esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Chlorophylll Fluorescence
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Foliar Spray**} \\ \cline{2-5}
**PEG** & **Non Treated** & **H\({}_{2}\)O** & **SA** & **MA** & **CA** \\ \cline{2-5}
**Level** & & & **N** & \\ \hline Control & 0.039 \(\pm\) 0.01 b & 0.034 \(\pm\) 0.01 ab & 0.031 \(\pm\) 0.01 ab & 0.033 \(\pm\) 0.01 ab & 0.031 \(\pm\) 0.01 ab \\ PEG 5\% & 0.018 \(\pm\) 0.0 a & 0.016 \(\pm\) 0.0 a & 0.018 \(\pm\) 0.0 a & 0.021 \(\pm\) 0.0 ab & 0.016 \(\pm\) 0.0 a \\ PEG 7.5\% & 0.016 \(\pm\) 0.0 a & 0.015 \(\pm\) 0.0 a & 0.019 \(\pm\) 0.0 ab & 0.02 \(\pm\) 0.0 ab & 0.015 \(\pm\) 0.0 ab \\ \hline \multicolumn{5}{c}{**P**} \\ \hline Control & 0.004 \(\pm\) 0.001 a & 0.003 \(\pm\) 0.001 a & 0.003 \(\pm\) 0.001 a & 0.004 \(\pm\) 0.001 a & 0.003 \(\pm\) 0.001 a \\ PEG 5\% & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a & 0.003 \(\pm\) 0.001 a & 0.003 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a \\ PEG 7.5\% & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.001 a & 0.003 \(\pm\) 0.001 a & 0.003 \(\pm\) 0.001 a & 0.002 \(\pm\) 0.001 a \\ \hline \multicolumn{5}{c}{**K**} \\ \hline Control & 0.072 \(\pm\) 0.006 c & 0.057 \(\pm\) 0.013 ab & 0.059 \(\pm\) 0.018 ab & 0.069 \(\pm\) 0.016 c & 0.058 \(\pm\) 0.011 ab \\ PEG 5\% & 0.039 \(\pm\) 0.009 ab & 0.04 \(\pm\) 0.001 ab & 0.044 \(\pm\) 0.004 ab & 0.051 \(\pm\) 0.002 ab & 0.04 \(\pm\) 0.004 ab \\ PEG 7.5\% & 0.041 \(\pm\) 0.003 ab & 0.034 \(\pm\) 0.006 a & 0.047 \(\pm\) 0.006 ab & 0.047 \(\pm\) 0.003 ab & 0.032 \(\pm\) 0.007 a \\ \hline \multicolumn{5}{c}{**Ca**} \\ \hline Control & 0.013 \(\pm\) 0.001 b & 0.011 \(\pm\) 0.001 ab & 0.01 \(\pm\) 0.003 ab & 0.013 \(\pm\) 0.002 b & 0.01 \(\pm\) 0.001 ab \\ PEG 5\% & 0.008 \(\pm\) 0.002 ab & 0.007 \(\pm\) 0.001 a & 0.008 \(\pm\) 0.00 ab & 0.008 \(\pm\) 0.001 ab & 0.006 \(\pm\) 0.001 a \\ PEG 7.5\% & 0.007 \(\pm\) 0.001 a & 0.006 \(\pm\) 0.001 a & 0.007 \(\pm\) 0.001 a & 0.007 \(\pm\) 0.00 ab & 0.006 \(\pm\) 0.0 a \\ \hline \multicolumn{5}{c}{**Mg**} \\ \hline Control & 0.005 \(\pm\) 0.0 c & 0.004 \(\pm\) 0.0 bc & 0.004 \(\pm\) 0.001 bc & 0.005 \(\pm\) 0.0 c & 0.004 \(\pm\) 0.0 bc \\ PEG 5\% & 0.003 \(\pm\) 0.0 ab & 0.002 \(\pm\) 0.0 a & 0.003 \(\pm\) 0.0 ab & 0.003 \(\pm\) 0.0 ab & 0.002 \(\pm\) 0.0 a \\ PEG 7.5\% & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a \\ \hline \multicolumn{5}{c}{**Na**} \\ \hline Control & 0.004 \(\pm\) 0.0 b & 0.003 \(\pm\) 0.0 ab & 0.003 \(\pm\) 0.001 ab & 0.004 \(\pm\) 0.0 ab & 0.003 \(\pm\) 0.0 ab \\ PEG 5\% & 0.003 \(\pm\) 0.0 ab & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 ab & 0.003 \(\pm\) 0.0 ab & 0.002 \(\pm\) 0.0 a \\ PEG 7.5\% & 0.002 \(\pm\) 0.0 a & 0.002 \(\pm\) 0.0 a & 0.003 \(\pm\) 0.0 ab & 0.002 \(\pm\) 0.0 ab & 0.002 \(\pm\) 0.0 ab \\ \hline \multicolumn{5}{c}{Values with the same letter do not differ significantly at \(p\) = 0.05.} \\ \end{tabular}
\end{table} 
Table 2: Effect of foliar application of salicylic acid (SA), maleic acid (MA), and citric acid (CA) on uptake of elements in aboveground parts of the plant (\(\mu\)g plant\({}^{-1}\)).
## 2 Related Work  
Table 1 summarizes research works on reliability, which considers the involvement of IoT, FC, SF, mechanisms for outliers detection, and mechanisms for data recovery when outliers happen.  
\begin{table}
\begin{tabular}{c l l l l l l l l} \hline \hline
**Ref** & **Description** & \multicolumn{5}{c}{**Relativity Definition**} & **LfT** & **FC** & **SF** & **Outlier Detection** & **Data Recovery** \\ \hline
[9] & A system architecture for monitoring the reliability of kfT & The ability of the system to prevent itself from failing by continuously & & & & & \\  & & & & & & & \\  & &  
Refs. [7; 8; 9; 10; 11; 12; 13; 14] introduced IoT applications, mostly in SF domains. These works focus on deploying specific applications such as monitoring for greenhouses, a control system for watering crops, and a system to minimize the use of fertilizer and pesticides. Although they highlight reliability as an essential feature for IoT-based SF, they did not design their solutions from a reliability perspective. Refs. [17; 18; 19] managed reliability in FC-based IoT by using optimization, self-adaptability, and re-transmission techniques. Nevertheless, none of these works included failure data detection and data recovery mechanisms to provide reliability in SF scenarios. Refs. [20; 21; 22; 23; 24; 25] introduced novel IoT-based SF applications using FC features, such as local processing, deploying control modules near the access network, minimizing the amount of data transferred to the cloud, and reducing delay. These works did not exploit the FC capabilities for data reliability improvement. Refs. [26; 27; 28] introduced mechanisms to detect anomalies in datasets. However, none of these works compared two or more methods for anomaly detection nor considered an SF environment based on FC and IoT. Ref. [29] proposed mechanisms for detecting and handling outliers by interpolation, but this work did not consider FC-and-IoT based-SF.  
There is no approach based on ML and interpolation techniques, to the best of our knowledge, aimed to provide data reliability to IoT-Fog-Cloud-based SF applications that support decision-making to farming stakeholders. Our approach seeks to address the shortcomings mentioned above by introducing a Fog-based and reliability-oriented architecture that incorporates a mechanism for detecting outliers in data and another mechanism for handling them to offer reliable data. In addition, it is to highlight that our approach operated with data collected in a real SF scenario.
## 2 Algorithm Principle
### INS+LEO2-Satellite Alternate Switching Ranging Integrated Navigation Algorithm  
#### 2.2.1 Algorithm Principle  
Two LEO satellites visible at any time of the carrier's flight position are chosen and named satellites #1 and #2. Unlike the traditional dual-satellite combination algorithm, our algorithm only has the true ranging value of one satellite at any time during the carrier's flight. However, there are two satellites' ranging values participating in INS integrated navigation filtering: one is the satellite's true ranging value, and the other is the virtual ranging value of the satellite. The real ranging value is the distance from the current satellite position calculated by the satellite ephemeris to the real position of the carrier. The virtual ranging value is the distance from the current satellite position calculated by the satellite ephemeris, to the current carrier position from real-time measurement by INS. The real range value and the virtual range value are alternately changed at alternating time intervals to realize the INS+LEO2-satellite alternate switching ranging integrated navigation and positioning.  
Figure 1: Schematic diagram of the standard INS+LEO2-satellite integrated navigation algorithm.
## 2 Methods: FMTL Framework
### Multi-Task Learning Framework  
As illustrated in Figure 2, the FMTL model adopts a shared-bottom architecture that comprises a unified feature extractor and two task-specific prediction heads. The model takes 30 s multichannel PSG signal segments as input. The shared feature extractor first applies a series of three one-dimensional convolutional layers to capture localized time-frequency patterns, followed by batch normalization to stabilize learning. The output is then processed by a bidirectional gated recurrent unit (GRU) to model temporal dependencies across signal sequences. To further enhance context awareness, a multi-head self-attention mechanism is applied to the GRU outputs, allowing the model to learn dynamic importance weights across time steps.  
Building on the shared representation, the model branches into two heads. The sleep staging head uses a Bi-LSTM network to extract sequential context, followed by dropout regularization and a dense SoftMax layer for five-class classification into the sleep stages W, N1, N2, N3, and REM. In parallel, the SDB severity classification head applies a GRU layer followed by attention pooling, a dense projection layer, and a SoftMax activation to output four-class prediction of SDB severity: normal (AHI < 5), mild (5 \(\leq\) AHI < 15), moderate (15 \(\leq\) AHI < 30), and severe (AHI \(\geq\) 30), in accordance with clinical AHI thresholds. The multi-task model is optimized using a joint loss function:  
\[\hat{y}_{stage}=Softmax\big{(}F_{stage}(f_{shared})\big{)} \tag{3}\]  
\[Loss=\lambda_{1}\cdot WeightedCrossEntropy\big{(}\hat{y}_{stage},\ y_{stage} \big{)}+\lambda_{2}\cdot WeightedCrossEntropy(\hat{y}_{sdb},\ y_{sdb}) \tag{4}\]  
where \(\hat{y}_{stage}\) and \(\hat{y}_{sdb}\) are the predicted outputs of the sleep staging and SDB classification heads, respectively, and \(y_{stage}\) and \(y_{sdb}\) are the corresponding ground truth labels. In our implementation, both task weights \(\lambda_{1}\) and \(\lambda_{2}\) are set to 0.5. The term WeightedCrossEntropy indicates that class-specific weights are applied to account for label imbalance in both tasks, ensuring that minority classes receive greater emphasis during training, which referred to previous research [17; 32].  
Figure 2: Detailed architecture of the shared CNN–GRU–attention encoder and task-specific heads used in the FMTL model.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 3 Fuzzy Sets Theory  
### Hesitant Fuzzy Set  
The HFS (Hesitant fuzzy set) method was put forward by [PERSON] [28] and [PERSON] and [PERSON] [29]. This method can solve the problem of different types of elements in a given set, including some multi-objective optimization problems. The following two definitions of the hesitant fuzzy element are given below.  
**Definition 1**.: _Refs. [28; 29] Assuming that \(X=\{x_{1},x_{2},x_{3},\ldots,x_{m}\}\) is a nonempty set, the HFS is a function of the subset from \(X\) to [0, 1]. For convenience, the HFS is expressed as_  
\[A=\{<x,h_{A}(x)>|x\in X\}\]  
_where, \(h_{A}(x)\) is a set of some numbers in the interval [0, 1] and it indicates the possibility that element \(x\) belongs to the set \(A\). At this time, we call \(h_{A}(x)\) a hesitant fuzzy element._  
**Definition 2**.: _Assuming that \(X=\{x_{1},x_{2},x_{3},\ldots,x_{m}\}\) is a nonempty set and \(h\) is a hesitant fuzzy element, we call \(s(h)=\frac{1}{l_{h}}\sum_{y\in h}y\) is the score of \(h\). Where, \(l_{h}\) is an element of \(h\). For two HFSs \(h_{1}\) and \(h_{2}\), if \(s(h_{1})>s(h_{2})\), then \(h_{1}>h_{2}\); if \(s(h_{1})=s(h_{2})\), then \(h_{1}=h_{2}\)._  
_For hesitant fuzzy elements \(h\), \(h_{1}\), \(h_{2}\), Refs. [27; 28] defined some operations:_  
\[h^{\lambda}=\cup_{\gamma\in h}\left\{\gamma^{\lambda}\right\} \tag{1}\]  
\[\lambda h=\cup_{\gamma\in h}\left\{1-(1-\gamma)^{\lambda}\right\} \tag{2}\]  
\[h_{1}\bigoplus h_{2}=\cup_{\gamma_{1}\in h_{1},\gamma_{2}\in h_{2}}\{\gamma_{ 1}+\gamma_{2}-\gamma_{1}\gamma_{2}\} \tag{3}\]  
\[h_{1}\bigotimes h_{2}=\cup_{\gamma_{1}\in h_{1},\gamma_{2}\in h_{2}}\{\gamma_ {1}\gamma_{2}\} \tag{4}\]
## 2 Implementation of FPGA-Based On-Board SAR Processor
### SAR Processor Architecture  
The major submodules of the SAR processor system are listed and detailed as below:  
1. Analogue-to-Digital Converter (ADC) and Digital-to-Analogue Converter (DAC) This module acts as the analogue interface of the system. The ADC digitizes the incoming analogue signal (radar return echo), and the DAC produces the baseband analogue radar signal, with the frequency typically modulated by a continuous wave pulse signal.
2. FPGA-based Embedded SAR Processor Acts as ADC and DAC control and data buffer. This portion of the system interfaces and controls data flow with the DAC and ADC chips. All signalling, including operating modes, are controlled by this block. The main task of the FPGA module is to execute the SAR image-formation algorithm process and output the SAR images. In this module, FFT/IFFT, complex multiplication and other operations are performed on the raw data and produce the correct SAR images.
3. Data Storage The solid-state device (SSD), which it is made of non-volatile memory chips, is used as data storage. The SSD is used to store the received SAR raw data, as well as the processed SAR images. A detailed functional block diagram of the embedded SAR processor is illustrated in Figure 1. The output of the SAR processor is the level 1 single-look complex (SLC) SAR image. The generated SAR image can be further processed to level 2 by performing advanced SAR processing, such as interferometry or polarimetric SAR processing.
## 5 Conclusions  
This paper proposes a control scheme combining IPSO and ALADRC to solve the HST speed tracking control problem. In the establishment of a HST model, a multi-mass point dynamic model with time-varying coefficients is considered. The running resistance, the additional resistance, the interaction force between carriages, and the time-varying coefficients are taken as disturbances to verify the effectiveness of the designed control scheme. In the design of a control scheme, LADRC is used as the HST speed tracking controller. The LESO can estimate and compensate for the total disturbance in real-time, meaning that each carriage can be independently controlled by LADRC without decoupling. In view of the difficulty in setting the LADRC parameters of different carriages, IPSO is introduced to optimize the LADRC parameters of different carriages according to the  
Figure 14: Displacement tracking error of the four carriages: (**a**) Displacement tracking error of the first two carriages; (**b**) Displacement tracking error of the last two carriages.  
Figure 13: Speed tracking error of the four carriages: (**a**) Speed tracking error of the first two carriages; (**b**) Speed tracking error of the last two carriages.  
optimization objective of minimizing the HST speed tracking error. This greatly simplifies the parameter adjustment process. In order to compensate for the observation error of LESO and further improve the control performance of LADRC when the HST encounters unknown disturbances during actual operation, APC is introduced. From the simulation results, it can be seen that it is feasible to use IPSO to optimize LADRC parameters, and the speed tracking error of the HST is small, which meets the requirements of HST operation. With the introduction of APC, ALADRC has better adaptability than LADRC with fixed parameters in the dynamic control process. From the simulation results of two examples, it can be seen that the HST using ALADRC has stronger anti-disturbance performance, faster response speed, and higher control accuracy when encountering disturbances. The HST speed mean absolute errors of ALADRC are smaller than LADRC. This can prove the effectiveness of the designed control scheme. At present, this paper is limited to theoretical analysis, and some practical problems are not considered, such as the traction and braking characteristics of the train traction motor, the energy demand of the actuator, and the signal transmission mode. These issues will be considered in the next work.  
[PERSON], contributed to the conception of the study, performed the experiment, performed the data analyses, and wrote the manuscript; [PERSON] and [PERSON], contributed to the conception of the study; [PERSON], [PERSON], [PERSON] and [PERSON], helped perform the analysis with constructive discussions. All authors have read and agreed to the published version of the manuscript.  
The authors received no financial support for the research, authorship, and/or publication of this article.  
Not applicable.  
The authors declare that they have no competing interests.
## 2 Methodology
### Optimization Procedure
#### 2.1.1 Building Performance Simulation Tool  
The establishment of the residential parametric model and performance simulation was based on the Rhino-Grasshopper visual programming platform. The building energy consumption, thermal environment, and solar radiation simulations were powered by the Honeybee and Ladybug plugins installed in Grasshopper. The Honeybee module can transform the building volume model into a thermal zoning model to form a parametric building hot zone model and directly invoke EnergyPlus (V 9.0) software to complete the simulation of the building's heating and cooling load and indoor ambient temperature. The simulation results of EnergyPlus were evaluated using the ANSI/ASHRAE standard 140-2007 (ASHRAE 2007) [30]. Many studies have been conducted using EnergyPlus [31; 32; 33]. Furthermore, in previous studies, the effectiveness of the simulation results was verified using the measured data [34]. The Ladybug module can read and process the climate information file; it has an independent simulation core and can provide various simulations of sunshine and thermal radiation, which can be used to calculate the solar irradiance received by the roof and facade, enabling calculation of the power generation of the solar photovoltaic system.  
Figure 1: Overall workflow for the multi-objective optimization of high-rise residential buildings.
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 1 Introduction
[27] presented a high-resolution air quality study over a large urban area of Brazil, encompassing the Metropolitan area of Sao Paulo, Campinas, Paraiba valley and the Sea Ridge, by employing the European Air Pollution Dispersion-Inverse Model (EURAD-IM) to simulate detailed features of aerosols and investigate air pollution cases based on particulate matter concentrations. The EURAD-IM study demonstrated exchange of pollutants between MASP and the Metropolitan area of Rio de Janeiro (MAR), which also affects the Metropolitan Area of the Paraiba valley (MAPV) by transportation of air pollutants. Furthermore, the MAPV can suffer from the impact of the large fleet of light and heavy-duty vehicles commuting every day by the Presidente Dutra Highway. Remote sensing techniques such as lidar, an acronym for light detection and ranging [28], are widely used to retrieve atmospheric features and monitor air pollution. In the MASP, the lidar technique is used to study aerosol optical properties and their impact on air quality [29; 30; 31], to monitor long-range aerosol transportation from biomass burning areas [32; 33; 34; 35], to study Cirrus cloud optical properties [36; 37] and volcanic ashes [38], and also to study the daily aerosol boundary layer behavior [39] and its relation with air quality [40]. In this context, a collaborative effort between three Brazilian research institutions, Instituto de Pesquisas Energeticas e Nucleares-IPEN, Universidade Federal Fluminense-UFF/Volta Redonda and Universidade de Sao Paulo-USP/Lorena, studied the vertical distribution of atmospheric aerosols by employing a remote sensing lidar system in two different periods and two different urban sites impacted by industries and highway road emissions, and also by aerosol transport episodes. The main contribution of this paper is to report the measurements of vertical aerosol distribution using a ground-based lidar in the Paraiba valley region. As far as we know, there are no articles in the literature using such a technique to study the aerosol optical properties in this region. The paper is structured as follows: The campaign sites and the instrumentation used in this work are described in Section 2. Section 3 presents the theoretical approach used to invert the lidar signal in order to obtain the aerosol optical coefficient profiles. The data retrieval, as well as the result analysis, are described in Section 4. Finally, the conclusions are presented in Section 5.
## 5 Experiment Validation
### Case 2: Bearing Rolling Element Fault
Figure 20: Plot of the MEVS for the bearing signal with rolling element defect. Figure 21: The comparison results of the roller fault signal by different methods: (**a**) the traditional vector entropy value sequence; (**b**) the proposed MEVS of 1-scale; (**c**) The proposed _MFEVS_. Figure 22: (**a**) The waveform of the reconstructed optimal short time sliding signal; (**b**) the corresponding frequency spectrum. In the end, the indicators of SNR(\(f_{bp}\)) and kurtosis are employed to quantify the comparison result. As illustrated in Table 4, the proposed method has the biggest SNR(\(f_{bp}\)) and kurtosis than other three methods. In summary, through the comparison of the four methods, the proposed method shows great advantage and excellent ability in extracting fault feature information from the roller defective signal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Raw Signal** & **Proposed Method** & **EEMD** & **WP** & **Fast Kurtogram** \\ \hline
**SNR(\(f_{bp}\))/dB** & \(-25.7\) & \(-15.4\) & \(-22.6\) & \(-19.6\) & \(-17.7\) \\
**Kurtosis** & \(3.1\) & \(4.5\) & \(3.3\) & \(4.1\) & \(3.4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison results of the rolling element fault signal by different methods. Figure 23: The comparison results of the roller fault signal by different methods: (**a,b**) the proposed method; (**c,d**) the EEMD method; (**e,f**) the WP method; (**g,h**) the filter method by fast kurtogram. Figure 24: The envelope waveform of the filtered signal in time domain.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Optimization of the MADDPG-VSC Algorithm for Control Design
#### 3.2.1 Reward Function Design
Thus, the energy consumption penalty is expressed as  
\[r_{\text{energy}}=\alpha_{\text{energy}}\cdot|\Delta v| \tag{19}\]  
Collision avoidance penalty \(r_{\text{collision}}\)  
The collision risk \(\text{C}_{\text{risk}}\) represents the likelihood of a collision based on the proximity to obstacles or other UAVs. It can be calculated using a function that evaluates the inverse distance, as follows:  
\[\text{C}_{\text{risk}}=\frac{1}{d+\epsilon} \tag{20}\]  
where \(d\) is the relative distance between UAVs or between a UAV and an obstacle, and \(\epsilon\) is a small positive constant used to prevent division by zero. This penalty discourages potential collision events, encouraging UAVs to avoid obstacles and maintain safe distances in complex environments [30]. The value of \(\alpha_{\text{collision}}\), which reflects the priority of collision avoidance, is typically set to 15. This value ensures that avoiding risks is prioritized, while maintaining a balance with other mission objectives. The collision avoidance penalty is then defined as  
\[r_{\text{collision}}=\alpha_{\text{collision}}\cdot\text{C}_{\text{risk}} \tag{21}\]  
The overall reward function \(R\) is expressed as  
\[R=r_{\Lambda}+r_{\text{formation}}-r_{\text{energy}}-r_{\text{collision}} \tag{22}\]  
This reward function system effectively balances positive reinforcement and penalties, guiding the algorithm toward optimal solutions that meet mission requirements [31]. By optimizing these reward components, the algorithm enhances the overall performance of UAV formations, ensuring that the best control strategies are learned and applied.