esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structural Description and Numerical Modeling
### Harmonic Response Analysis  
During fan operation, the dynamic imbalance of the rotating components induces a time-dependent harmonic load with sinusoidal characteristics on the frame structure, significantly affecting its stability. By applying the predefined harmonic load to the support structure, we can determine the steady-state response of the frame under this load excitation [26; 27]. Due to the small damping of the fan frame structure, the resonance frequency is like the modal frequency, and the axial modal frequency mentioned below is the axial resonance frequency.  
The modal analysis reveals that the critical speeds corresponding to the modal frequencies of the frame structure are 700, 1100, and 2600 rpm. The excitation frequency  
Figure 4: First six-order vibration mode of frame: (**a**) first-order; (**b**) second-order; (**c**) third-order mode; (**d**) fourth-order; (**e**) fifth-order; (**f**) sixth-order.  
\begin{table}
\begin{tabular}{c c c} \hline
**Order** & **Frequency/Hz** & **Mode Description** \\ \hline
1 & 11.2 & Lateral translation \\
2 & 17.9 & Vertical translation \\
3 & 41.4 & Motor plate axial lift \\
4 & 44.5 & Torsional vibration modes \\
5 & 55.0 & Symmetrical undulations in the axial \\  & & direction of the motor plate \\
6 & 68.4 & The motor plate tilts from side to side \\ \hline \end{tabular}
\end{table}
Table 3: Frame modal characteristics.  
spectrum of these three speeds is shown in Figure 5. Using the frequency spectrum data obtained from the excitation source vibration experiment as the load for harmonic response analysis, the vibration response was calculated within the range of 0-150 Hz, and the frequency velocity response curve was plotted, as shown in Figure 6. The results showed that the maximum amplitude occurred at 11.2 Hz laterally at 700 rpm, 17.9 Hz vertically at 1100 rpm, and 42.2 Hz axially at 2600 rpm, all exceeding 5 mm/s. These resonance frequencies are consistent with the first three modal frequencies.  
The modal frequencies of the frame structure should be as far away from the shaft frequency excitation near 2600 rpm as possible to ensure the safe use of the structure. Therefore, the third-order modal frequencies should be greater than 43.3 Hz. From the modal vibration mode in Figure 4, it can be seen that the axial vibration modes appearing on the motor board are related to the constraints of the cantilever structure. Therefore, the axial modal frequencies can be increased by increasing the rigidity of the motor plate, connecting tube, and mounting plate to avoid the axial resonance caused by the highest speed. This method still needs to be validated and analyzed.
## 1 Introduction  
One of the most critical phases in endodontics is the root canal preparation. This is not only performed to obtain the main objectives of the stage but also to facilitate the following important obturation to gain the paramount goal for successful endodontic therapy [1]. Besides the main duties, such as the removal or cleaning of canal content, the root canal preparation should maintain the original canal's shape and pathway, which is assessed by certain measurements [2]. The evaluation of root canal geometry on extracted human teeth before and after endodontic instrumentation is popular for canal transportation or centering ability in endodontics [3]. This assessment is commonly used for the technique, instrument, or other mechanical intervention in root canal preparation for its effectiveness, advantages and capability [4]. From the initial canal impression utilization for endodontic preparation assessment, many methods have been developed for this task along with the development of technology [4]. With the method of Bramante, the tooth is cross sectioned to evaluate the before and after instrumentation root canal perimeter at an acceptable certain precise level, but in an invasive way [3]. In search of excellent methods of evaluation, a non-invasive way is preferred, and radiographical imaging is prospective modality [4]. From the beginning of the use of this method, mainly in two-dimensional measurements with a periapical radiograph, it has been incessantly improved, gaining the ability for three-dimensional investigation using cone beam computed tomography (CBCT) or micro-CBCT [4]. Although the micro-CBCT is the gold standard nowadays, the accessibility of this has advanced. It is confined to investigation facilities in developing countries, regardless of the certain shortcomings of the time-consuming or ex vivo only methods [3]. The CBCT was used in the comparisons of root canal preparation using different techniques [1], or in working length determination [5; 6; 7; 8]. When using the CBCT, one of the most important parameters is the voxel size [6]. The smaller the voxel size of the scanning image, the more accurate the measurement [6]. However, the more precise the dimension, the more radiation the patient is burdened with, resulting in the limitation of CBCT manipulation in routine endodontic therapy [6; 9; 10]. The measurement of dimensions with both periapical radiograph and CBCT is commonly manipulated to evaluate root canal preparation. Both modalities achieve acceptable certain recognitions at certain precise levels [4]. However, the comparison between these two methods or the correlations between them have as yet not been adequately considered, and the data are limited [11].  
The aim of the present study is to compare cone beam computed tomography and periapical digital radiography for the evaluation of root canal preparation.
## 5 LMS Channel Models
As the works in the literature prove, the main used and proposed satellite channel model is represented by the three-state model that captures propagation channel variations while considering a LoS state and two other states concerning low-shadowing and high-shadowing conditions. Table 5 summarizes the channel models used in the specific reference papers, and makes it possible to see the main characteristics of each model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(a\) & Signal amplitude \\ \(\phi_{0},\phi\) & Phases uniformly distributed in \((0,2\pi)\) \\ \(b_{0}\) & Average scattered power due to multipath \\ \(p(a)\) & Probability density function (PDF) \\ \(\sqrt{d_{0}}\) or \(\sigma\) & Standard deviation of the signal \\ \(\mu\) or \(\pi\) & Mean of the signal \\ \(pj\) & Transition probabilities for Markov chain \\ \(G\) & Good state \\ \(B\) & Bad state \\ \(R\) & Transmission rate \\ \(v\) & Mobile terminal speed \\ \(\hat{\pi}\) & Stationary state vector for Markov process \\ \(p\) & Transition matrix for Markov process \\ \(M_{rx}\) & Mean multiple power \\ \(m\) & Mean of the signal \\ \(\sigma\) & Standard deviation of the signal \\ \(x\) & Received voltage \\ \(f_{1},f_{2},f_{3}\) & Cumulative distribution function \\ \(P_{1},P_{2},P_{3}\) & Occurrence probability of the three states \\ \(d\) & Distance \\ \(erf\) & Error function \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Symbols used in the LMS channel model section.
## 2 Model Description
### Market-Based DR Model  
In order to evaluate the impact of residential customers' participation in DR programs on load profile characteristics, an economic model of responsive loads is developed on the basis of elasticity factors. Elasticity is defined as demand sensitivity with respect to the electricity prices [26].  
\[E(t,t)=\frac{\rho_{0}(t)}{D(t)}\frac{\partial D(t)}{\partial\rho(t)} \tag{1}\]where, \(D(t)\) and \(\rho_{0}(t)\) are the nominal/initial value of demand and electricity price, respectively. Based on a single-period elastic load model, the customer changes his demand to achieve the maximum benefit from \(D(t)\) to \(D_{DR}(t)\) as:  
\[D_{DR}(t)=D(t)+\Delta D(t) \tag{2}\]  
The customer benefit for the \(t\)th time interval can be calculated as:  
\[S(D_{DR}(t))=B(D_{DR}(t))-D_{DR}(t)\cdot\rho(t) \tag{3}\]  
where, \(S(D_{DR}(t))\) and \(B(D_{DR}(t))\) represent customer benefit and income at time \(t\) after implementing DR programs, respectively. In order to maximize customer benefit, the following condition must be met [27]:  
\[\frac{\partial S(D_{DR}(t))}{\partial D_{DR}(t)}=0\Rightarrow\frac{\partial B (D_{DR}(t))}{\partial D_{DR}(t)}=\rho(t) \tag{4}\]  
Therefore, the customer utility function would get a quadratic form as follows [27]:  
\[B(D_{DR}(t))=B_{0}(t)+\rho_{0}(t)[D_{DR}(t)-D(t)]\times\left[1+\frac{D_{DR}(t )-D(t)}{2E(t,t)\cdot D(t)}\right] \tag{5}\]  
Differentiating (5) with respect to \(D_{DR}(t)\) and substituting the result in (4) yields:  
\[\rho(t)=\rho_{0}(t).\left[1+\frac{D_{DR}(t)-D(t)}{E(t,t)\cdot D(t)}\right] \tag{6}\]  
Therefore, a customer's consumption behavior over the time can be obtained as follows:  
\[D_{DR}(t)=D(t)\cdot\left[1+E(t,t)\cdot\frac{\rho(t)-\rho_{0}(t)}{\rho_{0}(t)}\right] \tag{7}\]  
In a multi-period elastic loads model, the price elasticity of the \(t\)th period versus the \(t\)th period can be defined as [26]:  
\[E(t,h)=\frac{\rho_{0}(h)}{D_{0}(t)}\cdot\frac{\partial D(t)}{\partial\rho(h)} \tag{8}\]  
Considering the linear relationship between the hourly demand level and the electricity prices, it can be expressed that:  
\[D_{DR}(t)=D(t)\cdot\left[1+\sum_{\begin{array}{c}t=1\\ t\
eq h\end{array}}^{T}E(t,h)\cdot\frac{\rho(h)-\rho_{0}(h)}{\rho_{0}(h)}\right] \tag{9}\]  
Combining (7) and (9), the responsive load economic model can be extracted as follows:  
\[D_{DR}(t)=D(t)\cdot\left[1+E(t,t)\cdot\frac{\rho(t)-\rho_{0}(t)}{\rho_{0}(t)} +\sum_{\begin{array}{c}h=1\\ h\
eq t\end{array}}^{T}E(t,h)\cdot\frac{\rho(h)-\rho_{0}(h)}{\rho_{0}(h)}\right] \tag{10}\]
## 2 Materials and Methods
### Deep Fuzzy Cognitive Map Model
#### 2.2.2. RGB-CNN  
A CNN indicates an algorithm that mimics humans' decision making. A CNN consists of input, hidden, and output layers. The hidden layer incorporates convolutional, pooling, dropout, and fully connected layers and aims to extract patterns from image data. A CNN can automatically extract features by employing a variety of filters on the input images, and via an advanced learning process, the most significant pixel values are retained. CNNs have extracted remarkable results from previous medical studies [2; 7; 18; 19].  
The convolutional layer is the first layer of a CNN, and its primary functionality is the creation of a feature map, which contains an abstract representation of the input image [2]. Pooling layers are applied after every convolutional layer to down-sample the data and reduce the computational complexity of the network. Pooling layers select the maximum or average value within a local window to retain the most important features [2]. Concerning the dropout layer, it helps to reduce unnecessary pixels and prevents overfitting. The dropout layer nullifies random pixel values to decrease the computational time of the training process. A flattening layer is applied next to convert multi-dimensional data into a vector. The last layer of the CNN is a sequence of fully connected layers, where each node is connected to the preceding one leading to the network's final prediction [7]. In most CNN models, the Rectified Linear Unit (ReLU) is utilized in convolutional and fully connected layers as an activation function, and sigmoid or softmax are used for output layers for binary and multiclass classification problems, respectively [7; 30; 31].  
After a thorough exploration process, we concluded with the ideal combination of parameters regarding pixel values, batch size, dropout rate, and number of nodes and layers of convolutional and fully connected layers. The proposed RGB-CNN rescales the input images to 300 \(\times\) 300 pixels and consists of three convolutional layers with 8,16, and 32 filters (kernels) accordingly. After each convolutional layer, there is a max pooling with a 2 \(\times\) 2 kernel size and a dropout layer with a drop rate of 0.2. Next, the flattening layer transforms multi-dimensional data into vectors to prepare data for fully connected layers. Regarding the fully connected layers, we selected two layers with 64 and 32 nodes, accordingly. The output layer is a single-node layer with a sigmoid activation function since we dealt with a two-class classification problem. Data augmentation was also utilized to increase the dataset size by generating altered versions of the original images. In our research, we applied rescaling to all images as a normalization technique, and regarding data augmentation, we employed width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0.1, and zoom_range = 0.1 to prevent overfitting and develop a generalizable model [2; 7; 32].
## 3 Long-Range, Low-Power IoT Communication Protocols and Their Security
According to [22], there are different approaches to providing authenticated encryption, which means to encrypt, authenticate the origin, and protect the integrity of the data. These steps can happen in different sequences:  
* Encrypt-and-MAC (E&M), where the encrypted data and the MAC are generated independently. * Encrypt-then-MAC (EtM), where the encryption is done first, and then the MAC is calculated using the encrypted data as one of the inputs. * MAC-then-Encrypt (MtE), where the MAC is produced first, and then it is encrypted together with the rest of the data. How are these approaches applied in IoT networks? To find out, this paper reviews three IoT communication architectures: LoRaWAN, NB-IoT, and Sigfox. To understand how they compare with other IoT communication protocols, Figure 5 shows a comparison of the different communication networks in terms of distance, data rate, and how much power they consume. This paper addresses the security of low-power wide-area networks (LPWA), which are in the upper left quadrant. Typical applications of short-distance communication networks, such as RFID, IEEE 802.15.4, Zigbee, and BLE, are IoT systems in smart buildings, homes, and factories. Other short-distance communication technologies such as Wi-Fi and 5G are not considered low-power but they are widely used in these smart home/building scenarios and industrial environments. Long-distance and low-power applications are well suited for IoT projects for smart cities, agriculture, transportation, and energy utilities. The distance factor makes security even more important since the IoT devices are placed over a large geographic area. The low data rate means that these devices save energy by sending small packets. Next, each protocol is explained in detail based on the methods they adopt to provide confidentiality, integrity, authentication, and their frame structures.
# Research on Tracking Control of Urban Rail Trains Based on Improved Disturbance Observer
## 3 Main Results  
This section proposes an improved observer-based control scheme. Firstly, the disturbance observer is designed to estimate and attenuate the total uncertainty, including the additional resistance and the unknown disturbance. Then stability analysis is conducted on the proposed disturbance observer and control method. Figure 3 gives the block diagram of the URTs tracking control based on the proposed method.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 5 Discussion  
### Estimation of m from Standard Deviation or from Coefficient of Variation  
Using the modified Robinson relation, it is now possible to estimate the Weibull modulus when only the average and standard deviation of a strength dataset are known. Two interlaboratory studies on mechanical testing standards that were cited earlier contain base data for many materials [24; 25]. These studies aimed to define the reproducibility, R, of mechanical tests conducted at different laboratories. The reproducibility includes interlaboratory deviations of strength calibration, deviations due to sample chemistry and process variables, test conditions, etc., since the main aim of these studies was to establish the accuracy of mechanical test results. The ASTM study [24] separately reported the standard deviation for within-laboratory precision, s\({}_{\text{r}}\), that for between-laboratory precision, s\({}_{\text{R}}\), and R. ASTM standard E8 provided these parameters for six alloys (two Al alloys, three steel alloys, and one Ni alloy) [107]. This ASTM E8 shows that the reproducibility R is three to six times larger than the standard deviation of a single series of tests at one location, s\({}_{\text{r}}\), while National Physical Laboratory (NPL) report [108] put the factor at four on average. Thus, the s\({}_{\text{R}}\) values can be used for estimating m values when samples come from a single set. When many sets of samples from multiple sources are tested at different sites, R values are appropriate. These two reports provided the average tensile strength and standard deviation for 20 different alloys (six for Al alloys, 12 for steel alloys, and three for Ni alloys). The m\({}_{\text{est}}\) values from the ASTM E8 [107] and NPL report [108] are shown in Table 7. While the m\({}_{\text{est}}\) values for Al (45 to 82) and A105 steel (75) are comparable to the previously known values in Table 3, the m\({}_{\text{est}}\) values for 316 and 51410 steels and Inconel(r) 600 are higher (91, 174, and 151). From NPL collected data, the m\({}_{\text{est}}\) of Al alloys are again comparable at 47 to 122, while steel and Ni alloy resulted in m\({}_{\text{est}}\) values of 44 to 169. Again, the m values reported fit the range found in Table 3, where the m\({}_{\text{est}}\) values of structural steels were from 44 to 110, while those of stainless steels and Ni alloys were from 37 to 175.
# Automatic Brix Measurement for Watermelon Breeding
## 3 Results
### Manipulation Parameters of the Refractometer  
In order to obtain feasible measurement parameters, we conducted a two-factor experiment, in which the first factor was the lateral gap, with four levels of 2 mm, 3 mm, 4 mm, and 5 mm, and the second factor was the vertical gap, with three levels of 2 mm, 3 mm, and 4 mm. There were 12 parameter settings in total. For every setting, we chose three new testing points on a watermelon's section, and for each testing point, we recorded the results 10 times to generate an average. The ground truths were obtained using PAL-1, manufactured by the Atago company and with Brix accuracy of \(\pm 0.2\%\), by manually dropping the sampled watermelon juice into its sensor via a pipette with three repeats for an average result. The experiments were finished within 5 days after watermelon samples were picked.  
As shown in Figure 7, the accuracies were turbulent, from 59.88% to 99.12%, when the lateral gap was 3 mm and 4 mm, and when the vertical gap was 2 mm, respectively. This is likely because the squeezed watermelon pulp rebounds slightly after release, when the vertical gap is small enough, the measurement light beam may be blocked. When the vertical gap was 3 mm and 4 mm, we found that the smaller the lateral gap, the higher the accuracy. This is because when the lateral gap is small, the watermelon juice is not easily lost through infiltration, so enough liquid can be maintained in the hole, keeping PEN-PRO functioning well. Therefore, we obtained the best Brix measurement parameters, namely  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type** & \(R^{2}\) & _RMSE_ \\ \hline RealSense D455 & 0.9975 & 1.136 \\ Laser rangefinder & 0.9902 & 2.163 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The correlation results of watermelon diameter measurement using different sensors.  
Figure 6: Linear regression plot for the measured diameters against the ground truths.  
2 mm for the lateral gap and 4 mm for the vertical gap, with an average accuracy of 98.74%.
# Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics
## 1 Introduction
Therefore, many studies have analyzed the kinematics and kinetics of ankles and knees, specifically in the sagittal plane [15; 34; 35; 36; 37; 38; 39]. This allows for a local analysis, however, for better assessment of abnormal gaits such as crouch gait, the other planes are necessary. These issues make it necessary for crouch gait analysis to define kinematic gait patterns over the three anatomical planes in both the joint space and in the workspace. In addition, gait kinematics research focuses on building body models to explain the functioning of the body system and provide solutions to improve the methods for GA. Acquiring and analyzing kinematic and kinetic data of the body segments and joints of interest is a common procedure [40]. Tired and repetitive routines as well as lack of feedback on performance and improvement during rehabilitation very often lead the patient to discontinue the therapy [41]. In these cases, visualization [42] and exergaming (exercise + game) [43; 44; 45] platforms may improve the results of the rehabilitation process. In this work, we propose a quaternions-based method to solve the forward kinematics of the position of the lower limbs during walking. We use an eight Degrees of Freedom (DoF) reduced gait model. In addition, we present an approach to solving the inverse kinematics for the same eight-DoF model and the visualization on the three anatomical planes of performance in both the workspace and the joint space. In this paper, forward and inverse kinematics analysis based on workspace and joint space metrics and visualization is carried out over the crouch gait. However, these methods could be extended for other abnormal gaits. This paper is organized as follows. Section 2 introduces the mathematics of forward and inverse kinematics modeling. Section 3 presents the main results. Section 4 provides a discussion, highlighting potential clinical applications. Finally, conclusions and possible future works are presented in Section 5.
## 3 Results and Discussion
### Dynamic Light Scattering (DLS) Analysis  
The particle size distribution was determined using dynamic light scattering (DLS). Figure 14 shows that the CNCs-PEG/NaOH had an average hydrodynamic size of 33.31 \(\pm\) 1.67 nm. The polydispersity index (PDI) for the sample was found to be 1.00, indicating that the particle size distribution was relatively homogeneous.  
Moreover, the DLS measurements showed larger particle sizes compared to the TEM results, which can be attributed to particle hydration. These findings are consistent with the previous study by [PERSON] and [PERSON] [66].