esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Battery Equivalent Circuit Model  
The LAB and LIB battery adopted for this study was based on parameters, which include power density, life cycle, charging and discharging rate, temperature, cost, weight, and safety. Figure 1 shows the battery equivalent circuit model with associated parameters. The battery equivalent circuit model was preferred for this study because it provides accurate results for the battery behaviour between computational requirements and determining the voltage precisely [36]. This model consisted of four important parameters named open-circuit-voltage (OCV), internal series resistance, and the second-order resistor-capacitor parallel branch.  
Figure 1 represents the battery equivalent circuit model, where OCV stands for battery open-circuit potential, Rs stands for the inner series resistance, and R1, R2, C1, and C2 are the parallel branch network to determine the transient response of the terminal voltage. R1 and C1 represent the small-time constant to provide battery cell feedback and are modelled as a double-layer capacitance and transfer charge. Whereas R2 and C2 provide lengthy time-constant to provide battery feedback and represent the cell diffusion procedure. Lead-acid and Lithium-ion battery models for the proposed research study were obtained from the MATLAB/Simulink library. This battery contained its parameters, which were altered according to the specification of the commercial battery. The battery parameters are estimated based on Figure 1 and by [38]. Moreover, battery depth-of-discharge directly impacts the SoC, which is important to help reduce battery stress. The battery SoC was determined as in Equation (1) [35]:  
\[SoC(t)=\frac{Q_{s}(t)}{Q_{b}(t)}; \tag{1}\]  
where \(SoC(t)\) represent the battery's dynamic state of charge, \(Q_{s}(t)\) is the battery stored charge or capacity and \(Q_{b}(t)\) stand for the actual battery capacity.  
However, the battery stored capacity is dependent on the battery charging current, as shown in Equation (2):  
\[Qs(t)=\int i_{b}(t)dt; \tag{2}\]  
where \(i_{b}\) stands for the battery charging current. The battery current is assumed positive when the battery discharges and negative when the battery is charging.  
Therefore, the battery dynamic SoC can be represented as in Equation (3):  
\[SoC(t)=SoC_{init}-\int\frac{i_{b}(t)}{Q_{batt}}dt; \tag{3}\]  
Figure 1: Battery equivalent circuit model with 2 nd-order RC branch, Reproduced from [37].  
where \(SoC_{init}\) is the battery's initial state of charge.  
The voltage across the RC branch was determined as in Equations (4) and (5) [37]:  
\[V =\frac{1}{s}\bigg{[}\bigg{(}\frac{1}{c}\bigg{)}-\bigg{(}\frac{V}{RC} \bigg{)}\bigg{]}; \tag{4}\] \[V_{b} =OCV-V_{1}-V_{2}-V_{s}; \tag{5}\]  
where \(V_{s}\) stand for a voltage drop in the internal series resistance. Thus, the total battery circulating current is shown as in Equation (6) [37]:  
\[i_{c}=\bigg{(}\frac{V}{RC}\bigg{)}+sCV; \tag{6}\]  
where \(i_{c}\) is the battery circulating current, \(\frac{V}{RC}\) and \(sCV\) are the currents flowing through the parallel RC branches.
# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 3 Proposed Method  
The proposed abnormal behavior analysis framework consists of two modules for: (i) detection and tracking pedestrians and (ii) analyzing abnormal behavior as shown in Figure 1. This section is organized as follows: (i) pedestrian detection and tracking method, (ii) a set of modules for analyzing abnormal behavior, (iii) detection algorithm for intrusion and loitering behavior, and (iv) detection algorithm for fall and violent behavior.
# Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
## 3 Results and Discussion
### CC between diatoms  
One final preprocessing process was proposed using the CC between diatoms to calculate \(\theta\). As is shown in Figure 7, two diatom images are needed for the CCauto and CC to be calculated. The images are converted to grayscale, resized (254 \(\times\) 254), and the PW is preprocessed. Basically, this preprocessing process is used to determine the similarity between two images.  
The similarity between diatoms is shown in a heat map in Figure 8. The heat map was generated \(\theta\) between all the previously selected diatoms where red represents a poor similarity and blue color a high similarity. The central line value is zero indicating total  
Figure 6: (**a**) Projection for each symmetry operation (**b**) score plot PC 1 vs. PC2 for all diatoms images using Nf, and projections parameters, inset: loading plot of PC1 vs. PC2.  
Figure 7: Scheme preprocessing steps using CC between diatoms to calculate \(\theta\).  
similarity; higher zero values \(\theta\) were found for any pair of diatoms; this shows that the \(\theta\) parameter suggests a good differentiator between diatoms.  
Diatoms 6 to 9 and 15 to 17 show high similarity (see Figure 8), but the \(\theta\) values, although close to zero, could be due to imperfections in the images and not show the fundamental differences between them, so it is essential to combine the \(\theta\) parameter with other ways of measuring the differences between it, as is the case of Nf. In any case, if we review the use of all the parameters for the classification in the principal components analysis, we see a total separation between the diatoms 6 to 9 and 15 to 17 (see Figure 6a).  
The Figure 8, the image is not two parts that mirror each other because the \(\theta\) value is calculated based on the CCauto of the reference image; when changing the role of the images, the CCauto is different, and therefore the value of the projection is a bit different. This is seen more markedly between very different diatoms than between similar ones, where the figure's mirror effect can be seen.  
This methodology to classify diatoms differs from others in that only simple parameters are determined, and complex algorithms are unnecessary, as deep learning [28; 32; 43]. Since these parameters are calculated based on the natural symmetry of the entities and the complexity of their structure, we can call this the diatom fingerprint.
###### Abstract  
This work aims to give an overview of wireless communication technologies (WCT) for underground applications. Difficulties regarding the harsh mining environment and operational constraints for WCT implementation and use are discussed. Selected technologies are then classified regarding underground mining-specific use cases in advanced mining operations. Use-case-based application categories such as 'automation and teleoperation', 'tracking and tracing' and 'Long-Range Underground Monitoring (LUM)' are defined. The use cases determine requirements for the operational suitability and also quantify evaluation criteria for the evaluation of WCT. The result is a comparison by category of the wireless technologies, which underlines potentials of different technologies for defined use cases, but it can be concluded that the technology always has to be evaluated within the use case and operational constraints.  
wireless communication technologies; underground mining; LPWAN; 5G; Wi-Fi; LoRa; advanced mining; mining 4.0
###### Abstract  
The objective of this paper is to present an analysis of Sentinel-1 derived surface soil moisture maps (S1-SSM) produced with high spatial resolution (at plot scale) and a revisit time of six days for the Occitanie region located in the South of France as a function of precipitation data, in order to investigate the potential of S1-SSM maps for detecting heavy rainfalls. First, the correlation between S1-SSM maps and rainfall maps provided by the Global Precipitation Mission (GPM) was investigated. Then, we analyzed the effect of the S1-SSM temporal resolution on detecting heavy rainfall events and the impact of these events on S1-SSM values as a function of the number of days that separated the heavy rainfall and the S1 acquisition date (cumulative rainfall more than 60 mm in 24 hours or 80 mm in 48 hours). The results showed that the six-day temporal resolution of the S1-SSM map doesn't always permit the detection of an extreme rainfall event, because confusion will appear between high S1-SSM values due to extreme rainfall events occurring six days before the acquisition of S1-SSM, and high S1-SSM values due to light rain a few hours before the acquisition of Sentinel-1 images. Moreover, the monitoring of extreme rain events using only soil moisture maps remains difficult, since many environmental parameters could affect the value of SSM, and synthetic aperture radar (SAR) doesn't allow the estimation of very high soil moistures (higher than 35 vol.%).  
S 2019 19 802 10.3390/s19040802
# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 2 Materials and Methods
We then developed a logistic regression model with unbalanced classes, and a logistic regression model with classes balanced using the SMOTE technique [28], which is an oversampling technique where synthetic data samples are generated for the class less represented (septic patients in our case). The oversampling technique was performed during the cross-validation phase, after the split into training and test datasets. Finally, we trained a random forest to identify patients at risk of mortality [29]. All models were built on two datasets based on early EHR data, with only one dataset including APACHE-II score, qSOFA score, or SOFA score as predictive features. Each dataset was split in the proportions of 0.7 training set and 0.3 test set, with a 10 k-fold cross validation to improve generalizability and reduce overfitting. Each fit was performed on a training set consisting of 90% of the total training set selected at random, with the remaining 10% used as a hold-out set for validation. Feature importance was calculated differently depending on the model used: for logistic regression we used the magnitude and the sign of the coefficients of the features given by the trained models, while for random forests we used the Mean Decrease in Impurity (MDI). This method calculates the average decrease in Gini impurity due to the splits in the analyzed feature across all the trees trained in the forest. Precision, recall, accuracy, F1-score, and ROC analysis with AUC were calculated to compare the performance of all developed models. Subsequently, we compared the performance of developed models with logistic regression models of the APACHE II, qSOFA, and SOFA scores [18; 19; 30].
# Structural Colors Based on Amorphous Arrays Comprised Solely of Silica Particles
## 2 Experimental Setup  
### Measurements and Materials  
The arrangement and particle shape of the silica particles were monitored using scanning electron microscopy (SEM, S-4800, Hitachi, Tokyo, Japan). The product synthesized through the sol-gel process was examined using x-ray diffraction (XRD, X'pert Pro MRD, PANalytical, Almelo, The Netherlands). The size of the silica particles was measured using a nano-micro particle size analyzer (PSA, Scatteroscope-I, Qudix, Seoul, Korea). The reagents used for silica synthesis were deionized (DI) water (extra pure grade, Duksan, Gyeonggi-do, Korea), ethanol (EtOH, 95%, Duksan), ammonia water (28.0-30.0%, Samchun, Seoul, Korea), tetraethyl orthosilicate (TEOS, 95%, Samchun), and phenol-formaldehyde resin. The phenol-formaldehyde resin was synthesized from phenol (99%, Samchun) and formaldehyde (35%, Samchun) in a ratio of 1:1.5 [19].
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### Communication Protocols and Interfaces
#### 3.2.2 AIS-Specific  
The AIVDM (reports from other ships) and AIVDO (reports from the \"own\" ship) message formats are used [89; 90] in the AIS. The AIS has 27 types of messages [20]. The messages are transmitted through four international frequencies to shore stations (i.e., base stations) and other ships, as follows [20]:  
* AIS 1 (Channel 87B, 161.975 MHz);
* AIS 2 (Channel 88B, 162.025 MHz);
* channel 75 (156.775 MHz), Message 27 transmission only;
* channel 76 (156.825 MHz), Message 27 transmission only.  
Other than those listed above, regional operating frequencies may be used [20]. Both Channel 87B and Channel 88B are used for standard operation of the AIS [20]. The AIS can transmit information over the AIS satellites; this technology is called Satellite AIS. Both Channel 75 and Channel 76 are used for the Satellite AIS [91]. Channel 75 and Channel 76 are used to send only long-range broadcast messages (i.e., \"_Message 27: Position report for long-range applications_\" [20]) to an AIS base station over an AIS satellite when the vessel is out of the coverage range of an AIS base station [20; 91]. At that time, the ship continues to transmit messages through Channel 87B and Channel 88B for vessels in the vicinity. The AIS stops to transmit Message 27 within an AIS base station coverage area [20]. Channel 87B and Channel 88B are used for bidirectional transfer of AIS messages between _ship and ship_ or between _ship and AIS base station_. Channel 75 and Channel 76 are only used in one-way communication to transmit Message 27 from ship to AIS satellite [91]. The AIS may use a Wi-Fi protocol (e.g., IEEE 802.11g/n, IEEE 802.11b) to allow connection with other Wi-Fi-enabled devices in the vessel, and may modify some parameters [92].  
Other than AIS messages, an AIS may also transmit or receive AIS Application-Specific Messages (AIS ASMs) including, but not limited to, meteorological and hydrographic information, dangerous cargo indication, berthing data, and number of persons onboard. Displaying AIS ASMs is not a mandatory function of AIS devices. External hardware and dedicated software could be required to receive and transmit AIS ASMs. AIS ASMs may be transmitted and received by AIS devices onboard ships and AIS base stations. AIS base stations can also distribute AIS ASMs to shore-based users after receiving them. The messages are divided into two classes, namely international functional messages and  
Figure 2: IEC 61162-1 (NMEA 0183) sentence structure.  
regional functional messages. In particular, binary messages are used for the transmission of ASMs and the message structure is specified in Recommendation ITU-R M.1371-5 [20; 93; 94; 95; 96].
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research Regarding Insertion Loss and Isolation of MEMS Switches in Different Bands
#### 3.1.4 MEMS Switches Based on T-Match and \(\pi\)-Match in Different Frequency Bands  
Table 4 summarizes the performance parameters of MEMS switches based on T-match and \(\pi\)-match. Two short high-impedance sections of transmission line were designed before and after the switch by T-match; these sections behaved as series inductors and provided an excellent match at the design frequency. In the case of \(\pi\)-match, a short section of high-impedance line was used between two shunt switches to result in an impedance match [PERSON] (2016).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & **Insertion Loss** & **Isolation** & **Actuation Voltage** & **Reference** \\ \hline Q & 0.25 dB@35 GHz & 35 dB@35 GHz & - & [(43)] \\ W & 0.3–1.3 dB & 30–50 dB & 30 V & [(38)] \\  & 2 dB@67 GHz & 25 dB@67 GHz & 22–23 V & [(44)] \\
180–250 GHz & 1.2–2.7 dB & \textgreater{}30 dB & 50 V & [(45)] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of MEMS switches based on T-match and \(\pi\)-match in different bands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & **Insertion Loss** & **Isolation** & **Actuation Voltage** & **Reference** \\ \hline \multirow{4}{*}{centimeter} & 0.11 dB@25 GHz & 55 dB@10 GHz & 11.75 V & [(39)] \\  & 0.1 dB@25 GHz & 47.75 [EMAIL_ADDRESS] GHz & 6.75 V & [(40)] \\  & 0.1 dB@20 GHz & 43 [EMAIL_ADDRESS] GHz & 20 V & [(41)] \\  & 0.11 dB@25 GHz & 40.7 [EMAIL_ADDRESS] GHz & 12.5 V & [(42)] \\ Q & 0.1–0.19 dB & 57 dB@36 GHz & - & [(36)] \\ V & 0.1–0.19 dB & 30–36 dB & - & [(36)] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of MEMS switches with a float metal layer on the dielectric layer in different bands.  
[PERSON] et al. [38] designed high-isolation MEMS shunt switches based on T-match and \(\pi\)-match on silicon substrates in the W band for the first time. The T-match design showed an insertion loss of 0.1-0.5 dB and an isolation of 21-30 dB in the W band, and its actuation voltage was 30 V. The \(\pi\)-match design showed an insertion loss of 0.3-1.3 dB and an isolation of 30-50 dB in the W band, and its actuation voltage was 30 V. [PERSON] et al. designed a MEMS capacitive switch with a high isolation of 35 dB at 35 GHz, low insertion loss of 0.25 dB at 35 GHz based on GaAs monolithic microwave integrated circuit (MMIC) technology, and T-match in the Ka band [43]. The capacitive type MEMS switch designed by [PERSON] et al. [44] realized a low insertion loss of 2 dB at 67 GHz and high isolation of 25 dB at 67 GHz based on a PZT/HfO\({}_{2}\) multilayered dielectric and \(\pi\)-match. A wideband MEMS switch was proposed by [PERSON] et al. [45] using standard BiCMOS technology and \(\pi\)-match, and its isolation was 30-50 dB and insertion loss was 1.2-2.7 dB at 180-250 GHz.
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 4 Simulation Study and Results
### Collision Avoidance in a Real Environment
#### 4.2.2 Actual Marine Environment Model  
The outer sea area of the harbor basin at Dalian Maritime University is selected as the marine environment modeling area. The marine environment in the electronic chart is shown in Figure 9. The electronic chart is provided by the Electronic Chart Display and Information System (ECDIS) of Dalian Maritime University.  
There are islands and coastlines in the electronic chart. It is very necessary to deal with the coastline. To determine whether there is convex areas in the coastline, these areas are considered as islands. After excluding the convex region, the coastline is fitted as a line segment, and the farthest point of the coastline is calculated. The circumcircle of the farthest point and two ends of the fitted line segment is the approximate circle of the coastline, as shown in Figure 10.  
Figure 8: The expanded obstacle.  
Figure 10: The coastline model after treatment.  
Figure 9: The marine environment in the electronic chart.  
These islands can be approximated as circles, for which the center point \((r_{x},r_{y})\) and radius \(r_{0}\) are calculated by:  
\[r_{0}=\sqrt{\left[\left(x_{\max}-x_{\min}\right)/2\right]^{2}+\left[\left(y_{ \max}-y_{\min}\right)/2\right]^{2}}\, \tag{15}\]  
where \(x_{\max}\), \(x_{\min}\) and \(y_{\max}\), \(y_{\min}\) are the maximum and minimum values of the islands on the \(x\) and \(y\) axes of the image coordinate system, respectively.  
The simulated marine environment is shown in Figure 11.  
Now, the environment model is completed, which provides the foundation for the CAS in the real environment.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 3 Camera Movement Errors
### Relative Rotation  
As shown in Figure 5, the relative rotation between two cameras can be treated as a process in which the left camera coordinate system is fixed while the right camera coordinate system is rotated. The rotation matrix \(R_{lr}^{\prime}\) between the two camera coordinate systems after the camera movement can be expressed as  
\[R_{lr}^{\prime}=R_{lr}R_{rr^{\prime}},\text{ with }R_{rr^{\prime}}=\left[ \begin{array}{ccc}\Delta r_{11}&\Delta r_{12}&\Delta r_{13}\\ \Delta r_{21}&\Delta r_{22}&\Delta r_{23}\\ \Delta r_{31}&\Delta r_{32}&\Delta r_{33}\end{array}\right] \tag{15}\]  
Figure 4: Relative translation between two cameras in a stereo-vision system.  
where \(\mathbf{R}_{lr}\) denotes the initial rotation matrix, \(\mathbf{R}_{rr^{\prime}}\) represents the transformation matrix between right camera coordinate systems before and after camera movement.  
According to Equation (3), the 3D coordinate error \(\Delta\mathbf{X}_{w}\) introduced by relative rotation can be expressed as  
\[\left\{\begin{array}{c}\Delta X_{w}=x_{nl}\Delta Z_{w}\\ \Delta Y_{w}=y_{nl}\Delta Z_{w}\\ \Delta Z_{w}=\frac{T_{x}-x_{nl}T_{z}}{x_{nr}\Big{(}x_{nl}\frac{3}{\sum\limits_ {i=1}^{3}{{r_{3}}\Delta r_{i1}+y_{nl}\sum\limits_{i=1}^{3}{{r_{3}}\Delta r_{i2} +\frac{3}{\sum\limits_{i=1}^{3}{{r_{3}}\Delta r_{i3}}}}}}\Delta_{i})-\Big{(}x_ {nl}\frac{3}{\sum\limits_{i=1}^{3}{{r_{1}}\Delta r_{i1}+y_{nl}\sum\limits_{i=1 }^{3}{{r_{1}}\Delta r_{i2}+\frac{3}{{r_{i1}}\Delta r_{i3}}}}}\Big{)}}\\ -\frac{T_{x}-x_{nr}T_{z}}{x_{nr}(x_{nl}\gamma_{31}+y_{nl}\gamma_{32}+r_{33}) -(x_{nl}\gamma_{11}+y_{nl}\gamma_{12}+r_{13})}\end{array}\right. \tag{16}\]  
Similar to Equation (9), \(\mathbf{R}_{rr^{\prime}}\) can be expressed by three rotation angles \(\Delta\alpha\), \(\Delta\beta\), and \(\Delta\gamma\). If the right camera is only subjected to one rotation angle movement, for instance, \(\Delta\alpha\
eq 0\), \(\Delta\beta=0\) and \(\Delta\gamma=0\), Equation (16) becomes  
\[\left\{\begin{array}{c}\Delta X_{w}=x_{nl}\Delta Z_{w}\\ \Delta Y_{w}=y_{nl}\Delta Z_{w}\\ \Delta Z_{w}=\frac{T_{x}-x_{nl}T_{z}}{(x_{nr}y_{nl}\gamma_{32}+x_{nr}\gamma_{3 }-y_{nl}\gamma_{32}-x_{nl}\gamma_{12}-r_{13})\cos\Delta+(x_{nr}y_{nl}\gamma_{3 2}-x_{nr}\gamma_{32}-y_{nl}\gamma_{13}+r_{12})\sin\Delta+x_{nr}x_{nl}\gamma_{3 1}-x_{nr}\gamma_{11}}\\ -\frac{T_{x}-T_{z}}{x_{nr}(T_{x}\gamma_{31}+y_{nl}\gamma_{32}+r_{13})-(r_{11} x_{nl}+r_{12}y_{nl}+r_{13})}\end{array}\right. \tag{17}\]  
Based on the auxiliary angle formula of the trigonometric function, the denominator of \(\Delta Z_{w}\) in Equation (17) varies with \(\Delta\alpha\) according to the shape of a sine-cosine curve, hence, the changes of 3D coordinate measurement error should have a sine-cosine trend. Furthermore, there will be a non-linear variation in the measured distance when the relative rotation occurs in the stereo cameras.