esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluations
### Effect of Actuator Duration  
It was shown that sustained vibrotactile feedback could be achieved using the proposed actuators and beam system but creating impulse \"button\" sensations was also desired. By running a continuous signal from one actuator then adding a short pulse signal from the other actuator, the vibration can be quickly increased or decreased, depending on the actuation frequencies and pulse duration. The maximum transient vibration response  
Figure 5: Acceleration along the beam, exciting a single actuator, varying the actuator’s magnitude between 50 and 100% at a frequency of: (**a**) 168 Hz; (**b**) 196 Hz.  
was measured up to 200 ms after introducing the second signal. Figure 6a shows the vibration along the beam using 168 Hz signals at different pulse time durations. The maximum vibration occurs at 15.2 cm, with no pulse signal being added by the second actuator. By adding a pulse in the range of 10-200 ms, the vibration response can be decreased from 61.19 to 8.14 m/s\({}^{2}\), depending on the duration of the pulse.  
Using this same technique, the vibration signal can be increased when using 181 Hz actuation signals. At this frequency, adding a second actuator increases the vibration response in the beam, as seen in Figure 6b. When exciting only one actuator at 181 Hz, the maximum vibration is about 14.71 m/s\({}^{2}\). By adding a pulse in the range of 25-200 ms, the vibration response can be increased up to 44.13 m/s\({}^{2}\), depending on the location of the beam and duration of the pulse.
# Functions of Representative Terpenoids and Their Biosynthesis Mechanisms in Medicinal Plants
## 6 Terpenoid Biosynthesis Pathway and Regulation  
Despite their substantial differences in structure and functionality, the terpenoids all originate from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). These IPP and DMAPP units can be biosynthesized via the mevalonate (MVA) or the methylerythnitol phosphate (MEP) pathway [87; 88; 89]. However, the downstream genes integrated into the terpenoid biosynthetic pathways, and the regulatory mechanisms, are specific to different medicinal plants (Figure 3). Gene tandem duplication is a prominent feature in the biosynthesis pathways of key secondary metabolites. Two strains of _A. annua_ with varying levels of artemisinin were used to assemble chromosome-level haploid maps. Genomic analyses revealed that multiple copies of the _AaADS_ (amorpha-4, 11-diene synthase) gene exist in the _A. annua_ genome. A strong positive association was found between artemisinin levels and the number of copies of _AaADS_[71]. ADS catalyzes the initial and critical step in the artemisinin biosynthetic pathway, the conversion of farnesyl diphosphate (FDP) to amorpha-4, 11-diene [90]. Overexpression of _AaADS_ can enhance artemisinin production in transgenic _A. annua_ plants, suggesting the important role of _AaADS_ in artemisinin biosynthesis [91; 92]. Then, the cytochrome P450 enzyme CYP71 AV1, along with the cytochrome P450 reductase CPR1 and cytochrome b5 (CYB5), effectively converts amorpha-4, 11-diene into artemisinin alcohol71 [93]. The overexpression of _AaCYP71 AV1_ and _AaCPR_ (redox partner for _CYP71 AV1_) in _A. annua_ can increase the artemisinin content by 2.4-fold in comparison to the wild-type [94; 95]. Several other genes which are important for artemisinin synthesis have also been identified, including _AaHMGR_, _AaDBR2_, _AaDXR_, and _AaFPS_[96; 97]. The co-transformation of the two important synthetic genes (e.g., _AaADS_ and _AaHMGR_ genes, _AaFPS_ and _AaHMGR_ genes) can significantly promote artemisinin content [96]. In particular, the co-overexpression of _AaFPS_, _AaCYP71 AV1_, and _AaCPR_ can dramatically increase the artemisinin concentration in _A. annua_[98]. In addition, most of these genes can be activated with UV light, cold, or heat shock, which correlates with higher artemisinin content [43; 99; 100; 101; 102; 103]. Figure 3: Biosynthetic pathways of terpenoids in several important medicinal plants. Artemisinin (purple), cannabinoids (orange), ginkgolide (pink), paclitaxel (yellow), tanshinones (green). One solid arrow indicates one step, two solid arrows represent more than one step, and the dashed arrows show hypothetical steps.
# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 2 Yolov8 Network Model  
YOLOv8 is the latest version of the current YOLO (you only look once) series of algorithms; YOLOv8 builds on the significant upgrade of YOLOv5, which is one of the commonly used target detection algorithm models. Built on the success of the previous one, YOLOv8 introduces new features and improvements to enhance performance, flexibility, and efficiency [7]. The network structure of YOLOv8 consists of an input end (input), backbone (backbone) network, neck (neck) network, and predictive head structure (head), which is composed of four parts, as shown in Figure 1.  
In comparison to the network structure of YOLOv5, the following specific improvements have been made: In the backbone network, the concept of CSP is still employed, but the initial C3 module has been substituted with the C2f module. This modification facilitates further reduction in the model's complexity while enabling the acquisition of more comprehensive gradient flow data, thus maintaining the overall lightweight nature of the network. YOLOv8 continues to employ the PAN concept but differs from YOLOv5 in that the CBS 1 \(\times\) 1 convolution structure in the upsampling stage has been eliminated, and the C3 module has been replaced by the C2F module. YOLOv8 abandons the previous anchor-based approach and instead employs the anchor-free concept. With regard to the loss function, the VFL loss is utilized for categorization, while the DFL loss + CIOU loss is employed for regression. The CIOU loss is employed as the regression loss and the previous version of IOU matching or the unilateral proportion of the allocation method is eschewed in favor of the task-aligned assigner matching method.  
YOLOv8 currently supports a full range of vision AI tasks, including detection, segmentation, pose estimation, tracking, and classification. It is available in five versions, YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x, with YOLOv8n having a smaller size and faster operation speed, which makes it more suitable for deployment at the edge. Considering that the problem to be solved is the actual problem of early fire detection and warning in structured spaces such as warehouses, the version adopted is YOLOv8n.
## 1 Introduction
Their clinical impact is further limited by significant toxicities (e.g., hypertension, hand-foot skin reactions, gastrointestinal disturbances), rapid immune clearance reducing effective tumor targeting, heterogeneous patient responses, and the emergence of drug resistance. All these factors underscore the urgent need for more effective and personalized treatment strategies in advanced HCC. In the second-line setting, regorafenib and cabozantinib have demonstrated survival benefits in patients who tolerated prior sorafenib therapy. However, their broader clinical application is constrained by the significant adverse events associated with their use. Ramucirumab provides benefits only for patients with elevated AFP (Alpha-fetoprotein) levels, while immunotherapy with nivolumab and pembrolizumab, although promising, has yet to demonstrate clear superiority over sorafenib in phase III trials. The majority of therapies recently investigated in phase II trials have failed to deliver outcomes superior to standard treatments [6]. This highlights the urgent need for innovative methods to enhance drug delivery to tumors while minimizing harm to healthy tissues. Recent advances in nanotechnology offer a promising path forward. Nanoparticles are emerging as valuable tools for precise drug delivery, cancer imaging, and diagnosis. They enable earlier detection of cancer, more accurate diagnostics, and the simultaneous treatment of disease, presenting a transformative potential for improving chemotherapy outcomes [7].
## 4 Conclusions  
This study has shown that NMR spectroscopy provides an improved approach for the monitoring and diagnostics of anaerobic processes after exposure to the disinfection agent, benzalkonium chloride. The NMR spectra of anaerobic microcosms provide a rapid and high-resolution molecular characterization of the full organic composition of wastewater samples, including fatty acids, amino acids, other metabolites, and the BAC itself in a single analysis. This approach allows for a detailed investigation of the ways in which BAC affects the microbial metabolism, leading to less-than-optimal biogas production, both in terms of total biogas and biogas composition. Improved understanding of the metabolic impacts of stressors on anaerobic digestion, as well as a rapid tool for monitoring and diagnostics, will lead to the future development of more efficient and effective operation of anaerobic digestors by giving us the ability to better identify and respond to potential fouling events.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12094620/s1](https://www.mdpi.com/article/10.3390/app12094620/s1)]([https://www.mdpi.com/article/10.3390/app12094620/s1](https://www.mdpi.com/article/10.3390/app12094620/s1)), Figure S1: 1H NMR spectra of the 0 mg/L BAC series day 1, 2, 5, 9, 12, and 18. (a) Aromatic Region. (b) Functionalized Alkyl Region. (c) Alkyl Region.; Figure S2: 1H NMR spectra of 16.7 mg/L BAC series day 1, 2, 5, 9, 12, and 18. (a) Aromatic Region. (b) Functionalized Alkyl Region. (c) Alkyl Region.; Figure S4: 1H NMR spectra of 66.7 mg/L added BAC series day 1, 2, 5, 9, 12, and 18. (a) Aromatic Region. (b) Functionalized Alkyl Region.; Figure S5: 1H - 1H TOCSY NMR spectra of control day 18.; Figure S6: 1H - 1H TOCSY NMR spectra of 16.7 mg/L added BAC day 18.; Figure S7: 1H - 1H TOCSY NMR spectra of 33.3 mg/L added BAC day 18.; Table S1: Biogas composition (CO2/CH4) of control, 16.7 mg/L added BAC, 33.3 mg/L added BAC, and 66.7 mg/L added BAC series.  
**Author Contributions:** Conceptualization, R.F., N.B., J.G., J.K. and J.G.L.; Methodology, R.F. and J.G.L.; Validation, R.F. and J.G.L.; Formal Analysis, R.F.; Resources, N.B. and J.G.; Writing--Original Draft Preparation, R.F.; Writing--Review and Editing, J.G.L.; Supervision, J.G.L.; Funding Acquisition, J.G.L. and J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science and Engineering Research Council of Canada (NSERC) through a Collaborative Research and Development Grant (CRDPJ-501109-16) and a Discovery Grant (RGPIN-2016-04493), and by Geosyntec Consultants.  
Not applicable.  
**Acknowledgments:** SiREM (Guelph, Ontario) is thanked for their assistance in providing the materials used to construct the microcosms used in this study.  
The authors declare no conflict of interest.
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 4 Results Analysis
### General Kinematic Performance Statistics
These differences range from \(-8\) m to \(20\) m, as seen in Figure 15b. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **L1/E1/B1 (GREC)** & & **L5/E5a (GE)** \\ \hline
**Measurement** & **Pseudorange** & **Carrier-Phase** & **Pseudorange** & **Carrier-Phase** \\ \hline
**Pre-fit residual rms (m)** & 11.7 & 11.2 & 10.3 & 10.1 \\ \hline
**Post-fit residual rms (m)** & 3.5 & 0.037 & 0.8 & 0.027 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Pre-fit and post-fit residual rms for single- and dual-frequency PPP processing. Figure 14: Correlation of the horizontal errors on the mean number of observations and PDOP. (**a**) Correlation of the horizontal errors on the number of observations. (**b**) Correlation of the horizontal errors on PDOP. To specify the benefits of incorporating smartphone IMU measurements under different environments, Figure 16 presents the horizontal rms with the number of satellites used in SFDF-PPP processing. On account of buildings and tunnels limiting portions of the sky, the minimum 4 satellites are not always available for smartphone GNSS positioning in suburban driving. Observing both Table 12 and Figure 16, the smartphone IMU can bridge these positioning gaps and significantly mitigate positioning errors when visible satellites are insufficient, e.g., a significant level of 98% and 87% horizontal positioning improvements is observed with the aid of the smartphone IMU measurements when the number of satellites are reduced to 4 and 5, respectively. The smartphone PPP/IMU solution is capable of providing stable and continuous positioning solutions, and the sensor fusion solution maintains the same grade of accuracy performance even with fewer satellites. The possible reason is that smartphone PPP/IMU solutions not only rely on GNSS satellite measurements, but also are relevant to predicted state covariance, previous estimated states, and IMU raw measurement quality.
# Preparation of Hybrid Molybdenum Disulfide/Single Wall Carbon Nanotube-n-Type Silicon Solar Cells
## 3 Results
### Optimisation of the Oxide Layer Thickness  
The thickness of the oxide layer on the silicon is also important to achieve the best efficiency. The exact thickness required is still a matter of some debate [41; 42; 43]. The second HF etch in the treatment series is used to control the oxide thickness in the final cells tested. In the normal procedure, the etch is carried out for 15 s. Further investigations were undertaken using etching for 30, 45, and 60 s. The comparison of the performance for these different cells is provided in the supplemental information in Table S3 and Figure S5. In all cases of extra etching, the overall performance is poorer. This is likely due to the lack of an oxide layer. A thin oxide layer was found to suppress dark saturation current, inhibit recombination, and lead to an increase in minority carrier lifetime and open circuit voltage (V\({}_{\text{oc}}\)) [42]. The passage of majority carriers across the junction is suppressed by the oxide, whereas the tunnelling-based passage of minority carriers is unaffected. The extra etching time leads to little change in V\({}_{\text{oc}}\) until 60 s where the V\({}_{\text{oc}}\) drops significant probably due to the complete removal of the oxide. For all other data presented, a 15 s etch will be used.
## 5 System Model
### THz Channel Model
#### 5.1.3 Misalignment Fading Effect  
THz transceivers with directional antenna systems are susceptible to building sway and environmental factors, resulting in misaligned antenna beams and pointing errors. Considering a receiver with a circular aperture area of radius \(a\), the transmitter emits a symmetric beam characterized by a waist \(w_{d}\) at a certain distance \(d\). Then the equivalent beam width is expressed as  
\[w_{eq}=\sqrt{w_{d}^{2}\frac{\sqrt{\pi}\text{erf}(u)}{2u\exp(-u^{2})}}, \tag{6}\]  
where \(u=\frac{\sqrt{\pi}a}{\sqrt{2}w_{d}}\). The misalignment gain is expressed as  
\[h_{m}(r)\approx A_{0}\exp\left(-\frac{2r^{2}}{w_{eq}^{2}}\right), \tag{7}\]  
where \(A_{0}\) represents the fraction of power collected by the receiver, expressed as \(A_{0}=(erf(u))^{2}\). The Probability Density Function (PDF) of the misalignment fading effect is expressed as \[f_{h_{m}}(x)=\frac{\xi^{2}}{A_{0}^{\xi^{2}}}x^{\xi^{2}-1},\quad 0\leq x\leq A_{0}, \tag{8}\]  
where \(\xi\) denotes the ratio between the equivalent beam width and the standard deviation of the pointing error at the receiver, expressed as \(\xi=\frac{w_{eq}}{2\sigma_{r}}\).
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 4 Model Validation and Comparison
### Model Validation  
In this proposal, experimental data on the fatigue of composites under CCF loading from literature [38] are introduced for model validation. The composite material used in the experiments is CFRP, consisting of T800 carbon fiber and epoxy resin. The specimen lay-up configuration is \([0/45/0/-45]_{25}\), and the laminate consists of 16 prepreg layers. The dimensions of the specimens are 200 mm \(\times\) 25 mm in length and width, respectively, with a thickness of 3 mm. The specimen gauge area is 100 mm. The LCF loading profile consisted of a trapezoidal spectrum with a stress ratio of \(R=0.1\). The maximum load levels were 80% and 75% of the tensile strength of the material, and the specimens were subjected to repeated loading cycles until failure. The test configuration and loading profile followed ASTM standard D3479 [39]. The CCF tests were conducted by superimposing HCF on the LCF loading profile. A single cycle of CCF included one LCF cycle and n HCF cycles, where the frequency ratio was maintained at \(R_{f}=10\). The applied stress levels for the HCF and LCF components were varied to evaluate the combined effects on fatigue life. The experimental condition parameters are shown in Table 1.  
[PERSON]'s rule, the M-H model, and the T-K model are applied to validate the proposed life prediction model, where 0.4 and 1.6 represent the material constants \(\beta\) and \(r\).  
The findings of this proposed model are compared to those of other models in Figure 6. Due to [PERSON]'s rule disregard for the interaction between HCF and LCF damage, the forecast tends to be nonconservative, and the error is within \(\pm 2\) times. The M-H model'sforecast understates the CCF life and is too cautious, with some data beyond the \(\pm\)2 life factor area. With several data surpassing the \(\pm\)2 life factor area and significant prediction errors, the T-K model's usefulness is restricted by the addition of extra material coefficients. With an inaccuracy of less than \(\pm\)1.5 times, the projected life of this proposed model has the best agreement with the experimental life, suggesting that it may be effectively used for high-precision fatigue life evaluation under CCF loading.
## 2 Materials and Methods
### EEG Data Analysis  
#### 2.4.1 The Correlation Matrix Analysis and S-Estimator  
Consider \(N\) sampled neuronal data (time-series), \([s_{1}(l),\,\ldots\,s_{N}(l)]\), with \(le\{1,2,\ldots,L\}\), where \(N\) and \(l\) denote the channel number and the number of data points contained in time-window \(L\), respectively. Let \(P\in R_{N}\times N\) be the calculated correlation matrix whose elements \(P_{ij}\) are represented as follows  
\[P_{ij}=\frac{\text{Cov}\big{(}s_{i},s_{j}\big{)}}{\sqrt{\text{Cov}(s_{i},s_{i} )\cdot\text{Cov}\big{(}s_{j},s_{j}\big{)}}} \tag{1}\]  
where Cov(\(\cdot\)) denotes the covariance calculation function. Assuming \(\{\lambda_{1},\ldots,\lambda_{N}\}\) are the eigenvalues of \(\mathbf{P}\), the Shannon entropy measure of the eigenvalues distribution can be calculated as [31]  
\[I=-\sum_{n=1}^{N}\frac{\lambda_{n}}{N}\log\biggl{(}\frac{\lambda_{n}}{N}\biggr{)} \tag{2}\]  
Note that the above measure \(I\) is inversely related to the cooperation strength in multivariate time-series and lies in the range of [0, \(\log(N)\)]. In this study, we use the S estimator to evaluate synchronization in multichannel EEG signals by using the distributions of the eigenvalues of the covariance matrix \(\mathbf{P}\). We normalize and take the complement to 1, the normalized index \(S\) based on S-estimator can be summarized as follows [25]:  
\[S=1-I/\log(N) \tag{3}\]  
This index \(S\) reflects the cooperation among the \(N\) sampled neuronal signals, i.e., \([s_{1}(l),\,\ldots\,s_{N}(l)]\)
## 2 Materials and Methods
### Data Collection  
An EMG system (Trigno Wireless EMG System, Delsys Inc., Boston, MA, USA) with a 2000 Hz sampling rate and two force plates (AMTI Inc., Watertown, MA, USA) with a 1000 Hz sampling rate were synchronized to collect EMG and ground reaction force data, respectively, using EvaRT software (Version 4.6, Motion Analysis Corporation, Santa Rosa, CA, USA). The force plates were 90 cm in length and 60 cm in width and placed 10 cm apart.  
Customized sand, turf, PU, and wood surfaces were used in the study. The turf, PU, and wood surfaces were cut to a size of 130 cm in length and 90 cm in width to fit flat on the two force plates. Additionally, to reduce the subject's fear when landing on the sand, a larger sand area was created, measuring 130 cm in length, 108 cm in width, and with a depth of approximately 10 cm. The sand had a base (130 cm in length and 90 cm in width) that matched the size of the two force plates. When each material surface was placed on the force plate, the force plate signal was re-calibrated and reset to zero to ensure no interference due to material surface noise or weight.
## 2 Methodology
### Physics-based thermal model  
When insufficient thermal sensors are available in practical applications, the VML must be constructed exclusively through numerical methods based on thermal models (LPTN/FE/CFD). However, this methodology imposes stringent requirements: precise knowledge of bearing geometric dimensions, grease material properties, and a comprehensive understanding of parameter variation mechanisms during long-term operation. Specifically, all thermal calculation parameters must be fully defined without ambiguity that could compromise result validity. Figure 2 illustrates a simplified 2D-LPTN model with multi-node topology for grease-lubricated traction motor bearings, featuring a deep groove ball bearing operating under pure radial load at rotational speeds. This model has evolved from an initial three-node configuration to a sophisticated multi-node structure incorporating lubricant rheological behavior, thermal expansion effects, and cage interactions [23; 39]. Critical factors in LPTN modeling include node network design, heat source quantification, and TR calculations.
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### GNSS Signal Characteristics  
#### 2.3.1 Simulation Models and Assumptions  
In our simulations we have modelled a GPS constellation consisting of 32 GPS satellites, allocated in the six orbital planes according to [21]. Realistic GPS L1 C/A signals are generated by our Spinert GSS8000 full constellation simulator, where the signal power level at the receiver \(P_{r}\) is modelled as [22]:  
\[P_{r}=P_{ICD}+O_{G}+20\times\log_{10}\left(\frac{R_{0}}{R}\right)-L_{tx}-L_{rx} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline ECI Initial position (km) & \(\left(\begin{array}{cc}2395.52&-5298.28&-3022.82\\ 10.19&3.58&1.72\\ \end{array}\right)\) \\ ECI Initial velocity (km/s) & \(\left(\begin{array}{cc}10.19&3.58&1.72\\ \end{array}\right)\) \\ Departure date & 2 July 2005, 00:34:18 \\ Mass of the spacecraft (kg) & 1000 \\ Reference surface (m\({}^{2}\)) & 20 \\ Radiation pressure coefficient & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial position and velocity of the considered receiver trajectory and spacecraft parameters.  
Figure 3: Relation between altitude and time during the considered MTO and GPS constellation altitude.  
Figure 2: STK representation of the considered MTO.  
where:  
\[P_{ICD}=-128.5\,dBm\] is the guaranteed minimum signal level for the GPS L1 C/A signals on the Earth according to [21].  
is the global offset, chosen to obtain in simulation the performance obtained when real signals are received. Typically, the transmitted signal power levels are from 1 to 5 dB higher than the minimum received ones [23], for this reason an intermediate value of 3 dB has been chosen.  
\[R_{0}\] is the reference range used for inverse-square variation calculation and equal to the range from a receiver to the GNSS satellite at zero elevation.  
\[R\] is the range from GNSS satellite to the receiver.  
is the loss from the GNSS satellite transmit antenna in the direction of the receiver. 3D GPS antenna patterns have been modelled, as described in our previous study [24; 25].  
\[L_{rx}\] is the loss from the receiver antenna in the direction of the GNSS satellite.