esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 4 Solution Concept
### Intersection Measurement  
As the calibration results and the laser stripe location are achieved, it is available to calculate the coordinate of points in the camera coordinate system by forward intersection. Generally, the camera coordinate system \(X_{C}Y_{C}Z_{C}\) is regarded as the world coordinate system.  
Figure 8: Regular image and laser stripe extraction result (red line).  
Suppose a point \(p(u,v)\) in laser stripe extraction \(LoL_{m\times 1}\), and the corresponding coordinate in the camera coordinate system is \((x_{c},y_{c},z_{c})\). The equation of the camera optical line is expressed as Equation (5), while the equation of the laser plane is not determined. In the system structure calibration, only the unit normal direction of the laser plane in the camera coordinate system is obtained.  
As it is mentioned in single camera calibration, the calibration cannot recover the last component \(t_{zi}\) of the translation vector \(t\), so \(t_{zi}\) can be set to any value, which means the camera coordinate system \(X_{C}Y_{C}Z_{C}\) is able to translate through the \(Z_{C}\) axis. Therefore, when the origin \(o_{C}\) of the camera coordinate system \(X_{C}Y_{C}Z_{C}\) is selected as the intersected point of the \(Z_{C}\) axis and the laser plane, the laser plane equation in the camera coordinate system \(X_{C}Y_{C}Z_{C}\) is determined as Equation (15).  
\[n_{p}^{T}(x_{c},y_{c},z_{c})^{T}=0 \tag{15}\]  
\(n_{p}=\left(n_{p1},n_{p2},n_{p3}\right)^{T}\) is the unit normal direction of laser plane in the camera system. Thus, combining Equations (5) and (15), the intersection measurement result of the laser line point is achieved in a camera coordinate system as follows. By the same method, the 3D coordinates of the located points on the laser line can be generated.  
\[P_{c}=[x_{c},y_{c},z_{c}]^{T}=\bigg{[}\frac{u-u_{0}}{m},\frac{v-v_{0}}{m},- \frac{n_{p1}\cdot(u-u_{0})+n_{p2}\cdot(v-v_{0})}{n_{p3}\cdot m}\bigg{]}^{T}. \tag{16}\]
# RF-EMF Exposure near 5G NR Small Cells
###### Abstract  
Of particular interest within fifth generation (5G) cellular networks are the typical levels of radiofrequency (RF) electromagnetic fields (EMFs) emitted by'small cells', low-power base stations, which are installed such that both workers and members of the general public can come in close proximity with them. In this study, RF-EMF measurements were performed near two 5G New Radio (NR) base stations, one with an Advanced Antenna System (AAS) capable of beamforming and the other a traditional microcell. At various positions near the base stations, with distances ranging between 0.5 m and 100 m, both the worst-case and time-averaged field levels under maximized downlink traffic load were assessed. Moreover, from these measurements, estimates were made of the typical exposures for various cases involving users and non-users. Comparison to the maximum permissible exposure limits issued by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) resulted in maximum exposure ratios of 0.15 (occupational, at 0.5 m) and 0.68 (general public, at 1.3 m). The exposure of non-users was potentially much lower, depending on the activity of other users serviced by the base station and its beamforming capabilities: 5 to 30 times lower in the case of an AAS base station compared to barely lower to 30 times lower for a traditional antenna.  
5G new radio; cellular network; exposure assessment; exposure limits; radiofrequency electromagnetic fields; small cell +
Footnote †: journal:  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
The surface molecular imprinting strategy is another approach proposed for developing MIPs. It is known to be able to generate a thin layer of imprinted materials on the surface of the support [27; 28; 29; 30; 31; 32; 33; 34]. By doing it this way, the prepared recognition sites are positioned near the surface within the channels and cavities. This would be beneficial and desirable for the efficient removal of templates and low defusing resistance, and it improves the accessibility of those sites in the rebinding process. Additionally, surface MIPs tend to be more physically robust due to the presence of the support. The support plays a key role for the performance of MIPs. Silica gel particles are established, and there exists popular support for surface imprinting due to the chemically stable non-swelling properties, ease of modification, and satisfactory compatibility with aqueous and biological systems [35; 36; 37; 38; 39]. Few MIPs using gossypol as the template were reported. In the work reported by [PERSON] et al. [40], a two-layer structure of poly(methacrylic acid)/SiO\({}_{2}\) bulks was designed and prepared for the detection of gossypol. The other two were developed by [PERSON] et al. [9; 41] for the recognition and adsorption of gossypol. Recently, MIPs via the sol-gel process have been prepared [42; 43; 44; 45; 46; 47; 48; 49]. However, a systematical study comparing bulk polymerization, sol-gel, and surface molecular imprinting has not been reported. It would be very helpful in practical applications to understand the strength and weakness of each above mentioned approach, especially for an interesting and important template such as gossypol. For the above mentioned reasons, three MIPs were prepared using gossypol as the template via bulk polymerization, sol-gel, and surface molecular imprinting. Namely, MIP1 was prepared with dimethylaminoethyl methacrylate (DMAEMA) as the functional monomer (FM) and ethylene glycol dimethacrylate (EGDMA) as the cross-linker by bulk thermal polymerization [9]. MIP2 was prepared by combining the advantages of surface imprinting and the sol\(-\)gel process. Silica gel was chosen as the support for surface imprinting, and (3-aminopropyl)triethoxysilane (APTES) and Tetraethoxysilane (TEOS) were used as the functional monomer and the cross-linker. As a control, MIP3 without silica support was prepared under the same reaction condition as that of MIP2. The prepared MIPs and non-imprinted polymers (NIPs) were characterized by SEM and surface measurement. The binding performances of the MIP1, MIP2, and MIP3 for the recognition and adsorption of gossypol were evaluated by static adsorption experiments. The adsorption kinetics, adsorption isotherms, adsorption selectivity, adsorption capacity, as well as the reusability of the obtained MIPs were also investigated.
# Fire Safety Risk Analysis of Conventional Submarines
## 3 Overview of Systems and Risks
### Fire Safety Risks for Conventional Submarines
#### 3.4.1 Current State--Australia's Collins Class  
Australia's Collins Class submarine has a traditional diesel-electric, conventional submarine configuration with three diesel engines providing electrical generation for four lead-acid battery packs that provide electrical power for main propulsion power and hotel load. The submarine's fire safety system features some important components [37]: (1) A halon 1301 fixed fire suppression system; (2) Zone-related smoke, flame and heat detectors; (3) Aqueous Film-Forming Foam (AAFF) system for manual firefighting; and (4) Various portable fire extinguishers.
## 6 Discussion  
In this section, the main findings of the study are discussed, in addition to its significance in both the industry and academia, and some limitations of the research are covered. This paper aims to improve the accuracy of shovel-force prediction for large-tonnage EVLs to enhance their operational efficiency. Through this study, a new method for predicting  
Figure 16: (**a**) Speed curve during linear driving process; (**b**) motor torque curve during linear driving process.  
Figure 17: Power curves: (**a**) no-load four-wheel-drive power curve; (**b**) full-load four-wheel-drive power curve.  
showel force was developed, one especially designed for large-tonnage EWLs. The combination of DEM and BP NNLA demonstrated high accuracy in predicting shovel force, which can provide strong support for the design and operation of EWL. The reliability and effectiveness of this paper's findings are demonstrated in three main aspects: model verification, consideration of various factors, and comprehensive data collection. The paper validated the simulation model through experiments and considered multiple factors influencing shovel force, including material parameters, loading steps, and the physical properties of loading materials. Through static and dynamic calibration experiments, the paper extensively collected and analyzed key parameters of the shovel-force prediction model. By improving the accuracy of shovel-force prediction, it is possible to better optimize the design and operation of EWL, thereby increasing production efficiency, reducing operating costs, and enhancing competitiveness. Additionally, the optimization methods mentioned in the study for motor selection and matching can also be applied in other applications within the EWL field. This holds significant importance for other construction machinery manufacturers and industries that require efficient material handling.  
Despite achieving some breakthroughs in the accurate prediction of shovel force for large-tonnage EWL in this paper, there are still some limitations. Firstly, the power matching in this paper was based on V-type operating conditions, and there is further room for optimization in real-world applications. Secondly, the discrete element simulation mainly focused on loose materials, and there might be some errors when extending the simulation to different material types. The accuracy of the simulation model can be affected by changes in actual operating conditions.
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 3 Digital Simulation Experiment and Discussions
### Effect of Sampling Pitch  
In the process of the sampling moire calculation, the most important parameter of input is the sampling pitch \(P_{S}\). For a certain fringe image, different sampling pitches affect the accuracy of the phase calculation [6]. In order to study the influence of the sampling pitch \(Ps\), we introduced the displacement field \(u^{\prime}\) of different amplitudes into the images with different fringe spacing \(P_{S}\) and analyzed the displacement calculation errors caused by different sampling pitches. In the experiment, the fringe spacing range was set to 6\(\sim\)14 pixels, the sampling pitch range was 5\(\sim\)17 pixels, and the displacement field was \(u^{\prime}=K*u\), \(K=\) [0.5, 1.0, 1.5, 2.0]. The calculation results are shown in Figure 7. As the displacement amplitude becomes larger, the fringe images become increasingly nonlinear, and the calculation error of sampling moire also becomes larger. It is worth noting that under different fringe spacing conditions, the calculation error reaches a minimum when the sampling pitch is close to the fringe spacing. In addition, as the deformation increases, the shape of the displacement error contour gradually changes from flat to concave, indicating that the calculation accuracy under a large deformation is more sensitive to the sampling pitch. In this situation, larger fringe spacing helps to make the calculation results of sampling moire more stable.
However, most studies have focused on determining the effects of individual stress factors, and data reported have referred mainly to acute stress status, while the combined effects of multiple biotic and abiotic stressors on fish at different organism levels are still insufficiently investigated [1; 7]. Turbot _Scophthalmus maximus_ is a benthic marine carnivorous species widespread in the Adriatic Sea, particularly in its northern part. It can grow up to 95 cm in length and a weight of 10 kg living at a depth of 20-70 m (usually between 10 and 15 m) [8]. Juveniles feed on mollusks and crustaceans, while the diet of adult fish consists of fish and cephalopods. Farming of this species began in the 1970s in Scotland and today has spread to many parts of the world (e.g., Chile, China, Korea, Japan), including other European countries. Cultivation can be carried out in onshore tanks (land-based farms) with flow-through or in recirculation systems and flat-bottom cages (floating or submerged at various sea levels) [9; 10; 11; 12; 13]. Many studies have assessed the effects of specific abiotic stressors such as temperature [14; 15], stocking density [16; 17]), ammonium [18]), pH [19]), or nitrite exposure on reared juvenile turbo [20]. However, there are no comparative studies on organismal responses of farmed and wild turbid that share the same geographical area. Consequently, the aim of this study was to (a) compare plasma biochemical, molecular, and histological profile of the farmed and the wild-caught turbo; (b) assess the impact of different living conditions on their health; and (c) establish a reference point for future studies of the effects of different endogenous and exogenous factors on this species.
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 1 Introduction
### Machine Learning for Intelligent Transport System Technologies  
The continued development of automation technologies and ITS applications implies using ML methods to analyze information about the transport system and make decisions based on selected contextual data [48]. ML methods are used in ITS applications to dynamically analyze traffic conditions, enhance operational precision, minimize delays, and ensure the safety of road users.  
The use of applications for analyzing traffic congestion and providing real-time alternative routes has become widespread in ITSs [49; 50; 51; 52]. The results of their application show that using ML methods can reduce the waiting time during peak loads by half and detect congestion during unexpected road closures almost five times faster [49]. ML techniques have been successfully used to reduce latency, manage resources, and optimize the performance of ITS applications [53; 54; 55]. They also help reduce power consumption and improve ITSs bandwidth efficiency [54].  
Improving highway safety is one of the most developed areas of ML application in ITSs. It includes security analysis of the road network and its elements [56; 57; 58], improving information security in ITSs [59; 60], and detecting anomalies in sensor data of connected and autonomous vehicles [61; 62; 63]. It also involves intelligent analysis of road traffic accidents to identify critical factors in their occurrence, essential for preventing similar incidents in the future [64]. ML for big data analysis enables the effective development and calibration of SMTFs. Integrating macroscopic modeling with ML techniques can enhance the accuracy of traffic flow modeling by improving prediction accuracy and adaptability to changing traffic conditions [65; 66; 67]. This hybrid approach can provide greater flexibility to changing traffic patterns by combining the overall traffic flow dynamics captured by macroscopic models with ML's ability to handle dynamic changes effectively. By leveraging ML methods instead of traditional statistical approaches, traffic algorithm modeling can achieve higher accuracy and adaptability [68].  
In [69], it is demonstrated that ML can be helpful in dynamically calibrating parameters in macroscopic traffic flow models. For instance, ML can generate a set of hyperparameters for calibration through generative adversarial simulation learning. However, a challenge arises in selecting a data source to timely calibrate the VTS to reflect changes in the PTS.  
Creating ML applications requires training and testing data corresponding to the real-world processes with which the applications interact [70]. Moreover, the data collection process is a non-trivial task requiring a separate approach.
## 1 Introduction
To emulate the lack of an absolute time synchronization between transmitters and receivers, we introduced a random time delay between different localization experiments. This paper is organized as it follows. Section 2 describes the proposed TDoA method, introducing the analytical model. Section 3 provides the experimental validation of the TDoA method. In particular it describes the design criteria of the positioning system architecture, and discusses the independent experimental validation results.
## 3 Results
### Biomarkers Analysis
#### 3.3.2 Glucose Levels  
The estimated marginal means of glucose (GLU) concentrations at each time point are summarized in Table 5 and visualized in Figure 3. Baseline values were comparable across groups: CTRL (98.125 \(\pm\) 1.282 mg/dL), OLT (97.833 \(\pm\) 1.333 mg/dL), and OLB (97.167 \(\pm\) 1.434 mg/dL).  
Figure 2: Postprandial insulin concentrations (Mean \(\pm\) SE) over time (baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups.  
Following the intervention, all the groups demonstrated a reduction in glucose levels at 1 h, with the lowest values observed at t2 in the OLB group (82.833 \(\pm\) 2.983 mg/dL), followed by CTRL (86.458 \(\pm\) 3.074 mg/dL) and OLT (87.208 \(\pm\) 3.753 mg/dL). Glucose concentrations remained relatively stable at 2 and 4 h, with slight fluctuations but no substantial rebounds or further declines.  
No statistically significant group-by-time interaction for glucose levels (_p_ = 0.546) indicates that the temporal pattern of glucose change did not differ meaningfully among the groups. The 95% confidence intervals for all time points across the groups showed considerable overlap (e.g., t3: CTRL [79.840, 92.077], OLT [81.817, 91.933], OLB [82.174, 94.826]).  
Overall, the intervention appeared to induce a modest and transient decline in glucose levels in all groups, but without a distinct differential effect between them.
# Dichotomization of Multilevel Variables to Detect Hidden Associations
## 2 Preliminaries
### Chi-Square Test  
This test of independence is used to determine whether there is a significant association between two variables [15; 16; 17; 18].  
The null hypothesis (H\({}_{0}\)) of the Chi-square test of independence is that there is no association between the variables. The alternative hypothesis (H\({}_{1}\)) assumes there is some association between V\({}_{1}\) and V\({}_{2}\).  
The Chi-square statistic is defined as follows:  
\[\chi^{2}=\sum_{i}\frac{(O_{i}-E_{i})^{2}}{E_{i}}\]  
where  
\(O_{i}\) is the number of samples in category (cell) \(i\),  
\(E_{i}\) is the expected value of category \(i\), i.e., \(E_{i,j}=\frac{\tau_{i}\tau_{j}}{N}\); \(T_{i}\) is the total of row \(i\), and \(j\) is the total of column \(j\).  
The value of \(\chi^{2}\) and the degrees of freedom are used to search the \(p\)-value in the table of distribution of probability of Chi-square. Typically, if the \(p\)-value is greater than 0.05 (level of significance), then V\({}_{1}\) and V\({}_{2}\) have no association (H\({}_{0}\) is accepted); otherwise; the variables have an association (H\({}_{0}\) is rejected).  
It is common in practice to consider that the test is reliable if \(E_{i}>5\) for at least 80% of the number of cells.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 7 Conclusions  
This article surveys the key concepts of linear interference alignment, surveys recent results on the topic and focuses on bringing the concept closer to implementation in CR networks, which promises to improve throughput. The IA algorithms/enhancements, as has been clearly described, remain the biggest challenge for this research. A number of enhancements have been incorporated in both paradigms of IA in CR to improve low SNR performance, enhance distributed computation and are more robust to CSI imperfections. To be more specific, under the first paradigm, enhancements applied to the LIF algorithm seem to yield the best performance in terms of sum rate capacity, regardless of CSI assumptions. Therefore, future research direction suggests more emphasis on developing algorithms that will not only achieve IA by progressively reducing LIF at each receiver while keeping an allowable interference level to the PU, but will also be focusing on closer practical implementation in CR networks. With the research done under the second paradigm, the combination of ST-WF algorithms at the PU optimization algorithms at the SU link achieved higher sum rate performance, owing to the ST-WFs ability to achieve higher capacity per antenna at low to moderate SNR regimes. While the sum rate results of the other endeavours where encouraging, it remains debatable whether the ST-WF algorithm with multiple SUs (incorporating CSS) will lead to achieving a more practical solution in CR networks. In addition, several practical challenges of these techniques have been identified in order to enable the future research in this domain.  
Conceptualization, Y.A., O.S. and Y.S.; Data curation, Y.A. and Y.S.; Formal analysis, Y.A., O.S. and Y.S.; Investigation, Y.A., O.S. and Y.S.; Methodology, Y.A., O.S. and Y.S.; Project administration, O.S.; Resources, O.S. and Y.S.; Software, Y.A. and O.S.; Supervision, Y.S.; Validation, Y.A. and O.S.; Writing--original draft, Y.A.; Writing--review & editing, Y.A. and Y.S. This research received no external funding. The authors declare no conflict of interest.
## 3 Implementation
### Storage Layer
#### 3.1.4 Local Data Gateway  
It is necessary to install a local data gateway on each storage node in order to exchange data with the data router and to report the node state to the nodes coordinator on the controller node. When a local data gateway receives the chunk data from the data router, it will serialize the data into a block device. It is also the local data gateway's responsibility to read the chunk data from the block device and send it to the data router when it receives the chunk retrieval request.  
Notably, the local data gateway does not have the ability to do data striping. It is possible for the user to create RAID volumes manually for their local data gateway in order to have data safety and to assure read/write performance.
## 2 Datasets and Methods
### Methods  
#### 2.3.1 Calculation of CUE  
The CUE was calculated as follows [4]:  
\[CUE=\frac{NPP}{GPP}=1-\frac{R_{a}}{GPP}, \tag{5}\]  
where CUE is carbon use efficiency, NPP is net primary production, GPP is gross primary production, and Ra is autotrophic respiration.  
To explore the contribution of PFIs and seasonal to positive and negative CUE extremes, we divided the year into four seasons: MAM (March, April, May), JJA (June, July, August), SON (September, October, November), and DJF (December, January, February). We classified the vegetation into eight plant function types: evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forests (MF), shrub (SHR), grass (GRA), and cropland (CRO).  
We adopted the integrated CUE (iCUE) to analyze the contributions of seasonal and PFIs to CUE extremes. iCUE was calculated by adding the monthly CUE values in the corresponding season or year. Then, the contribution of different seasons and PFIs to CUE extremes was statistically evaluated by weighted area.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 5 System Evaluation
### PHY and MAC Device Installation  
We employed IEEE 802.11 Wi-Fi Physical Layer (PHY) and MAC model. The AP network has 3 channels (Channel number 1, 6, and 11) to select, for minimizing the medium overlap between the adjacent APs. The ad hoc network uses a single channel reflecting the native communication pattern. Ad hoc network channel and the AP channels are distinctly instantiated without any expected interference, to follow the assumption in Section 3.1. Mobile nodes can change the AP connection safely since all APs use the same channel. Unlike the mobile nodes and GCS have one network device for the station and the ad hoc respectively, UAVs have two network devices for the AP and the ad hoc. The UAVs broadcast a beacon signal per second.