esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 4 Results and Discussion
### From Scratch Training on NaSC-TG2  
In this case, we trained all layers of the ResNet-50 model on the NaSC-TG2 dataset from scratch using random weights as the starting point. Figures 15 and 16 show the graphs for ResNet-50 and VGG-19, respectively, displaying the accuracy curves for training and validation. As expected, these models performed poorly when compared to previous techniques in which transfer learning was used. Without the presence of pre-trained weights as the starting point, random weights were initialized. Contrary to previous experiments, ResNet-50 showed more fluctuations than VGG-19 during the training process. The obvious reason for this behavior is that it has a deeper structure than VGG-19; however, these models slowly converged, and ResNet-50 yielded a better accuracy than VGG-19 on the test dataset.  
Figure 14: Confusion matrix for fine-tuning last 3 layers of ResNet-50.  
After the training and evaluation, we achieved an overall accuracy of 82.67% and 76.72% for ResNet-50 and VGG-19, respectively. Still, the ResNet-50 model performed better than the VGG-19 model but these models were far behind transfer-learning-based methods in terms of accuracy. Detailed class-wise results for the ResNet-50 model training (from scratch) are presented in Table 3 for further analysis. Looking at the table, the river class showed the worst performance in terms of the precision (60.21%), recall (43.50%), accuracy (91.48%), and F1 score (50.51%). The forest class showed the best performance in terms of the precision (97.63%), accuracy (99.02%), and F1 score (94.00%) while the beach class showed the best result in terms of recall (94.00%).  
Analyzing the confusion matrix of the ResNet-50 model training (from scratch) in Figure 17, we can clearly observe that the model yielded the lowest accuracy for the river class as many instances of the river class were confused as rectangular farmland, mountain, residential, or cloud, etc. The model also performed worse for snowberg as images for this class were confused with those of cloud, river, circular farmland, or others. The highest true positives were recorded for the forest class (TP: 370) and then for the desert class (TP: 368) out of 400 instances for each class in the test dataset. The highest false negatives were recorded for the river class (FN: 226) and then the snowberg class (FN: 114). The highest  
Figure 16: Training vs. validation accuracy curve during training of VGG-19 from scratch.  
Figure 15: Training vs. validation accuracy curve during training of ResNet-50 from scratch.  
false positives were recorded for the rectangular farmland class (FP: 175), for the cloud class (FP: 119) and then, for the mountain class (FP: 99).
# Traffic-Predictive Routing Strategy for Satellite Networks
## 5 Route Maintenance  
Due to the complex working environment of satellite networks, the instability of interstellar link connections often causes routing failure problems, including routing failure or becoming unavailable. This unstable connection can cause the router to choose unstable paths and ignore more stable connections. To solve this problem, the G-AODV routing protocol introduces an effective route maintenance and repair mechanism. During the maintenance phase of the traditional AODV routing algorithm, routers usually try to repair broken routes locally, which can improve overall network performance to some extent. However, when the upstream node at the interruption is moving, local repair by the broken node may cause the route to be interrupted again, reducing the reliability and stability of the route. Therefore, G-AODV introduces the concept of \"node stability\". In the route maintenance phase, a route repair strategy based on node stability is used to repair routes.  
In the G-AODV protocol, once a transmission path is established, the path enters the routing maintenance phase. Different from the original AODV, G-AODV uses an intelligent route repair mechanism to solve the problem of broken routes based on node stability. This mechanism selects appropriate nodes for source node repair or local repair by judging the fracture location. By comparing the stability of the upstream and downstream nodes at the break location, G-AODV can more wisely choose the node to repair the route, thereby reducing the risk of new routes being interrupted again.
## 2 Materials and Methods
### Autonomic Data Acquisition and Biosignal Analysis  
A portable X-pert2000 Biofeedback system with a MULTI radio module (Schuhfried GmbH, Modling, Austria) was employed to collect and record autonomic activity. A peripheral sensor was placed on the distal phalanx of the second finger on the non-dominant hand to gather data. The skin conductance level (SCL) value was recorded according to a current-current measurement acquired with an EDA gold electrode and a sampling frequency of 2 kiloHertz (kHz). Alternating voltage was used to lessen polarization. The SCL had a measurement resolution of 12 nanoseconds (nS) and was collected at a sampling frequency of 20 Hz.  
Photoplethysmography was used to measure heart rate (HR) at a sampling frequency of 500 Hz. To minimize interference from hand movements, an accelerometer within the sending unit (measured in m/s\({}^{2}\)) tracked motion in the non-dominant hand.  
Qualitative and quantitative data were reviewed to identify and exclude artifacts caused by motion or biological noise. Standard cardiac activity metrics, including heart rate (HR) and inter-beat interval (IBI) as well as heart rate variability (HRV), calculated as the standard deviation of IBI, were used to estimate vagal tone [45]. Before starting the tasks, a 120 s resting baseline was recorded to capture participants' autonomic responses at rest.
## 3 Results  
Beta energy spectra were acquired for each FTU steel sample using LSC. The Region of Interest (ROI) was set from channel 0 to 200 for \({}^{55}\)Fe and channel 0 to 400 for \({}^{63}\)Ni. Efficiency was evaluated using a blank sample and a traced standard, both treated using the same chemical procedure (refer to Tables 3 and 4 for details; the traced standard corresponds to vial #2 in Table 3 for \({}^{55}\)Fe and to vial #3 in Table 4 for \({}^{63}\)Ni).  
The measured spectra of the FTU samples showed no significant signal above the background in the defined ROIs for either radionuclide. This indicates that no detectable activity was observed above the critical level (L\({}_{\rm C}\)) during the selected acquisition periods.  
For \({}^{55}\)Fe, in all spectra except the one corresponding to the traced standard (named spiked sample), the observed values were consistent within the measurement uncertainty in the channel range 0-200 (Figure 6). The total number of counts in the 5-day blank measurement was 327,000, corresponding to a detection limit (L\({}_{\rm D}\)) of 2700 counts, according to the methodology defined in [16]. This equates to a Minimum Detectable Concentration (MDC) of 0.2 Bq/g. All four analyzed samples (FTU#1 to FTU#4) had count values below L\({}_{\rm C}\), confirming the non-detectability of \({}^{55}\)Fe at or above 0.2 Bq/g.  
Figure 6: Beta energy distribution of the Fe fraction, including blank, FTU samples, and traced sample: (**a**) shows the distribution in the whole channel range (0–1024) while (**b**) shows the distribution with error bars in the range 0–200, corresponding to the beta expected end-point energy for \({}^{55}\)Fe.  
Similarly, for \({}^{63}\)Ni, in all spectra except the one corresponding to the traced standard (named spiked sample), the observed values were consistent within the measurement uncertainty in the channel range 0-400 (Figure 7). The 6 h blank measurement yielded 33,300 counts, corresponding to an \(\mathrm{L_{D}}\) of 850 counts, equating to an MDC of 0.3 Bq/g. Again, all FTU samples exhibited count values below \(\mathrm{L_{C}}\), and thus, below the required MDC threshold.  
Figure 7: Beta energy distribution of the Ni fraction, including blank, FTU samples, and traced sample: (**a**) shows the distribution in the whole channel range (0–1024) while (**b**) shows the distribution with error bars in the range 0–400, considering that 320 corresponds to the beta expected end-point energy for \({}^{63}\)Ni.  
These results confirm that the activity concentrations of \({}^{55}\)Fe and \({}^{63}\)Ni in the investigated steel samples are below 0.3 Bq/g, meeting the regulatory requirements for clearance established by the Italian regulatory authority.
###### Abstract  
In response to the problem of vibration and noise reduction in equipment with cylindrical shell structures, this paper focuses on the micro-perforated laminated cylindrical shell structure and establishes its finite element model. Through comparative analysis with experimental results, the reliability of the finite element modeling method is verified. Based on this, the paper places particular emphasis on the vibration and acoustic radiation performance of the structure in the 1-1000 Hz frequency range under free conditions to understand the impact of different laminated shell structures, micro-perforation parameters (porosity, aperture), sound-absorbing foam materials, and placement methods. The results indicate that micro-perforated structures can efficiently reduce the structural radiated sound power level at specific frequencies, but the overall reduction in radiated sound power level is not significant. Various types of foam are effective in reducing the structural radiation acoustic power level, with polyurethane performing best among them. Changing the location of foam placement has a relatively insignificant impact on the structural radiation acoustic power level.  
ribbed laminated cylinder shell; micro-perforated shell; acoustic radiation; finite element analysis +
Footnote †: journal: Applied Sciences  
Article  
Study on the Vibration and Sound Radiation Performance of Micro-Perforated Laminated Cylindrical Shells
# Hardware-Based Architecture for DNN Wireless Communication Models
## 3 Background
### Conventional Detector  
In this section, three conventional digital signal processing (DSP) techniques to detect the MIMO OFDM signals at the receiver are introduced. These techniques are ZF, MMSE, and MLD [14].
## 3 Results  
### Evaluation of the Absorption Properties of CFs  
As mentioned before, drain-down tests were performed in order to assess the bitumen absorption properties of the CFs. The test was carried out in compliance with the [39] standard. This test allows the amount of drain-down in a loose bituminous mixture to be determined, when this is held at high temperature, compared to the amount obtained during the mixture in-plant production, storage and transportation. It is very useful for high bitumen content mixtures, where the addition of fibers is required precisely to prevent the separation of bitumen from aggregates.  
Four mixtures were tested, containing shredded CFs at a ratio of 0, 0.2, 0.3 and 0.4% of the weight of the aggregates, and two samples were produced for each mixture. The average results are presented in Table 4.  
Based on these results, the addition of experiment fibers equal to 0.4% of the weight of the aggregates seemed to be suitable, showing average drain-down values below the recommended 0.3% [46]. All in all, the shredded CFs exhibit similar absorption properties to the reference cellulose fibers, even if an increase by 0.1% in their dosage is required to achieve the same drain-down limit effect.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fibers Amount** & **Drain-Down (\%)** \\ \hline
0\% Fibers & 0.6 (\(\pm\)0.2) \\
0.2\% Fibers & 0.4 (\(\pm\)0.2) \\
0.3\% Fibers & 0.4 (\(\pm\)0.1) \\
0.4\% Fibers & 0.3 (\(\pm\)0.1) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average drain-down results for the shredded CFs.
## 4 CFD Modelling
### Computational Domain and Wind Farm Positioning  
The analysis is carried out with the use of the commercial CFD code ANSYS Fluent [31]. The size of the computational domain is 7 km wide, 800 m height, and 34.4 km long (Figure 3). The computational grid is a multi-block mesh of approximately 14 M hexahedral elements which was created in ANSYS ICEM software package. The area of interest that includes all the wind farms, has been well refined with uniform cubic elements of 43 m3 (Figure 4d). This area has been extended at a distance of five rotor diameters upstream of the first wind turbine to let the incoming flow develop. It starts at a height of 40 m up to a height of 160 m assuming 80 m-diameter wind turbines and hub heights at 80 m above the ground. At the blocks of the bottom (covering the height from ground to 40 m), as well as at the blocks of the upper part of the domain (from 160 m to 800 m), an exponential function has been used with a ratio \(R=1.10\). As a result the first cell height at the ground was 0.5 m. Downstream the area of interest a similar exponential function with ratio \(R=1.10\) was also used.  
Figure 3: The multi-block computational domain with a total size of 7 km \(\times\) 0.8 km \(\times\) 34.4 km.  
The internal cells that were chosen to model the wind turbines, represent the total size of the 80 m rotor diameters and were located according to the project layout and positioning of the wind farms (Figure 4d). The first wind farm that was located at the northern part of the island was not included since it was located 4 km away from the current arrays and approximately 20 km away from the south part of the island where the music trees under investigation were located.  
To extract the wind farms positioning, the initial map of the project has been projected on the Google maps [32] (Figure 4a,b). The final coordinates of the wind turbines were extracted as points, transformed to Cartesian coordinates, and used for the selection of the internal cells (Figure 4c,d).
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Spectral Data Processing  
ViewSpec Pro software was used to convert the original digital number (DN) values recorded in the spectral data into spectral reflectance values [17]. During the spectral measurement process, the original spectrum was affected by factors relating to the environment, human influence and instruments. The spectral curve was collinear and nonlinear, with evidence of data redundancy and other issues. Practice has shown that if the frequency of the noise is high and its magnitude is not large, it can be reduced by the smoothing method to a certain extent. In this study, the original spectrum was smoothed using a five-point weighted average. Practice has proved that this method has a good smoothing effect and has no effect on the original spectral characteristics.  
\[r_{i}\ =\ 0.1R_{i-2}+0.2R_{i-1}+0.4R_{i}+0.2R_{i+1}+0.1R_{i+2}\]  
where \(R_{i}(i\ =\ 3,\ 4,\ \ldots,N)\) is the reflectance of the original spectral curve measurement point and \(r_{i}(i\ =\ 3,\ 4,\ \ldots,N)\) is the reflectance of the five-point smoothed spectral curve measurement point.
# 2D/3D Multimode Medical Image Alignment Based on Spatial Histograms
## 1 Introduction
Therefore, it contains three kinds of image information: gray information, pixel coordinate information, and pixel number information, increasing the reliability compared with single-gray-level information registration. Others [4] have proposed 2D/3D registration methods combining machine learning and geometric transformation. The projection space based on the traditional projection algorithm contains three translation and rotation parameters [29]. The high complexity of projection space dramatically affects the timeliness of registration. [PERSON] et al. [30] considered that the 2D/3D registration problem needs to search the complex solution space, leading to many calculations, so they proposed a spatial parameter-decoupling method to achieve registration. According to the dimensional differences between the registration images, image registration can be divided into 2D/2D registration, 2D/3D registration, 3D/3D registration, and time-series registration [31]. The uses of these approaches are different. This paper mainly studies 2D/3D registration in image-guided surgery. This paper mainly proposes a 2D/3D registration algorithm based on a spatial histogram. The algorithm introduces a spatial histogram into the 2D/3D registration problem, and develops a weighted spatial histogram of gradient directions for registration, improving the registration accuracy and convergence range of translation transformation.
## 3 Results and Discussion  
### Model Validation  
Validation of the human model responses has been extensively investigated in the literature. Its biomechanics were compared against several cadaver and volunteer human responses reported in the literature [9]. Besides those reported, an additional validation was achieved in this study by comparing its global relative kinematics against the volunteer rear-end impact test conducted by [PERSON] et al. [22]. In their study, eight male volunteers were subjected to a 7 km/h velocity rear-impact profile and a mean acceleration of ~2 g. The test was performed using an adaptive seat with a head restraint backset gap of 150 mm. It is worth noting that a restraining harness was used instead of a 3-point seatbelt to ensure the volunteers' safety at a high head restraint backset gap. Figure 2 presents the head retraction (x-displacement) movement concerning the torso and the head's peak angular  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensions** & **Backrest** & **Base** & **Head Restraint** \\ \hline Cushion length (mm) & 585 & 200 & 640 \\ Cushion width (mm) & 530 & 250 & 530 \\ Cushion Thickness (mm) & 151 & 120 & 160 \\ Wing width (mm) & 130 & - & 100 \\ Wing Thickness (mm) & 53 & - & 30 \\ \hline
**Material Properties** & **Backrest/Base** & **Head Restraint** \\ \hline Density, \(\rho\) (kg/m\({}^{3}\)) & 60 & 40 \\ Stiffness, \(E\) (kPa) & 200 & 20 \\ Relaxation modulus, \(G_{1},\ G_{2}\) & 0.3003, 0.1997 & 0.0973, 0.174 \\ Relaxation time, \(\tau_{1},\ \tau_{2}\) (sec) & 0.010014, 0.1002 & 0.30639, 11.21 \\ Shear modulus \(\mu_{1},\ \mu_{2}\) (kPa) & 4.81, 3.6 & 44.185, 0.0037 \\ Exponent coefficient \(\alpha_{1},\ \alpha_{2}\) & 19.8, 19.8 & 21.4556, \(-\)6.89 \\ Decay constant \(\beta_{1},\ \beta_{2}\) (\(10^{-2}\)) & 0.0145, 0.65 & 0, 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Driver seat overall dimensions and soft-cushion material properties for Ogden hyper-elastic model.  
displacement. The resulted kinematic responses were found to be within the average motion corridors of the human subjects. Head retraction reached a maximum of 49.5 mm after 224 ms, whereas peak head extension was 33.1 degrees at 179 ms.
# Fractional Order Processing of Satellite Images
## 2 Definitions and Formulations
### Main Algorithm Description  
A main script was developed where the different edge detectors were introduced:  
* The image to be processed is read.
* A pre-processing step is performed where the figure is scanned for blue color pixels. A mask is formed attributing value zero to all blue pixels found. This mask is later used in post-processing (Figure 5), both for the case of grey-scale detection operators, and the case of color detection operators.  
Figure 4: Image number 38 to be evaluated with corresponding ground truth.  
Figure 3: Image number 27 to be evaluated with corresponding ground truth.  
* After contour detection it is necessary to close the contours, so that in the end segmentation of land and water is evident. For that, several closing morphological operations are performed.
* Firstly, using lines in \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\) directions a closing operation is carried out in order to close pixel gaps between nearby edges that the detector did not encounter.
* Next, in order to fill the parts of land that were not yet detected by the operator and the closing operations, a MATLAB built-in function is used (imfill).
* The blue mask obtained in the pre-processing is then applied to the result of the previous operations. As one can see in Figure 6, after the edge detection there is still a lot of noise in the water. The blue mask serves as a filter that eliminates this noise and guarantees better performance in segmentation.
* One last closing operation is performed. This is due to the fact that sometimes the color filter identifies small areas of land as blue. Using this closing operation, these small areas are set to white and are correctly identified. This step finalizes the processing part of the algorithm. An example of a processed image is shown in Figure 7.  
Figure 5: Original image and Blue Mask of Coast_D(23).jpg.  
Figure 6: Output of an edge detection operation.  
After post-processing, performance analysis is done. For this, the ground truth jpg image is read and converted to a logical array. Then, by comparison between the resultant processed image and the ground truth, four metrics of performance are computed. The results of this analysis are stored in an array and are ready for analysis.
## 5 The Concept of IoV
### Security Issues in IoV Communication
#### 5.5.4 Wireless Security Protocols  
The transmission of sensitive information via mobile phones, wireless cameras, and Radio Frequency Identification (RFID) is common in an IoT environment. As such, for the information to be protected against hackers and other intruders, the wireless communication must be protected. For instance, an unauthorized user can take charge of the network by locking out authorized users, thereby preventing them from using the network. This can be carried out by downloading applications that are irrelevant to the network. Some of the most effective solutions against these kinds of attacks include the use of Wi-Fi-protected access such as WPA2 and WPA3 [98; 99].  
Now more than ever, IoT networks are vulnerable to cyberattacks. The most common type of attack against IoV involves manipulating and exploiting connections. In other cases, the attackers can gain access to the on-board unit (OBU) and manipulate the data there, affecting functions such as the locking system, emergency brakes, cameras, and wireless system. As such, an effective security mechanism should be capable of protecting the system's data and nodes from external intrusion. The security of the IoV network is what determines the safety of the driver and passengers in a vehicle. The messages contained in IoV outline accident prevention procedures and methods on how to deal with malicious activity. As such, IoV networks must be protected at all costs due to their sensitivity. To maintain the integrity of the data being transmitted, they must not be altered or modified during transmission [74; 77].