esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 3 Results
### Characterization of Drained Graywater
#### 3.1.2 Characterization of Drained Graywater
\begin{table}
\begin{tabular}{l l c c c c c} \hline \multirow{2}{*}{**U**} & \multicolumn{6}{c}{**Treatment**} \\ \cline{3-6}  & & **RGW** & **GWT1** & **GWT2** & **GWT3** & **Ratio q** \\ \hline \multirow{2}{*}{pH} & M & 7.69 \(\pm\) 0.53 a & 7.73 \(\pm\) 0.59 a & 7.92 \(\pm\) 0.49 a & 7.94 \(\pm\) 0.29 a & \multirow{2}{*}{1.01} \\ \cline{2-6}  & C & 7.69 & 7.77 & 7.86 & 7.94 & \\ \hline \multirow{2}{*}{EC} & M & 2.41 \(\pm\) 0.45 a & 2.85 \(\pm\) 0.46 b & 3.43 \(\pm\) 0.39 c & 3.78 \(\pm\) 0.32 c & \multirow{2}{*}{1.16} \\ \cline{2-6}  & C & 2.41 & 2.80 & 3.26 & 3.79 & \\ \hline \multirow{2}{*}{TDS} & M & 1.40 \(\pm\) 0.45 a & 2.39 \(\pm\) 0.46 b & 2.6 \(\pm\) 0.39 c & 2.97 \(\pm\) 0.32 c & \multirow{2}{*}{1.31} \\ \cline{2-6}  & C & 1.4 & 1.84 & 2.41 & 3.16 & \\ \hline \multirow{2}{*}{Cl\({}^{-}\)} & M & 11.79 \(\pm\) 2.46 a & 14.9 \(\pm\) 2.65 b & 17.52 \(\pm\) 2.90 bc & 19.8 \(\pm\) 2.75 c & \multirow{2}{*}{1.19} \\ \cline{2-6}  & C & 11.79 & 14.03 & 16.69 & 19.86 & \\ \hline \multirow{2}{*}{SO\({}_{4}\)\({}^{2-}\)} & M & 4.65 \(\pm\) 2.12 a & 6.18 \(\pm\) 2.24 ab & 7.8 \(\pm\) 2.19 bc & 8.73 \(\pm\) 2.03 c & \multirow{2}{*}{1.24} \\ \cline{2-6}  & C & 4.65 & 5.75 & 7.11 & 8.8 & \\ \hline \multirow{2}{*}{HCO\({}_{3}\)\({}^{-}\)} & M & 5.93 \(\pm\) 2.42 a & 6.89 \(\pm\) 2.12 ab & 8.8 \(\pm\) 1.87 bc & 10.25 \(\pm\) 1.85 c & \multirow{2}{*}{1.20} \\ \cline{2-6}  & C & 5.93 & 7.13 & 8.56 & 10.29 & \\ \hline \multirow{2}{*}{Na\({}^{+}\)} & M & 13.18 \(\pm\) 3.97 a & 15.23 \(\pm\) 4.31 ab & 18.6 \(\pm\) 3.98 bc & 21.88 \(\pm\) 3.30 c & \multirow{2}{*}{1.18} \\ \cline{2-6}  & C & 13.18 & 15.61 & 18.49 & 21.9 & \\ \hline \multirow{2}{*}{Ca\({}^{2+}\)} & M & 5.96 \(\pm\) 1.34 a & 7.45 \(\pm\) 1.39 b & 9.14 \(\pm\) 1.76 bc & 10.03 \(\pm\) 1.43 c & \multirow{2}{*}{1.19} \\ \cline{2-6}  & C & 5.96 & 7.09 & 8.44 & 10.04 & \\ \hline \multirow{2}{*}{Mg\({}^{2+}\)} & M & 5.42 \(\pm\) 1.93 a & 6.91 \(\pm\) 2.12 ab & 7.75 \(\pm\) 1.82 bc & 9.38 \(\pm\) 2.25 c & \multirow{2}{*}{1.20} \\ \cline{2-6}  & C & 5.42 & 6.52 & 7.83 & 9.42 & \\ \hline \multirow{2}{*}{K\({}^{+}\)} & M & 0.67 \(\pm\) 0.33 a & 0.63 \(\pm\) 0.26 a & 0.92 \(\pm\) 0.39 ab & 1.04 \(\pm\) 0.31 b & \multirow{2}{*}{1.18} \\ \cline{2-6}  & C & 0.67 & 0.79 & 0.93 & 1.09 & \\ \hline \multirow{2}{*}{SAR} & M & 5.6 \(\pm\) 1.8 a & 5.7 \(\pm\) 1.6 a & 6.4 \(\pm\) 1.3 a & 7.1 \(\pm\) 1.3 a & \multirow{2}{*}{1.08} \\ \cline{2-6}  & C & 5.6 & 6.1 & 6.6 & 7.1 & \\ \hline \end{tabular}
\end{table} 
Table 2: Variation in physicochemical parameters based on treatment. C was calculated and M was measured.
## 5 Discussion  
The results show that the performance of FPHFA is best among all models tested for short-and long-time PM\({}_{2.5}\) forecasting. In comparison to other hybrid deep learning models and traditional deep learning models, the deep learning framework based on the attention mechanism becomes a more useful tool for handling spatiotemporal data.  
In terms of the temporal dimension, there was a significant seasonal variation in PM\({}_{2.5}\) concentrations, which show a declining sequence of winter, spring, autumn, and summer, with a U-shaped change on both seasonal and monthly scales. From the spatial dimension,  
Figure 12: Degree of fit between the observed and predicted PM\({}_{2.5}\) value throughout the course of a month (16 December 2016–15 January 2017) with different models (LSTM, GRU, CNN-LSTM, DAQFF, and FPHFA) for prediction horizons of 24 h and 96 h, in experiments on the Beijing dataset. (**a1**) LSTM model for prediction 24 h ahead; (**a2**) LSTM model for prediction 96 h ahead; (**b1**) GRU model for prediction 24 h ahead; (**b2**) GRU model for prediction 96 h ahead; (**c1**) CNN-LSTM model for prediction 24 h ahead; (**c2**) CNN-LSTM model for prediction 96 h ahead; (**d1**) DAQFF model for prediction 24 h ahead; (**d2**) DAQFF model for prediction 96 h ahead; (**e1**) FPHFA model for prediction 24 h ahead; (**e2**) FPHFA model for prediction 96 h ahead.  
PM\({}_{2.5}\) concentration was higher in the southeastern part of Beijing, lower in the northwestern part of the city, and gradually declined from the heart of the city towards the countryside.  
As a result of the experimental findings, the study shows that indicates that the FPHFA has the best prediction performance compared to other models for both short-term and long-term PM\({}_{2.5}\) prediction. Compared to other models, FPHFA is more accurate in predicting the peaks and valleys of PM\({}_{2.5}\) concentration at various time steps. In long-term PM\({}_{2.5}\) concentration prediction, FPHFA still outperforms other models despite sudden changes in pollutant concentration. Meanwhile, FPHFA can predict high PM\({}_{2.5}\) concentrations with accuracy, enabling a high consistency between predicted and observed values. After the experimental comparison with the DAQFF model, the outcomes revealed that FPHFA can learn long-term time-dependent features in PM\({}_{2.5}\) concentration data. Our proposed model performs so well due to (1) multi-channel 1D CNNs fully extracting the spatial features between sites and the spatiotemporal features between historical data; (2) Bi LSTM fully extracting the changing features of pollutant data by using the information features in both directions; (3) the attention mechanism according to assigning different weights to different moments of information enhanchethe role of important moment information and optimize the prediction results. In a word, the FPHFA model represents a helpful contribution to the prevention and management of air pollution.
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 4 Antimicrobial Mechanism of D-Limonene
### Antibacterial and Antifungal Mechanism of D-Limonene
#### 4.1.2 Effects on Lipids, Proteins, and Nucleic Acids  
Studies at the subcellular level, including lipids, proteins, and nucleic acids, similarly revealed the antimicrobial activity of D-limonene. A study shows that D-limonene prevents lipid molecules from being tightly packed and alters the properties of the condensed phase, resulting in smaller and fewer structural domains, which causes membrane fluidization in a bacterial model [111] and, in another, the normal modification of nascent proteins by the endoplasmic reticulum of _C. albicans_ was affected by 20 uL/mL of D-limonene treatment [118]. Notably, SDS-PAGE results showed a reduction in one plasmid band in D-limonene-treated _E. coli_ compared to the control group, presumably due to a change in the _E. coli_ DNA helix conformation, which may affect the stability and subsequent degradation susceptibility of _E. coli_[70].
## 3 Results and Discussion
### Preliminary Experiment for Determining the Optimum Composition Ratio of Layer Chip Batter
The following was the result of measuring the changes in puffing ratio and brittleness of the laver chip according to the additional amount of soybean oil (2\(\sim\)6 g%) to reduce the physical strength (brittleness) and process laver chip softer. The results were shown in Figure 2C. The brittleness of the laver chip significantly decreased with increasing the additional amount of soybean oil (\(p<0.05\)). However, there was no significant difference in brittleness (5.16-5.79 N) when the additional amount of soybean oil was 4 g or more. In the case of the puffing ratio, there was no significant difference between all samples, ranging from 111.83 to 113.97%. In conclusion, the addition of 4 g of soybean oil resulted in low brittleness and a high puffing ratio. [PERSON] and [PERSON] [36] found that the brittleness of Chamchwi (_Aster scaber_) snacks decreased with an increasing amount of soybean oil, but the oiliness also increased, leading to a negative sensory impact. Next, the changes in brittleness and puffing ratio of the laver chip according to the additional amount of corn syrup (4-12 g%) for flavoring the laver chip were shown in Figure 2D. Corn syrup is known to have a lower processing cost and a sweeter taste than those of refined sugar [37]. Therefore, the appropriate addition range of the corn syrup was determined based on sensory evaluation. The brittleness of the laver chip increased with increasing amounts of corn syrup, while the puffing ratio did not show a significant trend within the range of 111.68 to 113.89 N (\(p<0.05\)). In the case of adding 8 g of corn syrup, the brittleness was less than 7 N. In the sensory domain, the sweetness was evaluated as high when the additional amount of corn syrup was 8 g or more. Therefore, the additional amount of corn syrup in this experiment was selected as 8 g.  
To summarize the above results, the batter composition for processing the laver chip pellets was composed of 20% of laver, 20% of surimi, and 60% of rice flour. In addition, 3 g of GDL, 1.5 g of NaHCO\({}_{3}\), 4 g of soybean oil, and 8 g of corn syrup were added as levening agents and sweetener. In addition to the major components, 1 g of salt and 0.01 g of saccharin were added, and 140 mL of distilled water was used to process the batter for the laver chip.
# Superior Mechanical Behavior and Fretting Wear Resistance of 3D-Printed Inconel 625 Superalloy
## 3 Results and Discussion
### Tribological Tests
Such microstructural refinement can result in a further hardened surface and thus leading to the enhancement of the wear-resistance. In order to get reliable chemical and structural information from the wear tracks of AM-processed Inconel 625, high-resolution XPS spectra obtained on the tested roller specimens are shown in Figure 9. All the spectra were first calibrated from the C 1s peak at binding energy of 284.4 eV from the NISTXPS database. Additional smoothening was done by the Savitzky-Golay method. The background of the spectra was then subtracted followed by fitting on multiple peaks using the Voigt function. Considering the material transfer from the counterpart (42 CrMo4 stainless steel), the Fe 2\(p\) core level spectrum of the tribo-layer is shown in Figure 9a, which could be deconvoluted into five components. The peak \begin{table}
\begin{tabular}{c c c} \hline
**Normal Load (N)** & **Friction Coefficient** & **Dissipation Energy (J)** \\ \hline
**40** & 0.39 & 47.44 \(\times\) 10\({}^{4}\) \\
**62** & 0.42 & 62.50 \(\times\) 10\({}^{4}\) \\
**84** & 0.46 & 92.74 \(\times\) 10\({}^{4}\) \\
**106** & 0.50 & 127.20 \(\times\) 10\({}^{4}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Dissipated energy with the increasing normal load. at 712.2 eV [67] corresponds to Fe\({}_{3}\)O\({}_{4}\) which is formed through oxidation during the fretting wear, while the peak at 713.3 eV corresponded to 2\(p_{3/2}\) of Fe\({}^{3+}\) oxidation state [68]. The Fe 2\(p_{3/2}\) peak at 717.3 eV suggests the existence of Fe\({}^{2+}\) and Fe\({}^{3+}\) in wear tracks [69]. The peak at 724.0 eV [66] is identified as Fe\({}_{2}\)O\({}_{3}\), while 732.5 eV is Fe\({}_{3}\)O\({}_{4}\)[21]. The existence of both Fe\({}_{2}\)O\({}_{3}\) and Fe\({}_{3}\)O\({}_{4}\) in the tribo-layer confirms that the observed material transfer with tribo-oxidation reaction plays an important role in the formation of the tribo-layer. Figure 9b shows the Cr 2\(p\) core level spectra for the wear track of AM-processed Inconel 625. The peak at 576.1 eV [70] is identified as Cr\({}_{2}\)O\({}_{3}\), while the one at 585.7 eV is identified as Cr\({}^{3+}\) 2\(p_{1/2}\). The Mn 2\(p\) spectra of the wear track are shown in Figure 9c. A peak position is obtained at 641.7 eV [71] which corresponds to the oxidation state of Mn\({}_{2}\)O\({}_{3}\). The aforementioned results demonstrate that the well adhering tribo-layer is mainly composed of mixed oxides. Such layer is normally characterized by a nano-scale structure. [PERSON] and co-workers [72] reported the formation of similar metal oxide nano-particles on the wear track and found that a mild wear regime could be established for those oxides with the higher diffusion coefficients and observed coherent tribo-films. Therefore, the high wear-resistance observed on the AM-processed Inconel 625 shows strong dependence on the good mechanical properties including superior tensile and yield strengths, high hardness, as well as the nature of the tribo-layer formed during fretting wear.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Event-Based Sleep Analysis
#### 6.3.2 Sleep Event Pattern Extraction  
With each sleep event represented in the 12-dimensional feature space, we adopt unsupervised algorithms to cluster events into groups of similar behaviors. The resulting clusters demonstrate sleep behavior patterns of the target user.  
DBSCAN [47], a density-based, non-parametric clustering method, groups regions of densely connected samples (within a neighborhood of size \(\epsilon\)) into clusters. To allow varying densities across clusters, HDBSCAN [48; 49] conducts a hierarchical clustering scheme, where varying neighborhood distance thresholds are naturally selected. Specifically, selecting the most stable branches of the hierarchy allows the tree to be cut at varying heights, thus resulting in clusters of different density. Additionally, this algorithm does not require prior knowledge about the number of clusters.  
We conduct HDBSCAN on sleep events extracted from each user. Figure 4 visualizes the centers (median feature of all samples per cluster) of the resulting two clusters, extracted from the user shown in Figure 3. The first cluster represents the night sleep pattern with the sleep onset at around 10 p.m. and the getting up time at around 8 a.m., with some toss and turns, short awakenings, and toilet breaks. The second cluster illustrates a day-time nap pattern. The user typically takes a short nap in the early afternoon, with few toss and turns. Note, for visualization purposes we use the original time of day to represent the start/end time instead of the two-element sinusoidal cyclic representation. Figure 5 maps clustering results of the user on a 2-D t-SNE [50] space. We can see that the extracted sleep patterns align well with the densely distributed groups formed in the t-SNE space.
## 3 Methodology  
### Conceptual Framework  
The system design conceptual framework for this study consists of three main parts, namely, input data, process, and output data, as illustrated in Figure 1. The input data include extraneous variables, which represent the external factors that affect the sorting algorithm, the item timestamps during the user's shopping trips, and the unique behavioral patterns based on the item sequences from the timestamps of different shoppers. Meanwhile, the process stage involves machine learning analysis, leveraging the timestamps and unique behavioral patterns. It analyzes the data to identify the patterns, sequences, and relationships between grocery items.  
The proximity-driven sorting algorithm utilizes the patterns and sequences learned from the first step, then sorts the items according to the determined optimal order. Additionally, the dynamic sorting creates an adaptable design with the real-time data considering the factors such as first item selected, i.e., availability, and store dynamics. Lastly, the output data are the optimized and sorted list of grocery items designed to increase the shopper efficiency, thus reducing the amount of time spent in the supermarket.  
This study employs a comparative experimental research design to analyze the performance of the grocery shopping navigation that utilizes a sorted shopping list with agglomerative clustering, affinity propagation clustering, and a normal or unsorted shopping list. This allows for determination of the specific elements or mechanisms behind each algorithm that give it an edge over other algorithms, and to determine if it improves the performance of a shopper in terms of their dwell time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Key Contributions** & **Optimization** & **ML-Based Sorting** & **Simulation Environment** & **Dynamic Proximity** \\ \hline \multirow{4}{*}{[PERSON] et al. [32]} & Intelligent mechatronic & & & & \\  & system with shelf attraction & & & & \\ \cline{1-1}  & forecasting for indoor & No & No & Yes & No \\ \cline{1-1}  & navigation assistance & & & & \\ \cline{1-1}  & in retail environments & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Conceptual framework. _The system design provides the optimal grocery shopping list based on the proximity of items, reducing the dwell time of grocery shoppers._The method consists of one control group and one experimental group. The former is simulated without the supervision of software-based behavioral control mechanisms, while the latter is exposed to sorting algorithms using agglomerative clustering and affinity propagation clustering.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 2 Problem Description
### Problem Assumption  
The mathematical formulation of agile-satellite mission planning in actual satellite remote sensing scenarios is highly complex due to the flexibility of satellite maneuvering, the diversity of satellite loads, and the complexity of remote sensing tasks. In this study, we focus solely on the task decision component of agile-satellite mission planning. To make this feasible, we make some reasonable simplifications and assumptions based on actual engineering backgrounds [34]:Each task can only be executed once, and the profit can be obtained when the task is scheduled.
* The execution time windows of any two scheduled tasks cannot overlap and must be within the visible time window.
* Satellite storage, power constraints, and image download processes are not considered. Resource problems are not the key factor for short-term mission planning [6].
* Most of the tasks are regular with equal profit.
* Only the single-orbit scenario of a single satellite is taken into account.  
This paper primarily addresses the normalized monitoring scenario of remote sensing satellites, where regular tasks constitute the majority of actual satellite remote sensing observation tasks, with only a small number of emergency tasks. In this context, the profit differences between regular tasks are not apparent, making the direct assignment of task priority based on profit, as some current studies do, unreasonable. To assess the efficiency of regular task planning, this paper introduces the task scheduled rate, which is defined in Section 4.3. Future research will delve into a detailed analysis of the profit function.
## 4 Establishment of the PA-LM Model
### LM Model  
The architecture of LM model was developed through a trial phase in which we tested various model architecture settings such as layer count, size, and so on. The first layer is the input layer, including eight input variables, which are the well, geological, and operational parameters. The LSTM and dropout layers follow the first layer. A dense layer is used to encode the feature pattern of the input data and an output layer is the prediction of the COP. The addition of dropout layers improves the model performance by reducing overfitting [19].  
The LM model was used to improve the RNN model by introducing the cell state and gate structures, as shown in Figure 6[15; 16]. When the initial data sets are input into the LM cells, the gate structures select appropriate information for the cell state. Therefore, the LM can be used to solve time-series problems.  
Figure 5: The establishment process of the PA-LM model.  
Figure 6: Schematic of a typical LM cell.  
As shown in Figure 6, the symbols \(\times\) and + mean the multiplication and addition in the LM cell, respectively, and the arrow indicates the flow direction of initial data sets. The forget gate first applied to remove unnecessary information for the cell state is shown as follows:  
\[f_{t}=\sigma([h_{t-1},x_{t}]\times W_{f}+b_{f}) \tag{3}\]  
where \(\sigma\) is the activation function; \(f_{t}\) is the forgetting threshold at time \(t\); \(W_{f}\) and \(b_{f}\) are the weight and bias terms; \(h_{t-1}\) means the output values at time \(t-1\); and \(x_{t}\) is the input values at time \(t\).  
The input gate can select and store information in the cell state from the current initial data sets. The specific expressions are shown below:  
\[i_{t}=\sigma(i[h_{t-1},x_{t}]\times W+b_{i}) \tag{4}\]  
\[\widetilde{C}_{t}=tanh([h_{t-1},x_{t}]\times W_{\mathbb{C}}+b_{\mathbb{C}}) \tag{5}\]  
where \(i_{t}\) and \(\widetilde{C}_{t}\) are the input threshold at time \(t\); tanh is the activation function; \(b_{c}\) and \(b_{i}\) are bias terms; and \(W_{c}\) and \(W_{i}\) are the weights.  
The cell state is updated as follows:  
\[C_{t}=_{t}C_{t-1}\cdot\times f+\widetilde{C}_{t}\cdot\times i_{t} \tag{6}\]  
where \(C_{t-1}\) and \(C_{t}\) are the cell state at time \(t-1\) and \(t\), respectively.  
The output gate is used to output information at time \(t\), which is written below:  
\[o_{t}=\sigma([h_{t-1},x_{t}]\times W_{o}+b_{o}) \tag{7}\]  
where \(o_{t}\) denotes the output threshold at time \(t\); \(W_{o}\) and \(b_{o}\) are the weights and bias terms, respectively.  
The output value \(h_{t}\) and \(y_{t}\) at time \(t\) can be obtained as follows:  
\[h_{t}=y_{t}=o_{t}\times tanh(C_{t}) \tag{8}\]
## 4 Neuoprotective Peptides and Their Therapeutic Application in HIE
### Studies Using CARPs in Animal Models of HIE
#### 4.4.2 TAT-mGluR1  
mGluR1 is a 14-amino acid peptide (mGluR1: VIKPLTKSYQGSGK\({}_{929-942}\); net charge +3), corresponding to an intracellular region of the metabotropic glutamate receptor-1\(\alpha\) (mGluR1\(\alpha\)) protein that is cleaved by calpain in neurons following excitotoxicity [110]. It is proposed that TAT-mGluR1 (YGRKKRRQRRR-VIKPLTKSYQGSGK, net charge +11) blocks the ability of calpain to cleave mGlu\(\alpha\) and thus maintain the receptors ability to stimulate the neuroprotective phosphoinositide 3-kinase/protein kinase B (PI3K-Akt) signaling pathway and protect neurons from the toxic effects of excitotoxic calcium influx [110]. In a neuronal model of kainic acid mediated excitotoxicity, TAT-mGluR1 prevented mGlu\(\alpha\) truncation and reduced neuronal cell death [110]. In a P7 rat model of HIE [111] TAT-mGluR1 (58,590 nmol/kg; IP) administered 1 h before hypoxia significantly reduced cerebral infarction at 24 h post-HI.