esa-sceva/satcom-chunk-collection huggingface dataset:
# Extension of the Discrete Electron Transport Capabilities of the Geant4-DNA Toolkit to MeV Energies
## 3 Results and Discussion
### Range Simulations  
Another magnitude which depends solely upon the inelastic model (i.e., on the ionization and excitation cross sections) is the pathlength (see Equation (15)). Geant4 offers a ready-to-use example called _range_[17], which calculates (among other things) the average pathlength (or range) of the particle in a medium until its energy falls below a pre-determined cutoff value set by the user (called \"tracking cut\" in Geant4). Figure 9 presents simulation data of the electron range as a function of the (initial) electron energy with tracking cut set at 10 eV (i.e., \(T_{final}\) = 10 eV in Equation (15)) for DNA-Opt2, which extends up to 1 MeV [15; 17], the present model (DNA-Opt4X), which extends up to 10 MeV, and DNA-Opt6, which extends up to 256 keV [17; 45]. The statistical uncertainty of the data depicted is less than 1%. Range values from ICRU Report 90 are also depicted for comparison (59). The ICRU values are determined based on the continuous slowing down approximation (CSDA) whereby the range is defined as an integral over the reciprocal SP (see Equation (15)). In the bottom panel of Figure 9, the percentage difference in the simulated range data of each constructor from the ICRU values is presented, i.e., \(100\times\left(\frac{\text{R}_{\text{model}}-\text{R}_{\text{ICRU}}}{\text{R} _{\text{ICRU}}}\right)\). The comparison is limited to electron energies from 1 keV to 10 MeV where ICRU values are available. Note that the uncertainty of the range values given by ICRU increases rapidly below 10 keV. This is due to the limited accuracy of the Bethe SP formula (entering Equation (15)) for energies 1-10 keV (already discussed above) and, most importantly, to the crude (linear) extrapolation of SP from 1 keV down to 0 eV adopted by ICRU in the CSDA calculation via Equation (15). Thus, ICRU's range value below 10 keV should be considered only as qualitative.  
The results in Figure 9 indicate that, for energies from 10 keV to 10 MeV, where the ICRU values can be considered most accurate (assuming a similar uncertainty to the ICRU SP data, i.e., \(\sim\)1%), the present model (DNA-Opt4X) deviates from ICRU by less than 5% and, for energies up to 1 MeV by less than 2%. In contrast, the deviation of DNA-Opt2 from ICRU data is consistently higher (up to 10%) than the present model. The deviations of DNA-Opt6 from ICRU are within 1%. Thus, the present model (DNA-Opt4X) not only extends the application of Geant4-DNA to higher energies (up to 10 MeV) compared to DNA-Opt2 (upper limit 1 MeV) and DNA-Opt6 (upper limit 256 keV) but also offers much better agreement with the range data of the ICRU Report 90, as also observed for the SP data (Figure 6 of Section 3.2).  
Figure 8: The ratio of the electronic stopping power (SP) with the low-energy corrections (i.e., the Mott and/or the Coulomb corrections) to the SP without the corrections is calculated by the present model (DNA-Opt4X).
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 4 Firmware Remote Upgrade Design  
### Network Architecture of the Firmware Upgrade Protocol  
Figure 4 shows the network architecture of the remote upgrade system. The network architecture consists of three parts: the web server, remote upgrade configuration, and the DPU in the marine engine room simulator. The web server is a cloud-based, remote network server with a public IP address. Its main work is to transfer and store upgraded data files. The remote upgrade configuration is an application created in C# that is mostly used for sending upgrade instructions and receiving returned data [19].  
The DPU in the marine engine room simulator consists of a Flash memory and a communication module. Flash is mainly used to store program files and key informationr [20]. The communication module has a domestic TCP/IP protocol stack for communication with the web server [21].  
When the marine engine room simulator starts up, the DPU can connect to the web server via the communication module. It sends the agreed-upon commands to the web server, requests the program version, and receives information about the upgrade file. If a firmware upgrade is necessary, the device will set the upgrade flag \"UpgradeState\" to 1 and simultaneously switch the work area [22]. Then, the web server is requested to deliver the most recent version of the upgrade file. After a file is received, it is validated and written to the Flash runtime. At this moment, the remote upgrade is completed.  
Figure 3: 2D page of dual fuel engine generator in marine engine room simulator.
## 1 Introduction  
Precious metals are widely used in various industrial fields, including modern electronics, medicine and chemical catalysis [1; 2; 3; 4; 5; 6; 7]. The high value and depletion of precious metal reserves make the recovery of noble metals from the secondary resources highly desirable [8]. Waste electronic and electrical equipment (WEEE) and the spent catalysts are among the most important raw materials for precious metal recycling due to massive annual production and high element abundance. According to the statistics, the annual production of waste catalysts is about 500,000-700,000 tons [9; 10]. The rapid urbanization and change in our lifestyle have resulted in a large and still increasing number of WEEE. By 2021, about 52.2 million tons of WEEE will be generated [11; 12]. The recyclable noble metals in these waste materials are much more abundant than the natural ores, for example, the estimated concentration of gold in the WEEE is about 0.15 wt%.  
Currently, cyanide leaching is the major method to recycle noble metals from solid wastes, which possesses both simplicity and economic benefits. However, cyanide is extremely toxic to the human body and environment [5,13]. Therefore, new metal leaching systems based on environmental benign reagents, such as thiourea, thiosulphate, and KI/I\({}_{2}\) have been extensively studied [14, 15, 16, 17, 18]. However, problems still exist, such as inefficiency, high cost and the requirements of strict operation conditions have limited application of these systems in real circumstances.  
Ionic liquids (ILs) have attracted growing interest due to their unique physicochemical properties [19]. ILs are \"green\" solvents for extraction of precious metal cations and the common extraction mechanisms can be electrostatic interaction, cation exchange, and anion exchange [20, 21, 22]. In addition, ILs can form extended hydrogen bonding and have unique interactions both electronically and sterically with several precious metal species, which can stabilize the formation of several precious metal nanoparticles [23,24]. The aforementioned interactions made ionic liquids have the potential to become an ideal metal leaching agent.  
Moreover, trichloriosocyanuric acid (TCCA) is a readily available, stable, non-volatile, and inexpensive oxidant [25], which is a content in the majority of disinfectants for swimming pools, medical, and catering fields [26,27].  
Therefore, this study reports a novel, efficient, mild, low-toxic precious metals extraction system using a composite ionic liquid combining TCCA and tributyl monomethyl ammonium chloride (N\({}_{4441}\)Cl) together, for the leaching of precious metals from ore, spent catalyst, and WEEE. During the leaching process, TCCA acted as a precious metal oxidant, the oxidized metal ions were then captured by N\({}_{4441}\)Cl which coordinates well with the metal ions.
## 3 Results
### Assessing the Adequacy to the MTT Test  
To observe the performance of the spheroids on a MTT test, we evaluated the variation in cell density, incubation time, and formazan extraction time as technical factors affecting the assay. To assess if the incubation time would influence the results, we tested two times of incubation (2 and 4 h) with 20,000 cells spheroids. No significant difference can be seen between both times (\(p>0.05\)) (Figure 5A). With the aim to improve the extraction of formazan, different times were also tested. Figure 5B shows that the time of extraction is an important factor since the optical density (OD) of 1 h was greater than 10 min (\(p<0.05\)), but the results remained unsatisfactory, as readings remained rather low. There is no significant difference between the densities, and all of them are significantly smaller (\(p<0.05\)) than the 2D model in all tests (Figure 5C).  
We produced histological sections to analyze the interior of the spheroids after the time of exposure to tetrazolium and after the extraction step with DSMO for 1 h. Figure 5D shows that an incorporation of tetrazolium occurs, with its conversion into formazan up to the innermost layers of the spheroid. The section produced after the extraction step (5E) shows a decrease in the dye, but it is still present. Therefore, we can observe that there is not a reduction in cell metabolism, but a decrease in the ability of the dye to leave the cell aggregate during the extraction steps.  
Figure 5: (**A**) Optical density of the MTT assay for two different times of incubation (2 h and 4 h; (**B**)) Optical density for two different times of extraction (15 min and 1 h). Bars represent the mean \(\pm\) SD (n = 5) optical density at 540 nm; (**C**) Comparison of the optical density obtained by a MTT assay performed with different cell densities for the 3D model, and the seeding of 20,000 cells for the 2D model. Black bars indicate the results obtained with monolaminar cell culture of MC3T3-E1 cells form the same origin as those used in the aggregates. (**D**) 7 \(\upmu\)m section of spheroid with 20,000 cells after the incubation with Formazan for 2 h; (**E**) 7 \(\upmu\)m section of spheroid with 20,000 cells after the extraction with DMSO for 1 h. All experiments were performed with spheroids at the 4 th day after formation.
## 3 Results and Discussion
### Simulated Flue Gas Flow Rate  
The flue gas flow rate determines the flow velocity and residence time in the scrubbing tower. The effects of the simulated flue gas flow rate on the desulfurization and denitration efficiency levels are shown in Figure 11. In this process, the gas flow rate increases from 3 L/min to 9 L/min, the desulfurization efficiency gradually decreases from 100% to 98.4%, and the denitration efficiency decreases from 82.9% to 53.2%.  
Due to the absorbability and low concentration of SO\({}_{2}\), the flue gas flow rate has little effect on the SO\({}_{2}\) removal efficiency during the test. The influence of the flue gas flow rate on the denitration efficiency is more significant. This is mainly for two reasons:  
1. The increase in the flue gas flow reduces the gas-liquid mass transfer time of the flue gas in the scrubber, resulting in decreased mass transfer efficiency;
2. The increase in the flue gas flow rate decreases the specific energy density (SED, the energy obtained by unit volume of flue gas), the NO oxidation efficiency in the DBD reactor is reduced, and finally the denitration efficiency is significantly reduced.  
Figure 10: Effects of discharge power on NO, NO\({}_{2}\), and NO\({}_{x}\) concentrations and the oxidation degree of NO\({}_{x}\) at the outlet of the DBD reactor.
# Sustainable Operation of Active Distribution Networks
###### Abstract  
The present and future conditions in the energy market impose extremely high standards to the operation of building energy systems. Moreover, distribution networks face new operational and technical challenges as a result of the rapid penetration of renewable energy sources (RES) and other forms of distributed generation. Consequently, active distribution networks (ADNs) will play a crucial role in the exploitation of smart building prosumers, smart grids, and RES. In this paper, an optimization method for the sustainable operation of active distribution networks hosting smart residential building prosumers, plug-in electric vehicle (PEV) aggregators, and RES was developed. The thermal and electrical loads of the residential buildings were modeled in detail and an aggregation method was implemented to the hosted PEVs. Moreover, smart power dispatch techniques were applied at each building prosumer and PEV aggregator hosted by the active distribution network. Simultaneously, all the operational limitations of the active distribution network, building energy systems, and the hosted PEVs were satisfied. The constrained optimal power flow (OPF) algorithm was exploited to keep the voltages of the hosting distribution network between the permissible bounds. A significant operation cost reduction of 17% was achieved. The developed models were verified through detailed simulation results.  
active distribution network; sustainability; smart residential buildings; electric vehicles; renewable energy sources; optimization; power management; power dispatch Article
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Field Deployment  
The network was installed within the fabrication area of a manufacturing facility that produces heavy vehicles for construction and forestry. Work processes in the factory include welding, cutting,machining, grinding and abrasive blasting. The monitors in our network were deployed in the facility in a spatially optimized pattern to capture maximum spatial variability and reduce monitor redundancy [47]. While the network was deployed in stages, the complete network of 40 monitors was deployed for seven months. Here we provide examples of preliminary field measurements to display the type of data the sensor network can provide. Of particular importance are hazard maps, which display measured hazards throughout a facility or geographic area through maps [48; 49; 50; 51]. Presentation and analysis of the long-term temporospatial variability of hazards in the facility, sensor accuracy, and sensor precision are forthcoming [52]. Calibration curves developed in prior work were used to translate sensor signal in the field to concentration or sound pressure level [38; 40; 53].
# Methodology for the Assessment of Multi-Hazard Risk in Urban Homogeneous Zones
## 3 Results  
### Single-Hazard Risk Assessment of Homogenous Zones  
Single-hazard risk assessment is made for 14 homogeneous zones of the pilot site. Since the flood and extreme waves are affecting only a small coastal area, the seismic risk assessment will be presented here. The input data are calculated, or expert estimates are given for the following criteria:  
- Seismic hazard--PGA,  
- Buildings' seismic vulnerability,  
- Geology,  
- Communal infrastructure--electricity supply,  
- Communal infrastructure--water supply and drainage,  
- Road network,  
- Construction density (distance between buildings),  
Figure 9: The creation of homogenous zones: (**a**) four areas defined by specific urban characteristics; (**b**) four areas defined by main roads; (**c**) three areas defined by terrain (contours 5 and 10 m); (**d**) intersection of layers resulted with 15 homogenous zones.  
- Inhabitation density,
* - Importance factor (public, school, etc.),
* - Historical buildings.  
Since seismic hazard (PGA) and geology have the same values in each of the 14 homogeneous zones, they do not need be included in the numerical processing. The average seismic vulnerability has been calculated for each homogenous zone (Table 2).  
The criterion inhabitation density is generated from a digitized population census. For all other additional criteria a profound GIS analysis has been made, and criteria evaluations for each homogenous zone have been calculated or estimated (Figure 10).  
All the above-mentioned data have been collected into the decision matrix to be used by the PROMETHEE method with the help of Visual PROMETHEE software (Figure 11).  
The preliminary results of the PROMETHEE method are given in Figure 12, in which better rank represents higher risk. It means that the best-ranked homogenous zone HZ13 has the highest seismic risk in this case.  
Figure 11: Homogenous zones input data for PROMETHEE method (decision matrix).  
Figure 10: GIS analysis of additional criteria: (**a**) electricity supply; (**b**) water supply and drainage; (**c**) road network; (**d**) construction density; (**e**) importance factor (public, school, etc.); (**f**) historical buildings.  
Visual representation of the results has been made in GIS, where green represents low risk and red represents high risk (Figure 13).
## 4 Trends and Challenges
### Multidimensional
#### 4.1.3 Multi-Baseline
For example, deformation monitoring of the Muruntau Gold Mine in Uzbekistan plays a crucial role in assessing potential geological disasters and analyzing the mechanism of subsidence in mining areas, see Figure 23.  
Figure 16:0 PIESAT-1 satellite formation. Figure 16:1 DSM and DEM published by PIESAT Information Technology Co., Ltd. (**a**) Plain area data in Laos; (**b**) hilly area data in Laos; (**c**) mountainous and highland area data in Laos; (**d**) DEM of the central region of Bulgaria.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
###### Abstract  
Cloud Manufacturing enables the integration of geographically distributed manufacturing resources through advanced Cloud Computing and IoT technologies. This paradigm promotes the development of scalable and adaptable production systems. However, existing frameworks face challenges related to scalability, resource orchestration, and data security, particularly in rapidly evolving decentralized manufacturing settings. This study presents a novel nine-layer architecture designed specifically to address these issues. Central to this framework is the use of Apache Kafka for robust, high-throughput data ingestion, and Apache Spark Streaming to enhance real-time data processing. This framework is underpinned by a microservice-based architecture that ensures a high scalability and reduced latency. Experimental validation using sensor data from the UCI Machine Learning Repository demonstrated substantial improvements in processing efficiency and throughput compared with conventional frameworks. Key components, such as RabbitMQ, contribute to low-latency performance, whereas Kafka ensures data durability and supports real-time application. Additionally, the in-memory data processing of Spark Streaming enables rapid and dynamic data analysis, yielding actionable insights. The experimental results highlight the potential of the framework to enhance operational efficiency, resource utilization, and data security, offering a resilient solution suited to the demands of modern industrial applications. This study underscores the contribution of the framework to advancing Cloud Manufacturing by providing detailed insights into its performance, scalability, and applicability to contemporary manufacturing ecosystems.  
Cloud Manufacturing; innovative framework; layered architecture; system scalability; resource management; high-demand workloads +
Footnote †: journal: _Algorithms_
## 4 Integrated Design Methodology of \"Rood Cutting and Pressure Relief + Floor Grouting with Bolts\"
### Analysis of Results of Control Scheme  
#### 4.2.1 Analysis of Scheme O (Original Support Measures)  
This type of scheme analyzes and compares the stress distribution characteristics of the roof and floor, the force changes of the anchor ropes in the roof slabs with the amount of floor heave, and the amount of roof subsidence under the original support conditions in the field. Figure 17 shows the analysis results of the O-type scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **No.** & **Specific Parameters** \\ \hline Original support measures & O & — \\ (Comparison group) & A1 & 1.2 times \\ Floor grouting anchorage & A2 & 1.4 times \\ strength & A3 & 1.6 times \\ \hline  & B1 & \(0^{\circ}\) + 30 m \\ Roof cutting for & B2 & \(10^{\circ}\) + 30 m \\ pressure relief & B3 & \(15^{\circ}\) + 30 m \\  & B4 & \(20^{\circ}\) + 30 m \\  & B5 & \(15^{\circ}\) + 20 m \\ \hline  & C1 & Two bolts, roof cutting \(15^{\circ}\) + 20 m \\ Integrated control measures & C2 & Two bolts, roof cutting \(15^{\circ}\) + 30 m \\  & C3 & Five bolts, roof cutting \(15^{\circ}\) + 30 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed parameter for different control schemes.  
Figure 17: Analysis of results for scheme O: (**a**) stress distribution characteristics of roof and floor roadway; (**b**) force diagram of roof anchor cable support; (**c**) change curve of floor heave and roof subsidence; and (**d**) comparison results of roof and floor roadway parameters under original measure conditions.  
(1) Characterization of roof and floor stress distribution: Under the original conditions of the site, the difference in roof and floor stress is maintained within the range of 1.0-1.4 MPa, and the stress difference reaches the maximum of 1.4 MPa at a distance of 5 m from the roadway surface. The roof stress is 10.4 MPa and the floor stress is 9.4 MPa at a distance of 14 m from the roadway surface.  
(2) Analysis of roof anchor cable support force: Under the condition of original measures on site, the force of the roof anchor bolt and anchor cable in the range of about 200 m from the lagging working face is 44 kN and 86 kN, respectively, and the force of the anchor cable is 42 kN larger than that of the anchor bolt.  
(3) Analysis of the change in floor heave and roof subsidence: Under the conditions of original measures in the field, within the range of 50 m-225 m of the lagging face, both show a rapid growth trend, while within the range of 225 m-250 m of the lagging face, both tend to be stabilized. In the range of 250 m of the lagging face, the maximum amount of floor heave is 480 mm and the amount of roof subsidence is 181 mm, which is a difference of 299 mm.  
(4) Comparison results for roof and floor parameters under the original measures: When the difference in roof and floor stresses is close, the roof anchor bolts and anchor cables provide effective support force and control the deformation of the roof, which is manifested in the fact that the amount of floor heave is larger than the amount of roof sinking by 299 mm.
## 1 Introduction
The goal of this paper is to achieve the maximum communication coverage maintaining their connectivity with the limited number of underwater sensor nodes, especially in shallow water areas. The paper is organized as follows. Section 2 provides the descriptions of four sub-algorithms for optimal deployment of the sensor nodes: modeling of underwater acoustic channel impulse response; estimation of the communication performance; construction of the communication PS, and; optimal deployment of the sensor nodes. Section 3 presents the results of the optimal deployment positions for sensor nodes simulated for winter and summer seasons at a targeted area located off the east coast of Korea. Finally, Section 4 provides a summary and conclusion.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 2 Background
### Use Case and Communication Model  
The use case of the proposed method is an ad hoc network of IoT/sensor nodes monitoring, measuring, and collecting environmental parameters. The communication model assumes that all nodes keep sending data periodically to a single node, called a _sink_. This is a typical case for wireless sensor networks that represent a special class of ad hocnetworks [18]. The real-world applications of such networks include monitoring of air pollution, destruction phenomena, agricultural monitoring, and more [19]. The common property of all these use cases is that there is always one dedicated node collecting data from other nodes that act as sensors. These sensors cannot communicate directly with the sink, they need to use intermediate nodes to deliver their messages to the sink. Each route from a node to the sink is traversed periodically, so often that it does not expire.  
This communication model was used for performing an experimental evaluation of our solution, but the solution is not limited to this model. Other models of ad hoc communication are suitable for its application.