esa-sceva/satcom-chunk-collection huggingface dataset:
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 3 System Configuration and Procedure
### Procedures  
A flowchart of the hearing assistance system including the subject's movement (colored) is shown in Figure 13. If the voltage of the acoustic signal on one side of the ear microphone is less than the threshold value (\(V_{th}\)), we return to the beginning and repeat the signal detection. If the voltage exceeds the threshold value, we proceed to the next step. The threshold value is adjusted so that it is not affected by external noise. Acoustic signals are detected by the ear microphone, and the phase difference between the two ears is calculated for a short time. From this phase difference, we calculate the short-time interaural time difference and change the ratio of the vibration intensity of the two vibrators according to the direction of the sound source. In parallel with this process, we calculate the average value of the standard deviation of the variation of the short-time interaural phase difference along the frequency, and the vibration intensity of both shoulder vibrators is changed correspondingly. The subject was instructed to turn his face in the direction where the vibration intensity of both vibrators was equal. If the subject felt that the vibration intensity on the left shoulder was stronger, he turned his face to the left, and similarly for the right shoulder. This flowchart does not have a stop condition, as people always listen to sounds to make decisions. As long as the power is on, the system continues to detect sounds.  
Figure 12: Relationship between average value of standard deviation and output voltage of vibrators. If mean value of standard deviation (closer to the sound source) is smaller, the vibration intensity of the vibrators is stronger.
## 3 Results and Discussion
### Chloride Ion Diffusion Coefficient Model and Its Validation  
According to Formulas (2), (4), (5), and (7) and the relevant results of the test data, the calculation model of the chloride ion diffusion coefficient of concrete after high temperature-load coupling erosion was proposed. It is shown as follows:  
\[D_{t,T,\sigma}=\alpha\cdot\beta\cdot D_{t0}\cdot\left(\frac{t0}{t}\right)^{m_{T} \cdot n_{\sigma}} \tag{8}\]  
Presently, there are no reports on the chloride ion penetration mechanism in BFRC under the coupling effect of elevated temperatures and loads. In order to verify the accuracy and availability of this model, experimental data from [PERSON] [22] and [PERSON] et al. [23] was utilized. Variation between the chloride ion diffusion coefficients using the proposed model and the chloride ion diffusion coefficients from the test results in the two aforementioned studies for erosion days and temperature changes is shown in Figure 8a,b.  
Figure 6: Chloride ion diffusion coefficient of different erosion days with respect to different compressive stress ratios.  
Figure 7: Influence coefficient of the compressive stress ratio. (**a**) \(n_{\sigma}\) after 400 \({}^{\circ}\)C, (**b**) \(n_{\sigma}\) after 600 \({}^{\circ}\)C.  
In comparison with the references, we focused on the consistency of the changing trend of chloride ion diffusion coefficient, and the differences between them are reasonable. It can be seen that the model differs slightly from the result of [PERSON] [22] and [PERSON] et al. [23] due to fiber addition and experimental methods.  
[PERSON] [22] studied the chloride ion penetration of concrete with slag. Due to the excellent stick effect of basalt fiber, the calculated data from the model was slightly lower than the experimental data. Figure 8a shows a better chloride ion penetration resistance of BFRC than slag concrete.  
[PERSON] et al. [23] analyzed the chloride ion penetration of recycled aggregate concrete after elevated temperatures. The increasing trend shown in the model and the test results coincided. However, reasonable deviations appeared at elevated temperatures, especially after 300 \({}^{\circ}\)C. This is because the model was proposed based on BFRC, which greatly diminishes cracks in concrete when submitted to high temperatures.
# A Green Routing Protocol with Wireless Power Transfer for Internet of Things
###### Abstract  
The usually constrained resources and lossy links scenarios of Internet of Things (IoT) applications require specific protocol suite, as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Due to its flexibility, RPL can support efficiently vertical applications such as environmental monitoring, smart city and Industry 4.0. In this paper, we propose a new Objective Function (OF) for RPL based on a composite metric considering jointly the residual energy of a node (parent) together with the energy that a neighbor node (child) can transfer to the parent according to the Wireless Power Transfer (WPT) concept. Specifically, we consider simultaneous wireless information and power transfer (SWIPT) technique, which enables both the energy harvesting and information decoding from the same radio frequency (RF) signal, in order to influence the selection of the best path according to the proposed energy efficient metric in RPL. Performance evaluation on a realistic scenario pointed out a remarkable energy saving to prolong the network lifetime, by selecting the best path toward the sink node, with respect to the OFs usually considered in the literature.  
Low-Power and Lossy Networks; IPv6; Routing IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL); energy-harvesting; wireless power and information transfer +
Footnote †: journal: Sensor and Actuator Networks  
0
Footnote 0: _J. Sens. Actuator Netw._**2021**, _10_, 6. [[https://doi.org/10.3390/jsan10010006](https://doi.org/10.3390/jsan10010006)]([https://doi.org/10.3390/jsan10010006](https://doi.org/10.3390/jsan10010006))
## 5 Simulation Results and Implementation  
The comparison was conducted with the following characteristics:  
* Demand created within the model based on statistical distributions that are equal in both models.
* The following demand scenarios were performed are shown below in Table 3:
* Results are written in a \"KPI (Key Performance Indicator)\" Excel file and then compared between the models.  
Results for all the scenarios were better for the pull-control with higher difference for scenario 2 and less difference for scenario 3. The results in Table 4 are those for the base scenario, scenario 1, and show a clear competitive advantage for the pull control, as it had achieved better on-time delivery with less inventory and idle time at the same throughput. The biggest difference in the results happened when there was a large order intake. In this case, the pull control was able to smooth the demand with the determination of order release and completion dates, as it could act on the bottleneck early on, but the push-control could only react reactively when it was already too late:
## 3 Sound Field Simulation and Its Analysis Using Acoustic Parameters
### Simulation Test for the Reverberant Environment
#### 3.1.1 Simulation Setup
The source model in the FDTD simulation is a physically constrained source (PCS) [42], and the formula is as follows:  
\[\text{p}^{[\text{n+1}]}(\text{u},\text{w})= \text{p}^{[\text{n}]}(\text{u},\text{w})+\frac{\rho_{0}\text{c}^{ 2}\text{A}_{\text{s}}}{\text{f}_{\text{s,FDTD}}\text{8s}}\text{q}^{[\text{n}] }(\text{u},\text{w}) \tag{16}\] \[\text{q}^{[\text{n}]}(\text{u},\text{w})= \text{s}_{\text{p}}^{[\text{n}]}\ast\text{h}_{\text{m}}^{[\text{n}]}\] (17) \[\text{s}_{\text{p}}^{[\text{n}]}= \begin{cases}\omega_{\text{c}}&\text{if n}=0\\ \frac{(2\text{N}_{\text{p}}-1)!!^{2}\sin(\text{u}\omega_{\text{c}})}{\text{b} \,\text{n}\,\text{(2N}_{\text{p}}+\text{n}-1)!!}&\text{otherwise}\end{cases} \] (18) \[\text{H}_{\text{m}}\Big{(}\text{e}^{\text{j}\omega\text{n}}\Big{)}= \frac{\text{b}_{0}+\text{b}_{2}\text{e}^{-\text{j}\omega\text{n}}}{1+\text{ a}_{1}\text{e}^{-\text{j}\omega\text{n}}+\text{a}_{2}\text{e}^{-\text{j} \omega\text{n}}} \tag{19}\]  
where \(\text{p}^{[\text{n}]}(\text{u},\text{w})\) is the pressure node of the source, \(\delta\)s is the spatial resolution, \(\text{A}_{\text{s}}\)\(=4\pi\text{a}_{0}^{2}\) is the surface area of the sphere in volume velocity, \(\text{q}^{[\text{n}]}(\text{u},\text{w})\) is the velocity source, \(\text{s}_{\text{p}}^{[\text{n}]}\) is the maximally flat finite impulse response (FIR) filter, \(\text{h}_{\text{m}}^{[\text{n}]}\) is the mechanical filter of the source represented by a second-order infinite impulse response (IIR) filter, \(\text{H}_{\text{m}}\Big{(}\text{e}^{\text{j}\omega\text{n}}\Big{)}\) is \(\text{h}_{\text{m}}^{[\text{n}]}\) in the frequency domain, \(\text{b}_{0}=\beta/\Big{(}\text{M}_{\text{m}}\beta^{2}+\text{R}_{\text{m}} \beta+\text{K}_{\text{m}}\Big{)}\) and \(\text{b}_{2}\)\(=-\text{b}_{0}\) are the feedforward filter coefficients, \(\text{a}_{1}=2\Big{(}\text{K}_{\text{m}}-\text{M}_{\text{m}}\beta^{2}\Big{)}/ \Big{(}\text{M}_{\text{m}}\beta^{2}+\text{R}_{\text{m}}\beta+\text{K}_{\text{ m}}\Big{)}\) and \(\text{a}_{2}=1-2\text{R}_{\text{m}}\beta/\Big{(}\text{M}_{\text{m}}\beta^{2}+ \text{R}_{\text{m}}\beta+\text{K}_{\text{m}}\Big{)}\) are the feedback filter coefficients, \(\beta=\)\(\omega_{0}/\tan(\omega_{0}/2)\) is the bilinear operator, and (\(\ast\)) denotes the convolution. \(\text{M}_{\text{m}}\), \(\text{R}_{\text{m}}=\text{M}_{\text{m}}\cdot\omega_{0}/\text{Q}\), \(\text{K}_{\text{m}}=\text{M}_{\text{m}}\cdot\omega_{0}^{2}\), and Q are the mass, damping, elasticity, and quality factor constants characterizing the mechanical system of the source, respectively. \(\omega_{0}\) is the normalized low resonance frequency of the mechanical system, \(\text{M}_{\text{p}}\)\(=4\text{N}_{\text{p}}\)\(-\)1 is the FIR filter order, and \(\omega_{\text{c}}\) is the normalized cutoff frequency of the FIR filter. In this study, \(\text{M}_{\text{p}}\) was 16 samples, the normalized cutoff frequency was 0.05, the low resonance frequency was 300 Hz, \(\text{M}_{\text{m}}\) was 0.025 Kg, and Q was set to 0.6.
## 2 Circuit Design
### Transimpedance Amplifier  
The transimpedance amplifier represents a low ohmic input stage and acts as the central element of the current-based ECG circuit. It translates incoming current into a proportional output voltage. In the proposed circuit, the transimpedance amplifier is designed using a conventional voltage-controlled amplifier where the non-inverting input is connected to a reference electrode; it is thereby actively grounded and the inverting input is connected to the measuring electrode (Figure 3a). The low ohmic input is realized using a virtual shortcut at the input stage of the amplifier. Furthermore, the virtual shortcut provokes both inputs to be on an equal potential \(\Phi_{+}=\Phi_{-}\). Due to the low ohmic input of the measuring electrode, the current signal flows into the amplifier and is converted into voltage. A resistance is integrated in the feedback loop, in order to amplify the signal. According to [PERSON]'s and [PERSON]'s laws, the output is characterized as:  
\[U_{OUT}=-I\times R \tag{1}\]  
Besides the signal of interest \(I_{ECG}\), an interfering DC or slow drifting signal \(I_{noise}\) can occur and flow over the transimpedance amplifier, causing the amplifier and most likely the following filter stages to overload. Therefore, a feedback loop was implemented to counter the DC and low-frequency drifts by extracting and feeding the interfering signal that was back inverted and processed through a lowpass filter (Figure 3b). This feedback loop is only integrated in the wide frequency range circuit as a low-frequency drift or interfering DC can only occur there.
## 3 Methodology and Experimentation
### Proposed Methodology  
In this study, different mobility models such as RWM and RDM have been followed to evaluate the performance of three different routing protocols. Furthermore, the performance analysis of different routing protocols with different mobility models has been analyzed through different performance parameters. The proposed methodology is shown in Figure 3. It shows the proposed evaluation scheme with all operations. Firstly, the network will initialize with different sensors and data through the sink node. Based on different movements, pattern mobility models were applied in the network (RWM and RDM). Three different routing algorithms, as discussed above, are implemented on the network, and results of different QoS parameters are noted. A comparison of the network performance based on different routing algorithms for varying number of nodes in performance. This scheme will end up providing the best-suited routing algorithm for the WBAN scenario using entity mobility of nodes. Along with this, PDR, AE2 ED, and average throughput are the simulation metrics based on which 4 QoS parameters of 2 mobility models for 3 different routing protocols have been evaluated; the detailed stepwise explanation has been discussed in the following section of the paper.  
Following are the steps followed during analysis:  
Step 1: Do the initial setup and configuration of network where we decide the topological aspects along with access mechanism to be followed. (WBAN)  
Step 2: Generation of movement patterns for different mobility models viz., RWM and RDM is performed where we analyze the different movements of mobile nodes in simulation area.  
Step 3: Apply different proactive and reactive routing algorithms for data dissemination in WBAN and evaluate the values of different QoS parameters, viz. throughput, average end to end delay, and packet delivery ratio.  
Figure 3: The current working strategy of the proposed model.  
Step 4: Compare the values achieved with three different routing algorithms and figure out the best performing for WBAN.  
Step 5: End the Process.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 4 Constrained Stochastic Game  
When using appropriately the resources of two types of satellites by considering the operating environments, the delay to deliver the observation to the destination can be minimized and the average utilization of resources of the observation satellite can be kept below certain levels, which are the objectives of the formulated constrained stochastic game model [19]. In our model, observation satellites are the players. Thus, observation satellite \(i\) and player \(i\) are interchangeable in the formulation. The important notations are summarized in Table 2 (in this paper, the bold notation indicates the space).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\mathbf{S}_{i}\) & Local state space of observation satellite \(i\) \\ \hline \(\mathbf{S}\) & Global state space \\ \hline \(\mathbf{M}_{i}\) & Observation state space \\ \hline \(\mathbf{G}_{i}\) & Ground station state space \\ \hline \(\mathbf{A}_{i}\) & Local action space of observation satellite \(i\) \\ \hline \(\mathbf{A}\) & Global action space \\ \hline \(L_{T_{0,R}}^{W}\) & Latency between the observation satellite and the relay \\  & satellite without the compression \\ \hline \(L_{T_{0,R}}^{C}\) & Latency between the observation satellite and the relay \\  & satellite with the compression \\ \hline \(L_{T_{R,C}}^{W}\) & Latency between the relay satellite and the ground station \\  & without the compression \\ \hline \(L_{T_{R,C}}^{C}\) & Latency between the observation satellite and the ground \\  & with the compression \\ \hline \(L_{T_{0,C}}^{W}\) & Latency between the observation satellite and the ground \\  & station without the compression \\ \hline \(L_{T_{0,C}}^{C}\) & Latency between the observation satellite and the ground \\  & station with the compression \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of notations.
## 2 Technical Enablers  
### Deploying Intelligence on the Network Edge  
Edge computing, as originally envisioned in [46], is a major concept targeting reduced latency and communication delays. The fundamental idea of edge computing is to place computational resources closer to the raw data sources and users. To this end, edge computing [47] integrates components categorized as end-user equipment (e.g., smartphones), edge networking devices (e.g., border routers, bridges, base stations, wireless access points) and edge servers. This approach bounds the specific solution to the localized computing paradigm, but at the same time, it provides faster response to computational service requests and consequently eliminates the need for bulk raw data transfer to distant data centres located on the network core. Since it is indeed a step towards the right direction, certain deployment paradigms have been introduced in an attempt to combine the prime aspects of edge and cloud computing, namely Multi-access Edge Computing (MEC) and fog computing.
# An Integrated Decision Support Model for Evaluating Public Transport Quality
## 3 Results and Discussion
### AHP-BWM vs. SAW and SEM  
Since public transport quality evaluations are generally conducted by two mainstream modeling techniques, Simple Additive Weighting (SAW) [42] and Structural Equation Modeling (SEM) [43], we present the comparison of our proposed AHP-BWM model with them. Obviously, the survey procedure is very different for the three different methodologies, so the comparison is just based on qualitative aspects.  
Table 14 shows that the new method is competitive with the two other general methodologies, especially in the case when consistency is highly important for the decision makers. Obviously, SAW and SEM are well applicable and SAW can be recommended when low consistency is not a risk in the final derivation of the scores of attributes. SEM is suggested in those cases in which the interactions of the attributes are possible and important from the aspect of the final result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level** & **Criteria** & **Weight Scores** &
\begin{tabular}{c} **New Final** \\ **Weight Scores** \\ \end{tabular} & **New Ranking** & **Old Ranking** \\ \hline \multirow{4}{*}{Level 1} & Transport Quality & 0.1188 & 0.1200 & 4 & 4 \\  & Service quality & 0.2654 & 0.2600 & 2 & 2 \\  & Fare & 0.3754 & 0.3800 & 1 & 1 \\  & Tractability & 0.2404 & 0.2400 & 3 & 3 \\ \hline \multirow{4}{*}{Level 2} & Approachability & 0.0140 & 0.0141 & 13 & 13 \\  & Directness & 0.0233 & 0.0235 & 11 & 11 \\  & Time availability & 0.0568 & 0.0574 & 7 & 9 \\  & Speed & 0.0175 & 0.0176 & 12 & 12 \\  & Reliability & 0.0073 & 0.0074 & 14 & 14 \\  & Physical comfort & 0.0484 & 0.0475 & 9 & 8 \\  & Mental comfort & 0.0506 & 0.0496 & 8 & 7 \\  & Safety of travel & 0.1663 & 0.1629 & 2 & 1 \\  & Perspicity & 0.1196 & 0.1211 & 4 & 3 \\  & Info before travel & 0.1851 & 0.1873 & 1 & 2 \\  & Info during travel & 0.0707 & 0.0716 & 6 & 6 \\  & One-way tickets & 0.0389 & 0.0388 & 10 & 10 \\  & Weekly/monthly tickets & 0.0759 & 0.0758 & 5 & 5 \\  & Discounted tickets & 0.1256 & 0.1254 & 3 & 4 \\ \hline \multirow{4}{*}{Level 3} & Distance to stop & 0.0033 & 0.0034 & 10 & 9 \\  & Safety of stops & 0.0070 & 0.0071 & 6 & 8 \\  & Comfort of stops & 0.0036 & 0.0037 & 9 & 10 \\  & Need for transfer & 0.0073 & 0.0074 & 5 & 5 \\  & Fit connection & 0.0160 & 0.0161 & 3 & 3 \\  & Frequency of lines & 0.0296 & 0.0229 & 1 & 1 \\  & Limited time of use & 0.0272 & 0.0274 & 2 & 2 \\  & Journey time & 0.0039 & 0.0039 & 8 & 7 \\  & Awaiting time & 0.0081 & 0.0082 & 4 & 4 \\  & Time to reach stops & 0.0054 & 0.0055 & 7 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Weight scores and ranking after the sensitivity check for the public transport experts group.
## 4 Metabolic Pathway and Host Engineering for Advanced Biofuels Production  
Although ethanol is the most widely produced biofuel together with biodiesel, ethanol is not an ideal alternative fuel or blending fuel due to its low energy content (only about 70% of gasoline) and hygroscopic nature [19]. As a result, there has been significant demand for advanced \"drop-in\" biofuels that have better fuel properties and are compatible with the current engines and infrastructure.  
Good alternative transportation fuels would have similar chemical structures and properties to those found in existing transportation fuels (gasoline, diesel, and jet fuels). Metabolic pathways that produce desirable fuel-like molecules have been engineered: fermentative alcohols (butanol and isobutanol), non-fermentative alcohols from 2-keto acid pathways, fatty acids-derived fuels and isoprenoid-derived fuels.  
The general overview of the pathways for advanced biofuel production is summarized in Figure 3 and Table 1.  
Figure 3: Metabolic pathways to advanced biofuel molecules. ACP, acyl carrier protein; IPP, isopentenyl diphosphate, DMAPP, dimethylallyl diphosphate; FPP, farnesyl diphosphate; GPP, geranyl diphosphate; G3P, Glyceraldehyde 3-phosphate; PEP, phosphoenolpyruvate; CIT, citrate; ICT, isocitrate; OGA, oxoglutarate; SUC-CoA, succinyl-CoA; SUC, succinate; FUM, fumarate; MAL, malate; OAA, oxaloacetate; FAEEs, fatty acids ethyl esters; FAMEs, fatty acid methyl esters.
## 3 Fault-Tolerant Control Design for the Attitude System of HSV
### Fault-Tolerant Control Design for Slow-Loop of HSV
#### 3.1.2 Fault-Tolerant Controller Design under Unexpected Centroid Shift and System Fault
Take the derivative of (38) and we have  
\[\hat{D}_{s} =(\theta+\frac{p}{q}\zeta diag(\sigma_{0}^{\frac{p}{q}-1}))^{-1} (\sigma_{0}+l_{1}\sigma_{1}+l_{2}\text{sgn}(\sigma_{1}))+\hat{\phi}\text{sgn} (\sigma_{1})\]5 \[\hat{D}_{s} =(\theta+\frac{p}{q}\zeta diag(\sigma_{0}^{\frac{p}{q}-1}))^{-1} (\sigma_{0}+l_{1}\sigma_{1}+l_{2}\text{sgn}(\sigma_{1}))+\hat{\phi}\text{sgn} (\sigma_{1})\]6 \[\hat{D}_{s} =(\theta+\frac{p}{q}\zeta diag(\sigma_{0}^{\frac{p}{q}-1}))^{-1} (\sigma_{0}+l_{1}\sigma_{1}+l_{2}\text{sgn}(\sigma_{1}))+\hat{\phi}\text{sgn} (\sigma_{1})\]7  
Based on the convergence analysis of \(V_{\sigma}\), as shown in (31)-(36), combined with (39), we have that  
\[\hat{D}_{s} =(\theta+\frac{p}{q}\zeta diag(\sigma_{0}^{\frac{p}{q}-1}))^{-1} (\sigma_{0}+l_{1}\sigma_{1}+l_{2}\text{sgn}(\sigma_{1}))+\hat{\phi}\text{sgn} (\sigma_{1})\]8  
Therefore, due to the positive definition of \(V_{s}\), \(\dot{V}_{s}\) is negative semi-definite, which means \(V_{s}\) is an increasing function of time; that is, the tracking error \(e_{s}\), \(\tilde{\lambda},\sigma_{1}\) and \(\tilde{\phi}\) are all bounded. Next, the parameter projection algorithm is applied to modify the adaptive updating laws in both observer and controller to guarantee the boundedness of the designed adaptive parameters \(\hat{D}_{s},\tilde{\lambda},\tilde{\Phi}\). Furthermore, due to \(\dot{e}_{s}=-Ke_{s}+D_{s}-\hat{D}_{s}-\hat{\lambda}\), combined with Assumptions 3 and 4, we can infer that \(\dot{e}_{s}\) is also bounded. Subsequently, based on the Barbalat lemma and the bounded \(\dot{e}_{s}\) and definition of \(V_{s}\), it can be obtained that \(\lim_{t\to\infty}e_{s}=0\) to imply that the slow-loop system of HSV can track expected signals. In this way, Theorem 1 is proved and the argument projection is delivered as follows.