esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The performance of Wireless Sensor Networks (WSNs) faces a number of challenges. Of these challenges, energy consumption is considered a hot research area. Most WSN energy is used in transmitting the data from the sensor nodes either among each other or to a Base Station (BS). For this reason, many routing protocols have been developed to facilitate the data dissemination in the WSNs. One of these protocols, Low Energy Adaptive Clustering Hierarchy (LEACH) has provided a distinctive hierarchical approach that efficiently forwards the nodes data to the BS, but it suffers from increased energy consumption and a significant decline in the network performance in the case of large-scale networks. This paper aims to present a new approach for splitting the whole sensor network into several levels. Thus, every node will be acting accordingly on its position and status. Further, two techniques, a static one and a dynamic one, have been developed to route the data between the levels. The simulation results demonstrated that the proposed techniques prolong the lifespan, improve the stability and raise the throughput of the network compared with the LEACH, the Improved MHT-LEACH (IMHT-LEACH), and the Enhancing DMHT-LEACH (EDMHT-LEACH) protocols.  
wireless sensor network; clustering; lifetime; stability; throughput; LEACH protocol
## 1 Introduction  
Regenerative medicine aims at the restoration of tissue micro and nanostructures and functions by means of cells and synthetic or natural scaffolds [1; 2; 3; 4; 5]. Hence, the development of new materials capable of acting as supporting and temporary architecture for cell proliferation and extracellular matrix deposition, with subsequent tissue ingrowth, plays a critical role in the realization of a new functional tissue. Although various polymeric biomaterials have proved to be useful in the regenerative medicine, special attention has been given to aliphatic polyesters (APs) [6]. APs are biodegradable and/or bio-based, and their properties can be modulated both by chemical routes [7; 8; 9] and by physical means by fabricating nanocomposites [10; 11; 12; 13; 14; 15]. Despite their potential in various biomedical applications, APs lack surface epitopes, have poor biomechanical properties (e.g., low modulus or brittleness), and longer degradation times, e.g., poly(\(\epsilon\)-caprolactone), thereby necessitate the incorporation of additional molecules or nanofillers to modulate these characteristics.  
Efficient strategies have been studied to obtain polyesters applicable in biomedicine and many members of the family have been so far commercialized. Many efforts have been recently made to improve the properties and make APs more competitive as compared to petroleum-basedpolymers. In this respect, polymer blends, copolymerization strategies, and nanocomposites have been extensively studied [16; 17; 18; 19; 20].  
On the other hand, nanotechnology, which deals with materials of about 0.1 to 100 nm size, offers new methods to modify material properties. In particular, the recent combination of promising nanotechnology with biology, medicine, and material science has resulted in many innovative nanoengineering efforts applicable on the repair and regeneration of specific tissues, organs, and functions [11]. Hence nanoengineering interdisciplinary approaches applied to biomedicine may contribute to address the main issue of personal and global health care. Furthermore, the combination of chemical methods for modifying existing polymers with nanocomposite approach could open up new perspectives in advanced functional material development.  
This review highlights the recent advances in nanocomposites based on aliphatic polyesters in terms of design, synthesis, and applications in regenerative medicine. Structure-properties relationships of the resulted materials and their potential applications will be mainly investigated.
## 3 Validation of the Analytical Solutions
### Comparison to the Existing Analytical Solutions  
In this section, the suggested analytical model by [PERSON] [17] is compared with the introduced analytical solution. The calculation parameters for path 6 were as given in Table 2. Since the existing model can only be applied to homogeneous grounds, the parameters of the reinforced ground and the natural ground were used as in the [PERSON]'s solution [17]. On the other hand, [PERSON] [20] developed an analytical model for GRCs considering the ground reinforcement effect under dry condition. In Figure 7, the plotted results by the analytical solution were simultaneously compared with those of [PERSON]'s solution [17] and [PERSON]'s solution [20]. It can be seen that the GRCs obtained from the [PERSON]'s solution using the natural parameters and the reinforced parameters were, respectively, the upper and lower bounds. Further, the radial displacements of the analytical model were larger than those of [PERSON]'s solution, indicating the crucial role of the seepage forces in the tunnel radial displacement. In brief, the analytical model considering the effect of both reinforcement and seepage forces was superior to the existing model; therefore, it would be more suitable for predicting mechanical behavior of subsea tunnels crossing through unfavorable geological conditions.  
Through the calculation method in this paper, the development and distribution laws of displacements and plastic zones of the subsea tunnels can be rationally interpreted. Based on the abovementioned analysis, the determination method of reasonable reinforcement parameters are introduced in the next section.  
Figure 6: The validation of the analytical solution by comparing its results with those of the numerical simulation: (**a**) comparison of the GRCs, (**b**) comparison of the plastic radii of processing path 4; (**c**) comparison of the plastic radii of processing path 6; (**d**) comparison of the plastic radii of processing path 9.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 3 Hyper-Heuristic Approach for VRP
### Genetic Programming  
Genetic programming [50] is an optimisation and machine learning method that uses simulation of evolution to automatically discover symbolic solutions (functions and programs) to the problem at hand. The main idea behind GP is that the solution to the problem may be represented as a (computer) program, in most applications in the form of a syntactic tree. The elements of the programs (tree nodes) must be predefined by the user and must be sufficient to describe the solution to the problem (e.g., mathematical and logical functions, variables, actions such as move forward, turn left etc.). In this case, we restrict the solutions to priority functions consisting of arithmetical and logic operators, as well as variables corresponding to the current VRP state. The algorithm randomly generates a set of trees (potential solutions) and evaluates each one on a predefined set of test cases. Test cases describe how well the candidate solution solves the given problem, e.g., how well does the function represented in the tree describe the data. In this paper, test cases will be represented by different VRP instances. Each potential solution thus receives its quality estimate - the fitness value - which is then used in the selection process.  
The selection process imitates natural evolution where weaker solutions (individuals) are eliminated, and better individuals survive. Additionally, better individuals also participate in recombination, where two or more individuals are combined to form a new one. The algorithm also incorporates a mutation mechanism, where a single individual undergoes a random change, with a relatively small probability. The process continues, building new generations from old ones until a suitable termination criterion is reached.  
Termination criteria usually include finding a solution of the desired quality, evaluating a given number of potential solutions or running the algorithm for a predefined amount of time. The examples of human competitive results of genetic programming may be found in [51]. For priority functions evolution, the Evolutionary Computation Framework (ECF) [[http://ecf.zemris.fer.hr/](http://ecf.zemris.fer.hr/)]([http://ecf.zemris.fer.hr/](http://ecf.zemris.fer.hr/)) (accessed on 3 October 2022) was used.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 4 Discussion
In general, the technology related to the application of UAS as a platform for data ferrying (flying, integrating wireless communication devices, or retrieving datasets while flying) is a rather novel approach with additional work needed to design and evaluate the entire UWSN system (from node stations to UAS based data ferry). While the main purpose of the UWSN system was meant to perform real-time monitoring of soil moisture and crop canopy temperature for the purpose of irrigation management, significant time was spent developing, troubleshooting, and fine-tuning the technology and procedures used to retrieve the datasets. The methodology adopted in this study could be replicated/run on a testing framework such as FlockAI to investigate the result reproducibility of this experiment [45]. Future research should aim to investigate the scalability and adoptability of the proposed data ferrying system to strengthen the applicability of the UAS data ferrying system. The feasibility of mounting additional advanced instrumentation to the existing design of a data ferrying system for simultaneously capturing thermal/multispectral imagery could also be explored as it would aid in efficient irrigation and agronomic decision-making.
## 3 Results
### Accuracy Assessment of Atmospheric Corrections  
At CORS HBA1, a high-precision coordinate was utilized. The reference station UDUC solution model was applied to HBA1 to determine its true ZWD once ambiguities were resolved. Similarly, ZWD calculations using stations HBA2, HBB0, HBF7, HBE0, and HBH8 were conducted. Interpolation via inverse distance was then employed across the reference station network to estimate ZWD at HBA1. This interpolated value was compared with the true ZWD at HBA1 to assess the accuracy of tropospheric conditions. Additionally, UDUC PPP-AR enabled the calculation of true slant path ionospheric delays for each satellite visible at HBA1. Interpolation was also used to derive slant path ionospheric delays, facilitating further evaluation in this context.  
The slant path ionospheric and ZTD deviation time series on the HBA1 are shown in Figure 3. The ionospheric errors from different systems were similar, with a maximum error of around 5 cm due to afternoon ionospheric activity and satellite elevation angle variations in the Wuhan area. The RMS errors for GPS, Galileo, and BDS were 3.2 cm, 2.6 cm, and 3.8 cm, respectively, which are acceptable for PPP-RTK calculations. From the ZTD error time series shown in Figure 3, it can be seen that the ZTD error was relatively stable, with values ranging between \(-2.5\) cm and 2.5 cm, and the RMS of ZTD was only 1.8 cm. This difference may be attributed to the larger ionospheric isolation error and the stronger activity influenced by various factors, leading to greater errors in ionospheric interpolation.
# A Low-Cost Contactless Overhead Micrometer Surface Scanner
## 2 Materials
### Communication and Control  
The scanner control software runs on a personal computer connected wirelessly to the controller. This computer runs software mediating the image acquisition process. The software executes a scanning plan, containing the locations of image acquisition, encoded as 3D coordinates.  
Specifically, print bed and sensor are drawn to a designated relative position, due to a G-Code command to the controller, e.g., \"G1 X2.57 Y1.93 Z18.20\". The signal for image acquisition is then sent. The command sequence, encoded in HTTP, in Table 1 (top) is sent to trigger the acquisition of an image. Image acquisition failures and delays are treated as follows: The software checks if the image has indeed been acquired, using the HTTP command in Table 1 (bottom). This command acquires the list of stored images to be compared with a previously collected one. These two sequences are repeated until the image is acquired. Each filename is stored to later conveniently rename the acquired images.  
When all the pictures are acquired, the files are manually copied from the memory card of the sensor and automatically renamed by the software. The renaming includes the coordinates of image acquisition in the filename in the form of Z-Y-X.jpg, such as '018.20-001.93-002.57 jpg'. These 3D coordinates are only readings of the C3 Dp controllers, they are not regarded as absolute measurements but fused with visual cues, as discussed in Section 3.3.
## 2 Overview of Time-Series RSS Observation Outlier Detection Methods
### Z-Score Method  
This method calculates the difference between each RSS observation and the mean of the time-series RSS observations. Then, the result is divided by the standard deviation (SD) of the observation [27]. RSS observations that have a Z-score greater than a pre-determined threshold are considered outliers.  
Given the time-series RSS observations shown in Equation (1) obtained at the \(i\)-th reference location from the \(j\)-th wireless access point (AP):  
\[\mathbf{rss}_{i,j}=\left[\mathit{rss}(1),\mathit{rss}(2),\cdots\mathit{rss}(N )\right]\qquad\text{ for }1\leq n\leq N \tag{1}\]  
where \(\mathit{rss}(n)\) is the individual RSS observations.  
The Z-score of an RSS observation, \(\mathit{rss}(n)\) is calculated as follows [26; 28]  
\[Z_{score}=\frac{\mathit{rss}(n)-\mu}{\sigma} \tag{2}\]  
where \"\(\mu\)\" and \"\(\sigma\)\" are the mean and SD, respectively, of the time-series RSS observation.  
A Z-score of 0 indicates that the RSS observation is equal to the time-series observation's mean. A positive Z-score and a negative Z-score show that the RSS observation is above and below the mean, respectively. An RSS observation is considered an outlier if its Z-score value exceeds a predefined threshold. The most common threshold value for detecting outliers is \(\pm\)3. This means that when the Z-score value of an RSS observation is greater than \(\pm\)3, it is an outlier.
## 1 Introduction
Due to their ability to deliver enhanced quality of the transmission along with very high throughput, many research works advocating the use of mMIMO with OFDM can be found in the literature [21; 22; 23] and therefore mMIMO-OFDM becomes an attractive candidate for B5G and 6G networks [24]. However, to the best of our knowledge, no previous work regarding performance evaluation of the image transmission over mMIMO-OFDM link has been reported till date. The quality of image transmission depends on several factors such as received SNR, modulation technique and its order, and other channel-related parameters which can influence the system performance. The transmitter of mMIMO-OFDM is the same as that of conventional OFDM except that pre-coding and re-ordering of all users are carried out in the frequency domain before performing the inverse fast Fourier transform (IFFT). On the other hand, the receiver side of mMIMO-OFDM is similar to that of conventional OFDM [25]. In this work, the performance of mMIMO-OFDM systems based on FFT, DWT, and FrFT is evaluated by sending and receiving the reference baboon image under different SNR values, modulation orders, and a varying number of end-users. The PSNR and SSIM metrics have also been used in evaluating the quality of image transmission. The rest of this work is arranged as follows; Section 2 discusses the related works in the literature, while Sections 3 and 4 focus on the technical and mathematical analysis required to explain OFDM and mMIMO, respectively. The results are presented in Section 5 while, Section 6 contains the conclusive remarks.
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
###### Abstract  
This paper presents a dual-band 8-port multiple-input multiple-output (MIMO) antenna specifically designed for fifth-generation (5G) smartphones, featuring two open-slot metal frames. To enhance impedance matching and improve isolation between adjacent antenna elements, each antenna element employed a coupling feed. All simulation results in this paper come from Ansys HFSS. The operational frequency bands of the proposed antenna spanned 3.36-4.2 GHz for the lower band and 4.37-5.95 GHz for the higher band, covering 5G New Radio (NR) bands N78 (3.4-3.6 GHz) and N79 (4.4-4.9 GHz), as well as WiFi 5 (5.15-5.85 GHz). Notably, the antenna demonstrated outstanding isolation exceeding 16.5 dB within the specified operating bands. The exceptional performance positions the proposed antenna as a promising candidate for integration into 5G metal-frame smartphones.  
Keywords:dual-band antenna; MIMO antenna; metal frame; smartphone; 5G antenna +
Footnote †: journal: _Micromachines_
# Natural Creation of Large Rock Cavern: Can We Construct Them? Jenolan Caves as a Case Study
## 2 Case Study of the Grand Arch, Jenolan Caves  
### Location and General Geology of the Grand Arch, Jenolan Caves  
The Jenolan Caves are located in the Oberon region, about 175 km west of Sydney, New South Wales, Australia. The caves system is situated at an altitude of about 790 m in the deeply incised east-trending valley of the Jenolan River [40]. The caves system was formed in a limestone formation, called Jenolan Caves Limestone. This geological formation is dominantly a light-to-dark-gray bioclastic limestone, sometimes bedded, sometimes massive, with occasional mudstone lenses and minor dolomite. Cross-bedding and graded bedding can be found in the limestone at one locality behind the Caves House. The limestone outcrop extends in a north-south linear belt for about 11 km. Two main N-S trending faults, including the McKeown and Jenolan Faults, are present in the study area [40; 41], as shown in Figure 1.  
The Grand Arch is one of the spectacular limestone features of the Jenolan Caves. The available topographic map (Figure 2a) shows that the arch opening is formed below a saddle with an invert level, the same as the existing Jenolan Caves Road level, at approximately of +765 m (above sea level). The ground surface of the saddle is about +845 m. The Jenolan River flows through the arch, joining with the Blue Lake, which is located outside the arch, toward the southeast, and then continuously drains toward the east. Due to the Jenolan River flowing along the invert of the arch, it is assumed that the groundwater level is at the invert level of the arch and no significant water-pressure influence is present at the opening at the present day.  
The available images of the Grand Arch show that a thin soil layer of about 1-2 m in depth is expected. Underneath the soil, a jointed massive limestone bedrock is present. A pile of collapsed rock blocks rests aside the south wall of the opening, as shown in Figure 2b. As shown in Figure 2b, the limestone at the arch location is massive, with widely spaced joints. Two major joint sets, with variable spacing between 2 m and 3 m, are assumed.  
Note that no rock joints' survey and mapping, as well as laboratory testing of rock, were carried out in this study. However, based on the arch's opening, orientation, size, and shape, one of the two major joint sets is expected to be dipping south to southwest at about 100 m. The resulting map is shown in Figure 1. The resulting map is shown in Figure 1.  
\(56^{\circ}\), and the other one is expected to be dipping north to northeast at about \(20^{\circ}\). Given that the Grand Arch is 24 m high and 55 m wide [16], and considering the above assumptions, the shape of the opening used for numerical modeling was established as shown in Figure 3. The rock quality in terms of the Geological Strength Index (GSI) [42; 43; 44; 45; 46] and Q-value [47] at the arch opening's location is estimated to be about \(>\)55 and \(>\)4, respectively.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
###### Abstract  
In this paper, improvement in the power transfer capacity of transmission lines (TLs) by utilizing a multi-terminal high voltage direct current (MT-HVDC) grid is discussed. A multi-terminal HVDC grid designed for wind power can be used as an extra transmission path in interconnected systems during low wind conditions, and provides extra dynamic stability and security. This paper deals with the power transfer capacity as well as the small signal (SS) stability assessments in less damped oscillations accompanying inter area modes. Computation of the maximum allowable power transfer capability is assessed via DC optimal power flow-based control architecture, permitting more power transfer with a definite security margin. The test system is assessed with and without the exploitation of MT-HVDC grid. Simulation work is done using a generic computational framework i.e., international council on large electric systems (CIGRE) B4 test bench with a Kundur's two area system, shows that voltage source converters (VSCs) provide excellent control and flexibility, improving the power transfer capability keeping the system stable.  
AC networks; dynamic security; MT-HVDC systems; power transmission capability; security regions; wind farm integration +
Footnote †: journal: Applied Sciences