esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Results
#### 4.2.6 Price-Inducing Effects
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Scotens**} & \multicolumn{2}{c}{**South Korea \({}^{*}\)**} & \multicolumn{2}{c}{**Japan \({}^{*}\)**} \\ \cline{2-5} \cline{7-7}
**Value** & **Rank** & **Value** & **Rank** \\ \hline
1. & Agricultural, forest, and fishery goods & 0.00544 & 25 & 0.00136 & 27 \\
2. & Mineral and general goods & 0.00810 & 16 & 0.00005 & 32 \\
3. & Food, beverages, and tobacco products & 0.00730 & 20 & 0.00358 & 12 \\
4. & fertile and leather products & 0.00847 & 23 & 0.00256 & 18 \\
5. & Food, paper problems, printing, and reproduction of recorded media & 0.01196 & 8 & 0.00199 & 21 \\
6. & Petroleum and oral products & 0.00455 & 26 & 0.00203 & 31 \\
7. & Chemical products & 0.00927 & 13 & 0.00425 & 10 \\
8. & None-mitable mineral products & **0.0170** & **3** & **0.0070** & **3** \\
9. & Basic metal products & **0.01953** & 2 & **0.00713** & 2 \\
10. & Fabricated metal products, except machinery and furniture & 0.01237 & 6 & 0.00058 & 5 \\
11. & Computing machinery, electronic equipment, and optical instruments & 0.00417 & 27 & 0.00241 & 20 \\
12. & Electrical equipment & 0.00853 & 14 & 0.00251 & 17 \\
13. & Machinery and equipment & 0.00787 & 17 & 0.00032 & 15 \\
14. & Transport equipment, manufacturing services, and repair services of industrial equipment & 0.00728 & 21 & 0.00083 & 4 \\
15. & Other manufactured products & 0.00747 & 18 & 0.00181 & 23 \\
16. & Electricity and sound supply & **0.3700** & **1** & 0.00211 & 13 \\
17. & Water supply, sewage, waste treatment, and disposal services & 0.02101 & 7 & 0.00911 & 11 \\
18. & Construction & 0.00636 & 22 & 0.00280 & 16 \\
19. & Wholesale, retail trade, and commodity brokerage services & 0.00549 & 24 & 0.00453 & 8 \\
20. & Transportation & 0.00822 & 15 & 0.00199 & 26 \\
21. & Food services and accommodation & 0.01156 & 9 & 0.00838 & 1 \\
22. & Communications and broadcasting & 0.00300 & 28 & 0.00124 & 28 \\
23. & Finance and insurance & 0.00303 & 30 & 0.00101 & 29 \\
24. & Real estate services & 0.00304 & 31 & 0.00058 & 30 \\
25. & Professional, scientific, and technical services & 0.00111 & 10 & 0.00242 & 19 \\
26. & Business support services & 0.00319 & 32 & 0.00312 & 22 \\
27. & Public administration, defense, and social security services & 0.00300 & 29 & 0.00310 & 44 \\
28. & Education services & 0.00407 & 12 & 0.00532 & 7 \\
29. & Health and social care services & 0.01299 & 5 & 0.00456 & 9 \\
30. & Art, sports, and leisure services & 0.01058 & 13 & 0.00178 & 24 \\
31. & Other services & 0.00751 & 19 & 0.00754 & 6 \\
32. & Others & 0.01373 & 4 & 0.00146 & 25 \\ Sum (A) & 0.045313 & & 0.11434 & \\ \hline \hline \multicolumn{2}{c}{\({}^{*}\) The unit is percent.} & \multicolumn{1}{c}{0.02097} & \multicolumn{1}{c}{0.00834} \\ \end{tabular}
\end{table} 
Table 7: Impact of 10% increase in the price of natural gas supply sector in South Korea and Japan.
# Thermoregulation and Soil Moisture Management in Strawberry Cultivation Munched with Sheep Wool
## 2 Materials and Methods
### Methods  
The plantation was regularly tracked during strawberry growth and harvest and later during autumn and winter. At this time, the microclimate on the soil's surface was monitored. For this purpose, the HOBO MX Soil Moisture and Temperature (MX2307) data loggers were installed. The volumetric soil moisture and temperature measurements were carried out in the thin soil subsurface layer at a depth of 10 cm regularly in two-hour intervals. The logged data were transmitted wirelessly and collected on a computer.  
Figure 1: The experimental plot mulched with various materials in a strawberry plantation: (**a**) control without mulch wool; (**b**) wool; (**c**) straw; (**d**) bark.  
Data registration was initiated in May 2023, approximately one month before the strawberry harvest. In the following months, data on temperature and soil moisture were collected. Additionally, historical data on air temperature and falls registered in the local meteorological station were used to analyse the results.  
The paper presented the results of measurements during strawberry ripening and the harvest in June 2023 and dormancy in January 2024. The months were chosen for analysis to demonstrate the mulch's impact on soil thermoregulation and moisture in summer and winter, during and beyond the vegetation season.  
During the experiment, wool samples were periodically taken for microscopic observations. Before observations, the wool was mechanically cleaned from the remnants of soil and then covered with a thin layer of gold in the Leica EM ACE 200 low-vacuum coater (Wetzlar, Germany). The observations were carried out with the high-resolution Phenom ProX SEM microscope (PhenomWorld, Eindhoven, The Netherlands), operated in a backscattered electron mode.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 1 Introduction
Although existing studies have employed statistical analyses and vegetation indices to characterize the spatial and temporal variation in the response of regional vegetation to water surplus and deficit, the sensitivity of vegetation to changes in water deficit and how vegetation will adapt to or resist climate changes is not clear [29]. Furthermore, there are fewer studies regarding the impact of seasonal drought on land surface dynamics, and many uncertainties still need to be investigated regarding vegetation sensitivity to seasonal drought at the regional scale [13]. Therefore, a more comprehensive study of the effects of drought and climate change on vegetation in arid and semi-arid regions, such as the Loess Plateau, is necessary to develop strategies for the sustainable management of land and water resources. The objective of this study is to investigate the expanding trend of increasing vegetation water deficit in regards to climate change across diverse climatic zones, as well as the response mechanisms of vegetation to water deficit in these zones. The study aimed to achieve the following goals: (1) investigate the expansion trend of VWD in various climatic zones; (2) analyze the characteristics of the response of vegetation to water deficit from three perspectives, namely, different climatic zones, growing season stages, and vegetation types; (3) attempt to explain the reasons behind the variations in the expansion trend of VWD among different climatic zones based on the above perspectives; and (4) identify future sustainable changes in vegetation and moisture conditions and investigate the environmental factors that govern vegetation growth. Our study aims to comprehensively assess the resistance and resilience of vegetation to drought and its potential risks to vegetation health. Furthermore, the study intends to establish a foundation for formulating and implementing measures to alleviate vegetation drought.
## 2 Materials and Methods
### Extraction Procedures  
To explore the effects of various solvents on the chemical composition of the extracts, CS was extracted using two traditional methods, namely infusion and hydroalcoholic solvent extraction (with 50% EtOH and 70% EtOH), in conjunction with a modern technique, ultrasonic-assisted extraction (UAE) using a Polsonic ultrasonic bath. These extraction methods were compared with an optimized UAE (using all the solvents mentioned beforehand and an ultrasonicator). For every type of extraction applied, the following protocols have been used (based on traditional methods and/or Pharmacopoeia regulations):_Infusion:_ One part of the plant material was mixed with ten parts of boiling water (\(w/v\)) and then left for 15 min in the ultrasound bath. The final sample was abbreviated as **MSI**.  
_Hydroalcoholic extraction_: One part of plant material was mixed with ten parts of hydroalcoholic solution (\(w/v\)) in concentrations of 50 and 70%, respectively. The vials were kept in an ultrasound bath for 15 min at a constant temperature of 50 \({}^{\circ}\)C. The final samples were abbreviated as **MSE50** and **MSE70**.  
_Optimized ULAE:_ A D-optimal experimental design was employed, taking into account three distinct process variables: ethanol concentration (0%, 50%, and 80%, where 0% corresponds to pure water), ultrasonic amplitude (30%, 40%, and 50%), and extraction duration (20, 30, and 40 min). During UAE, the microtip was precisely immersed 2 cm into the extract within a 50 mL falcon test tube, and the temperature was rigorously maintained below 25 \({}^{\circ}\)C. A magnetic stirrer, operating at 300 rpm, ensured the consistent homogenization of the sample. The final sample was abbreviated **MSO**.  
Subsequently, all the samples were subjected to centrifugation and then filtered under pressure through paper filters. Afterwards, hydroethanolic extracts were subjected to a rotary evaporator to eliminate the ethanol, and the remaining aqueous solutions were lyophilized (\(-\)50 \({}^{\circ}\)C, 10 Pa, and 72 h) (Freeze Dryer BK-FD18S, Biobase, Jinan, China) to obtain dry powders. The final extracts have been kept in a dry place until further analysis in a desiccator. Before undergoing the biochemical assays, the dry samples were redissolved in either ethanol (for the antioxidant assays) or water (for the enzyme inhibition assays).
## 2 Materials and Methods
### GC-MS Analysis  
A head space solid-phase microextraction (HS-SPME) fiber assembly combined with a GC-MS system (GC-MS 2010 SE, SHIMADZU, Tokyo, Japan) was used for detecting volatile compounds from the selected samples. Briefly, bacon samples (3 g) were added into a headspace bottle for balance at 45 \({}^{\circ}\)C for 30 min, and an SPME fiber (50/30 \(\upmu\)m CAR/PDMS/DVB extraction head, Supelco, PA, USA) was used to adsorb the volatile compounds from the vial headspace at 60 \({}^{\circ}\)C for 30 min. After sampling the fiber was transferred to a gas chromatography inlet and desorbed at 250 \({}^{\circ}\)C for 3 min. Compounds were separated in a DB-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent J&W, Santa Clara, CA, USA). Helium (99.999%) was used as carrier gas at a flow rate of 1 mL/minand the injection volume was 1 \(\mu\)L. The temperature in the GC oven was maintained at 40 \({}^{\circ}\)C for 3 min, and then raised to 90 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\) and to 230 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\). The ion source was EI and the temperature was set at 200 \({}^{\circ}\)C. The interface temperature was set at 250 \({}^{\circ}\)C. A positive ionization potential of MS was at 70 eV, detection voltage of 1000 V and emission current of 100 \(\mu\)A. Semi-quantitative determination was obtained by using 2-methyl-3-heptanone as an internal standard. The volatile compounds acquired were identified for the reverse match factor (similarity > 700) and retention index (RI). The C7\(\sim\)C30 n-alkanes were employed to calculate linear RI of volatile compounds. Five replicates were applied for each product.
# Carbon Balance in Salt Marsh and Mangrove Ecosystems: A Global Synthesis
## 1 Introduction  
Salt marshes and mangrove forests are intertidal ecosystems comparable _sensu lato_ in that they both occupy the coastal land-sea interface; the former mostly in sheltered temperate and high- latitude coastlines, the latter along quiescent subtropical and tropical shores [1]. Both ecosystems are characterized by a rich mixture of terrestrial and marine organisms, forming unique estuarine food webs, and play an important role in linking food webs, inorganic and organic materials, and biogeochemical cycles between the coast and adjacent coastal zone. Structurally simple compared to other ecosystems, salt marshes and mangroves harbor few plant species, but they are functionally complex, having ecosystem attributes analogous to those of other grasslands and forests, respectively, but also functioning in many ways like other estuarine and coastal ecosystems [1; 2; 3].  
Drivers such as salinity, geomorphology, and tidal regime impose structural and functional constraints and foster adaptations and physiological mechanisms to help these wetland plants subsist in waterlogged saline soils. Tides and waves (to a much lesser extent) are an auxiliary energy subsidy that allows both ecosystems to store and transport newly fixed carbon, sediments, food and nutrients, and to do the work of exporting wastes, heat, gases and solutes to the atmosphere and adjacent coastal zone. This subsidized energy is used indirectly by organisms to shunt more of their own energy into growth and reproduction, making tidal power one of the main drivers regulating these intertidal systems [1]. Tidal circulation is complex, as marsh and forest topography and morphology and thetidal prism regulate the degree of mixing and trapping of water and suspended matter within adjacent tidal waters and the wetland communities [1].  
Food webs within these wetlands are composed of mixtures of terrestrial, estuarine and marine fauna and flora that help to actively cycle nutrients and carbon. Plankton communities in adjacent creeks and waterways are productive and abundant, and well-adapted to complex hydrology and water chemistry. These opaque tidal waters host organisms ranging in size from viruses to reptiles, such as alligators and crocodiles.  
Salt marshes and mangrove forests are carbon-rich ecosystems that are perceived to play a role in climate regulation, biogeochemical cycling, and in capturing and preserving large amounts of carbon that counterbalance anthropogenic CO2 emissions [4; 5; 6]. It is unclear to what extent both ecosystems constitute a significant carbon sink in the global coastal ocean, and whether restoring and replanting new marshes and mangroves will assist in ameliorating climate change. Thus, an improved understanding of carbon allocation and balance within these ecosystems is urgently needed. In this synthesis, similarities and differences in carbon cycling in both ecosystems are identified to better understand how they function, especially with regard to their role in carbon cycling in the global coastal ocean.
## Methods
### Cross Comparison between Predicted Maps and the CDL  
In addition to the accuracy assessment of the RFC output, we compared the predicted land cover maps with the reclassified CDL. The cross-comparison was reported as the map accuracy assessment (map AA). Although the CDL's accuracy ranged from higher for commodity crops to lower for non-agricultural classes, the CDL remains one of the more reliable land cover datasets for the US. Thus, cross-comparison between our predicted maps and the reclassified CDL should provide a good indicator of the accuracy of the ensemble land cover maps generated by the RFC. Note that ARD, HLS predicted land cover maps, and the reclassified CDL are in different projections and/or data. To allow cross-comparison, pixels from those datasets were co-registered to match perfectly with each other. Because off-sets between the CDL and ARD in latitude and longitude directions are small, co-registration between the two layers was just simple pixel snapping; ARD pixels were moved  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Practice** \\ \hline CxQ & Use only the 17 phenometrics from the Convex Quadratic model \\ HPLM & Use only the 14 phenometrics from the Hybrid Piecewise Logistic Model \\ SPL & Use only the 42 spectrally-based variables \\ CMB & Use the combination of 73 variables from CxQ, HPLM, and SPL \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input variables for Random Forest Classifier (RFC) modeling.  
to match the CDL pixels in the nearest direction. For cross-comparison between the CDL and HLS data, we examined four different adjustments to HLS pixels, moving the raster half pixel in up-right, down-right, up-left, and down-left directions. The up-right adjustment, which yielded the highest number of matched pixels between the CDL and HLS, was reported.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 4 Multiple Feature Extraction Methods for Target Identification
### Scale-Invariant Feature Transformation (SIFT) Feature Extraction Method  
Scale-invariant feature transformation (SIFT) is a machine learning method for local feature detection. The feature points of adjacent image frames are matched by the features obtained from the size and direction of the feature points in the image and their related descriptors. The extraction result of the SIFT feature on key point descriptors of the ship is shown in Figure 3.  
Figure 3: SIFT feature on key point descriptors of ship.  
Figure 2: Original image of ship.  
From Figure 3, it can be seen that the key point descriptors show the features of the scale size and direction of the descriptors. These features have a scale variance for changing image brightness, rotation angle, or shooting angle. These quantized features can be used to match the feature points of adjacent ship image frames, i.e., when the descriptors of the two adjacent ship frames are extracted, the key descriptors between the adjacent ship frames can be matched. When the direction and module length of the 128-dimensional vector are matched, the two frames are matched.
## 1 Introduction  
### Importance  
In contemporary power systems, instability presents a significant challenge, disrupting the normal operation of the system and impacting its security and financial aspects. Neglecting the timely resolution of power system oscillations can lead to either system instability, deviations from optimal operating conditions, or a complete blackout. Nevertheless, the widespread deployment and utilization of UPFCs in modern power systems has shown promising results in enhancing the capacity of transmission lines [1]. Currently, a wide array of Flexible Alternating Current Transmission System (FACTS) devices, including UPFCs and similar devices, has been introduced and integrated into practical power systems. The systematic application of a PSS in conjunction with a UPFC serves as an effective means to ensure power system stability and to mitigate the occurrence of Low-Frequency Oscillations (LFOs) that may arise during disturbances [1]. Furthermore, appropriately adjusting the parameters of both the PSS and UPFC in a coordinated manner to protect against cyber-physical attacks can offer a practical and advantageous solution for optimal and secure operation of the modern power system. This pertinent topic is further elucidated in the current paper. The following section delves into an exploration of relevant studies and research conducted in this specific field of interest.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 4 Results
### Privacy Findings: Was Sensitive Information Leaked into the Synthetic Records?
#### 4.3.1 Similarity between Real and Synthetic Documents  
To give a first indication of potential privacy leaks, we report summary statistics for the ROUGE-N recall between all pairs of real/synthetic documents (Table 8). On average, the low n-gram recall suggests that the synthetic data is substantially different from the real data. However, we also find \"high-risk cases\" with large n-gram overlap. In some rare cases, documents were reproduced exactly (maximum ROUGE-N recall of 1). We focus on the top 122 synthetic documents with highest risk in the user study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**PHI Tag**} & \multicolumn{2}{c}{**NUT**} & \multicolumn{2}{c}{**GPT-p**} & \multicolumn{2}{c}{**LSTM-p**} & \multicolumn{2}{c}{**NUT+LSTM-p**} \\ \cline{2-9}  & **Prec.** & **Rec.** & **Prec.** & **Rec.** & **Prec.** & **Rec.** & **Prec.** & **Rec.** \\ \hline Name & 0.967 & 0.951 & 0.810 & 0.875 & 0.897 & 0.945 & 0.960 & **0.959** \\ Date & 0.929 & 0.910 & 0.910 & 0.813 & 0.889 & **0.913** & **0.932** & **0.920** \\ Initials & 0.896 & 0.629 & 0.456 & 0.146 & 0.595 & 0.421 & 0.822 & **0.674** \\ Address & 0.888 & 0.814 & 0.460 & 0.654 & 0.716 & 0.680 & **0.901** & **0.878** \\ Care Institute & 0.742 & 0.681 & 0.321 & 0.116 & 0.414 & 0.245 & 0.705 & **0.718** \\ Organization & 0.743 & 0.596 & 0.159 & 0.052 & 0.340 & 0.257 & 0.717 & 0.559 \\ Internal Location & 0.784 & 0.527 & 0.273 & 0.055 & 0.188 & 0.055 & 0.757 & 0.509 \\ Phone/Fax & 1.000 & 1.000 & 1.000 & 0.563 & 0.000 & 0.000 & 0.941 & 1.000 \\ Age & 0.757 & 0.683 & 0.320 & 0.195 & **0.786** & 0.268 & **0.758** & 0.610 \\ Email & 0.909 & 1.000 & **1.000** & 1.000 & 0.000 & 0.000 & 0.833 & 1.000 \\ Hospital & 0.778 & 0.700 & 0.333 & 0.100 & 0.300 & 0.300 & **0.857** & 0.600 \\ Profession & 0.833 & 0.238 & 0.000 & 0.000 & 0.000 & 0.000 & **0.923** & **0.286** \\ URL/IP & 1.000 & 0.750 & 1.000 & 0.500 & 0.000 & 0.000 & 1.000 & 0.750 \\ ID & 0.714 & 0.400 & 0.500 & 0.080 & 0.000 & 0.000 & **0.786** & **0.440** \\ Other & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Entity-level precision and recall per PHI category. Comparing the baseline (_NUT_) with two models trained and validated on pure synthetic data (LSTM-p vs. GPT-p), as well as the mixed variant (NUT+LSTM-p) where the training set is composed of NUT and LSTM-p, but the validation set is the same as the one used in the baseline (real data). Highlighted values (bold) show improvements over the _NUT_ baseline.
## 4 Findings and Discussion
### Environmental Impact Produced
#### 4.4.3 Physical and Physico-Chemical Degradation
Furthermore, because of the presence of fiber in their composition, they may also produce MPs during domestic washing, which are released into wastewater and later reach the surface water due to the inability of wastewater treatment plants (WWTPs) to remove them with current treatment technologies [4; 16].