esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Characterization of the Capacitively Coupled RF-Plasma Thruster
## 2 Materials and Methods
### Diagnostics
#### 2.3.1 Emissive Probes
The floating potential is by definition the potential at current zero hence the value of the probe potential at zero current yields the value of the plasma potential. This can be easily measured by the SMU working in current source mode with a zero set value. The data in Figure 7a illustrate the potential difference of 6 volts between positions 70 mm and 190 mm away from thruster cut off. The parts of the VCC for the collecting mode (positive probe) should be independent from the heating power. This is confirmed by the curves in Figure 7a where the curves at different heating powers coincide in the range above plasma potential. In addition, the stability of the data is confirmed by the overlapping parts of the VCCs above the plasma potential. The measurements with two different emission currents were taken with one hour delay to each other. The good agreement of the curves justifies the stability of the RF discharge. For the estimation of the electron density the VCCs of the cold emissive probe, working in Langmuir probe regime, were compared to those of the hot probe for a limited amount of points, see example in Figure 7b. As was mentioned earlier the part of the emissive probe's VCC above the plasma potential should be equal to that of a cold Langmuir probe. Data in Figure 7b indicate that the hot probe delivers approx. Values of the electron saturation current 20-50% are higher than the cold (Langmuir) probe. Taking into account that the applicability of the Langmuir probe methods are limited in our case, only the estimation of the electron density can be obtained. The reasons are the presence of a magnetic field, a deviation of the probe form the cylindrical shape and space charge effects of the emitting probe. Nevertheless taking the hot probe's current at the voltage Vpl + 4 V we obtain an acceptable approximation for the electron saturation current I0 of a Langmuir probe. For a Langmuir probe the value of the electron current I0 at the plasma potential is proportional to the density of the electrons n. The latter can be calculated if the temperature of electrons Te and the surface area S of the probe are known:  
\[\text{n}=\text{I}_{0}/(\text{evS}), \tag{1}\]  
where v = sqrt(8 eTe/(m_e70)) is the thermal electron velocity, e is elementary charge and me is electron mass. For the estimation of electron density, the temperature of the electrons remains the only unknown parameter. Its influence can be checked by comparing the values of electron density calculated with Te values of 2 and 10 eV (which is the maximal possible range of expected electron temperatures in our case). Using the values of I0 at 70 mm from the front of engine in Figure 7b, results in an electron density estimation between 1.1 \(\times\) 1014 and 2.5 \(\times\) 1014 m\({}^{-3}\) for the values of the electron temperatures of 10 eV and 2 eV respectively. Given the limited applicability of Langmuir probe technique an average value of 1.8 \(\pm\) 0.7\(\cdot\)1014 m\({}^{-3}\) can be adopted.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Bus Faults
#### 3.1.3. ROCOC Algorithm  
This algorithm calculates the derivative of the current with respect to the sampling time, as described in Section 2.3. Then, the calculated value is compared to a pre-selected threshold. For this study, the selected threshold value for the ROCOC algorithm is 100 kA/s, in absolute values. The threshold of 100 kA/s is chosen to ensure that rapid fault events are detected without being affected by transient phenomena. This value is determined by considering the typical rate of change of current during fault events, particularly for high-resistance faults. It is also based on the system's expected maximum current levels, which are influenced by factors such as the grid's voltage level and the impedance characteristics of the fault location. The chosen threshold is sensitive enough to detect faults quickly while preventing unnecessary tripping due to non-fault conditions or transient events that could trigger false positives.  
In this case, as in Figure 4, the rate of change of the current is negative due to the change of polarity of the current when the fault occurs.  
Figure 5a shows the pole-to-pole fault scenarios, while Figure 5b shows the pole-to-ground fault scenarios. The algorithm presents fast operation in fault detection, as depicted in Table 3. For 0.01 \(\Omega\) and 2 \(\Omega\) faults, the detection time remains constant, 0.01 ms. When the fault resistance is 20 \(\Omega\), the detection time increases twice for pole-to-pole faults, and three times for pole-to-ground faults, as can be observed in Table 3. The algorithm shows a very good response to high-resistance faults.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Fault at Bus 2**} & \multicolumn{2}{c}{**Detection Time at R21 (ms)**} & \multicolumn{2}{c}{**Detection Time at R24 (ms)**} \\ \cline{2-6}  & **P2P** & **P2G+** & **P2P** & **P2G+** \\ \hline \multirow{3}{*}{UV} & R = 0.01 \(\Omega\) & 0.010 & 0.010 & 0.010 & 0.010 \\ \cline{2-6}  & R = 2 \(\Omega\) & 0.015 & 0.015 & 0.015 & 0.015 \\ \cline{1-1} \cline{2-6}  & R = 20 \(\Omega\) & 0.065 & 0.140 & 0.065 & 0.140 \\ \hline \end{tabular}
\end{table}
Table 2: Fault at bus 2, detection times at R21 and R24, for the UV algorithm.  
Figure 5: Fault at bus 2, ROCOC at R21 and R24: (**a**) pole-to-pole faults, and (**b**) pole-to-ground faults.
###### Abstract  
The automatic localization of audio sources distributed symmetrically with respect to coronal or transverse planes using binaural signals still poses a challenging task, due to the front-back and up-down confusion effects. This paper demonstrates that the convolutional neural network (CNN) can be used to automatically localize music ensembles panned to the front, back, up, or down positions. The network was developed using the repository of the binaural excerpts obtained by the convolution of multi-track music recordings with the selected sets of head-related transfer functions (HRTFs). They were generated in such a way that a music ensemble (of circular shape in terms of its boundaries) was positioned in one of the following four locations with respect to the listener: front, back, up, and down. According to the obtained results, CNN identified the location of the ensembles with the average accuracy levels of 90.7% and 71.4% when tested under the HRTF-dependent and HRTF-independent conditions, respectively. For HRTF-dependent tests, the accuracy decreased monotonically with the increase in the ensemble size. A modified image occlusion sensitivity technique revealed selected frequency bands as being particularly important in terms of the localization process. These frequency bands are largely in accordance with the psychoacoustical literature.  
spatial audio scene characterization; spatial audio information retrieval; convolutional neural networks; deep learning +
Footnote †: journal: Medical Imaging  
1
# Automatic Medical Image Segmentation with Vision Transformer
## 3 Method
### Infection Segmentation Network
#### 3.2.5 Loss Function  
We employed a weighted intersection over union (IoU) loss and a symmetric binary cross-entropy (BCE) loss for supervision during training. The weighted IoU loss allows  
Figure 3: Utilizing reverse attention module to learning edge features implicitly.  
higher importance to be assigned to certain classes or regions, helping the model prioritize clinically significant areas and improving segmentation accuracy in critical regions. It addresses class imbalance and differences in contextual importance. The asymmetric BCE loss allows customization based on clinical implications. It mitigates class imbalance issues and makes the model more robust to misclassifications. The weighted IoU guides the focus on salient areas, while the asymmetric BCE improves robustness. This makes the model more effective at delineating anomalies from normal tissues.  
Together, these losses align the model with clinical objectives by emphasizing critical regions, balancing class importance, and penalizing asymmetric errors tailored to medical imaging challenges. By combining losses tailored for medical images, the model becomes better equipped to handle complexities like class imbalance while improving performance in clinically valuable regions.  
As Equation (4) mentioned, we take the edge loss function \(\mathcal{L}_{edge}\) for supervision. The full loss function \(\mathcal{L}_{edge}\) comprises a weighted intersection over union loss(IoU) \(\mathcal{L}_{IoU}^{w}\), combined with a weighted BCE loss \(\mathcal{L}_{BCE}^{w}\) for segmentation supervision,  
\[\mathcal{L}_{seg}=\mathcal{L}_{IoU}^{w}+\mathcal{L}_{BCE}^{w} \tag{7}\]  
The proposed loss function consists of a weighted IoU and an asymmetric BCE loss. The global and local losses provide effective supervision at the image and pixel levels, respectively, for more accurate segmentation. Different from the standard IoU loss, the weighted IoU loss emphasizes hard pixels to highlight their importance, while the asymmetric BCE assigns greater weight to challenging mislabeled hard pixels, instead of treating all pixels equally. The formulation of these losses draws from successful applications in other domains like salient object detection [55,56]. Correntropy-induced losses [57,58] could also be explored here for improved robustness.  
Additionally, we adopt the global map \(S_{g}\) as well as three side-outputs (i.e., \(S_{3}\), \(S_{4}\), and \(S_{5}\)) with deep supervision by comparing their up-sampled versions (\(\mathcal{L}_{3}^{up}\)) to the ground truth segmentation mask during training. This provides direct optimization guidance at multiple encoder stages. The total loss is:  
\[\mathcal{L}_{total}=\mathcal{L}_{seg}\left(G_{s},S_{g}^{up}\right)+\mathcal{L}_ {edge}+\sum_{i=3}^{i=5}\mathcal{L}_{seg}\left(G_{s},S_{i}^{up}\right) \tag{8}\]  
where Gs is the labeled mask, and Ledge denotes cross-entropy loss for edge prediction. This benefits gradient flow across features and prevents overfitting.
## 2 Survey of Commercial Use of Radioactive Isotopes
### \({}^{90}\)Y  
Yttrium-90 (T\({}_{1/2}\) = 64.1 h) is a \(\beta\)-emitter with the maximum emission energy of 2.282 MeV, which makes it very attractive for therapeutic applications, especially for treatment of liver cancer [4]. This isotope is produced after the decay of \({}^{90}\)Sr fission product in fission reactors and alternatively through neutron irradiation of natural yttrium, through the \({}^{89}\)Y(n,\(\gamma\))\({}^{90}\)Y reaction [6], for 1 week. The latter option does not require additional purification of the \({}^{90}\)Y from \({}^{90}\)Sr traces. For human use, the reference specific activity of 1 Ci/g (3.7 \(\times\) 10\({}^{10}\) Bq/g) is applied in medical treatments [26]. The typical dose of \({}^{90}\)Y administrated to a patient is 20-30 mCi with a circulation of the substance in blood [27]. The maximum admitted dose for the implants is 3 GBq (\(\sim\)80 mCi) for one medical treatment. These data can be used to roughly assess the \({}^{90}\)Y radioisotope price: the medical implants for treatment cost USD 11,000-12,000 [28]. This includes \(\sim\)5-10% expenses for the isotope, that is, USD 550-1200/80 mCi or USD 186,500-405,400/TBq (USD 6900-15,000/Ci).
## 3 Research on Alkali-Activated Grout Materials
### Study on the Setting Time of Alkali-Activated Composite Grouting Material  
#### 3.3.1 Alkali-Activated Composite Grouting Material Setting Test Plan  
This section examines the setting time of alkali-activated composite-stabilized soil. Using single-factor control experiments, variables such as the soil-to-binder ratio, slaked lime content, and water-to-solid ratio were set, with the optimal strength ratio obtained from orthogonal experiments serving as the control group. The soil-to-binder ratios were set at 8.2, 7:3, 6:4, and 5:5; slaked lime contents at 0.0%, 1.5%, 3.0%, and 4.5%; and water-to-solid ratios at 0.26, 0.28, 3.0, and 3.2. The aim was to explore the influence of these variables on the setting time of alkali-activated composite-stabilized soil. The specific experimental scheme is shown in Table 10.  
Currently, there are no specific standards for the setting time of alkali-activated composite-stabilized soil. However, alkali-activated composite-stabilized soil is similar to cement mortar, with the stabilizer playing a role similar to that of cement. The in situ soil in Shenzhen contains a higher sand content, akin to sand in mortar. Therefore, the setting time can be tested by referring to the method for testing the setting time of mortar mixtures as outlined in the \"Standard for Test Methods of Basic Properties of Building Mortar\" JGJ/T70-2009 [45]. According to the \"Construction Plan for Karst Treatment in the Low-White Section of Work Area 2 of the Phase IV Project of Shenzhen Urban Rail Transit Line 3\", it is important to consider that some cavities may have fractures or channels connecting them to other cavities. The grouting material is prepared at the mixing station and transported to the construction site within 15 min. Therefore, for the application of alkali-activated composite grouting material, its setting time should be no less than 30 min and no more than 240 min. The setting time test for alkali-activated gro  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Number** & **Soil-to-Binder Ratio** & **Slaked Lime Content** & **Water-to-Solid Ratio** \\ \hline E-1 & 8:2 & 4.5\% & 0.26 \\ E-2 & 7:3 & 4.5\% & 0.26 \\ E-3 & 6:4 & 4.5\% & 0.26 \\ E-4 & 5:5 & 4.5\% & 0.26 \\ E-5 & 5:5 & 3.0\% & 0.26 \\ E-6 & 5:5 & 1.5\% & 0.26 \\ E-7 & 5:5 & 0.0\% & 0.26 \\ E-8 & 5:5 & 4.5\% & 0.28 \\ E-9 & 5:5 & 4.5\% & 3.0 \\ E-10 & 5:5 & 4.5\% & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Alkali-activated composite grouting material setting-time test.  
Figure 10: Alkali-activated composite grouting material setting-time test. (Pictured instrument: mortar setting time tester).
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 3 Single-Pixel Image Reconstruction  
The Single-Pixel Imaging (SPI) technique [21,22], reconstructs images by measuring correlated intensity on a detector that lacks spatial resolution. SPI cameras employ spatial light modulators (SLMs), such as Digital Micro-Mirror Devices (DMD), to generate spatially structured light patterns (resembling Hadamard patterns) that interrogate a scene. These cameras function according to two main architectures: structured detection and structured illumination, as illustrated in Figure 1.  
In structured detection, an object is illuminated by a light source, and the reflected light is subsequently modulated by a Spatial Light Modulator (SLM) before being measured by a single-pixel (bucket) detector. In contrast, in structured illumination, represented by the modulation pattern \(\Phi\), the SLM is used to spatially modulate incident light before it illuminates the object \(O\). The light reflected from the object is captured by the bucket detector and subsequently converted into an electrical signal \(y_{k}\), as defined in Equation (1).  
\[y_{k}=\alpha\sum_{i=1}^{M}\sum_{j=1}^{N}O(i,j)\Phi(i,j) \tag{1}\]Here, \(\alpha\) represents a constant factor influenced by the optoelectronic characteristics of the photodetector. The electrical signal generated by the photodetector is derived from the correlation between the spatial pattern of the light and the light reflected from the object. The corresponding sequence of electrical signals is produced by projecting a series of these spatial patterns. These signals are then computationally processed to reconstruct the image. Specifically, the image \(x_{k}\) is reconstructed from the captured signal \(y_{k}\) and the corresponding pattern \(\Phi\), as described in Equation (2) [22]  
\[x_{k}=\alpha\sum_{i=1}^{M}\sum_{j=1}^{N}y_{k}\Phi(i,j) \tag{2}\]  
In this study, we used an array of 32 \(\times\) 32 near-infrared LEDs (NIR-LEDs) that emit at a peak wavelength of 1550 nm for generating Hadamard-like patterns \(\Phi\) through active illumination. This wavelength was chosen to minimize the effect of water scattering and absorption coefficients. The LED array is positioned perpendicular to the lens's focal plane, allowing the light patterns to be projected toward infinity. However, due to the dimensions of the matrix, the effective projection range is limited to 0.3-2 m.
# Moment-Based Stochastic Analysis of a Bistable Energy Harvester with Matching Network
## 5 Linear System Analysis
The output voltage shows a maximum for \(L_{S}^{(\mathrm{opt})}=22.91\) H, and \(C_{P}^{(\mathrm{opt})}=8.53\) nF, with \(v_{o(\mathrm{rms})}^{\mathrm{max}}=20.59\) V. For comparison, the energy harvester with resistive load offers an output voltage \(v_{o(\mathrm{rms})}^{\mathrm{max}}=4.43\) V. The average output voltage, average output power and power efficiency for the two setups are summarized in Table 2.  
The high value for the inductance in the matching network is a numerical artifact, a consequence of the relatively high value assumed for the inertial mass \(m=1\) g. The inductance could be reduced considering a lighter inertial mass and of course also reducing the harvested power. An alternative solution could be the use of an active element (an inductance emulator) in the matching network that can produce high inductance values but would require an external power source. Notice that a significant increment of the harvested power can be obtained even using a sub-optimal value of the inductance. Finally, it is well known that piezoelectric transducers require high inductance values for shunting and matching. The realization of large inductances is an important research topic, and very promising results have recently been obtained, see, for example, [46]. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(m\) & \(1\) g \\ \(\gamma\) & \(0.012\) Ns/m \\ \(k\) & \(5.4046\cdot 10^{3}\) N/m \\ \(C_{\mathrm{pz}}\) & \(80\) nF \\ \(R_{L}\) & \(1\) M\(\Omega\) \\ \(\alpha\) & \(0.0042\) N/V (As/m) \\ \(\varepsilon\) & \(10^{-3}\) (dimensionless) \\ \hline \end{tabular}
\end{table} 
Table 1: Values of the energy harvester parameters.
## 2 Problem Description  
As shown in Figure 1, PCM fills the gap between the horizontal fins. Paraffin wax (Table 1) is considered as PCM. The heat flux enters through the lower surface. Due to the expanding the melted PCM, a space (air) above the heat sink is provided as shown in Figure 1.  
The density of PCM in the solid state is constant while in the liquid state, it is changed by \(\rho=\frac{760}{\beta(T_{\text{J}}-30)+1}\). Note that T\({}_{\text{I}}\) is the liquidus temperature (Table 1). Fins are made of aluminum alloy 6061 with constant thermophysical properties of \(c_{p}=875\) J kg\({}^{-1}\) K\({}^{-1}\) and \(k=177\) W m\({}^{-1}\) K\({}^{-1}\).  
The heat sink area \((A=WH)\) is considered to be constant (problem constraint). Input parameters are defined as the aspect ratio \(\left(AR=\frac{W}{H}\right)\), length ratio \(\left(LR=\frac{L_{f}}{W/2}\right)\) and fins number \((n)\). Note that the fin thickness was considered to be constant (\(H_{b}=0.25\) mm). The objective function was defined to maximize safe operation time (SOT). The applied heat flux changes so that the input energy per the total volume of heat sink remains constant. In other words, the value of \(\frac{q^{\prime\prime}W}{WH}\) is constant for all PCM filled heat sinks. The initial temperature is considered to be the same as the ambient temperature (27 \({}^{\circ}\)C). Following Table 1, at 27 \({}^{\circ}\)C, PCM is in the solid state. All walls (except the lower wall) are considered adiabatic. Under the worst conditions, all energy enter to the heat sink must be absorbed by the PCM. Note that the boundary condition at the lower surface is \(q^{\prime\prime}=-k\frac{QT}{g\psi}\). In studies involving PCM, the volume fraction of PCM \((\varphi_{PCM})\) indicates what fraction of the heatsink is occupied by PCM:  
\[\varphi_{PCM}=\frac{PCM\ volume}{PCM\ volume+Fin\ volume}=\frac{\left[HW-2\ H _{b}H-(W-2H_{b})H_{b}-2 nL_{f}H_{b}\right.}{HW} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \(\mathbf{T}_{\text{I}}\)/\({}^{\circ}\mathbf{C}\) & \(\mathbf{T}_{\text{S}}\)/\({}^{\circ}\mathbf{C}\) & \(\mathbf{h}_{\text{fg}}\)/\(\mathbf{k}\) & \(\mathbf{c}_{\text{p}}\)/\(\mathbf{k}\) & \(\mathbf{kg}^{-1}\) & \(\mathbf{k}\)/\(\mathbf{W}\) & \(\mathbf{m}^{-1}\) K\({}^{-1}\) & \(\mathbf{\beta}\)/K\({}^{-1}\) & \(\mathbf{\beta}\)/K\({}^{-1}\) & \(\mathbf{\beta}\)/K\({}^{-1}\) & \(\mathbf{\beta}\)/K\({}^{-1}\) \\ \cline{3-10}  & & & & & & & & & & & & \\ \hline RT-27 & 30 & 28 & 179 & 1800 & 2400 & 0.00342 & 0.15 & 0.24 & 0.0005 & 760 & 870 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PCM properties [12].  
Figure 1: PCM filled heat sink \((W\times H)\) equipped with two horizontal fins \(\left(L_{f}\times H_{b}\right)\).
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 3 Optimum Composition Ratios
### Optimum Proportion Based on Storage Stability
#### 3.3.2 Storage Stability Analysis  
Storage stability is mainly reflected as the viscosity changes during long-term storage. The 5 samples above were stored in a cylinder where the tube mouth was tightened. A BROOKFIELD viscometer was used to periodically measure the viscosity change at room temperature. Since the solution is very volatile, the volatilization during the sampling interferes with the accuracy of the data in the upper cylinder, so only the data in the central (C) and bottom (B) part were recorded. The results are shown in Figure 4.  
From Figure 4, after 3 and 7 days, the viscosity between the central and bottom were relatively small. The samples were stable, with slight changes mainly caused by temperature change. After 15 and 30 days, the viscosity showed a rapid increase because of the stratification; the viscosity gap between the central and bottom increased gradually. Comparing the 5 samples, the central and bottom viscosity difference of No. 1 and 4 were relatively small compared with 2, 3, and 5. Also No. 1 and 4 had less precipitation at the bottom, which was a controlled rock asphalt and reducing agent ratio between 30:20 and 25:25 with a penetrant and solvent oil ratio of 20:30, which resulted in better long-term storage stability and also less degradation and precipitation.  
In summary, based on Sections 3.1-3.3, the optimum components ratio was determined as, rock asphalt: reducing agent: penetrant: solvent oil = 30:20:20:30.