esa-sceva/satcom-chunk-collection huggingface dataset:
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 3 Related Work
### Supervised Learning Algorithms  
Supervised learning remains the dominant approach in phishing detection due to the availability of labeled datasets. Naive Bayes, Support Vector Machines (SVMs), Random Forests, and neural networks have been widely adopted. For example, Ref. [16] evaluated multiple classifiers in the Kaggle and Sklearn datasets and found that Naive Bayes achieved the best performance for spam detection tasks.  
Based on feature diversity, Ref. [17] integrated domain-based, stylometric, and structural features with a Random Forest classifier to address traditional and spear phishing attacks. Their system also introduced a verifiable secret-sharing protocol for internal sender authentication. However, the lack of publicly available spear-phishing datasets limited practical evaluation.  
Feature optimization has also been explored. In [18], a hybrid Cuckoo Search-SVM (CS-SVM) model was proposed to tune the RBF kernel parameters. The model, trained on emails from MillerSmiles, Enron, and the [PERSON] and [PERSON] corpus, achieved 99.52% accuracy--outperforming standard SVM configurations.  
Deep learning approaches have shown strong potential, particularly for semantic feature extraction. Ref. [19] combined word embedding with neural architectures such as CNN, RNN, LSTM, and MLP to detect phishing emails. The system achieved a 10-fold cross-validation accuracy of 0.991 using the IWSPA-AP 2018 dataset.  
Figure 4: HTML code of previous example.  
Figure 3: An example of a Zero-Font phishing technique.  
Other studies have focused on semantic cues and text structure. SEAHound [20] employed NLP and semantic analysis to detect patterns of social engineering in email text. It reduces false positives compared to commercial tools but remains limited to text-only attacks, excluding image-based or mixed-content phishing.  
Lightweight frameworks have also been proposed. Ref. [21] utilized feature selection and neural networks to detect phishing emails in real time. Their model, trained in domain and content features, achieved an accuracy of 93. 9% and included a blacklist for repeated threats.  
Convolutional [PERSON] have been effective in learning complex patterns from raw text. CNNPD, developed by [22], achieved 99. 42% precision in PhishingCorpus and SpamAssassin datasets using an embedding layer and convolutional filters without manual feature engineering.
## 3 The AgroTRACE Infrastructure  
Throughout the design and implementation of the AgroTRACE system, full compliance with GS1 standards and best practices related to them are ensured. The range of the tracing extends from the field to the consumer and back to the field by also monitoring waste treatment. Moreover, the modular structure offers flexibility and adaptability, while the use of open standards favors the interoperability and data sharing. Interoperability is one of the greatest challenges in the development of an integrated IoT system in agriculture [65]. The proposed system includes integrated information management based on a System-of-Systems (SoS) approach. This approach supports interconnection and interoperability of individual sensing systems in a single system that provides access to the user for the implementation of different traceability scenarios. It also allows existing information systems to continue to operate, by receiving data from partner systems.  
As can be depicted in Figure 3, AgroTRACE consists of three main elements: (i) the event capturing and IoT application platform, (ii) the transaction support and information management system and (iii) the data mapping system.
## 4 The Test Bench
### Governing Equation and Parameters  
The stiffness of the oscillator is determined by the position of the clamps in the guide rails and the material damping causes energy dissipation during the oscillation. The base is hung with ropes and therefore movable [47; 48]. Thus, the oscillator can be simplified theoretically as in Figure 13. There are two DoFs in the oscillator: \(x_{abs}\) for the moving mass and \(x_{base}\) for the base. The parameters \(m\), \(d\), and \(k\) are the mass, damping coefficient, and stiffness, respectively.  
The motion of the oscillator can be described mathematically by the following equation:  
\[m\ddot{x}_{abs}+d(\dot{x}_{abs}-\dot{x}_{base})+k(x_{abs}-x_{base})=0, \tag{17}\]  
because the excitation is only carried out on the base. Introducing the relation DoF \(x_{rel}=x_{abs}-x_{base}\), Equation (17) is:  
\[m\ddot{x}_{abs}+d\dot{x}_{rel}+kx_{rel}=0. \tag{18}\]  
Dividing Equation (18) by \(m\) and setting \(\omega_{0}^{2}=\frac{k}{m}\) and \(2D\omega_{0}=\frac{d}{m}\) yields:  
\[\ddot{x}_{abs}+2D\omega_{0}\dot{x}_{rel}+\omega_{0}^{2}x_{rel}=0. \tag{19}\]  
The calculated values of the parameters are listed in Table 3. Since errors accumulate into the theoretical values through the calculation, the listed theoretical values could vary to some degree from the true value. However, the numbers in Table 3 are useful to evaluate the order of magnitude of the identified parameters.
## 2 Related Work
The paper proposes a test platform that uses reproducible test methodologies to allow automated testing of military systems and applications over actual military radio equipment. Moreover, ref. [36] proposes a software testing method to evaluate the applicability, reliability, and durability of various communication equipment used in maritime satellite communications. Researchers [37] have developed an open-access wireless testing platform for testing communication protocols used in wireless communication networks. This platform comprises a large grid of ceiling-mounted antennas connected to programmable SDR devices operating at frequencies lower than 6 GHz. The system provides computational power and hardware support for testing complex communication systems and protocols such as MIMO (multiple-input and multiple-output) communication systems, cognitive radio, 5G cellular networks, IoT (Internet of Things), and more. A multiple-antenna evaluation and testing platform was proposed in [38]. The platform includes FPGAs, DSPs, and control processors to provide the necessary processing power and flexibility. The platform interfaces require high throughput since evaluating multiple antennas generates a large amount of real-time data.
# MA-Xnet: Mobile-Attention X-Network for Crack Detection
## 4 Experiment Study
### Ablation Experiments  
#### 4.2.1 Validation of the Validity of Different Modules  
Ablation experiments were performed on the modules to further verify that the revised method may increase the model's accuracy in segmenting cracks. The model contains a bottleneck residual depth-wise separable convolution block for feature extraction, a PA module, and a CA module. The contrasting object models include U-Net, M-UNet (a lightweight network using bottleneck residual depth separable convolution as feature extractor on U-Net), PAM-UNet (with only PA module added to M-UNet), CAM-UNet (with only CA module added to M-UNet), and MA-Xnet (with dual attention module (PA) and (CA) added to M-UNet). The tests were conducted in the same experimental environment and the experimental results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1-Score/\%** & **mIoU/\%** \\ \hline U-Net & 95.08 & 68.62 & 79.21 & 68.95 \\ M-UNet & 91.15 & 77.21 & 83.59 & 73.92 \\ PAM-UNet & 94.80 & 80.03 & 87.91 & 77.65 \\ CAM-UNet & 92.19 & 79.61 & 85.48 & 76.20 \\ MA-Xnet & 92.34 & 82.57 & 90.53 & 81.32 \\ \hline \end{tabular}
\end{table}
Table 3: Ablation experimental results.  
Figure 9: Detailed feature comparison diagram.  
The experimental data shown in Table 3 demonstrate that feature extraction using the bottleneck residual depth-wise separable convolution, which reduces the network parameters while taking into account the way the network extracts features, improves over the baseline network U-Net by 4.38% and 4.97% in the key metrics of the F1-Score and the mIoU, respectively. Further addition of the position attention module enables the network to capture the mapping of local and global dependencies of crack features, integrating remote contextual information in the crack pixel spatial and improving over the lightweight network M-UNet by 4.32% and 3.73% in the key metrics of the F1-Score and the mIoU, respectively. Further addition of the channel attention module allows the model to selectively enhance the weight of channels, with beneficial features for fracture information, and suppress the weight of useless features, improving the F1-Score and the mIoU by 1.89% and 2.28%, respectively, compared with the lightweight network MU-Net. The F1-Score of the final parallel incorporation of the two attention modules reached 90.53% and the mIoU reached 81.32%, which were substantial improvements in the key indices, thereby proving that the proposed model effectively improves the segmentation accuracy of cracks.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 3 Materials and Methods
### Sensing Terminal Subsystem Architecture  
The sensing terminal subsystem was installed on the fishing vessel to monitor and collect critical data. Figure 4 illustrates the structure of the sensing terminal subsystem.  
The subsystem comprises several key components:  
* **Microcontroller Unit (module STM32 MP157C): MP157C** (STMicroelectronics, Grenoble, France) is a dual-core Arm(r) Cortex(r)-A7 microcontroller (up to 800 MHz) featuring Wi-Fi, Bluetooth, and multiple communication interfaces (Ethernet, USB, CAN, SPI, I2C, UART). It is powered by 12-24 VDC and programmed via STM32 Cube Programmer. This study used Ethernet, USB, UART, and I2C ports. Its parameters are shown in Table 1.
* **Power management unit (PMU):** The PMU (Texas Instruments, Austin, TX, USA) is used to provide appropriate DC voltages for terminal components, for example, from 220 VAC to 12 VDC, 12 VDC to 5 VDC, and 5 VDC to 3.3 VDC.
* **Nine-axis sensor:** The JY901 provides real-time data on vessel movement, orientation, and speed. It combines a gyroscope, accelerometer, magnetometer, and digital motion sensor, delivering outputs at up to 200 Hz via the I2C interface.
* **Wireless module:** The RM500Q (Quetel Wireless Solutions Co., Ltd., Shanghai, China) 5G establishes cellular communication (5G/4G/3G) between the user and the terminal. It is connected to the central processing unit via a USB port and can switch to more reliable networks during severe weather or low signal conditions.
* **Temperature and humidity sensor:** SHT31 (Sensirion AG, Zurich, Switzerland) measures and reports real-time environmental temperature and humidity via the I2C interface, ensuring high accuracy and reliability [31].
* **GPS:** The BD-1612Z (BnStar Electronics Co., Ltd., Shenzhen, China) module determines the vessel's location. It operates at 2.7-3.6 VDC and connects to the MCU through a UART interface.
* **Storage module:** The micro-SD memory card module (Shenzhen Atom Technology Co., Ltd., Shenzhen, China) stores data when the system cannot send them wirelessly. We can store, retrieve, and copy data when the module has problems.  
Figure 4: Structure diagram of sensing terminal subsystem.  
* **Camera:** TL-IPC633P-A (TP-LINK Technology Co., Ltd., Shenzhen, China) camera provides a wide range of monitoring (horizontal angle 345\({}^{\circ}\), vertical angle 123\({}^{\circ}\)) to ensure comprehensive coverage, minimize vulnerabilities, and maximize vessel safety.  
The itemized budget for the sensing terminal subsystem is shown in Table 2. It can be seen that the total cost is USD 399.4. In addition, the cost can be reduced to USD 303.11, except the surveillance camera, due to many fishing vessels already having installed the electronic monitoring system [32].
# Assessment of a Substandard Reinforced Concrete Frame's Beam-Column Joint Using Shake Table Testing
## 1 Introduction
This suggests that the load-bearing potential of such joints can be significantly improved through the strategic optimization of the axial loads imposed on them. At present, the formulas employed for determining joint shear strength are based on findings from earlier experimental studies. According to the latest seismic code of Turkiye (TBEC 2018) [27], a minimum of 40% and 60% of the transverse reinforcements must be placed in the confinement zone of the column, distributed along the heights of the confined and unconfined joints, respectively. The code outlines that confinement for concrete is achieved through the use of transverse reinforcements, which include stirrups, hoops, and crossties. To ensure proper anchorage, it is essential that stirrups, hoops, and crossties incorporate a seismic hook that bends at least 135\({}^{\circ}\). Hoops are defined as closed ties that may consist of multiple reinforcing elements, each equipped with seismic hooks on both ends, and continuous wound ties possessing seismic hooks at both ends. Crossties, on the other hand, are continuous reinforcing bars featuring a seismic hook at one end and a hook bent at 90\({}^{\circ}\) at the opposite end; in this context, the hooks on the crossties should engage with the peripheral longitudinal reinforcing bars, as illustrated in Figure 2[28]. Recent advances in retrofitting methodologies have demonstrated that targeted interventions can significantly improve the seismic performance of deficient RC structures. Experimental investigations have particularly highlighted the efficacy of advanced materials and techniques in addressing specific damage modes observed in beam-column joints. For instance, [PERSON] et al. [29] conducted an in-depth experimental study on RC beam-column joints retrofitted with fiber-reinforced polymer (FRP) systems. Their findings clearly indicate that the application of FRP wrapping not only enhances the shear capacity of the joint but also improves confinement, thereby mitigating the propagation of X-shaped shear cracks--a common damage pattern in inadequately detailed structures. Complementary studies by [PERSON] et al. [30] further elucidate the cyclic behavior of RC joints strengthened with FRP, demonstrating significant improvements in energy dissipation and ductility. Additionally, [PERSON] and [PERSON] [31] have provided valuable insights into the effects of fiber composites on concrete joints, confirming that the application of such materials can substantially increase joint strength and delay failure mechanisms. These targeted retrofitting strategies, substantiated by empirical evidence and advanced analytical models, underscore the necessity of adopting a holistic approach to structural rehabilitation. Our work thus contributes to the evolving discourse on seismic retrofit interventions, providing a compelling framework for the application of scientifically validated retrofitting techniques in the modernization of aging RC infrastructure.
## 5 Analysis of Experimental and Simulation Results
### Uniform Water Body Conditions
#### 5.1.2 Case 2 Working Conditions
In terms of simulation results, horizontal movement dominates the process of the jet temperature diffusion while vertical changes are minimal, and dissipation characteristics are less pronounced. Furthermore, as jet velocity increases, horizontal movement becomes more dominant during the initial stages of discharge without significant upward floating. However, after some time has elapsed and with decreasing flow rates, upward diffusion begins but horizontal diffusion continues to prevail overall. Ultimately, though, it is noted that, despite efforts by increasing velocity inhibiting upward diffusion and slowing down decay speed due to reduced floating height, complete surface dispersion still could not be achieved.
# Preliminary Study of Recycled Aggregate Mortar for Electric Arc Furnace Dust Encapsulation
## 2 Materials and Methods
### Methodology  
To evaluate the mechanical properties of the manufactured mortars, the compressive strength and tensile strength were measured before and after immersion, following the procedure described in the XP X31-212:2011 standard.  
Cylindrical specimens of 80 mm in height and 40 mm in diameter were used, following the methodology proposed by [PERSON] et al. [31]. The mechanical strength data before immersion were determined after 28 days of mixing, and those corresponding to after immersion at 32 days, after the specimens had been immersed in deionized water for 96 \(\pm\) 4 h at a temperature of 20 \(\pm\) 5 \({}^{\circ}\)C, with L/S ratio = 10, as described in the XP X31-212:2011 standard.  
The water in contact with the submerged specimens was passed through a 0.45 \(\upmu\)m filter to determine the delitescence rate (XP X31-212:2011). The material retained on the filter was dried in an oven at a temperature of 105 \(\pm\) 5 \({}^{\circ}\)C. The delitescence rate was obtained by the following expression (1):  
\[t_{d}=100\times\frac{m_{d}}{m_{0s}} \tag{1}\]  
where:  
\(t_{d}\): delitescence rate (%)  
\(m_{d}\): dry mass of solid particles retained on the 0.45 \(\upmu\)m filter (g)  
\(m_{0s}\): dry mass of the specimen (g)  
A total of 3 repetitions were carried out for the tests of compressive and tensile strength before and after immersion. To determine the delitescence rate, 3 of the 6 specimens that were submerged in deionized water were randomly taken, after which the eluate was filtered and the delitescence rate was calculated. Once the specimens were broken in the compressive strength test, before and after immersion, their dry bulk density was calculated according to the UNE-EN 1015-10:2000 standard.  
To evaluate the concentration of heavy metals and anions that can leach from the encapsulated EAFD in the cement-based matrices, the method described in the XP X31-211:2012 standard was followed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mortar Type** & **CEM (g)** & **NA (g)** & **RCA (g)** & **SF (g)** & **EAFD (g)** & **Water (g)** & **w/c** & **Consistency (mm)** \\ \hline NA-REF & 1200 & 1600 & - & 1200 & - & 1050 & 0.88 & 236 \\ NA-EAFD/2:1 & 800 & 1067 & - & 800 & 1333 & 1194 & 1.49 & 224 \\ RCA-REF & 1200 & - & 1600 & 1200 & - & 1233 & 1.03 & 234 \\ RCA-EAFD/2:1 & 800 & - & 1067 & 800 & 1333 & 1628 & 2.04 & 236 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mortar mix proportions.  
Two cylindrical samples of each type of mortar were immersed for 24 h in deionized water (L/S = 10) in continuous movement by means of a magnetic stirrer. The water in contact with the samples was passed through 0.45 \(\upmu\)m filters, and the eluate from the filtering process was analyzed on an ICP-MS following the procedure described in the previous section. This method has been used in a previous study [31].
###### Abstract  
This study evaluates indoor environmental quality (IEQ) in childcare facilities, focusing on air quality and lighting--key factors affecting children's health and development. The analysis examines a nursery in Seville, Spain, where continuous monitoring revealed challenges in maintaining suitable indoor conditions. Carbon dioxide (CO\({}_{2}\)) levels often surpassed Spanish standards (770 ppm) and stricter thresholds (550 ppm) for sensitive groups, peaking at nearly 1900 ppm. These concentrations are linked to possible cognitive impairments and increased airborne pathogen risks, with Attack Rates (ARs) exceeding 70%. Passive ventilation strategies, such as window openings, proved insufficient, emphasizing the need for Controlled Mechanical Ventilation (CMV) systems to ensure consistent air renewal while maintaining thermal comfort. Lighting assessments identified insufficient circadian stimulus during key periods. Excessive lighting during nap times disrupted rest, while morning daylight levels failed to provide adequate circadian stimulation. These findings stress the importance of integrating solar protection and dynamic daylight and electric lighting systems to align with children's biological rhythms. This research highlights the urgent need for comprehensive IEQ strategies in childcare settings, combining advanced ventilation, hygothermal management, and circadian-friendly lighting to create safer and healthier environments for young children.  
air quality; risk of infection; airborne pathogens; CO\({}_{2}\); daylighting; circadian stimulus; nursery Article
# A CP-ABE Scheme Based on Lattice LWE and Its Security Analysis
## 3 Our Ciphertext-Policy ABE Scheme for NC\({}^{1}\) Circuits Based on LWE
Perform the following steps in order:  
* Sample vector \(\mathbf{s}\leftarrow\mathbb{Z}^{n}_{q}\) and error vector \(\{\mathbf{v}_{l}\}_{l\in\{2,\ldots,\mathit{mce}\}}\leftarrow\mathbb{Z}^{m}_{q}\). * In addition, continue to sample \(\{\mathbf{e}_{i}\}_{i\in[y]}\leftarrow\psi^{2m}_{l\mathit{mce}}\) and \(\{\hat{\mathbf{e}}_{i}\}_{i\in[y]}\leftarrow\psi^{m}_{big}\), for every \(i\in[y]\). Calculate \(\mathbf{c}_{i}\in\mathbb{Z}^{2m}_{q},\hat{\mathbf{e}}_{i}\in\mathbb{Z}^{m}_{q}\) as described below. * Output the ciphertext \(\mathbf{ct}=((\mathbf{G},\eta),\{\mathbf{c}_{i}\}_{i\in[y]},\{\hat{\mathbf{e }}_{i}\}_{i\in[y]},C=MSB(\mathbf{s}^{\top}\mathbf{x})\oplus\mu)\). \(\mathbf{Dec}(\mathbf{SK},\mathbf{ct})\): With the private key and a ciphertext as inputs, perform the following algorithm steps in order:
* In the case that the vector \((1,0,\ldots,0)\) cannot be represented by the rows of \(\mathbf{G}\) correlated to the attribute set \(U\), the decryption will fail. Conversely, let \(\Delta\) be a collection of the row labels of \(\mathbf{G}\) with the condition that for any \(i\in\Delta:\eta(i)\in U\). Hence, let \(\{\xi_{i}\}_{i\in\Delta}\in\{0,1\}\in\mathbb{Z}_{q}\) be scalars with the condition that \(\sum_{i\in\Delta}\xi_{i}\mathbf{G}_{i}=(1,0,\ldots,0)\), of which \(\mathbf{G}_{i}\) is the \(i^{th}\) row of \(\mathbf{G}\). 3. Calculate \(Y^{\prime}=\sum_{i\in\Delta}\xi_{i}(\mathbf{c}_{i}^{\top}\mathbf{h}_{u}+ \hat{\mathbf{c}}_{i}^{\top}\mathbf{w})\). 4. \(\mu^{\prime}=C\oplus MSB(Y^{\prime})\).
## 3 Results
### The Results of Reliability Methods  
The summary of reliability analyses performed by different methods is shown in Figure 9. In general, the errors of the Taylor Series and PEM methods are significantly higher, compared to other methods. Analytical FOSM, Taylor Series and PEM with the assumption that FS is normally distributed result in the highest errors, with values within the range of 0.91-1.87. The same methods result in a considerably less error, 0.06-0.6, when it was assumed that the FS is lognormally distributed. The results of direct integration, Monte Carlo, FORM and Simplified FORM methods are in very good agreement, with the maximum error of 0.05. The trend of decreasing the reliability index with an increase in the internal friction angle is visible in all cases. All methods except the PEM, with the assumption that the FS follows lognormal distribution, are below the control curve, i.e., give a conservative estimate of the reliability index.  
Figure 8: Comparison of pf and \(\beta\) calculated with the assumption that FS is normal and lognormal along with the results of Direct Integration of reliability integral.  
Figure 9: Summary of reliability analysis performed by different methods.