esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotics Applications in the Hospital Domain:
## 3 Robotics in Healthcare: An Overview
### Revolutionizing Hospitals  
In the future, robotics will play an important role in the digital transformation of healthcare, and integration will become more profound [17]. Collaboration between humans and robots will be part of everyday work in a hospital, i.e., robots will take part in all tasks and procedures in a hospital context, including cleaning and disinfection, transporting supplies and patients, surgical procedures, etc. [18; 19; 20]. The implementation of novel technologies, including robotics and AI applications, has led to a notable improvement in the safety of surgical procedures and a reduction in the length of hospital stays. Autonomous robotic systems (ARSs) are instrumental in hospital logistics, facilitating the management and delivery of supplies. An example of automation and enhanced operational efficacy is the TUG autonomous mobile robot, developed by Aethon. This system, which is already in operational use, facilitates the transport and logistics of materials in hospitals. For example, it is used at St. Olav's Hospital in Trondheim, Norway, where AGVs autonomously transport medicines, samples, supplies, and bed linens. This increases efficiency and reduces the probability of errors [21; 22; 23].  
An example of the implementation of automated processes and improved operational efficiency in hospitals is the use of the BirthSIM birth simulator, developed by Laerdal Medical, which is now in operation at University Hospital in San Antonio, Texas, to simulate uterine contractions and the mother's voluntary efforts [12]. A further illustration of the benefits of automation and enhanced operational efficiency and quality of care is the deployment of telepresence robots in hospitals. A notable example is the University Hospital in Nancy, France, where the UBBO telepresence robot, AXYN, is remotely operated by the family from a distance and provides the virtual presence of the family within the isolation room of a patient infected with the SARS-CoV-2 virus at the operational stage [23].
# 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
## 3 The Proposed Approach
### Encoding and Activity Classification
#### 3.2.1 Long Short-Term Memory (LSTM)  
To analyze the hidden sequential patterns, it is natural choice to use RNN to encode the temporal structure of extracted sequential features. In video, visual information is represented in many frames which help in understanding the context of an action. RNN can interpret such sequences, but in cases of long term sequences, it usually forgets the earlier input sequence. LSTM has been designed to mitigate the vanishing problem and to learn long-term contextual information from temporal sequences. LSTM is a kind of recurrent network which can capture long-term dynamics, and which preserves sequence information over time. In addition, the LSTM gradient does not tend to vanish when trained with back propagation through time. Its special structure with input, output and, control gates control long-term sequence pattern identification. The gates are adjusted by a sigmoid unit that learns during training when to open and close. We adopt LSTM for encoding and decoding to recognize human actions.  
The architecture of a LSTM unit is depicted in Figure 5. \(x_{t}\), \(c_{t}\), \(h_{t}\) and \(y_{t}\) stand for input vector, cell state, hidden state and output at the \(t\)-th state, respectively. The output \(y_{t}\) depends on hidden state \(h_{t}\), while \(h_{t}\) depends on not only the cell state \(c_{t}\), but also on its previous state. Intuitively, the LSTM has the capacity to read and write to its internal memory, and hence, to maintain and process information over time. The LSTM neuron contains an input gate \(i_{t}\), a memory cell \(c_{t}\), a forget gate \(f_{t}\), and an output gate \(o_{t}\). At each time step \(t\), it can choose to write, read or reset the memory cell through these three gates. This strategy helps LSTM to access and memorize information in many steps. Equations (4)-(9) demonstrate the operation of temporal modelling performed in LSTM unit.  
W and b are the parameters of the LSTM known as weights of the input vector and bias term. \(S\) means a sigmoid function, _tanh_ is the activation function and \(\otimes\) is the element-wise multiplication. The cell state and output are computed step by step to extract long-term dependencies. The input to LSTM is \(x_{t}\), which is the feature vector. A forget gate is used to clear the information from the memory unit, and an output gate keeps the information about the upcoming step. We also have \(g_{t}\), which is computed from the input of the current frame and state of the previous state \(h_{t,1}\). The hidden state of LSTM step is computed by using a _tanh_ activation function and memory cell \(c_{t}\).  
\[i_{t}=S(w_{xi}\ x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{4}\]  
\[f_{t}=S\Big{(}w_{xf}\ x_{t}+W_{hf}h_{t-1}+b_{f}\Big{)} \tag{5}\]\[o_{t}=S(w_{xo}\ x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{6}\] \[g_{t}=tanh(w_{xc}\ x_{t}+W_{ho}h_{t-1}+b_{c})\] (7) \[c_{t}=f_{t}\otimes c_{t-1}+i_{t}\otimes g_{t}\] (8) \[z_{t}=\ h_{t}=o_{t}\otimes tanh\ (c_{t}) \tag{9}\]
## 3 Results
### Model Verification Example
#### 3.1.2 Using the Algorithm for Model Verification  
Walk \(S_{0}\xrightarrow{f_{1}}S_{1}\xrightarrow{f_{2}}S_{2}\) can be represented as \(W=\{(0,f_{1}),(1,f_{2})\}\). Assuming that both actions associated to the reaction rules take 1 unit of time to complete, in Phase 2 both elements of set \(W\) will be transformed to form:  
Figure 4: The initial state of a system in the example of verifying model correctness. Figure 5: Reaction rules for the example of verifying a model. All residue functions are identities. (**a**) Reaction rule \(mov1\). (**b**) Reaction rule \(mov2\). 1. \((0,f_{1},\{(0,0),(1,1),(2,2),(3,3)\},\{(0,0),(1,1),(2,2),(3,3),(4,4),(5,5)\},6,( \{1\},1))\)
2. \((1,f_{2},\{(2,0),(3,1),(4,2),(5,3)\},\{(0,0),(1,1),(2,3),(3,2),(4,4),(5,5)\},6,( \{2\},1))\)  
The method of constructing \(m_{r}\) and \(m^{\prime}_{full}\) functions that result from _Trans_ function in Phase 2 is shown below. The rule of constructing \(m_{r}\) function:  
\[\forall x\in\{0,\ldots,n_{x}---1\}\quad m_{r}(x)=par^{-1}(m_{full}(x))\]  
where \(par^{-1}\) is the inverse function to \(par\) being an element of \(t_{tra}\). In this case, the functions \(f_{1},f_{2},\ldots,f_{8}\) correspond to the subsequent rows in Table 14.  
The rule of constructing \(m^{\prime}_{full}\) function:  
\[\forall x\in\{0,\ldots,n_{x}---1\}\quad m^{\prime}_{full}(x)=res^{-1}(m_{full }(x))\]  
\(res^{-1}\) is the inverse function of \(res\) which is an element of \(t_{tra}\). Table 16 lists the successive steps of the algorithm that will lead to a detection of an error in the model. The reason why this model is incorrect is not because the redex of the rule \(mov2\) is not in the \(0\) state but because the moved object is categorized as an element of the environment, thus we do not take into account the passage of time for it. As a result, the reaction rules create the appearance of being independent of each other when in fact the execution of \(mov2\) rule is dependent on the execution of the rule \(mov1\). To fix the model, the relocated object needs to be categorized as a passive object and one need to add a reaction rule allowing a robot that is in \(AT3\) area to wait until the object being moved is in \(AT2\) area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input State** & **Label** & **Output State** & **Par \& Res** \\ \hline \multirow{4}{*}{\(mov1\)} & \multirow{4}{*}{\(mov2\)} & \multirow{4}{*}{\(mov2\)} & \multirow{4}{*}{\(mov1\)} & \multirow{4}{*}{\(mov2\)} & \multirow{4}{*}{\(mov1\)} \\  & & & & \(\{(0,0),(1,1),(2,2),(3,3)\}\) \\  & & & & \(\{(0,0),(1,1),(2,3),(3,2),(4,4),(5,5)\}\) \\  & & & & \(\{(0,2),(1,3),(2,4),(3,5)\}\) \\  & & & & \(\{(0,2),(1,4),(2,5),(3,3),(4,0),(5,1)\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Tracking transition system for the first example. Figure 6: Incorrect state space for the task from the first example.
## 2 DFIG-Based Wind Turbine System
### Fault Characteristics of DFIG  
#### 2.3.1 Crowbar Circuit  
When grid faults occur, the voltage dips of the DFIG terminal can cause sudden increases in stator and rotor current, which may cause DC link overvoltage of the DFIG and damage to the RSC of the converter. These damages can lead to the direct disconnection of the DFIG from the grid [27; 29; 30].  
Thus, in the event of grid faults, the crowbar circuit is usually applied to suppress the rotor overcurrent of the DFIG and protect the RSC of the DFIG from the negative influence of rotor overcurrent. As shown in Figure 1, the crowbar circuit is composed of a diode rectifier circuit, an IGBT, and a crowbar resistor. Additionally, the working principle of a crowbar circuit is that when the rotor overcurrent of the DFIG is larger than 2 pu, the crowbar circuit will be activated by a gate signal from the IGBT. During the activation of the crowbar circuit, the fault overcurrent of the rotor will be limited by the crowbar resistance, which can avoid the rotor overcurrent from damaging the RSC of the DFIG. When the fault is cleared, the crowbar circuit is deactivated.  
Therefore, it is particularly important to analyze the fault characteristics of the DFIG when the crowbar circuit is activated during grid faults.
## 2 Background Study  
The most significant impacts on the propagation of slant microwave and millimetric wave links are hydrometeors such as thunder, hail, rain, ice, and snow. Among these parameters, rainfall attenuation is the most important phenomenon. The raindrops act as dielectric particle loss, which causes dispersion and depolarization.
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 2 Materials and Methods
### Triangle-Grid
#### 2.3.1 Surface Patch Approach: Overview
One of the primary issues is the specification of the excitation source, particularly when modeling waveguide ports. In such cases, the source and waveguide modes must be carefully matched to accurately represent the excitation. This can be difficult in MoM, as it may require solving a system of equations to determine the mode coefficients [318]. Another issue is the occurrence of singularities, which can arise when the object has a highly curved or irregular shape. This can lead to diverging surface integrals in MoM and result in an inaccurate or unstable solution [319]. There have been many attempts to solve this problem [320; 321]. While in [322; 323], the technique from the previous work was extended for the curvilinear RWG basis functions with the multilevel fast multipole algorithm (MLFMA). The same was done in [324], but with the use of a contemporary singularity-canceling integral transforms for highly efficient and accurate quadrature. The size and shape of the mesh cells also play a crucial role in determining the accuracy of simulation. If the cells are too small, the solution may be computationally expensive, and if they are too large, it may be inaccurate or unstable. To achieve a balance between accuracy and computational cost, it is important to choose an appropriate discretization [325]. For example, a technique for accelerating free-space MoM solutions for discrete BOR was demonstrated in [326] with a solution for overcoming the difficulty that arises when employing the RWG basis functions in periodic structures. Another solution is to use adaptive RWG basis functions [327]. Moreover, when modeling using RWG, which is a faceted approach, the geometry modeling errors can occur. This can be eliminated by using fully higher order curvilinear basis functions. They are used for this purpose in the SWITCH code, which is a hybrid formulation that solves a coupled system of equations involving FEM in interior dielectric regions along with the integral MoM equation on the exterior boundary surface [328]. Based on all the above research, many codes have been developed for solving electromagnetic problems using surface patches like in [329; 330]. Several programs have been built using such codes, for example, the program of geometric design of bodies shapes [331; 332], the program of electromagnetic modeling of composite wire and plate structures (WIPL, now WIPL-D) [333], the SWITCH code [328], and the fast Illinois solver code [334]. In addition, a MLFMA code was modified to be an application-independent library similar to ScaleME [322]. Moreover, with the help of the PDE toolbox to generate the mesh, a Matlab implementation of MoM antenna simulation code is presented in [335].
# Boosting of Denoising Effect with Fusion Strategy
## 6 Conclusions and Future Studies  
In this study, a denoising effect boosting method based on an image fusion strategy has been presented to combine two image denoising methods (i.e., NCSR and DnCNN) for a better denoising performance. It is based on two weight designs. The first weight design measures the importance of the pixel values according to the overall luminance, and it increases the weight when the neighboring pixel intensity changes largely. The second weight design reflects the importance of the regions with substantial variations in pixel values and suppresses the saturated pixels in the initial denoised images. By integrating the images denoised via NCSR and DnCNN into an optimally fused image, the final denoised output is produced. The experimental results confirm that the proposed method exhibits substantial quantitative improvements over the other state-of-the-art methods, in addition to producing high-quality fused denoised images with much better image structures and less visual artifacts.  
The proposed method is based on a general image fusion strategy. This indicates that it is not limited to image denoising problems. In future research, it is reasonable to extend the proposed boosting method to image de-blurring or image super-resolution problems. Future work could also involve choosing more efficient complementary algorithms or parallel implementations to further improve the computational efficiency of the proposed method. There is no single method that always performs better than others in complex imaging scenarios. Our method offers a solution to integrate individual methods that have complementary strengths into a stronger combined method. We also expect that a number of computer vision applications can benefit from the proposed denoising effect boosting method.  
Conceptualization, F.Y. and S.X.; methodology, S.X.; investigation, F.Y. and S.X.; writing--original draft preparation, F.Y.; software, C.L.; validation, F.Y. and C.L.; writing--review and editing, F.Y. and S.X.; resources, F.Y. and S.X.; supervision, S.X.; project administration, S.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grants 61662044 and 61163023, and in part by Jiangxi Provincial Natural Science Foundation under Grant 20171 BAB202017.  
The authors would like to thank the anonymous reviewers and AE for their constructive comments and suggestions, which improve the quality of the paper.  
The authors declare no conflict of interest.
## 4 Main Tools of the WSN-DPCM Platform
### Tool for WSN Application Planning and RF Simulation
#### 4.1.1 3D Building Reconstruction  
To improve the simulation accuracy, 3D reconstruction of physical environment is very desirable. When environment description is not available or is too expensive, the developers need to build it by themselves.  
However, for many radio propagation experts the preparation of the environment model is more challenging than analyzing the radio performance. Besides, most research results presented in academic conferences reuse the same environment to analyze their work and cannot test the robustness and accuracy for different environments.  
For this purpose, the WSN-DPCM toolset includes a functional component that can automatically reconstruct the third dimension from 2D images (Figure 4). It extends the algorithm proposed in [35] and uses sub-sampling and random feature selection techniques for iterative learning from images. The estimated confidence value of each class for each pixel can be reinterpreted as a probability distribution using soft max transformation to calculate texture layout potentials.  
The work flow of the method is shown in Figure 4. Meaningful object classes are recognized through a machine learning mechanism, which consists of a training phase followed by an evaluation phase. The training phase uses an image database through which the features of images are learned and discriminated for the concerned types of objects. After an image is properly recognized, the algorithm automatically assigns different volumetric information according to scale ratio and empirical material for each object, as it can be seen in Figure 4.
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 2 Materials and Methods
#### Channel Model  
The received signal model in wireless communication systems is generally represented in (1).  
\[\text{r(t)}=\text{x(t)}\otimes\text{h(t)}+\text{n(t)} \tag{1}\]  
In which  
* x(t) is the original signal;
* h(t) is the channel effect function;
* and n(t) is the summed-up noise.  
The total received signal is the resulting combination between the original signal x(t) convoluted with the channel effect function h(t) and additional noise n(t) summed up in the transmission. That noise is mostly implicating light surroundings, thermal noise, shot noise, and dark currents that are modeled as Additive White Gaussian Noise (AWGN). The encountering interferences and jamming threats upon RF signals are eliminated due to the high immunity of optical signals.  
One of the key installation parameters FSO links are prone to consider is the angle of transmission. For rooftop applications requiring LOS frequent availability, the light beam emission angle must be aligned with the receiver position at a horizontal (180\({}^{\circ}\)) angle with a slight acceptable error of \(\pm 5^{\circ}\) about the photo-detector area collecting the optical signal [24]. This gives total power (in watts) emitted from a uniform source, as represented in (2).  
\[P_{T}=BA_{S}\Omega_{S} \tag{2}\]  
In which  
* \(P_{T}\) is the total transmission power;
* \(B\) is the brightness function;
* \(A_{S}\) is the surface area;
* and \(\Omega_{S}\) is the emission angle.  
The above equations are valid for any free space communication link performance analysis. With the aid of a converging lens, the light from the source beam can be concentrated on a single point to reduce attenuation levels and scattering losses. For the used laser source, a focal lens is adopted to focus the output optical signal. The source has an  
Figure 4: Rough Power and Frequency Data Filtering according to their frequency band.  
Figure 3: Mapping Algorithm Block Diagram.  
aperture of 12 mm in a dot shape. The module is set perpendicular to the mirror interface at 90\({}^{\circ}\) so that the silicon-based photo-detector of 7.5 mm\({}^{2}\) and sensitivity angle \(\varphi=\pm 65^{\circ}\). For fixed transmission angle, sensitivity angle, and receiving surface area, the brightness becomes a function of the total transmission power.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 1 Introduction
### Research Contributions & Paper Organization  
The specific research contributions of this paper are summarized in the following.  
* We present a novel solution for securing intra-vehicle networks through the usage of a logical, centralized component that uses hybrid-cryptography to provide confidentiality and multi-source authentication: the _Crypto-Engine_. In comparison with similar solutions (e.g., the _Secure Gateway_ made available by several vendors (An example can be found in [[https://www.nxp.com/docs/en/white-paper/AUTOGWDEVWPUS.pdf](https://www.nxp.com/docs/en/white-paper/AUTOGWDEVWPUS.pdf)]([https://www.nxp.com/docs/en/white-paper/AUTOGWDEVWPUS.pdf](https://www.nxp.com/docs/en/white-paper/AUTOGWDEVWPUS.pdf)) (accessed on 27 November 2023))), the Crypto-Engine features can be distributed where needed, releasing manufacturers from adopting or interfacing with third-party components to secure communications;
* We provide a reference implementation of the hybrid-cryptography scheme for the adoption in intra-vehicle network components and a thorough analysis on its performance in a relevant real-world use case. While the presented implementation is software-based, the Crypto-Engine is designed to support HW acceleration as reported in Sections 3.2 and 5. This enables further performance improvements while maintaining the overall architecture unaltered.  
The rest of the paper is organized as follows. In Section 2 we introduce the context and review the state-of-art on the security aspects of intra-vehicle networks; in Section 3 we present our proposed solution, the Crypto-Engine; in Section 3.3 we present the results of the security analysis of our solution; in Section 4 we describe the implementation, the validation, and the performance evaluation steps of our solution in the context of the EMERGE project along with a discussion on the results obtained; finally, in Section 5 we report some ending remarks and on-going research activities.
# Architectural Design for Digital Twin Networks
## 3 Digital Twin Network
### Challenges  
In the networking domain, DTNs face distinct challenges due to the highly digital nature of network elements and their varied configurations, as summarized in Table 2. Effectively addressing these challenges requires a unified architecture for DTNs, as outlined in the previous section, which forms the foundation for developing and integrating key enabling technologies. While these challenges are not exclusive to DTNs and are present in many other fields, they become particularly pronounced when designing and implementing a DTN.  
For example, scalability is a common issue in large systems, but in the context of DTNs, where real-time operation is required for large networks, this challenge is magnified. Solutions may require multiple instances or a hierarchical structure of twins to maintain performance. Another challenge is accuracy, which depends on both the specific needs of the solution and budget constraints. Finally, often overlooked in research but crucial in practice, is security. Security introduces additional complexities, including latency issues in monitoring, which can exacerbate the challenges of scalability and accuracy.