esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Data Preprocessing
#### 2.3.4 Graph Structure Representation
(See Table 2 and Figure 4)  
In our brain MRI data graph, the prominently visualized central node, identified by its high degree centrality (Node ID 122 with a centrality of 0.01741), indicates a region of significant connectivity within the brain's network. This node, along with its neighboring cluster, highlights areas potentially crucial for brain function and structure. Nodes such as Node ID 1459 with the highest closeness centrality of 0.22366, Node ID 122 with the highest betweenness centrality of 0.07158, and Node ID 2692 with the highest eigenvector centrality of 0.16626, further underscore the importance of these regions. Such insights are invaluable for applying Graph Neural Networks (GNNs) in brain tumor segmentation, enhancing our understanding of the brain's architecture and advancing neuroimaging and neurological disorder diagnosis. \begin{table}
\begin{tabular}{l c} \hline \hline
**Property** & **Value** \\ \hline Number of Nodes & 3447 \\ Number of Edges & 31,637 \\ Average Clustering Coefficient & 0.42224 \\ Highest Degree Centrality (Node ID 122) & 0.01741 \\ Highest Closeness Centrality (Node ID 1459) & 0.22366 \\ Highest Betweenness Centrality (Node ID 122) & 0.07158 \\ Highest Eigenvector Centrality (Node ID 2692) & 0.16626 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Graph analysis of brain MRI data. Figure 4: Graph visualization centered on the most important node (Node ID [insert ID]). This representation highlights the node’s significant connectivity within the brain’s network.
# Damage Imaging in Lamb Wave-Based Inspection of Adhesive Joints
## 3 Results and Discussion
### WRMS Imaging
#### 3.3.2 Influence of Defect Shape and Position  
Figure 10 shows the WRMS maps for all of the considered specimens #1-#9. The parameters of the WRMS calculations were assumed as follows: the time window \(T\) = 3.00 ms and the weighting factor (\(w_{r}=r\)). The intensification of the WRMS maps could be observed at the disbond areas, where the values were greater than for the properly bonded parts. This means that the amplitudes of propagating waves vary more significantly if there is no direct connection between the bonded parts. It is worth noting that the clearness of the maps increased with the difference between values that were obtained for the disbonded and properly bonded areas.  
Figure 10: WRMS maps (values in (m/s)) for specimens #1–#9 (\(T\) = 3.00 ms, \(w_{r}=r\)).  
Figure 9: WRMS maps (values in (m/s)) for joints with internal defects for constant time window (\(T\) = 3.00 ms) and different weighting factors \(w_{r}=1\) (first column), \(w_{r}=r\) (second column), \(w_{r}=r^{2}\) (third column): (a) specimen #8; and, (b) specimen #9.  
The presence of defects was possible to determine in all cases, moreover, their shapes were also identifiable. The clearest damage image was obtained for specimens #4-#6, as well as specimens #2 and #3. Internal damages in specimens #7-#9 were also possible to identify; however, the damage maps had poorer quality. It is worth noting that the geometry of the identified defects was not in ideal agreement with the assumed one. In some cases, minor irregularities were visible, especially for samples that were intended as symmetric. This was an effect of inaccuracies in the process of the specimen preparation. The verification of the actual state of the adhesive layer was obtained after separating the joints (cf. Figure 3). It is clear that the adhesive leakages to the areas that are covered with PTFE tape were not identified in the maps, which means that the tape prevented the plates from being connected. On the other hand, the kissing defects were clearly noticeable (cf. Figure 3).  
Figure 11 also shows the WRMS maps for all of the considered specimens (#1-#9), but for the square weighting factor (\(w_{r}=r^{2}\)). The visual assessment of the presented maps led to conclusions that were similar to the ones resulting from analysis of maps for the linear weighting factor (\(w_{r}=r\)). The value of \(m\) power affected the clearness of images: the ones for the linear weighting factor appeared to be clearer. Based only on the visual assessment of the defect images, it was not possible to judge which variant of weighting factor provided the clearest results.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 4 Preliminary Performance Evaluation
### Issues to be Addressed
#### 4.2.4 Lack of Dithering  
uIP TCP does not have a dithering mechanism. This implies that many of the delivery failures that occur in spite of the maximum retry attempts may be due to a synchronization effect rather than due to congestion. Since each node backs off the retransmission timer based on the fixed RTO value of 3 s, the synchronization effect can be aggravated. We argue that, unlike typical TCP implementations for wired networks, TCP for wireless sensor networks needs to have a dithering mechanism like CoAP.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 2 WPT System Coupling Circuit Model
### System Frequency Splitting Analysis  
The frequency of the wireless power transmission systems splits under different operating conditions. The critical value of frequency splitting is \(\omega M=R_{\rm S}=R_{2}+R_{\rm L}\)[10; 12], and \(R_{\rm S}\) is the sum of the internal resistance of the receiver coil and the load resistance. When \(\omega M<R_{\rm S}\), the system operates under under-coupling conditions, and frequency splitting does not occur. When \(\omega M=R_{\rm S}\), the system operates under critical coupling conditions. When \(\omega M>R_{\rm S}\), the frequency-splitting phenomenon will appear when the system operates under an over-coupling condition.  
Figure 2 shows the relationship between the operating frequency of the system, the transmission efficiency, and the output power when the frequency splitting phenomenon occurs, as obtained from Equations (4) and (5). In the figure, splitting occurs at both the output power and transmission efficiency. The maximum output power obtains at the splitting point, and the maximum transmission efficiency obtains at the resonance point.  
The relationship between the phase difference \(\varphi\) and the operating frequency \(f\) is obtained from Equation (7), as shown in Figure 3. The coil distance is fixed. When the load \(R_{\rm L}\) value is large, the system is under an under-coupled state, no frequency splitting occurs, and there is only a zero point at the resonating frequency. When the load \(R_{\rm L}\) value is small, the system over-couples, and frequency splitting occurs. There are three zeros at the resonating frequency and two frequency splitting points. The resistance value is fixed. The frequency splits when the coil distance is close; the frequency does not split when the coil is far away. Since the PLL-based frequency-tracking technique tracks the zero point, the frequency splitting makes the frequency-tracking technique track the frequency of the splitting point rather than the resonating frequency.  
The above analysis shows that the frequency splitting of the system has a significant impact on the output power and a negligible impact on the transmission efficiency. It also affects the input phase angle of the transmitter, which further affects the nature of the system and makes the frequency tracking follow the frequency that is not the resonating frequency.
## 3 Preliminaries
### Basic Facts Regarding the Physical Geometric Space  
#### 3.2.1 Geometric Model  
A physical geometric model can be represented in two alternative forms: a boundary representation or a solid representation. For example, a ball can be modeled as a boundary representation through an equation of the form \(r^{2}=x^{2}+y^{2}+z^{2}\), where \(r\) is the radius of the ball and \(x\), \(y\), and \(z\) are values of the three-dimensional world, which is a 3D Euclidean vector space \(R^{3}\), or a solid representation can be formulated as the set of all points that are contained within the sphere. Both alternatives are considered in engineering applications.  
From the perspective of robot operations, there are two kinds of entities in the world: (1) obstacles, which are the occupied parts of the world, denoted by the obstacle region \(\mathcal{O}\), and (2) robots, which are geometrically modeled entities controlled via motion planning. During a collaborative process, a robot needs to perform motion planning within a certain time window to avoid the obstacle region that is occupied by another robot.
# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 4 Experiments
### Ablation Study  
In order to demonstrate the accuracy of the loss function in our parameter settings, we conducted ablation experiments by experimenting with the values of \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), and \(\lambda_{4}\) to investigate their impact on enhancing the images, as shown in Figure 10. From the results, we found that there were significant differences between the compared images, and some of the images had significant color deviation, such as the green book part of some images showing a yellowish hue. To better select experimental parameters, we evaluated the comparison results of various parameters using metrics, as shown in Table 3.  
According to the subjective visual comparison in Figure 10, it can be observed that the enhancement effect is closest to the reference image when \(\lambda_{1}=\lambda_{2}=0.1\) and \(\lambda_{3}=\lambda_{4}=0.01\). According to Table 3, the best results in terms of the SSIM, MSE, and NIQE metrics are achieved when \(\lambda_{1}=\lambda_{2}=0.1\) and \(\lambda_{3}=\lambda_{4}=0.01\). The algorithm also performs well in terms of PSNR, ranking among the top three. Based on the subjective and objective evaluations from Figure 10 and Table 4, we choose the parameter settings of \(\lambda_{1}=\lambda_{2}=0.1\), \(\lambda_{3}=\lambda_{4}=0.01\).  
Figure 10: Effect of different parameter values on the enhanced images.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Running Time (s)** \\ \hline LightenNet [16] & 0.983 \\ MBLLENI [15] & 3.975 \\ RetinexNet [5] & **0.469** \\ EnlightenGAN [17] & **0.012** \\ DSLR [18] & 0.942 \\ ExCNet [19] & 12.748 \\ RRDNet [14] & 78.386 \\ Zero-DCE [27] & 0.671 \\ Zero-DCE++ [20] & 0.747 \\ R2 RNet [6] & **3.704** \\ Our method & **0.633** \\ \hline \end{tabular}
\end{table}
Table 3: The average running time of the proposed method for enhancing images in the LOL dataset. The best results are highlighted in bold and underlined. The second-best results are in italics, and the third-best results are in bold.
## 4 Discussion  
Table 8 presents the accuracy performance of a collection of state-of-the-art methods proposed for the whole brain tumor segmentation problem. Although they are recent solutions, the vast majority still use the BraTS 2013 and BraTS 2015 data sets for evaluation purposes. An important requirement for the methods to be selected is that they apparently report results obtained by processing the whole data set, not only some records that give high accuracy. The Dice similarity coefficients shown in the table are written in the form published by the authors, with the intention to be interpreted correctly. We do not agree with publishing \(\overline{\text{DSC}}\) values expressed with two decimals only, because the half percent imprecision can hide an extremely large difference. One important thing reflected by this table is that it is much easier to obtain high accuracy with the BraTS 2013 data, which is probably caused by the reduced number of records in this set that cannot cover enough variation of tumor shapes and appearances. See for example the method of [PERSON] et al. [29], which obtains average Dice scores of 0.88 and 0.78 on BraTS 2013 and BraTS 2015, respectively. The proposed procedure with its achieved Dice scores belongs to the set of methods with top accuracy. Figure 7: One slice from 16 different MRI records, four from each data set, showing the four observed data channels and the segmentation result. The first four columns present the T1, T2, T1C, and FLAIR channel data of the chosen slices. The last column shows the segmented slice, representing true positives (\(|\Gamma_{i}^{(\pi)}\cap\Lambda_{i}^{(\pi)}|\)) in green, false negatives (\(|\Gamma_{i}^{(\pi)}\cap\Lambda_{i}^{(\
u)}|\)) in red, false positives (\(|\Gamma_{i}^{(\
u)}\cap\Lambda_{i}^{(\
u)}|\)) in blue, and true negatives (\(|\Gamma_{i}^{(\
u)}\cap\Lambda_{i}^{(\
u)}|\)) in grey, where \(i\) is the index of the current MRI record. From the point of view of efficiency, the main question is how much time a method needs to perform a whole segmentation on an MRI record of the same type that is never seen during the training of the method. Table 9 presents the performance against the clock of some selected state-of-the-art methods. Our goal is to report those runtime values, which reflect the total duration needed, starting from uncompressed.nii files and ending when the segmentation result is saved, but we cannot be sure that all methods included in this table report this kind of duration. It is not surprising at all that solutions based on CNN need a longer time even if they run on GPUs, because their architecture is usually more complex. However, other methods can also achieve fine segmentation quality in less time using CPUs only. This is probably because fine tuning complex architectures is much more difficult, and some of them may still run in a suboptimal state. With its reduced time complexity, our proposed method is very competitive and can be deployed without needing any special hardware.
# Research on Improved OEE Measurement Method Based on the Multiproduct Production System
###### Abstract
found that a raccoon family optimization algorithm could be used to solve the planning and scheduling problems of multiobjective manufacturing projects with limited sharing resources [20]. [PERSON] et al. presented a discrete-event simulation of continuous bioprocesses in a scheduling environment and proposed a novel look-ahead scheduling policy [21]. [PERSON] et al. identified the machine and/or parameters which could lead to the largest improvement on quality by extending the Markov model and carrying out the product sequence analysis [22]. [PERSON] et al. proposed a dynamic modeling method for task reliability in the multistate manufacturing system with multiproduction line [23]. However, few pieces of research considered the application of OEE in the multiproduct production system, especially for measuring production effectiveness from a holistic perspective. With the necessity of the multiproduct production system in modern factories, the research about the application of OEE in this system has become quite urgent. Thus, this study would like to address this gap. In this paper, based on the heuristic design idea, multiproduct production system effectiveness (MPSE), a novel method to calculate the effectiveness of the multiproduct production system, is proposed. Besides, this study further undertakes an empirical study to verify the reliability of this method. The research results broaden the application of OEE theory in the multiproduct production system and ameliorate OEE's measurement methods. Besides, the results are conducive to the measurement and improvement in the effectiveness of the multiproduct production system in practice. The main contributions of this paper can be listed as:  
* The deficiencies of mainstream OEE measurement methods in the multiproduct production system are concluded, and the characteristics of the multiproduct production system are summarized;
* MPSE is proposed to measure the production effectiveness of the multiproduct production system, improving the production effectiveness and operation performances of the multiproduct production system;
* The application framework of MPSE is introduced, which is based on the Plan-Do-Check-Action cycle (PDCA), is available for the improvement of the multiproduct production system continuously. The structure of this paper is organized as follows. Current OEE measurement methods and their deficiencies in the multiproduct production system are shown in Section 2. In Section 3, the methodology, which includes the characteristics of the multiproduct production system and the design ideas of MPSE, is discussed. Then, the MPSE measurement methods and the application framework are proposed in Section 4. In Section 5, the case study about the MPSE's application in enterprise Q is analyzed. In the end, the discussion about the main findings, research implications, and possible further research directions are mentioned in Section 6.
## 3 LOADng Protocol Overview  
The LOADng routing protocol proposes a simplification of the AODV [24], a well-known reactive routing protocol based on route discovery using messages of route request and route reply. In LOADng, some aspects are simplified with the aim of reducing the protocol complexity and the amount of computational resources required to execute it. Among the simplifications, it is possible to detach the restriction to the sending of intermediate route reply messages and the avoidance of the use of periodical control messages [16]. Thus, LOADng is specifically designed for networks composed of devices with strong hardware restrictions. Also, it allows the use of different addressing schemes such as IPv6, IPv4, and Rime [25].  
The following subsections present an overview of the LOADng protocol. These explanations are necessary to understand the approach proposed in this work.
## 3 Agent-Based Model for Co-Traveling
### Agent-Based Modeling  
An agent-based approach is used for evaluating the effects of an agent's behavior and for simulating the communications of autonomous entities. It is individual-centric and essentially distributed, and suitable for systems that display complex behavior. The proposed carpooling model is based on the Capacity, Role, Interaction, and Organization (CRIO) meta-model [4]. It provides organizational views for demonstrating dynamic structures in terms of their role and relations. This meta-model offers the transformation from organizational views to ones that are processed for generating an agent-based simulation. Accepting an organizational approach permits agents to dynamically adapt their actions without altering their core structure.  
According to the CRIO meta-model, the definition of an organization is \"a cluster of roles that participate in structured logical institutionalized designs of interactions with the other roles in a collective perspective. This perspective involves common knowledge, common rules or standards and common feelings, etc. and is considered as an ontology. The goal of an organization is to accomplish some requirements\". A group is used for splitting organizations. The group is an organizational object in which all associates are able to cooperate according to pre-defined interaction designations and rules. A role is defined as \"a predictable behavior where a set of role jobs are well-ordered by a strategy, and a set of privileges and responsibilities in the organization context.\" Each role contributes to the completion of the necessities of the organization within which it is defined.  
According to [28; 29], the CRIO meta-model is suitable because the carpoolers are dynamically altering their roles in the carpooling social network. For implementation, the Janus [5] multi-agent based platform is used which offers the well-organized application of agent-based and organizational-based models. Janus is built upon the CRIO organizational meta-model in which the concepts of organization and role are special entities. Each agent has the ability to play its role inside an instance (CPSG) of the organization. The organizational-based modeling permits the situations to be defined in an organized way. It provides the capability to determine where the associations between agents occur and how these associations influence the outcomes [4; 30].
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 4 Simulation Settings and Performance Comparison Results
### Simulation Setting  
The protocol we proposed is simulated using NS2 simulator, and we use the Simulation of Urban Mobility (SUMO) engine to establish an urban environment, which covers an area of 3000 m \(\times\) 4000 m, as shown in Figure 4. The area contains 28 junctions, 5 road segments with one-way lanes, 6 bidirectional road segments with 2 lanes, 23 bidirectional road segments with 4 lanes, and 10 bidirectional road segments with 6 lanes. It is assumed that each point on the road segment has an equal probability of being the initial position or destination of vehicles. Some other simulation parameters are shown in Table 4.
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 2 Materials and Methods
### Non-Orthogonal Multiple Access (NOMA)  
Power-Domain Non-Orthogonal Multiple Access (NOMA) combines the data of multiple users, and the data are separated using successive interference cancellation (SIC) [27]. In contrast to other multiple access techniques, such as OFDMA and CDMA, NOMA does not use channel spreading or compromise SNR performance. For PD-NOMA, different users use the same resources (frequency and time), which increases the overall system throughput; however, each user has a different power factor. The framework of NOMA is shown in Figure 7 for two users; it can be easily extended for a higher number of users.  
Figure 7: Framework of NOMA.  
The user at the cell edge is assigned higher power than that of the cell-centered user due to the relative difference in distance from the base station. The received signal can be written as follows [27]:  
\[\left[\begin{array}{c}y_{n,1}\\ y_{n,2}\end{array}\right]=\left[\begin{array}{c}H_{n,\ 1}\\ H_{n,2}\end{array}\right]\left[p_{1}x_{1}+p_{2}x_{2}\right]+\left[\begin{array}[ ]{c}n_{1}\\ n_{2}\end{array}\right], \tag{18}\]  
where \(y_{n,1}\) and \(y_{n,2}\) are the transmitters received at the \(n^{\text{th}}\) user; \(n=1,2\). At the receiver end, the data of user 1 are separated and demodulated as follows:  
\[\hat{s}_{1}=\frac{y_{n}}{p_{1}} \tag{19}\]  
After the data of user 1 are recovered, the data of user 2 are recovered by mitigating the interference from user 1 using SIC as follows:  
\[\hat{y}=y_{n}-p_{1}\hat{s}_{1} \tag{20}\]  
\[\hat{s}_{2}=\frac{\hat{y}_{n}}{p_{2}} \tag{21}\]