esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset
### Satellite Data  
Himawari-8 is a geostationary satellite operated by the Japan Meteorological Agency. It was launched on 7 October 2014 and carries the Advanced Himawari Imager (AHI) sensor, which is equipped with 16 bands from visible to infrared [36]. Himawari-8 releases AOD products at two levels, namely Level 2 (10 min temporal) and Level 3 (hourly and daily), which have been used for various applications including estimation of PM [96; 97; 98; 99],  
Figure 1: Locations of the 65 air quality monitoring stations across Malaysia.  
dust detection [100] and aerosol data assimilation [101]. The L3 product is an improved version of the L2 AOD product that minimized cloud contamination [102] and has a 5 km spatial resolution. Himawari-8 AOD at 500 nm is associated with quality assurance levels namely \"very good\", \"good\", \"marginal\" and \"no confident (or no retrieval)\" [99]. In this study, only the \"very good\" L3 AOD\({}_{500}\) retrievals were considered for PM\({}_{2.5}\) estimations, downloaded from the Japan Aerospace Exploration Agency (JAXA) website: available online: [[http://www.eorc.jaxa.jp/ptree/index.html](http://www.eorc.jaxa.jp/ptree/index.html)]([http://www.eorc.jaxa.jp/ptree/index.html](http://www.eorc.jaxa.jp/ptree/index.html)) (accessed on 10 May 2021) for the period January 2018-December 2019. Recently, Himawari-8 AODs were used to estimate the PM\({}_{2.5}\) concentrations over Hubei province, China [103]. Application of the Himawari-8 L2 AOD data over Malaysia revealed an overestimation by 24.2% [104], while the L3 AOD products displayed a better agreement with the Aerosol Robotic Network (AERONET) AODs with a coefficient of determination R\({}^{2}\) = 0.81, root mean square error (_RMSE_) of 0.13 and an overall overestimation of only 1% [92]. Moreover, Himawari-8 AODs presented a good agreement with AERONET AODs in China (R\({}^{2}\) = 0.41-0.83; _RMSE_ = 0.18-0.31) [105], Southeast Asia (R\({}^{2}\) = 0.64; _RMSE_ = 0.28), East Asia (R\({}^{2}\) = 0.83; _RMSE_ = 0.14) [106], Korea (R\({}^{2}\) = 0.69; _RMSE_ = 0.19) and Beijing-Tianjin-Hebei (R\({}^{2}\) = 0.76; _RMSE_ = 0.36) [107].
# Novel Fluidic Oscillator Evaluation Considering Dimensional Modifications
## 6 Results
### Comparison Between 3D and 2D Results  
In order to further increase the reliability of the 2D-CFD simulations, the unsteady outlet mass flow, the unsteady stagnation pressure at the mixing chamber lower converging wall, and the dynamic pressure, as well as the dynamic mass flow at the lower FC outlet, are compared for the standard fluidic oscillator configuration between the two-dimensional and the three-dimensional simulations; Figure 7a-d introduce these comparisons. The fluidic oscillator dynamic outlet mass flow, as well as the dynamic pressure at the MC's lower converging wall and at the lower FC outlet, shows good resemblance between the 2D and 3D models: the time-averaged values and peak-to-peak amplitudes are very close. The main differences appear when evaluating the dynamic mass flow and peak-to-peak amplitude at the FC outlet; see Figure 7d. The 3D simulations generate a much smaller FC mass flow peak-to-peak amplitude (about 35%) than the one obtained from the 2D simulations. The FC reverse flow is much smaller in the 3D simulations than in the 2D simulations. The authors believe this is likely to be due to the differences in the cross-stream velocity distribution at the FO's power nozzle. For the 2D simulations, such a velocity distribution is almost uniform at all points, while the 3D simulations generate a cross-stream velocity distribution which accentuates the boundary layer effect on both cross-stream sides of the power nozzle (not shown here). As a result, less flow particles impinge on the MC entrance width corners and flow along the FCs in the reverse direction. To clarify this point, Figure 8 introduces the MC velocity fields obtained for the 3D and 2D simulations at the respective instants at which the FC reverse flow is maximum. It can clearly be seen that the 2D model overestimates such reverse flow, as previously stated in Figure 7d. Four videos showing the velocity and pressure fields obtained from the 3D and 2D simulations are presented in the Supplementary Materials. Another effect of the FC reverse flow reduction is the increase in the oscillation frequency at the FO outlet. This increase was calculated to be about 11% higher for the 3D simulations; this frequency mismatch was already reported in [42]. Based on the observations made in the present document, it appears there is a link between the FO's output frequency and the FC reverse flow: the higher the reverse mass flow, the smaller the frequency of the FO outlet mass flow. This point is further assessed in the next subsections. The conclusion from the present subsection is that the 2D-CFD simulations are sufficiently accurate to compare the FO's performance when several MC inclination angles as well as several FC widths are considered, which is what is presented next.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters
### Step 7: Calculating the Maximum and the Minimum Voltage Gains of Reverse Mode  
According to (20) and (21), the maximum and the minimum gains of the reverse charging mode can be achieved.  
\[M_{max}=\frac{V_{DC\ Grid\_nom}}{N\cdot V_{Bat\_min}}=\frac{400}{1.2\cdot 280} \cong 1.19 \tag{20}\]  
\[M_{min}=\frac{V_{DC\ Grid\_nom}}{N\cdot V_{Bat\_max}}=\frac{400}{1.2\cdot 4 03}\cong 0.83 \tag{21}\]  
Step 8: Using the Resonant Device Parameters of Step 6 to Reversely Obtain the Parameters \(Q_{2}\), \(k_{2}\), \(g_{2}\), and \(m_{2}\) of the Reverse Discharging Mode  
Employing the resonant device parameters obtained from the forward charging mode and backwards, we can calculate the parameters \(Q_{2}\), \(k_{2}\), \(g_{2}\), and \(m_{2}\) of the reverse discharging mode.
# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 3 Results
### Assessment and Recommendations for Image Captruring Devices  
The following recommendations are suggested to evaluate the suitability of a mobile device camera for dermatological photography:  
* A larger sensor is preferable to a smaller one (Table 1) as it yields a higher-quality image. A camera's sensor determines the number of pixels in the photographs it produces. With between 8 and 14 million pixels, the dermatologist's photography needs are covered. More pixels would only be needed to maintain good resolution or size when cropping, or if TBP is applied.
* Digital cameras can save JPEG files in different sizes with varying degrees of compression. Use the highest quality JPEG setting (i.e., with the lowest compression of image information) possible to keep the detail.
* The camera should allow manual settings and white balance adjustment. Ensure the imaging device has an appropriate white balance setting to allow the most accurate colour rendition.
* For displaying an image preview on a camera/mobile device monitor, the following criteria should be considered: monitor resolution (and size) > 1920 \(\times\) 1080 px (14 cm) or more for a smartphone, and >1.000 k dots (14 cm) or more for a digital camera.
* Handheld dermoscopes must be supported with an adapter that ensures a firm fit and optimum position of the lens system in front of the camera to ensure the best image sharpness. Several handheld dermoscopes already produce universal attachments allowing for their compatibility with any smartphone. Lens size should be at least 20 mm in diameter, and a larger lens size is preferable. Some dermoscopes are already supported by a smartphone application for capturing and image management. It would be helpful to have integrated the assistance for automatic sharpness recognition [17].  
Our results (Tables 2-4) show that we have selected from all tested devices the iPhone 7 as a suitable device for capturing dermoscopic images. We made the decision based on measurements showing good sharpness of dermoscopic images and solid color reproduction with small mean absolute error in manual mode. This result is due mainly to the less aggressive sharpening algorithm implemented in this device. The device provides many manual settings. With the correct setting of the image capture parameters, the reproduction quality of skin colours is improved dramatically.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 5 Conclusions  
In this work, the design of a novel cryptographic protocol that guarantees the security services of confidentiality, authentication, and integrity to keep secure the information transmitted in a WBAN in medical applications has been presented. Security services in the proposed protocol were verified through automatic protocol verification tools. From the design point of view of the protocol, different primitives that help to achieve the security services mentioned before were considered. In addition, using a keyless authentication method provides the protocol with the advantage that generating additional cryptographic key pairs is not required. The results obtained through protocol verification tools show that the designed protocol is secure in the sense of three security services. In the same way, the results of the verification analysis confirm that the protocol design does not present flaws in its logic of sending messages. Results show that the construction of the protocol focuses on keeping the patient's sensitive data confidential and complete. Also, it was shown that the data communicated between the considered entities when they reached their destination were authentic and complete. Authentication between sensors is performed by ensuring they are placed on the same human body. Unlike the protocols presented in the related work, the developed protocol here is designed not to expose data during key generation, preventing attackers from obtaining information needed to perform spoofing attacks, resulting in security flaws found in previous protocols.  
Conceptualization, G.G.-G; Formal analysis, K.A.D.-V; Investigation, K.A.D.-V; Writing--original draft, K.A.D.-V. and G.G.-G.; Writing--review & editing, P.J.E.-A.; Supervision, G.G.-G.; Funding acquisition, G.G.-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONACyT grant number 321068, SIP-20221427 and SIP-20221495.  
Not applicable.  
**Data Availability Statement:** If necessary please contact the authors.  
Figure 14: Obtained results by AVISPA to the verified protocol.
## 4 Experiments
### Experimental Results
#### 4.4.3 Performance Comparison Experiment for Different Input Sequence Lengths  
The elevator fault prediction model predicts elevator faults based on historical operational data. The length of the input sequence directly affects the information that the model can learn and thus has a significant impact on the model's prediction performance. To determine the optimal input sequence length, we conducted a comparative experiment on the model's performance under different input sequence lengths. The experimental results are shown in Table 9 and Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Layers** & **Accuracy (\%)** \\ \hline
1 & 83.6 \\
2 & 86.3 \\
3 & 81.9 \\
4 & 75.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results for different numbers of layers in the calculation module.  
Figure 4: Training loss curve.  
From the experimental results, it can be observed that the accuracy increased with the input sequence length. This indicates that the proposed elevator fault diagnosis model can effectively extract information from long sequences. When the input sequence length was 150, the model achieved the best performance, with an accuracy of 86.3%. However, when the input sequence length exceeded 150, the model performance no longer improved significantly. In fact, the accuracy decreased by 0.1 of a percentage point when the input sequence length was extended to 180. This phenomenon may be due to two reasons: on the one hand, the main sequence patterns can already be effectively learned within a length of 150; on the other hand, features from excessively long-term sequences may have weaker relevance to the current state and could even act as misleading information.
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 2 Materials and Methods
### Test Method for Evaluating the Penetration Depth of Sulfate Ions  
As previously discussed, the first method presented in this article aims to evaluate the penetration depth of sulfate ions into cement paste samples. In this experimental work, salt precipitation was observed at the following intervals over a period of 24 weeks: 2, 4, 6, 8, 12, 16, and 24 weeks. The time periods were not chosen randomly. A preliminary study utilizing the ICP technique was conducted using the same formulations used in this work, which allowed us to apply the same drying times. There was a breakdown of the samples during the last period of time (24 weeks), which explains why the final period of time was chosen for the experiment.  
During the semi-immersion of the cylindrical specimens in the sulfate solution, sulfate ions are forced to move upward from the immersed portion of the specimen to the outer surface as a result of the capillary rise mechanism in semi-immersion [35].  
For each time interval, one 5 cm long cylinder was removed from the Na\({}_{2}\)SO\({}_{4}\) solution and cut in half (see Figure 3a,b) to obtain two parts that were placed in a climatic chamber at 50% RH and T = 20 \({}^{\circ}\)C for two to three hours. Then, salt crystallized on the surfaces exposed by the cut when the supersaturated solution evaporated from this newly air-exposed part of the specimen, resulting in the precipitation of white particles [36]. The formed white surface corresponds to the zone of crystallization of free sodium sulfate crystals (Na\({}_{2}\)SO\({}_{4}\)) due to evaporation conditions [37]. Under specific temperature and relative humidity conditions, the precipitate is caused by crystallization of thenardite (Na\({}_{2}\)SO\({}_{4}\)) and mirabilite (Na\({}_{2}\)SO\({}_{4}\).10H\({}_{2}\)O) under high concentrations of Na\({}_{2}\)SO\({}_{4}\) solution [38; 39]. This white zone should be examined in the future via SEM-EDX (scanning electron microscopy with energy-dispersive X-ray analysis) in order to identify all the phases that formed there.  
Finally, the width of the whitish precipitate zone was measured with a stainless-steel ruler at the end of the drying phase (see Figure 3c).
## 3 Error Characterization and Simulation Method
### Errors Description
#### 3.1.1 Instrument Uncertainties
(2018). Measurement noise, that is noise equivalent differential temperature (NEDT) is the most important error that worsens the precision of retrievals. It is a statistical uncertainty that cannot be calibrated. The standard deviation (STD) can be calculated from the radiometric sensitivity equation:  
\[\epsilon=\frac{T_{sys}}{\sqrt{\beta d\tau}}\, \tag{2}\]  
where \(T_{sys}\) is the system noise temperature, which is the sum of the receiver temperature and the atmospheric temperature received by the antenna, \(\beta\) is the noise equivalent spectral resolution, and \(d\tau\) is the integration time for measuring a single spectrum. The assumed instrument uncertainties are summarized in Table 3.
## 2 Access to Android Raw GNSS Measurements and GNSS Observation Generation
### Pseudorange Observation Generation  
The pseudorange observation is the travelling time of the signal to propagate from the satellite to the receiver (here smartphone). It is of the form [24]:  
\[P=(t_{Rx}-t_{Tx})\times 10^{-9}\times c, \tag{1}\]  
where \(P\) is the pseudorange observation in meter, \(t_{Rx}\) is the received time (measurement time) in nanosecond, \(t_{Tx}=ReceivedSvTimeNanos\) [ns] is the received GNSS satellite time at the measurement time in nanosecond reported in the CSV file (one of the variables in the GNSSMeasurement class) and \(c=299792458.0\) [m/s] is the speed of light. The measurement time \(t_{Rx}\) in GNSS time system in nanosecond is as follows:  
\[{}^{t}{}_{Rx\ GNSS}=TimeNanos+TimeOffsetNanos-(FullBiasNanos(1)+BiasNanos(1)), \tag{2}\]  
where \(TimeNanos\) is the GNSS receiver's internal hardware clock value, \(TimeOffsetNanos\) is the time offset at which the measurement was taken, \(FullBiasNanos\) is the difference between \(TimeNanos\) inside the GPS receiver and the true GPS time since 6 January 1980 and \(BiasNanos\) is the clock's sub-nanosecond bias. All of these variables can be found either in the GNSSMeasurement class or in the GNSSClock class. They all are reported in nanosecond. It should be noted that only the first value of \(FullBiasNanos\) and \(BiasNanos\)  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**App** & **Developer** & **Output Format** \\ \hline GnssLogger & Google & CSV, NMEA and RINEX \\ Geo++ RINEX Logger & Geo++ GmbH Company & RINEX \\ rinexON & FLAMINGO & NMEA, RINEX \\ GalileoVT & European Space Agency (ESA) & CSV and NMEA \\ G-Ritz logger & Ritsumeikan University & NMEA, RINEX \\ GNSS/IMU Android Logger & Universität der Bundeswehr & CSV, RINEX and IMU data \\  & München & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Available GNSS logger Android applications [20].  
must be used to compute all the received times (i.e., \(FullBiasNanos(1)\) and \(BiasNanos(1)\)) as long as there is no discontinuity in the internal received time. \(t_{Rx\ GNSS}\) and \(t_{Tx}\) must also be in the same time system for all GNSS systems which is not the case here as \(t_{Rx\ GNSS}\) is in the GNSS reference system while \(t_{Tx}\) is given for each GNSS system. Therefore, one must convert to other one (i.e., same GNSS time system). How to do this can be found in [20; 40].
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Monitoring Photosynthetic Process for Plant Growth  
Satellite radar remote sensing can provide effective measurements associated closely with ecosystem process modeling [67], such as the initial growth of plants or the timing of seasonal snow-melt. A remote sensor is used to produce an annual estimation of natural processes such as photosynthesis and autotrophic respiration rates of evergreen forests [68]. Many modern applications benefit from using satellite microwave remote sensing, e.g., to record the timing and bad weather effect on Primary Net Production (PNP), to perform seasonal measurements of maximum and minimum \(CO_{2}\) concentrations, and to observe global climate. This study is beneficial for the process of increasing vegetation greenery and yearly plant productivity [69; 70; 71; 72].
## 3 Topical User Modeling
### Analogy  
The underlying concept of the proposed method is that the users in an online community leave comments according to their own behavioral characteristics, affected by personal behavior such as interests and active time slots, and environmental factors such as social events and influential incidents. This is analogous to the concept of the classical topic-modeling method, in which words appear in documents according to their latent topics. We use this conceptual similarity between user behavior and text modeling to analyze user behavior in online communities by exploiting classical topic modeling methods. The analogies listed in Table 2 show the correspondence between the two models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Domain**} & \multicolumn{2}{c}{**Analogy**} & \multirow{2}{*}{**Main Goal**} \\ \cline{2-3} \cline{5-5}  & & **Document** & & **Word** \\ \hline
[23] & Java Program Code & Source file & Token & Extracting source code concepts \\
[24] & Word Network & Word & Adjacent Word & Topic modeling with short texts \\
[25] & Phenome Information & Drug & Phenotype & Searching similar drugs \\
[26] & Web Browsing History & User & Visited URL & Analyzing mobile user preferences \\
[27] & Purchase List & User & Item & Item recommendation \\
[28] & Twitter Network & User & Follower/Followee & User (friend) recommendation \\
[29] & Twitter Network & User & Follower/Followee & User grouping and labeling \\
[30] & Researcher Network & Author & Co-author & Community detection \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work using analogies from latent Dirichlet allocation (LDA).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classical Topic Modeling** & **User–Topic Modeling** \\ \hline Corpus & Community \\ Document & Article \\ Word & Comment Writer (User) \\ Topic & Behavioral Characteristic of Users \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analogies used in user-topic modeling.  
An online community is analogous to a corpus of textual documents. When an article (or post, thread) is uploaded to an online community, users read it and are involved by leaving comments to it. Although a comment has a textual content, we use its writer to represent it. Therefore, an article is represented as a sequence of users (comment writers), which is analogous to a document being represented as a sequence of words. Recalling the generative probabilistic model of standard topic modeling, we can describe the generative procedure of our probabilistic model as follows. Let \(K\) be a prespecified number of topics. For each article \(a_{i}\), we randomly pick a \(K\)-dimensional multinomial distribution \(P(\tau|a_{i})\) from the Dirichlet distribution with a prespecified parameter. For each comment, we randomly sample its latent topic \(\tau\in\{1,\cdots,K\}\) from the multinomial distribution; let \(\tau\) be the selected topic. Then a user is chosen from the distribution \(P(x|\tau)\). We need to estimate two probability distributions, \(P(\tau|a_{i})\) and \(P(x|\tau)\), from the observed user sequence.
## 4 Conclusions  
In the present study, the scandium doping effect on PrBaCo\({}_{2}\)O\({}_{5+\delta}\) (PBCO) was studied to improve the electrochemical performance of LT-SOFC by characterizing the structural, electrical, and electrochemical properties of PBCO and PrBaCo\({}_{1.9}\)Sc\({}_{0.1}\)O\({}_{5+\delta}\) (PBCSc). The substitution of Sc\({}^{3+}\) improves the electrochemical performance of PBCO by favoring structural properties related to O\({}^{2-}\) diffusion and oxygen reduction reactions. Based on these favorable Sc\({}^{3+}\) doping effects, the PBCSc cathode presents a low polarization resistance (0.44 \(\Omega\) cm\({}^{2}\)) at 500 \({}^{\circ}\)C with a maximum power density of 0.73 W cm\({}^{-2}\) which is 1.3 times higher than that of PBCO. Consequently, these results imply that the substitution of Sc\({}^{3+}\) into the PBCO could effectively enhance the cathodic electrochemical performance of low-temperature SOFC.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/11/2217/s1](http://www.mdpi.com/2076-3417/8/11/2217/s1)]([http://www.mdpi.com/2076-3417/8/11/2217/s1](http://www.mdpi.com/2076-3417/8/11/2217/s1)), Figure S1: The high-resolution transmission electron microscopy (HRTEM) images of (a) PBCO, (b) PBCSc, and (c) SP-PBCSc, Figure S2: Impedance spectra of single cells (Cathode/GDC/Ni-GDC) in a temperature range of 450 to 550 \({}^{\circ}\)C: (a) PBCO, (b) PBCSc and (c) SP-PBCSc, respectively.  
Formal analysis and investigation, D.J.; Rietveld refinement assistance, C.L.; TEM image analysis, O.K.; writing--original draft preparation, D.J., and J.K.; writing--review and editing, S.S., J.S., and G.K.; supervision, G.K.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20173020032120).  
The authors declare no conflict of interest.