esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Productions of Cell and Ethanol by BY4741 in Media Containing Wheat Bran
#### 3.4.1 Transcriptomic Analysis of Glycolysis  
Although the amounts of intracellular metabolites and proteins were less likely to be expressed as immediate cell responses, the gene transcription levels could be more easily expressed than those [37]. Thus, real-time PCR was performed to assess the cell responses in the different broths of WG+HLMU medium and YG+HLMU medium. According to the analytical results of the transcription levels in upstream glycolysis (Figure 4a), the transcription levels in WG+HLMU medium were overall higher than those in YG+HLMU medium; however, the levels showed characteristic differences between the preparatory and pay-off phases in glycolysis. Specifically, the transcription level of _FBA1/LOT1_ in WG+HLMU medium was 0.0317 times lower than that in YG+HLMU medium, and the levels of _TDH1/GLD3_, _TDH2/GLD2_ and _TDH3/GLD1_ in WG+HLMU medium were also 0.0560, 0.125, and 0.250 times lower than those in YG+HLMU medium, respectively.  
The gene transcription levels in the range from the downstream of glycolysis to the TCA cycle and to ethanol production were analyzed (Figure 4b). Even using the wheat bran instead of YNB, several transcription levels increased although the levels of the counterpart isozyme genes decreased in each metabolic reaction, with the result that the transcription  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Main Components (g.L\({}^{-1}\))**} & \multicolumn{4}{c}{**Minerals (mg.L\({}^{-1}\))**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Carbohydrate**} & \multirow{2}{*}{**Protein**} & \multirow{2}{*}{**Lipid**} & \multirow{2}{*}{**Na**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**Fe**} & \multirow{2}{*}{**Ca**} & \multirow{2}{*}{**K**} & \multirow{2}{*}{**Mg**} & \multirow{2}{*}{**Cu**} & \multirow{2}{*}{**Zn**} & \multirow{2}{*}{**Mn**} \\ \cline{2-13}  & **Total** & & & & & & & & & & & & \\ \hline Wheat bran & 15.1 & 1.7 & 6.1 & 0.0 & \(<\)10 & 69 & \(<\)1 & \(<\)10 & 98 & 26 & \(<\)0.1 & \(<\)0.5 & 0.7 \\ YNB & 0.0 & 0.0 & 0.0 & 0.0 & 40 & 227 & 0.07 & 36 & 287 & 101 & 0.02 & 0.2 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of wheat bran and YNB.  
levels basically showed few significant changes. However, there were some instances where the transcription levels that uniquely controlled a specific metabolic reaction were significantly changed: the levels of _PCKI_/_JPM2_/_PPC1_, a pair of _ACS1_/_FUN44_ and _ACS2_ as isozyme genes, a group of _ALD2_, _ALD3_, _ALD4_/_ALD7_, and _ALD6_/_ALD1_ as the isozyme genes in WG+HLMU medium were 56.1 (_p_ < 0.05), 303 (tendency existed), 106 (tendency existed), 9.34 (_p_ < 0.05), 15.7 (_p_ < 0.01), 6.11 (_p_ < 0.05), and 3.61 (tendency existed) times higher than those in YG+HLMU medium. Conversely, _ADH1_, which is a main ethanol producing enzyme in BY4741, in WG+HLMU medium was 0.189 times lower than that in YG+HLMU medium.
## 4 Results and Analysis  
We begin this section by determining the prior variance of the predicted clock result, following which we illustrate the numerical results, from which the major findings we identify are also detailed.  
We focus first on Figure 3, which depicts the clock offset time series obtained for two different estimation models of the clock offset at station BRUX. The data show that the noise from the clock offset with the between-epoch constraint model is smaller than the clock offset with the white noise model. Hence, the between-epoch constraint model is employed to estimate the clock offset parameter.  
To determine the accuracy of the clock offset prediction, 4400 tests were performed. The predicted error was for 10, 30, 60 and 90 min prediction periods. The prediction error of the adaptive clock prediction model at station BRUX is exhibited here in Figure 4 due to the limited space. Furthermore, the root mean squares (RMS) of the predicted errors are presented in Table 3. Taken together, we make three observations. First, the prediction error is extremely stable, which further explains the reliability of the adaptive clock prediction model. Second, considering the RMS values of the prediction error, they follow an order of increasing trend when the prediction period ranges from 10 min to 90 min, thereby indicating that our approach for short-term data interruption will obtain better results, but that this is not the case for long-term data interruption. We do not conceal this restriction. We recommend that the clock offset parameter be reinitialized if the data is interrupted for a long period. Third, the prediction accuracy of all the stations present similar qualities for the same prediction periods. Hence, the variance values will be set from a prior variance in this contribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **10 min** & **30 min** & **60 min** & **90 min** \\ \hline BRUX & 0.018 & 0.039 & 0.077 & 0.126 \\ IENG & 0.049 & 0.097 & 0.182 & 0.290 \\ NIST & 0.025 & 0.052 & 0.102 & 0.166 \\ OPMT & 0.029 & 0.057 & 0.112 & 0.183 \\ PTBB & 0.024 & 0.049 & 0.100 & 0.159 \\ WAB2 & 0.035 & 0.054 & 0.100 & 0.152 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The RMS values of the prediction error on six stations (ns).  
Figure 3: Clock offset time series using different models (red-5 ns).
## 1 Introduction
The fused OCV data are obtained by integrating the first-order backward difference of the low-current test results into the SOC interval corresponding to the incremental test control points, thus the polarization effect is reduced. The OCV curve is obtained by fitting the fusion data using a neural network with a strong non-linear mapping capability. Moreover, based on the fusion OCV curve, we design a state equation including SOC, polarization voltage, and model parameters. The unscented Kalman filter (UKF) is employed for the SOC and model parameters' co-estimation online, which reduces the effect of unexpected noise and improves the accuracy of SOC estimation. Finally, the comparison of SOC estimation with other literatures demonstrates the effectiveness of the proposed method and the SOC estimation is performed on the pulse constant current test and dynamic discharge test, respectively. The rest of this paper is as follows: the lithium-ion battery modeling and the experimental setup in this paper are described in Section 2. In Section 3, the OCV fusion method and the co-estimation method of SOC and model parameters based on UKF are proposed. In Section 4, the proposed SOC estimation method is compared with the traditional method, and the experimental results are analyzed and discussed. Finally, the conclusions are given in Section 5.
## 4 Experimental Methodology
### SRT Packet Loss Handling Mechanism Evaluation  
To validate the effectiveness of SRT's enhanced packet loss handling mechanisms, a controlled comparative experiment was designed to quantify the performance differences between three packet loss handling strategies:  
* Default ARQ (TCP-like): Traditional Automatic Repeat reQuest with strict in-order delivery, where any lost packet causes subsequent packets to be buffered until the missing packet is retransmitted and received.
* SRT Selective ARQ: SRT's selective retransmission mechanism that allows out-of-order delivery of non-critical packets while prioritizing retransmission of essential frame data (I-frames, motion vectors). This is mostly performed when the packet loss rate is low and the network conditions are mostly stable but still suffer from occasional packet loss.
* SRT Adaptive Drop: SRT's intelligent packet dropping strategy that discards packets when their retransmission would violate latency constraints, combined with Forward Error Correction (FEC) for graceful degradation. This is mostly performed when the packet loss rate is high but the required latency is low, such as in real-time video conferencing or live streaming scenarios.  
The experimental setup utilized a controlled network emulation environment using openWRT routers to simulate packet loss and latency variations. The SRT protocol was configured with given parameters to ensure consistent handling across all three strategies, and the performance of each strategy was evaluated under varying packet loss rates (0.5%, 1%, 2%, 5%, and 10%) to quantify their impact on end-to-end latency, playback continuity, and user experience.  
Figure 4: Delayed calculation flowchart.
## 8 Conclusions  
In this study, a coherent 265-DPPM/WMPC-OCDMA system was designed for underwater optical wireless communication. The design of the OCDMA transceiver architecture is based on hybrid M-DPPM modulation using the WMPC sequence scheme. The WMPC design methodology and its correlation characteristics were introduced. A mathematical model of the channel absorption, scattering, and attenuation was studied as a function of the operating wavelength and chlorophyll concentration coefficient. Furthermore, the effect of the concentration coefficient on the signal attenuation and transmission distance is presented. Variations in the operating wavelength and receiver aperture area were also investigated. The system multiplicity variation and coding parameters were considered in the BER analysis to enhance the transmission distance between the transmitter and receiver. Moreover, the results proved that the proposed 256-DPPM/WMPC system can accommodate 1310 users communicating over 930 m under pure seawater without repeaters at a data rate of 137 Mbps and \(10^{-9}\) BER at 2% EVM%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Modulation Scheme** & **Transmission Distance (m)** & **Throughput (Gbps*User)** & **Single/Multi-User** \\ \hline
[1] & 4-PAM & 130 & 1 & Single user \\
[2] & IM & 20 & 1.5 & Single user \\
[3] & NRZ-OOK & 60 & 2.5 & Single user \\
[4] & DFT-S DMT & 50 & 5 & Single user \\
[10] & NRZ-OOK & 100 & 5 & Single user \\
[12] & IM & 10 & Single user \\
[13] & NRZ-OOK & 35 & 27 & Single user \\
[14] & 256-PPM & 36 & 3.32 & Single user \\
[15] & OFDM & 26 & 5.5 & Multi-user \\ This work & 256-DPPM/WMPC & 930 & 180 & Multi-user \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the proposed 256-DPPM/WMPC-OCDMA system and the published results.  
Figure 14: EVM% versus OSNR of underwater OCDMA system for different modulation schemes.  
**Author Contributions:** Conceptualization, M.A.M.I.; Methodology, M.A.M.I.; Software, M.A.M.I.; Validation, K.S.; Formal analysis, M.A.M.I.; Investigation, K.S.; Resources, K.S.; Data curation, M.A.M.I.; Writing--original draft, M.A.M.I.; Visualization, M.A.M.I. and K.S.; Writing--review; M.A.M.I. and K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data supporting the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Abbreviations  
\begin{tabular}{l l} \(\beta_{v}\) & Momentum dust parameter \\ \(\beta_{t}\) & Thermal dust parameter \\ \(q_{r}\) & Radiative heat flux W-m\({}^{-2}\) \\ \(Rd\) & Radiation parameter \\ \(Pr\) & Prandtl number \\ \(\Omega\) & Angular velocity (sec\({}^{-1}\)) \\ \(Nu\) & Nusselt number \\ \(l\) & Mass concentration of dust phase \\ \(S_{1}\) & Thermal stratification parameter \\ \(\tau_{t}\) & Thermal relaxation time of dust phase \\ \(\tau_{v}\) & Momentum relaxation time of dust phase \\ \(Q_{0}\) & Heat generation absorption coefficient (m\({}^{2}\)s\({}^{-2}\)) \\ \(\mu_{f}\) & Dynamic viscosity of fluid (Pa-s) \\ \(\mu_{nf}\) & Dynamic viscosity of nanofluid (Pa-s) \\ \(\rho_{f}\) & Density of the fluid (kgm\({}^{-3}\)) \\ \(k_{uf}\) & Thermal conductivity of the nanofluid (W-m\({}^{-1}\)) \\ \(\rho_{nf}\) & Density of the nanofluid (kgm\({}^{-3}\)) \\ \(k_{CNT}\) & Thermal conductivity of carbon nanotubes (W/mk) \\ \(k_{f}\) & Thermal conductivity of the fluid (W/mk) \\ \(\delta\) & Density of nanofluid (kgm\({}^{-3}\)) \\ \(\delta_{1}\) & Heat source parameter \\ \(\gamma\) & Specific heat ratio \\ \(\theta_{w}\) & Temperature ratio parameter (K) \\ \(\alpha\) & Condensation or normalized velocity (m\({}^{2}\)/s) \\ \(V(u,\,v,\,w)\) & Velocity vector (ms\({}^{-1}\)) \\ \(V_{p}(u_{p},\,v_{p},\,u_{p})\) & Velocity vector for dust phase (ms\({}^{-1}\)) \\ \(\rho_{p}\) & Density of dust particles (kgm\({}^{-3}\)) \\ \(\overline{T}\) & Temperature of the fluid (K) \\ \(\overline{T}_{p}\) & Temperature of the fluid for dust phase (K) \\ \(\psi\) & Stefan-Boltzmann constant (W-m\({}^{-2}\).K\({}^{-4}\)) \\ \(\overline{k}\) & Average absorption coefficient (m\({}^{-1}\)) \\ \(\overline{T}_{w}\) & Temperature of the disk (K) \\ \(\overline{T}_{0}\) & Temperature of the surface of the film (K) \\ \(\overline{T}\) & Ambient fluid temperature (K) \\ \(\beta\) & The angle of inclination (radians) \\ \(\varphi\) & Nanoparticle volume fraction \\ \(\overline{g}\) & Gravitational acceleration (m/s\({}^{2}\)) \\ \end{tabular}
## 5 Simulation and Experiments
### Simulation  
Assuming that the coordinate frames of the IMU and the camera are aligned, the pose transformation between them can be omitted. To observe the convergence process, we deliberately assign random initial values to the prior covariance matrix \(P_{k|k}\). The starting point is [1,0,0]. The covariance matrix of measurement error \(R_{k}\) is designed as a skew-symmetric matrix with small random values. Gaussian noise is introduced to all the virtual measurement values. The coefficients are set as \(\mu_{\
u}=0.5\), \(\mu_{\omega}=0.5\), and \(\lambda=1\). The virtual IMU-camera rig is directed to move along a virtual helical trajectory, as described by the following equation:  
\[\left\{\begin{array}{l}x=\cos\Bigl{(}\frac{1}{2}\pi t\Bigr{)}\\ y=\sin\Bigl{(}\frac{1}{2}\pi t\Bigr{)}\\ z=t\end{array}\right., \tag{37}\]  
The three-dimensional position curves of the ICEKF and the ground truth are shown in Figure 4, which reveals that the estimated results align closely with the true values. The position errors of the ICEKF estimation against the ground truth of the IMU-camera rig along the three axes are depicted in Figure 5, and Figure 6 shows the orientation errors. The curve of \(\lambda\  
The norm errors between the ground truth and the simulation results, including the statistical values of the RMSE (root mean square error), mean error, and STD (standard deviation) for both coupled translation \(\mathbf{p}_{ic}\) and attitude \(\mathbf{\overline{q}}_{ic}\) across all three axes, are detailed in Table 2. The errors of the attitude are obtained by converting \(\mathbf{\overline{q}}_{ic}\) to roll-pitch-yaw.  
The curves and numerical statistics show that despite the randomized \(\mathbf{P}_{i|k}\) and \(\mathbf{R}_{k}\) alongside a high yaw angle velocity disturbing the initial convergence, the ICEKF ultimately converges without much specific parameter tuning. This indicates that the ICEKF possesses strong robustness while yielding reliable motion estimates.  
To further test the filter's ability to converge, an initial state estimate set is designed which conducts the virtual rig starting from the different three-dimensional points to the actual desired starting point [1,0,0]. The initial estimate set, varying from \([0,-1,-1]\) to [1,0,0], and the converging process are illustrated by Figure 8, which depicts how even when facing uncertain initial estimates, the filter can converge with a decent performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Translation** & **Translation** & **Translation** & **Attitude** & **Attitude** & **Attitude** \\
**RMSE** & **Mean Error** & **STD** & **RMSE** & **Mean Error** & **STD** \\ \hline
0.207 m & 0.1447 m & 0.1408 m & 0.1684 rad & 0.1348 rad & 0.1008 rad \\ \hline \hline \end{tabular}
\end{table}
Table 2: Norm errors of the translation and attitude between the ground truth and the simulation results.  
Figure 6: Orientation error of the simulation.  
Figure 7: Visual scale of the simulation.
## 2 Passive Wake-Up Radio
### Review of the Existing Passive Wake-Up Radio Hardware Design
#### Broadcast-Based Wake-Up Radio Design  
A 900 MHz passive CMOS-based RF transceiver chip was presented in [90] as WuRx. The microsystem consists of a digital baseband with NVM (nonvolatile memory) and an RF front-end. The RF front-end block comprises a modulator, demodulator, voltage multiplier, bias circuit, a ring oscillator, and a voltage limiter. The experimental result shows that with an oscillation frequency of 2.07 MHz, 2.64 \(\upmu\)W of power was consumed with a sensitivity of \(-\)17 dBm. A fully passive 868 MHz wake-up radio with high sensitivity is presented in [91]. The radio front end is designed on a 0.13 \(\upmu\)m CMOS including an antenna, voltage multiplier, impedance matching network and data slicer. For a 100 kbps data rate of the wake-up signal, the system is able to achieve a sensitivity of \(-\)33 dBm. Similarly, the authors in [92] presented another passive wake-up radio, which is also implemented in 0.13 \(\upmu\)m CMOS process. The design is developed for wearable medical gadgets, operating using FSK modulation at 8 Mbit/s data rate and a frequency of 915 MHz. The results show that sensitivity of \(-\)53 dBm and \(-\)78 dBm were achieved for the WuRx and the main receiver respectively. The power consumed by the main receiver was 640 \(\upmu\)W and the transmitter uses 1.4 mW from the 1.2 V supply. The wake-up receiver dissipates 0.2 \(\upmu\)W power at a sensitivity of \(-\)53 dBm. Authors in [93] described a 2.45 GHz passive wake-up circuit, which consists of an antenna, rectifier and matching network. The circuit design was optimized to achieve improved input voltage and maximum sensitivity and conversion efficiency. An output voltage of 1.8 V was achieved for a received RF power of \(-\)23 dBm. The rectifier circuit was implemented in UMC 180 nm CMOS process. Recently, the work in [94] studies the fundamental performance limits of the passive wake-up radios and shows the advantage of using the PWUR when a number of nodes are relatively small. Many other studies have proposed the wake-up radio schemes that are not fully passive. For example, the study in [95] proposed an ultralow power wake-up radio based on direct active RF detection with power consumption of just 4.5 \(\upmu\)W at 2.4 GHz, sensitivity of \(-\)50 dBm, data rate of 200 kbps, and 4.5 \(\upmu\)W consumption from a 0.8 V supply voltage. The work in [96] fabricated a circuit in a 0.18-\(\upmu\)m CMOS process with the sensitivity of \(-\)80.5 dBm and consumed 6.1 nW. The high sensitivity is realized by using a passive pseudo-balun envelope detector. Another similar design [15] achieved \(-\)60 dBm sensitivity while consuming 166 nW of power. To implement the sensitivity, a passive voltage-boosting network comprising of a high-quality factor (high-Q) piezoelectric MEMS resonator is used instead of an inductor to amplify the received voltages applied to a CMOS rectifier. Table 1 shows the performance characteristics and properties of the different passive wake-up radio design.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 2 Experimental Program
### AAS Mortar Manufacture
To capture the influence of adding calcium oxide to the bio-AAS composites, three mixtures were adopted. The first mixture was the control AAS mix \"C\" without any bacteria or calcium oxide addition. The second category of mixes was the AAS mix with bacteria incorporation \"C5\". C5 indicates the bacteria concentration, which was 10\({}^{5}\) cells/mL. The final category of AAS mix included bacteria and the 7% calcium oxide addition \"Ca-5\". Ca-5 refers to including 7% calcium oxide along with incorporating bacteria with a concentration of 10\({}^{5}\) cells/mL. Table 5 illustrates the adopted AAS mixes in this study. Figure 2: AAS manufacturing process: (**a**) fresh mixing; (**b**) hardened AAS specimens.
# A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks
## 2 Related Work
In our work, we choose OLSR, BATMAN, and BABEL because OLSR and BATMAN have been investigated and compared in different scenarios and networks. For example, the authors of [26] evaluated OLSR and BATMAN when low-power nodes transmitted VoIP data streams. In [27], the authors assessed the performance of OLSR and BATMAN by moving devices up and down different floors in a school building. As a novel routing protocol of the same type as OLSR and BATMAN, BABEL continues to demonstrate characteristics of some old routing protocols. Most closely related studies have not researched blockchain communication recovery in the ad hoc IoT-Blockchain system. In [28], the authors focused on combining IoT and blockchain for smart home applications. However, they only investigated an infrastructure-based network and considered static scenarios. In [29], combining IoT and blockchain for UAVs in a real deploymentwas investigated and a key management mechanism based on blockchain technology was proposed. Although the authors of [11] investigated the recovery time in an ad hoc IoT-blockchain system, they only focused on a single ad hoc routing protocol: OLSR. Moreover, the evaluation was conducted on a small-scale network. To highlight the differences between previous studies and ours, we summarize them in Table 1. An earlier version of this study was published in [12], which only considered the evaluation of two routing protocols (i.e., OLSR and BATMAN) in a static four-node emulated network. Here, we first extend the assessment with the new routing protocol, BABEL. Second, we deploy IoT-blockchain systems on actual IoT nodes with real blockchain and routing protocols. In addition, we investigate the performance in a larger network size for emulation. Finally, compared to the previous investigation, we also evaluate mobile scenarios that may occur in several IoT-blockchain applications.