esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 4 Radiomics in Osteoporosis Diagnosis
### Radiomics Based on MRI Images in Osteoporosis Diagnosis  
MRI is an imaging modality widely employed in the clinical evaluation of various diseases, including acute trauma, inflammation, metabolic disorders, tumors, and musculoskeletal diseases. Its application has been steadily increasing in recent years [71; 72]. By utilizing these clinically available MRI images for early osteoporosis diagnosis and intervention, it is possible to reduce costs and minimize exposure to ionizing radiation. The progression of osteoporosis was associated with the degradation of bone microstructure, alongside reduced bone blood flow and increased bone marrow fat, all leading to bone loss [73]. Unlike radiographs and CT images, MRI images not only show bone microstructure, but also reflect changes in bone marrow composition content [74; 75]. However, it was difficult to recognize these heterogeneous features through traditional visual inspection. Radiomics enhanced this process by quantitatively characterizing these features, providing valuable support for the accurate diagnosis of osteoporosis.  
Radiomics based on MRI images with different weighted sequences exhibited varying performance in detecting osteoporosis. However, integrating radiomics features from multimodal images might enhance diagnostic accuracy. Sagittal T1-weighted images (T1 WI) and T2-weighted images (T2 WI), commonly employed in routine spinal MRI protocols, have proven effective in osteoporosis detection [76]. T1 WI, with its high signal-to-noise ratio, was particularly sensitive to changes in bone marrow fat, while T2 WI provided insights into the changes in bone blood and water composition [48; 77]. Radiomics features extracted from multimodal T1 WI and T2 WI have demonstrated greater ability to distinguish between osteoporosis and non-osteoporosis patients compared to those derived from a single modality [50]. However, another study highlighted that while radiomics based on multimodal images generally outperforms T2 WI alone, it may not always surpass the diagnostic performance of T1 WI [49]. This indicated that the utility of radiomics features derived from different MRI sequences for osteoporosis diagnosis varied, and their combined application may yield the most effective results, contingent upon the specific images.  
Therefore, radiomics extracted from various MRI sequences serves as a reliable diagnostic tool for osteoporosis. By thoroughly extracting and analyzing imaging features from clinically available MRI images, it is possible to more comprehensively capture the changes in the bone microstructure and bone marrow composition associated with osteoporosis.
# Finite-Time Height Control of Quadrotor UAVs
## 2 Height Dynamic Model of Quadrotor UAV  
According to [21], a height dynamic model of the quadrotor UAV as shown in Figure 1 is established in the following form as  
\[\ddot{z}(t)=(\cos(\theta(t))\cos(\phi(t)))U_{1}(t)+\xi(t)-g \tag{1}\]where \(z(t)\) represents the actual flight height of the quadrotor UAV, \(\theta(t)\) and \(\phi(t)\) represent the current pitch angle and roll angle, respectively, \(U_{1}(t)\) is the total lift force, \(\zeta(t)\) represents the external disturbance, and \(g\) is the gravitational acceleration and can be approximated as \(9.8\) m/s\({}^{2}\). This scheme adopts the dual closed-loop design and divides the above height model into a height velocity inner loop and a height position outer loop.  
Letting \(x_{1}(t)=\dot{z}(t)\), the dynamics equation for the height velocity inner loop can be written as  
\[\dot{x}_{1}(t)=b_{0}u(t)+\zeta(t)-g \tag{2}\]  
where \(b_{0}u(t)=(\cos(\theta(t))\cos(\phi(t)))U_{1}(t)\), \(b_{0}\) is a positive adjustable parameter, and \(u(t)\) is the control input.  
To show the main results of this paper, the following lemma is presented. Note that the following lemma is used to promote follow-up analysis on convergence time of both the finite-time ESO and the finite-time super-twisting controller.  
**Lemma 1** ([22]).: _For the system of \(\dot{x}(t)=f(x(t))\), there are \(f(0)=0\) and \(x(t)\in\mathbf{R}^{n}\)._  
If the scalar function \(V(x(t))\) satisfies \(\dot{V}(x(t))\leq-aV^{b}(x(t))\), where \(a>0\) and \(0<b<1\), then the system is stable in the finite time and the converge time is calculated by \(t_{s}\leq\frac{V(0)^{(1-b)}}{a(1-b)}\). If the scalar function \(V(x(t))\) satisfies \(\dot{V}(x(t))\leq-a_{1}V(x(t))-a_{2}V^{b}(x(t))\), where \(a_{1}>0\), \(a_{2}>0\) and \(0<b<1\), then the system is stable in the finite time and the converge time is calculated by \(t_{m}\leq\frac{1}{a_{1}(1-b)}\ln\frac{a_{1}V(0)^{(1-b)}+a_{2}}{a_{2}}\).
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 3 Dynamic Characteristics of Positioning Stage  
### Structure Rigidity of the Stage  
A single-axis positioning stage was constructed to investigate the effect of the linear guide preload on the dynamic characteristics of the stage. As shown in Figure 4, the main components consisted of one pair of linear guides, a single ball screw, bearing supports, a stage or platform, and a test bed. The stage and tested base were made of carbon steel S45-C. The structural rigidity of the stage in the vertical loading direction was determined by the preloading of the sliding blocks on the rails. The rigidity of the stage was 1176, 1696, and 2020 N/\(\upmu\)m, when mounted on sliding blocks with a light preload (Z0), medium preload (Z1), and high preload (Z2), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Preload Level**} & \multirow{2}{*}{**Preload Amount (N)**} & \multicolumn{2}{c}{**Radial Rigidity (kN/\(\upmu\)m)**} \\ \cline{3-4}  & & **Hertz Theory** & **Experiments** \\ \hline Low, Z0 & 0.02 C & 699 & 295 & 308 \\ Medium, Z1 & 0.07 C & 2447 & 448 & 472 \\ High, Z2 & 0.12 C & 4195 & 536 & 536 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Preload and rigidity of the linear guides.  
Figure 4: A single-axis positioning stage: (**a**) solid model and (**b**) stage prototype for test.  
Figure 3: Load–deflection curves of the sliding blocks in radial direction.
# Overset Grid Assembler and Flow Solver with Adaptive Spatial Load Balancing
###### Abstract  
An overset mesh approach is useful for unsteady flow problems which involve components moving relative to each other. Since the generation of a single mesh around all components is prone to mesh stretching due to the relative motion of bodies, using the overset grid methodology, an individual mesh can be generated for each component. In this study, a parallel overset grid assembler was developed to establish connectivity across component meshes. Connectivity information was transferred to the developed parallel flow solver. The assembler uses multiple methods such as alternating digital tree and stencil walking to reduce the time spent on domain connectivity. Both the assembler and solver were partitioned spatially so that overlapping mesh blocks reside in the same partitions. Spatial partitioning was performed using a 3D space partitioning structure, namely octree, to which mesh blocks are registered. The octree was refined adaptively until bins of octree could be evenly distributed to processors. The assembler and solver were tested on a generic helicopter configuration in terms of load balance, scalability, and memory usage.  
computational fluid dynamics; numerical algorithms; overset grid methodology; load balancing
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
###### Abstract  
This research work proposes a compact four-port multiple-input multiple-output (MIMO) antenna that operates in the whole license free ultra-wideband (UWB) spectrum of 3.1-10.6 GHz. Spatial diversity has been introduced by arranging these antennas in close proximity without developing a strong mutual coupling. Antenna elements are evolved from a conventional rectangular patch antenna whereas a customized decoupling structure is introduced on the back side of the substrate to achieve the desired isolation level. The parasitic decoupling structure consists of different features which are resonant at different frequencies offering a whole UWB coverage. In addition to the decoupling structure a dumbbell shaped stub has also been introduced to the partial ground plane to suppress the mutual coupling. The overall measured isolation among elements is more than 20 dB. Different MIMO performance parameters have also been investigated from the measured results. Whole MIMO system measures 0.41 \(\lambda_{\mathrm{o}}\) \(\times\) 0.44 \(\lambda_{\mathrm{o}}\) at 3.1 GHz. The MIMO system is intended for high data rate and short-range communication devices used in wireless personal area networks.  
channel capacity loss (CCL); decoupling; envelope correlation coefficient (ECC); isolation; multiple-input multiple-output (MIMO); total active reflection coefficient (TARC); ultra-wideband (UWB) +
Footnote †: journal: Applied  
Article
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 2 Characteristics and Algorithms for Large-Scale Pointing Adjustment
### Algorithm of Active Vibration Isolation  
To ensure the pointing accuracy and stability of the optical load in the presence of disturbances, the pointing and stabilizing manipulator should be able to isolate the floor disturbance. Based on the above dynamic model, active vibration isolation of the feedforward method will be proposed in this subsection.
## 2 Fundamental Review on Vibration Isolation Systems
### Quasi-Zero Stiffness System  
The quasi-zero-stiffness (QZS) is a system where the stiffness approaches zero or stays around zero. Figure 1 shows a schematic sketch of one of the famous QZS systems, which consists of one vertical spring (\(k_{p}\)), and two oblique springs (\(k_{n}\)) that are inclined at a predetermined angle (\(\theta_{0}\)) from equilibrium position (the QZS is in its originally undeformed position).  
Figure 1 shows the QZS system at rest mode in its simplest form. However, such a design is vulnerable to many factors that may affect the range of stability around zero stiffness. These factors can be simply summarized in the geometrical factors and the stiffness ratio. The geometrical factors are the length of each spring (the oblique and vertical springs) and the inclination angle of the oblique springs. Putting all of these factors together helps in determining the distance between the suspension point of the oblique spring and the line of action of the vertical spring.  
To achieve better control of the zero stiffness, it is advised to adjust the location of the horizontal deflection component for the oblique spring \(x_{h}\) with the help of either a manual adjuster or linear actuator, which is fitted at the suspension point of the oblique springs, as shown in Figure 2.  
The factors that affect the range of stability of the QZS system are expressed as follows: geometric ratio \(=\frac{a_{0}-x_{h}}{2(L_{0}-a_{0}+x_{h})}\) and the stiffness ratio \(=\frac{k_{n}}{k_{p}}\).  
Figure 1: A simple model of the QZS system.  
Figure 2: QZS system with the horizontal deflection.
## 2 Materials and Methods
The results for different azimuth angles (\(-90^{\circ}\), \(-80^{\circ}\), \(-70^{\circ}\), \(-55^{\circ}\), \(-50^{\circ}\), \(-45^{\circ}\), \(-35^{\circ}\), \(-25^{\circ}\), \(-20^{\circ}\), \(-15^{\circ}\)\(-10^{\circ}\), \(-5^{\circ}\), \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\), \(25^{\circ}\), \(35^{\circ}\), \(40^{\circ}\), \(45^{\circ}\), \(50^{\circ}\), \(55^{\circ}\), \(70^{\circ}\), \(80^{\circ}\), \(90^{\circ}\)) were experimentally detected by comprehensive monitoring of operational conditions (ambient temperature, temperature of PV module surfaces, intensity of solar radiation, wind speed, output electric current and voltage, output electric power, energy production). The tilt angle was changed gradually by \(5^{\circ}\). Experimental data were collected, sorted, and numerically and statistically processed. The method of group data analysis was applied on huge data files. The time relations were detected in hourly, daily, and monthly time ranges. The measured data from the selected part model PV system (1 PV module) were compared with data from real PV power plant with the same tilt angle and azimuth angle. The results were converted to a unit of the PV module active area. The difference between each compared value was less than 0.46%. The experimental data from both PV systems with tilt angle \(35^{\circ}\) were also used for calculation of energy production. The correlation analysis confirmed 99.54% correlation between the values of energy produced by a model PV system and PV power plant. Then were created two-dimensional graphical dependencies and by regression analysis were detected suitable mathematical equations which describe the PV system energy production as a function of tilt angle and azimuth angle. In the next step polynomial approximation on the three-dimensional graphical dependencies was performed. The verification of mathematical models was made by iteration method. In the next part the PV power plant and the model PV system are described. Large data files were processed with spreadsheet software Microsoft Excel and Matlab(r) version R2015b. All data were analysed using analysis of variance (ANOVA). The comparison of the averages was carried out by [PERSON]'s test with a 95% confidence level. The arithmetic averages, medians, and standard error of the arithmetic average were computed from the data. The depth data analysis and the data extraction were applied on the data files obtained from real PV system for creating a mathematical model.
## 3 Overview of the Selected Techniques
### Gain Normalized Adaptive Observer  
This section briefly presents the gain normalized adaptive observer (GNAO) as published in Reference [27]. This observer uses the same dynamic model as presented in Section 3.1.1. For further development, let us define the unknown frequency \(\omega\) as,\(\omega=\omega_{n}+\Delta\omega\), where \(\Delta\omega\) is the frequency deviation. Let us consider the following state transformation:  
\[\Xi=\mathcal{T}\xi, \tag{24}\]  
where  
\[\mathcal{T}=\frac{1}{2\omega^{3}}\bigg{[}\begin{array}{cc}\omega&-1\\ \omega^{2}&\omega\end{array}\bigg{]}.\]  
Then, the transformed state and output matrices are given by:  
\[\mathcal{A}_{\Xi}=\mathcal{T}\mathcal{A}\mathcal{T}^{-1}=\mathcal{A}, \mathcal{C}_{\Xi}=\mathcal{C}\mathcal{T}^{-1}=\big{[}\begin{array}{cc}\omega ^{2}&\omega\end{array}\big{]}.\]  
Then, the following Luenberger type linear observer is designed:  
\[\hat{\Xi}=\hat{\mathcal{A}}_{\Xi}\hat{\Xi}+\mathcal{L}\big{(}U_{a}-\hat{U}_{ a}\big{)}, \tag{25}\]  
where \(\mathcal{L}=\big{[}\begin{array}{cc}\mathcal{L}_{1}&\mathcal{L}_{2}\end{array} \big{]}^{T}\) is the gain matrix for the observer and  
\[\hat{\mathcal{A}}_{\Xi}=\Bigg{[}\begin{array}{cc}0&1\\ -\big{(}\omega_{n}+\hat{\Delta\omega}\big{)}^{2}&0\end{array}\Bigg{]}. \tag{26}\]  
The frequency deviation estimation law is given by:  
\[\Delta\hat{\omega}=-\frac{\gamma(\mathcal{L}_{1}+\mathcal{L}_{2})\hat{\omega }^{3}\hat{\Xi}_{1}\big{(}U_{a}-\hat{U}_{a}\big{)}}{\sqrt{\big{(}\frac{\hat{ \Xi}_{1}2\hat{\omega}^{3}}{2}+\big{(}\hat{\Xi}_{2}2\hat{\omega}^{2}\big{)}^{2 }\big{)}^{2}}},\,\gamma>0. \tag{27}\]  
The denominator term in the right hand side of Equation (27) scales the frequency deviation update law by the estimate of the grid voltage signal amplitude. This makes the adaptation law less sensitive to large voltage sag/swell. This can be very suitable to provide low voltage ride through capability if the observer is used inside the control module of any grid-connected converter.  
Lyapunov function-based stability analysis as presented in Section 3.1.2 cannot be applied to the observer (25) due to the presence of gain normalization term in Equation (27). As such, only local stability results are presented in Reference [27]. From the estimated state vector \(\hat{\Xi}\), the state vector \(\hat{\xi}\) can be calculated through the following equation:  
\[\hat{\Xi}=\mathcal{T}^{-1}\hat{\xi}, \tag{28}\]  
where  
\[\mathcal{T}^{-1}=\bigg{[}\begin{array}{cc}\hat{\omega}^{2}&\hat{\omega}\\ -\hat{\omega}^{3}&\hat{\omega}^{2}\end{array}\bigg{]}.\]  
The same observer (25) can be applied to grid phase voltages \(U_{b}\) and \(U_{c}\). From the signals \(U_{a}\), \(U_{b}\), and \(U_{c}\) and their derivatives (i.e., quadrature signal) obtained through the observer (25), the sequences can be easily estimated through the Equations (7)-(9).
# Target Speaker Extraction by Fusing Voiceprint Features
## 3 Experiments and Results
### Experiment Settings
#### 3.1.2 Evaluation Method  
**Source-to-Distortion Ratio** The source-to-distortion ratio (SDR) [37] was used to evaluate the performance of the proposed Tse-FV model. It is a common evaluation method to assess the performance of speech separation models, which reflects the degree of similarity between the estimated speech \(\hat{\mathbf{y}}\) and the corresponding ground truth speech \(\mathbf{y}\). SDR can be computed using Equation (11).  
\[\text{SDR}=10\text{log}_{10}\bigg{(}\frac{\left\|\mathbf{y}\right\|^{2}}{\left\| \mathbf{y}-\hat{\mathbf{y}}\right\|^{2}}\bigg{)} \tag{11}\]  
The higher the similarity between \(\hat{\mathbf{y}}\) and \(\mathbf{y}\), the smaller the error \(\left\|\mathbf{y}-\hat{\mathbf{y}}\right\|^{2}\). Hence, the higher the SDR, the better the performance of the model. In this work, the mir_eval [38] python library was used to compute the SDR.  
**Mean Opinion Score** The mean opinion score (MOS) is a numerical measure of the human-judged overall quality of the speech, which is used to further evaluate the qualities of separated speech. In this experiment, 10 test audios were first randomly selected from the test set. Then, the target speech was separated using 14 speech separation models, which included DPCL, PITNet, and VoiceFilter, with and without integrating the voiceprint features using DC, CEC, and ECC feature fusion methods, as shown in Table 2. In the end, 140 segments of speech were obtained and used for evaluation. A questionnaire named _MOS evaluation questionnaire_ (see Supplementary Materials) was set up and 20 participants were invited to rank the qualities of the 140 segments of speech. The quality score for each ranked speech varied from one to five, where one corresponded to the worst quality and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **DPCL** & **PITNet** & **VoiceFilter** \\ \hline \multirow{3}{*}{LSTM} & num\_layers & 4 & 4 & 3 \\  & hidden\_size & 300 & 896 & 600 \\  & bidirectional & True & True & True \\ \hline \multirow{4}{*}{Optimizer} & activation & Tanh & Relu & Relu, sigmoid \\  & type & Adam & Adam & Adam \\  & learning\_rate & \(1.0\times 10^{-4}\) & \(1.0\times 10^{-3}\) & \(1.0\times 10^{-4}\) \\  & weight\_decay & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of three speech separation modelsfive corresponded to the best quality. The final _MOS_ value for the evaluated speech \(s_{i}\) was calculated by averaging the scores from 20 participants as,  
\[MOS_{s_{i}}=\frac{\sum_{n=1}^{N}r_{ni}}{N} \tag{12}\]  
where \(N=20\) is the number of participants and \(r_{ni}\) is the rating score of the \(i\)th speech \(s_{i}\) from the \(n\)th participant. For each model to be evaluated, its performance was represented by the average _MOS_ of 10 segments of evaluated speech which can be computed as,  
\[MOS_{model}=\frac{\sum_{i=1}^{M}\sum_{n=1}^{N}r_{ni}}{N\times M} \tag{13}\]  
where \(M=10\) is the number of the evaluated speech segments.
# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 3 Methodology
### Respiration Calculator Block
However, the respiration of infants can change suddenly both in amplitude and frequency, which misleads the FFT. Therefore, it is a common approach to use rate calculation methods based on peak detection [5; 31]. We have implemented a rate calculation method which is based on peak detection, and similar to the ones used in [31; 38]. This algorithm (called _CalcRate_) starts with the inversion of the signal and an adaptive thresholding step, where the threshold level is the local mean value of the input signal. In this way, we obtain a binary signal, where the value 1 represents the respirations (the positive peaks), and the starting of the respirations are indicated with the rising edges. In the last step, those respirations, which are closer than 0.3 s are merged, and the rate is calculated by averaging the time intervals between the peaks and converting the result into a frequency value. The process is shown in Figure 6.  
As we mentioned earlier, the calculated BR values can be used to generate alarm signal in apnea situations. In these cases, we can use a 10-s and a 120-s long version of calculation window simultaneously, in real time. The former is used to recognize apnea situations and respond quickly if it is necessary.
## 1 Introduction
In addition, the angular velocity of ladle tilting motion was controlled by the fuzzy rules referring to the filtered data. In the second estimation method, the flow rate was estimated using a visible camera [34; 35; 36]. In the approaches [35; 36], the liquid height in the target container was measured by the stereo camera or RGB-D camera for estimating the pouring state. The flow line of the inflow liquid in the clear target container was measured by the visible camera [37]. Therefore, in the case of the pouring situation using the clear ladle, the visible camera is able to estimate the flow rate of the outflow liquid by measuring the liquid volume in the ladle in real time. In the casting industry, since the ladle generally consists of gypsum and metal, detecting the liquid volume in the ladle using a visible camera is difficult. In this study, we used water as the target liquid and a clear acrylic container as a ladle. As such, we were able to discern the liquid volume in the ladle from the projected liquid area, as captured by the visible camera. This flow-rate estimation method cannot, however, be applied to practical pouring processes using molten metal. Nonetheless, we applied this flow-rate estimation method for verifying the appropriateness of the flow-rate estimation method by DKFs. Most practical automatic pouring machines have a rotary encoder for measuring the tilting angle of the ladle and a load cell for measuring the weight of the outflow liquid from the ladle. DKFs in which the flow rate can be estimated indirectly using the rotary encoder and the load cell are useful for the control of automatic pouring machine in practical use. The remainder of this paper is structured as follows. The tilting-ladle-type automatic pouring machine used in this study is introduced in Section 2. The mathematical model for the pouring motion is derived in Section 3, and compensation for the measured weight by the load cell is presented in Section 4. The flow-rate estimation method by DKFs is described in detail in Section 5. The additional two flow-rate estimation methods are discussed in Section 6. In Section 7, the appropriateness of the flow-rate estimation by DKFs is verified experimentally by comparing the process with two other flow-rate estimations. Concluding remarks are presented in the final section.