esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Experimental Method
### Experiment Scheme  
Although it is worth studying the breakage of shape-anisotropic non-spherical particles, the particle shape should be simple enough. For single-particle contact, the impact of particle shape on particle breakage is mainly at the contact point because crushing failure is sensitive to the relative curvature of the contact interfaces [27]. The particle shape is designed as a cone-hemisphere in this study (see Figure 4). Twenty-three group normal contact tests of cone-hemispherical particles with different contact diameters, cone angles and sphere diameters were carried out, as listed in Table 2. Each group test was repeated 10 times to minimize the random error. The rock rheological testing system (see Figure 5) was used for normal contact experiments, which can load the rock in two ways: force control or displacement control. The measurement and control precision for force and deformation are 5 N and 0.001 mm, respectively. The data acquisition frequency is approximately 1 Hz. The steel groove bases (see Figure 5c) are designed to support particles. A high-definition camera was fixed in front of the testing system to capture the particle breakage process per 10 s. The displacement control loading mode with a constant loading velocity of 0.002 mm/s was adopted. For a sphere diameter of 30 mm or a sphere diameter of 50 mm and a cone angle of 120\({}^{\circ}\), the predetermined displacements are 1.5 mm, or else the predetermined displacement is 2 mm, to make sure particles fully crush.  
Figure 4: Cone–hemispherical particle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**G-D-\(\theta\)-\(d\)** & **Material \(G\)** & \begin{tabular}{c} **Sphere Diameter \(D\)** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Cone Angle \(\theta\)** \\ **(\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Contact Diameter \(d\)** \\ **(mm)** \\ \end{tabular} \\ \hline G-30-90-3 & & & & 3 \\ G-30-90-5 & & & & 5 \\ G-30-90-7 & & 30 & 90 & 7 \\ G-30-90-8 & & & & 8 \\ G-30-90-10 & & & & 10 \\ G-50-60-5 & & & & 5 \\ G-50-60-7 & & & & 7 \\ G-50-60-8 & & & 60 & 8 \\ G-50-60-10 & & & & 10 \\ G50-60-12 & & & & 12 \\ G-50-90-5 & & & & 5 \\ G-50-90-7 & & 50 & & 7 \\ G-50-90-8 & & & 90 & 8 \\ G-50-90-10 & & & & 10 \\ G-50-120-3 & & & & 3 \\ G-50-120-5 & & & & 5 \\ G-50-120-7 & & & 120 & 7 \\ G-50-120-8 & & & & 8 \\ G-50-120-10 & & & & 10 \\ G-70-90-8 & & & & 8 \\ G-70-90-10 & & & & 10 \\ G-70-90-12 & & 70 & 90 & 12 \\ G-70-90-14 & & & & 14 \\ G-70-90-16 & & & & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particle groups used in the experiments.  
Figure 3: Relationship between critical normal force \(F_{\text{cr}}\) and the tensile strength.
## 3 Results
### Relationship between the RCOF (Initial Foot Contact) and the JRF during Slope Walking  
The results of the correlation analysis between the RCOF during the initial foot contact of the stance phase and the JRF of the lower-extremity joints during inclined and declined walking are shown in Figure 10. The results show a non-significant correlation between the RCOF and JRF at the ankle and knee during inclined walking (r = \(-0.341\) and \(-0.336\), respectively). The RCOF was significantly correlated with the JRF at the hip (r = \(-0.403;p<0.05\)). During declined walking, similar findings were found, in that the RCOF was significantly correlated with the JRF at the ankle, knee, and hip (r = \(-0.449\), \(-0.488\), and \(-0.446;p<0.05\)).  
Figure 10: Relationship between the RCOF (initial foot strike) and the JRF of the lower limb during slope walking. * Significant different at \(p<0.05\).  
Figure 9: Comparison of the COP-COM distance between level and slope walking. Note: L = level walking; 5i, 7.5i, 10i = inclined walking of 5\({}^{\circ}\), 7.5\({}^{\circ}\), 10\({}^{\circ}\); 5d, 7.5d, 10d = decline walking of 5\({}^{\circ}\), 7.5\({}^{\circ}\), 10\({}^{\circ}\); * = significant difference between level and inclined walking; ** = significant difference between level and declined walking.
## 2 Background
### Rpl  
The IETF Routing Over Low-power and Lossy networks (ROLL) working group standardized RPL, which is a gradient-based routing protocol for WSNs with bidirectional links. It can support a wide variety of different link layers, including those that are restricted, potentially lossy, or typically used in conjunction with host devices or routers with very limited resources [7].  
RPL is a lightweight proactive routing protocol and requires only small power and memory resources to operate, making it an excellent choice for networks with limitations, such as those of the IoT. RPL is flexible and customizable to adapt to the requirements of heterogeneous IoT applications. This is possible with the help of Objective Functions (OF). The OFs are based on metrics to indicate the best existing routes [8].  
Routing metrics are selected according to the network operation or application requirements. They try to maximize or, in some cases, minimize an OF to achieve the best performance. To avoid routing loops, the RPL uses weights associated with the classification of each node in the graph. Such weights are considered in such a way that the child node classification is always higher than that of the parent node, increasing as it moves away from the root node.
# Simulation Analysis and Experiment of Piezoelectric Pump with Tapered Cross-Section Vibrator
###### Abstract  
In order to meet the requirements of microfluidic transport in the fields of medical, health, and microelectromechanical integration, a valve-less piezoelectric pump with a tapered cross-sectional vibrator was designed according to the bionic principles of fish swimming. Through theoretical analysis, the pattern of fluid flow in the pump chamber caused by the vibration of the piezoelectric vibrator was derived. The flow field of the piezoelectric pump was analyzed through simulation based on multiple physical fields coupling using the software of COMSOL Multiphysics (version 6.1). The velocity field distribution and its change law were obtained, and the fluid disturbance and instantaneous motion suppression phenomena were acquired as well. Based on the analysis of flow field streamline, the rule of generating vortexes was found. Thus, the driving mechanism of the vibrator with the tapered cross-section, which was consistent with the swimming principle of a fish tail, was verified. A prototype pump was made, and the pump performance was tested. The experimental data showed that the tested flow rate changed in the same trend as the simulated flow rate. When the driving voltage was 150 V and the driving frequency was 588 Hz, the pump achieved a maximum output flow rate of 367.7 mL/min. These results indicated that the piezoelectric pump with the tapered cross-sectional vibrator has great potential of fluid transportation.  
Keywords: valve-less piezoelectric pump; tapered cross-sectional vibrator; fluid-solid coupling; pumping performance; test +
Footnote †: journal: Applied Sciences
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 2 Proposed Method
### Lung Segmentation  
After preprocessing, the lung in the image must be segmented to extract the region of interest for further processing. There are two types of CT scan images, i.e., lung windows and mediastinal windows, as shown in Figure 1. One may note that in a CT image of the lung window, there are three types of gray levels: a black background and gray regions for the chest wall and lung area. These three regions can be seen in the histogram of the image,as discussed in [20]. Figure 2a presents the histogram plot of the lung window image shown in Figure 1a. The first peak in the histogram corresponds to the black background in the image, which can be easily dropped. The second peak represents the lungs and the circular gray region, and the third peak separates the circular component from the lung region. Therefore, a threshold from the second valley can be used to extract the lungs.  
The histogram of the mediastinal window, on the other hand, is straightforward; see Figure 2b. The background and the lung intensities are concentrated around the lower end of the histogram and the region around the lungs is gray. This means that in order to binize the mediastinal window, we simply require an optimal threshold value from the first valley of the histogram.
## 3 Results and Discussion
### Antibacterial Activity
#### 3.4.2 Minimum Inhibitory Concentration (MIC) of Honey Extracts against Different Microorganisms  
Determining the minimum inhibitory concentration (MIC) was performed alongside the disk diffusion method in order to guarantee reliability and precision and evaluate the antibacterial capacity of the honey samples. Figure 3 illustrates the minimum inhibitory concentration (MIC) of the honey types required to completely prevent the microbiological development of the tested strains and the assessed relationship between bacterial strains and the evaluated honey types (Supplementary Table S1). Sugar solutions (i.e., fructose, glucose, and sucrose) were assessed to highlight the absence of microbial growth.  
The present analysis was carried out to guarantee that the honey's inherent antibacterial components may be responsible for the antimicrobial activity reported in the samples, instead of osmotic reactions (details of control solutions were excluded from Figure 3). Results of MIC revealed that the honey samples exhibited a targeted antimicrobial effect according to their botanical origin. Thus, high MIC values were recorded for the Gram-positive strains, particularly _S. aureus_ and _S. pyogenes_, whereas the lowest MIC values were observed for the Gram-negative _K. aerogenes_. The most susceptible were the honey samples from Bulgaria with predominant _Tilia_ sp. (H1) and polyfloral types (H2), but also the honey samples from Turkishye with predominant _Thymus_ sp. (H10) and honeydew honey samples from both Romania (H9) and Turkishye (H18). The most effective against Gram-negative bacteria, particularly _E. coli_, were several chestnut honey samples from Turkiye (i.e., H13, H16, and H17). Other reports describe the antimicrobial potential of honey in association with its botanical origin and nutritional and phenolic composition. Thus, as evidenced in the present report, thyme honey shows significant inhibitory activity against _P. aeruginosa_ and _S. epidermidis_, compared with manuka honey [65]. In the present report, _Fallopia_ sp. honey exhibited moderate inhibitory activity against the evaluated microorganisms, including Gram-negative _E. coli_ and Gram-positive _L. monocytogenes_. Romanian honey samples with predominant _Fallopia_ sp. pollen presented similar activity, with higher inhibitory potential against _S. aureus_ and moderate activity against _E. coli_[66]. Coniferous honey from Greece exhibited the highest inhibition against Gram-positive _S. aureus_ and Gram-negative _E. coli_ and _S. typhimurium._, followed by significant activity of thyme honey against Gram-positive  
Figure 3: Chord diagram based on the antimicrobial potential (i.e., MIC) of the evaluated honey samples against several microorganisms.  
S. aureus_ and _S. pyogenes_[67]. A different report revealed that _E. coli_ and _Enterococcus faecalis_ proved to be the most susceptible to chestnut honey compared with manuka honey [68].
# Preparation of Co-Ni Alloy Coating with Stable Composition by Jet-Electrodeposition
## 2 Experimental Details  
The sample substrate is made of brass sheet 24 mm in diameter and 6 mm thick. Before the electrodeposition, the surface of the sample is polished with an 800-mesh sandpaper, and is then electrochemically cleaned using a brush plating device.  
The jet electrodeposition equipment consists of a direct current (DC) supply power (produced by Huake Elektronic Equipment Co., Ltd.), a numerical control (NC) platform, an electrode plating solution reservoir, a spray gun, a control valve, a circulating pump and other units, as shown in Figure 1. The nozzle of the spray gun has an internal diameter of 6 mm. The spray gun reciprocates linearly and travels at a velocity of 1.2 mm/s. The vertical distance from the bottom of the nozzle to the sample surface is 10 mm.  
The total mass of CoSO\({}_{4}\cdot\)7H\({}_{2}\)O and NiSO\({}_{4}\cdot\)6H\({}_{2}\)O was fixed at 300 g/L, and the mass concentration ratio of Co\({}^{2+}\cdot\)Ni\({}^{2+}\) was referred to as M. Four kinds of plating solution were prepared with M 2:1, 1:1, 1:2 and 1:3. Additionally, the solution was mixed with NaCl at 20 g/L and HBO\({}_{3}\) at 30 g/L to maintain a pH value of 3-4. Jet electrodeposition was conducted at room temperature with a current density of 40 A/dm\({}^{2}\) and varying the flow rate from 2.0 to 4.5 L/min.  
The Nova Nano 450 field emission scanning electron microscope (SEM) was used to observe the surface morphologies of the coatings. The content of Co and Ni in the coatings was measured using an Energy Disperse Spectroscopy (EDS). The DX-2700 X-ray diffractometer was used to analyze the phase structure of the coatings. The surface roughness of the coating was measured by a 2300A-RC profile roughness measuring instrument at a scan speed of 0.5 mm/s and a scan length of 8 mm. Dry wear test was also conducted on a MFT-R4000 high-speed reciprocating friction wear tester. The friction pair was a GCr15 steel ball with a diameter of 4 mm, and the wear scar length was 5 mm at 5 Hz frequency for 10 min. The wear volume was determined from the wear scars using an OLS4000 3D laser microscope. A Buehler automatic microhardness tester was used to measure the microhardness of the coating at 50 g load and 15 s duration. The average of five tests was taken for each sample.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Scenario 1  
In the first scenario, as shown in Figure 6, PUs reappear in route \(k_{3}\) (\(fc_{s}-fc_{1}-fc_{4}-fc_{7}-fc_{d}\)) in a random manner. \(PU_{1}\), \(PU_{2}\), and \(PU_{3}\) interfere with channels \(c_{2}\), \(c_{6}\), and \(c_{9}\), respectively. The source node selects either D2D routes \(k_{2}\) or \(k_{4}\), or the backbone route \(k_{1}\). Since route \(k_{2}\) has a higher priority due to a lower number of hops, it is selected.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 4 Performance Tradeoffs Analysis
### Spatial Diversity Gain vs. Spatial Multiplexing Gain Tradeoff
The outage probability (\(P_{out}\)) is defined as the event when the instantaneous channel capacity is less than the data transmission rate._  
For massive MIMO system under imperfect CSI at high SNR regime, the instantaneous capacity is given by [36]  
\[R_{inst}=\max_{P_{dR}}\log_{2}\text{det}\left(I_{K}+Y\,G\,P_{d}\,G^{H}\right). \tag{74}\]  
Then, the outage probability is given by  
\[P_{out}=E\left[P\left(\log_{2}\text{det}\left(I_{K}+Y\,G\,P_{d}\,G^{H}\right) \right)<R_{rate}\Big{|}\hat{G}\right]. \tag{75}\]  
Under favorable propagation conditions, i.e., (19) and (20), which is the key property of massive MIMO system, then the expression (75) becomes  
\[P_{out}=P\left(\text{det}\left(1+Y\,M\,P_{d}\,D\right)\right)<R_{rate}, \tag{76}\]where \(R_{nte}\) is calculated depending on Theorem 1 involving the effect of imperfect CSI, consider \(\ell\)th cell and \(K\) users, then the following is obtained  
\[R_{nte}=\left(1-\frac{\tau}{T_{coh}}\right)G_{MUX}^{avg}\sum_{k=1}^{K}\log(Y_{ \ell k}), \tag{77}\]  
with unity power constraints, the outage probability under imperfect CSI is given by  
\[P_{out}=\left[P\left(\det\left(I+Y_{\ell k}\,M\,D_{\ell}\right)\right)<\left(1 -\frac{\tau}{T_{coh}}\right)\sum_{k=1}^{K}Y_{\ell th}^{avg}\right]. \tag{78}\]  
Assuming channel matrix is decomposed as  
\[G=\hat{G}+\varepsilon_{\Delta}, \tag{79}\]  
where \(\varepsilon_{\Delta}\) is the channel estimation error, \(G\) and \(\varepsilon_{\Delta}\) are independent, and \(\varepsilon_{\Delta}\) is i.i.d CN(0,\(\sigma_{\varepsilon}^{2}\)). The CSI quality (\(U_{CSI}\)) is defined as in following  
\[U_{CSI}=\lim_{Y\to\infty}\frac{\log_{2}\sigma_{\varepsilon}^{2}}{\log_{2}Y}. \tag{80}\]  
If \(\sigma_{\varepsilon}^{2}=0\) means perfect CSI, and if \(\sigma_{\varepsilon}^{2}=1\) means imperfect CSI. Following Theorem 3, Lemma 1, 2 and Corollary 1 in [41], the relation between \(\theta_{avg}\) and \(G_{MUX}^{avg}\) becomes as  
\[\theta_{avg}=-\lim_{Y\to\infty}\frac{\log E\left(P_{out}|\hat{G}\right)}{\log Y}. \tag{72}\]0  
where \(c-1\leq G_{MUX}^{avg}\leq c\leq K\) and \(c\) is a small integer value.
# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
## 2 Materials and Methods
### Proposed Automatic Facial Expression Recognition System
#### 2.2.4 Statistical Tests  
Statistical tests were performed in order to analyze the effects of the selected variables on the selected facial expressions of the students during the lecture. The multivariable ANOVA (MANOVA) test was performed in order to analyze the effect of multiple selected variables over selected facial expressions. The multiple analysis of variance is a technique to analyze the effect of independent categorical variables on multiple continuous dependent variables. The MANOVA essentially tests whether or not the independent grouping variable causes significant variance in the dependent variable. For a particular p-variable multivariate test, assume that the matrices H and E have h and e degrees of freedom, respectively. Four tests may be defined as follows. Let \(\phi_{i}\), \(\phi_{i}\), and \(\lambda i\) be the eigenvalues of \(\mathrm{H}(\mathrm{E}+\mathrm{H}-1\), \(\mathrm{HE}-1\), and \(\mathrm{E}(\mathrm{E}+\mathrm{H})-1\) respectively. Note that these eigenvalues are related as follows:  
\[\phi_{i}=1-\lambda i=\phi i/1+\phi i\]  
\[\phi_{i}=\phi_{i}/1-\phi_{i}=1-\lambda i/\lambda i\]  
Figure 5: The proposed automatic facial expression recognition system working in classroom.  
\[\lambda\dot{\rm i}=1-\theta\dot{\rm i}=1/1+\varphi\dot{\rm i}\]  
The MANOVA explains how the student's facial expressions change with respect to gender, difficulty of subject, lecture duration and department. We have calculated skewness and kurtosis for the normal distribution of data. According to the guidelines of [PERSON] in 2005, skewness and kurtosis are used to analyze the normal distribution of data. If the skewness coefficient is between \(\pm 1\) the data is considered suitable for normal distribution, while some other studies consider both kurtosis and skewness together and state that the data can be normally distributed if the values of kurtosis and skewness lie between \(\pm 2\)[74]. The kurtosis and skewness coefficient of the data collected in this study lies between \(\pm 2\) and can therefore be normally distributed.  
Furthermore, the [PERSON]'s M statistics and [PERSON]'s test was used to test the homogeneity of co-variance and variance respectively on the dependent variables [75,76].  
Moreover, the Bonferroni correction was used in order to analyze the effect of six selected facial expressions with respect to the five selected variables without pre-planned hypotheses. The value of \(p\) will be adjusted using Bonferroni correction to reduce the family-wise error rate.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 4 Homography Estimation for a Non-Center-Aligned Image Pair
### Training  
As shown in Figure 7, when training the hierarchical models, the training data of the current stage model depended on the predictions from the previous stage model. When training the three cascade models at the same time, we needed to do some data processing, such as warping image_a to generate image_patch_tmp, in real time, which resulted in a slow training speed. To speed up the training, we adopted a step-by-step training strategy and prepared training data for each stage model offline.  
Firstly, the training set generated by the method shown in Figure 6 was taken as Training Set 1 to train the Stage_1 model (a HomographyFpnNet_A model). Secondly, we used the predictions of the trained Stage_1 model to generate Training Set 2 offline, and then Training Set 2 was used to train the Stage_2 model (a HomographyFpnNet_B model). Thirdly, predictions from the trained Stage_2 model were used to generate Training Set 3 offline, and then Training Set 3 was used to train the Stage_3 model (also a HomographyResNet_B model).  
For simplicity, we used the same training parameters or the training of all stage models (i.e., one HomographyFpnNet_A model and two HomographyFpnNet_B models). For the training of each stage model, we used the same training parameters and methods as for the center-aligned image pair case, except that we used 130,000 training iterations rather than 90,000. During the training process, we also adopted the following data enhancement strategies for the image pairs. For image_patch_b (size of \(128\times 128\) pixels) in the image pair, we randomly adjusted its brightness and assigned random values to a randomly chosen small rectangle (size: \(w\times h,4\leq w\leq 16,4\leq h\leq 16\)). The main reason for using these two data enhancement strategies was to avoid issues that arise when the wall maps are used, such as illumination changes between the camera image and the wall map, the occlusion of the camera images by the robot, and so on. We used these data enhancement strategies to improve the estimation accuracy of the trained hierarchical HomographyFpnNet model on the test wall images.