esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Model Overview  
Concurrent design of satellite size and orbit configuration needs several design variables; without enough number of them, many aspects of the tradespace cannot be considered. On the other hand, considering too many variables would increase the tradespace beyond our computing power and slow down the optimization process. To achieve a balance between a coarse model and a fine model in fast prototyping, this study uses the following five data types: Design variables, parameters, internal variables, constraints, and objectives which will be explained in this subsection.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 4 Experimental Double Pull-Out Test on G40 Concrete and UHPFRC
### Experimental Test Result Analysis  
#### 4.2.1 Mechanical Test Result  
The evaluation of concrete compressive strength is crucial for ensuring the structural integrity of construction materials. The global standard BS EN 12390-3:2002 [26] emphasises the rigorous methodologies employed in this assessment. Adhering to precise specimen dimensions, calibrating compressive testing machines with specified failure load parameters (20% to 80% of full scale), and maintaining a stringent \(\pm\)1% relative error tolerance are critical aspects of the compressive strength testing process [27; 28]. The compressive mechanical test was conducted using a MATEST (MTS) machine. The compressive stress trend up to the first crack was observed for all the specimens and continued with full failure. According to BS EN 12390-3 \"Typical Failure Mode of Test Specimen\", G40 concrete exhibited a semi-explosive failure mode (Figure 11a), while UHPFRC displayed a more robust, non-explosive failure mode (Figure 11b). This characteristic is more desirable for structural applications [26]. Ultra-High-Performance Fibre-Reinforced Concrete (UHPFRC) demonstrates significantly greater strength than conventional concrete. It undergoes a rapid evolution of mechanical properties over time, achieving high early-age strength and stiffness, with a short-term hydration regime that is nearly complete within 90 days [20]. Experimental tests on UHPFRC have revealed substantial improvements in both compressive and flexural strengths [29]. The mechanical test results for G40 concrete and UHPFRC are presented in Figure 12. UHPFRC specimens were found to be more elastic, with 90-95 percent of the compressive strengths remaining elastic after strain hardening up to the maximum peak strength. This behaviour, however, is not consistently observed in all UHPFRC specimens and is related to the interaction between the steel fibres and the binder matrix. The ultimate outcome is ductile failure, and the concrete surfaces remain smooth even after strength loss.
## 2 System and Process Analysis
### Concrete Printing Process  
To describe and analyze the concrete printing process, a process map is developed where tasks, actors, and information exchange requirements are identified along the digital workflow for concrete printing. Synergies with the standardized requirements and process chain described in DIN SPEC 17071:2019-12 [30] and with data models developed for metal-based additive manufacturing [22], as well as the results of the survey, are used as the basis for developing the process map. Due to the material-process interactions within concrete printing, material-related tasks are considered part of the concrete printing process.  
Following the business process modeling notation [31], the process map is defined as focusing on the design and planning of concrete printing projects, as shown in Figure 2 in terms of an activity diagram. The actors in the activity diagram, defined as designer, engineer, material scientist, and machine operator, develop specific tasks or subprocesses and exchange information, following a sequence that translates digital models into printed components. The concrete printing process starts with design concepts and design specifications to generate geometric models and to specify the printing systems (i.e., machines) required to execute the concrete printing project. On the one hand, machine settings are initially defined and used to design the printing material (i.e., concrete) in an iterative process, which may include material testing, until the design specifications are satisfied and material specifications are derived. On the other hand, with respect to designing the geometry, the geometric models are sliced into layers considering machine specifications (e.g., nozzle sizes). For each layer, toolpaths are planned, and process parameters are assigned to the toolpath profiles according to the process data and the material specifications. Within toolpath planning, simulations are carried out to evaluate the manufacturing process, including the material behavior, to ensure a successful build. As an output of the toolpath planning, AM models are created and, if accepted, used as the basis to generate machine instructions (e.g., CNC code).  
Figure 1: System elements of a robot concrete printer.
## 3 Methods  
### System Architecture: Positioning and Orientation  
To better understand how these 3D positioning and orientation systems can be integrated and implemented for the purpose of this study, a system architecture was proposed. As shown in Figure 9, for the positioning estimations, the tag communicates with four anchors (i.e., reference points) through ultra-wideband RF signals. For orientation estimations, there are three sensors, acceleration, magnetic, and angular velocity, that can work together to estimate the tag orientation. The tag needs to be connected with a computing device such as a tablet to transfer the received data for analyzing and displaying to users. Using this information, the user can monitor the position and orientation of the tag. The first challenge is how can the tag be used for navigating the camera lens? The second challenge is how can data generated from the tag be displayed through a user interface for the purpose of monitoring the camera's position and orientation? To meet these challenges, a prototype was developed.  
Figure 9: Positioning and orientation system architecture.
## 3 Results and Discussion
### Prediction of Economic and Environmental Benefits  
As mentioned above, employing supplementary cementitious materials to replace or partially replace cement clinker is not only a method for saving energy, reducing emissions, and achieving sustainable development in the cement industry but also the primary choice for addressing the disposal of industrial solid waste. According to these objectives, the ratio of the CS-EMR-GBFS-based cementitious material was designed, and the CO2 emission and cost loss were predicted and analyzed. The energy consumption and electric carbon emissions generated by mechanical grinding were estimated at 40 kWh/t and 0.997 kg/kWh, respectively; the grinding time was 40 min, and the electricity cost was 0.6 RMB/kWh. The results show that the CS-EMR-GBFS-based composite cementitious material can effectively reduce costs and carbon emissions (Table 5 and Figure 11). The strength of the optimal group, EGC11, was higher than that of the cement group, and the cost and carbon emission of the cement group were only 88.70% and 72.44%, respectively. The cost price of the EMR raw material was determined internally by the research group, while the cost prices of other raw materials were determined according to local prices. Additionally, the information about carbon emissions was drawn from previous studies [32; 38].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cost (RMB/t)** & \
\begin{tabular}{c} **Carbon Emission** \\ **(kg/kg)** \\ \end{tabular} &
\begin{tabular}{c} **Compressive Strength** \\ **at 28 Days (MPa)** \\ \end{tabular} \\ \hline EMR & 20 & 0.007 & - \\ CS & 60 & 0.067 & - \\ GBFS & 300 & 0.083 & - \\ Cement & 400 & 0.8 & 42.5 \\ EGC0 & 370 & 0.5849 & 42.9 \\ EGC7 & 362 & 0.5843 & 39.2 \\ EGC8 & 361.2 & 0.5831 & 40.1 \\ EGC9 & 360.4 & 0.5819 & 43.2 \\ EGC10 & 359.6 & 0.5807 & 44.8 \\ EGC11 & 358.8 & 0.5795 & 45.5 \\ EGC12 & 358.0 & 0.5783 & 40.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation of economic cost and carbon emission of multicomponent solid waste cementitious materials.
# Recent Advances in Ultrafast Structural Techniques
## 4 Conclusions  
The future of fs-X-ray and fs-electron sources is very bright with several XFEL facilities readily available for use and a relatively small but quickly growing community of FED developers. There are other promising techniques for the generation of ultrashort electron pulses, which have not been discussed here. For instance, laser plasma electron accelerators [267; 268; 269; 270; 271; 272; 273] are quickly improving  
Figure 23: **Ultrafast low-energy electron diffraction (ULEED) setup and high-resolution diffraction pattern from 1T-TaS\({}_{2}\).****(a)** Schematic of the experimental setup for ULEED. Inset: electron micrograph of an electrochemically etched tungsten tip used as a photoemitter. (**b**) mm-sized laser-driven electron gun. Inset: tungsten tip, visible through the hole for laser illumination. (**c**) Electron pulse durations of the mm-sized electron gun (16.4 ps at 100 eV) and the \(\upmu\)m-sized electron gun (1.4 ps at 50 eV). (**d**) \(\upmu\)m-sized electron gun prepared by nanofabrication. (**e**) LEED pattern of the near commensurate CDW room-temperature phase, recorded with pulsed 100-eV electrons from the mm-sized electron gun (logarithmic colour scale). A retarding voltage of \(-\)20 V is applied at the detector front plate. (**f**) Line profile of the (1 \(\overline{1}\)) diffraction peak, illustrating high transversal coherence of the source. The fitted spot width of 0.03 Å\({}^{-1}\) (fwhm) corresponds to a transfer width of 21 nm. (**g**) Close-up of region marked in e, showing second-order CDW diffraction spots (**h**), Line profile of CDW diffraction spots shown in g, fitted with Lorentzian peak profiles. Figure adapted from Reference [262] with permission from Springer Nature.  
through the reduction of the large energy spread that has limited their application as FED sources. Another interesting candidate is the cold-atom electron source, [202; 274; 275; 276; 277; 278; 279; 280; 276; 277; 278; 279; 281] which has recently been proposed as an alternative to photoinjectors owing to the large reduction in electron beam emittance that should considerably improve the X-ray beam properties. Moreover, important advances have been made in the development and implementation of dynamic and ultrafast electron microscopes [3; 282; 283; 284; 285; 286; 287; 288; 289]. We are already transiting the era of atomically resolved movies, with more advanced experiments and exciting discoveries soon to come.  
G.S. would like to acknowledge support from the Canada Research Chair program, the National Science and Engineering Research Council of Canada, Canada Foundation for Innovation, and the Government of Ontario. G.S. is thankful for the support provided by WIN (through the Interdisciplinary Research Program Funds) and Transformative Quantum Technologies (via the Quantum Quest Seed Fund program).  
GS would like to thank Kostyantyn Pichugin and Tyler Lott (UeIL) for their very useful suggestions.  
The author declares no conflict of interest.
## 5 Case Study Analysis
### Calibration of Results  
This section will focus on the accuracy of individual passenger path selection. The travel habits of the original passengers changed significantly after the Line 543 adjustment, and there was no travel on July 20. For this reason, only Line 543 passengers who had the same travel requirements before and after the adjustment were sampled for calibration inspection, and their travel changes were analyzed; 395 trips were recorded, accounting for 17% of the total passenger flow for this line. Of these trips, 354 (direct, transfer) were correctly judged, with an accuracy rate of 89.6%. The two main sources of errors were in the  
Figure 9: Route map of bus line 543 and associated lines in Guangzhou.  
OD inference process and in what different passengers deemed to be acceptable walking distances.  
The passenger flow transfers between Line 543 and other lines were also examined. Passengers mainly transfer to five other routes, notably Line 238 and 539, with more than 150 transfer passengers recorded daily. Table 3 presents a comparison of line passenger flow for both pre-evaluation and actual scenarios (20 July 2023). The results indicate a close match, with a relative difference of less than 10%. Due to the inherent randomness of travel behavior, the actual passenger groups for these lines may vary before and after the adjustment; thus, the relative difference should be viewed as a numerical reference rather than a definitive error. This analysis suggests that the proposed method is effective in predicting the scale of passenger transportation after the adjustment.
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 5 Comparative Study
### Comparison of Access Time of the Proposed Scheme with Various Data Placement Schemes
This comparison highlights the efficiency of the proposed scheme in improving XML querying over wireless broadcast channels, providing faster query response times, reduced network traffic, and balanced resource utilization compared to centralized querying and unpartitioned broadcasting.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method
### Components
#### 3.2.3 Second-Order Channel Attention Module  
The second-order channel attention (SOCA) [23] module explores the attention of second-order feature statistics based on the first-order channel attention module. The global covariance pooling operation is adopted, and the Newton iteration method is used to solve the covariance normalization to reduce the required computational resources. As shown in Figure 6, after the feature map is input into the second-order channel attention module, the global covariance pooling operation is first performed. Reshape the input feature to a matrix \(X\) of \(C\times H\times W\), and calculate its corresponding covariance matrix \(\sum\). \(\sum\) can be obtained by Formulas (1) and (2):  
\[\sum=XIX^{\mathrm{T}} \tag{1}\]  
\[I=\frac{1}{s}(I-\frac{1}{s}O) \tag{2}\]  
where \(I\) and \(O\) represent the \(s\times s\) identity matrix and all-one matrix, respectively.  
We use \(\sum\) for covariance normalization. Since this matrix is a symmetric positive definite matrix, its eigenvalue decomposition is shown in Formula (3):  
\[\sum=U\Lambda U^{\mathrm{T}} \tag{3}\]  
where \(U\) is an orthogonal matrix, and \(\Lambda\) refers to the diagonal matrix when the eigenvalues are not increasing. The covariance normalization is converted to the power of the eigenvalue, as shown in Formula (4):  
\[\hat{Y}=\sum^{\alpha}=U\Lambda^{\alpha}U^{\mathrm{T}} \tag{4}\]  
where \(\alpha\) represents a positive real number, and \(\Lambda^{\alpha}\) represents a diagonal matrix. When \(\alpha=1\), no normalization is required, and when \(\alpha<1\), it will nonlinearly shrink eigenvalues greater than 1.0. Given previous work [24], we adopt \(\alpha=0.5\).  
The processed features go into SOCA. After two layers of convolution, the first layer of convolution is used for channel dimensionality reduction and the second layer of convolution is used for channel restoration, and the final second-order channel attention vector \(\omega\) is obtained. \(\omega\) can be obtained by Formula (5):  
\[\omega=f(W_{\mathrm{I}}\delta(W_{\mathrm{D}}z)) \tag{5}\]  
where \(W_{\mathrm{d}}\) and \(W_{u}\) are the weights of the convolutional layer, the feature channels are \(C/r\) and \(C\), \(f(\,\cdot\,)\) represents the sigmoid function, and \(\delta\) represents the ReLu function. Let \(\hat{Y}=[y_{1},\cdots,y_{c}]\), the channel-wise statistics \(z\in R^{\mathrm{C}\times 1}\) can be obtained by shrinking \(\hat{Y}\). Then the \(c\)-th dimension of \(z\) is computed as Formula (6):  
\[z_{c}=H_{GCP}(y_{c})=\frac{1}{C}\sum_{i}^{C}y_{c}(i) \tag{6}\]  
Figure 6: Illustration of the SOCA.  
where \(H_{GCP}(\,\cdot\,)\) represents the global covariance pooling function. Compared with first-order pooling, global covariance pooling can obtain higher-order feature information and more discriminative feature information.  
Finally, the second-order channel attention vector and the input features are multiplied by channel to obtain new features related to internal information.
# Generation of Beam Tilt through Three-Dimensional Printed Surface
## 4 Results and Discussion
### Fabrication of BDS Prototypes  
The multijet 3D printing method is adopted for realizing the proposed BDS surface where the Stratasys J750 3D printer was used, which is able to produce the part in a single print. It operates with Multijet Printing (MJP) method, which is a material jetting printing process that utilizes piezo print head technology to deposit materials layer-by-layer. These high-resolution 3D printers use separate, meltable or dissolvable support materials that can be completely removed in a virtually hands-free process, allowing even the most delicate and complex features to be thoroughly cleaned without damage. The MJP print head prints layers of UV curable liquid plastic onto a flat platform. Wax support material is also jetted  
Figure 11: Designed BDS prototype with four-layer cube arrangement. (**a**) Top view, (**b**) bottom view, (**c**) perspective view.  
Figure 12: Electric field patterns as observed with the use of the BDS prototype above the feed horn.  
to fill voids and other non-freestanding features. A UV lamp flashes to solidify material creating a fully cured plastic part and to crosslink a photopolymer. A printer head jets tiny droplets of photopolymers in the shape of the first layer. The UV lamp attached to the printer head crosslinks the polymer and locks the shape of the layer in place. In the MJP process, thousands of layers are built upon one another to create solid parts. Via hundreds of tiny jets, the MJP printer dispenses a photopolymer over a specific area. The print head then cures (solidifies) the photopolymer via a UV light. After a layer has been deposited and cured, the build platform drops down one layer thickness, and the process is repeated to build up a 3D part. This printer is capable of printing highly complex and high-resolution parts with fine features. The operation deposits the build material in a rapid and line wise fashion. Because of this, MJP printers are able to print multiple parts in a single line with no effect on build speed.  
A material having permittivity (\(\epsilon_{r}\)) of 2.8 and loss tangent (tan\(\delta\)) of 0.124 was used to 3D print the BDS prototype, whereas Acrylonitrile Butadiene Styrene (ABS) is used to fabricate the horn with 100% infill value and is used as a feed source underneath the BDS [31]. There is no gap between BDS and the aperture of the 3D printed horn used as a feed source, which decreases the overall height of the proposed structure. The VSWR measurements are performed using the Agilent PNA-X Network Analyzer, whereas the radiation measurements are performed in the NSI-700S-50 spherical near-field measurement system at the Australian Antenna Measurement Facility. The experimental setup is shown in Figure 13a,b, respectively, where on the left side, a block diagram of the experimental setup is depicted, and on the right side, fabricated BDS over the 3D printed feed horn is shown in the testing environment.
# Medium-Energy Synthesis Gases from Waste as an Energy Source for an Internal Combustion Engine
## 4 Experimental Results
### Integral Parameters of the Combustion Engine  
The following two graphs (Figures 3 and 4) show the torque and hourly fuel consumption profiles in the main speed characteristic for the various gaseous fuels classified into the two groups mentioned above. A preliminary analysis of the physical-chemical properties shows that SG10 has the highest volume heating value of the stoichiometric mixture of fuel and air (3.030 MJ/m\({}^{3}\)) and the synthesis gas SG3 has the lowest (2.619 MJ/m\({}^{3}\)). Therefore, the highest power parameters can be expected when burning SG10 synthesis gas, as the main factor influencing the output power parameters is the energy contained in the cylinder. Another factor is the value of filling the cylinder with fresh mixture, which is expressed as the volumetric efficiency. An equally important factor is the rate of fuel combustion as well as the volume of products after combustion. It can be seen from the graphs that the highest value of torque (39.2 N m) at operating speed 1500 L/min is achieved precisely during the combustion of the named synthesis gas SG10. The torque drop during SG10 operation is approximately 10% compared to methane operation in the full engine speed range. On the contrary, the lowest torque value (32.3 N m) is achieved when the engine is operating on SG1 fuel with a volume heating value of the mixture of 2.707 MJ/m\({}^{3}\).  
When comparing hourly fuel consumption, the lowest hourly consumption (1.56 kg/h) is for the reference fuel methane, which requires only 9.5% vol. to form a stoichiometric mixture with air. The value of the volume fraction of synthesis gases in the stoichiometric mixture ranges from 24.5% vol. for SG10 up to 37.5% vol. for SG2 or for SG5. The maximum hourly fuel consumption (8.17 kg/h) at operating speed of the micro-cogeneration unit 1500 L/min was when the unit was operating on SG2 synthesis gas, which had the highest volume fraction of fuel (37.5% vol.) in the stoichiometric mixture with air, and this gas also had a higher stoichiometric mixture density (1.090 kg/m\({}^{3}\)) than the SG5 gas. On the contrary, the lowest hourly fuel consumption (5.73 kg/h) was measured when operating a gas engine with the synthesis gas SG11, which had 26.7% volume in a stoichiometric mixture with a density of 1.131 kg/m\({}^{3}\) owing to the fact that the synthesis gas SG10 has a mixture density of 1.185 kg/m\({}^{3}\), though it has a smaller proportion in the air mixture than SG11 gas. The highest value of total effective efficiency (31.6%) at 1500 L/min was achieved during methane operation. Among the synthesis gases, the highest efficiency 31.2% was the synthesis gas with the designation SG10 followed by SG8 (31.1%), SG11 (31.1%), and SG9 (30.8%). On the contrary, the lowest effective efficiency (27%) occurred when operating on SG2 synthesis gas.