esa-sceva/satcom-chunk-collection huggingface dataset:
# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
## 4 Experimental Data
### Data Acquisition  
The data have been acquired with a prototype of a multi-sensor system developed at the German Aerospace Center (DLR, Institute of Transportation Systems, Braunschweig, Germany). The system comprises a GNSS receiver with external antenna, an inertial measurement unit and an analogue-to-digital converter with a three-axial analogue ABA sensor. The data acquisition and processing has been implemented in Robot Operating System (ROS). ABA data were recorded at a sampling rate of 20 kHz.  
The system was installed on a measurement car of the Osterreichische Bundesbahnen (OBB, Vienna, Austria, Figure 3). Data were gathered throughout a two-weeks measurement campaign in June 2019. During this campaign the measurement train was travelling at normal operating speed of up to 200 km/h.  
Figure 2: Cepstra of the synthetic data generated of the different models.
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 2 Conventional HARQ Mechanism  
3 GPP LTE and IEEE 802.16m adopted the multi-process stop-and-wait HARQ mechanism, which interlaces several independent stop-and-wait HARQ operations in time so as to fully utilize transmission resources [13; 14; 15]. Figure 1 describes the conventional HARQ scheme, where multicastpackets are transmitted to two users by interlacing three HARQ processes, and the decoding delay is one transmit time interval (TTI) in both the uplink and downlink transmission. In this case, the transmitter repeatedly sends a packet until ACK messages are received from all the multicast users or until the limit for the number of retransmissions is reached. For example, in Figure 1, the transmitter starts to send a new packet when TTI = 10 after receiving ACK messages from both users when TTI = 9 (ACK messages for Subpkt 2-1 were transmitted when TTI = 8). Since the conventional HARQ method for multicast is a direct extension of the conventional HARQ scheme for unicast, it can be easily implemented in practical communication systems. However, the overhead from ACK/NACK feedback rapidly increases as the number of multicast users increases.
## 2 Enhancement Methods for High-Resolution SAR Ship Wake Processing
### Ship Wake Enhancement Method Based on Equivalent Sequence Images
The specific calculation method is as follows:  
\[{I_{i,j}}^{\prime}=\big{(}{I_{i,j}}-\mu_{j}\big{)}\sigma_{0}/\sigma_{j}+\mu_{0}, \tag{2}\]  
After adjusting the grayscale values of the \(i\)-th OSMI image, denoted as \(I_{i,j}\), the mean \(\mu_{0}\), and standard deviation \(\sigma_{0}\) of each transformed image are calculated as follows [24]:  
\[\mu_{0}=\frac{1}{N}\sum_{j=1}^{N}\mu_{j}\quad and\quad\sigma_{0}=\left(\sqrt{ \prod_{j=1}^{N}\sigma_{j}}\right)^{\frac{1}{N}}, \tag{3}\]  
Here, \(\mu_{j}\) and \(\sigma_{j}\) are the original mean and standard deviation of the image. The grayscale values of pixels in the sea surface background images change slowly with azimuth imaging angle and imaging time sequence. By normalizing the histogram, the timeseries curves of each pixel can be obtained. Filtering in this three-dimensional direction can yield sea surface background images and eliminate strong reflection values caused by significant changes in moving ship targets. Due to the complexity of SAR images, statistical signal processing is widely used in SAR image processing and analysis. Many scholars have proved the effectiveness of log ratio operator and Gaussian distribution modeling techniques in real SAR and PolSAR data [26; 27]. In practice, the sub-sequence images are converted to dB units, and then, median filtering is applied to the OSMI images using the logarithmic operator. The specific formula is as follows [28]:  
\[I_{out}=medianfilter\bigg{\{}\mathop{\overset{N}{\underset{i=1}{\cup}}}\log[I(i) ]\bigg{\}},i=1,2\ldots N, \tag{4}\]  
Here, \(I_{out}\) represents the output result image after enhancement, and the image is represented in dB during the calculation process.
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 4 Two-Phase Scroll Expander  
### Working Principle of Two-Phase Scroll Expander  
A scroll expander contains two identical concentric spirals with a phase difference of \(180^{\circ}\), in which one is fixed, and the other moves following an orbiting motion (Figure 12a). The space between these two spirals creates multiple expansion chambers with increasing volume (Figure 12b). In this case, the incoming fluid is trapped near the axis of rotation, and it expands up to the peripheral zone of the involute, where the discharge occurs. Figure 12 displays the processes evolution of the fluid inside the machine, as follows: suction (1-2), expansion (2-3), and discharge (4-5). The mechanical power is available at the crankshaft linked with the orbiting spiral [10].  
The scroll expander does not need any control valves for the inlet and outlet, and it is characterized by low torque fluctuation, and low noise and vibration due to the simultaneous expansion chambers [33; 34].  
The volumetric efficiency is determined by radial leakages and flank leakages. They are due to the gap in the axial and radial directions between orbiting and fixed spirals, respectively [34].  
The back-pressure chamber can be used for the axial sealing, and the reduction in both the thrust bearing load and of the friction at the sliding portion between the orbiting and fixed spirals [35].  
Figure 11: High-temperature heat pump [30].
## 2 Mathematical Foundations
### Rotation of the Acoustic Field  
A spherical harmonic transformation of the plane wave expansion can be performed based on the Jacobi-Anger relation [24]. From the spherical harmonic representation, the rotation can be generated by the implementation of a rotation matrix. The derivation of a sound field rotation operator in the spherical harmonic domain proceeds as follows. A rotation in the azimuthal plane by \(\phi_{0}\) can be expressed as:  
\[p(r,\theta,\phi-\phi_{0},\omega)=\sum_{n=0}^{\infty}\sum_{m=-n}^{n}A_{nm}( \omega)j_{n}(kr)Y_{n}^{m}(\theta,\phi-\phi_{0}). \tag{4}\]  
Expanding the right-hand side of Equation (4) gives:  
\[p(r,\theta,\phi-\phi_{0},\omega)=\sum_{n=0}^{\infty}\sum_{m=-n}^{n}A_{nm}( \omega)j_{n}(kr)\sqrt{\frac{(2n+1)}{4\pi}\frac{(n-m)!}{(n+m)!}}p_{n}^{m}(\cos \theta)e^{jm\phi}e^{-jm\phi_{0}}, \tag{5}\]  
which yields:  
\[p(r,\theta,\phi-\phi_{0},\omega)=\sum_{n=0}^{\infty}\sum_{m=-n}^{n}j_{n}(kr)Y_ {n}^{m}(\theta,\phi)A_{\phi_{0}nm}(\omega), \tag{6}\]  
in which:  
\[A_{\phi_{0}nm}(\omega)=A_{nm}(\omega)e^{-jm\phi_{0}}. \tag{7}\]  
Equation (7) indicates that the azimuthal rotation of the sound field can be performed by taking the product of the complex spherical harmonic coefficients and a complex exponential whose argument depends on the angle of rotation. An inverse spherical harmonic transformation can be implemented to return to the plane wave domain after the rotation has been carried out.
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 2 Data and Methods  
### 2D WV Productions by GNSS Observations  
The 2D WV productions, called PWV, can be retrieved from GNSS observations. A commonly used tropospheric delay in the PWV processing is the tropospheric zenith total delay (ZTD), which is estimated by the observations, subtracted from the zenith hydrostatic delay (ZHD), and calculated by an empirical model (e.g., the Saastamoinen model [31]), and which yields the zenith wet delay (ZWD). The PWV of each site can be retrieved from the ZWD through the conversion coefficient. Additionally, in a relatively dense CORS network, the PWVs derived from multi-stations can provide a 2D WV field in the station coverage area by interpolation [13].  
\[\text{ZWD}\ =\ \text{ZTD}-\text{ZHD}, \tag{1}\]  
\[\text{PWV}\ =\ \prod\times\text{ZWD}, \tag{2}\]  
where \(\prod\) is the conversion parameter used to convert ZWD to PWV, and  
\[\prod\ =\ \frac{10^{6}}{\rho_{w}\times R_{v}\times(\frac{k_{3}}{T_{m}}+k^{ \prime}{}_{2})}, \tag{3}\]  
where \(\rho_{w}\) and \(R_{v}\) are the density of liquid water and universal gas constant, respectively; \(k_{3}\) and \(k^{\prime}{}_{2}\) are empirical constants [32]; and \(T_{m}\) is the weighted mean temperature, a function of surface meteorological factors (e.g., surface temperature, pressure, and relative humidity). In this study, we used the Bevis model [1] to obtain \(T_{m}\).
## 2 Materials and Methods
### Cell Tracking  
As cell tracking was not the main focus of this work, the algorithm used to create cell lineages is simple and effective. The problem of cell tracking is framed as a linear sum assignment optimization by calculating the combinatorial distances between all cell centroids in subsequent images. The optimization is solved using the Hungarian algorithm [43; 44; 45] which minimizes the sum of differences between all the paired centroids.  
This algorithm is formally described for two frames (\(t\), \(t+1\)) using the equation:  
\[\min\sum_{i}\sum_{j}C_{i,j}X_{i,j}, \tag{3}\]  
where \(C\) is a difference matrix describing the cost of pairing cell \(i\) in frame \(t\) and cell \(j\) in frame \(t+1\). In our case the cost is distance between the centroids of the two cells: \(i\) and \(j\). \(X\) is a Boolean matrix holding the assignments between cells. \(X_{i,j}=1\) if \(i\) and \(j\) are assigned to be the same cell.  
Tracking accuracy is defined using an F-measure statistic. The set of all true cell pairs between subsequent images is compared against the predicted cell pairs. The equation used for this metric is:  
\[F=2\cdot\frac{Precision*Recall}{Precision+Recall}. \tag{4}\]
## 3 Experiment and Discussion  
### Experiment Setup  
#### 3.1.1 Testing System  
In order to verify the comprehensive model, a defect localization experiment is performed using the Laser Scanning and Testing System (LSTS; see Figure 3) at the National Space Science Center, Chinese Academy of Sciences. The relevant parameters of the facility are shown in Table 3. The computer is used to monitor the current. As the laser beam scans the defect location, the abnormal \(\Delta I\) will be tested, and a red spot will be marked on the layout diagram. The test results supplied by the voltage source or current source have similar properties according to Equations (16) and (18), so a voltage source is used for the test.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 5 Experimentation
### Results and Discussion
#### 5.2.3 Motion Detection and Link Unavailability Prevention  
On-body sensors carried by the rescuers push data regularly to the IoT platform. Based on the type of recorded data, we can extract several human behaviors. For instance, gyroscope data recorded and depicted by the Labeeb-IoT platform in Figure 9 inform about human mobility. Sensors placed on the hand detect and send gyroscope variations tending to zero when the human has stopped and is not moving. Small variations may be distinguished in the first part of the figure when the human is walking and higher variations of the gyroscope when he/she is running. Figure 10 depicts the gyroscope angle variations over more than 2000 s. The gyroscope angle informs about the movement direction. Furthermore, some vital sign information may help the command center to switch rescue teams and send support there; we cite for example magnetometer and heart beat variations reflecting the stress level. All collected data on the IoT platform side could provide also the connectivity status for every deployed node, as can be seen in Figure 10. Disconnected nodes inform about the unavailable intermediate links or network over-saturation.  
Figure 8: ORACE-Net on-body mobile device behavior: round trip time delay and link quality estimation.
# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 3 Chinese Document-Level Emergency Event Extraction Dataset
### Dataset Summary  
Compared with existing partial event extraction datasets, our proposed dataset CDEEE has the following advantages. First, we solve the problem of missing datasets in the field of document-level emergency event extraction in Chinese, which allows the relevant models to be trained and validated. Second, our dataset is manually annotated with more documents than most of the existing DEE datasets at a size of 5000 documents. Third, we have annotated the role overlapping problem, which is more reflective of the real environment and reflects the text complexity.
# Visual MAV Tracker with Adaptive Search Region
## 5 Conclusions  
In this study, an SR-based MAV tracker was developed by integrating several methods to improve tracking accuracy, robustness, and computation speed. First, SC reduces the probability of losing the target from the SR. Second, SO optimizes the SR size by considering the effects of the SR size on the tracking accuracy. Finally, PP estimates a more accurate location of the SR by considering the target object motion. Although SC, SO, and PP were conducted with simple and existing operations, the combination of these methods led to significant tracking improvements (i.e., accuracy, robustness, and EAOC). Moreover, the proposed tracker can be applied to real-time tracking because of its high computation speed (410 FPS). Furthermore, the proposed tracker shows higher accuracy and robustness than a tracker named Ocean, which is one of the high-end trackers in the test dataset used in this study.  
Although the proposed model exhibits satisfactory tracking performance, additional work is required to address its limitations. First, the proposed tracker was verified using only an MAV. For use in other applications, its performance must be tested with other objects. Second, the proposed model must be updated to manage occlusion periods. When the target size is reduced by occlusion, the SR size can also decrease, which can lead to inaccurate tracking. Thus, an occlusion-detectable model should be developed. In the worst case, the target can be fully covered by other objects. Object detection algorithms can be modified to address this problem. In addition, the tracker should be updated using multiple SRs for multiple-object tracking.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app11167741/s1, Video S1: MAV-tracker.  
Conceptualization, W.N.; methodology, W.P.; software, W.P.; validation, W.P.; formal analysis, D.L.; investigation, W.P.; resources, W.P.; data curation, J.Y.; writing--original draft preparation, W.P.; writing--review and editing, W.N.; visualization, W.P.; supervision, W.N.; project administration, W.N.; funding acquisition, W.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1063463) and the Chung-Ang University Research Scholarship Grants in 2019.  
Figure 11: EAOC of tracking models.
## 3 Finite Element Simulation and Verification
### Finite Element Model Verification
#### 3.2.2 Load-Displacement Curve  
The _F-u_ curves of different cases obtained from the FEM are presented in Figure 7. Comparing the _F-u_ curves of different cases obtained from the FEM and tests, the differences in \(k\) and _Fu_ are less than 10%, indicating that the FEM can reflect the _F-u_ curves in the test. However, the elastoplastic stage of the numerical _F-u_ curve is different from the experimental one, which is reflected by the fact that the stiffness of the elastoplastic stage of the numerical _F-u_ curve reduces faster than that of the experimental one. This may be because the Mohr-Coulomb model is an ideal elastoplastic model and has some errors in the simulation of the nonlinear behavior of sand in the range of small strains and strain softening characteristics.
## 4 Discussion
### Distribution of Growth-Inhibiting Bacteria against _A. catenella_ (Group I)
strain MBEF04 of the class \(\alpha\)-proteobacteria, with homologies of 100%, 99.56%, and 100%, respectively (Figure 14). As these three strains were isolated from Stn. 1 and showed very high homologies, they are highly likely to be the same species. A similar presumption is possible for the strains AK34 (Stn. 1, leaf, homology 99.79%), AK52 (Stn. 1, PAB, homology 99.79%), and AK53 (Stn. 1, FLB, homology 99.59%), which all have base sequences close to that of _Alternomonas_ sp. KT1101 belonging to the class \(\gamma\)-proteobacteria (Figure 11). In the CFB group, the strains AK1 (Stn. 1, leaf), AK2 (Stn. 3, PAB), and AK21 (Stn. 3, PAB) are related to _Zobella laminariae_ (the homologies were 100%, 100%, and 99.92%, respectively), and the strains AK15 (Stn. 2, FLB), AK29 (Stn. 1, leaf), and AK49 (Stn. 1, PAB) are closely related to _Lewinella persicus_ with 100% homology for each strain (Figure 12). As described above, several strains of growth-inhibiting bacteria showed the same 16S rRNA gene sequences, and these strains were isolated from seagrass leaves and the seawater outside Akkeshi-ko Estuary. These results strongly suggest that the seagrass area is one of the significant habitats of growth-inhibiting bacteria against _A. catenella_ in the coastal area. The same situation was reported about the algicidal bacteria against the fish-killing raphidophyte _Chattonella antiqua_ (Ochrophyto, Raphidophyceae) from the seagrass (_Z. marina_) leaves and surrounding seawater in the Seto Inland Sea, Japan [43]. The distribution of three strains of algicidal bacteria _Alteromonas_ sp., i.e., strains S, K, and D [44], against the above mentioned raphidophyte _C. antiqua_ was investigated in the seagrass bed and surrounding sea area in Japan's Seto Inland Sea, and the study revealed the abundant existence of these algicidal bacteria in seawater from the seagrass bed compared to the seawater of the surrounding area [45]. This report suggested that the seagrass bed was a major source of algicidal _Alteromonas_ strains to the surrounding sea area. The action of algicidal bacteria on red tide plankton is roughly divided into two types, i.e., the direct attack type (mainly the phylum Bacteroides) and the algicidal substance-producing type [46; 47]. Algicidal/growth-inhibiting substances are thought to be involved in the algicidal/growth-inhibiting processes. As the algicidal substances, serine protease [48], prodigiosin [49], isatin [50], questiomycins [51], etc. were identified. These chemicals would be of the focus of interest for the investigations on understanding the basic mechanisms of controlling HABs.
## 2 Materials and Methods
### Performance Evaluation Matrices  
To assess the prediction effectiveness of models, a variety of performance matrices were employed such as mean absolute percentage error (MAPE), root mean square error (RMSE), R-squared (R\({}^{2}\)), and root mean standard deviation ratio (RSR). The MAPE quantifies the average size of the forecasting errors without taking into account their direction. It evaluates precision for continuous variables.  
\[MAPE=\frac{\sum_{i=1}^{N}\lvert y_{i}-x_{i}\rvert}{N} \tag{3}\]  
where \(y_{i}\) is the prediction, \(x_{i}\) is the actual value, and \(N\) is the number of samples.  
The root mean square error is a popular statistic for measuring the disparities between a model's anticipated and reported values (RMSE). The RMSE is calculated by adding up  
Figure 5: Structure of the proposed tree-based AI model.  
all the square roots of the second moment of the differences between the predicted and observed values in a given sample.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(x_{i}-y_{i}\right)^{2}}{N}} \tag{4}\]  
R\({}^{2}\) is a statistical measure that depicts the percentage of variance for a dependent variable in a regression model that is explained by one or more independent variables, which is given by the equation below:  
\[R^{2}=1-\frac{\text{{Sum}}\text{ {of}}\text{{squared}}\text{ {regression}}\left(SSR\right)}{\text{{Total}}\text{ {sum}}\text{ {of}}\text{{squares}}\left(SST\right)}=1-\frac{\sum \left(x_{i}-y_{i}\right)^{2}}{\sum\left(x_{i}-\overline{x_{i}}\right)^{2}} \tag{5}\]  
where \(\overline{x_{i}}\) is the mean value.  
Like R\({}^{2}\), the [PERSON]'s Index of Agreement (WIA) value suggests the correlation between the predicted and the actual outputs. The WIA can be expressed as:  
\[WIA=1-\frac{\sum\left|y_{i}-x_{i}\right|^{2}}{\sum\left(\left|y_{i}-x_{i} \right|+\left|x_{i}-\overline{x}\right|\right)^{2}} \tag{6}\]  
The root mean standard deviation ratio (RSR) is the ratio of the RMSE between the simulated and observed values to the standard deviation of the data. It is given by the following equation:  
\[RSR=\frac{RMSE}{STADEV_{obs}} \tag{7}\]  
where \(STADEV_{obs}\) is the observed standard deviation. RSR ranges from 0 to a big positive number. The lower the RSR, the lower the RMSE and the better the model simulation performance.
## 3 Results
### Boosting Options for PEMFCs  
Similarly to Internal Combustion Engines (ICEs) using boosting systems (air compressors (superchargers) or turbochargers) to increase their power density and efficiency, a boosting system can be used with a fuel cell stack to increase its performance. However, the requirements are not the same. Firstly, an air compressor for fuel cells needs a high pressure with a low air mass flow rate. Secondly, because of the battery, the stack, the control power unit, and the hydrogen storage tanks, the size of the boosting system needs to be considered in a transportation application. In most ICE applications, a turbocharger is used to recover the energy from high-temperature exhaust gas. Even if an expander is used, the operating temperature of the PEMFC (80 \({}^{\circ}\)C) is too low to recover enough power to drive the compressor. It implies that the air supply system uses power from the stack. As a consequence, a high efficiency is an important requirement.  
As seen in Table 2, centrifugal and Roots compressors are the most suitable for fuel cell application [28; 29; 30; 31; 32]. They are smaller and cheaper than screw and scroll compressors, and help to reduce the weight and the cost of PEMFCs vehicle, which is already increased by the use of platinum.  
Daimler, General Motor, and SAIC as an example use centrifugal compressors, as part of their electric-assisted turbochargers (E-turbochargers) [28]. The Honda FCX Clarity uses a screw compressor, but the new Honda Clarity fuel cell is now equipped with a two-stage centrifugal compressor, which has a 50% smaller sound absorber than the FCX screw compressor [4]. Toyota remains the only FCV manufacturer to use a Roots compressor. This type of compressor has a lower efficiency and pressure ratio, but higher power density than the centrifugal compressor. However, the pulsation noise implies the use of a bigger sound absorber. As a result, FCV manufacturers have adopted centrifugal compressors as the most suitable compressor type for fuel cell applications.  
Two-stage compressors and E-turbochargers are currently used for FCV applications. The Honda Clarity fuel cell two-stage compressor reaches a 4:1 pressure ratio [4]. An estimated pressure ratio value of 2.8:1 is given by a recent paper for an E-turbocharger [33]. Figure A1 in the Appendices provides an illustration of this two-stage system. This paper also proposed a mixed architecture with a two-stage compressor and a turbine generator to reduce the energy consumption of the boosting system.