esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Impact on Maintenance Task Prioritization, Planning, and Scheduling
Therefore, the maintenance planner has three different estimates; in addition, the predictive estimates can have several scenarios and estimates (low loading, high loading, extreme conditions). Figure 8: Maintenance task prioritization and its main emerging factors. Maintenance intervals are usually determined based on the mean time between failures, which is estimated using reliability analysis. However, emerging practices have affected this simple approach to determining maintenance intervals, as illustrated in Figure 10. The maintenance planner has two emerging ways to determine the maintenance intervals: (1) based on the monitored conditions and (2) based on the predicted useful lifetime. Prescriptive maintenance might also impact the determination of the maintenance interval in the future; however, none of the studied cases has indicated the use of a prescribed useful lifetime as a way to determine the maintenance interval.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 2 Experimental Design  
### Sensor Device for IAQ Monitoring  
The multi-sensor device was used for IAQ monitoring (see Figure 1). Its concept and prototype were developed at the Wroclaw University of Science and Technology, Poland. The equipment was designed to monitor several parameters of indoor air: temperature (T), relative humidity (RH), carbon dioxide (CO\({}_{2}\)) concentration, and total volatile organic compound (TVOC) content. The device was fitted with low-cost sensors. Their detailed measurement characteristics are provided in Table 1. Two sensors (SGP30 and SGPC3) were chosen to provide extensive information about VOCs in indoor air as they display different partial selectivity. Passive sensors' exposure to the test gas was realized in the device (no induced gas flow). The measurements were performed continuously, with a temporal resolution of 2 s. The data records were logged into a file, which contained the time series of all measured parameters stamped with the time index. The data files were stored on the SD card inside the device prepared for download. The GPS unit was installed inside the sensor device to know its location. The device is small in size (12 \(\times\) 8 \(\times\) 2.5 cm) and has a small weight. It operates automatically and does not require the user's attention. Batteries need to be replaced every five to seven days. Two devices of this kind involved in the IAQ monitoring study were taken into consideration for IAQ prediction in apartments using machine learning techniques and sensor data.  
Figure 1: The multi-sensor device for IAQ monitoring. The top cover was removed to show the inside.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 2 Proposed Methods
### Loss Functions  
We use a multilayer loss function to help the network effectively train and fit [28]. The time loss \(L_{T}\) is calculated by computing the L1 norm difference between the clean speech waveform and the model's output-enhanced waveform \(\mathbb{E}_{x,\hat{x}}\big{[}\|x-\hat{x}\|_{1}\big{]}\), with the time loss result being the average of the differences across all frames. The magnitude loss \(L_{M}\) is computed by first extracting the magnitude spectra \(X_{m}\) and \(\overline{X}_{m}\) from the clean speech and the enhanced speech, respectively, and then calculating the Mean Squared Error (MSE) between them \(\mathbb{E}_{X_{m},\widehat{X}_{m}}\Big{[}\big{\|}X_{m}-\widehat{X}_{m}\big{\|} _{2}^{2}\Big{]}\). The final loss value is the expected MSE across all frequency and time points. The function composition also includes the complex spectrum loss \(L_{C}\), which uses the ground truth and predicted complex spectra as inputs and calculates the MSE between the two spectral graphs \(\mathbb{E}_{X_{r},\widehat{X}_{r}}\Big{[}\big{\|}X_{r}-\widehat{X}_{r}\big{\|} _{2}^{2}\Big{]}+\mathbb{E}_{X_{i},\widehat{X}_{i}}\Big{[}\big{\|}X_{i}-\widehat {X}_{i}\big{\|}_{2}^{2}\Big{]}\), yielding the expected errors of the real and imaginary parts as the final output of \(L_{C}\).  
The polynomial of the above losses is used as the final loss function, and appropriate hyperparameters are set to optimize the model's performance.
## 3 Deep Learning in IoMT
### Augmenting Human Performance  
The increasing complexity of patient presentations and treatment regimens requires tools that enhance human decision making. DL integrated with the IoMT can improve clinicians' ability to manage and interpret medical information effectively.  
Experts' experience, knowledge, or other factors can heavily affect the results of the medical image interpretation. Machine learning (ML) models can assist experts to some extent, but the models may lack flexibility when faced with vast amounts of data with complex problems [112]. Convolutional neural networks (CNNs) in DL have shown exceptional performance in image classification tasks, such as identifying tumors in MRI scans or detecting fractures in X-rays [113]. Based on a systematical review from [PERSON] and [PERSON], a CNN is not a replacement for doctors but an optimization of routine medical tasks [114].  
DL can be utilized to improve efficiency in the following areas:  
* Rare and complex cases: DL models trained on diverse datasets are capable of providing decision support for rare diseases and atypical presentations, reducing diagnostic errors and delays. IoMT-enabled devices collect real-time data that DL algorithms can analyze to identify anomalies and suggest interventions [112; 113; 114].
* Multimorbidity management: The increasing prevalence of patients with multiple comorbidities complicates diagnosis and treatment. IoMT systems combined with DL can identify interaction effects and recommend optimized treatment pathways.
* Skill augmentation: DL-powered IoMT solutions can democratize knowledge by providing diagnostic and therapeutic recommendations informed by vast datasets, reducing disparities in care quality. For instance, [PERSON] et al. analyzed the diagnosis of skin diseases on dark skin. Without DL, the accuracy rate of board-certified dermatologists (BCDs) was 37%. Once DL was added, the accuracy rate of the BCDs was improved to 60%. The same result was applied to primary care physicians (PCPs), whose accuracy rate improved from 17% to 47% [115].  
DL improves almost every aspect of healthcare [116], such as its performance becoming more accurate [117] and the system being more robust [115]. With the framework being more secure, we intend to focus on studying and deploying different applications of deeplearning in healthcare to enhance its performance based on the unique requirements of medical CPSs.
# Type Hierarchy Enhanced Event Detection without Triggers
## 2 Related Work
### Event Detection without Triggers  
To reduce the massive annotation of triggers in new domains, several studies have been exploring event detection without triggers. [PERSON] et al. [30] constructed a deep learning model called BLMA, which uses bi-LSTM and multihead weighted attention to perform event detection on an automatically tagged screenplay dataset. [PERSON] et al. [10] propose the TBNNAM framework, which encodes a sentence based on event types via the attention mechanism on an LSTM model. This framework does not utilize bi-LSTM, which makes it capture limited contextual information. Moreover, individual event types represent fewer features, which leads to worse performance than trigger-based methods. [PERSON] et al. [31] feed inputs and a manually predefined template into a generation-based neural network to output a natural sentence for further event extraction. Nevertheless, The template is event-type-specific, manually created and the process is more complex. [PERSON] et al. [32] propose a sequence-to-structure model to avoid using labeled triggers to extract events in an end-to-end manner. Although the model does not use token-level annotations such as trigger offset, the framework still requires much effort to identify trigger words.  
In general, there are few methods for event detection without triggers. The above approaches either consider only context-level features or consider every word as a potential trigger when using word-level features. On the other hand, most embedding models typically represent each event type only using the literal meaning of the words or random initialization, which makes these models represent a limited semantic representation of event types.
# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 5 Conclusions  
In this research, we propose a new IMGCN model that uses the dependency relationship to capture the context dependency, and the semantic dictionary can increase the useful connection between words to supplement the shortcomings of the graph convolutional network in text representation. At the same time, a two-layer BiLSTM model is used to combine the local feature information obtained by the BERT model and the global information obtained by the GCN. Then mine the deep features of the text. Then add an attention mechanism to focus on keyword features. Furthermore, by introducing residual connections, the neural network degradation problem that occurs when the network model is stacked with multiple layers is solved. At the same time, let the model learn the residual so as to better obtain new features and improve the effect of text classification. We have conducted experiments on four datasets, which have achieved a certain degree of improvement compared with the baseline model.  
In future work, we will further explore the dependencies and construct graph features richer in semantic information to improve the performance of the model. In addition, our proposed model does not analyze the model complexity and how to reduce the model complexity, which is the direction of further research.  
Conceptualization, B.X., C.Z., X.W. and W.Z.; Methodology, B.X.; Software, B.X.; Validation, B.X.; Writing--original draft preparation, B.X.; Writing--review and editing, B.X. and C.Z.; Visualization, B.X.; Project administration, B.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 6: Average training time per epoch for different models on different datasets.
## 4 Uncertainty Quantification  
It was shown in the previous section that the stability threshold is affected by the parameters of the rotor-bearing system. The stability threshold is uncertain owing to the existence of inherent random parameters. In this situation, the uncertain stability threshold needs to be quantified to ensure that the rotating system is operating reliably. In view of the high computational costs of solving the characteristics of the finite-length HDB, the gPC expansion is employed to quantify uncertainty. The basic idea of a gPC expansion is to project a random variable onto a stochastic space, such that uncertain variables are represented by a series of orthogonal polynomials with unknown deterministic coefficients. Here, an uncertain variable \(Y\) defined in the random space \(\Gamma\) is represented as  
\[Y(\xi)=\sum_{i=0}^{\infty}y_{i}\psi_{i}(\xi)\approx\sum_{i=0}^{N}y_{i}\psi_{i} (\xi), \tag{26}\]  
where \(y_{i}\) denotes the unknown coefficients, \(\psi_{i}(\xi)\) represents the polynomials that are functions of the standard random variable vector \(\xi\), and \(N\) represents the finite number of truncated terms in the gPC expansion. This is identified from the expansion order \(m\) and the dimensionality \(n\) of the random vectors as  
\[N=\frac{(n+m)!}{n!m!}-1. \tag{27}\]  
In general, the unknown deterministic coefficients are determined by a Galerkin projection  
\[y_{i}=\frac{1}{\langle\psi_{i}^{2}(\xi)\rangle}\int_{\Gamma}Y(\xi)\psi_{i}(\xi )\rho(\xi)d\xi, \tag{28}\]  
where \(\langle\psi_{i}^{2}(\xi)\rangle\) stands for the inner product of the polynomials and \(\rho(\xi)\) represents the joint probability density function (PDF) of the vectors of random variables.
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.4.4 Self-Attention for the Encoder Feature Map  
We also investigate the effects of self-attention only on the low-level encoder feature itself. We conducted an experiment with the CVC-ClinicDB dataset. Table 7 reports the Dice coefficient for self-attention on the encoder feature map and our scAG, which obtains information for both the encoder and decoder feature map. We observe that only self-attention in the encoder feature can improve the performance over a classical skip connection in U-Net, but our scAG is even more effective. An explanation of the superior performance of scAG is its ability to seize useful information from the high-level decoder feature in order to guide the low-level encoder feature, while self-attention only on the encoder feature cannot exhibit effective performance due to poor information in the low-level encoder itself.
## 5 Efficiency Analysis  
Since HMBAC is targeted to fine-grained access for multi-authority, multi-domain environments, a practical implementation must be scalable to the number of authorities and domains. First, we analyze the scalability of the system in terms of system management. Then, we benchmark the performance of Janus for different configurations and access request rates. All measurements can be reproduced through the Janus github repository (Benchmarks are fully reproducable via an automated script - see the 'System Benchmark' section of the'readme' document on Janus repository [5]).
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 2 Related Works
### Particle Swarm Optimization  
PSO is a metaheuristic gradient-free optimization method based on the population dynamics of particles moving with a velocity that depends on both globally optimal and particlewise optimal solutions. PSO was introduced by [PERSON] and [PERSON] [34] and has been shown to be effective in solving optimization problems, even in high-dimensional search spaces. Although PSO has been implemented in various studies, alternatives are being developed to achieve higher performance. For example, researchers modified the structure of PSO, such as its size or shape, to construct improved approaches, such as the hybrid PSO method [37,41,42,43].  
The PSO algorithm was constructed on the basis of particles that simulate individuals in a group with two acceptance parameters, namely, positions and velocities. Assuming that the particles have a dimensionality of \(N\), the position of the \(i\)-\(th\) particle in \(N\) is denoted as \(X_{i}\), and its velocity is \(V_{i}\). The objective function is important in PSO, as it has a role in fitting each particle and obtaining the corresponding fitness value. Let the process obtain the best position (\(p-best\)) and the current position \(x_{i}\), which represents the experience of particle actions. \(g-best\) is defined as the best position found by all the particles in the entire group or the best position in \(p-best\). PSO uses both \(p-best\) and \(g-best\) to determine the next action in the next iteration.  
The PSO process begins with a random solution and then iterates to obtain the optimal solution. Each particle is updated under the conditions of \(p-best\) and \(g-best\) via Equations (1) and (2) to update the two parameters, as follows:  
\[v_{i}=c_{1}\times rand(|\text{B}|)\times(p-best_{i}-x_{i})+c_{2}\times rand(| \text{B}|)\times(g-best_{i}-x_{i}) \tag{1}\]  
\[x_{i}=x_{i}+v_{i} \tag{2}\]  
where \(i=1,\dots,N\), \(N\) is the total number of particles, and \(rand(|\text{B}|)\) is defined as a random number function resulting in random floats number in the half-open interval [0.0, 1.0) and has a size equal to the batch size of the input image (\(|\text{B}|\)) in the PE algorithm which is deployed by deep learning architecture in Section 2.1. \(c_{1}\) is the cognitive (individual) learning factor, \(c_{2}\) is a social (group) learning factor, and \(x_{i}\) is the current position. The expression of Equation (1) contains the magnitude and direction of the last speed, the best point between the current point and the particle, and the best point between the current point and the group. This combination determines the next action and a better \(v_{i}\) is defined by Equation (3) as shown below:  
\[v_{i}=\omega\times v_{i}+c_{1}\times rand(|\text{B}|)\times(p-best_{i}-x_{i})+ c_{2}\times rand(|\text{B}|)\times(g-best_{i}-x_{i}) \tag{3}\]  
where \(\omega\) is a nonnegative number that is used to define the weight of the PSO process, which affects the convergence speed and convergence precision of the PSO algorithm.
## 2 Materials and Methods
### Electrochemical Test  
Electrochemical tests were performed on the FCD-500 specimens, which were used as working electrodes in the test solutions. The working electrode with a surface area of 1 cm\({}^{2}\) was placed in a three-electrode corrosion cell connected to a potentiostat (VSP, Seyssinet-Pariset, France, Bio-Logic). A silver/silver chloride electrode (Ag/AgCl sat. KCl) and a graphite rod were used as reference and counter electrodes, respectively. The solution was naturally aerated and stirred continuously, using a magnetic stirrer, at 80 rpm, to maintain uniform mass transport conditions. Prior to the electrochemical test, the system was allowed to stabilize at open-circuit potential (OCP) for 1 h. In the potentiodynamic polarization test, all the samples were polarized at a scan rate of 0.5 mV/s, within a potential range of \(-\)250 to +250 mV versus the OCP. Tafel extrapolation was used to calculate the corrosion potential (\(E_{corr.}\)) and corrosion current density (\(i_{corr.}\)) from the polarization curves. The EIS test was conducted in 10 mV amplitude signal peak-to-peak corrosion potential conditions, over a frequency range from 50 mHz to 100 kHz. The EIS parameters were fitted using the EC-Lab\({}^{\circledR}\) software (Ver. 11.36).
## 2 Measurement Approach
### Actual Flow Transition
#### 2.1.2 Method B: Image Rotation and 1D Gradient Evaluation  
A more direct use of the a priori knowledge is achieved by an image rotation for each known transition line orientation in combination with a 1D gradient evaluation. The image is rotated to obtain directions of the 1D gradient evaluation perpendicular to one transition line segment, which provides maximal sensitivity and thus minimal uncertainty for localizing the position of the local temperature gradient maxima. Therefore, the original image is rotated three times to align each transition line segment parallel to the \(x^{\prime}\) axis of the rotated image, see Figure 3. After each rotation, the gradient component parallel to the \(y^{\prime}\)-axis is calculated for each image column (\(x^{\prime}\)-position) and the local maxima are identified. The results are the positions of local gradient maxima in the three images for the three known orientations of the different transition line segments. Using different thresholds allows for a detection of weak, low-contrast turbulence wedges with small gradients representing the premature flow transitions (low threshold) and a robust natural flow transitions detection in image columns without any premature flow transition (higher threshold). After image re-rotation and combination of the results in a single image, the actual flow transition line is finally obtained.
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 3 Materials and Methods
### Reference Video Material  
The reference video (see Figure 1) included dynamic scenes, namely a fragment of the start of a horse race [30].  
The video sequence was encoded using three codecs (H.264 [31], H.265 [32], and VP9 [33]), four image resolutions (360p, 480p, 720p, and 1080p), and 18 selected encoding rates (from 300 to 6000 kbps). The encoding speed range was selected based on previous research in such a way that the set of test video samples covered the entire quality scale of subjective ratings, i.e., from 1 to 5. There were a total of 216 video samples.
## 3 Results
### Results of Static Surveys Using a Single Receiver from Smart Station Set
The coverage factor k = 3 multiplied by the standard uncertainty is termed expanded uncertainty \(U_{99}\) for a position, considering both horizontal and vertical components together (3D), and is written as \(U_{99}=3\times U_{68}(3D)\). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Characteristics of the Results of**} & \multicolumn{1}{c}{**Value**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Scenarios of** \\ **Measurements** \\ \end{tabular} } \\ \hline \multirow{2}{*}{HDOP} & Minimal & 1.19 & 30 s G\&GL \\ \cline{2-4}  & Maximal & 1.95 & 15 s GL \\ \hline \multirow{2}{*}{VDOP} & Minimal & 1.88 & 30 s G\&GL \\ \cline{2-4}  & Maximal & 3.17 & 15 s GL \\ \hline \multirow{2}{*}{PDOP} & Minimal & 2.23 & 30 s G\&GL \\ \cline{2-4}  & Maximal & 3.74 & 15 s GL \\ \hline \multirow{2}{*}{RMS [m]} & Minimal & 0.010 & 1 s GL \\ \cline{2-4}  & Maximal & 0.021 & 30 s G \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The extreme value of DOP and RMS for different scenarios of rapid static surveys. The expected accuracy for the scenario of the rapid static surveys of nine measurement points were calculated and the results are presented in Table 2.  
The range of RMS errors from the characteristics of post-processing calculation (Table 1) matches well with the expected accuracy assessing the uncertainty interval calculated for horizontal and vertical components together U\({}_{68}\)(3D)-U\({}_{99}\)(3D) (uncertainty approximated with confidence level from 68% to 99%, in Table 2).
## 2 Materials and Methods
### Tissue-Simulating Phantom Validation
#### 2.3.2 Phantom Measurements  
DOI measurements were captured with a custom-built system that combines discrete wavelength FD and broadband CW techniques [16]. Four laser diodes (660, 690, 785, and 830 nm) were sequentially swept from 50 to 500 MHz (each \(\sim\)20 mW average power at the tissue) to acquire multifrequency amplitude and phase measurements using a 1 mm circular APD in contact with the phantom surface. For this study, only data at 200 MHz was used for reconstruction. The CW component of the DOI system consists of a tungsten-halogen white light source and a 1 mm core fiber coupled to the spectrometer to measure the reflectance spectra in the wavelength range of 650 to 1000 nm. More details of the imaging system have been provided elsewhere [39]. The DOI handheld imaging probe geometry, shown in Figure 5a, was used to make multiple measurements across the surface of the phantom with a source detector separation of 28 mm. Measurements are typically made by moving the probe in 1 cm increments along a rectangular grid coordinate system spanning the surface of the phantom. The dimensions of the rectangular grid pattern used were 7 cm \(\times\) 7 cm on both sides of the phantom. The grid size was chosen such that it fully encompasses the inclusion and captures the surrounding background composition. An example of the DOS (Diffuse Optical Spectroscopic) imaging grid locations on the phantom is shown in Figure 5b.