esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 5 The Proposed VLSI Architecture and Implementation  
According to the training-tracking method, the coefficients of the \(\alpha\)-\(\beta\) tracking approach were extracted from the KF tracking algorithm, which is a simplified form of the KF  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variance V** & **Steady KG of KF** & \begin{tabular}{c} **KG of 4.0/** \\ **Iteration Times** \\ \end{tabular} & \begin{tabular}{c} **KG of 8.0/** \\ **Iteration Times** \\ \end{tabular} & \
\begin{tabular}{c} **KG of 16.0/** \\ **Iteration Times** \\ \end{tabular} &
\begin{tabular}{c} **KG of 32.0/** \\ **Iteration Times** \\ \end{tabular} \\ \hline
5 & 5830 & 5830/26 & 5830/25 & 5830/22 & 5826/21 \\ \hline
10 & 5053 & 5053/29 & 5053/28 & 5053/23 & 5013/20 \\ \hline
15 & 4638 & 4638/32 & 4638/30 & 4638/24 & 4584/19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best Standard Value of Conditional Equation.  
Figure 5: Under the same large variance environment, use DKF and PKF individually.  
tracking approach. Namely, use the original fixed-coefficient feature. In decision mode, the proposed approach can prevent repeatedly calculating with the KG. In this part, PDK or DKF will be used according to the different environments. KG in the iteration will be stored in the registers. In terms of the threshold, as shown in Equations (23) and (24), according to the different conditions, the average of the past eight KGs (K\({}_{k-1}\sim\) K\({}_{k-8}\)) is compared with the current KG (K\({}_{k}\)). The other method is that the current KG (K\({}_{k}\)) is divided by the average of eight KGs (K\({}_{k-1}\sim\) K\({}_{k-8}\)). If the checked values are smaller than that of the threshold, the information of the KG will be recorded in the register. After the KG satisfied the threshold conditions, coefficients of \(\alpha\) and \(\beta\) are extracted from KF tracking approach to form the proposed approach, and then the inherent coefficients are implemented by the VLSI approach. Namely, after the training session, if the location tracking algorithm can make correct and reliable decisions, the position information can replace the training mode with their own decision mode of the tracking approach. In terms of the proposed filtering algorithm based on VLSI approach. In addition, the choice of the window size in the proposed approach is a key step. A too small filtering window may not provide a satisfactory result. As shown in Equations (23) and (24), the window size 8 of moving average filter was used. Then, effectively cycled with the position information between the prediction state and the correction state.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Rolling Bearing
#### 4.2.3 Vmd  
In this method, the observation signal is decomposed by VMD first, and then the Hilbert envelope spectra of each component signal can be obtained. The result is shown in Figure 13. In Figure 13a,b, the fault frequency of the outer ring, \(f_{ic}=70\) Hz, is extracted. The fault frequency of the inner ring, \(f_{oc}=106\) Hz, can be seen in Figure 13c. However, the amplitudes of other frequencies and the noise energy are still high.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.6 Muisne, Ecuador  
On 6 April 2016, a moment magnitude M 7.8 earthquake struck offshore of the west coast of northern Ecuador. As shown in Figure 16, high values of _MDI_ were experienced in grids with a heightened likelihood of liquefaction. However, these areas were beyond the reconnaissance extent. For this event, SMAP data might have been used by field reconnaissance teams to flag regions with a high soil moisture increase and to assess possible ground failures.  
Figure 15: \(\Delta\)\(SM_{\text{SMAP}}\) (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) _MDI_\(>\) 0.04, (**c**) _MDI_\(>\) 0.02, and (**d**) _MDI_\(>\) 0 cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Meinong, Taiwan 2016 earthquake.
# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels
## 2 Methods
### Converged Network  
The adaptive multitask decoder outputs the water leakage rough segmentation image, the edge information of the rough segmentation image is incomplete, and there is noise and other interference information. Therefore, this paper designs the Converged network to calibrate the rough segmentation image and obtain the fine segmentation image of the tunnel water leakage. Many previous pieces of research regard the UNet network as a standard network for image segmentation. UNet has many advantages, Unet network structure is an Encoder-Decoder structure, and the UNet shape is similar to the U shape. Its structure is simple and easy to implement. UNet encoder extracts low-resolution feature information (global feature) by down-sampling, the decoder recovers high-resolution feature information by upsampling (local feature such as edge and texture of water leakage area), and UNet's skip connection fuses low-resolution feature information extracted by the encoder with high-resolution feature information of the same size in the decoder, the problem of insufficient information in up-sampling is improved, and the model's performance is also improved. UNet network realizes pixel-level segmentation with high accuracy and output fine tunnel leakage images. We adopt shallow UNet as the Converged network based on the above advantages. The converged network based on the original U-Net fuses the segmentation images of water leakage from the adaptive multitask decoder. The convolutional component of the network is a standard convolution. The convolutional module is composed of 3 \(\times\) 3 conv, batch norm layer, and the ReLU activation function.
## 2 Materials and Methods
### Remotely Sensed NDVI  
Daily NDVI, computed by \((\rho_{\rm{nir}}-\rho_{\rm{red}})/(\rho_{\rm{nir}}+\rho_{\rm{red}})\), was applied in this study. \(\rho_{\rm{nir}}\) and \(\rho_{\rm{red}}\) are the spectral reflectances of near-infrared and red bands, respectively. MODIS MOD09 GA and MOD09Q1 products downloaded via the Google Earth Engine (GEE) platform [52] provided daily and 8-day composite spectral reflectances of these two bands with a 250 m spatial resolution, respectively. Considering that directly computed NDVIs from the original daily product have a low data quality and those from 8-day composite product have a low temporal resolution, the DAVIR-MUTCOP method [53] was utilized to reconstruct daily NDVI time series combining the MODIS daily and 8-day composite products. It has been demonstrated that the DAVIR-MUTCOP method can effectively reconstruct daily NDVI time series of varied land cover types, particularly for cropland [53]. The reason why MODIS NDVI data were selected was based on the fact that the ground-measured data in three NE sites, especially for the maize GPP and SLN measurements, were only available before 2012. Before 2012, other satellites like Landsat are hard to use to provide reliable daily NDVI time series considering their low temporal resolution (16 days for Landsat), although they can provide NDVI data with high spatial resolution (30 m for Landsat). Additionally, satellites like Sentinel 2 can provide NDVI data with both acceptable temporal and spatial resolutions (5-10 day and 10 m), but they only obtain global NDVI data after 2016.
# A Needs-Based Augmented Reality System
## 2 Background
### Technologies in Augmented Reality  
It is essential to understand the general process in an AR system, to realize where each of the technologies fits and how they function together. This section provides definitions for the various topics and technologies supporting the AR process [15] (Figure 1).  
The components in a vision-based AR process include:  
* Image acquisition device: this device, usually a camera, captures the marker or object to be recognized.
* Computing and storage device: the component responsible for processing the gathered input and producing the correct output.
* Tracking and location technology: tracks the user position, view direction, and motion to decide on the virtual object to display.
* User interaction technology: the interface that captures the user input.
* Virtual object database: a database that includes relevant virtual objects.
* Virtual-real fusion technology: combines the virtual objects and real environment to ensure correct occlusion, shadows, and illumination.
* System display technology: the display technology presents the augmented information integrated with the real environment. The display technology depends on the type of AR system, visual, auditory, haptic, etc.  
Figure 1: General process in an augmented reality system.  
The general process starts by acquiring an image from the real world and sending it to computing and storage devices. This image and user interaction gestures are sent to the tracking technology, which decides what virtual information to display based on the user's position, viewing direction, and state of motion. The virtual information is retrieved from the virtual objects database and sent to the fusion technology that ensures proper object registration in the real world. The virtual information is then displayed using the display technology [15]. Table 1 provides definitions for the core AR technologies that remain popular research topics: tracking, interaction and user interfaces, calibration and registration, display techniques, and AR applications [16].  
While Table 1 describes the core AR technologies in a general manner, these technologies differ with the type of AR used. For example, the tracking techniques for vision-based AR use a camera to identify markers, while the tracking techniques for auditory AR uses acoustic tracking systems that analyze sound waves [17].
## 5 System Model
### THz Channel Model
#### 5.1.2 Molecular Absorption Loss  
Molecular absorption loss refers to the attenuation of THz waves due to the absorption of specific molecules in the transmission window. Molecules such as \(O_{2}\) and \(H_{2}O_{g}\) absorb a portion of the energy emitted by THz signals, leading to divisions in the THz frequency spectrum [26]. When \(k_{a}(f)\) represents the absorption coefficient, the absorption loss can be modeled as  
\[h_{a}=e^{-\frac{1}{2}k_{a}(f)d}. \tag{5}\]
## 5 Simulation Results and Analysis
The DRNSM-APINT can select fewer reporting nodes than the other comparison methods. When the number of services is 300, the total number of reporting nodes in DRNSM-APINT decreases by 34% compared to FHINT, 20.1% compared to SRINT, and 42.8% compared to EEDA, which can significantly optimize the number of reporting nodes. Figure 8 presents a comparison of the number of combinations of virtual segment divisions using various methods. The combinations of virtual segment divisions affect the calculation complexity of DRNSM-APINT. The traditional solving algorithm traverses all possible segment partitions along the path, encompassing scenarios where segments are discontinuous. Therefore, with the growth of the path, the number of partition combinations grows exponentially. In this case, the number of combinations can be calculated as the second type of Stirling number [25], and the calculation equation is \(Y!\cdot\mathsf{S}\left(\mathrm{N_{pz}},Y\right)\), where \(N_{\mathrm{pz}}\) is the number of satellites and \(Y\) is the number of reporting nodes. The Q-learning algorithm also traverses all the possible outcomes, but it can optimize the problem into a combinatorial planning task of continuous segments division based on the reward function. The calculation formula of combination quantity is \(\left(\mathrm{N_{pz}}-1\right)!/\left((Y-1)!\cdot(\mathrm{N_{pz}}-Y)\,!\right)\). The dynamic partitioning of continuous segments division is conducted using a heuristic algorithm, resulting in a further reduction of the number of combinations to \(\mathrm{N_{pz}}\cdot Y\), but it is easy to getting trapped in local optimal solutions. The lightweight Q-Learning algorithm based on virtual boundary preplanning that proposed in this paper, combined with the APINT model's feature of being less influenced by the segment range in selecting reporting nodes, can markedly decrease the number of combinations. Compared with Q-Learning and heuristic algorithm, this method can reduce the number of combinations by one or two orders of magnitude. Meanwhile, considering that the figure only illustrates the calculation complexity involved in selecting reporting nodes within a single service path, the algorithm proposed in this paper demonstrates significant lightweight capabilities in massive service scenarios, alleviating the computational burden in large-scale satellite constellation.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Application of Conventional Distance Protection
#### 3.1.2 Phase-to-Phase Fault  
For the real phase-to-phase fault case mentioned in Section 2.3.2, the topology of wind farm is shown in Figure 4, but the voltage level of the transmission lines are 110 kV and the transformer T3 is out of operation. The phase-A-to-phase-B fault occurred on Line 2. However, the wind farm side phase-to-phase instantaneous distance protection on Line 1 maloperated. Obviously, the fault case is a maloperation accident. According to the real fault waveform records shown in Figure 6, the amplitude, phase and trajectories of the measured phase-A-to-phase-B impedance are calculated by the Fourier algorithm, as shown in Figure 8a-c, respectively.  
Figure 8: The phase-A-to-phase-B impedance of the wind farm side when a phase-A-to-phase-B fault occurs on the transmission line connected to a DFIG-based wind farm: (**a**) the amplitude of impedance; (**b**) the angle of impedance; and (**c**) the trajectory of impedance.  
It can be seen from Figure 8a,b that the amplitude and phase of the measured impedance are both constantly changing. In Figure 8c, the blue line is the impedance trajectory measured by conventional distance protection. Zone 1 is the polygon action area of wind farm side instantaneous distance protection on Line 1. 9.97 and 1.86 \(\Omega\) are the instantaneous distance protection setting values of the resistance and reactance, respectively. It can be seen that the trajectory of the impedance enters Zone 1 and causes the maloperation of wind farm side phase-to-phase instantaneous distance protection on Line 1. The conventional instantaneous distance protection of the wind farm side is no longer reliable when a phase-to-phase fault occurs on a transmission line.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 2 Localization Analysis of Single Array
### Range Precision  
The range is also a function of time delay \(\tau\), and the transmission error is:  
\[\sigma_{R}{}^{2}=(\frac{\partial R}{\partial\tau_{12}}\sigma_{\tau})^{2}+( \frac{\partial R}{\partial\tau_{13}}\sigma_{\tau})^{2}+(\frac{\partial R}{ \partial\tau_{14}}\sigma_{\tau})^{2}+(\frac{\partial R}{\partial\tau_{15}} \sigma_{\tau})^{2} \tag{12}\]  
Figure 2: Distribution of azimuth error of \(c\) and \(\sigma_{\tau}\).  
In Figure 3, the range error was quite big. In both cases, the error reduces with increasing array size, and it increases with both the range \(R\) and the time delay \(\sigma_{\tau}\). In general, however, the error is overall at a fairly high level. In Figure 3a, the relative error was almost 40% under the optimal condition, and the biggest error was 950%. The error distribution in Figure 3b is the same as in Figure 3a, with higher level, the optimal error was 50%, and the biggest one is twenty times.  
In summary, a single five-element cross array has good directional ability. The azimuth error can stay below 0.1\({}^{\circ}\) under reasonable conditions. However, the range ability is rather bad. The error is nearly 40% even under best conditions, which makes it impossible to achieve satisfactory sound source localization.
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 2 Materials and Methods
### Methods
#### 2.2.2 WVAT Calculation of DPC 910 nm  
The received signal of DPC in near-infrared channels can be expressed as [10]  
\[L_{sen}^{\lambda}=T_{g}^{\lambda}\left(L_{c}^{\lambda}+L_{0}^{\lambda}T_{ \uparrow}^{\lambda}T_{\downarrow}^{\lambda}\rho_{s}^{\lambda}\ \right) \tag{1}\]  
where \(L_{sen}^{\lambda}\) represents the radiance received at the TOA, \(T_{g}^{\lambda}\) represents the atmospheric absorption transmittance, \(L_{0}^{\lambda}\) represents the solar irradiance at the top of the atmosphere, \(T_{\uparrow}^{\lambda}T_{\downarrow}^{\lambda}\) represents the atmospheric total transmittance from the sun-surface-sensor path, \(\rho_{s}^{\lambda}\) represents the surface reflectance, and \(L_{c}^{\lambda}\) represents the atmospheric path radiance.  
Figure 2: Flow chart of DPC water vapor retrieval.  
Dividing the two sides of Formula (1) by solar irradiance \(L_{0}^{\lambda}\), the formula for calculating the TOA reflectance of the DPC near-infrared band can be obtained [14].  
\[R_{TOA}^{\lambda}=T_{S}^{\lambda}\left(\rho_{0}^{\lambda}+T_{1}^{\lambda}T_{1}^{ \lambda}\rho_{s}^{\lambda}\right) \tag{2}\]  
where \(R_{TOA}^{\lambda}\) represents the TOA reflectance and \(\rho_{0}^{\lambda}\) represents the atmospheric path reflectance.  
Due to the relatively close wavelengths of the DPC bands at 865 nm and 910 nm, the difference between the path reflectance and atmospheric total transmittance in these two bands can be ignored. In addition, the surface reflectance difference between the two bands is small. Based on these reasons, it can be assumed that the TOA reflectance of the DPC in the 910 nm band is equal to that in the 910 nm band without considering atmospheric absorption [22].  
In addition, the influence of atmospheric gases other than water vapor on the radiation absorption of DPC Band 8 is neglectable, which means that the WVAT in DPC Band 8 is equal to the atmospheric absorption transmittance. Since the influence of the atmosphere on the absorption at DPC 865 nm can be neglected, the WVAT at 910 nm can be calculated by dividing the TOA reflectance at 910 nm by the TOA reflectance at 865 nm.  
\[T_{g}^{910\ nm}=R_{TOA}^{910\ nm}/R_{TOA}^{865\ nm} \tag{3}\]  
where \(T_{g}^{910\ nm}\) represents the atmospheric absorption transmittance in the DPC 910 nm band, which can also be considered as the WVAT in the DPC 910 nm band, and \(R_{TOA}^{865\ nm}\) and \(R_{TOA}^{910\ nm}\) represent the TOA reflectance of the DPC in the 865 nm band and 910 nm band, respectively.
## Materials and Methods
### Experimental Characterization
#### 2.2.4 Analytical Calculation of Further Physical Parameters  
To calculate the acoustic parameters of the loose fibers in relation to their density using the formula (Section 2.2.1), it is necessary to know the variation of the physical parameters of the model with respect to density.  
In addition to porosity (Section 2.2.1) and airflow resistivity (Section 2.2.2), the following physical parameters were calculated:  
* Tortuosity \(\alpha_{\infty}\) using the analytical formula proposed in [31] as a function of the porosity: \[\alpha_{\infty}=\left(\frac{1}{\Phi}\right)^{0.9574}\] (11)  
Figure 7: Comparison between the experimental measurements (Exp) and analytical model (JCA) for four different combinations of thickness and density (constant sample weight).  
Viscous and thermal characteristic length \(\Lambda\) and \(\Lambda^{\prime}\) using the inversion technique [32] starting from the experimental acoustic measurements described in Section 2.2.3 and assuming the validity of the JCA equivalent dissipative fluid model. For each measurement of acoustic absorption relating to a specific density and thickness, the values of porosity, tortuosity, and airflow resistivity were fixed in the relationships (9), (10), and (11). The values of \(\Lambda\) and \(\Lambda^{\prime}\) were calculated through a procedure of inversion, minimizing the difference between the experimental absorption curve and the one calculated with the JCA model. The values of the viscous and thermal characteristic lengths obtained are shown in Figure 8 as a function of the apparent density; subsequently, to extend the results to any density through an analytical formulation, the following exponential interpolation equations were obtained:  
\[\Lambda=0.066(1-\phi)^{-1.309}\left[\upmu\right] \tag{12}\]  
\[\Lambda^{\prime}=0.790(1-\phi)^{-0.745}\left[\upmu\right] \tag{13}\]  
Figure 8 compares the interpolation curves and the experimental values as a function of the apparent density.  
The resulting average difference between the values of \(\Lambda\) and \(\Lambda^{\prime}\) obtained with the inversion technique and the analytical Formulas (12) and (13) is 2% and 8%, respectively.
## 6 Implementation and Evaluation
### Theoretical Calculation  
Let us assume that the probability of losing a packet when it is sent over any link in a lossy network is equal to \(l\). Thus the probability for success at any link-transmission in the network equals \(1-l\). If the communication is over N-hops, the number of links equals \(N\) and the probability that the communication succeeds over the \(2N\) link-transmissions (since every link has to be crossed once for the request and once for the reply) is:  
\[P_{N_{non}}=(1-l)^{2N} \tag{3}\]  
This communication success probability applies for multicast communication as well as for non-confirmable (non) CoAP unicast communication. However, when using confirmable (con) CoAP unicast communication as the case in our group communication solution, CoAP tries to achieve reliability by using a simple stop-and-wait retransmission with exponential back-off (see Section 2.1). This means that if a reply to a confirmable packet is not received within the back-off time, the CoAP sender will retransmit the packet. If a reply to the first transmission is not received, CoAP will retry the transmission until MAX_RETRANSMIT (by default 4) is reached. Again these retransmissions have the same probability for success as the first attempt. And thus the probability that \(r\) retransmissions are needed for successful transmission over \(N\) hops (\(r\) can go from 0 to 4) is:  
\[R_{N_{r}}=P_{N_{non}}\big{(}1-P_{N_{non}}\big{)}^{r} \tag{4}\]  
Thus the probability for success when using CoAP con communication equals the sum of the probabilities of successful communication of any of the five transmission attempts:  
\[P_{N_{con}}=\sum_{r=0}^{4}R_{N_{i}}=1-\big{(}1-P_{N_{non}}\big{)}^{5} \tag{5}\]  
In many group communication use cases, it is desirable to get answers from all members of the group. A complete group communication is considered successful when all members in the group also have successful communication:  
\[\begin{array}{l}P_{N_{non}}=\big{(}P_{N_{non}}\big{)}^{G}\\ P_{N_{con}}=\big{(}P_{N_{con}}\big{)}^{G}\end{array} \tag{6}\]  
This reliability of the unicast group communication is achieved by relying only on default CoAP retransmissions. If higher reliability is desired, the EM can perform its own retransmissions or fine-tune the default CoAP retransmission settings on an entity-wide level or per member.