esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 6 Strain-Life Fatigue Equation  
The strain fatigue life equation is characterized by the fatigue curve taking the following form:  
1. \(\varepsilon_{a}-N_{f}\) curve. Strain fatigue tests were conducted at different strain levels and under equal strain control to obtain the fatigue data, of the control variables \(\varepsilon_{a}\) is the true strain amplitude. 2. \(\varepsilon_{eq}-N_{f}\) or \(\sigma_{eq}-N_{f}\) curve. Fatigue data are obtained from fatigue tests that combine stress and strain. The control variables are not equivalent strain amplitude \(\varepsilon_{eq}\) or equivalent stress amplitude \(\sigma_{eq}\), which contain the mean stress. 3. \(S-N_{f}\) curve. The fatigue data comes from existing data from the S-N curve. The influence of notch and strain fatigue should also be considered. The second method needs a large amount of experimental data, and the third method was only applied to make a rough estimation, because of its low accuracy. The main \(\varepsilon_{a}-N_{f}\) curves are shown in Table 5.  
The Manson-Coffin equation is widely used for all of the \(\varepsilon_{a}-N_{f}\) curves by adopting a power function to characterize fatigue performance. The main parameter strain is calculated using the following equation:  
\[\varepsilon_{a}=\frac{\sigma^{\prime}{}_{f}}{E}\Big{(}2N_{f}\Big{)}^{b}+ \varepsilon^{\prime}{}_{f}\Big{(}2N_{f}\Big{)}^{c} \tag{21}\]  
Based on the proposed Manson-Coffin equation, direct tensile fatigue tests for HMAC were conducted to obtain the fatigue parameters. Estimation methods for fatigue parameters included the general slope method, modified four-points correlation, and four-points correlation [31; 32] (see Table 6). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Fatigue Constant** \\ \hline \multirow{4}{*}{General slope method} & \(b\) & \(-0.12\) \\  & \(C\) & \(-0.16\) \\  & & \(\sigma^{\prime}{}_{f}\) \(1.75\sigma_{\text{D}}/E\) \\  & & \(\varepsilon^{\prime}{}_{f}\) \(0.5\varepsilon^{0.006}_{f}\) \\ \hline \multirow{4}{*}{Four-points correlation} & \(b\) & \(-[0.0792+0.179\lg(\sigma_{f}/\sigma_{\text{D}})]\) \\  & \(c\) & \(-\lg(3.37\varepsilon^{0.25}_{f}\left[1-81.8\frac{\sigma_{\text{D}}}{E}\left( \frac{\sigma_{f}}{\sigma_{0}}\right)^{1.179}\right]^{-1/3})\) \\  & & \(\sigma^{\prime}{}_{f}\) \(1.12\sigma_{b}(\sigma_{f}/\sigma_{\text{D}})^{0.893}\) \\  & & \(\varepsilon^{\prime}{}_{f}\) \(0.413\varepsilon_{f}\left[1-81.8\frac{\sigma_{\text{D}}}{E}\left(\frac{ \sigma_{f}}{\sigma_{0}}\right)^{1.179}\right]^{-1/3}\) \\ \hline \multirow{4}{*}{Modified four-points correlation} & \(b\) & Same as four-points correlation \\  & \(c\) & Same as four-points correlation \\  & & \(\sigma^{\prime}{}_{f}\) \(1.18\sigma_{b}(\sigma_{f}/\sigma_{\text{D}})^{0.0946}\) \\  & & \(\varepsilon^{\prime}{}_{f}\) \(0.538\left[1-81.8\frac{\sigma_{\text{D}}}{E}\left(\frac{\sigma_{f}}{\sigma_{ 0}}\right)^{1.179}\right]^{-1/3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Estimation method for fatigue constant.
## 4 A Pricing Strategy for EVs on Account of the Supply-Demand Relationship of the Micro-Grid
### Dynamic Time-Of-Use Pricing Strategy for EV Charging
#### 4.2.2 Calculation of the Dynamic Time-Of-Use Charging Price
\[SOC_{i,t}=SOC_{i,t-\Delta t}+\int_{t-\Delta t}^{t}P_{ch,i}\eta_{ch}dt/C_{i} \tag{18}\]  
\[SOC_{i}^{\text{min}}\leq SOC_{i,t}\leq SOC_{i}^{\text{max}} \tag{19}\]  
\[P_{ch,i}^{\text{min}}\leq P_{ch,i}\leq P_{ch,i}^{\text{max}} \tag{20}\]  
where \(SOC_{i,t}\) is the state of charge of EV \(i\) at time \(t\), \(SOC_{i}^{\text{max}}\) and \(SOC_{i}^{\text{min}}\) are the higher/lower limits of SOC of the EVs participating in the dispatching respectively. \(P_{ch,i}^{\text{max}}\) and \(P_{ch,i}^{\text{min}}\) are the higher/lower limits of charging power. 2. The charging requirements constraint for EVsThe SOC at the leaving time from the grid should meet the minimum electricity required by the user for the next trip. \[SOC_{n,i}+\int_{t_{d,i}+n\Delta T}^{t_{i,j}}P_{ch,i}\eta_{ch}dt/C_{i}\geq SOC_{r,i} \tag{21}\]  
3. The number constraint of EV charging  
The number of EVs that participate in the dispatching should not be more than the number of controllable EVs in each time period. \[\Omega_{res}(k\Delta T)\leq\Omega_{sch}(k\Delta T) \tag{22}\]  
where \(\Omega_{sch}(k\Delta T)\) is the number of controllable EVs in the time period \(k\). 4. The higher/lower constraints of charging price for EVs  
The adjusted time-of-use charging price cannot be higher than the peak-valley price, or lower than the generation and transmission costs of renewable energy accordingly in each time period. \[c_{d}\leq p_{ch,k\Delta T}\leq p_{pv,k\Delta T} \tag{23}\]  
where \(c_{d}\) is the generation and transmission costs of renewable energy of the micro-grid, and \(p_{pv,k\Delta T}\) is the peak-valley price in the time period \(k\). 5. The constraint of user comprehensive satisfaction degree  
The user comprehensive satisfaction degree obtained by weighting sub-item satisfaction degrees should meet the minimum value that the user will be willing to participate in the power system dispatching. \[R_{USD,i}\geq R_{USD,i,\min} \tag{24}\]  
where \(R_{USD,i}\) is the user comprehensive satisfaction degree of EV \(i\), and \(R_{USD,i,\min}\) is the minimum comprehensive satisfaction degree of participating in the power system dispatching for EV \(i\), whose value in this paper is 0.8, and the minimum value of comprehensive satisfaction degree can be set according to the actual situation.
# Immobilised Humic Substances as Low-Cost Sorbents for Emerging Contaminants
## 4 Conclusions  
In this study, humic substances were immobilized, and three kinds of new materials were obtained: humic substances immobilized with (1) phenol (HSPh), (2) resorcinol (HSrz) and (3) iron compounds (HSFe). The characterizations of the obtained materials indicated that the immobilization was successful.  
The results showed that all of the immobilized HSs obtained could be used as sorbents to remove emerging contaminants (such as metals (Cr (III)) and pharmaceuticals) from water. At the same time, only the HSs immobilized using iron compounds were suitable for the purification of water from arsenic. To our knowledge, immobilized HSs have not been widely studied as sorbents up to now. Compared to the iron-modified natural materials described in other studies, the sorbents of immobilized HSs synthesized in this study showed similar pollutant sorption capacities [28; 29; 30; 31]. The synthesized sorbents of HSs immobilized with resorcinol, phenol and iron compounds showed promising results for the sorption of chlorpromazine HCl. We believe that these sorbents have potential for use in wastewater treatment from pharmaceuticals.  
The capacity of the immobilized HS sorbents to sorb As(V), Cr(III) and chlorpromazine was affected by the solution's pH value. Neutral and basic environments are appropriate for the removal of Cr(III) and chlorpromazine, while the highest sorbed amount of arsenates was observed in an acidic environment using the HSFe sorbent.  
The kinetic experiment results indicated that the process of sorbing Cr was rather fast, reaching the maximum sorbed amount within the first two hours and afterward becoming steady, while the removal of arsenic using the HGs immobilized with Fe compounds was slower. The sorption rate of chlorpromazine HCl onto HSph and HSrz is very high during the first hour but increased at a slower rate starting from the second hour and up to 24 h. All of the experimental data corresponded to the pseudo second-order reaction model.  
Conceptualization, M.K.; methodology, K.U., L.D., L.A.-B., J.K. and R.M.; software, J.K.; validation, L.D., K.U. and L.A.-B.; formal analysis, M.K.; investigation, K.U. and L.A.-B.; resources, L.D.; data curation, L.A.-B. and M.K.; writing--original draft preparation, L.A.-B.; writing--review and editing, M.K. and L.D.; visualization, L.A.-B.; supervision, M.K.; project administration, L.D.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Latvian Council of Science, Fundamental and Applied Research Project No. lzp-2018/1-0009 \"Properties and structure of peat humic substances and possibilities of their modification\".  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Real Dataset from Laboratory Experimental Evolution of _E. coli_  
We also obtained a real sequencing dataset from our bacterial experiments in this study. _E. coli_ cells (MDS42\(\Delta\)galk::Ptet-gfp-kan, [31; 32]) were experimentally evolved by adapting them in Kanamycin (50 ug/mL) and cell wall-targeting reagents-containing osmoprotective M63 media (15 mM (NH4)2 SO4, 39 mM KH2 PO4, 62 mM K2 HPO4, 136 mM NaCl, 1.79 M FeSO4, 14.8 uM thiamine-HCl, 1 mM MgSO4, 50 ug/mL Mecillinam, 100 ug/mL lysozyme). Briefly, the bacterial cultures (Stock-T2, P0) were gradually adapted with a series of passages in the above growth media. Bacterial cells (cell wall-less form, or L-form-like cell form) with abnormally bigger phenotypes [33; 34] were sorted in fluorescence-activated cell sorting (FACS) before each cell passage (P1-P16) [35; 36]. The following sorting gate and models were used: SSC-A threshold: 11, FSC-A threshold: 6000, FSC-H threshold: 900, FSC-A voltage: 35 \(\pm\) 3, SSC-A voltage: 299 \(\pm\) 3 (FACS, BD FACSMelody(tm) Cell Sorter, BD Life Sciences, San Jose, CA, USA). The sorted cells were cultured for 16-24 h to maintain them in the exponential phase (\(\sim\)10\({}^{6-8}\) cells/mL). After 16 rounds of the culture-sorting cycle, the resulting strains were obtained and sent for WGS. Since the contaminations in the final strains were detected, five single clones were also selected for WGS. Genome resequencing was performed by [PERSON] (Shanghai, China). Genomic DNA was extracted by MagentaBacterial DNA KF kit (Sango, Shanghai, China), and the gDNA library was constructed using NEBNext Ultra DNA Library Prep Kit from Illumina (NEB, Ipswich, MA, USA). WGS was performed on the NovaSeq 6000 (Illumina, San Diego, CA, USA) and MGISEQ-2000 platform (MGI, Shenzhen, China). Those genome sequencing results with or without unexpected bacterial contaminations were applied to CleanSeq for searching convincible genomic mutations.
## 3 Results  
In this work, a typical LEO satellite constellation is considered to generate the analysis data set, as shown in Figure 5, along continuous three moments with one-second intervals to mitigate the impact of randomness. There are 1638 objects in the FOV, which have the line-of-sight communication capability with the GEO data relay satellite. A medium-scale GEO communication satellite with 40 user beams is taken into account in the research. So, the optimization problem can be summarized as a total of 1638 objects being divided into 40 clusters by using the modified K-means clustering with adjustable parameters. Compared with the conventional K-means clustering, two parameters \(\alpha\) and \(\beta\) are introduced to the modified algorithm. The impact of the parameters on the clustering results is demonstrated in the following subsections.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
###### Abstract  
Diabetes is a global problem which impacts people of all ages. Diabetic retinopathy (DR) is a main ailment of the eyes resulting from diabetes which can result in loss of eyesight if not detected and treated on time. The current process of detecting DR and its progress involves manual examination by experts, which is time-consuming. Extracting the retinal vasculature, and segmentation of the optic disc (OD)/fovex play a significant part in detecting DR. Detecting DR lesions like microaneurysms (MA), hemorrhages (HM), and exudates (EX), helps to establish the current stage of DR. Recently with the advancement in artificial intelligence (AI), and deep learning(DL), which is a division of AI, is widely being used in DR related studies. Our study surveys the latest literature in \"DR segmentation and lesion detection from fundus images using DL\".  
diabetic retinopathy; deep learning; retinal blood vessel segmentation; lesion detection; retinal fundus images +
Footnote †: journal: Medical Imaging
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 4 Results and Discussion
### Simulation Results
#### 4.1.2 The Received Power
For example, in Table 5, for the smallest divergence angle at a distance of 1500 m, the received power is 4.2 dBm while at the same length for the angle 2.0944 \(\times\) 10\({}^{-4}\) the power is 2.1 dBm, and so on (see Table 5). Since the train operates within ETT and all of the train is covered with material (the laser cannot pass through it), and there is no glass, so, there are no effects of the laser beam on the eyes safety; therefore, it is possible to increase the power to more than 100 mW per cm\({}^{2}\). Figure 12 shows the received power in dBm calculated with a fixed transmitter power of 1 w \begin{table}
\begin{tabular}{c c c} \hline \hline
**Full Divergence Angle** & **Distance (m)** & **Transmitted Power in Watt** \\ \hline \multirow{4}{*}{6.944 \(\times\) 10\({}^{-5}\)} & 550 & 1.764 \(\times\) 10\({}^{-9}\) \\  & 1500 & 2.5 \(\times\) 10\({}^{-7}\) \\  & 14,200 & 1.2 \(\times\) 10\({}^{-4}\) \\  & 550 & 2.165 \(\times\) 10\({}^{-7}\) \\  & 1500 & 1.2 \(\times\) 10\({}^{-5}\) \\  & 14,200 & 6.54 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transmitted power required for 1.25 Gbps and variable distance with two types of divergence angles. Figure 11: Received power versus variable distance for two divergence angles (assuming a transmit power \(P_{tx}=27\) mW). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Full Divergence Angle** & **Distance (m)** & **Received Power in dBm** \\ \hline \multirow{4}{*}{6.944 \(\times\) 10\({}^{-5}\)} & 1500 & 4.2 \\  & 14,200 & \(-\)5.5 \\  & 550 & 4.2 \\
2.0944 \(\times\) 10\({}^{-4}\) & 1500 & 2.1 \\  & 14,200 & \(-\)17.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The received power for variable distance with two types of divergence angles (\(P_{tx}=27\) mW). Table 6 displays some of these values. The results are significantly enhanced compared to those obtained with a transmit power of 27 mW. The results were generated for both angles (6.944 \(\times\) 10\({}^{-5}\) and 2.0944 \(\times\) 10\({}^{-4}\)).
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 3 Experimental Results  
To train both the Mask R-CNN and the YOLACT networks, we collected 1600 intensity images of the track. All images came from a track inspection vehicle managed by Polish railway lines. The intensity images are captured by a fast camera installed under the floor of the inspection vehicle. This camera is equipped with a Gigabit Ethernet (1 GigE) interface. The captured images are sent via 1 GigE to a server installed on the board of this vehicle. The group of selected images with wooden and concrete sleepers included images recorded in good weather (900) as well as during rainfall (700). All images were pre-processed. In this process, each image pixel was normalized so that the average pixel intensity value in the image was zero. This pre-processing significantly improves the convergence of the gradient optimization used to train both the Mask R-CNN and the YOLACT networks. All the available images were randomly divided into three groups in the proportions 60%, 20% and 20%: training data hereinafter referred to as _train_data_, validation data hereinafter referred to as _valid_data_ and testing data hereinafter referred to as _test_data_.  
We did not train both networks from scratch. To improve the performance of both networks and speed up their training we decided to use transfer learning [36; 37]. This means that a model developed for one task is reused as the starting point for a model on a second task.
# Modeling and Resolving Uncertainty in DIKWP Model
## 2 Problem Description
### Uncertainty of Internal Conditions
#### 2.2.2 Differences in Content Understanding  
Differences in content understanding have complex implications for HFT, as they can both increase market volatility and provide opportunities for traders employing different strategies. For HFT firms, understanding the diversity in information interpretation within the market and leveraging advanced natural language processing (NLP) techniques and machine learning algorithms to enhance the accuracy and responsiveness of their information parsing are key to improving trading efficiency and profitability. Additionally, this underscores the need for regulatory bodies to be as clear and precise as possible when disseminating market-sensitive information to minimize unnecessary market fluctuations.  
* **Differences in market data interpretation (\(e_{13}\)):** Various HFT algorithms may interpret the same set of market data differently, leading to divergent or diversified trading decisions. For instance, during the release of significant information in the stock market, different HFT systems may have varied interpretations of the positive or negative impact of the data. Some algorithms may interpret it as a bullish signal and opt to buy related stocks, while others may perceive it as bearish and choose to sell. Such differences in content understanding can increase market volatility in a short period.
* **Diverse interpretations of news reports (\(e_{14}\)):** News reports and announcements often contain ambiguous or multi-interpretable language, prompting different trading systems to interpret this information based on their algorithms. For example, if a large tech company's financial report exceeds market expectations but its future revenue forecast appears slightly conservative, various HFT systems may react differently. Some may focus on the short-term bullish aspects and buy, while others may be concerned about the uncertainty in long-term revenue forecasts and choose to sell. Such diversity in news interpretation can lead to significant fluctuations in stock prices.
* **Differing interpretations of regulatory announcements (\(e_{15}\)):** Regulatory announcements from governing bodies typically have a direct impact on the market, but the complexity of their language and terms sometimes leads to varying interpretations and expectations. For example, if a regulatory agency issues new rules aimed at tightening oversight of HFT, some trading entities may interpret it as a direct threat to their business model and decrease trading activities. In contrast, others may seek gray areas within the new regulations, attempting to adjust their strategies to continue leveraging the advantages of HFT. Such differing interpretations of regulatory content may result in divergent behaviors among market participants, consequently affecting market structure and liquidity.
## 2 Related Concepts
### Practical Byzantine Fault Tolerance  
PBFT(Practical Byzantine Fault Tolerance) algorithm, proposed by [PERSON] in 1999, has a communication complexity of \(O(n^{2})\) (\(n\) refers to the size of the data scale, which is related to the number of nodes participating in the algorithm) and is used to build Byzantine fault-tolerant distributed systems. The PBFT algorithm is implemented by two types of nodes: master node and consensus node. The identity of the master node (denoted by \(p\)) is determined by the view number (\(v\)) and the number of nodes (\(R\)). The following Equation represents the election mechanism for the master node.  
\[p=v\;mod\;R \tag{1}\]  
The PBFT consensus process includes the following steps: request, pre-prepare, prepare, commit, and reply. After receiving a request message from a client, the master node creates a pre-prepare message and sends it to all consensus nodes. Upon receiving and validating the pre-prepare message, the consensus nodes send prepare messages to all nodes. When a consensus node receives more than \(2\mathrm{f}+1\) (\(\mathrm{f}\) is the number of Byzantine fault nodes in the system) valid prepare messages from non-self nodes, it sends a commit message to other nodes. When a node receives \(2\mathrm{f}+1\) valid commit messages from different nodes, it aggregates all the commit messages and sends a reply to the client. The consensus process concludes when the client receives reply messages from f + 1 different nodes.  
Most instant sharding blockchains use PBFT or its variants as their intra-shard consensus protocol [20]. These variants [23; 27; 34] adjust the validation content and propagation method to increase tolerance to node inflation and Byzantine failures.
## 6 Discussion  
### Acoustical Aspects  
The measurements in the flow duct have clearly shown the additional effect of the flexible cell structure with respect to the acoustic absorption of the novel liner structure. Additional measurements--firstly, of the isolated structure with cut-outs and flexible films and secondly, the in-cell-microphone results--have proven the involved movement of the flexible wall material. However, so far little is known about the actual deflection (e.g., spatial modes) of the flexible walls. There are several main research questions, which still have to be answered:  
* What is the optimum frequency for the excitation of the movement of the flexible wall? To answer this question, several efforts are required to obtain a profound description of the physical mechanism, which finally enables a precise design of the novel structures and the prediction of its performance. For the current design, it was aimed for eigenfrequencies of the flexible wall near the Helmholtz resonance of the cavity structure.
* How can the attached flexible film material be modelled? How reproducible is the assembly? Dedicated experiments measuring the spatial distribution of the actual displacement might be needed to answer these questions and model the flexible elements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(I_{z}\) & **mm** & **40** & **35** & **30** & **25** & **20** \\ \hline \(l_{x}=l_{y}\) & mm & 16.5 & 17.6 & 19 & 20.8 & 23.3 \\ \(\tilde{\delta}_{max}\) & mm & 1.01 & 0.92 & 1.02 & 0.95 & 0.96 \\ \(S_{\delta}\) & mm & 0.12 (12.5\%) & 0.14 (15.1\%) & 0.25 (24.7\%) & 0.18 (18.8\%) & 0.08 (8.2\%) \\ \(\overline{F}_{z}^{\mathit{c}u}\) & MPa & 3.79 & 3.6 & 3.12 & 2.86 & 2.34 \\ \(S_{F}\) & MPa & 0.14 (3.9\%) & 0.09 (2.6\%) & 0.28 (8.9\%) & 0.06 (2.3\%) & 0.06 (2.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of compression tests of honeycomb core structure.  
Figure 12: Deformation of honeycomb core structure samples under compression load.  
* How can the overall liner design be optimized? Currently, the inactive cells reduce the overall active liner area. What is the optimal size for the inactive cavities and can different inactive cells be coupled? The above questions cover only a small part of possible future research on the acoustical aspects of this novel liner configuration. However, answers to these questions are mandatory in order to understand the actual mechanisms involved and model the acoustic performance at least to a certain degree of accuracy. The effect of mean flow on the acoustic damping performance has not been shown in the current paper. However, measurements have been made with the existing samples. The observed behaviour coincides with the well-known phenomena for SDOF liners: a reduction of performance in mean flow and an increase in performance against the mean flow direction. Furthermore, it can be assumed that means to reduce non-linear effects of the face sheet resistance under grazing flow conditions, e.g., a fine wire mesh covering the face sheet of the liner, will also be applicable to the novel liner concept.