esa-sceva/satcom-chunk-collection huggingface dataset:
# Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
## 1 Introduction
It overcomes the shortcoming of traditional RNNs in handling long-term dependencies, making it a suitable choice for tasks that involve sequences with temporal dependencies, such as the motion of a robotic manipulator. On the other hand, XGBoost, an efficient implementation of gradient boosting machines, is appreciated for its capacity to capture complex non-linear relationships and its robustness against overfitting. The gradient-boosting algorithm focuses on minimizing the prediction error by adding new models to the ensemble sequentially. In the realm of manipulator stability research, the prevalent approach is to enhance the path planning algorithm to suppress vibrations. Most methods tend to view stability as an optimization index rather than a target for evaluation. Separately, there are studies that use vibration directly as a stability evaluation index. However, these primarily focus on detecting manipulator anomalies, overlooking the stability prediction under regular operating conditions. This paper proposes a different approach. We extract the cumulative characteristics of action from the manipulator's vibration signal subsequence. The command parameters of each action are processed to create a stability index evaluation system centered on a scorecard. Moving on, we construct a 'command-feature index' prediction model leveraging XG Boost. Subsequently, we introduce the variable-length LSTM algorithm to establish a 'feature index-stationarity score' prediction model. While the former predicts command-feature indices, the latter is used for forecasting stationarity scores. Lastly, we put our models to the test using the SR4B industrial manipulator data set. The results demonstrate that our models deliver accurate predictions, verifying their effectiveness.
## 2 Preliminaries  
### Federated Learning  
Federated learning is a machine learning technique that enables the training of models across multiple decentralized devices or servers, without transferring data to a central location [18; 19]. In federated learning, the data remain on local devices, and the model is trained locally, with only the updated model parameters being sent to a central server for aggregation. This approach creates robust and scalable models while maintaining data privacy and security, as the raw data never leave the local device. Federated learning finds applications in scenarios where data are both sensitive and decentralized.
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 2 Thermal Observations of Active Volcanoes  
An active lava flow emits more radiation than its surroundings and variations in the amount of radiated heat reveal a lot about the processes that take place within these flows. The temperature within a lava flow ranges between 400 and 1200 K [13]. According to Wien's displacement law, the peak emission of the radiance for blackbody surfaces at temperatures 600-1000 K is between 3 and 5 \(\upmu\)m (_i.e._, in the medium infrared (MIR)). For a temperature of 300 K (typical surface temperature of the Earth), which is the expected temperature to be measured by earth orbiting radiometers, the peak of the radiance emission is located at approximately 10 \(\upmu\)m (_i.e._, in the thermal infrared spectrum (TIR)).  
Satellite instruments and field thermal cameras can measure spectral radiance at different wavelengths. If the measured surface is isothermal on the pixel scale the spectral radiance can easily be converted to a temperature ([PERSON]'s law). However, the surface of an active volcano is composed of lava crust of different ages, possibly fresh lava, vegetation, water, _etc._ Considering [PERSON]'s law, it is also possible to detect and quantify volcanic thermal anomalies even if they do not cover an entire satellite pixel. The detected thermal anomalies are usually quantified by their temperature, area, volcanic radiant power (\(VRP\)), or time averaged lava discharge rate (_TADR_) [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. The uncertainty of single measured spectral radiances depends on numerous factors such as different sensor's point spread function and spectral response functions, different time of overpass, orbit geometry, and spatial resolution [25; 26].
# Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network
## 6 Conclusions  
Based on six constant fault models of power quality, this study proposed a power quality fault recognition method based on SDP and CNN. It can effectively identify the fault types, including voltage sag, voltage swell, voltage interruption, voltage harmonic, and voltage flicker. First, the signals were converted by SDP and imported into the CNN for recognition. The results indicate that the recognition rate of the proposed method reached 94.2%. The recognition accuracy was increased by almost 5% compared to other algorithms. Additional types of power quality events, such as voltage impulse, three-phase unbalance, and frequency drift, can be included in future research to construct power quality accident data signals more completely.  
Methodology, M.-H.W.; Validation, S.-H.C.; Resources, M.-H.W.; Writing--original draft, S.-H.C.; Writing--review & editing, S.-H.C.; Supervision, S.-D.L. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support of the Ministry of Science and Technology of Taiwan, under contract number: MOST 110-2221-E-167-008-MY3.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Results
### Performance in Frequency-Selective Channel  
As indicated in Table 3, we have chosen the ITU outdoor channel model A with six taps to produce the multipath components [52].  
performance is observed when using \(h=7/16\). When using the zero-forcing receiver, there is no value of \(h\) that beats the conventional GFDM, the closest being \(h=7/16\), as has been shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**AWGN Channel**} & \multicolumn{4}{c}{**Frequency-Selective Channel**} \\ \hline  & \multicolumn{2}{c}{_J=2_} & \multicolumn{2}{c}{_J=4_} & \multicolumn{2}{c}{_J=8_} & \multicolumn{2}{c}{_J=2_} & \multicolumn{2}{c}{_J=4_} & \multicolumn{2}{c}{_J=8_} \\ \hline \(h\)=\(\frac{p}{q}\) & MF & ZF & MF & ZF & MF & ZF & MF & ZF & MF & ZF & MF & ZF \\ \hline
1/2 & ✓ & - & ✓ & - & - & ✓ & ✓ & - & - & - & - & -  
Figure 13: BER performance of binary CPM-GFDM vs. BPSK-GFDM for zero forcing receiver over frequency-selective channels.  
Figure 14: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=4\) for matched filter receiver over frequency-selective channels.  
Figure 15: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=4\) for zero forcing receiver over frequency-selective channels.  
Figure 16: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=8\) for matched filter receiver over frequency-selective channels.  
Figure 17: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=8\) for zero forcing receiver over frequency-selective channels.  
The error performance of CPM-GFDM against QAM-GFDM with \(J=4\) is shown in Figures 14 and 15 for the two receivers. For the case of the matched filter receiver, there are five values of \(h\) for which the error performance of the proposed system is better than QAM-GFDM. The best performance is observed in the case when \(h=7/16\). The complete list of \(h\) values for which the proposed system outperforms QAM-GFDM is given in Table 4. When using a zero-forcing receiver, there are three values of \(h\) that perform better than QAM-GFDM. However, at high SNR values, \(h=3/10\) gives the best performance.  
Figures 16 and 17 depict the error performance with \(J=8\). It is observed that, in this case, there are four values of \(h\) that outperform conventional GFDM when using a matched filter receiver. The best performance is observed in the case when \(h=5/16\) and \(h=3/10\). Furthermore, it is noted in Figure 17 that, when using zero forcing receiver, the proposed system outperforms the conventional GFDM for three values of \(h\), i.e., \(h=1/4\), \(h=3/10\) and \(h=4/5\), with the best performance observed in the case of \(h=3/10\).
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.7 Laser + RF Coupling Measurement Technology  
Radio-laser coupling relative measurement technology is available in a variety of configurations [72]. Scheme one is to use the laser as the only measurement method, to obtain the distance and relative angle. The radio uses the laser-guided technique for cooperative measurement. Scheme two is that the radio acquires the baseline length and the laser acquires the relative angle to complete the information coupling, as shown in Figure 28.  
Scheme 1: The characteristics of wide radio coverage and its exploitation, along with radio detection, are first used to search and acquire targets. The obtained orientation information of the target spacecraft is used as the input of the laser measurement, to guide the  
Figure 27: Schematic diagram of visual + laser coupling measurement.  
laser for detection. Furthermore, if the target spacecraft is still in the laser beam range after the baseline measurement is completed, the target spacecraft will continue to be tracked and measured. This action is completed using the servo mechanism and the laser transmitting and receiving system. If the target spacecraft deviates, radio guidance is performed again to complete the state measurement. Using the four-quadrant method and the principle of phase-ranging, the precision of \(\upmu\)m-level distance measurement and arcsecond-level relative altitude measurement can be achieved. Scheme 2: First, the spacecraft is searched out and acquired using radio. The relative position and altitude of the target spacecraft are sent to the laser measurement system, and the laser is guided to measure the relative pose. At the same time, the distance measurement of the target spacecraft is realized using the radio microwave-ranging method. When the initial measurement is completed, if the target spacecraft is within the effective range of the signal, a closed-loop tracking measurement is assisted by the servo mechanism. If the target deviates from the laser beam coverage, radio guidance is performed again.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 1 Introduction
### Research Paper Organization  
The research paper is organized as follows: in Section 2, we present the literature review of the recent studies related to RPL rank attacks and RPL version number attacks. In Section 3, we describe the proposed SRPL-RP along with its design, description, and implementation. In Section 4, we give an overview of the simulation setup and present the analysis of the results of the proposed protocol and existing countermeasures. In Section 5, we present the discussion, which demonstrates the security analysis of the proposed SRPL-RP and justifies that SRPL-RP can significantly provide better results than the existing countermeasures in terms of network performance and accuracy. Finally, our conclusions are provided in Section 6, which wraps up the research, achieved objectives, and future works.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 6 Conclusions  
The problem of dual-functionality system design for joint radar and communications operation was considered. We further presented a two-step waveform optimization algorithm for the proposed system, which combines the waveform optimization and selection processes. The proposed algorithm is based upon constant learning of the radar scene at the receiver and adaptation of the probing codes to suit the dynamic target feature. The adaptive process ensures maximum information extraction from the target of interest. The effectiveness of the proposed technique and its superiority over existing techniques in terms of the BER performance and the target detection performance were demonstrated through extensive simulations. The proposed system would form a joint platform for future intelligent  
Figure 11: Probability of detection for adaptive waveform and static waveform.  
Figure 10: The SERs versus SNR for BPSK.  
transportation applications for which both environmental perception and establishment of data links are crucial. Future research will look into the tradeoff between the performance improvement offered by the proposed approach and the computational complexity involved.  
Conceptualization, Y.Y. and X.L.; Methodology, Y.Y.; Software, X.L.; Validation, Y.Y., X.L. and L.W.; Formal Analysis, Y.Y.; Investigation, Y.Y.; Resources, X.L.; Data Curation, X.L.; Writing-Original Draft Preparation, Y.Y.; Writing-Review & Editing, X.L.; Visualization, Y.Y.; Supervision, L.W.; Project Administration, L.W.; Funding Acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61761019, 61861017, 61861018, 61862024) and the Natural Science Foundation of Jiangxi Province (Jiangxi Province natural Science Fund) (20181 BAB211014, 20181 BAB211013) and Foundation of Jiangxi Educational Committee of China (GJJ180352).  
The authors declare no conflict of interest.
# Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model
## 4 Experimental Setup and Results
### Pose Estimation with Simulation Data  
For the performance validation of the proposed approach, we generated six poses; (1) stand and raise arms front; (2) stand and raise arms up; (3) walk with the left leg in front; (4) bend down and grab an object; (5) sit on a chair; and (6) kneel down.  
Figure 9 shows that the loss function profiles gradually minimize during 20 restarts for a given pose. It is apparent that the loss functions converge when the row length of the uDEAS matrices reach 10.  
Figure 10 shows that uDEAS successfully estimates the six poses under the current optimization configuration. Table 2 lists the MPJPE of each global minimum in Equation (7), the average angular difference between the 18 ground-truth joint angles in Equation (6), and the estimated ones for the six poses. The average MPJPE is 0.097 m, and the average angle difference per joint is 10.017 degrees, which is an acceptable result for pose estimation with a full-scale humanoid model. The body size was selected referring to average Korean women in their twenties [34]. It is worth noting that all poses were created with the body transverse angles (\(\psi_{bd}\)) between 20 and 40 degrees for generalization, which indicated that the camera view angle was set to the side of the subject.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & \(\gamma\) & \(\theta_{bd}\) & \(\phi_{bd}\) & \(\psi_{bd}\) & \(\theta_{tr}\) & \(\phi_{tr}\) & \(\psi_{tr}\) & \(\theta_{hp}^{l}\) & \(\theta_{kn}^{r}\) & \(\theta_{hp}^{r}\) & \(\theta_{kn}^{l}\) & \(\theta_{el}^{r}\) & \(\theta_{sh}^{l}\) & \(\theta_{el}^{r}\) & \(\phi_{hp}^{r}\) & \(\phi_{hp}^{l}\) & \(\phi_{sh}^{r}\) & \(\phi_{sh}^{r}\) \\ \hline \(\overline{\
abla}\) & 1 & 10 & 10 & 90 & 90 & 40 & 30 & 90 & 90 & 90 & 180 & 90 & 180 & 90 & 40 & 40 & 40 & 40 \\ \hline \(\underline{\
abla}\) & 1 & \(-10\) & \(-10\) & \(-90\) & \(-20\) & \(-40\) & \(-30\) & \(-20\) & \(0\) & \(-20\) & \(0\) & \(-180\) & \(0\) & \(-180\) & \(0\) & \(-40\) & \(-40\) & \(-40\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Upper and lower bounds of the optimization variables (in degrees).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pose** & **1** & **2** & **3** & **4** & **5** & **6** & **Avg.** \\ \hline MPJPE (m) & 0.0055 & 0.0099 & 0.0111 & 0.0049 & 0.0150 & 0.0116 & 0.097 \\ \hline Avg. ang. diff (deg) & 6.061 & 7.748 & 10.557 & 5.6 & 14.558 & 15.58 & 10.017 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MPJPE and average angular difference values for the best-fitted poses in Figure 10.  
Figure 9: Minimization aspect of loss function during 20 restarts of uDEAS with each restart colored differently.
# A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
###### Abstract  
To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing utilized resources. Due to the higher level of automation with respect to traditional agricultural systems, Controlled Environment Agriculture (CEA) applications generally achieve better yields and quality crops at the expenses of higher energy consumption. In this context, Digital Twin (DT) may constitute a fundamental tool to reach the optimization of the productivity, intended as the ratio between production and resource consumption. For this reason, a DT Architecture for CEA systems is introduced within this work and applied to a case study for its validation. The proposed architecture is potentially able to optimize productivity since it utilizes simulation software that enables the optimization of: (i) Climate control strategies related to the control of the crop microclimate; (ii) treatments related to crop management. Due to the importance of food security in the worldwide landscape, the authors hope that this work may impulse the investigation of strategies for improving the productivity of CEA systems.  
controlled environment agriculture; digital twin; productivity; architecture; optimization Article
## 1 Introduction
Today, the performance of AI-based CAD systems is comparable to that of experienced radiologists, owing to continuous technical developments and increased dataset quantity and quality [23; 24]. [PERSON] et al. evaluated the agreement of the diagnostic accuracy of five PI-RADS lesions as the ground truth between human readers and fully automated AI-based software and demonstrated that the AI-based software was able to identify highly suspicious lesions in image-guided prostate cancer screening [23]. [PERSON] et al. presented a CAD system for automated localization of clinically significant prostate cancer with multiparametric MRI and achieved a 0.882 area under the receiver operating characteristics curve (AUROC) in patient-based diagnosis [24]. For the GS classification, with the recent advancement of deep learning techniques, various GS prediction models have been proposed. [PERSON] et al. proposed a novel multi-class CNN, FocalNet, that jointly detects prostate cancer lesions and predicts their GS from multiparametric MRI and calculated an AUROC of 0.81 and 0.79 for the classifications of clinically significant prostate cancer (GS = 3 + 4) and prostate cancer (GS = 4 + 3), respectively [25]. We hypothesized that if the GS is estimated from MRI with high accuracy by 3D-CNN using semantic segmentation to crop the image and restrict the evaluation range to the prostate, the grade group can be determined without a highly invasive prostate biopsy, and as a result, the physical burden on the patient can be reduced. Furthermore, there is significant variability among observers depending on their experience in processing and interpreting prostate cancer diagnostics [11; 12; 26]. By using the quantitative features from MRI, an AI-based GS estimation system may not only automate and support the radiologist's workflow but also alleviate the workload of medical staff. Therefore, this study aimed to construct a system to estimate GS non-invasively from diagnostic MRI images. The present study is organized as follows: in Section 2, we describe MRI data and the technical framework for this study. Section 3 presents the results of precision, recall, overall accuracy and AUROC. In Section 4, we discuss the potential implications and extensions of this study, followed by concluding remarks.
## 3 Learning Model
### Main Idea  
The acquired power that is produced by a uniformly arranged beam can be considered as the image. Through this, low-resolution and high-resolution beam images can be seen as the gain powers of wide and narrow beams, respectively. The use of wide beam measurements to measure the beam quality of narrow beams is the main idea of the model. Thus, the goal that we aim for is to apply the super-resolution onto the beam-domain images. By combining the two models, the proposed beam-selection method is constructed. One is a model that estimates the quality of the beam, and the second is an estimation model that is based on predictions. The study of the first model is on the difference between low-resolution and high-resolution beams in the beam domain, and the output of the second model is the current high-resolution image of the laser in the beam domain, which requires some empirical values. At regular intervals, the receiver receives an uplink pilot signal.  
According to the uploaded signal, the BS can estimate the quality of the current beam, and it can also predict the beam quality within the coherence time of the next channel. Figure 3 shows the envisioned scheme framework for beam selection for high-resolution beamfield images and low-resolution images, in turn, and the unroll predictions from multiple low-resolution images, since the predicted result reduces the number of measurements per unit time, which means a reduction in the overhead, in turn. The goal is to acquire correlations in the spatial domain in images, and both models employ CNN techniques to achieve super-resolution. In addition, the prediction model in this paper adopts the deep-learning algorithm that is borrowed from the LSTM network, and by considering the time relationship of the beam quality, the computational cost is further optimized. In order to preserve useful information, the LSTM extracts the variation in the beam in the time domain, which can foresee the current quality value of the beam from the previous beam-measurement values. The rest of this section will cover the estimation and the prediction of the beam quality in detail.