esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and ANN Model Validation
### Architecture and Validation of the ANN Model
#### 5.3.2 ANN Model Validation  
In this section, the optimization data for the 20-year injection scenario was used to validate the ANN model using two error functions, \(R^{2}\) and RMSE. Figure 22 shows the CO\({}_{2}\) injection rate for 300 years, determined by the predicted model (ANN; red) and the validation model (simulation; blue). Although the two models initially yield a gap and at 20 years, it was found that the rate was well-predicted as it showed an \(R^{2}\) and RMSE of 0.9982 and 0.6681, respectively. In addition, \(R^{2}\) and RMSE values calculated from the injection well BHP show great acceptance with 0.9828 and 0.0497, respectively. Especially for the monitoring phase, the ANN accurately predicted BHP during the monitoring phase. When the analysis was extended to the trap mechanisms, an \(R^{2}\) of 0.9927 and an RMSE of 0.0136 for RTI, as well as an \(R^{2}\) of 0.9607 and RMSE of 0.0085 for STI were achieved, which implies that the developed ANN model has great accuracy in the prediction of trap mechanisms. On the other hand, the difference in the total CO\({}_{2}\) storage capacity was only 0.68% (2 kton), as the ANN and the simulation predicted 294 kton and 292 kton of CO\({}_{2}\) could be injected for 20 years, respectively. The predicted location of the relief well only had a distance of 20.1 m based on the ANN and simulation models (Figure 23).  
Figure 21: Results of the ANN model in (**a**) training and (**b**) testing with the optimal iteration number of \(R^{2}\) and RMSE.  
Figure 22: Comparison the (**a**) injection rate, (**b**) injection well BHP, and (**c**) RTI and (**d**) STI results between simulation and the ANN model.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 5 Future Directions
### Data Processing Problem  
Data processing has been a big challenge in deep learning and concept drift adaptation methods [4; 21]. Firstly, when inputting samples, we may face problems such as class-imbalance data, high-dimensional data, etc. For example, when performing online anomaly detection, most of the datasets are very unbalanced, and the abnormal data account for a very small part [109]. Secondly, when the model update is performed, we will face the problem of how to balance between new data and old data and the problem that the new data samples are not enough to support the update of the deep learning model after the concept drift occurs. These will lead to poor prediction, slow model convergence, delayed model update, and other consequences that are worthy of our consideration and research.
## 5 Case Study
### Results  
Figure 17 shows the radial deviations (\(R_{d}\)s) between the actual positions of the contour points and the theoretical ones. These deviations are illustrated for both verifications performed with the CMM (orange line) and those obtained with the described procedure (blue line).  
As shown, the identification of contour points using the proposed method covers the entire target layer, and the radial deviations are closely aligned with those obtained using the CMM.  
To quantify how comparable the obtained results are to a trusted reference, the same quality indicators defined in [29] were used. The coverage factor (\(C_{f}\)) represents the completeness of test-target reconstruction. The circular contour is divided into angular sectors, and \(C_{f}\) is defined as the percentage ratio of the number of sectors containing at least one valid contour point (\(n_{S_{p}}\)) to the total number of sectors considered (\(n_{S_{T}}\)) (360 for this research), as shown in Equation (6). The average of radial deviations (\(\overline{D}\)) is calculated as the mean absolute value of the deviations between the contour points obtained through image processing and those measured with the CMM, previously grouped into those 360 sectors,  
Figure 17: Representation of the radial deviations as calculated with the CMM (orange) and with the CIS-based procedure (blue) for the different test specimens: (**a**) \(H_{8}\); (**b**) \(H_{6}\); (**c**) \(H_{4}\); (**d**) \(H_{2}\).  
following Equation (7). Finally, the standard deviation of radial deviations (\(\sigma_{D}\)) represents the variability of the radial deviations along the contour, calculated as in Equation (8).  
\[C_{f}=\frac{n_{S_{v}}}{n_{ST}}\cdot 100=\frac{n_{S_{v}}}{360}\cdot 100 \tag{6}\]  
\[\overline{D}=\frac{1}{n}\cdot\sum_{i=1}^{n}\left(R_{d_{i}}\right) \tag{7}\]  
\[\sigma_{D}=\sqrt{\frac{1}{n-1}\cdot\sum_{i=1}^{n}\left|R_{d_{i}}-\overline{D} \right|^{2}} \tag{8}\]  
According to Table 2, the proposed method allows nearly full coverage, even for low-contrast specimens. Moreover, the average of the radial deviations is lower than 14 \(\upmu\)m for each artefact, with a maximum standard deviation of 13.35 \(\upmu\)m for the worst-case scenario (\(H_{2}\)).
# Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites
###### Abstract  
This paper presents an experimental study on detecting and monitoring of evolution of fatigue damage in composites under cyclic loads by using guided waves. Composite specimens fabricated by glass fiber/epoxy laminates and surface mounted with piezoelectric wafers are fatigued under tension-tension loads. A laser extensometer is used to obtain the degradation of longitudinal stiffness of the specimens under fatigue states to reflect the accumulation of internal fatigue damage. Meanwhile, at different fatigue cycles, one wafer acts as actuator to excite diagnostic guided waves, and the other acts as sensor to receive corresponding response waves. These guided wave signals are then processed by wavelet packet transform to extract characteristic features of energies in multiple frequency bands. A statistical multivariate outlier analysis is then performed to determine the existence of fatigue damage and to characterize their evolution using Mahalanobis squared distance. Experimental results have demonstrated the potential applicability and effectiveness of guided waves for continuous monitoring of fatigue damage in composite structures.  
structural health monitoring; fatigue damage in composites; guided waves; wavelet packet transform; statistical multivariate outlier analysis Article  
Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites
# A Hybrid Energy Equating Game for Energy Management in the Internet of Underwater Things
## 4 Hybrid Energy Efficient Clustering Game
### Network Architecture  
In our proposed framework, the N number of FNs are uniformly installed in the NSF, the SS is supposed to be outside the sensing area. As we are totally unaware of the inside situation, random node distribution is an effective strategy. In this framework, we utilize the following random distribution method to install the FNs in the NSF:  
\[P\big{(}N(A)=K\big{)}=\frac{\big{(}\lambda|A|^{K}\exp^{-\lambda|A|}\big{)}}{K!} \tag{7}\]  
where, \(\lambda\) and \(|A|\) are the FNs density and area of the NSF, respectively. In this framework, the FNs sense the information in the installed vicinity and transfer the collected information through a CH to the SS. Thus, in this framework, three types of sensors are taking part such as FNs, CHs and the SS. Suppose that N and CHs signify the set of FNs and CHs, respectively. Where, \(N~{}=~{}\{j~{}|~{}j\geq 1~{}\bigwedge~{}j~{}\leq~{}j_{max}\}\) and \(j_{max}\) is the maximum number of FNs. \(CHs=\{CH~{}|~{}CH\geq 1\bigwedge CH\leq CH_{\max}\}\), where \(CH_{max}\) is the maximum number of \(CHs\). All the FNs, CHs and the SS are independent of making connections with each other. From this discussion, we have the following connectivity expression:  
\[P_{(a,b)}=\begin{cases}1&\quad\quad\text{$If$ a establishes a connection with $b$}\\ 0&\quad\quad\text{$Otherwise$}\end{cases} \tag{8}\]  
The \(a,b\in N\), and \(a\
eq b\). Here, b is either a member node or a CH. If b is a CH, then a is a member node. The quality of links is directly related to the received signal strength and the distance. Before discussing the operations of our proposed framework, we are making some assumptions as follows:  
* The SS is an enriched resources device and it is equipped with both acoustic and radio modems. The radio modem is used for communication to the final processing center or offshore sink, while the acoustic modem is engaged in communicating with the CHs.
* The CHs are only relaying (working as a forwarder) the data of SMNs in their cluster and are not taking part in any sensing activity.
* The depth of the deployed FNs is different, it can be controlled with the help of sono-buoys. The FNs can freely move in the horizontal direction, but the movement in the vertical direction is negligible.
* All the sensor nodes, including the CHs, contain an equal amount of initial energies.
* The communication channel used is symmetric. This means that the amount of energy consumed by \(N_{i}\) to forward data to \(N_{j}\) is equal to the energy consumed by \(N_{j}\) to convey its data to \(N_{i}\) for a defined SNR.
* The FNs in the overlapping region contain the correlated data.
* FNs can perform sensing duties in sleep-mode or low-power listening mode, but they can communicate only in the active mode.
## 2 Materials and Methods
### Finger Real Motion  
#### 2.2.1 Experimental Setup  
To propose an improvement to the actual prototype, we performed several experiments to obtain real curves for the fingers' flexion-extension movements. These real curves were used to evaluate the accuracy of the elliptical trajectory proposed in the original prototype. The finger movements were recorded using the motion capture system Qualisys Track Manager (v2018, Qualisys, Goteborg, Sweden, 2018), which was used to process those records. A set of coordinates for each marker as a function of time was defined to analyze the data. Mokka (v0.6.2, BTK, 2019) and MATLAB software (v2018, MathWorks, Meudon, France, 2018) were used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vector** & **1** & **2** & **3** & **4** & **5** & **6** \\  & **From A to B** & **From B’ to B** & **From B to C** & **From C to F** & **From C to A** & **From B’ to D** \\ \hline Tag & \(a\) & \(d\) & \(b\) & \(f\) & \(c\) & \(e\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tags for vectors.  
The system was calibrated using a reference frame located in the corner on a flat surface. The volunteer placed their forearm, as shown in Figure 7. We recorded the flexion-extension movements from the index to small fingers. A total of six volunteers were involved in the experimentation, both men and women, in the age range of 25 to 40 years old without any finger injury.  
A set of 21 markers was used and installed on the right hand of each volunteer. Table 2 describes each marker's location and the label that was used to reconstruct the hand in digital form. The digital reconstruction of the hand is shown in Figure 8. The digital reconstruction was based on marker locations following the anatomy of the subject.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Finger** & **Marker** & **Articulation** \\ \hline \multirow{4}{*}{Thumb} & T1 & Tip \\  & T2 & IP \\  & T3 & MCP \\  & I1 & Tip \\  & I2 & DJ \\  & I3 & PIJ \\  & I4 & MCP \\  & M1 & Tip \\  & M2 & DJ \\  & M3 & PIJ \\  & M4 & MCP \\  & R1 & Tip \\  & R2 & DJJ \\   Ring & R3 & PIJ \\  & R4 & MCP \\  & S1 & Tip \\  & S2 & DJ \\   Small & S3 & PIJ \\  & S4 & MCP \\  & W1 & Carpal bones \\  & W2 & Carpal bones \\ \hline \hline \end{tabular}
\end{table}
Table 2: Localization and names of the markers in the hand.  
Figure 7: The experimental site with the Qualysis system.  
The existing prototype was designed only for the flexion-extension movements from the index to small fingers and considered only the trajectory of the distal phalanx; for this reason, only the marker placed in the distal phalanx for these fingers (I1, M1, R1, and S1) was analyzed.
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 2 Materials and Methods
### Culturing System  
The culturing system consisted of a peristaltic micropump with a dedicated controller, a medium bottle containing sterile distilled water and a lab-on-chip in a packaging with microfluidic connections (Figures 7 and 8). A Takasago peristaltic pump (model: RP-Q1.5S-P45Z-DC3V, Takasago Fluidic Systems, Japan) was used, which permitted flow control in the range of 0.1-0.45 mL/min. A flow management controller was specially designed and made to provide delivery of the medium at programmed intervals.  
Figure 6: Rotary Wall Vessel: (**a**) scheme, (**b**) laboratory version.  
The pump controller is a standalone unit based on an ATtiny48 microcontroller. It has two independent channels, so one unit can control two different culturing systems. The flow rate is regulated by PWM (pulse width modulation) with base frequency of 125 Hz and resolution of 0.1%. The PWM signal can be also switched on and off for some periods of time, with a maximum of approximately 16 min. on, set with 1 sec. resolution, and 100 h off, set with 6 min. resolution. Additionally, the first run of the pump can last for a different time than the normal mode (on-state) time in order to pre-charge the culturing system. All parameters can be set via 5 keys and a 3-digit LED display. Unit dimensions are approx. 80 \(\times\) 80 \(\times\) 20 mm.  
In our experiments, a pulse-based medium flow was applied and every 10 h fresh water entered the lab-on-chip microchannel at the minimal flow value (200 \(\upmu\)L/min). The  
Figure 8: Culturing system.  
Figure 7: Scheme of the culturing system.  
entire culturing system was placed in an incubator (ST 3/3 B Smart, Pol-Eko-Aparatura) to ensure homogeneous and optimal fungi growth at a temperature of 27 degC. Observation of the culture development was performed every 24 h, utilising an optical microscope (Leica DM750) with a Leica ICC50 W Microscope camera. Special attention was paid to indicating the start of germination and evaluating the physiological character of the mycelium growth.
# A Smart Parking Solution Architecture Based on LoRaWAN and Kubernetes
## 3 Analysis of Previous Works
### Analysis of Works That Emphasize Communication Protocols  
While Wi-Fi, Bluetooth Low Energy (BLE) and Zigbee are appropriate for short distances (less than 500 m). Network connectivity must reach long distances. In real conditions, parking slots are bigger and cover hundreds of square meters particularly in places like hospitals, universities, schools, cities. LPWAN protocols are suitable for covering long-range distances at low power consumption. Protocols like SigFox, LoRaWAN or NB-IoT could be considered for implementing a smart parking solution. Due to its versatility and openness, LoRaWAN is suitable protocol for a smart parking solution as discussed in [14; 16; 20].
## 2 Class-D PA for Polar-Mode Architectures
### Envelope Nonlinearities in Polar-Mode RF PAs
#### 2.1.2 Class-D Transformer-Coupled Current Switching  
The transformer-coupled current switching (TCCS) topology [16; 17], as shown in Figure 3b, is a widely used topology that helps to mitigate the nonlinearities related to the envelope leakage (\(v_{E}\) to \(v_{O}\)) effect. Its push-pull operation theoretically cancels the envelope current (\(i_{E}\)) baseband component before being applied (\(i_{RP}\)) to the low-Q resonator (\(L_{P}\) and \(C_{P}\)), as seen in the following:  
\[i_{RP}(t)=\frac{n}{m}i_{E}(t)s_{C}(t) \tag{3}\]  
where the envelope-independent term (\(i_{E}\)) was removed.  
However, the current-feeding inductor (\(L_{RFC}\)) required to operate from a voltage source (\(v_{E}\)) limits its bandwidth in wideband applications, resulting in spectral regrowth of the amplified signal (\(v_{O}\)).
## 2 Materials and Methods
### Data Collection  
Data were collected from schools in seven communities adjacent to the BKFFPA: San Isidro Poniente (primary), Venustiano Carranza (primary), Sabana San Francisco (primary), Puerto Arturo (secondary and high school), Candelaria (high school), San Felipe Primero  
Figure 1: Location of the communities where questionnaires were applied in the municipality of José Maria Morelos, Quintana Roo, Mexico. The red polygon corresponds to the BKFFPA area, and green polygons with numbers correspond to the ejidos of the schools: (1) Othón P. Blanco, (2) La Candelaria, (3) Puerto Arturo, (4) Sabana San Francisco, (5) San Felipe, (6) San Isidro Poniente, (7) Venustiano Carranza.  
(primary) and Othon P. Blanco (primary and secondary). This included five primary schools, two secondary schools, and two high schools (Figure 1). To obtain the data, we had the approval of the principals and teachers of the schools in which the environmental education workshops were given. Primary education is almost universal in these communities, each having at least one primary school. However, secondary and high schools are not as widespread, requiring students at these levels to travel several kilometres to neighbouring communities. This contributed to achieving spatial representativeness across the seven communities [33]. These communities were selected because they had previously been part of a wildlife monitoring project using camera traps, and some residents are part of the community rangers.  
For this cross-sectional study, students were categorised based on their years of schooling: lower primary (1 to 3 years), upper primary (4 to 6 years), secondary (7 to 9 years), and high school (10 to 12 years). The age range of the students was as follows: lower primary school 6 to 8 years, upper primary school 9 to 12 years, secondary 13 to 15 years and high school 16 to 18 years (Table 1). Lower primary students were asked to draw their favourite animals to explore their knowledge of the local fauna. Semi-structured interviews were conducted for upper primary, secondary, and high school students. These interviews included closed-ended, open-ended, and multiple-choice questions to gather comprehensive information. The topics addressed included demographics, knowledge about wildlife species and their importance, perceptions of animals' representative of their community, views on species that had disappeared and the perceived reasons for their decline, suggested actions to protect animals, and opinions on the main actors responsible for conservation efforts within their community. The activities were carried out during the second semester of 2019, as part of environmental education workshops to teach children the fundamentals of wildlife conservation in the Selva Maya.
## 3 Expert Systems for Features Classification
### Optimization of the Learning Process of ANN Expert System for IMs Fault Diagnosis
Between 60 and 80 percent of features are assigned to the training set, between 10 and 20 percent features for the test set, and the rest is assigned to the validation set. In the _'training/validation/test cross validation'_ method, it is important that the features selected on each set are representative of the whole range of values that the fault features can reach. In this research, this issue has been solved with the generation of a features space and its segmentation in a random sequence. As analyzed in [12], there are several learning algorithms based on a gradient back-propagation, where each one has its advantages and disadvantages. The scaled conjugate gradient back-propagation algorithm has been chosen in this work as it guarantees a global optimal learning for classification problems with a moderate computational complexity, and without over-training locally (for the specific ANN structure trained). The cost function used to minimize the gradient error is the _'cross entropy'_ formulated as:  
\[FC=-\frac{1}{n}\cdot\sum_{i=1}^{n}\left[y_{i}\cdot ln(a_{i})+(1-y_{i})\cdot ln (1-y_{i})\right] \tag{3}\]  
where the variable \(n\) denotes the total number of features for training, variable \(a\) stands for actual outputs and variable \(y\) stands for desired outputs.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 3 Radiation-Hardened SRAM Cell Design
### Quad-Node 10 T or Quatro-10 T Cell and Write-Enhanced Quatro-12 T Cell (WE-Quatro-12 T)
Due to this similarity, the read and write operations are also almost same as in Quatro-10 T.  
Figure 3: Schematic of (**a**) Quatro-10 T and (**b**) WE-Quatro-12 T SRAM cell. Similar to Quatro-10 T, PS-10 T and NS-10 T have only partial SEU robustness. In PS-10 T, 0 to 1 SEU cannot be recovered, while 1 to 0 SEU cannot be recovered in NS-10 T. For example, when NS-10 T stores datum \"1\", which means that the nodes' voltages are similar to A = VDD, B = 0 V, C = VDD, and D = 0 V, the node A can be accidently lowered by SEU. This results in the turning on of MP1 and MP4, leading to a rise in the nodes B and D. Thus, the pull-down path of A--MN1 and MN2--is enabled to statically drive A to 0 V, which means that the data are flipped. Figure 4c shows the radiation-hardened design of the 12 T SRAM cell (RHD-12 T) proposed in [3], which has a similar structure to PS-10 T. The difference in RHD-12 T from PS-10 T is that the sources P3 and P4 are not driven by S1 and S0, respectively. Instead, P2 and P5, whose gates are S0 and S1, are newly added to drive P3 and P4, respectively. The shallow trench isolation is inserted in the P2-P3 stack or P5-P4 stack. In this manner, the drain nodes of P2 and P5, where charges can be collected by SEU, can be isolated from the source nodes of P3 and P4, where the collected charge could have been injected to cause a node flip. However, as with PS-10 T, RHD-12 T also cannot be recovered from the SEU induced to \"0\" storing the Q or QN nodes.