esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
We evaluated the performance of our classification algorithm by testing it for 20 trials on \(n\) randomly selected species, (out of 659 species in the selected dataset) with \(n\) varying between \(n=10\) and \(n=300\). The results for the precision, AUC, mAP, recall, and accuracy are summarized in Figure 3.  
Box plots were estimated from 20 different randomized data sets for each \(n\). Box plots, also known as box-and-whisker plots, provide robust statistical summaries for the data, if the sample size is relatively small, i.e., here 20. The box plot divides the data into quartiles or fourths--two box panels and two whiskers. The middle 50% of data is spanned by the box with the 25 th percentile or 25% of the data falling below the lower edge of the box (first quartile) and 75% of data falling below the upper edge of box (third quartile). The edges of the box are often referred to as _hinges_, and the length of the box is called the _interquartile range_ (IQR). The median is indicated by the middle line of box. The whiskers mark the extremes for the remaining 50% of the data [37]. Surprisingly, we found no increase in the size of the interquartile range for the performance measures with increasing \(n\).  
There are several aspects of these results that deserve to be addressed in more detail.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 5 Discussions
### Effect of Class Wight in Data Imbalance  
We applied class weights to address the class imbalance in ISCX 2016 VPN/Non-VPN data in Section 3.2.1. Class weight represents a weight that reflects the proportion of classes in the data and can reduce the imbalance between classes. Figure 7 shows the distribution of data for each class before and after applying class weight.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Task** & **ST (ms)** & **PT (ms)** \\ \hline ET-BERT (Fine-Tune) [34] & STL (1 task) & 8.30–9.61 & 155.7 \\ XENTC [33] & STL (1 task) & - & 15.1 \\ \hline MATEC [39] & STL (1 task) & 2.10 & 1.3 \\ FastTraffic [8] & STL (1 task) & 0.25 & 0.59 \\ \hline Proposed & MTL (3 tasks) & 1.27 & 30.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results on efficiency evaluation.  
In Figure 7, the \(x\)-axis represents the classes per task and is organized the same as in Figure 3. For example, in Figure 7a, \"1\" and \"2\" represent [VPN, NonVPN], respectively, and in Figure 7b, \"1-6\" represent [Chat, Email, File Transfer, P2P, Streaming, VoIP]. Comparing the 'Before' and 'After' in Figure 7, we can see that the imbalance between each class is significantly reduced. However, in Figure 7c, we can see that there is still some imbalance as there are too few minority classes. These limitations will be tackled in the future with additional weighting and sampling techniques.
## 2 Related Work
It is a layering technique and each layer may contain multiple clusters. A single-hop and multi-hop communication were performed to cover larger zones effectively. In [39], a hybrid algorithm called whale optimization algorithm-moth flame optimization (MFO) is proposed to optimize the cluster formulation for IoT devices. MFO offers the advanced hybrid nature of CH selection, which is partially distributed and partially centralized to optimize the energy resources by saving the computational burden over network nodes. In our proposed model, some CHs use a centralized communication mechanism while others use a distributed communication scheme. In [40], another hybrid routing algorithm is proposed for mobile IoT nodes with different velocities. The proposed model is derived from multipath optimized link state routing (OLSR) and ad hoc on-demand multipath distance vector (AOMDV) protocol design to develop the hybrid clustering algorithm. After a review of the discussed articles [19; 40], we observed that there are different parameters used to define the performance of energy-constrained wireless sensor networks. Some related issues that are focused on in our proposed model are:  
* **Lifetime of Network:** The lifetime of a network can be measured in terms of the ratio between the available alive nodes and total nodes after specific periods. This is easy to measure, but special measures should be taken to improve its accuracy due to packet redundancy. * **Span of Covered Area:** The performance of a WSN can be determined using the span of its covered area. However, this metric may not be workable for mission-critical environments because all nodes cannot successfully deliver accurate data to the sink or base station (BS) node, although nodes have sufficient energy to communicate with the neighboring nodes. * **Connectivity Threshold:** A threshold can be used to measure connectivity or communication performance. This threshold is calculated using the parameters of alive nodes in terms of their residual energy, distance, and other communication factors. The threshold varies in different environments and applications. * **Network Performance:** The performance of a WSN can be related to how long a network can receive data from the physical environment. Network performance can also be defined as the duration in which all nodes can communicate successfully with each other. This characteristic encourages heterogeneity in WSNs in terms of the capabilities of wireless nodes. We observe in this section that most of the existing energy-efficient models are focused on data routing, data aggregation at CHs, and in successfully delivering the data packets to the BS. In our proposed model, we introduced a strategy to reduce packet transmission of cluster members to the CHs when the environment remains stable or changes gradually. We restrict member nodes of a cluster by using some conditions when communicating with CHs. This controlled transmission between member nodes and cluster heads will reduce the communication overhead of cluster heads and enhance the lifetime of the individual nodes in the network.
## 5 Experiments and Results
### Performance Evaluation
#### 5.2.2 Packet Loss Rate
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Time**} & \multirow{2}{*}{**Weather**} & \multirow{2}{*}{**Data Size**} & **Number of** & **Number of** & **Packet Loss** \\  & & & **Transmitted** & **Received** & **Rate** \\  & & & **Packets** & **Packets** & **Rate** \\ \hline
2020.11.27 8:20 & Rain & 54 B & 27 & 25 & 7.4\% \\
2020.11.27 19:02 & Rain & 54 B & 21 & 20 & 4.7\% \\
2020.11.28 8:20 & Rain & 54 B & 27 & 26 & 3.7\% \\
2020.11.28 19:02 & Rain & 54 B & 21 & 21 & 0 \\
2020.11.29 8:20 & Overcast & 54 B & 27 & 25 & 7.4\% \\
2020.11.29 19:02 & Overcast & 54 B & 21 & 19 & 9.5\% \\
2020.11.30 8:21 & Overcast & 54 B & 27 & 26 & 3.7\% \\
2020.11.30 19:03 & Overcast & 54 B & 21 & 21 & 0 \\
2020.12.1 8:21 & Overcast & 54 B & 27 & 26 & 3.7\% \\
2020.12.1 19:03 & Overcast & 54 B & 21 & 20 & 4.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Packet loss rate (without the packet creation mode). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Time**} & \multirow{2}{*}{**Weather**} & \multirow{2}{*}{**Data Size**} & **Number of** & **Number of** & **Packet Loss Rate** \\  & & & **Packets** & **Packets** & **Packets** \\ \hline
2020.12.9 8:19 & Sunny & 324 B & 54 & 51 & 6\% \\
2020.12.9 19:00 & Sunny & 324 B & 42 & 40 & 4.7\% \\
2020.12.10 8:19 & Sunny & 324 B & 54 & 50 & 7.4\% \\
2020.12.10 19:00 & Sunny & 324 B & 42 & 41 & 2.4\% \\
2020.12.11 8:19 & Sunny & 324 B & 54 & 51 & 6\% \\
2020.12.11 19:00 & Sunny & 324 B & 42 & 39 & 7\% \\
2020.12.12 8:19 & Overcast & 324 B & 54 & 52 & 3.7\% \\
2020.12.12 19:00 & Overcast & 324 B & 42 & 40 & 4.7\% \\
2020.12.13 8:19 & Overcast & 324 B & 54 & 50 & 7.4\% \\
2020.12.13 19:00 & Overcast & 324 B & 42 & 39 & 7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Packet loss rate (with the packet creation mode).
## 3 Results and Discussion
### Experimental Design and Analysis of Variance
#### 3.2.4 Validation of the Predictive Model  
The parameters were modified slightly in the verification experiment as follows: Extraction temperature of 60 \({}^{\circ}\)C, extraction time of 43 min, enzyme concentration of 2.5%, and pH of 6.0. The experimental value of 3.22 \(\pm\) 0.04% was obtained from three parallel experiments, compared with the prediction of 3.24%, this result confirmed that the response model was precise and satisfactory.  
Figure 3: Response surface (3D) showing the interactions between different extraction parameters. (**a**) Extraction temperature and extraction time; (**b**) Enzyme concentration and extraction time; (**c**) Extraction time and pH; (**d**) Enzyme concentration and extraction temperature; (**e**) Extraction temperature and pH; (**f**) Enzyme concentration and pH.  
Based on our previous research results, the yield of crude polysaccharides extracted from pumpkin seeds was 2.18% using HWE for 2.5 h [13]. Obviously, EAE showed higher efficiency with a shorter extraction time. At the same time, the yield of PSP (3.22%) using EAE was also higher than that of UAE (2.29%) with extraction time of 24 min and extraction temperature of 50 \({}^{\circ}\)C [14]. These data showed that EAE was an excellent method for polysaccharides extraction from pumpkin seeds.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 2 Motion and Distortion Induced Artefacts
### Involuntary Eye Motion
#### 2.2.1 Tremors  
Among all motion sources, tremors have the least weight on OCT volumetric distortion, as their amplitudes are around 8.5\({}^{\prime\prime}\), which corresponds to the diameter of a cone photoreceptor (approximately 6 \(\upmu\)m [23]). Taking into account that, on average, the lateral optical resolution of commercial OCT is approximately between 15-20 \(\upmu\)m, we would expect the amplitude of the tremors to fall within the range of the recording system noise, even though their frequency may range between 25-90 Hz [24]. Nevertheless, motion caused by tremors may become an issue on future OCT configurations such as in Adaptive Optics OCT (AO-OCT), where the lateral resolution is approximately 2-5 \(\upmu\)m [25; 26].
# Metal Mobility in Afforested Sites of an Abandoned Zn-Pb Ore Mining Area
## 3 Results
### Metal Concentrations  
#### 3.3.1 The Heap at Lgota  
The metal content varies depending on the profile depth and location. In the deepest Lgota I profile, located closest to the centre of the heap, there is a progressive increase in the concentrations of all investigated metals to the depth of about 130 cm. Below this horizon, concentrations drop several times. The maximum concentrations of Zn, Cd and Pb are 12,433 mg\(\cdot\)kg\({}^{-1}\), 138 mg\(\cdot\)kg\({}^{-1}\) and 3692 mg\(\cdot\)kg\({}^{-1}\), respectively. A similar regularity has also been observed for the Lgota II profile but the maximum metal concentrations appear there already at a depth of 37-47 cm. The maximum values are slightly lower than in the Lgota I profile (Zn\(\bullet\)10,923 mg\(\cdot\)kg\({}^{-1}\), Cd\(\bullet\)93 mg\(\cdot\)kg\({}^{-1}\) and Pb\(\bullet\)3117 mg\(\cdot\)kg\({}^{-1}\)). The Lgota III profile, located at the heap periphery, is characterised by lower metal contents than the previous ones, and for Zn, Cd and Pb they reach 6260 mg\(\cdot\)kg\({}^{-1}\), 77 mg\(\cdot\)kg\({}^{-1}\) and 3233 mg\(\cdot\)kg\({}^{-1}\), respectively. The most polluted strata there is at the depth of 5-20 cm (Table 3, Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **Unit** & **Lgota I** & **Lgota II** & **Lgota III** \\  & & **(n = 16)** & **(n = 9)** & **(n = 7)** \\ \hline
**fraction \textless{} 0.063 mm** & & & & \\ min-max & & 8.43–51.90 & 8.65–50.60 & 4.79–32.80 \\ _median_ & & 29.25 & 35.70 & 20.90 \\ (25 th; 75 th percentile) & & (19.58; 36.53) & (33.30; 40.50) & (12.83; 28.70) \\ \hline
**Cd** & & & & \\ min-max & & 12.50–138.20 & 10.18–92.95 & 8.13–77.20 \\ _median_ & & 75.02 & 61.57 & 15.69 \\ (25 th; 75 th percentile) & & (27.29; 116.35) & (23.65; 81.13) & (12.45; 31.04) \\ \hline
**Cu** & & & & \\ min-max & & 3.58–69.80 & 5.38–68.80 & 4.85–42.15 \\ _median_ & & 10.34 & 11.27 & 10.54 \\ (25 th; 75 th percentile) & & (7.69; 12.29) & (6.17; 11.93) & (8.62; 24.22) \\ \hline
**Pb** & & & & \\ min-max & & 532.0–3691.5 & 913.8–3116.9 & 158.8–3233.0 \\ _median_ & & 2376.6 & 2230.5 & 1053.1 \\ (25 th; 75 th percentile) & & (1324.4; 3262.2) & (1001.3; 2591.5) & (586.2; 1554.0) \\ \hline
**Zn** & & & & \\ min-max & & 559.6–12,433.0 & 1623.0–10,923.0 & 878.9–6260.0 \\ _median_ & & 8464.0 & 5829.0 & 2102.0 \\ (25 th; 75 th percentile) & & (2842.3; 9673.8) & (3551.0; 8362.0) & (1219.0; 3513.0) \\ \hline
**Mn** & & & & \\ min-max & & 557.1–2965.2 & 675.9–1377.4 & 465.8–1299.1 \\ _median_ & & 1105.9 & 962.6 & 919.1 \\ (25 th; 75 th percentile) & & (909.9; 1385.3) & (856.5; 1038.9) & (790.5; 1103.0) \\ \hline
**Fe** & & & & \\ min-max & & 500–45,893 & 18,646–37,880 & 16,977–50,935 \\ _median_ & & 34,360 & _28,406_ & _26,858_ \\ (25 th; 75 th percentile) & & (22,409; 37,930) & (26,882; 35,757) & (20,040; 34,537) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metals and silty-clay fraction concentration in the soil from profiles at the Lgota location.
# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
###### Abstract  
Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a \"public health emergency of international concern\" (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions.  
morkeypox; machine learning; time series; forecasting; stacking ensemble learning Article
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Case Study 2: Predicting the RUL of Bearings Using the XJTU-SY Dataset
#### 4.3.3 Ablation Experiment  
To assess the suggested method's ability for generalization, the design of the ablation trials aligns with the PHM 2012 dataset, and the test bearing selected is B1-2. Table 7  
Figure 14: Boxplots of MAE, RMSE, and scores on the XJTU-SY dataset.  
Figure 15: The XJTU-SY dataset contains RUL visualization prediction results for test bearings.  
displays the outcomes of the predictions, revealing that the _MAE_ and _RMSE_ outcomes of the proposed method exhibit a notable superiority over those of the comparative methods.
## 3 Materials and Methods
### Satellite Network Architecture
#### 3.2.1 Ground Segment  
The ground segment is a key terrestrial component of the satellite network, consisting of a _GCS_, \(GW_{1}\), \(GW_{2}\), an \(RT\), and an \(FW\) (Figure 2). This segment processes command and status data between satellites and user devices while managing network traffic to ensure efficient communication. The _GCS_ is responsible for satellite operations and control, including orbit management, data collection, command execution, and communication link establishment. Incoming packets from external networks pass through the \(FW\) for filtering before being forwarded to the _GCS_ via the \(RT\). The _GCS_ analyzes the received data and generates command data, which it transmits to \(MEO\) and \(LEO\) satellites using CCSDS. In actual satellite networks, each satellite connects to a dedicated _GCS_. However, for security modeling and experimental purposes, this study assumes a scenario where two satellites connect to a single _GCS_. Under this assumption, the _GCS_ can transmit satellite control commands to \(LEO\) satellites or send status data request commands to \(MEO\) satellites. \(GW_{1}\) facilitates data transmission between the \(IoT\) and a \(UAV\) using the MIOTY protocol and exchanges data with \(LEO\) satellites via the CCSDS. It collects environmental sensor data from the \(IoT\) and forwards it to \(LEO\) satellites while transmitting data from \(LEO\) satellites back to the \(IoT\). \(GW_{2}\) transmits data collected from the \(UAV\) to \(MEO\) satellites using the CCSDS and relays GPS data from \(MEO\) satellites back to the \(UAV\). The \(RT\) receives packets filtered by the \(FW\) and routes them to the _GCS_ or _GWs_ based on their destination addresses.
# A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks +
## 6 Conclusions  
A robust and energy-efficient weighted clustering algorithm on mobile Ad hoc sensor networks is put forward in this paper. Taking group mobility and residual energy into account, the algorithm focuses on the weight of nodes. In addition, the iteration times are decreased dramatically in the protocol, which can guarantee the energy-efficiency in the mobile networks. This protocol renders the nodes consuming energy more homogeneously and prolongs the network lifetime. In addition, we propose a periodic fault detection algorithm to enhance the fault tolerance and robustness of network. The topology maintenance is executed when fault nodes are detected or the average energy of all nodes is below a predetermined threshold value. Thus, the fault tolerance ability is enhanced and the throughput of network is more stable. The results of simulation also illustrate that the performance of throughput, lifetime and robustness of the network is much better than the other protocols.  
Our future work will be focusing on the robustness and trust estimation of the network. Currently, the security of the network is a hotspot. However, most of security authentications are trust value estimation through a frequent data aggregation. But clearly the energy consumption of data transmission is greater than the data computing. Therefore, we will consider a kind of trust estimation through minimum data transmission. We believe this field and direction will be made a big progress.  
Conceptualization, H.Q. and F.L.; Methodology, H.Q.; Software, T.X.; Formal Analysis, T.X.; Investigation, F.L.; Resources, S.J.; Data Curation, S.J.; Writing-Original Draft Preparation, T.X.; Writing-Review & Editing, F.L.; Project Administration, H.Q.; Funding Acquisition, T.X.  
This research has been sponsored by Natural Science Foundation of China (project number: 61572528).  
The work is also supported by Central South University of College students' free exploration project (project number: 201810533168) and Central South University of College students' research studying and innovative experiment project (project number: YC20180624).  
The authors declare no conflict of interest.
## 2 Theoretical Background
### The Use of a Complementary Filter for Estimation of Headwind Gusts  
The research hypothesis of this work assumes that the proposed method of estimating the value of the headwind component defined by Equation (1) and the temporary value of its change (gust) defined by Equation (12) can be used complementarily. Equation (1) can be used to determine the total value of the wind speed (headwind component). Due to the nature of the measured speeds GS and TAS (considerable delay of measurements), there are hard limitations related to the quick and precise detection of gusts. Equation (12), on the other hand, may be used to estimate the value of short-term changes in the headwind component, but it cannot be used to determine its absolute value. A natural solution in this situation seems to be the use of a complementary filtration system. The complementary filter (CF) is a computationally inexpensive and relatively efficient data fusion technique that consists of a low-pass and a high-pass filter [27]. The general structure of CF (Figure 2) consists of low- and high-frequency inputs of the composite signal.  
Figure 2: Complementary filter diagram.  
The complementary filter transfer function is represented by Equation (14). The amplitude of linear CF is unity (0 dB magnitude) over the complete frequency range. The phase shift is also linear and equals 0 deg (Figure 3).  
\[EST=\frac{1}{T_{c}\cdot s+1}\cdot LF+\frac{T_{c}\cdot s}{T_{c}\cdot s+1}\cdot HF \tag{14}\]  
_LF_--low-frequency signal,  
_HF_--high-frequency signal,  
_EST_--output of CF.  
On the basis of dependencies (1), (2), (12)-(14), the complete law of headwind estimation (including temporary gusts) can be formulated as (15), and finally, as Equation (16).  
\[U_{g\text{-}est\_CF}=\frac{1}{T_{c}\cdot s+1}\cdot U_{g\text{-}lo}+\frac{T_{c} \cdot s}{T_{c}\cdot s+1}\cdot u_{g\text{-}hi} \tag{15}\]  
\[U_{g\text{-}est\_CF}=\frac{1}{T_{c}\cdot s+1}\cdot(GS_{x}-TAS)+\frac{T_{c} \cdot s}{T_{c}\cdot s+1}\cdot(k_{1}\cdot\hat{q}-k_{2}\cdot q+k_{3}\cdot\delta_{ H}+k_{4}\cdot u) \tag{16}\]
## 3 Methodology
### Model Computational Time and Latency  
In addition to recognition performance, we evaluated the computational efficiency of each model by measuring both inference time and end-to-end latency. While inference time reflects the duration taken solely by the model to produce predictions, end-to-end latency accounts for the total processing time, including data acquisition and preprocessing. Table 14 presents the evaluation time and end-to-end latency.