esa-sceva/satcom-chunk-collection huggingface dataset:
# Editorial on Wireless Power Transfer (WPT): Present Advancements, Applications, and Future Outlooks
## 3 Challenges in Wireless Power Transfer  
**Efficiency and Energy Loss:** A major limitation of current WPT technologies is their efficiency, especially as the distance between the transmitter and receiver increases. While resonant inductive coupling achieves high efficiencies over short distances, far-field methods such as microwave- and laser-based WPT face significant losses due to scattering, absorption, and beam divergence. The efficiency of WPT systems is directly related to the alignment of their transmitter and receiver, and any misalignment results in significant energy losses. Researchers are currently focusing on developing adaptive systems that can dynamically adjust their transmission parameters to maximize efficiency under varying conditions.  
**Electromagnetic Interference (EMI):** Electromagnetic interference (EMI) is another critical challenge that affects the reliability and safety of WPT systems, particularly in environments with multiple electronic devices. High-power WPT systems can potentially interfere with sensitive electronic equipment, causing performance degradation or malfunction. This is especially important in healthcare settings where medical devices operate in close proximity to each other. To address this, researchers are investigating advanced shielding techniques and frequency management protocols that can minimize EMI and ensure the safe coexistence of WPT systems and other electronic devices.  
**Regulatory and Standardization Issues:** The adoption of WPT on a global scale is hindered by the lack of standardized protocols and regulatory frameworks governing the transmission of wireless power [9]. Currently, different regions have varying regulations regarding the frequency bands and power levels that can be used for WPT systems. This lack of harmonization complicates the development of globally interoperable systems. Organizations such as the Wireless Power Consortium (WPC) are working towards the creation of international standards for WPT technologies, which would enable their seamless integration into different markets and industries.
## 4 Registration Algorithms Based on Feature
### Feature Matching
When the spatial position of two pieces of point clouds differ significantly, color features are more accurate than spatial features. It is preferable to employ spatial characteristics to match the point clouds' geometric specifics after alignment. [PERSON] et al. [132] traversed to find the best correspondence by the angles of two pairs of straight lines to identify corresponding pairs of straight lines and derive a transformation that does not account for motion in the direction of gravity. By designing surface features that only require a pair of correspondences to determine the transformation matrix, [PERSON] et al. [140] reduced the number of transformations that needed to be verified. They also established a Kd-tree to find approximation features, discovered comparable transformations using the Kd-tree, and calculated the mean of certain related transformations. The majority of models often employ the inner product of feature or two-parametric distance of features to describe the matching similarity between two sets of features due to the K-nearest neighbors' inadequate computing efficiency in the network model. Nevertheless, this way ignores the difference of features on a certain channel. To improve this situation,Li et al. [133] learned a similarity measure for feature matching using distance-aware similarity matrix convolution to obtain better matching relationships. The network supports the use of fast point feature histograms (FPFH) or graph neural networks (GNN) to extract rotationally invariant geometric features. Furthermore, the distance augmented feature tensor is obtained by stitching it with a simple point-to-four-dimensional Euclidean feature, and then one-dimensional convolution is applied to the tensor to learn the feature matching metric. To further increase the robustness of the algorithm, the posture is fine-tuned once the initial parameters are obtained using the energy minimization function. [PERSON] et al. [134] suggested an overlapping mask network (OMNet). During each iteration, the overlapping masks of the two source and target point clouds are predicted individually, the non-overlapping parts are filtered. Afterwards, MLPs predict the relative motion parameters from the global features of two pieces of point clouds. OMNet achieves state-of-the-art results by removing the overlapping regions to prevent interference of the global features.
# An Efficient Automatic Midsagittal Plane Extraction in Brain MRI
## 2 Materials and Methods
### Estimation of Yaw Angle (\(\theta_{r}\))
#### 2.2.3 Cross-Correlation  
In the cross-correlation technique, the aligned image \(F\) acquired in the last step is reflected about the current vertical center line to produce a new reflected image \(F^{\prime}\). The reflected image \(F^{\prime}\) is rotated by \(2\theta_{2}\) (\(\theta_{2}\in[-10^{\circ},10^{\circ}]\)) with a \(0.5^{\circ}\) interval about the center of the image, cross-correlated with the image \(F\), and the maximum correlation score is noted. The value of \(\theta_{2}\) at which the cross-correlation score is maximum will be the required angle of bilateral symmetry with the vertical axis of the image. The cross-correlation is accomplished in the frequency space for greater efficiency. The detail of this method can be found in [18].  
If \(\theta_{2}\) is zero, it means that the PCA accurately aligns the brain bilateral symmetric axis with the vertical axis of the image, otherwise \(\theta_{r}\) (yaw angle) will be:  
\[\theta_{r}=\theta_{1}+\theta_{2} \tag{19}\]  
where \(\theta_{1}\) is the angle obtained from PCA and \(\theta_{2}\) is the angle yielded from the cross-correlation technique.  
After applying the cross-correlation technique, the brain bilateral symmetric axis is completely aligned with the vertical axis of the image, as displayed in Figure 5 (last column). Now, the angle \(\theta_{r}\) can be used to estimate the first two parameters of the normal of required MSP plane using Equation (7), i.e., \(a=\cos\theta\), and \(b=\sin\theta\).  
Figure 5: Estimation of symmetry axis orientation \(\theta_{r}\). First column: input images; second column: alignment of the brain bilateral symmetry axis with the vertical axis of the image using \(\theta_{1}\); last column: brain bilateral symmetry axis alignment using \(\theta_{r}=\theta_{1}+\theta_{2}\). Note that \(\theta_{2}=0\) in the first row.
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 3 Variable Voltage Regulator for LCRs to Balance the String LED Currents
### Dimming for Multiple-String N-LEDs  
For dimming multiple-string \(N\)-LEDs, multiple identical \(i_{DS}\)-\(v_{DS}\) planes, as shown in Figure 6, are simultaneously controlled by multiple LCRs with varying \(v_{GS}\) values, in which all \(v_{DS}\)'s are then placed at a constant low-dropout \(v_{DS}=V_{Q}\). For the convenience of analysis, all components, including MOSFETs, OPAs, LEDs, and source resistor \(R_{s}\), are assumed to be identical in an array of integrated circuits (ICs), in which only one \(VV_{dc}\), as shown in Figure 5b, is supplied in the planes of Figure 6. For the ease of analysis, two \(i_{DS}\)-\(v_{DS}\) planes are taken as being the same as each other, for example, in which \(V_{Q1}\) and \(V_{Q2}\) are the low-dropout voltages. Since the two MOSFETs are identical, the two operation points are almost the same, leading to the two string LEDs having the same currents. However, once there is a deviation from \(v_{DS}\), at least one of the MOSFET's \(v_{DS}\) should be maintained at the specified \(V_{Q}\), leaving the rest of \(v_{DS}\)'s as being greater than \(V_{Q}\), but still remaining on the same flat \(v_{GS}\)-characteristic curve; on which both of the drain currents of the MOSFETs in this example are almost the same, but are different for the \(v_{DS}\). Thus, the two string \(N\)-LEDs can still have the same currents, and thus emit the same luminance. This control theme is to be extended to the current balance of multiple string \(N\)-LEDs.  
Figure 5: (**a**) Predicted bias situation of MOSFET with a constant low dropout \(v_{DS}\), where the LCR is supplied with a variable \(V_{dc}\); (**b**) Bias construction for the synthesized \(VV_{dc}\) with low-dropout \(v_{DS}\).
## 3 Concepts, Models and Technologies
### Containers and Cloud Microservices Architecture  
In the case of applications controlling IoT devices within a Smart City or Smart Building, as well as in Smart Grid scenarios, an important middleware software component known as the Content Broker(CB) plays a key role. The CB acts as middleware between software systems, databases and various IoT applications. On the other edge, it manages IoT devices through protocols such as CoAP, SIP/RTP or MQTT, enabling real-time data collection and transmission with low latency.  
The European organization _Fiware.org_[29] has gained prominence in recent years for its reference architecture, which incorporates one Fiware Content Broker. In the subsequent case study on Smart Buildings, a simplified architecture will be adopted, leveraging CBs for integration with both Fog Computing and IEC.  
The integration of SDN with Fog or Intelligent Edge Computing is recommended due to SDN's decoupled control and data planes, which facilitate enhanced network flexibility and management. Extensive deployments have demonstrated the efficacy of this integration in Fog and Edge Computing environments, as evidenced by service providers' adoption of SD-WAN [13]. Consequently, this integration represents an important decision parameter within the Smart Building study case in Section 6.  
The rationale behind the research techniques proposed in this study lies in addressing the challenge of selecting the most suitable network architectures and solutions between Cloud and Edge Computing, given the current state of the art in technology. Additionally, a secondary objective is to contribute to the positioning and development of real-time multimedia applications at the network edge, using high-performance IoT devices. For example, the proposed application of VIoT gateways serves as a very interesting case study to understand its behavior on SBs [26].
## 3 The SLAM Framework of Low-Cost LiDAR and Vision Fusion
### United Error Function  
The traditional error function for the matching of image data or scan data is given in Section 2. In this part, for further optimization and mapping procedure, we introduce a united error function with the fusion of image data and scan data.  
Figure 4: The proposed SLAM framework.  
Considering two adjacent robot poses with an initial guess of translation and rotation parameters [**R,t**], which could get through scan matching, image feature matching or even encoder data individually. Based on the error formulas introduced in Section 2, the united error function could be expressed as:  
\[e=\beta\sum_{i=1}^{m}\left\|p_{i}-\frac{1}{Z_{i-1}}\mathbf{C}(RP_{i-1}+t)) \right\|\ +(1-\beta)\alpha\sum_{j=1}^{n}\left\|(P_{j}-(RP_{j-1}+t))\right\|, \tag{6}\]  
where \(m\) is the number of matched features; \(n\) is the number of corresponding scan points; \(\mathbf{C}\) is the camera parameter matrix;\(p_{i}\) is the image position of feature; \(P_{j}\) is the scan point position; \(\alpha\) is a parameter for unit conversion of the image pixel error and distance error, it is mainly judged by camera resolution and laser ranging range; \(\beta\) is the parameter for balancing the two measurements, it takes value between (0,1). It is mainly judged by the two sensors' reliability and precision, for example, if the robot is working in an environment full of image features, \(\beta\) can be larger so that we consider visual input more important. In contrast, if the scene is better for laser processing, \(\beta\) can be smaller.  
\((R,t)\) can be written in an algebra form, and the Lie Algebraic transformation formula is:  
\[RP+t=\exp(\hat{\xi})P. \tag{7}\]  
Then the error function could be reformulated as a Lie algebra form:  
\[e=F(\xi)=\beta\sum_{i=1}^{m}\left\|p_{i}-\frac{1}{Z_{i-1}}\mathbf{C}\exp(\hat {\xi})P_{i-1})\right\|+(1-\beta)\alpha\sum_{j=1}^{n}\left\|(P_{j}-\exp(\hat{ \xi})P_{j-1})\right\| \tag{8}\]  
It is a function with the variable, \(\xi\). For \(K\) pairs of multiple poses with their transformation relations, the total error could be written as:  
\[E=\sum_{i=1}^{K}e_{i}=\sum_{i=1}^{K}F_{i}(\xi_{i}), \tag{9}\]  
and such error can be minimized to find better transformations and rotations between multiple poses.
## 4 Materials and Methods
### Data Processing and Analysis  
The relative content of VOCs was calculated by the peak area normalization method, which was expressed as the ratio of peak area to the total. Excel and R were used for all statistical analyses. Excel was used only for data sorting and statistics. Volatile values were presented as mean \(\pm\) standard deviation (SD). The R version was 4.1.2. Analysis of variance (ANOVA) with Tukey's test was used to assess volatile difference analysis. Different letters (a-c) in Tables S1 and S2 represent significant differences between samples (\(p\leq 0.05\)). A Venn diagram was generated to visualize the shared and unique numbers of common and unique substances at different stages using R package \"VennDiagram\". The alpha diversity index was calculated using R package \"vegan\". Principal Component Analysis (PCA) was performed using the R packages \"FactoMineR\" and \"factoextra\". All the visualization figures were implemented by R package \"ggplot2\".
## 4 The Fast Second-Order Blind Identification (SOBI) Method for Diagonalization of Average Matrix
### The Fast SOBI Method Based on Diagonalization of the Average Matrix  
#### 4.2.1 Method Principle  
The structure of the correlation matrix with different time delays observed by the sensor is as follows:  
\[R(0)=E(x(t)x^{\star}(t)) \tag{14}\]  
\[R(0)=E(x(t)x^{\star}(t)),\forall\tau\
eq 0 \tag{15}\]  
Spatially whiten the correlation matrix \(R(0)\):  
\[\underline{R}(0)=\widehat{W}R(0)\widehat{W}^{H}, \tag{16}\]  
where \(\widehat{W}\) is the whitening matrix. The whitening correlation matrix \(\underline{R}(\tau)\) with different time delays is defined as  
\[\underline{R}(\tau)=\widehat{W}R(\tau)\widehat{W}^{H}\quad\forall\tau\
eq 0 \tag{17}\]  
The whitened correlation matrix forms a joint matrix \(M=\left\{M_{1},\cdots,M_{K}\right\}\), where  
\[\begin{array}{l}M_{1}=\underline{R}(\tau_{1})=\widehat{W}R(\tau_{1}) \widehat{W}^{H}\\ \vdots\\ M_{K}=\underline{R}(\tau_{K})=\widehat{W}R(\tau_{K})\widehat{W}^{H}\end{array} \tag{18}\]  
Due to  
\[\begin{array}{l}\sum\limits_{i\
eq j}(V^{H}(M_{1}+\cdots+M_{K})V)\\ =\sum\limits_{i\
eq j}(V^{H}M_{1}V+\cdots+V^{H}M_{K}V)\\ =\sum\limits_{i\
eq j}V^{H}M_{1}V+\cdots+\sum\limits_{i\
eq j}V^{H}M_{K}V\end{array} \tag{19}\]  
using the general diagonalization criterion  
\[C(M,V)=\sum\limits_{k=1}^{K}\sum\limits_{i\
eq j}\big{(}(V^{H}M_{k}V)_{ij} \big{)}, \tag{20}\]we can get  
\[\begin{array}{l}C(M,V)=\sum\limits_{i\
eq j}\left((V^{H}M_{1}V)_{ij}+\cdots+(V^{ H}M_{k}V)_{ij}\right)\\ =\sum\limits_{i\
eq j}\left(V^{H}(M_{1}+\cdots+M_{k})V\right)\\ \hskip 56.905512 pt=\sum\limits_{i\
eq j}\left(V^{H}\sum\limits_{k=1}^{K}(M_{i} )V\right)\end{array} \tag{21}\]  
Divide both sides by \(K\) and get the new diagonalization criterion:  
\[\begin{array}{l}C_{new}(M,V)=\sum\limits_{i\
eq j}\left(V^{H}(\frac{1}{K} \sum\limits_{k=1}^{K}M_{i})V\right)\\ =\sum\limits_{i\
eq j}\left(V^{H}mean(M)V\right)\end{array} \tag{22}\]  
The improved diagonalization method is based on the following optimization problems:  
\[\underset{V\in R^{non}}{\min}\sum\limits_{i\
eq j}\left(V^{H}mean(M)V\right). \tag{23}\]  
The joint diagonalization of the matrix is transformed into the diagonalization problem of the average matrix, which greatly simplifies the algorithm. The average matrix reflects the average feature structure of the matrix, which will inevitably cause the loss of some detailed features; this will affect the separation accuracy to some extent, but it can be seen from the subsequent experiments that this simplification does not seriously affect the separation effect. However, the separation speed was significantly improved.
## 5 Results and Discussion
### Discussion
Despite these advancements, the literature has also revealed ongoing challenges, such as managing interference and congestion in densely deployed networks. The studies stated a need for more advanced multi-agent systems and enhanced learning algorithms that can operate and adapt to the restrictions of the unlicensed spectrum used by LoRaWAN. These systems must not only manage internal network conditions but also dynamically mitigate external interference, which remains a notable challenge for reliable connectivity. While the body of work on LoRaWAN optimization through AI and machine learning is evolving and showing substantial benefits, it also highlighted clear directions for future research. This included the development of algorithms that can further minimize energy consumption, enhance adaptive responses to environmental and network changes, and improve interference management. These areas are important for the scalability and sustainability of LoRaWAN implementations. Future studies should also focus on the integration of real-world testing to validate these models and obtain better results.
## 4 Experimental Results
### 6 MWT and COT  
To quantitatively assess how much the developed KAEGO system aids in comfortable and stable walking for hemiplegic patients, a 6 min walking test was conducted, and the COT was calculated to compare the metabolic energy expenditure during walking. The experiment was conducted twice, considering the patient's condition, and the COT was measured before and after wearing the assistive device for each trial.  
Tables 4 and 5 present the results of the first and second 6 min walking test, respectively. The results from both the first and second trials showed a reduction in metabolic energy of approximately 7-8% when the assistive device was worn. The higher COT in the first trial is attributed to the patient's decreased condition.  
When wearing the developed KAEGO, walking speed increased by approximately 10% in both trials. In contrast, VO2 and heart rate (HR) increased by about 2% in the first trial but decreased in the second, particularly VO2, which showed a reduction of great than 10%. Therefore, the test results indicate that patients with hemiplegia can travel farther distances with a similar level of metabolic energy expenditure when wearing the KAEGO. In other words, assuming that the same distance is traveled, the findings indicate that movement occurs with lower metabolic energy consumption.
# Phase Offset Tracking for Free Space Digital Coherent Optical Communication System
## 5 Conclusions and Future Work  
In this paper, a new phase offset estimation method combining the Viterbi-Viterbi algorithm with the Kalman filter is presented. The number results demonstrate that this method can improve the performance of tracking the phase offset between the local laser in the receiver and the received space optical signal through the atmosphere effectively. This new method can produce less error obviously, especially with a low SNR, whenever the channel gain is constant and time-varying. In addition, this VVPE-KF algorithm has better robustness to atmosphere intensity and SNR. In engineering, the coherent optical receiver with a larger aperture can depress the optical irradiance scintillation effectively and support the high SNR. However, large aperture produces high cost and severe wave front aberration, which can degrade the communication performance inevitably. Another method to improve the communication performance is using the coherent receiver with multiple small apertures, in which the SNR is low, and the optical irradiance fluctuates seriously. The VVPE-KF can estimate the phase offset precisely for this multiple-aperture receiver, which is a research hotspot. For coherent diversity receiving, the frequency offset and channel gain are important information, thus the next work is to design an appropriate algorithm to estimate the frequency offset between the local laser and the received signal and the fluctuated channel gain caused by the atmosphere turbulence.  
Conceptualization, H.L. and Y.H.; methodology, H.L. and Y.H.; software, H.L. and Q.W.; validation, H.L. and D.H.; formal analysis, D.H.; investigation, D.H.; resources, H.L. and Q.W.; data curation, Q.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and Z.P.; visualization, H.L.; supervision, Y.H.; project administration, Q.W.; funding acquisition, Y.H. and Z.P. This research was funded by National Natural Science Foundation of China, grant number: 61571096.  
The authors declare no conflict of interest.
# An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
## 5 Conclusions  
The LQR-ORT controller presented in this work yields a better transient response and higher decoupling compared to the classical PR-Droop control approach as can be seen in the experiment results. The model presented in this work integrates V-I and power sharing dynamics in a single state-space model. Using this new model allows performing modern analysis methods of stability and robustness. Stability and robustness analyses show that the LQR-ORT controller has robust performance and stability under uncertainties in components and multiplicative uncertainties. The robustness against component variations is important for heavy duty applications, where environmental conditions may affect component specification through time. It is also important to remark that, unlike the classic PR-Droop controller, the LQR-ORT controller does not require low-pass filters to perform power calculation. Thus, the power dynamics are not separated from voltage-current dynamics allowing to obtain smoother and faster transient responses.  
Conceptualization, J.F.P.-M., J.D.V.-P., F.A., and L.F.; Formal analysis, J.F.P.-M., L.F.; Investigation, J.D.V.-P.; Methodology, J.F.P.-M., J.D.V.-P., L.F. and F.A.; Project administration, F.A.; Resources, F.A.; Validation, J.D.V.-P.; Writing--original draft, J.F.P.-M. and J.D.V.-P.; Writing--review & editing, J.F.P.-M and J.D.V.-P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the United States Department of Energy, grant number DE-SC0020281.  
The authors declare no conflicts of interest.