esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 3 Results
### Scenario 1: Canal Area  
In this case, we used two Planet images collected on 27 July 2017 and 5 August 2017 and two WV images collected on 26 July 2017 and 8 August 2017. The prediction scenario is summarized in Figure 5. Eight performance metrics were used. Due to resolution differences between Planet and WV images, we applied bicubic interpolation to upsample the Planet images to the same size of the WV images. Table 5 summarizes the performance metrics for RGB bands as well as individual bands. First, it can be seen that all of the three fusion methods have significant improvement (all metrics) over the Planet image at the prediction time. The PSNR values have been improved by 0.6 dB and the SSIM values improved by more than 0.11. Other metrics all show improvements over those of bicubic. Second, among the three fusion methods, HCM and FSDAF performed slightly better than STARFM. Figure 6 shows visual comparison of the three fused images with the ground truth (WV) and low resolution Planet images at the prediction time. It can be seen that the three algorithms can generate very high quality predictions. In particular, some small objects inside the green circles were correctly predicted  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Planet Image (3.125 m**} & & & \\
**Resolution) with 4 Bands** & & & & \\ \hline Blue & 455–515 nm & Coastal & 400–450 nm & Red & 630–690 nm \\ Green & 500–590 nm & Blue & 450–510 nm & Red Edge & 705–745 nm \\ Red & 590–670 nm & Green & 510–580 nm & Near-IR1 & 770–895 nm \\ NIR & 780–860 nm & Yellow & 585–625 nm & Near-IR2 & 860–1040 nm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Band specifications of Planet and Worldview images.  
using the three algorithms. In Figure 7, we also generated SSIM maps for this scenario. Bright indices show high similarity values. One can see that the HCM, FSDAF, and STARFM all have better SSIM values than that of bicubic interpolation.  
Figure 5: Two Planets images at 27 July and 5 August and one WV image at 26 July are fused to generate a prediction. The prediction is then compared to a WV image collected on 3 August 2017.  
Figure 6: Comparison of different fused images with the ground truth (WV) and the low resolution Planet image at the prediction time. Canal area.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
###### Abstract  
Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.  
dynamic environment; path planning; unmanned surface vehicles; modified genetic algorithms +
Footnote †: journal: Applied system  
url
## 1 Introduction
We evenly divide the target neighborhood into four blocks, then the maximum response patch is selected from each block as the context patches for collecting negative samples. Negative samples selected by this strategy are more informative. Secondly, to gain robustness against partial occlusion, we construct a structural correlation filter by learning both the holistic and local model. The final response of the correlation filter can be obtained by integrating every component with adaptive weight based on the Peak to Sidelobe Ratio (PSR) that is used to measure the reliability of each component. The new location of target is predicted according to the final weighted response. Finally, we propose an adaptive updating scheme by using a fluctuation parameter. The parameter is calculated by the deviation of PSR between two consecutive frames, which can describe the degree that appearance changing is alleviating or deteriorating. The updating scheme can adaptively adjust the updating rate for different frames, thereby updating the filter more accurately. We evaluate our proposed method on OTB-100 benchmark. The benchmark annotated 100 video sequences with 11 sub-categories (including occlusion, background clutter, illumination variation, scale variation, deformation, motion blur, fast motion, in-plane rotation, out-of-plane rotation, out-of-view and low resolution) that describe the different challenges in the tracking problem. The precision plot and success plot are used to carry out quantitative analysis in the benchmark. The procedure of the adaptive context-aware and structural correlation filter shows in Figure 1. Extensive comprehensive experiments on OTB-100 datasets demonstrate that our approach performs favorably against state-of-the-art methods. The rest of the paper arranged as follows. In Section 2, we discuss recent visual tracking algorithms. Section 3 elaborates the proposed adaptive context-aware and structural correlation filter exhaustively. Section 4 evaluates the performance of our proposed tracker by Visual Tracking benchmark [8] datasets, and compares it with several state-of-art trackers, and Section 5 concludes this paper.
## 2 Algorithm Principle
### Error and Noise Model  
#### 3.3.1 GNSS Environmental Error Model  
(1) Ionospheric and tropospheric error models  
Satellite signals will be refracted in the ionosphere and troposphere, which will cause signal delay, and will affect the measurement of pseudorange and pseudorange rate, therefore, we need to correct the pseudorange and pseudorange rate by using the following ionospheric and tropospheric approximate models [29; 30; 31]:  
\[\sigma_{I}\sim\left[1-(\frac{R_{0}\cos\phi}{R_{0}+h_{0}})^{2}\right]^{-1/2} \tag{30}\]  
\[\sigma_{T}\sim\left[1-(\frac{\cos\phi}{1.001})^{2}\right]^{-1/2} \tag{31}\]  
where \(\sigma_{I}\) and \(\sigma_{T}\) are the code ionospheric and tropospheric errors, respectively; \(R_{0}\) is the average earth radius, \(\phi\) is the satellite elevation angle, and \(h_{0}\) is the average height of the ionosphere.  
(2) Multipath and NLOS error model  
Due to the multipath effect, NLOS causes pseudorange measurement errors and carrier phase measurement errors, which affect the accuracy of satellite navigation and positioning, therefore, according to the reference [32], we model according to the direct signal relative to the multipath signal amplitude (Signal-to-Multipath Ratio, SMR), which is different from the reference [32]. We model the multipath and NLSO together. This can indicate that the multipath and NLOS phenomena occur separately or at the same time, although they are different physical phenomena. This model is simple and practical, avoiding the complexities of multipath and NLOS modeling, ignoring the number of multipath paths and reducing the unnecessary confusion with NLOS. The model was created as follows.  
\[SMR=\frac{1}{\alpha_{\textit{Mult}+NLSO}}\overset{\text{[def]}}{=}\frac{1}{\alpha} \tag{32}\]  
where \(\alpha\) is the amplitude of the multipath or NLOS relative to the direct signal. Here, we have normalized the direct signal. To facilitate analysis, we define the multipath signal relative to the direct signal amplitude (Multipath- to -Signal Ratio, MSR), and according to (32), we get:  
\[MSR=\frac{1}{SMR}=\alpha \tag{33}\]  
Usually, we think that when the amplitude of the multipath or NLSO signal is equivalent to the direct signal, i.e., \(\alpha=1\), we cannot identify whether the received signal is a direct signal or a multipath/NLOS signal [32]. When \(\alpha=0\), which is an ideal situation, we considered there to be no multipath or NLSO signal at this time, therefore, the value of \(\alpha\) was usually between 0 to 1.
The optimum conditions at which the acids can be suitably applied, resulting in successful acid treatment, are established. The detailed optimization analysis and modeling will be carried out based on parametric study. The experiments include matrix acidizing lab experiments combined with analysis of the mineralogy and geochemistry (elemental composition) of the cores before and after treatment. Solubility and density tests will be also conducted. The core samples are from the Qamchuqa Formation in the Kirkuk oilfield, northern Iraq (Figure 1). This formation was first defined by [23] at the Qamchuqa Gorge in NE Iraq. It is composed of thick-bedded limestones and strongly domoitized limestones [24; 25]. Several studies have been reported on the Upper Qamchuqa Formation, but mostly focused on the characteristics of reservoirs, while others used core plugs with wireline logging (porosity, gamma rays, resistivity) to measure the permeability, porosity and resistivity [26; 27; 28]. This study will add, for the first time, two different lines of sophisticated analyses: a new in-situ micro-model setup to draw microscale sizes of the porous media of the reservoir at Qamchuqa Formation and simulation of matrix acidizing in core samples. An adopted in-situ acid injection into thin sections of the carbonate rock samples under optical microscope at different acid concentrations and acidizing temperatures is carried out to observe and quantify the evolution of pore spaces as a function of the acidizing operating variables.
## 5 Challenges and Future Research Directions
### Future Research Directions  
With the advancement and integration of technology, UAV-assisted SAGINs will drive innovation across many industries. In this subsection, we will analyze their future research directions from the perspectives of future applications, security, scale, and more.
# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 1 Introduction  
### Background  
In recent decades, the protection of the environment made governments throughout the world change the existing electrical grid to a smart electrical grid. The regeneration of a traditional electric grid is needed based on many weaknesses. Particularly, the traditional power grid uses nonrenewable power resources which leads to gas emissions and climate changes. Additionally, to control the outage of power, a high amount of unused energy and energy that cannot be stored is produced and consequently wasted. In addition, the traditional electric grid lacks advanced communication techniques and monitoring [1]. SG seeks to improve the efficiency, flexibility, and reliability of the traditional electric grid [2]. The key feature of SG is the use of renewable energy generators like photovoltaic power systems (PPS). The PPS may be deployed on the roof of buildings or the ground, such as solar farms. More importantly, SG is based on a set of different communication technologies that range from wired to wireless technology. It allows the components of the grid, such as generators and substations, to communicate to exchange different types of data considering the following requirements: latency, reliability, bandwidth, security and privacy, scalability, and interoperability. The installation of the energy generators in harsh and open environments arises many problems in the maintenance of the infrastructure and the real-time monitoring of the grid components became more difficult. The energy generators are facing many risks, such as fires leading to burned panels, ice, electric shock,etc. In this context, WSNs are a promising solution that can be deployed in the case of serious and complex situations where manual operations cannot be applied. The major tasks of WSNs are target tracking and monitoring [3]. WSNs applications allow sharing of information, such as humidity, voltage, and ambient temperature, that helps in real-time monitoring to observe the physical processes and detect problems. Compared with traditional wireless communication, they are characterized by easy deployment, low cost, and large area coverage [4].
## 3 Results and Discussion
### Plastome Diversity Analysis through Phylogenetic Tree  
To better explore the phylogeny of the newly assembled _A. japonicum_, a phylogenetic ML tree was constructed using 25 plastomes with 33 shared genes. These plastomes used to build the ML tree contained multiple genera, including seven _Adenophora_ species and six _Campanula_ species in the Campanulaceae family. A phylogenetic tree is useful for inferring evolution in different taxonomic species [52], and increased plastome resources enable more precise phylogenetic analysis in Campanulaceae species [53].  
The ML trees were divided into three clades, except for _H. annuus_ as an outgroup (Figure 6). In all clades, different genus species were mostly separated with high bootstrap values. In clade I, _A. japonicum_ was classified into a single cluster with strong supports (bootstrap value = 99%), and it was closely related to _H. asiatica_ and the _Adenophora_ group (bootstrap value = 100%). Clades I and II showed that most species were classified into the _Adenophora_ and _Campanula_ groups, respectively. Clade III was split into two major subclades corresponding to the _Codonopsis_ and _Cyclocodon_ groups. These findings show that _A. japonicum_ is in the closest evolutionary position to _H. asiatica_ and that the genus _Asyneuma_ is more distantly related to other genera of Campanulaceae species.  
Figure 6: A phylogenetic maximum-likelihood (ML) tree using 25 plastomes with 33 commonly shared genes. The ML bootstrap values and NCBI accession numbers are presented on each node. Excluding the outgroup, 24 plastomes belong to the Campanulaceae family. Clades I, II, and III contain 9, 6, and 9 species, respectively.
## 5 Discussion
### On-Boarding  
As we have mentioned earlier, our on-boarding procedure has been evaluated in a data analysis study with six data sharing institutions to prove its ability to foster collaborations between data premises. The evaluation has been conducted with different host OS and network settings which gives us a broad overview of its applicability. One of the main drawbacks of the state-of-the-art has been the lack of automatism during the configuration of a new analysis endpoint. The consequence is the increased deployment time which has a direct impact on the duration of the clinical study. Using our workflow, each partner has on-boarded the endpoint within less than 15 min. Further, our exemplary clinical study--as described above in Section 4--has covered around one month. The only partially comparable number we have found in the literature has been presented by [PERSON] et al. [15]. They have stated that they connected eight healthcare institutes in five countries within four months. To what extent these numbers are comparable remains open but even this presented ad hoc approach has required multiple user interactions showing the need for automated configurations. During the installation, we have experienced no crucial problems during the deployment of the station software and all stations have been brought to life in a timely manner. One advantage has been the OS-independence of the actual software since our analysis endpoint component is fully _dockerised_. Therefore, each station admin had the freedom to pick any software (and hardware) which meet the local regulations and preferences. Especially, the installation wizard for the configuration has the benefit that one-to-one support did not occur in our scenario. As an exception, the build-in connectivity check to the CS has detected firewall issues at two institutions, which, however, were resolved efficiently. Despite the successful case study and the decentral deployment of software, our approach could exhibit some limitations, which are discussed in the next section.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 3 Proposed Methodology  
In this section, the SDN-based flow routing application is presented. Figure 1 illustrates the stages of the proposed methodology. These stages are detailed hereinafter.  
The proposed method starts by capturing the OpenFlow traffic statistics from the SDN switch and saving them as raw data. Then, the proposed application takes over the pre-processing of the input data to prepare them for the next step, which uses unsupervised ML.  
The aim of utilizing unsupervised ML techniques is to characterize the flows based on network performance metrics. This step includes applying PCA and K-means. The PCA technique is applied to reduce dimensionality. PCA is a technique that uses a linear transformation concept. The technique starts with arranging the data based on their variance and representing the arranged data as coordinates. In the end, the coordinates with the highest variance values will be kept, while those with the lowest values will be ignored. Consequently, the dimension of the dataset will be reduced. K-means has attracted wide attention due to its simplicity and effectiveness in building robust cluster labeling. It is considered one of the efficient and fast techniques that contributes to solving practical clustering problems [31]. K-means as an unsupervised clustering method can create clusters of flow without the need for predefining the classes. After that, to identify flows as mice or elephants, the thresholds for the flow features are pre-defined.  
After that, the flows are identified as mice and elephants for each cluster that results from the clustering process. The thresholds for predefined flow features are determined to achieve an automated identification process that can optimize the forwarding of these flows in the network [32].  
Finally, two topologies of data center networks (DCN) are used for the SDN deployment. The main objective of this work is to select the best path for each of the elephant and mice flows. The selection of the best path for a flow is performed depending on the requirements of the flow, such as low latency for mice flows and high bandwidth for elephant flows. Therefore, the links that fulfill the conditions are determined, and the paths that contain the appropriate bandwidth and latency will be selected. To find the best path for mice and elephant flows, a developed algorithm was proposed that leverages the inverse relationship between the bandwidth and latency to calculate the cost of the link.  
The rest of this section is organized as follows: In Section 3.1 the architecture of the proposed SDN-application is presented. Afterward, the implemination of the architecture is provided in Section 3.2.
## 4 Discussion
### qPCR Analysis of XO Gene Expression  
The three-fold increase in XO gene expression in the negative control might be due to the increase in inflammatory cytokines. Elevated SUA level was shown to be associated positively with inflammatory cytokines [28], and the increase in inflammatory cytokines in turned upregulate the XDH/XO gene expression [29]. The present study showed that both doses of F10 downregulated the XO gene expression. This result confirmed our inference about the XO gene expression downregulation by F10, with 50 mg/kg dosage being more effective, comparable to that of allopurinol. The expression of XO gene was probably down-regulated due to the lower level of ROS, and subsequently lower concentration of inflammatory cytokines. [PERSON] et al. (2005) reported that lower level of ROS could directly down-regulate the xanthine dehydrogenase (XDH) and XO gene expressions [30]. They also indicate that lower hydrogen peroxide level reduces calcium ions release from the endoplasmic reticulum, subsequently supressing the conversion of XDH to XO.  
_Chrysanthemum_ flower oil has been shown to suppress uric acid production by inhibiting the XO enzyme in the liver and accelerating the renal uric acid excretion [1]. The findings of [PERSON] et al. demonstrated that the up regulation of uric acid transporter genes in the kidney increases the excretion of uric acid [1]. However, the regulation of liver XO gene expression was not reported. In the present study, the down-regulation of the XO gene expression might be related to the reduced oxidative stress in the treated rats attributed to the high antioxidant activity of the F10.
## 1 Introduction
### Motivation  
Recently published works investigate the quality of the routing trees in WSNs in order to extend network lifetime. These improvements include balancing the tree to distribute the load among nodes [4] and using specific metrics (residual energy, link quality, proximity to the sink) to construct the routing paths [1]. Little attention has been paid to efficient tree construction. Traditionally, the efficiency of distributed algorithms is measured by runtime and the number of messages exchanged among the nodes [6]. Runtime is an important metric for WSNs, because some applications require a fast reconstruction of the routing structure if sensor nodes fail or exhaust their batteries. For instance, in safety and mission-critical applications where sensor nodes are deployed to detect oil/gas leaks or structural damage, delays can cause unexpected and catastrophic events [3]. Moreover, if the network requires frequent reconfiguration, the routing tree may not have enough time to be updated. The number of messages exchanged among nodes is another important metric, because most of the energy of sensor nodes is consumed by data communication [7, 8, 9]. As sensor nodes are usually battery-powered, energy is a very crucial resource.  
Shortest path trees (SPTs) are the most common routing trees in the many-to-one networks and particularly in WSNs, because they represent an efficient routing structure in terms of time and energy [1,9]. There are many algorithms to construct SPTs, including the algorithms of [PERSON] [10] and Bellman-Ford [11]. However, these algorithms are centralized solutions that must be avoided, because of the unacceptable communication overhead involved in network topology discovery, since sensor nodes have scarce computational resources. Moreover, WSNs in general do not have centralized management and fixed infrastructure. In [12], [PERSON] and [PERSON] proposed a distributed asynchronous version of the Bellman-Ford algorithm for distributed systems. The distributed Bellman-Ford (DBF) presents fast convergence, the absence of any synchronization overhead and easy adaptation to topologicalnetwork changes, but it is not efficient for networks composed of a large number of nodes or dense networks, because of the excessive number of messages required for the tree construction [13]. In [9], the authors propose an algorithm called shortest hop multipath (SHM) in order to overcome the communication overhead of the DBF and to construct multiple paths between nodes and the sink to be used as mechanisms for fault tolerance and load balancing. However, sensor nodes have to partially know the network topology, and the algorithm cannot construct the routing tree in the event of message losses or node failures. Message loss is a common event in wireless networks, and sensor nodes are prone to failures.