esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
## 2 Materials and Methods
### AI-DES Development
#### 2.1.1 AI Network
The generator attempts to produce virtual low-energy images that resemble the real low-energy images from high-energy images. The discriminator aims to distinguish between the pairs containing virtual images and the real image pairs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Type** & **Norm 1, Dropout** & **Activation** & **Input Shape 2** & **Output Shape 2** \\ \hline \multirow{4}{*}{Encoder} & Layer1 & & – & & \(1024\times 1024\times 3\) & \(512\times 512\times 64\) \\  & Layer2 & & & & \(512\times 512\times 64\) & \(256\times 256\times 128\) \\  & Layer3 & & & & \(256\times 256\times 128\) & \(128\times 128\times 256\) \\  & Layer4 & Conv2d (4,2,1) & BN & & \(128\times 128\times 256\) & \(64\times 64\times 512\) & \(32\times 32\times 512\) \\  & Layer6 & & & & \(32\times 32\times 512\) & \(16\times 16\times 512\) \\  & Layer7 & & & & \(16\times 16\times 512\) & \(8\times 8\times 512\) \\  & Layer8 & & & & \(8\times 8\times 512\) & \(4\times 4\times 512\) \\ \hline \multirow{4}{*}{Decoder} & Layer9 & Deconv2d (4,2,1) & BN & & \(4\times 4\times 512\) & \(8\times 8\times 512\) \\  & Layer10 & & & & \(8\times 8\times 512\) & \(16\times 16\times 512\) \\  & Layer11 & & BN+Dropout & & \(16\times 16\times 512\) & \(32\times 32\times 512\) \\  & Layer12 & & & & \(3\,2\times 32\times 512\) & \(64\times 64\times 512\) \\  & Layer13 & ReLU+Deconv2d (4,2,1) & & & \(64\times 64\times 512\) & \(128\times 128\times 256\) \\  & Layer14 & & & & \(128\times 128\times 256\) & \(256\times 256\times 128\) \\  & Layer15 & & & & \(256\times 256\times 128\) & \(512\times 512\times 64\) \\ \cline{1-1} \cline{3-4}  & Layer16 & & – & Tanh & \(512\times 512\times 64\) & \(1024\times 1024\times 3\) \\ \hline \hline \end{tabular} \({}^{1}\) Normalization. 2 Width \(\times\) height \(\times\) channel.  
\end{table} 
Table 1: Generator architecture in AI-DES. Figure 2: Generator network used in AI-DES. The network has a 16-layer U-Net structure. The skip connections concatenate the mirrored encoder and decoder layers to recover high-frequency components in the generated image quality.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Mathematical Foundations  
In the realm of cryptographic key management, the effectiveness and security of the Transparent Key Management Algorithm are underpinned by several crucial mathematical constructs. At its core, the algorithm leverages Merkle trees, hash functions, and digital signatures to create a transparent and verifiable system for managing cryptographic keys [15]. These elements work in concert to ensure the integrity and authenticity of key management processes. Merkle trees, with their hierarchical structure, facilitate efficient, and secure verification of key inclusion, while cryptographic hash functions provide robustness against tampering. Digital signatures further enhance security by enabling the verification of the authenticity of updates to the transparency logs [16]. This foundation is critical in addressing the challenges associated with traditional key management systems, such as transparency and scalability. The following section delves into the specifics of Merkle trees, illustrating their role and benefits in the context of key management [17]. Figure 1 shows a deeper and more detailed binary Merkle tree with multiple levels of internal nodes, which better illustrates how hash values propagate through the tree.  
The Merkle tree graph illustrates a hierarchical structure used in cryptographic key management, depicted mathematically as follows. At the base level, the leaf nodes represent the hash values of individual data elements. Let \(L_{1}\), \(L_{2}\), \(L_{3}\), \(\ldots\), \(L_{8}\) be the data elements at the leaf level of the tree. These data elements are each hashed by a cryptographic function \(h_{i}=H(L_{i})\) for \(i=1\), \(2\),\(\ldots\), \(8\). Moving up to Level 2, internal nodes are computed by concatenating the hashes of their child nodes. For instance, hashes such as \(H_{AB}\), \(H_{CD}\), \(H_{EF}\), and \(H_{GH}\) are derived from  
\[H_{AB}=H(H_{1}\|H_{2})\text{, }H_{CD}=H(H_{3}\|H_{4})\text{, }H_{EF}=H(H_{5}\|H_{6})\text{, }H_{GH}=H(H_{7}\|H_{8}) \tag{1}\]  
Similarly, as \(H_{AB}\), \(H_{CD}\), \(H_{EF}\), and \(H_{GH}\) are computed from their respective child hashes. At Level 1, the internal nodes \(H_{AB\,|\,CD}\) and \(H_{EF\,|\,GH}\) are calculated as  
\[H_{AB\,|\,CD}=H(H_{AB}\|H_{CD})\text{, and }H_{EF\,|\,GH}=H(H_{EF}\|H_{GH}) \tag{2}\]  
The root node RRR, representing the entire dataset, is computed as  
\[R=H(H_{AB\,|\,CD}\|H_{EF\,|\,GH}) \tag{3}\]
## 1 Introduction
### Analysis of Test Results of Cutter-Chip Contact Area  
Theoretically, the calculation of the contact area between the cutter and the chip is very complicated for the micro-textured ball-end milling cutter. Therefore, in the milling process, the contact diagram method was used to fit the contact area between the ball-end milling cutter and the chip. After the milling, the titanium alloy, nine texture density combinations and non-texture cutters were observed through an ultra-depth microscopy, and the cutter-chip contact area on the front of the cutter was measured. The contact length and width of the cutter-chip contact were approximated by the contact diagram method. Taking a micro-textured cutter with a texture density combination of 0.09-0.09-0.09 as an example, the experimental data of the tool-chip contact length and width obtained by measuring and fitting are shown in Table 4.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 5 Proposed Solution
### Integration Overview  
Figure 5 presents an overview of the integration of the proposed solution in the context of 5G networks. NSSF DAF was initially designed to function as an MEC service, being one of the main contributions of this work. However, the objective was to evaluate the implementation in a local datacenter close to the gNodeB in the RAN, or in the 5 GC. It seeks to verify whether there is a significant difference in the performance of slice selection at different points of the network slicing architecture. Its integration with the data plane (UPF--User Plane Function) is essential for traffic recognition and the differentiation of service classes, which allows for its operation.  
Among the main functional and non-functional requirements (\(R\)) of the NSSF DAF we identified the following:  
1. Deployable on servers with different virtualization solutions (NFVI-NFV Infrastructure);
2. Facilitate deployment in production networks, in a transparent way;
3. Facilitate the integration of later modules, adding new features, through the available documentation;
4. Compliance with the specifications and models of the standards bodies (e.g., 3 GPP and ETSI);
5. Act independently of specific hardware manufacturers and models;
6. Use open-source solutions;
7. Use DevOps--Development and Operations--philosophy, practices, and tools;
8. Make the mobile application available for download from repositories.  
The client application is cross-platform and can be embedded in several UEs, such as UAVs, vehicles, smartphones, robots, or even in IoT brokers in smart homes. The application uses a set of APIs, providing the necessary signaling for the NSSF DAF. The UE sends messages with its location (GPS coordinates), checking if there is an occurrence of NSSF DAF on the Edge or RAN to which it is linked. From this signaling and the profile of the application used, the NSSF DAF selects the best slice available, and returns with the best option for the UE, as detailed in the modules included in Section 5.2.  
The functional and non-functional requirements (RA--Requirement Application) of the client application are detailed below.  
\(RA1\): Be able to search for the available slices (RAN and Edge);  
\(RA2\): Direct a request for the best slice option to NSSF DAF, through HTTP requests and API consumption;  
\(RA3\): Execute the handover to the best available slice;  
\(RA4\): Facilitate the setting of the user's profile (preferences), which can be as follows: VoD 4K/8K, AR/VR, V2X, among others;  
\(RA5\): Run in automatic (service) or manual mode, according to the UE.  
Regarding integration with cloud computing tools, the NSSF DAF operates with standardized data models, allowing for the required integration. The specification of the functionalities of all modules is detailed in Section 5.2.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 3 Accurate Registration
### Key Points Extraction  
In the original point cloud, there are some representative points with rich feature information. Extracting these points to form a key point set can improve registration accuracy and speed. [PERSON] et al. [25] used the mean angle between the normal vectors of one point and its k-nearest neighbor points as the angle-invariant feature, which has strong sensitivity to the local surface change, but do not consider the spatial distribution of these neighbor points. Therefore, the feature cannot correctly reflect the surface change in a local area where the points are sparse.  
As shown in Figure 2, when a different number of neighbor points is taken to calculate angle-invariant features of point in uneven area, there is a big difference in the result. This is because the more neighborhood points are taken, the more points in the flat area will be included into calculation. This will significantly influence the evaluation of convexity degree of area around the point.  
Therefore, we introduce neighborhood weighting to optimize the algorithm. The improved algorithm is used to extract key points with rich feature information from the point cloud of unstructured terrains. The key points are extracted as follows:  
\[f_{i}=\frac{\sum\limits_{j=1}^{k}d_{ij}}{m}\cdot\sum\limits_{j=1}^{m}\left( \frac{n_{i}\cdot n_{j}}{|n_{i}|\cdot|n_{j}|}\cdot\frac{1}{d_{ij}}\right) \tag{3}\]  
where \(f_{i}\) is the neighborhood weighted angle-invariant feature, \(n_{i}\) and \(n_{j}\) are the normal vectors of point, \(d_{ij}\) is the spatial distance between the point and the neighbor point, \(m\) is the number of neighbor points.  
We set an appropriate threshold \(\varepsilon\) here. If \(f_{i}>\varepsilon\), it means the local surface change of the point is small and the point should be discarded; if \(f_{i}<\varepsilon\), it means the local surface change of the point is large and the point should be kept as a key point. Through this way, we can obtain the key point sets of source point clouds and target point clouds.
## 2 Literature Review and Conceptual Context
### Situation-Aware Route Planning
#### 2.2.1 Routing Using Traffic Conditions
However, since it relies solely on sensor data, it is prone to malfunctions. One of the previous studies used the A* algorithm for vehicle routes based on the spatiotemporal correlation of roads in a traffic network [54]. It used real-time sampling data to compute a congestion coefficient from the number of state vectors sent by monitored cars.
## 2 Production of CNTs
### Multi-Walled CNTs (MWCNTs)  
Multi-walled carbon nanotubes (MWCNTs) are cylindrical nanostructures composed of multiple layers of carbon atoms arranged in a hexagonal lattice, consisting of several SWCNTs nested within each other. In 1991, [PERSON] reported for the first time the synthesis of graphitic carbon needles (MWCNTs) that were 4-30 nm in diameter and up to 1 micrometer in length [47]. The structure of MWCNTs can be envisioned as multiple graphene sheets rolled into concentric cylinders, with the interlayer spacing approximately close to the distance (\(\sim\)0.35 nm) between graphene layers in graphite; the diameter of MWCNTs usually ranges between 3 and 30 nm; however, it often grows above the value of 30 nm (Figure 2) [48; 49; 50; 51].  
Figure 1: Possible wrappings of the two-dimensional graphene sheet into tubular forms. The red dots represent metallic (metal, conductive) CNTs and the black dots represent semiconductive CNTs [38].  
MWCNTs have exceptional tensile strength of 50-150 GPa and elastic modulus, although these properties can vary depending on the number of walls and the defects within and between the layers. The presence of multiple walls enhances the durability and impact resistance, making MWCNTs easier to work with in various applications compared to SWCNTs [51,52].  
MWCNTs are known for their high thermal conductivity (\(2.2\times 10^{4}\) S/cm) along the tube axis. This property, combined with their structural stability, makes MWCNTs suitable candidates for thermal management [53]. Among the MWCNT production methods, CVD is popular for producing MWCNTs with controlled growth parameters, allowing for the tailoring of dimensions and properties for specific applications.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 2 Research Progress in Smart Mobility and Gap Analysis
### Regional, Governance and Citizen Centric Papers
Moreover, an analysis of different properties and uses related to smart mobility and real-time traffic management systems has been given. The paper identifies and addresses the major challenges related to smart cities and smart mobility, such as unequal geographic advancement, privacy concerns and the lack of collaboration. Furthermore, significant gaps have been identified in the domain of smart mobility regarding \"Vehicular Ad hoc Networks (VANETs)\" and \"Smart Traffic Lights\". In [41], the authors have illustrated the implementation of \"Service-Dominant Business Model Radar (SDBM/R)\" in the context of smart mobility. The proposed framework is aimed at designing mobility related business models for the mobility of travelers and cargo in a collaborative fashion. In a multi-stakeholder business context, the authors have designed the system as a central element in the development of complex digital novelties that provide value to users. A survey has been conducted to validate the model and the input from the participants indicates the relevance of the proposed model and its potential for practical use. In [42], the authors have explored the correlation between urban intelligence and sustainable mobility forms for the municipalities in Australia, to explore whether the intelligence of cities contributes to sustainable mobility forms. The impact of growing broadband internet availability on sustainable mobility mode is analyzed using a multivariate multiple regression model, and it is observed that growing broadband internet availability decreases the use of active, public mobility means, while increasing the use of private commuting means. An analysis of the association between the deployment of the smart city notion and the sustainable mobility notion is presented in [43]. The authors have also analyzed the effect of carbon dioxide release from smart city components as a determining factor of mobility. The United Nations' \"ForFITS (For Future Inland Transport Systems)\" model has been used to predict the possible carbon dioxide release resulting from the implementation of the Warsaw transport system. The findings indicate that a thorough change in the mobility and energy domains is necessary to achieve the reduction goals identified by the \"European Union 2011 White Paper on Transport\". In [44], the author has focused on the notion of shared mobility and has presented an analysis of the present literature. The paper observes that most of the literature is aimed at evaluating the effect of shared mobility on factors such as modal transition, congestion, transit clientele, vehicle possession and environmental aspects, while little consideration is given to female safety, accessibility and comfort during travelling, revealing a growing gender parity in urban mobility.
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
## 2 Design and Optimization of the Power Amplifier
### Size Determination and Analysis of the Neutralized Capacitor
Further, the maximum stable gain (MSG), MAG, and stability factor (K) are calculated as [6]:  
\[MSG=\frac{\left|y_{21}\right|}{\left|y_{12}\right|}=\frac{\left|g_{m}-s\left(C_ {gd}-C_{neu}\right)\;\right|}{\left|-s\left(C_{gd}-C_{neu}\right)\;\right|}= \frac{\sqrt{g_{m}\,^{2}+\omega^{2}\left(C_{gd}-C_{neu}\right)\,^{2}}}{\omega \left|C_{gd}-C_{neu}\right|} \tag{7}\]  
\[MAG=MSG\cdot\left(K-\sqrt{K^{2}-1}\right) \tag{8}\]  
\[K=\frac{2\text{Re }\left\{\;y_{11}\right\}\text{-Re }\left\{\;y_{22}\right\} \text{-Re }\left\{\;y_{12}\text{-}y_{21}\right\}}{\left|y_{12}\right|y_{21}\right|^{2} \left|y_{21}\right|^{2}\left|y_{21}\right|^{2}} \tag{9}\]  
\[K>1\;,\quad\text{Re }\left\{\;y_{11}\right\}>0\;,\quad\text{Re }\left\{\;y_{22}\right\}>0 \tag{10}\]  
Figure 4: Schematic representation of (**a**) differential common source power cell, (**b**) small signal analysis model of (**a**). To conveniently determine K, a series parasitic resistance of the gate was equivalent to a parallel parasitic resistance, \(g_{g}\), to satisfy the condition in Equation (10). The simulation results of K, MSG, and MAG, based on the values of \(C_{neu}\) in a single-stage differential PA at 28 GHz, are shown in Figure 5. As \(\mid C_{gd}-C_{neu}\mid\) decreases, K and MSG increase with smaller denominators and achieve the highest value when \(C_{gd}\) and \(C_{neu}\) are equal. Thus, adjusting the \(C_{neu}\) can deliver additional gain and stability in the PA. Consequently, although the oscillation risk is particularly high in the high-gain three-stage PA, this can be solved by analyzing the parasitic capacitance of the transistors and the subsequent optimization of capacitive neutralization.
# Multi-Objective Motion Control Optimization for the Bridge Crane System
## 3 Control Design
### LQR Control Based on Trajectory Planning  
The TP algorithm is easy to apply, which can take the moving efficiency and maximum payload swing into account. However, it is difficult to adaptively adjust the swing for the payload in practical conditions, and the oscillation cannot be suppressed when external disturbance exists. Hence, the TP algorithm combined with the closed-loop control can effectively solve these problems.  
For the underactuated system, there exists multiple motion states, and thus, it is necessary to attain the feedback control. According to Equation (6), the LQR control law from the feedback input \(u_{LQR}=-KX\) yields:  
\[\dot{X}=(A-BK)X \tag{25}\]  
where \(K\) can be calculated by minimizing the energy function of LQR \(E_{LQR}\), as:  
\[E_{LQR}=\frac{1}{2}\int_{0}^{\infty}\Big{(}X^{T}QX+u_{LQR}^{T}Ru_{LQR}\Big{)}dt \tag{26}\]  
where \(Q\) is a semi-definite matrix of \(4~{}\times~{}4\), and \(R\) is a positive define matrix constant of \(1~{}\times~{}1\). Then, \(K\) can be calculated as:  
\[K=R^{-1}B^{T}P \tag{27}\]  
where \(P\) is a positive definite symmetric matrix, which is evaluated from the Algebraic Riccati equation (ARE) as:  
\[A^{T}P+PA+Q-PBR^{-1}B^{T}P=0 \tag{28}\]By this manner, the gain of LQR feedback \(K\)=\([k_{1},k_{2},k_{3},k_{4}]\) can be estimated, and the crane acceleration \(u\) can thus be determined. In order to accomplish the position control accurately, the differential and integral control is introduced for the crane's position adjustment. Hence, the improved controller can be expressed as:  
\[u(t)=K_{I}\int_{0}^{t}e_{T}dt+K_{D}\dot{e}_{T}-k_{1}x_{T}-k_{2}\dot{x}_{T}-k_{3 }\theta-k_{4}\dot{\theta} \tag{29}\]  
where \(u(t)\) is the control input; and \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\), belonging to \(K\), are defined as feedback gains for the crane position and velocity, the payload swing, and the angular velocity, respectively. \(e_{T}=x_{d}-x_{T}\) is the error trajectory of the crane system, and \(K\) can be determined based on \(Q\) and \(R\) by engineering experience. However, it is difficult to find the optimal solution in this manner. \(K_{I}\) and \(K_{D}\) are integral and differential gains, which are designed by the response optimization, and the constraint of the response optimization can be expressed as:  
\[\begin{split} optimal(K_{I},K_{D})=\min\left[\max_{0\leq t\leq 1 0}(x_{Tsim}-x_{Tbmd})\right]\\ s.t.\left\{\begin{array}{cc}0\leq x_{T}\leq 0.320&0\leq t \leq 1.2\\ 0.285\leq x_{T}\leq 0.302&1.2\leq t\leq 10\end{array}\right.\end{split} \tag{30}\]  
where \(x_{Tsim}\) is the simulated response of the crane position, and \(x_{Tbmd}\) is the piecewise linear bound of the crane position.
## 1 Introduction
Ray tracing is a method of computing all the direct and reflected paths from a transmitter to a receiver and computing the distance between them using points in the reflections by summing up these values to arrive at the resultant value. Therefore, implementing ray tracing in a sound simulation would make the process much less resource-intensive and more robust. This is due to the geometry-based approach, which calculates the Transmission Loss (TL) pattern in its three components: geometrical [19] loss (the space between the sender and recipient), dissipation loss (the atmosphere's characteristics), and reflection loss (the reflection surfaces) [20]. In a ray tracing simulation, acoustic energy is modelled to propagate from the source to the receiver, similarly to the behaviour of light beams or rays. Additionally, specular reflections are considered for all surfaces [21]. When a ray observes a barrier, a reflected ray is created and tracked until it reaches the receiver. Different forms and orientations can be tested by adjusting the reflecting surfaces until the rays are uniformly dispersed among the audience. The source's beams are perpendicular to the wavefronts, which are a spherical surface transmitted by the source [20]. The surface orientation is now allowed for one source site [21]. The method is repeated for each conceivable source site until the design is complete. Even when performed manually, two-dimensional ray tracing can be a highly effective approach for simulating the acoustics of a small room [21]. Currently, the ray tracing method is widely used in simulating mmWave Bands [22], ultrasonic array imaging [23], and light rays in digital animation [24]. While the focus has been on other applications, recently, there have been a few new studies on acoustic ray tracing. [PERSON] et al. [25] investigated acoustic modelling for indoor spaces using the ray tracing method. However, this study was theoretical, and limited research focuses on real-world applications. There are also studies on underwater sound propagation [26] and underwater positioning [27]. Many studies are conducted in 2D, neglecting the floor and ceiling, which significantly impacts the acoustics of small enclosed buildings [28]. However, the 3D ray tracing method is not common for simulating and assessing factory noise. The application and testing of this method in simulating factory noise floors integrated with ISO standards and safety recommendations are minimal. Therefore, this study aims to develop a user-friendly noise evaluation tool for factories using the 3D ray tracing method with ISO standards integration and safety recommendations. It focuses on remodelling and developing the ray tracing engine in MATLAB for acoustic applications. It is then verified with manual calculations. The tool is equipped with an intuitive graphical user interface (GUI). This easy-to-use tool would be the first line of defence for those small manufacturing factories to evaluate and predict their noise floors.