esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The exposure ratio \(\lambda_{f}^{j}\) may be defined as [20]  
\[\lambda_{f}^{j}=\left(\frac{E_{f}^{j}}{E_{lim,f}^{j}}\right)^{2}, \tag{1}\]  
where \(E_{lim,f}^{j}\) is the corresponding reference level; the latter is defined by the International Commission of Non-Ionizing Radiation Protection (ICNIRP) [21]. However, it should be noted that, according to Greek legislation [22; 23], the established reference levels for general public exposure in the vicinity (i.e., 300 m from the perimeter) of schools and kindergartens in Greece are set to 60% of the 1999 EU Council's reference level values [24]. Thus, \(E_{lim,f}^{j}\), in Equation (1), is taken as equal to the Greek reference level in all calculations presented in this paper (Table 2). The total exposure ratio, for point \(j\), is given by:  
\[\Lambda_{j}=\sum_{f}\lambda_{f}^{j} \tag{2}\] \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Frequency Band** & \multicolumn{4}{c}{**E-Field (\(E_{f}^{j}\) (mV/m)**} & \multicolumn{4}{c}{**Eposure Ratio (\(\lambda_{f}^{j}\))**} & \multicolumn{2}{c}{**Model’s Test**} \\ \cline{2-10} (**MHz)** & **Min** & **Max** & **Mean** & **SD** & **Median** & **Min** & **Max** & **Mean** & **SD** & **Median** & \(p\)**-Value** \\ \hline
27–879 & 36.93 & 2382.0 & 444.15 & 259.14 & 344.00 & 0 & 1.250 & 0.058 & 0.119 & 0.026 & 0.155 \\
879.1–961 & 34.17 & 316.3 & 65.75 & 42.90 & 48.26 & 0 & 0.010 & 0.001 & 0.001 & 0.000 & 0.004 \\
961-1709 & 61.32 & 963.0 & 175.04 & 104.95 & 134.65 & 0 & 0.085 & 0.004 & 0.008 & 0.002 & 0.042 \\
1709.1–1881 & 30.32 & 1070.0 & 112.19 & 99.54 & 77.93 & 0 & 0.059 & 0.001 & 0.004 & 0.000 & 0.002 \\
1881.1–1919 & 29.05 & 275.5 & 47.11 & 29.04 & 36.06 & 0 & 0.004 & 0.000 & 0.000 & 0.000 & 0.042 \\
1919.1–2171 & 92.65 & 902.2 & 156.10 & 101.62 & 158.0 & 0 & 0.037 & 0.002 & 0.004 & 0.001 & 0.012 \\
2171.1–3000 & 19.93 & 2482.0 & 441.88 & 272.71 & 336.35 & 0 & 0.277 & 0.012 & 0.026 & 0.005 & 0.042 \\ \hline
**Total** & 36.93 & 3861.0 & 683.03 & 418.38 & 524.15 & 0.006 & 1.720 & 0.078 & 0.162 & 0.034 & 0.042 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics for \(E_{f}^{j}\) and \(\lambda_{f}^{j}\) for all measurement data and \(p\)-values obtained from the Mood’s median tests between urban and suburban environments.
## 2 Previous Studies in Auto-Vision of Inspection System
The maximum error is determined by measuring the values for the outer and inner boundaries. The inner boundary, outer boundary, and circular center are represented by \(xi,~{}x0,\) and \(c\), respectively, as described by [49]. The distance is measured by establishing a reference circle from the measurement data. In the case of shafts, the largest circle that can be contained within the workpiece's profile is used, known as the MIC. Conversely, the smallest circle that can just contain the profile is used for holes, referred to as the MCC. To find the roundness error, it is necessary to find a pair of concentric circles between which the narrowest annular zone, known as the MZC, can be established. The circle that minimizes the sum of the squares of the radial distances of the data points is selected as the LSC. Accurate determination of the center position and radius of the reference circle is crucial in assembly operations. The methods MIC, MCC, and MZC, along with their respective algorithms and mathematical calculations, have been previously discussed [20]. Therefore, the primary objective of this paper is to concentrate on the mathematical calculations with algorithms of the MZC technique for measuring roundness error through the aid of computer vision. Additionally, the study aims to integrate the data collected using MQTT protocols with the Internet of Things (IoT) based on Virtual Network Computing (VNC).
## 3 Methods
### Sea Surface Temperature Retrieval Equations  
The MCSST [15] and NLSST [16; 17] algorithms, which are subdivided into various formulas, have been operationally used in the estimation of SST and understanding diverse oceanic phenomena, and used as input data in the numerical weather prediction models, as shown in Table 3. The NLSST equations are somewhat different in terms of the consideration of satellite zenith angle and the first-guess SST as suggested by ACSPO (Advanced Clear Sky Processor for Ocean) [50], NAVO (Naval Oceanographic Office) [51], Pathfinder [18], NRL (Naval Research Laboratory) [52], and OSI SAF (Ocean and Sea Ice Satellite Application Facility) [20; 53]. One study to evaluate the performance of existing SST algorithms pointed out that the NLSST algorithm of OSI SAF was better than the other algorithms in the estimation of SST for Visible Infrared Imaging Radiometer Suite (VIIRS) data on board the Suomi National Polar-Orbiting Partnership (Suomi-NPP) [22].  
However, the following equations of MCSST and NLSST, Equations (1)-(4), have been widely used for over the past decades. Considering many aspects, we selected the algorithms in this study. In order to suggest the SST coefficients of the COMS/MI, we selected the NLSST formulation of OSI SAF to test its performance capability by the comparison with the Equations (1) and (2), as follows:  
\[MCSST_{split}=a_{0}+a_{1}BT_{IR1}+a_{2}(BT_{IR1}-BT_{IR2})+a_{3}(BT_{IR1}- BT_{IR2})(sec\theta-1) \tag{1}\]  
\[NLSST_{split}=a_{0}+a_{1}BT_{IR1}+a_{2}SST_{FG}(BT_{IR1}-BT_{IR2})+a_{3}(BT_{ IR1}-BT_{IR2})(sec\theta-1) \tag{2}\]  
\[MCSST_{Triple}=a_{0}+a_{1}BT_{IR1}+a_{2}(BT_{SWIR}-BT_{IR2})+a_{3}(BT_{SWIR}- BT_{IR2})(sec\theta-1) \tag{3}\]  
\[NLSST_{Triple}=a_{0}+a_{1}BT_{IR1}+a_{2}SST_{FG}(BT_{SWIR}-BT_{IR2})+a_{3}(BT_{ SWIR}-BT_{IR2})(sec\theta-1) \tag{4}\]  
where \(BT_{SWIR}\), \(BT_{IR1}\), and \(BT_{IR2}\) are BT measured by COMS/MI SWIR, IR1, and IR2 channel, respectively; \(SST_{FG}\) is a first-guess SST; and \(\theta\) is the SZA; \(a_{0}\), \(a_{1}\), \(a_{2}\), and \(a_{3}\) are coefficients estimated by the regression scheme with in situ measurements. Equations (3) and (4) represent the MCSST and NLSST equations based on the triple windows using IR1, IR2, and SWIR bands. The MCSSTs and NLSSTs of COMS/MI data were estimated in the daytime (solar zenith angle \(\leq\) 80\({}^{\circ}\)) and nighttime (solar zenith angle \(>\) 80\({}^{\circ}\)) under good satellite-sun geometry conditions.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Results
#### 5.3.5 Classification Accuracy  
The graphs in Figure 7(a),b show classification accuracy of various event types exploiting the Bayes Theorem according to Algorithm 1. At the beginning, sensory data generated by the UEs are used as normal data for communication with eNB in an NB-IoT network. Subsequently, all collected data and their co-relations are used by eNB to classify the events. The eNB identifies event-wise training data, which serve as prior information for determining the likelihood of events, and then Bayesian learning assists in determining the probability of events using prior knowledge and associated conditions. The Figure 7(a) shows that as the number of UEs grow, the accuracy level gradually decreases. Note that when the number of UEs increases, many sensory inputs may not reach the eNB in a timely manner, causing the event relationships to be misinterpreted. If available data are not present, it can be difficult to locate relevant data for a comparable critical event; nevertheless, there is no trouble identifying a normal occurrence. As a result, regular events are identified with high accuracy, followed by critical events and relevant events. We did not compare classification accuracy with others because they do not have a classification system. Figure 7(b) depicts categorization accuracy as a function of simulation time, from 5 to 50 s. In the case of probabilistic determination, training data accumulate over time, allowing for more precise estimations of occurrence likelihood. As time passes, training data accumulate, resulting in a more accurate type of event and a more accurate event association. In this diagram, the accuracy for normal events has remained constant since the beginning, whereas critical and relevant event accuracies have been increased over time.  
Figure 7: Delays Experienced by different packet types vs. User Equipment.
## 2 Materials and Methods
### Model Training
#### 2.6.2 Deep Learning Models  
To further explore the potential of deep learning in FRP prediction, we employed two advanced architectures: 1D Convolutional Neural Networks (1D-CNNs) and Bidirectional Long Short-Term Memory networks (BiLSTMs). These architectures were chosen due to their proven capabilities in handling sequential data, which is characteristic of protein sequences, and their ability to capture complex hierarchical and temporal dependencies, respectively. While simpler models like Multilayer Perceptrons (MLPs) or Artificial Neural Networks (ANNs) can also process sequential data, they typically lack the specialized architectural inductive biases that make 1D-CNNs and BiLSTMs particularly effective at extracting meaningful features from long and intricate sequences like proteins. Therefore, we focused on these more advanced deep learning models to maximize the predictive power for FRPs.  
**1D-CNNs** are powerful architectures adept at processing sequential data such as protein sequences [40]. Our **1D-CNN** architecture (Figure 3a) comprises convolutional layers, max-pooling layers, and fully connected layers, incorporating ReLU activation functions and dropout regularization to prevent overfitting. This design facilitates the extraction of hierarchical feature representations, capturing both local and global patterns within the sequences.  
BiLSTM networks, a specialized variant of Recurrent Neural Networks (RNNs), are designed to capture long-range dependencies within sequential data [41]. BiLSTM processes sequences in both forward and backward directions, enabling it to learn comprehensive representations of the input sequences. Our BiLSTM architecture (Figure 3b) consists of  
Figure 3: Architectures of the proposed deep learning models for FRP prediction. (**a**) Architecture of the proposed **1D Convolutional Neural Network (1D-CNN)** for FRP prediction. The model includes multiple convolutional and pooling layers to improve feature extraction, followed by fully connected layers and a sigmoid output layer for binary classification. (**b**) Architecture of the proposed Bidirectional Long Short-Term Memory (BiLSTM) network for FRP prediction. The model comprises multiple BiLSTM layers that process the input sequences bidirectionally, capturing long-range dependencies within protein sequences. These recurrent layers are followed by fully connected layers, and a sigmoid activation function is employed in the output layer for binary classification.  
two bidirectional LSTM layers followed by a fully connected layer, effectively capturing temporal dependencies and contextual information critical for accurate prediction [42].  
For both 1D-CNN and BiLSTM models, we extensively utilized dropout layers (with a rate of 0.5) within the network architectures and implemented Early Stopping during training. Early Stopping monitored the validation loss, halting training when no further improvement was observed, thereby ensuring that the models generalized well to unseen data.
# Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
## 1 Introduction
Ray tracing enables the simulation of absorbed radiation for individual canopy elements (\(E_{al}\)) and holds significant potential to scale \(P_{g}\) estimates from the stand level to finer scales where experiments can be increasingly controlled. Virtual 3D canopies can be generated using tree-growing algorithms that are based on a recursive application of a set of growing rules [22, 23, 24]. To limit data dimensionality, a clump of leaves may be represented by its convex hull [24] or as a planar polygon or plate-like shoot model that exhibits scattering properties similar to the assembly of individual leaves or needles [25, 26]. Recent studies have demonstrated the combined use of tree-growing algorithms and time-of-flight laser scanning technology [27, 28] for the reconstruction of virtual forest canopies [29, 30, 31, 32, 33]. Ray tracing simulations are computationally expensive and require the use of accelerating structures that group scene elements into hierarchical bounding volumes or regular grids to speed up ray-intersection tests [34, 35]. These accelerating structures provide performance gains for fixed illumination conditions; however, they do not provide additional gains for cases where illumination conditions are dynamic, _i.e._, when the distribution of radiation across leaves is to be studied over time and for different sun angles and cloudiness conditions. As a result, the application of ray tracing for scaling between \(P_{gl}\) and \(P_{gs}\) estimates over extensive time series remains challenging. The work presented herein relies on the modelling of time series of canopy radiation transfer using stochastic ray tracing. To speed-up ray tracing simulations under dynamic lighting conditions, we compute a look-up table designed to hold the probabilities of absorbing radiation from over a thousand hemispherical directions for thousands of scene elements. Once this look-up table is populated, the absorption of photosynthetically active radiation is computed for each element, with only array multiplications, thereby significantly reducing computing costs. To improve computation time, the virtual forest scene is modelled at a reduced resolution of 50,000 elements, representing soil, bark and foliage. We demonstrate the inversion of this model and estimate empirical leaf-level photosynthesis parameters from EC-estimates of \(P_{gs}\) and meteorological measurements. We conclude by discussing the limitations of this reduced scene fidelity and challenges and opportunities ahead for the proposed technique.
Given this scenario, multiple researchers have explored the displacement and stress in observation windows through the incorporation of theoretical calculation methods, parametric analysis, and experimental research [18; 19; 20]. However, the predominant method for the long-term analysis of PMMA windows is based on tensile tests, despite the fact that PMMA windows are predominantly subjected to complex compression states. [PERSON] et al. [21] applied the creep time hardening model for finite element parametric analysis on the viewport window, demonstrating the model's suitability for PMMA windows. Nevertheless, this model does not take into account the influence of temperature. [PERSON] et al. [22] investigated the impact of temperature and non-linearity on acrylic windows through finite element analysis and tests. In summary, there is still a lack of parametric analysis considering the temperature creep behavior of PMMA viewport windows. The aging theoretical model proposed by [PERSON] et al. [23] accounts for the temperature factor, with model parameters derived from compression creep tests. In summary, there is still a need for a parametric analysis that takes into account the temperature-dependent creep behavior of PMMA observation windows. The above research requires further refinement to yield more robust conclusions, as the creep behavior influenced by temperature effects on the window has not been comprehensively examined. In the present study, the commercial software ABAQUS 2022 was employed to build a finite element calculation model for the frustum observation window. Subsequently, improved creep model parameters were determined based on the fundamental properties of the PMMA material of the observation window. Furthermore, the agreement between the calculation results based on the creep model and the experimental data is analyzed and the simulation model is verified. Finally, the creep characteristics and structural properties of the observation window under various loading speeds, friction coefficients, temperature variations, and different loading levels are investigated. In the process of long-term deep-sea operations, the influence of creep on the displacement of the observation window becomes very significant, so the research on creep experiments and finite element simulations is very valuable to lay a foundation for further service life research on the observation window structure.
## 4 Results
### Accuracy Validation  
To comprehensively evaluate the accuracy of the water depth results obtained from GF-1, the retrieved water depth data were validated using ICESat-2 data, and a scatter plot was generated. The validation results are illustrated in Figure 5. From the figure, it can be observed that within the water depth range of 0-20 m across the four study areas, the average \(R^{2}\) value is 0.9266, the average RMSE is 0.7125 m, and the average MAE is 0.55 m. Although a small number of outliers can still be observed on some islands in the figure, these errors are likely due to the inherent limitations of the DBSCAN method in denoising from ICESat-2 data, which are considered unavoidable systematic errors. Further processing of the bathymetric data may be required [30]. However, overall, the validation results indicate that the inversion results obtained from this study indicate a high level of correlation and low error levels, demonstrating the effectiveness and reliability of this method.  
Among the four study areas, the bathymetric inversion results of Emae Island exhibit the best performance, with an RMSE of 0.56 m and an MAE of 0.43 m, significantly outperforming the other regions. This indicates that the method employed in this study is capable of effectively capturing the actual variations in water depth in this area, demonstrating high spatial accuracy. Furthermore, the fitted curves in the scatter plots for all study areas are generally close to the 1:1 line, with \(R^{2}\) values exceeding 0.85. This suggests a high level of accuracy in the water depth inversion results, with minimal differences between the predicted values and the true values. Overall, through comparisons with ICESat-2 data, the water depth inversion method proposed in this study demonstrates strong adaptability and reliability, effectively retrieving water depth information and showing broad application prospects.  
Figure 4: Scatterplot comparison of profiles in the four study areas: (**a**) Onotoa Island; (**b**) Ant Atoll; (**c**) Emae Island; (**d**) Vuthovutho Island.
## 4 Network Comparative Analysis
### Training Set Analysis  
The logging data from Y3 wells is used to train Inception, BiLSTM, and the hybrid network, separately. Splitting the dataset into 70% for training and 30% for testing allows sufficient data for training while preserving enough information for evaluation. The training phase loss errors of the three networks are shown in Figure 9. It is evident that as the number of training times increases, the loss error decreases and eventually stabilizes at a constant value, which indicates that the network has converged to an optimal state. This stabilization also reflects the robustness of the model, as it demonstrates consistent performance across multiple training epochs without significant fluctuations in loss. Furthermore, the convergence of training and validation losses shows that the model can be effectively generalized to other data, minimizing the risk of overfitting. Overall, the stable loss values indicate that the model is reliable and capable of maintaining its predictive accuracy under varying conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline The number of time steps & 9 \\ The number of the LSTM units & 16 \\ The number of the training epochs & 200 \\ The number of training batch size & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the proposed hybrid network for the comparative experiment.  
Figure 9: _Cont._The proposed network exhibits a lower loss error compared to the Inception and BiLSTM networks, indicating its effective utilization of the correlation between spatio-temporal features in conventional logging data and Vs. This improvement highlights the network's enhanced sensitivity to critical spatio-temporal features.
# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 2 Related Works  
### Semi-Supervised Objection Detection  
In the field of supervised learning, object detection algorithms have made considerable advancements. However, the high cost of manual annotation remains a significant bottleneck to their widespread application. To address this issue, semi-supervised learning methods have gradually become a focal point of research. These methods aim to make full use of limited labeled data in conjunction with a large amount of easily accessible unlabeled data, thereby enhancing model performance and offering great potential for the application of semi-supervised algorithms.  
Semi-supervised object detection algorithms can be broadly categorized into two types: consistency-based methods [17] and pseudo-labeling methods [18]. STAC proposes a semi-supervised learning framework for visual object detection, which combines self-training and strong data augmentation consistency regularization, effectively leveraging unlabeled data to significantly boost model performance [19]. Unbiased Teacher, through a mutual learning mechanism between the teacher and student models, integrates techniques such as exponential moving average (EMA) training and focal loss, effectively mitigating issues of class imbalance and foreground/background disparity [20]. Soft Teacher introduces an innovative pseudo-label generation and selection mechanism, along with an efficient end-to-end training process, providing an effective new approach to address the challenges of semi-supervised object detection [21]. Efficient Teacher, specifically designed for one-stage anchor-based detectors, presents a semi-supervised object detection framework that optimizes the allocation of pseudo-labels through a Pseudo-Label Assigner. It also introduces an Epoch Adaptor to adjust the threshold and distribution adaptation of pseudo-labels, ensuring the efficiency and quality of the student model's training process [22]. Humble Teacher [23] replaces hard pseudo-labels with soft pseudo-labels in semi-supervised learning, enabling the student to extract more comprehensive information from the teacher. MixTeacher [24] introduces a new approach involving mixed-scale teachers, aiming to improve pseudo-label generation and achieve scale-invariant learning, ultimately addressing the issue of scale variation during training. Subsequently, several studies [18, 25] extended these existing methods to single-stage detectors, in pursuit of simpler and more effective solutions. Recently, Semi-DETR [26] explored the SSOD framework based on DETR, bringing new possibilities to the field of semi-supervised object detection.