esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result Analysis
### Experimental Design and Environment
#### 4.1.1 CAE Comparison Experiment  
A convolutional autoencoder (CAE) is also constructed for comparative experiments. A convolutional autoencoder is an autoencoder (AE) implemented using a convolutional neural network (CNN). An autoencoder is an unsupervised learning model designed to reconstruct input data through an encoder and a decoder and to learn the hidden features of the data.  
The difference between a convolutional autoencoder and a traditional autoencoder is that it replaces the fully connected layer with a convolutional layer and a pooling layer (Pooling Layer), which allows it to better process two-dimensional data such as images. The convolutional layer can extract local features, and the pooling layers can reduce dimensions and increase invariance. The network structure of the convolutional autoencoder is shown in Figure 8.  
The network consists of an encoder and a decoder. The encoder is composed of five convolutional layers and max pooling layers. The convolutional layers are used to extract features from the input image, and the max pooling layers are used to reduce the spatial dimensions of the features. The decoder consists of five deconvolutional layers, which are used to recover the features of the input image and up-sampling to increase the spatial dimensions of the features, resulting in the final reconstructed image.  
Figure 8: CAE network structure.
## 7 Conclusions  
This paper presented a new approach based on the energy market and reactive power dispatch considering the reactive power cost of DERs in VVC in distribution networks. For this purpose, a new pricing framework was presented to determine the cost of reactive power produced by DERs including synchronous machine-based DGs and WTs. In the proposed method, the initial scheduled active powers of Disco and DERs were determined in the day-ahead active power market. Then, the results  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Different Parts of Objective Function**} & **Case 1** & **Case 2** \\ \cline{2-3}  & **Benders Decomposition** & **DICOPT Solver** \\ \hline Number of switching operations of OLTC & 0 & 4 \\ Number of switching operations of C1 & 9 & 12 \\ Number of switching operations of C2 & 7 & 16 \\ Total active power losses (kWh) & 998.134 & 954.506 \\ Depreciation cost of OLTC (\$) & 0 & 11.16 \\ Depreciation cost of Sh.Cs (\$) & 2.592 & 4.536 \\ Total cost of active power losses (\$) & 59.64 & 57.07 \\ Total cost of reactive power (\$) & 387.03 & 404.995 \\ Total cost (\$) & 449.26 & 477.761 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of optimal results in cases 1 and 2.  
\begin{table}
\begin{tabular}{c|c c c|c c|c c|c c|c c} \hline \hline \multirow{2}{*}{**Hour**} & \multicolumn{3}{c|}{**Disco**} & \multicolumn{3}{c|}{**DG1**} & \multicolumn{3}{c|}{**DG2**} & \multicolumn{3}{c}{**WT**} \\ \cline{2-13}  & \(\Delta P^{L}\) & \(\Delta P^{Adj}\) & \(\mathbf{Q}\) & \(\Delta P^{L}\) & \(\Delta P^{Adj}\) & \(\mathbf{Q}\) & \(\Delta P^{L}\) & \(\Delta P^{Adj}\) & \(\mathbf{Q}\) \\  & **(kW)** & **(kW)** & **(kVar)** & **(kW)** & **(kW)** & **(kVar)** & **(kW)** & **(kW)** & **(kVar)** & **(kW)** & **(kW)** & **(kW)** & **(kVar)** \\ \hline
18 & 125.73 & 478.79 & 1300 & 0 & 0 & 400 & 0 & \(-478.79\) & 895.76 & 0 & 0 & 366.61 \\
19 & 174.714 & 461.01 & 1920 & 0 & 0 & 400 & 0 & \(-461.01\) & 892.20 & 0 & 0 & 366.61 \\
20 & 147.39 & 172.38 & 1860 & 0 & 0 & 400 & 0 & \(-172.38\) & 834.48 & 0 & 0 & 366.61 \\
21 & 96.68 & 286.48 & 1050 & 0 & 0 & 400 & 0 & \(-286.48\) & 857.3 & 0 & 0 & 366.61 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Daily optimal reactive power dispatches of generation units for peak hours for case 3.  
were surrendered to the DSO, who performed daily VVC testing based on the proposed reactive power dispatch in order to determine the optimal dispatches of VVC devices. Due to the coupling between active and reactive power of DERs, in the proposed model, this issue was addressed by considering the capability diagram of DERs defined in the PQ plane. A Benders decomposition technique was applied to cope with the complexity of solving the daily VVC problem as a MINLP problem. It was concluded from the research results that incorporating the cost of reactive power support of generation units in the developed model would encourage the DERs to actively contribute to the reactive power support as an ancillary service.
## 3 Strategic Development of Ultrafiltration Membrane
### Membrane Materials and Molecular Weight Cutoff  
Various categories of membrane material were implemented in UF process. Inorganic membrane such as ceramic-derived is simpler in terms of construction, has less replacement cost, vapor-sterilizable, and able to achieve high CF [33; 177; 178]. Nevertheless, organic membranes, in particular, those that have hydrophilic features, are endowed by lower fouling tendencies. In fact, hydrophobic organic membranes like PES are still predominant in industrial use for their chemical resistances, robust mechanical strength, and thermal stabilities [178; 179]. Although various laboratory works of membrane UF modification are well-established, those which used complex protein mixtures as testing solution are still scarce and therefore going to be showcased in this part.  
One work researched the effect of material and molecular weight cutoff (MWCO) on the UF performances of saithe (_Pollachius virens_) protein hydrolysate using five tubular membranes (material variations: PES, modified PES (m-PES), and PS, and MWCO: 4, 6, 8, and 9 kDa) [180]. In that experiment, the objective was to separate peptides lower than 2 kDa that possess antioxidant activities. They concluded that considerable hydrophobic feature of PES created less permeable surface which is unfavorable for pertaining peptide separation. Permeate flux was more dependent on MWCO, and this value increased with the increase of MWCO. It is relevant to mention that depending on the hydrolysate the UF and membrane materials were working with, higher MWCO was not always directly correlated to the increase of permeate flux as membrane morphology (surface area, pore size and density) can drive different water permeability within the same membrane materials [181]. In addition, another work showed no connection between MWCO and yield-purity [182] since the homogeneity of membrane pore distribution is one of the keys to enhance selectivity [183].  
Related to MWCO, because its actual value decreased from their nominal value due to the tested substances, operating conditions, and solute-solute-membrane interactions, researches to figure out the main effect of MWCO toward the UF performance needed to operate UF with low pressure and carefully choose membrane with MWCO above the smallest and below the biggest separated molecules [184]. The impact of MWCO on the characteristics of fouling is also relevant to the discussion. It is exemplified that while operated to fractionate aqueous extract of soy flour [185], lower MWCO of 50 kDa exhibited 0.2 um foamular deposits in contrast to that of 100 kDa membrane with 0.4 um thickness. Foulant was observed to have viscoelastic attributes, high resistance to shear stress in flat plate module, and low solid content (21.5% wt). The finding of thinner cake of smaller pore membrane is also coherent with the observation that for hydrolysate from corn ethanol process, smaller pore size (5 kDa) maintained slower fouling rate than 100 kDa membrane [182].
###### Abstract  
This paper presents a carrier waves phase shifting method to reduce the dc-link capacitor current for a dual three-phase permanent magnet synchronous motor drive system. DC-link capacitors absorb the ripple current generated at the input due to the harmonics of the pulse width modulation (PWM). The size, cost, reliability, and lifetime of the dc-link capacitor are negatively affected by this ripple current flowing through it. The proposed method is especially appropriate for common dc-link capacitors for a dual inverter system driving two PMSMs. In this paper, the input current of each inverter is analyzed using Double Fourier Analysis, and the harmonic components of the dc-link capacitor current are determined. The carrier wave phase shifting method is proposed to reduce the magnitude of the harmonics and thus reduce the dc-link capacitor current. Furthermore, the optimum angle between the carrier waves for the maximum reduction in the dc-link capacitor current is analyzed and simulated for different scenarios considering the speed and load torque of the PMSMs. The proposed method is verified through experiments and PMSMs are driven by three-phase voltage source inverters (VSIs) modulated with Space Vector Pulse Width Modulation (SVPWM), which is the most common PWM strategy. The proposed method reduces the dc-link capacitor current by 60%, thereby significantly decreasing the required dc-link capacitance, the volume of the drive system, and its cost.  
carrier waves phase shifting; SVPWM; dc-link capacitor; three-phase VSI; dual PMSMs; double Fourier series; harmonic analysis 2025 16 39
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 2 Pseudo-Periodic Extraction of Satellite Telemetry Data  
### Pseudo-Period Analysis of Satellite Telemetry Data  
Satellite telemetry data provide the operational state information for each system on the satellite and are received by the ground TT&C station in the time sequence. The typical telemetry data \(x(t_{i}),i\in[1,N]\) can be described as:  
\[x(t_{i})=s(t_{i})+n(t_{i}) \tag{1}\]  
where \(t_{i}\) is the time point of the telemetry data acquisition, \(s(t_{i})\) is the ideal telemetry value, and \(n(t_{i})\) is the noise. The composition of the noise can be expressed as:  
\[n(t_{i})=\varepsilon(t_{i})+d(t_{i})+o(t_{i}) \tag{2}\]  
where \(\varepsilon(t_{i})\) is the random noise widely distributed in the data, \(d(t_{i})\) is the noise in the numerical trend pattern, \(o(t_{i})\) is the outlier that only occurs at few time points, and the absolute value of \(o(t_{i})\) is significantly greater than 0 and significantly deviates from the data.  
The satellite runs in its own orbital cycle, so the telemetry data transmitted by the satellite mostly follows the cyclic law of approximate periodic change, which is defined as the pseudo-period [16]. Due to the non-stationarity of the pseudo-periodic change, the change law cannot be accurately determined and has no rigorous and unified mathematical definition. To date, there have been few pseudo-period-based studies of telemetry data. According to the characteristics of the telemetry data, the pseudo-period of the satellite telemetry data \(T_{fi},i\in[1,N]\) is defined as:  
\[T_{fi}=T_{r}+\Delta t_{i} \tag{3}\]  
where \(T_{r}\) is the ideal period of the telemetry data, \(\Delta t_{i}\) is the time deviation of the different pseudo-period whose value fluctuates in a certain range, \(\Delta t_{i}\in[\tau_{a},\tau_{b}]\) and \(\Delta t_{i}\ll T_{r}\). If \(\Delta t_{i}\)= 0, then \(T_{fi}=T_{r}\), namely, the period is an ideal period. For simplicity, a single ideal period is defined as a standard pseudo-periodic sequence \(\overline{X}_{fT}\). Figure 1 shows the measured data of the bus current of the solar panels of a satellite.  
These data exhibit a typical law of approximate periodic variation. According to the turning point of the data shape, 13 approximate periods can be determined, as shown in Figure 1.
# Spring Failure Analysis of Mining Vibrating Screens: Numerical and Experimental Studies
## 5 Conclusions  
This study reports a proposed approach for the spring failure diagnosis of mining vibrating screens by using numerical and experimental studies. The following conclusions are drawn. The spring failures of spring stiffness decrease have a specific influence on the mining vibrating screen dynamic characteristics. The system displacements in the established finite element model are sensitive to variable spring stiffness changes. The spring failure influence rules obtained by numerical simulations indicate that the AVCs of the four spring seats in the \(x\), \(y\), and \(z\) directions may decrease, increase, or stay the same (i.e., indeterminate) under different spring failures. Therefore, the AVCs can reveal single spring failure and double spring failure, which provides a promising approach for spring failure diagnosis. Furthermore, the accuracy of this approach has been experimentally verified. The key of the approach lies in extracting information from orthogonal dynamic displacements to reveal the spring failure, namely obtaining the AVCs of the four spring seats in the \(x\), \(y\), and \(z\) directions. This information provides guidance for the routine maintenance of mining vibrating screens. However, further work will be required to identify whether this novel finding can be extrapolated to structural health monitoring and predictive maintenance.  
Conceptualization, Y.L. and G.M.; Methodology, Y.L., S.S., A.W., and D.L.; Validation, Y.L. and X.C.; Writing--Original Draft Preparation, Y.L.; Writing--Review and Editing, Y.L., G.M., and S.S.; Visualization, Y.L. and J.Y.  
This research was supported by the National key research and development program of China [grant number 2016 YFC0600900]; the National Natural Science Foundation of China [grant number U13611151]; and the Yue Qi Distinguished Scholar Project, China University of Mining & Technology, Beijing.  
The authors declare no conflicts of interest.  
Figure 15: The amplitude variation coefficients (AVCs) of each spring seat in the \(x\), \(y\), and \(z\) directions under spring failure kind 6, including the simulation results and experimental results.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### FLGR Forwarding Mode  
Owing to the inaccurate location of the destination node, the greedy forwarding mode does not fit into the FLGR protocol. The next forwarding node can be selected by the node parameters of CN. Taking an example in which EN \(n_{1}\) sent a data message to the destination node \(n_{8}\), the FLGR forwarding mode is shown as follows.  
As shown in Figure 1, the location of node \(n_{8}\) is the current accuracy location, and the location of node \(n_{8}^{\prime}\) is the fuzzy location by looking up the local network location table of EN \(n_{1}\). When EN \(n_{1}\) has a data message to be sent to the destination node \(n_{8}\), the previous location and update time of node \(n_{8}\) can be obtained through the local network location table of EN \(n_{1}\). Next, the fuzzy location region of node \(n_{8}\) can be obtained, according to the time interval \(t_{18}\) from the previous update time to the current moment. The fuzzy location region can be denoted by radius \(r_{18}\), which can be calculated as follows:  
\[r_{18}=t_{18}*v_{\max}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Node ID & 2 Bytes \\ Node location & 16 Bytes \\ Update time & 2 Bytes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Local network location table.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Num\_seq & 2 Bytes \\ Data message ID & 2 Bytes \\ Destination node ID & 2 Bytes \\ Destination node location & 16 Bytes \\ Update time of destination node & 2 Bytes \\ Node IDs\_pre & 2 Bytes \({}^{*}\) Num\_pre \\ Nodes locations\_pre & 16 Bytes \({}^{*}\) Num\_pre \\ Data & 1280 Bytes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Format of data message.  
According to the fuzzy location region of destination node \(n_{8}\), we can get the CNR A of the EN \(n_{1}\). The CNR A is denoted by angle \(\theta_{18}^{zero}\in(0,\pi)\), which can be calculated as follows:  
\[\theta_{18}^{zero}=2\times\arccos\frac{r_{18}}{\left\|l_{1}-l_{8}\right\|_{2}} \tag{2}\]  
To select the better forwarding node in the CNR of EN \(n_{1}\), a comprehensive assessment of node \(n_{2}\) and \(n_{7}\) should first be obtained.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 5 Learning-Based Asynchronous Stream Sensing
### Training Methodology  
#### 5.4.1 Training Procedure  
The overall procedure is summarized in Algorithm 1, where \(N_{\text{data}}\) is the number of data. In this work, \(N_{\text{data}}\) is batch size. The feature extractors and the classifier are updated sequentially.
## 2 System Model  
### OFDM System Model  
This section describes the OFDM system. We consider an OFDM system with \(N_{a}\) active subcarriers and \(N\)-point inverse fast Fourier transform (IFFT). The samples of baseband OFDM symbol in the time domain can be expressed as follows,  
\[x_{i}[n]=\frac{1}{\sqrt{N}}\sum_{k=0}^{N_{a}-1}X_{i}[k]e^{2\pi nk/N_{a}},\;n=0, \,1,\,\ldots,\,N_{a}-1, \tag{1}\]  
where the \(X_{i}[k]\) is an \(M\)-ary phase shift keying (MPSK) or \(M\)-ary quadrature amplitude modulation (MQAM) modulated symbol. The received signal after passing the channel and removing the guard interval is as follows,  
\[y_{l}[n]=\sum_{k=0}^{N-1}H[k]X_{l}[k]e^{2\pi kn/N}+w[n], \tag{2}\]  
where the \(H[k]\) is the channel coefficient and the \(w[n]\) is the additive white Gaussian noise (AWGN) with zero-mean [5].
## 2 Materials and Methods
### Data Resources
#### 2.2.2 Remote Sensing Data  
Five types of remote sensing data during 2018-2020 (including vegetation, topography, meteorological, human activity intensity, and air quality) were downloaded from the NAI observing sites (Table 1). Figure 1: The distribution of 107 stable monitored NAI sites and the land-use types across Zhejiang province. (1) Vegetation datasets  
In this study, photoelectric-related vegetation factors including the SIF, leaf area index (LAI), and gross primary productivity (GPP), were collected from the GOSIF and MODIS datasets, respectively (Table 1). LAI, the fraction of absorbed photosynthetic active radiation (FPAR), and GPP datasets are available from MCD15A2H, MYD16A2, and MYD17A2H on NASA's LAADSDAAC official website ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)), accessed on 4 January 2021). These datasets have a spatial resolution of 500 m and a temporal resolution of 8 days. Only high-quality data were used based on the quality assurance (QA) layers in the MODIS datasets. Since these datasets may have unavoidable gaps caused by the cloud, GLASS products were used here to fill these gaps in the MODIS datasets. The widely used GOSIF product was selected to provide regional-scale SIF (with a spatial resolution of 0.05\({}^{\circ}\)). These datasets were produced based on OCO-2, MODIS, and MERRA-2 datasets and were updated in December 2020. The GOSIF version 2 product was downloaded from the Global Ecology Data Repository ([[https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)]([https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)), accessed on 4 January 2021). (2) Meteorological datasets  
Meteorological factors consist of evapotranspiration (ET), air temperature (T), relative humidity (RH), wind speed (W), solar radiation flux (R\({}_{\rm S}\)), vapor pressure deficit (VPD), cloud cover (CC), and dew point temperature (DEWT). These parameters were collected from the AgERA5 datasets. The data can be obtained from the Copernicus Climate Change Service ([[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)), accessed on 4 January 2021). The AgERA5 is meteorological re-analysis data based on hourly ECMWFERA5 surface data. Firstly, these datasets were aggregated to a daily temporal scale and resampled from 0.1\({}^{\circ}\) to 500 m.  
(3) Topographic datasets
## 3 Problem Formulation and Use Case Description
### Aircraft Engine Use Case  
Typically, aircraft engines have five main components: the fan, the low-pressure compressor (LPC), the high-pressure compressor (HPC), the low-pressure turbine, and the high-pressure turbine (HPT). These aero-engines typically undergo different types of failure related to the degradation of their main components (LPC, HPC, LPT, HPT, and fan), which have to be replaced to avoid failures [44].  
In this work, we use the C-MAPSS dataset, which is a benchmark dataset [10] for RUL estimation on aircraft engines, provided by NASA [10]. It was obtained through the commercial modular aero-propulsion system simulation (C-MAPSS) [45], and contains degradation trajectories of aero-engines, divided into four subsets (FD001-FD004), each one considering a specific number of failure modes and operating conditions, as shown in Table 1. Training instances contain run-to-failure sensor measurements, while testing instances include measurements up until a certain point before failure. When utilized to test the RUL prediction model, the goal is to predict the RUL in cycles for the last working cycle of each engine in the testing set. Each instance contains 26 columns, including the engine ID, cycle index, and three columns containing operational settings and sensor measurements.  
For this study, we focus exclusively on the subset FD001, which was utilized for the demonstration and validation of the maintenance scheduling framework. For these purposes, we need run-to-failure data. Therefore, we exclusively utilize the training instances, as they are the only ones that contain such data. To ensure that the approach is only validated on unseen data, we adopt an 80-20% split for training and testing, respectively. That is, the first 80 engines are used for training purposes, while the remaining 20 are reserved for testing the approach.  
The main objective is to schedule maintenance interventions at a maintenance slot just before a failure occurs to optimize the useful life of the engines and to avoid failures, a critical requirement to ensure the safety of the aircraft staff.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 5 Experimental Analysis
### Data Utility  
In the experiments in this subsection, we measured the data quality by the distortion ratio and the aggregate query answering error. In addition, we conducted an experiment on classifier learning of the published datasets. We observed that the Merging method has lower data loss, less relative error and better classification accuracy than the all other methods.