esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques
## 4 Late-Fusion Model Design with VGGish and YAMNet
### Introduction to the Late-Fusion Model  
The fusion method combines the information extracted from different data sources or models to achieve a better performance than independent models. More cases can be learned by combining information from models with information from feature extraction methods, which are also used to integrate information from multiple sensor data.  
This study selected a late-fusion method to input the same data into YAMNet and VGGish, learn in parallel, and appropriately combine the obtained information to improve the classification results. Figure 7 shows the area activated using Grad CAM in the layer before classification, when the same spectrogram image is input to each model. Owing to the structural differences between the models, VGGish achieves a high location resolution by extracting locational features in the time-frequency domain of the image. Conversely, YAMNet is designed to identify overall features of an image and does not rely on locational features to analyze information. Consequently, there is a difference in analyzing information.  
Figure 8 shows the designed model that extracts patterns and features for emotion classification from preprocessed data and integrates information to classify the emotion classes. New feature information is generated by adding the information obtained from each layer of VGGish. Three types of data are synthesized, and a network that synthesizes the feature information and classifies the emotions in the final classification layer is designed. When combining features in a fusion model, the addition or multiplication method is mainly used. However, in the above network, to reduce the influence between the feature channels, a depth-combining method is used to combine the features extracted using the two models. This method not only maintains the information but also classifies it through the interaction of various information in the fully connected layer.  
Figure 6: Feature extraction and combination in the fusion transfer learning model.  
Figure 7: Feature extraction resolution comparison: YAMNet vs. VGGish.
# Vibration Energy Harvesting from Raindrops Impacts: Experimental Tests and Interpretative Models
## 6 Discussion  
The measurement of the voltage generated by the novel harvester hit by actual rain confirmed the results obtained by means of artificial rain [21]. Outdoor tests showed a wide dispersion of results, which was related to the variable sizes of actual raindrops, to the presence of wind, to the variable impact position, and to the non-zero initial conditions related to previous impacts. These effects have already been highlighted by other researchers that dealt with cantilever harvesters without spooms [3,4,18].  
Video analysis has been extensively used to study the impacts of drops on liquid surfaces [19] and there are some examples of the use of this technique in the field of raindrop harvesters [16,17]. In the framework of this research, video analysis proved to be a powerful tool that made quantitative measurements possible (e.g., of crater height) that were useful for the development of the mathematical model.  
The proposed mathematical model allows one to simulate only one raindrop impact along the midline of the harvester, but results are in good agreement with the experimental ones, with a small error in resonance frequency (1.7%) and an acceptable error in peak-to-peak voltage (6%). It is worth noting that nowadays the impact of drops on liquid  
Figure 17: Force acting on the harvester due to a drop impacting on a thin water layer.  
Figure 18: Comparison between the experimental (red) and simulated (blue) harvester voltage in the time (**a**) and frequency (**b**) domain for a drop impacting on a spoon full of water.  
surfaces can be simulated by means of powerful computational fluid dynamics (CFD) codes. The proposed interpretative model gives approximated results, but it is much simpler than CFD codes, can be quickly integrated with existing harvester models and, hence, is useful for design purposes.  
The electrical load that is included in the model is a simple resistance that in the simulations of Section 5 represents the resistance of the acquisition module (\(R=305\) k\(\Omega\)). An estimation of the energy harvested by the device can be made by assuming in the model a resistive load equal to the optimal resistance \(R_{opt}\)[12], i.e., the one that maximizes the power generated by a harvester with capacitance \(C_{pu}\) and the natural frequency of the first vibrational mode \(\omega_{1}\):  
\[R_{opt}=\frac{1}{\omega_{1}C_{pu}} \tag{32}\]  
Under such an assumption, the collected energy is:  
\[E=\frac{1}{R_{opt}}\int_{0}^{t_{max}}v^{2}(t)dt \tag{33}\]  
where the upper limit of the integral \(t_{max}\) is chosen to take into account the fact that the signal is transient and vanishes after some periods of oscillation. Making use of the parameters in Appendix A and Table 1, and of Equations (32) and (33), the energy generated by the impact on the empty spoon is 0.15 uJ; this value is in agreement with the values reported in the literature [2,31]. Conversely, the energy generated by the raindrop impacting on the filled spoon is 4.6 uJ. This result confirms the validity of the novel harvester design.
## 4 Discussion  
### Artificial Lighting  
The two tested artificial lighting systems displayed two distinct light spectra that were expected to induce changes in _H. portulacoides_ development. Nonetheless, according to the results, no differences could be associated with the type of lighting system. In contrast, previous studies showed that some types of artificial lighting and their associated light spectra can affect different stages of halophytes' development. For instance, the seedling germination rates and shoot development of _Atriplex halimus_ L., _Atriplex hotensis_ L., and _S. europaea_ were higher when irradiated with a combined red and blue LED system compared with fluorescent lighting [36], and the vegetative development of _A. hotensis_ also improved under red and blue LEDs [45]. Similarly, the edible _Mesembryanthemum_ C. (ice plant) showed improved vegetative development under red and blue LEDs compared to red or blue LEDs alone [35], as well as under red and white LEDs compared with other combinations of white, blue, red, and far-red LEDs [37]. The effect of LED lighting on plant hydroponic production has been more intensively studied using more traditional crops [29; 46]. The use of blue and red LEDs (alone or combined) seems to improve the quality and yields of several vegetables and fruit when compared with fluorescent lighting [31]. In aquaponics, for instance, the productivity of kale (_B. oleraea_) and Swiss chard (_Beta vulgaris_ L.) is higher with LEDs [47] and some studies suggest that blue LEDs seem to stimulate leaf area enlargement and the aboveground development of lettuce (_Lactuca sativa_ L.) and other vegetables (e.g., Chinese cabbage, spinach and coriander) [28; 48]. In Table 4, a non-exhaustive summary of hydroponic trials looking at the different effect of LEDs and fluorescent lighting in the vegetative growth of leafy greens is presented, but the results were varied and inconclusive. For instance, _L. sativa_ var. \"crispa\" grew better under red LEDs compared to either blue or red and blue LEDs [49]; meanwhile, the var. \"capitata\" seemed to grow better under blue LEDs [50]. The reality is that the effects of light quality in regulating growth processes and physiology in plants is tremendously complex. Even within the same species, outcomes can be different among varieties and time points [51; 52]. The analysis of pigment concentrations suggested that the photosynthetic pigments in _H. portulacoides_ leaves were unaffected by the type of light spectra after a long-term exposure of 10 weeks. The only indication of an effect came in fact from the planting density, where the concentrations of antheraxanthin and zeaxanthin were higher in low-density units. Being products of the photoprotective xanthophyll cycle [53], one possible explanation for this
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 5 Conclusions  
The Qinghai-Tibet Plateau features many high and extremely high mountains, where glaciers and glacial lakes have widely developed. In this area, 35 GLOF disasters have been recorded, which threatened human life and property. With the change of the global climate and ecological environment, the frequency of GLOF disasters is increasing. In particular, debris flow from glacial lake outbursts in some cross-border basins have caused certain damage to local areas and downstream countries, creating international disaster events. Therefore, this study developed a model for predicting the possibility of glacial lake outburst by employing logistic regression. The following conclusions can be drawn.  
(1) The prediction model of glacial lake outburst risk was established by selecting seven indexes: the glacier area, area of glacial lake, distance of the glacial lake from the glacier, slope of the glacier, slope of the glacial tongue, ratio of glacial lake water surface to dam crest height to dam body height, ratio of dam body width to thickness, and dam backwater slope. The indexes can truly reflect the risk of glacial lake outburst.  
(2) The ROC curve and AUC were used to test the accuracy of the model. The ROC curve showed a correct rate of 70%, and the AUC was greater than 0.5, indicating the high accuracy and generalization ability of the model.  
(3) According to the probability equation of the model, the area of the glacial lake, the distance of the glacial lake from the glacier, the slope of the glacier, the slope of the glacier tongue, and the dam backwater slope are all directly proportional to the possibility of glacial lake outburst. Therefore, larger index values indicate a higher possibility of glacial lake outburst.  
**Author Contributions:** Conceptualization, J.G.; methodology, J.D.; software, J.G. and Z.Y.; resources, J.G.; data curation, Z.Y.; writing--original draft preparation, J.G.; writing--review and editing, J.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The Key Project of the Natural Science Foundation of Tibet Science and Technology Department, Grant No. XZ0220201 ZR0001G, The second Tibetan Plateau Scientific Expedition and Research Program (STEP), Grant No. 2019 QZKK0206, and The Opening Project of Institude of Tibetan Plateau and Polar Meteorology, Chinese Academy of Meteorological Sciences (ITPP2021K01).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank the Key Project of the Natural Science Foundation of Tibet Science and Technology Department, The second Tibetan Plateau Scientific Expedition and Research Program (STEP) and The Opening Project of Institude of Tibetan Plateau and Polar Meteorology for supporting this work. We also thank reviewer for their constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### CMC Solutions Preparation  
Stock solutions at 20 g/mL were prepared by dissolving each CMC powder sample in deionized water and left stirring at room temperature until complete dissolution as follows: 20 \(\pm\) 0.1 g of each CMC sample was used \"as-received\" with a careful and slow addition to deionized water at 25 \({}^{\circ}\)C to assure a good dispersion. The solutions were then gently stirred for 4 h after which the pH was adjusted to 6.5-7.5 using 0.1 M NaOH or 0.1 M HCl, as appropriate, to minimize the possible impact of pH.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 2 Detection System in the Peruvian Navy
### Phase 1: Plot Extractor
#### 2.1.3 Plot Detection  
This process involves the extraction of plots using detection algorithms. The detection algorithm used by the Peruvian Navy for the last 20 years is based on the CFAR statistical method, designed with a variation and simplification to one line. The CFAR algorithm is used in a single dimension, applying the detector to each echo in a given angular direction, from 0\({}^{\circ}\) to 360\({}^{\circ}\). This detector model is enabled on many commercial ships and defense systems. The CFAR detector receives the echoes, detects pixel strings, and obtains probabilities to determine whether it is a contact. If the detected strings in one line are adjacent to any string in the following line, an object is formed; when the object no longer has adjacent strings, the object detection process is terminated. This process is executed in real-time and ends when the extracted plots are sent together with the metadata, center of gravity, and area to the second phase, called Tracking-Prediction. However, the CFAR detection algorithm presents certain deficiencies reflected in the data obtained experimentally from the radar installed on the vessel, equivalent to a false alarm rate of 0.05 and false negatives of 0.10.  
Our research is framed within this plot detection process. This process is crucial because it requires extracting as many plots as possible, which will then be processed to determine the defense operations. The improvement in this process results in the accurate detection of plots that will be extracted to continue their course in the next phase. The processes corresponding to each phase are shown in Figure 2. The process that frames our research is highlighted.
## 4 Discussion
### ORL Database Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Occlusion Rate and Method** & \multicolumn{3}{c}{**Other Measurement Model**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & **Predinance Indicators** & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{4}{*}{10\% (Random Occlusion)} & Precision \textgreater{} 0.7 & 39 & 24 & 33 & 29 & 37 & **40** \\  & Recall \textgreater{} 0.7 & 38 & 27 & 32 & 27 & 37 & **40** \\  & F1 Score \textgreater{} 0.7 & 39 & 18 & 30 & 25 & 37 & **40** \\  & Precision, recall, and F1 Score = 0 & 0 & 1 & 0 & 1 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 38 & 14 & 27 & 13 & 36 & **40** \\
20\% (Random Occlusion) & Recall \textgreater{} 0.7 & 38 & 13 & 26 & 22 & 37 & **39** \\  & F1 Score \textgreater{} 0.7 & 37 & 9 & 23 & 10 & 36 & **40** \\  & Precision, recall, and F1 Score = 0 & 0 & 4 & 1 & 2 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 24 & 11 & 15 & 12 & 36 & **38** \\
30\% (Random Occlusion) & Recall \textgreater{} 0.7 & 26 & 10 & 17 & 15 & 34 & **38** \\  & F1 Score \textgreater{} 0.7 & 16 & 5 & 8 & 7 & 34 & **38** \\  & Precision, recall, and F1 Score = 0 & 1 & 7 & 1 & 6 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 14 & 5 & 18 & 5 & 39 & **39** \\
40\% (Random Occlusion) & Recall \textgreater{} 0.7 & 18 & 6 & 18 & 11 & 37 & **37** \\  & F1 Score \textgreater{} 0.7 & 9 & 1 & 10 & 2 & 37 & **37** \\  & Precision, recall, and F1 Score = 0 & 7 & 13 & 6 & 12 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 36 & 12 & 19 & 15 & 38 & **40** \\
10\% (Vertical Occlusion) & Recall \textgreater{} 0.7 & 36 & 15 & 24 & 25 & 36 & **39** \\  & F1 Score \textgreater{} 0.7 & 36 & 8 & 15 & 11 & 36 & **39** \\  & Precision, recall, and F1 Score = 0 & 0 & 3 & 0 & 1 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 9 & 5 & 8 & 2 & 35 & **35** \\
20\% (Diagonal Occlusion) & Recall \textgreater{} 0.7 & 9 & 6 & 14 & 8 & 34 & **34** \\  & F1 Score \textgreater{} 0.7 & 4 & 1 & 4 & 1 & 33 & **33** \\  & Precision, recall, and F1 Score = 0 & 25 & 27 & 21 & 27 & 0 & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The number of categories that identified performance indicators to an extent greater than 0.7 or single categories that were wrongly identified from ORL datasets for different methods.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## Appendix A  
MSAC, MLESAC, and PROSAC can be formulated by modifying the baseline RANSAC approach. MSAC and MLESAC are designed to improve the outlier rejection accuracy, whereas PROSAC is to reduce the computation time.  
MSAC and MLESAC modify weights for samples to better reject outliers. In the RANSAC procedure, we solve the equations below to find a candidate rotation matrix that maximizes the number of inliers, \(L\):  
\[\begin{gathered} L=\sum_{i}^{N}l_{i},\\ l_{i}=\left\{\begin{array}{c}1\left(\theta_{i}\leq c\right)\\ 0\left(\theta_{i}>c\right)\end{array}\right.,\end{gathered} \tag{1}\]  
where \(l_{i}\) is an indicator of inlier or outlier, \(\theta_{i}\) is the vector angle evaluated from the given rotation matrix (defined by Equation (9) in Section 3.2.3), \(c\) is a threshold, and \(i\) represents a pair index (1, 2,..., \(N\)). While original definition of \(l_{i}\) is 1 or 0, we can modify the algorithm to be more robust by allowing \(l_{i}\) take continuous value [9]. In MSAC, in this study, we define \(l_{i}\) for evaluating \(L\) as follows:  
\[l_{i}=\left\{\begin{array}{c}1-\frac{\theta_{i}^{2}}{c^{2}}\left(\theta_{i} \leq c\right)\\ 0\left(\theta_{i}>c\right)\end{array}\right. \tag{2}\]  
In MLESAC, we define \(l_{i}\) as follows:  
\[l_{i}=\frac{\gamma}{\sqrt{2\pi\sigma^{2}}}\exp\left(-\frac{{\theta_{i}}^{2}}{ 2\sigma^{2}}\right)+(1-\gamma)\frac{1}{\
u}, \tag{3}\]  
where \(\gamma\) is an inlier ratio for a given iteration step, \(\sigma\) is a standard deviation of inliers which in practice is determined in advance from several tests, and \(\
u\) is a size of error range of outliers. In this study,we set \(\sigma=0.02\) and \(\
u=20\) based on preliminary experiments. MLESAC maximizes the sum of probability, instead of maximizing the number of inliers, which was the objective of the original RANASAC. RANSAC in general can provide outlier rejection poorer than that of MSAC and MLESAC when we cannot set an appropriate threshold, since RANSAC treats all possible inliers (there should be both more plausible inliers and less plausible ones) with an equal weight [9].  
PROSAC utilizes the property about samples, in our case the similarities between features of each pair, to reduce repetitions for rejecting outliers. PROSAC sorts the order of feature pairs based on the similarities measured by the SURF descriptors, then search inliers semi-randomly according to that order. In other words, PROSAC modified Step 1 of the baseline procedure to choose three matched pairs from completely random to semi-random. Since PROSAC uses the same objective function (Equation (A1)) as the baseline RANSAC, its main benefit is not accuracy, but fast screening of inliers.
# Shrinkage-Induced Response of Composite Steel-Concrete Slabs: A State-of-the-Art Review
## 4 Time-Dependent Modelling of Composite slabs
### Design Model  
Different design models were proposed in the literature to capture the non-linear shrinkage gradients that develop in composite slabs.  
Reference [65] presented a design expression to evaluate the shrinkage induced curvature that accounted for the influence of the following parameters: a factor that depends on  
Figure 7: Experimental and numerical mid-span deflections considering different shrinkage profiles [68].  
the profile of the steel sheeting; a parameter denoting the reinforcement ratio produced by the steel sheeting; the shrinkage strain induced at the top drying surface of the slab; and the total slab thickness. The authors also considered the cases of composite slabs cast on trapezoidal steel sheeting, for which they proposed some additional modification factors to be used in the calculations.  
An expression for the shrinkage strain profile for a composite slab with steel sheeting was presented in [66]. This was written in terms of the following variables: the shrinkage strain at any height of the cross-section; the shrinkage strain calculated following available design codes over the time of interest; the steel sheeting profile; and for trapezoidal sheeting profiles, the ratio between a trough height and the slab thickness. A modification to the definition of the hypothetical thickness used in AS 3600-2009 [11] was proposed in [55] to account for the response of composite slabs.  
The occurrence of the non-uniform shrinkage profile in composite slabs was considered in [18; 19; 70] and it was estimated to be taken as 1.2 and 0.2 times of the design shrinkage strain at the top and bottom surfaces of the slab, respectively [15; 16]. Design shrinkage strains over time were predicted using concrete design specifications by considering the case of a solid slab with the same thickness as the composite slab and exposed to dry from both its top and bottom surfaces. The long-term predictions of the composite slab response have also been presented in [68] based on these shrinkage gradients.  
A comprehensive design guideline for calculating the instantaneous and long-term deflections of composite slabs considering the non-uniform shrinkage profile through the slab thickness was presented in [12]. The instantaneous deflection was obtained from available design codes, whereas deflection due to creep was evaluated from the effective modulus method. The reference shrinkage strains over time were calculated following design guidelines [11], while adopting the hypothetical thickness equal to the thickness of the composite slab, and the gradient was assumed as the same gradient reported in [15; 16]. This design procedure was adopted in AS/NZS 2327:2017 [13] under its simplified approaches for the calculation of the composite slab deflection.
## 3 Results and Discussion
### Gc-Ms
#### 3.1.1 General Aromatic Composition of the Raw Distillate
The first component (PC1) explained 52.93% of the variance, and it was strongly positive correlated ([PERSON]'s R \(\geq\) 0.7) with methyl esters, benzenoids, ethyl esters, aldehydes and acetated esters. The second component explained 18.72% (PC2) of the variance, and it was strongly positive correlated with alcohols. No strong negative correlations ([PERSON]'s R \(\leq-\)0.7) were observed between the first two principal components and the original variables. From this PCA, we could recognize some differences between the test and the treated samples. The test sample was the richest in aldehydes, acetated esters, ethyl esters, benzenoids and methyl esters and poor in alcohols. Samples 13, 10, 4, 2 (heavy toasting level) and 12 (not toasted) showed a medium-high concentration in compounds strongly correlated with PC1 together with a medium-high concentration in alcohols. Samples 5, 3, 8 (light or not toasted) and 7 presented a medium-low presence in compounds highly correlated with PC1 with a medium-high presence of alcohols. Samples 14, 6, 9 and 11 (all light or not toasted) displayed a medium-low concentration of compounds related to the PC1 with a medium-low concentration of alcohols. Figure 1: PCA biplot representing spirits aged with different alternative products. The graph highlights the effect of toasting on the spirit composition on volatile compounds grouped by chemical structure. Yellow, NT, non-toasted wood; red, heavy toasting; orange, light toasting. Sample 1 (control) had the lowest concentrations in alcohols, in contrast with all the samples treated with wood pieces that were high-toasted, which showed a greater concentration of alcohols. From these results, it is possible to hypothesize an absorbing effect of wood on some important fermentative compounds belonging to the family of methyl esters, benzenoids, ethyl esters, aldehydes and acetated esters. No clear differentiations were observed for poplar and oak, nor among the different formats of woods.