esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 3 Application and Experiments
### Experimental Setup  
In order to determine whether the proposed two-step ensemble cost-sensitive diagnosis model (MC-LGB) can effectively play a role in the imbalanced FPG fault diagnosis compared with the diagnosis models employed on the entire UAV (the baseline GBDT and FCNN; the superb XGBoost; the extremely fast LightGBM; and the balanced modified CNN) [27], our experiment analyzed the performance of the proposed MC-LGB model in its overall diagnosis ability, total misdiagnosis cost optimization, and computing resource occupation. The performance of these classifiers was compared through the following metrics: accuracy, precision, recall, F1-score, MCC, AUC, and training time.  
All the experiments were performed using an Intel Core i7-10700F 2.90 GHz machine with 16 GB RAM and NVIDIA GeForce GTX 3070. The codes of methods were implemented in the R-studio 1.3 and Python 3.7, with Keras 2.3.1; Scikit-learn 0.22.2.post1; Tensorflow 2.1.0; Lightgbm 2.3.1; and Xgboost 1.0.2. The hyperparameters related to the proposed model were optimized by Grid Search and manual adjustment, with the main hyperparameters of the control group being shown in Table 3. All models were evaluated in a 10-fold cross-validation.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 3 Simulation  
The simulation environment used in this paper is exactly the same as in our previous work [5]. The parameters of the model and the controller are shown in Table 1. Figure 3 shows the definition of displacement error and heading error. Their detailed explanation can be found in [5].  
The simulation was divided into three groups. The first group is the comparison between the multilayer MPC controller, the nonlinear MPC controller and the switching MPC controller. The second group is the comparison between the two topologies of the multilayer MPC controller, and the third group is a performance test of the controller with positioning errors. In all simulations, most of the parameters were fixed. Each group simulation had the same computer model of articulated vehicles and the same reference path. The reference path consisted of straight lines and arcs, where the radius of the arcs was 10 m. The radius of these arcs is 5 m smaller than the radius of the arcs in [5].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(L_{f}\) & 2.468 m & \(\dot{v}_{lim}\) & 2m/s\({}^{2}\) & **Q** & \(10^{-2}\)**I\({}^{1}\)** \\ \(L_{\tau}\) & 3.439 m & \(T\) & 0.05s & **R** & \(10^{-4}\)**I** \\ \(\gamma\) & (\(-0.70\) rad, 0.70 & \(N_{p}\) & 30 & \(\rho\) & \(10^{-4}\) \\ \(\omega_{\gamma}\) & (\(-0.14\) rad/s, 0.14 rad/s) & \(N_{c}\) & 1 & \(\mu_{1}\) & 2 \\ \(v_{ref}\) & (1 m/s, 5 m/s) & \(N_{vp}\) & 100 & \(\mu_{2}\) & 1 \\ \hline \hline \end{tabular}  
\({}^{1}\) I is the unit matrix.  
\end{table}
Table 1: Parameter values.
## 1 Introduction
The FAO definition also includes a reference to a long-term reduction in tree cover, which, strictly speaking, excludes certain types of forest cover loss from the definition of deforestation, e.g., if a primary forest is logged and then replanted by other trees. A more precise terminology for EO-derived information services is, therefore, to use forest cover loss or tree cover loss to describe the observed loss of trees, without making any assumptions about the drivers of the loss (human or natural) and about the longevity of this loss. Forest cover loss monitoring from EO data has been demonstrated from a variety of sensors and satellite platforms. The two primary sensor types are Synthetic Aperture Radar (SAR) and multispectral imaging sensors. The main SAR applications for detecting forest cover loss include the JIFAST algorithm (JICA-JAXA Forest Early Warning System in the Tropics') [10] from the Advanced Land Observation Satellite (ALOS-2) Phased Array L-band Synthetic Aperture Radar (PALSAR-2) and the RAdar for Detecting Deforestation (RADD) alerts [11; 12] based on Sentinel-1, a C-band radar satellite constellation by the European Space Agency under the Copernicus Programme. Multispectral forest cover loss applications include the Global Land Analysis and Discovery (GLAD) alert system by Global Forest Watch [7] based on [PERSON] et al. 's [13] global tree cover change monitoring method at 30 m resolution, and the Sentinel-2-based Python for Earth Observation (PyEO) forest alert system [14], which was developed by the UK National Centre for Earth Observation at the University of Leicester together with the Kenya Forest Service and the REDD+ Stakeholder Round Table in Kenya. PyEO has been adopted by the Government of Kenya under its National Forest Monitoring Programme. The PyEO forest alert system has also been applied in other countries including Mexico and Colombia [15] and Brazil and Guatem (this paper). Recently, some authors have suggested the adoption of combined SAR/multispectral forest cover loss monitoring systems [16; 17]. While many of the published forest cover loss detection systems described above aim at large-scale or global mapping, forest monitoring solutions from space are still lacking for many parts of the world (e.g., [18]). This is especially the case for farm-scale monitoring of forest cover loss. This study in the ForestMind project aimed to answer the research question of whether an adaptation of PyEO's forest alerts system is usable for supply chain monitoring at the farm scale. The objectives of the study were to (i) train a random forest machine learning model of forest cover, (ii) enhance the quality of the forest loss detections from post-classification change detection by combining the algorithm with differential NDVI data, (iii) produce forest loss reports in an operational scenario, and (iv) assess the accuracy of the company reports provided by the system.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Chaotic Image Encryption  
A common use for neural networks in chaotic image encryption is to generate the key streams. This can be seen in a proposal by [PERSON] et al. [175], where a Hermite chaotic network was trained from a logistic map, and in a proposal by [PERSON] et al. [251].  
In a different proposal by [PERSON] and [PERSON] [252], the hyperchaotic properties of cellular neural networks were leveraged to achieve chaotic image encryption. Simulation results in MATLAB demonstrated strong cryptographic properties.
## 2 Materials and Methods
### Algorithmic Approach (Figure 1)
#### 2.2.2 Feature Extraction  
The application of discrete wavelet transforms was used to identify statistical features from each level of the wavelet. A comparison was drawn between the level 8 wavelet method and the level 5 wavelet. Wavelet selection was based on results from [PERSON] et al., which identified the most responsive wavelet given each signal type.  
For both GSR and ECG, a Daubechies wavelet was determined to be the most accurate; for RESP, a symlet wavelet was indicated [10; 23].
# Improving Congestion Control of TCP for Constrained IoT Networks
## 5 Proposed Scheme
### Exponential Backoff with Variable Limits  
We made a modification to uIP TCP so that the retransmission timer backs off based on the estimated RTO instead of the fixed RTO. Several studies argued that the mechanisms which use different backoff policies according to the RTO value are useful to avoid setting the retransmission timer to an excessively large value and to improve fairness between close nodes and distant nodes from the border router [1; 19]. For this reason, CoCoA employs a variable backoff factor (VBF) mechanism which uses three backoff factors (3, 2, and 1.3) with two RTO thresholds of 1 and 3 s.  
However, when an RDC mechanism is enabled, RTO may be set to a very large value due to RTT fluctuation, as seen in the preliminary test result. This makes the CoCoA-like variable backoff factors unsuitable for uIP TCP in networks with an RDC mechanism enabled. Although we examined the effect of VBF mechanism where the thresholds were adapted to our network conditions, the result was not so good as expected. Thus, we consider varying the maximum number of backoffs according to RTO values. In choosing the maximum number of backoffs and the thresholds, we borrowed some numbers from the original uIP TCP. Recall that, in the original uIP TCP, the fixed RTO of 3 s backs off up to four times and, therefore, RTO increases up to 48 s (3 \(\to\) 6 \(\to\) 12 \(\to\) 24 \(\to\) 48). We simply chose the thresholds of 3, 6, and 12 s and then adjusted the maximum number of backoffs (4/3/2/1) so that RTO did not back off over 48 s. For example, if RTO is less than 3 s, RTO backs off up to four times. For RTO greater than 12 s, backoff is allowed only once. Table 2 shows the details.
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
## 3 Distributed GNSS Receiver Concept with Two 2 \(\times\) 1 Antenna Sub-Arrays
### System Calibration
#### 3.2.2 CAL Signal Distribution
A photograph of one sub-array is shown in Figure 6.The sub-array module consists of two stacked printed circuit boards, one containing the antenna network and the other the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**CAL Signal Distribution** & \(\ldots\)**at IF** & \(\ldots\)**at RF** \\ \hline \multirow{6}{*}{Hard- and software effort} & \multirow{6}{*}{(o) usage of internal ASIC PLLs} & \multirow{6}{*}{(\(-\)) 2 additional PLL modules} \\  & & (o) usage of internal ASIC PLLs & \\  & (+) programming via ASIC & (\(-\)) additional control \\ \cline{1-1} \  
RF frontend including the ASIC. The size measures about 10 cm \(\times\) 5 cm \(\times\) 2 cm. With these dimensions, an integration into typical automotive installation spaces appears feasible.
## 4 Experiment Results and Discussion
### Performance and Feasibility of the ESG in Suppressing Road Noise
The actuator worked properly with the proposed ARNC algorithm in the real-time control experiment with the reference sensors and the error microphone and reduced the three dominant road booming noises that existed in 100-250 Hz by about 6.0 dB and 8.3 dB on average with 4 and 12 reference signals, respectively, at the car speed of 60 kph. The ESG-cowl pair and its supportive hardware and software including the ARNC algorithm can be implemented in a stand-alone system, which is completely separated from a car audio system. Thus, the proposed ARNC system could be an option for suppressing road noise inside a car cabin, although the controllable frequency range is limited (100-250 Hz) and the suppressible road noise level, which is correlated with the car speed, is confined below a car speed of 60 kph. As an extended study, the proposed ARNC system was also tested at a car speed of 100 kph to assess the stability and performance when the road noise level was increased dramatically. It was observed that the ARNC system became unstable. As the ESG was commanded by the control filter to produce much stronger anti-sound beyond the excitation force limit (about 5 N) of the ESG to suppress the increased road noise, the produced anti-sound from the ESG-cowl pair became distorted gradually and diverged. This made the ARNC system unstable against the increased road noise at 100 kph. This problem can be solved by applying another ESG-like actuator with an increased excitation force limit, for example, 10 N or 20 N. A new cowl panel as a better sound radiator with the actuator against road noise could be helpful.
# Compact 50 W All-Solid-State Picosecond Laser System at 1 kHz
## 3 Discussion and Conclusions  
In summary, this paper proposes a compact, high-power and high repetition picosecond laser system. Seed pulses were obtained via a Nd:YVO\({}_{4}\) oscillator with etalon in cavity. The seed pulses were input into a homemade regenerative amplifier, which can overcome the limitation of the beam waist radius due to the thermal lens effect under high power pumping. Maximum output power of the regenerative amplifier was 6.5 W at 1 kHz, and the beam quality of M\({}^{2}\) factor was about 1.3. The homemade regenerative amplifier worked in the state that output power was 5 W, as a pre-amplifier for a subsequent amplifier. The pulses were amplified to 10.8 W via an end-pumped single-pass amplifier. In order to compensate for the thermal depolarization of the side-pumped amplifier, we made a theoretical analysis of the depolarization compensation and applied a more flexible depolarization compensation structure. After the picosecond laser pulses traversed the side-pumped amplifier, the pulses' power was amplified to more than 50 W at 1 kHz. This is a simple and compact structure in which the output power can achieve more than 50 W at 1 kHz. The compact high-power picosecond laser will be a perfect source for high power OPCPA systems in the future.  
[PERSON] designed and supervised the research; [PERSON] and Z.C. performed the research; [PERSON] and P.Z. analyzed the data; and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Sailing Program (18 YF1425900); National Natural Science Foundation of China (11774364); Bureau of International Cooperation, Chinese Academy of Sciences (181231 KYSB20170022); Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. (XDA25020302).  
The authors declare no conflict of interest.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
###### Abstract  
Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such as precipitation, soil moisture, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments and at various incidence angles, spatial resolutions, and radiometric characteristics, a mere direct integration of them from different microwave sensors would not necessarily provide consistency. However, when appropriately harmonized, they can provide a complete dataset to estimate the diurnal cycle. This study first constructs the diurnal cycle of land TBs using the non-sun-synchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations by utilizing a cubic spline fit. The acquisition times of GMI vary from day to day and, therefore, the shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of measurements. This diurnal pattern is used as a point of reference when intercalibrated TBs from other passive microwave sensors with daily fixed acquisition times (e.g., Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2) are used to modify and tune the monthly diurnal cycle to daily diurnal cycle at a global scale. Since the GMI does not cover polar regions, the proposed method estimates a consistent diurnal cycle of land TBs at global scale. Results show that the shape and peak of the constructed TB diurnal cycle is approximately similar to the diurnal cycle of land surface temperature. The diurnal brightness temperature range for different land cover types has also been explored using the derived diurnal cycle of TBs. In general, a large diurnal TB range of more than 15 K has been observed for the grassland, shrubland, and tundra land cover types, whereas it is less than 5K over forests. Furthermore, seasonal variations in the diurnal TB range for different land cover types show a more consistent result over the Southern Hemisphere than over the Northern Hemisphere. The calibrated TB diurnal cycle may then be used to consistently estimate the diurnal cycle of land surface emissivity. Moreover, since changes in land surface emissivity are related to moisture change and freeze-thaw (FT) transitions in high-latitude regions, the results of this study enhance temporal detection of FT state, particularly during the transition times when multiple FT changes may occur within a day.  
brightness temperature; passive microwave; diurnal cycle; diurnal temperature range; global precipitation measurement mission 20213817. [[https://doi.org/10.3390/rs13040817](https://doi.org/10.3390/rs13040817)]([https://doi.org/10.3390/rs13040817](https://doi.org/10.3390/rs13040817))
## 1 Introduction
Stratifying patient groups by patterns of dysfunction or in the presence of various risk factors (e.g., anti-NR2A/B antibodies) may help to identify subgroups with specific underlying pathogenesis [24]. Conventional magnetic resonance imaging (MRI) remains the gold standard imaging technique for the diagnosis of NPSLE [26]. Cross-sectional and longitudinal studies have revealed that T2-weighted hyperintensities and cerebral atrophy are the most common abnormalities associated with cognitive dysfunction [12, 27, 28, 29]. These T2-weighted hyperintensities are small punctuate lesions localized mainly to the periventricular and subcortical white matter, suggesting that white matter tract damage might be a potential mechanism for cognitive dysfunction [24, 27]. SLE patients with no focal neurological symptoms who underwent \({}^{18}\)F-fluorodeoxyglucose (\({}^{18}\)FDG) positron emission tomography (PET) had increased \({}^{18}\)FDG uptake in the white matter, indicative of inflammatory activity [30]. The exact cause of cerebral atrophy is not clear but SLE patients with severe cognitive impairment had greater white and grey matter reduction compared to patients with moderate or no cognitive impairment [28]. One potential explanation of grey matter disease is a form of disconnection-induced diaschisis, in which diffuse loss of white matter structural integrity causes cortical functional decline in remote regions [30]. Hence, cognitive dysfunction in SLE may result from white matter inflammation as an inciting pathology and T2-weighted hyperintensities and cerebral atrophy develop over time [30]. In this review we will discuss the recent advances in cognitive dysfunction research from murine models and human SLE where a role for autoantibodies, molecular and cellular mechanisms has emerged. The choice of mechanisms considered in this review has been driven by earlier, albeit controversial, reports from the medical literature [16]. However, affirmatively, a recent study screening a multimarker panel for cognitive dysfunction in childhood-onset SLE identified anti-NR2A/B antibodies and elements of blood-brain barrier (BBB) disruption and neutrophil activation as potential biomarkers [31]. Hence, key to this discussion is the idea that when considered individually, the impact of these mechanisms might be muted but they likely function together, as an ensemble, to contribute to disease pathogenesis of lupus-related cognitive dysfunction.
## 2 Spectral UAV Sensors
### Spectral 2D Imagers
#### 2.3.2 Sequential 2D Imagers  
Sequential band systems record bands or sets of bands sequentially in time, with a time lag between two consecutive spectral bands. These systems have often been called image-frame sensors [43; 44; 59; 60]. An example of such a system is the Rikola hyperspectral imager by Senop Oy [61] that is based on the tunable Fabry-Perot Interferometer (FPI). The camera weighs 720 g. The desired spectral bands are obtained by scanning the spectral range with different air gap values within the FPI [44; 62]. The current commercial camera has approximately 1010 \(\times\) 1010 pixels, providing a vertical and horizontal FOV of 36.5\({}^{\circ}\)[61; 63]. In total, 380 spectral bands can be selected with a 1-nm spectral step in the spectral range of approximately 500-900 nm, but in typical UAV operation, 50-100 bands are collected. The FWHM increases with the wavelength if the order of interference and the reflectance of the mirrors remain the same [64]. However, in practical implementation of the Rikola camera, the resulting FWHMs are similar in the visible and near-infrared (NIR) ranges: approximately 5-12 nm. The Rikola HSI records up to 32 individual bands within a second; so, for example, a hypercube with 60 freely selectable individual bands can be captured within a 2-s interval. Recently, a sort-wave infrared (SWIR) range prototype camera was developed, with a spectral range of 0.9-1.7 um and an image size of 320 \(\times\) 256 pixels [65; 66].  
Benefits of the sequential 2D imagers are the comparably high spatial resolution and the flexibility to choose spectral bands. At the same time, the more bands that are chosen, the longer it takes to record all of them. In mobile applications, the bands in individual cubes have spatial offsets that need to be corrected in post-processing (c.f. Section 3.3.2) [44; 67; 68]. The frame rate, exposure time, number of bands, and flying height limit the flight speed in tunable filter-based systems. The FPI cameras have been used in various environmental remote sensing studies, including precision agriculture [44; 69; 70; 71], peat production area moisture monitoring [65], tree species classification, forest stand parameter estimation, biodiversity assessment [72; 73; 74], mineral exploration [68], and detection of insect damage in forests [60; 75].