esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### DA-Sensor for CC Sensing  
The high sensitivity of the DA-sensor enabled the construction of a calibration curve for CC in the solution. In the concentration range of 0.05 mM to 10 mM, both anodic and cathodic peak currents increased proportionally with the CC concentration, exhibiting an excellent linear correlation for the anodic (Equation (3)) and cathodic (Equation (4)) peaks (Figure 7). The linear regression equations were expressed as follows:  
\[\mathrm{i_{pa}/\upmu A}=(32.16\pm 0.06)[\mathrm{CC}]/\mathrm{mM}+(14.5\pm 0.2);\mathrm{R}^{2}=0.9999999, \tag{3}\]  
\[\mathrm{i_{pc}/\upmu A}=(-24.4\pm 0.1)[\mathrm{CC}]/\mathrm{mM}+(-7.4\pm 0.4);\mathrm{R}^{2}=0.9999999. \tag{4}\]The limit of detection (LOD) and limit of quantification (LOQ) were subsequently estimated through calculation using the following established formulas (Equations (5) and (6)):  
\[\text{LOD}=\frac{3\text{S}_{\text{a}}}{\text{b}}, \tag{5}\]  
\[\text{LOQ}=\frac{10\text{S}_{\text{a}}}{\text{b}}, \tag{6}\]  
where S\({}_{\text{a}}\) is the error of the intercept and b is the slope of the calibration curves. The LOD and LOQ for CC were determined to be 18.6 uM and 62.1 uM, respectively, with a linear dynamic range from 62 uM to 10,000 uM, thus encompassing around three orders in the concentration level. For two levels in the CC concentration, 250 uM and 3 mM of CC in 50 mM PBS at pH 7.4, the relative standard deviation (RSD) obtained was 0.8%.
## 3 Jamming Attack Models  
A jammer is defined in [14] as \"...an entity who is purposefully trying to interfere with the physical transmission and reception of wireless communications\". There is a number of models for jamming, and some of them will require more resources than others when they are carried out in a real scenario. Budget, effectiveness, technical complexity and energy consumption are some of the factors that are considered when defining an attack strategy.  
In constant jamming, for instance, a constant emission of a radio signal is used to block a node from receiving signals from others. In addition, if that target node MAC layer is based on a carrier sense scheme, then it will not be able to transmit either, since the medium channel will always be busy. This kind of jamming produces an effective denial of service (DoS). Yet, it may require purchasing specialized hardware, such as a waveform generator or a power amplifier, since, often, a power supply or a battery with enough capacity is needed in order to provide the energy demanded by the jamming equipment. This makes jamming deployment less practical, but more importantly, it eases the identification of a suspicious device. Indeed, when a DoS becomes so evident, constant jamming can be easily detected; therefore, it might not be a reasonable long-term attack strategy.  
In the process of defining how a jamming attack should be attempted, one hence may consider special hardware or even a special configuration of a wireless node. However, from the attacker's point of view, for the sake of both simplicity and inexpensiveness, the hardware that can best interfere with the channel frequencies of the target node is one that is originally intended to serve as a legitimate node of the WSN, and since, in most cases, such a device is low-cost, the attack can be very practical. In our work, we shall consider a practical scenario where the jamming is produced by a device with the same MAC and physical layer as the rest of the nodes of the WSN. Thus, the packets produced by the jamming device will have the same structure as the packets generated by legitimate nodes.  
In the comparative development of this work, we have used two strategies for jamming: random and reactive. The rationale behind this selection decision is as follows. Random jamming is, on the one hand, one of the simplest types of jamming to implement and fairly difficult to detect. On the other hand, reactive jamming is both complex to implement, but rather difficult to detect. We introduce our jamming models below.
## 4 Results  
### Deformation Results  
#### 4.1.1 Mean Deformation Rate Map  
Figure 5 shows the average annual ground deformation from 2015 to 2018 in the Erhai region by using SBAS-InSAR. The figure was superimposed by the reflection of the intensity information of the SAR image, where the positive values of the rate map indicate that the surface was uplifting in the vertical direction, and the negative values denote surface deformation in the same direction. The SBAS-InSAR-derived results are shown in Figure 4; the annual average deformation rates in the Erhai region ranged from \(-33\) to \(9\) mm/year. In the northern part of the Erhai region, the surface did not show obvious deformation; however, ground deformation was mainly concentrated in the southern Erhai region, with the most serious observation points reaching an average of \(-33\) mm/year.
# Key Roles of Plasmonics in Wireless THz Nanocommunications--A Survey
## 6 Summary and Conclusions
\begin{table}
\begin{tabular}{c c c} \hline
**THz Band Transceiver** & ***SoA** & **Beyond *SoA** \\ \hline \hline \multirow{7}{*}{Antenna} & & Graphene based nanoantennas (patch antenna, dipole, MIMO) \\ \cline{3-3}  & Silicon integrated antennas, CNT & Hybrid graphene-dielectric antennas (H2G- two graphene monolayers separated by a thin dielectric) \\ \cline{3-3}  & based antennas, ultra-wide broadband (UWB) and multi-band antennas & Plasmonic antenna with dielectric waveguides (single/dipole loop plasmon nanantenna, plasmonic horn nanoantennas, single/double/array Vivaldi plasmon antenna/hybrid Plasmon dielectric array) \\ \cline{3-3}  & & Plasmonic nanopatch antenna, with hybrid metal \\ \cline{3-3}  & & Insulator metal (HMIM) plasmonic waveguide \\ \hline \multirow{7}{*}{THz band transmitters} & & THz plasmonic lasers (plasmonic quantum cascade lasers (QCLs), THz QCLs with metallic cavities, single-mode metal-clad plasmonic lasers) \\ \cline{3-3}  & QCLs, SiGe-heterojunction bipolar transistors, HBTs) & Hybrid THz transmitters III-V semiconductor based HEMT, enhanced with graphene \\ \cline{3-3}  & & Plasmonic slot waveguides-nanocouplers with on chip sources (nanoLEDs, nanolasers) \\ \hline \multirow{7}{*}{THz band receivers} & & Hybrid plasmon THz wave detectors (POH slot waveguide with a bowtie-antenna, graphene slot photodetector on SOI) \\ \cline{3-3}  & & Plasmonic teraFET, graphene-FET \\ \cline{3-3}  & & Plasmonic internal photoemission detectors (PIPED) \\ \cline{3-3}  & transceiver package & Plasmonic lateral photoemission detectors (Tx, Rx) integrated on si-photomic chip platform \\ \hline THz band transceiver & & Graphene (GFET) based switch, LPF, BPF, phase shifter, graphene THz modulator \\ \hline \multicolumn{3}{c}{*SoA—state-of-the-art.} \\ \end{tabular}
\end{table} 
Table 2: SoA and beyond SoA technologies for THz band transceiver components. This comprehensive survey paper has highlighted such an objective, by qualitatively presenting in such detail, the latest plasmonic and accompanied photonics technologies on equipping future competitive THz nanoscale communication systems, hosting wireless THz nanoapplications, namely NoCs, WNSNs and beyond 5G communications, providing at the same time, motivation for research academia to seek for efficient solutions towards this direction. The author declares no conflict of interest.
# Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment
## 2 Materials and Methods
### Functions and Processes of System Software and Data Acquisition Systems
#### 2.2.2 Discharge Waveform Measurement  
The expert system for diagnosing damage to substation transmission and transformation equipment focuses on the online recording of raw discharge waveforms for oil-immersed transformers, as shown in Figure 7. It utilizes noise-suppression techniques and improves the identification rate of PD to ensure the reliable operation of the transmission and transformation equipment, reduce maintenance costs, enhance safety, ensure a stable power supply, and prevent power interruptions caused by unknown faults. PD is an important indicator for assessing the insulation health of substation transmission and transformation equipment. Continuous PD may lead to further aging or damage of insulation materials, thereby increasing the risk of equipment failure.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 7 Conclusions  
In this paper, the pitch and head systems are evaluated via several experimental tests with different gain values for stabilizing the system. We have generated the environmental disturbances (i.e., variation in depth, uncertainties of hydrodynamic coefficients) for the proposed controller using the Marine GNC toolbox in Simulink. It has been analyzed that the proposed HLQR controller shows a satisfactory response to hydrodynamic disturbances in comparison to PID and lead-compensator. Moreover, it has been analyzed that the HLQR controller provides an optimal response, better response time, and gets settled more easily because of the adaptive complexion of the controller. In addition to its durability in a variety of underwater settings, the suggested adaptive control technique can track the goal route consistently while maintaining an acceptable precision. Furthermore, the use of dual LQR controllers in the proposed HLQR, the step response and handle disturbances have been improved. Last, but not least, because of the interchangeability of our proposed control technique, it meets the industrialization criterion well.  
Conceptualization, H.T. and M.R.; methodology, M.A.H.; software, M.A.H.; validation, H.T. and M.R.; formal analysis, M.R.; investigation, S.S.A.; resources, S.S.A. and M.H.S.; data curation, M.H.S.; writing--original draft preparation, H.T.; writing--review and editing, S.S.A. and M.R.; visualization, H.T.; supervision, S.S.A.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Our Controller**} & **[PERSON] et al. [4]** & **[PERSON] et al. [23]** \\ \cline{2-7}  & **Pitch** & **Head** & **Pitch** & **Head** & **Pitch** & **Head** \\ \hline Rise Time (sec) & 0.94 & 0.792 & 2.0 & 7.365 & 3.07 & 4.25 \\ Settling Time (sec) & 10.1 & 9.8 & 20 & 30 & 19.8 & 24.8 \\ Error (\%) & 0 & 0 & 0 & 0.0028 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of results with state-of-the-art.
## 5 Implications and Recommendations  
The smart home can enhance the lives of older people in a modern society where digital technologies can provide much needed solutions. Smart home innovations can however, also be associated with new or compound existing problems. Based on insights from the literature and the preceding discussion the following recommendations are offered:1. Smart homes should be introduced in planning residences for older people with careful considerations of their strengths and potential risks. Importantly, older users need to be assured that any potential problems will be assessed, risks managed and feedback valued [30].
2. Technical and psychological accessibility can be addressed by fully investigating the views and needs of older people when implementing smart homes as spaces for aging in place. Close consultations with potential users need to take place before, during and after the construction of smart homes. The needs of older users can change during this process and should be accommodated so that a smart house can become a smart home (See for example [34]).
3. Financial accessibility or affordability should be considered. There is little point in building a smart home which is beyond the reach of its potential users. Suggestions made by The Aged and Community Services of Australia [9] provide some guidance for developing policy in this area when they recommend: 1. Stronger and better directed funding for public and non-profit housing; 2. A new incentive for private investment in low rent housing; 3. Expansion of the capability and contribution of the non-profit housing sector; 4. Better provision of residential infrastructure in high growth areas (p. 11).
4. As acknowledged by technologists, \"vulnerable users may not understand what the technologies they use can and cannot do--most ethical codes for computer development assume that users are knowledgeable and sophisticated in their use of computer technologies\" [30], it is recommended that some aspects of informative discussions and training should include not only the users but also their immediate social networks such as family members, friends (p. 13) and service providers.
5. A high level of public engagement associated with the introduction of smart homes can help inform the community and garner support.
6. Further research is needed around the technical and psychological accessibility to smart home technology for older users, the perceptions of caregivers, because of their direct and indirect involvement in long-term care provision.
# Investigating Bayesian Parameter Identification Using Non-Standard Laboratory Specimens
## 2 Modeling of Fracture Propagation
### Constitutive Law
Thus, the modeling errors \(\mathbf{e}_{\text{model}}\) were modeled as heteroscedastic measurement noise, where the variance is an input-dependent function of the chosen quantities. For the proxy modeling error \(\mathbf{e}_{\text{proxy}}\), the standard deviation is estimated from the variance of proxy errors evaluated on an independent validation dataset, i.e., input-output pairs not used during proxy model training. The main idea of the Bayesian method is to update the prior distribution of the parameters \(\pi(\mathbf{z})\) using the obtained measurements \(\mathbf{d}_{m}\) by applying [PERSON] theorem of conditional probability [23; 51; 52]:  
\[\pi(\mathbf{z}|\mathbf{d}_{m})=\frac{\pi(\mathbf{d}_{m}|\mathbf{z})\pi( \mathbf{z})}{\pi(\mathbf{d}_{m})}. \tag{17}\]  
Here, \(\pi(\mathbf{d}_{m}|\mathbf{z})\) represents the likelihood function, which quantifies the probability of obtaining the measurement \(\mathbf{d}_{m}\) given a specific set of parameter values \(\mathbf{z}\). This likelihood function is defined by the probability that the error \(\mathbf{\epsilon}\) equals \(\mathbf{d}_{m}-\mathbf{u}\)[23], where \(\mathbf{u}\) is computed from the forward operator \(\mathbf{u}=\mathcal{G}(\mathbf{z})\). The term \(\pi(\mathbf{d}_{m})\) is referred to as the evidence function. It serves as a normalization constant in [PERSON] theorem, ensuring that the posterior distribution \(\pi(\mathbf{z}|\mathbf{d}_{m})\) integrates to 1. The expression \(\pi(\mathbf{z}|\mathbf{d}_{m})\) represents the posterior distribution of the parameter values \(\mathbf{z}\). This distribution is the final result of the Bayesian inverse procedure, representing the updated understanding of the parameter values based on the used measurements \(\mathbf{d}_{m}\). A common challenge with the Bayesian inverse method is that, in most cases, the posterior distribution cannot be formulated explicitly. To address this, various mathematical techniques, such as sampling methods, have been developed to approximate its shape. Sampling methods generate samples that approximate a stationary distribution matching the desired posterior distribution. In this work, the Markov Chain Monte Carlo (MCMC) method, based on the Metropolis-Hastings algorithm, is chosen as a robust, simple, and model-independent technique [19; 50; 51; 53; 54]. This method employs a random walk approach to efficiently generate samples from the posterior distribution. However, when the posterior distribution exhibits a complex shape, the effective application of the MCMC method requires evaluating hundreds or even thousands of proposed parameter candidates. The most computationally intensive part of this process is constructing the likelihood function, which involves computing the forward model solutions \(\mathcal{G}(\mathbf{z})\) for each candidate parameter. For complex numerical models, such as models to simulate fracture processes, this represents a significant computational burden. To mitigate this, mathematical proxy models are often employed.
## 3 Results and Discussion  
### Extraction of The Seed Oil of Prunus mahaleb L. Using Different Solvents  
Oil can be extracted from plant seeds using various solvents, such as ethanol, isopropanol, chloroform, \(n\)-hexane, diethyl ether, petroleum ether, and \(n\)-heptane [84, 85]. Selecting a solvent is the key step during extraction because different solvents presumably provide different compositions and yields [47, 86]. In this research, two different solvents were used to extract oil from the seed of _Prunus mahaleb_ L., which were petroleum ether and \(n\)-hexane, since \(n\)-hexane is one of the most common solvents to extract oils from plant sources due to the miscibility of the oils in this solvent and easy recovery [58]. However, both solvents have a low polarity index (0.1) to extract the seed oil and a low boiling temperature, which is in favor of the heat-sensitive compounds [87]. In this study, the percentage (g/100 g) of _Prunus mahaleb_ L. seed oil extracted with petroleum ether (PMSOEPE) was (30.5%), which was approximately equal to the yield provided by [PERSON] et al., (2009) [45]. However, the contents of the extracted oil were significantly higher than the previous works achieved by [PERSON], (2005) (4.7-18.5%) [46] and [PERSON] et al. (1997) (12-16%) [88]. The yield of _Prunus mahaleb_ L. seed oil extracted with \(n\)-hexane (PMSOENH) was (26%), while the earlier studies by [PERSON], (2014) and [PERSON] et al., (2015) reported a slightly higher percentage of the extract (31%) [41, 55]. Finally, PMSOENH was provided with a bright yellowish color compared with the dark yellow PMSOEPE color (Figure 2).
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 5 EA-FSW of Similar Joints  
A similar joint refers to a joint with the same base materials welded together. In such a case, the EA-FSW can be divided into two groups [45]. The electrical current was applied to the base materials and on the joint line throughout the tool. The collection data about various joint types in EA-FSW of similar joints are presented in Table 3. The Inconel 601 was the thinnest plate (2 mm) welded by EA-FSW, and the AA2219-T87 aluminum alloy was the thickest plate (6 mm). Most joints currently use electrical tools to add extra heat to the joint line; in other cases, the preheating EA-FSW case was mostly used. On the other hand, the output results of EA-FSW joints were mostly focused on material flow and hardness of the joint.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Raw Material** & **Thickness** & **Type of Joint** & **Output Results** & **Ref.** \\ \hline AA6061-T6511 aluminum & 0.25 in & Combination & Force, temperature, and hardness & [26] \\ \hline AA6061 aluminum & 3.175 mm & Combination & Force and torque & [27] \\ \hline AA6082-T6 aluminum & 4 mm & Simple & Heat and material flow & [28] \\ \hline AA6082-T6 aluminum & 4 mm & Simple & Heat and material flow & [29] \\ \hline AA2219-T87 aluminum & 6 mm & Simple & Material flow and mechanical properties & [30] \\ \hline AA7075 aluminum & 5 mm & Preheat & Microstructure and hardness analysis & [31] \\ \hline AZ31B magnesium & 5 mm & Preheat & Microstructure and hardness analysis & [31] \\ \hline AZ31B magnesium & 3 mm & Preheat & Material flow and mechanical properties & [32] \\ \hline AZ31B magnesium & 5 mm & Simple & Material flow and mechanical properties & [33] \\ \hline Ti-6 Al-4V titanium & 3 mm & Simple & Material flow and mechanical properties & [34] \\ \hline Ti-6 Al-4V titanium & 3 mm & Simple & Microstructure analysis & [35] \\ \hline Low-carbon steel & Not mentioned & Preheat & Hardness analysis & [36; 37; 38] \\ \hline Inconel 601 & 2 mm & Simple & Tensile strength & [39] \\ \hline \end{tabular}
\end{table}
Table 3: Used joint types during EA-FSW of similar joints.
# MUXER--A New Equipment for Energy Saving in Ethernet
## Materials and Methods
### MUXER in Network Topology  
As mentioned above, MUXER has to know when data signals come to it, where these signals need to forward, and how these signals should be transferred. In network topology, there are three positions that could be put MUXER: before the end-terminal, behind end-terminal and before network devices, behind network devices. In here, we proof which position is the best one to put MUXER in network topology. Obviously, MUXER could not be put before the end-terminals, because of the scalability and the deployment capability. In the case of the third position, when MUXERs are put in there, they cannot recognize the traffic coming from end-terminals to network devices, so that they don't know how to make decisions to turn off or turn on network devices. Hence, with the second position, when MUXER(s) are between end-terminals and network devices, all defined designed and functioned principles are satisfied. MUXER can receive the data signals from end-terminals, and determine which actions will be performed later (turn on devices, turn off devices, or transfer data signals). Figure 2 shows the position of MUXER in a sample network topology.  
In above figure, one MUXER (MUX 1, MUX 2) can receive data signals from many end-terminals (E1, E2, E3, E4 for MUX 1, and E5, E6, E7, E8 for MUX 2). Beside it, each MUXER controls a group of network devices (MUX 1 controls switch SW1 and SW2, MUX 2 controls switch SW3, SW4, SW5). To communicate between MUX 1 and MUX 2, they use a control line (red line), and a data line (black line). The roles of the lines are described in the next section.  
In the case of hierarchical network topology, where the data signals can leave from child network devices, and come to upper network devices, the position of MUXER(s) can be difference. Normally, MUXER(s) also can be put in the same position described in the previous sample. Beside it, MUXER(s) can be put behind the children nodes and before their parent nodes. In this case, we consider the role of children nodes looks like end-terminals in the previous sample, and parent nodes are network devices. Hence, the components of MUXER should need to be simple, so that it can be applied for all network topologies. The next section describe details about MUXER's components.