esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 STATCOM Performance and Results
### Disintegration Loads
Table 1 describes the THD percentage of STATCOM currents under inductive and capacitive operations for continuous, cumulative, and disintegration loads of the system at reference reactive power dynamic changes with satisfactory performance results. Figure 9 shows the performance of seven level VSC based STATCOM at terminal voltage variations at various dynamic conditions. At the starting point, the reference terminal voltage is set to \({}^{\prime}1.0^{\prime}\) pu up to 0.2 s, and then the STATCOM performance is observed. Then, the reference terminal voltage is moved to \({}^{\prime}1.03^{\prime}\) pu during the time interval between 0.2 s and 0.4 s, and the STATCOM performance is verified with waveforms at this stage. STATCOM sends the reactive power; then, the DC-Link voltage and the AC converter output voltage are increased to minimize the system losses. Afterwards, the reference terminal voltage is moved to \({}^{\prime}1.0^{\prime}\) pu during the time interval between 0.4 s and 0.6 s, then the STATCOM performance is observed (similar to the initial stage). Thus, the reference terminal voltage is moved to \({}^{\prime}0.97^{\prime}\) pu during the time interval between 0.6 s and 0.8 s, then the STATCOM performance is verified. STATCOM receives the reactive power, then the DC-Link voltage and the AC converter output voltage are decreased to minimize the system losses. Further, the reference terminal voltage is moved to \({}^{\prime}1.0^{\prime}\) pu during the time interval between 0.8 s and 1.0 s. Then, the overall STATCOM performance is observed with system voltage, system current, load current, converter current, reactive power, DC-Link voltage, per unit terminal voltage, and phase angle waveforms by all the stages from starting to final time periods at various inductive loads and capacitive loads on the system. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**STATCOM Current Operation**} & **Loads** & **\%Total Harmonic Distortion (THD)** \\ \hline Under inductive & Continuous & 3.83 \\ Under capacitive & & 5.10 \\ \hline Under inductive & \multirow{2}{*}{Cummulative} & 3.74 \\ Under capacitive & & 5.01 \\ \hline Under inductive & \multirow{2}{*}{Disintegration} & 4.51 \\ Under capacitive & & 5.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: STATCOM current THD at various loads. Figure 8: STATCOM currents harmonic spectra during disintegration loads of inductive and capacitive operations. Table 2 designates the THD percentage of the system, load, and STATCOM currents under inductive and capacitive operations. The dynamic performance of system, load and STATCOM current conditions at reference terminal voltage changes, with satisfactory results.
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 2 Related Work  
This paper leverages previous works providing communication reduction with error reset [15], asynchronous federated optimization [16], and Byzantine tolerance [17; 18; 19; 20; 21]. The authors of [15] presented a technique called _error reset_, which adapts arbitrary compressors to distributed SGD and corrects for local residual errors, but the proposed algorithm and the corresponding theoretical analysis are limited to the classic synchronous SGD rather than the federated optimization. The authors of [16] presented a combination of asynchronous training and federated optimization or local SGD, but it lacked guarantees in security and privacy. For the security, we focus on the Byzantine failures [22] in this paper. The authors of [17; 20; 21] presented Byzantine-tolerant SGD algorithms based on robust statistics such as the trimmed mean. However, it is argued in [18] that these previous approaches are not specially designed for gradient descent algorithms, which results in the potential vulnerability to several specific types of attacks. To resolve the potential issues of the previous approaches based on robust statistics, the authors of [19] presented score-based approaches, which validated the updates with a standalone validation dataset, but they used a definition of Byzantine tolerance similar to that in [21]. There are also other approaches to Byzantine-tolerant SGD. For example, DRACO [23] uses redundant workers as pivots to distinguish Byzantine workers, which provides strong guarantees for Byzantine tolerance, at the cost of additional computation resources for the redundant workers. To make things worse, adding redundant workers is infeasible in federated learning scenarios. While there are many different kinds of differential privacy (DP) [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], we focus on local differential privacy when releasing the individual updates from the workers to the servers, which is more important in the federated learning scenarios.
## 3 Datasets and Evaluation Metrics
### NIMA  
The Neural Image Assessment metric uses a CNN to predict the statistical distribution (e.g., histogram) of quality scores that humans would assign to the image. The final score is usually the weighted average of this predicted distribution. As it uses a neural network, this metric cannot be represented by a single mathematical formula [100]. Its training on real data at the pixel level aligns this assessment with human perception. The predicted average score (usually from 1 to 10) indicates quality; higher values are better.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 4 Experiments
### Datasets  
The dataset is collected using a DJI Mavic Air 2 drone with an onboard camera. Several flight plans were designed to address various scenarios, including suburban residential areas, university campuses, surface parking lots, and farmlands. We force UAS to fly over  
Figure 9: Illustration of the UAS flight height and scale relationship (blue). At each height, we collected over 200 observations with descriptive statistics visualized as candlesticks (green).  
100 m above the ground to ensure flight safety and sufficient features to be collected. Over a flight altitude of 100 m, little variation such as moving vegetation due to wind could be ignored. Please see Figure 10 for sample images from each flight plan. The images are acquired every 3 m at a platform speed of 2.5 m/s. The attitude and altitude of UAS are allowed to change during flight with a maximum angular velocity of 250 \({}^{\circ}\)/s, an ascent speed of 4 m/s, and a descent speed of 3 m/s. The UAS speed is limited to 18 m/s. The UAS reference geoposition position has been extracted within a hovering accuracy range of \(\pm\)1.5 m horizontally and \(\pm\)0.5 m vertically.  
We stored an approximate initial take-off geoposition (\(\pm\)10 m) and orientation (\(\pm\)10\({}^{\circ}\)) of UAS for each flight plan. The scaling relationship discussed earlier is embedded into the geopositioning algorithm. We should note that, in practice, the proposed approach has high initial position and attitude tolerances. Experimental evaluations show that the initial geoposition and attitude accuracy can be within \(\pm\)120 m and \(\pm\)40\({}^{\circ}\), respectively. We would like to note that, compared with our approach, alternative geopositioning methods, such as variations of SLAM, require highly accurate initialization.
## 2 Materials and Methods  
### Materials and Powder Characterization  
The gas-atomized EOS NickelAloy HX powder used in this study had the chemical composition shown in Table 1. This composition places the alloy in the category of heat-resistant nickel superalloys, with significant amounts of chromium (20.5-23 wt%), iron (17-20 wt%), and molybdenum (8-10 wt%).  
The powder was examined for particle size distribution using a Horiba LA-9500 laser diffraction system and for morphology using a Hitachi TM-1000 scanning electron microscope (SEM). The powder exhibited a predominantly spherical morphology with some satellite particles, as shown in Figure 1. The particle size analysis revealed a mean diameter of 32 um, with 80% of particles falling within the 18-48 um range, which conforms to the manufacturer's requirements for the EOS M290 system. It is worth noting that the powder exhibited high quality for 3D printing due to its high sphericity and a uniform, near-Gaussian particle size distribution (18-48 um). A potential issue, however, could be the presence of satellite particles, which are typical for gas atomization processes.
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 5 Conclusions  
In this paper, we proposed an industrial motor bearing fault diagnosis algorithm based on multi-local model decision fusion conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. The proposed method is based on Bi-LSTM and first uses the noise reduction encoder to clean the vibration signal and current signal of the industrial motor bearing, respectively, to obtain the vibration reconstruction signal and the current reconstruction signal. Afterward, the reconstructed vibration signal and the reconstructed current signal are input into the local fault diagnosis model based on Bi-LSTM and the attention mechanism, and the characteristic information of the reconstructed signal in each fault state is deeply mined to form a local diagnosis decision. Finally, the local diagnostic decisions are fused using the D-S evidence theory to obtain the final diagnostic results. Performance in accuracy, PI score, training time, and the false positive rate was evaluated by experiments, which showed the superiority of the proposed method. However, there are still some points that could be improved. Several of the directions are listed as follows and will be further developed in our future work. First, more evaluation indicators may be considered to increase the objectivity of the results. Second, in the actual production process, for some special industrial motor platforms, such as centrifugal pumps, cryogenic pumps, mine hoisting motors, etc., their bearing signal duration is usually short, or the monitoring conditions are relatively harsh, so that the bearing sensor can only collect a small amount of sample data. When the bearing fault diagnosis method based on deep learning and information fusion is directly adopted, it will lead to overfitting problems in the model training process, which will seriously affect the recognition accuracy of the fault diagnosis model. Maintaining the accuracy of bearing fault diagnosis and recognition under the condition of small samples is another critical issue facing the development of industrial motor bearing fault diagnosis technology.  
Conceptualization, G.H.; methodology, X.W., A.L. and G.H.; software, X.W.; validation, X.W.; formal analysis, X.W.; investigation, X.W., A.L. and G.H.; resources, X.W., A.L. and G.H.; data curation, X.W.; writing--original draft preparation, X.W. and A.L.; writing--review and editing, A.L. and G.H.; visualization, X.W. and A.L.; supervision, A.L. and G.H.; project administration, G.H.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 3 Experimental Procedures and Methods
### SAR Image Processing Procedure
#### 3.3.2 Extraction of the Observed Polarization Scattering Matrix for ARCs  
To eliminate the complex factor \(Ae^{j\varphi}\) before matrix multiplication in Equation (4), the distortion matrix was solved by using the ratio of the quad-polarization matrix elements of the same ARC to represent it, such that the three matrices, \(X\), \(Y\), and \(Z\), were normalized, and the one with the largest magnitude value was used as the reference to normalize; the results were as follows:  
\[\hat{X}=\begin{pmatrix}X_{hh}/X_{h\upsilon}\\ X_{h\upsilon}/X_{h\upsilon}\\ X_{\upsilon\upsilon}/X_{h\upsilon}\end{pmatrix}=\begin{pmatrix}\delta_{2}/f_{ 1}\\ f_{1}/f_{1}\\ \delta_{2}\delta_{3}/f_{1}\\ \delta_{3}\end{pmatrix}=\begin{pmatrix}\delta_{2}/f_{1}\\ 1\\ \delta_{2}\delta_{3}/f_{1}\\ \delta_{3}\end{pmatrix} \tag{7}\]  
\[\hat{Y}=\begin{pmatrix}Y_{hh}/Y_{\upsilon\upsilon}\\ Y_{h\upsilon}/Y_{\upsilon\upsilon}\\ Y_{\upsilon\upsilon}/Y_{\upsilon\upsilon}\\ Y_{\upsilon\upsilon}/Y_{\upsilon\upsilon}\end{pmatrix}=\begin{pmatrix}\delta _{4}/f_{2}\\ \delta_{4}\delta_{1}/f_{2}\\ f_{2}/f_{2}\\ \delta_{1}\end{pmatrix}=\begin{pmatrix}\delta_{4}/f_{2}\\ \delta_{4}\delta_{1}/f_{2}\\ 1\\ \delta_{1}\end{pmatrix} \tag{8}\]  
\[\hat{Z}=\begin{pmatrix}Z_{hh}/Z_{h\upsilon}\\ Z_{h\upsilon}/Z_{h\upsilon}\\ Z_{\upsilon\upsilon}/Z_{h\upsilon}\end{pmatrix}=\begin{pmatrix}(-1+\delta_{2} -\delta_{4}+\delta_{2}\delta_{4})/(-\delta_{1}+f_{1}-\delta_{4}\delta_{1}+ \delta_{4}f_{1})\\ 1\\ (-\delta_{3}+\delta_{2}\delta_{3}-f_{2}+\delta_{2}f_{2})/(-\delta_{1}+f_{1}- \delta_{4}\delta_{1}+\delta_{4}f_{1})\\ (-\delta_{1}\delta_{3}+\delta_{3}f_{1}-\delta_{1}f_{2}+f_{1}f_{2})/(-\delta_{ 1}+f_{1}-\delta_{4}\delta_{1}+\delta_{4}f_{1})\end{pmatrix} \tag{9}\]  
After normalization, the system distortion matrices \(R\) and \(T\) are calculated from the 9 observations.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 2 Design of the Proposed System
### Pre-Processing  
The raw tri-axial accelerometer data were pre-processed by linear de-trending and by a second-order Butterworth band pass filter [21; 28]. The cut-off frequency of the implemented filter was from 0.5 to 5 Hz, which included the variability and consistency of the gait pattern [29].
## 4 Analysis and Evaluation
### Efficiency  
We experiment to show the approximate time used to build the polynomial, as shown in Table 1. With a prototype implemented on a machine with Intel Core i7 10700f CPU, we can see that building a polynomial with a Lagrange form for 20,000 degrees needs around 2 s, while the polynomial with degree 10,000 only takes less than 0.5 s. These experimental results show that our scheme is quite efficient. Although some nodes in the blockchain network may be equipped with lower end processors, it should be capable of handling the work of building a polynomial with thousands of nodes according to our results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Degree** & 10 & 50 & 100 & 500 & 1000 & 5000 & 10,000 & 20,000 & 50,000 \\ \hline
**Time** & 513 ns & 0.012 ms & 0.05 ms & 1.25 ms & 5.20 ms & 0.12 s & 0.49 s & 1.99 s & 12.38 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Running time used to build the polynomial with Lagrange form.  
Figure 2: Distribution of random data points generated in 1000 rounds.
## 3 Materials and Methods
### Validation of the Modified Miami Model  
For approximation and validation of the model, four different species were chosen, two of them adapted to cool climates, Norway spruce and need canary grass (_Phalaris arundinacea_ L.), and two highly productive species with a low frost resistance, _Paulownia_ spp. and _Miscanthus_ spp. Typical MAT\({}_{\mathrm{nat}}\) values and very high BM\({}_{\mathrm{max}}\) values for the four species were found in the literature as input data for the model. The modeled data (ABM) were compared (\(r^{2}\)) with the literature data (BM\({}_{\mathrm{max}}\)), and the result is presented in Table 1. The species-specific parameters were chosen as follows:  
Norway spruce is a native species of taiga and montane forests, but cultivation has extended its area in Northern and Central Europe [29]. As the species is native to the boreal forest, a MAT\({}_{\mathrm{nat}}\) of 1 \({}^{\circ}\)C was suggested. The BM\({}_{\mathrm{max}}\) was chosen as 15 Mg ha\({}^{-1}\)a\({}^{-1}\) in accordance with the findings of [PERSON], [PERSON] [20]. Reed canary grass is native to nearly all of Europe, except above the Arctic Circle and some southern regions. The MAT\({}_{\mathrm{nat}}\) for reed canary grass was set at 2 \({}^{\circ}\)C, as it grows best under cool and moist conditions [30], and the BM\({}_{\mathrm{max}}\) was chosen as 14 Mg ha\({}^{-1}\)a\({}^{-1}\)[31]. _Paulownia_ is a species originating from Asia with an average MAT\({}_{\mathrm{nat}}\) of about 17 \({}^{\circ}\)C [26]. While some species within this genus show frost resistance at about \(-\)20\({}^{\circ}\)C, others tolerate no frost [27]. Estimations based on growing tests gave values of 35 to 90 Mg ha\({}^{-1}\) in two to three years [32]. For this study, 36 Mg ha\({}^{-1}\)a\({}^{-1}\) (90 divided by 2.5) was chosen as BM\({}_{\mathrm{max}}\). _Miscanthus_ is a highly productive species native to Southeast Asia with a MAT\({}_{\mathrm{nat}}\) of about 18 \({}^{\circ}\)C [23,33]. Growth tests on _Miscanthus_ showed that yields of more than 40 Mg ha\({}^{-1}\)a\({}^{-1}\) could be achieved [34]. Comparison of the BM\({}_{\rm max}\) values from the literature with the modeled BM\({}_{\rm max}\) shows an underestimation of BM\({}_{\rm max}\), especially at higher MAT. The reason is that the BM\({}_{\rm max}\) values from the literature are extremely high values, partly achieved in growing tests. These extreme values can be much higher than average values, because of regional and experimental variation in nutrients and water supply. The difference increases with increasing MAT, because the importance of water as a limiting factor is intensified. The inclusion of MAP and E in the model therefore leads to an underestimation of BM\({}_{\rm max}\) by the model compared to the literature data. In the case of Norway spruce, for example, the high BM\({}_{\rm max}\) values were achieved by fertilization and irrigation. As modeling was based on a MAP of 718 mm, the impact of the water-limitation increased with increasing MAT.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 3 System Model and Routing-Related Metrics
### Connectivity Probability  
During the transmission, the sender hopes the selected next-hop relay node can stably connect to its nearby nodes when the next-hop relay node is out of the transmission range of the destination. That way, the next-hop relay node can probably forward the packet to its next-hop relay node when it received the packet from the sender. If the selected next-hop relay node cannot stably connect to its nearby nodes, even if it has received the packet, it may not be able to successfully deliver the packet to the destination.  
Therefore, the connectivity probability should be considered when selecting the next-hop relay node. According to [49], if vehicle intensity is less than 1000 \(\mathrm{veh}/\mathrm{h}\), the inter-vehicle distance is subject to exponential function. For the vehicle pairs \((\phi_{i},\phi_{j})\), the sender \(\phi_{i}\) hopes the receiver \(\phi_{j}\) can stably connect to its nearby vehicles. The connectivity probability of the vehicle \(\phi_{j}\) can be defined as:  
\[P_{j}=\begin{cases}1-e^{-\lambda\phi}&\text{if }\phi_{j}\in\mathcal{M}_{i}\\ 0&\text{if }\phi_{j}\
otin\mathcal{M}_{i}\end{cases}. \tag{6}\]
## 2 Modeling
### Kinematics
#### 2.2.2 Rigid Body Transformation  
According to the assumptions in Section 2.1.1, Tilt-X is assumed to be geometrically symmetric, and it has its moment of inertia expressed as:  
\[I=\begin{bmatrix}m\left(\frac{\sqrt{2}}{2}d\right)^{2}&0&0\\ 0&m\left(\frac{\sqrt{2}}{2}d\right)^{2}&0\\ 0&0&md^{2}\end{bmatrix}. \tag{5}\]  
Considering the position vector and the attitude kinematics, the rigid body model of the Tilt-X is written as:  
\[\left\{\begin{array}{l}\dot{p}^{e}=v^{e}\\ \dot{v}^{e}=\frac{F_{x}^{b}}{m}R_{b}^{e}e_{1}+\frac{F_{z}^{b}}{m}R_{b}^{e}e_{3 }+ge_{3}\\ \dot{\Theta}=A_{b}^{e}\omega^{b}\\ I\dot{\omega}^{b}=-\omega^{b}\times(I\omega^{b})+\tau_{g}+M^{b}\end{array}. \tag{6}\]Based on the roll angle \(\varphi\) and pitch angle \(\theta\) of Tilt-X being very small, the transformation matrix of angular velocity in Equation (4) can also be considered a diagonal matrix, then Equation (6) can be re-written as:  
\[\left\{\begin{aligned} \ddot{x}&=\frac{F_{x}^{b}}{m}c \theta c\varphi+\frac{F_{z}^{b}}{m}(c\varphi s\theta c\varphi+s\psi s\varphi)\\ \ddot{y}&=\frac{F_{x}^{b}}{m}c\theta s\varphi+\frac{ F_{z}^{b}}{m}(s\psi s\theta c\varphi-c\psi s\varphi)\\ \dot{z}&=-\frac{F_{x}^{b}}{m}\theta+\frac{F_{z}^{b} }{m}c\varphi c\theta+g\\ \ddot{\varphi}&=\frac{2M_{x}^{b}}{md^{2}}-\omega_{ y}^{b}\omega_{z}^{b}\\ \ddot{\theta}&=\frac{2M_{y}^{b}}{md^{2}}c\varphi+ \omega_{x}^{b}\omega_{z}^{b}c\varphi\\ \ddot{\psi}&=\frac{M_{z}^{b}c\varphi}{md^{2}c\theta} \end{aligned}\right.. \tag{7}\]  
To simplify the rigid body model, gyroscopic torque is typically neglected. The expressions for the sum of thrust force \(F_{x}^{b}\), \(F_{y}^{b}\), \(F_{z}^{b}\) and the sum of moment \(M_{x}^{b}\), \(M_{y}^{b}\), \(M_{z}^{b}\) in the \(x\), \(y\), and \(z\) directions are presented in Section 2.3.2.
## 1 Introduction
Gain estimation scheduling can be computationally intensive, especially when a large number of operating points must be considered, and it often requires detailed knowledge of the system dynamics. Although the required decomposition of the true plant may seem restrictive, it is one of the strongest attributes. This becomes especially apparent when the true system is derived from a non-linear model linearized at a nominal operating point. If the system subsequently undergoes a health-related change--such that the original non-linear model no longer accurately captures the dynamics--the theory still guarantees that the error states will converge to a neighborhood around zero, provided that the altered system continues to satisfy the structured decomposition. However, it is essential to note that there may still be numerical and practical limitations. While the theorem is often framed in the context of health monitoring, it can also be interpreted as an adaptive observer that tracks changes in the true system across different localized operating regimes. Additionally, given that the proposed estimator is non-invasive--meaning that none of the estimated states are fed back into the true system--the control scheme can be utilized without adversely affecting the true system. The robustness proof depends on two stability criteria: Strictly Positive Real (SPR) and Almost Strictly Dissipative (ASD). For a formal definition, along with SPR and ASD assumptions and constraints, refer to [16; 21].