esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As one of the most sensitive indicators of global climate change, seasonal ice-covered lakes are attracting gaining attention worldwide. As a large seasonal ice-covered lake located in Northern Mongolia, Lake Khovsgol not only provides important freshwater resources for the local population but also serves as a means of water transportation in summer and an important land-based activity for residents in winter. In this study, we used the sub-pixel offset technique with multi-temporal Sentinel-2 optical images to estimate the time-series displacement of lake ice in Lake Khovsgol from 7 December 2020 to 17 June 2021. With the processing of 112 Sentinel-2 images, we obtained 27 pairs of displacement results at intervals of 5, 10, and 15 days. These lake ice movement results covered three stages from ice-on ice-off. The first stage was the lake ice growth period, which lasted 26 days from 7 December 2020 to 3 January 2021. Ice formation started from the south and extended northward, with a displacement of up to 10 m in 5 days. The second stage was the active phase of the ice cover, which took place from 3 January 2021 to 18 April 2021. Maximum displacement values reached 12 m in the east and 11 m in the north among all observations. The value of the lake ice movement in the north-south direction (NS) was found to be larger than in the east-west direction (EW). The third stage was the melting period, which closed on 17 June 2021. In comparison to the freezing date of November in past years, our results demonstrate the ice-on date of Lake Khovsgol has been delayed to December, suggesting a possible reason that the seasonal ice-covered lake located at the middle latitude has been affected by global warming. In addition, the lake ice movement of our results can reveal the regional climate characteristic. This study is one of the few cases to reveal the distribution characteristics and changing trends of lake ice on the Mongolia Plateau, providing a rare reference for lake ice research in this region.  
Lake Khovsgol; Sentinel-2 optical images; sub-pixel offset tracking; time series ice movement; ice ridge 2021
# Analysis of Electroencephalograms Based on the Phase Plane Method
## 5 Conclusions  
The main task of signal reception is to recover as much useful information about the signal as possible, which is distorted during propagation and received along with interference.  
The amplitude spectrum is widely used in signal processing, and the PP is undeservedly forgotten in this processing.  
The most-important quantitative indicator that characterizes the quality of the useful signal is the SNR.  
For the first time, using an additive mixture of the signal and noise, it can be seen that, at a sufficiently low noise level (\(SNR\geq 23.9\) in our case), the PP has the shape of an ellipse, and the ellipse line width is determined by the value of \(8\sigma\).  
The free space inside the ellipse disappears with an increase in noise power, and the PP has the form of a noise PP.  
Figure 14: Amplitude spectrum of detail coefficients \(d2\) and \(d3\).  
Figure 12: Detail coefficients of the PP of the EEG WT.  
Figure 13: PP of the detail coefficients \(d2\) and \(d3\) (more details).  
The condition when the phase portrait of the additive mixture of the harmonic signal and normal noise has the form of an ellipse has been obtained.  
EEGs of ten awake adults were considered. By constructing the EEG PP of an awake adult, an ellipse was obtained. This form of the PP allows assuming that the mathematical model of the EEG is an additive mixture of harmonic components and Gaussian noise.  
Currently, wavelet analysis is widely used in applied problems in the analysis of nonstationary signals. The application of the discrete WT and PP to the EEG makes it possible to determine the harmonic components. The main harmonic components are at frequencies \(f=5;10;20;40\,\mathrm{Hz}\) for the EEG under consideration. The EEG noise power is shown to be not evenly distributed over the frequency range, which opens up the prospects for the use of this method in the diagnosis of diseases and studies of the functional activity of the brain. These methods can be installed in the form of the software and hardware implementations of modern encephalographs, which will increase the reliability of the analysis and ensure the reproducibility of the results.  
Conceptualization, O.K. and Z.K.; methodology, O.K.; software, O.K.; formal analysis, O.K., Z.K. and V.A.; investigation, O.K. and Z.K.; writing--original draft preparation, O.K., Z.K. and V.A.; writing--review and editing, V.A., O.K. and Z.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant from The Simons Foundation International (grant No. 992227, IMI-BAS).  
Ethical review and approval were waived from this study because of patient anonymity.  
Patient consent was waived due to the patient anonymity.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.
## 3 Issues in Implementing URLLC
### Handover Issues for URLLC  
Handover (handoff) is one of the most integral parts of any telecom infrastructure. NR for 5G must be able to support the mobility requirements illustrated in Table 4.  
Figure 5: User-specific transmission buffer [20]. UE, user equipment.  
In 5G NR, the basic handover process is quite similar to that of the LTE handover [23]. NR supports handover at two different levels to manage seamless handover. Cell level mobility is managed using a radio resource control (RRC) layer in the same way as in the LTE handover. In addition, the beam level mobility is handled using physical and medium access control (MAC) layers without involving RRC for low latency [24]. As NR adopts the same handover signaling procedures as the LTE [25], it inherits two unresolved issues of mobility robustness and mobility interruption time (MIT). Because of the mentioned concerns, enhancements in NR are considered and proposed to achieve zero handover interruption time (HIT) and handover failure (HOF) for URLLC [24]. Further studies are still needed to satisfy the requirements of URLLC QoS.
## Materials and Methods
### Input Datasets
#### Aboveground Biomass Productivity  
Net Primary Productivity (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. NPP is the GPP minus autotrophic respiration, with the losses caused by the conversion of basic products (glucose) to higher-level photosynthesis (starch, cellulose, fats, proteins) and the respiration needed for the maintenance of the standing biomass. The NPP, as defined in WaPOR, is expressed as  
\[\text{NPP}=Sc\cdot Rs\cdot\varepsilon_{p}\cdot FAPAR\cdot SM\cdot\varepsilon_{ lue}\cdot\varepsilon_{T}\cdot\varepsilon_{CO_{2}}\cdot\varepsilon_{AR}[\varepsilon_{RES}] \tag{4}\]  
where \(Sc\) [-] is the scaling factor from dry matter productivity (DMP) to NPP, \(Rs\) is the total shortwave incoming radiation (MJT/ha/day), \(\varepsilon_{p}\) is the fraction of photosynthetically absorbed radiation (PAR) (0.4-0.7 \(\upmu\)m) in total shortwave with a value of 0.48 (\(\text{J}_{\text{P}ar}\))/Total-sw). _fAPAR_ (-) is the PAR-fraction absorbed by green vegetation. \(SM\) (-) is the soil moisture stress reduction factor. \(\varepsilon_{lue}\) (-) is the light use efficiency (LUE) (DM = dry matter) at optimum (kgDM/GJPA), \(\varepsilon_{T}\) (-) is the normalized temperature effect, \(\varepsilon_{CO_{2}}\)(-) is the normalized CO\({}_{2}\) fertilization effect, the \(\varepsilon_{AR}\)(-) is the fraction kept after autotrophic respiration, and \(\varepsilon_{RES}\) (-) is the fraction kept after residual effects (including soil moisture stress). A look-up table based on the land use classification is used to determine the LUE for a given pixel.  
When total biomass (_TBP_) or aboveground biomass productivity (_AGBP_) is derived from the continental NPP data (without prior information on crop type), the following conversions are used in the WaPOR database [36].  
\[TBP=22.22\cdot\text{NPP} \tag{5}\]  
\[AGBP=0.65\cdot 22.22\cdot\text{NPP} \tag{6}\]  
where 0.65 is the conversion fraction from total to above ground biomass, and 22.22 is the conversion from gC/m\({}^{2}\)/day NPP to DMP (above and below ground dry biomass) in kg/ha/day, assuming a carbon fraction of 0.45 in the organic matter.
## 3 Results and Discussion  
Considering two different input-output spaces, results from Directional Distance Function are proposed with the aim to obtain two efficiency scores with two different meanings: Model#1 presents holistic scores, while Model#2 describes environmental efficiency scores.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Building** & **Input/** & & & & & & \\
**Typology** & **Output Space** & & & & & \\ \hline \multirow{6}{*}{SFH} & Input (Model\#1) & LCC (mean value, \(\epsilon\)/m\({}^{2}\)) & 143.10 & 178.47 & 219.18 & 234.50 \\  & Input (Model\#2) & Total costs (\(\epsilon\)) & 18,158 & 20,006 & 21,457 & 22,336 \\  & Undesirable output & CO\({}_{2}\) emissions (kg/m\({}^{2}\)) & 9.24 & 12.25 & 16.15 & 17.12 \\  & Good output & Final energy saving per year (Mwh) & 13.50 & 21.57 & 25.45 & 36.94 \\ \hline \multirow{6}{*}{MFH} & Input (Model\#1) & LCC (mean value, \(\epsilon\)/m\({}^{2}\)) & 115.64 & 155.71 & 189.20 & 199.21 \\  & Input (Model\#2) & Total costs (\(\epsilon\)) & 32,258 & 46,631 & 49,920 & 51,519 \\ \cline{1-1}  & Undesirable output & CO\({}_{2}\) emissions (kg/m\({}^{2}\)) & 8.81 & 10.75 & 14.05 & 14.97 \\ \cline{1-1}  & \multirow{2}{*}{Good output} & Final energy saving per year (Mwh) & 31.69 & 33.80 & 41.05 & 58.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input-output space descriptive statistics (mean values) (mean values are presented, but in model, all values for each solution of POS were used).  
In both cases, scores equal to 0 represent the optimal observation that is placed on the efficient frontier. If the value of score increases, also the inefficiency grows.  
Building typology and climate zone are the key variables used for presenting results with the aim to identify the more efficient solution(s) within the POS of each group.
## 4 Simulations and Discussions
### Discussions
However, due to the complexity of these structures, it is unrealistic to evaluate the performance of these structures using theory and simulations. The other method is by enhancing the intensity sensitivity of the detector to achieve a high RI resolution of the sensor. For example, adding the pixel number is a method to achieve this goal, which can be proved by [PERSON]'s experiment, where the RI solution increases from \(1.83\times 10^{-3}\)RIU to \(5.96\times 10^{-4}\) RIU [16]. Moreover, in other experiments, using a smart phone camera instead of a CMOS camera increases the RI resolution of the sensor from \(6.1\times 10^{-4}\)RIU [17] to \(7.4\times 10^{-5}\)RIU [25]. As for our model, if the minimum detected photocurrent is 0.4 nA and the maximum dark current is 0.3 nA, the minimum detected loss can be improved to \(5.7891\times 10^{-7}\) dB. Then, according to the sensitivity shown in Table 2, the RI resolution of our sensor, using the LP\({}_{11}\) mode (LP\({}_{21}\) mode), increases to \(6.8\times 10^{-5}\) RIU (\(6.4\times 10^{-5}\) RIU). Here we list a commercial SPR instrument (Biosuplar 6), whose RI resolution is \(2.7\times 10^{-5}\) RIU [25] as a comparison. It shows that, through the proposed methods, it is promising to improve the performance of our sensor at a low cost, making it comparable with the above-mentioned instrument in terms of the RI resolution. Based on this model, it is possible to fabricate a sensor using a higher-order mode to achieve high sensitivity and resolution of the RI at a low cost.
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 3 Removal Mechanism of Major Pollutants by Biochar
### Physical Adsorption  
Physical adsorption means that biochar utilizes its surface characteristics, namely porosity and large specific surface area, so that pollutants such as heavy metals or organic substances could be adsorbed on its surface or diffused into the micropores. The diameter of the heavy metal ions is smaller than the average pore diameter of the biochar. Generally, the smaller the diameter of the heavy metal, the more the pores penetrate into the pores of the biochar, thereby increasing the adsorption capacity [81; 82]. The intensity of physical adsorption is closely related to the properties and specific surface area of biochar, the properties and concentration of pollutants, and the temperature during adsorption process. Physical adsorption kinetics is usually fitted by pseudo-first-order and pseudo-second-order kinetic models [83; 84]. Physical adsorption can be either single-layer adsorption or multi-layer adsorption, which is usually fitted by Langmuir and [PERSON] model [85; 86].
## 3 System Model
### The Proposed MAC Protocol  
The proposed MAC protocol is time slotted and its operation is separated into intra-cluster communication and inter-cluster communication. The inter-cluster communication is further divided  
Figure 1: Selection Relaying Cooperative WSN.  
into communication rounds as illustrated by a complete inter-cluster communication frame in Figure 2. Each round is formed by Cluster Construction (CC) and a number of sessions. The duration of a communication round is fixed and number of sessions is decided on implementation depending on data traffic of the application.  
A communication session consists of contention (Cont.) period, a schedule (Sch.) slot, data communication (Dt.) slots and variable sleep slot period. The sleep period varies depending on the traffic available during each session. Each cluster follows its independent intra-cluster communication frame.
###### Abstract  
In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.  
Quadrotor aircraft; attitude control; ADRC; parameter optimization; fractional-order fuzzy particle swarm optimization
## 5 Conclusions  
1. A numerical model of the idealized geometry flow through a mycelium porous zone was researched. The results show that the average air flow rate is equal to 2.7 m/s, 1.5 m/s and 0.7 m/s in the mycelium pores in the cases of 50 \(\upmu\)m, 20 \(\upmu\)m and 10 \(\upmu\)m pore diameters, respectively, when the inflow rate through the air channel is equal to 0.5 m/s.
2. Mycelium filling, which is promising for air flow filtration, depending on the size of the pores in the range of 10-50 \(\upmu\)m in diameter, makes a pressure drop from 240 Pa to 1800 Pa throughout the air flow path. The largest local pressure losses in the system are located at the end of the mycelium pores and at the outflow into the outflow channel.
3. Research shows that, when particles pass through an air filter, their interaction at a low angle to the interacting surface results in sticking. This is important when choosing a mycelium material for making a filter.
4. The question of filter clogging time remains open, so further research should focus on both filter clogging and mycelium pore/hole size. The pores determine both the efficiency of the filter and its clogging rate.  
Conceptualization, V.V., A.C. and R.J.; methodology, V.V., A.C. and R.J.; software, A.C. and R.J.; validation, A.C. and R.J.; formal analysis, V.V., A.C. and R.J.; investigation, A.C. and R.J.; resources, A.C. and R.J.; data curation, A.C. and R.J.; writing--original draft preparation, A.C. and R.J.; writing--review and editing, A.C. and R.J.; visualization, A.C. and R.J.; supervision, V.V., R.J. and A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Autonomous Wireless Sensor Networks in
## Materials and Methods
### Communication Module
#### 2.3.3 LoRa Communicaiton Module  
The LoRa module can be interconnected with the communication ports (Port 1-5; Figure 2) of the PentaSense wireless sensor node. The LoRa module is based on Microchip's RN2483 Low-Power Long Range LoRa Technology Transceiver module provides an easy to use, low-power solution for long-range wireless data transmission. The RN2483 radio complies with the LoRaWAN Class A protocol specifications. It integrates RF, a baseband controller, command Application Programming Interface (API) processor, making it a complete long-range solution.  
The communication module houses an onboard LoRaWAN protocol stack which with ASCII command interface over a universal asynchronous receiver-transmitter (UART) and possible device firmware upgrade (DFU) over it. The compact size of the module makes it easy to attach and house within the same environmental shelter as the smart gateway with Castellated SMT pads for PCB mounting. This module is operational in the same range as the RF communication module making it easy to combine, it has lower power consumption with programmable RF communication bit rate up to 300 kbps with frequency shift keying modulation. The model has an integrated microcontroller unit, radio transceiver with an analog front end and 14 general-purpose input/outputs for control and status. The module is equipped with the RF analog feature able to operate on 866 MHz frequency bands hence eliminating the need for integrating both modules for low-range point to point within the mesh as well as long-range gateway-to-gateway communication. The TX power is adjustable up to +14 dBm with high-efficiency power amplifiers. The range can go up to 15 km at a suburban and 5 km in an urban area of course depending on the elevation profile.
## 5 Results  
### Evaluation Indicators  
This article introduced a set of evaluation metrics to assess the performance of the experimental results, namely, accuracy, precision, recall, and F1 score. The specific formulas associated with these evaluation metrics are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[F1=\frac{2*Precision*Recall}{Precision+Recall} \tag{13}\]  
The abbreviation \"TP\" stands for instances correctly predicted as aviation turbulence by the classifier when they are indeed turbulent. \"FP\" represents instances incorrectly predicted as aviation turbulence by the classifier when they are non-turbulent, indicating the false positive count. \"TN\" indicates instances correctly identified as non-turbulent by the classifier when they are indeed non-turbulent. \"FN\" denotes instances incorrectly predicted as non-turbulent by the classifier when they are turbulent, indicating the false negative count. Another evaluation metric is the Receiver Operating Characteristic (ROC), utilizing True Positive Rate (TPR) and False Positive Rate (FPR) as key indicators. It measures the performance of classification accuracy and plots the False Positive Rate (FPR) on the Y-axis and True Positive Rate (TPR) on the X-axis. The TPR represents the proportion of actual positive instances correctly classified, while the FPR indicates the proportion of false positive instances incorrectly predicted as positive. The corresponding formulas for these evaluation metrics are as follows:  
\[TPR=\frac{TP}{TP+FN} \tag{14}\]  
Figure 9: Training process of turbulence identification model.  
\[FPR=\frac{TN}{FP+TN} \tag{15}\]  
The ROC curve of an ideal machine learning model would overlap with the Y-axis, although achieving this scenario is unrealistic for any model. The Area Under the Curve (AUC) is the region enclosed by the ROC curve and the X-axis. It is consistently bounded between zero and one, a visual metric to evaluate the classifier's performance. A higher AUC indicates stronger classification performance for the machine learning model.
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 3 Results and Analysis
### Confusion Matrix  
The confusion matrices with and without ROI's estimation for four classes of motor imagery using ITD are presented in Tables 2 and 3, respectively. Different classification behaviors of the motor images were observed. For ITD with ROI estimation, the classification accuracy was 95.08% for both feet, 94.40% for the right hand, 91.70% for the left hand and 89.63% for both hands; whereas for ITD without ROI estimation, these values were 80.08%, 82.94%, 79.01% and 76.03%, respectively. Thus, ITD with ROI estimation showed 12.76 to 15.17% higher classification accuracy compared to ITD without ROI estimation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Predicted Class**} \\ \cline{2-6}  & & **Right Hand** & **Left Hand** & **Both Hands** & **Both Feet** \\ \cline{2-6} True class & Right hand & 94.40\% & 4.03\% & 0.75\% & 0.82\% \\ Left hand & 6.43\% & 91.70\% & 1.54\% & 0.33\% \\ Both hands & 6.03\% & 3.93\% & 89.63\% & 0.41\% \\ Both feet & 3.01\% & 1.07\% & 0.84\% & 95.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of ITD with ROI.  
Figure 7: Subject 3’s motor imagery topographic map for (**a**) the right hand, (**b**) the left hand, (**c**) both hands, and (**d**) both feet.