esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Estrone Concentrations  
Estrone presence was determined by LC/MS. Data exceeding the detection limits (LOD) were observed only for estrone treatments, while the estrone content in controls was <LOD in all cases.  
Figure 2: Enzymatic activity.  
Estrone concentration changes in each soil were individually determined with the Friedman test, covering data from the entire experiment, while for soil comparisons, the Brown-Forsythe test was employed for 0 and 28 days, followed by one-way ANOVA and Tukey post hoc pairs comparisons.  
The soils showed a significant difference in estrone concentrations between the start and the end of the experiment, with thermozem presenting the highest variation (from 28.71 ug\(\cdot\)kg\({}^{-1}\) to 0.94 ug\(\cdot\)kg\({}^{-1}\)), and fluvisol presenting the lowest (from 20.48 ug\(\cdot\)kg\({}^{-1}\) at 4 h to 5.09 ug\(\cdot\)kg\({}^{-1}\) at 28 days). At the same time, fluvisol had the highest median, 13.93 ug\(\cdot\)kg\({}^{-1}\), and the clearest transition in the phases of estrone at 24 h; chernozem had the lowest median (8.10 ug\(\cdot\)k\({}^{-1}\)) and presented several transition phases (4 h to 12 h, 24 h to 3 days, and the last one between 7 and 28 days), while cambisol showed no significant differences between consecutive sampling times.  
Soil comparison regarding the entire experiment gave _p_-value = 0.94. (Table 2--Estrone). However, analysis of the first (0 day) and last day (28 day) separately presented a more detailed insight into the differences in estrone transformation in each soil. For day 0, although the overall _p_-value was 0.10, there were already differences between soils, although not yet significant ones. Pairwise comparisons confirmed that the most different soil was fluvisol and the highest degree of variation was with chernozem (_p_-value 0.08). By day 28, the differences became significant: fluvisol presented a marked contrast with both chernozem (_p_ = 0.00001) and cambisol (_p_-value 0.00002). On the other hand, cambisol and chernozem were more similar at 28 days than at 0 days (_p_-value 0.76 vs. 0.56).
## 6 Rational Antimicrobial Use Practices
### Antimicrobial Stewardship  
Antimicrobial stewardship (AMS) programs promote and supervise rational antimicrobial use within healthcare institutions and can include different core elements, depending on the setting [73]. For example, the focus of the AMS program can shift slightly, for hospitals managing inpatients [73; 74], for outpatient departments [73; 75] or for long-term nursing facilities [73; 76]. According to the US Centers for Disease Control and Prevention, core components of hospital AMS programs should include: leadership commitment to dedicate the required resources, accountability by appointing one leader of the AMS team, pharmacy expertise by appointing a clinical pharmacist as co-leader, action through implementing interventions to control antimicrobial use, tracking and surveillance of antimicrobial prescribing practices, reporting surveillance data and AMS program outputs, as well as education of healthcare staff at all levels on optimal prescribing practices [77].  
Different toolkits have been designed and are readily available, to aid in implementing AMS programs throughout healthcare facilities [78] and bedside tools can be used to predict the risk of Gram-negative drug-resistant infection in hospitalized patients [79]. Furthermore, clinical decision support systems can facilitate therapeutic choices, by discriminating between cases that might indeed require antibiotic treatment and those that would not, and by allowing the choice of the most appropriate agent based on patient risk profiles [80; 81].  
Figure 2: Opportunities for intervention to ensure appropriate antimicrobial prescribing practices throughout the continuum of care (created with BioRender.com). AMS = antimicrobial stewardship; CDSS = clinical decision support system.
## 3 Results
### Engine Performance  
Figure 6 shows the brake specific fuel consumption (BSFC) values for all test fuels at different engine loads. As shown in Figure 6, it is obvious that the BSFC of all test fuels shows a decreasing trend with the increase of engine load. For example, when the engine burns D100, D95E5, D90E10, and D85E15 blended fuels at 60 Nm, the BSFC decreases by 23.58%, 21.89%, 22.97%, and 25.82% compared with 30 Nm, respectively. At 90 Nm, the BSFC of D100, D95E5, D90E10, and D85E15 is decreased by 25.40%, 27.72%, 29.40%, and 32.30% compared with that at 30 Nm, respectively. This can be attributed to the increase of engine load, which leads to the increase of air flow intensity, and the increase of temperature and pressure in the cylinder, which is conducive to the full combustion of fuel and the improvement of combustion efficiency, and finally leads to the reduction of BSFC. Similar results were reported by [PERSON] et al. [32]. They also pointed out that the reason for the decrease of BSFC is related to the increase of total energy release with the  
Figure 4: Heat release rate for (**a**) D100, (**b**) D95E5, (**c**) D90E10, and (**d**) D85E15.  
increase of engine load. On the other hand, the BSFC of diesel engines increases with the proportion of ethanol in the fuel blend. At 30 Nm, the BSFC of D95E5, D90E10, and D85E15 is increased by 3.71%, 7.42%, and 12.88% compared with that of D100, respectively. At 60 Nm, the BSFC of D95E5, D90E10, and D85E15 is increased by 6.00%, 8.29%, and 9.57% compared with that of D100, respectively. At 90 Nm, the BSFC of D95E5, D90E10, and D85E15 is increased by 0.49%, 1.66%, and 2.44% compared with that of D100, respectively. This may be mainly related to the low calorific value of ethanol. Increasing the mixing ratio of ethanol in diesel reduces the calorific value of the blended fuel, which requires more fuel to meet the working requirements of the CRDI diesel engine [25].
## 3 Results  
### Autofluorescence Images of Lung Tissues  
Figure 1 demonstrated four HE images on stained lung tissue samples (left column) and autofluorescence images of the corresponding unstained samples (middle and right columns). Figure 1a showed several alveolar structures in normal tissues. Figure 1b,c presented representative adenocarcinoma and squamous carcinoma tissues. It is well known that NADH and FAD emit fluorescence of 450-560 nm and porphyrins emit fluorescence of over 590 nm [35]. Thus the middle column in Figure 1 showed the autofluorescence of NADH and FAD in lung tissue samples, and the right column showed the fluorescence images of porphyrins. It can be seen that the autofluorescence images demonstrated the cellular morphology and tissue structure, as well as the HE staining images. In Figure 1d, a blood vessel wall with erythrocytes (marked by a white arrow) can be seen in lung tissue. Considering the vascular wall and erythrocytes can influence the autofluorescence spectra, the areas with blood vessels or erythrocytes should be avoided during the further spectra study.  
To study the fluorescence imaging capability on thick tissues, the 3D images of two samples were acquired and shown in Figure 2a,b; normal tissue and cancerous tissue. The cellular morphology features were clearly different between normal and cancerous tissue from the surface of the tissue (0 \(\upmu\)m) down to the deep position (\(-24\)\(\upmu\)m).  
Figure 1: HE and autofluorescence images of lung tissue samples: (**a**) normal tissue; (**b**) adenocarcinoma; (**c**) squamous carcinoma; and (**d**) blood vessel in lung tissue. Scale bar is 50 \(\upmu\)m.
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 2 Methodology
### Methods and Models  
The trigger frequency was calculated as the ratio of the number of UA events divided by the total amount of air traffic of each airport, and it was modeled as a spatial analysis unit in this paper. The spatio-temporal distribution patterns of UA events were explored by temporal heatmaps,spatial distribution maps, and spatio-temporal cubes. Correlation analysis was used to make a preliminary analysis of the potential influencing factors of UA events, such as airport terrain and meteorological factors.
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 4 MIPS Architecture
### Operating Modes:
#### Address Translation:  
With virtualization arises the need for translation of the virtual guest address to an actual physical address. This is met with the help of an additional layer, as shown in Figure 3. The first address translation layer is used by the hypervisor and the second by the guest OS. The guest OS translates from a virtual address to a pseudo-physical address in the second layer and the hypervisor layer translates from a pseudo-physical address to a physical address in the first layer.  
Figure 3: Two-level address translation in the MIPS virtualization mode [31].
# Enhancement of Lycopene Biosynthesis Using Self-Assembled Multi-Enzymic Protein Cages
## 3 Results  
### Construction of IPP Synthetic Multi-Enzyme Nanocage Using \(\alpha\)-Carboxysome Shell Assembly  
The \(\alpha\)-carboxysome simplified gene module from _P. marinus_ MED4 was used for the assembly of carboxysome shells (CBs) (Supplementary Figure S1). To establish an IPP synthetic enzyme immobilized on carboxysome shells, we selected an exogenous IPP synthetic enzyme cascade based on the isoprenoid alcohol pathway [31; 32; 33; 34]. This enzyme cascade involves the synthesis of isoprenyl pyrophosphate (IPP) from the exogenous addition of isoprenol by choline kinase from _Saccharomyces cerevisiae_ (ScCK) and isoptenyl phosphate kinase from _Arabidopsis thaliana_ (AtIPK). Subsequently, the interconversion of IPP and dimethylallyl pyrophosphate (DMBPP) is catalyzed by an IPP isomerase (IDI), accumulating IPP for the production of lycopene via the catalysis with CrtE, CrtB, and CrtI [44] (Figure 1a). The SpyTag/SpyCatcher (ST/SC) is a system for site-specific protein conjugation linked by intermolecular isopeptide bonds [45]. For the assembly of IPP biosynthetic enzymes on the exterior of the carboxysome protein cage, we fused the ST to the C-termini of ScCK, AtIPK, and MxanIDI with a (GGGGS)\({}_{2}\) linker. The SC protein was then connected to the major carboxysome shell protein CsoS1A, which was also linked via a (GGGGS)\({}_{2}\) sequence at its C-terminus (Figure 1b).
## 3 Recent Advances in Different Configurations of STSCs
### Cermet Composite STSCs
[68] studied the impact of Y\({}_{2}\)O\({}_{3}\) content on the corrosion resistance of 316L/TiC coatings. Their results declared that integrating 1 wt% of rare earth oxide produced the polarization of a dynamic potential curve characterized by a high corrosive potential and a low density. The authors attributed this to the refining effect of Y\({}_{2}\)O\({}_{3}\) on the coating structure, which led to the formation of a more compact oxide film. In a similar study conducted by [PERSON] et al. [69], Y\({}_{2}\)O\({}_{3}\)-doped cermet-based single-layer nanocomposite coatings were prepared using laser cladding. The addition of Y\({}_{2}\)O\({}_{3}\) improved the optical performance of the coatings and enhanced their wear and corrosion resistance. [PERSON] and [PERSON] [70] incorporated Pr and Nd into Co-WC cemented carbide through alloy infiltration. This process refined the alloy grains and increased the alloy density, resulting in improved corrosion resistance in alkaline environments. Functional absorptance characteristics are also essential for improving the thermal tolerance of coatings. [PERSON] et al. [71] developed a WTi-Al\({}_{2}\)O\({}_{3}\) cermet-based STSC that exhibited a solar absorptance of approximately 93% and an emissivity of 10.3% at 500 \({}^{\circ}\)C, even after being annealed at 600 \({}^{\circ}\)C for 850 h. Their research showed that the surface segregation of solute Ti atoms from the parent alloy nanoparticles, along with their partial oxidation to form a protective layer, exhibited the outwarded diffusion of W elements, minimized nanoparticle agglomeration, and maintained the hybridization of the interface structure, thereby enhancing the heat-tolerance of the paintings. [PERSON] et al. [72] proposed the synthesis of STSCs using Mo and SiO\({}_{2}\) and evaluated the impact of different substrate treatments--electropolishing, chemical cleaning, and passivation--on the performance of STSC. The maximal absorptance achieved was 98.20% for a 45 nm SiO\({}_{2}\)/Mo on an electropolished substrate. However, the desired solar selectivity was obtained with the SiO\({}_{2}\)/Mo on a passivated substrate. In summary, metal particles within Al\({}_{2}\)O\({}_{3}\) cermet are prone to oxidation and diffusion at elevated temperatures, resulting in reduced spectral efficiency. It is found that certain innovative coatings, such as W-Ni-Al\({}_{2}\)O\({}_{3}\) and W-Ti-Al\({}_{2}\)O\({}_{3}\), exhibit robust thermal stability up to 600 \({}^{\circ}\)C, which typically withstand up to 400 \({}^{\circ}\)C. However, double cermet STSC may be more effective than textured surface coatings and multilayer interference stacks. Therefore, these coatings are recommended as a promising future research trend due to their high thermal performance, high-temperature durability, and suitability for large-scale fabrication.
## 3 Detection Method of Ionospheric Anomaly  
To detect ionospheric disturbance responses in TEC measurements of BDS-GEO satellites, a descriptive statistical analysis method is used on daily hourly TEC. [PERSON] et al. [48] have first examined the ionospheric TEC observations from GPS receivers in Taiwan regions to study ionospheric electron density variations during the Chi-Chi earthquake. They have further computed a 15-day running median of the TEC and the associated inter-quartile range as a reference for detecting abnormal signals during severe earthquakes. It has demonstrated that a statistical analysis method is useful to register pre-earthquake ionospheric anomalies before significant earthquakes. Then, many researchers have started to use GPS TEC from the global ionospheric maps to statistically analyze the ionospheric disturbance responses before worldwide earthquakes [49; 50; 51; 52; 53; 54]. In this work, the median of 30 days before the observed day is calculated to construct the anomaly bounds. In Equation (3), the first and third quartiles are also calculated to quantify the anomalous disturbance responses. There is a positive (greater than upper bound) or negative (smaller than lower bound) disturbance response when the value of BDS-GEO TEC is outside of the upper and lower bounds.  
\[\left\{\begin{array}{l}\text{ TEC}_{i}>\text{Upper Bound}=M+1.5(UQ-M)\quad \text{ positive}\quad\text{response}\\ \text{ TEC}_{i}<\text{Lower Bound}=M-1.5(M-LQ)\quad\text{ negative}\quad\text{response}\end{array}\right. \tag{3}\]  
where \(M\) is the median of TEC series from global ionospheric map (GIM) model; and \(LQ\) and \(UQ\) are the first and third quartiles, respectively.  
The critical frequency (\(f_{o}F_{2}\)) and peak height (\(h_{m}F_{2}\)) of the ionospheric F2 layer are collected from four ionosonde stations over low-latitude regions to investigate the ionospheric disturbance responses during the different stages of the storm. Meanwhile, the relative deviations of the \(f_{o}F_{2}\) and \(h_{m}F_{2}\) are used to measure the disturbance intensity of F2 layer signatures, which is defined by equation \(Df_{o}F_{2}=(f_{o}F_{2}(i)-f_{o}F_{2}(q))/f_{o}F_{2}(q)\) and \(Dh_{m}F_{2}=(h_{m}F_{2}(i)-h_{m}F_{2}(q))/h_{m}F_{2}(q)\), where \(f_{o}F_{2}(i)\) and \(h_{m}F_{2}(i)\) represent the measurement of \(f_{o}F_{2}\) and \(h_{m}F_{2}\) during the storm time, respectively; and \(f_{o}F_{2}(q)\) and \(h_{m}F_{2}(q)\) represent the quiet value of \(f_{o}F_{2}\) and \(h_{m}F_{2}\) calculated from the five-day average before the storm, respectively. It can be considered that positive deviation corresponds to the positive ionospheric responses, and negative deviation corresponds to the negative ionospheric responses.
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 4 Discussion
### Limitations of the Study
#### 4.5.2 Omission of Severity of Rockfalls  
Another limitation of the current study is that it classifies data solely as rockfall or non-rockfall points, without considering the severity and impact of the rockfalls. This limitation arises from the nature of the news reports used, which are often written by general reporters or based on witness accounts rather than experts in rockfall analysis. Consequently, these reports typically lack detailed information on the severity or magnitude of the rockfalls, and there is currently no reliable method to infer this information accurately from the news. Therefore, we chose not to perform this level of analysis to avoid drawing misleading conclusions.
## 2 Materials and Methods
### Dataset  
Address records for the raw data were manually collected in 2019 from various government departments. The geographical area to which this data refers is Shangcheng District, Hangzhou, Zhejiang Province, China. The address dataset contains a variety of location description types, including standard addresses, nonstandard addresses, POIs, road intersections, place name abbreviations, and so on. The preprocessed address datasetamounted to 1,552,532, consisting of three fields of address records, longitude, and latitude, which served as the address corpus for the pretraining phase.  
To use the address data for semantic address matching, we created a dataset of address pairs with labels based on the address corpus. Based on the set of addresses filtered with the same coordinates, we performed manual matching using a standard address database. In addition, to give the model better prediction performance and generalization capabilities, we augmented the dataset with easy data augmentation [51] methods for text classification tasks, mainly using synonym replacement, address element deletion, and address element insertion. To improve the robustness of the model, we constructed mismatched address pairs in the set of address pairs with Jaccard similarity coefficients [11] greater than zero. We finally obtained a dataset of 64,358 address pairs and corresponding labels, a sample of which is shown in Table 1. The statistical features of the dataset used for semantic address matching are shown in the Table 2, where we used the difference in the number of characters, Levenshtein distance [8], and Jaccard similarity coefficient [11] to show the similarity of address pairs in the dataset. Unmatched address pairs will perform worse in terms of text similarity, in line with our common sense.
## 4 Discussion
### XRF Soil Fractions Comparison  
Linear regressions on the three heavy metals of concern (Hg, As, and Sb) showed strong correlations, suggesting no need for semi-log and log-log regressions. Both Sb and Hg XRF soil fraction comparisons showed that ex situ-sieved vs. ex situ-bulk had a strong correlation, whereas ex situ-bulk vs. in situ had a poor correlation. Arsenic XRF soil fraction comparisons showed that both regressions (ex situ-bulk vs. in situ, and ex situ-sieved vs. ex situ-bulk) have strong correlations. The ex situ-bulk vs. in situ regressions were most likely poor correlations for Hg and Sb because the soil analyzed in the XRF in situ readings was not the same soil as the ex situ samples analyzed by XRF locations (Section 4.2). The in situ results were randomly selected XRF surface readings on a sub-area, while ex situ results are random soil aliquots of that same area. Arsenic's ex situ-bulk vs. in situ regression had a strong correlation, which has been demonstrated in previous studies [17].  
The ex situ-sieved vs. ex situ-bulk regressions had a strong correlation across all three heavy metals (Hg, As, and Sb). This may be because the only difference in samples is the selection of particle size, with ex situ-sieved more likely to have higher concentrations of heavy metals [6]. Interestingly the Hg ex situ-sieved vs. ex situ-bulk had a conversion factor of 1.09, nearly a one-to-one ratio. This contradicts findings in other studies [18; 19]. Unlike Hg, ratios for As and Sb were not one-to-one. This may be because of the mining process; both As and Sb are byproducts of the process in the form of particulates, whereas Hg is emitted from the smelter stack as vapor molecules that can subsequently bind to fine particulates. Because Hg is likely bound to fine soil particulates on a molecular level, it does not increase in concentration due to sieving.
# The Computability of the Channel Reliability Function and Related Bounds
## 2 Definitions and Basic Results
### Lower and Upper Bounds on the Reliability Function for the Typewriter Channel  
As mentioned before, [PERSON], [PERSON], and [PERSON] assumed in [13] that the expurgation is bound tight. [PERSON], [PERSON], and [PERSON] showed in [42] a counterexample for the symmetric \(q\)-ary channel when \(q\geq 49\). [PERSON] and [PERSON] found a simpler counterexample in [43]. They showed that the conjecture is already wrong for the \(q\)-ary typewriter channel for \(q\geq 4\). We would like to briefly present their results here.  
**Definition 13**.: _Let \(\mathcal{X}=\mathcal{Y}=\mathbb{Z}_{q}\) and \(0\leq\epsilon\leq\frac{1}{2}\). The typewriter channel \(W_{\epsilon}\) is defined by_  
\[W_{\epsilon}(y|x)=\begin{cases}1-\epsilon&y=x\\ \epsilon&y=x+1\mod q.\end{cases} \tag{12}\]The extension of the channel \(W_{\epsilon}^{n}\) is defined by_  
\[W_{\epsilon}^{n}(y^{n}|x^{n})=\prod_{k=1}^{n}W_{\epsilon}(y_{i}|x_{i}). \tag{13}\]  
For the reliability function of this channel, the interval \((C_{0}(W_{\epsilon}),C(W_{\epsilon}))\) is of interest. The capacity of a typewriter channel \(W_{\epsilon}\) has the formula  
\[C(W_{\epsilon})=\log(q)-h_{2}(\epsilon),\]  
where \(h_{2}\) is the binary entropy function. [PERSON] showed in [40] that \(C_{0}(W_{\epsilon})\) is positive if \(q\geq 4\). He showed that for even \(q\), it holds that \(C_{0}(W_{\epsilon})=\log\left(\frac{q}{2}\right)\). It is difficult to get a formula for odd \(q\). [PERSON] proved in [44] that [PERSON]'s lower bound for \(q=5\): \(C_{0}(W_{\epsilon})=\log\sqrt{5}\) is tight. For general odd \(q\), [PERSON] proved  
\[C_{0}(W_{\epsilon})\leq\log\frac{\cos(\pi q)}{1+\cos(\pi q)}q.\]  
It is only known for \(q=5\) that this bound is tight. In general, this is not true. For special \(q\), there are special results outlined in [44; 45; 46; 47].  
[PERSON] and [PERSON] provide upper and lower bounds on the reliability function in [43]. They observed that the zero-error capacity of the pentagon can be determined by a careful study of the expurgated bound.  
They present an improved lower bound for the case of even and odd \(q\), showing that it also is a precisely shifted version of the expurgated bound for the BSC. Their result also provides a new elementary disproof of the conjecture suggested in [13] that the expurgated bound is asymptotically tight when computed on arbitrarily large blocks. Furthermore, in [43], [PERSON] and [PERSON] present a new upper bound for the case of odd \(q\) based on the minimum distance of codes. They use [PERSON]'s linear programming method [48] (see also [49]), combining the construction used by [PERSON] [44] for bounding the graph capacity with the construction used by [PERSON] [50] for bounding the minimum distance of codes in Hamming spaces. In the special case \(\epsilon=1/2\), they give another improved upper bound for the case of odd \(q\), following the ideas of [PERSON] [51] and [PERSON] [52], which in turn are based on estimates for the spectra of codes originated by [PERSON] [53].