esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Distortion and Perceptual Measures
### Perceptual Measures in Information Theory
#### 2.2.3 Wasserstein Distance  
The Wasserstein distance, rooted in the theory of optimal transport, is a powerful tool with broad applications in deep learning as a perceptual measure [33]. It has also garnered significant attention in information theory, particularly following the pioneering work of [PERSON] on distance-divergence inequalities [34; 35].  
**Definition 2**.: _Given \(p\geq 1\) and a Polish space \((\mathcal{X},c)\), the \(L_{p}\)-Wasserstein distance is defined as_  
\[W_{p}(P_{X},P_{Y})=\inf\left(\mathrm{E}[c^{p}(X,Y)]\right)^{\frac{1}{p}},\]  
_where the infimum is over all jointly distributed random variables \((X,Y)\in\text{Prob}(\mathcal{X}\times\mathcal{X})\)._  
For each \(p\geq 1\), \(W_{p}(,)\) defines a metric on \(\text{Prob}(\mathcal{X})\). In practice, \(W_{1}\) and \(W_{2}\) are most widely used.
###### Abstract
It shows a strong ability in pattern recognition and classification, which provides a reference for the progress of flow pattern identification technology. More and more scholars have applied machine learning technology to the field of flow pattern recognition and developed a series of well-performing flow pattern recognition methods. [PERSON] [22] used ultrasonic echoes and RBF neural network to identify the flow patterns in a horizontal pipe. [PERSON] [23] utilized the complex network theory to identify the flow patterns in water pipelines. [PERSON] [12] applied a neural network with the temperature fluctuation on pipe walls in classifying flow patterns. In addition to neural networks, support vector machine (SVM) technology is also widely used in flow pattern recognition. [PERSON] [24] identified the oil-gas two-phase flow pattern based on SVM and electrical capacitance tomography technique. [PERSON] [25] used doppler spectrum analysis and SVM to identify the flow pattern of oil-water two-phase flow. [PERSON] [26] used void fraction time series and SVM to classify the two-phase flow pattern in a vertical pipe. It can be seen that the flow pattern recognition method combining signal feature extraction and machine learning technology has been introduced on some occasions. Unfortunately, compared with the importance of two-phase flow pattern identification in porous media, the research on this technology is far from enough. The present study proposes a new method to identify the gas-liquid two-phase flow pattern in porous media based on differential pressure signals and machine learning technology. The time domain characteristics and time-frequency domain (EMD) characteristics of the differential pressure signal are obtained. Using these features, a variety of flow pattern identification models based on SVM and BP neural networks are constructed. The comparison of different models verifies the performance of SVM and BP neural networks in porous media flow pattern recognition. Finally, an intelligent online flow pattern identification system for porous media is constructed by using three optimal models and integrated recognition technology. Compared with the single model, the identification ability of the system has been improved. This paper provides a new idea for the development of flow pattern identification methods in porous media in the fields of the chemical industry, agriculture, petroleum, and nuclear engineering.
# On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs
## 7 Conclusions  
With an increasing concern for the environment, it is becoming a key aspect for governments to boost smart and sustainable transportation systems. While the literature on rich vehicle routing problems (VRPs) is extensive, some issues remain to be addressed. For example, most works assume that the traveling times between pairs of nodes are both known in advance and static. In contrast, this paper has focused on the dynamism characterizing many real-world routing applications, where traveling times are dynamic in nature.  
Taking into account the high number of factors that might affect the traveling time (from accidents to the weather), we introduce the VRP with dynamic traveling times. In particular, these times vary dynamically as the routing plan is being generated. Therefore, they need to be re-evaluated dynamically during the optimization process. As far as we know, this is the first work addressing this version of the dynamic VRP. In order to deal with this problem, a hybrid solving approach has been presented. Our approach integrates a statistical learning technique into a multi-start framework. A comprehensive set of computational experiments have been carried out to quantify the differences between ignoring or not the dynamism in traveling times among many realistic scenarios. The results show an average gap around 12.02%, with all gaps being positive (i.e., it always pays off to take this dynamism into account while designing the routing plan).  
Several lines of research stem from this work. For instance, we have employed a linear model as an emulator of a real system that provides data for building/updating our model. Thus, analyzing the convenience of building a more sophisticated emulator would be an interesting research line. If the function was not linear, it could make sense to apply and compare the performance of different statistical learning techniques. In case of requiring time-consuming techniques, the use of a distributed computing system could be tested. Finally, it would be interesting to model richer VRPs (heterogeneous fleet, multiple depots, etc) considering also dynamic traveling times influenced by the manager decisions.  
**Author Contributions:** Conceptualization, Formal Analysis, Supervision, and Writing, A.A.J. and I.S.; Software Implementation and Validation, Q.A.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work has been partially carried out in the context of the Erasmus+ program (2017-1-ES01-KA103-036672).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# UAV Support for Mission Critical Services
## 3 UAV Characteristics, Classifications and Commercial Deployments Status
### UAVs as a Part of the Communication Infrastructure
The preferred UAVs for UAVs are multi-rotor UAVs as the speed and range is not essential in the mentioned case. The available average transport capabilities of commercial Micro, Mini and Small UAVs are sufficient to carry a UAV-based node with computing and heterogeneous networking capabilities (5G/4G, WiFi, Bluetooth). Such node weight can be estimated to be 2-5 kg (a combination of smartphone hardware with ultrabook hardware with batteries but without displays). The battery lifetime of Mini and Small UAVs (1-5 h) is sufficient for MCS, as UAVs can be replaced by new ones when the battery is exhausted.
## 2 Finite Element Modeling for Thermal Analysis  
In this research, a high-fidelity thermal analysis model is constructed to conduct precise thermal analysis for the LPBF process simulation using ABAQUS [20]. Figure 1 shows the finite element model and scan strategy, which are used to estimate the melt pool size from thermal analysis. Figure 2 shows the result of the melt pool size with respect to the number of tracks obtained from the test thermal analysis with the process parameters and analysis parameters in Table 1. This test thermal analysis is performed to determine the dimension of the finite element model by identifying the tendency of the thermal analysis results. The result shows that the melt pool size almost converges to a constant after the fifth track. According to [PERSON] et al. [4], the melt pool size of each track stays almost constant after about 2 mm from the beginning of the track. Considering these, the model is composed of 6 tracks with a length of 3 mm. The dimensions of the model are 5 \(\times\) 4 \(\times\) 1 mm, and the dimensions of the scan area where the laser scans the powder bed are 4 \(\times\) 3 \(\times\) 1 mm. As shown in Figure 1, 6 lines are sequentially scanned in the x direction, and they are continuously scanned without pause after scanning each track.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Process parameter} & Power (W) & 120 \\  & Speed (mm/s) & 150 \\  & Radius (mm) & 0.2 \\  & Hatch spacing (mm) & 0.3 \\ \hline \multirow{4}{*}{Analysis parameter 1} & Convection coefficient \(\left(\text{W}/\text{m}^{2}\text{K}\right)\), \(h_{c}\) & 10 \\  & Emissivity, \(\varepsilon\) & 0.3 \\ \cline{1-1}  & Conductivity coefficient, \(\alpha\) & 0.015 \\ \cline{1-1}  & Porosity, \(\varphi\) & 0.4 \\ \cline{1-1}  & OPD, \(\left(\text{\,\upmu m}\right)\), \(H\) & 140 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Process and analysis parameters used in the analysis of Figure 2.  
Figure 1: Finite element analysis model and scan strategy.  
According to [PERSON] et al. [21], if the element size is one-fourth of the laser beam diameter, steep temperature gradients can be simulated sufficiently. Therefore, the element size of the scan area is set to 0.1 \(\times\) 0.1 mm, and the other regions are filled with coarser elements for computational efficiency. The size of elements in the thickness direction becomes smaller toward the top surface. Considering the thick powder bed and low conductivity of the powder, the base plate is not modeled, and AISI 316L stainless steel powder, which is widely used in the LPBF process, is used as the process simulation material [4].
## 2 Materials and Methods
### Database
#### 2.2.1 Saarbrucken Voice Database (SVD)  
The speech files were extracted from the German Saarbrucken Voice Database (SVD), available online by the Institute of Phonetics at the University of Saarland [36].  
The database contains voice signals from over 2000 subjects with voice disorders as well as normophonic voices (controls). Each subject has the recording of phonemes /a/, /i/, and /u/ at the low, neutral/normal, and high tones, varying between tones, and the German phrase. \"Guten Morgen, wie geht es Ihnen?\" (\"Good morning, how are you?\"). The size of the sound files is between 1 and 3 s, recorded with 16 bits resolution, mono, and a sampling frequency of 50 kHz.  
In this analysis, 10 control subjects (5 male and 5 female) speech files were used, and another 10 subjects (5 male and 5 female) with the 3 voice disorders were also used. Patient subjects consisted of four subjects with chronic laryngitis, two male and two female; four with dysphonia, of which two were males and the other two females; and two with paralysis of the vocal folds, one of each gender. According to [PERSON] et al., 2020 [13], there is no statistically significant difference in Autocorrelation, HNR, and NHR between the audios of subjects with chronic laryngitis, dysphonia, and paralysis of the vocal folds.  
The control subjects were between 40 and 65 years of age, with a mean of 55 years and a standard deviation of 8 years, and the patient subjects were between 16 and 77 years of age, with a mean of 51 years and a standard deviation of 19 years. Only subjects with the 9 audio files without audible disturbances related to awful voice quality (unvoiced speech) or technical issues were selected.  
The set of extracted features (Autocorrelation, HNR, NHR, and others) using the algorithm can be found at [[http://www.ipb.pt/~joaopt/products/CuredDatabase/base_d](http://www.ipb.pt/~joaopt/products/CuredDatabase/base_d)]([http://www.ipb.pt/~joaopt/products/CuredDatabase/base_d](http://www.ipb.pt/~joaopt/products/CuredDatabase/base_d)) e_dados_curada.xlsx, (last access on 18 September 2022).
## 5 Conclusions  
In this study, we evaluated the ability of three different approaches, SIF, LUE-based MuSyQ-GPP, and process-based BEPS, to estimate the diurnal courses of GPP at a maize site. In general, all three approaches adequately estimated the GPP, and the process-based BEPS model had the best performance, followed by the LUE-based MuSyQ-GPP algorithm and the SIF-based statistical model. The MuSyQ-GPP algorithm underestimated the GPP at high productivity, which can be attributed to its weakness in characterizing the underlying ecological processes of vegetation and the contribution of shaded leaves to total GPP. In contrast, the BEPS model that was established based on the two-leaf theory and physiological mechanisms can provide more accurate estimations. Our study indicated that the SIF\({}_{760}\)-based statistical model can provide reasonable estimates of GPP in the short term without additional information such as climate inputs, which are required by the process-based and LUE models.  
The observational approach revealed that both SIF\({}_{760}\) and GPP exhibited similar diurnal patterns as PAR, both SIF\({}_{760}\) and GPP were linearly correlated with APAR, and a linear function could well characterize the relationship between SIF\({}_{760}\) and GPP for maize over our study period. Despite its limitations in applying to large area and long-term studies, SIF provided a proxy for GPP not available from any other remote sensing measurements of vegetation.  
The following are available online at www.mdpi.com/2072-4292/9/12/1267/s1, Text S1: Estimation of water stress factor in the MuSyQ-GPP algorithm, Text S2: Performance of the SIF\({}_{686}\)-based GPP model, Figure S1: Diurnal patterns of PAR, GPP and SIF\({}_{686}\) during the experiment, Figure S2: Relationship between APAR and individual SIF\({}_{686}\) and averaged SIF\({}_{686}\), Figure S3: Relationship between individual SIF\({}_{686}\) and GPP, averaged SIF\({}_{686}\) and GPP, Figure S4: Relationships between SIF\({}_{686}\)-based GPP and EC-based GPP during the experiment period.  
This work was supported by the National Key R&D Program of China (2017 YFA0603002) and the National Natural Science Foundation of China (61661136006001, 41471349, and 41531174). We would like to thank [PERSON] and [PERSON] from University of Toronto for providing the BEPS model.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] implemented the experiments; [PERSON] preprocessed and analyzed the data; and [PERSON] and [PERSON] prepared the paper.  
The authors declare no conflict of interest.
## 4 Numerical Examples  
In this section, we consider the numerical examples of the proposed method (IDSM) for solving the distributed matrix game problem of the following form:  
\[\min_{\mathbf{u}\in\Delta_{p}}\max_{\mathbf{v}\in\Delta_{r}}\sum_{i=1}^{m}F_{i}( \mathbf{u},\mathbf{v}),\]  
where the constrained sets \(\Delta_{p}\subset\mathbb{R}^{p}\) and \(\Delta_{r}\subset\mathbb{R}^{r}\) are standard simplices in \(\mathbb{R}^{p}\) and \(\mathbb{R}^{r}\), respectively, and the component coupling function \(F_{i}:\mathbb{R}^{p}\times\mathbb{R}^{r}\rightarrow\mathbb{R}\) for each \(i=1,\ldots,m\) is given by  
\[F_{i}(\mathbf{u},\mathbf{v})=\left\langle\mathbf{u},\mathbf{A}_{i}\mathbf{v} \right\rangle+\frac{1}{2}\|\mathbf{u}\|^{2}-\frac{1}{2}\left\|\mathbf{v}- \frac{1}{m}\mathbf{1}_{r}\right\|^{2},\]  
where \(\mathbf{1}_{r}\) is the vector in \(\mathbb{R}^{r}\), all of whose components are \(1\). We generated \(p\times r\) matrices \(\mathbf{A}_{i}\) for each \(i=1,\ldots,m\) randomly, where its entries were independently and uniformly distributed in the interval \((-10,10)\). We compared the IDSM with the existing methods NO-09 and AN-23. We set the delay sequences with the cyclic order delays \(\tau_{i}^{k}=\mu_{i}^{k}=k\mod(\tau_{\max}+1)\) for all \(k\geq 0\) and \(i=1,\ldots,m\), with the delay bounds \(\tau_{\max}=0,5\), and \(10\) for both the IDSM and AN-23. We set the step-size constant as \(\left(\frac{1}{2+(\tau+1)^{2}}\right)^{1/0.99}\) for the IDSM and \(\left(\frac{2}{1+2(\tau+1)^{2}}\right)^{1/0.99}\) for AN-23 and NO-09. We performed \(100\) independent random tests and terminated all tested methods when the number of iterations reached \(100\). We examined the considered problem in various problem sizes \(p\times r\), as detailed below.
## 3 Methodology
### Datasets
#### 3.1.2 Limitations  
The referred datasets are lacking in the type and number of categories for urban sounds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**BDLib Dataset**} & \multicolumn{2}{c}{**ESC-10 Dataset**} & \multicolumn{2}{c}{**UrbanSound Dataset**} \\ \hline
**Categories** & **Total Time (s)** & **Categories** & **Total Time (s)** & **Categories** & **Total Time (s)** \\ \hline Airplane & 100 & Dog barking & 200 & Air conditioner & 6577 \\ Alarms & 100 & Baby crying & 200 & Car horn & 4819 \\ Applause & 100 & Clock tick & 200 & Children playing & 13454 \\ Birds & 100 & Person sneezing & 200 & Dog bark & 8399 \\ Dogs & 100 & Helicopter & 200 & Drilling & 4864 \\ Footsteps & 100 & Chainsaw & 200 & Engine idling & 3604 \\ Motorcycles & 100 & Rooster & 200 & Gun shot & 7865 \\ Rain & 100 & Fire cracking & 200 & Jackhammer & 4328 \\ Sea waves & 100 & Sea waves & 200 & Siren & 4477 \\ Wind & 100 & Rain & 200 & Street music & 6453 \\ Rivers & 100 & & & & \\ Thunderstorm & 100 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the most popular open source datasets for urban sound and ESR.  
* **Large number of categories per dataset**: Whereas the UrbanSound dataset only contains 10 urban sound categories, the ESC-50 dataset contains 50 categories, but many are not audio recordings from the urban environment. A larger number of categories would enable the recognition of more urban sounds.
* **Small number of audio samples per categories**: The datasets do not contain a large number of audio samples, which would benefit the training of the classifiers.
* **Irrelevant categories**: Sound categories such as _sea waves_ or _applause_ available in the BDLib dataset are not of interest for urban sound recognition.  
These limitations can be alleviated by rearranging or combining datasets. Sounds belonging to the same category can be grouped and similar categories can be fused to create new categories. The creation of a new dataset from the existing datasets is detailed in Section 5.4.1.
In other words, screening out a small number of wavelengths using appropriate algorithms can improve modeling efficiency and prediction accuracy. The typical wavelength selection algorithms such as competitive adaptive reweighted sampling (CARS), successive projections algorithms (SPA), random frog, and variable combination population analysis (VCPA) have been applied in spectral data analysis [21; 22]. Neighborhood rough set (NRS) is a feature selection method with strong applicability. Its biggest advantage is to use the equivalence relationship between the upper and lower approximation sets to obtain the core knowledge of the data without losing any effective information [23], thereby reducing information and completing data dimensionality reduction [24]. [PERSON] [25] utilized hyperspectral technology and the NRS algorithm to select feature bands and build a classification model for soybean varieties. [PERSON] [26] successfully combined NRS with hyperspectral images for detecting the degree of apple powdering. However, the complexity of most models can be further simplified, and accuracy and stability still need to be improved. In addition, the research on spectral feature wavelength selection based on NRS algorithms is relatively limited, and a complete set of extraction methods has not been formed. The application modes facing different samples are still in the exploratory stage. In this study, the NRS algorithm is introduced into the field of the spectral non-destructive detection of paddy quality and safety, and further applies it to effectively extract spectral features of different types of paddy. In particular, using the storage state of paddy as a classification standard and establishing a qualitative model for identifying it is rarely reported in the literature. Based on the description above, this study takes japonica paddy as the research object, and uses NIR technology combined with feature selection methods such as neighborhood rough sets to establish classification models and discriminate the suitable storage status of paddy, achieving rapid non-destructive testing of the safety and quality of the stored paddy. The specific aims were (1) to collect the NIR data (1000-1800 nm) of paddy samples during storage; (2) to detect the fatty acid of paddy and identify three grades (storage status) by these values; (3) to extract the spectral features using NRS, SPA, and VCPA algorithms and compare their differences; (4) to construct classification models using random forest (RF), extreme learning machine (ELM), and soft independent modeling of class analogy (SIMCA) classifiers with spectral features to distinguish the different storage statuses of paddy; and (5) to compare performances of different models.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
###### Abstract  
Accurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic growth in computational cost as image resolution increases. To address these issues, we propose HCMUNet, a novel medical image segmentation model that innovatively combines the local feature extraction capabilities of CNNs with the efficient long-range dependency modeling of Mamba, enhancing feature representation while reducing computational cost. In addition, HCMUNet features a redesigned skip connection and a novel attention module that integrates multi-scale features to recover spatial details lost during down-sampling and to promote richer cross-dimensional interactions. HCMUNet achieves Dice Similarity Coefficients (DSC) of 90.32%, 81.52%, and 92.11% on the ISIC 2018, Synapse multi-organ, and ACDC datasets, respectively, outperforming baseline methods by 0.65%, 1.05%, and 1.39%. Furthermore, HCMUNet consistently outperforms U-Net and Swin-UNet, achieving average Dice score improvements of approximately 5% and 2% across the evaluated datasets. These results collectively affirm the effectiveness and reliability of the proposed model across different segmentation tasks.  
medical image segmentation; Mamba; Convolutional Neural Networks; hybrid architectures; State Space Model +
Footnote †: journal: Medical Image Segmentation