esa-sceva/satcom-chunk-collection huggingface dataset:
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 3 Methods
### Node Layout
#### 3.2.1 Spring Force Constraint  
Spring force acts on nodes connected by one edge. The difference from the spring force of attribute community layout is that the ideal distance \(L_{ij}\) between the two nodes \(i\) and \(j\).  
When the attributes of two nodes are the same, the two nodes belong to the same attribute community. Therefore, the formula for calculating \(L_{ij}\) is as follows:  
\[L_{ij}=L_{max}\cdot\sqrt{\frac{n_{c_{p}}}{n}}-r(\sqrt{\frac{deg_{i}}{deg_{max} }}+\sqrt{\frac{deg_{j}}{deg_{max}}}) \tag{33}\]  
where \(r\) is the default radius of the node, \(deg_{i}\) is the degree of the node \(i\), \(deg_{max}\) is the maximum degree of all nodes.  
When the attributes of two nodes are different, the two nodes belong to different attribute communities \(p\) and \(q\). Therefore, the formula for calculating \(L_{ij}\) is as follows:  
\[L_{ij}=\sqrt{\left(x_{c_{p}}-x_{c_{q}}\right)^{2}+\left(y_{c_{p}}-y_{c_{q}} \right)^{2}} \tag{34}\]
# Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications
## 4 Applications  
There are numerous potential applications for PDMS-based flexible resistive strain sensors such as virtual reality and entertainment technology, human-machine interfaces, personal healthcare, and sports performance monitoring, etc. These kinds of strain sensors can be attached to clothing or directly laminated on the human skin because PDMS flexible substrates are both stretchable and biocompatible. An avatar in the computer virtual environment was successfully controlled by using smart gloves integrated with AgNWs/PDMS sandwich-structured strain sensors. Each sensor was fixedly attached to the back of each finger with the fingers bending, leading to the bending of the avatar finger (Figure 4a) [68]. Meanwhile, an intelligent glove was fabricated with nine carbon particle/PDMS sensors and wearable electronic module; the nine-digit binary keys were based on the bending/unbending states of the nine sensors. These keys were associated with 26 letters of the American Sign Language (ASL) alphabet. The corresponding gestures of the glove successfully translated all letters into text displayable on a computer screen (Figure 4b) [81]. Human-machine interface is another potential application. The signals from PDMS-based flexible resistive strain sensors can be utilized to detect or actuate smart robots. The graphene/PDMS-based sensors were directly fixed across the movable joints of a robot to detect elbow rotation, waist side-bending, and stepping (Figure 4c) [82]; those strain sensors successfully accomplished the real-time movement detection during the robot dancing \"Gangnam Style\". As demonstrated in Figure 4d, the carbon paper (CP) /PDMS strain sensor was employed to control the movement of a mobile robotic platform. The forward and backward motion of the moving robot depends on whether the CP/PDMS strain sensor is being stretched or relaxed [83]. The CP/PDMS strain sensor shows excellent stability when used in a man-robot interaction scenario. Personal healthcare monitoring is promising to deal with poor medical services. A wearable strain sensor has been assembled by graphene woven fabrics (GWFs)
## 3 The Proposed Method
### Student Ability Extraction Module
#### 3.2.4 Prediction Module  
After the information difficulty extraction module, we obtained the skill difficulty representation vector \(d_{s}\in R^{d}\) and the question difficulty representation vector \(d_{q}\in R^{d}\). After the student ability extraction module, we obtained the knowledge state vector \(K\in R^{2d}\) and the question-solving ability vector \(L\in R^{2d}\). Finally, by concatenating the above vectors with \(q_{t}\) (current predicted question embedding vector), we obtained the embedding vector \(\mathrm{Z}\in R^{2d}\):  
\[\mathrm{Z}\ =\left(d_{s}+d_{q}\right)\oplus(K+L)\oplus q_{t} \tag{27}\]  
Then, the vector \(\mathrm{Z}\) was input into the multi-layer perceptron (MLP) to predict the questions to be answered by the current student.  
\[layer_{t}^{l}=\mathrm{ReLU}\left(\mathbf{W}^{l}layer_{t}^{l-1}+\mathbf{b}^{l}\right) \tag{28}\]  
\[y_{t}=\mathrm{Sigmoid}\left(\mathbf{W}^{l}layer_{t}^{l-1}+\mathbf{b}^{l}\right) \tag{29}\]  
where \(l\) denotes the layer of the multilayer perceptron, \(layer_{t}^{0}=\mathrm{Z}\), and \(\mathbf{W}^{l}\) and \(\mathbf{b}^{l}\) denote the weight matrix and bias vector of the \(\mathrm{l}\mathrm{l}\mathrm{l}\mathrm{l}\mathrm{l}\mathrm{y}\), respectively. The error between the predicted value \(y_{t}\) and the true value \(\widetilde{y_{t}}\) was calculated using a binary cross-entropy loss function, and the model parameters were optimized with the Adam optimizer. The loss values are calculated as follows:  
\[L=-\sum_{i=1}^{t}(\widetilde{y_{i}}\log y_{i}+(1-\widetilde{y_{i}}))\log(1-y_ {i})) \tag{30}\]
## 3 Results and Discussion
### Calculated Core-Level Binding Energies, XP Spectra, and Work Function Changes
#### 3.3.3 Coverage Dependence
This is illustrated in Figure 8, where we show the change in electrostatic  
Figure 6: Core level shift, \(\Delta\mathrm{E_{Cls}}\), (circles) and work function change \(\Delta\Phi\) (square symbols) for systems I, III, IV, and VI as a function of coverage (varied between full, a quarter, and 1/16 coverage, where the latter approaches the single molecule limit, see Supplementary Materials). All energies are reported relative to the values of the non-interacting reference system VIa. energy due to the formation of the bond between the pyridine-docked SAM and the metal substrate. Consequently, Figure 8 does not actually reflect the impact of the entire interface dipole, but only of the part resulting from the bond dipole. This quantity can, however, be calculated conveniently by subtracting the electrostatic energies of the subsystems from that of the bonded interface, which is not possible for the full interface dipole. It is also well suited to qualitatively illustrate the difference between the local and the far-field situation, even if one cannot expect quantitative agreement with the data from Figures 6 and 7, which are still determined by the entire interface dipole. For the full coverage system in the bottom panel of Figure 8, the main drop in electrostatic energy occurs at the immediate metal-pyridine interface and the electrostatic energy in the region of the top ring is essentially the same as far above the sample (despite a minor potential variation at the distance of the topmost C-H bond). Conversely, at 1/16 coverage the far-field electrostatic energy far above the interface (relevant for the work function) is several tenths of an eV higher than in the region of the upper carbon atoms (determining the position of the XPS peak). Figure 7: Core level shift, \(\Delta\)E\({}_{\text{Cls}}\), as a function of the work function change, \(\Delta\Phi\), for system I (**blue**), system II (**red**), system III (**orange**), system IV (**green**), and system VI (**violet**). Results for full, 1/4, and 1/16 coverage are compared. The black lines are fits to all systems for specific coverages, while the colored lines were fitted to the coverage-dependence for a specific system.
## 4 Data Quality Control in TasMAN Using Fuzzy Rules
### Using Fuzzy Assessments for Generating Errors Bars  
The defuzzification process used to provide an overall estimate of data quality assigned uncertainties to the green, yellow and red components of the overall membership function. By combining these uncertainties, an overall quantitative assessment of uncertainty for each measurement was generated via Equation (3):  
\[\begin{split}\text{Error}=\Sigma\;\alpha_{l}\times\text{Overall} _{i}\\ i=\text{small, medium, large}\end{split} \tag{3}\]  
\(\alpha_{\text{small}}\) was assigned a value equal to the sensor accuracy quoted by the manufacturer; the other coefficients were multiples of \(\alpha_{\text{small}}\) that were chosen empirically by trial and error using early 2008 data as a training set. Table 3 lists the values of \(\alpha\) obtained.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Minimum value accepted** & **Maximum value accepted** \\ \hline Temperature (\({}^{\circ}\)C) & 5 & 25 \\ Conductivity (\(\mu\)S/cm) & 25,000 & 60,000 \\ \hline \end{tabular}
\end{table}
Table 2: Thresholds applied to physical parameters of temperature and conductivity.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 5 Measurements and Results
Compared to Ref. [38], this work shows higher relative sensitivity and provides 2.8% measurement accuracy. From the comparison, it is seen that the proposed design is suitable for measurement of high-permittivity and high loss liquid samples. However, it has to be mentioned that for low-loss liquid samples, the accuracy is yet to be verified. In addition, since the sample is contained in a glass tube, samples are not in direct contact with sensors, which will decrease \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & \begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} & **RS** & **Accuracy** & \
\begin{tabular}{c} **Complex** \\ **Permittivity** \\ \end{tabular} &
\begin{tabular}{c} **Corrosive** \\ **Sample** \\ \end{tabular} \\ \hline
[24] & 2.620–3.816 & 0.348 & / & no & yes \\ \hline
[25] & 0.86–0.91 & 0.069 & 11.0\% & no & no \\ \hline
[26] & 3.180 & 0.488 & / & yes & no \\ \hline
[27] & 2.189 & 0.98 & / & yes & no \\ \hline
[28] & 1.200–2.335 & 0.623–0.879 & 4\% & yes & no \\ \hline
[29] & 2.30–2.35 & 0.024 & / & yes & yes \\ \hline This work & 2.850–2.960 & 0.041 & 2.8\% & yes & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various liquid microwave microfluidic sensors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Ref. [38]**} & \multicolumn{2}{c}{**This Work**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-7}  & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime}}\) & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime\prime}}\) & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime}}\) & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime\prime}}\) & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime}}\) & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime\prime}}\) & \(\mathbf{\varepsilon_{\mathrm{r}}^{\prime\prime}}\) \\ \hline Water & 79.0 & 10.9 &the sensitivity for low-loss samples. However, for high-loss samples, this is a good method. With this connection, there are many aspects for further study, such as the development of a sensor for the measurement of both high-loss and low-loss samples. Other topics may include the design of a sensor for very small volume samples.
## 2 Fundamentals
### Dynamic Response Statistical Moment (DRSM)  
If an excitation force subject to a stationary Gaussian random process distribution is applied to a linear structural system, then the output response \(x\) of the system will be subject to a stationary Gaussian random process distribution [15]. To this end, the \(n\)th-order statistical moment of the dynamic response \(x\) can be expressed as:  
\[DRSM_{n}=\frac{1}{\sigma\sqrt{2\pi}}\int_{-\infty}^{+\infty}\left(x-\mu\right) ^{u}e^{-\frac{\left(x-\mu\right)^{2}}{2\
u^{2}}}dx \tag{5}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of the response \(x\), respectively. From Formula (5), it is easy to verify that when \(n\) is odd, \(DRSM_{n}=0\) and when \(n\) is even, \(DRSM_{n}=1\cdot 3\cdot 5\cdot\ldots\left(n-1\right)\sigma^{n}\).  
The above formula works under the condition that the excitation force on the structure obeys a stationary Gaussian random process distribution. Under general excitation, the expression of \(DRSM_{n}\) in the time domain can be expressed as the following formula:  
\[DRSM_{n}=\frac{1}{N}\sum_{r=1}^{N}\left(x_{r}-\mu\right)^{n} \tag{6}\]  
where \(N\) is the number of the sampling points.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 5 Results
### Number of Transmissions According to Selection Combining  
To form conclusions about which could be the best order modulation and power transmission to use with selection combining, we analyze, based on a Bernoulli distribution, the probability to receive a BER lower than \(10^{-4}\) after several retransmissions. Analyzing the results in Figure 12. Number of transmissions according to selection combining, we can see that making use of a 4 PSK with a power transmission of 12 W, 25 W, and 50 W, we have probabilities of 99% to achieve a BER lower than \(10^{-4}\) if we retransmit the packet. In the case of using a 4 PSK with a power transmission of 6 W or an 8 PSK with a power transmission of 50 W, we have nearly the same results. To achieve a 99% probability to obtain a BER lower than \(10^{-4}\), it would be necessary to have 4 transmissions. With two transmissions, we can obtain probabilities of 91% to achieve a BER lower than \(10^{-4}\), and with three transmissions, probabilities of 97% can be obtained to achieve a BER lower than \(10^{-4}\).
## 2 Materials and Methods
### FerryBox Installation and Other Oceanographic Parameters Measured  
The water intake of the FerryBox was realized in the moonpool of the research vessel (in approx. 4 m depth) close to the measuring head of the submersed 'Kallemeter' to ensure a comparability of the measurements between the three types of instruments. Besides the FDOM data, readings of standard oceanographic parameters, such as temperature, salinity, and chlorophyll \(a\) fluorescence were also obtained. All FerryBox data were averaged on minutely values and smoothed by a moving mean filter (window size 11).
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 2 Related Work
### Domain Adaptation in Remote Sensing Applications
Multiple classifications [43; 48; 50] of TTA methods exist but the types of interest here are _white-box_ (where model parameters are accessible for adaptation) and _online_ (where unlabeled target data are ingested in a stream and processed once). Examples of white-box online TTA include:  
* Test-time Entropy Minimization (Tent) [69]: This method adapts a probabilistic and differentiable model by minimising the Shannon entropy of its predictions. For each batch normalisation (BN) [70] layer in a DNN, Tent updates (i) the normalisation statistics \(\mu,\sigma\) in the forward pass, and (ii) the affine transformation parameters \(\gamma,\beta\) in the backward pass. See Section 6.2.2 for more details. * Dynamic Unsupervised Adaptation (DUA) [71]: This method modulates the \"momentum\" of BN layers with a decay parameter, which helps stabilise the adaptation process. See Section 6.2.1 for more details. DUA shows similar adaptation performance to Tent [71]. There is a lack of reported works on applying TTA to EO applications; this shortage accentuates the novelty of our work. Table 2 includes two sample applications of TTA to multi/hyperspectral image classification, but of the TTA methods used, (i) contrastive prototype generation and adaptation (CGPA) entails offline optimisation over multiple epochs, and (ii) attention-guided prompt tuning requires a resource-intensive vision foundation model, unlike the efficient DUA-based and Tent-based online methods in Sections 6.2.1 and 6.2.2.
# Eccentric Wear Mechanism and Centralizer Layout Design in 3D Curved Wellbores
## 2 Materials and Methods
### Dynamics Model for SRS
#### 2.2.2 Geometric Description of 3D Curved Well Trajectory  
Figure 3 is a geometric relationship of the 3D curved well trajectory [24]. The geometric position of any point on the wellbore axis in three-dimensional space can be described by vector diameter. Deviation angle and azimuth angle are applied to describe the tangential direction vector of the well axis trajectory. The \(s\) is the vector diameter; \(r_{0}\) is the borehole  
Figure 2: 3D curved wells. (**a**) 3D buckling in vertical wells; (**b**) 3D buckling in directional wells.  
ace length; \(\alpha\) is the deviation angle; \(\varphi\) is the azimuth angle; and \(\tau_{o}\) is the unit vector in the tangential direction.  
The spatial geometric relations are formulated as follows:  
\[\mathbf{r}_{o}(s)=x_{o}(s)\mathbf{i}+y_{o}(s)\mathbf{j}+z_{o}(s)\mathbf{k}, \tag{1}\]  
\[\frac{dx_{o}}{ds}=\sin\alpha\cos\varphi, \tag{2}\]  
\[\frac{dy_{o}}{ds}=\sin\alpha\sin\varphi, \tag{3}\]  
\[\frac{dz_{o}}{ds}=\cos\alpha, \tag{4}\]  
\[\mathbf{\tau}_{o}=(\sin\alpha\cos\varphi)\mathbf{i}+(\sin\alpha\sin\varphi) \mathbf{j}+(\cos\alpha)\mathbf{k}, \tag{5}\]  
According to the basic principle of differential geometry, the geometric relationship between the curvature and torsion of the corresponding borehole trajectory curve, the unit vector in the tangential direction, the unit vector in the main normal direction, and the unit vector in the secondary normal direction of the borehole axis trajectory are as follows:  
\[k_{o}=\bigg{|}\frac{d\mathbf{\tau}_{o}}{ds}\bigg{|},\ T_{o}=\frac{1}{k_{o}^{2 }}\bigg{|}(\mathbf{\tau}_{o}\frac{d\mathbf{\tau}_{o}}{ds}\frac{d^{2}\mathbf{ \tau}_{o}}{ds^{2}})\bigg{|}. \tag{6}\]  
\(k_{o}\) is the curvature of the well trajectory curve; \(T_{o}\) is the torsion of the well trajectory curve; \(\mathbf{n}_{o}\) is the unit vector in the main normal direction; and \(\mathbf{b}_{o}\) is the unit vector in the secondary normal direction.  
Substituting Formula (5) into the above expression (6) and simplifying yields results in the following:  
\[k_{o}^{2}=\bigg{(}\frac{d\alpha}{ds}\bigg{)}^{2}+\sin^{2}\alpha\bigg{(}\frac{d \varphi}{ds}\bigg{)}^{2}, \tag{7}\]  
\[T_{o}=\frac{1}{k_{o}^{2}}\Bigg{\{}\bigg{(}\frac{d\alpha}{ds}\frac{d^{2}\varphi }{ds^{2}}-\frac{d\varphi}{ds}\frac{d^{2}\alpha}{ds^{2}}\bigg{)}\sin\alpha+ \bigg{[}2\frac{d\varphi}{ds}\bigg{(}\frac{d\alpha}{ds}\bigg{)}^{2}+\sin^{2} \alpha\bigg{(}\frac{d\varphi}{ds}\bigg{)}^{3}\bigg{]}\cos\alpha\Bigg{\}}, \tag{8}\]  
\[\mathbf{n}_{o}=\frac{1}{k_{o}}[\frac{d}{ds}(\sin\alpha\cos\varphi)\mathbf{i}+ \frac{d}{ds}(\sin\alpha\sin\varphi)\mathbf{j}+\frac{d\cos\alpha}{ds}\mathbf{ k}], \tag{9}\]  
\[\mathbf{b}_{o}=-\frac{1}{k_{o}}(\sin\varphi\frac{d\alpha}{ds}+\frac{\sin 2 \alpha}{2}\cdot\frac{d\sin\varphi}{ds})\mathbf{i}+\frac{1}{k_{o}}(\cos\varphi \frac{d\alpha}{ds}+\frac{\sin 2\alpha}{2}\cdot\frac{d\cos\varphi}{ds})\mathbf{j}+( \frac{\sin^{2}\alpha}{k_{o}}\cdot\frac{d\varphi}{ds})\mathbf{k}. \tag{10}\]  
Figure 3: Geometric relationship of the 3D curved well trajectory.
###### Abstract  
The study of the aerodynamics of a flight vehicle under Martian air conditions is experimentally difficult due to its characteristics such as low air density and temperature, making the vehicle operate at an ultra-low Reynolds number, which in turn introduces a complex flow field. In this paper, to find a proper computational fluid dynamics (CFD) method with which to better understand the aerodynamics of rotor-type aircraft under Martian air conditions, the aerodynamic performance of a rotor with rectangular blades under ultra-low density Martian air conditions is studied. A simulation scheme using a large eddy simulation solver and sliding meshing technology is established, and the method is verified based on experimental results from a Mars Air Simulator (MAS). In addition, the influence of the test bench and chamber is investigated through flow field analysis. The results show that the established method can predict lift in a very accurate manner, but that the torque prediction is not so promising. The study also determines that the fixture and the chamber wall of an MAS has little influence on the prediction of aerodynamic performance due to the quickly decreasing of flow speed and dissipation of vortexes. The test bench has about 5% influence on lift prediction, possibly due to the ground effect of the bench. In addition, simulation under actual Martian air conditions shows that the results agree well with the MAS experiment's results, indicating that the temperature difference has little influence on the lift performance, and therefore that the MAS is a good tool for the lift prediction of Martian helicopters.  
M +
Footnote †: journal: Applied  
_Acticle_  
Numerical Investigation on Aerodynamics of a Rectangular Blade Rotor under Mars Air Conditions Using Large Eddy Simulation
## 2 Materials and Methods
### Human Imaging Dataset Acquisition and Post-Processing
Ethical approval was obtained for use of retrospectively collected, anonymized data (IRB #MDACC-RR-03-800). This dataset was selected because it contains a moderately sized cohort of patients who had undergone scans which employed comparable (though not identical) acquisition parameters in terms of b-value and number of gradient directions. Diffusion-weighted images were acquired using single-shot echo-planar (EPI) spatial encoding pulse sequence with voxel size of 0.86 \(\times\) 0.86 \(\times\) 3.5 mm, 27 gradient directions plus b-zero, and b-value of 1200 s/mm\({}^{2}\). Diffusion-weighted and accompanying T1-weighted and/or T2-weighted sequences wereacquired using 3.0T GE Signa HDxt MRI scanners with 8-channel high resolution brain coil (GE Healthcare, Waukesha, WI, USA). Of 79 patients treated for glioblastoma between 2008 and 2013, \(n=66\) comprised the final analysis cohort after excluding those with insufficient preoperative imaging for the present analyses, infratentorial tumor locations, and/or intraventricular tumor extension. The same image postprocessing methods as described above were employed, except that for cases of multifocal disease, only the largest was segmented and used to generate tumor-intersecting tractography. Additionally, the number of projecting tract bundles was also quantified as an additional metric, again using DSI Studio software. In addition to QSI-derived quantitative features associated with observed tractography patterns, intra-tumoral duMRI metrics including quantitative anisotropy (QA), fractional anisotropy (FA), mean diffusivity (MD), and axial diffusivity (AD) were calculated within the segmented tumor ROI. For this retrospective anonymized dataset, molecular characterization of tumors including MGMT promotor methylation and IDH mutation status was not available, though age and basic treatment details are reported in a pooled fashion.