esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Correction and Calibration
#### 2.3.2 Baseline Measurement  
In obtaining the VSFs of hydrosols, the methodology consists of performing measurements for a sample containing pure water only; such measurement is hereinafter referred to as \"baseline measurement.\" Performing this measurement is essential because the contribution for pure water (or pure sea water) \(\beta_{w}(\theta)\) to the total VSF \(\beta(\theta)\) should be removed to allow the determination of particle contribution \(\beta_{p}(\theta)\).  
\[\beta_{p}(\theta)=\beta(\theta)-\beta_{w}(\theta) \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mu_{\text{ND}}\) (\(\upmu\)m) & \(\mu_{D}\) (\(\upmu\)) & \(\delta_{D}\) (\(\upmu\)) & \(\sigma_{D}\) (\(\upmu\)) & \(n\) (\(n_{r}\)@520 nm/@532 nm + \(n_{i}\)) \\ \hline
0.2 & 0.203 & 0.004 & 0.0059 & 1.5999/1.5982 (+ 0.00035 \(\pm\) 0.00015) i \\
2 & 2.02 & 0.015 & 0.02 & 1.5999/1.5982 (+ 0.00035 \(\pm\) 0.00015) i \\
3 & 2.994 & 0.029 & 0.03 & 1.5999/1.5982 (+ 0.00035 \(\pm\) 0.00015) i \\
5 & 5.027 & 0.047 & 0.05 & 1.5999/1.5982 (+ 0.00035 \(\pm\) 0.00015) i \\
11 & 11.1 & 0.5 & 0.6 & 1.5999/1.5982 (+ 0.00035 \(\pm\) 0.00015) i \\ \hline \end{tabular}
\end{table}
Table 1: The specification of polystyrene beads used in this study. Beads of a nominal diameter \(\upmu_{\text{ND}}\) are assumed to be normally distributed with an actual mean diameter of \(\mu_{D}\) and a standard deviation of \(\sigma_{D}\). \(\sigma_{D}\) represents the uncertainty in determining \(\mu_{D}\) at 95% confidence level. The complex refractive index (\(n\)) at 532 nm and 520 nm are also listed.  
When measuring VSF of standard beads, pure water was prepared by the Milli-Q advantage A10 water purification system (Millipore Inc., Burlington, MA, USA) and then filtered through a polycarbonate cartridge filter of pore size 0.2 \(\upmu\)m (PN 12991, Pall Co. Ltd., Port Washington, NY, USA) to further remove residual particle contaminations. During offshore observations, the filtered seawater was prepared for baseline measurement. Additionally, a Liqui-Cel Membrane Contactor with a vacuum pump was used to remove the bubbles inside the pure water samples so as to obtain correct baseline measurements. Finally, these baseline measurements were subtracted from the subsequent measurements taken on particle suspensions.
# Commuted PD Controller for Nonlinear Systems: Glucose-Insulin Regulatory Case
## 5 Conclusions and Future Work  
This paper proposes a new strategy to control the blood glucose concentration in a pancreatic system, and with the control objective to reduce the error between this concentration with respect to the basal one. As is well-known, the main idea is to administrate insulin to prevent hyperglycemia. But this insulin dose could be too strong, falling into the hypoglycemia region. To mitigate this effect, we suggest ingesting some controlled quantity of glucose. The method used to define the controllers is the proportional-derivative one, which is able to stabilize the glucose concentration around its basal value. The key feature is to avoid an unhealthy glucose concentration range, combining insulin injection with glucose administration. Then, two control actions are defined: one to modify the insulin dynamics (and prevent hyperglycemia), and the other one to improve the performance of the glucose dynamics (and prevent hypoglycemia).  
Figure 10: White noise on measurements. In this case, white noise is considered to simulate a worse case of glucose measurement error. It is observed that the controller regulates the glucose concentration to a healthy one of 70 mg/dL, but slower than without error measurements. Hence, for this noise environment, if external ingestion occurs, almost all patients cases are regulated, except case 2 (hypoglycemic patient). (**a**) Without external perturbation. (**b**) With ingestion at 230–250 min.  
The simulations test the closed-loop performance on four different human behaviors. Moreover, external perturbations are considered meal ingestion. The results are comparable or improved with respect to other works. The purpose of this work is to study the behavior of this novel strategy, and opens the possibility of testing this switched strategy on another type of controller. Also, as theoretical future work, discontinuities of state variables can be considered by working with a piecewise smooth differential system. Moreover, the time delays in insulin administration can be also an interesting case to study [40].  
Finally, two complementary studies are presented. One is based on modifying the glucose absorption dynamic, to test the robustness of the commuted PD controller. The other study was to include an integral part of our controller approach, defining a PID with a resetting integrator action, showing that despite the rise-time of closed-loop dynamics being reduced, chattering on the controller appears inevitably.  
Conceptualization, L.A.; Methodology, G.P.-V., L.A. and J.G.-B.; Software, G.P.-V., Validation, G.P.-V., L.A. and J.G.-B.; Formal analysis, G.P.-V. and L.A.; Investigation, L.A.; Data curation, J.G.-B.; Writing--original draft, G.P.-V. and L.A.; Writing--review & editing, J.G.-B.; Visualization, L.A. and J.G.-B.; Supervision, G.P.-V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No data were created during the study.  
The authors declare no conflict of interest.
## 6 Case Study  
### Prediction Model of Real-Time Physical and Mechanical Indices  
The high core-wall rockfill dam of X hydropower station located in Sichuan Province, China, is selected as the case study. The maximum dam height is 295 m which is the third highest earth-rock dam in the world. The RTCM and PDA collection systems are applied here to realize all-weather real-time intelligent control for compaction quality and information collection of material sources and spot tests [6]. The core wall district with gravel-mixed cohesive soil is selected as the research object, which is compacted using ten roller passes with padfoot rollers the vibration frequency of which is 27 Hz and the exciting force can reach 416 kN. The physical indices (i.e., \(K\) and \(\rho_{d}\)) are obtained by the random spot tests. To minimize the effect of material disturbance, a slight location offset is adopted. One hundred samples are applied to establish the prediction model of physical indices using SVR with CFA, and 20 additional samples are used for cross validation. The comparison between the actual values and measured values is shown in Figure 5. The linear correlation coefficient (\(R\)) [Equation (26)] is adopted as the accuracy criteria.  
\[R=\frac{N\sum yy^{\prime}-\sum y\sum y^{\prime}}{\sqrt{N\sum y^{2}-\left(\sum y \right)^{2}}\sqrt{N\sum\left(y^{\prime}\right)^{2}-\left(\sum y^{\prime} \right)^{2}}}, \tag{26}\]  
where \(y\) is the actual value, \(y^{\prime}\) is the calculated value, and \(N\) is the data bulk. In Figure 5a, the maximum relative absolute error of \(K\) is 2.94%, and the coefficient \(R\) reaches 0.977; in Figure 5b, the maximum relative absolute error of \(\rho_{d}\) is 4.32%, and the coefficient \(R\) reaches 0.917, which denotes the prediction model performs well.  
Figure 5: Cross validation of physical indices: (**a**) \(K_{i}\) (**b**) \(\rho_{d}\).  
Like the prediction of physical indices, 105 samples are applied to establish the prediction model of mechanical indices, and 21 additional samples are used for cross validation. In Figure 6a, the maximum relative absolute error of \(E_{\text{rd}}\) is 9.69%, and the coefficient R reaches 0.916; in Figure 6b, the maximum relative absolute error of \(E_{0}\) is 8.22%, and the coefficient R reaches 0.921; in Figure 6c, the maximum relative absolute error of \(K_{30}\) is 11.13%, and the coefficient R reaches 0.899, which indicates the availability of the prediction model. Referring to the different maximum relative absolute errors in Figures 5 and 6, we find the prediction accuracy of the mechanical indices is lower than that of the physical indices. The reason may be that the unevenness of the working face leads to inadequate contact between the roller's drum and the dam materials, which makes the drum vibration acceleration create a bigger noise than the compaction and material parameters. It is important to reduce the noise by combining wavelet analysis and an intelligent algorithm to further improve the prediction accuracy in the future [35].  
Figure 6: Cross validation of physical indices: (**a**) \(E_{\text{nd}}\); (**b**) \(E_{0}\); (**c**) \(K_{30}\).
## 2 Principle
The relevant term \(\cos(2\theta)\sin\phi\) is loaded into \(\omega_{2}\), \(2\omega_{1}+\omega_{2}\) and \(2\omega_{1}-\omega_{2}\) signals, and the relevant term \(\sin(2\theta)\sin\phi\) is loaded into \(\omega_{1}\), \(2\omega_{2}+\omega_{1}\) and \(2\omega_{2}-\omega_{1}\) signals. By solving the differential frequency signal components of dual photoelastic modulation, simultaneous measurement of the retardance and the fast-axis azimuth can be realized. Digital phase-locking technology is used to control the analog-to-digital (AD) sampling frequency through the field programmable gate array (FPGA), and the digital signal sequence of the detected light intensity after AD conversion is input into the FPGA [11,15,16]. The fundamental frequency harmonic terms of the two PEMs, \(V_{\omega_{1}}\) and \(V_{\omega_{2}}\), and the different frequency harmonic terms, \(V_{\omega_{2}-\omega_{1}}\), are simultaneously demodulated. \[\left\{ \begin{array}{l}V_{\omega 1}=I_{0}J_{0}(\delta_{20})I_{1}(\delta_{20}) \sin(2\theta)\sin\phi\\ V_{\omega 2}=I_{0}J_{0}(\delta_{10})I_{1}(\delta_{20})\cos(2\theta)\sin\phi\\ V_{\omega 2-\omega 1}=I_{0}I_{1}(\delta_{10})I_{1}(\delta_{20})\cos\phi\end{array} \right. \tag{7}\]  
For the convenience of data calculation, the ratio of the fundamental frequency signal (\(V_{\omega_{1}}\), \(V_{\omega_{2}}\)) to the differential frequency signal (\(V_{\omega_{2}-\omega_{1}}\)) of the two PEMs is calculated, and the ratios of the two channels are defined as \(R_{\rm I}\) and \(R_{\rm II}\). \[\left\{ \begin{array}{l}R_{\rm I}=\frac{V_{\omega 1}I_{1}(\delta_{20})}{V_{ \omega 2-\omega 1}I_{0}(\delta_{20})}=\sin(2\theta)\tan\phi\\ R_{\rm II}=\frac{V_{\omega 1}I_{1}(\delta_{10})}{V_{\omega 2-\omega 1}I_{0}(\delta_{10})}= \cos(2\theta)\tan\phi\end{array} \right. \tag{8}\]  
Using the above ratio, phase retardance \(\phi\) and fast-axis azimuth \(\theta\) are simultaneously solved. \[\left\{ \begin{array}{l}\theta=\frac{1}{2}\arctan(\frac{R_{\rm I}}{R_{\rm II }})\\ \phi=\arctan(\sqrt{{R_{\rm I}}^{2}+{R_{\rm II}}^{2}})\end{array} \right. \tag{9}\]  
The phase retardance of the waveplate is usually expressed by the retardance, and \(\Phi=\phi\lambda/2\pi\). The scheme in this study can simultaneously measure the waveplate parameters through a single measurement of the polarization analysis system with dual cascade differential frequency photoelastic modulation. Combined with a motorized scanning stage to push the waveplate, parameter distribution measurements on the entire aperture of the waveplate are performed.
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 4 Diagnosis: Tissue Characterization in Later Disease  
Tissue characterization on CMR has led to the concept of a staged progression of disease, through accumulation of sphingolipid to a hypertrophic phase, ultimately resulting in fibrosis and inflammation. In the final phase of fibrosis, elevated T2 values have been found in regions of fibrosis identified on LGE, that corresponded closely to elevation in high sensitivity troponin release [38]. T2 mapping, like T1 mapping, provides both a visual and quantitative assessment of intracellular and/or extracellular water, and is able to detect focal and diffuse myocardial oedema (see Figures 8 and 9). The interesting feature in FD is that T2 signal is high in areas of LGE, consistent with oedema, raising the possibility of inflammation as a final pathway in the progression of cardiomyopathyopathy. This elevation in T1 was not found in other diseases causing LVH, such as pressure overload (aortic stenosis), extracellular expansion (amyloid) or cellular hypertrophy and disarray (sarcomeric HCM).  
Figure 8: (**Left**) CMR T2 map with increased relaxation times representing oedema and inflammation within the myocardium in a patient with advanced Fabry cardiomyopathy; (**Right**) CMR LGE images with areas of LGE in the inferior and inferolateral walls of the myocardium which correspond with areas of high T2.  
Figure 9: (**Left**) CMR T2 map with increased relaxation times representing oedema and inflammation within the myocardium in a patient with advanced Fabry cardiomyopathy; (**Right**) CMR LGE images with areas of LGE corresponding with areas of increased T2.  
These CMR data build on data from other forms of advanced cardiovascular imaging, specifically 18F-FDG-positron emission tomography, that myocardial inflammation may in fact even precede development of LGE and, therefore, could be a more sustained process [39]. This was supported by data in heterozygous women with FD but no LVH, in whom focal myocardial uptake of 18F-FDG was found on PET scanning which correlated with reduction in GLS [40]. This study, therefore, linked early inflammation in FD cardiomyopathy with functional decline, before the hypertrophic phase, a finding consistent with elevation of plasma inflammatory and cardiac remodelling biomarkers [41]. The importance of these data from CMR and FDG PET are that they suggest potential alternative avenues of therapy in FD that go beyond replacement or reactivation of \(\alpha\)-GAL enzyme.  
Summary points:  
* Staging disease requires a combination of echocardiography and CMR data to define a pre-hypertrophy stage when both structural and functional abnormalities can be subtle;
* Following hypertrophy, there is increasing recognition from biomarker and imaging studies that inflammation plays a role in progression to fibrosis and scar, that may coincide with altered perfusion detectable on CMR and nuclear medicine techniques.
###### Abstract  
The aim of this pilot study was to investigate the feasibility of markerless tracking to assess upper body movements of children with and without human immunodeficiency virus encephalopathy (HIV-E). Sagittal and frontal video recordings were used to track anatomical landmarks with the DeepLabCut pre-trained human model in five children with HIV-E and five typically developing (TD) children to calculate shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension and trunk lateral sway. Differences in joint angle trajectories of the two cohorts were investigated using a one-dimensional statistical parametric mapping method. Children with HIV-E showed a larger range of motion in shoulder abduction and trunk sway than TD children. In addition, they showed more shoulder extension and more lateral trunk sway compared to TD children. Markerless tracking was feasible for 2D movement analysis and sensitive to observe expected differences in upper limb and trunk sway movements between children with and without HIV. Therefore, it could serve as a useful alternative in settings where expensive gait laboratory instruments are unavailable, for example, in clinical centers in low- to middle-income countries. Future research is needed to explore 3D markerless movement analysis systems and investigate the reliability and validity of these systems against the gold standard 3D marker-based systems that are currently used in clinical practice.  
machine learning; deep learning; clinical gait analysis; arm swing; posture; trunk; DeepLabCut +
Footnote †: journal: Applied sciences
## 3 Act
### Energy  
Transmission of data is one of the most energy expensive tasks that a node undertakes and using data compression to reduce the number of bits sent reduces energy for transmission. However, compression requires the computation, which also consumes the energy. Trading the computation for transmission can save the energy since typically on the order of 3,000 instructions can be executed for the energy cost required to transmit one bit over a distance of 100 m by radio [44]. Moreover, usually sensor nodes are deployed in the large area and thus packets are relayed through multiple sensor nodes. Therefore, the reduced amount of data with compression can reduce the energy consumption on the compressing node and the relaying nodes.  
Figure 13 presents the relation between the compression and the energy consumption. The amount of data generated by the compressor is in inverse proportion to the compression ratio [Figure 13-(A)] and the amount of energy consumed by the compressor is also in inverse proportion to the compression ratio [Figure 13-(B)]. In the ACT, workloads of DWT and ADPCM are constant regardless of the data, however the workload of RLC is in inverse proportion to the difference among data. Therefore, there is the minimum level of the consumed energy in the ACT. The amount of energy consumed by the packet transmission is in proportion to the amount of data transferring [Figure 13-(C)] and to the hop count  
Figure 12: Priority and quantization.  
which a packet is relayed [Figure 13-(D)]. As a consequence, ACT can reduce the total consumed energy by reducing the amount of data to send [Figure 13-(E)].
## Appendix B Parameter Estimation for Distribution Functions  
To accurately simulate patient characteristics using various statistical distributions, the model calculates distribution-specific parameters--such as shape (\(\alpha\)) and scale (\(\lambda\))--using the user-provided mean (\(\mu\)) and standard deviation (\(\sigma\)). The formulas vary by distribution type as follows:  
1. Gamma Distribution * Shape (\(\alpha\)): \[\alpha=\frac{\mu^{2}}{\sigma^{2}}\] * Scale (\(\lambda\)): \[\lambda=\frac{\sigma^{2}}{\mu}\]
2. Beta Distribution * Shape \(\alpha\): \[\alpha=\mu\bigg{(}\bigg{(}\frac{\mu(1-\mu)}{\sigma^{2}}\bigg{)}-1\bigg{)}\] * Shape \(\beta\): \[\beta=\bigg{(}\frac{\mu(1-\mu)}{\sigma^{2}}-1\bigg{)}-\alpha\]  
Note: Only \(\alpha\) is derived in the original function, with \(\beta\) computed via \(\beta=\alpha\Big{(}\frac{1-\mu}{\mu}\Big{)}\) if needed.  
3. Log-Normal Distribution * Mean of log (\(\mu\)_log): \[\mu_{\text{log}}=\ln\Bigg{(}\frac{\mu^{2}}{\sqrt{\mu^{2}+\sigma^{2}}}\Bigg{)}\]\(\bullet\) Standard deviation of log (\(\sigma\_\)log): \[\sigma_{\text{log}}=\sqrt{\text{ln}\bigg{(}1+\frac{\sigma^{2}}{\mu^{2}}\bigg{)}}\]
4. Normal Distribution \(\bullet\) Mean (\(\alpha\)): \[\alpha=\mu\] \(\bullet\) Standard Deviation (\(\lambda\)): \[\lambda=\sigma\]  
These equations ensure the accurate parameterization of distribution functions, facilitating valid stochastic sampling, particularly for the inverse cumulative distribution (quantile) functions used in simulations and probabilistic modeling  
Appendix C Constructing and Resolving the Dependency Matrix Importance of Parameter Resolution Order  
In this section, we provide a detailed description of constructing and resolving the dependency matrix--including algorithmic steps, examples, and the handling of circular dependencies. In the cohort-generation model, the sequence in which parameters are computed is essential for logical consistency and clinical plausibility. This sequence is especially critical when the model employs user-defined hard limits or linked equations, creating explicit dependencies among parameters, for instance, the following:  
* Menopause cannot be resolved before knowing both age and sex.
* Hypertension diagnosis depends upon previously computed values, such as systolic blood pressure (SBP).
# FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses
## 5 Discussion  
Different individual CNN models have different performances for image classification and identification. In this study, by combining the results of these models, we developed a better system with higher accuracy to detect rosacea. A comparison between the results of our proposed system and the results produced by other studies is shown in Table 4. Our proposed system shows better performance (92.27%) than that of the study with the highest accuracy (90.2%, Ros-Net).  
For rosacea identification and classification, we utilized clinical photos taken by a digital camera covering the whole face. However, a single photo might not cover the proportion of the skin lesion, thereby hardly representing or even distorting the features of rosacea. To overcome such limitations, we took photos from at least three different angles (i.e., multi-view learning), which contain the whole lesion of rosacea. Moreover, clinical photos were segmented and augmented to integrate different-scaled images to provide both microscopic and macroscopic characteristics of rosacea to improve the performance of FACES.  
Figure 8: Parametric analysis in different functions with respect to the evaluation factor (\(\alpha\)) of the FACES. (**a**) Effects of \(\alpha\) on accuracy values with different degrees of polynomial order (n). (**b**) Average accuracy values of each function: linear (n = 1), quadratic (n = 2), cubic (n = 3), and biquadratic (n = 4) functions.  
Figure 9: Comparison of performance of FACES with that of individual CNNs and the majority rule in the testing phase.  
There are still limitations to be addressed and factors to be explored in the future to improve FACES's ability to detect rosacea. First, we did not compare the performance between FACES and experienced dermatologists to validate our method. Hence, comparison tests are required to warrant using our detection tool in clinical settings. In addition, we did not consider several important parameters, such as race, age, or gender, which can be highly associated with the occurrence of rosacea, thus, affecting FACES's classification abilities. Early studies demonstrated that rosacea is an age-related disease that occurs more frequently at an older age, particularly above the age of 65 years [40; 41]. Moreover, women under the age of 49 were found to be more affected by rosacea, while rosacea was more prevalent in men over 50 [40]. Rosacea's prevalence is also highly dependent on race. Prior research illustrated that Hispanics and Latinos are more susceptible to rosacea compared to African Americans or Asians [42]. Taken together, these limitations, as well as factors such as age, gender, or race, should be considered in future studies to enhance the performance of artificial intelligence. Such an approach will shed light on new effective diagnoses and rosacea treatment in clinical practice.