esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 IoV-Based Collision-Avoidance Architectures  
The operating procedure for an infrastructure in Figure 1 is described as follows:  
* For an IoV-based collision avoidance architecture, the real-time video is captured by the camera mounted on the infrastructure.
* By using the real-time object detection, we can obtain all objects that interest us, such as vehicles, motorcycles, and pedestrians.
* By utilizing the real-time dynamic prediction, we can get the future position of a moving object.
* Lastly, according to the future position of two moving objects, we can predict whether the collision will happen or not, and then warn them by using communication ability and/or the traffic sign.  
Note that we will only consider the scenario of moving vehicles in the text.  
For instance, in Figure 2, there are two cars that come from distinct directions to the same intersection. If the collision happens, they will be alerted to slow down by the system via its communication ability and/or the traffic sign. Particularly, the vehicle with its communication ability can be warned by the IoV message and the roadside traffic sign. On the other hand, the vehicle without communication ability can be warned by the roadside traffic sign. It is practical to make up for the deficiency of the assumption that all vehicles must have communication ability.
## 4 Numerical Validation
### Example 1: Dynamic System with Cubic Nonlinearities
The corresponding Largest Lyapunov Characteristic Exponent (LLE) curves plotted versus the driving frequency, \(\omega_{f}\), are exhibited in Figure 5. It is evident from Figure 5 that the LLE curve computed from the equivalent uncoupled expressions (29) is closed to that estimated from Equation (30). In this case, the RMSE does not exceed the value of 0.0056. This confirms that the proposed conjecture is numerically true. As a second example to investigate the validity of the proposed conjecture, a forced, damped nonlinear dynamic system with cubic nonlinearities was next examined.
# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 3 Materials and Methods
### Methods
#### 3.3.2 Response Process  
The dataset is considered imbalanced if one label/class of the data appears significantly more often than others. Imbalanced classifications can be slight or severe; for example, the difference ratio between PDO, injury, and death was found to be 1726:1518:262 in the present research. Figure 3 illustrates death as a minority classification, while PDO and injury are majority classifications. The ML models face difficulty with regard to extracting accurate information from imbalanced data and producing biased results. There are various methods to overcome this problem, for instance, resampling techniques and the synthetic minority oversampling technique (SMOTE).  
Resampling can be done through either over-sampling or under-sampling. While performing under-sampling, the run time and storage problems can be improved by reducing the amount of training data, but this might result in a loss of potentially important information. Over-sampling, however, does not lead to information loss but does increase the likelihood of overfitting since it replicates minority class events. Using SMOTE, overfitting is mitigated as synthetic examples are generated instead of replicating instances. In addition, no useful information is lost.  
SMOTE, however, does not consider neighboring examples from other classes when generating synthetic examples. Consequently, classes can overlap, and noise can be introduced. Furthermore, SMOTE does not support categorical data in Python yet; even if categorical data are transformed into numerical; SMOTE would generate data that did not make sense. Therefore, a resampling technique with a typical random over-sampling was adopted in this research to overcome the ML bias problem.
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 2 Materials and Methods
### Frame Selection for Effective Learning  
In this study, the Mask R-CNN model, the Xception model, and the proposed regression artificial neural network model were used [12; 13]. Supervised learning compares the ground truth with the model output and updates the weights according to the difference between the two. The weight update value is proportional to the input, and if there is a large amount of data, the characteristics of common features of the target class can be learned correctly. However, the repetition of similar data impedes learning and increases the risk of overfitting. The study data consisted of a video at 30 FPS, and the data for the continuous frames every 1/30 th of a second were essentially the same because the difference was quite insignificant. Data were collected through a histogram difference-based approach to avoid a decrease in the efficiency of the learning process due to the similarly repeating images. The amount of data through histogram variation can be changed by the threshold value. In addition, each cow has a different degree of movement when measuring weight, so  
Figure 3: Collected research data: (**a**) examples of top-view image data; (**b**) weight distribution statistics.  
it is necessary to select a threshold that can collect appropriate data for each cow. The threshold value was determined to collect approximately 100 images per cow from the training dataset. A total of 9359 images were extracted from about 125,550 images using this method. The process was as follows.  
1. The image was converted into a grayscale image and the histograms were computed.
2. The sum of the histograms was calculated by adding all the values in each histogram.
3. Steps 1 and 2 were executed for successive frames, and the images were selected only when the difference between the histograms of the two images exceeded the threshold value of 50,000.  
Because this method utilizes only the difference of the histogram summed values, its operation is simple. Examples of the selected and discarded data are shown in Figure 4a. The difference between the three images can be seen in Figure 4b. The composition of the dataset after frame selection is presented in Table 1. Validation was performed by randomly selecting 20% of the training dataset every epoch.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 2 Description of the Algorithm
### Dynamic Phase Comparison Algorithm
The expression for the satellite's motion distance is  
\[d=v\Delta t. \tag{6}\]  
By combining the above expressions, the azimuth angle of the radiation source signal can be obtained as  
\[\alpha\approx\arccos\left[\frac{c(2\pi f\Delta t+\Delta\varphi)}{2\pi fv\Delta t }\right]=\arccos\left[\frac{c}{v}\left(1+\frac{\Delta\varphi}{2\pi f\Delta t} \right)\right], \tag{7}\]  
where the range of values for the phase difference and signal azimuth angle are  
\[ \begin{cases}-\pi\leq\Delta\varphi<\pi\\ \quad 0\leq\alpha<\pi.\end{cases}  \tag{8}\]
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 1 Introduction
Using the numerical simulation based on the combination of the finite element method (FEM) and the boundary element method (BEM), broadband holographic imaging be achieved, and the excellent imaging effects confirm the feasibility of our design scheme.
## 5 Discussion
### Applications and Limitations of the Proposed CRBS Method  
Considering that regenerative braking is relatively weak compared to normal braking, the initial scope of this study was to evaluate the effective performance of the proposed CRBS method when the vehicle was moving in a straight line. The results of the CRBS method showed that the jerk profile of the vehicle was generated by considering the comfort domain as a prime constraint.  
However, there are some limitations of the proposed CRBS method for regenerative braking, specifically when it is used in turning or reversing situations. In these situations, the vehicle may need to stop quickly to avoid a collision, which could require a rapid application of braking force. In such cases, the performance of the CRBS method may be limited, and it may be necessary to rely on conventional brakes to provide the necessary stopping power. In the future, it will be important to carefully consider the performance of the CRBS method in various driving conditions, such as turning, uphill, downhill, and reversing, to ensure that it provides adequate braking force and safety for the vehicle and its occupants.  
In addition, the major challenges of implementing an AI-based regenerative braking system in practice, mainly concerned with the requirement of high precision equipment to control heavy vehicle when subjected to the extensively high force. In addition, cost and feasibility of implementing such a system on a large scale. Furthermore, robust and accurate sensors were required to collect data on the vehicle's speed and acceleration to implement AI algorithms and make control decisions in real time. Finally, very careful integration with the existing control systems in a vehicle is required. Overall, this type of evaluation can help to further validate the system and demonstrate its potential value in practical applications.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Case Study
#### 2.2.3 Assessment of the Change Impacts  
The land cover data created for the before and after images during the thematic change detection were used to quantify how much and what type of land cover changed. The fused DEM provided input for assessment of changes in slope, surface water flow patterns, stream channels, and watershed boundaries caused by the new development. Slope maps for the original and fused DEMs were calculated using the GRASS GIS module r.slope.aspect. Surface water flow patterns were simulated with GRASS GIS module r.sim.water. The module solves the bivariate form of shallow water flow equation using the Green's function Monte Carlo method (path sampling approach [54]), which makes the simulations feasible even on high resolution, noisy surfaces.  
The model was parameterized to simulate an extreme rainfall event with a steady, spatially uniform rainfall excess rate of 100 mm/hr, the infiltration rate of overland flow set to 0 mm/hr, and the Manning's n roughness coefficient uniformly set to 0.1. The model was run with 1 million walkers over a 60 min period producing six, ten minute water depth output maps. The simulations were performed with USGS NED DEM (3 m), UAS DEM (3 m), and the fused DEM (3 m) as input elevation datasets. Changes to the watershed boundaries and stream channels were evaluated using the GRASS GIS module r.watershed.
# Improving Convolutional Neural Networks' Accuracy in Noisy Environments Using k-Nearest Neighbors
## 4 Conclusions and Future Work  
In this work, we have proposed a hybrid method that replaces the softmax layer by a kNN classifier in the inference stage without modifying the training stage. Different datasets, network topologies and noise levels have been evaluated, performing statistical tests to get rigorous conclusions.  
We show that replacing softmax by kNN significantly outperforms traditional CNN architectures with noisy labels. The optimal number of neighbors is higher in larger datasets; therefore, the proposed method can benefit from large amounts of annotated data. In these large datasets, the performance gain is remarkable, as can be seen in the CIFAR experiments.  
It has been statistically verified that a non-linear classifier such as kNN used on the CNN representations significantly increases the accuracy with low noise levels. However, with high label noise levels, softmax and kNN get similar average results. This is because training with label noise forces the CNN to learn an incorrect transformation of data, which generates noise at the feature level. Although the softmax layer may learn to correct this problem, the noisy features confuse the kNN classifier. It has also been statistically verified that adding \(\ell_{2}\) normalization is beneficial at any noise level.  
An advantage of using kNN is that, besides the class, the most similar prototypes are also obtained, making it suitable for similarity search tasks. In addition, we observed that replacing the softmax layer by other classifiers such as SVM or RaF is also beneficial in terms of accuracy, yet to a lesser extent than  
Figure 4: Average \(F_{1}\) (%) accuracy (\(y\)-axis) with respect to the noise level (\(x\)-axis) considering several supervised classification schemes. RaF, Random Forest.  
considering kNN. On the other hand, computational cost is higher with kNN, but given the efficiency of the recent approximate methods, this may not be a severe issue.  
As future work, the reported results could be improved by using networks that aim to maximize the kNN accuracy by jointly training the CNN with kNN in order to optimize the classification error on training data [38] or introducing metric learning techniques. Furthermore, it would be interesting to compare kNN with other classifiers such as SVM or RaF using neural codes.  
[PERSON], [PERSON], and [PERSON] conceived and designed the methodology; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the results; [PERSON], [PERSON], and [PERSON] wrote the paper.  
This work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through the HISPMUS project (Ref. TIN2017-86576-R, partially funded by UE FEDER funds).  
The authors declare no conflict of interest.
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.6 QoS-Aware Peering Routing for Delay-Sensitive Data (QPRD)  
In [38] the authors proposed QPRD, which intends to improve the EPR discussed in [37] by classifying the patients' data packets into two categories: Ordinary Packets (OP) and Delay Sensitive  
Figure 7: **(a)** EPR framework, **(b)** Timing diagram [37].  
Packets (DSP). It uses the same framework used by EPR [37]. As shown in Figure 8, redrawn from [39], the routing architecture of the QPRD is divided into seven modules: MAC receiver, delay module, packet classifier, hello protocol module, routing service module, QoS-aware queuing module, and MAC transmitter. The data packets from the other nodes are received by the MAC receiver module, while their classification as hello packets and data packets is done at the packet classifier module. The delay module monitors the different types of delays and forwards the results to the network layer to find out the node delay. The hello protocol module is responsible for sending/receiving the hello packets. The routing service module receives the data packets from the upper layers and packet classifier, categorizes them as ordinary packets or delay sensitive packets, and chooses the best path for each category. QoS-aware queuing module forwards the received data packets to their corresponding queue while the MAC transmitter module store the received data and hello packets in a queue on a First-Come-First-Serve (FCFS) manner and transmits them using the CSMA/CA approach.
## 2 The Lunar South Pole
### Candidate Locations for Future Lunar Settlements  
The choice of the possible future landing and exploration sites on the lunar south pole is not an easy task and requires a careful analysis between aspects. Regions of interest (ROIs) should be located near permanently shadowed regions, offer a long duration of access to sunlight, direct-to-Earth communication and a surface slope and roughness favorable for landers and astronauts. Topographically high crater rims exposed to almost constant sunlight and located near these unilluminated crater floors seem to be the best location due to their proximity to potential vital resources and areas exploitable for solar power installations. Refs. [17; 23] have proposed a series of main drivers in the selection of possible ROIs in the vicinity of the lunar south pole and they are combined in the following list:  
* The availability of water. In particular, a Diviner average temperature lower than 110 K. In this way, water ice is supposed to be stable at the surface and enhanced H signatures > 100 ppm by weight so ice should be present close to the surface; this last value derives from the LPNS, where a lower value means that the presence of water ice might be located deeper and so be harder to reach.
* The slopes of the terrain should be less than 20\({}^{\circ}\). Based on the state of the art, lunar mission slopes greater than 15\({}^{\circ}\) can lead to the overturn of a lander while slopes up to 20\({}^{\circ}\) could guarantee safer extra vehicular activities.
* Usable energy source. Regions with a relatively higher average solar illumination (>50% of the lunar day) for power generation by solar panels.
* Communication link. Semi-continuous visibility of Earth is essential for the remote control of robotic operations and crew safety during the initial phase of lunar exploration programs when relay communication infrastructures will not yet be implemented.
* The base site should have enough areas for regular support ground operations and expansion (e.g., extra vehicular activities, EVAs)
* Abundance of mineral resources.
* Scientific interest to deepen the origin of our natural satellite.  
Clearly, the most binding driver is that related to illumination, which largely impacts the design and duration of a mission considering that existing datasets suggest that there are no flat areas > 1 km\({}^{2}\) with illumination > 50% at latitudes > 80\({}^{\circ}\). Despite everything, a tradeoff is necessary and the Artemis program has proposed 13 potential candidate sites, as shown in Figure 5. Accurate geodetic coordinates of each potential landing site can be accessed through NASA's Moon Trek software [24].  
Robotic missions scheduled for the next few years will surely pave the way to make the best choice among them.
## 4 Results
### Validation: Application on Thermograms of an In-Service Wind Turbine
(**a**) shows the preprocessed thermogram with the turbulence wedges numbered with roman numerals. (**b**) shows the reference transition line, which was estimated manually, as well as the CNR of the wedges noted above the respective wedge. (**c**) shows the result of applying the gradient-based algorithm (GBA) of [PERSON] et al. (2014) to the image and (**d**) shows the result of the application of the model-based algorithm (MBA) on the thermogram. Note the reduced color scale. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total Wedge Area \(A\)** & \(F_{A}\) in \% & \(N\) \\ \hline Reference Value & \(3814.5\) & \(0\) & \(5\) \\ GBA & \(1364.9\) & \(-64.2\) & \(2\) \\ MBA & \(3520.5\) & \(-7.7\) & \(5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Areas determined by the model-based algorithm (MBA) as well as the state-of-the-art, gradient-based algorithm (GBA) for all turbulence wedges in Figure 13. The area values are given in unit pixel\({}^{2}\) and are summed over all turbulence wedges. \(N\) is the found or true number of wedges in the image. wedge to the true area would show the error in the area determination more clearly. On the other hand, the MBA detects ten wedges, which includes, in addition to the nine referenced wedges, the turbulence wedge with \(\text{CNR}=0.2\) between wedges VII and VIII. However, the determined areas are always slightly smaller than the true areas of the wedges, which in total leads to a relative deviation of \(-20.2\%\). The validation demonstrates that the proposed model-based wedge detection algorithm is capable of detecting turbulence wedges and their areas on real thermograms of wind turbines in operation. The algorithm was able to determine wedges with CNR values in a range of \(0.2\leq\text{CNR}\leq 20\). The state-of-the-art algorithm consistently detects fewer wedges than actually present in the thermogram due to low CNR values, but overestimated the areas for each found wedge. While the model-based algorithm detected the number of wedges correctly, it underestimated the area by 7% to 20% depending on the CNR of the turbulence wedges, which is in agreement with results from the characterization of the algorithm.
# On-the-Fly Sleep Scoring Algorithm with Heart Rate, RR Intervals and Accelerometer as Input
## 3 Results
### Motion-Aware On-the-Fly Sleep Scoring Algorithm  
The following results were obtained: For each night session, the medical and the algorithm scoring overlapped second by second, as shown in the example in Figure 3.  
In this paper, all figures in which medical sleep scoring and algorithm sleep scoring are compared refer to the same subject, the first subject. This decision was made in order to compare the same scoring representative of the data collected in the experimentation with the different algorithms shown. Table 1 shows, for each stage, how many times (in percentage) the stage was recognized as another phase. The main diagonal (in cyan) represents the percentage of successful identification for each stage.  
The algorithm was evaluated also in terms of the sleep-wake pattern, taking into account the difference between Sleep Onset time, Awake time, Total Sleep Time (TST), and Wakeup After Sleep Onset (WASO). Results are shown in Table 2. Sleep Onset time and awakening time were compared for all the subjects, it was calculated how far ahead of medical scoring the times obtained with the algorithm were, and all values were averaged. The same was performed with TST and WASO.  
Figure 3: Medical sleep scoring and motion-aware on-the-fly algorithm sleep scoring compared for the 1 st subject.  
Finally, the confusion matrix was performed also for the sleep-wake pattern, finding the following results:  
\[Sensitivity=93.52\%,Precision=91.42\%,Accuracy=88.46\%\]
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 3 Proposed Fault-Free Protection Level Equations for CLAS PPP-RTK Service  
To develop a fault-free protection-level equation for a PPP-RTK system, the current protection-level equations of SBAS are used as the baseline equations because both SBAS and PPP-RTK broadcast the correction and quality messages in SSR formats. This section provides an overview of the SBAS fault-free protection level equation, followed by the proposed PPP-RTK protection level equation.