esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical and Simulation Results
### Performance of the HSTMAC Protocol with Dynamic Pre-Backoff Scheme  
As shown in Figure 11, we validate the analytical analysis of DPBO algorithm through simulation results. For DPBO algorithm, jitters can be found in the resource utilization ratio and average request number. The reason is that the pre-backoff window size of DPBO is changing dynamically according to the traffic load, which results in the dynamically changing of the transmission interval \(I_{\text{max}}\) and the distribution of devices in different transmission stages.  
When the arrival rate of H2H packets is \(\lambda\) = 20, the reserved RBs are sufficient to satisfy the QoS requirement of M2M traffic. Shown in Figure 12a, almost all the M2M devices can send their packets successfully, and the resource utilization ratio of M2M traffic shown in Figure 12b is increasing linearly with the number of M2M devices. Under this condition, the pre-backoff window size in DPBO algorithm would remain close to zero. The time interval \(I_{\text{max}}\) is increasing with the pre-backoff and backoff window size. Hence, as shown in Figures 10 and 11, the resource utilization ratio and the average request number in the DPBO algorithm is higher while the average delay is much lower than that in the UPBO algorithm.  
When the arrival rate of H2H packets is \(\lambda\) = 50, the system quickly becomes congested due to the growth in the number of M2M devices. Shown in Figures 12a and 13a, the performance results without backoff scheme are the worst. On the other hand, though the average success probability is decreasing with the number of M2M devices, the results in DPBO algorithm is better than that in scenarios with UPBO algorithm. In a scenario with high system load, as shown in Figures 12b and 13a, the resource utilization ratio in DPBO algorithm is higher while the average request number is lower than that in UPBO algorithm. Moreover, as shown in Figure 13b, the average delay in DPBO algorithm is lower or in the same order with that in UPBO algorithm.  
As a conclusion, we can derive that pre-backoff scheme can improve the success probability at cost of lowering the resource utilization ratio and increasing the delay. However, the proposed DPBO algorithm can improve the system performance at a much lower cost when compared to the UPBO algorithm.  
Figure 11: Performance comparisons of HSTMAC protocol between analytical and simulation results (\(\lambda\) = 50, Q = 10, W\({}_{\text{BO}}\) = 20): (**a**) Average success probability of M2M traffic; (**b**) Average resource utilization ratio of M2M traffic.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Iso 27017:2015  
ISO 27017 was created as part of the ISO 27000 series to address the particular security problems associated with cloud computing. It is based on the larger ISO 27001 standard, which specifies the criteria for implementing an information security management system (ISMS). ISO 27017 tailors these criteria explicitly for cloud service providers and clients, giving additional guidelines and controls to improve cloud security. ISO/IEC 27017 provides guidelines for information security controls relevant to the use of cloud services by giving further implementation instructions for the 37 controls listed in ISO/IEC 27002 as well as seven additional cloud-related controls (As shown in Figure 4) that address the following.
# Intelligent Interference Management in UAV-Based HetNets +
## 3 System Model
### Brute Force Algorithm  
The brute force algorithm investigates the entire search state space of all possible values of UABS locations and individual eICIC and FeICIC parameters and returns the state with the best mean or median SE. This is illustrated in Algorithm 1. Since it individually searches for all possible parameter values, it is computationally infeasible for large areas and large number of MBSs and UABSs.  
```
1: Best state, \(\mathbf{S^{\prime}}\leftarrow\mathbf{NULL}\)
2:\(bestSE\leftarrow-1\)
3:for all State \(\mathbf{S}\)do
4:\(currentSE\gets C(\mathbf{S})\)
5:if\(currentSE>bestSE\)then
6:\(bestSE\gets currentSE\)
7:\(\mathbf{S^{\prime}}\leftarrow\mathbf{S}\)
8:endif
9:endfor
```  
**Algorithm 1** Brute force algorithm.
## 4 Location Algorithm Based on Ranging  
### Composition of Ranging Errors  
Since ranging needs to calculate the signal flight time, it is obvious that the propagation channel will reduce the ranging accuracy. The distance measurement error (DME) can be defined as:  
\[\varepsilon_{W}(d)=\widehat{d_{W}}-d \tag{5}\]  
where \(\widehat{d_{W}}\) is the distance value calculated by the anchor based on DS-TWR, and \(d\) is the true distance between TAG and ANCHOR. The DME comes from multipath errors, errors in the process of measuring the real distance between the tag and the anchor, NLOS errors, and device antenna delay errors [26]. The random variable \(\xi\) is used to model the occurrence of NLOS conditions, and the random variable takes a value of 1 when the NLOS condition occurs, otherwise the value is 0. Specifically, it can be expressed as:  
\[\varepsilon_{W}(d)=\varepsilon_{A}+\varepsilon_{T}+\varepsilon_{M,W}(d)+\xi* \varepsilon_{N,W}(d) \tag{6}\]  
where \(\varepsilon_{A}\) is the error caused by the UWB device error, including antenna delay, crystal error, etc. Its value fluctuates to some extent. \(\varepsilon_{T}\) is the error caused by the operation or reading of the measuring instrument during the actual measurement of \(d\), which approximately obeys the normal distribution, \(\varepsilon_{M,W}(d)\) is the error caused by multipath, and \(\varepsilon_{N,W}(d)\) is the error caused by NLOS condition. Figure 8 shows the NLOS condition and how the multipath error is generated.  
Figure 7: The structure of the MLP and CNN models. The number in parentheses after the layer name is the number of neurons contained in the layer. (**a**) Structure of the MLP, (**b**) structure of the CNN.
## 5 Conclusions  
This paper proposes a hybrid compensation method to address the mixed nonlinearities of piezoelectric actuated machine tools (PAMTs) under S-shaped trajectory driving voltage. Based on capacitance theory, the relationship between speed change and actuator deformation was analyzed, leading to a non-uniform creep difference (NCD) processor. NCD and hysteresis were modeled using a polynomial model and a clockwise operator model. Subsequently, a hybrid feedforward control strategy integrating these models effectively mitigated velocity-dependent NCD and inherent hysteresis. The experimental results show that the maximum MRE and MMSE of this method are 5.48% and 0.28%, respectively, and the modeling accuracy is improved by about 70% compared with the NCD uncompensated model. The S-shaped trajectory tracking accuracy was significantly improved.  
This method not only improves the control accuracy of PAMTs, but also applies to other piezoelectric devices such as cell probes, especially in the case of combined motion trajectories. The clockwise operator model simplifies hysteresis modeling and reduces computational complexity by directly dealing with inverse relationships, and can also be used with other control algorithms. Future work will focus on developing a real-time strategic workflow, from automated analysis of NCD trends and model building to adaptive optimization of voltage compensation drives, to the integration of piezo actuator output performance under special conditions. Therefore, it is possible to extend the applicability of this hybrid compensation method to a wider range of applications.  
Conceptualization, Z.Q.; methodology, Y.Y.; software, Z.Q.; validation, Z.Q., Y.Y. and X.Y.; formal analysis, Z.Q. and X.Y.; investigation, Z.Q. and Y.Y.; resources, Y.Y. and C.L.; data curation, D.A., Y.Y. and Z.Q.; writing--original draft preparation, Z.Q.; writing--review and editing, D.A., Z.Q., Y.Y. and C.L.; visualization, Z.Q. and X.Y.; supervision, D.A. and Y.Y.; project administration, D.A. and C.L.; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Liaoning Province of China No. 2024-MSLH-390, the National Key Research and Development Program of Liaoning Province of China No. 2024 JHz/102400045, and the National Key Research and Development Program of China No. 2024 YFB3410205.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
Thanks for ultra-precision motion control and measurements engineering and research center of shanghai.  
The authors declare no conflicts of interest.
## 5 Applications
### Application 2: Draft Tube
Figure 12: Draft tube example: geometry, boundary conditions and loads. Comparison of the different curves shows that the results were less sensitive to the variation of \(f_{y}\), similar to example 1 considered in Section 3. Using Equation (6), and resistance values for each analysis, it is possible to compute \(V_{R}=0.09\), which is higher than the value of 0.05, found in [3] using ANFEA. This result is consistent with the results of previous Section 3. Using Equation (5) and \(\beta_{min}=4.25\), the computed value of the global resistance factor is \(\gamma_{G}=1.36\) which is slightly larger than the value of 1.19 obtained in [3] using ANFEA (without considering model error \(R=\overset{\sim}{R}\)). The design value of the draft tube resistance is accordingly shown in Figure 14. It is above the ultimate design load level, indicating that the draft tube is safe for the considered load case. The design value found in this study is 15% lower than the one found in [3] using ANFEA, confirming that SRAM results are always on the conservative side. Figure 14: Draft tube force-displacement results for (**a**) Rosenblueth’s analyses; (**b**) analyses with mean material properties and comparison with previous works [3]. Figure 13: Draft tube example results: (**a**) Maximum concrete tensile strain field; (**b**) \(\beta\) field in a roof at ultimate load level and at failure; (**c**) Compressive stresses in a roof at ultimate load level and at failure. Finally, Figure 14b compares the results using mean input parameters with and without thermal loads. Consideration of thermal loads decreases by 14% the strength of the RC structure according to SRAM results of this study, whereas a 7% reduction was observed with ANFEA. The differences between SRAM and ANFEA results for this example are believed to result from the influence of the fracture energy \(G_{F}\) on ANFEA results. As pointed out in [3], \(G_{F}\) is responsible for stable crack propagation in RC structures, which has a beneficial effect on the resistance (\(R_{m}\)) and a _damping effect_ on its variability (\(V_{R}\)). However, this beneficial effect may be counteracted by an increase in the variability of results (\(V_{R}\uparrow\)) when considering the fracture energy as an additional third independent input parameter in [PERSON]'s analyses (\(N=3\)). This is still difficult to achieve since not only a good estimation of the mean fracture energy is required, but also an assessment of its variability (\(cov(G_{F})\)) is required.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 6 Conclusions and Recommendations  
From the research presented above, the following conclusions are drawn:  
* The developed code allows the connection between a design software and the KUKATM robotic arm for multiple perimeter elements without the need to generate a new printing code for each figure. Tests and comparisons conducted show that the code responds to a variety of different geometries, allowing precise movements at speeds easily controllable by the user.
* By developing a continuous print path, the concrete is intended to spread evenly along the print cordon. This is why checking the precision with which the robotic arm responds to the parameters entered in the control code is essential. In this sense, this research has found confidence levels that show the precision of the robotic arm is approximately between 98% and 99%.
* It was possible to test trajectories of a series of elements with different geometries and sizes by using the developed code, which was capable of modifying the elevation, the printing speed, and the separation of points that define smoothness in the curves easily, facilitating the 3D concrete printing processes.
* A theoretical way of calculating the maximum speed of printing was determined using an empirical equation, which helps users to choose speeds lower than the maximum speeds, allowing the robotic arm to be better coupled to the designer's requirements.  
Figure 11: Graph of maximum speed for different point spacings.  
* For future research, it is necessary to develop elements of greater complexity, in addition to generating a filling pattern to give them greater resistance, and to include reinforcement for the construction of structural elements used in seismic places. In addition, future research should include a comprehensive comparison between the findings of the present study and traditional methods.  
Conceptualization, E.F., R.G.-A. and F.O.; methodology, J.P. and A.V.; software, J.P., A.V. and J.S.; validation, E.F., R.G.-A., F.O. and J.S.; formal analysis, J.P. and A.V.; investigation, J.P. and A.V.; resources, J.S.; data curation, J.P. and A.V.; writing--original draft preparation, J.P., A.V. and E.F.; writing--review and editing, E.F., R.G.-A. and F.O.; visualization, J.S.; supervision, J.S. and E.F.; project administration, E.F.; funding acquisition, E.F. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Universidad del Bio-Bio (research project No. GI194703).  
Not applicable.  
The authors declare no conflict of interest.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 3 Overview of Previous Studies on IRS-Assisted UAV Communications  
In this section, we provide an overview of previous studies relevant to IRS-aided UAV communications. Most of the works focused on the optimization of various parameters, such as UAV altitude and speed, IRS location, IRS phase shift, and beamforming weights, to achieve a certain goal. Primary performance criteria include spectral efficiency, energy efficiency, and security. We categorized the previous works according to the main performance criteria and provide technical features of each work in the following subsections.
## 3 Proposed DVC Framework for Online Correlation Noise Model
### General Concept of Proposed Online Correlation Noise Model  
Figure 1 depicts the proposed DVC framework for calculating the online correlation noise model CNM for the DVC frameworks [10; 15]. In [15], the motion estimation is not exploited at the decoder, and the actual frame is also not available at the decoder. Therefore, online CNM computation is quite a challenging task in such a DVC framework. Keeping that in mind, we proposed a novel online CNM based on the mean of band coefficients. In this framework, the Band Mean Calculator (BMC) is deployed at the encoder and decoder to perform the necessary calculations which are used in the CNM.  
Figure 1: Proposed DVC framework with novel online correlation noise model.  
At the encoder, the BMC calculates the mean of the coefficients of each band of the WZF and sends them to the decoder. The BMC deployed at the decoder calculates the mean of each coefficient band of side information (SI) in a similar way as it does on the encoder. The Laplacian distribution is used to model the residual between the means of the original WZ and the corresponding SI frame for each band. The CNM calculation details and mathematical representation are given in Section 3.2. The SI generation process from [15] and conventional interpolation in [10] were used in this study. A brief overview of the phase-based SI generation algorithm from [15] is presented in Algorithm 1. We deployed and verified the performance of this novel online CNM concept for the DIS codec and named it _D_istributed _V_ideo Coding with Online Band Mean _C_orrelation Noise _M_odel (DIVCOM). Next, the same online CNM concept is deployed for the DVC codec with the phase-based SI [15], and we called the implementation _P_hase-based _D_istributed _V_ideo Coding with Online Band Mean _C_orrelation Noise _M_odel (PDIVCOM).
## 1 Introduction
In this system, the discharge gas injected into the high-voltage electrode tube is heated by Joule heating generated from the electrode, so that discharge can occur in the discharge gap. In addition, it serves to cool the high-voltage electrode. Because ozone is easily decomposed by heat, it is important to cool the electrode. The coaxial structure of this system, in which ambient air is injected into the high-voltage electrode as a third collision partner, decreases the temperature of the electrode. The electrode can be maintained at a low temperature owing to the self-cooling effect of the discharge gas and can generate a high concentration of ozone as ozone decomposes rapidly at high temperatures. To prove this, we measured the optical and electrical properties of compact air-cooled coaxial DBD plasma. The optimized discharge conditions were derived according to the flow rate of the discharge gas and the change in power consumption. In contrast, in a previous study, plasma was generated using water-based cutting fluids and sterilized microorganisms [5]. Underwater plasma has an advantage in that the active species and radicals of the plasma directly react with water-based cutting fluids. However, the electrodes and components of the underwater plasma devices are damaged when operated for a long period in water-based cutting fluids containing approximately 10% oil. Thus, long-term plasma generation in water-based cutting fluids and equipment maintenance are difficult. To overcome these limitations, high concentrations of ozone were efficiently dissolved in water-based cutting fluids. A rectangular bubbler was added to the gas outlet of the compact air-cooled coaxial DBD to dissolve the ozone in the water-based cutting fluids. Ozone and bubbles dissolved in water-based cutting fluids can effectively react with impurities and microorganisms. Consequently, a high concentration of ozone and numerous bubbles containing ozone can effectively treat water-based cutting fluids. We performed sterilization experiments with various bacteria in waste-cutting fluids using a compact air-cooled coaxial DBD plasma with a bubbler under optimized conditions. Additionally, we analyzed the changes in the concentration, pH, and composition of water-based cutting fluids after ozone treatment.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **WL** & **Operating** & \multirow{2}{*}{**Average** & **Polarization** & \multirow{2}{*}{**Used** & **Dimensions (length** & **Anti-** \\  & **(in nm)** & **Frequency** & **Absorption** & **of Layers** & **Angular** & **Materials** & \(\times\)**width \(\times\) height)** & **Layer** & \\ \hline
[32] & 200–1300 & 230–1500 & 8\% & 3 & N/A & Cr\({}_{\text{G}}\)SiO\({}_{2}\) & 3\(\times\) 3\(\times\) 1.3\(\lambda\) & No & 88.50\% \\
[22] & 580–1000 & 300–516 & 95.2\% & 2 & \(\begin{array}{c}\text{Independent},\\ \text{0}=30^{\circ}\end{array}\) & Ge\({}_{2}\), Sz\({}_{\text{G}}\), Te\({}_{\text{S}}\), 0.4\(\lambda\)\(\times\) 0.4\(\times\) 0.21\(\lambda\) & No & N/A \\
[36] & 450–650 & 460–665 & 91.82\% & 3 & Independent, \(\begin{array}{c}\text{Int},\\ \text{0}=40^{\circ}\end{array}\) & Tu\({}_{\text{S}}\),1 & 1.8\(\times\) 1.8\(\times\) 0.4\(\lambda\) & No & N/A \\
[26] & 280–700 & 428–1070 & 90\% & 3 & \(\begin{array}{c}\text{Independent},\\ \text{0}\leq 30^{\circ}\end{array}\) & \(\begin{array}{c}\text{Tungsten},\\ \text{0}\leq 30^{\circ}\end{array}\) & \(2\times 2\lambda\times 0.4\lambda\) & No & N/A \\
[37] & 450–3000 & 100–666 & 90\% & 3 & \(\begin{array}{c}\text{Independent},\\ \text{N/A},\\ \text{0}=30^{\circ}\end{array}\) & Fe\({}_{\text{S}}\),Au & 0.6\(\times\)0.6\(\lambda\)\(\times\) 2\(\lambda\) & No & N/A \\
[38] & 435–615 & 450–650 & \(-\) & 4 & \(\begin{array}{c}\text{Dependent},\\ \text{0}=30^{\circ}\end{array}\) & rsd3, SiO\({}_{2}\),Al & 1\(\lambda\)\(\times\) 1\(\lambda\)\(\times\) 1.5\(\lambda\) & No & N/A \\
[39] & 500–1700 & 175–600 & 82\% & 6 & \(\begin{array}{c}\text{Independent},\\ \text{0}=\leq 30^{\circ}\end{array}\) & \(\begin{array}{c}\text{SiO},_{\text{T}},\\ \text{Min},\text{TiO}_{2}\end{array}\) & 0.6\(\lambda\)\(\times\) 0.6\(\lambda\)\(\times\) 1\(\lambda\) & No & N/A \\
[31] & 300–700 & 428–750 & 92\% & 3 & \(\begin{array}{c}\text{Independent},\\ \text{0}=\leq 45^{\circ}\end{array}\) & Tungsten, \(\begin{array}{c}\text{3.2}\times\) 3.2\(\lambda\)\(\times\) 0.77\(\lambda\) & No & N/A \\
[3 pt] & 400–2400 & 125–750 & 92\% & 3 & \(\begin{array}{c}\text{Independent},\\ \text{0}=\leq 45^{\circ}\end{array}\) & Tungsten, \(\begin{array}{c}\text{Total},\\ \text{SSO}_{2}\end{array}\) & 0.5\(\times\) 0.5\(\lambda\) & Yes & 90.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed MA with the recent design.
## 2 Data and Method
### Data  
#### 2.2.1 Rain Gauge Observations  
The actual precipitation used in this study is the hourly precipitation measured by 3045 rain gauges over the Yangtze River Basin. These rain gauges measured the precipitation with an intensity above 0.1 mm/h at their locations. The measured precipitation datasets were processed and administrated by the China Meteorological Administration (CMA). The spatial distribution of these rain gauges is presented as red points in Figure 1. The dense rain gauge network over the study area could effectively capture the rainfall event over this area and provide sufficient samples to establish and validate rain area delineation methods.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
###### Abstract  
The Smart Grid (SG) aims to transform the current electric grid into a \"smarter\" network where the integration of renewable energy resources, energy efficiency and fault tolerance are the main benefits. This is done by interconnecting every energy source, storage point or central control point with connected devices, where heterogeneous SG applications and signalling messages will have different requirements in terms of reliability, latency and priority. Hence, data routing and prioritization are the main challenges in such networks. So far, RPL (Routing Protocol for Low-Power and Lossy networks) protocol is widely used on Smart Grids for distributing commands over the grid. RPL assures traffic differentiation at the network layer in wireless sensor networks through the logical subdivision of the network in multiple instances, each one relying on a specific Objective Function. However, RPL is not optimized for Smart Grids, as its main objective functions and their associated metric does not allow Quality of Service differentiation. To overcome this, we propose \(OFQS\) an objective function with a multi-objective metric that considers the delay and the remaining energy in the battery nodes alongside with the dynamic quality of the communication links. Our function automatically adapts to the number of instances (traffic classes) providing a Quality of Service differentiation based on the different Smart Grid applications requirements. We tested our approach on a real sensor testbed. The experimental results show that our proposal provides a lower packet delivery latency and a higher packet delivery ratio while extending the lifetime of the network compared to solutions in the literature.  
Smart Grid; WSN; RPL; routing; QoS; objective function; metric +
Footnote †: This work is an extended version of the conference paper published in PEWASUN 2017 entitled “Towards Multi-instances QoS Efficient RPL for Smart Grids”.