esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Reality: Types and Applications
## 7 Conclusions  
The performed analysis of accessible publications and research reports in extended reality issues related to robotics revealed the following main findings:  
* Virtual reality (VR) systems are mostly used in entertainment and education based on the established hardware conditions.
* The education area is predominant due to the number of studies on extended reality technologies; this is a definite marker of the underdevelopment of industrial and military area in XR.
* The development of user-friendly 3D engines attracts a greater amount of research and a wider use of specialized applications in industry, medicine, and all other areas of activities.
* The development of technologies allows us to make affordable and technologically advanced devices, such as Meta Quest 3. Using these, it is possible to develop XR technologies and expand the scope of application.
* The use of modern technologies and algorithms such as eye tracking for foveal rendering, frame generation, DLSS, and FSR, which can reduce the computing load on the device and network load, increase battery life based on device optimization.
* The future development of XR technologies can be aimed at optimizing computing processes, reducing delays, and increasing the autonomy of devices, which can expand the scope of application of this technology.  
Conceptualization, O.S. and V.B.; methodology, A.D. and M.P.; validation, A.D., O.S. and M.P.; formal analysis, O.S. and A.D.; investigation, O.S. and V.B.; resources, V.B. and A.D.; data curation, M.P.; writing--original draft preparation, O.S.; writing--review and editing, V.B., A.D. and O.S.; visualization, O.S.; supervision, V.B.; project administration, A.D.; funding acquisition, V.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission as part of the European Union's Horizon 2020 research innovation program, through the ECSEL Joint Undertaking project AI4 CSM, under Grant Agreement No. 101007326.  
Not applicable.  
The authors declare no conflicts of interest. The funders had no role in the study's design; the collection, analysis, or interpretation of data; the writing of the manuscript; or the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**3D Engine** & **Virtual Reality** & **Augmented Reality** & **Mixed Reality** \\ \hline Unity 3D & 1866 & 1377 & 1001 \\ \hline Unreal Engine & 351 & 182 & 164 \\ \hline \end{tabular}
\end{table}
Table 5: The popularity of 3D engines for XR is based on the number of articles from 2020 to 2024.
# The Influence of the Load Term of the Strain Energy Sensitivity in the Shell Shape Optimization
## 3 The Derivation of the Strain Energy Sensitivity  
### The Sensitivity Derivation by the Traditional Way  
It is assumed in literature [8,9] that the change of design variables does not affect the nodal load vector \(\mathbf{F}\) of the structure. For the problem shown in Equation (1), the strain energy (Equation (2)) and the finite element equation (Equation (3)) can be used to deduce the expression of the strain energy sensitivity to the coordinate \(z_{j}\) of the \(j\)th node, as shown in equation (4).  
\[\mathbf{C}=\frac{1}{2}\mathbf{F}^{T}\mathbf{U} \tag{2}\]  
\[\mathbf{K}\mathbf{U}=\mathbf{F} \tag{3}\]  
where \(\mathbf{K}\) is the structural stiffness matrix of the curved shell, \(\mathbf{U}\) is the displacement vector of the structure, and \(\mathbf{F}\) is the load vector.  
\[\frac{\partial\mathbf{C}}{\partial z_{j}}=-\frac{1}{2}\mathbf{U}^{T}\frac{\partial\bm {K}}{\partial z_{j}}\mathbf{U} \tag{4}\]  
where, \(z_{j}\) represents the \(z\)-coordinate of node \(j\).
# Analysis and Comparison of Electromagnetic Microrobotic Platforms for Biomedical Applications
## 3 The Electromagnetic Microrobotic Systems
### Three-Dimensional Manipulation
#### 3.2.7 Platform 9E  
[PERSON] et al. [96] have investigated the design of an EMA system composed of nine electromagnetic coils that is capable of independently controlling identical and nonidentical microrobots in the 3D workspace, and named BatMag as illustrated in Figure 18b. The independent control is achieved by exploiting the in-homogeneity of the strong fields that the system can generate. The control strategy for the independent 6 DOFs control of microrobots has been tested and evaluated. Furthermore, a thermal management technique is developed and quantitatively analyzed to prevent overheating during continuous operations. In fact, the Joule heat occurred by the large input current flowing the coil is an important safety factor for EMA setups. This nine-coil EMA system with the independent control could be used for precise collaborative tasks in MIS fields, microassembly, micromanipulation, tissue engineering, and lab-on-a-chip applications.  
Figure 18: EMA platform developed with independent control: (**a**) a photograph of the EMA setup comprising 8 electromagnetic coils [95]; and (**b**) a photograph of the nine-coils EMA system [96].
# Interference-Aware Opportunistic Dynamic Energy Saving Mechanism for Wi-Fi Enabled IoTs
###### Abstract  
The wireless local area network (WLAN) is one of the most popular wireless technologies offering connectivity today, and one of the candidates for the internet of things (IoTs). However, WLAN's inefficiency in terms of complexity and relatively large power consumption compared with other wireless standards has been reported as a major barrier for IoTs applications. This paper proposes an interference-aware opportunistic dynamic energy saving mechanism to improve energy efficiency for Wi-Fi enabled IoTs. The proposed scheme optimizes operating clock frequencies adaptively for signal processing when the mobile station transmits packets in partial sub-channels. Evaluation results demonstrate that the proposed scheme improves energy efficiency by approximately 34%.  
dynamic energy saving; multi-channel; power consumption; WiFi; IoTs +
Footnote †: journal:  
_Future Internet_**2017**, 9, 38; doi:10.3390/f9030038
# UAV Formation Trajectory Planning Algorithms: A Review
## 7 Future Research Focus and Direction
### Allocation of Resources  
In the future, drone formations will be widely used in battlefields and anti-terrorism operations. The environment in which UAVs perform these tasks may become very complex, resulting in increased mission difficulty, and the environment may provide extremely strong support for UAV formations, such as satellite links and energy supplies for continuous flight. How to reasonably allocate available resources to each UAV during mission execution is also a challenging problem.
## 3 Long-Term Feature Points Localization
### Pantograph Horn and Spark Localization
\(l_{i}^{*}\) and \(g_{i}^{*}\) are, respectively, the classification and position ground-truth corresponding to anchor \(i\); \(h_{i},x_{i}\) are the confidence and location obtained in ARM, respectively; and \(c_{i},t_{i}\) are the values predicted in ODM. \(\mathcal{L}_{\mathbf{b}}\) is a binary classification loss equation, which can obtain the binary classification loss of positive and negative samples through cross-entropy loss. \(\mathcal{L}_{\mathbf{m}}\) is a multi-category classification loss equation based on softmax loss, which can output the confidence level of multi-class synchronously. \(\mathcal{L}_{\mathbf{r}}\) is an L1-loss for position regression. \(\left[l_{i}^{*}\geq 1\right]\) is the confidence level used to filter positive samples, and the loss calculation is only performed for positive samples. \(N_{\text{ARM}},N_{\text{ODM}}\) are the number of positive samples in each module. Training Datasets: Based on the given network model, in the pantograph catenary image three objects are set: left horn, right horn, and sparks. The double side of the pantograph horns are selected and distinguish the left and right horns so that a horn missing fault can be detected. This method can effectively solve the accurate identification of the target under the shelter of one side of the horn during train operation and can easily locate the endpoint of the pantograph head by obtaining the vertex of the detected box. The possible arcing phenomenon is also trained synchronously to conduct multiple class detection. After training, it is possible to determine the positioning of the endpoint of the pantograph and detect possible arcing. On the one hand, through the results of the arc detection, the continuous arcing contact problems that may occur during train operation can be detected and analyzed. At the same time, the arc position itself can be used to verify the contact points in the neighbor frames. Based on the detected image features, an analysis scheme of the actual contact state is next proposed.
## 3 Results and Discussion
### ROS Generation of the _E. coli_ incubated with the Cys-AuNCs and Cys-AgNCs  
To further evaluate the ROS generation, EPR spectroscopy with a spin trap of DMPO was applied for the measurement and characterization of free radicals and other species with unpaired electrons. As shown in Figure 6a, there was no significant ROS generation for the _E. coli_ solution. In Figure 6b, for the _E. coli_ incubated with the Cys-AuNCs (0.175 mg/mL), there was also no obvious ROS generation, which corresponds to the high biocompatibility of the Cys-AuNCs with _E. coli_. However, as shown in Figure 6c, for the _E. coli_ incubated with the Cys-AgNCs (0.175 mg/mL), the EPR spectra revealed the spin adduct (DMPO-OH) with a characteristic 1:2:2:1 quartet of EPR lines [51]. The reason for this can be ascribed to the hydroxyl radicals generated from the incubation of the _E. coli_ with  
Figure 4: Growth curves of the _E. coli_ incubated with (**a**) Cys-AuNCs, (**b**) Cys-AgNCs, and (**c**) AgNPs. OD600 indicates the optical density of a sample measured at a wavelength of 600 nm. All data are presented as means \(\pm\) SD, \(n=3\) per group.  
Figure 5: (**a**) Photographs of the growth of the _E. coli_ that were incubated with sterilized water (control) and the Cys–AuNCs, Cys–AgNCs, and AgNPs on LB agar plates. (**b**) The viabilities of the _E. coli_ that were separately incubated with sterilized water (control) and the Cys–AuNCs, Cys–AgNCs, and AgNPs. In the control experiment, the viability of the _E. coli_ incubated with sterilized water was set to 100%. All data are presented as means \(\pm\) SD, \(n=3\) per group.  
the Cys-AgNCs, which interacted with the nitrogen nucleus and a proton present on the spin trap of the DMPO. Overall, the results of the EPR analyses demonstrated that the high biocompatibility of the Cys-AuNCs can be attributed to the use of the cysteine ligand as an ROS scavenger to eliminate the ROS after the incubation of the _E. coli_ with the Cys-AuNCs.
## 3 Optimal Beamwidth Maximizing Uplink Coverage Probability Scheme
### Beam Boresight Direction  
In the QEFC satellite system, the network and satellite need to calculate and decide the beam boresight direction during the beam-fixed time. This section introduces the beam topology defined in [18] and formulates the beam boresight direction. The beam position is expressed as Earth-Centered Earth-Fixed (ECEF) coordinates \([x_{l}\,y_{l}\,z_{l}]^{\text{T}}\). Our beam topology follows [18] with the adjacent beam spacing is 3.82\({}^{\circ}\), half-power beamwidth is 4.4127\({}^{\circ}\), and the distance of the adjacent beam is around 40.2 km. With the pre-defined beam topology and the satellite orbit, the beam boresight direction \([\phi_{l}(t)\ \theta_{l}(t)]\) can be calculated based on the satellite angular speed \(\omega\) and relative beam position \([x_{l}(t)\ y_{l}(t)\ z_{l}(t)]^{\mathrm{T}}\) at time \(t\) as follows:  
\[\omega=\sqrt{\frac{G_{E}\cdot M_{E}}{(R_{E}+h)^{3}}}=\sqrt{\frac{3.98\times 10 ^{14}}{(R_{E}+h)^{3}}} \tag{9}\]  
\[\mathbf{v}_{l}(t)=\begin{bmatrix}\delta_{l}(t)\\ \hat{y}_{l}(t)\\ \hat{z}_{l}(t)\end{bmatrix}=\begin{bmatrix}\cos(\omega t)&0&-\sin(\omega t)\\ 0&1&0\\ \sin(\omega t)&0&\cos(\omega t)\end{bmatrix}\begin{bmatrix}x_{l}\\ y_{l}\\ z_{l}\end{bmatrix} \tag{10}\]  
\[\begin{bmatrix}\hat{x}_{0}(0)\\ \hat{y}_{0}(0)\\ \hat{z}_{0}(0)\end{bmatrix}=\begin{bmatrix}x_{0}\\ y_{0}\\ z_{0}\end{bmatrix}=\begin{bmatrix}0\\ 0\\ R_{E}\end{bmatrix} \tag{11}\]  
\[\delta_{l}(t)=\arccos\frac{\hat{z}_{l}(t)}{R_{E}} \tag{12}\]  
\[\phi_{l}(t)=\arccos\frac{R_{E}+h-R_{E}\cos\delta_{l}(t)}{\sqrt{R_{E}^{2}+(R_{ E}+h)^{2}-2R_{E}(R_{E}+h)\cos\delta_{l}(t)}} \tag{13}\]  
\[\theta_{l}(t)=\arctan\frac{\hat{y}_{l}(t)}{\hat{x}_{l}(t)} \tag{14}\]  
\[\epsilon_{l}(t)=\frac{\pi}{2}-\lambda_{l}(t)-\phi_{l}(t) \tag{15}\]  
where \(G_{E}\) is the earth's gravitational constant, \(M_{E}\) is the earth's mass, \(\phi_{l}(t)\) and \(\theta_{l}(t)\) are the zenith and azimuth angle of departure, \(l=0\) denotes the center beam, and \(t=0\) denotes the time when \(\epsilon_{0}(0)=\frac{\pi}{2}\). The example for (12) to (15) is shown in Figure 2. With the defined beam boresight direction, the angular difference between beam \(l\) and user \(s\) can be calculated as follows:  
\[\varphi_{l}^{s}(t)=\arccos\frac{||\mathbf{x}_{sat}(t)-\mathbf{x}_{s}(t)||^{2 }+||\mathbf{x}_{sat}(t)-\mathbf{v}_{l}(t)||^{2}-||\mathbf{x}_{st}(t)-\mathbf{ v}_{l}(t)||^{2}}{2||\mathbf{x}_{sat}(t)-\mathbf{x}_{u}(t)||\cdot||\mathbf{x}_{ sat}(t)-\mathbf{v}_{l}(t)||}. \tag{16}\]
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 1 Introduction
SDN separates the entities of the control plane and the data plane. The control plane software is run on general-purpose hardware. SDN enables the self-service deployment of control and traffic  
Figure 1: V2X communication infrastructure. routing [13]. The SDN controller has some default flow values pre-configured. As soon as the switch is turned on, the traffic flow is pre-programmed. SDN controllers and switches exchange information flow over the network via a secure channel, such as the secure socket layer (SSL) or the transport layer security (TLS) protocols, whereas the OpenFlow manages communication between the network and control layers [13]. In addition, multi-access edge computing is deployed through small cell linked to RSUs. With the design of the next vehicular network enabled by advanced technology, such as SDN, small-cells-assisted RSU to preserve latency requirements for data dissemination in 5G network for V2X is proposed. We propose a novel distributed vehicular network architecture-based SDN and small cells linked-up RSUs (small cells linked-up RSU that abstracts the multi-access edge computing approach). This article provides the following contributions:  
* We propose a distributed based software-defined small cells-linked RSU vehicular network (diSRsVN) architecture as the new, next 5G V2X architecture. * We describe the logical structure of the control plane of the small cell linked-up RSU as well as the software-defined wireless vehicle. * We provide the discovery topology for reliable routing over the proposed architecture. * We provide a simulation of the proposed diSRsVN architecture for data dissemination of V2X messages and discuss the relevant improvements of using SDN and small cells-linked RSU to fit the latency requirements of V2X services in a 5G-enabled vehicular network. The results of the proposed scheme are analyzed through network simulation tools that integrate frameworks capable of supporting cellular networks. We compose the remainder of the paper as follows. Section 2 presents the related works. Section 3 presents the system architecture of the distribution of software-defined small cell-linked RSU as well as the description of components of the control plane layer. Section 4 presents the simulation results of the proposed architecture. Section 5 concludes with some conclusions.
## 2 Analytical Model of the Drive and Transmission Mechanism
### Torque Characteristics  
The graphical representation of the expression of the equilibrium of torques acting on the vehicle during driving in the form of torque characteristics shows the dependence of the engine torque \(T_{e}\) considering the full fuel supply on the engine revolutions \(n_{e}\).  
The engine torque values at the full fuel supply can be obtained directly from the external revolution characteristics of the engine. Another important relation that has to be considered during the creation of the torque, traction and power characteristics of the engine and vehicle is Equation (7) for the calculation of the current vehicle speed:  
\[v=\frac{2\pi\frac{n}{60}3.6r_{d}}{i_{c}} \tag{7}\]  
The torque characteristics of the IVECO F1 CE0489C engine shown in Figure 3 can be obtained as the dependence of the torque on the vehicle speed related to the external engine characteristics, considering the dynamic radius of the driving wheel \(r_{d}\) obtained from its static value \(r_{s}\) given in the vehicle's technical documentation and the overall gear ratios \(i_{c}\) for every gear.  
Torque characteristics have a wide range of application possibilities. However, for the purposes of this part of the research, they are primarily used to verify the torque characteristics obtained numerically. Due to the simplifications of the analytical method (especially the absence of a hydrodynamic member and different initial conditions), the course of the values of the analytical method was modified by a numerical tool for the purpose of this comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(n_{\text{e}}\) & 0 & 1000 & 1500 & 1800 & 2500 & 2900 & 3000 & 3500 & 3650 & 3850 & 4000 \\ \hline \(T_{\text{e}}\) (Nm) & 0 & 225 & 380 & 490 & 490 & 490 & 480 & 390 & 355 & 275 & 0 \\ \hline \(P_{\text{e}}\) (kW) & 0 & 25 & 65 & 88 & 120 & 140 & 140 & 140 & 140 & 110 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the external revolution characteristics of the IVECO F1 CE0489C engine.  
Figure 2: External revolution characteristics of the IVECO F1 CE0489C engine.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 3 Method  
This section outlines the architecture of MLKD-Net, a lightweight U-Net-based model tailored for single image dehazing. As illustrated in Figure 1a, MLKD-Net incorporates multiple Multi-Head Large Kernel Dehaze (MLKD) blocks to extract and refine features efficiently. The network synergizes the expressive capacity of Vision Transformers with the computational efficiency of CNNs by employing a Multi-Head Large Kernel Attention (MLKA) mechanism for spatial modeling coupled with a Channel Attention (CA) module [11] for selective feature enhancement.  
The CA module adaptively emphasizes feature channels that are more relevant to haze characteristics (e.g., color distortion or low contrast) while suppressing less informative ones. In feature maps produced by convolutional layers, not all channels contribute equally to the task of haze removal. Some channels may capture haze-relevant visual cues, while others may represent redundant or irrelevant information. The CA module addresses this by applying learnable weights to each channel, allowing the model to focus its attention on meaningful features. This improves both the discriminative power and overall representation quality of the network. The effectiveness of CA has also been proven in previous studies [11,18], showing measurable improvements over baseline models without CA.
# Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Modulation**} & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Data Rate** \\ **(Gbps)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Fiber Length** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Energy Eff. (p/bit)** \\ **(* = no LO)** \\ **Included** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Total Chip** \\ **Area (mm\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{**Reference**} \\ \hline CP-FSK & 120 & 17.7 & 1.0 & 4.0 * & N/A & [3] \\ CP-FSK & 140 & 12 & 1.0 & 19.2 & 2.31 & [7] \\ ASK & 60 & 6 & 2.0 & 4.7 & N/A & [8] \\ ASK & 135 & 27 & 1.0 & 4.8 & 1.94 & [9] \\ PAM-2 & 131 & 30 & 1.0 & 4.1 * & 0.83 & this work \\ PAM-4 & 131 & 22 & 1.0 & 5.6 * & 0.83 & this work \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with other PMF links, with BER \(<10^{-12}\).
## 1 Introduction
In the context of seismic inversion, a major challenge lies in addressing the inconsistent scales of inversion parameters, which can lead to inaccuracies in the inversion process. To overcome this issue, we propose a novel inversion equation that transforms the parameters into a uniform scale, thereby improving the reliability of the inversion results. To demonstrate the effectiveness of our method, we perform validations using both synthetic model data and actual field data. The results underscore the practical applicability of our approach to enhance seismic inversion accuracy in anisotropic media.
## 3 Theoretical Background
### Voice Activity Detector  
Voice activity detection (VAD) is a process that detects the presence of a voice in a noisy signal. It is an essential component in speech processing applications like voice and speech detection, speech recognition, speech enhancement, and speaker recognition [56].  
In this work, a VAD detector based on the work presented in [56] is utilized. Figure 5 shows an overview of the VAD. According to this model, the four different feature streams are used as follows:  
1. Spectral Shape: The short-term power spectrum envelope of the speech signal.
2. Spectro-temporal modulations: The spectral scales and temporal rates of speech.
3. Voicing: The vibrations of the vocal cords (folds) that appear in speech as fundamental and harmonic components of the pitch (frequency).
4. Long-term variability: The variations in speech caused by successive phone generation.  
These feature streams are extracted from the input audio stream. These streams are divided into small frames, then the DCT of each feature stream is taken. The DCT contains a large number of coefficients. However, the first five DCT components are sufficient to extract the most relevant context information from those frames [56].  
The resulting five-dimensional vectors from each of the four feature streams are normalized and combined into a single twenty-dimensional frame feature vector.  
Finally, these frame feature vectors are used to train a multilayer perceptron (MLP) classifier to detect the presence of speech. Speech segments are detected by thresholding the ratio of speech and non-speech outputs of the MLP.
## 3 Actuation and Control
### Actuation  
The robot, excluding its gripper, is actuated using two motors: a 20 mm motor to drive the MA along the links and a 25 mm motor to actuate the joints. Both motors operate within a voltage range of 6-9 volts, have different gear ratios, and come with a built-in encoder, thus enabling precise control of the actuators' movements. The 20 mm motor is equipped with a built-in gear with a ratio of 1:488, providing a torque of 3.5 kg-cm at 24 rpm under optimal working conditions. The 25 mm motor has a gear ratio of 1:4.4, which delivers a torque of 0.22 kg-cm at 1800 rpm. The motors and the gear transmissions were carefully selected to meet two primary requirements:  
1. Ensure that the MA can travel against gravity while carrying a load of 1 kg. The system can carry a total load of 3.2 kg.
2. Ensure that the joints can rotate even when the robot carries a 1 kg load. In the current design, the motors can rotate the joints even when a load of 2.64 kg is applied at the proximity of the last link.