esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Based Speech Synthesis
## 3 Statistical Parametric Speech Synthesis  
A complete SPSS system is generally composed of three modules: a text analysis module, a parameter prediction module which uses a statistical model to predict the acoustic feature parameters such as fundamental frequency (F0), spectral parameters and duration, and a speech synthesis module. The text analysis module mainly preprocesses the input text and transforms it into linguistic features used by the speech synthesis system, including text normalization [28], automatic word segmentation [20], and grapheme-to-phoneme conversion [29]. These linguistic features usually include phoneme, syllable, word, phrase and sentence-level features. The purpose of the parameter prediction module is to predict the acoustic feature parameters of the target speech according tothe output of the text analysis module. The speech synthesis module generates the waveform of the target speech according to the output of the parameter prediction module by using a particular synthesis algorithm. The SPSS is usually divided into two phases: the training phase and the synthesis phase. In the training phase, acoustic feature parameters such as F0 and spectral parameters are firstly extracted from the corpus, and then a statistical acoustic model is trained based on the linguistic features of the text analysis module as well as the extracted acoustic feature parameters. In the synthesis phase, the acoustic feature parameters are predicted using the trained acoustic model with the guidance of the linguistic features. Finally, the speech is synthesized based on the predicted acoustic feature parameters using a vocoder.
###### Abstract  
The Chinese BeiDou navigation satellite system (BDS) aims to provide global positioning service by 2020. The combined use of BDS and Global Positioning System (GPS) is proposed to provide navigation service with more stringent requirements. Actual satellite data, signals and measurements were collected for more than one month to analyze the positioning service qualities from both BDS and GPS. In addition to the conversions of coordinate and timing system, five data quality analysis (DQA) methods, three signal quality analysis (SQA) methods, and four measurement quality analysis (MQA) methods are proposed in this paper to improve the integrated positioning performance of BDS and GPS. As shown in the experiment results, issues related to BDS and GPS are resolved by the above proposed quality analysis methods. Thus, the anomalies in satellite data, signals and measurements can be detected by following the suggested resolutions to enhance the positioning performance of the combined use of BDS and GPS in the Asia Pacific region.  
Global Navigation Satellite System (GNSS); BeiDou Navigation Satellite System (BDS); Global Positioning System (GPS); navigation data; ephemeris; almanac; signal; measurement +
Footnote †: This paper is an extension of work presented at the Institute of Navigation GNSS+ 2015 Conference, Tampa, FL, USA, 14-18 September 2015.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 4 Discussion  
In this section, numerical simulations for the controller proposed in this paper are conducted to demonstrate its performance.  
First, the system parameters are set as follows:  
\[\mathbf{J}=diag(50,75,100)\mathrm{kg}\cdot\mathrm{m}^{2},\mathbf{d}=5\times 10^{-3 }\mathbf{randn}(3,1)\mathrm{N}\cdot\mathrm{m}\] \[\mathbf{\omega}(t_{0})=\left[\begin{array}{cc}-0.1&-0.05&0.04\\ \end{array}\right]^{T}\mathrm{rad}/\mathrm{s},\mathbf{q}_{v}(t_{0})=\left[\begin{array} []{cc}\sqrt{2}&\sqrt{3}&\sqrt{6}\\ \end{array}\right]^{T} \tag{40}\] \[t_{sample}=0.5\mathrm{s}\]  
Next, the numerical simulation results would be given.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 3 Proposed Speech Separation Algorithm  
Generally, speech separation algorithms based on a microphone array can be divided into two categories based on whether the sound source position, related transfer function (RTF), or steering vector is required. The representative methods of these two categories are beamforming and ICA in blind source separation (BSS). Beamforming is the most common speech enhancement algorithm based on microphone array measurements, which has the advantages of being easy to implement and a low computation cost, whereas the performance of beamforming depends on the accuracy of the sound source localization, especially in the scene where the interference sound source exists. To completely and accurately suppress the interference, the algorithms not only need the positioning information, but the steering vectors or even RTF which is hard to estimate [20]. From the perspective of actual implementation and use of BSS algorithms like ICA or complex Gaussian mixture model (cGMM), the sound source localization algorithms may not be essential. However, such algorithms are often cumbersome in actual applications. First, ICA obtains the separation matrix by iterating the objective function, which notably increases the computation cost. Although there are online stochastic gradientdescent algorithms for ICA, the convergence rate is still a problem. Second, algorithmic modeling is not flexible to handle with the scenes of sound sources joining and moving. Third, without counting and localization algorithms, only using PCA for dimensionality reduction, it is difficult for BSS algorithms to iteratively converge in an appropriate dimension. Because of the sparsity of the speech signals in the time-frequency domain and the presence of noise, PCA could retrieves a dimension reduction that less than the existing number of sound sources and more than the existing number of sound sources, respectively. The different dimensions across the frequency bins lead to increased difficulty in subsequent process of permutation problem. Iterating in high dimension also leads to increased computation costs, especially for IVA, which iterates simultaneously at all frequency bins.  
Therefore, an adaptive fast speech separation algorithm based on multiple sound source localization as preprocessing to select between beamforming and frequency domain ICA according to different mixing conditions per frequency bin is proposed in this section. Moreover, the proposed framework is not limited to ICA, IVA, cGMM, or other algorithms that iterate with a large computation cost can also use it to reduce calculation cost. Furthermore, the proposed method is not sensitive to the inaccuracy of the positioning result.
## 4 Experimental Results and Analysis
### Word Vector Pre-Training  
In this work, we used Google's open-source Skip-Gram with Negative Sampling model to generate pre-trained word vectors. The training window size of the Skip-Gram model was set to 15 (the farthest distance between the current word and the predicted word), the learning rate of the model was set to 0.05, the negative value was set to 5, the min_count was set to 1 (indicating that no words would be discarded), and the number of iterations was set to 10. The parameters of the pre-trained word vector were set as shown in Table 2. The word similarity of the pre-trained word vector is shown in Table 3.
## 5 Conclusions  
In this paper, we have proposed a DLaaS concept to implement various DL slices in distributed vehicular environments through virtualization on NTNs. We have analyzed various technologies, including edge computing, distributed computing/communication, network slicing, and different DL methods. An end-to-end functional decomposition of typical DL methods and their possible implementation as slices over the distributed networking platforms is discussed. A detailed case study of a typical vehicular scenario is considered. Through simulation, it has been shown that the proposed DLaaS concept can have several advantages in terms of user satisfaction, flexibility, scalability, performance boosts, and added intelligence. Some key challenges of the proposed DLaaS concept are also provided to motivate future research.  
Conceptualization, D.T.; methodology, D.N., S.S.S. and D.T.; software, D.N.; validation, D.N. and S.S.S.; formal analysis, D.N. and S.S.S.; investigation, D.N.; resources, D.T.; data curation, D.N.; writing--original draft preparation, D.N.; writing--review and editing, D.T., D.N. and S.S.S.; visualization, D.N.; supervision, D.T.; project administration, D.T.; funding acquisition, D.T. All authors have read and agreed to the published version of the manuscript.  
This work was co-funded by the European Commission under the \"5G-STARDUST\" Project, which received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101096573 and by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE00000001--program \"RESTART\"). The views expressed are those of the authors and do not necessarily represent the project. The Commission is not liable for any use that may be made of any of the information contained therein. This work has received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI).  
Not applicable.  
Data Availability Statement:Data available on request due to restrictions e.g., privacy or ethical.  
The authors declare no conflicts of interest.  
Figure 8: Reconfigurabilty vs the number of slices.
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 2 Description of the Protocol Protecting Private Keys with MFA  
### Factors of Authentication for the MFA  
In addition to passwords, multiple factors of choice can be selected to protect a private key. Examples include but are not limited to the following:  
A physical token with a SRAM-based physical unclonable function (PUF) that can support a CRP mechanism. The challenges are a set of \(n\times n\) addresses pointing at the SRAM; the responses are the reading of the SRAM at these \(n\times n\) positions. Each SRAM device is unique when submitted to power-off power-on cycles, each cell awaking either as a consistent \"0\" or \"1\". Small portions of these cells are flaky, awaking randomly as a \"0\" and a \"1\", which we recorded with a third state, \"x\".  
A biometric scheme supporting a CRP mechanism. For example, the biometric images are described by a set of L landmarks. The challenges are a set of \"n\" addresses pointing at the image. The responses are the reading of the distances between a position in the image and the L landmark. The distances can be converted into an s-bit data stream; thereby the total size of the responses is \(n\times(s\times L)\). When submitted to repetitive enrollment cycles, the streams of responses are consistent \"0\" or \"1\"s Small portions of the information are flaky, awaking randomly as a \"0\" and a \"1\"; they can be recorded with a third state, \"x\".  
A virtual token can be generated from a digital file resulting from the computation of a chain of information and its blockchain, which supports a CRP mechanism. The challenges are a set of \(n\times n\) addresses pointing at the digital file; the responses are the reading of the digital file at these \(n\times n\) positions.
# Thermal Fatigue Properties of Ultrasonically Bonded Copper Joints
## 3 Results and Discussion  
### Effect of Thermal Fatigue Fracture on Mechanical Properties  
Figure 4a shows the results of the peel strength test and the value of the bonding area of the C1020 joints before and after the thermal cycling test. The bonding area was obtained from the results of the fracture tests, as shown in Figure 4b. The strength after the thermal cycling test was 240 N, while the peel strength before the thermal cycling test was 409.5 N (Figure 4a). The bonding area changed from 12.6 mm\({}^{2}\) to 3.69 mm\({}^{2}\) after the thermal cycling test. Figure 4b shows the stereomicroscope images of the fractured surface obtained after the peel strength test. There were bright or dark regions on the fracture surface; the bright regions were present as stripes, and the number decreased after the thermal cycle tests. Figure 4c shows the SEM image of the boundary of a stripe. We observed dimples indicating ductile fracture inside the stripe, while the surface of the dark region was smooth. Hence, the bright and dark regions corresponded originally to the bonded and unbonded regions, respectively. To define the bonding area after the thermal cycling test, the sample was dipped into a dye penetrant before starting the thermal cycling test, which could show either the initial unbonded region or that produced during the thermal fatigue test based on the difference in oxidation behavior. For example, Figure 4d shows the representative backscattered electron (BSE) image of the fractured surface after the thermal cycling test; the stripes corresponding to the initial bonded region are shown in dark color. Although the center of the fracture surface exhibits a bright oval region, the surrounding region is dark, indicating that the surrounding region was oxidized during the thermal cycling test. In this case, the bonded area can be estimated as the area of the stripes inside the bright oval region. Thus, the bonded area should decrease after the thermal cycling test. From these results, we inferred that the decrease in the peel strength could be attributed to the reduced bonding area due to the thermal fatigue generated during the thermal cycling test.
## 2 Network Model
### Traffic Model  
We assume the traffic model that we used in [27], which was developed based on [32]. In this traffic model, the data which are carried by traffic flows over the packet transport network have a constant bit-rate. The data are sent by RUs, DUs, and CUs, periodically, as bursts of Ethernet frames. Each remote unit periodically generates the bursts containing radio data and destined to its DU. After processing in DU, the data are again periodically sent in the form of a burst of Ethernet frames to the central unit, in which the radio processing is completed. Finally, CU sends the IP traffic encapsulated into Ethernet frames towards the DC for further 5G core and content processing.  
The bursts are not divided in the network into individual frames, but are switched as entire. The frames have the payload of a fixed size equal to 1500 bytes [8]. Additionally, each frame has 42 bytes of overhead [8,32].  
The bit-rates of FH, MH, and BH flows have been estimated according to the model provided in [33]. For evaluation purposes, a radio system consisting of four antennas with MIMO and 100 MHz channels was considered. As discussed in Section 2, we assumed functional split Options 7.2 and 2, respectively, in fronthaul and midhaul. The obtained bit-rates of flows are shown in Table 1. Additionally, in Table 1, we present the size of the burst of Ethernet frames (i.e., number of frames) for particular flows. For more details on the traffic model, refer to [27].
## 2 Materials and Methods
### Amplicon Sequencing and Bioinformatics Analysis  
The microbial communities colonizing the analyzed samples were examined by the sequencing of the V3-V4 region of the 16S rRNA gene. The 16S rRNA gene fragment was amplified with the PCR primers recommended for the Illumina technique. The primers were developed by adding Illumina adapter overhang nucleotide sequences to the PCR primers given by [PERSON] et al. [27]. Amplicons were indexed using the Nextera(r) XT Index Kit (Illumina, Inc. San Diego, CA, United States) according to the manufacturer's instructions. DNA was sequenced in Illumina MiSeq in the \(2\times 250\) paired-end mode. Demultiplexing and FASTQ file generation were performed using Miseq Reporter v.2.4. (Illumina, Inc. San Diego, CA, United States).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Microorganism** & **Medium** & **Incubation Conditions** \\ \hline _Lactobacillus_ spp. & MRS agar (Merck) & 37 \({}^{\circ}\)C, 48 h, anaerobic incubation \\ \hline _Lactococcus_ spp. & M17 agar (Merck) & 30 \({}^{\circ}\)C, 48 h \\ \hline \multirow{2}{*}{_Leuconostoc_ spp.} & Sucrose agar: (sucrose—50 g/L; yeast extract—10 g/L; agar—15 & 30 \({}^{\circ}\)C, 72 h \\  & g/L; pH 7.2–7.4) & \\ \hline \multirow{2}{*}{_Propionibacterium_ spp.} & Lactate agar: (peptome—10 g/L; yeast extract—5 g/L; calcium lactate—10 g/L; agar—15 g/L; pH 7.0–7.2) & 30 \({}^{\circ}\)C, 72–96 h, anaerobic incubation \\ \hline Enterobacteriaceae & VRBG (Merck) & 37 \({}^{\circ}\)C, 24–48 h \\ \hline _Enterococcus_ spp. & Slanetz–Bartley agar (Merck) & 37 \({}^{\circ}\)C, 48 h \\ \hline _Staphylococcus_ spp. & RPF agar (Merck) & 37 \({}^{\circ}\)C, 48 h \\ \hline _Clostriidium_ spp. & RCM agar (Oxoid) & 37 \({}^{\circ}\)C, 48 h, anaerobic incubation \\ \hline _Bacillus_ spp. & Nutrient agar (Merck) & 30 \({}^{\circ}\)C, 48 h \\ \hline Yeasts and molds & YGC agar (Merck) & 25 \({}^{\circ}\)C, 72–96 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Culture media and incubation conditions applied in the study.  
Sequencing results in the form of FASTQ files were uploaded to the MetaGenome Rapid Annotation Subsystems Technology (MG-RAST) server for analysis [28]. Each file underwent quality control (QC), which included quality filtering (removing sequences with \(\geq\)5 ambiguous base pairs) and length filtering (removing sequences with a length \(\geq\)2 standard deviations from the mean). The UCLUST algorithm was used to cluster the identified rRNA sequences. A representative sequence of each cluster was used for taxonomic identification based on the Greengenes reference database. Illumina metagenomic datasets are available at MG-RAST under accession numbers 4,730,840.3, 4,730,841.3, 4,730,842.3, and 4,734,160.3. Additionally, sequencing data were deposited in the Sequence Read Archive (SRA) NCBI ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/))) as a Bioproject under the accession code: PRJNA637891.
# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
## 6 Conclusions  
Against the background of a large number of EVs connecting to the grid in an unorganized manner, there are great difficulties involved in grid scheduling.  
1. We propose a multi-timescale EV cluster division strategy for the day-ahead and intraday phases, which better solves the drawbacks of the EV cluster as a whole and the single EV as the scheduling object in the power grid at the current stage. First, in the day-ahead phase, EV charging loads are modeled based on historical EV travel data; in the intraday phase, EVs with similar attributes are placed into the same cluster by collecting EV access and departure times from the grid and real-time demand. This kind of clustering can ensure the user's traveling demands and thus has strong practical application significance.  
Figure 7: Clusters and curve.  
2. We established a two-layer, real-time optimal scheduling model for EVs that are capable of and willing to participate in V2G. First of all, in the upper layer model, the electric vehicle cluster charging and discharging power are obtained to minimize the variance of the load curve of the distribution network; in the lower model, the charging and discharging schedules of individual EVs are optimized to minimize the cost to the EV owners and are made to be as consistent as possible with the upper scheduling schedule. This cluster scheduling strategy is compared with disordered charging; the proposed model makes full use of the low grid load rate to charge EVs and keep the system at a higher and more stable load rate, while ensuring the safe operation of the distribution network and user satisfaction.  
The simulation results show that the model has a good optimization effect and a fast solution speed for dealing with large-scale electric vehicle access problems. In order to consider customer satisfaction, the model effectively smooths the load fluctuation, and the economy of the electricity consumption by vehicle owners is also effectively improved. In the subsequent research work, issues such as the leakage of the vehicle owners' privacy will be emphasized.  
Conceptualization, Q.Z. and Y.Y.; methodology, Q.Z. and Y.Y.; software, Q.Z. and Y.Y.; validation, Q.Z., Y.Y., J.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data in the text relates to the privacy of electricity use in the regional distribution grid.  
The authors declare no conflict of interest.
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 2 Monocentric Imager with FOPs as Relay Image Transmission Elements  
### Fiber Optic Plate Relay Image Transmission Scheme  
An FOP's structural characteristics determine its own image transmission mechanism: a space target is imaged on the imaging focal plane coupled with the FOP input end surface by the monocentric imager front imaging system; each fiber on the incident end surface transmits a pixel independently through its own optical fiber channel; that is, the FOP divides the signal into millions of pixels arranged in a honeycomb [6]; each divided signal is transmitted separately along its own channel and finally imaged on an image sensor coupled with the FOP exit end surface [7,8]. This method of using FOP to achieve inter-stage coupling is called the fiber optic plate relay image transmission (FOPRIT) scheme (see Figure 1). In some complex optical system structures, this scheme can greatly reduce the size of entire imaging system, can replace the traditional relay imaging scheme and has an otherwise unattainable advantage.  
Figure 1: The two-stage discrete coupling model of a spherical focal surface (fiber optic plate—FOP) image sensor, establishing a rectangular coordinate system \(O\)-\(xy\) on the spherical focal surface and setting the centroid position of the imaging spot to \((x_{0},y_{0})\); establishing a rectangular coordinate system \(O^{\prime}\)-\(x^{\prime}y^{\prime}\) on the FOP exit end surface and setting the centroid position of the imaging spot to \((x^{\prime}_{0},y^{\prime}_{0})\); establishing a rectangular coordinate system \(O^{\prime\prime}\)-\(x^{\prime}y^{\prime\prime}\) on the image sensor surface and setting the centroid position of the imaging spot to \((x^{\prime\prime}_{0},y^{\prime\prime}_{0})\).