esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
To determine the effect of the adjusted pH on cell viability and proliferative activity and to study the effect of the chitosan formulations on colon cancer progression, three incubation periods (24, 48, and 72 h) were studied in the colon cancer cell line. _Cell proliferation and metabolite activity_: For the identification of viable, proliferate, and invasive cancer cells, the 3-(4,5-dimethylthiazolyl-2)-2, 5-diphenyltetrazololium bromide (MTT) assay was used. MTT (5 mg/mL) was dissolved in PBS (Sigma-Aldrich, Germany) and a volume of 50 \(\mu\)L was added to each well. After 3 h of incubation at 37 \({}^{\circ}\)C, 150 \(\mu\)L of MTT solvent was added to each well and again incubated for 15 min at room temperature in the dark in a shaker for the complete dissolution of MTT formazan crystals. The absorbance was measured at 570 nm using a Zenyth 200 rt microplate reader (Biochrom). The cell viability (percentage) of two or more dependent and independent experiments was calculated using the following equation:  
Cell viability (%) = (OD of treated cells)/(OD of control cells) \(\times\) 100,  
where OD is the optical density of the culture. _Cell viability:_ Cell viability was analyzed by trypan blue staining method with automated counting, using an Automated Cell Counter (Invitrogen, OR, Waltham, Massachusetts USA). Summarily, 10 \(\mu\)L from cells suspension were mixed with 10 \(\mu\)L trypan blue in concentration 0,4% and the mixture was incubated for 30 seconds on room temperature. After that 10 \(\mu\)L from mixture was placed in a Countess(R)(Invitrogen) glass chamber and the cell viability was counted automatically. _Statistical analysis._ All results are expressed as the mean \(\pm\) standard deviation (SD) from three independent experiments with two parallel samples for each experiment (n = 6 for experiments with acids and n = 10 for experiments with chitosan particles). The comparisons between samples/groups were performed using Student's \(t\)-test and XL Stat for the Excel-One-test. Values of less than 0.05 were considered statistically significant.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 4 Performance Evaluation
### Result of Experiments  
In this subsection, we will show the result of experimental evaluation on varying aspects such as performance changes of HI-Sky by the np, indexing time, and skyline computation time of each method.
## 4 Experiments and Analysis  
### Experimental Description  
The multi-feature set extracted from the visible imagery and the emissivity retrieved from the thermal infrared hyperspectral imagery using FLAASH-IR were used to form the SSE feature set. In this paper, the VDVI is calculated with the red and green band from the visible image. The GLCM texture, that is Haralick measures (mean, homogeneity, contrast), is used to characterize the texture information with a window size 3, considering the time efficiency when calculating the GLCM texture of a large imagery. For the object-based feature, the scale of mergence and segmentation is selected based on some additional experiments. The object-based feature image is obtained with the different scales; then, they were input to the classifier to get a classification map. A high classification accuracy represents the optimal scales. In the paper, the scale of mergence and segmentation is (80, 90). At the same time, the emissivity information was resampled to the same resolution with the visible imagery and the first five components of the MNF was used in the SSE feature set. All the classification experiments were conducted by supervisor classification method using the training samples, and these samples are shown in Figure 1c, and the classification results were assessed by calculating the overall accuracy and kappa coefficient using the test samples shown in Figure 1d.
# Depth Image Completion through Iterative Low-Pass Filtering
## 3 Experimental Results
### Real-Scene Results  
In this section, we showcase completion depth images obtained from a Kinect for Xbox 360 (Kinect 1.0) and a Kinect 2.0. Kinect 1.0 employs IR structured light to acquire depth images [11].  
Figure 7a-c showcases the completion results obtained from the Kinect 1.0. In Figure 7a, captured in an indoor setting, we deliberately introduced disruption by placing a halogen lamp to interfere with the infrared structured light. The halogen lamp, visible as overexposed in the infrared image, caused signal loss, creating a hole with a diameter of 15 cm in the depth image. This resulted in a 9.46% loss in the operation block. Figure 7b presents the completion result, and Figure 7c displays the point cloud of the completion result, with yellow points representing accurate completion alignment.  
Moving on to Figure 7d-f, we present the completion results from the Kinect 2.0, which utilizes the Infrared Time-of-Flight (IR ToF) technique for depth image acquisition. Capturing both RGB and depth images under sunlight illumination and on a glossy granite floor tile, which disrupts the Kinect 2.0 IR signal, presents challenges. However, with proper segmentation of the operation blocks, our method proved effective in recovering highly sparse data. It is important to note that the experiments considered a defect rate below 42.77%. The point cloud results affirm the correct positioning of the recovered point cloud, demonstrating our method's capability to recover depth information accurately from both shiny surfaces and non-reflective materials in Figure 7d.  
Figure 6: Analysis of a cylinder with a 6.5 mm standard deviation of noise. (**a**) Accuracy for the cylinder depth image with and without noise. (**b**) Precision for the cylinder depth image with and without noise.  
Figure 8, captured by the Kinect 1.0, illustrates the depth image completion of a random surface converted into a point cloud. The random surface, obtained from curved wallpaper illuminated by a halogen lamp, showcases the correct alignment of the completion result on the surface.
# Calculation of the Center of Mass Position of Each Link of Multibody Biped Robots
###### Abstract
In order to define its position, the authors of the paper proposed an analytic formula that gives the real position of the CoM of the platform and is based on the application of a procedure that requires only the values of the force-torque sensors, applied on the feet of the humanoid robot, and the values of the motors torque. This procedure standardizes the calibration procedure in order to minimize the errors and it can be applied to every biped platform. The formulation approach has been implemented on the SABIAN robot with dimensions comparable to humans (height: 1500 mm; weight: 64 kg) giving very good results. The limits underlined in the papers [2; 3] were based on the approximation used to put the error mass in the determined CoM with the proposed formula. The approximation has been justified because the real position of the error mass is not known and if an external mass is positioned in the real CoM, its negative influence is reduced. However, the problem remains because an approximation has been used on behalf of exact calculation. In this paper, the limits underlined in [2; 3] are bypassed with the optimization of the formula based on the determination of the CoM position of each link of the robot. With the proposed solution, the error mass is distributed on each link of the biped robot. Another advantage of the formulation presented in this paper is that if the total CoM position of the platform is known a priori, the first formulation proposed in [2; 3] can be bypassed and the CoMpositions of each link of the platform can be calculated analytically without using force-torque sensors and the motor's torque. The paper is structured as follows: Section 2 presents, in synthesis, the first validated theoretical formulation proposed in [2; 3]; Section 3 shows results and discussion on the second theoretical formulation to determine the center of mass position of each link of the platform. Section 4 presents validation of the second theoretical formulation. The paper ends with a conclusion and future works.
## 7 Nanotechnology in Food Science
### Bioavailability of Bioactive Compounds  
Bioactive compounds have been used more frequently in supplements and food products due to their diverse beneficial effects on health; however, the challenge in the industry lies in the solubility, stability, and susceptibility to degradation of these compounds. In this sense, nanotechnology has found an area of opportunity in the development of methods to solve this problem and improve their bioavailability. Nanoencapsulation is a technology that allows the encapsulation of bioactive compounds on a nanometer scale [32; 81] using nanoemulsions, nanosuspensions, nanoparticles, liposomes, and micelles as nanocarriers [82]. The stability of these delivery systems is related to their internal and external morphology, size, and area-volume ratio on the surface of the nanostructure, and the above characteristics are determinants of sedimentation stability, aggregation stability, solubility, and bioaccessibility.  
At the same time, bioaccessibility is the first stage of oral bioavailability and considers the release of the bioactive compound (which depends on the texture of the food matrix), physicochemical characteristics of the carrier delivering the bioactive compound, and the presence of enzymes in the digestion process [83]. Therefore, stability is also accompanied by the choice of the matrix material and its physicochemical properties to evaluate the effect on the entrapment efficiency as well as to understand the mechanism of release and diffusion through different kinetic models [32; 84]. Therefore, the molecular weight and chemical structure of bioactive compounds are parameters involved mainly in the bioavailability solubility, lipophilicity, and permeability and have an influence on the bioaccessibility and absorption process by cells [6; 83; 85]. The main interactions responsible for the structural organization of food components are hydrophobic and electrostatic interactions, hydrogen bonding, and entropy effects [86]; these molecular forces are essential in colloidal systems, which is why it the important to characterize these interactions to improve the release processes of additives or molecules of interest.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
# Machine Learning Based Representative Spatio-Temporal Event Documents Classification
## 3 Materials and Methods
### Machine Learning Models  
#### 3.2.1 Convolutional Neural Network  
A convolutional neural network (CNN) is a model to prevent loss of spatial information in data input to a neural network, which is mainly used to analyze visual images. CNN is a deep learning model that can effectively process images because it effectively analyzes the characteristics of adjacent data while maintaining the spatial information of the data. CNNs are also used for classification tasks [24; 25; 26].  
In a previous study, we proposed a representative spatio-temporal document classification model using CNN. The layers of the proposed CNN model are shown in Figure 1. The CNN model consists of one word embedding layer, nine convolution layers (conv1d and maxpooling1d), and three dense layers. Among the parameters of the convolution layer, filters are set to 256, kernel to 7, strides to 3, and pool size to 3. Among the parameters of the two dense layers, unit is set to 100 and activation is set to relu. For binary classification, the unit of the last dense layer is set to 1, and the activation is set to sigmoid. We found that the training of the CNN model was slow at the beginning of training, so the model did not learn when stopping patience was set low. Therefore, we set epochs to 300 and stopping patience to 200 in model training. Dropout rate is 0.6, batch size is 64, and learning rate is 0.00001.
## 4 Propagation Characteristics and Phenomena of LMS Communication
### Multipath Fading  
Small-scale variability is the one that best characterizes fading and that takes the countless multiple paths present in urban environments into account, which can produce additional attenuations in amplitude and the phase of a signal, typically over a short time period. This phenomenon is often caused by obstruction due to foliage or construction, resulting in diffraction, reflection, and dispersion. It is also known as multipath fading, and it is a typical problem of mobile radio communications studied and analyzed with characteristics typical of a random process. The Rayleigh distribution is used to characterize multipath fading, and the PDF of the signal envelope is given as:  
\[p_{Rayleigh}(a)=\frac{a}{\sigma^{2}}exp\left[-\frac{a^{2}}{2\sigma^{2}}\right], \tag{5}\]  
where \(a\) is the received signal level, \(\sigma^{2}\) represents the average power received, caused by the multipath.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 4 Results and Discussion
### Comparison with Previous Studies  
The comparison summary of nineteen machine learning algorithm applications for mental workload assessment, as well as the current study, is outlined in Table 2. This includes details on author names, publication years, participant numbers, class numbers, measurement tools, methodologies, and accuracy rates. The performance results of the current study surpass those of several other studies [14; 15; 22; 25; 26], as shown in Table 2. Conversely, it has been observed that certain studies [12; 18] have achieved better results  
Figure 9: Importance ranking of the 34 features selected by the LightGBM algorithm.  
than this study. Upon examining EEG studies with superior outcomes, it is evident that the device used in those studies had a higher number of channels, which differs from the current study. When eye tracking studies that yield better results are examined, higher participant numbers are noteworthy. Although the number of participants used in this study is sufficient for the reliability of the analyses, increasing the number of participants is deemed crucial for obtaining even more dependable data to be incorporated into the analysis.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 2 Basic Theory  
### MOMEDA Method  
MED can only extract a single pulse. In order to improve this defect, [PERSON] proposed maximum correlated kurtosis deconvolution (MCKD), but the length, noise reduction interval, and shift number of the algorithm parameter filter have a great influence on the effect. Furthermore, MCKD can only effectively extract a limited number of pulses, which limits the detection efficiency. Therefore, in 2016, [PERSON]. [32] successfully extracts continuous periodic impact by solving the idea of optimal filter by the non-iterative method. Named MOMEDA, the idea of this method is as follows:  
Vibration signal collected by the sensor \(y(n)\):  
\[y(n)=h(n)x(n)+q(n), \tag{1}\]  
where \(q(n)\) is noise, \(x(n)\) is the impact sequence, and \(h(n)\) is the transfer function. The purpose of MOMEDA is to obtain an FIR (finite impulse response) filter, making the output \(y(n)\) match the original impact signal \(x(n)\) to the maximum extent.  
MOMEDA is a deconvolution algorithm for multi-pulse identification of signals. In order to obtain continuous impact pulses, the concept of MDN (Multiple D-norm) is introduced in Equation (2), and its maximum value is the minimum entropy deconvolution (MOMEDA) of multi-point optimization in Equation (3):  
\[MDN(\overset{\rightarrow  
Let Equation (5) equal 0 and it will become the following:  
\[\|\overrightarrow{y}\|^{-1}X_{0}\overset{\rightarrow}{t}-\|\overrightarrow{y}\| ^{-3}\overset{\rightarrow}{t}\overset{\rightarrow}{y}X_{0}\overset{ \rightarrow}{y}=\overset{\rightarrow}{0},\]  
which is \(\frac{\overset{\rightarrow}{t}\overrightarrow{y}}{\|\|^{2}}X_{0}\overset{ \rightarrow}{y}=X_{0}\overset{\rightarrow}{t}.\)  
Because \(\overset{\rightarrow}{y}=X_{0}^{T}\overset{\rightarrow}{f}\) and the assumption \(\left(X_{0}X_{0}^{T}\right)^{-1}\) exists,  
\[\frac{\overset{\rightarrow}{t}\overset{\rightarrow}{y}\overset{ \rightarrow}{y}}{\|\|\overrightarrow{y}\|^{2}}\overset{\rightarrow}{f}= \left(X_{0}X_{0}^{T}\right)^{-1}X_{0}\overset{\rightarrow}{t}. \tag{7}\]  
The MOMEDA filter and output solution can be summarized as follows:  
\[\overset{\rightarrow}{f}=\left(X_{0}X_{0}^{T}\right)^{-1}X_{0}\overset{ \rightarrow}{t}, \tag{8}\]  
\[X_{0}=\left[\begin{array}{cccccc}x_{L}&x_{L+1}&x_{L+2}&\ldots&\ldots&x_{N} \\ x_{L-1}&x_{L}&x_{L+1}&\ldots&\ldots&x_{N-1}\\ x_{L-2}&x_{L-1}&x_{L}&\ldots&\ldots&x_{N-2}\\ \vdots&\vdots&\vdots&\ddots&\ldots&\vdots\\ x_{1}&x_{2}&x_{3}&\ldots&\ldots&x_{N-L+1}\end{array}\right]_{L\;by\;N-L+1}, \tag{9}\]  
\[\overset{\rightarrow}{y}=X_{0}^{T}\overset{\rightarrow}{f}. \tag{10}\]  
As the multiple of the filter is the solution of Equation (10), the multiple of \(\overset{\rightarrow}{f}=\left(X_{0}X_{0}^{T}\right)^{-1}X_{0}\overset{ \rightarrow}{t}\) is the solution to the MOMEDA problem. This method completely avoids the effect of iterative operations and periodicity on the noise reduction effect of integers.
## 3 Results
### Adeuate Training Data Volume Was Necessary and Sufficient to Obtain High Detecting Performance  
Although polynomial-SVM algorithm had exhibited high performances in classification events, this model had speed limitation for training larger datasets [24]. However, excessively reducing the volume of training data might affect the classification performance of the polynomial-SVM model. A reasonable training data volume should not only guarantee the running efficiency but also retain the high classification performance. To study the effect of the training data volume on the classification performances, 75% (_Case 0_), 50% (_Case 1_) and 25% (_Case 2_) of morphometric data were selected as training datasets (see \"Methods\" section; please note that the results of _Case 0_ were presented in Table 4). As shown in Table 5, although the performance of the training accuracy was not affected by the training datasets volume, the performances of testing accuracy, testing sensitivity, and testing specificity were sensitive to the volume of training datasets. In addition, when the volume of training data was sufficient (\(\geq\)50%), all the performances of the polynomial-SVM model reached the best (close to 100%) and were not affected by the training datasets volume and the sampling method (Tables 4 and 5 (_Case 1_)). These results suggested that adequate training datasets volume (\(\geq\)50%) was necessary and sufficient to obtain high detecting performance. To examine the effect of the input features dimension (features number of a combination) on the classification performances, all the combinations of the six morphological features were examined with the polynomial-SVM model. When the dimension was \(k\), there were \(C_{6}^{k}\) possible combinations that can be used to build model (see \"Methods\" section). Each scatted point in Figure 3a-d represented a detection result for the specific combination. The results showed that the performances of training accuracy, testing accuracy, testing sensitivity, and testing specificity exhibited a similar trend. The classification performances of the model were improved as the dimension of input features increased (Figure 3). When the dimension \(\geq\)4, all the classification performances achieved a high level (over 95%) and were not susceptible to the combination approach. However, when the dimension \(<\)4, the classification performances increased rapidly as the number of the dimension increased, and the classification performances exhibited large dispersion among different feature combinations for a specific dimension. This suggested that the feature combination approach was also a factor affecting the classification performances of the machine learning model when the dimension \(<\)4.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 2 System Dynamics and Problem Statement
### Problem Statement  
The aim of this research is to enable the precise interception of multiple intruders by pre-calculating maneuvers for a UAV. The following section provides a detailed specification of the problem under investigation.  
Define \(\sigma(t)\) as the translation variable of a quadcopter, including its position, velocity, and acceleration thrust components:  
\[\sigma(t)=(\boldsymbol{p}(t),\boldsymbol{v}(t),\boldsymbol{a}_{t}(t))\in \mathbb{R}^{9}. \tag{9}\]  
Let \(T\) be the target duration of the motion, and \(\vartheta\) be the component of the state translation variable required at the end of the motion. The users can set it flexibly according to different task requirements:  
\[\sigma(T)=\vartheta. \tag{10}\]  
In this paper, the interception task is used as an example. As illustrated in Figure 2, the UAV is required to track the velocity of the target when it reaches the target position. It is assumed that both sensors and interceptors are installed on the centroid of the UAV and follow the body coordinate system of the UAV.  
This research seeks to address the following challenges: Given a quadrotor drone that initiates from an initial state set, comprising position, velocity, and attitude at \(t_{0}\), within the time interval [\(t_{0}\), \(T\)] it is necessary to determine suitable inputs \(f_{l}(t)\), \(\mathbf{\omega}(t)\) such that the drone's terminal state at the termination time \(T\) aligns with equation (10). Concurrently, throughout the entire flight trajectory, the drone's movement must adhere to the quadrotor dynamics Equations (1), (2) and (5), in addition to input constraints (7) and (8).
## 3 Experiments and Results  
### Experiment Setup  
To evaluate the proposed control method for various walking conditions, we designed three walking cases: level-ground walk, uphill walk, and loaded walk. Each experiment was repeated for 10 min. To avoid the impact of human fatigue on the case, there was a 30 min interval between each case. For uphill walking, the tilt angle of the treadmill was changed to imitate different slopes. In the case of the loaded walk, the subject wore different load vests. The load weight was evenly distributed throughout the subjects. A wireless EMG system was used to measure the sEMG signals on the able-bodied legs. During the experiment, the subject first experienced walkingwithout the proposed control method for 10 min and was then allowed to use the proposed method. Each test lasted for 2 min to ensure a steady optimization process, and the data were recorded for 60 s in 2 min.