esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Efficiency Analysis
### End-to-End Cost  
In order to assess the overall (computation and communication) end-to-end cost of the secure communication protocol, we simulated 50 RSUs, each equipped with a single CPU, while the SA is equipped with 10 CPUs working in parallel. This is a reasonable assumption since the SA will be equipped in practice with much higher processing power than RSUs. We assume all messages are pending on infinite capacity FIFO queues before they are processed. For the end-to-end cost we add the average wait in queues (for the SA and the RSUs) and the computational cost for each processing step (composition of a message, signature etc)--see Figure 6,b. We omit the the average waiting time of the RSU FIFO queues since it is zero in all cases. The computational cost of each vehicle is roughly the cost of encryption 1.05 ms.  
As expected, the processing costs related with the SA is the potential bottleneck of our scheme, which however can be easily avoided by assigning proportionally higher parallel processing power to the SA, with respect to the number of covered RSUs.
## 1 Introduction
[10] analyzed land use and cover changes in gold ore areas in the Brazilian Amazon rainforest using Landsat images and the RF classifier. [PERSON] et al. [7] evaluated the extent of soil degradation caused by gold mining in the Magadan region of China and its changes in the 21 st century, based on Landsat/Sentinel-2 satellite data. [PERSON] et al. [40] used the kNN machine learning algorithm to estimate mineral resources (predicting the gold grade in the Quartz Ridge area) and analyze the impact of its unregulated extraction on land use and land cover. The integration of RS techniques and machine learning algorithms, such as kNN, combined with spectral sensitivity studies of targets using indices, emerge as essential tools in monitoring land cover in mining areas, enabling spatiotemporal analysis with precision and effectiveness [41; 42]. This methodological approach allows for identifying and quantifying changes in vegetation and soil moisture, providing crucial data to assess the environmental impacts resulting from mining activities. [PERSON] et al. [43] demonstrated the applicability of this technique in mapping areas affected by forest fires in Portugal, efficiently using kNN to classify Landsat-8, Sentinel-2, and Terra satellite images. [PERSON] and [PERSON] [44] highlight that, although kNN may be slightly more sensitive to training sample size compared to other algorithms like SVM, it still presents high Overall Accuracy, especially when the sample size is sufficient. In this context, this study proposes a decision support model for sustainable monitoring of mining activities in semi-arid regions of Brazil, offering an innovative integrated analysis of sensing and machine learning using high-resolution orbital images to spatially analyze environmental variability within and around areas impacted by mining. The approach combines the kNN classifier with spectral indices such as NDVI and NDWI, derived from PlanetScope satellite images, covering the period from 2018 to 2023. This model aims to address existing gaps in the literature by offering an integrated analysis that tackles both the socio-environmental impacts and the efficiency in monitoring landscape transformations in semi-arid scenarios, with an emphasis on the Serita-Cedro region. However, it is noteworthy that the methodology proposed in this study can be implemented to evaluate the spatiotemporal behavior of land cover in other mining regions with arid and/or semi-arid climatic characteristics.
# Targeted Universal Adversarial Examples for Remote Sensing
## 3 Methodology
### Targeted Universal Adversarial Examples  
#### 3.1.1 Problem Definition  
According to [21], universal adversarial examples are designed to be effective for all images and mislead the victim model \(f\) to make wrong predictions. Formally, given the dataset, e.g., \((x,y)\in\mathcal{D}\), the victim model has been trained well and has a high accuracy for classification. So, we have \(f(x)=y\) in most cases. We use \(x_{ua}\) to represent the corresponding adversarial counterpart of \(x\), where \(x_{ua}=x+\delta\). To generate universal adversarial examples from \(\mathcal{D}\), we seek perturbation \(\delta\), satisfying  
\[f(x+\delta)\
eq y\quad\text{subject to}\quad\|\delta\|_{p}\leq\epsilon, \tag{7}\]  
where \(\epsilon\) is the maximal magnitude for the \(l_{p}\) norm of the generated perturbations \(\delta\), and \(p\) can be \(0,2,\) and \(\infty\). We set \(p=\infty\) in our paper.  
Similar to universal adversarial examples, targeted universal adversarial examples are also image-agnostic. However, instead of simply fooling the victim model, we expect them to mislead the victim model to output the desired prediction class \(t\). We use \(x_{tau}\) to represent the targeted adversarial counterpart of \(x\), where \(x_{tau}=x+\delta_{t}\). Correspondingly, the perturbation \(\delta_{t}\) is supposed to satisfy  
\[f(x_{tau})=t,\quad\text{subject to}\quad\|\delta_{ct}\|_{p}\leq\epsilon. \tag{8}\]
## 3 Two Case Studies Using Web 2.0 Technologies for Distance Education
### Case Study 2, Principles of Coastal Resource Management
Team wikis, those wikis created by the student groups with an assignment brief, but without a pre-defined structure, were accessed a total of 5205 times by students (average 70 hits per student) over the Session, with one student (the top performing student mentioned before) accessing their team wiki 503 times (only slightly fewer times than the Associate Lecturer (tutor) who accessed this content area a total of 535 times over the same period). How students used the free-for-all and team wikis as well as group pages incorporating file exchange and discussion boards is summarised in Table 4. The usage and hit rates suggest active engagement from students with the wiki environment. Thirteen of the fifteen groups submitted their collaborative work using wiki structure. Three groups uploaded a file for download into the wiki. One group chose to present their collaborative output both as a wiki, and as an embedded file for download. Of the thirteen teams to submit using wiki structure, five used hyperlinking, five used embedded graphics, and four teams included embedded tables. Despite a list of fourteen potential non-engagers (based on LMS site activity in the lead up to Week 5) only five groups experienced problems around participation, with six students designated as non-participators. Staff allocated each group one potential non-engager, despite this one group  
Figure 4: The Home page of the ‘Chaucer Bay Council Wiki’ wiki used in PCRM to situate students in their scenario-based learning approach, and provide introductory resources on how to use wiki’s. Students created their own wikis, according to their own design, for their major group work. experienced two drop-outs. The average grade for the group work was 75.5% and team grades ranged between 65% and 96.3% (Table 4). The teacher in charge of marking this assignment reflected that the quality of the group work exceeded previous years' individual efforts. Communication between group members in PCRM was a common theme in the students' reflection blogs and clearly some groups were more successful at communicating than others:  
\"A couple of issues presented themselves during the process, mainly stemming around the lack of real conversation, and human contact. Working in this type of situation is difficult when trying to manage timeframes. When working as a group face to face, you are able to throw ideas around straight away, but the problem we had was that it took a few days before you found out the opinions of others.\"  
\"This project has been a challenge with some of us being in different areas and timezones. There has been the same effort applied by all in the group at different times, due to this, and overall this has been a success. Personally I have found this project to be similar to the ones I have been involved in within my professional life in remote areas where there is little personal interaction and the life of the project being decided by emails and other electronic communications that I have never personally met.\"
## 1 Introduction
Performance evaluations have demonstrated that the EGO algorithm based on the PEI criterion exhibits strong robustness and effective global optimization capability. However, it is known that a major limitation of the parallel EGO algorithm based on the PEI criterion is that as the number of candidate points increases within a single optimization cycle, the improvement in parallel optimization efficiency gradually slows down. A possible explanation proposed by researchers is that as more IFs are multiplied by the initial EI function, the reliability of the PEI function gradually decreases. Consequently, the likelihood of identifying high-quality candidate points diminishes. Currently, no systematic study has been conducted to investigate the impact of the number of candidate points  
Figure 1: The fundamental process of surrogate-based optimization (SBO). per optimization cycle on the performance of the EGO algorithm. Key aspects such as the quality of the converged solution, convergence speed, and variations in algorithmic diversity remain unexplored. Furthermore, existing numerical studies are based on the ordinary kriging (OK) model, without exploring how the number of candidate points affects the performance of the EGO algorithm under different kriging models, such as the gradient-enhanced kriging (GEK) model. This study aims to enhance the practical applicability and further development of the EGO algorithm based on the PEI criterion. A comparative analysis was conducted on nine analytical functions with different characteristics to investigate the impact of the number of candidate points on the performance of the PEI-based EGO algorithm using both the OK and GEK models. A systematic and in-depth evaluation was performed from multiple perspectives, including global optimization capability, convergence speed, optimization efficiency in achieving the desired convergence criteria, algorithmic diversity, and the balance between global exploration and local exploitation. The remainder of this paper is organized as follows: Section 2 provides a brief overview of the methodology of the parallel EGO algorithm and details the design of the comparative numerical experiments. Section 3 presents the experimental results and discussion. Finally, Section 4 presents the conclusions and recommendations.
## 5 Conclusions  
This study enhanced NEP estimation by incorporating developmental factors (growing-season GPP and SOS) into autumn phenology model and using the modified phenology outputs to constrain the CASA model. The integration of developmental factors significantly improved the autumn phenology model's performance, resulting in lower RMSE and higher correlation. The CASA model, constrained by phenological information, exhibited markedly better alignment with MODIS NPP products. Moreover, compared to the original model, the NEP produced by the phenology-modified CASA model achieved a 15.34% reduction in RMSE and a 74% improvement in R\({}^{2}\) against flux tower observations. From 2003 to 2021, the annual average total NEP of China's terrestrial ecosystems was 489.67 \(\pm\) 38.27 Tg C/yr and significantly increased with an average growth rate of 1.75 g C/m\({}^{2}\)/yr\({}^{2}\). Among vegetation types, evergreen broadleaf forests maintained the highest NEP, while shrublands exhibited the lowest values. However, the NEP growth rates across all four economic regions remained insufficient to offset the concurrent increases in industrial carbon emissions, with eastern China showing the highest net carbon emissions and facing significant challenges in achieving carbon neutrality. This study proposes a new and more accurate framework for NEP estimation, which provides valuable insights for understanding and predicting China's carbon sink strength and offers scientific support for the formulation of carbon neutrality policies.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17030487/s1](https://www.mdpi.com/article/10.3390/rs17030487/s1)]([https://www.mdpi.com/article/10.3390/rs17030487/s1](https://www.mdpi.com/article/10.3390/rs17030487/s1)), Figure S1: Correlation of interannual fluctuations between different GPP datasets.  
Conceptualization, S.J. and S.R.; methodology, S.J. and S.R.; investigation, S.J.; writing--original draft preparation, S.J. and S.R.; writing--review and editing, L.F., J.C., G.W. and Q.W. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Key R&D Program of China (No. 2022 YFC3204400) and the National Natural Science Foundation of China Key Program (No. 52339002).  
All data used in this study could be obtained freely online.  
We acknowledge the data support from National Earth System Science Data Center, National Science & Technology Infrastructure of China.  
The authors declare no conflicts of interest.
## 2 Problem Formulation
### Numerical Simulation Modeling  
The thermal characteristics of LED lighting devices have been studied using finite element method (FEM) simulation tools. However, numerous FEM tools could be equally effective in thermal simulations [20, 21]. This study involved performing numerical simulation by using COMSOL Multiphysics software (COMSOL, Inc., Burlington, MA, USA) [18] to analyze CLRDL thermal management [22].  
The simulation module included the predefined non-isothermal flow model of Multiphysics coupled to simulated systems for density variations with temperature. The \"conjugate heat transfer in non-isothermal flow\" model from COMSOL Multiphysics was used for the ALFHS mounted in the CLRDL, and the coupled \"free and porous media flow and heat transfer in porous media\" model was used for the CUFHS mounted in the CLRDL. The stationary models involving steady-state \"free fluid and solid coupling with heat\", and \"free and porous media flow coupling with heat\", as well as general heat transfer models were set up for the simulation.  
Heat transfer in natural convection was applied according to the conditions of commercially available CLRDLs. Passive methods that use natural convection are generally characterized by high reliability [23, 24]. Natural convection is widely implemented in the lighting industry because of its low cost, low noise, simplicity, small size, and reduction of reliability issues [18] caused by active convection part failure. Heat sinks that employ natural convection require no additional power supply. Additionally, they are almost maintenance free, which is suitable for long lifetime devices, such as LEDs [25].  
However, the use of heat dissipation through natural convection heat transfer introduces the problem of low heat reduction efficiency. Air flow velocity is quite slow, especially in enclosed areas. Thus, relying on conducting repeated experiments is inadequate for meeting the design requirements for an effective, high-quality heat sink. For natural convection, a high volume of space is essential because larger spaces lead to high air velocity. To investigate the relations between space and the natural convection around solid state lighting systems, simulations with various dimensions of typical enclosures [26] were performed. Numerical simulation software facilitates enhancing the heat management design performance [27].  
We applied the specified models for the ALFHS and CUFHS mounted to the CLRDL and installed in the test box individually. The models were applied to conditions in which the high temperature in the test box is reached in a steady state. Designing heat sinks with heat dissipation capabilities that satisfy the condition that the LED junction temperature remains below a specified temperature is imperative.
###### Abstract  
Gully erosion poses severe problems for land degradation in several areas worldwide. This study aims to evaluate the accuracy and robustness of the maximum entropy (MaxEnt) method for assessing gully erosion susceptibility. We selected the catchment of the Mesima stream as the test site, which is situated in the southwest sector of the Calabria region (South Italy). An inventory map of gully erosion was realised and 12 predisposing factors, such as lithology, soil texture, soil bulk density, land use, drainage network, slope gradient, aspect, length-slope (LS), plan curvature, stream power index (SPI), topographic position index (TPI), and topographic wetness index (TWI), were selected to implement the dataset in the MaxEnt method. The accuracy and uncertainty of the method were tested by 10-fold cross-validation based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and related area under curve (AUC). The dataset was randomly divided into 10 equal-sized groups (folds). Nine folds (90% of the selected dataset) were used to train the model. Instead, the remaining fold (10% of the dataset) was used for testing the model. This process was repeated 10 times (equal to the number of the folds) and each fold was used only once as the validation data. The average of 10 repeated processes was performed to generate the susceptibility map. In addition, this procedure allowed the reliability of the susceptibility map to be assessed, in terms of variables, importance and role of predisposing factors selected, prediction ability, and accuracy in the assessed probabilities for each pixel of the map. In addition to exploiting the 10-fold cross-validation, the mean value and standard deviation for the probability estimates of each pixel were computed and reported in the susceptibility and uncertainty map. The results showed that the MaxEnt method has high values of accuracy (>0.90), of the kappa coefficient (>0.80), and AUC (>0.92). Furthermore, the achieved findings showed that the capacity of the method used for mapping gully erosion susceptibility is quite robust when the training and testing sets are changed through the 10-fold cross-validation technique.  
gully erosion-prone areas; forecast models; machine-learning algorithm; k-fold cross-validation; Mediterranean area 2020
## 3 The ML Strategy within Network Slicing
### The Role of Machine Learning
#### 3.1.1 Supervised Learning  
Supervised learning involves training a model with a labelled dataset, allowing it to learn between input features and the respective outputs. After the training, the model is tested with unlabelled data and generates output based on input-output pairings [82]. These algorithms rely on external guidance for the learning process.  
Supervised learning, focusing on classification, finds particular efficacy in various security applications such as intrusion detection systems (IDS), malware detection, spam filtering, and anomaly detection [72]. Through labelled data, supervised learning models can accurately categorize incoming data instances. This proficiency efficiently identifies potential intrusions, malicious software, unsolicited emails, and abnormal patterns within datasets. Notably, supervised learning exhibits high accuracy in detecting known threats and can continually improve through feedback and retraining. However, it is not without its drawbacks. Dependency on labelled datasets and susceptibility to overfitting, wherein the model performs well on training data but struggles with unseen data, are among the challenges associated with this technique [83].
## 4 Tracking
### Experiment and Analysis
#### 4.3.2 Case Two  
In this case, the EKF, UKF, CKF, and PF were used to track the trajectory in Figure 7, in which the adaptive model was used. The tracking covariances of each estimation methods are shown in Table 4.  
It can be seen that the filters have similar performance. The estimated covariance of EKF is the largest, which shows the disadvantage of EKF when dealing with strong nonlinearity. UKF and CKF have the similar tracking covariance, the same conclusion has been gotten by other research works [84; 85]: they both have higher accuracy than the EKF. A similar conclusion was reached in Ref. [86] for bearings-only tracking (BOT) problem. [PERSON] and [PERSON] [87] also compared a UKF and CKF using an extensive set of positioning benchmarks, including real and simulated data from GPS and mobile phone base stations, and resulting in conclusion that in tested scenarios no particular filter in this family is clearly superior. Therefore, although the CKF was developed later than the UKF, we find that it does not have the obvious advantages than UKF.  
As to the PF, in this simulated system, it did not show superior performance because the Gaussian noise was used. In fact, for the practical system with non-Gaussian noise, PF can be much better than other methods based on accurate modeling of the noise, while the performance of PF depends heavily on the modeling methods in Section 4.1. It means that if we use the inaccurate model, the tracking performance will decrease greatly.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 3 Virtual Network Embedding Problem Formulation
### Measurements of Substrate Network Resources
#### 3.4.1 VNR Acceptance Ratio  
The VNR acceptance ratio is defined in Equation (6). It is determined by the number of successfully embedded virtual networks and the number of total virtual network requests, which can directly reflect the ability of the virtual network embedding algorithm, and it is an important metric to evaluate the virtual network embedding algorithm.  
\[R_{\text{accept}}=\frac{N_{\text{success}}}{N_{\text{total}}}, \tag{6}\]  
where \(N_{\text{success}}\) represents the number of successfully embedded virtual network requests, and \(N_{\text{total}}\) represents the number of total virtual network requests.