esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Finite Element Simulation under the Realistic Rotator Cuff Muscle Loading  
The realistic muscle force was applied to the validated finite element shoulder joint model to calculate the displacement at the fracture site, bone stress, plate stress, and mean screw stress for various positions of the three shoulder motions (Table 1, Figure 7). The maximum deformation was found at the humerus distal end. The displacement at the fracture site was maximum at 90\({}^{\circ}\) abduction position, 65\({}^{\circ}\) flexion position, and 50 and 70\({}^{\circ}\) horizontal flexion positions in the corresponding motions. Overall, the horizontal flexion movements had relatively larger displacements than the flexion and abduction movements. The maximum stress in the plate, screw, and bone that appeared at the locking screw holes of the plate, screws around the fracture site, and at the humeral head of the bone around the screw hole were much lower than the yield strength of the implant and bone for all configurations of the models. For all positions of the abduction and flexion motions, the maximum stresses in the plate, screw, and bone for the GT fracture model did not significantly change; however, the maximum stress for the horizontal flexion motion varied significantly. When compared to flexion and abduction movements, horizontal flexion movements had relatively larger maximum stresses in the plate, screw, and bone.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Rehabilitation** \\ **Movement** \\ \end{tabular} & \begin{tabular}{c} **Position (\({}^{\circ}\))** \\ \end{tabular} & \begin{tabular}{c} **Displacement** \\ **at Fracture** \\ **Site (mm)** \\ \end{tabular} & \begin{tabular}{c} **Max. Bone** \\ **Stress (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Max. Plate** \\ **Stress (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Mean Screw** \\ **Stress (MPa)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Abduction} & 15 & 0.144 & 15.8 & 10.8 & 15.6 \\  & 40 & 0.133 & 23.8 & 13.1 & 24.4 \\  & 65 & 0.075 & 22.2 & 12.1 & 22.0 \\  & 90 & 0.208 & 17.1 & 7.6 & 13.7 \\  & 15 & 0.155 & 19.6 & 12.7 & 17.5 \\  & 40 & 0.338 & 25.1 & 14.1 & 23.5 \\  & 65 & 0.573 & 23.4 & 17.8 & 21.3 \\  & 90 & 0.450 & 31.5 & 18.6 & 21.7 \\  & 35 & 0.491 & 40.5 & 14.8 & 12.6 \\ Horizontal & 50 & 1.022 & 60.6 & 29.1 & 37.3 \\ flexion & 70 & 1.163 & 56.1 & 29.0 & 34.0 \\  & 90 & 0.486 & 33.0 & 16.4 & 21.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of the finite element models under realistic muscle loading.  
Figure 7: The deformation and stress distributions of the finite element models under the shoulder joint activity position of 15\({}^{\circ}\) for abduction.
## 2 Methodology: Data Pre-Processing and Models
### Artificial Neural Network  
The ANN makes the discrete power predictions without the knowledge of time, since the LSTMs already do the temporal predictions of the weather conditions (Figure 5). The ANN uses the LSTMs' outputs and predicts what would the community power load be for given weather conditions. The only difference between the ANN's predictions of the short or the long-term power load is in the weather condition predictions it uses as its inputs--for the short term predictions, the ANN uses the LSTMs' short term weather predictions and for the long term predictions it uses the LSTMs' predictions farther out in time.  
The ANN is trained using the current weather conditions to predict the community power load for the current conditions. Once trained, the ANN's inputs are the LSTM outputs of the predicted future weather predictions. The network's inputs are the outputs of multiple LSTMs, where each temporal model predicts the future weather condition of a single weather factor. This ANN configuration integrates the predictions of multiple weather features.  
The ANN's cost function is the mean squared error (MSE) between the expected power load prediction (network's output) and the actual power load recorded in the collected data. The network uses the error back-propagation to adjust its weights. The weights were updated using an Adam optimizer-an extended version of the gradient descent optimization algorithm [20]. The network parameters were manually tuned and the final parameter values that produced the highest accuracy of power prediction used: the number of hidden layers = 4, the number of neurons in each hidden layer = \(300,500,500,300\), the number of instances in each batch = 500, and the number of epochs = 200. Recall that the batch instance was defined as a weather feature vector and a power load readout. Each neuron in the hidden layer used the rectified linear unit (ReLU) activation function [21].
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.1. Dice Similarity Coefficient [40]  
It is used to assess each model's segmentation performance, known as the dci similarity index (DSI). This assessment is conducted by comparing the segmentation results produced by the model with manually segmented images created by radiologists, which serve as the ground truth.  
The formula for calculating the dice coefficient is:  
\[DICE=\frac{2\times|A\cap B|}{|A|+|B|}, \tag{1}\]  
* \(A\) represents the set of pixels classified as part of the predicted segmentation.
* \(B\) represents the set of pixels part of the ground truth segmentation.
* \(|A\cap B|\) denotes the number of standard pixels to both sets.
* \(|A|+|B|\) denotes the number of pixels in the predicted and ground truth segmentations.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 3 Methods
### Water Classification
The buffered water histograms typically have two peaks, one characteristic of land pixels (lower NDWI) and the other of water pixels (higher NDWI). We identified the 100% land threshold (\(LL\)) and 100% water threshold (\(WL\)) as:  
\[LL=height_{lp}-0.9\times prom_{lp} \tag{1}\]  
\[WL=height_{wp}-0.9\times prom_{wp} \tag{2}\]  
where \(height_{lp}\) and \(height_{wp}\) are the heights of the land/water peak respectively and \(prom_{lp}\) and \(prom_{wp}\) are to the prominences of the land/water peaks. We then calculated the water fraction (\(WF\)) for each pixel as [29]:  
\[WF=100\times\frac{NDWI-LL}{WL-LL} \tag{3}\]  
The water fraction is thus the percent of each pixel that is inundated or water covered and is particularly useful in this region where many pixels contain inundated vegetation. All NDWI pixels \(\geq\)\(WL\) were considered 100% water and all NDWI pixels \(<\)\(LL\) were considered 100% land. Figure 2: Water fraction classification workflow for Planet imagery. Rounded boxes represent manual steps and rectangles are fully automated steps.
## 3 THz Wave Modulation and Devices Based on ATR
### Polarization Modulation  
Similar to the amplitude modulation, high-performance and broad-band and active polarization modulation can be realized with grating structures. [PERSON] et al. [24] proposed a THz polarization converter operating in TIR geometry with a metal wire grating, which can achieve three functions, including achromatic 45\({}^{\circ}\) polarization rotation, quarter-wave and half-wave retardance. These states can be easily transferred by rotating the wire grating. The performance of this device was achromatic over 0.1-0.7 THz. Then, they applied the VO\({}_{2}\) film and grating structure into TIR geometry and further realized an active THz polarization controller (Figure 2d). This device shows an average modulation depth of 99.75% in the range of 0.2-1.1 THz [27]. Unlike the former, the operation state of this device is controlled by the state of VO\({}_{2}\), which is a phase-changed material. When VO\({}_{2}\) was in insulating phase, the device worked as a linear rotator with an almost linear \(p\)-polarized output from 0.8-1.5 THz (as shown in Figure 2e). While in the metallic phase, this device became a quarter-wave converter with a circular polarized output from 0.8-1.5 THz (as shown in Figure 2f). Thus, the polarization state of the reflect wave is determined by both the grating structure and the conductivity of the interface. Similar active polarization may also be realized by using material with tunable conductivity.  
Figure 2: Illustration of the THz wave modulation geometries and devices and results. (**a**) Time-domain results for the quartz reference and the ion-gel graphene-on-quartz with different voltages. (**b**) Modulation depth (MD) from 0.1 to 0.6 THz as a function of gate voltage. (**c**) The ion-gel graphene-on-quartz device. (**d**) The VO\({}_{2}\) device and its combination with the prism. The polarization states from 0.8 to 1.5 THz, when the VO\({}_{2}\) was in (**e**) insulating and (**f**) metallic states, respectively. Reproduced with permission from [28], Copyright © 2016, WILEY-VCH VERLAG GMBH & CO. KGAA, WEINHEIM, [27], Copyright © 2018, AIP Publishing.
## 4 Discussion: Application to the Current Probability in Italy
In Figure 9, the variation of the 10% probability time interval, expressed in days, with time after each of the last seven major earthquakes that hit the Italian territory since 1978 (Table 1) is shown. I limited this analysis to the last seven, since there were only eight major events with M \(\geq\) 6.00 for which a CMT magnitude estimate was available, and the method I propose requires at least two events to estimate the occurrence probability of the next one. The event rate is calculated for each event on the basis of DS5--the validated CPT115 dataset--excluding the more recent, yet to occur, events. The occurrence timing of the next event is shown with a dashed vertical line. It is noteworthy that the events all occur during graph minima, which actually correspond to increased probability time intervals. This means that, according to this pattern, the next event will likely occur either in the next few months, during spring-fall 2022, or after 2035, the latter two being highlighted by gray areas. It is obvious that we are simply discussing probabilities and that events can occur even with a highly improbable timing; nevertheless, we have an indication. \begin{table}
\begin{tabular}{c c c} \hline
**Event Date** & \(\Delta t\)**Previous Event (Days)** & \(\overline{\Delta t}\) (Days)** \\ \hline
15 April 1978 & 709 & 2371 \\ \hline
23 November 1980 & 953 & 2350 \\ \hline
6 April 2009 & 10,360 & 2337 \\ \hline
20 May 2012 & 1140 & 2301 \\ \hline
24 August 2016 & 1557 & 2264 \\ \hline
26 October 2016 & 64 & 2371 \\ \hline
30 October 2016 & 3 & 2350 \\ \hline \end{tabular}
\end{table} 
Table 1: Date of occurrence of the _seven_ last major events in Italy. The other columns are \(\Delta t\) with respect to the previous event and the average \(\overline{\Delta t}\) from _1600_ up to the current event, both in days. Figure 8: Survival probability relative to DS1, DS5, and DS6. The solid line refers to DS5, the dashed line refers to DS6, and the dotted line refers to DS1. There is a final remark. The approach I propose works well for the Italian territory, as its \(b\) value is probably very similar to the world one. It should work for all those areas with similar characteristics. However, it could probably also be applied in areas where it is rather different, as the majority of events are anyway earthquakes whose magnitudes are nearer to the lower cut-off thresholds and, in this case, any change of the Gutenberg-Richter slope should not be too dramatic.
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
## 5 Finite Element Analysis Results
### Distribution Law of Beam Temperature Difference  
The daily temperature difference of the beam calculated by the finite element model is shown in Figure 13.  
According to ANSYS thermal analysis, the maximum daily beam temperature difference in the Tuotuhoe region is 10.2 \({}^{\circ}\)C, and the maximum daily beam temperature difference in Za'gya Zangbo region is 17.8 \({}^{\circ}\)C, which exceeds the value of 15 \({}^{\circ}\)C specified in _Code for Design of Railway Continuously Welded Rail_[3]. The time when the beam daily temperature difference in Za'gya Zangbo region is greater than 15 \({}^{\circ}\)C is 20 days, accounting for 5.31% of the total number of samples, mainly from November to March of the second year.  
The larger value of beam temperature difference appears in winter, which is mainly related to the ground's effective radiation and surface reflectivity. The ground effective radiation of the Qinghai-Tibet Plateau is large in autumn and spring and small in winter and summer [32]. The surface reflectance is smaller in summer, larger in winter, and the second largest in spring. In summer, the ground effective radiation and the surface reflectance are low, and the change in beam temperature difference is also low. In winter, the ground effective radiation appears to have a low value, but the surface reflectance appears to have a large value, so the change in beam temperature difference remains high.  
Based on the measured data and heat transfer simulation analysis, the temperature difference load mode suitable for concrete two-piece T-beam in typical areas of Qinghai-Tibet railway is calculated, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Season**} & \multicolumn{2}{c}{**Tuotuhoe Region**} & \multicolumn{2}{c}{**Za’gya Zangbo Region**} \\ \cline{2-5}  & **Daily Beam Temperature** & **Daily Air Temperature** & **Daily Beam Temperature** & **Daily Air Temperature** \\  & **Difference (\({}^{\circ}\)C)** & **Difference (\({}^{\circ}\)C)** & **Difference (\({}^{\circ}\)C)** & **Difference (\({}^{\circ}\)C)** \\ \hline Spring & 9.76 & 18.30 & 16.84 & 21.90 \\ Summer & 5.96 & 10.90 & 10.91 & 13.20 \\ Autumn & 8.35 & 17.10 & 15.36 & 21.30 \\ Winter & 10.19 & 20.50 & 17.81 & 23.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature difference load mode.  
Figure 13: Beam temperature difference calculated by finite element software.
## 4 Nanotechnology and Cancer  
Recent advancements in nanotechnology provide promising strategies to circumvent the nonspecific harm to healthy tissues resulting from conventional cancer therapies, such as chemotherapy, radiotherapy, and immunotherapy [39]. Nanotechnology's distinctive benefits center on augmenting the effectiveness of drug and radiation therapy while minimizing adverse reactions. Multifunctional nanocarriers, for instance, can exploit the discrepancies between tumor and normal tissues to selectively improve drug delivery and retention [39]. Moreover, the tumor microenvironment (TME) exhibits several physical and chemical characteristics, such as abnormal temperature gradients, reductive conditions, mild acidity, hypoxia, and overexpressed proteins and enzymes. Exploiting these features can enable the controlled release of drugs from nanocarriers, furthering the potential for targeted, effective cancer treatments. With the rapid advancement of nanotechnology over the past few decades, countless nanomaterials have been developed. However, only a limited number of nanoparticulate-based systems are applicable in the biomedical field, and even fewer meet the stringent requirements set by the US FDA (United States Food and Drug Administration) for clinical applications. The use of engineered nanoparticles (NPs) for targeted cancer therapy, including breast cancer, is a growing field of research [39]. The goal is to improve the efficacy of treatment, minimize side effects, and elevate the overall quality of life for patients. These NPs can be customized to bind specifically to cancer cells, thus ensuring that therapeutic drugs are delivered to the targeted location [39].
# Influence of Porosity on Drag Coefficient of Perforated Road Signs
###### Abstract  
To ensure the structural stability of road signs against strong gusts of wind, perforated road signs with sign boards perforated at regular intervals have been adopted for use in urban road infrastructure. However, ASCE 7-22 standards do not consider the drag coefficient of signs with low porosity, i.e., less than 30%. Therefore, here, computational fluid dynamics (CFD) analysis was performed to analyze the drag coefficient of perforated road signs with a porosity of less than 30%. The aspect and clearance ratios, which are the width/height ratio of a perforated road sign and the height of the perforated road sign/its distance from the ground, respectively, were set as parameters. Subsequently, the drag coefficient of perforated road signs with a porosity of 30% was calculated, and the effect of porosity and wind speed on the drag coefficient of perforated road signs was analyzed. Results show that the drag coefficient of perforated road signs decreased as the porosity increased, with a drag coefficient reduction effect of up to 0.85 times. Furthermore, perforated road signs with a high clearance ratio exhibited significant decreased drag coefficient. For perforated road signs, the drag coefficient does not show a clear correlation with wind speed.  
perforated road sign; drag coefficient; porosity; wind speed Article
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Task Reliability Modeling Design
#### 4.1.3 Fault Modeling  
A reconfigurable failure model of the ship electronic information system is constructed from the failure model and dynamic structural model of the mission based on the fault transmission mechanism. The failure model focuses on modeling the behavior, transmission, and types of failures [52]. The specific analysis includes the following three points: failure modes of reconfigurable ship electronic information system, fault transmission methods for reconfigurable ship electronic information system, and common mode failure analysis of reconfigurable ship electronic information system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Submodule** & **Components** & **AADL Model Type** \\ \hline Detection and & RF integrated equipment & Device \\ Perception Devices & Optical frequency equipment & Device \\ \hline  & Computing service equipment & System \\  & Display device & Device \\  & Network devices & Device, Bus \\  & Dot generation software & Process \\ Command and & Target recognition software & Process \\ Control System & Track generation software & Process \\  & Situation display software & Process \\  & Threat assessment software & Process \\  & Decision allocation software & Process \\  & Target accusation software & Process \\ \hline  & Remote air defense equipment & Device \\ Weapons Module & Short range air defense equipment & Device \\  & Electronic interference & Device \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correspondence between the system component modules and the AADL meta-model.
# Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification
## 5 Conclusions  
In summary, a novel compact, label-free single and low-cost cell characterization methodology was implemented based on laser self-mixing interferometry technology. Compared with the bulky and expensive devices in use for conventional flow cytometry systems, our novel optofluidic cytometry system only consists of a commercial laser diode, a homemade circuit and a PDMS microfluidic chip. This approach allowed us to achieve high-sensitivity target-by-target characterization of various micro-scale biological samples. Moreover, this method does not need fluorescence staining or any other operations which can be harmful to the valuable samples. A series of measurements were applied using both living cells and artificial polystyrene beads. The characteristics of the SMI signal in the presence of the target particle were investigated in detail, and a statistical approach was used for micro object classification. The results were consistent with our expectation, validating the cell or particle size differentiation capability of our system.  
Considering this work is the first attempt to exploit the SMI technique on the single cell characterization, there are some shortcomings in our system: it can classify the cell type by size differentiation but it is still inadequate for accurate cell size characterization with significant resolution, and more effort must be made in data processing and theory. In spite of the various shortcomings, this method open new opportunities for in on-site bio-medical detection or other sensing applications.  
The contribution of the authors to this work is detailed as follows: investigation, conceptualization and writing: [PERSON], [PERSON], [PERSON]; experimental validation and visualization: [PERSON], [PERSON], [PERSON]; Reviewing: [PERSON], [PERSON]; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, Grant Number 61905005; Beijing municipal Natural Science Foundation, Grant Number 4194071 and L182011. Key project of Science and Technology of Beijing Municipal Education Commission, Grant No. KZ201910005009. Scientific Research Project of Beijing Educational Committee (Grant No. KM201910005004).  
Figure 12: The histogram of MCF7 cells (in red); 293 cells (in blue). (**a**) Size parameter \(K\) distribution; (**b**) SMI signal amplitude distribution.  
**Acknowledgments:** The authors acknowledge [PERSON] to PDMS microfluidic channel fabrication processing. The authors also express gratitude to [PERSON] and [PERSON] for cells culture.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Security Analysis
### Mutual Information  
In the presented CV quantum conferencing scheme, all the Bobs receive their entangled modes and then perform homodyne detection on the \(p\)-quadratures of them, respectively. The legitimate users pick the measurement results of an arbitrary \(i\)th Bob's mode as the reference of the reconciliation procedure. Each of the other users (noted as the \(j\)th Bob) can generate a secret key with the \(i\)th Bob to establish secure communication between them.  
The Bobs perform homodyne detections on the \(p\)-quadratures of their received modes, respectively. The conditional CM of the \(i\)th and the \(j\)th Bobs' modes \(B_{i}\) and \(B_{j}\) after homodyne detections is given by  
\[\mathbf{V}_{B_{i}|B_{j}}=\mathbf{\Lambda}-\mathbf{\Gamma}(\mathbf{\Pi} \mathbf{\Lambda}\mathbf{\Pi})^{-1}\mathbf{\Gamma}^{T}, \tag{26}\]  
where  
\[\mathbf{\Pi}=\begin{pmatrix}0&0\\ 0&1\end{pmatrix}. \tag{27}\]  
The mutual information between the \(i\)th and the \(j\)th Bobs' results is generated by \[I(B_{i}:B_{j})=\frac{1}{2}log_{2}\frac{\mathbf{\Lambda}^{p}}{\mathbf{V}_{B_{i}|B_{j}}^{p}}. \tag{28}\]  
We also consider each user performs heterodyne detection on both \(x\)-quadrature and \(p\)-quadrature of their received mode. The conditional CM of the the \(i\)th and the \(j\)th Bobs' modes after one of them performs heterodyne detection is given by  
\[\mathbf{V}_{B_{i}|B_{j}}^{\prime}=\mathbf{\Lambda}-\theta^{-1}\mathbf{\Gamma}(\Theta\mathbf{ \Lambda}\Theta^{T})\mathbf{\Gamma}^{T}, \tag{29}\]  
where  
\[\theta=det\mathbf{\Lambda}+Tr\mathbf{\Lambda}+1, \tag{30}\]  
and  
\[\Theta=\begin{pmatrix}0&1\\ -1&0\end{pmatrix}. \tag{31}\]  
The mutual information between the \(i\)th and the \(j\)th Bobs' heterodyne results is given by  
\[I^{\prime}(B_{i}:B_{j})=\frac{1}{2}log_{2}\frac{\mathbf{\Lambda}^{x}}{\mathbf{V}_{B_{ i}|B_{j}}^{rx}}+\frac{1}{2}log_{2}\frac{\mathbf{\Lambda}^{p}}{\mathbf{V}_{B_{i}|B_{j}}^{ p}}. \tag{32}\]