esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Machine-Learning Algorithms  
Based on the GEE platform, Sentinel-2 hyper-spectral images were utilized for processing. A large number of high-quality samples were selected by visual interpretation, and the three popular machine-learning algorithms of SVM, CART, and RF were employed to identify and map winter wheat in a large-scale region.  
\[y_{i}(wx_{i}+b)\geq 0 \tag{2}\]  
where \(x_{i}\) is the point lying on the boundary of hyperplane, \(w\) is the weight vector and \(b\) is called the bias.  
SVM is a classification algorithm based on statistical learning theory, which was first proposed by [PERSON] [47]. The basic theory is to find the optimal hyperplane of the feature space in order to determine the maximum interval between different classes (Equation (2)) [47]. The hyperplane of SVM is defined by Equation (2) [47], and these points on the boundary of the hyperplane are called support vectors, which directly affect the final classification performance. Owing to this feature, many previous works have proved that SVM performs well in small data and multi-feature data sets [33; 48].  
Figure 3: Overall workflow of this study.  
At present, it is very common to employ SVM to conduct land cover research, crop identification, and classification [49; 50; 51; 52].  
\[G=\sum\sum_{j\
eq i}(f(C_{i},T)/|T|)\Big{(}f\Big{(}C_{j},T\Big{)}/|T|\Big{)} \tag{3}\]  
where \(T\) is a given training set, \(f(C_{i},T)\) is the probability that the selected case belongs class \(C_{i}\).  
CART is a tree-based machine learning algorithm, which [PERSON] discussed in detail [53]. It is a non-parametric computationally intensive algorithm that can be used for both classification and regression [54]. The basic mechanism is based on a split criterion to perform binary recursive classification on data sets, which can process continuous or discrete attributes as targets or predictors [53]. The Gini index defined by Equation (3) is used to select the feature at each internal node of the decision tree [55]. CART is a popular machine-learning algorithm, which is also widely used in disaster monitoring [56], vegetation growth [26], and crop classification [15].  
The RF algorithm was first proposed by [PERSON] [57]. Its essence is that a classifier is ensembled by many decision tree classifiers \(\{h(x,\Theta_{k})\), \(k=1\), \(\ldots\}\), where the \(\{\Theta_{k}\}\) are independent identically distributed random vectors and \(x\) is an input pattern [57; 58]. Each tree votes on the results, and the classification result with the most votes is chosen as the final classification result. Moreover, the Gini index is chosen for RF, owing to its simplicity [55]. Because RF assembles many trees as the basis for classification, the more independent the trees are, the smaller the generalization error is [57]. Owing to the fact that RF has high classification efficiency, even though the number of samples is large, increasingly researchers employ RF to carry out related research in agricultural fields [9; 50].
## 4 Mmpl3 Transporter: The Other Hot TB Target of the Moment
### Mmpl3: A Mycolic Acid Transporter
Surprisingly, the 3D structure of this transporter is different from other RND transporters like AcrB, MexB, CusA, MtrD, CmeB and HpnN. The structure shows that the \(N\)-terminal and C-terminal halves are assembled in a twofold pseudo symmetrical fashion and the two periplasmic domains (PD1 and PD2) interact with each other and are also flexible. MmpL3 possesses a cavity that extends from the outer leaflet of the inner membrane up to the periplasmic domain. This cavity is large enough to fit a TMM molecule. Fortuitously, MmpL3 co-purified with n-dodecyl-\(\beta\)-d-maltoside (the detergent used for solubilization) and phosphatidylethanolamine (PE) bound to it. The former was found in the hydrophobic pocket created by TMs 7-10 while the latter was bound to the periplasmic domain within the large space between PD1 and PD2. These results reinforce the idea of MmpL3 channeling its substrate from the outer leaflet of the inner membrane up to the periplasmic space. The authors further demonstrated that TMM binds to purified MmpL3 with a K\({}_{\text{d}}\) of \(3.7\pm 1.3\) uM while there was no binding when TDM was assayed. In addition to PE and TMM, both phosphatidylglycerol and cardiolipin interacted with MmpL3. Whether these phospholipids act as substrates for MmpL3 in whole cells remains to be determined. Native mass spectra, Small-angle X-ray scattering (SAXS) data and Blue-Native PAGE of the purified MmpL3 transporter indicate that it is a monomer in detergent solution [25; 87]. It has to be noted that in both cases MmpL3 was purified devoid of the \(C\)-terminal domain due to its instability in solution and this domain was shown to be important for MmpL3 oligomerization [83]. This is in contrast to the quaternary structure of CmpL1, a _Corynebacterium glutamicum_ ortholog, which has been shown to possess a trimeric quaternary structure [81]. There is a great variety of quaternary structures among the RND superfamily of transporters, while the efflux subfamily of RND transporters, namely, AcrB [88], MexB [89] and MtrD [90] exist as trimers, the hopanoid biosynthesis-associated transporter (HpnN) from _Burkholderia multivorans_ is a dimer [91] and human NPC1 is a monomer [92]. Due to this high variability, we cannot rule out that MmpL3 has a higher oligomerization state in native conditions found in the mycobacterial cell membrane.
## 3 Results and Discussion
### Writing Desk by [PERSON]
Figure 5: Elemental maps of the first area of the drawer obtained by the INFN-CHNet MA-XRF scanner. (**a**) Visible. The maps presented are: (**b**) Ca K\(\alpha\) transition; (**c**) Pb L\(\beta\) transition; (**d**) Pb L\(\alpha\)/As K\(\alpha\) transitions; (**e**) As K\(\beta\) transition; (**f**) Cu K\(\alpha\) transition; (**g**) Fe K\(\alpha\) transition; (**h**) Mn K\(\alpha\) transition. Figure 6: Elemental maps of the second area of the drawer. (**a**) Visible. The maps presented are: (**b**) Ca K\(\alpha\) transition; (**c**) Pb L\(\beta\) transition; (**d**) Pb L\(\alpha\)/As K\(\alpha\) transitions; (**e**) Fe K\(\alpha\) transition; (**f**) Cu K\(\alpha\) transition. Figure 7: Spectra of the two points analysed with XRF: (**a**) red tone; (**b**) black tone. The presence of Ca probably due to ivory was detected in both. On the external side, the two scans have been merged. As a result, elemental maps of the whole area indicated in Figure 4b were realised and are shown in Figure 8. In the area are green leaves, a dark blue-black stem and different flowers of red, black-blue and brown-orange tones. Some similarities with the drawer can be noted. In the shading of the red flower, mercury has been detected (Figure 8d), possibly due to the use of cinnabar-vermilion (HgS). Regarding the black-blue flowers, the same elements as in the black-blue flowers of the drawer--iron (Figure 8g) and lead (Figure 8c)--are present. Therefore, the decoration may have been realised with Prussian blue and lead white. In the blue-black stem area, the signals of iron and manganese are present in the elemental maps due to the probable use of iron-oxide-based compounds enriched with manganese oxides or other ochres-earths. Regarding the dull-yellow flower, no map shows evidence of the presence of a characteristic element; therefore, the use of organic compounds can be hypothesised. Conversely, regarding the green parts in the drawer, a strong signal of copper (Figure 8e) associated with chlorine traces (Figure 8f) is present in the leaves on the side, whereas the presence of arsenic was not detected. This result led to the hypothesis of the use of different pigments in the green areas on the external side compared with the same colour in the drawer. Because the MA-XRF analysis does not allow the identification of compounds, it is not possible to determine which of the copper-based pigments has been used, or even if it was due to degradation effects as reported in [44]. A summary of the pigments hypothesised by the use of MA-XRF analysis is reported in Table 3.  
Figure 8: Elemental maps of the external area of the _Scrivania con scansia_ obtained by the INFN-CHNet MA-XRF scanner. (**a**) Visible. The maps presented are: (**b**) Ca K\(\alpha\) transition; (**c**) Pb L\(\alpha\) transition; (**d**) Hg L\(\alpha\) transition; (**e**) Cu K\(\alpha\) transition; (**f**) Cl K\(\alpha\) transition; (**g**) Fe K\(\alpha\) transition; (**h**) Mn K\(\alpha\) transition.
# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Introduction to Automated Machine Learning (AutoML)  
Definition and Scope  
AutoML represents a significant advancement in artificial intelligence, aiming to simplify and democratize the machine learning process. Traditionally, developing machine learning models has required a high level of expertise in data science, including deep knowledge of algorithms, data preprocessing techniques, model selection, and hyperparameter tuning [11, 26]. AutoML addresses these challenges by automating the entire workflow, enabling non-experts to create and deploy high-quality machine-learning models with minimal manual intervention. [PERSON] et al. [27] describe AutoML as an emerging sub-area of machine learning where machine learning models are automatically selected, composed, and parametrized to ensure optimal performance. [PERSON] et al. [28] explain that this is achieved by introducing functions such as no-code or low-code features requiring minimal machine learning and statistics knowledge. The rise of AutoML can be associated with the increased number of software companies that provide customizable software solutions, allowing businesses to tailor their models for specific workloads.  
The scope of AutoML extends across various industries and applications, from optimizing marketing strategies to improving supply chain operations and enhancing customer experience. [PERSON] and [PERSON] [12] explain that AutoML automates complex and time-consuming tasks involved in machine learning, allowing businesses to focus on leveraging the insights gained from their data rather than getting bogged down in the technical details of model development. This is evidenced in [PERSON] et al. [28] research, which found that companies without tech expertise often purchase user-friendly software solutions to create custom machine learning models used in logistics, production, and supporting procedures, such as natural language processing (NLP). This broad applicability makes AutoML a versatile tool for organizations looking to utilize the power of data-driven decision-making.
# A General Model for Side Information in Neural Networks
## 5 Materials and Methods  
As a proof of concept of our model of side information, we consider two datasets. The first is the _Modified National Institute of Standards and Technology_ (MNIST) dataset [16]. It is a handwritten, \(28\times 28\) pixel image, digit database consisting of a training set of 60,000 instances and a test set of 10,000 instances. These are the standard sizes of training and test for MNIST, which result in about 85.7% of the data being used for training and about 14.3% reserved for testing. This is a classification problem where there are 10 labels each depicting a digit identity between 0 and 9.  
Our second experiment is based on the House Price dataset, which is adapted from the Zillow's home value prediction Kaggle competition dataset [25], which contains 10 input features as well as a label variable [26]. The dataset consists of 1460 instances. The main task is to predict whether the house price is above or below the median value according to value of the label variable being 1 or 0, respectively, so it is therefore a binary classification problem.  
For both experiments we utilised a _fully connected feedforward neural network_ (FNN) containing two hidden layers, as well as a softmax output layer; see also Figure 1 and Algorithm 1 regarding the modification of the neural network to handle side information. Our experiments mainly aim at evaluating whether or not the utilisation of side information is beneficial to the performance of the learning algorithm, i.e., whether it is useful. This is achieved by evaluating the difference between the respective NN losses without and with side information. The experiments demonstrate the effectiveness of our approach in improving the learning performance, which is evidenced by a reduction in the loss (i.e., improvements in the accuracy) of the classifiers, for each of the two experiments, during the test phase.  
In both experiments, all the reported classification accuracy values and usefulness estimates (according to (5) and (6)) are averaged over 10 separate runs of the NN. For every experiment, we also report the statistical significance values of the differences between the corresponding classification accuracy values, without and with side information. To this effect, we perform a paired \(t\)-test to assess the difference in accuracy levels obtained, where the null hypothesis states that the corresponding accuracy levels are not significantly different.  
For each run out of the 10 repetitions, the parameters of the NN are initialised randomly. In addition, the order via which the input data are presented to the network is also randomly shuffled during each run. Also, missing side information values are replaced with the average value of the side information for the respective class label. The number of epochs used is 10, with a batch size of 250. The adopted loss used in both experiments is the cross-entropy loss.  
Before presenting the results in Section 6, we describe some additional detail for the two datasets in Sections 5.1 and 5.2.
###### Abstract  
A Recurrent Neural Networks (RNN) based attention model has been used in code-switching speech recognition (CSSR). However, due to the sequential computation constraint of RNN, there are stronger short-range dependencies and weaker long-range dependencies, which makes it hard to immediately switch languages in CSSR. Firstly, to deal with this problem, we introduce the CTC-Transformer, relying entirely on a self-attention mechanism to draw global dependencies and adopting connectionist temporal classification (CTC) as an auxiliary task for better convergence. Secondly, we proposed two multi-task learning recipes, where a language identification (LID) auxiliary task is learned in addition to the CTC-Transformer automatic speech recognition (ASR) task. Thirdly, we study a decoding strategy to combine the LID into an ASR task. Experiments on the SEAME corpus demonstrate the effects of the proposed methods, achieving a mixed error rate (MER) of 30.95%. It obtains up to 19.35% relative MER reduction compared to the baseline RNN-based CTC-Attention system, and 8.86% relative MER reduction compared to the baseline CTC-Transformer system.  
speech recognition; code-switching; Transformer; multi-task learning; language identification
# Laboratory Performance Evaluation of Hot-Mix
## 6 Summary and Conclusions  
This study aimed to systematically explore the influence of three different design parameters, namely aggregate gradation, \(N_{\rm des}\), and asphalt type, on the properties of HMAs. The high- and low-temperature, fatigue, and dynamic modulus properties of the mixture were included for evaluation. Based on the findings, the following conclusions can be drawn:  
1. The high-temperature resistance of HMAs increased with the rise in \(N_{\rm des}\), the reduction in asphalt penetration grade and the finer gradation. In addition, the contribution of increasing gyration number for shear resistance enhancement is able to make up for the shear resistance loss caused by increasing asphalt penetration grade.  
2. The flexural-tensile strain of HMAs at low temperatures increased with the lower \(N_{\rm des}\) or coarser gradation. In addition, when the asphalt penetration grade increased to one higher level, the flexural-tensile strain of the HMA increased by around 17 \(\sim\) 25%. Moreover, the low-temperature resistance loss caused by harder asphalt binder can be restored through using lower \(N_{\rm des}\) or coarser gradation by increasing asphalt content.  
3. The fatigue life of HMAs increased by about 32% when the \(N_{\rm des}\) decreased by 25. The HMA fatigue life rised around by 12 \(\sim\) 15% when the asphalt grade decreased to one lower level. Moreover, when both \(N_{\rm des}\) and asphalt grade decreased by one level together, the fatigue life could be significantly improved by approximately 36 \(\sim\) 41%. This may indicate that the combination of hard asphalt and low \(N_{\rm des}\) has more potential than traditional one.  
4. The dynamic modulus of HMAs decreased significantly with the reduction in \(N_{\rm des}\), the rise in asphalt binder grade, or coarser gradation. Specifically, the dynamic modulus of HMA decreased by about 4 \(\sim\) 26% when the \(N_{\rm des}\) decreased to one lower level. Moreover, the HMA modulus declined by 10 \(\sim\) 14% when the asphalt grade increased to one higher level.  
5. Under the combination of design parameters, more design possibilities can be obtained to achieve the expected performance compared with traditional methods, and thus the flexibility in the design of HMAs was further improved.  
Conceptualization, L.S.; Data curation, Y.Z.; Formal analysis, Y.Z.; Funding acquisition, L.S.; Methodology, L.S. and H.C.; Visualization, Y.Z.; Writing--Original draft, Y.Z.; Writing--Review & editing, L.S. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant from the National Key R&D Program of China (2018 YFB1600100), Natural Science Foundation of China (NSFC, 51678443), and Science and Technology Innovation Program of Education Commission of Shanghai.  
The authors declare no conflict of interest.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 3 Results and Discussion  
### S-Parameters  
Figure 6 presents the simulated and measured S-parameters results for port 1, including S\({}_{11}\) and S\({}_{21}\). It is exhibited that the measurement results are different from the simulation results because of SMA soldering and unavoidable tolerances in the fabrication and measurement process. To be specific, it is observed that the measured \(-10\) dB impedance band  
Figure 4: Reflection coefficients of the proposed MIMO antenna (**a**) with different values of R\({}_{2}\) and (**b**) with different values of H\({}_{1}\).  
Figure 5: Current distributions of the proposed MIMO antenna system at (**a**) 2.5 GHz and (**b**) 4.7 GHz.  
can cover the n7 (2.5-2.69 GHz), n38 (2.57-2.62 GHz), n41 (2.496-2.690 GHz), and n79 bands (4.4-5 GHz) in Figure 6a. From the transmission curves, it can be seen that S\({}_{21}\) is below \(-20\) dB over the whole operating frequency band (2.0-6.0 GHz).
# Denoising Directional Room Impulse Responses with Spatially Anisotropic Late Reverberation Tails
## 4 Denoising by Tail Resynthesis
### Mixing Time Estimation by Incoherence  
In order to replace the nondecaying noise floor with a prolongation of the reverberation tail resynthesized as independent zero-mean Gaussian noise signals, it must be verified that the DRIR has reached maximum incoherence before falling below the noise floor. Verifying this condition requires two quantities: the moment maximum incoherence is reached, which can be seen as an analogue of the mixing time; and a global, broadband value of the moment the reverberation tail falls below the noise floor.  
The first may be determined by analyzing the evolution of the incoherence measure \(\Gamma\) over the length of the DRIR, as was done with measures of the sound field's diffuseness in the SHD in [7] (and similarly by [PERSON] et al. [28]). The mixing time will be marked by the incoherence measure reaching a maximum value after an initial period of instability due to the arrival of coherent early reflections, and can be estimated algorithmically using \(\Gamma(t)\) (see Section 5.2 below).  
In the context of spatial PWD analysis, an overall estimation of the noise floor time limit requires taking into account the possibility that the noise floor may not be reached at the same time in all directions, due to the assumed anisotropic nature of the late reverberation tail and the possibly anisotropic or localized nature of the noise source(s). Furthermore, since the mixing time estimated using the incoherence measure \(\Gamma(t)\) is a broadband value, the frequency-dependent \(t_{\text{lim}}^{(d)}(f)\) values returned by the EDR analysis must be reduced to a single value for each look direction in an appropriate manner.  
As proposed in [7], the frequency dimension can be reduced in a perceptually-informed manner by performing an average over Bark-scale frequency bands weighted by the ITU-R 468 standard noise filter. If this is done for each look direction, the condition described above can be verified by making sure that the minimum of the averaged \(\tilde{t}_{\text{lim}}^{(d)}\) values is greater than the estimated mixing time.  
Note that since the EDR analysis is done in a direction-dependent manner, the proposed method also has an additional advantage over the SHD-based denoising procedure originally proposed in [7] in the case of localized noise sources; indeed, these would contribute to the nondecaying noise floor of multiple SH components, whereas they risk affecting only a limited number of PWD look directions.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
###### Abstract  
In the traditional layered division multiplexing (LDM) system, by simply adjusting the injection level, the reception performance of the core layer (CL) mobile services will be decreased significantly, resulting in the deterioration of system coverage performance. Thus, it is necessary to improve the performance of the enhanced layer (EL) service reception without affecting the reception threshold of CL service. To achieve this, in this paper, an enhanced LDM (En-LDM) scheme that supports multi-service transmission is proposed for the next-generation broadcasting network. In this scheme, at the transmitter end, part of the low-density parity-check (LDPC) coded data stream of fixed service conveyed in the EL will be extracted out through puncturing, and then, it will be transmitted over the CL of the LDM signal along with the original CL data in a frequency domain multiplexing (FDM) manner. At the receiving end, the punctured data of the EL fixed service will be recovered with a higher signal-to-noise ratio (SNR). Compared to the traditional LDM scheme, the proposed En-LDM scheme can significantly improve the reception performance of fixed services without decreasing the SNR threshold of mobile services. Moreover, the En-LDM can achieve a higher channel capacity than that of the traditional LDM for both the fixed services and the overall services. The superiority of the proposed En-LDM scheme over the traditional LDM scheme is validated by the simulation under the additive white Gaussian noise (AWGN) and fading channels.  
LDM; multi-service transmission; LDPC; FDM; broadcasting +
Footnote †: journal: Sensors  
0000-0002-4030-3330][PERSON]  
0000-0002-0002-3895][PERSON]
## 3 Materials and Methods
### Technical Details  
The implementation was conducted in a controlled environment to ensure consistency and reproducibility. The computational setup consisted of the following hardware and software specifications:  
Hardware Configuration:  
* Number of devices: 5 PCs;
* CPU: Intel Core i5-8400 (Intel, Santa Clara, CA, USA) (6 cores, 6 threads, base clock 2.8 GHz, turbo up to 4.0 GHz);
* Memory: 8 GB RAM per device.
* Software Environment:
* Operating Systems: Windows and Linux;
* Programming Language: Python 3. All computations and network operations were executed from a single IP address.