esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 2 Methods
### The Proposed DIES for Improving SC Analysis  
In general, the route of the proposed DIES for diagnosing an inter-shaft bearing fault is depicted in Figure 8. Specifically, the flowchart of the proposed DIES method is depicted in Figure 9. It includes 5 steps that are stated as follows:  
Figure 8: Technique route of the proposed DIES method.  
Figure 7: Illustration of the improved adaptive threshold denoising method: (**a**) positive values of the \(NIES_{sub}\), (**b**) denoising result using the conventional adaptive threshold, (**c**) denoising result using the improved adaptive threshold.  
Step 1: Perform fast SC analysis on both the faulty and healthy signals to generate the improved envelope spectrum;  
Step 2: Compute NIES, which is utilized for spectral subtraction;  
Step 3: Derive the subtracted NIES by subtracting the healthy NIES from the faulty NIES;  
Step 4: Calculate an adaptive threshold for subtracted NIES by using a two-straight-line-based change point detection model;  
Step 5: Determine the frequencies of the fault components by using the subtracted NIES and its adaptive threshold. Frequencies of spectral lines whose amplitudes are larger than the threshold can be ascertained as frequencies of fault components.  
From these steps, it can be concluded that the core thought of the proposed DIES method is as follows. First, the improved envelope spectrum module demodulates the hidden periodic fault signal components. The resultant IES provides the critical basis for solving the multi-source interference with a wide-band distribution problem under dual-frequency excitation. Subsequently, the spectral subtraction module removes the influence of inherent interference frequencies in the spectrum. Finally, an improved adaptive threshold denoising module is applied to further eliminate noise and interference components from the spectrum, allowing for the extraction of fault characteristic frequency components more clearly.  
Overall, the proposed method focuses on efficiently reducing noise and enhancing the clarity of fault signatures, thereby improving its effectiveness in diagnosing inter-shaft bearing faults.
## 4 Classification and Features of Smartphone Apps for Solar PV Energy
### Potential Assessment of PV Systems
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name of App**} & \multirow{2}{*}{**Decopler/Company**} & \multirow{2}{*}{**Platform**} & \multirow{2}{*}{**Price \({}^{\bullet}\)**} & \multirow{2}{*}{**Function**} & \multicolumn{3}{c}{**PV Power Output**} & \multirow{2}{*}{**For**} & \multirow{2}{*}{**Root/Rootrop**} & \multirow{2}{*}{**Reference**} \\  & & & & & & & **Hourly** & & **Daily or** & & **Yearly** \\ \hline SolarUp\(-\)PV Solar & Earth Wise LLC & iOS & Free & & & & & & & & Unknown & [59] \\ Design Tool & Lewis O’Leary & Android & Free & & & & & & & NASA & [60] \\ Solar HV Optimal Spot & [PERSON] & Android & Free & & & & & & PVG’s & [61] \\ PV Solar Calculator & Skegret & Android & Free & Simulation & & & & & User input & [62] \\ PV OrbitMizer & Emvail Energy Energy & Android & Free & & & & & & NOAA [63] \\ SolarV & Hursh Hazari & iOS & Free & & & & & & Unknown & [64] \\ SolarTester & Optiekov & Android & Free & & & & & NASA & [65] \\ Scan The Sun & Scientific Mobile & Android & Free & & & & & NASA & [65] \\ \hline Solar Engineering & Green & Android & Free & & & & & & SolarGIS [6] & [67] \\ Calculator & Engineering & Imps & & & & & & & & & \\ SolarMeter \({}^{\dagger}\) Sun Energy & VisTech.projects & iOS & 53.99 & Analysis \& & & & & Measurement & [68] \\ Planetary & LLC & & Simulation & & & & & & & \\ Solar Solar Solar Panel & Vistech.projects & Both & & & & & & & Measurement & [69] \\ Planetary & & & & & & & & & & \\ Solar Panel Simulator & & & & & & & & NASA & [70] \\ PV System 3D & & & & & & & & & & \\ SolarMore & [PERSON] & iOS & 514.99 & & & & & Measurement & [71] \\ SolarT-Solar PV & M. N. N. & Android & Free & & & & NASA and & [72] \\ Systems & & & & & & & & NREL & \\ SolarTester Pro & Optiekov & Android & 58.45 & & & & & NASA or & \\ \hline \hline \end{tabular}  
* Price in USD\({}^{\dagger}\) PVG’s [[https://cc.europas.eu/me/m/pygis/](https://cc.europas.eu/me/m/pygis/)]([https://cc.europas.eu/me/m/pygis/](https://cc.europas.eu/me/m/pygis/)); NOAA: National Oceanographic & Atmospheric Administration for the Japan Meteorological Agency, \({}^{\ddagger}\) SolarGIS; [[https://solarys.com/maps-and-gis-data/overview](https://solarys.com/maps-and-gis-data/overview)]([https://solarys.com/maps-and-gis-data/overview](https://solarys.com/maps-and-gis-data/overview)) (accessed on 27 February 2021).  
\end{table} 
Table 6: Summary of smartphone applications for potential assessment of PV systems.
## 6 Simulation Results and Discussion
#### ROC Curve  
The receiver operating characteristic (ROC) curve is a graphical plot used to show the diagnostic abilities of binary classifiers. The main objective of this paper was to identify the family violence and compare the results of a balanced data set and imbalanced data set. From Figure 12, we can observe that the diagnostic abilities for three ML algorithms are meager when we considered the imbalanced data set. It means that fitting the ML algorithm with the imbalanced data set then decreases the diagnostic ability while increasing the classification error. Although with an imbalanced data set, the RF algorithm is the best performer compared to the other ML algorithms, such as LR and NB.  
After using SMOTE for balancing our imbalanced data. From Figure 13, we can observe that the diagnostic abilities of three ML algorithms increased with balanced data when compared to the imbalanced data. As a result, the classification error decreased. Here, the RF algorithm provided better results for the balanced data set when compared to both LR and NB algorithms.  
Figure 12: The diagnostic result with imbalanced data.
## 3 Results and Discussion
### COD Mass Balance  
Based on Equations (1)-(3), a COD mass balance was used to assess the bioconversion process of organic matter during spring, summer, autumn and winter (Figure 5). Approximately 43.5-52.5% of influent COD was converted to methane (including gaseous methane and dissolved methane). Similar COD conversions were reported for an anaerobic filter treating on-site domestic wastewater [37]. In addition, 10.2-31.0% of influent COD was converted to VFAs, while 25.5-37.3% of influent COD was transformed to biomass, CO\({}_{2}\) and non-biodegradable organic matters. During the spring, about 28.3% of influent COD was converted to gaseous methane and 18.7% of influent COD was accounted for VFAs. With an increase of temperature in summer, more organic matter was converted to gaseous methane (41.1% of influent COD) than VFAs (10.2% of influent COD). In contrast, more VFAs were accumulated in winter. Meanwhile, the results demonstrated that dissolved methane represented about 17.7%, 11.4%, 16.4%, and 23.7% of COD during spring, summer, autumn, and winter, respectively, indicating that more methane dissolved in effluent with a decrease of temperatures. As a potential energy resource, future studies should focus on the recovery of dissolved methane in the AWFR system to avoid the loss of dissolved methane.  
Figure 4: Biogas production in the system during the whole operation.  
Figure 5: COD balance in different seasons.
###### Abstract
Some studies quantified the residuals after excluding the ecological effects of natural factors, such as the effects of human activities on vegetation and climate, and some other studies, such as [PERSON] et al. [16], [PERSON] et al. [17] and [PERSON] et al. [18], used residual analysis to indirectly derive the effects of human activities, none of which can quantify human activities. The concept of human footprint was introduced by [PERSON] et al. [19] in 2002 to reflect the impact of human activities on ecosystems, climate and human health [20]. Existing free global human footprint datasets are mapped based on eight categories of variables: built environment, population density, electrical infrastructure, agricultural land, grazing land, roads, railways and navigable waterways [21; 22]. In this paper, we introduce the Human Footprint dataset to quantitatively study the impact of human activities on vegetation ecological quality. The influence of climate, topography and human activities on vegetation ecology has obvious regional characteristics. The classical Geographically Weighted Regression (GWR) model can reflect this pattern at one scale (same bandwidth), but it ignores the effect of drivers at different scales. Multiscale Geographically Weighted Regression (MGWR) can quantitatively assess the spatial variability of the effects of drivers at different scales. Based on this characteristic, MGWR has been applied in several areas, such as exploring the spatial heterogeneity of PM2.5 forcing factors [23; 24], modeling urban ozone drivers [25], and integrating nighttime lighting with multi-source urban data [26]. However, it has not yet been applied to explore the driving factors of vegetation ecological quality. Therefore, we use MGWR to construct a spatial impact model of climate, topography, and human activity factors on EQI. During the 14 th Five-Year Plan period, Anhui Province is firmly promoting the construction of ecological civilization. As the primary producer of the ecosystem, the evaluation of vegetation quality can be regarded as the core content of ecological quality assessment. In this study, the Ecological Quality Index of Terrestrial Vegetation (EQI) calculated via NPP and FVC was used to reflect the vegetation function and cover status of terrestrial ecosystems in Anhui Province during the growing season (April-October) [27]. Using the GEE platform, the time series of EQI in Anhui Province for the past 20 years were constructed to analyze the spatial and temporal variation characteristics of EQI. Partial correlation analysis, spatial bivariate autocorrelation and MGWR were used to quantitatively evaluate the spatial variability of the effects of natural and anthropogenic activities on EQI. This study aims to provide scientific and reasonable information on the construction of ecological civilization in Anhui Province in terms of climate, topography and human activities.
## 2 Calorimetric Analysis, Aging and Compatibility Studies on Industrial-Grade Fatty Acid Mixture  
The PCM was supplied by the chemical product trading company Altichem(r). This product is an industrial-grade mixture of different fatty acids in the proportions listed in Table 1, mainly stearic acid and palmitic acid.  
Stearic acid (SA) or octadecanoic acid, CH3(CH2)16 COOH, is a fatty acid with interesting chemical characteristics (non-toxic, non-corrosive, MSDS with only one symbol: Xn). It is a white to yellowish-white amorphous waxy solid, with a tallow-like odor and taste, that is very slightly soluble in water but soluble in alcohol, ether and acetone. It is a combustible when exposed to heat or flame. Palmitic acid (PA) or hexadecanoic acid, CH3(CH2)14 COOH, has the same characteristics. PA is naturally present in palm oil and various animals or plants, and widely used in the food industry and soap fabrication. SA is used in a wide range of applications, such as in candles; in chemicals; in dispersants; as a softener in rubber compounds, food packaging and PVC lubricant; a direct food additive; a clarifying agent (wine); a textile auxiliary; an intermediate for pains; an emollient/superfatting agent in cosmetics; a paper sizing ingredient; a masticatory substance in chewing gum; in pharmaceuticals [23]. SA's latent heat of fusion is equal to 200 J/g and its density is 940 kg/m\({}^{3}\), thus, its volume storage capacity is about 52 kWh/m\({}^{3}\)[24]. This value is quite close to that of PA, 51.5 kWh/m\({}^{3}\), with a latent heat of fusion equal to 206 J/g and a density of 900 kg/m\({}^{3}\)[25]. Their melting temperatures are respectively 70 and 63 \({}^{\circ}\)C.  
Although the properties of pure or binary fatty acid are well documented [25], data regarding their industrial-grade use are missing, and a complete experimental characterization of the PCM mixture must be performed. Furthermore, the stability of the key thermo-physical properties over thermal cycling and extended temperature exposure periods must be studied.  
Moreover, as the encapsulation technique has been chosen to ensure the interface between the HTF and the PCM in the TES system, the design of the test bench (Section 3) will include spherical polyolefin capsules filled with the PCM mixture. These capsules, provided by the industrial partner, are called nodules and are made of polyolefin (outer diameter 98 mm, width 2 mm). Thermal characterization, aging and compatibility studies of the industrial-grade SA-PA mixture with theses nodules are thus necessary.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Energy Consumption Analysis  
In this section, we evaluate the energy consumption of ACE and E-ACE.  
The transmission energy \(ET\), for a message of size \(L\) bits through a channel with bandwidth, \(B\) kbps is given as [31]:  
\[ET=P_{t}\times\frac{L}{B} \tag{39}\]  
Similarly, the receiving energy, \(ER\), is defined as:  
\[ER=P_{r}\times\frac{L}{B} \tag{40}\]  
where \(P_{t}\) and \(P_{r}\) are transmit and receive power respectively. Let \(N\) be the total number of nodes in the network and all nodes always have data to transmit. The total transmission energy \(ET_{TX}\) with m relays involved in data transmission becomes,  
\[ET_{TX}=(m+1)\times ET \tag{41}\]  
Here the term \((m+1)\), \(m\) indicates the number of relays involved in retransmission and \(1\) indicate the transmission from source node. In case of ACE, \(m=1,\ 2\). If no retransmission is performed, \(m=0\). Similarly the total reception energy, \(ER_{RX}\) is derived as:  
\[ER_{RX}=(m+1)\times ER \tag{42}\]  
In reception energy, \(1\) indicates the reception at master node.  
Figure 7: Data transmission flow chart for Enhanced-Adaptive Cooperation in Energy (E-ACE).  
The transmission from source to sink takes place in a multi-hop manner. The number of hops can be different for different nodes depending on node's location. Let \(h_{i}\) be the number of hops for data packet of node \(N_{i}\). The energy required for data from node \(N_{i}\) travelling through \(h_{i}\) hops is given as:  
\[ET_{TXi}=N_{i}\times h_{i}\times(m+1)\times ET \tag{43}\]  
where, \(i=1:N\). The total transmission energy of the network becomes:  
\[ET_{TX}=\sum_{i=1}^{N}N_{i}\times h_{i}\times(m+1)\times ET \tag{44}\]  
Similarly the total reception energy, \(ER_{RX}\) is given as:  
\[ET_{RX}=\sum_{i=1}^{N}N_{i}\times h_{i}\times(m+1)\times ER \tag{45}\]  
In ACE and E-ACE, besides data transmission, we have energy spent by control messages. Let \(E_{CTRL}\) be the total energy used in control messages. The total energy consumption for ACE and E-ACE is,  
\[E=ET_{TX}+ER_{RX}+E_{CTRL} \tag{46}\]  
where, \(E_{CTRL}\), with length of control message \(LC\), is defined as:  
\[E_{CTRL}=(\sum_{i=1}^{N}N_{i}\times h_{i})P_{i}\times\frac{LC}{B}+(\sum_{i=1 }^{N}(N_{i}+m)\times h_{i})P_{r}\times\frac{LC}{B} \tag{47}\]
## 2 Results
### Overexpression GmOCS in Transgenic Soybeans Led to Enhanced Resistance to Spodoptera litura  
To better investigate the _GmOCS_ role in anti-herbivore, it was stably transformed into soybean P3 by routine Agrobacteriumium infection. Five transgenic soybean lines were selected for further analysis. However, no obvious phenotypic changes were observed (Figure 5A). RT-qPCR (reverse-transcription quantitative PCR) analysis validated that _GmOCS_ had  
Figure 4: **In vitro and in vivo characterization of GmOCS.** (**A**) In vitro enzymatic analysis of GmOCS using four acyclic prenyl diphosphate substrates. X-axis represented retention time, and the y-axis represented product abundance. The contents of enzymatic products were detailed in Supplementary Table S2. (**B**) Volatile terpen released from _N. benthamiana_ leaves transiently overexpressing _GmOCS_. Agrobacteriumium containing _GmOCS_ in pEAQ-HT backbone was infiltrated into _N. benthamiana_ leaves. The leaves were sampled and analyzed by SPME-GC/MS analysis. _N. benthamiana_ leaves infiltrated by _Agrobacteriumium_ containing empty pEAQ-HT were used as control.  
different expression levels in these lines (Figure 5B). Whereafter, the leaves were harvested and further analyzed by SPME-GC/MS (solid-phase microextraction gas chromatography/mass spectrometry). When compared to the control, the five transgenic lines overexpressing _GmOCS_ released more (_E_)-\(\beta\)-ocimene and \(\alpha\)-farresene (Figures 5C and S5). To further investigate the effects of transgenic soybeans overexpressing _GmOCS_ on herbivores, _S. litura_ was employed in the following dual-choice feeding preference test. Interestingly, more gaps were observed from the control leaves when compared with transgenic leaves, indicating that _S. litura_ was repelled by leaves overexpressing _GmOCS_ (Figure 5D). To quantify the deterrence, the amount of leaves eaten by _S. litura_ was assessed, indicating that _GmOCS_ might be a good candidate gene for repelling soybean herbivores represented by _S. litura_ (Figure 5E).  
Figure 5: **The overexpression of _GmOCS_ in soybean plants led to enhanced resistance against _S. litura_. (**A**) Control and transgenic soybean plants overexpressing _GmOCS_. (**B**) The relative expression levels of _GmOCS_ in transgenic soybean leaves compared with the control. (**C**) Volatile terpen detected in the control and transgenic soybean leaves overexpressing _GmOCS_. (**D**) _S. litura_ attacked wild leaves and transgenic leaves, overexpressing _GmOCS_ in a dual-choice feeding-preference experiment. (**E**) Leaves of wild and _GmOCS_ overexpressed lines eaten by _S. litura_ in dual-choice feeding-preference experiment. Data represented the mean \(\pm\) SD of three biological replicates, and the student’s _t_-test was used to analyze the significance of differences (**_p_ < 0.01) in (**B**,**E**).
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 3 A Genetic Algorithm (GA) for the UA-Flp
### Solution (Chromosome) Representation  
For the proposed GA, the solution or chromosome is represented as a permutation of the facilities \(\{1,2,\ldots,N\}\). For example, randomly generating a chromosome for a UA-FLP with six facilities (\(N=6\)) may produce the following chromosome \(\pi=\{2,3,6,5,1,4\}\).  
In other words, facility 2 is in position 1 (i.e., \(\pi(1)=2\)), facility 3 is in position 2 (i.e., \(\pi(2)=3\)), and so on. That is, facility 2 will be placed on the plant floor first. Next, facility 3 will be placed on the plant floor and so on.  
Figure 1: Optimal layout for small UA-FLP instance.  
In stage 1 mentioned above, a set of chromosomes are generated randomly for the initial population \(P_{0}\). Note, the solution representation (chromosome) does not specify the location (\(x_{i}\), \(y_{i}\), \(l_{i}\), \(w_{i}\)) and orientation (\(o_{i}\)) of each facility \(i\). The BSH presented in [PERSON] and [PERSON] (2016) is used to construct the layout and determine TWD for each chromosome in \(P_{0}\).
# Magnetic Properties of Nanocomposites
## 4 Ceramic Nanocomposites
** This strongly affects both the features of the ceramic matrix and the number, dimension, distribution, and morphology of magnetic metal nanoparticles. In actual fact, different zeolites exhibit different Si/Al ratios, frameworks, and cation locations. It is evident that the Si/Al ratio affects the ceramic matrix, whereas the cation location in the framework of the zeolite affects the features of the TM magnetic nanoparticles as they arise from the migration of the newly formed TM atoms from their original cation sites. 2. **Cation exchange procedure**. Through cation exchange, different TM cations (also more than one contemporaneously) may be introduced in the zeolite framework to a various extent, which affects the magnetic metal(s) nanoparticles. Moreover, through cation exchange as well as alkaline-earth cations (e.g., Ba\({}^{2+}\) and Sr\({}^{2+}\)) may be introduced into the zeolite framework, thus giving rise to ceramic phases, which modify the features of the ceramic matrix [70; 71]. 3. **The temperature and time of the thermal treatment under the reducing atmosphere. ** These affect the extent to which the reduction of TM cation(s) to 0 oxidation number and the thermal collapse of the zeolite framework occur. Moreover, the migration of the newly formed TM atoms and, therefore, the dimension and morphology of the magnetic nanoparticles, are also strongly dependent on these key parameters. It must be noted that this simple and cheap process appears to be strongly related to a peculiarity of zeolites. In actual fact, it is well known that cations are bound to the zeolite framework by ionic bonds weakened by the action of zeolitic water acting as a dielectric medium [72; 73]. The thermal treatments at relatively moderate temperatures (600-850 \({}^{\circ}\)C), under a reducing atmosphere, to which the TM-exchanged zeolites are subjected, are able to reduce the TM cations only on account of such peculiarity. Thus, other different classes of ceramic functional materials do not seem, at least at a first sight, as likely precursors for metal-ceramic nanocomposites. Typical values of static magnetic quantities measured in ceramic nanocomposites are reported in Table 1 (bottom).
## 3 Results
### Results and Analysis of Habitat Quality Assessment  
Resistance factors and other data were input into the InVEST model (Table 7), employing the habitat quality module to measure the study region's habitat quality index, yielding a distribution range between 0 and 0.98. Based on the quantitative results and relevant literature references [52,53], the habitats in the study area were categorized into high-quality (0.76-0.98), medium-quality (0.43-0.75), and low-quality (0-0.42) habitats. The habitat quality analysis diagram is shown in Figure 7. Analysis of habitat quality ratios (Table 10) showed that low-quality habitats comprised 76.30%, medium-quality habitats accounted for 14.19%, and high-quality habitats accounted for the smallest proportion at 9.51%, exhibiting a polarized distribution trend. The trend could be attributed to the slow expansion of land used for construction in recent years and the overabundance of forest cultivation. Large-scale, high-quality habitats were concentrated around the Baiyang Lake wetland, with a significant aggregation in the eastern part of the wetland, resulting in a scattered overall distribution of high-quality habitats.  
Figure 6: MSPA landscape type analysis results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Landscape Type** & **Area/km2** & \
\begin{tabular}{c} **Account for the Proportion of** \\ **Foreground Elements** \\ \end{tabular} &
\begin{tabular}{c} **Account for the Proportion of the** \\ **Study Area** \\ \end{tabular} \\ \hline core area & 432.58 & 70.22 & 24.5 \\ islet & 17.44 & 2.83 & 0.99 \\ perforation & 16.88 & 2.73 & 0.95 \\ edge & 106.30 & 17.26 & 6.02 \\ loop & 4.38 & 0.71 & 0.25 \\ bridge & 11.02 & 1.79 & 0.62 \\ branch & 27.45 & 4.46 & 1.56 \\ Total & 616.05 & 100 & 34.89 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Area and proportion of each landscape type based on MSPA.  
The Baiyang Lake wetland is the largest macrophyte-dominated shallow freshwater wetland in the North China Plain [54]. It not only provides habitat for wildlife to support biodiversity but also interacts with surrounding green spaces to create synergistic effects (Figure 8), offering extensive ecosystem services to the adjacent areas [55]. The wetland ensures benefits for the surrounding environment [56], resulting in a distribution pattern of larger, high-quality habitats along the Baiyang Lake wetland within the Xiongan New Area.