to the log-likelihood function in the optimization function. In most applications, regularized estimation repeatedly estimates the IRT model on a grid of regularization parameters \(\lambda\). The final model is selected for the parameter that minimizes the Akaike or Bayesian information criterion (AIC or BIC). In recent work, it has been proposed to directly minimize a smooth approximation of the AIC or the BIC for regularized estimation. This approach circumvents the repeated estimation of the IRT model. To this end, the computation time is substantially reduced. The adequacy of the new approach is demonstrated by three simulation studies focusing on regularized estimation for IRT models with differential item functioning, multidimensional IRT models with cross-loadings, and the mixed Rasch/two-parameter logistic IRT model. It was found from the simulation studies that the computationally less demanding direct optimization based on the smooth variants of AIC and BIC had comparable or improved performance compared to the ordinarily employed repeated regularized estimation based on AIC or BIC.  
regularized estimation; item response models; smooth information criterion; differential item functioning; multidimensional item response model; Rasch model; SCAD penalty
## 2 Methodology
### Principal Component Analysis  
Principal component analysis (PCA) is a linear feature extraction method that is widely used for linear dimensionality reduction. It uses the variance of each feature to find new features to maximize the separability of categories for dimensionality reduction [26]. Its principle is to delete closely related variables and create as few new variables as possible to make the transformed variables uncorrelated. Meanwhile, the transformed variables should reflect the original information as much as possible [27].  
Given a training set \(X=x_{1},x_{2},\ldots,x_{N}(x_{i}\in\mathbb{R}^{D},i=1,2,\ldots,N)\) and a lower dimension \(d\), we can calculate the average of the training set and the covariance matrix. Then, we can obtain the spectral decomposition of the covariance matrix to obtain the eigenvalues (\(\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{D}\)) and corresponding eigenvectors (\(\xi_{1},\xi_{2},\ldots,\xi_{D}\)). For any \(x\in\mathbb{R}^{D}\), its new low-dimensional representation can be represented as Equation (1)  
\[y=(\xi_{1}{}^{T}(x-\overline{x}),\xi_{2}{}^{T}(x-\overline{x}),\ldots,\xi_{d}{ }^{T}(x-\overline{x}))\in\mathbb{R}^{d} \tag{1}\]
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach
### Final Accuracy and Position Estimation  
Position estimations coming from the different data sources are provided with a timestamp, an identifier of the technology used and the associated estimated accuracy. This way it is possible to know the accuracy level and validity time of the estimated position. Depending on hybridization policy scheme, the position estimations are combined to get a final location.  
Our hybrid approach denotes the usage of different ways of approximating the user position, but the most important part is how they are combined to get a better approximation minimizing the error. It is determined by a set of predefined handover policies. Initially the handover policy was very simple, selecting the most accurate technology at each moment and discarding the rest. However, in a second stage of the development we realized that technologies can support each other to improve accuracy. The strategy followed is illustrated next, with a representative example about how fingerprinting can provide a valuable help to the proximity algorithm to reduce the error.  
Figure 2 shows a first case without combining technologies on the left. Here, the positioning system uses the Bluetooth beacon depicted in the figure to locate the person. The distance between the person and the beacon can be determined using the perceived signal strength. However, the positioning system is only able to figure out that the user is probably located at any point of the dashed blue circumference surrounding the beacon. As the mean position of all possibilities in a circumference is the middle point, the algorithm returns this location as result, with an accuracy/error equals to the calculated distance between the person and the beacon. In contrast, in the second case illustrated in Figure 2, due to the fact that Wi-Fi fingerprinting is integrated with Bluetooth proximity, in addition to the distance between the person and the beacon, the location of the nearest fingerprint can be used to choose a best estimate location within the circumference. Hence, as can be seen, the final error is greatly reduced. As we cannot know the exact value of this new error, the same accuracy/error blue circumference is also shown in Figure 2b, but centered in this case in the new approximated position. This assumption is valid if we have at least three surrounding fingerprints. This hybridization is just an example of the possibilities offered by the platform, which is open to new merging possibilities.  
In addition, the handover mechanism has to support the effective change between technologies, in order to determine what is the main and supporting technologies at each moment. This approach can be used for other purposes like battery saving and preparing the technology beforehand. This is particularly interesting in our case when using GPS, since it can be disabled indoors and activated when the mobile device is detected at especially identified handover fingerprints close to the main door of the building.
## 3 Results and Discussion  
Figure 3 shows the stress-strain response curve of the foam material under the uniaxial state. Although foam is non-continuum, it also changes from linear to nonlinear. When the loading is small, the stress-strain response is linear. With the increasing of loading, the micro part of the foam begins to yield making the increase in amplitude of stress decrease with the increasing of strain. And when more of the foam yields, the stress-strain response curve of the foam material becomes nonlinear. In order to verify, we give the stress-strain response curves along the x, y, and z axis of the RVE cubic. Figure 3 shows that the stress-strain response curves along the x axis is almost coincided with that along the y and z axis. It is generally known in [15; 16; 19; 29; 30; 31] that the yield points under the multiaxial loadings are mainly determined by two methods. One is obtained at 0.2% characteristic strain offset on the characteristic stress-strain curves (Figures 4 and 5). The other is given in terms of the plastic dissipation energy.  
\begin{table}
\begin{tabular}{l l l l l} \hline \(\sigma_{1}\) & \(\sigma_{2}\) & \(\sigma_{3}\) & \(k\) & \(\cos(3\theta)\) \\ \hline \(1\) & \(1\). & \(-2\) & \(-\infty\) & \(-1\) \\ \(2\) & \(-1\) & \(-1\) & \(-\infty\) & \(1\) \\ \(0\) & \(0\) & \(-1\) & \(-3\) & \(-1\) \\ \(1\) & \(-1.366\) & \(-3.732\) & \(-3\) & \(0\) \\ \(1\) & \(-2\) & \(-2\) & \(-3\) & \(1\) \\ \(1\) & \(1\) & \(-4\) & \(-1.5\) & \(-1\) \\ \(-0.134\) & \(-1\) & \(-1.8661\) & \(-1.5\) & \(0\) \\ \(0\) & \(-1\) & \(-1\) & \(-1.5\) & \(1\) \\ \(-1\) & \(-1\) & \(-2.5\) & \(-1\) & \(-1\) \\ \(-1\) & \(-2.367\) & \(-3.733\) & \(-1\) & \(0\) \\ \(-1\) & \(-4\) & \(-4\) & \(-1\) & \(1\) \\ \(-4\) & \(-4\) & \(-7\) & \(-0.6\) & \(-1\) \\ \(-1.9364\) & \(-3\) & \(-4\) & \(-0.6\) & \(0.0534\) \\ \(-1\) & \(-2\) & \(-2\) & \(-0.6\) & \(1\) \\ \(-3\) & \(-3\) & \(-4\) & \(-0.3\) & \(-1\) \\ \(-1\) & \(-1.375\) & \(-1.375\) & \(-0.3\) & \(1\) \\ \(-1\) & \(-1\) & \(-1\) & \(0\) & \\ \hline \end{tabular}
\end{table}
Table 4: The stress ratios used in the numerical simulations.  
Because there is a large number of yield points of different RVE models, we exclusively utilize Kelvin RVE with relative densities of 0.20 and 0.43, and Models 1 and 6 (in Table 1) with relative densities of 0.4 and 0.3, respectively, to investigate the yield surface of closed-cell foam. This can make the data comparison with the experiments more clear. So we use these RVEs to explore the yield definition under multiaxial loadings in the following section.  
Figure 4: The characteristic and principal stress-strain curves under uniaxial compression loading. Note: Point A is the yield point determined by 0.2% plastic strain offset in the characteristic stress-strain curve; Point B is the yield point determined by 0.2% plastic strain offset in the uniaxial stress-strain curve.  
Figure 3: The stress-strain curves of Model 1 along the x, y, and z axis under uniaxial compression loading.
## 2 Current Capabilities of Modern FPGAs
### Partial Reconfiguration  
It is worth noting the dynamic reconfiguration capability of several FPGAs. Dynamic reconfiguration means modifying the system at run-time in order to change the content of the logic blocks as well as the interconnection configuration. This allows to reconfigure either portions or the whole content of the FPGA. Hence, the advantages of dynamic reconfiguration are twofold. First, it is possible to minimize the circuit area via the reconfiguration of the sequential parts of an algorithm, loading one step at a time. Second, it is possible to reduce the power consumption to the same extent as in the first case. However, not all vendors support the same type of reconfiguration. Moreover, reconfiguration is carried out according to different interfaces depending of the vendor. For instance, Xilinx Virtex FPGAs (4-7 series) support partial reconfiguration as well as the Artix-7 and Kintex-7 platforms. However Spartan and older Virtex series only allow to reconfigure sections of the FPGA based on differences of two bitstreams. Moreover, Xilinx allows to change the behaviour of several HIPs such as clock managers, Phase-locked Loops (PLLs), Analog to Digital Converters (ADCs) and transceivers using a set of memory-mapped registers.  
Likewise, Altera's series V (including Stratix, Arria and Cyclone) support partial reconfiguration and dynamic reconfiguration of serial transceivers. On the other hand, since Actel platforms are not based on SRAM, partial reconfiguration is not supported. Nevertheless, Actel provides the reconfiguration of the FPGA's PLLs at run-time.