esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation
### Error Probability
#### 3.1.2 Numerical Approach  
After analyzing the signal detection in the presence of imperfect reference phase recovery, the formulas for BER in detecting BPSK and QPSK signals can be, respectively, written in the form of [24]  
\[P_{e}^{\mathrm{BPSK}}=\frac{1}{2}\int\limits_{r=0}^{+\infty}\int\limits_{\varphi =-\pi}^{\pi}\text{ erfc}\bigg{(}\frac{r\cos\varphi}{\sqrt{2}\sigma}\bigg{)}p_{R}(r)p_{ \varphi}(\varphi)\,\mathrm{d}\varphi\mathrm{d}r, \tag{38}\]  
Figure 2: Relative truncation error magnitude during computation of a SER value for typical system paramters: BPSK, \(K=10\,\mathrm{dB}\), \(\Gamma=0.5\), \(\sigma=10^{\circ}\), \(\mathrm{SNR}=20\,\mathrm{dB}\).  
\[P_{e}^{\text{QPSK}}=\frac{1}{4}\int\limits_{r=0}^{+\infty}\int\limits_{\varphi=- \pi}^{\pi}\Bigg{\{}\text{erfc}\bigg{(}\frac{r\cos(\pi/4-\varphi)}{\sqrt{2}\sigma }\bigg{)}+\text{erfc}\bigg{(}\frac{r\cos(\pi/4+\varphi)}{\sqrt{2}\sigma}\bigg{)} \Bigg{\}}p_{R}(r)p_{\varphi}(\varphi)\,\mathrm{d}\varphi\mathrm{d}r, \tag{39}\]  
where \(\text{erfc}(\cdot)\) is notation for the complementary error function defined as ([18], Equation (7.1.2)).  
In order to calculate a numerical value of BER, it is necessary to evaluate twofold integrals in (38) and (39). The problem of accurately calculating BER is not to be underestimated, as the procedure includes a double integral over a function that includes an infinite alternating series. Obviously, the first concern is the sum, and it is easily proven that the series converges since terms tend to zero and are decreasing. For computing the PDF involved, we use partial sum and the alternating series remainder theorem [25] to determine the number of summands necessary. The precision of summation is set to the level of \(10^{-8}\). As for double integrals, we employ integration using the Cartesian rule in which the cubature abscissas are the Cartesian product of two independent quadrature rules for variables \(r\) and \(\varphi\). Since the dimensionality is only of second order, the number of points at which the functions must be computed is not forbiddingly large. We set the goal for computing the values of the integrals to at least two significant digits. Since the number of Gaussian points in quadrature rules is a significant parameter, in Figure 3, we explore this influence on the time required for computing a single BER value for the following parameters: BPSK modulation, \(K=10\,\mathrm{dB}\), \(\Gamma=0.8\), \(\sigma_{\varphi}=30^{\circ}\), and SNR = 24 dB, using the Mathematica 13 software package. From Figure 3b,c, we conclude that the least amount of time is taken when we choose the number of points to be 7 in the direction of \(r\) and 15 in the direction of \(\varphi\). Applying the numerical integration in this sense requires less than 10 s of processor time for computing BER for the given SNR on a 3 GHz i7 processor. When computing multiple values to show the BER curve for a range of SNR values, parallelization can be used to significantly reduce the required time.
# An Experimental Strain-Based Study on the Working State of Husk Mortar Wallboards with Openings
## 4 Data Analysis
### The Characteristic Parameters  
Typically, the first cracks are a local failure of the wallboard. With cracking propagation, local failure becomes structural failure. This development might raise the question of how to define structural failure based on the propagation of local failure/cracking, that is, how to delineate the boundary between structural and local failures. In other words, the stressing state of a structure should be expressed by the response data of key points from the experimental measurement or numerical simulation. This issue leads to an updated definition of structural failure. In this definition, the structural failure state is  
Figure 10: Numerical and test results of specimen 1-3.  
Figure 9: FE model and deformation pattern of specimen 1-3.  
defined as the working state after the structure loses its stable/normal working state, which is not a structural collapse corresponding to the ultimate load. Here, the wallboards are analyzed to reveal their unseen working characteristics and to define their failure loads based on an empirical modelling of the experimental data.  
First, a characteristic parameter \(E_{j,\text{norm}}\) is defined as the normalized approximate strain energy (NASE) sum of the wallboard to the \(j\)th load level \(F_{j}\); that is,  
\[E_{j,\text{norm}}=\frac{\sum\limits_{i=1}^{N}E_{ij}}{E_{M}},\qquad E_{ij}= \frac{1}{2}\overline{E}\varepsilon_{ij}^{2} \tag{1}\]  
where \(E_{ij}\) is the ASE value of the \(i\)th measuring point to the \(j\)th load increment; \(N\) is the number of measuring points distributed on the wallboard; \(E_{M}\) is the maximum sum of strain energy among the measuring points to the individual load increments or the ASE sum to the last (\(M\)th) load increment; \(\overline{E}\) is the elastic modulus; and \(\varepsilon_{ij}\) is the strain value at the \(i\)th point to the \(j\)th load increment.  
For Equation (1), two points must be indicated:  
1. \(E_{ij}\) is just an approximate strain energy parameter of the wallboard since it is calculated using a limited number of experimental strain values. Furthermore, \(E_{j,\text{norm}}\) approximately reflects the global behavior of the wallboard under lateral load. However, \(E_{j,\text{norm}}\) might better reflect the real working behavior of the wallboard than conventional numerical simulation because of the direct application of experimental data.
2. At present, a large amount of experimental strain data has not been fully used in the field of structural analysis. Hence, the introduction of \(E_{j,\text{norm}}\) represents a methodological attempt to reflect some unseen global working behavior of the wallboard (it could be as a structure) by applying experimental strain values.
## 3 Experiments and Analysis
### Experiment Details  
To assess the effectiveness of the algorithms, all experiments were conducted within the PyTorch framework and executed on NVIDIA GeForce GTX 3060 GPUs (12G). Data augmentation techniques, including random rotations and horizontal flips, were applied during the training of MTUW-GAN. Additionally, all input images in the training set were resized to dimensions of 256 \(\times\) 256 pixels. The adversarial training involved simultaneous and progressive optimization of \(G_{T}^{M}\), \(G_{T}^{A}\), and \(G_{S}\), as the entire optimization process follows an online knowledge distillation framework. The Adam optimizer was utilized for optimization, with an initial learning rate of \(2\times 10^{-4}\). The parameters \(\beta_{1}\) and \(\beta_{2}\) were set to 0.5 and 0.999, respectively. The batch size was set to 1, and a total of 200 epochs were trained.  
To demonstrate the robustness of our proposed method, we conducted training and testing using two publicly available underwater datasets: EUVP [19] and UIEBD [17]. The training set comprises real underwater images paired with their corresponding clear images. Specifically, we employed 11,435 images from the EUVP dataset to train our model, encompassing both degraded and high-quality underwater images. For testing, we randomly selected 300 pairs of images from the EUVP test set, representing various underwater scenes.  
The UIEBD dataset offers a diverse collection of underwater scenes and encompasses a wide range of degradation types. The images within this dataset cover various domains such as marine ecology, divers, submarine corals, and coral reefs. We utilized a random subset of 800 images from the UIEBD dataset for training purposes, while the remaining 90 images were reserved for testing.
## 2 Materials and Methods
### Water Quality Indices (WQI) Analysis  
Water Quality Indices (WQI) serve as a fundamental framework for the evaluation and reporting of water quality conditions across diverse aquatic environments [23; 24; 25; 26; 27; 28]. Over time, various adaptations of WQI have been developed to address specific regional and methodological needs, with CWQI and SWQI emerging as widely utilized frameworks [29; 30; 31; 32; 33; 34; 35; 36]. Their adaptability across different geographical conditions underscores their significance in scientific research.  
This study employs a systematic approach to evaluating reservoir water quality, utilizing data from the Serbian Environmental Protection Agency (2021-2022). To ensure a standardized and comprehensive assessment, the Serbian Water Quality Index and the Canadian Water Quality Index were applied, providing an objective framework for analyzing key water quality parameters.  
The selection of the Serbian Water Quality Index (SWQI) and the Canadian Water Quality Index (CWQI) in this study is based on their complementary strengths in water quality assessment. The SWQI, developed by the Serbian Environmental Protection Agency, is designed to reflect local environmental conditions and regulatory standards, incorporating key parameters for assessment of organic and nutrient pollution ensuring alignment  
Figure 1: Locations of sampling profiles (A1, B1, C1) and geographic position of Ribnica Reservoir. Source of satellite imagery: [22].  
with Serbia's water protection policies and long-term monitoring practices [37]. In contrast, the CWQI, established by the Canadian Council of Ministers of the Environment, provides a broader, internationally recognized framework for evaluation. It integrates factors such as heavy metal concentrations and deviations from established objectives to assess suitability for various uses (e.g., drinking, aquatic life, irrigation, and livestock) [38]. By combining both indices, this study enhances the depth and reliability of water quality assessments, enabling a more comprehensive understanding of ecological conditions and potential risks in the reservoir.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 2 System Model  
In this section, we formalize our system model. We start with the deployment and then proceed to introduce relaying, antenna locations, and propagation assumptions. Finally, we define our metrics of interest. The notation utilized in this paper is summarized in the abbreviations section.
## 4 Conclusions  
In summary, novel 2D Co (II) porphyrin-containing hypercrosslinked polymeric nanosheets were efficiently prepared using a RAFT emulsion polymerization approach, a nucleophilic substitution reaction, and a FeCl\({}_{3}\)-promoted Friedel-Crafts reaction, where the nanosheets could be readily transformed into Co-doped porous carbon nanosheets using direct pyrolysis treatment. The structures and morphologies of the as-prepared materials were characterized systematically by FTIR, XPS, TEM, and SEM measurements. The resulting porous carbon showed a high specific surface area of up to 272.5 m\({}^{2}\) g\({}^{-1}\), which was beneficial for contact with the electrolyte, as well as for the rapid transfer of electrolyte ions. Used as electrode materials in a supercapacitor, these graphene-coupled porous carbon materials exhibited outstanding supercapacitive behavior and high stability. Notably, the specific capacitance of the Co-GPC could reach 455 F g\({}^{-1}\) at a specific current of 0.5 A g\({}^{-1}\). The incorporation  
Figure 6: (**a**) Specific capacitance of the Co-GHCP, Co-GPC, GHCP, and GPC. (**b**) Electrochemical impedance spectroscopy (EIS) plots of the Co-GHCP, Co-GPC, GHCP and GPC, Inset: the low frequency region; (**c**) Cycling performance of the Co-GHCP, Co-GPC, GHCP, and GPC at the specific currents of 1 A g\({}^{-1}\); (**d**) Ragone plot of the Co-GHCP, Co-GPC, GHCP, and GPC.  
of cobalt ion into the framework of the porous carbon not only provided a high pseudocapacitor property, but it also enhanced the conductivity of the porous material. Therefore, our approach opens a facile avenue for the preparation of metal-doped 2D porous carbon nanosheets, which could serve as promising electrochemical electrodes for a broad range of applications in electrochemical energy storage and conversion.  
The following are available online at [[http://www.mdpi.com/2073-4360/10/12/1339/s1](http://www.mdpi.com/2073-4360/10/12/1339/s1)]([http://www.mdpi.com/2073-4360/10/12/1339/s1](http://www.mdpi.com/2073-4360/10/12/1339/s1)). Supplementary data of the TGA, XRD, Electrochemical results related to this article can be found at [[https://doi.org/10.1016/](https://doi.org/10.1016/)]([https://doi.org/10.1016/](https://doi.org/10.1016/)).  
Data curation, C.L.; Funding acquisition, X.Z.; Investigation, D.Z.; Methodology, X.Z.; Project administration, S.H.; Software, J.K.; Validation, F.L.; Visualization, F.Q.; Writing - review & editing, Y.C. and F.Q.  
This work was financially supported by the National Natural Science Foundation of China (21504057, 21574080, 21774072 and 5171101862), National Natural Science Foundation of China for Excellent Youth Scholars (51722304), Shanghai Committee of Science and Technology (15)C1490500, 16 JC1400703, and 17 ZR1441700), Shanghai Pujiang Talent Program (18 PJ1406100), Shanghai Excellent Technology Leaders Program (17 XD1424900).  
The authors declare no competing financial interest.
## 7 Discussion and Further Work
### Utilization in IoT System Architectures  
Several system models and architectures have been proposed for IoT. We have discussed a few of them in Section 6, such as the Open IoT [34] and IoT-A [36] architectures. One may also find domain-specific system architectures in the literature related to grids, vehicular systems, eHealth, _etc._ These architectures detail technological, system or application perspectives of IoT. They categorize physical objects, applications and services to distinguish and model consumer and service operations. They may describe the means and methods that can be used to communicate between the possible interfaces and how the architecture can be employed as a cloud service or in a particular business or public domain, _etc._ EDAS proposes a security perspective and architecture detailing how a particular service, _i.e._, security adaptation, can be modeled, such that it can be adopted in any of the mentioned or related IoT system architectures.  
As an example, Figure 18 instantiates how EDAS can be utilized in the OpenIoT architecture [34] in the context of IoT-eHealth. The first row represents the OpenIoT architecture itself and the distribution of different things, services, applications and utilities it employs at each plane it describes. The second row shows the IoT-eHealth and its corresponding elements as a possible application archetype of the OpenIoT architecture. The last row specifies the possible implementation of EDAS and its components in the OpenIoT. The physical plane will encompass the event sources, _i.e._, the monitored assets, such as sensors, smart devices and their event frameworks. The virtualized plane will consist of the methods and tools necessary for risk analysis and response, _i.e._, the EDAS platform. It can either be implemented in the cloud or as a dedicated server inside the hospital-controlled environment. Utilities, such as local adopters, security dashboards and other admin tasks, can be implemented in the utility-app plane. The figure dictates that if adaptive security is desired in an OpenIoT-related architecture, EDAS and its components can be readily implemented in it as illustrated. Furthermore, as previously mentioned, EDAS offers independent and extensible components that can be distributed and employed as per the requirements of a given IoT system architecture, such as the OpenIoT.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Feature Extraction Technique  
Transformers have seen tremendous growth in recent years and excel in feature extraction techniques rather than traditional methods. Due to its multilayer architecture, the  
Figure 3: The fusion word and char representations [22].  
transformer extracts multiple features without any intervention through its hierarchical structure and layer-aware and attention-based feature extraction approaches used to extract features. The BERT and CharBiGRU can extract features from raw datasets using hierarchical, layer-aware, and cross attention-based feature extraction techniques, which allow them to model the relationships between tokens at multiple levels of granularity. In hierarchical feature techniques, it identifies patterns at various levels, from smaller units (like characters or sub-words) to larger structures (like words or full URL segments). By tokenizing URLs into sub-words, BERT can identify associations at the phrase and sub-word levels. By incorporating character-level features, Char2B improves and can identify fine-grained patterns, including identifying subtle differences in a URL.  
In a layer-aware technique, features extracted at lower, middle, and upper layers of the transformer architecture. In the lower layer, it extracts common URL prefixes (\"http://\", \"https://\", and \"www:\"), sub-words (\"login-secure\"), and special characters (dots, hyphens, and slashes). In the middle layer, it emphasizes capturing broader relationships and multi-token dependencies, which includes identifying sequences of tokens that imply their intent or specific phrases like suspicious word combinations (\"secure-login\" and \"account-verify\") and keyword positioning (\"secure-update\" + \"account-check\"). In the upper layers, an understanding of the structure and purpose of a URL is enhanced by aggregating broader contextual information across the sequence. This captures high-level intent and the overall meaning, such as the global structure of the URL (for instance, legitimate URLs are often straightforward, while phishing URLs may use numerous subdomains or nested paths to mimic legitimate sites, e.g., \"account-update.bank-name.example.com\").  
Additionally, it identifies top-level domain relevance, detecting anomalies and verifying whether the overall URL structure aligns with known patterns of legitimate or phishing URLs. The CLS token embedding further summarizes the entire sequence, capturing the holistic meaning of the URL. The cross-attention feature extraction method integrates and correlates characteristics from two different embedding's, BERT and Char2B, using attention mechanisms. This method efficiently combines data from multiple feature spaces, incorporating both character-level representations from Char2B and sub-word-level representations from BERT to enhance the model's understanding and detection capability.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 2 Data and Instrument
### Solar Irradiance  
Solar irradiance measurement revolves around three components, that is, global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI). The GHI is the total solar irradiance that reaches the earth's surface, which is the combination of DNI and DHI. The GHI can be measured using a pyranometer that has been designed to receive solar irradiance from all directions. In this study, the GHI was measured using a EKO pyranometer MS-80 in Kookmin University (see Figure 2a,b). The pyranometer was then installed with ventilation units that cover the dome of the pyranometer to protect it from dew and frost during the morning and to reduce the effects of raindrops and dust. The pyranometer has a response time of less than 0.5 s with an approximate sensitivity of 10 \(\upmu\)V/Wm\({}^{-2}\) and collects the GHI in 1-min, 10-min, 1-hr, and 1-day intervals. The pyranometer was calibrated following the ISO 9060:2018 standard.  
Figure 1: An instrument to record sky image data. (**a**) Total Sky Camera J1006; (**b**) sky camera calibration using the checkerboard image.
## 3 Simulation and Experimental Results
### Experimental Results  
Based on the above theories, an OOK-OOK experiment in a commercial MZM was conducted as a proof-of-concept. Figure 6 shows the overall experimental scheme. However, because the MZM in the laboratory had an internal polarizer to decrease the \(Y\)-axis power for MZM performance, the experiment could not be conducted under optimal power-dividing conditions. Therefore, more power was allocated to the \(Y\)-axis and amplified by the EDFA. In addition, the RF amplifier in the laboratory had a limited range; thus the experiment could not be conducted under the optimal \(3V_{\pi}\) condition depicted in Figure 3. Therefore, the experiment was conducted within \(2V_{\pi}\) range. A 2.5 Gbps experiment was conducted for each axis with a back-to-back system.  
Figure 5: Relative optical power vs. BER graph for the proposed technique and MPAM. (**a**) PAM4 vs. OOK–OOK; (**b**) PAM8 vs. PAM4–OOK; (**c**) PAM16 vs. PAM8–OOK.  
Figure 6: Experimental scheme.  
Figure 7 depicts the eye diagram for each polarization. As shown in Figures 2 and 3, the \(X\)-axis has only two levels, but the \(Y\)-axis has more levels.  
The received optical power vs. BER graph for theoretical OOK-OOK and experimentally measured OOK-OOK is depicted in Figure 8a. In this graph, the received optical power is the total received power of each axis. The theoretical value is the simulated BER when the voltage swing is in a \(2V_{\pi}\) range for the given noise power. It can be seen that the measured OOK-OOK performance is less than the theoretical value. This is because, as mentioned above, the experiment could not be conducted under optimal conditions owing to the internal polarization of the MZM in the laboratory and the limitations of the RF amplifier. The measured voltage-intensity transfer curve for each axis is shown in Figure 8b. It can be observed that the \(Y\)-axis transfer curve is not an ideal transfer curve. This limits the performance of the proposed technique. However, these were all experimental errors. If the experiment is performed under optimal conditions, modulation with a higher spectral efficiency can be conducted with a single MZM.