esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution and Analysis of Longitudinal Deformation Curve of Surrounding Rock
### Data Comparison and Equation Optimization Analysis
#### 3.2.2 Equation Optimization and Improvement
The relationship between the expanded convergence function, shear strength and Mohr circle is shown in Figure 6.  
By solving the relationship between the expanded convergence function, shear strength and the intersections \(A(a_{1},b_{1})\) and \(B(a_{2},b_{2})\) of the Mohr's circle Equation (Equation (9a)), the fitting parameter expression Equation with parameters can be obtained (Equation (9b)):  
\[\left\{ \begin{array}{l}\xi~{}=~{}c+a_{1}\tan\varphi-\exp(-\eta a_{1}/R)\\ \left[\xi+\exp(-\eta a_{2}/R)\right]^{2}\\ =1/4(\sigma_{1}+\sigma_{3})^{2}-\left[a_{2}-1/2(\sigma_{1}+\sigma_{3})\right] ^{2}\end{array} \right. \tag{9a}\]  
Figure 5: M-C yield strength criterion of surrounding rock. Figure 6: Relationship between expanded convergence function, shear strength and M-C.  
\[\left\{ \begin{array}{l}k(x)\ =\ \xi+\exp(-\eta x/R)\\ \tau\ =\ c+\sigma\tan\varphi\\ \tau^{2}+[\sigma-1/2(\sigma_{1}+\sigma_{3})]^{2}\ =\ 1/4(\sigma_{1}-\sigma_{3})^{2} \end{array} \right. \tag{9b}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) are the maximum and minimum principal stress of rock mass and their values can be obtained through tests, the elastic modulus is \(E\), the internal friction angle is \(\varphi\), cohesion is \(c\) and the blasting parameters can be obtained through a geological survey. Parameters \(a_{1}\) and \(a_{2}\) are the fitting parameters. Through the above analysis, the proposed Equation (9) of the \"expanded convergence function\" is combined with the theoretical Equation (8) given by [PERSON] and [PERSON] to obtain the modified Equation (10):  
\[\lambda(x)\ =\ \left\{ \begin{array}{l}(0.22\
u+0.19)\exp\bigl{[}-(0.73\
u+0.8 1)\bigr{]}\frac{x}{R}\bigr{]}\cdot\cdot\cdot x<0\\ \bigl{[}\xi+\exp(-\eta x/R)\bigr{]}\Bigl{\{}(0.22\
u+0.19)+(0.81-0.22\
u)[1-( \frac{0.39\
u+0.65}{39\
u+0.65+x/R})^{2}]\Bigr{\}}\cdot\cdot\cdot x\geq 0 \end{array} \right. \tag{10}\]  
The relevant parameters contained in the \"expanded convergence function\" can be obtained by test and fitting, and it is reasonable to take \(\xi\) = 0.001 and \(\eta\) = 2.8 here.
###### Abstract  
In this study, a novel two-parameter, three-dimensional chaotic system is constructed. The system has no linear terms and its equilibrium is a line, so it is a system with hidden attractors. The system is first studied by computation of its bifurcation diagrams and diagram of Lyapunov exponents. Then, the system is applied to two encryption related problems. First, the problem of secure communications is considered, using the symmetric chaos shift keying modulation method. Here, the states of the chaotic system are combined with a binary information signal in order to mask it, safely transmit it through a communication channel, and successfully reconstruct the information at the receiver end. In the second problem, the states of the system are utilized to design a simple rule to generate a bit sequence that possesses random properties, and is thus suitable for encryption related applications. For both applications, simulations are performed through Matlab to verify the soundness of the designs.  
chaos; hidden attractor; line equilibrium; secure communications; CSK modulation; random bit generation; PRBG +
Footnote †: This paper is an extended version of our paper published in 9 th International Conference on Modern Circuits and Systems Technologies (MOCAST) 2020, Bremen, Germany, 7–9 September.
## Appendix A Details of Proposed AI Model Architecture and Process  
### Dataset Preparation  
The generator, based on the U-Net architecture, is designed for image-to-image translation tasks. The down-sampling path, which is an encoder, is composed of 8 layers of U-Net down block. Each block performs a convolution followed by instance normalization (if normalization is true) and leaky ReLU activation. Starting with an input image of size 3 \(\times\) 256 \(\times\) 256, each down-sampling step halves the spatial dimensions (height and width) and potentially increases the number of channels. Dropout is applied in the later down-sampling layers to prevent overfitting [59; 60]. The up-sampling path, which is a decoder, is composed of 8 layers of U-Net up block. Each block performs a transposed con volution to increase the spatial dimensions and decrease the number of channels. After the transposed convolution, the output is concatenated with a corresponding feature map from the down-sampling path (skip connection), followed by instance normalization and leaky ReLU activation. The final block restores the image to its original size of 3 \(\times\) 256 \(\times\) 256 and applies a Tanh activation.  
The discriminator is designed to evaluate the authenticity of the generated images based on given input conditions. It processes concatenated pairs of images, with the input comprising 6 channels each of degraded and the original data information. Each block in the discriminator includes a convolutional layer followed by optional instance normalization and leaky ReLU activation. The convolutional layers use a 2D convolution with a specific kernel size, stride, and padding to reduce spatial dimensions while increasing channel numbers. Instance normalization, applied in certain blocks, aids in stabilizing the training process [61; 62], while the leaky ReLU activation function allows for a small gradient when inactive [63; 64; 65]. The discriminator's structure consists of several stages, each increasing the number of channels while reducing spatial dimensions, focusing on more abstract features. The final layer is a convolutional layer that reduces the depth to 1 channel, producing a patch output to indicate the authenticity of each patch of the input images. A sigmoid activation is applied to this output.  
The architecture of both the generator and the discriminator is detailed in Figure 11, providing a visual representation of their structures. Additionally, the combined structure of a single cGAN network unit, comprising both the generator and the discriminator, is illustrated in Figure 12.  
Figure 11: Common architecture of the generator and the discriminator for both frequency and spatial domains in the hybrid domain learning framework. The numbers above each tensor (N, M\({}^{2}\)) represent the tensor structure, with N dimensions and a size of M \(\times\) M.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 3 Acquisition Procedure and Data Transmission  
The data acquisition phase spans a duration of half an hour and is activated one or more times throughout the day, aligning with the monitoring requirements of most bridge SHM systems [25].  
Before starting the data acquisition phase, each sensor node initiates the process by turning on its GNSS module. The node proceeds to establish a connection with the satellites to synchronize its internal clock and retrieve accurate time information. Once synchronized, the ESP32 waits for the scheduled time to trigger the start of the data-gathering phase. This synchronization mechanism ensures coordinated data acquisition across the entire network.  
During each activation period, sensor nodes engage in the collection of data and store it into the ESP32 memory. Following the completion of the one-hour acquisition period, sensor nodes switch to the data transmission phase, wherein collected data are sent to the central gateway.  
Upon the conclusion of the sending phase, each sensor node transitions into a deep sleep mode, a low-power state optimized for energy conservation. In this mode, the node deactivates nonessential functions and enters a state of dormancy. The duration of the sleep period is predetermined by the node's internal clock, calculated based on a fixed interval established within the system architecture. Figure 3 gives a graphical representation of the evolution of the node state during the acquisition routine.  
The segmentation of data acquisition and transmission into two discrete phases within the acquisition strategy is implemented to optimize energy utilization and operational  
Figure 3: Evolution of the node state.  
Figure 2: Network architecture.  
efficiency. During the acquisition phase, sensor nodes perform data collection, with the Wi-Fi functionality temporarily deactivated to conserve energy. Then, during the transmission phase, Wi-Fi is briefly activated to enable MQTT communication, while the GNSS and the accelerometer are turned off. The efficiency of the system hinges significantly on the transfer speed of this protocol, which allows the data-sending duration to be in the order of a few minutes.  
The repeater node follows the same logic, but unlike the sensor nodes, it has only two states. The first one is the deep sleep mode and the other one is the hotspot generation. The switching between the two is dictated by the internal clock of the ESP32 and based on the current state of the network. Once the transmission process is completed for all nodes, the Raspberry Pi can undertake local data processing tasks and eventually transmit the processed data to the central server via FTP.
## 3 Results and Discussion
### Effect of SDS Concentration  
In MEKC, the SDS concentration is significant for separation. To evaluate the effect of SDS concentration on the separation of analytes, the SDS concentration was varied from 15 to 30 mM in 5 mM phosphate buffer at pH 3.7. The CMC of SDS in water is \(\sim\)8.2 mM [30]. The positive charged analytes interacted with negative charged SDS micelles to form ion pairs. The migration time of each analyte decreased with increasing SDS concentration because of the strong interaction with micelles (Figure 3). The log \(K_{\text{ow}}\) values of T, 5-HT, TA, DA, Trp, E, 5-HT, NE, and Tyr were 1.55, 0.21, 0.86, \(-\)0.98, \(-\)1.05, \(-\)1.24, \(-\)1.37, \(-\)1.70, and \(-\)2.26, respectively [31]. The order of migration is related to the polarity of the ion pair formed with SDS which, in turn, is strongly related to the polarity of the different analytes. With the addition of 20 mM SDS, analytes were baseline separated (Figure 3). Optimum MEKC conditions were 5 mM phosphate buffer containing 20 mM SDS at pH 3.7. Figure 3B shows the electropherogram for 1.0 mM analytes standards using the optimum MEKC conditions. All analytes were successfully detected within 11 min.  
Figure 1: Effect of the concentrations of the phosphate buffer (pH 3.5) containing 20 mM SDS on the separation of the nine analytes: (**A**) 1, (**B**) 5, (**C**) 10, and (**D**) 15 mM. Sample (1.0 mM) injection was performed at a height of 20 cm for 10 s. A 50-cm capillary with an effective length of 40 cm was used. Separation was conducted at \(-\)15 kV. Peak identities: (**1**) T, (**2**) 5-HT, (**3**) TA, (**4**) DA, (**5**) Trp, (**6**) E, (**7**) 5-HTP, (**8**) NE, and (**9**) Tyr.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
###### Abstract  
Sun-Induced chlorophyll Fluorescence (SIF) relates directly to photosynthesis yield and stress but there are still uncertainties in its interpretation. Most of these uncertainties concern the influences of the emitting vegetation's structure (e.g., leaf angles, leaf clumping) and biochemistry (e.g., chlorophyll content, other pigments) on the radiative transfer of fluorescent photons. The Caatinga is a large region in northeast Brazil of semiarid climate and heterogeneous vegetation, where such biochemical and structural characteristics can vary greatly even within a single hectare. With this study we aimed to characterize eleven years of SIF seasonal variation from Caatinga vegetation (2007 to 2017) and to study its responses to a major drought in 2012. Orbital SIF data from the instrument GOME-2 was used along with MODIS MAIAC EVI and NDVI. Environmental data included precipitation rate (TRMM), surface temperature (MODIS) and soil moisture (ESA CCI). To support the interpretation of SIF responses we used red and far-red SIF adjusted by the Sun's zenith angle (SIF-SZA) and by daily Photosynthetically Active Radiation (dSIF). Furthermore, we also adjusted SIF through two contrasting formulations using NDVI data as proxy for structure and biochemistry, based on previous leaf-level and landscape level studies: SIF-Yield and SIF-Prod. Data was tested with time-series decomposition, rank correlation, spatial correlation and Linear Mixed Models (LMM). Results show that GOME-2 SIF and adjusted SIF formulations responded consistently to the observed environmental variation and showed a marked decrease in SIF emissions in response to a 2012 drought that was generally larger than the corresponding NDVI and EVI decreases. Drought sensitivity of SIF, as inferred from LMM slopes, was correlated to land cover at different regions of the Caatinga. This is the first study to show correlation between landscape-level SIF and an emergent property of ecosystems (i.e., resilience), showcasing the value of remotely sensed fluorescence for ecological studies.  
chiorophyll fluorescence; SIF; drought; spectral vegetation indices; Caatinga; GOME-2; abiotic stress 2020 IEEE/RSJ Web Conference (2020); 12 3264 10.3390/rs12193264
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 3 Results and Discussion
### TG-DSC Analysis  
Figure 8 shows the TG-DSC curves of cement mortar specimens with 7.2% SO\({}_{3}\) content at 56 d, 120 d and 240 d under 20 \({}^{\circ}\)C maintenance. Because the heat absorption decomposition temperature of each phase is different, the heat absorption peaks in different temperature intervals can reflect the changes of the phases. In the DSC curves, the dehydration temperatures of calcium alumina, gypsum and calcium hydroxide ranged from 80 to 120 \({}^{\circ}\)C, 120 to 150 \({}^{\circ}\)C and 390 to 470 \({}^{\circ}\)C, respectively. From Figure 8a, it can be seen that there are heat absorption peaks of calcium alumina, gypsum and calcium hydroxide at 56 d, 120 d and 240 d. Combined with Figure 8b, the contents of calcium alumina, gypsum and calcium hydroxide can be calculated according to Equations (2)-(4).  
\[AFt(\%)=W_{AFt}\cdot\frac{M_{AFt}}{26M_{H_{2}O}} \tag{2}\]  
\[G(\%)=W_{G}\cdot\frac{M_{G}}{2M_{H_{2}O}} \tag{3}\]  
\[P(\%)=W_{P}\cdot\frac{M_{P}}{2M_{H_{2}O}} \tag{4}\]  
\(W_{AFt}\), \(W_{G}\) and \(W_{P}\) are the dehydrated mass loss of calcium alumina, gypsum and calcium hydroxide, respectively, and \(M_{AFt}\), \(M_{G}\), \(M_{P}\) and \(M_{H_{2}O}\) are the relative molecular masses of calcium alumina, gypsum, calcium hydroxide and water, respectively.  
Figure 9 shows the contents of calcium alumina, gypsum and calcium hydroxide for cement mortar specimens with 7.2% SO\({}_{3}\) content at 56 d, 120 d and 240 d under 20 \({}^{\circ}\)C maintenance. The results showed that the contents of calcium alumina were 9.40%, 12.28% and 17.75%, of gypsum they were 1.42%, 1.11% and 0.65% and of calcium hydroxide they were 8.79%, 8.96% and 8.87% at 56 d, 120 d and 24 d, respectively, which were similar to the results of XRD quantitative analysis. This indicates that during the internal sulphate attack, the gypsum content decreases, the calcium alumina content increases, and the calcium hydroxide content remains relatively stable.  
Figure 8: TG–DSC curves of cement pastes added with 7.2% SO\({}_{3}\).
## 2 Modeling of Compromised LFC System and False Data Injection Attacks
### Basics of a Compromised LFC System
Based on the area control error (ACE), the commands of the Area \(i\) of the LFC system are generated using the following equation:  
\[u_{i}=-K_{Pi}ACE_{i}-K_{li}\int ACE_{i} \tag{5}\]  
\[ACE_{i}=\Delta p_{i}^{f}+\Delta p_{i}^{tm} \tag{6}\]  
where \(K_{Pi}\) and \(K_{li}\) are the proportional and integral gains of the controller, respectively; \(\Delta P_{i}^{tm}\) represents the measured data uploaded by the RTU of tie-line power; and \(\Delta p_{i}^{f}\) represents the data of the equivalent active power deviation caused by \(f_{i}\). Attackers can mislead the controller by compromising the input data of the ACE. Considering the fact that the compromised \(\Delta p_{i}^{f}\) can be rapidly detected by cross-checking the data with other normal ones, we only focus on FDI attacks on \(\Delta P_{i}^{tm}\) in this paper.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
These limitations underscore the need to augment Wazuh with ML models, which can enhance its detection capabilities by automating the learning process, reducing false positives, and providing more nuanced threat analyses. This study demonstrates significant improvements in threat detection and operational efficiency, specifically within the Wazuh SIEM system, contributing to the broader field of SOCs and Security Information and Event Management (SIEM) systems. **Enhanced Threat Detection with ML Integration:** Demonstration of successful integration of machine learning models into Wazuh, achieving a high accuracy of 0.972 with Random Forest (RF), significantly improving the system's ability to detect sophisticated cyber threats compared to traditional rule-based methods. **Reduction in False Positives:** Addressing Wazuh's high false-positive rates, with RF achieving a low false-positive rate of 0.03 and DBSCAN clustering at 0.0821, thereby reducing alert fatigue and enhancing operational efficiency in SOCs. **Comprehensive Model Evaluation:** Providing a thorough evaluation of multiple ML models (RF, SVM, KNN, Logistic Regression, Gaussian Naive Bayes) and clustering algorithms (DBSCAN, K-means, Isolation Forest), offering comparative insights into their performance for cybersecurity applications, with RF and DBSCAN outperforming others. **Improved Alert Management:** Enabling more efficient real-time threat detection and alert management by streamlining the alerting mechanism, allowing SOC analysts to focus on genuine threats and reducing manual effort. **Framework for Adaptive Security:** Establishing a framework for an adaptive security system by integrating ML with Wazuh, paving the way for more responsive and intelligent threat detection in dynamic cybersecurity environments. The following sections provide a detailed exploration of this study: Section 2 reviews related work; Section 3 outlines the materials and methods, including the ML-SOC architecture and data processing; Section 4 presents results and discussion with performance metrics; and Section 5 concludes with future directions.
## 4 Results and Discussion
### Short-Time Fourier Transform Implementation
Those features increase the complexity of the wave propagation causing scattering [17] and mode conversion [18; 19], for instance, which consequently increases the number of wave packets in the received signal with possibly different sensitivities to strain, which in turn can potentially increase the effectiveness of the technique.
## 4 Material and Methods
### Identification and Retrieval of Genes Associated with AsA Biosynthesis  
The genes encoding the 4 AsA biosynthesis pathways (D-mannose/L-galactose, L-glucose, D-galacturonate, Myo-inositol) corresponding to GDP-mannose pyrophosphylase (GMP), GDP-L-galactose phosphorylase (GGP), L-galactose-1-P phosphatase (GPP), L-galactose dehydrogenase (GalDH), L-galactono-1,4-lactone dehydrogenase (GalLDH), GDP-D-mannose 3\({}^{\prime}\),5\({}^{\prime}\)-epimerase (GME), L-gluconolactone oxidase (GuLO), galacturonate reductase (GalUR), and Myo-inositol oxygenase (MIOX) were primarily identified in the genomic and cDNA data of _Arabidopsis_ from the GenBank database ([[http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov)]([http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov))) accessed on 1 April 2023. Subsequently, via BLASTn (E-value threshold: 1 \(\times\) 10\({}^{-1}\)), their orthologous cDNAs were searched in the _G. max_ genome, also available in the GenBank database. The cDNAs and corresponding proteins for each gene were retrieved from RefSeq mRNA and/or protein databases and used in gene expression and phylogenetic analyses, respectively. Detailed information about the sequences is provided in Supplementary Table S1.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 2 Network Model and Spectral Efficiency
### Downlink Data Transmission  
All APs treat the channel estimation in (4) and apply CB precoding to data signals to the users that are served. If AP \(l\) serves all users, then the transmitted signal from AP \(l\) is given by  
\[x_{l}=\sqrt{\rho_{d}}\sum_{k=1}^{K}\eta_{lk}^{1/2}\hat{h}_{lk}^{*}q_{lk}. \tag{6}\]  
The power constraint at each AP is given by  
\[\mathrm{E}\Big{\{}|x_{l}|^{2}\Big{\}}\leq\rho_{d}. \tag{7}\]  
Then power constraint (7) can be rewritten as follows,  
\[\sum_{k=1}^{K}\eta_{lk}\gamma_{lk}\leq 1,\ \ \text{for all}\ l, \tag{8}\]  
Figure 1: Network model of user-centric cell-free massive MIMO system.  
where  
\[\gamma_{lk}\triangleq\mathrm{E}\left\{\left|\hat{h}_{lk}\right|^{2}\right\}=\sqrt{ \gamma_{p}\rho_{p}}\theta_{lk}l_{lk}. \tag{9}\]  
When using the above formulas, the received signal at the user \(k\) is given by  
\[\begin{split} r_{d,k}&=\sum_{l=1}^{L}h_{lk}x_{l}+ \omega_{d,k}\\ &=\sqrt{\rho_{d}}\sum_{l=1}^{L}\sum_{k^{\prime}=1}^{K}\eta_{lk}^{ 1/2}h_{lk}\hat{h}_{lk}^{*}q_{k^{\prime}}+\omega_{d,k}.\end{split} \tag{10}\]  
Then, \(q_{k}\) is detected from \(r_{d,k}\).  
In this paper, the following spectral efficiency (12) as in [8] is used. Then, the obtained downlink spectral efficiency is given by (12). The (12) indicates the original cell-free massive MIMO downlink spectral efficiency.  
Throughout this paper, \(\mathcal{M}_{k}\) is defined as a subset of APs serving user \(k\). Then, \(\mathbf{\alpha}\) can be rewritten as follows,  
\[\mathbf{\alpha}_{lk}=\begin{cases}\mathbf{I}_{N}&ifl\in\mathcal{M}_{k}\\ \mathbf{0}_{N}&ifl\
ot\in\mathcal{M}_{k}\end{cases}. \tag{11}\]  
Finally, the downlink spectral efficiency in the user-centric cell-free massive MIMO also can be described easily as (13) by applying the matrix \(\mathbf{\alpha}\) in (11).