esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Co-Inertia between Soil Parameters and Microbial Activities Dataset  
The co-inertia analysis (Figure 3a) showed that soil parameters (six variables) had a highly significant correlation (\(p<0.001\)) with the microbiological dataset (eight variables) with 41.86% of total variance explained. Total organic carbon was positively correlated to enzyme activities with the exception of acid and alkaline phosphatase. Soil pH, fine sand and clay content were negatively related to the microbiological dataset.  
Discriminant analysis of co-inertia (Figure 3b) showed that soil samples were significantly distinguished (\(p<0.001\)) according to their spatial distribution in terms of the quality of ecological restoration. In the axis 1 direction, soil samples collected from restored sites (Bel3 and Yao3) were predominated on the positive axis, and were clearly distinguished from non-restored sites, all on the negative side.  
According to these data, the microbial biomass and all enzyme activities tested except for phosphatases would be correlated exclusively to the organic carbon concentration. However, we observed a negative correlation of the microbiological data with the concentration of clays and fine sands. Texture and structure are among the most important factors influencing the activity of telluric enzymes [39]. Enzyme activity is generally greater in fine textured soils than in coarse textured soils [40; 41] and negative correlations between enzyme activity and clays appear to show opposite results. However, while the nature of clays also impacts enzymatic activities [42; 43], it has been shown that the structural stability of soil aggregates, linked to the organic matter content, could play a more important role  
Figure 3: (a) Co-inertia analysis between soil physico-chemical parameters and microbiological variables. The variables linked by lines refer to the physico-chemical variables, the other variables refer to the different enzymes activities and microbial biomass in the soil sample; (b) discriminant analysis on co-inertia variable of soil samples. Boxes represent the projected co-ordinates of physico-chemical dataset and microbial dataset of each rehabilitated site, respectively. The length of the arrow is proportional to the divergence between the data sets. Eigen values 86.74% and 9.08% for axis 1 to 2, respectively. Randtest: simulated \(p\)-value: 0.0019. Explained variance: 41.81%.  
than soil texture alone [40, 44, 45]. Organic matter stabilizes aggregate structure, increases soil retention capacity and improves nutrient bioavailability [46, 47], thus stimulating soil microbial flora [48].  
In this study, the major factor of microbial activity was organic matter. In these rehabilitated soils, the contribution of organic matter is exclusively conditioned by the nature of the plant cover and therefore by the success of ecological restoration. The choice of the implantation of fabaceous species, fast-growing nitrogen-fixing species, seems judicious for their ability to bring a large amount of organic matter to the soil [49, 50].
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 3 Results
### Regression
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** \\ \hline Linear regression & 2.89 (0.77) & 1.92 (0.64) & 70.65 (35.17) & 0.19 (0.28) \\ SVM & 2.91 (0.78) & **1.68 (0.61)** & **48.37 (31.27)** & 0.23 (0.32) \\ Random forest & **2.81 (0.75)** & 1.87 (0.62) & 69.56 (34.54) & **0.24 (0.35)** \\ XGBoost & 3.00 (0.83) & 1.99 (0.66) & 72.07 (36.35) & 0.19 (0.24) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of the regression models in terms of RMSE, MAE, MAPE, and R\({}^{2}\). The results of each evaluation score are averaged over 10 different experiments using repeated random train/test split. The main values represent the evaluation of the models trained on the entire dataset with the full range of LOS (1–21 days), while the values in parentheses represent the metrics for the models considering data with only actual short stays, i.e., LOSs between 1 and 4 days. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Long Stays** & **Predicted Short Stays** \\ \hline
**Actual Long Stays** & 9108 (9007) & 516 (617) \\
**Actual Short Stays** & 2469 (1873) & 29,380 (29,976) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrices for random forest (the main values) and XGBoost (the values in parentheses) classifiers used prior to regression. Note that these classifiers, given by the maximum G-mean, partition the entire dataset into short/long stays. The predicted short stays are then used for step-wise regression.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 4 Performance Evaluation  
In this section, we compare the retrieval effectiveness and the search cost of our routing approach RLQR vs Controlled Flooding (CF) [2] and Learning Peer Selection (LPS) [9]. It is important to note that we assess three RLQR instances, noted with respect to the used 1-NS learning algorithm: RLQR-EGNS, RLQR-UCBNS, and RLQR-TSNS. To this end, different simulation scenarios are performed using the PeerSim simulator [28]. The latter is an open-source Java simulator widely used for implementing and testing prototypes of P2P protocols. As the dataset, we use the \"Big Dataset\" collection [29]. The latter consists of \(25,000\) documents and \(4000\) queries distributed among \(1000\) peers using a benchmarking software developed by [PERSON] et al. [29]. The Time-To-Live of the query (i.e., the \(TTL\) parameter) and the number of neighbors to be selected by the forwarder peer (i.e., the \(k\) parameter) are set to \(5\) and \(3\), respectively.
## 2 Materials and Methods  
### Sample Collection and Parameters Analyzed Without Shown Results  
Samples were collected by fishers from the small-scale octopus fishing fleet of the Algarve. Two areas of the Algarve were considered for sampling purposes: Barlaven-to/Windward (samples collected by vessels registered at Alvor harbour) and Sotaven-to/Leeward (samples collected by vessels registered at the Fuzeta harbour) and analyzed separately due to the well-known differences in the fishing grounds of the two areas [5; 14], with western Algarve more exposed to the Atlantic coast and eastern Algarve to the Mediterranean Sea [15; 16; 17]. A total of eight samples (three to four individuals per sample) of octopus were collected every three months in each region, from December 2022 to December 2023, with an approximate weight of 1.5 kg per individual. Octopus were eviscerated and frozen immediately after landing, replicating the common practice of the industry, and shipped properly to prevent defrosting. The analysis was carried out in ANFACO-CECOPESCA laboratories that work under the UNE-EN ISO/IEC 17025:2017 standard [18], and it is accredited by the National Accreditation Entity (ENAC) that is a member of the European Accreditation (EA).  
To simplify the understanding of the work and visualize the most relevant results, no values or tables referring to the quality of the octopus have been included. However, all the contaminants established in the legislation were analyzed, as well as those that could be considered a risk. Then, we have analyzed microbiological parameters as total viable count (TVC), Enterobacteriaceae, _Escherichia coli_, _Salmonella_ spp., _Vibrio parahaemolyticus_, _V. cholerae_ and _V. vulnificus_; chemical contaminants: mercury, cadmium and lead, inorganic arsenic, dioxins, polychlorinated biphenyls (PCBs) and dioxins like PCBs; biogenic amines, histamine, cadaverine and spermidine; nutritional compounds as fatty acids, moisture, proteins, ash, salt, carbohydrates; fresh and quality parameters: trimetilamine (TMA), thio-barbituric acid (TBA), peroxides index, K index and marine biotoxins including, lipophilic, paralytic shellfish poisoning (PSP), amnesic shellfish poisoning (ASP), and tetrodotoxin (TTX). In this context, it is worth mentioning that all contaminants evaluated are either absent or found in an extremely low concentration and very well below the legislated limit. The results obtained in those parameters that are not regulated were also in all cases below those levels published as a reference in the bibliography.
## 2 Materials and Methods
### Optimization Algorithm  
The NSGA-II optimization algorithm has been selected for the bucket's geometry optimization, where the hydraulic efficiency [24] and the EIC60041 standard friction and windage losses are the objective functions. Besides other optimization algorithms, the NSGA-II is characterized by fast non-dominated sorting, a parameter-free crowding distance, and its use of a candidate elitism selection scheme. A Pareto front is used to show the optimal candidates of each iteration of the optimization process, where the horizontal and vertical axes correspond to the hydraulic efficiency and friction and windage losses, respectively.  
The fast non-dominated sorting approach considers two entities: the number of solutions dominating each candidate (\(np\)) and the number of solutions dominated by each candidate (\(sp\)) for each one of the objective functions. The solutions where \(np=0\) conform to the first front, while the subsequent fronts are filled up iteratively by decrementing the domination counts of solutions \(sp\). To maintain population diversity, the implemented parameter-free crowding distance metric estimates the density of solutions surrounding a particular candidate in the population, calculating the average distance of two solutions along each of the objective functions. Finally, the candidate elitism selection scheme controls the most suitable candidates of each generation being the only ones available for crossover and mutation.  
The bucket's geometrical parameters considered part of the candidate solution genome are as follows: \(\beta_{1}\), \(\beta_{2}\), \(L\), and \(B\), and their corresponding values were selected between the ranges described in Table 3. The casing dimensions \(B_{io}\), \(B_{iu}\), \(R_{io}\), and \(D_{P}\) are also considered.  
The implemented NSGA-II starts with an initial population, referred to as parents. Each parent is evaluated with both defined objective functions and sorted regarding the non-dominance approach, seeking the best solution candidates. These candidates are considered the \"elite\" and are used for the generation of new solutions through genetic operators such as crossover and mutation, always keeping the diversity. This process continues iteratively for a defined number of generations.  
The initial population number is 100 and consists of different combinations of the above-mentioned design parameters. The stopping criterion considered for the algorithm is the maximum number of generations; this number must be considered adequate when the solutions converge into a specific value (considered the optimal solution). The initial parameters of the algorithm are described in Table 4.  
Figure 4: EIC60041 standard geometric parameters for the friction and windage losses calculation.
## 2 Theoretical Analysis of Tunnel-Face Support Pressure  
### Mechanism of Support Pressure on Tunnel Face During Pipe Jacking  
The mud-water balance theory is a pipe-jacking theory that balances the soil pressure and groundwater pressure in the soil layer where the tunneling machine is located. It does this using the pressure inside the mud compartment of the tunneling machine (i.e., the support pressure on the tunnel face). Theoretically, when the support pressure on the tunnel face is in balance with the soil and water pressure in front of it, the pipe jacking will not lead to deformation of the stratum in front of the tunnel face.  
When the support pressure on the tunnel face is less than the soil and water pressure in front of it, the stratum in front of the tunnel face moves towards the tunnel. The interaction between the soils has a tendency to inhibit soil sliding, and the shear resistance increases. When the support force on the tunnel face decreases to a certain critical point, the displacement of the tunnel face suddenly increases sharply, and the tunnel face undergoes active damage. Established research results show that the damage pattern manifests as a chimney-like shear zone [33; 34] when active damage occurs on the tunnel surface during shallow buried pipe jacking in sandy soil layers. The damage surface arises from the location of the tunnel's elevated arch, directly from the sides in the vertical direction, with horizontal movement almost never occurring. The damage body is in the shape of a column with a small difference in width between its top and bottom (as shown in Figure 1a). When the water level is above the surface, the upper stratum in front of the tunnel face undergoes overall damage and slides downward to the surface [35], as depicted in Figure 1b.  
When the tunnel-face support pressure is greater than the resistance, the soil body in front of the tunnel face shows an upward sliding displacement tendency. The interactions between soil grains inhibit the sliding tendency of the soil body, and the shear resistance increases. When the support pressure increases to a critical point, the displacement of the tunnel face suddenly increases sharply, and the pipe-jacking tunnel face undergoes passive damage. Existing research results show that when the tunnel surface in shallow buried pipe jacking in a sandy soil layer is passively damaged, the shape of the damaged area of the formation in front is similar to an inverted cone that is wide at the top and narrow at the bottom with a certain angle of inclination [36] (as shown in Figure 1c).
## 5 Discussion
### Vulnerabilities Mitigation
#### 5.2.4 Phys, Port, Stat: Losses  
Losing a device is greatly facilitated by its mobility, as an employee may take it anywhere and be robbed or mislay the portable equipment or document, possibly containing sensitive company data. In that regard, Bring Your Own Device (BYOD) has increased the potential for such occurrences. It refers to using employee-owned mobile devices to access business enterprise content or networks. Similar portability concepts are Choose Your Own Device (CYOD), Company-Owned and Personally Enabled (COPE), and Company-Owned Business Only (COBO), and they all raise security concerns.  
[PERSON] et al. [77] list some challenges these mobile devices bring to hospitals, which may also apply to companies in general. They categorize these challenges as related to technology, human factors, and policies. They also provide possible solutions to these challenges. Table 9 presents these challenges and solutions.  
In addition to BYOD, teleworking and co-working spaces may pose a security threat to companies. These work models, which have emerged since the COVID-19 pandemic, also imply new security gaps similar to the BYOD-related ones.  
Geofencing is another suitable security control in this scenario, which refers to triggered actions in response to a device leaving a pre-defined geolocation. Such actions could  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Technical** & **People** & **Policy** \\ \hline \multirow{3}{*}{Challenges} & \multirow{3}{*}{Insecure device} & \multirow{3}{*}{Inappropriate behavior} & \multirow{3}{*}{Lack of policy} \\  & & & \\ \cline{1-1}  & & & Lack of awareness & Compliance \\ \cline{1-1}  & & \multirow{3}{*}{Insecure network} & Poor user experience & Sanctions for breaches \\ \cline{1-1}  & & & Skills shortage & \\ \cline{1-1}  & & & Mobile device management & Security culture & BYOD strategy and governance \\ \cline{1-1}  & & & & User agreement \\ \cline{1-1} Solutions & & & Identity and access management & Skills improvement & BYOD policy \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & Endpoint security tools & \\ \cline{1-1}  & & & Secure communication platforms & \\ \hline \hline \end{tabular}
\end{table}
Table 9: BYOD challenges and solutions [77].  
be, for instance, disabling its network interface card or remotely wiping the device to prevent data leakage upon exiting an authorized area.  
On this subject, [PERSON] [78] evaluated the effectiveness of remotely wiping data, considering the deleted data may be forensically retrieved, as explained in Section 5.2.5.  
Encryption is also recommended for data protection, and, for mobile devices, File-Based Encryption (FBE) is mandatory in Android since its 10 th version [79]. For notebooks, BitLocker and VeraCrypt are some available options.  
One appropriate access control method for BYOD is Attribute-Based Access Control (ABAC). As per this paradigm, the company can deny and concede access to an identity based on attributes of the request, such as location, hour of the day, and object being accessed.
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results of First-Principles Studies of Localization
#### 7.5.1 Application to \(\mathrm{K_{y}Fe_{2-x}Se_{2}}\)  
For example, among the iron-based superconductors, \(\mathrm{K_{x}Fe_{2-y}Se_{2}}\) has been studied intensely because of its unique properties. It has a relatively high \(T_{c}\) of 31 Kelvin [216] and an exotic type of antiferromagnetic order. It was the first iron-based superconductor that only has electron pockets  
Figure 44: A schematic phase diagram in the disorder-energy plane of the Anderson-Hubbard model showing a disorder-driven QCP separating a Fermi liquid from an as yet unidentified Phase-2. Reprint from [189].  
Figure 43: The negative of the imaginary part of the low-frequency (\(\omega\)) self-energy as a function of \(\omega\) on a linear (a-panel) and log-log scale (b-panel) for various disorder values (legends), \(N_{c}=38\) and \(U=1.6\). The left panel shows that the \(-\mathrm{Im}\Sigma(\omega)\) is quadratic close to the Fermi level and crosses over to a power law form (see more clearly in the right panel) with an exponent \(\alpha(W)<2\), that is disorder-dependent.  
and no hole pockets. Moreover, K\({}_{x}\)Fe\({}_{2-y}\)Se\({}_{2}\) is strongly disordered due to a significant amount of Fe vacancies and it is the only iron-based superconductor whose parent compound is an antiferromagnetic insulator instead of an antiferromagnetic metal [217]. Like other iron-based superconductors, it is quasi two dimensional which makes it more sensitive to the disorder. This leads to the question whether it can be an Anderson insulator. Due to the presence of the strong disorder, the precise number of electrons in K\({}_{x}\)Fe\({}_{2-y}\)Se\({}_{2}\) is difficult to quantify, we consider two extreme cases with fillings of 6.0 and 6.5 electrons per Fe. The true electron concentration should fall in between these cases. As shown in Figure 45, the calculated DCA and TDOS indicate that despite the strong Fe vacancy disorder and the low dimensionality, for both fillings, there are very few states that are Anderson localized in the Fe bands. Since those states reside far away from the Fermi level it can be concluded that K\({}_{x}\)Fe\({}_{2-y}\)Se\({}_{2}\) is not an Anderson insulator.
# Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency
## 3 Fabrication of Random Microlens Array (rMLA)  
Fabrication of the rMLA is realized by the combination of chemical etching and lithography technology. Before the glass substrate is chemically etched, the chemical etching solution needs to be prepared and the substrate needs to be pretreated. The composition and proportion of the solution are super important, which will significantly affect the chemical etching rate and the profile of the rMLA in the experiment, and further affect the beam homogeneity as well. For substrate pretreatment, the surface of the substrate is necessary to be coated with chromium film as the masking layer of chemical etching. In particular, the fabrication of the rMLA requires that the random micropores array is processed on the surface of the chromium film, so that the chemical etching solution is able to react with the glass substrate.  
Figure 1: (**a**) Schematic diagram of random microlens array (rMLA); (**b**) structural parameters of microlens unit; (**c**) honeycomb rMLA; (**d**) rectangular rMLA.
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 4 Conclusions  
* Taking the 10 factors that affected the occurrence of rock bursts in the Yanshitai Coal Mine as the main influencing factors, the PCA method was used to extract the dimension reduction feature information of the evaluation index, from which six principal components were extracted as the input vectors for extreme learning machine, eliminating the interaction between the various factors and simplifying the network structure of the model.
* Using the PSO algorithm, the initial weights and hidden layer neuron thresholds of the ELM were optimized. Therefore, a combined early warning PSO-ELM model for rock bursts was ultimately established, which overcame the disadvantage of randomness of the input weights and hidden layer thresholds of the ELM and enhanced the accuracy of the model prediction.
* Through the precision analysis and comparison between the prediction results of the BP neural network, radial basis function, and ELM, the research findings indicated that the prediction accuracy of the PSO-ELM model on the test set was as high as 100%, and the prediction accuracy was far better than the other three models.
* In summary, the rock burst PCA-PSO-ELM prediction model had the advantages for good prediction performance, fast learning speed, firm generalization ability, and satisfied robustness, which provided an idea for the early warning research of rock burst disasters and exhibited favorable engineering significance.
* It is worth noting that rock bursts are not only affected by the conditions of geology and mining technology, but also influenced by the hidden factors of Class II mining technology and so on during the prediction process of rock burst, such as the progress degree of the working face or the expansion and repair of roadway walls, prompting the risk rank assessment of rock bursts to become a difficult subject. Consequently, the investigation on intelligent early warning for rock bursts requires further research and discussion.  
Conceptualization, S.J. and H.Y.; funding acquisition, H.Y.; writing--original draft, S.J.; writing--review and editing, G.L., L.X., H.L., Z.C. and Z.X. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support for this research from the National Natural Science Foundation of China (No. 51874112), the National College Students Innovation and Entrepreneurship Training Program (No. 202210359040S), and the University Synergy Innovation Program of Anhui Province (No. GXXT-2020-055).  
Not applicable.  
The authors have declared that no competing interest exist.