esa-sceva/satcom-chunk-collection huggingface dataset:
# Traditional Grain-Based vs. Commercial Milk Refirs, How Different Are They?
## 2 Materials and Methods
### Cultivation-Independent Analysis of Microbial Composition of Kefirs
#### 2.2.2 Ngs  
For the identification of all bacterial and all yeast species, the bacterial 16 S gene and the fungal internal transcribed spacer (ITS) regions were analyzed. Barcoded bacterial primers (27 F: AGRGTYGATYMTGGCTCAG & 1492 R: RGYTACCTTGTTACGACTT) [10] and fungal primers (ITS9 MUNngs: TACACACCCGCCGCCG & ITS4 ngsUni: CCTSCSTTANTDATATGC) [11] were used for PCR-amplification for SMRTbell(r) library preparation and sequencing (Pacific Biosciences of California, Inc., Menlo Park, CA, USA). In brief, amplification was performed using 2x KAPA HiFi HotStart ReadyMix (Roche Applied Science, Penzberg, Germany), 0.375 uM primer, and 1 ng genomic DNA in a volume of 25 uL. After initial denaturation (3 min, 95 \({}^{\circ}\)C), 20 cycles of 30 s, 96 \({}^{\circ}\)C denaturation, 30 s, 57 \({}^{\circ}\)C annealing and 60 s, 72 \({}^{\circ}\)C elongation were run (Pacific Biosciences of California, Inc., Menlo Park, CA, USA). Amplicon sizes were determined on a 5200 fragment analyzer using the NGS fragment kit (Agilent Technologies, Santa Clara, CA, USA). Amplicon concentrations were quantified with a Qubit fluorometer (Invitrogen, Waltham, MA, USA). Amplicons were pooled at equimolar concentration and then processed according to the SMRTbell(r) Library Preparation and Sequencing protocol. After final purification, the library was sequenced on a Sequel Ile instrument (Pacific Biosciences of California, Menlo Park, CA, USA).  
For bacterial taxonomy, raw PacBio 16 S circular consensus sequences (CCS) were exported and demultiplexed using lima ([[https://github.com/PacificBiosciences/barcoding](https://github.com/PacificBiosciences/barcoding)]([https://github.com/PacificBiosciences/barcoding](https://github.com/PacificBiosciences/barcoding)), accessed on 1 February 2022). Data were processed in R (4.1.1, R Core Team 2021) using dada2 [12]. Primers were removed (remove Primers, max.mismatch = 0, orient = TRUE). Sequences were filtered according to quality (filterAndTrim, settings: minQ = 22, minLen = 1000, maxLen = 1600, maxN = 0, rm.phix = FALSE, maxEE = 3). Error rate estimation was performed with the maximum possible number of bases. Data were dereplicated and amplicon sequence variants (ASV) were acquired. Taxonomy annotation was performed using Silva [13] (Nr. 99 v138.1).  
For fungal taxonomy, raw Pacbio ITS CCS were imported and processed under QIIME 2 software [14]. Briefly, trimming primers and barcodes was conducted using Cut-adapt. Subsequently, QIIME DADA2 plugin was applied to denoise sequencing reads and acquire ASVs. The representative sequences were assigned to the UNITE 8.3 database ([[https://unite.ut.ee/](https://unite.ut.ee/)]([https://unite.ut.ee/](https://unite.ut.ee/)), accessed on 01 October 2021) for taxonomic classifications utilizing the feature-classifier plugin [15; 16]. ASVs of bacterial sequences which were identified as 'unassigned' were excluded.
## 1 Introduction
Therefore, an attribute filtering method is designed that removes attributes with low importance based on the idea of classification tree models. Second, the correlation between attributes is sophisticated and hard to mine comprehensively and accurately. Thus, a classification method based on correlation coefficient is proposed. As a result, the proposed scheme selects a small number of attributes to differentiate failures, and these features are also the most crucial ones. It should be noticed that, to the best of our knowledge, we are the first to conduct feature selection from the perspective of mining the relationship between attributes in this area. The main contributions of this work are as follows:  
* From the perspective of the data in ML/AI-based hard disk failure prediction, this paper discusses and analyzes improving the prediction accuracy and timeliness of modeling by effectively selecting crucial attributes;
* An attribute filtering scheme is designed to remove unimportant attributes based on the idea of classification tree models, where entropy and the Gini index are the kernel techniques of these models; thus, both entropy and the Gini index are considered and employed to determine the importance of attributes;
* An attribute reduction scheme is proposed, to reduce the number of attributes in deep learning.First, this classifies features into different groups by mining their mutual correlation, where [PERSON]'s correlation coefficient and [PERSON]'s correlation coefficient are considered to mine the correlation between attributes. Then, it selects the most critical attribute from each group for modeling;
* We employ real disk health data to evaluate our scheme. The experimental results show that the proposed scheme can improve the prediction accuracy of ML/AI-based prediction models, and it can reduce the training and prediction latency by up to 75%and 83%, respectively, compared with the baseline methods. Our proposed scheme based on the Gini index and Pearson's correlation coefficient performs best when using RF and LSTM. This paper is organized as follows: Section 2 introduces the background and related work; Section 3 presents the motivation; Section 4 presents the design; Section 5 evaluates the experimental results; and Section 7 concludes the paper.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Other Signals
#### 3.4.1 Engine Operating Conditions  
[PERSON] et al. [40] developed a system that used five different machine learning techniques to predict knock. Their study collected engine data from a number of sensors on the engine, including the data from the vibration knock sensor. They then used different machine learning algorithms, including AutoEncoder Dense and Convolutional, Feature Extraction Classifier, Support Vector Machines, and Isolated Forest. Of these techniques, the Feature Extraction Classifier, which involves extracting relevant features from the raw data and using them to train a model for classification tasks, achieved the highest level of precision; however, they found that even with this technique, they were only able to detect 81 percent of the knocking cycles.  
[PERSON] et al. [41] tried an alternative approach, using three machine learning approaches to detect knock in the racing field. These models incorporated factors such as the engine speed, air-fuel ratio, internal cylinder pressure, combustion timing, and air conditions. Their study found that they could accurately predict knocking conditions through these models, concluding that they could enhance the engine performance under diverse operating conditions without extensive computational efforts or the need for a knock sensor.
## 5 Further Application Examples
### Graph Coloring with Additional Constraints  
The well-known task of graph coloring means that we need to assign a color to each vertex of the graph such that neighboring vertices have different colors. If a coloring satisfies this requirement, it is called a _proper coloring_.  
The standard question regarding graph coloring is: how many colors are needed for a proper coloring? A simple basic fact is that in a graph with maximum degree \(\Delta\), it is always enough to use at most \(\Delta+1\) colors: index the colors by \(1,2,\ldots\) and color the vertices one by one, always using the lowest indexed color that has not occurred among the already colored neighbors of the considered vertex. It is easy to see that this leads to the use of at most \(\Delta+1\) colors.  
What if, however, we want a coloring that also satisfies some extra requirements? Such an extra requirement is that the frequency of color occurrences in the neighborhood of any vertex is limited, as defined below.  
**Definition 1**.: _Let \(\beta\geq 1\) be an integer. A proper coloring is called \(\beta\)-frugal if no color appears more than \(\beta\) times in the neighborhood of any vertex._  
[PERSON], [PERSON], and [PERSON] [7] analyzed the number of colors needed for \(\beta\)-frugal colorings. They found a relationship between the maximum degree and the number of colors that suffice, but this is much more complex than the simple \(\Delta+1\) bound mentioned above for conventional coloring. Using the asymmetric Lovasz Local Lemma, they proved the theorem below (we present a simplified version given by [PERSON] [8]):  
**Theorem 5**.: _Let \(G\) be a graph. If the maximum degree \(\Delta\) of \(G\) satisfies \(\Delta\geq\beta^{\beta}\), then \(G\) has a \(\beta\)-frugal proper coloring using at most \(16\Delta^{1+1/\beta}\) colors._
## 5 Conclusions  
This study developed a multi-spectral sensor-based edge sensor node integrated with the XGBoost machine learning model for the rapid and accurate detection of milk components. The findings demonstrate the model's high accuracy in predicting protein and fat content, emphasizing its superiority over other machine learning techniques. This portable, cost-effective, and energy-efficient technology offers significant potential for on-site quality control in the dairy industry.  
The results highlight the feasibility of this integrated approach to revolutionize quality control and production processes. Future research should focus on evaluating the device across diverse milk varieties and environmental conditions, as well as optimizing energy consumption and data transmission for scalability in larger facilities.  
This work represents a promising step toward advancing food safety and quality monitoring technologies, contributing to more efficient and sustainable food production practices.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the author upon request.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ APDS & Ambient Light Proximity Detection Sensor \\ GBM & Gradient Boosting Machine \\ IoT & Internet of Things \\ LED & Light-Emitting Diode \\ ML & Machine Learning \\ MSE & Mean Squared Error \\ nm & Nanometer \\ RF & Random Forest \\ RMSE & Root Mean Square Error \\ \(R^{2}\) & Coefficient of Determination \\ XGBoost & Extreme Gradient Boosting \\ \end{tabular}
## 5 Simulation and Experimental Results
### Experimental Setup  
_Heudiasyc laboratory_ (In the Universite de Technologie de Compiegne) has developed a PC-based simulator-experiment framework (This framework is primarily designed and developed by engineer [PERSON] under the help of other colleagues and PhD students in Heudiasyc laboratory) for controlling quadrotors in real-time. The programs (written in C++) running in the quadrotors are the same, both in the simulator and in the embedded processors of real quadrotors. The goal of the framework is to firstly validate a program on the simulator before implementing on real quadrotors, in order to avoid destroying the real drones. It is important to note that within this framework, the control algorithms are implemented on the quadrotors rather than on a PC. There does not exist a central controller that sends control signals to the quadrotors.  
In the simulator, the complete quadrotor dynamics are used. The flight of the quadrotors are animated in a virtual 3D environment, which permits us to observe the behavior of the quadrotors under some formation control laws. This framework permits the simulation to reflect better the real-time experiment. Both the simulator and the real-time experiment share the ground station interface on the PC, which is responsible for displaying and sending instructions such as taking off and landing.  
The experimental setup is shown in Figure 4. In the experiments, the motion capture system Optitrack is used to localize the quadrotors in the formation. The ArDrone2 quadrotors manufactured by _Parrot_ are used for real-time experiments.  
The procedure of using the simulator-experiment framework is stated as follows:  
* Program the algorithms by using C++.
* Compile the program into executable files for the quadrotors in the simulator and for the real quadrotors.
* Test the algorithm in the simulator, adjust the parameters of the controller.
* Send the executable file to each quadrotor.
* Carry out the real-time experiment.  
**Remark 6**.: _Although the system Optitrack is a global localization system, we just use it to obtain the coordinates or inter-distances of the quadrotors. For instance, a quadrotor can use its on-board cameras to detect other quadrotors and calculate their inter-distances. Additionally, the self-localization can be realized by using GPS or other sensors (laser, etc.). The system Optitrack is used here to simplify the study._  
Figure 4: The experimental setup of the real-time experiments.
# Flexible Antennas: A Review
## 2 Materials for Flexible Antennas  
Flexible antennas are fabricated using various conductive materials and substrates. The substrate is chosen based on their dielectric properties, tolerance to mechanical deformations (bending, twisting, and wrapping), susceptibility to miniaturization, and endurance in the external environment. In contrast, the selection of conductive material (based on electrical conductivity) dictates the antenna performance, such as radiation efficiency.
## 3 Data Analysis Method
### Flame Length and Height of Center of Mass of the Flame  
In order to evaluate the flame dynamics, two critical parameters of the flame are obtained based on the CH* chemiluminescence images: the flame length (\(L\)) and the center of mass of the flame (\(P_{fy}\)). The flame length is defined as the axial length of the flame, determined by the CH* chemiluminescence images whose pixel gray value (g) is greater than \(0.1g_{max}\). Here, \(g_{max}\) represents the maximum gray value for a CH* chemiluminescence image of the flame. The height of the centre of mass (COM) of the flame is defined by the following Equation (4):  
\[P_{fy}=\frac{\int g(x,y)ydy}{\int g(x,y)dy} \tag{4}\]  
where \(g(x,y)\) is the gray value of the image at position (\(x,y\)). The symbol \"\(x\)\" is used to indicate the horizontal dimension, whereas the symbol \"\(y\)\" represents the axial dimension. (\(x,y\)) is the actual coordinate of the flame corresponding to the image pixel.  
Figure 2 displays the flame length and height of COM for a time-average image of the flame. A total of 2000 images were utilized for temporal averaging, representing a duration of 1 s. The subsequent images for temporal averaging were also subjected to the same processing method. The influence of the excitation frequency and amplitude on the two parameters will be discussed in Section 4.2.
## 2 Materials and Methods
### Governing Equations and Model Parameters  
The governing equation for heat transfer calculation in the transient state is given by Equation (1). For steady-state calculations, the terms with time and time derivatives are omitted. \[\rho C_{p}\frac{\partial T}{\partial t}+\rho C_{p}\mathbf{u}\cdot\
abla T+ \
abla\cdot\mathbf{q}=Q+Q_{p}+Q_{\mathrm{vd}}+Q_{\mathrm{ted}}, \tag{1}\]  
\[\mathbf{q}=-k\
abla T, \tag{2}\]  
where \(T\) is the dependent variable for temperature (K), \(t\) is time (s), \(\rho\) is the density (kg/m\({}^{3}\)), \(Cp\) is the specific heat capacity at constant stress (J/(kg\(\cdot\)K)), \(\mathbf{q}\) is the heat flux (W/m\({}^{2}\)), \(\mathbf{u}\) is the velocity vector (m/s), \(k\) is thermal conductivity (W/m\(\cdot\)K), \(Q\) contains other heat sources (W/m\({}^{3}\)), \(Q_{\mathrm{vd}}=\boldsymbol{\tau}:\
abla\mathbf{u}\) is heat sources for viscous dissipation in fluid (W/m\({}^{3}\)), \(Q_{\mathrm{P}}=\boldsymbol{\alpha}T\frac{\partial p}{\partial t}+\mathbf{u} \cdot\
abla\mathrm{p}\) is total point heat sources resulting in pressure work (W/m\({}^{3}\)), \(Q_{\mathrm{ted}}=\boldsymbol{\alpha}T:\frac{\partial S}{\partial t}\) is thermoelastic dumping in solids (W/m\({}^{3}\)), \(\boldsymbol{\tau}\) is viscous stress tensor (Pa), \(p\) is pressure (Pa), \(S\) is the second Piola-Kirchhoff stress tensor (Pa), and \(\alpha=-\frac{1}{\rho}\frac{\partial p}{\partial T}\) is the coefficient of thermal expansion (1/K). The thermal insulation boundary condition for the tunnel floor (see Figure 2d) is defined by Equation (3), while the other tunnel boundaries are subject to changes defined by the ambient temperature, as defined in Equation (4). \[\mathbf{n}\cdot\mathbf{q}=0,\]  
Figure 2: CAD drawing depicting the applied materials: (**a**) parts made of aluminum, (**b**) parts made of acrylic plastic, and the critical boundaries: (**c**) heat sources, (**d**) thermal insulation. \[T=T_{amb}, \tag{4}\]  
The theoretical model involves diffuse surface equations for the transparent part made of acrylic plastic, which are given by Equations (5)-(9). Equations defined for the aluminum surface are given by Equations (10) and (11).
# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 2 Related Work
### Single Image-based Methods  
Single image-based methods have limited access to information compared to video-based methods, which makes it more challenging to remove the rain streaks. Single image-based methods include low-rank approximations [3; 19], dictionary learning [4; 5; 20], and kernel-based methods [21]. In [4], the authors decomposed the image into high- and low-frequency components and recognized the rain streaks by processing the high-frequency components. Other mechanisms have used gradients [22] and mixture models [18] to model and remove rain streaks. In [18], the authors introduced a patch-based prior for both clean and rainy layers using Gaussian mixture models (GMM). The GMM prior for rainy layers was learned from rainy images, while for the clean images, it was learned from natural images. Nonlocal mean filtering and kernel regression were used to identify rain streaks in [21].
# Entity-Centric Fully Connected GCN for Relation Classification
However, the traditional GCN treats all nodes in the graph as equally important, ignoring the importance of entities in the sentence. And because GCN can only capture the information of direct neighbor nodes in one convolution process, GCN can only capture short-range context information. This problem can only be solved by adding a GCN layer. However, current research shows that increasing the number of GCN layers for relational classification tasks will make the model more complex [22]. Meanwhile, increasing the number of GCN layers will also cause excessive smoothness of node features, which will cause local features to converge to similar values. Therefore, it is worth exploring and applying an extended GCN, which can capture the key information in the graph and eliminate some unnecessary noise. In order to solve the above GCN problem, we use the entity node as a global node, and propose an entity-centric fully connected GCN (FCGCN) model. The model in this paper uses an effective graph convolution operation to encode the dependency relation structure of the input sentence, and then extracts the entity-centric representation for reliable relation prediction. We evaluate the model on the popular SemEval 2010 Task 8 dataset and TACRED dataset. Compared with previous models on these two data sets, the proposed model can make better use of the importance of entities in relation classification and obtain better results. When combined with the pruning strategy, the effect is further improved. In short, the main contributions of this paper are as follows:  
* We proposes a relation classification neural model based on graph convolutional network, using two entities as global nodes, that is, entity nodes have corresponding edges to other nodes. * We use the difference vector of the entity pair as part of the relation classification constraint to make the relation classification result more accurate. * A detailed analysis of the model and pruning technology shows that the pruning strategy and the proposed model have complementary advantages.
## 3 Modeling and Trajectory Planning
### Forward and Inverse Dynamics
The inverse \(\mathbf{D}^{-1}\) of the input matrix always exists for a fully actuated MBS and thus the inverse model can be easily computed for a prescribed trajectory. Note that for a 3D motion, the challenge is given by describing the prescribed trajectory in the joint space considering all possible configurations, avoiding collisions and singular configurations, but this is beyond the scope of the paper. In the case of an exact model of the manipulator and no disturbances, the feed-forward controller calculated in Equation (12) would be sufficient for tracking the desired trajectory, but in a real application, the performance of the feed-forward controller depends on the quality of the derived model. Typically, the feed-forward control law (12) is used to control the MBS with an additional PD or PID feedback controller such that the decoupled dynamics of the tracking error is stable, see for example Reference [1] for details. However, in this contribution, an MPC is used to compensate model uncertainties and disturbances.
## 3 Security and Privacy Issues
### Privacy Issues
Thus leakage of prescription data becomes a privacy issue. _Eavesdropping on patient medical information_: patient medical information drifts on the wireless links, which are easily monitored. The monitoring system records patient data from communication channels and extracts the valuable patient information. Thus eavesdropping is very simple for an attacker, while the patient data is transmitting from the body area network to the caregiver device. Hence, the patient privacy breached. _Social implications for the patient_: Another privacy issue concerns the social implications, where a patient is not able to make decisions for their own privacy. For instance an older person (_i.e._, age 65 and above), especially if he/she suffers from dementia (_i.e._, dementia is a loss of mental skills that affects daily life [76]). Other authors have discussed difficulties in managing privacy settings and lack of support in designing privacy-sensitive applications; for more details readers may refer to [74]. This section has shown that security and privacy issues directly influence patient life and the healthcare system, if there is any loophole in the application security, so it is recommended to the forthcoming research that robust security should be considered from the beginning of the application design. Furthermore, as we have seen above the security and privacy issues could breach patient physiological information, so there is a need for stringent regulation and laws that can act to mandate rigorous standards for protecting patient sensitive information. The next section discusses such stringent laws for healthcare providers and related organizations.
## 3 Results and Discussion
### Matrix Interference  
We developed simple and cost-effective dilute and shoot sample preparation procedures for evaluating and comparing sixteen important essential and non-essential trace elements. Urine samples could be analyzed directly in the simple dilution described above. The direct introduction of a highly concentrated organic matrix in the plasma often results in matrix interferences and spectral interferences from polyatomic ions. Spectral interferences have been reported for several elements such as \({}^{40}\)Ar\({}^{35}\)Cl\({}^{+}\) for \({}^{75}\)As\({}^{+}\), \({}^{35}\)Cl\({}^{16}\)O\({}^{+}\) for \({}^{51}\)V\({}^{+}\), \({}^{43}\)Ca\({}^{16}\)O\({}^{+}\) for \({}^{59}\)Co\({}^{+}\), \({}^{40}\)Ar\({}^{23}\)Na\({}^{+}\) for \({}^{63}\)Cu\({}^{+}\), and \({}^{32}\)S\({}^{16}\)O\({}^{18}\)O\({}^{+}\) for \({}^{66}\)Zn\({}^{+}\). Another prominent interference in urine analysis is \({}^{95}\)Mo\({}^{16}\)O\({}^{+}\) for \({}^{111}\)Cd\({}^{+}\)[39; 40]. Al, V, Co, Cr, Cu, Zn, As, and Cd were determined using an octupole-based collision cell with helium and hydrogen as the collision gas in order to avoid interferences of molecules [39].