esa-sceva/satcom-chunk-collection huggingface dataset:
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 2 Experimental Procedure
### Passive Thermography  
In the passive thermography experiment, the suction side of the blade was monitored outdoors during a sunny day from morning until the afternoon. The blade's position was not changed relative to the IR camera during this time. This experiment, whose setup is depicted in Figure 2, sought to determine the most favorable conditions to reveal the most defects and to evaluate the fault detection capability of thermography when the blade is heated by the sun.  
The experiment was conducted on a sunny day in July 2017 from 9:00 a.m. to 7:30 p.m. Sunrise and sunset on this day were 5:53 a.m. and 9:30 p.m., respectively. During the experiment, the sky was clear, the humidity was almost 36%, and the temperature varied between 16 \({}^{\circ}\)C and 26 \({}^{\circ}\)C. A T1030 Sc IR camera made by FLIR Systems was located 4 m from the blade section and equipped with a 21.2 mm lens, resulting in a spatial resolution of 4 mm per pixel. ResearchIR, a software package developed  
Figure 1: (**a**) The damaged wind turbine blade. (**b,c**) The defect plate with flat-bottomed holes. All holes were drilled from the rear and did not penetrate the outer surface.  
by FLIR Systems that provides high-speed data recording and image analyzing capabilities was used to record thermograms at a frequency of 1 Hz.
# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 4 Experiment
### Ablation Test  
Our model is characterized by data augmentation and feature augmentation. Therefore, we designed an ablation test to verify the advantages of each module. Firstly, the data augmentation module was removed from the model, and the model only extracted the fact description features from word and sentence levels, and the variant model was named no-data. Subsequently, the feature augmentation module based on DGCN was removed,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **AUC** \\ \hline HAN & 61.3 \\ \hline Few-attribute & 73.9 \\ \hline Cross-loss & 74.6 \\ \hline Ours & 79.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: AUC values of different models on Criminal-L.  
Figure 7: Performance comparison of our model and Few-attribute on specific charges.  
and the model did not introduce external knowledge such as charge information, and the variant was named no-feature. Finally, the category prior loss function was replaced by the ordinary cross-entropy loss function, and the variant was named cross-loss. Experiments were conducted on three datasets and the results are shown in Table 8. It is observed that the prediction performance decreases when any of the modules is removed. Specifically, when the Mixup data augmentation module is removed, the performance decreases significantly on the Criminal-S dataset because the training samples can be implicitly augmented by Mixup strategy, thus improving the model's training effect. When the feature augmentation module is removed, the model performance decreases significantly because this paper extracts features from the charge information into the model by DGCN, which provides external knowledge for the low-frequency and confusing charges existing in the dataset, thus effectively improving the representation capability of the model. Finally, when ordinary cross-entropy loss is used instead of the optimized loss in this paper, the accuracy is slightly reduced, but the F1 value decreases significantly. By analyzing the results, the decrease in performance mainly originates from the performance on low-frequency charges, which accounts for a low proportion of the dataset, so the accuracy rate does not fluctuate much, further proving the effectiveness of the category prior loss function on few-shot charges.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing  
Images from the UAV and satellites were uploaded to Google Earth Engine (GEE) as private assets. All subsequent image processing was performed in GEE, with some post analysis on data exported from GEE performed using Python. GEE allows quick and powerful cloud-based spatial data analysis, and includes algorithms for image co-registration, clustering and supervised classification [18].
## 4 Conclusions  
The performance of the proposed methodology GSLEO-AEFA with its counterpart algorithms were tested on 23 benchmark functions by conducting overall effectiveness, convergence analysis, Friedman and Wilcoxon rank-sum statistical tests as well as computational analysis. Even in the varying dimension sizes, GSLEO-AEFA has achieved the best optimization accuracy among the algorithms tested.  
The performance of GSLEO-AEFA was then validated on real engineering design problems including antenna optimization. In here, it has been outlined that the success of the proposed algorithm was maintained at the same level for all of the design problems. The proper use of local escaping operator (LEO), Gaussian mutation (GM) and specular reflection learning (SRL) techniques in the algorithm GSLEO-AEFA provides a better balance in exploration and exploitation by overcoming not only the original algorithm which is derived from but the other algorithms used in the comparison.  
Although its performance is quite satisfactory, it can be acknowledged that the computational time is not always satisfactory. This is mainly caused because of the structure  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & & **GSLEO-AEFA** & **AEFA** & **PSO** & **DE** & **JAYA** & **SCA** & **CS** \\ \hline s1 & AVG & \(\mathbf{0.000}\) & 1.351 & \(1.083\times 10^{-2}\) & 1.603 & 5.448 & \(1.370\times 10^{1}\) & 5.477 \\  & STD & \(\mathbf{0.000}\) & \(2.469\times 10^{-1}\) & \(5.946\times 10^{-4}\) & \(2.752\times 10^{-1}\) & 4.379 & 1.915 & \(5.862\times 10^{-1}\) \\ \hline s2 & AVG & \(\mathbf{8.611\times 10^{-7}}\) & 6.839 \(\times 10^{1}\) & 7.255 \(\times 10^{1}\) & 3.407 \(\times 10^{1}\) & 9.047 \(\times 10^{1}\) & 8.684 \(\times 10^{1}\) & 8.040 \(\times 10^{1}\) \\  & STD & \(\mathbf{1.756\times 10^{-6}}\) & 9.564 & 4.589 & 5.161 & 1.687 \(\times 10^{+1}\) & 1.192 \(\times 10^{1}\) & 3.057 \\ \hline \end{tabular}
\end{table}
Table 12: Results of antenna S-parameter optimization problem, significant values are in bold.  
of the GSLEO-AEFA, since it was generated by having three individual methods as well as AEFA. However, researchers may debate for a fine balance between the optimization accuracy and the computational time for the problem to be solved.  
O.R.A.; designing experiment, doing tests, data analysis and writing. E.D.U.; project administration, data analysis, writing, revising and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki. The approval of the Institutional Review Board was exempted due to the use of publicly available data.  
Patient consent for the use of anonymized biological material was in accordance with Article 34 of the Human Research Act, The Federal Assembly of the Swiss Confederation.  
The data obtained through the experiments are available upon a reasonable request from the first author, [PERSON].  
The authors declare that there are no conflict of interest.
## 1 Introduction
### Intelligent Control System Combined with the MOPSO Algorithm  
Figure 2 shows the structure diagram of the system. Its main control variables are the output content of the system, specifically including the aeration volume, return sludge ratio, and excess sludge discharge in the aeration tank. The sequencing batch activated sludge process is a biological metabolism process that requires oxygen. Microbes in the reactor can decompose substances in the sludge through COD generated during aeration, and factors such as influent concentration, aeration volume, and sewage concentration will directly affect the change in COD. In general, COD can be detected online using a detector [16], so that COD can be used as the control parameter in the SBR method. In the system, air is directed to the aeration tank through the blower to add dissolved oxygen to the tank. Then, the fan speed controller is adjusted by recording the COD value online, the air inlet volume of the aeration tank is adjusted, and finally the chemical oxygen demand is made to be within the appropriate range for the whole reaction process.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 5 Application to Predictive Maintenance of LNG Tank Refilling by Means of Trucks
### Prognosis of Remaining Time to Refill Tank Using [PERSON]'s Double-Filter Method  
The double aliasing of Brown, explained in Section 4.2.2, is very useful for the prognosis of the remaining time to refill the tank. Figure 10 shows the temporal pattern of the decreasing level in the blue line, and the two filtered signals, \(y_{1}\) in red and \(y_{2}\) in green, with a prefixed parameter \(\alpha=0.05\).  
Figure 11 shows remaining useful time \(h_{p}\) to achieve the minimal level in the tank in the blue line, with a confidence interval of \(+/-5\%\), in the red and green line, respectively. The results showed that [PERSON]'s algorithm can predict the situation of achieving the minimal tank level in 9 days. Reliable remaining useful time obtained in this scenario is 9 days, practically 50% of the time needed to empty the tank. This result is useful information to outline the process for. managing a request for a new tank refill. Comparing the Brown double-aliasing method of prognosis with the FIR method for the same scenario, both were both quite good, with 9 and 14 days of anticipation time, respectively, but the FIR method gave better anticipation of days. The same results were obtained for other scenarios, and this better performance of FIR is quite reasonable because it is a nonlinear method, uses other variables such as tank temperature, and it needs a learning period of data to produce good results.  
Figure 9: Prognosis stated at \(t=1\) day (blue) and at \(t=2\) days (red).  
Brown double aliasing is a linear method but it does not need any extra information (e.g., temperature) or any learning period of data. Similar results were obtained in any period of the year, but time anticipation was always around 50% of the cycle, which allows to predict with a minimum of days to achieve the minimal tank level, this being useful information for a predictive-maintenance plan to refill the tank by means of trucks.  
Figure 11: Remaining useful time to achieve minimal tank level in March 2018.  
Figure 10: Temporal pattern of NVT real and Brown-filtered signals for \(\alpha=0.05\).
# ECOTIRE: A New Concept of a Smart and Sustainable Tire Based on a Removable Tread
## 4 Experimental Study of the Casing-Tread Mechanical Joint
### Methodology  
As mentioned, a sample of rubber with the suggested arrangement and dimensions undergoes testing under several conditions. To accomplish the goal of optimizing force transmission, a specialized testing machine illustrated in Figure 6, is employed. This device, designed and manufactured specifically for this purpose, facilitates measurements by integrating two load cells, one dedicated to measuring the normal load (vertical force and inflation pressure) and the other tasked with measuring the tangential tensile force (longitudinal or lateral force). The testing protocol begins with the application of an initial normal load onto the tire tread assembly. Following this, the tensile force is increased until it reaches the predetermined allowable displacement in tangential direction, this being 15 mm, at a speed of 5 mm/min. This progressive increase in force allows for a comprehensive assessment of the sample behavior under different initial normal force conditions. By monitoring and analyzing the data collected from the load cells throughout the testing procedure, it is possible to understand the behavior of the tire tread assembly. The aim of this testing and analysis is to refine the design, improving the geometric models and validating the simulations performed with these models, thereby enhancing their efficiency in transmitting forces and improving overall performance.  
Figure 5: (**a**) Manufactured test samples, (**b**) holds with the geometry of the test samples, (**c**) and one tested sample representation with the detail of the boundary conditions.  
Figure 6: Image of the “virtual contact patch models” test rig (**a**) and the device mounted in the universal testing machine for applying normal and tangential forces simultaneously (**b**).  
The specialized testing machine (including the \"virtual contact patch\" test device) has been designed to apply normal load without constraining the tangential displacement of the samples, being a fundamental condition to evaluate the behavior of the joint including displacements and free deformations both in longitudinal and lateral directions. It is also essential not to keep the normal force/load constant in order to study its evolution when applying tangential force. This provides fundamental information on how the mechanical link behaves and whether the surfaces are well connected or trying to separate from each other. Additionally, it is important to note that during testing, longitudinal and lateral forces are not simultaneously applied and measured; rather, they are tested individually, one at a time.
## 3 System Model
### Channel Model  
Radio waves are affected by various factors during propagation, such as free space propagation loss, rain, fog, poor angle of inclination, etc. As satellite-ground links operate at high-frequency bands, atmospheric attenuation will be the major effect for propagation channels. The Shadowed-Rician fading model is widely adopted in the current literature [23; 24], and the channel fading coefficient is:  
\[\tilde{I}_{p,q}=A_{1}\exp\left(j\psi_{p,q}\right)+A_{2}\exp\left(j\phi_{p,q} \right), \tag{1}\]  
where \(\tilde{I}_{p,q}\) is the fading coefficients of the channel between satellite \(q\) and corresponding terminal \(p\), including scattered and line-of-vision (LOS) components. \(\psi_{p,q}\in[0,2\pi)\) denotes the stationary random phase, \(\phi_{p,q}\) denotes the deterministic phase of LOS component, and \(A_{1}\) and \(A_{2}\) represent the amplitudes of the scattering component and LOS component, which are independent stationary random processes following Rayleigh and Nakagami-m distributions, respectively.  
Let \(x_{p,q}\) be the signals transmitted by satellite \(q\) and \(N\) be the set of satellites. The received signal \(y_{p,q}\) at user \(p\) can be formulated as  
\[y_{p,q}=\sqrt{P_{p,q}G_{re}}\tilde{I}_{p,q}x_{p,q}+\sum_{q^{\prime}\in N\backslash q }\!\!\!\sqrt{P_{p,q^{\prime}}G_{re}}\tilde{I}_{p,q^{\prime}}x_{p,q^{\prime}}+ \text{NOISE}_{p}, \tag{2}\]  
where \(P_{p,q}\) is the transmit power of satellite \(q\). \(G_{re}\) denotes antenna receiving gain. NOISE\({}_{p}\) denotes additive Gaussian noise at the receiver \(p\), where \(\sigma_{p}^{2}\) denotes the corresponding noise power. The first part of \(y_{p,q}\) is the useful signal, and the second part of \(y_{p,q}\) is the interference from other LEO satellites.  
According to (2), the Signal to Interference plus Noise Ratio (SINR) of the link between user \(p\) and satellite \(q\) can be expressed as [25; 26]  
Figure 1: GEO/LEO heterogeneous satellite system.  
\[\gamma_{p,q}=\frac{P_{p,q}G_{re}\big{|}\tilde{I}_{p,q}\big{|}^{2}}{\sum_{q^{ \prime}\in N\setminus q}P_{p,q^{\prime}}G_{re}\big{|}\tilde{I}_{p,q^{\prime}} \big{|}^{2}+\sigma_{p}^{2}}. \tag{3}\]
## 3 Monitoring System Architecture and Software Architecture  
### Monitoring System Architecture  
The proposed cloud server-assisted remote monitoring and fault identification system for DTPPF is mainly composed of monitoring terminals, a cloud server, and devices such as computers or cellphones. The system architecture is shown in Figure 3.  
As shown in Figure 3, the monitoring terminal consists of DTPPF\({}_{1}\)-DTPPF\({}_{y}\), ST\({}_{1}\)-ST\({}_{y}\), and DC\({}_{1}\)-DC\({}_{y}\). DTPPF\({}_{1}\)-DTPPF\({}_{y}\) are the \(y\) installed DTPPFs, while ST\({}_{1}\)-ST\({}_{y}\) are the temperature acquisition circuits of the core devices responsible for monitoring the temperatures of DTPPF\({}_{1}\)-DTPPF\({}_{y}\); the data acquisition modules DC\({}_{1}\)-DC\({}_{y}\) communicate with the controllers of PDF\({}_{1}\)-PDF\({}_{y}\) to obtain their electrical parameters.  
The data collected by DC\({}_{1}\)-DC\({}_{y}\) are transmitted to the back-end service layer of the cloud server through a wireless communication network (WCN) and stored in a database after parsing. The front-end application layer of the cloud server uses a local computer to read data from the database and visualize these data to achieve human-machine interaction. In addition, users can browse the running state parameters of the DTPPF and the fault information of the core devices for further analysis.  
Figure 4 presents the hardware used in the data acquisition module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type** & **Fault Device** & **Symbol** \\ \hline \multirow{4}{*}{Overcurrent fault} & Filter capacitor C1 & \(F_{\text{C1S}}\) \\  & Filter capacitor C2 & \(F_{\text{C2S}}\) \\  & Filter capacitor C3 & \(F_{\text{C3S}}\) \\  & Filter capacitor C4 & \(F_{\text{C4S}}\) \\ \hline \multirow{4}{*}{Overvoltage fault} & Filter capacitor C1 & \(F_{\text{C1V}}\) \\  & Filter capacitor C2 & \(F_{\text{C2V}}\) \\  & Filter capacitor C3 & \(F_{\text{C3V}}\) \\  & Filter capacitor C4 & \(F_{\text{C4V}}\) \\ \hline Open-circuit fault & ECFR thyristor & \(F_{\text{L1S}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fault type parameters of DTPPFs.  
Figure 3: Architecture of cloud server-assisted remote monitoring and fault identification system for DTPPFs.  
In Figure 4, adopted the microprocessor is of the STM32F405 VGT6 type and the communication module is a GPRS communication module. The temperature is detected by LM75 temperature sensors, which have a measurement range of \(-\)55-125 \({}^{\circ}\)C and a measurement accuracy of 0.125 \({}^{\circ}\)C. These are responsible for obtaining the temperature signals T\({}_{1}\) of the ECFR and T\({}_{2}\)-T\({}_{5}\) of the filter capacitors.
###### Abstract  
Passive wireless transducers are used as sensors, probed by a RADAR system. A simple way to separate the returning signal from the clutter is to delay the response, so that the clutter decays before the echoes are received. This can be achieved by introducing a fixed delay in the sensor design. Acoustic wave transducers are ideally suited as cooperative targets for passive, wireless sensing. The incoming electromagnetic pulse is converted into an acoustic wave, propagated on the sensor substrate surface, and reflected as an electromagnetic echo. According to a known law, the acoustic wave propagation velocity depends on the physical quantity under investigation, which is then measured as an echo delay. Both conversions between electromagnetic and acoustic waves are based on the piezoelectric property of the substrate of which the sensor is made. Investigating underground sensing, we address the problems of using GPR (Ground-Penetrating RADAR) for probing cooperative targets. The GPR is a good candidate for this application because it provides an electromagnetic source and receiver, as well as echo recording tools. Instead of designing dedicated electronics, we choose a commercially available, reliable and rugged instrument. The measurement range depends on parameters like antenna radiation pattern, radio spectrum matching between GPR and the target, antenna-sensor impedance matching and the transfer function of the target. We demonstrate measurements at depths ranging from centimeters to circ 1 m in a sandbox. In our application, clutter rejection requires delays between the emitted pulse and echoes to be longer than in the regular use of the GPR for geophysical measurements. This delay, and the accuracy needed for sensing, challenge the GPR internal time base. In the GPR units we used, the drift turns out to be incompatible with the targeted application. The available documentation of other models and brands suggests that this is a rather general limitation. We solved the problem by replacing the analog ramp generator defining the time base with a fully digital solution, whose time accuracy and stability relies on a quartz oscillator. The resulting stability is acceptable for sub-surface cooperative sensor measurement.  
acoustic transducer; surface acoustic wave; cooperative target; wireless sensing; ground-penetrating RADAR +
Footnote †: journal: Sensors  
08.18.246. doi:10.3390/s18010246
## 3 Controller Design Approach
### Proposed GA-Fuzzy-Based Control Schemes  
#### 3.3.1 Governor Control System  
The basic construction of the governor control approach designed for the conventional generators is presented in Figure 2. The deviation of rotor speed of each conventional generator and its variation during a sampled time are considered as two input signals for the fuzzy controller, while its output is the control signal to adjust the governor set point of each generator. The governor has non-linear characteristics, but can be modeled with a first-order lag transfer function where the time constant is 0.08 s. Also, the value of \(R\) is 2.4 Hz/pu.Mw. Fuzzy membership functions for the input and output patterns of the proposed FLC technique are addressed in Figure 3. Also, Table 1 shows the associated control rules. The membership functions corresponding to the input and output variables are set as Negative Large (NL), Negative Medium2 (NM2), Negative Medium1 (NM1), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium1 (PM1), Positive Medium2 (PM2), and Positive Large (PL). They have been arranged based on triangular membership functions. The antecedent part of each IF-THEN rule is created using the And function. Also, Mamdani fuzzy interference system is used in this control approach. Finally, resultant fuzzy subsets representing the controller output are converted to the crisp values. Unlike the traditional control theorems, which are based on the linearized models of the controlled system, the fuzzy control scheme tries to build the proposed controller in a direct way based on long-term experience, knowledge of domain operators, and measurements. Then, the GA is used to tune the input and output membership function parameters of the proposed fuzzy controller to improve its performance using integral absolute error criteria (IAE) utilizing the MATLAB(r) (Release 2016a, The MathWorks, Inc., Natick, MA, USA) environment based on the following equation:  
\[\text{Fitness}\quad\text{Function}=\int_{0}^{t}|\Delta f|\;dt. \tag{1}\]  
The optimal values for membership function parameters obtained from the GA are then listed in Table 2. In Table 2, unit of the input membership functions parameters (\(A_{1}\)-\(A_{14}\)) is Hz while the unit of the output membership functions parameters (\(A_{15}\)-\(A_{21}\)) is pu.Mw.  
Figure 3: **(a)** Fuzzy membership functions for the governor controller’s first input; **(b)** Fuzzy membership functions for the governor controller’s second input; **(c)** Fuzzy membership functions for the governor controller’s output. NL: Negative Large; NM2: Negative Medium2; NM1: Negative Medium1; NS: Negative Small; Z: Zero; PS: Positive Small; PM1: Positive Medium1; PM2: Positive Medium2; PL: Positive Large.  
Figure 2: Governor control scheme.