esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Band Structures
### Three-Orbital Tight-Binding Model: Previous Studies
In calculation, we generate all positions \(\{\mathbf{r}_{i}\}\) for a given \(x\) without shifting any A site, and obtain the transfer integrals \(t_{mm^{\prime}}^{ij}(\{\mathbf{r}_{i}\})\) according to the modified distances \(|\mathbf{r}_{i}-\mathbf{r}_{j}|\) for all pairs of nearest neighbors. The band structure in our tight-binding model is summarized in Figure 2. Panel (a) shows the result calculated from \(\hat{H}_{\text{TB}}^{0}\) (1) in the absence of a PLD. Panel (b) shows the same result, but the bands are folded by taking a hexagram as a unit cell. Panel (c) shows the result calculated from \(\hat{H}_{\text{TB}}\) (3) with distortion \(x=-1.5\). This case shows a large band restructuring, and one narrow band separates from the others and extends near the Fermi energy. This isolation becomes clearer for larger \(|x|\) with \(x<0\). Note that we consider the case of \(x<-1\), while the value \(x=-1\) was used in [38] with referring to the experimental values. This is because the value \(|x|=1\) is not large enough to obtain a splitting of the narrow band within our treatment of the electron-electron interactions discussed below. The discrepancy of our value \(x=-1.5\) from the experimental one \(x=-1.0\) could be decreased if we corrected the approximate law for the transfer integral change (2). In this paper, we do not go into detail in this direction and keep using Equation (2). \begin{table}
\begin{tabular}{c c} \hline \hline
**Crystal Field** & **Slater–Koster Parameters** \\ \hline \(d_{3z^{2}-r^{2}}=1.489\) & dd\(\sigma=-0.7955\) \\ \(d_{x^{2}-y^{2}}=d_{xy}=1.339\) & dd\(\pi=0.2595\) \\  & dd\(\delta=0.1080\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of crystal field energies and Slater–Koster parameters in our model. All values are shown in the unit of eV.
## Material and Methods
### RNA Extraction  
Twelve paired independent _L. plantarum_ WCFS1 batch cultures (50 mL each) were grown in MRS to an OD\({}_{600}\approx\) 0.8-0.9. Then, a culture of each pair (twelve cultures) was centrifuged at 9000\(\times\)_g_ for 5 min at 20 \({}^{\circ}\)C and cells suspended in MRS containing HXT medium (10 mM final concentration) (Seproxbiotech, Madrid, Spain), which was freshly prepared prior to use. After 10 min of exposure, the HXT-induced cells and their respective controls were centrifuged at 4 \({}^{\circ}\)C. The pellet was mixed with 2 mL of quenching buffer (60% methanol, 66.7 mM HEPES, pH 6.5, \(-\)40 \({}^{\circ}\)C). Following quenching, the cells were centrifuged at 9000\(\times\)_g_ for 10 min at \(-\)10 \({}^{\circ}\)C and suspended in an extraction mixture (500 \(\upmu\)L 1:4 chloroform-acid phenol, 30 \(\upmu\)L of 10% SDS, 30 \(\upmu\)L Na-acetate 3M pH 5.2, 400 \(\upmu\)L Tris-EDTA buffer [10 mM Tris(hydroxymethyl) amino methane, 1 mM EDTA] pH 7.4, 15 mg of polyvinylpolypyrrolidone, and 500 mg of glass beads (\(\phi\), 75-150 m). The cells were broken in a FastPrepTM FP120 (Thermo Savant, Holbrook, NY, USA) by applying three cycles at 5000 rpm for 40 seconds with chilling in dry ice for 1 min between cycles. The suspension was then centrifuged at 4 \({}^{\circ}\)C at 10,000\(\times\)_g_ for 2 min. After two extractions with 500 L of chloroform, the supernatant containing the RNA was immediately frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C [18]. NanoDrop ND1000 instrument (Thermo Scientific, Madrid, Spain) was used for RNA quantification. The A\({}_{260}\)/A\({}_{280}\) and A\({}_{260}\)/A\({}_{230}\) ratios were measured to check RNA purity. Integrity and quality of RNA samples were determined by electrophoresis on agarose gels. Two treatments with DNase I (Ambion, Inc., Carlsbad, CA, USA) were applied and the absence of genomic DNA was confirmed by PCR [18].
## 5 Innovative Approaches for Enzyme Immobilization
### Carrier-Free Immobilized Enzymes  
Carrier-free immobilized enzymes reduce costs by eliminating solid supports, using cross-linking agents to bind molecules, thereby enhancing enzyme stability and reusability [218]. These enzymes are typically smaller and more uniformly distributed in the solvent. The two primary carrier-free immobilization methods include cross-linked enzyme aggregates (CLEAs) and cross-linked enzyme crystals (CLECs) [219; 220], with CLEAs being more widely applicable, as they do not require high enzyme purity.  
In contrast to CLECs, CLEAs streamline the process by combining purification and immobilization. The preparation of CLEAs eliminates the need for solid supports by precipitating proteins and cross-linking them with a bifunctional chemical (such as glutaraldehyde) Despite being commonly utilized, glutaraldehyde can impair performance because of its toxicity and mass transfer limits [221]. Studies revealed that the carrier-free enzymes, such as type A feurolyl esterase, _Candida rugosa_ lipase (CRL), and urase, exhibited high bioconversion rate, stability, and higher affinity and activity over a broad pH and temperature range [220; 222; 223; 224]. Figure 7 illustrates two approaches for preparing CLEAs.  
While CLEAs generally outperform carrier-bound enzymes in terms of activity, handling, and recovery, they face challenges such as poor reproducibility, mechanical instability, and difficulties in handling gelatinous forms [225]. Glutaraldehyde-based cross-linking can also reduce enzyme activity and environmental stability [226]. To address these limitations, hyperbranched polymer cross-linked lacase aggregates (HPCLEA) have been developed,which offer enhanced cross-linking sites and improved catalytic control due to their tunable internal structure [227].  
Additional issues regarding CLEAs include their inconsistent particle size and cross-linking, which can limit enzyme performance. A Y-shaped millifluidic reactor was used to produce uniform cellulase CLEAs (200-400 nm) by optimizing reactant concentrations and flow rates [228]. Additionally, the dense structure of CLEAs limits their use with macromolecular substrates. To overcome this, porous co-precipitating enzymes with starch (later removed via amylase) or using dextran polyaldehyde as a cross-linker, significantly improved their activity toward macromolecular substrates [229].
## 5 Transmission Model of Multipath QKD Network-Based Zero-Trust Relays  
In [50], the authors proposed a formula to quantify the security rate in multipath networks for QKD. The QKD network topology is generally designed to reflect real-world conditions, prioritizing adaptability to various scenarios. As illustrated in Figure 5, node A performs a multipath key distribution to node B, utilizing various path combinations over time. The optimal paths are dynamically selected using an optimization algorithm that considers network conditions and pre-defined security metrics. These paths facilitate the distribution of multiple keys while enhancing overall security through redundancy and spatiotemporal diversification.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.15 The Indicator to Measure Energy Efficiency (of the Public Facilities)  
This indicator encourages energy efficiency in public spaces, by reducing energy consumption and energy-consuming systems (i.e., public lighting and dynamic control systems). It has been promoted by SBTool\({}^{\text{PT}}\) Urban, SNTool, BREEAM C, and LEED ND, while addressed by ISO 37120, and SDGs. Therefore, it is recognized as a prioritized environmental concern. In the case of this indicator, the SBTool\({}^{\text{PT}}\) Urban Guide indicates a verification checklist to calculate it. The list's criteria could be \"PARTIALLY\" calculated through the CIM. The first criterion is a Monitoring and Maintenance Plan, and similar to the Environmental Monitoring indicator, this criterion cannot be assessed through the CIM model. However, the other criteria can be calculated using the model and the Dynamo routine.
## 5 Conclusions  
In this survey article, we discuss several TLS-encrypted NTA methods and their deployment models in the context of malware detection and family classification for security experts in SOCs. We observe that while TLS interception is widely used in industry, the rise of privacy issues leads for researchers and some vendors to recommend inspection without decryption. Another approach to utilize searchable encryption is promising, but the current generation of SOCs has no incentive to deploy such solutions owing to performance issues and an implausible assumption for malware. Thus, we discuss the state-of-the-art methods which are suitable for SOCs which inspect TLS-encrypted traffic without decryption, focusing on the machine learning-based methods. Especially, we emphasize the current trend in TLS fingerprinting in industry and academia, which can be helpful for security experts who intend to introduce machine learning-based methods in SOCs.  
While a substantial number of studies have been conducted in this field, including some groundbreaking works in recent years, there is still room for further improvement as follows:  
* The existing proposals have been validated in different and small datasets. While lack of diverse, large, and sharable datasets with labels is a persistent problem in NTA [115], sharing TLS fingerprints in OSINT feeds seems to be relatively plausible. Thus, designing OSINT-friendly TLS fingerprinting techniques with more features optimized for machine learning-based NTA can be a promising research direction.
* With the fast adoption of TLS 1.3, visibility of TLS-encrypted traffic using TLS interception is rapidly decreasing in many SOCs, even though the enhanced flow records are collected. It is because that in TLS 1.3, many features in TLS handshake metadata are no longer collectible due to inherent secure design. It implies that more features in TLS-encrypted traffic should be collected with novel feature representations, well-designed machine learning algorithms, and model optimization techniques, under the diverse constraints of SOCs (privacy, cost, automation, scalability, etc.). Recent advances in deep learning-based NTA can be a potential research direction.
* The current academic literature lacks consideration in real-time and online processing for NTA. Considering the higher requirements of deep learning-based methods, we may need to be aware of systematic and holistic approaches in NTA.  
Conceptualization, H.R. and C.O.; methodology, H.R.; investigation, H.R. C.O., and J.H.; writing--original draft preparation, H.R., C.O. and J.H.; writing--review and editing, C.O. and H.R.; funding acquisition, H.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the Korea Institute of Science and Technology Information (KISTI).  
Not applicable.
## 7 Security and Privacy Analysis  
In this section, security and privacy concerns are discussed, and how the proposed scheme is mitigating them.  
**Proposition 1**.: _The proposed scheme protects the privacy of voters._  
Proof.: A voter can interact with the blockchain with a _one-request-only_ blockchain address obtained using blind signatures. The votes' privacy is protected by encrypting them using the Verifiable Aggregator Oblivious Encryption. Only the voter who submitted his/her vote can know the exact vote and no one including the blockchain nodes can get access to the private information of the voters.  
**Proposition 2**.: _Our scheme ensures election fairness._  
Proof.: This is because the votes are encrypted and no one can access the votes that have been cast. Only the election results can be obtained by using the _AggrDec_, as explained in detail in Section 5.4. Because the votes are in an encrypted format, only the tally of the election can be obtained to protect the individual votes of the voters.  
**Proposition 3**.: _The proposed scheme is secure against single-point-of-failure attacks._  
Figure 7: Varying transaction send rates’ impact on Transaction Error Rate. (**a**) GetBallot; (**b**) CastVote. (Author’s own processing).  
Proof.: This is due to the underlying blockchain that is used to run the scheme. Our proposed e-voting scheme allows small businesses and governments alike to run an election in a transparent, decentralized, and secure manner.
## 4 Optimization of Measurement Parameters
### Phase Angle  
The lock-in amplifier is an important tool for detecting photoacoustic signals as it can extract weak signals from noisy backgrounds and measure them accurately. A certain phase difference exists between the detected signal and the original reference signal because of (1) the relaxation of exited gas molecules, (2) the energy loss due to the friction of the photoacoustic cell wall, and (3) the hysteresis of the electronic circuit. The phase difference \(\theta\) of the lock-in amplifier should therefore be adjusted to maximize the signal. Figure 7 plots the signal of 50 ppm methane recorded by scanning the laser injection current at varying phase angles. With the given experimental conditions, the signal at a current value of 62.35 mA is well fitted with a cosine function and reaches a maximum when the phase angle is 12.1\({}^{\circ}\). In addition, the phase angles of the other two gases (ethylene and ethane) are very close to that of methane when the maximum signal is reached. Therefore, for simplicity, the detection phase angle is set at 12.1\({}^{\circ}\) throughout the whole scanning process for all three gases.
## 3 Extracting Influencing Factors
### Weather Information  
Among all meteorological factors, rainfall, air temperature, visibility, and wind are significantly relevant for understanding travel time. Snow and fog might increase the travel time for a driver from departure to arrival point. Therefore, we explore the effect of these factors on link travel time to improve prediction accuracy. A heavy rain waterlogs roadways, and makes the road surface slippery, thus traffic will be rather slow during and after rains. However, the traffic on sunny days is smoother. Heavy fog reduces visibility and might affect traffic. Hence, weather conditions must be considered when estimating and predicting travel time accurately.  
Meteorological information was obtained from the public weather website [[http://tianqi.2345.com/wea_history/57494.htm](http://tianqi.2345.com/wea_history/57494.htm)]([http://tianqi.2345.com/wea_history/57494.htm](http://tianqi.2345.com/wea_history/57494.htm)) and included date, day of week, minimum air temperature, maximum air temperature, weather, wind direction and wind force as depicted in Table 2. The weather information included visibility, and precipitation. However, there were no data for continuous wind direction or wind force in Wuhan City from April to July 2014. Meanwhile, annually, there is little fog and snow in Wuhan, and therefore it is unnecessary to consider the influence of wind, fog, and snow on link travel time estimation or prediction, in this instance. We just considered the influence of temperature and rainfall on traffic. Average air temperature and rainfall were chosen as inputs for the model, denoted by \(temp\) and \(rain\), respectively. Table 2 demonstrates meteorological information by day.  
Based on this analysis, we designed an algorithm which extracted features or influence factors between target link and adjacent links. The travel time ratio between target link and adjacent links was regarded as output of model among all the extracted features and influence factors. Others were all regarded as inputs in our designed model. We conducted experiments using these extracted features and influencing factors.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Land Cover Classification System  
The classification system of China land cover for carbon budget [35] was adopted in this study, which includes two levels (Table 1). The first level has six land cover classes, including woodlands, grasslands, wetlands, croplands, artificial surface lands and bare lands. The basic 38 classes at the second level are formed by taking into full consideration the interpretation abilities of satellite imagery  
Figure 1: The location of Southwestern China.  
Figure 2: The flowchart of the HC-MMK (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge) approach.  
and the application demands of the Carbon Special Program and Ecological Decade Project. The land cover classes in this system are able to easily reorganize according to application requirements, and also are compatible with international land cover classification systems, such as FAO LCCS [35]. For more details on each land cover class, refer to the article by [PERSON] _et al._[35].
###### Abstract  
Background: This study evaluated a custom algorithm that sought to perform a radiogenomic analysis on lung cancer genetic and imaging data, specifically by using machine learning to see whether a custom clustering/classification method could simultaneously identify features from imaging data that correspond to genetic markers. Methods: CT imaging data and genetic mutation data for 281 subjects with NSCLC were collected from the CPTAC-LUAD and TCGA-LUSC databases on TCIA. The algorithm was run as follows: (1) genetic clusters were initialized using random clusters, binary matrix factorization, or k-means; (2) image classification was run on CT data for these genetic clusters; (3) misclassified subjects were re-classified based on the image classification algorithm; and (4) the algorithm was run until an accuracy of 90% or no improvement after 10 runs. Input genetic mutations were evaluated for potential medical treatments and severity to provide clinical relevance. Results: The image classification algorithm was able to achieve a >90% accuracy after nine algorithm runs and grouped subjects from a starting five clusters to four final clusters, where final image classification accuracy was better than every initial clustered accuracy. These clusters were stable across all three test runs. A total of thirty-eight genes from the top hundred across each subject were identified with specific severity or treatment data; twelve of these genes are listed. Conclusion: This small pilot study presented a potential way to identify genetic patterns from image data and presented a methodology that could group images with no labels or only partial labels for future problems.  
l 2025 15 4053
## 4 Discussion
### Extending Techniques to Determine the Viability
#### 4.2.1 Amount of ATP in the Viability Assessment  
Estimating an amount of intracellular ATP (iATP) in _H. pylori_ cells as an indicator of the viability of these bacteria appears to be problematic. It has been observed that coccoid _H. pylori_ forms have changed physiology and the amount of iATP is over 100-fold to 2000-fold lower than in spiral forms (Figure 6) [20; 71; 72]. Moreover, the same studies excluded the presence of extracellular ATP (eATP) in culture media, indicating the compactness of cellular structures and suggesting that measuring the amount of eATP may be a better indicator of the viability of coccoid forms treated with antimicrobial substances.  
It should be kept in mind that bacteria have an ability to convert ATP to polyphosphate, which constitute an alternative source of energy [73]. Although guanosine tetraphosphate (ppGpp), an alarmone, is responsible for the production of polyphosphates and transition of bacteria to persistent forms, these processes can also occur in the absence of this signal compound [74]. The first report indicating the ability of _H. pylori_ to synthesize pyrophosphate granules has been shown in 1993 [75], and these observations have then been confirmed later in many independent studies [19; 23; 24; 71; 76]. The presence of numerous polyphosphate granules was noticed in long-term incubated coccoid _H. pylori_ forms (27% and 11% of spherical forms with polyphosphate granules after 45 days and 28 months, respectively) [71]. Based on these observations, it seems that counting the amount of polyphosphate granules of spherical _H. pylori_ forms may be as important for determining a bioenergetic level as assessing the amount of iATP and eATP.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 2 Data and Preprocessing  
### Study Area  
The North-South Seismic Belt is one of the most frequently seismically active regions in mainland China, with a complex geologic structure in which the collision of the Indian Ocean plate with the Eurasian plate led to the uplift of the Tibetan Plateau and the formation of a series of tectonic fracture zones. Among them, the southern section of the North-South Seismic Belt is the convergence boundary of the relative tectonic movements of the Tibetan Plateau block and the South China block, so the seismic activity and the probability of strong earthquakes in this region are high. Researchers have differed in understanding the extent of each segment of the North-South Seismic Belt [26; 27; 28]. This study tentatively takes the area within (20\({}^{\circ}\) N\(\sim\)30\({}^{\circ}\) N, 96\({}^{\circ}\) E\(\sim\)106\({}^{\circ}\) E) as the southern segment of the North-South Seismic Belt (Figure 1), which includes the Xiaojiang fault zone, the Zhongdian-Honghe fault system, the Chenghai fault zone, the Chuxiong-Jianshui fault zone, the Yuanmou-Luzhijiang fault zone and the Southwestern Yunnan seismic tectonic zone, etc. These fault zones have different tectonic movement characteristics, such as sinistral slip, dextral slip, etc. They are closely related to many large earthquakes, such as the 2008 Wenchuan 8.0 magnitude earthquake.
## 4 Experimental Results and Analysis  
Experiments were performed on problem sets of increasing complexity with problem instances using the methodology from [35]. In each case the results obtained were compared with results obtained by the Bayesian Interval Mapping method using Windows QTL Cartographer (WQTLC) Ver. 2.5 [36], a popular tool in the bioinformatics field of study.  
A back-cross obtained from inbred lines was simulated, composed of \(n=100\), \(n=200\) or \(n=300\) progeny, each with nine chromosomes of length 100 cM and each having 11 equally spaced markers (at a spacing of 10 cM) for a value of \(k=99\). We assumed that there is no crossover interference in recombination process and the marker data are complete. Since the algorithm is stochastic, we created 100 replicates (datasets) for each sample size \(n\).  
\begin{table}
\begin{tabular}{l l} \hline \hline _population size_ & 200 \\ _mutation probability_ & 0.05 \\ _crossover operator_ & uniform crossover \\ _crossover probability_ & 0.90 \\ _genome representation_ & binary vectors \\ _stopping criterion_ & max iterations (200) \\ \hline \hline \end{tabular}
\end{table}
Table 1: **Algorithm Parameters:** Parameters used for the Modified Extinction Evolutionary Algorithm (MEEA).