esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Results
#### 5.2.2 Attestation Overhead  
Figure 6a,b shows the overhead of our proposed scheme in comparison to the baseline scheme. To achieve the same efficiency, our scheme needs less attestations. Our scheme only needs around \(10\%\) of the attestations in comparison to the baseline scheme. As we can see from the figure, the verification efficiency of our proposed attestation scheme remains the same as the baseline scheme because compromised nodes keep the same attestation, and only reliable nodes get less attestation. Our scheme can detect almost all of the attacks in the network. As shown in Figure 6c, because our proposed attestation scheme is based on the vulnerability risk of nodes, nodes that have a highly probability of being compromised will have a high probability of being verified. For nodes that have a low risk of being compromised, an appropriate verification will be used. In contrast, all nodes will have the same verification when the baseline scheme is used, leading to a higher attestation overhead. From the figures, we observe that our proposed attestation scheme achieves the same efficiency with only \(10\%\) verification overhead in comparison to the baseline scheme.  
Figure 6: The attestation overhead comparison. (**a**) Successful attestation; (**b**) the number of attestations; (**c**) the number of undetected attacks.
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Target Detection Based on Deep Learning Methods
Since use of the genetic algorithm (GA) in this field has not been fully realized, [PERSON] et al. [26] further discussed its application potential in fish tracking based on GA. On the other hand, the deep learning algorithms recurrent neural network (RNN) and long short-term memory (LSTM) have been used in several visual track prediction methods to predict targets, including pedestrians, vehicles, mobile robots, fish, etc. The results from these methods were shown to be better than most tracking methods, and thereby underwater video fish tracking research has been carried out based on RNN-LSTM. The results showed that trajectory prediction using LSTM is more accurate than the use of a genetic algorithm, but both showed an acceptable accuracy and the average absolute percentage errors of GA and LSTM were 2.8-30.5% and 3.33-17.74%, respectively. LSTM has been widely used in trajectory prediction in many fields while the genetic algorithm has seldom been used as a trajectory prediction method. The results of GA can be improved through the use of additional variables or fitness functions, such as [PERSON]'s equation of motion and quadratic regression. Three-dimensional coordinates have been shown to provide more accurate prediction results for GA and LSTM, so it can be further extended for two-dimensional and three-dimensional path prediction in the future, such as the use of GA and LSTM in fish tracking and marking or investigation of its combination with other tracking algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Accuracy** & **Accuracy Evaluation** \\ \hline Laplacian [23] & 68.9\% & Normal \\ Sobel X [24] & 79.26\% & High \\ Sobel Y [24] & 79\% & High \\ Combined Sobel [25] & 88.9\% & Very high \\ Canny [3] & 89.13\% & Very high \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of edge detection algorithms. Intelligent target recognition and positioning using deep learning methods is powerful. However, the accuracy of underwater target recognition is affected by the image clarity, and deep learning methods are only applicable in waters that are similar to the training set image, so this method has some limitations. Therefore, the combination of good image restoration methods and deep learning methods can make target detection and recognition in turbid waters more effective.
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 4 Machine Learning Methods
### Semantic Segmentation  
Semantic segmentation is similar to categorical classification, with the difference that it assigns a unique class to each pixel in an image [36]. In the example used previously, when this type of model is inputted with a street view image showing a car and a bus, the model would assign the class 'car' to the pixels belonging to the car and the class 'bus' to the pixels of the bus. In this task, losing detail in the output image is an unwanted effect of the bottleneck of convolutional networks. A new type of machine learning model has recently been developed to address this issue, the HRNet [25]. Here, the image is copied, and multiple branches are processed at the same time. Each branch has a different resolution, thus having at least one high-resolution branch which helps maintain all the detail in the output image and a low-resolution branch that provides the bottleneck effect.  
The branches are combined through cross-convolutional layers that upscale or downscale each resolution path and concatenate all branches together over their feature dimension.
# Bias Effects on g- and s-Factors in Westcott Convention
## 3 Results and Discussions
### Bias on s-factor by the joining Function  
Biases on s-factor originating in a joining function are compared among \({}^{241}\)Am, \({}^{151}\)Eu, \({}^{197}\)Au, \({}^{59}\)Co, \({}^{103}\)Rh, \({}^{115}\)In, \({}^{177}\)Hf, and \({}^{226}\)Ra in Figure 14. The ratios \(\mathrm{s}_{4}^{W}/\mathrm{s}_{2}^{W}\) and \(\mathrm{s}_{4}^{H}/\mathrm{s}_{2}^{W}\) are shown by black bars and red-hatched bars. The results for neutron temperature of 300 K, 600 K, and 1000 K are shown in Figure 14a-c, respectively.  
Figure 13: (**a**) Westcott g-factor for \({}^{226}\)Ra as a function of neutron temperature \(T_{n}\); (**b**) Expanded figure of (**a**); (**c**) s-factors, \(\mathrm{s}_{2}^{W}\) (solid line), \(\mathrm{s}_{4}^{W}\) (dotted line), \(\mathrm{s}_{4}^{H}\) (dashed line) for \({}^{226}\)Ra as a function of neutron temperature \(T_{n}\); (**d**) Expanded figure of (**c**); (**e**) Ratios of s-factors, \(\mathrm{s}_{4}^{W}/\mathrm{s}_{2}^{W}\) (dotted line) and \(\mathrm{s}_{4}^{H}/\mathrm{s}_{2}^{W}\) (dashed line) for \({}^{226}\)Ra; (**f**) Expanded figure of (**e**).  
Figure 14: (**a**) Ratios of s-factors, \(\rm s_{4}^{W}/s_{2}^{W}\) (black bar), and \(\rm s_{4}^{H}/s_{2}^{W}\) (red dashed bar) for \({}^{241}\)Am, \({}^{151}\)Eu, \({}^{197}\)Au, \({}^{99}\)Co, \({}^{103}\)Rh, \({}^{115}\)In, \({}^{177}\)Hf, and \({}^{226}\)Ra at the neutron temperature of 300 K; (**b**) Same as (**a**) at the neutron temperature of 600 K; (**c**) Same as (**a**) at the neutron temperature of 1000 K.  
As shown in Figure 14a, large biases exceeding 10% occur in the cases of \({}^{241}\)Am, \({}^{151}\)Eu, and \({}^{226}\)Ra at the neutron temperature of 300 K. The largest deviation from the unity is observed in the case of \({}^{151}\)Eu for \({\rm s}_{4}^{H}/{\rm s}_{2}^{W}\), corresponding to about 29%. In the case of the neutron temperature of 600 K, the largest deviation from the unity is also observed for \({}^{151}\)Eu, though for \({\rm s}_{4}^{W}/{\rm s}_{2}^{W}\), corresponding to about 37%. In the case of the neutron temperature of 1000 K, the largest deviation from the unity is again observed for \({}^{151}\)Eu, for \({\rm s}_{4}^{W}/{\rm s}_{2}^{W}\), exceeding 50%.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 2 Characteristics of the Underwater Acoustic Channel
### Noise  
The ambient noise in an acoustic channel is modeled using four constituent sources: shipping activity noise (Equation (6)), turbulence (Equation (7)), breaking waves (Equation (8)), and thermal noise (Equation 9). Equations (6)-(9) give the power spectral density of the four noise sources in dB re \(\mu\) Pa per Hertz for frequency \(f\) in kilohertz:  
\[10\log N_{s}(f)=40+20(s-0.5)+26\log f-60\log(f+0.03) \tag{6}\]  
\[10\log N_{t}(f)=17-30\log f \tag{7}\]  
\[10\log N_{w}(f)=50+7.5w^{1/2}+20 logf-40\log(f+0.4) \tag{8}\]  
\[10\log N_{th}(f)=-15+20\log f \tag{9}\]  
Figure 2: Absorption coefficient as a function of frequency [11].  
The power spectral density of the overall ambient noise in underwater acoustic channel is given by Equation (10):  
\[N(f)=N_{s}(f)+N_{t}(f)+N_{w}(f)+N_{th}(f) \tag{10}\]  
The signal to noise ratio (in dB) experienced at a receiver in the underwater acoustic channel is given by Equation (11):  
\[SNR=SL-TL-NL-DI \tag{11}\]  
where, \(NL\) represents the level of ambient noise in dB, \(TL\) represents the transmission loss (dB), \(DI\) or directivity index is set to 0. \(SL\) or source level of the transmitter in dB can be computed using Equation (12) as follows:  
\[SL=10\log\left(\frac{I_{t}}{0.67\times 10^{(-18)}}\right) \tag{12}\]  
where \(I_{t}\) represents the intensity of transmission power. For shallow waters, \(I_{t}\) can be calculated in Watts/m\({}^{2}\) using Equation (13):  
\[I_{t}=\frac{P_{t}}{2\times\pi\times 1m\times z} \tag{13}\]  
In case of deep waters, Equation (14) can be used to calculate \(I_{t}\):  
\[I_{t}=\frac{P_{t}}{4\times\pi\times 1m\times z} \tag{14}\]
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 4 Energy-Efficiency Model of the Centrifugal Pump
### Analytical Modelling of the Pump Efficiency Characteristics for Variable-Speed Operation  
#### 4.1.1 Manufacturer-Supplied Pump Characteristics  
Pump manufacturers usually provide \(Q\)-\(H\) diagrams containing efficiency data for specific pump models at specific speeds. For example, the Etanorm 150-125-250 pump model is characterised by two isospeed curves at 1450 rpm and 960 rpm, assuming that the pump is driven by a four- or six-pole induction motor connected to a 50 Hz mains [33].  
Further curves are available for 1750 rpm and 1160 rpm when the pump is driven by motors connected to a 60 Hz mains [34].  
In the diagram in Figure 9a, the isospeed curves specified by the manufacturer for a pump with an impeller diameter of 237 mm are compiled from the available \(Q\)-\(H\) diagrams. The four isospeed curves are also labelled with discrete efficiency points. Dotted approximation curves connect points with the same efficiency and form isoefficiency curves that can be modelled mathematically with quadratic functions.  
The annual dataset of OPs for the WCS is represented by the cloud of grey dots in Figure 9. Some of these points are within the range specified by the manufacturer, which allows the use of interpolation tools such as Matlab's Curve Fitting Toolbox (R2023b, MatWorks) to accurately determine the efficiency of the pump and the required speed at these points.
## 3 Ad Hoc Networks
Centralized control via SDN not only enhances the network's ability to handle high traffic volumes but also allows for the rapid deployment of security protocols, such as DDoS detection and response strategies. As a result, our approach is particularly suitable for intelligent transportation systems, where reliable, real-time communication and high security are paramount. The integration of SDN within VANETs provides a pathway toward more resilient and adaptable vehicular networks, paving the way for secure and efficient intelligent transportation infrastructures.
# Prediction of Damage Level of Slab-Column Joints under Blast Load
## 4 Numerical Analysis Results and Discussion
### Fracture Volume  
In general, support rotation and displacement are used as criteria for evaluating the blast-resistant performance of RC members, but as shown in the previous analysis, the support rotation and displacement alone were not sufficient to accurately evaluate the performance of the slab-column joint. Moreover, it was difficult to use the support rotation or displacement to predict the blast-resistant performance of the slab-column joint because the support rotation and displacement according to the blast load condition did not have a certain tendency. Therefore, it is necessary to examine additional factors to evaluate the performance of the joint subjected to a blast load.  
In this study, fracture volume was analyzed from the analysis results as an additional evaluation factor. Fracture volume was expressed as a percentage of the volume lost due to the explosive load relative to the total volume for the joint region within a scaled distance of 0.1 m/kg\({}^{1/3}\) in Figure 3. It should be noted that this fracture volume is the effective volume of joint excluding the concrete cover.  
Figure 7 shows the effective fracture volume of the joint according to the scaled distance for each TNT weight of 10 kg, 20 kg, and 30 kg. Not surprisingly, as the weight of TNT increased, more damage occurred in the joint region. Interestingly, in all cases, the effective fracture volume decreased almost uniformly as the scaled distance increased.
###### Abstract  
The discrimination of river blockages is very important for the risk assessment of landslide disasters and secondary hazards. Experimental studies and statistical analyses were carried out to explore the formation process and discriminant criteria of river blockages caused by landslides. An adjustable slide chute was designed and built to conduct forty-five landslide experiments. According to the experimental results, river blockage was identified as having six types based on the differences between the water depth and the height of the landslide dam, and the degree of river blockage increased from 70% to 100% as the chute angle, particle size, and landslide volume increased. It is also found that landslide volume controls the landslide dam height and degree of blockage, and particle size and slide angle control both the landslide velocity as it enters the river and the cross-section shape of the landslide dam. To investigate more influence factors, a statistical investigation of 60 real landslide cases was carried out, and it revealed that some geometric attributes related to landslide volume have the highest correlation with river blockage, especially landslide thickness. Finally, an improved probability model was proposed to assess the possibility of complete blockage, and it has overall accuracies of 91.1% and 83.3% when applied to predict experimental landslide cases and real landslide cases, respectively.  
landslide dam; river blockage; formation process; laboratory test; river blockage criteria +
Footnote †: journal: Applied Sciences
## 5 Results
### Final Results
#### 5.2.4 Performance Analysis of Visual Coding Values  
An error ranking of visual values was undertaken from data obtained automatically from the application developed for the evaluation test. This ranking was compared with the difficulty ranking for each visual value to identify target items in the proposed tasks, and the difference in the positions in both rankings was calculated. In total, 471 data errors were collected automatically, while 478 answers for the identification problems were obtained.  
The primary purpose of the comparison was to find visual values with low and high discrepancy positions considering both rankings. The comparison results could indicate true correlations (if the visual item is good or bad for the identification task), false positives,  
Figure 12: The ranking of visual variables to identify the target items more easily. The ranking presented from the visual variable considered easier to the visual variable considered more difficult to identify.  
and false negatives. From the 35 different values of visual coding used, those with more than ten errors were considered to have a significant identification problem.  
Figure 13 presents the two rankings, highlighting the values with the most significant differences between the number of errors registered using the testing tool and the number of poor perceptions registered by participants through the applied questionnaires.  
The first analyses showed that the hue, lightness, saturation, texture, and orientation visual variables presented the highest levels of position variance. The only values with more than ten recordings of errors were the 29 th (saturation-test tool 34 records and perception questionnaires 8 records) and 31 st (orientation-test tool 39 records and perception questionnaires 19 records). Additionally, some visual items were reported as worse perception by the participants, contrary to the fact that the test tool did not register significant errors.  
A second analysis was undertaken to determine when the visual coding values that recorded more than ten errors from the test tool indicated a direct relationship with the participants' perception. Figure 14 shows both rankings, emphasizing the visual coding values that recorded more than ten occurrences of errors by the test tool.  
The results of the second analysis demonstrated a more effective relationship between the objective and the subjective data. The analysis enabled the determination of the visual coding values that presented the highest identification problems. At this point, it should be emphasized that many of the visual coding values of the saturation, orientation, and texture visual variables had elevated identification problems.  
A visual coding value that diverged from the standards and stood out in this analysis was \"K\" (text visual variable), which presented a high number of testing tool errors (23 errors) and worse perception indications (20 indications).
Also, in remote areas, such as mountains, deserts and oceans, where rain gauges are unable to be installed and radar systems are not available; the satellite-derived rainfall may be the only tool to provide precipitation information with relatively high spatial and temporal resolutions ([PERSON] et al., 2016). Therefore, the present study aims to evaluate the IMERG precipitation dataset over the Mexico region by analyzing different time scales of precipitation, such as hourly, daily and seasonal cumulative precipitation. The automatic meteorological network located within this region has been used as the reference since it provides rain gauges at time scales smaller than the analysis scales. This investigation, which is to our knowledge, the first to evaluate the IMERG precipitation data over Mexico, will contribute to a better understanding of the IMERG dataset and it will also open the doors to future studies with hydrological applications over the region. This article is organized as follows: Section 2 will describe the data, methods and the area of study. In Section 3 results will be presented through three sub-sections that analyze the IMERG data with different time scales of the precipitation. To complement the evaluation study, IMERG is analyzed as a function of the elevation given the location of the automatic meteorological stations. The discussion and conclusions are presented in Sections 4 and 5 respectively.
## 4 Empirical Study
### Experimental Results
#### 4.4.4 Comparison of Classification Methods  
In the phase of feature selection, we compared the classification effectiveness of One R, C5.0, Random Forests, Naive Bayesian and Bayesian network, and Bayesian network performed best in this case. When applying a predictive model for detecting the cargo theft in practice, comprehensibility and interpretability of the applied techniques is important, because it helps the port workers and the port authority to identify risky deliveries faster since they know exactly which features to look for. Therefore, we compare our proposed technique to the commonly known and widely used classification technique, i.e., logistic regression which is known for being both intuitive and easily interpretable, showing clear indication of how the features affect the prediction. As the dataset we used in this case is imbalanced, i.e., that bulk cargo theft has a much smaller sample size than normal operations, the ROC curve is utilized to illustrate the performance of the binary classification. Noticeably, Bayesian network performed better than logistic regression. The results can be seen in Figure 11, which shows values at the tangent point of ROC curve, indicating the higher sensitivity and specificity of Bayesian network.  
Figure 10: The results of the model testing.  
The AUC of Bayesian network, at 0.834, is higher than the AUC of logistic regression, with 0.771. This result also indicates that Bayesian network performs better for this binary classification.
# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 3 Results
### Experimental Setup
For the designed detection tool, the \(l_{e}\) distance (Equation (24)) was 159.7 mm. The software architecture of the whole system is presented in Figure 9. Data from the detection tool are read by the control program running in Matlab. This program, based on the current and previous detection data, calculates the next detection tool position, i.e., where the robot arm should be moved. Calculated robot joint positions are sent (via Ethernet) to the robot controller. On the controller, there are two programs running, the main one and the so-called User Task [39]. User Tasks are additional programs that can run in parallel with the main program. In the presented case, the User Task developed for the purpose of this research was responsible for two-way communication with the PC. It must be noted that although the User Task can perform various tasks associated with the robot control, it cannot contain commands that execute the physical moves of the robot arm. Moves can only be performed by commands included in the main program. Due to this, the User Task writes joint angles received from the PC to internal registers of the robot controller and the main program reads them and moves the robot to the desired position. Additionally, the developed User Task can also return current robot position as joint angles or Cartesian coordinates, operate the gripper, and perform some additional robot settings. Communication between the Nachi CFD robot controller and PC is provided via Ethernet and TCP protocol.