esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 3 Simulation Tool and Settings
### Main Settings
#### 3.2.1 Minimum SINR Thresholds  
At the PHY layer, a threshold model on SINR is adopted, as done in several similar works (e.g., [31; 32; 33]). Indeed, especially for LTE-V2X, reference curves relating the SINR to the packet error rate (PER) are not available and the few presented (e.g., in [5; 34]): (1) do not cover all possible combinations of MCS and packet size; and (2) all have an almost threshold behaviour.  
In order to set the thresholds, in IEEE 802.11p, we used the transmission power, noise power density, and minimum sensitivity level indicated in [35]. In IEEE 802.11p, the threshold only depends on the MCS, whereas the packet length has no impact. Regarding LTE-V2X, we adopt the procedure detailed in ([36], Appendix A), which starts from the Shannon capacity and considers a parametric loss as suggested by 3 GPP in [37]; this approach lets us set the threshold for any possible combination of MCS and packet size.  
The values obtained with the detailed approaches are listed in Tables 2 and 3. Both for IEEE 802.11p and LTE-V2X, our assumptions might be restrictive and real devices could perform better. However, the performance of the devices is strongly affected by the specific hardware. In any case, it can be noted that, if we focus as a specific example to MCS 3 of IEEE 802.11p (QPSK, coding rate 1/2) and MCS 6 of LTE-V2X (QPSK, coding rate 0.48), we have approximately 7 dB difference, which is somehow in the middle between what is indicated in [5; 6]. In addition, in order to overcome this aspect and verify the performance of the two systems if they performed the same at the PHY layer, we also simulate what we call _IEEE 802.11p with LTE PHY layer_, as detailed in the following subsection.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Vehicular Wi-Fi** & **Sidelink Cellular-V2X** & **Scenarios** & **Main results** \\ \hline [PERSON] et al. [7] & IEEE 802.11p QPSK-1/2 & LTE-V2X, Mode 4, \(p_{\text{rx}}=0\), & Highway, 120 v/km & LTE-V2X mostly provides better performance, but IEEE 802.11p with 16 QAM-3/4 might be preferable with higher channel load \\ \hline [PERSON] et al. [8] & IEEE 802.11p QPSK-1/2 & Mode 3 \& LTE-V2X, Mode 4 & Manhattan, dense, \
## 4 Optimization
### Optimization of the Expander  
Since the aim of the optimization in the present work is to maximize the total-to-total efficiency for the given design specification, the isentropic total-to-total efficiency of the expander at the design point was considered the objective function, as Equation (5) shows:  
\[\mathbf{\eta_{TT}}=\frac{\mathbf{h_{T0}}-\mathbf{h_{T6}}}{\mathbf{h_{T0}}-\mathbf{h_{T6}^{seen}}} \tag{5}\]  
where \(\mathbf{h_{T0}}\) is the expander inlet total enthalpy, \(\mathbf{h_{T6}}\) is the expander outlet total enthalpy, and \(\mathbf{h_{T6}^{seen}}\) is the expander outlet total enthalpy in isentropic conditions of the expander inlet. The hub and shroud meridional contours in the z-r plane and the blade \(\beta\) angle distributions at the hub and shroud were chosen as the design parameters of the 3D radial expander rotor. The hub and shroud meridional contours and the control points of each Bezier curve are shown in Figure 9. The locations of the inlet and outlet points were fixed. To maximize the shape profiles with the minimum number of test cases, and \(Z_{H2}\), \(R_{H3}\), \(Z_{52}\), and \(R_{S3}\) were selected as the design parameters for the optimization of the meridional contours. Figure 10 shows the blade \(\beta\) angle distributions at the hub and shroud and the control points of each Bezier curve. \(\beta_{H3}\), \(\beta_{H4}\), \(\beta_{S3}\), and \(\beta_{S4}\) were selected as the design parameters for the optimization of the blade angle distributions. For the 4 design variables, 25 test cases were designed using the Box-Behnken design method using Minitab software. The design of 25 test cases alternated between the shape of the impeller meridional contours and the blade \(\beta\) angle. In this study, the optimization process for the blade \(\beta\) angle distributions was first conducted with the initially given geometry for the hub and shroud meridional contours. An optimal design for the blade \(\beta\) angle distributions was achieved using the results of the 25 test cases. The next optimization process was performed for the hub and shroud meridional contours with the obtained geometry of the blade \(\beta\) angle distributions from the first optimization process. The 25 CFD test cases presented an optimized geometry for the impeller meridional contours. As such, the optimization process alternated between optimization of the blade \(\beta\) angle distributions and that of the impeller hub and shroud meridional contours. In this study, eight optimization cycles were executed. The metal models of optimizations are summarized in Table 5, and the enhancement of the isentropic total-to-total efficiency of the expander is presented in Figure 11. Figures 12 and 13 show comparisons of the blade \(\beta\) angle distributions and meridional contours obtained by optimization. Figure 9: Hub and shroud meridional contours controlled by Bezier curves.
## 5 Simulation Results and Discussion
### Energy of the Network  
The total network energy is the sum of the energies of all the nodes in each round. Figure 5 shows the total network energy on a 500 m \(\times\) 500 m terrain, while Figure 6 shows the total network energy on a 1000 m \(\times\) 1000 m terrain. Using the CH-active algorithm, due to the fact that the sensors are active for a longer period, the total energy of the network does not decrease so quickly as with LEACH. The total network energy for CH-active and CH-partially-active algorithms does not differ greatly.  
Figure 4: Number of active nodes per round (1000 m \(\times\) 1000 m terrain).  
Figure 3: Number of active nodes per round (500 m \(\times\) 500 m terrain).  
Figure 7 depicts the average energy per node on a 500 m \(\times\) 500 m terrain, while Figure 8 depicts the average energy per node on a 1000 m \(\times\) 1000 m terrain. The best energy balance in the network is achieved, when the CH-active algorithm is used. Figure 7 shows that with the CH-active algorithm, the nodes have residual energy for more than 400 rounds, after the network is exhausted under LEACH algorithm (500 m \(\times\) 500 m terrain) and for more than 200 rounds (1000 m \(\times\) 1000 m). The WSN with the CH-active algorithm is active twice as long as the WSN with LEACH algorithm in the 500 m \(\times\) 500 m terrain, and it is active for 60% longer in the 1000 m \(\times\) 1000 m terrain. When the terrain is bigger as in the second case, the sensors consume more energy to communicate with the BS.  
Figure 5: Total network energy (500 m \(\times\) 500 m terrain).  
Figure 6: Total network energy (1000 m \(\times\) 1000 m terrain).
## 6 System Evaluation
### Complexity Analysis  
In this section, we measure the complexity of our proposed scheme in terms of computing time and communication cost. For computing time complexity, at the first round of _[PERSON]_'s side, encryption represents the most expansive operation, which is bounded by \(O(k)\) or 70 ms for each image, where \(k\) is the number of descriptors in the input image. At _[PERSON]_'s side, the secure dot product subroutine is run \(log_{R}(m)\) times, and each time it takes the complexity of \(O(k.p)\). Thus, the overall computing time complexity of this step is \(O((k.p)log_{R}(m))\), which is equivalent to 162.3 ms. Decryption represents the most expansive operation in the second round of _[PERSON]_'s side, and it takes around 65 ms for each similarity score. Thus, the total computing time of this step is bounded by \(O(M.k.p)\) operations, where \(M\) is the number of similarity scores. With respect to the communication cost, we can summarize it as follows: in the first round, _[PERSON]_ sends \(k.n\) values to _[PERSON]_ and _[PERSON]_ sends back \(M.p.k\) values to _[PERSON]_. Suppose that each value has \(b\)-bit long, then the total complexity is bounded as \(O(b(k.n+M.p.k))\) bits.
# Path Loss Measurements of Indoor LTE System for the Internet of Things
## 2 Principles
### Coverage Range  
The coverage radius is derived as [16]:  
\[R=10^{-(P_{r,\min}+FM-A^{\prime})/10N}, \tag{2}\]  
where the parameter \(A^{\prime}\) is generated from the link budget equation and Equation (1).  
\[A^{\prime}=P_{t}-L_{t}+G_{t}+G_{r}-\left[20\log_{10}(f)-28\right]-L_{r}. \tag{3}\]  
\(P_{t}\) (dBm) is the transmit power; \(P_{r,min}\) (dBm) is the minimum receive power; \(FM\) (dB) is the fade margin; \(L_{t}\) (dB) is the transmitter loss; \(G_{t}\) (dBi) is the transmit antenna gain; \(G_{r}\) (dBi) is the receive antenna gain; and \(L_{r}\) (dB) is the receiver loss.  
This paper considers the SR requirements of indoor small cell fading environments. The received signal strength \(W\) at the UE is log-normally distributed. The coverage probability of \(W\) being greater than the threshold \(W_{0}\) from the eNB to a UE at a distance \(d\) is:  
\[P_{W_{0}}(d)=p[W\geq W_{0}]=\int\limits_{W_{0}}^{\infty}p(W)dW=\frac{1}{2}- \frac{1}{2}erf\bigg{(}\frac{W_{0}-K+10\alpha\log(d/R)}{\sqrt{2}\sigma_{W}} \bigg{)}, \tag{4}\]  
where \(R\) is the femtocell radius, \(K-W_{0}\) (dB) is the Fade Margin (FM) at \(d=R\), which is used to guarantee the reliability at the cell edge, and \(K\) is the average signal strength (dBm) at the cell edge.  
The percentage of the UE in a cell of radius \(R\) for \(W\) greater than the receiver threshold \(W_{0}\) is defined as the SR, which is given as [16]:  
\[\text{SR}=\frac{1}{2}\bigg{\{}1+erf(p)+\exp\bigg{(}\frac{1+2 pq}{q^{2}}\bigg{)} \bigg{[}1-erf\bigg{(}\frac{1+pq}{q}\bigg{)}\bigg{]}\bigg{\}}, \tag{5}\]  
where  
\[p=\frac{FM}{\sqrt{2}\sigma_{W}}, \tag{6}\]  
\[q=\frac{N\log_{10}e}{\sqrt{2}\sigma_{W}}, \tag{7}\]  
and \(\sigma_{W}\) is the standard deviation of the received signal strength in a given cell. In addition, the standard deviation in the indoor office environment is set to 10 dBm [17]. Substituting the standard deviation and the path loss exponent into Equations (5)-(7) yields the FM for 90% SR.
# Grasp Analysis for the Robot-Based Manipulation of Pre-Assembled Cables with Electrical Connectors
## 4 Results
### Comparison of Different High-Voltage Cables  
In order to analyze the effects of using different cable parameters on the course of the required grasp force, the previously used high-voltage cable made of copper is compared with one made of aluminum. The effective cable parameters of the two instances are depicted in Table 2. The initialized trajectories presented in Section 4.1 are applied to both cable instances. The results can be found in Figure 11.  
The remaining cable-gripper combinations are shown in Figure A2 in Appendix A. For Figure 11a, it can be observed that, for a predominant bending load with only minor values in the other wrench components, similar courses are obtained. This can be explained by the fact that mainly the similar bending stiffness \(EI_{1/2}\) takes effect, whereas the tensile stiffness \(EA\) and the torsional stiffness \(GJ\) have only a minor impact. In addition to bending, cable 3 in Figure 11b exhibits further deformation components to gripper 1. Therefore, different values for both cables result.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Conductor** & \(EA\) & \(EI_{1/2}\) & \(GJ\) \\ \hline Copper & 13,406,400 N & 0.095109 Nm\({}^{2}\) & 0.0922964 Nm\({}^{2}\) \\ Aluminum & 4,483,500 N & 0.0955892 Nm\({}^{2}\) & 0.580454 Nm\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 2: Effective parameters of two high-voltage cables (©fleXstructures GmbH 2021) from an exemplary database of _IPS Cable Simulation_. Both cables feature a cross-section of 50 mm\({}^{2}\). Due to their circular cross-section, both cables have equal bending stiffnesses \(EI_{1}\) and \(EI_{2}\) along both material directions [31] and, thus, are summarized to \(EI_{1/2}\). The cables differ significantly in their tensile stiffness \(EA\) and torsional stiffness \(GJ\), whereas the bending stiffness \(EI_{1/2}\) only deviates slightly. Note that the effective parameters cannot be derived solely from the conductor since the material mix of conductor and isolation is crucial.  
Figure 11: Comparison of the required grasp forces for two different high-voltage cables made of copper and aluminum, each with a cross-section of 50 mm\({}^{2}\). Both cables are manipulated along the same trajectories. WP 16* represents the superposition of WP 15 with the force for mating the plug to the socket.
## 3 Results and Discussion
### NACA0012 Models  
Based on 12 m/16 m/20 m far-field distances, the SST k-omega and SA turbulence models, and ROE and AUSM flux types, we can make the following simulation configurations: SST + 12 m (ROE/AUSM), SA + 12 m (ROE/AUSM), SST + 16 m (ROE/AUSM), SA + 16 m (ROE/AUSM), SST + 20 m (ROE/AUSM) and SA + 20 m (ROE/AUSM). Therefore, for each NACA0012 model described in Section 2.1.1, applying the parameters in Table 8, twelve groups of simulations is performed. The numerical results of P/P\({}_{\rm t}\), T/T\({}_{\rm t}\), and U/U\({}_{\rm t}\) and the corresponding reference data are illustrated in Figure 5, Figure 6, Figure 8, Figure 9, Figure 11, Figure 12, Figure 14, Figure 15, Figure 17, Figure 18, Figure 20 and Figure 21. We then calculate the related mean error ratios of P/P\({}_{\rm t}\), T/T\({}_{\rm t}\), and U/U\({}_{\rm t}\) and the results are provided in Tables 12-17. The optimal results and the related accuracy improvements under different configurations for each NACA0012 model are obtained, which are shown in Figures 7, 10, 13, 16, 19 and 22.
# Critical Study Quality Management for the Anti-Seepage System in Macau's Landfill Area
## 1 Introduction
### Synthesis of Domestic and International Research  
Over the past few decades, the field of quality management for landfill anti-seepage system projects has garnered extensive attention and research. Scholars and experts from around the world have extensively discussed and researched quality management methods and practices for similar projects. Internationally developed countries, such as the United States, Canada, and Australia, have established relatively comprehensive quality management systems for landfill anti-seepage system projects, accumulating rich experience.  
In China, with the increasing awareness of environmental protection, research on landfill anti-seepage system projects has been on the rise. Different regions have proposed various solutions based on distinct geological, climatic, and environmental characteristics. For instance, the methods used in the anti-seepage system projects in Guangdong Province to cope with extreme weather conditions, such as typhoons and heavy rainfall, are noteworthy [3]. Simultaneously, some studies focus on the application of novel materials, such as geosynthetic liners and biodegradable materials, to enhance the effectiveness and sustainability of anti-seepage systems [4].  
In the study of anti-seepage system projects, both internationally and domestically, various quality management methods and experiences have been employed to ensure their successful execution. International insights highlight the significance of early-stage geological surveys and environmental assessments in upholding project quality. Additionally, a scientifically sound design and meticulous material selection are pivotal for the functionality of anti-seepage systems [3; 4].  
During project implementation, a well-structured construction plan and efficient management processes play a vital role in coordinating resources, guaranteeing construction quality, and maintaining progress. Some domestic projects have embraced modern engineering technologies, such as remote sensing and Geographic Information Systems (GIS), for real-time monitoring and project management [5].  
Furthermore, quality inspections and assessments are indispensable. The utilization of non-destructive testing methods, such as electrical resistivity testing and ground-penetrating radar, allows for continuous monitoring, enabling timely issue identification and resolution. Regular quality assessments contribute to a repository of lessons learned, offering guidance for future projects.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 2 Framework of VMD-Based LFO Identification
### Processing and Detrending  
DC offset and trend components affect the estimation results, so we need to remove the trend component by linear fitting. If some data are lost, we can use linear interpolation to solve this problem; this processing method does not affect the accuracy of the analysis results.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 4 Challenges  
The reconfiguration and re-routing of paths can cause significant data processing delays. An FSO system requires a robust routing protocol with minimum delay and small number of hops [6]. This challenge can and should be mitigated with the use of DTN routing protocols.  
A possible challenge of the approach FSO+LTP is related to processing speed. Ironically, it actually stems from the major benefit of FSO, i.e., a transfer rate that is 10 times, or even 100 times, to its RF equivalents. Current DTN protocols have not been designed with this speed in mind; thus, their processing, instead of available bandwidth, could represent the new bottleneck.  
Artificial intelligence approaches have been used to predict the performance of FSO networks [55], and also as a tool to enhance it, by training an FSO antenna with previous measurements, providing predictions with minimal time delays [56].  
In order to do so, a system with DTN protocols and optical wireless links should be emulated, with the use of the platform _Virtual Bricks_[57]. When challenges arise, solutions can be found during an initial phase, before implementing the system for a real setup. This stage allows to develop LTP modifications, although real machines are necessary to test the system with higher data-rates.  
Another possible challenge, also related to processing speed, could derive from the adoption of packet-layer FEC, as suggested in Reference [45; 46]. These codes could be highly beneficial on interplanetary links, where retransmissions would be very costly in terms of delay, due to the extremely long propagation delay (3-23 min for an Earth to Mars link).  
When implementing a new technology, it is of primary importance to create a robust yet simple system, to both overcome the issues that surface and be able to pinpoint the parts of the structure that need improvement.
## 2 Related Work
They presented the offloading issue as an optimization problem. They developed a polynomial-time-complexity method using the Lyapunov optimization method, which evaluates if and where to offload to reduce the IoT device's energy consumption. They also demonstrated that the cloud could be used as a long-term data processor for computation-intensive jobs, whereas simulations confirmed the solution's efficacy. Validating real-world blockchain networks with smart agents was required. Finally, [PERSON], [PERSON] [35] suggested a cooperative edge-assisted blockchain network, in which Proof-of-Work (PoW) mining operations might be delegated to Base Stations (BSs), and block data could be kept by a Cloud Service Provider (CSP). They used a 3-stage Stackelberg game to model their network's interaction process between IoT objects, BSs, and CSP. Within every step, they also looked at the subgame optimization problem. They developed the Stackelberg game's backward induction-based iterative approach for computing offloading, block storage strategies, and resource service prices for all equipment, BSs, and CSPs. They found that their proposed backward induction-based iterative method had a high convergence rate and discussed the interactions between the three stages of the proposed game. In addition, the analysis results revealed that the two-cell method had significant advantages over the two non-cooperative methods. Finally, a side-by-side analysis of state-of-the-art IoT offloading methods is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{3}{*}{**Mechanism**} & \multirow{3}{*}{**Main Idea**} & \multicolumn{6}{c}{**Parameters**} \\  & & **Man** & **Co** & **S** & **S** & **Energy** & **Latency** & **Task** & **Security** & **Network** & **Using TLZ** \\ \hline
**Aljanabi** & \multirow{3}{*}{ \begin{tabular}{c} Proposing a Q-learning-based \\ method to fix the model and select \\ the optimal offload policy. \\ \end{tabular}  } & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & Fog-cloud & No \\
**[PERSON]** & & & & & & & & & & \\
**[PERSON]** & & & & & & & & & & \\
**[PERSON]** & & & & & & & & & & \\
**[PERSON]** & **[PERSON]** & & & & & & & & & \\
**[PERSON]** & & & & & & & & & & \\  
Using Deep Neural Network (DNN) approaches on IoT objects can result in overhead; plus, finding ways to remove this overhead or lighten these algorithms on more capable devices, such as edge nodes, can help to overcome these flaws. Additionally, considering security and privacy mechanisms, blockchain technology can improve the security of a system. Moreover, we are using an EH module to extend the system's battery life. However, we are attempting to build an intelligent and online/offline dynamic approach for secure and cost-effective task offloading that outperforms the smart and dynamic methods, employing the most up-to-date techniques and expertise.
## 5 Field Application  
Field tests were carried out in the 91-101 working panel of Wang-Zhuang Coal Mine in Chang Zhi City. Through underground field observation, it could be seen that the deformation of surrounding rock in the roadway advance longwall area and the concrete pouring area could be controlled using the following methods: (1) temporarily strengthening the support and grouting the rock mass modification in the roadway advance longwall area; (2) rapidly connecting the concrete-filled steel tube column and roof; (3) modifying the concrete with early strength agents, etc. The structural deformation in the active zone of the roof is the deformation that appears in the later stage of gob-side entry retaining, which can be controlled by adjusting the parameter of the concrete-filled steel tubes and optimizing the span of the columns. In this study, specific construction technical parameters are proposed after field investigation and calculation to achieve the purpose of optimizing construction process, improving working quality, optimizing overburden structure, increasing surrounding rock strength, and reducing the load acting on hydraulic props. The specific support forms and parameters of gob-side entry retaining in the 91-101 ventilation roadway are as follows.