esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Ecological Implications  
Soil volatilomics has emerged as a potential diagnostic tool for comprehensive soil health assessments through the qualitative and quantitative characterization of volatile organic compound profiles in soil matrices [14]. The classical HS-SPME-GC-MS analytical approach, combined with exploratory, supervised, and differential analyses, enables the maximal separation of complex matrix classes, thereby elucidating the dynamic patterns of soil volatilome profiles across land use transects. This study helps us obtain insights into complex metabolic activities and ecosystem stability parameters, offering unprecedented resolution in monitoring soil biochemical processes. In diverse land use scenarios, distinct volatilome profiles manifest as unique molecular signatures, reflecting the intricate interplay between underlying biochemical pathways and microbial community dynamics [41]. These profiles serve as sensitive indicators of ecosystem functions, enabling the detection of subtle perturbations in soil metabolic networks [42]. The methodology's high resolution allows for the capture of dynamic changes in soil biochemistry, providing crucial information about ecosystem responses to environmental stressors and anthropogenic influences.  
Forest ecosystems consistently present rich and heterogeneous volatilome profile compositions characterized by complex molecular signatures that indicate robust microbial interactions and optimized nutrient cycling processes. This diversity in their volatile profiles is strongly correlated with elevated levels of ecosystem resilience and functional redundancy within soil microbial communities [9; 11]. The intricate volatilome patterns observed in these natural systems serve as valuable reference points for assessing ecosystem health and stability. Conversely, soils under intensive agricultural management regimes typically present more uniform volatilome composition signatures, suggesting reduced microbial diversity and altered biochemical functionality [3; 5]. This homogenization of volatile profiles often correlates with decreased ecosystem resilience and modified carbon cycling pathways, highlighting the potential of volatilomics in identifying early indicators of soil degradation and ecosystem stress [4].  
The integration of volatilomics with complementary omics methodologies, including metabolomics, metagenomics, and proteomics, enables a comprehensive analysis of soil biochemistry [23]. This multiomics approach facilitates the elucidation of the complex relationships between metabolic processes and broader ecological outcomes, providing a mechanistic understanding of soil ecosystem dynamics [26]. This integrated analysis supports the development of precise, targeted interventions for ecosystem restoration and sustainable land management practices. Furthermore, this assessment tool's capacity to differentiate between subtle variations in volatile organic compound profiles makes it an indispensable tool for monitoring soil health, guiding conservation efforts, and informing strategic environmental management policies.
###### Abstract  
The three spacecraft of the space gravitational wave antenna employ heterodyne interferometry to mitigate the effects of Doppler shift. Constrained by laser relative intensity noise (RIN) and the sampling frequency constraints of phase readout circuits, the widespread adoption of fixed offset frequencies effectively regulates the frequency of heterodyne interferometric beat notes within a reasonable frequency domain of [5 MHz, 25 MHz]. In this work, a high-precision fitness genetic algorithm for heterodyne interferometry is utilized to generate the initial offset frequency distribution scheme. To address issues with unreasonable switching times and offset frequency settings in the initial scheme for partial frequency domains, optimization strategies are proposed from three aspects: frequency domain selection extension, switch times control, and numerical low frequency. Results demonstrate that the optimization of frequency domain selection extension narrows the reasonable frequency domain to [5 MHz, 15 MHz] and [7 MHz, 17 MHz]. Optimization of switch times control ensures that switching times of offset frequency distribution scheme generated under the settings of [6 MHz, 17 MHz] and wider frequency domains can be controlled within a reasonable range of 6 to 13 times. Fixed offset frequency settings are generally reduced by 24.3% after low-frequency optimization. This methodology and result can provide a reliable reference for Program Taiji and even related space gravitational wave antenna projects.  
gravitational wave antenna; Taiji project; laser heterodyne interferometry; algorithm optimization
## 2 Experimental Study
### Time-Frequency Analysis
#### 2.4.2 Wavelet Transform  
Wavelet transform uses the mother wavelet function to express the original signal, which can be expressed as:  
\[\mathrm{WT_{z}}(a,b)=\frac{1}{\sqrt{a}}\int_{\mathrm{R}}z(t)\varphi(\frac{t-b }{a})\mathrm{d}t, \tag{9}\]  
where \(z(t)\) is the original signal. The \(\varphi(\frac{t-b}{a})\) is the mother wavelet function. The \(a\) and \(b\) are the scaling and translating parameters, respectively. The scaling parameter represents the dilation or compression of the mother wavelet, and the translating parameter determines the mother wavelet moving along the time domain. The dilative mother wavelet corresponds to low-frequency resolution. The compressive mother wavelet corresponds to high-frequency resolution [18]. By calculating the cross-correlation of the original signal \(z(t)\) and the mother wavelet function under different scales, the frequency components are obtained. The moment that a certain frequency appears is obtained by moving the mother wavelet along the time domain.  
For the STFT, when the width of the time window is determined, the resolution of time and frequency on the whole time-frequency range is constant. However, in signal analysing, the high-frequency resolution is needed on the low-frequency range, while the high-time resolution is needed on the high-frequency range. On this aspect, the wavelet transform meets the requirements more closely.  
Figure 11 shows the contour maps of the two conditions analysed by Cmor wavelet transform. Except for the characters that are shown in Section 2.4.1 by STFT, new phenomena are found in Figure 11. When the void exists, the frequency components between 1000 Hz and 1200 Hz appears from 0 s to 0.005 s, which is the period of the primary peak in Figure 5. In addition, the frequency components above 1200 Hz occurs from 0.005 s to 0.01 s, which is the period of the secondary peak in Figure 5.  
Figure 10: Time–frequency spectra by STFT.  
For the STFT, it could only be roughly determined when the frequency components appear because of the low time resolution. However, for the wavelet transform, the moment that a certain frequency component appears could be clearly figured out. This means that the wavelet transform can tell more detail about the characters.
## 3 Results and Discussion
### Effects of Environmental Media on Dissipation Energy Characteristics
#### 3.1.2 Hysteresis Loops of Torque versus Torsion Angle  
Figure 2 shows evolutions of hysteresis loops of torque versus torsion angle during tension-torsion cyclic loading of steel wire with fretting contact in distinct environmental media. The hysteresis loop of torque versus torsion angle (\(T\)-\(\theta\)) indicates friction dynamics characteristics of steel wire during cyclic torsional loading [24]. The linear hysteresis loop indicates the adhesion state at the fretting contact surface; the relative motion is mainly coordinated by the elastic deformation. The elliptical hysteresis loop reveals the partial slip state; relative motion is mainly coordinated by combined elastic and plastic deformations. In each environment medium, hysteresis loops of torque versus torsion angle present the ellipses, which indicates that the torsion angle lags behind the torque. The hysteresis phenomenon is more obvious at the stabilized stage as compared to the initial stage of the test. Increases of loading cycles cause increased degree of openness of hysteresis loop, which reveals that shear stress attributed to cyclic torsional loading with fretting contact causes more irreversible work absorbed by material internal friction and more severe wire surface damage.  
At loading cycles 20,000, dissipation energies caused by cyclic torsional loading with fretting contact in cases of air, deionized water, acid, neutral and alkaline solutions are \(9.26\times 10^{-3}\) J, \(7.0\times 10^{-3}\) J, \(9.97\times 10^{-3}\) J, \(7.89\times 10^{-3}\) J and \(7.98\times 10^{-3}\) J, respectively. Therefore, the dissipation energy decreases  
Figure 1: \(F_{t}\)-\(D\) loops during tension–torsion cyclic loading of steel wires with fretting contact in different environmental media. (**a**) air; (**b**) acid solution; (**c**) neutral solution; (**d**) alkaline solution; (**e**) deionized water; (**f**) comparison of hysteresis loops at loading cycles of 20,000.  
in the order: acid solution, air, alkaline and neutral solutions, and deionized water, which indicates most severe and slightest damages at fretting surfaces of cyclic loaded wires in cases of acid solution and deionized water, respectively.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.6 Skin Protection  
One of the challenges raised in [4] is concern over skin protection. Wearable devices, which are in certain instances implanted into human body tissues, are prone to cause harm or other undesirable effects to the skin and other bodily systems. Researchers and other stakeholders need to take the absorption rate of radiation from wireless sensors into careful consideration during the conceptualization, design, prototyping, and deployment of WBAN devices in order to avoid causing more harm than good.
## 2 Materials and Methods
### Analyzed Navigation Systems  
Two optical transit identification methods for vehicle navigation have been considered, both of which use matte black tape to define the path of movement. The first solution  
Figure 1: Three-dimensional model of the AGV used in this study.  
Figure 3: Main dimensions of the AGV vehicle.  
Figure 2: Drive transmission system for one of the AGV’s drive wheels.  
employs two reflective transmitter-receivers mounted on a head that moves along the surface on which the vehicle is operating. This solution is shown in Figure 4. The block diagram of the control system with reflective optocouplers is presented in Figure 5.  
Two reflective optocouplers (CNY 70--VISHAY Telefunken, Wien, Austria) [32] were used for line detection. They were placed in a shared housing at a distance of 18 mm (Figure 6), ensuring a consistent distance between the reflective optocouplers and the designated line.  
Figure 4: The AGV with reflective optocouplers mounted (indicated by the blue arrow) on the head monitoring the position of the track tape.  
Figure 5: Block diagram of the AGV control system with reflective optocouplers.  
Figure 6: Reflective optocoupler head with a mounting bracket for installation on a guide rail.  
In the second solution, a camera (Figure 7) is used to determine the guiding vector based on its position relative to the tape. The block diagram of the AGV control system with a camera is presented in Figure 8.  
Selected Technical Parameters of the Pixy2 Camera (SparkFun, Niwot, CO, USA) [33]:  
* Processor: NXP LPC4330, 204 MHz, dual-core;
* Image Sensor: Aptina MT9M114, 1296 \(\times\) 976 resolution with integrated image flow processor;
* Lens Field-of-View: 60 degrees horizontal, 40 degrees vertical;
* RAM: 264 KB;
* Flash: 2 MB;
* Available Data Outputs: UART serial, SPI, I2C, USB, digital, analog;
* Integrated Light Source: Approximately 20 lumens;
* Minimum Distance of the Camera Lens from the Object: 50 mm [34].  
Both types of sensors are mounted on a bracket that allows for adjustment of the distance between the sensors and the vehicle's drive axis (Figure 9). In both cases, the output data from the sensors is processed by an electronic control system built using an Arduino Uno module with an AtMega 328 processor running at a clock frequency of 16 MHz. The microcontroller's output signals are transmitted to a module with an H-bridge based on the L298N chip, which controls the vehicle's two DC motors. Each motor is controlled separately using a PWM signal (responsible for motor power) and two logic signals, which  
Figure 8: Block diagram of the AGV control system with a camera.  
Figure 7: The AGV with the camera mounted (indicated by blue the arrow).  
enable reversing the motor's direction and braking. The control system allows for changes in wheel rotation direction, significantly enhancing the vehicle's maneuverability.  
Table 1 presents the settings of the Pixy2 camera used in the conducted research.
## 4 Results and Discussion
### Comparisons of Different Deep Learning Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Well** & **Model** & **RMSE** & **MAE** & **MAPE** \\ \hline \multirow{6}{*}{Well1} & \multirow{3}{*}{LSTM} & LSTM-L & 181.75 & 134.33 & 7.4 \\  & & LSTM-M & 134.40 & 95.16 & 5.6 \\  & & LSTM-S & 102.31 & 85.97 & 3.8 \\  & & GRU-L & 165.91 & 112.83 & 6.3 \\  & & GRU-M & 133.87 & 99.30 & 5.2 \\  & & GRU-S & 120.03 & 105.30 & 4.7 \\  & & GRU-MLP-L & 151.35 & 81.93 & 5.4 \\  & GRU-MLP & GRU-MLP-M & 125.12 & 83.67 & 4.8 \\  & & GRU-MLP-S & 66.73 & 33.59 & 1.6 \\ \hline \multirow{6}{*}{Well2} & \multirow{3}{*}{LSTM} & LSTM-L & 182.70 & 114.54 & 6.0 \\  & & LSTM-M & 181.46 & 82.84 & 5.0 \\  & & LSTM-S & 64.30 & 58.23 & 2.7 \\  & & GRU-L & 204.26 & 145.77 & 7.0 \\  & & GRU-M & 178.14 & 80.88 & 4.0 \\  & & GRU-S & 61.00 & 52.97 & 2.5 \\  & & GRU-MLP-L & 175.06 & 105.15 & 5.0 \\  & GRU-MLP & GRU-MLP-M & 174.73 & 63.03 & 3.6 \\  & & GRU-MLP-S & 59.80 & 50.43 & 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Assessment metrics for various methods in gas production prediction. Upon scrutiny of Figure 9, it becomes palpable that the short-term predicted production trajectory derived from the GRU-MLP model aligns better with the factual shale gas production. Moving further, Figures 10 and 11 spotlight the outcomes for medium-term and long-term predictions, as rendered by both the GRU and GRU-MLP models. These visual comparisons underscore the consistency between the model predictions and the in situ data, which are notably evident in the long-term predictions produced by the GRU-MLP model. This comprehensive scrutiny substantiates the efficacy of the GRU-MLP model in both short-term and long-term forecasting endeavors.
## 2 Materials and Methods
### Chemical Analyses  
As an important driver of metal sorption on and desorption from soils and sediments, the pH values of the analysed samples were determined with the help of GOST 26423-85.  
Soil samples weighing 30 g, weighed with an error of not more than 0.1 g, will be placed in conical flasks. Then, 150 mL of distilled water will be added to the samples with a dispenser. The soil and water will be mixed for 3 min with a stirrer and left for 5 min to settle. A 20 mL portion of the soil suspension will then be poured into a 50 mL beaker and used to measure the pH. The instrument readings will be taken no earlier than 1.5 min after the electrodes are immersed in the measured solution to measure the soil pH using a HANNA HI83141 pH meter (Hanna, Woonsocket, RI, USA).  
During operation, the instrument is periodically calibrated against a pH 6.86 buffer solution [43].  
The first stage of the laboratory method for heavy metals determinations is as follows: (1) scattering each sample on paper, (2) drying under air-dry conditions, (3) kneading large lumps using a pestle, pre-cleaning the inclusions (stones, branches and other foreign particles) and (4) using the quartering method, the soil weighing 0.2 kg is ground with a mortar and pestle and sifted through a sieve with a hole diameter of 1 mm. A sample with a mass of about 5.0 g was taken from the obtained air-dried soil sample.  
The extraction of mobile forms of metal compounds from the soil samples was carried out according to this method [44], with a soil pretreatment using acetate-ammonia buffer solution with a pH of 4.8.  
In total, 50 mL of acetate-ammonium buffer solution with a pH of 4.8 (ratio soil:solution = 1:10) was added to a 50 mL cylinder, and the soil sample was gently moistened through stirring. The soil sample in the acetate-ammonium buffer solution was incubated for 24 h at room temperature with a brief agitation of the soil sample.  
After 24 h, the soil sample containing the solution was stirred and transferred for filtration through a funnel with a folded paper \"white tape\" filter into a clean measuring flask, washing the soil residue through the filter with buffer solution (about 50 mL). Then, the volume of the filtrate in the volumetric flask was supplemented with a buffer solution of 100 mL to conduct the further determination of the mobile forms of metals through the use of the atomic absorption spectrometric method [44; 45].  
The heavy metals present in the samples were determined using the flame atomic absorption spectrometric method using an AA-7000 atomic absorption spectrophotometer (Shimadzu, Kyoto, Japan) (Figure 2).  
The assessment of the results of the content of metals in the soil was carried out in accordance with the regulatory documents [43; 44] established in the Republic of Kazakhstan, which are updated annually by the JSC \"National Center for Expertise and Certification\" of the Republic of Kazakhstan.  
These guidelines describe the assurance and quality control of metal content results.
## 2 Materials and Methods
### LC-MS/MS Instrumental Condition  
The pesticide analysis was carried out on an LCMS-8040 triple quadrupole mass spectrometer coupled with a Nexera liquid chromatograph, both sourced from Shimadzu (Kyoto, Japan). For the separation of analytes from interferences, a Cadenza CD-C18 column (3 \(\upmu\)m, 2 \(\times\) 150 mm; Imtakt Corp., Kyoto, Japan) was used. Mobile phase A consisted of 0.1% FA in water, and mobile phase B consisted of 0.1% FA in methanol. The gradient for mobile phase B was initiated at 80% for 0.2 min, then rose to 92% over the next 3.3 min. This was followed by a rapid increase to 98% over 0.1 min and a 2.4 min duration to cleanse the column of impurities. Afterward, the mobile phase was adjusted from 98% back to 80% within 0.1 min and held for another 3.4 min to re-equilibrate the column for the next sample injections. The overall runtime for the analysis was 9.5 min. The column oven temperature was steady at 40 \({}^{\circ}\)C, with a flow rate set at 0.2 mL/min and an injection volume of 1 \(\upmu\)L.  
The tandem mass spectrometer utilized the positive electrospray ionization (ESI+) mode. A pure argon gas (99.999%) was employed for collision-induced dissociation (CID). The temperatures of the desolvation line (DL) and heat block within the ion source were set at 250 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, respectively. The flow rates of the drying gas and nebulizing gas were 15 L/min and 3 L/min, respectively. Data analysis was conducted using the Shimadzu LabSolutions software (version 5.60 SP2).  
For the trace analysis via multiple reaction monitoring (MRM), the optimal transition patterns for tricyclazole were established as follows. Firstly, a mass-to-charge ratio (\(m/z\)) of the precursor ion was determined using a full scan analysis. Subsequently, the fragmentation of the precursor ion at various collision energies (CE) was verified through a product ion scan. Two MRM transitions with high selectivity and sensitivity were chosen as the quantifier and qualifier, respectively.
## 3 Results
### Association between Clinical Symptoms and Final Diagnosis of SSS/CMA Pathologies  
As demonstrated in Table 6, the prevalence of asymptomatic maxillary sinusitis was significantly higher in the patients diagnosed as SSS (both pSSS and iSSS) or SSS-CMA pathologies (\(p<0.012\)), whereas CRS presence was a characteristic clinical symptom for both forms of CMA (\(p<0.043\)). The reason why the authors know about CRS from a clinical point of view is that the CBCT database was labeled with the main cause for each patient who was sent for a radiographic imaging, the CBCT evaluation.  
Based on the current literature findings ([PERSON]; [PERSON] et al.; [PERSON]; [PERSON] et al.; [PERSON] et al.) (1977; 1978; 1979; 2010), we analyzed the association between additional clinical symptoms such as mid-facial asymmetry, maxillary asymmetry, facial pain/pressure, headaches, and nasal obstruction with/without taste and smell variations and final diagnosis of SSS/CMA pathologies. The mid-facial asymmetry symptom significantly increased in both forms of SSS (pSSS and iSSS, \(p<0.029\)). A headache symptom was characteristic for pure CMA and SSS-CMA (\(p<0.042\)), and nasal obstruction with/without taste and smell variations was significantly higher in both forms of CMA (pCMA and iCMA) and in SSS-CMA pathologies (\(p<0.012\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SSS**} & \multicolumn{3}{c}{**CMA**} & & \\ \cline{2-7}  & **Pure** & **In-pure** & **Pure** & **In-Pure** & **SSS-CMA** & **MSH** & \\  & **/pSSS** & **/iSSS** & **/pCMA** & **/iCMA** & **(n = 1)** & **(n = 1)** & **\(p\)-Value** \\ \hline MS atelectasis & 5 (100.0) & 5 (100.0) & 1 (100.0) & 1 (100.0) & 1 (100.0) & 1 (100.0) & 1.000 \\ Unilateral MS opacification & 5 & 0 & 1 & 0 & 1 & 0 & 0.002 * \\  & (100.0) & (0.0) & (100.0) & (0.0) & (100.0) & (0.0) & 0.002 * \\ MS volume reduction & 5 & 5 & 1 & 1 & 1 & 1 & 1 \\  & (100.0) & (100.0) & (100.0) & (100.0) & (100.0) & (100.0) & 1.000 \\ Blockage of the osteomeatal complex & 5 & 0 & 1 & 0 & 1 & 0 & 0 \\  & (100.0) & (0.0) & (100.0) & (0.0) & (100.0) & (0.0) & 0.002 * \\ MS walls deformation & 5 & 5 & 1 & 1 & 1 & 1 & 1 \\  & (100.0) & (100.0) & (100.0) & (100.0) & (100.0) & 1.000 \\ MS asymmetry & 3 & 3 & 1 & 0 & 1 & 0 & 0.341 \\  & (60.0) & (60.0) & (100.0) & (0.0) & (100.0) & (0.0) & 0.341 \\ Unilateral MS hypoventilation & 5 & 0 & 1 & 0 & 1 & 0 & 0.002 * \\  & (100.0) & (0.0) & (100.0) & (0.0) & (100.0) & (0.0) & 0.002 * \\ Osteomeatal complex (OMC) with or & 4 & 0 & 1 & 0 & 1 & 0 & 0 \\ without blockage & (80.0) & (0.0) & (100.0) & (0.0) & (100.0) & (0.0) & 0.015 * \\ \hline \hline \end{tabular} SSS: silent sinus syndrome; CMA: chronic maxillary atelectasia; MSH: maxillary sinus hypoplasia *: statistically significant.  
\end{table}
Table 5: Relationships between CT/CBCT radiological features and occurrence of SSS/CMA cases in both pure and in-pure forms. Descriptive data were presented as number of positive observations (percent). [PERSON]’s exact test was used for analysis of data.
## 6 Performance Evaluation
### Evaluation for the Designed Protocol
#### 6.1.3 Path Construction Successful Probability  
In Figure 5a, we first conduct the comparison of the path construction successful probability among the four compared routing protocols. It can be seen that all four mentioned protocols show relatively identical performance, which might be due to exactly the same vehicular topology information being used in the simulations. Moreover, with the increase of the vehicular traffic density, a higher successful probability can be obtained in constructing a multi-hop path between the SUV and the SOV. When the vehicular traffic density is higher than 15 vehicles per kilometer, the path construction successful probability approaches one as the most anticipated value.