esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Consensus Measure in Group Decision Making
Furthermore, Algorithm 3 does not disturb the consistency of HPMPRs, i.e., if the individual HPMPRs \(H_{k}=(h_{ij,k})_{n\times n}\)\((k=1,2,3,...,m)\) are consistent, then the output of Algorithm 3, the adjusted HPMPRs \(H_{k}^{(t)}=(h_{ij,k}^{(t)})_{n\times n}\), are also consistent. **Theorem 9**. : _Consider \(H_{k}=(h_{ij,k})_{n\times n}\)\((k=1,2,3,...,m)\) HPMPRs given by decision-makers. Let \(\{H_{k}^{(t)}\}\) and \(\{H_{S}^{(t)}\}\) be the sequences obtained from Algorithm 3. If \(\max_{k}\{CI(H_{k}^{(t)})\}\leq CI_{r}\), then_  
\[\max_{k}\{CI(H_{k}^{(t+1)})\}\leq\max_{k}\{CI(H_{k}^{(t)})\}\leq CI_{r}. \tag{36}\]  
_(please see Appendix A for Proof 9)._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(d\)} & \multirow{2}{*}{\(GCI_{r}\)} & \multicolumn{5}{c}{\(\beta\)} \\ \cline{5-8}  & & & & **0.2** & **0.4** & **0.7** & **0.9** \\ \hline
4 & 4 & 4 & 1.01 & 1.967 & 2.715 & 5.969 & 18.828 \\  & & & 1.05 & 1 & 1.271 & 2.747 & 8.101 \\  & & & 1.1 & 0.97 & 0.998 & 1.692 & 4.61 \\  & 3 & 3 & 1.01 & 1.878 & 2.574 & 5.75 & 18.261 \\  & & & 1.05 & 0.999 & 1.296 & 2.773 & 8.116 \\  & & & 1.1 & 0.938 & 0.967 & 1.589 & 4.311 \\  & 2 & 2 & 1.01 & 1.759 & 2.525 & 5.756 & 18.47 \\  & & & 1.05 & 0.884 & 0.927 & 1.552 & 4.394 \\  & & & 1.1 & 0.793 & 0.771 & 1.248 & 3.292 \\
5 & 4 & 5 & 1.01 & 1.892 & 2.393 & 5.471 & 17.564 \\  & & & 1.05 & 1 & 1.165 & 2.616 & 7.644 \\  & & & 1.1 & 0.99 & 0.998 & 1.714 & 4.569 \\  & 3 & 4 & 1.01 & 1.896 & 2.512 & 5.652 & 17.993 \\  & & & 1.05 & 1 & 1.611 & 3.203 & 9.615 \\  & & & 1.1 & 0.89 & 0.868 & 1.235 & 2.911 \\  & 3 & 3 & 1.01 & 1.964 & 2.759 & 6.044 & 19.299 \\  & & & 1.05 & 0.999 & 1.057 & 2.278 & 6.513 \\  & & & 1.1 & 0.86 & 0.871 & 1.133 & 2.655 \\ \hline
6 & 5 & 6 & 1.01 & 1.946 & 2.342 & 5.529 & 17.46 \\  & & & 1.05 & 1 & 1.121 & 2.563 & 7.507 \\  & & & 1.1 & 0.995 & 0.994 & 1.5 & 3.896 \\
5 & 5 & 1.01 & 1.932 & 2.457 & 5.723 & 17.95 \\  & & & 1.05 & 1 & 1.213 & 2.642 & 7.891 \\  & & & 1.1 & 0.999 & 0.999 & 1.523 & 3.994 \\
4 & 4 & 1.01 & 1.959 & 2.548 & 5.897 & 18.231 \\  & & & 1.05 & 1.01 & 1.254 & 2.742 & 7.982 \\  & & & 1.1 & 0.891 & 1.212 & 2.745 & 4.121 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average values of iterations in Algorithm 3.
## 3 Method
### Performance Evaluation  
The performance of the learned policy is evaluated in a real-world scenario in terms of success rate \(S_{rate}\), which specifies the ratio of the times of successfully achieving the desired task within the allowed error \(\delta\) to all testing times \(N\) consumed.  
\[S_{rate}=\frac{\sum\limits_{i}^{N}\hbar(d_{e}^{i}\leq\delta)}{N}, \tag{12}\]where \(\hbar(\cdot)\) is a indicator function outputting 1 when taking _True_ as input and outputting 0 when taking _False_ as input, and \(d_{e}^{i}=\left\|p_{f}-p_{d}\right\|\) is the distance error measured by the Euclidean distance between the target position \(p_{d}\) and the final robot (or gripper) position \(p_{f}\) at the end of the \(i\)th episode.  
```
1:Capture RGB image \(I_{rgb}\) and depth image \(I_{dpt}\) from RGB-D camera.
2:Obtain semantic image \(I_{sem}\) based on FCN.
3:Construct a task-related simulated environment with \(I_{sem}\) and coordinate transformation.
4:Design a policy network.
5:Train policy with PPO in the constructed simulated environment.
6:for\(k=1,2,\cdots,do\)do
7: Collect trajectory \(\tau\).
8: Update policy parameter \(\theta\) by maximizing the surrogate objective \(L^{clip}(\theta)\).(Equation (7))
9: Fit value function \(V_{\varphi}\).(Equation (10))
10:endfor
11:Until policy converges.
12:Sim-to-real inference phase:
13:for\(k=1,2,\cdots,T\)do
14: Semantic image \(I_{sem}\) is segmented from the captured real-world RGB image.
15: Synthesize simulated-like images \(I_{syn}\).
16: The trained policy takes synthetic images as input, and output actions controlling the real-world robot.
17:endfor
18:Until finish the target task.
```  
**Algorithm 1** RSR transfer method
# Voice Communication Systems Impact on Navigating Officers
## 5 Research Limitations  
This research has a limitation that has to be emphasized. The collected data referring to the frequency of incoming calls and their duration were based solely on the questionnaire survey. The participants of the survey have answered the questions based on their subjective opinion and the ability to recall the call particulars. Such an approach cannot provide data of high reliability and accuracy on the matter, hence, the results obtained by this research should be considered as estimated and used carefully. Further research of this kind should be based on more objective data sources, primarily on detailed and extensive phone logs for all types of communication systems studied. Another challenge experienced during this research that refers to confidentiality issues was the willingness of shipping companies to provide such call logs for the research purposes.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 4 Energy Management Model Mathematical Formulation
### Assumptions Related to the Energy Management Model  
The proposed adaptive energy management model is summarized in Figure 5. To provide a reasonable implementation of this proposal, the following assumptions (simplifications) have been considered:  
1. Use of a discrete-time model, in which the time horizon (\(\{0,\dots,W\}\)) is divided into slots of equal duration. This time model provides flexibility to energy management solution because the size of each time slot can represent different time units (e.g., a time slot equal to 10 min, or a time slot equal to 1 h) according to the applicability scenario.
2. Use of a finite and discrete-time window for performing the time shifting (forward and backward) for the service execution. In this regard, in a preliminary study [19], we proved that the use small time shifting intervals (e.g., intervals up to two or three time slots) is enough to achieve considerable improvements in energy use (e.g., 100% energy utilization and improved service processing). In addition, a small time shifting interval is preferred because increasing this parameter produces a non-polynomial increase in the complexity of the optimal energy management problem, as demonstrated in [19].
3. Use of discrete values for service quality degradation. To provide a feasible energy management model and maintain the linear condition of the problem, in the proposal, the values of degradation,also called levels of degradation that can be applied to a service, are restricted to a finite set of possibilities. Analogously to the time shifting value, this sequence of values should be small (e.g., up to three or four degradation levels) because an increase in levels corresponds to a non-linear increase in the complexity associated with optimal energy management.
4. The proposed energy management model assumes the processing of entire (complete) services. If \(P_{A}\) is insufficient to meet the total power demanded by a service, this service in analysis is considered as not processed or rejected. In this regard, the partial processing of a service and related consumption can be discussed in future work.
5. In 5G network slicing, a customer (user equipment) may be served by at most eight network slices at a time [1]. However, in our proposal for simplicity, a customer can only belong to one network slice. Moreover, the customers in each network slice are different from each other (i.e., different network slices specified to different customers).
6. If needed, the MNO can deploy multiple network slices of the same slice/service type (e.g., eMBB with the same features but for different groups of customers). In this case, the MNO is able to differentiate the slices according to the network slice identifier, as shown in the example in Figure 1a.
# Dynamic and Active THz Graphene Metamaterial Devices
## 3 Applications of Graphene Metamaterial
### THz Phase Modulation  
The graphene and the metamaterial combination present an effective solution to achieving tunable phase shift. Table 2 summarizes the progress of graphene-based terahertz phase modulators. It remains a big challenge to independently control the amplitude and the phase responses and even increase the modulation rate, requiring exploration of new modulation mechanisms and improved material fabrication techniques. [PERSON] et al. applied graphene capacitors integrated with metallic SRRs, demonstrating a novel electrically tunable phase modulator [72]. The graphene capacitors consisted of two large-area graphene electrodes separated by the dielectric, resulting in efficient mutual gating between the electrodes for high carrier densities. The graphene electrodes introduced additional electrical losses to the SRR due to graphene's sheet resistance, and it changed the overall impedance required to achieve resonance by capacitive coupling. Based on tunable high mobility carriers on graphene, the resonance of hybrid metamaterials can be tuned efficiently, achieving a phase shift of more than 90\({}^{\circ}\) with a gated voltage range from \(-1.5\) V to \(1.5\) V. The 90\({}^{\circ}\) phase modulation range cannot meet the wave front shaping, requiring a  
Figure 4: Optically pumped terahertz amplitude modulator based on graphene. (**a**) The metasurface is composed of graphene deposited on SU-8 substrates, the absorption spectra of three structures for perfect absorption conditions graphene conductivity (\(\sigma_{1}\)) and for another case (\(\sigma_{2}\)) that does not fulfill above conditions, the photoexcited spectra of a graphene-based structure under fluence from 0 to 0.690 mJ/cm\({}^{2}\); (Reprinted with permission from ref. [51] Copyright 2019, American Chemical Society) (**b**) The periodic array of split-ring resonators made of photoinduced monolayer graphene, the magnetic resonances of graphene SRRs under optical pumping, transmission spectra for unpumped and pumped graphene with different Fermi levels. (Reprinted with permission from ref. [65] Copyright 2018, American Chemical Society). more significant phase shift. A multi-layer split ring metasurface composed of graphene and perovskite film was proposed by [PERSON] [69]. Without external stimuli, the carriers in perovskite and graphene are in thermal equilibrium with little effect on higher-order Fano resonances. The optical pump excites the perovskite to generate charge carriers, breaking the thermal equilibrium and correspondingly the photoconductivity of the perovskite increases under pumping. This is also the case with graphene. Therefore, compared with perovskite only, DOM-GPPMM changes the optical conductivity more significantly at the same optical pump. Additionally, the modulation depth is further increased under the external bias voltage. Finally, the resonance can be tuned in the 0.2-1.0 THz band and achieve a maximum modulation depth of 346\({}^{\circ}\), as shown in Figure 5a.
## 4 Smart Contract Vulnerability Detection Tools
### Formal Verification  
One of the most accurate techniques for evaluating a system's accuracy and for verifying intelligent saving behavior in its early stages is formal verification [31]. Using mathematical language to fully define the desired behavior of the system to help increase correctness, formal verification works by first providing a system specification and then determining whether each step of the implementation complies with the specification. The steps below are typically included in the formal verification process to find smart contract vulnerabilities:  
1. Modeling: Turn the smart contract into a formal model by transforming the contract code into a form of a formal specification language (e.g., Solidity);
2. Property specification: Specify the security characteristics that must be examined, such as that assets cannot be tampered with or that they are immune to reentrant assaults. Predicate logic or temporal logic are two examples of formal logic that can be used to define these property designations;
3. Formal verification: Check the smart contract using formal verification techniques. Typically, this entails verification using instruments like model detectors and automated theorem provers;
4. Analytical feedback: Examine the validation results, if flaws are discovered, inform the developer so that they may be corrected.  
A mathematical justification for the system's lack of certain vulnerabilities is provided through formal verification. This method's advantage is its capacity to demonstrate the system's completeness without omitting any weaknesses, to identify prospective issues. However, formal verification has several drawbacks, like being more challenging to execute, requiring specialized expertise and experience, and having some restrictions, such as being unable to address all types of vulnerabilities. As a result, formal verification is frequently seen as a cutting-edge method for systems with stringent security needs.  
Solidity compilers, according to [PERSON] [32] are unreliable and might carry out attacks on bytecode rather than Solidity code. As a result, smart contracts' source code and bytecode are both converted into the F* language [33] (a functional language for the purpose of program review).
## 1 Introduction  
Fish resources, some of the most critical living resources, play a crucial role in preserving the planet's ecological balance, providing food for human beings, and serving as a material foundation for the long-term growth of human society [1; 2; 3]. As a result, the world has increasingly emphasized investigating, researching, and exploiting fishes [4; 5]. Fish species are diverse and numerous, so monitoring their numbers, distribution, and behavioral habits is crucial. As a result, completing fish detection in the underwater images or videos is vital, which can provide essential data for research and conservation efforts.  
Fish detection using computer vision modeling presents many challenges, such as illumination variations, low contrast, high noise, fish deformation, and a dynamic background [6]. Over the years, much work has been performed to address these challenges [7; 8; 9]. However, occlusion has always been a difficult problem in fish detection [6]. In underwater photographs and videos, other fish or environments, such as underwater plants, occluded many fish objects, making their detection challenging. Detecting these occluded fish objects is vital for a more precise understanding of fish abundance, activity, and ecology. Unfortunately, despite years of research, there is still no perfect solution to the occlusion problem in object detection [10; 11; 12; 13], making it extremely difficult to deal with the occlusion problem in fish detection.  
Many state-of-the-art object detection models have emerged, each with unique improvements in speed and accuracy breakthroughs. You Only Look Once v8 (YOLOv8) [14]and Real-time Detection Transformer (RT-DETR) [15] are the latest state-of-the-art models. This study significantly improves the YOLOv8 detection accuracy by integrating innovative parts of RT-DETR into YOLOv8. The loss function plays a crucial role in the object detection model, and different loss functions can cope with varying detection tasks and scenarios. Repulsion Loss is a specialized loss function specifically useful for occlusion scenes. In this work, Repulsion Loss replaces the original loss function of YOLOv8 to further improve YOLOv8 performance in fish detection under occlusion scenes. Overall, these main models and methods make the following significant contributions:  
First, the basic YOLOv8 model is responsible for the initial extraction of fish features in the images. Second, the integrated RT-DETR modules are responsible for encoding and decoding complex multi-scale fish features to make the model learn as many complex fish features as possible to cope with the occlusion scenarios. Finally, Repulsion Loss is used to make the model more accurately find the direction of the gradient descent on the occlusion dataset, which makes the trained model more robust in occlusion scenarios.
## 6 Conclusions  
IoT systems consist of wired and wireless communication, various heterogeneous devices, and various software. WSNs are widely used in the design of IoT systems. WSNs consist of sensor nodes with limited energy and the performance of sensor nodes affects IoT systems. Extending the lifetime of WSNs by increasing the transmission energy efficiency of sensor nodes is an important task. LEACH is a well-known protocol for WSNs. Many studies have been conducted that have revised the limits of LEACH to enhance the performance of WSNs. In this study, we proposed the D-LEACH protocol to reduce unnecessary transmission energy consumption caused by the reverse transmission when the sensor node is located between the BS and the CH. D-LEACH is a routing protocol that transmits data using the relative distance based on the RSSI between the sensor node and the BS. D-LEACH divides the network area into several layers using the relative distance between the sensor node and the BS. Each layer has a CH selection probability. The sensor nodes become the CH using the CH selection probability in the layer. The sensor nodes compare the relative distances of CH and BS it becomes a member of the CH or becomes a normal node. When the cluster configuration of WSNs is completed, the sensor node data are transmitted to the BS using direct transmission, single-hop transmission, or multi-hop transmission.  
To evaluate the performance of D-LEACH, we confirmed the lifetime, average energy consumption, and network throughput of WSNs according to the network size. Performance comparison showed that D-LEACH extends the lifetime of WSNs relative to LEACH and MH-LEACH. In addition, the average energy consumption of D-LEACH is less than that of LEACH and MH-LEACH. In particular, when the size of the WSNs increases, D-LEACH shows better performance. This shows that the energy balance of sensor nodes in D-LEACH is superior to that of LEACH and MH-LEACH. D-LEACH reduces the consumption associated with reverse transmission energy and increases the efficiency of data transmission energy, improving the network performance of IoT systems.  
In this study, we focused on reducing the consumption of transmission energy in WSNs. The security and reliability of the data were also considered. Future research tasks will determine how to secure enhance security and reliability by proposing cryptographic transmission of data in D-LEACH and how to reduce packet size to increase energy efficiency.  
**Author Contributions:** Formal analysis, K.M.N. and J.S.P.; funding acquisition, J.S.P.; methodology, K.M.N. and J.S.P.; project administration, J.S.P.; resources, K.M.N. and J.H.P.; writing-original draft, K.M.N.; writing-review and editing, K.M.N., J.H.P. and J.S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2017R1D1A1B03035833).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 5 Data Processing
### Gauging Performance of Both Filtering Methods  
To compare the proposed filtering method with the standard SOR procedure, the organized point cloud resulting from the steps described earlier is first processed with _pcdenoise()_ (Matlab implementation of SOR). With default settings, this procedure calculates for each datapoint a mean distance to the four nearest neighbors and then the standard deviation \(\gamma\) of all mean distances. Outliers are labeled by selecting all datapoints with a mean distance larger than \(\gamma\). Only a top part of the point cloud, relevant in bin-picking applications, is processed. Then, the proposed filtering is applied to the same top portion of the 3D data. The organized format of the point cloud is preserved by changing the z components of the labeled 3D points, as described in the previous subsection.  
The outcomes of both procedures applied to six different datasets are visually inspected for the existence of false negatives (i.e., outliers clearly mislabeled as inliers) and false positives (i.e., inliers incorrectly identified as outliers). Since no ground truth for outliers is available, the absolute filtering accuracy for each method (defined as a ratio of the number of filtered points to the number of true outliers) could not be calculated. To provide a quantitative comparison of both filtering procedures, only relative metrics can be determined. They are based on the three parameters: (1) the number of outliers \(N_{both}\) identified by both procedures; (2) the number of outliers \(N_{1}\) identified by SOR but missed  
Figure 10: Example of removal of the points belonging to a table and the bin: (**a**) original dataset with segmented top of the bin (marked in blue)—all points on XY plane outside of the segmented top of the bin are labeled as belonging to a table; (**b**) segmented parts in the bin (marked in black) and registered model of the bin (marked in blue).  
by the new procedure; and (3) the number of outliers \(N_{2}\) identified by the new procedure but missed by SOR. Based on the three numbers, the following metrics are determined:  
\[\alpha=N_{1}/N_{2}, \tag{3}\]  
\[\beta_{n}=N_{both}/N_{n},\ n=1,2. \tag{4}\]  
The meaning of \(\alpha\) is as follows: if \(N_{2}>0\) and \(\alpha=0\), then \(N_{1}=0\) and all outliers identified by SOR are also identified by the new procedure. When \(\alpha\) is small but nonzero, only a small fraction of all outliers found by SOR are not detected by the new procedure. Larger values of \(\alpha\) signal an increasing number of outliers identified by SOR only and missed by the new procedure. Therefore, a small \(\alpha\) is a sign of better performance of the new method when compared with SOR. The interpretation of \(\beta_{n}\) is as follows: its small value indicates that for the \(n\)-th method (SOR or the new one), only a small portion of detected outliers is found by both methods, i.e., a large fraction of outliers is found exclusively by the \(n\)-th method. Thus, a smaller \(\beta_{n}\) corresponds to a better performance of the \(n\)-th method.