esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 4 Performance Evaluation
### System Model and Experiment Setup  
To evaluate the performance of the proposed system, it is assumed that the sensor nodes move randomly within a square target area while capturing photos of the target points of interest (i.e., BoIs) from various locations and directions. Figure 6 shows a layout of the system model.  
Any contact between two nodes results in photo sharing between them and the assumption is that, via free network links, the nodes are both able to store and exchange photos between them [11].  
The Cambridge dataset simulates DTNs [25]. To evaluate the performance of the proposed system, the Cambridge dataset is pre-processed such that all contacts between any pair of nodes are gathered and sorted according to the contact time in ascending order. MATLAB was employed in the stimulation of DTNs. Here, 1000 nodes carry local information of photos in the network, and the location of the command center is outside the target area. In disrupting contact with the command center, the presumption is that only 15 sensor nodes, selected randomly, are able to contact the server, and thus, only 15 nodes can deliver information about the target area.  
In this section, we present the selection patterns of the BoIs scheme in our proposed work and the PoIs scheme in [11]. Concerning the BoIs model, two cases are considered:  
1. Case 1: The coverage disk radius, \(r\), is 5 m, while the sub-regions have an area of 50 m\({}^{2}\) each. Considering a target area of 100 m \(\times\) 100 m, the number of sub-regions is thus 200, resulting from (100 \(\times\) 100)/50. Meanwhile, the number of BoIs is 800, resulting from 200 \(\times\) 4. Nonetheless, owing to redundancy between adjacent sub-region BoIs, the actual number of points is 220.
2. Case 2: The coverage disk radius, \(r\), is 4 m, while the sub-regions have an area of 32 m\({}^{2}\) each, resulting in 288 generated sub-regions. There are 1152 BoIs (288 \(\times\) 4), but owing to redundancy between adjacent sub-region BoIs, there are 312 points.  
The PoIs model does not apply boundary points to each sub-region; instead, it distributes a set of random PoIs within the target area. Hence, two cases are considered for the PoIs model, whereby in case 1, the target area is divided based on \(r=5\) m. In each sub-region, one random point is allotted and extra points are added in the target area so that case 1 would reach 220 points. For case 2, the target area is divided based on \(r=4\) m. In each sub-region, a random point is allotted, and extra points are added in the target area so that case 2 would reach 312 points. The number of photos is 500 per hour. Each time, a random direction is selected for each photo.  
Figure 6: The system model of the proposed framework.
## 2 Materials and Methods
### Characterization
#### 2.3.4 Water Vapor Permeability (WVP)  
The moisture barrier properties of film composites are assessed using water vapor permeability (WVP), a key metric calculated based on the water vapor transmission rate (WVTR). These parameters were measured using a Labthink WVTR machine (C390H, Labthink Inc., Jinan, China), which operates according to the ASTM F1249 standard [32]. The experimental setup employed an infrared sensor to detect moisture transmission across two cells, with the film composite mounted between them. One cell contained humidified nitrogen gas, while the other contained dry nitrogen. Moisture transmitted from the humid side to the dry side was detected by the infrared sensor [33].  
The samples were prepared for testing as follows. Each specimen was cut into 5 cm \(\times\) 5 cm squares and mounted into aluminum masks with a 4 cm \(\times\) 4 cm window. To prevent moisture leakage and ensure data integrity, the samples were sealed with high vacuum silicone grease (Dow Corning, Midland, MI, USA) and secured with sheathing tape. The tests were conducted at 38 \({}^{\circ}\)C and 68% relative humidity in the humidified cell [34; 35]. Prior to the test, a 2 h preheating time was implemented to stabilize the setup and avoid inconsistencies. Each specimen underwent nine measurement cycles, each lasting 30 min, to ensure the reproducibility of WVP measurements.
# Features of Structure and Absorption in the Jet-Launching Region of M87
## 4 Discussion  
### Jet Recollimation with Absorption  
As we have mentioned in Section 3.1, recollimations between \(d=0.90\) and 1.30 mas and between \(d=3.65\) and 4.25 mas could possibly be the representations of the persistent recollimations suggested by [8] at our observed epoch. By comparing our obtained transverse jet structure and the 22-43 GHz spectral-index map, we find these two recollimations are spatially close to the inverted-spectrum features.  
The collimations are nonequilibrium behaviors in the AGN jet-launching region before the hydrodynamic or magnetohydrodynamic equilibrium is achieved, and they reminds us the reconfinement nodes in the simulations of a jet propagating in the external medium with pressure decreasing slower  
Figure 7: The pseudo-color maps of spectral-index distribution with color bars superimposed on the total intensity contour plots. Panel (**a**) shows 22–43 GHz spectral-index map superimposed on the contour plot of 22 GHz. Panel (**c**) shows the 43–86 GHz spectral-index map superimposed on the contour plot of 43 GHz. Their noise maps showing distribution of uncertainty are also presented in panel (**b**) and (**d**) respectively.  
than the thermal pressure of jet [21; 22; 23; 24; 25; 26; 27; 28]. As to the external medium, since these features are between 300-2000\(R_{s}\) from the central engine (consider the viewing angle of the jet in M87 as 17\({}^{\circ}\), as used in [8]), in the hot accretion flow models like ADIOS (see [29] for an overview), the external medium interacting with an AGN jet on this scale is believed to be \"winds\", i.e., the moderately magnetized, non-relativistic un-collimated, extremely hot and generally fully ionized gas outflows launched from the accretion disk.  
Since stationary shocks at the reconfinement node may enhance the magnetic fields locally [26; 27; 28], thus increase the opacity of Synchrotron Self-Absorption, SSA could be a possible mechanism responsible for the spectral turnover at the recollimations. But in a SSA-only case, according to Equation (2) of [30], for a region with intensity of 0.02 Jy Beam\({}^{-1}\) (a typical value of intensity of jet in M87 between 300-2000\(R_{s}\)), turnover frequency higher than 43 GHz requires a unreasonably intense magnetic filed of \(10^{5}\)\(G\). So contribution of other mechanisms like free-free absorption from the external medium (\"winds\") must be substantial, therefore spectral turnover at the recollimations may also indicate a large free-free opacity, i.e., high density of external medium there, suggesting either the jet collimations locally enhance the density of the external medium, or the locally high density of external medium induces the collimations of the jet. (by looking back upon literatures, we find that the 22-43 GHz spectral-index map in [31] also showed an association of collimation and absorption at locations between \(d=1\) and 2 mas, but it was not reported explicitly then).
## 3 Results and Discussion
### Changes in Substances during Fermentation
#### 3.3.2 Free Amino Nitrogen  
Wort contains a large amount of nitrogenous substances, but not all of these substances are utilized by yeast in cellular metabolic pathways. Nitrogen-containing compounds in word are mainly composed of free amino nitrogen, ammonia ions, and small molecular peptides (dipeptides and tripeptides), and yeast produces different volatile aromas due to the utilization of different ratios of amino acids, i.e., amino nitrogen is the main source of variations in aroma substances in beer. The Table 8 shows the amino acid contents in LS, OWBL, word, and sugarcane syrup. It can be inferred that LS has a higher and more comprehensive utilization of amino nitrogen as compared with OWBL. The amino nitrogen content of LS was significantly lower than that of normal whole-barley beer. It is hypothesized that this is due to the addition of sugarcane syrup at the fermentation stage altering the yeast metabolic pathway, allowing the yeast to newly utilize amino acids (e.g., histidine) that are not readily available in OWBL. LS utilizes amino acids at a higher rate than OWBL. Fewer amino acids remain, which makes LS less susceptible to contamination by spoilage bacteria that utilize amino acids for growth, such as lactobacilli and lactococci. Fruit juices contain 19 of the 20 essential amino acids (without cysteine), and just as word utilizes sugar, the amino acids are taken up by yeast cells in the order in which they are taken up [30]. The amino acids of yeast are classified into two groups (a, b) based on the percentage of amino acids utilized by the yeast; the a group of amino acids is more readily utilized by the yeast and utilized in a higher percentage, and the b group of amino acids is utilized in a smaller percentage. It can be said that the addition of sugarcane syrup can promote the utilization of b-amino acids by yeast and change the fermentation pattern of yeast, thus producing different flavor substances. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Glucose (g/L)** & **Fructose (g/L)** & **Maltose (g/L)** & **Sucrose (g/L)** & **Maltotriose (g/L)** \\ \hline
10\({}^{\circ}\)P Wort & 8.39 \(\pm\) 0.26 & 2.43 \(\pm\) 0.11 & 80.6 \(\pm\) 6.8 & 1.42 \(\pm\) 0.02 & 5.28 \(\pm\) 0.42 \\
10\({}^{\circ}\)P Cane Juice & 0.63 \(\pm\) 0.03 & 0.79 \(\pm\) 0.05 & 0 & 11.58 \(\pm\) 1.35 & 0 \\
10\({}^{\circ}\)P OWBL (mg/100 mL) & 0.68 \(\pm\) 0.02 & 0.12 \(\pm\) 0.01 & 3.07 \(\pm\) 0.22 & 0.87 \(\pm\) 0.03 & 0.29 \(\pm\) 0.01 \\ LS (10\({}^{\circ}\)P substrate) (mg/100 mL) & 1.22 \(\pm\) 0.03 & 0.36 \(\pm\) 0.01 & 2.62 \(\pm\) 0.12 & 0.92 \(\pm\) 0.12 & 0.38 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Measurement of the amount of fermentable sugars before and after fermentation.
# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
###### Abstract  
Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED septic patients, but these models often lack pre-hospital data and lack validation against early sepsis identification scores (such as qSOFA) and scores for critically ill patients (SOFA, APACHE II). Methods We conducted an electronic health record (EHR) study to test whether interpretable and scalable ML models predict mortality in septic ED patients and compared their performance with clinical scores. Consecutive adult septic patients admitted to ED over 18 months were included. We built ML models, ranging from a simple-classifier model, to unbalanced and balanced logistic regression, and random forest, and compared their performance to qSOFA, SOFA, and APACHE II scores. Results: We included 425 sepsis patients after screening 38,500 EHR for sepsis criteria. Overall mortality was 15.2% and peaked in patients coming from retirement homes (38%). Random forest, like balanced (0.811) and unbalanced logistic regression (0.863), identified patients at risk of mortality (0.813). All ML models outperformed qSOFA, APACHE II, and SOFA scores. Age, mean arterial pressure, and serum sodium were major mortality predictors. Conclusions: We confirmed that random forest models outperform previous models, including qSOFA, SOFA, and APACHE II, in identifying septic patients at higher mortality risk, while maintaining good interpretability. Machine learning models may gain further adoption in the future with increasing diffusion and granularity of EHR data, yielding the advantage of increased scalability compared to standard statistical techniques.  
sepsis; septic shock; emergency medicine; critical care; machine learning; random forest; prognostication; model +
Footnote †: journal:
## 3 Results
### Microbiological Evaluation  
The microbiological results of the HHP-treated blood plasma suspensions were evaluated according to the Hungarian Decree 4/1998 (XI. 11.) [52]. In general, two limit values are considered for the evaluation of the number of microorganisms, which are marked in Figure 4. The value \"C\" represents the compliance limit, and the value \"R\" indicates the rejection limit. A sample is compliant if it stays below the \"C\" level. It is acceptable if it meets or exceeds this level but stays below \"R\". If it reaches or exceeds \"R\", it is rejected [52].  
Figure 4 shows the development of the mesophilic aerobic bacterial count of control and HHP-treated blood plasma samples during the 44-day storage period. A decrease in the number of mesophilic aerobic bacteria measured on day 0 was observed because of HHP treatment. Compared to the control sample (3.28 log CFU/mL), the microbial number of the sample treated at 600 MPa was reduced by half (1.68 log CFU/mL). [PERSON] et al. [53] investigated the effect of non-thermal technologies (PEF, UV-C) like HHP on the microbial load of Asian seabass gills. They found that the application of high PEF intensity (17.5 kV) for 5 min reduced the initial microbial load from 8.0 log CFU/g to 5.5 log CFU/g. Subsequent UV-C exposure for 15 min further decreased the microbial count to 3.0 log CFU/g. This means that the application of these non-thermal technologies effectively contributes to the shelf life of food critical for food safety, enhancing consumer health safety.  
Figure 4 shows that the control sample exceeded the rejection limit on the last day of storage. The HHP-treated samples were all below the \"R\" limit. The HHP-treated samples at 550 and 600 MPa did not exceed the \"C\" value. HHP effectively inactivates mesophilic aerobic microorganisms by disrupting their cellular structure, including membranes and proteins, leading to cell death [54]. The rate of microbial inactivation increases with  
increasing pressure levels. Based on the slope indicators obtained from the equation of straight lines, it can be concluded that in the case of a sample treated at the lowest pressure value (300 MPa), there is already a decrease in the growth of the surviving microbes. For samples treated at 400 MPa and 450 MPa, there is no difference in the slope, so in this case, a 50 MPa pressure increase has no effect on microbial growth. In the case of the sample treated at 600 MPa, a 29% decrease in the growth rate of microorganisms was observed compared to the control sample. A different conclusion was reached by [PERSON] et al. [55], who treated fresh porcine blood plasma for 15 min at 450 MPa at different temperatures (5 \({}^{\circ}\)C, 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C). They found that treatments at 5 \({}^{\circ}\)C reduced the microbial counts by 90% and reduced the ability of surviving microorganisms to reproduce by 20-50%.
## 4 Computational Experiments
### Computational Results
#### 4.2.3 Validation and Efficiency of the Adaptive and Diversification Mechanisms  
To verify the performance of the adaptive weight adjustment and diversification mechanisms of our proposed EATS algorithm, we select one of two 2L-VRPSC instances under the probability of 0.8. Under the same parameters combination, the proposed EATS-MOBFS is compared with ATS-MOBFS (without a diversification mechanism) and ETS-MOBFS (without adaptive weight adjustment), with the comparison results presented in Table 10. For conciseness, these three algorithms are abbreviated as EATS, ATS and ETS, respectively.  
We can observe that the presented EATS approach shows superiority with respect to solution quality compared with the ATS and ETS algorithms. Compared with ATS and ETS algorithms, the proposed EATS algorithm is capable of producing better solutions for 8 and 6 out of 10 instances, which indicates the adaptive weight adjustment and diversification mechanism have a positive impact on the interplay between intensification and diversification. The better performance of ETS over ATS may be due to the fact that the diversification mechanism can avoid trapping into a local optimum to some extent. Additionally, the average computational time of ATS is slightly shorter than EATS and ETS, which is attributed to the fact that the diversification process is time-consuming to destroy and repair the solution structure. This slight increase in computation time is acceptable given that the diversification mechanism can diversify the search and guide the algorithm search for better solutions.
## 3 Results
### Antimicrobial Activity of CsE and NmE  
The results showed a noticeable antimicrobial activity against a wide range of the test organisms (Table 3). CsE was effectively more than _Nm_E against _Aspergillus flavus_, _Cryptococcus neoformans_, _Salmonella typhimurium_, _Klebsiella pneumonia_, _Streptococcus mutants_, and _Candida lipolytica_, while _Nm_E showed positive antibacterial activity against _Escherichia coli_ and MRSA. The most susceptible test organism is _Salmonella typhimurium_ followed by _Streptococcus mutants_. The most resistant strain is _Penicillium expansum_. While CsE could not affect the growth of _Escherichia coli_ and MRSA, the _Nm_E did.  
Figure 11: Effects of _Nm_E on protein expression of Cyclin D1, Cyclin E1, and in HepG2, MCF-7, and Caco-2 cancer cells, which were treated with each _Nm_E concentration and harvested 48 h after treatments. The immunoblots shown are representative of three independent experiments, which all gave similar results, only representative, cropped, and unedited images are provided here. Bars represent the means \(\pm\) SD. * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001 and *** \(p\) <0.0001 vs. control (DMSO-treated) cells.  
Figure 12: Effects of \(N\)_m_E on ROS production in HepG2 cancer cells. The maximum effect occurred at 48 h post-treatment. Bars represent the means \(\pm\) SD. \({}^{***}\)_p_ < 0.001 vs. control (DMSO-treated) cells.  
Figure 13: Schematic drawing of the mechanism of action of \(N\)_m_E. This carton is based on the current available data throughout 474 the present study.  
All the values resulting from the sponge crude extracts are lower than those obtained by the positive standard; however, it was noticed that CsE had an antibacterial potential against _Klebsiella pneumonia_ while, ketoconazole as a positive control, had no antibacterial activity against this strain so, this is a very promising result.  
Entirely, with a careful look at Table 3, it is observed that CsE has a superior antifungal rather than antibacterial activity, and therefore might be classified as a moderate potent antifungal agent and as a mild to moderate antibacterial, and vice versa with _Nm_E, which has only a limited antibacterial activity, and therefore might be classified as weak to a mild antibacterial agent.
## 5 Conclusions and Recommendations  
Order statistics has become increasingly significant in the performance analysis of wireless communication systems based on advanced diversity techniques over fading channels. These advanced techniques require novel and more complicated order statistical results. However, existing analytical approaches are not useful owing to their high complexity. For example, in the recently proposed d-CDD, the use of order statistics was inevitable, but the related previous analytical results mostly relied on asymptotic analysis, even in the case of i.i.d. channel environments. In this study, by deriving the new key common functions necessary for the analysis, we provided novel and insightful statistical results of the partial sums of ordered Gamma-distributed RVs. We performed detailed mathematical manipulations and introduced new representations to obtain generic results (e.g., joint MGF and related joint PDF) for i.i.d. Gamma distribution in a compact form. In this regard, we showed that our results are useful for obtaining the required analytical results of the combined received SNR of the d-CDD CP-SC scheme. In the future, we will analyze the i.n.i.d. case to consider more general and versatile fading environments. The main challenge in generalizing the results of this study to i.n.i.d. general fading cases is that the joint PDF of ordered i.n.i.d. RVs are considerably more complicated than ordered i.i.d. RVs.  
Conceptualization, S.S.N. and S.C.; methodology, S.S.N.; software, S.C.; validation, S.C., D.H., and S.S.N.; formal analysis, S.S.N.; investigation, D.H.; resources, D.H.; data curation, S.C.; writing--original draft preparation, S.S.N.; writing--review and editing, S.C.; visualization, S.C.; supervision, S.S.N.; project administration, D.H.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Wonkwang University in 2020.  
Figure 2: PDF of \(S\), \(f_{S}(x)\), for various values of \(m_{k}\) when \(M=6\), \(K=3\), and \(\overline{\gamma}=1\).