esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### SST Prediction  
The methods of SST prediction are classified into two categories [8]. One is the numerical-based method, which relies on multiple physical formulas to make predictions. With a large number of elements other than SST as parameters, it predicts SST through correlation analysis. For example, after discovering a potentially useful predictive relationship between winter North Atlantic Sea Surface Temperature Anomaly (SSTA) and the subsequent summer (July-August) Central England Temperature (CET), [PERSON] used it to build a regression equation for predicting subsequent summer CET based on the strength of the eigenvectors [9]. In general, this method requires the use of external elements in large numbers but is low in the accuracy of prediction, as a result of which it is rarely used in practice to predict SST.  
The other is a data-driven method, which does not require any prior knowledge but has the capability to learn the rules from the input data automatically. This kind of method is dominated by machine learning and deep learning. Machine learning methods, such as genetic algorithms, support vector machines, and Markov chains, have produced excellent time series prediction results [10; 11; 12]. For example, [PERSON] et al. [13] proposed the SSTP model, which relies on dynamic time warping (DTW) to mine the similarity of the historical SSTseries and then predicts SST via SVM, while [PERSON] et al. [14] applied a multilevel vector autoregressive model (VAR-L) to achieve a satisfactory result in forecasting sea surface temperature. In general, these machine learning models outperform the numerical-based method in accuracy and simplicity.  
In recent years, deep learning has experienced rapid development. Due to its capability to extract complex features from data, neural networks are widely applied in various fields. Recurrent neural networks (RNNs), long short-term memory units (LSTMs), and gated recurrent units (GRUs) [15; 16; 17] have been successful in time series forecasting because they can capture nonlinear relationships in time series data. Due to the effectiveness of these models in time series forecasting, neural networks are also commonly used in marine science. For example, [PERSON] et al. [18] applied a gated recurrent unit (GRU) neural network algorithm to predict SST, which led to good results. [PERSON] et al. [19] combined a convolutional neural network (CNN) and LSTM to extract complex features, and the resulting model had high generalization ability. [PERSON] et al. [20] used the attention mechanism to predict the surface temperature of the ocean, and the model had an excellent performance in terms of long-scale and long-term predictions.  
Despite the high accuracy reached by the above-mentioned deep learning models, the data of the entire prediction area are inputted into the model when predictions are made, which ignores the special data, thus leading to the inaccuracy of predictions.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 3 Challenges  
Despite the great development of disambiguating with various methods, there remain some unresolved challenges and gaps between research and practical applications. The challenges of eliminating depth ambiguity mainly have six aspects.  
* Existing methods based on camera parameter constraints generally simplify the imaging model and directly treat the estimation task as a coordinate regression without adequate consideration of the inherent kinematic structure of the human body, which may lead to invalid results.
* Due to the lack of the original input image, the two-stage methods place overreliance on the 2D detector and discard the rich spatial information in the image when estimating the 3D pose. The error of the first stage will be amplified in the 3D estimation. The algorithm is ultimately limited if the 2D pose estimation is not updated with the 3D.
* The one-stage methods usually fix the scale of the 3D pose. The 3D pose is constructed from the 2D pose and depth, which may make estimation fail when the height of the subject is far from the height in the training set.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Dir.** & **Disc.** & **Eat** & **Greet** & **Phone** & **Photo** & **Pose** & **Purch.** & **Sit** & **SitD.** & **Smoke** & **Wait** & **WalkD.** & **Walk** & **WalkT.** & **Avg** \\ \hline [PERSON] et al. [31] & 60.6 & 54.1 & 60.0 & 59.6 & 60.7 & 78.8 & 56.2 & 52.6 & 58.0 & 82.9 & 66.7 & 62.0 & 51.5 & 60.9 & 42.8 & 60.1 \\ [PERSON] et al. [32] & 48.5 & 54.4 & 54.4 & 52 & 59.4 & 65.3 & 49.9 & 52.9 & 65.8 & 71.1 & 56.6 & 52.9 & 60.9 & 44.7 & 47.8 & 56.2 \\ [PERSON] et al. & 48.6 & 54.5 & 54.2 & 55.7 & 62.6 & 72 & 50.5 & 54.3 & 70 & 78.3 & 58.1 & 55.4 & 61.4 & 45.2 & 49.7 & 58 \\ [PERSON] et al. [34] & 49.2 & 55.5 & 53.6 & 53.4 & 63.8 & 67.7 & 50.2 & 51.9 & 70.3 & 81.5 & 57.7 & 51.5 & 58.6 & 44.6 & 47.2 & 57.8 \\ [PERSON] et al. [35] & 34.9 & 40.8 & 37.5 & 47.2 & 41.5 & 46.6 & 35.9 & 39.5 & 52.6 & 72.5 & 42.3 & 45.8 & 42 & 31.6 & 33.8 & 43.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of performance on Human3.6 Protocol #1 (MPJPE: mm).  
Figure 6: Illustration of [PERSON]’s two-stage approach based on depth ranking.  
* The interframe continuity and stability constraints used by methods based on temporal consistency constraints lead to smoothness effects, which may cause inaccurate estimation of each frame. The estimated results will have floating, jitter, and sliding problems.
* Some kinematic constraints belong to simple structural constraints and are commonly treated as auxiliary losses in research, such as symmetry loss, joint angles limit, e.g., could only make marginal improvements to the estimation results, and limited disambiguating effect.
* The monocular image belongs to a two-dimensional representation and carries no depth information, which is challenging for image cue methods to learn depth information. In addition, as the depth is highly sensitive to camera parameters, translation and rotation will make joint depth prediction more difficult.
## 4 Discussion
### Limitations and Future Research Directions  
This review has several limitations:  
* A focus on post-2020 studies, which limits historical comparison.
* Geographical bias toward developed regions.
* Potential publication bias toward positive results.
* Variability in measurement protocols.  
A significant limitation identified in the reviewed literature is the short duration of most studies, with an average research period of only 8.2 months. This temporal constraint raises concerns about the long-term reliability of ventilation performance data and energy consumption patterns in educational buildings. The absence of longitudinal studiestracking ventilation systems for periods exceeding 5 years creates a critical knowledge gap regarding system degradation, maintenance requirements, and sustained energy efficiency over a building's operational lifespan.  
Future research should prioritize the integration of life cycle assessment (LCA) methods when evaluating ventilation strategies in educational facilities. LCA would provide a more comprehensive understanding of the environmental impacts and energy consumption patterns throughout the entire lifecycle of ventilation systems--from material extraction and manufacturing to installation, operation, maintenance, and eventual disposal or replacement. This approach would offer valuable insights into the long-term sustainability of different ventilation strategies beyond the typical short-term performance metrics currently reported in the literature. Particularly, LCA studies spanning 5-10 years would help capture the effects of seasonal variations, system degradation, and changing occupancy patterns on ventilation performance and energy consumption.  
Priority areas for future research include the following:  
* The development of standardized measurement protocols.
* The integration of occupant feedback mechanisms.
* Long-term studies of modified ventilation strategies.
* Cost effectiveness analysis of comprehensive monitoring.
## 3 Experimental Results
### Effect of Extracted Features  
This subsection presents an analysis of the effects of different features on the average accuracies for emotion recognition based on the EEG signal. When the extracted features alternate among PSD, DE, DASM, RASM, and DCAU, the mean precisions obtained by LORSAL, SVM, and NB for the classification of LV/HV and LA/HA are shown in Figures 7a-f and 8a-f. All the LORSAL, SVM, and NB classifiers performed best for the DE features. Among all classifiers, LORSAL obtained the highest accuracies of 77.17% and 77.03% in valence and arousal dimensions, respectively, for the DE features extracted from total frequency bands. In addition, the highest precisions obtained by SVM are 69.55% and 69.92%, respectively, for DE from total frequency bands. The DE features denote the complexity of continuous random variables [55; 56; 57]. The EEG signals are characterized by higher low-frequency energy over high-frequency energy, and consequently, DE can distinguish EEG sequences according to low- and high-frequency energy. These results agree with the findings in [47; 48], and further show the superiority of the DE features in EEG-based emotion classification.  
Figure 6: Fisher ratio of features extracted on different frequency bands by averaging the values over all subjects in **(a)** valence and **(b)** arousal dimensions, respectively.  
Figure 7: Effects of the different features (PSD, DE, DASM, RASM, and DCAU) on the classification precisions for LV/HV emotions obtained by the LORSAL, SVM, and NB classifiers from the six different frequency bands: (**a**) Delta, (**b**) Theta, (**c**) Alpha, (**d**) Beta, (**e**) Gamma, (**f**) Total.  
Figure 8: Effects of the different features (PSD, DE, DASM, RASM, and DCAU) on the classification precisions for LA/HA emotions obtained by the LORSAL, SVM, and NB classifiers from the six different frequency bands: (**a**) Delta, (**b**) Theta, (**c**) Alpha, (**d**) Beta, (**e**) Gamma, (**f**) Total.  
Moreover, the DASM and DCAU features provide relatively ideal performance compared to the PSD and DE features. DASM and DCAU are asymmetric features and the former findings showed the effectiveness of asymmetrical brain activity along left-right and frontal-posterior directions in emotion analysis. It is noted that the dimensions of the DASM and DCAU features are 70 and 55, respectively, which are fewer than that of PSD and DE with 160-dimension features. This makes DASM and DCAU more competitive in computational complexity. The experimental results were also consistent with the findings by [PERSON] and [PERSON] [47; 48].
# Planning MAR Schemes Using Physical Models: Comparison of Laboratory and Field Experiments
## 2 Materials and Methods
### Statistical Evaluation of Infiltration Experiments  
Only data from complete HLCs were used and if incongruences could be attributed to human or technology failure, the data from a cycle was not regarded in the statistical analysis. HLC were further separated into wetting phases (time of infiltration) and subsequent drying phases.  
To evaluate the performance of the laboratory setups compared to the FIU, the statistical parameters root mean square error (_RMSE_) and Nash-Sutcliffe coefficient (NSE) were calculated. The _RMSE_ is a measure of the exactness of the model using accurate units (Equation (1)). _RMSE_ values close to zero indicate good model performance [29].  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\bigl{(}\psi_{i}-\psi_{pi}\bigr{)}^{2}}, \tag{1}\]  
where \(\psi\) = measured pressure head, \(\psi_{p}\) = predicted pressure head, and \(N\) = the number of data points.  
The NSE determines the relative magnitude of residual variance compared to the variance of measured data [30]. For an NSE over zero, the model results are more accurate than the mean of measured data with one indicating a perfect match of modelled and observed data (Equation (2)).  
\[NSE=1-\frac{\sum_{i=1}^{N}\bigl{(}\psi_{i}-\psi_{pi}\bigr{)}^{2}}{\sum_{i=1}^{ N}\bigl{(}\psi_{i}-\overline{\psi}\bigr{)}^{2}}, \tag{2}\]  
where \(\overline{\psi}\) = mean of observed pressure head data.  
For each HLC, the minimum and mean tension values of the wetting phase and the maximum tension values of the drying phase were determined and illustrated with R software [31]. The software was further used to calculate the mean differences between FIU and laboratory experiments.  
Next to simple multipliers or addends, linear regression was used to predict the value of an outcome variable [32]. In this case, the FIU tension values, \(\psi_{F}\), were predicted based on the input predictor variables, \(\psi_{L}\), which are the tension measurements of the laboratory experiments (tank and column). The aim was to establish a mathematical formula (Equation (3)), so when only the predictor values \(\psi_{L}\) are known, the tension values of the FIU can be estimated.  
\[\psi_{F}=\beta_{1}+\beta_{2}\times\psi_{L}+\epsilon, \tag{3}\]  
where the regression coefficients are \(\beta_{1}\) (intercept) and \(\beta_{2}\) (slope) and \(\epsilon\) is the error term.  
The calculated mean differences and regression functions were then used to adapt the laboratory tension values and compare those to the measured tension values of the FIU. Representativeness of the function and coefficients was assessed by calculation of NSE and RMSE. The adapted laboratory values were mapped with a 95% confidence interval. In a second step, the regression factors calculated from the laboratory tension measurements of scenario 1 were used to adapt the laboratory tension measurements of scenario 2 and compare those to the respective FIU measurements to assess their representativeness for various scenarios.
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 4 Experimental Results  
### Datasets  
* ICDAR2013 The ICDAR 2013 [31] consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark for evaluating near-horizontal text detection. Some examples of the ICDAR2013 dataset are shown in Figure 8.
* Street View Text (SVT) The SVT dataset is harvested from Google Street View. Image text in this data exhibits high variability and often has low resolution [32]. In autonomous driving, the text in these Street View images helps the system confirm its position. Compared to the ICDAR2013 dataset, text in the SVT dataset has lower resolution, more complex light conversion, and relatively smaller text, which is more challenging. Figure 9 shows some examples of the SVT dataset.  
Figure 8: Several examples of difficulties in text detection in the ICDAR2013 dataset. (**a**) Background destruction font structure. (**b**) Perspective transformation and uneven illumination. (**c**) Text is too small and low contrast. (**d**) Complex background and low contrast.  
Figure 7: Examples of CRF processing effects. (**a**) Two lines of text are placed in one bounding box. After CRF processing, each word has a separate bounding box. (**b**) The arrow is erroneously detected as text and the arrow bounding box is removed after CRF processing.
# Training Spiking Neural Networks with Metaheuristic Algorithms
###### Abstract  
Taking inspiration from the brain, spiking neural networks (SNNs) have been proposed to understand and diminish the gap between machine learning and neuromorphic computing. Supervised learning is the most commonly used learning algorithm in traditional ANNs. However, directly training SNNs with backpropagation-based supervised learning methods is challenging due to the discontinuous and non-differentiable nature of the spiking neuron. To overcome these problems, this paper proposes a novel metaheuristic-based supervised learning method for SNNs by adapting the temporal error function. We investigated seven well-known metaheuristic algorithms called Harmony Search (HS), Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Grammatical Evolution (GE) as search methods for carrying out network training. Relative target firing times were used instead of fixed and predetermined ones, making the computation of the error function simpler. The performance of our proposed approach was evaluated using five benchmark databases collected in the UCI Machine Learning Repository. The experimental results showed that the proposed algorithm had a competitive advantage in solving the four classification benchmark datasets compared to the other experimental algorithms, with accuracy levels of 0.9858, 0.9768, 0.7752, and 0.6871 for iris, cancer, diabetes, and liver datasets, respectively. Among the seven metaheuristic algorithms, CS reported the best performance.  
spiking neural network; metaheuristic; classification
## 3 Results
The VL-ST pair demonstrated a \(p\)-value of 0.384, while the LG-TA pair showed a \(p\)-value of 0.645, indicating no statistically significant differences between the groups. Figure 3 presents the linear regression analysis of peak EMG during a series of 15 continuous roundhouse (_bandal chagui_) kicks, which demonstrated significant trends in specific muscles: BF (slope = 2.082, intercept = 61.890, R\({}^{2}\) = 0.661, \(p\) < 0.001), LG (slope = 0.866, intercept = 73.331, R\({}^{2}\) = 0.288, \(p\) = 0.039), and TA (slope = \(-\)1.192, intercept = 81.024, R\({}^{2}\) = 0.457, \(p\) = 0.006). The BF and LG exhibited upward trends, while the TA showed a downward trend. The other muscles did not present significant changes in peak EMG trends (\(p\) > 0.05). Figure 3: Peak EMG trends during a series of continuous roundhouse (bandal chagui) kicks. BF: _biceps femoris_; LG: _lateral gastrocnemius_; RF: _rectus femoris_; SO: _soleus_; ST: _sentientinosus_; TA: _tibialis anterior_; VL: _vastus lateralis_; VM: _vastus medialis_. Figure 2: Mean differences in muscle co-contraction for specific muscle pairs between novice and advanced Taekwondo athletes. BF–RF: _biceps femoris\(-\)_rectus femoris_; VM-BF: _usus medialis\(-\)biceps femoris_; VL–ST: _vastus lateralis\(-\)sentientinosus_; LG–TA: _lateral gastrocnemius—tibialis anterior_; SO–TA: _soleus—tibialis anterior_; \({}^{*}\)_p_ < 0.05; \({}^{**}\)_p_ < 0.01. Fixed effects explain a range of variance between 0.8% and 11.6%, while the complete models (fixed and random effects) explain 50.4% to 55.8% of the variance. The PCA component of fat mass (loading coefficient = 0.707) and muscle mass (loading coefficient = \(-\)0.707) showed a significant and positive effect in the BF, RF, VL, SO, and TA muscles, indicating that higher fat mass and lower muscle mass are significantly associated with an increase in the trend of peak EMG change. The experience level parameter presented a negative and significant impact in the RF, VM, and VL muscles, showing that being a novice is significantly associated with a lower tendency to increase the quadriceps peak EMG. Additionally, the number of kicks was significant in the BF and TA muscles, suggesting that EMG activity increases with successive roundhouse (_bandal chaguu_) kicks in the BF, while at the same time, it decreases in the TA. The results of the mixed-effects model are presented in Table 2.
## 3 Results and Discussion
### Spectroscopic Characterization of DBD Plasmas over the Water Surface  
Figure 3 presents the OES results for DBDs with various gases in an interaction with the distilled water. Based on the OES results, in addition to argon and oxygen spectra, hydroxyl appears in argon and oxygen DBDs in interaction with water molecules. In contrast, the spectra for air and nitrogen DBDs provide mainly nitrogen species, and the spectra patterns are almost identical. Nevertheless, a weak signal of the nitrogen spectra is observed in the cases of argon and oxygen DBD plasmas (Figure 3a,b) due to the interaction with the ambient air. We take this advantage to estimate neutral gas and electron temperatures based on the second positive line of the nitrogen band.  
Table 1 shows the neutral gas and electron temperatures, estimated by Specair software, in the gas phase. The calculation of Specair software is based on modeling, which describes the energy level of the included species. Two estimated parameters: electron temperature and rotational temperature, were determined by the respective Boltzmann population distributions of the internal energy levels [22]. The rotational temperature represents the neutral gas temperature at atmospheric pressure. The fitting, including the convolution of instrumental function of the spectrometer, was performed in the range of 370-384 nm (second positive of nitrogen band) to estimate the rotational temperature [23].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**DBD Plasma** & **Consumed** & **Consumed** & **Averaged** & **Neutral Gas** & **Electron Temperature** \\
**(Gas Species)** & **Energy—One** & **Power-One Cycle** & **Consumed** & **Temperature (eV)** & **(eV)** \\ \hline Argon & 4.64 & 93 & 18.6 & 0.03 \(\pm\) 0.004 & 0.7 \(\pm\) 0.14 \\ Oxygen & 9.82 & 200 & 40 & 0.05 \(\pm\) 0.01 & 0.6 \(\pm\) 0.12 \\ Nitrogen & 9.82 & 200 & 40 & 0.09 \(\pm\) 0.018 & 0.9 \(\pm\) 0.18 \\ Air & 9.95 & 200 & 40 & 0.09 \(\pm\) 0.018 & 0.9 \(\pm\) 0.18 \\ \hline \end{tabular}
\end{table}
Table 1: Neutral gas and electronic temperatures are measured via OES in various gas species of DBD plasmas, including the consumed energy and power for one cycle time and the averaged consumed power during the discharge.  
Figure 3: Optical emission spectroscopy for (**a**) argon, (**b**) oxygen, (**c**) nitrogen, and (**d**) dry air DBD plasmas in the gas phase.  
The fitting to evaluate the electron temperature was performed with the nitrogen peak wavelength of 337 nm. The gas temperature range was estimated between 0.03 to 0.09 eV, while the electron temperature range was 0.7 to 0.9 eV for all DBD plasmas. The electron temperature is much higher than the gas temperature for all DBD plasmas shown in Table 1. Therefore, the DBD plasmas are considered non-thermal plasmas for all gas feeding, although the gas temperature is relatively high for air and nitrogen plasmas. The energetic electrons, in this investigation, can make dissociation and ionization that is responsible for the generation of radicals and reactive species [24; 25].