esa-sceva/satcom-chunk-collection huggingface dataset:
# Volatile Metabolome and Aroma Differences of Six Cultivars of _Prunus mume_ Blossoms
## 3 Materials and Methods
### HS-SPME-GC-MS Analysis Method  
An amount of 0.300 g (\(\pm\)0.002 g) of the sample powder was weighed into a 15 mL SPME bottle (CNW Technologies GmbH, North Rhine-Westphalia, Germany), 5 \(\upmu\)L of 0.816 \(\upmu\)g/\(\upmu\)L 2-methyl-3-heptanone solution (in methanol) was added with 10 \(\upmu\)L of 0.521 \(\upmu\)g/\(\upmu\)L 1,2-dichlorobenzene solution (in methanol) as internal standards to correct the retention time and conduct quantitative analysis of the volatile metabolites. The bottle cap was tightened with a PTFE/silica gel septum (JIEDAO Technology Instrument Co., Ltd., Nanjing, China) and placed on an SPME Sampling Stand (PC-420D, Corning Inc., Corning, NY, USA) to equilibrate at 40 \({}^{\circ}\)C for 20 min. A SPME fiber (50/30 \(\upmu\)m, DVB/CAR/PDMS, SUPELCO, Bellefonte, PA, USA) was inserted to extract for 60 min at the same temperature, and then the fiber was immediately inserted into the GC inlet for thermal desorption.  
The volatile metabolites were analyzed by GC-MS (7890B-7000C, Agilent Technologies Inc., Palo Alto, CA, USA) equipped with an HP-INNOWax column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.5 \(\upmu\)m; Agilent Technologies Inc., Palo Alto, CA, USA). The inlet temperature was set to 250 \({}^{\circ}\)C and spitless injection was adopted. High-purity helium with a flow rate of 1 mL/min was the carrier gas. The initial oven temperature was held at 40 \({}^{\circ}\)C for 5 min, and was then programed to 230 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and maintained for 10 min; the total time was 78.33 min. The mass spectrometer adopted an electron ionization source with 70 eV electron energy, and the temperature of the transfer line and the ion source were both 250 \({}^{\circ}\)C. The scanning range was 30-450 \(m/z\) conducted on the first quadrupole with a scan time of 600 ms.  
Figure 6: Pictures of six varieties of _P. mume_ blossoms. YD (Yudie), DFZS (Dongfang Zhusha), GLH (Gulihong), JM (Jiangmei), LE (Ive), GF (Gongfen).  
The HS-SPME-GC-MS conditions employed here were optimized in advance. Each sample was sampled twice in duplicate and the QC samples were injected every 9 intervals to assess the stability of the analysis system. A blank control sample in which only internal standards were included was analyzed for signal verification. Additionally, the mixed standard solution of C7-C40 \(n\)-alkanes and the mixed authentic standards solution were analyzed at the same conditions for calculation of the retention index (RIs) and assisting qualitative analysis of volatile metabolites in samples.
## 4 Results and Discussion  
### Current Situation Analysis  
As a result of the current model FEA, the pick force was 56,427 N, and the absorbed energy was 840 J. The force-displacement curves of the current model and optimized model are shown in Figure 14. On the basis of literature reviews regarding the mechanical behavior of the Al 6082-T6 profile [55; 56], it was assumed that the current model can meet the test requirements of annex 8D of the ECE R100 regulation when a complete battery pack including the top cover and the bottom base is considered. The approach in this study is to avoid over-engineering, which does not provide value, and the overcost structure is simplified. In other words, resources such as material, time and money are generally wasted in terms of value engineering.  
Figure 14: Force–displacement curves of the current model and optimum model.  
Figure 13: Schematic representation of the matrix–fiber ratio.  
Figure 17: Force–displacement curves of cases 10–18 and the optimized model.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Case No.**} & \multicolumn{2}{l}{**Design Variables**} & \multicolumn{2}{l}{**Objectives**} & \multicolumn{2}{l}{**Absorbed Energy (J)**} \\  & \multicolumn{1}{l}{p1 (mm)} & \multicolumn{1}{l}{p2 (mm)} & \multicolumn{1}{l}{p3 (degree)} & \multicolumn{1}{l}{**Mass Gain (g)**} & \multicolumn{1}{l}{**Pick Force (N)**} & \multicolumn{1}{l}{**Absorbed Energy (J)**} \\ \hline case 1 & 60 & 6 & 15 & 871 & 51,772 & 730.6 \\ case 2 & 80 & 6 & 15 & 1164 & 48,611 & 643.6 \\ case 3 & 100 & 6 & 15 & 1392 & 42,438 & 509.2 \\ case 4 & 60 & 6 & 75 & 1829 & 46,099 & 688 \\ case 5 & 80 & 6 & 75 & 2209 & 34,529 & 505.9 \\ case 6 & 100 & 6 & 75 & 2195 & 32,985 & 462.5 \\ case 7 & 60 & 6 & 135 & 1785 & 50,652 & 727.4 \\ case 8 & 80 & 6 & 135 & 2129 & 42,363 & 569.1 \\ case 9 & 100 & 6 & 135 & 2048 & 35,636 & 478.8 \\ case 10 & 60 & 9 & 15 & 43 & 66,924 & 870.7 \\ case 11 & 80 & 9 & 15 & 269 & 54,424 & 754.8 \\ case 12 & 100 & 9 & 15 & 549 & 45,387 & 604.7 \\ case 13 & 60 & 9 & 75 & 1184 & 48,855 & 719.6 \\ case 14 & 80 & 9 & 75 & 1738 & 34,801 & 511.1 \\ case 15 & 100 & 9 & 75 & 1736 & 31,783 & 474.5 \\ case 16 & 60 & 9 & 135 & 1063 & 54,937 & 789.3 \\ case 17 & 80 & 9 & 135 & 1624 & 44,212 & 599.8 \\ case 18 & 100 & 9 & 135 & 1517 & 36,577 & 497.2 \\ case 19 & 60 & 12 & 15 & \(-\)619 & 80,400 & 1014 \\ case 20 & 80 & 12 & 15 & \(-\)465 & 64,581 & 859.2 \\ case 21 & 100 & 12 & 15 & \(-\)206 & 47,021 & 643.6 \\ case 22 & 60 & 12 & 75 & 521 & 50,080 & 741.1 \\ case 23 & 80 & 12 & 75 & 1277 & 35,033 & 513.2 \\ case 24 & 100 & 12 & 75 & 1300 & 32,178 & 482.1 \\ case 25 & 60 & 12 & 135 & 287 & 55,177 & 818.5 \\ case 26 & 80 & 12 & 135 & 1107 & 45,232 & 615.3 \\ case 27 & 100 & 12 & 135 & 1014 & 37,242 & 508.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: FEA results for 27 different cases.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 2 Theory and Design  
### Design of the RF MEMS Switch  
In high-frequency communications, the low insertion loss of RF MEMS switches helps to maintain the RF signal quality, high isolation can reduce crosstalk between signals, high linearity can maintain signal integrity, and small size facilitates integration. It has outstanding benefits. In addition, the processing techniques for RF MEMS switches are relatively mature [18], while the integration with the antenna is relatively high in terms of the manufacturing process. It shows great potential in designing millimeter wave reconfigurable antennas [19]. RF MEMS switches are therefore used to electronically control the antenna frequency in this paper.  
The RF MEMS switch designed in this paper consists of a borosilicate glass substrate, a straight-plate cantilever-beam top electrode, an actuation electrode, coplanar waveguide (CPW) lines, and an air bridge, as shown in Figure 1. Details of the switch sizes are given in Table 1. The actuation electrode is positioned below the top electrode and activates the switch by electrostatic actuation. A voltage is applied to the actuation electrode to operate the switch. When the top electrode is pulled down to \((2/3)g_{0}\) by electrostatic force, the increase in electrostatic force is much greater than the increase in restoring force, resulting in a rapid drop-down of the top electrode [20]. At this point, the top electrode contacts the bottom electrode, and the signal is transmitted. After removing the voltage, the switch returns to its original state, and the signal is then disconnected. The voltage that pulls the top electrode down to \((2/3)g_{0}\) is the driving voltage. Its expression is:  
\[V=\sqrt{\frac{8k}{27\varepsilon_{0}A}\delta_{0}^{3}} \tag{1}\]  
where \(\  
The simulation results of the S-parameters of the MEMS switch, including isolation and insertion loss, are shown in Figure 2. When the switch is on, the insertion loss value is \(\leq\)0.17 dB and the return loss value is \(\geq\)26 dB in the 40-60 GHz band. When the switch is off, the isolation is \(\geq\)15 dB in the 40-48.5 GHz band and \(\geq\)13.4 dB in the 48.5-60 GHz band. As can be seen, the MEMS switch has better RF performance in high-frequency bands and can support the design of additional reconfigurable antennas.
# Linomycin-Induced Transcriptional Alterations in the Green Alga _Raphidocelis subcapitata_
## 4 Discussion
### Genes Related to Xenobiotic Metabolism  
Biodegradation, including phase I, II, and III detoxification processes, is an effective way to excrete xenobiotics in algae [39, 40]. In this study, after exposure to high LIN concentrations, the genes involved in all phases were nearly downregulated. In phase I, the expression of _Cyt p450_ and _Cyt b5_ genes were decreased. _Cyt b5_ functions as a catalyst in adjusting the activity of _cyt p450_[41]. Downregulation of _cyt b5_ may thus reduce the activity of _cyt p450_, which converts the lipophilic xenobiotics to more water-soluble compounds by facilitating the excretion of certain compounds [42]. In line with this, ERY, which has the same characteristics as LIN, inhibited the _cytochrome p450_ activity in _R. subcapitata_[10]. The amidase family, which is involved in mycothiol-mediated detoxification, can be used to regenerate mycothiol, which is a common protective agent under environmental stress conditions and it mainly serves to improve cell robustness and survival [43]. _AOs_ catalyzing the oxidation of amines to aldehydes have been proposed as sources of reactive oxygen species (ROS) in the apoplast [44]. Surprisingly, AOs were up-regulated in the high-LIN group, suggesting the elevated ROS in algae, which is a marker of oxidative stress. In phase II (conjugation progress), expression of _gpx_ was decreased in high-LIN groups. _Gpx_ shows a potent effect on the detoxification of peroxides from the cells and/or tissues, which protect various organisms from oxidative stresses [45, 46]. Hence, the decreased _gpx_ may reduce the detoxification ability in algae. In phase III of detoxification in photosynthesizing lives, exogenous substances are discharged into the cell wall or vacuole through exocytosis [47, 48]. Surprisingly, _ABC transporter family_ genes (_ABC transporter family A_, \(B\), \(I\), _G_), acting as an ATP-powered efflux pump that transport xenobiotic across biological membranes, were down-regulated. This may lead to higher toxicity by prolonging dwell time and the effect of LIN in _R. subcapitata_[49, 50]. In accordance with this, a reduction of ABC transporter family genes was detected in both 80 (_ABC transporter family A_, _G_) and 120 \(\upmu\)g L\({}^{-1}\) ERY-dosing groups (_ABC transporter family A_, \(B\), \(I\), _G_) [10].
## 4 Sensitivity Investigations and Numerical Simulations
### System Dynamics without Control (\(\beta_{j}=0,j=1,2,\ldots,10\))  
The non-linear dynamics of the considered Jeffcott-rotor have been discussed within this sub-section before control via solving the non-linear system (44) to (47) when \(\beta_{j}=0,(j=1,2,\ldots,10)\). Figure 4 has been obtained via solving Equations (44) to (47) when the rotor eccentricity \(E=0.01,\ 0.02,\) and \(0.03\) utilizing \(\sigma\) as the main bifurcation parameter at \(\beta_{j}=0,\ (j=1,2,\ldots,10)\). The figure shows that the vibration amplitudes (\(a_{1}\) and \(a_{2}\)) are symmetric and monotonic increasing functions of the shaft eccentricity \(E\). In addition, the figure depicts that the Jeffcott-rotor responds as a linear system as long as the eccentricity \(E<0.01\). However, increasing \(E\) beyond \(0.01\) results in nonlinearity dominance of the system response where the system exhibits hard spring characteristics, bistable solutions, and a jump phenomenon. In addition, Figure 4 demonstrates that the uncontrolled system may be subjected to rub and/or impact force between the rotor and the 8-pole actuator when \(E>0.01\) because \(a_{1}>1\) and \(a_{2}>1\) at some values of the angular speed \(\Omega=1+\sigma\). In Figure 5, the eccentricity \(E\) has been utilized rather than the detuning parameter \(\sigma\) as a bifurcation parameter to plot what is known eccentricity-response curve (i.e., \(E\) versus \(a_{1}\) and \(a_{2}\)) at \(\sigma=0,\ 0.05\). The figure shows that the rotor oscillation amplitudes (\(a_{1}\) and \(a_{2}\)) are a monotonic increasing function of \(E\). In addition, the figure demonstrates that the Jeffcott-rotor may respond with a mono-stable periodic solution regardless of the shaft eccentricity magnitude (i.e., \(0<E\leq 0.1\)) as long as the angular speed is equal to the rotor's natural frequency (i.e., \(\Omega=1+\sigma,\ \sigma=0\)). However, the increase of the rotor angular speed beyond the system's natural frequency (i.e., \(\Omega=1+\sigma,\ \sigma=0.05\)) results in nonlinearity dominance of the system response, where the bi-stable solution region appears (i.e., when \(0.02<E<0.05\)).
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 4 Simulation Results and Discussion
### Environmental Conditions
#### 4.1.3 Air Density  
Air density directly impacts UAVs aerodynamically and in terms of engine performance. Air density affects nearly every aspect of a UAV's flight. In less dense air, standard measurements, such as take-off distances, rate of climb, landing distance, would all be increased, thus reducing the performance. Atmospheric density, in general, is defined as the mass per unit volume of a planet's atmosphere.
## 3 Results and Discussions
### Soil Enzyme Activities  
During the remediation process, soil dehydrogenase activity, FDA hydrolase activity, urease activity, and phosphatase activity were monitored, and the results are shown in Figure 3.  
As shown in Figure 3a, the changing trend of dehydrogenase activity is consistent with the diesel degradation process (Figure 2a), indicating that dehydrogenase is a crucial enzyme in the degradation of diesel pollutants. The enzyme activities of B and N-B systems increase rapidly, and their growth trends are the same, indicating that the metabolism of pollutants in the soil was mainly completed by the added degrading bacteria in the  
Figure 2: The changes of diesel contents and functional genes during different bioremediation processes.  
beginning bioremediation stage. After 18 days, the enzyme activity of the N-B group was gradually higher than that of the B group, indicating that the addition of nutrient sources is beneficial to the growth of both degrading strains and indigenous microorganisms and can promote the increase of dehydrogenase activity. The dehydrogenase activity of the N group increased rapidly after 15 days, indicating that the degrading bacteria in the indigenous flora played a role.  
It can be seen from Figure 3b that during the first six days of all treatment processes, the FDA hydrolase activity is low, which shows that diesel pollutants have an adverse impact on microbial activity.  
During the experiment, the FDA hydrolase of the C group has been maintained at a low level (less than 55 \(\upmu\)g fluorescein/g/h), indicating that the microbial activity in the soil is low and remains unchanged. The N group and N-B group trends were similar in the early stage, and the degradation ability was higher than that of the N group due to the addition of degrading bacteria in the later stage of degradation. The results of groups N and B are opposite to the results of dehydrogenase activity. The overall results show that biological stimulation remediation measures can better promote the restoration of soil microbial activity.  
Soil urease and phosphatase can reflect the nutrient cycling capacity of the soil, which is an indicator that needs attention in the process of remediation of barren contaminated soil. Figure 3c,d give kinetics of urease and phosphatase activities of soil during different remediation methods. The effect of the N group is the best, and the urease activity and phosphatase activity increase rapidly during the whole degradation process, which shows that the proper ratio of nutrient addition can significantly restore the soil nutrient cycle.  
Figure 3: The changes of enzyme activity during different bioremediation processes.  
Moreover, from the measurement results of the two enzymes, the effect of biological stimulation on different enzyme activities is different. The urease activity increased from 0.95 \(\pm\) 0.02 to 8.94 \(\pm\) 0.01 mg NH4\({}^{+}\)-N/g dry soil/h, and phosphatase activity increased from 0.02 \(\pm\) 0.01 to 0.17 \(\pm\) 0.01 mg phenol/g dry soil.
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 4 Data  
Since ML-based models require a large number of samples for proper training and assessments, and based on the automotive standards that were used as the basis for assessments of the results, it was decided that the data used for training the model and final validation will use both recordings and simulations to fulfil the requirements.  
To do so, three types of data were used: (i) Purely simulated data used for the simulation of all spoofing and PRN jamming datasets; (ii) Recorded base GNSS signal + simulated RFI used for all jamming types other than code/spectrum-matched jammers; and (iii) Recorded signals containing both GNSS + RFI used only for our final validation tests.  
It should be noted that the broadcasting of jamming and/or spoofing signals is illegal. All the broadcasting and subsequent recordings of RFI in the real field was carried out  
Figure 1: Detection and classification models used in Antiference for jamming (**left**) and spoofing (**right**).  
on a military training ground in Austria by OHB Digital Solutions GmbH under a special permit. The difficulty in recording these datasets and their subsequent scarcity is the reason they were used for our final validation. To record the required signals, two data-collection campaigns were performed: firstly, Real-field RFI recordings, collected on the military training ground in Austria within a period of two days. The recordings consist of a collection of jamming and spoofing datasets in static and dynamic conditions. The recorded jammers were five COTS jammers, and broadcasted simulated jamming signals using an IZT S1000 signal generator with a connected power amplifier and transmit antenna. Secondly, Base (clean) GNSS signal recordings collected in the city of Graz and its surrounding areas in Austria. Twelve datasets containing around 8 h of recording in different urban and suburban environments from a car were collected.
## 2 Theoretical Background of the Uncertainty Theory
### Uncertainty Theory: Measure, Variable, and Distribution
#### 2.1.2 Uncertain Variables  
Uncertain variables have been used to model demand fluctuations, supply chain disturbances, and deployment time fluctuations in SCM. Advanced applications include dynamic pricing models, where uncertain variables represent market demand, and inventory management systems that adjust stock levels based on uncertain times and demand patterns [6; 17]. In the domain of uncertainty, an uncertain variable is a function that maps outcomes from uncertainty spaces onto real numbers. Any Borel set \(B\) must be measurable, meaning it must belong to the sigma-algebra of an event set \(\{\xi\in B\}\). This concept is useful when discussing logistics elements that typically lack precise calculations, such as demand, supply, and order fulfillment periods.  
In other words, an uncertain variable \(\xi\) is a mapping or correspondence from the uncertainty space \((T,\mathcal{F},\mathcal{M})\) to the set of real numbers, such that the pre-image of each Borel set \(B\), where \(\{\xi\in B\}\), is in \(\mathcal{F}\).
# Fuzzy Frankot-Chellappa Algorithm for Surface Normal Integration
## 6 Conclusions  
We have presented a fuzzified Frankot-Chellappa method for surface normal integration, which incorporates fuzzy logic to handle uncertainty in the input normals. The fuzzy Poisson equation with Dirichlet boundary conditions was formulated and solved using a fuzzy discrete sine transform. The improved fuzzified Frankot-Chellappa method can reconstructs surfaces with normal vectors that are uncertain or noisy, with performance comparable to the classic method on clean data and improved behavior on noisy data. In terms of accuracy, for smooth surfaces, the defuzzified fuzzy solution is essentially equivalent to the classical solution. In terms of noise suppression, the use of fuzzy concepts (in particular the inherent filtering effect of the \(F\)-transform) led to reconstructions with slightly lower error than the classical approach.  
Overall, the developed fuzzy Frankot-Chellappa algorithms represent a promising extension of the classical approach. They maintain the computational efficiency of the original algorithm while providing improved noise tolerance and resilience to missing or ambiguous data.  
Empirical evaluations demonstrate that the proposed strategy consistently improves reconstruction accuracy in noisy scenarios compared to both the classical FC method and the original fuzzy approach in [19].  
Despite its promising performance, the proposed fuzzy FC method has certain limitations. First, the current model relies on predefined fuzzy parameters (\(\lambda\)) which are empirically tuned and may not adapt well to all types of noise distributions. Second, the method assumes a regular grid structure and Dirichlet boundary conditions, which might not generalize to arbitrary or irregular domains. Future work will focus on automatic tuning of fuzziness parameters (e.g., determining optimal spreads or thresholds based on data characteristics) and on extending the approach to non-rectangular domains or irregular integration regions, which would further enhance its applicability in real-world scenarios.  
The authors declare that the main idea and experiments were proposed by [PERSON] and [PERSON] and that the manuscript was written together with [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that upon reasonable request, the data and the code are available from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TFNs & Triangular Fuzzy Numbers \\ gH & Generalized Hukuhara \\ PDEs & Partial Differential Equations \\ sT & Discrete Sine Transformation \\ FsT & Fuzzy Discrete Sine Transformation \\ FC & Frankot-Chellappa \\ RMSE & Root Mean Square Error \\ \end{tabular}