esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.  
thick LWSN; network self-organizing; network clustering; routing protocol; energy consumption +
Footnote †: journal: Sensors
# Unit Cell Optimization of Groove Gap Waveguide for High Bandwidth Microwave Applications
## 2 Materials and Methods
### Design of Unit Cell
To model the new design of the unit cell, a parametric refinement was conducted on the proposed unit cell, incorporating a large air gap (4.036 mm) and minimizing the mushroom head to a very small dimension in relation to the unit cell's entire length, in contrast to the mushroom structure that is employed for microstrip lines, in which size of the mushroom head is comparatively very large in relation to its tail that joins the ground surface. Thus, the size ratio between the head and tail of the proposed mushroom-like unit cell is easy to fabricate with good physical strength for commonly used metals like copper, aluminum, and brass, and it can perform equally well for high-power applications. In the proposed mushroom-like EBG unit cell, a mushroom head is modeled additionally with a bed-of-nails unit cell. The dimensions of the mushroom head mirror each other precisely on both sides of a straight pin. The bandgap, in the lower cut-off frequency region, is increased by adding a small head to the straight pin. Figure 3a illustrates the physical dimensions of the proposed unit cell, while Figure 3b depicts its attained dispersion characteristics. Figure 2: (**a**) Bed-of-nails unit cell characterized by the following dimensions, air gap g1 = 4.036 mm, w1 = 10 mm, h1 = 30 mm, h2 = 34.036 mm (WR284 reference), and p = 20 mm; (**b**) dispersion characteristics of finite length of periodic structure based on a bed-of-nails unit cell, as specified by the dimensions in (**a**). The bandwidth enhancement was analyzed from dominant propagating modes in dispersion characteristics. A broader bandwidth was exhibited by the mushroom unit cell's flattened dispersion curve that was achieved by slowing down the waves' group velocity through the engineering of the unit cell structure. The compression of the dispersion curve was accomplished by adding more resonances and couplings. Consequently, the waveguide's bandwidth increased, permitting it to accommodate a greater range of frequencies with reduced attenuation and distortion of the signal. The dispersion results indicate that the proposed unit cell has a considerably large bandgap as compared to the prior bed-of-nails unit cell. The bandgap results are summarized in Table 2.  
From the results, it can be observed that the proposed mushroom-unit-cell-based EBG design provides 191 MHz extra bandwidth in comparison to the bed-of-nails-based EBG structure. The proposed unit cell design results suggest that it is suitable for wide-band microwave applications as it significantly increases the bandwidth. Additionally, it provides ease of fabrication for the proposed design as it can be fabricated using conventional milling tools.
###### Abstract  
Background: A BMI \(>\) 25 is the most decisive, albeit modifiable, risk factor for knee osteoarthritis (KOA). This study aimed at assessing the efficacy of intra-articular injections of hybrid hyaluronic acid (HA) complexes (Sinovial(r) H-L) for the treatment of KOA in overweight patients in terms of disease severity, cardiocirculatory capacity, and quality of life. Materials: In this single-site, open-label, prospective trial, 37 patients with symptomatic knee OA were assessed at baseline and 3 months after ultrasound-guided intra-articular injection of hybrid HA complexes (Sinovial(r) H-L). Results: Primary variables displaying a statistically significant improvement after treatment were pain (VAS), disease severity (WOMAC), and cardiopulmonary capacity (6 min walk test). Among secondary variables, quality of life (SF-12) improved significantly, as did analgesic intake for pain control. No statistically significant difference was observed in body fat and muscle mass percentage measured by bioelectrical impedance analysis. Conclusions: Intra-articular hybrid HA injections are significantly effective in improving OA-related disease severity, cardiopulmonary function, and analgesic intake. This supports the role of hybrid HA viscosupplementation as a nonpharmacological treatment to relieve pain, reduce disability, improve quality of life, and limit the risk of polypharmacy in overweight patients with knee OA.  
knee osteoarthritis; hybrid hyaluronic acid; viscosupplementation; obesity; overweight
## 2 Materials and Methods
### Location, Environmental Conditions, and Animals  
The study was carried out in the Nuble region (Chile), South zone 36\({}^{\circ}\)4432\" S 72\({}^{\circ}\)1755\" W. This region has annual temperatures that fluctuate between 13.5 \({}^{\circ}\)C and 14 \({}^{\circ}\)C with a maximum in the hottest month (January) of 33 \({}^{\circ}\)C and an average minimum in the coldest month (July) between \(-\)2 \({}^{\circ}\)C and 5 \({}^{\circ}\)C. Annual rainfall is 1025 mm. The study was conducted between July 2017 and June 2020. Handling, feeding, and health conditions of the herd were standardized according to the requirements of high-producing dairy cows (NRC).  
High-producing Holstein-Friesian cows (_Bos taurus taurus_, \(n=3209\)) were selected. From that population, only those animals at first calving were selected for this study (\(n=385\)). After an initial selection of animals based on production and udder health characteristics using Research Randomized (RR v.4.0, SPN, Pennsylvania, PA, USA) and G*Power (v.3.1.9.7, Universitat Dusseldorf, Dusseldorf, Germany) for random sampling and random assignment tools; 100 animals were used, divided into 2 groups: one with low inbreeding (LI: \(<\)2.5%) and one with high inbreeding (HI: \(\geq\)2.5% and \(\leq\)5.0%) according to genomic inbreeding traits. The classification of both groups was carried out based on previous approaches including the assessment of thousands of cows and establishing an inbreeding coefficient of \(\sim\)2.6 on average for Holstein breed [16; 26; 27]. Moreover, the highest threshold was established on 5.0 (two-fold), based on [PERSON] et al. [29] observations to avoid possible outliers due to the extremely high inbreeding index values (II \(>\) 5.0). Regarding animal selection by production characteristics, having into account that a Holstein primiparous cow is on average \(\sim\)24-26 L/day [35; 36], only high-producing individuals above this threshold (+20% L/day) were selected. Finally, after leaving out some animals with unspecific problems, a total of 80 high-producing cows were used based on the number of births (primiparous) with an average age between 23 and 26 months with \(\geq\)30 L average milk/cow/day, adjusted to 305 days of lactation. Genome trait data were obtained from Zoetis-Clarifide, (San Diego, CA, USA) which provides genomic evaluations for dairy cattle. Data on phenotypic traits were obtained from first lactation production records and breeding records associated to first calving in each genotyped animal and recorded in the farm's milk control software (DairyComp 305, Winnipeg, MB, Canada). The yields of milk, fat, protein, and somatic cells were standardized to 305 lactation days. This study included records of all nulliparous cows that became pregnant and calved regardless of whether conception occurred at first or more inseminations.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 2 Novel Hybrid Data-Fusion System
### Single PNN Damage Detection Model
#### 2.2.3 PNN Decision  
It is well known that neural network is a kind of machine learning method that imitates the human brain. A neural network usually consists of an input layer, a hidden layer and an output layer. In this paper, PNN is chosen to detect damage. PNN as a feed-forward neural network was first proposed by [PERSON] [24]. The PNN implements the Bayesian decision rule by representing the probability density functions (PDFs) of the known data sets with a non-parametric estimator, then judges which set of known data is most likely the source of the unknown datum. PNN uses Bayesian probabilistic approach to describe measurement data, which fundamentally determines that PNN has prominent merits among neural network models in noisy condition and pattern recognition.  
The essence of the PNN classifier for damage identification is to judge the pattern class of damage types into which the testing vectors of unknown source could be categorized. For the above-mentioned PNN, assume the input vector \(X=\{x_{1},x_{2}\dots\ x_{n}\}^{T}\) in the input layer consists of \(p\) eigenvectors. In the pattern layer, there are \(s\) pattern classes, each representing a possible damage type. The number \(s\) of pattern classes depends on a specific structure. After entering training vectors of all pattern classes as weights between the input and pattern layers, the PNN for damage type is configured. If \(n_{i}\) training vectors are prepared for the \(k\)th pattern class, the total number of neurons in the pattern layer is \(\sum_{k=1}^{s}n_{k}\), where \(s\) is the total number of pattern classes. When given a new input vector (testing vector) \(X\) consisting of measured data of unknown source, the configured PNN outputs the PDF estimates for each pattern class at the testing vector point. The pattern class with the largest PDF implies the class of the current input (testing) vector and indicates the damage class.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 4 Conclusions  
In this paper, a feature detection and description method based on consistent edge structures known as DDCE was proposed for infrared and visible image match. First, consistent edge structures were detected to address nonlinear intensity variations and local detail discrepancies of infrared and visible images. Second, common feature points of infrared and visible images were extracted based on the consistent edge structures to improve the repeatability of feature points. Lastly, feature descriptions were established according to edge attributes including length and direction to enhance the description ability. In order to validate the performance DDCE, two public datasets CVC and LWIR were employed for matching test and several state-of-the-art methods were used for comparison. Experimental results showed that DDCE could achieve superior matching performance compared with PIIFD, MMSURF,  
Figure 6: Feature match after RANSAC. (**a**) Matching Results of LGHD. (**b**) Matching Results of DDCE.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & PIIFD & MMSURF & PCEHD & LGHD & RIFT & MSCB & DDCE \\ \hline
**ART** & 6.5 & 2.4 & 7.2 & 8.5 & 13.5 & 3.7 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average running time.  
MSCB, PCEHD, and RIFT. Although LGHD achieved the highest percentage of correct matches, DDCE could obtain better RANSAC estimation performance than LGHD.  
In the future, more infrared and visible images including different kinds of targets and scenes will be acquired under a variety of meteorological conditions. Specific matching strategies for different targets and scenes will be designed to improve the matching reliability of DDCE. DDCE will be modified to be an invariant to rotation and scale. DDCE will be attempted to apply on practical platforms like unmanned aerial vehicles and remote sensing satellites.  
Writing--original draft, Q.W. Writing--review & editing, X.H. and F.W. Software, X.G. and Z.J. All authors have read and agreed to the published version of the manuscript.  
The National Major Special Funding Project, grant number 2018 YFA0704605, and 13 th Five-Year Major Special Funding Project, grant number 2017 ZX05064, funded this research.  
The authors declare no conflict of interest.
## 2 Designs for the Artificial Magnetic Conductor and Antenna
### The Proposed AMC-Combined Spidron Fractal Dipole Antenna  
Figure 4a presents an illustration of the quasi-self-complementary Spidron fractal dipole antenna. The Spidron fractal is composed of triangles that are sequentially scaled down and added to the hypotenuse of a larger triangle [19]. To meet the specifications of a quasi-self-complementary structure, the two sides of the largest triangle are set to half of the values of \(S_{W}\) and \(S_{L}\), which are 250 mm and 200 mm, respectively. The quasi-self-complementary configuration is utilized in the design of the antenna, which operates over a wide frequency bandwidth [22; 23]. The Spidron fractal structure consists of 13 triangles which are scaled down with a scale factor (\(\delta\)) of \(\overline{\sqrt{2}}/8\), which is ratio of the area, \(T_{n+1}/T_{n}\) (\(n=1,2,3...12\)). The quasi-self-complementary is realized by two Spidron fractal structures on a 1.57-mm-thick Taconic RF-43 substrate (shown in green in Figure 4a), with a dielectric constant of 4.3 and a loss tangent of 0.0033 and with side lengths of \(S_{W}=500\) mm and \(S_{L}=400\) mm. Each side length of the substrate is set considering the size of the manpack application referred to in the literature [24]. The Spidron fractal dipole antenna elements are located symmetrically on the upper and  
Figure 2: Geometry of the proposed unit cell of the artificial magnetic conductor (AMC): (**a**) 3D view; (**b**) boundary condition.  
lower sides of the substrate. The upper and lower elements are indicated in white and grey, respectively. Figure 4b shows the configuration of the AMC, which has a \(4\times 5\) unit cell structure. The number of unit cells and arrays of unit cells are also set considering the size of a manpack application. For the insertion of a coaxial cable, the holes shown in orange in Figure 4b with a radius of 0.85 mm were formed on the substrates and ferrite of the AMC. The quasi-self-complementary Spidron fractal dipole antenna with AMC is illustrated in Figure 4c. The distance between the Spidron fractal dipole antenna and the AMC is \(gap=1.5\) mm.  
Figure 4: **(a)** Designed Spidron fractal dipole antenna; **(b)**\(4\times 5\) AMC; **(c)** side view of the Spidron fractal dipole antenna with the AMC.  
Figure 3: Simulated reflection phase of the proposed unit cell of the artificial magnetic conductor (AMC).
# A Survey on Satellite Communication System Security
## 7 Future Research Directions
### Software-Defined Satellite Networks  
Managing SCSs can be challenging due to their highly dynamic nature, such as various topologies and intermittent links. One of the significant difficulties faced is the cost and effort required for reconfiguration and resource allocation [133; 134]. To address this issue, some researchers have suggested using software-defined network (SDN) features, which include flexible scalability, virtualization, and interoperability [135].  
More specifically, research on software-defined satellite networks (SDSNs), which are SDN-enabled SCSs, has been proposed, such as Dijkstra and dynamic algorithms for enhancing network QoS and security reliability in SCSs [136; 137; 138]. Also, a few studies [139; 140; 141; 142] have proposed a new load balancing algorithm to reduce congestion and bottlenecks in an SCS network. These studies can be applied to SCS security to mitigate DDoS attacks in an SCS network.  
These approaches are particularly beneficial in managing the complex, dynamic, and heterogeneous nature of modern satellite constellations. However, there is still room for improvement. First, integrating SDSNs with terrestrial SDN systems presents significant challenges due to the differing technological demands and the unique requirements of space-based infrastructure. Additionally, as satellite constellations, such as Starlink, have been expanded to include thousands of satellites, the complexity and demand for efficient management of these vast networks have increased considerably. Lastly, the inherent issues of a variable latency and link instability in satellite communications can further impede the performance of SDSNs, potentially degrading the quality of service and the overall network reliability [133].  
In particular, the integration of ground and satellite networks through SDSNs is crucial for creating a unified global network that can seamlessly support various applications, from global internet coverage to specialized services like disaster management communications. However, this integration is challenging due to the architectural and operational discrepancies between terrestrial and satellite networks. Moreover, SCSs inherently have a widerange of coverage due to their characteristics. This can make them vulnerable to attacks such as eavesdropping, requiring encrypted traffic during handover and upload/download processes. However, encryption in satellite communication systems faces difficulties due to high computational resource requirements and traffic loss.  
A few studies [143; 144] are attempting to address the above issues, but they still face challenges in resolving frequent handovers and delays, which are unique features of SCSs.
# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 2 Materials and Methods  
### Study Area  
This study was carried out on a 22-ha field located in a typical valley area (at Porto Felloni Co.) in Ferrara, North Italy (Figure 1). The study field lies within the Mediterranean climatic zone and the total rainfall during the corn growing season was 266 mm on average. The soil texture is silt loam and the study field has been irrigated by a center pivot irrigation system and cultivated with corn for two decades, with the exception of 2015, when it was cultivated with wheat. This work focused on three years of corn cultivation from 2016 to 2018. The corn planting dates were 30 March 2016, 22 March 2017 and 11 April 2018 for the study field and harvest was done at a crop age of 160 days on average. The company cultivated the same corn seed variety (Pioneer P1916) during the study period with 75 cm of row spaces and 16 cm between seeds, on average. Additionally, they used a variable rate of nitrogen application with an average urea application (nitrogen 46%) of 344 kg/ha in 2016 and 2017 and 367 kg/ha in 2018. The prescription map of applied nitrogen was the same for the 2016 and 2017 seasons and modified for the 2018 season.
## 3 Empirical Setting: Context of the Research and Methods
### Sample and Data  
The data used in this research are drawn from a survey that was carried out among firms belonging to the Spanish ceramic tile cluster. Based on a structured questionnaire, the survey aimed to gather detailed information on how ceramic tile firms faced the adoption process of digital printing technology, a technological disruptive innovation. This innovation stirred up the decoration tile stage of the manufacturing process, as explained by [PERSON] et al. [45]. Consequently, interviews were addressed to the wall and floor ceramic tile manufacturing firm category among the different categories of firms which are part of the cluster (such as equipment manufacturers, raw material providers or auxiliary services suppliers). Indeed, only the wall and floor ceramic tile manufacturers were those members of the cluster who were able to adopt the disruptive technology under study. Specialized or integrated firms are, consequently, out of the scope of the research.  
Two polling rounds were conducted from October 2016 to February 2018. As a result, a total of 88 completed questionnaires were obtained. At that time, the universe of firms in the cluster (total amount of independent tile manufacturers) totaled 118 independent manufacturers.  
The targeted group of respondents is mainly comprised of CEOs or R&D managers. These respondents were considered by the researchers as the most suitable profiles because they are expected to have first-hand information about the impact of the new technology as well as the innovative patterns in their own companies. Nevertheless, to a lesser extent, some questionnaires were answered by different profiles of respondents such as marketing managers or technical production managers. They are similarly involved in the innovation processes of their corresponding companies.  
Moreover, secondary information was gathered from the SABI database (Iberian Balances Analysis System) in order to obtain other relevant features of the companies as well as their business and performance information. Besides, data regarding total assets, total revenues, and the number of employees were gathered for the respondent companies from 2007 to 2013. This period of time was selected so as to cover a representative period around 2010 due to the fact that this year has been chosen as the threshold for a clustered company to be contemplated as an early adopter of the disruptive technology as described in the previous section.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 8 What Should 6G Standardization Do Differently?
The combination of computing, AI, and sensing will enable the creation of digital twins, providing a unique and valuable asset for operators [95];Lesson #5: The traditional 3 GPP release-style enhancements have difficulty keeping up with the increasing pace of innovation. As seen in the previous sections of this retrospective, 3 GPP evolution depends on the release cycle, where enhancements are bundled and introduced every 18-24 months. Fixed schedules for 3 GPP releases have the drawback of delaying the deployment of new features, forcing the industry to wait for standardization processes. This release-cycle evolution approach is increasingly misaligned with the rapid pace of AI-driven innovation. AI technologies are advancing rapidly, often delivering significant breakthroughs within months or even weeks. The rigid cadence of 3 GPP releases means that AI-driven capabilities may be delayed in reaching deployment. Waiting for the next release cycle undermines AI's ability to optimize network performance. 6G standardization should enable continuous enhancements and innovations. 6G should start to gradually move away from release-bound updates, enabling innovations to be deployed dynamically as they are developed. Shifting to an AI-native system design in 6G will enable the system to evolve dynamically through self-learning and adaptation, facilitating continuous improvements without waiting for standardization cycles. Additionally, software-driven enhancements can reduce deployment costs and time-to-market for innovations.