esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Methodology  
### System Overview  
The proposed system model focuses on secure communication between a set of SMs and MDMS using a permissioned blockchain platform. The proposed scheme is composed of the following components: (1) a set of smart meters (\(SM_{i}\)), (2) a set of pre-selected data concentrators (\(DC_{j}\)), (3) Smart Contract (SC), and (4) Meter Data Management System (MDMS). Figure 4 depicts the abstract view of the proposed system model architecture.  
\(SM_{i}\) and MDMS represent the ith smart meter and meter data management system in the proposed system model, respectively. These are registered in the permissioned blockchain regulated by \(DC_{j}\). For permissioned blockchain, we used Hyperledger Fabric (HLF); \(DC_{j}\) is considered as the client nodes, and SC plays the role of registration and key generation instead of any trusted third party or registration authority. DC only relays the SM and MDMS's registration to the deployed SC, and verifies transaction requests to the HLF ordering service. The ordering service follows the PBFT consensus mechanism. The ordering service performs a proper transaction validation. The transactions are integrated into a block and added to the blockchain. After a certain period, the blocks are released to the database, which the pre-registered MDMS retrieves.
# An Overview of Quantum Machine Learning Research in China
## 3 Algorithms and Applications
### Algorithms
Other quantum algorithms include quantum canonical correlation analysis (QCCA), QSAM, quantum autoencoder (QAE), quantum multi-classification classifier (QMCC), quantum continual learning (QCL), quantum capsule network (QCN), variational shadow quantum learning (VSQL), quantum state clustering (QSC), quantum linear discriminant analysis (QLDA), quantum hierarchical agglomerative clustering (QHAC), quantum support vector regression (QSVR), quantum neighborhood preserving embedding (QNPE), and so on [74; 75; 76; 77; 78; 79; 80]. All these QML algorithms can generally be categorized into supervised learning and unsupervised learning based on their learning modes. Supervised learning includes classification and regression tasks, while unsupervised learning mainly involves tasks such as clustering, dimensionality reduction, data generation, and correlation analysis. The different types of machine learning tasks, the corresponding QML algorithms, and the associated references are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning Mode** & **Task** & **Algorithm** & **References** \\ \hline \multirow{8}{*}{Supervised} & \multirow{8}{*}{Classification} & QNN & [42; 43; 44; 45; 46; 47; 48; 49; 50] \\  & & QCNN & [51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 6
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Method Validation on Diverse Data
#### 3.3.3 Prediction Experiments in the Meteorological Field  
In the weather forecasting experiment, we used the Weather dataset, a common dataset in time series prediction, for comparative experiments. This dataset, sourced from the meteorological field, includes 21 weather-related indicators. Each record covers a 10-min time span, and the entire dataset spans one year.  
The experimental results of different models on the Weather dataset are shown in Table 7 and Figure 8. Since the Weather dataset has 21 correlated variables, it contains a large volume of information and poses higher prediction difficulty. In the experiment with a prediction step of 192, none of the models mentioned in this paper  
As shown by the experimental results in multiple fields, MD-Autoformer can capture time information at different scales more precisely, especially on datasets with significant periodicity and trends. Compared to Autoformer, it enhances prediction stability and accuracy while maintaining the ability to capture sequence trends.
## 4 Discussion
### Directional Asymmetry in Cross-Lingual Transfer  
One of the most striking findings from our experiments is the significant directional asymmetry in performance gains. The retrieval-based example selection consistently yields substantial improvements in the X\(\rightarrow\)English direction while showing more modest and variable gains in the English\(\rightarrow\)X direction. This asymmetry provides valuable insights into the nature of cross-lingual transfer in LLMs.  
We hypothesize that this phenomenon stems from two key factors. First, LLMs generally have stronger generation capabilities in English due to the dominance of English data in their training corpora. When the target language is English, providing semantically relevant examples helps the model extract and organize key information from the source article, leveraging its strong English generation abilities. Second, the correlation analysis between example similarity and performance (\(r=0.908\) for X\(\rightarrow\)English versus \(r=0.295\) for English\(\rightarrow\)X) suggests that semantic similarity plays a fundamentally different role depending on the direction of transfer.  
This asymmetry has important practical implications for cross-lingual applications. It suggests that different strategies may be optimal depending on the direction of translation. For the X\(\rightarrow\)English direction, focusing on semantic similarity yields substantial benefits. For the English\(\rightarrow\)X direction, other factors such as target language fluency and stylistic considerations may need to be prioritized alongside semantic relevance.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Calibration and Validation--Cal/Val
#### 3.2.4 Pre-Launch Validation of the ADCS  
Validating the ADCS ensures that the satellite's instruments can accurately capture data, perform measurements, and achieve the mission's scientific objectives. To validate the Uvsq-Sat NG ADCS on the ground, a new test facility (Helsa) was developed by LATMOS with the support of SATT Paris-Saclay. One use of the Helsa test facility with its Helmholtz cage is to effectively cancel out the Earth's magnetic field for the tuning and characterization of the Uvsq-Sat NG ADCS sensors. The Helsa Helmholtz cage allows for the adjustment and manipulation of the magnetic field within its volume. The Helsa air-bearing table is another tool used to test the Uvsq-Sat NG's ADCS on the ground, simulating 'zero-g' conditions. The combination of these two items allows us to perform Attitude Control testing of the Uvsq-Sat NG satellite in an environment which simulates operation in space. Here is a step-by-step description of the test process: set up the Helsa Helmholtz cage, prepare the Uvsq-Sat NG proto-flight model, verify that the satellite is not magnetized, calibrate all sensors, establish the Helsa air-bearing table, initialize satellite systems, perform orientation tests, test Reaction Wheels, perform magnetic field testing, and conduct Fine Sun Sensor and Star Tracker tests. The functionality of the Star Tracker in the satellite will be validated using another setup, which relies on a star simulator and a screen that projects stars onto the Star Tracker.
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 3 Methods
### Data Collection and Preprocessing
Before inputting the selected variables into the prediction model, min-max normalization was employed to eliminate the relative variance of each variable and to establish a benchmark for subsequent analysis. All selected variables were assigned a value between 0 and 1 according to Equation (1). \[X_{nom}=\frac{X-X_{min}}{X_{max}-X_{min}}\in[0,1] \tag{1}\]  
where \(X_{max}\) and \(X_{min}\) are the maximum and minimum values in the data, respectively.
## 2 Materials and Methods  
### Plant Material  
Corsican wild hops were found in different Corsican riverbeds and planted in an experimental field in Patrimonio (Corsica, France). Leaves sample were collected for the DNA analysis. Rizhomes of Cascade, Tettnanger and Smaragd cultivars were obtainedfrom German grower (Locher-Hopfen-Tettnnag, Missenhard, Germany), and planted in the experimental field. The hop cones of all the studied hops were picked two times: three months after planting and the following picking year.  
To have the evidence of the varieties and to be sure to compare the same varieties, leaves samples of the German hops cultivated in Patrimonio and in Tettnnag region were genetically analyzed. The climatic conditions of the Tettnnag valley and the four studied Corsican areas have been gathered from the open database \"climate-data.org\" for which the results shown are averaged from 1999 to 2019, and from the database \"meteoblue\" for which the data are averaged from 2010 to 2020. The \"Patrimonio\" data can be considered common to \"Oletta\" due to the geographical proximity. During the growth period of hop from May to September, the Tettnnag valley is characterized by its very high rain content (90-130 mm/month; av: 114 mm/month), low temperatures (13-18 \({}^{\circ}\)C; av: 15.8 \({}^{\circ}\)C), strong south-west to north-east winds (5-50 km/h) and a low solar exposition (4-7 days/month; av: 5.6 days/month). For the same period, the Corsican planting site (Patrimonio) and Oletta are characterized by their low rain content (10-54 mm/month; av: 30 mm/month), high temperatures (16-24 \({}^{\circ}\)C; av: 21.2 \({}^{\circ}\)C), strong south-east to north-west winds (5-50 km/h) and a very high solar exposition (10-20 days/month; av: 14.2 days/month). Ajaccio is characterized by its low rain content (15-55 mm/month; av: 30 mm/month), high temperatures (17-24 \({}^{\circ}\)C; av: 21.4 \({}^{\circ}\)C), medium west-south-west to east-north-east winds (5-19 km/h) and a high solar exposition (5-14 days/month; av: 9 days/month). Corte is characterized by its medium rain content (15-60 mm/month; av: 38 mm/month), medium temperatures (15-21 \({}^{\circ}\)C; av: 18.6 \({}^{\circ}\)C), weak north to south winds (0-38 km/h) and a high solar exposition (7-16 days/month; av: 11.4 days/month).  
In the first part of this study, wild hops (Table 1), located in Ajaccio, Corte, Patrimonio and Oletta, were characterized by a morphological, genetic and chemical approach.  
In the second part of the study were compared three hops cultivars: Cascade, Smaragd and Tettnnager. In this case, the hops have been identified with the commercial name of the cultivar followed by the geographical origin, for example, Cascade Tettnnag, Cascade Corsica, etc. The geographical characteristics of the two growing sites (Tettntang valley, Tettnnag, Germany; Patrimonio, Corsica, France) are shown in Table 2.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 3 Component-Based IoT Architecture
### External Data Input Component  
External data input component represents any reference data that is not directly read from the end devices, for example API data from weather forecast station, solar activities' data from an open-source website or smart devices computational data. In this component, we have two sub-components: (i) web API data extractor; and (ii) data extractor scheduler.  
* Web API data extractor: A web API data extractor is a sub-component to curl the data from any designated API. It also includes decode of the data downloaded through web API into JSON format through a curl script.
* API data extractor scheduler: The API data extractor scheduler is a function that automatically repeats the data extraction process by a fixed interval set by the user to receive the latest data from the API data provider.  
After the data processing, the essential data are transferred and stored in the same server as the other data from the end devices component. These external data are also visualized under the same platform as the other data through the software application component.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario  
The proposed model is a synergistic integration of RBAC, ABAC, and a sophisticated risk engine (RiBAC), organized into a three-layered structure. Through the combination of these access control approaches, the proposed model utilizes the strengths of the individual models while mitigating their inherent limitations. This integration ensures adaptability to changing circumstances and conditions, granting precise access control decisions, and fostering enhanced information security in modern healthcare systems.  
This section discusses the implementation of the proposed model. It starts by presenting the real-world scenario that will be used in the implementation to identify various parameters and values needed for the implementation. Then, the implementation of the proposed model will be presented by discussing the implementation steps of each one of the models and their integration to provide an effective and accurate access control decision for the healthcare access control scenario.
## 3 Results
### Ablation Experiment  
From the perspective of the algorithmic framework, this algorithm is based on the differential evolution algorithm, thus sharing evident similarities with traditional single-objective search algorithms. The algorithm is specifically designed to address the distribution characteristics of the sphericity error. It proposes that the resolution of the sphericity error can be treated as a unimodal function in the local solution space. Subsequently, the local enhancement method for solving this unimodal function is synergistically integrated with the differential evolution algorithm, demonstrating higher precision and robustness.  
To delve into the pivotal aspects of the algorithm in this paper, we conducted ablation experiments, employing the same parameter configuration outlined in Section 3.1. We designated the algorithm with the removal of local enhancement steps as algorithm A and designated the algorithm retaining these steps as algorithm B. Subsequently, ten experiments were performed for each algorithm, with the standard deviation of the results computed. The experimental findings are documented in Table 10, from which it is evident that removing local enhancement methods significantly diminishes the algorithm's robustness, reducing the standard deviation from a magnitude of \(10^{-14}\) mm to a magnitude of \(10^{-2}\)-\(10^{-7}\) mm. This experiment's results substantiate the favorable outcome achieved by amalgamating the proposed local enhancement method and differential evolution algorithm in this paper.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results
### Physical and Chemical Analysis
#### 3.1.7 The Results of Phytochemical Analysis
[42] pointed out. By fermentation, the cell wall of apples was broken down by releasing lycopene from the tissue matrix and causing several important related bioavailability changes. An apparent increase in lycopene, a trans-isomer, was observed on day 21 of fermentation (0.57 \(\pm\) 0.62 to 0.82 \(\pm\) 0.00 mg/g d.w. for NaCl brine, 0.63 \(\pm\) 0.00 to 0.90 \(\pm\) 0.00 mg/g d.w. for KCl, and at least 0.65 \(\pm\) 0.00 to 0.93 \(\pm\) 0.01 for MgCl\({}_{2}\)). Nonetheless, the \(\beta\)-carotene content decrease is almost always associated with lycopene increase, as our study has revealed, similar to [PERSON] and [PERSON] [43]. Even so, lycopene bioavailability increases due to fermentation or any other type of processing owing to its unique property of being hydrophobic and somewhat lipophilic [8], which on the other hand makes it unstable for digestion. The phase marked by fermentation from day 7 to day 28 registers a chlorophyll content decrease of almost 16% for NaCl brine, 13.5% for KCl brine, and the highest percentage of 26% for MgCl\({}_{2}\) brine. Possible physiological and metabolic changes, such as the hydrolysis of starch, degradation of chlorophyll, and CO\({}_{2}\) gas diffusion, could occur through apple tissue; these expected phenomena are visible due to the color changes identified [44]. Unexpectedly, on day 35 of fermentation, the chlorophyll content increased up to values almost comparable with those registered on day 7 of fermentation. This instant phenomenon could be attributed to the formation of some compounds or complexes of compounds originating from cellular enzymatic and non-enzymatic reactions produced under the action of lactic acid bacteria, the fermentation environment, and conditions.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 4 Taxonomy of Vulnerability Detection Approaches and Findings Analysis
### Matching-Based Approach
#### 4.1.1 Matching-Based Approach Methods Description
Method Based on HermeScan  
The current method discussed an approach to detect taint-style vulnerabilities (security issues in data flow) in Linux-based IoT firmware by applying the reaching definition analysis (RDA) technique [54]. HermeScan starts with extracting firmware and libraries to identify user input and system operations, as shown in Figure 15 below. Then, it builds a comprehensive Control Flow Graph (CFG) to precise untrusted data enters and sink points (critical operations occur). Next, the method employs fuzzy matching technique between the front-end and back-end to uncover untested candidate functions. By using Reaching Definition Analysis (RDA), the core of HermeScan analyzes and tracks data flows between functions to handle control complexity. Finally, the taint inspection engine verifies any security policy violations. Table 3 below provides additional information about the different methods used in this category.
# Optimization and Preparation of Tallow with a Strong Aroma by Mild Oxidation
## 2 Results and Discussion
### SPME-GC-MS Analysis Results of Oxidized Melled Tallow under Different Conditions
** & **CAS** & **Component** & **RI x** & **Odor** & **25 Min** & **35 Min** & **45 Min** & **55 Min** & **65 Min** \\ \hline \multicolumn{8}{c}{Aldehydes} \\ \hline
1 & 110-62-3 & 1-pentanal & 962 & almond & 3.72 \(\pm\) 0.78 \({}^{\ast}\) & 14.18 \(\pm\) 0.25 \({}^{\ast}\) & 9.29 \(\pm\) 1.91 \({}^{\ast}\) & 22.13 \(\pm\) 2.09 \({}^{\ast}\) & 33.17 \(\pm\) 0.51 \({}^{\ast}\) \\
2 & 66-25-1 & 1-hexanal & 1063 & grassy & 12.23 \(\pm\) 2.76 \({}^{\ast}\) & 45.66 \(\pm\) 0.59 \({}^{\ast}\) & 31.02 \(\pm\) 1.53 \({}^{\ast}\) & 40.79 \(\pm\) 11.90 \({}^{\ast}\) & 68.54 \(\pm\) 29.8 \({}^{\ast}\) \\
3 & 111-71-7 & (_E_)-2-pentanal & 1128 & fruity & 0.79 \(\pm\) 0.17 \({}^{\ast}\) & 11.46 \(\pm\) 1.02 \({}^{\ast}\) & 6.97 \(\pm\) 2.65 \({}^{\ast}\) & 14.55 \(\pm\) 6.88 \({}^{\ast}\) & 30.46 \(\pm\) 6.62 \({}^{\ast}\) \\
4 & 6728-26-3 & 1-heptanal & 1182 & fatty & 11.78 \(\pm\) 7.72 \({}^{\ast}\) & 36.79 \(\pm\) 4.01 \({}^{\ast}\) & 33.17 \(\pm\  
Quantitative results  
A heat map was drawn according to the content of aroma compounds in the oxidized melted tallow under different conditions, as shown in Figure 4. In the figure, blue represents content below the average level, red represents content above the average level, and red also indicates higher content. It could be seen that the overall content of aldehydes at 120 \({}^{\circ}\)C was higher than other temperatures in the reaction temperature, especially 1-hexanal (green, grassy), 1-nonanal (fatty), 1-octanal (fatty), (_E_)-2-nonenal (cucumber), (_E_)-2-decenal (fatty) and 2-undecenal (orange peel). The 1-heptanal (fatty), benzaldehyde (nutty), and 2-methylpyrazine (nutty) at 150 \({}^{\circ}\)C were more prominent. However, according to the sensory evaluation and determination of the oxidation index, it was found that although the overall substance content was high at 120 \({}^{\circ}\)C, the flavor was not very popular, largely because the temperature did not reach the melting point during the melting process, resulting in a slight fishy and greasy taste of the overall flavor and, thus, the aldehyde content was higher than that of samples at other temperatures. The overall content of aldehydes was the highest in the reaction time of 65 min, especially 1-nonanal (fresh), (_E_)-2-nonenal (cucumber), (_E_)-2-decenal (fatty), 2-undecenal (orange peel), and (_E_,_E_)-2,4-decadenal (fried). In addition, it also contains high acid substances, such as acetic acid (vinegar), octanoic acid (putrid), nonanoic acid (waxy),  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Compound Information**} & \multicolumn{4}{c}{**Relative Content (ng/g)\({}^{\mathbf{Y}}\)**} \\ \hline
**No.
# The Two-Parameter Bifurcation and Evolution of Hunting Motion for a Bogie System
## 3 Method of Investigation
### Two-Parameter Co-Simulation Theory  
The two-parameter co-simulation method is developed based on cell mapping, which involves discretizing the state space into multiple cells and determining the final state of each cell by calculating its motion state. This approach enables the calculation of attractors and domains of attraction for the system in the state space. For a single-degree-of-freedom dynamical system, once the state space comprising displacements and velocities is discretized into numerous cells, it becomes possible to calculate the final attributed states of phase trajectories with different initial values of displacements and velocities. However, since a two-dimensional plane can only satisfy two sets of parameters as a state space, there are significant limitations in applying cell mapping to multi-degree-of-freedom dynamical systems.  
In two-parameter co-simulation, two critical parameters among the system parameters are selected to construct a two-parameter plane. In the schematic diagram of the two-parameter co-simulation method shown in Figure 3, the system parameters \(u_{1}\) and \(u_{2}\) form the (\(u_{1}\), \(u_{2}\))-plane and are discretized into a finite number of grids. The mode of motion at the grid can be determined by calculating the number of periods and impacts of stable phase trajectories under the system parameters at the grid center point \(a(i,j)\). The patterns and distributions of period motions on the two-parameter plane can be obtained by identifying each grid's motion pattern (\(n\)-\(p\)-\(q\)) and marking grids with the same pattern in the same color. The more grid divisions, the smoother and finer the depiction of neighboring periodic motion lines in the (\(u_{1}\), \(u_{2}\))-plane, but computational volume will increase rapidly. In this paper, a \(400\times 400\) grid was used for calculation. The numerical simulation was conducted on the C++ platform, and the fourth-order Runge-Kutta method was adopted for the numerical solution of the differential equations.  
Figure 3: Theory of two-parameter co-simulation.
## 4 Experiments
### Comparison of Different Experiments
#### 4.3.3 Classification Results of Different Network Structure  
The basic network structure of the model proposed in this paper is based on Inception v3. For this reason, in our experiments, we used the parameters from the Inception v3 model pre-trained on the ILSVRC2012 dataset to initialize our model's parameters. For the CUB200-2011 dataset, we used a single network structure to classify fine-grained images, such as Inception v3 and DenseNet.  
For the bilinear model, we used B-CNN proposed by [PERSON] et al. [25]. The experimental results are shown in Figure 11. Although the single network structure can improve the accuracy of image classification to some extent when increasing the depth of the network, its performance is still weaker than the bilinear model. Hence, we can derive the conclusion that the bilinear deep neural network can make better use of the relationship between regional features and global features. At the same time, our method runs with only 5M parameters, while achieving a classification speed of 48 frames per second. From the derived experimental results, our proposed method obtained a better performance than B-CNN, reaching a classification accuracy of 85.1%.