esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Detection Performance  
#### 4.2.1 Accuracy of Frame Loss Differentiation  
To demonstrate the efficacy of CUBIA in distinguishing the selfish carrier sensing problem, we consider three testing scenarios: (i) selfish carrier sensing, (ii) hidden node problem, and (iii) collisions.  
Figure 9 shows the temporal behavior of CUSUM change detection filter \(c_{k}\) for the three testing scenarios. Figure 9 plots the results of CUBIA over time for the case of selfish problem in the topology that is depicted in Figure 9. We can observe that the detection filters of APs in the interference range continue to increase, where the selfish node starts transmissions at 5 s.  
Figure 9,c show CUBIA's ability of filtering the collisions and the hidden node problem, respectively. To simulate an abrupt change in the network state, initially, 10 AP-clients pairs are considered until an additional 40 nodes are abruptly activated at 3 s. Figure 9 demonstrates that  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Transmission range & 75 \\ Carrier range & 150 \\ Data rate/ACK rate & 54 Mbps/6 Mbps \\ CBR rate per AP-client pair (UDP) & 20 Mbps \\ payload size of UDP, TCP & 1000, 1500 bytes \\ \(T_{FER}\) (in Equation (3)) & 0.05 \\ \(\lambda\) (in Equation (1)) & 0.1 \\ \(m\) (number of transmissions for \(p_{k}\) in Equation (1)) & 10 \\ the maximum allowed latency of detection & 2 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in performance evaluation.  
Figure 9: The dynamics of CUBIA toward three difference types of frame losses.  
CUBIA effectively filters out the MAC-layer collisions. We generated ON/OFF traffic on a hidden node in order to simulate the impact of the hidden node problem, as illustrated in Figure 9c. In the figure, we can see that the detection filter of CUBIA promptly reacts to the hidden node, increasing above \(\theta_{AS}\), but the value of \(c_{k}\) remains low and does not exceed \(\theta_{S}\). This is because the effect of a hidden node is mitigated by adaptive RTS/CTS changes, thus imparting a negative drift to the detection filter.  
Overall, our proposed scheme CUBIA accurately distinguishes the selfish misbehavior with CCA manipulation from collisions and the hidden node problem.
# Analysis of Volatile Components in Different _Ophiocordyceps sinensis_ and Insect Host Products
## 3 Materials and Methods
### GC\(\times\)GC-QTOFMS Analysis for Volatile Components  
The analysis of volatile composition and analytical method validation were referenced by the method of previous reports [19; 21; 24]. The volatile constituents of _O. sinensis_ and host insects were analyzed by comprehensive two-dimensional gas chromatography (7890B-SSM1800, Agilent Technologies, Santa Clara, CA, USA and J&X Technologies, Shanghai, China) coupled with a high-resolution quadrupole time-of-flight mass spectrometry (QTOFMS) (7250, Agilent Technologies). First, 100 mg of samples were accurately weighed into a 20 mL vial, and then the SPME fiber that was equilibrated at 270 \({}^{\circ}\)C for 30 min in an autosampler (PAL RSI 120, CTC Technologies, Alexandria, VA, USA) was exposed to the headspace of the bottle for 20 min at 60 \({}^{\circ}\)C. Then, the SPME fiber was introduced into the GC splitless injector and kept there for 3.0 min to allow thermal desorption of the analytes. All samples were conducted in triplicate to check the repeatability and reliability of the method development. Reproducibility is expressed as the relative standard deviation (RSD). To compare the techniques of GC-MS and GC\(\times\)GC-MS, a quality control sample (mix of each collected sample) was analyzed by both techniques under the same chromatographic conditions. The analytical system was equipped with simultaneous 1 DGC and GC\(\times\)GC in one instrument, which can conduct both techniques at the same time without any change of columns. The samples were introduced by a splitless injector (SSL) system equipped with an autosampler. Peak separation was performed on a weak-polar column HP-5 MS (5% phenyl-95% dimethylpolysiloxane, 30 m \(\times\) 250 \(\mu\)m, 0.25 \(\mu\)m) in the first dimension and a more polar column DB-17 MS (50% phenyl-50% dimethylpolysiloxane, 1.2 m \(\times\) 180 \(\mu\)m, 0.18 \(\mu\)m) in the second dimension (both from Agilent Technologies, USA).  
The 1 DGC and GC\(\times\)GC conditions were the same. The GC injector was kept at 250 \({}^{\circ}\)C in splitless mode. The carrier gas was helium with a flow rate of 1.0 mL/min for the first dimensional column. The initial oven temperature was 50 \({}^{\circ}\)C; it was held for 3 min and then ramped at a rate of 4 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C and held for 1 min. For the GC\(\times\)GC system, the carrier gas was helium with a flow rate of 3.14 mL/min for second dimensional column, and the cold zone temperature of modulator was set at \(-\)50 \({}^{\circ}\)C. The temperatures of the entry hot zone and exit hot zone were +30 and +120 \({}^{\circ}\)C relative to oven temperatures, respectively, with a cap temperature of 320 \({}^{\circ}\)C for both hot zones. The modulation period was 4 s.  
The MS transfer line temperature was kept at 280 \({}^{\circ}\)C, and the ion source temperature was kept at 200 \({}^{\circ}\)C. Electron impact ionization was 70 eV. Data were collected as a mass range of 50-500 \(m/z\) at a sampling rate of 50 scan/s, and a solvent delay of 8 min was used.
## 4 Results
### Inventory Related Costs
#### 4.1.3 Holding Cost
The biggest relative increase in average \(\mathrm{C_{H}}\) in a 5-day WS for low demand variability occurs for R + LT = 3/2, with a value of 36.7%, whereas for high demand variability, the maximum relative increase is noticed for R + LT = 4/3, with a value of 25.8%. In a 7-day WS, the biggest relative increase in average \(\mathrm{C_{H}}\) for SD 10 occurs for R + LT = 2/1, with a value of 80.7%, whereas for SD 30, the maximum relative increase is noticed for R + LT = 2/1, with a value of 67.0%. On average, with the increase in R + LT value for single time unit, average \(\mathrm{C_{H}}\) increases 7.7% in a 5-day WS and 11.6% in a 7-day WS regardless of demand variability. However, it is worth noting that 17 groups with an average reduction of 2.0% are noticed regardless of WS and demand variability. When comparing the average \(\mathrm{C_{H}}\) for a 7-day WS to a 5-day WS for each SC setup, the average \(\mathrm{C_{H}}\) for a 7-day WS is lower due to lower AIL, as visible in Figure 9a. On average, the relative reduction for SD 10 is 32.2%, and for SD 30, it is 26.2%. The relative difference between the 7-day and 5-day WSs in \(\mathrm{C_{H}}\) reduces with the increase in the R + LT. Maximum reductions are experienced for R + LT = 1, where for SD 10, they are 65.0%, and for SD 30, 57.3% on average. Minimal relative differences occur in the R + LT = 30 group, with values of 7.7% and 5.5% for SD 10 and SD 30, respectively. Figure 8: (a) Average \(\mathrm{C_{H}}\) per R + LT group for a 5-day WS and FR 99.9%. (b) Average \(\mathrm{C_{H}}\) per R + LT group for a 7-day WS and an FR 99.9%. The average \(\mathrm{C_{H}}\) in every combination of R + LT and WSs is higher for an FR of 99.9% compared to 89.9%, as visible in Figure 9b. In a 5-day WS, they are 48.6%, while in a 7-day WS, 36.4% higher, regardless of demand variability. However, considering demand variability, average \(\mathrm{C_{H}}\) is significantly higher for higher demand variability (58.2% in a 5-day WS and 53.6% for a 7-day WS) than lower demand variability (39.0% for a 5-day and 19.2% for a 7-day WS).
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 4 Semiconductor Hydrogen Sensors
### Schottky Diode Hydrogen-Sensitive Materials and Sensors  
Non-resistive semiconductor hydrogen sensors sense hydrogen according to the response of the potential barrier and capacitance of the material about the hydrogen concentration. Depending on the principle and structure, they can be divided into Schottky diode-type and metal oxide semiconductor-type field-effect transistors (MOSFET). As shown in Figure 6, Schottky diode-type hydrogen sensors generally deposit a layer of metal (usually Pd or Pt) on the semiconductor material to form a Schottky junction. A metal oxide insulating layer is also usually added between the two components to improve the stability and sensitivity of the sensor to hydrogen. When the metal comes into contact with hydrogen, the hydrogen molecules decompose into hydrogen atoms and diffuse into the semiconductor layer, causing a change in the Schottky barrier, and the hydrogen concentration is detected through detecting current or voltage. [PERSON] et al. [55] and [PERSON] et al. [56] reviewed Schottky diode-type hydrogen sensors using nitride-based semiconductor materials, with GaN and AlGaN showing better performance with very high sensitivity and fast response times, respectively. The sensing signal of the GaN hydrogen sensor is affected by the ambient temperature, and [PERSON] et al. [57] introduced a reference diode to compensate for the temperature of the GaN hydrogen sensor.  
The advantages of semiconductor-type hydrogen sensors are their simple structure, small size, easy integration, low cost, fast response, and suitability for mass production. However, they are affected by electromagnetic interference and are prone to signal drift, making them difficult to use in harsher environmental conditions. In addition, sparks may be generated, while there are combustion, explosion, and other safety issues.  
Figure 6: Schematic diagram of Schottky diode hydrogen sensor structure.
# RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets
## 2 300 GHz FMCW Radar  
Current commercial vehicle radars use multiple input multiple output (MIMO) technology at 77-79 GHz with up to 5 GHz bandwidth and a very limited range--typically 35 cm--and azimuth resolution--typically 15\({}^{\circ}\)[6]. This equates to a cross range resolution of \(\approx\)4 m at 15 m, meaning that a car will occupy only one cell in the radar image. In this work, we use a 300 GHz frequency modulated continuous wave (FMCW) scanning radar designed by the University of Birmingham [13; 14]. The assumed advantage of the increased resolution is a better radar image which may lead to more reliable object classification. The 300 GHz radar has a bandwidth of 20 GHz, which equates to a 0.75 cm range resolution. The azimuth resolution is 1.2\({}^{\circ}\), which corresponds to 20 cm at 10 m. Figure 2 shows 300 GHz radar images with different bandwidths. As can be seen, the 20 GHz bandwidth provides much better resolution compared to current commercial sensors, and as such is considered for classification in this paper.  
The raw data captured by the 300 GHz radar comprise a time-domain signal at each azimuth direction. To transform the raw signal into an image, a Fast Fourier Transform (FFT) is applied to create a range profile at each azimuth, which is then converted to decibels (dB). The original polar image is converted to Cartesian coordinates to ensure consistent object dimensions at all ranges.  
Figure 1: RADIO—radar data augmentation—is a data augmentation technique based on the physical properties of the radar data. The methodology is developed for both object detection and classification. CNN: convolutional neural network. DBSCAN: density-based spatial clustering of applications with noise.
# Low-Cost Three-Quadrant Single Solar Cell _I-V_ Tracer
## 2 Methods and Materials  
### Solar Cell I-V Curve Characteristics  
The general _I-V_ curve characteristics of a solar cell are well known and can be predicted from the simplest electrical model, namely the one-diode model (Figure 1).  
Within this model, a current source (\(I_{P}\)) is used to consider the photogenerated current of the solar cell and a diode (\(D\)) to model the fact that a cell is, in essence, a P-N junction between two semiconductors. Finally, a parallel resistor (\(R_{p}\)) and a series resistor (\(R_{s}\)) model show that the diode and the source are not ideals (contact resistances, impurities,  
Figure 1: One-diode model for a photovoltaic cell. \(I_{P}\): current source; \(D\): diode; \(R_{p}\) parallel resistor; \(R_{s}\): series resistor.  
annihilation between conduction electrons and holes, scattering, and collisions with the crystalline net, etc.).  
Since the usual \(R_{p}\) has a very high value and the \(R_{s}\) has a low one, from this model it is easy to induce that, for moderate reverse voltages or forward ones under the conduction voltages of the diode, the external current is approximately equal to \(I_{P}\) (the forward current), since the diode is in an off-state and no (or very small) currents flow through it. This region corresponds to the flat sections within the first and second quadrants of the curve in Figure 2b, where the I\({}_{\rm P}\) is labelled as \(I_{\rm SC}\) (solar cell short circuit current), as they are the same. However, if the reverse voltage is applied enough, the avalanche (Zener) voltage of the diode could be reached and the reverse avalanche current through the diode will be added to the forward external current, resulting in currents rapidly increasing over the I\({}_{\rm P}\) value (the left region of the curve (b) in Figure 2). On the forward voltage side, if the conduction voltage of the diode is reached, the forward current through the diode will be subtracted from \(I_{P}\), and the output current will be reduced. From this point, if the forward voltage continues to rise, the output current will reach zero and will even be reversed, exponentially, to negative values, corresponding to the right region of the curve in Figure 2b, within the first and fourth quadrants.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 3 Background
### Design Challenges  
A surface gateway deployment for an UWSN must address the following major challenges:
# Front-End Amplifiers for Tuning Forks in Quartz Enhanced PhotoAcoustic Spectroscopy
## 3 Quartz-Enhanced Photoacoustic Sensor  
The QEPAS sensor setup employed to investigate the performance of the proposed amplifiers is depicted in Figure 3.  
A single-mode continuous-wave distributed-feedback QCL with a central emission at 7.72 \(\upmu\)m was used as the light source. The QCL operates in the 20-30 \({}^{\circ}\)C temperature range and in the 140-280 mA injected current range. The laser beam was focused between the prongs of the QTF by using a ZnSe lens with a focal length of 75 mm (L1 in Figure 3). Its transmittance is 95% at the laser wavelength. The employed QTF has prongs spaced by 800 \(\upmu\)m in order to minimize possible optical noise due to the light hitting the QTF prongs [17]. The frequency of the fundamental resonance mode is f\({}_{0}\) = 12456.16 Hz at atmospheric pressure. The QTF is mounted in an acoustic detection module  
Figure 3: Schematic of the experimental apparatus. ADM—acoustic detection module, QTF—quartz tuning fork, L1, L2—lenses, PC—personal computer.  
(ADM) equipped with two ZnSe windows for the laser beam's entrance and exit. Beyond the ADM, a ZnSe lens (L2 in Figure 3) collects the light on the sensitive element of a power meter for optical alignment. A standard air cylinder and a Nafion humidifier were used to generate a gas mixture with a constant water vapor concentration of 2%. A gas flow controller was used to fix the mixture flow to 30 sccm, while a vacuum pump and a pressure controller were used to fix the gas pressure to 760 Torr. Wavelength modulation spectroscopy with second harmonic demodulation was employed as a detection scheme [30]. Laser wavelength modulation was achieved by dithering the current driver with a sinusoidal wave with a frequency of f = f\({}_{0}\)/2. A ramp with a frequency of 5 mHz was added to the laser current driver in order to scan the wavelength emission across the absorption line. Both the fast modulation and the ramp were provided by a waveform generator (Tektronix AFG102). The QEPAS sensor architecture allowed for an easy interchange of the QTF front-end amplifiers, without altering the experimental conditions. A National Instrument Data Acquisition (DAQ) card (NI USB-6008) was used to acquire the QTF signal demodulated by a PerkinElmer 7265 lock-in amplifier. The lock-in integration time was set to 100 ms and the signal acquisition time to 300 ms. The reference signal for the lock-in amplifier was provided by the TTL output channel of the waveform generator.  
At a laser operating temperature of 20 \({}^{\circ}\)C within its current dynamic range, the wavelength emission of the QCL ranges from 1296 to 1298 cm\({}^{-1}\). In this spectral range, the HITRAN database was used to simulate the absorption cross-section spectrum of standard air [31] as shown in Figure 4.  
For our investigation, we selected the two highest water vapor absorption features, which peaked at 1296.48 and 1296.71 cm\({}^{-1}\) with absorption cross-sections of \(4.7\times 10^{-23}\) and \(3.1\times 10^{-23}\) cm\({}^{2}\)/ molecule, respectively.
## 3 Results
### The Third Hypothesis  
From the results of the third hypothesis shown in Figure 7, we observe that all position parameters correlate positively between the Max and Submax zones. Among these, the AH down tilt position has the strongest positive correlation coefficient (0.86), while the anterior tilt has the weakest (0.31). The other positioning parameters also correlate well, each with a coefficient above 0.6. The results support the alternative hypothesis (H\({}_{\text{a}}\)), indicating that most positional variables correlate strongly between the Max and SubMax zones. This highlights the consistency in positional performance across different levels of force control. Overall, these results reject our H\({}_{0}\) hypothesis that there is no strong positive correlation between most of the variables (less than four with coefficients \(\leq\) 0.6). Table 5 shows all the Spearman correlation values.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variable** & **Median (IQR)** & **Median (IQR)** & **\(p\)-Value** & **N USCP** & **N Non-USCP** \\  & **USCP** & **Non-USCP** & **\(p\)-Value** & **N USCP** & **Non-USCP** \\ \hline MVC & 8.18 (3.73–12.63) & 10.48 (7.96-15.09) & 0.15 & 62 & 21 \\ Posterior Max & 7.75 (3.42–12.09) & 5.68 (2.53–9.19) & 0.31 & 62 & 21 \\ Anterior Max & 1.1 (0.19–2.0) & 1.11 (0.71–2.36) & 0.73 & 23 & 14 \\ NAH Forward & 3.42 (0.61–6.23) & 2.31 (1.18–3.78) & 0.15 & 58 & 20 \\ Rotation Max & 1.3 (0.26–2.34) & 0.81 (0.67–1.27) & 0.15 & 34 & 14 \\ AH Down Tilt Max & 5.45 (1.63–9.28) & 3.67 (1.10–5.85) & 0.15 & 48 & 17 \\ NAH Down Tilt Max & 2.87 (\(-\)0.18–5.92) & 2.64 (0.84–3.51) & 0.40 & 42 & 15 \\ Posterior SubMax & 4.4 (0.61–8.18) & 3.78 (3.07–6.67) & 0.97 & 62 & 20 \\ Anterior SubMax & 1.51 (0.75–2.26) & 2.55 (1.61–3.80) & 0.05 & 43 & 18 \\ NAH Forward & 2.81 (0.52–5.1) & 1.74 (1.39–2.69) & 0.04 & 58 & 21 \\ AH Forward & & & & & \\ Rotation SubMax & 1.29 (0.29–2.3) & 1.34 (0.54–1.63) & 0.17 & 48 & 17 \\ AH Down Tilt SubMax & 3.59 (0.21–6.97) & 3.52 (1.44–5.71) & 0.60 & 50 & 18 \\ NAH Down Tilt SubMax & 2.29 (0.11–4.47) & 2.18 (1.44–3.33) & 0.57 & 43 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of median (IQR) between USCP and non-USCP groups.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Max Zone** & **SubMax Zone** & **Spearman Correlation** \\ \hline Posterior Tilt & Posterior Tilt & 0.74 \\ Anterior Tilt & Anterior Tilt & 0.31 \\ NAH Forward Rotation & NAH Forward Rotation & 0.69 \\ AH Forward Rotation & 0.70 \\ AH Down Tilt & AH Down Tilt & 0.86 \\ NAH Down Tilt & NAH Down Tilt & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spearman correlation between Max and SubMax zones for the whole group.
## 5 Conclusions  
Our findings identified the candidate proteins that participated in isoprenoid biosynthesis and the potential transcription factor families, which may play key roles in the leaf development of linalool-type _Cinnamomum camphora_ by proteomic analysis. These results implicate that the process of isoprenoid biosynthesis in _C. camphora_ is regulated by a complicated network consisting of many conserved synthetases and leaf development by transcription factors regulation. This study provides valuable proteomic information for further investigation of the regulatory mechanisms underlying the isoprenoid metabolism of plants.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f13091487/s1](https://www.mdpi.com/article/10.3390/f13091487/s1)]([https://www.mdpi.com/article/10.3390/f13091487/s1](https://www.mdpi.com/article/10.3390/f13091487/s1)), Figure S1: The mevalonate (MVA) and methyleryth-thiritol phosphate (MEP) pathway; Figure S2: Metabolite analyses of Cinnamomum camphora leaves; Figure S3: Functional characterization of leaf proteome in Cinnamomum camphora; Table S1: Expression and annotation of all leaf proteins; Table S2: Expression and annotation of differentially expressed proteins; Table S3: Expression of proteins related to isoprenoid biosynthesis.  
**Author Contributions:** Conceptualization, methodology, investigation, writing original draft, and funding acquisition, C.Z.; conceptualization, methodology, data curation, writing original draft, validation, and visualization, F.Z.; investigation, resources, and methodology, S.C.; methodology and validation, K.W.; investigation and resources, G.X.; methodology, investigation, and data curation, X.L.; investigation, resources, and visualization, J.A.; conceptualization, methodology, investigation, resources, and supervision, K.L.; conceptualization, methodology, investigation, resources, supervision, writing review, and editing, L.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Funds of Guangxi Key Laboratory of Special Non-wood Forest Cultivation and Utilization (no.19-B-04-01, JA-20-04-04, JA-20-04-07); Fundamental Research Funds for Guangxi Forestry Research Institute (no.201806, 202102); Guangxi Science and Technology Project (no.1517-06); Guangxi Special Expert Program.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.