esa-sceva/satcom-chunk-collection huggingface dataset:
# New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots
## 4 Numerical Simulation  
### System Parameters  
In this case study, the Fanuc M-20 iA robot for machining and assembly shown in Figure 1 is simulated. The reference frames of the robot are drawn in Figure 3. Only the deflections of the first three joints are considered since the moment arms for the external force acting at the Tool Center Point (TCP) about the last three joints are much shorter than those about the first three joints. Note that the modeling approach presented in Section III is generic and can be adopted to robots with over three flexible revolute joints. The inertial frame [_oxyz_] is established at the robot base with its positive \(Z\) axis vertically upward and its \(Y\) axis perpendicular to the plane containing the second and third links. The \(z_{i}\) axes of the local frames are in the rotation directions of the \(i\)th joints; when the robot is stationary, the \(x_{1}\) axis is parallel to the \(X\) axis, and the \(x_{2}\) and \(x_{3}\) axes are along the link length directions. The directions of other reference axes can be determined by the right-hand rule.  
The link length vectors of \(\alpha_{1}\omega_{2}\), \(\omega_{2}\omega_{3}\), and \(\omega_{3}\omega_{\text{E}}\) can be written in the local frames as \(l_{1b}=[0,\ 0,\ l_{1}]^{T}\), \(l_{2b}=[0,\ 0,\ l_{2}]^{T}\) and \(l_{3b}=[0,\ 0,\ l_{3}]^{T}\), where the link lengths are \(l_{1}\)\(=0.2\) m and \(l_{2}\)\(=l_{3}\)\(=0.8\) m. The masses, position vectors of the centers of gravity (CG), and inertia tensors of the links written in the corresponding local frames are listed in Table 1. The torsional stiffness of the joints is given as: \(k=2.4\times 10^{5}\) N-m/rad, \(k_{2}\)\(=k_{3}\)\(=10^{5}\) N-m/rad. A damping ratio of 0.06 is used to compute the damping matrix in this simulation based on the modal test data for industrial robots given in [36]. The ranges of the joint angles are  
Figure 3: Reference frames for simulated robot.  
\(\theta_{1}\in[-\pi,\ \pi]\), \(\theta_{2}\in[-\pi/2,\ \pi/2]\), and \(\theta_{3}\in[-\pi/3,\ \pi/3]\). For this assembly application, the robot moves within a cuboid shaped operating workspace that can be represented by the width a = 1.8 m, the height \(h\) = 0.8 m, and the depth \(b\) = 0.5 m shown in Figure 3. The coordinates of the center of the operating workspace in the inertial frame are \(x\) = 0.75 m, \(y\) = 0 m, and \(z\) = 0.65 m. The Denavit-Hartenberg parameters of the robot are listed in Table 2. Lastly, the mass of the rivet gun is 5.8 kg. More information about this robotic application can be in [37].
# Evaluation of Rock Brittleness Index under Dynamic Load
## 3 Results
### Brittleness Response Characteristics under Different Loading Modes  
As can be seen from Table 3, when the load on the rock changed from static to dynamic, the brittleness of the rock was enhanced, while the relative brittleness relationships  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Brittleness**} & \multirow{2}{*}{**Looding**} & \multicolumn{6}{c}{**Brittleness Value of Rock**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Fine**} & \multicolumn{2}{c}{**Coarse**} & \multirow{2}{*}{**Shale**} & \multirow{2}{*}{**Marble**} & \multirow{2}{*}{**Sandstone**} \\  & & & **Granite** & & & & \\ \hline \multirow{3}{*}{\(B_{1}\)} & Static & 13.982 & 18.978 & 22.039 & 23.996 & 16.068 \\  & dynamic & 15.096 & 20.118 & 24.044 & 24.361 & 19.031 \\  & Static & 31.400 & 22.868 & 17.648 & 14.834 & 13.596 \\  & dynamic & 75.967 & 48.373 & 30.218 & 27.264 & 25.079 \\  & Static & 0.753 & 0.723 & 0.794 & 0.566 & 0.722 \\  & dynamic & 0.910 & 0.852 & 0.883 & 0.735 & 0.785 \\  & Static & 0.477 & 0.472 & 0.471 & 0.467 & 0.466 \\  & dynamic & 0.479 & 0.460 & 0.445 & 0.426 & 0.448 \\  & Static & 79.147 & 66.440 & 55.159 & 47.940 & 35.883 \\  & dynamic & 199.832 & 141.231 & 93.343 & 81.101 & 69.249 \\  & Static & 49508 & 48858 & 49296 & 40372 & 34552 \\  & dynamic & 43943 & 25126 & 32766 & 21907 & 37047 \\  & Static & 0.931 & 0.863 & 0.751 & 0.679 & 0.820 \\  & dynamic & 0.805 & 0.821 & 0.805 & 0.813 & 0.759 \\  & Static & 1.690 & 1.630 & 1.544 & 1.768 & 1.309 \\  & dynamic & 2.159 & 2.033 & 1.885 & 2.452 & 1.619 \\  & Static & \(-\)0.690 & \(-\)0.630 & \(-\)0.544 & \(-\)0.768 & \(-\)0.309 \\  & dynamic & \(-\)1.159 & \(-\)1.033 & \(-\)0.885 & \(-\)1.452 & \(-\)0.619 \\  & Static & 1.552 & 1.515 & 1.389 & 1.603 & 1.209 \\  & dynamic & 1.933 & 1.848 & 1.712 & 2.181 & 1.470 \\  & Static & \(-\)0.552 & \(-\)0.515 & \(-\)0.389 & \(-\)0.603 & \(-\)0.209 \\  & dynamic & \(-\)0.919 & \(-\)1.120 & \(-\)0.724 & \(-\)0.978 & \(-\)0.576 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Brittleness index values of different rocks under dynamic and static loading.  
between different rocks did not change. A rock with stronger brittleness under static load must be stronger than other rocks under dynamic load, with a \"strong and constant strength\" feature.  
In this paper, the difference in brittleness index values of rock samples under dynamic load and static load is defined as brittleness value difference. Figures 6 and 7 show the relationship between the brittleness value difference and the static brittleness value. Each black dot represents a type of rock. It can be seen that the greater the brittleness value under static load, the greater the brittleness value difference. In other words, for a rock with strong brittleness under static load, its brittleness value was increased more significantly under dynamic load. This also results in the relative brittleness relationship between different rocks not changing according to the loading method.
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods
### Skip-Squeeze-and-Excitation Module
#### 2.3.3 Excitation  
In order to alleviate the vanishing gradient of the network with the increase of the depth of the model, we choose the feature map \(\text{FM}_{j}\) from deeper layer interaction with the channel descriptor \(\text{d}\) from the shallow feature map. However, the number of channels in \(\text{FM}_{j}\) and \(\text{d}\) is generally different. To make them possible to be multiplied, we have to post-process the channel descriptor \(\text{d}\). Squeeze operator establishes the global information of each channel in spatial dimension, but it does not consider the connection between channels. Therefore, excitation operator adopts the gating strategy [32] to focus on establishing the connection between channels, the formula is shown in Equation (4):  
\[\text{d}^{\prime}=\text{F}_{ex}(\text{d},\text{W}_{1},\text{W}_{2})=\delta( \text{W}_{2}\delta(\text{W}_{1}\text{d})), \tag{4}\]  
where \(\text{d}^{\prime}=\left[d^{\prime}_{1},d^{\prime}_{2},\dots,d^{\prime}_{c}, \dots,d^{\prime}_{j}\right]\delta\) refers to the Rectified Linear Unit (ReLU) activation function [34], \(\text{W}_{1}\in\mathbb{R}^{\frac{C_{j}}{r}\times C_{i}}\) and \(\text{W}_{2}\in\mathbb{R}^{C_{j}\times\frac{C_{i}}{r}}\), \(\text{r}\) is reduction ratio. To build up the correlation between channels, we take the channel descriptor \(\text{d}\) as the input of two fully-connected layers. According to Equation (4), the first fully-connected layer changes the number of channels from \(C_{i}\) to \(\frac{C_{i}}{r}\), and the second changes the number of channels from \(\frac{C_{i}}{r}\) to \(C_{j}\), which is same as the channels number of feature map \(\text{FM}_{j}\). Besides, both of the fully-connected layer uses the ReLU activation function.  
The output of SSE module is obtained by the following formula:  
\[\text{SSE}_{c}=\text{F}_{sc}\Big{(}\text{FM}_{jc},d^{\prime}_{c}\Big{)}=d^{ \prime}_{c}\text{FM}_{jc}, \tag{5}\]  
where \(\text{SSE}=\left[\text{SSE}_{1},\text{SSE}_{2},\dots,\text{SSE}_{c},\dots, \text{SSE}_{j}\right]\), \(\text{F}_{sc}\Big{(}\text{FM}_{jc},d^{\prime}_{c}\Big{)}\) refers to channel-wise multiplication between the channel weights \(d^{\prime}_{c}\) and the feature map \(\text{FM}_{jc}\).  
The SSE module essentially introduces the skip-connection strategy and depthwise separable convolution: we select the feature maps of different depths as input, and use the channel weights generated by the shallow feature map to multiply the deeper feature map to enhance the gradient transmission ability of the network; the squeeze operator aggregates feature maps in the spatial dimension to obtain the global information of each channel; the excitation operator uses the gating strategy to establish the correlation between the channels, and converts the channel descriptor into the channel weights, which can be used to recalibrate the input feature map with the global information considering the channel relationship.
## 2 Related Work
### Current Methodologies for Predicting Fall Risk  
A wide range of sensor-based technologies have been used to assess fall risk in older adults. Current ones include inertial sensors such as accelerometers and gyroscopes used to quantify center of mass (CoM) motion [42; 43; 44; 45], motion capture cameras (Kinect(tm) system) [46; 47], pressure sensing platforms such as the Wii board [48; 49] and environmental motion sensing technologies such as radar or laser [50]. Overall, these devices have the potential to provide an accurate, cost-effective, and easy-to-implement fall risk assessment. However, variation in measured parameters, assessment tools, sensor placement sites, motion tasks, and modeling techniques preclude a firm conclusion about their ability to predict future falls [41].  
In addition to those mentioned, different movements have been used as functional evaluation mechanisms (gait, transfer from sitting to biped and from biped to sitting, Test up and Go, Berg Balance Scale, etc.). At the same time, the characteristics extracted to implement the methods are also different (movement, duration, movement speed, acceleration amplitude, measurements in the frequency, domain, reaction time, etc.), and the modeling methods are diverse. This may explain why the current literature describes a diverse range of performance (accuracy: 47.9-100%, sensitivity: 16.7-100%, specificity: 40-100%, AUC 0.65-0.89) [41].
## 2 Materials and Methods
### Allocation  
Allocation procedure is defined in the ISO 14041 (Environmental management--Life cycle assessment--Goal and scope definition and inventory analysis) as partitioning the input and/or output flows of a process to the product systems under study. In order to evaluate the environmental performance of the cob-based products, the allocation between grains and cob represents a necessary step. The ISO 14044 [44] recommends to avoid allocation, whenever possible. However, if not avoidable, the ISO series recommends to use methods that reflect the physical relationships--such as mass or energy content--or other relevant variables, e.g., the economic value of the products [45]. In this study, the corn cob have been assumed to be a co-product of the main production--the maize grains--and the physical allocation approach based on the mass values has been adopted. Starting from the data collected on the case study farm, the mass percentages of grains and cobs, with respect to the total production mass, have been calculated. The mass ratio between grains and cobs residues is about 8:1 with a cob production about one ton every eight tons of grains production (dry matter at 14% of humidity). Therefore, the allocation percentages used for the cultivation processes have been 88% and 12% for grains and cobs, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Emission to Soil** & **Amount (kg)** \\ \hline Carbon & \(-\)453 \\ \hline Copper & \(-\)0.0033 \\ \hline Iron & \(-\)0.19 \\ \hline Magnesium & \(-\)0.008 \\ \hline Nitrogen & \(-\)3.7 \\ \hline Phosphorous & \(-\)0.3 \\ \hline Potassium & \(-\)11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data of avoided emissions by cobs removal from field, based on the production of one ton of corn cob.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature Data** & **Average (t/ha)** & **Minimum (t/ha)** & **Maximum (t/ha)** \\ \hline Maize grain production (14\% of humidity) & 15.1 & 7.8 & 19.1 \\ \hline Maize Corn Cob production (42\% of humidity) & 3.0 & 1.8 & 4.1 \\ \hline Maize Corn Cob production (dry matter—14\% of humidity) & 1.9 & 1.3 & 2.4 \\ \hline Ratio (com cob/maize grain) & 18.7 & 12.2 & 35.2 \\ \hline Ratio (dry corn cob/maize grain (14\% of humidity)) & 11.6 & 6.7 & 25.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data on maize grain and maize corn cob productions.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 4 Detection of Various Damage Types Using ABT-ABT on a 3-mm Quasi-Isotropic Composite Plate
### Pure Mode Generation
#### 4.2.1 Pure S0 Mode Generation  
The ABT-ABT was used to generate a pure S0 mode at 300 kHz in the 3-mm quasi-isotropic CFRP composite plate. A pitch-catch experiment was conducted to measure the group velocity. For the excitation of pure S0 mode at 300 kHz, the incident angles were set to 26\({}^{\circ}\) (based on Snell's law). The excitation signal applied to the transmitter ABT was a narrow-band three-count tone burst at a central frequency of 300 kHz.  
To validate the generated guided wave, the response signals at five different locations were measured, as shown in Figure 18a. Strong response signals with high signal-to-noise ratios were obtained. It was noted that the wave packet was nondispersive. The TOF of the direct wave packet was determined and plotted as a function of distance, as shown in Figure 18b. Linear regression was used to estimate the experimental group velocity, yielding a value of 5.84 mm/\(\upmu\)s, which agreed with the theoretical S0-mode group velocity of 5.87 mm/\(\upmu\)s at 300 kHz obtained from the SAFE approach [5]. Therefore, pure S0 mode at 300 kHz was successfully generated in the 3-mm quasi-isotropic composite plate.  
Figure 17: Dispersion curves of a 3-mm [\(-\)45/90/45/0]\({}_{\text{3s}}\) quasi-isotropic composite plate: (**a**) phase velocity; (**b**) tuning angle.  
Figure 18: Experimental group velocity measurements for the S0 mode excitation at 300 kHz on a 3-mm quasi-isotropic composite plate: (**a**) waveforms; (**b**) correlation between distance and time of flight (TOF).
# Chemical Signatures of Ge in Magnetic of Wugang BIF, China
## 2 Geological Setting and Methods  
### Geological Setting and Samples  
The North China Craton (NCC) is the oldest craton in the world and the largest craton and has experienced several geological events, including the removal of deep cratonic mantle roots, multiple episodes of continental crust growth, and precipitation of mineral resources [23; 24; 25]. The NCC is composed of the eastern and western blocks of the Trans-North China Orogen (TNCO) based on lithology, structure, metamorphic grade, and geochronological data [26; 27]. The TNCO is a Paleoproterozoic subduction-collision belt, and Neoarchena terranes are distributed in the southern areas, including the Dengfeng, Zhongtao, and Taiwanic complexes [26; 27; 28]. The Taiwanic Complex consists mainly of early Precambrian medium- to high-grade metamorphic rocks, tonalite-trondjemite-granodiorite (TTG) graisses, amphibolites, mignatites, and spracrustal rocks. The Huashan, Xiaoshan, Luoning, Lushan, and Wugang terranes are distributed from northwest to southeast (Figure 1a) [23; 29]. The Wugang BIF is hosted in the Wugang block, which is located in the easternmost part of the Taiwanic complex in the southern part of the NCC, and is considered a representative iron ore in the Central Plain [23]. The Taiwanic complex is divided into the Paleoproterozoicoic upper Taihua unit (metapelitic graisses, marbles, quartzites, and banded iron formations) and the Neoarchean lower Taihua unit (TTG graisses and amphibites) [29]. Two major iron ores, the Tieshanmiao-type iron ore and the Zhaoanzhuang-type magmatic-origin iron ore, are distributed in the Taihua complex (Figure 1b). These two Fe deposits have contrasting geochemical and mineralogical signatures. The Zhaoanzhuang-type magmatic-origin iron ore is spatially and temporally related to ultramafic rocks of magmatic origin [30]. The Tieshanmiao-type Fe deposits are included in the BIF and display general geochemical signatures of other BIFs, including low concentrations of Al\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\), and REE + Y patterns [23; 31; 32]. Macroscopic and microscopic analyses revealed that the iron ore from the Wugang BIF exhibited a layered or massive texture (Figure 2). The banding texture consisted of alternating magnetite-pyroxene-rich and quartz-rich bands at various scales. The massive texture was composed of pyroxene and magnetite.  
Figure 1: (**a**) Map of the North China Craton with the location of the study area, modified after [27]; (**b**) Simplified geological map of the sampling area [29].
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 2 Basic Principles
### Ati  
Figure 1 illustrates the geometric configuration of an ideal ATI system. Two antennas, \(R_{a}\) and \(F_{a}\), were mounted in line with the flight direction to sequentially image the sea surface current area within a certain time interval. The phase difference between the two complex images is proportional to the radial velocity of the sea-surface scatterers. Assume that at imaging time \(t\), the distance between the sea-surface scatterer and the forward antenna \(F_{a}\) is \(R_{1}\), and after a certain time interval \(\Delta t\), the distance to the backward antenna \(R_{a}\) is \(R_{2}\). During this interval, with the scatterers moving at a radial velocity \(V_{r}\), the phase difference between \(F_{a}\) and \(R_{a}\) attributable to the sea surface scatterers can be calculated as follows:  
\[\phi_{AT}=-\frac{4\pi}{\lambda}[R(t+\Delta t)-R(t)]=-\frac{4\pi}{\lambda}v_{d} \Delta t. \tag{3}\]  
For a single-transmit dual-receiver system, such as LuTan-1, the effective baseline length was half the physical baseline length; therefore, the time delay \(\Delta t=B_{AT}/2v_{p}\), where \(B_{AT}\) is the physical baseline length and \(v_{p}\) is the platform velocity. Furthermore, the radial velocity of the sea surface scatterer can be obtained as follows:  
\[v_{d}=\frac{\phi_{ATI}\lambda v_{p}}{2\pi B_{AT}}. \tag{4}\]  
The one-dimensional component of the sea surface current along the ground range direction can be calculated as follows:  
\[u_{d}=\frac{v_{r}}{\sin\theta}. \tag{5}\]  
where \(\theta\) denotes the incidence angle.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 4 Materials and Methods
### The Privacy-Preserving Scheme
#### 4.2.2 Adversary Model  
According to the system architecture (Figure 2), the adversaries can be located within or outside of the Vehicular Ad-Hoc Network (VANET), in any other network (e.g., a mobile network), or the cloud service network. In other words, they can be part or not of a network and can be an authorized or unauthorized user. Depending on the location of the adversaries, they can perform different attacks. For instance, when adversaries are in the middle of the communication between the vehicles and roadside units, they can perform a Man-in-the-Middle (MitM) attack. Similarly, they can perform a replay attack by eavesdropping the communication channel to intercept messages, which they will then fraudulently resend. Additionally, they can perform a tampering attack by maliciously altering the messages exchanged between vehicles and roadside units.  
Our adversary model considers adversaries Type I, II, III, and IV with their assumptions and capabilities. These adversaries can act like honest but curious or malicious users. Table 2 presents the adversary model. A description of those types is presented as follows.  
* Type I is an internal adversary who is an authorized user of a VANET. They can act like an honest and curious adversary and listen to the communication channel to intercept messages.
* Type II is an external adversary who is not an authorized user of a VANET and is located between the VANET and a roadside unit. They can perform Tampering, Impersonation, MitM, Denial of Service (DoS), Replay, and other attacks.
* Type III is an external adversary who uses any other network, for instance, the Internet. They can perform DoS, Malware Injection, and other attacks.
* Type IV is an internal adversary who is an authorized user of the cloud service provider network. Similarly to Type 1, they can act like an honest and curious adversary.
## 3 Implementation of RUL Prediction Based on TCNMABG
### Model Verification and Comparison
#### 3.3.2 Multi-Domain Mixed Feature Extraction  
Due to the small number of original bearing data samples, it is difficult to meet the requirements of neural network model training, and generally, the raw signals are long; in order to reduce the calculation cost, the time window division method is used to expand the number of samples. In this paper, the length of the time window is selected as 4096 (32,768/8), and the spacing between Windows is also 4096; that is, the samples do not overlap, and the signal after noise reduction is segmented into multiple samples on the premise of not losing data information. This method is shown in Figure 6.  
Taking bearing1_1 as an example, the sampling frequency is 25.6 kHz, 32,768 data points are obtained once per minute, the window length is 4096, and the total number of samples of bearing1_1 is 984 (123 \(\times\) 32,768/4096). For each time step sample data, the 29-dimensional features in Section 2.3 are extracted from horizontal and vertical vibration signals, respectively, and a total of 58-dimensional multi-domain mixed features are obtained.  
The obtained multi-domain mixed feature set is selected according to monotonicity, correlation, and comprehensive evaluation index. The evaluation index results are shown in Figure 7. Taking 0.9 times the maximum value of the comprehensive evaluation index as the threshold, feature visualization was performed on the top 20 features larger than the threshold, and the results are shown in Figure 8.  
Figure 6: Data sample expansion.  
Figure 7: Comprehensive evaluation results of 58-dimensional features of Bearing1_1.