esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 6 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Replaced Sensor**} & \multicolumn{2}{c}{**Reconstruction Error**} & \multicolumn{2}{c}{**Excess Error**} \\ \cline{2-5}  & **ASC** & **ASC\({}^{\text{SEL}}\)** & **ASC** & **ASC\({}^{\text{SEL}}\)** \\ \hline temperature (\({}^{\circ}\)F) & 0.47 \(\pm\) 0.012 & **0.38 \(\pm\) 0.009** & 0.34 \(\pm\) 0.010 & **0.22 \(\pm\) 0.009** \\ humidity (\%) & 0.53 \(\pm\) 0.016 & **0.47 \(\pm\) 0.014** & 0.42 \(\pm\) 0.014 & **0.31 \(\pm\) 0.011** \\ dew point (\({}^{\circ}\)F) & 0.47 \(\pm\) 0.012 & **0.44 \(\pm\) 0.009** & 0.37 \(\pm\) 0.010 & **0.25 \(\pm\) 0.009** \\ wind speed (mph) & 5.04 \(\pm\) 0.061 & **4.83 \(\pm\) 0.059** & 4.36 \(\pm\) 0.052 & **3.71 \(\pm\) 0.055** \\ wind gust (mph) & 6.28 \(\pm\) 0.052 & **5.61 \(\pm\) 0.045** & 4.75 \(\pm\) 0.041 & **3.96 \(\pm\) 0.042** \\ pressure (Pa) & 3.17 \(\pm\) 0.19 & **1.68 \(\pm\) 0.18** & 2.68 \(\pm\) 0.19 & **1.04 \(\pm\) 0.18** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Individual sensor changes on weather data with many sensors. Each entry shows the average reconstruction error and the corresponding standard error. The best performing method(s) (statistically significant up to one standard error) are in bold font.
###### Abstract  
Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.  
adaptive neuro-fuzzy inference system; firefly; forecasting; hybrid firefly and particle swarm optimization; particle swarm optimization; photovoltaic; wavelet transform +
Footnote †: journal: Applied Sciences
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 4 Stable Throughput of Cooperative System (COG)
### Stay and Switch Mode  
In this system, \(N=1\) and the success rates of packet transmission for \(j\in\{\texttt{R}0,\texttt{HY}\}\)\(v_{j,1,0}\) and \(v_{j,1,1}\) expressions are given in Appendix A.1, where \(p_{d,\texttt{HY}}\) and \(p_{f,\texttt{HY}}\) are detection and false alarm probabilities of HY transmitter, respectively.  
The average service rate of \(\mu_{j,1}\) is given in (8) with \(v_{j,1,0}\) and \(v_{j,1,1}\) in (A1)-(A4). In that model, the average service rate depends on the probability of the buffer state of the pair borrowing the frequency. In this case, the domain system is used to find a stable region. In the domain system, one buffer is always filled with packets, and the average service rate does not depend on the probability of the buffer state.  
The first dominant system is identical to the original system, except that the RF transmitter transmits a dummy packet whenever \(Q_{\texttt{R}0}\) empties. All other assumptions remain unchanged in the dominant system. Let \(\mu_{\texttt{HY}}^{(1)}\) and \(\mu_{\texttt{R}0}^{(1)}\) be the service rates of \(Q_{\texttt{R}0}\) and \(Q_{\texttt{HY}}\) in first dominant system, respectively, then they are given by  
\[\mu_{\texttt{HY}}^{(1)} = v_{\texttt{HY,1},} \tag{19}\] \[\mu_{\texttt{R}0}^{(1)} = \left(1-\frac{\lambda_{\texttt{HY}}^{(1)}}{\mu_{\texttt{HY}}^{(1 )}}\right)v_{\texttt{R}0,0}+\frac{\lambda_{\texttt{HY}}^{(1)}}{\mu_{\texttt{ HY}}^{(1)}}v_{\texttt{R}0,1}. \tag{20}\]  
Thus, the stable region for the first dominant system is obtained by  
\[R_{\texttt{COG,SAS}}^{(1)}=\{\lambda_{\texttt{HY}}^{(1)},\lambda_ {\texttt{R}0}^{(1)}|\lambda_{\texttt{HY}}^{(1)}<\mu_{\texttt{HY}}^{(1)}, \lambda_{\texttt{R}0}^{(2)}<\mu\texttt{R}0^{(2)}\}. \tag{21}\]  
The second dominant system is identical to the original system, except that the HY transmitter transmits a dummy packet whenever \(Q_{\texttt{HY}}\) empties. All other assumptions remain unchanged in the dominant system. Let \(\mu_{\texttt{HY}}^{(2)}\) and \(\mu_{\texttt{R}0}^{(2)}\) be the service rates of \(Q_{\texttt{R}0}\) and \(Q_{\texttt{HY}}\) in second dominant system, respectively, then they are expressed as \[\mu^{(2)}_{\texttt{RO}} = v_{\texttt{RO,1}}, \tag{22}\] \[\mu^{(2)}_{\texttt{HY}} = \left(1-\frac{\lambda^{(2)}_{\texttt{RO}}}{\mu^{(2)}_{\texttt{RO}}} \right)v_{\texttt{HY,0}}+\frac{\lambda^{(2)}_{\texttt{RO}}}{\mu^{(2)}_{\texttt{ RO}}}v_{\texttt{HY,1}}. \tag{23}\]  
Thus, the stable region for the second dominant system is obtained by  
\[R^{(2)}_{COG,\text{SAS}}=\{\lambda^{(2)}_{\texttt{HY}},\lambda^{(2)}_{\texttt{ RD}}|\lambda^{(2)}_{\texttt{HY}}<\mu^{(2)}_{\texttt{HY}},\lambda^{(2)}_{\texttt{RO}}< \mu\texttt{RO}^{(2)}\}. \tag{24}\]  
Finally, the stable region for COG with SAS is given by  
\[R_{COG,\text{SAS}}=\{R^{(1)}_{COG,\text{SAS}}\cup R^{(2)}_{COG,\text{SAS}}\}. \tag{25}\]
## 3 Results and Dissscussion
### Thermal Analysis  
The results of the thermal analysis are presented in Figure 8.  
By comparing the DTA curves recorded for the alloy and for the composite, it turns out that in the case of the composite, the value of the melting heat (energy) is lower (314.8 J\(\cdot\)g\({}^{-1}\)) than in the case of the alloy (372.9 J\(\cdot\)g\({}^{-1}\)). This difference corresponds to the volume of alloy replaced by the embedded SiC particles. As it is known, silicon carbide melts at much higher temperatures.  
Figure 8: Differential thermal analysis (DTA) curves during heating for aluminum alloy and for the composite aluminum alloy—SiC particles.  
Figure 7: Comparative dilatometric analysis of the alloy and the composite.  
The metallic coating of SiC particles, in addition to the effect of increasing their wettability by molten metal, also has a role in preventing the formation of aluminum carbide in case of heating above 700 \({}^{\circ}\)C.  
[PERSON] et al. [43] reported that a concentration of Si between 7 to 15% in the alloy prevents the formation of the Al4C3 phase as a result of the chemical reaction of Al with SiC according to chemical relation (1).  
\[4\text{Al}+3\text{SiC}\rightarrow\text{Al}_{4}\text{C}_{3}+3\text{Si} \tag{1}\]  
If this reaction takes place extensively, the formation of the Al4C3 compound leads to a degradation of the composite properties (for example its strength decreases due to the formation of weak phases at the interface).  
To confirm this fact, the thermal analysis was performed up to 800 \({}^{\circ}\)C (Figure 9). It can be easily noticed on the DTA curve that during heating no transformations occur after the aluminum melts, which means that the metal coating protects the reinforcement particles during the heating process.
## 1 Introduction
A second argument is based on the known characteristic of antiferromagnetic nanoparticles of having uncompensated  
Figure 1: Sketch of the AFM structures encountered in hole-doped CMR manganites at different doping levels. (**a**) Representation of a simple perovskite REMnO\({}_{3}\), in which each Mn cation (at the center of octahedra) interacts magnetically with six first neighbors through oxygen atoms (small gray balls); the RE cation (red ball) is at the center of the unit cell. (**b,d**) Most common AFM structures (only the Mn species are represented, with magnetic moments shown as arrows pointing up and down): (**b**) G-type structure, typical of x = 1 compositions with only not Jahn–Teller active Mn\({}^{4+}\) ions, in which all six first neighbors are antiferromagnetically coupled through Anderson superexchange interactions. (**c**) A-type and (**d**) C-type AFM structures, with, respectively, four over six and two over six ferromagnetic interactions, found when all (or the great majority) of Mn species are instead Jahn–Teller active Mn\({}^{3+}\). (**e**) CE-type charge and orbitally ordered AFM phase typical of half-doped compositions. Mn\({}^{3+}\) and Mn\({}^{4+}\) magnetic moments are represented by arrows of different colors (Mn\({}^{4+}\) blue arrows, Mn\({}^{3+}\) yellow arrows). surface spins, not sharing the core magnetic ordering, the importance of which is the greater the higher the surface to volume ratio [22; 23; 24; 25]. Moreover, the increase of unit cell volume with a decreasing particle size was proposed to affect the Mn e\({}_{\text{g}}\) bandwidth, responsible for a changed balance between FM and AFM interactions [26]  
In this work, we studied how the structural and magnetic properties are modified by particle size reduction in the particular case of Ho\({}_{0.5}\)Ca\({}_{0.5}\)MnO\({}_{3}\). Herewith, we shall consider the case of a very small particle size of about 20 nm.
# Distortion of Thomson Parabolic-Like Proton Patterns Due to Electromagnetic Interference
## 5 Conclusions  
Distortions of parabolic ion tracks in a TP spectrometer were reported in the literature [5; 6]. Unstable ion trajectories resulting into wiggles in the detector plane were assigned to the variation of the spectrometer's electric field due to the EMP or to the pointing of the laser-accelerated proton beam itself.  
On the basis of these observations, we carried out a series of measurements that showed correlation between the frequency spectrum of the EMP and the distortion of the parabolic-like tracks of protons on the recording system of the TP ion spectrometer at the PALS laser facility. In particular, frequencies extracted from the measured EMP signal were used to estimate the modulation of the electric field in our model. The parabolic tracks of protons obtained by Equation (3) and the measured ones presented similarities both in the time and the frequency domain. Frequency analysis of both the modulated and the measured parabolic curves showed that the frequency spectra had similar profiles, with peaks occurring at the same frequencies. Particularly, modulation frequencies between 50 and 200 MHz were found. In addition, the integrals of the frequency spectrum corresponding to both the modulated and the recorded parabola (i.e., the energy carried by the electrical signals) were alike. The ratios of such signal energies showed values in the range of 0.7-1.3. These observations led to the conclusion that the distortion of the spectrometer tracks detected at the PALS laser facility is caused by the EMP generated during and immediately after the high-energy laser-target interaction.  
Conceptualization, J.K. and A.V.; methodology, F.G., J.K., A.V., M.D.M., J.D., M.P. and D.M.; software, F.G.; validation F.G., A.V. and D.M.; formal analysis, F.G.; investigation, F.G., J.K., A.V., M.D.M., J.D., M.P. and D.M.; resources, D.M.; data curation, F.G., A.V. and M.D.M.; writing--original-draft preparation, F.G.; writing--rew and editing, F.G., J.K. and D.M.; visualization, F.G.; supervision, D.M.; project administration, D.M.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education, Youth, and Sports of the Czech Republic through the project \"Advanced Research Using High-Intensity Laser-Produced Photons and Particles\" (CZ.02.1.010.00.016_0190000789).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors gratefully acknowledge technical support provided by PALS staff. The authors declare no conflict of interest.  
Figure 7: Frequency spectrum of extracted and modulated curves in Figure 6. Peaks occurring at the same frequencies are also plotted. Additionally, the subplot shows values of the same frequencies at which peak overlapping occurred.
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 2 Materials and Methods
### Collision Localization Model  
In our previous work, we explored collision localization models based on a CNN [3] and an ESN [4], respectively. Drawing inspiration from these two approaches, we proposed a collision localization model that integrates a CNN and an ESN as feature extractors. In this model, the CNN demonstrates a propensity for capturing salient features along the line of sight, making it a prevalent choice for feature engineering. Meanwhile, the ESN exhibits the capability to unfold in accordance with the temporal sequence, finding widespread applications in time series analysis. To enhance the ultimate localization performance, we also integrate an SVM model as the final region classifier. Capitalizing on distinctive attributes of the CNN and ESN, we formulate a collision localization model based on a CNN-ESN-SVM (CE-SVM) architecture.
## 2 Materials and Methods
### Expression and Purification of Esterase EstA  
The complete ORF of the identified _estA_ was amplified by PCR (Peltier Thermal Cycler, Bio-Rad, Hercules, CA, USA) using primers EstA-F (5\({}^{\prime}\)-GGGGTACCATGATGAAATTAGCATCTC-3\({}^{\prime}\), _Kpn I_ (the restriction site is underlined) and EstA-R (5\({}^{\prime}\)-CCCTCAGATTACAAATTAGCTTCTC-3\({}^{\prime}\), _Xho I_ restriction site was underlined) based on the _estA_ sequence. The amplified product was digested with KpnI and XhoI and ligated into the KpnI and XhoI sites of pET32a (+), resulting in the recombinant plasmid pET32a-_estA_. The culture, induction, and harvest of recombinant _E. coli_ BL21 (DE3) cells carrying pET32a-_estA_ were performed according to the standard method [31]. The positive clone was identified by PCR, and after IPTG (1 mmol/L) was added, those positive colonies were inoculated overnight until its OD\({}_{600}\) reached 0.4-0.6. The culture suspensions were broken by sonic oscillator and examined by SDS-PAGE. After expression, the recombinant protein was purified using a Ni-Agarose His Kit (CWbiotech, Shanghai, China), according to the manufacturer's instructions. Briefly, the cultures were harvested by centrifugation and lysed with solution (10 mM imidazole,300 mM NaCl and 50 mM NaH\({}_{2}\)PO\({}_{4}\)). The protein solution was washed and balance by binding buffer in the kit (20 mM NaH\({}_{2}\)PO\({}_{4}\), 500 mM NaCl, pH 7.8) to make sure the sample adhered to the column completely after putting the filtered solution into the column. Then, a linear gradient concentration of imidazole was used to elute the recombinant protein adhered on the column, and then purified protein was dialyzed by Tris-HCL (pH = 7.4). His-tag was removed by incubating in Bovine Enterokinase overnight. The purified protein was analyzed via 1.5% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Protein concentrations were measured using the Bradford method with standard bovine serum albumin (BSA) [32].
# Beyond Human Vision: Unlocking the Potential of Augmented Reality for Spectral Imaging
## 2 Method
### Step 1: Data Acquisition  
As depicted in Figure 1, the first step is to acquire the necessary datasets to construct the 3D digital model of the object and integrate it with spectroscopy information.  
For the acquisition of the 3D model of the sample, we utilized a photogrammetry technique [7,24] as previously explored in a recent work [6]. Note that although we opted for this method due to the lower cost of the solution, there are other techniques such as laser-based scanning [25,26] that can be utilized if better resolution models are needed, in particular, if the object is small (e.g., below centimeter scale). For the photogrammetry technique, the 3D reconstruction of the sample was generated by capturing a video using a smartphone, which was then loaded into the Polycam platform to generate a realistic three-dimensional model of the sample and the respective texture.  
Regarding the spectral imaging dataset, any spectral imaging technique could be explored, as long it is utilized to scan a sample surface, resulting in a dataset of \(N_{x}\times N_{y}\times N_{\lambda}\), where \(N_{x}\) and \(N_{y}\) relate to the transverse spatial dimensions, whereas \(N_{\lambda}\) is associated with the spectral part (i.e., wavelength or wavenumber). For the present case, we utilized a Laser-Induced Breakdown Spectroscopy technique [6] to scan a sample surface point by point, obtaining a spectral signal for each point that contains emission lines to be related with the elements present in the sample [27].
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 3 GSFedSec: GS-Based SA for Privacy Preservation in FL
### System Setup
#### 3.1.5 Verification  
The server can identify the signature of the member by its group signature. If a member is registered as a group member and its membership is valid, it would sign with the latest group signature. The server can verify the latest group signature keys with the admin as given in step 4 of Figure 4. If the signature of the client is not matching with the latest group signature, it will be rejected by the server. In this way, the server ensures the identity of the client. However, as we can see, the server knows the group of the client from its signature, not the actual signer. The server computes the challenger from the group keys received from the admin, and ciphertext variables and signing variables received from the client. The client also sends the hashed challenger. If the two challengers, one sent by the client and one computed by the server, are not matching, the signature is rejected.  
When the server receives any message from the edge device, it first decrypts it, obtains message \(Upd_{s}\), and verifies the signature. To verify the signature, the server first collects the latest GS from the admin. Then, it computes the verification variables as (formula given in Equations (10a)-(10c)):  
\[\vec{B}_{1} \leftarrow u^{s_{\alpha}}c_{1}^{-c},\qquad\vec{B}_{2} \gets v^{\delta_{\beta}}c_{2}^{-c}, \tag{10a}\] \[\vec{B}_{3} \leftarrow\hat{e}(c_{3},g_{2})^{s_{x}}\hat{e}(h,w)^{-s_{\alpha}- s_{\beta}}\hat{e}(h,g_{2})^{-s_{\delta_{1}}-s_{\delta_{2}}}e(c_{3},w)/\hat{e}(g_{1},g_{ 2}))^{c},\] (10b) \[\vec{B}_{4} \gets c_{1}^{s_{x}}u^{-s_{\delta_{1}}},\qquad\vec{B}_{5} \gets c_{2}^{s_{x}}v^{-s_{\delta_{2}}}. \tag{10c}\]  
Then, the server computes the challenger \(c\) again as formula given in Equation (11):  
\[\hat{e} \leftarrow H(M,c_{1},c_{2},c_{3},\vec{B}_{1},\vec{B}_{2},\vec{B}_{3}, \vec{B}_{4},\vec{B}_{5}). \tag{11}\]  
If \(c=\hat{c}\), the signature is verified. Otherwise, the server rejects the message received. In this way, the server knows message \(M\) but does not know the signer of the message. The server receives the model updates from all participating members in this manner.
## 4 Optimization Method of MPDQN Based on DRL  
### Framework for Optimization Problems  
Based on the defined system model, the optimization problem is formulated to minimize the weighted sum of the average AoI and energy consumption of vehicles in the system. Since the AoI and energy consumption of vehicles depend on RRI \(\Gamma\) and power \(p\), the optimization problem can be expressed as follows:  
\[\min_{\Gamma^{t},p^{t}}\left[\omega_{1}\overline{E}+\omega_{2} \overline{\Phi}\right] \tag{15}\] \[s.t.\quad p^{t}\in[0,P_{max}],\forall t\in\mathcal{T},\] (15a) \[\Gamma^{t}\in\{20,50,100\},\forall t\in\mathcal{T}, \tag{15b}\]  
where \(\omega_{1}\) and \(\omega_{2}\) are non-negative weight factors.  
Since the channel conditions in the NR-V2X system are uncertain, we employ the Multi-Pass deep Q-Networks (MPDQN) method based on DRL to solve this optimization problem. In this method, the RSU serves as the agent, and its observed state at time slot \(t\) is as follows:  
\[S^{t}=\big{[}s_{1}^{t},\cdots,s_{i}^{t},\cdots,s_{N_{o}}^{t}\big{]}. \tag{16}\]  
The state of each vehicle is defined as follows:  
\[s^{t}=\Big{[}N^{t},\vec{d}^{t},P^{t}(u^{t}=1),RC^{0}\Big{]}, \tag{17}\]  
where \(N^{t}\) is the total number of other vehicles within the range \(w\), defined as receivers. The average distance to these receivers is \(\vec{d}^{t}\). \(P^{t}(u^{t}=1)\) is the probability of successful message reception by the receivers. \(RC^{0}\) represents the total number of times that vehicles use reserved resources.  
The action assigned by the RSU to vehicles at the time slot \(t\) is as follows:  
\[a^{t}=\big{(}\Gamma^{t},p_{\Gamma}\big{)}, \tag{18}\]  
where \(p_{\Gamma}\) represents the parameter that converts the continuous action \(p\) into a discrete action \(\Gamma\). They act as two sub-actions, and the tuple they form constitutes the complete action assigned.  
The objective of the optimization problem is to minimize the AoI and energy consumption in the system. Therefore, the reward function is defined as follows:  
\[r_{i}^{t}=-\Big{(}\omega_{1}E_{i}^{t}+\omega_{2}\overline{\Phi_{i}^{t}}\Big{)}. \tag{19}\]  
where \(\overline{\Phi_{i}^{t}}\) is the mean AoI of the receivers for vehicle \(i\) over a certain period of time:  
\[\overline{\Phi_{i}^{t}}=\frac{1}{T}\frac{1}{N_{v}}\sum_{t=1}^{T}\sum_{j\in N_{ o}}\Phi_{i\to j}^{t}. \tag{20}\]
## 4 Conclusions  
In this study, experimental and numerical work on the plasma stimulation technique is presented. Four rock samples are employed to study plasma stimulation under confined and unconfined rock conditions. Moreover, numerical study is presented to investigate the effect of plasma fracturing on natural fractures and to examine the plasma stimulation using nano-second pressure pulses. The results show the following:  
1. Stimulating confined rock using the plasma technique requires more energy and repetitive pulses to fracture than the unconfined case of the same rock type. It is noticed that fracturing the confined sandstone sample took four plasma pulses of more energy than the unconfined sandstone which was cracked after one pulse of less energy.
2. The rock type affects the development of plasma fractures. The confined sandstone sample required the release of plasma energy four times. However, the confined limestone sample was fractured after three pulses. This may be attributed to the rock tensile strength that is higher in sandstone than limestone.
3. The natural fractures numerical case shows that the presence of natural fractures can arrest the propagation of the plasma fractures. However, new plasma fractures may propagate from the natural fracture tips.
4. The extension of the plasma pressure pulse on the time axis has a greater effect on the developed fractures than the value of the peak pressure.  
Conceptualization, M.S. and S.M.F.-A.; Methodology, M.K.; Software, M.K.; Writing--original draft, M.K.; Writing--review & editing, M.S. and S.M.F.-A.; Supervision, M.S. and S.M.F.-A.; Funding acquisition, C.C. and R.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hess and Halliburton.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Hess Corporation and author [PERSON] was employed by the company Halliburton. The authors declare that this study received funding from Hess and Halliburton. The funders had the following involvement with the study: modification of objectives, set general direction of the research, provide field examples, and review the manuscript.  
Figure 25: Pressure pulse (**left**) and results (**right**) of the nanosecond pressure profile—case 1.