esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
The measurements were carried out for samples made of several grades of 3% SiFe grain-oriented electrical steel sheets. Figure 1 shows the rotation machine operating in four axes (X, Y, Z and A) with the attached transducer placed over the sample. The transducer combines two sections: an excitation section and a measurement section. The transducer is made up of a C-shaped core with an excitation coil and a coil for measuring the magnetic flux density (B-coil). The MBN signal measurement section consists of two coils wound on a ferromagnetic core (MBN-coil). The details of the transducer and the measuring system were given in [22; 23; 30].  
Conventional (CGO) and high grain-oriented (HGO) steel grades were utilized for sample preparation. The steel sheets had different thicknesses of 0.23 mm, 0.27 mm and 0.30 mm, and were subjected to various surface engineering methods, including coating with and decoating of oxide layer, laser scribing of the surface or nitriding. A detailed description of the tested samples, along with the metallographic assessment of their properties, can be found in the previous paper [26]. The set of the inspected samples with their basic description is given in Figure 2.  
For each sample, the measurements were made for different angular positions of the transducer between 0\({}^{\circ}\) and 180\({}^{\circ}\) using an angular step of 1.8\({}^{\circ}\) (for 0\({}^{\circ}\) position the main axis of the transducer is along the axis transverse (TD) to the rolling direction of steel; for 90\({}^{\circ}\) position the main axis of the transducer is along the rolling direction of steel--RD). The excitation signal was sinusoidal with a frequency of 10 Hz. The field strength obtained during the measurements was about 1.6 kA/m. MBN signals were collected via a data  
Figure 1: View of the rotation machine with sample and transducer (with dimensions given in mm).  
Figure 2: Samples and measuring procedure: (**a**) designation of the samples, (**b**) testing procedure; note: #1 and #2 in indexes refers to the producer no. 1 and no. 2 of the utilized steel sheets.  
acquisition (DAQ) card (NI-PCI-6251) using a sampling frequency of 250 kS/s. Prior to the acquisition, first, the signals were bandpass filtered in the frequency range of 2 kHz to 100 kHz. Next, the gathered MBN signals were transformed into _TF_ representation using short-time Fourier transform (STFT). It allows for the determination of the content of sinusoidal components of different frequency and phase in local sections of the base signal as it changes over time [22, 23, 30]. In the transformation process, the Kaiser function with a size of 512 points was used, which resulted into a resolution in time of 512 us and a frequency of 488 Hz.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 2 Microbial Chassis as Cell Factories for Edible Terpenoids
### Prokaryotic Microorganisms  
Prokaryotic microbial cells, characterized by their simple cellular structures, rapid reproduction, robustness, ease of genetic manipulation, and metabolic regulation, are frequently used in the construction of cell factories. Most prokaryotic microorganisms, including gram-negative bacteria and cyanobacteria, possess an endogenous MEP pathway capable of producing IPP and DMAPP [13]. A stoichiometric and redox balance analysis reveals that the MEP pathway exhibits high carbon efficiency. For each molecule of IPP produced, the pathway consumed 1.196 molecules of glucose, representing a carbon consumption 14% higher than the MVA pathway [23]. Consequently, prokaryotic microorganisms are extensively employed for the synthesis of terpenoid compounds (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Organism.** & **Chemicals** & **Strategy** & **Yield** & **Reference** \\ \hline _Escherichia coli_ & Lycopene & \
\begin{tabular}{c} Multiplex automated genome engineering (MAGE) of twenty endogenous genes to \\ increase the flux of DXP pathway. \\ \end{tabular} & 9 mg/g DCW & [24] \\ \hline \multicolumn{4}{c}{Taxadiene} & Multivariate-modular approach to metabolic-pathway engineering. & 1.25 g/L & [23] \\ \hline \multicolumn{4}{c}{Lycopene} & Reconstitution of MVA Pathway. & 40 mg/L \\ \hline \multirow{4}{*}{Geraniol} & \multirow{4}{*}{Geraniol} & (1) & Screening of different geranyl diphosphate synthetase (GGPS) and gerani & \multirow{4}{*}{2.124 g/L} & \multirow{4}{*}{[25]} \\  & & synthetase (CES). & & \\  & (2) & Screening of \(E\). _coli_ hosts; & & \\  & & (3) & Using truncated leader peptides as fusion tags to improve geraniol & \multirow{4}{*}{13.19 g/L} & \multirow{4}{*}{[26]} \\  & & production. & & \\ \hline \multirow{4}{*}{Myrcene} & \multirow{4}{*}{Geraniol} & (1) & Introducing an alcohol acyltransferase; & \multirow{4}{*}{13.19 g/L} & \multirow{4}{*}{[26]} \\  & & (2) & Using another \(E\). _coli_ expressing acetyl esterase, which hydrolyzes geranyl & & \\  & & acetate to gerani. & & \\ \hline \multirow{4}{*}{Myrence} & \multirow{4}{*}{
\begin{tabular}{c} (1) \\ (2) \\ \end{tabular} } & Using a linaol
## 6 Discussion
### Significance of Security Metadata  
We presented compelling security metadata and topic-based security evidence that were highlighted during table-top exercises. Experts examined the topic from various attack vectors and security-awareness perspectives, leading to a rigorous discussion of a comprehensive set of security controls. They not only identified essential security metadata but also proposed additional relevant metadata based on existing and partly standardized technologies that remain underutilized in current systems.  
Experts provided positive feedback on the concept of adding security metadata in the provenance graphs by noting _\"...it is timely and important...\"_ and mentioned that inclusion would be helpful to make the system more trustworthy and transparent. The identified security metadata are significant and can be made available to the end-user. The experts recommended a list of security controls as tangible security metadata to be reported in security-aware provenance graphs. Thus, this exercise shows the viability of the security-aware provenance graph by showing that substantial security metadata are available for use.  
Furthermore, the simulation results revealed vulnerabilities in IoT devices, particularly within the sensing layer of the healthcare scenario, which collects and transmits sensitive medical data. By simulating three types of attacks (code manipulation, denial-of-service, and node impersonation), this study highlighted how security metadata can help detect and reduce these threats. For example, changes in hash values were useful for identifying code tampering and configuration changes, while digital signatures from trusted platform modules (TPMs) helped detect unauthorized devices pretending to be legitimate nodes. These results show that security metadata are an important tool for real-time monitoring and improving cybersecurity awareness, allowing users to make better decisions about data integrity and system trustworthiness.
## 2 Proposed Method
### Revisiting Feature Maps and Feature Spaces  
Feature maps and feature spaces are similar in several ways: CNN-based FSR methods utilize complex convolution layers to project LR images into inner \"feature maps\" and then into HR ones; on the other hand, manifold learning-based FSR methods, which belong to the traditional FSR method, project LR images into \"feature spaces\" and then into HR ones, assuming that LR and HR spaces share the same local geometry [46]. Many traditional FSR methods have been developed based on manifold learning [47; 48; 49], focusing on further enhancing the LR and HR space relationship. However, with the rise of CNNs, manifold learning has gained less attention due to the complicated nature of CNN structures, which are difficult to deliberate on. [PERSON] et al. [50] proposed a manifold localized deep external compensation (MALDEC) network that references online big data to provide an accurate localization and mapping to the HR manifold for image super-resolution. [PERSON] et al. [51] traversed the HR manifold spaces to search for images that suit the original LR image, then feed the downscaling loss to a generative model for image reconstruction. [PERSON] et al. [52] proposed a homogenization projection in LR space and HR space to formulate FSR in a multi-stage framework. [PERSON] et al. [39] claimed that limiting mapping spaces would benefit image super-resolution, and introduced a closed-loop dual regression network (DRN) containing an additional constraint. The above methods try to merge traditional manifold learning with CNN-based methods; however, they overlook the key role of inner feature maps (Figure 1), limiting their image super-resolution performance. Therefore, how to effectively handle inner feature maps is vitally important for a high-quality image reconstruction process.
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods
### System Experimental Method  
To verify the final effect of the algorithm, the system used the most commonly observed microscopic blood cells to conduct the experiment. First, the whole blood cells were diluted 1:10,000 with buffer solution, then the diluted blood samples were injected into the microchannel of the microfluidic chip. The dilution concentration mainly ensures that the cells can be evenly distributed in the observation window and that there is a certain distance between them to ensure that the diffraction patterns do not overlap. Then, the image sensor can be used to capture multi-frame images. Finally, the microfluidic chip was loaded into the lensless imaging system and the distance between the microchannel and the image sensor was fixed; then, the collected image was sent to MATLAB software for processing, and the image was registered using the local image registration. After the normalized convolution super-resolution algorithm and the de-blur and diffraction algorithm, the high-resolution image of each cell was obtained, and the final result is provided in Section 3.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 3 Concrete Plastic Damage Model  
### Fundamentals  
The CDP model was provided by ABAQUS. Assuming that the concrete material mainly fails due to tensile cracking and compressive crushing, the evolution of yield or failure surfaces is controlled by two hardening variables, \(\tilde{\varepsilon}_{t}^{pl}\) and \(\tilde{\varepsilon}_{c}^{pl}\). \(\tilde{\varepsilon}_{t}^{pl}\) and \(\tilde{\varepsilon}_{c}^{pl}\) denote the equivalent plastic strains in tension and compression, respectively [17]. The stress-strain curves are shown in Figures 3 and 4 [18]. The tensile and compressive stress-strain curves of the CDP model were entered into ABAQUS in the form of \(\sigma_{t}-\tilde{\varepsilon}_{t}^{ck}\) and \(\sigma_{c}-\tilde{\varepsilon}_{c}^{ck}\), respectively, and the relationships between the equivalent plastic strains \(\tilde{\varepsilon}_{t}^{pl}\) and \(\tilde{\varepsilon}_{c}^{pl}\) and the corresponding inelastic strains \(\tilde{\varepsilon}_{t}^{ck}\) and \(\tilde{\varepsilon}_{c}^{ck}\) when hardened in tension and compression are as follows [19]:  
\[\begin{cases}\tilde{\varepsilon}_{t}^{pl}=\tilde{\varepsilon}_{t}^{ck}-\frac{ d_{t}}{(1-d_{t})}\frac{\sigma_{t}}{E_{0}}\\ \tilde{\varepsilon}_{c}^{pl}=\tilde{\varepsilon}_{c}^{ck}-\frac{d_{c}}{(1-d _{c})}\frac{\sigma_{c}}{E_{0}}\end{cases} \tag{1}\]  
Among them, \(d_{t}\) and \(d_{c}\) are the tensile and compressive unloading stiffness reduction factors, respectively, and \(E_{0}\) is the initial damage-free elastic modulus of the material.  
Figure 5 illustrates the stiffness recovery process under uniaxial cyclic loading. Concrete, while susceptible to tension-induced cracking, sees crack closure when the load shifts from tension to compression. This closure augments the support area, thereby reviving stiffness. The OAB segment demonstrates tension application and the subsequent damage, then unloading and reverse loading lead to point C on the BC segment--this point marks  
Figure 4: Uniaxial tensile stress–strain diagram of concrete [18].  
Figure 3: Uniaxial compression stress–strain diagram of concrete [18].  
the shift from tension to compression. Subsequent compression along the CF segment returns the stiffness to its initial state. When subjected to a dual-cycle load, transitioning from compression to tension, the curve follows the GH path, with G indicating the pivot from compression to tension. Given prior tensile and compressive damages, there is a slight decrease in stiffness. \(W_{c}\) and \(W_{t}\) are the coefficients of recovery of compressive and tensile stiffness, respectively, which are generally taken as \(W_{c}=1\), \(W_{t}=0\).
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 3 Analysis of the Delay Performance
### SNC in the SNR Domain
#### 3.3.1 Mellin Transform over the Arrival Process in the SNR Domain  
As shown in Equation (24), the UB-QDVP is related to the arrival process on the Mellin transform in the SNR domain. Consequently, deriving the Mellin transform of \(a_{k}\) is essential for evaluating the UB-QDVP. We consider the traffic class of \((\delta(s),\lambda(s))\)-bounded arrivals, whose arrival processes characterized in the SNR domain for some \(s>0\) on the Mellin transform are bounded by [37]  
\[\mathcal{M}_{\mathcal{A}_{k}}(s,\tau,t)=\big{(}\mathcal{M}_{\varphi_{k}}(s) \big{)}^{t-\tau}\leq e^{(s-1)\cdot(\lambda(s-1)\cdot(t-\tau)+\delta(s-1))}. \tag{25}\]To simplify the notation, we consider the case where \(\delta(s)=0\) and \(\lambda(s)\equiv\lambda\); namely, \((\delta(s),\lambda(s))\) are independent of \(s\), which is true for constant arrivals [54]. Thus,  
\[\mathcal{M}_{\varphi_{k}}(1+s)\leq e^{\lambda s}, \tag{26}\]  
where \(\lambda\) is the constant number of arrival bits that arrive successfully at one slot, as well as the constant bit arrival rate.
# Simplified Fly Tower Modeling for Preliminary Acoustic Predictions in Opera Houses
###### Abstract  
The acoustic field of an opera house is much more difficult to predict than those of concert halls because, in the fly tower, the absorption characteristics vary from time to time, according to the opera piece layout. For this reason, the paper aims to find a simplified fly tower model to be used as a fixed reference in a preliminary acoustic prediction for opera houses. Firstly, referring to a case study, the effects of the fly tower Depth and absorptive characteristics are investigated to identify the simplified model. As a traditional opera is set on an empty stage, and modern pieces are supported by a virtual projected environment, the influence of the variable stage elements on Reverberation Time RT, Clarity C80, and Strength G is considered, comparing the traditional Semiramide opera to a modern digital one, according to the Just Noticeable Difference JND. Results confirm the utility of the suggested fly tower model, which does not require any set definition.  
acoustic modeling and analysis; room acoustics prediction; acoustic design process; opera theaters Article
## 9 Hybridization of EHO-BFA: Bacterial Foraging Algorithm  
The _E. coli_ bacterium species' behavior when looking for food served as the model for the BFA. The bacteria look for food in the surroundings while it is foraging. The neighbors are informed when plentiful food is found. The educated bacteria swarm towards the direction of food. The following swarm behaviors are seen throughout this process.  
The meal is covered in bacteria;  
* Chemotaxis motion help a bacteria identify food in their environment (tumble and swimming);
* The bacterium with insufficient nonurishment perishes and vanishes;
* Bacteria that successfully proliferate and live reach the food. Additionally, they entice others by creating chemicals.  
In the quest for new food sources, the germs disperse once more. The fitness of randomly chosen bacteria is assessed once the population has been initiated in the EHO-BFA. The BFA process is then used, which involves removing unhealthy bacteria, conducting a tumble, and swimming toward the food supply. With every instance of tumbles or swimming, the bacteria move either closer to the food or farther away from it. Every step in the water or during a tumble updates the population. The procedures are carried out for the entire population, returning a list of the top bacteria. The population on the list is brand new and is broken up into 'w' sub-populations or clans. Algorithm 1 is used to update the sub-populations. The upgrade is carried out with the help of the matriarch (local best). When another ingredient proves to be more suitable than the matriarch, the matriarch is updated. Each clan is updated, and Algorithm 2 only preserves the best components while eliminating the remainder of the undesirable components from the population. Each clan's result is combined to create a new list, and the cost of the items on the new list is determined. The nodes that have the closer position towards the sink are placed into less expensive time slots. As a result, nodes are placed in the optimal locations. The EHO-BFA stages are listed in Algorithm 5. For the number of nodes with an energy status larger than the average energy of all shiftable nodes, the temporal complexity of the proposed EHO-BFA is (length (NBeacons > AvgPr)). Hence, the complexity of EHO-BFA is O(length(NBeacons > AvgPr) ) > BP).  
Due to their conceptual similarities, BFA and FA's scheduling habits are comparable. Due to the allure of the dominating components, scheduling behavior in the proposed EHO-BFA, and EHO-FA is comparable. It is possible to use a hybrid of BFA and FA for scheduling and routing mechanisms in underwater environments, as both algorithms are general-purpose optimization algorithms that can be applied to a wide range of problems.  
Scheduling and routing problems in underwater environments typically involve finding the optimal order or sequence of tasks or routes based on certain constraints and objectives. These problems can be formulated as optimization problems, and a hybrid of BFA and FA can be used as an optimization algorithm to find an optimal or near-optimal solution.
# Human Behavior Analysis: A Survey on Action Recognition
## 3 Datasets
### Pixel-Level Benchmarks  
**UCF-101-24**[95] is the second version of ground-truth labels from the original UCF-101, where they provide the bounding box annotations of the humans present in the videos. Although there are 101 classes, these pixel-level labels only represent 24 classes of them. This dataset is one of the most popular benchmarks among action recognition methods at the pixel-level.  
**J-HMDB-21**[101] is the second version of ground-truth annotations from the original HMDB-51, where they provide the bounding box labels of the humans present in the videos. These labels at the pixel-level represent 21 action categories from the original 51. This benchmark is also one of the most popular datasets for evaluation at the pixel-level.  
**AVA**[54] (atomic visual actions) is composed of 430 video clips (15 minutes each) from different movies, containing 80 atomic visual actions. Following the same activity hierarchy as previously mentioned (Section 1), the ground-truth labels (provided at the pixel-level) of this dataset represent the atomic body movements or object manipulations at its lowest possible level of natural descriptions, such as the pose action (sit, stand, run, etc.), object interaction (if applicable, carry, write, ride etc.), and person-to-person (if applicable, talk to, listen to, watch, etc.).  
**NTU RGB+D**[102] consists of 56,880 video samples with 60 action classes. This dataset was captured from highly restricted camera views providing 3D skeleton and RGB-D data for each video sample. This benchmark was built for the purpose of exploring the skeleton dynamics of the human body, not only for its estimation but also to recognise the action performed, being one of the most popular for evaluation of skeleton-based action recognition methods. A second version of this dataset was recently released, NTU RGB+D 120 [103], adding 60 classes and 57,600 video clips to the original version.  
**Kinetics-Skeleton**[97] was introduced by skeleton-based action recognition methods, ignited by the challenging diversity of the Kinetics-400 dataset, action recognition methods based on skeleton data started employing multi-person pose estimators [61; 62; 63; 65] in order to extract its skeleton data to feed their models.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 3 Traffic Flow and Its Characteristics
### Classical Traffic Flow Theories  
The development of classical traffic flow theory began in the 1950s, marking the start of systematic efforts to understand and model traffic dynamics. A significant milestone in this field was the publication of a comprehensive bibliography in 1961 by the Committee on Theory of Traffic Flow, which was featured in a letter to the Editor of the Journal of Operations Research [28]. This bibliography categorized important publications into ten sections, including hydrodynamic analogies, kinematic waves, car-following dynamics, probabilistic flow models, traffic distributions over networks, and various statistical studies. The foundational topics identified in this early work continue to influence traffic flow research today.
## 4 Model Training  
Training a model is an optimization problem equivalent to finding the minima of the loss function, which measures how 'good' the architecture is. During the training process, all neuron weights in each layer are updated using the backpropagation algorithm, Adam [32]. An L2 regularization technique is employed in all the convolutional and fully connected layers to prevent overfitting, limit model complexity, and thereby improve the model's generalization ability [33]. The random batch gradient descent method is used to improve the generalization and reduce the risk of overfitting [34]. A total of 300 samples are randomly selected for each training epoch, and 50 iterations are required. Each model was trained using a GPU (NVIDIA GeForce RTX 3070, NVIDIA Corporation, Santa Clara, CA, USA) of desktop workstation (Processor: 12 th Gen Intel(R) Core(TM) i9-12900K 3.20 GHz, Memory: 64 GB, Intel Corporation, Santa Clara, CA, USA), with the 200 epoch training process taking approximately 5 min.  
Figure 5 shows the evolution of accuracy and recall rate with the number of iterations for the training/test datasets. As the number of epochs increased, the loss on the training set gradually decreased, while the loss on the test set initially decreased but then gradually increased, indicating the occurrence of overfitting. This overfitting is attributed to the small and homogeneous sample size and, for each depth sampling point, we extracted 40 m of data, leading to many samples with similar information. A viable solution is to employ early stopping, as indicated by the gray vertical line in Figure 5, to enhance the model's predictive accuracy and generalization ability.  
Figure 5: The loss evolution of CNN (**a**) and RESNET (**b**).  
In the Random Forest algorithm, the parameter estimators and random states are essential [30]. The number of estimators defines the number of trees in the forest. Generally, increasing the number of trees enhances model performance; however, beyond a certain threshold, adding more trees results in diminishing returns. The random state parameter sets the seed for the random number generator, ensuring the reproducibility of the results. The selection of the number of estimators and random states has a significant impact on the model's performance. In this study, optimal values for these parameters were determined through grid search based on their performance on the test set [35] (Figure 6).