esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Autonomous Landing Method
### Trajectory Generation and Tracking
#### 4.3.3 Model Predictive Control  
To achieve accurate trajectory tracking, MPC is used to perform trajectory tracking of the FPLIR. MPC is an online optimal feedback control strategy that uses the model to predict the future behavior of a system within a limited forecast horizon. The optimal input is obtained by solving a constrained optimization problem for the objective function along the prediction horizon and applying only the first control action to the system. These steps are repeated several times while shifting the prediction horizon at each time step.  
After linearizing and discretizing the dynamics model of the FPLIR, the linear state-space model can be represented by the following:  
\[\begin{array}{l}\mathbf{x}(k+1)=\mathbf{A}\mathbf{x}(k)+\mathbf{B}\mathbf{u}(k)\\ \mathbf{y}(k)=\mathbf{C}\mathbf{x}(k)\end{array} \tag{21}\]  
where \(k\) is the sampling instant, \(\mathbf{x}(k)\) is the state of the system at time \(k\), \(\mathbf{u}(k)\) is the system input at time \(k\) and \(\mathbf{y}(k)\) is the system output at time \(k\). The matrices \(\mathbf{A},\mathbf{B},\mathbf{C}\) characterize the coefficient matrices of the system.  
In the case of a given reference trajectory, the cost function is the error between the predicted state and the reference state over the time horizon. The optimization problem with constraints can be expressed as follows:  
\[\begin{array}{l}J=\underset{\mathbf{u}}{argmin}\left(\mathbf{x}_{N}^{T}\mathbf{F}\mathbf{x} _{N}+\sum\limits_{i=1}^{N-1}\left(\mathbf{x}_{i}^{T}\mathbf{Q}\mathbf{x}_{i}+\overline\mathbf{{u}_{i}}^{T}\mathbf{R}_{u}\overline{\mathbf{u}_{i}}+\widetilde{\mathbf{u}_{i}}^{T}\mathbf{R}_ {\Delta}\widetilde{\mathbf{u}_{i}}\right)\right)\\ \text{subject to}:\ \widetilde{p}_{dmin}\leq\widetilde{p}_{d}\leq \widetilde{p}_{dmax}\\ -\mathbf{O}_{max}\leq\mathbf{O}_{\mathbf{\eta}}\leq\mathbf{O}_{max}\\ -\mathbf{v}_{max}\leq\mathbf{v}_{e}\leq\mathbf{v}_{max}\\ \mathbf{u}_{min}\leq\mathbf{u}\leq\mathbf{u}_{max}\end{array} \tag{22}\]  
where \(\mathbf{x}_{i}=\mathbf{x}(k+i|k)-\mathbf{x}_{ref}(k+i|k)\), \(\overline{\mathbf{u}}_{i}=\mathbf{u}(k+i\mid k)-\mathbf{u}_{ref}(k+i\mid k)\), and \(\overline{\mathbf{u}}_{i}=\mathbf{u}(k+i\mid k)-\mathbf{u}_{ref}(k+i\mid k)\). \(\mathbf{x}(0)\) is the initial state of the system and \(f(\mathbf{x}(k),\mathbf{u}(k))\) is the system dynamics model for discrete time. \(\mathbf{Q}\), \(\mathbf{R}_{\mathbf{u}}\), \(\mathbf{R}_{\Delta}\) and \(\mathbf{F}\) are the weight matrices for the states, inputs, and the control transformation rate, respectively. In order to ensure the safety of the inspection robot when dropping the line, and considering its considerable inertia, the acceleration, angular velocity, speed and output of the inspection robot need to be constrained.
## 3 Methods and Materials Processing
### Wavelet Transform
#### 3.2.3 Wavelet Coherence  
As regards consistency, XWT may produce misleading results due to the fact that when the CWT spectrum of one of the time series exhibits a strong peak, the absolute value of XWT between the two will be high, even if there is no strong correlation between the two-time series. To overcome this problem, [PERSON] et al. [45] proposed a normalizedmetric for the relationship between \(X_{n}\) and \(Y_{n}\), namely, wavelet coherence (WTC). WTC is defined by the following equation:  
\[R_{n}{}^{XY}=\left|\frac{\varsigma[W_{n}{}^{XY}]}{\sqrt{\varsigma[W_{n}{}^{X}] \varsigma[W_{n}{}^{Y}]}}\right| \tag{3}\]  
where \(W_{n}{}^{XY}\) denotes the corresponding XWT, \(\varsigma\) denotes the smoothing factor, and \(R_{n}{}^{XY}\) denotes the WTC of the time series \(X_{n}\) and \(Y_{n}\). The value range of wavelet coherence is \([0,1]\), and the closer the value of WTC is to \(1\), the stronger the correlation between time series \(X_{n}\) and \(Y_{n}\) is.
## 3 Data Used
Out of these, the lithological map was classified into six groups based on lithological characteristics including Group 1--acid-neutral igneous magmatic rocks and tuff; Group 2--acid-neutral intrusive magmatic rocks; Group 3--terrigenous sedimentary rocks with rich aluminosilicate components; Group 4--mafic-ultramafic magma rocks; Group 5--carbonate rocks; and Group 6--quaternary deposits [48, 49]. Meteorological data were collected from global weather data for SWAT [29, 50] for a 31 year period (1984-2014) to generate a rainfall map. Distance to features (i.e., roads, rivers, faults) and feature density were constructed using feature extraction from the topographic map and geological maps. In order to use the datasets for modeling, the conditioning factors were reclassified into various sub-classes [43] on the basis of the frequency analysis of landslides occurring in the area [8, 9, 10]. Figure 2: _Cont._  
Figure 2: Maps of the landslide affecting parameters: (**a**) slope map, (**b**) aspect map, (**c**) profile curvature map, (**d**) curvature map, (**e**) plan curvature map, (**f**) elevation, (**g**) distance to rivers map, (**h**) river density map, (**i**) distance to roads map, (**j**) road density map, (**k**) distance to faults map, (**l**) fault density map, (**m**) land use map, (**n**) lithology map, and (**o**) rainfall map.
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 3 Results
### Prediction of the Spread of COVID-19 in Kuwait after the Repatriation of Citizens back to Kuwait
The uncertainty is due to several parameters including the latent period in the patient infected with COVID-19, the period of infectivity, and patients' recovery time. It is therefore crucial to consider these uncertainties when interpreting the prediction results. To address these uncertainties, a stochastic SIR ICM approach was utilized to simulate the spread with a variable number of contacts before and after the repatriation plan. The approach will be effective in estimating the peak value and time of the epidemic due to simultaneous changes in the number of susceptible and infected individuals at time t. Also, to assess whether the intervention measures, such as partial curfew and social distancing, are effective in responding to the outbreak. Consequently, a combination of 18 simulations were carried out to illustrate the impact of adherence to social distancing in controlling the spread of COVID-19 in Kuwait. To demonstrate the effect of simultaneous changes in the number of susceptible individuals, the stochastic SIR ICM model was simulated with a varying population size. Similarly, to illustrate the effect of the rapid changes in the number of infected individuals at a given time, different infection rates and individual contact numbers were simulated simultaneously. As a result, various infection rates were assumed for the simulation scenarios. The base infection rate was selected as 0.38 and increased further to 0.405 and 0.43 to examine the effect of the variation of infectivity on the epidemic peak value. Furthermore, an individual's number of contacts was chosen to be 2, 6, and 10 contacts to represent low, medium and high social mixing, respectively. Figures 7 and 8 present the stochastic simulation of the Kuwaiti population before and after the repatriation plan, respectively, and the associated simulation results are presented in Table 7.  
_Appl. Sci._**2020**, _10_, 3402 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Infection Rate**} & \multicolumn{2}{c}{**Contacts Per Day before Repatriation**} & \multicolumn{2}{c}{**Contacts Per Day after Repatriation**} \\ \cline{2-7}  & **2** & **6** & **10** & **2** & **6** & **10** \\ \hline \multirow{2}{*}{\(\beta=0.38\)} & 97 cases & 211 cases & 348 cases & 108 cases & 223 cases & 399 cases \\  & Day 54 & Day 51 & Day 49 & Day 56 & Day 53 & Day 49 \\ \hline \multirow{2}{*}{\(\beta=0.405\)} & 189 cases & 352 cases & 520 cases & 199 cases & 398 cases & 601 cases \\  & Day 48 & Day 47 & Day 46 & Day 48 & Day 47 & Day 46 \\ \hline \multirow{2}{*}{\(\beta=0.43\)} & 198 cases & 401 cases & 599 cases & 215 cases & 460 cases & 698 cases \\  & Day 46 & Day 45 & Day 45 & Day 46 & Day 45 & Day 45 \\ \hline \end{tabular}
\end{table} 
Table 7: Stochastic SIR simulation results. Figure 7: Stochastic simulation results of SIR model with various infection rates and contacts before the repatriation plan.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 3 Voice Spoof Detection and Algorithm Recognition System  
The framework of our system is shown in Figure 1.  
To prevent voice spoof detection and spoof algorithm identification from interfering with each other, we divided them into two separate modules. First, we input the test voice into the voice spoof detection module to identify the authenticity of the voice. If the judgment was true, the test was completed. If the voice was judged to be spoofed, it was input into the voice spoof algorithm recognition module for spoof algorithm recognition.  
The following section describes the methods used in these two modules. Section 3.1 describes the methods for detecting the authenticity of a voice, and Section 3.2 gives the recognition methods used for the voice spoof algorithm in incremental scenarios.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### XPCS at Free-Electron Laser Sources
#### 4.2.1 Role of SASE Fluctuations  
In a first XPCS study performed at LCLS, [PERSON] et al. measured slow dynamics of gold nanoparticles suspended in polymer melts [160]. The experiment was performed at the XCS instrument [26] using a monochromatized beam of 8.7 keV, which resulted in about \(10^{9}\) photons per pulse. Speckle patterns were measured in SAXS geometry using a PI CCD camera, that only allowed to access a minimum lag time of \(\Delta t_{\rm min}=7\) s. Nevertheless, dynamics could be measured between 7 s and 1000 s and showed the expected behaviour. Therefore, significant impact from radiation damage could be ruled out, the estimated temperature increase due to absorption of X-rays [59; 156] was on the one hand well below the damage threshold. On the other hand, the heat relaxed faster than 100 \(\upmu\)s, so that even with the 120 Hz repetition rate of the LCLS the temperature has been equilibrated before the next pulse. Although the correlation times matched the expected trends, the \(g_{2}\) functions only showed a speckle contrast of about 0.1. From the experimental parameters a single-pulse contrast above 0.8 was estimated [152; 153]. The authors connected this with the SASE fluctuations, leading to an effective reduction of the speckle contrast in the \(g_{2}\) functions.  
A study at SACLA proved this by comparing XPCS series of static and dynamic samples [161] using a repetition rate of 30 Hz at BL3 [162]. While the contrast of single patterns using a pink beam was in the range of 0.4 to 0.5 [154; 161], the \(g_{2}\) functions showed values around 0.1. This reduced contrast could be modelled assuming variations of beam size and pointing positions, e.g., average variations of about 20% of both, corresponding to about 0.3 \(\upmu\)m on a pulse-to-pulse level would already result in the observed difference. Nevertheless, the diffusion dynamics of nanoparticles could be obtained from series of several 100 to 1000 pulses without any indication of radiation damage. This study highlighted the importance of measuring the maximum speckle contrast using, e.g., static samples as a starting point for obtaining dynamics information. Otherwise, potential faster processes may not be identified, or a wrongly assigned speckle contrast leads to incorrect correlation times and KWW exponents.  
In order to address the question of the radiation damage on soft matter samples, the dynamics of radiation-sensitive PNIPAm nanogel systems have been studied as well at SACLA in a sequential XPCS experiment with 30 Hz repetition rate [163]. Similar to the previous study, the speckle contrast was determined using static samples. Analysis of structure and dynamics of pure PNIPAm nanogels and silica-PNIPAm core-shell systems showed that below a critical dose in the range of \(10^{5}\) Gy the dynamics of dense systems could be obtained. This threshold is similar to the ones observed at storage-ring based experiments [133], proving that similar models are valid for both type of experiments.
## 2 TTRS and Mathematical Modeling
### Mathematical Modeling  
As mentioned previously, TTRS is aligned with geometric dimensioning and tolerancing (GD&T) standards. However, it does not support a description of deviated geometries that is detailed enough to quantify their effects on part location/orientation in the assembly. For this reason, TTRS concepts must be enriched with additional mathematical constructions, which are a basic part of analysis models. This work proposes the combination of TTRS concepts with the application of a small displacement torsor (SDT) [26] model to support the computation of deviated geometries. The values of the SDT define the maximum deviations applicable to each variant DoF, calculated according to the bounded geometries and the TZ, depending on its type and size.  
The TTRS model references each surface using RGEs, which can be a point, a line, or a plane. These basic geometric elements must be defined by an appropriate mathematical expression: in this case, a combination of points and vectors expressed in a local Cartesian system, specific to each part considered. This local geometric description enables the independent specification of each part, but it is not compatible with the definition of assembly relationships. To this end, all the geometries involved in the analyzed assembly must be expressed with respect to a common global system, enabling the definition of mathematical expressions that support Pseudo TTRSs. For this reason, the local definition of the geometries must be transformed to a global definition, changing the reference system. This transformation can be supported by a transformation matrix that is characteristic of each part. Once all the geometries of every part are expressed with respect to a common system, all TTRS restrictions can be implemented as mathematical expressions between them (fundamentally vector and scalar products), supporting both part specifications and assembly relationships.  
Our proposal conforms to the need to model nominal and deviated geometries, as well as expressing them with respect to local and/or global coordinate systems, by adopting the following mathematical processing from the nominal geometry of each part with respect to local coordinate systems: (a) computation of deviated geometries by applying an SDT vector that is compatible with the limits imposed by the TZ and for which the deviated geometries are also expressed with respect to the local coordinate systems; (b) coordinate transformation of deviated geometries to a global coordinate system by applying a paramet  
Figure 2: The 44 association types between MGEs.  
ric transformation matrix whose values must be calculated from the assembly relationships that restrict the position/orientation of each part.
## 4 The Proposed Real-Time Monitoring System  
To enable mobility of cardiac patients and improve their life quality, herein we propose a tracking and supervision real-time system that monitors the vital signs of a cardiac patient. The system analyzes the received data from the patients and then performs machine learning operations to classify the status of the patient whether it is stable, unstable non-critical, or unstable critical, illustrated in Figure 1. In case of a stable state, the patient should be advised to stay at home and maintain monitoring, while in the case of an unstable state, the patient should be taken to the nearest associated healthcare facility or hospital to receive the necessary medical therapy at the appropriate time. In the event of an emergency, the system sends out alert signals to indicate that medical assistance is urgently required. Patients in the ICU require proper monitoring of their blood circulatory and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Factors** & **Applicable Sensors** & & \\ Blood Pressure (Systolic and Diastolic) & & Blood Pressure Monitor (Oscillo-metric) & \\ Heart Rate & Heart Rate Sensor (e.g., photoplethysmography) & \\ Respiratory Rate & Respiration Sensor (e.g., chest belt or nasal airflow sensor) & \\ Glasgow Coma Scale & & Clinician Assessment (e.g., EEG optionally for brain activity) & \\ SPO2 & & Pulse Oximeter & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Factors** & **Min.** & **Max.** & **Mean (\#)** & **Std. Dev. (S)** \\ \hline Gender & 0 & 1 & 0.627 & 0.484 \\ \hline Age & 23 & 87 & 54.159 & 12.267 \\ \hline BMI & 22 & 35 & 28.86 & 2.375 \\ \hline Chest pain & 0 & 1 & 0.665 & 0.472 \\ \hline Shortness of breath & 0 & 1 & 0.536 & 0.5 \\ \hline Smoking & 0 & 1 & 0.541 & 0.499 \\ \hline Systolic & 50 & 200 & 133.33 & 32.421 \\ \hline Diastolic & 50 & 130 & 80.536 & 18.821 \\ \hline Heart rate & 45 & 140 & 87.181 & 17.2 \\ \hline Respiratory rate & 10 & 22 & 14.231 & 2.808 \\ \hline Glasgow coma scale & 8 & 15 & 13.067 & 1.926 \\ \hline SPO2 & 52 & 98 & 91.752 & 6.01 \\ \hline Class & 0 & 2 & 1 & 0.817 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical summary of the collected dataset for cardiac patients.  
respiratory systems since these are time-critical processes that must be monitored regularly and intensively. Therefore, this article proposes two machine learning approaches that can be employed to predict the status of cardiac patients, listed in the lower portion of Table 1. Next, the proposed models are discussed.
###### Abstract  
Drought risk analysis can help improve disaster management techniques, thereby reducing potential drought risk under the impacts of climate change. This study analyses observed and model-simulated spatial patterns of changes in drought risk in vulnerable eco-regions in China during 1988-2017 and 2020-2050 using an analytic hierarchy process (AHP) method. To perform a risk assessment and estimation of a drought disaster, three subsystems--namely hazard, vulnerability and exposure--are assessed in terms of the effects of climate change since the middle of the 21 st century: (i) Hazards, represented by climate anomalies related to the drought process, such as changes in rainfall averages, temperature averages and evaporation averages; (ii) vulnerability, encompassing land use and mutual transposition between them; (iii) exposure, consisting of socioeconomic, demographic, and farming. The results demonstrated that high hazards continue to be located in the arid zone, high vulnerability levels occur in the Junggar Basin and Inner Mongolia Plateau, and high exposure levels occur Loess Plateau and southern coastal area. In this way, the results provide exhaustive measures for proactive drought risk management and mitigation strategies.  
drought; risk assessment; risk estimate; hazard; vulnerability; exposure +
Footnote †: journal: _Journal of the Royal Society B: Climate Change_
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 3 Watermark Embedding  
The watermarking algorithm, analyzed in this paper, is based on embedding a random bitstream in the domain of one of the transforms described in Section 2. It is assumed that only the grayscale image, represented by the luminance channel, is processed. As it is usually done, forward and inverse transform operations are performed on smaller subimages, having the size of 8 \(\times\) 8 pixels. As a result, we obtain a lot of processed blocks, each containing 64 spectral coefficients. This is the standard procedure, which is followed before the subsequent steps are realized. This base procedure for watermark embedding is depicted in Figure 1. The blocks highlighted in orange are optional. The block highlighted in green is the one for which different embedding solutions can be used. Since the aim of this paper is to gain some knowledge about the applicability of the novel orthogonal transforms in digital watermarking, several approaches, both simple and those more sophisticated, are investigated in order to learn which transform types and which embedding methods are the most suitable for specific cases and goals that we want to achieve. Thus, five approaches have been examined and verified. Methods 1 and 4 are classic techniques used as a reference, while methods 2, 3, and 5 are proposed as their adaptive extensions that aim at the efficient use of the presented matrix types. These approaches are presented in the following subsections.
## 4 Cell Segmentation
The sequence of the different locations of the mode across the convergence path is given by:  
\[y_{j+1}=\frac{\sum_{i=1}^{n}x_{i}g((y_{j}-x_{i})^{T}\;H^{-1}(y_{j}-x_{i}))}{ \sum_{i=1}^{n}g((y_{i}-x_{i})^{T}\;H^{-1}(y_{j}-x_{i}))}\;j=1,2,..... \tag{13}\]  
where \(g\) is the derivative of the Kernel function. Practically the mean shift segmentation is composed of the following steps:  
1. Segment the feature space into a region. 2. Choose the initial location of the mode in each region. 3. Compute the new locations of the modes by updating them using the shift step. 4. Repeat step 3 and 4 until convergence. 5. Merge the neighboring modes and their associated pixels. The convergence is reached when the shift step approaches zero. Figure 9(c), shows the mean shift segmentation outcome for some cells considering both the gray level and spatial information. We observe that the segmentation produces several non-compact regions that do not fit the desired target (e.g., The nucleus and cytoplasm). Statistically, we found that the number of regions varies between 3 and 6. However, one case of the under-segmentation is depicted in Figure 10(a). This is because the gray level distribution does not exhibit distinctive peaks as in Figure 10(b), thus causing the pixels inside the cell area to converge into a single style. In the next phase we perform a rule-based region merging. First from each region, represented as a binary image, we extract the largest connected patches (excluding the isolated and tiny ones). Afterwards, we perform the region merging as in Figure 9(d) subject to the following constraints:  
a: The darkest region is part of the nucleus. b: The clearest region is part of the cytoplasm. c: Regions on the borders are part of the cytoplasm. d: The final number of regions must be equal to 2. In the final phase, we performed basic hole-filling morphological operations to get the fully compact regions corresponding to the nucleus and cytoplasm as in Figure 9(e). In the experiment we compared the performance of two variants of the mean shift method (with and without spatial component) with the Hopfield Neural Network (HNN) employed in [7,15]. We used the same assessment procedure as in the sputum extraction that is we compare the effect of the segmentation of the ground truth data composed of the manually segmented nucleus and cytoplasm in the set of test images. Table 3 summarizes the performances of the three methods. We can see that the mean shift clearly outperforms the HNN technique. In addition, we notice that the integration of the spatial data in the mean shift boosts further the performance.