esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation of Proposed Scheme
### Simulation Results
The proposed method consistently outperforms both strategies employed in MRMQoS, specifically the multicast tree with fixed delay and fixed profit, showcasing performance enhancements with reductions of up to 18% and 15%, respectively. Crucially, MRMQoS encounters challenges in networks exceeding 25 nodes due to prolonged running times, whereas our proposed method efficiently identifies optimal solutions within a reasonable timeframe, thus surpassing the performance of both MRMQoS strategies. The performance improvement gain of the proposed model against the avg. cost multicast tree fixed delay and fixed profit is given in Table 3.
## 6 Results and Discussion
### Our Results  
Figure 14b illustrates the confusion matrix of the classification model using SNNs. In the case of the detection model, our SNNs have no mislabeled traffic, and the total network traffic classified as normal is 38,596. However, 423,096 of the traffic is classified as anomaly traffic. In the case of the classification model, only twelve of the traffic in the total network traffic were mislabeled. Therefore, the overall accuracy of the two models reached 100%.  
The performance of DT is shown in Figure 14c, which shows the confusion matrix of the DT classification model. There are no misclassified traffic in the detection model, and only thirteen network activities were misclassified in the classification model. Therefore, the overall accuracy of the two models reached 100%.  
The evaluation performance of BT is represented in Figure 14d, which shows the confusion matrix of the BT classification model. There was no misclassified traffic using the detection model, and only sixteen traffic samples were misclassified using the classification model. In brief, the accuracy of the detection and classification models is 100%.  
The performance response of SVM is shown in Figure 14e, which illustrates the confusion matrixes of the SVM classification model. There was no misclassified traffic using the detection model, and only 487 out of 461,696 network activities were misclassified using the classification model. In summary, the accuracy of the detection model is 100%, and the overall accuracy of the classification model reached 99.80%.  
The performance of KNN models is shown in Figure 14f, which illustrates the confusion matrix of the KNN classification model. The accuracy of the detection model was 100%, and the overall accuracy of the classification model reached 99.40%. Overall, SSNs, DT, and BT recorded 99.99% better performance than SVM and KNN.  
Figure 14: Confusion Matrix: (**a**) Detection model (For all models), (**b**) SNNs Classification model, (**c**) DT Classification model, (**d**) BT Classification model, (**e**) SVM Classification model, (**f**) kNN Classification model.
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 2 Background and Motivation  
### Depalletizing Systems in Industrial Environments  
A pallet is a structural element used to provide stability to a set of items when they are moved as a unit by mechanical means, such as a forklift. Typical pallets are wooden frames, but other materials are also used. There exist dimensional standards to facilitate handling at different locations (source, transportation, destination).  
Depalletizing systems play a crucial role in modern industrial environments, where efficient material handling and inventory management are vital for streamlining operations and minimizing downtime [15; 16; 17]. These systems are designed to automate the process of either moving entire pallets or removing specific objects from a pallet, reducing the need for manual labour and mitigating potential safety risks associated with repetitive tasks.  
Manual depalletizing can be labour-intensive, time-consuming, and prone to errors. Additionally, it may expose workers to potential hazards, such as repetitive strain injuries and accidents caused by the improper handling or lifting of heavy objects. Automated methods can employ single-purpose depalletizer machines, working on regular arrangements (see e.g., [18] for a description), or robotic arms. The latter, in turn, can be either programmed (e.g., [19]) or semi-autonomous, based on machine vision (e.g., [20]).  
Focusing on machine vision-guided robotic arms, they have been one of the first pioneering experiments in AI-based robotics [21; 22] and are currently widely available for traditional applications, as well as being the object of much current research [23; 24; 25; 26; 27; 28].  
However, when targeting resource- and cost-constrained computing as required in such environments as retail delivery automation or small-scale production, the options become more limited.
## 1 Introduction
In general, this detection is affected by color similarities between grapes andbackground, the different size and scale of the grapes, occlusion originating from leaves or/and branches, weather conditions, light variations and reflections and shadows. For example, the proposal of [5] was to characterize the diameter and the ripeness of grapes in vineyard images. The segmentation process was performed in the HSI (hue, saturation and intensity) color space by applying a threshold segmentation level and two additional restrictions: the image regions must have a predefined range of intensities and the objects must have a feasible diameter. In [25], the proposal to avoid illumination variability was to perform the grape segmentation procedure in the CIELab color space. This segmentation considers the lightness component of the pixels in relation to the color characteristics of the crop allowing a segmentation quality of 87.2%. In [26], an automatic system to detect berries and to determine their size and weight was proposed. In this case, the segmentation technique was based on applying a Bayesian discriminant model by using as inputs the RGB pixels from two classes of objects selected in the image; background and fruit (peel/stem). Then, these results were stored in a look up table (LUT) to perform a fast segmentation. The system estimates the berry weight (R2 > 0.96) and size (R2 > 0.97) properly, extending the suitability of the system to other types of fruits and vegetables. The use of artificial illumination at night was proposed in [27], where color mapping combined with morphological operators was used to localize clusters of red and white grapes, obtaining a cluster detection efficiency of 97% and 91%, respectively. More recently, [28] showed the first complete system for vineyard yield estimation, which has been evaluated with artificial illumination at night over several years and a large quantity of vines of different vineyards. In this case, the system captures 75% of the spatial yield variance with average errors between 3% and 11% of the total yield, values that represents the state-of-the-art in this field. In a similar direction, the proposal of this paper is to perform yield estimation by applying controlled artificial illumination at night in a vineyard in order to avoid the color variability and changes induced by daylight natural illumination. The main goal is to assess different methods suitable for grape segmentation in vineyard images, perform an estimate of the area and volume of the cluster of grapes and, finally, estimate the vineyard yield.
## 2 Materials and Methods
### Quantitative Assessment of Image Quality  
In order to determine the image quality in these CTA protocols, the image quality was quantitatively evaluated by measuring the signal-to-noise ratio (SNR) of the proximal and middle descending aorta. Regions of interest (ROI) were placed in the true and false lumens with an area of 25 mm\({}^{2}\) (containing at least 300 voxels) to measure SNR. Figure 4 shows the ROI measurement of SNR in the aorta of the model. To minimize the difference between observers, the SNR measurement was repeated three times at each location, and the average value was used to minimize the difference between observers, and the average value between two observers was used as the final result.
## 4 Results  
### Relationship between JRC and Seepage Flow Q  
Figure 6 shows the relationship between the JRC and the seepage flow \(Q\) under different hydraulic pressure and normal pressure values. The normal pressure, the hydraulic pressure, and the roughness of the fractures affected the change in the seepage flow in the rough fractures. According to the changes of the curve, it was found that:  
1. Under the same hydraulic pressure and normal pressure, the fluid seepage flow decreased with the increase in the JRC. The larger the JRC was, the more uneven the rough fracture was. When the fluid passed through the fracture channels, more energy was needed to overcome the seepage resistance caused by the fracture roughness, which led to a decrease in fluid seepage flow.
2. Under the condition of constant normal pressure and JRC, the seepage flow of the fluid increased with the increase in hydraulic pressure. The greater the hydraulic pressure, the greater the energy of the fluid in the fracture channel. More fluid flowed through the fracture channel.
3. In the case of constant hydraulic pressure and JRC, the seepage flow of the fluid decreased with the increase in normal stress. As shown in Figure 6, when the JRC was 11.70, and the hydraulic pressure was 0.6 MPa, the seepage flow of the fracture decreased from 2.6 \(\times\) 10\({}^{-6}\) to 8 \(\times\) 10\({}^{-6}\) m\({}^{3}\)/s with the increase in normal pressure.  
Figure 6: _Cont._Based on the above analysis, the nonlinear relationship between the JRC value and the seepage flow was affected by hydraulic pressure and normal pressure. With the increase in JRC, fluid seepage flow decreased under the different normal pressure and hydraulic pressure, and the amplitude of the decrease was different. Table 4 shows the rate of the decline in fluid seepage flow under different hydraulic pressures and normal pressures. The rate of decline in fluid seepage flow decreased with the increase in hydraulic pressure, whereas the rate of decline in fluid seepage flow increased with the increase in normal pressure. The greater the normal pressure on the rough fracture, the greater the rate decrease in the fluid seepage flow, indicating that the greater the rate of decrease in the fluid seepage flow, the more pronounced was the influence of normal pressure on fluid seepage flow.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 1 Introduction  
Unmanned aerial vehicles, sometimes generically called drones, are becoming increasingly popular to provide mobility solutions for congested smart cities [1, 2]. Equipped with a wide range of sensors and real-time processing capabilities, drones combine the key principles of technological modernity: data processing, networking, autonomy, and mobility. In smart city environments, drones are called to play a major role in a variety of applications, including package delivery [3, 4], data collection [5], traffic policing [6], surveillance [7], Search and Rescue (SAR) operations [8, 9, 10], and even as transportation devices [11]. The integration of drones into densely populated areas presents many challenges due to safety, security, and privacy considerations. This is especially true when considering autonomous operations with little or no human intervention.  
To fulfill their intended purposes, drones must be provided with the ability to explore complex urban environments and search for mission-dependent targets. Motivated by this fact, [12] recently considered a multi-resolution probabilistic approach for searching for a landing site in an urban environment by maintaining a probability map quantifying the probability of different areas being suitable for landing. Note that in that paper, as well as in the current one, \"multi-resolution\" refers to the fact that information is gathered from different altitudes, giving increased _spatial_ resolution when flying at lower altitudes. This is not to be confused with other usages of multiple-resolution, such as image processing tools (e.g., a pyramid parametrization) or employing a sequence of images as in super-resolution. These probabilities are updated based on images taken from a downward-looking camera and by re-visiting certain areas from different viewpoints. In order to do this efficiently, a trajectory planning scheme is required that selects which areas should be re-visited and then computes an efficient and safe trajectory.  
In this work, we address the trajectory planning problem outlined above. Following [13], we propose a solution for the Global Path Planning (GPP) problem that aims to plan safe trajectories to explore an area at a constant altitude. Due to time and energy constraints, the trajectories are necessarily limited in length and must cover areas with a high probability of being appropriate for landing.  
The remainder of this paper is organized as follows: Section 2 reviews existing work regarding well-known coverage path planning algorithms and related computational geometry problems. The formulation of the GPP problem is provided in Section 3. Section 4 presents a baseline algorithm for the GPP problem based on an exhaustive search. Section 5 introduces SST--an efficient heuristic algorithm for the GPP problem. Performance results are reported in Section 6, and Section 7 concludes the paper.
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 2 Channel Model
### Atmospheric Model
#### 2.1.1 Rainfall Weather  
Mie scattering theory and the ITU-R model are both commonly used approaches to calculate terahertz link degradation in falling rain [16; 17]. The former can provide a better agreement with measurements when an exact distribution of raindrop particles is offered. The latter is proposed based on a wide range of measurements and regarded as an empirical model, which is employed in our calculation due to its wider applicability. In the standard defined by the ITU-R, attenuation by falling rain can be expressed as \(\gamma_{R}=kR^{a}\) [dB/km], with the parameter \(R\) as the rainfall intensity [mm/h]. Coefficients \(k\) and \(\alpha\) are frequency-dependent and polarization-dependent, respectively, with their values can be obtained in the ITU-R P.838-3 protocol [17]. In the following calculation, we set a link distance of 1 km in falling rain and a rainfall rate of 25 mm/h corresponding to heavy rain. In the simulation, we consider it as a static parameter.
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 2 Methodology  
The purpose of this work is to emulate the impact of the low salinity waterflooding process in naturally fractured reservoirs using numerical simulation. We have approached this objective by using two steps. The first is collecting the coreflood experimental results and estimating the change in the relative permeability curves due to low salinity brine injection using a similar approach to our previous work [2]. The alteration to the relative permeability has been captured through saturation exponents of oil and water phases, as well as by the saturation endpoints. Then, Corey formulae for the oil-water system were employed to calculate new flow curves to represent the region flooded by the low salinity brine.  
Meanwhile, in the second step, an appropriate weighting factor formula is proposed to calculate the average salinity of the brine mixture in the mixing region accurately. A precise weighting factor is essential because it controls the salinity of the brine mixture, which, in turn, determines the brine properties such as density, viscosity, compressibility, and formation volume factor. Besides, the weighting factor determines the mobility of the brine mixture. In this part, a normalised brine salinity has been used to obtain a range between zero and one with a proper increment step, which enables capturing of the soaring of the salinity mixture in the mixing zone. Then, the weighting factor and brine properties calculated accordingly using the provided equations, as illustrated in Table 1.  
In the later work, both water and brine will be used interchangeably for the injected water or formation water, and they have the same meaning and the term low salinity has been shortened in the figures to LoSal for simplicity. However, the above-explained methodology has demonstrated the following points:
## 3 Vehicle Pose Estimation Method
### Multi-Layer Edge Points of Frontal Vehicle  
A total of ten edge feature points are encompassed within the framework, spanning various critical locations. These include the left and right contact points between the front and rear wheels and the ground, as well as two edge points positioned at the rear light of  
Figure 2: The block diagram of the vehicle pose estimation.  
the vehicle. Furthermore, two edge points reside on the vehicle's roof, with an additional two on the apex of the front wheel. Notably, eight of these points are directly discernible in Figure 3. When the camera pitch angle is fixed, the longitudinal distance \(D_{1}\) can be measured according to the height difference between the camera and the reference point. The calculation is as follows:  
\[D_{1}=\frac{H_{i}}{\tan(\arctan(\frac{|v-v_{0}|}{f})+\theta)}+k \tag{1}\]  
In Equation (1), \(f\) is the focal length of the camera; \(v\) and \(v_{0}\) are pixel coordinates corresponding to the edge point and the center of the optical axis, respectively; and \(k\) is the offset value relative to the selected calibration area. Let \(k=0\) when \(H_{i}\) corresponds to the bottom edge point. Other edge points can provide reliable pixel information for the distance measurement model when bottom points are invisible or blurred, but \(D_{1}\) is greatly affected by the change in camera pitch angle.  
When the frontal vehicle's slant \(I_{c}\) relative to the camera is horizontal, the vehicle distance \(D_{2}\) can be measured according to the actual width \(W_{i}\) between the left and right edge points on the same layer. The calculation is as follows:  
\[D_{2}=\frac{fW_{i}}{w}+k \tag{2}\]  
where \(w\) is the pixel width corresponding to the edge points of each layer. \(D_{2}\) will not be affected by the pitch\(-\)angle change, so it is complementary to Equation (1).  
After obtaining an accurate \(D_{2}\), the camera pitch angle \(\theta\) can be reversely calculated with Equation (1), and then the target vehicle's yaw angle \(\alpha\) can be calculated as follows:  
\[\alpha=\arcsin(\frac{D_{1}^{f}-D_{1}^{r}}{L}) \tag{3}\]  
where \(f\) indicates the front wheel and \(r\) indicates the rear wheel. \(D_{1}^{f}\) is the distance between the camera and the front wheel that has been corrected by \(\theta\), and \(D_{1}^{r}\) is the distance between the camera and the rear wheel. \(L\) is the distance between the front and rear wheels. Both  
Figure 3: Distribution of vehicle tail edge points and corresponding ranging models. A total of ten edge feature points are encompassed within the framework, spanning various critical locations.  
sets of formulas presented necessitate two essential inputs: the pixel\(-\)position data for the edge points and the unchanging rigid structural parameters pertaining to the target vehicle. The former can be predicted through the utilization of a convolutional neural network, harnessing its predictive capabilities. Meanwhile, the latter can be refined and optimized within the context of an ongoing tracking sequence, allowing for the iterative enhancement of accuracy.
## 3 Modeling Analysis of Bowden Cable-Fabric Force Transfer
### Modeling of Force Transfer and Friction Compensation for Bowden Cable  
A Bowden cable consists of a wire rope and an outer wire sheath. Because the wire rope is located in the wire sheath, there is friction in different directions during reciprocating movement. In the application of real-time control, we usually choose to use the static force transfer model based on the Coulomb friction model. Therefore, the force transmission of the Bowden cable was analyzed using the element method, and the following assumptions were made in the modeling:  
1. The mass and inertia of the Bowden cable are ignored;
2. The wire rope moves axially along the inner wall of the wire sheath, and there is no extra-axial movement;
3. The friction between the wire rope and the wire sheath is Coulomb friction;
4. The elastic deformation of the wire rope is in line with [PERSON]'s law and within a certain range.  
The static force analysis of Bowden cable is shown in Figure 7. According to the basic relationship expression of the force on the element segment of the Bowden cable [20], the transmission model of the output tension was derived as follows:  
\[F_{out}=\left\{\begin{array}{ll}F_{in}e^{-\mu_{1}\theta(t)}&\dot{v}>0\\ F_{in}&\dot{v}=0\\ F_{in}e^{\mu_{1}\theta(t)}&\dot{v}<0\end{array}\right. \tag{3}\]  
where \(F_{in}\) and \(F_{out}\) are the input tension and output tension at both ends of the Bowden cable with length L, \(\mu_{1}\) is the friction coefficient between the rope and the sheath in the Bowden cable, \(\theta(t)\) is the corresponding central angle at time \(t\), and \(\dot{v}\) is the acceleration.  
Based on the static tension transfer model derived from Formula (3), it is easy to obtain the tension relationship between the input end and the output end of the Bowden cable. Therefore, as long as the static tension transfer model is established, the corresponding friction compensation value is designed and added to the input signal, so as to ensure that the output tension can statically track the expected tension curve. So, the given input tension signal should be equal to the expected output force plus the offset of friction.  
Figure 6: Expected force curve of knee joint under three different peak values.  
According to the difference between \(F_{in}\) and \(F_{out}\), the friction force when the output force is \(F_{out}\) can be obtained; then, the friction force when the output force is \(F_{d}\) can be expressed as follows:  
\[f_{L}=\frac{F_{in}-F_{out}}{F_{out}}F_{d}=\left\{\begin{array}{ll}F_{d}[e^{\mu _{1}\theta}-1]&\dot{v}>0\\ 0&\dot{v}=0\\ F_{d}[e^{-\mu_{1}\theta}-1]&\dot{v}<0\end{array}\right. \tag{4}\]  
where \(f_{L}\) is the friction force of the Bowden cable under the expected tension, \(F_{d}\) is the expected tension, and \(\theta\) is the bending angle of the Bowden cable.  
Therefore, the actual tension signal given by the Bowden cable should be  
\[F_{A}=F_{d}+f_{L} \tag{5}\]  
where \(F_{A}\) is the actual given tension of the Bowden cable.