esa-sceva/satcom-chunk-collection huggingface dataset:
# Automating Visual Blockage Classification of Culverts with Deep Learning
###### Abstract  
Blockage of culverts by transported debris materials is reported as the salient contributor in originating urban flash floods. Conventional hydraulic modeling approaches had no success in addressing the problem primarily because of the unavailability of peak floods hydraulic data and the highly non-linear behavior of debris at the culvert. This article explores a new dimension to investigate the issue by proposing the use of intelligent video analytics (IVA) algorithms for extracting blockage related information. The presented research aims to automate the process of manual visual blockage classification of culverts from a maintenance perspective by remotely applying deep learning models. The potential of using existing convolutional neural network (CNN) algorithms (i.e., DarkNet53, DenseNet121, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, VGG16, EfficientNetB3, NASNet) is investigated over a dataset from three different sources (i.e., images of culvert openings and blockage (ICOB), visual hydrology-lab dataset (VHD), synthetic images of culverts (SIC)) to predict the blockage in a given image. Models were evaluated based on their performance on the test dataset (i.e., accuracy, loss, precision, recall, F1 score, Jaccard Index, region of convergence (ROC) curve), floating point operations per second (FLOPs) and response times to process a single test instance. Furthermore, the performance of deep learning models was benchmarked against conventional machine learning algorithms (i.e., SVM, RF, xgboost). In addition, the idea of classifying deep visual features extracted by CNN models (i.e., ResNet50, MobileNet) using conventional machine learning approaches was also implemented in this article. From the results, NASNet was reported most efficient in classifying the blockage images with the 5-fold accuracy of 85%; however, MobileNet was recommended for the hardware implementation because of its improved response time with 5-fold accuracy comparable to NASNet (i.e., 78%). Comparable performance to standard CNN models was achieved for the case where deep visual features were classified using conventional machine learning approaches. False negative (FN) instances, false positive (FP) instances and CNN layers activation suggested that background noise and oversimplified labelling criteria were two contributing factors in the degraded performance of existing CNN algorithms. A framework for partial automation of the visual blockage classification process was proposed, given that none of the existing models was able to achieve high enough accuracy to completely automate the manual process. In addition, a detection-classification pipeline with higher blockage classification accuracy (i.e., 94%) has been proposed as a potential future direction for practical implementation.  
convolutional neural networks; visual blockage of culverts; intelligent video analytic; image classification
## 3 Results and Discussion
### Part Classification Results
#### 3.1.3 Results of Local Feature Extraction  
Using k-means clustering, the targeted region and the background were image segmented. The image was modified so that local features could be obtained through morphological skeletonization. Numbers of local features of SIFT and SURF were compared in images of 80 \(\times\) 80 pixels in width and length of 30 fruits, nodes, leaves, and suction regions, respectively. Figure 14 shows numbers of local features extracted using SIFT. For fruits, the median number and average number of local features were 121 and 123, respectively, with a maximum value of 129 and a minimum value of 111. In the case of suckers, the median number and average number of local features were 105 and 101, respectively, with a maximum value of 123 and a minimum value of 81. In the case of leaves, the median  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parts** & **Actual Number** & **Segmented Number** \\ \hline Fruit & 682 & 676 \\ Sucker & 345 & 308 \\ Leaf & 8169 & 5981 \\ Node & 11,856 & 10,435 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numbers of parts retained after image segmentation.  
Figure 13: Image segmentation of steam, leaves, suckers (a), and fruits (b) by k-means clustering.  
number and average number of local features were 103.5 and 104, respectively, with a maximum value of 115 and a minimum value of 92. For stems, the median, the average, the maximum, and the minimum were 111.5, 113, 133, and 99, respectively.  
Figure 14b shows the number of local features extracted using SURF. For fruits, the median number and the average number of local features were the same at 193, with a maximum value of 201 and a minimum value of 186. In the case of suckers, the median number and average number of local features were 107 and 112, respectively, with a maximum value of 143 and a minimum value of 90. In the case of leaves, the median number and average number of local features were 126.5 and 127, respectively, with a maximum value of 139 and a minimum value of 116. For stems, the median, the average, the maximum, and the minimum were 154, 153, 172, and 136, respectively. In the case of suckers, numbers of local features of SIFT and SURF were extracted from overlapping ranges. The average, maximum, and minimum values showed high SURF. In the case of fruits, leaves, and stems, the number of local features of SURF was higher than that of SIFT without overlapping ranges. Therefore, the SURF algorithm was selected as the classification method for extracting local features of sweet pepper fruits, stems, leaves, and suckers.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC2: Urban Environment
#### 5.2.2 Urban Serving Distance  
When employing the joint (FL) scenario, the \(p_{90}\) servicing distance is significantly decreased (40 m as opposed to 100 m in other instances), as there is a higher chance of serving an EN located nearby. This is also due to the optimisation method that aims to minimise the path loss to maximise the supported bit rate. Vegetation only reduces the \(p_{90}\) serving CPE distance by 3%, while heavy rain reduces it by 8%. Oppositely, the \(p_{90}\) backhaul connecting distance increases by 6% for the FL scenario due to the optimisation algorithm that aims to maximise network size and minimise hop count. Here, rain significantly impacts the distance since it shortens the backhaul-connected distance by 20% and 6% during a heavy and moderate rainfall, respectively.
## 3 Results and Discussion
### Prediction Model Based on Electric Nose Data  
The fitting correlation coefficient (R\({}^{2}\)) and the root-mean-square error (RMSE) are the two most important indexes for judging the correlation effects of different regression models. According to previous research results, the prediction value has a high correlation with the actual value when R\({}^{2}\) is greater than 0.8. If R\({}^{2}\) is closer to or further from 1, the correlation is higher or lower, respectively. However, if R\({}^{2}\) is lower than 0.8, the prediction value has a low correlation with the actual value, and the prediction is unfeasible. In addition, the closer the RSME value is to 0, the better the prediction effect [27]. The RPD is used as a statistical indicator. The RPD should generally be greater than 3. Furthermore, R\({}^{2}\) and RPD cannot be very high when the variance in the reference data is low [47].  
The prediction performances of the MLR and PLRS methods based on the electric nose data are shown in Table 5. The R\({}^{2}_{\text{c}}\) values of the electronic nose ranged from 0.7419 to 0.8936, and the RMSEC values ranged from 1.9716 to 3.0709. Because the R\({}^{2}\) value was larger than 0.8, the application of the electronic nose for predicting the concentration of SDPP mixed with WPC is feasible. In addition, the optimal algorithm for the electronic nose was the MLR algorithm with the mean center preprocessing method, which confirmed the accuracy of the CR analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Regression Model**} & \multirow{2}{*}{**Preprocessing Model**} & \multicolumn{6}{c}{**Performance**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Cross-Validation Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{3-10}  & & \(\mathbf{R}^{2}_{\text{c}}\) & **RMSEC** & \(\mathbf{R}^{2}_{\text{cp}}\) & **RMSECV** & \(\mathbf{R}^{2}_{\text{p}}\) & **RMSEP** & **RPD** \\ \hline \multirow{5}{*}{MLR} & RAW & 0.8907 & 1.9985 & 0.8721 & 2.1631 & 0.9183 & 1.8212 & 3.3317 \\  & MSC (mean) & 0.7827 & 2.8174 & 0.7525 & 3.0111 & 0.7314 & 3.1408 & 1.9319 \\  & Mean-center & 0.8936 & 1.9716 & 0.8736 & 2.151 & 0.9247 & 1.7441 & 3.479 \\  & S-G 1 st and & & & & & & & \\  & Mean-center & 0.8899 & 2.0055 & 0.8713 & 2.1701 & 0.9167 & 1.8383 & 3.3007 \\ \hline \multirow{5}{*}{PLSR} & RAW & 0.7873 & 2.7874 & 0.7743 & 2.8721 & 0.8917 & 2.1611 & 2.8077 \\  & MSC (mean) & 0.7419 & 3.0709 & 0.721 & 3.1945 & 0.6906 & 3.3508 & 1
## 6 Performance Evaluation Strategy
### Simulation Setup  
To conduct our experiments, we used the well-known ns-2 network simulator. We assumed a mobile sink on each of the test scenarios. The sink, while relying on the sequence of estimated intruder coordinates, will constantly move towards the intruder, simulating a scenario where the security personnel chases the intruder in an attempt to seize him.  
The default simulation parameters used in the different experiments are shown in Table 1, unless stated otherwise. We deployed 200 nodes according to a regular grid topology. The distance between sensors was set to 10 m [35]. Since radio communications are based on the IEEE 802.15.4 standard, the transmission range used for all nodes was also set to 10 m. This means that we obtain a connected network (no partitioning effects) with the lowest possible number of sensors. Notice that achieving a connected set with a random sensor deployment would require more sensors for that same area, which would contribute to improve tracking accuracy. Thus, our scenario is a near worst case scenario in terms of tracking accuracy for the sensor communications range defined. The radio propagation model adopted was the two-ray ground. Concerning the sink location, it was initially located on the top left of the scenario. From there, and based on the estimation of the intruder location, it constantly moved towards the intruder.  
The routing protocol used in our experiments is MRLG, introduced in Section 5.  
The methodology used to conduct the tests was the following: first, we generated the intruder mobility pattern along the monitored area. Then, we calculated the time instant when different sensors were triggered due to the intruder proximity. Since we adopted binary detection sensors, each sensor sent an intruder detected report to the sink upon detection. If the intruder remained within the sensor's detection area, the latter continued reporting the intruder's presence to the sink every 5 s. Based on the different intruder reports gathered, the sink periodically estimated the current intruder location, and dynamically adapted its target, linearly moving towards the last estimated location.
## 5 Discussion
### Limitations  
Despite the fact that this study can be very helpful in situations where dataset creation and careful model selections are needed, it has a number of limitations that should be noted. The generalization of the results may be limited by the fact that, although we tested our models on four benchmark datasets, these datasets might not accurately reflect the variety of real-world situations, such as variances in lighting, backdrops, and cultural gestures. Furthermore, the datasets did not include professional people making motions, which resulted in inconsistent data that would have impacted the models' functionality. It is possible that our dependence on the MHL and OpenPose model as the main feature extractor skewed the findings in favor of models that fit this particular representation well, possibly ignoring alternative feature extraction techniques.  
Additionally, the main emphasis of this investigation was solely focused on temporal-level data processing, without consideration of any segmentation techniques, which might not adequately represent or consider the factors contributing due to the continuous data frames coming in feed to the system. Meanwhile, we did not thoroughly examine other important aspects like latency, energy consumption, and resistance to adversarial inputs, all of which are essential for real-time deployment in safety-critical applications, like drone control and other HCI systems. We also solely focused on performance evaluation matrices, including subjective as well as objective testing. The thoroughness of our findings may be limited since, although our exploration included six lightweight models, alternative designs like enhanced fine-tuned MobileNet [23] or ShuffleNet [49] were not taken into account. Lastly, even though this study focused on hand gesture detection for vision-based control systems, it is unclear if it will apply to other fields, like virtual reality or healthcare. These drawbacks point to areas that need more investigation to improve the scalability and resilience of gesture recognition systems.
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 2 Materials and Methods
### Retinal Datasets  
The proposed procedure was tested on fundus images obtained from three widespread public datasets.  
1. The DRIVE (Digital Retinal Images for Vessel Extraction) dataset [27] was established to allow comparative studies on the segmentation of blood vessels in retinal images. In total, 40 retinal images from the DRIVE dataset, which were obtained from a diabetic retinopathy screening program in the Netherlands, were randomly selected from 400 people between 25 and 90 years of age. The images were captured using a Canon CR5 non-mydriatic 3 CCD with a forty-five-degree field of view (FOV)  
Figure 2: Main steps of the proposed method. CLAHE: contrast-limited adaptive histogram equalization.  
and saved in JPEG format. The size of each image was (584 \(\times\) 565) pixels. Diabetic retinopathy was detected in 7 images, while 33 images showed no signs of the disease.  
2. The DIARETDB0 (Standard Diabetic Retinopathy Database Calibration level 0) dataset [28] was established to evaluate the success of automatic diabetic retinopathy detection and compare the performance of the developed methods. The dataset consists of 130 colored fundus images, of which 20 are normal and 110 include symptoms of diabetic retinopathy. The fundus images were captured with a fifty-degree field of view (FOV). The dataset was titled as \"calibration level 0 fundus images\", and the size of each image was (1152 \(\times\) 1500) pixels.  
3. The DIARETDB1 (Standard Diabetic Retinopathy Database Calibration level 1) dataset [29] is a public dataset for evaluating the performance of automatic diabetic retinopathy detection methods. The dataset consists of 89 colored fundus images, of which 84 include mild non-proliferative signs (Ma) of the diabetic retinopathy and the remaining 5 fundus images contain no sign of the disease. The fundus images were captured with a Nikon F5 fundus camera and with a fifty-degree field of view. The dataset was titled as \"calibration level 1 fundus images\", and the size of each image was (1152 \(\times\) 1500) pixels.  
The detailed specifications for the DRIVE, DIARETDB0, and DIARETDB1 datasets are given in Table 1.
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 3 Results and Discussion
### Comparison with Different Methods  
To verify the effectiveness of the proposed method in predicting the coal MC further, comparative experiments were carried out using typical machine learning regression models, including random forest (RF), support vector regression (SVR), partial least squares (PLS), AdaBoost, XGBoost, decision tree (DT), and kNN. In all experiments, the NCV method was used to adjust the parameters of the models. To avoid accidental errors, each experiment was repeated six times, and the average results were used to evaluate the performances of the models, as shown in Table 4.  
As shown in Table 4, the overall average prediction result of the DW-kNN model was the best among all the models; it achieved \(R^{2}\) of 0.9106, MAE of 0.2374, and RMSE of 0.2940; still, the \(R^{2}\) values of the other models were all less than 0.9. The repeated experimental results of different models are presented in Figure 6, where it can be seen that the results of all comparison models were acceptable except for the DT model, which had a slightly worse performance than the other models--both single-sample results and average results were poor. When the sample MC is high, the electromagnetic waves react violently with moisture and thus show natural fluctuations in high frequency bands. These fluctuations will lead to the generation of unstable decision trees in the process of model training, which will seriously affect the robustness of the model. The results obtained by AdaBoost and kNN models are second only to those obtained by DW-kNN models. This shows that the microwave signals corresponding to similar coal MCs are highly correlated. Proper weight distribution can overcome the influence of fluctuation and improve the measurement effect. Moreover, the box size and the extreme value difference obtained by the DW-kNN model were small, which indicated that this model had good stability. This result further demonstrates that the DW-kNN model can achieve good results in coal MC prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline RF & 0.8874 & 0.2674 & 0.3306 \\ SVR & 0.8745 & 0.2851 & 0.3507 \\ PLS & 0.8735 & 0.2877 & 0.3504 \\ AdaBoost & 0.8965 & 0.2601 & 0.3172 \\ XGBoost & 0.8850 & 0.2697 & 0.3333 \\ DT & 0.8665 & 0.2895 & 0.3586 \\ kNN & 0.8956 & 0.2592 & 0.3180 \\ DW-kNN & 0.9106 & 0.2374 & 0.2940 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average results of different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Preprocessing** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline Untreated & 0.8962 & 0.2564 & 0.3182 \\ MD-ICM & 0.9106 & 0.2374 & 0.2940 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of experimental results before and after data preprocessing.
## 3 Materials and Testing  
### Raw Materials  
Local bitumen with a penetration grade of 60/70 and crushed limestone aggregates are the main components of asphalt mixes used in this study. Table 1 shows the physical characteristics of the bitumen, which comply with the ASTM D946 [40] requirements for a 60/70-penetration-grade bitumen. Since this study aims to measure the RSI values of asphalt mixtures while considering the influence of some aggregate and mixture properties, including the variables Ab%, AC%, Va%, and F%, it was imperative to look for aggregate materials with different properties. Therefore, the aggregates were collected from various local quarries across Jordan. Five available quarries were found to be suitable sources of aggregates with different absorption properties and filler proportions in the fine-size aggregate. The collected aggregates from each source were dried, sieved, and combined individually to meet the gradation limits of the specification of the Ministry of Public Works and Housing (MPWH) in Jordan [41] for wearing asphalt mixes, as shown in  
Figure 1: Research methods.  
Figure 2: Gradations of combined aggregate mixes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Result**} & \multicolumn{2}{c}{**ASTM D946 Criteria**} \\ \cline{3-4}  & & **Min.** & **Max.** \\ \hline Specific gravity at 25 \({}^{\circ}\)C & 1.02 & na & na \\ \hline Ductility at 25 \({}^{\circ}\)C, cm & 127 & 100 & na \\ \hline Flash Point (\({}^{\circ}\)C) & 306 & 230 & na \\ \hline Softening Point (\({}^{\circ}\)C) & 51.8 & 46 & na \\ \hline Penetration (0.1 mm) & 67.3 & 60 & 70 \\ \hline Loss on heating (\%) & 0.26 & na & 0.8 \\ \hline Penetration of residue, \% of original & 63.1 & 54 & na \\ \hline Rotational viscosity at 135 \({}^{\circ}\)C, Pa\(\cdot\)s & 0.458 & na & na \\ \hline Rotational viscosity at 165 \({}^{\circ}\)C, Pa\(\cdot\)s & 0.158 & na & na \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bitumen properties.
## 3 A Routing Protocol Based on Energy and Link Quality (REL)
### Path Selection and Load Balancing
Algorithm 1 shows the route selection process, where there are three basic rules. The _ShiftToRoute_ function represents a switch between the active route and candidate route. The three rules filter the routes in accordance with the decision structures shown in Lines 5, 11, and 17. These structures evaluate the candidate route (\(\textit{R}_{b}\)) and categorize it as their energy level, comparing it with the current active route (\(\textit{R}_{a}\)). Line 17 shows the use of \(\textit{E}_{th}\) as a tolerance parameter for the difference that is acceptable if \(\textit{R}_{b}\) has less energy. After analyzing the energy level, the algorithm calculates the number of hops and evaluates the quality of the links. This evaluation occurs in Lines 7, 12, and 18, where the threshold used is \(\textit{HCdiff}_{max\_allow}\). According to this algorithm, the next stage of the evaluation needs only be analyzed if \(\textit{R}_{a}\) is a route that has more hops (Lines 6 and 12). The rule described in Line 17 is a special case in the selection process, since it analyzes the cases where \(\textit{R}_{b}\) has less energy than the active route. In this case, \(\textit{R}_{b}\) has to replace \(\textit{R}_{a}\) if the energy difference with regard to the threshold \(\textit{E}_{th}\) and \(\textit{R}_{b}\) is considerably smaller (Line 18). In this algorithm, the use of energy and number of hops thresholds is shown as a fine tuning system, able to configure the way the protocol operates in accordance with the purpose and goals of the IoT application. For example, imagine a scenario with \(\textit{HCdiff}_{max\_allow}\) = 7. This allows load balancing operations, because there are a larger number of possible alternative routes. However, it causes an increased delay in packet delivery since (long) routes with more hops are used. ```
1:Let \(HCdiff_{max\_allow}\) = Hop Count Maximum
2:Let \(E_{th}\) = Energy Threshold
3:Let \(R_{a}\) = Active Route
4:Let \(R_{b}\) = Alternative Route
5:if\(R_{a}\).energy = \(R_{b}\).energy then
6:if\(R_{a}\).hopCount \(>R_{b}\).hopCount + \(HCdiff_{max\_allow}\) then
7:if\(R_{a}\).weakLinks \(\geq R_{b}\).weakLinks then
8: ShiftToRoute(\(R_{b}\))
9:endif
10:endif
11:elseif\(R_{a}\).energy \(<R_{b}\).energy then
12:if\(R_{a}\).hopCount + \(HCdiff_{max\_allow}\geq R_{b}\).hopCount then
13:if\(R_{a}\).weakLinks \(\geq R_{b}\).weakLinks then
14: ShiftToRoute(\(R_{b}\))
15:endif
16:endif
17:elseif\(R_{a}\).energy \(>R_{b}\).energy and \(R_{a}\).energy \(\leq R_{b}\).energy + \(E_{th}\) then
18:if\(R_{a}\).weakLinks \(\geq R_{b}\).weakLinks and \(R_{a}\).hopCount \(>R_{b}\).hopCount + \(HCdiff_{max\_allow}\) then
19: ShiftToRoute(\(R_{b}\))
20:endif
21:endif
```  
**Algorithm 1** Selection Route Algorithm of REL protocol.