esa-sceva/satcom-chunk-collection huggingface dataset:
# Overexpression of _PtDXS_ Enhances Stress Resistance in Poplars
## 2 Results
### Protein Expression, Purification, and Western Blot Analysis  
To study the function of PtDXS protein, we firstly needed to obtain PtDXS protein. By using prokaryotic expression and protein purification technology, we successfully expressed and purified PtDXS protein (Figure 3). To identify whether the prokaryotic expression system could express the target protein, the SDS-PAGE analysis was performed. IPTG-induced bacteria showed a specific band of the expected size, 78.6 kDa (including the 6\(\times\)His tag, Figure 3A). This was consistent with the predicted protein (77.72 kDa). In addition, SDS-PAGE analysis has been carried out to identify whether the expressed PtDXS protein exists in the supernatant or in the precipitation. The result showed that the target protein was detected in the precipitate, suggesting its sequestration in inclusion bodies (Figure 3B). The processes of denaturation and renaturation were used to release inclusion bodies. Inclusion bodies were denatured in 2, 4, 6, and 8 M urea (Figure 3C). Continuously, we obtained the pure target protein PtDXS through the combined techniques of dilution, renaturation phase changes in dialysis, re-dissolution and finally, Ni-IDA resin. To isolate PtDXS fusion protein, we captured His-tagged protein using Ni-IDA resin, washed frequently in buffer containing 20 mM imidazole, and eluted in buffer containing 250 mM imidazole (Figure 3D,E). Western blotting of PtDXS showed the presence of His-DXS for confirming that the produced peptide in _E. coli_ BL21 (DE3) was DXS (Figure 3E). Figure 1: Predicted tertiary structures of AtDXS and PtDXS Green, cyan, and yellow, \(\alpha\)-helices, \(\beta\)-strands, and random coils, respectively. Red and magenta, TPP and DRAG domain motifs, respectively. (**A**) Tertiary structure of AtDXS; (**B**) Tertiary structure of PtDXS. Figure 2: Phylogenetic tree of _Populus trichocarpa_. Amino acid sequences of PtDXS (XP_024463484.1) and other DXS proteins. The tree was constructed using the neighbor-joining method in MEGA 5.1 and was bootstrapped 1000 times. Bootstrap percentages are indicated at the branch points. In all cases, the tree topologies obtained using the neighbor-joining, minimum-evolution, and maximum-parsimony methods were identical. The GenBank accession numbers of the DXS sequences are as follows: _Alpinia officinarum_ (AEK69518.1), _Medicago truncatula_ (CAD22530.1), _Pinus taeda_ (ACJ67021.1), _Catharantlus roseus_ (ABI35993.1), _Jatropha curcas_ (XP_012076628.1), _Fragaria vesca subsp. [PERSON]_ (XP_011459218.1), _Colwellia psychoreythraea_ (KGJ90592.1), _Hever brasiliensis_ (ABD92702.1), _Bixa orellana_ (AMJ39459.1), _Elieus guineensis_ (XP_019710182.1), _Helianthus annuus_ (OTG31837.1), _Plonetix dactylifera_ (XP_008795130.1), _Prunus persica_ (XP_007225144.1), _Ricinus communis_ (XP_015573388.1), _Taraxacum kok-saghyz_ (AMB19705.1), _Thebroma cacao_ (XP_017975597.1), _Aquilaria sinensis_ (AHI62962.1), _Astragalus membranacus_ (AID51428.1), _Bixa orellana_ (AMJ39460.1), _Catharantlus roseus_ (CAA09804.2), _Crataegus pinnatifida_ var.
# Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques
## 5 Results and Discussions
### Answers to the Research Questions  
RQ01: What are the purposes and reasons for SRPM research? Software Risk Prediction is very important to develop software with fewer hassles within an efficient budget and time. The main purpose of the study is to reduce the risks during SDLC using Machine Learning models or algorithms. RQ02: What is the average number of SRPM studies each year? Figure 3 illustrates a year-by-year presentation of the studies that were selected. From 2007 to 2021, a total of 15 years' worth of data for 16 articles was displayed in this section. Each year, there is a noticeable disparity in the distribution of articles. Our first acquired research publication on SRPM was published in 2007. Since then, a large number of research articles on this topic have been published. During the year 2013, three papers were published. In the years that followed, the rate of publication decreased substantially, with five new publications being produced until 2019. The figure also shows the vast majority of articles published in 2020 and 2021, with article counts ranging from 4 to 3 in each of those years. The overall picture implies that published papers are unequally distributed. We have not detected any pattern of sequential distribution over time, which is consistent with this conclusion. RQ03: What kinds of data sets are used in the detection process? Depending on the type of dataset used, we divided the studies into two categories: public and private data. In contrast to public datasets, which are made available to the public, private datasets are collected and used by individuals rather than being made available as public datasets. As a result of our investigation, we learned that 37.5% of the datasets utilized in the research were publicly accessible and 62.5% were private. An important challenge is the use of a private dataset to train models for the detection or prediction. As a rule, exposure to a private dataset is confined, making it hard to compare the outputs of different machine learning models in practice.
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 4 Discussion
### Covert Channel Attack  
To further expand the research in the direction of heat propagation, we created a small Covert Channel Attack. The idea of a covert channel attack is based on bypassing the current security measures in order to transmit sensitive data without breaking them. In more details, a person (called A) has access to secret data in the interior of an entity; however, due to the security measures, it cannot transmit the data in the exterior to another person (called B). To avoid the security measures, [PERSON] can use a covert channel to transmit the data to the exterior, which will look like a normal operation from inside, and [PERSON] will listen from the exterior and will try to extract the secret values. Furthermore, we decided that our secret data are the text \"_hello world_\" which will be transmitted by [PERSON] from Core 7 (the interior) to B (the exterior) which has access to a nearby core, but not to Core 7. The transmission is performed by sequences of executing the (value of 1) and sleep (value of 0) on Core 7. We transformed the secret text in binary values so that it can be sent using the rule presented above, when the core executes (increasing the CPU load to 90%) for 10 s, it sends a value of 1, and when it is idle for 10 s, it sends a 0. The listening part will be performed by [PERSON], who is monitoring the temperature of the nearby core, reading the temperature of its core every second. As can be seen in Figure 24, the heat is propagated to the nearby core. We presented both cores in this figure so that the propagation can be seen better. However, [PERSON] only has access to the data in blue, which represent the propagated data. A further thermal analysis must be applied to these data to extract the secret text. A technique called _binarization_ is applied, using a filter which will divide data into values of 0 and 1. After this, we have the binary values, and we will extract the final values by searching for patterns that will help us distinguish the time for transmitting one bit. The results of this experiment are that we were able to reconstruct 71 of 88 bits of the secret text, which means 80.6% of the original text sent from A to B was recovered.
## 4 Conclusions
### Challenges  
The most challenging problem in the MIMO-OFDM system is the elimination of the ICI and ISI interferences. One cumbersome solution would be to use a CP-OFDM variant. Nonetheless, with massive MIMO architecture and with some complex signal processing algorithms (channel estimation, equalization, coding, decoding) the process would cause a lot of latency, which would alter the communication with a lot of delays and errors. In fact, when the CP length is less than the channel impulse response, ISI and ICI degrade the performance of the receiver. The inherent idea is to choose a suitable channel estimator and a key equalization technique to fade away the interference effect.
## 3 Improved NSGA-II  
In multi-objective optimization, each optimization objective is in conflict with the other and they cannot be optimized at the same time. Therefore, the task of multi-objective optimization is not to find an optimal solution, but to obtain a set of Pareto solution sets. The NSGA-II was proposed by [PERSON] et al. [36] on the basis of the NSGA, and the core involves non-dominated ordering and introduces the concept of congestion. With the advantages of fast operation, good robustness, and good convergence, it has become an important basis for handling multi-objective optimization problems. However, there are several problems in applying the NSGA-II to solve the NHFSP, as follows. (1) The selection process essentially chooses solutions with a non-dominance rank of 1 and a congestion of 0, which leads to the easy and accelerated convergence of the Pareto solution set to a local optimum. (2) Crossover and variation are important steps to generate new individuals, but the traditional NSGA-II crossover and variation operators have poor local and global search capabilities.  
Therefore, in this paper, we improved the NSGA-II to ensure its optimization capability in a multi-target NHFSP, named as NSGA-II-V. Firstly, the initial population was generated with \"random\" and \"kind of consecutive\" encoding. Second, a crossover operator combining \(POX\) and \(LOX\) with a new mutation operator was designed. Then, a combination of elite selection and tournament selection strategies were used to determine the parent population. Finally, to ensure that the Pareto solution set was optimal in the neighborhood, a variable neighborhood search algorithm was used to perform a local search on the solution set. Figure 3 shows the flow of the algorithm.
# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 1 Introduction
Due to the multiple inputs in these systems (ambient temperature (T\({}_{\text{amb}}\)), relative humidity (HR), thermal load, air velocity, usage habits, etc. ), it is difficult to develop a mathematical model that can accurately describe the behavior of the system within a wide operating range [18]. In this sense, nonlinear controllers that are based on fuzzy logic, expert systems, and artificial neural networks, can overcome these problems and, therefore, have shown to be a viable alternative to achieve better results in the control of variables than conventional controllers [19]. Based on the aforementioned, the main objective of this work is to present a comprehensive review of the application of fuzzy logic in the design and implementation of controllers in RACs. Detailed information about fuzzy controllers and controller design is discussed regarding input and output variables, the number and type of membership functions, and inference rules. Additionally, factors that can be used in the implementation of fuzzy controllers and offer the potential of energy savings are presented.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 4 PH-Responsive Nanocarriers
The higher retention time in the colon may be due to the drug loading efficiency of nanoparticles, ensuring colon-specific drug release. Further studies are needed to test the efficiency of the nanoconjugates in humans [18]. Dual azo-polyurethane and pH-sensitive Eudragit S100 (ES-Azo.pu) nanoparticles were prepared by mixing pH-sensitive Eudragit S100 with enzyme-sensitive azo-polyurethane [19]. The compound aims to obtain site-specific controlled drug release via enzymatic degradation. Single pH-sensitive nanoparticles were prepared using Eudragit S100 in order to compare their ability in terms of colonic drug delivery with that of ES-Azo.pu NPs. A hydrophobic model drug, coumarin-6, was embedded into nanoparticles for the treatment of IBD. The size, shape and drug load capacity of nanoparticles was assessed and the novel ES-Azo.pu NPs were prepared by the oil-in-water modified emulsification solvent evaporation technique. The drug was retained inside the NPs at acidic pH conditions and only released in a sustained and controlled manner at a physiological pH and in the presence of cecal contents of rats. These observations were noted using in vitro drug release tests and in vivo localization of the drug in the gastrointestinal tract of the rat models. The results demonstrated that the novel ES-Azo.pu NPs were highly superior to ES NPs in terms of coumarin-6 levels in the colon, being a promising colon-targeted drug delivery system [19]. More recently, budesonide was loaded into pH/Time_NPs to reduce premature drug release in the stomach and small intestine and support the sustained and controlled release in the colon [20]. Colon-targeted drug delivery and drug distribution were more effective in the case of pH/Time_NPs, with premature drug release in the stomach and small intestine for Time_NPs and pH_NPs, as shown by in vitro drug release and in vivo distribution tests. In vivo assessment of the disease activity index (DAI), changes in body weight, colon length, histological imaging and immunohistochemistry of colon tissue indicated that pH/Time_NPs mitigated the effects of DSS-induced colitis in mice to a greater extent than pH_NPs. Thus, the novel pH/Time_NPs could be efficiently used in the treatment of colitis as an oral colon-targeted delivery system, being superior to single pH-dependent drug-delivery systems [20].
# Longer Ubiquinone Side Chains Contribute to Enhanced Farnesol Resistance in Yeasts
## 3 Results  
### UQ9 is the Major Ubiquinone in C. albicans  
The diversity of ubiquinone presence in genus _Candida_ was studied in detail by [PERSON] and [PERSON] [23,24] where UQ9 was identified as the major ubiquinone in _C. albicans_ strain JCM 1542 (IFO 1385). Ubiquinones are known to be localized in different types of cell membranes. Due to the distinct structural and chemical compositions in different morphologies of _C. albicans_, we analyzed the ubiquinone pool in _C. albicans_ cells grown under different physiological conditions and/or in different morphological states, i.e., yeasts, hyphae, anaerobically grown cells, and both white and opaque cells (Figure 2). We confirmed that UQ9 is the major type of UQ in _C. albicans_ in two widely used laboratory strains SC5314 and WO-1 (Table 1) as analyzed by HPLC-DAD with UQ10 as the internal standard [21]. Figure 2a shows the retention profile of ubiquinones in wild type _C. albicans_ grown in minimal media and Figure 2b summarizes the ubiquinone types and total ubiquinone content under different physiological and morphological states. These data confirm the predominance of UQ9 in all cell types except for the anaerobically grown cells which lack detectable ubiquinones. In particular, UQ9 was the dominant ubiquinone in both rich medium (YPD) and minimal medium (GSB), for both white cells and opaque cells, and for both mycelial cells (37 \({}^{\circ}\)C) and yeast cells (30 \({}^{\circ}\)C). These observations show that UQ9 is a general characteristic of _C. albicans_ regardless of the cell morphology. We did not detect any UQs in anaerobically grown cells; this absence is expected because mitochondrial respiration is absent in anaerobically growing cells.
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 2 Materials and Methods
### Data Pre-Processing
#### 2.5.5 Leave One Subject out Cross-Validation  
Leave-one-subject-out cross-validation has been applied, in order to achieve a robust result. As shown in Table 5, at each iteration, one microphone data is used as the testing data, and the remaining data are used as training data, until all the microphones have been used once as testing data. Therefore, five microphones lead to five iterations of each training process. The final evaluation will be averaged over the five iterations.  
The whole process is called leave-one-subject-out cross-validation. The subject refers to different microphones here. Leave-one-subject-out cross-validation makes sure that the model does not have subject bias [42].
## 5 Discussion  
To verify the effectiveness of the VMD-EWT algorithm in decomposing Raman spectral sequences for spectral denoising, comparative experiments were conducted with different modal decomposition-based spectral denoising algorithms. The results demonstrate that utilizing VMD to decompose the original sequence into several sub-sequences and a residual sequence, followed by further decomposition of the residual sequence using EWT, and categorizing the noise and signal sequences based on the correlation coefficient and zero-crossing rate index, can resolve the signal mode mixing problem and maintain good denoising performance.  
In the comparative denoising experiments, different state-of-the-art denoising algorithms are evaluated using MSE and SNR as objective metrics. The proposed neural network model demonstrates superior denoising performance over other methods, validating the efficacy of the EBiLSTM architecture design. The incorporation of two short-term and long-term neural structures captures the correlations and spectral features in the Raman data.  
To thoroughly validate the denoising performance of the VMD-EWT-EBiLSTM network architecture, comprehensive performance analysis is carried out on the test set under 20 dB and 30 dB of random noise conditions, by examining the denoising outcomes from each algorithm. It is evident from the results that the VMD-EWT-EBiLSTM network architecture achieves a lower MSE and a higher SNR compared to other benchmark denoising algorithms, demonstrating superior denoising performance. Furthermore, the analysis of the average MSE and average SNR metrics over the Raman spectral test set provides further evidence that the network architecture yields improved denoising results.  
The proposed method in this paper does not always perform ideally. As the noise level increases, the MSE and SNR metrics for noise estimation deteriorate, as shown in Table 4. Under 30 dB of Gaussian noise, the denoising performance of the VMD-EWT-EBiLSTM network declines, mainly due to the following reasons: at high noise levels, the noise intensity becomes comparable to the signal, making it difficult to differentiate between the signal and the noise.  
The proposed method in this paper adopts a VMD-EWT-EBiLSTM network architecture for denoising Raman spectral signals. Since it involves relatively complex algorithms, the neural network-based denoising process requires a certain amount of training time and computational resources. To reduce the subsequent time and computation costs, future research could focus on model compression and pruning of the structure and parameters, as well as leveraging GPUs to accelerate model training and inference procedures, in order to further improve the denoising performance for Raman spectra.
# High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation
## 6 Use-Cases: Modeling the Emilia-Romagna and Lombardy Regions
### Restrictions  
In Emilia Romagna's model, there were six different levels of restrictions to represent as precisely as possible the division into bands implemented by the Italian government during the simulation period taken into account:  
1. White: no restrictions
2. White from 10/18 to 10/24: represents the restrictions introduced on 18 October 2020: no limitations regarding work, but high schools and universities at 50% in attendance. The number of daily interactions is reduced by 40% to shape the closure of businesses such as bars after 9 pm and restaurants after midnight.
3. White from 10/25 to 11/05: represents the latest restrictions introduced before the zoning: no limitations regarding work, but schools and universities still at 50% in attendance. Closing of activities such as gyms, theaters and cinemas, closing restaurants after 6 pm, also the recommendation not to move. The number of social interactions is reduced by 50% compared to the initial value.
4. Yellow: high schools and universities closed (100% distance learning). Curfew from 22.00. Closure to the public of exhibitions, museums and other places of culture such as archives and libraries. To model these additional restrictions, the number of social interactions is reduced by 60%.
5. Orange: in addition to the limitations of the yellow area, the prohibitions on moving between municipalities except for proven work needs and the closure of catering activities (excluding take-away) are added. The use of smart working is encouraged and recommended even for workers, excluding essentials. In the model, social interactions are reduced by 70% compared to the initial values and cannot take place outside the municipalities (except those related to work activities).
6. Red: in addition to the limitations of the orange zone, the prohibition of movement within the municipality. For this, the daily interactions are reduced by 80% and reduced to zero those regarded as usual ones.  
Figure 7: Distribution of work classes.