esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Open-Source Serverless Platforms for Kubernetes
## 2 Materials and Methods
### Experiment Setup  
The experiments were conducted using an OpenStack ([[https://openstack.org/](https://openstack.org/)]([https://openstack.org/](https://openstack.org/)) accessed on 27 May 2022) instance hosted by the GWDG ([[https://gwdg.de/](https://gwdg.de/)]([https://gwdg.de/](https://gwdg.de/)) accessedon 27 May 2022). OpenFaaS and Nuclio were each installed into a Kubernetes cluster of four VMs with four virtual CPU cores and 8 GB of memory per VM. Additionally, one more VM with 32 vCPU cores and 64 GB of memory was used as the function director (FD). The physical CPUs were AMD EPYC 2 7542 32-Core processors ([[https://www.amd.com/en/products/epyc](https://www.amd.com/en/products/epyc)]([https://www.amd.com/en/products/epyc](https://www.amd.com/en/products/epyc)) accessed on 27 May 2022).  
The network was handled by OpenStack and offered a theoretical upper speed limit of 10 Gbit/s between any two nodes. The speed between the FD VM and a container running in one of the Kubernetes clusters was measured at around 700 MB/s using iPerf ([[https://iperf.fr/](https://iperf.fr/)]([https://iperf.fr/](https://iperf.fr/)) accessed on 27 May 2022). HPC workloads commonly use remote direct memory access (RDMA) technologies that enable distributed processes to bypass the CPU when writing to another host's memory. Such technologies were not used here, as the serverless platforms under study do not support RDMA; therefore, it was expected that the CPU speed of the VMs would limit the throughput rather than the network's bandwidth capacity.  
Each request from the FD VM to a function instance running on any of the serverless platforms in the setup was handled as follows. First, the request is send out from the FD VM targeting the HTTP endpoint of a serverless function. The IP address of the endpoint resolves to the OpenStack load balancer, which in return forwards the request to the VMs running the Kubernetes cluster. Inside the cluster, the request is again load balanced by Kubernetes to the endpoints of the serverless platform, commonly referred to as Gateways. The gateways are responsible for resolving the URL path that identifies the target serverless function and load balancing incoming requests among all available serverless function instances. The experiment setup here used two gateway instances per cluster. Finally, the request reaches the VM running an available function instance, and is received by the web server of that function instance.
# Water Temperature and Salinity Measurement Using Frequency Comb
## 5 Conclusions  
In this work, a frequency comb, for the first time, is applied to measure water temperature and salinity. We achieved the target of temperature and salinity measurement, respectively. By a simple Michelson interferometer, based on the optical distance difference change between two arms, the refractive index change can be obtained, which empirical formulas can convert into temperature and salinity. Compared with the reference values, our results show differences of below 0.12 \({}^{\circ}\)C for temperature measurements, and 0.06% for salinity measurements. Owing to the limitation of our experimental equipment, we performed the experiment in a limited range. However, our method can be applied to a larger range, which provides a powerful scheme for future temperature and salinity measurements.  
[PERSON] came up with this idea. [PERSON]. ([PERSON]) designed the experiment. [PERSON]. ([PERSON]), [PERSON] and [PERSON]. ([PERSON]) performed the experiment. [PERSON]. ([PERSON]) and [PERSON] analyzed the data. [PERSON]. ([PERSON]) wrote the original manuscript. [PERSON], [PERSON] and [PERSON] did the review and editing.  
**Funding:** This research was funded by the Projects from Tianjin University of Technology and Education, grant number KJ15-24; the Natural Science Foundation of Tianjin, grant number 2018F2-0004.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: (**a**) Results obtained using salinity sensor and our method. Blue solid circle: our method results, black dashed line: the salinity sensor results; (**b**) the difference between our method and the salinity sensor.
## 4 Survey Findings
### Difficulties
Taking into account the nature of organizations' ownership, 84.01% are private while 15.99% public. It can be highlighted that public administrations are more satisfied with the EMAS. Just above 13% of them intend to drop out, while the percentage grows to over 25% for private organizations (Figure 3). Organizations were asked to indicate if the monetary investment made for EMAS has been paid back. Public administration and private firms' opinion differ substantially. Looking at Figure 4, public administrations (Figure 4a) seem slightly less satisfied than private enterprises (Figure 4b), and only 51.25% is deemed fully satisfied about the investment made, in respect of the 62.9% of private companies. Public administrations encountered wider organizational and economic difficulties in the implementation of the EMS, and were generally more enthusiastic about benefits achievable with EMAS than enterprises. Figure 4: Investment pay-off of EMAS for Private sector organizations (**a**) and Public Administrations (**b**). Source: Author’s elaboration on survey database. Figure 3: Organizations’ ownership and intention to renew EMAS. Source: Author’s elaboration on survey database. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**EMAS Renewal**} \\ \cline{3-4}  & & **No** & **Yes** \\ \hline \multirow{4}{*}{Dimension} & Large & 4 (4.04\%) & 95 (95.96\%) \\  & Medium & 17 (9.09\%) & 170 (90.91\%) \\  & Small & 43 (20.28\%) & 169 (79.72\%) \\  & Micro & 9 (22.50\%) & 31 (77.50\%) \\ \hline \hline \end{tabular} Source: Author’s elaboration on survey database.  
\end{table} 
Table 9: Organizations’ size and intention to renew EMAS. Comparing Figures 3 and 4 it is interesting to note that while public organizations have an higher propensity to renew, the monetary investment payback is greater for private firms. It is also interesting to note that even though public administrations perceive a major negative difference between drivers and benefits of EMAS, they declare an higher propensity to renew EMAS and an higher rate of investment pay off (Tables 4-6 and Figures 3 and 4).
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 4 Conclusions  
Carbon fiber-reinforced composite materials occupy a critical position in many structural material applications, and their EM shielding performance has also attracted great attention. This study mainly investigated the EMI shielding anisotropy of CFRP materials.  
The electrical conductivity of unidirectional CFRP composites varies due to the fiber orientation angles, and the calculated results by the formulas proposed in this study were highly consistent with the experiment values. It was found that the skin depth of unidirectional CFRP was different at various orientation angles and frequencies. At a frequency of 15 GHz, the CFRP exhibited the lowest skin depth value of 0.035 mm in the 0\({}^{\circ}\) direction. All the characteristics mentioned above are key issues leading to the EMI shielding anisotropy of CFRP composites. The obvious EMI shielding anisotropy of unidirectional CFRP composites is clarified by the experimental results using the specified set-up of free-space measurement. The shielding results predicted by the theoretical formula are highly consistent with the experimental results. The maximum difference between the predicted and experimental values at a fiber orientation of 45\({}^{\circ}\) is only about 1.9 dB.  
A comparison of the free-space measurement and the coaxial transmission line method was also conducted, and the influence of electric anisotropy on the test results was further discussed. The SE values obtained by the coaxial transmission line method was much lower than that obtained by the free-space measurement tested in the fiber direction, which indicated that special attention should be paid to the influence of the anisotropy of CFRP composites on the shielding results when evaluated by various measurements. With these results, the mechanism of EMI shielding anisotropy of CFRP composites is clarified, which will provide an effective design of EMI shielding products with a designable shielding direction and frequency.  
Conceptualization, [PERSON]; methodology, [PERSON] and P.X.; validation, [PERSON]; formal analysis, J.H.; investigation, [PERSON] and P.X.; resources, [PERSON]; data curation, [PERSON] and P.X.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and P.X.; supervision, [PERSON]; project administration, [PERSON] All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Figure 8: EMI SE of CFRP by the transmission line method and free-space method.
## 5 Discussion  
In this study, the system configuration of a UGV used for ground-aerial cooperation was briefly introduced, and an efficient path-planning method for autonomous driving of the UGV in real time was introduced. For real-time path planning, not only the stable avoidance of obstacles but also the calculation speed is very important. Although a simple and efficient potential field algorithm was selected, the computation time was insufficient. To overcome this, a method to reduce the amount of computation by using the vehicle's steering characteristics was proposed.  
Through the simulation, it was shown that the results of path generation and the existing method were not significantly different, and the calculation time was reduced compared to the existing method by measuring the calculation time. Then, a driving experiment was performed by applying the algorithm to the UGV, and it was confirmed that the UGV avoids obstacles in the driving path.  
Figure 17: Obstacle avoidance during operation of multiple USVs/UAVs: (**a**) UGV #1; (**b**) UGV #2.  
Despite the existence of various path-planning algorithms, path-planning methods are still needed to be used in a situation in which the controller needs to perform various tasks, including path planning, or a controller is needed with low computational performance. The method proposed in this paper is expected to be particularly suitable for controlling a vehicle equipped with a low-spec controller.  
Conceptualization and methodology, M.K. and S.Y.; formal analysis and software, M.K. and D.L.; data curation, writing--original draft, and writing--review and editing, M.K.; validation, S.Y. and G.-H.L.; supervision, project administration, and funding acquisition, G.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Research Project of KIMM (No. NK230H) and the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (Grant No. 2020M3C1C1A02086421).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 3 RFID Link Budget Calculation
### Downlink (Reader to Tag) Communication  
#### 3.1.1 Background  
In general, the radiation power flux density averaged over time, or the average Poynting vector, of an electromagnetic wave is given by \(\phi=e^{2}r_{rms}/120\pi\) (W/m\({}^{2}\)), where \(e_{rms}\) is the root mean square (rms) value of the electric field [39]. The effective aperture of a receiving antenna--i.e., the area over which the antenna gathers the energy of the incoming electromagnetic waves--is shown in specialized texts [39] to be \(a_{off}=g_{R}\lambda^{2}/4\pi\) (m\({}^{2}\)), where \(g_{R}\) is the gain of the antenna and \(\lambda\) (m) is the wavelength of the wave. It holds that the mean power \(p_{R}\) delivered to the receiving antenna is the product of the Poynting vector at the antenna by its effective area \(a_{off}\).  
\[p_{R}=\phi\cdot a_{eff}=\frac{g_{R}\lambda^{2}}{4\pi}\frac{e_{rms}^{2}}{120\pi} [W]. \tag{1}\]  
For a free-space propagation scenario, the Poynting vector at a distance \(r\) (m) from a transmitter can be also calculated, for a transmission power \(p_{T}\) (W), as \(\phi=p_{T}g_{T}/(4\pi r^{2})\), where \(g_{T}\) is the gain of the transmitter antenna in the direction of the receiver. By equating both expressions for \(\phi\), we obtain that [39]  
\[e_{rms}=\frac{\sqrt{30p_{T}g_{T}}}{r}[V/m]. \tag{2}\]  
Assuming, as a convenient simplification, that the dependence with time of the electric field is given by a sine or cosine function with peak amplitude \(\sqrt{2e_{rms}}\), the instantaneous field at this point is given by the wave  
\[e(t)=\frac{\sqrt{60p_{T}g_{T}}}{r}\cos(\omega_{c}t^{\prime})[V/m], \tag{3}\]  
where \(\omega_{c}\) (rad/s) is the carrier's angular frequency. Equation (3) reflects the fact that, as the propagation of the radio-waves is not instantaneous, the field received at time \(t\) was emitted at a different and earlier instant \(t^{\prime}\). Noting that \(t\)-\(t^{\prime}\) is the time took by the field to propagate over the distance \(r\), we can determine \(t^{\prime}\) by the equation  
\[t^{\prime}+\frac{r(t^{\prime})}{c}=t, \tag{4}\]  
where \(c\) is the speed of light. Substituting in (3) we get  
\[e(t)=\frac{\sqrt{60p_{T}g_{T}}}{r}\cos(\omega_{c}t-k_{c}r)[V/m], \tag{5}\]  
where \(k_{c}=\omega_{c}/c=2\pi f_{c}/c=2\pi/\lambda\) (rad/m) is called the 'wavenumber', and \(f_{c}\) (Hz) is the frequency of the wave.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### End-to-End Delay Optimization  
This section studies how a node's cache impacts end-to-end delay. We abstract the transmission procedure as the Markov procedure and analyze the packet loss depend on bottleneck nodes' remaining cache. Subsequently, we calculate the dispersion degree of the remaining cache size of the bottleneck nodes. Finally, we provide the multi-path data distribution algorithm depending on the remaining cache size of the bottleneck nodes.
## 3 Results and Discussion
### Biological Assessments
#### 3.3.3 Cytotoxic Activity
The mechanism of the cytotoxicity of metal nanoparticles against WI38, HepG2, and MCF7 is not fully understood, but it is thought to involve a combination of factors, including surface morphology of the nanoparticles, size, shape, surface defects, and bioaccumulation [63]. Precisely, the surface chemistry [64] of metal nanoparticles can affect their toxicity, in which positively charged nanoparticles are more likely to interact with cell membranes and cause damage. Smaller [65] and needle-shaped [66] nanoparticles are more likely to enter cells and cause damage. The presence of surface defects [67] on metal nanoparticles can also affect their toxicity. These defects can act as sites for the generation of reactive oxygen species (ROS), which can damage cells. The relative importance of these mechanisms may vary depending on the metal nanoparticle, the cell type, and the exposure conditions. For example, some metal nanoparticles may be more cytotoxic to WI38 cells than HepG2 cells [68]. In addition to these mechanisms, metal nanoparticles may also have other effects that contribute to their cytotoxicity. For example, metal nanoparticles  
Figure 8: Comparison of the cytotoxic results against the various tumor and normal cell lines. can induce inflammation, which can damage cells. Silver nanoparticles can generate ROS, which can damage DNA and other cellular components [69]. Silver nanoparticles can also induce apoptosis by activating caspases [33]. It is important to note that the cytotoxicity of metal nanoparticles can vary depending on the specific nanoparticle and the exposure conditions [70]. Additionally, the toxicity of metal nanoparticles can be increased by the presence of other chemicals, such as organic solvents or surfactants.
## 3 Improved Algorithm
### Determining Relevant Modes Based on Autocorrelation  
CEEMDAN obtains each mode by adding white noise and calculating residual at each decomposition stage. Given a discrete signal \(x(k)\), the steps of CEEMDAN can be summarized as follows [25]:  
Step1: Add white Gaussian noise to the original signal as follows:  
\[x^{m}(k)=x(k)+\varepsilon s^{m}(k) \tag{1}\]  
where \(\varepsilon\) is the noise factor and \(s^{m}(k)\) is the white noise obeying \(N(0,1)\) distribution. \(m=1,2,\cdots,M\), \(M\) is the times of adding noise.  
Step2: Decompose each \(x^{m}(k)\) by EMD. In EMD, the local minimum and maximum of each \(x^{m}(k)\) are calculated by the cubic spline method. The new mode \(IMF^{m}\) is the average of the minimum and maximum values [26]. The first mode of CEEMDAN is obtained by averaging the new modes. The first residual is obtained by subtracting this first mode calculated from the previous signal.  
\[IMF1(k)=\frac{1}{M}\sum_{m=1}^{M}IMF^{m} \tag{2}\]  
\[c_{1}(k)=x(k)-IMF1(k) \tag{3}\]  
Figure 2: Flowchart of the improved algorithm.  
where IMF1 is the first mode, and \(c_{1}\) is the first residual.  
Step3: Repeat all the above steps for the residual and terminate the algorithm when there are no more than two local minimums or maximums in the residual. IMFs and a residual can be obtained as follows:  
\[x(k)=\sum_{i=1}^{K}IMFi+c \tag{4}\]  
where \(i\) is the number of the modes, and \(c\) is the residual.  
The ACF of a discrete series \(y(k)\) is calculated as follows [24]:  
\[R(\tau)=\frac{1}{n}\sum_{k=1}^{n}y(k)\cdot y(k+\tau) \tag{5}\]  
where \(R(\tau)\) is the ACF for \(y(k)\) at time interval \(\tau\), \(n\) is the length of \(y(k)\).  
The effective signal is autocorrelated, and its ACF value does not decay rapidly with increasing time intervals. Therefore, its ACF value is highly discrete with respect to the mean value, which means that the RMS value is larger. The RMS of the ACF for each mode \(R_{RMS}^{i}\) is calculated as follows:  
\[R_{RMS}^{i}=\sqrt{\frac{\sum\limits_{\tau=0}^{N}R^{i}{(\tau)}^{2}}{N}} \tag{6}\]  
\(\beta_{1}\) is the set threshold. According to the principle of ACF, when choosing the value of \(\beta_{1}\), the amplitude range of the effective signal should be taken into account, as should the possibility of mode mixing [25], a phenomenon in which the effective signal is decomposed into multiple modes, resulting in small values for some of the relevant modes. When \(R_{RMS}^{i}>\beta_{1}\), the IMF is a relevant mode.
## 4 Results and Discussion
### Field Application of Investigation Findings  
To verify the results obtained from the above two methods, it is necessary to compare residual gas content, calculated according to the result of extraction ratio, with the residual gas content measured on site. The field application location is chosen as at the 14\(\mu\) mining face for replacement mining (as shown in Figure 6), and the residual gas content of the 6m coalbed after CBM extraction is calculated according to Equation (13).  
\[W_{cy}=\frac{W_{0}G-Q\eta}{G} \tag{13}\]  
In the equation, \(W_{cy}\) is the residual gas content of 6m coalbed after CBM extraction, m\({}^{3}\)/t; \(W_{0}\) is the maximum original gas content of 6m coalbed measured. According to the geological data revealed by on-site drilling and mining, the maximum original gas content of the 6m coalbed measured in the field at the 14\(\mu\) mining face is 12.47 m\({}^{3}\)/t; \(Q\) is the volume of CBM extraction from multi-seams. When conducting CBM extraction from multi-seams in the 14\(\mu\) bottom lane, the CBM extraction volume within the 90-day extraction period is 12,608.43 m\({}^{3}\); \(\eta\) is the extraction ratio of the 6m coalbed, the average value of the results determined by the above two methods on the 90 th day of extraction, which is 60.81%; \(G\) is the coal reserve within the extraction influence range that participates in the calculation, which is calculated according to Equation (14).  
\[G=4 mn\gamma R^{2} \tag{14}\]  
In the equation, \(R\) is the radius of CBM extraction for the layer-through boreholes, 2.5 m; \(n\) is the number of layer-through boreholes, 16; \(m\) is the average thickness of the coalbed, 1.8 m; \(\gamma\) is the bulk density of the coalbed, 1.70 t/m\({}^{3}\).  
Using the average value of the results determined by isotope geochemistry and the measured flow method, the residual gas content of the 6m coalbed is calculated to be 6.21 m\({}^{3}\)/t (as shown in Table 3). The maximum residual gas content of the 6m coalbed measured in the field is 5.84 m\({}^{3}\)/t.  
The deviation rate of the residual gas content between the calculated value and measured value is 5.9%.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 6 Simulation Results
### Sixth Scenario  
In this scenario shown in Figure 14, the jammer is located in quadrant EG23, the CH of each cluster varies notably from the previous scenarios. The most affected cluster, the blue, changes its CH selection to E3. This change permits that node in position D3, which was previously from the green cluster, to now associate with the blue cluster. Additionally, several nodes in column H change their association to the yellow cluster.  
The number of isolated nodes is 23.44% of total nodes in the network. As a consequence of the high impact of the jammer, the relay nodes will have a higher load to provide communication. On the other hand, the algorithm calculates that, with a transmission power of 23.45 dBm, the isolated nodes can communicate with the CH.  
The information transmitted for FP and PA is only 199,171 kb, due to the high transmit power. The residual energy is 30.66 J for FP and 559.28 J for PA. The cooperation strategies use several relay nodes to connect all the nodes to the CH. This is explained by the relative distance between the jammer and the isolated nodes in column G. Note that  
Figure 13: Fifth scenario: where the blue cluster is notably the most affected, with multiple isolated nodes.  
as the associations that a relay can provide are reduced, the load per relay increases. The information transmitted for cooperation strategies is 94,937 kb for CP and 159,142 kb for PAC. The residual energy for these strategies is 559.28 J for CP and 566.80 J for PAC, with the worst tradeoff for the analyzed scenarios. Finally, the information transmitted for FCPA is 914,288 kb with a residual energy of 424.34 J. The main difference between the PAC cooperation strategy is the balanced use of the relays. As a consequence, the CH nodes exhaust their batteries first. However, in this scenario, the number of hops increases significantly for isolated nodes in column H.