esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Classification and Analysis
### Mathematical Models  
The models of kinematic nature are a description of the motion without regard to the forces that cause it. They can usually be represented using transformation matrices. The general formula for forward kinematics using Denavit-Hartenberg (DH) parameters is  
\[T=\ T_{1}\cdot\ T_{2}\cdot\ldots\cdot\ T_{n}, \tag{37}\]  
where \(\ T_{i}\) is the transformation matrix for the \(i\)-th joint, defined as  
\[T_{i}=\begin{bmatrix}\cos\Theta_{i}&-\sin\Theta_{i}\cos\alpha_{i}&\sin\Theta_ {i}\sin\alpha_{i}&a_{i}\cos\Theta_{i}\\ \sin\Theta_{i}&\cos\Theta_{i}\cos\alpha_{i}&-\cos\Theta_{i}\sin\alpha_{i}&a_{ i}\sin\Theta_{i}\\ 0&\sin\alpha_{i}&\cos\alpha_{i}&d_{i}\\ 0&0&0&1\end{bmatrix} \tag{38}\]  
where \(\Theta_{i}\) is the joint angle, \(d_{i}\) is the link offset, \(a_{i}\) is the link length, and \(\alpha_{i}\) is the link twist. This matrix multiplication results in the transformation matrix \(T\), which describes the position and orientation of the end effector relative to the base frame. With some given joint settings, this framework defines the procedure for computing the end-effector's pose.  
The inverse kinematics method involves determining the settings of the joints that will provide the desired location for the robot's end effector. This can be represented as  
\[f(\theta)=(x,y,z), \tag{39}\]where \(f\) is a function that maps joint angles to end effector locations. This solves for the joint angles \(\theta\) given a desired position (\(x,y,z\)).  
Polynomial inequalities are usually applied to provide limits and restrictions in robotic motions. These inequalities can represent various physical scenarios such as collision avoidance, joint limits, and workspace boundaries.  
Geometric reasoning is used to solve the problems of robot motion and configuration based on the utilization of geometrical characteristics and relationships. It typically employs equations of lines, planes, and curves in space, such as for a 3D plane.  
\[ax+by+cz+d=0, \tag{40}\]  
Topological models help in understanding a robot's configuration space and the relations between different configurations. Topological spaces can express notions such as homotopy and connectedness, which are not usually expressed in straightforward equations.  
The findings of the recent simulation prove that the developed mathematical model effectively supports designing an adaptive control system with artificial intelligence features, especially for autonomous mobile robots with manipulators that are inherently complicated and networked systems. By cross-connecting control channels, the model makes it easy to design adaptive control algorithms and improves the system's efficiency.
# Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing
## 2 Preliminaries  
### A Review of Compressed Sensing  
CS compresses the signal at the time of sampling while maintaining the ability to reconstruct the original signal. For a signal \(x\in\mathbb{R}^{N}\) that has at most \(k\) terms as nonzero components in some bases \(\Psi\), the compressed signal \(y\) is obtained through the following linear transform:  
\[y=\Phi\Psi x \tag{1}\]  
where \(y\) is an \(M\times 1\) vector and \(\Phi\) denotes an \(M\times N\) random measurement matrix with \(M\ll N\).  
In general, \(M\) is much larger than \(N\), so the reconstruction \(x\) from the measurements \(y\) can be solved by forming an underdetermined set of linear equations. Thus, the CS reconstruction is generally an ill-posed problem. To guarantee an exact reconstruction of every \(k\) sparse signal, one of the most important assumptions of CS is that the measurement matrix \(\Phi\) satisfies the restricted isometry property (RIP) [27; 28] with parameters \((k,\delta)\)[29; 30; 31].  
\[(1-\delta_{k})\|x\|_{2}^{2}\leq\|\Phi x\|_{2}^{2}\leq(1+\delta_{k})\|x\|_{2}^{2} \tag{2}\]  
where \(\delta_{k}\) is the RIP constant and \(0<\delta_{k}<1,k<M\).  
When a matrix satisfies the RIP, the lengths of all sufficiently sparse vectors are approximately preserved under the matrix transformation [29]. In [19; 21], it was demonstrated that if \(\delta_{2K}<\sqrt{2}-1\),, then the signal can be exactly reconstructed via a finite number of iterations.  
The CS reconstruction aims to find the sparsest possible solution that satisfies Equation (1). Then, the CS model [1; 31] is represented as:  
\[\min\|\Psi x\|_{0}\quad\text{ subject to }\quad y=\Phi\Psi x \tag{3}\]  
where \(\|\Psi x\|_{0}\) is the \(l_{0}\) norm and denotes the number of nonzero components in \((\Psi x)\).
## 2 Related Work
For the M-GEAR algorithm, the center of the sensing field has a gateway sensor node if the distance from a sensor node to the BS is greater than a predefined threshold and the communication between the sensor node and the BS is built on the basis of clusters; otherwise, the sensor node directly communicates with the BS. The PEGASIS, EEUC, and M-GEAR algorithms only consider the node distance property, which cannot completely represent characteristics of WSNs, and fails to effectively balance a greater energy consumption of the sensor nodes. Hence, the issue of maximizing WSN longevity is unsolved. More recently, joint optimization of the node energy and node distance properties has been extensively explored to identify the optimal network structure, and has triumphantly inspired people to put forward many effective routing protocols to improve WSN longevity. A HEED algorithm [10] is the typical representative routing protocol. Specifically, the HEED algorithm synthetically considers the mixture of the node residual energy and a secondary parameter (i.e., the sensor node proximity to its neighbors or the sensor node degree) to periodically select CHs. Despite that the HEED algorithm has improved the speed of clustering and reduced the cost of communication between clusters, it excludes some sensor nodes from joining into any clusters because of the competition between clusters [19]. Leader election with load balancing energy (LELE) [20] is a routing protocol promoted over the LEACH algorithm, which selects CHs by considering the amount of node residual energy and the distance between the sensor node and its neighbouring nodes. Although the LELE algorithm slightly improves the WSN longevity, the issue of unevenly distributed CHs still exists, which is not useful for maximizing WSN longevity. Furthermore, a framework to dynamically optimize clusters of WSNs is provided by a GASONeC method [21], in which the node residual energy, the expected energy expenditure, the distance to the BS, and the number of sensor nodes in the vicinity are considered. Balancing these factors is the key to dividing sensor nodes into clusters and designating surrogate sensor nodes as CHs, which contributes to the maximizing of WSN longevity. However, its computational complexity increases dramatically for a large size of iteration generations. In this paper, the DHCO algorithm aims to balance the energy consumption of sensor nodes and improve WSN longevity by synthetically taking into account the node energy and the node distance properties. Specially, the DHCO algorithm utilizes the node distance property to construct a hierarchical network structure that is the vital foundation of the feasible routing set for each sensor node, with the feasible routing set supplying a solution space to obtain the optimal route in consideration of the node energy expenditure.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 4 System Models
### LiCi Block Cipher  
LiCi block cipher: LiCi [24] uses a 64-bit plaintext encryption key and 128-bit plaintext encryption. In comparison with other ciphers, LiCi consumes only 1051 gate equivalents. A Virtex 6 FPGA makes ciphers that consume 25 mW of power. LiCi is an excellent option for applications requiring a low footprint and low power among the many ciphers available. The cipher has a key-scheduling feature. LiCi utilizes the PRESENT key schedule as an AES algorithm. A LiCi cipher goes through 31 iterations or rounds until the optimum security level is reached. LiCi has developed a lightweight S-box with a shift mechanism [25].
## 5 Discussion and Future Works  
The architecture we developed offers an end-to-end framework for aircraft semantic segmentation, characterized by strong scalability. Notably, it makes it possible for aircraft that were not in the initial training dataset to be automatically recognized, removing the requirement for the model to be retrained. For researchers struggling to obtain a sizable amount of labeled actual data for aircraft picture segmentation, our model offers a workable option. The findings imply that a fine-tuning strategy for initializing the encoder's weights in the network can improve the performance of the proposed U-Net model.  
One limitation of our research pertains to the number of images utilized, which is determined by the DLRSD. While the number may appear modest compared to those employed in other studies, our proposed model mitigates the necessity for a large volume of labeled data and obviates the manual extraction of aircraft features. For instance, in the study presented in [40], the researchers assembled a dataset comprising 600 images, achieving the highest accuracy for U-Net at 95%, which is inferior to the accuracy attained in our investigation (i.e., 98.24%).  
Similarly, the authors in [39] utilized the NWPUVHR-10 dataset, containing 650 target images and 150 background images, totaling 800 images across 10 target types, with 80 images featuring aircraft. Despite this larger dataset, their accuracy only reached 93.10%. Likewise, in the study conducted in [4], the DLRSD encompassed 21 broad categories, each with 100 images, resulting in a total of 2100 images. Despite this extensive dataset, their accuracy stood at 81.77%. Thus, while our dataset may be smaller in comparison, our study achieved superior accuracy levels.  
One additional limitation of our study is the exclusive evaluation of the U-Net architecture without considering other architectures, such as VGG 16 or ResNet-50. While this choice may impact the generalizability of our findings, the results attained, when juxtaposed with those of other studies, can serve as a foundational framework for subsequent research endeavors.  
By solely assessing the U-Net architecture, our study may have overlooked potential insights that could have been gleaned from exploring alternative architectures like VGG 16 or ResNet-50. However, the improvement to our work may require using transfer learning methods for more advanced pre-trained encoders like ResNet34 or VGG16. The use of ensemble learning methods is a further possible enhancement route. To construct an aggregate prediction, ensemble learning combines predictions from many pixel-wise classification networks. This strategy can lessen the bias brought on by certain models, which enhances the network's overall performance.
# Sensing in Inland Waters to Promote Safe Navigation: A Case Study in the Aveiro's Lagoon
## 5 Discussion  
The results obtained from this research reflect the needs and preferences of sailors concerning data sharing and the use of electronic instruments on their vessels. Participants showed interest in sharing and accessing meteorological and oceanographic information, acknowledging its significance for maritime safety. The identification of important data to be shared, in both coastal and inland waters, emphasizes the significance of this information for sailors and future research within the field.  
Understanding tidal movements is crucial for anticipating and mitigating navigational risks, especially in areas such as the Ria de Aveiro in Portugal. Water levels and tidal movements must be carefully monitored. To address these challenges, it is essential to leverage technology. Real-time access to meteorological and oceanographic data can enhance navigational safety by providing sailors with updated information on weather and water levels conditions. Integrating such technology into maritime data sharing systems can help sailors make more informed decisions, reducing the risk of grounding and maritime accidents in intricate environments such as the Ria de Aveiro (Aveiro's lagoon).  
With the interviews and survey, it was possible to emphasize the importance of the data sharing and access to meteorological and oceanographic information, especially in coastal and inland waters where navigation can be challenging. Also, the results of this survey allowed us to understand the importance of human participation in designing a visually appealing and usable UI, especially in contexts where presenting critical information can make a real difference between life and death. It is crucial to consider the insights obtained from individuals who navigate complex situations, as their firsthand experiences often reveal nuanced challenges that may not be immediately apparent. The results about the UI of the maritime data sharing system include opinions from the sample on the best choice of UI for integration into a maritime data sharing system.  
The results of this study provide valuable insights for the development of maritime data-sharing systems, highlighting the importance of accessible and reliable solutions that are tailored to user needs. Further research could explore these findings to improve existing technologies and develop new approaches to enhance maritime safety and efficiency.
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
## 3 RLFMB Rotational Dynamics Modeling  
### RLFMB Equivalent Magnetic Circuit Model  
The inner and outer magnets in the RLFMB magnetic circuit provide the magneto-motive force. Along the radial direction, from the magnetic resistance, the outer magnetic conductive ring, outer magnetic steel, working air gap, inner magnetic steel, and inner magnetic conductive ring follow. The inner and outer magnetic rings are joined as a single unit, and the inner and outside magnetic steel form an open sector-shaped magnet. The magnetic induction intensity at the working air gap are oriented in the same direction for the two groups of inner and outer magnets because they are radially symmetrical. Figure 6 depicts the analogous magnetic circuit of one side of the RLFMB, and radial symmetry on the other side is the same.  
Ignoring the influence of magnetic circuit leakage magnetism and the magnetic field generated by the coil, according to the equivalent magnetic circuit method, its magnetic flux can be expressed as follows:  
\[\phi=\frac{\frac{\sum\limits_{s}F_{S}}{\sum\limits_{s}R_{S}+\sum\limits_{i=1}^{4 }R_{i}}}{\sum\limits_{s}R_{S}+\sum\limits_{i=1}^{4}R_{i}} \tag{1}\]  
\[s\in(O-,O+,I-,I+)\]  
In the above formula, \(F_{O-},F_{O+},F_{I-},F_{I+}\) is the magnetomotive force of four permanent magnets, \(R_{1},R_{2}\) is working air gap magnetoresistance, \(R_{3},R_{4}\) is the magnetoresistance of the inner and outer magnetic rings, \(R_{O-},R_{O+},R_{I-},R_{I+}\) is the magnetoresistance of four permanent magnets, \(H_{c}\) is the coercive force of radially magnetized permanent magnets, and \(\phi\) is the magnetic flux at the operating air gap.  
The magnetic induction intensity is expressed as follows:  
\[B=\phi/S \tag{2}\]  
This can be brought into Equation (1):  
\[B=\frac{H_{c}(2l_{p1}+2l_{p2})}{(\frac{l_{1}}{5_{1}l_{0}l_{r}}+\frac{l_{2}}{5_ {2}l_{0}l_{r}}+\frac{2\delta}{\mu_{0}S}+\frac{2l_{p1}}{5_{p1}l_{0}l_{r}}+\frac {2l_{p2}}{5_{p2}l_{0}l_{r}})S} \tag{3}\]  
In the above formula, \(l_{p1}\) and \(l_{p2}\) are the magnetization lengths of the inner and outer permanent magnets, \(l_{1}\) and \(l_{2}\) are the thicknesses of the inner and outer permanent magnets, \(S_{1}\) and \(S_{2}\) are the circumferential surface areas of the magnetic conductive ring surrounding the inner and outer permanent magnets, \(\delta\) is the length of the working air gap magnetic field, \(S\) is the circumferential surface area of the coil, \(S_{p1}\) and \(S_{p2}\) are the circumferential surface area of the inner and outer permanent magnets, and \(\mu_{0},\mu_{r}\) are the permeability of air and the relative permeability of soft magnets. In summary, the optimal magnetic induction intensity can be obtained by designing the parameters of the permanent magnet to meet the uniform distribution of magnetic induction intensity at the working air gap.  
Figure 6: RLFMB equivalent magnetic circuit.
###### Abstract  
The provision of high data rate services to mobile users combined with improved quality of experience (i.e., zero latency multimedia content) drives technological evolution towards the design and implementation of fifth generation (5G) broadband wireless networks. To this end, a dynamic network design approach is adopted whereby network topology is configured according to service demands. In parallel, many private companies are interested in developing their own 5G networks, also referred to as non-public networks (NPIs), since this deployment is expected to leverage holistic production monitoring and support critical applications. In this context, this paper introduces a 5G NPN architectural approach, supporting among others various key enabling technologies, such as cell densification, disaggregated RAN with open interfaces, edge computing, and AI/ML-based network optimization. In the same framework, potential applications of our proposed approach in real world scenarios (e.g., support of mission critical services and computer vision analytics for emergencies) are described. Finally, scalability issues are also highlighted since a deployment framework of our architectural design in an additional real-world scenario related to Industry 4.0 (smart manufacturing) is also analyzed.  
5G; private networks; O-RAN; network monitoring and telemetry; AI/ML based network optimization; time sensitive networking +
Footnote †: journal: Sensors
# Interleaved Boost Converter with ZVT-ZCT for the Main Switches and ZCS for the Auxiliary Switch
###### Abstract  
A soft-switching interleaved topology is presented herein and applied to the boost converter. The basic operating principle is that the main power switches are turned on at zero voltage and turned off at zero current via the same auxiliary resonant circuit whose switch is turned on from zero current. Furthermore, as compared to the traditional boost converter, the proposed topology has three additional auxiliary diodes, two additional auxiliary capacitors, one additional auxiliary inductor, and one additional auxiliary switch. On the other hand, since the interleaved control is adopted herein, the difference in current between the two phases exists. Hence, the cascaded control is utilized to regulate the output voltage to the desired voltage via the first phase, whereas the current-sharing control, based on half of the input current as the current reference for the second phase, is employed so as to make the load current extracted from the two phases as evenly as possible. In this paper, the effectiveness of the proposed topology and control strategy is demonstrated by some experimental results.  
Keywords:boost converter; soft switching; zero voltage transition; zero current transition; zero current switching; cascaded control; interleaved control +
Footnote †: journal: Applied Sciences
# Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
## 3 Proposed Control Strategy
### Application to the Studied System  
The presence of nonlinear or unbalanced loads leads to the generation of periodic harmonic components in the output voltage waveform. To counteract these effects, we have proposed a control method based on an intelligent proportional-integral (iPI) controller with adaptive gain, while respecting the control objectives. Unlike traditional approaches, the proposed control does not rely on the system model but simply uses the inputs and outputs of the system without needing to know the internal details. This approach has the advantage of an equation that is independent of system parameters, which guarantees a significant rejection of disturbances. This makes our method more flexible and adaptable to real system conditions. Throughout this work, \(v=1\). The controller design based on the free model is shown in Figure 6. It includes two separate control loops:  
* Outer loop: this loop uses an iPIa to regulate the output voltage and provides the internal current references and uses an iPIa controller.
* Inner loop: this loop uses a simple Proportional gain controller to generate the converter input control signal.  
The application of the proposed control law gives the following reference currents:  
\[i_{d}^{*}=\frac{{i_{d}}^{\prime}-F_{e}(t)+k_{p}\varepsilon_{vd}+k_{i}\int \varepsilon_{vd}}{\alpha_{0}+\Delta\alpha} \tag{19}\]  
\[i_{q}^{*}=\frac{{i_{q}}^{\prime}-F_{e}(t)+k_{p}\varepsilon_{vq}+k_{i}\int \varepsilon_{vq}}{\alpha_{0}+\Delta\alpha} \tag{20}\]
# Adjacent Vehicle Number-Triggered Adaptive Transmission for V2V Communications
## 5 Conclusions  
In this paper, we proposed a dynamic transmission scheme with a variable data rate according to the number of connected vehicles and the random topology in VANET. The numerical results verified that the proposed scheme was able to provide more continuous and reliable V2V communication and to show better performance in terms of throughput compared to the conventional scheme in 802.11p. Note that the only objective is not the error performance but the throughput performance. When the Eb/N0 is larger, the proposed method generally shows much benefit compared to the conventional methods. Therefore, the proposed scheme with the adaptive transmission is proven to be generally better for the frequently changing topology and the channel environments in the VANET and, thus, may improve the continuity and the reliability of the V2V communication, even though the complexity may be rather increased. For future research, the effect of a power boost on system performance will be analyzed to further improve power efficiency. Also, the different probabilities for N = 0, 1, 2, 3, and 4 can be considered.  
[PERSON] designed the system model, performed performance evaluations by simulation, and prepared for the manuscript. [PERSON] set up a basic simulator for the system model and carried out the simulation. [PERSON] and [PERSON] discussed the main ideas and analyzed the results together. All authors approved the publication. The authors declare no conflict of interest.