esa-sceva/satcom-chunk-collection huggingface dataset:
# Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval
## 1 Introduction
QaRank [20; 21] demands that query-adaptive weights should minimize intra-class similarities and maintain inter-class relations among the top similar images. QsRank [22] directly considers a floating point query sample and a target nearest neighbor radius as inputs, and it is designed only for principal component analysis hashing. In WhRank [23], bitwise weights are adaptive to training samples and sensitive to query data. QRank [24] learns query-adaptive weights by exploiting the discriminative power of each hash function and their complements. Although the aforementioned hash methods and bitwise weights methods have shown their efficacy for ANN search tasks, we would argue that most of them treat data points at different ranking positions equally. Furthermore, few of them explore learning hash functions and bitwise weights functions simultaneously. To this end, we propose a novel top-position-sensitive ordinal-relation-preserving bitwise weight (TORBW) method, as shown in Figure 1. We iteratively learn binary codes and bitwise weights and demand them to satisfy the top-position-sensitive relative-similarity-preserving restriction. For the ANN search task, we utilize weighted Hamming distances to distinguish data pairs that share the same similarity degree. The main contributions of this work are concluded as follows:  
1. [leftmargin=*,noitemsep,topsep=0 pt]
2. When learning binary codes and bitwise weights, we propose to penalize data points without preserving an ordinal relation at the top position of a ranking list more than those at the bottom. This measure helps to reduce the probability of chaos ranking occurring at the top position. 2. Unlike a general two-step mechanism, we simultaneously learn binary codes and bitwise weights by an iterative mechanism, and they can feedback into each other. When the mechanism converges, the binary codes and bitwise weights are effectively adapted to each other. 3. A tensor ordinal graph (TOG) is precomputed to represent a relative similarity relationship of any two data pairs, which can effectively reduce the training complexity. The rest of this paper is organized as follows: Section 2 describes the proposed TORBW method and the adopted iterative optimization mechanism. In Section 3, we show and discuss experimental results. Finally, we conclude this paper in Section 4.
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 3 Results
### Vegetation Productivity Loss Probability under Different Drought Scenarios
Our results showed that arid and semiarid areas are more likely to suffer different levels of damage than are humid and semi-humid areas under different drought scenarios. As the degree of drought increases, the conditional probabilities of GPP loss also increase in different climate zones (similar results were also evident in other seasons (Figures S10-S12). By comparing the probability of vegetation productivity losses under different drought scenarios in different land cover types when the GPP \(\leq\) 40 th percentile (Figures 10 and S13-S15), it can be seen that as the degree of drought increases, the productivity loss probability of EBF, DNF, OS, SAV, and GRA show an increasing trend in different seasons. From March to August, for other land cover types, such as ENF, compared with extreme drought, severe drought has the greatest effect on the degree of damage to vegetation productivity, indicating that different land types respond differently to drought during different seasons. Figure 8: Conditional probabilities of vegetation productivity losses under different drought scenarios in March–May. (**a–d**) represent the conditional probabilities when the GPP \(\leq\) 10 th, \(\leq\) 20 th, \(\leq\) 30 th, and \(\leq\) 40 th percentiles in the moderate drought scenario, respectively. (**e–h**) represent the conditional probabilities when the GPP \(\leq\) 10 th, \(\leq\) 20 th, \(\leq\) 30 th, and \(\leq\) 40 th percentiles in the severe drought scenario, respectively. (**i–l**) represent the conditional probabilities when the GPP \(\leq\) 10 th, \(\leq\) 20 th, \(\leq\) 30 th, and \(\leq\) 40 th percentiles in the extreme drought scenario, respectively. Figure 10: _Cont._  
Figure 9: Conditional probabilities of vegetation productivity losses under different scenarios in various climatic regions in March–May. (**a–c**) represent conditional probabilities of vegetation productivity losses under moderate, severe and extreme drought, respectively. GPP loss1, GPP loss2, GPP loss3 and GPP loss4 represent GPP \(\leq 10\)th, \(\leq 20\)th, \(\leq 30\)th, and \(\leq 40\)th percentiles, respectively. The error bars indicate \(\pm 1\) standard error.
## 4 Experiments
### Database
#### 4.1.1 IEMOCAP  
The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database [57] is one of the most comprehensive multimodal datasets available for emotion recognition. It comprises approximately 12 h of audio-visual data, including speech, text transcriptions, and facial movement. The dataset features five sessions, with a total of ten professional actors (five males and five females) performing both scripted and improvised emotional scenarios. Each session was annotated by at least three people, providing categorical labels (neutral, happiness, sadness, anger) as well as continuous labels (valence, activation, dominance). The distribution of emotions across categories is imbalanced (Figure 5), which may lower the recognition accuracy for the under-represented samples.  
In our experiments, data from four sessions were used for training, while the remaining session was reserved for testing. The study focused on recognizing four basic emotion categories: anger, happiness, sadness, and neutral. To address data imbalance, happiness and excitement categories were combined into a single class. The number of samples in each emotional category is shown in Table 2, highlighting the uneven distribution of emotions, which could impact recognition accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Number of Samples** \\ \hline Anger & 1090 \\ Happiness & 1627 \\ Neutral emotion & 1704 \\ Sadness & 1077 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of utterances across different emotion classes in the IEMOCAP dataset.  
Figure 5: Comparison of emotion distribution between the IEMOCAP and CREMA-D datasets.
## Materials and Methods
### Dispersion Protocols  
The composites employed in this study were manufactured under numerous dispersion protocols with different filler weight content/type and dispersion duration. Two types of systems were manufactured: (i) binary systems, that is, two-part systems, consisting of resin and CNTs or CB; and (ii) ternary systems, that is, three-part systems, where the synergy effects of CNTs and CB in the resin were evaluated. Two binary systems and one ternary system were studied in this work. The first binary system consisted of CNTs in epoxy resin with concentrations of 0.1 wt. %, 0.3 wt. % and 0.5 wt. %. The second binary system was produced by dispersing three different concentrations of CB, 0.5 wt. %, 1% wt. % and 2 wt. %, in resin. Finally, the ternary system, which consisted of CNT, CB and epoxy resin, was studied in terms of two different concentrations of the fillers, 0.5 wt. % CNTs with 1 wt. % CB and 0.5 wt. % CNTs with 2 wt. % CB. It should be noted that the concentration of the CNTs ranges from 0.1 wt. % to 0.5 wt. %. Preliminary studies with the specific epoxy system showed that the concentration of 0.5 wt. % CNTs is an upper limit as, beyond this concentration the increased viscosity makes the system almost unusable. At the same time, CB concentration above 2 wt. % did not alter the electrical behavior of the system. Additionally, as previous studies have indicated [14; 30], electrical percolation is reached at ca. 0.5 wt. % CNTs [14]. To this end, a maximum concentration of 0.5 wt. % was chosen for CNTs and 2 wt. % was chosen for CB.  
The aforementioned concentrations were also studied regarding the dispersion duration. This is a critical parameter for defining the optimal mixing duration as two mechanisms take place during the dispersion process. Initially, the de-agglomeration of the agglomerates, which is the desired result of the process, occurs. On the other hand, the structural degradation of the fillers could lead to a decrease of the mechanical and the electrical properties of the composite. The dispersion duration studied for each system ranged from 1 to 4 h, with a step of 1 h. A more analytical review of the filler content and dispersion duration is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Sample Name** & \begin{tabular}{c} **Dispersion** \\ **Duration (h)** \\ \end{tabular} & \
\begin{tabular}{c} **CNTs** \\ **(wt. \%)** \\ \end{tabular} &
\begin{tabular}{c} **CB** \\ **(wt. \%)** \\ \end{tabular} \\ \hline \multirow{8}{*}{Binary 1} & CNTs0.1-I & 1 & 0.1 \\  & CNTs0.1-II & 2 & 0.1 \\  & CNTs0.1-III & 3 & 0.1 \\  & CNTs0.1-IV & 4 & 0.1 \\  & CNTs0.3-I & 1 & 0.3 \\  & CNTs0.3-II & 2 & 0.3 \\  & CNTs0.3-III & 3 & 0.3 \\
## 3 Fault Diagnosis Method Based on LSTM and Multi-Sensor Data Fusion
### Attention Mechanism  
For different types of input data, the feature extraction ability of the LSTM network is different. In other words, some components of the input data may dominate the network data. With the attention mechanism introduced, the proposed network can focus on essential hidden features. Therefore, the outputs from different depths of the residual module are fed into the attention layer, which extracts multiscale features and enhances the proposed network prediction accuracy efficiently. The details of the attention block are displayed in Figure 4.  
After full feature extraction from CNN-LSTM module, the characteristics of multi-sensor information acquired are sent to the proposed attention mechanism to optimize feature extraction. In addition to improving the depth of feature extraction, the proposed model can effectively identify specific information and adaptively adjust channel characteristics, which independently learn the critical information of various sensor features in  
Figure 3: Dilated Convolution layer with different dilation rates. (**a**) Kernel size of Dilated Convolution is 3 \(\times\) 3 and dilation rate is 1, which is same as the normal convolution process. (**b**) 2-dilated conv with kernel size is 3 \(\times\) 3 and zero padding; (**c**) 4-dilated conv.  
the fault diagnosis of an A-class insulation panel production line. Hence, the proposed attention block effectively enhances the feature extraction ability.
## 3 The PML-V2 Model
### The Estimation of GPP  
In PML-V2, the photosynthesis rate of leaves, \(A_{g}\), estimated from radiation intensity (\(I\), \(\mu\)mol m\({}^{-2}\)s\({}^{-1}\)) and the concentration of CO\({}_{2}\) (C\({}_{a}\), ppm) using a rectangular hyperbola function ([PERSON] et al., 2018) are shown as follows:  
\[A_{g}=\frac{A_{m}\beta I\eta C_{a}}{A_{m}\beta I+A_{m}\eta C_{a}+\beta I\eta C _{a}} \tag{1}\]  
where \(\beta\) is the quantum efficiency, defined as the initial slope of the response curve of assimilation rate of leaves to light (\(\mu\)mol CO\({}_{2}\) (\(\mu\)mol \(PAR\))\({}^{-1}\)), \(\eta\) is the carboxylation efficiency, defined as the initial slope of the response curve of assimilation rate of leaves to CO\({}_{2}\) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\))\({}^{-1}\)), \(A_{m}\) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) is the maximum photosynthetic rate obtained when both \(I\) and \(C_{a}\) are saturated, which is temperature-dependent ([PERSON] et al., 2016; [PERSON] et al., 2016), shown as follows:  
\[A_{m}=\frac{1}{2}\frac{V_{m,25}\exp[a(T-25)]}{1+\exp[b(T-41)]}, \tag{2}\]  
where \(V_{m,25}\) is a parameter that has the same dimension with \(A_{m}\), and \(a\) and \(b\) are temperature coefficients, which are taken as 0.031 and 0.115, respectively ([PERSON] et al., 2016). The PML-V2 model estimates the canopy carbon assimilation rate \(A_{cg}\) by integrating the \(A_{g}\) of leaves to the canopy scales using \(LAI\), shown as the following:  
\[\begin{array}{ll}A_{cg}=\int_{0}^{LAI}A_{g}dl&=\frac{A_{m}\beta I\eta C_{a} }{k(A_{m}\beta+\beta I\eta C_{a})}\Big{\{}kLAI+ln\frac{(\beta A_{m}+\beta I \eta C_{a})I_{0}+\eta A_{m}C_{a}}{(BA_{m}+\beta I\eta C_{a})I_{0}\exp(-kLAI)+A _{m}\eta C_{a}}\Big{\}}\\ &\qquad\qquad\qquad=\frac{P_{1}C_{a}}{k(P_{2}+P_{4})}\Big{\{}kLAI+ln\frac{P_{ 2}+P_{3}+P_{4}}{P_{2}+P_{3}\exp(kLAI)+P_{4}}\Big{\}}\end{array} \tag{3}\]  
where  
\[P_{1}=A_{m}\beta I_{0}\eta,\quad P_{2}=A_{m}\beta I_{0},\quad P_{3}=A_{m}\eta C _{a},\quad P_{4}=\beta I_{0}\eta C_{a}, \tag{4}\]  
The ecosystem GPP is finally determined using \(A_{cg}\) and a constraint function that reflects the effect of atmospheric dryness (\(D_{a}\), vapor pressure deficit, kPa) on GPP, where \(D_{max}\) and \(D_{min}\) are parameters.  
\[\begin{array}{ll}GPP=f(D_{a})A_{cg}\\ f(D_{a})=\left\{\begin{array}{ll}1,&D_{a}\leq D_{\rm min}\\ \frac{D_{\rm max}-D_{c}}{D_{\rm max}-D_{\rm min}},&D_{\rm min}<D_{a}<D_{\rm max }\\ 0,&D_{a}\geq D_{\rm max}\end{array}\right\}\end{array} \tag{5}\]
## 4 Validation and Discussion
This is because these three parameters vary depending upon the temperature changes and soil properties (e.g., thermal conductivity, soil density, and moisture content). Further research is needed to quantify the effects of temperature changes and soil properties on the developed model. Moreover, the continuous measurement of air temperature is required for this model. If air temperature is not measured for a few days, the accuracy of the proposed model will be reduced. Additionally, the model may have prediction errors at shallow depths (depths \(<15\) cm from the ground surface) due to the spatial variability of soil properties (e.g., soil structure, fabric, or texture). In most cases, it was found that the proposed model can predict soil temperatures for areas where there are differences in soil water contents because it can be assumed that the spatial variability is somewhat uniform at shallow depths. Therefore, it is possible to predict the soil temperatures of areas adjacent to locations at which the air temperature has been measured with some degree of spatial heterogeneity using the soil water content and the measured air temperature. The proposed model can be used to predict long-term daily mean soil temperature for daily mean air temperature and to evaluate changes in soil temperatures caused by the reactions of spatially heterogeneous (e.g., soil properties and thermal conductivity) soils to the climatic changes expected over a regional scale. The accuracy of the proposed model was improved by including the soil water content, which can reasonably represent soil properties. Therefore, it is expected that the model will be applied to evaluate temperature changes due to soil reactions (soil water content), which require further understanding [19], as well as to predict daily mean soil temperatures under the influence of climate change, and the resultant changes in soil hydraulic properties (i.e., soil water content).
# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 4 Discussion  
### Advantages and Limitations of the Attention U\({}^{2}\)-Net Model  
In this study, we proposed and applied an attention U\({}^{2}\)-Net model for mangrove extraction from high-resolution satellite images. The results indicate that our model performs well in mangrove classification. The precision is 92.0%, which is much higher than that of the RF model (52.2%). As shown in Table 3, the attention U\({}^{2}\)-Net model may outperform some other machine-learning or deep-learning methods [25; 26; 27; 28]. One possible reason is that the U\({}^{2}\)-Net can learn from data and optimize the algorithm and parameters automatically, without relying on manual feature extraction or selection. In addition, the attention U\({}^{2}\)-Net model can improve the accuracy and efficiency of mangrove mapping by using a complex and nonlinear network that can capture the spectral and spatial features of mangroves. The attention gate allows the model to focus on the most relevant regions and pixels for mangrove classification. The use of high-resolution images may also contribute to the improvement of model performance.  
Figure 6: GF-1 image with 2 m resolution (**a**), mangrove label image (**b**), and classification results from four models: (**c**) RF, (**d**) U-Net, (**e**) U\({}^{2}\)-Net, and (**f**) attention U\({}^{2}\)-Net.  
A potential limitation of the attention U2-Net model is that it was developed based on satellite data acquired in three cities of Guangdong Province. Is the model applicable to the other regions? This has not been verified yet. This will be completed in the near future after more high-resolution satellite data are collected
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 3 Hierarchical Phoneme Classification
### Voiced and Unvoiced Consonants Classification  
Several pairs of different consonants are distinguished by being voiced or unvoiced. For voiced sounds, the glottis is closed or nearly closed, whereas for voiceless sounds it is wide open, with the distance between the folds actually amounting to only a fraction of a millimeter [18]. The voiced sounds carry periodic pulses generated by the glottis vibration, while almost no periodicity but turbulent noise is observed in the unvoiced sounds. Phonemes [b], [p], [d], and [t], all belong to the stops group in the phonetic classification. However [b], [d] are voiced phonemes and [p], [t] are unvoiced ones. Phonemes [z] and [s] are both fricatives, but [z] is voiced and [s] unvoiced. These pairs of examples are usually hard to distinguish because they are generated at the almost same place in the oral cavity area. Similarly, [p] and [b] are produced at the same place in the mouth and the only difference is being voiced or unvoiced. If we add a feature that helps distinguish voiced and unvoiced phonemes especially when recognizing consonants and create a model that can classify them, the performance may be improved. Therefore, we use autocorrelation functions to add the periodicity feature of the phoneme sound if the found phoneme falls into consonant categories  
Voiced and unvoiced sounds can be distinguished by pitch, or equivalently fundamental periodicity values [47]. Autocorrelation measures the similarity function of the time lag with itself in the signal [19; 20]. Due to the vibration of the vocal cords, the voiced sound is periodic and this vibration interval is pitch. Autocorrelation is a unique case of cross-correlation [48; 49]. The cross-correlation measures the similarity of the two discrete signals, \(x[n]\) and \(y[n]\), defined by [50]:  
\[\sum_{n=-\infty}^{\infty}x[n+\tau]y[n]\, \tag{1}\]  
where \(\tau\) is an integer time lag that takes values from \(\{0,\pm 1,\pm 2,\dots,\}\). If two discrete signals are identical, this process is defined as autocorrelation, which is given by:  
\[ac(x,\tau)=\sum_{n=-\infty}^{\infty}x[n+l]x[n]. \tag{2}\]  
Algorithm 1 is the pseudo code for the autocorrelation computation. The array \(x\) is the speech signal, \(N\) is the size of signal, and the autocorrelation function is denoted by _ac_. Figure 5(a) shows the waveform of a voiced consonant phoneme [d] and its autocorrelation computed by Algorithm 1. Likewise, Figure 5(b) is for an unvoiced consonant phoneme [t]. The autocorrelation of phoneme [d] shows nonzero values, while that of phoneme [t] has almost zero, meaning that there is no significant pitch. From these results, autocorrelation can help in distinguishing voiced and unvoiced phonemes.
## 2 Materials and Methods
### SXR Full-Field Microscope  
To focus the SXR radiation from the nitrogen plasma an ellipsoidal, axi-symmetrical SXR condenser (Rigaku, Prague, Czech Republic) coated with nickel film was used, as depicted in Figure 1c. A titanium filter (200 nm thick, Lebow Co., Goleta, CA, USA), positioned downstream the condenser, selects the He-like nitrogen line at \(\lambda\) = 2.88 nm from nitrogen plasma emission, suitable for the objective optic. Filtered SXR radiation illuminates the sample/object, positioned 140 mm downstream the condenser, in the second focal point. A FZP objective (Zoneplates Ltd., Claverings Industrial Estate, London, UK) was used to form a magnified image onto a back-illuminated SXR-sensitive CCD camera (Andor, iKon-M DO-934-BN, 1 Mpixel, 13 \(\times\) 13 \(\upmu\)m\({}^{2}\) pixel size) in transmission mode. A silicon nitride FZP (400 nm thick, 250 \(\upmu\)m in diameter, outer zone width of 30 nm) with a focal length \(f\) = 2.6 mm at 2.88 nm wavelength, was used as an objective. The numerical aperture of the zone plate was matched to the numerical aperture of the condenser, providing incoherent illumination [35]. The image plane was located 57 cm from the zone plate resulting in the geometrical magnification of the system of \(\sim\)220\(\times\). It was already demonstrated that such a system is capable to achieve a half-pitch spatial resolution of \(\sim\)60 nm [36] (corresponding to half-period of the grating imaged under incoherent illumination conditions for a given numerical aperture of the objective--smallest feature that the microscope can resolve). More details about the microscope and its technical component can be found in [37]. The photograph of this system is depicted in Figure 2a.  
Figure 2: Photographs of three EUV/SXR imaging systems: (**a**) photograph of the SXR full-field microscope, (**b**) photograph of the EUV full-field microscope, and (**c**) photograph of the contact SXR microscope developed at the Institute of Optoelectronics, Military University of Technology, Warsaw.