esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description  
The basic principle of capacity regulation of a reciprocating compressor is that the movement of the suction valve is controlled by external forces which delay its closure [6]. A part or all of the gas in the cylinder flows back to the inlet line before it is compressed, and only the required gas is compressed. The power consumption and actual volume gas are directly proportional [1]. Therefore, the power consumption of the compressor is reduced when it is not under full load.  
The reciprocating compressor pressure system with SCRS can be called the variable capacity reciprocating compressor pressure system (VCRCPS). The VCRCPS consists of a reciprocating compressor, buffer tanks, an outlet valve, and a SCRS which is integrated with a hydraulic system, an intelligent control system, and an electro-hydraulic actuator, as shown in Figure 2. The gas is compressed by a reciprocating compressor to realize a pressure boost. The hydraulic system provides the hydraulic driving force. The intelligent control system records the exhaust pressure, temperature, and the signal of the top dead center (TDC) of the compressor to calculate the load and output-corresponding control signal. The electro-hydraulic actuator responds according to the control signal to make part of the gas return into the inlet line without compressing, so as to realize capacity regulation.  
The object studied in this paper is a two-stage reciprocating compressor with SCRS, as shown in Figure 3, including a control system, hydraulic system, gas pipeline, buffer tank, electro-hydraulic actuator, etc. The main operating parameters are listed in Table 2.  
The load regulation experiments were carried out on the test bench to verify the modified multi-subsystem mathematical model. The performance optimization method proposed in this paper was also verified on this test bench.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Rated speed (r/min) & 300 \\ Stage number & 2 \\ Number of cylinders & 2 \\ Buffer tank capacity (m\({}^{3}\)) & 0.3/0.16 \\ Spring stiffness (kN/m) & 38 \\ Hydraulic pressure (MPa) & 7 \\ Suction pressure (kPa) & 101 \\ Exhaust pressure (kPa) & 27/700 \\ Exhaust volume (m\({}^{3}\)/min) & 12 \\ Number of suction valves & 4/4 \\ Number of exhaust valves & 4/4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operation parameters of the compressor and the SCRS.  
Figure 3: Two-stage reciprocating compressor with a stepless capacity regulation system (SCRS).  
Figure 2: The variable capacity reciprocating compressor pressure system.
## 1 Introduction
According to the multi-model fusion strategy, using real sample points for interpolation to obtain synthetic data has more consistent probability distribution characteristics compared to traditional sample point interpolation methods, making it more advantageous [40]. At present, in the field of well logging data mining, there are also studies that use stacking models combined with the SMOTE algorithm to obtain lithology identification results [41]. These studies all focus on over-sampling the minority class to enrich the information content of the dataset. The above review indicates that data balancing is crucial for enhancing the performance of classifiers and improving the predictive capability for sparsely represented classes in the samples. Well logging data often suffer from imbalanced sample distributions. Data on fine scale reservoir intervals are relatively scarce, and models tend to misclassify corresponding samples into more abundant lithology classes in order to maintain high overall accuracy. This can ultimately lead to missing highly economical reservoir intervals during industrial exploitation [42; 43; 44]. To enhance the accuracy of identifying lithology classes with scarce samples, it is necessary to construct balanced datasets with an equal number of samples for each class. On the other hand, to adopt sample balancing methods for balancing the frequency distribution of data across different classes, it is necessary to consider both the information loss issue of under-sampling methods and the overfitting risk of over-sampling methods. Based on the analysis of the above issues, this study proposes a novel approach that first utilizes over-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic sampling (ADASYN) to generate synthetic samples for lithology classes with scarce samples. Then, the study applied the edited nearest neighbors (ENN) process to remove marginalized samples and maintain the balance in the count of samples between lithology classes. After enhanced sampling, several machine learning models, including support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), were applied to test the improvement of this sampling workflow on the lithology discrimination results. According to the experimental results, discussions were held to determine the optimal process of enhanced sampling and model application to achieve efficient and balanced enhanced sampling and intelligent and accurate lithology identification. This study provides a method for synthesizing logging data for scarce sample lithologies and explores an efficient workflow for the accurate identification of a fine-scale reservoir.
## 2 Materials and Methods
### Model Formulation  
#### Basic Model Setup  
VegET monitors soil water levels in the root zone through a daily (or longer time step) water balance algorithm to estimate ETa in rainfed landscapes. The key input data to VegET are precipitation (PPT), ETo, WHC, and Kcp. ETa is calculated as the product of ETo, soil stress coefficient (Ks), and Kcp, as shown in Equation 1. Figure 1 shows a simplified schematic representation of the VegET model.  
\[ETa=Kcp*Ks*ETo \tag{1}\]  
Where ETa is actual evapotranspiration in mm; Kcp is LSP-derived crop coefficient (-); Ks is soil-water stress coefficient (-) and ETo is reference ET in mm.  
Ks is determined from a soil water balance model similar to the one developed by [PERSON] and [PERSON] [8] for USGS/FEWS NET early warning applications using Equation 2. The dimensionless Ks coefficient varies from 0.0 to 1.0 depending on the soil water level in the modeling unit root zone.  
\[Ks=\frac{SWi}{MAD}\to SWi<MAD;Ks=1.0\to SWi>=MAD \tag{2}\]  
SWi is soil water of current time step in depth unit. MAD is the Maximum Allowable Depletion level of soil water in the root zone, below which the vegetation ET is less than \"potential\" and will be constrained by the availability of soil water according to Equations 3. Although MAD varies by crop/vegetation type, a nominal value of 50% of the soil WHC can be used for most generalized crops such as cereals and natural vegetation. Thus, MAD was estimated as 50% of the WHC derived from STATSGO.  
The soil water level is determined using a daily soil water balance using Equation 3 where ETa is estimated iteratively employing Equations 1 and 2. The model estimates a combined runoff and deep drainage based on the principle of saturation excess where soil water in excess of the water holding capacity (WHC) of the soil is considered to be unavailable for plant-use in the root zone; thus SWi is set to a maximum of WHC and a minimum of 0.0 during the modeling time-step. More detailed information is available on the setup and initialization of the soil water balance model for operational crop monitoring in Senay and Verdin [8].  
\[SW_{i}=SW_{i-1}+PPT_{i}-ETa_{i} \tag{3}\]  
Where SWi is soil water of current time step (mm); SWi-1 is soil water of the previous time step (mm); PPTi is precipitation (mm) and ETa is actual ET (mm); \"i\" is the modeling time step.  
The innovative approach in VegET is on the calculation of the Kcp (Figure 1). The Kcp is comparable to the Kc widely used by agronomists [6]. The key difference is that Kcp is a parameter derived from remotely sensed data as opposed to site-specific field experiments. Kcp represents both the spatial and temporal dynamics of the landscape water-use patterns on a grid-cell basis. LSPs are characterized and converted into Kcp parameter functions for each modeling grid cell using LSPs generated from NDVI climatology.
Driven by breakthroughs in biotechnologies and polymer synthesis, developed polymeric nanocariers can currently address most of the above-cited challenges related to free-form antibiotic delivery. Various nanocarier platforms have been explored to improve the physicochemical properties of antimicrobials (dissolution and stability), precisely release them at the infectious sites in a controlled manner, and co-deliver the antibiotic combinations (Figure 1). Besides the potential therapeutic benefits, polymer nanocarriers are commonly synthesized from biodegradable and biocompatible materials, facilitating the transfer of these novel antibiotic dosage forms to clinical practice. Figure 1: Overview highlighting the main advantages of a nanocarier-based antibiotic delivery approach for the treatment of bacterial infections. Depending on the targeted therapeutic applications, nanocarriers may be engineered to improve different pharmacokinetic parameters of antibiotics, resulting in increased therapeutic efficacy and reduced adverse side effects. In this review, we will describe the essential aspects in which polymeric nanocarriers can be engineered to offer better local bioavailability and maximize antibiotic-therapeutic efficacy. The intrinsic antibacterial mechanisms of nanomaterials have been summarized in several recent reviews [8; 9; 11] and, thus, will not be reiterated in detail here. The engineering approaches discussed will include dissolution and stability enhancement of antimicrobial compounds, Controlled antibiotic delivery, Targeting delivery of antibiotics to sites of infection, and Co-delivery platforms for combination antibiotic therapy. The current challenge and future direction of nanocarrier-based drug delivery systems for antimicrobial therapy are also discussed.
## 4 Discussion
### Laying Period
#### 4.2.3 Influence of Stocking Density and Access to Free Range  
The stocking density had a significant effect in the univariate analysis of the first laying period. This result corresponded with other studies that found the stocking density to be a risk factor of plumage damage [34]. Other resource-based factors, such as 'perch length per hen', were fixed in the barn, and therefore directly connected to the stocking density. The univariate analysis showed a significant effect of 'perch length per hen' for two visits in the first laying period (LV1 and LV2). This variable was chosen for the model, but was not significant. This finding is not surprising, because in Germany minimum requirements for stocking densities and resource-based parameters, such as 'number of hens per drinker' and the perch and feeding trough lengths, are regulated by law [67]. Therefore, as expected,there was little variance in these variables between the flocks, which were all housed on conventional (not organic) commercial farms in Bavaria.  
Six farms provided free range for their laying hens. Laying-hen flocks with access to free range had less plumage damage in our study--a result also found in the study by [PERSON] [33]. The availability of free range was significant in the univariate analysis for LV1 and LV3 of the first laying period, and had large effects ([PERSON]'s \(d\)) in the second laying period. The model over both laying periods confirmed the significant positive influence of free range on the plumage condition for LV1 (\(p\) = 0.006) and LV3 (\(p\) = 0.020). The second laying visit was in the cold season, during winter in most cases. This could explain the missing effect for LV2, because free range may not have been used as much in winter and may not have been available all the time. Some authors have pointed out that not just the availability but also the actual use of free range is crucial for preventing SFP [1,41,69]. The usage of free range could not be investigated in this study, but during our visits we noticed that free-range areas were sometimes closed during bad weather in winter, and that large free-range areas had little or no structure at all. Free-range areas are better-used if they have vegetation or shelters, or both [41].
## 4 Results  
### Low-Frequency Abnormal Signal of Seismic Wave  
The maximum value of the amplitude frequency envelope is tracked for the continuous seismic waveform data recorded at the Wuhai (WUH), Shizuishan (SZS) and Dongshengmiao (DSM) seismic stations from 0:00 on 1 April to 0:00 on 16 April 2015. The results show that the maximum values of the amplitude frequency envelopes of the three stations have obvious morphological differences (Figures 6-8). On the basis of the relatively gentle changes since 1 April 2015, the curve shape of WUH, closest to the epicenter of the Azuqi Ms5.8 earthquake, began to fluctuate from 6:00 on 10 April 2015. It is considered that this morphology from WUH corresponds to the theory of fracture nucleation, which may be the manifestation of sub-instability. The result from DSM is relatively stable and the same sharp pulse signal as that from WUH before 6 April appears only at some individual times. Moreover, the intensity of this sharp pulse signal weakened after the continuous curve fluctuation at WUH on 6 April. It is speculated that in the process of the rock fracture and nucleation under WUH, the rock fissures are squeezed due to the increasing stress, so the low-frequency signal is blocked and cannot be better transmitted to the area below DSM. The difference in the low-frequency signals tracked by the three stations may be related to the epicentral distance. Assuming that a region is equipped with enough dense seismometers, the epicenter location of moderate and strong earthquakes can be predicted through this difference feature.  
Figure 5: Schematic diagram of data processing flow.
## 2 Diffraction-Based Optical Switching
We tested our LCoS-based optical switch with a QPSK (Quadrature Phase Shift Keying) signal and examined the clustering of the demodulated signal with and without the switch in the transmission line. The constellation diagrams for both \(x\) and \(y\) incident polarizations are presented in Figure 7. No signal degradation was observed for the diffraction by a zone plate containing up to 600 rings, i.e., 600 2\(\pi\) phase shift. Figure 3.0 Phase shifting of the phase shift keying (PSK) symbol induced by (**a**) the LCoS SLM (Liquid Crystal on Silicon Spatial Light Modulator); (**b**) the difference in path length. Figure 3.1 Constellation diagrams of a 25 Gbaud/s signal. (**a**) no switch, \(x\)-polarization; (**b**) no switch, \(y\)-polarization; (**c**) with switch, \(x\)-polarization; (**d**) with switch, \(y\)-polarization. \(\lambda=1550\) nm, phase pattern: 600 rings zone plate.
# In-Band Pumped Thulium-Doped Tellurite Glass Microsphere Laser
## 3 Theoretical Results and Discussion
The obtained results may serve as a guide for designing Tm-doped microlasers pumped by readily available pump sources at a telecommunication wavelength. The model describing single-mode laser generation works well for not very high excess of pump power over the threshold. To describe multi-mode laser generation in different mode families, which is observed for high excess over the threshold, the model can be modified.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Drying and Sample Extraction  
VFD samples were pre-frozen at \(-80\)\({}^{\circ}\)C for 12 h (DW-86-386, Haier Biomedical Co., Ltd., Qingdao, China) to ensure uniform solidification. Lyophilization was conducted at \(-20\)\({}^{\circ}\)C under a pressure of 7.3 Pa for 48 h, with the condenser temperature maintained at \(-88.7\)\({}^{\circ}\)C (FB-1B-80+, Bo Yikang Instruments Co., Beijing, China). For HD, the samples were dried at 40 \({}^{\circ}\)C for 48 h in a forced-air drying oven (DHG-9245A, Shanghai Yiheng Technology Co., Shanghai, China), with air circulation at 1200 rpm and 15 air exchanges per hour. VD was executed in a vacuum chamber (VO29, Memmert GmbH + Co., KG, Schwabach, Germany) at 40 \({}^{\circ}\)C under a pressure of 20 Pa for 16 h. For MD, the operation was carried out in a microwave oven (XMJD6 SW-2, Xiaomi Mechanical Instrument Co., Ltd., Nanjing, China) for a duration of 15 min, with a microwave power of 700 W and a microwave frequency of 2.45 GHz. ND samples were spread uniformly on perforated stainless-steel trays and dried naturally at ambient temperature (20-25 \({}^{\circ}\)C) for 25 days. Overnight, trays were transferred to an incubator (DHP-9082, Shanghai Jinghong Experimental Equipment Co., Shanghai, China) maintained at 23 \(\pm\) 1 \({}^{\circ}\)C and 45 \(\pm\) 5% RH. Subsequently, all dried samples were ground at 25 \(\pm\) 2 \({}^{\circ}\)C using a high-speed blender (FW100, Taisite Instrument Co., Ltd., Tianjin, China) in five cycles (10 s grinding with 30 s cooling intervals). Powders were sieved (3.35 mm), vacuum-packed (\(-0.08\) MPa) using a vacuum packaging machine (DZ-260 PD, Hualian Machinery, China), sealed in low-density polyethylene bottles, and stored at \(-80\)\({}^{\circ}\)C until analysis.  
Regarding the extraction process of the dried fruit powder, we employed an ultrasound-assisted extraction method. Specifically, approximately 1 g of the sample was accurately weighed and diluted to a final volume of 50 mL with 70% (_v/v_) aqueous ethanol. The mixture was then subjected to ultrasonic treatment at 300 W for 1 h and subsequently stored at 4 \({}^{\circ}\)C for further use.
# Fake Base Station Detection and Link Routing Defense +
## 5 Our Scheme
### Link routing vs. networking packet routing  
Our link routing is different from networking packet routing in terms of the triggering condition, routing decision maker, and route selection procedure. In networking packet routing, intermediary devices such as routers and switches take part in route selection decisions during an event of change in communication links such as link discontinuation, link cost change, etc. On the other hand, in link routing, the user equipment, i.e., the end device, decides which route or path to select for connection switching after a fake base station detection. For the route selection procedure in link routing, the user equipment selects the best route (more specifically a base station) from a reputation vector containing known legitimate base stations for connection switching. However, in networking packet routing, the routers and switches use different metrics such as the shortest distance, lowest delay, or greater bandwidth along a path to select the best route to switch the data packets.
## 4 Conclusions  
Based on the work presented here, the following conclusions have been derived:  
From the simulation, the efficiency is negatively affected when the diameter of the nanoparticle increases, because the global heat transfer area between the nanoparticle and the base fluid decreases. Furthermore, the efficiency decreases when the solar radiation and the inlet temperature rise. Likewise, the efficiency enlarges when the volume fraction is higher.  
For the production of Fullerene C60, it is important to first evaluate the temperature profile inside the microwave oven, to identify the maximum temperature points. To ensure that the thin carbon film obtained is useful, a silver-gray layer and a characteristic soot of macromolecule formation should be visualized. The separation of carbon molecules can be accelerated by the manual grinding and sonication of carbon diluted in alcohol. Using high-resolution electron microscopy, it can be verified that the thin film contains up to approximately 90% of Fullerene C60, with a diameter of less than 20 nm.  
From the evaluation of the low-temperature solar collector performance using carbon nanofluids, it can be concluded that the efficiency of the low-temperature collector, using C60 fullerene nanofluids, can be improved by up to 47.22%, at 0.063% volume fraction and 0.0194 LT/s, with respect to one that uses water as the working fluid.  
Conceptualization, E.E.B., A.M., L.G.D.-B. and A.Z.; methodology, E.E.B. and A.Z.; software, J.J.R., J.M.C. and A.Z.; validation, J.J.R., M.D.V., N.G., M.V. and A.Z.; formal analysis, A.M., L.G.D.-B., G.L.G., M.D.V., N.G., M.V. and A.Z.; investigation, E.E.B., A.M., L.G.D.-B., M.V. and A.Z.; resources, A.M., L.G.D.-B. and A.Z.; data curation, E.E.B., J.d.J.R. and A.Z.; writing--original draft preparation, E.E.B. and A.Z.; writing--review and editing, E.E.B., L.G.D.-B., J.d.J.R., J.M.C., M.V. and A.Z.; visualization, G.L.G., M.D.V. and N.G.; supervision, A.Z.; project administration, A.Z.; funding acquisition, A.M., L.G.D.-B. and A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Polytechnic Institute of Mexico (IPN), Project (SIP20220182). E.E.B. thanks CONACYT for the scholarship granted.  
Figure 15: Efficiency of the CSAD using Fullerenes C60, with respect to the concentration at two flow rates.
###### Abstract
The apparatus of electrospinning has a simple structure including a high-voltage power source having a positive or negative polarity, a syringe pump having a capillary tube or a tube to transport the solution from the syringe or pipette to the spinneret, and a conductive collector such as aluminum foil [10]. The electrospinninging process uses a high-voltage electric field to produce a charged jet from a polymer solution that produces nanofibers when dried by evaporation of the solvent. The highly charged fibers are directed toward a collector that connects to the ground. During electrospinninging, spinning of a fiber is mainly achieved by inducing a tensile force generated in the axial direction of the polymer flow in the presence of an electric field [10; 11; 12]. Poly (lactic acid) (PLA) is a thermoplastic, high-strength, high-modulus polymer made from renewable resources in the form of particles, and is widely used for industrial packaging or biocompatible/bioabsorbable medical devices [13; 14]. It is easy to process using standard plastic equipment to produce molded parts, films or fibers. Shellac is a kind of resin secreted by the female lac bug on trees in Indian and Thai forests. Due to its excellent water resistance, fixing abilities, film-forming as well as biocompatibility properties, shellac has been widely used in medicine, paint, food, military, dyes and other fields [15]. Compared with traditional technologies, electrospinninging is an efficient, simple, and low-cost method for preparing various nanofibers. As mentioned above, shellac is soluble in anhydrous ethanol to make an electrospinninging solution. After being treated with the ethanol vapor, the transparency of the shellac electrospinninging membrane will be greatly improved [15]. Moreover, PLA electrospinninging fibers have been used as stents for drug delivery and tissue engineering due to their high specific surface area [16]. Therefore, the PLA electrospinninging membrane was used as a fixed layer, and the shellac electrospinninging membrane was combined with the PLA membrane, which is more stable than the shellac membrane itself, and is advantageous for preparing the membrane of the alcoholic vapor sensor with a low molecular weight. The present study aims to produce an alcoholic vapor sensor with a low molecular weight that is easy to process and test accurately using the PLA/shellac/PLA sandwich-structural membrane via an electrospinning method. In this paper, we report that the PLA/shellac/PLA sandwich-structural membrane will become transparent when exposed to alcoholic vapors with a low molecular weight, in order to detect these at room temperature. Thus an effective and biodegradable sensor can be made to detect the alcoholic vapor with a low molecular weight.