esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 5 Discussion  
In the digital realm, 3D architectural surfaces are typically composed of multiple planar polygons, which contain crucial information regarding the geometric shape of the building. Hence, we can employ geometric methods to construct high-precision constraint outlines for missing regions, thereby rectifying incomplete architectural point cloud 3D models. However, while the overall shape of the building point cloud encoded by the encoder \(\varphi\) remains intact, the encoded shape in the missing regions often deviates from the original geometric positions. Consequently, we amalgamated these two characteristics by merging the building-outline-constrained shape latent code with the building's original point cloud shape latent code. This yields a shape latent code that more closely conforms to the architectural geometric shape, thus more effectively constraining the point cloud completion model to generate point clouds that correctly fill the missing regions.  
Figure 13: Visualization of ablation experiment point cloud completion results: The point clouds visualized in the figure include, in sequence, the input incomplete building point cloud, the building point cloud completed by the Baseline Model, the building point cloud completed by the model with only the fusion of contour-constrained shape latent code \(C\), the building point cloud completed by the model with only the fusion of contour-constrained shape latent code \(M\), and the completed point cloud obtained by our method.  
In this undertaking, the construction of precise constraint outlines plays a pivotal role in enhancing the accuracy of point cloud completion. Achieving high geometric accuracy in constraint outlines primarily focuses on two critical aspects: 1 Improving the geometric shape accuracy of the planar polygons extracted from the building surface point cloud; and 2 rationalizing the grouping of building surface polygons.  
Therefore, future work will emphasize the following two aspects: firstly, extracting planar polygons with high geometric accuracy, ensuring they accurately reflect the geometric shape of the building surface, and secondly, optimizing the grouping method of polygons to eliminate interference from irrelevant polygons and generate more rational constraint outlines within blocks that have missing regions.
## 2 Structure of the Proposed Antenna  
The structural diagram of the antenna is depicted in Figure 1. The overall physical size is 50 mm \(\times\) 50 mm \(\times\) 2.8 mm and the electrical size is 0.9\(\uplambda_{0}\times 0.9\uplambda_{0}\times 0.05\uplambda_{0}\). The antenna is composed of two dielectric substrates. The upper-layer dielectric substrate is Rogers4003C with a relative permittivity of 3.5 and a thickness of 2 mm. The MTS, parasitic patches, and mode-suppressing patches are printed on its upper surface, as shown in Figure 1a. The MTS consists of metal patches with dimensions of \(p\times p\). Among them, eight unit patches located at the corners have a triangle with a side length of b truncated from each, and the gap between each unit patch is \(g\). The area of the parasitic patches is half that of the metal patches, and the spacing between them and the nearest metal patch is \(g\). The mode-suppressing patches are arranged in a triangular pattern around the four corners of the dielectric substrate. Each patch has a side length of \(p_{1}\times p_{1}\), and the spacing between the patches is \(g\). The lower-layer dielectric substrate is FR4 with a relative permittivity of 4.4 and a thickness of 0.8 mm. Its upper surface is a metal ground plane etched with a ring slot. The inner radius of the ring slot is \(r_{1}\), and the outer radius is \(r_{2}\), as shown in Figure 1b. The lower surface is an L-shaped microstrip feed line with a quarter-wavelength impedance transformer. The length and width of the impedance transformer are \(l_{2}\times w_{2}\), and the width of the L-shaped microstrip feed line is \(w_{1}\), as shown in Figure 1c. The two substrates of the antenna are closely attached to each other, and its side view is shown in Figure 1d. To facilitate the determination of the positional relationship between the feed line and the circular slot, its overlapping view is shown in Figure 1e.  
After completing the structural design of the antenna, ANSYS HFSS 2022R2 software is employed to conduct optimization scans on the dimensional parameters with the aim of achieving the best ARBW and gain performance while maintaining a low-profile characteristic. Through a series of optimization operations, the final optimized parameters have been compiled and are presented in Table 1.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 6 Conclusions and Future Work  
This paper proposes an improved in-motion coarse alignment method based on position loci and an Optimal-REQUEST algorithm. The proposed algorithm only uses position information to construct the observation vector, which greatly expands the application scope of the coarse alignment algorithm because all GPS receivers can output the position information. Moreover, the effects of random noise contained in the vector observation are suppressed by the Optimal-REQUEST algorithm which can choose the filter gain adaptively. The results of simulation tests showed that the proposed OP-PL algorithm converged faster and had better accuracy than the comparison algorithms. The pitch angle, roll angle and yaw angle calculated by the OP-PL algorithm were reduced by approximately 51.95%, 53.80% and 63.03%, respectively. However, our work still has some limitations and areas that can be improved. In future work, we plan to verify our proposed algorithm in real vehicle experiments and to extend the application scope of the algorithm to underwater environments.  
Conceptualization, J.W.; Supervision, H.H.; Writing--original draft, J.W.; Writing--review & editing, H.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Jiangsu Province (BK20221500), the National Natural Science Foundation of China (61703098), and the Fujian Provincial Key Laboratory of Coast and Island Management Technology Study (F)CIMS2019-03). This work is also supported in part by London Tech Bridge, the APEX Undersea Challenge, the European Regional Development Fund--Industrial Intensive Innovation Programme, the Research Fund from the Science and Technology on Underwater Vehicle Technology Laboratory (2021 JCJQ-SYSJ-LB06905), and the Water Science and Technology Project of Jiangsu Province under grant (2021072, 2021063).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Conclusions  
The 5G communication network is currently one of the best wireless transmission platforms, that would be a part of future cellular networks and can be incorporated in other communication systems. The employment of 5G in a typical smart grid communication system will offer high-speed data transfer with low latency and ensure enough bandwidth for various users. However, scheduling data packets and resource distribution and allocation processes are some of the major issues that must be overcome to ensure the efficient assigning of available Resource Blocks (RB) to smart grid users. Although several scheduling techniques have been employed to carry out these tasks, most of them fall short of maintaining an efficient scheduling approach that ensures that a series of tasks need to be executed before their deadlines. Amongst the most popular scheduling techniques is the EDF (Earliest Deadline First) technique, which is employed to meet the timing constraints of a given set of tasks. It unfortunately does not meet time constraints when the system is unaware of the future release patterns of the tasks. In order to improve the efficacy of the EDF scheduling technique, we improved on this technique by fusing a \(\
icefrac{{CH}}{{\rho}}\) policy into an existing EDF scheduling technique to ensure that the deadlines are known upon arrival and also boost its performance. A DSC scheduling approach is also evaluated in this paper, while a novel Optimal Usage and Dropping Scheduling (OUD) technique is proposed. The proposed technique utilizes a smart schedule and drop mechanism in the system and ensures that the needed task remains in the queue while maintaining a predefined order.  
The OUD scheduling technique is compared with the DSC technique as well as the improved EDF scheduling technique over several performance indexes so as to evaluate their performance. Simulation results show that the OUD scheduling technique gives a better performance when compared to the improved EDF technique and the DSC scheduling approach. However, the DSC scheduling technique displays poorer schedulability performance when compared to the improved EDF scheduling technique. It is expected that the OUD scheduling approach can be employed in actual smart grid communication systems that require real-time guarantees. When employed in these systems, it will improve its schedulable performance, and the system can accommodate more sets of tasks in real-time.  
In the future, artificial intelligence tools like the Genetic Algorithm may be used to modify the proposed OUD algorithm so as to make the proposed algorithm more general, flexible, and smarter.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, and writing--original draft preparation--E.F.O., writing--review and editing, visualization, supervision, project administration, funding acquisition--K.A.-A.-E. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors are not allowed to publish the data.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Data Analysis  
#### 3.1.1 Data  
For the purpose of this article, we used hourly energy consumption data for the years 2002-2018, which is freely available on Kaggle and belongs to PJM Interconnection LLC (PJM) (Norristown, PA, USA). The datasets are hourly energy consumption data from PJM, a regional transmission organization in the United States that supplies electricity to Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia. We did not use the entire dataset but a part of it in our study. The data we use is the energy consumption rate of PJM East Region (PJME). PJM Interconnection LLC (PJME) provides a time series dataset consisting of 145,366 rows and 2 features. The consumption data is in megawatts (MW) [47].
## 5 Evaluations
\begin{array}{c}\text{R}^{2}\left(\%\right)\text{,}\end{array} \) & 0.812 & 0.790 & 0.787 & 0.814 & 0.828 & 0.792 \\  & \( \begin{array}{c}\text{RMSE (kWh)}\text{,}\end{array} \) & 22.93 & 24.10 & 24.27 & 22.77 & 21.89 & 23.94 \\  & \( \begin{array}{c}\text{CVRMSE (\%)}\text{,}\end{array} \) & 0.252 & 0.265 & 0.267 & 0.250 & 0.241 & 0.263 \\ \hline \multirow{3}{*}{3} & \( \begin{array}{c}\text{R}^{2}\left(\%\right)\text{,}\end{array} \) & 0.759 & 0.786 & 0.804 & 0.823 & 0.829 & 0.817 \\  & \( \begin{array}{c}\text{RMSE (kWh)}\text{,}\end{array} \) & 25.32 & 24.32 & 23.33 & 22.25 & 21.86 & 22.61 \\  & \( \begin{array}{c}\text{CVRMSE (\%)}\text{,}\end{array} \) & 0.278 & 0.268 & 0.257 & 0.245 & 0.240 & 0.249 \\ \hline \multirow{3}{*}{4} & \( \begin{array}{c}\text{R}^{2}\left(\%\right)\text{,}\end{array} \) & 0.829 & 0.787 & 0.821 & 0.857 & 0.853 & 0.847 \\  & \( \begin{array}{c}\text{RMSE (kWh)}\text{,}\end{array} \) & 21.83 & 24.22 & 22.30 & 19.96 & 20.23 In addition, to verify the robustness and applicability of the configurations of the previously trained models, we compared the performance of the daily forecast provided by the model with respect to unseen data from the testing-set. In Figure 8, some samples are shown with the forecast of the energy consumption of the building's heat pump obtained for one day with these configurations. For these forecasted samples, we have also evaluated and calculated the relative error of the average energy consumption and the relative error of the maximum energy consumption of the building's heat pump. Tables 8-11 show each configuration of the model obtained. Figure 8: Comparison of the observed values with those predicted by the configurations obtained (C1-T50, C2-T60, C3-T50, C4-T50) for the daily energy consumption of the heat pump. Figure 7: Evaluation of training and validation for the best configurations obtained. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Forecast Day**} & **Avg. EC \({}^{*}\)** & **Avg. EC \({}^{*}\)** & **Relative Error** & \multirow{2}{*}{**Max. EC \({}^{*}\)**} & **Relative Error** \\  & **by Day (Y \({}^{*}\))** & **by Day (\(\hat{Y}\) \({}^{*}\))** & **(Avg. EC \({}^{*}\)** & **by Day (Y \({}^{*}\))** & **by Day (\(\hat{Y}\) \({}^{*}\))** & **(Max. EC \({}^{*}\)** \\  & & & & & & \\ \hline
1 & 99.24 & 85.18 & 14.16\% & 141.60 & 132.07 & 7.58\% \\
2 & 82.15 & 85.36 & 3.91\% & 142.07 & 129.79 & 8.65\% \\
3 & 92.41 & 84.75 & 8.28\% & 142.08 & 128.39 & 9.64\% \\
4 & 89.93 & 84.80 & 5.70\% & 142.40 & 127.69 & 10.33\% \\
5 & 90.87 & 84.56 & 6.94\% & 141.19 & 127.79 & 9.49\% \\ Avg. & 90.92 & 84.93 & 7.80\% & 141.87 & 129.15 & 9.14\% \\ \hline \hline \end{tabular} Avg. EC \({}^{*}\): Average energy consumption (\(\&\)W); Max. EC \({}^{*}\): Maximum energy consumption (\(\&\)W); Y \({}^{*}\): Expected value; \(\hat{Y}\) \({}^{*}\): Forecasted value.  
\end{table} 
Table 10: Calculation of the relative error of the daily energy consumption (average and maximum) with respect to the testing-set (model: C3-T50).
# Effective Attention-Based Mechanism for Masked Face Recognition
## 4 Experiments and Results
### Evaluation Metrics  
To assess the proposed method, four evaluation parameters, accuracy, precision, recall and F1 score are adopted.  
**Accuracy.** The accuracy is an intuitive performance measure, and it is defined to describe the accuracy of the algorithm for recognition and classification problems. It represents the ratio of the correctly predicted sample to the total of sample, which can be computed as shown in Equation (7).  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}), \tag{7}\]  
where TP, TN, FP, and FN are true positive, true negative, false positive, and false negative, respectively.  
**Precision.** The precision is a metric that determines the number of accurate positive predictions. Therefore, precision computes the accuracy for the minority class. It is computed as the ratio of correctly predicted positive samples divided by the predicted number of positive samples. Precision can be computed as defined in Equation (8).  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{8}\]  
**Recall.** The recall is a metric that measures the number of correct positive predictions made from all positive predictions that could have been made. This is as opposed to precision, which only considers the correct positive predictions out of all positive predictions. Recall can be computed as defined in Equation (9).  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{9}\]  
**F1 score.** The F1 score allows for precisions and recalls to be combined into a single measure that captures both properties. It can express high precision with poor recall or, alternately, terrible precision with perfect recall. The F1 score can be computed as defined in Equation (10).  
\[\text{F1}=2\times(\text{Precision}\times\text{Recall})/(\text{Precision}+ \text{Recall}) \tag{10}\]  
Figure 6: Sample images of training and validation dataset. CASIA-WebFace is a training dataset whereas the rest are validation datasets. Each dataset has two columns of corresponding images.
## 3 Fault Localization Methods
### Methodology  
In order to exemplify the utility of the edge-cloud framework proposed for the operation in a real LTE-based environment, we selected the method proposed in [11] due to its robustness, low number of required PMUs and previous experience with its implementation in a laboratory environment. However, it is worth noting that any of the methods presented in Section 3.2 could be used with the proposed framework. At this point, it seems reasonable to provide a brief summary of the selected method to acquaint the reader with its main philosophy.  
The goal of the method is to estimate the location of events in DNs with as few as two PMUs, utilizing the compensation theorem from circuit theory. It is worth noting that only faults, causing the de-energization of the feeder, are considered as events in this paper, whereas the method itself is sensitive enough to localize events much less severe than faults (as proven in [11]). Either way, the measurements of the networks' pre-event and post-event steady-state synchro-phasors are used alongside known load profiles, network topology and line parameters to calculate the voltages of every bus as seen from each PMU. Those two sets of voltages are then compared and the bus where the difference is minimal is identified as a source of the event.  
Optimal observability of the feeder using just two PMUs is achieved when one of them is installed at the beginning of the main feeder (primary substation) and the other is placed at the end of the feeder (secondary substation). However, it is worth pointing out that in configuration with just two installed PMUs only the faults on the main feeder can be correctly identified. When a fault occurs on a branch that is not part of the main feeder, the bus, from which the lateral stems off, will be identified as faulted instead. In DNs, such localization is usually sufficient since laterals are generally short and the exact localization does not help reduce the fault mitigation time significantly. Nevertheless, if more precise localization was needed, one could achieve it with the introduction of PMUs at the laterals' ends. The extension of the method for the case with an arbitrary number of PMUs is presented in [36].  
To summarize, the main features relevant for the implementation of the FLA on the proposed edge-cloud framework are:  
* model-driven method, meaning that the topology and parameters of lines are assumed to be known;
* synchro-phasor measurements of voltages and currents acquired from as few as two PMUs are sufficient for localizing the fault on the main feeder;
* the rest of the information collected from the feeder, such as load consumption, can be in the form of pseudo-measurements from either power quality meters (PQMs) or smart meters (SMs). The term \"pseudo-measurement\" is used for these devices to emphasize the lower reporting rate compared to the PMU and to highlight that the measurements of PQMs and SMs are not as precisely time-stamped as in the case of a PMU.
## 4 Discussion
### Effects of eHMI Information Contents on MW and SA  
Under conditions of full eHMI penetration, no significant differences in MW were observed across the tested eHMI information content configurations. This finding may be attributed to the complexity of multi-vehicle traffic scenarios. Prior research indicates that variations in eHMI information content influence MW in simpler scenarios involving one or two AVs [33; 56]. However, in more complex multi-vehicle environments, different eHMI designs did not yield significant MW differences [32; 57; 58].  
In contrast, significant MW differences emerged under partial eHMI penetration conditions. This may be due to the increased cognitive demands of partial penetration scenarios, where pedestrians must adapt their crossing decisions to account for both AVs equipped with eHMIs and TVs without eHMIs [11]. In such contexts, the impact of eHMI information content on MW is likely amplified. Notably, the two-level medium-high configuration significantly increased MW compared to the three-level low-medium-high configuration, potentially due to the perceived urgency of medium- and high-risk signals [12].  
Additionally, participant feedback highlighted that the three-level eHMI configuration may elevate MW. Specifically, participants (e.g., P16, P19) reported that interpreting three distinct risk levels increased cognitive complexity, while rapid transitions between levels made it challenging for pedestrians to react (e.g., P14). This observation aligns with ranking results under full eHMI penetration, where 33% of participants ranked the three-level configuration as the most preferred, yet another 33% ranked it as the least preferred (6 th). These findings are consistent with theory of [17], which posits that high information volume and rapid data changes can overwhelm individuals due to limited cognitive processing capacity.  
In terms of SA, no significant differences were observed across the six information content conditions under the full eHMI penetration condition. Although vehicle kinematics, eHMI, and environmental factors are critical for SA [59], prior studies emphasize vehicle kinematics--particularly speed and distance--as the primary cues for pedestrian crossing decisions, e.g., [12; 19; 60; 61; 62].  
However, significant SA differences were detected under partial eHMI penetration, likely due to the heightened cognitive demands of this scenario amplifying the influence of eHMI content. As pairwise comparisons revealed no significant differences, further investigation into eHMI's impact on SA is warranted.
## 4 Architecture
The initial phase is called _Smart Object Authentication_ (in blue color), and it involves the required messages until the smart object and the Authentication Agent share the Master Session Key (MSK), which is sent by the AAA server after finishing the EAP-PSK method. Then, during the _MUD Obtaining_ phase (red color), the Authentication Agent gets the MUD file (and the associated signature file) from the MUD File Server through the IoT Controller. This MUD file is translated into an MSPL policy in the _MUD Translation_ phase (green color) by the Policy Interpreter, which receives the required information through the IoT Controller and SDN Controller, as part of the MUD Manager entity. Then, such intermediate policies are translated to specific security configurations, which are sent by the SDN Controller to the SDN Switch in the _MUD Enforcement_ phase (yellow color). Based on these phases, the next section provides a detailed description of the message exchange required in our approach.
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
###### Abstract
Moreover, while several review articles have been published in recent years, they have predominantly focused on technological advancements in the imaging of plant roots and biological processes [11; 12], as well as the evolution of microstructures [13]. There is a relative scarcity of comprehensive case study reviews that delve into the application of computed tomography (CT) in the integrated fields of soil and plant research [14; 15]. Specifically, there is a dearth of review articles that concentrate on the application of CT in the study of the microstructure of loess within geotechnical bodies. Against this backdrop, the objective of this research is to offer a contemporary assessment of the utilization of computed tomography (CT) within the sphere of loess microstructure investigation. This paper begins with an exposition of the essential concepts and historical context of CT, complemented by an overview of the fundamental tenets of image processing techniques. Subsequently, it assesses the noteworthy progress achieved in the realm of CT scanning over the past years, and it synthesizes an array of specialized methodologies applied in the context of CT scanning and image processing for pertinent applications. Furthermore, bibliometric analysis is applied to delineate the research trends and hotspots for CT applications. This review also systematically revisits the application instances of CT technology in the study of loess microstructures in recent years.