esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
estimated a temperature of about 6000 K for the gliding arc plasmatron by fitting synthetic spectra to the SPS \((0,0)\)- and FNS \((0,0)\)-transitions [35]. According to the discussion given in [35], if the rotational temperature of the excited nitrogen molecule and the rotational temperature of the ionized nitrogen molecule are similar, both states are populated by an electron impact exitation,corresponding to conditions of high electric field and low electron density. This is the case for the discharge under study, considering the results in Figure 6. Furthermore, [PERSON] et al. analyzed a commercially available plasma system operated at a pulse frequency of 19 kHz with nitrogen flowing at 30 L/min and a peak input power of about 1.5 kW in [36], reported rotational temperatures of about 5000 K inside the plasma generator and between 3000 K and 4000 K at nozzle exit. Similarly to previous references and also to this work, the authors based the estimation on the second positive \((0,0)\)-transition of molecular nitrogen. For the same plasma system, also pulsed at 19 kHz but working with a compressed air gas flow rate of 76.6 L/min, [PERSON] et al. estimated a rotational temperature of about 1760 K at nozzle exit using the \((0,2)\)-transition spectra of the second positive system [3]. If a linear relationship between gas flow and temperature would be assumed in a first approximation for this result, than a rotational temperature of approx. 4500 K at an air flow rate of 30 L/min could be expected. Heavy particle temperatures between 5500 K [43] and 5800 K [56] can be found in further references for low-current discharges driven by direct current under atmospheric pressure. A gas temperature of \(\sim\)5500 K was reported in [71] by [PERSON] et al., who modelled the plasma generator investigated by [PERSON] et al. in [28]. The heavy particle temperatures given in Section 4 are in a similar temperature range compared to the above references, and therefore the temperatures presented in this paper are considered plausible, with the exception of the values estimated for an axial distance of 1 mm, where the results were most probably affected by imaging errors, as discussed above.
## 4 State of Art of the Smart Energy Management Systems
### Smart Energy Management System Architecture  
In this section, we have based our design on an architecture proposed in recent work [3], where the SEMS is presented in Figure 6 as a data flow from different open-pit sections. In the field, all monitored machines and equipment, such as draglines, bucket wheel reclaimers and conveyors, are instrumented by current and voltage sensors, depending on the values of the equipment, regarding the accuracy, drift, linearity, phase shift, integration, and price, of course. The sensors installed are interfaced with power meters, which communicate directly with PLC in the control layer, the PLC interface with the SCADA system to collect, store and visualize the data. In the artificial intelligence layer of the SEMS, the database feeds the defects diagnosis and the load forecasting algorithms, which report the current and the predicted state of the demand response [28].  
In the operational layer, the SEMS manages the load and sources in order to optimize the energy efficiency and improve the system as well, these operations reports to management layer where the energy manager can oversee all changes and can take real time decisions [22].  
Figure 7 shows the general subsystems of a SEMS that can be implemented in industries, typically chemical processing and the mining industry. The data management subsystem collects energy consumption and generation data and runs the regressors to forecast the energy profile and demand then uses the classification algorithms to diagnose the system, detect abnormal consumption and anomalies and predict critical grid failures [8; 13].  
The supervisory control and data acquisition system is implemented and connected to energy meters and the process system which allows an efficient and fast data acquisition and processing.  
Figure 5: A scheme of energy metering, storing, predicting and real-time monitoring for industry. Reprinted with permission from Ref. [3]. Copyright 2022 IEEE.
# Ionic Liquids for Development of Heterogeneous Catalysts Based on Nanomaterials for Biocatalysis
## 4 Contribution of Nanomaterials to Effectiveness of Supported Ionic Liquid Phases
Additionally, a chemically modified magnetite nanomaterial surface, with hydrophobic ionic liquids, provides high enzyme stability and prevents leaching of the enzyme. Moreover, high ionic liquid loading on magnetite nanomaterials was reported, which can be easily explained by their large surface area [68; 69; 70; 71]. The next significant group of nanomaterials that are used as matrices in SILP/SILLP, is carbon nanotube. Immobilized on carbon nanotubes, lipases exhibit high activity, due to the hydrophobicity of the MWCNTs outermost shells. There are reports where the ionic liquid is covalently grafted, or physically adsorbed, on the MWCNTs surface, which causes the increase in hydrophobicity of the SILP or SILLP matrixes. Therefore, lipases that are immobilized on SILP and SILLP, based on carbon nanotubes, showed great activity and enzyme loading. It is worth emphasizing the interesting approach with the D-glucose-based ionic liquid used for SILP synthesis. In consequence, bio-based SILP was obtained and successfully used in esterification reactions [75; 76; 77; 78; 79]. The most widely used in supported ionic liquid phases is silica. The surface of the silica nanomaterials can be easily functionalized. Moreover, the porosity of silica nanoparticles increases ionic liquid loading on the surface, and so on the efficiency of enzyme immobilization. The high activity and thermal stability of the enzymes were observed after immobilization on silica-based SILLP or SILLP [67; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94]. For all the presented examples, nanomaterials improved the ionic liquid and enzyme loading caused by the large surface area:volume ratio. All of the described nanomaterials can be functionalized, which is key for the supported ionic liquid-like phase method. Nanomaterials based on supported ionic liquid phases, enabled the activity and stability of the employed enzymes to be increased. It was due to the imidazolium ionic liquids containing long alkyl chains and proper anions grafted to the nanomaterial, which increased the hydrophobicity of the support. CALB, PPL, and BCL are the most common lipases used in nanomaterial-based SILP and SILLP. Immobilized enzymes on the nanoparticles that are modified with ionic liquids, catalyzed in hydrolysis, esterification, transesterification and oxidation reactions, provide high yields and conversions. Moreover, biocatalysts could be easy separated from the reaction mixture, especially those with magnetic properties, and used in many cycles.
## 9 Materials and Methods  
### The Node Hardware  
The nodes consisted of Wi-Fi6 routers for our signal quality experiments. The Asus RT-AX53U router was chosen for its ease of accessibility, low-cost, and support of the open-source OpenWrt [44] operating system. The Asus RT-AX53U is a 2 \(\times\) 2 dual-band Wi-Fi6 router that provides 80 MHz channel bandwidth and 1024 QAM for fast wireless transmissions. The device was limited to an 80 MHz channel bandwidth due to an OpenWrt limitation. It supports OFDMA, BSS Colouring, and two spatial streams, which allows it to cope well with multi-hop performance in a stope environment. The throughput and jitter results were recorded using the iperf3 utility within OpenWrt, whilst the latency was obtained using the ping utility within OpenWrt. The utilized topology for signal quality evaluation is shown in Figure 4.  
To achieve an accurate power consumption comparison, the nRF7002 dk [45] device was used to evaluate both Wi-Fi6 and OpenThread protocols. The nRF7002 dk is shown in Figure 5. The nRF7002 dk was flashed with an open-source operating system known as Zephyr OS [46]. The boards were configured to transmit data via the 2.4 GHz channel for both protocols. By using the same OS, same transmission frequency, same input voltage, and same development kits, accurate power consumption comparison was achieved. The power evaluation setup is shown in Figure 5.  
Figure 4: Asus RT-AX53U Wi-Fi6 router.  
Figure 5: Power evaluation setup.  
The Nordic Power Profiler Kit 2 (PPK2) was used to analyse the power consumption of the nRF7002 dk. The PPK2 provides measurement with a resolution of 0.2 uA and allows for sampling at 100 KHz. Zephyr's zperf utility was used to transmit data at specific throughput values. The PPK2 was then used to record the power consumption for the different transmission rates. Nordic Power Profiler software [47] was used to analyse power consumption. The PPK2 powered the nRF7002 dk with 3.3V for both of the Wi-Fi6 and OpenThread tests. The PPK2 only recorded the power consumption of the nRF7002 dk that it was connected to during each test. At the time of performing the experiments, Zephyr OS did not support Wi-Fi directly, so an additional Wi-Fi6 router was used for the Wi-Fi6 test, but the results are still accurate since the receive power was obtained directly from the nRF7002 dk and the transmit power was also obtained directly from the nRF7002 dk during transmissions. The two nRF7002 dk boards alternated between being transmitters and receivers. The PPK2 recorded the power consumption that the development kits used to send/receive 10 MB of data at different transmission rates. Each test case was performed several times, and the average results were recorded.
# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
## 2 Materials and Methods
### SignFi Dataset  
The SignFi dataset contains the CSI data, which were extracted by the 802.11n CSI-Tool on the Intel WiFi Link 5300 device with three antennas. The dataset was collected through a transmitter with three external antennas and a receiver with one internal antenna. Figure 1 shows the measurement scenes of the lab and home environments. The 802.11n CSI-Tool provides CSI values of 30 sub-carriers, which were sampled approximately every 5 milliseconds. The duration of each gesture is about 1 s, so there were 200 CSI samples for each gesture. The CSI data was stored as a 3D matrix of complex values representing amplitude and phase information. The size of the 3D matrix is \(200\times 30\times 3\). The 3D amplitude and phase matrices are similar to digital images with spatial resolution of H \(\times\) W and C color channels. Thus, the CSI data can be regarded as images. The three color channels correspond to the three antenna signals.  
The SignFi dataset consisted of two parts. The first part included 276 gestures, a total of 8280 instances from the same user. Among them, 5520 instances and 2760 instances were obtained in the laboratory and home. Each gesture had 20 and 10 instances in the laboratory and home, respectively. The second part included 150 gestures with 7500 instances collected from five users in the laboratory, 50 instances of each gesture and 10 instances of each user. The dataset was further divided into four groups to train and  
Figure 1: Measurement scenes of the lab and home environments. (**a**) Lab environment. (**b**) Home environment.  
evaluate our method including Home276, Lab276, Lab+Home276, and Lab150 groups. The number of gestures were 276 and 150. Table 1 shows the statistics of the SignFi dataset.
# An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN
## 5 Simulation Experiment
### Simulation Environment Settings
#### 5.2.3 Migration Cost  
The cost of switch migration was compared, and the results are shown in Figures 7 and 8. The migration cost of UMS is higher than that of MOS and LBSNB because it only considers resource utilization as a single factor in selecting the target controller and does not consider the migration cost. Both MOS and LBSNB strategies can maintain a short migration time and migration cost. The experimental results show that LBSNB is slightly better than MOS in terms of migration time, indicating that the firefly algorithm in this strategy is superior to the MOS genetic algorithm in optimization time. This paper's strategy thus performs better in multi-objective optimization.
As shown in our previous study, it is possible to extract astaxanthin directly from these zoospores into a solvent. Thus, mechanical cell wall disruption, drying, and extraction with supercritical CO\({}_{2}\) could be replaced by a liquid-liquid extraction step, using a liquid-liquid chromatographic unit [26; 27]. Nevertheless, the cultivation conditions to convert cyst cells  
Figure 1: The cell cycle of _H. pluvialis_: (**a**) motile, flagellated cell, (**b**) aplanospore, (**c**) astaxanthin-accumulating aplanospore, (**d**) red cyst cell, (**e**) dividing cells (sporangium), (**f**) astaxanthin rich zoospores released from the sporangium, (**g**) aplanospore derived from astaxanthin containing zoospore. Scale bar: 10 \(\upmu\)m.  
into zoospores, keeping the astaxanthin content high within the zoospores after release, are not yet fully investigated [26; 28]. In previous studies, it could be determined that the nitrate concentration [25], the light intensity [29] and composition [30], and the age of the cyst cells [31] influence the germination, i.e., the proportion of released zoospores compared to non-germinated cysts and the astaxanthin content in the zoospores. In these studies, germination was performed at phototrophic conditions. It could be shown that at higher light intensities in comparison to lower ones, more zoospores were usually released from the cyst cells [25; 29]. However, this was also accompanied by a degradation of astaxanthin at higher compared to lower light intensities. In addition, a stronger decrease of astaxanthin in the zoospores released was observed at higher compared to lower nitrate concentrations in the medium at the same light intensity [25]. In contrast, after the initiation of germination, an increase in the astaxanthin concentration of 20% at 12 h and a decrease of 30% at 24 h has also been reported [28]. In addition, compared to red or white light, it has been shown that germination with blue light has a positive effect on the number of zoospores released and their astaxanthin content [30]. The aim of this work was to develop a germination method that initiates rapid germination of as many cysts as possible while maintaining a high astaxanthin content in the released zoospores for the subsequent downstream process. Therefore, the influence of the CO\({}_{2}\) content on photo- and mixotrophic germination was examined. In addition, the influence of an external glucose source in comparison to cultivation without glucose was studied for two heterotrophic germinated cultures. The influence of light on germination was discussed using mixotrophic and heterotrophic cultivation.
## 5 Conclusions  
In this study, we used the Framingham dataset to apply five types of machine-learning models (XG Boost, Random Forest, Logistic Regression, Ensemble Learning, and Ensemble Stacking) and compared their accuracy in predicting the risk of death from heart disease. The results showed that XG Boost had the highest prediction performance (AUC = 0.83). Furthermore, among the 15 parameters extracted from the dataset, it was confirmed that coronary artery disease (PREVCHD), glucose level (GLUCOSE), and diastolic blood pressure (DIABP) were important factors strongly associated with the risk of death. The choice between continuous monitoring and regular home monitoring considers the possibility of intervention in specific risk groups. The significance of this study lies in the fact that it integrates existing knowledge using machine learning to explore the possibility of its application to health management and other areas.  
**Author Contributions:** Conceptualization, E.Y.; methodology, E.Y. and I.K.; software, D.H.; validation, D.H.; formal analysis, D.H.; investigation, E.Y.; resources, D.H. and I.K.; data curation, E.Y.; writing--original draft preparation, E.Y.; writing--review and editing, E.Y.; visualization, D.H.; supervision, E.Y.; project administration, E.Y.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This study involves the analysis of open data. The open data does not contain personally identifiable information (such as addresses or names), and since it does not involve human subjects, ethical review board approval is not required.  
**Informed Consent Statement:** In this study, informed consent is not required as the research involves the analysis of open data that does not contain personally identifiable information. The data used in this study is anonymized and does not pertain to human subjects directly, thus making the informed consent process unnecessary.  
**Data Availability Statement:** The data used in this study is publicly available from Kaggle's Framingham Heart Study dataset. The dataset can be accessed at the following URL: [[https://www.kaggle.com/datasets/](https://www.kaggle.com/datasets/)]([https://www.kaggle.com/datasets/](https://www.kaggle.com/datasets/)) (accessed on 30 June 2025). The data is provided under the terms of the relevant license and can be freely accessed for research purposes.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Design of SocialCode
### The Flow of SocialCode  
Figure 5 illustrates the main flow diagram of SocialCode, which consists of two kinds of operations depending on whether the vehicular node can receive the wireless signal of the RSU or not. Assume the maximum period for the vehicular node to stay in contact with the nearby RSU is \(t\).  
1. Whenever a node has received a SLNC-coded content piece broadcast by the RSU during \(t\), it executes the first kind of SocialCode operations as follows. It will use its interest and selfishness value on this received piece to recognize itself as which type of vehicular node according to rules of Table 2.
2. If it identifies itself as the intermediate or irrelevant node, it will discard this received piece. On the other hand, it will store this piece in its queue if it is the destination or overhearing node. Afterward, it continues receiving the content piece broadcast by the RSU and goes back to step 1.
3. If the node does not stay in the wireless signal scope of any RSU at step 1, which means it has not received any content piece broadcast by any RSU for the period of \(t\), it will start the second kind of SocialCode operations to perform the SLNC-based CCD process between it and nodes also losing contacts with RSUs.
4. As soon as the node receives the SLNC-coded content piece, the beacon message or the ACK packet from any nearby intermediate or destination node during this process, it will invoke the CCD reception procedure to receive decode all SLNC-coded pieces of a generation.
5. If the node has received and decoded all content pieces of a data object, SocialCode has completed its flow. Otherwise, this node goes back to step 3 to repeat the SLNC-based CCD process.
6. Oppositely, if the node receives a fresh content piece broadcast by any RSU, it stops executing the SLNC-based CCD process and goes back to step 2, which recognizes itself as which type of vehicular node as described above.
7. If the node has not received a fresh content piece broadcast by an RSU and its backoff timer has been expired, this intermediate or destination node will invoke the CCD broadcast procedure to forward or re-broadcast content pieces stored in its queue. Afterward, this node goes back to step 3 to repeat the SLNC-based CCD process. Details of the CCD broadcast and reception procedures will be explained as follows.  
Figure 5: The main flow diagram of SocialCode.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 5 Bridge Digital Twin Modeling Using Multi-Source Heterogeneous Data
### Expert Knowledge Modeling and Integration  
Expert knowledge modeling plays a vital role in the DTM of bridges. It is not merely the formalization of rules from the expert knowledge base; more significantly, it integrates with spatial and temporal models to create a comprehensive evaluation system for more accurate and holistic assessments.  
The expert knowledge model establishes a rule engine, which is applied to real-time data and predictive trends derived from spatial and temporal models. Based on different input conditions, such \"if-then\" rules enable damage assessment and risk prediction. For example, \"If the crack width surpasses 5 mm, the structural integrity may be at risk\".  
The expert knowledge model also incorporates a data linkage engine based on the rule engine and expert experience. When data from the spatial and temporal models are integrated into the expert knowledge model, it combines expert experience with multi-source data around the anomaly points to assess the condition of the bridge. This allows for a more comprehensive evaluation of the bridge's state by considering anomalies in context with nearby data.  
The expert knowledge model integrates the rule engine and data linkage engine with expert knowledge to analyze and predict the types and locations of potential damage to the bridge. First, the rule engine detects potential anomaly points in the sensor data, triggering system alerts. Then, the data linkage engine further examines these anomalies by incorporating historical data, environmental data, and inputs from the spatial and temporal models to precisely locate the problem and predict damage development in that area.  
The expert knowledge model dynamically adjusts the rules according to expert knowledge in different environments. For example, in extreme temperatures or high-humidity conditions, the aging process of certain bridge materials may accelerate significantly. The expert knowledge model accounts for the influence of environmental factors and updates the rules to address new damage patterns or accelerated damage progression. The dynamic adjustment of rules based on expert knowledge enables the model to respond with greater flexibility and precision to real-time data.