esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling
### Physical Model  
The influences of three factors are concerned, respectively: time, flow velocity, and height-to-diameter ratio. The range of height-to-diameter ratio is between 0.4 and 1.4, and the variation is 0.1. The flow velocities are 425, 525, 625, and 725 m\({}^{3}\)/h during the charging process and 850, 1050, 1250, and 1450 m\({}^{3}\)/h during the discharging, respectively. The variation of temperature distribution with time under different conditions is simulated.  
In this paper, the cylindrical storage tank is vertically placed. Before discussing the storage tank, the following assumptions are proposed:  
1. The fluid flow and heat transfer in the tank are one-dimensional. Under this assumption, the thermocline is axisymmetric regardless of radius. Water is evenly introduced into and led out of the storage tank.  
2. The insulation of the tank body is extraordinarily good, so the heat loss caused by heat transfer between the tank body and outside is extremely small and can be ignored. Under this assumption, the change of thermocline in the tank is mainly caused by the heat conduction and mixing of cold and hot fluids, but there is nothing to do with the heat conduction of the tank.
3. During the charging process, hot water is injected from the top of the storage tank, and the temperature of the incoming water is higher than that of the water already stored in the tank. During discharging process, cold water is injected from the bottom of the tank. Consequently, the mixing between cold and hot water caused by density differences can be avoided due to different water temperatures.
4. The temperature variation of water in the whole process is large, so it is considered that the physical properties change in flow process.
5. The whole process is regarded as unsteady flow to obtain the solution at each time.  
Because of assumption1, the three-dimensional model is simplified to two-dimensional. Figure 2 shows the charging mode of the storage tank. The storage medium in the storage tank is water. At this time, the high-temperature water (98 \({}^{\circ}\)C) enters from the top of the storage tank at a fixed flow rate. According to Figure 2, the low-temperature water (55 \({}^{\circ}\)C) flows out from the bottom of the storage tank. During the discharging process, the direction of the flow is opposite.
# Mathematical Background and Algorithms of a Collection of Android Apps for a Google Play Store Page
## 5 Conclusions
### Novel Torch App Which Can Be Switched by Double-Tapping Anywhere on the Screen
\begin{tabular}{l l} \(\mathbf{a}_{i}\) & attention mechanism \\ \(\mathbf{s}_{i}\) & decoder states \\ \(\mathbf{m}_{i}\) & Mel spectrogram frames \\ RNN & Recurrent Neural Network \\ CTC & Connectionist Temporal Classification \\ \(\mathcal{L}_{\text{CTC}}\) & CTC Loss \\ GRU & Gated Recurrent Unit \\ LSTM & Long Short-Term Memory \\ \(\mathbf{p}_{i}\) & prediction \\ \(\mathbf{W}\) & character probabilities \\ GSM & Global System for Mobile Communications \\ UCS-2 & Universal Coded Character Set--2-byte \\ UTF-8 & Unicode Transformation Format--8-bit \\ \(N_{s}\) & number of septets \\ \(M\) & string \\ \(N_{p}\) & number of parts \\ \(RS\) & Reed-Solomon code \\ \(n\) & block length, B-tree keys, complete count of files, specific time points, \\  & dimension of vector \\ SMSC & Short Message Service Center \\ SMPP & Short Message Peer-to-Peer \\ \(L_{msg}\) & total message length \\ \(L_{handler}\) & length of the SMPP header \\ \(L_{body}\) & length of the encoded message \\ \(s(t)\) & modulated signal \\ \(A\) & amplitude \\ \(m(t)\) & message signal \\ \(f_{c}\) & carrier frequency \\ \(\phi\) & phase \\ \(x\) & input text, B-tree node \\ \(F(x)\) & feature extraction function responsible for transforming text \\  & into linguistic features \\ \(S\) & synthesis function responsible for creating audio signal \(y(t)\) \\  & using the features, state matrix, total storage capacity \\ \(a_{i}\) & amplitude for the \(i\)-th sine wave \\ \(f_{i}\) & frequency \\ \(\phi_{i}\) & phase offset \\ \(N\) & count of sine waves employed in the synthesis process \\ \(y(t)\) & audio signal input \\ \(G\) & function responsible for transcribing the audio signal into text \\ Enc & function responsible for extracting features from the audio signal \\ Dec & function responsible for converting features into text \\ \(x(t)\) & time-dependent function, signal's amplitude \\ \(t_{n}\) & sampling \\ \(\Delta t\) & interval between samples \\ \(x_{n}\) & sampled values of the signal \\ DFT & discrete Fourier transform \\ \(\mathbf{X}=\) & vector of Fourier coefficients \\ \([\delta_{0},\hat{x}_{1},\ldots,\hat{x}_{N-1}]^{T}\) & projection matrix, diagonal matrix \\ \(\mathbf{Y}\) & compressed signal \\ \(C_{0}\) & original battery capacity \\ \(P_{\text{transmitted}}\) & signal power in the phone speaker \\ \(P_{\text{received}(d)}\) & power received at a distance \(d\) \\ \end{tabular}
## 3 Results and Discussion  
### Fruit Characteristics and Spectral Interpretation  
Increased granulation in honey pomelo affects both mass and volume. Dependent on the changing trend of the mass and volume, the width and length also showed a downward tendency, as shown in Figure 6. This changing trend can also be seen in Table 1. The changes of fruit characteristics indicate that the external feature extraction of samples can be used to discriminate fruits' degree of granulation. From Class A to Class E, with an increased granulation level of samples, the contents of SSC and TA gradually decreased (Table 1). Granulation often results in an accelerated decrease in SSC and TA in citrus fruit, including pomelo fruit, navel oranges, and Valencia oranges [7; 46]. The evidence of granulation is that the juice vesicles become tough, dry, and colorless [4; 47], and this can be easily seen in honey pomelo. The MC of samples decreased with increased dryness and toughness, and the pulp became less red and more white in color (Figure 6). As shown in Figure 7a,b, within the visible spectral region (400-780 nm), no obvious absorption peaks were detected in the raw spectra and mean spectra. In the near-infrared spectral region, typical overlapping absorption peaks correspond to C-H, O-H, and N-H chemical bonds of water and carbohydrates in fruits [48]. In the short-wave near-infrared (SW-NIR) spectral region (780-1100 nm), an overlapping peak was detected at approximately 813 nm. The mean transmittance spectra of samples at 750-820 nm is related to the third and fourth overtone of C-H stretching vibrations and the first overtone of C=C stretching vibration [49]. The two spectrometers have overlapping regions of measurement (900-1100 nm), but because the instrument parameters are different, the peaks in this range are centered at 947 nm and 948 nm, corresponding to water absorption bands (O-H) [50]. As shown in Figure 7c,d, the long wave near-infrared (LW-NIR) spectra included a region of 1100-1700 nm, with a maximum absorption peak at approximately 1283 nm, which is the second overtone of the C-H functional group vibration band [51]. The absorption peak around 1410 nm corresponds to lignin-related absorption, and methylene C-H and C-H of R(CH\({}_{2}\))\({}_{n}\)R groups [52]. Citrus
## 1 Introduction
In addition, we provide a simple yet clear illustration of how the hybrid CARS technique can be exploited for imaging and sensing of a gas flow escaping from a nozzle by performing multi-shot measurements along with a raster-scanning an area in a collinear configuration. In the future, the same ideas can be applied to remote detection and sensing of trace amounts of gases [36]. Figure 1: (a) CARS (coherent anti-stokes Raman scattering) level diagram. CARS signal is generated from the probe pulse scattering off the molecular vibration, coherently prepared by the pump (\(\omega_{\text{pump}}\)) and Stokes (\(\omega_{\text{Stokes}}\)) pulses, which are resonant with the Raman frequency of the molecule (\(\Delta\omega_{\text{Raman}}\)). \(\
u\), \(J\), \(\
u^{\prime}\), \(J^{\prime}\)—initial and final vibrational and rotational states correspondingly; (b) Phase matching CARS scheme for collinear beams configuration; (c) Experimental setup. DS1,2—delay stages. BS1,2—beamsplitters, L1–3—lenses (\(f=100\) mm), ND + BPF—set of neutral density and bandpass filters.
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 1 Introduction
In order to ensure that the loss of GDOP value is within the acceptable range of the user, at the same time, the real-time performance of the positioning system is greatly improved, and the algorithm is verified by the real test through the sports car experiment.
## 2 Related Works, Definitions, and Preliminaries
### General Shop Minimum-Length Unit-Time Scheduling Problems
The subset \(\,E\setminus E^{*}\) of the edges determines \(\,|M|\,\) cliques \(\,\Big{\{}v_{i_{1}},v_{i_{2}},\ldots,v_{i_{|V_{i}|}}\Big{\}}\) in the subgraph \(\,(V,\varnothing,E\setminus E^{*})\), where all operations \(\,\Big{\{}v_{i_{1}},v_{i_{2}},\ldots,v_{i_{|V_{i}|}}\Big{\}}\) must be processed on the same processor \(\,M_{i}\in M\). The precedence constraints between operations belonging to different jobs are determined by the directed subgraph \((V,A\backslash A^{*},\varnothing)\) of the mixed graph \(G=(V,A,E)\). If the completion time \(c_{k_{p}}\) of the operation \(v_{k_{p}}\), \(J_{k}\in J\), must precede the start time \(s_{l_{q}}\) of the operation \(v_{l_{q}}\) belonging the job \(J_{l}\in J\) with \(k\
eq l\), then the mixed graph \(G=(V,A,E)\) must contain both arc \((v_{k_{p}},v_{l_{q}})\in A\) and edge \([v_{k_{p}},v_{l_{q}}]\in E\); see Definition 1. We denote this completion-start constraint as \(v_{k_{p}}\Rightarrow v_{l_{q}}\). If the constraint \(v_{p}\Rightarrow v_{q}\) holds for the unit-time scheduling problem \(G_{c}|t_{ij}=1|C_{\max}\), then the implication (1) holds for the arc \((v_{p},v_{q})\in A\) (the implication \((v_{p},v_{q})\in A\Rightarrow[v_{p},v_{q}]\in E^{*}\) holds for the edges \(E^{*}\subseteq E\)). If the start time \(s_{k_{p}}\) of the operation \(v_{k_{p}}\) of the job \(J_{k}\in J\) must precede the start time \(s_{l_{q}}\) of the operation \(v_{l_{q}}\), \(J_{l}\in J\), where \(k\
eq l\), then the desired mixed graph \(G=(V,A,E)\) contains arc \((v_{k_{p}},v_{l_{q}})\in A\). We denote this weak precedence constraint as follows: \(v_{k_{p}}\to v_{l_{q}}\). If the precedence constraint \(v_{p}\to v_{q}\) holds for the problem \(G_{c}|t_{ij}=1|C_{\max}\), then the implication (1) does not hold for the arc \((v_{p},v_{q})\in A\). Due to the use of weak precedence constraints, it is possible to require that a subset \(V(k)\subset V\) of the unit-time operations must be processed simultaneously. To represent such a requirement, it is necessary to construct a circuit containing all vertices of the set \(V(k)\). As shown in [9], the constructed mixed graph \(G=(V,A,E)\) completely determines the input data for a general shop unit-time problem \(G_{c}|t_{ij}=1|C_{\max}\). Definition 4: If a mixed graph \(G=(V,A,E)\) completely determines the input data for the scheduling problem \(\alpha|\beta|\gamma\), then this individual problem \(\alpha|\beta|\gamma\) is called a scheduling problem \(\alpha|\beta|\gamma\) on the mixed graph \(G\). Theorem 3 was proved in [9]. Theorem 3 ([9]): A general shop unit-time problem \(G_{c}|t_{ij}=1|C_{\max}\) on the mixed graph \(G=(V,A,E)\) reduces to the problem of determining the optimal colouring \(c(G)\). It should be noted that there are problems in finding optimal colourings of the mixed graph that cannot be represented as a general shop unit-time scheduling problem \(G_{c}|t_{ij}=1|C_{\max}\). In Appendix A, we solve Example A1 of the unit-time scheduling problem \(G_{c}|t_{ij}=1|C_{\max}\) to illustrate Theorem 3.
# Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination
## 3 Results
### Modified Algorithm to Obtain the Combined Signal from Raw Signals
The algorithm first finds the peaks \(\overset{\rightarrow}{P}_{i,j}\) of the sinusoidal raw signals by Equation (8). We implemented this with the MATLABTM function _peaks_, but a customized implementation via a local maxima detection is also possible. \[\overset{\rightarrow}{P}_{i,j}=\text{peaks}(\text{CH}_{i}[k\cdot(j-1)+1:k \cdot j],\quad\quad i=1\ldots 4,\ j=1\ldots m \tag{8}\]  
Then the sample length of the phase offset \(a_{i,j}\) is calculated in Equation (9) by using the first detected peak \(p_{i,j}=\overset{\rightarrow}{P}_{i,j}(1)\). \[a_{i,j}=p_{i,j}-\min\bigl{(}p_{1,j},p_{2,j},p_{3,j},p_{4,j}\bigr{)},\quad\quad i =1\ldots 4,\ j=1\ldots m \tag{9}\]  
with the calculated sample length of the phase offset \(a_{i,j}\) the individual raw signals are phase shifted according to Equation (10). \[\text{CH}_{i,\text{PS}}[k\cdot(j-1)+1:k\cdot j]=\text{CH}_{i}[k\cdot(j-1)+1+ a_{i,j}:k\cdot j+a_{i,j}],\] (10) \[\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quadAn example of a small section of one block of the raw signals before and after correction, is shown in Figure 8:0  
In the selected section of the raw signal, the first detected peak amplitude belongs to CH1, consequently all other channels are aligned accordingly. In this case CH2 is shifted by three samples, CH3 by five samples and CH4 by eight samples. Afterwards, the samples before and after the transition points are interpolated to correct missing samples due to the phase shift and discontinuities due to the clockwise combination. In Figure 10, a small section of the combined demodulated velocity signal around a transition point of two blocks (between j = 1 and j = 2 with k = 1000) is shown before and after interpolation. Due to this preceding method, we can minimize the impact of both the discontinuities and the phase offset without distorting the original signal, as can be seen by the magnified section in Figure 8:1  
This is possible, because these errors always occur in the same locations around the transition points. Errors similar to the one shown in Figure 5 are random and therefore much harder to compensate. Figure 8:2 Section of the combined, demodulated velocity signal before and after interpolation to correct discontinuities and error due to phase shifting. Figure 8:3 (a) Raw signals of the individual channels (CH1–CH4) without phase correction; (b) Raw signals with phase correction (samples with dotted lines are only shown for visualization of the shift).
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Health Services
Predicting how a health value will change in the future enables counteracting an adverse trend at an early stage [55]. The potential of such services is therefore virtually unlimited, including education, diagnostics, and treatment [56]. In such an application scenario, the accuracy of the data is key, as it affects the health of patients. Thus, the data must be refined assiduously in order to achieve the highest possible accuracy and eliminate measurement errors [57]. However, such services also raise privacy concerns, since in addition to the health data, which intrinsically contain a vast amount of sensitive information, a vast amount of knowledge about, e.g., lifestyle, environment, and social life is disclosed [58]. As smart devices have become an integral part of our lives, users no longer even notice their presence. Sensors are embedded in everyday objects and enable a comprehensive and almost invisible permanent monitoring of everyone--both the users of the smart devices or accidental bystanders [59]. For this reason, particularly high privacy standards must be applied in such an application scenario [60]. The inherent opportunities and privacy threats of smart health services can be broken down as follows:  
Opportunities:  
* IoT technologies enable the self-quantification of health-related values, which means that frequently recurring health checks in particular can be performed at home by the patients themselves. This relieves the burden on both patients and physicians. * The non-intrusive nature of the smart devices allows the permanent monitoring of patients without disturbing their daily routine. This enhances safety, as no health measurements can be forgotten, and health problems can be detected at an early stage. Figure 2: Three Types of Time-Series Analyses Applied in Health Services. Since the smart devices that feed smart health services with data are ubiquitous and capable of capturing a variety of health values, they can be used to provide 360-degree health views on the patient. Privacy Threats:  
* Health data are among the most sensitive data, so the large-scale collection and processing in itself is a privacy threat. * As smart devices are ubiquitous, data subjects are no longer aware that health data are collected permanently, which makes them unaware of the privacy threat. * In addition to inferences about diseases, the collected health data also allow insights into other aspects, such as unhealthy behaviors, e.g., whether the data subject is a smoker or carries out little physical activities.
## 2 Theory, Requirements and Synthesis
### Micro-Ring Resonator as a Bandstop Filter
#### 2.2.2 DFB Wavelength Tuning by TEC  
Instead of tuning the laser source and/or the MRR to a fixed absolute wavelength, we could also seek to tune one to the other, i.e., to tune a laser for its optimal (relative) alignment with one of the MRR's resonances or vice versa. As a mechanism for tuning a DFB laser in terms of wavelength, we established an approach that involves a thermoelectric cooling (TEC) element, also known as a Peltier heat pump. This mechanism serves as the basis for all the experimental work reported in this paper. As anticipated in Figure 2, the proposed system is time dependent, thus the tuning should be continuous. This calls for a CLC system.
## 5 Concluding Remarks  
* The long-term performance of the combined VFF-HFF system developed in this study was satisfactory in terms of organic matter and nitrogen removal (>90% and >70%, respectively). No signs of clogging or deterioration in either organic matter or Tot-N removal were observed during the 126 weeks of operation.
* Under increasing HLR, the combined VFF-HFF filter system still achieved high removal of organic matter (90-93%) and nitrogen (>50% removal), which showed robustness of the system.
* The combined VFF-HFF system achieved substantial removal rates of _E. coli_ and _Salmonella_ spp. (3.0 log\({}_{10}\) units), but the safety of the effluent will depend on the initial concentration of these microorganisms in the incoming wastewater. At higher levels of microbial contamination, playing or bathing in water close to points where the effluent is released should be avoided. Protective measures, e.g., wearing gloves and mask and washing hands, are recommended for at least three weeks when handling filters after cases of infection.
* The design of the filter system with respect to depth: width: length of the horizontal flow system has not yet been investigated and more research is needed on this aspect. The system also needs to be investigated at extreme HLR, e.g., 200 L m\({}^{-2}\) day\({}^{-1}\).
* Multi-criteria analysis to assess the sustainability of the biochar filters in terms of technical performance, environmental impacts and social and economic aspects is needed.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/24/5386/s1](http://www.mdpi.com/2076-3417/9/24/5386/s1)]([http://www.mdpi.com/2076-3417/9/24/5386/s1](http://www.mdpi.com/2076-3417/9/24/5386/s1)), Figure S1.  
**Author Contributions:** S.S.D., Y.S. and A.A. collected the data, S.S.D. carried out the data analysis and wrote the manuscript. A.A. revised it.  
**Funding:** This project was funded by Swedish Marine Agency grant number [1:11 Atgarder for havs- och vattenmilj0] and the Swedish Foundation for international cooperation in research and education grant number [PT2016-6875].  
**Acknowledgments:** We gratefully acknowledge [PERSON] for his efforts in the laboratory work. Thanks for Swedish Marine Agency and the Swedish Foundation for international cooperation in research and education for their financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamic and Stability Analysis of Multibolt Plane Joints under Normal Forces
## 6 Conclusions  
The surface topography has a significant effect on the vibration characteristics and stability of the contact interface system. In this article, an improved model of contact stiffness is developed to supply a study foundation. Furthermore, based on the contact model, a single-freedom dynamic model of the joint parts with the consideration of fractal surface has been established. In addition, the influences of excitation force and key design parameters of the machined bolt joint interface are discussed by amplitude-frequency curves, bifurcation diagrams, 3-D frequency spectrums, time histories, phase plane diagrams, and Poincare sections. Theconclusions are as follows:  
1. The stiffness of the system increases nonlinearly with the deflection, which shows the hardening type nonlinearity in the positive direction and the softening type nonlinearity in the opposite direction. However, the system performs softening behavior under harmonic excitation.
2. The surface topography is controlled by fractal parameters \(D\) and \(G\). With the increase of \(D\) or the decrease of \(G\), the stiffness increases if the displacement is positive, and the bolt joint becomes relatively unstable.
3. The system is sensitive and unstable when it has a higher excitation force. When the bolt pre-tightening is controlled and the interface has not been separated, the joint parts present a poor stability with a smaller bolt pre-tightening force. Moreover, as the apparent contact area increases or the number of bolts decreases, subharmonics appear.  
Conceptualization, Z.L., Y.Z., and C.L.; Funding acquisition, Y.Z., and C.L.; Investigation, Z.L., and Z.T.; Methodology, Z.L., and C.L.; Supervision, Y.Z., and C.L.; Writing--Original Draft, Z.L.; Writing--Review & Editing, Z.L., and C.L.  
This study is funded by National Natural Science Foundation of China (grant Nos. U1708254, 51575095) and China Postdoctoral Science Foundation (2017M610180).
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 4 Proposed System Setup
### Source Coding Arrangements  
The raw video captured at the source encoder is compressed at the transmitter side using the H.264/AVC compression standard. The video encoder encodes the input video into a stream of \(x_{k}\) or \(k\) information bits. Consequently, the coded stream is then processed by the DEMUX block, which further segregates the encoded stream into their respective type A, B, and C data partitions that are represented as \(x_{a}\), \(x_{b}\), and \(x_{c}\), which are further concatenated in different orders, depending on the applied error protection schemes, as in Tables 2 and 3. Thereafter, the concatenated bitstream \(x_{i}\) is presented to the channel encoder for further processing.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parametric Findings** & **Values** \\ \hline Source Compression Standard & H.264/AVC \\ \hline Bit Rate & 64 kbps \\ \hline Per frame Slices & 9 \\ \hline MBs Per Slice & 11 \\ \hline MB Update Intra-Frame & 3/frame \\ \hline Channel Code & IRCC \\ \hline Overall bit rate budget & 0.7 \\ \hline Modulation Scheme & Sphere Packing \\ \hline MIMO Scheme & DSTS \\ \hline Transmitter Antennas & 2 \\ \hline Receiver Antennas & 1 \\ \hline Channel & Rayleigh fading (fd = 0.01) \\ \hline Spreading Code & Walsh Code \\ \hline Spreading Factor & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters and their values.