esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Data Preparation
#### 3.3.1 Environmental Factors  
The distances from road and main road, river network, residential areas, agriculture areas, and refugee camps merit special attention Accessibility is key to locating a hospital, so, the decision makers are usually interested in locations close to roads, particularly main roads; the nearer to the road a hospital is sited, the better for ease of service delivery and facility maintenance. Beyond the advantages of proximity to a road is also the concern about noise pollution from motor vehicles and/or a railway line. Therefore, there is always a compromise between how far or close to roads the ideal location should be.  
The distance from a river is considered another related conditioning factor. As water runoff increases and drains into streams/rivers, there is a risk of floods in areas abutting the river, particularly at a lower elevation and slope. In this respect, the distance of a potential site from streams/rivers is an important criterion.  
Healthcare facilities are sited to serve the people; thus, residential areas are favorable targets for locating a healthcare facility. The nearer a hospital is to a residential area, the better. The residential factor is normally examined in terms of the population data and distribution, which is converted to a thematic map layer in raster format. The better the information provided, the better the resulting factor contributes to the overall accuracy.  
Food security is important to the well-being of any society. So, policies exist all over the world to prohibit the location of facilities on land designated as agricultural lands. For this, a specified distance away from agricultural areas is imposed as a condition to evaluate whether a candidate piece of land that is suitable for building a hospital is outside of agricultural land.  
Political crisis and civil unrest across the world gave birth to a completely new settlement called a refugee camp. Such refugee camps are an important factor to be considered in the site selection for suitable space to construct a hospital and other healthcare facilities. The reason being that refugee camps are always overcrowded, often unorganized, and without the provision for basic infrastructure. This is the case in the Gaza Strip, which has been in crises for decades.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 1 Introduction
[28] showed that drought had short-term lagged impact on NEP in eastern humid and warm regions of China and a longer lagged effect on NEP in western cold and arid regions. A central challenge for assessing the sensitivity of global terrestrial carbon fluxes to climate variability is that the dominant factors limiting terrestrial carbon sequestration have significant spatial variations [3; 39; 40; 41; 42]. Generally, in some humid regions and high latitudes, terrestrial carbon sequestration is dominantly regulated by solar radiation and temperature. The increase in precipitation is normally associated with a decrease in solar radiation [43] and consequently carbon sequestration might decrease [44]. A recent study suggested that ecosystem sensitivity to drought peaked over semi-arid ecosystems [45]. Meanwhile, [PERSON] et al. [19] reported that for aboveground net primary production, the highest sensitivity to interannual variations in rainfall was found at the driest sites, and that the wettest sites displayed the lowest sensitivity. The sensitivity of productivity to drought was found to be inversely related to mean annual precipitation [46]. Further studies are needed for deepening our insight into how global terrestrial carbon fluxes respond to drought. Previous research has shown that drought may cause a decline in GPP without a change in vegetation greenness [47]. The immediate and lagged effects of drought on carbon fluxes and their sensitivities in different regions of the globe are still unclear. Therefore, this study aimed to quantify the response of global terrestrial carbon fluxes to drought over the time period from 1981 to 2016 by addressing three objectives: (1) to assess the maximum correlations between global terrestrial carbon fluxes and droughs; (2) to analyze the lagged effects of drought on global terrestrial carbon fluxes; and (3) to determine the changes in the sensitivity of carbon fluxes to drought in different regions of the globe. These objectives were achieved using global terrestrial carbon fluxes simulated using the BEPS model. Drought was assessed using the standardized precipitation evapotranspiration index (SPEI) [48]. SPEI is an extension of the widely used standardized precipitation index (SPI) [49]. It was designed to account for both precipitation and potential evapotranspirationration (PET) in determining drought. Unlike SPI, SPEI includes the effects of increased temperature on atmospheric water demand.
## 3 Numerical Analysis
### Dynamic Responses to Piston Slaps  
In the case of the piston side slaps being considered alone, the dynamic response of the liner measured from the same sampling point shows more complex and abundant patterns, as seen in Figure 13a, under the operating condition of 1800 rpm and 40 Nm. In Figure 13a, it is possible to identify six separate and transient responses to highs and troughs of piston side-thrust force, and these are marked Impact A to Impact F. For the most part, these responses are in the frequency band relating to the first three modes, and that is particularly so for responses to slaps away from combustion.  
Three other dynamic events are also observable in Figure 13a, and these are marked as Impact G, H and I. These events arise from secondary impacts caused by sustained high-amplitude side-thrust force, rather than by any particular force peak or trough, indicating that a sustained high-amplitude side-thrust force might cause additional impacts.  
The frequency range between 500 and 3000 Hz has been chosen by earlier studies [9; 10; 21; 22; 23] to identify and analyze the piston slap-induced events. As Figure 13 shows, there is probably some truth to this, which may stem from mode characteristics inherent in liner structures. As this study shows, piston slap-induced dynamic responses occur for the most part in the 600-3500 Hz range, near the first four modes' frequency band, suggesting that the way cylinder liners respond dynamically may depend on their structural modes to a high degree.  
In the case of the piston lateral slap being considered alone, the dynamic responses of the liner burning biodiesel show a similar pattern in STFT results to that of diesel, under the same operating condition of 1800 rpm and 40 Nm. As can be seen in Figure 14a, the overall energy distribution of slap-induced responses fueled with biodiesel is significantly reduced compared to that of diesel in Figure 13a. This shows that the piston movement driven by the smoother combustion excites fewer mode components in low frequency ranges.  
It should be noted that distinguishable differences in the local response features between the two fuels can be clearly observed around the combustion TDC and exhaust TDC, marked with red and blue rectangles in Figure 14. The STFT result of biodiesel shows much more localized dynamic responses about the exhaust TDC compared with those of diesel. Conversely, the diesel presents more intense dynamic responses around the combustion TDC than the biodiesel. These complex and even reverse trends of local features can hardly be interpreted using the differences in the physical and chemical  
Figure 13: Short-time Fourier transform (STFT) of responses to piston slaps under 40 and 1800 Nm; (**a**) STFT result of predicted displacement response; (**b**) Piston side-thrust force, predicted displacement and acceleration responses.  
properties of fuel types. Therefore, it is necessary to perform some contrastive and diagnostic analyses on these features under different operating conditions to understand the generating mechanisms behind them in depth.
## 4 Secondary Metabolites
### Analytical Methods
#### 4.2.1 Spectrophotometric Assays  
The extraction of secondary metabolites is followed by their qualitative and/or quantitative analysis. Although the separation techniques, coupled with different detectors,play a key role in the analysis of bioactive compounds, a number of spectrophotometric methods have been developed, in order to verify their presence or to quantify them in plant-based foods [87]. These assays are based on different principles and are mainly used to determine polyphenols and flavonoids. For the rough quantification of polyphenols in apple fruits extract, colorimetric methods are widely used, due to their simplicity and high sensitivity. These include the Folin-Ciocalteu methods, used for the analysis of total polyphenols content (TPC), and the aluminum chloride (AlCl3) assay for flavonoids determination [5; 10; 88; 89]. The Folin-Ciocalteu assay is based on an electron-transfer reaction between the Folin-Ciocalteu reagent, which is a mixture of phosphomolybic and phosphotungstic acid, with the polyphenol-based extract. In presence of phenols, under basic conditions, phosphomolybic and phosphotungstic acid reduce themselves. Their reduced forms confer to the sample a blue color (\(\lambda_{\text{max}}=495\) nm). By evaluating the absorbance of the extract at 765 nm, it is possible to determine the total phenols concentration [10]. [PERSON] et al. [10] used this assay in order to quantify the TPC in Fuji, Galaxy and other traditional Brazilian apples varieties. A great variation in terms of total phenolic content was observed by the authors among the apple cultivars (105.4-269.7 mg of gallic acid equivalents per 100 g of fresh matter). One of the main limits related to the quantification of total polyphenols through spectrophotometric assay, is due to interfering compounds, such as sugars, amino acids and ascorbic acid, which absorb in the same polyphenols region [90]. To avoid this problem, [PERSON] et al. [88] made a cleanup procedure on a C-18 cartridge, prior TPC analysis. The average content of total polyphenols in the apple, evaluated by the Folin-Ciocalteu assay, was 110.2 mg/100 g of fresh fruit with significant differences depending on the apple variety. Instead, the principle involved in AlCl3 colorimetric method is that AlCl3 forms acid stable complexes with the C-4 keto groups and either the C-3 or C-5 hydroxyl group of flavones and flavonoid, which show maximum of absorbance at 415 nm. The total flavonoid content of _Malus_ fruits is generally, around 110-120 mg of catechin equivalents per 100 g of fresh matter [89]. Although these spectrophotometric assays are often used as preliminary test to evaluate the content of a specific metabolite class of apples, they are not selective and not useful for a sensitive quantification of individual compounds. Therefore, in the last years, more sensitive and selective methods, based on chromatographic techniques, have been continuously developed.
## 3 Shaft Furnace System Modeling
### Mass Balance Model  
According to the model of HDRSF in Figure 1, it can be seen that the incoming materials of the shaft furnace include iron ore, reducing gas, and low-temperature cooling gas, while the discharged materials are DRI, top gas, and high-temperature cooling gas. According to the law of conservation of mass, the mass of the materials entering the furnace should be equal to the mass of the materials leaving the furnace. Hence, the mass balance model of the HDRSF is shown in Equation (9).  
\[M_{iron\ ore}+M_{reducing\ gas}+M_{cooling\ gas\ in}=M_{top\ gas}+M_{DRI}+M_{ cooling\ gas\ out} \tag{9}\]  
In Equation (9), the mass of DRI depends on the actual production need. According to assumption (5), the mass of discharged high-temperature cooling gas should be the same as the mass of incoming low-temperature cooling gas, and the actual mass of cooling gas needs to be determined according to the DRI yield. In addition to the above three items, the other items in Equation (9) can be calculated by Equations (10)-(12).  
\[M_{iron\ ore}=\frac{M_{DRI}\times W_{Fe}}{TFe\times\lambda_{Fe}} \tag{10}\]  
\[M_{reducing\ gas}=V_{reducing\ gas}\times(\rho_{H_{2}}x_{H_{2}}+\rho_{CO}x_{ CO}) \tag{11}\]  
\[M_{top\ gas}=V_{top\ gas}\times(\rho_{H_{2}}X_{H_{2}}+\rho_{H_{2}O}X_{H_{2}O}+ \rho_{CO}X_{CO}+\rho_{CO_{2}}X_{CO_{2}}) \tag{12}\]  
In Equations (10)-(12), it can be seen that the mass of iron ore is related to its total iron content, the yield and metallization rate of DRI, and the mass percentage of Fe in DRI. The mass of the reducing gas is related to the volume of the reducing gas and the ratio of H\({}_{2}\) and CO in the reducing gas. The top gas includes the surplus reducing gas that is not involved in the reaction, the H\({}_{2}\)O and CO\({}_{2}\) generated by the reduction reaction, and the vapor of water carried in the iron ore. According to the principle of conservation of elements, the extra oxygen in the top gas is equal to the oxygen lost in the iron ore. Based on the mass of oxygen lost in iron ore, the proportion of each component in the top gas in Equation (12) can be calculated by Equations (13)-(17).  
\[X_{H_{2}O}=\frac{x_{H_{2}}O_{loss}/16+M_{iron\ ore}\times w_{H_{2}O}/18}{V_{ top\ gas}/22.4} \tag{13}\]  
\[X_{CO_{2}}=\frac{x_{CO}O_{loss}/16}{V_{top\ gas}/22.4} \tag{14}\]  
\[X_{H_{2}}=\frac{V_{reducing\ gas}\times x_{H_{2}}/22.4-x_{H_{2}}O_{loss}/16}{ V_{top\ gas}/22.4} \tag{15}\]  
\[X_{CO_{2}}=\frac{V_{reducing\ gas}\times x_{CO_{2}}/22.4-x_{CO_{2}}O_{loss}/16}{ V_{top\ gas}/22.4} \tag{16}\]  
The mass of oxygen lost of iron ore in the reduction reaction is the difference between Fe\({}_{2}\)O\({}_{3}\) mass in iron ore and Fe mass in DRI and can be calculated by Equation (17).  
\[O_{loss}=M_{iron\ ore}\times\frac{w_{Fe_{2}O_{3}}}{160}\times 16-M_{DRI} \times W_{Fe}/56\times 16 \tag{17}\]
## 1 Introduction
It utilizes multiple-layer architectures or deep architectures to extract inherent features in data from the lowest level to the highest level, and they can discover huge amounts of structure in the data [38]. Deep learning is derived from the study of artificial neural networks. The most common neural network models include multilayer perceptron (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), etc. [39]. For time series, RNN is often employed to characterize the association of hidden states and capture the data characteristics of the entire sequence. Nevertheless, simple RNN has long-term dependence problems and cannot effectively utilize long-interval historical information. Therefore, long short-term memory (LSTM) network has emerged to unravel the problem of gradient disappearance, which has been used for stock price forecasting [40], air quality forecasting, sea surface temperature forecasting [41], flight passenger number forecasting, and speech recognition [42]. The results illustrated that the model had achieved excellent performance. In this study, we deployed an IoT-based noise monitoring system to acquire the urban environmental noise, and proposed an LSTM network to predict the noise at different time intervals. The performance of the model was compared with three classic predictive models--random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM) on the same dataset. This study also explored the impact of monitoring point location on prediction results and policy recommendations for environmental noise management.
# A2C: Attention-Augmented Contrastive Learning for State Representation Extraction
## 3 Methodology
### State Representation Learning
#### 3.1.1 Contrastive Learning  
As shown in Figure 1a, there are two encoders used to learn the efficient state representation from images. The key encoder \(\theta_{k}\) is responsible for generating a key vector \(k\) that can summarize the information of an image, while the query encoder \(\theta_{q}\) generates a vector \(q\) to query its similarity between the key vector. If the query vector can successfully obtain the similarity, it means that the encoders are capable of extracting the efficient similarity information from images. To train the contrastive learning model, firstly, there are thousands of images sampled from the environment. Each of them is augmented twice to form an image pair and stored in an image buffer, which is the source of training data. Secondly, at every epoch, a batch of image pairs is sampled from the buffer. The query and the key encoders respectively take the images from the same pairs as input. The key vector obtained here is defined as positive sample \(k_{+}\). At every epoch, the key vectors are pushed into a queue to provide a large number of negative samples, which is helpful in identifying the similarity information. According to [17], each query vector will be queried with one positive example and all the negative examples. The training target of contrastive learning is to maximize the similarity between the query vector and the positive sample, as well as minimize the negative samples. Therefore, the loss function \(\mathcal{L}\) is defined as follows:  
\[\mathcal{L}=-\log\frac{\exp\left(q\cdot k_{+}\right)}{\sum_{i=0}^{K}\exp\left( q\cdot k_{i}\right)} \tag{1}\]  
where \(K-1\) is the capacity of the queue which contains all the negative samples. Then, the loss is backpropagated to update the parameters of the encoders. Note that the gradient is only propagated along the query encoder, and the key encoder is updated with \(\gamma\theta_{k}+(1-\gamma)\theta_{q}\) where \(\gamma\) is a constant [17].
### Some Studies about Differentiation and Standardization  
Various educators and researchers have been interested in conducting studies on differentiated instruction, while others have been attracted to standardized assessment. The studies presented here exemplify the academic community's interest in both techniques and their effectiveness in various contexts.  
For example, in the Republic of Cyprus, a study was done to assess differentiated instruction's impact on students' learning in classrooms of students with various skills [26]. The results indicated that the students showed more learning progress in the classes where differentiated instruction was conducted systematically. Therefore, according to this research, this methodology promotes equity and optimizes education quality and effectiveness.  
On the other hand, [PERSON], [PERSON], and [PERSON] [27] conducted research in Germany to evaluate differentiated instruction effects on students' reading comprehension. The results showed that considering each student's particular needs to develop their reading competency improved only their reading speed.  
In the Netherlands, research was carried out to learn students' particularities that teachers considered when they applied differentiated instruction [28]. The results showed that the teachers prioritized students' backgrounds over their learning styles or their interests when designing and implementing study lessons under the differentiated instruction method.  
These studies are evidence of the importance being given to the differentiated instruction method. There have been studies conducted that show the importance of standardized assessment as well. The standardized test PISA (Program for International Student Assessment) has been used as a framework for various studies. In Spain, [PERSON] and [PERSON] [29] conducted research to correlate students' performance on the PISA with the teachers' characteristics and practices. They concluded that traditional teaching methods positively influence students' performance in mathematics, while dynamic and innovative learning strategies negatively impact the results.  
One research study conducted in England [30] uses the PISA results to validate whether students' performance in science improves when using the Research-based Learning model, i.e., when students are allowed to conduct their own experiments, and the teacher offers little guidance. The results showed that this method does not favor the PISA test results; on the contrary, when more guided teaching is used, the student performance improves on this standardized test.  
Like the study conducted in Colombia, in Malaysia, research was done where PISA was used to compare and study the assessment score gaps between South Korea, Singapore, and Malaysia [31], the latter having the lowest test scores. Using Blinder-Oaxaca Decomposition (an outcomes decomposition method), the authors concluded that, despite socio-economic and school factors reasons, there were unknown and unexplainable causes why the assessment scores in Malaysia were lower than in the other two countries.
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 3 Model Construction
### Health Index Degradation  
The C-MAPSS dataset defines the remaining useful life of a turbofan engine as the number of remaining operating cycles until a fault occurs. Current research primarily centers on predicting the remaining operating cycles of turbofan engines [6; 7; 8; 9; 34; 35; 36]. However, the remaining operating cycles are influenced by various external and internal factors, resulting in significant errors and a low coefficient of determination between the predicted and actual values. Most prediction modeling studies that focus on the number of remaining operating cycles struggle to capture the degradation of an engine with a substantial RUL greater than 100 cycles.  
This study differs from traditional \(RUL\) prediction by utilizing a remaining health index (\(RHI\)) evaluation criterion for turbofan engines. The \(RHI\) is constructed based on \(RUL\), and the health of each brand-new engine is initially recorded as '1'. The RHI decreases by a certain amount for each operating cycle until the engine stops due to a malfunction, at which point, the \(RHI\) becomes '0' [37]. In simpler terms, the \(RHI\) is the ratio between the current number of operating cycles remaining for the engine and the maximum number of operating cycles for this engine (\(Max_{c}\)), as calculated by Equation (5).  
\[RHI=\frac{RUL}{Max_{c}} \tag{5}\]  
Figure 3: Flowchart of ESO-BP model. The temperature (\(Temp\)) and food quantity (\(Q\)) is defined in Equations (1) and (2).  
Figure 4 illustrates a visualization that compares the \(RHI\) degradation function with existing segmented \(RUL\) degradation functions.