esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Aerodynamic Design of a Rotor
## 4 Results and Discussion
### Effect of Tip Sweep  
The blade tip plays an important role in rotor aerodynamic performance, since the highest aerodynamic pressure and the strong tip vortex are in this region. A well-designed tip planform can enhance rotor performance significantly. A blade planform with tip sweep is shown in Figure 11.  
Figure 10: Induced power loss and profile power loss with blade taper variation.  
Figure 9: Section drag with twist variations in \(\mu=0.18\).  
The study chooses the tip sweep angle \(\Lambda\) and sweep start station \(r_{\Lambda}\), where the blade starts to sweep, as design parameters for investigating the effects of tip sweep on rotor performance. It should be noted that the tip sweep may induce the center of aerodynamic pressure to move towards trailing edge. It is possible to intensify the structure coupling between blade pitch and flap in a larger tip sweep. The structure coupling effect is beyond the scope of this study; hence, sweep angle is constrained to within 20 degrees, and its start position is constrained to between 0.85 and 0.95 radius station.  
With a blade tip sweep, the effective chord \(\hat{c}\) in the tip region can be expressed as  
\[\hat{c}=c\cos\Lambda \tag{28}\]  
Mach number of flow normal to airfoil leading edge is expressed by  
\[Ma_{n}=Ma\cos\Lambda \tag{29}\]  
The surface plots of Figure 12 depict the relationship among power loss, sweep angle and sweep start station, in hover and forward flight. It can be seen that in the majority of conditions, the required power decreases with sweep angle increases, both in hover and forward flight. From Equation (28), the sweep angle reduces the effective chord of the blade tip, hence making the blade section lift lower, and its distribution more uniform. The benefit of sweep to profile power is attained by the lower effective Mach number. The lower Mach number normal to the leading edge of the blade section can weaken the compressibility effect, decreasing the drag coefficients in the tip region. It should be noted in Figure 12 that the tip sweep has more influence on rotor performance in forward flight. Thus, a prominent improvement of power loss in forward flight may be attained by a well-designed blade tip.  
Figure 11: Blade planform.  
Figure 12: Power losses with tip sweep variations.
## 5 Conclusions  
In this paper, we propose a Wheatstone bridge readout IC with chopper-stabilized multipath operational amplifier. The proposed Wheatstone bridge readout IC is fabricated using a standard 0.18 \(\upmu\)m 1P6M CMOS process and has an area of 8.6 mm\({}^{2}\). It dissipates 1.5 mW of power using a 1.8 V supply voltage. The proposed Wheatstone bridge readout ICs consist of a three-operational amplifier IA, LPF, buffer, and 12-bit SAR ADC. The three-operational amplifier IA achieves a gain of 38-70 dB using two 5-bit programmable resistors. The chopper-stabilized multipath operational amplifier, which forms the input stage of the three-operational amplifier IA, consists of an HFP and an LFP  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **This Work** & **[11]** & **[18]** & **[2]** & **[29]** \\ \hline Year & 2019 & 2016 & 2016 & 2012 & 2004 \\ \hline Technology (\(\upmu\)m) & 0.18 & 0.065 & 0.18 & 0.7 & 0.5 \\ \hline \multirow{2}{*}{Techniques for IA} & chopping + & correlated & & \\  & multipath & double & chopping & chopping & conventional \\ \hline Supply voltage (V) & 1.8 & 1 & 2.7 & 5 & 2.5 \\ \hline Supply current (\(\upmu\)A) & 833 & 12.3 & 27.65 & 270 & 61 \\ \hline Gain of IA & 38–70 & 100 & 40 & 40 & 20 \\ \hline Input range (mA) & \(\pm\)20 & \(\pm\)9.38 & \(\pm\)8.8 & \(\pm\)0 & - \\ \hline Input referred noise & & & & & \\ (nV/\(\upmu\)Hz) & 24.88 & 347.85 & 84.08 & 16.2 & 175 \\ \hline RRL & Y & N & N & Y & N \\ \hline NEF \({}^{*}\) & 27.61 & 46.9 & 17 & 10.23 & 52.54 \\ \hline \end{tabular} NEF \({}^{*}\) = V\({}_{\text{ni}}\)/(2-I\({}_{\text{no}}\)/\(\upmu\)U-4 kT-BW).  
\end{table}
Table 3: Performance summary of the Wheatstone bridge readout IC and comparison with other studies.  
with a chopping technique applied to reduce noise. In addition, a RRL is implemented to effectively attenuate the ripple generated by chopping in the LFP. The input noise voltage density of the proposed Wheatstone bridge readout IC was shown to be 24.88 nV/v/Hz over the 0.5-50 Hz range and the input range was \(\pm\)20 mV.  
Conceptualization and supervision, H.K. ([PERSON]); validation, J.L.; investigation and writing--original draft preparation, K.H.; writing--review and editing, H.K. ([PERSON]), J.K., D.Y., H.H., and Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018 ID).  
The chip fabrication and EDA tool were supported by the IC Design Education Center (IDEC), Republic of Korea.  
The authors declare no conflict of interest.
## 3 Experimental Results and Discussion
### Experimental Platform  
3.2.1. Experiment Configuration  
Our experimental configuration is shown in Table 1 below.
## 3 Description of the Test Stands
### CDNE Method  
The required (according to [6]) test setup configuration for measuring the conducted disturbance emission of lighting lamps in the range of 30 to 300 MHz using the CDNE method is presented in Figure 2. The LED lamp under test was placed in a socket on an insulating sub-base 0.1 m high and supplied with 230 V AC (PS) via the CDN-M2 artificial network. The disturbance signal via a 6 dB attenuator (T) was measured by an ESU-26 receiver (R). The components of the test setup, such as the LED lamp under test, the artificial network, the attenuator, and the required isolation pads, were placed on a metal plane (MP) connected to the reference ground of the 3 M TDK anechoic chamber. In accordance with the requirements of the standard [2], the socket in which the LED lamp under test was  
Figure 1: Test stand for measuring the radiated disturbance emissions generated by the LED lamp under test in the frequency range of 30 to 300 MHz using the SAC method.  
placed was connected to the CDNE network with a 0.2 m long two-wire cable. To ensure its required distance to the metal plane, it was placed on an insulated spacer 0.04 m high.  
Although tests using the CDNE method do not require an anechoic chamber, the measurements also were made in a 3 M TDK chamber (Figure 3).  
As previously mentioned, the signal output of the CDN network was connected to the ESU-26 measurement receiver by means of a 6 dB attenuator and RF cable system. The attenuation characteristics of the filter, the attenuation of the RF cables, and the voltage division factor of the CDN-M2 network were included in the total signal balance of the measurement path. The voltage division factor was measured according to the guidelines in the standard. A test stand was designed for this purpose. The SML100A signal generator, Rohde & Schwarz power probe NRP-Z21, was used to measure the division factor (Figure 4). The measured value of the voltage division factor for the network used for the measurements is presented in Figure 5.  
Figure 3: Measuring stand for determination of conducted disturbance emissions generated by the LED lamp under test in the range of 30 to 300 MHz using the CDNE method.  
Figure 2: Test setup for testing lighting devices using the CDME method in accordance with EN 55015:2013: PS—power supply, R– measuring receiver ESU-26, CDN-M2—coupling and decoupling network, T—attenuator 6 dB, EUT—tested LED lamp, MP—grounded metal plate.  
As in the SAC method, in the CDNE method, the measuring system was also controlled by specialised software EMC32.
## 2 Data and Methods
### Simulation Experiment
#### 2.2.3 Building Regression Model
The second step is a sequential selection of features in the forward mode. That means the model starts to learn from one feature and sequentially adds features to improve the quality of the model. As a result of this selection, the feature sets for each IGBP class have been generated, as shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Blue** & **NDMI** & **SAVI** \\ \hline
27 December 2009 & 344.9 & 327.6 & 2314.2 \\
28 December 2009 & 346.8 & 345.2 & 2335.3 \\
29 December 2009 & 338.3 & 315.5 & 2276.5 \\
30 December 2009 & 342.1 & 317.5 & 2289.9 \\
31 December 2009 & 355.2 & 355.5 & 2392.7 \\
1 January 2010 & 351.3 & 356.4 & 2354.5 \\
2 January 2010 & 349.3 & 325.1 & 2346.3 \\
3 January 2010 & 351.4 & 361.2 & 2373.4 \\
4 January 2010 & 355.4 & 363.2 & 2382.4 \\
5 January 2010 & 352.2 & 348.9 & 2367.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of the aggregated dataset for each feature for each date. Note that the values are scaled by 10,000. \begin{table}
\begin{tabular}{c c} \hline \hline
**Date** & **NEE, gC/m\({}^{2}\)/Day** \\ \hline
27 December 2009 & \(-\)5.77 \\
28 December 2009 & \(-\)7.43 \\
29 December 2009 & \(-\)10.24 \\
30 December 2009 & \(-\)5.49 \\
31 December 2009 & \(-\)7.83 \\
1 January 2010 & \(-\)5.47 \\
2 January 2010 & \(-\)5.24 \\
3 January 2010 & \(-\)3.87 \\
4 January 2010 & \(-\)5.52 \\
5 January 2010 & \(-\)3.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of “target” NEE from eddy covariance stations for each date. Model training and hyperparameters tuning processes were based on \(N\)-fold cross-validation. In our case, \(N\) equals 3, 5, or 7 depending on the size of the available dataset per IGBP class. The original dataset was divided into \(N\) nearly same-sized folds with a unique set of Fluxnet sites inside. Then, the model was trained on \(N-1\) folds and validated on the remaining fold after the procedure was repeated \(N\) times with different combinations of folds. Each fold contains a unique set of stations without overlapping with other folds [51], which ensures spatial scalability. Thus, \(N\) times independent validation was performed on the sets of stations that were not involved in the training of the model, which ensures spatial scalability of the model predictions. Similarly to [19], hyperparameters for each \(N\) fold were tuned separately for a fixed set of features. As a result, we have not just one model per land cover but an ensemble of models with their respective metrics trained on a different dataset configuration.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 3 Methodology and Machine Learning Methods (MLMs)  
The research was executed using 628 samples, covering various types of actual faults collected from multiple sources, including laboratories, the literature, Al-Dorra Refinery Company, and other electricity stations in Iraq. A variety of samples were used to confirm the reliability of the fault diagnosis. The methodology for this study is shown in Figure 1.  
All algorithms were implemented using the Orange(r) Data Mining package and Python programming language.
# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System
### Machine Learning Monitoring and Maintenance  
A wide range of machine learning (ML) mechanisms have been defined for both selection and extraction as well as inference. For the three, the different techniques can be differentiated by their supervised or unsupervised nature. On the one hand, supervised mechanisms require labeled data, this means, additionally to the values of the indicators/features themselves, they require pre-existent datasets where the values of the feature are labeled as being obtained under certain performance status or class (e.g., if the values were gathered during a specific network failure or configuration).  
On the other hand, unsupervised techniques only require the values of the indicators themselves, and they then use their statistical characteristics and the relation between them. As an example, one of the most widely applied unsupervised ML technique for extraction is Principal Component Analysis (PCA) [21]. This is a multivariate technique that analyzes inter-correlated data with the goal of extracting the most relevant information and representing it as a new data set of orthogonal variables or principal components. Unsupervised techniques exist also for selection and classification, although for the latest they consist in clustering the multi-variate feature values into unlabeled classes.  
In both unsupervised and supervised ML, one of the main issues of fully automated machine-learning mechanisms is their common degradation with time: once a system is deployed, radical or even gradual changes in the features' values distributions lead the defined mechanisms to degrade their performance [25]. This is especially relevant for FE, as the selection allows to also reduce the number of variables being gathered as well as the indicators to be generated. However, this might lead to missing metrics that become relevant when the network changes or if new failure cases appear.  
In order to avoid this issue, a machine-learning monitoring and maintenance functionality, MLMM, is deemed to be necessary. As represented in Figure 2, MLMM is dedicated to periodically obtain the original complete set of indicators and check if the selection mechanisms indicate the same relevance for the indicators or the list of the chosen metrics needs to be updated. Equally, the MLMM oversees the training and re-training of the extraction and inference algorithms, the statistical characteristics of the current indicators being gathered and the availability of new labeled cases that can be obtained from confirmed diagnosis.
## 1 Introduction
[PERSON] et al. [26] introduced a dual-stream attention-based LSTM containing a visual attention mechanism that enables selectively focusing on key elements in the image frames by applying varying levels of attention to each individual deep feature map's output. The deep feature correlation layer embedded in their framework is, indeed, relevant to our work, and it contributes towards enhancing the robustness of the action recognition. The validation in [26] was, however, in experimental scenarios, different from that considered in this work. Unlike the existing related methods reviewed above, the proposed research combines the robust pose detection ability of YOLOv8 with temporal sequencing ability of the ConvLSTM to propose an effective and efficient approach aimed specifically at aerial action recognition. In fact, the proposed framework offers a reliable recognition of human actions from an aerial perspective by utilizing the convolutional LSTM's capacity to parse temporal sequences. Specifically, the proposed method extracts the body pose keypoints from the frames and classifies actions at the frame level utilizing the customized convolutional LSTM network model. The reason behind relying on the extraction of the target body pose or keypoints is the lower computational cost as compared to the spatial features. Moreover, we use the LSTM network due to its demonstrated effectiveness for sequential data classification [23; 24; 27; 28]; plus, it is not well explored in the literature for the problem under consideration. We showed the effectiveness of the proposed method in terms of encouraging performance accuracy and computational cost when compared on a public dataset (containing a wide range of action types) with several existing related approaches. The organization of the paper is as follows. The proposed method for action recognition is described in Section 2. Section 3 provides details of the experimental results and analysis, which is followed by the conclusions in Section 4.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 3 Experiments and Results
### Feature Prediction  
The Mel spectrogram visualizes the effects of synthesized speech, and the more detailed the Mel spectrogram, the higher the potential quality of the synthesized audio. Mel spectrograms synthesized by each model are depicted in Figure 6. We observed that the mixture alignment FastSpeech2 model exhibits an advantage in predicting Mel spectrogram details.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module Name** & **Hyperparameters** \\ \hline Embedding & 256 \\ Letter/Character Encoder Layers & 4 \\ Encoder Hidden Size & 256 \\ Encoder Conv1D Kernel & 5 \\ Encoder Conv1D Filter Size & 1024 \\ Predictor Conv1D Kernel & 3 \\ Predictor Conv1D Filter Size & 256 \\ Predictor Dropout & 0.5 \\ Decoder Layers & 6 \\ Decoder Hidden & 256 \\ Decoder Conv1D Kernel & 5 \\ Decoder Conv1D Filter Size & 1024 \\ Encoder/Decoder Dropout & 0.2 \\ Total Number of Parameters & 29.19 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of Model Modules.  
In the analysis of the attention alignment graph, dots represent attention weight values; the higher these values, the brighter the dots. The alignment graph reflects the stability of the speech synthesis model. The clarity of the lines in the graph indicates higher accuracy of speech synthesis and enhanced model stability. The alignment graph generated by the mixture alignment FastSpeech2 model is depicted in Figure 7, its clear and bright lines indicate good alignment capabilities.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 3 Proposed Scheme
### Overview of the Proposed Scheme
Therefore, the proposed scheme implements the concept of the multi-party computation (MPC) model, using multiple VENs to distribute the task of computation and ensure computation privacy. The selection and deployment of different VENs are based on the physical location of IoMT devices; a hospital has hundreds of patients, each using multiple sensors located in varying geographical areas within the building. Physical edge nodes are avoided for data calculation operations due to their lack of resources for managing the big data generated from healthcare applications. Different cloud services are used to acquire resources and dynamically provide resources to the DU. Selection of varying cloud services prevents a single untrustworthy third-party service provider from storing performed additional and multiplicative computational operations and learning the data analysis process. Compared to cloud services, virtual edge nodes performing computation operations on homomorphically encrypted data provide results closer to the devices, resulting in reduced latency in transmitting time-sensitive output to the DU. VENs benefit the DU by providing dynamic allocation of nodes and serving as resource provisioning services. A single or selection of nearby physical edge nodes, such as base stations, cannot offer real-time services due to limited inbuilt computational resources. This paper assumes that the healthcare service deploys cloud-based resource services such as Amazon Web Services and Google Cloud, using on-demand payment services. On-demand payment-based services provide a highly scalable selection of edge nodes, ensuring complex operations are performed without delay.
# Trepenoids in Marine Heterobranch Molluscs
###### Abstract  
Heterobranch molluscs are rich in natural products. As other marine organisms, these gastropods are still quite unexplored, but they provide a stunning arsenal of compounds with interesting activities. Among their natural products, terpenoids are particularly abundant and diverse, including monoterpenoids, sesquiterpenoids, diterpenoids, esterterpenoids, triterpenoids, tetraterpenoids, and steroids. This review evaluates the different kinds of terpenoids found in heterobranchs and reports on their bioactivity. It includes more than 330 metabolites isolated from ca. 70 species of heterobranchs. The monoterpenoids reported may be linear or monocyclic, while sesquiterpenoids may include linear, monocyclic, bicyclic, or tricyclic molecules. Diterpenoids in heterobranchs may include linear, monocyclic, bicyclic, tricyclic, or tetracyclic compounds. Sesterterpenoids, instead, are linear, bicyclic, or tetracyclic. Triterpenoids, tetraterpenoids, and steroids are not as abundant as the previously mentioned types. Within heterobranch molluscs, no terpenoids have been described in this period in tylodinoideans, cephalaspideans, or pteropods, and most terpenids have been found in nudibranchs, anaspideans, and sacoglossans, with very few compounds in pleurobranchoideans and pulmonates. Monoterpenoids are present mostly in anaspidea, and less abundant in sacoglossa. Nudibranchs are especially rich in sesquiterpenes, which are also present in anaspidea, and in less numbers in sacoglossa and pulmonata. Diterpenoids are also very abundant in nudibranchs, present also in anaspidea, and scarce in pleurobranchoidea, sacoglossa, and pulmonata. Sesterterpenoids are only found in nudibranchia, while triterpenoids, carotenoids, and steroids are only reported for nudibranchia, pleurobranchoidea, and anaspidea. Many of these compounds are obtained from their diet, while others are biottransformed, or _de novo_ biosynthesized by the molluscs. Overall, a huge variety of structures is found, indicating that chemodiversity correlates to the amazing biodiversity of this fascinating group of molluscs.  
marine natural products; Gastropoda; chemical ecology; bioactivity +
Footnote †: journal: Marine drugs
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 5 Conclusions  
In this paper, we present the theoretical analysis of the error bounds by selecting the single, double and JMART measurements as the metric for indoor localization. The purpose of the analysis is to better design an indoor localization system, either through the judicious selection of the metrics for localization or though the optimal AP deployment. Based on the extensive simulations, we find that the JMART is able to provide higher Fisher information, as well as achieve lower error bounds compared with the ones by using the metrics of single and double measurements. Furthermore, we find that the increase of AP number decreases the error bounds or keeps the error bounds unchanged, while as the noise power increases, the error bounds with respect to the single, double and JMART measurements will also increase. In general, since the smaller distance between the AP and testing locations results in the lower error bound, we are able to optimize the AP locations towards the lowest error bound. Our future work is to rely on the analytical relations of the Fisher information, error bounds, metrics used for localization and AP deployment to design a more effective indoor localization  
Figure 12: Error bounds by using the hybrid (**a**) AOA/RSS, (**b**) TOF/AOA and (**c**) RSS/TOF.  
Figure 13: Sum of diagonal elements in the FIM (**a**) and error bounds (**b**) by the JMART.  
system, and meanwhile, we will continue to study the error bounds for the biased localization problem by using different metrics for the indoor localization.  
The authors wish to thank the reviewers for the careful review and valuable suggestions. This work was supported in part by the Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), the National Natural Science Foundation of China (61301126 and 61471077), Young Science Research Program of Chongqing University of Posts and Telecommunications (A2013-31), the Postgraduate Scientific Research and Innovation Project of Chongqing (CYS16157) and the Special Fund of Chongqing Key Laboratory (CSTC).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] helped in data analysis; [PERSON] helped in paper revision.  
The authors declare no conflict of interest.