esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### FDS Geometries  
The performances of three flow diverting stents with different designs and porosities were compared along with the different artery configurations. In particular, the typical T-screen and W-screen stents were compared with a recently designed stent of higher porosity [28] (X-screen). The three-dimensional geometries of the stents can be seen in Figure 1. The W-screen-based stent has rectangular cross-sectioned struts and is manufactured by laser-cutting of a thin-walled tube. It has a porosity of about 80%. The T-screen-based stent is a round sectioned strut manufactured by double-helical woven wires, and has a porosity of about 70% (see Figure 1). The newly designed stent has a zig-zag configuration (X-screen), manufactured with a strand diameter down to 0.4 mm; it was realized at the University of Saskatchewan [38]. It has porosity of about 90% and was fabricated with the emerging dispensing-based rapid prototyping (DBRP) technique, which facilitates the fabrication of stents with excellent repeatability [38]. Hereafter, the three stents are named STENT 1 (X-screen), STENT 2 (W-screen) and STENT 3 (T-screen). Stent porosity was calculated by the percent ratio of the void surface to the total surface of the stent cylinder [13,28]. The three-dimensional geometries of the stents were created with the commercial package SolidWorks\({}^{\circledR}\) (see Figure 1). In a second stage, the stents were merged with the straight configuration of the artery that was finally curved and made tortuous using its centerline, as explained above. The three stents were generated to have the same length and external vessel diameter for the purposes of comparison. The main dimensions of the three stents are summarized in Table 1.  
Finally, the geometries were exported as IGES files (Initial Graphics Exchange Specification) and imported into the commercial software package Ansys Inc., Version 16.0 (ANSYS Inc., Canonsburg, PA, USA) for creating the computational meshes and numerical models and performing the computations.
# Effect of Substrates on the Dynamic Properties of Inkjet-Printed Ag Thin Films
## 3 Measuring the Dynamic Properties of Ag Ink on Flexural Substrates
### Morphology and Characteristics of Ag Films  
To calculate the average thickness, the morphology of inkjet-printed thin films was measured using a 3D optical profiler (Nano View NV-P2020, NanoSystem, Daejeon, Korea). The degree of densification during the sintering process determines the Young's modulus of the ink. With an identical overprint number and sintering process, densification by grain growth induces thickness reduction [16]. Figure 3 shows typical surface images and profiles on the PI, silicon wafer, and glass substrates. The mean value calculated by surface profiles at five locations was utilized as a representative thickness of the Ag film printed on each beam. The sintering process changes the rectangular cross-sectional shape into a rough semi-circular shape due to central collapse during densification [30]. The silver formation, i.e., the grain size, the surface roughness, and the thickness of the Ag film, is affected by the sintering temperature and the number of overprints [16]. As the high sintering temperature helped reduce the film thickness, the grain growth depended on the substrate. The thickness of the Ag film decreased with a soft surface during the sintering process. The bending stiffness was proportional to the third power of thickness, but the rough surface was not included in the thickness. When the Ag film became thinner, densification was greater under the same sintering conditions.  
The 3D optical profiles indicated substrate influence on the surface roughness and thickness of the Ag film. The thin films on the PI had a larger thickness than those on the silicon wafer and the glass. The average thickness and standard deviation at five locations on the PI, silicon wafer, and glass were 1.91 \(\pm\) 0.11 \(\upmu\)m, 1.85 \(\pm\) 0.05 \(\upmu\)m, and 1.53 \(\pm\) 0.04 \(\upmu\)m, respectively.  
Figure 3: Surface morphology of inkjet-printed Ag thin films sintered on various substrates. (**a**) The typical surface image and (**b**) profiles of the substrates. PI: Polyimide.  
Figure 2: Experimental set-up to measure wave propagation on the specimen. The inset shows a cross section of the specimen bilayer structure. FFT: Fast Fourier transform.
## 2 False Image Injection Attacks  
The idea behind false image injection attacks originates from the original false data injection attack [7]. In [7] the mathematical representation is provided and this holds true for the false image injection attacks.  
* Original image vector, \(I\{i_{1},i_{2},\ldots,i_{n}\}\)
* Observed image vector, \(I_{a}\{i_{a},i_{a_{2}},\ldots,i_{a_{n}}\}\)
* False image vector, \(a\{a_{1},a_{2},\ldots,a_{n}\}\)
* \(I_{a}=I+a\); when \(a\
eq 0\).  
When there is a false image injection attack, \(a\
eq 0\). Though, this definition is originally devised for smart grid, it can be seen from the healthcare perspective as long as the healthcare domain is now heavily dependent on sensor networks [7]. Figure 2 reflects the false image injection attacks in a dental scan. Figure 2a shows the original image and Figure 2b reflects the false image injection attack on the original image. The false image hides one of the wisdom teeth which is in horizontal position. Consider a patient going to their dentist with dental pain but the false image will mislead the dentist and result in a subsequent wrong treatment for the patient. The example of this dental scan is quite simple, the consequences will be much more dangerous if the images are related to more sensitive organs, such as CT scans of the brain, lungs etc. We highlight a few scenarios where **FIIA** leads to dangerous consequences:  
* **Misleading patient diagnosis**: In an unexpected case when the medical equipment which is part of a sensor network is compromised, the injected false image will mislead the physicians [7]. For example, refer to Figure 2.
* **Counterfeit insurance claim**: Injecting false imaging records may cause the insurance provider to pay unnecessarily as the patient data is not legitimate [7]. For example, if a cyber attacker, injects a false image for a surgery which is supposed to be covered by insurance, then the beneficiary will get paid without having any surgery.
* **Emergency situations**: While a complicated tele-surgery is going on and the physicians are dependent on the real time images, even a slight variation of these images (as a consequence of **FIIA**) can lead to loss of life [7].
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results  
In this section, the limits achieved for detecting the defect signal using each method is reported. The SNR of the defects in each test is demonstrated in Table 1. These values are generated using the following equation:  
\[\text{SNR}\ =\ 20\log_{10}\left(\frac{\text{energy}(\text{Signal})}{\text{ energy}(\text{Noise})}\right) \tag{15}\]  
The SNRs are calculated with respect to the overall noises in the region. In the case of test numbers 1-6, only the energy of Defect 1 and the pipe end signal is removed for calculating the energy of the noise; however, for test numbers 7-12, as well as the Defect-1 and pipe end signals, the Defect-2 signal is also removed for calculating the noise. As demonstrated in Table 3, the defect signals are within the noise level, and identification of them using the inspection routine would not be accurate, especially for a 2% CSA defect. Nonetheless, the overall SNR of Defect 2 is much higher due to both the increment of CSA size, and the existing coherent noises of the region.
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
###### Abstract  
The present work introduces a new scheme of data cryptography in the context of emerging trends due to the challenge of defending critical network infrastructure against new exploit systems based on artificial intelligence or defending against quantum threats. In this paper, we will present an innovative cryptographic system composed of keys coming from fractals and prime numbers that are additionally manipulated through mathematical operations using matrices and quantum security. This technique ensures a high level of security, as demonstrated by the NIST \(p\)-values calculated on the key. This paper works upon the foundation on the previous work F&NIF (Fractal & Numerical Information Fusion), as we will discuss in the paper. In this work, we take this procedure and expand it with these new added features, using new fractal schemes and, in particular, implementing a novel quantum security procedure. This algorithm creates a security key applicable to cryptography that is resistant to quantum attacks since this procedure is quantum-crypto-agile.  
fractal cryptography; quantum cryptography; data encryption; prime numbers +
Footnote †: journal: Applied sciences  
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# The CYGNSS Mission: On-Going Science Team Investigations
## 3 Conclusions and Final Remarks
**Acronyms**  
Above Ground Biomass (AGB)  
Advanced Land Observing Satellite-2 (ALOS-2)  
Advanced Microwave Scanning Radiometer (AMSR-2)  
Advanced SCATerometer (ASCAT)  
Artificial Neural Network (ANN)  
Bistatic Radar Cross Section (BRCS)  
Climate Data Record (CDR)  
Contiguous United States (CONUS)  
Cyclone Global Navigation Satellite System (CYGNSS)  
Delay Doppler Map (DDM)  
Delay Doppler Mapping Instrument (DDMI)  
Digital Elevation Model (DEM)  
Disaster Monitoring System-1 (DMC-1)  
Earth Observation (EO)  
Earth System Science Pathfinder (ESSP)  
End-to-End Simulator (E2 ES)  
European Centre for Medium-Range Weather Forecasts (ECMWF)  
European Space Agency (ESA)  
Flight Model (FM)  
Food and Agriculture Organization (FAO)  
Fully Developed Seas (FDS)  
Physical Oceanography Distributed Active Archive Center (PODAAC)  
Geophysical Model Function (GMF)  
Global Forecast System (GFS)  
Global Navigation Satellite Systems (GNSS)  
Global Positioning System (GPS)  
Institute of Space Sciences (ICE)  
Institute of Space Studies of Catalonia (IEEC)  
Intermediate Frequency (IF)  
International Soil Moisture Network (ISMN)  
Jet Propulsion Laboratory (JPL)  
Land Information Systems (LIS)  
Low Earth Orbit (LEO)  
Madden Julian Oscillation (MJO)  
Maxwell Model 3D (NMM3D)  
Mean-Square Slope (MSS)  
Modern-Era Retrospective analysis for Research and Applications (MERRA-2)  
Multi-satellite Retrievals for GPM (IMERG)  
National Aeronautics and Space Administration (NASA)  
NASA-ISRO (NISAR)  
National Oceanic and Atmospheric Administration (NOAA)  
Normalized Difference Vegetation Index (NVDI)  
OceanSat Scatterometer (OSCAT)  
Pseudo-Random Noise (PRN)Research Opportunities in Space and Earth Science (ROSES)  
Science Data Record (SDR)  
Signal-to-Noise Ratio (SNR)  
Soil Moisture Active Passive (SMAP)  
Soil Moisture Content (SMC)  
Soil Moisture Ocean Salinity (SMOS)Space-borne Imaging Radar-C (SIR-C)
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.5 Evaluation of the Weighted Averaging Fusion Factor (\(\alpha\))  
The experimental results reported in Figure 3 show the contribution of the fusion factor value (\(\alpha\)) to the rank-1 accuracy in score-level fusion in the OSNet, MGH, and PiT architectures, as described by Equation (9).  
For OSNet, the rank-1 accuracy first increases slightly with \(\alpha\) until reaching a rank-1 accuracy of approximately 70% for \(\alpha=0.66\). For the highest values of \(\alpha\), the accuracy decreases.  
For MGH, the rank-1 accuracy (starting from 86%) also increases with the value of \(\alpha\) and reaches an accuracy of 90% for \(\alpha=0.6\), then decreases.  
The PiT model starts with a high rank-1 accuracy (90%), showing high performance without the integration of additional information. We can see that this accuracy increases slightly with \(\alpha\) until reaching 93% for \(\alpha=0.7\). For the highest values of \(\alpha\), the accuracy strongly decreases, suggesting potential overfitting or information saturation.  
These results suggest that adding additional information through the use of a fusion factor can increase the performance of the models. The choice of \(\alpha\) is, thus, important in increasing the predictive power of these models. For optimal results, it seems we can choose a fusion factor of \(\alpha\in[0.6,0.75]\) for all tested architectures.
## 5 Validation Results
### Comparison between the Difference Consumption and Energy PV Generation  
Figure 17 shows that the size of the PV system that can be supported on the rooftop area available on all section 16 houses is in the range of 5 kW to 15 kW.  
Figure 16: Average daily solar potential estimation of the community for one year.  
Figure 15: Average solar potential estimation in section 16 for a day.  
The analysis of the results shown considers the solar impact on the photovoltaic generation systems using three days of photovoltaic generation: a typical day of irradiation, a clear or maximum irradiation day, and a cloudy or minimum irradiation day [14,16].  
Figures 18-20 show the results of PV system estimation of 1 kW, 2 kW, and 4 kW. The profiles represent the Typical energy PV (green), Maximum energy PV (orange), Minimum energy PV (purple), and the consumption of House energy (blue).  
In Figure 18, it is observed that the 1 kW PV system is not sufficient for the demand of the one-bedroom house profile. The cloudy day or with minimum irradiance only generated 20% of the house energy demand. Further, the day with typical and maximum irradiance only generated around 63% and 96%, respectively.  
In Figure 19, it is observed that the 2 kW PV system is sufficient for the demand of the one-bedroom house profile. The day with minimum irradiance generated 36% of the house energy demand. The day with regular PV generation supplied 100% of the house energy demand.  
Figure 17: Maximum system size supported per home.  
Figure 18: Profile 1 vs. 1 kW system (day).  
In Figure 20, it is observed that the 4 kW PV system is enough to supply the demand of the three-bedroom house profile on a day with regular irradiance. However, the PV system is oversized; thus, the energy generated is wasted.  
Table 5 presents the summary of the analysis performed for the 1 kW, 2 kW, and 4 kW PV systems considering the different household profiles.  
It is highlighted that a size of 4 kW would cover the needs of the profiles of two and three bedrooms, which represent approximately 74% of the characteristics of the households in the community. On the other hand, the 2 kW size is considered to cover the basic needs of a household, such as a backup or emergency system.  
Figure 19: Profile 1 vs. 2 kW system (day).  
Figure 20: Profile 3 vs. 4 kW system (day).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**House**} & **DC System** & **DC System** & **DC System** \\  & **Size 1 kW** & **Size 2 kW** & **Size 4 kW** \\ \hline One bedroom & \(-211.74\) & \(1257.72\) & \(4173.51\) \\ \hline Two bedrooms & \(-2134.12\) & \(-664.65\) & \(2274.28\) \\ \hline Three bedrooms & N/A & \(-1468.27\) & \(1470.67\) \\ \hline Four bedrooms & N/A & N/A & \(-1249.08\) \\ \hline Five or more bedrooms & N/A & N/A & \(-1771.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Differences in demand profiles vs. PV power generation.
## 1 Introduction  
Semantic segmentation is an important road detection application for autonomous driving. This application must be both accurate and able to operate in real-time to ensure passenger's safety. Existing convolutional neural networks (CNNs) are effective for road semantic segmentation. However, variations in road conditions, weather, and the time of the day affect the detection stability and reduce the accuracy of the semantic segmentation.  
To achieve a stable and accurate semantic segmentation under diverse road conditions, we propose a convolutional neural network (CNN) with a model-switching architecture (MSA) that was trained using data with variations in terms of road conditions, weather, and time of day. The proposed CNN optimizes multi-model deep learning by using diverse data, and the model-switching architecture enables it to select the most appropriate model for the context detection on the basis of CNN classifiers. Training using multiple models avoids the problem of reduced detection performance due to a mutual suppression between weights, which occurs when a single-model CNN is trained using the data of multiple contexts.  
However, semantic segmentation using a multi-model CNN with classifiers for different contexts and model-switching results in a tremendous computation load. There-fore, we additionally propose a lightweight method to facilitate CNN-based semantic segmentation by using CNN classifiers and multiple models to achieve a specific level of performance, reduce the number of calculations, and increase the calculation speed. The article is organized as follows: Section 2 introduces different types of road semantic segmentation. Section 3 presents proposed methods. Section 4 is the experiment results. Section 5 is our conclusion.  
The main contributions of this paper are:Mutual suppression between weights, which occurs in a single-model CNN, is addressed using a multi-model CNN with model-switching architecture for semantic segmentation given diverse road situations.
* Through the use of lightweight processes in the CNN, the model size and computational load were reduced to increase the execution speed.
# Isokinetic Strength in Peritoneal Dialysis Patients: A Reliability Study
###### Abstract  
Although there are studies assessing the effects of interventions on the knee strength of patients undergoing dialysis, there are no previous studies investigating the test-retest reliability of isokinetic measures in people undergoing peritoneal dialysis. The objective of this study was to determine the relative and absolute reliability of peak torque and work measurements for isokinetic concentric knee and elbow extension and flexion in peritoneal dialysis patients. Thirty-one patients undergoing peritoneal dialysis (19 males) participated in the current study. All isokinetic tests were performed using a Biodex System 3. Participants performed three concentric repetitions of each test (flexion or extension) with the dominant limb (knee and elbow) at 60\({}^{\circ}\)/s. Peak torque (Nm) and work (J) were extracted. The intraclass correlation coefficient (ICC), standard error of measurement (SEM), and smallest real difference (SRD) were calculated. The results showed that all knee peak torque and work measures had an ICC of >0.90. On the other hand, the ICC for peak torque and work in the elbow concentric extension was <0.90, while the remaining elbow-related variables achieved an excellent reliability. Therefore, isokinetic dynamometry is a reliable technique to evaluate peak torque and work for concentric flexion and extension in both the knee and elbow joints in patients undergoing peritoneal dialysis.  
kidney; torque; exercise; physical fitness; peritoneal dialysis +
Footnote †: journal: Applied Soft. 2019, 9, 3542; doi:10.3390/app9173542
## 2 Previous Works  
Initial sentence classification models based on deep learning were n-gram models using convolutional neural networks (CNNs) [1; 2; 3; 4; 5]. The authors of [3] proposed a CNN architecture using diverse versions of pre-trained static word vectors and variable size convolution filters. It was shown in [2] that simple convolutions of word n-grams could contribute to improving the performance of sentence classification by fine-tuning pre-trained static word vectors like Word2 Vec [6]. These n-gram models were effective in exploring the regional syntax of words, but they could not account for order-sensitive situations where the order of words was critical to the meaning of a sentence. To overcome this problem, [7] proposed a classification model combined with a recurrent neural network (RNN) and a CNN. Then, some studies demonstrated that sub-word units like character n-grams could contribute to improving the performance of downstream natural language processing (NLP) tasks [8; 9; 10; 11; 12; 13]. The authors of [12] proposed a part-of-speech tagging model based on an RNN in which each word is represented by a combination of Korean alphabet embeddings for making the model robust to typing errors. The authors of [13] proposed a character-level CNN model for text classification which showed that the character-level CNN model could achieve state-of-the-art or competitive results. In addition, [14] demonstrated that domain embeddings (i.e., embeddings of predefined categories) could contribute to improving the performance of large-scale domain classification. Recently, bidirectional encoder representations from transformers (BERT) was proposed [15], which is deeply bidirectional, unsupervised language representation that is pre-trained using a large amount of plain text corpus. BERT has shown state-of-the-art performance in many downstream NLP tasks such as classification, sequence labeling, and span prediction by learning task-specific vectors through fine-tuning. In sentence classification tasks such as sentiment analysis and semantic textual similarity analysis, BERT also outperformed the previous state-of-the-art models.