esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Open Source 3D Printers  
In this study, we use an open-source 3D printer. Hence, it is essential to explore the various parameters that influence the print performances of such printers. The calibration of such printers also depends on the specific model. [PERSON] et al. [4] show that even when the temperature in the software is set to be the same, the extrusion temperatures of the two printers will be different. The behavior of the parts printed using these two printers also might differ due to the heterogeneous properties of the material, mainly resulting from the slip experienced by the filament over the hobbed pulley [28]. In other words, the individual components and their assembly can influence the performance of such a printer [28]. With this understanding, each component and its assembly are carefully tested for the printer before the experiment is conducted.  
Several prior studies have explored the dimensional accuracy of FDM parts. One of the studies that compared the printed dimensions with the dimensions of the CAD model [29] showed that the length varied by 0.51%, width by 1.71%, and thickness by 6.05% on average. Another study [30] showed that several models of desktop 3D printers are less likely to undersize parts (less than 5%) but likely to oversize them by at least 5%. [PERSON] et al. [31] showed that three print parameters affect dimensional accuracy the most: extrusion temperature, layer height, and build direction. They showed that lower extrusion temperatures are preferable for better dimensional accuracy. At the same time, higher temperatures are preferable for better mechanical properties.  
Another study by [PERSON] et al. [32] investigated the effects of infill orientation, layer thickness, and the number of shell perimeters on the mechanical strength of parts 3D printed with desktop printers. The study found a positive correlation between mechanical strength and layer thickness. A similar positive correlation was observed between strength and the number of perimeters.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 5 Technological Trends and Challenges
### Trends in Cobots' Hardware Related Capabilities  
In recent years, significant hardware innovations have enhanced the safety, adaptability, and operational efficiency of cobots, accelerating their adoption in industrial and human-centric environments [75]. A primary development has been the widespread integration of high-resolution torque sensors and series elastic actuators (SEA), both of which contribute to improved compliance and force control at the joint level, allowing safer physical interactions with human workers [105]. These systems provide fine-grained feedback that supports adaptive responses during unstructured tasks.  
Moreover, advances in lightweight composite structures--notably the use of carbon fiber-reinforced polymers and 3D-printed aluminum alloys--have yielded arms with lower inertia and greater payload-to-weight ratios [106], enabling higher-speed operation while maintaining ISO/TS 15066-compliant safety profiles [107]. Complementing these are compact, backlash-free harmonic drives [108] and miniaturized servo systems [106], which allow for precise motion in tight workspaces and reduce the physical footprint of cobots [109].  
The development of back-drivable actuators and soft robotic joints has further increased cobot usability in kinesthetic teaching and physical guidance tasks [110]. These allow the human operator to intuitively manipulate the cobot without motor resistance. To facilitate broader task versatility, many systems now include modular end-effector interfaces with quick-change couplers [111] and embedded multi-axis force-torque sensors at the wrist, enhancing manipulation sensitivity during delicate assembly operations [112].  
Also notable are the soft grippers made from pneumatic or tendon-driven designs [113]. Soft grippers enable the safe handling of deformable or irregular objects, expanding the range of applications [114]. In parallel, energy-efficient and regenerative actuator modules have emerged, capable of harvesting energy from deceleration phases, contributing to improved energy consumption metrics over long operational cycles [115].  
Lastly, the deployment of redundant joint configurations (e.g., 7-DOF arms) has improved task flexibility and enhanced cobot maneuverability [116]. This is especially important for avoiding collisions in cluttered or collaborative spaces [117] without depend ing on external sensors for collision avoidance. These hardware advances are laying the foundation for the next generation of scalable, safe, and human-friendly cobots.
# Construction and Application of Safety Management Scenarios at Construction Sites
## 2 Materials and Methods  
### Ideas for Building Construction Site Safety Management Scenarios  
The management of safety on construction sites for engineering projects depends on several factors, such as the natural geography, construction conditions, and the construction process. Therefore, conducting an in-depth exploration and understanding of Geographic Information System (GIS), geographic elements, and spatial models, among other things, is essential to ensure safety during construction.  
The construction of geographic scenes based on the concept of three-dimensional reality is becoming increasingly prevalent with the ongoing development of GIS technology. A geographic scene consists of a highly complex integrated region comprising various elements, including human society, natural phenomena, and geographic data. These elements interweave and interact with one another within a specific time and space scope, enabling the scene to fulfill its unique functions. By integrating the surrounding topography with building-related data, a virtual geographical environment is created [27]. Geo-scenarios can visualize differences between locations and patterns of human activity and can meet a variety of planning needs [28]. Accordingly, several academics have begun researching the optimal utilization of this technology to enhance the advantages of urban planning and design.  
In the field of construction engineering site safety management, this paper proposes the concept of geographic scene construction as a means to integrate various information elements, such as personnel, materials, machinery, management, and the environment throughout the construction project. By analyzing the interactions between these elements, incorporating computer information technology and other analytical tools, the project can ultimately achieve the goal of producing a safe work environment.  
This study carries out research on the construction and application of safety management scenarios at construction sites of building projects. Figure 1 illustrates the technical route.
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 2 Conventional Approaches
### Pathway Analysis Methods
#### 2.3.1 Over-Representation Analysis (ORA)  
Over-representation analysis (ORA) is among the most used pathway analysis approaches for the interpretation of metabolomics data needed as input, once the type of annotations to examine is chosen. One obtains a collection of annotations and their associated \(p\)-value as outputs since a statistical test is applied to determine whether a set of metabolites is enriched by a specific annotation (e.g., a pathway) in comparison to a background set. Different statistics can be applied to obtain information about the studied biological mechanisms and on the specific functionality of a given metabolite set. Among the most used statistics, we would like to mention the well-known binomial probability, [PERSON]'s exact test and the hypergeometric distribution [84; 85].  
Three are the necessary inputs in ORA analysis: (i) a set of pathways (or metabolite collections); (ii) a catalog of investigating metabolites and, (iii) a background collection of compounds. The list of investigating metabolites usually comes from experimental data after applying a statistical test to determine those metabolites whose signals can be associated with a precise result by choosing a threshold value usually associated to the \(p\)-values [74]. The background collection includes all metabolites that can be revealed in the considered measurement. If the p-value corresponding to each pathway is obtained by means of the right-tailed [PERSON]'s exact test based on the hypergeometric distribution, the probability to find k metabolites or more in a pathway can be written as [74]:  
\[P(X\geq k)=1-\sum_{i=0}^{k-1}\frac{\binom{M_{i}}{i}\binom{N-M_{i}}{n-i}}{ \binom{N}{n}}, \tag{6}\]  
Figure 10: Conceptual map about the topology-based pathway analysis method.  
where \(N\) corresponds to the number of background compounds, \(n\) is the number of the measured metabolites, \(M\) is the number of background metabolites mapping the \(i\)th pathway, and \(k\) represents the overlap between \(M\) and \(n\). A scheme of the ORA principle is displayed in Figure 11 as a 3D Venn diagram. Finally, multiple corrections are usually applied, as calculations are made for many pathways, thus obtaining a collection of significantly enriched pathways (SEP).  
Before applying ORA, one has to verify if the metabolomics dataset is sufficiently big to furnish proper statistical significance. For instance, usually MS-based techniques can observe more metabolites than NMR-based methods, such as the mono-dimensional NMR ones commonly used for profiling [86]. Indeed, the choice of the most suitable background collection is the real challenge and still remains an open subject because it strictly depends on the situation [74].
## 4 Results  
### Initial Implementation and Breakdown of the Asset Information Model  
The initial implementation followed the 1192 suite of standards discussed in Section 3.1. All the processes related to information exchange on the project were stipulated in the post-contract BIM Execution Plan (BEP). Overall, processes such as document control on site and the archiving of data within the project Common Data Environment (CDE) were set out (Figure 5). Also, more detailed processes such as raising and documenting Non-conformance Reports (NCRs) were established, as shown in the example process map in Figures 6 and 7.  
Following the procedure that was agreed upon and executed led to the development of the Project Information Model (PIM), which contained 26,401 individual files (these included 3D models, documentation, drawings, and images). Based on the Exchange Information Requirements (EIRs) set by the asset operators, an Asset Information Model (AIM) was produced, which contained 5549 files. A breakdown of the AIM can be seen in Figure 8.  
Figure 6: Process for communicating and closing the Non-Conformance Report (NCR)—Part 1.  
Figure 7: Process for communicating and closing the Non-Conformance Report (NCR)—Part 2.  
The results confirmed the observations made in previous studies as, more than 90% of the AIM was document based [3; 4]. Furthermore, given the asset management systems that were being used, this was the most suitable form of information exchange as the documentation contained certificates, drawings, and maintenance manuals, which are an integral part of the information to be handed over.  
As a part of the EIR, a federated 3D model was produced, along with linked non-graphical information. To produce the 3D model, manufacturers were given Product Data Templates (PDTs), which contained the required attributes to be attached to the 3D objects. This was a relatively manual process that involved manufacturers filling in spreadsheets and then the modellers attaching this information to the relevant objects. This was time consuming and prone to human error given the scale of the project and the volume of information being produced.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 3 Mathematical Analysis
### Estimation for a Single Building
#### 3.1.4 Overall System-Level Performances
\right)\left(\sigma_{\text{aTMS, non-orth}}^{\text{sys,all SBSs}}/Q\right) \tag{16}\]  
From Equations (15) and (16), it can be found that data transmitted per Hz increases, whereas the energy required per bit transmission decreases by the factor \(\underline{\kappa}_{\text{reuse}}\) when employing non-orthogonal allocation principle as compared to that when employing orthogonal allocation principle for sharing the satellite spectrum with the same number of small cells in a building.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 2 Methodology  
A flow chart of an on-board parameter optimization method based on ADS-B is shown in Figure 1. Firstly, the typical infrared signature of civil aircraft was simulated combined with different marine cloud backgrounds. Next, a space-based infrared detection model was established and the system detection band was optimized using signal-to-noise ratio (\(SNR\)), signal-to-clutter ratio (\(SCR\)) and the joint signal-to-noise/clutter ratio (\(JSNR\)) as the evaluation standard. Then, an automatic parameter adjustment algorithm was used to maximize the integration time based on real-time ADS-B flight speed. Finally, a switch to a more suitable integration capacitor gear was executed so that the noise was further suppressed. The output parameter set contained the optimized spectral band, the integration time and the integration capacitor gear.
## 3 Safety risk entropy  
The essence of entropy [76; 77] is considered as a measure of the degree of disorder in the system. Currently, there are three typical definitions: Clausius entropy, Boltzmann entropy, Shannon entropy. Therefore, in this paper, safety risk entropy is defined as the measure of all random factors in system safety risk. Through the previous system's safety risk analysis, it was found that the randomness mainly derives from the probabilistic characteristics of each step of the fault propagation process. Therefore, according to the definition of Shannon entropy, it is assumed that the fault propagation from resource element \(r_{i}\) to resource element \(r_{j}\) requires n steps, and the probability of fault propagation for each step is \(p_{1}\), \(p_{2}\),..., \(p_{n}\) (based on the direct propagation matrix \(C_{d}^{P}\)). Then, based on Shannon entropy, \(H_{ij}\) means the effect of safety risk entropy that from resource element resource \(r_{i}\) to the resource element \(r_{j}\), as shown in Equation (16). In other words, \(H_{ij}\) represents the uncertainty risk of fault propagation from resource element \(r_{i}\) to resource element \(r_{j}\). Moreover, total safety risk entropy \(H_{i}\) of resource element \(r_{i}\), which effects the overall system calculated in Equation (17). The higher the safety risk entropy value is, the greater the uncertainty risk caused by the fault in this element effects on the system is.  
\[H_{ij}=-\sum_{m=1}^{n}p_{m}\ln p_{m} \tag{16}\]  
\[H_{i}=\sum_{j=1}^{n}H_{ij} \tag{17}\]  
According to comprehensive analysis on results of Pareto rule, ALARP principle and safety risk entropy, aimed at the serious risk region and critical risk factors, the coupling correlations are further researched to propose preventive measurements, so as to reduce the risk level and improve system safety.  
Figure 4: As Low As Reasonably Practicable (ALARP) model.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 3 Methodology
### Discrete Wavelet Transform (DWT)  
The DWT represents a signal in multiple resolutions through various scaled and shifted coefficients. The DWT is employed to reduce the dimension of datasets and computational cost. Additionally, it provides information across different frequency bands. DWT utilizes a function known as the mother wavelet [33], denoted as \(\psi(t)\), along with a scaling function, represented by \(\varphi(t)\). Considering a signal \(x[n]\), the wavelet coefficients, denoted as \(X(a,b)\), are determined by:  
\[X(a,b)=\sum_{n}x[n]\psi_{a,b}[n], \tag{4}\]  
where the function, \(\psi_{a,b}[n]\), corresponds to the wavelet function with scale \(a\) and displacement \(b\). The wavelet function takes the form:  
\[\psi_{a,b}[n]=\frac{1}{\sqrt{a}}\psi\left(\frac{n-b}{a}\right). \tag{5}\]  
The function \(\psi(.)\) can be one of the wavelet functions such as Gabor, Mexican Hat, Haar, Daubechies, Morlet, and Symmlet, among others. The dyadic DWT is a special case of the DWT [34], in which \(a=2^{-j}\) and \(b=k2^{-j}\), therefore:  
\[\psi_{j,k}(t)=2^{\frac{j}{4}}\psi(2^{j}t-k),\hskip 28.452756 ptj,k\in\mathbb{Z}. \tag{6}\]  
Substituting Equation (10) into Equation (8), we obtain the expression that allows calculating the wavelet coefficients:  
\[X(j,k)=\sum_{n}x[n]2^{j/2}\psi[2^{j}n-k], \tag{7}\]where \(j\) represents the scale level and \(k\) represents the displacement level at scale \(j\). The wavelet coefficients are split into two parts; low-frequency approximation coefficients \(D_{i}\) and high-frequency detail coefficients \(A_{i}\)[35].
## 3 Analysis of Results
### Full Load Torsional Working Condition
The stress increases rapidly when it is close to the outer axle bracket, which means that the maximum stress position at the midline of the upper surface of the front axle is at \(l=297\) mm, and the maximum stress is 107.04 MPa. When the stress curve crosses the highest point, the stress decreases rapidly from 107.04 MPa at \(l=297\) mm to 25.11 MPa at \(l=315\) mm, and then decreases slowly. Due to the action of the inner and outer axle support, the stress curve will show a small range of stress fluctuations in the process of falling. Figure 11: Stress nephogram under torsional conditions. Figure 12: Stress curves of main components under torsion condition. (**a**) Axle stress curve, (**b**) crossbeam stress curve, and (**c**) longitudinal beam stress curve. As can be seen from Figure 12b, the maximum stress position of the front and rear crossbeams occurs at the contact position between the crossbeam and the outer side of the longitudinal beam (at \(l\) = 490 mm and \(l\) = 1189 mm), and then decreases rapidly. The maximum stress of the front crossbeam and rear crossbeam is 15.53 MPa and 13.71 MPa, respectively. The stress fluctuates in a certain range at the interface between the crossbeam and the longitudinal beam. In the middle of the crossbeam, the maximum stress of the front crossbeam is larger than that of the rear front crossbeam. Figure 12c represents the stress variation trend in the midline of the upper surface of the left and right longitudinal beams, and the stress variation fluctuation is obvious. At the connection between the longitudinal beam and the other components, the stress appears to be obviously reduced and then rebounded, and the change trend of the left and right longitudinal beams is approximately equal. Due to the asymmetric support of the frame, there are differences in the individual stress peaks between the left and right longitudinal beams, and the maximum stress value of the left longitudinal beam is higher than that of the right longitudinal beam, with the maximum stresses of the left longitudinal beam and right longitudinal beam being 55.74 MPa and 47.77 MPa, respectively. The maximum stress at each part of the frame is much less than the allowable stress of its material, and so the strength of the frame under torsional conditions meets the design requirements.
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
## 2 Materials and Methods
### Anticancer Activity  
Antitumor activity of samples was evaluated employing four human tumor cell lines: T47D (breast cancer), DU145 (prostate cancer), NCI-H1299 (lung carcinoma) and HT29 (colorectal cancer). A nontumor Vero cell line from kidney epithelial cells of the African green monkey was also used.  
Each algal extract and fraction was initially tested at two sample concentrations to allow the detection of a high to moderately-high anticancer activity [18], at final concentrations of 20 \(\upmu\)g/mL and 50 \(\upmu\)g/mL, freshly prepared by diluting from sample stocks kept at \(-\)20 \({}^{\circ}\)C.  
The determination of the cytotoxic activity of the samples was performed following the MTT (tetrazolium salts) method [19]. The selected tumor cell lines were grown in 75 cm\({}^{2}\) Nunc\({}^{\text{TM}}\) culture flasks (Thermo Fisher Scientific, Roskilde, Denmark), with the corresponding nutrient medium i.e., T47D and NCI-H1299 with RPMI medium; DU145 with DMEM medium, and HT29 with McCoy's medium (Biowest, Miami, FL, US). To perform the assay, 100 \(\upmu\)L of cells with the appropriate nutrient medium were seeded in a 96-well plate (5000 cell/well for DU145 and 10,000 cells/well for the other three cell lines), similar to the method described by [PERSON] et al. [20]. To determine the concentration of viable cells for the bioassays, an ADAM cell counter (NanoEnTek Inc., Waltham, MA, USA) was used following the manufacturer's instructions. Plates were incubated at 37 \({}^{\circ}\)C, 95% humidity and 5% CO\({}_{2}\), for 24-72 h depending on the time division of the cell line.  
Assays were performed in triplicate for both concentrations and controls. A 5.43 \(\upmu\)g/mL (10 \(\upmu\)M) solution of the commercial anticancer drug adriamycin was used as positive control, together with vehicle and negative controls.  
After 48 h incubation, 10 \(\upmu\)L of freshly prepared MTT solution tempered in the incubator was added to each well (final concentration: 0.3 mg/mL) and allowed to react for 1.5-2 h, followed by solubilization and homogenization of formazan. Finally, absorbance was recorded at 595 nm using the above-mentioned MultiSkan Go spectrophotometer.  
Analysis of the collected results was conducted by calculating the mean value and standard deviation of the three replicates. The data blank was subtracted to the absorbance values of treated cells, positive and negative controls. The percentage of cell survival was calculated as:  
\[\text{Survival (\%)}=[(\text{Abs untreated cells}-\text{Blank})\times 100]/\text{mean (Abs untreated cells}-\text{Blank})] \tag{2}\]  
Samples displaying the lowest cell survival rates were reassay in the same manner as above but in a range of concentrations up to 80 \(\upmu\)g/mL, obtained by serial dilution, in order to allow the determination of the IC\({}_{50}\) values, i.e., the concentration that causes a 50% growth inhibition of the cell population.