esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Complex-Valued Conditional Generative Adversarial Network
### Generator Network  
The generator network is mainly used to generate noise-free signals and achieve direct mapping from noisy signals to noise-free signals, whose inputs are dynamic noisy signals. In order to suppress noise as much as possible without compromising the RFF features of the original signals, we incorporated complex-valued convolution and symmetric architecture [29] into the generator network. The complex-valued convolution can better characterize signals, while symmetric upsampling and downsampling structures can effectively remove random noise from the signals and restore the detailed RFF features in the original signals. The downsampling process was implemented through complex-valued convolutions without using pooling operations to avoid losing valuable information. The upsampling process is implemented by complex-valued inverse convolution operations, also known as complex-valued transposed convolution, which recovers the original signals from the higher-dimensional feature maps.  
The generator network consists of seven network layers. The first to third layers are utilized for extracting latent characteristics from dynamic noisy signals, while the fourth to seventh layers are responsible for restoring the extracted features back into noise-free signals. The structures of the first to third layers of the generator network are consistent, each comprising complex-valued convolutional layers with a common kernel size of \(3\times 1\), complex-valued batch normalization layers, and zReLU activation functions. The structures of the fourth to sixth layers of the generator network are consistent, each comprising complex-valued transposed convolutional layers with a common kernel size of \(3\times 1\), complex-valued batch normalization layers, and zReLU activation functions. The last layer of the generator network consists of a complex-valued transposed convolution layer and a complex-valued Tanh activation function, which is mainly used to generate noise-free signals which are the same size as the dynamic noisy signals.
###### Abstract  
Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.  
Bayesian model updating; structural damage detection; delayed rejection adaptive metropolis; transitional Markov Chain Monte Carlo; differential evolutionary adaptive metropolis
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 1 Introduction
Then, a certain amount of noise is added to these data to test the robustness. The results obtained from the proposed algorithm are compared with those of the convolutional neural network (CNN), long short-term memory (LSTM), support vector machine, and extreme learning machine (ELM) classification algorithms. The main contributions of this study can be explained as follows. 1. When noise from sensors is not taken into account in real-time operations, various problems occur in both signal processing and machine learning applications. The main problems are misclassification and low accuracy. However, in this study, noise and outlier effects are taken into account for fault detection. 2. When the studies in the research are examined, Matlab is generally used for the realization of fault type and location detection in single-bus transmission systems. However, the work done in Matlab needs to be compared with real-time systems. In this study, the IEEE 39-bus system, which is a small model of a distribution network, is realized using the RTDS simulator and a real-time study is performed. 3. When refs. [18; 19; 40] are analyzed, it is found that six new attribute vectors that were not previously used in fault type data detection in distribution networks have been created. Thanks to these feature vectors, the accuracy of fault type and location detection in high-impedance short-circuit faults is improved by 10%. 4. In the literature [16; 17; 18; 19; 20; 21; 22; 23; 24], high-impedance short-circuit faults are evaluated together with other faults and an average detection accuracy rate is given. In general, the detection of high-impedance faults is not considered separately in previous research. The paper is organized as follows. Section 2 presents the ORR-RVFLN algorithm, feature vector extraction, and formulation; Section 3 presents the IEEE 39-bus system in the RTDS and the single-bus transmission system in PSCAD; and Section 4 presents the fault type and location detection values. Finally, the paper concludes in Section 5.
## 6 Result and Discussion
### Ablation Study  
Three essential modules--Channel-Attention and Spatial-Attention (CBAM), Transformer, and Joint Pyramid Upsampling (JPU)--were gradually added to improve the baseline model. Every module was designed to tackle distinct problems in retinal vascular segmentation, including multi-scale feature fusion, contextual comprehension, and feature representation.  
The CBAM module was included in the baseline architecture in the initial version of our model. Segmentation performance improved right away thanks to CBAM, which is renowned for its capacity to adaptively recalibrate feature maps by capturing inter-channel relationships and emphasizing interesting spatial regions. This first improvement showed how well attention processes work to improve feature representation and capture distinguishing traits.  
By incorporating the Transformer module, we were able to further enhance the model's performance, building on the success of CBAM. The Transformer module enabled self-attention and context modeling in the network, drawing inspiration from the Transformerarchitecture in natural language processing tasks. This resulted in modest improvements in segmentation accuracy as the model was able to capture long-range dependencies and improve feature representations.  
We added the JPU module, along with CBAM and Transformer, to the model architecture in the last phase of our ablation investigation. The JPU module yielded further improvements in segmentation accuracy since it was made for multi-scale feature fusion and contextual information aggregation. The JPU module substantially enhanced the model's capacity to capture complex vessel architectures and precisely segment retinal vessels by combining characteristics from several pyramid scales and improving feature representations across many scales.  
Table 1 summarizes the effects of adding these modules to the baseline model. The table indicates that the addition of each module resulted in improvements in segmentation measures; the best results were obtained when all three modules were merged. These results highlight the combined benefits of using cutting-edge methods for context modeling and feature representation in retinal vascular segmentation tasks.
# Improving Named Entity Recognition for Social Media with Data Augmentation
## 4 Experimental and Analysis
### Baseline  
To evaluate the effectiveness of our proposed approach in social media NER, we compared it with other state-of-the-art models. Below is a brief description of these models.  
* **Bi-LSTM-CRF**[14] uses a bi-directional LSTM (Bi-LSTM) to model the language and obtain both left-to-right and right-to-left sentence encodings. This enables the model to capture the context of a word from both directions. Then, a Conditional Random Field (CRF) layer is added to capture the dependencies between the tags and make predictions based on the entire sentence.
* **BERT**[12]. BERT is a language model that is specifically designed for pre-training deep bidirectional representations from unlabeled text. It does so by conditioning on both left and right contexts in all layers, making it highly effective in capturing contextual information in natural language processing tasks.
* **XLNET**[22] is an autoregressive model that implements bidirectional contextual information by per-training language models. It predicts the possible words at a certain position by randomly arranging the input sequence and then trains a contextualized word vector with context.
* **AESINER**[24] improves entity recognition by leveraging syntactic information or semantically relevant texts.
* **InfenNER**[25] is a method designed for Named Entity Recognition (NER) in short texts. It utilizes word-, character-, and sentence-level information without relying on external sources. Additionally, it can incorporate visual information and includes an attention component that computes attention weight probabilities over textual and text-relevant visual contexts separately.
* **HGN**[26]. HGN obtains more local features and location information through multi-window loops and combines global information and multiple local features to predict entity labels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **NE Types** & & **Train** & **Dev** & **Test** \\ \hline \multirow{2}{*}{WN16} & \multirow{2}{*}{10} & Se. & 2.4 K & 1.0 K & 3.9 K \\  & & NE. & 1.5 K & 0.7 K & 3.5 K \\ \hline \multirow{2}{*}{WN17} & \multirow{2}{*}{6} & Se. & 3.4 K & 1.0 K & 1.3 K \\  & & NE. & 2.0 K & 0.8 K & 1.1 K \\ \hline \multirow{2}{*}{ON5e} & \multirow{2}{*}{18} & Se. & 59.9 K & 8.5 K & 8.3 K \\  & & NE. & 81.8 K & 11.1 K & 11.3 K \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of all datasets with respect to the number of sentences (Se.) and named entity (NE.).
## 2 Adaptive Filtering Systems for Active Control
### Limitations  
Many studies have modified the LMS algorithm to address defects such as misadjustment and excessive error with respect to the reference inputs. Hybrid structures were proposed by combining the LMS method with feedback control [37; 38]. The vibration isolation was improved at main frequencies and the speed of the convergence was increased, but the error over a broadband frequency range could not be managed. An alternative feedback structure was suggested with internal model control. This method achieves a better transient response and faster convergence, but an incorrect secondary path model seems to degrade the total control performance [39]. An LMS algorithm with adaptive step size was investigated to increase the convergence rate [40; 41]. Similarly, a variable-step LMS scheme was applied along with a creep model to accomplish rapid convergence and stability and to identify the hysteresis parameters [42].  
A variable convergence coefficient corresponding with an input signal can improve the performance, but it may result in noisy step sizes with unwanted disturbances and thus influence the mitigation at other frequencies [43]. Another LMS scheme prevents instability with impulsive noise, and the reference is pre-defined to assure stability and convergence, particularly when the magnitude of the signal is larger than a certain threshold [44]. An anti-saturation LMS algorithm was introduced to overcome the controller output with large disturbance, which showed improved performance. A different cost function is needed to obtain an enhanced recursive formulation [45].  
An extra filter has been used to suppress disturbances in the primary path resulting from uncorrelated noise in the secondary path. This method accomplishes faster convergence, but additional filters increase the complexity and noise in the control system [46]. A new LMS algorithm with phase compensation was introduced to deal with signals that include DC components. The traditional LMS may not be effective for such signals, and when a high pass filter is used to eliminate the DC components, it can produce a phase variation [47].
###### Abstract  
Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50 th percentile of D\({}_{\text{PT}}\), the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size.  
human papillomavirus; oropharyngeal squamous cell carcinoma; magnetic resonance imaging; diffusion magnetic resonance imaging; machine learning; radiomics 2022
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 5 Motivating Examples
### Example 2: Asymmetric Topology  
Consider still, the topology shown in Figure 1, but assume for this example an asymmetric traffic load: node 3 generates an average of 20 packets; node 4 generates an average of 10 packets; and node 5 generates an average of 0.5 packets.  
Figure 2: Cumulative route costs using the LS-based procedure, the LQI-based procedure, and the U-RR procedure (defined in Section 6) in topology of Figure 1. Results in (**a**,**c**,**e**) correspond to Example 1 in Section 5.1. Results in (**b**,**d**,**f**) correspond to Example 2 in Section 5.2.  
Because node 4 generates more packets than node 5, the chance that node 1 is receiving a packet from node 4 when node 3 transmits to node 1 is higher than the chance that node 2 is receiving a packet from node 5 when node 3 transmits to node 2, which means that \(c_{3,1,0}>c_{3,2,0}\) and it is desirable that node 3 chooses route 3,2,0.  
Considering all other parameters as before, we simulated this topology with both the LS-based and LQI-based procedures.  
For the LS-based procedure, Figure 2b shows the cumulative cost at node 3 at every RREQ received. It is possible to observe that, in some instances, the LS-based procedure was able to obtain \(\hat{c}_{3,1,0}^{(LS)}(t)>\hat{c}_{3,2,0}^{(LS)}(t)\) and recognize that route 3,2,0 is preferred over 3,1,0; however, \(\hat{c}_{3,1,0}^{(LS)}(t)=\hat{c}_{3,2,0}^{(LS)}(t)\) most of the time. At those instances, node 3 would randomly choose between the two routes and could choose the suboptimal route 3,1,0 until the next RREQ arrives. For this particular simulation run, node 3 chose route 3,1,0 35% of the time.  
For the LQI-based procedure, the cumulative costs at node 3 were similar to the costs shown in Figure 2c; i.e., both routes 3,1,0 and 3,2,0 were estimated to have the same cumulative cost 2, meaning that the LQI-based procedure was not able to recognize that route 3,1,0 had more instances of hidden node problems. Seeing both routes with the same cumulative cost, node 3 chose between routes 3,1,0 and 3,2,0 randomly. For this particular simulation run, node 3 chose route 3,1,0 64% of the time.  
Sending over the suboptimal route 3,1,0 is undesirable because it increases the probability of packet losses, causing unnecessary MAC retransmissions and traffic load in the channel. For these particular simulation runs, there was an average of 108 unnecessary MAC transmissions per 1000 messages transmitted by node 3 when the LS-based procedure was used; and this number increased to 128 when the LQI-estimator was used.
###### Abstract  
The monitoring of farming processes can optimize the use of resources and improve its sustainability and profitability. In fish farms, the water quality, tank environment, and fish behavior must be monitored. Wireless sensor networks (WSNs) are a promising option to perform this monitoring. Nevertheless, its high cost is slowing the expansion of its use. In this paper, we propose a set of sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process. The WSN is based on physical sensors, composed of simple electronic components. The system proposed can monitor water quality parameters, tank status, the feed falling and fish swimming depth and velocity. In addition, the system includes a smart algorithm to reduce the energy waste when sending the information from the node to the database. The system is composed of three nodes in each tank that send the information though the local area network to a database on the Internet and a smart algorithm that detects abnormal values and sends alarms when they happen. All the sensors are designed, calibrated, and deployed to ensure its suitability. The greatest efforts have been accomplished with the fish presence sensor. The total cost of the sensors and nodes for the proposed system is less than 90 \(\cels\).  
a 201818750 doi:10.3390/s18030750
# Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites
## 1 Introduction  
Cultural heritage sites reflect the architectural, artistic, and social achievements of past civilizations. They offer invaluable insights into the evolution of societies, their cultural practices, and technological advancements. However, due to a variety of factors--ranging from the fragile state of many historical structures to ongoing conservation efforts and geographic remoteness--numerous heritage sites remain inaccessible to the general public [1]. Furthermore, excessive human intervention can accelerate the corrosion of delicate artifacts and structures, making it essential to balance access with preservation. As a result, there is a growing need to explore alternative methods of engaging with these sites without compromising their integrity.  
Mixed reality (MR) offers a transformative solution to this challenge, providing a remote immersive cultural experience. With MR, users can virtually explore intricate details of historically significant locations, from ancient temples to modern museums all over the world [2], and engage with digital reconstructions, multimedia narratives, and contextual information. Additionally, a shared multi-user experience enhances cultural learning in virtual heritage by enabling collaboration and social interaction [3]. This not only bridges the gap between physical and virtual experiences but also provides a deeper understanding of historical contexts.  
The implementation of digital transformation (DT) technology in cultural sites can have varying economic and social results by integrating advanced technologies like MRthat attract a diverse range of visitors [4]. Additionally, the adoption of DT can contribute to sustainable tourism by providing immersive experiences and educational opportunities for a deeper understanding of the site and encouraging responsible visitation. Ultimately, such initiatives can drive economic growth while preserving the cultural heritage and promoting long-term development in the region.
# Ground Simulation Tests in Two-Dimensional Dynamic Acceleration Environment
## 4 Synchronous Loading of 2-D Dynamic AE
### Centrifugal Turntable AE Loading System Based on 2-D Vector Distribution  
The centrifugal turntable simulation test system includes an arm control subsystem, a 2-D vector distribution subsystem, and an acceleration test subsystem. The 2-D vector distribution subsystem and the acceleration test subsystem are mounted on the arm. The arm is driven by the variable-frequency adjustable-speed motor. Both the unit being tested and the accelerometer are fixed on the turntable. The turntable is driven by the servo motor. The acceleration of the unit being tested is fed back to the computer through the accelerometer. The control and test signals of the arm are transmitted through the conductive slip ring.  
Based on the axial and radial acceleration requirements of the unit being tested, the combined acceleration of the arm can be calculated, and the rotation of the arm can be controlled. When the arm is accelerated, the servo motor controls the angular displacement of the turntable and the unit being tested, and the resultant acceleration of the arm can be decomposed along the axial and radial directions of the unit being tested to achieve the synchronous loading of the 2-D dynamic AE. If the axial direction of the unit being tested is along the direction of the resultant acceleration of the arm, the radial acceleration of the unit being tested is zero \(g\), which simulates the impact overload environment. By decomposing the resultant acceleration of the arm in two dimensions and keeping the axial acceleration of the unit being tested constant, the flight overload environment can be simulated.