esa-sceva/satcom-chunk-collection huggingface dataset:
# Strain Rate of Metal Deformation in the Machining Process from a Fluid Flow Perspective
## 1 Introduction
The Deborah number, from the definition of rheology, points out the difference between a solid and a fluid and is represented as \(\text{D}_{\text{b}}=\text{time}\) of relaxation/time of observation. If the time of relaxation of a material under observation is very small, the material flows. In the metal cutting process, the strain rate can reach \(10^{5}\) s\({}^{-1}\) and above, and the metal behavior is more like a fluid than a solid; thus, high speed machining can be better modeled as fluid flow than as solid. [PERSON] studied the cutting process from the aspect of fluid flow, and designed an experimental apparatus to model the cutting process as the flow of a fluid around the tool tip R. (1998) (Figure 1). Fluid is pumped into place A and flows past tools with different rate angles at B. Powdered bakelite is introduced at C to make the streamlines visible, and photographs are taken by the camera at D. [PERSON] (2017) used Bingham liquid to model the plastic flow between the chip and rake face and study the friction of the second shearing zone. [PERSON] (2018) presented a potential flow analysis approach in ultra-precision machining to predict the contact length and cutting forces. [PERSON] (2018) believes that the cutting fluid melt layer between the chip and the cutting edge induces the chip to easily flow, thereby decreasing the high-speed cutting force. The strain rate is very high during metal cutting, and the relationship between the shear stress and shear strain rate appears to be linear under high strain rate conditions because of the fluid characteristics K. (2018); K. (2018). According to this linear relationship, [PERSON] (2018); [PERSON] (2018) modeled high-speed machining according to the potential flow problem of one source and two sinks; he considered the high-speed cutting process as a simple closed polygonal boundary flow and transformed the process into fluid flow issues. Some metals exhibit similar characteristics to fluids at room temperature. [PERSON] (2018) used a hard steel wedge plow to cut a copper surface and studied the sliding of the metal interface. The surface slip of copper showed laminar flow, and folding and laminar flow interruption occurred. Full-scale sinuous flow simulations were used by [PERSON] (2018) to study metal cutting; he finds that folding and sinuous flow can occur even at a low friction and at very low cutting speeds. However, the reason why the relationship between the shear stress and shear strain rate is linear under high strain-rate conditions is unknown and must be studied with respect to the microscopic control mechanism of plastic deformation; this paper answers this question through dislocation motion analysis. Based on this theory, the strain-rate distribution in the cutting process is studied from the perspective of fluid flow by means of simulation and experimental verification, so as to provide new ideas for the research of the cutting process.
## 6 Result Discussion
### Result Accomplished
#### 6.3.2 Analysis of Energy Consumption  
Energy is one of the essential resources while performing any form of node operation, be it routing or security implementation. Higher energy retention is always a necessary demand in the computation of game models to this end, owing to the mathematical computation involved. Apart from this, the proposed system performs three stages of operation, synchronised with private and public clouds, including multiple sequences of computing-updated \(GT\) and \(QM\) based on the dual roles of nodes (CIN and RIN). For this purpose, the mobile nodes are assigned with an initial energy of 10 J, while they will consume 50 nJ for forwarding 1 bit of data. This condition is based on the first-order radio energy model [60]. The mathematical expression used for computing total energy, \(E_{T}\), consumption is written in Equation (8):  
\[E_{T}=E_{TX}+E_{RX}+E_{agg}+E_{amp} \tag{8}\]  
In Equation (8), the dependable variables for the computation of energy are transmittance energy, \(E_{TX}\), receiving energy, \(E_{RX}\), aggregation energy, \(E_{agg}\), and amplification energy, \(E_{amp}\)[60]. Figure 7 showcases that this scheme offers reduced energy consumption compared to other models observed over the entire iteration.  
The prime reason for the consumption of energy during data propagation of nodes is mainly due to more retransmission attempts of control messages, \(C_{msg}\), in the case of non-availability of adjacent nodes, more usage of amplified energy, \(E_{amp}\), during the condition of signal attenuation, or higher fluctuation of mobility of IoT nodes. Considering the possibility of any of these circumstances, or all of them, the proposed system introduces a concept where IoT hotspots and CIN reduce the extensive load on route discovery and data propagation. Apart from this, the allocation and updating of payoffs are carried out via TRM, which is highly distributed, reducing the effort of either confirming the presence of misbehaved nodes or performing data delivery. Hence, there is less fluctuation from an identified path of propagation by mobile IoT nodes in the proposed system,  
Figure 6: Comparative analysis of identification accuracy.  
resulting in higher retention of residual energy. On the other hand, all of the existing game models include highly sophisticated logical conditions for evolving with a secured strategy, resulting in extensive energy depletion.
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
## 1 Introduction
Therefore, determining the \(\mathrm{G_{mb}}\) or air void content of asphalt layers, i.e., assessing the compaction's properness, is important when evaluating the pavement quality and durability. The two commonly employed approaches for the evaluation of compaction quality are (1) laboratory assessment of cores and (2) field density gauging, including nuclear and non-nuclear density measurements. Previous studies have suggested that Ground Penetrating Radar (GPR) can also be utilized as a non-nuclear method to assess the compaction quality and monitor the compaction process during asphalt pavement construction [22; 23]. However, in many countries, in situ nuclear gauge measurements are still used in standard applications [24; 25; 26; 27]. The disadvantage of this latter method is that it gives low spatial resolution and at-a-point information on \(\mathrm{G_{mb}}\), and the operation of the device requires special knowledge and permissions. The first aim of the present research was to apply different GPR techniques in the assessment of dielectric constants and to determine the most reliable and convenient way of conducting in situ measurements using validation data obtained from nuclear gauge and laboratory pavement core tests. The extensive measurements during the repeated compaction of the investigated pavement also allowed the establishment of a function between dielectric constants and \(G_{\text{mb}}\). Consequently, by also involving literature data, we attempted to establish a more general model for the GPR assessment of \(G_{\text{mb}}\) and relative density (RD) on dolomitic pavements.
## 3 Results and Discussion
### Imaging  
Based on the Monte Carlo model results (Figures 4 and 5), light intensities in Pathlengths 1 and 2 are expected to be about equal, and the intensity of light in Pathlength 3 should be about 50% of that in Pathlengths 1 and 2. Light intensities in Pathlengths 1 and 2 were not statistically different, but the experimental results also show a greater intensity of light in Pathlength 3 than the Monte Carlo simulation predicted (Figures 4 and 13). Nevertheless, this increase in Pathlength 3 intensity (compared to the simulation results) is beneficial because greater balance among the pathlengths yields higher quality composite images and a greater dynamic range for scattering angle analysis. Sample SAR-OCT images are also shown in Figure 14.  
Figure 12: (**A**) The peak intensity of each pathlength as a function of angular deviation due to the dual-axis MEMS mirror raster scanning. Several images were stitched together to form a more complete dataset. (**B**) The azimuthal average of each pathlength intensity. The blue dashed line matches the location of the blue line in Figure 11. The red dashed line matches the location of the red line in Figure 11.  
Figure 13: Distribution of normalized intensity of each pathlength is shown for 32 mouse retinal images. The mean normalized intensity was 0.99 for Pathlength 1, 0.97 for Pathlength 2, and 0.69 for Pathlength 3.
## 4 Evaluation
### Ranging Accuracy
#### Indoor Short Range Test  
The accuracy of the Wi-PoShardware system is tested in indoor lab environment conditions for small distances (between 1 and 5 m). The nodes are placed on tripods at a height of 1.5 m. The groundtruth distances for this measurement are determined with a 1.5 mm-accuracy laser meter, and the results of the experiment can be found in Figure 9b. For these small distance indoor experiments, the influence of multipath effects is rather small as the reflected waves path length is significantly larger than the line of sight path. The accuracy of these measurements are below the 10 cm level.  
Figure 8: Ground reflection model.
# Performance Assessment of Waste Cooking Oil-Modified Asphal Mixtures
## 4 Results and Discussions
### Performance Assessment of Waste Cooking Oil on Balanced Asphal Mixtures
#### 4.2.2 Putting Performance Evaluation of WCO-Modified Asphalt  
Figure 5 presents the mean results of HI-IDT\({}_{\text{strength}}\) with the rutting threshold (i.e., a minimum of 110 KPa). The error bars indicate a plus/minus one standard deviation. It can be observed that the control mixture (i.e., 0% WCO) had an average HI-IDT\({}_{\text{strength}}\) of 114 KPa, which satisfies the performance threshold. The data showed that HI-IDT\({}_{\text{strength}}\) has a good indirect linear relationship (i.e., HI-IDT\({}_{\text{strength}}\) = \(-\)1020[WCO%] + 96.78) with the variation WCO dosage (i.e., R\({}^{2}\) of 0.73). Such relationships indicated that HI-IDT\({}_{\text{strength}}\) decreased by 10.20 KPa for each 1% increment in WCO dosage. The WCO dosages of 0%, 2%, 3%, 4%, 5%, and 7% had HI-IDT\({}_{\text{strength}}\) of 114.7, 65.8, 54.6, 48.4, 43.6, and 39.0, respectively. Such findings indicate that mixture resistance to rutting declined with the increment in WCO dosages. ANOVA and [PERSON]'s tests revealed a statistically significant discrepancy between the control mixture and all modified mixtures. However, no significant difference existed between consecutive WCO dosages (i.e., 2% vs. 3% vs. 4% vs. 5% vs. 7%). Furthermore, the data showed a low variability (i.e., average COV less than 15%). This finding agrees with reported results in the literature [24,86,87].
## 3 Data Assimilation
### Data Assimilation Setup  
The purpose of DA is essentially to characterize uncertainties [42]. The hydrodynamics of a system is modelled based on deterministic equations and the model initial conditions. However, boundary conditions are associated with uncertainties that adversely affect the performance of any calibrated model [19]. To address this issue, we included stochasticity and added noise to both velocity components as well as the water level in boundary conditions. Observations (i.e., velocity) to be assimilated were perturbed at observation locations to support the measurement errors. Ensemble forecasting produces a number of model realizations from stochastic perturbations applied to model boundary forcings and observations. These model realizations are then propagated using available observational information, which is required to reduce model uncertainty and improve its accuracy.  
We introduced stochasticity to the deterministic model by using the OpenDA noise model. To fully capture the model uncertainty, an ensemble was created with a reasonably wide spread [43]; to this end, error statistics (Table 2) were designated to sufficiently spread the ensemble members.
## 1 Introduction
### Related Surveys  
In this subsection, selected recent survey papers are analyzed in the context of 6G networks. To this end, the work in [28] focuses on cloud RAN (CRAN), which is considered one of the major concepts in 5G/6G wireless networks since it decouples RAN software execution from specific hardware deployments. In this context, CRAN approaches can leverage efficient and dynamic network configurations, improve latency and resource allocation, and support the connectivity of a mass number of devices, since scaling can be performed on demand. When it comes to the 6G concept, resource optimization can be a highly demanding task due to the number of devices involved and the amount of data exchanged. Therefore, the article also highlights the importance of AI/ML algorithms towards effective resource management.  
The work in [29] analyzes all key aspects of machine-to-machine (M2M) communications in 6G networks. To this end, various issues are discussed, such as secure communications and power management of the devices involved, scalability, and interoperability of the connected devices. The work in [30] is focused on terahertz (THz) communications and sensing for 6G networks. Integrated sensing and communication (ISAC), also referred to as joint communication and sensing (JCAS), is a technology candidate with promising potential. ISAC integrates sensing and spatial location of passive (not connected) objects into the mobile communication network, expanding the network's functionality beyond just communication. Hence, advanced applications can be supported, such as robotics fleet management, AR/VR, and autonomous driving.  
In [31], all cutting-edge technologies in the framework of 6G networks are presented, such as THz communications, ultra-massive MIMO, artificial intelligence (AI), ML, quantum communication, and reconfigurable intelligent surfaces (RISs). Emphasis is given to AI/ML technologies and their integration in 6G networks. In the same context, potential use cases are presented as well. Finally, the work in [32] summarizes all key aspects of  
Figure 1: Paper structure.  
AI/ML deployments in future 6G networks. The key outcomes of each work are also highlighted in Table 1.
## 3 Results and Discussion
### Gelling Characteristics
#### 3.4.5 FTIR Spectra  
The FTIR spectra of gels of unwashed mince and surimi washed with different washing cycles are shown in Figure 3. The FTIR technique is widely used to figure out a protein's secondary structure [5]. There was a decrease in \(\alpha\)-helix structures and an increase in \(\beta\)-sheet structures after heating for creating unwashed mince/surimi gel, which was attributable to salt solubilization of protein [58]. During the manufacture of surimi sol, approximately 2.5 % (\(w/w\)) salt was added prior to heating to be gelled [7]. Amide III, II, I, and amide B/A vibrational modes in protein molecule are indicated by absorptions in the infrared ranges of 1200, 1500, 1700, and 3100-3300 cm\({}^{-1}\), respectively [59]. The identical spectra were observed in all samples in Figure 3, showing that the gel structure contained the same protein functional groups. In the FTIR spectra, the primary bands were identified as amide I, II, III, A, and B. At 1640 cm\({}^{-1}\), a significant amide I peak in the spectra of all gels generated from the C=O stretching vibration, and extensively employed as a probe for protein conformation, was found, indicating a supremacy of the \(\beta\)-sheet and thus a more stable structure [60]. N-H bending and C-N stretching vibrations were attributed to the amide II band, which occurred at 1530 cm\({}^{-1}\). At 1236 cm\({}^{-1}\) (N-H deformation and C-N stretching vibration), amide III can be found. The N\(-\)H stretching vibration of amide A was measured at 3273 cm\({}^{-1}\), while the CH stretching vibrations of -CH\({}_{2}\) group of amide B were measured at 2925 cm\({}^{-1}\). Increasing the number of washing cycles with cold tap water did not appear to impact the chemical makeup of myofibrillar proteins.
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology
### Digital Twin
#### 2.2.1 Signal Modeling: The First Step to Design the Digital Twin
\[K(\theta_{i},\theta)=e^{(-\frac{1}{2\sigma^{2}}\|\theta_{i}-\theta^{2}\|)}. \tag{18}\]  
In addition, the function's bias \((\varnothing)\) is made known to the following preparation. \[\varnothing=\frac{1}{|S|}\sum_{s\in S}\big{[}Z_{S}-\sum_{i\in S}\big{(}\delta_ {i}^{+}-\delta_{i}^{-}\big{)}\times K(\theta_{i},\theta_{S})-(\mathbb{C} \times sign\big{(}\delta_{i}^{+}-\delta_{i}^{-}\big{)}\big{)}\big{]}. \tag{19}\]  
Here, \(S,\ Z_{S}\), and \(\mathbb{C}\) are the support vector of approximation, the signal which is defined in the range of the support vector, and the accepted boundary of the support vector to allow for better signal compensation, respectively. Furthermore, if \(\omega\) is defined as a constant, the support vector of approximation, \((S)\), is defined using the following definition. \[S=\big{\{}i\big{|}0<\delta_{i}{}^{+}-\delta_{i}{}^{-}<\omega\big{\}}. \tag{20}\]  
Thus, the proposed technique of the digital twin to model the bearing using the combination of the MAT technique, Equation (10) and the SAL algorithm, and Equation (15), (henceforth called MS) is represented as the following definition. \[\left\{ \begin{array}{l}W_{MS}(k+1)=\big{[}\gamma_{MS_{1}}W_{MS}(k)+\gamma_{MS _{2}}Z_{MS}(k)+\Delta_{MS}(k)\big{]}+e_{MS}(k)\\ Z_{MS}(k)=(\beta_{MS})^{T}W_{MS}(k)\end{array} \right.. \tag{21}\]  
Furthermore, the unknown condition and the error of the bearing modeling using the digital twin algorithm that was computed using the MS procedure are represented as the following function. \[\left\{ \begin{array}{l}\Delta_{MS}(k)=Z(k)-Z_{MS}(k)\\ e_{MS}(k)=Z_{MS}(k+1)-Z_{MS}(k)\end{array} \right. \tag{22}\]  
Here, \(W_{MS}(k),e_{MS}(k),\Delta_{MS}(k),\ Z_{MS}(k)\), and \(\big{(}\gamma_{MS_{1}},\gamma_{MS_{2}},\beta_{MS}\big{)}\) are the state of the bearing signal modeling based on the MS technique for the bearing's digital twin, the error of the bearing's digital twin modeling using the MS technique, the unknown condition of the bearing's digital twin modeling that was computed using the MS technique, the bearing's digital twin output modeling using the MS technique, and the coefficients for the state-space bearing's digital twin performance tuning using the MS method, respectively.
## 2 Aerodynamic Phenomena
### Limited Amplitude Phenomena
#### 2.1.1 Vortex-Induced Vibrations (VIVs)  
Vortex-induced vibrations (VIV) are motions experienced by structures due to their interaction with the flow, caused by vortex shedding in the wake. This leads to fluctuating pressures on the structure's surface, causing oscillations perpendicular to the flow direction. The frequency of these vortices depends on flow velocity, the shape of the body, and fluid viscosity. Lock-in, a phenomenon in long-span bridges, occurs when the vortex shedding frequency aligns with one of the bridge's natural frequencies, leading to strong, sustained oscillations for a specific wind-speed range. Past examples of bridges experiencing VIVs include the Great Belt Bridge, the Trans-Tokyo Bay Highway, the Niteroi Bridge, the Volgograd Bridge, and the Arches of Alconetar bridge during the construction [25; 26; 27; 28].
## Results  
### Papers Selection  
The electronic search procedure is presented in Figure 1. The search was conducted before 20 December 2020, and a total of 843 articles published by the Implant Retrieval Center Laboratory of University \"G.D'Annunzio\" of Chieti-Pescara were identified and evaluated. A total of 43 literature reviews were excluded from the present investigation, and the full text was analyzed to evaluate the qualitative synthesis eligibility. A total of 770 papers were excluded for the following reasons: topic research (\(n=763\)), book chapters (\(n=3\)), and case reports (\(n=6\)). A total of 27 manuscripts were included for the qualitative synthesis: 8 animal studies [13; 14; 21; 22; 23; 24; 25; 26], 17 human studies [13; 13; 15; 16; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39], and 2 in vitro articles. The in vitro studies evaluated the osteogenic gene expression BMP2, RUNX2 and ALP and the mechanical characteristics of the experimental membranes (Table 1). The histological new bone formation (NBF) represented the most diffused evaluation of the included in vivo studies on animals and humans (Tables 2 and 3).
## 3 Results and Discussion  
### WRF-Chem Meteorological Performance Evaluation  
The French National Weather Service (Meteo France) [85] maintains a network of meteorological stations throughout France that continuously monitor standard meteorological variables as part of the World Meteorological Organization network. Twelve of their stations fit within the bounds of our 1 km WRF domain, as shown in Figure 13 in Appendix A. We obtained corresponding measurements of temperature, wind speed, wind direction, and relative humidity. To help validate the WRF-Chem model, we compared the simulated values from the corresponding pixels, including the relevant pressure level, against the simultaneous measurements. Following the methodology of [49], a calm wind threshold of 1 ms\({}^{-1}\) was set such that wind values at or below that threshold were excluded from the analysis, as the wind direction for calm conditions is undefined. This filtered out 526 of 2604 (20.4%) of the summertime measurements, 197 of 2688 (7.3%) of the wintertime measurements, and 31 of 2604 (1.2%) of the springtime measurements. The exclusion of these undefined measurement values allows for a more robust comparison with the model, making the agreement more evident. For example, without this filtering the MAE values for Summertime 2016 wind speed and direction would have been 35% and 21% larger, respectively. The ME and MAE values were calculated for each station and for each variable, following the equations in Section 2.4, then the mean was taken across all stations.  
Table 2 shows the results of the ME and MAE analyses for the daytime hours (12-18 UTC) of the Summertime 2016, Wintertime 2017, and Springtime 2017 periods. The model performs especially well in the Wintertime and Springtime periods. When compared against [PERSON] et al. [49], whose model setup we emulated, we find our values to be comparable to theirs for the 1 km domain of Indianapolis in the WRF-Chem simulation without Four-Dimensional Data Assimilation, which had MAE numbers of 30 degrees and 1 ms\({}^{-1}\) and ME numbers of 6 degrees and 0.2 ms\({}^{-1}\), respectively. The Summertime 2016 values show a wider range of disagreement, which is most likely a result of the model having difficulty accurately capturing specific features of atmospheric dynamics of this coastal area, e.g., sea and land breeze processes. Further studies may want to address whether the worse agreement in the model during summertime is a recurrent event, and if so whether CO\({}_{2}\) studies in this region should account for the increased uncertainty during this period of the year. Overall, the observed values are considered to be within the expected range for model performance for an analysis of this type in a region with such complex terrain.