esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for High-Throughput and Spatial Multi-Omics Integration
#### 2.3.2 OmicsSuite: A GUI-Based High-Throughput Multi-Omics Integration Platform  
In contrast to SM-Omics's command-line nature, OmicsSuite provides a more accessible approach by offering a graphical user interface (GUI) while maintaining its capacity for high-throughput multi-omics integration. OmicsSuite is a desktop-based application that supports high-throughput multi-omics integration across various omics types, including genomics, proteomics, transcriptomics, metabolomics, and single-cell data. It is built to assist experimental biologists who lack advanced computational skills by offering a graphical user interface (GUI) [25]. OmicsSuite is equipped with 175 sub-applications across different omics categories and offers pipelines for data integration, statistical analysis, and visualization tools such as heatmaps, PCA, and dimensionality reduction techniques like tSNE and UMAP. This makes the platform highly versatile for large-scale omics projects. Although it does not have a web interface, its comprehensive nature and GUI make it more accessible than command-line tools like SM-Omics. One limitation is that it requires local installation, which might be challenging for users without sufficient computational resources or technical expertise.
## 2 Materials and Methods
### Trend Analysis  
Theil-Sen estimation method was used to reveal the temporal and spatial variations of vegetation and air temperature indices over the TP. Theil-Sen Median selects the median of all line slopes of a pair of points for linear fitting that is not sensitive to outliers, especially for data with large variance. Compared with the robust linear regression fitting method, its accuracy is significantly improved. A positive value of T-Sen illustrates an increase in the variable of interest and vice versa [28].  
The coefficient of variation (CV), also known as the dispersion coefficient, is a mathematical indicator to measure the degree of dispersion between observed values and unit mean value [29]:  
\[CV=\frac{1}{NDVI}\sqrt{\frac{1}{n-1}{\sum_{i=1}^{n}(NDVI_{i}}-\overline{NDVI} )^{2}}\, \tag{1}\]  
where \(NDVI_{i}\) is the NDVI of year \(i\) and \(\overline{NDVI}\) is the inter-annual average value from 1982 to 2017. The larger the CV value, the more dispersed the data and the greater the interannual variation of plant growth.
## 1 Introduction  
Heat treatment can significantly change meat's composition, as well as its flavor components and physicochemical properties. When meat composition is combined with a specific cooking methodology, it majorly affects the final quality of meat products [1]. Furthermore, heat treatment can result in decreased nutritional value because of the oxidative modifications of proteins, which finally leads to decreased protein digestibility [2] and changes in fatty acid (FA) composition. Steak is generally sliced across the muscle fibers and grilled. Beef steaks can be cooked at different temperatures and lengths of time using dry heat, and the resulting cooked steaks can range from very rare to overdone. Searing means \"burning\" or \"strongly scorching\" and refers to a technique of cooking the surface of food at high temperatures to form a brown crust. The temperature of the meat's surface should exceed 150 \({}^{\circ}\)C to attain the desired brown crust. [PERSON] [3] found that the primary pathway for non-volatile meat to generate flavor via heating is the oxidation and decomposition of fat and the pyrolysis and interaction of proteins, peptides, amino acids, sugars, and nucleotide target substances, and the secondary pathway is the Maillard reaction. It was reported that the searing process using the Maillard reaction secures the surface of the meat and reduces the loss of moisture, fat, and cooking due to the brown skin formed on the surface of the steak [4]. Reverse searing is the opposite method to strong searing and involves cooking meat evenly in an oven. This means that meat is slowly cooked in an oven at a relatively low temperature compared with searing and then seared over strong heat to finish. Recent reports suggest that reverse seared steak (RSS)has a more tender and juicy texture than conventionally seared steak (SS) and is, therefore, preferred by some meat lovers and chefs. The complicated relationship between texture and altered tenderness during cooking is regulated by the interactions between heat and time in various components, such as myofibrillar proteins, connective tissues, and the lipid matrix. It was learned in previous studies that protein denaturation and the loss of water-holding capacity within the meat are a result of dry cooking [5]. Therefore, pan-fried meat is initially softer but increases in toughness during prolonged cooking [6]. However, the differences between RSS cooked at low temperatures for an extended duration and conventionally SS have yet to be reported. Thus, this study was conducted to determine the effect of heat treatment (searing and reverse searing) on the volatile compounds (VCs) and physicochemical and sensory characteristics of beef tenderloin.
## 2 Data and Methods
### Model Development
#### 2.3.2 Simulations under Extreme Climate Conditions  
To analyze the applicability of the models under extreme climate conditions, we specifically evaluated the model performance in simulating NEE under extremely hot, cold, wet, or dry years. The procedures were as below. First, we selected each of the EC sites with more than 10 years of observational data. For each selected site, we calculated the respective standard deviations (SD) for the mean annual temperature (MAT) and annual total precipitation (ATP). The year is considered to be extremely cold if its MAT is less than the multi-year mean temperature by two SDs, and extremely hot if its MAT is greater than the multi-year mean temperature by two SDs. We can find the extremely dry and extremely wet years in the same way. Finally, 5 sites with extreme heat years, 7 sites with extreme cold years, 4 sites with extreme wetness years, and 3 sites with extreme dryness years were selected, details in Table S2. For each extreme situation, we selected the sites with different biomes for evaluation. If there were multiple sites with the same biome in the same extreme conditions, we randomly selected one of them. In order to maintain a balance between the number of extreme and normal samples, we selected a non-extreme year closest to the extreme year to represent the normal year. The samples of extreme year and the normal year were split into training samples (70%, merged the extreme and normal training samples for training) and validation samples (30%, representing extreme and normal samples of testing) with \"random_state\" value of 420, respectively. A comparison of prediction NEE for extreme and normal samples with the same modeling approach as that of biome above is shown in Section 2.3.1. Note that, in order to maintain the integrity of the data, we did not use the quality mark of each feature for sample exclusion as in Section 2.3.1.
## 6 Multi-Fault Classification
#### Parameters Selection  
Parameters for feature calculation are set in a heuristic manner. The first parameter corresponds to \(\tau\) that is used for calculation of CCM features. The CCM features are calculated by varying \(\tau\) between 1 and 60 for the vibration signal A1. The fault classification is performed by using the EBT algorithm, and the accuracy is plotted as a function of \(\tau\). The results are shown in Figure 19. The accuracy increases as \(\tau\) increases, and it settles at \(\tau=45\). In consequence, we can set \(\tau=50\) for the rest of experiments. Similarly, calculation of SD features requires previous construction of the lagged Poincare plot according to the lag \(\tau\). In this case, \(\tau\) is varied between 5 and 60. For each lag value, the SD features are calculated from signal A1, and the fault classification model uses the EBT algorithm. The accuracy is plotted with respect to the lag value, and the lag value \(\tau=15\) corresponds to the maximal accuracy obtained in classification. The plot is shown in Figure 20.  
The parameters for the classification models are the maximum number of splits and the number of learning cycles for the EBT algorithm. In this case, the maximum number of splits is set as \(n-1\) where \(n\) is the number of samples in the training set. The number of learning cycles is 36. The algorithm ESK considers 30 learning cycles, and the number of predictors to sample is set as half the number of predictors of the features set.  
Figure 19: Accuracy according to \(Lag=\tau\) for CCM features.  
Figure 18: Methodological framework for multi-fault classification by using SD, CCM and RF models.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### SMS of Telecom Customers Factor Analysis
#### 5.3.5 Component Matrix  
The component score coefficient matrix is shown in Table 16. F6 had more significant loads for China Unicom's MMS quantity and CRBT. Therefore, MMS and CRBT were resumed as the second factor F6. Moreover, the first factor F5 showed more significant  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Eigenvalue Starting Value**} & \multicolumn{3}{c}{**Squared Sum Extraction Loading**} & \multicolumn{3}{c}{**Square Sum Rotation Loading**} \\ \cline{2-11}  & **Sum** & **Variance \%** & **Accumulative \%** & **Sum** & **Variance \%** & **Accumulative \%** & **Sum** & **Variance \%** & **Accumulative \%** \\ \hline
1 & 1.458 & 29.151 & 29.151 & 1.458 & 29.151 & 29.151 & 1.313 & 26.265 & 26.265 \\
2 & 1.047 & 20.937 & 50.087 & 1.047 & 20.937 & 50.087 & 1.191 & 23.822 & 50.087 \\
3 & 0.971 & 19.423 & 69.51 & & & & & & \\
4 & 0.815 & 16.291 & 85.801 & & & & & & \\
5 & 0.71 & 14.199 & 100 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Total interpretation variance.  
Figure 4: Scree plot.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Initial Value** & **Extraction Value** \\ \hline China Unicom SMS quantity & 1 & 0.580 \\ China Mobile SMS quantity & 1 & 0.594 \\ China Telecom SMS quantity & 1 & 0.545 \\ China Unicom MMS quantity & 1 & 0.556 \\ CRBT & 1 & 0.629 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Common factor variance—adapted from [PERSON] [6].  
loads for China Unicom's SMS quantity, China Mobile's SMS quantity, and China Telecom's SMS quantity. Therefore, the SMS quantity can be explained by the first factor F5.  
Therefore, it can be concluded that F5 characterizes the SMS, while F6 characterizes MMS and CRBT. The used formulas were as follows:  
F5 = 0.596 \(\times\) China Unicom SMS quantity's SMS + 0.570 \(\times\) China Mobile SMS quantity's SMS + 0.295 \(\times\) China Telecom SMS quantity's SMS + 0.011 \(\times\) China Unicom MMS quantity's MMS \(-\) 0.120 \(\times\) CRBT.  
F6 = \(-\)0.106 \(\times\) China Unicom SMS quantity's SMS + 0.035 \(\times\) China Mobile SMS quantity's SMS \(-\) 0.034 \(\times\) China Telecom SMS quantity + 0.614 \(\times\) China Unicom MMS quantity + 0.685 \(\times\) CRBT.
## 3 Results  
### Identification of VOCs  
The data from GC-MS analysis showed that more than 34 different volatile compounds were separated and identified from the infested and non-infested citrus fruit and differential associated emissions attributed to herbivore activity were found for all fruit species (Table 1). The two naturally presented compounds in non-infested lemon, orange and mandarin, d-limonene and \(\beta\)-ocimene were significantly increased in the infested fruit (Table 1). However, \(n\)-hexadecanoic acid was exclusively produced by infested lemon fruit, and 5,4-di-_epi_-aristolochene was decreased after infestation, while 3,7-dimethyl-(_E_)-2,6-ocatdienal and \(\alpha\)-bulnesene were only present in non-infested lemon and were not detected in infested lemon. There were no significant differences between the non-infested and infested mandarin fruits, but the compounds of \(\gamma\)-terpinene, alloocimene and alloaromodendrene were significantly increased in infested mandarins (Table 1). For orange, compounds were changed only in infested fruit, e.g., three more compounds, alloaromodendrene, 4,8-dimethyl-(_3E_)-1,3,7-nonatriene, and hexyl caproate were increased, and five compounds, acetic acid hexyl ester, alloocimene, \(\alpha\)-terpineol, nerolidol and (_3E_,7_E_)-4,8,12-trimethyltrideca-1,3,7,11-tetraene were exclusively present in infested fruit (Table 1).  
The PCA showed that the volatile compound profiles varied with infestation and citrus fruit species (Figure 1). d-Limonene and \(\beta\)-ocimene were the major VOCs released from all tested fruits. d-Limonene emitted 26.42%, 35.92% and 8.50% and \(\beta\)-ocimene emitted 32.83%, 31.51% and 17.46% of total volatile emissions from lemon, mandarin and orange fruit, respectively. Moreover, there were some major VOCs released from infested orange fruit such as alloaromodendrene, which accounted for 24.79% of total volatile emissions, and 19.65% and 23.48% of eudesma-4(14),7(11)-diene from lemon and orange fruit, respectively.
## 3 Blocked Distance Estimation
### Gaussian Process Regression  
Gaussian process regression is a machine learning method that utilizes kernel functions and is grounded in statistical principles and Bayesian theory. It is particularly well-suited for handling regression problems with small sample sizes and exhibits robust learning and generalization abilities. Operating as a non-parametric regression technique, Gaussian process regression arranges data in such a manner that any subset of the organized data consistently conforms to a Gaussian distribution [36]. The Gaussian regression process can be outlined as follows:  
For a set of training data, \(D:(x,y)\) and \(f(x_{i})=y_{i}\), assuming they all follow a multivariate joint Gaussian distribution.  
\[f(x)=[f(x_{1}),f(x_{2}),f(x_{3}),\dots,f(x_{n})]-GP[\mu(x),\sum(x)] \tag{21}\]  
\[Y=f(x)+\varepsilon-N[\mu(x),\sum(x)+\sigma^{2}I] \tag{22}\]  
\[\mu(x)=[\mu(x_{1}),\mu(x_{2}),\mu(x_{3}),\dots,\mu(x_{n})] \tag{23}\]  
\[\sum(x)=K=\begin{bmatrix}K(x_{1},x_{1})&K(x_{1},x_{2})&\cdots&K(x_{1},x_{n})\\ K(x_{2},x_{1})&K(x_{2},x_{2})&\cdots&K(x_{1},x_{n})\\ \vdots&\vdots&\vdots&\vdots\\ K(x_{n},x_{1})&K(x_{n},x_{2})&\cdots&K(x_{n},x_{n})\end{bmatrix} \tag{24}\]  
In the formula, \(GP\) represents a multivariate Gaussian distribution, \(Y\) is the predicted value, and \(N[\mu(x),\sum(x)+\sigma^{2}I]\) is used to represent the mean and variance. \(\mu\) is the vector composed of the mean of \(f(x_{1}),f(x_{2}),\dots,f(x_{n})\). \(K\) is the covariance matrix. The set of \(x_{i}\) to be predicted is defined as \(X^{*}=({x_{1}}^{*},{x_{2}}^{*},{x_{3}}^{*},\dots,{x_{n}}^{*})\), and the corresponding predicted value is as follows:  
\[y^{*}=f(x^{*})+\varepsilon \tag{25}\]
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### Experiment Setup
#### 4.1.4 Compared Algorithm  
In this work, we not only compared RLSchert with the traditional heuristic scheduling policies, we also compared DeepRM based on the runtime prediction before job runs and give the results of each scheduler using and not using the kill policy. The details of each policy are as follows:  
1. **Random:** The scheduler randomly selects a job from the waiting queue and allocates resources;
2. **SJF:** This is the shortest job first. The SJF assigns jobs in increasing order of their predicted time before the job runs;
3. **SAF:** This is the smallest area first. Using the predicted runtime before the job runs and request resources, the SAF orders jobs by their area (runtime\(\times\) resources);
4. **Tetris [25]:** Taking job request resources and cluster available resources as indicators, the scheduler selects the job with the smallest value, which is an implementation of packing [c];
5. **Tetris* [15]:** This is a scheduler based on Tetris, which balances preferences for short jobs and resource packing in a combined score;
6. **DeepRM [15]:** This is based on the predicted time before the job runs as information, using the PPO algorithm to learn the model.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 1 Introduction
By observing the above findings, a thermal simulation is essential for the proper prediction of PV operation in orbit, and it should include the satellite's dynamics. When proposing a control methodology for a battery's temperature using a heater, the most common method, the engineer must have access to an integrated thermal and electrical methodology. As observed in the work of [16], a purely numerical simulation has limitations in the depth of insight into the satellite's real performance. However, it is a crucial tool for satellite development at design phases. Its implementation costs are attractive compared to hardware simulations and may be refined with experimental results to provide more accurate results. Examples of CubeSats' thermal simulations are found in [17; 18; 19], whose authors solved the transient temperature of critical components of the satellite, such as the battery, but the simulations neither had active thermal control nor integration with electrical models of the satellite. On the other hand, the work of [7] studied the state of charge estimation for the battery of satellites, but the authors did not assess it considering typical transient irradiance and temperature profiles found in orbit. There are also papers in the literature concerning specifically the design and management of satellites' power systems (electrical models) [20; 21; 22; 23; 24; 25]; however, they do not present any generalized method that CubeSat engineers can further develop in order to satisfy the mission constraints and requirements that they are focused on in that moment. In this regard, this work presents an integrated thermal-electrical framework capable of taking into account the thermal and electrical effects of the battery and photovoltaic panels for each instant of time in a given orbit. In this way, the operator can evaluate the effects of a proposed thermal control on the battery's temperature by turning on a heater and visualizing the effects of the solar panels' varying temperatures during the orbit. It is also possible to assess the battery charge status due to the power to keep the heater activated. Therefore, this work's main contribution is an integrated and self-contained model for orbit, attitude, irradiance, thermal, electrical, and battery models for CubeSat mission analysis, especially useful for readers without access to commercial software for simulation of satellites. The model is versatile enough to simulate the thermal and electrical behavior under diverse types of orbit and attitude, including user-defined control dynamics, and does not require external simulators. This paper is divided as follows: Section 2 presents the methodology to calculate the thermal and electrical parameters along the orbit; those equations are used to compose an integrated model presented in Section 3; simulation results are described in Section 4; the final remarks about the paper are drawn in Section 5.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 1 Introduction
* We present our distributed merging approach, named Ring Reunion, in Section 3; this Ring Reunion encompasses steps for identifying new overlays, coordinates the initialization of the merging process in the subnetwork, merges two disjoint rings from that elected starting point (in parallel), and terminates reliably. * In Section 4, we evaluate our Ring Reunion approach in both simple mode and parallel mode (\(2^{2}\)-\(2^{4}\) instances) in comparison to Chord-Zip [6] and two versions of [PERSON] et al's Ring-Unification [7] approach (Simple and Gossip). We show that only our approach, Ring Reunion, is capable of merging the various Chord rings in demanding scenarios, such as merging multiple rings or having only very few contact points with other rings. * Finally, we perform a parameter study with Ring Reunion and show the impact of parallelism (i.e., \(2^{2}\) to \(2^{6}\) parallel merging instances) and the impact of \(\alpha\), a parameter that specifies how many nodes in a subnetwork (of size _size_,) should be in charge of initiating the merging. Our approach for merging two or more ring-based overlays, Ring Reunion, is capable and efficient in merging disjoint rings after a network separation, such as that which occurs during an internet shutdown. Thus, local rings in countries can prevail during a local internet shutdown and are capable of rejoining after internet connectivity with other countries is reestablished. This general scenario can also be applied in other cases of network partitioning; thus, this is a concept that stabilizes ring-based peer-to-peer overlay networks.
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 5 Formal Verification
### Formal Verification via ProVerif
#### 5.2.3 Assertions
Available online:_ [[https://www.secg.org/sec1-v2.pdf](https://www.secg.org/sec1-v2.pdf)]([https://www.secg.org/sec1-v2.pdf](https://www.secg.org/sec1-v2.pdf)) (accessed on 10 December 2023). * _Research (2010)_ _Research, C. SEC 2: Recommended Elliptic Curve Domain Parameters. Standards for Efficient Cryptography, 2010. Available:_ [[https://www.secg.org/sec2-v2.pdf](https://www.secg.org/sec2-v2.pdf)]([https://www.secg.org/sec2-v2.pdf](https://www.secg.org/sec2-v2.pdf)) (accessed on 10 December 2023). * _3 GPP (2020)_ _3 GPP. 3G Security Architecture TS 33.102 (Release 16). Technical Report, The 3 rd Generation Partnership Project. 2020. Available:_ [https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2262](https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2262) (accessed on 10 December 2023). * _ISO (2023)_ _ISO. ISO/IEC 29128-1:2023 Information Security, Cybersecurity and Privacy Protection--Verification of Cryptographic Protocols--Part 1: Framework. Technical Report, the International Organization for Standardization, 2023. Available online:_ [[https://standards.iteh.ai/catalog/standards/iso/5a8C7c4d-434f-4816-a4e0-b33660](https://standards.iteh.ai/catalog/standards/iso/5a8C7c4d-434f-4816-a4e0-b33660) dd311c/iso-iec-29128-1-2023]([https://standards.iteh.ai/catalog/standards/iso/5a8C7c4d-434f-4816-a4e0-b33660](https://standards.iteh.ai/catalog/standards/iso/5a8C7c4d-434f-4816-a4e0-b33660) dd311c/iso-iec-29128-1-2023) (accessed on 10 December 2023). * _[PERSON] et al. (2022)_ _[PERSON]; [PERSON]; [PERSON]; [PERSON] A Beyond-5G Authentication and Key Agreement Protocol. In Proceedings of the Network and System Security--16 th International Conference, NSS 2022, Denarau Island, Fiji, 9-12 December 2022; Berlin/Heidelberg, Germany, 2022; Volume 13787, pp. 249-264. [CrossRef]_

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