esa-sceva/satcom-chunk-collection huggingface dataset:
# Linomycin-Induced Transcriptional Alterations in the Green Alga _Raphidocelis subcapitata_
## 2 Materials and Methods
### RNA-seq Data Analyses  
The output Image data were transformed by software to obtain raw data in FastQ format. We used Cutadapt (v. 1.1) to trim the sequences of 3 'end bands and discard the low-quality value (QV \(<\) 20) in the raw data [23]. To make the different genes and gene expression levels comparable between different samples, we adopted Fragments Per Kilo bases per Million fragments (FPKM) to normalize the expression of genes. We applied Hisat2 (v. 2.1.0) to estimate the matching degree of the filtered reads to _Raphidocelis subcapitata_ 1.0 (GCA-003203535.1) [24], then the HTSeq was applied to count the read value for each gene (v. 0.11.1) [25]. The total number of reads mapped to unique locations for each sample varied from 35,303,248 to 40,270,036, and the matching coverage ranged from 95.46% to 96.08%. Afterward, Pearson correlation coefficients among each group were calculated and visualized by using the R package. Expressed genes with the |log2 Fold Change (FC)| > 1 and adj \(p\) value < 0.05 in comparison of the control to LIN-treated group were identified to differentially expressed genes (DEGs) with the \"DESeq\" package [26]. Samples in each treatment group were clustered using the \"DESeq\" package in R software to conduct principal component analysis (PCA) [26]. Volcano plots and heatmap were plotted with \"ggplot2\" and \"Pheatmap\" packages, respectively [27; 28]. The amount of DEGs in each group was illustrated by plotting a Venn diagram. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were applied to analyze DEGs.  
The \"TopGO\" package was used for GO enrichment analysis, whose results were classified in terms of molecular function (MF), biological process (BP), and cell component (CC). The top 10 GO terms were chosen by the minimal \(p\) value in each GO classification were chosen for display. The enriched pathways were distinguished by a condition of \(p\) value < 0.1 in KEGG pathway analysis.
## 2 Material and Methods
### Multi-Output Spectral Mixture Gaussian Process  
Given an EEG trial, the Gaussian Process (GP) probabilistic framework computes the mixture parameters by maximizing the data likelihood as the cost function. A Gaussian Process (GP) is a real-valued stochastic process \((f(t))\) over a input set \(\mathbf{t}\), such that for any finite subset of inputs \(t\in\{1,\dots,T\}\), the random variables \(f(t)\) are jointly Gaussian [20]. Additionally, the GP is uniquely determined by its mean function \(m(\mathbf{t}):=E_{t}(f(t))\), typically assumed \(m(\mathbf{t})=0\) and its covariance function \(\kappa(\mathbf{t},\mathbf{t}):=cov(f(t),f(t^{\prime}))\in\mathbb{R}^{T\times T}\) known as the kernel.  
Then, the multivariate extension of GPs is derived by assembling \(C\) different scalar-valued stochastic processes, one for each EEG channel. Any finite collection of values across all such processes are jointly Gaussian, termed multiple-output Gaussian Process (MOGP). This extension results in a vector-valued process \(\mathbf{f}\sim\mathcal{GP}(\mathbf{m},\mathbf{K})\), where \(\mathbf{m}(\mathbf{t})\in\mathbb{R}^{TC}\) is a concatenated vector from the mean vectors associated to the outputs and \(\mathbf{K}\in\mathbb{R}^{TC\times TC}\) a block partitioned matrix of the form [20]:  
\[\mathbf{K}(\mathbf{X},\mathbf{X})=\begin{bmatrix}K(\mathbf{X}_{1},\mathbf{X}_{1})&\cdots&\mathbf{K}( \mathbf{X}_{1},\mathbf{X}_{C})\\ K(\mathbf{X}_{2},\mathbf{X}_{1})&\cdots&\mathbf{K}(\mathbf{X}_{2},\mathbf{X}_{C})\\ \cdots&\cdots&\cdots\\ K(\mathbf{X}_{C},\mathbf{X}_{1})&\cdots&\mathbf{K}(\mathbf{X}_{C},\mathbf{X}_{C})\end{bmatrix}, \tag{9}\]  
where each block \(\mathbf{K}(\mathbf{X}_{i},\mathbf{X}_{j})\) is a \(T\times T\) matrix denoting the covariance between output channels \(i,j\). Furthermore, a multivariate kernel \(\mathbf{K}(t,t^{\prime})\) is stationary if \(\mathbf{K}(t,t^{\prime})=\mathbf{K}(t-t^{\prime})\). In this case, the kernel becomes \(\kappa_{ij}(t,t^{\prime})=\kappa_{ij}(\tau)\) if substituting \(\tau=t-t^{\prime}\). Therefore, we use the MOSM kernel in Equation (7) as the covariance function to be implemented within the MOGP. By defining such a process as the MOSM kernel, the model adjustment to the data is performed by maximizing the data log-probability. Since the observations in the multiouput case are jointly Gaussian, they are concatenated into the vector \(\mathbf{y}=[\mathbf{x}_{1}^{\top},\mathbf{x}_{2}^{\top}\cdots,\mathbf{x}_{C}^{\top}]^{\top} \in\mathbb{R}^{CT}\) the channel observed value. Then, the negative log-likelihood (NLL) can be expressed as in Equation (10).  
\[-\log p(\mathbf{y}|\mathbf{t},\mathbf{\Theta})=\frac{CT}{2}\log 2\pi+\frac{1}{2}\log|\mathbf{K} |+\frac{1}{2}\mathbf{y}^{\top}K^{-1}\mathbf{y}, \tag{10}\]  
with \(\Theta=\{w_{i}^{(q)},\mu_{i}^{(q)},\sigma_{i}^{(q)},\theta_{i}^{(q)},\phi_{i}^ {(q)},\sigma_{i}^{2}\}_{i=1,q=1}^{C,Q}\) holding the complete set of parameters. As a result, minimization of NLL with respect to \(\Theta\) designs an spectral kernel quantifying the EEG channel relationships at automatically tuned frequency bands.
## 3 Compressive Sensing Based Preamble Channel Estimation  
In this section, we present a novel sparse adaptive channel estimation method based on CS for FBMC/OQAM systems. We first review the two classical preamble structures and CS theory for channel estimation. We briefly introduce the conventional CS signal recovery algorithms, OMP, CoSaMP and SAMP [36]. Then, we propose the new CS algorithm. Along with the algorithm process, we present numerical evidence showing that our proposed algorithm provides attractive results.
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 4 Experiment
### Final Results on MOT20  
For the performance of TraPeHat on the MOT20 training set, we ran and evaluated in our local devices. Because the MOT20 testing set does not expose its ground truth, our results were uploaded to the MOT Challenge website [65] for evaluation. The overall performance results of the training set and test set are shown in Table 6.  
We compared TraPeHat with some other trackers on the MOT20 dataset, the running results of which were from the MOT Challenge website [65], as shown in Table 7, from which we can see that our algorithm achieved a comparable effect. Our method is superior to the other methods in Table 7 in most of the multiobject tracking indicators. From this, we can learn that TraPeHat achieved higher MOTA, HOTA, and IDF1 scores, and the scores of FP, FN were lower. However, the ML and ID-Switch indicators of FlowTracker were slightly better than TraPeHat. We speculate that the reason for this phenomenon is as follows. FlowTracker used optical flow to realize multiobject tracking, and the principle of multiobject tracker based on optical flow method is that the appearance features of the same pedestrian do not change significantly in two adjacent frames. TraPeHat, on the other hand, did not use target appearance information in the matching stage. Therefore, FlowTracker used more comprehensive appearance information than TraPeHat, and this also gives FlowTracker an advantage when dealing with continuous targets and ID Switch. But TraPeHat integrates head tracking which enforced the overall tracking performance on MOTA, HOTA, and so on. It is worth mentioning that we did not use any deep learning tricks to improve the accuracy in the whole experiments.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **MOTA\(\uparrow\)** & **IDEuCl\(\uparrow\)** & **IDF1\(\uparrow\)** & **MT\(\uparrow\)** & **ML\(\downarrow\)** & **ID Sw\(\downarrow\)** \\ \hline SORT [1] & 46.4 & 58.0 & 48.4 & 49 & 216 & **649** \\ V\_IOU [71] & 53.4 & 34.3 & 35.4 & 80 & 182 & 1890 \\ Tracktor++ [6] & 58.9 & 31.8 & 38.5 & 125 & 117 & 3474 \\
**TraPeHat(ours)** & **63.6** & **60.3** & **57.1** & **146** & **93** & 892 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison between different head tracking methods on HT21 dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **AP/\%** & **MR\({}^{-2}\)/\%** \\ \hline Crowd Human Benchmark [10] & 85.0 & 50.4 \\ Adaptive NMS [72] & 84.7 & 49.7 \\ Soft-NMS [73] & 88.2 & 42.9 \\ PBM [74] & 89.3 & 43.3 \\
**TraPeHat(ours)** & **90.7** & **41.4** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison between different body detection methods on CrowdHuman dataset.
## 3 Results
### Machine Learning Analysis for Pressure Estimation
#### 3.2.2 Analysis of Correlation Between Consolidated Temperature and Pressure Prediction  
An additional analysis was conducted to explore the potential correlation between consolidated temperature data from the entire foot, measured at the five anatomical points, and the pressure metrics at a single point. To test this hypothesis, the dataset was augmented to include the consolidated thermal time series data from these five anatomical points. These combined temperature data were then used to predict the average pressure at each of the anatomical points, focusing on the three pressure metrics. The objective of this analysis was to determine whether temperature information from multiple locations on the foot could enhance the prediction of pressure at a specific site. Given the extensive results collected from five anatomical points and three pressure metrics, Tables 5-7 provide a representative sample of the findings. These tables focus on the first metatarsus targeting average peak pressure, the fifth metatarsus targeting average peak plantar pressure, and the lateral midfoot targeting peak plantar pressure. As observed, the results indicate that the correlation between the consolidated temperature and the pressure metrics did not produce promising outcomes. The analysis shows negative R\({}^{2}\) values and high errors across various evaluation metrics, suggesting that the consolidated temperature data are not sufficient for accurately predicting the pressure values at these specific anatomical points. The regression models employed in this analysis were consistent with those used in the previous sections, and similar metrics were utilized to evaluate the performance of these models. The objective was to determine whether consolidated temperature data from multiple anatomical points could improve the prediction of the pressure metrics at specific locations on the foot. However, the analysis reveals a weak correlation between the consolidated temperature data and the pressure metrics, as indicated by consistently negative R\({}^{2}\) values and high error metrics across all the models. In Table 5, which presents the results for the first metatarsus targeting average peak pressure, all the regression models demonstrate poor performance. The Light Gradient Boosting Machine and Dummy Regressor, which produced identical results, recorded a mean absolute error (MAE) of 24.3803 and a root mean square error (RMSE) of 29.9025, with a negative R\({}^{2}\) value of \(-0.6947\). This suggests that these models, like the others, failed to capture any meaningful relationship between the consolidated temperature data and the
## 3 Results and Discussion
### Deployment  
After training the model and confirming its viability, the following step is to integrate it into the real system. This step, which is in the initial phase, consists of deploying a Cloud system model, which can be accessed by the different centers of San Juan de Dios Hospital (see Figure 2). In our case, we use SPSS modeler to test and select the model we want to implement, and we deploy it in IBM cloud. We based our deployment on application programming interface (API); basically, an API defines which variables we will use to call the model and which format will receive the answer. API permits distributed systems to be easily connected. Normally, this API uses a standard formatting called JSON. A JSON, or JavaScript Object Notation, is a minimal, readable format for structuring data. It is used primarily to transmit data between a server and web application. The advantage of this type of deployment is that we create a web-based service, and anyone with the required access will be able to call this API via HTML call or a web browser, and passing the parameter in the URL, he will be able to see the result. The system can be used easily, because any hospital can call the API and obtain the prediction, as the hospital will pass only the parameter without any private information (PI) data of the patient: it will protect our patients' PI.  
In our case, we call the JSON API microservice with a JSON containing the value of the forty explanatory variables. Section 2.3.  
This service would answer by predicting the classification integrated into another JSON. The proposed architecture, based on a microservice, would facilitate its integration into the work flow by reducing the complete process to a single call.  
The advantage of deploying this type of microservice in the cloud is that everyone with access will be able to call the service and obtain the prediction. If the workload grows, we will be able to easily escalate the system, adding additional power to our prediction cloud infrastructure.
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 4 Measurement Results and Discussion
### Modify the Model of the Designed Filter  
The physical dimensions for the SU8 fabricated filter are measured using a confocal lens, as shown in Table 2. It shows that the measured dimensions are about 23.7 \(\upmu\)m smaller than the designed size on average in the two-dimensional plane directions of \(x\) and \(y\). The average sidewall steepness calculated by the tangent formula is 1\({}^{\circ}\). The exposure dose used in this paper is slightly larger than the optimal exposure dose. This causes a part of the UV light to be reflected so that the SU-8 resist is exposed again. Therefore, the steepness of the devices fabricated can be further addressed by reducing exposure doses. The various tolerances mentioned above should be considered in the model design to ensure that the re-simulated and measured results with the pressure block I match perfectly, as shown in Figure 13. Compared with the originally designed structure, the modified model uses a scaling factor of 1.0065, which is attributed to the fact that the actual size of the machined filter is smaller than the design size. Through the above analysis, the sidewall is the main parameter that affects the performance of the device. There are two methods to improve the test results: First, the tolerance caused by the steepness can be compensated by the change of the size during the design. Second, a method without standing waves in the direction of the steepness is adopted so that the frequency of the resonator can be adjusted to the steepness of the sidewall of the resonator, thereby reducing the sensitivity of the device performance to the steepness.
## 4 Experimentation and Evaluation
### Feature Selection Methodology
#### 4.1.1 Data Preparation  
Initially, for the training dataset, records containing an unknown service, written as \"-\", were removed. Next, the features were transformed into their corresponding types, resulting in three categorical features: \"proto\", \"service\", and \"state\". OneHotEncoder [4] encoding was chosen for these categories.  
After preprocessing, we observe different anomaly and non-anomaly distributions for binary classification (Table 1) and attack distributions for multiclass classification (Table 2).
## 2 Theory and Experimental Method
### MR Fluid Power Distribution System
#### 2.1.4 Multi-Plate Clutch Fluid Mode Torque Modeling  
Magnetic field control torque refers to the torque that is transmitted by the MR fluid between the friction surface of the input and output shaft, and there is a torque that is transmitted by viscosity and a torque that is transmitted by the magnetic field. The torque transmitted by the magnetic field is the MRF's friction, which acts upon the surface between the disc, the plate, and the external annular part. The MR single-plate clutch's magnetic control torque \(T_{MR}\) is caused by the magnetic field and is determined by the MR fluid's yield shear stress \(\tau_{y}\) and the friction area, and it can be derived from the MRF, as shown in Equation (6).  
\[T_{MR}=\int_{A}\tau rdA=2\pi\int_{r_{i}}^{r_{o}}\tau r^{2}dr. \tag{6}\]  
The MR fluid used in this study is MRF-132 DG from the LORD Company (Cary, NC, USA). In a MR multi-plate clutch, shear stress affects the MR fluid at the end annular part of the disc and plate. The average magnetic flux density, which is calculated using numerical integration and is similar to that in the cross-sectional space of the MR fluid, allows us to derive the following equation (this equation assumes that the shear rate of the MR fluid is linearly distributed):  
\[T_{th}=2\pi r^{2}l\left(\tau_{y}+\eta\frac{r\omega}{d}\right), \tag{7}\]where \(l\) and \(r\) denote the annular part's radius and length. The transmission equation of the MR single clutch in the magnetic field off mode (when a magnetic field is not applied to the MR fluid) is as follows.  
\[T_{0}=\frac{\pi\eta_{0}}{h}\Big{(}r_{o}^{4}-r_{i}^{4}\Big{)}\omega+\frac{4\pi r _{y0}}{3}\Big{(}r_{o}^{3}-r_{i}^{3}\Big{)}+2\pi r_{o}^{2}t_{d}(\tau_{y0}-\eta_ {0}\frac{\tau_{o}\omega}{h}). \tag{8}\]  
Here, \(h\) refers to the gap between disks, and \(t_{d}\) refers to the thickness of the disk and plate. The torque calculation equations for the single-plate clutch can be used to determine the torque modeling equation for the multi-plate clutch that is to be designed. Since the MR multi-plate clutch has \(N\) number of friction surfaces, the multi-plate clutch's mode transmission torque \(T_{M\ldots m}\) can be expressed as shown in Equation (9). However, in the fluid mode, torque transmission is performed by the MR fluid between the discs and the plates; therefore, it is necessary to compute the torque values at all locations. Accordingly, the fluid mode transmission torque \(T_{MR\ldots m}\) is the value obtained by adding all the torque values acting on the spaces between the discs and the plates, as shown in Equation (10).  
\[T_{M\ldots m}=T_{M}+T_{0}, \tag{9}\]  
\[T_{MR\ldots m}=\sum_{l=0}^{n}\{T_{MR}+T_{th}+T_{0}\}. \tag{10}\]  
Here, \(n\) denotes the number of spaces between the discs and the plates. In the torque calculation equation, the transmission torque of the MR friction clutch can be obtained by ignoring the torque caused by the friction of the sealing and the bearings [19; 21; 22].
## 3 Results
### Drive Test  
As mentioned in the first section, the second category of the used data in this research is the data related to drive test measurements. The drive test is a real test of the network to obtain detailed information about the location of the fault, how the antennas around the location signal, and to check the redistribution status in the routes. To perform this test, the operators send a group of human resources to the site. The responsibility of human resources is to check the network quality from different perspectives. Various scenarios, such as short calls, long calls, and non-calling mode or idle mode, are proposed by human power. The non-calling mode analyzes the mobile device's signal exchanges with the receiving station. The test is a method to measure and check the coverage status, network capability, and quality from a common point of view. The test is conducted by makinga call through several mobile phones with software to control and record the reported measurements and parameters from the BTS side and the Global Positioning System (GPS). The measurements are transferred from the mobile phone to the computer. The measurements only report network mode from different perspectives and cannot identify the fault type and cause. In general, the drive test process consists of the following steps:  
1. Setting the devices for the required measurements
2. Defining routes
3. Determining the testing time and type
4. Performing the test
5. Collecting data and related reports  
Figure 12 shows the scope of conducting a drive test according to the division of urban areas and the boundaries between the central controller stations.  
By analyzing the conducted measurements in the drive test, some results, such as determining blind spots and non-coverage, determining the displacement of the sector or the feeder, determining an area with interference, interruption of conversation, and failure in the transfer, are determined. In general, the identified faults list in the drive test is as follows:  
1. Problems related to sector and feeder changing
2. Call interruption problems caused by interference and sector signal overshooting.
3. Interference problems that require checking assigned frequencies to nearby cells.
4. Problems related to outsourcing, which can be traced by adequately setting the neighborhoods, coverage area, and related parameters to outsourcing.
5. Problems related to the coverage area, which, by adjusting the height, power, and angle of the antenna, lead to a change in the design and, finally, the installation of a new site.
# GTMesh: A Highly Efficient C++ Template Library for Numerical Schemes on General Topology Meshes
## 1 Introduction
GTMesh makes extensive use of template metaprogramming, together with advanced concepts, such as SFINAE [31], on account of which it can provide generic data structures and mesh algorithms that compile for the desired use case and provide maximum run time performance. Using GTMesh, it is possible to write the dimension-agnostic code of a complete numerical solver, including the numerical scheme, the data association with the mesh and the data I/O. The dimension of the problem is then specified as a template parameter at a single point in the code. Currently, GTMesh is a relatively small open-source project, which is publicly available, together with introductory documentation (see Data Availability Statement at the end). This paper serves as an introduction to the concepts used in GTMesh. In Section 2, the theoretical description of general topology meshes in \(\mathbb{R}^{d}\), \(d\in\mathbb{N}\), is laid out. Section 3 presents the general data structure for the mesh representation used in this project. The implementation details are explained in Section 4, including the data structures (Sections 4.1 and 4.2) and the mesh algorithms (Section 4.3). In this section, the ideas are often presented in the context of FVM schemes. For simplicity, geometrical considerations are demonstrated on 2D polygons or 3D polyhedra, despite the possibility of generalization into \(\mathbb{R}^{d}\), \(d>3\). Section 5 is devoted to a useful concept of class reflection, which finds utility in generic numerical and data I/O algorithms. Its implementation is based on original ideas combining C++ class templates and preprocessor macros, in order to create a transparent and comfortable interface for the user. Finally, a simple example program that solves heat conduction in 3D, by the FVM scheme, is demonstrated in Section 6.