esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 4 Results
### Comparison with State-of-the-Art Methods
In particular, compared to the more extensive network MSRN proposed by ECCV, the MCFN network shows higher PSNR and SSIM values by 0.21 dB and 0.0041, respectively, on the Set14 test set with a scaling factor of 2. On Set5, with a scaling factor of 3, compared to the MIPN, the MCFN also improves its PSNR and SSIM values by 0.1 dB and 0.0009. As the scaling factor increases, the low-resolution image loses more high-frequency information, limiting the high-  
In Figure 5, a significant difference in the clarity of the letters reconstructed by the different algorithms can be observed when the magnification factor is two times. For example, the letters reconstructed by SRCNN and CARN could be more precise and quieter. Although IDN, MSRN, and MIPN methods have improved the clarity, some details of the letter shape still need to be recovered. In contrast, the letters reconstructed by MCFN are more transparent and less noisy. In Figure 6, a building at sunset at 3\(\times\) magnification shows that MCFN performs better in preserving the edge texture and reducing the artifacts. Figure 7 shows a car roof image at 4\(\times\) magnification. MCFN demonstrates less distortion and effectively reduces ringing effects, with richer information on the edge contours. In addition, in Figure 8, the selected sign text in the scene is displayed under a magnification factor of 4, and our method improves the edge clarity while also improving the brightness to obtain a better visual effect. In general, our network performs well in objective indicators and shows significant advantages in subjective visual effects.
## Methods  
### Data Collection  
The data for this study were collected at a six-lane divided urban section in Doha, Qatar. The site details are shown in Figure 1. The data were collected using four video cameras for two days. A total of 12 consecutive hours were recorded each day from 6:00 a.m. to 6:00 p.m. (total of 24 h). This time period was selected to ensure daylight condition and clear view when analyzing the videos. The pedestrian data were extracted to obtain 1094 observations for pedestrians crossing without a conflict (no vehicles were present when the pedestrian crossed the road) and 972 observations for pedestrians crossing with a conflict (some vehicles were present when the pedestrian crossed). Of the 972 observations, 602 pedestrians crossed the road using a perpendicular path, and 370 pedestrians crossed on an oblique path. This study focused on accepted gaps for pedestrians crossing in a perpendicular path. Different variables were extracted for each pedestrian crossing case. More details regarding the data collection process are available in the preliminary work of the authors [28].  
Figure 1: Layout of the study setup.  
The gap was computed as the time for a critical vehicle to arrive at the point of crossing. The critical vehicle is described as the closest vehicle to the pedestrian. The location of the critical vehicle was recorded once a pedestrian started to cross the road. The critical vehicle can be positioned in any lane according to the crossing situation. The critical distance is defined as the distance from the crossing point to the critical vehicle. The speed of the critical vehicle was calculated using distance and time. The video data were played frame by frame to obtain the time taken by each vehicle to traverse a known trap length. The speed was then calculated using the difference in time between the vehicle passing the first line and the second line of the trap.  
The pedestrian waiting time was measured from the time a pedestrian arrives at the curb or median until the crossing begins. The pedestrian speed was calculated based on crossing time and the traveled distance. Due to the presence of a median, separate observations were recorded for the case of crossing from the median to curb and vice versa.  
In addition to the previous variables, variables related to pedestrians such as age, gender, type of clothing, crossing in a group, carrying bags, and use of mobile phones were collected as shown in Table 1. Pedestrian age was extracted from the videos based on visual appearance. To minimize the errors, the videos were processed by two different investigators. In case of any disagreement, both investigators discussed each case until reaching an agreement.
## 1 Introduction
The final surgical result of these reconstructions, also in term of an accurate prosthesis-to-bone contact, largely relies also on how bone reactions are performed, and thus on the design and manufacturing of the so-called patient-specific cutting guides [1; 15; 30; 32; 33; 34; 35]. The anatomical design of these implants based on digital bone models should imply the knowledge of the shape of both the affected and unaffected hemipels; the former must provide an accurate location and extension of the tumor, the latter should be considered a best possible target for the final reconstruction [15; 27; 36; 37]. The exact definition of the tumor, together with the skeletal and soft tissue structures, requires complete computer-based anatomical models, to be obtained via multimodal medical image scans using computed tomography (CT) and magnetic resonance imaging (MRI), and accurate 3D reconstructions. These final 3D models, observed separately and also registered superimposed on one another in 3D space, allow visualization, identification and localization of all important anatomical structures, necessary for a careful pre-surgical planning and implant design [3; 38; 39]. For these procedures, and for the following virtual surgery planning, a close collaboration between surgeons, radiologists, bioengineers, and technicians is necessary. Despite these techniques must have been exploited massively for modern custom-made implants, only a few papers have reported in detail the procedures implied in this modelling part of the personalisation [15; 27; 30]. The aim of the present work is to report on an original procedure in orthopaedic oncology for the 3D design of custom-made implants for the pelvis. In particular, the procedure is here shown for a single clinical case and follows the full process from medical imaging to final 3D computer-based models of the tumor resection and bone reconstruction. All these steps are based on personalised models of the pelvis of the patient, including the bones and the tumor, as derived from of CT and MRI images using state-of-the-art semi-automatic segmentation tools. The steps of this procedure are also defined under careful indication of the surgical team, including of course the critical decision on the osteotomies for tumor resection. This technical procedure includes for the first-time spatial registrations and also mirroring of the unaffected hemipelvis to the affected one, as a reliable subject-specific homologous reference for a possible most accurate definition of these cutting planes. In every step, the main accuracy parameters are tracked.
## 3 Results
### Numerical Modelling Augmentation
#### 3.7.3 Sensitivity Analysis  
Accuracy is crucial for biomechanical applications of numerical models. Thus, it is of great relevance to perform sensitivity analysis to understand how the conditions chosen in developing numerical models impact the results. [PERSON] and [PERSON] [44] evaluated that, in CFD simulations, using pulsatile inlet conditions produces better results, and they suggested that these should be as physiologically accurate as possible. [PERSON] et al. [33] performed FSI simulations on idealised aortic models and studied the effect of grid spatial resolution. They suggested that the undesirable effects of numerical dissipation and diffusion are mainly ruled by spatial discretisation. Mesh sensitivity analysis is recommended in FEM modelling and it was also suggested that higher-order discretisation schemes assist in accuracy improvement. [PERSON] et al. [38] studied the influence of inlet BC on Low-Density Lipoprotein (LDL) transport and found that realistic inlet BC produced closer agreement with in vivo data. [PERSON] et al. [27] focused on studying the numerical results sensitivity of CFD and FSI simulations to the inlet waveform (cardiac cycle duration and stroke volume). The results proved that stroke volume strongly influenced the numerical results. Contrary to the stroke volume, the period of the cardiac cycle had a moderate impact and the interacting effect of the two was insignificant. Ascending aorta presented less sensitivity to period and stroke volume than the aortic arch and descending aorta. Moreover, when the wall motion was considered, the standard deviation was not significantly changed. The same group published two other works on the same topic, where they studied the influence of inlet waveform uncertainties [120] and the spatial distribution of inlet velocity fields [121].
## 4 Case Analysis
### Forecast of Displacement and Deformation of Ground Surface  
The probability integration parameters are the bases of the forecast of the displacement and deformation of the ground surface through the probability integration method. The surface topography and geomorphology, the physical and mechanical properties of the strata, the coal seam occurrence characteristics, the coal mining technologies and other aspects all impact the surface displacement and deformation. Therefore, those above factors should be taken into full account during the selection of probability integration parameters. Through the reference to observation data on the surface displacement and deformation near similar mining areas, and the comparison of stratigraphic characteristics in the targeted area, the initial mining subsidence coefficient was taken as 0.6 and the repeated mining subsidence coefficient was taken as 0.72, 1.2 times the initial mining. In view of the influences from the coal seam dip angle and surface topography, the uphill angle was taken as 69\({}^{\circ}\); the downhill angle was taken as 63\({}^{\circ}\); the strike displacement angle was taken as 65\({}^{\circ}\); and the probability integration parameters were selected as shown in Table 3:  
The mining damage assessment system, based on the independently researched and developed probability integration method, was adopted to determine the displacement and deformation of the ground surface [42]. Surface displacement and deformation caused by coal mining is a spatial and temporal process. Therefore, time works as a key factor for surface displacement and deformation during each round of mining. The \"Knothe\" time function was introduced in the displacement and deformation forecast system based on the probability integration method in this research, so as to correct the displacement and deformation. In order to ensure long-term building stability and a stabilized ground surface, the displacement and deformation values after 20 years of initial mining were selected for the deformation forecast. By virtue of Arc GIS 10.2, the Kriging algorithm was adopted to interpolate the points on the ground surface with a 1 m step, and then the point-to-raster function was adopted to convert the displacement and deformation data at each point in the targeted area into 1 \(\times\) 1 m raster cells and to generate the deformation cloud chart. Due to the paper length, only the subsidence cloud chart is shown in Figure 9.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 2 Study Site and Database
### Vegetation parameters  
Vegetation cover measurements are carried out to characterize several parameters: the leaf area index (LAI) and canopy height (H), as described in the following.  
**Leaf Area Index (LAI)**  
The LAI is defined as the total one-sided area of leaf tissue per unit ground surface area. According to this definition, the LAI is a dimensionless quantity. For each test field, 20 hemispherical digital images were used. These were processed by analyzing the canopy gap fraction, in order to retrieve this vegetation parameter [53]. The measurements were applied four times during each agricultural season. During all measurement campaigns, the computed value of LAI ranged between 0 and 5 (Table 2). The highest values of LAI were observed mainly in irrigated test fields, between the end of March and the beginning of April.  
**Vegetation Height (H)**  
Approximately 20 vegetation height measurements were considered for each reference field and for each date. These were averaged, to retrieve a mean height value for each test field. In the case of H, measurements were carried out four times during each agricultural season. The tallest vegetation heights lie in the range (90-110 cm) and are reached towards the end of March or the beginning of April (Table 2).  
**Land use**  
Land use maps were produced in 2017. This process relies on the decision-tree method proposed in [30] and was applied to four LANDSAT images (recorded in late autumn, early winter, spring and summer). Ten classes of land-use classification were identified: non-irrigated olive trees; irrigated trees; irrigated winter vegetables; irrigated summer vegetables; cereals; urban areas; sebkha areas; landforms; dams, and; oueds. These remotely sensed classifications were validated during a ground verification campaign, involving more than 100 fields with various types of land use. The results reveal an identification accuracy of approximately 75%. Figure 4 shows the Resulting Land Use Map for the 2016-2017 season.
# Attention-Guided Network Model for Image-Based Emotion Recognition
## 2 Materials and Methods
### Attention Modules
#### 2.2.1 Squeeze and Excitation (SE)  
Squeeze and excitation (SE) blocks are simple in structure and implementation. They enhance the representation power of the network by refining the channel-wise features without drastically affecting computational performance [5].  
In SE blocks, the features generated by the convolution layers are first squeezed to find the channel statistics, which is achieved by implementing global average pooling (GAP) on the spatial features across the channel dimension. An advantage of GAP is that it enforces correlation between feature maps and the classes, making them class-agnostic confidence maps. Equally, since there are no optimization parameters at this layer, over-fitting can be avoided [38]. A gating mechanism is then added by means of two FC layers to bottleneck the data. To reduce the dimensionality of the information, the first FC layer was assigned to output a feature vector with a size of C/R, where R is a reduction coefficient and C is the total number of channels. The output then passes through an ReLU activation function to threshold each feature element. Afterward, it passes through the second FC layer, where the feature space is scaled back up to the input dimension, the excitation phase, and ends with a Sigmoid activation function before being multiplied channel-wise with the input feature space to form the newly refined features. Figure 2 shows a schematic drawing of the SE block architecture.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 6 Performance Evaluation
### Packet Loss Rate Evaluation  
In order to analyze the network stability of each scheme in the authentication phase, we conducted simulation experiments on the data packet loss rate (PLR). We utilize OMNeT++ 5.6.2, combined with SUMO 1.8.0, inet 4.2.5, and Veins 5.2, on a Windows 11 operating system. SUMO (Simulation of Urban MObility) is an open source, highly portable, microscopic and continuous multi-modal traffic simulation package designed to handle large networks. The main parameters of the simulation environment are shown in Table 5.  
In our simulation experiments, the map and all road configurations adopt the default settings provided by SUMO. Figure 8 shows the simulation process of our scheme. The yellow rsu[0] in the figure represents the RSU, the green nodes represent vehicles that have completed mutual authentication with the RSU, and the red node represents a vehicle in the process of mutual authentication. The blue dotted lines indicate the process of information transmission.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Single Message (Bytes)** & **n Messages (Bytes)** \\ \hline
[19] & \(128\) & \(128n\) \\
[20] & \(136\) & \(136n\) \\
[21] & \(144\) & \(144n\) \\
[22] & \(192\) & \(192n\) \\ Ours & \(108\) & \(108n\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The communication overhead.  
Figure 8: Simulation process of packet loss rate evaluation in the authentication phase.  
We define the packet loss rate \(PLR\) as shown in Equation (3) [24]. \(N_{l}\) represents the total number of RSU lost packets, and \(N_{r}\) represents the total number of RSU accepted packets.  
\[PLR=(\frac{N_{l}}{N_{l}+N_{r}})*100\% \tag{3}\]  
For each scheme, when the number of vehicles in the simulation scene was 20, 40, 60, 80 and 100, we conducted a set of experiments, respectively. Each set of experiments consisted of ten trials, during which we counted the packet loss rate for each trial, and then calculated the average packet loss rate for the ten trials. The simulation results of each scheme with four different vehicle numbers were obtained, as shown in Figure 9. From the simulation results, compared with other algorithms, our scheme exhibits obvious advantages in terms of packet loss rate. The main reason is that our scheme has the lowest communication and calculation costs during the authentication stage. Meanwhile, as the number of vehicles in the simulation scenario increases, the packet loss rate also rises accordingly. This is because when the number of vehicles increases, the communication load in the entire scene rises sharply, and network congestion and mutual interference between pieces of information will also increase.
## 6 Case Study
### Experimental Setup  
The experimental setup employed in this study features an industrial fan, as shown in Figure 4, monitored through ENGING's online platform, which enables the continuous extraction of motor current signals and spectral components essential for belt fault detection (Tables 2 and 3).  
While the framework was tested on a single asset, it was designed to be scalable and transferable. Since it uses only electrical data, it can be applied to a wide range of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Power** & **Voltage** & **Current** & **Frequency** \\ \hline
200 kW & 400 V & 346 A & 50 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main nominal parameters of the motor.  
Figure 4: Scheme of experimental setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{D}_{motor\_pulse}\) & _fr_ & _Lbelt_ \\ \hline
300 mm & 24.83 Hz & 5600 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Industrial fan information.  
motors and assets with minimal adaptation. This makes it well-suited for integration across different industrial environments.  
Nevertheless, scaling to multiple motors may present challenges such as variations in load, operational profiles, or motor characteristics. However, these can be addressed through model retraining or parameter tuning. The method's modularity and data-driven nature support this flexibility.
# An Advanced Physiological Control Algorithm for Left Ventricular Assist Devices
## 2 Materials and Methods
### LVAD Estimator Model  
Our research group has developed and validated a dynamic model for estimating the average pump flow (\(Q_{p}\)) of an LVAD [28]. Through this development, two auto-regressive (ARX) models with exogenous input were utilized for system modeling and configuration. Pulse-width modulation (\(PWM\)) signal was used as input signal for the first ARX model to estimate the pulsatility index of pump rotational speed (\(PI_{\omega}\)). Then, the \(PI_{\omega}\) was utilized as the input for the subsequent ARX model to estimate \(Q_{p}\). A recursive least squares approach  
Figure 1: The CVS–LVAD rotary pump interaction model. \(R_{in}\): inlet cannula resistances; \(R_{out}\): outlet cannula resistances; \(L_{in}\): inlet cannula inertances; \(L_{out}\): outlet cannula inertances; \(R_{suc}\): suction resistance; and \(P_{thr}1\) and \(P_{thr}2\): intrahoracic pressures.  
was used to estimate the system parameters. Accordingly, the obtained dynamic estimator model can be expressed as follows:  
\[\begin{array}{l}\chi(n+1)=A\chi(n)+\delta A\chi(n)+Bu(n)+\zeta(n)\\ Q_{p}(n)=C\chi(n)\end{array} \tag{4}\]  
where \"\(\chi\in R^{n}\)\" represents the states of the system; \"\(\delta A\)\" is the system parameter variation; \"\(u=PWM\)\" represents the control input; \"\(\zeta\)\" is the system disturbance; \"\(Q_{p}\)\" refers to the system output; and \"\(A\), \(B\), and \"\(C\)\" represent matrices of appropriate dimensions.
## 4 Indoor Testing Area
### Intel RealSense Depth Camera and Vision-Based Localisation Method  
In this study, an Intel RealSense depth camera is used for the experiments [49], as it is a low-cost localisation system [8; 50]. There are several advantages to using this particular depth camera in terms of its size, camera specifications and 3D Cartesian coordinate detection. Moreover, it has a \(69^{\circ}\times 42^{\circ}(\alpha\times\beta)\) field of view (FoV) for the RGB sensor, \(1280\times 720\) px\({}^{2}\) resolution, and a 30 fps RGB frame rate.  
To find the localisation of the UAV, RGB colour detection is used to track it. A red-coloured circle piece is put on the region of interest of the area, and the colour parameters are measured as shown in Table 3. These colour parameters are in the hue, saturation, and value colour space (HSV). Moreover, the HSV values have maximum and minimum limits, which are shown in Table 3.  
After detecting the HSV parameters, some trigonometric formulas are used to find the transformed centre points of the UAV. Figure 9 shows the three-axis sizes of the testing area \((x,y,z:200,150,250,\) respectively), camera position, UAV altitude level \((z_{a}:90\) cm), and detected surface on the UAV.  
In Equation (13), the raw centre points of the UAV (\(\hat{x}_{px}\) and \(\hat{y}_{px}\)) are defined, which are converged to the transformed centre points. In the matrix, \(s_{x},s_{y}\), \(sh_{x},sh_{y}\) and \(t_{x},t_{y}\) stand for scale factors, shear factors, and translation factors, respectively. Moreover, these  
Figure 8: Fuzzy logic-based position control system.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**HSV** & **Hue** & **Saturation** & **Value** \\ \hline Maximum Values & 160 & 60 & 0 \\ Minimum Values & 200 & 255 & 255 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Camera HSV parameters.  
Figure 9: Camera trigonometric formation scheme and the UAV position.  
parameters are determined by using \"Computer Vision Toolbox for Matlab\" [44] and using the following model:  
\[\begin{bmatrix}\hat{x}_{px}\\ \hat{y}_{px}\\ 1\end{bmatrix}=\begin{bmatrix}s_{x}&sh_{y}&0\\ sh_{x}&s_{y}&0\\ t_{x}&t_{y}&1\end{bmatrix}\begin{bmatrix}x_{px}\\ y_{px}\\ 1\end{bmatrix}. \tag{13}\]  
Normally, this calculation is set for the two axes because the z-axis is fixed. For the calculation for the three axes, the trigonometric formula is used via Equation (14). In this equation, \(M_{x}\) and \(M_{y}\) show the total number of pixels for the x-y axes, and also \(\sigma_{x}\) and \(\sigma_{y}\) are defined as the relative mounting angles of FoV. Lastly, \(h\) (or z-axis) is used for the distance between the camera and the ground or floor. In Figure 9, the x-y-z-axes dimensions are given as 200 cm, 150 cm, and 250 cm, respectively, and by accounting for the following relations:  
\[\begin{array}{l}x_{obs}=-z\text{tan}\big{(}\sigma_{x}+\beta\big{(}\hat{y}_{ px}/M_{x}-1\big{)}\big{)},\\ y_{obs}=-z\text{tan}\big{(}\sigma_{y}+\alpha\big{(}\hat{x}_{px}/M_{y}-1\big{)} \big{)}.\end{array} \tag{14}\]