esa-sceva/satcom-chunk-collection huggingface dataset:
# Positron Emission Tomography Image Segmentation Based on [PERSON]'s Intuitionistic Fuzzy Sets
## 5 Results
### Comparison with Different Segmentation Algorithms  
In this section, the proposed algorithm is compared to existing algorithms using the IoU and pixel accuracy measures.  
We evaluated our algorithm against existing similar methodologies, fuzzy C-means (FCM), K-means, and the affinity propagation (AP) algorithms. Since our algorithm detects and delineates the tumor, having as a final result a binary image, we adapted the existing algorithms to produce similar results. In each method, we also used different parameter values in order to choose the best performance setup.  
The FCM and K-means algorithms need to define a seed and a number of clusters to perform the segmentation. With the objective to have a binary image as a result, we set the number of clusters at 2 and handcrafted the seed position to the max intensity of the image  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**T Max** & **PET 1** & **PET 2** & **PET 3** & **PET 4** &... & **Average** \\ \hline
**30\%** & 0.33777 & 0.82292 & 0.21323 & 0.72679 &... & 0.29735 \\
**32\%** & 0.39137 & 0.87486 & 0.24328 & 0.76301 &... & 0.32694 \\
**34\%** & 0.47370 & 0.92075 & 0.2837 & 0.8021 &... & 0.36072 \\
**36\%** & 0.57546 & 0.96107 & 0.33(assuming the best possible seed position), resulting in a binary image. To achieve a binary image using the AP, we tested different weighting parameters, \(m\) and \(n\), achieving the best result when \(m=1\) and \(n=6\).  
Figure 6 shows the results obtained with the \(IoU\) method, and Figure 7 shows the pixel accuracy. Each algorithm output result is compared to the ground truth.  
The obtained results show that the fixed threshold \(t50\%\) had better performance than the fixed threshold \(t40\%\) considering any of the two evaluation measures, and the fixed threshold \(t40\%\) had the worst segmentation results of the tested methods. The fixed threshold \(t58\%\) obtained the second-best \(IoU\) and accuracy values (Tables 2 and 3). These are expected results because this threshold was obtained for our data set. According to both measurement values, the FCM had the highest distribution of values (as can be seen in Figure 6) and the lowest \(IoU\) values. The K-means and AP methods showed similar performance, with AP achieving better \(IoU\) scores despite the higher distribution of the accuracy results. The proposed algorithm outperformed all of the other methodologies, and this result is even more noticeable (around at least \(10\%\) increase in performance) when using an evaluation measure (\(IoU\)) not biased by the numerous background pixels, and with a low distribution of values in accuracy. Considering that our methodology is unsupervised, in the 40 images, some produced a wide range of \(IoU\) values. However, it is the method that obtained the best global results. The observed results indicate that A-IFSs has more precision and repeatability.  
Figure 6: Algorithm comparison with \(IoU\).
## 4 Experiments
### Computation Cost Study  
For the ASTGCN, ASTGNN, and STGNN models, the computational complexity of attention operation is O(\(N^{2}\)-C) in the spatial dimension. In the temporal dimension, the attention operation complexity for ASTGCN and ASTGNN is O(\(L^{2}\cdot D\)), while for STGNN, it is O(LlogL-\(D\)). Table 5 shows the recorded inference time for one epoch during the training stage for the four spatial-temporal models in two different prediction steps. STG-NODE has a relatively long computation time because it uses ordinary differential equations for  
Figure 7: Components analysis. These three figures demonstrate the impact of different model components on RMSE and MAE, respectively.  
spatial-temporal modeling. STGNN utilizes sparse attention and a generative decoder, resulting in significantly faster inference time than ASTGCN and ASTGNN.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 5 Conclusions  
In this work, an implementation for MBSS using SDR communication devices was presented. This development was done in Python language, and the algorithm works sequentially updating the information every 100 ms. The HackRF One device showed good  
Figure 23: Samples in error for the energy detector.  
Figure 24: SDR–UAMI–MBSS application.  
performance in (i) correctly detecting noise or a possible _PU_ transmission, and (ii) locating the start and end of a possible _PU_ transmission. However, this performance cannot be significantly improved due to the noise cancellation module. Modifying the approximation coefficients for a neighboring colony indirectly affects the precision in detecting _PU_ transmission. Practically, using this methodology, the precision for detection is sacrificed to improve the probability of success.  
The impulse noise elimination module presented in this work showed good performance in eliminating high-frequency noise and abrupt changes in the signal. It was concluded that the implemented MBSS technique performed well, with similar results to the simulated results (0.98 of _PS_ and two (mean) samples in error when locating the _PU_ transmission) [12]. Nevertheless, this performance may be improved if this device uses a higher-gain antenna and if the SDR deployment is done in parallel, permitting the execution time to be reduced, as well as computing resources to be used more efficiently.  
This MBSS technique also presented better performance than a conventional ED, in accurately detecting the transmission of the _PU_ and in detecting its location. Moreover, with this algorithm, it is possible to use seven SDR devices seen as _SU_s. As future work, it is possible to develop a network for the MBSS using the same principles outlined in this work.  
[PERSON] and [PERSON] conceptualized the original idea of how to use different SDR devices and designed the proposed methodology. [PERSON] improved these proposals by adding some ideas on how to obtain the data and how to develop the necessary software. [PERSON] also performed all the experiments of this work. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). This work was also supported by the Program for the Professional Development of Teachers (PRODEP).  
The authors are grateful to CONACYT, PRODEP, and the Department of Electrical Engineering of UAM-I for providing the necessary support for this work.  
The authors declare no conflict of interest.
## Appendix A Basic Concept of Allan Variance
### Representation of Noise Terms in Allan Variance
#### Random Walk Frequency  
The random walk frequency or rate random walk is a stochastic process with uncertain origin. It is possibly involved in a limiting case in which an exponentially correlated noise that embraces a very long correlation time exists. The corresponded PSD for this noise term is given by  
\[S_{\Omega}\left(f\right)=\left(\frac{K}{2\pi}\right)^{2}\frac{1}{f^{2}} \tag{12}\]  
where \(K\) denotes the coefficient of the random walk frequency. Substituting (12) to (11) gives  
\[\sigma^{2}\left(\tau\right)=\frac{K^{2}\tau}{3} \tag{13}\]  
Equation (13) indicates that a slope line of \(\frac{1}{2}\) on a log-log plot of \(\sigma\left(\tau\right)\) versus \(\tau\) can describe random walk frequency as shown in Figure 12. Then, \(K\) can be read from the slope line at \(\tau=3\).  
Appendix A.2.3. Exponentially Correlated or Markov Noise  
The exponentially correlated noise is identified and quantified with an exponential decaying function with a finite correlation time. The PSD for this type of noise is given by  
\[S_{\Omega}=\frac{\left(q_{c}T_{c}\right)^{2}}{1+\left(2\pi fT_{c}\right)^{2}} \tag{11}\]  
where \(q_{c}\) denotes the noise amplitude; \(T_{c}\) stands for the correlation time. Substituting (11) to (16) gives  
\[\sigma^{2}\left(\tau\right)=\frac{\left(q_{c}T_{c}\right)^{2}}{\tau}\left[1- \frac{T_{c}}{2\tau}\left(3-4e^{-\frac{\tau}{T_{c}}}+e^{-\frac{2\tau}{T_{c}}} \right)\right] \tag{12}\]  
Figure 11 shows a log-log plot of the square root of the Allan variance for the time-correlated noise.  
Supposing that \(\left[\bar{x}_{GM},\;\bar{y}_{GM}\right]\) is the coordinate vector that can represent the peak of the curve in Figure 11, \(q_{c}\) and \(T_{c}\) can be derived as follow  
\[T_{c}=\frac{\bar{x}_{GM}}{1.89},\;\;\;q_{c}=\frac{\bar{y}_{GM}}{0.437\sqrt{T_{ c}}} \tag{13}\]  
When used with the clock drift error in units of meters per second, \(q_{c}\) in (13) must be multiplied by the speed of light, i.e., 299792458.0 m/s.
_applied sciences_  
_Article_  
Study on the Mechanical Properties and Hydration Behavior of Steel Slag-Red Mud-Electrolytic Manganese Residue Based Composite Mortar  
[PERSON]\({}^{\copyright}\), [PERSON] 1,2, [PERSON] 2,3,*, [PERSON] 2 and [PERSON] 2  
1 College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China; [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (P.C.)
2 College of Civil and Architectural Engineering, Guilin University of Technology, Guilin 541004, China; [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (Z.L.)
3 Guangqi Engineering and Technology Center for Utilization of Industrial Waste Residue in Building Materials, Guilin University of Technology, Guilin 541004, China
* Correspondence: [EMAIL_ADDRESS]  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
**Abstract:** The functional and mechanical properties of steel slag (SS)-red mud (RM)-electrolyte manganese residue (EMR)-based composite mortar under different matching ratio conditions were investigated in this paper to examine the synergistic cementing effect among multiple solid wastes. The hydration characteristics of the composite mortar and its microstructure were characterized by the heat of hydration assessment, X-ray diffraction, thermogravimetric analysis, and other tests. The results of the study showed that compared with the pure cement group, 30% SS alone will inhibit the hydration reaction of the slurry, thus reducing the mechanical properties of the mortar, while compounding the appropriate amount of RM, and EMR can effectively reduce the negative impact of SS on the mechanical properties of the mortar. The flexural and compressive strengths of the composite mortar at 28 d were the highest when 15% of SS, 12% of RM, and 3% of EMR were mixed, which were 7.2 MPa and 41.4 MPa at 28 d, respectively. Compared with the test group with 30% SS alone, the flexural and compressive strengths increased by 18.0% and 25.5%. This is mainly because the incorporation of RM and EMR not only plays the role of physical filling, but the free alkali in RM and sulfate material in EMR can also compoundly stimulate the hydration activity of SS to produce more calcium alumina (AFt) and hydrated calcium silicate (C-S-H gel), thus improving the microstructure of mortar, which makes the overall decrease of 26.35% of multiharmful and harmful pores and the overall increase of harmless and less harmful pores of composite mortar specimens of 43.57%.  
**Keywords:** composite mortar; mechanical properties; hydration characteristics; microstructure
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 1 Introduction
This may indicate that the use of olfactory stimuli to regulate emotions is effective, as exogenous stimuli can enhance emotional self-monitoring [26], thereby facilitating the understanding of emotions. Despite these advancements, existing research in emotion recognition has primarily focused on the valence dimension. In reality, each emotional state is a linear combination of various dimensions. Accurate emotion recognition requires the simultaneous consideration of multiple emotional dimensions. The widely used VA (valence-arousal) dimensional emotional model links valence to the degree of pleasure and arousal to the degree of excitement, with higher dimension values corresponding to greater emotional intensity. This model treats emotional experiences as a continuum of related but often ambiguous states, effectively capturing the nuances of emotional expression [27]. This study investigates the emotional effects of inhaling SEO, which is believed to induce relaxation in olfactory aromatherapy, and BEO, which is thought to enhance concentration. By combining subjective self-report scores with objective EEG spectral analysis, we assessed and validated the emotional effects induced by inhaling SEO or BEO. Using EEG data collected after the inhalation of SEO or BEO, we introduced a novel feature construction method, namely extracting features from six frequency bands (\(\delta\), \(\theta\), \(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2) across all electrodes in five brain regions, and incorporating the \(\beta\)/\(\alpha\) ratio as an initial feature. This approach led to the development of a high-performance model for classifying low-arousal (SEO) and high-arousal (BEO) emotions. The model enables rapid and accurate differentiation between various emotional states, facilitating timely monitoring of emotional changes. It provides an effective tool for screening essential oils that can evoke distinct emotional responses in various applications, such as aromatherapy, human-computer interaction, and multimedia.
###### Abstract  
Due to its excellent physical properties and availability directly on a semiconductor substrate, epitaxial graphene (EG) grown on the (0001) face of hexagonal silicon carbide is a material of choice for advanced applications in electronics, metrology and sensing. The deposition of ultrathin high-k insulators on its surface is a key requirement for the fabrication of EG-based devices, and, in this context, atomic layer deposition (ALD) is the most suitable candidate to achieve uniform coating with nanometric thickness control. This paper presents an overview of the research on ALD of high-k insulators on EG, with a special emphasis on the role played by the peculiar electrical/structural properties of the EG/SiC (0001) interface in the nucleation step of the ALD process. The direct deposition of Al\({}_{2}\)O\({}_{3}\) thin films on the pristine EG surface will be first discussed, demonstrating the critical role of monolayer EG uniformity to achieve a homogeneous Al\({}_{2}\)O\({}_{3}\) coverage. Furthermore, the ALD of several high-k materials on EG coated with different seeding layers (oxidized metal films, directly deposited metal-oxides and self-assembled organic monolayers) or subjected to various prefunctionalization treatments (e.g., ozone or fluorine treatments) will be presented. The impact of the pretreatments and of thermal ALD growth on the defectivity and electrical properties (doping and carrier mobility) of the underlying EG will be discussed.  
epitaxial graphene; atomic layer deposition; high-k insulators +
Footnote †: journal: Applied Physics Letters
## 6 Designing a Distributed Questions and Answers System
### Data Discovery Using a Trustless Distributed Protocol  
To discover data in our open and distributed system, we propose the use of a query protocol. The queries of the system state the constraint that the responses must satisfy. For instance, a question that contains a given text can be searched in a Q&A system. The query can also constrain the structure of the response (e.g., it has more than one answer and more than one positive vote).  
Additionally, a score function can be defined to sort the valid responses. For instance, the questions containing some text can be ranked by the number of positive votes.  
Next, the protocol interactions (Figure 4) are described as follows:  
1. An agent sends a query (with constraints and a score function).
2. Any agent can reply, with a response consisting of a content-centric link to the data satisfying the query and its corresponding score.
3. The querying agent accesses the data and verifies the responses and scores. This protocol presents the following characteristics:Lightweight communication: Responses consist of a short link and a numeric value. Their length is then a few bytes long, even though they may represent complex, large data structures.
2. Early distributed ranking: Responses may be ranked without accessing their data.
3. Trustless ranking and validity: Similarly to how any node can verify that all the transactions in a Blockchain are valid without needing to trust a third party, the validity and ranking of the responses in the proposed protocol can be assessed without trusting the agents providing the responses or the data (e.g., checking the digital signatures for authorship and the validity of the linked votes).  
The protocol can be implemented using the following: (1) Merkle-linked data distributed over IPFS; (2) JavaScript pure functions to express query constraints and score functions, using the JavaScript implementation of IPFS; and (3) a bus model for distributed systems communication [58] over IPFS pub-sub channels. Thus, the protocol would enable the implementation of distributed open systems with different consistency and availability requirements (see Table 1 for a summary of the guidelines for those different requirements).
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 2 Materials and Methods
### MinIO Benchmarks in HPC  
To create a reference number for the performance of S3 and explore the possible ways to optimize performance, we first use the MinIO server (release: 2020-08-18T19-41) to accomplish our tests using the modified benchmarks inside our HPC environment.  
MinIO Deployment  
MinIO supports the following modes of operation:* Standalone (_sa_): runs one MinIO server on one node with a single storage device. We test configurations from tmpfs (in-memory fs/shm) and the local ext4 file system.
* Distributed servers (_srv_): runs on multiple nodes, object data and parity are striped across all disks in all nodes. The data are protected using object-level erasure coding and bitrot. Objects are accessible from any MinIO server node. In our setup, each server uses the local ext4 file system. Figure 0(a) illustrates the deployment.
* Gateway (_gw_): adds S3 compatibility to an existing shared storage. On Mistral, we use the Lustre distributed file system as the backend file system as seen in Figure 0(b).  
Alongside these three modes, we introduce two modes by inserting the Nginx [28] (v1.18.0) load balancer in front of the distributed and gateway configurations, and we refer to these setups as _srv-lb_ and _gw-lb_, respectively. Both variants can utilize a cache on the Nginx load balancer (_-cache_).
# Preventing Black Hole Attacks in AODV Using RREQ Packets
## 2 Black Hole Attack
### Construction  
Before describing the behavior of a black hole attack, we discuss how AODV is routed. In AODV, the sender issues what is called a route request (RREQ) packet at the timing of sending data to the destination, flooding all nodes within the transmission range of the source node. This RREQ packet contains the IP address of the destination node; the node that receives this packet can verify whether it is the destination. If a relay node determines that it is not the destination node, it floods the RREQ packet again, and the process repeats. Finally, the RREQ packet reaches the destination node, which sends an RREP packet to the source node back along the same route that the RREQ packet followed. Thus, a route is established when the RREP packet reaches the source node.  
Next, the behavior of black hole attacks is described. An attacking node that receives an RREQ packets broadcast to the network (hereinafter called a black hole node), then forges and route replay packets (RREP) with a large sequence number and a small hop value and sends them back to the source node. In AODV, the priority of a route is evaluated based on the sequence number of the packet and the hop value. Therefore, when a black hole node receives an RREP packet returned by a black hole node, it misidentififies the route through the black hole node as the latest route to the destination. Figure 1 shows an example of a black hole attack. The nodes are indicated as follows: S is the source node, A is the relay node, D is the destination node, and BH is the black hole attacker. The solid line shows the flow of RREQ packets, and the dashed line shows the flow of RREP packets. Inthis example, before the RREQ packet reaches the destination node D via the relay node A, the black hole node BH near the source node S sends a forged RREP packet back to the source node S. As a result, the source node S mistakenly believes that the black hole node BH is the route to the destination node D and establishes a route. All data packets sent after the establishment of the route are relayed to the black hole node BH, and the black hole node discards all data received. This causes a situation where the route is established, but the data do not reach the destination. Even if the source node S tries to establish the route again, it will end up in the same situation as long as the black hole node BH is present.
## 3 Results
### Interrelationships among Productivity, Phenotypic Traits, Thermal and Humidity Indices, and CWSI  
Investigating the relationships between productivity, phenotypic traits, trunk and canopy thermal and humidity indices, and CWSI, we examined a total of 23 variables. It is important to mention that certain variables were employed in the construction of composite variables and were not analyzed individually (e.g., PS, US, cavities, healthy leaves). Additionally, no categorical variables (OLS) were included in the analysis due to the nature of the correlation analysis, which primarily deals with continuous variables. The productivity of centennial trees exhibited significant associations, either positive or negative, with a total of 18 variables. Among these, six pertained to phenotypic traits, eleven related to trunk thermal and humidity indices, and two were associated with canopy-related indices (T\({}_{\text{IQR}}\) canopy, CWSI) (Figure 6). Delving into the specifics, concerning phenotypic traits, productivity displayed noteworthy positive correlations with SR (\(r=0.54\), \(p<0.05\)), CA (\(r=0.32\), \(p<0.05\)), and HC (\(r=0.69\), \(p<0.05\)). In contrast, it exhibited negative correlations with DBH (\(r=-0.59\), \(p<0.05\)), Age (\(r=-0.59\), \(p<0.05\)), CR (\(r=-0.64\), \(p<0.05\)), and P\({}_{\text{range}}\) (\(r=-0.71\), \(p<0.05\)). Turning to the trunk thermal and humidity indices, productivity demonstrated negative relationships with all of them, with correlation coefficients ranging from \(-0.26\) to \(-0.66\) (\(p<0.05\)). Moreover, it exhibited negative correlations with T\({}_{\text{IQR}}\) canopy (\(r=-0.66\), \(p<0.05\)) and CWSI (\(r=-0.60\), \(p<0.05\)).  
Figure 5: Box plots showing the differences between the two sides of the olive trees’ trunk: (**a**) humidity interquartile range (H\({}_{\text{IQR}}\)), (**b**) humidity outer percentile range (H\({}_{\text{OR}}\)), (**c**) temperature interquartile range (T\({}_{\text{IQR}}\)), and (**d**) temperature outer percentile range (T\({}_{\text{OR}}\)). Horizontal lines: medians; boxes: interquartile ranges (25–75%); whiskers: data ranges.  
The examined phenotypic traits displayed both direct and inverse relationships among themselves, as illustrated in Figure 6. In contrast, trunk thermal and humidity indices primarily exhibited positive associations, and this pattern was consistent between T\({}_{\text{IQR}}\) canopy and CWSI.