esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study introduces a novel technique for relative attitude estimation of non-cooperative targets using point cloud data. The proposed method enhances inter-frame alignment accuracy by selecting local regions of the point cloud for planar fitting, projecting noisy points at various distance scales, and implementing noise suppression techniques, including a noise filtering method based on moving least squares microplane projection (mpp-MLS) that preserves local features.  
A key advancement presented in this research is the enhancement of the ICP alignment algorithm, referred to as the J-ICP algorithm. The J-ICP algorithm incorporates small-angle transformed poses between neighboring frames, effectively mitigating error growth in position estimation and reducing computational time. This approach significantly improves the accuracy and efficiency of the pose estimation process.  
A semi-physical experimental platform was established to validate the methodology proposed in this paper. The experimental findings demonstrate that during coarse alignment, the method exhibited strong robustness towards noise interference encountered during point cloud acquisition. Furthermore, in fine alignment, the J-ICP method outperformed the traditional ICP approach by minimizing translation and Euler angle errors. Specifically, the cumulative translation error remained below 1.57 mm, while the cumulative Euler angle error stayed within 0.071 degrees, achieving a reduction of maximum translation and rotation errors by 56% and 65%, respectively. The total elapsed time for the process was approximately 98 ms, which enabled a pose tracking frequency up to 10 Hz.  
Conceptualization, Y.L. and J.Y.; methodology, Y.L. and Z.S.; validation, Y.L., Z.S. and Y.H.; formal analysis, J.Y.; investigation, Y.L.; resources, Y.W.; data curation, Y.L., Z.S., and J.Y.; writing--original draft preparation, Y.L.; writing--review and editing, Z.S. and Y.H.; supervision, F.Z.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant numbers: 2023 YFC2604900 (sub-project grant number: 2023 YFC2604904); the National Key Research and Development Program of China, grant number: 2023 YFF0719800 (sub-project grant number: 2023 YFF0719801).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
## 4 Experimental Results
### Experimental Results
#### 4.2.1 Different Ratios of Application Resource Demands  
To evaluate the performance on different application resource demands, three ratios for resource demands, ([1:2], [1:4], and [1:6]) were assumed in the first experiment. We used these different ratios in experiments to evaluate the performance of heterogeneous degree. The higher the ratio we applied, the more heterogeneous the resource demands will be. The ratio of [1:2] represented that one application demand requires a resource with 1 CPU core and 2 GB memory for running Spark PageRank, and another application demand requires 2 CPU cores and 1 GB memory for running Spark Pi. Furthermore, [1:4] and [1:6] represented that the ratio of CPU cores and memory capacity is ((1,4) and (4,1)), and ((1,6) and (6,1)), respectively, for two different applications. Both Spark Pi and Spark PageRank applications were used in the Spark system and different resource demands from these two kinds of applications were submitted to the system as processing jobs. The number of submitted jobs for each application was increased from 4, 8, 16 to 32 for each round of the ratio.  
The experimental results are depicted in Figures 3-5. These results illustrated that, when the resource demand of an application was closer to the available resources of the server, resource utilization was higher and performance improvement was obvious. The proposed MRG algorithm improved the average performance with 6.5%, 8.5%, and 1.9% for each ratio. Particularly, the performance gain was up to 12.4% in the ratio of [1:2]. The performance gain did not increase with the number of submitted jobs because all application demands exceeded the total system resources. In addition, when the resource demands of an application do not match the available resource of a server, MRG does not waste any unused resource, unlike the load-balanced algorithm.  
Figure 4: Ratio of resource demand is [1:4] in which one application requires 1 CPU core and 4 GB RAM and another application requires 4 CPU cores and 1 GB memory.  
Figure 3: Ratio of resource demand is [1:2] in which one application requires 1 CPU core and 2 GB RAM and another application requires 2 CPU cores and 1 GB memory.  
Overall, the results of this experiment proved that system performance improved with MRG compared with the load-balanced algorithm. The more jobs submitted to the system will increase the waiting time for available resources for processing the application jobs. In addition, a resource gap occurred while some resource capacities were exhausted and other resource capacities on the same server were still available. Furthermore, higher resource utilization was observed in the MRG algorithm so as to reduce the completion time. As a result, improving the resource utilization by the MRG algorithm can reduce the overall completion time.
## 2 Results and Discussion
### Reactivities of Model Bio-Oil Components
Levulinic acid was derived from the acid-catalyzed conversions of both d-glucose (Scheme 7) and 2-hydroxymethylfuran (Scheme 10), which are both known pathways. Levulinic acid has been obtained by dehydration of hexoses to 5-hydroxymethylfurfural (HMF) and its subsequent hydration in acidic media [2,28]. Acid-catalyzed conversion of 2-hydroxymethylfuran to levulinic acid in aqueous solutions also has been recently reported [29]. **Scheme 10. ** Acid-catalyzed conversion of 2-hydroxymethylfuran to levulinic acid in aqueous solution [29]. Butyl levulinate shows the extent of levulinic acid formation. Table 13 demonstrates that it originates from both d-glucose and 2-hydroxymethylfuran. Increasing butyl levulinate formation was observed when the amount of 2-hydroxymethylfuran and d-glucose were increased in the model bio-oil [22]. Trace amounts of 3-methyl-2-hydroxycyclopent-2-enone were generated from hydroxyacetone over Cs\({}_{2.5}\)/K10, A15 and DX2 catalysts. Among the new products formed, 1-octene oligomers were the most abundant components over Cs\({}_{2.5}\)/K10 or A36. However, phenol alkylates became the most abundant products for the other three catalysts. Trace amount of 2-(2-furylmethyl)furan, formed by furfuryl alcohol polymerization, was detected over the Cs\({}_{2.5}\)/K10 catalyst. No 2-(2-furylmethyl)furan was detected over other catalysts, because all the reactive furan products were consumed by these more acidic catalysts. \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{3}{c}{**Addition amounts (g)**} & \multicolumn{2}{c}{**Butyl levulinate yields**} \\ \hline
**2-Hydroymethylfuran** & **d-glucose** & **1-Butanol** & **(Area \%)** \\ \hline
0.15 & 0.15 & 0.15 & 0.22 \\
0.15 & 0.30 & 0.30 & 0.34 \\
0.15 & 0.30 & 0.30 & 0.98 \\
0.30 & 0.15 & 0.30 & 1.39 \\
0.30 & 0.30 & 0.60 & 2.45 \\ \hline \end{tabular}  
* Reaction conditions: (cataly, 0.15 g; 1-octene: 1-butanol: phenol: water: acetic acid: acetaldehyde: hydroxyacetone: d-glucose: 2-hydroxymethylfuran (g) = 1.35: (0.15–0.6): 0.94: 0.15: 0.15: 0.12: 0.12: (0.15–0.3): (0.15–0.3); these results were first noted in the preliminary communication of this work found in Reference [22].  
\end{table} 
Table 13: Butyl levulinate yields obtained from reactions of 1-octene/1-butanol with model bio-oils containing different amounts of d-glucose, 2-hydroxymethylfuran and 1-butanol at 120 \({}^{\circ}\)C in 3h over Dowex50 WX2 [FOOTNOTE:]Footnote : Reaction conditions: (cataly, 0.15 g; 1-octene: 1-butanol: phenol: water: acetic acid: acetaldehyde: hydroxyacetone: d-glucose: 2-hydroxymethylfuran (g) = 1.35: (0.15–0.6): 0.94: 0.15: 0.15: 0.12: 0.12: (0.15–0.3): (0.15–0.3); these results were first noted in the preliminary communication of this work found in Reference [22].
## 3 Results
### Analysis of Surface Quality and Parameter Effects
[4] and [PERSON] et al. [41]. The presence of this lubricant film reduces friction and prevents excessive plastic deformation during the burnishing process, resulting in a more homogeneous surface structure. In contrast, the dry-processed surface in Figure 8b shows more pronounced scratches and irregularities, with deeper micro-indentations and a higher surface roughness (Ra = 0.727 \(\upmu\)m). These observations indicate that the lubrication provided by the MQL environment reduces friction and deformation, resulting in a superior surface finish compared to the dry condition. This deterioration in surface quality under dry conditions can be explained by the increased friction and thermal effects at the tool-workpiece interface, as discussed by [PERSON] et al. [42]. The absence of lubrication leads to higher localized temperatures and more intense plastic deformation, resulting in more aggressive material displacement and surface damage. The microscopic data support prior results that MQL considerably enhanced surface integrity by better controlling the tribological conditions in the burnishing zone. Although this study did not directly investigate tool life, the observed reduction in surface roughness and the improved uniformity of the burnished surface suggest the potential for longer tool life. This hypothesis is based on the established relationship between smoother surfaces and reduced tool wear, as reported in similar studies. However, further experimental studies are required to confirm this assumption. These findings are especially important in  
Figure 7: Impact of ball burnishing parameters on surface roughness. industrial applications where surface quality directly influences component performance and durability. The results clearly demonstrate MQL burnishing's apparent superiority in creating improved surface qualities, underscoring its promise as a sustainable and efficient production strategy, providing both environmental and technical benefits over dry burnishing processes.
## 3 Radius of Plastic Zone and Peripheral Displacement
### Effect of Tunnel Burial Depth  
The burial depth of the tunnel is reflected in the vertical initial stress, \(\sigma_{y}\). The variation curve of the radius of the plastic zone with burial depth (tunnel radius = 4 m, internal friction angle = 23.5\({}^{\circ}\), cohesion = 0.2 MPa) is shown in Figure 1a. As can be seen from Figure 1a, the radius of the plastic zone increases with the increase of tunnel burial depth. For example, when the support resistance is assumed to be 0.4 MPa, the radius of the plastic zone of the tunnel is 6.2 m at a depth of 100 m, and 12.9 m at a depth of 300 m.  
The variation curve of peripheral displacement with burial depth (tunnel radius = 4 m, internal friction angle = 23.5\({}^{\circ}\), cohesion = 0.2 MPa) is shown in Figure 1b. It can be seen from Figure 1b that the peripheral displacement increases sharply with the increase of tunnel depth. For example, when the support resistance is assumed to be 0.4 MPa, the peripheral displacement of the tunnel is 9.1 cm at a depth of 200 m, and 96.7 cm at a depth of 500 m. It is evident that the depth of the tunnel has a great influence on radius of the plastic zone and the surrounding displacement. From the later research, we can see that this property will be even more significant in soft rock tunnels.
## 5 Data Analysis and Hypothesis Testing  
### Reliability and Validity Test  
Experiments 1 and 2 were only different in terms of their video content, and the measured variables were the same. Therefore, we combined the data from these two experiments for the reliability and validity tests.  
All data were analyzed by SPSS (version 25.0), which was developed by IBM in Amund City, NY, USA. The reliability coefficients of the variables in the questionnaire ranged from 0.843 to 0.954, fully satisfying the requirement of being greater than 0.7 (see Table 4), indicating that the experiments had good internal consistency.  
All items on the measurement table were taken from similar studies with good content validity. The combined reliability of all variables was greater than 0.9, and all average variance extracted (AVE) values were greater than 0.7, indicating that the measurements have good structural validity and convergent validity (see Table 5). Furthermore, all coefficients of the variables were greater than 0.9. The square root of the AVE of all variables was greater than the correlation coefficient of this variable with other variables, indicating that this measurement has good discriminative validity (see Table 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **Perceived** & **Perceived** & **Purchase** \\  & **Information** & **Risks** & **Value** & **Intention** \\ \hline
**Perceived Information** & & & \\
**Volume (PIV)** & 0.9215 & & \\
**Perceived Risks (PR)** & \(-\)0.063 & 0.9211 & \\
**Perceived Value (PV)** & 0.776 ** & \(-\)0.127 & 0.8398 & \\
**Purchase Intention (PI)** & 0.676 ** & \(-\)0.134 & 0.827 ** & 0.8796 \\ \hline \hline \end{tabular}  
* was significantly correlated at 0.01 level (bilateral).  
\end{table}
Table 4: Reliability analysis of variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & **Reliability Coefficient** & **Item Number** \\ \hline
**Perceived Information Volume (PIV)** & 0.940 & 4 \\
**Perceived Risks (PR)** & 0.843 & 6 \\
**Perceived Value (PV)** & 0.954 & 10 \\
**Purchase Intention (PI)** & 0.902 & 4 \\
**Overall Reliability** & 0.926 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Combined reliability and AVE.
## 4 Implementation
### Libp2p Host  
To mitigate the weaknesses of Bluetooth Mesh at the network level, it is possible to make use of P2P solutions. For instance, the integration of libp2p with Bluetooth Mesh provides significant advantages, especially when it comes to opportunistic communications and lowering the inherent limitations of Bluetooth Mesh. The following are the benefits of using libp2p in conjunction with Bluetooth Mesh to address the limitations of the technology:  
* Mesh networks are limited to short-range communications, so while Bluetooth Mesh would handle local broadcast, libp2p would handle a broader broadcast to the rest of the nodes.
* Mesh networks are not designed to withstand sudden disconnections; in an opportunistic network, this is essential so libp2p can help data persist on the network by allowing message propagation even if the source nodes go offline.
* If by any chance the Bluetooth transport is limited, Libp2p acts as a higher level of connection management, allowing interoperability between different transport technologies (not only Bluetooth Mesh, but also Wi-Fi or LTE, if available).
* Although Bluetooth Mesh is designed for low-power nodes, message retransmission within the mesh network can be somewhat inefficient due to continuous retransmissions, libp2p can optimize global routing and avoid redundant retransmissions, preventing messages from propagating further than necessary.  
In addition, other reasons to use libp2p in opportunistic applications include the following:  
* The networking stack may be customized to fulfill unique requirements thanks to its modularity.  
It provides a collection of standards that may be tailored to support a variety of transport protocols.
* It offers a range of discovery mechanisms and data storage.
* It contains multiple security features such as peer identity verification using public key cryptography and encrypted communications between peers using modern cryptographic algorithms.
* It includes features such as peer discovery and content routing that assist in guaranteeing that the network remains available and accessible even if some peers are offline or unreachable.
* It uses publish/subscribe messaging (Pub/Sub) to send a message to several receivers without the sender knowing who they are.
* The decentralized design enables the applications to function without a central authority.
# Inductive Imaging of the Concealed Defects with Radio-Frequency Atomic Magnetometers
## 2 Experimental Setup
The observed amplitude and phase of the signal describe the components of the secondary field orthogonal to the primary field axis, i.e., the components parallel to the surface of the object. These field components are only produced in the vicinity of a defect by asymmetries in the flow of the eddy currents. Figure 2 shows the changes of the amplitude (b) and phase (c) of the rf signal over a 64 \(\times\) 64 mm\({}^{2}\) area of a 6 mm thick aluminium plate with a single recess, whose depth changes from 0 mm to 5 mm (right to left) [23]. The red dashed line marks the position of the edge of the recess. The coil starts from a known position relative to the defects. With a known pixel/step spacing, positioning can be validated. The image was recorded in the self-compensated configuration with the atomic magnetometer operating at 1.9 kHz. It is worth pointing out that a non-zero signal is not only produced in the immediate vicinity of the recess edge, but also along the sloping area of the plate. Intuitively, this can be understood as a reflection of the rf primary field from the tilted recess surface, creating field components orthogonal to the bias field. In contrast to the phase image recorded at a higher frequency, shown in [26], the phase recorded in the vicinity of the edge [Figure 2c] contains information about the depth of the recess edge (phase changes with depth). There is a faint modulation visible in Figure 2b-f. These 'ripples' are caused by the movement of the trolley that supports the plate as it is moved by the arm of the translation stage, the ball barrings of which are slightly magnetic. The trolley only moves on one axis which accounts for the asymmetry in these ripples. Figure 2: (colour online) The measured change of the amplitude (**b**,**e**)/phase (**c**,**f**) of the rf signal over a 64 \(\times\) 64 mm\({}^{2}\) area of a 6 mm- thick aluminium plate with a single recess, whose depth changes from 0 mm to 5 mm (right to left). Images (**b**,**c**) are recorded with the recess on the top surface, while in (**e**), (**f**) the recessed surface is facing away from the sensor. Change of the recess depth, and inversely the plate thickness, is shown in (**a**,**d**), respectively. To record images (**b**,**c**,**e**,**f**), the same plate was simply flipped by 180\({}^{\circ}\). Red dashed line marks a position of the edge of the recess.
## 5 Performance Analysis and Optimization
### Optimization Performance on Service Guarantee Rate  
In this subsection, we aim to investigate the optimization effect of the DDAR scheme in different circumstances. The optimization regarding service guarantee rate is shown in this subsection.  
From Table 6, it can be seen that the optimization performance exists in all networks. The ratio of \(\rho\) is the average value of \(\rho\) in DDAR scheme divide the value of \(\rho\) in CDAR scheme with different value of \(P_{\alpha}\), \(\lambda\) and \(\eta\). DDAR scheme shows a better performance which the service guarantee rate increases by 25.01% on average.  
To find the parameter that dominates the performance of optimization, the networks whose service requirement is [3; 50; 10] is added in the simulation. As can be seen in Figure 9, the ratio decreases with the value of requirement increases and is saturated at 0.88 eventually. The performance of the network with the average gap of 10 is worse than that of the network with the average gap of 30. The added scenario shows that with the same number of requirements, the larger the average gap the service requirements, the better performance DDAR scheme has. In fact, the optimization performance is determined by the average gap of the requirements. If the requirement is smaller than the average requirement, there is a positive optimization effect. And if the requirement is larger than the average, there is a negative impact on optimization. Larger the absolute gap value, more obvious the impact of optimization. Additionally, the positive impact is obviously greater than the negative impact when the absolute value of gap is the same, this is the reason that DDAR performs better. Therefore, the parameter that determines the optimization performance is the average value of the difference between the average value of requirement and each value of requirement. DDAR has a good performance when the network has a large average differences of service requirements.  
Figure 10 demonstrates the optimization regarding \(\rho\) under the environments with a different value of \(\eta\). With the increase of \(\eta\), the optimization performance increases and decreases slightly. The networks with a \(\eta\) value of 0.1 have the best performance.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathcal{Q}=\mathbf{3}\) & \(\mathcal{Q}=\mathbf{5}\) & \(\mathcal{Q}=\mathbf{7}\) \\ \hline Ratio of \(\rho\) (\%) & 132.7 & 122.65 & 119.68 \\ \hline \end{tabular}
\end{table}
Table 6: Optimization of service guarantee rate.  
Figure 11 shows the delay distribution of the data packets with each service requirement under the networks with various \(\mathcal{Q}\). The number of data packets increases rapidly at first and gradually decreases to 0. In the networks with DDAR, the networks with different \(\mathcal{Q}\) have different delay distributions. The smaller the delay requirement, the more packets concentrated toward the small delay part and more sharply the number rises and falls. The packet delay distributions are the same for all \(\mathcal{Q}\) in all networks with CDAR.