esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### Edge-Industrial IoT (IIoT) and Wireless Sensor Networks (WSNs)  
Edge computing is a term indicating data processing closer to the source of information. These systems prioritize local computing and routing, minimizing latency and reducing the load on centralized servers. Ref. [39] describes edge networks as an architecture consisting of two components: edge servers and edge devices, where the latter forward computationally intensive tasks and data to the former, closer-positioned servers, rather than a central cloud or other servers.  
While edge computing disperses the load of central computing to many different nodes, these edge devices have limited resources, leading to worse efficiency in encryption/decryption, and have an overall lower quality of data connected from these nodes, as highlighted in [40]. It represents a decentralized computing paradigm where data processing occurs as close to the source of data generation as possible, rather than relying solely on centralized infrastructure. This shift toward edge computing addresses several challenges faced by IoT deployments, particularly those requiring low-latency responses and real-time decision-making.  
IIoT networks are considered to be the numerous sensors and interconnected devices that monitor and control critical systems, such as manufacturing processes, manufacturing plants, power grids, etc. Combined with Edge, they form the category of Edge-IIoT networks, as discussed in [41; 42; 43]. Ref. [43] describes IIoT networks as larger-scale and more machine-based compared to small-scale home networks. The paper also shows their disadvantages, such as how the heterogeneity of devices and protocols often complicates integration.  
Wireless Sensor Networks (WSNs), as described by [44], consist of spatially distributed sensors that monitor and collect data about their environment. The paper highlights problems and solutions of using the MQTT protocol; specifically, these networks are widely used due to their scalability and ease of deployment. Energy efficiency is a primary concern, however, as sensor nodes are often powered by batteries with limited lifespans. [PERSON] et al. [44] describe utilizing a technology that had its limits. It took several minutes before the test case anomaly was discovered, which could lead to major losses in real life. Their findings concur that while simulation has been very helpful in gathering data and identifying abnormalities, there are still many other routes to explore to enhance this research.  
IIoT, Edge-IoT, and WSNs highlight the diversity of infrastructure approaches in IoT deployments, each addressing specific performance, scalability, and security needs. Edge-IoT prioritizes low-latency processing, IIoT emphasizes reliability and uptime in industrial contexts, while WSNs provide a flexible and scalable framework for distributed data collection. Despite their distinct focuses, these architectures often overlap in practice, creating hybrid networks that leverage their combined strengths.
## 3 Spatial-Temporal Dependency Modeling  
### The Adjacent Matrix  
The adjacent matrix, which reflects the geographical dispersion of the wind farms, is very important for the spatial-temporal dependency modeling and there are many methods to construct the adjacent matrix. As the wind farms are located in a region and the spatial dispersion is mostly based on distance, we define the adjacent matrix based on the Gaussian kernel threshold distance function of the wind farms.  
\[A_{i,j}=\left\{\begin{array}{cc}e^{-\frac{dist(i,j)^{2}}{std^{2}}},&\textit{ if dist}(i,j)\leq\\ 0,&\textit{otherwise}\end{array}\right.\varepsilon \tag{6}\]  
where \(dist(i,j)\) is the geographical distance between wind farm \(i\) and wind farm \(j\). \(std\) is the standard deviation of the distance between n wind farms and \(\varepsilon\) is the threshold. Here, we choose the half of the mean distance as the threshold. If the distance is smaller than the threshold, we assess that there is no connection between the two wind farms to guarantee the sparsity of the adjacent matrix.
# Design, Development and Use of a Digital Badges System in Higher Education
## 1 Introduction  
Digital badges are electronic symbols used to document performance [1]. Recently, they have gained interest in education as a tool for promoting engagement, motivation, participation and achievement [2]. Open badges are intended to provide additional information via metadata, in order to verify issuer details, evaluation criteria and evidence.  
The use of badges as credentials implies the importance or standardization, in order to provide credibility for skill acquisition [3]. In 2012, the Mozilla Foundation introduced the Open Badges Infrastructure [4] as a valuable technology for educational settings. Since then, other sites emerged with similar goals and approaches, such as Credy [5], BadgeOS [6], Acceldible [7], etc, and smart badges systems have been implemented in several educational institutions around the world. Although different badges environments may accommodate varying degrees of interoperability, the private and open source developers were and are aware of the need for portability of credentials and the expectations that users want to accumulate and display badges.  
Since the middle of the last decade, several studies have emerged on how badges can best be purposed [8, 9] and on their value and efficacy in educational settings. Related to higher education (see Section 2), its future may benefit from open badges used as mobile credential systems. They are a way of recognizing informal learning that occurs outside the traditional classrooms. Moreover, they have the potential to strengthen traditional programs to support competency-based programs and to link earners to employers and professional organizations.  
Nevertheless, more evidence is needed to support the value and contribution of these systems in high educational levels. In this work we describe the features of the Badgesmodule of our custom made Social Learning Environment (SLE) SocialWire [10] and an example of its use in a gamified edition of a master level course on computer networks.  
The rest of the paper is organized as follows. Section 2 summarizes some recent related work. Related modules in SocialWire are presented in Section 3. Section 4 describes in detail the badges module and Appendix A its integration with Open Badges. The example of use is explained in Section 5. Finally, some concluding remarks are included in Section 6.
## Materials and Methods
### Earth Observation LAI Estimates  
In this study we evaluate three global EO LAI products. MODIS 500m product (MOD15A2H) provides estimates of LAI at 500 m spatial resolution with an 8-day interval from 2000-present [10]. GEOV2 1 km [11] and GEOV3 300 m LAI [12] were derived from the SPOT/VEGFATION sensor data at a 10-day interval, at spatial resolutions of 1/112\({}^{\circ}\) and 1/336\({}^{\circ}\), respectively (approximately 1-km and 300 m at the equator), in the Plate Carree projection. For the MODIS 500 m LAI product, we consider only the products derived from the main algorithm, which is based on the use of Look Up Tables (LUTs) built for six different plant functional types, with simulations from a three-dimensional radiative transfer model [61]. For the GEOV2 1 km and GEOV3 300 m LAI products, LAI is estimated using Neural Networks(NNTs) applied on Top-of-Canopy (TOC) input reflectance in the red, near-infrared, and shortwave infrared bands, at 1 km resolution and 300m, respectively [11; 12]. The period of MODIS 500 m LAI products and GEOV2 1 km LAI products is from 2005 to 2017 and 2014 to 2017 for the GEOV3 300 m LAI product (released from 2014).  
The definition of uncertainty information varies between LAI products. For MODIS LAI, the standard deviation is used to measure the uncertainty of pixel LAI values, which is calculated over all acceptable solutions of a look-up table (LUT) retrieval method [17]. For GEOV2 1 km and GEOV3 300 m LAI products, the uncertainties are computed as the RMSE between the final decadal value and the daily NNTs estimates in the compositing period [12; 62]. In this study, they are both treated as the LAI products' uncertainty. The quality assessment information is used to clean the LAI and inform uncertainty information at the pixel level for all of these LAI products. For MODIS 500 m products, data effected by cloud shadow, internal cloud masks, or aerosol are removed and only main retrievals  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sites** & **Latitude** & **Longitude** & **Altitude** & **MAT** & **AP** & **Gradient** & **Forest Types** \\ \hline BNF & 21.91 & 101.2 & 730 & 21.8 & 1506 & 18–25\({}^{\circ}\) & Tropical seasonal rainforest \\ HSF & 22.67 & 112.89 & 70 & 21.7 & 1761 & 18–23\({}^{\circ}\) & Mixed coniferous forest \\ DHF & 23.16 & 112.53 & 300 & 21 & 1996 & 25–35\({}^{\circ}\) & Mixed coniferous forest \\ ALF & 24.54 & 101.01 & 2488 & 11 & 1931 & 5–25\({}^{\circ}\) & Natural wet evergreen \\  & 29.57 & 101.98 & 3160 & 4.2 & 2175 & 30–35\({}^{\circ}\) & Obadalpine Emei fir forest \\ SNF & 31.3 & 110.47 & 1650 & 10.6 & 1722 & 10–70\({}^{\circ}\) & Evergreen and deciduous \\  & & & & & & mixed broad-leaved forest \\ \hline \hline \end{tabular}
\end{table}
Table 1: Background information for each site. MAT: mean annual temperature; AP: annual precipitation.  
were used in the analysis. For Copernicus LAI data (GEOV2 1 km and GEOV3 300 m), pixels that were filled or interpolated, or out of LAI range, were removed.
# Numerical Investigation of the Relationship between
## 3 Results
### Optimization of Anastomosis Angle
#### 3.1.4 Recirculation  
The presence of recirculation is associated with the development and progression of cardiovascular diseases. Due to flow stagnation caused by recirculating flow, substances found in the blood can easily adhere to and accumulate on blood vessel walls. As such, recirculation is linked to atherosclerosis, thrombosis, endothelial damage, as well as intimal thickening [43; 44; 45; 46; 47]. The optimal graft design must therefore be able to induce blood flow with minimal recirculation regions.  
Figure 11 shows the areas affected by recirculation (negative axial velocity), as seen in the cross-section of the artery one millimeter and five millimeters away from the anastomosis toe. Recirculation regions were found in the upper regions of the artery. This is in line with the flow separation in the pressure and axial velocity contours, and it corresponds to the dead zone observed in the region. Since the centrifugal force due to inertia shifted the bulk motion outwards toward the host artery bed, a low-velocity region formed within the toe region that allowed for recirculation.  
The size of recirculation is influenced by the anastomosis angle as higher anastomosis angles tend to have larger recirculation regions. This was observed in the two monitoring planes. Of particular interest is the total elimination of recirculation in the second monitoring plane at lower anastomosis angles. This size dependence of the area of recirculation on the anastomosis angle is also consistent with the pressure and velocity contours observed for the models.  
Figure 11: Areas affected by recirculation 1 mm and 5 mm away from the anastomosis toe.  
Figure 10: Extent of the area affected by abnormal WSS. The 30-degree anastomosis angle resulted in the total elimination of abnormally high WSS, so it is not present in the chart on the right.  
The location of recirculation regions and the dependence of their size on the anastomosis angle is consistent with findings in the literature [26; 48; 49], indicating the advantage of smaller anastomosis angles in bypass graft design through the minimization of recirculation regions.
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 3 Smart Road Traffic Accidents Reduction Strategy
### Entities  
The entities involved in the traffic accident reduction strategy are as follows:  
1. Smart Vehicles
2. Road Side Units (RSUs)
3. Trusted Authority (TA)
4. Governmental Authority (GA).  
1. _Smart Vehicles_ In VANETs, a communicating device called the On-Board-Unit (OBU) is installed in each vehicle which allows it to communicate with other entities in the system. Vehicles communicate with other vehicles and the RSU through the OBU to obtain a better awareness of their surroundings in order to react in a timely manner to any unusual situation, for example, accident, traffic jam, road blockage, and so on.
2. _Road Side Units (RSUs)_ The RSU keeps the information of the automobiles (vehicles) moving in its transmission range so that it has a global view of the traffic. This feature enables the RSU to maintain the efficient traffic flow and, hence, accidents are also avoided.  
Figure 1: Taxonomy of Literature Review.  
_Trusted Authority (TA)_  
The Trusted Authority (TA) is a trusted administrative authority. Both vehicles and RSUs are registered with the TA. The TA assigns unique identities to both the RSUs and vehicles. The TA has a record of all the registered entities so that they remain liable to it.  
_Governmental Authority (GA)_  
The Governmental Authority (GA) plays the role of a passive entity. The passive entity is not directly involved in the execution of TARS; however, it plays a vital role if a non-registered vehicle is encountered so that it should not affect the security of the VANET system at all.
## 4 Parallel Multi-Objective Energy Valley Optimizer
### Optimization Considering Forecast Errors  
A classic example of energy production planning involves creating a model under the assumption that input data are accurately known and fixed at certain nominal values, using evolutionary algorithms to obtain near-optimal solutions [30]. Building on this, some scholars have already conducted research considering the significant uncertainty and randomness inherent in actual energy load [5]. However, it leads to inevitable discrepancies between actual demand and forecasted values when using time series prediction models for demand forecasting.  
In the initial energy-production-planning process, the solution candidate set calculates the fitness of each solution candidate based solely on predicted demand. As iterations progress, the solution candidates gradually adapt to predicted energy load, leading to near-optimal solutions. However, when actual demand deviates from the predicted values, the performance of the near-optimal solutions may decline sharply.  
To enhance the robustness of the near-optimal solutions against prediction errors, this paper modifies the basic EVO by shifting from serial iteration of a single solution candidate set to parallel iteration of multiple solution candidate sets targeting different predicted demands. This modification is designed to improve the robustness of the obtained near-optimal solutions. The strategy is shown in Algorithm 1.  
The flowchart of the Algorithm 1 is shown in Figure 3.
## 5 Simulation Analysis  
In this paper, the distribution network of a city in northeast China is used as a reference area. At 7:00 on 7 November 2018, freezing rain began to appear in the large city. The distribution lines began to trip and overlap at 11:30. After 15:00, the wind speed gradually expanded and the precipitation gradually decreases.  
The distribution network of reference area consists of 4 substations (CG substation, Y substation, RB substation, and GZ substation), including a total of 74 nodes and 73 distribution lines with a voltage level of 66 kV, 4 BPS, and 4 tie-lines. Reference region power distribution system node diagram is shown in Figure 5. There are three types of nodes in the reference region power distribution system diagram. The yellow nodes are circuit breaker nodes, the blue nodes are branch nodes which can lead multiple feeders, and the black nodes are ordinary nodes which including load switches and 66/10 kV substation.  
This paper selects 8 important distribution lines which are highlighted in red in the Figure 5. The basic information of the 8 lines are shown in Appendix C. Among them, three dedicated lines (DL) DL-A, DL-B, DL-C are used for business hospital and confidentiality, respectively. In addition, these BPSs are installed at nodes 9, 45, 61, and 65. ZL1-ZL4 are 4 tie line switches that used to transfer the loads. Z1-Z4 are switches of BPS which are opened in normally state. There is a segment switch between each two nodes such as Z50. In the normal state, ZL1-ZL4, Z1-Z4 are shown as dotted lines and segment switches are shown as solid lines. Among them, the solid line indicates the switch is closed and the dotted line means the switch is opened.
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 6 Pharmacological Activity of D-Limonene: A Potential Natural Medicine
### Antiviral Activity  
The antiviral efficacy of plant essential oils and their constituents has attracted the interest of researchers due to their safety and reliability. Dozens of herbs and hundreds of natural compounds have been reported to exhibit antiviral effects by modifying the immune system and inhibiting viral replication [177; 178].  
D-limonene has been considered to have antiviral properties. [PERSON] et al. [179] speculated that the antiviral activity of D-limonene may be attributed to the presence of cyclo-hexenyl. According to the paper, D-limonene has an inhibitory effect on the replication of herpes simplex virus type I; treatment with the maximum non-cytotoxic concentration of D-limonene before infection reduced the infection rate by 100%, but the treatment only slightly reduced plaque formation after the virus penetrated the cells, suggesting that D-limonene primarily targets the free herpes simplex virus type I. This result indicates the potential medicinal value of D-limonene in treating recurrent herpes labialis [180].  
In addition, the influenza A virus H1N1 inhibition assay demonstrated that D-limonene achieved a virucidal activity log reduction that was 4.32 and 3.94 after 250 and 125 \(\mu\)g/mL (0.025% and 0.0125%), respectively, effectively killing 99.99% of the virus. This efficacy is comparable to that of household disinfect sodium hypochlorite used at 0.21% [46]. [PERSON] et al. [181] utilized molecular docking analysis to show that the inhibitory effect of D-limonene on Lassa virus L polymerase, an enzyme essential for viral transcription and replication, was comparable to ribavirin, which is currently the most effective drug for treating Lassa virus fever.  
COVID-19, caused by SARS-CoV-2, remains a significant public health concern. ACE\({}_{2}\) is the human host factor or cell entry receptor for SARS-CoV-2 [182]. Molecular docking revealed that D-limonene has structural similarity to thymine in the SARS-CoV-2 viral genome and can potentially inhibit the receptor binding domain of the SARS-CoV-2 spike protein from fusing with the ACE2 protein. This could reduce SARS-CoV-2 host cell binding, cellular internalization, and pathogen release [53; 183]. Moreover, cytokine storm is a major cause of death in COVID-19 patients. D-limonene, by mediating multiple inflammatory pathways and mediators, might inhibit cytokine storm [27] and regulate the expression of various signaling pathways, such as PI3K/ Akt/IKK-\(\alpha\)/NF-\(\kappa\)B P65, making it a potential candidate for preventing or treating COVID-19-related pulmonary fibrosis [184].  
Current research shows the potential of D-limonene in the development of novel antiviral drugs. However, as different viruses cause varied infection symptoms, further in-depth studies are necessary to understand the mechanisms and efficacy of D-limonene against different viral infections.
## Materials and Methods
### Methods  
#### 2.3.1 Harmonic Analysis  
Harmonic analysis is a method of expressing a function or signal as a superposition of fundamental waves as Equation (1). In this study, to estimate the variation of multiple variables (e.g., SSM, precipitation), a harmonic model \(H(t)\) was built with elements of a constant band (\(\beta_{0}\)), a linear term of slope (\(\beta_{1}\)) and harmonic terms of amplitudes (\(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\) and \(\beta_{5}\)). The constant band \(\beta_{0}\) represents the extent of consistency of the time series. \(\beta_{1}\) shows linear trend of a time series in spite of seasonal variations. Therefore, the term \(\beta_{1}\) represents the annually increasing/decreasing trend. Additionally, \(f\) represents the fundamental frequency and harmonic terms of amplitudes (\(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\) and \(\beta_{5}\)) are used to simulate seasonal variations.  
\[H(t)\ =\ \beta_{0}+\beta_{1}t\ +\ \beta_{2}\ \cos(2\pi ft)\ +\ \beta_{3}\ \sin(2\pi ft)\ +\ \beta_{4}\ \cos(4\pi ft)\ +\ \beta_{5}\ \sin(4\pi ft) \tag{1}\]  
Following the methods in [38; 39], harmonic analysis was applied to show a yearly trend followed by precipitation, ET and surface soil moisture in global drylands from 1 January 2000 to 31 December 2019 using monthly values (units in mm). The units of SSM/ET outputs from FLDAS and GLDAS products were also transformed into mm to compare with MODIS products.
## 5 Conclusions  
To improve the accuracy and performance of 3D face imaging, an effective coarse-to-fine spatiotemporal stereo matching scheme using speckle pattern projection was proposed, which was accelerated by STBF. Comparing our 3D face reconstruction results with the ground-truth data collected by high-precision 3D industrial scanning equipment for global errors, the impact of the projected speckle-pattern number and matching windows on measurement accuracy and reconstruction speed were further researched. Through quantitative and qualitative analyses of the experimental data, the optimum combination of parameters needed to provide a balanced strategy of reconstruction speed and accuracy was made. It was demonstrated that the system can achieve accuracy with an average measurement error of 0.071 mm and standard deviation of 0.091 mm with N = 12 speckle stereo-image pairs. Experimental results showed that increasing the number of projectedspeckle patterns yields better measurement accuracy, while the optimal matching window will continue to shrink and the computation time will increase slightly. To meet the requirement of rapid 3D face imaging under slight motion, this study provides a compact 3D face acquisition device with three fixed speckle projectors. Using the optimal combination parameters for three-speckle stereo images, the spatiotemporal correlation stereo-matching process, which takes the longest time among the components, is accelerated by the GPU through the use of a parallel pipeline strategy between each core processing unit; the purpose of real-time data acquisition and real-time 3D reconstruction. In the future, the authors intend to apply the proposed scheme to 3D face verification and recognition in practical scenarios.  
Conceptualisation, W.X., K.F. and P.Z.; Data curation, J.Z. and P.Z.; Investigation, H.Y., J.Z. and P.Z.; Software, W.X. and P.Z.; Writing--original draft, W.X. and K.F.; Writing--review and editing, W.X. and K.F. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the National Natural Science Foundation of China (Grant Nos. 61901287, 61703077), Sichuan Province Key Program (Grant Nos. 2020 YFG0112, 2020 YFG0306), Sichuan Province Science and Technology Program (Grant Nos. 2019 ZDZX0039 and 2018 GZDZX0029), Chengdu Key Research and Development Support Program (Grant No. 2019-YF09-00129-GX) and SCU-Luzhou Municipal People's Government Strategic Cooperation Project (Grant No. 2020 CDLZ-10).  
Not applicable.  
We thank the anonymous reviewers for their insightful suggestions and recommendations, which led to the improvement of the presentation and content of the paper.  
The authors declare no conflict of interest.