esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Analysis of Geologic Hazards
## 4 Mines  
[PERSON] et al. [35] treated the measurement of ground deformation caused by mining activities in the Fengfeng area (Eastern China) in 2015 and 2016. They adopted MTInSAR applied to multiple satellite SAR images, i.e., TerraSAR-X, Sentinel-1, Radarsat-2, and PALSAR-2, thus increasing the data adopted to build the time series. They focused their study on vegetated areas, where MTInSAR observations are usually critical. They measured subsidence of almost 800 mm year\({}^{-1}\), and registered a root mean squared deviation of 83 mm year\({}^{-1}\) compared to terrestrial observations.  
[PERSON] et al. [36] applied MTInSAR in a mining area in southern Italy to measure the deformation time series caused by subsidence in the period 2016-2018. They found areas with deformation rates up to 250 mm year\({}^{-1}\). Moreover, they manually mapped more than 100 sinkholes that occurred in the area between 1956 and 2018 using nine orthoimages acquired across this period, and they analyzed the deformation in correspondence of the sinkholes. In their work, quite homogeneous subsidence rates (10-20 mm year\({}^{-1}\)) were measured, except for the more recent sinkholes, where the velocities were more heterogeneous and higher (up to 80 mm year\({}^{-1}\)).
## 2 Fundamentals of Aerial Wireless Sensor Networks for Oceanographic Monitoring.
### Monitoring Application  
The information gathered by the sensor nodes has to be transmitted to a base station with a massive data storage system (relational databases are the commonest solution) which can also be used for the necessary studies using the existing oceanographic theoretical models. Having integrated monitoring tools makes it possible to maintain permanent communication with the sensor network deployed and access to the stored data via Internet. The information displayed by these tools usually consists of the number of nodes deployed, the parameters analysed, the geographical location of each node, the most recent data gathered by the sensor nodes, and a visualization of a data historical table.  
An example of a monitoring application is shown in Figure 4, where we can see the elements that are normally incorporated: (1) a data area (left) displaying statistical data on a particular WSN subset for a user-selected time range, and (2) a two-dimensional representation of the deployment along with tools for visualizing the latest data gathered by the selected sensors in that graphic area (the example chosen uses Google Maps(r) and the APIs necessary to include the points of interest).  
Figure 4: Example of an application for real-time monitoring of data from an oceanographic A-WSN.
## 5 Legal Challenges of Organ Replacement Technology  
There are two primary legal risks related to commercializing a 3D-printed human organ: products liability and patentability. The case of products liability is well understood. Any time a manufacturer produces a medical device for implantation in the human body, they run the risk of a legally identifiable defect resulting in rejection or complications that accrue tort liability. These risks, however, can be readily mitigated via appropriate research and development. The serious legal impediment to commercializing bioprinted organs is the question of whether or not such organs are patentable.  
The primary case law relevant to bioprinting's patentability is [PERSON] v [PERSON], a Supreme Court case from 1980 [66]. Under the two-prong test of [PERSON], a genetically engineered bacterium capable of breaking down crude oil was found to be patentable because it was (A) non-naturally occurring and (B) the product of human ingenuity. Under this test, one could readily argue that a 3D printed organ constructed, layer-by-layer, from human stem cells may resemble a natural organ but contains more than ample structural differences to be considered non-natural. Also, as the assembly occurs exclusively due to the instructions of complex, human-written, computer code guiding the 3D printer, the process can also readily be argued to be the result of significant human ingenuity.  
However, the elephant in the room for patentability is the America Invents Act (AIA) of 2011 [67]. This particular law has a section that states that no patent may issue on a claim directed to or encompassing a human organism. Because the AIA is more recent law, it expands upon the understanding established in [PERSON]--meaning that the test today is now a three-prong test. To be patentable, a 3D bioprinted organ must be non-natural, the result of human ingenuity, and not a human organism. However, to date, neither the Act nor the Courts have defined \"human organism\" within the meaning of the AIA, so it remains up in the air whether the United States Patent Office (USPTO) will allow a patent on a bioprinted organ. Due to the statutes' ambiguous language, investors and researchers working with bioprinted organs should expect a legal challenge prior to patent issuance [68].
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 3 Methodology
### Appliance Identification Based on Multi-Feature Integrated Classification (MFIC)
#### 3.6.1 Similarity Index of Single Feature  
Intrinsic features are determined by the internal structure of appliances, which are not affected by the user' behavior habits, and relatively stable with slight fluctuations. The similarity indices of these features can be quantified as:  
\[S_{(\cdot)}(v)=\exp\left(-k\frac{|v-v_{man}|}{H(v-v_{man})}\right) \tag{13}\]  
\[H(\Delta v)=\left\{\begin{array}{ll}|v_{\max}-v_{\rm mean}|,&\Delta v\geq 0 \\ |v_{\min}-v_{\rm mean}|,&\Delta v<0\end{array}\right. \tag{14}\]  
where, \((\cdot)\) denotes the intrinsic feature of detection. \(v\) represents the detected value of certain feature, and \(v_{\rm mean}\) denotes the mean value of certain feature recorded in the feature library. Considering the slight fluctuations in these intrinsic features, \(v_{\max}\) and \(v_{\min}\) are used to represent the limits of upper and lower fluctuation bound. \(H(\cdot)\) is a piecewise function. \(k\) is a calibration parameter to ensure that the similarity index is almost 0 when the detected value \(v\) exceeds \(v_{\max}\) and \(v_{\min}\). \(k=1\) in this paper.  
Statistical features are expressed as a range rather than a fixed value. The similarity calculation of statistical features is defined as:  
\[S_{(\cdot)}(x)=\left\{\begin{array}{ll}1,x\in R_{(\cdot)}\\ 0,x\
otin R_{(\cdot)}\end{array}\right. \tag{15}\]  
where, \((\cdot)\) stands for the statistical feature of detection. \(x\) is the statistical value of specific feature. \(R_{(\cdot)}\) denotes the range of possible values for a certain feature.
## 4 Hybrid Fiber Composition
### Steel--Basalt Hybrid Fiber  
#### 4.2.1 Physical Characteristics  
[PERSON] et al. [70] performed flowability tests of UHPC mixes with steel fibers of length/diameter 12 mm/0.3 mm and basalt fibers of 12/0.45 mm, with a total fiber content of 2%. They found that as the basalt fiber content increased from 0.5% to 1.5%, the flowability decreased from 30.7 to 43.6% compared to the fiberless mixture. This was due to the blocking properties of the basalt fibers, which tend to clump and curl, causing the matrix to become sticky. Basalt fibers have a rough structure that increases the fiber-matrix contact area, unlike steel fibers, which are typically smooth. This structure increases friction, leading to a reduction in flowability as the basalt fiber content increases. The mixture with 1.5% basalt fibers and 0.5% steel fibers did not achieve the minimum flow of 200 mm, and a decrease in strength was also recorded. The general reduction in flowability was caused by the increased difficulty in the movement of particles within the matrix and the increase in friction between the ingredients. Fibers act as a skeleton that has difficulty shifting position between the components. Furthermore, basalt fibers absorb a small amount of water during mixing, reducing the mobility of the mix. Steel fibers, with a smoother surface, exert less impact on the flow of fresh UHPC.  
Reduced flowability was also observed in a hybrid UHPC with 2% steel fibers of a length/diameter of 13/0.2 mm and 0.5% basalt fibers of 12 or 30/0.02 mm [76]. The flowability was 34 to 35% lower than the fiber-free mix and lower for the shorter basalt fibers. The flowability of the hybrid mixes was lower than that of the mix with 2.5% steel fibers of 13 mm length. These results confirmed the behavior of UHPC mixes with basalt fibers. A summary of the results of the workability test is presented in Table 7. The results indicate that basalt fibers have a negative effect on the flowability of hybrid mixes, regardless of the fiber lengths used.  
Figure 4: Dynamic test results of hybrid UHPC reinforced with steel fibers depending on impact velocity: (**a**) dynamic compressive strength, (**b**) fracture energy.
## 2 Theoretical Background
### Parallel Network Structure  
The time-domain features contain the time-dependent vibration characteristics, and the time-frequency domain features can effectively reveal the nonstationary characteristics of the bearing degradation signals. The collected bearing vibration signals are one-dimensional time series, while the time-frequency images are two-dimensional, and the forms of the features are quite different. The traditional processing method is to manually extract partial degradation features and combine them into a one-dimensional sequence for analysis [16; 23] or only use one domain feature for analysis [12; 13], which cannot guarantee the comprehensiveness of degradation information.  
Figure 1: Residual block.  
In this paper, the time-domain features and the time-frequency images are combined as input by using the P-ResNet. One-dimensional convolution operation of the time domain features and two-dimensional convolution operation of the time-frequency images are carried out to ensure the comprehensiveness of the bearing degradation information. The structure of the P-ResNet model is shown in Figure 2. The corresponding networks of time-frequency images include seven two-dimensional convolutional layers, a two-dimensional pooling layer, two residual layers, and a flattened layer. The corresponding networks of the time domain features include four one-dimensional convolutional layers, two one-dimensional pooling layers, and a flattened layer. Then the two flattened layers are combined into a one-dimensional vector as the input of the fully connected layer. Finally, the RUL is predicted through three fully connected layers.
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 3 Experimental Test of Output Torque for a PMSM  
### Experimental Setup  
The test platform to measure output torque of the PMSM is shown in Figure 6. It mainly consists of a drive circuit, a control circuit, a computer, and a MEMS gyroscope sensor. The MPU6050 is chosen as the core component of the MEMS gyroscope sensor and is used to obtain rotor dynamics data which is then processed by the computer.  
Figure 4: Boundary settings of stator and rotor.  
Figure 5: Simulated torque with the increase of current.  
Figure 6: Test platform for torque measurement.  
Figure 7 shows the installation location of the MEMS gyroscope sensor and the initial position of the rotor in the experiment. The MEMS gyroscope sensor is mounted on the top of the output shaft as shown in Figure 7a. As shown in Figure 7b, the demo board containing MPU6050 is placed inside the sensor housing. The sensor housing is mounted on the top of the output shaft and two screws are used to hold it in place. The reset key of the demo board is on the sensor housing. When the rotor is adjusted to the initial position, the reset key will be pressed to avoid drift errors and to ensure that the current position is the initial position for the sensor. The initial position of the rotor shown in Figure 7c is a relative position between the rotor and the stator, which is consistent with that in the coordinate system of the simulation model in Figure 2. A detachable limit device shown in Figure 7b is utilized to ensure that the rotor can be adjusted to the initial position before the experiment is conducted. The stator static coordinate OXYZ is coincided with the simulation coordinate. When the PMSM is driven, the relevant dynamic data are measured by the MEMS gyro sensor and are kept in the computer.  
In the experiment, the oscilloscope was connected to the AC/DC current probe of A622 100 Amp type and then used to monitor the current of each coil winding. The PWM current waveform of No. 2 stator coil with a duty cycle of 90% is shown in Figure 8, and the measured current data are shown in Table 1.  
Figure 7: Installation of the MEMS gyroscope sensor: **(a)** The installation diagram; **(b)** The MEMS gyroscope sensor; **(c)** The initial position.
# Candidate Waveforms for ARoF in Beyond 5G
## 1 Introduction
A possible way to mitigate impairments and to address a more complex channel lies in implementing advanced waveform formats. In terms of waveform, consider now LTE fourth-generation (4G) cellular networks, where orthogonal frequency-division multiplexing (OFDM) is the digital modulation technique adopted in the standards [10]. However, the spectral efficiency of OFDM is limited by the inclusion of a cyclic prefix (CP) and by its large side lobes, which require some null guard tones at the spectrum edges [11]. Furthermore, OFDM signals may suffer from large peak-to-average-power ratio (PAPR) values. Moreover, due to frequency deviations, the subcarriers will be no longer orthogonal, causing inter-carrier interference (ICI). Similar effects arise when the OFDM technique is affected by Doppler spread for the case of non-linear time-invariant (non-LTI) channels [12]. For those reasons, OFDM should be enhanced by using another alternative waveform format for next generation mobile  
Figure 1: Radio over fiber (RoF) configuration for mmWave cells over ultra-dense wavelength division multiplexing-passive optical networks (UDWDM-PONs). networks (5G and beyond). Thus, in this manuscript, we offer an analysis and comparison of different candidate waveform formats for future mobile networks. This paper is organized as follows. Section 2 introduces the requirements for waveforms to be used in high-bandwidth mmWave signals and ARoF transport. Section 3 shows and describes potential heirs of OFDM for beyond 5G in mmWave over UDWDM-PONs. Section 4 displays the state-of-the-art (SoA) on waveforms in ARoF experiments. Section 5 compares the presented candidate waveforms based on the requirements detailed in Section 2. Finally, Section 6 provides some concluding remarks.
## 3 Results
### Model Parameterization and Validation  
Based on the quantitative dependencies of spatiotemporal variability in \(RE\) to these biotic and environmental factors, we developed a semi-empirical \(RE\) model described in 2.3. The long-term flux tower-observed \(RE\) was used for parameter estimation and validation. Detailed parameter values for \(R\)_eref_ and \(f(T,P,W)\) in the two grasslands are presented in Table 2. These parameters are mostly empirical coefficients varying with the vegetation types and structures, site conditions, and histories. One of the parameters for _Reref_, for example, \(p_{3}\), has the opposite sign for the temperate grasslands (negative) relative to the alpine grasslands (positive). This phenomenon implies a negative correlation between the spatial variation of \(RE\) and mean annual temperature in Inner Mongolia. Among the parameters for \(f(T,P,W)\), the activation energy \(E_{0}\) and the half saturation constant \(k\) are two key parameters that have a special physiological implication. In the alpine grasslands, \(E_{0}\) has a higher value than the temperate grasslands. This result accurately reflects the increasing temperature sensitivity with the decreasing temperature. Regarding the half saturation constant, \(k\), the value is higher (0.207) in the temperate grasslands than the alpine grasslands (0.021). The drie ecosystem has a higher half saturation point, accurately characterizing the different responses of \(RE\) to moisture among the alpine and the arid and semi-arid regions. The model parameter estimation reveals that approximately 77.8% and 74.9% of the variation in the observed \(RE\) across the alpine and temperate grasslands, respectively, could be explained by this model, albeit with a small bias (Figure 5; Table 3, joint-sites). Specifically, the model could explain more than 55% of the spatial variability of \(RE\) (_Reref_) in the two regions. The simulation error in the Tibetan alpine grasslands (RMSE = 0.62 g C m\({}^{-2}\) day\({}^{-1}\)) is higher than that in the Inner Mongolian temperate grasslands (RMSE = 0.11 g C m\({}^{-2}\) day\({}^{-1}\)). The temporal variability (\(f(T,P,W)\)) is generally explained to a greater degree (more than 65%) compared to the spatial variability, while the simulation error in the temperate grasslands (0.91 g C m\({}^{-2}\) day\({}^{-1}\)) is higher than that in the alpine grasslands (0.35 g C m\({}^{-2}\) day\({}^{-1}\)). The cross-validation among subtypes in the alpine and temperate grasslands shows that approximately 75.6-91.7% and 57.5-80.3%, respectively, of the variation in the observed \(RE\) could be predicted by the model, with the values of RMSE ranging from 0.841 to 1.544 g C m\({}^{-2}\) day\({}^{-1}\) and 0.405-1.021 g C m\({}^{-2}\) day\({}^{-1}\) for the alpine and temperate grasslands, respectively (Table 3). Except for the typical steppe, where the parameters derived from the limited sites in the meadow steppe and
## 1 Introduction
Some image processing-based approaches have been proposed to size defects automatically. These approaches are based on contour detection or image segmentation. Classic contour detectors, such as the Canny operator and the Sobel operator, have been employed in IR thermography in the natural image processing literature [21; 22]. Recently, a new approach to detecting the tips of cracks in inductive thermography was developed based on sparse greedy-based principle component analysis [23]. These contour detectors can provide accurate contour information for distinct defects. However, defects are not always distinct in IR thermography due to the effects of non-uniform heating and the depths of defects. Low observable defects are usually over-sized when applying these classic contour detectors. Besides contour detection algorithms, image segmentation is another kind of image processing-based sizing approach to distinguish defects from sound regions. Image segmentation algorithms applied in IR thermography include thresholding [24; 25; 26; 27; 28], region growing [29], superpixel segmentation [30], and anomaly detection [31]. Thresholding is the most commonly used method because of its easily understandable and simple programming. However, sizing accuracies of these image segmentation-based approaches are usually sensitive to the quality of thermal images. Our work aims to develop a fast approach which can size defects with a satisfactory accuracy not only for distinct defects but also for blurry ones in square pulse thermography. The proposed approach is based on the oriented gradient of histograms, which is used to calculate gradient in the probability of boundary (Pb) contour detector used in the natural image literature [32; 33]. Testing results on experimental thermal images demonstrate that the sizing accuracy in square pulse thermography can be improved significantly through the presented approach. Especially blurry defects, which may be over-sized greatly by existing methods, are sized with high accuracies in the proposed approach.
## 3 Performance Analysis
### Performance of Suboptimal Receiver
It can be seen that the optimal value of \(\gamma\) changes dynamically with \(E_{b}/N_{0}\); thus, in order to obtain the best demodulation performance, the value of \(\gamma\) should be adjusted dynamically.
# Bioacoustic Classification of Antillean Manatee
## 3 Results and Discussion
### Results for Experiment #1--End-to-End Training with Different Representations and Architectures  
Table 3 shows the results for training and testing, both in the accuracy and loss values for each representation/network architecture combination. All models were suitable for the classification task, with no combination having a striking different result. In terms of accuracy all models were able to achieve over 94% results after 50 epochs for the testing set, with a few 100% for the linear spectrogram representation. A similar value of over 94% was achieved for the validation set. In terms of the loss function, resulting errors were less the 0.1 for testing and were below 1.00 for the validation set. Accuracy and loss curves for linear with dropout architecture for (a) binary, (b) linear normalized and (c) logarithmic normalized spectrogram representations are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spectrogram Type** & **Network Architecture** & **Accuracy** & **Loss** & **Val. Accuracy** & **Val. Loss** \\ \hline \multirow{4}{*}{Binary} & Linear & 100 & \(3.79\times 10^{-4}\) & 96.35 & 0.5023 \\ \cline{2-6}  & Pyramidal & 100 & \(7.93\times 10^{-4}\) & 98.54 & 0.6116 \\ \cline{2-6}  & Linear w/Dropout & 96.53 & 0.0906 & 94.16 & 0.2677 \\ \cline{2-6}  & Pyramidal w/Dropout & 97.99 & 0.0542 & 94.16 & 0.1555 \\ \hline \multirow{4}{*}{Linear} & Linear & 100 & \(1.38\times 10^{-5}\) & 98.54 & 0.4383 \\ \cline{2-6}  & Pyramidal & 100 & \(2.48\times 10^{-4}\) & 98.54 & 0.4216 \\ \cline{2-6}  & Linear w/Dropout & 99.09 & 0.0311 & 97.81 & 0.3838 \\ \cline{2-6}  & Pyramidal w/Dropout & 99.13 & 0.022 & 97.81 & 0.9781 \\ \hline \multirow{4}{*}{Logarithmic} & Linear & 100 & \(1.95\times 10^{-4}\) & 97.08 & 0.162 \\ \cline{2-6}  & Pyramidal & 99.27 & 0.02 & 93.43 & 0.9343 \\ \cline{2-6}  & Linear w/Dropout & 95.98 & 0.1316 & 98.54 & 0.041 \\ \cline{2-6}  & Pyramidal w/Dropout & 94.33 & 0.1488 & 98.54 & 0.0294 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for Experiment #1 end-to-end training with different spectrogram representations and networks architectures.  
Figure 5: Experiment #1, accuracy and loss curves for linear with dropout architecture for (**a**) binary, (**b**) linear normalized and (**c**) logarithmic normalized spectrogram representations.  
The time performances of the architectures and representation combinations are shown in Table 4. As expected, the training time was higher than the time spent in doing the inference. This behavior was seen on both the time for all epochs and the time per epoch. More importantly for our assessment, regardless of the spectrogram type, the linear architecture consumed longer computational time than the pyramidal architecture. Even when the dropout rate was fixed at 50%, it added over 100 s for the complete training, which translate to roughly 2 s per epoch.