esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 4 Short-Term Frequency Support of a DFIG
### Conventional Short-Term Frequency Support Method of a DFIG  
To heighten the system frequency nadir while preventing the rotor speed from stalling, \(P_{n\!f}\) in this method includes deceleration and acceleration periods. In the deceleration period and the acceleration period, \(P_{n\!f}\) is defined in the rotor speed domain and in the time domain, respectively.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 5 Implementation Issue
### Optimal Parameters  
In the proposed scheme, if the number of the symbols in a superposed signal is increased, the sum throughput can be increased linearly. However, the sum throughput cannot be increased without limit since there is a limitation that a receiver can detect the symbols. As more symbols are transmitted, the power allocated to the symbols is reduced. Therefore, if too many symbols are transmitted, the receiver can not detect each symbol. Furthermore, there is also the degradation of sum throughput because of the interference in performing SIC. Therefore, the important issue is to find a near-optimal parameter between the number of users and the number of symbols in a superposed signal in the overall system.
## 4 Discussion
### Reasons for the Variations in Precipitation Frequency and GPP Values
#### 4.1.2 Variation in GPP Value  
Drought is one of the key factors that weakens vegetation productivity [61], which leads to a reduction in the GPP of vegetation to different degrees, especially for barren land. A declining trend in annual GPP was also observed in cropland and shrubland. The reduction in GPP for cropland is partly attributed to the late summer harvest, and the reason for reduction in shrubland GPP is complicated. The findings of reduced summer GPP in shrubs align with previous observations of \"browning\" in the Arctic [62; 63]. The Arctic method effect could lead to shrub invasion and expansion, and the new shrub GPP is lower [64]. The rising temperatures and extreme drought events result in shrubs mortality [65]. Climate change can also induce alterations in land cover types, causing shrubs with high biomass to gradually transform into forests [51].  
Tundra in the Northern Hemisphere primarily exists in the pan-Arctic region. The tundra is undergoing shrubification while expanding towards the high-latitude Arctic. Consequently, tundra vegetation GPP is increasing, especially when the restrictions imposed by low temperatures are alleviated in summer, leading to a more rapid increase in GPP. The growth limitations of grasslands in the Northern Hemisphere have large uncertainty. In high latitudes, the growth mechanism resembles that of tundra, while in low latitudes, such as Mongolian grasslands, they are generally driven by drought events.
## 1 Introduction
The low pH environment facilitates the protonation of DOX, which weakens the \(\pi\)-\(\pi\) stacking and hydrophobic interactions between DOX and the nanocar, thereby facilitating the release of DOX from the graphene complex [25]. In addition, in a study using PEGylated GO, a more effective release of DOX was facilitated by treatment with glutathione (GSH) compared to the release from GO-PEG alone [23; 26]. External stimuli have also been employed to release DOX from graphene nanocarriers, such as the work by [PERSON] et al., who developed GO wrapped mesoporous silica NPs loaded with DOX that released DOX \"on demand\" under NIR laser irradiation [48]. [PERSON] et al. worked on utilising GO decorated with iron oxide NPs and functionalised by PEG for the magnetically targeted drug delivery of loaded DOX. The application of an externally applied magnetic field resulted in the high uptake of GO-IONP-PEG-DOX by breast cancer cell line 4T1 directly within the applied field and the selective cytotoxicity to these cells by the released DOX, with the cells outside the magnetic field not affected [49]. The research effort into DOX-loaded graphene nanocarriers for drug delivery has been extensive and has seen a lot of progress in recent years; however, research is still limited to pre-clinical stages. Further, information on the release kinetics of DOX from different nanocarriers has not been studied; it is still unclear what the best release mechanism of DOX is for therapeutic efficacy. Understanding the most effective release of DOX from graphene nanocarriers may facilitate further focused research and accelerate the transfer of this technology to clinical applications. Driven by these needs, the current study aims to undertake a systematic review of the previous literature published on graphene-based drug delivery vehicles loaded with DOX for ovarian and breast cancer treatment, and analyse extracted data using mathematical models to identify the most successful and clinically relevant release mechanism. In addition, the limitations of published literature and any missing data are considered. In addition, recommendations for future work are addressed.
## 5 Conclusions  
This paper focuses on the RUL prediction of the PCCS in the railway system. The LM distribution is analyzed in the first place. It shows that the wear development trend is distinctive for each PCCS, even for the same type of PCCS. After that, a Wiener-process-based RUL prediction method considering unequal data interval is proposed. Firstly, the method of unit time difference coefficient is applied to convert the unequal interval wear data into the equal interval ones which are used for wear modeling. Then, the model parameters are estimated by the EM algorithm, and the UIWGLRCM is introduced to restore the estimations of model parameters and the wear loss. Finally, the PDF and the prediction of RUL are obtained.  
The proposed method is further applied to the RUL prediction issue of the PCCS. By comparing with the Si method (the method in [8]), the proposed method in this article achieves a more accurate prediction of wear path and RUL in considering the unequal interval wear data. So the proposed method has attractive features: the degradation coefficient is update recursively by the KF algorithm; the EM algorithm is adopted to update the model parameter; the exact PDF of the RUL is deduced based on FHT. The comparative study demonstrates that the proposed method is practical and achieves a better performance in solving the unequal interval problem. The improved method provides a practical alternative for the RUL prediction of the PCCS.  
Conceptualization, Q.G. and X.W.; Methodology, Q.G. and X.W.; Software, Q.G. and X.W.; Validation, Q.G. and X.W.; Formal analysis, L.J.; Investigation, X.W.; Resources, Y.H. and H.Z.; Data curation, Y.H. and H.Z.; Writing--original draft preparation, Q.G.; Writing--review and editing, Q.G. and X.W.; Supervision, L.J.; Project administration, X.W.; Funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (contract no. 2017 YFB1201202). Thanks to the Guangzhou Metro Group Co. for providing data.  
The authors declare no conflict of interest.
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 3 Experiments Setup
### Evaluation Metrics  
To assess the effectiveness of each model, we use performance metrics such as classification accuracy, precision, recall, and the F1 score.  
Accuracy (Acc) serves as our primary metric for evaluating the correct classification of data samples, taking into account all predictions made by the model, as described by the following equation:  
\[Acc=(TP+TN)/(TP+TN+FP+FN), \tag{3}\]  
where _TP_ represents true positives, indicating the number of correctly predicted anomalies, _TN_ represents true negatives, indicating the number of correctly predicted normal instances, _FP_ represents false positives, indicating the number of normal instances incorrectly classified as anomalies, and _FN_ represents false negatives, indicating the number of anomalies misclassified as normal.  
Precision is used to measure the accuracy of correct predictions, calculated as the ratio of correctly predicted samples to the total number of predicted samples for a particular class, as illustrated by the following equation:  
\[Precision=TP/(TP+FP) \tag{4}\]  
Recall, also known as the true positive rate (_TPR_), is used to determine the ratio of correctly predicted samples for a specific class to the total number of instances of that class, as shown by the following equation:  
\[TPR(Recall)=TP/(TP+FN) \tag{5}\]Finally, the F1 score calculates the balance between precision and recall, evaluating the trade-off between these two metrics, as given by the following equation:  
\[F1=2*\left(\left(Precision*Recall\right)/\left(Precision+Recall\right)\right) \tag{6}\]
###### Abstract  
In this paper, we propose robust image-smoothing methods based on \(\ell_{0}\) gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the \(\ell_{0}\) gradient, i.e., the number of nonzero gradients in an image, and the \(\ell_{2}\) data fidelity results in a smooth image. However, this optimization often leads to undesirable artifacts, such as pseudo-edges, known as the \"staircasing effect\", and halos, which become more visible in image enhancement tasks, like detail enhancement and tone mapping. To address these issues, we introduce two types of gradient constraints: box and ball. These constraints are applied using a reference image (e.g., the input image is used as a reference for image smoothing) to suppress pseudo-edges in homogeneous regions and the blurring effect around strong edges. We also present an \(\ell_{0}\) gradient minimization problem based on the box-/ball-type gradient constraints using an alternating direction method of multipliers (ADMM). Experimental results on important applications of \(\ell_{0}\) gradient minimization demonstrate the advantages of our proposed methods compared to existing \(\ell_{0}\) gradient-based approaches.  
Keywords:image smoothing; detail enhancement; total variation; \(\ell_{0}\) pseudo-norm; alternating direction method of multipliers +
Footnote †: This paper is an extended version of our paper published in Proceedings of the IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7-10 October 2018.
# Optimization of Shape Design of Grommet through Analysis of Physical Properties of EPDM Materials
###### Abstract  
Ethylene propylene diene monomer (EPDM) has superior mechanical properties, water resistance, heat resistance, and ozone resistance. It can be applied to various products owing to its low hardness and high slip resistance properties. A grommet is one of the various products made using EPDM rubber. It is a main component of automobiles, in which it protects wires throughout the inside and outside of a vehicle body. The grommet, made of EPDM, has different mounting performance depending on the process parameters and the shape of the grommet. This study conducted optimization to improve the mounting performance of a grommet using EPDM materials. The physical properties of the main molding materials were investigated according to process parameters. A grommet was fabricated according to the process parameters of fabrication. Insertion force and separation force were examined through experiments. Nonlinear material constants were determined through uniaxial and biaxial tensile tests. The nonlinear analysis of the grommet was conducted, and a compound design that incorporated the shape parameters for the minimum load of each part was derived. Then, additional nonlinear analysis was performed. This was followed by a comparative analysis of the actual model through experimental evaluation.  
Keywords:Ethylene-propylene diene monomer rubber EPDM; grommet; physical properties; optimization of shape design +
Footnote †: journal: Applied Materials Science
# Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots
## 2 Establishment of Vehicle Delay Model  
### Overview of Delay Indicators  
There are many evaluation indicators describing traffic impact [14], such as queue length, delay, and traffic capacity. Vehicle delay can not only reflect the congestion severity, fuel consumption, and travel time loss of dynamic traffic flow but also measure the rationality and reliability of traffic channelization design. At the same time, drivers are most sensitive to the change of time when queuing in congestion. In addition, vehicle delay is widely used in traffic congestion evaluation, intersection signal control, and route guidance. Considering that delay is quite significant for dynamic traffic and static traffic, this paper selected the average vehicle delay as the evaluation index of dynamic and static traffic impact analysis of parking lot.  
Vehicle delay refers to the difference between the actual travel time of the vehicle and the travel time under the ideal state [15]. Generally, it includes fixed delay, operation delay, parking delay, and queuing delay.
## 3 Results and Discussions
### Cash Flow Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Option**} & \multirow{2}{*}{**Total Capital Cost**} & \multicolumn{2}{c}{**Loan**} & \multicolumn{2}{c}{**Equity**} & \multirow{2}{*}{**Annual Loan Payment**} & \multirow{2}{*}{**Total Savings**} & \multirow{2}{*}{**Payback Period**} \\ \cline{3-4} \cline{6-7}  & & **Year \(\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{ \boldsymbol{ \boldsymbol{ \boldsymbolboldsymbol{ \boldsymbolboldsymbol{ \boldsymbol }}}}}}}}}}}}}} & & \multicolumn{1}{1} & \multicolumn{1}{c}{} \\ \hline ITES & 8.3 million & 4.1 million & 4.1 million & - & 612k & 7.8 million & 10 years \\ BS & 11.2 million & 5.6 million & 3.6 million & 2.0 million & 830k & 4.8 million & 13 years \\ \hline \end{tabular}
\end{table} 
Table 3: Key economic findings (Currency: USD). 10 th year. This decreases the cumulative savings by USD 2 million and hence increases the payback period. For ITES, a period of 10 years is enough to pay back the investment. However, this time increases to 13 years for the BS due to larger capital size and higher annual loan of the associated system. Hence, the ITES storage system is a better option out of two considered alternatives in terms of cumulative savings and payback period.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 2 Method
### Map Generation and Update  
Occupancy grid maps, which are a location-based environment model, are one of the most commonly used map models in mobile robot environment modeling. The basic idea of an occupancy grid map is to discretize the environment into a series of grids of the same size according to the map resolution, and each grid is set with an occupancy probability value to indicate the probability of occupation [35]. In this paper, a multiresolution occupancy grid map is used to represent the environment, which is similar to the image pyramids used in the field of computer vision. We first set the total number of occupied grid map resolutions and then divide it from top to bottom according to the different resolutions until it is divided into the minimum resolution grid; the map center and map boundary also need to be set at the beginning. When new LiDAR scan data are to be inserted into the multiresolution occupancy grid map, we first perform a scan matching search on the lower-resolution map layer to obtain a rough pose and then on the higher map layer to achieve a high-precision result, which can greatly reduce calculation pressure. The pose obtained from the scan-to-map matching process is used to update the entire map synchronously; thus, we can ensure that maps of different resolution are always consistent.  
We use the same occupancy grid state update strategy as Google's Cartographer. Whenever new LiDAR scan data are to be inserted into the occupancy grid map, we first compute a group of grid points for hits and another disjoint group of grid points for misses. Figure 1 shows the relationship between the scanning point and the grid points. For each hit, the nearest grid point will be inserted into the hit group, and for each miss, we insert the grid points that intersect the line between the scan origin and each scan point (not including the grid points that are already in the hit group) [12]. The occupancy probability of the grid point will be set if it has not been observed before and is in the hit set or miss set. If a grid point has been observed before, the occupancy probability for hits and misses is updated as follows.  
\[\begin{split} odds(x)=\frac{p}{1-p},\\ M_{new}(x)=\textit{clamp}\big{(}odds^{-1}(odds(M_{old}(x)) \cdot odds(p_{hit}))\big{)}\end{split} \tag{1}\]
# Metal Bioaccumulation by Carp and Catfish Cultured in Lake Chapala, and Weekly Intake Assessment
## 1 Introduction  
Lake Chapala is the largest freshwater lake in Mexico, with a total surface area of 114,659 ha [1]. A shallow, alkaline body of water, it has a mean depth of less than 7 m. Lake Chapala belongs to the Lerma-Chapala Basin. Its main tributary input comes from the Lerma River, which supplies about 80% of its waters (Figure 1). This river is also the main source of anthropogenic pollution, including heavy metals, because \(\sim\)3500 industries pour their treated wastewaters into it [2, 3]. Sediments from Lake Chapala are rich in heavy metals, and previous studies have shown that lead (Pb) and cadmium (Cd) are present in the exchangeable fraction of water, and possibly available for absorption by living organisms, including fish [4]. The sediments, however, have a metal adsorption-desorption dynamic that is strongly influenced by pH and marked seasonal fluctuations in water levels, so low concentrations of metals in the water have also been reported [5, 6].  
For humans, consuming fish is an excellent source of proteins and n-3 polyunsaturated fatty acids, but concern has arisen over the potential danger of heavy metals [7]. Human diets are a significant source of exposure to heavy metals that carry a toxicological risk because they cannot be degraded by biological processes. Due to this potential for harm, the Joint FAO/WHO Expert Committee on Food Additives (JECFA) has established a Provisional Tolerable Weekly Intake (PTWI) for metals that estimates the amount of a given contaminant that can be assimilated weekly per unit of body weight (bw) over a lifetime, without constituting an appreciable health risk [8].  
Contaminated fish are the principal source of heavy metals such as mercury, lead, and arsenic for humans. Previous reports in fish from Mexican water reservoirs have shown borderline-to-high quantities of heavy metals, making it essential to maintain constant monitoring of their intake through fish consumption in the diet [9; 10; 11; 12].  
The aim of the present study was to evaluate the bioaccumulation of metals (Zn, Cu, Cd, Hg, As, Pb) along time in fish cultured in a natural environment to evaluate the risk of dietary intake of metals in humans.
# Influence of Inflow Turbulence on the Flow Characteristics around a Circular Cylinder
###### Abstract  
To study the influence of turbulence on the wind pressure and aerodynamic behavior of smooth circular cylinders, wind tunnel tests of a circular cylinder based on wind pressure testing were conducted for different wind speeds and turbulent flows. The tests obtained the characteristic parameters of mean wind pressure coefficient distribution, drag coefficient, lift coefficient and correlation of wind pressure for different turbulence intensities and of Reynolds numbers. These results were also compared with those obtained by previous researchers. The results show that the minimum drag coefficient in the turbulent flow is basically constant at approximate 0.4 and is not affected by the turbulence intensity. When the Reynolds number is in the critical regime, the lift coefficient increased sharply to 0.76 in the smooth flow, indicating that flow separation has an asymmetry; however, the asymmetry does not appear in the turbulent flow. Drag coefficient decreases sharply at a lower critical Reynolds number in the turbulent flow than in the smooth flow. In the smooth flow, the separation point is about \(80^{\circ}\) in the subcritical regime; it suddenly moves backwards in the critical regime and remains almost unchanged at about \(140^{\circ}\) in the supercritical regime. However, the angular position of the separation point will always be about \(140^{\circ}\) for turbulent flow for the Reynolds number in these three regimes. Turbulence intensity and Reynolds number have a significant effect on the correlation of wind pressures around the circular cylinder. Turbulence will weaken the positive correlation of the same side and also reduce the negative correlation between the two sides of the circular cylinder.  
circular cylinder; wind tunnel test; turbulence intensity; Reynolds number; turbulent flow Article
## 4 Hybrid Approach Based on RNNs and Topic Modeling (LSTM-LDA)
### Model Structure  
This section explains the complete structure of the model. Here in the proposed model, LSTM is used to model the topic sequences, i.e., \(\mathrm{p}\) (\(\mathrm{zt}|z\mathrm{t}-1,\;z\mathrm{t}-2,\ldots,\;z\mathrm{1}\)), while LDA models word sequences, i.e., \(\mathrm{p}\) (wij\(\mathrm{zi}\)). The parameters for LDA are presented in Table 1.  
Let us consider we have topics K, dictionary of size V, collection of documents D where each document d consists of N words. By using all these representations, we can formally define an algorithm for our hybrid model, as shown below in Algorithm 1.  
```
for topic \(k=1\)\(\rightarrow\)\(K\) select topic \(\varphi_{\mathrm{k}}\)\(\sim\) Dir(\(\beta\)) for document \(d=1\)\(\rightarrow\)\(D\)  LSTM model initialization with s\({}_{0}=0\) for words at \(t=1\)\(\rightarrow\)N in document \(d\) LSTM update as s\({}_{\mathrm{t}}=\) LSTM (\(z_{\mathrm{d,t-1}}\), s\({}_{\mathrm{t-1}}\)) Calculate \(\theta=\mathrm{softmax}_{\mathrm{K}}(\mathrm{W}_{\mathrm{p}}\mathrm{s}+ \mathrm{b}_{\mathrm{P}})\) (for topic distributions)  Select a topic \(z_{\mathrm{d,t}}\) from \(\theta\)  Select word \(w_{\mathrm{d,t}}\) from \(\varphi_{\mathrm{zd,t}}\)
```  
**Algorithm 1**Generative model for the hybrid approach  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Type** & **Definition** \\ \hline K & Integer & Number of topics \\ V & Integer & Dictionary/Vocabulary size \\ D & Integer & Number of documents \\ N & Integer & Total number of words in document d \\ alpha & K-dimensional vector & Prior weight of a topic k in a document \\  & [+ve real] & Prior weight of a word in a topic \\ beta & [+ve real] & Prior weight of a word in a topic \\ Theta & Float [0,1] & Probability \\ z & N-dimensional vector of integers & Topic assignments \\ \hline \hline \end{tabular}
\end{table}
Table 1: LDA parameters and definitions.  
Subsequently, we compute the probability of the topic for the next word based on the topics of preceding words. In Figure 5, the architectural overview of our proposed model is presented. The input w(t) is the word vectors generated for the comment at time t, and z(t) is the latent vector generated as a result of the LDA process. This latent vector z(t) is fed to the LSTM model. After training, LSTM calculates the topic category of any given comment. The ground truth data is composed of comments along with category labels.  
The prediction module uses the LSTM based topic model to predict the topic class for the next word in the document. The input comments are preprocessed and are converted to word vectors. LDA learns topics, and LSTM is trained based on this pre-trained model. The second module is the recommendation module, which suggests some projects based on the topics discovered by incorporating the investor's preferences.