esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources and Processing  
The dataset used in this study included Boreal Ecosystems Productivity Simulator (BEPS)-driven data, agricultural statistical data, NDVI data, and land use and land cover data.  
Climate forcing data from the year 2000 to 2017 for driving the BEPS model include air temperature, precipitation, relative humidity, surface downward shortwave radiation, and wind speed. These meteorological observing station data were obtained from the China meteorological data service center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 2 November 2020)), and were used to generate the 500 m meteorological fields by means of the weighted inverse distance. In the interpolation of temperatures, a lapse rate of 0.6 \({}^{\circ}\)C per 100 m was assumed. The pattern of the multi-year average temperature and precipitation over eastern Inner Mongolia is shown in Figure 2. Overall, the average temperature and precipitation increase from southwest to northeast, and range from 0.45\({}^{\circ}\)C to 2.9\({}^{\circ}\)C and 262 mm to 535 mm, respectively.  
Besides climate datasets, the leaf area index (LAI) and land cover data are also needed. The long-term LAI used in this study was generated from the surface reflectance datasets (MOD09A1) of the Moderate Resolution Imaging Spectroradiometer (MODIS) by driving an LAI inversion algorithm based on a four-scale geometrical optical model [30; 31; 32]. The  
Figure 1: The geographical location and topography of eastern Inner Mongolia.  
Figure 2: Spatial distribution of annually averaged temperature (**a**) and precipitation (**b**) during 2000–2017 in eastern Inner Mongolia.  
yearly land cover data were obtained from the 500 m MODIS land cover data (MCD 12Q1), and reclassified into eight vegetation cover types according to the land cover classification scheme of the International Geosphere Biosphere Programme (IGBP). For the initialization of BEPS, the fractions of clay, silt, and sand in the soil are necessary for determining the physical parameters of the soil, including the wilting point, field capacity, porosity, hydrological conductance, and so on. These characteristics of the soil were retrieved from the harmonized global soil database ([[http://www.fao.org/](http://www.fao.org/)]([http://www.fao.org/](http://www.fao.org/)), accessed on 1 October 2022). The MODIS 500 m NDVI data (MOD13A1) were obtained from the NASA Distributed Active Archive Center (DAAC), and the yearly MOD13A1 datasets are 16-day composites using the maximum value composite (MVC) method. In addition, the numbers of livestock data in the study area were taken from the Inner Mongolia statistical yearbook ([[http://tj.nmg.gov.cn/datashow/index.htm](http://tj.nmg.gov.cn/datashow/index.htm)]([http://tj.nmg.gov.cn/datashow/index.htm](http://tj.nmg.gov.cn/datashow/index.htm)), accessed on 1 October 2022).
## 3 Preliminaries
### Trapezoidal Fuzzy Number and a-Cut
#### 3.1.2 The \(\alpha\)-Cut of Trapezoidal Fuzzy Numbers  
For the \(\alpha\)-cut of the trapezoidal fuzzy number \(\widetilde{a}=[a_{L},a_{M1},a_{M2},a_{R}]\), it can be represented as \(\widetilde{a}(\alpha)=\{x\in R|\widetilde{a}(x)\geq\alpha\}\), where \(\alpha\in[0,1]\). For different \(\alpha\), the \(\alpha\)-cut of the trapezoidal fuzzy number \(\widetilde{a}\) varies. However, any \(\alpha\)-cut of a trapezoidal fuzzy number can be represented by an interval number in which \(\widetilde{a}(\alpha)=[a_{L}(\alpha),a_{R}(\alpha)]\). Specifically, when \(\alpha=1\), the \(\alpha\)-cut is \(\widetilde{a}(1)=[a_{M1},a_{M2}]\), and when \(\alpha=0\), the \(\alpha\)-cut is \(\widetilde{a}(0)=[a_{L},a_{R}]\). Based on the \(\alpha\)-cut formula, the trapezoidal fuzzy number \(\widetilde{a}=[a_{L},a_{M1},a_{M2},a_{R}]\) can be rewritten in the following \(\alpha\)-cut form:  
\[\begin{split}\widetilde{a}(\alpha)&=[a_{L}(\alpha ),a_{R}(\alpha)]=[a_{L}+\alpha(a_{M1}-a_{L}),a_{R}+\alpha(a_{M2}-a_{R})]\\ &=[(1-\alpha)a_{L}+\alpha a_{M1},(1-\alpha)a_{R}+\alpha a_{M2} ]\end{split} \tag{2}\]  
where \(a_{L}(\alpha)\) and \(a_{R}(\alpha)\) represent the lower and upper bounds of \(\widetilde{a}(\alpha)\) for the \(\alpha\)-cut, respectively. The trapezoidal fuzzy number and its \(\alpha\)-cuts are illustrated in Figure 2.  
Figure 3 shows an arbitrary triangular fuzzy number and its \(\alpha\)-cut. Compared to the triangular fuzzy numbers, the trapezoidal fuzzy number function is characterized by a gradual decrease after it first rises and keeps the maximum affiliation constant over an interval. This characteristic makes the trapezoidal fuzzy number more flexible to fit the uncertainty in many realistic scenarios. Also, when the maximum affiliation interval of the trapezoidal fuzzy number is short enough, it is practically equal to the triangular fuzzy number. In view of these advantages of trapezoidal fuzzy numbers, this paper chooses trapezoidal fuzzy numbers to improve Shapley value with trapezoidal fuzzy numbers in order to deal with the problem of profit distribution in cooperative games more accurately.  
Figure 3: Arbitrary triangular fuzzy number and its \(\alpha\)-cut.  
Figure 2: Arbitrary trapezoidal fuzzy number and its \(\alpha\)-cut.
# CVE2 ATT&CK: BERT-Based Mapping of CVEs to MITRE ATT&CK Techniques
## 1 Introduction
[PERSON] [12] highlights the importance of combining CVEs with the ATT&CK framework to achieve threat intelligence. Years ago, it was considerably harder for security teams to understand the attack surface, thus reducing their capacity to protect the organization against cyber attacks. With the emergence of the ATT&CK project, the security teams have a better overview of the CVEs based on known attack techniques, tactics, and procedures. Vulnerability management can be divided into three categories, namely: the \"Find and fix\" game, the \"Vulnerability risk\" game, and the \"Threat vector\" game. The first one is a traditional approach where the vulnerabilities are prioritized by CVSS Score; this is applicable for small organizations with less dynamic assets. The second category consists of risk-based vulnerability management where organizational context and threat intelligence (such as CVE exploited in the wild properties) are considered; this applies to organizations that have security teams, but the number of CVEs is too large. The \"Threat Vector\" game includes the understanding of how the hackers might exploit the vulnerabilities while accounting for the MITRE ATT&CK framework mappings between CVEs and techniques, tactics, and procedures. The third category is the most efficient model of threat intelligence, with inputs delivered to the vulnerability risk management process from cyber attacks that have occurred and are trending. As such, security teams should take into account risks for building the vulnerability management program, but also threat intelligence to have a better understanding of vulnerabilities and to discover the attack chains within the network [13]. The aim of this paper is to develop a model that leverages the textual description found in CVE metadata to create strong correlations with the MITRE ATT&CK Enterprise Matrix techniques. To achieve this goal, a data collection methodology is developed to build our manually labeled CVE corpus containing more than 18,100 entries. Moreover, state-of-theart Natural Language Processing (NLP) techniques that consider BERT-based architectures are employed to create robust models. We also target addressing the problem of a severely imbalanced dataset by developing an oversampling method based on adversarial attacks. Efforts have been already undertaken to interconnect CVEs to the MITRE ATT&CK Framework. However, we identified limitations of existing solutions based on the research gap in the literature regarding the identification of correspondences between CVEs to the corresponding techniques from the MITRE ATT&CK Enterprise Matrix. The following subsections details existing state-of-the-art techniques relevant for our task.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
###### Abstract  
Augmented reality (AR) is rapidly becoming a popular technology for exhibitions. The extended content provided through virtual elements offers a higher level of interactivity and can increase the appeal of the exhibition for younger viewers, in particular. However, AR technology in exhibition settings is typically utilized to extend the effects of exhibits, focusing solely on individual experiences and lacking in shared social interactions. In order to address this limitation, in this study, we used AR technology to construct a participatory exhibition-viewing system in the form of an AR mobile application (app), \"Wander Into Our Sea\". This system was developed as a component of the 2022 Greater Taipei Biennial of Contemporary Art exhibition titled \"Log Into Our Sea\". The app features two modes: exhibition-viewing mode and message mode. The first embodies passive exhibition-viewing while the second offers channels for active participation. The app has three functions: (1) in exhibition mode, visitors passively view the exhibition content through the AR lens, (2) in message mode, visitors can use the AR lens to leave messages in the 3D space of the exhibition to become part of the exhibit, and (3) during the use of either mode, the app collects data on visitor behavior and uploads it to a cloud to create a research database. The third function allowed us to compare the behaviors of exhibition visitors while they used the two modes. Results revealed that without restricting the ways and sequences in which AR content was viewed, there were no significant differences in the duration of viewing, or the distance covered by visitors between the two modes. However, the paths they took were more concentrated in the exhibition-viewing mode, which indicates that this mode encouraged visitors to view the exhibit in accordance with the AR content. In contrast, in message mode, visitors were encouraged to leave text messages and read those left by others, which created disorganized unpredictable paths. Our study demonstrates an innovative application of AR positioning within an interactive exhibition-viewing system, showcasing a novel way to engage visitors and enrich their experience.  
augmented reality; participatory exhibition viewing; visitor behavior analysis Article
# Topology Optimization of an Aerospace Bracket: Numerical and Experimental Investigation
## 2 Materials and Methods
### Additive Manufacturing of Parts  
After topologically optimizing the bracket, the optimized models were converted into STL files and subsequently sliced using the Ultimaker Cura 5.3.0 software [33]. In the Cura Software, the printing material, printing parameters, and printing conditions were carefully selected according to the desired functionality of the printed part. The printing material used in this work is Ultimaker PLA [34] due to its desirable qualities. According to Ultimaker, PLA (polylactic acid), which is available in 11 colors, is very versatile and easy  
Figure 2: Domain regions for optimization (blue) along with the excluded regions (red) for the four studied scenarios.  
to use; it is reliable, prints with high dimensional accuracy, and produces surface finish of high quality, thereby making it a good option for different applications such as detailed prototypes, simple manufacturing jigs, and gauges [34]. As noted in relevant literature, the following printing parameters affect the quality of the printed part as well as the total cost of production; hence, they must be carefully selected.  
1. The printing speed, which defines the material deposition rate and printing time [6; 35; 36].
2. Layer height influences the quality and dimensional accuracy of the printed parts [36; 37; 38; 39; 40].
3. Extrusion temperature, which is the temperature of the nozzle, affects the viscosity of the melted polymer and so impacts the quality [38; 41; 42].
4. The infill density and its pattern affect the mechanical properties of the FDM 3D-printed part [43].  
Based on the studies above [6; 35; 36; 37; 38; 39; 40; 41; 42; 43], the following printing parameter values were selected: printing speed of 65 mm/s, 50% infill density, Triangle infill pattern, and layer height of 0.2 mm. The printing was performed using the Ultimaker ([[https://ultimaker.com/3d-printers/ultimaker-s5](https://ultimaker.com/3d-printers/ultimaker-s5)]([https://ultimaker.com/3d-printers/ultimaker-s5](https://ultimaker.com/3d-printers/ultimaker-s5)), accessed on 8 September 2023) S5 3D printer, and the recommended temperatures of 60 and 205 \({}^{\circ}\)C were set for the build plate and extruder, respectively. After selecting the appropriate printing parameters and slicing the initial and optimized models using the Cura software, the generated printer codes were transferred to the printer where the models were built. In total, 15 samples of the bracket were printed: five for the initial model, and five for each of the level-set and density-based optimized results.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation
### Computational Overheads
#### 6.1.1 Theoretical and Experimental Delays

Table 5: Theoretical computational overheads. It is noteworthy that, for all blockchain-based schemes, we omitted the blockchain indexing time in the computational overhead calculation. Blockchain indexing is a process for gNBs to categorize recently downloaded blocks. It can improve the blockchain random access speed to a constant time. Since indexing can be performed in the background parallelly at any time, it does not impose a significant negative effect on authentication. Hence, only the average blockchain transaction read time \(T_{\text{BC. }read}\) and write time \(T_{\text{BC. }write}\) were included in the calculation. For the experimental computational delays shown in Figure 5, the 5 GSBA was slower than the standardized 5G-AKA by \(5.1377-3.4683=1.6694\) milliseconds. This is because the 5 GSBA employs asymmetric ECDH to replace the less secure symmetric key-based key derivation function (KDF) in the 5G-AKA [4]. By introducing this tiny computational overhead, the 5 GSBA can ensure session key perfect forward secrecy (PFS), backward secrecy, and linkability attack prevention. On the other hand, the computational time of the 5 GSBA and the SE-AKA [20] are comparable because both schemes employ ECDH, but the 5 GSBA offers an enhanced security function in terms of DoS attack prevention and DDoS alleviation. The BB-AKA 5G [10] consumes much more time than the 5 GSBA because the BB-AKA 5G needs two ECDSA signatures and verifications at the gNB and the AMF, while in contrast, the 5 GSBA only requires one lightweight one-way hash secret verification in the gNB. Thus, the 5 GSBA saves \((10.4107-5.1377)/10.4107=50.6\%\) of computational overhead compared to the BB-AKA 5G. Consequently, although the 5 GSBA protocol has unavoidably introduced tiny computational overhead, it is still the most efficient protocol in terms of balancing between security and performance.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
###### Abstract  
This study aims to explore the joint usage of multisource scatterometer measurements in polar sea water and ice discrimination. All radar backscatter measurements from current operating satellite scatterometers are considered, including the C-band ASCAT scatterometer on board the MetOp series satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT). By performing seven experiments that use the same support vector machine (SVM) classifier method but with different input data, we find that the SVM model with all available HSCAT, CSCAT, and ASCAT scatterometer data as inputs gives the best performance. In addition to the SVM outputs, we employ the image erosion/dilation techniques and area growth method to reduce misclassifications of sea water and ice. The sea ice extent obtained in this study shows a good agreement with the National Snow and Ice Data Center (NSIDC) sea ice concentration data from the years 2019 to 2021. More specifically, the sea ice areas are closer to the sea ice areas calculated using 15% as the threshold for NSIDC sea ice concentration data in both Arctic and Antarctic. The sea ice edges acquired by the multisource scatterometer show a close correlation with sea ice edges from the Sentinel-1 Synthetic Aperture Radar (SAR) images. In addition, we found that the coverage of multisource scatterometer data in a half-day is usually above 97%, and more importantly, the sea ice areas obtained on the basis of half-day and daily multisource scatterometer data are very close to each other. The presented work can serve as guidance on the usage of all available scatterometer measurements in sea ice monitoring.  
multisource scatterometer; radar backscatter measurements; sea ice extent; support vector machine; classification 2023  
1
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 3 LoRaWAN Specifications and Applications  
Considering The OSI stack, LoRAWAN and LoRa are referring to the physical, and a part of the data link layers, where LoRaWAN operates on top of LoRa. In other words, LoRa physically transmits the data by using its proprietary modulation, while LoRaWAN is a scheme to make the connection of several end-devices to gateways possible and practical, therefore, the network architecture is called LoRaWAN [7; 8; 25]. In this system when the messages leave the gateways towards the network server, the communication is no longer based on LoRaWAN, but high-throughput backhauling such as Ethernet [26]. In other words:  
* LoRa is a proprietary, physical layer (PHY) modulation technique and it uses chirp spread spectrum (CSS).
* LoRaWAN is an open-source protocol for medium access control (MAC).
## 6 Recent Advances and Future Trends in AI-Based PdM  
Recent advances in AI-based PdM have improved the performance and accuracy of predictive maintenance predictions and increased the autonomy and adaptability of machines in complex and dynamic working environments. Some recent advances in this field include the following:  
1. Integration of advanced machine learning algorithms;
2. Edge and cloud computing for real-time analysis and data storing;
3. Predictive analytics with big data;
4. XAI for transparency;
5. IoT sensor integration;
6. Digital twin, AR, VR, MR, and extended versions.  
The field of AI-based PdM has been developing and improving, as shown in Figure 10. The future research topics in the field are given below:  
1. Big data and analytics are used to collect, analyze, and interpret large amounts of data.
2. The exponential growth of cyber-physical systems of digital twins, AR, VR, XR, metaverse, and human-driven industrial metaverse solutions to both physical and virtual work environments allows smooth collaboration and communication between employees, machines/robots, and AI.
3. Development of autonomous maintenance systems that are capable of self-diagnosis, decision making, and proactive interventions without human intervention.
4. Evolving toward zero-touch maintenance operations where AI systems automate the maintenance process from detection to resolution.
5. Extraction of actionable insight advancements in AI algorithms to predict failures and provide actionable insights and recommendations for optimal maintenance strategies.
6. Integrating experiential learning and reinforcement learning techniques to improve AI models based on ongoing data and continuous feedback.
7. Implementation of blockchain technology for data security to enhance the security and integrity of PdM data, ensuring trust and transparency.
8. Development of trustworthy AI algorithms and human-centric AI interfaces for better collaboration between AI systems and human operators, facilitating seamless interaction and decision making.
9. Development of energy-efficient AI-based PdM to minimize resource consumption while maintaining high prediction accuracy.
10. Development of collaborative robots (cobots), IIoT, edge and cloud computing, and 5G/6G connectivity for next-step PdM autonomy and smart factory that can adjust to shifting circumstances and changing conditions and streamline manufacturing processes.
11. Development of generative AI models to contribute to the above items. For example, they can provide failure warnings, present encompassing instructions for repair and replacement methodologies, achieve suggestions to optimize energy consumption and cut down the carbon footprint to human operators by simulating the real system and/or analyzing maintenance logs and sensor data, and facilitate better collaboration between automated systems and human operators through natural language communication in automated maintenance planning.
## 3 Results
### Optimization of the Enzymatic Treatment
#### 3.2.2 Optimization of the Enzymatic Cocktail Composition  
The optimization of the enzymatic cocktail was performed to identify the best combination for an improved extraction. TPC and TFC were determined after the application of different enzymatic combinations, as well as at different enzymatic concentrations. The yield of TPC and TFC was analyzed to select the best conditions (Table S2). By coupling the enzymatic pretreatment and the hydroalcoholic extraction (EH + HA), the extraction yield seemed higher than the single extractants (EH or HA). No differences were appreciated among enzymes at different concentrations (100 and 200 U/g DM). This suggested that the concentration of 100 U/g DM could be sufficient to pretreat the plant material Table S2. All the enzyme combinations seemed to display similar performances, highlighting that all four enzymes could have an action on the structural breakdown of plant matrices. Nevertheless, the enzymes contributing most to the extraction yield of phenolics and flavonoids appeared to be mainly cellulase and pectinase, suggesting that the combined use of cellulase and pectinase could lead to an effective enzyme cocktail for extraction of bioactive compounds from _V. nigrum_ (Table S2).  
Noteworthy higher extraction yields have been reported by other authors using conventional extraction techniques such as homogenizer-assisted extraction (TPC up to 42 mg/g DM and TFC up to 39 mg/g DM for an extraction in methanol [38]) or maceration (TPC up to 61 mg/g DM and TFC up to 50 mg/g DM for maceration in methanol [39]), with most studies reporting an extremely high variability in extraction yields from different _Verbascum_ species, highlighting the effect of biomass composition [31; 40]. Importantly, several works exploring the use of other green methods for extraction of bioactive compounds, such as supercritical fluid extraction, pulsed electric field extraction, microwave-assisted extraction or ultrasound-assisted extraction, showed extraction yields in the range of 2-120 mg/g DM [40], suggesting that the tandem enzymatic and hydroalcoholic extraction proposed in the present work can be a valuable methodology.  
The extracts obtained for each condition were then analyzed through HPLC-DAD/MS, to preliminarily highlight potential compound enrichment. Five peaks were observed with or without the preliminary enzymatic treatment. Quantification of verbascoside was performed for different enzyme combinations, as a proxy for extraction efficiency. The extraction yield of the analyzed compound could generally be improved by coupling the enzymatic treatment with the hydroalcoholic extraction, if compared to the enzymatic treatment or the hydroalcoholic extraction alone.