esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Positive Matrix Factorization  
Positive matrix factorization (PMF) is a powerful method for studying soil contamination, providing valuable information about the origins, levels, and distribution patterns of pollutants [1]. In addition to PMF, the following models are often used for source identification and quantification: chemical mass balance (CMB), Unmix models, and receptor models.  
Positive matrix factorization (PMF) allows for identifying the various factors that may be causing the variability in the observations and assigning a percentage of the incidence of each element in each factor. This way, it is possible to characterize and attribute a specific source to it through the most prevalent metals [1]. A specified dataset can be viewed as a bidimensional data matrix X, which represents the number of samples and the chemical species that were measured, with uncertainties referring to the level of uncertainty or error associated with the measurement of each chemical species in each sample. These uncertainties are essential to environmental data, as they reflect the confidence and reliability of the observed concentrations. Receptor models aim to solve for chemical mass balance (CMB) between measured species concentrations and source profiles [20], a critical component of receptor models used to analyze environmental pollution data, particularly in chemical mass balance modeling. It shows how the concentration of measured pollutants at a receptor (like a sampling station) can be expressed as a sum of contributions from different pollution sources. Equation S1 shows the mathematical formulation of positive matrix factorization.  
The evaluation was divided according to the basins to be studied, Aconcagua and Casablanca, where the latter was also separated according to the type of crops present in the study areas, such as vegetables and fruits. Therefore, three matrices were prepared with their respective uncertainties, which were determined to be 10% for all cases. For the construction of the model, the relationship between concentration and uncertainty was analyzed for the three cases. All S/N ratios (signal-to-noise ratio indicating whether the variability of the measurements is natural or within the noise of the data) were above 1.0 and were therefore categorized as \"strong\".  
In the execution of the model, a seed value of \"100\" was used in all cases and many iterations of 20. The Qtrue/Qstrong values were evaluated, and for the three cases, it was determined that the number of adequate factors corresponded to 3. The residual values were analyzed with this, which were between \(-3\) and 3. Three samples of theAconcagua basin presented anomalies, so they were extracted from the database so they did not contaminate the model.
# Field Investigation and Rapid Deterioration Analysis of Heavy Haul Corrugation
## 5 Conclusions  
The corrugation growth properties including wavelength, depth, and evolution process are investigated and a rapid deterioration phenomenon affected by grinding works was found by the 69-days long continuous test on the sharp curve of the heavy haul railway. To analyze the reason for the rapid deterioration of corrugation and the influence of the grinding marks, numerical methodologies based on the FEM and the fracture mechanics were conducted. The main conclusions are as follows:  
1. The measured data show that the on-site rail corrugation has a significant plastic flow pattern and RCF cracks on the railhead. At the end of the field test, the maximum amplitude reached 350 \(\upmu\)m, with the main wavelength around 160 mm.
2. After grinding, the trend in corrugation amplitude growth is moderate for a period of time. The residual surface grinding marks caused by a poor grinding process accelerate the evolution of corrugation so that the deterioration process can progress rapidly. During this process, the average peak-to-peak values can increase up to 1.4 \(\upmu\)m/day.
3. In the circular curve section, rail surface cracks tended to expand more easily compared to those in the straight line and transition sections. A higher SIFs value appear in the intersectional area between grinding marks and cracks. Grinding marks from poor grinding works will increase the SIFs of cracks, promote the rapid expansion of surface cracks, reduce the plastic resistance of contact area of rails indirectly, and result in the rapid development of plastic flow corrugation.  
Our study on the impact of poor grinding quality on corrugation indicates that fatigue cracks on a railhead should be eliminated thoroughly in the process of rail grinding. As the  
Figure 14: CDF contrast in SIFs under different conditions: (**a**) contrast in type II SIFs; (**b**) contrast in type III SIFs; GM—grinding marks; SL—straight line; TC—transition curve; CC—circular curve.  
bad influence of poor grinding quality on rail corrugation shows, the intersections between grinding marks and fatigue cracks should be avoided to prevent rapid deterioration.  
Funding acquisition, H.X.; Conceptualization, F.J. and H.X.; methodology, F.J.; validation, F.J., H.Y. and W.L.; formal analysis, F.J.; writing--original draft preparation, F.J.; writing--review and editing, F.J., M.M.N. and H.X.; visualization, F.J.; supervision, H.X.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (Grant No. 2020 YS127), National Natural Science Foundation of China (Grant No.51978045), and Scientific and Technological Innovation Project of China Shenhua Energy Company Limited (Grant No. SHGF-16-50).  
The authors declare no conflict of interest.
## 4 Conclusions  
In conclusion, a kind of SCLPF with dielectric sheets loaded has been designed for checking the influence of surface microstructure on the multipactor threshold. The device possesses a 1 mm gap at the middlemost position, in which the vertical RF electric field reaches the maximum, serving as the multipactor sensitive region. Alumina and PTFE have been chosen as the loaded dielectric sheets for partly filling the narrow gap, and a surface groove structure has been constructed to verify the effect of microstructure on electron avalanche. The simulation work indicates that the transmission characteristics of the SCLPFs are little affected by introducing groove structures on the dielectric surface. The influence of groove depth, groove number, and aspect ratio on the multipactor threshold has been discussed. The results show that constructing grooves is an effective method to improve the multipactor threshold, and it is found that there is a saturation effect for the multipactor threshold as the groove depth and aspect ratio increase. By analyzing the mechanism of the phenomenon, it is found that the three feature parameters of the groove, namely, groove depth, groove number, and aspect ratio, have a combined modulation effect on the multipactor threshold, and several rules are summarized to further explain the saturation effect. The quantitative study shows that a groove structure with high-density, large aspect ratio grooves is able to increase the multipactor threshold of the original PTFE-loaded SCLPF device from ~300 W to ~1100 W. The contour map obtained has practical significance for developing the surface microstructure to raise the multipactor threshold for microwave devices, and the work makes engineering sense for improving the reliability of space microwave systems.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13158586/s1](https://www.mdpi.com/article/10.3390/app13158586/s1)]([https://www.mdpi.com/article/10.3390/app13158586/s1](https://www.mdpi.com/article/10.3390/app13158586/s1)).  
Conceptualization, J.S. and W.Z.; methodology, J.S. and Y.Z.; software, W.Z. and B.S.; validation, J.S. and D.W.; investigation, B.S., Y.L., and T.S.; data curation, J.S. and J.Z.; writing--original draft preparation, J.S. and W.Z.; writing--review and editing, Y.Z. and W.Z.; visualization, Y.Z. and Y.L.; supervision, W.Z.; project administration, T.S. and X.Z.; funding acquisition, W.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author.  
The authors declare no conflict of interest.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 2 Related Work  
To respond to the intensive computational requirements of massive dataset analysis, a number of frameworks are already available in the literature that deal with continuous queries such as Spark Streaming and Flink [35]. Continuous queries were introduced for the first time in the TQL language in Tapestry [36], for content-based filtering of an append-only email and message posting database. Conceptually, a restricted subset of the SQL was used, and it was converted into an incremental query that was defined to retrieve all answers obtained in an interval of \(t\) seconds. The incremental query was issued continuously, every \(t\) seconds, and the union of answers returned constituted the answer to the continuous query. An incremental evaluation approach was used, to avoid the repetitive computations and to return only the new results to the users. Besides this, other architectures have been proposed as well to process continuous queries, such as the NiagaraCQ [37] and the OpenCQ [38]. Both systems use continuous queries over changing data, similarly to Tapestry, periodically executing individual queries. However, all aforementioned approaches focus on relational databases and thus do not deal with issues that appear when moving to semi- and unstructured data.  
STREAM [26] is an all-purpose framework that focuses on addressing the demands imposed by data streams on data management. The authors pay attention to memory management in order to enable approximate query answering. In particular, one of the project's goals is to understand how to efficiently run queries given a bounded amount of memory. The system can process streaming data, but it is designed as a centralized system.  
Our approach is a coherent integration of Apache Spark, as a computational engine, with semantics of HIFUN that can be used to rewrite continuous queries for optimization. Spark can tackle different paradigms (e.g., structured and unstructured processing) based on a common set of abstractions. The data itself can be stored in a centralized or distributed manner with different data models (e.g., relational and unstructured sources). In other words, HIFUN queries are agnostic to the evaluation layer and the nature and location of the data.
## 2 Materials and Methods
### Transmit Subsampling Strategy  
By using fewer transmit cycles, the measurement time can be improved. Our goal is to use the information derived from the raycasting procedure and forward modelling to get an optimal choice of transmit elements for imaging a region of interest. The steps of  
Figure 1: Sketch illustrating how rays are rasterized and projected to the equispaced voxels used for imaging by a line tracing algorithm.  
the algorithm are also summarized in Figure 2. The model in Equation (3) can be used to derive the linear forward model. The following equation  
\[\overset{\wedge}{\mathbf{g}}\ =\ \mathbf{M}_{fmc}^{H}\overset{\wedge}{\mathbf{t}} \tag{11}\]  
yields a synthetic dataset \(\overset{\wedge}{\mathbf{g}}\) given by a superposition of all point scatterer responses modeled by \(\mathbf{M}_{fmc}^{H}\overset{\wedge}{\mathbf{t}}\). We now choose \(\overset{\wedge}{\mathbf{t}}\) such that all entries corresponding to voxels in our region of interest have value 1 and 0 elsewhere. In consequence, \(\overset{\wedge}{\mathbf{g}}\) now holds data which, though following a simplified model, reflects a scenario where the full region of interest is reflecting.  
We can now sum these theoretical responses to get a measure of how much the excitation of each transducer element contributes to imaging the region of interest as  
\[[\mathbf{c}]_{m_{tx}}\ =\ \sum_{m_{tx}}\sum_{m_{t}}|[\overset{\wedge}{\mathbf{g}} _{m_{tx},m_{tx}}|m_{t}| \tag{12}\]  
with \(\mathbf{c}\in\mathbb{R}^{N_{cl}}\). We assume here that the elements indexed by \(m_{tx}\) are ordered such that for adjacent transducer elements the contributions are given by \([\mathbf{c}]_{i}\) and \([\mathbf{c}]_{i+1}\). In order to get the best trade off between collecting the maximum energy from the region of interest (choosing only the center elements) and using an as wide as possible aperture (using only the outer elements) we use the following heuristic. We divide \(\mathbf{c}\) into \(N_{tx}\) sections where each section has the same area under curve. We then use the center transducer element corresponding to the center index of each section as transmitter. This can be expressed by the following computation steps. We first compute the cumulative sum \(\mathbf{c}^{(c)}\in\mathbb{R}^{N_{cl}}\) of the elements of \(\mathbf{c}\),  
\[\left[\mathbf{c}^{(c)}\right]_{m_{tx}}\ =\ \frac{1}{\mathbf{1}^{T}\mathbf{c}} \sum_{i\ =\ 0}^{m_{tx}}\ [\mathbf{c}]_{i} \tag{13}\]  
normalized such that the maximum is 1. We then find our \(k\)-th transmitter element index \([\mathbf{p}]_{k}\) with \(\mathbf{p}\in\mathbb{N}^{N_{tx}}\)  
\[p_{k}\ =\ \min_{\xi}\ \xi\ \text{s.t.}\ \left[\mathbf{c}^{(c)}\right]_{\xi} \geq\ \frac{1}{N_{tx}}\left(\frac{1}{2}+k\right) \tag{14}\]  
which is the smallest index for which \(\mathbf{c}^{(c)}\) exceeds the given threshold.
## 2 Outdoor Positioning in the IoT Ecosystem
### Location-Enabled IoT Technologies
#### 2.3.4 LTE-M  
To provide cellular connectivity for machine type communication, Long-Term Evolution for Machines (LTE-M) was introduced by 3 GPP [29]. Like NB-IoT, LTE-M operates in licensed LTE spectrum and uses synchronous communication for QoS guarantees. LTE-M can work with a bandwidth that is equal to a single GSM channel (200 kHz). The most relevant characteristic of LTE-M is low cost, specifically on the IoT device side, due to the possibility of using less expensive radio modules [30]. Compared with NB-IoT, LTE-M supports up to 1 Mbps in uplink and up to 384 kbps in downlink. In addition, LTE-M has lower latency, but it typically consumes more power, leading to the decrease of the battery lifetime [31]. LTE-M is compatible with LTE or GSM, and it has a similar medium access control mechanism.
## 3 The Proposed Solution Method Using the PA-Jaya Algorithm
### Computational Complexity Analysis for PA-Jaya  
As described above, the population size is set as \(M\), the number of variables in each candidate solution is \(2N\), and the maximum number of inner iterations is \(K\).  
According to the flow shown in Algorithm 2, it is obvious that our proposed PA-Jaya algorithm needs to use a triple loop for each iteration update. The number of the outermost loop is set as the population size \(M\), which indicates that each individual solution in the population is traversed to ensure that all solutions in the population are updated. The number of the middle loop is set as a predefined constant \(K\). The function of this loop is to minimize the generation of infeasible solutions in the updated population, so as to accelerate the overall convergence speed of the algorithm. The number of the innermost loop is set as the number of variables \(2N\) in each candidate solution. This loop is essential because our PA-Jaya algorithm needs to generate two random numbers, \(r_{1}\) and \(r_{2}\), for each of these variables when updating each candidate solution.  
Then, in the worst case during each iteration, the time complexity required for our proposed PA-Jaya algorithm is \(\mathcal{O}(M\times N\times K)\), which seems to be \(K\) times the time required by the traditional Jaya algorithm. However, considering that \(K\) is a relatively small constant, such an increase in time is acceptable. In addition, PA-Jaya requires fewer iterations to converge, which also makes up for the increase in time required for each iteration to some extent. Moreover, by using a parallelized structure, our algorithm makes full use of existing computing resources while greatly reducing the runtime of the algorithm.
## 3 Numerical Results and Discussion
### Ga and In Doping Dependence of the Magnetization and Band Gap Energy in CFO  
To further substantiate the capability of the proposed model to accurately describe the properties of doped CFO, we extend our analysis to include the band gap energies of CuAlO\({}_{2}\), CuGaO\({}_{2}\), and CuInO\({}_{2}\), as experimentally investigated by [PERSON] et al. [19]. The ionic radius of Al\({}^{3+}\) (0.675 \(\AA\)) is smaller than that of Ga\({}^{3+}\) (0.76 \(\AA\)), which in turn is smaller than that of In\({}^{3+}\) (0.94 \(\AA\)), whereas the ionic radius of Fe\({}^{3+}\) is 0.69 \(\AA\)-positioned between those of Al and Ga. Let us consider, firstly, the case of Ga doping of CFO. Ga has a larger radius compared to that of Fe, i.e., there appears a tensile strain. We have the relation \(J_{d}<J\) which leads to a decrease in \(M\) and from Equations (11) and (12) an increase in the band gap energy \(E_{g}\) (see Figures 1 and 2, curve 2). In analogy, the magnetization of In-doped CFO is smaller whereas the band gap energy is larger than that of undoped CFO (see Figures 1 and 2, curve 3). It must be noted that because the difference between the radii of In and Fe is larger than that between Ga and Fe, we have to choose a smaller \(J_{d}\) value compared to the previous case. The observed results are in very good agreement with those reported by [PERSON] et al. [19], [PERSON] et al. [35,36], and [PERSON] et al. [37] for CuAlO\({}_{2}\), CuGaO\({}_{2}\) and CuInO\({}_{2}\). It can be seen from Figure 2 that for \(x=1\) we would observe that the band gap \(E_{g}\) increases from CuAlO\({}_{2}\) to CuGaO\({}_{2}\), and further to CuInO\({}_{2}\), as reported by [PERSON] et al. [19].  
In principle, we would obtain the same result, an increasing band gap energy \(E_{g}\) from Al to In for \(x=1\), also by substituting CuAlO\({}_{2}\) with Ga, as well as CuGaO\({}_{2}\) with In, where tensile stress and an increase in the band gap are also observed. Further quantitative studies incorporating strain-tuned exchange parameters could deepen our understanding of structure-property correlations in these systems.  
Let us note that this increasing band gap from M = Al, Ga, to In, is an interesting result not seen in conventional semiconductors. Contrary to our and to the experimental results, the direct band gap of CuAlS\({}_{2}\) (3.49 eV), CuGaS\({}_{2}\) (2.43 eV), and CuInS\({}_{2}\) (1.53 eV) decreases when the atomic number of the group-III elements increases [19]. This case will be studied in a next paper.
# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods
### Machine Learning Classification Algorithms
#### 2.3.2 Convolutional Neural Network (CNN)  
To predict categorical classes, a convolutional neural network (CNN) was employed based on the raw images. CNNs are specialized artificial neural networks crafted specifically for processing and analyzing images, as well as other forms of multidimensional data, such as audio signals or 3D structures. A distinguishing characteristic of CNNs lies in their capability to acquire hierarchical representations of data by applying a sequence of filters or kernels to the input data. Each filter executes a convolution operation on the input, generating a feature map that accentuates specific patterns or features. These feature maps are subsequently forwarded to successive layers of the network, which further employ additional filters to extract more intricate features. To further improve the model's fit, we used the Xception-network, a 71-layer convolutional neural network (CNN) with pre-trained weights on the ImageNet dataset, to improve accuracy. Following the pre-trained network, a global average pooling layer was applied; this converts the feature maps into a single vector by averaging across each channel. After global pooling, a fully connected (dense) layer with 2048 units was added. This layer served as a high-dimensional representation of the input image features extracted by the base CNN, which was followed by a ReLU activation function, which introduces non-linearity, making the model capable of learning complex patterns. The CNN also had a dropout layer with a dropout rate of 0.2, which helps prevent overfitting by randomly setting 20% of the units to zero during training. The final layer utilized a softmax activation function, which outputs probabilities for each class. We used the Adam optimizer, which is a variant of a stochastic gradient descent that adapts the learning rate of each parameter, known for its efficient convergence in training deep networks. For the loss function, categorical cross-entropy, which is commonly used for multi-class classification tasks, was used. The CNN was trained for 20 epochs, where saturation in the validation set was observed, as the validation set's accuracy had a tendency to decline after that.  
Every image of colon cancer in our CNN setup had a uniform dimension of 150 \(\times\) 150 pixels. In our case, for the evaluation of the model, we split the data into train, validation, and test subsets for the 8 different types of tissue: (a) tumor epithelium, (b) simple stroma, (c) complex stroma, (d) immune cells, (e) debris, (f) normal mucosal glands, (g) adipose tissue, and (h) background (no tissue). The train set consisted of the majority of images, the validation set amounted to 30% of the training set, and the test set had 12 images of each type of tissue. Once we had trained the final layer of the model, the test images were passed through the network to identify and predict the tissue label. The accuracy was reported to validate the model.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 3 Experiments
### Evaluation Metrics  
PESQ: Perceptual Evaluation of Speech Quality is a standardized objective metric used to assess the quality of speech signals. Initially developed by the International Telecommunication Union (ITU), it was primarily designed for evaluating speech quality within telephony systems. PESQ aims to provide an objective quantification of speech quality by simulating aspects of the human auditory system's response to audio signals.  
STOI: Short-Time Objective Intelligibility is an objective metric designed to assess the intelligibility of speech signals. Similar to PESQ, STOI aims to provide an objective and quantitative method for measuring the understandability of speech signals. This metric primarily focuses on the intelligibility of speech signals in noisy environments and is applicable to speech communication, speech recognition, and other applications that involve conveying speech information in the presence of background noise.  
SI-SDR: Scale-Invariant Signal-to-Distortion Ratio is an objective metric used to measure the quality of separating audio sources. It is primarily employed to assess the performance of separation algorithms in audio source separation tasks. SI-SDR considers a balance between the scale-invariant proportion between the estimated and true source signals and the level of distortion, making it a scale-invariant measure.  
CSIG: Composite Speech Intelligibility Index is a metric used to assess the performance of speech processing systems, such as speech enhancement and speech coding. This metric is primarily employed to measure the intelligibility and quality of speech signals. The design of CSIG aims to provide a comprehensive evaluation that reflects the impact of speech processing systems on the original speech signal. It considers not only the clarity of the speech signal but also the combined effects of factors such as background noise and distortion.