esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control System Design
### Following under Strong Interference
#### 6.2.2 Curve Path Following under Strong Interference  
Other design parameters remain unchanged except for \(c_{r}=7.6\) and \(c_{u}=1.8\). In addition, \(p_{d}(\theta)=[20\sin(\theta/20)+\theta,\theta]^{T}\) denotes the desired curve geometrical path.  
The simulation research of curve path following under strong interference is plotted in Figure 7.  
As revealed in Table 4, considering \(x_{e}\) and \(y_{e}\), the control policy proposed in this paper has notable performance advantages. The simulation research of curve path following under strong interference is plotted in Figure 7a-g. Under strong interference, Figure 7f shows that for estimations \(\hat{\Phi}_{u}\), \(\hat{\Phi}_{r}\), the model has about 20% deviation. Under strong interference, the model deviation parameters are effectively compensated, which brings good control effect to curve path following. The overall control effect is similar to that of curve path following under weak interference. Consequently, the numerical simulations of straight-line path geometrical following and curve geometrical path following have realized excellent results, testifying to the validity and superiority of the IAILOS guidance scheme and control scheme proposed in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Indicator** & **AFTC** & **ANNC** \\ \hline IEA\(x_{e}\) & 0.450 & 0.926 \\ IEA\(y_{e}\) & 9.17 & 13.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indicator of straight-line path following under strong interference.  
Figure 6: The results of the straight-line path following: (**a**) Path following result. (**b**) Along-track and cross-track error. (**c**) Profile of the heading and surge velocity tracking. (**d**) Estimations of disturbance. (**e**) Profile of the control inputs. (**f**) Estimations \(\hat{\Phi}_{r}\) and \(\hat{\Phi}_{r}\). (**g**) Estimations of sideslip angle.  
Figure 7: _Cont._
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 3 Validation
### Product Validation
While the spread of the matchups (as measured by the standard deviation) is similar for GT_MYD_2P and MYD11_L2, there is a reduction in overall bias for GT_MYD_2P compared with MYD11_L2 both during the day and night. For almost all combinations of day/night and site the RMSE is lower for the GT_MYD_2P product. Another noticeable feature is that the MYD11_L2 is primarily negative biased with respect to the in situ measurements. This \"cool\" bias of the operational MYD11_L2 product with respect to in situ data is consistent with previous studies [3; 61], whereas GT_MYD_2P is more evenly spread across the sites between positive and negative differences. The implication is that the new approach to determining retrieval coefficients for the GSW algorithm implementation for Aqua-MODIS LST is better able to eliminate any systematic \"cool\" bias for MODIS. The generally higher number of matchups for GT_MYD_2P is a result of the selection of optimum cloud flags we have made. Possible explanations for this reduction in bias include: (i) an improved representation of the surface emissivity; and (ii) an improved stratification of the retrieval coefficients by water vapour and satellite zenith angle. The statistical improvement from MYD11_L2 to GT_MYD_2P is dependent on the site, with bias reductions at some sites larger than others, which indicates the surface emissivity estimation is the most likely factor influencing the product improvement.
# Optimized Design of Flexible Quick-Change System Based on Genetic Algorithm and Monte Carlo Method
## 3 Results and Discussion
### Finite Element Simulation Analysis of a Flexible Quick-Change Process System
#### 3.3.2 Flexible Quick-Change Process System Extreme Working Condition Error Analysis  
In the machining process, the cutting force under difficult working conditions is employed as the applied load, and the coupling simulation of clamping force and cutting force is used to study the deformation and error change. Due to the symmetrical structure of the flexible quick-change process system, 16 constant cutting force load points are determined on the upper end of the workpiece, and the contact surface between the fixture plate and the workpiece is divided into eight areas, as shown in Figure 11a.  
When the cutting load is machined at various points, the applied load causes the maximum deformation in the corresponding area of the fixture plate, accompanied by a dip angle of the fixture plate. This will affect the positioning error of the flexible quick-change process system. The adaptable fast change process system's error flowchart for each load point under challenging circumstances is shown in Figure 11b. The error diagram for each load point can be used to determine the flexible quick-change process system's error range under extreme operating conditions.  
\[\mathrm{q_{w}}=\begin{bmatrix}\Delta\mathrm{x}\\ \Delta\mathrm{y}\\ \Delta\mathrm{z}\\ \Delta\mathrm{\alpha}\\ \Delta\mathrm{\beta}\\ \Delta\mathrm{\gamma}\end{bmatrix}=\begin{bmatrix}0\mathrm{mm}\\ 0\mathrm{mm}\\ -6.31\times 10^{-4}\mathrm{mm},-6.47\times 10^{-4}\mathrm{mm}\\ -3.4736\times 10^{-6}\mathrm{rad},2.921\times 10^{-6}\mathrm{rad}\\ \pm 4.7631\times 10^{-6}\mathrm{rad}\\ 0\mathrm{rad}\end{bmatrix}. \tag{12}\]  
The figure demonstrates the symmetrical distribution of errors at each load point under extreme conditions. The misregistration and deflection angles are both higher in the D and H zones, which are primarily related to the cutting force's direction and the zero point clamping system's distribution position. Nevertheless, the adaptable quick-change process system's maximum error under extreme operating conditions is within the acceptable tolerance range and able to satisfy the technical criteria. The planned adaptable quick-change process system complies with the requirements for error accuracy, according to the finite element simulation analysis.
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 3 RF-Access
### Motion Direction Detection
#### 3.2.1 Signal-Based Sensing
Given a tag, we collect \(n\) responses by the \(m\)th antenna and build a tag response matrix following the timestamp order, which is described as: \(D^{m}=\left(D_{1}^{m}\ D_{2}^{m}\ \cdots\ D_{n}^{m}\right)^{T}\), where \(D_{i}^{m}\) is a tag's RSSI record, which is actually a triple \(D_{i}^{m}=\left(e,r_{i}^{m},t_{i}^{m}\right)\), where \(e\) is EPC (tag ID), \(r_{i}^{m}\), \(t_{i}^{m}\) are the measured RSSI value and corresponding timestamp obtained by the \(m\)-th antenna. So, the response matrix of the \(m\)-th antenna can be written as follows:  
\[D^{m}=\left(D_{1}^{m}\ D_{2}^{m}\ \cdots\ D_{n}^{m}\right)^{T}= \begin{pmatrix}e& r_{1}^{m}&t_{1}^{m}\\ e&r_{2}^{m}&t_{2}^{m}\\ \vdots&\vdots&\vdots\\ e&r_{n}^{m}&t_{n}^{m}\end{pmatrix} =\left(E,R^{m},T^{m}\right), \tag{3}\]  
Figure 4: System deployment for the RSSI-based model. Figure 5: RSSI values with respect to the time; (**a**) moving in; (**b**) moving out. where the timestamp meets \(t_{i}^{m}\leq t_{j}^{m}\) (\(0<i<j\leq n\)), \(E\) is the broadcast of \(e\), \(R^{m}\) is the vector of RSSI values measured by the \(m\)-th antenna, and \(T^{m}\) is the vector of timestamps corresponding to \(R^{m}\). The changing trend of the RSSI curve is approximate to the parabola with the opening downward. We fit the RSSI and timestamp in the response signals of two antennas with a quadratic curve, respectively. Let the second-order coefficient and the first-order coefficient in the fitting results be \(a\) and \(b\); the time \(t_{peak}\) corresponding to the peak is:  
\[t_{peak}=-\frac{b}{2a}. \tag{4}\]  
By comparing the time of the two antenna's peaks, we can determine the motion direction. The signal-based model functions properly in an ideal scenario with modest multi-path effects and slow speed. However, in a real indoor scenario, this model is not robust to more general scenarios. In Figure 6, we let a volunteer carrying a tag walk through the system and use the signal-based model to detect the direction. As we can see, since the human body blocks the tag, almost all the tag signals in the second half are absorbed, resulting in the incomplete image of the RSSI, which further affects the detection accuracy. According to the fitting results, we will obtain a wrong result of the direction. The main reason is that RF signals are vulnerable to environmental changes, which motivates us to seek a more robust way.
## 3 Experiment and Results
### Experiment 3: Cartilage Change Prediction at the Top Horizontal Line  
This experiment trained models using the top three CDI locations (#7, #8, and #9) and slice X3 for manual segmentation to predict the change of cartilage. Table 5 listed the distribution of the 'change' and 'no-change' categories. Table 6 summarized the results and Figure 5 plotted the ROC curves.  
Again, the CDI generated better performance (AUC = 0.671) than manual segmentation (AUC = 0.438). For this experiment, the sample rebalancing strategies helped improve the performance of CDI but did not affect the performance of manual segmentation by much.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quantification Method** & **Precision** & **Recall** & **F-Measure** & **MCC** & **AUC** \\ \hline CDI & 0.500 & 0.111 & 0.182 & 0.192 & **0.766** \\ \hline Manual Segmentation & 0.270 & 0.205 & 0.234 & 0.082 & 0.494 \\ \hline CDI with rebalance & 0.222 & 0.222 & 0.222 & 0.111 & 0.696 \\ \hline Manual Segmentation with rebalance & 0.257 & 0.444 & 0.325 & 0.116 & 0.570 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction performance of ANN using center CDI locations and manual segmentation on X2 slice.  
Figure 4: ROC plot of middle CDI (#4, #5, and #6) and manual segmentation on X2 slice.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantification Method** & & \multicolumn{2}{c}{**‘Changed’**} & \multicolumn{2}{c}{**‘No Change’**} \\ \hline \multirow{4}{*}{CDI} & \multirow{2}{*}{Original} & Train & 29 & 142 \\ \cline{3-4}  & & Test & 19 & 53 \\ \cline{2-4}  & & Train & 130 & 142 \\ \cline{2-4}  & & Test & 19 & 53 \\ \hline \multirow{4}{*}{Manual Segmentation} & \multirow{2}{*}{Original} & Train & 1905 & 6645 \\ \cline{3-4}  & & Test & 753 & 2847 \\ \cline{1-1} \cline{2-4}  & & Train & 1905 & 1905 \\ \cline{1-1} \cline{2-4}  & & Test & 753 & 2847 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample distribution in experiment 3.
# Improving Multi-Label Learning by Correlation Embedding
## 4 Experiment
### Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{6}{c}{**One Error \(\downarrow\)**} \\ \cline{2-9}  & **MLLCE** & **NL-MLLCE** & **ML-LSS** & **JFSC** & **BR** & **Glocal** & **MLL-FLSDR** \\ \hline rcv1 subset1 & 0.415 \(\pm\) 0.013 & **0.414**\(\pm\) 0.016 & 0.426 \(\pm\) 0.006 & 0.446 \(\pm\) 0.008 & 0.450 \(\pm\) 0.014 & 0.417 \(\pm\) 0.008 & **0.414**\(\pm\) 0.016 \\ rcv1 subset2 & 0.408 \(\pm\) 0.005 & 0.416 \(\pm\) 0.016 & **0.406**\(\pm\) 0.010 & 0.413 \(\pm\) 0.014 & 0.467 \(\pm\) 0.014 & 0.411 \(\pm\) 0.012 & 0.444 \(\pm\) 0.055 \\ enron & 0.217 \(\pm\) 0.009 & 0.219 \(\pm\) 0.023 & 0.227 \(\pm\) 0.019 & 0.249 \(\pm\) 0.018 & **0.212**\(\pm\) 0.018 & 0.246 \(\pm\) 0.014 & 0.216 \(\pm\) 0.014 \\ recreation & 0.443 \(\pm\) 0.008 & **0.439**\(\pm\) 0.018 & 0.452 \(\pm\) 0.013 & 0.465 \(\pm\) 0.023 & 0.482 \(\pm\) 0.012 & 0.512 \(\pm\) 0.026 & 0.456 \(\pm\) 0.013 \\ stackex-coffee & 0.484 \(\pm\) 0.081 & **0.458**\(\pm\) 0.083 & 0.569 \(\pm\) 0.043 & 0.573 \(\pm\) 0.038 & 0.551 \(\pm\) 0.065 & 0.533 \(\pm\) 0.040 & 0.636 \(\pm\) 0.041 \\ stackex-ches & **0.405**\(\pm\) 0.009 & 0.421 \(\pm\) 0.034 & 0.423 \(\pm\) 0.018 & 0.463 \(\pm\) 0.015 & 0.435 \(\pm\) 0.023 & 0.474 \Based on the experimental results, we can observe the following conclusions. * The linear and the nonlinear versions of the method MLLCE have comparable performance. In addition, the nonlinear MLLCE is better than the MLLCE method in terms of average precision, ranking loss, one error, and AUC, which indicates that the proposed nonlinear method can improve classification performance to some extent. * Compared to the the five comparison methods, MLLCE achieves competitive performance in terms of ranking loss, Micro F1, AUC, one error, average precision, Example-based F1 on the 15 data sets, and these results clearly show the effectiveness of MLLCE in multi-label learning. * In Hamming loss, the performance of all the comparing algorithms are not significantly different. However, according to Table 2, it is noted that MLLCE still achieves a relatively good performance. * MLLCE outperforms ML-LSS and Glocal on all evaluation metrics except hamming loss, Micro F1 and Example-based F1 Since ML-LSS adds sample similarity to the model, ML-LSS has better performance in Micro F1 and Example-based F1 metrics. These results verify the feasibility of our proposed method MLLCE through graph embedding to model label correlation.
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 3 Methodology
### Data Preprocessing  
In the filed of deep learning, data preprocessing represents the phase of data cleaning that improves the input data for tasks. This procedure is important for many reasons. First, it ensures the generalization of a given model, especially when tested on datasets outside its training cohort. Additionally, preprocessing reduces data noise and/or distortions and allows the network to operate more efficiently and faster. Because of the use of the transfer learning technique, the data must be rescaled to fit the input of the model. In our work, we initially employed data normalization where all pixel values were rescaled to \([-1,1]\) using a pixel-wise multiplication factor of \(1/255\), giving a set of grayscale images as follows:  
\[R_{i}=\frac{I-I_{min}}{I_{\text{max}}-I_{\text{min}}} \tag{1}\]  
where \(R_{i}\) is the normalized data, \(I\) is the original data, \(I_{max}\) and \(I_{min}\) are the maximum and minimum values in the input data, respectively. Then, histogram equalization [33] was applied to enhance the images' contrast. After that, images were resized to 200 \(\times\) 200 before training began. Besides, some transformations were also applied to the images, including rotation by 25 degrees, a zoom range equal to 0.2 and setting the fill-mode to nearest. Data augmentation was used to improve our model's generalization ability because it mitigates overfitting by adding variations to the dataset.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 3 Performance Analysis
### Control Effort  
According to Equations (1) and (6), when the pseudo-error \(e^{\prime}(n)\) converges to zero, it holds that  
\[y^{\prime}(n)=c(n)-d(n) \tag{26}\]  
where \(y^{\prime}(n)\) is the counter noise signal corresponding to the control effort. In the narrow-band ANC systems, the command and disturbance signals can be considered as cosine waves. The phase error can be set as the difference in phase between the disturbance and command signals. Without loss of generality, the phase of the disturbance signal can be set to zero, and the phase of the command signal can be set as \(\phi\in[0,2\pi)\). The phase error between the command and disturbance signals is \(\phi\). The disturbance signal and the command signal can be written as  
\[d(n)=D\cos(\omega n) \tag{27}\]  
\[c(n)=C\cos(\omega n+\phi) \tag{28}\]  
where \(\omega\) is the circular frequency, and \(D\) and \(C\) are the amplitude of the disturbance signal and command signal, respectively. Substituting Equations (27) and (28) into Equation (26) yields  
\[\begin{array}{l}y^{\prime}(n)=C\cos(\omega n+\phi)-D\cos(\omega n)\\ =C[\cos(\omega n)\cos\phi-\sin(\omega n)\sin\phi]-D\cos(\omega n)\\ =[C\cos\phi-D]\cos(\omega n)-C\sin(\omega n)\sin\phi\\ =Y^{\prime}(C,\phi)\cos(\omega n+\phi^{\prime})\end{array} \tag{29}\]  
where \(Y^{\prime}(C,\phi)\) and \(\phi^{\prime}\) are amplitude and phase of the output \(y^{\prime}(n)\), respectively, and they are expressed as follows  
\[Y^{\prime}(C,\phi)=\sqrt{C^{2}+D^{2}-2 CD\cos\phi} \tag{30}\]  
\[\phi^{\prime}=\arctan\left(\frac{C\sin\phi}{C\cos\phi-D}\right) \tag{31}\]The control effort is positively related to the amplitude of the output signal. Therefore, according to Equation (30), the control effort of the output is extremely relevant to the phase error \(\phi\). When \(\phi:0\rightarrow\pi\), the control effort increases, and when \(\phi:\pi\to 2\pi\), the control effort decreases. Thus, it is obvious that the control effort reaches a peak when \(\phi=\pi\). These conclusions are identical to those presented in reference [13], and this paper presents an idea to estimate the control effort with the influence of phase error.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 4 Application
### Image Recognition and Tracking  
During steps 6 and 7 of the assembly procedure, the user needs to fasten two labels on the product (see Figure 12). These labels cannot be treated as previous objects for validation purposes because they have text and images. So, Vuforia's image tracking capabilities become useful for solving the issue, since Image Targets have no limitations in the number of objects that are recognizable at the same time. Object recognition of the assembly was thus combined with image recognition of the label to create a robust validation procedure.  
This validation procedure excludes the correction of placement for components with text/image facing down. Only loop audio guidance saying to turn the label upside-down is offered to the user, and the Model Target validation does not start until the label text/image is recognized.
## 4 Scenario and Implementation of Test-bed
### Behavior Data between Nodes  
The node for the test-bed is TIP 700 CM sensor, which senses illumination, humidity, and temperature with the following message format.  
The normal data collected through the structure above has the following format depicted in Figure 5.  
\begin{table}
\begin{tabular}{|l|} \hline Header Description \\ \hline Length (1 byte): message length in bytes not including header \\ Fcf (2 byte): IEEE 802.15.4 frame control field [reserved] \\ Dsn (1 byte): IEEE 802.15.4 data sequence number [reserved] \\ Destpan (2 byte): IEEE 802.15.4 Destination personal area network identifier [reserved] \\ Addr (2 byte): TinyOS destination address \\ Type(1 byte): TinyOS active message number \\ Group (1 byte): TinyOS group id \\ \hline Multihop Message Description \\ \hline Sourceaddr (2 bytes): address of the previous hop \\ Originaddr (2 bytes): address of the origin of the message \\ Seqno (2 bytes): sequence number of the previous hop of multihop messages \\ Origiseqno (2 bytes): sequence number from the origin of multihop messages \\ Hopcount (2 bytes): hopcount \\ \hline Surge Message Description \\ \hline Type (2 bytes): type, type 0 is ’sensor reading’ \\ Reading (2 bytes): ADC reading from the sensor \\ Parentaddr (2 bytes): address of the parent in the multihop tree \\ Seq_no (4 bytes): sequence number of Surge messages \\ \hline \end{tabular}
\end{table}
Table 1: TIP 700 CM sensor message format.  
Figure 4: A sudden rise in the number of messages due to the attack from Sensor 3.  
For the analysis of node behaviors, BS verifies the behaviors requested from each sensor and saves the data. Figure 6 shows the process of verifying and saving behaviors between nodes.  
According to the result of the test-bed, Sensor 3 keeps requesting/sending data from/to Sensor 1, 2, and 4. By confirming messages of a sudden increase, we can detect which node requests a message and the ID of the sensor node generating unnecessary traffic.  
Figure 7 shows the result of confirming an abnormal node by collecting data of the sensor nodes. Under 'State', 'anomaly_number' shows which node makes the greatest number of abnormal requests and we can see that the node of id 3, 'anomaly 0', makes the most frequent message requests and generation. The following numbers help to figure out whether or not each sensor makes malicious attacks or is being attacked. Therefore, based on this information, the system will report to the network manager and remove Sensor 3 from the service so as to monitor changes in other nodes.  
Figure 5: Sensor data format.  
Figure 6: Process of verifying behaviors between nodes.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Progress Reasoning
[64] automatically derive topological relationships between solid objects or geometric primitives with a 3D solid CAD model. [PERSON] et al. [25] attributed these relationships to technological dependencies and represented these dependencies with graphs (nodes for building elements and edges for dependencies). However, there is controversy about the use of ancillary information. For example, [PERSON] et al. [32] pointed out this approach would not be totally reliable, since the only way to truly gain confidence that a component is finished is to visually verify it. They suggested a combination of multiple sources of image to increase the overall reliability.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 5 Discussion  
### Technical Limitations in Current Paradigms  
Pavement distress detection has evolved through three key phases: manual inspection, semi-automated systems, and non-destructive automated solutions. China's Highway Performance Assessment Standard (JTG 5210-2018) [85] reflects this progression by incorporating automated detection technologies and standardized distress quantification methods. However, current technologies still face several limitations when evaluated against such standards:  
First, most object detection models only address common distress types (e.g., linear cracks, potholes), while standards define over a dozen distinct forms for asphalt and concrete pavements. Less frequent but critical distress patterns remain understudied.  
Second, deep learning performance varies significantly across imaging conditions. Models optimized for drone/smartphone images show notably reduced accuracy when processing data from professional inspection vehicles--the primary source for official assessments.  
Third, training these models requires large annotated datasets. While pavement images are abundant, the labeling process remains resource-intensive.  
Finally, even advanced object detection networks achieve only ~70% accuracy. Though pixel-level segmentation offers higher precision, its computational demands hinder large-scale implementation.