esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 5 Experiment Results and Analysis
### Evaluation Index
#### 5.2.3 Evaluation Index for Unique Identification Similarity  
When assessing whether map data have been infringed upon, it is crucial to compare the similarity between the identifiers of the potentially infringing data and the original data to accurately match the unique identifiers. The assessment of unique identifier similarity primarily incorporates metrics such as Hamming distance (HD), signal-to-noise ratio (SNR), and bit error rate (BER). Among these, the bit error rate directly quantifies the error rate of unique identifiers during transmission or processing, offering an intuitive and straightforward measure of the similarity between unique identifiers. Consequently, this paper opts to use the bit error rate (BER) as the metric for evaluating similarity between two unique identifiers. Specifically, the bit error rate (BER) denotes the ratio of unequal values between the unique identifier in the blockchain identifier library and the constructed unique identifier to the total number of values, as depicted in Equation (16).  
The bit error rate (BER) is a critical metric in evaluating the similarity between unique identifiers, with values ranging from 0 to 1. A higher BER signifies lower similarity between two unique identifiers, thereby reflecting the model's stronger uniqueness but potentially weaker robustness. Drawing on extensive experimental data, as well as insights from related studies such as those by [PERSON] [43] and [PERSON] et al. [44], this paper determines a threshold of 0.1 for BER. Through initial experiments and benchmarking against other approaches, a threshold of 0.1 was established to strike an optimal balance between robustness and accuracy. When the BER remains below 0.1, the blockchain system can reliably and efficiently match unique identifiers, ensuring both effective copyright protection and practical applicability. This selection ensures a solid practical performance across diverse scenarios while aligning with theoretical considerations in digital watermarking and data integrity applications.
## 4 Results and Discussion
### Unsteady Simulation Results
#### 4.2.1 Characteristics of Diffuser Stall  
To explore the unsteady behaviors of diffuser stall, the surface static pressure fluctuations were extracted and analyzed on the near shroud side (0.95 span) at seven different locations (Figure 14). The diffuser vane leading-edge (point 1) and middle position of flow passage are distributed equidistantly to the diffuser vane exit (points 2-7). The TVD and SVD spatial distribution of FFT results at different locations under the near-stall operating point are presented in Figure 13. The discrete frequency peaks are observed at 86.645 Hz, the fundamental and two times blade-passing frequency (BPF) in TVD and SVD. The discrete frequency 86.645 Hz, which is approximately equivalent to 35.7% of impeller-passing frequency (IPF). According to [PERSON]'s study [24], the stall frequency is 1/5 to 1/2 of IPF, so 86.645 Hz was considered the stall frequency of TVD and SVD.  
In TVD, the discrete frequency amplitude corresponding to 86.645 Hz of the diffuser vane inlet (point 1 and point 2) is slightly higher than the fundamental frequency, representing that the pressure fluctuation is more influenced by the diffuser stall cell. As the monitoring point move towards the vane exit, the amplitude of the fundamental frequency is higher than that of the discrete frequency (86.645 Hz), demonstrating that the pressure fluctuation in diffuser passages is mainly affected by the fundamental frequency. It is noteworthy that at point 3 (diffuser passage throat) of TVD, pressure fluctuation caused by the diffuser stall cells is still significant, which indicates that the diffuser throat of TVD may be blocked by stall cells. The evolution of the leading-edge vortex in the TVD passage will be emphasized below. However, there is no such phenomenon in SVD.
## 5 Discussion
### Influence of Chamber Earth Pressure and Thrust Force on Surface Deformation  
As described in Section 4.1, the chamber earth pressure and thrust force have a great impact on the surface deformation before the shield arrival section. [PERSON] et al. [21] concluded that through the centrifugal model test, the double diameter of the tunnel excavation surface is the tunneling influence range. Therefore, the values of the chamber earth pressure and thrust force around the first five rings of the cutter are selected to establish a relationship diagram with the corresponding surface deformation, as shown in Figures 16 and 17.  
As shown in Figure 16a, before the cutter reaches the monitoring section, the surface deformation shows an increasing trend, and the thrust force also shows an increasing trend. When the ring number is 335-339, it increases first and then decreases. This is because the proportion of highly weathered argillaceous siltstone increases, the proportion of round gravel stratum decreases, the friction between soil and shield shell decreases, and the soil still leaves. To ensure small surface deformation, the thrust force needs to reduce. In Figure 16b, ground settlement is observed before the shield cutter reaching the monitoring section, and the thrust force tends to increase to maintain small surface deformation. The thrust force of the north line is significantly greater than that of the south line, and the surface deformation is in the state of settlement. Combined with the analysis of crossing  
Figure 15: The relationship between grouting pressure and grouting volume: (**a**) south line; (**b**) north line.  
stratum, it is found that the proportion of other strata with natural unit weight greater than round gravel stratum in the south line is less than that in the north line, so the static earth pressure of soil in the north line is greater, and the thrust force required for propulsion is also greater.  
As shown in Figure 17a, when the cutter reaches the monitoring section, the surface is heated, and the chamber earth pressure shows an increasing trend, which is in line with the basic law of chamber earth pressure and surface deformation. In Figure 17b, the surface deformation tends to decrease, but the chamber earth pressure fluctuates. This is because the change in chamber earth pressure has a greater impact on the surface deformation than the thrust force. At the same time, the geological situation of the crossing section is highly complex. To ensure small surface deformation, the chamber earth pressure needs to be continuously adjusted. Therefore, the fluctuation of chamber earth pressure on the north line is greater than that on the south line.  
In soft soil areas, the relationship between the thrust force, chamber earth pressure, and surface deformation is similar to that of rounded gravel strata, both being positively correlated [9; 22].
## 5 The Proposed Protocol
### Communication Overhead of the Proposed Protocol
#### 5.6.3 Device Deployment Redundancy and Redundant Coverage Optimizations  
In fact, when developing an efficient data fusion grouping protocol for IoT networks, we must consider the redundancy of device deployment and the difficulties associated with redundant coverage within the service area. Overlapping coverage and redundant devices can result in excessive energy consumption and ineffective network resource utilization. The network's longevity and efficacy are greatly improved by our implementation of the proposed protocol, which takes these aspects into account. To guarantee stability and fault tolerance, redundant device deployment basically involves the positioning of several SNs in close proximity to one another. Redundancy can improve the resilience of a network, but it can also lead to redundant data transfers and inefficient energy usage. The proposedprotocol further employs data fusion techniques to consolidate redundant information from multiple SNs, thereby reducing the volume of transmitted data and conserving energy. By aggregating data at designated fusion points, it eliminates duplicate and non-critical information while maintaining the integrity and accuracy of the dataset. As a result, the proposed protocol ensures that the DFG density will not surpass the network density as shown in Table 3 and will be equal to it. We prevent the deployment of duplicate devices by doing this. On the other hand, redundant coverage describes the situation in which overlapping sensing zones result from many SNs covering the same area. Increased communication overhead and needless data replication may result from this redundancy. The protocol addresses this by employing intelligent data fusion mechanisms that merge overlapping sensing data into unified datasets, effectively mitigating redundancy. In the proposed protocol, SNs are only in active mode within their TS. They only transmit data to the related PDFHs during this particular TS. After that, they use a TDMA schedule to transition back to sleep mode. A PDFH will not accept data from an SN if it is sent to another PDFH because SN is not one of the PDFH's DFGMs. In this manner, intra-DFG interference is avoided. However, DSSS is utilized to reduce inter-DFG interference. This means that every DFG is given a distinct spreading code that is different from the codes issued to other DFGs. This distinction in spreading codes prevents DFGs from interfering with each other, ensuring efficient data fusion and enabling the network to utilize its resources optimally while maintaining robust fault tolerance and operational stability.
## 1 Introduction
Three-dimensional stereophotogrammetry was extensively tested for its reliability and validity: high precision and accuracy were shown [12; 24], and good accuracy was found when facial scans (FSs) were superimposed on CBCTs [25]. However, there is a great diversity in soft tissue analysis and FS analysis has never been compared with the analysis of CBCT. In the analysis of FSs, there is still a lack of a standardized reference system that could allow for better analysis for longitudinal studies. In 2D cephalometry, the cranial base serves as the reference structure for superimposition. However, in 3D soft tissue images, there are no stable structures available for superimpositions to measure growth. One study explored using an alternative reference frame for facial scans, utilizing the mid-pupil point as the origin, specifically applied to infants [26]. This reference system for children proved clinically applicable for soft tissue analysis but was not evaluated with respect to CBCT, stating that the mid-pupil reference point can be easily affected by the position of the pupils at the time of scanning. Hence, due to the importance of soft tissue variations after orthodontic treatment, the aim of this study is to evaluate the accuracy and repeatability of two reference systems for three-dimensional facial scans (FSs), compared with a reference system based on CBCT scans, that can be useful as reference systems for soft tissue analysis. The hypothesis to be assessed is that these new reference systems are valid and reproducible with respect to a CBCT reference system.
This representation allows us to formulate the schedulability test problem as a reachability problem: any task of a real-time system may miss its deadline if the \"bad state\" corresponding to this situation is reachable in the corresponding Kripke model of the system. Using the inverse transition relation of the Kripke model for real-time systems in which there is one less processor than there are tasks, we distinguish 14 possible cases to analyse the backward reachability from the bad state. Five of these cases are unreachable from the initial state, which means that the system is schedulable. The remaining cases show that the missed deadline state is reachable, so in these cases, the system fails the schedulability test. Each of these cases can be described in terms of comparisons of task parameters, namely their execution times and deadlines. We integrate these comparisons into two first-order logical inequality predicates that take into account all the tasks in the system. The truth of these predicates corresponds to a negative verdict of the schedulability test for the real-time system under test. Thus, an algorithm that checks the truth of these predicates for the tasks of the system performs an exact schedulability test. The time complexity of this exact test is quadratic in the number of tasks. Since these predicates correspond to the exact non-schedulability of real-time systems with one less processor than there are tasks, we show that they can be used for an algorithm that partially solves the non-schedulability problem for systems with an arbitrary ratio of processors to tasks. This algorithm, which tests the sufficient condition for non-schedulability, also has quadratic time complexity. In addition, we propose an algorithm for partitioning the system into clusters of \(k\) tasks and \(k-1\) processes, while simultaneously applying our schedulability test. Comparative experiments show that our schedulability and non-schedulability tests have a significant advantage in execution speed even compared to sufficient tests, and the non-schedulability testing algorithm is almost as accurate as the state-of-the-art algorithm from [28]. The rest of the paper is organized as follows. In Section 2, we recall the basic definitions of real-time systems and scheduling and formalize real-time systems with non-preemptive tasks and a global fixed priority scheduler as Kripke models. Section 3 presents the main results of our work: an analysis of the backward reachability from bad states and algorithms for schedulability and infeasibility tests that exploit this analysis. We describe our experiments in Section 4, providing details of the implementation. In Section 5, we discuss related work and our results. Our conclusion is given in Section 6.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Experimental Setup
#### 4.1.3 Implementation Details  
Our method was implemented on an Nvidia GeForce RTX 3090 20 GB GPU, using input 3D patches of size 128 \(\times\) 128 \(\times\) 128 for 400 epochs, with the batch size set to 2, a learning rate of 0.01, and a weight decay of \(3\times 10^{-5}\). The initial values of the curvature matrix in CFA were set to \(\alpha=-\frac{1}{16}\), \(\beta=\frac{5}{16}\), according to [PERSON] [34]. The curvature diffusion is best when \(\alpha=-\frac{1}{16}\), \(\beta=\frac{5}{16}\), which are the most favorable for curvature learning. The values in the loss function are set to \(w_{1}=0.5\) and \(w_{2}=0.5\).
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 3 System Applications  
In this section, the various applications utilizing the special characteristics of the COACH techniques are discussed. Due to a limited space of this review, we confine the discussion only to the applications of 3D imaging, super-resolution imaging, and imaging through scatterers.
## 4 Discussion
#### 4.1.2 Limitations  
With only five unfocused transmit beams (ultrafast ultrasound imaging), injection of contrast agent microbubbles is required to generate beacon signals because synthetic transmit focusing of speckle with only five plane waves does not generate a strong enough coherent wavefront.  
A real-time implementation was achieved with focused transmit beams (three frames/s abdominal scanning [40]) without injection of a contrast agent. However, real-time implementation with injection of a contrast agent and using bubble echoes as beacon signals (post-processing in Ref. [37]) was never reported, and is likely very challenging because automatic detection of bubble position would be necessary. The stronger the aberration, the smaller the size of the isoplanatic patch, and the more isoplanatic patches are required to improve image quality, which means that many APs must be estimated (one per isoplanatic patch; see the change in the quality of the image of scatterers #2 and #5 in Figure 8b,c). This imposes a tradeoff between the image quality and computational time.  
The PS method does not improve localization error, especially in depth (panel g in Figures 5, 6 and 8). This is because the PS method uses the information of an already miss-located scatterer (acting as the beacon signal), and, therefore, it does not compensate for the overall error in the calculated travel-time caused by the higher wave-speed in bone. As shown in Figure 9, the PS leads to a travel-time similar to that calculated in the conventional reconstruction. This ambiguity in the overall travel-time (depth ambiguity) leads to suboptimal image reconstruction.  
As indicated by the numerical results in Figure 6, accurate estimation of the AP with the beamsum channel correlation method when the brain tissue has a heterogeneous wave-speed is very challenging if the amplitude of the beacon signal is not high enough. The image quality obtained by the PS method in this scenario is poor (panel c in Figures 5 and 6).  
Figure 9: The travel-time estimated for scatterer 1 (solid lines) and scatterer 3 (dashed lines) positioned in patch 1 and 3, respectively (see Figure 3c), with the three different reconstruction techniques when a steered plane wave at (**a**) \(-\)15 degree and (**b**) 0 degree is used for transmission. The bone and background models were flat and homogeneous, respectively.  
Advantages and Limitations of the Geometry-Based Phase Aberration Correction Technique for Ultrafast Transcranial Ultrasound
## 3 Delay Propagation Model for Route Networks
### Suitability of the SEIR Model  
[PERSON] and [PERSON] [23] In 1991, the SEIR model was proposed based on the SIR model with the addition of the latency state E of the epidemic, which increased the applicability of the model by the latency period not being infectious. Currently, with the popularization of the model, in addition to epidemiology [24], they are also used in information [25] or social networks [26] of transmission mechanisms, etc., and the state iterative transitions are shown in Figure 3.  
(1) A delayed node of a route network can transmit delays to its associated nodes, which is equivalent to an infected person I transmitting a virus to a susceptible person S in the SEIR model.  
(2) Normal nodes, after being affected by delayed nodes, do not have delayed propagation capability immediately because the nodes themselves have a certain capacity and will be infected only after a period of time, which is equivalent to the susceptible person S being infected by the infected person I in the SEIR model, which is transformed into the latent person E first and then into the infected person I with the capability of infection.  
(3) The delayed nodes of the route network will be delayed and dissipated after a certain period of time by way of route network optimization, flight deployment, etc., and transformed into non-delayed nodes, which is similar to that of an infected person I who recovers from the disease and transforms into an immune person R.  
From the above analysis, it can be seen that the route network delay propagation process is similar to the infectious disease model propagation process, which indicates that the SEIR model is borrowed for the study of the route network delay node-to-node propagation applicability is better and more in line with the actual situation than the SIR model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Section Number** & **Name** & **Distance** & **Recovery Probability** \\ \hline
193 & JNVOR -WFVOR & 171.0139039 & 0.552342289 \\
194 & LAGAL -LYGVOR & 126.2095388 & 0.440802791 \\
195 & ATVIM -XZVOR & 34.06497851 & 0.238785375 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 3: Schematic of the SEIR Model.
## 3 Using a Knowledge Graph for a Home Service Robot
### Object Knowledge Reasoning Based on SWRL Rules  
It is difficult for the system to identify the color, shape, and material properties of each object instance without making mistakes. Thus, some properties are lacking, and reasoning is an effective method for obtaining missing knowledge. This section describes the dependencies between the object attributes. SWRL rules are used to establish the acquisition mechanism for missing attributes of objects and to perform reasoning using object knowledge. This strategy enhances the logic of the ontology model and enables the robot can infer the missing attributes based on the existing attributes of the object instance.  
As shown in Figure 4, based on the acquisition of various attributes of object instances in the initial stage of the system, the mechanism includes the following rules:  
The first rule type is the inference acquisition of the functional properties of objects. It includes two cases. The first case is to obtain functional attributes based on the category, physical, and visual attributes, including rules such as: Category (?x, Cup) \(\rightarrow\) hasAffordance (?x, Containable), indicates that if the instance?x is a \"Cup\", then it has the functional attribute \"Containable\". The second case is to use co-occurrence and infer missing functional attributes from known functional attributes, including rules such as: hasAffordance (?x, Openable) \(\rightarrow\) hasAffordance (?x, Closable), indicating that if instance?x has the functional attribute \"Openable\", then it also has another functional attribute \"Closable\".  
The second rule type is the inference acquisition of the operational properties of objects. The operation attribute is obtained by functional attribute inference. It contains rules such as: hasAffordance (?x, Graspable) \(\rightarrow\) hasOperational Attribute (?x, Grasp), indicating that if the instance?x has the functional attribute \"Graspable\", then it also has the operational attribute \"Grasp\".  
In addition, as shown in Figure 5, a mechanism for acquiring missing category, physical, and visual attributes of the object is established. The reasoning relationship between attributes is formally represented to achieve mutual reasoning for the three attributes  
Figure 4: The acquisition mechanism of missing attributes of objects.  
and the individual attributes. It includes rules such as: Category (7x, Ball) \(\rightarrow\) hasShape (7x, Sphere), indicates that if the instance 7x is a \"Ball\", then it has a visual attribute, i.e., the shape is a \"Sphere\"; hasShape (7x, cubic) \(\char 66\) hasWidth (7x, 20) \(\rightarrow\) hasHeightcm (7x, 20) \(\char 66\) hasLengthcm (7x, 20) means that if the shape of instance x is a cube and its width is 20 cm, then its length and height are also 20 cm.