esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project Overview
### Parameters of Tunneling  
The topography of the project area is low in the north and high in the south, with the lowest elevation of 204.01 m and the highest elevation of 221.50 m. The subway tunnel is arranged in the form of parallel twin tubes, and the shape of the tubes is circular. The length of the tunnel is 1213.25 m, the outer diameter of the tubes is 6.0 m, and the axial spacing of twin tubes ranges from 14.3 m to 17.0 m. The maximum longitudinal slope of the tunnel is 25%, and the buried depth of the tunnel ranges from 14.6 m to 22.5 m. According to the stratum conditions of the project area, an earth pressure balance (EPB) shield machine with a minimum turning radius of 250.0 m and a maximum slope of 5% was employed for tunneling in the project. The total length of the shield machine is 80.0 m, of which the length of the main machine is 9.0 m. The shield machine adopts the middle supported composite-steel cutter head made of Q345 + Q690, and the excavation diameter is 6280.0 mm. The double-edge hob, single-edge hob, tearing cutter, and soft rock cutter can be replaced to meet the needs of different geological conditions. The main drive system adopts an inner and outer double lip seal and a frequency conversion motor drive. The total length of the screw conveyor is 12.0 m, which is driven by hydraulic pressure. The belt conveyor is electrically driven and the conveying speed is 2.2 m/s. Synchronous grouting adopts single slurry grouting, and the injection pipeline type is a segmented detachable pipeline. A centrifugal pump cooling system is adopted; the flow rate of the pump is 900.0 L/min, and the pump lift is 70.0 m. The liquid capacity of the foam system is 750.0 L, and the foam is injected by a single-screw pump. The circular lining is composed of six prefabricated reinforced concrete segments assembled in a staggered manner, with a lining width of 1200.0 mm and a thickness of 300.0 mm. Table 2 lists the design parameters for tunnels, Table 3 presents the parameters of the shield machine, and Table 4 gives the initial parameters of shield tunneling. Figure 1: Location of this project. Figure 2: Geological longitudinal profile of the south section. The location circled by the blue dotted line is the area studied in this article.
## 3 Industrial Applications
### Oil and Gas  
The world economic forum has estimated that AM could eventually save costs of approximately AU $44 billion (US $30 billion) in the oil and gas industry [98]. This is a significant potential in the application of AM technologies across the upstream and midstream oil and gas value chain. Another benefit of metal AM in the Oil and Gas industry is reducing lead times and consequently improving the supply chain efficiency [99]. However, this industry has been slow in embracing metal AM for manufacturing end-use products [100], there are some successful applications. For example, the AM gateway manifold of Titanium for pipelines was designed by Safer Plug Company (SPC) and printed by the AM production company 3T RPD using a PBF system. Lloyd's [101] claims that the production of this part with conventional manufacturing systems is nearly impossible due to complex internal channels. Today, GE uses the ability of metal AM techniques to manufacture control valve parts with complex shapes and configurations such as hollow structures, curved shapes, meshes, etc. for use across different applications in the energy sector [102].  
Siemens is producing a number of metal AM products for the oil and gas industry such as nickel alloy gas turbine blades, fuel nozzles, gas turbine burners, heat shields impellers, and swirlers. One of the successful examples of metal AM application at Siemens is the production of an advanced burner swirl for the SGT-750 gas turbine. For this component, metal AM enabled manufacturing of a swirl shape. Another effective application of metal AM technology is manufacturing a water pump impeller of the fire protection system at Slovenia power station [98]. Siemens also designed metal additively manufactured sealing rings of SST-300 steam turbines, which were used in India in 2018. Figure 9 shows some of these examples [103].  
Figure 8: Lightweight AM antenna [96].  
It can be seen that the oil and gas industry continues to drive the industrialisation of metal AM products. [PERSON] and [PERSON] (2012) believed that AM also has the potential to manufacture lightweight offshore facilities such as separators, pumps, and deck structures through topology optimisation and use of lattice/cellular structures. It should be highlighted that application of metal AM in the oil and gas industry requires feasibility analysis, strategic planning, and careful examination to avoid excess costs and enjoy AM benefits such as lead-time reduction, enhanced design complexity, improved functionality, and increased supply chain efficiency.
## 6 Results and Discussion
### Embankment B  
#### 6.2.1 Settlement Predictions  
Settlement-time curves are given in Figures 14 and 15 for settlement plates at the surface--point A at the center of the embankment and point B 15 m from the center of the embankment, respectively.  
Figure 14: Comparison of settlement-time curves for Embankment B—Point A.  
Figure 12: Comparison of excess pore pressure-time curves for Embankment A—Point \(P_{2}\) (5.0 m below the surface).  
Figure 13: Comparison of excess pore pressure-time curves for Embankment A—Point \(P_{3}\) (8.3 m below the surface).  
The discussion and considerations that apply to Embankment A are applicable to Embankment B. The MCC model highly underpredicts the settlements (60% compared to the measured), while the HASP soil model gives a good prediction for point A and slightly overpredicts for point B. For Embankment B, differences in the strain magnitude are more pronounced for the two models than for Embankment A. An explanation can be found in Figure 16, which illustrates the effective stress paths. The stress path for the MCC model remains in the elastic zone during the entire loading process, which represents one of the main drawbacks of the MCC model for use in analysis with overconsolidated clays. On the other hand, the HASP model predicts the development of plastic deformations from the very beginning of the loading, and its predictions are consistent with the field data.
## 3 Polymerization Methods for IIP Synthesis
The polymerization reaction begins with an increase in temperature or incidence of UV radiation in a nitrogen atmosphere. * The template can be easily removed due to it is only on the surface of the material, making the cavities selective and more accessible to metal ion adsorption. * In this strategy, polymeric support with spherical pores is used as the base of the IIP. The template, bifunctional monomer, crosslinking agent, and initiator are dissolved in the porogenic solvent and added to the polymeric support until it fills its pores. * The polymerization reaction begins with an increase in temperature or incidence of UV radiation in a nitrogen atmosphere. * After polymerization, the microspheres are suspended, separated, and washed. * Grat polymerization has advantages such as the possibility of producing materials with a two-dimensionally organized monolayer structure due to the immobilization of the monomer, as well as three-dimensionally through the imprinting step [71]
* The distribution of binding sites is considerably better compared to bulk polymerization. \begin{table}
\begin{tabular}{c c c} \hline \hline
** Polymerization Methods** & **General Features** & **Examples of Polymeric Particle Morphology** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._In the synthesis of IIP by the sol-gel process, initially, the reaction occurs between the metal ion and the selective group of the functional precursor. The precursor (organosilica) and the cross-linking agent (tetraethoxysilane-TEOS) are initially hydrolyzed to form a colloidal solution (sol) of very small particles (1-100 nm), which then interconnect (polycondensation) to form a rigid porous network (gel) [81]. * The most common precursors for this procedure, which act as a cross-linking agent, are 3-aminopropyltriethoxysilane (APTES), phenyltrimethoxysilane (PTMS), tetramethylissilane (TMOS), and tetreatethoxysilane (TEOS), and are responsible for the polymeric network formation. * Acids and bases can be used as catalysts for the synthesis. * The presence of surfactants allows controlling the size of the selective cavity, promoting the access of the metal ion due to the fast mass transfer. This type of procedure allows obtaining polymers with double imprinting; therefore, the formation of the selective cavity depends on the metal ion-functional precursor interaction and the surfactant. * Emulsion polymerization is heterogeneous and allows obtaining particles with sizes in the order of micrometers. In this polymerization, the organic phase (template, monomer, and cross-linking agent) is dispersed in water (polar phase). For this, a surfactant such as sodium dodecyl sulfate is used, which promotes the formation of micelles, where the radical initiator will cause polymerization. * The advantage of this polymerization strategy is that it does not require the use of organic solvents. * Similar to suspension polymerization, water can interfere with the imprinting process.
## 6 Seismic Performance of the Case Study
### DL Limit State  
Figure 16 illustrates the inter-storey drift ratios for different seismic analysis methods applied to the structure, including time-history (TH) analysis with bidirectional ground motions (XY and YX components), response spectrum analysis (RSA) with behavioural factors (\(q\) = 1.5 and \(q\) = 3.0), and pushover analysis (uniform and triangular loading). The vertical black line at 0.5% forms the Damage Limitation (DL) threshold, which serves as a benchmark for the seismic drift performance evaluation of the building. In pushover analysis, the target displacement corresponding to each limit state will be worked out by finding the intersection between the pushover curve and the demand spectrum. Based on this target displacement, it will be possible to estimate the other demand parameters such as drift ratios and capacities for the corresponding performance criteria. In the X direction plots (top row of Figure 16), TH analysis results show higher drift demands near the DL threshold, which are particularly noticeable in floor 2 at the top side of the building, mass centre, and bottom nodes. In the X direction, RSA analysis with both \(q\) = 1.5 and \(q\) = 3.0 exhibits smaller drift ratios than TH analysis, staying well below the DL threshold. This indicates that in this case, RSA is more conservative than TH. On the contrary, pushover analysis against both uniform and triangular loading patterns presents the drift demands concentrated at floor 2, which are close to but not exceeding the DL threshold. This suggests that the updated loading patterns in pushover analysis better estimate the real structural behaviour for seismic loading in the X direction compared to the RSA analysis.  
In the Y direction plots (bottom row of Figure 16), the TH analysis again produces higher drift demands than RSA, particularly at the mass centre and right side of floor 2. The pushover analysis, especially with triangular loading, closely mirrors the drift distribution patterns observed in TH analysis, albeit with slightly lower values. Both the RSA and pushover analyses show that the maximum drifts remain below the DL  
Figure 16: Inter-storey drift distributions at the DL limit state for various analysis methods (TH, response spectrum, and pushover).  
threshold, demonstrating conservative design estimations in the Y direction. Overall, these results indicate that while TH analysis provides a dynamic and realistic assessment of drift demands, the pushover approach with uniform and triangular loading patterns captures critical drift distributions effectively, providing a conservative yet reliable alternative for estimating drift demands at the DL limit.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 4 Steam Leak Detection  
In this section, we introduce the actual heat-powered [15] device designed for detecting steam leaks (Figure 4). It features a highly efficient thermoelectric generator (TEG) coupled with a DC/DC up-converter and an edge-computing, battery-free device. The system incorporates a LoRaWAN wireless protocol chipset for communication. It also includes temperature detection via \(\Delta\)T (temperature differential) and ultrasound for comprehensive leak detection. The device connects to cloud computing or SCADA systems through a LoRaWAN gateway for data transmission/analysis.  
In detail, Figure 5 shows the stand-alone heat-powered IIOT device composed by:  
1. Edge-computing IIoT, using LoRaWAN with three sensor interfaces (Section 4.2).
2. TEG device with a housing and radiator, which converts heat into electricity (Section 4.1).  
Figure 4: Proposed scenario from the heat-powered IoT device to the cloud platform.
## 3 Route Determination for Ship Passage Using Capability Plots
### Ship Route Without Using the Algorithm  
From the navigational scenario shown in Figure 5, it can be inferred that the distance between the current position of the ship and the anchoring point is 2.4 NM (approximately 4.5 km). Over such a short distance, manual control with a constant course can be used to reach the endpoint, as illustrated in Figure 11.  
To determine the impact of environmental forces on a ship moving on a course of 35\({}^{\circ}\) over a distance of 4500 m, a simple coefficient was introduced to enable later comparison with the route determined by the algorithm. The coefficient is expressed as follows:  
\[T=\sum_{i=1}^{n}d_{i}\cdot k_{i} \tag{28}\]  
where \(T\)--route cost function [-]; \(n\)--number of transitions between waypoints [-]; \(i\)--current transition between waypoints [-]; \(d\)--distance between waypoints [m]; \(k\)--cost of transition between waypoints [-]  
In Equation (28), the cost of transition between waypoints is read from the table shown in Figure 6.  
For the navigational scenario depicted in Figure 11, after substituting the distance values between points and the transition costs from Figure 7 (taking into account wind direction and the ship's course), the route cost function yielded a value of 846.  
The energy consumption required to navigate the route shown in Figure 11 can be determined from the thruster data (Table 2) and the ship information (Table 1). The total energy consumption is given in Table 3.  
In the table above:  
* Phase--current route segment.
* S--distance between points.
* V--speed of the ship.
* t--the duration of the navigation of the given segment.
* Course--ship's course.
* True wind angle--angle of wind impact on the vessel.
* Env. Force vector--environmental forces affecting the vessel over a given distance.
* Sum of thrust--thrust generated by propellers.
* Fuel consumption--fuel consumption per route section.
* Energy consumption--energy consumption per route section.
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 3 Theoretical Foundations  
### Game Theory  
Game theory is a mathematical framework for studying interactions between decision-makers, focusing on formulating strategies to achieve optimal outcomes. In game theory, decision-makers are referred to as players, who choose their actions based on the behavior of their opponents, leading to specific outcomes or payoffs. The primary goal of game theory is to analyze and predict players' behaviors under various scenarios and identify optimal decision strategies.  
Stochastic games, a model within game theory, are often represented as a tuple \(\mathcal{M}_{\text{SG}}=\{I,S,A^{i},O,obs,\delta,R,b_{0}\}\) to describe interactions among multiple participants where the outcomes of actions involve randomness. In these games, each participant faces multiple possible actions at every step, with results influenced by probability distributions. The randomness can arise from uncertain environments, random events, or the actions of other participants. Unlike deterministic games, stochastic games are closer to real-world scenarios, as they allow the modeling of uncertainty and risk [31]. Analyzing stochastic games often involves probabilistic and statistical methods to determine optimal strategies and potential outcomes for participants.  
Two important subtypes of games in game theory are incomplete information games and imperfect information games. In incomplete information games, players lack full knowledge about certain elements of the game, such as participant types, preferences, payoff functions, or strategy spaces. In imperfect information games, players do not have full visibility of all the previous actions or decisions made by others, meaning some historical information is unavailable.  
A Partially Observable Stochastic Game (POSG) is an advanced model in game theory combining features of incomplete and imperfect information games with elementsof stochastic dynamic systems. It describes interactions among multiple agents in dynamic, uncertain environments [32]. In POSGs, players have partial observations of the environment or other participants, and the environment itself evolves dynamically with randomness. POSGs are widely applied in fields such as robotics, autonomous driving, and military decision-making, where complex decision-making is required in dynamic and uncertain settings. These characteristics make POSGs highly relevant for modeling intricate human-machine interactions and competition or cooperation among intelligent systems.
# Features Engineering to Differentiate between Malware and Legitimate Software
## 4 API Call Analysis  
The public data set in [42], consists of 43,876 API call sequences divided into two classes--malware, with 42,797 samples, and legitimate, with 1079 samples. The data set was produced after running Portable Executable (PE) files through Cuckoo Sandbox. After obtaining the dynamic analysis report, the API call sequences were obtained and processed in order to transfer the API calls into ordered values. Additionally, the sequences were only taken from the parent process, and the first 100 non-consecutive repeated API calls were taken into consideration.  
To extract certain crucial statistics for initial comprehension of the data set, Python programs were built. The data set contained 264 distinct API calls in total. The distribution of these API calls between malware and goodware reveals the correlation between the two classes. Table 1 shows that 252 API calls were shared in the malware class. Although the legitimate class had a small number of samples compared to the malware class, 200 API calls were observed in this class. This shows that the majority of API calls present in the data set were shared by both classes. Figure 1 depicts the name of unique API calls found in the legitimate class with their number of occurrences, while Figure 2 shows the 64 unique API calls that exist in the malware class.  
Examining the most frequently used API calls of each class may uncover the thinking trend of malware creators. Figure 3 shows the top 30 most used API calls for each class. The interesting observation about Figure 3 is that both classes share 23 API calls. Table 2 lists the unique API calls for each class.  
The statistical analysis above shows that the majority of the 264 API calls were shared between malware and legitimate samples, making it difficult to separate classes. In order to uncover any potential hidden patterns, additional statistics and visualisations are necessary.  
\begin{table}
\begin{tabular}{c c} \hline
**Class** & **Name of API Calls** \\ \hline \multirow{3}{*}{Malware} & LoadStringW, GetSystemInfo, NtDelayExecution, \\  & NtProtectVirtualMemory, DrawTextExW, RegQueryInfoKeyW, \\  & SetUnhandledExceptionFilter \\ \multirow{2}{*}{Legitimate} & GetFileType, NtOpenSection, GetFileAttributesExW, FindFirstFileExW, \\  & GetFileAttributesW, NtCreateSection, RegEnumKeyW \\ \hline \end{tabular}
\end{table}
Table 2: The unique API calls in each class (out of 30).  
Figure 3: The top 30 API requests that are used the most: (**a**) Malware class; (**b**) legitimate class.  
Figure 2: The unique API calls of malware class.
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 3 Materials and Methods
### Method
#### 3.2.2 Selection of Criteria and Adoption of Rules for Model Assessment  
Based on the literature review, the proposed solution is to adopt the following criteria for model assessment:  
1. Boundary conditions are met. A model representing the full range of traffic conditions should meet the following conditions: BC1: \(\mathrm{v}\to\mathrm{v}_{\mathrm{sw}}\) when \(\mathrm{k}\to 0\) and \(\mathrm{q}\to 0\) BC2: \(\mathrm{v}\to 0\) when \(\mathrm{k}\to\mathrm{k}_{\mathrm{max}}\) and exclusively in the case of two-regime models: BC3: \(\mathrm{v}_{\mathrm{sw}_{1}}=\mathrm{v}_{\mathrm{sw2}}\), \(\mathrm{v}_{\mathrm{opt}_{1}}=\mathrm{v}_{\mathrm{opt}_{2}}\), \(\mathrm{k}_{\mathrm{opt}_{1}}=\mathrm{k}_{\mathrm{opt}_{2}}\),\(\mathrm{q}_{\mathrm{max}_{1}}=\mathrm{q}_{\mathrm{max}_{2}}\), \(\mathrm{k}_{\mathrm{max}_{1}}=\mathrm{k}_{\mathrm{max}_{2}}\) The third boundary condition applies to identical values of boundary parameters in free-flow and congested traffic (input boundary parameters from the free-flow model can be input into the congested traffic model as constant values). Please note that BC3 excludes non-continuous models (with BC3, it is possible to compare single and two-regime models).
2. Simplicity, which is measured with the number of parameters and equations in the model. It was assumed that the fewer the equations and parameters, the simpler, more practical, and more adaptable the model will be for the given road and traffic conditions.
3. Empirical accuracy, which is measured with absolute and relative measures of goodness of fit, the RMSE, and MAPE, which are classical accuracy measures. The RMSE is expressed with units of a dependent variable, which means that it will be possible to establish the average difference in km/h between real and estimated values of speed. The MAPE expresses the average percentage difference between a real and estimated value.
4. Values of boundary parameters are assessed mainly by comparing estimated boundary parameters with expected ranges as determined. If the value of an estimated parameter fits in within the expected range, we can say that the model is a good estimator of the particular parameter.