esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Nevertheless, a thorough impact analysis of climatic variation and human interventions on GPP is necessary and still worth studying, owing to the complex interactions between the carbon cycle and environmental factors. Since GPP provides important ecological services for human survival [23; 24], grounded insights into the short-term prediction of GPP lays the basis for untangling how the terrestrial carbon cycle will evolve in the near future. The autoregressive integrated moving average (ARIMA) model is recognized as a functional statistical tool for analyzing and predicting time series data. The model is valued for its powerful performance [25], with its strength mainly reflected in the recognition and consideration of seasonality and sequence correlation in data [26]. At present, the ARIMA model has been applied in many disciplines such as aerosols [27; 28], energy [29], climatology [30], and hydrology [31]. However, the currently available GPP products are inconsistent in terms of periodicity change, space distribution, and data availability [32], and such inconsistencies makes it a challenge to obtain a coherent prediction analysis. Accordingly, whether the ARIMA model can reasonably delineate and predict spatial-temporal patterns of GPP remains unknown. In recent decades, China's ecological construction has been increasingly conducted because of the rapid growth of the economy and population [33; 34]. In view of the important role of GPP in the ecosystem, the study of GPP in China is of great significance in deepening the understanding of the terrestrial ecosystem and providing basic support for China's ecological construction and policy-making. In light of this, our study aims to analyze the variations, trends, and impact factors of GPP dynamics over more than three decades (from 1982 to 2015) in China, and further predict short-term GPP dynamics. The specific objectives of our study are: (1) to characterize the GPP dynamics in nine major river basins in China at both seasonal and annual scales; (2) to assess the response of GPP tendencies to climatic variations and human activities; and (3) to examine the feasibility of the ARIMA model in simulating and forecasting GPP profiles and to pinpoint the potential sources of uncertainty associated with model performance.
# Exchange Rate Analysis for Ultra High Bypass Ratio Geared Turbofan Engines
## 2 Problem Statement  
Given the fact that UHBR engines is a promising solution for near future aircraft propulsion, the engine's thermal management system needs to manage any increased heat loads.  
The objective was to develop an optimized representative UHBR-GTF (as the best candidate for dealing with limitations set by governments and organizations for optimal future air travel) engine performance computational model assuming a 2025 entry into service level of technology.  
The problem was addressed by two main steps: The first one is the design of optimized engine parameters. For this purpose, the engine design point (DP) is set at the take-off (T/O) condition to achieve a maximum net thrust value (FN). The cycle is then optimized to obtain the maximum efficiency for the components at cruise conditions by satisfying pre-defined constrains. The outcome of this step is an accurate, optimized model for the UHBPR-GTF engine, which is used for the subsequent steps.  
The second step is to perform SFC exchange rate analysis to relate changes in component performance levels to change in engine parameters. This is done in both scenarios of design point and synthesis exchange rates. More specifically, the following cases are examined for both scenarios:  
* Effect of bypass duct pressure loss of 1% to 4% (i.e., reflecting the air oil heat exchanger (AOHE) installation in the bypass duct)
* Effect of bypass duct mass flow loss of 1% to 4% (i.e., AOHE effect)
* Effect of core mass flow into bypass of 1% to 4% (i.e., core bleed effect)
* Added heat of 50 kW to 150 kW either to the bypass duct (reflecting AOHE installation) or to the fuel (reflecting the fuel oil heat exchanger (FOHE) utilization to pre heat the fuel).  
For the synthesis scenario, an additional case was also investigated to consider the effect of power offtake from a high pressure turbine (HPT) by extracting extra power on top of the DP (reference) value. This is the case when more power is required by the auxiliary systems due to the sizing of specific components such as the power gearbox (related to the fan size and bypass ratio (BPR)), shaft bearings, auxiliary gearbox, etc. Therefore, 20 kW, 30 kW, 40 kW, and 50 kW were added on top of the 37.5 kW (DP value).
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
###### Abstract
Hence, in an Internet of EVs in which different entities are interconnected with the charging infrastructure, the charging service provider (CSP) needs to ensure the authenticity of the EVs requesting the dynamic charging service and to verify the corresponding billing credentials before initiating the charging service. This needs to be achieved while preserving the privacy of EV user information and minimizing the computational overhead to ensure fast and lightweight communications during the vehicle's motion. This work particularly focuses on the authentication and billing aspects of EVs utilizing the DWC infrastructure. Given the stringent requirements for low latency and high reliability due to the short lane-crossing duration, and assuming an efficient underlying communication network, this paper proposes a fast, secure and lightweight authentication and billing scheme for EVs requesting dynamic wireless charging. The proposed scheme allows each EV user to receive a single, aggregated bill instead of segment-by-segment payment, in a DWC network consisting of multiple charging lanes owned by different pad owners (PO) and coordinated using a Centralized Management and Coordination System (CMCS) in an IoEV within a smart grid. A charging service company (CSC) is responsible for EV registration and authentication with the DWC network. This scheme uses pseudonyms, symmetric and asymmetric encryption, and lightweight hashing to authenticate the EVs to the charging segments, after registration and authentication with the CSC and the PO. In the proposed model, the amount of energy demanded by the EV needs to be announced before the EV enters the charging lane. A heterogeneous communication network that integrates different communication technologies has been adopted, including 5G cellular networks, wired connections and Direct Short Range Communications (DSRC)to meet the communication requirements of the different entities. The performance of the proposed model is analyzed in terms of security, communication overhead, computational cost and system delay. The remainder of this paper is organized as follows: Section 2 provides a description of some of the cryptographic techniques used in this work. Section 3 then outlines the related works on authentication and billing of EVs in DWC systems. The system model, including the network entities and the communication and cryptographic protocols, is then presented in Section 4, and the proposed authentication and billing scheme is detailed in Section 5. Security analysis and performance evaluation of the proposed scheme are presented in Sections 6 and 7, respectively, before the paper is concluded in Section 8.
## 3 Results and Discussion
### Economic Analysis for the STPP (Wet-Cooled Model) for the Six Potential Sites in India
In the case of the Srikakulam and Erode sites, the effect of increasing sales tax on the LCOE of the wet-cooled model was almost insignificant as it followed the same trend with a minimal difference. The impact of sales tax on LCOE was observed to be more impactful on the dry-cooled plant than that of the wet-cooled module. Figure 10 presents the effect of the up-front fee on the cost of energy from the power plants for all sites. It shows an increase in LCOE for all sites. However, its effect on the LCOE is very minimal at all the six sites for the wet-cooled model as compared to that for the dry-cooled model. Similarly, the highest cost of electricity is expected to occur at the Jalandhar site, which shows an increase in the up-front fee for both models. Bhopal, however, recorded a lower LCOE for both models and an increase in the up-front fee. Figure 11 shows the effect of SM on the NCC of the various power plants; it can be seen from the figure that the SM has a major effect on the NCC of the STPP for all selected sites. The NCC for the dry-cooled model increases sharply from an SM of 1 to 2; however, it decreases after reaching an optimum SM. The maximum NCC achieved for the dry-cooled model at the Bhopal, Jhansi, Jalandhar, Srikakulam, West Medinipur, and Erode sites are 6.83789 USD/W, 6.83777 USD/W, 6.83777 USD/W, 6.8377 USD/W, 6.83754 USD/W, and 6.83636 USD/W at the SM range of 2. On the other hand, the maximum NCC for the wet-cooled model are 7.32521 USD/W, 7.32519 USD/W, 7.32399 USD/W, 7.3247 USD/W, 7.95507 USD/W, and 7.3243 USD/W for the various STPP sites, respectively. Figure 9: Effect of sales tax rate on the LCOE of both models, (**a**) dry-cooled and (**b**) wet-cooled, at 6 h TFS for SM of 2.
## 3 Numerical Experiments
### FM Signal Simulation Research  
FM signals usually exist in vibration signals of mechanical operation signals. In this paper, the FM signal is adopted to conduct the simulation analysis of noise reduction, in order to verify whether the algorithm is suitable for FM signals, namely mechanical signals. The simulation FM signal is shown in Equation (22).  
\[x(t)=\cos(80\,\pi t)\big{[}1+\sin(20\,\pi t)\big{]} \tag{22}\]  
The sampling frequency of the above signal is 4096 Hz, and the sampling point is 4096, with the addition of Gaussian white noise, the SNR of which is 2. The time domain plots of the original signal and our noisy signal are shown in Figures 15 and 16. The time domain plot of the denoised FM signal with noises is shown in Figure 17.  
It can be seen from Figures 15-17 that the proposed smooth local subspace projection method can achieve a good denoising result. There is no extra noise in the denoised signal in time, three evaluation indicators of SNR, Cm and MSE can be utilized to measure the effectiveness of the smooth local subspace projection denoising method quantitatively. The SNR, C\({}_{\text{m}}\), and MSE values change along with iterations, and can be shown in Figures 18-20, respectively. Furthermore, to show the effectiveness of the proposed approach, different noise levels for the FM signal are adopted to verify its denoising effect using the logarithmic weighting function. The values of three indicators of denoising performance are given in Table 2.  
Figure 16: Time domain plot of noisy signal.  
Figure 15: Time domain plot of FM signal.  
Figure 17: Time domain plot of denoised FM signal with noises.  
Figure 18: SNR values along with iterations.  
It can be seen from Figures 18-20 that the FM signal is denoised by the proposed approach effectively, according to the three denoising indicators mentioned above. Furthermore, it can be seen from Table 2 that the proposed approach can achieve a good denoising effect under the condition of different added noise levels. Hence, it can be concluded from the simulation researches that the proposed smooth local subspace projection method based on SOD can achieve a good denoising effect towards FM signals. Owing to the reason that the mechanical vibration signals usually contain the characteristics of FM signals, the proposed approach can be applied to deal with mechanical vibration signals.
## 2 Related Works  
### PBFT  
PBFT (Practical Byzantine Fault Tolerance) is a consensus algorithm designed to function in asynchronous networks. It guarantees consensus when there are more than \(2f+1\) nodes in total, even if \(f\) of them are malicious nodes (known as Byzantine nodes). This means the PBFT algorithm can achieve consensus even if up to 33% of the total nodes are malicious. PBFT is a representative non-competitive consensus algorithm used in private blockchains. Figure 1 illustrates the typical operation process of PBFT.  
As depicted in Figure 1, when a new request is generated, the client sends a consensus request message to the primary node. Upon receiving this message, the primary node initiates the pre-prepare phase. The primary node then broadcasts the consensus request message to all other replica nodes participating in the consensus, excluding itself. The replica nodes, upon receiving the message from the primary node, validate it. If the message is deemed valid, the replica nodes broadcast prepare messages to the other nodes. Upon receiving \(2f+1\) prepare messages, each node transitions to the prepared state and initiates the commit phase. Nodes in the commit phase broadcast commit messages to other nodes. When a node receives \(2f+1\) commit messages, it transitions to the commit certificate state and updates its blockchain ledger.  
During the execution of the consensus process, PBFT requires each node to broadcast messages twice to all other nodes, excluding itself, during both the prepare and commit phases to achieve consensus. Consequently, PBFT's communication complexity is \(O(n^{2})\) when there are \(n\) total nodes. This results in scalability challenges for PBFT as the number of nodes participating in the consensus grows [15; 21].
## 5 Results and Discussion
### Results of Implementing the AI Assessment and Scoring Application  
To realize and demonstrate the application of the scoring system, we developed an AI-based assessment and scoring application. Users are first required to fill in basic information such as name, birthdate, and test date and upload hand-drawn graphics of children. Figure 6 is an example image of a participant completing the test, uploaded to the application as input for the model. After completing these steps, users can activate the internal model for automatic scoring of the graphics. The model extracts features and scores each uploaded graphic, and once scoring is complete, the software displays important data on the interface, including raw scores, standard scores, and achievement levels, which helps assess the child's overall performance in the test. Additionally, the software provides a dedicated area showing the scoring rules that the child did not meet, which is significant for parents and teachers to improve targeted teaching and training. The performance of the application in the experiment demonstrated its feasibility and effectiveness in practical applications. Through precise data processing and a user-friendly interface design, the software not only enhances scoring accuracy and efficiency but also provides unmet standards, offering strong support for the assessment and development training of children. Figure 7 shows the initial screen of the software application.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy (\%)** & **Epoch** & **Training Time (mins)** & **Testing Time (s)** \\ \hline
**Improved DenseNet** & **90.99** & 12 & 57 & 10 \\
**Improved DenseNet** & 90.45 & **3** & **Feature extract: 22** & **Feature extract: 9.8** \\
**+ SGD classifier** & & & **Classification: 1** & **Classification: 0.027** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of improved DenseNet (90% frozen) combined with SGD classifier.
###### Abstract  
The normal vector estimation of the large-scale scattered point cloud (LSSPC) plays an important role in point-based shape editing. However, the normal vector estimation for LSSPC cannot meet the great challenge of the sharp increase of the point cloud that is mainly attributed to its low computational efficiency. In this paper, a novel, fast method-based on bi-linear interpolation is reported on the normal vector estimation for LSSPC. We divide the point sets into many small cubes to speed up the local point search and construct interpolation nodes on the isosurface expressed by the point cloud. On the premise of calculating the normal vectors of these interpolated nodes, a normal vector bi-linear interpolation of the points in the cube is realized. The proposed approach has the merits of accurate, simple, and high efficiency, because the algorithm only needs to search neighbor and calculates normal vectors for interpolation nodes that are usually far less than the point cloud. The experimental results of several real and simulated point sets show that our method is over three times faster than the Elliptic Gabriel Graph-based method, and the average deviation is less than 0.01 mm.  
normal vector; large-scale; scattered point cloud; hash table; interpolation 1
## 4 Discussion
[39] reported the highest antimicrobial activity of LAB against _L. monocytogenes_ and _E. coli_. In a study by [PERSON] et al. [40], all lactic bacteria tested inhibited the growth of _S. aureus_, and one strain additionally reduced the growth of _E. coli_ and \(S\). Typhimurium. [PERSON] et al. [44] demonstrated that _L. lactis_ inhibited the growth of _E. coli_, _Pseudomonas fluorescens_ and _S. epidermidis_. On the other hand, _L. casei_ decreased the abundance of _P. fluorescens_, _S. epidermidis_ and _S. aureus_[44]. [PERSON] et al. [48] showed high antimicrobial activity for _L. paracasei_ sub. _Paracasei_, _L. casei_ and two strains of _L. rhamnus_ against _S. sonnei_, _E. coli_ and \(S\). Typhimurium. High antimicrobial activity against 12 selected pathogenic bacteria and _Aspergillus flavus_ strains of the genus _Lactobacillus_ isolated from conventional curd was demonstrated by [PERSON] et al. [49]. In turn, [PERSON] et al. [50] showed that _L. brevis_ and _L. plantarum_ species isolated from local Malaysian fermented foods showed antimicrobial activity against _Porlyomonas gingivalis_ (a periodontal pathogen). Different results, compared to our on the decrease in the number of pathogenic microorganisms in the presence of LAB were presented by [PERSON] et al. [51]. This may be due to the fact that the study used a cell-free supernatant (_L. casei_, _L. delbrueckii_, _L. fermentum_, _L. plantarum_ and _L. pentosus_). In a trial using _S. aureus_, _L. monocytogenes_, _E. coli_ and _Klebsiella pneumoniae_, the supernatant showed no inhibitory activity against the listed pathogens. Weak activity was noted against _Pseudomonas aeruginosa_ and _Proteus mirabilis_, while moderate activity was noted against _Bacillus cereus_, \(S\). Typhi and _Shigella flexneri_[51]. The action of LAB is related to antimicrobial peptides that penetrate the outer membranes of Gram-negative bacteria and the cell walls of Gram-positive bacteria, and produce cavities, causing the efflux of intracellular contents [52]. LAB activity may also result from organic acid production [2]. The evaluation of the mechanisms of LAB action on pathogenic bacteria requires further research. The presence of LAB in dairy products can benefit human health but also contributes to the reduction or elimination of foodborne bacteria. The limitation of this study was the small number of yoghurt samples and _Lactobacillus_ spp. strains isolated from the yoghurt samples, which could be related to the use of one type of medium (the Rogosa agar, which is a medium designed to culture _Lactobacillus_ spp., among other things, from dairy product samples) and incubation conditions. Future studies should consider a wider range of isolation methods, including molecular methods. Additionally, it would be valuable to assess the mechanisms influencing the reduction in pathogenic bacteria by _Lactobacillus_ bacteria.
## 4 Data and Methods
### Image Restoration Algorithm
Based on the given remarks, the final determination of the most appropriate pixel \(C_{i,j}\) for the removal of a cloudy pixel \(C_{x,y}\) is given by:  
\[C_{i,j}=CFV\cdot IDW \tag{4}\]  
In case two or more pixels gained the same weight due to the estimation of their feature vector and their spatial distance, the pixel with the smallest spatial distance is chosen as appropriate fill pixel for \(C_{x,y}\). We implemented two different approaches to fill cloudy pixels based on the determined CFV. A standard approach, which was used in this study, is based on the assumption that the spectral information of a clouded pixel \(C_{x,y}\) is completely lost for all spectral bands. Therefore the information of all spectral bands \(k\) is replaced by the spectral information of the respective spectral band \(k\) of the CFV. An alternative approach is useful in case only single bands of a certain pixel are corrupted. In this case only certain bands of this pixel are reconstructed. This scenario applies for the Landsat ETM+ SLC-Off effect, for example.
## 2 Literature Review
### Vision-Based Methods for Construction Equipment Monitoring  
Traditional observational approaches to construction sites are time-consuming, costly, error-prone, and sensitive to the complexity of construction sites and available manpower for monitoring [34; 50]. Numerous vision-based methods for the detection, tracking, and activity recognition of objects, humans, or construction equipment have been reported [51; 52]. Histograms of oriented gradients (HOG) classifiers and CNN-based faster R-CNN, R-CNN-based IFaster R-CNN, and Region Proposal Network (RPN) techniques have been reported for the detection of objects, workers, non-hardhat-use of the construction workers, and excavators to maintain a safe environment at construction sites [22; 53; 54]. The potential of computer vision techniques for the pose estimation of the construction equipment has also been explored using Cascaded Pyramid Network and Stacked Hourglass Network [55; 56; 57]. A CNN-based algorithm has been proposed to enhance the accuracy of the 3D pose estimation of the excavator by using synthetic images during the training process and by utilizing hybrid datasets that integrate simulation and laboratory data under diverse scenarios and camera parameters [58]. Faster R-CNN, Deep Simple Online and Realtime Tracking (SORT), and 3D ResNet classifier have been used to detect, track, recognize activities, and perform productivity analysis using surveillance videos of the construction site [19].