esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Considering this, a more stable and promising topology for the MT-HVdc grid to connect offshore wind farms with the mainland grid is proposed in this paper and termed as two wind farms and substation ring topology (2 WF-SSRT). This new MT-HVdc grid topology offers benefits of reduced length, rating, and the number of dc circuits; no extra offshore substation is required with the least number of dc breakers. 2 WF-SSRT exhibit features of 2 WF-ISST [30] and SSRT [28]. Furthermore, the proposed topology is able to improve the stability, utilization, and redundancy while fulfilling the maximum loss criterion in the normal and abnormal conditions by providing an alternate path for the power flow. The main contributions of this paper are:  
1. A new promising topology for the MT-HVdc grid is proposed and named as two wind farms and substation ring topology (2 WF-SSRT). 2. MT-HVdc transmission grid topologies are investigated with stress on wind farm power (\(P_{\text{WF}}\)) loss during dc grid and mainland ac grid faults via simulations. 3. The core contribution of this paper is a comparison of different topologies of [27; 28; 29; 30] with the proposed 2 WF-SSRT topology based on the techno-economic factors such as length, number, and ratings of dc cables, number, and ratings of VSCs and DCCBs, additional offshore substation requirements, flexibility, capital, and running costs, stability, redundancy, and loss of VSC station in case of a fault. Such a comprehensive comparison was missing in the literature. Topology with the least number of offshore stations, DCCBs and reduced dc-link length with maximum flexibility, stability, utilization, and redundancy is checked to endure the MT-HVdc grid codes without a communication system, via simulations--a step to meet the HVdc grid codes by following the standards recommended by the GBSQSS [31]. 5. EMTDC/PSCAD tool is used to analyze and compare the transmission circuit topologies. In [27; 28; 29], the authors just proposed the circuit layout for MT-HVdc grids without simulations. However, in this paper, time-domain simulated configurations are experienced with the dc line-line faults and ultimate disconnection of the grid side VSC (GS-VSC) to assess the real-time evaluation of topologies. 6. Finally, annotations on MT-HVdc grid topological evaluation are provided which may serve as a guideline for the researchers to understand different norms in this field. The rest of the paper is organized as follows: multi-terminal HVdc transmission systems and its necessities are described in Section 2. Various topologies for the MT-HVdc grid are discussed from the literature in Section 3. A new topology is proposed in Section 4 for MT-HVdc transmission systems and stability of this newly proposed topology is assessed via two dc fault scenarios. A general case study based on techno-economic analysis is presented in Section 5. Simulations are conducted in Section 6 and each topology is tested for (i) GS-VSC disconnection and (ii) dc line-line fault. In Section 7, annotation on the topological evaluation of HVdc circuits is provided. Finally, remarks and conclusions are drawn in Section 8.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 3 Results
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Reference** & **Year** & **Objective** \\ \hline [PERSON] et al. [22] & 2009 & Evaluate the use of goniometry in estimating the joint angle of the catch simultaneously with inertial sensors. \\ \hline [PERSON] et al. [23] & 2012 & Implement a Kalman filter using a smartphone to estimate 3-D angulation of the trunk. \\ \hline [PERSON] et al. [24] & 2012 & Study if wearable sensors can detect differences in balance and gait between people with MS with normal walking speeds and healthy controls. \\ \hline [PERSON] et al. [25] & 2013 & Develop a wearable motion analysis system to evaluate gait that consists on six IMUs. \\ \hline [PERSON] et al. [26] & 2014 & Describe a wearable pressure-sensitive inside sensor for lower-limb amputees feedbacks. \\ \hline [PERSON] et al. [27] & 2014 & Assess suitability of instrumented gait and balance measures for PD diagnosis and estimation. \\ \hline [PERSON] et al. [28] & 2014 & Develop gait and balance analysis algorithms to gather quantitative data considered early indicators of AD. \\ \hline [PERSON] et al. [29] & 2014 & Compare a mobile technology application with a commonly used subjective balance assessment. \\ \hline [PERSON] et al. [30] & 2014 & Describe a system for continuous remote monitoring of patients with PD. \\ \hline [PERSON] et al. [31] & 2014 & Investigate whether detection of gait initiation in transfemoral amputees can be useful for voluntary control of lower extremity prostheses. \\ \hline [PERSON] et al. [32,33] & 2015 & Develop a biomechanically based quantification of the BESS using inertial sensors data. \\ \hline [PERSON] et al. [34] & 2015 & Evaluate IMU-system when assessing movement dysfunctions of concurrent validity and reliability. \\ \hline [PERSON] \& [PERSON] et al. [35,36] & 2015 & Describe a smartphone-based application to quantify gait variability. \\ \hline [PERSON] et al. [37] & 2015 & Investigate the use of a wearable sensor compared to laboratory reference. \\ \hline [PERSON] et al. [38] & 2015 & Measure the knee joint angle using IMUs and reference it with a motion capture system. \\ \hline [PERSON] et al. [39] & 2015 & Present a method for calculating continuous heel and toe clearance and foot angle in the sagittal plane without knowing shoe dimensions. \\ \hline [PERSON] \& [PERSON] et al. [40,41] & 2015 & Design and validate a smartphone-based system for motor assessment using IMUs. \\ \hline [PERSON] et al. [42] & 2015 & Present and evaluate the step count performance of a smart insole system. \\ \hline [PERSON] et al. [43] & 2015 & Develop a system to objectively record ground reaction forces, acceleration and direction of the feet using wearable sensors. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Literature on Wearable Sensors in Gait, Balance, and RoM Research Included in the Review.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Datasets and Preprocessing  
First, we collected data on maize growth and development from monitoring stations in Fengqiu, Yucheng, and Luancheng from 2004 to 2019 (chinaFLUX). In the NCP, the maize emergence period is primarily around mid-to-late June, and the harvest period is in late September. We concentrated on this time period in our investigation. In this study, data on maize, satellite data, and climatic data were employed.
Due to the limited speech and speaker information contained in short speech, the model's feature extraction capability is required to be stronger, as it needs to extract effective speaker features from the limited audio information. However, the ECAPA-TDNN model mainly focuses on modeling local spatial features and lacks effective integration of global context information, making it unable to fully handle long-range dependencies. As a result, this model does not perform optimally in air-ground communication scenarios. To improve the performance of speaker identification systems in air-ground communication scenarios and overcome the limitations of the ECAPA-TDNN model proposed by [PERSON] et al. [5] in such environments, this paper presents an easy-to-implement and effective speaker identification network model--Chrono-ECAPA-TDNN (CET). Using ECAPA-TDNN as the baseline network, three improvements are made to address the challenges of complex noise and short speech in air-ground communication. The main contributions of this paper can be summarized as follows:  
1. Addressing the challenge of complex noise: data augmentation techniques, including speed perturbation, spectral masking, and random noise suppression, are applied to the audio to enhance the model's robustness in complex noise environments. 2. Addressing the challenge of short speech: a parallel branch architecture is introduced in the air-ground communication speaker identification model (Chrono-ECAPA-TDNN). Using raw air-ground communication speech as input, a bidirectional long short-term memory (Bi-LSTM) network is added to the existing residual neural backbone of the ECAPA-TDNN model to capture speaker information at both global and local levels. A multi-head attention mechanism is also incorporated for feature enhancement. 3. Improving model accuracy: to improve the accuracy of the air-ground communication speaker identification model, the Sub-center AAM-Softmax (Angular Additive Margin Softmax) is used instead of the AM-Softmax (additive margin Softmax). This modification tightens features within the same class and increases the separation between classes in the feature space, thus improving the model's classification and discrimination ability, especially for categories with similar features. In this paper, the Chrono-ECAPA-TDNN model was pre-trained on the VoxCeleb2 [7] dataset and then tested on the air-ground communication dataset, as well as the TIMIT [8] dataset, LibriSpeech [9], VoxCeleb1 [10], and CN-Celeb [11]. Performance comparisons were made with baseline networks including ECAPA-TDNN, Residual Network 34 (ResNet34), and Multi-scale Feature Aggregation Conformer (MFA-Conformer). The test results demonstrate that Chrono-ECAPA-TDNN outperforms existing models in both air-ground communication scenarios and across domain open-source datasets, with a significant reduction in Equal Error Rate (EER) and a notable improvement in accuracy.
## 4 Discussion
### Time Dependency of the Best Performing Inversion Method  
The objective was to be able to monitor the study site over multiple dates with or without available field data. As field data are usually scarce, inversion methods are often calibrated using data from only a single date. While this guarantees that the optimal method for this date has been found, the validity of extrapolations to images acquired at different times is not a given. Indeed, in Section 3.3's Table 7, TCARI/OSAVI, GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms the others with a lower RMSE and higher R\({}^{2}\) (Table 9). This highlights the need for multiple validation dates to properly select the inversion method if one aims to monitor canopies over time.  
The number of best matches \(q\) to keep when doing the LUT-based inversion also varied depending on the validation date, and a generalizable method to determine it was not found. There is still no clear methodology to determine this number. [PERSON] et al. (2018) used three different values for the inversion (\(q=50\) when using 6 bands from AVIRIS-C images, \(q=150\) when using their full spectrum, and \(q=200\) when using Landsat images; LUT size: 115,000); [PERSON] and [PERSON] (2018) only considered the best match when using 8 bands from CHRIS images (LUT size: 1,056,000); [PERSON] et al. (2018) used \(q=50\) when trying to inverse digital data sets (LUT size: 280,000); and [PERSON] et al. (2018) considered \(q=20\) when using AISA images (LUT size: 3500). This is unfortunately not satisfactory, as much room is left for subjectivity and overfitting of the inversion method.
## 3 Results and Discussion  
### Specificity  
#### 3.1.1 Elution Time and Separation Capacity of DEHP  
Figure 1 shows the chromatogram of the standard solution, in which DEHP elutes at retention time of 14.3 min.  
In the chromatogram, Figure 2, of the real Ouzo sample, a peak of the same shape is observed at the same time, 14.3 min. In this case it was considered unnecessary to calculate the separation capacity because the DEHP peak is clear and there are no other peaks around it.  
Additionally, it is worth noting that in the pure solvent chromatogram, Figure 3, no peak is observed at the elution time of DEHP. Therefore, we conclude that satisfactory separation of the DEHP peak in Ouzo samples is achieved.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{8}{c}{**Parameter**} \\ \cline{2-7}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline
**1** & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline
**2** & 1 & 1 & 1 & 2 & 2 & 2 & 2 \\ \hline
**3** & 1 & 2 & 2 & 1 & 1 & 2 & 2 \\ \hline
**4** & 1 & 2 & 2 & 2 & 2 & 1 & 1 \\ \hline
**5** & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\ \hline
**6** & 2 & 1 & 2 & 2 & 1 & 2 & 1 \\ \hline
**7** & 2 & 2 & 1 & 1 & 2 & 2 & 1 \\ \hline
**8** & 2 & 2 & 1 & 2 & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 7-parameter (A, B, C, D, E, F, G) two-level (1, 2) experimental design.
## 3 Results and Discussion
### Effect of Air Compressibility  
In this section, we take interest in the gas compression effect of alternating fluid flow. We carried out isothermal flow tests (no initial temperature gradient) in order to solely investigate the fluid gas compression effect due to the regenerator's presence.  
The ideal gas model is used to approximate gas density. The resulting equation for density estimation is:  
\[\rho=\frac{P}{rT}\, \tag{13}\]  
where \(r=287\) J kg\({}^{-1}\) K\({}^{-1}\) for the air.  
The effect of frequency on the gas density \(\rho\) at both ends of the regenerator for a porosity of \(\varepsilon\) = 40% is plotted in Figure 10(a) (similar results are obtained for other porosities; [PERSON] et al. [34]). We observe an increase in the gas density at high frequencies. We also observe a significant variation in the density (1.08 kg m\({}^{-3}\) < \(\rho\) < 1.50 kg m\({}^{-3}\)), even if the measurements were carried out for isothermal flow.  
The density variation ratio for the three types of tested regenerators is calculated with respect to the average density \(\rho_{avg}\) over a complete cycle, which is defined by [PERSON] et al. [34]:  
\[\frac{\Delta\rho}{\rho_{avg}}(\%)=\frac{\rho_{max}-\rho_{min}}{\rho_{avg}}100, \tag{14}\]  
where \(\rho_{min}\) and \(\rho_{max}\) are the minimum and maximum densities over a complete cycle, respectively.  
We can observe that the density variation ratio is significant at a high frequency for any structure (Figure 10b) as it reaches 30%. For the most disturbed structure (Str_30%), this compressibility is already obtained at low frequencies. For Str_35% and Str_40% structures, we observe that this ratio increases with frequency until it stabilizes at a maximum value (28%), beyond which this ratio remains almost constant.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 3 Preliminaries
### The CKKS Scheme  
As a typical fully homomorphic encryption (FHE), the Cheon-Kim-Kim-Song (CKKS) scheme [34] is a cryptographic technique in which addition and multiplication operations on plaintext are equivalent to corresponding operations on cipher-text. With the unique encoding, decoding, and rescaling mechanisms, CKKS can encrypt float numbers and vectors. CKKS consists of the following functions:  
1. _Key Generation_: \((sk,pk,evk)\gets KeyGen(1^{\lambda})\). This procedure generates a public key \(pk\) for encryption, a corresponding secret key \(sk\) for decryption, and a key \(evk\) for evaluation.
2. _Encoding_: \(m\gets Ecd(\mathbf{z},s)\). Taking an \(N/2\)-dimensional vector \(\mathbf{z}=(z_{j})_{j\in\mathcal{T}}\) and a scaling factor \(s\) as inputs, the procedure transforms \(\mathbf{z}\) to a polynomial \(m\) by encoding, i.e., \(m=\sigma^{-1}\circ\pi^{-1}(\mathbf{z}\cdot s)\), where \(\sigma^{-1}\) and \(\pi^{-1}\) are the inverses of \(\sigma\) and \(\pi\), respectively.
3. _Encryption_: \(c\gets Enc(m,pk)\). Taking the given polynomial \(m\in\mathcal{R}\) and public key \(pk\) as inputs, the procedure outputs the cipher-text \(c\in\mathcal{R}_{q_{L}}^{k}\).
4. _Addition_: \(\hat{c}\leftarrow Add(c_{1},c_{2})\). Taking the cipher-texts \(c_{1}\) and \(c_{2}\) as inputs, the procedure outputs \(\hat{c}=c_{1}\oplus c_{2}\).
5. _Multiplication_: \(\hat{c}\leftarrow Mult(c_{1},c_{2},evk)\). Taking the cipher-texts \(c_{1}\) and \(c_{2}\) and the evaluation key \(evk\) as inputs, the procedure outputs \(\hat{c}=c_{1}\otimes c_{2}\in\mathcal{R}_{q_{L}}^{k}\).
6. _Rescaling_: \(c^{\prime}\gets RS(c)\). Taking a cipher-text \(c\in\mathcal{R}_{q_{L}}^{k}\) at level \(l\) as inputs, the procedure outputs the cipher-text \(c^{\prime}\leftarrow\lfloor\frac{q_{L}}{q_{L}}c\rceil\) in \(\mathcal{R}_{q_{L}}^{k}\), where \(\lfloor x\rceil\) represents the integer closest to the real number \(x\).
7. _Decryption_: \(m\gets Dec(c,sk)\). Taking the cipher-text \(c\) and the secret key \(sk\) as inputs, the procedure outputs the polynomial \(m\).
8. _Decoding_: \(\mathbf{z}\gets Dcd(m,s)\). Taking the plaintext polynomial \(m\in\mathcal{R}\) as inputs, the procedure transforms \(m\in\mathcal{R}\) into a vector \(\mathbf{z}\) using \(\mathbf{z}=\pi\circ\sigma(s^{-1}\cdot m)\).
## 3 Results
### Student Engagement: Physical Assessment of Blood Pressure and Heart Rate  
Before the study, participants were told to abstain from alcohol, caffeine, nicotine, chocolate, soda, and energy drinks and engage in vigorous physical activity [54]. During the procedures, participants wore a cuff blood pressure and pulse monitor (Omicron). The systolic blood pressures and heart rates recorded pre-test (BH-PT) for the control and VR groups were very similar (Figure 9). However, during the first VR module, the VR group's systolic, diastolic, and heart rates rose markedly compared to the control group. Furthermore, VR modules 2 and 3 recorded elevated systolic, diastolic, and heart rates. Implying the immersive simulated radiopharmaceutical preparation was life-like and stimulated the participant's senses more than the video training. VR can stimulate the users' vision, hearing, and, in some cases, even touch.  
Figure 8: Practical and experimental gamification of learning. (**A**) Number of correct responses (out of 3 targets) for the control group, virtual reality simulation, and hands-on labeling quality control training. (**B**) Targeting accuracy (%) for the training simulation and hands-on labeling of the control and virtual reality simulation training groups. (**C**) Targeting precision (%) for the training simulation and hands-on labeling of the control and virtual reality simulation training groups. The data are presented as the mean \(\pm\) standard deviation (\(n=30\)). Statistical significance is indicated as ns (insignificant) when \(p>0.05\).  
Figure 9: Physical assessment of blood pressure and heart rate for student engagement responses: (**A**) systolic blood pressure; (**B**) diastolic blood pressure; (**C**) heart rates are presented for the following conditions: pre-test (BH-PT), same-day post-test of self-study (BH-S), before watching video 1 of the control group (BH-C1), after watching video 1 of the control group (BH-C2), after watching video 2 of the control group (BH-C3), after watching video 3 of the control group (BH-C4), before gamification of the control group (BH-CG1), after gamification of the control group (BH-CG2), before VR 1 of the VR group (BH-V1), after VR 1 of the VR group (BH-V2), after VR 2 of the VR group (BH-V3), after VR 3 of the VR group (BH-V4), before gamification of the VR group (BH-VG1), after gamification of the VR group (BH-VG2) (\(n=30\)).  
Satisfaction in VR-Enhanced Learning Experience: Prioritizing Educational Significance and Practiality in VR-Assisted Teaching  
The results of the five-question Likert questionnaire, evaluating students' satisfaction with both video and VR training, are illustrated in Supplementary Table S3. It is evident that there is a significant difference (*** \(p\leq\) 0.001) in six out of the eight questions. VR training was deemed more understandable, engaging, and accessible to follow. Moreover, it enhanced understanding with better examples and exercises and achieved greater confidence compared to video training.
## 1 Introduction
### Scientific Contributions and Structure of the Paper  
The amount of available studies addressing privacy issues in VANET-based emergency vehicle warning systems is rather small. Our main scientific contribution is constituted by the proposed privacy preserving concept and by the feasibility study of the application protocol for the emergency vehicle warning system. Specific contributions of this work can be summarized as follows:  
* Formulation of the requirements and the concept of the application protocol for an emergency vehicle warning system based on VANET, with low risk of EV location disclosure;
* Application of the federated telco-traffic simulator and benchmarking of the communication performance of the Ad hoc On-Demand Distance Vector (AODV), GPSR, and Dynamic MANET On-demand (DYMO) routing protocols using realistic scenarios;
* Evaluation of the proposed system in the transport domain in terms of reaction time available to drivers.  
The rest of the paper is organized as follows. System requirements and assessment of communication technologies are given in Section 2. Section 3 presents the concept of the proposed system. Simulation scenarios and simulation results are presented in Sections 4 and 5, respectively. Discussion and summary of conclusions are provided in Section 6.
# Diagnosing Phosphorus Limitation in Subtropical Forests in China under Climate Warming
## 4 Discussion
### Comparison of Past and Future Phosphorus Limitations in Subtropical China  
By the end of this century, the model-predicted NPP in the four forest sites will continue to increase under RCP8.5. To compare the intensity of P limitation in 1951-2010 with that in 2018-2099, we calculated the AAPD/\(\Delta t\) in the two periods, where \(\Delta t\) is the length of the time period. The AAPD/\(\Delta t\) averaged over the four sites for S\({}_{1}\) and S\({}_{2}\) are, respectively, 2.58 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) and 0.26 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) for 1951-2010, and 7.62 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) and 0.77 mg\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\) for 2018-2099. The AAPD/\(\Delta t\) for 2018-2099 is about 2.95 times more than that for 1951-2010, which indicates P limitations will be greatly enhanced in subtropical forests in China under RCP8.5.  
The intensity of P limitation in Qian Yanzhou is the highest among the forest sites for the period of 1951-2010, and the future AAPD/\(\Delta t\) in Qian Yanzhou is also the highest, which implies the plantations in central subtropical China are likely to face the highest risks of P limitation in the future.
## 5 Machine Learning Framework for Dimension Optimization
### The Proposed DNN Framework
#### 5.2.2 Output Layer  
The proposed DNN framework aims at obtaining the solution of problem \(\mathbf{P1}\), i.e., \(\{s_{1}^{\star},...,s_{G}^{\star}\}\). The number of eigenvalues for the \(g\)-th group is variable dependent on the channel statistics, i.e., covariance matrices, whose range is \(r_{g}\leq M\). Thus, the maximum number of classes for the optimal dimensions considering all groups becomes \(M^{G}\), which is almost impossible to establish the DNN model in practice for massive MIMO systems. We adopt the parallel DNN framework for each group as shown in Figure 4. Then, the desired output vector \(\bar{\mathbf{o}}_{g}\) for the \(g\)-th group is expressed by  
\[\bar{\mathbf{o}}_{g}=\left[0,...,0,\underbrace{1}_{(\bar{s}_{g}^{\star}\text{ th component})},0,...,0\right]^{T}\in\mathbb{R}^{M}, \tag{34}\]  
where (34) is comprised of the \(s_{g}^{\star}\)-th component one and the other components zero. For the output layer, the Softmax activation function is used for outputs in the interval \((0,1)\). Thus, the position of \(1\) in the \(g\)-th output vector becomes the dimension for the \(g\)-th group.