esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Since SAR distinguishes objects by different scattering mechanisms and surface roughness, the above two factors are basically the same in saltwater and river, making it difficult to distinguish between them. Therefore, the spectral characteristics of optical images are required to improve the PA, UA, and F1 scores of water bodies. For synergetic classification, the PA, UA, and F1-score are above 90% as most phenological features are captured by the SAR backscatter coefficients and OHS spectral information. Although there is an overall increase in the Kappa coefficient, OA, UA, PA, and F1-score for different wetlands with synergetic classification, the PA, UA, and F1-score of shrub, grass, and _Suaeda salsa_ are abnormal, respectively. The decrease in the UA, PA, and F1-score could be due to the fact that the sample pixels used for training are insufficient. Considering the complexity of wetlands in the study areas, these levels of accuracy prove the robustness and high performance of the proposed synergetic classification in different study areas with various ecological characteristics. Misclassification commonly occurs in the process of image classification. The fewer misclassified categories and misclassified pixels, the better the results of the classification. Figure 11 is a graphical representation of the confusion matrix. Most off-diagonal cells have low values, indicating that most pixels are reasonably well classified. In particular, the results of the ML synergetic classification show that part of the tidal flats were wrongly classified as _Suaeda salsa_, grass, river, and farmland. In a few cases, saltwater was also misclassified as shrub and river. The biggest omission was the misclassification of _Suaeda salsa_ as tidal flat and farmland. In general, there is extensive confusion between adjacent succession groups, such as saltwater vs. river, farmland vs. tidal flat, and shrub vs. grass.
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 1 Introduction
### Our Contributions  
In this paper, to model the air transportation system more accurately, from physical facilities to traffic applications, three-layer networks, including air route networks, city-pair airline networks (airport networks), and flight operation networks, are built, where air route networks are regarded as the physical layer, and airport networks and flight operation networks are the application layers. Moreover, considering the path diversity of airlines, a simulated annealing-based framework is proposed to optimize the routing paths for city-pair airlines (airlines between airports) on the three-layer networks, such that the traffic congestion of air route network can be alleviated. The main contributions of this paper are outlined as follows.  
1. The three-layer networks, including air route network, city-pair airline network and flight operation network, are built to model the air transportation system from physical facilities to application, where the air route network is regarded as the physical layer, and the city-pair airline network and flight operation network are the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors Discussed in Research** & **Air Route** & **City-Pair Airline** & **Flight Operation** \\ \hline
[8; 10; 12; 13; 15; 16; 17; 18; 19; 20; 21; 22; 23] & ✓ & - & - \\ \hline
[9; 25; 27] & - & ✓ & - \\ \hline
[2; 24; 26; 28; 29] & ✓ & ✓ & - \\ \hline This paper & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three different factors of air transportation network and most researches hardly cover flight operation network.  
application layers. In addition, the topology characteristics of the three-layer networks are analysed using the complex network theory.
2. A simulated annealing-based method is proposed to optimize the routing paths of each city-pair airline based on the three-layer air transport networks, such that the congestion issue of air route segments in the air route network can be reduced.  
Experimental results show that, compared with the default routing paths, the shortest routing paths, and random routing paths, the proposed routing paths optimization strategy can reduce the maximum traffic flows of air route network by 2.4%, 4.6%, and 4.8%, respectively.  
The remainder of the paper is organized as follows. Section 2 provides the construction method of air transport networks and the mapping relations between the three-layer networks. Furthermore, the problem definition of optimizing routing paths for airlines is also introduced. Section 3 discusses the simulated-annealing based method to optimize the routing paths for airlines in detail. Experimental results and conclusions are shown and discussed in Section 4 and Section 5, respectively.
## 3 System Model
OemR has to store the hash value of each software cluster in addition to existing image metadata such as image name, version, size, hashing function used, and any additional proprietary OEM information, such as the download URL for the image file located in the image repository. OemR is trusted and responsible for protecting the harmonization among software clusters as well as the integrity of each software image to prevent any tampering. * Image repository \"ImgR\": This repository contains the software binary images to install as well as the signatures (authentication tags) for these images. These signatures are generated by OemR. * OTA Master \"OtaM\": This entity is responsible for receiving software images in the vehicle and distributing them to other ECUs in the vehicle. OtaM is capable of connecting to backend repositories through wireless channels, such as Wi-Fi or cellular communications. * End-Target ECU \"EcuX\": This is the ECU in the vehicle where software image is installed or updated. Upon reception of new image software, EcuX validates the integrity and authentication of the image as well as proves complete possession and compatibility of the software image in question. EcuX can be directly connected to OtaM through in-vehicle communication channels, e.g., CAN or Ethernet, or can be indirectly connected through a gateway or domain controller. In case the software image is intended for the OTA Master itself, OtaM will play the role of EcuX. Figure 2: OTA System Architecture: The OEM and image repositories contain all the information about the software update process and the software images to be wirelessly transferred to the vehicle. The OTA Master manages the update process for all ECUs within the vehicle. It receives the images from the repositories and transfers the image to the target ECU.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Sp
#### 2.1.1 Load Detection  
This step must set a threshold limit to decide when a certain server is over-utilization or under-utilization. The load detection step can be divided into overload (hotspot) detection and underload (coldspot) detection.  
* Hotspot Detection: In the existing works, when the monitoring value or predictive value such as resource utilization, waiting time, and user mobility information exceeds the set threshold, this is considered a hotspot [16] in CDCs. Getting rid of hotspots can ensure the QoS and minimize resource wastage.
* Cold Spot Detection: If a specific VM detects low utilization than the set threshold, this state can be termed a \"Cold Spot.\" It is the aim of SP to remove this state through migration to other active hosts. For efficient SP, it needs to appropriately see that VMs are selected to solve the over-loaded or under-loaded host.
## 2 Materials and Methods
### Procedures
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Variables**} & **AEL** & **CMJ** & **CON** & **P** & **F** & **Value** & \(\mathbf{\eta^{2}p}\) \\  & **(n = 9)** & **(n = 10)** & **(n = 10)** & **(n = 10)** & **(n = 10)** & **(n = 10)** \\ \hline \multirow{2}{*}{**Age (years)**} & \multirow{2}{*}{\(26.6\pm 4.6\)} & \multirow{2}{*}{\(26.8\pm 4.5\)} & \multirow{2}{*}{\(27.5\pm 4.8\)} & \multirow{2}{*}{\(0.93\)} & F\({}_{(2,27)}\) & \multirow{2}{*}{\(0.04\)} \\  & & & & & & \(0.21\) \\ \hline \multirow{2}{*}{**Height (cm)**} & \multirow{2}{*}{\(1.75\pm 0.02\)} & \multirow{2}{*}{\(1.74\pm 0.06\)} & \multirow{2}{*}{\(1.75\pm 0.06\)} & \multirow{2}{*}{\(0.53\)} & F\({}_{(2,27)}\) & \multirow{2}{*}{\(0.04\)} \\  & & & & & & \(1.65\) \\ \hline \multirow{2}{*}{**Body mass (kg)**} & \multirow{2}{*}{\(76.9\pm 8.3\)} & \multirow{2}{*}{\(77.2\pm 11.8\)} & \multirow{2}{*}{\(73.9\pm 11.7\)} & \multirow{2}{*}{\(0.75\)} & F\({}_{(2,27)}\) & \multirow{2}{*}{\(0.02\)} \\  & & & & & \(1.62\) \\ \hline \multirow{2}{*}{**Back squat 1 RM (kg)**} & \multirow{2}{*}{\(112\pm 1\)} & \multirow{2}{*}{\(110\pm 1\)} & \multirow{2}{*}{\(110\pm 2\)} & \multirow{2}{*}{\(0.95\)} & F\({}_{(2,27)}\) & \multirow{2}{*}{\(0.003\)} \\  & & & & & \(0.57\) \\ \hline \multirow{2}{*}{**Relative strength (kg/kg)**} & \multirow{2}{*}{\(1.5\pm 0.2\)} & \multirow{2}{*}{\(1.4\pm 0.1\)} & \multirow{2}{*}{\(1.5\pm 0.2\)} & \multirow{2}{*}{\(0.42\)} & F\({}_{(2,27)}\) & \multirow{2}{*}{\(0.007\)} \\  & & & & & \(0.90\) \\ \hline \end{tabular}
\end{table} 
Table 1: Participant demographic and baseline strength characteristics. Values are presented as mean \(\pm\) standard deviation. AEL: accentuated eccentric loading; CMJ: countermovement jump; CON: control group; 1 RM: one-repetition maximum; \(\eta^{2}p\): partial eta squared.
## 2 Background
### Description of IRRT* Algorithm  
Algorithm 1 provides the steps of the IRRT* algorithm. It firstly behaves as an RRT* algorithm until a first solution \(c_{best}\) is found in line 13, and adds the solution to a list of possible solutions (line 15). Then the IRRT* algorithm uses the minimum solution of this list (line 6) to calculate and continue sampling \(X_{f}\) (line 7), and repeat the steps as above. Thus, the minimum value of \(c_{best}\) of this list can be calculated and updated during iterations. IRRT* algorithm first acts similarly to RRT* algorithm to find an initial solution and calculate the path length. Then it structures an ellipse with the path length \(c_{best}\) as its transverse diameter and the start point \(x_{start}\) and goal point \(x_{tar}\) as the focuses. Finally, it improves the sampling probability of the extended node in the ellipsoidal domain, and decreases the ellipse area by constantly iterating until the optimal solution is found. The iterative process can be shown in Figure 2.  
The IRRT* algorithm restricts the search domain to a subset of the initial domain based on the current optimal solution, which improves the rate of convergence. It helps the robot find the near-optimal solution faster than the original RRT* algorithm does. However, due to the disadvantages of the randomness of sampling and non-real time process, the IRRT* algorithm still generates unnecessary extended nodes in invalid regions, which limits the search efficiency and convergence rate.  
Figure 1: The heuristic sampling ellipse domain. The \(x_{start}\) and \(x_{tar}\) are the focal points. \(c_{min}\) and \(c_{best}\) are the theoretical minimum cost and the current solution cost between the start point and target point, respectively.  
```
1:\(V\leftarrow\{x_{start}\}\) ;
2:\(E\leftarrow\varnothing\) ;
3:\(X_{soln}\leftarrow\varnothing\) ;
4:\(T=(V,E)\) ;
5:foriteration \(=1\) to n do
6:\(c_{best}\gets min_{x_{soln}\in X_{soln}}\{Cost(X_{soln})\}\) ;
7:\(x_{rand}\gets Sample(x_{start},x_{target},c_{best})\) ;
8:\(x_{near}\gets Nearest(T,x_{rand})\) ;
9:\(x_{new}\gets Get(x_{rand},x_{near},StepSize)\) ;
10:ifCollisionFree\(x_{new}(x_{new},x_{new})\)then
11:\(X_{near}\gets Nearest(T,x_{new},r_{RRT^{*}})\) ;
12:\(x_{parent}\gets Parent(x_{near})\) ;
13:ifInEllipseRegion\((x_{new})\)then
14:\(X_{soln}\gets X_{soln}\cup\{x_{new}\}\) ;
15:\(T.addNode(x_{new})\) ;
16:Return\(T\) ;
```  
**Algorithm 1**IRRT\({}^{*}\) (\(x_{start}\),\(x_{target}\))
# Disseminating the Past in 3D: O Corro dos Mouros and Its Ritual Landscape (Galicia, Spain)
## 1 Introduction
### Objectives and Technological Approach  
This study proposes an integrated methodology combining UAV-LiDAR and SfM photogrammetry to demonstrate the complementary capabilities of this technological fusion at archaeologically significant sites and their surrounding landscapes, showing how their synergy overcomes the individual limitations of each technique in complex environments with dense vegetation. LiDAR captures hidden microtopographies beneath the vegetation cover, while photogrammetry provides realistic textures and detailed surface geometry, together generating a comprehensive 3D model with threefold application: scientific (centimetre-level accuracy for archaeological analysis), analytical (detection of subsurface features through LiDAR processing), and outreach-oriented (photorealisticvisualisation for heritage purposes). The main objective is to demonstrate its potential as a comprehensive solution for archaeological documentation in similar environments, thereby establishing a workflow that contributes to both research and digital heritage preservation.
## 1 Introduction  
Adequate nutrient supply is of greater relevance during childhood than at any other period in life. Due to their high basal and anabolic requirements and limited metabolic reserves, pediatric patients are particularly sensitive to energy and protein restriction [1]. Sufficient nutrients must be provided not only for the maintenance of body tissue, but also for growth. Starvation, even for few days, may be detrimental [1; 2; 3].  
Parenteral nutrition (PN) has become a valid therapeutic option for patients with intestinal failure (IF) who require long-term PN and home-PN (HPN) [1; 4; 5]. The provision of PN to pediatric patients represents a distinct clinical challenge, as metabolic demands and nutritional needs change according to age, growth velocity, and clinical situations [3]. Moreover, complex and rapidly changing fluid and electrolyte requirements are a major challenge to PN delivery [6].  
In children, IF is defined as a critical reduction in functional gut mass below the minimum needed for adequate growth [7; 8]. The leading cause of IF in childhood is short bowel syndrome as a consequence of extensive small bowel resection. Long-term PN and home-PN (HPN) are the mainstay for the management of chronic IF, and allow children to grow in their familiar environment [9].  
This narrative review aims to give an overview of specific quantitative and qualitative nutritional intake in pediatric patients including term newborn infants, infants/toddlers, children, and adolescents receiving PN. Preterm infants with their highly specific demands are not included, as this would be beyond the scope of the present article. Moreover, it explores the clinical experience regarding the use of a composite intravenous lipid emulsion (ILE) containing fish oil (FO) (SMOFlipid(r), Fresenius Kabi, Bad Homburg, Germany) and a pediatric crystalline amino acid solution (Vaminolact(r), Fresenius Kabi, Bad Homburg, Germany) in the management of pediatric patients with acute or chronic IF depending on PN.  
To this end, we conducted a literature search sourcing published research investigating the use of SMOFlipid and Vaminolact in the management of pediatric patients with acute or chronic IF depending on PN. Databases searched were PubMed (without any time restrictions), Embase (papers published since 2016), and Google Scholar (papers published since 2018), with the last search conducted in June 2022, and an update in January 2024. Retrospective studies, prospective studies, and RCTs conducted on the age groups of newborn infants, infants/toddlers, children, or adolescents and reporting efficacy and/or safety results were eligible for this narrative review. Studies conducted exclusively on preterm newborns, studies on adults, abstracts, case reports, studies not investigating PN effects/the off-label use of SMOFlipid or Vaminolact, and studies with unclear data regarding the actual use of SMOFlipid/Vaminolact were excluded.
## 2 Materials and Methods
### Characterization  
The crystal structures of the CN and FCNwere analyzed by X-ray diffraction (XRD, Philips X'pert Pro). FT-IR spectra were recorded on a Perkin Elmer FT-IR Spectrometer. The Brunauer-Emmett-Teller (BET) surface area was determined using a Quantachrome NOVA 2000e surface area analyzer. The composition of CN and FCN was analyzed by X-ray photoelectron spectroscopy (XPS). Solid-state ultraviolet-visible (UV-vis) diffuse reflectance spectra (DRS) were measured by the Shimadzu UV-2600 UV-vis spectrophotometer. The photoluminescence (PL) spectra were measured by the HORIBA Jobin Yvon LabRAM HR system. Morphology of the FCN surface was observed by field emission scanning electron microscopy (FESEM, Hitachi S-4800) and the chemical composition were recorded by energy dispersive spectroscopy (EDS).Structure of FCN was analyzed by high-resolution TEM (HRTEM). Electron spin resonance (ESR) spectra were acquired by ESR spectrometer (JEOL JES-FA200 ESR spectrometer) at room temperature.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 4 Performance Analysis
### Decoding Success Probability  
In this subsection, we study the performance of LT codes in terms of decoding success probability (DSP). In general, we use the analysis proposed by [PERSON] et al. to measure the DSP [29; 30]. In brief, the analysis defines three main parameters at each decoding step: (1) the number of unrecovered source symbols at this step, \(u\); (2) the ripple, \(r\), which is the number of degree-one encoded symbols at this step; (3) the cloud, \(c\), which is the number of output symbols that have a degree higher than one at this step. Thus, the decoder will be in state \((c,r,u)\) with probability \(P(c,r,u)\), which is given by Equation (5) (at the bottom of the page). \(\Phi((c+j,r+1+i-j,u);(c,r,u-1))\) in the equation is the transition probability from state \((c+j,r+1+i-j,u)\) to state \((c,r,u-1)\), and it is given by Equation (6) (at the bottom of the page). Finally, \(p_{u}\) in Equation (6) represents the probability of releasing a degree-one encoded symbol from the cloud (\(c\)) given that there are \(u\) unrecovered source symbols, and it is given by Equation (7) (at the bottom of the page).  
\[P(c,r,u-1)=\sum_{i,j\geq 0,j-i\leq r}P(c+j,r+1+i-j,u)\Phi((c+j,r+1+i-j,u);(c,r,u -1)). \tag{5}\]  
\[\Phi((c+j,r+1+i-j,u);(c,r,u-1))=\binom{c+j}{j}p_{u}^{j}(1-p_{u})^{c}\binom{r+i -j}{i}\bigg{(}\frac{1}{u}\bigg{)}^{i}\bigg{(}1-\frac{1}{u}\bigg{)}^{r-j}. \tag{6}\]  
\[p_{u}=\frac{\sum\limits_{d}\Omega_{d}d(d-1)\frac{(u-1)(k-u)...(k-u-d+3)}{k(k-1 )...(k-d+1)}}{1-\sum\limits_{d}\Omega_{d}\frac{(k-u)...(k-u-d+1)}{k(k-1)...(k-d +1)}-\sum\limits_{d}\Omega_{d}d\frac{u(k-u)...(k-u-d+2)}{k(k-1)...(k-d+1)}}. \tag{7}\]  
The decoder fails to recover all the source symbols once it approaches a state of \(r=0\) and \(u\geq 1\). Thus, for a given degree distribution, \(\Omega(x)\) (such as RSD) and \(n\) received encoded symbols, the DSP is given by  
\[\underset{\Omega(x),\ n}{DSP}=1-\sum_{r=0,u\geq 1}P(c,r,u). \tag{8}\]
###### Abstract
The spinal stability induced by a PS or CS has been compared in many studies. However, most of them focused only on lumbar fusion [20; 21; 22]. Only a few of them, including the thoracic spine, compared the results of fixing different segments. Therefore, this study numerically compared spinal stability in terms of screw type, motion, and the spine fixing method among four types of spinal fixations using a PS and CS in a human spine FE model with a thoracolumbar burst fracture. This study aimed to numerically evaluate the stability of the spine and safety of the implant by calculating the equivalent stresses applied to the peri-implant bone (PIB), and IVD of the spine using FEA.
## 6 Simulation Results
### Analysis of the Crack Detection Results of the OMP Algorithm
#### 6.2.2 Analysis of Results for Two Cracks  
The simulated estimation of crack positions for two cracks is shown in Figure 7. It can be observed that when the distance between the two cracks is close, the localization error increases. This indicates that the resolution for the performance of crack identification is degraded, which may be related to the wavelength of the transient wave, i.e., it is difficult to identify two cracks if the distance between them is smaller than the wavelength of the transient wave. The cumulative distribution functions (CDFs) of the estimated crack position errors are shown in Figure 8.  
Figure 5: Monitoring results for a single crack.  
Figure 6: CDF of estimated error in single-crack position.  
Figure 7: Monitoring results for two cracks.  
As shown in Figure 8, the CDF analysis of the error using the CS-based OMP algorithm for two cracks reveals that the detection error for two cracks is obviously greater than that for a single crack. During the simulation process, the distance between the two crack points has a significant impact on the error. The closer the crack points are, the larger the error will be, and it may even fail to detect the two cracks.
## 2 Phytonutrients
#### 2.1.3. Biomedical effects  
Anticancer  
Turmeric extracts and isolated curcumin have been extensively studied for cancer applications. Since 1985, turmeric extracts have demonstrated potent cytotoxic activity against cancer in vitro and in vivo studies [49]. Then, it also entered clinical studies for the treatment of cancer [50]. Curcumin has been shown to diminish tumor growth effectively, prevent tumor formation, angiogenesis, migration, and invasion by modulating several cell signaling pathways related to adhesion molecules, cell survival proteins, growth factors, transcription factors, cytokines, kinases, and receptors [51]. Different studies demonstrated that curcumin downregulates cyclin D1, cyclin E, and MDM2, and upregulates p21, p27, and p53 [52]. Due to the low bioavailability of pure curcumin, some researchers prefer to continue studies using turmeric extracts, co-administration with other phytochemicals, or the development of drug delivery systems. For example, [PERSON] et al. reported that turmeric extracts (200 mg/kg) induced in vivo tumor growth inhibition and anti-metastatic effects using colorectal CT26, HT29, and HCT116 cancer cells [53]. Furthermore, in combination with the phytochemical quercetin, it reveals a synergistic effect against lung, skin, colorectal, and breast cancer cells [54]. In addition, [PERSON] et al. designed a model that encapsulated curcumin in a chitosan polymer nanoparticle (115 nm) to increase its anticancer activity. This curcumin-chitosan nanoparticle showed a sensitive release in a more acidic pH environment, such as in cancer cells [55]. Moreover, several studies using curcumin as an anticancer agent include possible mechanisms of action [56; 57; 58; 59; 60].  
Antioxidant  
Curcumin is an extremely potent antioxidant by inhibiting the formation of reactive oxygen species [61]. In an in vitro study, [PERSON] and [PERSON] demonstrated the potent radical scavenging activity of curcumin by inhibiting \(>\)95% of lipid peroxidation [62]. [PERSON] et al. investigated the antioxidant and neuroprotective effects of curcuminoids on neurons from Sprague-Dawley rats as a potential treatment for dementia. Turmeric extract (200 mg/kg) prevents spatial memory deficits, and its effects were comparable to the standard dementia medicine, citicoline [63]. In addition, [PERSON] et al. demonstrated the antioxidant properties and protective effects to hepatic organs in orally supplemented rats through a combination of curcumin (62%), flavonoids (37%), and ascorbic acid (10%). The possible mechanism of action was through antioxidant enzyme upregulation and lipid peroxidation inhibition, providing protective effects [64].