esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles
## 5 BSD Radar System Performance Test
### Designed BSD Radar Performance  
The performance of the BSD radar system was verified using a target simulator for BSD detection during vehicle operation. The evaluation criteria included the maximum detection range, maximum detection speed, response time, maximum detection angle, and distance resolution. The testing methodology for each criterion is described below.  
1. Maximum detection range: The simulator was set up at distances of 50 m, 52 m, and 55 m to simulate the distance of a forward vehicle. A passenger car size of 10 m\({}^{2}\) was assumed as the target size. The results were compared with the simulation distance to verify whether the detection accuracy was within \(\pm\)0.5 mm.
2. Maximum detection speed: The target simulator was set up with a forward vehicle at a distance of 50 m, driving at speeds of 130 km/h, 140 km/h, and 150 km/h. The target size was assumed to be a passenger car with a size of 10 m\({}^{2}\). The results were compared with the simulation speed to verify whether the detection accuracy was within \(\pm\)1 km/h.
3. Response time: The target simulator was set up with a forward vehicle at a distance of 50 m, driving at a speed of 100 km/h. The target size was assumed to be a passenger car with a size of 10 m\({}^{2}\). The results were compared with the target detection response time to verify whether the detection occurred within 50 ms.
4. Maximum detection angle: A 10 m\({}^{2}\) corner reflector for vehicle modeling was installed at points corresponding to \(\pm\)75\({}^{\circ}\) in the left and right directions. The results were compared with the sensor detection angle to verify whether the detection accuracy was within \(\pm\)5\({}^{\circ}\).
5. Distance resolution: Two 10 m\({}^{2}\) corner reflectors for vehicle modeling were installed at a distance of 1.5 m apart in the forward and backward directions. The results were compared with the sensor detection distance to confirm the distance resolution (1.5 m).  
The performance test results for the five items of the designed BSD radar showed that the maximum detection distance was 55 m, the maximum detection speed was 150 km/h, the response time was 34.64 ms, the maximum detection angle was 150\({}^{\circ}\), and the distance resolution was 1.5 m. The results indicate that the BSD radar installed in the vehicle can meet the performance requirements for ensuring a safe and stable driving environment.  
Figure 14: Geometry of BSD Radar Installation and Warming Area.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
###### Abstract  
Since Landsat-1 first started to deliver volumes of pixels in 1972, the volumes of archived data in remote sensing data centers have increased continuously. Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and revisit periods of these archived data are vastly different. In addition, the remote sensing data received continuously by each data center arrives at a faster code rate; it is best to ingest and archive the newly received data to ensure users have access to the latest data retrieval and distribution services. Hence, an excellent data integration, organization, and management program is urgently needed. However, the multi-source, massive, heterogeneous, and distributed storage features of remote sensing data have not only caused difficulties for integration across distributed data center spatial infrastructures, but have also resulted in the current modes of data organization and management being unable meet the rapid retrieval and access requirements of users. Hence, this paper proposes an object-oriented data technology (OODT) and SolrCloud-based remote sensing data integration and management framework across a distributed data center spatial infrastructure. In this framework, all of the remote sensing metadata in the distributed sub-centers are transformed into the International Standardization Organization (ISO) 19115-based unified format, and then ingested and transferred to the main center by OODT components, continuously or at regular intervals. In the main data center, in order to improve the efficiency of massive data retrieval, we proposed a logical segmentation indexing (LSI) model-based data organization approach, and took SolrCloud to realize the distributed index and retrieval of massive metadata. Finally, a series of distributed data integration, retrieval, and comparative experiments showed that our proposed distributed data integration and management program is effective and promises superior results. Specifically, the LSI model-based data organization and the SolrCloud-based distributed indexing schema was able to effectively improve the efficiency of massive data retrieval.  
multi-sourced remote sensing big data; data integration; data management; distributed data centers; OODT
## 3 Results
### Mechanically Induced Hemolysis Analysis in Bovine and Porcine Blood  
Using bovine and porcine anticoagulant-treated blood, mechanically induced hemolysis was evaluated with a roller pump. In both bovine and porcine blood, pfHb concentrations increased over time, while the number of RBCs with morphological integrity decreased (Table 2 and Figure 4). The pfHb increase rate was considerably higher in bovine blood than in porcine blood. However, there was no significant difference in the ratio of RBCs with morphological integrity or the rate of decrease between bovine and porcine blood.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Time (Hours)**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**Hct**} \\ \cline{3-3} \cline{5-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & & \\ \hline \multirow{4}{*}{**Porcine** **(n = 3)**} & \(\Delta\)_p_ff_hp_ (mg/dL) & 11.4 & 22.8 & 34.7 & 47.7 & 57.9 & 74.0 & \multirow{4}{*}{12.1} & \multirow{4}{*}{0.997} \\  & & \(\pm\)5.1 & \(\pm\)8.8 & \(\pm\)14.7 & \(\pm\)19.5 & \(\pm\)22.3 & \(\pm\)29.7 & & \\ \cline{2-10}  & \multirow{2}{*}{Percent of} & 72.4 & 51.7 & 40.7 & 26.9 & 19.5 & 11.9 & \multirow{2}{*}{14.1} & \multirow{2}{*}{0.943} \\  & & \(\pm\)7.3 & \(\pm\)12.5 & \(\pm\)8.9 & \(\pm\)8.3 & \(\pm\)4.6 & \(\pm\)4.1 & & \\ \hline \multirow{4}{*}{**Bovine** **(n = 3)**} & \(\Delta\)_p_ff_hp_ (mg/dL) & 181.6 & 312.4 & 493.9 & 575.7 & 768.4 & 909.7 & \multirow{4}{*}{148.8} & \multirow{4}{*}{0.995} \\  & & \(\pm\)27.5 & \(\pm\)20.6 & \(\pm\)51.4 & \(\pm\)30.2 & \(\pm\)58.1 & \(\pm\)56.4 & & \\ \cline{2-10}  & \multirow{2}{*}{Percent of} & 69.8 & 61.4 & 50.6 & 43.6 & 36.4 & 14.7 & \multirow{2}{*}{12.2} & \multirow{2}{*}{0.943} \\  & & \(\pm\)10.9 & \(\pm\)12.4 & \(\pm\)15.0 & \(\pm\)7.4 & \(\pm\)11.3 & \(\pm\)4.6 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of the hemolysis tests.  
Figure 4: Hemolysis test. (**A**) Plasma-free hemoglobin (\(\Delta\)_p_ff_hp_) over time. (**B**) The percentages of morphologically intact red blood cells over time. (**C**) Light microscopy observations of red blood cells over time (20\(\times\)).
## 3 Results and Discussion
To protect the sound source from possible damage (design considerations, diaphragm deflection), the measurements in this experiment were limited to the selected frequencies. It was experimentally proved that the propagation of acoustic waves with assumed parameters, including frequency and modulation, affects air particles. From the point of view of physics, any propagated acoustic wave causes local pressure changes, with the phenomenon occurring if the level of the resulting turbulence is sufficient to extinguish the flame (once the critical frequency value is reached) (see Figure 3). In practice, turbulence depends on the frequency and the modulation used. This is due to the fact that sound pressure depends on the power applied to the speaker, and this relationship is not linear (linearity is observed only in a limited range). The wave motion of molecules also interacts  
Figure 5: P [W] vs. f [Hz] for modulated and unmodulated acoustic waves. favorably with the surface of the flame (by propagating the acoustic wave and increasing the speed of air movement at the edge of the flame, there is a reduction in the area over which the combustion process occurs, thus spreading the flame over a larger area). In addition, as previously mentioned, the wave motion of the particles has a beneficial effect on the temperature of the flame (if it decreases and is lower than the ignition temperature, extinguishment will occur). The consequence of acoustic waves is the distribution of heat over a larger area, and thus the disruption of combustion (continuous exposure of the flame to acoustic waves will result in its deviation from its original position, breaking into pieces, and then being completely extinguished). As shown, the extinguishing effect is affected by the frequency of the wave and modulation, which translates into a difference in the necessary power that must be delivered to the sound source for complete extinguishment of the flames to occur. It is worth noting that both attempts to extinguish the flames with modulated and unmodulated waves were successful. In this experiment, the sound pressure level at which the phenomenon of acoustic flame extinction was observed ranged from about 116 dB to 127 dB. Because the extinguishing capabilities of acoustic waves have not been fully recognized to date, this technique is still undergoing testing and development. The presented method may make it possible to replace traditional fire protection measures with it, especially in the case of extinguishing firebreaks, in the future.
# Surface Engineering of Woodworking Tools, a Review
## 4 Surface Technologies  
The development of tool materials looks to combine high hardness with high toughness, to improve their mechanical, tribological and thermophysical properties, leading to an improvement in the durability of cutting tools. One of the most important properties of the coating is good adhesion to the substrate and between the substrate layers. Other important properties are chemical stability and high wear resistance in working conditions. One can specify two groups of technologies that improve the wear resistance of tools.  
The first one includes methods for improving the mechanical properties of the material tools (such as heat or thermo-chemical treatment). Heat treatment is to give them the desired mechanical properties by changing their structure, while the thermo-chemical treatment is to enrich the surface layer of the alloy in a certain element, such as C, N, Al, Cr, Si, or group of elements, e.g., C and N, N and S, N and O. The aim of these treatments is to give the surface layer specific physical properties--mainly resistance to abrasion, or chemicals--usually by resistance to oxidation at high temperatures. The quality of the tool is improved by applying heat treatment that allows it to obtain the desired hardness of the blade, and the appropriate fine-grained structure of steel and toughness.  
Thermo-chemical treatment, especially nitriding, has a beneficial effect on the performance characteristics of tools. Many scientists indicate an increase in the wear resistance of nitrided tools for woodworking. Dependent on the type of technology used, this increase is as high as 100% [14; 23; 39].  
The second group of technologies includes working surface modification techniques for tools by applying coatings with special properties. Among these, CVD (chemical vapor deposition) [42] and PVD (physical vapor deposition) [43] methods and surfacing using a submerged arc welding (SAW) technique and a mixture of alloying elements spread on the surface under industrial flux [44] can be listed.  
The properties of the coatings can be modified by the type and roughness of the substrate, the deposition temperature, the composition and pressure of the gaseous atmosphere in the vacuum chamber, substrate bias voltage, and arc current or magnetron power, dependent on the deposition method. The above parameters influence the chemical and phase composition of the coatings, crystallite size, density of the coating, and surface quality (roughness). As an effect of properties such as hardness, elastic modulus, and also adhesion, toughness and wear resistance can be improved. The findings of [PERSON] et al. [45]  
Figure 1: The subject of woodworking and woodworking tools in scientific articles, 1980–2022.  
indicate that increase in substrate hardness results in higher adhesion of the coatings, but increase in substrate roughness results in lower adhesion of the coating investigated.
## 2 Study Area  
The field studies were conducted in the territory of the Volchya kimberlite pipe, which is located in the central part of the Zimneberezhny diamondiferous district within the Verkhotinsky uplift of the Ruchyevskiy protrusion of the crystalline basement (Arkhangelsk region, European part of Russia) (Figure 1) [32].  
The temporal sections reveal the presence of three seismo-stratigraphic strata, which are clearly distinguished from one another by the boundaries of the basement surface and  
Figure 1: Study area and pipe contour.  
the Riphean. The Riphean is overlain by the Vendian sediments. The surface of the Ust-Pinezhskaya Formation is the most stable within the Vendian sediments, as indicated by seismic data [32]. Explosion pipes have been observed to penetrate the Vendian sediments, which are situated at depths of 50-70 m below the surface [33].  
The Volchya pipe belongs to small pipes with poor diamondiferousness [34]. The Volchya pipe is manifested as a weakly contrasting subvertical area of increased electrical conductivity. The Volchya pipe is confined to the area of Quaternary paleodoline development, where the watered sediments display lenticular areas of increased electrical conductivity in the upper part of the section. As evidenced by electrical exploration data, the pipe exhibits a distinct eastward inclination [35]. The surface area of the crater facies rocks was estimated at 4.15 hectares. Following prospecting and appraisal work, it was determined that the excitation object of the magnetic anomaly was not a single large kimberlite body but two converging pipes named Volchya southern and Volchya northern [32] (Figure 2). The observed separation into two distinct objects may be indicative of a complex geological structure. The plan view of the site reveals two distinct oval-shaped structures of varying dimensions, extending along a north-northeast axis. The Volchya northern kimberlite pipe penetrates terrigenous Vendian sediments in close proximity to the Volchya southern pipe. However, it possesses significant distinctive features that allow it to be considered an independent kimberlite body. This is corroborated by the presence of two epicenters of magnetic anomalies and different petrographic compositions, which fulfill the criteria for kimberlite beccias [32].  
The vent and crater facies formations are integral to the structure of the pipes. The vent facies are represented by tufobreccia and xenotuffobreccia, while the crater facies include  
Figure 2: Geological section of the Volchya pipe [32].  
tuff sandstones, tuff siltstones, sedimentary breccias, tuffs, and tuffites (Figure 2). The vent is characterized by the presence of tuffaceous breccias in its central region, while xenotuffaceous breccias are observed in the periphery. The boundaries between the rock types within the pipe are relatively conventional, as evidenced by the gradual transitions observed in the majority of boreholes penetrating the vent, which progressed from xenotuffborecia to tuffaceous rocks [32].
## 2 Materials and Methods
### Introduction of The GNSS SDR Using the A-VTTCES-KF  
The architecture of the GNSS SDR using the proposed A-VTTCES-KF technique is illustrated in Figure 9, where red lines denote the VT feedbacks, blue blocks represent the SV ephemeris and the implementations which rely on ephemeris. Since the coherent integration time of the tracking process is set to 5 ms in this research, and the general LR is the reciprocal of the integration time, which is equal to 200 Hz. On the other hand, the VT LR with the numerical values of 5 Hz and 10 Hz are both applied to the subsequent field test. The carrier NCO is assisted by the A-VTTCES-KF while the code NCO is dependent on the carrier NCO aiding. Under this circumstance, both the PLL and delay lock loop (DLL) are working under the assistance of the VT algorithm.
## 4 Discussion
Although perfect superimposition is still difficult to obtain with digital comparison software, the technique using best-fit alignment significantly amended the merging accuracy and dwindled the quantification fallacy [42]. Also, many researchers quoted outcomes for measuring change using the best fit alignment [14; 43; 44; 45; 46; 47] although other authors relied on the zero-method technique for calculating deviations [11; 12; 18; 21; 35]. The zero method relies on the implant center to calculate angulations and distance deviations. In addition, some preceding studies used a coordinate measuring machine (CMM) instead of desktop scanner [11; 12; 18; 21; 35]. Even though the CMM is a repeatable measuring method, it shows abbreviated exactitude in assessing small areas due to its probe size and shape [37]. In our previous work and in a similar study by [PERSON] and [PERSON] similar conclusions were found showing favorable results for photogrammetry even when the latter used the zero-method technique [35; 36]. In contrast, [PERSON] and her colleagues contradicted the results of this investigation and stood out among other previous clinical reports stating that photogrammetry provided the least accurate values, with the highest discrepancy [34]. It is critical to mention that a different DPG system called Icam Imetric was used, which may have a different capture complexity than Pic. Despite the significant findings of this study, limitations do exist. Correlating findings of this in vitro study to clinical situation should be carried out with attentiveness as there are contributing factors that although standardized are different in the oral environment [9]. This includes different light reflectivity, presence of saliva, and limited access during scanning. In addition, upon importing the STL files into Geomagic software, inconsistencies in the mesh quality were noticed between the groups. Since DPG STL files were imported from Pic library, they had the least irregularities in comparison with DIOS. This may be the reason behind the underestimation of the intraoral scanning technology which may have influenced the RMS 3D deviation. However, angular deviations were sufficient to reflect the validity of the results. Further analysis should investigate the effect of mesh topography when weighing up STL files and determining accuracy. Also, the current study only attempted to assess the data acquisition step of the workflow and did not investigate what effect this may have on the manufacturing procedures, such as the processing and production of the definitive full arch framework. Challenges remain in identifying the appropriate methodology for comparing these techniques since alignment of datasets is still highly prone to errors and these techniques vary in their workflow and strategy. Future studies should be directed towards evaluating the influence of various comparison methodologies, implant angulation, photogrammetry systems, and ISBs on the legitimacy of the impression techniques.
# Value of Information Based Data Retrieval in UWSNs
## 4 Path Planning Algorithms
### Hybrid Path Planner-HPPP  
The algorithms for path planners HPP and HIPP were devised after studying results from our simulation experiments. What we observed was that the LPP would perform best, in terms of VoI accumulation, when there where no catastrophes, i.e., VoI profiles were similar across all the sensor nodes. Contrarily, GPP and GIPP performed better in the scenario where the sensor nodes were reporting catastrophes.  
```
1:procedureHPP(\(S,s_{o}\))
2:\(S\leftarrow\{s_{1},s_{2},...,s_{n}\}\)\(\triangleright\) Set of sensor nodes
3:\(V\leftarrow\emptyset\)\(\triangleright\) Visitation sequence
4:\(S_{HP}\leftarrow\{s_{m}\mid s_{m}\in S\wedge s_{m}\text{ is high-priority}\}\)\(\triangleright\) Set of high-priority sensor nodes
5:\(S_{LP}\gets S-S_{HP}\)\(\triangleright\) Set of low-priority sensor nodes
6:\(V\leftarrow\)GPP(\(S_{HP},s_{o}\))
7:\(V\gets V+\)LPP(\(S_{LP}\))
8:return\(V\)
9:endprocedure
```  
**Algorithm 4** Hybrid Path planner-**HPPP**.  
So the thought process behind this path planner is to use a greedy algorithm (GPP or GIPP) for retrieving data from nodes that are reporting catastrophes, while, using TSP like algorithms (LPP) for retrieving data from the rest of the nodes. This implies switching a greedy and a shortest path algorithm and so the name Hybrid Path Planner. HPP switches between GPP and LPP.  
The algorithm for HPP is given as Algorithm 4. The complexity of this algorithm is \(\mathcal{O}(h^{2}+l)\) where \(h\) is the number of high-priority sensor nodes while \(l\) is the count of low-priority sensor nodes. \(\mathcal{O}(l^{2})\) is the complexity of GPP being used within HPP while \(\mathcal{O}(l)\) is the complexity of LPP. As \(h\leq n\) and \(l\leq n\), therefore, the complexity of HPP is determined as \(\mathcal{O}(n^{2})\).