esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 3 Materials and Methods
### Detection Scheme
#### 3.4.1 Secondary Classifier  
One of the major challenges encountered was the significant number of false positives obtained during detection, especially in the presence of objects that could be mistaken for weapons, such as mobile phones or keyboards. To enhance confidence in detecting small weapons and effectively filter out these false positives, a secondary classifier was implemented. This classifier comes into play when the first detector exhibits low confidence in its detection.  
In this setup, the detection with low confidence from the primary detector is forwarded to the secondary, more robust classifier, which determines whether the identified object is a weapon or some other item. For this secondary classifier, state-of-the-art classifiers in the well-known ImageNet-1k dataset [62] were considered. This dataset encompasses diverse classes, including representations of weapons.  
It has been tested with state-of-the-art architectures such as CAFormer [63] and EVA02 [64], both renowned for their robust performance on the ImageNet-1k dataset. CAFormer employs a hybrid architecture that integrates convolutional and attention mechanisms without conventional activation functions, demonstrating proficiency in extracting both local and global features. This capability potentially enhances its ability to differentiate between weapons and non-weapons, particularly in challenging scenarios where the primary detector may encounter difficulties. In contrast, EVA02 employs advanced vision transformers, which facilitate superior contextual comprehension and granular classification capabilities. This results in the accurate identification of weapons, even in complex visual environments where visually similar objects could otherwise be misclassified. The integration of these architectures aims to reduce the number of false positives while maintaining an acceptable level of detection for real weapons. Moreover, both CAFormer and EVA02 have been designed with computational efficiency as a key consideration, thereby potentially limiting the impact on overall system performance.  
Despite achieving high accuracy, ImageNet-1k has an extensive array of classes that are unnecessary. Consequently, a modified version of this dataset was created to fine-tune the model for improved accuracy in weapons detection and to reduce the occurrence of false positives. This dataset includes specific classes, each having an equal number of images: assault gun, mobile phone, computer keyboard, laptop, computer mouse, and water bottle.  
It is noteworthy that attempts were made initially using both the full dataset and a dataset containing only weapons. However, both versions, while demonstrating reasonable confidence in detecting weapons, lacked the ability to effectively filter out false positives.
# Experimental Investigation on Jet Vector Deflection Jumping Phenomenon of Coanda Effect Nozzle
## 2 Experimental Setup and Methods  
### Coanda Effect Nozzle  
The structure and mechanism of the Coanda effect nozzle are shown in Figure 1. The nozzle was comprised of a pair of Coanda walls with inclination, a secondary flow passage, a secondary flow control valve, and a central jet flow passage. As shown in Figure 1a, the secondary flow control valves at the upper and lower sides remained open when the jet remained neutral. At this time, two secondary flow fields were located at the upper and lower sides of the main jet and in the same direction as the main jet, respectively. The secondary flow was generated by the ejection effect of the main jet. This secondary flow was passive and did not need external energy injection. As shown in Figure 1b, when the jet deflected downward to the attached wall, the secondary flow control valve on the upper side of the nozzle remained open, and the passive secondary flow control valve on the lower side of the nozzle was closed. The Coanda effect was generated between the main jet and the lower side wall. At the same time, the secondary flow on the upper side of the jet generated a downward pressure difference, which eventually caused the jet to attach to the Coanda wall and complete the deflection attachment.
## 3 Results and Discussion
### Effect of Temperature Distribution on Interfacial Bonding Process
Figure 10: Interfacial bonding processes and \(x\) and \(y\) direction temperature gradient distributions at different moments when the polymer is unfilled: (**a**) 7.00 s, (**b**) 7.09 s, (**c**) 7.14 s, and (**d**) 7.24 s.  
To reveal the motion of the molten polymer layer during the interfacial bonding process, the observations and temperature gradients in a single laser scan were analyzed, that is, the laser direct joining processes from 6.10 s to 7.90 are shown in Figure 12. The laser scanning direction was from right to left, and the laser irradiation position was on the sides of the aluminum alloy at 6.10 s and 7.90 s, while the laser irradiation position was on the sides of the observation area at 6.90 s and 7.09 s. From 6.10 s to 6.90 s, when the laser scanned the right side of the outer observation area, the bubble in the molten polymer appeared to move to the \(x+\) direction, whereas the bubble position was unchanged when the laser scanned the observation area from 6.90 s to 7.09 s. Furthermore, the bubble moved to the \(x-\) direction when the laser scanned the left side of the outer observation area from 7.09 s to 7.90 s. The \(x\) direction temperature gradients in the observation area followed the same trend as those of the bubble movement, that is, the \(x\) direction temperature gradient was positive when the bubble moved to the \(x+\) direction, gradually decreased to zero when the bubble position was unchanged, and there was a negative \(x\) direction temperature gradient when the bubble moved to the \(x-\)direction at 7.09 s. The above results proved that the direction of the temperature gradient was the key to determine the flow of molten polymer, and the molten polymer flowed faster when the direction of the temperature gradient was the same as the laser scanning direction.
# A Phased Array Antenna with New Elements Designed Using Source Transformations
## 2 Theoretical Model of \"Pinwheel\" Antenna Array
\right) \tag{6}\]  
\[\text{where }v=\ \Delta\theta\Big{(}1-\frac{\rho}{R_{1}}\Big{)}-\theta,\rho=\sqrt {x^{2}+y^{2}},\text{ and }\theta=\tan^{-1}\frac{y}{x}.\]  
The current distribution for the \"pinwheel\" antennas in the transformed array is \(\mathbf{I}^{\prime}_{n}=\overline{\mathbf{J}^{\prime}}.e^{i(n-1)\varnothing}\), where \(n=1,2\dots N\) and \(\varnothing\) is the phase between the adjacent \"pinwheel\" antenna elements. It is important to note that the transformations do not affect the fundamental antenna quantities such as complex power and impedance [20]. It is desired that the \"pinwheel\" shape antenna has a radiation field pattern similar to the dipole antenna, but its impedance and complex power will be preserved under the \"pinwheel\" transformations. Similarly, the material parameters can be obtained using (2), (4), and (5) as:\[\mathbf{\varepsilon}^{\prime}=\ \mathbf{\mu}^{\prime}=\left( \begin{array}{cc}1+\dfrac{y* \Delta\theta\left(\frac{2R_{1}x}{R_{1}^{2}}+y*\Delta\theta\right)}{R_{1}^{2}}&- \dfrac{\Delta\theta\left(xy\Delta\theta+R_{1}\rho cos(2\theta)\right)}{R_{1}^{2 }}&0\\ -\dfrac{\Delta\theta\left(xy\Delta\theta+R_{1}\rho cos(2\theta)\right)}{R_{1}^{2 }}&1+\dfrac{x*\Delta\theta\left(-\frac{2R_{1}x}{\rho}+x*\Delta\theta\right)}{ R_{1}^{2}}&0\\ 0&0&1\end{array} \right). \tag{7}\]  
Equation (7) results in anisotropic and inhomogeneous permittivity and permeability tensors. Both electromagnetic parameters \(\varepsilon\) and \(\mu\) have the same behavior, as shown in Figure 2. It is also worth noting that the results lead to a perfect impedance matching with no reflection at the boundaries of the material region and free space. Such an anisotropic and inhomogeneous transformation medium can be realized by discrete metamaterials and structures such as periodic split ring resonators (SRRs) [21]. Now, the material parameters from (7) and the transformed current from (6) were used to transform each element of the \"reference array\" into a \"pinwheel\" antenna element and realize the linearly transformed \"pinwheel\" antenna array, as shown in Figure 1b. The dimensions and edge-to-edge distance between the array elements were kept the same in the transformed \"pinwheel\" antenna array as in the \"reference\" linear dipole array. It is worth noting that while designing the \"pinwheel\" antenna elements of the transformed array, the current distribution from (5) and the material parameters from (7) were translated as the coordinates were no longer at the origin. The coordinates of the \"pinwheel\" elements changed along the y-direction, and the coordinates remained constant in the x-direction (\(x_{n}=0\)).
# Study of Gravelly Soil Core Material Using a Large-Scale Triaxial Wetting Test
## 2 Materials and Methods
### Sample Preparation and Experimental Program  
The samples were prepared using the compaction method. The sample size was \(\Phi 300\times 700\) mm. The dried sample was divided into six groups according to the following gradations: 0-1 mm, 1-5 mm, 5-10 mm, 10-20 mm, 20-4 0 mm, and 40-60 mm. Note that the total mass of each specimen was calculated according to the cylindrical specimen size, and then the mass of each group could be calculated according to the particle distribution curve (Figure 1). To prepare the samples, the soils from each group were mixed together, and water was added to achieve the desired moisture content. Each mixture was divided into five equal parts and compacted in the mold in layers using a vibrator.  
Figure 2 illustrates the wetting apparatus used in this experiment, which was designed by the Nanjing Hydraulic Research Institute. In order to carry out the wetting deformation test, the strain-controlled triaxial shear instrument that is usually used should instead be stress-controlled.  
The single-line method was used to conduct the wetting test on the gravelly soil core material. In the triaxial wetting tests, the specimen prepared in the split mold was placed onto the pedestal. After the confining pressure was applied to the sample until the consolidation was stable, axial stress was applied at the predetermined level, while the confining pressure and axial stress were kept constant. After the primary deformation of the sample reached a stable state, the sample was saturated via soaking, starting from the bottom of the sample. During the saturation process, the axial deformation and the external volume deformation of the sample were measured until the primary deformation of the sample was completed (the strain was less than 1 \(\times\) 10\({}^{-4}\) within 30 min), and then the axial  
Figure 1: Grain size distributions of the prototype and tested material.  
Figure 2: Large-scale triaxial wetting testing apparatus.  
pressure was applied to the sample to continue shearing until the sample was destroyed or reached 15% of the axial strain of the sample.  
Each group of tests consisted of confining pressures and different stress levels for the wetting experiments. In total, 12 tests were completed. The specific test plan is presented in Table 1, in which SI represents the stress level, defined as the ratio of the shear stress at the start of wetting to the shear stress upon failure under the same conditions.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 1 Introduction
### Our Contribution  
This paper revisits the re-keying scheme. First, we point out that the previous works [4; 5; 6] did not give a formal security model. For example, [PERSON] et al. [6] discussed the security of their schemes with different security models. In addition, their models did not take the SCA resistance into consideration. In fact, as we give a concrete SCA attack, their model did not capture the security against the SCAs. Hence, we introduce a security model in considering the SCAs.  
Second, we give two concrete first-order SCA-resistant re-keying schemes and discuss their security in our model. The first scheme is naturally derived from the combination of [PERSON] et al.'s second scheme [6] and [PERSON] et al.'s tweakable block cipher [8]. Unlike [PERSON] et al.'s abstract scheme, it is possible to discuss the SCA resistance with our concrete scheme. Our second scheme is a modification of the first scheme, which cannot be SCA resistant if it is used in the decryption device, which reveals the plaintext. However, it is useful for some applications in IoT systems, where the decrypted data are not revealed. We also discuss another scheme that is secure against the higher order SCA.  
The paper is organized as follows. In the following subsection, we explain a related work recently reported. Section 2 reviews the definitions of the block cipher and the tweakable block cipher. In Section 3, we provide a message recovery attack with the SCA to the previous re-keying schemes. In considering such an attack, we introduce a new security model for the re-keying scheme in Section 4. We then give our concrete schemes and discuss their security in Sections 5 and 6, respectively. We also discuss the application of the re-keying scheme and the component of the re-keying function in Section 7. Finally, Section 8 concludes this paper.
## 3 Antenna Characteristics
### Effect of the Slots in the Radiating Patch on Impedance Matching  
The graphical representation depicted in Figure 3 illustrates how different slots in the radiating patch affect the impedance matching of the radiating patch. Ant-1 exhibits two resonance frequencies at 8.37 GHz and 12.09 GHz, along with corresponding reflection coefficients of \(-12.09\) dB and \(-18.44\) dB. Ant-2 also demonstrates two resonance frequencies at 8.72 GHz and 9.05 GHz, while showing improved impedance matching at these resonant frequencies. Ant-3, the second iteration of the CMFA, reveals three resonant frequencies at 8.68 GHz, 10.02 GHz, and 12.73 GHz, resulting in reflection coefficients of \(-33.01\) dB, \(-11.56\) dB, and \(-40.04\) dB, respectively. It is worth noting that the addition of a fractal structure to the radiating patch enhances both impedance matching and bandwidth.
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 2 Materials and Methods
### Navigation Controller Design
#### 2.4.1 Calculating the Target Front Wheel Angle  
Generally speaking, orchard vehicles always complete various operations at low speed. This experiment does not consider the deformation and lateral slip of the tire due to the pressure. The vehicle kinematics model shown in Figure 10 is established.  
Figure 8: Navigation information parameter extraction schematic.  
Figure 9: Schematic diagram of orchard vehicle path-tracking control system.  
According to the vehicle kinematics model in Figure 10, the front turning angle of vehicle can be obtained:  
\[\delta=\arctan(l/R) \tag{9}\]  
As show in the Figure 11, a pure pursuit model under the line tracking condition is established. According to the geometric relationship in Figure 11, the vehicle's turning curve \(\gamma\) and the target's abscissa \(x_{g}\) in the car body coordinate system can be obtained:  
\[\gamma=2\frac{x_{g}}{L_{d}^{2}} \tag{10}\]  
\[x_{g}=d\cos\theta-\sqrt{L_{d}^{2}-d^{2}}\sin\theta \tag{11}\]  
Combined with the above Equations (9)-(11), the front turning angle of vehicle under pure pursuit model can be obtained:  
\[\delta=\arctan(\frac{2l(d\cos\theta-\sqrt{L_{d}^{2}-d^{2}}\sin\theta)}{L_{d}^ {2}}) \tag{12}\]  
When the forward-looking distance is selected to be larger, the vehicle will drive along the smaller curvature to the desired path, resulting in a longer control response time [41]. When the  
Figure 11: Pure pursuit model. Note: \(S\) is the navigation tracking path; \(G\) (\(x_{g}\), \(y_{g}\)) is the target point on the path; \(O\) is the turning center; \(\gamma\) is the turning rate of the vehicle (m\({}^{-1}\)); \(L_{d}\) is the forward looking distance (m); \(R\) is the instantaneous turning radius (m); \(d\) is Lateral deviation (m); \(\theta\) is the heading deviation (rad); \(\
u\) is the forward speed of the vehicle (m\(\cdot\)s\({}^{-1}\)); \(l\) is the vehicle wheelbase (m); \(\delta\) is the front wheel angle (rad); \(\
abla\) is the angle of course change when the vehicle reaches the target point along the steering arc (rad).  
Figure 10: Vehicle kinematics model. Note: \(M\) (\(x_{r}\), \(y_{r}\)) and \(N\) (\(x_{r}\), \(y_{j}\)) are the coordinates of the axle of the rear axle and the front axle of the vehicle respectively; \(P\) is the instantaneous steering center of the vehicle; \(\partial\) is the heading angle of the current position of the vehicle (rad); \(\delta\) is the front turning angle (rad); \(v_{r}\) is the center speed of the rear axle of the vehicle (m\(\cdot\)s\({}^{-1}\)); \(v_{f}\) is the center speed of the front axle of the vehicle (m\(\cdot\)s\({}^{-1}\)); \(l\) is the wheelbase (m); \(R\) is the turning radius of the vehicle (m).  
forward-looking distance is too small, the system will cause the vehicle to approach the set route along the large arc curve, causing the system to generate large oscillations. Therefore, this paper uses fuzzy controller to adaptively adjust the forward-looking distance in the pure pursuit algorithm to overcome the above shortcomings.
## 3 Results
### An Example: AR Application of the Archaeological Site \"La Matilla\"
One of the methods currently available to launch the 3D representations in the AR applications is the use of 2D markers. These markers are basically 2D images which are recognized by the application to begin the visualization of a 3D model. In our proof of concept, we assigned one image with a text for each of the 3D models. Figure 7 depicts two of the markers. 4. Generate the application for the mobile devices. Since we have used Unity software to create the AR application, it is relatively easy to reorient the developed content to be compiled for different types of hardware. So far, we have only generated an application for Android mobiles. Figure 7: Two of the markers designed to be used in the AR application. Figure 8 shows some images of the use of our AR application and a view of the interior of one of the ovens that can be visited in the experience. Regarding the level of presence (point 8 of the decalogue), it is undeniable that AR applications for cell phones do not offer a presence comparable to the immersion offered by a VR equipment. In any case, although the 3D scenarios have had to be optimized for mobile devices, the quality of the textures produces a very realistic effect in the visualization of the site, which definitely contributes to increasing the level of presence in the experience. Since the application is still in the prototype phase, it has not yet been disseminated in conferences, social networks, or through institutional announcements (point 10 of the decalogue). This is an issue that is considered of great relevance in order to achieve the application's own objective: raise awareness of the remains, particularly in the case where people may not know of their existence, and, at the same time, make these people participants in the revolution that the application of technologies for the dissemination of heritage, the learning about history, and the digital preservation of valuable remains that have survived the passage of time entails. In the medium term, we plan to first launch a local information campaign among the inhabitants of the site's surroundings and then to extend this campaign to the whole region. Figure 8: Two moments of the use of our AR app: (**a**) the user focuses the camera on the marker; (**b**) the 3D model of the site is shown over the image of the marker; (**c**) the 3D model of one of the ovens displayed in the AR application.
## 4 Results and Discussion
### The Seismic Fragility Analysis of Isolated Bridge Components
Comparative Analysis of Seismic Vulnerability between the Isolated Bridge System and General Bridge System  
The comprehensive analysis from the previous section showed that there are significant dependences among component seismic demands since the components interact with each other under earthquake excitation. Therefore, the copula technique was applied to evaluate the seismic damage probability for the bridge structure system. In order to compare the effect of the seismic isolation system, the seismic fragility curves of the general bridge and the isolated bridge were devised based on the copula function method, as shown in Figure 9.  
From Figure 9, under frequent earthquake action (PGA = 0.11 g), the probability of minor damage and moderate damage to the general bridge were 18 and 6%, respectively. The isolated bridge remained basically intact. Under rare earthquake action (PGA = 0.51 g), the probability of serious damage to the general bridge was 68%, and the probability of collapse 29%, but the probability of minor damage, moderate damage, severe damage, and collapse of isolated bridges were 92, 56, 20, and 10%, respectively. Therefore, the probability of different damage states of the bridge structure was greatly reduced with the seismic isolation scheme. Figure 9: Comparison of the seismic fragility curves of (**a**) the isolated bridge and (**b**) the general bridge.