esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
###### Abstract
To achieve sustained reconnaissance in hostile scenes, in this study we first proposed the Threat Avoidance and Reconnaissance FANET Construction algorithm (TARFC). Then, taking into account the movement of monitored targets and the overall changes in the hostile area, the Total Edit Distance (TED) is defined as a measure of those variations. Finally, the Dynamic Threat Avoidance and Continuous Reconnaissance FANET operation (TA&CRFO) is proposed by incorporating the TED indicator into TARFC. The algorithm reduces the complexity of TARFC by making UAVs execute adaptively and has good application value in actual scenes. The contributions of this paper are as follows:  
* We introduce a constraint on the threat probability density function (threat PDF) to model the changing threats in the scene. By transforming the constrained problem into an unconstrained problem using the Lagrange Multiplier method, the PSO-based TARFC algorithm is proposed to find optimal UAV locations that stay away from threats and maintain network connectivity. * The TED metric is put forward to measure the variation degrees of the FANET and reconnaissance scenarios over different periods of time. According to the TED value, the control center will determine whether to execute overall coordination by sending control commands or to allow each node to perform distributed adaptive adjustments based on their local information. In this way, the dependence of UAVs on the control center can be reduced. * Combined with the above two, the TA&CRFO algorithm is designed. It can adaptively adjust the topology of the FANET in realistic scenarios and realize the dynamic continuous reconnaissance goal of the FANET with low complexity, even if the monitored targets or scenario' threats are time-varying. The structure of this paper is organized as follows: Section 2 mainly introduces a hierarchical architecture of the heterogeneous FANET and presents the problem-framing process. In Section 3, the PSO-based TARFC algorithm is proposed to achieve the construction of the FANET reconnaissance network and the threat avoidance in the scenario. Then, the TED metric is designed to measure the relevant changes. Finally, TA&CRFO is proposed to achieve the on-demand collaborative management of UAV nodes during continuous reconnaissance. Subsequently, Section 4 provides some analysis of simulation results, and some conclusions and future directions are described in Section 5.
# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 5 Conclusions  
The overall objectives for network control protocols are always throughput, latency, and packet error rate. These objectives remain invariant regardless of the number of layers involved in the optimization process. At the same time, these objectives can be achieved in a variety of ways. This variety is very wide, so a useful classification is needed for it. The existing surveys offer the only way to classify cross-layer ad hoc network control methods based on the involved layers. This classification is simple but not very useful because the number of the involved layers does not give us any information on the goals of the considered methods.  
We have introduced the new classification of cross-layer methods in ad hoc networks, which is not based on combinations of used OSI layers. It is based on the main goal of the cross-layer method instead. Cross-layer methods can be divided into two large groups: first, optimization methods for tasks of one layer with information usage from other layers, and second, methods for the collaborative optimization of multiple layers (Figure 7). The methods of one-layer optimization are divided into corresponding layers: physical, channel, network, transport, and application. The methods of multiple layer optimization are divided into, first, the optimization of layers by a separate independent entity (this approach corresponds to the \"CrossTalk\" architecture [27]), and second, the independent optimization of layers by themselves, but with information sharing (this approach corresponds to the \"MobileMan\" architecture [26]).  
The proposed classification helps to systematize the cross-layer ad hoc network control methods based on the purpose of these methods. Thus, the proposed classification is not morphology-based but is goal-based.  
Most cross-layer methods use the first three layers of the OSI model. Network, channel, and physical layers form the basis of ad hoc networks. Without these layers, ad hoc networks cannot exist. Meeting the requirements for the delivery of data flows (and the possibility of delivery) depends mostly on the bottom three layers. The studies of cross-layer methods for upper layers are rare. The optimization of the application layer does not matter, as this layer creates data streams for the network to deliver. Data from the application layer should be treated as an input parameter for cross-layer methods. The transport layer is responsible for the delivery guarantee and congestion control, but the confirmation of delivery can be performed at the application layer, and congestion can be avoided by the routing protocol (network layer), which chooses paths with the least busy lines.
## 2 Materials and Methods
### Flux Tower Site Data  
Observations from three flux tower datasets were utilized for site-level validation in this study. These datasets include FLUXNET2015 [44], FLUXNET-CH4 [45], and Drought-2018 [46], collectively comprising a total of 280 sites worldwide. They provide various variables such as GPP, RECO, and NEE. Initially, daily values were extracted from these datasets, spanning the timeframe from 1999 to 2018. Subsequently, the extracted data were aggregated into monthly averages to ensure their consistency with the time intervals of other products.
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 4 Experimental Results and Comparative Analyses
### Summary of Experiments  
The experiments described above prove that the novel hybrid-copy algorithm can effectively reduce the number of page faults, especially when the migrated VM runs a read-intensive workload. Based on the analysis of the experimental results, we arrive at the conclusion that as the \(\alpha\) increases, fewer iterative copy are executed. In this situation, more bandwidth resources can be saved, but the page faults and the downtime caused by page faults may increase. Therefore, if a VM runs a write-intensive workload, the \(\alpha\) should be set to a large value. For example, it is appropriate to set the \(\alpha\) to 0.7 in SPEC VIRT_SC 2013 workload. In this case, we spent an additional 0.4% live migration time to reduce about 10% number of page faults. On the contrary, a small value of \(\alpha\) is a good choice for a read-intensive workload. More iterative copies can significantly reduce the page faults, and only a little more migration time is spent. For instance, in a Linux Kernel Compile workload, when the \(\alpha\) is set as 0.3, almost 75% number of page faults are reduced, and only an additional 9.5% of migration time is spent. Page faults always seriously affect the performance of the migrating VM. Sometimes, the services provided by the migrating VM are totally unavailable because of the page faults. Therefore, reducing the number of page faults will significantly improve the user experience.
## 1 Introduction
In this regard, several algorithms have also been introduced, as follows: the particle swarm optimisation (PSO) algorithm, in combination with the use of modal data to determine the damage in the structures, including two beam structures and two truss structures [42]; the Genetic Algorithm (GA), applying residual force methods based on the theory of specific vibration analysis to identify failures in elastic structures [43]; combining a strain energy-based index with PSO to determine the location and extent of various failures [44]; Ant colony optimisation (AC), applied to determine the damage location of the 2-story frame and 3-story steel frame structures corrected on measurements [45]; a combined GA algorithm and information synthesis technique, to determine the location and extent of damage at many locations in the structure [46]; a GA algorithm, to detect damage in frames and cantilever beams using an objective function, including natural frequency, mode shapes, and both [47]; and finally, using both the GA and dynamic characteristics of the structure [48]. These applications demonstrated the potential of combining artificial intelligence and vibration-based methods in SHM, but were mostly performed on small, simple structures. With a huge structure, operation and maintenance often consume a lot of resources. Solutions for visual monitoring or static load testing significantly increase maintenance costs. It would also be extremely costly to make too many measurements of the overall vibration of the structure. This study has presented and tested a comprehensive solution on an actual steel truss bridge. To determine and quantify the damage from the steel truss bridge, efforts were made to combine artificial neural network and vibration measurement results. Hopefully, this solution can help engineers to save time, resources, and cost.
# Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization
## 2 Portfolio Optimization
### Mean-Variance Portfolio Optimization  
A portfolio model is a mathematical description of the behavior of a portfolio of assets given market-related information. The portfolio model is optimized, typically, by adjusting the weights of the assets in the portfolio. A popular portfolio model is the mean-variance model, which is formally defined as  
\[\text{minimize}\;\lambda\sigma-(1-\lambda)R \tag{3}\]where \(\lambda\) is used to balance risk (\(\bar{\sigma}\)) and return (\(R\)). The \(\lambda\) coefficient is bound in \([0,1]\), where smaller values favor return and larger values favor risk. Risk is calculated as the weighted covariance between all \(n\) assets in the portfolio:  
\[\bar{\sigma}=\sum_{i=1}^{n}\sum_{j=1}^{n}w_{i}w_{j}\sigma_{ij} \tag{4}\]  
where \(w_{i}\) and \(w_{j}\) are weightings of assets \(i\) and \(j\), respectively, and \(\sigma_{ij}\) is the covariance between assets \(i\) and \(j\). \(R\) is calculated using  
\[R=\sum_{i=1}^{n}R_{i}w_{i} \tag{5}\]  
where \(R_{i}\) is the return of asset \(i\).  
The mean-variance model is subject to two constraints: (1) The summation of all asset weights must be equal to one, and (2) the weight of each asset must be non-negative. These constraints are expressed as  
\[\sum_{i=1}^{n}w_{i}=1, \tag{6}\]  
and  
\[w_{i}\geq 0. \tag{7}\]  
The mean-variance model (Equation (3)) is optimized by tuning the weights, i.e., \(\mathbf{w}\), to return the lowest value for a given \(\lambda\) value. A diverse set of optimal portfolios can be obtained by repeating the optimization process for different \(\lambda\) values. Multi-objective portfolio optimization, however, is the simultaneous maximization of return (Equation (5)) and minimization of risk (Equation (4)) by tuning the weights to balance these conflicting objectives.
## 4 Results and Discussion
### Limitations  
Despite the promising results obtained with our proposed EEG-pain detection framework using KCS-FCnet and FCAM, several limitations should be acknowledged. First, although the model shows competitive subject-independent performance, especially when compared to traditional methods, the variability in EEG responses across individuals remains a significant challenge. Inter-subject variability related to genetic, psychological, and neurodevelopmental factors can result in distinct neural signatures of pain, which limits the generalizability of DL models trained on limited cohorts. Furthermore, while sex-specific grouping enhanced cross-subject pain detection performance in our experiments, the model's ability to generalize across other demographic dimensions--such as cultural background, pain tolerance, or cognitive state--was not explored and may affect its robustness in real-world clinical scenarios [3].  
Another key limitation concerns the availability and diversity of EEG datasets for pain assessment. Most publicly available databases, including the one used in this study, rely on controlled experimental pain stimuli (e.g., laser-evoked pain) that may not fully capture the complexity of chronic or spontaneous pain experienced by patients in clinical settings [24]. Additionally, the relatively short duration of EEG recordings and the binary nature of the classification task (no-pain vs. high-pain) do not reflect the continuous and subjective nature of real pain experiences. This underscores the need for future work to explore multimodal data fusion (e.g., EEG with fNIRS, facial expressions, or physiological signals), develop more nuanced pain intensity scales, and validate models in naturalistic and clinical environments. Moreover, interpretability methods such as FCAMs, while useful, still require domain expert validation to ensure the clinical relevance of the identified patterns.  
Finally, the laterality of stimulus application is an important consideration in pain neurophysiology. Although our study focused on brain responses regardless of the stimulus side, we recognize that stimulation of the left versus right hand can modulate EEG patterns due to hemispheric differences in pain processing. Although early components of the laser-evoked potential, such as the N1 wave, tend to be more strongly contralateral, later components (N2, P2) associated with pain perception and salience are more bilaterally distributed [55; 56].  
Figure 14: KCS-FCnet-derived Gaussian functional connectivity patterns for representative subjects: female (Subject 29) and male (Subject 15). 2 KS-based pruning is carried out to preserve discriminative FC with \(p\)-value \(<0.05\). Then, connections exceeding 97% of the maximum relevance value are retained for visualization. Colored brain regions within the graph are related to Figure 1.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 6 Evaluation of Algorithm Performance
### Power Efficiency
When we use the PCEB-MS algorithm, the sensor nodes only require one hop or two hops to transmit data to the sink, therefore reducing the transmitting energy, and they also receive fewer broadcast messages from the sink, thus effectively reducing the total energy consumption of sensor nodes. In this experiment, the energy consumption of the parameters are set as \(\varepsilon_{mp}\) = 0.0013 pJ/bit/m\({}^{4}\), \(E_{elce}\) = 50 nJ/bit, \(\varepsilon_{fs}\) = 10 pJ/bit/m\({}^{2}\), and \(E_{DF}\) = 5 nJ/bit/signal, which are the same values as those set in [40; 41]. According to the relationship between the sink transmission power, transmission radius, and energy consumption presented in [42; 43; 44; 45], Figure 9 shows a comparison between the CNP algorithm and the PCEB-MS protocol in relation to maximum power. This shows that the PCEB-MS protocol is superior in saving energy consumption; and thus, with an increase in sink nodes the energy consumption would be lowered. When there are 12 sinks, the total energy consumption is reduced by approximately 70% compared to maximum transmission power, and by approximately 64% with the CNP algorithm.
## 3 Results and Discussion
### Potential Implications of Gasifier Cook Stove Uptake
#### 3.3.2 Increased Food Production  
Application of biochar to the soil (Figure 7) results in increased crop yield and water retention capacity of the soil [36]. For instance, the average yields more than doubled after farmers applied the biochar produced from the gasifier to their maize plots at Kwale [37]. Increased yields could help the households to have enough food to feed their members and hence be more food-secure as well as save money spent on purchasing food. In instances where more food is produced, the excess could be sold to raise some income for the household.
## 2 Endophytes-Current State of the Art
### Endophytes as Inhabitants of Agricultural Plants
#### 2.2.3 Plant Phytohormones Connected with Plant Growth and Yielding  
Phytohormones are signaling molecules that coordinate cell activity and control plant growth and developmental processes [4]. The major plant hormones include auxins, ethylene, gibberellins, cytokins and abscisic acid [76]. Auxins are particularly important. They play a significant role in almost every stage of the plant's growth and life. They regulate many important physiological processes such as seed germination, cell division and elongation, and root development. They are also involved in organogenesis and gene regulation [4]. The most common naturally occurring auxin is indolyl-3-acetic acid (IAA), which can be produced by plants, bacteria and fungi in tryptophan-dependent and tryptophan-independent pathways [77]. Areas of IAA action mainly include cell signaling, cell division, elongation and differentiation, initiation of root, leaf and flower systems, and fruit development and aging [78]. IAA also induces plant defense systems, mediates responses to stimuli, affects photosynthesis and biosynthesis of metabolites, and may even control the synthesis of other plant hormones, e.g., ethylene [28; 77]. It is a fact that IAA is commonly secreted by endophytic microorganisms. The most active strains of IAA-producing endophytic bacteria include the following genera: _Pseudomonas_, _Rhizobium_, _Azospirillum_, _Enterobacter_, _Azobacter_, _Klebsiella_, _Alcaligenes_, _Pantoea_, _Acetobacter_, _Herbospirillum_, _Burkholderia_, _Bacillus_, _Rhodococcus_ and _Streptomyces_ [4; 77]. Endophytic fungi are also capable of synthesizing auxins, including IAA. For example, the endophytic fungus _Apergillus awamori_ W11 isolated from _Withenia somifera_ leaves was capable of producing important secondary metabolites including IAA, phenols and sugars. Furthermore, this strain effectively colonized maize roots and improved the growth of the host plant and finally maize yields [79; 80].  
Other plant hormones are also commonly synthesized by endophytic microorganisms [80]. [PERSON] et al. (2022) showed that endophytic _Trichoderma_ strains produce cytokins that stimulate plant growth, influence colonization strategies by symbiotic fungi and increase plant resistance to pathogens [81]. As endophytic microorganisms can synthesize cytokins, they can also influence the increase in cytokin levels in both the soil and the plants growing on it [81]. [PERSON] and [PERSON] [82] isolated more than 60 endophytic bacteria belonging to the genera _Pseudomonas_, _Stentrophonomas_, _Bacillus_, _Pantoea_ and _Serratia_, capable of producing gibberellins and auxins. The strains tested evidenced inhibitory effects on bacterial phytopathogens, significantly increased root length and promoted plant growth [83].
## 2 Literature Review
### Risk Assessment Methods  
As part of risk management, there are several methods of assessing risk factors, including importance index methods (e.g., the relative importance index) [20] and machine learning (e.g., artificial neural network (ANN)) methods [15]. Recently, a risk assessment utilizing a Monte Carlo simulation-based risk interdependency network was carried out by [PERSON] et al. [21]. However, this method depends on the interpretative structural model (ISM) and the conditional probabilities of risks, and the method may not be capable of considering quantitative and qualitative analyses. The PLS-SEM has the advantage of examining the interrelationships among the groups. The PLS method is centered on estimating latent variables and identifying relationships between those variables and observed indicators [22]. The PLS approach was further developed by [PERSON] and [PERSON] [23] to handle latent variables in three-way data structures. They explained how the PLS-SEM was used to estimate parameters and evaluate relationships in complex models. The PLS-SEM was called the \"silver bullet\" for solving challenging research issues [24]. The PLS-SEM's benefits and uses were reviewed by [PERSON], [PERSON], and [PERSON] [24], including its capacity to handle small sample sizes and non-normal data.  
Furthermore, [PERSON], [PERSON], and [PERSON] [25] highlighted the PLS-SEM's stringent applications and enhanced outcomes. They addressed the significance of model assessments, model measurement evaluations, and the use of bootstrapping to verify and validate the importance of relationships. [PERSON] et al. [26] thoroughly reviewed the PLS-SEM with respect to when to apply it and how to present its findings. The authors presented additional recommendations, including metrics for model comparisons and the PLS-SEM's prediction for an out-of-sample prediction [26]. Therefore, the PLS-SEM is suitable for complex research models. The method is also distinguished in handling samples of relatively small sizes. Modeling via the PLS-SEM is a statistical technique which combines regression analysis and structural equation modeling elements. The technique can connect risk assessments (qualitative data) with the value of an issue, such as cost overrun or a time delay. Therefore, the PLS-SEM can be utilized to develop a time and cost risk interdependency model which considers quantitative and qualitative data. The PLS-SEM has been successfully utilized in different disciplines. Table 1 reports examples of the application of the PLS-SEM in different disciplines.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
###### Abstract  
Improving the durability of flexible pavements and constructing new roads on weak soil foundations present significant challenges, prompting designers to explore alternative methods to prolong pavement lifespan. Geosynthetics have emerged as a promising solution for soil stabilization, with various materials developed for this purpose. The current study concentrates on using the finite element (FE) method to examine the effectiveness of geogrid-incorporated flexible pavements on soft soil substrates. A three-dimensional layered pavement is constructed with an FE model, incorporating subgrade layers of varying strengths based on their California bearing ratio (CBR) values, with a geogrid layer implemented to enhance subgrade stability. Additionally, attention is also given to investigating the effect of base course thickness. The findings reveal that the geogrid layer primarily influences the formation of plastic strains in the subgrade rather than resilient strains, effectively reducing vertical compressive strain by approximately 40%. With increasing CBR values, there is a reduction in vertical strain, although the influence zone extends up to a depth of 300 mm within the subgrade. At the surface of the subgrade, vertical strain decreases by around 17%, 39%, and 49% as the CBR values increase from 1% to 3%, 5%, and 8%, respectively.  
geogrid; weak subgrade; finite element method; CBR; compressive strain Article