esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 4 Proposed Algorithm for Dynamic UE Scenario
### The Proposed DDPG-Based Algorithm
#### 4.2.2 Structure of the Actor and Critic Networks  
In the proposed DDPG algorithm, both the actor and critic networks are fully connected deep neural networks (DNNs) with the same structure. Each network is composed of five layers, as shown in Figure 2(b), i.e., one input layer, one output layer, two hidden layers, and, in between, a normalization layer. The input and output dimensions of the actor are equal to the state cardinality and action set cardinality, respectively. Meanwhile, in the critic network, these dimensions change to the cardinality of the state plus action for the input and one for the output (i.e., representing the Q-value function). Note that the state and action are treated independently and then concatenated within the critic to be processed as one input. The hidden layer neurons are set to 400 for each layer, which is found to be suitable for the number of BS transmitting antennas, the number of active UEs, and the number of IRS elements considered in our system model [26]. Between the hidden layers, a normalization layer is used to speed up the algorithm's convergence and reduce the training time. As for the activation functions, the tanh and relu models are utilized, which can facilitate backpropagation and gradient descent. In addition, the networks are trained according to the Adam optimizer with decay rates of the gradient and squared gradient moving averages that are set as 0.9 and 0.999, respectively. To ensure the adequate exploration of the action set, the noise model \(\mathcal{N}\) is added to the action, which is chosen as complex Gaussian noise with a mean of zero and a variance meeting \(Variance*\sqrt{T_{s}}=(1\%\) to \(10\%)\) of the action range, where \(T_{s}\) is the sampling time. Other hyper-parameters used in the model are listed in Table 1, which were found to be the best tuning values for our optimization problem after some trials.
## 2 Materials and Methods
### Modeling in ABAQUS/CAE
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Magnitude** & **Parameter** & **Magnitude** \\ \hline Shaft length & 150 mm & Density & \(7.83\times 10^{-6}\) kg/mm\({}^{3}\) \\ Shaft diameter & 100 mm & Young’s modulus & 207,000 MPa \\ Disk diameter & 300 mm & Poisson’s ratio & 0.3 \\ Disk width & 100 mm & Structural damping & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the rotor system. Figure 3: Modeled rotor systems: (**a**) unbalanced and (**b**) bowed. It is crucial to mention that the values presented in Table 1 have been selected due to their close alignment with the dimensions of potential rotating systems that can be installed and studied in laboratory settings by future researchers. On the contrary, opting for smaller dimensions would restrict the achievable rotational speed, which hinders the analysis by diminishing the critical speed. When the rotation pace was 75 and 100 rad/s, the vibration signals of the bowed and unbalanced systems were in the same range according to Figure 4. While in the slowest rotational speed, e.g., 50 rad/s, the vibration of the bowed system was more intensive than the unbalanced one; on the assumption of the fastest velocity (150 rad/s), on the contrary, the amplitude of the unbalanced system is much higher than the bowed rotor while the magnitude of the bowed systems remained approximately constant in all operational conditions. These behaviors find their roots in the unbalancing force's nature, which is characterized by an increase in magnitude with the square of the rotational speed, as well as the shaft bow's introduction of a roughly constant force [15,48]. Similar observations were noted in industrial scenarios, where the augmentation of rotational speed was found to be ineffectual in yielding a proportional increase in the resultant force of a shaft deflection [49]. Figure 4: Time domain signals at four rotational speeds. Figure 5: Fourier transforms of signals at four rotational speeds. In the frequency spectrums, the first frequency harmonic was observed at approximately the same rotational speeds that the systems operated; moreover, there are not any other higher harmonics neither in the unbalanced system nor in the bowed one. It should be noted that a second frequency component appeared when the Fourier transform's sampling frequency was increased, but its amplitude and occurrence frequency were constant across all four operational speeds, indicating that it represented the system resonance frequency rather than the fault's effect.
## 3 Experimental Design and Implementation
### Experimental Procedure
#### 3.3.5 Traffic Parameter Calculation  
Based on identified vehicle paths, various traffic parameters are calculated:  
1. Link flow calculation: Defining the list of links to be analyzed, calling the selectVinfoMain() function with the parameter set to 'volume', counting vehicle numbers passing through each link in each time period, and calculating the mean and standard deviation of flow.
2. Speed and travel time calculation: Using dual calculation methods (based on instantaneous speed and time difference), extracting speed and time parameters simultaneously through the selectVinfoMain() function, fusing results from both methods to improve estimation accuracy.
3. Approach flow analysis: Identifying turning relationships at each intersection, calculating left-turn flow using the tpVolume3() function, and generating intersection-level flow statistics tables.
4. OD matrix construction: Counting OD pairs based on vehicle path data, generating both basic and detailed OD matrices, and calculating time period averages of OD flows.
## 4 Testing Exercise  
### Test Scenario and Exercise Execution  
An exercise to test the ability to correctly assess a situation around a ship using radar equipment was carried out in open water with restricted visibility.  
The following hydrometeorological conditions were simulated:  
* Restricted visibility to 0.5 NM;
* Air temperature of 20 \({}^{\circ}\)C;
* Atmospheric pressure 1080 hPa;
* Wind direction 270\({}^{\circ}\), speed 15 kn;
* Water temperature 18.7 \({}^{\circ}\)C;
* Wave height 1 m, direction 090\({}^{\circ}\).  
Figure 5: An example of an evaluation sheet generated in the SEA program.  
A bulk carrier model (BULKC06L--see Table 1, Item 1) was used as a mathematical model of the own ship. The movement of five other vessels (targets) was simulated in the area (Figure 6). There was no interaction between the own ships, i.e., if more own ships participated in the simulation, then they did not maneuver relative to each other, starting the simulation from the same initial position.  
It was assumed that tested persons must be familiar with the basics of the navigational and ship control equipment available on the bridge, as well as the principles of analyzing the surrounding situation and proceeding in collision situations according to the International Regulations for Preventing Collision at Sea (COLREG) rules.  
Before the exercise, the person taking part was instructed about the general conditions of the simulation and the required basic safety and evaluation criteria (in this case, a minimum passing distance of 1 NM had to be respected, and the anticollision maneuvers had to be in accordance with the COLREG recommendations and good maritime practice).  
During the exercise, the tested person was required to:  
* Turn on and adjust the ARPA device properly according to described conditions;
* Properly acquire targets;
* Assess the situation;
* Plan and execute anticollision maneuvers (if necessary);
* Plan and execute a returning maneuver (if necessary).  
The simulation ran for about 25 min.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 3 Materials and Methods
### Fault Signal Processing Method  
The signal decomposition algorithm is employed to reduce part of the noise in the fault signal, retain the impact part of the original signal, and achieve the goal of improving the accuracy of model diagnosis. The fault signal processing flow based on improved VMD is shown in Figure 3.  
This paper proposes an improved VMD algorithm combining the VMD algorithm with the reweighted kurtosis value. Firstly, in the basic parameter setting of the VMD algorithm, the penalty factor \(\alpha\) is set to 2000 and other parameters are set to default values. The role of \(\alpha\) is reflected in the control mode bandwidth: the larger the \(\alpha\), the narrower the bandwidth of each mode function, and the more concentrated the time-frequency characteristics of the mode, but it may lead to excessive smoothing and sacrifice part of the reconstruction accuracy. VMD common code implementation (such as MATLAB/python Toolkit) usually sets the value of \(\alpha\) to 2000. This numerical value has been verified by many experiments, which can achieve a good balance between modal bandwidth and reconstruction accuracy and is suitable for mechanical vibration signals. The default value of noise tolerance (tau) is applicable to scenes where the noise distribution is not clear. The default value of convergence tolerance (tol) can ensure that the algorithm converges in a reasonable time without adjustment. After that, the fault signal is decomposed and reconstructed by decomposing the number of decomposition K from 2 to 9. The reconstructed signal  
Figure 2: 1 DCNN-GAP pooling process.  
is divided into 12 segments, and the value and weight of each segment are calculated to obtain its reweighted kurtosis value. The reweighted kurtosis value is employed to evaluate each reconstructed signal, and the reconstructed signal with the largest reweighted kurtosis value is selected as the fault signal. Compared with the kurtosis value, the reweighted kurtosis value as the evaluation index can eliminate the impact of some strong shocks in the fault signal and further improve the diagnostic accuracy of the model. After the improved VMD algorithm processing, it can remove part of the interference in the fault signal while retaining the impact part of the original signal.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Payload Summary
#### 2.2.1 Ionospheric Plasma Brake (IPB) Payload  
The IPB payload is a system developed by the Finnish Meteorological Institute based on Coulomb drag propulsion (CDP) technology. It is a novel deorbiting technology for LEO satellites [32]. As of April 2023, the IPB remains an unconfirmed technology for LEO and interplanetary applications [34]. Based on the in-orbit experience of ESTCube-1 and Aalto-1 missions [8; 9], several improvements to the IPB design were introduced. Most notably, the tether deployment mechanism of the ESTCube-2 IPB payload was completely re-designed (Figure 4).  
Figure 4: A render of the Ionospheric Plasma Brake module.  
Figure 3: Deorbiting with ionospheric plasma brake in the low-Earth orbit ionosphere [33]. As the satellite moves through the relatively stationary ionosphere, the charged tether creates a braking force to deorbit the satellite. Given the satellite’s orbital parameters, the plasma brake deorbiting effect would take weeks to months to be noticeable. Artwork credit: [PERSON], [PERSON], and [PERSON].  
While ESTCube-2 is designed for IPB in-orbit demonstration, it will measure both interactions: (i) a negatively charged tether interacting with the relatively stationary ionosphere for deorbiting purposes and (ii) a positively biased tether interacting with charged solar wind particles for electric sail propulsive effect. The payload hardware of both applications is identical, except that positive polarity operation (i.e., E-sail) requires an electron emitter, which is included in the ESTCube-2 IPB payload.  
The technicalities of the IPB integration in the ESTCube-2 were presented by [PERSON] et al. [18], and a detailed description, performance evaluation, and risk assessment has been published by [PERSON] et al. [7]. Our team collaborates with the Foresail-1p team in cross-validating in-orbit ionospheric plasma brake results [10]. Foresail-1p will launch a similar ionospheric plasma brake demonstration payload in 2023. The main difference between ESTCube-2 and Foresail-1p is the positive E-sail mode with electron emitters on board the ESTCube-2.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 3 Results
### Nasal Obstruction Assessment
#### 3.1.4 Nasal Obstruction Symptom Evaluation Questionnaire (NOSE)  
The NOSE questionnaire is a patient-reported outcome measure designed to evaluate the degree of nasal obstruction symptoms and how they affect the patient's quality of life [4]. It was utilized in three studies included in this review [22; 23; 25]. In the first one [22], mean NOSE scores showed significant improvements at one and three months post-surgery compared to baseline (\(p<0.001\)). Significant differences were also observed between one- and three-month scores (\(p<0.0014\)). The second study [23] found that postoperative NOSE ratings were significantly lower than preoperative values in all patient groups operated on for septal deviation (\(p=0.001\)). The third study [25] found significant improvements in NOSE ratings after surgery for all participants (\(p<0.001\)), including those in Group A (severe septal deviation) and Group B (narrowed nasal airway).
## 2 Experiment Details
### Data Acquisition with Smartphone  
The camera quality of commercially available smartphones has improved a lot over the last decade and offers excellent potential utility as a biomedical apparatus for data acquisition [32]. Nearly all smartphones can be used for the capture of PPG signals. However, the quality of the acquired signal can vary widely depending on the resolution and number of frames recorded by the camera per second. It is possible to extract PPG signals from a subject's fingertips or earlobe. In our study, the fingertip was chosen for data acquisition due to the convenience and comfort in the process for the subject. [PERSON] et al. have previously shown earlobe pulse oximetry to be less reliable than fingertip pulse oximetry [33]. During the recording, the subject was asked to remain relatively still and comfortably place their index finger on the smartphone camera. Video data were  
Figure 1: System diagram of noninvasive glucose detection using smartphone camera (**top**) compared with the conventional glucometer-based technique (**bottom**).  
subsequently collected for 60 s, with the participant's right index finger placed on the horizontal camera array and the LED (Light Emitting Diode) flash. The duration of 60 s allowed for a greater likelihood of obtaining acceptable PPG data, and allowed enough time in the beginning and the end of the segment for motion artifacts that are common with biomedical signal measurements. A maximum of five trials were collected from each participant. Each video was captured using the default camera application of the smartphone at 30 frames per second (fps) with a resolution of 1280 \(\times\) 720 pixels.  
To enhance the generalizability of the regression models trained, and to test the effectiveness of our denoising protocol, we initially shortlisted four smartphones to collect data in this study. Three of these phones were on Android platforms, whereas the fourth was on iOS. The four phones had different camera software running and the built-in cameras and optical setups of each offer a wide range of specification for data collection. After a preliminary study on the performance of each phone, the data collection was narrowed down to two phones--one based on an iOS platform and a second on Android.
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 5 Discussion  
Classification methods for hyperspectral images have been proposed by many scholars in their respective research fields, but most of them focus on the feature extraction of single features, such as farmland, buildings, and forests, over large areas. Applying such models to the survey and investigation of small features such as those in desert grasslands will reduce the robustness of the models. This study proposes a new rathole identification model based on a low-altitude UAV hyperspectral remote sensing platform to collect desert steppe rathole spectral data, representing a successful exploration of the application of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Classification Result**} \\ \cline{2-4}  & **Rathole** & **Non-Rathole** & **Sum** \\ \hline Rathole & 143 & 10 & 153 \\ Non-rathole & 12 & 335 & 347 \\ Sum & 155 & 345 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of rathole and non-rathole areas.  
Figure 7: Image of non-rathole area based on NDVI.  
low-altitude remote sensing platforms to identify and classify desert steppes and establish early warning mechanisms for grassland rodent damage. Moreover, this study introduces basic research for the subsequent application of this method in large-scale spatial grassland degradation research and grassland rodent damage prevention and control. In the next step, we will apply deep learning technology to spectral data of desert steppe rodent holes and conduct in-depth research on single image elements, soil types, and other factors scattered in the spectral data of desert steppe rodent holes to achieve a more accurate identification and classification. We still need to carry out a lot of experiments and model optimization to conduct in-depth research on grassland degradation and grade evaluation.
## 4 Results and Discussions
### Evaluation of Land-Atmosphere Interactions  
Improving the representations of land surface conditions and cloud will improve the simulations of surface (energy and water) budgets and turbulent heat fluxes, which will eventually improve the representation of land-atmosphere interactions in the model. Radiosonde observations of temperature and humidity in the planetary boundary layer, which reflect average turbulent fluxes over the observation domain, are commonly used to determine the degree to which the land affects the atmosphere, and vice  
Figure 5: Hourly variation of spatial correlations calculated from model simulated cloud top temperatures and IR cloud top temperatures for 6 June 2006.  
versa. Radiosonde soundings obtained at Niamey station were used to investigate the model performance in simulating the land-atmosphere feedback mechanism over the model domain.  
Figure 6a,b compare the observed potential temperature and specific humidity soundings with the simulated soundings from ARPS, LDAS-A, and CALDAS at 1030 UTC (1130 Local Time (LT)) on 6 June 2006. During this time, the land was heated by the sun and turbulent mixing was created close to the land surface (from surface to \(\sim\)875 hPa), as shown by the observed temperature soundings. ARPS underestimated the potential temperature sounding (Mean Bias Error (MBE) = \(-\)0.52 K, Root Mean Square Error (RMSE) = 2.00 K) and overestimated the humidity sounding (MBE = 1.85 g/kg, RMSE = 2.92 g/kg) from the surface to \(\sim\)700 hPa. In the case of LDAS-A, the assimilation of surface soil moisture resulted in marginal improvements in both the temperature (MBE = \(-\)0.48 K, RMSE = 1.94 K) and the humidity (MBE = 1.82 g/kg, RMSE = 2.88 g/kg) profiles, whereas improvements were seen in CALDAS for the humidity profile (MBE = 1.32 g/kg, RMSE = 2.01 g/kg).  
Figure 7 compares the observed soundings with the simulated soundings at 2230 UTC (2330 LT) on the same day. The observed atmospheric conditions at this (night) time, as shown by the temperature profile, were characterized by statically stable air with neutrally stratified layers. In the case of the ARPS and LDAS-A models, profiles with higher humidity and lower temperature were simulated. Further investigation of the model results indicated that these two models produced higher soil moisture during this period (not shown). However, the observed trend was captured well by CALDAS and it compared well with the observed soundings of potential temperature (MBE = 0.26 K, RMSE = 1.02 K) and specific humidity (MBE = 1.06 g/kg, RMSE = 1.31 g/kg). The improvement extended from the surface to the middle atmospheric layers (\(\sim\)650 hPa) in the CALDAS simulation. These results show that the synchronized land and atmosphere re-initialization resulted in better prediction of the atmospheric sounding (_i.e._, land-atmosphere interactions) during the forecast period of the model.  
Figure 6: Comparison of observed and model soundings at 1030 UTC on 6 June 2006; (**a**) potential temperature (K) and (**b**) specific humidity (g/kg).
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 6 Discussion  
### AIS Trajectory Reconstruction  
As indicated by the results, the proposed method provides a significant improvement over the straight-line interpolation technique, especially for larger temporal gaps. More precisely, our experiments show that if the AIS gap lasts between 3 and 6 h, the proposed method provides an improvement up to 16.91% over the straight-line interpolation results, while this number goes up to 26.37% for even larger gaps (6-24 h). The inclusion of the historical information during the trajectory reconstruction is quite beneficial; a factor of 0.3 upon the respective weights results in a better accuracy. Lastly, since the incorporated graph is based solely on grid cells that include sea areas, transitions over land is avoided in most cases. This fact deems the proposed approach as more suitable for all AIS gaps over 1 h than the straight-line interpolation.  
Using the proposed mechanism for improving a dataset with AIS gaps, we experimented on SAT-AIS messages from October 2021 and extracted a new set of trajectories (Figure 11). Density maps for both the original as well as the improved datasets (Figure 17) were generated using a 10 km grid cell resolution. As seen in the results, our approach creates meaningful trajectories in place of the AIS gaps. Major pathways between ports are either uncovered or enhanced in the improved dataset, while a clearer image about traffic in less traversed waters is also provided.