esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography
## 2 Infrared-Thermogram Processing
### Edge Detection of Infrared Thermogram
Since the thermal imager can convert the temperature value of the slab surface by detecting the infrared radiation emitted from the slab surface, a temperature-value matrix, which contains the temperature value of all pixels in the infrared thermogram, can also be obtained. The temperature-value matrix of the infrared thermogram is defined as:  
\[\text{TEM}= \begin{bmatrix}tem_{1,1}&\cdots&tem_{1,col}\\ \vdots&\ddots&\vdots\\ tem_{row,1}&\cdots&tem_{row,col}\end{bmatrix}  \tag{13}\]  
where \(tem_{1,col}\) is the temperature value of the pixel at the coordinate \((1,col)\) and \(row\times col\) is the size of the image. The transverse temperature-gradient matrix of infrared thermogram is defined as:  
\[gradx= \begin{bmatrix}gdx_{1,1}&\cdots&gdx_{1,col-1}\\ \vdots&\ddots&\vdots\\ gdx_{row,1}&\cdots&gdx_{row,col-1}\end{bmatrix}  \tag{14}\]  
where \(gdx_{row,col-1}=tem_{row,col}-tem_{row,col-1}\). The vertical temperature-gradient matrix of infrared thermogram is defined as:  
\[grady= \begin{bmatrix}gdy_{1,1}&\cdots&gdy_{1,col}\\ \vdots&\ddots&\vdots\\ gdy_{row-1,1}&\cdots&gdy_{row-1,col}\end{bmatrix}  \tag{15}\]  
where \(gdy_{row-1,col}=tem_{row,col}-tem_{row-1,col}\). According to the pixel coordinates in the crack area obtained in Step 4, three scalar attributes are given to each pixel in the area, temperature value, transverse temperature gradient value, and vertical temperature-gradient value, so that a dataset with three-dimensional scalar attributes \(\{AT_{1},AT_{2},\cdots\}\) is formed. The \"\(k\)-means\" clustering algorithm [22] is used to classify all elements in the dataset, and the crack area is refined according to the classification results and the coordinates of the elements in the dataset. The effect of crack-edge extraction is shown in Figure 5f. Note that the category number \(clu\) is set to 4 after several trials. In Figure 5, the whole process of the crack-edge-detection algorithm based on phase congruency is presented. It can be observed that a complete binary image of cracks from the infrared thermogram of the track slab can be extracted, which can be used for subsequent measurement and analysis of surface cracks.
## 4 Experimental Results
### VGGNet on CIFAR-10  
Table 1 shows the result of pruning VGG-16 on the CIFAR-10 dataset achieving a 34% FLOPs reduction rate using different filter selection criteria. The first row in the table is the baseline accuracy of pretrained network before pruning. We experiment with the pruning of this pretrained network using different filter selection criteria and empirically found that norm magnitude and euclidean similarity criteria are effective in retaining the accuracy of the model. According to the provided \(w_{mag}\) value in Algorithm 1 the proposed pruning algorithm will use either magnitude criteria or similarity criteria or a combination of both. From the Table 1, we can see that among similarity criteria, euclidean similarity produces  
a better result. The magnitude information seems to be the crucial information even in similarity-based filter selection criteria because cosine and NCC similarity are invariant to filter magnitude. The best result is obtained while removing half of the network FLOPs with \(l_{1}\)-norm criteria and another half with euclidean similarity criteria for the given FLOPs reduction rate. As we can see in the Table 1, the original accuracy of the pruned model is recovered with just 40 fine-tune epochs, and if we further increase the fine-tune epochs, the accuracy of the pruned model surpassed the baseline accuracy. Table 2 shows comparison of accuracy results with other similar state-of-the-art pruning methods. With a similar pruning rate, the proposed algorithm achieved 94.02% accuracy of the pruned network which is better as compared to results reported by PFEC [24], MFP [16], and FPGM [15]. Note that PFEC [24], and FPGM [15] use fixed-rate pruning while our method use loss-aware variable rate pruning. Furthermore, even after reducing the VGGNet FLOPs by more than 50% using the proposed algorithm, the pruned network is still able to retain its original accuracy.
## 3 Improvements in HY-2B POD
### Single-Receiver Ambiguity Resolution  
Reprocessing of the raw GPS data from the HY-2B satellite covering the period from the launch (11 November 2018) to end of 2019 was performed for this study to support single-receiver ambiguity resolution. The CNES-CLS wide-lane satellite bias (WSB) product, complementary to these biases, and the CNES-CLS clock product were used in the GPS-based precise orbit determination. Details of this process and successful implementation of the Swarm GPS receiver and the Sentinel-3 GPS receiver are described in [16; 29].  
Over the 1-year timeframe displayed in Figure 10, average fixed rates of 92.19% for wide-lane ambiguities and 73.93% for narrow-lane ambiguities were obtained for HY-2B. Simple integer rounding is sufficient in practice to solve the mixed-integer problem [16; 20], and the acceptance criterion of 0.24 and 0.12 cycles, respectively, was used. No notable variations in the success rate for wide-lane and narrow-lane ambiguity fixing could be recognized.  
Figure 10: Fraction of passes with resolved wide-lane and narrow-lane ambiguities for the HY-2B satellite from November 2018 to the end of 2019.  
Figure 9: SLR residuals for HY-2B precise orbit determination using the nominal phase center (blue dot) and PCO/PCV correction (red dot) over a 14-month period.  
SLR measurements are used as a totally independent technique to evaluate the accuracy of GPS-based orbits for the HY-2B satellite. RMS SLR residuals of 15.7 and 13.3 mm were obtained for the float ambiguity (FA) solution and the ambiguity-fixed (AF) solution, respectively (see Table 5). The overall reduction of 15.3% in the RMS SLR residuals proved that AF POD solutions are a powerful strategy for improving orbit accuracy. A well-established SLR validation depends heavily on the station coordinates and station-specific ranging biases [7; 51]. For a better interpretation, the mean and standard deviation of the SLR residuals for each ILRS station are presented in Table 5. An overall mean bias of about 2 mm was found in the SLR validation. In all computations, observations above the 8\({}^{\circ}\) elevation were employed, and an outlier threshold of 15 cm (only 0.88% of the SLR data were rejected) was applied.
## 5 Results: Impact of Faults in LSHT Machines
### Evaluation of Faults Specific to PMSMs: Partial Demagnetization
In contrast, Level 4, although exhibiting a higher degree of demagnetization, has reduced symmetry and periodicity, resulting in a lesser impact on the machine's cogging torque, hence not contributing to additional ripple variations. The variation in the performance metrics of the 16-pole LSHT machines is summarized in Table 9.  
Similar trends to those observed for the 10-pole machines are observed for the 16-pole machines. Demagnetization levels 1 and 2 have a mild effect on torque capacity, whereas a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Symbol** & **Healthy** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline Mean Torque & T\({}_{\rm mean}\) [Nm] & 1360.9 & 1346.5 & 1298.6 & 1244.4 & 1132.7 \\ Torque Ripple & T\({}_{\rm rp}\) [\%] & 4.5 & 6.55 & 8.85 & 13.31 & 9.36 \\ Efficiency & \(\eta\) [\%] & 97.3 & 97.3 & 97.3 & 97.3 & 97.1 \\ Active Losses & L\({}_{\rm cp+fe+edd}\) [W] & 2320 & 2315 & 2283 & 2247 & 2173 \\ Power Factor & pf [\(-\)] & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Main performance data comparison of optimized 10-pole PMSM LSHT machines including partial demagnetization. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Symbol** & **Healthy** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline Mean Torque & T\({}_{\rm mean}\) [Nm] & 1522 & 1515 & 1479 & 1396 & 1254 \\ Torque Ripple & T\({}_{\rm rp}\) [\%] & 2.7 & 4.2 & 7.0 & 14.3 & 10.6 \\ Efficiency & \(\eta\) [\%] & 96.2 & 96.2 & 96.1 & 96.0 & 95.8 \\ Active Losses & L\({}_{\rm cp+fe+edd}\) [W] & 2358 & 2355 & 2338 & 2282 & 2175 \\ Power Factor & pf [\(-\)] & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Main performance data comparison of optimized 16-pole PMSM LSHT machines including partial demagnetization. significant impact is observed for levels 3 and 4. When \(\Delta B_{\rm r,total}=10\%\), torque reduction is 8.3%, and when \(\Delta B_{\rm r,total}=20\%\), torque reduction is 17.6%. In turn, the reduction in losses is 3.2% and 7.7% respectively, the decrease in losses being always lower than the torque reduction. Trends for torque ripple are similar to those of the 10-pole machines. It is important to note that, regardless of the demagnetization level and pole count, the decrease in losses is always lower than the torque reduction. This means that restoring the torque to match the rated load would lead to higher thermal stress.
###### Abstract  
In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O\({}_{2}\)-B and the deep O\({}_{2}\)-A absorption bands present a high sensitivity to detect SIF, these regions are also largely influenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., first an AC and second a Spectral Fitting technique to disentangle SIF from reflected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reflectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reflectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reflectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefficient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reflectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation.  
solar-induced chlorophyll fluorescence; atmospheric correction; oxygen bands; apparent reflectance; spectral fitting method; FLEX +
Footnote †: journal: Remote Sensing  
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Footnote †: journal: Remote Sensing
# Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
## 3 Problem Description and an MIP Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(i\) & index for jobs (\(i=1\), \(2\), \(\ldots\), \(n\)) \\ \(k\) & index for machines (\(k=1\), \(2\)) \\ \(t\) & index for positions in a sequence \\ \([t]\) & index of the job in the \(t\)th position in a sequence \\ \(p_{k}\) & processing time of job \(i\) on machine \(k\) \\ \(r_{i}\) & release time of job \(i\) (=0 if job \(i\) is a normal job) \\ \(w_{i}\) & limited waiting time of job \(i\) \\ \(d_{i}\) & due date of job \(i\) (=\(r_{i}+p_{i}\) for \(i\in J_{A}\), and a large number for \(i\in J_{B}\)) \\ \hline \(y_{i}\) & =1 if job \(i\) is a normal job, and 0 otherwise \\ \(x_{it}\) & =1 if job \(i\) is placed in the \(t\)th position in a sequence, and 0 otherwise \\ \(c_{tk}\) & completion time of the \(t\)th position on machine \(k\) \\ \(c_{i}\) & completion time of job \(i\) on machine 2 \\ \(\tau_{i}\) & tardiness of job \(i\) \\ \(Z\) & the makespan of normal jobs \\ \(\alpha\) & weight for total tardiness of urgent jobs (\(0\leq\alpha\leq 1\)) \\ \(1-\alpha\) & weight for the makespan of normal jobs \\ \(M\) & a large number \\ \(S\) & (partial or complete) schedule \\ \(Si\) & a schedule in which job \(i\) is added to the end of \(S\) \\ \(C(S)\) & completion time of \(S\) \\ \(U_{A}\) (\(U_{B}\)) & set of urgent (normal) jobs not scheduled yet \\ \(obj\)(S) & objective function value of \(S\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation for parameters and decision variables. pletion time of each job on machine 2. Constraints (11) and (12) define the makespan of normal jobs and the tardiness of urgent jobs. Constraints (13) and (14) define the domain of decision variables.
# System for Evaluation and Compensation of Leg Length Discrepancy for Human Body Balancing
###### Abstract
In order for the body's CoM to be moved into its expected position (the middle of the body), it is necessary to raise the force plate under the shorter leg to reduce the difference in load. For this purpose, the Human Body Balancing Algorithm (HBBA) was developed. Based on a calculation of the human body's CoM, the HBBA determines the needed change in height of the force plate under the shorter leg. We propose an iterative procedure to achieve the needed change in height, comprising an adaptation constant that makes the whole process more comfortable for the patient. After the HBBA completes the process of moving the body into equilibrium, the difference in the height of the left and right force plate serves as an evaluation of LLD. Based on this assessment, shoe insoles can be made for the LLD patient which compensate the effects of LLD so that the load on both legs can be equal. Moreover, in the case of clinical treatments (slowing down or stopping the growth of the longer leg, shortening the longer leg and lengthening the shorter leg), practitioners will have the opportunity to track the status of a patient, validate the quality of surgical treatment and take necessary steps to improve the quality of life for a person suffering LLD. Since the LLD evaluation is affected by the posture of the human body during the compensation of LLD, we propose a vision system consisting of  
Figure 1: Measuring leg load distribution by using two equal commercial scales. two cameras and 15 markers placed on predefined anatomical points of the human body to monitor the posture and further help the clinician determine the patient's status. The methodology and system described in this paper were made based on the authors' previous work [16; 17; 18; 19]. A CAD design of the mechanical part of the system is given in [16], and an animation of the CAD system is available via YouTube [17]. In [18], a simple simulation model of dynamic LLD measuring is shown while the first prototype of a mechatronic system for human body's CoM assessment is presented in [19]. This paper is organized as follows: Section 2 describes the process of Human Body Balancing with the goal of evaluating and compensating LLD. A mathematical model of a 3-RPS parallel manipulator with force plates is given in Section 3. The HBBA with simulation results is shown in Section 4. Section 5 features the mechatronic design of the system for the evaluation and compensation of leg length discrepancy for human body balancing, as well as a description and analysis of experimental results. A brief conclusion and plans for future work are given in Section 6.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 4 Trajectory Synthesis
### Privately Synthesizing Obfuscated Trajectories
#### 4.4.1 Constructing Obfuscated Trajectory Synthesizer  
CRF is a discriminative undirected graphical model that supports auxiliary dependency within a sequence and performs well over many problems, such as sequence inference [31]. Recent works have shown that CRF can capture individual movement patterns from spatial, temporal, and semantic aspects, even though the trajectory data are sparse [23]. In particular, CRF is good at learning the transition patterns between locations, bringing about good preservation of data utility, such as periodic movement patterns.  
In this work, we consider the most important example of modeling sequences, i.e., a linear chain CRF, which models the dependency between the actual trajectory and a mobility feature sequence. We consider the following mobility features concerning each actual location \(l\) in the actual trajectory; that is, the time when the individual visits \(l\), the day of the week when the individual visits \(l\), the time elapsed since the previous time instant, and the category of \(l\). We extract the mobility features for each actual location in the actual trajectory to obtain the mobility feature sequence.  
As shown in line 1 of Algorithm 4, the obfuscated trajectory synthesizer is essentially the CRF trained over the actual trajectory \(I\). The way to train the CRF is following our previous work [23]. The idea is as follows. First, we split both the actual trajectory and the corresponding mobility feature sequence into a number of sub-sequences by one week to enrich the training data. Then, we train the CRF by estimating the parameters that maximize the probability of the actual trajectory conditioned on the mobility feature sequence. After the training, the individual movement patterns of the actual trajectory \(I\) have been \"remembered\" by the parameters of CRF.
## 1 Introduction
Findings demonstrate that an MDM-FSO link employing MDB with NRZ modulation exhibits superior performance and greater tolerance to degradations caused by self-phase modulation (SPM) and modal dispersion. Further analysis reveals that MDB MDM-FSO systems usingRZ signals show higher received power sensitivity than those using NRZ signals, making them particularly effective for long-distance transmissions. To enhance the performance of the proposed MDM-FSO systems utilizing duobinary modulation, the following step-by-step procedure is adopted in this study:  
* The process begins by systematically adjusting the bias voltage applied to the two LiNbO\({}_{3}\) modulators to achieve the optimal operating point, which maximizes signal fidelity while minimizing error rates. This adjustment is critical for maintaining stable modulation performance, as deviations from the optimal bias can lead to signal distortion and increased bit error rates. * A structured approach, involving iterative testing and analysis, is employed to identify the bias point that provides the best modulation characteristics until the optimal bias voltage is established. This process includes continuously monitoring key performance metrics such as extinction ratio, signal-to-noise ratio (SNR), and bit error rate (BER) to ensure precise modulation. Advanced optimization techniques, such as machine learning algorithms and artificial intelligence (AI) as well as adaptive control loops, can further enhance the accuracy of bias voltage tuning, leading to improved system stability and overall performance which will be considered for our future research. * The next step is to evaluate the duobinary modulation scheme, which is recognized for its spectral efficiency and resilience to intersymbol interference. Its implementation can be systematically benchmarked against more advanced multi-dimensional modulation free-space optical (MDM-FSO) architectures by analyzing key performance parameters such as bandwidth utilization, power efficiency, and error vector magnitude. Additionally, simulation and experimental validation can be conducted to assess its feasibility under different atmospheric conditions, ensuring optimal performance for high-speed optical communication systems. * Additional performance metrics, such as BER and overall system capacity, are thoroughly assessed in this study to evaluate the effectiveness of the proposed modulation scheme. These metrics provide critical insights into signal integrity, transmission reliability, and spectral efficiency.
## 5 Conclusions  
This paper presents an integrated control method including pre-emptive braking control for emergency collision avoidance. It aims to prevent vehicle instability by adjusting the vehicle speed to a reasonable value, while enhancing the vehicle stability through 4 WS and DYC. The effectiveness of the proposed control system is validated through a joint simulation of Matlab/Simulink and CarSim under a double lane change emergency collision avoidance scenario. To address the problem, four different control systems are constructed for comparison: 1 2 WS, 2 pre-emptive braking control (PBC) + 2 WS, 3 PBC + 4 WS, and 4 PBC + 4 WS + DYC. The following conclusions can be drawn:  
1. System 1, which adopts 2 WS alone, exhibits poor vehicle stability during emergency collision avoidance.
2. Compared to System 1, the maximum stable vehicle speeds of System 2 with PBC increase by 63.15%, 55.71%, and 48.67% on low, medium, and high friction road, respectively.
3. The maximum stable vehicle speeds of System 3 and System 4 are very close. Compared to System 1, the maximum stable vehicle speeds of System 3 and system 4 increase by 73.7%, 64.9%, and 56.9% on low, medium, and high friction road, respectively.
4. Compared to System 3, System 4 with DYC achieves a reduction in vehicle sideslip angle by 35.5%, 23.5%, and 22.0% on low, medium, and high friction road, respectively.  
This study is limited to the given scenarios of constant road friction. Future investigation should be focused on the influences of different scenarios and variable road friction on vehicle stability and safety. In addition, experimental verification is also an important research task for the future.  
Conceptualization, F.L.; methodology, formal analysis, F.L. and X.W.; software, validation, X.W.; writing, F.L. and X.W.; supervision, project administration, F.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Research Program of Chongqing Education Commission of China (202101105).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.