esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Context-Aware Positioning Model
### Management of Contextual Information
#### 3.2.3 Manage Positioning Information  
This task is conducted just after the context sensing has determined which positioning strategy will be used, based on the set of recommendations. If the recommendation is to perform self-positioning, then the context information manager would receive a request to access the corresponding peripherals  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & **S1** & **S2** & **S3** & **S4** & **S5** & **C1** & **C2** & **C3** \\ \hline
**Scenario Feature Vector 1** & 0.35 & 0.15 & 0.05 & 0.10 & 0.30 & 0.0 & 0.0 & 0.05 \\
**Scenario Feature Vector 2** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 1.00 \\
**Scenario Feature Vector 3** & 0.80 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.20 \\... & & & & &... & & & \\ \hline \end{tabular}
\end{table}
Table 4: Training data example.  
Figure 3: Example of randomized decision trees.  
(e.g., a GPS transceiver) in order to determine the position of the device. Note that the model only determines which strategy is to be used, and has no control on whether the peripherals manage to obtain the required position or not; this is entirely dependent on the device's capabilities. If all goes well, the position is obtained and processed. In the event of a time-out (_i.e._, no positioning estimation could be obtained), a new request is sent by the model to the peripherals related to the next available strategy from the recommendations vector, and so on.  
On the other hand, if the recommendation is to perform collaborative positioning, the model would request the context-aware positioning component to perform positioning based on that recommendation. Unlike self-positioning, this process is entirely dependent on the model, and no peripherals are required since all the required information is already in the summary. Either way, once the position of the device has been determined, it is stored for future reference, and redirected towards the consumer application.
## 2 Development of Proposed Controller
### PI-PD Controller  
A PI-PD controller is a widely used feedback system in industrial settings. It merges proportional-integral (PI) and proportional-derivative (PD) control to regulate the system output by measuring the difference between the desired set point and the measured process variable, known as the error signal [5]. The PI parameters of the controller include two control processes: Proportional control (P) adjusts the output relative to the current error signal, providing an immediate corrective response. Integral control (I) integrates the error signal over time, adding corrective action to remove the steady-state error. It continually adjusts the output based on historically accumulated errors to ensure long-term accuracy and stability. The PI control components are highly effective at tracking set points and minimizing steady-state errors. They are an invaluable tool for industrial processes that require precision control and monitoring. The PI-PD controller's block diagram is shown in Figure 2, and the control signal is given as follows:  
\[U(s)=\bigg{(}K_{p}+\frac{K_{i}}{s}\bigg{)}E(s)+\bigg{(}1+K_{d}s\bigg{)}Y(s). \tag{3}\]  
The PD parameter is a combination of proportional control and derivative control operations. The proportional control (P) responds to instantaneous error, while the derivative control (D) responds to the rate of change of error. This allows for a damping effect that reduces overshoot and improves the system response to error signal changes. By predicting future trends and adjusting the output accordingly, the PD controller component is instrumental in systems where a fast response to error signal changes is desired or damping is critical to prevent oscillations and instability. When the PI and PD parameters are combined, PI-PD controllers offer a balanced approach to control systems. PI parameters  
Figure 2: Block diagram of a PI-PD, FOPI-PD, and COPI-PD controllers.  
provide long-term accuracy and steady-state performance, while PD parameters improve transient response and damping characteristics. PI-PD controllers are widely used in various applications, such as process control, robotics, and motion control systems, as they provide flexible and robust control solutions.
## 4 Experimental Setup and Analysis of Results  
### Datasets  
The experiments in this paper were conducted using the large vocabulary dataset LRS2 [19]. The LRS2 dataset is one of the largest publicly available continuous speech recognition datasets, containing both speech and video data extracted from YouTube videos, primarily sourced from BBC news and talk shows. It includes approximately 1000 different speakers and is widely used to study the impact of visual information on speech recognition. The dataset is divided into training, validation, and test sets, and also includes a pretraining set with longer segments, which can be accessed by requesting permission from BBC R&D. Details about the LRS2 dataset are provided in Table 1, and a partial screenshot of the dataset is shown in Figure 7.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
Rank & & & & & & & & & & \\ (Var) & 5.00 & 7.33 & 6.11 & 7.22 & 4.67 & 3.00 & 9.78 & 3.00 & _7.67_ & **1.22** \\ \hline Covtype & 83.13 (5) & 74.14 (9) & 84.97 (3) & **87.77 (1)** & 82.19 (6) & 84.23 (4) & 67.30 (10) & 80.83 (7) & 77.78 (8) & 85.09 (2) \\ Shuttie & 00.07 (5) & 83.18 (1) & 46.58 (2) & 0.00 (7) & 37.78 (5) & 35.18 (6) & 0.00 (7) & 40.16 (3) & 0.00 (7) & 38.94 (4) \\ hypothoid & 10.35 (7) & 30.05 (2) & 34.48 (2) & 2.26 (9) & **43.81 (1)** & 15.44 (4) & 0.00 (10) & 13.71 (6) & 13.95 (5) & 18.32 (9) \\ competat\_4 & 43.01 (6) & 48.49 (4) & 47.11 (36) & 36.50 (8) & **32.84 (1)** & 52.77 (2) & 28.40 (10) & 41.03 (7) & 33.16 (9) & **47.44 (4)** \\ diabetes & 39.87 (7) & 43.08 (2) & 42.12 (4) & 38.27 (9) & 41.05 (4) & 42.41 (3) & 40.19 (6) & 38.97 (7) & 35.99 (10) & **45.36 (1)** \\ CIC\_ISC & 97.57 (9) & 94.06 (8) & 97.49 (6) & **98.69 (19)** & 98.57 (2) & 98.09 (4) & 86.44 (10) & 95.79 (7) & 92.69 (9) & 98.20 (2) \\ \hline Avg. Rank & & & & & & & & & & \\ (Real) & 6.17 &  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \multicolumn{1}{c}{**Data Stream**} & \multicolumn{6}{c}{**Supervised Learning Algorithms**} & \multicolumn{6}{c}{**Active Learning Algorithms**} \\ \cline{2-11} \multicolumn{1}{c}{**Lab Stream**} & \multicolumn{1}{c}{**LB**} & \multicolumn{1}{c}{**MOOB**} & \multicolumn{1}{c}{**ARFre**} & \multicolumn{1}{c}{**SRF**} & \multicolumn{1}{c}{**CSARF**} & \multicolumn{1}{c}{**ROSE**} & \multicolumn{1}{c}{**ROALE-DI**} & \multicolumn{1}{c}{**CALMID**} & \multicolumn{1}{c}{**McFoul-E**} & \multicolumn{1}{c}{**AdaAL-MID**} \\ \hline Fixed\_Stream\_1 & **96.0** (0) & 92.31 (8) & 95.37 (5) & 94.37 (6) & 93.12 (7) & 95.80 (3) & 88.23 (10) & 95.58 (4) & 89.71 (9) & 95.97 (2) \\ Fixed\_Stream\_2 & 95.67 (2) & 91.90 (8) & 95.35 (5) & 94.10 (6) & 92.97 (7) & 95.36 (4) & 88.68 (10) & 95.54 (5) & 90.63 (9) & 96.23 (1) \\ Fixed\_Sud\_1 & 92.78 (5) & 83.63 (9) & 93.69 (4) & 92.66 (6) & 90.86 (7) & 93.75 (3) & 78.42 (10) & 93.94 (2) & 88.25 (8) & **94.82 (1)** \\ Fixed\_Sud\_2 & 91.95 (6) & 83.23 (9) & 93.81 (4) & 92.29 (9) & 90.90 (7) & 94.60 (7) & 81.06 (10) & 93.95 (5) & 83.23 (7) & **94.66 (1)** \\ Fixed\_G\_1 & 90.23 (6) & 81.61 (9) & 93.25 (8) & 89.74 (7) & 90.35 (5) & 93.03 (4) & 79.98 (10) & 94.41 (2) & 86.37 (8) & **94.93 (1)** \\ Fixed\_G\_2 & 92.11 (5) & 80.83 (9) & 93.21 (8) & 86.94 (7) & 82.55 (9) & 94.26 (1) & 77.64 (10) & 92.96 (4) & 85.82 (8) & 94.05 (2) \\ Fixed\_MS\_IS8 & 84.63 (7) & 71.04 (10) & **89.98** (8) & 85.26 (6) & 98.45 (8) & 89.22 (7) & 73.56 (8) & 87.71 (4) & 71.66 (9) & 88.05 (3) \\ Fixed\_Mix\_IGG1 & 86.15 (5) & 72.38 (9) & **90.11 (1)** & 81.82 (7) & 88.70 (6) & 89.10 (3) & 67.79 (10) & 86.58 (4) & 77.49 (8) & 89.91 (2) \\ Fixed\_Mix\_SIG & 91.18 (8) & 80.28 (9) & 90.39 (5) & 89.04 (6) & 87.78 (7) & 90.75 (4) & 77.24 (10) & 91.21 (2) & 82.94 (8) & **92.17 (1)** \\ \hline Avg.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 6 Simulation Results and Analyses
### Convergence of the Algorithm  
We first analyze the convergence of the algorithm. Figure 4 shows the real-time encoding rates of seven videos under three different total channel rates (1 Mbps, 3 Mbps and 6 Mbps). We can see that, after nearly 20 iterations, all curves become smooth and stable. This means that the proposed algorithm can make the system converge to equilibrium state in a very short time and obtain Nash equilibrium. When _Chand_ = 6 Mbps, this means that the channel bandwidth is relatively adequate. As shown in Figure 4a, each video can be encoded and transmitted at its maximum rate \(R_{i}^{\text{max}}\), and faster and more complex video naturally takes up more resources. When _Chand_= 3 Mbps, the total bandwidth is limited. In Figure 4b, the videos, Mobile, Football and Costguard reduce their rates accordingly, thus keeping the total utility function within a reasonable range. When _Chand_ = 1 Mbps, the overall channel environment is extremely poor. In Figure 4c, Football and Mobil can only maintain the minimum rate \(R_{i}^{\text{min}}\), and the rates of the rest videos are below 100 kbps. Next, we analyze the time-varying curve of the total utility value obtained by the whole system under different channel bandwidth conditions, as shown in Figure 4d. It can be seen that, after finite iterations, the total utility value also converges to the stable value. As long as the total bandwidth provided by the AP stays the same and the characteristics of each video remain unchanged, both the data rates of all UAVs and the total utility of the system will finally remain stable. The algorithm can converge rapidly under different bandwidth conditions and has very good stability.  
Figure 4: _Cont._
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 4 Discussion
(e) Performance on small datasets: XGBoost can perform well on small datasets, whereas deep learning models typically require large amounts of data to perform well. We utilized Sentinel-2 median mosaics due to cloud coverage frequency at the study site. This process allowed us to reduce the dimensionality of the input array consisting of satellite daily images and adjust the periods of temporal mosaics covering field campaigns at the Biebrza Valley. Results on the quality of the compositions from Sentinel-2 satellite images by [PERSON] et al. 2020 [86] confirmed the use of improved cloud-free and composeddaily, weekly, or monthly mosaics for regular land monitoring. Regarding unfavorable weather conditions in the study area, Sentinel-2 median mosaics were taken into account as well. In our study, we did not explicitly consider the impact of view zenith angle difference and its derivative consequences on the model performance in temporal pattern prediction [87]. However, this is an important factor to consider in future studies. Therefore, we recommend that future studies investigating temporal patterns in remote sensing data should consider this factor and explicitly evaluate its impact on model performance. However, some limitations must be mentioned in next future studies. Even though the XGBoost algorithm effectively detected ChF patterns at the local site and the method presented might be applied to other areas, the parameters require further optimization. In this context, the XGBoost model will be developed applying additional parameters characterizing high water-level fluctuations, e.g., surface roughness and soil moisture establishments from satellite radar data. Additionally, regarding weather and optical satellite daily image constraints, the obtained median mosaics were few, and the coverage was limited. Therefore, this study did not focus on large-scale environmental monitoring or detecting stress periods. However, including meteorological information such as air temperature, precipitation, and humidity derived from ERA-5 Land reanalysis could be valuable for future research. Our study aimed to emphasize the possibility of applying free-of-charge Sentinel-2 imagery for mapping chlorophyll fluorescence with fine resolution. Considering the reports on the state of European wetlands from the EEA and the anthropogenic influence on the wetland biodiversity and sustainable management of the Biebrza Valley in Poland [25; 84], the proposed study might support biodiversity conservation and management practices that are the subject of investigations from other academic units [88; 89].
## 5 Results
### Results of SEM
#### 5.1.2 Results of the Structural Model  
As shown in Table 7, the model fit indices conform to the recommended thresholds, with \(\chi^{2}/df=1.841\), RMSEA = 0.065, CFI = 0.917, TLI = 0.901, and SRMR = 0.057. It is observed that complexity (\(\beta\) = 0.071, \(p\) > 0.05) and compatibility (\(\beta\) = 0.082, \(p\) > 0.05) are not significantly related to implementation success. Therefore, H1 and H2 are not supported. Top management support (\(\beta\) = 0.177, \(p\) < 0.05), technology competence (\(\beta\) = 0.199, \(p\) < 0.05), supplier support (\(\beta\) = 0.289, \(p\) < 0.01), and pilot project (\(\beta\) = 0.252, \(p\) < 0.05), on the other hand, are significantly related to implementation success. Hence, H3, H4, H5, and H6 are supported. A summary of the results is presented in Table 8.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Item** & **Loading** & **CR** & **AVE** & **Cronbach’s Alpha** \\ \hline \multirow{4}{*}{Supplier support} & Supplier1 & 0.740 & 0.892 & 0.675 & 0.893 \\  & Supplier2 & 0.778 & & & \\  & Supplier3 & 0.885 & & & \\  & Supplier4 & 0.873 & & & \\ \hline \multirow{4}{*}{Pilot Project} & Pilot1 & 0.638 & 0.829 & 0.551 & 0.824 \\  & Pilot2 & 0.760 & & & \\  & Pilot3 & 0.857 & & & \\  & Pilot4 & 0.696 & & & \\ \hline \multirow{4}{*}{Implementation success} & Success1 & 0.702 & 0.800 & 0.503 & 0.778 \\  & Success2 & 0.839 & & & \\ \cline{1-1}  & Success3 & 0.638 & & & \\ \cline{1-1}  & Success4 & 0.639 & & & \\ \hline \hline \end{tabular} Note: * \(p\) < 0.05; ** \(p\) < 0.01.  
\end{table}
Table 6: Correlations and square root AVE of the constructs.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Indices** & \(\chi^{2}\)_diff_ & **RMSEA** & **CFI** & **TLI** & **SRMR** \\ \hline Value & 1.841 & 0.065 & 0.917 & 0.901 & 0.057 \\ Threshold & \(\leq\)3 & \(\leq\)0.08 & \(\geq\)0.9 & \(\geq\)0.9 & \(\leq\)0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model fit indices.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Item** & **Loading** & **CR** & **AVE** & **Cronbach’s Alpha** \\ \hline \multirow{4}{*}{Supplier support} & Supplier1 & 0.740 & 0.892 & 0.675 & 0.893 \\  & Supplier2 & 0.778 & & & \\  & Supplier3 & 0.885 & & & \\  & Supplier4 & 0.873 & & & \\ \hline \multirow{4}{*}{Pilot Project} & Pilot1 & 0.638 & 0.829 & 0.551 & 0.824 \\  & Pilot2 & 0.760 & & & \\  & Pilot3 & 0.857 & & & \\  & Pilot4 & 0.696 & & & \\ \hline \multirow{4}{*}{Implementation success} & Success1 & 0.702 & 0.800 & 0.503 & 0.778 \\  & Success2 & 0.839 & & & \\ \cline{1-1}  & Success3 & 0.638 & & & \\ \cline{1-1}  & Success4 & 0.639 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
## 5 Conclusions  
In this study, a stress grading method based on spectral index reconstruction was proposed, which automatically selects one spectral index to reduce the dimension of contiguous narrow bands and sets the thresholds of adjacent levels with RISIs. Compared with the traditional grading methods based on the multi-class classifier, it showed stronger interpretability. The performance was evaluated by using six types of hyperspectral SIson a cotton field infected with aphids at the plot scale, and the results demonstrated that the spectral index distance between the healthy cotton and cotton infected by aphids had great potential in detecting the sensitive bands of cotton aphids. In addition, instead of the traditional pairwise class discrimination, the disease severity grading can be simplified by a comparison between adjacent levels. Furthermore, the applicability of the proposed approach was established based on three types of two-band SIs and three types of three-band SIs, among which the three-band RIRSI (R732, R878, R712) achieved the best grading performance and the red-edge region was sensitive to cotton aphids. The proposed approach provides a new way to automatically grade diseases and pests through remote sensing technology.  
Conceptualization, X.H.; Methodology, X.H.; Validation, H.Q. and B.C.; Formal analysis, X.H. and H.S.; Investigation, H.Q.; Resources, H.Q.; Data curation, B.C.; Writing--original draft preparation, X.H.; Writing--review and editing, B.C.; Visualization, H.S.; Supervision, H.S.; Project administration, H.Q.; Funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Funds of the National Natural Science Foundation of China (U2003119).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
However, these techniques involve high energy costs and may degrade thermally sensitive substances due to the use of high extraction or separation temperatures, depending on the boiling point of the solvent [13; 14]. Supercritical fluid extraction (SFE) using carbon dioxide is a widely employed technique for extracting oils and compounds from plants, including _S. terebinthifolius_. This method offers an environmentally friendly approach by pressurizing and heating CO\({}_{2}\) to its supercritical state, ensuring the efficient extraction of the desired compounds from the plant material while minimizing degradation. Compared to conventional methods, SFE eliminates the need for chemical solvents and results in a high-quality product with improved flavor and aroma profiles. Moreover, the use of supercritical CO\({}_{2}\) in the extraction process preserves the natural chemical composition of the extract and enhances its stability [13; 15]. Overall, SFE with carbon dioxide is a promising and sustainable extraction method for obtaining valuable compounds from _S. terebinthifolius_. The objective of this study was to conduct a technical and economic evaluation of the production of bioactive compounds from _Schinus terebinthifolius_ using supercritical CO\({}_{2}\). In light of the findings, it can be concluded that the utilization of supercritical fluid extraction techniques, specifically employing carbon dioxide, holds immense potential for extracting valuable compounds. This approach provides a sustainable and environmentally friendly extraction method and ensures the preservation of the integrity and quality of the extract. Consequently, it is a highly advantageous option for industrial-scale production and various commercial applications.
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 2 Related Work
### Findings and Contributions  
From the aforementioned state-of-the-art review, we see that most researchers have implemented the intrusion detection system within the classification framework. Even though this approach is effective, it has limited application since the inference happens in real-time and not proactively. We also see that multivariate time series prediction is an area of active research where new deep learning approaches are frequently proposed for various tasks of different thematic areas. From the related work review, we observe that some researchers have tried to frame this problem as a time series prediction with promising results. When it comes to the UNSW-NB15 dataset, most researchers choose an incomplete version of the dataset (approx. 100.000 samples instead of \(\sim\)2.5 million samples). This dataset is tailored (unbalance is removed, only samples with strong correlation with the target label are kept) to machine learning applications and is not a representative version of real-world scenarios. For the reasons above, our work differs significantly since we (i) use the whole version of the dataset to emulate real-world applications, (ii) frame the intrusion detection system as a time series problem, and (iii) conduct multiple experiments to thoroughly compare against the common approaches.
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 2 Methods  
The overall framework of this paper is shown in Figure 1. Firstly, a data set of traffic flow of two-way two-lane roads was established, and the vehicles in the data set were classified as forward or opposite vehicles. Secondly, a vehicle detection model of two-way two-lane roads was trained by using YOLOv5s neural network to detect the forward and opposite vehicles and calculate the feature points of vehicles. After that, the rotation model of the imaging plane of the camera will be established. The simulation test was designed to analyze the influence of the roll angle of the camera on ranging results using the traditional geometric ranging method, and the improved geometric ranging method considering the roll angle of the camera was proposed. The feature point of the vehicle and parameters of the camera were used as the input of the vehicle distance measurement model, and the model output the vehicle category and the longitudinal distance between the vehicle and the forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle. Finally, an experiment with actual vehicles carried out on the actual two-way two-lane roads was designed and compared with other methods to verify the feasibility and ranging accuracy of this method.
# A Novel Method of Seismic Signal Detection Using Waveform Features
###### Abstract  
The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.  
STA/LTA; detection performance; waveform features; joint probability model; Gaussian process +
Footnote †: journal: Applied Sciences  
Article