esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Distributed Energy-Efficient Clustering (DEEC)
#### 1.3.1. DEEC Principles and Operation  
Traditional clustering protocols, such as LEACH (Low-Energy Adaptive Clustering Hierarchy), assume homogeneous energy distribution among nodes and select CHs randomly. However, sensor nodes often exhibit energy heterogeneity in real-world deployments due to different initial energy levels or energy harvesting capabilities. Random CH selection in such scenarios can lead to premature node failures and suboptimal network performance. DEEC is proposed to address these limitations by considering both nodes' initial and residual energy during CH selection [27; 28; 29; 30; 31; 32]. This approach ensures that nodes with higher energy are more likely to become CHs, balancing energy consumption across the network and extending its operational lifetime.  
* _Network Model:_ The Distributed Energy-Efficient Clustering protocol operates in a WSN comprising \(n\) sensor nodes, which may have different initial energy levels. The network is assumed to be deployed randomly over a target area, with a stationary BS located either inside or outside the sensing field. Nodes periodically sense the environment and transmit data to the BS via CHs.
* _Cluster Head Selection:_ The main aim of DEEC lies in its CH selection mechanism. Unlike protocols that assign equal probability to all nodes, DEEC assigns a higher probability to nodes with greater residual energy. The probability \(P_{i}\) for node \(i\) to become a CH in round \(r\) is given by Equation (1): \[P_{i}=\frac{P_{opt}\cdot E_{i}(r)}{E^{\prime}(r)}\] (1) where \(P_{opt}\) is the optimal probability of a node becoming a cluster head, \(E_{i}(r)\) is the residual energy of node \(i\) at round \(r\), and \(E^{\prime}\) is the average residual energy of all nodes at round \(r\). This adaptive probability ensures that energy nodes are preferentially selected as cluster heads, distributing the energy load more evenly.
* _Data Transmission and Cluster Formation:_ Once CHs are selected, they send advertisement messages to nearby nodes in the network. Nodes that are not selected as CHs will join a CH with the strongest received signal (typically the closest CH) and join its cluster. During the data transmission phase, member nodes send their sensed data to the CH, which aggregates data to eliminate redundancy before forwarding the aggregated data to the BS.  
* _Energy Model:_ DEEC employs a first-order radio energy model where the energy consumed for transmitting a \(k\)-bit message over a distance \(d\), as shown in Equation (2): \[E_{TX}(k,d)=E_{else}\cdot k+\in_{amp}\cdot k\cdot d^{2}\] (2) and the energy for receiving is shown in Equation (3): \[E_{RX}(k)=E_{else}\cdot k\] (3) where \(E_{else}\) is the energy dissipated per bit to run the transmitter or receiver circuitry and _eamp_ is the energy for the transmit amplifier.
# Phase Separation Study on the Storage of Technically Specification Natural Rubber Modified Bitumen
## 1 Introduction
Epoxy natural-rubber-modified bitumen showed improved asphalt binder performance at low temperatures. The storage stability of epoxy natural-rubber-modified bitumen can only be achieved if the epoxy natural rubber content is less than 6% [26; 27]. Even with small amounts of natural rubber latex added, the storage stability of natural rubber latex-modified bitumen is poor [28]. The use of incompatible raw materials or manufacturing processes can contribute to the storage instability of polymer-modified bitumen. Many previous studies have investigated the effects of asphalt and polymer or natural rubber, and their compatibility, on the morphology and storage of polymer-modified asphalt or natural-rubber-modified asphalt [16; 27; 29; 30]. However, these studies found it difficult to pinpoint the key parameters controlling the phase separation process due to the complexity of the process [16]. Because all possible mass transfer and effects coexist and are interconnected, it would be difficult to investigate a complex process using only experimental methods to distinguish the influence of one factor over another. Although numerical approaches are frequently used to investigate simplified systems, they allow each factor to be studied independently. As a result, in this study, a two-dimensional model for predicting the storage stability of natural-rubber-modified bitumen was developed, with the goal of creating a numerical model that can describe and predict the phase separation behavior of natural-rubber-modified bitumen. The model was developed using COMSOL Multiphysics version 5.5.0 with a combined Navier-Stokes and phase field model, to simulate the phase separation that occurs in natural-rubber-modified asphalt, which is influenced by the natural-rubber-to-asphalt ratio, modified asphalt storage temperature, and type of additives added.
## 2 Related Works
### SWIPT in WSN  
SWIPT has been applied to WSNs in various ways to overcome their energy limitations. Numerous surveys of SWIPT in WSNs have been published recently [29; 30; 31]. For instance, [29] summarized the current research on SWIPT-based cooperative sensor networks, in which SWIPT is applied to WSNs in terms of dual-hop and multi-hop relays. Meanwhile, [30] focused on the integral aspects of SWIPT in other prominent networks, such as device-to-device networks, vehicular ad hoc networks, wireless body area networks, WSNs, and so on, and presented open issues and challenges in SWIPT application. In [31], an overview of SWIPT/WPT-enabled WSNs was provided and future directions were suggested.  
Meanwhile, [32] described a WSN consisting of multiple clusters and a sink node, in which the CH of each cluster performs SWIPT to give energy to relay nodes with low energies. In [33], a SWIPT-powered sensor network was considered in which each source node operates SWIPT as both an information transmitter and an energy transferrer, and the destination node works as an information receiver while the other nodes work as energy harvesters. Furthermore, [34] focused on the deployment of a WSN and its routing strategy when SWIPT is applied to the WSN. The basic idea is to reduce the total recharging cost to enhance the lifespan of the WSN. In addition, [35] proposed a dynamic routing algorithm for a renewable WSN with SWIPT. SWIPT has been adopted in many studies to overcome the energy limitations of WSNs, but SWIPT has not yet been applied in the WPCNenvironment. Furthermore, these previous studies were focused on increasing the lifetimes of WSNs rather than guaranteeing ENO.
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 2 System Model and Problem Formulation
### Problem Formulation  
We assume that the relay's buffer operates under first-in-first-out (FIFO) mode. Thus, the data transmission from relay cannot exceeds the data received and the overall throughput of the system is determined by the rate at the second hop.  
Our aim is to maximize average throughput i.e., \(\lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=1}^{T}\sum_{i=1}^{N}(1-\alpha_{t})R_ {i,t}^{2}\) for an OFDM-based BAR network under the constraints that total output at the buffer should not exceeds the total input data and only one link is selected at a given time. The power allocation under a sum power constraint over source and relay may provide analysis and makes the problem simple [28]. However, the solution proposed under global power constraints is not practical, as the relay and source cannot share common energy storage. Thus, we consider separate power limitations at the source and relay nodes to make our solutions more practical. The problem can be stated, mathematically, as;  
\[\max_{P_{i,t}^{BS},P_{i,t}^{BS},\alpha_{t}} \lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=1}^{T}\sum_{i=1}^{N} (1-\alpha_{t})R_{i,t}^{2}\] (4) s.t. \[\underbrace{\lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=1}^{T} \sum_{i=1}^{N}\alpha_{t}R_{i,t}^{1}}_{\mathbf{A}}+\bar{A}\geq\] \[\underbrace{\lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=1}^{T} \sum_{i=1}^{N}(1-\alpha_{t})R_{i,t}^{2}}_{\mathbf{D}} \tag{5}\] \[\sum_{i=1}^{N}P_{i,t}^{BS}\leq P_{T}^{BS},\quad\forall t,\] (6) \[\sum_{i=1}^{N}P_{i,t}^{R_{b}}\leq P_{T}^{R_{b}},\quad\forall t,\] (7) \[P_{i,t}^{BS}\geq 0,\quad P_{i,t}^{R_{b}}\geq 0,\quad\forall i,t,\] (8) \[\alpha_{t}(1-\alpha_{t})=0, \tag{9}\]  
where \(\bar{A}\), \(P_{T}^{BS}\) and \(P_{T}^{R_{b}}\) are the amount of data in the buffer before the start of current transmission, total available powers at \(BS\) and at \(R_{b}\), respectively. Please note that inclusion of \(\bar{A}\) in our model makes the proposed solution more realistic i.e., instead of considering the buffer empty at the start of transmission (as considered in most of literature), this solution is valid starting at any instant of time. According to the law of conservation of flow, state of the buffer is shown by the constraint in (5). The conditions in (6) and (7) represent that sum power at each of the source and relay node can not exceed the total available power at that node. Moreover, (8) and (9) ensure that power at each transmitting node cannot be zero and only one link will be active in a given time slot, respectively.
## 4 Reliable V2C Communication Using Multiple Paths with Redundancy Mitigation
To enable the cloud server to track the packet loss status in V2C communication, the packet delivery schedule information includes the start time, end time, and frequency  
Figure 3: Algorithm implemented at the cloud server. of packet delivery (e.g., 100 ms) from the vehicle to the cloud server. Upon obtaining the adjustment information from the cloud server, the vehicle updates the multipath setup to maintain reliability and reduce redundancy. In cases where the cloud server instructs the vehicle to increase the number of paths to improve reliability, the vehicle increases the number of paths if the current number of paths is less than the maximum number of active paths. In cases where the cloud server instructs the vehicle to keep the current number of paths, the vehicle does not make any changes to the number of paths used. In cases where the cloud server instructs the vehicle to decrease the current number of paths, the vehicle proceeds to decrease the number of paths if the current number of paths is greater than one. We further examine the basic properties of the proposed method in terms of reliability. Let the maximum number of paths for a multipath connection be \(N\). Let \(P_{L}[i]\) denote the packet loss probability. Assume that the reliability of V2C communication is measured by the successful delivery rate of packets. The reliability of using \(n\) paths, \(1\leq n\leq N\) to send the same packet can be expressed as  
\[Relability[n]=1-\prod_{i=1}^{n}P_{L}[i]. \tag{1}\]  
The improvement in reliability compared to using a single path can be expressed as  
\[Reliability Improvement = Reliability[n]-Reliability[1]\] \[= P_{L}[1]-\prod_{i=1}^{n}P_{L}[i]. \tag{2}\]  
Figure 4: Algorithm implemented at the vehicle side. The effectiveness of improving reliability by adding a path can be expressed as  
\[ \begin{split} ReliabilityAdd&=\mathit{Reliability}[n+1]- \mathit{Reliability}[n]\\ &=(1-\prod_{i=1}^{n+1}P_{L}[i])-(1-\prod_{i=1}^{n}P_{L}[i])\\ &=(1-P_{L}[n+1])*\prod_{i=1}^{n}P_{L}[i].\end{split}  \tag{3}\]  
The effectiveness of changing reliability by removing a path can be expressed as  
\[ \begin{split} ReliabilityDecrase&=\mathit{Reliability}[n]- \mathit{Reliability}[n-1]\\ &=(1-\prod_{i=1}^{n}P_{L}[i])-(1-\prod_{i=1}^{n-1}P_{L}[i])\\ &=(1-P_{L}[n])*\prod_{i=1}^{n}P_{L}[i].\end{split}  \tag{4}\]
## 3 Results
### Economic Analysis  
All amounts will be rounded to present worth for proper comparison. The capital expenditure (CAPEX) for the baseline buses over 15 years lifespan is summarized in the following investment timeline in Figure 5:  
Figure 5: Investment timeline for baseline buses over 15 years.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Electric Consumption in Service Buildings & 10,908,570 kWh/Year \\ Electric Consumption due to Streets Lighting & 800,230 kWh/Year \\ Annual CO\({}_{2}\) Emissions due to Electric Consumption & 8,231,286 kg CO\({}_{2}\)/Year \\ \hline \end{tabular}
\end{table}
Table 8: Environmental impacts of baseline electrical consumption.  
\begin{table}
\begin{tabular}{c c} \hline
**Summary for Emissions-Baseline** \\ \hline Total Annual Emissions due to Diesel Buses & 18,972,398 kg CO\({}_{2}\) \\ Total Annual Emissions due to Electricity & 8,231,286 kg CO\({}_{2}\) \\ Total Annual Emissions & 27,203,684 kg CO\({}_{2}\) \\ Total Emissions during Project’s Lifetime (15 Years) & 408,060 ton CO\({}_{2}\) \\ \hline \end{tabular}
\end{table}
Table 9: Overall environmental impact of the baseline.  
CAPEX for the renewable energy model is higher than the baseline case. This is due to the more expensive electric buses and the installation of photovoltaic systems. The salvage values of all components within the alternative system are taken into consideration.  
The following investment timeline depicted in Figure 6 shows the capital expenditure (CAPEX) of the PV supplied electric bus upgrade over 15 years:  
PV system inverters have an expected lifetime of 8-10 years. Therefore, for this study, inverters are replaced after 9 years of operation. In general, inverters share 20% of the PV system's capital cost.  
The operating expenses (OPEX) for the baseline case include fuel, maintenance, lubricants, fuel station maintenance, electricity for streets lighting, and electricity for service buildings and stations, in addition to the expected increment in diesel and grid electricity prices. These factors are summarized in Figure 7.  
On the other hand, OPEX for the PV electric bus model is much lower due to use of electric buses and electricity generated from the PV system. Savings from social cost and carbon trade of emissions are included. Servicing of a PV system is included as 3% of the capital cost per year for general maintenance, and 400 USD/MWp per month for panel cleaning. Figure 8 shows the OPEX of the PV electric bus model.  
Figure 6: Investment timeline for PV electric buses over 15 years.  
Figure 7: Operational costs for baseline buses.  
The nominal interest rate of 7% is used in the calculations; 3.1% is the average inflation rate in Jordan. Assigning these rates in the net present value formulas and applying the same on the CAPEX and OPEX of both baseline and PV electric bus model results in the following NPV values in Table 10.
# Numerical Investigations of Pile Group Foundations under Different Pile Length Conditions
## 1 Introduction
[PERSON] [18] conducted three-dimensional finite element simulations to examine the effects of sloping ground on the vertical pile-to-pile interaction for end-bearing piles embedded in homogeneous soil. [PERSON] [19] utilized numerical simulations to study the influence of pile groups on soil arches. [PERSON] [20] analyzed bucket foundations and group piles using the ABAQUS 2020 software. [PERSON] [21] proposed an MSS constitutive model for structured soils and analyzed the advantages of this model. [PERSON] [22] used a modified Cam-Clay failure criterion to analyze the distribution of failure load and displacements in different soil depths. This article presents the depiction of the slope's basic appearance based on actual topographic maps. A model is established and imported into the FLAC3D numerical simulation software for calculation. In this particular group pile foundation project, the foundation piles are relatively long and penetrate deep into the rock. Additionally, the bottom rock layer has a strong embedding capacity. To meet the basic stability requirements of the group pile foundation while conducting numerical simulations, the author reduces the length of the foundation piles based on the load conditions. This approach provides an effective piling scenario and enhances the economic efficiency of the project.
## 3 Geometric Error Measurement Algorithm  
According to the reference ISO 230-7, there are 6-DOF geometric errors in the rotary axis, which are usually caused by defects in component manufacturing [13]. Therefore, using the rotary A-axis as an example, the 6-DOF geometric errors of the A-axis consist of the axial motion error \(\delta_{xu}\), the radial motion errors \(\delta_{yu}\) and \(\delta_{za}\) in the y and z directions, respectively, and the angular deviations \(\varepsilon_{xa}\), \(\varepsilon_{ya}\) and \(\varepsilon_{za}\), around the X-, Y- and Z-axes, respectively. For the purposes of identifying the 6-DOF geometric errors of the rotary table, a measurement algorithm is presented in this subsection through analyzing the deviations of the spatial coordinate positions of the reflectors. It must be emphasized that executing the proposed measurement methodology is not needed to drive the other three linear axes. In other words, the 6-DOF geometric errors of the rotary table can be determined without three-axis machine tools by using the proposed measurement method.  
In the ideal case, the central lines of the reflector standard fixture must be located along the average lines of the rotary table during measurement. However, it is difficult to make the reflector standard fixture position and the rotary table position agree within an acceptable tolerance due to installation issues. According to our past experiences, the set-up errors, including two linear offset errors \(O_{yu}\) and \(O_{za}\) in the y and z directions, respectively, and two the angular errors \(S_{yu}\) and \(S_{za}\) around the Y- and Z-axes, respectively, affected the accuracy of the measured value. As a result, the set-up errors are identified in Section 3.1.
###### Abstract  
In North America and Europe, asphalt shing waste created during the installation of roofing membranes and tear-off shingles retrieved at the end of the membrane's life cycle are two major sources of municipal solid waste. Since almost 15-35% of recycled asphalt shingles (RAS) consist of an asphalt binder, the effective recycling of RAS into asphalt mixtures could also allow a reduction in the consumption of non-renewable resources such as asphalt binders. In this context, several studies investigating the use of RAS in asphalt mixtures can be found in the literature, although they exhibit widespread and sometimes conflicting information about the investigated materials, the mix preparation and testing methodologies and the experimental findings. Given this background, this review paper aims at summarizing the existing information and research gaps, providing a synthetic and rational picture of the current literature, where similar attempts cannot be found. In particular, different research studies show that the use of RAS in asphalt mixtures is an economical as well as an eco-friendly option. RAS with up to 20% by weight of binder or 5% by weight of aggregate/mixtures (eventually in combination with 15% reclaimed asphalt pavement aggregate) were found to be relatively suitable to improve the performance properties of asphalt mixtures, both in the laboratory and in the field. Adding RAS to asphalt mixtures could enhance their stiffness, strength and rutting resistance (i.e., high-temperature properties), while negatively affecting the mixtures' fatigue and thermal cracking resistance. However, the addition of specific biomaterials (e.g., bio-binders, bio- oils) or additives to asphalt mixtures can mitigate such issues, resulting in lower brittleness and shear susceptibilities and thus improving the anti-cracking performance. On the other hand, the literature review revealed that several aspects still need to be studied in detail. As an example, RAS-modified porous asphalt mixtures (fatigue, rutting, moisture susceptibility and thermal cracking) need specific research, and there are no comprehensive research studies on the effects of the RAS mixing time, size and mixing temperature in asphalt mixtures. Moreover, the addition of waste cooking/engine oils (biomaterials) as asphalt binder rejuvenators in combination with RAS represents an attractive aspect to be studied in detail.  
recycled asphalt shingles; circular economy; smart pavements; asphalt mixtures +
Footnote †: journal: Applied sciences
# Experimental Study on Engineering Properties of Cemented Soil with High Water Content
## 3 Test Results
### UCS Test Results  
The UCS tests were carried out after the cemented soil specimens were cured to the planned time. The test results of the specimens with different cement content and different curing times are shown in Table 3. The influence of curing time on the UCS of cemented soil is shown in Figure 7. As shown in Figure 7a, the strength of the cemented soil with different cement content all increased with curing time. Nevertheless, the growth rate gradually slowed down as the curing time increased. For the cemented soil specimen with 12% cement content, the UCS increased from 900 kPa to 1330 kPa when the curing time increased from 3 to 7 days, and the strength increased by 32.3%. The UCS increased from 1330 kPa to 1694 kPa when the curing time increased from 7 to 28 days, and the strength increased by 27.3%. As shown in Figure 7b, the UCS of cemented soil with a different cemented content had an approximate linear correlation with curing time under the semi-logarithmic coordinate.  
Figure 6: Variation in the brittleness index with confining pressure ( curing time: 28 days).  
The correlation between the UCS of the cemented soil and cement content is presented in Figure 8. This figure indicates that there was a linear correlation between the UCS and cement content. Figure 8 also shows that when the curing time was 7 days, the UCS of the cemented soil with 12% cement content increased by 98.8% compared with that of the cemented soil with 6% cement content, and the UCS of the cemented soil with 18% cement content was 26.5% higher than that of cemented soil with 12% cement content.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Curing Time/d** & **3** & **7** & **14** & **28** \\ \hline Cement content/\% & \(q_{u}\)/kPa & \(q_{u}\)/kPa & \(q_{u}\)/kPa & \(q_{u}\)/kPa \\ \hline
6 & 447 & 669 & 818 & 889 \\ \hline
12 & 900 & 1330 & 1519 & 1694 \\ \hline
18 & 1190 & 1683 & 2013 & 2353 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The UCS test results.  
Figure 7: Correlation between UCS and curing time.  
The correlation between the deformation modulus and UCS of cemented soil is shown in Figure 9. The deformation modulus \(E_{50}\) is the ratio of the 50% peak strength of cemented soil to the corresponding strain. Figure 9 shows that when the cemented soil strength was in range of 447-2353 kPa, the corresponding deformation modulus was in the range 47.2-225.3 MPa. The ratio of the deformation modulus \(E_{50}\) and cemented soil strength \(q_{u}\) was in the range 71-119. The local specification suggests that the ratio of the deformation modulus \(E_{50}\) and the cemented soil strength \(E_{50}\) was in the range 100-120. It could be considered that the test result in this study was close to the proposed values of the local specification [22]. Moreover, the recommended value of \(E_{50}\)/\(q_{u}\) was 95 according to the experimental results.