esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Optimized Micro-Scale Models through Genetic Algorithms  
The Bezier curves of fourteen nodules of the models obtained with the proposed methodology are shown in Figure 7. These nodules were chosen due to their irregular shapes and their different sizes, since they are critical geometric shapes that can be found in the microstructure of a DCI. The models were obtained using genetic algorithms to optimize the selection of the Bezier curves degree to represent the graphite nodules contours. For the implementation of the genetic algorithm, it was considered a population with eight individuals (degrees), which were randomly selected in a range of Bezier degrees between 4 and 25. The genetic algorithm chose the Bezier curve degree, which has the relative error that is smaller than the desired relative error, which in this paper has a value of 0.45. Once the optimal Bezier degrees were selected for each graphite nodule, the matrix system in Equation (2) was evaluated in the points to be approximated, \(\mathbf{A}=[a_{0},\,a_{1},\,a_{2},\,...,\,a_{k}]\) and the solution of the system yield the control points, \(\mathbf{C}=[p_{0},\,p_{1},\,\ldots,\,p_{n}]\). These points define the Bezier curves for each nodule. Once the control points have been obtained, they are joined by polylines and the curves were written in a DXF format file, this was achieved by the MATLAB drawing tools and the corresponding libraries to draw in a DXF format. As can be observed in the nodules of Figure 7, the Bezier curves (red contours) that were obtained by the proposed methodology approach smoothly to the digital  
Figure 6: (**a**) Original micrography; (**b**) Binarized image; (**c**) Image without the edge nodules nor the smallest nodules; (**d**) Image with overlapped nodules separated; (**e**) Labeled image; and, (**f**) Graphite nodules contours extracted.  
contours (black contours), avoiding the errors that are caused by the discretization process, for example: corners at \(90^{\circ}\) and the completely flat segments, which can be seen more clearly in the black outlines of the nodules shown in Figure 7.
# Entropy-Based Algorithms for Signal Processing
### Applications
Thus, the authors design a loss function for the expectation-maximization method based on relative entropy. In the EM iteration frame, the sparse coefficients can be estimated with the denoising-based approximate message passing algorithm. Their experimental results have shown that the proposed algorithm can significantly outperform existing CS techniques for image recovery.
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 3 Results and Discussion
### Real Field Conditions  
In order to validate the performance and reliability of the standalone HF OFDM communication applications developed using GNU Radio and the Qt library, a real field-testing setup was deployed. The configuration comprised a fixed transmission system and a mobile receiving system. The principal objective was to assess the efficacy of the system in transmitting and receiving messages under conditions that approximate those encountered in the HF band, which are often characterized by variable propagation, interference, and noise.  
Before all tests, measurements were taken at different frequencies to identify the frequency that provided the best SNR. Once the optimal frequency/frequencies with the highest SNR was identified (minimum 20 dB for BPSK modulation), the real-world test was performed.  
The fixed transmitter system comprises the following hardware components:  
The Universal Software Radio Peripheral (USRP) N210 was employed in this configuration. The USRP N210 was employed as the principal hardware for the generation and transmission of OFDM signals. This device is renowned for its high performance and flexibility, rendering it an optimal choice for real-world communication testing.  
Laptop: A laptop was connected to the USRP N210 via a Gigabit Ethernet interface. The laptop operated the standalone HF OFDM transmitter application, which was responsible for controlling the modulation, channel aggregation, and transmission processes.  
A 4-W power amplifier was used to ensure that the transmitted signal would have sufficient power to reach the mobile receiver over a considerable distance. The amplifier augmented the signal power prior to its transmission via the antenna.  
Figure 17: Monitoring received PER on Channel 1—QPSK modulated channel.  
Figure 18: Monitoring received PER on Channel 2–8-PSK modulated channel.  
An inverted V antenna (Figure 19a), mounted on a mast, was employed to transmit the RF signal from the amplifier. This type of antenna is particularly well suited to HF communication, offering an optimal balance between performance and ease of installation. The antenna was tuned to operate within the target frequency range for HF transmission, which was used for the OFDM signal.
# Preparation of an Asymmetric Membrane from
###### Abstract  
Asymmetric cellulose acetate membranes have been successfully fabricated by phase inversion, using sugarcane bagasse (SB) as the starting material. SB is a raw material with high potential to produce cellulose derivatives due to its structure and morphology. Cellulose was extracted from SB by pretreatment with solutions of 5 wt% NaOH, 0.5 wt% EDTA; then bleached with 2 wt% H\({}_{2}\)O\({}_{2}\). Cellulose acetate (CA) was prepared by the reaction between extracted cellulose with acetic anhydride, and H\({}_{2}\)SO\({}_{4}\) as a catalyst. The obtained CA exhibited a high degree of substitution (2.81), determined with \({}^{1}\)H-NMR spectroscopy and titration. The functional groups and thermal analysis of the extracted cellulose and the synthesized CA have been investigated by Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). The change in the crystallinity of the extracted cellulose and CA was evaluated by X-ray diffraction (XRD) spectroscopy. Asymmetric membranes were fabricated using dimethyl sulfoxide (DMSO) as the solvent, with a casting thickness of 250 \(\upmu\)m. The obtained membranes were studied by scanning electron microscopy (SEM), DSC and atomic force microscopy (AFM). The hydrophilicity of the membranes was evaluated, as demonstrated by the measurement of water contact angle (WCA) and water content. Furthermore, the antifouling properties of membranes were also investigated.  
Keywords: sugarcane bagasse; cellulose acetate; DMSO; phase inversion; asymmetric membrane +
Footnote †: journal: Applied Physics Letters
## 3 Input Data  
In this paper, we used 3D seismic data and available well data (Figure 3). Since only two wells are drilled in the researched gas field, one is used for computation pseudo-wells regarding the variation of porosity and reservoir thickness, while the second one is used as a blind well.  
Figure 2: Lithost stratigraphic division of the Pliocene, Pleistocene, and Holocene sediments in the Croatian part of the Po Depression (after [16]).  
Seismic data in the case of the Adriatic basin are frequently and significantly affected by anomalies caused by gas saturation, usually at multiple levels. That makes accurate time-to-depth conversion difficult and uncertain. For the same reason, well-to-seismic data correlation is challenging, as well as direct implementation of seismic volume properties in 3D reservoir model properties in the depth domain. On the other hand, DHIs on seismic data are crucial for the interpretation of the lateral extent of gas saturation.  
Well logs acquired in two wells included Gamma Ray, Resistivity, FMI, Attenuation, Density, Neutron, Sonic, and Nuclear Magnetic. Apart from Gamma Rays, the response of all other measurements is strongly affected by the gas saturation in the reservoir zone.
## 3 Identification Algorithms
### The Main Convergence Result  
The main convergence result of the proposed AM-MISG algorithm for periodically non-uniformly sampled-data Hammerstein systems is given in the following theorem.  
**Theorem 1**.: _Assume that the noise sequences \(v_{i}(k)\ (i=0,\ 1,\ 2,\cdots,\ q-1)\) satisfy:_  
1. \(\mathrm{E}[v_{i}(k)]=0\)_,_ \(\mathrm{E}[v_{i}^{2}(k)]\leqslant\sigma^{2}<\infty\)_,_ \(\mathrm{E}[v_{i}(k)v_{i}(j)]=0,k\
eq j\)  
Figure 2: The flowchart of computing the parameter estimate.  
and there exist a positive constant \(\alpha\) and an integer \(N\) such that the following persistent excitation condition holds: (A2) \(\sum_{j=0}^{N}\sum_{l=0}^{p-1}\frac{\hat{\mathbf{\varphi}}_{j}(k+j-l)\hat{\mathbf{\varphi} }_{l}^{*}(k+j-l)}{r_{i}(k+j)}\geqslant\alpha\mathbf{I}\). Then, the parameter estimation vector \(\hat{\mathbf{\theta}}_{i}(k)\) given by the AM-MISG algorithm consistently converges to the true parameter vector \(\mathbf{\theta}_{i}\) in the mean-square sense._  
Theorem 1 can be proven in a similar way to [42]. Therefore, its detailed proof is omitted here.
## 5 Experimental Analysis
### Analysis of Results
#### 5.3.2 TaxiBJ  
As with the experiment discussed above, for the TaxiBJ case study, the parameters of the models have been set according to the specifications given in the respective publications. In the case of STREED-Net, the hyperparameters are kept unchanged in the two experiments, except for the number \(L\) of encoding and decoding blocks, which has been increased to 3 because the grid is larger (\(32\times 32\)) in this experiment and more convolutional layers are needed to map the input tensor of the model. Also for this experiment, the kernel size, batch size, and learning rate parameters have been optimized with _Bayesian optimization_ and the best values found were 3, 16, and 0.0001 respectively. The number of epochs has been set at 150. Notice that these values are the same used in the BikeNYC experiment.  
As can be seen from Table 2, STREED-Net outperforms all other methods, in particular, reducing MAPE and APE by 2.9%, and 2.8%, respectively, compared with the second-best approach. The difference in performance in favor of the proposed model, in this experiment is more appreciable because the data set used for the training process is more significant but also because the number of regions is higher. This last consideration highlights how the proposed model seems suitable to be applied in real-world scenarios, i.e., where high model accuracy and dense tessellation are required (i.e., the city is partitioned into a large number of small regions).
## 6 Performance Evaluation
### Baseline  
To evaluate the performance of DIFTOS, we have compared its performance with the following baselines:  
CD: Since most of the recent state-of-the-art navigation systems in the industry [2; 3; 4; 5] are using [PERSON]'s shortest path algorithm based on real-time congestion statistics. In addition to other state-of-the-art works, such as [15; 29], compared their systems with Dijkstra based congestion avoidance system as a benchmark. Therefore, we have implemented a centralized system and used it as a comparing baseline; we will refer to it as CD (Centralized Dijkstra).  
SAINT: The system presented in [15], where a centralized traffic control system maintains the road reservation matrix. The vehicles solicit the server to get the least congestion path to their destination based on the road reservation matrix (Unlike CD, which does not consider road reservations).  
DIVERT: The hybrid system presented in [16], where the path calculation is computed by a centralized server, and some of the computing is done by the vehicles (similar to DIFTOS approach). However, a preinstalled infrastructure is required (RSU, cellular network). Unlike DIFTOS, there are not hierarchical levels of the vehicular servers.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Scheduling and Resource Allocation
#### 6.3.2 Resource and Time Allocation  
Proper allocation of resources in BackCom enhanced the life of the network. An algorithm for the allocation of resources was proposed to maximize the energy efficiency, while considering data transmission [101; 141; 142]. [PERSON] et al. suggested three schemes for resource allocations, which consider equal time allocation, minimum throughput maximization, and proportional time allocation, to enhance the fairness in a wirelessly powered cooperative cognitive radio network [143]. Efficient energy resource allocation for WPCN can address the energy efficiency maximization problem for WPCNs [144]. [PERSON] et al. optimized three resources together: (i) the transmission power of the reader, (ii) the reflection ratio, and (iii) the time allocation for semi-passive and passive tags [145]. Optimal allocation of time and energy was considered to maximize the total throughput. This allocation scheme also saved energy in the tags of the upcoming data blocks [146].  
Optimal time allocation was achieved for a full-duplex and half-duplex BAWPCN, to maximize the system throughput while considering H-AP [95]. Similarly, optimal time and energy allocation techniques were found to enhance the sum throughput in BackCom [100]. The model based on the time block bifurcated the time block into two pieces. One was for data backscattering in BackCom and the other was for data transmission in HTT [101]. Moreover, optimal time allocation policies were shown to improve the system throughput. That work considered optimization to maximize performance. The authors also suggested time allocation policies for energy harvesting and data backscattering while transmitting data.
# Multi-Granularity User Anomalous Behavior Detection
## 6 Discussion
### Real-Time Detection Analysis  
Real-time detection of internal threats is an important factor in determining whether MG-UABD can be deployed for applications. There are three main factors that affect the efficiency of real-time detection: data push and basic processing speed, model incremental training speed, and representation and detection time of the test samples. Among them, data push and basic processing speed depend on the processing capabilities of deployed devices, and using high concurrency processing for streaming data can improve efficiency. Incremental training of the model refers to regularly tagging newly collected samples to maintain the model's capability to recognize new threats and alleviate the problem of concept drift. This part is a trade-off between training complexity and detection efficiency, and a suitable compromise needs to be found. After training the basic model, the time complexity of detecting each subsequent behavior sample in our method is \(O(d)\), where \(d\) is the depth of the decision tree. Due to the much lower complexity of random forests compared to complex models such as neural networks, better real-time detection efficiency can be achieved while meeting the requirements for parallel data processing.  
Apart from detection efficiency, real-time detection accuracy will vary depending on the domain and the model's generalization ability. After running the algorithm for a period of time, concept drift may occur, leading to a decrease in real-time detection accuracy. It is necessary to consider using methods such as continuous learning to incrementally train the model and regularly update its parameters. Enterprises with a single business, few personnel, and homogeneous behavior patterns require lower model construction costs to meet their needs, while enterprises with complex personnel and diverse behavior types require higher model construction costs.
## 3 Results
### Monomer Composition of PHAs Produced by P. putida LS46123  
_P. putida_ LS46 can synthesize MCL-PHA polymers containing 3 hydroxyhexanoate, 3 hydroxyoctanoate, and 3 hydroxydecanoate subunits as major components in cells grown with glucose, octanoate, decanoate, and REG-FFAs [10]. However, the monomer compositions of PHA polymers synthesized by _P. putida_ LS46123 were very different from the compositions of polymers synthesized by the parent strain, _P. putida_ LS46, on the same substrates. _P. putida_ LS46123 added both short chain and medium chain length monomers to the PHA polymers it synthesized (Figure 1). In RMM-glucose cultures, _P. putida_ LS46123 synthesized polymers consisting of 32% 3 HB, 50% 3 HHx, and 16% 3 HO subunits, whereas in RMM containing hexanoic, octanoic and decanoic acid as substrate it accumulated 30%, 52%, and 45% of 3 HB monomer. RMM-REG-FFA cultures the polymers synthesized by _P. putida_ LS46123 consisted of up to 52% 3 HB.  
Figure 1: Monomer composition of PHA polymers synthesized by _P. putida_ LS46123 grown on different carbon sources: (**A**) Glu, Glucose; Hx, Hexanoic acid; Oct, Octnoic acid; Dc, Decanoic acid; FFA, Biodiesel-derived free fatty acids. Subunit components: C4, 3-hydroxybutanoic acid; C6, 3-hydroxyhexanoic acid; C8, 3-hydroxyoctanoic acid; (**B**) Non, Nonanoic acid. Subunit components: C5, 3-hydroxyvaleric acid; C7, 3-hydroxyheptonic acid; C9, 3-hydroxymonanoic acid.  
With nonanoic acid as the substrate, it produced a polymer with 34 mol % 3 HV, 56 mol % 3 HHp, and 10 mol % 3 HN. These monomer compositions were not only different from the polymers produced by parent strain _P. putida_ LS46 but also different from all other known polymers produced by _P. putida_ or _Ralstonia eutropha_ strains.
## 3 Results
### Self-Reported Overall Fatigue and Musculoskeletal Pains by the Degree of Hazard Exposure
#### 3.3.3 Self-Reported Upper Limb Pain by the Degree of Hazard Exposure  
Table 8 shows the distribution of self-reported upper limb pain by the degree of hazard exposure. There were significant differences between the upper limb pain complaining group and the non-complaining group in most of the hazards except manual material handling exposure (\(\chi^{2}\) = 4.169, \(p\) = 0.124) and infection exposure (\(\chi^{2}\) = 4.735, \(p\) = 0.094). The proportion of the upper limb pain complaining group increases as the degree of hazard exposure increases. Longer exposure to tobacco smoke shows the highest rate of complaining of upper limb pain (47.4%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**ADHE**} & \multirow{2}{*}{\(\chi^{2}\) **Test**} \\ \cline{3-5}  & & \(<\)**2** & **2–4** & & \(\geq\)**4** \\ \hline \multirow{4}{*}{Physical hazard} & Vibration & 5.8\% & 5.0\% & 9.6\% & \(\chi^{2}\) = 2.680, \(p\) = 0.262 \\ \cline{2-5}  & Noise & 3.8\% & 9.5\% & 11.9\% & \(\chi^{2}\) = 8.923, \(p\) = 0.012 * \\ \cline{2-5}  & High temperature & 6.1\% & 1.8\% & 14.5\% & \(\chi^{2}\) = 11.152, \(p\) = 0.004 * \\ \cline{2-5}  & Low temperature & 7.0\% & 5.7\% & 12.5\% & \(\chi^{2}\) = 2.638, \(p\) = 0.267 \\ \hline \multirow{4}{*}{Ergonomic hazard} & Awkward posture & 3.6\% & 4.9\% & 12.2\% & \(\chi^{2}\) = 10.545, \(p\) = 0.005 * \\ \cline{2-5}  & Manual material handling & 7.5\% & 3.7\% & 12.7\% & \(\chi^{2}\) = 7.082, \(p\) = 0.029 * \\ \cline{2-5}  & Standing posture & 5.0\% & 4.5\% & 9.1\% & \(\chi^{2}\) = 2.732, \(p\) = 0.255 \\ \cline{2-5}  & Repetitive motion & 8.5\% & 6.0\% & 7.7\% & \(\chi^{2}\) = 0.246, \(p\) = 0.884 \\ \hline \multirow{4}{*}{Chemical and biological} & Fumes and dust & 5.5\% & 8.5\% & 12.7\% & \(\chi^{2}\) = 5.625, \(p\) = 0.060 \\ \cline{2-5}  & Vapor & 6.8\% & 9.8\% & 12.2\% & \(\chi^{2}\) = 1.919, \(p\) = 0.383 \\ \cline{2-5}  & Skin contact with chemical & 6.7\% & 6.3\% & 17.1\% & \(\chi^{2}\) = 5.744, \(p\) = 0.057 \\ \cline{2-5}  & Tobacco smoke & 6.8\% & 10.9\% & 15.8\% & \(\chi^{2}\) = 2.833, \(p\) = 0.243 \\ \cline{2-5}  & Infection & 7.4\% & 0.0\% & 21.1\% & \(\chi^{2}\) = 6.786, \(p\) = 0.034 * \\ \hline \hline \end{tabular}  
* * Significant at 0.05. ADHE = adjusted daily hazard exposure time.  
\end{table} 
Table 7: Distribution of subjective backache by the degree of hazard exposure.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 3 Materials and Methods
### Network Architecture
#### 3.3.1 Convolutional Layer  
Each neuron in a CNN is arranged in a feature map and connected to a set of filters or kernels in the convolutional layer, which consists of weight matrices in the complex-valued domain. These kernels slide across the feature maps and perform convolutions between the input elements and the kernel weights. The resulting convolution is passed through an activation function to compute a new feature map [57].  
Convolutional layers act as feature extractors, where each filter captures specific local patterns in the input. They detect edges, colors, and gradient directions, enabling the model to understand input images comprehensively. Due to the spatial correlation in image data [58], the same filter group is shared across the same feature map channel. Important features are typically fixed in certain parts of the image, making it essential to scan with the same filters across different regions.  
In CV-CNN, a complex-valued convolution unit is calculated by convolving all output feature maps from the previous layer. A complex-valued number, \(d\), is defined as  
\[d=a+ib \tag{1}\]  
\[a=\text{Re}(d),\quad b=\text{Im}(d) \tag{2}\]  
where \(a\) and \(b\) represent the real and imaginary parts of the complex-valued number, respectively.  
Instead of applying real-valued convolutions separately to the real and imaginary parts, we perform true complex-valued convolution. Let the complex-valued filter be \(W=X+iY\), where \(X\) and \(Y\) are real-valued filters, and let the complex-valued input be \(d=a+ib\). Using the distributive property of convolution [30], this operation becomes  
\[W*d=(X+iY)*(a+ib)=(X*a-Y*b)+i(Y*a+X*b) \tag{3}\]  
This convolution can be expressed in matrix form as  
\[\begin{bmatrix}\text{Re}(W*d)\\ \text{Im}(W*d)\end{bmatrix}=\begin{bmatrix}X&-Y\\ Y&X\end{bmatrix}*\begin{bmatrix}a\\ b\end{bmatrix} \tag{4}\]  
To mitigate vanishing gradients and achieve faster convergence, we employ the complex-valued ReLU activation function, defined as  
\[\text{ReLU}(d)=\begin{cases}d&\text{if }d\geq 0\\ 0&\text{otherwise}\end{cases} \tag{5}\]
# An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing
## 3 Major Functions
### Ray Tracing  
The ray tracing in this system is based on the IASP91 standard Earth velocity model [16; 30]. The common seismic phases in natural seismic signals, as determined by the International Association of Seismology and Internal Geophysics, are P, S, PcP, ScS, PKP, SKS, etc. Using the ray tracing method adopted in this system, by setting the Phase and the Depth of the source in the Settings and executing Processing\(\rightarrow\)RayTracer, the theoretical traveltimes of the various seismic phases can be calculated and adopted as a reference for phase identification.