esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Life Cycle Impact Assessment  
A life cycle impact assessment (LCIA) was conducted to determine which processes or materials contributed the most to the environmental impacts. The inventory data were gathered and analyzed using the SimaPro 9.0 software, while the selected background inventory sources were EcoInvent 3.4 and the USLCI databases. The ReCiPe 2016, used to calculate the LCI category indicators, was developed by [PERSON] [28] for the evaluation of 18 impact categories at the midpoint level: stratospheric ozone depletion (ODP), global warming (GWP), ozone formation (human health) (HOFP), ionizing radiation (IRP), ozone formation (terrestrial ecosystems) (EOFP), fine particulate matter formation (PMFP), freshwater eutrophication (FEP), terrestrial acidification (TAP), terrestrial ecotoxicity (TETP), marine eutrophication (MEP), marine ecotoxicity (METP), freshwater ecotoxicity (FETP), human non-carcinogenic toxicity (HHTnc), human carcinogenic toxicity (HTPc), mineral resource scarcity (SOP), land-use change (LUC), water consumption (WCP), and fossil resource scarcity (FFP). The impact categories were divided into three damage assessment categories: (1) damage to human health (HH), (2) damage to resource availability (RA), and (3) damage to ecosystem quality (ED).  
Similar categories in the CML baseline 2000, IMPACT 2002+, and ReCiPe Midpoint (H) methods were compared to identify the \"hot spots\" in each impact category. The results were presented at the midpoint and endpoint levels. The impact assessment results were presented in the following stages: characterization, damage assessment, normalization, and weighting.  
During the characterization stage, the LCI results were converted and combined into representative indicators of environmental and human health impacts. The characterization served as a way of making direct intra-category comparisons of the LCI results [29].  
In the normalization stage, the impact indicator data were expressed to allow easy comparison among the impact categories. An indicator's result was normalized by dividing it by a predetermined reference value. By multiplying the normalization factors by the impact or damage category values, the values could be expressed as person equivalents [29].  
The weighting stage involved assigning relative values or weights to the calculated impact categories to consider their perceived relevance/importance. Weighting was an essential process because the goals of the study had to be reflected in the impact categories. Weighting was accomplished by multiplying the weighting factors by the normalized values of a weighted milli-person (1/1000 person) equivalent [29]. The LCIA results were interpreted to provide recommendations and suggestions for government agencies and policymakers working toward sustainable agricultural practices.
### Related Work
In our work, the proposed prediction process uses two learning models, LSTM and ANFIS, with the aim to perform accurate forecasting of solar energy radiation in west-central Jordan based on meteorological data for the last five years. LSTM can memorize the data sequence and contains a set of modules where the data streams are captured and stored. In contrast, ANFIS is a hybrid model that uses numerical and linguistic knowledge. Its advantages include abilities of adaptation, nonlinearity, and rapid learning. The proposed prediction process aims to accurately forecast solar radiation in west-central Jordan based on meteorological data for the last five years. The two models are compared with each other. The Principal Component Analysis (PCA) is used to filter the input signals. New parameters not previously addressed in other studies in the literature are considered; it is concluded that some of these parameters used, such as direct short-wave radiation, diffuse short-wave radiation, and evapotranspiration, greatly influence solar radiation. This research study is organized as follows: in the introduction, a brief analysis of the different machine learning classifiers proposed in the literature to predict solar radiation was provided, indicating the used parameters and obtained forecast results. Section 2 describes the methods used in this study; data standardization, Principal Component Analysis (PCA) for noise filtering, Pearson Correlation Coefficient (PCC) for feature selection, and the application of LSTM network and ANFIS for the prediction process. Section 3 is devoted to the obtained results related to the solar radiation forecasting for five years with the consideration of seasonal variation. Section 4 provides a comparison of results obtained using ANSIF and LSTM. Finally, Section 5 presents the work's conclusions.
### Flat Plate Solar Collectors  
In a conventional flat plate solar collector (FPC), solar radiation passes through a transparent cover and strikes the blackened absorber surface, which has high absorptivity. Much of this energy is absorbed by the plate and transferred to the fluid in the tubes for storage or use. The absorber plate's underside and sides are well insulated to reduce conduction loss. The liquid tubes, either welded to the absorbing plate or integral to it, are connected by large diameter header tubes. The transparent cover reduces convection losses by restraining the stagnant air layer between the absorber plate and the glass. Advantages of flat plate collectors include their low manufacturing cost, ability to collect both beam and diffuse radiation, and fixed position, which eliminates the need for sun tracking [11].  
Recent comprehensive studies on the state of the art of flat plate solar collectors (FPC) are published in the reviews presented by [12,13]. These studies discuss heat transfer enhancement, the use of phase change materials for storage, geometry modifications for cost reduction, design aspects of major components, and the use of thermal stratification structures. They also cover the use of heat transfer fluids in solar systems, includingwater and antifreeze (water/glycol mixtures), as well as the application of nanofluids in solar systems.  
Using an appropriate concentration of a heat transfer fluid to prevent pipe freezing is a simple and feasible method for large-scale solar heating projects. Commonly utilized heat-transfer fluids include water and ethylene glycol. A recent study [7] compares the thermal performance and hydraulic performance of water, ethylene glycol, propylene glycol, and dihydric alcohol in an FPC. The volume concentration of ethylene glycol and propylene glycol was 50%. They conclude that water performs better, and that the lower the volume concentration of ethylene glycol in the heat-transfer fluid, the better the performance of the solar collector system, while ensuring the freezing point is maintained.  
Previous works have compared different heat transfer fluids that includes water and ethylene glycol. Many theoretic and experimental studies are well summarized in [14], where the authors developed a comparison of the thermal performance of FPSCs using various heat transfer fluids in Romania. They found that the concentration and type of nanoparticles significantly affect efficiency, and that ionic liquid performed better than water as a base fluid under the analyzed conditions. FPC energy and exergy efficiency was compared by [15] using EG, propylene glycol (15.6%), and water. The results were better in the case of water, establishing a limitation that it is not possible to use it due to environmental conditions such as freezing in winter.
Appl. Sci._**2015**, \(5\), 1846-1868; doi:10.3390/app5041846  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Numerical Simulation of Laminar Forced Convection of Pin-Fin Heat-Sink Array in a Channel by Using Porous Approach  
**[PERSON] and [PERSON] ***  
Department of Mechanical Engineering, Chienkuo Technology University, Changhua 500, Taiwan;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-4-7111111 (ext. 3192); Fax: +886-4-7357193.  
Academic Editor: [PERSON]  
_Received: 30 July 2015 / Accepted: 7 December 2015 / Published: 16 December 2015_  
This work used a porous approach model to numerically investigate the fluid flow and heat transfer characteristics of the pin-fin heat-sink array in a rectangular channel with in-line arrangement. The air flow through the channel was laminar. The pin-fin heat sinks with various porosities and pin-fin numbers were employed. The relative center-to-center longitudinal and transverse distances between adjacent heat sinks were changed. The results indicate that the Nusselt number of various heat-sink arrays increased with decreasing the relative center-to-center transverse distance, but not varied with the relative center-to-center longitudinal distance. For the typical pin-fin heat-sink arrays, the Nusselt number changed slightly for the heat sinks with 0.358-0.556 porosity, but increased by 11.7%-24.8% when the porosity increased from 0.556 to 0.750, and then dropped obviously when the porosity exceeded 0.750. Increasing the number of pin fins continuously could increase Nusselt number. However, when the number of pin fins was large, the Nusselt number increased with the number of pin fins slowly. The present numerical simulation has been validated by the typical experiment. Finally, a semi-empirical correlation of Nusselt number for each heat sink in the heat-sink array was proposed.  
porous approach; pin-fin heat sink; heat-sink array; heat transfer
## 2 Materials and Methods
### Frequency Measurement and Calculations for String Tension Force
Equation (1) shows that the frequencies of the higher modes are harmonically related to f0. \[\sigma_{Si}=\frac{T}{A} \tag{2}\]  
where \(Si\): String number = 1, 2, 3, 4, 5, 6; \(T\): Tension force of the string (N); \(A\): Cross-sectional area of the string (mm\({}^{2}\)) and \(\sigma_{Si}\): Cross-sectional stress of the corresponding string (MPa). Sound recording, sample frequency measurement and calculations related to string tension force are given in Figure 2 and Table 1, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**String Group**} & **Playing Tuning** & **String** & **String** & **Measured Frequency** & **Corresponding** & **Corresponding** \\  & **(End Tune)** & **Diameter** & **Note** & **(FFT Size 1024-** & **String Tension** & **Cross Section** \\  & & & & & **Force** & **String Stress** \\ \cline{2-6}  & **(Reference Tune:** & & & & **(Hz)** & **(N)** & **(MPa)** \\  & **A4 (LA) @ 440 Hz)** & & & & & & \\ \hline \multirow{2}{*}{Lower String} & B2 (SI) (123.5 Hz) & 0.16 & B4 (SI) & 493.14 \(\pm\) 0.10 & 30.194 & 1502.488 \\  & C3 (DO) (130.8 Hz) & 0.16 & C5 (DO) & 523.54 \(\pm\) 3.24 & 34.032 & 1693.471 \\  & D3 (RE) (146.8 Hz) & 0.16 & D5 (RE) & 587.42 \(\pm\) 0.43 & 42.843 & 2131.917 \\ \hline \multirow{2}{*}{Middle String} & B2 (SI) (123.5 Hz) & 0.25 & E4 (MI) & 329.10 \(\pm\) 0.25 & 32.831 & 669.160 \\ Group (\(\lx@sectionsign\)3 and \(\lx@sectionsign\)4) & C3 (DO) (130.8 Hz) & 0.25 & F4 (FA) & 349.9 \(\pm\) 2.15 & 37.112 & 756.415 \\  & D3 (RE) (146.8 Hz) & 0.25 & G4 (SOL) & 392.78 \(\pm\) 1.95 & 46.765 & 953.162 \\ \hline \multirow{2}{*}{Upper String} & B2 (SI) (123.5 Hz) & 0.16 & A4 (LA) & 440.5 \(\pm\) 0.20 & 24.092 & 1198.846 \\ Group (\(\lx@sectionsign\)5 and \(\lx@sectionsign\)6) & C3 (DO) (130.8 Hz) & 0.16 & B4b (SI) & 466.11 \(\pm\) 3.30 & 26.975 & 1342.307 \\  & D3 (RE) (146.8 Hz) & 0.16 & C5 (DO) & 523.32 \(\pm\) 0.14 & 34.003 & 1692.028 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frequency measurements and calculations pertaining to string tension force. Figure 2: Illustrative example of the experimental process for sound recording and monitoring.
###### Abstract  
This study investigated which sizes of double-J stents are more effective in achieving an acceptable urine flow through stenotic and stented ureters. Sixty four computational fluid dynamics models of the combinations of two different gauge ureters (4.57 mm and 5.39 mm in diameter) with four different levels of ureteral and four different sizes of double-J stents were developed for the numerical analysis of urine flow in the ureter. Luminal, extraluminal, and total flow rates along the ureter were measured, and the flow patterns around the ports and side holes were investigated. For the 4.57-mm ureter, the total flow rate for each gauge of stent was 23-63 mL/h (5 Fr), 20-47 mL/h (6 Fr), 17-35 mL/h (7 Fr), and 16-26 mL/h (8 Fr) and for the 5.39-mm ureter, the total flow rate for each gauge of stent was 43-147 mL/h (5 Fr), 36-116 mL/h (6 Fr), 29-92 mL/h (7 Fr), and 26-71 mL/h (8 Fr). With a 74% stenosis, all stents allowed a low flow rate, and the differences in flow rates between the stents were small. At the other levels of stenosis, 5 Fr stents allowed greater flow rates than the 8 Fr stents. The luminal flow rate increased just before the area of stenosis and decreased after the stenosis because of the increase and decrease in the luminal flow through the side holes before and after the stenosis. Therefore, a larger double-J stent is not favorable in achieving an acceptable urine flow through the stenotic and stented ureters. The results in this study could not be necessarily correlated with clinical situation because peristalsis, viscosity of the urine and real format of the ureter were not considered in our model. In vivo experiments are necessary for confirmation of our findings. Double J stents are commonly used in the ureteral stenosis or occlusion, especially due to ureter stones which obstruct the flow of urine. Clinicians choose the size of double J stent on the basis of their clinical experience. Here, we tried to know which sizes of double J stents are better for sufficient urine flow. According to various documents that try to determine the optimal shape of double J stents to increase the urine flow through the ureter, mostly bigger stent is recommended to occur maximum urine flow. However, in case of ureter with stenosis or occlusion, the right size of the double J stent may vary depending on the degree of stenosis in the ureter. To find appropriate stent size for the ureter with stenosis, computational fluid dynamics was conducted. This study shows that smaller diameter stents are more appropriate than larger diameter stents depending on the situation.  
**Keywords:** ureter; double-J stent; urine flow; computational fluid dynamics
## 3 Results
### Complexity Analysis
#### 3.1.4 Dynamic MSL Signs Classification  
Algorithm for Dynamic Signs (k, rr, ll) Detection:  
* Description: This algorithm uses a sliding window of five frames to detect the presence of dynamic signs based on movement patterns.
* Complexity: Let \(N\) be the total number of frames in a video. The sliding window approach results in a complexity of \(O(N)\), as each frame is processed once to detect patterns. Checking for conditions within the window also adds a small constant factor but does not affect the overall linear complexity.
## 3 An Implementation of the Proposed Architecture
### Device Integration Layer and Basic Operation Layer  
Layer 1 is where various kinds of devices are located. In the proposed technology integration model, all direct device control operation need to be implemented in layer 1. In this subsection, physical and logical connections between controlled devices and Raspberry Pi boards in H204 are described in Table 11. As shown in Table 11, devices with various kinds of control interfaces, such as RS232, RS485, Modbus, TCP, GPIO, are linked to Raspberry Pi boards. And control commands corresponding to device actions are sent from Raspberry Pi boards, by shell commands, programs, python scripts, or PHP CLI scripts, to the controlled devices, including a projector, a motorized projector screen, four light sets, an interactive board, a temperature sensor, a CO\({}_{2}\) sensor, an exhaust fan, an air conditioner, and electromagnetic door lock.  
After control commands corresponding to actions on devices are implemented and tested on Raspberry Pi boards in layer 1, layer 2 basic operations can be defined as a web application interface calls and implemented with Python and Flask. The mappings of layer 1 device actions to the corresponding layer 2 basic operation web application interface calls in H204 are shown in Table 12. With layer 2 basic operations web application interface calls and proper network configurations, device actions in H204 can be called from the central web server. Then layer 3 combo operation web application interface calls be defined and implemented on the central web server.
# Trepenoids in Marine Heterobranch Molluscs
## 3 Sesquiterpenoids
### Monocyclic Sesquiterpenoids  
Nudibranchs, anaspideans, sacoglossa, and pulmonates present monocyclic sesquiterpenoids. In sea slugs, tangylides A (11) and B (Figure 3) are found in the dorsal mantle of the doridacean _Sclerodoris tanya_ from California, being used as effective fish-feeding deterrents [88]. In addition, some known sesquiterpenes, including dendrolasin (12) (Figure 3), are found in the Patagonian doridacean _Tyrinna_  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group (Species)/Compounds** & **Nudibranchia (51)** & **Anaspidea (7)** & **Sacoglossa (2)** & **Pulmonata (1)** \\ \hline Linear Sesquiterpenoids & 1 & 0 & 0 & 0 \\ Monocyclic Sesquiterpenoids & 5 & \(>\)5 [1] & 3 & 2 \\ Bicyclic Sesquiterpenoids & \(>\)60 [1] & \(>\)6 [1] & 0 & 0 \\ Tricyclic Sesquiterpenoids & \(>\)5 [1] & 3 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of sesquiterpenoids in different Heterobranch groups in the reviewed period. In brackets: number of species with sesquiterpenoids.  
_nobilis_[89]. Furthermore, (5R,6Z)-dendrolasin-5-acetate was isolated from _Hypselodoris jacksoni_ from Australia [90]. The dendronotid slug _Doto pinnatifida_ from the Atlantic presents dotofide (13) (Figure 3), a guanidine-interrupted terpenoid with an unknown ecological role [91].  
In sea hares, the Sri Lankan species _Aplysia coulifera_, possesses compounds obtained from the red algae _Laurencia_, such as srilankenyne (14) (Figure 3), while the digestive gland of Indic and West Pacific specimens presents brominated acetylenes instead [92; 93]. _Aplysia argus_ from Bahamas presents, among other compounds, the sesquiterpene ethers dactyloxene-B (15) (Figure 3) and dihydroxydeodactol monoacetate in whole body extracts [94; 95; 96; 97; 98; 99].  
Figure 3: Structures of selected sesquiterpenoids from Heterobranch molluscs: (**11**) tanyolide A; (**12**) dendrolasin; (**13**) dotofide; (**14**) srilankenyne; (**15**) dactyloxene-B; (**16**) volvatellin; (**17**) crispatening; (**18**) onchidal; (**19**) anicstrodial.  
The modified sesquiterpenoid, volvatellin (16) (Figure 3), was found in the Indian shelled sacoglossa _Volvatella sp._, along with the previously mentioned monoterenoid, caulerpenyne (see above) [70]. The configurational assignment of volvatellin and its putative origin from oxytocin-1 was further reported [100]. _Ellysia crispata_ from Venezuela, instead, was found to contain, among other compounds, crispatening (17) and onchidal (18) (Figure 3), the latter also found in the pulmonate _Onchidella_[69; 101; 102]. The case of _E. crispata_ is extremely intriguing because it includes different types of chemistry and chemical strategies: bioaccumulation, biotransformation, and biosynthesis. The pulmonate _Onchidella binneyi_ contains onchidal, which is further secreted in its active form, anicstrodial (19) (Figure 3), to elicit deterrence against predators [103].
## 3 Results and Discussion
### Transport Properties
It can be concluded that both hexagonal perovskites (Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\) and Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\)) are promising proton conductors. Further modification of the composition of these compounds will make it possible to achieve more significant values of the oxygen-ion and proton conductivities. It can also be said that the class of hexagonal perovskites characterized by the structure of coherent intergrowthing is a new promising class of proton conductors for which it is necessary to establish their own specific regularities of ion transport.
## 5 Experimental Case Planning, Results, and Analysis
### Measurement of Experimental Data  
The measurement platform system device for this experiment is shown in Figure 6, and the equipment items are shown in Table 3. This measurement platform system can control the load (No. 6) through the dynamometer control box (No. 3), to measure the data of the induction motor (No. 4) under different loads. The values of the tachometer (No. 1) and torque meter (No. 2) could be observed during measurement to judge the induction motor operating condition. The three-axis vibration signals of the 2 HP induction motor were measured in the experiment, and the operating states were the healthy state and four fault types. The fault models included stator winding short circuit, rotor broken bar, bearing outer ring damage, and misalignment failure. The motor data were measured under 50% load and 100% load. The sampling rate of each data signal was 20 kS/s, the sampling time was 5 s, and there were 100,000 sample points.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Optimal Estimation Algorithm
#### 3.2.2 Photon Path Length Probability Density Function Algorithm  
The photon path length probability density function algorithm, or PPDF algorithm [66], is a simplified estimation method. In the atmospheric radiative transfer, thin cirrus clouds and aerosol scattering can cause lengthening or shortening of the optical radiative transfer path and changes in the photon optical range. The PPDF achieves a correction for the scattering problem in atmospheric CO\({}_{2}\) inversions by varying the photon optical range in atmospheric radiative transfer. Thin clouds are considered in the retrieval of CO\({}_{2}\) from space-based reflected solar observations in the near-infrared region [86].  
A typical atmospheric scenario consists of clouds and aerosol layers, with at least three layers having different photon path length distributions: within the aerosol layer, between the aerosol layer and clouds, and above the clouds. Here the PPDF algorithm divides the whole atmosphere into two layers [86]. The lower layer contains clouds and aerosols, where sunlight is scattered many times resulting in longer light paths, and the upper layer of the atmosphere contains no aerosols, where sunlight is reflected from cloud tops resulting in shorter light paths. The effective transmittance model for the whole atmosphere can be expressed as:  
\[\overline{T}=\alpha\cdot T_{2}(h_{\alpha})+(1-\alpha)\cdot T_{1}(\gamma,\rho, h_{\alpha})\cdot T_{2}(h_{\alpha}) \tag{7}\]  
where \(\alpha\) is the reflectivity of the cloud top, \(T_{1}\) and \(T_{2}\) are the transmittance of the lower and upper atmosphere respectively, \(h_{\alpha}\) is the height of the top of the lower atmosphere, \(\rho\) is the correction factor for the path of photons in the lower atmosphere, and \(\gamma\) is the linear correction factor of the PPDF in the lower atmosphere. \(\alpha\), \(h_{\alpha}\), \(\rho\) and \(\gamma\) are the four parameter factors introduced to describe the scattering effect, i.e., PPDF factors, and the transmittances of the two layers of the atmosphere \(T_{1}\) and \(T_{2}\), are functions of the PPDF factors. The basic principle of the PPDF algorithm is to obtain the PPDF factors by inversion in a specific band, and then modify the atmospheric effective transmittance model accordingly in order to estimate the scattering effect.