esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Intelligent Identification of Structural Surfaces
### Structural Surface Detection Using YOLOv5  
YOLOv5 is a lightweight single-stage object detection network known for its fast inference speed and high accuracy. To enhance the model's robustness and generalization capability, a heterogeneous dataset containing 1260 images was constructed, incorporating tunnel face images, natural rock outcrops, and concrete surface cracks (see Figure 1). The dataset was divided into training, validation, and testing sets at an 8:1:1 ratio.  
All images were resized to 640 \(\times\) 640 pixels and denoised using Gaussian and bilateral filters. Structural surface regions were manually annotated using the LabellImg tool. During training, the Complete IoU (CIoU) loss function and a cosine annealing learning rate scheduler were employed to enhance convergence stability and mitigate overfitting. The YOLOv5 model was trained with a batch size of 16 and an initial learning rate of 0.001, using the Adam optimizer for a maximum of 150 epochs. Early stopping was implemented based on validation loss plateauing for 10 consecutive epochs. To improve generalization under varying geological textures and lighting conditions, a range of data augmentation techniques was applied, including random horizontal flipping, rotation (\(\pm 15^{\circ}\)), scaling (\(\pm 10\%\)), color jittering, and brightness perturbation. The training was conducted on an NVIDIA RTX 3090 GPU using PyTorch 1.13, with a total training time of approximately 5 h. The default anchor boxes provided by YOLOv5 were used throughout. An example of the detection results is shown in Figure 2.  
Figure 1: Examples of heterogeneous structural surface images. (**a**) Tunnel face images; (**b**) natural rock outcrops; (**c**) concrete surface cracks.  
Figure 2: Structural surface localization results using YOLOv5.
# Characteristics of Electric Field Induced by Oscillating Metal Underwater Vehicle
## 4 Maritime Verification Experiment
As can be seen from the figure, along the speedboat sailing direction, the 1-30 Hz **frequency band** electric field component peak value in the boat speed is lower than 20 knots, relatively small; the electric field amplitude is less than 20 \(\mu\)V/m at speeds of 5 knots and 10 knots, andapproximately 30 \(\mu\)V/m at a speed of 20 knots; the amplitude of the interference electric field spectrum perpendicular to the navigation direction (y-direction) increases significantly with the increase in navigation speed, which is caused by the cutting of the geomagnetic field by the wire. The equivalent length of the geomagnetic field wire cut perpendicular to the navigation direction is longer, and it will increase significantly with the increase in navigation speed. Calculated at 20 knots, the spectral density of the background interference electric field of the speedboat in the frequency band of 1-30 Hz is about 0.4 \(\mu\)V/\(\sqrt{\mathrm{Hz}}\), which is still lower than most of the target electric fields of the ship, so it is possible to detect the electric field signals of the ship by using this speedboat with the electric field detection system on board. Figure 10: The electric field at different speeds. (**a**) The electric field component in the x-direction. (**b**) The electric field component in the y-direction.
# Effect of Particle Size on Composet Analysis by Portable X-ray Fluorescence
## 3 Results and Discussion  
### _Compost Characteristics_  
The general characteristics of the composots shown in Table 1 indicate that they were different from each other, having variable contents of organic matter between 310 and 726 g kg\({}^{-1}\) and different granulometric compositions. The sewage compost presented a prevalence of fine particles, with more than 92% of its particles being smaller than 2 mm. The MSW compost presented a balanced granulometric distribution of the size fractions 2-5, 0.5-2, and \(<\)0.5 mm. The alperujo compost presented a predominance of intermediate particles 0.5-2 mm with few fine particles \(<\)0.5 mm. Table 2 shows the aqua regia soluble elemental contents of the composots. Relating to the different origins and nature of the three composots, wide variations in the composition can be observed. The highest concentrations of V, As, Ba, Mn, P, Mg, S, Al, and Fe were found in the sewage compost. The highest concentrations of Cd, Cr, Cu, Ni, Pb, Sr, Sn, Zn, and Ca were found in the MSW compost. The concentrations of Cd and Ni in this compost exceeded the tolerance limits in force in Europe for soil improvers (2 and 50 mg kg\({}^{-1}\), respectively) and growing media (1.5 and 50 mg kg\({}^{-1}\), respectively) [19]. The alperujo compost generally showed the lowest concentrations of trace elements and heavy metals, which are indicators of anthropogenic contamination, such as Cd, Cr, Cu, Ni, Pb, Sn, and Zn, and the highest concentration of K, a plant nutrient from the olive. The concentrations of the elements Cd, Hg, Ni, Pb, and As, which are considered in the European legal regulation, both in the alperujo compost and in the sewage compost did not exceed the tolerance limits in force in Europe. It is worth noting here that the aqua regia procedure and XRF do not necessarily have to provide the same element concentrations because XRF measures total contents while aqua regia does not dissolve the entire sample (these are often referred to as pseudo-total contents), and this depends on the characteristics of the sample (for example of elements that are associated with silicates) [3; 20]. As a measure of the variability among the three composots used, the RPR was 194% for Cd, 189% for Sn and Pb, 184% for As, 177% for Zn, 165% for Ni, 163% for Cr, 159% for Fe, 147% for S, 136% for Mg, 130% for Ba, 125% for V, 113% for Mn, and 102% for K. Only for the elements Al (93%), Cu (87%), Sr (78%), and Ca (59%), was the RPR less than 100%. The choice of these three composots for carrying out this study is supported by their different origins and their varied characteristics.
## 2 Dynamic Modeling
### Control Forces and Moments  
The two propellers of the coaxial HAUV are fixed on the vector tilting device, and the vector platform controls the output of force and torque by controlling the tilt angle. The control force and torque generated by them can be expressed as follows [36; 41]:  
\[T=\begin{bmatrix}T_{x}\\ T_{y}\\ T_{z}\end{bmatrix}=R\begin{pmatrix}\delta_{x},\delta_{y}\end{bmatrix}\begin{bmatrix} 0\\ 0\\ k_{\alpha}\omega_{1}^{2}+\sigma k_{\beta}\omega_{2}^{2}\end{bmatrix} \tag{13}\]  
\[R\begin{pmatrix}\delta_{x},\delta_{y}\end{pmatrix}=\begin{bmatrix}\cos\delta_ {y}&-\sin\delta_{y}\sin\delta_{x}&-\sin\delta_{y}\cos\delta_{x}\\ 0&\cos\delta_{x}&-\sin\delta_{x}\\ \sin\delta_{y}&\cos\delta_{y}\sin\delta_{y}&\cos\delta_{x}\cos\delta_{y}\end{bmatrix} \tag{14}\]  
\[\tau_{\Theta}=\begin{bmatrix}u_{2}\\ u_{3}\\ u_{4}\end{bmatrix}=\begin{bmatrix}-d\sin\delta_{x}\begin{pmatrix}k_{\alpha} \omega_{1}^{2}+\sigma k_{\beta}\omega_{2}^{2}\end{pmatrix}\\ -d\sin\delta_{y}\cos\delta_{x}\begin{pmatrix}k_{\alpha}\omega_{1}^{2}+\sigma k_ {\beta}\omega_{2}^{2}\end{pmatrix}\\ \gamma_{\alpha}\omega_{1}^{2}+\gamma_{\beta}\omega_{2}^{2}\end{bmatrix} \tag{15}\]  
where \(k_{\alpha}\) and \(k_{\beta}\) are the lift coefficients of the upper and lower propellers, respectively, \(\sigma\) is the lift loss coefficient of the lower propeller, and \(d\) is the distance from the lift center of the propeller to the center of mass. \(R\begin{pmatrix}\delta_{x},\delta_{y}\end{pmatrix}\) represents the mapping transformation matrix from the propeller thrust to body coordinate system; \(\delta_{x}\) and \(\delta_{y}\) represent the deflection angle of the vector tilt device.  
Remark 2 ([41; 42]): _From a practical flight control point of view, considering that the tilting platform angles are all small, we can obtain \(\cos\delta_{x}\approx 1\), \(\cos\delta_{y}\approx 1\), \(\sin\delta_{x}\approx\delta_{x}\approx 0\), and \(\sin\delta_{y}\approx\delta_{y}\approx 0\). The control model can be simplified, where \(T_{x}\) and \(T_{y}\) are very small compared to each other, \(T_{z}\), and the effect of this and can be ignored. For convenience of presentation, \(T_{z}\) is redefined as \(u_{1}\)._  
The control force generated in the body coordinate system is rewritten as  
\[\tau_{v}=\begin{bmatrix}\tau_{vp}^{\mathsf{T}}&\tau_{v\Theta}^{\mathsf{T}} \end{bmatrix}^{\mathsf{T}}=\begin{bmatrix}0&0&u_{1}&u_{2}&u_{3}&u_{4}\end{bmatrix}^{ \mathsf{T}} \tag{16}\]  
Remark 3: \(\tau_{cp}\) and \(\tau_{v\Theta}\) are the actual control inputs of the position subsystem and attitude subsystem, respectively. It is obvious that the position subsystem has only one control input to control the 3-DOF translational motion, which is a typical underdriven system.
# Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology
## 4 Discussion  
In this paper, important results for the DNI estimation over the Antarctic region are presented. The valuable observations in all three components of solar radiation were conducted during the XXVIII scientific expedition carried out in the austral summer of 2012/2013 at the Mario Zucchelli base. Those observations made it possible to find the fine-tuning model between direct and global irradiance, which was then extrapolated to the entire observation dataset in this area.  
The fine-tuning model offered the possibility for a feasibility study and evaluation of a possible CS plant in this location. Within presented fine-tuned model we reduced the error in calculated DNI from observed GHI values in Antarctica from 12.9% considering the known Boland-Ridley coefficients for Australia to 2.8%. The DNI levels estimated for the Antarctica region suggest a positive outcome from designing and installing a renewable power plant that uses CS technology at the Mario Zucchelli  
\begin{table}
\begin{tabular}{c c c} \hline
**Location and** & **Mario Zucchelli Station** & **Mario Zucchelli Station** \\
**Time Frame** & **(Entire Year)** & **(Austral Summer)** \\ \hline DNI average [kWh-m\({}^{-2}\)] & 2014.0 & 1795.0 \\ \(E_{\rm sol}\) [kWh] & 193,545.5 & 172,499.5 \\ \(Q_{\rm sol}\) [kWh] & 176,126.3 & 156,974.5 \\ \(E_{\rm el}\) [kWh] & 36,985.5 & 32,964.6 \\ \hline \end{tabular}
\end{table}
Table 4: Performance of parabolic dish, annually and in the summer season.  
Figure 12: Parabolic dish, (**a**) Conceptual scheme (A-dish solar concentrator, B–receiver and micro gas turbine) and (**b**) parabolic dish at ENEA [14].  
Station. This type of power plant is studied at ENEA. This type of renewable power plant with environmentally friendly characteristics of transfer fluid is suitable for use in Antarctica.  
We considered Reference [16], where the multi-predictor approach is used in diffraction model. The multi-predictor approach was applied to the dataset where no significant improvements are found and where calculation error has not decreased below presented 2.8%. In this sense, the usage of two parameters model is correct where presented calculation error is included in observational error. The usage of simpler model with two predictors was found to be more efficient with respect to multi-predictor model in this specific region where ground albedo have high values due to present snow cover for entire year. Until there is no unique and precise diffraction model for all global locations, with this work we added an important contribution with two parameters in the diffraction model for the Antarctic region.
## 5 Numerical Results
### Multipath Error Envelope  
Figure 5 shows the multipath error envelopes of the meta-signals and current GAL signals when assuming an all-pass transmit characteristic, NELP with an early-late spacing \(\Delta=0.01\times T_{\text{C,BPSK-R(1)}}\), and a \(24.552\) MHz brick-wall receive filter centrally placed at \(f_{\text{IF}}\). The meta-signal using E1D-b/e with the longer chip duration has a poorer multipath rejection than the other signal candidates as the envelope is non-zero up to approximately \(586\) m in multipath tap length. The meta-signal combinations E1C+E1D-a/b/c/g perform poorer than using the E1C signal as a stand-alone signal in the region from \(0\) to \(50\) meters of NLOS tap length. The other meta-signals with E1D-d/e/f have a tighter multipath envelope  
\begin{table}
\begin{tabular}{r r r r r r r r} \hline \hline  & **E1D-a** & **E1D-b** & **E1D-c** & **E1D-d** & **E1D-e** & **E1D-f** & **E1D-g** \\ \hline BPSK-R(1) & \(-82.08\) & \(-85.09\) & \(-81.58\) & \(-96.06\) & \(-99.07\) & \(-94.91\) & \(-87.78\) \\ Sine BOC(1,1) & \(-82.08\) & \(-85.09\) & \(-75.31\) & \(-96.06\) & \(-99.07\) & \(-89.13\) & \(-87.78\) \\ MBOC(6,1,1/11) & \(-81.58\) & \(-84.59\) & \(-75.53\) & \(-94.91\) & \(-97.92\) & \(-89.19\) & \(-86.57\) \\ BPSK-R(10) & \(-72.59\) & \(-72.55\) & \(-72.85\) & \(-96.06\) & \(-99.07\) & \(-89.14\) & \(-76.59\) \\ Sine BOC(10,5) & \(-81.79\) & \(-82.12\) & \(-79.59\) & \(-96.06\) & \(-99.07\) & \(-90.30\) & \(-83.35\) \\ Cosine BOC(15,2.5) & \(-94.91\) & \(-96.49\) & \(-89.98\) & \(-87.13\) & \(-90.14\) & \(-80.70\) & \(-87.36\) \\ Sine BOC(14,2) & \(-90.07\) & \(-93.08\) & \(-84.60\) & \(-91.11\) & \(-94.12\) & \(-80.86\) & \(-87.65\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral separation coefficients in \(\text{dB}/\text{Hz}\) of currently transmitted waveforms (with \(f_{0}=1575.42\) MHz) with the investigated added E1D signal candidates within the upper L-Band placed off-center.  
but possess more ripples than the current signals' envelopes. According to these results, choosing a larger frequency offset would be beneficial for an E1D signal. Two-correlator NELP of a E1C+E1D-d/e/f signal can reduce the influence of multipath, but due to the sheer number of side peaks in the ACF, an oscillating multipath error can be observed up to the longer of the two chip durations of the stand-alone signals constituting the meta-signal. The minima of this oscillating multipath error of the E1C+E1D-a/b/c/d/e/f meta-signal are approximately located at \(1/f^{\Delta}\).
## 3 UAV Flying Base Station Properties
### Controlling Mobility and Positioning  
A UAV flying base station implements the mobility functionality to better serve mobile users, while the traditional base stations are stationary and have a fixed location. While this mobility presents an opportunity to better serve the user (approaching the user for greater channel reliability and data communication rate/bandwidth), it also presents unique engineering problems and risks. Because flying base station research and development is in its infancy, its user-dependent mobility control is new.  
**Security implications:** New security risks can involve a malicious user, including those violating the integrity of the positioning control, to misplace the base station and launch a denial-of-service (DoS) of the base station's connectivity provision. The vulnerabilities for such security threats are due to the mobility capability of the flying base stations and therefore do not apply to the stationary base stations.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 6 Conclusions and Future Research Directions
### Decision and Planning Strategies  
As previously mentioned, self-driving vehicles and AVs must achieve Level 5 automation in order to be able to drive without any human intervention [7]. As a result, vehicles must be intelligent enough to communicate within the traditional transport system and the surrounding [8]. To achieve this, the AVs need to undergo a transition period in which they will have to co-exist with the existing non-autonomous vehicles. Therefore, implementation requires careful and gradual planning both at vehicular and infrastructure level [38]. For the vehicular communication network development, a key factor is generating a heterogeneous model in which different AV communication technologies can be stacked together to perform versatile tasks [40]. For this purpose, DSRC technology seems to be the most appropriate technology for V2V communication, while LTE technology can be used for the implementation of V2I communication. Nevertheless, there are many open issues such as handover, big data management, cross layer design and vehicular cloud computing systems, which still require improvement [38]. Various driving strategies that the AVs must adapt in order to connect and coordinate the information, received from the environment and from other vehicles, are presented in [211]. These driving strategies not only facilitate the implementation of AVs, but also define the basic vehicular competencies needed for traffic flow and safety. They are explained in Table 9.  
In addition, there are three main challenges for V2X communication in AVs, which need to be taken into account in any future implementation strategy [212]. Firstly, there is the high cost required for developing and deploying the proper road infrastructure for an ITS. Secondly, VANETs expose critical vehicular information and, thus, require better privacy protection mechanisms. Thirdly, it can be very challenging to build robust
Finally, with regards to the infrastructure and environmental aspects, the investment in development of smart cities should incorporate the development of AVs and ITS [213]. In addition, to achieve an ITS, the existing infrastructure is lacking features and requires significant up-gradation, further research and development along with financial investment [214]. Conceptualisation, M.N.A. and Q.Z.A. ; Methodology, M.N.A. and Q.Z.A. ; Writing--original draft preparation, M.N.A. ; Writing--review and editing, Q.Z.A., F.A.K. and M.H. ; and Supervision, Q.Z.A. and M.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 2 Methodology
### Optimizing Cutting Paths Using Improved NSGA-II  
#### 2.3.1 Objective Function Formulation for Path Optimization  
The cutting space is derived from joint space parameters, encompassing spatial coordinates of the end effector and its envelope surface. The trajectory points embody spatial joint information, eliminating the need for frequent inverse kinematics solutions during control, thereby enhancing control efficiency and accuracy.  
\[\min F(x)=\min(F_{1}(x),F_{2}(x)) \tag{10}\]  
where the key parameters include the shortest path \(F_{1}(x)=L(\pi)\) and the minimum turning angle \(F_{2}(x)=T(\pi)\).  
The trajectory point set \(\pi=(p_{\pi(1)},p_{\pi(2)},\cdot\cdot\cdot,p_{\pi(m)})\) is defined as the collection of all path points during roadheader operation. The total length of the cutting trajectory, \(L(\pi)\), is the sum of distances between consecutive path points.  
\[L(\pi)=\sum_{k=1}^{n-1}d(p_{\pi(k)},p_{\pi(k+1)})+d(p_{\pi(n)},p_{\pi(1)}) \tag{11}\]  
The total turning angle, \(T(\pi)\), is the sum of angle differences between consecutive path points, calculated using a method based on spatial vectors of joint variables. For any consecutive trajectory points corresponding to joint variables \(\theta_{\pi(k-1)}\), \(\theta_{\pi(k)}\), \(\theta_{\pi(k+1)}\), \(2\leq k\leq n-1\), the direction vector \(v_{k}\) from \(\theta_{\pi(k)}\) to \(\theta_{\pi(k+1)}\) is defined, and based on these vectors, the total turning angle \(T(\pi)\) of the entire path is calculated.  
\[T(\pi)=\sum_{k=2}^{n-1}T_{k-k+1}+T_{n\to 1} \tag{12}\]  
Figure 5: Design of cutting trajectory space under target cross-section constraints.  
where \(T_{k\to k+1}=\begin{cases}\left|\cos^{-1}(\frac{v_{k^{*}}v_{k+1}}{|v_{k}| \cdot|v_{k+1}|})\right|_{t}&\frac{v_{k^{*}}v_{k+1}}{|v_{k}|\cdot|v_{k+1}|}<1\\ 0,&\frac{v_{k^{*}}v_{k+1}}{|v_{k}|\cdot|v_{k+1}|}=1\end{cases}\).  
Optimization of the cutting trajectory must meet the section forming cutting characteristics: the path should cover all trajectory points, ensuring each is traversed accurately once for completeness; trajectory points should be adjacent to ensure cutting continuity, as the target section is composed of solid coal walls and cannot jump; the starting point should be located on the outermost cutting plane of the section.
# Roughness Evaluation of Burnished Topography with a Precise Definition of the S-L Surface
## 2 Materials and Methods  
### Analysed Details  
Two types of surface finishing were analysed--plateau-honed, with a cross-_hatch_ pattern equal to 60\({}^{\circ}\), turned, and isotropic details. Surfaces containing dimples of different sizes were considered. The dimple's average width and average depth were between 0.2 and 0.8 mm and between 10 and 100 \(\upmu\)m, respectively. The length and width of the dimples were equal and they were circular, so, correspondingly, their width was only considered. More than 20 measured surfaces were studied, but few of them are presented in detail.  
In Figure 1, the contour map plots (a-c), isometric views (d-f), material ratio curves (g-i), autocorrelation function graphs (j-l), and ISO 25178 surface topography parameters (including Sk family) are presented for three various surfaces. The first surface, presented in the left column, was a plateau-honed cylinder liner with additionally created oil pockets received by the burnishing technique, where correspondingly, the dimple width and depth were, on average, around 0.3 mm and 5 \(\upmu\)m. The second surface, located in the middle column, was an isotropic cylindrical surface with modelled dimples with a width of around 0.8 mm and a depth of 60 \(\upmu\)m. The last and third surface was a turned cylindrical surface containing additionally burnished dimples with a 0.7 mm and 100 \(\upmu\)m width and depth, respectively. The area density of the oil pockets was smaller than 20% (this has been studied in many previous papers considering tribological performance) for all of the surfaces studied.  
The values of the following ISO 25178 surface topography parameters were measured, calculated, and considered: arithmetic mean height _Sa_, auto-correlation length _Sal_, surface bearing index _Sbi_, core fluid retention index _Sci_, root mean square gradient _Sdq_, developed interfacial areal ratio _Sdr_, core roughness depth _Sk_, kurtosis _Sku_, maximum peak height _Sp_, arithmetic mean peak curvature _Spc_, peak density _Spd_, reduced summit height _Spk_, root mean square height _Sq_, upper material ratio _Sr1_, lower material ratio _Sr2_, skewness _Ssk_, texture direction _Std_, texture parameter _Str_, maximum valley depth _Sv_, valley fluid retention index _Svi_, reduced valley depth _Svk_, and the maximum height of surface _Sz_.  
Figure 1: Contour map plots (**a–c**), isometric views (**d–f**), material ratio curves (**g–i**), 3D ACF (**j–l**) and the ISO 25178 surface topography parameters (**m–o**), received from a cylindrical surface with additionally burnished dimples with different sizes and densities.