esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Efficient Approach towards Computational Fluid Dynamics Simulations on Quantum Devices
## 3 Preliminaries
### Reducing Model Complexity
#### 3.3.2 Reduced-Order Linearized Model
\begin{split}\frac{\partial}{\partial t}\hat{x}_{i}^{\prime}(t)& =\hat{A}_{i}\hat{x}_{i}^{\prime}(t)+\hat{B}_{1i}u(t)+\hat{B}_{2i}\\ \hat{y}_{i}(t)&=\hat{C}_{i}\hat{x}_{i}^{\prime}(t)+C _{0i},\end{split}  \tag{39}\]  
where the reduced-order matrices are given by  
\[ \begin{split}\hat{A}_{i}&=V^{T}A_{i}V\\ \hat{B}_{1i}&=V^{T}B_{1i}\\ \hat{B}_{2i}&=V^{T}B_{2i}\\ \hat{C}_{i}&=C_{i}V.\end{split}  \tag{40}\]  
Each ROM has its own range of validity, as seen in the updated Figure 4 denoted by the orange circles. The orange dashed line represents the approximation made to the training trajectory A calculated along the points \(x_{1}^{{}^{\prime}}\), \(x_{2}^{{}^{\prime}}\) and \(x_{3}^{{}^{\prime}}\) in the reduced space, which correspond to \(x_{1}\), \(x_{2}\) and \(x_{3}\) points in the original system. The last step is to apply a weighting function on the ROMs. Using the procedure described in [48], the piecewise-linear ROM becomes  
Figure 4: Collection of reduction points \(x_{1}^{{}^{\prime}}\), \(x_{2}^{{}^{\prime}}\) and \(x_{3}^{{}^{\prime}}\), along with the previously highlighted linearization points \(x_{0}\), \(x_{1}\), \(x_{2}\) and \(x_{3}\) in a 2D state-space. Orange circles denote suitable regions for use for each reduced-order model, whereas the orange dashed line represents an approximation to the training trajectory A.  
Figure 3: Collection of linearization points \(x_{0}\), \(x_{1}\), \(x_{2}\) and \(x_{3}\) in a 2D state-space. Blue circles denote suitable regions for use at each linearization point. Trajectory A is called the training trajectory. \[ \begin{split}&\sum_{i=0}^{s-1}w_{i}(x)\bigg{\{}\frac{\partial}{ \partial t}\hat{s}_{i}^{\prime}(t)=\hat{A}_{i}\hat{x}_{i}^{\prime}(t)+\hat{B}_{ 1i}u(t)+\hat{B}_{2i}\bigg{\}},\\ &\sum_{i=0}^{s-1}w_{i}(x)\bigg{\{}\hat{y}_{i}(t)=\hat{\mathcal{ C}}_{i}\hat{x}_{i}^{\prime}(t)+\mathcal{C}_{0i}\bigg{\}},\end{split}  \tag{41}\]  
where \(w_{i}(x)\) are state-dependent weights. A typical choice is to let \(w_{i}(x)\) be large for \(x=x(t)\) close to \(x_{i}\), and small otherwise. For other, more advanced schemes, refer to [50].
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Performance Analysis
#### 6.2.1 Feature Comparison  
To analyze the functional advantages of the proposed sharing scheme, this section compares it with existing medical data sharing schemes. As shown in Table 3, we compare our proposed scheme with the schemes in [29; 30; 31; 32] in terms of original data storage location, anonymity, resistance to data mining threats, decentralized identity authentication, and conditional query functionality. Resistance to data mining threats indicates the ability to withstand threats to patient privacy exposure due to data mining.  
Refs. [29; 30; 32] and our proposed scheme all use pseudo-identity information for patients to protect patients' privacy. In contrast, ref. [31] chose to enter patients' real identifying information into the system and relied on access control policies to determine whether different users are authorized to access patients' personal information. This scheme provides security but shows limitations in maintaining user anonymity.  
Compared to the other literature, ref. [29] and the approach proposed in this paper are more closely aligned in terms of methodology, as both adopt a dual-channel mechanism for managing medical data, thus enhancing data security. However, refs. [29; 30; 32] still directly associates the pseudo-identity information used on the blockchain with the medical record number, and the medical record number has not undergone appropriate privacy protection. These schemes fail to fully defend against the risk of data mining attacks. The scheme proposed in this paper has better protection measures for privacy.  
In terms of identity verification, both [31] and the scheme proposed in this paper conduct decentralized user authentication on the blockchain, providing stronger privacy protection. Simultaneously, our scheme optimizes the efficiency of blockchain conditional queries, significantly reducing the time cost required for such queries. These improvements ensure that the sharing of EMR is both secure and efficient, offering a novel solution for the field of medical data sharing.
## 3 Conceptual Framework and System Overview
### Theoretical and Developmental Basis of the User Experience Design
#### 3.1.2 Educational Foundations for Emotionally Supportive Interaction  
To address these developmental challenges, this system draws on educational and psychological models that promote emotionally meaningful support and guided interaction. These models position the child not as a passive recipient of control, but as an active participant who can grow within supportive interpersonal and environmental structures.  
This aligns with the self-regulated learning framework, which views children as metacognitively, motivationally, and behaviourally active participants in their own learning process [61]. [PERSON]'s concept of the Zone of Proximal Development (ZPD) forms a key theoretical foundation, describing the gap between what a child can do independently and what they can achieve with suitable support [62]. In this framework, self-regulation is seen not as a static ability but as a skill nurtured through support from a more capable other. The system functions within the ZPD of the child as an emotionally attuned guide, offering cues, feedback, and narrative framing to scaffold the regulatory behaviour of the child over time [29].  
This support is provided through symbolic play, a developmentally appropriate way for preschoolers to explore roles, internalise rules, and regulate emotions. By framing screen-time transitions as a form of narrative role-play, the system turns a potentially forced separation into a self-initiated, emotionally positive experience, allowing the child to engage with the transition through imagination, agency, and symbolic meaning. This aligns with the view that symbolic play is a natural and effective means of learning self-regulation in early childhood [63].  
The character in the system also functions as a behavioural model, based on social learning theory [64]. Through repeated, emotionally engaging interactions, the child is encouraged to observe, imitate, and internalise appropriate behaviours for managing transitions. The positive and empathetic tone of the character further fosters trust and emotional safety, resembling the affective security of a secure attachment [65]. Thus, the UX design is cognitively supportive and relationally grounded.  
Collectively, these educational foundations inform the focus of the design on empathy, agency, and emotional resonance. Rather than issuing fixed commands, the system engages the child in a developmentally scaffolded, playful, and pedagogically sound interaction that fosters self-regulation.
###### Abstract  
The main objective of this research work was to evaluate the variation in nutritional profile, antioxidant, anti-proliferative and antibacterial activities of selected species of _Brassica_. Five locally grown _Brassica_ species (cauliflower, broccoli, red cabbage, white cabbage and Chinese cabbage) were collected from Ayub Agriculture Research Institute (AARI), Faisalabad, Pakistan. Polyphenolic rich extracts of these Brassicaceae species were prepared by Soxhlet extraction technique using ethanol. Phenolic acids, flavonoids and sugar contents of the investigated species were determined and quantified by RP-HPLC. Antioxidant activity was carried out by measurement of total phenolic contents (TFC), total flavonoid contents (TFC), reducing potential and 2, 2-diphenyl-1-picry/hydraz1 (DPPH) radical scavenging capacity. Anti-proliferative activity of all the extracts was determined by MTT assay on lung cancer cell line A549. Antibacterialarily was tested against the two bacterial strains, i.e., _Bacillus_ cereus (_B. cereus_) and Escherichia coli (_E. coli_). HPLC analysis revealed the presence of gallic acid, _p_-cumaric acid, chlorogenic acid and benzoic acid as the major phenolic acids, whereas catechhe was the major flavonoid in most of the extracts. The TPC ranged from 9.7 to 32.8 mg/g of dry plant material, measured GAE and TFC ranged from 7.7 to 23.7 mg/g of dry plant material, measured as CE. Higher TPC and TFC were found in red cabbage extract followed by cauliflower, broccoli, white cabbage and Chinese cabbage. Red cabbage extract also showed higher DPPH radical scavenging activity (IC\({}_{50}\) = 2.3 mg/mL) followed by cauliflower, broccoli, white cabbage and Chinese cabbage. Maltodextrose was the major sugar followed by fructose in all species of _Brassica_. Promising anti-proliferative and antibacterial activities were also recorded by the selected _Brassica_ extracts.  
Keywords:antioxidant capacity; Brassicaceae; anti-proliferative; antibacterial activities +
Footnote †: journal: _Applied Sciences_
## 3 Results and Discussions
### The Importance of Non-Geological Methods in Urban Flood Control
For example, for the urban flood disaster caused by the \"720\" rainstorm in study area, the combined efforts of the two methods can only remove 49.14% of the rain and flood. This is the reason behind the extremely serious consequences caused by the \"720\" torrential rainstorm in Zhengzhou. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Rainfall** \\ **Intensity/Return** \\ **Period** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Rainfall** \\ **(mm/d)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Combining Two** \\ **Methods Can** \\ **Solve the** \\ **Rainfall (\%)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **The Proportion of** \\ **Geological** \\ **Methods Solved** \\ **(\%)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Reduced** \\ **Rainwater Storage** \\ **Capacity (10\({}^{6}\) m\({}^{3}\))** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Combining Two** \\ **Methods Can** \\ **Solve the** \\ **Raposition of** \\ **Rainfall (\%)** \\ \end{tabular} } \\ \hline Light rain & 10 & 480.91 & 19.75 & 20.50 & 406.36 \\ (upper limit) & 25 & 224.84 & 23.72 & 24.19 & 189.66 \\ Heavy rain & 50 & 139.48 & 28.27 & 30.32 & 117.43 \\ (upper limit) & 76 & 110.28 & 31.45 & 36.71 & 92.72 \\ Rainstorm & 100 & 96.80 & 33.56 & 42.60 & 81.31 \\ (upper limit) & 111 & 92.57 & 34.35 & 45.30 & 77.74 \\ Once in 5 years & 127 & 87.73 & 35.35 & 49.23 & 73.63 \\ Once in a decade & 149 & 82.77 & 36.49 & 54.63 & 69.43 \\ Once in 30 years & 184 & 77.32 & 37.91 & 63.22 & 64.82 \\ Once in 50 years & 200 & 73.35 & 38.47 & 67.15 & 63.25 \\ Once in a century & 222 & 73.35 & 39.09 & 72.55 & 61.46 \\ Once in 200 years & 244 & 71.61 & 39.64 & 77.95 & 59.99 \\ Heavy rainstorm & 250 & 71.07 & 39.84 & 79.42 & 59.52 \\ (upper limit) & 273 & 67.24 & 41.77 & 85.07 & 55.90 \\ Once in a millennium & 295 & 64.13 & 43.50 & 90.47 & 52.98 \\ The 3-day rainfall of & 617.1 & 49.14 & 58.06 & 199.08 & 37.40 \\ the “720” & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The important role of geological prevention and control methods of flood and waterlogging disasters in the study area.
## 4 Privacy and Security Enhanced Methods
### Credit-Heterogeneity-Based Executor Scheduling Strategy
#### 4.2.1 Quantification of Executor Heterogeneity and Credibility  
**Heterogeneity Calculation.** Assume the executor pool contains \(n\) heterogeneous executors, denoted as \(P_{1},P_{2},\ldots,P_{n}\). The heterogeneity between executors \(P_{i}\) and \(P_{j}\) is defined as  
\[H_{i,j}=\frac{\text{max}(v_{i},v_{j})-v_{i,j}}{\text{max}(v_{i},v_{j})}, \tag{12}\]  
where \(v_{i}\) is the cardinality of vulnerability set \(\mathbf{V}_{i}\) for executor \(P_{i}\), and \(v_{i,j}\) is the cardinality of common vulnerability set \(\mathbf{V}_{i,j}\) between executors \(P_{i}\) and \(P_{j}\). This formula yields values in the range [0,1], with higher values indicating greater heterogeneity.  
**Credibility Calculation.** The credibility index \(R_{i}\) for executor \(P_{i}\) integrates vulnerability assessment with operational history:  
\[R_{i}=1-\frac{a_{i}\times\sum_{k=1}^{v_{i}}\text{CVSS}(V_{k}^{i})-\sum_{k=1}^ {v_{i}^{\prime}}\text{CVSS}(V_{k}^{i^{\prime}})+\sum_{k=1}^{v_{i}^{\prime}} \text{CVSS}(V_{k}^{i^{\prime}})}{\sum_{k=1}^{v_{i}}\text{CVSS}(V_{k}^{i^{ \prime}})}, \tag{13}\]  
where \(a_{i}\) is the weight coefficient for patched vulnerabilities:  
\[a_{i}=a\times n\times\frac{\sum_{k=1}^{v_{i}}\text{CVSS}(V_{k}^{i})}{\sum_{j= 1}^{n}\sum_{k=1}^{v_{i}^{\prime}}\text{CVSS}(V_{k}^{i^{\prime}})}. \tag{14}\]  
Here, \(a\) is the system-wide weight coefficient, and \(\text{CVSS}(V_{k}^{i})\) represents the common vulnerability scoring system value for vulnerability \(V_{k}^{i}\).
## 1 Introduction
To meet the explosive traffic growth and provide a better quality of experience (QoE), dense deployed HCNs will provide a higher system throughput and spectrum utilization than conventional networks. To achieve the above key performance metrics, scholars have proposed various spectrum sharing technologies [39; 40; 41]. In dense deployed HCN, the number of public telecommunication users and dedicated telecommunication users is very large, so the traffic characteristics of public telecommunication systems and dedicated telecommunication systems are inconsistent in time and space. In such a way, the application of spectrum sharing technology in HCN is very meaningful [40]. In this paper, we focus on the EE of the two-tier MHCP-MHCP network. We first derive the asymptotic coverage probability of the MHCP-MHCP network. Based on the works in [12; 13; 14; 15; 16], we extend the ASAPPP method to analyze and obtain the coverage performance of MHCP-MHCP. Unlike the work in [20], we adopt the maximum receiving power criterion instead of the nearest BS criterion. In this paper, we approximate each MHCP tier to PPP tier, so we call this approximation method as \"each tier ASAPPP (ET-ASAPP)\". On the basis of the coverage analysis, we derive and formulate the EE for the MHCP-MHCP network. The main contributions of this paper are as follows. First, we derive the asymptotic SIR gain in the MHCP model relative to that in the PPP model. Based on this, we derive the coverage probability of the MHCP-MHCP network with the use of the \"ET-ASAPPP\" method. Then, we derive and formulate the EE of the MHCP-MHCP network. Finally, we compare the coverage probability and EE of the MHCP-MHCP network with that of the PPP-PPP network. The remainder of this paper is organized as follows. Section 2 introduces the system model. Section 3 gives the ASAPPP Approach for Single-Tier MHCP Network. The coverage probability and EE of the MHCP-MHCP network are derived in Sections 4 and 5, respectively. Simulation results are discussed in Section 6 and the conclusion is given in Section 7.
# UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
## 1 Introduction
### Problem Statement  
The problem we aim to solve here is the selection of a set of representative features that can appropriately approximate the original feature space. The challenge is associated with the incremental updating of the set of selected representative features amongst all of the views clustered to date so that: (i) the number of views, which are different sets of features, increases over time so that existing instances can be represented by new additional views; (ii) the number of instances can also increase (i.e., online learning), and the new instances are shown in all the current views; and (iii) different instances are shown by different combinations of views. This paper contributes to (i) and (ii), and (iii) will be investigated in future work.  
There are two possible applications that we can describe to illustrate the problem we are addressing here. A healthcare application that deals with the prediction of early symptoms of a heart attack provides a good illustrative example. We could have a group of patients (i.e., instances) with different groups/views of medical tests (i.e., features). Let us assume that we initially have two groups/views of medical tests, namely medical tests belonging to a heart view and medical tests belonging to a physical therapy view (Figure 1a). We then have three possible scenarios: (1) new patients undergo all the medical tests of existing views (Figure 1a); (2) the current patients undergone additional medical tests, which relate to diabetes (additional view) (Figure 1b); and (3) different patients can undergo different medical tests presented in different views, but not all of them. In this paper, we have addressed the first and second scenarios, while the third one will be addressed in future work. This application example shows that both the number of views and the number of instances can increase at any time.  
The other application we consider is web page clustering, where a set of pages (i.e., instances) is presented in both text view and image view. The set of pages might be updated later on, in terms of views, with an additional video view. Additionally, the number of web pages can increase.  
The proposed method is not limited to the above-mentioned examples but is also applicable to broad applications of multi-view data where the instances can be represented by different feature spaces. Example applications could be web text translation by different languages [17], visual concept recognition [16], and gene selection [19].  
Formally, we consider \(\{X^{(v)},v=1,2,\dots\}\) a dataset of different views such that views can increase over time. Following the online learning, \(N=\{inst_{1},inst_{2},\dots\}\) is a group of instances in \(n_{v}\) views, where \(X^{(v)}\in R^{(N_{inst}\times D_{v})}\) and \(D_{v}\) is the dimension of the instances in the \(v^{th}\) view.
## 4 Results and Discussion
### Hydrological Evaluation of Satellite Precipitation Estimates
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Product** & **CC** & **RMSE (mm)** & **RB (\%)** \\ \hline Grid scale & IMERG-UC & 0.57 & 3.10 & \(-\)46.65 \\  & IMERG-C & 0.61 & 3.30 & \(-\)12.05 \\  & GSMaP-MVK & 0.52 & 5.78 & 59.25 \\  & GSMaP-Gauge & 0.75 & 2.41 & \(-\)3.19 \\ Basin scale & IMERG-UC & 0.74 & 1.65 & \(-\)46.07 \\  & IMERG-C & 0.79 & 1.60 & \(-\)12.19 \\  & GSMaP-MVK & 0.74 & 2.70 & 48.01 \\  & GSMaP-Gauge & 0.93 & 0.82 & \(-\)4.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical summary of the precipitation products at grid and basin scales for the year of 2015 in the upper Yellow River basin. GSMaP-Gauge performed the best again, while the NSE value of IMERG-C reached 0.63. However, the satellite-only products still had unsatisfactory performance with negative NSEs, suggesting they have low hydrological utility for this region. The simulation accuracy could be improved if the hydrology model was calibrated with different precipitation inputs. Subsequently, we recalibrated the model parameters using each satellite precipitation dataset during 2015. This scenario is also an alternative strategy for hydrological applications in ungauged basins where only satellite precipitation estimates are available [78; 79]. As shown in Figure 11, the simulation performances were effectively improved after the model was recalibrated. For example, the daily NSE of IMERG-C significantly increased from 0.18 to 0.63. Furthermore, the RMSE also significantly decreased for all satellite products. As summarized in Table 6, simulations of IMERG-C and GSMaP-Gauge products had good statistical agreement with observed streamflow at daily and monthly scale. However, the NSE values of both satellite-only products was still below zero at the daily scale, further confirming that the undesirable hydrological utility of these two satellite-only products is mainly due to the unreliable precipitation estimates. The errors existing in these two precipitation datasets was propagated to simulated streamflow, and could not be removed  
Figure 10: Daily observed and simulated streamflow with gauge benchmarked parameters over the upper Yellow River basin. Figure 9: Observed and Variable Infiltration Capacity (VIC) model simulated streamflow with the CCDPA precipitation for the calibration period (2009–2011), and validation period (2012–2014) over the upper Yellow River basin. upon model-parameter recalibration. Generally, the recalibration of the model parameters effectively improved the hydrological potential of satellite precipitation, especially for precipitation products with small errors; however, this recalibration approach should be taken with a grain of salt because it may result in unrealistic parameter values in some cases ([PERSON] _et al._, 2018; [PERSON] _et al._, 2018).
## 3 Thermal and Innovative Technologies for PPO Inactivation
### Ultrasound Processing  
Food products that are free of additives or preservatives have become a consumer demand for high quality and fresh products, especially juice products. This demand has led researchers or producers to employ ultrasonic technology in the production of fruit juice. Ultrasonic or ultrasound is a promising technology that can be used to replace traditional thermal treatment processes. It has the potential to improve the quality and safety of juice by converting electrical energy into mechanical energy through piezoelectric materials [47]. When sound waves pass through the fluid, continuous compression and relaxation will occur, and then the negative pressure overcomes the tensile force, the transmission of sound waves to the relaxed position causes the formation of micron-sized bubbles and spaces [49]. Ultrasound is also transmitted at frequencies higher than the audible frequency of 20 kHz [50]. The frequency of ultrasonic equipment is usually 20 kHz to 10 MHz [51].  
The applications of ultrasound in the food industry include low-energy and high-energy ultrasound. Low energy intensities of ultrasound, less than 1 W-cm\({}^{-2}\), and frequency greater than 100 kHz [47] can be used to monitor food composition and physicochemical properties of food during processing and storage, for quality control purposes. High-energy ultrasound is also called power ultrasound because the intensity is higher than 1 W-cm\({}^{-2}\), and the frequency range is 20 to 100 kHz [47]. Ultrasound can be applied to a product using two methods: submergence in an ultrasonic bath [52] or direct treatment to the product using a probe sonicator [53, 54].  
Figure 6 summarises PPO inactivation by ultrasound in fruit juice [28, 39, 55]. Ultrasound occurs at frequencies ranging from 20 kHz to 24 kHz, inactivating PPO by more than 80%. Ultrasound combined with heat, also known as thermosonication (TS), is another emerging preservation method. TS treatment on apple juice of different cultivars shows an inactivation PPO of more than 80% at various temperatures (ranging between 60 \({}^{\circ}\)C and 70 \({}^{\circ}\)C) and time (6 to 20 min) [30, 55, 56]. A comparison of apple juice shows that different parameters used such as sound intensity, time, and temperature would affect the inactivation of PPO in the fruits. The higher the intensity (or amplitude) of ultrasound applied is, the more enzyme is inactivated, because high intensity releases high energy. The wide range of data on PPO inactivation in fruits shows that enzyme inactivation is influenced by various parameters, including source, sub-type, environment, and physicochemical conditions such as pH and temperature.  
Figure 5: Inactivation of PPO (\(\geq\)80%) by HPP [9, 29, 35, 43, 48].