esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology  
### Overview  
In this paper, we propose a DCGAN-based WBM augmentation model as a data augmentation method for improving the performance of WBM defect pattern classifiers on extremely imbalanced WBM datasets. Figure 2 shows a schematic of the proposed methodology. First, (a) the WBMs to be used for augmentation for each defect pattern are selected. Here, the number of WBMs for each defect pattern selected for augmentation was limited to not exceed 20% of the corresponding defect pattern to maintain the imbalance between each defect pattern and the deficiency of the number of defects. Next, (b) a DCGAN-based WBM augmentation model is trained using the selected WBM. Then, (c) the wafer augmented with a DCGAN-based model is refined using a masking process. This step corrects errors in the augmented WBM without manual intervention and improves the quality of the generated WBM. After this, (d) the refined WBM is used to train a CNN-based WBM defect classifier. We constructed a CNN-based WBM defect pattern classifier to determine the quality of WBMs generated by CAE-based augmented methods and DCGAN-based augmented methods, comparing the two types of methods based on the accuracy of the model. Additionally, we propose an index for numerically estimating the polymorphism of generative models and compare the performance of generative models using the index, which we named the polymorphic generative index (PGI).  
For defect pattern recognition and classification of the experiments, we defined each WBM via classification as one of the nine defect patterns represented in Figure 1. We used the WM-811k dataset [35] for our experiments. The WM-811k dataset was used to train and test the WBM augmentation model and the CNN-based WBM defect pattern classifier.
## 4 Experimental Results
### Experimental Setup
#### 4.3.2 Comparison of Subjective Performance  
To obtain the subjective inspection of the reconstructed frames, we show the 8 th reconstructed frame of the \"City\" sequence at 896 kbps in Figure 8. From this figure, we can see that the visual quality of the reconstructed frame by the proposed method is conspicuously better than those by the other four methods. It can be plainly discerned that the frame processed by our codec presents less blocking artifacts in the homogeneous regions, better preserved textures, and sharper appearance than other codecs. In particular, it is worth noting that the regions with high spatial details (those enclosed by red rectangles on buildings) are well preserved by the proposed algorithm whereas they are not very clear by other reference codecs.  
Figure 7: The average standard deviations of PSNR for various test sequences.
## 1 Introduction
Several studies, such as those in [26; 27], have been developed based on emission observations generated by various electrical devices supplied with nominal voltage. In contrast, low voltage distribution systems typically offer fluctuating mains voltage levels over time as a result of a variety of factors, including changes in the load connected to the system. The standard EN50160 regulates the mains voltage level change in the public distribution system. The minimum allowable voltage level for a nominal voltage of 230 V is 207 V, and the maximum allowed voltage level is 253 V. This means that the mains voltage variation is restricted to \(\pm 10\%\) of the nominal voltage. There is a relative gap in methodical investigations showing in a complex way the real impact of mains voltage supply on spectrum components or concentration of characteristic frequency bands. By examining an experimental scenario of the influence of supply voltage variation within allowed limits on the conducted emission in the frequency range up to 150 kHz delivered into the power grid by certain devices, this research seeks to extend the state-of-the-art of the topic. Section 2 presents our motivation, while Section 3 contains a review of the literature. In Section 4, the methodology for the case study is described. Section 5 consists of two subsections. Section 5.1 summarizes measurement findings in the harmonics range of frequency (up to 2 kHz). Section 5.2 presents the results in the supraharmonics emissions range (9-150 kHz). This subsection is divided into three case studies, each for different equipment under test (EUT). In the final section of the paper, conclusions are formulated and discussed. For future research, novel directions on this topic are proposed.
# Differential Signalling in Free-Space Optical Communication Systems
## 3 Lossy Channel and Background Noise  
As was mentioned earlier, \(h_{i}\) can represent the total loss in the channel including geometrical loss, fog, or other particle induced scattering. The geometrical loss, which depends on the optical link configuration, is a constant quantity for a fixed point-to-point FSO link. On the other hand, losses due to fog/smoke, which are classified as slow fade events, can be assumed to be relatively constant over the channel for a given time. Under these conditions, both \(h_{1}\) and \(h_{2}\) can be considered constant, and therefore the DTL average and its variance can be determined using Label (6). Note that, since \(h_{i}\) is constant, we can therefore substitute in Label (8) \(f_{h}(h)=\delta(h-h_{\text{DSS}})\), where \(\delta(\cdot)\) is the Dirac delta function. Thus, the averaging integral operation is simplified to the Gaussian \(Q\)-function.
## 7 Performance Evaluation
### Simulation Configurations
#### 7.2.4 EDF, DRRF, and EDF-DRRF  
There are three main simulation configurations in order to show the fairness behavior among all MSs and the delay constraints for all three algorithms.  
First (7.2.4a), an under-load scenario with three video flows with 1.35 Mbps average rate each, and one Constant Bit Rate (CBR) flow with 3 Mbps. The purpose of CBR flow is to measure the unused space in the frame. We treated the CBR flow as a lower priority so that the CBR flow acquires transmission opportunity only if there is an unused space within the WiMAX frame. The second case (7.2.4b) is an overload scenario with five video flows with 1.35 Mbps average rate each.  
Third (7.2.4c), we used three video flows and one CBR flow; however, one of the video flows is ill-behaved or sending more traffic. Note that because of the effect of link overload, CBR flows do not really get any transmission opportunities in this particular case.  
Again, for all video flows, we used the SAM traffic generator [13] to produce a video stream with an average rate of 1.35 Mbps and 3.3 Mbps (for the ill-behaved flow). The SAM traffic generator was based on the video trace from Lord of the Ring movies: Episode I, II, and III. We also randomly chose 10-second scenes for both under-load and overload scenarios. The full trace is available for the research community as shown in the reference [18].  
Although we used three to five flows to show the effect of fairness, the results are expected to be similar with a greater number of MSs and higher MCS levels. Note that the video frames were packetized, and so RTP (Real-Time Transport Protocol), UDP (User Datagram Protocol), and IP header overheads were added. All video flows' deadlines were set to 20 ms.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 4 Results and Discussion
### Average Channel Capacity Simulation Results  
Figure 5 presents the average channel capacity as a function of the SNR in dB. The figure demonstrates that exact expression, closed-form expression, and Monte Carlo simulation are in close agreement throughout the SNR range, confirming the validity and precision of the closed-form expression derived for the average capacity. This makes it particularly useful for system-level analysis where rapid capacity evaluations are required, such as in adaptive modulation or real-time link adaptation strategies.  
The results in Figure 5 indicate that the capacity increases logarithmically with SNR, consistent with [PERSON]'s capacity formula, indicating higher data rates achievable under improved channel conditions. At low SNR values (below 0 dB), the capacity increases gradually, showing the noise-limited behavior of the channel where improvements in SNR yield only modest gains. Conversely, in the high-SNR region, the logarithmic nature of the curve leads to more substantial gains, demonstrating the benefit of operating in power-rich environments. From a design standpoint, understanding capacity trends across SNR values helps optimize power allocation and guide adaptive coding to meet target data rates.
## 2 Fault Detection Method
### Signals Collection
The sampling frequency is 20 kHz, and each acquisition is 10 s, that is, 200,000 sampling points for each working condition. At the above speeds and sampling frequencies, there are 600 sampling points per cycle. In order to ensure that each sample contains at least 3 cycles, the data length of each sample point is set to 2048. In order to ensure the training accuracy of the fault detection model, the total sample size is 1000, and the data is enhanced by overlapping sampling techniques, as shown in Figure 6. If we move the window of the first signal along the timing direction, the slip is less than the length of the signal segment, and there will be a certain overlap between the first segment and the second segment. In this way, more samples can be generated from a long signal, which achieves data enhancement. Figure 5: Physical diagram of a healthy rotor and a cracked rotor: (**a**) healthy rotor; (**b**) cracked rotor. In this paper, the convolution neural network is adopted to extract the fault feature of the experimental signal to complete the classification task of crack location. To extract the fault feature, the convolution layer and the fully connected layer are needed. In order to measure the error of fault detection, it is necessary to define the loss function, also known as the objective function. In the training process of the network, an optimization algorithm is needed to update the hyperparameters of the network so that the hyperparameters in the network can be updated stably in the direction of minimizing the loss function to ensure the fault detection accuracy of the model. The loss function and optimization algorithm of a convolutional neural network are introduced below.
# Selecting a Proper Microsphere to Combine Optical Trapping with microsphere-Assisted Microscopy
## 2 The Selection of Microsphere Types
### The Super-Resolution Imaging Ability of Index-Different Microspheres  
Next, we consider the super-resolution imaging performances of index-different microspheres under white light illumination, including the SiO\({}_{2}\), PS, MF and BTG microsphere. Many researchers have demonstrated that theses spheres in different immersion mediums maintain the discerning ability to achieve super-resolution imaging. The SiO\({}_{2}\) sphere can realize super-resolution imaging in air or semi-immersing in ethanol droplet [6,7], but it loses such ability with full immersion in liquid [11]. Large PS spheres (diameters \(>\) 30 um) were used to overcome the diffraction limit in air condition [9], and it has also been demonstrated that the PS sphere with a 10 um diameter can discern a sub-diffraction nanopattern with a line width of 45 nm in water [10]. The MF sphere together with the Mirau interferometry has achieved label-free nano-3D imaging in air condition [8]. The super-resolution imaging of nanostructures was realized by fully immersing high-refractive-index BTG spheres in water [11,12]. Although the MF sphere in water is rarely used for super-resolution imaging, it should keep the super-resolution discerning ability, because its refractive index is between that in the PS and BTG sphere, which has been demonstrated by our next experimental results. In general, except for the SiO\({}_{2}\) sphere, the PS, MF and BTG sphere can realize super-resolution imaging in water.  
The optical trapping performances and the super-resolution imaging ability of these four types of microspheres in water are summarized in Table 1. Considering the optical trapping properties and the super-resolution imaging ability of index-different microspheres in water guides us in selecting a proper microsphere for the combination of the optical tweezers and the microsphere-assisted microscopy. From Table 1, the PS and MF sphere are selected, because that they can be optically trapped and they also maintain super-resolution imaging ability in water.
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 4 Experiment
### Baselines
* Davids [43], a Twitter-based online abusive language detection dataset, which includes three classes, Hate, Offensive or Neither based on the hate speech lexicon from Hatebase. * Multifeatures with RNN [49], a hybrid character-based and word-based recursive neural network (RNN) model was used to detect abusive language. In addition, BiLSTM and BiLSTM Attention, the baseline method of text classification in this paper, were also used for further comparison. * Character-based transformer [50], a character-level transformer-based classifier for harassment detection on Twitter posts. * BERT [9], a fine-tuned BERT model, consistent with the encoder used in our model. We used the maximum pooling of the output of the last layer as the document characteristic and the output features were used to train the text classifier. * HateBERT [10], a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful. * MTL [1], a multitask framework for emotion detection and abuse detection based on BiLSTM, which proposes three different BiLSTM-based decoder structures. _Hard_ consists of an encoder shared and updated by two tasks, followed by task-specific branches. _Double Encoder Model_ has a task-specific two-layered BiLSTM encoder for each task. _Gated Double Encoder Model_ uses gate to control information flow between different task encoder. The three variants are written as MTL\({}_{Hard}\), MTL\({}_{DEncoder}\) and MTL\({}_{GatedDEncoder}\), respectively. To be fair, we extended MTL with our label generation approach to verify the effectiveness of our proposed multitasking approach.
## 2 Numerical Calculation Model
### Mesh Division and Independence Validation  
In order to improve the reliability of numerical simulation, grid independence validation research was carried out. The grid was generated by Gambit software, and was divided into six groups, as shown in Table 1, which increased from 2.9 million to 16.13 million in turn. Preliminarily judging by the nondimensional velocity profile trend at the monitoring point (Figure 1b), the nondimensional velocity profile of the Middle2 precision grid condition was seen to be in good agreement with that of the higher precision grid condition, as shown in Figure 3a. At the same time, the nondimensional velocity ratio was defined by the velocity of the Middle2, dividing the other cases at the same height. Due to the maximum of the nondimensional velocity ratio, ranged from 1.01 to 1.20, and 1.01-1.02 represents the relationship between the Middle2 and Fine1, Fine2. Furthermore, it can also be seen from Figure 3b that the accuracy of the Middle2 precision grid calculation was higher. Considering the numerical simulation accuracy and calculation cost, the Middle2 precision grid was finally selected as the calculation grid, and the number of grids was 11.5 \(\times\) 10\({}^{7}\). Triangular mesh was used on the ground, and triangular prism mesh was used as a whole. Taking into account the subsequent roughness, based on [PERSON]'s suggestion [24], the first mesh boundary layer was 5 m and the growth rate was 1.15, for a total of 10 layers. The last layer of the mesh was 17.6 m and the total height of the boundary layer was 101.5 m, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Grid Precision** & **Coarse1** & **Coarse2** & **Middle1** & **Middle2** & **Fine1** & **Fine2** \\ \hline Grid quality & 2,909,100 & 5,380,410 & 8,050,575 & 11,494,320 & 13,202,820 & 16,131,445 \\ Maximum of the & & & & & & \\ nondimensional & 1.20 & 1.06 & 1.06 & 1.00 & 1.02 & 1.01 \\ velocity ratio & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Calculation grid.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
Furthermore, the reliability and resilience of IoT systems are essential, as disruptions can disrupt essential services and affect citizens' daily lives. Amid these challenges and complexities, anomaly detection is a critical component of smart city operations. Anomalies take various forms, from sudden spikes in energy consumption to unusual patterns in transportation data or cyber-attacks on city infrastructure [11]. Quickly identifying and responding to anomalies is vital to maintaining smart cities' functionality, security, and efficiency. This work focuses on the contributions of anomaly detection models in the framework of IoT-enabled smart cities. It investigates how these models can be effectively applied to various urban scenarios, ranging from energy grid management and transportation systems to environmental monitoring and cybersecurity. These models promise real-time anomaly detection and proactive problem resolution by leveraging advanced data analytics and machine learning techniques. A substantial part of this work involves a comprehensive evaluation of various anomaly detection models in the framework of smart cities. The performance of these models is rigorously evaluated, considering factors such as accuracy, precision, recall, and scalability. Additionally, a qualitative analysis delves into the advantages and limitations of each model, shedding light on its suitability for specific urban applications. The results obtained from this research have far-reaching implications for advancing smart cities. By improving the safety and efficiency of urban systems, anomaly detection models enable cities to address challenges quickly and proactively [12]. The ability to detect anomalies in real-time safeguards critical infrastructure and improves the quality of life of urban residents. This work aligns with the growing body of literature that recognizes the crucial role of anomaly detection in smart city operations. Furthermore, it highlights the importance of conducting research that supports and informs the evolution of these advanced urban environments.
## 3 Methods
### V-RTI: Data Acquisition
#### 3.1.1 Photogrammetric Image Acquisition of Ellesiya Bas-Relief  
Orthogonal point-of-view images were obtained using the tools listed in Table 1, with the camera located at approximately 1.20 m from the wall. The object under study is a bas-relief measuring around 170 (width) \(\times\) 180 (height) cm. Due to the dimension and placement of the wall relief, the application of RTI was prevented by the close proximity to the floor, ceiling, and adjacent wall (Figure 4a). The acquisition required a progression by vertical and horizontal orthogonal photographs, with a 70% overlap in both directions (Figure 4b). A further set of images was collected at different distances and inclinations, supporting the correct alignment of the first dataset. Camera parameters were set as follows: exposure time \(1/160\), aperture value f/11, and ISO 400.
## 4 Conclusions  
This study examined the effects of seasonal variation of rain heights on relevant radio propagation parameters for circularly polarized Earth-space satellite links in Pretoria. The results showed that XPD varies directly with percentage of time exceedance and reduces as frequency and co-polar attenuation levels increase. However, XPD starts declining at frequencies higher than 20 GHz, which demonstrates the intricate links between frequency and polarization effects. This implies that communication system design requires attention to rain height variation caused by seasonal changes which contributes to signal degradation and depolarization. This study revealed that XPD achieves peak values in winter and minimum values in summer seasons, while spring and autumn seasons maintained moderate values at all the selected frequencies. Sufficient XPD must be provided for radio signals operating above 25 GHz to compensate for depolarization caused by heavy raindrops. The deployment of adaptive modulation and coding techniques in summer months is recommended to boost link reliability because they improve system performance in changing atmospheric conditions. Development and application of the recommended depolarization mitigation techniques in the study location are recommended for further research.  
Conceptualization, Y.B.L. and P.A.O.; methodology, Y.B.L.; software, Y.B.L., C.T. and P.A.O.; validation, Y.B.L. and P.A.O.; formal analysis, Y.B.L., C.T. and J.S.O.; investigation, Y.B.L.; resources, C.T. and E.V.W.; data curation, Y.B.L.; writing--original draft preparation, Y.B.L.; writing--review and editing, Y.B.L., P.A.O. and J.S.O.; visualization, Y.B.L. and J.S.O.; supervision, P.A.O., C.T. and J.S.O.; project administration, C.T. and E.V.W.; funding acquisition, Y.B.L. and E.V.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tshwane University of Technology in South Africa.  
Not applicable.  
The sources of the data used for this research are provided in the Section 2.  
The authors declare no conflicts of interest.
# Probabilistic Seismic Hazard Assessment of the Southwestern Region of Saudi Arabia
## 2 Methodology  
Our approach in this evaluation follows the established PSHA methodology outlined in previous works [28; 29], further developed in [30], and refined in [31], commonly referred to as the classical approach by several authors [32]. A PSHA aims to determine the likelihood of experiencing a specified ground motion level at a particular site within a defined timeframe. Following the Cornell-McGuire approach [28; 29; 31], several key steps are necessary for conducting a PSHA: first, identifying and delineating seismic source boundaries, typically within a range of 300 to 400 km from the site, adjusted based on the tectonic setting; next, defining recurrence rates of earthquake frequency and magnitude for each identified seismic source; then, selecting an appropriate GMPE to probabilistically describe hazard parameters; finally, calculating hazard curves by integrating across all magnitudes and distances for all identified source zones. Here, the assessment procedure consisted of several key steps: (a) considering potential seismic sources in the region, as indicated by recent publications such as those by [33] for Ethiopia and [34] for the Arabian Peninsula; (b) choosing appropriate GMPEs; and (c) estimating hazard curves, which display the likelihood of exceeding specific ground motions at specific locations [35; 36]. This was accomplished by using a tried-and-true logic tree approach.  
Here, we employ the most widespread method for computing seismic hazard presently. As previously stated, our objective is to enhance previous seismic hazard assessments to inform updates to the earthquake load specifications within both local and national building codes in Saudi Arabia. This requires the use of a probabilistic approach rather than a deterministic one. This PSHA method aligns with the approach adopted in the International Building Code [37] and advocated in Eurocode 8 [38], alongside other standards. It is the methodology best suited to address uncertainties, as articulated by [39], making it the preferred method for handling uncertainty. Subsequent sections will delve into detailed explanations of each of these phases.