esa-sceva/satcom-chunk-collection huggingface dataset:
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 3 The LHP Based Functional Devices
### Photodetectors
#### 3.2.3 Flexible Photodetectors  
Due to the ease of combining LHP with other materials, hybrid detectors with good flexibility have also been developed. [PERSON] et al. reported a MAPbI\({}_{3-x}\)Cl\({}_{x}\) lead halide perovskite/poly-(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (LHP/PEDOT:PSS) vertical photodetector which maintained a good performance after being bent to a radius of 7 mm for 300 times, covering the wavelength range from UV to NIR [100]. The responsivities were in the level of \(\sim\)10\({}^{9}\) A/W and detectivities in the level of \(\sim\)10\({}^{14}\) Jones [100]. Another flexible UV-NIR photodetector was put forward by [PERSON] et al., in which a conjugated-polymer (PDPP3T) is used in collaboration with LHP [93]. The two materials could complement each other very well: the polymer could be utilized to broaden the response band of LHP, while the presence of the LHP heterostructure leads to longer carrier diffusion length and enhanced carrier separation; in addition, the PDPP3T layer is found to resist moisture and oxidization in the LHP layer. After 1000 cycles bending/striatening to a curvature radius of 7 mm, a retention of 90% or 85% of the responsivity was found under 650 nm or 835 nm illumination, respectively, demonstrating a good flexibility.
## 3 Research Model and Hypothesis Development
### Technological Factors
#### 3.1.2 Compatibility  
Compatibility, defined as \"the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters\" [99], plays a pivotal role in technology implementation processes. It entails a mutual adaptation process, wherein both the technology and the organization undergo changes to better align with each other [102]. A lack of compatibility can pose a significant challenge to the successful implementation of Industry 4.0 initiatives [82].  
The level of compatibility between a new technology and existing technologies, processes, and work styles directly influences the amount of adaptation work required. When a technology seamlessly integrates with the organization's current setup, it minimizes the need for extensive modifications. Conversely, if a technology is incompatible with the organization's practices, infrastructure, or culture, it can deter manufacturing firms from adopting smart manufacturing digital technologies [103]. The fear of facing substantial adaptation efforts and the risk of failure often discourages firms from pursuing such implementations.  
Supporting this perspective, the study conducted by [PERSON] and [PERSON] [58] highlighted compatibility as a critical success factor for the successful implementation of AR technologies. Based on these observations, this paper proposes the following hypothesis:  
**Hypothesis 2 (H2).**_Compatibility positively influences the implementation success of Industry 4.0 technologies._
## 2 BIT* Algorithm Procedure
### Improved BIT* + Energy Consumption Equation + TD3 Hybrid Path Planning Strategy Model
#### 3.3.5 Path Optimization and the Energy Consumption Equation  
In the initial path planning of the BIT* algorithm, the algorithm preemptively optimizes the path's turning cost by leveraging the energy consumption equation. Through the introduction of the turning weight \(T_{\theta}\), the algorithm is enabled to account for the turning energy consumption of the Unmanned Surface Vehicle (USV) in path planning, thereby generating smoother and more energy-efficient paths. The calculation formula for the turning cost is as follows:  
\[C_{turning}=T_{\theta}\times Z_{angle}, \tag{7}\]  
where \(C_{turning}\) represents the turning cost, \(T_{\theta}\) is the turning weight, and \(Z_{angle}\) denotes the turning angle between three consecutive nodes, calculated using the vector dot product formula. A smaller turning angle corresponds to a smoother path, while a larger turning angle indicates a sharper turn, resulting in higher energy consumption. By adjusting the turning weight \(T_{\theta}\), the algorithm can control the influence of the turning cost on path planning, prioritizing paths with smaller turning angles and consequently reducing the energy expenditure associated with the USV's turning maneuvers.
# Design and Analysis of the Rolling and Jumping Compound Motion Robot
## 1 Introduction  
For deep space exploration, the primary task of the detector is to reach the target area. However, the complex topography requires the space robot to have flexible movement mode and obstacle crossing ability. For flat terrain, rolling robots have the characteristics of fast-moving speed and wide range of motion [1]. Compared with wheeled, tracked, or legged structures [2, 3, 4], they have smaller volume and higher fault tolerance. When facing rough terrain or large obstacles, jumping robots are obviously the most reasonable choice, which can surmount an obstacle of their own size, or even several times their own size [5].  
In order to realize the rolling or jumping function of robot, several methods have been developed. In the field of rolling robots, [PERSON] proposes a mass driven robot \"sprerobot\", which can realize rolling forward by changing the mass distribution [6, 7]. [PERSON] proposed a modular closed-chain rolling robot with flexible shape memory alloy actuators, which can simulate the behavior of rolling animals [8]. For a jumping motion, NASA designed a jumping robot to meet the requirements of interstellar exploration across rugged terrain. The robot is mainly driven by a motor and stored in a compression spring, which can adjust the direction, takeoff, and landing [9, 10, 11]. [PERSON] designed a kind of jumping robot \"Minerva\", the robot uses the motor to rotate the torsion device inside the robot, and the reaction force on the ground makes it jump [12]. [PERSON] designed a torsion spring legged jumping robot, which has strong continuous jumping ability [13]. However, a single mode of movement creates difficultly in dealing with the complex and changeable deep space environment. If two or more motion modes are integrated to make up for the limitations of each motion mode, the motion ability of the robot will be greatly improved. Relevant studies have gradually been reported, [PERSON] has developed a wheeled jumping robot, which is driven by two wheels, when encountering obstacles, the internal spring legs can release energy to realize jumping [14], and [PERSON] ofChongqing University developed a crawling and rolling integrated robot composed of six crawling legs and multi-link auxiliary legs. Through the shape change of six legs of the robot, the robot has two motion modes of crawling and rolling [15].  
In this paper, a rolling and jumping robot is designed and analyzed, both two modules of the motion mode are integrated into the robot structure which are controlled by wireless remote control. The rolling and turning movements of the robot are realized by changing the internal eccentric moment, and the spring energy storage device hidden in the sphere is used to provide the jumping power.
# Pattern Formation on Alloys by Cluster Ion Beam Irradiation
## 3 Results and Discussion
### Volume Exchange
The case of the pure Au substrate (Figure 5b) confirms previous findings: at intermediate angles, redistribution predominates, while at grazing angles, sputtering is the dominant effect [20,21]. Undoubtedly, the defects generated by irradiation enhance the local mobility of atoms. In this sense, local displacements would be larger in amorphous solids. However, the effect we are concerned with is not local, but long-range. It is important to highlight some differences between the sputtering curves of previous systems. In the case of Au, the sputtering is minimal for an incidence close to normal (30-40\({}^{\circ}\)). This leads to a higher redistribution factor for intermediate angles. The horizontal displacement curves indicate that, for 50% Au-Cu, the maximum displacement occurs at an angle of 70\({}^{\circ}\). In fact, the redistribution factor, without reaching values that make it significant against sputtering, is the highest for this angle (Figure 5a). We must also analyze whether there are differences in the relocation of species with different mobility in the wave pulse. Figure 6 analyzes the atoms displaced to different positions in a 50% alloy. This allows us to analyze the tendency of species to move in a certain direction. However, these curves may vary slightly depending on the angle of  
Figure 5: Redistribution factor \(F_{rd}\) for two surfaces of two compositions (at.% Cu): (**a**) 50 and (**b**) 0 and different angles of incidence: 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\), and 70\({}^{\circ}\) as a function of time. incidence. Regarding sputtering, it can be observed that Au has preferential sputtering compared to Cu, at least for the grazing angles, but not for the intermediate angles, where the difference is very small. This fact leads to an enrichment of Au on the surface and specifically on the ridges [3]. The Au also has a tendency to return to the groove; in this way, the boundary atoms are fundamentally Cu atoms responsible for the amorphization of the lattice. These in-depth phase separation processes lead to the formation of patterns [33]. In short, it appears that Au bounces off the amorphous Cu lattice and either deposits on the ridge or disperses and returns to the groove.
## 3 Proposed Methodology
### Data Preprocessing
#### 3.2.2 Data Normalization  
Data obtained from various sensors frequently display uneven scales, and the existence of outlying samples in the data can result in extended training durations and potential challenges in reaching convergence. To mitigate the impact of variations in the values of each channel, min-max normalization was implemented for each feature, projecting their values into the [0, 1] interval.  
As expressed in Equation (1), the initial sensor data undergo normalization prior to processing, bringing them within the range of 0 to 1. This method addresses the challenge in model learning by standardizing the entire range of data values. This standardization facilitates faster convergence of gradient descents.  
\[x_{i}^{norm}=\frac{x_{i}-x_{i}^{min}}{x_{i}^{max}-x_{i}^{min}},i=1,2,3,\dots. \tag{1}\]  
where \(x_{i}^{norm}\) represents the normalized data, \(n\) represents the number of channels, and \(x_{i}^{max}\) and \(x_{i}^{min}\) are the maximum and minimum values of the \(i\)-th channel, respectively.
## 2 Materials and Methods
### Stage One: Identification and Validation Process
#### 2.1.4 Meshing Results and Validation  
A mesh analysis was performed over the chosen stem by varying the element size in a 0.1 mm ratio, as previously reported [2; 28], until the variation in the obtained results was minimum and stable; see Figure 3. Then, the mesh size was selected, and its results were compared to those published by [29].  
[PERSON] et al. [29] reported 240,272 elements and 314,658 nodes in their work, resulting in the maximum equivalent stress of von Mises of 247.8 MPa. An element size of 2.4 mm (Figure 3) gives (in our analysis) a von Mises stress of 247.52 MPa, generating 181,320 elements and 259,863 nodes, representing an 0.11% error when compared with [PERSON] et al. [29]. The maximum stress for both assessments was located in the same area (medial-neck area), which validates the results obtained here. Therefore, for this work, a 2.4 mm element size, 10-node quadratic tetrahedron elements, the material and loads from  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Modulus of Elasticity (GPa)** & **Poisson Coefficient** \\ \hline SS316L & 220 & 0.3 \\ CoCrMo & 210 & 0.3 \\ T16 Al4V & 114 & 0.3 \\ CoCr & 200 & 0.3 \\ Concrete & 30 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the implants and the assembly components [1; 11; 29; 49; 50].  
Figure 3: Mesh analysis results between the element size and the maximum equivalent stress (\(\sigma\)) of the Exeter stem.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Number** & **Activity** & **Angle** & **FFH (N)** \\ \hline
1 & Normal walking (75 kg) & \(\theta_{1}=20\) & 3000 \\
2 & 1 leg stand & \(\theta_{1}=13\) & 1938.36 \\
3 & Normal walking (80 kg) & \(\theta_{1}=13\) & 5242.26 \\
4 & Down stairs & \(\theta_{1}=12\) & 5015.96 \\
5 & Knee bend & \(\theta_{1}=16\) & 5503.63 \\
6 & 2-1-2 legs stand & \(\theta_{1}=7\) & 2757 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Loads for different and daily activities. The weight of 75 kg was used for activity 1 ([2; 29; 46] and 80 kg for activities 2–6 [20; 51]). Force on the femoral head (FFH): corresponds to the resultant reaction force (N) acting on the femoral head.  
Tables 2 and 3, and the assembly features and the types of contacts described in Section 2.1.3, were used for the prosthesis simulation and assessment of the remaining stems.
# Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites
## 2 Materials and Methods
### Localization System
#### 2.4.1 GPS-Dependent Localization  
The GPS-dependent localization system leverages the user's real-world geographic coordinates (latitude, longitude, and heading) to precisely align the virtual building with its real-world counterpart. This method ensures that the virtual site is placed accurately, making it ideal for outdoor applications where GPS data are accessible. The process begins by calculating the distance between the user's current position and the target building using the Haversine equation, which determines the shortest distance between two points on the Earth's surface based on their latitude and longitude:  
\[d=2r\cdot\text{asin}\left(\sqrt{\text{sin}^{2}\left(\frac{\Delta\phi}{2}\right) +\text{cos}(\phi_{1})\cdot\text{cos}(\phi_{2})\cdot\text{sin}^{2}\left(\frac{ \Delta\lambda}{2}\right)}\right),\]  
where \(d\) is the distance between the user and the building, \(r\) is the Earth's radius (\(\approx\)6378 km), \(\phi_{1},\phi_{2}\) are the latitudes of the user and the building, and \(\Delta\lambda\) is the difference in longitude. Additionally, the bearing equation is used to compute the angle between the user and the building, with respect to the north:  
\[\theta=\text{atan2}(\text{sin}(\Delta\lambda)\cdot\text{cos}(\phi_{2}),\text{ cos}(\phi_{1})\cdot\text{sin}(\phi_{2})-\text{sin}(\phi_{1})\cdot\text{cos}( \phi_{2})\cdot\text{cos}(\Delta\lambda)),\]  
where \(\theta\) is the bearing angle between the user and the building. By knowing the user's heading (rotation) and the bearing angle between them and the building, the relative orientation is calculated. Using this angle and the distance, a polar-to-Cartesian conversion is applied to calculate the building's virtual location relative to the user:  
Figure 4: The virtual guide functionality in the application. The guide offers dynamic, educational content about POIs and follows predefined paths to enhance users’ interaction and engagement in the virtual heritage environment. Capture from Quest 3 on the left; capture from Unity’s scene view on the right. (**a**) The guide provides details about a user-selected POI. (**b**) The guide navigates through predefined paths based on the user’s selection.  
\[x =d\cdot\cos(\theta-\text{user\_heading})\] \[y =d\cdot\sin(\theta-\text{user\_heading})\]  
The calculated coordinates in the virtual world \((x,y)\) are then used to place the building within the MR environment, ensuring that the virtual model aligns with its true location in the real world. This GPS method is particularly beneficial for large-scale outdoor environments, as it maintains accurate positional and rotational alignment between the real and virtual worlds.
# Practical Inner Product Encryption with Constant Private Key +
## 1 Introduction  
Inner product encryption (IPE), first introduced by [PERSON] et al. [1], is a type of predicate encryption [2] in which a ciphertext and a private key correspond to an attribute vector \(\mathbf{x}\) and a predicate vector \(\mathbf{y}\), respectively. In particular, the decryption will be correct if and only if the attribute vector and the predicate vector satisfy the inner product predicate, meaning that the inner product operation of \(\mathbf{x}\) and \(\mathbf{y}\) equals zero (\(\langle\mathbf{x},\mathbf{y}\rangle=0\)). Over the past decade, many IPE schemes have been proposed, such as those based on pairing [3; 4; 5; 6; 7] and lattice [8; 9; 10; 11]. The security definition of an IPE scheme [1] can be naturally extended from the IND-CPA security of identity-based encryption [12; 13; 14]. More precisely, under the security approach of IPE, an adversary learns nothing about the encrypted message from a ciphertext associated with an attribute vector \(\mathbf{x}\) if they do not own the private key associated with a predicate vector \(\mathbf{y}\) such that \(\langle\mathbf{x},\mathbf{y}\rangle=0\). Such a definition is also called the IND-CPA security for IPE scheme in some papers [15] and is defined as the payload-hiding property in [1]. Alternatively, the security definition defined in [1], called the attribute-hiding property, states that a ciphertext reveals nothing about the corresponding ciphertext attribute \(\mathbf{x}\). However, we emphasize that the attribute-hiding property is not an absolutely necessary property for IPE. Many IPE schemes proposed in the literature achieve only IND-CPA security/payload hiding, such as that in [15; 16; 17].  
In addition to their usefulness in fine-grained access control, IPE schemes can be used to construct various cryptographic primitives or can be converted to more complex primitives,such as identity-based encryption [12; 13; 14], hidden vector encryption [2; 18] and subset predicate encryption [19; 20]. We refer readers to the work presented in [1; 19] for details.  
Although many IPE schemes have been introduced, the computing cost and communication cost of these schemes are high. In particular, the pairing operation required by existing pairing-based IPE schemes is typically linearly related to the vector length; therefore, the computational efficiency of these schemes is low. Moreover, the size of the private key of most schemes is linearly related to vector lengths. However, although the existing lattice-based IPE schemes are considered quantum-resistant, the key size of almost all schemes is too large or the message space is too small. In addition, Internet of Things devices are gradually becoming common in daily life; however, the problems mentioned in the preceding discussion make the application of an IPE scheme impractical for these resource-constrained devices. Thus, an unresolved question remains: can we obtain an efficient IPE scheme by reducing the cost of decryption and optimizing the length of the private key?
## 2 Materials and Methods  
### Copula Function  
In 1959, [PERSON] [21] proposed that an n-dimensional joint distribution function could be decomposed into \(n\) edge distribution functions and a copula function, which describes the correlation between variables. [PERSON] [22] provided a strict definition of the copula function in 1999. The copula function is a connecting function that connects the joint distribution function \(F(x_{1},x_{2},\cdots x_{N})\) of random variables \(X_{1},X_{2},\cdots X_{N}\) with their respective edge distribution functions \(F_{X_{1}}(x_{1}),F_{X_{2}}(x_{2}),\cdots F_{X_{N}}(x_{N})\), that is, function \(C(\mu_{1},\mu_{2},\cdots\mu_{N})\),  
\[F(x_{1},x_{2},\cdots x_{N})=C[F_{X_{1}}(x_{1}),F_{X_{2}}(x_{2}),\cdots F_{X_{N }}(x_{N})] \tag{1}\]  
When the edge distribution of each random variable is known, it is easy to calculate their joint distribution function using the copula function. At present, the copula function is mainly used in financial fields [23; 24; 25], for measurements such as that of value-at-risk of multiple financial assets.  
Table 1 lists some commonly used copula functions.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis
### Experimental Results
#### 4.2.4 Results of Ship Detection with HISEA-1 Image  
With the successful operation of HISEA-1 in orbit, we have obtained some actual results of on-board ship detection. We selected a relatively representative image for  
Figure 10: Examples of false alarms (**a**–**e**) and missing ships (**f**–**h**).  
Figure 9: Ship detection results of the simulated SAR image.  
verification purposes due to the limited data. Table 5 shows the information for the HISEA-1 test image. Figure 11 shows the test results.  
There were relatively few ship targets in this test image, and it was obvious that our method was able to detect all of the ships on the sea, as shown in Figure 11. However, near the harbor, the model triggered a false alarm, which may have been caused by the building having a similar backscattering signature to the ships.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 3 Problem Formulation
### Orthogonal Frequency Division Multiplexing  
OFDM has been used over a decade and has proved its robustness in multi-carrier technologies, such as Wi-Fi and cellular technology. It uses multiple smaller subcarriers to avoid the Inter-Channel Interference (ICI) and Inter-Symbol Interference (ISI) over the network. It adds a Cyclic Prefix (CP) to demodulate the signal effectively on the receiver side. It is assumed that the transmitter and receiver are synchronized properly to avoid the misinterpretation of symbols [77]. It uses the Inverse Fast  
Figure 2: System architecture. DLC, Data Link Control; PDSCH (Physical Downlink Shared Channel); PDCCH (Physical Downlink Control Channel); PUSCH (Physical Uplink Control Channel); PUSCH (Physical Uplink Shared Channel).  
Fourier Transform (IFFT) to convert the symbol from the time domain to the frequency domain at the transmitter, while applying the Fast Fourier Transform (FFT) to transform the symbols from the frequency domain to the time domain at the receiver. Since OFDM uses the Cyclic Prefix (CP) to cancel out ISI, there are \(N_{sym}^{long}\) OFDM symbols prefixed with a long CP of duration \(T_{CP}^{long}\). Furthermore, there are \(N_{sym}^{short}=N_{sym}-N_{sym}^{long}\) symbols prefixed with a short CP of duration \(T_{CP}^{short}\). Thus, for OFDM systems, the fixed time slot size is:  
\[T_{s}^{OFDM}=(N_{sym}\times T_{p})+(N_{sym}^{long}\times T_{CP}^{ long})+(N_{sym}^{short}\times T_{CP}^{short}) \tag{1}\]  
where \(T_{p}\) is the symbol duration and \(T_{CP}\) is the symbol duration with CP. Hence, OFDM is spectrally inefficient since it adds \((N_{sym}^{long}\times T_{CP}^{long})+(N_{sym}^{short}\times T_{CP}^{short})\) cyclic prefixes, which consume additional resources in the frequency domain. The CPs usually consume about 25% of the subcarrier bandwidth \(\Delta f\)[78].  
Filter Bank Multi-Carrier  
In 5G, a base station needs to serve a large number of users; thus, it needs a spectrally-efficient PHY waveform. FBMC uses a chain of filters at each subcarrier to make it spectrally efficient, unlike OFDM, which uses CPs. Hence, FBMC can enhance the spectral efficiency and improve the network performance. For FBMC, the fixed time slot size \(T_{s}^{FBMC}\) is:  
\[T_{s}^{FBMC}=N_{sym}\times T_{p} \tag{2}\]  
We assume that the SRA process happens at the beginning of a Transmission Time Interval (TTI) between two consecutive time slots, similar to [13]. By comparing Equations (1) and (2), we observe that FBMC achieves higher spectral efficiency compared to OFDM due to the absence of CPs. However, FBMC applies a filter chain at each individual subcarrier. Hence, the base station needs to spend more time and computational resources compared to OFDM.