esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
###### Abstract  
A metamaterial-based non-uniform dipole array antenna is presented for high gain 5G millimeter-wave applications with a wideband characteristic. Initially, a non-uniform dipole array is designed on a 0.202 mm thick Rogers RO4003C substrate, offering a wide operating bandwidth ranging from 23.1 GHz to 44.8 GHz. The dipole array antenna emits unidirectional end-fire radiation with a maximum gain of 8.1 dBi and an average gain of 6.7 dBi. Subsequently, to achieve high gain performance, a \(5\times 7\) metamaterial structure is designed in the direction of the antenna radiation. The implemented metamaterial structure is optimized for the operating frequency, enhancing the directivity of the antenna radiation and resulting in a gain increment of more than 3 dBi compared to the dipole array alone. The developed metamaterial-integrated dipole array antenna offers an operating bandwidth (S11 \(<-\)10 dB) of more than 21 GHz (63.92%), ranging from 23.1 GHz to 44.8 GHz, covering the most commonly used 5G millimeter-wave frequency bands (n257, n258, n259, n260, and n261). Furthermore, the presented antenna yields a stable high gain with a peak gain of 11.21 dBi and a good radiation efficiency of more than 64%. The proposed antenna is an excellent option for millimeter-wave 5G systems due to its overall properties, particularly its high gain and end-fire radiation characteristics, combined with a wide operating bandwidth.  
mm wave antenna; dipole array; end-fire radiation; metamaterial; high-gain antenna +
Footnote †: journal: Applied  
0000 000-0001-6697-2233]M.A.A.S.
## 2 Statistical Analysis for the Determination of Operating Parameter Requirements
### Overview of Operating Parameter Requirements  
A database for the regression analysis was established, consisting of data from 10 projects worldwide collected from various sources [7; 8; 9; 10; 11; 12; 13; 14; 15]. The projects were conducted in various ground conditions. As most data were not clearly stated, they were obtained indirectly using the \"Engauge Digitizer\" that converts graph to data points to graph. Earth pressure balanced (EPB) shield TBMs were applied in nine projects, and one project used a slurry TBM. For nine projects, machine diameters ranged between 6 and 10 m, and the tenth was larger. The information for each project includes more than one dataset for different ground types; also given, are each machine's specifications (diameter, maximum thrust, maximum torque, and maximum rotation speed) and data for at least one operating parameter.  
Each dataset originally included more than one operating parameter: thrust (kN), torque (kN\(\cdot\)m), cutterhead rotation speed (rev/min), and advance rate (mm/min). The first three were normalized to related TBM specifications and expressed as percentages. These normalized parameters, with the advance rate, are required operating parameters. Based on the established database, a simple regression analysis was conducted. Considering the TBM's diameter and the maximum capacities of three other specifications, allowed estimation of the requirements for four TBM operating parameters (Figure 1). Aside from the advance rate, it appears possible to estimate three operating parameters using three TBM specifications (TBM diameter, maximum thrust, and maximum torque). These specifications show proportional relationships with the thrust and cutterhead torque requirements and are inversely proportional to the rotation speed requirement. However, all of the coefficients of determination (R2) obtained from the red regression lines are less than 0.50. Therefore, it is difficult to estimate the four TBM operating parameter requirements reliably by simple regression analysis.  
Although TBM operators endeavor to maintain the operating parameters within specific ranges [7], they inevitably vary depending on the ground conditions and TBM specifications. Therefore, it is essential to consider both the ground condition and TBM specifications together; multiple regression analysis could feasibly facilitate reliable estimation of the TBM operating parameters.
# Biomechanical Rationale for a Novel Implant Design Reducing Stress on Buccal Bone
###### Abstract  
Modern implant designs should allow for adequate primary stability but limit mechanical stress on buccal bone in order to prevent initial marginal bone loss. A dental implant characterized by a shift in core diameter and thread geometry was evaluated. Polyurethane foam was used as bone surrogate material and implant placement was performed measuring insertion torque and strain development on buccal bone using strain gauges as well as primary stability by using damping capacity assessments. An existing tapered bone-level implant was used as a control while the novel experimental implant described above (\(n=10\)) was used in the test group. Statistical analysis was based on t-tests (\(\alpha=0.05\)). Both the maximum insertion torque (\(p=0.0016\)) and maximum strain development in buccal bone (\(p=0.1069\)) were greater in control implants as compared to the novel implant design. Moreover, in the control group, these were reached at a significantly later timepoint of the insertion process, i.e., when the implant was almost fully seated (maximum insertion torque \(p=0.0001\), maximum strain development \(p<0.00001\)). The final insertion torque (\(p<0.00001\)) and final strain development (\(p=0.0137\)) were significantly lower in the novel implant design while the primary stability of both implant types did not differ significantly (\(p=0.219\)). The novel implant design allowed for a greater undersizing of osteectomies while not mechanically overstressing buccal bone. Comparable primary stability was obtained from trabecular bone instead of compressing cortical bone as occurs in conventional, existing tapered implant designs.  
insertion torque; strain development; implant design; bone damage +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl3010666](https://doi.org/10.3390/appl3010666)]([https://doi.org/10.3390/appl3010666](https://doi.org/10.3390/appl3010666))
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Classifiers
#### 3.2.1 Svm  
In SVM, the sensor nodes are identified and classified in hyperplane in a non-probabilistic manner. The data collected are given to training and the same is labeled as a supervised learning mechanism. This hyperplane has divided the plane into two sub-classes. The SVM classifier has parameters such as the epsilon (\(\varepsilon\)), regularization, and kernel parameters.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 2 Overview of the Three Path Planning Algorithms
### The HAUCS Path Planning Algorithm  
HPP is a path-planning strategy that enables drone platforms to monitor aquaculture farms in a back-and-forth pattern using a geometric approach. It consists of two phases: clustering and routing. In the clustering phase, k-means is used on the set of nodes to produce evenly spaced clusters, with k set to the number of drones to be used. Convex polygons are then created around each cluster, and the antipodal pairs of these polygons are calculated using the Shamos algorithm [35]. In the routing phase, the Optimal Coverage Algorithm (OCA) is used to determine the shortest optimal back-and-forth path that starts and ends at one of the antipodal point pairs [36]. The route is then assigned to follow the optimal back-and-forth pattern as closely as possible. If a cluster is too small to form a polygon, it is expanded by adding the closest nodes from neighboring clusters until all clusters have a valid size and shape. The performance of HPP is demonstrated by the evenly spaced clusters of approximately equal size shown in Figure 2, despite the irregular outline shape of the node locations. The effectiveness of this approach is dependent on the assumption that the distribution of nodes is generally even. If nodes are placed randomly or have areas of higher density, this assumption may not hold, and the method may be less effective in producing efficient routes. Further details of the HPP implementation can be found in our previous works [32; 36; 37].
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
###### Abstract  
Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user's queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user's queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.  
ant colony optimization; clustering; virtualization; wireless sensor networks +
Footnote †: journal: Sensors
## 4 Results
### Modification Analysis of the Mudstone Shear Strength  
#### 4.2.1 Determination of In Situ Mudstone Shear Strength  
Prepare multiple sets of mudstone samples with different water contents and use a quadruple direct shear tester to test the shear strength of mudstone at these varying water contents. Conduct direct shear experiments on four samples with the same moisture content in each group. The results are shown in Figure 5.  
Figure 4: The combined determination method of Atterberg limits the results of modified specimens. (a) unmodified by the addition of 10% AlCl\({}_{3}\) solution; (b) modified by the addition of 10% AlCl\({}_{3}\) solution.  
Figure 5: Shear stress–shear displacement curves of mudstone under different water content. (a) \(w=25\%\); (b) \(w=20\%\); (c) \(w=15\%\).  
As the water content decreases, both the shear strength and residual shear strength show an upward trend, and the shear stress on the specimen increases at an accelerated rate. The shear stress-shear displacement charts for the three mudstones with varying water content follow essentially the same loading path, with a short elastic phase (rapid increase in shear stress), a long plastic phase (slow increase in shear stress), and a residual shear phase (basic shear stress stability).  
According to Figure 5, the \(\tau\)-\(\sigma\) curves of mudstone under different water content conditions were plotted and linearly fitted to obtain the equations between shear stress and normal stress, as shown in Figure 6.  
According to the fitted equations, the \(C\) and \(\varphi\) of mudstone under three water content conditions can be obtained, as shown in Table 2.  
When the specimen's water content increased from 15% to 20%, the shear strength parameter cohesion \(C\) and the internal friction angle \(\varphi\) decreased by 32.7% and 13.3%, respectively. When the water content increased from 20% to 25%, the internal friction angle \(\varphi\) value decreased to 14.9\({}^{\circ}\), a decrease of 28%, while the decrease in cohesion \(C\) was 21.7%, which slowed down compared to the previous range. This indicates that the relationship between mudstone's \(C\) and \(\varphi\) values and its water content is nonlinear.
## 4 Discussion  
The correlation analysis between LVDT and LiDAR revealed that Wall A showed a high similarity of 99.73%, while Wall B had 98.86%, as shown in Figure 14. In both wall experiments, the displacement noise level of LiDAR was similar in size to the small displacement generated during the initial low lateral load application, resulting in an error with LVDT. However, for Wall B, the drift ratio between LiDAR and LVDT during the middle of the load application (110-160 min) showed an error of about 0.0028, as illustrated in Figure 14b. The LiDAR equipment scanned the left interface (reference to the rear of the wall) opposite the right wall equipped with LVDT #1. However, Wall B has an opening, which resulted in different behavior patterns on the left and right sides, leading to a low accuracy of 1%. When conducting a comparative experiment using an attached LVDT sensor through a LiDAR measuring instrument, which is a non-contact measurement method, excellent accuracy is expected.  
Figure 15 presents the correlation coefficient between the measurements obtained from LVDTs and LiDAR in vertical displacement. Notably, the correlation coefficient was calculated as 99.88% for the PC-C specimen and 99.94% for the PC-P specimen. The results indicated that the proposed method is totally reliable when comparing the measured LVDT data. This high correlation coefficient signifies a remarkably accurate relationship between the two measurement methods. In instances where the displacement values were relatively low, there was an observable increase in errors due to the presence of fine noise; this was particularly evident in situations involving low displacements. However, as the displacement values escalated, the accuracy of the measurements proportionally improved. This trend highlights the robustness of the LiDAR technique, particularly  
Figure 14: Drift ratio correlation between LVDT and extraction from LiDAR: (**a**) Wall A and (**b**) Wall B.  
Figure 13: The displacement measurements during the experiments, showing the comparison between all point distributions by LiDAR (in boxes) and LVDTs: (**a**) PC-C; (**b**) PC-P.  
for larger displacement values, while acknowledging its susceptibility to minor noise interference when measuring smaller displacements.
## 4 Global Error Model Verification by Numerical Simulations  
The correctness and accuracy of the global positioning error (d\(L\)) calculated by the proposed error model were verified by numerical simulations. When the error model was valid, the total increment of the positioning error was approximately equal to the total differential (\(\Delta L\approx\mathrm{d}L\)).  
According to the foundational principles of calculus, when the change in the independent variable was small, the total increment could be effectively approximated by the total differential. This study showed that when the absolute value of the measurement deviation was less than or equal to \(5^{\circ}\) ( \(\mid\Delta A_{\mathrm{s}}\mid\leq 5^{\circ}\), \(\mid\Delta h_{\mathrm{s}}\mid\leq 5^{\circ}\)), the total increment could be approximated as a total differential. Under these specified conditions, the error model established in Section 3 was validated.  
The presence of multiple variables in Equation (9) could make an effective representation of analysis results using a single two-dimensional graph challenging. Therefore, this study employed a case-by-case discussion to reduce the number of variables, thus facilitating a clearer presentation of the results. The discussion was conducted for two distinct cases: \(\Delta A_{\mathrm{s}}=\Delta h_{\mathrm{s}}\) and \(\Delta A_{\mathrm{s}}\
eq\Delta h_{\mathrm{s}}\). The simulation calculations were performed using MATLAB (Matrix laboratory) software.
## Materials and Methods
### Optical Multispectral Sensor Dataset  
We used MODIS data to calculate SM, VI, and GPP (Table 2). First, SM was calculated using LST of MOD1A1 and land surface albedo of MCD43A3. The details are introduced in Section 2.4. For VL we used the NDVI of MOD13A1 and analyzed the CO\({}_{2}\) generation according to vegetation activity through GPP of MOD17A2H. A method for CO\({}_{2}\) analysis using GPP is introduced in Section 2.3. All MODIS data used in this study had a spatial resolution of 0.5 km \(\times\) 0.5 km. As for the temporal resolution, MOD11A1 and MCD43A3 data were provided daily; MOD17A2H eight days, and MOD13A1 16 days. Various MODIS land products have different temporal resolutions. As these products were combined to calculate the drought index, they must be unified using the same time frame. Temporal resolution is unified on a daily scale. For the eight-day product, the same value is matched for the remaining seven days from the starting point. Similarly, the 16-day product matches the same value for the remaining 15 days from the starting point. All MODIS data from 2001 to 2019 can be obtained from NASA Earthdata website [32]. For this study, the results were obtained for 2001 to 2015.
## 2 Related Works
### Data-Sharing Mechanism  
The authors of [19; 20] focus on algorithm design to explore the cache-sharing strategy in distributed edge environments. To address the problem of MUs' difficulty in discovering required IoT resources due to device heterogeneity, a Fog Computing-based resource discovery solution named FDS-RD is proposed [19]. FDS-RD employs a Distributed Hash Table (DHT) to map resources to a structured peer-to-peer (P2P) network, facilitating resource discovery in large-scale IoT environments.  
VCNS [20] is a content-sharing network, tailored for the vehicular ad hoc network (VANET) scenario. It presents an edge caching scheme based on cross-entropy and dynamically adjusts caching based on content popularity within the request scope. Additionally, it designs a collaborative content delivery mechanism for RSUs to further reduce system latency overhead. Meanwhile, the authors of [21; 22] focus on network logical topology design, exploring both structured and unstructured network topologies. The emphasis is on managing resource nodes to organize decentralized sharing networks, providing cached content to users to optimize system overheads.  
Considering cooperative caching among edge servers, ref. [21] proposes a distributed edge data indexing system called EDIndex. In this system, any server maintains a hierarchical counting bloom filter (HCBF) tree index structure, which indexes the data stored in nearby edge servers, enabling fast querying at the edge. SA-Chord [22] is a pure edge computing-based adaptive distributed overlay network architecture. Based on the chord protocol, it designs a two-layer circular routing overlay composed of peer nodes and super nodes. Peer nodes only participate in content reception and transmission without routing, while super node clusters are responsible for maintaining routing tables for message-forwarding, achieving decentralized content-sharing based on the dual-layer structure.  
From the above works, it can be observed that decentralized application caching and retrieval based on distributed hash can efficiently achieve data sharing, effectively reducing various overheads of the system, considering the limited storage capacity of geographically distributed edge service nodes. Therefore, this paper will explore the optimal task scheduling strategy based on a heuristic algorithm. Additionally, it will leverage the edge backhaul links' path in existing multi-server-multi-user MEC networks to construct a structured data-sharing mechanism based on DHT.
## 3 Results and Discussion  
### Definition of Average Wall Shear Viscosity  
An average wall shear viscosity \(\overline{\eta}\) that was used in the temperature correction method (Figure 3a) is defined here. For a straight capillary of uniform cross-section, we postulateda solution of the form \(v_{z}=v_{z}(r)\), \(v_{\theta}=0\), \(v_{r}=0\), \(P=P(z)\), and \(T(r,\ z)\). The \(z\)-component of the equation of motion, in terms of \(\tau\), is [1; 37]  
\[-\frac{dP}{dz}=\frac{1}{r}\frac{d}{dr}(r\tau_{rz}) \tag{8}\]  
This may be integrated to give  
\[\tau_{rz}=-\frac{dP}{dz}\frac{r}{2}+\frac{C_{1}}{r} \tag{9}\]  
The constant \(C_{1}\) has to be zero, since one does not expect to have an infinite momentum flux at the axis of the tube. Here, \(dP\) is the pressure drop over a distance \(dz\) along the capillary. Equation (9) is therefore the result of the application of the equation of motion and is suitable for conditions where the fluid properties change appreciably along the capillary. At \(r=R\), Equation (9) becomes  
\[\tau_{\rm w}=-\frac{R}{2}\frac{dP}{dz}\bigm{|}_{r=R} \tag{10}\]  
where \(\tau_{\rm w}\) is the wall shear stress. By integrating this equation over the capillary of length \(L\) and then applying the mean value theorem for definite integrals, we obtain the capillary pressure drop \(\Delta P_{\rm cap}\) in nonisothermal flow  
\[\Delta P_{\rm cap}=\frac{2}{R}\int_{0}^{L}\tau_{\rm w}(z)dz=\frac{2L}{R}\tau_ {\rm w}(z^{*})=\frac{2L}{R}\tau_{\rm w}(T^{*}) \tag{11}\]  
where \(z^{*}\) exists in \([0,L]\) and \(T^{*}\) is the local fluid temperature at \(z^{*}\). We assume that \(T^{*}\) is close enough to the mean fluid temperature \(\overline{T}_{\rm w}\), averaged over the wall surface along the tube, so that  
\[\Delta P_{\rm cap}\approx\frac{2L}{R}\tau_{\rm w}(\overline{T}_{\rm w}) \tag{12}\]  
Hereafter, the mean value along the die wall is designated by an overbar. Substitution of the material function \(\tau_{\rm w}\big{(}\overline{T}_{\rm w}\big{)}=\eta\big{(}\overline{T}_{\rm w }\big{)}\dot{\gamma}\) (\(\overline{T}_{\rm w}\)) into Equation (12) gives the following definition of the average shear viscosity \(\overline{\eta}\) of fluids:  
\[\overline{\eta}=\eta\big{(}\overline{T}_{\rm w}\big{)}\approx\frac{R\Delta P_ {\rm cap}}{2L\dot{\gamma}\big{(}\overline{T}_{\rm w}\big{)}} \tag{13}\]  
We emphasize that the development here has been given for mean rheological properties of the fluids at the wall. As suggested by Equation (13), \(\overline{\eta}\) may be better evaluated by using the mean fluid temperature at the wall \(\overline{T}_{\rm w}\), instead of the bulk temperature, as previously assumed. Throughout the iterative process described next in Figure 3a, Equation (13) was used to calculate \(\overline{\eta}\), in which \(\overline{\tau}_{\rm w}\) (\(=\tau_{\rm w}\big{(}\overline{T}_{\rm w}\big{)}\)) is set equal to the experimental wall shear stress and \(\overline{\dot{\gamma}}\) (\(=\dot{\gamma}(\overline{T}_{\rm w})\)) is determined from the simulation. It is worth mentioning that our simulation results presented later seem to lend support to the use of Equation (13) in nonisothermal capillary flows.
## 4 Simulation and Analysis
### Analysis of Time Consumption

Table 2: Time consumption values under different numbers of selected satellites. Figure 16: Comparison of the difference in average time consumption under different numbers of selected satellites. algorithm leads to a slight increase in computational time; in contrast, integrating the EWM algorithm results in a relatively smaller increase in computational time. In both cases, the computational time remained significantly lower when compared to the traditional exhaustive search method.