esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Last but not the least, we survey state-of-the-art energy-efficient and QoS-aware communication protocols including MAC protocols and routing protocols. To be precise, MAC protocols with prioritization and service differentiation functionalities and disjoint multipath routing protocols are studied. For each topic we not only sum up the pros and cons of each surveyed protocol but also we provide a look into its future trends. To the best of our knowledge, our work is the first to achieve all three at the same time. The rest of our paper is organized as follows. The characteristics of WMSNs are discussed in Section 2, along with the imposed challenges and respective existing solutions. Section 3 studies MAC protocols designed for WMSNs while Section 4 investigates a series of disjoint multipath routing protocols. Lastly, we conclude our work in Section 5.
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 6 Workflow for Practical Implementation with Other Low-Cost Techniques  
Given the results of Section 5, a pipeline was developed where the model and workflow utilizing small datasets can practically be integrated along with other low-cost methodologies for the assessment of pavement distresses in a road network. This pipeline is depicted in Figure 18, with the objective being to better plan decisions on maintenance interventions within the network.  
Within the workflow, the process begins with the collection of network data followed by the feature selection and model development as explained and portrayed in this study. Following this, the model can be utilized to generate a schedule of road sections where interventions are likely to be needed over time. The model could be run to predict where interventions would be scheduled over the next ten year period. It must be noted however that all of the features utilized in this study would have to found for each of the roads in the network before the model is run. Whilst this still requires some data collection, it would be significantly easier to do so than having to physically survey the entire network. The schedule produced as a result of this would indicate the particular geographical areas where interventions are likely needed and the roads that are allocated to a particular year group of intervention. To validate this schedule, conditional surveys can then be done on the sections highlighted by the model. Whilst these surveys can be costly, the pipeline utilizes low-cost surveys using object detection models embedded on smartphones to detect the presence of pavement distresses. This would subsequently produce a hotspot  
Figure 18: Workflow for the practical implementation of the methodology.  
analysis of the roads covered in the surveys as identified in an earlier study [39]. This survey would yield information about the location and frequency of the distresses and be able to validate the work carried out by the model within this study predicting which roads need intervention. The surveys would be run on the roads as deemed necessary from the results of the model in this study.  
Following this, detailed measurements can be determined, where necessary, with low-cost 3D modelling techniques using smartphones and drones to produce metric models of the distressed areas as validated by previous work [81]. It is significant to note that each step of this workflow utilizes a low-cost technique or system to not only acquire the information needed but to process it. This is vital to help road authorities with their under-budgeted scenarios whilst still maintaining a high level of accuracy and adequate planning practices. At each stage of the pipeline, the data and analyses would be fed towards the road asset database to allow for updated records of road assets and continual low-cost network monitoring by the road authority. The workflow and the combination of low-cost measures within it are what offers the greatest contribution to the work.
###### Abstract  
GPP is the most important component of terrestrial carbon flux. Red-edge (680-780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, \(\frac{\rho 783}{\rho 705}-1\)) multiplied by PARin and GPPEC had the highest correlation (\(R^{2}=0.77\), root-mean-square error (RMSE) = 0.81 gC-m-2-day-1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.  
Sentinel-2; red edge; canopy chlorophyll content; time-series data; photosynthesis; grassland; evergreen broadleaf forests +
Footnote †: journal: Remote Sens. **2019**, _11_, 1303; doi:10.3390/rs11111303
## 3 Analysis of I2O WBAN Systems  
In this section, the proposed communication system is considered in likely operating scenarios, such as in hospitals or homes, where a patient with an in-body implant device/sensor transmits data to a wearable device placed at the maximum distance of the on-body region. A communication link is established for an I2O body scenario between the Tx located in the liver tissue and the Rx placed 2 cm from the human body surface. The BER performance for the I2O shadow fading channel employing BPSK, QPSK, 16 PSK and 16 QAM modulation schemes is obtained. The relationship between the data transmission rates, transmitting power and the achievable communication distance is discussed under the link budget analysis.
## 2 Materials and Methods
### NPP Data  
Net primary productivity (NPP) characterizes vegetation vigor and is an important indicator of ecosystem productivity and energy conversion efficiency [26]. Currently, there are varied data products on NPP. According to existing studies, the model used for Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data performs superiorly, as the model has been calibrated with large samples of data from around the globe and the product has been proven to retrieve grassland NPP on the TP with close to real values as well as a more stable performance [36; 37]. Thus, we selected the NPP data from the MODIS product MOD17A3 HGF dataset (at NASA Earth Science Data Systems, [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 12 May 2024)) to simulate grassland productivity on the TP.  
The data were derived from the Terra satellite with a spatial resolution of 500 m. Key steps in the generation of the MODIS NPP include the calculation of each 8-day set of gross primary productivity (GPP) data based on the fraction of photosynthetically absorbed radiation (FPAR), incident solar radiation, minimum temperature, and daylight-averaged water vapor pressure difference (WVDD) data, as well as the calculation of annual maintenance respiration (MR) and growth respiration (GR) data based on the annual maximum leaf mass and temperature. NPP is the difference between the annual sum of 8-day GPP and MR and GR ([[https://modis-land.gsfc.nasa.gov/pdf/MOD17C61](https://modis-land.gsfc.nasa.gov/pdf/MOD17C61) UsersGuideV11 Mar112021.pdf]([https://modis-land.gsfc.nasa.gov/pdf/MOD17C61](https://modis-land.gsfc.nasa.gov/pdf/MOD17C61) UsersGuideV11 Mar112021.pdf)(accessed on 12 May 2024)). This dataset has been improved by calibration changes and the use of climatological LAI/FPAR as a backup to the operational LAI/FPAR, improving data quality compared to previous versions. In addition, the dataset cleaned the poor-quality inputs from the 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the quality control label for every pixel, and its value was replaced through linear interpolation, if any LAI/FPAR pixel did not meet the quality screening criteria. Prior to data processing and analyzing, we also resampled the NPP data to a 1 km resolution.
## 3 Results
### Hydrological Evaluation of Discharge Simulation Using Two SPPs
#### 3.3.3 Event of 3 May 2016  
The hydrological model results showed a reasonable fit between the shape of the simulated and observed hydrographs. Figure 6 shows a chronological comparison of simulated and observed streamflow at the watershed outlet for a calibration period of 3-5 May 2016 (we limit ourselves to modeling short-duration floods for which the evapotranspiration process is negligible). Although the measured peak flow values did not exactly match the simulated peak flow values for both products, there was a slight improvement for IMERG V05. The model tended to underestimate the streamflow, since the river flow was already high due to snowmelt, and this was added to the runoff caused by the flood, which the model did not take into account.  
On the other hand, the volumes were well respected and the rise and fall curves were generally well reproduced with a slight improvement on the IMERG V05 side. The results of the evaluation criteria for both products 3B42V7 and IMERG V05 are satisfactory with NSE ranging from 76.80% to 79.30%, respectively.  
After following the validation procedures previously mentioned in Section 3.3.4, the comparison of the observed and simulated hydrographs showed that the model underestimates the point flows, due to the non-conclusion of the snowmelt process. However, Figure 7 shows a good trend in the reproduction of the observed and simulated discharge curves, with an NSE of 61.30% for 3B42V7, and 72.90% for IMERG V05, which are good results.  
Figure 6: Calibration of the episode 3 May 2016, using 3B42V7 and IMERG V05 rainfall products and observed runoff data as input.
# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 10 Conclusions and Future Work
### Conclusions  
1. **Security Integration**: Most reviewed approaches incorporate security considerations such as confidentiality, integrity, and availability directly into the scheduling process. This integration ensures that workflows are executed securely without compromising the performance of cloud resources.
2. **Adaptability and Efficiency**: The use of diverse algorithms--from heuristic and metaheuristic approaches to advanced deep learning models--demonstrates the flexibility and robustness required to manage cloud environments' dynamic and often unpredictable nature.  
Figure 9: **(a).** Percentage of security and without security. **(b)** Percentage of methods used.  
3. **Trade-offs in Optimization**: These methodologies focus on balancing the trade-offs between performance metrics such as makespan and security measures. This balance is crucial in scientific workflows, where execution time and data protection are of paramount importance.
4. **Resource Management**: Techniques like VM utilization maximization and energy-aware scheduling address resource efficiency and environmental sustainability goals, catering to user needs and global ecological concerns.
# An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
## 1 Introduction
revealed that the removal efficiency of quartz glass increases obviously and the surface roughness decreases significantly as the mass fraction of abrasive increases [10]. [PERSON] et al. investigated the influence of pH on the polishing performance of quartz glass, and they analyzed and characterized the silica sol used in CMP by dynamic light scattering and Zeta potentials. Additionally, they improved the surface quality and removal rate of quartz glass by adding guanidine carbonate as a co-solvent to the polishing solution [11]. [PERSON] et al. found that the polishing efficiency of the polycrystalline diamond polishing pad on quartz glass was higher than that of the single crystal diamond polishing pad, and the polycrystalline diamond polishing pad would cause less subsurface damage [12]. [PERSON] et al. explored the relationship between dressing parameters of the polishing pad and surface defects of the workpiece after CMP. They found that excessive dressing of polishing pads would cause more polishing fragments on the surface, which would cause damage to the workpiece [13]. However, the focus of most previous studies is placed on improving the technological parameters, polishing particles, and polishing discs of CMP to reduce the subsurface damage and improve the efficiency and surface roughness. There are fewer studies on the approaches to detect and repair the damage and sub-surface damage layer caused by CMP. In this study, photomask quartz glass substrates were etched with the hydrofluoric (HF) acid etching method after chemical mechanical polishing (CMP), so that the polishing hydrolysis layer could be completely removed. In addition, the surface scratches caused by CMP were observed with a surface quality detection device and a white light interferometer. Second, the high-efficiency array-type magnetorheological polishing technology was employed to eliminate the surface-shape error on the surface of quartz photomask glass substrates. Then, the removal function rotation angle based magnetorheological polishing technology was utilized to evenly remove the mid-frequency ripples and scratches caused in the preceding technological process. Finally, much surface damage is removed and there is no mid-frequency ripple on the surface. In this method, a new technology route of photomask quartz glass substrate was proposed, which could realize rapid damage removal and high-quality processing of photomask glass substrates.
# Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
## 3 Available Solution Using Particle Filtering  
As described in Section 1, the dynamic localization problem can be solved using the well-known particle filtering method [27]. In this section, we describe this approach.  
The Bayesian filtering equations are initialized by \(p(x_{1}|Z_{1})=\delta(x_{1})\), and proceed as follows  
\[p(x_{t}|Z_{t-1}) =\int p(x_{t}|x_{t-1})p(x_{t-1}|Z_{t-1})dx_{t}, \tag{5}\] \[p(x_{t}|Z_{t}) =\frac{p(z_{t}|x_{t})}{p(z_{t}|Z_{t-1})}p(x_{t}|Z_{t-1}). \tag{6}\]  
Equations (5) and (6) are called the prediction and update steps, respectively. These equations do not have close form expressions, in general. Hence, a numeric method is needed to approximately compute them. This can be done using particle filtering. More precisely, we start with  
\[p(x_{1}|Z_{1};m) \simeq\sum_{i=1}^{l}\alpha_{1|1}^{i}\delta\Big{(}x_{1}-x_{1|1}^{i }\Big{)}, \tag{7}\] \[x_{1|1}^{i} =0,\] \[\alpha_{1|1}^{i} =\frac{1}{l}.\]  
Then, at time \(t>1\), the prediction step is computed by  
\[p(x_{t}|Z_{t-1};m)\simeq\frac{1}{l}\sum_{i=1}^{l}\delta\Big{(}x_{t}-x_{t|t-1}^{ i}\Big{)}, \tag{8}\]  
with \(x_{t|t-1}^{i}\) obtained by drawing it from the following distribution  
\[x_{t|t-1}^{i}\sim\sum_{i=1}^{l}\alpha_{t-1|t-1}^{j}p\Big{(}x_{t}|x_{t-1|t-1}^{ i}\Big{)}\delta\Big{(}x_{t}-x_{t-1|t-1}^{j}\Big{)}.\]  
Also, the update step is computed by  
\[p(x_{t}|Z_{t};m) \simeq\sum_{i=1}^{l}\alpha_{t|t}^{i}\delta\Big{(}x_{t}-x_{t|t}^{ i}\Big{)}, \tag{9}\] \[x_{t|t}^{i} =x_{t|t-1}^{i},\] \[\alpha_{t|t}^{i} \propto p\Big{(}y_{t}|x_{t}^{i}\Big{)}.\]  
with \(\alpha_{t|t}^{i}\) normalized so that \(\sum_{i=1}^{l}\alpha_{t|t}^{i}=1\).  
The use of particle filtering for solving the localization problem permits accommodating the accuracy by increasing the number of particles. As mentioned in Section 1, a problem that often occurs is that the desired accuracy requires a very large number of particles. This can result in a numerically very expensive algorithm. To address this issue, in the next section we propose a variant of the particle filtering algorithm, which drastically reduces the number of particles required to achieve a given accuracy.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods  
A two-stage framework is proposed to address path planning optimization for UAV relay positioning, as depicted in Figure 1. The first stage aims to predict the likely locations where MR services are required to aid victims experiencing low mobile coverage. Notably, the location of nearby cellular towers and a digital representation of the terrain elevation are combined to model the coverage of relevant cell-towers. This is achieved by computing the signal propagation pathloss at each point along popular walking and hiking routes using the Longley-Rice propagation model. Areas of the tracks with undesirable coverage are subsequently identified as areas of interest.  
The second stage focuses on optimally planning the UAV relay path to pass through the areas of interest. Firstly, the traveling salesman problem is solved using the areas of interest to determine the optimal visiting order that minimizes the total path distance. A visibility analysis is then conducted to identify visible points from a specific altitude and prevent the relay from passing through areas with no coverage. Weather conditions are also taken into account to (a) determine hazardous areas, (b) modulate the throttle based on air speed, and (c) boost the autonomy of the UAV through energy awareness.
###### Abstract  
Predicting human future motion holds significant importance in the domains of autonomous driving and public safety. Kinematic features, including joint coordinates and velocity, are commonly employed in skeleton-based human motion prediction. Nevertheless, most existing approaches neglect the critical role of dynamic information and tend to degrade as the prediction length increases. To address the related constraints due to single-scale and fixed-joint topological relationships, this study proposes a novel method that incorporates joint torques estimated via Lagrangian equations as dynamic features of the human body. Specifically, the human skeleton is modeled as a multi-rigid body system, with generalized joint torques calculated based on the Lagrangian formula. Furthermore, to extract both kinematic and dynamic joint information effectively for predicting long-term human motion, we propose a Multiscale Mixed-Graph Neural Network (MS-MGNN). MS-MGNN can extract kinematic and dynamic joint features across three distinct scales: joints, limbs, and body parts. The extraction of joint features at each scale is facilitated by a single-scale mixed-graph convolution module. And to effectively integrate the extracted kinematic and dynamic features, a KD-fused Graph-GRU (Kinematic and Dynamics Fused Graph Gate Recurrent Unit) predictor is designed to fuse them. Finally, the proposed method exhibits superior motion prediction capabilities across multiple motions. In motion prediction experiments on the Human3.6 dataset, it outperforms existing approaches by decreasing the average prediction error by 9.1%, 12.2%, and 10.9% at 160 ms, 320 ms, and 400 ms for short-term prediction and 7.1% at 560 ms for long-term prediction.  
motion prediction; pose dynamics; graph neural networks; multiscale modeling +
Footnote †: journal: Applied sciences
## 2 Kinematic Constraint Equations
### Constraint Equations of Four-Wheeled Robot with Mecanum Wheels  
Assuming that \(\delta=\pi/4\), the constraint Equation (9) become:  
\[\begin{split} V_{C\xi}-V_{C\eta}-(\rho+l_{C})\dot{\psi}& =R_{C}\dot{\varphi}_{1}\,,\\ V_{C\xi}+V_{C\eta}+(\rho+l_{C})\dot{\psi}&=R_{C} \dot{\varphi}_{2}\,,\\ V_{C\xi}+V_{C\eta}-(\rho+l_{C})\dot{\psi}&=R_{C} \dot{\varphi}_{3}\,,\\ V_{C\xi}-V_{C\eta}+(\rho+l_{C})\dot{\psi}&=R_{C} \dot{\varphi}_{4}\,.\end{split} \tag{10}\]  
Combing the four Equation (10), yields the equivalent form:  
\[\begin{split} V_{C\xi}&=\frac{R_{C}}{2}\left(\dot{ \varphi}_{1}+\dot{\varphi}_{2}\right),\\ V_{C\eta}&=\frac{R_{C}}{2}\left(\dot{\varphi}_{3}- \dot{\varphi}_{1}\right),\end{split} \tag{11}\]  
Figure 3: Model of Mecanum wheel.  
and  
\[\begin{split}\psi=&\frac{R_{\rm C}}{2(\rho+l_{\rm C})} \left(\phi_{2}-\phi_{3}\right),\\ \phi_{1}+\phi_{2}=\phi_{3}+\phi_{4}\,.\end{split} \tag{12}\]  
Taking into account expressions (2), we can represent the Equation (11) as follows:  
\[\begin{split}\dot{x}_{\rm C}&=\ \frac{R_{\rm C}}{ \sqrt{2}}\cos\left(\psi-\frac{\pi}{4}\right)\phi_{1}\,+\,\frac{R_{\rm C}}{2}( \cos\psi\,\phi_{2}-\sin\psi\,\phi_{3})\,,\\ \dot{y}_{\rm C}&=\ \frac{R_{\rm C}}{\sqrt{2}}\sin\left( \psi-\frac{\pi}{4}\right)\phi_{1}\,+\,\frac{R_{\rm C}}{2}(\sin\psi\,\phi_{2}+ \cos\psi\,\phi_{3})\,.\end{split} \tag{13}\]  
Therefore, the considered system has four constraint equations: two non-holonomic (non-integrable) (13) and two holonomic constraints (12).
## 4 Discussion
To date, due to the complexity of casting post and cores and the wide range of available materials, few studies have quantitatively evaluated the fit and accuracy of custom post and cores using micro-CT scanning, nor have they evaluated the fit and accuracy of custom post and cores via a visual inspection or direct measurements of the gap filled with cement material, as the latter has only been used to evaluate the internal fit and adaption of dental restorations [55; 56]. The limitations of this study and recommendations for future ones are listed below:  
1. Thermocycling was used for a short period. Further studies with longer thermocycling periods should be conducted;
2. In the current study, only one type of luting cement was used. Additional studies using different types of cements should be performed to assess the effect of the type of cement on the retention of these post and cores;
3. The effects of saliva and temperature changes in the oral cavity were not replicated in this study. The simulated clinical situations might have affected the results; hence, further studies that simulate the oral environment are recommended.