esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study developed a few-shot learning framework to address the challenges associated with depth completion, particularly in scenarios where dataset availability is limited. Our method leverages self-training with noise injection to enhance the depth completion algorithm. Incorporating noise significantly enhanced the model's diversity and generalization capabilities, enabling robust performance across diverse input conditions. Furthermore, including a pixel-wise KD module allowed the model to preserve and reinforce previously learned features, ensuring continuity and stability in its performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE** & **REL** & \(\delta_{\textbf{1.25}}\) & \(\delta_{\textbf{1.25}^{2}}\) & \(\delta_{\textbf{1.25}^{3}}\) \\ \hline RGB Gaussian blur only & 0.240 & 0.048 & 97.1 & 99.5 & 99.8 \\ Depth perturbation only & 0.184 & 0.032 & 99.2 & 99.9 & 100.0 \\ Dual noise & 0.090 & 0.011 & 99.7 & 99.9 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of independent effects of noise types.  
Experiments performed on the NYUv2 dataset underscore the substantial benefits of the proposed framework. The results demonstrate that our approach significantly outperforms the original model, validating the effectiveness of integrating self-training with noise and pixel-wise KD. By addressing the limitations posed by small datasets, this approach enhances the robustness and scalability and ensures accurate depth completion. Therefore, our approach exhibits its practical applicability and establishes a strong foundation for future advancements in depth completion research.  
Formal analysis, S.Z. ([PERSON]); investigation, S.Z. ([PERSON]); methodology, S.Z. ([PERSON]); writing--original draft preparation, S.Z. ([PERSON]); supervision, J.Z. and H.D.; writing--review and editing, J.Z. and H.D. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Key R&D Program of China (Grants 2023 YFC3806000 and 2023 YFC3806002), the National Natural Science Foundation of China (Grants 61936014 and U23A20382), the Shanghai Municipal Science and Technology Major Project (Grant 2021 SHZDZX0100), the Shanghai Science and Technology Innovation Action Plan Project (Grant 22511105300), and the Fundamental Research Funds for the Central Universities.  
Not applicable.  
The data are presented within the article.  
We thank LetPub (www.letpub.com.cn, accessed on 24 March 2025) for its linguistic assistance during the preparation of this manuscript.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Analysis of Harvester Positions and Canopy Cover  
To estimate the percentage of the canopy above the harvester, a buffer zone of 10 m for each of the positions was calculated in Arcgis Pro. To evaluate the extent to which the canopy cover affected the positional accuracy of the Trimble R12, the CHM was recalculated, excluding 4 m from the lower part of the canopy. The canopy cover was calculated as a percentage of the area covered by pixels above 4 m in the CHM within a 10 m radius from the harvester position point.  
Viewshed analysis [26; 27] was used to evaluate the impact of canopy cover on the GNSS's positioning accuracy by determining the areas where the GNSS signals were obstructed by the canopy. The analysis was carried out separately for each position in Arcgis Pro, using the harvester positions and the DSM as the input data. Analyzing the viewshed from the GNSS's receiver positions made it possible to identify the areas where the line-of-sight to the satellite was blocked by the canopy. These areas were then used to evaluate the impact of the canopy on the GNSS's accuracy by comparing the GNSS's signal quality in the obstructed areas to the signal quality in areas without canopy obstruction. This allowed the extent to which the canopy cover reduced the accuracy of the GNSS measurements to be determined. For this purpose, a 10 m radius around the observation point was used to calculate the percentage of the sky that was visible from the receiver vantage point. The results were used to compare against the positioning accuracy and to identify the areas where the GNSS signals had been blocked or degraded by the forest canopy.  
Figure 3: The digital surface model reconstructed using structure-from-motion process and several ground control points.
## 3 Results and Discussion
### The Assessment of Health Risk-Sediments
#### 3.3.2 Hazard Quotient (HQ) and Hazard Index (HI)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Sites** & **Toxic Metals** & **HI** \\ \cline{2-3}  & **[mg/kg]** & **Children** & **Adults** \\ \hline \multirow{5}{*}{S1} & Cr & \(1.14\times 10^{-1}\) & \(2.74\times 10^{-1}\) \\ \cline{2-3}  & Pb & \(1.84\times 10^{-2}\) & \(4.13\times 10^{-2}\) \\ \cline{2-3}  & Zn & \(3.56\times 10^{-4}\) & \(7.77\times 10^{-4}\) \\ \cline{2-3}  & Cu & \(1.59\times 10^{-4}\) & \(3.66\times 10^{-4}\) \\ \cline{2-3}  & Fe & \(3.75\times 10^{-1}\) & \(8.35\times 10^{-1}\) \\ \hline \multirow{5}{*}{S2} & Cr & \(3.63\times 10^{-1}\) & \(8.70\times 10^{-1}\) \\ \cline{2-3}  & Pb & \(4.70\times 10^{-2}\) & \(1.06\times 10^{-1}\) \\ \cline{2-3}  & Zn & \(4.38\times 10^{-4}\) & \(9.56\times 10^{-4}\) \\ \cline{2-3}  & Cu & \(1.81\times 10^{-4}\) & \(4.18\times 10^{-4}\) \\ \cline{2-3}  & Fe & \(5.05\times 10^{-1}\) & \(1.13\times 10^{0}\) \\ \hline \multirow{5}{*}{S3} & Cr & \(1.60\times 10^{-2}\) & \(3.85\times 10^{-2}\) \\ \cline{2-3}  & Pb & \(1.39\times 10^{-2}\) & \(3.12\times 10^{-2}\) \\ \cline{2-3}  & Zn & \(2.30\times 10^{-4}\) & \(5.02\times 10^{-4}\) \\ \cline{2-3}  & Cu & \(1.50\times 10^{-4}\) & \(3.45\times 10^{-4}\) \\ \cline{2-3}  & Fe & \(2.41\times 10^{-1}\) & \(5.37\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: HI for the sediments at all the studied sites: S1 “Blekitina Laguna”, S2 “Huta”, and S3 “Mala Szwecja” [mg/kg].
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 4 Test Procedure
### Experiment 3--Mobile Repeatability for Robot Applications  
The aim of the final experiment was to assess the performance of the FAGPS approach for a moving target, as would be expected in a field robotics application. A base-ball diamond area was chosen as it has a well defined shape which varies in latitude and longitude. The start position (marked in Figure 5) was where the compensation on a geolocated point was performed. The experiment involved traversing around the edge of the grass in a clockwise direction as a speed of approximately 1.2 m/s. A total of 26 laps of the baseball diamond were performed over a period of 30 min.
###### Abstract  
Widespread usage of unmanned aerial vehicles (UAVs) in new and emerging applications needs dynamic and adaptive networking. The development of routing protocols for UAV ad hoc networks faces numerous issues because of the unique characteristics of UAVs, such as rapid mobility, frequent changes in network topology, and limited energy consumption. The Optimized Link State Routing (OLSR) protocol seems to be a promising solution as it offers improved delay performance. It is expected that OLSR will satisfy the strict demands of real-time UAV applications such as \"search and rescue\" (SAR) missions as it involves the most recent update of routing information. The classical OLSR routing protocol and its enhanced versions, D-OLSR, ML-OLSR, and P-OLSR, use different techniques to make an appropriate decision for routing packets. These routing techniques consider the quality of a wireless link, type of antenna, load, and mobility-aware mechanism to select the best UAV to send the message to the destination. This study evaluates and examines the performance of the original and modified OLSR routing protocols in UAV ad hoc networks for three SAR scenarios: (1) increasing mobility, (2) increasing scalability, and (3) increasing the allowed space of UAVs. It analyzes and validates the performance of the four OLSR-based routing protocols. It determines the best OSLR routing protocol by taking into account the packet delivery ratio, latency, energy consumption, and throughput. The four routing protocols and the SAR scenarios were simulated using NS-3.32. Based on the simulation results, ML-OLSR outperforms OLSR, D-OLSR, and P-OLSR in the considered measures.  
U  
2023
# Non-Magnetic Circulator Based on a Time-Varying Phase Modulator
###### Abstract
Its size small allows it to be integrated into the integrated circuit (IC). Inspired by previous studies, the concept of the time-varying phase modulator (TVPM) is proposed in the paper. The TVPM can be used to modulate the phase of a carrier wave at different times for breaking time-reversal symmetry. A gyrator is composed of two different time-varying phase modulators and appropriate buffering units. The proposed topological structure is used to construct a gyrator. The proposed gyrator and several reciprocal devices [18] can be combined to construct the circulator. Compared with the  
Figure 1: Different types of gyrators or isolators and summary topology. (**a**) N-path filter. (**b**) Optical isolator based on tandem modulators. (**c**) Conductivity modulation. (**d**) The general structure of nonreciprocal devices. gyrator circuit composed of an N-path filter in Reference [15], a larger bandwidth can be achieved with the proposed gyrator. Compared with the gyrator in the method of conductivity modulation in Reference [16], the proposed gyrator in the paper reduces one degree of freedom, which makes the volume smaller and the application involves fewer inductors.
## 2 Collective Detection Approach
### Working Principle of Collective Detection
Then, the pseudorange can be converted to an equivalent estimated code phase \(\hat{\tau}_{k}\), at a hypothetical location \(R_{i},\theta_{j}\), a clock bias \(\Delta B_{n}\), and the maximum code phase error, as [7]:  
\[\hat{\tau}_{k}=\frac{\Delta\rho_{k}\big{(}R_{i},\theta_{j},\Delta B_{n},\beta \big{)}}{c\cdot T_{code}}\cdot N_{code} \tag{6}\]  
Then, the correlation output, which is the individual detection metric, is expressed as [7]:  
\[D_{ind}\big{(}R_{i},\theta_{j},\Delta B_{n},\beta\big{)}=|S(\hat{\tau}_{k})|^{2} \tag{7}\]  
Finally, the CD metric is obtained by the sum of individual detection metrics for all satellites in view as [7]:  
\[D_{\text{CD}}\big{(}R_{i},\theta_{j},\Delta B_{n},\beta\big{)}=\sum_{k}D_{ind }(\hat{\tau}_{k}) \tag{8}\]  
Once the CD metric is performed for all candidate points, many approaches can be used to decide which set of values corresponds to the best estimation of the true MS position coordinates and clock bias. If the CD metric exceeds a pre-defined threshold, the GNSS signal could be detected. Thus, the code phase and Doppler frequency corresponding to the detected signal could be obtained. The benefit of the CD application is shown in Equations (7) and (8). In fact, weak satellite signals may not be detectable in conventional GNSS receivers with only the individual correlator output value given by Equation (7). Nevertheless, the accumulation of all individual correlation values for each visible satellite can increase the receiver sensitivity using Equation (8) in which the summation operator represents the term \"collective\" in collective detection.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### User  
On the basis of users, hybrid beamforming provides services to both single and multi-user systems.
## 1 Introduction
These technologies are capable of providing better coverage with high throughput, improved spectrum efficiency, greater bandwidth and ultra-low latency. The 6G IoT system is sustainable for high-accuracy localization and sensing, which are necessary for most of the envisioned highly computationally intensive applications. Figure 1: Cisco Annual Report from 2016 to 2022 [2]: (**a**) Cisco Visual Networking Index Global Mobile Data Traffic from 2016 to 2022; (**b**) Global Growth of Smart Mobile Devices and Connections Excluding Low-Power Wide-Area (LPWA).
## 5 Experimental Results  
The PMSM motion control experiment and machine tool thread milling experiment were used to validate the PCB-ILCC approach developed in this study. An industrial computer with a steel chassis, backplane, and CPU card manufactured by Advantech Co., Ltd. was used in this study. The CPU card, which utilized a 3.4-GHz INTEL CORE i7-2600 CPU and 16-Gigabyte memory, is the main component of the industrial computer. The computer used the Linux operating system; moreover, programming language C was used to realize the control system that included a human-machine operating interface, signal measurement, feedback control, and recording and displaying of experimental data. Furthermore, the computer utilized the EtherCAT fieldbus industrial network system for sending control commands to and receiving feedback signals from the PMSM drivers at a sampling period of 0.5 ms so that the feed drive axes perform helical motions. In the experiments, the thread milling path of M40 \(\times\) 3.5 ISO metric external threads was adopted as the helical path. Here, Equation (29) was used to calculate the helical radius \(R_{H}\):  
\[R_{H}=\frac{1}{2}\Bigg{(}(D_{M}+D_{B})-\Bigg{(}\frac{3\sqrt{3}}{8}\cdot 2\cdot P _{H}\Bigg{)}\Bigg{)}, \tag{29}\]  
where \(D_{M}\), \(P_{H}\), and \(D_{B}\) denote the major diameter, helical pitch of the external thread, and the blade diameter of the thread milling tool, respectively.  
The following control structures were adopted in the experiments for validating the feasibility and evaluating the motion performance of the PCB-ILCC approach developed in this study.  
* PMSM-PPIC: the proportional-proportional-integral control extensively used in commercialized PMSM drivers and CNC controllers.
* PMSM-DC: the PMSM driving control developed in this study (Section 4).
* PCB-ILCC: the PCB-ILCC (Section 2) with the integration of the CEV estimation and ILCC design (Section 3) and the PMSM driving control (Section 4).  
The following performance indices were also adopted:  
* AVG: average value.
* SD: standard deviation.
* RMS: root-mean-square value.
* MAX: maximum value.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF), one of the three main releasing pathways of vegetation-absorbed photosynthetic active radiation, has been proven as an effective monitoring implementation of leaf photosynthesis, canopy growth, and ecological diversity. There exist three categories of SIF retrieval methods, and the principal component analysis (PCA) retrieval method is obtrusively eye-catching due to its brief, data-driven characteristics. However, we still lack a lucid understanding of PCA's parameter settings. In this study, we examined if principal component numbers and retrieval band regions could have effects on the accuracy of SIF inversion under two controlled experiments. The results revealed that the near-infrared region could remarkably boost SIF's retrieval accuracy, whereas red and near-infrared bands caused anomalous values, which subverted a traditional view that more retrieval regions might provide more photosynthetic information. Furthermore, the results demonstrated that three principal components would benefit more in PCA-based SIF retrieval. These arguments further help elucidate the more in-depth influence of the parameters on the PCA retrieval method, which unveil the potential effects of different parameters and give a parameter-setting foundation for the PCA retrieval method, in addition to assisting retrieval achievements.  
solar-induced chlorophyll fluorescence; retrieval methods; principal component; band regions +
Footnote †: journal: Applied Sciences  
12 April 2021  
Accepted: 20 May 2021  
Subject: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems
## 2 Methodology
### Calculation Tools
#### 2.1.1 Power Production  
Aerodynamic analysis is based on ordinary blade element/momentum (BEM) theory. A detailed description of the process with an extensive explanation of its fundamental equations can be found in Reference [19]. AEP is computed using a Rayleigh distribution in which mean speed varies with the hub height. An exponential law is used to calculate the average wind speed at different hub heights as follows:  
\[V(h)=V(h_{0})\left(\frac{h}{h_{0}}\right)^{\alpha} \tag{1}\]  
where \(h_{0}\) is the reference height, \(V(h_{0})\) is the average wind speed at \(h_{0}\) and \(\alpha\) is the wind shear exponent.
# Identifying Energy-Use Behavior and Energy-Use Profiles of Hotel Guests
## 5 Integrated Energy-Use Framework Implementation
### Identifying Energy-Use Behavior of Hotel Guests  
The most influential questions (i.e., questions with high correlation coefficients) were selected from the energy behavior reliability analysis to be used for K-means clustering algorithm to identify energy-use behavior of hotel guests. To achieve this objective, K-means clustering analysis was conducted using RapidMiner software to organize hotel guests in groups with similar clusters to identify energy-use behavior of hotel guests for each of the three MOA levels [83,84]. In this study, the value of K was tested four times with K = 3, K = 4, K = 5, and K = 6; to find the best K value (i.e., number of clusters) for this study. The result showed that the K value of 4 was the best fit for the integrated energy-use framework implementation due to its lowest error rate (\(-150\)). Accordingly, the hotel guests (i.e., energy-use survey participants in this study) were divided into four groups of energy-use behavior for each of the three MOA levels of hotel guests named: Low, Medium-Low, Medium-High, and High energy-use behavior. Low indicated hotel guests' lowest energy-use behavior and High indicated hotel guests' highest energy-use behavior (see Figures 4-6).  
Figure 4: Energy-Use Behavior of Hotel Guests’ Motivation Level.  
The energy-use behavior results of hotel guests' Motivation level illustrated that about 26% of the hotel guests (97 out of 370) had Low energy-use behavior Motivation level, about 25% of the hotel guests (90 out of 370) had Medium-Low energy-use behavior Motivation level, about 28% of the hotel guests (105 out of 370) had Medium-High energy-use behavior Motivation level, and about 21% of the hotel guests (78 out of 370) had High energy-use behavior Motivation level (see Figure 4).  
The energy-use behavior results of hotel guests' Opportunity level illustrated that about 26% of the hotel guests (96 out of 370) had Low energy-use behavior Opportunity level, about 25% of the hotel guests (93 out of 370) had Medium-Low energy-use behavior Opportunity level, about 30% of the hotel guests (111 out of 370) had Medium-High energy-use behavior Opportunity level, and about 19% of the hotel guests (70 out of 370) had High energy-use behavior Opportunity level (see Figure 5).  
The energy-use behavior results of hotel guests' Ability level illustrated that about 19% of the hotel guests (71 out of 370) had Low energy-use behavior Ability level, about 34% of the hotel guests (127 out of 370) had Medium-Low energy-use behavior Ability level, about 20% of the hotel guests (73 out of 370) had Medium-High energy-use behavior Ability level (see Figure 6). Overall, at least 50% of the hotel guests' have Low or Medium-Low  
Figure 5: Energy-Use Behavior of Hotel Guests’ Opportunity Level.  
Figure 6: Energy-Use Behavior of Hotel Guests’ Ability Level.  
Levels of Motivation (187 out of 370), Opportunity (189 out of 370), and Ability (198 out of 370) (see Figures 4-6).
###### Abstract  
(1) Background: Accurate preoperative diagnosis of ovarian masses is crucial for optimal treatment and postoperative outcomes. Transvaginal ultrasound is the gold standard, but its accuracy depends on operator skill and technology. In the absence of expert imaging, pattern-based approaches have been proposed. The integration of artificial intelligence, specifically deep learning (DL), shows promise in improving diagnostic precision for adnexal masses. Our meta-analysis aims to evaluate DL's performance compared to expert evaluation in diagnosing adnexal masses using ultrasound images. (2) Methods: Studies published between 2000 and 2023 were searched in PubMed, Scopus, Cochrane and Web of Science. The study quality was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2). Pooled sensitivity and specificity for both methods were estimated and compared. (3) Results: From 1659 citations, we selected four studies to include in this meta-analysis. The mean prevalence of ovarian cancer was 30.6%. The quality of the studies was good with low risk of bias for index and reference tests, but with high risk of bias for patient selection domain. Pooled sensitivity and specificity were 86.0% and 90.0% for DL and 86.0% and 89.0% for expert accuracy (\(p\) = 0.9883). (4) Conclusion: We found no significant differences between DL systems and expert evaluations in detecting and differentially diagnosing adnexal masses using ultrasound images.  
adnexal mass; deep learning; transvaginal ultrasound; ovarian cancer; artificial intelligence