esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ultrasonic Pulse Velocity  
The strength of a mortar can be estimated based on the principle that more compact materials present higher wave propagation speeds and resistance values. Therefore, with the ultrasonic pulse velocity (UPV) test, it is possible to assess the compactness of mortars and consequently its state of internal degradation (discontinuities, voids). As expected, prior to heat exposure the values of the UPV of thermal mortars were lower than those of sand mortars. As depicted in Figure 4 and Table 3, exposure to elevated temperatures had a reduced influence in the UPV of the expanded clay (A\({}^{\text{EC}}\)) and granular expanded cork (B\({}^{\text{GEC}}\)) mortars.  
The silica aerogel mortar (C\({}^{\text{AERO}}\)) was sensitive to high temperatures, with a reduction of UPV of 25% after exposure to 250 \({}^{\circ}\)C. After exposure to elevated temperature, sand mortars also experienced UPV reductions, mainly for the mortar with admixtures (above 100 \({}^{\circ}\)C), with a maximum reduction of 34% (E\({}^{\text{AD}}\)) after exposure to 250 \({}^{\circ}\)C. The reduction of UPV after heat exposure in both insulating and conventional mortars may reflect the development of mortar discontinuities, namely increasing porosity and micro-cracking [33].
# Microstructural Evaluation of Dental Implant Success Using Micro-CT: A Comprehensive Review
## 6 Micro-CT Application in Bone Quality Assessment  
In general, the jawbone structure is composed of dense cortical bone and cancellous bone with a trabecular bone structure. As has been previously established, the quality of the jawbone is a critical determinant of the success of implant placement [75]. Micro-CT has emerged as a valuable tool for evaluating bone quality in dental implant planning and assessment. It offers high-resolution, non-destructive imaging of bone microarchitecture, facilitating comprehensive analysis of parameters such as bone density, porosity, and trabecular characteristics [23; 76]. The utilization of micro-CT devices was initiated with the introduction of slice thicknesses in micrometers. Micro-CT exhibits excellent cross-sectional resolution, facilitating more comprehensive examination and enabling the scanning and construction of three-dimensional reconstructions of biological samples [22; 76].  
The use of micro-CT enables the classification of bone quality based on morphometric values, thus facilitating the development of personalized treatment plans and the evaluation of osseointegration [23; 76]. Although the method is primarily used ex vivo, there is potential for in vivo applications, which could improve clinical decision-making in implantology. The quantitative approach of micro-CT enhances the precision and reliability of dental research. Researchers and clinicians can make evidence-based decisions regarding treatment strategies and patient management, relying on the quantifiable data provided by micro-CT [77].  
The utilization of micro-CT in bone analysis can furnish a crucial scientific foundation for an array of experimental research designs pertaining to bone analysis [23]. It is anticipated that micro-CT will become an increasingly pivotal tool in advancing our comprehension of dental pathology, enhancing treatment efficacy, and, ultimately, optimizing patient care in the domain of dentistry [77].
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 5 Conclusions  
Limited communication and computational resources onboard are significant factors for negotiation, which is not getting enough attention. There usually exists cooperation in the micro-satellite cluster, which means the effect of time delay caused by communication and computational cannot be negligible.  
Figure 10: Cost-effectiveness ratio of each mode.  
Figure 9: Profit of each mode.  
In this paper, a neural-network-based multi-granularity negotiation method under decentralized architecture is proposed, which is proven to be effective. Advantages of the proposed method are summarized as follows:  
1. The three levels of granularity we allocated work for different situations. We combine the advantages of the levels and use the best level of granularity according to the situation, which brings about better profits and a higher cost-effectiveness ratio.
2. Complex situation analysis for granularity selection also brings about time delay; therefore, a neural network is trained for granularity selection in real-time.
3. The framework of satellites is decentralization, which means it is suitable for a large satellite cluster containing failure nodes and malicious nodes.  
Conceptualization, Y.D. and X.D.; methodology, X.D. and Y.D.; software, X.D. and S.X.; validation, X.D., S.X. and Y.D.; formal analysis, X.D. and S.X.; investigation, X.D.; resources, Y.D.; data curation, X.D.; writing, X.D.; visualization, X.D.; supervision, Y.D.; project administration, X.D.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (number 2016 YFB0501102).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target ID** & **Position** & **Course Angle** & **Velocity** & **Mission Status** \\ \hline  
& \(-\)73.5\({}^{\circ}\) & 21.8
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 3 Satellite Coverage Traffic Volume Modeling and Prediction Approximation  
In this section, SCTV is described as the objective function to be modeled with respect to the ground sites as the input based on the historical data. To improve the balance between the SCTV prediction accuracy and data transmission as well as storage efficiency, an effective surrogate ensemble modeling method is proposed to approximate the objective function, which mainly includes two parts. First, the global earth surface domain is divided into multiple sub-domains according to specific SCTV distribution features. Second, for each sub-domain, multiple different candidate surrogates are established, and a multi-surrogate management method is developed to adaptively select the contributing surrogates and combine them into a single ensemble with better performance.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 1 Introduction
Key Contributions and Paper Highlights: This work makes three primary contributions to mobile acoustic sensing: (1) a novel dual-thread concurrent architecture that enables simultaneous high-quality audio capture and responsive motor control on resource-constrained platforms, (2) an adaptive compression algorithm with a mathematical optimization model that maintains audio fidelity while meeting real-time bandwidth constraints, and (3) a comprehensive noise mitigation methodology addressing electromagnetic interference, mechanical coupling, and wireless communication challenges specific to mobile acoustic sensing. The framework achieves 90-95% packet delivery rates with sub-150 ms control latency, demonstrating the feasibility of mobile acoustic sensing applications previously thought impossible on embedded platforms.
###### Abstract  
Sweet cherry is highly appreciated by its characteristic flavor, which conditions the consumer's preference. In this study, four sweet cherry cultivars (Durona, Lapins, Summit, and Van cultivars) were characterized according to biometric (fruit and stone weights, length, maximum and minimum diameters, pulp/stone mass ratio), physicochemical (CIELAB color, penetration force, titratable acidity, and total soluble solids), and potentiometric profiles (recorded by a lab-made electronic tongue with lipid polymeric membranes). Biometric and physicochemical data were significantly cultivar-dependent (\(p\)-value \(<\) 0.0001, one-way ANOVA). Summit cherries had higher masses and dimensions. Lapins cherries had the highest penetration force values having, together with Summit cherries, the highest CIELAB values. Van cherries showed the highest total soluble solids contents. No significant differences were found for fruits' acidity (similar titratable acidities). The possibility of discriminating cherry cultivars was also evaluated using a linear discriminant analysis/simulated-annealing algorithm. A discriminant model was established based on nine non-redundant biometric-physicochemical parameters (using a low-level data fusion), with low sensitivity (75 \(\pm\) 15% for the repeated K-fold cross-validation). On the contrary, a discriminant model, based on the potentiometric fingerprints of 11 selected sensors, allowed a better discrimination, with sensitivities of 88 \(\pm\) 7% for the repeated K-fold cross-validation procedure. Thus, the electronic tongue could be used as a practical tool to discriminate cherry cultivars and, if applied by fruit traders, may reduce the risk of mislabeling, increasing the consumers' confidence when purchasing this high-value product.  
sweet cherry; biometric data; CIELAB color scale; chemical data; potentiometric taste sensor; analysis of variance; linear discriminant analysis; simulated annealing algorithm; cultivar discrimination
## 5 Simulation Results and Discussion
Figure 4: The CDF of the average energy efficiency (EE) of a 36-cell network using different transmission schemes under different average target SINR. Figure 3: The CDF of the average energy efficiency (EE) of a 12-cell network using different transmission schemes under different average target SINR. **Effect of the target SINR variance among users** (i.e., when the running applications among users are entirely different):  
Not only do the average of users' target SINRs affect the EE of the full duplex schemes, but also the variance of these target SINRs affects the EE. Contrary to the half duplex scheme, the EE of the full duplex schemes decreases as the variance of the target SINRs among the users increases (see Figure 7). This phenomenon occurs because of the increase in the average of the required transmitted power as  
Figure 5: The CDF of the average transmit power in a 12-cell network using different transmission schemes under different average target SINR. Figure 6: The CDF of the average transmit power in a 36-cell network using different transmission schemes under different average target SINR. the spread of the interference distribution increases. Figure 8 shows that, in case the average target SINR among users is 50, the average transmit power exceeds 100 mW 90% of the time when the target SINR variance is 33.3. On the other hand, the average transmit power exceeds 100 mW only 20% of the time in low target SINR variance among the users.
# UAV Communication Recovery under Meteorological Conditions
## 1 Introduction
Consequently, we employ an allowable path loss threshold to dynamically adjust the UAV's position. In comparison to the signal-to-noise ratio (SNR) threshold outlined in Ref. [21], our approach streamlines certain calculation steps for improved efficiency. Thirdly, this paper takes into account the recovery of the coverage of the UAV across diverse weather conditions. Thus, our study aims to fill a gap by examining the effects of various weather conditions on UAV coverage, which are not considered in the three mentioned references. Moreover, some studies have explored the specific attenuation of rain, fog, or snow in communication systems. For instance, Refs. [22; 23; 24] examine the impact of rain-specific attenuation on traditional or satellite communications. Fog-specific attenuation is primarily investigated in the context of optical communication, as seen in the literature [25; 26; 27; 28]. Additionally, Refs. [29; 30] study the attenuation caused by snow in wireless communications. Nonetheless, limited research has been conducted on the effects of various weather conditions on communication between UAVs and ground users. This paper presents a strategy to restore communication coverage between UAVs and ground users under rain, fog, or snow conditions considering two frequencies (28 GHz and 71 GHz). The reason that we focused on these two frequencies is that they are critical and representative frequency bands adopted by future wireless communication systems such as 5G and beyond [31]. Numerical results demonstrate that, under different weather conditions, the UAV-ground communication coverage can be effectively restored to a level comparable to that of a clear day through compensated path loss and UAV height adjustment. In this paper, we summarize our contributions in the following aspects. 1. Drawing on ray tracing simulation data from previous work, we provide a new LoS probability expression for four distinct 3D urban environments. 2. Using the selected four 3D urban environments, we conduct ray tracing simulations and obtain excessive path loss data for these environments, considering one, two, and three reflections. 3. We present expressions and associated parameters that illustrate the relationships between the coverage area, optimal UAV height, and compensated path loss in relation to the rainfall rate, liquid water density, and snowfall rate across four urban environments at two distinct frequencies (28 GHz and 71 GHz). 4. We propose an algorithm for UAVs to restore communication with ground users under varying weather conditions and analyze the time required for communication recovery. The remainder of this paper is structured as follows. Section 2 presents the system model. Section 3 provides the methods. In Section 4, we propose a UAV communication recovery strategy to address the challenges posed by adverse weather conditions for UAV communications and give the results. Lastly, Section 5 provides the concluding remarks.
## 3 Precoding Matrix Design Based on Type I 5G-NR for SU-MIMO
where \(\mathbf{w}_{ULA}(l_{d})\) and \(\mathbf{w}_{ULA}(m_{d})\) are the DFT-based ULA precoders in the horizontal and vertical direction, respectively, given by  
\[\begin{array}{cc}\mathbf{w}_{ULA}(l_{d})=&\frac{1}{\sqrt{N_{1}}}\bigg{[}1&e^{ /2\pi\frac{l_{d}}{O_{1}N_{1}}},\...\,e^{/  
considering the secondary lobes which is a very effective way of illustration used also in [30; 31]. The grid is composed of a total of \(A_{c}=64\) beams. The number of orthogonal DFT beams without the oversampling factors is given by \(N_{1}N_{2}=4\) and they are represented by the red-filled circles. The remaining 60 circles represent the oversampled or rotated beams. The wideband precoding matrix \(\mathbf{W}_{1}^{[d]}\) selects a beam per layer from the predefined DFT grid. In this example, it is assumed that \(N_{s}=2\), \(i_{11}=6,i_{12}=2\), and \(i_{13}=2\). The striped circles illustrate the \(N_{s}=2\) orthogonal beams that are chosen according to the indexes \(l_{d}\) and \(m_{d}\). The beam of the first layer is obtained with \(l_{1}=i_{11}=6\), and \(m_{1}=i_{12}=2\). Then, assuming that \(i_{13}=2\), the beam for the second layer is given by \(l_{2}=6\), and \(m_{2}=6\) (see Tables 5.2.2.1-3 and 5.2.2.2.1-6 in TS 38-214 [25] for details). It can be noted that the beams  
Figure 3: Grid of beams of a single-panel UPA with \(N_{1}=2,N_{2}=2\), and \(O_{1}=O_{2}=4\). It is assumed that \(N_{s}=2\), \(i_{11}=6,i_{12}=2\), and \(i_{13}=2\). Figure 2: Radiation pattern of all the beams obtained with a 5G-NR antenna array designed as a DP single-panel UPA with \(N_{1}=N_{2}=2\). are orthogonal in the vertical dimension. Finally, these two beams are combined with the co-phasing factors selected per subband. Note that the UE only needs to report the coefficients and co-phasing factor of the beam in the first layer. Then, the gNB is capable of computing the beams and the co-phasing factors for the remaining layers. This strategy reduces feedback overhead guaranteeing an efficient transmission. Commonly, the same matrix \(\mathbf{W}_{1}^{[d]}\) is used for the entire bandwidth. However, codebook mode 2 enables a special case for \(N_{s}=1\) or \(N_{s}=2\) where \(\mathbf{W}_{1}^{[d]}\) may vary for different subbands. In this case, the selected beam also depends on the frequency-selective coefficient \(i_{2}^{n}\). This represents a more flexible design at the cost of requiring more bits for reporting \(i_{2}^{n}\). Therefore, it is only implemented for a low number of layers (i.e., \(N_{s}=1\) or \(N_{s}=2\)) to ensure that the overhead is not too high. There is another special design of the matrix \(\mathbf{W}_{1}^{[d]}\) enabled for (\(N_{t}\geq 16\)) when \(N_{s}=3\) or \(N_{s}=4\). The main goal is to prevent the beam in the horizontal dimension from becoming narrower as the number of antenna ports increases, which can be harmful to some propagation models. Therefore, the available values of \(i_{11}\) are reduced to \(i_{11}\in\left\{0,1,\ldots,\frac{N_{t}O_{1}}{2}-1\right\}\) to widen the beam [25; 31].
## 4 Discussion
This is verifiable in the case of scans performed on an object along a circular path; furthermore, varying the distance to the object being scanned (e.g., due to obstacles) increases the possibility of the occurrence of a drift effect. The phenomenon just described can also be observed in the scans of two structures collected for documentation purposes. Both structures were scanned with the iPAD Pro 2021 with a resolution of 1.5 cm, as shown in Figure 16.  
In the first structure (Figure 16a), due to tight spaces, the operator scanned by standing still in the centre of the structure and rotating in place. In the second structure (Figure 16b), the operator moved actively inside the structure to collect every detail. The scans of the structures are generally good, but when analysing the sections, it is evident that the section of the first structure (Figure 16c) has fewer problems (or no problems) with surface splitting compared to the second (Figure 16d), which has surface splitting at the entrance to the  
Figure 16: Scans of two structures collected for documentary purposes; scalar field refers to the surface variation value: first structure (**a**), second structure (**b**), section of first structure (**c**), section of second structure (**d**). structure, where the scan closed. This is an example of how movement can generate drift problems that practically result in surface splitting. Finally, through the third and fourth case studies, it can be seen that in general, the accuracies are on the order of 1-3 cm, but scanning problems such as surface splitting can degrade these values to more than double their original values (see Table 7). For these reasons, it is very important to monitor these issues in scans performed with smartphones and the analysis methods described in this paper.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 5 Heal Coverage Holes Algorithm  
In this section, we will integrate the several subproblems we have solved above and propose the Heal Coverage Holes Algorithm( HCHA) detailed. The network we research is hybrid, which consists of both static and mobile sensors. During the network initialization, static sensors construct the DT.  
Pseudo-code is given in Algorithms 1 and the process is illustrated in Figure 7.  
Figure 6: Node \(P_{1}\) lies in the angle bisector.  
Figure 7: Position of assisted sensor.  
Step 1. Select the node with maximum energy as the head node in each triangle. In order to balance the energy consumption, we set the sensor which is selected as head node will not attend again in head selection of other neighbour triangles. The head node gathers the relevant information including current energy, position from the remaining two static sensors of the triangle and estimates coverage holes, the number of the assisted sensors as well as the position of assisted sensors. Then head node broadcasts the information of calculation results with a distance threshold \(d_{max}\) which is used to limit the hop ( Package Information ).  
Step 2. Once mobile sensor \(M_{j}\) receives the data package, it will compare the distance \(d(M_{j},P_{i})\) with the \(d_{max}\), if \(d(M_{j},P_{i})<d_{max}\), \(M_{j}\) send message which contains \(M_{j}^{\prime}s\) location, energy information and \(M_{j}^{\prime}s\) ID back to the head node by multiple hops, and continue to broadcast Package Information. If \(d(M_{j},P_{i})>d_{max}\), \(M_{j}\) transfers the message back to the head node only and stops broadcasting Package Information.  
Step 3. Head node receives the information of mobile nodes around, chooses the optimal node based on the energy and position information, then sends command.  
Step 4. Lastly, Mobile node judges whether itself needs to move by the command.
## 3 Results
### Effects of Liner Thickness and Head-Helmet Friction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Impact Location** & **Head–Helmet Friction Coefficient (\(\mu\))** & **Linear Thickness (Th)** \\ \hline \multirow{4}{*}{Inclined-crown} & \(\mu\leq 0.23\) & Th \(\geq 10\) mm \\  & \(0.23<\mu\leq 0.28\) & Th \(\geq 13\) mm \\  & \(0.28<\mu\leq 0.33\) & Th \(\geq 15\) mm \\  & \(0.33<\mu\leq 0.36\) & Th \(\geq 17\) mm \\ \hline \multirow{2}{*}{Inclined-front} & \(\mu\leq 0.65\) & Th \(\geq 10\) mm \\  & \(\mu\leq 0.21\) & Th \(\geq 10\) mm \\  & \(0.21<\mu\leq 0.23\) & Th \(\geq 13\) mm \\  & \(0.23<\mu\leq 0.25\) & Th \(\geq 15\) mm \\  & \(0.25<\mu\leq 0.27\) & Th \(\geq 18\) mm \\ \hline \multirow{4}{*}{Inclined-side} & \(\mu\leq 0.33\) & Th \(\geq 10\) mm \\  & \(0.33<\mu\leq 0.4\) & Th \(\geq 13\) mm \\ \cline{1-1}  & \(0.4<\mu\leq 0.5\) & Th \(\geq 15\) mm \\ \cline{1-1}  & \(0.4<\mu\leq 0.65\) & Th \(\geq 16\) mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Suggested ranges of a suitable friction coefficient and liner thickness. For the crown location, the maximum allowable head-helmet friction coefficient was 0.36 with the liner thickness greater than 17 mm. For a liner thickness of 10 mm, the friction coefficient must be 0.23 or lower. The friction coefficient could be as high as 0.65 for the side location but the thickness must be greater than 16 mm. However, the critical location was the rear location--the maximum friction coefficient was allowed to be 0.27 and the thickness must be greater than 18 mm to maintain the rotational acceleration within 8000 rad/s\({}^{2}\).