esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results  
The EA-ILS hyper-heuristic has been tested on all instances of each problem in the extended HyFlex suite. The stopping criterion for the EA-ILS hyper-heuristic was set according to the execution time returned by a program on the machine used for the experimentation. The time limit for the EA-ILS hyper-heuristic on the machine used for an experiment is 530 s, which is the equivalent of 600 s on a standard testing computer according to the CHeSC 2011 organizers. The performance of the EA-ILS hyper-heuristic on the extended HyFlex domains was benchmarked against those of other hyper-heuristics that have been tested on the domains. The other competing algorithms include FS-ILS, NR-FS-ILS, AdapHH, EPH, SR-IE, SR-AM, and SSHH. Both SR-IE and SR-AM select LLHs at random but differ in their acceptance mechanisms. SR-IE accepts solutions with equal or better quality than the incumbent solution while SR-AM accepts every proposed solution regardless of its quality. The data generated for the performances of the three domains were obtained from the webpage ([[https://github.com/[PERSON]/hyflext](https://github.com/[PERSON]/hyflext)]([https://github.com/[PERSON]/hyflext](https://github.com/[PERSON]/hyflext)) accessed on 10 September 2022), with the exception of that of the SSHH. The median objective function values (ofvs) of the solutions obtained for the SSHH can be found in a paper [25]. Tables 3 and 4 respectively highlight the performance of the EA-ILS hyper-heuristic in terms of its overall best and median best ofvs obtained across the 30 instances of the problem domains in comparison with the top six performing hyper-heuristics reported in [25], where the values in bold style denote the best values. In Table 3, the percentage deviation in the best ofv of the EA-ILS hyper-heuristic in each instance from the best-known values reported in [24], denoted by \(\Delta\)(%), is presented. Figure 2: Illustration of LS-Seq update procedure.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 6 Conclusions  
This work proposes a multi-sensor indoor localization approach which works on different smartphones in a similar fashion. The magnetic data from the smartphone magnetometer is utilized to make the database; however, instead of magnetic field values, magnetic patterns are used to achieve device dependence. Since the magnetic signature similarity in the indoor environment may lead to higher errors, the database search space is narrowed down using a scene recognition classifier. A deep learning based convolutional neural network (CNN) classifier is used for scene recognition. A modified K nearest neighbor (mKNN) is presented which considers magnetic signature similarity, as well as the spatial closeness of selected candidates. The results demonstrate that mKNN improves the localization accuracy and locates a person within 1.08 m at 50% irrespective of the smartphone used for localization. We aim to reduce the labor time by adopting the crowdsourcing-based data collection and extend the approach to test more complicated environments. Scene recognition is affected by camera image resolution and it is computationally complex. Similarly, varying light conditions, dark environments or scene recognition in low light conditions can severely limit its performance. We intend to research scene recognition under low light conditions in the future. The current study does not consider different orientations of smartphone and we intend to work with various orientations in the future.  
Conceptualization, I.A. and Y.P.; Data curation, S.H.; Funding acquisition, Y.P.; Methodology, I.A.; Software, S.H.; Writing--original draft, I.A.; Writing--review & editing, Y.P.  
This research was supported by the SK Telecom, Korea. This work was supported by the Brain Korea 21 Plus Program (No. 22A20130012814) funded by the National Research Foundation of Korea (NRF). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2017R1E1A1A01074345).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range Free Technique
#### 3.2.1 Centroid-Based  
The center of the geometric structure is calculated by finding the average of all the points those who generated the structure defined as  
\[\left(u_{pred},v_{pred}\right)=\left(\frac{u_{1}+u_{2}+\ldots+u_{P}}{P},\frac{ v_{1}+v_{2}+\ldots+v_{P}}{P}\right) \tag{1}\]  
where \(P\) represents a total number of sensor nodes and \(\left(u_{pred},v_{pred}\right)\) are an unknown node's coordinate. Most of the approaches in this technique are dependent on the center and the anchor node coordinates for estimating an unknown node's position [31].  
Node density in the network does not affect the performance. The computational complexity of centroid-based techniques is \(O(n)\), which is very low, and it achieves comparatively better precision in networks with uniform anchor node distribution. However, for the random distribution of anchor nodes, accuracy is very low. In weighted centroid localization (WCL) [32], initially, the anchor node communicates its position to all other nodes, then they determine their position with respect to the centroid. In WCL, weights are used to improve the calculations in which the weight is a function dependent on the distance and the receiver's features. Thus, it is reliant on communication range, and it does not require any extra hardware.
## 4 Conclusions  
This work presented a new method for the reduction of the bauxite process tailings (red muds), through blast furnace sludges. Both blast furnace sludges and pure graphite were used as reducing agents. Many different starting mixes were reduced at \(1200\), \(1300\), and \(1500\,\,^{\circ}\)C, with a different carbon concentration and lime content, for a total of sixteen mixes. On the basis of the products obtained, the following conclusions can be drawn:  
* the chemical behavior of the two different batches, with graphite or blast furnace sludges, is equivalent. This confirms that B.F. sludges can be used instead of coke or other carbonaceous materials as reducing agent;
* the Fe\({}_{2}\)O\({}_{3}\) in the red muds can be reduced through a pyrometallurgical process, using blast furnace sludges;
* value of metal/charge ratio of at least \(17\%\) was obtained through blast furnace sludges;
* the reduction yield though blast furnace sludges is \(1.5\) times higher than through graphite;
* high quality of DRI or cast iron can be obtained with blast furnace sludges;  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Sample**} & **Moist** & **B.E.** & **Coke** & **Lime** & **Cast** & **Transportation** & **Raw** & **B.E.** & **Costs** & **Revenues** & **Margin** \\  & **RM** & **Studges** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **Costs** & **Costs** & **Costs** & **[**]** & **[**]** \\  & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** & **[**]** \\ \hline \multirow{2}{*}{1a} & \multirow{2}{*}{C2} & \multirow{2}{*}{1.25} & \(0.00\) & \(0.25\) & \(0.003\) & \(0.18\) & \(125\) & \(42.77\) & \(190\) & \(0\) & \(29\) & \textasciasci{}{}29 \\ \  
* blast furnace sludges induce a vitrified structure of the slag hindering the toxic element leaching.  
By considering all the aspects listed before, the best mix suggested is a 1:1 red mud/B.F. sludge, equivalent to 0.85 C/Fe\({}_{2}\)O\({}_{3}\).  
To conclude, all the trials and experiments carried out in this work led to the optimisation of the reducing process which can be implemented in a large-scale plant, i.e., arc transferred plasma furnace or direct reduction shaft furnaces, by replacing coke with blast furnace sludges.  
Conceptualization, C.M.; methodology, C.M. and D.M.; investigation, D.M.; resources, D.M. and A.G.; writing--original draft preparation and editing, D.M.; writing--review, S.B.; supervision, C.M.  
This research received no external funding.  
Authors would like to thank [PERSON] (Prosimet, [PERSON] (BG)) for TG-DSC tests, Eng. [PERSON] (Dilab, Crema (CR)) for iron wet analysis, [PERSON] and the Centre des Materiaux des Mines d'Ales (C2 MA) (Ecole des Mines d'Ales) for XRD analysis.  
The authors declare no conflict of interest.
# A Methodology to Improve Energy Efficiency and Sustainability in Urban Environments
## 5 Active and Passive Building Interventions
As a result, the building achieves an energy class of A4, which is substantially higher than the initial energy performance class (Figure 15). These measures enhance energy efficiency, reduce energy consumption, and promote sustainability within the building. In this particular case, the building's non-renewable global energy performance index is measured at 22.24 kWh/m\({}^{2}\) annually. The energy performance indicates that the building exhibits a high energy performance during the winter season, whereas its performance level during the summer season is considered average. The planned photovoltaic system, therefore, has a total nominal power of 157 kW. Based on this capacity, it is estimated that the system will consume approximately 157.230 kWh/year. The projected electricity production from the photovoltaic system is estimated to be around 169.024 kWh/year, taking into account various inputs and monthly production variations. It is important to note that the quantitative results obtained for the solar potential estimation are calculated with caution over a one-year time frame. Additionally, it is worth highlighting that the application of photovoltaic systems only on building facades or roofsis not the only possibility. There are increasingly innovative methodologies emerging that integrate photovoltaic systems into various elements of urban design and less conventional building elements. These approaches aim to enhance energy efficiency and maximize the utilization of solar energy resources in urban environments.
# Analysis of Shield Tunnels Undercrossing an Existing Building and Tunnel Reinforcement Measures
## 6 Effect of Grouting Pressure on Mechanical Behaviors of Soil, Segment, and Building
### Effect of Grouting Pressure on Displacements of Soil  
The cross-section in the middle of the model was taken to study the law of soil subsidence above the double tunnels. Figure 23 shows the change curves of soil subsidence at each position above the tunnels with different grouting pressures. From Figure 23, it can be found that the change laws of soil subsidence were the same for the different grouting pressures. The subsidence of soil directly above the double shield tunnel was the largest. As shown in Figure 24, the maximum subsidence of soil reduced linearly with the increase of grouting pressure. For the grouting pressures of 50 kPa and 550 kPa, the corresponding maximum subsidence of soil were 23.93 mm and 6.37 mm, with a reduction of 73.38%. According to the calculation, the maximum subsidence value of the soil mass would decrease by 3.512 mm when the grouting pressure increased by 100 kPa. From this, it is clear that increasing the grouting pressure appropriately during the excavation of double shield tunnels can effectively reduce the subsidence of soil, which was consistent with the conclusion in reference [32].  
Figure 23: Subsidence of soil at cross-section in the middle of the model with different grouting pressures.  
Figure 24: Variation of the maximum subsidence of soil with grouting pressure.
# Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
###### Abstract
Predicting mortality is important in determining the appropriate level of care and treatment for the patient and providing appropriate support for end-of-life care decisions. Therefore, the predictions of heart failure and mortality are important for the management of cardiac patients, but they serve different purposes. Predicting heart failure helps identify patients at risk of developing heart failure, whereas predicting mortality helps identify patients at risk of dying from their heart disease. A summary of the major contributions of this study follows:  
1. A decision support system (\(\chi^{2}\_\)RF) is proposed for mortality prediction in cardiac patients;
2. The problem with the biased ML model due to imbalanced classes in the dataset is addressed by deploying SMOTE;
3. The constructed decision support system (\(\chi^{2}\_\)RF) outperforms other cutting-edge machine learning models for the prediction of mortality in cardiac patients, and boosts the performance of the conventional random forest model by 5.5% for the prediction of mortality in cardiac patients;
4. The proposed method (\(\chi^{2}\_\)RF) has reduced temporal complexity since it uses fewer features;
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 5 Applications for Two Party Cryptography
### Bit Commitment
#### 5.1.2 Security Analysis  
The correctness of the protocol does not need to be proven because the protocol is designed according to the definition of bit commitment protocol. Its \(\varepsilon\)-hiding is guaranteed by the security of the ROT protocol.  
**Lemma 8** (Security for [PERSON]).: _n represents the number of bits transmitted during the protocol. Let \(n\to\infty\), we have: (1) \(\delta_{cB^{\prime}}\approx_{\varepsilon}\tau_{\{0,1\}}\otimes\rho_{B^{\prime}}\), (2) \(\mathrm{H}_{\min}(c|B^{\prime})\geq 1-\varepsilon\)._  
**Proof.** Our Commitment protocol is adopted from the 1-2 ROT, and according to Definition 5, we have \(\mathrm{H}_{\min}(c|B^{\prime})\geq 1-\varepsilon\).  
**Lemma 9** (Security for Bob).: _n represents the number of bits transmitted during the protocol. Fix \(\delta\in[0,\frac{1}{4}]\), and exist \(\varepsilon\to 0\), we have:_  
\(\mathrm{Pr}(\text{ans}=\text{accept}|\mathrm{A}^{\prime})\leq\varepsilon\)_._  
**Proof.** According to Lemma 5,  
\[\mathrm{H}_{\min}(y^{n}|A^{\prime})\geq-n\mathrm{log}_{2}\left(\frac{1}{2}+ \frac{1}{2\sqrt{2}}\right),\]  
because  
\[\mathrm{Pr}(\text{y}^{n}|A^{\prime})=2^{-\mathrm{H}_{\min}(y^{n}|A^{\prime})} \leq\left(\frac{1}{2}+\frac{1}{2\sqrt{2}}\right)^{n}.\]we can easily get \(\Pr(\text{ans}=\text{accept})|\Lambda^{\prime})\leq\varepsilon\).
## 3 Results and Discussion
### Assessment of Cd Pollution Risk in Agricultural Soils of Hainan Island  
According to the criteria of GB 15618-2018, we assessed the heavy metal pollution status in agricultural soils in the study area using the single-factor pollution index (PI), Geo-accumulation Index (Igeo) and the Potential Ecological Risk Index (ER). Results are shown in Figure 8. From the perspective of the single-factor pollution index, the agricultural soils on Hainan Island are generally classified as clean, but cadmium pollution is evident in some areas.  
According to the \(I_{\text{geo}}\), Cd levels in most of Hainan's agricultural soils remain below 1, indicating an unpolluted or unpolluted to moderately polluted classification, covering approximately 80.44% of the area. Heavy and above pollution (\(I_{\text{geo}}>3\)) accounts for around 10.87%, while a minimal portion of sites (2.17%) shows severe pollution.  
Based on the ER (Figure 8), about 89.36% of the area falls under low ecological risk (Level I). Additionally, 8.51% of the area is classified as having considerable or higher ecological risk, while 2.13% faces extremely high risk (Level V).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**City** & **Mean (mg/kg)** & **City** & **Mean (mg/kg)** \\ \hline Danzhou City & 0.13 & Wenchang City & 0.04 \\ Dongfang City & 0.04 & Lingshui County & 0.08 \\ Changjiang County & 0.06 & Wanning City & 0.08 \\ Baisha County & 0.03 & Baoting County & 0.02 \\ Ledong County & 0.04 & The city of Sanya & 0.25 \\ Five Fingers Group & 0.05 & Qionghai City & 0.11 \\ City & 0.04 & Tunchang County & 0.03 \\ Chengmai County & 0.09 & Ding’an County & 0.03 \\ Haikou City & 0.23 & Qiongzhong County & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical summary of the sample-number-weighted Cd concentration of different cities in Hainan Island.  
Figure 7: Spatial distribution of Cd concentrations in agricultural soils across Hainan Island.  
While the majority of agricultural soils in Hainan Island exhibit low Cd pollution and ecological risk, the presence of heavily polluted areas and extreme ecological risks necessitates focused attention. Continuous monitoring and proactive remediation strategies are critical for mitigating the cumulative and localized risks associated with Cd contamination.
## 3 Materials and Methods
### Geodetic Analysis  
GNSS and DInSAR are two techniques of satellite geodesy, which have been widely used in recent years to measure crustal deformation associated with a number of natural phenomena, such as earthquakes, volcanism, landslides, and subsidence (e.g., [51; 52; 53; 54; 55; 56; 57; 58]). GNSS can provide millimetre-level accuracy in horizontal directions and centimetre-level accuracy in the vertical direction [59] with unprecedented time resolution, but only at selected points, even if in dense networks. On the contrary, DInSAR techniques can be used to measure areal displacements in the direction of radar line-of-sight (LoS) with an accuracy of up to a portion of the microwave wavelength, namely an accuracy of several millimetres to centimetres [60]. For realistic crustal deformation monitoring, these two techniques perform better when used jointly. Due to the scarce availability of permanent GNSS stations in the studied area, hereafter we used GNSS only for validation purposes and cross-checking of the DInSAR data. Details of geodetic data used in this study are presented in Table 2.
# Area and Performance Estimates of Finite State Machines in Reconfigurable Systems
###### Abstract  
Modern reconfigurable systems are typically implemented in field-programmable gate arrays (FPGAs) based on look-up tables (LUTs). Finite state machines (FSMs) perform the functions of control devices and are integral to reconfigurable systems. When designing reconfigurable systems, the problem of optimizing the area and performance of FSMs often arises. The FSM synthesis and state encoding methods generally use only one estimate of the FSM area or performance. However, regardless of the computational complexity of the FSM synthesis or state encoding method, if the estimate incorrectly reflects the optimization aim, the result is far from the optimal solution. This paper proposes several estimates of the area and performance of FSMs implemented in LUT-based FPGAs. The effectiveness of the proposed estimates was investigated using the sequential method for FSM state encoding. Experimental studies on benchmarks showed that the FSM area decreases on average from 3.8% to 6.5%, compared to known approaches (for some cases by 33.3%), while the performance increases on average from 3.5% to 7.3% (for some cases by 27.6%). Recommendations for the practical use of the proposed estimates are also provided. The Conclusions section highlights promising directions for future research.  
finite state machine (FSM); area; performance; estimate; field-programmable gate array (FPGA); state encoding method Article
# Effect of Slag Particle Size on Fracture Toughness of Concrete
## 2 Research Significance  
Laboratory investigations have shown that when the slag particle size is reduced, its mechanical performance in concrete is improved. The finer slag with the larger surface area has an intensive reaction which may lead to higher enhancement of the strength, the fracture energy, and the critical stress intensity factor of concrete. Other than the research by [PERSON] and [PERSON] [PERSON] and [PERSON] (2009), who observed a dependence of the fracture energy on the aggregate type and independence from the compressive strength of concrete, this study found that an increase in concrete compressive strength of 10% resulted in an increase in fracture energy of around 18%.
# Enhanced Adsorption of Methyl Orange by Mongolian Montmorillonite after Aluminum Pillaring
## 2 Materials and Methods
### Adsorption Kinetic and Isotherm Models
#### 2.5.2 Adsorption Isotherm  
The basic assumption of the Langmuir isotherm model is that, when a monolayer of adsorbate is saturated on a homogenous surface of an adsorbent, the adsorbent can reach the maximum amount of adsorption [35]. The Langmuir model equation can be written as the following non-linearized form [36; 37]:  
\[q_{e}=q_{m}C_{e}K_{L}/(1+C_{e}K_{L}) \tag{4}\]  
where \(C_{e}\) is the equilibrium concentration of the adsorbate in the solution, mg/L; \(q_{m}\) is the monolayer adsorption of the adsorbate onto the adsorbent, mg/g; and \(K_{L}\) is the constant related to the free energy of adsorption, L/mg.  
The Freundlich isotherm model is commonly used to express the adsorption which occurs on the heterogeneous surface of an adsorbent, with different affined adsorption sites towards the adsorbate [38]. This isotherm model can define the surface heterogeneity and the distribution of the adsorption sites and their energies [39]. This model can be expressed in the following equation:  
\[q_{e}=k_{F}C_{e}^{1/n} \tag{5}\]  
where \(k_{F}\) is the Freundlich constant related to the adsorption capacity of the adsorbent, (mg/g)/(mg/L)\({}^{n}\), and 1/_n_ is the adsorption strength.  
The Redlich-Peterson isotherm model, incorporating three parameters, combines both the Langmuir and Freundlich models, and explains the adsorption equilibrium results obtained from a wide range of adsorbate concentrations [40]. The Redlich-Peterson isotherm model is described in the following non-linearized equation [41]:  
\[q_{e}=K_{R}C_{e}/\left(1+\alpha_{R}C_{e}^{\beta}\right) \tag{6}\]  
where \(K_{R}\) is the Redlich-Peterson isotherm constant; L/g; \(\alpha_{R}\) is also a constant, L/mg; and \(\beta\) is the exponent that lies between 0 and 1.  
At relatively high concentrations of the adsorbate in an aqueous solution, the isotherm equation (Equation (6)) can be reduced to the Freundlich equation.  
\[q_{e}=(K_{R}/\alpha_{R})C_{e}^{1-\beta} \tag{7}\]  
when \(\beta=1\), the Redlich-Peterson model can be reduced to the Langmuir equation. In contrast, when \(\beta=0\), the Redlich-Peterson model can be reduced to [PERSON]'s equation.