esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Two-Step Parametrization Method
### Parameter Sensitivity Analysis
 \tag{19}\]  
The identifiability of the parameters is theoretically derivable from the inverse of \(\mathbf{F}_{\mathrm{norm}}\). Unfortunately, this is not always feasible because the FIM is often ill-conditioned or even singular. Using a singular value decomposition (SVD) of \(\mathbf{F}_{\mathrm{norm}}\) resolves this issue and enables further analysis [32; 33; 34]:  
\[\mathbf{F}_{\mathrm{norm}}=\mathbf{U}\Sigma\mathbf{V}^{\mathrm{T}} \tag{20}\]  
\(\mathbf{U}\) denotes the matrix containing the left singular vectors, \(\Sigma=\mathrm{diag}(\sigma_{1},\sigma_{2},\ldots,\sigma_{n_{\theta}})\) the singular value matrix, and \(\mathbf{V}\) the matrix containing the right singular vectors. The left and the right singular vector matrix are identical because \(\mathbf{F}_{\mathrm{norm}}\) is a symmetric matrix. The analysis utilizes the right singular vector matrix \(\mathbf{V}=[\mathbf{v}_{1},\mathbf{v}_{2},\ldots,\mathbf{v}_{n_{\theta}}]\), where \(\mathbf{v}_{l}\) for \(l\in\{1,2,\ldots,n_{\theta}\}\) denotes a singular vector corresponding to the singular value \(\sigma_{l}\). The singular values are interpretable as the amount of information, and the corresponding singular vectors determine the direction in the parameter space. The Euclidean norm of the vectors is \(1\). Thus the relative direction share of the vector component \(v_{l,i}\) is \(v_{l,i}^{2}\)[35]. The relative direction \(v_{l,i}^{2}\) multiplied with the singular value \(\sigma_{l}\) is the amount of information showing into the parameter \(\theta_{i}\)'s direction. Summing up all the information of one parameter from each singular value yields the parameter's total information  
\[\sigma_{\theta_{i}}=\sum_{l=1}^{n_{\theta}}\upsilon_{l,l}^{2}\sigma_{l}. \tag{21}\]  
The parameter's total information \(\sigma_{\theta_{i}}\) indicates if a parameter is identifiable with the given model and measurement data. The most significant parameters \(\theta_{\text{ms}}\) are determinable by sorting the parameters according to their total information in descending order. The most significant parameters are determined by summing up the first \(n_{\theta_{\text{ms}}}\) parameters until the following inequality holds:  
\[\frac{\sum_{i=1}^{n_{\theta_{\text{ms}}}}\sigma_{\theta_{\text{ms},i}}}{\sum_{ i=1}^{n_{\theta}}\sigma_{\theta_{\text{s}}}}\geq\gamma \tag{22}\]  
Note that \(\gamma\in[0,1]\) denotes the threshold and is adaptable for different purposes. This work uses \(\gamma=0.99999\). In this case, the most significant parameters \(\theta_{\text{ms}}\) describe \(\geq\)99.999% of \(\mathbf{F}_{\text{norm}}\), and the least significant ones \(\theta_{\text{ls}}\) describe \(\leq\)0.001%, which makes the latter negligible for parametrization.
# Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users
## 1 Introduction
Perhaps at a very basic level there are the usual limitations defined by a community and low-resource environment: tools need to be low-cost, portable, simple to use, and easy to understand even for the occasional and inexperienced user. Outcomes measures (such as \"no risk\", \"low risk\", \"high risk\") should translate directly into improved clinical decision-making, and categorical cutoffs should be clearly defined, and outcome based. Moreover, measurements and outcomes should be consistently reproducible, and longitudinal evaluation of changes (for example, in lesion risk levels) should be possible. To improve the accuracy of adjunct screening tools, researchers are pairing emerging optical imaging modalities with novel artificial intelligence-based approaches to provide \"smart\", inexpensive, robust, non-specialist chairside screening platforms [42; 43; 44]. Artificial intelligence approaches combined with imaging can have considerable impact on OC outcomes, with modalities ranging from low-cost screening with smartphone-based probes to algorithm-guided detection of oral lesion heterogeneity and margins using optical coherence tomography [42; 45; 46; 47; 48]. Our long-term goal is to improve OC outcomes by developing a smartphone-based oral screening platform for non-specialist use that informs on OC risk levels based on multimodal intraoral imaging, clinical signs and symptoms, and risk factors. The specific goal of this study was to evaluate the effect of tobacco use on the accuracy of an imaging-and risk factor-based screening algorithm for OC that is being developed in our laboratories.
## 3 Types and Characteristics of Sensor Faults
### Types of Sensor Faults
#### 3.1.1 Hard Fault  
A hard fault refers to the damage or failure of the agricultural sensor's hardware, resulting in a sensor fault. A hard fault prevents the sensor from collecting and transmitting data normally, including a transmitter circuit fault, receiver circuit fault, microcontroller fault, sensor circuit fault, and power fault. A sensor with poor circuit contact is highly susceptible to short-circuit faults. Circuit disconnection results in an open circuit sensor fault. The poor working environment causes sensor hardware to malfunction. Most of these faults are permanent and require the replacement of defective hardware or maintenance circuits.  
Researchers report that power supply failures are the primary cause of sensor data errors [65]. The primary factor limiting wireless sensors is the power supply [66]. Wireless sensors must operate for years or even decades after deployment, particularly when some nodes are deployed in remote locations, making it difficult to regularly meet the power maintenance requirements. A power failure causes the sensor to be unable to collect  
Figure 3: Types of sensor faults in agricultural sensor systems.  
information correctly, or transmit data normally, and may even cause the failure of the entire agricultural sensor network. The energy consumption of wireless sensors is primarily attributable to monitoring, data transmission, and data reception. In the paper [67], the impact of power faults on WSNs is categorized into four groups: node, link, routing path, and global fault. A node fault may occur when the power supply falls below the node's operating level. When a node's power supply is insufficient, its communication range is diminished, resulting in a link fault. When one or more nodes have link faults, the WSN becomes less efficient, resulting in routing path faults. When node or link faults occur in key nodes, WSN communication is interrupted, resulting in a global fault.
## 2 Materials and Methods
### Study Area and Datasets
#### 2.1.2 Eddy Covariance Flux Tower Observations  
We used the eddy-covariance (EC) observations and the corresponding meteorological data collected from six flux tower sites to evaluate six LE products. Table 1 lists the flux tower sites information in detail. The data for Daxing1 (DX1) site was provided by FLUXNET Lathuile network, the data for Yucheng (YC) site was provided by Chinaflux  
Figure 1: Geographical location and topographic map of the Haihe River basin (HRB) and distribution of the 6 flux tower sites.  
network [44], and the data for Dxing2 (DX2), Guantao (GT), Huailai (HL), and Miyun (MY) sites was provided by the National Tibetan Plateau Data Center (TPDC) [45; 46; 47; 48; 49]. These sites cover two global land-surface biomes: Mixed forest (MF) and crop land (CRO). The flux towers used the eddy-covariance (EC) system for measurement of the half-hour latent heat flux (LE), sensible heat flux (H), surface net radiation (Rn) and soil heat flux (G). We linearly aggregated the surface flux from half-hour into daily mean values and then aggregated daily values into 8-day mean values. We removed zero values and invalid values when the amount of missing data exceeds 20% of the responsible half-hourly measurements. The problem of unclosed energy in the flux towers data were reported in some studies, and we used the method of [PERSON] et al. [50] to correct LE values of six flux towers.
## 3 Modeling and Linearization of the System
### Stator Algebraic Equations  
In order to write the algebraic equations of the model, the equivalent circuits associated with the negative and zero positive sequence networks are analyzed. The following denomination is used to make reference to the voltage and current of the system. For any given machine \(i\), it is considered that:  
* \(V_{\beta(Rc)_{i}}^{\rho}\) the real part of the voltage \(\beta\) phase sequence \(\rho\) (pu);
* \(I_{\beta(Rc)_{i}}^{\rho}\) the real part of the current \(\beta\) phase sequence \(\rho\) (pu);
* \(V_{\beta(Im)_{i}}^{\rho}\) the imaginary part of the voltage \(\beta\) phase sequence \(\rho\) (pu);
* \(I_{\beta(Im)_{i}}^{\rho}\) the imaginary part of the current \(\beta\) phase sequence \(\rho\) (pu);
* \(\theta_{i}^{\beta}\) the current gap of a \(\beta\) phase (rad).
## 2 Numerical Setup
### Mesh Quality Study  
For all meshes used in this study, two different mesh quality parameters were studied, such as the mesh orthogonality and the skewness. The obtained values for these two parameters are shown in Table 1 for the six VG meshes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**VG Case** & **Non-orthogonality (Max)** & **Non-orthogonality (Avg.)** & **Max.Skewness (-)** \\ \hline
1.2H & 66.58\({}^{\circ}\) & 12.71\({}^{\circ}\) & 2.87 \\ H & 65.32\({}^{\circ}\) & 12.69\({}^{\circ}\) & 3.33 \\
0.8H & 64.85\({}^{\circ}\) & 12.77\({}^{\circ  
The non-orthogonality (nO) measures the angle between a line that connects the center of two adjacent cells and the normality of their common face; any non-orthogonality value satisfying the condition of \(nO<70\) is a safe value and for all the VG cases, the used meshes nO values lie in this safe zone. The skewness represents the distance between the point where the common face of two cells is crossed by the line that connects the center of those two cells and the center of this common face. The smaller the value for skewness, the better, and any value bigger than 20 may suggest lack of accuracy. Both meshing parameters, non-orthogonality and skewness, are in the range explained ahead for all meshes.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification
### Influence of the Location of Concentrated Load
#### 4.2.2 Damage Degree Identification  
The deflection difference of the girder (instead of the deflection values measured in real bridges) obtained when the concentrated force acts on different measuring points is used as the basis to identify the damage degree of the cable. The damage index is identified according to the damage degree identification method, and the results of four kinds of double-cable damage identification in working conditions can be obtained. The results show that when the concentrated force acts near the measuring point of the tower, the discernable damage index value is significantly different from the actual damage index (the preset damage index) value (the maximum error is 3.76%). It can be seen that the location of the concentrated force will affect the accuracy of identifying the cable damage degree based on the Kriging proxy model. To sum up, the point of operation of concentrated force should be selected in the mid-span as far as possible.  
Figure 16: Distribution curves of the first derivative of the deflection difference of the girder when the concentrated force acts on different measuring points.
## 2 Passive Wake-Up Radio
### State-of-the-Art in Passive Radios  
Besides RF energy transfer and harvesting, electric energy is harvested by resonance coupling [47] and magnetic inductive coupling [48]. However, the inductive and magnetic resonance coupling wireless transmission techniques are considered as a near-field [49], whereby the magnetic coupling can be so weak at long distance that the receiver coil's effect on the sender coil is almost negligible. Although these techniques have high conversion efficiency and power density, the efficiency of power transmission is dependent on the coupling coefficient, which decays as the inverse cube of the distance between the resonators [50]. Moreover, both resonance coupling and inductive coupling need the coils at the transmitter and receiver to be calibrated and aligned. This makes them unsuitable for remote charging as in the case of wake-up radios. However, such constraints are not present in the RF energy transfer. RF energy transfer is considered as a far-field energy transfer technique.  
Within the previous decade, low-power transfer using RF has been attracting research attention. These low-power RF energy harvesting circuits are used to power the radios of wireless sensor nodes as well as the wake-up radios. Due to the easy accessibility of the RF energy harvesting source and reduced dependency on environmental changes, its energy supply is more predictable than the other techniques. The RF transmission signal strength is inversely proportional to the distance between transmitter and receiver. Although large amount of ambient RF power is transmitted, the energy at the receiver is usually very low because as the signal spreads further from the energy source, it is attenuated gradually. Therefore, wake-up radios with energy harvesters should be sited near the RF source to scavenge RF energy.
## 4 Conclusions  
In this study, SHS was extracted from surface sediment that contained organic matter discharged from livestock wastewater. The AD samples had a lower pH value, TOC, and OM level, which could be due to the mineralization of readily biodegradable OM in the AD process. The DOC concentrations of the AD SHS were significantly lower than the FD SHS, but the OC mass fractions of the size-fractionated SHS were insignificantly different between the AD and FD SHS. The SUVA\({}_{254}\) and HIX reflected a low degree of aromaticity and humification in the SHS samples. The AD SHS had higher aromaticity (SUVA\({}_{254}\)) and extent of humification (HIX) than the FD SHS. The HHS had high SUVA\({}_{254}\), but the LHS had high HIX. The HIX had a significantly positive correlation with the ratio of P\({}_{\Pi\!,\alpha}\)/P\({}_{\Pi\!,\alpha}\) which suggested that high humification of the LHS was attributed to the high extent of fulvic acid-like substance in the LHS. The size separation method successfully distinguished the chemical composition and structure of the size-fractionated SHS.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11178220/s1](https://www.mdpi.com/article/10.3390/app11178220/s1)]([https://www.mdpi.com/article/10.3390/app11178220/s1](https://www.mdpi.com/article/10.3390/app11178220/s1)), Table S1: The optical indicators (S275-295, SR, A250-400, FI, and BIX) of size-fractionated SHS for AD and FD samples.  
Conceptualization, C.-W.C., W.-S.H. and T.-C.C.; methodology, C.-Y.H. and T.-C.C.; validation, W.-S.H.; formal analysis, W.-S.H. and Y.-Y.L.; writing--original draft preparation, C.-W.C. and T.-C.C.; writing--review and editing, C.-Y.H. and T.-C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Pingtung University of Science and Technology, Taiwan.  
Not applicable.  
Data Availability Statement: Data are available through the request to the corresponding author.  
The authors declare no conflict of interest.  
Figure 4: The linear correlation of optical indicators HIX and SUVA\({}_{254}\) with P\({}_{\Pi\!,\alpha}\)/P\({}_{\Pi\!,\alpha}\).
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 4 Case Study
### Truss Structure  
In this section, the FE model of truss structure is used for model updating, and considering the results under the interference of noise at the same time, so as to verify the feasibility of the model updating method using FRFs based on Sherman-Morrison formula.
## 4 Experiments and Results
### Results and Analysis  
#### 4.4.1 Experimental Results with Different Parameter Settings  
In the proposed model, two layers of self-attention were used to obtain the global sentence vectors. One layer of convolution and one layer of attention were combined to circulate. If the length of the question and answer are beyond 100, the output is also a vector of fixed length. When the training of the model is finished, the accuracy and BLEU values of the model are output. Table 5 shows the results of accuracy of our model with different parameters.  
Rows A to D are the results of our model with different parameters. The first parameter refers to the learning rate and the next is the hidden size. The learning rate for the parameter update is determined by trying different learning rates. Different learning rates were tried in steps of 10 until the cost function converged to the lowest position. The hidden size was gradually increased to reach the operating range of the computer. The more neurons, the better the answer selection. The best results are obtained when the value of similar knowledge k is set to 5 and the dropout is set to 0.35. It can be seen from Table 5 that compared to rows A, B, and C, row D achieved the best result when the learning rate was set to 0.3 and the hidden size was 130.  
Comparative experiments were performed on the datasets so as to compare the effects of different self-attention layers. The performance of different self-attention layers on our model is shown in Figure 7. As is revealed by Figure 7, when the number of layers was two, we observed the greatest impact on our answer selection model. As the number of layers increased, there was not a significant impact on our model. Therefore, two layers of self-attention is the best choice in the model.
## 2 Framework Design of Gateway
### Gateway: Functional Requirements Analysis
#### 2.1.2 Ability to Communicate with Remote Server via GSM  
Many military warehousing facilities are located a distance from the main base because they are used to store flammable or explosive materials, such as ammunition or fuel. Ammunition warehouses in particular may be located in remote, unpopulated mountainous regions. Due to cost considerations, these warehouses may not be equipped with Ethernet, which means operators are limited to using wireless communication methods (usually mobile phone networks) for remote monitoring.  
Security considerations, however, prevent military officers from using mobile phone network services. Also, Figure 1 shows that remote mountainous regions have poor mobile phone coverage, limiting users to voice calls and SMS. We therefore designed the gateway to be able to communicate with the server via GSM messaging.
## 5 Circuit Implementation
### Circuit Implementation of Master-Slave Systems  
Meanwhile, the circuit of slave system (3) is given in Figure 15, and only variables \(x,y\) of the master system are transmitted and used to synchronize the slave system. In Figure 12b, the consensus results of the master-slave systems are shown based on Multisim 14.0. Obviously, the variable \(x_{1}\) of slave system (3) is synchronized with the variable \(x\) of master system (1).
## 3 Methods
### Extraction of Human Pose Euler Angles
#### 3.2.1 Initial Attitude Angle Calculation
 \tag{6}\]  
To begin with, the magnetometer data is utilized to compensate for the yaw angle in the original attitude angle by determining the direction of the horizontal plane of Earth's magnetic field. Since the magnetic field component perpendicular to the horizontal plane does not impact the calculation of the yaw angle, the orientation of the magnetic field within the horizontal plane is employed for compensation. The formula for compensating the yaw angle is given as follows:  
\[\varphi_{t}=0+atan2\big{(}m_{y},m_{x}\big{)} \tag{7}\]  
Additionally, the original Euler angles are compensated using gyroscope data from various body parts, following a similar calculation approach. The compensation method for the torso's original Euler angle using gyroscope data is described below. Gyroscope data can be integrated to compensate for the torso's Euler angles. Specifically, the gyroscope data compensation formula is as follows:  
\[\left\{ \begin{aligned} & roll_{comp}=roll_{prev}+\frac{ gyro_{v}\cdot dt}{cos\big{(}pitch_{prev}\big{)}\cdot cos\big{(} yaw_{prev}\big{)}}\\ & pitch_{comp}=pitch_{prev}+\frac{ gyro_{v}\cdot dt}{cos(roll_{prev})\cdot cos\big{(} yaw_{prev}\big{)}}\\ & yaw_{comp}= yaw_{prev}+\frac{ gyro_{v}\cdot dt}{cos(roll_{prev})\cdot cos \big{(}pitch_{prev}\big{)}}\end{aligned} \right. \tag{8}\]  
where, \(dt\) represents the sampling time interval, \(roll_{prev}\), \(pitch_{prev}\), and \( yaw_{prev}\) are the Euler angles of the torso at the previous sampling moment. These formulas employ the integration of gyroscope data to update the current Euler angles of the torso, thereby compensating for the gyroscope data. Generally, the Euler angle principle is an early method for attitude determination due to its straightforward physical interpretation. However, it involves complex operations with many trigonometric calculations. To simplify the practical application process, we introduce the quaternion method as an optimization of the Euler angle approach.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 4 Experimental Test and Analysis
### Test and Analysis of the Factors Affecting the Quality of Download
#### 4.2.2 Measurement under Different Distances(d) between Electrodes  
Keeping other parameters unchanged, different electrode distances were selected for the experimental test, and the relationship between BER and point electrode distance was obtained, as shown in Figure 13.  
As can be seen from Figure 13, fixed other parameters, the distance between electrodes is directly proportional to the transmission BER, that is, the smaller the distance between electrodes, the lower the BER of the analog communication system. In Figure 13, the BER when the distance between electrodes d = 5 cm and d = 8 cm is far less than that when the distance between electrodes is d = 10 cm and d = 15 cm. It can be seen that reducing the distance between electrodes is beneficial to reducing the BER of information transmission. However, when the distance between electrodes is too small, as shown in Figure 13, when d = 1 cm, the BER is greater than that generated by other electrode spacing. The result has demonstrated that the current flowing along the drill string decreases rapidly when the distance between the electrodes is too small, which leads to the low current at the bottom of the drill string, and the anisotropic magnetoresistance sensor cannot detect the magnetic field around the drill string, which leads to the increase of BER. It is suggested that the distance between electrodes should not be too large or too small in practical application. According to the experimental conditions in this paper, when the distance between electrodes is controlled about 5 cm, the BER of information transmission is the lowest.