esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study focused on the characteristics of sparsely distributed forest land in the new round of GGP stands in Inner Mongolia. We developed the optimal tree height-diameter at breast height growth models for the major tree species and constructed a high-quality AGB sample dataset by integrating airborne LiDAR data and tree species information. With the assistance of Sentinel-2 images and a gradient boosting regression tree model, the AGB of major tree species in these areas was estimated. The study draws the following conclusions:  
1. The high-quality AGB sample set constructed using LiDAR data and optimal growth models showed excellent agreement with field-measured data, making it a reliable substitute for traditional ground-based measurements in model development.
2. This study used the AGB sample set, combined with Sentinel-2 images, to establish an AGB estimation model for the main tree species in the GGP stands. This can obtain the AGB of the main tree species in the GGP stands, with an R\({}^{2}\) of 0.96 and an RMSE of 560 g/m\({}^{2}\), which can provide reference and support for the evaluation of the effectiveness of GGP and ecological benefits.
3. The tasseled cap transformation index (TCG) and vegetation indices such as RENDVI and VDVI were identified as key variables sensitive to the AGB estimation model. These variables have been demonstrated to be effective for extracting vegetation information in arid and semi-arid regions.
4. The AGB of the major tree species in the new round of GGP stands in Inner Mongolia ranged from 120 to 9253 g/m\({}^{2}\). The mean AGB of poplar was 978 g/m\({}^{2}\), the mean AGB of Mongolian Scots pine was 622 g/m\({}^{2}\), and the mean AGB of Chinese red pine was 313 g/m\({}^{2}\). This distribution characteristic provides a reference for the follow-up conservation and management of the GGP.  
Conceptualization, G.Y. and X.L.; methodology, G.Y., X.L. and C.J.; software, G.Y. and T.W.; validation, G.Y. and T.W.; formal analysis, G.Y.; investigation, G.Y., T.W., Y.Z., C.C. and L.Z.; resources, X.L.; data curation, G.Y. and T.W.; writing--original draft preparation, G.Y.; writing--review and editing, G.Y., X.L. and C.J.; visualization, G.Y.; supervision, X.L. and G.G.; project administration, X.L. and T.S.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Monitoring and Evaluation of Ecological Effectiveness of Grain for Green Program in Inner Mongolia Autonomous Region and Establishment of Database (Phase II) and the Capacity Building-MFST-Establishment and Demonstration Application of Land Degradation Neutrality Decision Supporting System of Mongolian plateau, grant number 42261144748.  
Not applicable.  
Data Availability Statement:Data will be made available upon reasonable request.  
The authors declare no conflicts of interest.
###### Abstract  
Image dehazing has always been one of the main areas of research in image processing. The traditional dark channel prior algorithm (DCP) has some shortcomings, such as incomplete fog removal and excessively dark images. In order to obtain haze-free images with high quality, a hybrid dark channel prior (HDCP) algorithm is proposed in this paper. HDCP first employs Retinex to remove the interference of the illumination component. The variant genetic algorithm (VGA) is then used to obtain the guidance image required by the guided filter to optimize the atmospheric transmittance. Finally, the modified dark channel prior algorithm is used to obtain the dehazed image. Compared with three other modified DCP algorithms, HDCP has the best subjective visual effects of haze removal and color fidelity. HDCP also shows superior objective indexes in the mean square error (_MSE_), peak signal-to-noise ratio (_PSNR_), structural similarity index (_SSIM_), and information entropy (_E_) for different haze degrees.  
image dehazing hybrid dark channel prior (HDCP); variant genetic algorithm (VGA); Retinex +
Footnote †: journal: Medical Image Generation
## 3 Results and Discussion
### Micro-Oxygenation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entry** & **O\({}_{2}\) (\%vol \(\pm\) 0.01)** & **H\({}_{2}\)S (ppm\({}_{v}\))** & **H\({}_{2}\)S Removal (\%)** \\ \hline
1 & 0.0 & 860 & // \\
2 & 0.1 & 506 & 41.2 \\
3 & 0.2 & 319 & 62.9 \\
4 & 0.3 & 264 & 69.3 \\
5 & 0.4 & 268 & 68.8 \\
6 & 0.5 & 262 & 69.5 \\
7 & 0.6 & 268 & 68.8 \\
8 a  & 0.3 & 161 & 81.3 \\
9 a  & 0.4 & 137 & 84.1 \\
10 a  & 0.5 & 135 & 84.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of O\({}_{2}\) insufflation test. 9-10), the plateau level was reached with an H\({}_{2}\)S removal efficiency of about 84%. In these cases, a higher digester headspace volume allowed for a greater contact time between O\({}_{2}\) and biogas H\({}_{2}\)S for the sulfide oxidation process and a larger surface area colonized by sulfur-oxidizing bacteria for the H\({}_{2}\)S biological oxidation. Indeed, it is known [25] that sulfur-oxidizing bacteria grow on the walls of the digester headspace where elemental sulfur is biologically produced and deposited. Consequently, the greater colonizable surface area resulted in higher bacterial activity and H\({}_{2}\)S removal efficiency. On the other hand, increasing the digester headspace implied a smaller useful volume for the AD process with consequent lower biogas production and economic income. In all of the tests carried out, an O\({}_{2}\) concentration equal to 50-55% of that introduced into the digester headspace was measured in the outgoing biogas flow. Therefore, it can be deduced that just 45-50% of the O\({}_{2}\) supplied to the digester headspace was used for the H\({}_{2}\)S oxidation process. These data were in agreement with those reported by the study of [PERSON] et al. [38] according to which just 30-40% of the total O\({}_{2}\) supplied to the digester serves H\({}_{2}\)S oxidation.
## 1 Introduction
If the result is judged to be an attack, the keys can only be generated from single-photon pulses, and the secure key rate will be estimated by the existing decoy-state method. Otherwise, all pulses with the same basis leading to a successful Bell state measurement (BSM) event including both single-photon pulses and multiphoton pulses can be used to generate keys, and we give the formula of the secure key rate in this case. Furthermore, we use the real experimental data in [36] to verify our method, and the analytical results show that our method is credible (e.g., a significance level of 5%). The structure of this paper is organized as follows. In Section 2, we briefly review the typical decoy-state MDI-QKD and related notations. In Section 3, we describe our method for detecting the PNS attack in the decoy-state MDI-QKD via statistical hypothesis testing in detail. In Section 4, the correctness of our method is verified with the real experimental data from the existing literature. Finally, we discuss and draw conclusions in Section 5.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 2 Research Approach and Design
### Activity-Based Classification of IoT Applications
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activity** & **Domain Examples** & \
\begin{tabular}{c} **5G Service** \\ **Classification** \\ \end{tabular} &
\begin{tabular}{c} **Application Examples** \\ \end{tabular} \\ \hline \hline Ticketing & Smart Transportation and Logistics & mMTC & POS Terminal [4] \\ \hline \multirow{4}{*}{Monitoring} & Smart Healthcare & uMTC & Health condition [97] \\ \cline{2-4}  & Smart Buildings & uMTC & Structures (buildings, tunnels, etc.) [12; 25; 213] \\ \cline{2-4}  & Smart Buildings/Smart City & mMTC & Parking spaces \\ \cline{2-4}  & Smart Buildings/Smart Environment & mMTC & Home video [12] \\ \cline{2-4}  & Smart food/water monitoring & mMTC & Food growth condition [10] \\ \hline \multirow{4}{*}{Tracking} & Smart Healthcare/Sport and Leisure & mMTC & Medical assets, wearables [200; 213] \\ \cline{2-4}  & Smart Transportation and Logistics & mMTC & Transport fleet [200; 213] \\ \cline{2-4}  & Smart Industry/Social Networking & mMTC & Shipping of products \\ \cline{2-4}  & Smart Healthcare & mMTC & People in science museum \\ \hline \multirow{4}{*}{Managing/controlling} & Smart Healthcare & uMTC & Remote surgery [213] \\ \cline{2-4}  & Smart food/water monitoring & uMTC & Food processing facilities [96] \\ \cline{1-1} \cline{2-4}  & Smart Transportation and Logistics & uMTC & Traffic, driving [12; 213] \\ \cline{1-1} \cline{2-4}  & Smart Energy & mMTC & Energy distribution [12; 98] \\ \hline \hline \end{tabular} Legend: 5G (Fith Generation), POS (Point Of Sale), mMTC (massive Machine Type Communication), uMTC (ultra-reliable Machine Type Communication).  
\end{table} 
Table 3: Activity-based classification of IoT applications. IoT stakeholders that can participate in more than one layer. Based on their role in the IoT ecosystem, telecom operators usually take a part in the network communication layer. In this regard, they follow four evolutionary business models [23]: (1) selling connectivity services only; (2) selling third-party products; (3) selling internal products; (4) providing a broad menu of IoT products. Each of these IoT business models includes traditional telcos strength, i.e., connectivity which determines the how dimension of the IoT business model. In this sense, our activity-based classification of IoT applications indirectly allows the construction and distribution of performance as IoT value proposition using enabling technologies as IoT resources in telco's internal value chain.
###### Abstract  
In large-scale wireless sensor networks (WSNs), nodes close to sink nodes consume energy more quickly than other nodes due to packet forwarding. A mobile sink is a good solution to this issue, although it causes two new problems to nodes: (i) overhead of updating routing information; and (ii) increased operating time due to aperiodic query. To solve these problems, this paper proposes an energy-efficient data collection method, Sink-based Centralized transmission Scheduling (SC-Sched), by integrating asymmetric communication and wake-up radio. Specifically, each node is equipped with a low-power wake-up receiver. The sink node determines transmission scheduling, and transmits a wake-up message using a large transmission power, directly activating a pair of nodes simultaneously which will communicate with a normal transmission power. This paper further investigates how to deal with frame loss caused by fading and how to mitigate the impact of the wake-up latency of communication modules. Simulation evaluations confirm that using multiple channels effectively reduces data collection time and SC-Sched works well with a mobile sink. Compared with the conventional duty-cycling method, SC-Sched greatly reduces total energy consumption and improves the network lifetime by 7.47 times in a WSN with 4 data collection points and 300 sensor nodes.  
sensor network; energy efficient; mobile sink; asymmetric communication; wake-up radio +
Footnote †: journal: Sensors
# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Discussion about Robotic Autonomy  
Robots have been progressively moving the frontier of tasks that can be conducted remotely, collaboratively, or autonomously. The raising autonomy of robots helped humans secure and optimize basic tasks but also opened the way to new applications which are hardly-feasible or not feasible manually such as in several micro-scale surgical tasks [51; 53; 377; 378], some maxillofacial surgeries [360], some dental surgeries [93], and some ophthalmic operations [148; 378]. There remain several operations that are challenging for robots especially those including the unpredictability of the environment (such as autonomy on soft and deformable tissues) and those requiring a high level of domain knowledge for fast reflexive reactions (in extremely sensitive operations). Research in robotics has augmented sensorimotor skills of human and at the same time provided insight on ways to cooperatively perform tasks which are not feasible by robot only or human only.  
Thanks to the arrival of new telecommunication paradigms such as 5G and beyond, a new generation of teleoperated applications can be envisioned with ultimate fidelity and safety of force feedback thanks to unprecedented quality of service (very low bandwidth, delays, and jitters) of novel communication systems and performance of advanced control algorithms.  
It can be imagined that the level of autonomy of medical telerobots and patient safety will progressively increase along with technological progress.  
Based on the current trend, we can envision that autonomous telerobots will not aim to replace surgeons but to expand human capability through more efficient, adaptive, high-level, and safe operation offered by robotic dexterity and artificial intelligence.
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 4 Conclusions  
In this paper, we presented a novel approach to the initial specification of the noise component in a Gaussian mixture model, offering a new and effective methodology for the challenging problem of anomaly detection. Our proposed approach involves an automatic procedure for selecting the initial outlying observations to be used in the EM algorithm for Gaussian mixture models with a noise component. Specifically, the initialization of the noise is based on a comparison of the contribution of each data point to the entropy of the Gaussian mixture with the contribution arising from a uniform distribution over the hyper-rectangle that encloses the data.  
We demonstrated the effectiveness of our proposal by successfully identifying anomalies in both simulated and real-world datasets, even in the presence of significant levels of noise and outliers. However, there is still much room for improvement and future research in this area. A comprehensive comparison with other state-of-the-art methods would be valuable for evaluating the effectiveness of our proposal. Another promising avenue for future research is to expand the scope of our study by using simulated data from a broad parameter landscape. This would allow for the investigation of the sensitivity and specificity of the procedure under a wider range of conditions and the identification of areas where further improvements are needed. In particular, close attention should be devoted to the specificity of the procedure, which is a critical measure of its performance in distinguishing normal data from anomalous data without mistakenly flagging normal data as anomalous.  
Finally, we mentioned that our proposal is applicable to a wide range of applications, making it a valuable contribution to the field of anomaly detection using Gaussian mixture  
Figure 11: Scatterplots of selected features for the Wisconsin diagnostic breast cancer data with data points marked according to the classification from the GMM(EVI,2) with the noise component model and ellipses representing estimated cluster covariances. The 1 st cluster is represented using the \(\bullet\) symbol and it is made by all malignant cases, while the 2 nd cluster is represented using the \(\square\) symbol and it is mainly composed by benign cases. Finally, data points classified as noise are represented using a + symbol.  
models. Overall, this paper can help advance the state of the art in robust and effective anomaly detection, and it will be of interest to researchers and practitioners working in this field.  
This research received no external funding.  
All the analyses have been conducted in R [18] using the mclust package [19; 20]. Code to reproduce the analyses is available in a GitHub repository at [[https://github.com/luca-scr/GMM_Entropy_Anomaly_Detection](https://github.com/luca-scr/GMM_Entropy_Anomaly_Detection)]([https://github.com/luca-scr/GMM_Entropy_Anomaly_Detection](https://github.com/luca-scr/GMM_Entropy_Anomaly_Detection)). Last access on 2 April 2023.  
The author declares no conflict of interest.
## 4 Experimental Data and Simulation Results
### Simulation Results  
This paper conducted simulation analysis for the RSM. The no-load excitation current and the winding current with load under various DC bias currents were calculated by the time domain field-circuit coupling FEM and these results will be presented in Section 4.3 for making a comparison with the experimental data. The flux distribution in Figure 14 clearly shows that the core was severely saturated under DC bias current, especially in the main leg and the T-joint area.
## 4 Evaluation
### Performance Metrics  
We evaluate FDM's ability to identify outliers by using the metrics involved in the confusion matrix: Accuracy, Recall, Precision, FAR, and F-Score [58]. We use the classical metrics used by other works in the literature for evaluating ML algorithms and Interpolation techniques [28; 37; 59]. Table 4 presents the confusion matrix, the terms \"positive\" and \"negative\" refer to the classifier's prediction (i.e., normal or outlier). The terms \"true\" and \"false\" refer to whether the prediction corresponds to the proper observation. The True Negative indicates the outliers detected correctly. False Positive denotes values classified as normal that were outliers. False Negative exposes outliers incorrectly identified. True Positive states the well-classified normal values. Accuracy is the proportion of normal and outlier values correctly classified among the total number of classifications (see Equation (1)). Accuracy answers the question: how many classifications of the algorithm were identified correctly? Recall refers to the percentage of total normal values classified correctly by the algorithm (see Equation (2)). Recall answers the question: how many instances of the algorithm were identified correctly? Precision is the fraction of normal values that are properly-identified among the instances classified as normal (see Equation (3)). Precision answers the question: how many instances of the algorithm were predicted correctly? F-Score is the harmonic mean of Precision and Recall (see Equation (4)). F-Score is best if there is a balance between Precision and Recall. False Alarm Rate is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Timestamp** & **Value\_temp** & **Test\_1** \\ \hline (dd/mm/yyyy hh:mm:ss x.x.) & (int) & (float) & (float) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset structure.  
the percentage of falsely detected normal values of the instances classified as outliers (see Equation (5)). FAR answers the question: how many outliers of the algorithm were identified incorrectly?  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{1}\]  
\[Recall=\frac{TP}{(TP+FN)} \tag{2}\]  
\[Precision=\frac{TP}{(TP+FP)} \tag{3}\]  
\[F-Score=\frac{(2\times Recall\times Precision)}{(Recall+Precision)} \tag{4}\]  
\[FAR=\frac{FP}{(FP+TN)} \tag{5}\]  
We evaluate FRM's ability to infer data to replace outliers by using RMSE [60]. RMSE is a method of measuring the difference between values predicted by a model and their actual values. RMSE measures the amount of error between any two datasets. In this vein, we calculate the RMSE of the original temperature datasets versus the datasets interpolated to identify the most accurate technique. The less RMSE, the better the performance of the interpolation technique.
## 3 Results and Discussions
The absolute antenna efficiency of the proposed design is represented in Figure 2:7 The circular disk-shaped MIMO antenna and CSRR metamaterial- shaped MIMO antenna represent good radiation efficiency. The circular-shaped MIMO describes more than 0.92 of absolute radiation efficiency over the 4 to 15 GHz frequency span, and the CSRR-shaped MIMO antenna means more than 0.90 of absolute radiation efficiency over the 4.2 to 15 GHz frequency span. Figure 2:8 Radiation pattern of presented design structure. (**a**) Circular disk-shaped patch structure. (**b**) CSRR-shaped patch structure. Figure 2:9 Antenna gain pattern of presented design structure. (**a**) Circular disk-shaped patch structure. (**b**) CSRR-shaped patch structure.
## 5 Latency Mitigation Strategies
### Control Latency Approaches
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Domain** & **Teleoperation Entity** & **Algorithm** \\ \hline
[53] & UGV teleoperation & Predict human steering behaviour & Two-point visual steering model based on PI controller, adaptive control of thought–rational (ACT-R) cognitive model \\ \hline
[75] & Teleoperating autonomous vehicles, flight simulator & Control prediction & PID control model to predicted motion \\ \hline
[76] & Robotic teleoperation for surgery & Motion scaling for control & Constant, position and velocity scaling to improve performance and decrease errors during delay \\ \hline
[72] & Tele-driving & Control prediction & Model predictive control (MPC) and Kalman filter \\ \hline
[77] & Robotic bilateral teleoperation & Control & Radial basis function (RBF) neural network (NN)-based four-channel wave-based time-domain passivity approach (TDPA), Lyapunov control \\ \hline
[71] & Robotic teleoperation & Control prediction & Model predictive control (MPC) and linear interpolation to predict state and avoid obstacles \\ \hline
[78] & Connected vehicles & Connected cruise control & Optimal control using linear quadratic regulation and minimising a cost function \\ \hline
[79] & Bilateral teleoperation & Control & TDPA and time delay power network (TDPN) to achieve position synchronisation \\ \hline
[55] & Teleoperated ground vehicles & Control prediction & Predicted trajectory guidance control (PTGC) and deep-learning-based LSTM model to predict trajectory \\ \hline
[43] & Bilateral teleoperation & Control prediction & Survey of predictive control approaches \\ \hline
[126] & Road vehicle teleoperation & Control and haptic prediction & Two-stage predictive approach environment model (Bayesian filters) and haptic feedback to compensate delay, warn collisions and assists \\ \hline
[80] & Bilateral teleoperation & Adaptive control, reduce haptic data during delay and improve QoE & Delay-adaptive control switching scheme between TDPA and MMT \\ \hline
[81] & Bilateral teleoperation & Adaptive control & Radial basis function (RBF) neural network-based controller \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 2 System and Signal Model
### RF Link  
The received RF signal at the relay is given by  
\[y_{SR_{i}}^{RF}=\sqrt{P_{r,SR_{i}}G_{SR_{i}}}I_{SR_{i}}x_{rf}+n_{RF}, \tag{4}\]  
where \(x_{rf}\) is the modulated signal transmitted by the RF transmitter; \(P_{r,SR_{i}}\) is the transmitted RF power of the source. The RF channel coefficient of the first hop is \(I_{SR_{i}}\), and \(n_{RF}\) is the AWGN with zero-mean and variance \(\sigma_{rf}^{2}\). \(G_{SR_{i}}\) denotes the path loss for the source-to-relay RF link.  
The instantaneous received SNR at \(R_{i}\) can be expressed as  
\[\gamma_{SR_{i}}^{RF}=\frac{P_{r,SR_{i}}G_{SR_{i}}}{\sigma_{rf}^{2}}I_{SR_{i}}^{ 2}=\overline{\gamma}_{SR_{i}}^{RF}I_{SR_{i}}^{2}, \tag{5}\]  
where \(\overline{\gamma}_{SR_{i}}^{RF}\) is the average SNR for the source-to-relay RF link. Moreover, the instantaneous SNR expression for the relay-to-destination RF link is given by  
\[\gamma_{R_{i}D}^{RF}=\frac{P_{r,R,D}G_{R,D}}{\sigma_{rf}^{2}}I_{R,D}^{2}= \overline{\gamma}_{R_{i},D}^{RF}I_{R_{i}D}^{2}, \tag{6}\]  
where \(\overline{\gamma}_{R_{i},D}^{RF}\) is the average SNR for the relay-to-destination RF link, and \(P_{r,R_{i}D}\) is the average transmitted power of the relay; \(G_{R_{i}D}\) and \(I_{R_{i}D}\) indicate the channel coefficient and path loss of the second hop, respectively.