esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Bending Test  
The bending strength (MOR) and modulus of elasticity in bending (MOE) were determined according to ISO 13061-3 [16] and ISO 13061-4 [17]. The test was performed by means of a ZWICK/ROELL equipment type BT1 FB050 TN.D30/2007 (Figure 5a), endowed with a device (support) for positioning the specimen (the two bearings being situated at a distance of 280 mm), a device for applying the load, and a deflection-measuring device. The MOE determination (Figure 5b) consists of measuring the deformation of the specimen at mid-length during the application of progressively increasing force within the region of proportionality between the load and the deflection, while the MOR determination (Figure 5c) consists of measuring the maximum load required to break the specimen under a static load applied transversely in the middle of the simply supported specimen. The MOE and MOR measurements used the same samples.  
The force was applied radially on half of the samples originating from the solid wood boards (Figure 6a) and tangentially (Figure 6b) on the other half, with a view to compare the values obtained for the bonded boards with both cases, considering that in the case of the glued boards, the force direction relative to the annual rings could be neither radial nor tangential (Figure 6c). A total of 48 glued samples (12 from each half log) and a total of 120 solid wood samples (15 radially + 15 tangentially loaded, from each half log) were used in the experiment.  
Figure 4: Reconstituted board after edge-cutting at 45\({}^{\circ}\), anti-parallel rotation at 180\({}^{\circ}\), and gluing of two tapered mirrored sideboards.  
The machine software records and displays all measured data automatically. A data correction depending on the real moisture content was performed in order to assess \(\rho_{12}\), \(\text{MOE}_{12}\), and \(\text{MOR}_{12}\).  
Then, the collected data were statistically analyzed by means of one-way ANOVA. The statistical test was performed by means of the Microsoft Excel and Data Analysis Tool Pack. First, a characterization of the three properties, variation in log length, and cross-section was undertaken. Then, for each log, the mean MOE and MOR values obtained for the bonded samples were compared to the mean values obtained for the solid wood samples from the same log, and then from both logs all together. In order to have a fair comparison, only the values for the outer zone were taken into account for the solid wood samples (considering that the bonded boards were all achieved from material originating from the outer area of the logs). The final outcome was to evaluate if there are significant differences between the bonded elements and the solid wood elements in terms of MOE and MOR.  
Figure 5: ZWICK/ROELL equipment type BT1 FB050 TN.D30/2007 (**a**) used for the determination of MOE (**b**) and MOR (**c**).  
Figure 6: Direction of the bending force relative to the annual rings: (**a**)—solid board, radially loaded; (**b**)—solid board, tangentially loaded; (**c**)—bonded board.
## 4 Results and Discussion
The main purpose of Test 2 was the possible detection of different cable behaviors in terms of the sag-angle relationship during a step-by-step increasing of tensile load (instead of decreasing it, as in Test 1). Again, a higher discrepancy can be observed at higher tensile loads, where smaller angles must be detected by the sensor. The solution can be improved in these terms, as already pointed out previously, by adopting a MEMS accelerometer characterized by a higher amplitude resolution. An example can be represented by the IIM-42351 from TDK InvenSense, which allows an amplitude resolution up to 0.5 mg (using the \(\pm\)2 g full scale range). Despite of this, the validity of the proposed method in detecting the sag variation using angular measurements carried out with wireless sensors was verified with experimental tests. Therefore, the developed system can represent an integrated, low-cost, and robust solution to be used both for vibration monitoring of wind-induced phenomena on conductors and for sag monitoring during the line operativity. Figure 8: Comparison of the sag values estimated through the proposed method (red curve) with the reference measured values (blue curve) and the ones from the parabolic model (yellow curve) during Test 2.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 1 Introduction
### Notation  
The definitions of the acronyms that will be frequently used in this paper are summarized in Table 1 for ease of reference.
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 2 Study Materials and Method
### Automatic Classifier Using the Random Forest Algorithm
#### 2.2.2 Time-Domain Attributes
Four significant attributes are calculated from the SI function: the mean for SI (\(\mu\)SI), the standard deviation for SI (\(\sigma\)SI), the scintillation index ratio (SIR), and the ratio of the mean value to the standard deviation for SI (\(\sigma\)SI/\(\mu\)SI). In Figure 5, it is easy to find that the variations of MA and SI for the noise case are less than those for earthquake and landslide cases, so the attribute values, including \(\sigma\)MA, \(\sigma\)SI, MAR, and SIR, are lower. Due to the normalization, the ranking of \(\mu\)MA for different cases follows the rule \(\mu\)MA\({}_{\mathrm{noise}}>\mu\)MA\({}_{\mathrm{LS}}>\mu\)MA\({}_{\mathrm{EQ}}\). The ranking of \(\mu\)SI is \(\mu\)SI\({}_{\mathrm{EQ}}>\mu\)SI\({}_{\mathrm{LS}}>\mu\)SI\({}_{\mathrm{noise}}\), since SI is associated with the signal magnitude. In addition to the above eight attributes, the mean of an absolute seismic signal itself is an additional time-domain attribute to facilitate the efficiency of classification.
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
###### Abstract  
Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography.  
Keywords:GNSS; tomographic technique; water vapor; minimum bounding box +
Footnote †: journal: Remote Sensing, 2020, 12, 2744; doi:10.3390/rs12172744
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 5 Discussion
The power output increased due to the decrease in transient response in torque control, and this robust performance had the potential lead to an increase in the annual energy production (AEP) of a turbine in the rated wind speed region. As shown in Figure 8b, a failure situation in which the pitch actuator of one blade (named the second blade) becomes out of control at about 105 s was applied to both controllers, and this scenario is represented by a straight red line. As a case requiring the use of pitch control, the experiment was conducted in the region where the wind speed was higher than the rated wind speed. Prior to the pitch actuator failure, the \(H_{\infty}\) controller had superior regulating performance compared to the LQR controller, but the load reduction performances were similar for the two controllers. After the pitch actuator failure of one blade, however, the power-regulating performance of the LQR controller dropped, while the regulating performance of the \(H_{\infty}\) controller was maintained as if no failure happened. As a result, the thrust force was also reduced. Figure 8: Results from wind tunnel experiments of the target wind turbine. The left-hand plot (**a**) shows the results near the rated wind speed region (mean 6.2 m/s) for the Pitch-\(H_{\infty}\) controller (black solid lines) and the \(H_{\infty}\) controller (green dashed lines). The right-hand plot (**b**) shows the results in the blade failure situation above the rated wind speed region (mean 8.0 m/s) for the LQR controller (black solid lines) and the \(H_{\infty}\) controller (green dashed lines). To quantitatively compare the control performance of the proposed controller with the LQR controller for the blade failure situation in Figure 8b, the performances of both controllers are presented in Figure 9 as a spider plot. While LQR control did not reduce the standard deviation of the power and rotor speed in the blade failure situation, the proposed \(H_{\infty}\) control reduced the standard deviation of those by 48.5% and 38.9%, respectively. Also, the thrust load of the rotor was reduced by 1.6% using the \(H_{\infty}\) controller. However, the standard deviation of the blade pitch angle was increased by about 12.4% as a trade-off with the \(H_{\infty}\) controller, resulting in blade pitching with larger angles. As a result, the average power with the \(H_{\infty}\) controller was higher than that with the LQR controller by 2.7%. This clearly shows that the proposed \(H_{\infty}\) controller can cope with unexpected failure situations, such as blade failure, more effectively than the LQR controller (i.e., in terms of robustness).
## 2 Materials and Methods  
### Datasets  
#### 2.1.1 OCO-2 Dataset  
Orbiting Carbon Observatory-2 (OCO-2) is a satellite launched by NASA dedicated to monitoring the distribution and variability of atmospheric CO\({}_{2}\) concentrations on Earth. Its primary product is XCO2 data, which represents the average concentration of CO\({}_{2}\) in the dry air column extending from the Earth's surface to the top of the atmosphere, measured in parts per million (ppm). The OCO-2 spacecraft collects reflected sunlight in the A-band of molecular oxygen centered at 765 nm and the CO\({}_{2}\) bands centered at 1610 and 2060 nm in high-resolution spectra. OCO-2 XCO2 data have a spatial resolution of 2.25 km \(\times\) 1.29 km, a 16-day revisit period, and a satellite observing footprint of approximately 10 km. Despite the OCO-2 satellite can acquire nearly a million observations per day, only about 10% of these measurements are valid due to cloud cover and optically thick aerosols that impede observations of the entire atmospheric column [32]. Aerosol was the largest source of error for OCO-2 XCO2, followed by spectroscopy and calibration. The total terrestrial error due to all error sources is \(\sim\)1.5-3.5 ppm [33].  
In this study, we collected OCO-2 XCO2 data (OCO2_L2_Lite_FP V10r) from EARTH-DATA ([[https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/)]([https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/)) [last access: 18 March 2023]) from 2015 to 2020. Then, we filtered high-quality data with \"xco2_quality_flag = 0\" and removed outliers for each month according to the 3\(\sigma\) criterion. Considering the uneven spatial distribution of XCO2 data, which may introduce scale biases to the model results, we calculated the average values of XCO2 data within each 0.25\({}^{\circ}\) grid in China and generated the monthly 0.25\({}^{\circ}\) grid-scale XCO2 data in China for the period from 2015 to 2020, with a total of 211,116 data points.
## 2 System Model
### FSO System Model  
The FSO channel between node S and node R is modeled as Gamma-Gamma distribution with PE [8]. The Gamma-Gamma turbulence model characterizes the statistical behavior of received optical irradiance. This model has been widely used in recent literature to simulate FSO channels, depending on its double random scintillation model. The probability distribution function (PDF) of channel coefficient \(h_{FSO}\) is given as  
\[f_{h_{FSO}}(h_{FSO})=\frac{\xi^{2}}{h_{FSO}\Gamma(\alpha)\Gamma(\beta)}G_{1,3} ^{3,0}\left(\alpha\beta\frac{h_{FSO}}{I_{FSO}M_{o}}|_{\xi^{2},\alpha,\beta}^{ \varepsilon+1}\right) \tag{1}\]  
where \(\Gamma(.)\) is the well-known Gamma function, \(G_{p,q}^{m,H}\left(x|_{b_{1},\ldots,b_{q}}^{\varepsilon+1,\ldots,\varepsilon}\right)\) is the [PERSON]'s G-function. The AT causes the channel gain of the FSO link to be weakened, denoted as \(I_{FSO}\) dealt with S and path loss. Suppose the elements of \(I\) are modeled as independent and identically distributed random variables. \(\xi=\overline{\omega}_{e}/2\sigma_{s}\) is the ratio of equivalent beam radius and zero boresight displacement standard deviation at the photo detector (PD). The constant term \(M_{o}\) is the power fraction received when there is no detector. \(1/\alpha\) and \(1/\beta\) are the variances of large turbulent vortices and small turbulent vortices, respectively. The FSO system model is shown in Figure 2.  
The parameters \(\alpha\) and \(\beta\) are distance-dependent fading variables in the presence of AT. Parameter \(\alpha\) and \(\beta\) can be expressed as  
\[\alpha=\left\{\exp\left[\frac{0.49\sigma_{R}^{2}}{\left(1+1.1\sigma_{R}^{\frac {12}{6}}\right)^{\frac{1}{6}}}\right]-1\right\}^{-1} \tag{2}\]  
and  
\[\beta=\left\{exp\left[\frac{0.51\sigma_{R}^{2}}{\left(1+0.69\sigma_{R}^{\frac {12}{6}}\right)^{\frac{1}{6}}}\right]-1\right\}^{-1} \tag{3}\]  
where \(\sigma_{R}^{2}=1.23C_{n}^{2}v^{7/6}d_{SR}^{11/6}\) represents the Rytov variance, and \(C_{n}^{2}\), \(v\) and \(d_{SR}\) represent the refractive index structure constant, wave number, and link length, respectively. \(C_{n}^{2}\) is typically taken as a value in the range of \(10^{-17}\sim 10^{-13}\) for weak to strong turbulence conditions.  
Figure 1: RIS-assisted multi-user UAV communication system model.  
Figure 2: FSO system model.
## 5 Conclusions  
This paper proposed a QoS-oriented CR-OFDMA protocol based on CR. Firstly, high-priority and low-priority patients compete fairly for subchannels, guaranteeing the fairness of low-priority patients. Then high-priority patients who fail to compete can transmit data on the remaining subchannels, which ensures the QoS of high-priority patients. Lastly, low-priority patients who fail the competition use the feedback slots and the next round of the backoff slots of PU to transmit data, reducing the waste of channel resources. In addition, based on the proposed system, we established a two-dimensional Markov chain model and derived the expressions for throughput, channel utilization, and fairness index. Furthermore, numerical analysis is performed. An important result is that the number of users affects the system's performance. When the number of system users is small, increasing the number of users can improve the system throughput and channel utilization, but when the number of system users is large, increasing the number of users leads to system performance degradation instead. However, the CR-OFDMA proposed in this paper outperforms conventional OFDMA and DRA-OFDMA, regardless of the number of users in the system. In addition, the CR-OFDMA has the best throughput for high-priority patients compared with the conventional OFDMA and the DRA-OFDMA. This verifies that CR-OFDMA provides good QoS for high-priority users. Another important result is that collision probability affects the fairness index. When the collision probability is particularly large, the protocol is unfair, but the CR-OFDMA protocol in this paper is relatively fair as long as the collision probability does not exceed 0.9. The system considered in this paper simply divides patients into minor and critical patients, but the patient's condition is complex in reality. For future work, this study can be extended to multi-class user cases. In addition, combining NOMA with OFDMA will be considered to further improve spectrum efficiency.  
[PERSON] researched the literature, provided the mathematical models, designed the algorithm, completed numerical simulations, took charge of the original draft preparation, and edited the manuscript; [PERSON] conceived the study concepts, improved the systematic research and analysis methodology, and supervised the completion of the refinement of the paper; [PERSON], [PERSON] and [PERSON] gave valuable suggestions for revision and checked formula deducing and English grammar. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 61663024 and in part by the Hongliu First Class Discipline Development Project of Lanzhou University of Technology (225305).  
Not applicable.  
The data supporting this article are from previously reported studies and datasets, which have been cited.  
The authors declare no conflict of interest.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 3 Link Quality Measurement and the Metric for Evaluating
### Burstiness Distribution Metric
#### 3.2.1 Calculate Burstiness Distribution List  
First, the neighbor list (nbrlist) and the BDL are set to empty. When sensor node x receives a probe packet from node y, it saves node y's ID and sequence to its nbrlist if node y is not already on node x's nbrlist. If node y is on node x's nbrlist, node x will calculate the burstiness value for node y as follows:  
\[\text{burstiness\_value}(y)=\text{seqno}(y)-\text{nbrlist}[y].\text{seqno}(y)-1\]  
In which, seqno(y) is the sequence number in the new incoming probe packet from node y, and nbrlist[y].seqno(y) is the previous sequence number of node y saved in the neighbor list.  
The parameter burstiness_time_count(y) refers to a burstiness_value(y) set to 1, and the pair {burstiness_value(y), burstiness_time_count(y)} will be saved in node y's BDL if this value is not already on node y's BDL. If burstiness_value(y) is on node y's BDL, node x will increase the burstiness_time_count(y) referring to burstiness_value(y) by 1. Then, it updates the seqno(y) value in the neighbor list.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bursitness Value** & **0** & **1** & **2** & **3** & **4** \\ \hline
**Bursitness Time Count** & 634 & 129 & 31 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Burstiness distribution list (BDL) of a link after finishing the measure link quality (MLQ) period.  
Figure 2: The distribution of Burstiness compared with link Packet Reception Rate (PRR).  
This process will repeat during the measure link quality period. The detailed algorithm is in Algorithm 1, and the flowchart is in Figure 3.  
```
Input : Incoming probe P Output : BDL
1 : Initialize nbrlist is empty, BDL is empty
2 : When node y send probe packet:
3 : P = (y, seqno(y));
4 : When node x received probe packet from node y:
5 : If (y not belong to x's nbrlist)
6 : nbrlist \(\gets\) y, y's seqno;
7 : Else
8 : burstiness_value(y) = seqno(y) - nbrlist[y].seqno(y) - 1;
9 : If(burstiness_value(y) not in y's BDL)
10 : burstiness_time_count(y) = 1;
11 : (burstiness_value(y), burstiness_time_count(y)) \(\rightarrow\) y's BDL;
12 : Else
13 : (BDL[burstiness_value(y)].burstiness_time_count(y)++;
14 : Endif
15 : (nbrlist[y].seqno(y) \(\leftarrow\) seqno(y);
16 : Endif
```  
**Algorithm 1**Calculate burstiness distribution list
# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 3 Experiment and Results Analysis
### Analysis of Experimental Results
#### 3.4.2 Fusion Experiments  
The YOLOv5s-MobileNetV3 model is sequentially integrated with C3-Faster, CA, CBAM, and the EIOU loss function for fusion experiments. They undergo training and validation on the dataset, and the results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{4}{c}{**Backbone**} & \multirow{2}{*}{**FLOPs/G**} & \multirow{2}{*}{**Param/M**} & \multirow{2}{*}{**Size/MB**} & \multirow{2}{*}{**mAP/\%**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{6-9}  & & & & & & & & \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 15.8 & 7.03 & 14.5 & 85.5 & 149 \\
2 & \(\surd\) & \(\times\) & \(\times\) & 29.6 & 4.32 & 9.1 & 82.7 & 93 \\
3 & \(\times\) & \(\surd\) & \(\times\) & 6.0 & 3.26 & 7.0 & 78.6 & 107 \\
4 & \(\times\) & \(\times\) & \(\surd\) & 5.9 & 3.53 & 7.5 & 80.3 & 136 \\ \hline \hline \end{tabular} \(\surd\) denotes the use of this backbone; \(\times\) denotes the absence of this backbone.  
\end{table}
Table 2: Horizontal comparison experiments of backbone networks.  
The experimental results illustrate that each improvement made to the YOLOv5s-MobileNetV3 model contributes positively to the final outcome. Specifically, the following outcomes are noted.  
1. The integration of the C3-Faster module not only reduces the model computation, parameters, and size but also enhances the mean Average Precision (mAP) and detection speed.
2. The inclusion of attention mechanisms incurs minimal computational and parameter costs while improving the recognition accuracy.
3. The fusion of these modules results in positive optimizations across the overall performance metrics.  
When all three modifications are combined, the best optimization effect is achieved, resulting in average accuracy of 83.7%. Additionally, employing the Efficient Intersection over Union (EIOU) as the model's localization loss function further improves the accuracy by 0.3%, reaching final accuracy of 84%. Despite a slight reduction in accuracy compared to the YOLOv5s model, the final model exhibits significant decreases in computation by 68%, parameters by 55%, and model size by 54%. Moreover, the detection speed reaches 112 frames per second (FPS).  
These results emphasize the more lightweight design of the YOLOv5-Sewer model compared to other models and its acceptable accuracy loss. The substantial decrease in computational requirements and model size makes the optimized YOLOv5-Sewer model highly suitable for deployment on on-site operational devices for sewer defect detection, addressing real-world demands for rapid and efficient detection in sewer environments.
# Evaluation of Unfilled Sheath in Concrete Structures Using Response Waveform in Time Domain
###### Abstract  
The impact elastic wave method (IEW) has been applied to evaluate the thickness and internal defects of the target structure based on the dominant frequency of the response wave that is formed by the repeated reflections in the thickness direction. However, it is difficult to evaluate the size and position of the defect by IEW if the size and depth are relatively small and deep, respectively, and further, it is known that the technique is inapplicable if the target is not a plate-like structures. Therefore, the authors propose a new technique that uses Difference value as a new evaluation index to overcome the limitations of the conventional methods. Difference value shows the change of the response waveform in the time domain; it is computed by using a response waveform of the structures in sound condition as a reference. In this paper, the practicality of the Difference value is investigated by performing experiments using concrete specimens. The results of the experiments demonstrate that Difference value changes by the influence of internal defects, and Difference value evaluates the location of the relatively small defect that is difficult to evaluate by the conventional technique.  
non-destructive testing; concrete; impact elastic wave method; impact-echo; difference value; internal defects; unfilled sheath