esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 6 Limitation and Future Scope  
This study highlights the potential of various machine learning algorithms in analyzing textual clinical data for the early detection of lung cancer. However, it is essential to note that the study was conducted on a relatively small dataset, which primarily relies on patients and symptoms. Future research should focus on applying these techniques to larger datasets to assess variability in algorithm performance. Additionally, employing a more comprehensive and authentic dataset containing at least 16 essential parameters would enhance the robustness of classification, as the current approach is based on symptoms and behavioural attributes. Moreover, advanced machine learning models, such as multilayer perceptrons (MLPs), warrant further exploration when applied to larger datasets. Observational insights from this study suggest that male patients with a history of alcohol consumption, chest pain, and allergies may have a higher likelihood of developing lung cancer. However, these findings require expert validation, which could lead to the establishment of a weighting system for specific attributes in lung cancer detection, thereby improving diagnostic accuracy.  
The integration of Electronic Health Records (EHRs) can play a crucial role in the early detection of lung cancer by leveraging clinical data to identify patterns among patients. This approach enables a comparative analysis of relevant attributes and allows for a more reliable assessment of the Area Under the Curve (AUC) achieved by different machine learning models. Further studies are needed to validate the model adoption process and refine classification strategies for improved real-world applications. In hospitals with limited resources, EHR-based automated risk assessment tools can assist doctors in decision-making by flagging high-risk lung cancer cases based on patients' medical history. However, further validation through clinical trials and real-world deployments is necessary to assess the feasibility and ethical considerations of such implementations.  
P.M.G., H.K. and M.M.J.; Methodology and Validation, P.M.G., H.K. and M.M.J.; Writing--original draft, P.K.; Supervision, P.K. and P.V. All authors have read and agreed to the published version of the manuscript.  
Open access funding provided by Data Meghe Institute of Higher Education and Research. No funding is involved while preparing the manuscript.  
Not applicable  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Experimental Results
### Experimental Results from Rural Areas
Metric-based results were in line with the visual interpretation. The CIELab method also provided the highest accuracies according to the QAVG and PSNR metrics. In addition, the proposed method provides the value 1 for the SSIM metric, which is the best possible value. Moreover, the IHS method provides worst results for all quality indexes, with respect to Table 14. Lastly, Ehlers, GIHS and HCS methods provide lower accuracies in some cases. This unstable manner of these methods across different scenes is another problem that should be considered. To assess the spatial quality of Pan sharpened images, the CC, the Zhou index, Sobel RMSE and spatial ERGAS indexes that are presented in Section 3.3, were calculated by comparing the Pan image and the intensity component of the Pan sharpened images. Numerical results of these metrics are presented in Table 15. According to comparative evaluation, the Pan sharpened image from the proposed CIELab method provided the highest spatial CC and Zhou values and lowest SRMSE and SP ERGAS values. These results indicate that the proposed method has the best spatial performance among all methods tested. Ehlers, HIS and HCS methods provided the lowest spatial performances according to the values presented in Table 15.  
As a result, the proposed CIELab method provided the best performance for the rural scenes based on the visual interpretation and spectral and spatial quality metrics results.
## 1 Introduction  
Fractional order models are generalizations of models described by differential equations or state space descriptions in which classical derivatives are replaced by fractional derivatives [1; 2; 3]. These models are now widely used to characterize the behavior of many systems in diverse areas. For example:  
* electrochemistry in which charge diffusion in batteries can be described by Randles' models [4; 5], or other kinds of fractional models [6];
* thermal conduction where the exact solution of the heat equation in a semi-infinite medium links the heat rate to the surface temperature by a fractional differentiation of order 0.5 and applied to the thermal modeling of buildings [7; 8];
* biology for modelling complex dynamics in biological tissues [9];
* mechanics with the dynamical property of viscoelastic materials and for wave propagation problems in these materials [10];
* acoustics where fractional differentiation is used to model visco-thermal losses in wind instruments [11];
* robotics through environmental modeling [12];
* electrical distribution networks [13];
* modelling of explosive materials [14].  
The main reason for the use of fractional order models is their ability to generate long memory behaviors (time power law relaxations) in the same way as the systems previously mentioned. However, this interesting property comes at the price of several defects that have important consequences in the field of dynamical system analysis:the physical consistency of the state concept is questionable, making it necessary to introduce the notion of a pseudo-state [15; 16];
* they are defined on an infinite time interval or on an infinite space domain (depending on the representation used) [15; 16];
* as a result, they are adapted to studying the input-output behavior of a system, but not its internal properties [17] (initialization [18], internal stability, exact controllability [19], exact observability, exact flatness);
* the implementation of these models requires an approximation step [8; 20].  
This work looks at this approximation step. There are several methods in the literature that allow such an approximation. Among all these methods, a very well-known one because of its efficiency and the simple algorithm that it implements is based on the approximation of the fractional order differentiation or integration operator using a recursive (geometric) pole and zero distribution [21; 22]. In this paper, this method is analyzed in terms of sub-optimality and a simple solution is proposed to improve the approximation accuracy. Some stability issues resulting from the approximation of a fractional model are also discussed. The results presented in this paper will contribute to obtaining more accurate and stable approximations of a fractional model, and above all will help the reader to understand that the geometric distribution of poles and zeros (also called \"recursivity\" in the literature) for the approximation of a fractional order integrator and differentiator is one among an infinity of other permitted distributions and cannot be interpreted as the physical reason for the observed long memory behaviors.
## 3 Results
### Toxicity of BPA in Activated Sludge
#### 3.1.3 BPA Reduces the Microbial Activity of Activated Sludge  
The use of fluorescent dyes can determine the activity of microorganisms based on the integrity of the microbial cell membrane. Under the selective stress of BPA, the viable bacteria level of the activated sludge microorganisms in the SBR system was evaluated, and the viability of the overall microbial population was assessed. Compared with the sludge in the control group (62.32 \(\pm\) 1.13%), the proportions of viable bacteria in stages L, II, and III of the reactor in the BPA group were 57.75%, 49.32%, and 40.25%, respectively (Figure 3B). The results showed that BPA had a significant effect on bacterial death and cell membrane damage. In addition, with the increase of BPA concentration, an increasing number of damaged cell membranes were observed in activated sludge microorganisms, the degree of damage to cell membranes increased, and the proportion of viable bacteria was significantly reduced.  
In all analyzed samples, the viable bacteria of the control activated sludge were mainly arranged around flocs. The areas that fluoresced red to indicate cell death or damage in the activated sludge in the BPA group increased significantly. Greater differences were observed with increasing BPA concentrations (Figure 3A). The results showed that BPA led to the death or damage of a large number of bacteria, and the microbial structure of activated sludge was destroyed. Studies have shown that looser microflocs and bacteria attached to the outer surface of these flocs are more susceptible to external factors [49]. In addition, the increased aggregation of dead bacteria can be observed, which may be a consequence of protection against environmental stresses, of cellular lysis attributed to increased released deoxyribonucleic acid under stress conditions, or the loss of viability [50].  
Figure 3: The effect of bisphenol A (BPA) on the microbial activity of sequencing batch reactor (SBR) activated sludge. (**A**) Fluorescence staining of activated sludge microorganisms in different reaction stages; (**B**) The ratio of viable microorganisms in activated sludge in different reaction. Note: The three images are parallel samples of the same stage.  
Under the stress of a low concentration of BPA, activated sludge exhibited strong shock load resistance, and BPA exerted less toxicity toward microorganisms in activated sludge. This was due to the self-protection mechanism of activated sludge, which reduced the impact of environmental pressure. Once the BPA concentration exceeded the capacity of the activated sludge, BPA caused permanent damage to the microbial community structure of the activated sludge. It is worth noting that although the methods used in this study are advanced, they cannot be used to monitor the community structure of activated sludge and the proportion of viable bacteria in real time, and complete data cannot be obtained using these methods in a short period of time.
## 8 New Technologies
### Nanoparticles  
Nanotechnologies contribute in several ways to the development of innovative methodologies for CH conservation [43]. In particular, new strategies based on nanoparticles have provided some promising alternatives, even as biocides, in the consolidation and protection of damaged CH materials.  
Due to their large variety and multifunctional addressing (mechanical, hydrophobic, biocides), nanoparticles (NPs), such as SiO\({}_{2}\), ZnO, TiO\({}_{2}\), Mg(OH)\({}_{2}\), Ca(OH)\({}_{2}\), ZrO\({}_{2}\), TiO\({}_{2}\), Ag, etc., in suspension, combined with appropriate resins or as precursor formulations, have been widely applied in stone material restoration [44; 45; 46]. Featuring good penetration properties, NPs significantly contribute to the consolidative/protective processes of the substrate.  
By using proper strategies, for example, the combination with low surface energy polymer matrices, nanoparticles have demonstrated high capabilities in producing biomimetic micro/nanostructured surfaces on a variety of stone substrates with controlled wettability and roughness [47; 48; 49]. For this, NPs are useful as alternatives to synthetic organic polymers for restorations.  
Among NPs, semiconductor TiO\({}_{2}\) photocatalysts have played the main role. TiO\({}_{2}\)-based materials are currently used as self-cleaning and antibacterial coatings. Their application is limited because near-ultraviolet irradiation is required for photocatalytic activation. TiO\({}_{2}\) doping or formation of heterostructures produce useful means to access to VIS-light driven reactions [45].  
Although very promising, the use of nanomaterials and nanotechnologies in the field of CH conservation, over time, has raised serious concerns both in terms of human health and environmental risks, as well as in terms of efficiencies against microorganisms, and long-term effects on the material [50; 51].
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 6 Conclusions  
This paper provides, in its first part, an overview of the two main wireless access technologies for V2X communications and of their roadmap and evolutions: IEEE 802.11p is ready for deployment and is gaining momentum; concurrently, C-V2X is running to be ready as well, having the advantage that its infrastructure is already deployed. For both technologies, we have briefly discussed the work ongoing for a next generation, namely IEEE 802.11 bd and 5G NR, respectively.  
Then, we have presented a performance comparison in realistic scenarios to evaluate which conditions and parameters mainly affect the performance of the two technologies. To summarize: (1) LTE-V2X has an improved PHY layer allowing wider range than IEEE 802.11p, an aspect that is well known and considered for IEEE 802.11 bd; (2) LTE-V2X Mode 3 outperforms IEEE 802.11p and LTE-V2X Mode 4, since resources are reserved by the network, which has a wider knowledge of the node positions and allocations; (3) LTE-V2V Mode 4 outperforms IEEE 802.11p in most cases, but it suffers from higher update delay (i.e., higher probability of several consecutive errors). Thinking of the evolution of IEEE 802.11p, we have also evaluated its performance with an improved PHY layer, demonstrating that, in such case, the performance in terms of packet delivery rate becomes similar to LTE-V2X Mode 4, still granting a quite lower update delay.  
Conceptualization, A.B., G.C., B.M.M. and A.Z.; Data curation, A.B. and G.C.; Formal analysis, A.B.; Funding acquisition, A.B., B.M.M. and A.Z.; Investigation, A.B., G.C. and M.M.; Methodology, A.B. and A.Z.; Project administration, A.B.; Software, A.B., G.C. and M.M.; Supervision, A.B.; Validation, A.B.; Visualization, A.B. and G.C.; Writing - original draft, A.B., G.C. and M.M.; Writing, review and editing, A.B., B.M.M. and A.Z.  
This research received no external funding  
The authors declare no conflict of interest.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 4 Experiment Evaluations
### Experiment Results
#### 4.4.2 Regression Performance  
In the regression/final positioning phase, the DNLoc algorithm finds the user's final location in the already-predicted area and we compare its performance in terms of accuracy and online computational time with the best papers published on the same dataset like TransLoc [21], DFMIC [22], SmartLoc [23], and aslo with some other papers in the literature like ViVi [45], KAAL [46], UFL-ECLS [47], EMSS [48], and MSSE [49]. We utilized both third and fifth floor datasets over the span of 15 months. Table 2 shows the number of samples used to train and test the proposed MVR-DNN model in each area. The training time is also presented in the last column. Table 3 shows the AED and percentile error of the DNLoc model. Table 4 shows the response time and AED comparison with that of the literature. Figure 10 depicts the AED of DNLoc as compared to other Wi-Fi RSSI-based best techniques in the literature and shows that DNLoc outperforms the other methods, reporting an AED of 2.09 m. TransLoc [21], SmartLoc [23], EMSS [48], Wi-Fi-FAGOT [47], VIVI [45], and KAAL [46] incur an AED of 2.21 m, 2.588 m, 3.9 m, 2.68 m, 2.7 m, 3.23 m, and 3.25 m, respectively. Figure 11 shows the cumulative distribution function (CDF) of different techniques juxtaposed with DNLoc. DNLoc reduces the 85 th percentile of DIFMIC, Wi-Fi-FAGOT, KAAL, MMSE, and MUCUS by 15.7%, 37.64%, 45.8%, 48.9%, and 66.8% respectively, demonstrating that DNLoc outperforms all other methods.  
Figure 10: Comparison of AED with different methods for 15 months.  
Figure 9: Classification accuracy of full, reduced, and SDR-refined dataset (a) SVM; (b) RF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Floor** & **Sub-Area** & **Offline Samples** & **Online Samples** & **Training Time (s)** \\ \hline
3 & 1 & 1800 & 9000 & 45.7 \\
3 & 2 & 1800 & 9000 & 33.4 \\
3 & 3 & 1800 & 9000 & 42.1 \\
3 & 4 & 1800 & 9000 & 20.15 \\
5 & 1 & 1800 & 9000 & 23.6 \\
5 & 2 & 1800 & 9000 & 59.2 \\
5 & 3 & 1800 & 9000 & 37.3 \\
5 & 4 & 1800 & 9000 & 66.27 \\ Total/Average & 8 & 14,400 & 72,000 & 327.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of samples and training time for DNLoc.  
Figure 11: CDFs of different methods compared with DNLoc in the library environment.
## 3 Results and Discussion
### Gene Expression  
The effect of GNPs on the expression of thirteen genes (genes _SOD1_, _CAT_, _MMP1_, _TGFB1_, _GPX1_, _FN1_, _HAS2_, _LAMB1_, _LUM_, _CDH1_, _COL4A1_, _FBN_ and _VCAN_) in human keratinocytes cells at two non-cytotoxic concentrations (0.005 and 0.01 ug/mL) exposed for 24 h are shown in Figure 6.  
Figure 5: Proliferative activity of GNPs in human keratinocyte HaCaT cells treated with graphene nanoplatelets (GNPs) at non-cytotoxic concentrations (0.005 and 0.01 ug/mL) for 72 (**a**) or 96 (**b**) h. Data are expressed as the mean \(\pm\) standard deviation of six replicates. The results of the statistical analysis with respect to control and to the epidermal growth factor (EGF) are indicated in the graph: **\(p<0.01\); ***\(p<0.001\); ***\(p<0.0001\); n.s: not significant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Exposure (h)** & **EC\({}_{\text{50}}\) (\(\upmu\)g/mL)** & **95\% CI** & **R Square** \\ \hline
12 & 1.142 & 0.837–1.564 & 0.8961 \\
24 & 0.760 & 0.585–1.004 & 0.9176 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Median effective concentration (EC\({}_{\text{50}}\)) in ug/mL of keratinocytes (HaCaT) treated with GNPs for 12 and 24 h. Confidence limits (95% CI) and goodness of fitness (R square) are also presented.  
Figure 6 shows that GNPs can up-regulate the expression of six genes in HaCaT cells (_SODI_, _CAT_, _TGFB1_, _FN1_, _CDH1_, and _FBN_) in human keratinocytes cells after 24 h.  
Thus, multi-layer graphene, GNPs, are able to up-regulate two more genes (_SODI_ and _FBN_) than multi-layer graphene oxide, GO, at 0.01 \(\upmu\)g/mL in human keratinocytes [13]. The _SODI_ gene encodes an isozyme that binds copper and zinc ions destroying free superoxide radicals [30]. The expression increases of _FBN_ enhance the skin structure and firmness [31]. Therefore, these results reinforce the idea of using these nanomaterials for biomedical applications such as wound healing and skin tissue engineering. Nonetheless, it is important to mention that other carbon materials such as 1D filamentous hollow carbon nanofibers showed effective results in up-regulating eight (_FN1_, _MMP1_, _CAT_, _CDH1_, _COLA1_, _FBN_, _GPX1_, and _TGFB1_) of the thirteen analyzed genes [14].  
Recent studies have tested GNPs not only in keratinocytes but also in mice, the toxicity and activation of inflammatory processes, using the same homogenization technique as in the present study [32]. They concluded that GNPs were not skin sensitiizing. Exposure did not induce any type of inflammatory reaction after measuring different interleukins and TNFa, a fact that does not occur in all trials according to the verified bibliography, which does not allow a comparison to be made with our results [33].
## 2 Multifocused Analysis Methodology
### Groundwater Agriculture Problem: Multifocused Analysis
#### 2.1.3 Energy Analysis  
The environmental issues not only affect the water balance of the river basins, but are also involved in one of the main water problems in agriculture: aquifer overexploitation with high energy consumption [47]. Once a well is built, the energy required to raise water to the surface is the most relevant annual cost for these systems. This cost depends mainly on: \((i)\) the unit price of energy, \((ii)\) the depth of the phreatic level, \((iii)\) the generator-pump system efficiency, and \((iv)\) the hydrogeological characteristics of the aquifer. The high energy dependence of fossil fuels poses an international problem for any sector, and it usually involves high costs. The agriculture sector also suffers from these consequences, as it is a demanding sector of fossil fuels. To solve this, geopolitical and economic factors must be considered to find a suitable solution. For a specific crop, a decreasing phreatic level is closely related to the corresponding energy requirements. Indeed, proper hydric maintenance requires pumping from a deeper source of water and thus more energy is required in the process. Reducing the phreatic level requires a large amount of energy to raise, transport, and distribute water to crops. Increased energy demand involves major production costs for farmers, regardless of their country. This is a difficult problem to be borne by farmers, since it implies more economic effort to pay for fuel for pumping. Different contributions have been devoted to solving this energy problem [48]. The different solutions depend on proper water management to meet high energy demands at low cost [49]. Other inefficiencies, such as poorly performing irrigation methods, a lack of maintenance, or oversized facilities, can mean an excess of energy demand and high economic costs. To solve these problems, some countries have developed different energy policies for agriculture, aiming to reduce the cost of energy production. In some cases, national policies advocate the subsidization of fossil fuels for any sector or exclusively focused on agricultural use. Other policies are based on fiscal subsidiesof hydrocarbon taxes on farmers and ranchers, whereas in other countries, the price of fuel is totally regulated by the government.
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 3 Results and Discussion
### Optimization of Isomaltulose Production  
#### 3.4.1 Optimization of Enzyme Dosage for Isomaltulose Biosynthesis  
Enzyme dosage usually affects the production of isomaltulose directly, which was investigated in this work. As shown in Figure 4a, with the dosages of 10-25 U/g sucrose, isomaltulose production was enhanced with the increase in Pal-2 dosage and reached the maximum at 25 U/g of enzyme. However, with further increases in dosage, the isomaltulose conversion rate exhibited a stable tendency. Therefore, 25 U/g is the optimal amount of enzyme for the enzymatic synthesis of isomaltulose. The result is similar to that of _P. dispersa_ UQ68J [39] and _S. plymuttica_ AS9 [41].