esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### System Support
#### 4.1.2 Architectural Enhancements Support  
To facilitate vehicular communications (via PC5 and Uu) for eV2X services, the specification document (_TS 23.285 Rel-16_) provides the architecture reference model enhancements supporting the service requirements and use cases stated in earlier specification documents (i.e., _TS 22.185_ and _TS 22.186_). The older releases of the specification (_TS 23.285 Rel-14_ and _Rel-15_) did not focus on the enhancements for 5G such as EPC controlled NR PC5 operations, support of NR PC5 quality-of-service (QoS) under EPC, NR PC5 QoS modeling, etc. These enhancements introduced advancements for V2X applications, known as advanced-V2X (aV2X) services. The aV2X services are comprised of advanced scenarios such as advanced driving, remote driving, platooning, extended sensors, general aspects, and vehicle QoS support. Therefore, to summarize the support for each communication scenario, we present the performance requirements provided by 3 GPP in the (_TS 22.186_) in Table 2. In what follows next, we provide an overview of the support for aV2X services available in _Rel-16_ and _Rel-17_.  
3 GPP discusses the architectural enhancements for EPS and 5G systems in (_TR 23.786 Rel-16_) to support V2X services. Moreover, the TR also specifies the assumptions, requirements, and 15 key issues. Some of the important key issues are network slicing, interworking for eV2X, edge computing, NR-based PC5 communication, sensor sharing over PC5, service authorization, provisioning, etc. Furthermore, the TR provides 29 solution approaches and some of the potential solutions are network slicing, application function influence-based edge computing for V2X, interworking for eV2X, eV2X impacts to 5 GC procedures, and network-controlled QoS mechanism for PC5 communication. Four variants of eV2X architecture including architecture reference models have also been discussed.  
In _Rel-17_, the architectural enhancements i.e., assumptions and requirements for advancing V2X services are provided in the technical report (_TR 23.776 Rel-17_). In this regard, the 5 GS enhancements are investigated for advanced operations and applications of eV2X services. For example, power-efficient communication between UEs and pedestrians. The key issues identified in _Rel-16_'s technical report (_TR 23.786_) are considered with new key issues in _Rel-17_'s technical report (_TR 23.776_). One of the important issues is the support of QoS-aware NR PC5 power efficiency for pedestrian UEs. Similarly, some of the potential solutions are QoS-aware power-efficient PC5 communication for pedestrian UEs, V2P parameter provisioning, QoS support for V2X messages sent by pedestrian UEs, etc., reported in the technical document (_TR 23.776 Rel-17_). It goes without saying that 3 GPP standardization will continue supporting V2X services with architectural enhancements in future releases i.e., _Rel-18_.
## 5 Conclusions and Further Work  
This research aimed to predict the degradation of the communication signal transmitted from a satellite until it reaches the ground station. The signal copes at the beginning, with attenuation caused by climate phenomena, and later, when it meets ground constructions surrounding the ground station. We applied LSTM twice to provide two prediction models to cover what the signal is experiencing. This prediction's impact optimizes the energy consumed by a satellite transmitter to properly propagate microwave signals towards its associated ground station, enabling broadband communication. We proposed a prediction mechanism using LSTM neural networks. We collected and simulated data from the Genesis satellite and built a sampling dataset to serve as the training dataset loaded to the LSTM system. We executed the predicting models for a wide frequencies spectrum and reached excellent predictions. We intend to expand the prediction timeframe (more than one second) further and apply it to other obstacles causing signal attenuation and loss.  
Conceptualization, M.M.D.A.; Data curation, G.L.; Funding acquisition, M.M.D.A.; Investigation, M.M.D.A.; Software, G.L.; Writing--original draft, M.M.D.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
### Porosity Monitoring  
Most research agrees that porosity can be divided into three categories which are (1) keyhole, (2) gas pores, and (3) lack of fusion [31; 44]. A keyhole porosity occurs when there is an excess in the energy density in the melt pool, typically as a result of high LP and low scanning velocity, as a consequence the melt pool becomes deep with a depression zone of vaporized material [45]. Gas porosities are created as a result of the entrapment of vapors within the melt pool [46]. If a substrate or preceding layer experiences insufficient penetration from the melt pool of a succeeding layer, Lack of fusion may be produced [47; 48].
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 4 Comparison between DEM and Experimental Results
### Two Elements under Edge-Edge Contact Mode
#### 4.1.2 Non-Collinear  
From Figure 10, it can be observed that the variation trend of the contact force-time curves obtained from simulation and experiment under different impact loads is roughly consistent, and the amplitude of the contact force is similar. The relative error between the peak contact force obtained from the discrete element simulation and the experimental value is within 14%. The experimental results in the figure exhibit a phenomenon of \"anomalous waveform\". This is due to the non-collinear nature of the contact, where the contact form is an edge-edge contact. In reality, two polyhedra make contact at a single point. This type of contact poses significant challenges for the installation of thin-film  
Figure 9: Comparison of DEM and experimental contact force–time curves under edge-to-edge contact mode (collinear).  
pressure sensors. Improper installation may result in uneven force distribution on the sensor, consequently causing waveform anomalies.
## 4 Results
### Bus Bunching Process  
Route 113 is selected as an example to reflect the process of bus operation during peak time (7:00 to 9:00) and off-peak time (13:00 to 15:00), as observed in Figure 4. The results in Figure 4 clearly indicate that the adjacent vehicles BS37580D and BS06015D bunched together at stop 25 with 99 s headway, roughly a quarter of the departure interval of 346 s. Similarly, BS02935D and BS05185D arrived at stop 25 at nearly the same time, with 13 s headway.  
The phenomenon of \"large and small interval\" could be clearly seen as well. As the bus departed from the terminus at a fixed interval, bus bunching at stop 25 resulted in a large interval before and behind it, remarkably increasing the passenger waiting time. Vehicle BS06015D may serve fewer passengers because of the small interval with vehicle BS37580D; thus, its transport capacity could not be fully utilized in the case of no overtaking. On the contrary, affected by the large interval between BS06015D and BS02935D, BS02935D may face severer operational pressure.  
Figure 4 shows the same principles, but the fluctuation of bus headway is relatively gently affected by the reduced passenger demands and lengthened departure interval. However, the phenomenon of \"large and small intervals\" still exists.
## 5 Evaluation Protocol and Results
### Experiment Results
The results of confusion matrices in Table 9 represent testing each subject using a majority voting by predicting three trials for each subject and classifying the subject based on the majority voting of the three trials. Figure 5 shows the ROC curve of testing the seventh fold of the testing data without majority voting and with majority voting, respectively. It can be noticed from the curve that AUC reaches a high value (0.99) for the method based on majority voting, which means that the model, in this case, is able to distinguish between schizophrenic and normal controls. To measure the time complexity of this model, we used an Intel Core i7 device with 16 GB RAM for testing (Intel, Santa Clara, CA, USA). We tested the system for classifying 2100 test samples of 6 s belonging to one-fold. The average time to classify one example was 3 ms. \begin{table}
\begin{tabular}{c c c c} \hline
**Fold Number** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Fold1 & 100\% & 100\% & 100\% \\ \hline Fold2 & 100\% & 100\% & 100\% \\ \hline Fold3 & 100\% & 100\% & 100\% \\ \hline Fold4 & 100\% & 100\% & 100\% \\ \hline Fold5 & 100\% & 100\% & 100\% \\ \hline Fold6 & 100\% & 100\% & 100\% \\ \hline Fold7 & 98.81\% & 100\% & 97.44\% \\ \hline Fold8 & 100\% & 100\% & 100\% \\ \hline Fold9 & 100\% & 100\% & 100\% \\ \hline Fold10 & 100\% & 100\% & 100\% \\ \hline
**Avg** & **99.88\%** & **100\%** & **99.74\%** \\ \hline \end{tabular}
\end{table} 
Table 8: Results of testing the best model using majority voting. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Fold 7** & \multicolumn{3}{c}{**Fold 10**} \\ \hline \multicolumn{3}{c}{True} \\ \hline
**Predicted** & 45 & 0 & 45 & 0 \\ \hline
1 & 38 & 0 & 39 \\ \hline \end{tabular}
\end{table} 
Table 9: Confusion matrices for testing fold 7 and fold 10 using majority voting.
## 2 Theoretical Research on Indirect Measurement Method Based on Vehicle-Bridge Coupling
### Theoretical Research on Vehicle-Axle Coupling Vibration  
The whole mathematical model can be regarded as two subsystems; one is the vehicle subsystem and the other is the bridge subsystem. In the solution of vehicle-bridge coupling vibration equation [19], bridge mass, damping and stiffness matrices are generally constant matrices, bridge damping is selected as Rayleigh damping, and differential equations for bridge and vehicle vibration can be expressed as:  
\[M_{b}\ddot{Z}_{b}(t)+C_{b}\dot{Z}_{b}(t)+K_{b}Z_{b}(t)=P_{b}(t) \tag{1}\]  
where: \(M_{b}\) is the bridge quality matrix, \(C_{b}\) is the bridge damping matrix, \(K_{b}\) is the bridge stiffness matrix, \(Z_{b}\) is the bridge displacement matrix, \(\dot{Z}_{b}\) is the bridge speed matrix, and \(\ddot{Z}_{b}\) is the bridge acceleration matrix. \[[M_{v}]\left\{\ddot{u}_{v}(t)\right\}+[C_{v}]\left\{\dot{u}_{v}(t)\right\}+[K _{v}]\left\{u_{v}(t)\right\}=\left\{P_{v}(t)\right\} \tag{2}\]  
where \([M_{v}]\), \([C_{v}]\), and \([K_{v}]\) are vehicle generalized mass matrix, generalized damping matrix, and generalized stiffness matrix, respectively, \(\left\{\ddot{u}_{v}(t)\right\}\), \(\left\{\dot{u}_{v}(t)\right\}\), and \(\left\{u_{v}(t)\right\}\) are vehicle acceleration vector, velocity vector, and displacement vector, respectively, and \(\left\{P_{v}(t)\right\}\) is load vector. In the vehicle-bridge coupling vibration system, the solution process of differential equation of vehicle-bridge vibration is based on the geometric compatibility and mutual constraint of force balance at the contact point between wheel and bridge surface, the force acting on the wheel is obtained by using the relative displacement between the axle, and the vehicle matrix operation is used to obtain the force of the vehicle on the bridge. The displacement of the wheel relative to the road surface can be expressed as:  
\[\Delta_{i}=Z_{wi}-Z_{i}-r_{i}(x) \tag{3}\]  
where \(Z_{wi}\) is the vertical displacement of the wheel, \(Z_{i}\) is the vertical displacement of the bridge at the contact point between the bridge and the wheel, and \(r_{i}(x)\) is the pavement roughness at the bridge and point. Figure 1: Mathematical model for vehicle scanning method. There is a pair of forces with equal size and opposite direction at the contact point between the wheel and the bridge. The gravity of the vehicle on the bridge should be considered in calculation and programming. The force balance can be expressed as:  
\[F_{wi}=-F_{bi}=c_{wi}(\Delta_{i}^{\prime})+k_{wi}(\Delta_{i}) \tag{4}\]  
where \(c_{wi}\) is wheel damping, \(k_{wi}\) is the wheel stiffness, and \(\Delta_{i}\) is the vertical relative displacement between wheel and pavement. The equation selects the Newmark-\(\beta\) method for solving the vehicle-bridge coupling system. The displacement and velocity of vehicles calculated by the Newmark-\(\beta\) method are used as the initial conditions for the next time interval to solve the vehicle-bridge coupling system.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 6 Conclusions  
In designing the security of the earth dams, it is necessary to evaluate the slopes in the effect of earthquakes accurately. Therefore, measurement and prediction of this issue in the designs are considered as an important prerequisite. In this study, an attempt was made to predict the value of the slope deformations in the earth dams. To the end, through gathering a set of data from the recorded changes in the real samples, including the parameters of earthquake magnitude (Mw), maximum horizontal earthquake acceleration (amax), yield acceleration of earth dam slope (ay), predominant earthquake period (TP), and fundamental period of earth dam (Td), the results and models were evaluated and analyzed. These parameters were selected as important and effective parameters and introduced as the input to the intelligent models. In this study, the two models of GMDH and MARS were developed and implemented to evaluate the slope deformations in the earth dams created in the effect of an earthquake. These models were evaluated by different examinations, and the effective  
Figure 10: Comparison between RE of different models.  
parameters of each one were identified and evaluated. In the end, a comparison was made between the best models of GMDH and MARS to select the chosen model. The new MARS model could predict the values of Dave with higher performance. The accuracy of the suggested model was obtained R\({}^{2}\) = 0.8905 and R\({}^{2}\) = 0.8956 for the test and training sections, respectively. The performance of this model shows the acceptable precision for predictions. After this section, the impact of different parameters on the output was obtained using the sensitivity analysis. Mw and ay were determined as the most effective and the least effective parameters in the prediction models. In the end, a comparison was made between the models developed in this study and the old models. These results show that the present model can predict the value of Dave in the earth dams arising from earthquakes with higher precision.  
Conceptualization, D.J.A.; data curation, B.T.P.; formal analysis, M.C. and M.K.; supervision, D.J.A.; writing--review and editing, M.K., M.C., and B.T.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors confirm that this article content has no conflict of interest.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 1 Introduction
### The Need for Automated Detection Systems for Condition Monitoring
The study utilizes the use of harmonized distress categories based on industry standardized distress types with associated specified severities so that the final model can give the user a better understanding of the level of distresses along a network and the required interventions. This differs from previous work as the model developed not only seeks to highlight the location of the distress and type but also a quick but accurate severity assessment. Using this, a pipeline for future integrations within the PMS is proposed.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 3 System Model and Problem Formulation
### System Model and Proposed BSs Switching On/Off Mechanism
#### 3.1.1 High Traffic Load (0.4 < \(\lambda\)\(\leq\) 1)  
Mobile users' demand can be summarized as a high data rate with QoS and full radio frequency coverage. Hence, in order to meet subscribers' QoS expectations, all BSs inclusive of LTE and 5G are required to be active and functional. This will lead to the provisioning of optimal network performance meeting mobile subscribers' expectations. Priority should be given to 5G cells because of the small cells involved. 5G BSs should perform traffic monitoring periodically, along with data delivery. This will enable the 5G BSs to switch off in case the mobile wireless traffic load decreases under a certain threshold, \(\lambda\)\(\leq\) 0.4, and remains under the threshold for a certain period. This denotes the low mobile wireless traffic use case (energy-saving case). However, LTE BSs are active in a high traffic case to assist 5G BSs with the provisioning of extra coverage to subscribers.
## 2 Materials and Methods
### Sample Pre-Treatment
The residue was dissolved in 1 mL of methyl-terz-butyl-ether (MTBE)/methanol (1:1, _v/v_), filtered through 0.45 \(\upmu\)m PTFE, and analyzed by HPLC following a method we used for other carotenoid determinations [31; 32]. The analyses were carried out using an HPLC system (Shimadzu, Milan, Italy) equipped with an SPD-M20A photo-diode array detector, two LC-20 AD pumps, a CBM-20A controller, a DGU-20A3 degasser, and a SIL-20 AC auto-sampler. The data were processed with the software LabSolutions ver. 5.10.153 (Shimadzu, Milan, Italy). For MS analyses, the system was coupled to a mass spectrometer detector (LCMS-2020, Shimadzu, Milan, Italy), equipped with an APCI interface, both in positive and negative ionization mode. Carotenoids were separated on a C30 YMC column (250 \(\times\) 4.6 mm; 5 \(\upmu\)m), and the injection volume was 20 \(\upmu\)L. The mobile phase was a binary gradient of methanol/methyl-terz-butyl ether/water (MeOH/MTBE/H\({}_{2}\)O) (90:8:2, _v/v/v_) (A), and MeOH/MTBE/H\({}_{2}\)O (8:90:2, _v/v/v_) (B): 0.01-20.00 min, 0-30% B; 20.01-35.00 min, 30-80% B; 35.01-65.00 min, 100% B; 65.01-75.00, 100% B. Then, the gradient was re-equilibrated to the initial condition in another 5 min before the next analyses. The flow rate was 1 mL/min, and the analyses were performed at 20 \({}^{\circ}\)C. Pigments (\(\alpha\)-carotene, \(\beta\)-carotene, and lutein) in cheese samples were identified, comparing elution order, UV/vis, and APCI-MS spectra with those of available standards. The UV/vis spectra were acquired in the range of 250-600 nm, while the chromatograms were extracted at 448 nm for \(\beta\)-carotene, 444 for lutein, and 325 for \(\alpha\)-carotene (sampling frequency: 1.5625 Hz; time constant: 0.64 s). The MS/APCI parameters were set as follows: Acquisition Mode, Scan; Interface Temperature, 350 \({}^{\circ}\)C; Interface Voltage: 4.5 kV; Heat Block, 300 \({}^{\circ}\)C; CDL Voltage: 0 V; CDL temperature, 300 \({}^{\circ}\)C; _m/z_ range, 300-1200; Nebulizing gas flow (N\({}_{2}\)): 4 mL/min; Event Time: 1 s; Detector Voltage: 0.8 kV; Q-array: 0.0 V; RF: 90 V; Sampling: 2 Hz. The identified compounds were also confirmed by comparing the spectral data of authentic standards, while quantification was performed with the external standard method. The data on the pigment levels referred to the cheese samples before the lyophilization process and were obtained as average values of triplicate analyses, and the results are expressed as mean \(\pm\) standard deviation. The coefficient of variation (CV%) of the three analyses was always lower than 5%.
###### Abstract  
The challenge of mixing high-viscosity materials is a common issue encountered in the manufacturing process of food materials. The advantages of the internal meshing screw mixer have led to its adoption in various manufacturing processes, but it has yet to be implemented in the food industry. The paper presents a design method for an internal meshing screw mixer based on kinematic principles. The mixer features a helical chamber created by alternating volumes formed by the stator and rotor, establishing an extension-dominated environment for mixing high-viscosity fluids. A kinematic model based on the internal cycloid principle was established, providing trajectories and equations for key points on the rotor, simulating both its rotation and revolution processes, and revealing the velocity variations at different points on the rotor. Based on the kinematic analysis results, a stator and rotor design method was developed according to relevant functional divisions. To achieve the desired motion effects, transmission and support devices were designed, and the relationship between the transmission device and the internal cycloid surface of the fixed rotor was established. The mixer's application and mixing effectiveness in the food industry were validated using corn syrup and flour. Experimental results showed that the extensional mixer described in the paper effectively mixed high-viscosity fluids while also efficiently blending fine powders. Slurry viscosity was tested with a rheometer at different speeds with an eccentric rotor mixer. Results showed that viscosity decreased with increasing shear rate, with a more pronounced decrease at higher shear rates. The apparent viscosity trend remained consistent at different speeds, although variations were observed at lower shear rates, especially concerning speed. The non-Newtonian fluid index exhibited minimal variations at different speeds, while the consistency coefficient showed significant fluctuations. The mixing uniformity index of the slurry was used to evaluate the mixing uniformity and dispersion uniformity of this extensional mixer. At different rotational speeds, the density of the slurry changes little. The uniformity index of mixing decreases gradually with the increase of rotational speed, reaching its maximum at 15 r/min. The overall trend of the uniformity index decreases with increasing rotational speed, indicating a decrease in density uniformity. A peak appears at 45 r/min, possibly due to the maximum values of elongation rate and shear rate at this speed. As the rotational speed increases, the residence time of the material in the mixer decreases, which may be the main reason for the decrease in mixing uniformity. These findings provide valuable insights into the design and utilization of extension-dominated screw mixers within the food industry, laying a solid foundation for future research and practical applications in this field.  
kinematics design method; extension dominated; screw mixers; high-viscosity fluid; food industry +
Footnote †: journal: Applied Sciences  
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Footnote 1: _Applied Sciences_