esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolution Accelerator Designs Using Fast Algorithms
## 3 Architecture Design
### Strassen-Winograd Design  
As shown in Section 2.4, the Strassen and Winograd algorithms can be used together in the matrix convolution. The architecture of the convolution based on the Strassen-Winograd algorithm is shown in Figure 10.  
Figure 8: Schematic representation for the architecture of the FilterNxN module.  
Figure 9: Dataflow of the convolution based on the Winograd algorithm.  
As shown in Figure 10, the design based on the Strassen-Winograd algorithm is divided into three states. In Stage 1, the input data are transformed before being sent to the FilterNxN module. In Stage 2, seven convolution FilterNxN modules are instantiated in parallel. The FilterNxN module is the same as those in Figure 7. The output data should be then transformed in Stage 3 before being sent out. The FilterNxN module needs 16 multipliers; thus the design based on the Strassen-Winograd algorithm requires a total of \(7\times 16=112\) multipliers.
# A Survey on Change Detection and Time Series Analysis with Applications
## 4 Change or Breakpoint Detection within Non-Stationary Time Series
### Continuous Change Detection and Classification (CCDC)  
The CCDC is like BFAST Monitor with the aim of detecting changes in near-real-time; however, CCDC uses a robust regression technique, called the Least Absolute Shrinkage and Selection Operator (LASSO) [73]. Unlike OLS used in BFAST and BFAST Monitor, LASSO is used in CCDC to fit the season-trend model to the history period, which minimizes over-fitting by limiting the total absolute value of the coefficients [74; 75]. The Root Mean Square Error (RMSE) of the fitted history model and residuals of incoming observations are used to detect a change, so if the new residuals deviate from the fitted model about six times in a row, the breakpoint location and magnitude will be identified. Then a sliding windowing strategy is used to determine the next stable period [69; 73].  
The advantages of CCDC include its fully automated monitoring, capability of monitoring many kinds of land cover change as soon as new data become available, and its independence from empirical or global thresholds. The thresholds in CCDC are calculated through the original observations. Furthermore, LASSO used in CCDC can also prevent the over-fitting issue, but it is more likely to under-fit the seasonal curve and unlikely to overestimate the number of breaks [69]. CCDC has high computational complexity, and it requires high temporal frequency of clear observations to reliably estimate the model coefficients. Furthermore, CCDC does not explicitly consider the observational uncertainties into its model and may not be reliable for time series with more complex intra-annual variation [73]. Noisy data are more likely to affect the efficacy of CCDC in the correct detection of breakpoints than missing values [69].
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 6 Discussion  
This paper aims to solve the zero-bias problem caused by sampling distortion in BOC signal pseudorange measurement. From a statistical point of view, a new indicator for evaluating the bias performance is proposed, which is conducive to eliminate the effects of the spreading sequence and the initial phase. An analytical method of equivalent reference waveform is proposed, and the analytical expression of the standard deviation of the zero-bias is derived. A quantizable zero-bias suppression method based on receiver parameter design is provided. Some research results are achieved, but the following aspects still wait to be explored.  
1. When the sampling frequency of the baseband signal is limited to an equal sampling frequency, the phase resolution error of the digital discriminator is incapable to be avoided. In this case, the elimination of the influence of phase resolution error on pseudorange accuracy is inseparable from further study.  
Figure 12: Zero-bias and thermal noise error of BOCs (1, 1) signal (\(F_{c}\) = 1.023 MHz, \(F_{sc}\) = 2.046 MHz, \(2d_{0}^{sc}\) = 1 chip, \(2d_{0}^{sc}\) = 0.25 chip, \(T_{coh}\) = 1 ms). (a) Long period code. (b) Short period code.  
2. The article simulates the BOC (1, 1) signal, and the conclusion can be directly extended to the BOC (m, n) signal, but the applicability to the MBOC signal is still worth exploring.
3. This paper is confined to the influence of signal Doppler frequency on the statistical standard deviation of zero-bias. In the case of non-zero Doppler, the initial phase of the received signal changes continuously, and the zero-bias of the digital discriminator changes. For the phase discriminator of short-period codes, the elimination of the bias by means of the Doppler effect design algorithm is needed to be studied further.
4. When multiple signals are simultaneously transmitted in the channel, the multiple access interference has a significant impact on the high precision measurement of the pseudorange. How to quantify the impact of multiple access effects on pseudorange accuracy is still worth studying.
## 3 Results
### LBP Experimental Analysis  
(1) Parameter analysis of foam image processed by rotation-invariant LBP operator  
When processing foam images with a rotation-invariant LBP operator, the parameters \(P\) and \(R\) affect the image processing effect and thus the final recognition accuracy. Set \(P\) to take the values in the set [4,6,8,16], and \(R\) to take the values in the set [1,2,3,4]. The images are processed using the rotation-invariant LBP operator, and then the dynamic texture feature vector is calculated. The average recognition accuracy of the six experiments is shown in Figure 9. When experimenting with P, R, the optimal value of 3 is chosen; when experimenting with \(R\), \(P\), the optimal value of 8 is chosen.  
Figure 9: Recognition accuracy under different LBP parameters.  
As shown in Figure 9, with the change in \(P\) and \(R\) parameters, the recognition accuracy also shows some floating. The value of \(P\) has a great influence on the image-processing effect; when \(P\) is small, the image contrast is lower, and when \(P\) is large, more factors interfering with texture feature extraction are obtained. Figure 10 shows the image processing effects of \(P\) and \(R\) with different values. When \(P=8\) and \(R=1\), the foam boundary in the image is not clearly depicted, and when \(P=6\) and \(R=3\), the image contrast is low. These comparison results show that the extraction effect is better when \(P=8\) and \(R=3\), and the texture features of foam image are more obvious, which lays a good foundation for feature extraction. The experimental results show that the highest accuracy of work condition recognition is obtained by choosing \(P=8\), \(R=3\) for image processing.  
(2) LBP-TOP feature extraction  
The data set was rotation invariant LBP-processed to enhance texture visibility while reducing the effect of high brightness, and then the processed foam was placed in the tri-orthogonal plane to extract dynamic temporal features. In the foam image sequence, the number of frames in the continuous foam image sequence was N; the time axis radius RT = L was set according to the data, where N \(\geq\) 2 L + 1 and the L + 1 frame in the foam image sequence was taken as the center frame. The eigenvalues of LBP were calculated on the X-Y plane, and then L frames were taken from the foam images before and after, according to the center frames on the X-T and Y-T planes, respectively. Finally, the LBP-TOP features of the foam image sequence were cascaded together according to the order of X-Y, X-T and Y-T planes. \(P\)XY = \(P\)XT = \(P\)YT = 8, \(R\)Y = \(R\)X = 3, \(R\)T = 1 were selected to divide the image sequence into 2 \(\times\) 2 blocks and calculate the LBP-TOP features of foam image sequences corresponding to various working conditions or performance states. The X-Y, X-T, Y-T planes each have 708 features dimensions, and the three planes add up to a total of 2124 dimensions. The LBP-TOP histogram is shown in Figure 11.  
Figure 10: The processing effect of different values.
## 4 Results
### Sizing of Estimable Items
In Table 3, an example of applying the above approach to RTBPM-E is represented; for calculations, the following values of the parameters were chosen by the authors: \(p=168\frac{\mathrm{man\_hour}}{\mathrm{EIP}}\), \(u_{1}=0.1\), \(u_{2}=0.2\). Therefore, the RTBPM-E NDEs are the following:  
\[ \begin{split}\text{RTBPM-E}\big{[}\mathrm{NDE}_{\mathrm{basic}} \big{]}&=2520.0\,\mathrm{man\_hours},\\ \text{RTBPM-E}\big{[}\mathrm{NDE}_{\mathrm{opt}}\big{]}& =2007.6\,\mathrm{man\_hours},\\ \text{RTBPM-E}\big{[}\mathrm{NDE}_{\mathrm{psm}}\big{]}& =3544.8\,\mathrm{man\_hours},\end{split} \]  
Figure 4: Defining EIPs and EIUs for the RTBPM-E estimable items. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IE Identifier** & **EIP** & \
\begin{tabular}{c} **Basic NDE,** \\ **Man-Hours** \\ \end{tabular} & **EIU** &
\begin{tabular}{c} **Optimistic NDE,** \\ **Man-Hours** \\ \end{tabular} & **Passinistic NDE,** \\ \hline EI-1 & 2 & 336.0 & 2 & 268.8 & 470.4 \\ EI-2 & 1 & 168.0 & 1.5 & 142.8 & 218.4 \\ IE-3.1 & 3 & 504.0 & 3 & 352.8 & 806.4 \\ IE-3.2 & 3.5 & 588.0 & 2.5 & 441.0 & 882.0 \\ IE-4 & 1 & 168.0 & 1.5 & 142.8 & 218.4 \\ IE-5 & 1.5 & 252.0 & 2 & 201.6 & 352.8 \\ IE-6 & 1.5 & 252.0 & 1 & 226.8 & 302.4 \\ IE-7 & 1 & 168.0 & 1 & 151.2 & 201.6 \\ IE-8 & 0.5 & 84.0 & 0.5 & 79.8 & 92.4 \\ \hline Total & – & 2520.0 & – & 2007.6 & 3544.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NDEs of the RTBPM-E estimable items. where the optimistic and pessimistic estimates form the range \(-20.3\%\ldots+40.7\%\) (relative to the \(\text{NDE}_{\text{basic}}\)), which, from authors' perspective, is acceptable for the preliminary estimation.
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
## 2 The Components of U-Cities and Hazards
### Information and Communication Technologies (ICTs)
#### 2.3.3 Satellite/Aerial Network  
The satellite networks designed for telecommunications services are low earth orbit and geostationary satellite networks, which able to cover almost everywhere on Earth's surface. Aerial navigation network is a collection of various airborne applications using different aircraft, which provide services such as air surveillance, search, and rescue. Geographic information system, global navigation satellite system, remote sensing, and radar are examples of Satellite/Aerial Networks.  
Geographic Information System (GIS): GIS incorporates the ability to store, edit, analyze, share, and display data that is geographically accurate. This allows the ability for the citizens to obtain location-specific information for example map data and spatial references. This data can be used in the amplitude of scenarios the most prevalent being route guidance but also management tasks and monitoring. It works in unison with the sensors capable of monitoring pollutants and references the data geographically allowing for a sensical data collection [43].  
Global Navigation Satellite System (GNSS): Global Navigation Satellite System (GNSS) is a well-known geographic locating technology that can be used in all weather and anywhere in the world. A GNSS receiver can lock on the signal from three or more satellites circulate on the Earth orbit, to determine the precise position through latitude, longitude, and altitude [44]. Multiple-use cases have been applied to this technology including speed detection, navigation, earthquake studies, and telecommunication network synchronization [27].  
Remote Sensing (RS): RS is the acquisition of information about an object or phenomenon without making physical contact with the object and thus in contrast to on-site observation, the sensors can collect data from a satellite or mounted on aircraft by detecting the energy that is reflected from Earth surface [45].  
Radar: Radar is referred to as radio detection and ranging, which using a transmitted radio signal emitted by an antenna and using a receiver to detect the signals echoes bounce from objects. Radar technology is commonly used in military applications, traffic control, and weather observations [46].
## Materials and Methods
### GRACE/GRACE-FO Derived TWS  
GRACE satellite provided global monthly TWS anomalies (TWSAs) by measuring the minute distance changes between twin satellites caused by changes in the Earth's gravity field [36]. The GRACE Science System continuously release monthly GRACE solutions from three different processing center, the GeoForschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas, Austin (CSR), and the Jet Propulsion Laboratory (JPL), with each utilizing different parameters and solution strategies [31; 44]. Five different TWSA products were utilized in this study: (1) three spherical harmonic coefficients from CSR, GFZ, and JPL (provided at 1\({}^{\circ}\)); (2) JPL mascon solutions (provided at 0.5\({}^{\circ}\)) [45]; and (3) CSR mascon solutions (provided at 0.5\({}^{\circ}\)) [46]. All products cover the GRACE period from April 2002 to June 2017 and the GRACE-FO [47] period from June 2018 to December 2020. There are nineteen months missing TWSA data points during the GRACE and GRACE-FO periods that are supplemented by the nearest neighbor interpolation approach. All products are resampled at 0.25\({}^{\circ}\) using a bilinear approach to keep them consistent. This study utilized the GRACE TWSA solutions from 2002 to 2019 (downloaded from [[https://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land/](https://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land/)]([https://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land/](https://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land/)), accessed on 11 July 2022).
# Soliton Content of Fiber-Optic Light Pulses
## 3 Extensions to the Nonlinear Schrodinger Equation
### Cases Involving Several Solitons
#### 3.3.1 Soliton Molecules  
In DM fibers, stable compounds of solitons exist, which have been called soliton molecules [41]. The alternating dispersion creates a rapid to-and-fro of the chirp of DM solitons and, thus, a varying mutual force. If two DM solitons are in a particular mutual separation, the net force is zero, and an equilibrium is obtained [42; 43]. For larger separations, there is attraction, for smaller, repulsion: it is a stable equilibrium. Soliton molecules also exist for more than two DM solitons and have been discussed as information carriers in data transmission [44; 45]. As it turns out, there can be several equilibrium positions [46].
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 2 Vehicle Model Establishment
### Establishment of the Equation of Motion  
The dynamic differential equation of the three-mass vehicle model can be obtained according to the D'Alembert principle. Figure 1: 3-DOF seat model with time-delay control system. Seat vertical motion equation:  
\[m_{p}\ddot{x}_{p}+c_{1}\big{(}\dot{x}_{p}-\dot{x}_{s}\big{)}+k_{1}\big{(}x_{p}- x_{s}\big{)}=0 \tag{1}\]  
Body mass vertical motion equation:  
\[m_{s}\ddot{x}_{s}-c_{1}(\dot{x}_{p}-\dot{x}_{s})-k_{1}\big{(}x_{p}-x_{s}\big{)} \tag{2}\]  
Vertical motion equation of unsprung mass:  
\[ \begin{array}{l}m_{u}\ddot{x}_{u}-c_{2}(\dot{x}_{s}-\dot{x}_{u})-\\ k_{2}(x_{s}-x_{u})+k_{3}(x_{u}-x_{r})-f=0\end{array}  \tag{3}\]  
where \(\ddot{x}_{p}\) is the seat vertical beating acceleration; \(\dot{x}_{p}\) is the seat vertical beating speed; \(\ddot{x}_{s}\) is the body mass vertical beating acceleration; \(\dot{x}_{s}\) is the body mass vertical beating speed; \(\ddot{x}_{u}\) is the unsprung mass vertical beating acceleration; \(\dot{x}_{u}\) is the vertical beating speed of the unsprung mass; \(f=gx_{u}(t-\tau)\), where \(g\) and \(\tau\) represent the feedback gain and time-delay control amounts of the suspension active control force. If the feedback gain of the suspension active control force and the time-delay control amount is 0, the active suspension system with time-delay control can be simplified to a traditional passive suspension system. To transform Formulas (1)-(3), we can get:  
\[\left\{ \begin{array}{l}\ddot{x}_{p}=-\big{(}c_{1}\big{(}\dot{x}_{p}-\dot{x} _{s}\big{)}+k_{1}\big{(}x_{p}-x_{s}\big{)}\big{)}/m_{p}\\ \ddot{x}_{s}=\big{(}c_{1}(\dot{x}_{p}-\dot{x}_{s})+k_{1}\big{(}x_{p}-x_{s}\big{)} -c_{2}(\dot{x}_{s}-\dot{x}_{u})-k_{2}(x_{s}-x_{u})-f\big{)}/m_{s}\\ \ddot{x}_{u}=\big{(}c_{2}(\dot{x}_{s}-\dot{x}_{u})+k_{2}(x_{s}-x_{u})-k_{3}(x_ {u}-x_{r})+f\big{)}/m_{u}\end{array} \right. \tag{4}\]  
The single-input multiple-output state-space method of the multiple-degree-of-freedom vibration system is used to analyze and modify the suspension system:  
The state variable is \(X=\big{[}\dot{x}_{p},\dot{x}_{s},\dot{x}_{u},x_{p},x_{s},x_{u}\big{]}^{T}\); the seat acceleration is \(y_{1}=\ddot{x}_{p}\); the body acceleration is \(y_{2}=\ddot{x}_{s}\); the suspension dynamic deflection is \(y_{3}=x_{s}-x_{u}\); the tire dynamic deformation is \(y_{4}=x_{u}-x_{r}\); the output is \(Y=\big{[}y_{1},y_{2},y_{3},y_{4}\big{]}^{T}\); the input vector is \(w(t)=[x_{r}]\); the active control force of the suspension is \(U=[f]\). The state space equation of the active suspension control system with time-delay control shown in Equation (1) is:  
\[\left\{ \begin{array}{l}\dot{X}=AX+BU+Ew(t)\\ Y=CX+DU+Fw(t)\end{array} \right.
## Objectives  
Determine vocal fatigue levels among college professors in Manuela Beltran University in Bogota (Colombia).  
**Introduction**  
For some professionals, the voice is the main tool of work. Therefore, voice disorders impact their daily performance and quality of life greater proportion. These workers are called \"voice occupational users\" [1]. Among those, teachers, singers, speakers, among others have been widely investigated. Generally, voice problems are identified through laryngoscopic examinations, perceptual examinations by speech and language pathologists, self-report or acoustic characterization of voice parameters [2]. In the case of voice occupational users, self-report is one of the most common evaluation methods. Through this has been detected a high occurrence of vocal fatigue in these occupational groups. However, there is no objective measure that quantifies the degree of vocal fatigue [3]. That is the reason why we propose to determine the degree of vocal fatigue in teachers. This will allow voice professionals to identify and recommend healthy vocal use conditions.  
**Methods**  
For this study, technological resources will be used to register and characterize the signals obtained. Therefore, differentiate the changes or visible variations in the waves. In order to do this, we will recruit college professors from a private university in Bogota, Colombia. The signals will be obtained at the beginning of the academic day and at the end of the day (repeated measures -intrasubject-). This will show changes that take place during a work day and will allow to detect short-term vocal fatigue. The first step will be to identify parameters that allow to determine variations in the voice signals obtained through digital signal processing protocols. The second step is to quantify variations in the identified variables to obtain vocal fatigue degree. The last step was to define the relation between the identified variables and vocal fatigue control variables (self-report, standard deviation of the fundamental frequency and standard deviation of the level of vocal sound pressure).
## 5 Limitations  
Despite the promising outcomes of this study, some limitations must be acknowledged. Firstly, the datasets used, ADNIMERGE and ADNI-1, despite being extensive and informative, still represent a specific patient population. Results could potentially vary with other datasets or populations. This study also relies heavily on the accuracy of cognitive tests and the feature selection method. Variations in test administration, subject response, and selection algorithms can introduce errors.  
Another limitation is the need for neuroimaging in the combined features. While combined features provided slightly better results, the requirement for neuroimaging makes this less feasible in many real-world clinical settings. Cognitive testing is generally easier to administer, cheaper, and more accessible, especially in low-resource settings. Hence, a focus on further improving performance with cognitive features alone would be beneficial.  
Additionally, while the AdaBoost model performed well in this study, it might not be the optimal model for all scenarios. The performance of machine learning models can vary based on the specific characteristics of the dataset. Therefore, exploring other potential models could be valuable. Lastly, the issue of overfitting must be considered, particularly when dealing with a large number of records, as seen with the ADNIMERGE dataset. Techniques to mitigate this problem and ensure the model's generalizability should be considered in future studies.
## 2 Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions
### Sea Surface Monitoring through SAR-Based Approaches
Current interest has also been oriented to inland seas, where currents occur in a much-limited range. The Mediterranean Sea represents an interesting case study for estimating sea surface currents with the SAR techniques [275; 281; 283; 284]. Furthermore, recently, the use of SAR images has been applied for the analysis of inland waters. In the literature, SAR applications on lakes are mainly limited to the analysis of the backscattered signal amplitude. In these cases, the SAR amplitude is used as a support for optical or thermal imagery [285; 286; 287]; moreover, there are some studies that show the potential of using SAR images to recover the size of surface eddies in large lakes [288] and information distributed in space on the intensity of the wind [289]. Finally, a preliminary study was also presentedin which the possibility of recovering surface water velocity from SAR Doppler analysis in medium-sized lakes is investigated [265]. To show the potential of these methodologies in an operational context, we show in Figure 3 some results related to an acquisition performed in January 2013 in an area along the south coast of Italy that includes the Volturno river estuary (see the zoomed-in area of Figure 3). The test area covers about 7 km \(\times\) 3 km (azimuth and range) along the coastline. Data were collected in January 2013 by an airborne SAR system, the InSAe54 system (also called TELAR), an Italian-operated, InSAR-oriented airborne platform operating at the X-Band [290,291]. The area encompasses the Volturno river estuary (see the zoomed-in area in Figure 3). The test area covers about 7 km \(\times\) 3 km (azimuth and range) along the coastline. On the right side of Figure 3 is the estimated sea surface velocity map. The mean value decreases toward the coast, which is expected behavior for a marine environment. A further example of applying the DCA technique in the Mediterranean Sea can be found in [275]. The analysis proposed in this work was oriented to understand the influence of wind on the sea surface radial velocity estimated from the Doppler analysis of SAR. This methodology is based on the joint use of three modules exploiting SAR data: the DCA map, wind information (e.g., speed and direction), and the Doppler map associated with wind. The results demonstrate that the use of DCA for estimating surface speed can provide valuable results for the internal seas, and the integrated use with wind analysis represents a helpful methodology for a precise estimation of the sea surface velocity. The results shown in Figure 4 and obtained from DCA evidence there is a relatively good correspondence between the surface velocity obtained from DCA and the surface velocity due to the wind contribution. Figure 3: Test site presented in [281], situated along the coast of southern Italy. The middle panel displays a zoomed-in view of the area of interest. The right panel shows the sea surface velocity map obtained from the ATI technique [281].