esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In particular, machine learning has proved successful in applying a multitude of variables for predicting forest fire risk [8; 9; 26]. Main input variables to the forest fire risk prediction include satellite-derived vegetation indices, anthropogenic factors such as proximity to roads, meteorological factors such as temperature, and hydrological factors such as rainfall [26]. Many studies have modeled forest fire susceptibility based on the binary information with forest fire location or its surrounding areas as 1 and non-fire areas far from forest fires as 0. A variety of machine learning approaches including artificial neural networks, random forest (RF), support vector machines, generalized additive model, and boosted regression trees have been used for forest fire risk modeling [7; 8; 20; 27]. However, many of the studies have used fixed parameters and focused on long-term (i.e., yearly, monthly) fire risk. Nowadays, studies on dynamic models focusing on short-term (i.e., daily, sub-daily) fire risk are becoming active [5]. Since forest fire risk has its diurnal cycle, it is necessary to derive a forest fire risk index at very short time scale (i.e., every hour) to provide practical information for decision making on forest fire management. Thus, there is still room for improvement such as providing forest fire susceptibility and risk hourly or sub-hourly. So far, machine learning-based forest fire susceptibility studies have focused on a few large forest fire cases or are not updated with time (not hourly) [5; 28]. When an approach is proposed for fire risk prediction, its transferability should be evaluated on both spatial and temporal domains. Therefore, this study aims to propose a new hourly forest fire risk index (HFRI) at 1 km resolution over South Korea. Specifically, the measurable objectives of this research were to (1) develop a machine learning-based ensemble approach that combines two models with the topographic and anthropogenic factors as well as weather factors, (2) analyze the impact of each factor on HFRI, and (3) to evaluate the transferability of the proposed approach on a temporal domain. As mentioned above, forest fires in South Korea have mainly been caused by anthropogenic factors. Thus, the proposed HFRI that synergistically integrates weather, topographic, and anthropogenic factors is expected to better predict the forest fire risk in South Korea than other risk indices based solely on weather conditions such as DWI.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
Footnote †: This study concentrates on physical layer security (PLS) in UAV-aided Internet of Things (IoT) networks and proposes an innovative approach to enhance security by optimizing the trajectory of unmanned aerial vehicles (UAVs). In an IoT system with multiple eavesdroppers, formulating the optimal UAV trajectory poses a non-convex and non-differentiable optimization challenge. The paper utilizes the successive convex approximation (SCA) method in conjunction with hypograph theory to address this challenge. First, a set of trajectory increment variables is introduced to replace the original UAV trajectory coordinates, thereby converting the original non-convex problem into a sequence of convex subproblems. Subsequently, hypograph theory is employed to convert these non-differentiable subproblems into standard convex forms, which can be solved using the CVX toolbox. Simulation results demonstrate the UAV's trajectory fluctuations under different parameters, affirming that trajectory optimization significantly improves PLS performance in IoT systems.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 4 Architecture and Procedures for MEC-Based Mobile Services
Each SDN controller is responsible for configuring its network components using network-specific protocols. The MEC system acts as an external data network that is accessible through the 5 GC. It is composed of several MEC Hosts, deployed at the network edge, and providing virtualized resources. MEC Hosts allow the deployment of MEC applications, which provide services for the end user, including, e.g., augmented reality, video analytics, and IoT applications. In the augmented reality domain, a typical scenario where a stateful service is run in edge servers is assisted surgery [36]. Considering the IoT, a mobile application can be used to remotely control sensors and actuators through a service running on an MEC Host. Video analytics applications can be exploited for autonomous driving [37]. Several MEC Hosts are managed by a logically centralized MEC Orchestrator (MEO), which manages the deployment, scaling, and lifecycle of MEC applications across multiple MEC Hosts. It optimizes resource utilization based on user demands and network conditions. In this work, we have not studied policies and algorithms for selecting the service instance in order to optimize the performance and resources. However, considering the growing adoption of a new paradigm, known as computing-aware traffic steering (CATS), in future work, we intend to study orchestration solution matching that new vision. In CATS, networking and computing capabilities and their dynamic status must be jointly taken into consideration to carry out the proper decision in terms of traffic steering and service instance selection [38]. The MEO decisions may require the 5 GC to be configured accordingly. For this purpose, the MEC communicates with the AMF and SMF through the NEF. The integration of the above components may introduce new vulnerabilities in the system, each one inherited by the added elements. In this respect, within the literature, there are several works that describe and solve the possible security threats of 5G, SRv6, MEC, and SDN controllers. The potential risks and typical attacks on SDN networks are comprehensively outlined in [39], which also presents solutions for protecting the network. The work in [40] presents the most common security issues in 5G networks, providing possible countermeasures as well. ETSI MEC vulnerabilities are described in [41], also illustrating the solutions available in the literature. Finally, the authors in [42] deal with security and risks in SRv6 networks.
# Transcriptomic Analysis of the Candidate Genes Related to Aroma Formation in _Osmanthus fragrans_
## 2 Results
### Identification of UnigenesInvolved in Scent Metabolism  
In previous studies, the _O. fragrans_ floral constituents have been found to be volatile terpenoids. The terpenoid metabolic pathway genes, and many of the biosynthetic enzymes involved, have been identified and functionally characterized. The key genes involved in the terpenoid pathway were identified according to the KEGG annotation and a local BLASTx search (Table 3). A total of nineunigenes, involved in terpenoid biosynthesis, were selected to validate the sequencing data. The qRT-PCR was carried out with independent samples collected from the different flowering stages to analyze their differential expression pattern (Figure 6A). The primer sequences used arelisted in Table S2.  
Figure 4: Cluster analysis of the differentially expressed genes. The profiles in color indicate statistical significance (\(p<0.01\)). The number on the topis the profile number and assigned genes. The black lines represent the model expression profiles of different genes.  
Figure 5: Analysis of differentially expressed unigenes (DEGs) during flowering in _O. fragrans_ are listed with different comparisons, including S1 versusS2, S1 versusS3, and S2 versusS3. Red indicates up-regulated unigenes, while green indicates down-regulated unigenes. The \(x\)-axis indicates the absolute expression levels (Log Conc). The \(y\)-axis indicates the log-fold changes between the two samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**KEGG Pathway**} & **All Genes with Pathway Annotation** & **DGEs Genes with Pathway Annotation** & **Pathway ID** \\ \hline \hline \begin{tabular}{c} Phenylpropanol \\ Monoterpenoid biosynthesis \\ \end{tabular} & 319 & 178 & Ko00940 \\ \begin{tabular}{c} Torpenoid backbone biosynthesis \\ \end{tabular} & 17 & 8 & Ko00902 \\ \begin{tabular}{c} Sesquietrepenoidal tritrepenoid biosynthesis \\ \end{tabular} & 184 & 78 & Ko00900 \\ \begin{tabular}{c} Diterpenoid biosynthesis \\ \end{tabular} & 69 & 45 & Ko00909 \\ \
\begin{tabular}{c} Diterpenoid biosynthesis \\ Limonene and pinene degradation \\ \end{tabular} & 45 & 25 & Ko00904 \\
\begin{tabular}{c} Limonene and pinene degradation \\ \end{tabular} & 15 & 10 & Ko00903 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The KEGG enrichment of DEGs among the three flowering stages.
## 3 Results
### Spatial and Temporal Characteristics
#### 3.2.3 Spatial and Temporal Characteristics of WUE  
This study analyzed the interannual variation in vegetation water-use efficiency (WUE) in the alpine grassland of Qinghai Province from 2001 to 2018, including the changes in annual and growing season average values. The WUE across the study area shows significant spatial variation. Higher WUE values were distributed around lower elevation areas near Qinghai Lake, the source of the Lancang River, and the source of the Yellow River. Lower WUE values were mainly found around the Qaidam Basin and near the Tanggula Mountains. The WUE of the alpine grassland generally increased from northwest to southeast, with the WUE in the Sanjiangyuan region being significantly higher than in the sparsely vegetated areas of the northwest.  
In the annual mean WUE spatial distribution map, the WUE of the alpine grassland mainly ranged from 0 to 1.5 gC/mm, with areas where the WUE exceeded 0.5 gC/mm accounting for 34.56%, as shown in Figure 8a. The spatial distribution of WUE during the growing season was basically consistent with that of the entire year. The areas where the WUE exceeded 0.5 gC/mm during the growing season accounted for 68.12%, and the areas where the WUE exceeded 1.0 gC/mm accounted for 37.9%. This indicated that the mean WUE during the growing season in the alpine grassland was generally higher than 0.5 gC/mm. In areas with lower elevation around Qinghai Lake, the WUE during the growing season even reached 1.5 gC/mm, as shown in Figure 8b.  
Figure 7: Interannual variation in evapotranspiration: (**a**) time series, (**b**) multi-year average monthly ET.  
This study statistically analyzed the annual WUE of the Qinghai Plateau alpine grassland from 2001 to 2018 and examined the interannual variation trend, as shown in Figure 9a. From a long-term perspective, the WUE trend from 2001 to 2018 was not significant, but there were noticeable fluctuations between years. The annual WUE fluctuated between 0.82 and 1.07 gC/mm, with the highest value in 2006 and the lowest in 2013. The growing season WUE fluctuated between 0.94 and 1.17 gC/mm, and the fluctuation range was similar to that of the annual WUE. Considering the interannual variations in NPP and ET, it was evident that the lower WUE in 2003 was primarily driven by the higher ET in that year. Figure 9b illustrates the multi-year average monthly WUE from 2001 to 2018 for the alpine grasslands of Qinghai Province. An analysis of the intra-annual variation indicates that WUE values were consistently below 0.5 gC/mm during January to April and October to November. In contrast, from May to September, WUE exceeded 0.5 gC/mm, with a gradual increase from May to July, reaching its peak in July at 1.46 gC/mm, before gradually decreasing from August to September. This was likely due to the characteristics of the alpine region, where from October onward, low temperatures and reduced precipitation lead to soil freezing. Vegetation enters a dormant state and becomes desiccated, preventing photosynthesis and transpiration. As a result, WUE approaches zero.
## 2 Literature Review  
### Axial Turbines for Turbocharging  
Honeywell Turbo Technologies (HTT) presented a report detailing a breakthrough in the development of an axial turbine for improved transient response with their DualBoostTM turbocharger. The results demonstrated a reduction in turbo lag of 25% by reducing the mass moment of inertia by 50% [9]. Figure 1 shows the comparison of the total to static efficiency between the newly developed DualBoostTM turbine and the baseline radial. The figure demonstrates that the axial turbine is more effective under low-velocity (blade tip to absolute gas flow velocity) ratios in recovering energy from exhaust gas. However, the theoretical maximum efficiency of the axial turbine is lower than that of the radial turbine.  
This technology, however, is not relatively new. Ford Motor Company (Dearborn, MI, USA) conducted a testing in 1985 to assess the performance of an axial turbine when compared to a radial turbine on a 2.3-L engine under transient conditions. The study identified that the axial turbine could produce the same \"flow and peak efficiency characteristics\" as its radial counterpart at different engine speeds but with a lower inertia. The results of the study demonstrated a 25% to 40% improvement on the transient response of the turbocharger, with just under 50% mass moment of inertia reduction. Thisprovides a target for the design considered in the present work to achieve at least a 25% improvement in transient response [3].  
Despite the discussed advantages in the implementation of the axial turbines, there are few challenges associated with it. Axial turbines are predominantly found in aerospace and large power generation applications. For automotive turbochargers, as the power range is in kilowatts, blade height for axial turbines is comparatively smaller than radial turbines and would result in disproportionate increase in leakage flow effects. Therefore, a key challenge is the corresponding decrease in efficiency with a decrease in size. This calls for finer manufacturing tolerances and, in turn, makes the employment of axial turbines for turbocharger applications more costly [10]. Hence, increases in aerodynamic efficiency are of paramount importance in the successful employment of such designs. In addition, for the successful implementation of an axial turbine design, a detailed flow field and rotor dynamic analysis of the turbine needs to be performed followed by transient performance testing of the turbocharger and engine. This type of research is ongoing but outside the scope of this paper.
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 4 Discussion
### Data Availability and Coverage  
Lastly, Figure 7 illustrates electric power lines, roads, and FAA obstacles in southern Rhode Island. The electric power line is parallel to the road and the FAA reported obstacles are co-located with the power line. These obstacles are the towers supporting the power lines themselves. As described in Section 2.2, each obstacle is represented as a circle with a radius defined by the horizontal position uncertainty. The larger the circle, the greater the uncertainty. A challenge is that many datasets do not guarantee complete coverage of all features. This issue exists for both federally managed and open sourced datasets. The information available across datasets varies too and correlating datasets can be challenging.  
Figure 5: Features near Rugby in northern North Dakota.  
Figure 6: Features near Boston in eastern Massachusetts.  
Figure 7: Features near Portsmouth in southern Rhode Island.  
In Figure 7, the DHS HIFLD electric power line datasets do not specify the locations of the towers supporting the lines. The FAA DOF simply specifies the location of a tower and does not designate if a tower is used to support electric power lines. However, from a UAS operations perspective, an inspection of electrical systems could include both the tower and power lines. The consequence is that our results may skew towards closer smaller distances, as we do not delineate if two features are components of a single system.
## 3 Results and Discussion
### Length of Partitions as a Proxy of the Number of Classifications  
We wondered whether the number of classifications and the length of the VP may change the statistical significance of the \(PQA\) score because the less the number of items in the VP, the greater the chance to group each item with any order. We then tested such an effect by calculating a Z-score from ordered synthetic partitions increasing both the number of classifications and the number of items up to 100. We also kept constant the number of classifications for the sake of this analysis. We noticed that only the length of the partition has a true effect on the Z-score, but that is not the case for the number of classifications. We observed that every partition less than 13 could be considered as pure noise, however, we consider a Z-score cutoff of greater than 3 (\(p\)-value of 0.002). We also observed Z-score values still greater than 2 with a length of 12, 11, and 10, but less than with lengths between 2 and 9 (Figure 2). If we were more flexible, we could have laid out a length cutoff on those values without losing statistical significance, since a Z-score of 2 corresponds roughly to a \(p\)-value of 0.05. The results of this analysis were expected by intuition because the probability of an item to occupy a position in the VP increases the number of items doing the same.
## 6 Discussion
### Research Issues and Challenges
Adaptive MAC protocols are capable of learning additional information about the type of data that are being communicated. However, the learning algorithms are not designed for medical applications. Accuracy of these learning-based MAC protocols should be demonstrated for medical purposes, and error free implementation of these algorithms is yet to be studied. Incorporating WuR to these adaptive MAC protocols can greatly reduce the power consumption, where nodes can benefit from the low power operation of WuR when establishing newer wake-up schedules. Use of WuR for the learning phase of these adaptive MAC protocols will have significant improvement in terms of energy-efficiency and QoS performance. Furthermore, co-optimization of MAC and the PHY layer between the main radio and WuR can improve energy-efficiency in dual radio IBSN devices. Incorporating WuR at the MAC level rather than using only the wake-up interrupt will improve the reliability of the data communication.
## 2 Materials and Methods
### Study Areas
#### 2.1.1 Haditha Dam  
The Haditha Dam area, as defined in this study, extends along the Euphrates river roughly from the north of the archaeological site of Khirbet al-Diniye (34\({}^{\circ}\) 33\({}^{\prime}\) 47.1168\({}^{\prime\prime}\) N, 41\({}^{\circ}\) 34\({}^{\prime}\) 32.6064\({}^{\prime\prime}\) E) to the south-east of the city of Haditha (34\({}^{\circ}\) 5\({}^{\prime}\) 12.3144\({}^{\prime\prime}\) N; 42\({}^{\circ}\) 30\({}^{\prime}\) 4.7592\({}^{\prime\prime}\) E) (Figure 4). The dam was completed in 1985, resulting in the formation of the al-Dadissyah lake, covering an area of approximately 500 km\({}^{2}\) with a capacity of 8.2 km\({}^{3}\). As mentioned before, the main periods of water reduction were recorded in the years 1991, 2001, 2009,2011, and 2015. The Haditha lake is prone to strong surface reductions: for example, during the droughts recorded in 2007-2009 and 2013-2014, the surface lake area dropped by 72% and 62%, respectively [27] (pp. 271, 272).
# A Review on Bitumen Rejuvenation: Mechanisms, Materials, Methods and Perspectives
## 6 Forefront/Vanguard Techniques Facing Complexity in Bitumen
### Probing Complexity
#### 6.2.5 Applications of Relaxometry Nuclear Magnetic Resonance to Bitumen  
Generally, regarding types of bitumen, the T\({}_{2}\) relaxation time distribution exhibits two peaks. Direct correlation can be made between T\({}_{2}\) and the rigidity of structures in these materials [43], as well as the molecular constraint, which causes dynamic hindrance [46]. The shorter T\({}_{2}\) times (around 10 ms) reasonably corresponds, therefore, to more rigid supra-molecular aggregates; hence, they are attributed to asphaltenes. Conversely, high T\({}_{2}\) times (around 100 ms) can be attributed to low intra-molecular interactions; they can be referred to the maltene fraction of the sample under examination. This finding supports the colloidal model of the bitumen. In fact, if the polar fluid model suggested by [PERSON] [110] were applicable to our system, the ILT result would demonstrate a sole broad peak referred to as a continuous T\({}_{2}\) time. Figure 30 shows a typical Probability Density Function obtained through an ILT transform of T\({}_{2}\) relaxation time data determined by analyzing neat and modified 100/130 penetration grade bitumen supplied by Highway Research Institute (Almaty, Kazakhstan) [111]. The measures of T\({}_{2}\) were made at a temperature 15 \({}^{\circ}\)C lower than transition temperature (solid-liquid) measured by dynamic temperature ramp test experiment. In Figure 30 the panels refer to the following samples: SD refers to PAV bitumen + 2 wt% HR (the green rejuvenator), SC refers to PAV bitumen + 2 wt% VO (Vegetable Flux Oil), SB PAV bitumen, SA Neat Bitumen.  
The T\({}_{2}\) relaxation time distribution can be considered a structural fingerprint of the bitumen where changes in the T\({}_{2}\) relaxation times evidence modification in the structure of colloidal binder. In particular, the powerful ILT, applied to the echo decay, can be used to verify the effectiveness of the real rejuvenator. The same or better similar profile of T\({}_{2}\) relaxation times prove that the structure of aged bitumen has been regenerated reorganizing the same distribution of asphaltene micelles in the maltene phase. In general, by ILT NMR relaxometry it is possible to follow the structural evolution of bitumen when additives (polymer surfactants, etc.) are added, in fact, the relaxation distribution is strongly affected by the supramolecular organizations present in the colloids.
## 3 Catalytic Advanced Oxidation Processes (AOPs)
### Sulfate-Radical AOPs  
SDB has been extensively investigated as a heterogeneous catalyst for peroxymonosulfate (PMS) and peroxydisulfate (PDS) activation. Its carbon matrix facilitates single-electron transfer, resulting in the generation of sulfate radicals (SO\({}_{4}\)\({}^{-}\)) or even non-radical oxidants like singlet oxygen [42]. SDB catalysts have been shown to be very effective in sulfate-radical AOPs, typically removing the contaminants completely within minutes to hours. The PMS/SDB system achieved a 94.63% removal of sulfamethoxazole (SMX) within 30 min, exhibiting remarkable stability, interference resistance, and adaptability across different pH conditions [43]. A noteworthy advance was the preparation of metal-bearing SDB, i.e., by pyrolyzing Fe-rich SDB under reductive conditions. For instance, [PERSON] et al. [44] obtained ZVI-SDB by one-step preparation with no external iron source that rapidly activated persulfate, achieving the highly efficient degradation of acid orange. Similarly, [PERSON] et al. [45] reported that SDB produced from iron-rich sludge exhibits excellent performance in removing tetracycline (TC) from both synthetic and real pharmaceutical wastewater via combined adsorption and oxidant activation processes. The activation of PMS and H\({}_{2}\)O\({}_{2}\) was primarily attributed to the presence of Fe(II), which facilitated the generation of -OH and SO\({}_{4}\)\({}^{-}\). Such composite SDBs exploit the synergistic effects of added metal ions. Another study reported that SDB effectively activates PDS for efficient SMX degradation, highlighting that SDB catalysts can favor non-radical routes [46]. This non-radical dominant mechanism reveals the propensity of SDB to drive contaminant breakdown via electron transfer pathways, thereby mitigating performance limitations caused by radical scavengers in complex wastewater matrices. Thus, selecting appropriate modification strategies is critical to optimize the catalytic route for different pollutant classes.