esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Structural Optimization of PIPR's End Face
### Experimental Scheme Based on LHSD Optimized by Greedy Strategy  
Although the improved model of PIPR can reduce the pressure impact compared to the original model, turbulent vibration caused by pressure fluctuation has also influenced the stability of the plugging process. Therefore, the end face of PIPR is optimized to reduce the pressure fluctuation. The response surface method is widely used for structural optimization, but the experimental schemes designed by this method are limited, and the polynomial fitting is not suitable for complex nonlinear relationships. For these problems, LHSD is proposed for the design of the experimental scheme. In order to make the sample distribution more uniform, the greedy strategy is introduced for global optimization, which can ensure the space-filling properties and equilibrium properties [26]. Aiming at the structure of PIPR's end face, the thickness of the surface (s), bottom height (\(h\)), the radius of the connection groove (\(r\)), and shape parameter (\(S_{p}\)) are selected as optimization variables. The ranges of each optimization variable are shown in Table 2.  
The pressure gradient of the flow field is an index for measuring the pressure fluctuation [27]. The average absolute pressure values of 12 planes at different positions are extracted; the coordinates are shown in Table 3. Pressure gradient values between adjacent points can be obtained, as shown in Equation (11). The average pressure gradient (\(\frac{\Delta P}{\Delta L}\)) of all positions is the optimization objective, which is related to the stability of the flow field.  
\[\frac{\Delta P}{\Delta L}=\left|\frac{P_{2}-P_{1}}{L_{2}-L_{1}}\right| \tag{11}\]  
where \(P_{1}\) and \(P_{2}\) are the pressure values of different positions; \(L_{1}\) and \(L_{2}\) are the coordinates of different positions.  
Within the range of optimization variables, 300 sets of experiments are designed using the LHSD optimized by greedy strategy. According to the experimental schemes, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optimization Variable** & **Range** \\ \hline \(s\) & 14–24 cm \\ \(h\) & 2–12 cm \\ \(r\) & 2–8 cm \\ \(S_{p}\) & 0.01–0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ranges of each optimization variable.  
Figure 18: The flow velocity distribution of the original plugging state of the improved model.  
average pressure gradient of each group is calculated through numerical simulation. Partial results are shown in Table 4.
## 2 Materials and Methods
### Calculating FSM Area  
Step 4 of Algorithm 2 suggests different area calculation modes for transition functions: CPLD, FPGA, ASIC, diff_w, and max_w. For example, in LUT-based FPGAs, the area of a transition function \(d_{r}\) is calculated as:  
\[C_{FPGA}(d_{r})=\left\{\begin{aligned} & 1,\text{ when }q\leq n\\ &\left\lceil\frac{q-n}{n-1}\right\rceil+1,\text{ when }q>n\end{aligned}\right., \tag{6}\]  
where \(q\) is the rank (number of arguments) of \(d_{r}\), and \(n\) is the number of LUT inputs.  
When implemented in CPLDs, the area C\({}_{\text{CPLD}}(d_{r})\) of the transition function \(d_{r}\) is equal to the number of product terms in the Sum-of-Products (SOP) representation of \(d_{r}\):  
\[C_{CPLD}(d_{r})=H(d_{r}), \tag{7}\]  
where \(H(d_{r})\) is the number of product terms in the SOP of the function \(d_{r}\).  
When the FSM is implemented in an ASIC, the area \(C_{\text{ASIC}}(d_{r})\) of the function \(d_{r}\) is determined by the number of literals in the SOP of \(d_{r}\):  
\[C_{ASIC}(d_{r})=\sum_{i=1}^{H(d_{r})}\left(X_{i}(d_{r})+R_{i}\right)+H(d_{r}), \tag{8}\]  
where \(X_{i}(d_{r})\) is the number of input variables in the \(i\)-th product term in the SOP of the function \(d_{r}\), and \(R_{i}\) is the number of feedback variables in the \(i\)-th product term.  
The total area of the FSM implemented in an FPGA is defined as:  
\[C_{FSM}=\sum_{r=1}^{R}C_{FPGA}(d_{r}). \tag{9}\]  
It is important to note that the actual area occupied by a function is influenced by various factors such as the techniques and algorithms employed in the design tool. These include methods for logical synthesis, the optimization of both logical and physical synthesis, mapping the design logic to the physical structure of the electronic component, and ensuring compliance with time constraints during the design process. Consequently, the area estimated using Equations (6) to (9) may frequently diverge from the actual area occupied by the implemented FSM.
## 1 Introduction
In as much as that, the poor management of the resources of 5G can also lead to performance degradation in a communication system. Hence, supplementary UL can be used as low bands complement to the cell's UL when operating in high-frequency bandsand a supplementary DL in a cell's DL in the spectrum [12]. Radio resource scheduling and management play a vital role in a typical smart grid communication system since it is responsible for allocating radio spectrum resources while guaranteeing the several types of available services. Since 5G networks are not particularly intended for smart grids and because requirements are different amongst devices in a typical smart grid network, it is imperative to formulate and develop efficient scheduling algorithms to meet the requirements of smart grids. In this paper, the best possible packet scheduling approaches are proposed and examined, and their suitability for smart grid communications is ascertained and established. The main contributions and highlights of this study are as follows:  
* Elucidate the differences between the operation mode and execution of different scheduling algorithms employed in various environments, including computing, process systems, and smart grid environment. * Develop a Deadline Scheduling with Commitment algorithm and evaluate the DSC technique as a possible scheduling candidate for smart grid communications over 5G networks. * EDF scheduler is a popular scheduling approach employed in various queuing models; however, its performance has not been too superior due to its inability to prioritize packets. In this work, the present EDF scheduling technique (I-EDF) is improved and modified by including an optimal queuing technique and the knowledge of servicing time upon arrival and other favorable characteristics to enhance its performance. * Propose an optimal usage and dropping scheduler that can conveniently accommodate smart grid communications and allocate resources to achieve maximum rewards. * Develop a suitable RB allocator that is responsible for allocating the available channels to packets. The rest of this paper is organized as follows: In Section 2, a review of the works that are related to this area is conducted. Section 3 presents the system model, and Section 4 illustrates the packet scheduling architecture. The simulation results obtained are discussed in Section 5, while Section 6 concludes the work. Figure 2: Typical illustration of 5G new radio frame structure and resource block [12].
# Gaussian Process Modeling of Specular Multipath Components
## 4 Results
### GPR of SMC Amplitudes
Figure 3b-e show clearly that the predicted mean values of the SMC GP amplitudes vary significantly with the angle \(\phi_{k}(\mathbf{p})\). Those variations originate mostly from the angle-dependency of the reflection coefficient \(\Gamma_{k}(\phi_{k}(\mathbf{p}))\), because the angle-dependency of the antenna pattern \(A(\phi_{k}(\mathbf{p}))\) is slow over the entire observation angle. As expected, the predicted standard deviation is much larger than the one for the LOS amplitude. (A more detailed verification of the variance regression performance is found in Section 4.3.2.) For the SW (at \(\mathbf{a}_{3}^{(2)}\)) and the NGW (at \(\mathbf{a}_{5}^{(1)}\)), the effect of varying reflection coefficient due to different building materials can be seen; c.f. Figure 3d,e, respectively. In Figure 3d, sections \(A^{*}\), \(C^{*}\) and \(B^{*}\) correspond to the whiteboard, the metal door and a small plasterboard section in between, c.f. Figure 1. It can be seen that, for angles between \(-260\) to \(-230\) degrees, the fluctuations are small because the SMC relates to the strong reflection by the metallic whiteboard. For angles above \(-230\) degrees, the amplitude fluctuates severely, because the point-of-reflection moves to sections \(B^{*}\) and \(C^{*}\) comprising of other materials. Also note that the variance of the data points fluctuates. In general, data from positions which are quite far away from the VA \(\mathbf{a}_{3}^{(2)}\) have larger variances. This is partly the result of a more significant impact of the DMC on those data but it is also the effect of multiplying the DMC with the longer travel distance to obtain the normalized SMC amplitude. In Figure 3e, the amplitude fluctuations due to varying reflection materials can be seen again. The various materials are noted accordingly, whereas sections \(A\), \(C\) and \(E\) are glass windows. Even with the same material, separate angle ranges are covered where the normalized amplitude, and thus the reflection coefficient, is significantly different, for example, for lower angles, an increased coefficient can be seen.
## 2 Seismological Data and Earthquake Sequence Analysis
### Hypocentral Relocation of the Earthquake Sequence
Furthermore, five (5) cross-sections were performed in order to see the impact of the relocation procedure on the sequence hypocentral depths and the discrimination of the local activated structures. Cross-sections 1-2 have an NNE-SSW orientation, and 3-5 WNW-ESE direction, perpendicular to the NE trending faults. The geometry of the hypocenters as they appear in the performed cross-sections, reveal the activation of a fault, dipping \(\sim\)60\({}^{\circ}\) to the WNW, and a smaller antithetic structure, possibly connected to Galatas N-S striking normal fault (Figure 5). An almost sub-vertical structure makes an appearance in the cross-sections north of the epicenter of the mainshock (sections B-B\({}^{\prime}\), D-D\({}^{\prime}\); Figure 5). The earthquake activity that belongs to cluster 4, started on 16 January 2022, with an event of M\({}_{\rm L}\) = 3.3 and continued throughout the first two months of 2022 as the latest contribution to the seismic sequence. Figure 4: (**a**) Location of the 2021–2022 Arkalochori sequence for 4750 events (**b**) relocated events of the aftershock sequence using hypoDD. The locations of the permanent (red triangles) and the temporary (blue triangles) are presented (see text for details). The M \(\geq\) 4.0 earthquakes are depicted by yellow stars. Faults are marked as red lines (see text and [30] for details). The hypocenters appear to be located in deeper parts of the crust and they have an apparent dip towards the WNW. Figure 5: Presentation of the map of the 5 cross-sections, 5 km wide (**A**–**A**\({}^{\prime}\), **B**–**B**\({}^{\prime}\), **C**–**C**\({}^{\prime}\), **D**–**D**\({}^{\prime}\), and **E**–**E**) on the left side of the figure and the results of the SSW-NNE oriented cross-sections (upper panel) and the results of WNN-ESE oriented cross-sections (lower panel) on the right side. The projection of the 27 September mainshock is depicted by yellow star on sections (**B**–**B**\({}^{\prime}\)) and (**C**–**C**\({}^{\prime}\)).
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 3 Modified Nonlinear Fatigue Damage Accumulation Model Considering the Vibration Load Effect  
As is described in [23], the damage curve model proposed by [PERSON] and [PERSON] presented prediction results close to experimental data. However, in particular for this paper, the load sequences and load interactions are induced by the effect of the random vibration. Then, to determine the fatigue damage accumulation, the damage curve approach model [17], as described by Equation (11), is modified to consider the random vibration PSD load effect, also.  
Since in the damage curve model, the exponent value of 0.4 is based on the crack growth and it is constant, it does not consider the intensity effect of the loading change induced by a random vibration (PSD). Thus, to consider it, the exponent value 0.4 is replaced by \((\sigma i-1_{vb}/\sigma i_{vb})\). This relation represents a nonlinear continuum damage function of the vibration bending stress induced.  
That vibration bending stress is obtained from the PSD response acceleration function, as shown in Figure 1, where \(A\) is the speed for a given time \(t\) and \(B\) is the displacement for a given time \(t\). \(W_{n}\) is the natural frequency and \(\Phi\) is the phase angle. Thus, the vector \(C\) represents the maximum amplitude (acceleration) of the movement, which, by its cyclical movement, induces fatigue damage.  
The vector C and the phase angle \(\Phi\) are obtained as  
\[C=\sqrt{A^{2}+B^{2}} \tag{12}\]  
\[\Phi=\tan^{-1}\frac{B}{A} \tag{13}\]  
Now, regarding the damage curve model, and considering the vibration bending stress, the proposed damage accumulation model under two level loading conditions is represented by,  
\[D=\sum_{i=1}^{2}D_{2}=\left[\frac{n_{2}}{N_{2,f}}\right]^{(\frac{N_{2,f}}{N_{1, f}})[\frac{\sigma_{1,bb}^{2}}{2\sigma_{bb}}]} \tag{14}\]  
This model presents the proposed nonlinear damage accumulation model that considers the effect of the sequence and interaction effect induced by a random vibration PSD. Here, it is highlighted that the total cycles to failure \(N_{i}\) value is determined by using the Basquin's equation defined in Equation (25), and that it is determined by placing into Equation (25) the corresponding vibration bending stress \(\sigma_{ib}\). It is used instead of the constant tensile stress \(\sigma_{i}\) value that is obtained from the material's S-N diagram.  
Thus, the proposed model for damage accumulation is applied, starting from calculating the bending stress caused by the effect of random vibration, as follows.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 2 Background
### Related Work  
Information hiding in mobile networks has already been a subject of research, especially within LTE systems. In 2013, [PERSON] et al. [2] evaluated the capabilities of covert channels in LTE advanced (LTE-A). The authors analyzed the underlying protocols to determine how and where secret information could potentially be hidden. Based on this analysis, [PERSON] and [PERSON] [15] designed the covert channel LaTEsteg, which uses physical layer padding to hide data in an LTE network. [PERSON] et al. [16] improved this method by combining padding bits and sequence numbers in order to enhance robustness and flexibility. The resulting covert channel is called LaSPsteg. [PERSON] et al. [17] also analyzed the protocol stack of LTE-A and created HyLTEsteg by utilizing a covert timing channel as well as a covert storage channel.  
Only a small number of publications have analyzed the capabilities of covert communication in 5G. [PERSON] proposed a covert channel called SPARROW [18] that exploits the broadcast signals of the MAC layer in LTE and 5G. After the vulnerability was responsibly disclosed to the GSM Association in CVD-2021-0045, [PERSON] and [PERSON] published a framework for the denitrification and mitigation of the SPARROW covert channel [19]. As part of a comprehensive security analysis of the A1 interface in a 5G Open RAN system, [PERSON] et al. [20] evaluated the feasibility of covert channels with O-RAN's management components, the so-called RAN Intelligence Controllers (RICs). The authors described a timing channel that encodes information by setting and removing a preshared policy as well as a storage channel that utilizes a preshared key value within a policy to transmit the hidden information. [PERSON] and [PERSON] [21] described an overshadowing attack that can be made \"stealthy\"; yet, they did not use this to describe covert communication, although they can block access to a cell temporarily and thus signal information. [PERSON] et al. [6] described an attack on 4G/5G communication that introduces a delay on the Radio Link Control (RLC) layer to increase the uplink (UL) application traffic's latency by sending a falsified RLC Negative Acknowledgement (NACK) inside a false RLC STATUS report via overshadowing. While the authors did not mention steganography, we use their approach to sketch a timing covert channel in Section 4.6.  
Even though covert communication in mobile networks is a subject of current research, covert channels specific to 5G New Radio have not yet been evaluated to the best of our knowledge. Considering that 5G New Radio has already been deployed in public mobile networks, this is a substantial research gap that is partly addressed by our contribution. Themost promising part of the 5G New Radio protocols are those that exceed the 4G protocols and are accessible, which, for example, excludes Layer 1.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Fabric-Based Graphene ECG Electrodes
#### 2.2.3 Nanofiber-Based Graphene ECG Electrode  
Nanofibers are a kind of one-dimensional (1D) materials with unique physical and chemical characteristics [48]. They can form nano-scale networks with high porosity and ultra-light weight. Most significantly, their huge specific surface area can provide a large number of binding points that can combine with other functional materials to enable the nanofibers to have customized functions [49]. Electrospinning is a fast and simple way to produce nanofibers [50]. By applying an electrostatic field to the solution of the desired polymer, the corresponding polymer nanofiber can be \"spun out\". Based on electrospinninging polyvinylidene dilfluoride (PVDF)/poly (3,4-ethylene dioxythiophene) polystyrene sulfonate (PEDOT: PSS) nanofibers, [PERSON] et al. realized a carbon-nanofiber sensing electrode and applied it in smart clothing for ECG monitoring [51] (Figure 2c). A conductive film consisting of reduced graphene oxide and carbon black was used to collect the electrospun nanofibers. After electrospinninging, the conductive film was combined with the nanofibers to create a nanofiber-based electrode with high conductivity (25 \(\Omega\)/square) and high stability (can sustain over 3000 repeated uses). The nanofiber-based graphene ECG electrodeincreases the contact area between the electrode and skin, and shows good hydrophobicity while realizing high stability and excellent electrical conductivity, making it suitable for long-term ECG monitoring. Moreover, its performance in intelligent clothing shows its excellent stability for monitoring ECG signals while running.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 2 Design Issues and Routing Challenges in Green Industrial IoT Networks  
Industrial wireless networks are deployed with self-organizing, flexibility, and inherent smart processing ability. In order to make the industrial network green, the underlying communication technologies and routing strategy need to optimize that can satisfy the energy hunger of resource-constrained smart devices. In this paper, the green IoT concept is considered in a viewpoint of how the routing approach can achieve energy efficiency in the resource-constrained IoT system. While state-of-the-art technologies being considered, the industrial wireless network still presents different challenges, for instance, unstable communication links, limited resources (low bandwidth, storage, energy, etc.), and varied traffic pattern. These unique characteristics pose a significant challenge to the design of reliable and efficient routing solutions. Based on the purpose and requirements of the application, routing protocols in IIoT networks may face the following challenges:
## Appendix A  
Considering the independence and symmetry between the real and imaginary parts of the MQAM signal, the probability of the symbol being correctly received for the in-phase and quadrature branches of the MQAM signal are equal, \(P_{C,R}=P_{C,I}\). Therefore, the SER for MQAM signal is  
\[P_{S}=1-P_{C,R}^{2} \tag{1}\]  
Because the symbol estimator tends to assign received symbols to their closest possible constellation point, the probability of the transmitted symbol \(S_{i}\), which is correctly estimated as \(\hat{x}=S_{i}\) by the receiver, can be expressed as (only the real part is considered)  
\[\begin{array}{l}P_{C,R}=\frac{1}{k+1}\sum\limits_{i=0}^{k}P_{C,R}(S_{i,R})\\ =\frac{1}{k+1}\sum\limits_{i=0}^{k}P(\hat{x}_{R}=S_{i,R}|x_{R}=S_{i,R})\\ =\frac{1}{k+1}\sum\limits_{i=0}^{k}\int_{D_{i,R}}f(y_{R}|x_{R}=S_{i,R})dy_{R}= \frac{1}{k+1}\sum\limits_{i=0}^{k}\int_{D_{i,R}}\frac{1}{\sigma_{n}}\varphi( \frac{y_{R}-\alpha S_{i,R}}{\sigma_{n}})dy_{R}\end{array}, \tag{2}\]  
where \(D_{i,R}\) is the decision region for symbol \(S_{i,R}\). By using \(v=y_{R}-S_{i,R}\), then \(\widetilde{D}_{iR}=D_{iR}-S_{iR}\), \(y_{R}-\alpha S_{iR}=v-(-S_{iR}+\alpha S_{iR})\). Letting \(\lambda_{ii,R}=-S_{i,R}+\alpha S_{i,R}\), Equation (2) can be rewritten as  
\[\begin{array}{l}P_{C,R}=\frac{1}{k+1}\sum\limits_{i=0}^{k}\int_{\widetilde{D }_{iR}}\frac{1}{\sigma_{n}}\phi(\frac{p-\lambda_{ii}}{\sigma_{n}})dv\\ =\frac{1}{k+1}Q(-\frac{\lambda_{ik}}{\sigma_{n}})+\frac{1}{k+1}Q(\frac{b- \lambda_{ik}}{\sigma_{n}})+\frac{1}{k+1}\sum\limits_{i=1}^{k}\left(Q(\frac{-b- \lambda_{ii}}{\sigma_{n}})-Q(\frac{b-\lambda_{ii}}{\sigma_{n}})\right)\end{array}. \tag{3}\]  
Comparing Equations (14) and (3), we can get  
\[P_{C,R}=\frac{\xi[k]^{2}-F}{2\sigma_{n}^{2}}+\frac{1}{k+1}\sum\limits_{i=1}^{ k}\left(Q(\frac{-b-\lambda_{ii}}{\sigma_{n}})-Q(\frac{b-\lambda_{ii}}{\sigma_{n}}) \right), \tag{4}\]where  
\[\begin{array}{ll}F=&\frac{2}{1+k}\{\sum\limits_{j=0}^{k}\left[\sigma_{n}(-b+ \lambda_{jk,R})\varphi(\frac{-b-\lambda_{jk,R}}{\sigma_{n}})+\lambda_{jk,R}^{2} Q(\frac{-b-\lambda_{jk,R}}{\sigma_{n}})\right]+\sum\limits_{j=0}^{k-1}\sigma_{n}^{2}Q( \frac{-b-\lambda_{jk,R}}{\sigma_{n}})\\ &+\sum\limits_{j=0}^{k}\left[\sigma_{n}(b+\lambda_{jk,R})\varphi(\frac{b- \lambda_{jk,R}}{\sigma_{n}})+\lambda_{jk,R}^{2}Q(\frac{b-\lambda_{jk,R}}{ \sigma_{n}})\right]+\sum\limits_{j=0}^{k-1}\sigma_{n}^{2}Q(\frac{b-\lambda_{jk,R}}{\sigma_{n}})\\ &+\sum\limits_{i=1}^{k}\sum\limits_{j=0}^{k}\left[\sigma_{n}(-b+\lambda_{ji,R })\varphi(\frac{-b-\lambda_{ji,R}}{\sigma_{n}})+(\lambda_{ji,R}^{2}+\sigma_{n }^{2})Q(\frac{-b-\lambda_{ji,R}}{\sigma_{n}})\right]\\ &-\sum\limits_{i=1}^{k}\sum\limits_{j=0}^{k}\left[\sigma_{n}(b+\lambda_{ji,R })\varphi(\frac{b-\lambda_{ji,R}}{\sigma_{n}})+(\lambda_{ji,R}^{2}+\sigma_{n }^{2})Q(\frac{b-\lambda_{ji,R}}{\sigma_{n}})\right]\}\end{array} \tag{10}\]  
Substituting Equation (10) into Equation (11), the SER as a function of NDA-EVM is  
\[P_{S}=1-\{\frac{\xi[k]^{2}-F}{2\sigma_{n}^{2}}+\frac{1}{1+k}\sum\limits_{i=1}^ {k}\left[Q(\frac{-b-\lambda_{ii}}{\sigma_{n}})-Q(\frac{b-\lambda_{ii}}{\sigma _{n}})\right]\}^{2}. \tag{11}\]