esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 2 Finite Element Model of Eccentric Casing in an Elliptical Wellbore
### Variable Definition  
We define ellipticity, eccentricity, and stress non-uniformity separately. In numerical simulation, we will independently vary the ellipticity and spatially uneven stress while keeping other parameters constant to investigate their respective effects on the stress distribution of the casing.  
Ellipticity is a parameter that describes the degree to which the cross-sectional shape of a wellbore deviates from a circular shape. The definition of ellipticity \(s\) is as follows:  
\[s=\frac{a_{3}}{b} \tag{1}\]  
where \(a_{3}\) represents the long axis of the elliptical wellbore. The value of eccentricity \(s\) is defined as 1.0, 1.1, 1.2, 1.3, 1.4, or 1.5 [18].  
Stress non-uniformity is a parameter that describes the degree of uneven distribution of in situ stress around the wellbore. The definition of stress non-uniformity \(\lambda\) is as follows:  
\[\lambda=\frac{\sigma_{H}}{\sigma_{h}} \tag{2}\]  
where \(\sigma_{H}\) represents the maximum horizontal stress and \(\sigma_{h}\) represents the minimum horizontal stress. The values for stress heterogeneity are defined as 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, and 1.6.  
Eccentricity is a parameter that describes the degree of deviation between the wellbore axis and the major axis of the ellipse. To simplify the calculations, this study only considers the presence of eccentricity and does not take the influence of the eccentricity angle into account. Specifically, it assumes that the center of the casing and the center of the elliptical wellbore are located on the same line. Based on this assumption, we define the eccentricity as \(\varepsilon\) as follows:  
\[\varepsilon=\frac{e}{b-a_{1}} \tag{3}\]  
where \(e\) represents the eccentricity, which is the distance between the center of the casing and the center of the elliptical wellbore. \(b\) represents the major axis of the elliptical wellbore. \(a_{1}\) represents the outer diameter of the casing, as shown in Figure 3.  
In the subsequent numerical simulations, we will keep the elastic parameters of the casing and formation constant while varying the elastic modulus and Poisson ratio of the cement sheath. This is performed to investigate the impact of cement sheath elasticity on the distribution of casing stresses. The numerical values of the elastic parameters of the cement sheath are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & & & & & **Numerical Value** & & \\ \hline \(E_{2}/\mathrm{GPa}\) & 10 & 15 & 20 & 25 & 30 & 35 & 40 & - \\ \(\mu_{2}\) & 0.12 & 0.15 & 0.18 & 0.21 & 0.24 & 0.27 & 0.3 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Changes in the elastic parameters of cement sheath.  
Figure 3: Overview of eccentricity.
# Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning
###### Abstract  
In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.  
Keywords: traffic congestion; traffic light cycle configuration; machine learning; reinforcement learning; Q-learning +
Footnote †: journal: Applied Soft. 2019, 9, 4558; doi:10.3390/app9214558
## 3 Results
### Self-Reported Overall Fatigue and Musculoskeletal Pains by the Degree of Hazard Exposure
#### 3.3.4 Self-Reported Lower Limb Pain by the Degree of Hazard Exposure
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**ADHE**} \\ \cline{3-6}  & & **<2** & **2-4** & \(\geq\)**4** & \(\chi^{2}\) **Test** \\ \hline \multirow{4}{*}{Physical hazard} & Vibration & 10.2\% & 7.5\% & 14.0\% & \(\chi^{2}=2.811\), \(p=0.245\) \\ \cline{2-6}  & Noise & 7.5\% & 8.1\% & 18.8\% & \(\chi^{2}=12.197\), \(p=0.002\) * \\ \cline{2-6}  & High temperature & 8.2\% & 5.4\% & 23.6\% & \(\chi^{2}=20.706\), \(p<0.001\) * \\ \cline{2-6}  & Low temperature & 10.9\% & 5.7\% & 20.3\% & \(\chi^{2}=6.669\), \(p=0.036\) * \\ \hline \multirow{4}{*}{Ergonomic hazard} & Awkward posture & 7.2\% & 2.4\% & 19.3\% & \(\chi^{2}=21.148\), \(p<0.001\) * \\ \cline{2-6}  & Manual material handling & 6.5\% & 12.5\% & 20.0\% & \(\chi^{2}=12.692\), \(p=0.002\) * \\ \cline{2-6}  & Standing posture & 5.0\% & 7.9\% & 14.1\% & \(\chi^{2}=5.604\), \(p=0.061\) \\ \cline{2-6}  & Repetitive motion & 4.3\% & 2.0\% & 14.0\% & \(\chi^{2}=8.951\), \(p=0.011\) * \\ \hline \multirow{4}{*}{Chemical and biological} & Fumes and dust & 6.6\% & 8.5\% & 27.5\% & \(\chi^{2}=32.208\), \(p<0.001\) * \\ \cline{2-6}  & Vapor & 9.0\% & 19.6\% & 24.4\% & \(\chi^{2}=11.939\), \(p=0.003\) * \\ \cline{1-1} \cline{2-6}  & Skin contact with chemical & 9.4\% & 12.5\% & 29.3\% & \(\chi^{2}=14.101\), \(p=0.001\) * \\ \cline{1-1} \cline{2-6}  & Tobacco smoke & 10.2\% & 17.4\% & 26.3\% & \(\chi^{2}=6.179\), \(p=0.046\) * \\ \cline{1-1} \cline{2-6}  & Infection & 10.9\% & 17.4\% & 21.1\% & \(\chi^{2}=2.593\), \(p=0.274\) \\ \hline \hline \end{tabular}  
* Significant at 0.05. ADHE = adjusted daily hazard exposure time.  
\end{table} 
Table 9: Distribution of subjective lower limb pain by the degree of hazard exposure.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Evaluation of the System without Optimization
#### 3.4.5 The Goal Effectiveness from the Delay Perspective  
First, it is worth noting that the E2E delay depends on the computation delay, the PER and the interference (cf. (8) and (10)). In Section 3.4.3 (Figure 3), we empirically illustrated the independence between PER and computation delay. On the other hand, the communication delay is highly dependent on PER and received interference. Indeed, at fixed channel conditions and GO user transmit power \(P_{g}\), by increasing the DO user's transmit power \(P_{d}\), the data rate of the latter increases, thus generating more interference to the GO system, which causes higher delay in the case of fixed-target PER. To show the effect of the E2E delay on the effectiveness, we consider a delay threshold \(D_{\max}=50\) ms, and we plot, in Figure 5, the probability for a batch to be classified by the deadline as a function of the DO user data rate loss (i.e., the goal cost in (18)) for a subset of target PER \([10^{-7},10^{-4},10^{-3},10^{-2}]\) (for ease of readability). This is obtained by increasing the DO user transmit power \(P_{d}\) by \([0,200]\) mW. As we can notice, contrarily, as before, lower PER leads to lower effectiveness (as the effectiveness only entails delay, i.e., the GO user does not care about receiving high entropy results, provided that they are received within the predefined E2E delay). This result is due to the strong dependence of the communication delay (and thus the E2E delay) on PER and interference. Indeed, as an example, let us show, in Figure 6, the empirical probability density function of the GO system E2E delay (communication and computation) for the left-hand point of Figure 5, i.e., for a single value of \(P_{c}=200\) mW. As we can notice, the distribution of the delay experiences longer tails for lower PER, as expected from (8). From the last results, we can easily conclude that:  
* From a goal-value perspective, the goal effectiveness is only affected by the PER (although not strongly, depending on the PER value), and a higher PER (i.e., lower communication reliability) leads to lower (partial) goal effectiveness; also, from \(\gamma=10^{-7}\) to \(\gamma=10^{-3}\), stable performance is experienced.
* From an E2E-delay perspective, the goal effectiveness is affected by the PER and the DO user interference, and higher PER (i.e., lower communication reliability) leads to higher (partial) goal effectiveness.  
The goal of this work is to analyze and optimize performance by taking into account the overall definition of a goal (and goal effectiveness), entailing the goal value and the end-to-end delay but also the goal cost. Therefore, in what follows, we finally present the results in terms of goal effectiveness, as per its overall definition in (15), together with the corresponding goal cost.  
Figure 5: Probability of guaranteeing the delay constraint vs. goal cost (DO user relative data loss).
## 4 Risk Analysis for Current Protocols
### TCP/IPv6
#### 4.1.4 Replay/Man-in-the-Middle  
The protocol is also vulnerable to replay attacks [27], where an active inline device is used to complete the TCP three-way-handshake with the client and server devices, while intercepting (and potentially modifying) the data in-between.
## 3 Results and Discussion
### Spatial Distribution of Land Slope and Roughness, and Correlations with VARI  
The spatial analysis carried out in the experimental areas showed that the slope of land subjected to post-fire treatments was significantly different (32.2 \(\pm\) 5.26% for LEB and 36.2 \(\pm\) 2.61% for CFD) compared to the BNA (23.1 \(\pm\) 6.22%), but similar to the UB land (36.6 \(\pm\) 3.71%) (Table 3 and Figure 9a). This may be due to the fact that landscape managers prioritized steeper areas when post-fire treatments were planned, since the zones with higher slopes are more prone to erosion and hydrogeological risks compared to flat areas.  
According to the land map of Figure 9b prepared using spatial analysis, the terrain roughness of the burned areas was significantly different (0.08 \(\pm\) 0.03 \(\upmu\)m for LEB, 0.09 \(\pm\) 0.01 \(\upmu\)m for CFD, and 0.06 \(\pm\) 0.03 \(\upmu\)m for BNA) compared to UB zone (0.28 \(\pm\) 0.21 \(\upmu\)m) (Table 2). This means that the vegetation growing in the burned and treated zone do not have a significant effect on the terrain roughness compared to the area without treatment, and that these values are well below the roughness of the unburned soil. A high terrain roughness is beneficial to reduce surface runoff and thus, sediment transport downstream [65; 66; 67]. Soil preparation (e.g., by tillage, conditioning, and terracing) after wildfire may be suggested, in order to enhance vegetation growth and improve the soil's resistance to erosion [68; 69; 70; 71].  
A fair and significant linear correlation was found regressing VARI on land slope under all soil conditions (r\({}^{2}\) = 0.52, \(p\) < 0.05) (Figure 10a), while the linear regression between VARI and terrain roughness was much lower (r\({}^{2}\) = 0.15) and not significant (\(p\) = 0.49) (Figure 10b). This correlation becomes significant (\(p\) < 0.05) although, again, low (r\({}^{2}\) = 0.35 to 0.38), adopting exponential or logarithmic equations, whose physical meaning is, however, difficult to be justified (data not shown).  
Figure 9: Spatial distribution of land slope (**a**) and terrain roughness (**b**) surveyed by UAV images of 2016 among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Legend: Fajinas = log erosion barriers; cordones = contour-felled log debris; sin tratamiento = burned and no action; sin fuego = unburned.  
The first correlation suggests that the effectiveness of post-fire treatments on vegetation regeneration (measured using VARI) increased with slope, and thus, the strategy of landscape managers that have prioritized steeper slopes for treatments was successful. By contrast, the lower, or absence of, significance of correlations between VARI and terrain roughness implies that vegetation growth, at least in the experimental conditions, played a limited influence on soil surface conditions. However, vegetation regeneration remains a key factor to reduce soil exposure to erosion in wildfire-affected areas.
## 4 WSN Platforms and Energy Harvesting Techniques Investigations
### Energy Harvesting Techniques
#### 4.2.2 Solar Energy Harvesting Systems  
Solar energy is a traditional ambient energy source that has already been widely investigated for its abundance and renewability. Solar energy has the highest power density among all the ambient energy sources. Twelve Photovoltaic (PV) cells are the key components of solar energy harvesters that generate electricity from the ambient sunlight [24]. Table 12 presents a summary of the solar energy harvesting techniques. The second column identifies the output voltage. The size of the photovoltaic modules (PV) is shown in the third column. The fourth column presents the performance of the conversion process in terms of the maximum output power and the conversion efficiency. The fifth and sixth columns express the rectification and integration processes. As can be seen in Table 12, the output power and voltage are directly proportional to the size of the PV module used in the systems. For example, [PERSON] et al. [69] proposed a solar energy harvesting system with maximum output power and output voltage of 28.4.4 W and 29 V, respectively, which are generated from a PV module with a size of 140 cm\({}^{2}\), whereas the system proposed by [PERSON] et al. [78] is capable of generating only 3 W and 3.7 V with a 78 cm\({}^{2}\) PV module.
## 4 Implementation of Off-Chip Components  
### Primary Coil, Secondary Coil and Detection Coil  
Power delivery from the external control unit to the internal implant unit depends on the coupling of the primary and the secondary coils. Good link efficiency requires high quality factors for both coils. As shown in Figure 9a,b, all the three coils are two-layer designs printed on PCBs to have larger inductance within the limited area. The primary coil and the detection coil have outer diameters of 2 cm and 3 cm, respectively, and both are designed on the same PCB. The diameter of the PCB of the implant unit is 5 mm with a thickness of 0.3 mm. The outer diameter of the secondary coil is only 4.4 mm, and based on HFSS simulation, the inductance of the secondary coil is 170 nH and resonates with a 90 pF on-chip tuning capacitor at 40.68 MHz.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 8 Statistical Models
### Hidden Markov Model  
The authors [PERSON] and [PERSON] [50] use a three-phased approach to improve the Quality of Service (QoS) in optical WDM networks. The proposed Load Balancing Technique to improve QoS for Lightpath Establishment (LBIQLE) evaluates a network based on throughput, blocking probability, and delay metrics. A Hidden Markov Model (HMM) was first used to predict the next future traffic load. An HMM has two layers, a hidden layer and an observable layer. To determine the most likely future state of the system, a sequence of probabilities in the hidden layer is calculated and output to the observable layer. This series of probabilities is known as the Markov chain, which assumes that the next state is only dependent on the current state of the system and not any prior past states [51]. Phase 2 of LBIQLE utilizes wavelength order to reduce crosstalk. Finally, in phase 3, a proprietary algorithm is used to modify the virtual topology to best meet the predicted traffic demand in phase 1 from the HMM. Simulated results using LBIQLE are compared with a GRASP approach and found to have higher network utilization rates. LBIQLE also reduces the blocking probability and delay while increasing the traffic throughput.  
Moreover, the authors of [52] propose Monte Carlo Tree Search as a solution to the Markov Model to enable traffic prediction in inter-data center optimization problems that use Cross-Stratum Optimization architecture for provider-centric use cases.
## 3 Results
### Sensitivity Analysis
#### 3.2.2 Material Properties
* Material properties that also influence the mean power (i.e., the total heat transferred): all remaining material properties (\(\rho\), \(L\), \(c_{l}\), \(c_{s}\), \(T_{m}\)) also affect the mean power when varied, but \(c_{l}\), \(c_{s}\) and \(T_{m}\) only slightly. The power over time curve during charging is shown in Figure 9 for the reference case and an increased melting enthalpy. Throughout the whole simulation, the deviation is positive--the mean power is increased. In contrast, Figure 10 shows that an increase in thermal conductivity (also for charging) leads to a different type of deviation. First, the power is increased for the variation compared to the reference, but later it decreases--the mean power is only affected slightly. The same phenomenon can be seen for discharging and the contrary effect can be seen for lowering the melting enthalpy and heat conductivity (Figures A1 and A2 in the Appendix A). This behavior is, in general, identical for all material properties that either affect only the maximum deviation of the power and the global liquid fraction or also the mean power. Figure 8: Absolute maxima of the relative variation of the global liquid fraction for charging and discharging when varying the material properties by \(\pm\)10% or \(\pm\)1 K.
## 4 Performance Evaluation
### Simulation Results
Because a node's energy consumption in the routing protocol is mostly dependent on the how many paths are established and how many packets are transmitted on a node, longer network lifetime than MK-AG is observed in the proposed scheme which is based on multiple paths which lead to distribute the packets along the multiple paths according to DBP values. In addition, adaptive path selection algorithm leads to prevent significant battery drain on nodes along the path. In the aspects of (\(m\),\(k\))-firm requirement, strict requirement is usually met by one or at most two paths. This limited number of path accelerates the battery drain on nodes. Therefore, short network lifetime is naturally observed in more strict requirement. In the other hand, network lifetime can be affected by the number of control message. Because our approach is based on path search through flooding, larger number of control messages lead to reduce network lifetime. In addition, reply message with DBP value is proportional to number of sent message. Despite of this shortcoming, longer network lifetime is achieved by the adaptive routing and traffic shaping. This implies that network lifetime is more dependent on the data packet than control one. Finally, we conduct the simulation for SDSR for varying deadline as illustrated in Figures 11 and 12. In both figures, lower SDSR is observed shorter deadline is assumed in both schemes. However, higher SDSR is measured in the proposed scheme rather than MK-AG. Due to adaptive path selection and traffic adjustment algorithm lead to find the more suitable path than MK-AG. Even though larger gap between two schemes are measured in short deadline, almost identical SDSR is observed in the long deadline. This implies that two schemes are able to meet real-time delivery in appropriate way. In the aspects of (\(m\),\(k\))-firm requirement, less strict (\(m\),\(k\))-firm requirement induces higher SDSR than strict one. If the requirement is not strict, more available paths are likely to be established. This is mainly because packet loss and queuing delay caused by congestion are reduced. Figure 9: Lifetime in (2,3)-firm application as a function of (\(m\),\(k\))-firm sets. As a result, we can summary the performance of the proposed scheme and MK-AG protocol in Table 2. We present the relative value according to (_m,k_)-firm requirement and given the metrics. As described in the Table 2, our proposed scheme is observed to provide more comparative performance than the existing scheme. Figure 11: SDSR in (2,3)-firm application as a function of deadline. Figure 3:0 Lifetime in (2,5)-firm application as a function of (_m,k_)-firm sets.
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
## 1 Introduction
To our knowledge, only one work reported the application of MIP for the phenolic acid analysis in urine, but with a different purpose, the assessment exposure of toluene and xylenes [22]. In our study, 4-hydroxyphenylacetic acid (4-HPA), excreted catabolite of polyphenols compounds, was chosen as a template of molecularly imprinted polymers. Synthesized MIP was used as sorbent in a SPE procedure (MISPE) for extraction and isolation of 4-HPA and analogues. Once the MISPE parameters were optimized, the procedure was validated using spiked human urine samples.
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 3 The Smart Collaborative Routing Protocol for IoT
### Preliminary Foundation
#### 3.1.1 GEAR Protocol  
The GEAR protocol combines the ideas of the directed diffusion routing and GPSR routing. Moreover, it takes the node energy factor into consideration for solving the uneven energy consumption problem. Each node in GEAR protocol can perceive its own location information and residual energy information, which can be exchanged between all neighbor nodes through a simple Hello message. Finally, an optimal path from the sink node to the event area is established based on these information, which avoids flooding propagation and reduces overhead of establishing routes.  
Generally, the GEAR protocol utilizes the idea of greedy algorithm in GPSR. The path from the sink node to the event area is built based on the location and energy information of nodes. The spread of packets in GEAR protocol includes three stages: First, the sink node issues a query command. According to the geographical location of the event area, the query command is transmitted to the node which is the closest one to the sink node inside the event area. Then, an iterative forwarding mechanism is triggered to propagate the query command from the node to all other nodes in the area. Finally, the monitored data is transmitted to the sink node along the reverse path, which are shown as (a), (b) and (c) in Figure 1.  
However, since the GEAR protocol relies on the location information of nodes, the attackers can launch the Sybil attack by forging the location information. Moreover, the GEAR protocol utilizes the remaining energy of nodes as a factor to select the path. The attacker can deceive the surrounding nodes to elect it as the next hop by broadcasting forged energy information. By obtaining the forwarded packets of the surrounding nodes, the attacker can implement the selective forwarding attacks. Inspired by the above issues, we introduce the IN mechanism to the GEAR protocol.