esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 4 Pre-Evaluation and Health Monitoring System of FAST
### Pre-Evaluation System  
The rainflow counting method is an efficient method used for fatigue data analysis. It divides the measured historical load data of the structure into several load cycles and estimates the fatigue life of the structure based on these cycles. The fatigue test load spectra are also compiled. This method widely used nowadays in fatigue-life calculations [19].  
In the daily observation process of FAST, the obtained stress of each cable is stored in the database in chronological order. Through the rainflow counting method, the fatigue-load spectrum analysis of the stress of all cables can be utilized to obtain the actual stress amplitude of all cables. Therefore, the Miner linear cumulative damage theory can be used to obtain the incremental fatigue damage degree to date [20].  
Supposing that the fatigue damage of the cable under each stress level is performed independently, the total damage can be linearly superimposed. For a certain level of stress cycles, the number of cycles corresponding to the \(i\)th equivalent stress amplitude is recorded as \(N_{i}\), and then the damage of one cycle is \(1/N_{i}\), so the total damage of all cycles is:  
\[D=\sum_{i=1}^{n}\frac{1}{N_{i}} \tag{6}\]  
where \(D\) is the total fatigue damage suffered by the cable to date. From the perspective of engineering applications, when \(D=1\), the corresponding cable-fatigue failure occurs according to the Miner rule. Therefore, according to the force-time curve of each cable, the fatigue life of all cables can be evaluated in advance.  
After the analysis, the fatigue stress amplitude of the FAST cable net under working conditions is up to 445 MPa. Taking into account the service life of FAST and the complexity of the working conditions, the cables of the structure are required to meet: 1. 500 MPa fatigue-stress amplitude; 2. Upper limit of stress of 0.4 \(F_{\text{ptk}}\), where \(F_{\text{ptk}}\) refers to the standard value of the strength of the cable; 3. At least two million stress cycles. Once it is discovered that some cables are at risk of fatigue failure, on-site staff can order suitable cables from the manufacturer in advance and replace them at the right moment to ensure the orderly progress of FAST observations.  
Figure 7: PHM framework of Fast.
## 7 Experimental Results
### Performance Comparison with the Existing Schemes
#### 7.2.2 Impact of End-to-End Distance  
Figure 6a shows a comparison of DSR according to the distance from the source to the sink. The moving speed of the sink is fixed at 20 m/s. As the end-to-end distance increases, the DSRs of SPEED and THVR are decreased rapidly but that of EAR2 is decreased more gently than those of the other schemes. This result is derived from the difference of data delivery speed calculation mentioned in Section 3.1. Because EAR2 forwards data with the modified data delivery speed that considers sink mobility according to end-to-end distance, the DSR is higher than those of the other schemes.  
Figure 5: Performance comparison of EAR2 with the existing schemes SPEED and THVR according to sink speed: (**a**) deadline success ratio and (**b**) energy consumption.  
We compare the EC of the proposed scheme with that of the other schemes in Figure 6b. The EC of all schemes is proportional to the distance, because more sensor nodes participate in forwarding according to the distance. EAR2 consumes an amount of energy that is slightly higher than the other schemes because it exploits restricted flooding. The difference of the EC between SPEED and THVR indicates the cost of the initial beacon message for neighbor discovery. The EC of the three schemes has a similar increasing pattern because the end-to-end distance does not affect the frequency of the sink location update, the data path for footprint chaining, and the extent of _EA_.  
Figure 6: Performance comparison of EAR2 with the existing schemes SPEED and THVR according to end-to-end distance: (**a**) deadline success ratio and (**b**) energy consumption.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Data  
#### 2.2.1 MODIS Data  
Since rubber trees cycle through mature leaf, red leaf, defoliation and fresh leaf periods (Figure 2), the lowest NDVI value can be found according to the MODIS-NDVI time series curve to determine the specific times of defoliation and leaf extraction in rubber forests. This method has been verified by [PERSON] et al. [8] and [PERSON] et al. [7]. The MODIS data in this study were MOD13Q1-NDVI products obtained from January to December 2019 with a 16-day return period and a 250-m spatial resolution; these products are typically used to study land surface and vegetation conditions. The MODIS images taken in Hainan Province were denoted H28V07, and a total of 23 images were collected in 2019. These data were downloaded from the Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center Website ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)), accessed on 14 August 2019). The NDVI time series for 23 rubber forests in the study area were acquired to provide a basis for determining the optimal remote sensing monitoring time.
## 2 Artificial Cobweb Routing Algorithm
### Artificial Cobweb Routing Algorithm
#### 2.2.2 Example of the Routing Algorithm  
The single-phase power distribution network in Figure 1 consists of one BS and 36 terminal nodes. After the artificial cobweb initialization algorithm, the MAC layer of the network reflects the network structure shown in Figure 4. One radio broadcast of the BS is intercepted by nine nodes, and these nine nodes will then respond to the BS one by one to receive a logical ID. The BS selects the node, we assume the logical ID 5, to be the central node of layer 1, and then, these nodes compose a cobweb in the MAC layer based on the steps 1, 2. The node with the logical ID 5 continues to broadcast and is intercepted by 15 nodes from different physical branches, which are then assigned logical IDs 10-24. The node with the logical ID 13 is selected as the central node for networking. In case the broadcast of this central node is not intercepted by a node with a logical ID assigned in another branch, this node will send a message to node 5. Node 5 will then reselect node 19 as the central node to complete the network of layer 2. According to the actual environment and redundant calculation of time delay, we stipulate that node 5 wait 10 s to collect all the replies before establishing central nodes for layer 2.  
Central nodes 13 and 19 search with the same algorithm until the rest of the nodes obtain logical IDs to complete the network in layer 3.  
After completing the cobweb route initialization, each central node records the logical ID and layer of the nodes in the same subnet. The peripheral nodes record the logical IDs and layers of their own nodes and the central nodes. The complexity and length of the routing table is determined by the number of the nodes and the layers in the network.
## 4 Experiment and Discuss
### Comparative Analysis and Experimental Results
#### 4.3.3 Performance Evaluation of the Diagnostic Framework
The time cost of classifying the testing set which is composed of 800 samples is shown in Figure 11. It indicates that the execution time of the HMLC-SBELM-based model is only 39.4 ms, which is nearly 4 times faster than other models. In practice, the execution time of a single sample is extremely small. The extremely fast execution speed of the HMLC-SBELM-based model is chiefly due to the minimum hidden layer scale obtained through the sparse Bayesian framework. To further measure the stability and validity of the proposed intelligent diagnostic model based on HMLC-SBELM, the model was run for 100 trials. In each trial, the entire  
Figure 11: Comparison of the execution time. Figure 10: Comparison of the training times. data set is shuffled and reassigned to obtain a diverse training set, v set, and testing set. The diagnostic accuracies of 100 trials are shown in Figure 12.  
As illustrated in Figure 12, the diagnostic results of 100 trials are relatively stable which are between 93% and 96% with no obvious fluctuations. It indicates that the proposed intelligent HMLC-SBELM-based model based on is a high-precision and efficient diagnostic model, which can effectively solve the simultaneous fault mode diagnosis.
# Initial Results from SQUID Sensor: Analysis and Modeling for the ELF/VLF Atmospheric Noise
## 3 Data Preprocessing  
A continuous observation during seven days for ELF/VLF atmospheric noise was conducted at Hoxidolgay, China in March, 2014. As the vertical magnetic field component is usually much smaller than the horizontal ones near the ground ([PERSON] et al., 2016), only a single SQUID sensor was chosen for this investigation. It was placed in a dewar made of fiberglass full of liquid helium at a temperature of 4.2 K and the atmospheric noise in the south-north direction in the horizontal plane was recorded during 08:00\(\sim\)11:00 UT from 2 nd to 7 th March. The cryostat was half flush-mounted in the Earth and the wind was shielded. The atmospheric noise during this time were quite low. Limited by the storage ability, the data were sampled at a 100 kHz sampling rate and only stored twice an hour with a length of 20 s each time. The measurement site is far from cities but with a power transmission line about 5 km away. As the measurement was conducted under magnetically unshielded environment, the measured data suffered from baseline wandering caused by geomagnetic activity (daily variation and vibration may also cause baseline wandering) and harmonics radiated from the power line nearby. In order to get an accurate analysis and modeling for the ELF/VLF atmospheric noise, the baseline wandering and the harmonics has to be eliminated before further data processing.
## Abstract  
The model of bearingsings-only tracking is generally described by discrete-discrete filtering systems. Discrete robust methods are also frequently used to address measurement uncertainty problems in bearings-only tracking. The recently popular continuous-discrete filtering system considers the state model of the target to be continuous in time, and is more suitable for bearings-only tracking because of its higher mathematical solution accuracy. However, the sufficient evaluation of robust methods in continuous-discrete systems is not available. In addition, in the different continuous-discrete measurement environments, the choice of a robust algorithm also needs to be discussed. To fill this gap, this paper firstly establishes the continuous-discrete target tracking model, and then evaluates the performance of proposed robust square-root continuous-discrete cubature Kalman filter algorithms in the measurement of uncertainty problems. From the simulation results, the robust square-root continuous-discrete maximum correntropy cubature Kalman filter algorithm and the variational Bayesian square-root continuous-discrete cubature Kalman filter algorithm have better environmental adaptability, which provides a promising means for solving continuous-discrete robust problems.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Data Processing  
In this study, multiple key features were extracted from the original speech data, including content representation (\(I_{content}\)), speaker voiceprint (\(I_{spk}\)), pitch (\(I_{pitch}\)), energy (\(I_{energy}\)), prosody (\(I_{prosody}\)), and the target mel spectrogram. The content representation, \(I_{content}\), was extracted by using a pre-trained automatic speech recognition model, SenseVoice [46], which provides high-precision linguistic features. Speaker identity features \(I_{spk}\) were obtained by using the pre-trained speaker verification model Camplus [44], which captures speaker-dependent characteristics. Pitch information \(I_{pitch}\) was extracted by using the pre-trained neural pitch estimation model RMVPE [42], which directly derives pitch features from raw audio, ensuring high accuracy and robustness. Energy \(I_{energy}\) was calculated as the root-mean-square (RMS) energy of each frame in the speech signal. Prosodic features \(I_{prosody}\) were extracted by using the neural prosody encoder HuBERT-Soft [43], which captures rhythm, intonation, and other prosodic cues in speech. The target mel spectrogram was computed with a standard signal processing pipeline consisting of pre-emphasis, framing, windowing, Short-Time Fourier Transform (STFT), power spectrum calculation, and mel filterbank projection. The configuration details were as follows: a sampling rate of 32,000 Hz, a pre-emphasis coefficient of 0.97, a frame length of 1024, a frame shift of 320, the Hann window function, and 100 mel filterbank channels.
## 3 Materials and Methods
### Reference Measurements
#### 3.4.2 Terrestrial Laser Scanning  
The accuracy of a UAV-derived model was compared with that of the TLS data model, using a Riegl LMS-Z210 scanner recorded from multiple stations with high and medium resolution. TLS is a reference data model--its point cloud referred to known control points received from the RTK total station survey from benchmarks given by the Royal Thai Survey Department. The TLS device was set up at first at known XYZ coordinates, which were initiated from the total station. Finally, the result was applied as a reference for the UAV model. The accuracy of TLS is 3-5 mm. The 3D pagoda model was developed using the standard procedure implemented by CloudCompare software.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Area of flight & 1 ha & Baseline, b & 32 m \\ Scale, \(S\) & 1:50 & Overlap, \(p\) & 80\% \\ GSD & 20 mm & Sidelap, \(q\) & 40\% \\ Footprint & 80 \(\times\) 60 m & Spacing between strips, t & 36 m \\ Flight height, \(H\) & 50 m & Number of strips & 3 \\ Orientation of strips & West–East & Image per strip & 10 \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of unmanned aerial vehicle (UAV) flight planning.  
Figure 5: Overlap and sidelap images established from flight planning and control.
## 4 Conclusions  
A methodology was developed that enabled a distinction of work sequences from non-work sequences using a few parameters retrieved from a tractor's ISOBUS. A field part distinction was applied as a prerequisite for a differentiated area-related analysis. A modeling of the cultivated tracks, based on the way the plow covered the area step-by-step, was implemented. Based on that, the cultivated area was quantified and the field boundary was detected in terms of location and size. Finally, different indicators were developed and calculated for the overlapping. An assessment with a more common indicator proved the plausibility of the results. Incomplete information about the work state of the implement, erroneous D-GNSS positioning and a limited shrinking of the boundary were identified as the main sources of error.  
Automated, detailed detection of the cultivated area and the field boundary can be very valuable, e.g., for documenting and invoicing of agricultural tasks within networked software infrastructures. A differentiated analysis of overlapping can serve as an indicator of efficiency. In future work, the presented parameters for the overlapping analysis should be validated on fields of different sizes and geometries.  
If there were more detailed information about the actual work state of the plow, more reliable results could be expected. The new generation of ISOBUS-enabled plows is very promising in delivering this information. Furthermore, the use of more accurate positioning and data acquisition instrumentation is recommended when a higher level of accuracy is required. One possibility would be using real time kinematic GNSS and robotic total stations [25]. Another option would be fusing information from GNSS receivers and inertial sensors, a technology which is already implemented in commercial automated steering systems. Considering these possible improvements, the slight overestimation of 1.4% in terms of the boundary's size is promising for the potential use of more sophisticated applications, e.g., automated steering. With a few adaptions, the used methodology for an area-related analysis could be transferred to a wide range of mounted soil tillage implements and plant production processes.  
Conceptualization, D.S.P.; methodology, D.S.P. and A.H.; software, A.H.; writing--original draft preparation, review and editing, A.H and D.S.P.; and supervision, D.S.P. and H.W.G.  
This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant numbers 2815 ERA01H and 281B300116.  
The insightful discussions with [PERSON] are gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 Mathematical Formulation of LIRP with EPQ Model  
Given a set of the potential depots with fixed locations and customers with known demands and returns, the LIRP with EPQ model in reverse logistics is designed to determine the optimal location and number of open depots, the production quantity produced at each open depot as well as the routing of the assigned customers with known demands and returns to the open depots. The depotswill function as a distributor as well as a collector. At the depot, the condition of the returned products will be inspected and classified into two categories: defect and non-defect items. The production setup cost will be affected where the non-defect items re-enters into the market, while the defect items will need to be counted in the process of production planning. The objective of the LIRP is to minimize the cost of establishing facilities, cost of inventory including production setup and holding, and cost of distance travelled by vehicles. Figure 1 shows an example of LIRP in the supply chain with three depots and nine customers. It can be seen from Figure 1 that customers 1, 2, and 3 are served by depot 1, while the remaining 6 customers are served by depot 2 using two vehicles. Depot 3 remains closed. The mathematical formulation of LIRP with the EPQ model is given as follows. The objective and constraints are given below:  
_Sets:_  
\(I\) = set of all depots \((i\ =\ 1,\ 2,\ \ldots,\ I)\). \(J\) = set of all customers \((j\ =\ 1,\ 2,\ \ldots,\ \ J)\). \(K\) = set of all vehicles \((k\ =\ 1,\ 2,\ \ldots,\ K)\). \(U\) = set of depots and customers \((u\ =\ 1,\ 2,\ \ldots,\ I+J)\). _Input Parameters:_  
\(D_{j}\) = demand of customer \(j\). \(r_{j}\) = non-defect items returned by customer \(j\). \(s_{j}\) = defect items returned by customer \(j\). \(R_{j}\) = returned products by customer \(j\) (\(R_{j}=r_{j}+s_{j}\)). \(d_{ij}\) = distance from \(i\) to \(j\). \(VC_{k}\) = capacity of vehicle \(k\). \(N\) = number of customers. \(F_{i}\) = fixed operating cost of depot \(i\). \(DC\) = distance cost per mile. \(VD_{i}\) = maximum through at the depot \(i\). \(KC\) = setup cost per production. \(h\) = holding cost per unit inventory. \(P\) = production rate. \(TL_{ijk}\) = total load of customer \(j\) at depot \(i\) by vehicle \(k\). _Decision Variables:_  
\(z_{i}\ =\ \left\{ \begin{array}{ll}1,&\text{if depot $i$ is open}\\ 0,&\text{otherwise}\end{array} \right.,\)  
\(y_{ij}\ =\ \left\{ \begin{array}{ll}1&,\ \ \text{if depot $i$ is assigned to customer $j$}\\ 0&,&\text{otherwise}\end{array} \right.,\)  
Figure 1: Example of a LIRP with 3 depots and 9 customers. Based on the objective function given in Equation (1), the total cost of inventory (TCI) consists of production setup cost and holding inventory cost.
## 3 Search Results and Study Selection Process  
Our initial search yielded 102 studies. The initial screening steps (duplicates, language) excluded 34 studies. Abstract screening was thus conducted on 68 studies, of which 30 were retrieved for full-text evaluation, and 18 studies were excluded (not screening adults, not comparing mTBI adult patients and healthy controls, not providing information on the demographic description of the population samples). During the data extraction process, 12 studies were excluded (not reporting in the Montreal Neurological Institute (MNI) or Talairach space or not providing detailed coordinates for volumetric changes). Thus, the present voxel-wise meta-analysis was finally conducted on five relevant studies [22; 23; 24; 25; 26].  
All the studies used structural T1 MRI for their brain imaging analyses.  
PRISMA flowchart was constructed to describe the selection process (Figure 1).
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 4 Experiments
### Results
#### 4.3.3 Baselines and Comparison  
To compare our proposed method with other related works in multiple player tracking, here we list four baselines which have reported their MOTA or GMOTA scores based on the same 1500-frame APIDIS basketball dataset. In order to a fair comparison, we reset weight factors (\(c_{1}=c_{2}=1\), \(c_{3}=log_{10}\)) in MOTA and GMOTA according to [11]. The baseline methods are briefly described as follows and their comparison results are shown in Table 2.  
**KSP tracker**[9]: The original KSP tracking framework was firstly proposed by [PERSON] etc. It can track multiple objects efficiently but is prone to identity switches due to the neglect of appearance features.  
**T-MCNF tracker**[11]: It is a tracklet-based multi-commodity network that it uses the tracklets as graph nodes instead of individual locations. It needs to run the KSP algorithm two times: the first time is used to generate tracklets being composed of obviously connected nodes and the second time is used to solve the multi-commodity network built for multiple appearance-groups.  
**MOT-SA tracker**[58]: The method uses geometric information regarding 3D scene structure rather than appearance information, and it is very robust for 3D tracking of multiple objects with similar appearances.  
**MOT-CE tracker**[59]: The algorithm is designed to track rigid objects in 3D scene and it also uses the geometric information to track.  
\begin{table}
\begin{tabular}{c c c} \hline
**Trackers** & **MOTA** & **GMOTA** \\ \hline MOT-CE [59] & 0.799 & - \\ MOT-SA [58] & 0.855 & - \\ KSP [9] & 0.959 & 0.714 \\ KSP-AF (60\%Color+40\%Num) & 0.963 & 0.720 \\ KSP-AF (70\%Color+50\%Num) & 0.970 & 0.791 \\ T-MCNF [11] & 0.92 & 0.86 \\ KSP-AF (90\%Color+50\%Num) & 0.981 & 0.923 \\ \hline \end{tabular}
\end{table}
Table 2: The comparison of MOTA and GMOTA scores in APIDIS dataset.  
Figure 11: The comparison of MOTA and GMOTA scores between original KSP and our proposed KSP-AF in STU dataset: (1) 70%Color+50%Number is provided for KSP-AF tracking while original KSP considers neither of them; (2) both MOTA and GMOTA scores are clearly improved in KSP-AF compared to original KSP; (3) numbers in brackets are the frame lengths of each period respectively.