esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparative Analysis of HS-GC-IMS and HS-SPME-GC-MS in Identifying VOCs  
#### 3.4.1 Comparative Analysis of VOC Quantities and Categories  
As illustrated in Figure 4A, a total of 178 VOCs were identified in this study using two distinct analytical methodologies. Specifically, HS-GC-IMS detected 44 VOCs, while HS-SPME-GC-MS identified 144 VOCs. Among these, 10 volatiles were found to be common across both methods, namely linalool, linalool oxide II, linalool oxide I, 6-methyl-5-hepten-2-one, benzaldehyde, methyl salicate, octanal, (E)-2-heptenal, 1-octen-3-ol, and hexanal. These shared compounds are integral to the aroma profiles of fresh tea leaves and various tea products [4; 24].  
VOC classification further revealed that HS-GC-IMS grouped the detected volatiles into seven categories, with aldehydes representing the most abundant class. By contrast, the VOCs identified via HS-SPME-GC-MS were categorized into ten groups, with alcohols being the dominant class. Notably, HS-GC-IMS did not to detect acids, alkanes, or alkenes, and the number of VOCs in most other categories, except for heterocyclic and aromatic compounds, was significantly lower compared to those identified by HS-SPME-GC-MS (Figure 4B). This disparity aligns with previous findings in the literature, wherein the two techniques were applied for the analysis of tea aroma compounds. HS-GC-IMS predominantly detects highly volatile and low-boiling-point compounds, whereas HS-SPME-GC-MS is more proficient in enriching and identifying higher-boiling-point volatiles. Moreover, the limited selectivity of IMS, attributed to ion-molecule and ion-ion competition reactions within the ionization chamber [45; 46], may further contribute to the lower number of detected volatiles in comparison to HS-SPME-GC-MS.  
Figure 4: Comparative analysis of VOC quantities and categories: (**A**) Venn diagram of VOC numbers; (**B**) column graph of VOC categories.
## 2 Upper-Bound Limit Analysis Theorem  
According to the upper-bound theorem, if an external load is applied to a system with a kinematically admissible failure mechanism, the rate of external work equals the rate of internal energy dissipation due to plastic deformation. If this balance is achieved, collapse is considered imminent, and the applied load represents an upper estimate of the true failure load. Conversely, the lower-bound theorem states that if a stress field can be found that satisfies equilibrium conditions and no material point exceeds the yield criterion, then the structure remains stable under the applied load. This load then serves as a conservative lower estimate of the actual failure load [16; 17; 18; 19]. Therefore, the principle of the upper limit theory is described as follows:  
\[\sum F_{u}\cdot\delta w_{u}=\int_{v}\sigma_{u}\cdot\delta\varepsilon_{u}dv \tag{1}\]  
To obtain the upper limit, \(F_{u}\), of the external load, the internal initial stress, \(\sigma_{u}\), the boundary displacement, \(\delta w_{u}\), and the internal strain increment, \(\delta\varepsilon_{u}\), are generated along with the external load. The left side (work performed by the external forces) and the right side (work performed by the internal stresses) will be equal; therefore:  
\[\sum F_{c}\cdot\delta w_{c}=\int_{v}\sigma_{c}\cdot\delta\varepsilon_{c}dv \tag{2}\]  
where \(F_{c}\) and \(\sigma_{c}\) are the external load and internal initial stress when failure occurs. The subscripts \(u\) and \(c\) represent the ultimate condition and the failure condition, respectively.  
[PERSON] (1981) [16] applied the upper- and lower-bound principles of limit analysis to evaluate the ultimate bearing capacity of foundation soils. In his study, soils were classified into two main types: purely cohesive soils and cohesive-frictional (c-phi or c-\(\varphi\)) soils. By analyzing different assumed failure mechanisms, it can be derived for the ultimate bearing capacity corresponding to each soil type and failure geometry. At the limit state, the observed failure mechanisms were found to align with the full shear failure patterns previously described by [PERSON] (1973) [4]. In the context of upper-bound analysis, particularly for load-induced failure in soils, the classification into cohesive and C-phi soils is essential. This distinction enables the selection of appropriate failure mechanismsand energy dissipation models for accurately estimating collapse loads under varying soil conditions.
# A Wide-Ranging NMR Probe for Multiscale Structures
## 4 Experimental Details
### Sample Preparation  
For conventional \({}^{129}\)Xe NMR measurements, samples are in the form of sealed NMR glass tubes (usually 5 mm or 10 mm outer diameter) with gaseous xenon inside. It is important to select tubes with increased wall size, depending on the required final pressure. Prior to the preparation, samples must be degassed under vacuum, at a high temperature if necessary, to remove air and other adsorbed species. This can be done directly inside the NMR tube. Then, xenon gas is inserted inside the tube with a proper apparatus, for example a Schlenk line, and kept inside by freezing with liquid nitrogen. Lastly, the tube containing xenon and the sample is hermetically sealed, either with a flame or with a valve.  
In HP \({}^{129}\)Xe NMR, instead, the sample is directly placed into the NMR tube and inserted into the instrument. In this case, Magic Angle Spinning (MAS) probes and rotors can also be used, because the hyperpolarized xenon gas is produced and delivered directly into the sample, eliminating the need of using sealed tubes. No further preparation is required.
## 3 Performance Evaluation of RAN Network Slicing  
In this study, the effects of bandwidth utilisation, application type difference and numerology change on data rate and delay were investigated in terms of RAN network slicing when the physical resource block was planned according to network slices as shown in Figure 3. In the simulation tests, the effects of QoS, bandwidth and numerology changes on throughput and latency were investigated using the open-source network simulator NS-3 with the 5G test library '5G-Lena' plug-in.
## 4 Process and Result
### Identifying Unknown Artifact Types Based on Multiple Linear Regression Model
#### 4.3.1 Testing for the Presence of Heteroskedasticity in the Perturbation Term  
Perturbation term \(\mu_{i}\) is unobservable and requires certain conditions to be met. Our model defaults to a spherical perturbation term, which generally has to satisfy \"no autocorrelation\" and \"homoskedasticity\" because if the perturbation term is \"correlated with the independent variable\", i.e., endogenous, it will make the correlation regression coefficient inaccurate; if there is \"heteroskedasticity\", it will cause the hypothesis test statistic we constructed to be invalid, and the \(OLS\) estimator cannot be treated as the optimal linear unbiased estimator [61]. Therefore, we performed the BP test and White test on the perturbation term to verify the presence of heteroskedasticity, as shown in Table 7 [62; 63; 64].  
The above two hypotheses were tested for heteroskedasticity, and the original hypothesis \(H0\) was that there is no heteroskedasticity in the perturbation term. However, the \(p\)-value is greater than 0.05, so \(H0\) is accepted, and we can assume that there is no heteroskedasticity in the perturbation term.
## 5 Conclusions  
In this paper, a PLL prototype with a programmable VCO and automatic band selection was presented. The PLL chip was fabricated using the TSMC 0.18 \(\upmu\)m 1P6M process. The programmable VCO frequency output was 330 MHz, while a PLL output frequency of 33 MHz was achieved for an input reference frequency of 25 MHz. The PLL prototype is designed for wide-range frequency selection with a low VCO gain to obtain low jitter. The architecture provides multiple overlapped band switching, as well as band twisting, with a programmable current DAC to obtain the desired central frequency operation. The chip measurement results show a 19.10 ps rms jitter. The Gaussian histogram and smooth bathtub curve show no cross-talk. The prototype also has an additional safety feature of a power down mode. It was shown that a differential design approach also reduces noise, possibility in the analog block. The proposed PLL is designed for \(-40\) to 885 \({}^{\circ}\)C, a wide temperature range.  
Conceptualization, N.A. ([PERSON]); formal analysis, N.A. ([PERSON]); methodology, N.A. ([PERSON]) and N.A. ([PERSON]); supervision, C.-W.L.; validation, C.-W.L.; visualization, M.O.-e.; writing original draft, N.A. ([PERSON]); writing review and editing, N.A. ([PERSON]), and C.-W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by grants from the Ministry of Science and Technology, Taiwan, under Grants MOST 108-2221-E-007-094-MY2,108-2622-M-007-006-CC1 and 109-2622-M-007-007-CC1.  
The authors express their best thanks to Tyrafos Technology Company, Ltd., Taiwan, for providing facilities, and for the continuous encouragement, constant help, esteemed guidance and anonymous reviews about the manuscript. We are also thankful to the Chip Implementation Center (CIC), Taiwan, for the chip measurement and testing.  
The authors declare no conflict of interest.  
Figure 21: (**a**) Bathtub histogram (BER Tj litter); (**b**) Gaussian histogram (Rj litter).
## 2 Dataset Description
### Satellite Data and in situ Measurements
#### Soil Measurements  
Volumetric soil moisture measurements were conducted only in the first top 5 cm using calibrated TDR (Time Domain Reflectometry) probes; this was because the radar signal penetration depth in the soil surface is only a few centimetres at X-band [76]. Due to high evaporation rates, the soil moisture measurements were collected within a time window of 2 h around the satellite overpass time. Between 25 and 30 soil moisture measurements were performed for each training plot along regular transects. The volumetric soil moisture was then calculated for each training plot using the mean of all soil moisture measurements collected on the training plot, except for training plots where high spatial heterogeneity of the soil moisture was observed. This heterogeneity is frequent when the plot is under irrigation or when irrigation was finished a few hours before measurements were taken. In this case, several homogenous areas within the training plot were defined. The soil moisture content of each plot (or part of a plot) ranged from 0.10 cm\({}^{3}\)/cm\({}^{3}\) to 0.47 cm\({}^{3}\)/cm\({}^{3}\) (Table 2), with standard deviations between 0.01 and 0.05 cm\({}^{3}\)/cm\({}^{3}\).  
Measurements of soil roughness were carried out only once for each training plot using a needle profilometer 1 m in length with 2 cm sampling intervals. Ten roughness profiles were established in each training plot during the period where the vegetation was the lowest (in April). The following two surface roughness parameters were then calculated from these measurements: the average root mean square surface height (_Hrms_), which specifies the vertical scale of the roughness, and the correlation length (\(L\)), representing the horizontal scale [76]. The _Hrms_ values varied between 0.35 and 0.55 cm. The correlation length (\(L\)) ranged from 2.00 to 4.60 cm. In general, the precision on the roughness measurements is influenced mainly by the length of the roughness profiles, the number of profiles, and the sampling interval of the profiles. It was demonstrated that significant errors are observed when short profiles with a low sampling interval are used [77,78]. For our smooth soils and X-band SAR data, roughness measurements with a sampling interval better than 1 cm would have been more precise. Given the homogeneous surface roughness in our study site, roughness parameters will not be considered in the sensitivity analysis of the X-band radar signal to soil moisture and vegetation parameters. However,these will be used in an upcoming work dedicated to model the radar signal according to soil and vegetation parameters.
## 3 Results and Discussion
### Pd(II) Sorption in Mono Metal System
#### 3.2.2 Uptake Kinetics
The values of intercept C are non-zero, suggesting that the sorption process is not only controlled by intraparticle diffusion: the resistance to film diffusion plays a role in the overall uptake kinetics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **LD/PEI** & **CC/PEI** \\ \hline Experiment & q\({}_{\text{deplap}}\) (mmol g\({}^{-1}\)) & 0.81 & 1.36 \\ \hline \multirow{3}{*}{PFORE} & q\({}_{\text{deplap}}\) (mmol g\({}^{-1}\)) & 0.77 (0.041) & 1.30 (0.038) \\  & k\({}_{1}\times 10^{3}\) (min\({}^{-1}\)) & 7.51 (1.07) & 3.03 (0.32) \\  & R\({}^{2}\) & 0.94 & 0.97 \\ \hline \multirow{3}{*}{PSORE} & q\({}_{\text{deplac}}\) (mmol g\({}^{-1}\)) & 0.82 (0.037) & 1.42 (0.032) \\  & k\({}_{2}\times 10^{7}\) (g mmol\({}^{-1}\) min\({}^{-1}\)) & 14.42 (2.13) & 3.08 (0.42) \\  & R\({}^{2}\) & 0.97 & 0.99 \\ \hline \multirow{3}{*}{IPDE} & k\({}_{\text{d,1}}\) (mmol g\({}^{-1}\) h\({}^{-0.5}\)) & 0.267 (0.008) & 0.319 (0.006) \\  & C\({}_{1}\) & 0.047 (0.008) & 0.022 (0.01) \\ \cline{1-1}  & R\({}_{1}^{2}\) & 0.99 & 0.99 \\ \cline{1-1}  & k\({}_{\text{d,2}}\) (mmol g\({}^{-1}\) h\({}^{-0.5}\)) & 0.105 (0.001) & 0.041 (0.003) \\ \cline{1-1}  & C\({}_{2}\) & 0.400 (0.001) & 1.047 (0.02) \\ \cline{1-1}  & R\({}_{2}\)\({}^{2}\) & 0.99 & 0.99 \\ \hline \end{tabular}
\end{table} 
Table 1: Uptake kinetics—Modeling parameters for PFORE, PSORE, and IPDE (agitation speed: 60 rpm). LD/PEI: _L. digitata_-bPEI-GA composite; CC/PEI: C. _crispus_-bPEI-GA composite; PFORE: pseudo-first order rate equation; PSORE: pseudo-second order rate equation; IPDE: intraparticle diffusion equation. Figure 4: Uptake kinetics for Pd(II) sorption using LD/PEI and CC/PEI beads: (**a**) Modeling with PSORE (pseudo-second order rate equation); (**b**) Modeling with IPDE (intraparticle diffusion equation) at an agitation speed of 60 rpm (C\({}_{0}\): 0.5 mmol L\({}^{-1}\); SD: 0.2 g L\({}^{-1}\); pH: 1, adjusted by H\({}_{2}\)SO\({}_{4}\) or NaOH; room temperature).
# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
###### Abstract  
The You Only Look Once (YOLO) series of object detection models is widely recognized for its efficiency and real-time performance, particularly under the challenging conditions of underwater environments, characterized by insufficient lighting and visual disturbances. By modifying the YOLOv9s model, this study aims to improve the accuracy and real-time capabilities of underwater object detection, resulting in the introduction of the YOLOv9s-UI detection model. The proposed model incorporates the Dual Dynamic Token Mixer (D-Mixer) module from TransXNet to improve feature extraction capabilities. Additionally, it integrates a feature fusion network design from the LocalMamba network, employing channel and spatial attention mechanisms. These attention modules effectively guide the feature fusion process, significantly enhancing detection accuracy while maintaining the model's compact size of only 9.3 M. Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent real-time performance. This model significantly improves the ability of underwater target detection by introducing advanced feature extraction and attention mechanisms. The model meets portability requirements and provides a more efficient solution for underwater detection.  
YOLOv9s-UI; underwater object detection; TransXNet; Dual Dynamic Token Mixer; LocalMamba; channel attention; spatial attention; model lightweight +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion
As can be seen from Figures 9 and 10, compared with the equal-weight midperpendicular algorithm, the four radii obtained by the weighted midperpendicular algorithm have smaller fluctuations and smaller differences with the true radii. Especially in engineering experiments, when the error is large, the optimization effect of the proposed algorithm is more obvious. Therefore, according to the above theoretical verification experiment and engineering experiment results, it can be concluded that the center of the circle fitted by the weighted iterative algorithm is more accurate.
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 1 Introduction
[13] proposed a novel hybrid humidification HDH and AD system. The system was designed to produce freshwater and to obtain chilled water for an air-conditioning purpose. Two HDH-AD schemes were proposed: First, where seawater was precooled in the AD evaporator before feeding the HDH system; second, where seawater was used to cool the adsorption process. The gained output ratio (GOR) for the HDH-AD system was >7.6 and the coefficient of performance (COP) was >0.45. [PERSON] et al. [14] proposed a modified configuration for an energy-efficient adsorption desalination and cooling system. The proposed system was made of a double-cycle multi-bed dual-evaporator with internal heat recovery. The specific daily water production of the proposed system was 20% higher in comparison to a single-cycle multi-bed dual evaporator system. It was also stated that the system could produce considerable amounts of high- and low-grade cooling. [PERSON] et al. [15] proposed a new design of a vapor compression (VC) refrigeration system integrated with HDH desalination. The proposed system used low-temperature indoor air to cool the condenser, which significantly enhanced the system performance. It was stated that the highest COP of the proposed system was 2.16, the highest GOR was 6.11, and the highest EUF was 8.27. Dew point cooling (i.e., indirect evaporative cooling) utilizes the renewable psychrometric energy resulting from the latent heat of water evaporating into the air. Dew point cooling has been proven a low energy-consuming cooling method for air-conditioning applications with COP (defined as cooling capacity divided by electric power input) reaching the level of 50 or higher [16; 17; 18]. [PERSON] et al. [19] proposed the integration of an indirect evaporative cooler (IEC) and HDH system for the simultaneous production of cooling and freshwater. It was stated that the GOR of the IEC-HDH system was 1.6-2.5 and the COP was 2.1-2.5. So far, dew point cooling has been proposed as an addition to the standard water production systems. However, no stand-alone unit based solely on the dew point cooling for combined cooling and water production has been proposed. Recent studies show that both cooling and drinking water production are critical issues worldwide. Many regions experience both limited access to freshwater and high energy demand for cooling purposes, which means that the search for low-energy methods of combined cooling and water production is most justified. This study provides the analytical investigation of a novel system for combined Dew Point Cooling and Water Recovery (DPC-WR system). So far, the system has been analyzed only for water recovery in the desalination process [20], but no studies of combined cooling and water recovery by the system have been done to date. The goal of this study is to present the operating principles of the DPC-WR system, provide the analytical study of the system performance, and determine its applicability in various climatic conditions.
## 4 Simulation Results
### Case Study 2  
In this case, the output power of all DG units including PV and WT are controlled by the EMS within their constrains and power limits. The results of this case, which are shown in Table 4, show a huge reduction in the total cost of energy compared to that of case 1. The reason for this is that the EMS fully controls DG units aiming to reduce the total cost. This leads to higher dependency on the utility grid during low energy bids.  
Results of both cases show the HHO algorithm functions properly during different modes of operation for the EMS. Figure 5, which indicates the conversion curve sample of the HHO algorithm, clearly shows the effectiveness of the proposed method as the conversion rate is fast during both cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (h)** & **Load (KW)** & **MP (cent/KW h)** & **WT (KW)** & **PV (KW)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: The forecast values of load demand, market price, wind and photovoltaic (PV) power production.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multicolumn{6}{c}{**Power (KW)**} & \multirow{2}{*}{**Cost (f ct/h)**} \\ \cline{2-2} \cline{4-7}  & **PV** & **WT** & **DEG** & **FC** & **Battery** & **Utility** & \\ \hline  
0008 & 10.1657 & 9.8369 & 29.9966 & 17.2679 \\ \multicolumn{7}{c}{**Total cost (fct)**} & **75.7596** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The best solutions obtained for solving EOM problem for Case Study 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multicolumn{6}{c}{**Power (KW)**} & \multirow{2}{*}{**Cost (f ct/h)**} \\ \cline{2-2} \cline{4-7}  & **PV** & **WT** & **DEG** & **FC** & **Battery** & **Utility** & \\ \hline
1 & 0 & 1.785 & 6 & 29.9998 & \(-15.7848\) & 30 & 14.379 \\
2 & 0 & 1.785 & 6 & 30 & \(-17.785\) & 30 & 12.419 \\
3 & 0 & 1.785 & 6.0004 & 29.9992 & \(-17.7845\) & 30 & 10.919 \\
4 & 0 & 1.785 & 6 & 30 & \(-1
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 2 Literature Review
Another study used the hybrid PSO-GA algorithm to optimize different types of model coefficients, such as linear, exponential, and quadratic models, when forecasting energy demand in China [50]. This indicates that PSO-GA can adapt to multiple model structures, provide optimal weights for different types of models, and increase the flexibility and adaptability of models. The above findings provide strong support for the selection of PSO-GA, it as the optimization algorithm determine the optimal weight of the combined model in this study. The improved PSO-GA algorithm proposed in this study, by combining the global search capability of the PSO algorithm and the local search capability of the GA algorithm, is not only able to determine the optimal weights of the combined model more accurately, but also helps to improve the generalization capability of the combined model, so as to solve the problem of determining the optimal weights of the combined model in more price-forecasting fields, thus expanding the applicability of the combined model for price forecasting and enhancing the flexibility and adaptability of the combined model for price forecasting. This expands the applicability of the combination model for price forecasting and strengthens the accuracy of the combination model's prediction. Supported by the above literature, this study proposes a novel optimized forecasting combinatorial framework based on the improved PSO-GA algorithm to achieve more accurate and stable forecasting of scallion prices in Shandong Province. This not only fills in the research gap regarding the application of the combination forecasting framework in the field of scallion price forecasting in Shandong Province but also provides a new direction and reference for the field of small agricultural product price forecasting.