esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Type I Error Analysis
#### 3.1.2 Quality Control #2  
Now, data are considered to have bias (expressed by the median, see Table 5). Intervals are built as: _Median \(\pm\) tolerance_, where the tolerances are the same as for the QC1 case.  
Consequently, three categories of errors are defined:  
* CoE1: \(E_{i}\in[-0.039,-0.012]\) m.
- CoE2: \(E_{i}\in[-0.058,-0.039]\) m OR \(E_{i}\in(-0.012,\ 0.007]\) m.
- CoE3: \(E_{i}\in(-\infty\,-0.058)\) m OR \(E_{i}\in(0.007,\ \infty)\) m
* CoE1: \(E_{i}\in[-0.179,\ 0.050]\) m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Infrastructure** & **Urban** & **Natural** \\ \hline  & 20 & 0.733 & 0.0000 & 0.0037 \\  & 50 & 0.980 & 0.0000 & 0.0010 \\ Sampling size & 100 & 1.000 & 0.0000 & 0.0000 \\  & 200 & 1.000 & 0.0000 & 0.0000 \\  & 500 & 1.000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Proportion of times that the null hypothesis has been rejected for the QC1 in each study case.  
* CoE2: \(E_{i}\in[-0.252,-0.179)\) m OR \(E_{i}\in(0.050,\ 0.123]\) m.
* CoE3: \(E_{i}\in(-\infty,-0.252)\) m OR \(E_{i}\in(0.123,\ \infty)\) m.
* Natural study case. The tolerances for the 3D case define the following intervals:
* CoE1: \(E_{i}\in[-0.212,\ 0.017]\) m.
* CoE2: \(E_{i}\in[-0.284,-0.212)\) m OR \(E_{i}\in(0.017,\ 0.090]\) m.
* CoE3: \(E_{i}\in(-\infty,-0.284)\) m OR \(E_{i}\in(0.090,\ \infty)\) m.  
The results obtained by the simulation following the process described at the beginning of this section are shown in Table 8. Here, the situation is somewhat different from QC1. Study cases 2 and 3 behave almost as they did in QC1 and the null hypothesis is always accepted. Study case 1 reduces its rejection noticeably because when including the bias in the calculations, there are more error cases within the tolerances. For this study case, the rejection values are close to the significance level (5%), and this is logical since the values of the tolerances for this case are very close to the quantiles of the observed distribution (Table 5).
## 2 Ultrasonic Guided Wave Characteristics
### Axisymmetric Ultrasonic Guided Waves  
Outer diameter, wall thickness, and the material of the pipe are the factors that influence dispersion, which should be input in advance before PC_disp programs [29] are run. An aluminum pipe with an outer diameter of 56 mm, a wall thickness of 3.5 mm, and a length of 4 m was used, for which the dispersion curves of axisymmetric UGWs were drawn in Figure 1. It can be observed from Figure 1a,b that L(0,1), L(0,2), T(0,1), and T(0,2) UGWs exist in the frequency range of 0 to 600 kHz, and the propagation velocities of UGWs (except T(0,1) UGW) are dependent on frequency, namely dispersion. However, there are specific non-dispersive frequency bands where the propagation velocities of UGWs change little as frequency varies, and it can be noticed that the non-dispersive frequency band of L(0,2) UGW is 90-150 kHz.  
UGWs manifest different sensitivities to defects under the relationship between UGW propagation directions and defect distributions, where L(0,2), L(0,1), and T(0,1) UGWs are dominated by axial, radial, and circumferential displacements (Figure 1c). In this way, L(0,2) UGW is mostly suitable for circumferential defect detection. In consideration of UGW testing efficiency, dispersion, and energy attenuation comprehensively, fast-speed L(0,2) UGW with the non-dispersive excitation frequency of 100 kHz was determined, and the corresponding phase and group velocities were 5.372 km/s and 5.261 km/s, respectively.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Crop Identification and Detection
[87] introduces a deformable large kernel attention mechanism at the network's output to enhance detection capabilities for occluded and deformed targets. Frequency Domain Feature Distillation (FreeKD) is used to transfer knowledge from large models to lightweight models. This multi-kernel dynamic convolution module enhances the model's feature extraction capabilities for tomatoes of different sizes and shapes by adaptively aggregating convolution kernels of varying sizes, thereby achieving high-precision tomato detection. Traditional models often struggle to accurately detect small fruits with variable shapes. To address this, ref. [89] focuses on target detection research for small red pears. The proposed method incorporates a Spatial-Channel Decoupled Downsampling (SCDown) module to optimize the downsampling process while preserving fine details of small targets. The research expanded an original dataset of 395 images through data augmentation, ultimately creating a dataset of 1580 images. However, the dataset has several limitations: It contains few occluded samples; the images were primarily captured during daytime with insufficient representation of extreme lighting conditions; and the experiments were conducted in a single orchard environment without cross-regional or cross-crop validation of model generalizability. Kiwifruit detection also faces challenges such as dense growth, leaf obstruction, and fruit overlap. Therefore, ref. [92] focuses on kiwifruit identification and localization. This study combines the Coordinate Attention Mechanism (CA) with YOLO for feature extraction and optimizes the loss function by integrating Focal Loss (to address class imbalance), EIoU Loss (to improve bounding box localization accuracy), and confidence loss. It uses stereo vision localization and 2D coordinates to output the real-world location of kiwifruit. This achieves real-time recognition and localization of kiwifruit, facilitating subsequent management and harvesting.
## 3 Fatigue Life Calculation in Frequency Domain
### Stress Rainflow Range PDF Model  
(1) Dirlik model for Gaussian case  
Dirlik approximated the rainflow cycle PDF of wideband random stresses using one exponential distribution and two Rayleigh distributions. The expression for the rainflow cycle PDF of Gaussian wideband random processes is [1]\[p_{\text{DK}}(Z)=\frac{D_{1}}{Q}e^{-\frac{Z}{Q}}+\frac{D_{2}}{R^{2}}Ze^{-\frac{Z^{ 2}}{2R^{2}}}+D_{3}Ze^{-\frac{Z^{2}}{2}}\] (19) where \[\begin{array}{cc}Z=\frac{S}{2\sqrt{m_{0}}}&\gamma=\frac{m_{2}}{\sqrt{m_{0}}m _{4}}&X_{m}=\frac{m_{1}}{m_{0}}\sqrt{\frac{m_{2}}{m_{4}}}\\ R=\frac{\gamma-X_{m}-D_{1}^{2}}{1-\gamma-D_{1}+D_{1}^{2}}&Q=\frac{125(\gamma-D_ {3}-D_{2}R)}{D_{1}}\\ D_{1}=\frac{2(X_{m}-\gamma^{2})}{1+\gamma^{2}}&D_{2}=\frac{1-\gamma-D_{1}+D_{1 }^{2}}{1-R}&D_{3}=1-D_{1}-D_{2}\end{array} \tag{2}\]  
Tovo-Benasciuti model for Gaussian case  
The Tovo-Benasciuti PDF model is a combination of the Narrow-Band and the Range-Count counting PDF methods, as shown in the following formula [9]  
\[p_{\text{TB}}(S)=bp_{\text{LCC}}(S)+(1-b)p_{\text{RC}}(S) \tag{20}\]  
where, \(p_{\text{LCC}}(S)\) is a Rayleigh-type PDF of the Narrow-Band process  
\[p_{\text{LCC}}(S)=\alpha_{2}\frac{S}{\sigma_{X}^{2}}\exp\biggl{[}-\frac{1}{2} \biggl{(}\frac{S}{\sigma_{X}}\biggr{)}\biggr{]} \tag{21}\]  
\(p_{\text{RC}}(S)\) is the Range-Count counting PDF  
\[p_{\text{RC}}(S)=\frac{S}{\sigma_{X}^{2}\alpha_{2}^{2}}\exp\biggl{[}-\frac{1}{ 2}\biggl{(}\frac{S}{\sigma_{X}\alpha_{2}}\biggr{)}^{2}\biggr{]} \tag{22}\]  
And the weight \(b\) depends on the spectral density of the process.  
\[b=\frac{(\alpha_{1}-\alpha_{2})[1.112(1+\alpha_{1}\alpha_{2}-(\alpha_{1}+ \alpha_{2}))e^{2.11\alpha_{2}}+(\alpha_{1}-\alpha_{2})]}{(\alpha_{2}-1)^{2}} \tag{23}\]  
In Equation (23), \(\alpha_{1}\) and \(\alpha_{2}\) are spectral width parameters described in Equation (10).
3. Bi-Gamma model for non-Gaussian case  
The rainflow range PDFs of the wideband non-Gaussian stress processes proposed in this paper follow a bi-Gamma distribution; the expression is  
\[p_{BG}(Z)=c\frac{\lambda_{1}{}^{\beta_{1}}Z^{\beta_{1}-1}e^{-\lambda_{1}Z}}{ \Gamma(\beta_{1})}+(1-c)\frac{\lambda_{2}{}^{\beta_{2}}Z^{\beta_{2}-1}e^{- \lambda_{2}Z}}{\Gamma(\beta_{2})} \tag{24}\]  
where \(\Gamma(\cdot)\) is the Gamma function, and \(\beta_{1},\beta_{2},\lambda_{1},\lambda_{2},c\) are parameters of the bi-Gamma distribution, which can be optimized through fitting to the stress rainflow cycle PDF values. The optimization object is expressed as  
\[\text{min}\left[\sum_{i=1}^{M}\left(p_{i}^{R}-p_{i}^{Z}\right)^{2}\right] \tag{25}\]  
where \(p_{i}^{R}\) represents the PDF sequence values of rainflow counts, and \(p_{i}^{Z}\) is the sequence values calculated from Equation (24). By initializing \(\beta_{1},\beta_{2},\lambda_{1},\lambda_{2},c\) and utilizing a non-linear optimization algorithm to minimize Equation (25), the optimal solution obtained corresponds to the parameters of the bi-Gamma distribution in Equation (24).
## 2 Materials
### Sentinel-1 SAR Data  
In this study, a total of 288 SI SAR images operating at the C-band frequency of 5.405 GHz were utilized. These images were acquired by S1A and S1B satellites in both ascending mode (afternoon at 18:00 UT) and descending mode (morning at 06:00 UT). Specifically, 72 images (36 ascending and 36 descending) were obtained for each of the four reference plots (6-day revisit time) located in Montpellier (P1) and Tarbes (P2, P3, and P4). The data spanned from April to October for each year, with 2017 for P1 and 2018, 2019, and 2020 for P2, P3, and P4, respectively. This period corresponds to the irrigation period for these reference plots. The SAR images were acquired in the interferometric-wide (IW) swath with two polarizations: VV (vertical-vertical) and VH (vertical-horizontal). For the purpose of this study, only the VV and VH polarizations were considered in the SSM (surface soil moisture) estimation algorithm. The 288 images were derived from the ground-range-detected (GRD) product, featuring a pixel spacing of 10 m \(\times\) 10 m. These images were downloaded via the European Space Agency (ESA) website ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)), accessed on 9 November 2022). Figure 3 illustrates the repetitiveness of S1 data in both the ascending \"A\" and descending \"D\" acquisition modes across four study sites: Montpellier (P1) in June 2017 and
## 4 Performance Evaluation
### Evaluation of Beam-Quality-Prediction Models  
In this section, we evaluate the property of beam selection in consistent time for all of the above schemes, without transmitting the pilot signals. The input to the prediction model is the low-resolution image of the last three pilot signals. Figure 10 shows the CDF of the SNR. As is shown in Figure 10, when using convolutional LSTM (Conv-LSTM) and the convolutional gating unit (Conv-GRU) with our proposed scheme, our SNR is better than that of the no-prediction exhaustive-only approach. The reason is that our scheme has the advantage of capturing the motion state of the UE, so as to predict the acquired power more accurately. Furthermore, our scheme results in little difference in the CDF performance, regardless of whether Conv-GRU or Conv-LSTM is used. The reason for this phenomenon is that both the Conv-RELU and Conv-LSTM neural networks have the  
Figure 8: RMSEs with different quantization parameters: \(P_{u}\), \(P_{t}\), and \(r_{CQI}\), and specifically with consideration to some combinations of \(P_{u}=45,40,35,30\) dBm and quantization granularity (\(r_{CQI}=0.1,\ 0.2,\ 0.5,\ 1,\ 2,\ 5,\ 10\) dBm).  
Figure 9: Comparison of regression error \(\left|y_{pred}-y_{true}\right|\) to quantization granularity (\(r_{CQI}=1,2,5\)) and no quantization granularity.
## 4 Results
### Input Interactions  
#### 4.1.1 Reaction Time  
Participants' reaction time (the interval between the appearance of the IESM questions to participants' selection) was calculated using the stored live video to better understand the performance of each input interaction, as faster input helps to keep the driver involved in NDRT.  
We measured the average reaction time for each input across all scenarios and two IESM. The one-way repeated measure ANOVA test showed a statistical significance, \(F\) (2, 62) = 3.81, \(p<0.027\). As Figure 5 shows, G-HI had the fastest reaction time (\(M\) = 3.76, \(SD\) = 1.10), followed by TI (\(M\) = 4.14, \(SD\) = 1.23) and VI (\(M\) = 4.60, \(SD\) = 1.54). Reaction time for VI was about 1 s greater than the G-HI \(p<0.029\).  
To understand which scenario most influenced reaction time, we analysed the reaction time in each scenario for each input. The ANOVA test shows a significant difference in the changing lanes action \(F\) (2, 62) = 7.39, \(p\) < 0.001 but no significant differences in the other actions as shown in Figure 6. When changing lanes, reaction time using VI (\(M\) = 4.54, \(SD\) = 1.47) was about 1.4 s greater than G-HI (\(M\) = 3.19, \(SD\) = 1.43, \(p\) < 0.002), where reaction time in G-HI was 1.16 s faster than TI (\(M\) = 4.35, \(SD\) = 1.64, \(p\) < 0.01).
## 2 Reviewing Background and Related Work
### Blockchain Technology Improves Security by Recording Events  
During the data transmission stage, preventing tampering with data message packets and causing data changes is a dilemma. Blockchain technology solves this problem by having each node in the application network keep a digital ledger containing a list of all transactions performed on the web [26]. Public and private key pairing is used to ensure security. When executing a blockchain transaction, it is recorded as a ledger update. The transaction is encrypted using a unique private key, a digital signature. This means that no node can tamper with transaction data without invalidating its digital signature, thereby rendering the original transaction invalid [27; 28]. Therefore, when exchanging IoT data through MQTT, data tampering can be avoided by signing the data using the data encryption feature of block training. If different essential processing can be included for various nodes in the vital part, each node can update the critical management with other nodes through the subscription mechanism. This can eliminate the technical bottleneck caused by standard keys. Since each node has a public key shared with all other nodes, this solves the problem. When a node receives a ledger update message from another node, it verifies it using the public key corresponding to the initiating node. If the ledger has not been tampered with, the verification is successful, and the node updates its catalog copy. This ensures that every transaction is propagated through the blockchain network and becomes part of the ledger stored at each network node.  
The main objective of blockchain technology is to utilize smart contracts to establish secure communication channels and record events, thereby addressing security and privacy concerns that arise from decentralized data exchange. This technology can offer immutable distributed data when stored, making it highly suitable for safeguarding critical data. The research considers that when using MQTT's broadcast characteristics, any IoT device must authenticate the connection with the broadcast center to ensure that the subscriber and sender have synchronized data information. This allows for the integration of data before exchanging it. The application of blockchain technology builds a secure communication encryption framework. The blockchain security agent integrates edge computing into a fast encryption framework to perform group operations. This strengthens the security of large-scale data transmission and storage under the Internet of Things.
## 3 CPN Models
### Power-Related Functions  
The CPN models introduced in the previous sections use three auxiliary functions that we have defined to compute power-related measurements: addEnergy(), calcTime() and addResourceEnergy(). Functions addEnergy()and calcTime() are responsible for calculating the power consumption and performance, respectively, of an operator modelled. On the other hand, function addResourceEnergy()is used to calculate the power consumption of the resource (radio and LEDs).  
The pseudo-code (Due to the complexity of the syntax of CPN ML, we adopted a pseudo-code to facilitate the understanding of the function codes.) of the function addEnergy is presented in the following:  
```
1 Function addEnergy(realmean,realvariance){
2 varenergy=normal(mean,variance);
3 energy_app=(!energy_app)+energy;
4 returnenergy; }
```  
Listing 1: The _scheduler_ is a _scheduler_. The _scheduler_ is a _scheduler_.  
The parameters mean and variance are used to calculate a random value following the normal distribution (variable energy in line 2) that represents the power consumption of a transition. It is worth observing that function normal() belongs to the CPN Tools library. The generated value is then added to the variable (energy_app) used to store the application's power consumption. Function calcTime is similar to addEnergy, but it generates a random time associated to each transition.  
The proposed infrastructure supports many distribution probabilities, among them exponential, Erlang, polynomials, normal, and so on [27]. The system considered in this work, however, being an embedded system in which each mote is mainly devoted to few local non interfering activities, makes the system quite stable as can witnessed through the measures collected. Nevertheless, variation still exits, since the system is not deterministic. In this study, the normal probability distribution was the one that best fitted the measured data. Finally, the pseudo-code of function addResourceEnergy is shown in the following:  
```
1 function addResourceEnergy(realtime){
2 if(radiolsOn){
3 varenergy=radio_power*time;
4 energy_app=(!energy_app)+energy;
5}
6}
7 if(led0 IsOn){
8 varenergy=led0_power*time;
9 energy_app=(!energy_app)+energy;
10}
11 if(led1 IsOn){
12 varenergy=led1_power*time;
13 energy_app=(!energy_app)+energy;
14}
15 if(led2 IsOn){
16 varenergy=led2_power*time;
17 energy_app=(!energy_app)+energy;
18} }
```  
This function calculates the power consumption of the resources (e.g., LEDs) available in the sensor node. In this particular case, the adopted mote (IRIS mote) has one radio and three LEDs. Each resource has associated a global variable that indicates if the resource is active (on) or not. In the case the resource is on, the power consumption due to the resource is calculated and added to the variable that stores the application's power consumption (energy_app). It is worth observing that as we do not know how long the resource is on, this function is associated to each transition present in the model.
# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
## 4 Discussion
The results are shown in Table 7. The correlation coefficient for B-WIM measurements varied from 0.67 to 0.87. The weigh station's correlation coefficient values varied from 0.64 to 0.80. This shows a high correlation between the portable scales, and B-WIM, and weigh stations. The lowest values of the correlation coefficient ranging from 0.60 to 0.76, which were moderate correlations, are obtained in the case of the WIM data set. On the other hand, the correlation coefficient was calculated in comparison to truck weigh station measurements for GVW, 1 st axle weight, and two groups of tandem axles. The strongest correlation was noticed with portable scales, where the value of the correlation coefficient was between 0.64-80 (moderate/high correlation). The B-WIM and truck weigh station correlation varied between 0.59-0.77, which was also a moderate/high correlation. The WIM measurement correlation was the weakest, providing a correlation coefficient between 0.45-52 (modest/moderate correlation). The stronger linear correlation was reported by comparing the measurements to portable scales. A high linear correlation coefficient was noticed for the first axle weight and first tandem axle, but the weakest for GVW and the second tandem axle. This study can be beneficial for state agencies, bridge owners, consulting companies, weight measurement equipment producers, and researchers. The comparative analysis of measurement techniques provides the advantages and disadvantages of considered techniques. Accurate traffic measurements are critical for all stakeholders involved. Therefore,  
Figure 11: Measurement tolerances compared to portable scales. Figure 10: Measurement tolerances compared to portable scales. the same vehicles' comparative measurements can help verify the measuring systems, calibrate them, and improve the performance. Systematic static weight comparison between the truck weigh station, and portable scales allow maintaining the good accuracy of the heavy traffic measurements. Similarly, in pavement and bridge WIM systems, measurements can be calibrated by the comparative analysis. The efficient, scheduled calibration of weighing systems is vital to maintain infrastructure safety and adequately manage the repair and replacement work. The traffic measurements are critical for the bridge and road inspections, live load model development, a site-specific update of safety factors for design and evaluation. The weigh-in-motion technology continuously monitors traffic-induced loads and allows us to assess their impact on bridge and pavement conditions. Accurate measurements allow improving the infrastructure life cycle analysis. In this study, it was noticed for all considered measurement techniques that the further from the steering axle, the bigger error was captured. It may be concluded, the more significant number of axles, the more probable to have a larger measurement error. The analysis indicated that axle 4 and 5 measurements, and accordingly, the second tandem provided the biggest variation and tolerance error.
## 1 Introduction
Then, three groups of distinct features are composed for the image. The first-order statistics (FOS) group contains 4 features per channel, giving rise to 12 features for the 3 channels. The higher-order statistics (HOS) group contains 14 features per channel per displacement, giving rise to 112 features for the 3 channels and 8 displacements. The histogram of oriented gradient (HOG) group contains 81 features. The three groups thus collectively contain 429 features that can discern between the three classes we consider. However, to make the processing more efficient, a genetic algorithm (GA)-based scheme is leveraged to select the most relevant and informative of these features, successfully reducing them to 105. To evaluate the feature selection process and the proposed system's performance in general, we use four machine learning (ML) classifiers: decision tree (DT), naive Bayes (NB), \(k\)-nearest neighbor (\(k\)NN), and linear discriminant analysis (LDA). Based on our experimentation with other classifiers, such as support vector machine (SVM), random forest (RF), and Adaboost, and based on our previous work [12] with artificial neural networks (ANNs) in the same image processing context, the four chosen classifiers provide higher accuracy, easier setup, fewer training images, and less training and/or testing time. It is possible, however, that a classifier that we did not experiment with performs better using the proposed features than the chosen four. The article is organized as follows. Section 2 surveys previous work on retinal CAD systems, especially feature extraction and image classification. Section 3 describes the retinal fundus datasets used in our experiments and the methodology adopted in feature extraction and selection. Section 4 provides the results of the experimental work and discusses their implications and the gained insights, with the discussions of those results presented in Section 5. Finally, concluding remarks are given in Section 6.