esa-sceva/satcom-chunk-collection huggingface dataset:
# Thurstonian Scaling for Sensory Discrimination Methods
## 4 Discussion
### Future Trends  
Overall, the use of sensory evaluation methods for detecting food contamination is evolving alongside technological advancements, offering both speed and precision in food quality control. [PERSON] [72] accounts for the interest and importance of food quality and safety in food production by summarizing the volume of work published in _Food Quality and Safety_ (FQS) between 2017 and 2022. The future of sensory evaluation for monitoring food safety is driven by advancements in technologies and the increasing demand for real-time, accurate assessments. One of the most effective uses of sensory evaluation for food quality and safety monitoring is the establishment of data relationships between perceptual sources of data and instrumental assessments [73]. Several trends are shaping the future of this field, including the following: (1) integration of digital technologies that mimic human sensory systems (electronic noses and tongues) [74]; artificial intelligence (AI) and machine learning (ML) with algorithms to interpret sensory data [75]; (2) portable and real-time sensors allowing real-time monitoring for immediate feedback along stages of the supply chain [76], though these are often still used in combination with traditional sensory methods for assessing and understanding food quality [77]; Internet of Things (IoT) integration (smart sensors) [76]; (3) nanotechnology (nano-biosensors to detect contaminants at molecular levels) [74]; (4) enhanced consumer sensory feedback (crowdsourcing sensory data, integration of real-time data with smart devices); (5) non-destructive imaging testing techniques to assess food quality and safety without damage to the product [76; 78]; (6) personalized food safety monitoring (wearable and smart devices) for real-time assessments in food production, at home, or in restaurants [79; 80]; (7) food microbiome detection to predict spoilage or contamination even before it becomes perceptible through traditional sensory methods [76], and advances continue for shelf-life prediction models with development of new models foreseen [81].  
In summary, future trends in sensory evaluation for food safety will rely heavily on emerging technologies such as AI, IoT, nanotechnology, and non-destructive testing methods, enabling faster, more precise, and more accessible monitoring systems. These innovations aim to improve food safety at all stages of the supply chain and empower researchers and consumers with better tools. These emerging techniques should benefit from analysis in conjunction with predictive perceptual models to provide precise, quantitative assessments of food safety and help in developing novel methods for detecting food contamination or adulteration [82]. Such applications are critical for both research and industry, where maintaining high standards in food quality is essential. Applied use of Thurstonian modeling of sensory discrimination results serves to contribute to this evolution.
# Order-Preserving Multiple Pattern Matching in Parallel
## 3 Parallel Algorithms for the OPMPM Problem
### Parallel OPMPM Algorithm Using the Aho-Corasick Automaton  
Our parallel algorithm for the OPMPM problem using the Aho-Corasick automaton consists of the following steps: In the preprocessing phase, we create prefix tables using \(O(M)\) threads in \(O(\overline{m})\) time. That is, the prefix table of each pattern is calculated in parallel by assigning \(|P_{a}|\) threads for each pattern \(P_{a}(1\leq a\leq k)\). In the prefix table \(\mu_{P_{a}}\) of pattern \(P_{a}\), each thread \(t(1\leq t\leq|P_{a}|)\) linearly searches \(P_{a}[1..t-1]\) to calculate \(\mu_{P_{a}}[t]\). See Algorithm 1. Thus, a set of prefix tables for all patterns can be calculated in \(O(\overline{m})\) time using \(O(M)\) threads. To create an automaton using \(\mu_{P_{a}}\), the existing algorithm [1] method is employed.  
```
Input: A set of strings \(\{P_{1},P_{2},\ldots,P_{k}\}\) Output: A set of prefix tables \(\{\mu_{P_{1}},\mu_{P_{2}},\ldots,\mu_{P_{k}}\}\)
1 parallel for\(a\gets 1\)to\(k\)do
2 parallel for\(t\gets 1\)to\(|P_{a}|\)do
3\(\mu_{P_{a}}[t]\gets 1\)for\(i\gets 1\)to\(t-1\)do
5 if\(P_{a}[i]<P_{a}[t]\)then
6\(\mu_{P_{a}}[t]\leftarrow\mu_{P_{a}[t]+1}\)
```  
**Algorithm 1** Preprocessing phase (parallel calculation of prefix tables for a pattern set).  
In the search phase, \(T\) is divided into \(b\) blocks and each block is searched in parallel using \(b\) threads (Figure 2). Thread \(t(1\leq t\leq b)\) searches each block of \(T\) for the location of the substring that is order-isomorphic to \(P_{a}\) using the automaton created in the preprocessing phase. See Algorithm 2. Since the substring of \(T\) that is order-isomorphic to \(P_{a}\) can occur across two adjacent blocks, all threads except the last one set the block size to  
Figure 1: Example of the Aho–Corasick automaton for OPMPM.  
\(\lceil n/b\rceil+\overline{m}-1\) (Figure 2). Since the insertion, deletion, and rank calculation operations in the order-statistics tree require \(O(\log\overline{m})\) time during the search phase, they are conducted in \(O((n/b+\overline{m})\log\overline{m})\) time. If we set \(b=\lceil n/\overline{m}\rceil\), the search phase can be conducted in \(O(\overline{m}\log\overline{m})\) time. Thus, this parallel algorithm can be solved as an OPMPM problem in \(O(M+\overline{m}\log\overline{m})\) time using \(O(max(M,\lceil n/\overline{m}\rceil))\) threads.
# Predicting Runoff from the Weigan River under Climate Change
## 2 Data Sources and Methods
### Research Methodology
#### 2.3.2 \(r\)-Multi-Model Ensemble  
Due to the systematic errors and uncertainties in the data of each climate model, the effects on different meteorological elements are not consistent. A single \"optimal\" model has a large bias, which often has a large impact on the simulation and assessment of future climate change. [PERSON] et al. [26] and others have shown that the results of ensemble averaging of model data are better than most individual models. Therefore, this study adopted the Multi-Model Ensemble (MME) averaging method based on Pearson's \(r\)-correlation coefficient (\(r\)-MME) to eliminate previous variability in and uncertainty of individual model data, reduce systematic errors and, thus, improve the accuracy of future climate change prediction. The calculation formula is as follows:  
\[F_{r-MME}=\sum_{i=1}^{n}F_{i}\times\left(r_{i}/\sum_{i=1}^{n}r_{i}\right) \tag{5}\]  
where \(F_{r-MME}\) is the result of the \(r\)-MME averaging method based on Pearson's \(r\)-correlation coefficient; \(F_{i}\) is the simulation result of each single mode; \(r_{i}/\sum_{i=1}^{n}r_{i}\) is the weight based on the correlation coefficient \(r\) for the corresponding pattern; and \(r_{i}\) is the correlation coefficient between each single model and the observation data within a historical period.
# Exploring External Peak Demands: The Influence of Contextual Factors on Male Basketball Players
## 3 Results  
The mean values, standard deviations, and coefficients of variation for the PL variables in each group, based on the contextual factors, are presented in Table 1. These values are shown for time windows of 30 s, 1 min, and 3 min. Table 2 presents the results of the data analysis, including \(p\)-values for the differences between groups and ES with 95% confidence intervals. Regarding the playing time factor, the core group obtained significantly higher results than the occasional group for both the 1 min (\(p<0.05\), ES = 0.28) and 3 min windows (\(p<0.05\), ES = 0.36).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PLAYING TIME** & & \\ \hline _Sample Duration_ & _Effect Size_ & \(95\%CI\) & \(p\) \\ \hline \multicolumn{4}{c}{30 s Time Window} \\ \hline Top vs. Core & 0.03 & \(-\)0.31, 0.36 & 1.000 \\ \hline Top vs. Occasional & 0.14 & \(-\)0.22, 0.50 & 0.962 \\ \hline Core vs. Occasional & 0.10 & \(-\)0.21, 0.41 & 0.941 \\ \hline \multicolumn{4}{c}{1 min Time Window} \\ \hline Top vs. Core & \(-\)0.03 & \(-\)0.36, 0.30 & 1.000 \\ \hline Top vs. Occasional & 0.27 \({}^{*}\) & \(-\)0.10, 0.62 & 0.240 \\ \hline Core vs. Occasional & 0.28 \({}^{*}\) & \(-\)0.03, 0.59 & 0.040 \\ \hline \multicolumn{4}{c}{3 min Time Window} \\ \hline Top vs. Core & \(-\)0.10 & \(-\)0.43, 0.24 & 1.000 \\ \hline Top vs. Occasional & 0.39 \({}^{*}\) & 0.03, 0.76 & 0.270 \\ \hline \multicolumn{4}{c}{**TEAM VENUE**} \\ \hline _Sample Duration_ & _Effect Size_ & \(95\%CI\) & \(p\) \\ \hline \multicolumn{4}{c}{30 s Time Window} \\ \hline Home vs. Away & \(-\)0.01 & \(-\)0.29, 0.27 & 1.000 \\ \hline Home vs. Neutral & 0.15 & \(-\)0.29, 0.59 & 0.729 \\ \hline \multicolumn{4}{c}{Away vs. Neutral} & 0.15 & \(-\)0.29, 0.59 & 1.000 \\ \hline \multicolumn{4}{c}{1 min Time Window} \\ \hline Home vs. Away & 0.07 & \(-\)0.21, 0.35 & 1.000 \\ \hline \multicolumn{4}{c}{Home vs. Neutral} & 0.07 & \(-\)0.37, 0.51 & 1.000 \\ \hline \multicolumn{4}{c}{Away vs. Neutral} & 0.00 & \(-\)0.45, 0.44 & 1.000 \\ \hline \multicolumn{4}{c}{3 min Time Window} \\ \hline Home vs. Away & 0.19 & \(-\)0.10, 0.47 & 0.145 \\ \hline \multicolumn{4}{c}{Home vs. Neutral} & 0.16 & \(-\)0.28, 0.59 & 1.000 \\ \hline \multicolumn{4}{c}{**SCORE OUTCOME**} \\ \hline _Sample Duration_ & _Effect Size_ & \(95\%CI\) & \(p\) \\ \hline \multicolumn{4}{c}{30 s Time Window} \\ \hline Win vs. Loss & 0.00 & \(-\)1.60, 1.60 & 0.699 \\ \hline \multicolumn{4}{c}{1 min Time Window} \\ \hline Win vs. Loss & \(-\)0.03 & \(-\)0.33, 0.27 & 0.784 \\ \hline \multicolumn{4}{c}{3 min Time Window} \\ \hline Win vs. Loss & \(-\)0.10 & \(-\)0.40, 0.20 & 0.305 \\ \hline \multicolumn{4}{c}{**MATCH NATURE**} \\ \hline _Sample Duration_ & _Effect Size_ & \(95\%CI\) & \(p\) \\ \hline \multicolumn{4}{c}{30 s Time Window} \\ \hline Official vs. Non-Official & 0.15 & \(-\)0.27, 0.57 & 0.228 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pairwise comparison of peak Player Load demands across different contextual factors analyzed.
## 3 Timed Petri Net and Retention-Free Petri Net
On the other hand, with the firing of transition \(t_{O}\), token amounts flowing out of place \(p\) per unit of time are called \"output token-flow\" and are denoted by \(TF_{p,t_{O}}\). \(TF_{t_{1},p}\) and \(TF_{p,t_{O}}\) (shown in Figure 6) are defined by the following equations, respectively:  
\[TF_{t_{1},p} = f_{1}\beta_{1} \tag{3}\] \[TF_{p,t_{O}} = f_{O}\alpha_{O}, \tag{4}\]  
where \(f_{1}\) and \(f_{O}\) are the firing frequencies of \(t_{1}\) and \(t_{O}\), respectively, and \(\beta_{1}\) and \(\alpha_{O}\) are the weights of \(e=(t_{1},p)\) and \(e=(p,t_{O})\), respectively. Based on this definition, the following proposition holds. Proposition 1: (23) Let \(p\) be a place with input transitions \(\{t_{I_{1}}|t_{I_{1}}\in\)\({}^{\bullet}p\}\) and output transitions \(\{t_{O_{j}}|t_{O_{j}}\in\)\(p^{\bullet}\}\). Then, \(\sum_{i=1}^{m}TF_{t_{1},p}\) and \(\sum_{j=1}^{n}TF_{p,t_{O_{j}}}\) are the total input token-flow and the total output token-flow for place \(p\), respectively. Furthermore, when firing frequency \(f\) is at the maximum firing rate, \(\overline{f}\), input token-flow \(TF_{t_{1},p}\) and output token flow \(TF_{p,t_{O}}\) become the maximum, \(\overline{FT_{t_{1},p}}\) and \(\overline{FT_{p,t_{O}}}\), respectively. These maximum token-flows satisfy the following equations. \[\sum_{i=1}^{m}TF_{t_{1},p} \leq \sum_{i=1}^{m}\overline{FT_{t_{1},p}} \tag{5}\] \[\sum_{j=1}^{n}TF_{p,t_{O_{j}}} \leq \sum_{j=1}^{n}\overline{FT_{p,t_{O_{j}}}} \tag{6}\]  
The following requirement is trivial. Figure 6: Input and output token flows. Proposition 2: [23] _In a timed Petri net, the total output token-flow is not more than the total input token-flow for each place \(p\):_  
Figure 5.0  
In a signaling pathway, a reaction not only modifies the formation of molecules, as mentioned above, but also controls the amounts of produced substances. Although the amount of a substance will change along the signal propagation, it cannot be increased indefinitely; otherwise, an abnormal accumulation of a substance may cause a crucial problem in the signaling pathway. Based on this observation, we introduced the notion of a \"retention-free\" pathway for a timed Petri net. In a retention-free Petri net, the total token amounts flowed-in and flowed-out for each place per unit of time are equivalent. Definition 8: [23] A timed Petri net \(TPN\) is called a retention-free Petri net (\(RFPN\)) (satisfying Proposition 1) if the total input token-flow and total output token-flow are equivalent at any place of \(TPN\), that is,  
Figure 5.1  
In fact, there exists at least one T-invariant in an \(RFPN\), which indicates a firing counting vector of transitions in a periodic firing sequence [26] that leads back to the marking where it starts. This is because actually, the firing frequencies of all of the transitions construct a T-invariant.
# Implementation of a Blood Cold Chain System Using Blockchain Technology
###### Abstract
We describe the proposed architecture, based on which blood transactions between medical institutions can be completed in a fully distributed manner. Due to the functionality of blockchain technology, the proposed blood cold chain system in blockchain can enhance information visibility. Blood banks and blood test centers, transportation vehicles, and medical institutions are participants in the blood cold chain system. Because blood has a large demand uncertainty, supply agility among cold chain participants is required to quickly respond to changes in demand. Our main contributions are summarized as follows:  
* We propose an innovative blockchain-based blood cold chain system in order to overcome the limitations of current centralized blood management systems. Compared with the conventional centralized system, the proposed system can significantly increase the visibility of the information of the blood cold chain system by recording the overall information of blood supply and detailed information such as consumption and disposal in the distributed ledger (see Sections 3.2.1 and 3.2.2). * We designed a new type of blood supply system that directly transacts blood among medical institutions when a medical institution with an urgent demand cannot receive blood quickly from a blood bank (see Section 3.2.4). In a centralized blood supply system, it is virtually impossible to receive from a supply point other than the blood bank. A fatal problem exists that endangers a patient's life when an emergency occurs in a hospital far from the blood bank. * We implemented a comprehensive prototype of the blockchain-based blood cold chain system using Hyperledger Composer, of which the implementation demonstrates that information visibility can be gained by creating and tracking a single record per supply point and reducing blood supply time in emergencies (see Section 4). The rest of the paper is organized as follows: Section 2 reviews the previous research on blood cold chains and the features, types, and techniques of blockchain. It also provides examples of blockchain applications in the supply chain and healthcare sectors. Section 3 presents the new blood cold chain system methodology based on Hyperledger Fabric technology and describes the design method according to two environmental configurations. Section 4 presents the development environment and the characteristics of Hyperledger Composer along with its implementation results. Finally, Section 5 concludes the paper.
## 5 Conclusions  
This paper proposes block iteration with the parallel method to improve the efficiency of the greedy selection based on RBF mesh deformation. The block iteration method is proposed based on the fact that the specific selection way has the iterative feasibility. It could reduce the computational complexity of matrices construction and inversion from \(O(n^{3})\) to \(O(n^{2})\) in the process of the greedy selection. The parallelization addresses the load imbalance problem of the selection process and improves the efficiency of data reduction, based on the independence of the boundary point. To adopt the block iteration with the parallel method, the efficiency of greedy selection in RBF deformation is greatly improved while keeping the data accuracy. Three models are tested via this proposed method and validate the good performance of the method. The model tests show that the total speedup ratio of each test could achieve 17.35 on average via the block iteration with the parallel method.  
Conceptualization, R.Z. and C.L.; data curation, R.Z., S.F. and Y.W.; formal analysis, R.Z., C.L. and X.G.; funding acquisition, C.Y.; investigation, R.Z., C.L. and X.G.; methodology, R.Z. and C.L.; project administration, R.Z. C.Y. and C.L.; resources, R.Z., C.L. and X.G.; software, R.Z.; supervision, C.L. and X.G.; validation, R.Z. and C.L.; visualization, R.Z., S.F. and Y.W.; writing--original draft, R.Z.; writing--review and editing, C.Y. and C.L.  
This research was funded by the National Natural Science Foundation of China Grant No. 61872380, 61802426 and 61806213  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Time Comparison (s)** & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) & \(f\) \\ \hline Origin (\(1.5\times 10^{-4}\)) & 9.28 & 125.54 & 3.64 & 745.05 & 348.93 & 50.01 \\ Block iteration (\(1.5\times 10^{-4}\)) & 0.06 & 1.21 & 3.54 & 3.45 & 361.26 & 65.21 \\ Block with parallel (\(1.5\times 10^{-4}\)) & 0.06 & 1.16 & 3.57 & 0.34 & 36.41 & 83.74 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time comparison of three-dimensional Super-cavitating Hydrofoil (value of \(\xi_{tol}\) is in the brackets, number of cores is 12).  
Figure 12: Time cost and speedup of three-dimensional Super-cavitating Hydrofoil. (**a**) Time Comparison of three-dimensional Super-cavitating Hydrofoil. (**b**) Speedup Ratio and Parallel Efficiency of three- dimensional Super-cavitating Hydrofoil.  
**Acknowledgments:** The proposed methods are tested via OpenFOAM.  
**Conflicts of Interest:** 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.
# Research Developments of Aerostatic Thrust Bearings: A Review
## 3 Advances in Static Performance
### Structure Improvements
#### 3.3.2 Introduction of Surface Structures  
The static performance of the ATBs is not only closely related to the type of restrictor but also the surface structure. Scholars have found that the static performance of bearings can be improved by changing the surface profile of ATBs. Among these surface structures, the most widely used is the pressure-equalizing groove structure [67,68], as depicted in Figure 10. The studies of surface structures are introduced separately below.  
4. Pressure-equalizing Grooves  
Compared with the traditional ATBs, the static performance of ATBs with pressure-equalizing grooves has been greatly improved. [PERSON] investigated the static performance of aerostatic bearings with trapezoidal pressure-equalizing grooves. The load capacity of the bearing was enhanced by \(18\%\)[67]. [PERSON] designed an aerostatic bearing with an elastic equalizing pressure groove. The bearing stiffness was enhanced by \(59\%\) compared to bearings without equalizing pressure grooves [68]. [PERSON] carried  
Figure 9: Aerostat thrust bearings with micro holes [33]: (**a**) configuration of ATBs with micro holes, (**b**) the pressure simulation results.  
out numerical simulations on the shapes of pressure-equalizing grooves and studied the static performance of bearings [69]. Six kinds of pressure-equalizing grooves were compared, including the line-shape, the extended-shape, the S-shape, the oblong-shape, the X-shape, and the reticular-shape. The load capacity and stiffness of the bearings can be greatly improved after optimizing the structure of the pressure-equalizing groove, and the pressure-averaging effect can be strengthened. Among the six shapes of pressure-equalizing groove, the reticular shape had the best static performance.
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 5 Discussion
We demonstrate that the uncertainty model displays a good fit with the standard deviation of the in situ differences. However, the conceptual cone shape may well be an overly simplistic representation of the true relationship between the total uncertainty and the in situ differences. For instance, LST validation has a non-negligible site component, which is a function of factors such as its heterogeneity and elevation. Further quantification of these factors is necessary in order to evolve the LST uncertainty budget with the aim of meeting climate quality standards.
## 4 Results of System Performance
### Results of Fusion Approach of Deep Features Maps  
These are the results of performing diagnosis of WSIs of cervical abnormal cells by ANN according to integrating features extracted by VGG-16 and GoogLeNet models. Because CNN models obtained no satisfactory results, the features were extracted from both GoogLeNet and VGG-16 and combined into the same feature vectors. When the deep features of the VGG-16 and GoogLeNet models were combined, the hybrid feature vectors were high-dimensional. Therefore, the PCA method was used for reducing the fused features. Finally, ANN received feature vectors of low dimension for its classification.  
Table 4 shows the results of the ANN with the hybrid features of both VGG-16 and GoogLeNet for diagnosing WSIs of abnormal cells for the cervical cancer data set. This  
Figure 8: Results of the diagnosis of WSIs of abnormal cells of the cervix by (**a**). VGG-16 + SVM and (**b**). GoogLeNet + SVM.  
technique reached an accuracy of 98.5%, specificity of 99.8%, sensitivity of 98.4%, an AUC of 98.92% and precision of 98.2%.  
Figure 9 shows the confusion matrix generated by ANN performance with hybrid features extracted by VGG-16 and GoogLeNet models to classify five types of abnormal cervical cancer cells. Class 1 refers to cervix_dyk, class 2 to cervix_koc, class 3 to cervix_mep, class 4 to cervix_pab, and class 5 to cervix_sfi. The ANN network based on features fused achieved an accuracy for each class of 98.1%, 99.1%, 98.4%, 98.2% and 98.6% for the classification of cervix_dyk, cervix_koc, cervix_mep, cervix_pab and cervix_sfi, respectively.  
Regarding samples that the ANN based on the fused of the CNN features failed to diagnose correctly: For the cervix_dyk class, the system failed to diagnose 19 images correctly. For class cervix_koc, the system failed to diagnose 9 images correctly. For class cervix_mep, the system failed to diagnose 16 images correctly. For the class cervix_pab, the system failed to diagnose 18 images correctly. For the cervix_sfi class, the system failed to diagnose 14 images correctly.