esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 5 Conclusions  
This work focuses on the EDC placement problem, providing a novel solution based on machine learning (specifically, unsupervised clustering) and the Apache Spark framework, emphasizing its feasibility and effectiveness in distributed computing environments. Throughout the study, it has been demonstrated that integrating the K-means algorithm for the EDC placement problem into Apache Spark is feasible and highly beneficial for scenarios requiring efficient workload distribution in large-scale data processing systems.  
The research highlights that combining Apache Spark's distributed processing capabilities with a specific edge node placement algorithm offers a robust solution to scalability and efficiency challenges in edge computing environments. This integration significantly enhances performance in managing and processing large numbers of data, which is increasingly critical in smart city environments.  
Moreover, the proposed approach makes a unique contribution to the existing literature by offering a novel method for optimizing the deployment of edge nodes. Its implementation within Spark not only enhances the algorithm's adaptability and flexibility with various distributed system architectures but also maximizes the utilization of computational resources in scalable platforms.  
Conceptualization, J.M.H. and J.C.-C.; methodology, M.E.P.; software, C.C.; validation, J.C.-C., C.C. and R.P.-V.; formal analysis, M.E.P.; investigation, C.C.; resources, R.P.-V.; data curation, J.M.H.; writing--original draft preparation, J.C.-C. and C.C.; writing--review and editing, M.E.P.; visualization, J.M.H.; supervision, J.M.H.; project administration, J.C.-C.; funding acquisition, J.M.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Consejeria de Economia, Ciencia y Agenda Digital of the Junta de Extremadura and the European Regional Development Fund (ERDF) of the European Union under Grant GR21040, in part by the European Regional Development Fund (ERDF) of the European Union Interreg V-A Espana-Portugal (POCTEP) 2021-2027 program, under Grant 0206_RAT_EOS_PC_6_E, and in part by the Spanish Ministry of Science and Innovation under the project \"TED2021-1316998-I00/ MCIN/AEI/10.13039/501100011033/\" and European Union NextGenerationEU/PRTR.  
The original data presented in the study are openly available in [[http://sguangwang.com/TelecomDataset.html](http://sguangwang.com/TelecomDataset.html)]([http://sguangwang.com/TelecomDataset.html](http://sguangwang.com/TelecomDataset.html)) (accessed on 18 September 2024).  
The authors declare no conflicts of interest.
## 1 Introduction  
Vertebral augmentation procedures are efficient, offering the benefit of being minimally invasive and performed under local anesthesia. Introduced by [PERSON] et al. in 1987, verteboplasty (VP) is the first percutaneous vertebral augmentation system (PVAS) [1]. It involves injecting low viscosity polymethyl methacrylate (PMMA) bone cement at high pressure into the collapsed verteb, which has been shown to restore vertebral strength and help prevent further kyphotic deformities. Although VP effectively relieves pain, it does not restore vertebral height and carries a risk of cement leakage into surrounding tissues, including intervertebral disks, and, in rare cases, the spinal canal [2].  
The challenges of first-generation PVAS led to the development of balloon kyrophplasty [3]. In this second-generation technique, a balloon is inflated inside the collapsed verteb to regain its height, followed by the stabilization of the fracture using bone cement,which can be injected a at lower pressure, reducing the risk of leakage [3,4]. However, vertebral height recovery may be temporary, as partial or complete collapse can occur after balloon deflation [5].  
The challenges with height restoration led to the development of third-generation PVAS devices, which use mechanical kyphoplasty without traditional balloons to provide long-term vertebral body height restoration [6]. These spinal implants consist of expandable titanium mesh cages, designed for the reduction and stabilization of vertebral fractures. The cage's expansion mechanism allows for compression of the trabecular bone, restoring vertebral height and correcting kyphosis. The internal cavity of the cage is filled with PMMA bone cement, which promotes interaction with the host bone, contributing to the healing process and structural integrity of the implant [7].  
The most commonly used devices include the Vertebral Body Stenting System(r), SpineJack(r), and OsseoFix Spinal Fracture Reduction System(r) [8]. While these systems serve similar purposes, they differ in their technical specifications. The aim of this review is to evaluate and compare the efficacy, safety, and technical advancements of third-generation PVASs, with a particular focus on their ability to restore vertebral height, minimize cement leakage, and provide long-term stabilization in patients with vertebral compression fractures. Additionally, the rationale of this review is to provide a critical synthesis of the most recent developments in the context of the existing literature, analyzing the effectiveness and biomechanical impact of third-generation devices, and any potential combined treatments, and how these may influence future clinical practice.
## 2 Study Area and Dataset
### Reference Data  
More than 150 locations were randomly selected for the training dataset based on the RRD land use map of 2012 with the proportion of 75 locations for rice and 75 locations for non-rice areas. The testing dataset, based on an integrated sampling of two-stage cluster sampling and stratified sampling, includes 549 points for rice and 849 points for non-rice (Figure 2a). Stratified sampling was adapted at the provincial level. Cluster sampling was used at the district and commune level to select randomly sampled locations. There was no overlap between the training and test samples. Training and test data were labeled using very high resolution (VHR) satellite images derived from Google Earth. In the RRD, permanent water was grouped mainly into two types of regular water surface and aquaculture. To assess the quality of permanent water maps, 362 locations were randomly selected by a stratified sampling method for two strata with a confidence level (CL) of 95% and confidence interval (CI) of 5% for regular water surface and aquaculture. Also, 500 points for non-water areas were selected (Figure 2b). The data were also labeled based on VHR satellite images from Google Earth. GSO statistical data of rice area at the provincial level in the RRD for the 2018 winter-spring season were used to assess the quality of the rice map. The quality of the flooding maps was assessed by GSO inundated rice areas of provinces and cities which were seriously affected by the tropical storm Son-Tinh, such as Bac Ninh (Tien Du, Thuan Thanh and Luong Tai), Thai Binh (Tien Hai, Thai Thuy and Kien Xuong), Nam Dinh, Ha Noi, and Ninh Binh.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Duration** & **RoN** & **Number of Images** & **Channel** & **Product Type** & **Tasks** \\ \hline
1 February 2018–31 May 2018 & 91 and 55 & 10 & VV, VH & GRD & Rice mapping for winter-spring season \\
1 January 2018–31 December 2018 & 91 and 55 & 31 & VV & GRD & Permanent water map \\
1 July 2018–30 October 2018 & 91 and 55 & 9 & VH & GRD & Flood map and flood frequency map \\ \hline \hline \end{tabular}
\end{table}
Table 2: S1A data used in the study.
# Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow
## 3 Controller Design and Numerical Simulation Verification
### Algorithm Numerical Verification
#### 3.2.2 The Influence of Terminal Cost Coefficient  
The time required to solve the MPC problem is one of the issues of concern when designing MPC. Based on this, the effect of different terminal cost weights on the solution time is verified by different terminal cost coefficients. The experimental results are shown in Table 2. The experimental results show that with the terminal cost coefficient increasing, the solution time will be longer. Considering the solution time and solution accuracy, the terminal cost coefficient is usually less than 10.
## 4 Performance Analysis
### The Blocking Probability  
When there are already \(r_{k}\) tasks queued in the \(k\)th-tier MEC server, if a new task arrives, the queue is full and the new task cannot be queued and is blocked. Assuming that the blocked task does not return, the current \(k\)th-tier MEC server is in a state of not receiving tasks. The probability of this state is the blocking probability, also known as the loss probability.  
\[P_{b,k}=\pi_{k,c_{k}+r_{k}}=\frac{c_{k}^{c_{k}}\rho_{k}^{c_{k}+r_{k}}}{c_{k}! }\pi_{k,0} \tag{20}\]  
Therefore, the system blocking probability \(P_{b}\) is given as follows  
\[P_{b}=\prod_{k\in K}p_{o,k}P_{b,k}=\prod_{k\in K}p_{o,k}\frac{c_{k}^{c_{k}} \rho_{k}^{c_{k}+r_{k}}}{c_{k}!}\pi_{k,0} \tag{21}\]
# A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
###### Abstract  
Image denoising poses a significant challenge in computer vision due to the high-level visual task's dependency on image quality. Several advanced denoising models have been proposed in recent decades. Recently, deep image prior (DIP), using a particular network structure and a noisy image to achieve denoising, has provided a novel image denoising method. However, the denoising performance of the DIP model still lags behind that of mainstream denoising models. To improve the performance of the DIP denoising model, we propose a TripleDIP model with internal and external mixed images priors for image denoising. The TripleDIP comprises of three branches: one for content learning and two for independent noise learning. We firstly use a Transformer-based supervised model (i.e., Restormer) to obtain a pre-denoised image (used as external prior) from a given noisy image, and then take the noisy image and the pre-denoised image as the first and second target image, respectively, to perform the denoising process under the designed loss function. We add constraints between two-branch noise learning and content learning, allowing the TripleDIP to employ external prior while enhancing independent noise learning stability. Moreover, the automatic stop criterion we proposed prevents the model from overfitting the noisy image and improves the execution efficiency. The experimental results demonstrate that TripleDIP outperforms the original DIP by an average of 2.79 dB and outperforms classical unsupervised methods such as N2V by an average of 2.68 dB and the latest supervised models such as SwinIR and Restormer by an average of 0.63 dB and 0.59 dB on the Set12 dataset. This can mainly be attributed to the fact that two-branch noise learning can obtain more stable noise while constraining the content learning branch's optimization process. Our proposed TripleDIP significantly enhances DIP denoising performance and has broad application potential in scenarios with insufficient training datasets.  
image denoising; deep image prior; content learning; noise learning; internal and external mixed priors +
Footnote †: journal: Applied  
Acticle  
A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning [PERSON] Hu  
School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China;  
[EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (X.C.);  
[EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (Y.H.)  
* Correspondence: [EMAIL_ADDRESS]
## 4 Developed Management System  
Generally, the energy management system is used to control power production and schedule programs for a group of power grid applications. However, it may be considered as another way to control the electrical loads in microgrids. The present system is designed to meet the loads. As renewable resources are intermittent sources, the battery is used as a back-up system, and this is also designed to meet the load as well. The developed system includes three different types of loads (industrial, residential, and commercial loads), and in the developed model, the industrial load is assumed to be a priority load. The system comprises two renewable sources and a storage system. The net power generated \(\rm P_{G}\) and the load power \(\rm P_{L}\) are calculated as follows:  
\[\rm P_{G}=P_{PV}+P_{wind} \tag{25}\]  
\[\rm P_{L}=P_{Ind}+P_{Res}+P_{Com} \tag{26}\]  
where \(\rm P_{G}\) is the power generated from the renewable energy sources; \(\rm P_{PV}\) is the power produced from PV; \(\rm P_{wind}\) is the power produced from wind; \(\rm P_{L}\) is the load power; \(\rm P_{Ind}\) is the industrial load power; \(\rm P_{Res}\) is the residential load power; and \(\rm P_{Com}\) is the commercial load power.  
The battery bank will charge when there is excess production and will discharge when the power generated is not able to handle the load demands. Based on this architecture, the power generation will supply the load through four conditions and by the support of the battery bank.  
Firstly, when the power generation is equal to the load demands, in this case, the loads are supplied by the power generation from PV and wind without any disruption.  
Secondly, when the power generation exceeds the load demands, in this case, the power generation is more than the demand of the loads; thus, the loads are supplied by the power generation and the surplus of the production is used to charge the battery. When the battery is fully charged, the remaining excess of production is located to an auxiliary load.  
When the power generation becomes less than the demand of the loads, then the loads are supplied with the help of the battery bank. The battery is discharged until its SOC reaches its minimum value.  
\[\rm SOC_{min}<SOC_{batt}<SOC_{max}=20\%<SOC_{batt}<100\%\]  
When the \(\rm SOC_{batt}<20\%\), the battery is disconnected from the system, and at this time, the priority load, which is the industrial load, is supplied by the power generation from the renewable energy sources. If the power production exceeds the industrial load, in this case, the excess of the production is used to charge the battery. When the power production falls below the priority load, then the power generated from the renewable energy sources is used to charge the battery. The system will permanently verify the power generation and the battery SOC until the power generation meets the loads or the battery becomes able to handle the loads.
## 5 Conclusions  
In cellular IoT networks, there is a need for computationally non-demanding assessment of OFDM transmission performance that is either expressed by or related to the irreducible BER floor, which needs to be estimated by end-points of limited processing capacity, and directed upwards through the protocol stack to higher-layer protocols, thus influencing their error detection and correction actions.  
The standard framework that the model targets is LTE, specifically its downscaled versions LTE-M and NB IoT, but its validity stretches to 5G NR as well.  
Specifically, in indoor or small-cell outdoor environment, short propagation paths justify the assumption of high SNR, and enable the transmitter HPA to operate almost in a linear regime, thus effectively removing the excessive PAPR from the short list of the main BER influencers; quite alike as the time dispersion caused by inter-symbol interference was prevented by a large enough OFDM symbol cyclic prefix. This retains just the CFO (including the PAPR-induced additional CFO) as the sole OFDM impairment determining the BER floor in indoor or small-cell outdoor environment.  
Furthermore, in contrast to earlier analytical models demanding considerable processing power for estimating BER, in this paper, after identifying the optimal sample instant in a power delay profile, we abstract the CFO by equivalent time dispersion that would produce the same BER degradation as the CFO. This finally provided the novel CFO-inclusive BER floor estimation proportional to delay variance of the power delay profile, as well as the CFO-exclusive BER estimation.  
The proposed BER estimation is verified by the common AWGN link abstraction, and by means of the industry-standard software simulator in the LTE FDD downlink environment.  
Conceptualization, A.L., V.L., and B.M.; methodology, A.L.; software, A.L.; validation, A.L.; formal analysis, A.L.; investigation, V.L.; resources, A.L., V.L., and B.M.; data curation, A.L. and V.L.; writing--original draft preparation, A.L.; writing--review and editing, V.L.; visualization, A.L.; supervision, V.L.; project administration, A.L.; funding acquisition, A.L. and V.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 2 Related Work
### Presentations and Emotions
The collected data were related with a subjective evaluation of the engagement coming from a questionnaire with four dimensions: challenge, skill, engagement, and perceived learning. The authors found that the less stressed and more relaxed students are, the more engaged they appeared to be. Furthermore, they demonstrated that the more excitement and engagement the students felt during a presentation (TED videos), the more engagement was perceived. Although our work also takes place in a learning environment, a virtual seminar held at multiple universities simultaneously, it differs from the studies which analyze the relationship between emotions and the learning process in that we investigate the relationship between the perceived quality of a presentation and the emotions among the audiences and the presenter. In other words, we focus on finding an emotional pattern that leads to a great presentation.
## 2 Principle
From [PERSON]'s law of \[n_{0}\sin\theta_{0}=n_{1}\sin\theta_{1}=n_{2}\sin\theta_{2}\] (5) and Fresnel formulas for the reflection coefficients of s- and p-polarization: \[1\to 0\ r_{10S}=\frac{n_{1}\cos\theta_{1}-n_{0}\cos\theta_{0}}{n_{1}\cos \theta_{1}+n_{0}\cos\theta_{0}}\] (6) and \[1\to 0\ r_{10P}=\frac{n_{0}\cos\theta_{1}-n_{1}\cos\theta_{0}}{n_{0}\cos \theta_{1}+n_{1}\cos\theta_{0}},\] (7) where the condition (a) tells that \(\theta_{0}\) or \(\theta_{1}\) should be large (close to \(\pi/2\)). In addition, it is necessary that the light is incident from \(n_{0}\) to \(n_{1}\) [A: \(n_{0}\sin\theta_{0}=n_{1}\sin\theta_{1}\)] and TIR occurs from \(n_{1}\) to \(n_{2}\) [B: \(n_{1}\sin\theta_{1}>n_{2}\)]. If \(n_{1}>n_{0}>n_{2}\), these conditions are satisfied by bringing \(\theta_{0}\) closer to \(\pi/2\). If \(n_{0}>n_{1}>n_{2}\), these conditions are satisfied by bringing \(\theta_{1}\) closer to \(\pi/2\). However, multiple interferences at a nearly \(\pi/2\) reflection angle within the thin film severely deteriorates visibility of interference when the beam size is finite. If \(n_{1}>n_{2}>n_{0}\), when [A] is satisfied, then there is a real angle \(\theta_{2}\) to satisfy \(n_{1}\sin\theta_{1}=n_{2}\sin\theta_{2}\) (TIR does not occur). Therefore, the necessary condition for SCCPA utilizing TIR is \(n_{1}>n_{0}>n_{2}\). Under the premise that a nearly transparent (\(q\ll 1\)) dielectric material is used as the \(n_{1}\) thin film, critical coupling, or SCCCPA, requires an incidence angle \(\theta_{0}\) from the substrate to the thin film close to \(\pi/2\), i.e., grazing incidence.
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Remote Sensing Data Collection
#### 2.2.1 Reference Data  
A total station survey was conducted to obtain coordinates of topographic checkpoints that would be used as topographic reference for the DTM evaluation, and also to obtain coordinates of stabilized wooden stakes which were further used to georeference close-range remote sensing data (UAVimage, TLS and PLSh). Two total station surveys were conducted in total, first in February 2019 and, second, in January 2021. A Topcon es65 total station (Topcon Positioning Systems Inc., Tokyo, Japan), with 5\" angle measurement accuracy, was used for both surveys. A permanent geodetic reference was established within the forest area during the first campaign, and polygons were stabilized in the cut tree stumps using metal bolts. A number of ground points on the plot were recorded within the same campaign; as well, wooden stakes (5 \(\times\) 5 \(\times\) 40 cm) were stabilized within the plot area. In total, 167 randomly chosen checkpoints and 18 stakes were recorded in six plots. The previously established geodetic reference was used in the second survey. The goal of the second survey was to determine coordinates of newly stabilized wooden stakes (5 \(\times\) 5 \(\times\) 40 cm) used as UAVimage control points and a TLS reference. The geodetic reference established in the first survey was used for the resection in the second survey, and the average resection positioning root mean square error (RMSE) value was 0.5 cm in the horizontal direction and 0.3 cm in the vertical direction. The small resection error indicated that the geodetic reference displacement between two epochs was small and could be neglected considering the values observed in this study.
## 2 The Standard PTP  
### PTP Operation  
The standard PTP provides a synchronization mechanism for the clocks of participating nodes in a system to a high degree of accuracy and precision. The PTP defines several kinds of clocks, as follows:  
* Ordinary Clock (OC): a clock that has one PTP port in a single domain and that maintains the timescale used in the domain. It may serve as a source of time (i.e., the GMC) or it may synchronize to another clock (i.e., an SC).
* Boundary Clock (BC): a clock that has multiple PTP ports in a single domain or multiple domains and that maintains the timescale used in the domain. It may serve as the source of time (i.e., an MC) and it may synchronize to another clock (i.e., an SC).
* Transparent Clock (TC): a clock that measures and calculates the elapsing time (called the residence time) for PTP event messages, including measurement of the processing and queuing delays. Furthermore, TC measures and calculates the link-propagation delay between similarly equipped ports at the end of the communication path.  
The standard PTP is based on a master/slave principle. There are two phases in the normal execution of PTP: (1) establishing a master-slave hierarchy and (2) synchronizing the clocks.