esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This investigation experimentally studies the concept of trailing edge jet control to modify the vortex shedding mode for alleviating the fluctuating amplitude of surface pressure, aerodynamic force, and the unsteady flow structure. The non-dimensional jet momentum coefficient (\(J_{sj}\)) serves as a monitor to evaluate the strength of the wake jet flow. The surface pressure and the PIV measurements were conducted to investigate the characteristics of aerodynamic force and flow field of the single box girder (SBG) model with various \(J_{sj}\). Some significant conclusions are given as follows.  
The trailing edge jet that flows into the wake flow can obviously mitigate the fluctuating amplitude of the drag, lift, and moment forces. Based on the surface pressure measurement results, the saturation of the control effectiveness for lift force was obtained when the \(J_{sj}\) is set as 0.0094. The unsteady drag, lift, and moment forces are estimated to decrease about 16.23%, 36.34%, and 38.74%, respectively, compared to the uncontrolled case. Moreover, the vortex shedding frequency of the case \(J_{sj}=0.0094\) exhibited a difference.  
The PIV measurement results explained the phenomenon that were obtained by the pressure measurement. The TKE distribution behind the SBG model with trailing edge jet were smaller than the case \(J_{sj}=0\), leading to the more stable wake. The instantaneous wake flow shows that the control method can change the antisymmetric mode into the symmetric mode, which suppresses the alternating vortex shedding and mitigates the unsteady aerodynamic force. Moreover, the unstable wake region of case \(J_{sj}=0.0094\) was narrowed by about 71.84%, compared to the uncontrolled case.  
Conceptualization, G.C. and W.C.; methodology, W.C.; software, G.C.; validation, W.C., and G.C.; formal analysis, G.C.; investigation, G.C.; resources, W.C.; data curation, G.C.; writing--original draft preparation, G.C.; writing--review and editing, W.C.; visualization, G.C.; supervision, W.C.; project administration, W.C.; funding acquisition, W.C. and G.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China grant number 51978222, 51808173, 51722805 and U2106222; The Fundamental Research Funds for the Central Universities grant number HIT.BRETIV 201803.  
Not applicable.  
This research work is supported by the National Natural Science Foundation of China (51978222, 51808173, 51722805 and U2106222) and the Fundamental Research Funds for the Central Universities (HIT.BRETIV 201803). [PERSON] also acknowledges the financial support from China Scholarship Council (CSC 202106120222).  
Figure 14: The imaginary part of the critical point at various positions (X/B) for different test cases.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis  
Hereafter, we evaluate the proposed chaos-based cryptosystem's performance. First, we estimate the number \(r\) of rounds required to have a secure system, then we assess the performance of the proposed chaotic generator, histogram chi-square, NIST tests on the generated sequences, and finally, we examine the security of the cryptosystem up against the usual cryptographic attacks.  
All the simulations were implemented using MATLAB (R2016b) on a PC with a 2.5 GHz processor Intel Core i5-7300 HQ CPU, 16 GB RAM, under Windows 10 Professional, and a 64-bit operating system.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 3 LED to Ambient Light Ratio Optimization
### A Brief Introduction to the Optimization Problem  
The performance of a VOCC system is evaluated through two aspects: Data rate and BER. Regarding the former, a higher data rate without scarifying the BER of the VOCC system can be achieved by using cameras with higher frame rates or larger LED panels to accommodate more LED chips. Cameras with higher frame rates and more powerful processors are obviously costly, and vehicles might not have enough space to install a large LED panel. Consequently, increasing the data rate in the VOCC system usually involves the problem of balancing between the required data rate and the implementation costs of particular systems; this is not the concern of this paper. The second aspect--the BER--on the other hand, can be improved by optimizing the system parameters without having to increase the implementation costs. Therefore, this paper is concerned with optimizing VOCC system parameters to obtain the optimal BER at the same implementation costs.  
Among the many parameters, L2A ratio and LED spacing are the ones with decisive effects on BER. As mentioned earlier, the L2A ratio is the ratio between LED and ambient light. LED spacing is the amount of space between LEDs in the captured LED panel image. Figure 3 shows a top-down explanation on the deciding effects of the L2A ratio and LED spacing on the LED bit detection accuracy. More specifically, the LED bit detection accuracy is determined by the characteristics of LED features extracted from the received image. These characteristics include between-class variance (Bvar) and within-class variance (Wvar) of the On and Off LED features. These variances are in turn determined by the characteristics of On and Off LEDs in the received image. These imaging characteristics of LED includes LED extinction ratio and LED interference. The characteristics of LEDs in the received image are in turn determined by system parameters including the L2A ratio and LED spacing. The following subsections provide details of the relationships shown in Figure 3.
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 3 Mathematical Model  
Based on LightGBM model, this paper establishes a heart failure prediction framework of bootstrap sampling and a weighted fusion LightGBM sub-model. After preprocessing, feature selection, bootstrap sampling, and sub-model prediction based on LightGBM classification and weighted fusion, the prediction result is finally obtained. The data pre-processing includes two aspects: feature discretization and feature coding. Through feature discretization, continuous features are reasonably divided to better meet the processing requirements of subsequent models. On the other hand, feature coding is a kind of standardized coding conversion applied to different types of features. This ensures that the data form is convenient for the model to analyze and operate. The feature selection part focuses on correlation analysis and feature combination exploration. Correlation analysis aims at revealing the degree of correlation between each feature and the target variable. It filters out features with weak correlation and the least contribution to the prediction results, thus reducing the data dimension and eliminating redundant information. The study of feature combination exploration attempts to combine existing features from different angles. The purpose is to explore a more expressive and predictive feature combination forms, further optimize the data features of the feed-in model, and improve the accuracy and efficiency of model prediction. Bootstrap sampling sub-model prediction based on LightGBM classification is one of the core steps, and involves several key operations. Firstly, the data set is divided into the training set and verification set, respectively, to ensure the scientificity and effectiveness of model training and verification. Then, the model training is carried out through bootstrap sampling, and the performance of the model under different parameter combinations is comprehensively explored by means of cross-validation and parameter combination traversal. After the training of each sub-model, the prediction results of multiple sub-models are integrated through voting. For each parameter combination, the corresponding evaluation index is calculated. This enables the determination and fine-tuning of the model parameters according to these indexes, allowing the model to achieve a better prediction performance. For the test set, the weighted fusion method is adopted, and the weights of the corresponding sub-models under each parameter combination are determined according to the evaluation indexes obtained in the previous calculation. These weights are used to carry out weighted fusion prediction on the test set, and finally, a comprehensive and accurate prediction result is integrated to achieve the goal of the whole prediction process. The overall framework is shown in the Figure 1.
## 3 Our Advanced Proposed Methodology  
In our previous work [8], we conducted experiments using 15 physical sensors and achieved very promising results. The SSWSN model demonstrates strong performance across various fire-related scenarios. The highest accuracy of 95% was achieved in the \"No fire\" and \"Smoke\" scenarios, and 90% accuracy was achieved in the \"Fire about to Start\" scenario with, highlighting its effectiveness in detecting safe conditions and early warning signs of fire. Accuracy decreases in more critical fire scenarios, with 80% in \"Fire\" and 65-70% in \"Fire Increasing\" and \"Intensive Fire\", suggesting challenges in detecting and distinguishing intense fire conditions. The model also performs moderately in combined scenarios like \"Fire with Thundering\" (85%) and \"Fire with Thundering and Lightning\" (65%), indicating that more complex environmental factors introduce data challenges. These results suggest that while the SSWSN model is effective at early detection, further refinement is necessary to improve its accuracy in dynamic and intense fire conditions.  
Building on our achieved success, this study explores the integration of virtual sensor data to further enhance our SSWSN model's predictive capabilities and covering fire field size. Specifically, we detail the methodology for generating 15 virtual sensors using polynomial regression and integrating them into our previous research physical sensors supervector, producing a hybrid supervector (HSPV) constructed from the original 15 physical and the new 15 virtual sensors. This approach enhances the model's ability to capture complex interactions between sensor readings, leading to improved prediction accuracy across various phenomena.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### Reconfigurable Intelligent Surfaces  
Reconfigurable intelligent surface (RIS) is a groundbreaking technique for reconfiguring the wireless propagation environment through software-controlled reflections to achieve enhanced spectral and energy efficiency in a cost-effective manner [205; 206; 207; 208; 209]. Some alternative terms are also used for RIS, such as intelligent reflecting surface (IRS) or software-controlled metasurfaces [211; 212]. By changing the phase and/or amplitude, the elements of RIS can separately reflect the incident signal. In this way, beamforming for directional signal augmentation or null placement is performed. Radiating elements, control circuitry using PIN diodes or varactors, and a controller or field-programmable gate array (FPGA) [213; 214]. RIS technology is still in its infancy; however, most of the development aspects of RIS are presented in detail in [215; 216]. Furthermore, beamforming development with RIS is studied in [217; 218; 219].  
RIS provides a unique and cost-effective method for proactively combating wireless channel impairments. In this view, RIS-aided industrial communication is an emerging research area with huge potential [208; 220]. RIS-aided wireless communication carries the promising potential to be an enabling technology to assist smart communication in Industry 4.0 and Industry 5.0 [25]. Due to densely distributed industrial equipment (such as metal machinery, unpredictable movement of things (robots and trucks), wooden structures, and thick pillars), wireless signals are easily blocked and reduce performance reliability. When the direct communication link is impeded, RIS is a viable choice to provide an alternate transmission link [21; 221; 222; 25]. The communication link can be created using a RIS mounted on the factory ceiling or the wall, delivering mission-critical industrial communication with seamless, reliable connectivity. A vision of RIS-enabled communication in Industry 4.0 and beyond seems to become a reality in the near future.
## 4 Evaluation Parameters  
This section provides comprehensive definitions of the parameters used for the evaluation of the aforementioned schemes.  
Classification accuracy (CA) measures the percentage of overall correctly detected class instances, as shown in Equation (9) [41]. Higher CA values show better capability of the proposed system in a real-time environment.  
\[CA=\frac{TP+TN}{TP+FN+FP+TN}*100 \tag{9}\]  
The Misclassification rate (MCR) provides the percentage of wrongly predicted instances. A lower MCR value shows better robustness of the trained model against unseen data samples. The MCR can be computed using Equation (10).  
\[MCR=100-\frac{TP+TN}{TP+FN+FP+TN}*100 \tag{10}\]  
Figure 7: Gossip learning (GL)-based classifier model sharing.  
Sensitivity is also known as the true positive rate (TPR) and can be measured using Equation (11). Sensitivity is used to measure the efficiency of the trained model to predict true instances only.  
\[Sensitivity=\frac{TP}{TP+FN}*100 \tag{11}\]  
Specificity measures the proportion of correctly negative instances and is also known as the true negative rate (TNR), as presented in Equation (12). This value should be higher for a well-trained model.  
\[Specificity=\frac{TN}{TN+FP}*100 \tag{12}\]  
The NPV measures correctly predict negative instances as negative. The NPV can be computed using Equation (13), which comprises the ratio of true negatives (TNs) to the sum of true negatives (TNs) and false negatives (FNs).  
\[NPV=\frac{TN}{TN+FN}*100 \tag{13}\]  
FPR provides a measure of false positive prediction by the trained model and can be computed using Equation (14). Lower values of FPR show higher classification accuracy.  
\[FPR=100-NPV \tag{14}\]  
FNR provides a measure of false negative prediction and can be computed using Equation (15), and should also have a smaller value for a well-trained model.  
\[FNR=100-Sensitivity \tag{15}\]  
Precision is computed to provide the ratio of true instances over true positive and true negative instances using Equation (16). This represents the ability of the trained model to correctly predict a true instance as a true instance. A higher value of precision demonstrates higher performance efficiency of the trained model.  
\[Precision=\frac{TP}{TP+FP}*100 \tag{16}\]
# Dynamic Packet Duplication for Industrial URLLC
## 2 Background
### Industrial Networks
#### 2.1.2 Critical Applications in Industry 4.0  
In the Industry 4.0 paradigm, agility is a key objective in the design of factories. Agility means the flexibility of the system to changing requirements by replacing or improving separated modules. Some of the main technologies that allow such agility in factories are the following:  
* Rearrangeable modules in production lines [23]: traditionally, production lines have been made up of static modules that perform specific operations. These modules, each controlled by a Programmable Logic Controller (PLC), are interconnected via wired to the Manufacturing Execution System (MES). By enabling the mobility of these modules, new combinations of elements into new types of production lines are possible.
* Automated Guided Vehicle (AGV) [24]: it is common that vehicles driven by workers perform tasks, such as moving stocks and supplies in factories. In the Industry 4.0 paradigm, due to the customization of production, these kinds of movements increase exponentially. It is harder to provide supplies in batches; therefore, smaller vehicles are required with an increase in the number and variety of trips. To achieve this without increasing the workload, AGVs do this without the need for human drivers.
* Drones [25]: drones are a new category of vehicle that enables novel possibilities in factories. Applications, such as emergency assistance, surveillance or rapid point-to-point delivery can be highly optimized with these vehicles.
* Autonomous robots [26]: robots have been extensively adopted in industry since commercial variants have been available. Nevertheless, early iterations of robotics technologies were limited in the number of tasks that they could perform and depended strongly on operators programming them correctly. Currently, AI and ML, along with Simultaneous Location and Mapping (SLAM) and navigation technologies, are enabling novel functionalities on robots that are much more autonomous and perform tasks that were previously reserved for workers.
* Connected workers solutions [27]: the development of consumer electronics in the last years has had a higher impact in the professional area. Gadgets, such as Augmented Reality (AR) glasses, tablets, haptic interfaces and sensors have shown a productivity boost in factories.  
All of these technologies rely on wireless connectivity. Some of them, such as production lines, are already mature technologies where a mobility component is added in the Industry 4.0 paradigm. In that cases, wired connections need to be changed to wireless [28].
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## Materials and Experimental Preparation
### Experimental Process  
#### 2.2.1 Upward Flow System  
The experimental flow system consisted of three main parts: a syringe pump, an acrylic cylinder (diameter of 3.5 cm; length of 12 cm), and a pressure data logger (Figure 3). Jumunjin sand was compacted in the acrylic column before setting the HBL on top of the specimen. The syringe pump pumped NiCl2.6H2O solution from a 60 mL syringe to the soil column in the acrylic cylinder at a constant flow rate via the inlet system. The bottom and top caps include inlet and outlet systems. The solution from the outlet was collected to measure the change in hydraulic conductivity of the soil column and the Ni\({}^{2+}\) removability of the soil column.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GG Content [\%]** & 0.0 & 0.5 & 1.0 & 2.0 \\ \hline
**Symbol** & 0.0 HBL & 0.5 HBL & 1.0 HBL & 2.0 HBL \\ \hline
**Dry density [g/cm\({}^{3}\)]** & 1.61 & 1.57 & 1.54 & 1.53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dry density of the specimens.  
Figure 2: Particle size distribution of jumunjin sand.  
The voltage values in mV, which were monitored at the top and bottom of the soil column by the pressure data logger, were converted to pressure (kPa), and then they were converted to head difference (m). The drained water was collected to measure its volume with testing time. The data of the head difference, the volume of water, and the time were collected and used to calculate the hydraulic conductivity of the soil column.  
In this study, to demonstrate the action of HBL under adverse conditions, the HBL was fully saturated by an upward flow of NiCl\({}_{2}\).6H\({}_{2}\)O solution. The Ni\({}^{2+}\) adsorption tests were conducted on saturated specimens. The experimental conditions are summarized in Table 2.
## 5 Conclusions  
The evaluation of the photocatalytic activity for hydrogen generation of the best-performing photocatalyst at varying organic concentrations allows the identification of an adsorption equilibrium constant of 0.22 M\({}^{-1}\). A beneficial effect is recorded with raising the system temperature, with an increase in hydrogen productivity of nearly 4.5 times with respect to the lowest temperature. Such a temperature effect is related to the higher reaction rate of hydrogen generation and the easier desorption of the products from the catalyst. As regards the effect of solution pH, alkaline conditions exert a beneficial effect on photocatalytic hydrogen generation. Under the best conditions tested (i.e., glycerol as a scavenger, pH = 12.0, T = 80 \({}^{\circ}\)C, and direct sunlight irradiation), a value of light-to-chemical energy efficiency of 8% in the UV range is observed. Therefore, the simple photocatalytic system is suited to visible-light-driven hydrogen generation. Despite the good performances detected under visible light irradiation only, the UV component in the solar spectrum is able to rise by 40% the photocatalytic hydrogen generation with respect to the visible light irradiation only.  
Conceptualization, M.M. and R.A.; methodology, M.M.; validation, L.C. and L.D.S. investigation, M.M. and M.R.; data curation, M.R. and L.C.; writing--original draft preparation, M.M., R.A. and R.M.; supervision, R.M. and R.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 3 Challenges
### Data Management Challenges  
The identified requirements impose significant challenges also to the network data management and distribution. It is widely recognized that entirely centralized solutions to collect and manage data in industrial environments are not always suitable [24; 25]. This is due to the fact that in order to assure quick reaction, process monitoring and automation control may span among multiple physical locations. The fact that automation control may span multiple physical locations and include heterogeneous data sources also pushes towards decentralization. Moreover, the adoption of IoT technologies with the associated massive amounts of generated data makes decentralized data management inevitable. Cloud technologies are considered a great opportunity to implement different types of data-centric automation services at reduced costs, but deploying control-related services in clouds also poses significant challenges. Sending data back and forth between the cloud and the factory over the Internet might result in loss of control over the data by the legitimate owners. A significant challenge is that when data are managed across multiple physical locations, data distribution needs to be carefully designed, so as to ensure that industrial process control is not affected by the well-known issues related to communication delays and jitters [26; 27]. While several approaches have been proposed to optimize data management and distributed processing, smart data distribution policies, e.g., data replication to locations from where they can be accessed when needed within appropriate deadlines, are still to be investigated.