esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Influence of Wind Speed Variability on Wind Power
The solid blue arrows marking the scatter in instantaneous wind speed values at \(\overline{v}\) = 5, 7, and 9 m/s qualitatively demonstrate the monotonic increase in wind speed variability with mean speed; i.e., the case of multiplicative variability. Before proceeding, we note a nuance concerning the time-average for \(\overline{P}\) and \(\overline{v}\). IEC standard 61400-12-1 [3] requires that each value of \(\overline{v}\) and \(\overline{P}\) in the power curve be determined by averaging a 10 min time record (sampled at 1 Hz or higher) of \(v(t)\) and \(P(t)\), respectively. This translates to an average over 600 s \(\times\) 1 Hz = 600 samples--at least--for each time-averaged value in the power curve. Since the Howard data set (see Table 1) was sampled at 0.2 Hz, a pertinent question to ask is whether 600 s \(\times\) 0.2 Hz = 120 samples are sufficient to achieve statistical convergence of the mean. We performed a bootstrap protocol [17] for the Howard time series to generate ten different time series by randomly shuffling values of the originally measured time series for \(v(t)\) and \(p(t)\). We then measured the mean for each bootstrap time series as a function of increasing sample size for wind speed and wind power. Figure 2 shows how the mean wind speed (Figure 2a) and mean wind power (Figure 2b) converge as a function of increasing sample size. Since each sample is measured 5 s apart in our data set, 120 samples equate to a 10 min interval prescribed by the IEC standard. As is evident from Figure 2, asymptotic statistical convergence of the mean values is achieved only at about 400 samples. Following from this statistical convergence test, we constructed the power curve in Figure 1 by averaging over 400 samples (i.e., a 33.3 min time record for each value of \(\overline{v}\) and \(\overline{P}\)). We emphasise that the bootstrap protocol tests for statistical convergence in the absence of correlations. The successive values in the measured time series are correlated; i.e., the \(n\)th value will lie within a certain band relative to the \((n-1)\)th and \((n+1)\)th values, depending upon correlation strength and correlation time. When this measured series is randomly shuffled to generate a synthetic series for the bootstrap test, the correlations between successive values in the time series are lost. On the other hand, when using the actual time series for \(v(t)\) and \(P(t)\) in constructing the power curve from averages, such correlations persist and reveal themselves in the variability, as we discuss below.
# Arc-Completion of 2-Colored Best Match Graphs to Binary-Explainable Best Match Graphs
## 2 Notation  
We consider simple directed graphs (digraphs) \(G=(V,E)\) with vertex set \(V\) and _arc_ set \(E\subseteq V\times V\setminus\{(v,v)\mid v\in V\}\) and rooted (undirected) trees \(T\) with root \(\rho\). Correspondingly, we write \((x,y)\) for directed arcs from \(x\) to \(y\), and \(xy\) for undirected tree edges. We write \(G[W]\) for the subgraph of \(G\) induced by a set of vertices \(W\subseteq V\). Given a tree \(T\), we write \(V(T)\) and \(E(T)\) for its set of vertices and edges, respectively; \(L(T)\) for the set of leaves; and \(V^{0}(T)=V(T)\setminus L(T)\) for the set of inner vertices.  
A vertex coloring of a graph is a map \(\sigma:V\to S\), where \(S\) is a non-empty set of colors. A vertex coloring of \(G\) is _proper_ if \(\sigma(x)\
eq\sigma(y)\) for all \((x,y)\in E(G)\). We will also consider _leaf-colorings_\(\sigma\): \(L(T)\to S\) for trees \(T\) and denote by \((G,\sigma)\) and \((T,\sigma)\) vertex-colored (dijgraphs and leaf-colored trees, respectively.  
Given a rooted tree, we write \(x\preceq_{T}y\) if \(y\) is an _ancestor_ of \(x\), i.e., if \(y\) lies along the unique path from \(\rho\) to \(x\) in \(T\). We write \(x\prec_{T}y\) if \(x\preceq_{T}y\) and \(x\
eq y\). The relation \(\preceq_{T}\) is a partial order on \(T\). If \(xy\in E(T)\) and \(x\prec_{T}y\), then \(y\) is the unique _parent_ of \(x\), denoted by \(\mathsf{par}_{T}(x)\), and \(x\) a _child_ of \(y\). The set of children of a vertex \(u\in V(T)\) is denoted by \(\mathsf{child}_{T}(u)\). A rooted tree \(T\) is phylogenetic if every inner vertex \(x\in V^{0}(T)\) has at least two children. All trees in this contribution are assumed to be phylogenetic. Furthermore, we write \(T(u)\) for the subtree rooted in \(u\), i.e., \(V(T(u))=\{y\in V(T)\mid y\preceq_{T}u\}\). The _last common ancestor_ of a non-empty subset \(A\subseteq V(T)\) is the unique \(\preceq_{T}\)-minimal vertex of \(T\), that is, an ancestor of every \(u\in A\). For convenience, we write \(\mathrm{lca}_{T}(x,y,\dots)\) instead of \(\mathrm{lca}_{T}(x,y,\dots)\).  
A triple \(xy|z\) is a rooted tree with the three leaves \(x\), \(y\), and \(z\) such that \(\mathrm{lca}_{T}(x,y)\prec\mathrm{lca}_{T}(x,y,z)\). If \(\epsilon\in E(T)\), we denote by \(T_{\epsilon}\) the tree obtained by contracting the edge \(e\). We will only be interested in contractions of inner edges, i.e., those that preserve the leaf set. We say that \(T\)_displays_ a tree \(T^{\prime}\), in symbols \(T^{\prime}\leq T\), if \(T^{\prime}\) can be obtained from \(T\) as the minimal subtree of \(T\) that connects all elements in \(L(T^{\prime})\) with root \(\mathrm{lca}_{T}(L(T^{\prime}))\) and by suppressing all inner vertices that only have one child left, which can, e.g., be achieved by a stepwise contraction of one of their two incident edges until no such vertices remain.
## 3 Results and Discussion
### Deployment and Optimization of Advanced Energy Storage Systems
#### 3.3.1 Comparative Analysis of Storage Technologies (BESS, Hydrogen, PHS)  
Among the available storage solutions, BESSs have received significant attention due to their high energy density, rapid response capabilities, and modular deployment options [12; 14; 15]. Despite these technical advantages, challenges persist regarding high capital costs, limited service life, and environmental concerns associated with battery disposal [26; 41; 82].  
Hydrogen-based storage systems are gaining interest as a long-term solution, particularly for large-scale and seasonal storage applications [19; 21]. These systems offer substantial energy capacity and the potential for integration with power-to-gas technologies; however, efficiency losses and the requirement for advanced hydrogen infrastructure present notable challenges [20; 55].  
Pumped hydro storage (PHS) remains one of the most mature and cost-effective technologies for large-scale energy storage [16; 17]. PHS provides excellent long-term storage capabilities and high efficiency but is geographically constrained, limiting its deployment in certain regions [36; 83].  
The comparative analysis of these technologies underscores the importance of selecting appropriate storage solutions based on the specific operational requirements and geographic conditions of each microgrid.
## 3 Case Study
### Data Completion and Analysis
The experimental results with different missing ratios simultaneously verify this inference and confirm that effective completion and rolling prediction of displacement monitoring data can be achieved by using global time series datasets. After analyzing the reasons, the TRMF model only analyzes and calculates the matrix structure and does not use the time domain smoothness of multivariate time series and the potential correlation information between the series in the time and space dimensions. MLATC and HALRTC both use the tensor structure to complete the time series prediction through the method of quantitative completion. In particular, the MLATC model introduces the autoregressive norm, which not only preserves the structural information of the original time series through the tensor structure, but also makes full use of the correlation and trend characteristic information between the time series. Similarly, the experimental results of the four sensors numbered SZY-08, SZY-02, SZY-03 and SZY-06 are shown in Figures 6 and 7 after RM 40% processing of the dataset of the Shuizhuyuan landslide; the data completion performance are shown in Table 2. The prediction model has a very good recovery effect on missing data and shows a better data fit in terms of time series trend prediction. Figure 5: Completion of displacement data (NM40%). (**a**) Point SZY-02; (**b**) point SZY-03; (**c**) point SZX-06. The blue curve represents the measured data, the yellow dots represent the missing data and the red curve represents the recovered data. Figure 6: Completion of the displacement data of SZY-08 (RM40%). The blue curve represents the measured data, the yellow dots represent the missing data and the red curve represents the recovered data. Figure 7: Completion of displacement data (RM40%). (**a**) Point SZY-02; (**b**) point SZY-03; (**c**) point SZY-06. The blue curve represents the measured data, the yellow dots represent the missing data and the red curve represents the recovered data.
## 4 Results: Manual Service Composition
### Performance during Manual Composition  
A total correctness score was calculated for the design compositions created by the participants to measure their performance (as shown in Table 3). This score added together the individual scores, where each user's solution was graded using the following scheme: a complete solution with no help receives three marks, a solution with some help receives two marks, a solution with substantial help receives one mark, and a wrong solution or no solution receives 0 marks. The manual composition tasks yielded varying performance results between the two user groups, with the non-programmers performing worse than the programmers. All 10 non-programmers who managed to define the conditional branch, required substantial help from the experimenter. In contrast, only two out of nine experts required substantial help to complete the same task. Indeed, these low completion rates and scores support hypothesis H1b. All non-programmers successfully bound an activity with the correct service, with only three of them requiring some level of assistance. The programmers exhibited similar behaviour.  
Specifying data flow connections between two services triggered the highest number of failures in both groups, with five non-programmers unable to complete the task. Five of the seven non-programmers who completed the data flow task required some assistance, as opposed to only three out of seven experts. The non-programmers saved their compositions without any help.  
Moreover, the non-programmers spent significantly more time adding the parallel branch to the composition, (\(t\)-test (16) = 2.94, \(p\) = 0.009), and specifying the data flow connections between services, (\(t\)-test (12) = 3.37, \(p\) = 0.006), than the programmers. These figures confirm our hypothesis H1c. Both user groups spent the longest time creating the conditional branching, parallel branching,and specifying dataflow, (F (4, 84) = 22.74, \(p\) < 0.001). These tasks encompass more steps and require strong analytical skills; thus, ensuring longer completion time.
###### Abstract  
As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. However, this practice has sparked apprehensions amongst users concerning the privacy and safety of their personal data. Federated learning emerges as an evolution of centralized machine learning, enabling a collective training of machine learning models by multiple users on their respective devices. Crucially, this is achieved without the direct submission of data to a central server, thereby significantly mitigating the hazards associated with privacy infringements. Since the machine learning algorithms act locally in federated learning, passing just the local model back to the central server, the users' data remain locally. However, current research work indicates that local models also include user data privacy-related components. Moreover, current privacy-preserving secure aggregation schemes either offer insufficient accuracy or need significantly high computing resources for training. In this work, we propose an efficient and secure aggregation scheme for privacy-preserving federated learning with lower computational costs, which is suitable for those weak IoT devices since the proposed scheme is robust and fault-tolerant, allowing some of the users to dynamically exit or join the system without restarting the federated learning process or triggering abnormal termination. In addition, this scheme with the property of result verification in the situation when the servers return incorrect aggregation results, which can be verified by the users. Extensive experimental evaluations, based on real-world datasets, have substantiated the high accuracy of our proposed scheme. Moreover, in comparison to existing schemes, ours significantly reduces computational and communication costs by at least 85% and 47%, respectively.  
federated learning; data aggregation; privacy preserving; verifiable computation Article
## 4 Applications
### Ultra Compact Elliptic-function Lowpass Filters Based on SIRs  
Elliptic-function lowpass filters are characterized by the presence of transmission zeros that improve the frequency selectivity of the filter. These transmission zeros can be achieved by using grounded series resonators. Therefore, SIRs loading a CPW transmission line are specially suited for this purpose. Figure 20 shows a designed elliptic lowpass filter and its frequency response [46]. The filter characteristics are good, and the device dimensions are very small. The small size of the SIR is in this case the most relevant aspect of the proposed structure.
# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
## 3 Results
### Experimental Results from Capacitively-Coupled ECG and Bio-Impedance
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subj.**} & \multirow{2}{*}{**Traffic**} & \multicolumn{4}{c}{**Before Motion Artefact Algorithms**} & \multicolumn{4}{c}{**After Motion Artefact Algorithms**} \\ \cline{3-10}  & & **R Peak Detection** & **R Peak Detection** & **HR Mean Absolute** & **R Peak Detection** & **R Peak Detection** & **Hr Mean Absolute** & **\% of Signal** \\  & & **Sensitivity** & **Positive Predictivity** & **Error (ppm)** & **Sensitivity** & **Positive Predictivity** & **Error (ppm)** & **Removed** \\ \hline \multirow{2}{*}{\(1\)} & highway & 94.67\% & 95.15\% & 2.28 & 97.88\% & 98.26\% & 0.86 & 10.3\% \\ \cline{2-10}  & city & 89.46\% & 92.34\% & 2.18 & 97.11\% & 97.23\% & 1.28 & 29.7\% \\ \hline \multirow{2}{*}{\(2\)} & highway & 76.71\% & 84.65\% & 3.06 & 98.28\% & 99.03\% & 1.57 & 47.3\% \\ \cline{2-10}  & city & 59.27\% & 66.53\% & 12.68 & 99.36\% & 100.00\% & 0.93 & 82.5\% \\ \hline \multirow{2}{*}{\(3\)} & highway & 98.05\% & 98.04\% & 1.10 & 99.85\% & 99.49\% & 0.36 & 9.5\% \\ \cline{2-10}  & city & n/a & n/a & n/a & n/a & n/a & n/a & n/a \\ \hline \multirow{2}{*}{\(4\)} & highway & 99.63\% & 99.89\% & 0.05 & 99.82\% & 99.92\% & 0.01 & 2.8\% \\ \cline{2-10}  & city & 99.28\% & 98.97\% & 0.35 & 99.31\% & 99.08\% & 0.22 & 0.3\% \\ \hline \multirow{2}{*}{\(5\)} & highway & 89.92\% & 86.57\% & 3.37 & 96.65\% & 91.51\% & 2.30 & 20.6\% \\ \cline{2-10}  & city & 92.74\% & 87.45\% & 6.56 & 93.99\% & 88.64\% & 5.80 & 11.2\% \\ \hline \multirow{2}{*}{Median} & highway & 94.67\% & 95.15\% & 2.28 & 98.28\% & 99.03\% & 0.86 & 10.29\% \\ \cline{2-10}  & city & 91.10\% & 89.90\% & 4.37 & 98.21\% & 98.14\% & 1.11 & 20.41\% \\ \cline{2-10}  & overall & 92.74\% & 92.34\% & 2.28 & 98.28\% & 99.03\% & 0.93 & 11.15\% \\ \hline \multirow{2}{*}{Mean} & highway & \({}^{*}\) & \({}^{*}\) & 1.97 & \({}^{*}\) & \({}^{*}\) & 1.02 & \({}^{*}\) \\ \cline{2-10}  & city & \({}^{*}\) & \({}^{*}\) & 5.44 & \({}^{*}\) & \({}^{*}\) & 2.06 & \({}^{*}\) \\ \cline{2-10}  & overall & \({}^{*}\) & \({}^{*}\) & 3.51 & \({}^{*}\) & \({}^{*}\) & 1.48 & \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: R peak detection and heart rate performance before and after motion artefact algorithms. The percentage of signal removed is a result of the quality-based motion artefact handling. \({}^{*}\) The mean is not calculated for numbers expressed as percentages since the sample sizes are not identical.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 3 Condition Monitoring
### Methodology  
In the following section, all the signals used for damage detection are presented. The signals were resampled as described in the Section 3.3. To present the signals, an arbitrary time section from an undamaged gear is chosen. The abscissas of the diagrams shows the time in seconds. The periods of meshing and the rotation of the gears are shown with different colors on the following figures. Each coloured strip, followed by a white field, stands for one period of meshing or rotation. The legend for the colors is presented below each figure.  
The raw acceleration signals in g from the two sensors, IEPE1 (upper diagram) and IEPE2 (lower diagram) are presented in Figure 7. It can be seen that the signals are very similar, with almost no noise. The amplitude modulation of the ring gear is distinguishable. The meshing frequency is also conspicuous in the signal. Considering how clearly the meshing can be seen in these signals, we can conclude that both can be used for monitoring the meshing and the damage to the gears.  
In Figure 8, the torque signal, measured at the pinion gear (\(M_{D7}\)), and the TE signal are depicted. The torque signal is shown in the upper part of the diagram, and it is scaled in % of rated torque. TE is presented below, in degrees, and it is calculated based on the ring gear rotation, using the Formula (13) derived from [27]. The period of the ring gear rotation (gray area) can be seen on both diagrams well. The meshing (green stripes) can be seen in the torque and TE diagrams with the calculated meshing frequency. Both diagrams show clear signals with almost no noise. The long wave component of both signals with the ring gear rotation is caused by run-out of the ring gear. This can be either radial or axial run-out. Due to the scaling of the TE, the meshing component cannot be seen well on the diagram. To be able to see the meshing frequency, the signal has been high-pass filtered both with the ring gear and the pinion gear frequency. The filtered signals are presented in Figure 9.  
Figure 7: Raw acceleration signals in g vs. time in seconds.  
\[TE_{Out}=\phi_{Out}-\frac{1}{i}\phi_{ln} \tag{13}\]  
In the upper part of Figure 9, the frequency of the pinion gear is very distinct, together with the meshing frequency. Like in the case of the ring gear described above, this is the result of pinion gear run-out. After high-pass filtering the signal with the pinion gear speed, the frequency of the meshing can be seen more clearly. Summarizing the torque and TE signals, it can be said that due to the low level of noise and the clearness of the meshing in the signals, and it is suspected that these features can be utilized for damage detection.
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 2 Mathematical Model of Proportional Valve with HSVB as Pilot Section
### Main Valve Model  
Assuming that the right side is the positive direction of the spool movement, according to the working principle of the DPV shown in Figure 1, the force balance equation of the main spool can be obtained as follows:  
\[\begin{array}{l}A_{5}p_{5}-A_{4}p_{4}-K(x_{0}+x_{m})-F_{\text{f}}-F_{\text{sh1 }}-F_{\text{sh2}}=m\ddot{x}_{\text{m}}\\ F_{\text{sh1}}=2C_{\text{v}}C_{\text{d}}a_{xm}(p_{\text{K}}-p_{\text{A}})\cos \theta_{1}\\ F_{\text{sh2}}=2C_{\text{v}}C_{\text{d}}a_{xm}(p_{\text{B}}-p_{\text{T}})\cos \theta_{2}\\ F_{\text{f}}=B_{x}x_{\text{m}}\end{array} \tag{1}\]  
\(A_{5}\) represents the effective area in the left control chamber of the main spool, while \(p_{5}\) represents the pressure in the same chamber. \(A_{4}\) represents the effective area in the right control chamber of the main spool, and \(p_{4}\) represents the pressure in the right control chamber of the main spool. \(K\) represents the spring rate, and \(x_{0}\) represents the pre-compression. \(F_{\text{f}}\) is the friction force acting on the spool. The viscous damping coefficient is \(Bx\). \(F_{\text{sh1}}\) represents the flow force on the spool when the hydraulic fluid flows from the pressure port to the load, and \(F_{\text{sh2}}\) represents the flow force on the spool when the hydraulic fluid flows from the load to the oil return port. \(C_{\text{v}}\) is the flow velocity coefficient, while \(C_{d}\) is the flow rate coefficient. The valve port flow area is \(a_{xm}\). The inlet pressure of the main valve is \(p_{K}\). The pressure in port A of the load is \(p_{A}\), the pressure in port B of the load is \(p_{B}\), and the pressure of the oil return port is \(p_{T}\). The mass of the main spool is \(m\), and the displacement of the main spool is \(x_{m}\). The effective areas at both ends of the main spool are equal, so \(A_{4}\) and \(A_{5}\) are equal. \(\theta_{1}\) represents the jet angle of the flow from the pressure port towards the load, and \(\theta_{2}\) represents the jet angle of the flow from the load towards the oil return port. The valve port arrangement is shown in Figure 2. The angles of \(\theta_{1}\) and \(\theta_{2}\) can be acquired in the following way with reference to Figure 2.  
\[\begin{array}{l}\theta_{1}=\tan^{-1}(\frac{L_{1}}{B_{1}})\\ \theta_{2}=\tan^{-1}(\frac{L_{2}}{B_{2}})\end{array} \tag{2}\]
## 4 System Performance Discussion
### Parameter Discussion on System Performance
#### 4.2.3 Signal-to-Noise Ratio
The whole system is made up of four antennas and the four channel signals are processed by the pulse-compressed modules. Finally, the results are obtained through the STAP element.