esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Implementation and Performance Evaluation
### Implementation Considerations  
Deploying the enhanced GN-AK protocol in real-world systems requires attention to implementation strategies, platform compatibility, and cryptographic library support. This section outlines key factors that influence practical deployment.  
Point halving is particularly efficient in binary fields (\(\mathbb{F}_{2^{m}}\)), which should be prioritized when implementing the protocol. Recommended curves include:  
* NIST B-163, B-233, B-283 (binary fields).
* Koblitz curves, which further optimize arithmetic.  
The DBC-based scalar multiplication with point halving is especially advantageous on constrained hardware:  
* 8-bit microcontrollers: Reduction in field inversions translates directly to less CPU usage.
* IoT devices: Battery-powered nodes benefit from energy-efficient ECC operations.
* Smart cards: Limited RAM and ROM favor arithmetic simplifications.  
Most ECC libraries (e.g., micro-ecc, RELIC, TinyECC) do not yet support point halving natively. For integration, one must  
1. Extend the scalar multiplication function to parse and apply DBC representation.
2. Implement field-specific point halving routines.
3. Ensure constant-time execution to resist timing attacks.  
Cryptographic software must adhere to side-channel attack mitigation practices:  
* Use of constant-time conditional logic for DBC traversal.
* Randomized representation of scalars to prevent pattern leakage.
* Disabling hardware acceleration (e.g., DMA) if it introduces timing side-channels.  
By designing DBC routines as modular components, existing protocols using ECC can adopt the optimized scalar multiplication without altering higher-level logic. This facilitates incremental adoption and reuse in TLS, VPN, or key management systems.  
Since the GN-AK protocol employs public/private key pairs and identity-bound authentication, it aligns well with existing PKI systems. Integration steps include:  
* Binding public keys to X.509 certificates.
* Managing key lifetimes and session expiration securely.
* Supporting revocation and rotation of long-term keys.
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 3 Experimental Validation  
To verify the path planning capability of the improved RRT-KF algorithm in different environments, five experimental environment maps are constructed in this experiment: an obstacle-free map, a multi-channel map, a single-channel map, a complex environment map, and a 3D environment map. The first four scattered maps represent various 2D complex environments, while the 3D scene can validate the algorithm's performance in high-dimensional space. The size of the first four two-dimensional maps is uniformly \(30\times 30\), and the algorithm step size \(step_{1}=1\); the 3D environment map size is \(1000\times 1000\times 1000\), and the algorithm step size \(step_{2}=20\). In order to minimize the influence of chance error on this experiment, 40 groups of repeated experiments are conducted, and the collected average path length, average planning time, average number of collision points, average number of sampling points, and average number of valid points (rounding off) are used as experimental evaluation indicators.
# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
## 2 Methodology
### Data Augmentation  
Domain adaptation refers to learning when the feature distributions of the source and target domains are inconsistent [37]. It aims to narrow the distribution gap between the two domains to achieve a better learning performance in the target domain. Based  
Figure 1: Unet network architecture. Gathers are input features, and the outputs are velocity models. Each box represents the output feature map of the convolutional layer. The number at the top of each box indicates the channel number in the corresponding feature map. The encoder consists of a convolution layer with a 3 \(\times\) 3 convolution kernel size (blue arrow), a batch normalization (BN) layer, a leaky rectified linear unit (LeakyReLU), and a 2 \(\times\) 2 maximum pooling layer and the Dropout layer (yellow arrow). Each decoder replaces the maximum pooling layer with a 5 \(\times\) 5 transposed convolution layer (black arrows). Skip connections indicate the corresponding channel feature maps connecting the encoder and decoder sections (green arrows).  
on the idea of domain adaptation, data augmentation is achieved by linear operations of correlation and convolution operations between synthetic and real data [38]:  
\[\overline{d_{s}^{i}}(t)=d_{s}^{i}(t)\otimes d_{s}^{k}(t)*d_{r}^{ij}(t)\otimes d_{ r}^{ij}(t) \tag{3}\]  
where \(i\) is the index of the single trace, \(j\) is the index of an arbitrary event of the real data, \(k\) is the index of the reference trace and we set \(k=1\), \(\overline{d_{s}^{i}}(t)\) is the new augmented data, \(d_{s}^{i}(t)\) is the single trace of the synthetic data, \(d_{s}^{k}(t)\) is the reference trace of the synthetic data, \(d_{r}^{ij}(t)\) is the single trace of the real data, \(\otimes\) is the correlation operator, and \(*\) is the convolution operator.  
Here, we randomly select one reference field event for each synthetic event corresponding to each stage and set the first trace as the reference trace. The high-frequency information in the real data can be implicitly introduced through the operations in Equation (3). The correlation operation can eliminate the effects of recording time delays between the synthetic and real data. The data augmentation operation can reduce the feature difference between the training (source) synthetic data and the (target) real data and will finally contribute to enhancing the performance of the neural network model when applying to the real data.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 2 Related Work
### Inertial Sensor-Based Fall Detection Systems  
Sensor-based systems use wearable sensors such as accelerometers, gyroscopes, EMG, and EEG to record features such as angles and distances [18]. Among them, [PERSON] et al. developed a non-linear classification and Kalman filter-based human fall detection system where they reported 99.4% accuracy with the Sisfall dataset [19,20]. [PERSON] et al. also developed a fall detection system with high accuracy within short times of 0.25 s [21]. [PERSON] et al. tested k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) on the Sisfall dataset, achieving accuracies of 99.8%, 99.78%, and 99.56%, respectively [22]. Another author combined federated learning with an extreme learning machine to develop a human fall recognition system where they showed 99.07% accuracy in experiments with only elderly subjects. In the same way, [PERSON] et al. [23] developed a human fall detection system aiming to handle a variety of real-world scenarios where they evaluated the model with a combination of three datasets: UMA fall [24], WEDA fall [25], and UP fall [26]. They achieved a recall rate of 90.57% by combining three datasets. [PERSON] et al. developed a smartwatch-based real-time fall detection system [27]. The system achieved an F-score of 85% using an LSTM model and transfer learning.
## 4 Results and Discussion
### Synthetic Image Analysis  
The synthetic images of these three methods look similar to the reference Landsat 8 images acquired 4 September 2014 (Figure 7a). Figure 7b-d shows that land cover with large area, e.g., water bodies, residential areas and wide roads, can be clearly identified. From the subplots of the synthetic images (Figure 7f-h), the synthetic image in the heterogeneous region produced by the unmixing method appears somewhat \"blurry\" and a homogeneous \"plot\" in which we could not discern the small objects. In contrast, areas from the images generated by the USTARFM and STARFM represent more spatial details (Figure 7g-h). Compared with the image predicted by STARFM (Figure 7g), the result produced by the USTARFM has a lower spectral deviation to the actual spectrum (Figure 7e) in the fragmentation region. This is because the directly resampled data used in STARFM has uniform spectral reflectance, which is unable to reveal the heterogeneity within the MODIS pixel.  
Figure 6: Scatterplots of three bands generated by the three methods in the region with the heterogeneity level of 1.
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 3 Proposed Algorithm
### Adaptive Tone Remapping  
The dehazing algorithm often results in a low-dynamic range image, owing to the fact that computations can produce results out of the normalized range from 0 to 1. [PERSON] et al. [16] employed a simple tone mapping that solely operates on the luminance channel to resolve this problem. We make use of an efficient adaptive tone remapping method, proposed by [PERSON] et al. [31], in this work. Not only is the luminance enhanced, the color also is emphasized, according to the ratio of the enhanced luminance to the original luminance.  
\[\mathbf{EL}(x,y)=\mathbf{L}(x,y)+\mathbf{G_{L}}(x,y)\mathbf{W_{L}}(x,y), \tag{25}\]  
\[\mathbf{EC}(x,y)=\mathbf{C}(x,y)+\mathbf{G_{C}}(x,y)\mathbf{W_{C}}(x,y)+0.5, \tag{26}\]  
where \(\mathbf{EL}\) denotes the enhanced luminance, \(\mathbf{L}\) the input luminance, \(\mathbf{G_{L}}\) the luminance gain, \(\mathbf{W_{L}}\) the adaptive luminance weight, \(\mathbf{EC}\) the emphasized color, \(\mathbf{C}\) the input color, \(\mathbf{G_{C}}\) the color gain, and \(\mathbf{W_{C}}\) the adaptive color weight. [PERSON] et al., [31] adopted an adaptive limit point, which is calculated according to the cumulative distribution function of the input luminance, to formulate the luminance gain \(\mathbf{G_{L}}\) as a nonlinear power function. The adaptive luminance weight \(\mathbf{W_{L}}\) is a simple linear function where the input luminance is the independent variable and \(\mathbf{W_{L}}\) itself is the dependent variable. The color gain \(\mathbf{G_{C}}\) is defined as the multiplication of the input color and the ratio between the enhanced luminance and the input luminance. The adaptive color weight \(\mathbf{W_{C}}\) is the piecewise linear function consisting of three line segments, in which the first and the third lines are two thresholds. Readers can refer to [PERSON] et al., [31] for a more detailed description and computational formulae.
## 1 Introduction
Various factors, including the volume fraction of aggregates, aggregate type, aggregate gradation, coarse-to-fine aggregate ratio, aggregate shape and texture, water-to-cement ratio, cement content, type and content of any supplementary cementitious materials, and type and dosage of chemical admixtures [38; 39; 40; 41; 42], influence the compressive strength of concrete. Due to the complex and nonlinear interrelationship between these factors and compressive strength, general linear equations are often ineffective [43; 44; 45]. In recent years, machine learning algorithms have been increasingly used to predict the performance of concrete with both natural aggregates [46; 47] and with recycled aggregates [4]. Studies have focused on predicting various properties of concrete with recycled aggregates, such as strength [43], elastic modulus [19], chloride resistance [1], and durability [48], using machine learning models. However, there remains insufficient established predictive information regarding the relationship between the compressive strength of concrete and the moisture content of RCA. Therefore, this study aims to address this gap by conducting a literature review to investigate the relationship between compressive strength and moisture content in RCA. To achieve this, a database was constructed consisting of 752 entries, considering parameters such as the moisture content, water-to-cement ratio, replacement ratio of recycled aggregates, composition ratio of natural aggregates, curing age, etc. Seven machine learning methods, including linear regression, support vector regression (SVR), k-nearest neighbors (KNN), decision tree, random forest, LightGBM, and XGBoost, were employed to develop a predictive model for the compressive strength of RCA. The models were compared using evaluation metrics, and the final model was selected. Furthermore, feature importance and interaction plots were utilized to analyze the relationship between moisture content and compressive strength. This study contributes to the field by addressing the lack of consideration of RCA moisture content in predicting concrete performance metrics, making it a novel endeavor in the realm of machine learning studies in concrete technology.
# LVRT and Stability Enhancement of Grid-Tied Wind Farm Using DFIG-Based Wind Turbine
## 6 Conclusions  
The current study proposed partial integration of the DFIG with the proposed rotor-side controller based on the PI controller to improve the LVRT effectiveness of the SCIG-based WF. Furthermore, a comparison of the suggested and conventional rotor side controllers was performed. According to the simulation outcomes and performance evaluations, the following aspects of the proposed approach were remarkable:  
1. The proposed controller of DFIG with a minimum power rating could stabilize the SCIG with a higher power rating during a fault state.
2. The implementation expense could be minimized by integrating a limited number of DFIGs with the suggested control scheme and a wide scale of SCIGs into a WF.
3. The proposed RSC controller of the DFIG system could keep the terminal voltage at the rated condition during the transient state by transferring an adequate amount of reactive power into the grid.  
Therefore, the combined installation of the proposed DFIG consisting of relatively low power rating with SCIG based WF could enhance the LVRT capability as well as the system stability.  
In future work, the frequency control method of hybrid WF could be a strong candidate.  
[PERSON], [PERSON] and [PERSON] prepared the theoretical conceptions and designed the proposed RSC controller of the DFIG system. [PERSON] and [PERSON] executed the simulation studies and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Active Power response of DFIG.  
Figure 17: Rotor Speed performance of DFIG.
# A Component-Based Model for Novel Modular Connections with Inbuild Component
###### Abstract
The horizontal joint between modular units was considered as a bolted steel plate, and the elastic behavior was assumed by modeling the connection as an element with strength in bending and shear 30% greater than the adjacent beam section. [PERSON] [13] developed a combined horizontal and vertical connection model. The vertical intermodule connections were modeled by beam element with consideration of the possible positions of plastic hinge. Spring models were placed between horizontal modular units to transfer lateral load. The spring stiffness was determined as the shear stiffness of the adjacent intermodule connection. According to the above analysis, intramodule connections are usually considered to be rigid. The selection of stiffness and strength parameters of intermodule connections mainly depends on traditional design assumptions and experience. Moreover, due to the construction complexity of modular connections, it is difficult to relate the mechanical properties of real connections to the simplified model. The component method that adopted in Eurocode 3 [14] and Eurocode 4 [15], also known as mechanical model or spring model. It describes the mechanical behavior of connections through a series of rigid and elastic elements. This method has wide applicability and can be extended to all connection structures theoretically. Considering the flexibility of the component method, many researchers  
Figure 1: Assembling process of modular construction. have been dedicated to developing a component method for different types of connections [16; 17; 18]. Some of the studies have also investigated connection mechanical properties under various complex loading conditions using the component method, such as cyclic loading [19], axial load-bending moment interaction [20; 21] and fire [22]. However, there are a few component method studies for modular connections in the present study. The aim of this paper is to develop a component-based model for novel modular connections with superimposed beams shown in Figure 2. The novel modular connection eliminated gaps between ceiling and floor beams in order to ensure more continuous force transmission between modular units. The basic mechanical properties of joints [23] and superimposed beams [24; 25; 26] were previously studied. In this paper, a more comprehensive parameter study was fist implemented using elaborate finite element models. We then followed the component method procedure:  
1. the component-based model for novel modular connections was developed based on parameter analysis results. 2. the force-deformation response of each component was determined with the help of the finite element method. 3. assembly of all components to overall rotational joint. At last, the results of the simplified model were compared with the results of the elaborate finite element model.
## 2 Results and Discussion
### Response to Oxidative Stress  
It has been observed that rhizosphere bacteria are exposed to high levels of reactive oxygen species (ROS) that are formed along the plant root surface during the reaction of redox-active plant secondary metabolites, such as phenols, quinones, flavins, and phenazines, with molecular oxygen [90]. Additionally, upon contact with bacteria, one of the plant defense reactions is the production of ROS, nitric oxide and phytoalexins; thus, rhizosphere bacteria must survive in a highly oxidative environment during the colonization of plant tissues. Moreover, the degradation of aromatic compounds leads to the generation of ROS such as H\({}_{2}\)O\({}_{2}\), OH\({}^{-}\), and O\({}_{2}\). Furthermore, heavy metal ions have been reported to promote increased formation of ROS [26; 91] As part of adaptation, bacteria employ various enzymes, including catalases, peroxididases, and superoxide dismutases, for defense against reactive oxygen species (ROS). In the genome of ZCR6, we found a wide variety of enzymes that help to cope with oxidative stress (Table 7). Genome analysis of the ZCR6 strain revealed genes encoding enzymes such as hydrogen peroxide catalases (_katE_ and _katG_), superoxide dismutases (_sol2_), glutathione S-transferases (_GST_), gamma-glutamyl transpeptidase/glutathione hydrolase (_ggt_), glutathione peroxidase (_gpx_), and glutathione reductase (_gor_). It is also worth emphasizing that the genome of the ZCR6 strain carried the _oxyR_ gene, which encodes the hydrogen peroxide sensor OxyR activating the expression of a regulon of hydrogen peroxide-inducible genes. The presence of the abovementioned genes suggests that ZCR6 possesses the potential to overcome oxidative stress, which was also confirmed by phenotypic assays.
## 5 Evaluation Results and Discussion
### Analysis of the Impact of Candidate Paths  
In the first set of experiments concerning the Flex-O scenario, we analyzed the overall network cost (\(z\)) and the complexity of solving the MILP model in a function of the number of candidate paths (\(k\)) in the networks considered.  
Figure 4a shows the values of \(z\), obtained and averaged for traffic loads \(\rho\in\{1.0,1.5,2.0\}\) and WDM capacity \(W\in\{4,8\}\), as a function of \(k\). Moreover, Figure 4b presents the cost savings, defined as a relative difference in \(z\), with respect to the single-path case (\(k=1\)). The results indicate that the availability of a larger set of candidates routing paths allows to decrease the network cost, which is achieved due to increased opportunities for the aggregation of wavelengths at remote sites. The cost savings are between 9 and 10% in MESH-9/MESH-20, and above 13% in MESH-38, and they stabilize for \(k\geq 4\), \(k\geq 10\), and \(k\geq 12\) in the respective networks.  
Figure 5 presents the CPLEX computation times of solving the MILP model (2)-(25) as a function of \(k\) in the largest MESH-38 network for different traffic loads (\(\rho\)) and assuming \(W\in\{4,8\}\). We can see that the MILP model is scalable, and optimal solutions are found within less than 40 s, even for the largest problem instance (\(k=16\), \(\rho=1.5\), \(W=8\)). In general, the computation times increase with \(k\) and \(W\), which transl  
Figure 4: (**a**) Overall network cost \(z\) and (**b**) relative difference in \(z\) vs. the single-path scenario as a function of the number of candidate routing paths (\(k\)) in the networks analyzed; the results obtained and averaged for \(\rho\in\{1.0,1.5,2.0\}\) and \(W\in\{4,8\}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Number of Channels**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **8** \\ \hline OADM attenuation [dB] & 0.8 & 1.4 & 2.1 & 2.8 & - \\ MUX attenuation [dB] & 0.0 & 0.9 & – & 1.2 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attenuation of OADMs and MUXs; the values do not comprise connector losses.  
problem variables and constraints. The impact of \(\rho\) on the MILP solving times is related to the amount of available options for aggregation of wavelengths in the network, which are considered by the solver during the search for an optimal solution. Namely, for lower values of \(\rho\) at which the remote sites have smaller connection (wavelength) requests, it is possible to aggregate the wavelengths from a larger number of sites on a single TP, and the amount of possible combinations of such sites is high. On the other hand, for higher \(\rho\), the remote sites either cannot be served by other TPs since they request numbers of wavelength connections that are too large, or the aggregation of wavelengths is possible just for a few sites, whereas direct TPs are established for the others. It leads to the reduction in the solution space and smaller CPLEX runtimes.  
In the remainder, we assume \(k=4\) in MESH-9, \(k=10\) in MESH-20, and \(k=12\) in MESH-38.