esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 2 State of the Art
### Team Situation Awareness  
Situation Awareness is an individual's awareness in a situation--one person's understanding of \"what is going on\" in that exact situation. The SA is different from person to person even if people have the same information available and the same working conditions. This is because cognitive factors such as experience, mental models, schemata, and qualifications differ between individuals.  
A team can be characterized as a group of people with a shared goal. Working in a team may give several advantages over single operators, such as sharing the workload between operators, contributing with expertise on subtasks and there may be an advantage in safety considering that the operators can check each other's work.  
Several papers have been published with good theoretical accounts of situation awareness in teams [8; 9; 10; 16]. In particular, [PERSON] defines situation awareness as \"the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future.\" She defines TSA as \"the degree to which every team member possesses the SA required for his or her responsibility,\" distinguishing this from SA which is shared [8]. [PERSON] et al. [9] and [PERSON] et al. [10] place a greater emphasis on the shared understanding of a situation in TSA and also consider the issue of how team process influences situation assessment as an intrinsic component of TSA. The application of the concept of our study combines elements of the two approaches. It is close to the one by [PERSON] in that shared understandings are not emphasized, however, the role of team communication in developing TSA is explicitly considered, in line with [PERSON] et al. [9]. In short, this experiment draws on both theories and does not seek to discriminate between them. As a result, the findings are readily interpretable within either approach.
###### Abstract  
Camera and LiDAR data fusion has been a popular research area, especially in the field of autonomous vehicles. This study evaluates the efficiency and accuracy of different depth point extraction methods, including Point-by-Point (PbyP), Complete Region Depth Extraction (CoRDE), Central Region Depth Extraction (CeRDE), and Grid Central Region Depth Extraction (GCRDE), across object categories such as person, bicycle, car, bus, and truck, and occlusion levels ranging from 0 to 3. The approaches are assessed based on extraction time, accuracy, and root mean squared error (RMSE). Bounding box-based methods, such as PbyP and CoRDE, consistently show slower extraction times compared to segmentation mask methods, with CeRDE being the most efficient in terms of computational speed. However, segmentation mask methods, particularly CeRDE and GCRDE, offer superior accuracy, especially for complex objects like trucks and cars, where bounding box methods struggle, particularly at higher occlusion levels. In terms of RMSE, segmentation mask methods consistently outperform bounding box methods, providing more precise depth estimations, particularly for larger and more occluded objects. Overall, segmentation mask methods are preferred for applications where accuracy is critical, despite their slower processing speed, while bounding box methods are suitable for real-time applications requiring faster depth extraction. GeRDE offers a balance between speed and accuracy, making it ideal for tasks needing both efficiency and precision.  
Keywords:computer vision; LiDAR; light detection and ranging; object detection; multi-sensor fusion; distance measurement; real-time depth extraction; semantic depth sensing; autonomous vehicles +
Footnote †: journal: Medical  
Traditionally, LiDAR-camera fusion involves projecting LiDAR point cloud data onto the camera image plane using extrinsic and intrinsic calibration parameters, where the LiDAR points within the camera's field of view are used to enhance perception tasks. This fusion typically includes combining spatial information (i.e., the 3D positions) from LiDAR with appearance features from camera images, followed by applying object detection algorithms to locate and classify objects. After detecting objects, the challenge lies in associating LiDAR points with object bounding boxes to determine their precise distances.  
This research proposes three novel approaches for extracting object distances using camera-LiDAR sensor fusion: Complete Region Depth Extraction (CoRDE), Central Region Depth Extraction (CeRDE), and Grid Central Region Depth Extraction (GCRDE). Each method focuses on improving distance estimation accuracy by leveraging sparse depth maps generated from LiDAR data and refining object-specific distance calculations using object detection results. By comparing the three methods, this study aims to highlight the strengths and limitations of each approach, providing insights into more accurate and efficient distance estimation for real-time applications such as autonomous driving.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method  
AIDA is an iterative method that aims to minimize the number of unconnected SMs at every execution. A set of unconnected SMs is submitted to the method to be processed at each iteration. After the current iteration is finished, the percentage of unconnected SMs (\(P_{u}\)) is evaluated. A new iteration starts if this percentage is greater than the stopping criteria (\(P_{u}^{max}\)), which establishes the maximum of unconnected SMs accepted by the simulation. A list of remaining unconnected SMs is processed in the subsequent run. The AIDA method includes the following steps (Figure 2):
## 2 Methods
### The Issue of Optimization  
The analysis used two optimization methods in the form of a standard genetic algorithm (GA), and a simulated annealing (SA) algorithm. Ready libraries for the DELPHI software environment were used.  
The procedure for the numerical determination of the optimal parameters can be summed up in the following steps:  
1. Calculations of the eigenvalues and eigenvectors in Finite Element (FEM) software, adopting baseline frequency \(\omega_{i}\), to which MTMDs will be tuned;
2. Adopting a point for reducing a system to an equivalent SDOF system, normalization of eigenvectors and determination of the parameters for the Equation (7);
3. Adopting a TMD number and initial parameters \(m_{k^{\prime}}^{t}\), \(c_{k^{\prime}}^{t}\), \(k_{k}^{t}\) for the TMDs;
4. Adopting variables, which will be subject to the optimization process, e.g., \(c_{k^{\prime}}^{t}\), \(k_{k}^{t}\) for a constant value of \(m_{k}^{t}\) (different constant parameter options possible);
5. Selection of the optimization method (GA or SA) and the optimization issue, calculations of the equivalent system \(\mathrm{FRF}\left|\overline{H}_{jj}(j\omega)\right|\), and the determination of values using the Equations (61) or (62) at each optimization step;
6. After reaching the desired calculation accuracy (change of the \(J1\) or \(J2\)) value, saving the obtained MTMD parameters, and calculations of the equivalent system \(\mathrm{FRF}\left|\overline{H}_{jj}(j\omega)\right|\) associated with the \(j\) degree of freedom, which was adopted as the equivalent system reduction point.
## 3 Results and Discussion
### Adsorbents Characteristics
#### 3.1.3 Surface Morphology of Adsorbents  
Scanning electron microscopy was used to image the surface morphology of investigated carbon and polymer materials (Figures 10 and 11). The morphology of both carbons C3 and C4 is differentiated (Figure 10). The surface morphology of the carbon C3 is characterised by uneven cavities and fine open pores including layered systems and crevices. In the case of the carbon C4, symmetrical forms of porous structures are visible, and the entire carbon material is much more compact and tight. The morphological images are in close correlation with the porosity analysis using low-temperature nitrogen adsorption-desorption and SAXS results. The SEM analysis combines the conclusions regarding the larger pore sizes for sample C3 (Figures 5B and 8A), the amount of closed porosity (the largest for sample C4), and the size of the interfacial surface (also the largest and with the sharpest edge for the sample C4), which is shown in Figure 9.  
Morphologically, the polymer materials P1 and P2 constitute spheres with dimensions of \(\sim\)500 \(\upmu\)m and \(\sim\)300 \(\upmu\)m. The difference concerns the quality of the spheres for the sample P2 and the quality of their formation. The polymer material P1 consists of spheres with a uniform and smooth surface, under which, as shown in Figure 11B, there is a porous structure. The polymer P1 consists of spheres with a greater number of deformations and a disturbed spherical structure. Similar to the sample P1, the interior of the sphere is also made up of a porous polymer structure (Figure 11D).  
Figure 9: Porod plots were calculated for investigated porous carbons (**A**) and polymer materials (**B**). The asymptotic range of the Porod curve was approximated by a linear function for the Porod constant. Specific surface S/V was calculated from I(0), Porod invariant, and asymptotics. The S/V values were used for the calculation of S\({}_{\text{SAXS}}\) from the equation: S\({}_{\text{SAXS}}\) = (10,000\(\times\frac{\xi}{\xi}\)) / \(d\) where d is the bulk density of the materials.  
Figure 11: SEM images for the porous polymer P1 (**A**,**B**) and P2 (**C**,**D**) at two selected magnifications. SEM images (**B**,**D**) are an enlargement of the area marked with a square.  
Figure 10: SEM images for the carbon materials C3 (**A**,**B**) and C4 (**C**,**D**) at two selected magnifications.
# Theory of Movement of Machine-Tractor Unit with Trailer Haulm Harvester Machine
## 1 Introduction  
The most significant technological operations in the sugar beet harvesting process are digging the root crops out of the soil and removing the uncut leaves [1, 2, 3]. Sugar beet haulms have suitable nutritional properties and can be used as feed in animal husbandry in green form and silage [3, 4]. Sugar beet haulms are now widely used as a raw material in the production of biogas and organic fertilizer, which can be applied to the soil immediately after its cutting and disintegrating.  
The use of asymmetric machine-tractor units in harvesting sugar beet and fodder beet haulm is quite justified. In this case, the stable motion of such a harvesting unit should be understood as the one in which the deviation of its trajectory under the influence of random disturbing factors (perturbed trajectory) from the trajectory without the actionof these factors (unperturbed trajectory) is within the limits of permissible values. These permissible values can be considered as such agotechnical requirements, which ensure the quality of the technological process of harvesting the haulm while reducing energy costs, reliability of operation, and the same.  
The theoretical study of motion stability of agricultural machines and machine units is based on the basic provisions of motion stability of complex tracking dynamic systems. It is based on the study of perturbed and unperturbed motions, described by systems of differential equations of their motion in the horizontal plane [5; 6; 7; 8].  
The papers [5; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] are devoted to studying the motion stability of agricultural machine-tractor units using computational mathematical models of their motion (functioning). The theoretical study of the plane-parallel motion of asymmetric machine-tractor unit and some questions concerning the stability of its motion in the horizontal plane are thoroughly considered in the article [18; 19].  
The effective use of trailed haulm harvesters in the harvesting of haulm is caused by the need to ensure their stable movement in the horizontal plane because, in this case, such harvesting unit is usually asymmetrical. The solution to this issue is an urgent scientific and technical problem, which still does not have a sufficiently complete solution. Part of the difficulty in solving it lies in how the trailed haulm harvester is connected to the energy vehicle (tractor) used. Of the two possible options for connecting a trailed harvester to the rear tractor unit, rigid and articulated, both lead to the creation of asymmetric machine-tractor units, each of which has its features and disadvantages. Therefore, improving the trajectory performance and efficient operation is relevant for all trailed haulm harvesters.  
The study aims to determine the parameters of stable plane-parallel motion of asymmetric haulm machine and tractor unit based on numerical computer simulation of the obtained analytical dependencies.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 4 Numerical Results
### Abstracted Physical Layer  
To abstract the physical layer, we made use of so-called decoding regions [44]. Based on a certain interference pattern, we decided whether the decoding of a replica was successful or not, simply by checking whether the corresponding noise plus interference vector fell within the decoding region. An \(m\)-dimensional decoding region can be seen as an extension of a threshold model to channels with \(m\)-dimensional channel parameters, such as block fading channels.
## 3 Results and Discussion
This highlights the transient nature of storm surge hazards, dependent on the typhoon's relative position to the nearshore region. As shown in Figure 13, only the Yangtze River and Giantang Estuary area were affected by the typhoon. The extreme water level rise was between 0 and 4.5 m. The flow direction in this area deflected from W to E. There was no significant rise in the coastal water level in southern and central Jiangsu and Quanzhou, Fujian. The flow direction in southern Jiangsu was W. The rivermouth regions appeared to be more susceptible to storm surge disturbances due to their concave-shaped coastlines. Jiangsu Province, with a coastline of 945 km long, was most seriously affected by the storm surge. In Typhoon In-fa (2021), taking the south of Jiangsu as an example, the typhoon process exceeded the warning water level, as shown in Figures 15 and 16. The exceeding time process was located from 15:00 on 24 July to 16:00 on 24 July 2021 and from 4:00 on 25 July to 5:00 on 25 July 2021. Typhoon In-fa (2021) affected the southern region of Jiangsu Province for 3 days, and the maximum water increase lasted for 2 h at a time. During the storm surge of Typhoon In-fa (2021), the variation range of the water level was up to 5 m, with the gradient roughly uniform. The water increase gradient varied substantially within a certain range from the typhoon center. What is more, the area of water increase was smaller than that of water decrease, and the water increase gradient was larger than that of water decrease gradient during the typhoon period. There was some water reduction after the water increase. [PERSON] et al. conducted a numerical analysis of the impact of levee openings on storm surge in coastal Louisiana [24]. They found that levee openings can effectively reduce storm surges in adjacent basins. The levee openings can inform coastal management strategies in the East China Sea; by analyzing the results, coastal managers can assess the potential impacts of levee breache  
Figure 4:2 Water level during Typhoon In-fa (**left**: 25 July 2021 4:00, **right**: 25 July 2021 5:00). Figure 4:3 Water level during Typhoon In-fa (**left**: 24 July 2021 15:00, **right**: 24 July 2021 16:00). and plan accordingly. This information can guide the design and construction of leveee systems, ensuring that they are resilient and capable of withstanding storm surge events. In addition, the stormwater increased dramatically in front of the typhoon, and the water reduction phenomenon occurred after it; that is, the water reduction phenomenon generally occurred behind the typhoon. The stormwater increment in the southern sea area of Jiangsu and the Yangtze River Estuary changed most violently, followed by the central sea area, and the northern sea area had the least impact, which means special attention should be paid to the southern sea area of Jiangsu for disaster reduction due to storm surges, and it is also necessary to consider the impact of storm surges in the design of marine buildings.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 3 Results  
The databases Web of Science (364), PubMed (337), and Scopus (543) yielded a total of 1244 publications. Removing 589 duplicates produced 655 items. Following an analysis of their abstracts and titles, 488 entries were removed. The remaining 167 papers were successfully obtained by the writers, who were able to verify their validity. This method resulted in the elimination of 148 articles that were deemed off subject. This study includes the qualitative analysis of the final 19 articles (Figure 2). The results of each investigation are shown in Table 2.  
Figure 2: PRISMA flowchart of literature search and article inclusion process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Type of Study** & **Aim of Study** & **Matherials and Methods** & **Results** \\ \hline \multirow{8}{*}{[PERSON] et al. (2024) [220]} & \multirow{8}{*}{Feasibility interventional study} & Determine the feasibility of recruiting, retaining, and treating individuals with mild dementia and periodontitis; assess cognition during dental visits. & Non-randomized clinical study, cognitive and periodontal assessments over 24 months, involving professional and personalized care. & In total, 18 participants enrolled, 15 completed 12 months, 8 completed 24 months. Significant improvement in periodontal health indicators (e.g., reduced bleeding and plaque levels). Cognition declined initially but stabilized later.
## 3 Results and Discussion
### The Impact of Different Influencing Factors on Carbon Emissions in Wastewater Treatment
#### 3.4.4 The Impact of Influent Water Quality and Emission Standards on Carbon Emissions  
No notable correlation was found between the influent COD and TN and the indirect carbon emission intensity. However, a significant positive relationship was observed with the direct emission intensity. At lower influent COD and TN levels, the direct emission intensity remained relatively low. On the contrary, as influent COD and TN increased, so did the direct emission intensity. A higher influent COD supplied more degradable organic matter, favoring methane production, while a higher TN led to increased N\({}_{2}\)O emissions during nitrification and denitrification processes, both contributing to the rise in direct emission intensity. When the influent COD was below 100 mg/L and TN was 20-30 mg/L, the total emission intensity was the lowest. Appropriately controlling the influent water quality can effectively reduce the direct emission intensity, but the impact of increased carbon sources needs to be considered.  
Statistics regarding the influent water quality of the 49 WWTPs revealed that the average value of COD stood at 217.3 mg/L. The average BOD was 78.5 mg/L, and the average TN measured 34.95 mg/L. The BOD/TN ratio was calculated as 2.25, falling below the minimum of 3.0, which is essential for the denitrification process. Due to the insufficient carbon source, 43 out of the 49 WWTPs needed to add carbon sources, accounting for as high as 87.8%, which significantly increased the chemical carbon emission intensity. In addition, the stricter the emission standard, the higher the indirect emission intensity (Figure 8). This trend was consistent with the research conclusion of [PERSON] and others [15; 21; 41; 42]. It was analyzed that the strict emission standards required wastewater treatment plants to reduce the effluent COD, BOD, and ammonia nitrogen concentrations, which not only required higher aeration volumes and more chemicals but also increased the amount of sludge produced and the consumption of electricity.  
Figure 7: The impact of load rate on emission intensity.
## 3 Results and Discussion  
### Physical and Chemical Characteristics of Fe-CMB  
Figure 1 shows the surface morphologies of Fe-CMB obtained from FE-SEM, revealing a rough and porous surface with round lumps. Interestingly, the porous structure of Fe-CMB was maintained despite Sb(V) adsorption. Furthermore, Fe-CMB is mainly composed of C, O, Cl, Fe, and Ca (Table 1). After the adsorption experiments, the Cl and Ca contents of Fe-CMB reduced significantly, indicating their loss during adsorption. The Fe on the surface of Fe-CMB did not decrease and detach despite the adsorption experiments, indicating that Fe was firmly bound to the biochar surface.  
Figure 1: Surface morphologies of Fe-CMB (**a**) before and (**b**) after Sb(V) adsorption. Analyzed using a field emission scanning electron microscope.  
Fe-CMB and Sb(V)-adsorbed Fe-CMB displayed several distinctive peaks, as shown in Figure 2. The broad band at 3229 cm\({}^{-1}\) corresponds to the O-H bending vibration [10]. The peaks observed at 2920-2930 and 2860-2865 cm\({}^{-1}\) correspond to the methylene C-H asymmetric/symmetric stretch, while the peak at 1454 cm\({}^{-1}\) also corresponds to the methylene C-H bend [26]. The peaks at approximately 1135 and 1020-1028 cm\({}^{-1}\) represent the skeletal C-C vibrations [26]. The peaks near 600 cm\({}^{-1}\) are attributed to O-H out-of-plane bending [26]. The peaks observed between 650 and 1275 cm\({}^{-1}\) are ascribed to \(\equiv\)Fe-O [27]. The changes in the peaks after Sb(V) adsorption differ from those in previous studies, where Sb(V) was removed using metal oxides. [PERSON] et al. [28] reported that the intensity of the peaks at approximately 1100 and 3200 cm\({}^{-1}\) increased while Sb(V) was adsorbed onto iron oxide by inner-sphere complex with the contribution of hydroxyl groups from adsorbed antimonate. Although the results of this study were dissimilar to those of a previous study [28], peak improvement at 1020 cm\({}^{-1}\) evidently indicated the adsorption of Sb(V). The peak at 1020 cm\({}^{-1}\) is generated by the 8(OH) of hexahydrox antimonate salt, which may probably be enhanced by the adsorption of Sb(V) [27]. This suggests that Sb(V) is adsorbed onto Fe-CMB via electrostatic attraction or ion exchange.