esa-sceva/satcom-chunk-collection huggingface dataset:
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research Category** & **Publication Year** & **Author** & \begin{tabular}{c} **Features/Neural** \\ **Network Input** \\ \end{tabular} & **Method** \\ \hline Detection & 2020 & \begin{tabular}{c} [PERSON] et al. [8] \\ [PERSON] et al. \\ [9] \\ [PERSON] [10] \\ [PERSON] et al. [11] \\ \end{tabular} & \begin{tabular}{c} Statistical Features \\ Statistical Features \\ Statistical Features \\ Statistical Features \\ \end{tabular} & \begin{tabular}{c} LGBM, Random Forest \\ Random Forest, Naive \\ Bayes, SVM, LSTM \\ Bi-RNN \\ Transformer \\ Decision tree, Random \\ Forest, Logistic \\ Regression \\ \end{tabular} \\ \hline Detection and Identification & 2021 & \begin{tabular}{c} [PERSON] et al. [13] \\ [PERSON] et al. [14] \\ \end{tabular} & \
\begin{tabular}{c} Statistical Features \\ Statistical Features \\ Statistical Features \\ \end{tabular} &
\begin{tabular}{c} LGBM, XGBoost \\ Stacked Random Forest \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research on DoH-encrypted DNS covert channel detection and identification. By summarizing the existing research on DoH-encrypted DNS covert channel detection, we identify the following limitations:  
* There are few works related to DoH-encrypted DNS covert channel detection and identification in existing studies, and the performance in this area still needs to be improved;
* Most existing studies use statistical features as the basis for detection, which makes it easy for attackers to evade detection by using single features;
* The role of byte sequence features and combining multiple features is ignored, thus failing to meet the requirements of encrypted DNS covert channel detection. In summary, there is a lack of work related to DoH-encrypted DNS covert channel identification in existing studies, and the detection performance still needs to be improved. Therefore, in this paper, we focus on the detection and identification of DoH-encrypted DNS covert channels and calculate the correlation between statistical features and session byte sequence features globally through Multi-Head Attention to obtain weighted fusion features as a basis for detection and identification. As the correlation between global features is extracted and the key features are highlighted, detection and identification performance have been further improved.
## 3 State-of-the-Art MIMO-SDR Systems  
In this section, we will discuss the main innovative work concerning MIMO systems: the hardware/software, the different existing waveforms, the channels, and the equalization algorithms.
## 4 Experiments and Numerical Results
### Experimental Setup
#### 4.1.2 Evaluation Metrics  
In link prediction, the aim is to rank all entities in the triple according to their probability of being the correct target. The model's performance is evaluated based on the ranking position of the actual target entity. To assess the model's overall recall, we employed Mean Reciprocal Rank (MRR), which measures the average proportion of correctly ranked entities across all test cases. We report the MRR and ranking results in a filtered setting, where entities other than the ground-truth target are filtered out.
## 2 Materials and Methods
### Liquid Chromatography--Mass Spectrometry  
The ultrahigh performance liquid chromatography-mass spectrometry (UHPLC-DAD-ESI/MS\({}^{\text{n}}\)) analysis was performed using a Thermo Scientific Ultimate 3000 RSLC (Dionex, Sunnyvale, CA, USA) equipped with a Dionex UtiMate 3000 RS diode array detector and coupled to a mass spectrometer. The column used was a Thermo Scientific Hypersil GOLD C18 (Part No. 25002-102130; Dim 100 mm \(\times\) 2.1 mm; Lot 14913; SN 10518298) with a part size of 1.9 \(\mu\)m and its temperature was maintained at 30 \({}^{\circ}\)C. The mobile phase was composed of (A) acetonitrile and (B) 0.1% formic acid in water (\(\upsilon/\upsilon\)), both degassed and filtered before use. The flow rate was 0.2 mL/min. For the first 14 min the mobile phase had 5% of (B). Next, the gradient of (B) increased to 40% for 2 min and then to 100% for 7 min. From 23 min to the end it was set back to 5% (B). The injection volume was 2 \(\mu\)L. The equipment was operated in negative-ion mode with an electrospray ionization source of 5.00 kV and ESI capillarity temperature of 275 \({}^{\circ}\)C. The full scan covered a mass range of 50 to 2000 \(m/z\). Collision-induced dissociation and MS\({}^{2}\) experiments were simultaneously acquired for precursor ions. The quantification of the individual compounds in the plant extracts was performed by the external standard method, using reference compounds for each family. A calibration curve was obtained by the injection of at least six different known concentrations and with a correlation coefficient (R\({}^{2}\)) higher than 0.99 (see detailed information in Supplementary Materials).
# Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks
###### Abstract
High traffic density introduces data traffic overload, particularly when using broadband access services such as transferring a video from a vehicle or a roadside unit (RSU) to solve a transport management problem [16, 17]. Thus, new technologies, such as mobile edge computing (MEC), software-defined networking, SDN, and blockchain, should be used to provide the required network scalability, reliability, and availability [18]. One of the main challenges with VANETs is the handover process [19]. Handover, or handoff, is the process in mobile communication and telecommunication systems in which a connected cellular call or data session is transferred from one base station or cell site to another, without the session becoming disconnected. Cellular services are based on mobility management; thus, handover allows users to move from one cell site range to another or switch to the nearest cell site for better performance [20]. Handoff can also be described as a process in which a link is transferred from one base station to another base station due to a lack of signal strength. Several operations are included in the handoff process, such as initiating the handoff, allocating the channel, breaking the connection with the old base station, and choosing the new base station [21]. In VANETs, due to high mobility, users will move from the coverage of one base station, i.e., an RSU with an eNB, to another, and thus a handover mechanism should be deployed efficiently and in a seamless way [22, 23]. This work provides a novel handover mechanism to perform, manage, and control the handover process of a VANET that runs over a 5G cellular system. Mobile edge computing (MEC) is a recent communication technology that cellular network operators have introduced to improve network efficiency [24]. This can be achieved by offloading computing workloads to nearby clouds instead of going through the core network to the remote cloud [25]. Deploying MEC for communication systems achieves various benefits, including higher system reliability, availability, and spectrum efficiency [26]. Furthermore, MECenables the network operator to introduce new services and provide a new means of service innovation [27]. In this work, we use MEC as part of a vehicular network in which cars can communicate with smart traffic lights, surrounding objects, other vehicles on the road, and other infrastructure. The main contributions of this article can be summarized in the following points:  
1. Designing and developing a reliable system structure for a VANET over a 5G system, with the deployment of MEC technology, that supports V2X and V2V communications. 2. Designing and developing a novel seamless, fast handover mechanism for the introduced VANET system structure. 3. Implementing the developed handover algorithm over an MEC server. 4. Conducting a performance evaluation of the proposed system and the developed handover mechanism.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 2 Literature Review
### Comparison and Integration of Road Safety Methods  
Each road safety analysis method--reactive, proactive, and predictive--has unique strengths that can be leveraged individually or in combination to achieve more accurate identification of high-risk road segments. Integrating these approaches maximizes the effectiveness of safety interventions and resource allocation.  
Studies show the value of combining these approaches. For example, integrating statistical models with GIS enhances the spatial accuracy of risk assessments [19; 20; 21], while hybrid methods balance the strengths of different approaches [22; 23]. This synergy enables tailored interventions that address specific network conditions and data availability, ultimately improving road safety management and contributing to sustainable transport systems, as demonstrated in Figure 1.
# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 4 Implementation  
### Used Databases and the Exemplary Driver Model  
For representing real human driving behavior, the open-source dataset inD ([[https://ind-dataset.com/](https://ind-dataset.com/)]([https://ind-dataset.com/](https://ind-dataset.com/)), accessed on 1 February 2023) was selected, which provides recordings of four German unsignalized intersections from a birds-eye perspective shown in Figure 3 (right) [35]. The dataset is composed of _tracks data_ describing the spatiotemporal motion of all road users, _meta data_ describing dimensions and classifications of road users, and the respective openDRIVE map for the location. For creating the human behavior database, all recordings except recording 12 were selected, whereby this recording was retained for validation purposes as described in Section 3.5 (as referred to inD12). For testing the proposed metric, artificial driving data were created on two intersections, which are shown in Figure 3 (left). For testing on a large scale of interactive situations, data were created with the help of the simulation framework _Spider_ at BMW [36]. Driving behavior is generated by the BMW proprietary driver model TRM. The model follows a hierarchical and heuristic structure, categorizing the driving task into maneuvers like car-following, changing lanes, overtaking, stopping due to an obstacle or red lights, slowing down for speed limits, or curved roads. Based on the situational and environmental information, multiple maneuver evaluators identify their need for action in each time step. The chosen maneuver is subsequently performed by applying the respective motion model such as the Wiedemann-following model for car-following [1]. Behavioral and contextual parameters are computed according to Section 3 for all datasets employing the fusion and interaction identification algorithms presented in our previous work [8]. The algorithms first fuse the time-series and map data by assigning all vehicles to the respective lane they are driving on. Based on this assignment and the knowledge from the map, interactions can be identified and described by semantic parameters.
# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
## 1 Introduction
According to the most recent IPCC report, floodplain forests are one of the most threatened ecosystems because of the undergoing \"savannization\" feedback to extreme drought events [37]. In this scenario, the carbon and methane sink services they provide could be compromised [38]. Furthermore, recent reports have demonstrated considerable tree mortality on central Amazonian floodplains due to changes in flooding quota by upstream dam construction [39]. Although induced by indirect changes to the flood pulse, direct effects of extreme droughts also revealed the sensibility of this ecosystem. The droughts of 1997 and 2005 have also shown that fires have a stronger and longer-lasting impact on floodplain forest structure than in upland forests; moreover, these floodable areas are located at the core of the Amazon forest, threatening the resilience of the entire system [40]. Most Amazonian floodplain studies have been located in the central Amazon basin, surrounding the Solimoes and Amazonas rivers. Nonetheless, a different pattern of climate change is expected to occur at the southern portions of the basin, with an increase in temperature and decrease of precipitation during wet seasons [41]. In the southeast region, bordering the Amazon basin, there is an LBA (large-scale biosphere-atmosphere) eddy flux tower site (Bananal site) located on a seasonal floodplain forest, at the transition between the Amazon and Cerrado biomes. Previous studies have suggested that this forest shows two periods of water stress--one due to excess of water in the peak of the rainy season--and another due to water deficit, at the end of the dry season [42; 43; 44]. During the ElNino drought of 2005, decreased rates of evapotranspiration [42] and increasing rates of tree mortality were reported in this region [44]. Given the uncertainties of floodplain environmental response to seasonal cycles of flood and dry periods, this study aims to evaluate the seasonal productivity of this floodplain forest located in the Amazon-Cerrado transition, as well as its environmental controls, based on field observations and on enhanced vegetation index (EVI) derived from MODIS (MAIAC) surface reflectance data. We asked the following questions: (i) How does the Bananal seasonally flooded tropical forest respond to flood and drought cycles in terms of evaporative fluxes and gross primary productivity (GPP)? (ii) Do phenological patterns observed from field data agree with landscape-level remote observations (EVI from MODIS/MAIAC)? (iii) Does flooding affect the EVI signal of these forests?
## 2 Measurement Methods and Equipment: Laboratory Drum
### Which Test Should Be Used: Rolling on a Flat Runway or on a Steel Drum?  
The previously presented measurements confirm that the thermal evolution of the tire tread is mostly influenced by the skidding angle followed by the velocity and loading. These results are in agreement with the results presented by [PERSON] et al. [15]. They discussed the effect of the skidding angle, velocity and loading on the temperature of the tire tread rolling on a flat runway.  
In a previous study [5], the same aircraft tire was tested on flat runway. The rolling conditions were identical for the skidding protocol; the velocity was limited to \(70\ \mathrm{km/h}\). The following figures illustrate the temperature distributions in the contact zone, along the interface line after rolling on a flat runway (Figure 10) and on a steel drum (Figure 11). The rolling conditions are similar (vertical loading about \(200\ \mathrm{kN}\), velocity lower than \(60\ \mathrm{km/h}\), and skidding angle up to \(8^{\circ}\)).  
Figure 10: Experimental measurements: (**a**) thermal evolution of an aircraft tire tread rolling on a standard runway, (**b**) image of contact zone, infrared camera acquisition, (**c**) typical S-profile of thermal evolution of tire tread rolling on a flat runway.  
A typical S-profile of temperature evolution is shown in Figure 10c by the orange curve. Figure 11a represents the thermal evolution of the tire tread in the case of rolling on a steel drum with the same S-profile tendency. Figures 10a and 11a present the temperature distribution starting from the hottest outside part (zone 1), followed by the temperature of the middle part (zone 2) and finished by the coolest inside part (zone 3). Rolling on a regular runway or on a steel drum, the inside groove temperatures seem to be slightly affected (Figure 10a or Figure 11a) by the frictional or inelastic heating of the rubber; the low conductivity of the rubber could explain this phenomenon. In conclusion, it seems that the main part of the tire tread heating results from friction phenomenon, which leads to similar thermal evolutions of the tire rolling on a standard runway or on a steel drum.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 2 Materials and Methods
### PlanetScope Data  
In the areas circumscribed in the field surveys, high-resolution satellite images were acquired to characterize the trees by calculating vegetation indices. In this study, we used PlanetScope nano-satellite images acquired between 15 July 2019 and 15 July 2023. These images were derived from the second generation of PlanetScope satellites (PS2.SD), which are available from 2019 and include 4 spectral bands and 8 quality bands. The 4 spectral bands are reported in Table 2.  
The quality bands (Table 3) were used for cloud mask and noise correction [26; 38]. The noise due to changes in sun angle depending on acquisition time was removed by calculating weekly composites by averaging all images acquired for each week [39].  
Figure 3: Overlapping georeferenced points (each point represents a tree) and PlanetScope false-color images: (**a**) Villa Lazzaroni (excluded from the analysis) and (**b**) Villa Borghese (included in the analysis).  
Images were co-registered using the PlanetScope co-registration tool, which enables the spatial alignment of a series of images within a specified time series ([[https://developers.planet.com/apis/orders/tools/](https://developers.planet.com/apis/orders/tools/)]([https://developers.planet.com/apis/orders/tools/](https://developers.planet.com/apis/orders/tools/)) accessed on 20 August 2024). The order--and, thus, the co-registration, clipping, and downloading of the imagery--was performed in sequence for 21 study areas. This was important, as, to properly apply the co-registration tool, there had to be consistent overlap between different PlanetScope scenes; thus, small study areas were needed. The final resulting number of downloaded co-registered and clipped images was 30,481.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.4 EfficientNet B7 Model  
In the current research, EfficientNet B7 is adopted for wetland remote sensing segmentation, owing to its sophisticated architecture and capacity for managing high-resolution data. Distinct from conventional models, this approach deploys parallel sub-models to analyze two-channel data subsets independently, thereby optimizing learning processes and discerning complex data correlations. The incorporation of a distinctive batch normalization technique contributes to model stability, augmenting resilience for tasks involving high-resolution criteria [33]. The amalgamation of these varied feature sets enables EfficientNet B7 to provide an exhaustive and subtle interpretation of multichannel data, establishing it as a superior solution for enhancing the accuracy of high-resolution wetland image segmentation [34].  
Given the model's three-channel input prerequisite, four-channel image data in this study are subdivided into subsets. Each subset contains two channels, resulting in an ensemble of data slices. Each generated slice then feeds into sub-models, which are based on the EfficientNet B7 architecture. These sub-models accommodate three channels of data, simplifying the challenges typically associated with direct multichannel data processing. Batch normalization is applied before the data is inputted into the EfficientNet B7 network [35], enhancing the model's learning environment and promoting better generalization. The sub-models produce corresponding feature sets, which undergo global pooling to achieve uniform dimensions, prior to the concatenation phase. These layers consist of 256 and 128 units, each activated by the'relu' function. The study implemented 140 epochs, a parameter established through cross-validation outcomes, instrumental in assessing the intricacies of model complexity, learning rate, and the potential for both overfitting and  
Figure 5: The architecture of the RegNet model.  
underfitting. To counteract overfitting, an amalgamation of CE_Loss and FocalLoss was employed, as substantiated by references [28,29]. The EfficientNet B7 model's architecture for this research is depicted in Figure 6.
# Emerging Trends in Sustainable Biological Resources and Bioeconomy for Food Production
## 4 Blue Bioeconomy Resources
### Algal-Derived Resources
This structural specificity contributes to the superior bioavailability of algal-derived omega-3s compared to synthetic or non-marine sources. Omega-6 fatty acids, while also crucial for human health, must be balanced with omega-3s to prevent pro-inflammatory effects, underscoring the importance of maintaining a proper ratio between these two essential fatty acids in the diet. Omega-9 fatty acids, found in some algal oils, support cardiovascular health and are increasingly utilized in skincare products for their embclient and anti-aging properties [66]. Together, these fatty acids, combined with eco-friendly cultivation practices, make algal oils a sustainable and nutritionally beneficial alternative to traditional sources of omega-3 fatty acids, offering valuable applications in food, nutraceuticals, and cosmetics. Moreover, the extraction of algal oils can be performed using several methods, including cold pressing, organic solvent extraction, and more advanced techniques such as supercritical CO2 extraction. Supercritical CO2 is particularly advantageous for preserving thermolabile compounds and achieving high selectivity and purity without solvent residues, aligning with green chemistry principles. Moreover, emerging techniques like enzyme-assisted extraction and ultrasound- or microwave-assisted extraction have been developed to enhance yield and reduce energy inputs, although their scalability remains under investigation [67, 68]. Algal oils are increasingly utilized in high-value sectors such as food, nutraceuticals, cosmetics, and the bioindustry due to their rich content of long-chain polyunsaturated fatty acids, particularly DHA and EPA. In the nutrition sector, they serve as plant-based alternatives to fish oil, used in supplements and fortified foods like infant formulas and dairy substitutes. In cosmetics, their antioxidant and moisturizing properties support applications targeting skin aging, inflammation, and barrier repair. Additionally, their high lipid productivity renders certain microalgal strains promising for industrial use, including the production of biofuels, bioplastics, and eco-friendly lubricants. Moreover, advanced encapsulation systems using prebiotic compounds such as inulin and chitosan have demonstrated the potential to enhance the oxidative stability, solubility, and functional properties of algal oils, positioning them as sustainable, eco-friendly functional ingredients for use in food and nutraceutical applications [69, 70, 71]. In conclusion, algal oils, particularly from microalgae, represent a promising and sustainable source of high-value lipids for nutritional, pharmaceutical, cosmetic, and industrial applications. Continued innovation and investment in algal research and bioprocessing technologies will be key to unlocking their full commercial potential in the context of the growing demand for clean-label and eco-friendly products.