esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Properties of Asphalt Mixtures under Triaxial Compression
###### Abstract  
In order to study the influences of confining pressure and temperature on the shear properties of asphalt mixtures, triaxial tests were conducted at 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, with the confining pressure ranges from 0 to 1 MPa for the widely used continuous-graded AC (Asphalt Concrete)-13, open-graded OGFC (Open-Graded Friction Course)-13, and gap-graded SMA (Stone Mastic Asphalt)-13 asphalt mixtures in China. A nonlinear regression/prediction model of triaxial strength for asphalt mixtures was proposed. The results show that confining pressure and temperature had a significant effect on the shear resistance of asphalt mixtures. With increasing temperature, the shear strength of the asphalt mixture gradually decreased due to the decreasing of cohesion strength; the shear strength of the asphalt mixture increased with the increase of confining pressure. Meanwhile, the cohesion strength increased and the friction angle decreased gradually with the increase of confining pressure. When the confining pressure was close to 0.6 MPa, the Mohr-Coulomb failure envelope bended down, so the linear Mohr-Coulomb criterion is not suitable to describe the failure behavior of asphalt mixtures. Therefore, a power function failure envelope was put forward to characterize the nonlinear shear properties of asphalt mixtures. The nonlinear evolutional laws of shear parameters, which includes cohesion strength and friction angle, were also proposed for asphalt pavement material and structure design. Among these asphalt mixtures, the gap-graded SMA-13 asphalt mixture exhibited better performance on the resistance to shear failure, and it was recommended as the upper layer material to improve the shear performance of asphalt pavement.  
Keywords: asphalt mixtures; triaxial test; shear properties; nonlinear Mohr-Coulomb failure envelope +
Footnote †: journal: Applied
# Carbon Balance in Salt Marsh and Mangrove Ecosystems: A Global Synthesis
## 6 Whole-Ecosystem Carbon Mass Balance
The budget assumes a global salt marsh area of 54,951 km\({}^{2}\)[49]. Solid blue arrows depict mean values based on empirical data (see text for explanation and references). Dashed red arrows represent mean values estimated indirectly by difference. The \(\text{C}_{\text{ORG}}\) pool (both live and dead roots and other organic matter) in soils to a depth of 1 m is presented in a box in the marsh floor with units of Tg C\({}_{\text{ORG}}\). Abbreviations: GPP = gross primary production; NPP = net primary production; \(\text{R}_{\text{A}}\) = algal production at soil surface and aboveground biomass; \(\text{R}_{\text{C}}\) = canopy respiration; \(\text{R}_{\text{S}}\) = soil respiration at soil surface; \(\text{R}_{\text{WATER}}\) = waterway respiration; \(\text{POC}\) = particulate organic carbon derived from litter and exported to tidal waters; DIC = dissolved inorganic carbon; \(\text{DOC}\) = dissolved organic carbon; \(\text{CH}_{4}\) = methane. As defined in [53], net ecosystem production (NEP) can be derived by subtracting all respiratory losses (ecosystem respiration, \(\mathrm{R_{E}}=\mathrm{R_{C}}+\mathrm{R_{S}}+\mathrm{R_{WATER}}+\mathrm{R_{MICROALGAE}}\)) from all salt marsh/magnrove, benthic algal and plankton gross primary production (GPP). NEP is 54 Tg C a\({}^{-1}\) for mangroves and 21 Tg C a\({}^{-1}\) for salt marshes (Table 6). Both ecosystems contribute only a small proportion of GPP and \(\mathrm{R_{E}}\) to the global coastal ocean due to their small global area, but mangroves are more productive in terms of NEP on a per area basis than the other coastal habitats (Table 6). Macroalgae contribute nearly 94% of NEP to total NEP (2217 Tg C a\({}^{-1}\)), the latter derived by summing the NEP of the global benthic coastal ocean (\(-\)165 Tg C a\({}^{-1}\)) and all the other habitats (2382 Tg C a\({}^{-1}\)). Seagrass meadows also contribute a larger share to total NEP (4.5%) than either mangroves (2.4%) or salt marshes (1.0%) due to their larger global area. On a per area basis, mangroves produce and respire more carbon than the other coastal habitats. Coral reefs are on average less productive in terms of GPP than their coastal ocean counterparts but have roughly equivalent global NEP to seagrass. All vegetated ecosystems are net autotrophic (\(\mathrm{P_{GPP}}\)/\(\mathrm{R_{E}}\) = 1.09-1.37), with macroalgae being the most autotrophic; the global coastal ocean is net heterotrophic (\(\mathrm{P_{GPP}}\)/\(\mathrm{R_{E}}\) = 0.98). The sum of plant respiration, surface and subsurface soil respiration, and respiration in tidal waterways equates to about 82% of salt marsh and 85% of mangrove GPP (Table 6). The remaining carbon, including that fixed by algae, is stored in vegetation and soil and, to a smaller but vital extent, is probably lost to fisheries, food webs, birds, and other organisms, including humans.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 1 Introduction  
The aim of connected and autonomous vehicles is to improve traffic flow, reduce fatalities on the road, and reduce the environmental impact of modern mobility, among others [1]. Vehicle-to-Everything (V2X) communication enables the sharing of various types of information (such as traffic, safety and entertainment) with other vehicles, infrastructures and pedestrians using the road through wired and wireless networks. It is essential for ensuring low latency and high dependability [2, 3]. V2X communication is made up of three different types of communication: vehicle to vehicle (V2V), vehicle to network or infrastructure (V2N and V2I), and vehicle to pedestrian (V2P) [4].  
The 3 GPP C-V2X technology, in accordance with the fifth generation (5G) NR, ensures higher efficiency connectivity [5]. Up until now, significant resources have been put into enhancing the efficiency of direct V2V communications using the sidelink interface, which allows for a network infrastructure bypass [6]. In order to facilitate minimum latency V2X communications, 3 GPP release version 15 added two additional sidelink transmission modes, Mode 3, and Mode 4 (shown in Figure 1) [7]. In addition to C-V2X Modes 3 and 4, two more sidelink resource allocation NR Modes (1, 2) are now available. In the Release 16 by 3 GPP, Mode 1 refers to the network-controlled allocation of Radio Resources (RR)s for sidelink communications. In 3 GPP Release 16 Mode 2, individual vehicles can choose their own RRs using a distributed process [8].  
Several improvements to the C-V2X standard are included in the NR V2X specifications under the development in Release 16 [9]. These improvements impact both the sidelink PC5 interface that facilitates direct V2V connectivity and the traditional uplink unicast (Uu) cellular interface. NR Mode 1 is also known as a network-controlled mode. In this mode, the base station schedules resources for vehicular UEs to use for sidelink communications. Similar to Mode 3 based on Release 14, Mode 1 only applies to in-coverage conditions. In NR Mode 2 (also known as user equipment autonomous), the network preconfigures a set of sidelink red scenarios, and UEs independently choose how to use them. Similar to Mode 4 in Release 14, Mode 2 can be considered for both in-coverage as well as out-of-coverage cases [10]. This work primarily focuses on Mode 2. Mode 2 follows the sensing-based Semi Persistent Scheduling (SPS) technique described in Release 14. Using this approach, UEs can detect the channel before transmission to select the resources that are less likely to be interfered with and possibly reserve them for a randomized number of consecutive messages.
# Thermal Performance Analysis of a Double-Pass Solar Air Collector: A CFD Approach
## 2 Materials and Methods
### U-Shape Double-Pass CPC Description and Physical Model
#### 2.1.2 Mesh  
A hexahedral structured mesh was generated according to the proposed computational domain. The near-wall model approach was used to accurately predict the hydrodynamic behavior of the flow and the heat transfer in the system. The method was to implement 15 cells to cover the viscous and buffer sublayer to have accurate results in a reasonable computation time.  
The mesh refinement was carried out considering the shear stress for the hydrodynamic phenomenon and the Nusselt number (Nu) for the thermal boundary layer. The Nusselt number represents the dimensionless temperature gradient in the wall of interest.  
The mesh size was refined until the variation of the shear stress and the average Nu was less than 1%. Next, the size of the viscous sublayer and the buffer sublayer were calculated for the interval \(0.5<y^{+}<5\) using Equation (1), and for the thermal sublayer, the Nu was monitored. In addition, \(y^{+}\) values of 35 and 60 were applied to the turbulent sublayer to carry out the mesh independence study; this monitoring was carried out to describe the viscous sublayer. Then, to obtain the final mesh size used in this work, the mesh was refined in the \(z\)-axis from 100 divisions to 1600. Through the analysis, Nu varied 0.2% with \(y^{+}\) values of 0.8 and 0.5, selecting \(y^{+}\) 0.8.  
\[y=\frac{y^{+}\mu}{\rho u_{T}} \tag{1}\]  
Additionally, the mesh size was verified in the direction of the entrance flow with a cavity mesh refinement in the longitudinal axis (\(z\)-axis), as shown in Figure 3. The analysis found that the Nusselt had a variation of less than 0.1% from 550 divisions onwards. In Figure 3a, a cross-section of the U-shape double-pass CPC solar heater is shown, with the magnified detail of the mesh in the receiver. Figure 3(b1,b2) present the longitudinal section, where the coarse and refined mesh in the cavity are presented.  
The k-\(\omega\) models are \(y^{+}\) insensitive treatments; therefore, the \(\omega\)-equation can be integrated without additional terms through the viscous sublayer. Nevertheless, the Transition SST k-\(\omega\) model requires a more stringent grid resolution to solve the thin laminar boundary layer upstream of the transition location. For this reason, using a near-wall mesh with \(y^{+}\approx 1\), especially for heat transfer predictions, is recommended [26].
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 4 Experimental Setup
### Emulation Environment  
To test and analyze the performance of the four protocols previously mentioned, we use the network emulator Common Open Research Emulator (CORE) [64]. Developed by Boeing's Research and Technology division, CORE is a real-time, open source, emulator. The CORE is chosen because it enables the use of real-world routing protocols and applications using Linux system virtualization. The e-RLRP code must be executed on a Linux platform. Each node in the emulator is a virtual machine with network interface and resources shared with the host machine. The e-RLRP, RLRP, BATMAN and OLSR routing protocols are installed to be used by network nodes.  
The network performance metrics obtained in the tests were throughput, _Probability of Packet Loss (Ppl)_, the Round Trip Time (RTT) and Overhead. The throughput and _Ppl_ values are calculated using Iperf tool [65]. It is capable of generating UDP and TCP traffic streams at defined rates. To calculate RTT, the UDP stream is replaced by an ICMP stream generated by the native Linux PING command. The PING command itself returns the RTT value. The Overhead is measured using the WireShark [66] tool. In addition to the aforementioned tools, the native Linux shell script is used to shutdown nodes on a programmed or random basis.  
Finally, the speech quality of a VoIP communication is evaluated. To this end, the network parameters, such as _Ppl_ and delay were used as inputs of the E-model algorithm to estimate the communication quality.
# Foams in Wastewater Treatment Plants: From Causes to Control Methods
## 4 Evaluation Method
### Indirect Methods
#### 4.2.3 Hydrophobicity  
The bacteria causing foaming in activated sludge plants are hydrophobic, and their hydrophobicity is assumed to be a crucial factor in their foam-forming ability. The method of cell surface hydrophobicity (CSH) involves partitioning the mixed liquor biomass in a hydrophobic organic solvent and the absorbance of the water layer is measured before and after treatment: the difference is used to calculate the hydrophobicity [62]. Therefore, there is a relationship between CSH, as determined by microbial adherence to hydrocarbons, and the ability of microorganisms isolated from activated sludge foamsto produce stable foams [71; 94]. [PERSON] et al. [2], to evaluate the hydrophobicity of cell surfaces, estimated the microbial adherence to hydrocarbons (MATH) according to the modified Rosenberg's method, using n-hexadecane as the solvent [95]. [PERSON] et al. [78], applied the same method, extending the measure of hydrophobicity not only to the superficial cells of mixed liquor (CSHml) but also to those in foams (CSHf).
## 4 Buck Converter
### Loss Resistance, Loss Voltage and Voltage Gain Determination  
With the aim of identifying \(R_{X}\), \(V_{X}\) and \(A_{Vi}\) according to the converter parameters given in Figure 1, we proceeded as follows. Comparing (44) and (74) the following is obtained:  
\[R_{X}=\frac{r+R_{S}}{\delta+k_{1}\beta}, \tag{75}\]  
\[V_{X}=\bigg{[}\frac{k_{2}\beta}  
Then, (75) and (77) can be approximated respectively to:  
\[R_{X}\simeq\frac{\delta+\delta_{f}}{\delta^{2}}(r+R_{S})\simeq\frac{\delta+ \delta_{f}}{\delta^{2}}r, \tag{82}\]  
and  
\[A_{Vi}\simeq 1-\frac{\delta_{f}}{\delta+\delta_{f}}. \tag{83}\]  
Finally, regarding (76), considering in this order: (25), (56) with (17) and (24), and (35) with (18), it can be rewritten as:  
\[V_{X}\simeq\frac{\delta_{f}}{\delta+\delta_{f}}V_{\gamma} \tag{84}\]  
If the converter works in CCM \(\left(\delta_{f}=1-\delta\right)\), (82) and (83) lead to the literature common expressions [24; 36; 37; 38; 39]:  
\[R_{X}|_{CCM}\simeq\frac{1}{\delta^{2}}(r+R_{S})\simeq\frac{r}{\delta^{2}}, \tag{85}\]  
and  
\[A_{Vi}|_{CCM}\simeq\delta. \tag{86}\]  
On the other hand, substituting (82) and (83) in (51):  
\[R_{i}\simeq\frac{\delta+\delta_{f}}{\delta^{2}}r+\left(\frac{\delta+\delta_{ f}}{\delta}\right)^{2}R_{L}, \tag{87}\]  
that for CCM condition \(\left(\delta_{f}=1-\delta\right)\) leads to:  
\[R_{i}|_{CCM}\simeq\frac{r+R_{L}}{\delta^{2}}. \tag{88}\]  
Similarly, (84) can be written in CCM as:  
\[V_{X}\cong(1-\delta)V_{\gamma}. \tag{89}\]  
Regarding approximate efficiency determination, it is enough to substitute (82)-(84) in (53) with the condition that \(\left|A_{Vi}V_{g}\right|\gg\left|V_{\gamma}\right|\):  
\[\eta\simeq\frac{1}{1+\frac{r}{\left(\delta+\delta_{f}\right)R_{L}}}. \tag{90}\]  
This efficiency for CCM condition can be written as:  
\[\eta|_{CCM}\simeq\frac{1}{1+\frac{r}{R_{L}^{2}}}. \tag{91}\]
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Free Localization Techniques
#### 2.3.1 Proximity Detection  
It is one of the simplest position estimation algorithms and provides relative location information based on the range of known transmitting nodes. The unknown receiving node's position is estimated based on the strongest signal strength, and the relative location is derived from the antenna grid when it is within the radio signal range. Its accuracy is proportional to the transmitting nodes' density. Specifically, each transmitting node creates a theoretical circle, which indicates its coverage region, as shown in Figure 6. The circle diameter is determined by the signal power. The receiving nodes scan the radio signal channel and are classified as being within the region (i.e., \(Rx_{1}\)) or outside the region (i.e., \(Rx_{2}\)). It can only provide room-level accuracy for indoors and cell-level for outdoors. For instance, the GSM-based localization is within a range of 50-200 m coverage, which corresponds to the size of the GSM cell [20]. Moreover, proximity is utilized by the RFID-based anti-theft systems, as well as the BLE beacons, such as the iBeacon and Eddystone introduced by Apple [3] and Google [5], respectively, with various applications including hotels and airports.
## 5 Performance Analysis and Discussion
### Evaluation Results
The average success rate achieved for the workflows are shown in Figure 9. It can be seen that the performance of the proposed DSB is better in terms of cost achieved under user-defined deadline as compared to the other baseline algorithms.
# Shear Strength Improvement of Clay Soil Stabilized by Coffee Husk Ash
###### Abstract  
Finding alternatives to natural resources is important for a sustainable future and is essential to infrastructure projects. Among these replacements is the use of coffee waste as soil stabilizers. Coffee husk ash (CHA) is a solid waste obtained by the processing of coffee beans on a farm or factory. The main aim of this study is to determine the geotechnical properties of clay soil treated with CHA to develop a low-cost, environmentally friendly alternative composition. Laboratory tests were conducted to investigate the influence of CHA on the physical properties and the mechanical properties of clay. The CHA concentration was adjusted from 5% to 25% by the dry weight of clay in 5% increments. The clay classification of the mixture becomes coarser following the addition of the CHA. At 25% CHA, a peak UCS of 130.83 kN/m\({}^{2}\) was measured compared with the untreated clay of 89.17 kN/m\({}^{2}\). In addition, the cohesion values and internal friction angles of soil for 0% and 25% CHA increased from 80.1 kN/m\({}^{2}\) to 148.7 kN/m\({}^{2}\) and from 16.1\({}^{\circ}\) to 25.8\({}^{\circ}\), respectively. It was found that CHA can improve the strength of clay by forming a pozzolanic and hydration process that fills soil voids and binds particles together.  
clay; coffee husk ash; environment; stabilization; sustainability Article
## 3 Results and Discussion
### Effect of DAP Concentration  
The pollutant concentration is an important parameter in wastewater treatment. To evaluate the influence of this parameter on the efficiency of the oxidation process the pH was fixed at 3.2 keeping [H\({}_{2}\)O\({}_{2}\)]/[Fe\({}^{2+}\)] and [H\({}_{2}\)O\({}_{2}\)]/[DAP] mass ratios equal to 20 and 10, respectively. The effect of DAP concentration in the range 40-140 mg/L is presented in Figure 6. This figure indicates that the DAP degradation yield achieved more than 92% after 120 min for all DAP concentrations. The increase of DAP concentration from 40 to 140 had no significant effect on DAP degradation yield by Fenton oxidation. The lower the DAP initial concentration, the shorter the reaction time needed to completely degrade DAP. For example, the DAP degradation yield decreased from 99.7% to 75.9% with an increase in DAP concentration from 40 to 140 mg/L after 45 min. This decrease in degradation rate may be due to the competitive reactions between \({}^{\bullet}\)OH and the intermediates formed during DAP degradation by Fenton oxidation.  
Figure 5: Effect of H\({}_{2}\)O\({}_{2}\) concentration on the DAP degradation kinetics during the Fenton process (H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)). Experimental conditions: [DAP]\({}_{0}\) = 100 mg/L; [H\({}_{2}\)O\({}_{2}\)] = 0–3000 mg/L; [H\({}_{2}\)O\({}_{2}\)]/[Fe\({}^{2+}\)] = 20; pH = 3.2.
# Hybrid Energy Routing Approach for Energy Internet
## 7 Simulation and Results  
In this section, various study cases, implemented in MATLAB, were conducted to validate the effectiveness of the proposed energy routing approach. Additionally, the comparison to an existing routing algorithm is carried out to evaluate the performance of the proposed approach. The ER-based EI represented in Figure 1 is treated as a simulation case where the energy system parameters are:
## 3 Results and Discussions
### Analysis of Variance  
#### 3.2.1 Analysis of Variance Experimental Data for AA5083/Silicon Carbide Composite  
1. Ranking of Contributing Factors  
Analysing the factors that influence each response factor of the fabricated composite joints is essential. Utilizing the delta ranking approach, MINITAB software Version 18 displays the most significant factors influencing the response factor of the fabricated AA5083/Silicon carbide composite joint. Tables 8-10 show that the fabricated AA5083/Silicon carbide composite joint's ultimate tensile strength, percentage elongation, and microhardness are significantly impacted by rotational speed (rank 1), traverse speed (rank 2), and tilt angle (rank 3). Based on the results, it can be observed that parameters for maximizing ultimate tensile strength and percentage elongation in AA5083/Silicon carbide composite joints are traversal speed at level 1, rotational speed at level 2, and tilt angle at level 3, as seen in Tables 8 and 9. The best parameters for optimum microhardness are traversal speed at level 2, and rotational speed at level  
carefully considering research objectives and data characteristics is crucial when selecting between these methods to ensure accurate and reliable results. It was discovered that rotational speed and traverse speed, with percentage contributions of 60.36% and 16.62%, respectively, were significant parameters for percentage elongation (see Table 12). These results are consistent with the ranks that were shown in Table 9. Research by [PERSON] et al. (2013), [PERSON] et al. (2016), [PERSON] et al. (2015), and [PERSON] et al. (2014) also showed a similar pattern, identifying tool rotational speed as the main factor influencing ultimate tensile strength. Furthermore, it was shown that rotational speed and traverse speed were important factors in microhardness with percentage contributions of 48.17% and 27.40%, respectively (see Table 13). These results are consistent with the ranks that were shown in Table 10. Research by [PERSON] et al. (2016), [PERSON] et al. (2015), and [PERSON] et al. (2015) also showed a similar pattern, identifying tool rotational speed as the main factor influencing microhardness. For percentage elongation and microhardness, the percentage contributions and F-values match the ranks as well.
## 3 Recording of Atmospheric Conditions Using a Weather Monitoring Station to Predict RSSI Value
### Square Root Mean Square Error  
If we want to calculate the measurement accuracy, we need to determine the standard deviation. This is often used to determine the differences between values (real and predicted) in machine learning methods to predict values. We chose square root mean square error (_SqRMSE_) [23; 24].  
\[SqRMSE=\sqrt{\frac{1}{n}\left(RSSI_{r}-RSSI_{p}\right)^{2}}\left[\text{dBm}\right] \tag{5}\]  
Formula (5) represents the square root of the square averages of these differences. \(n\) in this formula represents the number of all samples, \(RSSI_{r}\) represents the real values of the \(RSSI\) and \(RSSI_{p}\) stands for predicted values. In our case, we decided to calculate this deviation for individual measurements and then averaged it according to the number of total samples. _SqRMSE_ is used to compare prediction errors for a particular data set and mainly depends on the scope of the data. The _SqRMSE_ value can never be negative. In general, the lower the _SqRMSE_ value, the better it is compared to the higher value [23].