esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Network Performance Test
Positive and negative sample pairs were input to the network to assess the accuracy of the network for targets with different SNRs. The results of the evaluation are shown in Figure 7. The horizontal axis is the factor \(\mu\) that controls the SNR, and the vertical axis is the accuracy of the network. As shown in Figure 7, the Siamese network has a high accuracy for 10 types of new targets and 16 SNRs when \(\mu\leq\) 1.8. As \(\mu\) increase, the accuracy of the network decreases rapidly, but it remains above 80% for \(\mu\leq\) 2.0. Experiments showed that when the SNR is within a certain range, the network with CNN identifies underwater targets accurately. The recognition rate of the Siamese network with MLP is relatively low. The classification results for when \(\mu\) was 2.2 are shown in Figure 8. The calculated value of \(F1_{\textit{average}}\) was 0.6403 using the same method. Figure 7: The performance of the proposed Siamese network for SNRs. * Performance evaluation of interference. Dataset GG was used to evaluate the performance of the Siamese network in recognizing new targets with different levels of interference. Positive and negative sample pairs were input to the network to assess the accuracy of the network for targets with different parameters. The results of the evaluation are shown in \[precision_{n}=\frac{TP_{n}}{TP_{n}+FP_{n}} \tag{13}\]0 The horizontal axis shows the number of interference occurrences (negative data indicate the loss of line spectra), and the vertical axis shows the accuracy of the network. As shown in Figure 9, our proposed network identifies targets well with various interference levels. Experiments showed that the network could accurately identify underwater targets when the number of line spectra increased or decreased within a certain range. The recognition rate of the Siamese network with MLP was relatively low. The classification results when the number = 2 are shown in \[precision_{n}=\frac{TP_{n}}{TP_{n}+FP_{n}} \tag{13}\]1 The calculated \(F1_{\text{average}}\) was 0.7423 using the same method. \[precision_{n}=\frac{TP_{n}}{TP_{n}+FP_{n}} \tag{13}\]2 Classification result of samples with \(\mu\) = 2.2. \[precision_{n}=\frac{TP_{n}}{TP_{n}+FP_{n}} \tag{13}\]3 The performance of the proposed Siamese network for interferences.
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 4 Experiments
### Qualitative Results  
**Synthesis:** the qualitative results of our proposed synthesis method are illustrated in Figure 6. For both types of inputs provided to the synthesis model, the generated images show high visual quality overall content of the input image, such as blood vessels, even though no explicit supervision is provided. In comparison to the input images, the images generated by both U-Net and ResNet trained with \(\mathcal{L}_{cyc}\) only appear darker, noisy, and introduced artifacts without any noticeable improvement. On the contrary, the proposed method with a perceptual loss showed improved visual quality without a significant loss of information. Moreover, translation results confirm the benefit of including additional losses to maintain visual quality and produce images that may be indistinguishable from real images.  
**Segmentation:** Figure 7 shows the segmentation results on the test set containing the real catheter. Overall, models that trained with synthetically generated images showed better visual segmentation compared to models trained on manually annotated data only. Among the evaluated methods, Linknet [42] showed consistent and improved performance over the rest. However, failure cases were noted in some samples where surgical sutures or stipples were segmented as a part of catheter. Moreover, although PSPNet [43] and PAN [44] showed improvement in removing the surgical sutures, they tend  
Figure 6: From top to bottom: inputs, U-Net \(\mathcal{L}_{cyc}\), ResNet \(\mathcal{L}_{cyc}\) and ResNet \(\mathcal{L}_{cyc}+\mathcal{L}_{perpp}\). (Red arrows) highlight the initial position of the composited catheter in X-ray angiograms and with the generated images using different methods presented in each subsequent rows. Synthetic catheters are represented with green dotted region, whereas the camera catheter is shown in the blue dotted region.  
to over-segment the catheter. We infer that this behavior is mainly due to the lack of samples with surgical sutures in the training data, thus the segmentation models treat the surgical sutures/stipples as a catheters.  
Figure 7: Test image results of a segmentation models trained with 30,000 generated images. From top to bottom: input (1 st row), the label images (2 nd row), U-Net [26] trained with 140 labeled images (3 rd row), U-Net [26] trained with 30,000 generated images (4 th row), PSPNet [43] trained with 140 labeled images (5 th row), PSPNet [43] trained with 30,000 generated images (6 th row),PAN [44] trained with 140 labeled images (7 th row), PAN [44] trained with 30,000 generated images (8 th row), Linknet [42] trained with 140 labeled images, (9 th row), and Linknet [42] trained with 30,000 generated images (10 th row).
## 3 Verification Method  
In this study, we propose a heart rate estimation method based on the principle of ballistocardiography (BCG) using a sensor that combines a three-axis accelerometer and a three-axis gyroscope, referred to as a six-axis Heart Rate Sensing Device. This method estimates heart rate by detecting body movements or vibrations associated with heartbeats.  
This falls within the typical error range of 3-8 bpm observed in commercial wearable devices. Table 1 presents the heart rate measurement accuracy of several representative wearable devices.  
An error level of 4-5 bpm is generally considered acceptable in consumer-grade wearable devices. At this level of accuracy, the method can be applied to a range of practical applications, such as heart rate tracking during exercise, stress or sleep estimation, daily health reporting, and exercise intensity control based on heart rate zones. Although it does not meet clinical-grade standards, it offers sufficiently competitive accuracy for  
\begin{table}
\begin{tabular}{c c c c} \hline
**Device/Study** & **MAE (bpm)** & **MAPE (\%)** & **Remarks** \\ \hline Apple Watch Series 3 & \(\sim\)4.7 & \(\sim\)5.9 & Clinically acceptable during indoor workouts. Shows robustness in light activity. \\ \hline Fitbit Charge 2 & \(\sim\)4.7 & \(\sim\)6.0 & Good accuracy during rest and walking. Some drop-off in intense exercise. \\ \hline Fitbit Charge 4 & 6–11 & \(<\)10 & Acceptable during low-to-moderate activity. Accuracy decreases during high-intensity workouts. \\ \hline Other Consumer Devices & \(\sim\)3–8 & — & Varies by brand. Typical range for fitness use; not suitable for medical-grade applications. \\ \hline \end{tabular}
\end{table}
Table 1: Heart rate measurement accuracy of several representative wearable devices.  
non-medical purposes. Accordingly, our goal is to achieve this level of bpm accuracy using only a six-axis sensor.  
To validate the heart rate estimated using the proposed approach, we performed a comparative analysis by converting the data obtained from the six-axis Heart Rate Sensing Device and collecting reference data from a photoplethysmography (PPG) sensor. The procedure for receiving and comparing data from both devices is illustrated in Figure 1. For the six-axis Heart Rate Sensing Device, an additional step--Changing angle measurements''--is required, while the remaining processing steps are applied identically to both the six-axis device and the PPG sensor.
## 4 Evaluation
### Dataset  
Real-life Abilene network traffic data were used in the experiments [25; 26]. The data from [25] are based on measurements of origin-destination flows taken continuously over a period of seven days, starting from 22 December 2003 to 29 December 2003. These data represent aggregate flow volumes for \(12\times 24\times 7=2016\) consecutive 5 min time intervals over the week, across \(11\times 11=121\) origin-destination pairs (including self-loops) of the \(11\) nodes of the Abilene network. The dataset from [26] is Abilene traffic measurements starting from 1 March 2004 to 10 September 2004 at 5 min time intervals. The datasets were cleaned and three sub-datasets with a period of 7 d each were selected randomly from them for the three experiments.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 4 Methodology  
In this section, the method proposed in this paper for improving the RRT-Connect algorithm will be detailed, including the improved Bridge Test, dynamic sampling strategy, and path-smoothing techniques. Our goal is to achieve efficient, high-quality path planning.
# Structural Features Guiding the Design of Liquid-Crystalline Elastomeric Fluorescent Force Sensors
## 2 Discussion
### Structural Factors That Impact the Mechanofluorescent Behavior
#### 2.2.4 Anchoring Mode (End-on Versus Side-on)  
To show the effect of the anchoring mode, i.e., end-on or side-on, two nematic liquid single crystal elastomers will be considered: EM4_CBZO6_10 and EM4_CBZN6_OMe_10 (in Figure 5a,b) [58]. The former elastomer has its carbazole fluorophores connected by a lateral alkoxyl chain in an end-on fashion. The latter, which resembles elastomer EM4_CBZN6_10 (see Figure 2), is connected to the main polymer backbone by the nitrogen atom of the heterocycle in a side-on mode. It has to be noticed that the fluorophores of LSCE EM4_CBZN6_OMe_10 contain a methoxy group in one of the _meta_ positions of the carbazole ring in order to have identical transition moments for both elastomeric systems. DRX and DSC experiments confirm that the two elastomers exhibit a monodomain nematic mesophase, which is stable under ambient conditions.  
Figure 5: Chemical composition (**a,b**) and mechanofluorescent response (**c,d**) for the end-on (**a,c**) and side-on (**b,d**) LSCEs EM4_CBZO6_10 and EM4_CBZN6_OMe_10. Mechanofluorescent response for end-on elastomers with a different flexible spacer length (**e,f**) [58]. (Copyright from ACS Applied Polymer Materials 2019).  
A comparison between the mechanofluorescent response observed for these two elastomers reveals that the system where the carbazole fluorophores are attached end-on (EM4_CBZO6_10) is significantly more efficient (\(\Delta I_{\text{Max}}=-93\%\), in Figure 5c) than its side-on counterpart EM4_CBZN6_OMe_10 (\(\Delta I_{\text{Max}}\) = \(-28\%\), in Figure 5d) [58]. According to this, an end-on connection of the carbazole fluorophores to the polymer chains, the very same as the mesogens, enables a much stronger interaction with the surrounding mesogen molecules upon deformation and leads, in turn, to more efficient optical force sensors. In this way, the mode by which the carbazole fluorophores are connected to the polymer coil is essential to achieve LSCE-based optical force sensors with improved sensing abilities.  
It should be also mentioned that, as for side-on LSCEs, the mechanofluorescence exhibited by end-on LSCEs depends also on the length of the flexible alkyl chain connecting the carbazole fluorogenic moieties to the polysiloxane network. In this case, when short or medium spacers are used, the force applied to the elastomeric material is effectively transmitted from the polymer chains to the pendant carbazole units producing, thus, a major decrease in the material luminescence (\(\Delta I_{\text{Max}}\) values of \(-87\%\) and \(-93\%\) for \(n=3\) and \(n=6\), respectively, in Figure 5c,e). As the conformational freedom of the flexible spacer increases (\(n=11\)), the linker can dissipate the mechanical input and compromise the efficiency of the resulting optical force sensor (\(\Delta I_{\text{Max}}=-55\%\), in Figure 5f) [58].
## 3 System Model Design
### Using the Fuzzy Inference System to Compute the Transmission Preference
#### 3.3.1 The Fuzzifier Component  
The fuzzifier is a common component that is used to evaluate the membership degree of fuzzy sets in the fuzzy inference system. In our fuzzifier component, there are two different input variables (the social and mobile similarities) and we define three different fuzzy sets (low, medium, and high) for each variable. Moreover, each fuzzy set in the fuzzifier can be computed by a membership function. Consequently, we need to define three different membership functions for the fuzzy set in the fuzzy inference. In the traditional membership function, there exist many different membership function paradigms that can be applied to different scenarios, such as rectangular, triangular, trapezoidal, or inverse. Due to the normal distribution [28] of the movement of nodes in opportunistic social networks, the normal membership function (15) [29] will be defined to assess the membership degree of each input in our inference system.  
\[f_{\mu}(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-(\frac{(x-x)^{2}}{2\sigma^{2}})} \tag{15}\]  
\[\left\{\begin{array}{c}f_{low}(x)=\frac{1}{\sqrt{2\pi}\times  
Additionally, for the sake of the requirement of low computation in mobile devices, the three different membership functions (low, medium, and high) for the node similarity can be defined as the above system of Equations (16), and their geometric graph is shown in Figure 2. In mathematical expressions of the three membership functions, different values of the node similarity correspond to three different levels in the above system of Equations (16). For instance, when \(MS(u,v)=1/2\), it corresponds to \(f_{low}(1/2)=0.8066\), \(f_{medium}(1/2)=1.33\), and \(f_{high}(1/2)=0.8066\) for the three different fuzzy sets of mobile similarity.
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 2 Related Work
### Fog-Based Hybrid Trust Models  
[PERSON] et al. [31] propose a Fog-based hierarchical trust model to evaluate trustworthiness in sensor-cloud systems. In this model, the behavior trust is formed among nodes in the wireless sensor layer, and the data trust of nodes is formed in the Fog layer. The Fog layer plays a significant role and acts as a trust buffer zone between the things layer and the cloud layer. This model is based on direct interaction and recommendations from other nodes. It constructs the trust level in the Fog layer as follows. First, the Fog layer obtains the trust state of the IoT things periodically. Then, the Fog layer performs data analysis to reveal hidden data attacks and ensure the credibility of edge nodes. Finally, the Fog layer establishes trust relationships between cloud service providers and sensor service providers through collecting pieces of evidence. [PERSON] et al. [32] propose a three-layer Fog-based detection system that utilizes a trust evaluation method to detect and solve hidden data attacks. The first is the direct trust layer in Wireless Sensor Networks (WSNs). The second layer is the preliminary decision layer among underlying Fog nodes, and the third layer is the data analysis layer in the Fog platform. Their approach for evaluating the trustworthiness of nodes is based on direct trust and recommendation. The considered trust metrics are packet success rate, forwarding delay, and routing failure rate.  
[PERSON] et al. [43] present a hybrid trust management system that uses centralized and distributed techniques to assess the trustworthiness of nodes. The nodes execute in-network processing and convey only the fine-grained values to the fog node for assessment to satisfy the scalability requirement in the centralized method. The fog nodes forward their calculated reputation values of IoT devices to the cloud for global reputation computation. The proposed model meets the adaptivity, survivability, and scalability requirements of the trust management system for the IoT environment.  
A trust management method IoT edge computing system in smart cities is presented by [PERSON] et al. [44]. The evolutionary game theory improves the validity and stability of the trust management system. The validity and stability of the trust management system are proved using the Lyapunov theory. The suggested approach enhances IoT device collaboration in edge computing. Their approach primarily examines the trust relationships of end-users. The approach is then extended to include end users and Edge service providers. They maintain a blocklist and allowlist methods as part of their method. By the end nodes, the reputable nodes will be added to the allowlist, while the suspicious nodes will be added to the blocklist. During path selection, the network tries to entail the nodes in the allowlist.
# A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets
## 2 Innovation  
Our paper makes the following contributions to the field of fuzzy co-location pattern mining:  
1. In the fuzzy processing of corresponding attribute information based on the original type-1 fuzzy membership function, a granular type-2 fuzzy membership function based on an elliptic curve is generated from the evaluation data. We adopt a gradual method to adjust the parameters of the function so that the type-2 membership function not only meets the connectivity [11] but also makes its confidence reach the given threshold (set to 85%).
2. Based on the abovementioned granular type-2 fuzzy membership function, we define the concepts of fuzzy membership interval, upper bound participation ratio, lower bound participation ratio of fuzzy features, upper bound participation index, and lower bound participation index for FCP mining. Further, to smooth the \"sharp boundary\" of pattern prevalence and reduce the deviation caused by the subjective understanding of the prevalence degree of FCPs, we propose the concepts of absolutely prevalent FCPs, FCPs with a prevalence tendency degree, and absolutely non-prevalent FCPs.
3. Based on spatial cliques, a method of mining FCPs is proposed. First, an algorithm for generating spatial cliques from spatial datasets is presented, and then an Apriori-like algorithm and two pruning strategies are proposed to discover the absolutely prevalent FCPs and FCPs with a prevalence tendency degree. In addition, we apply interval type-2 fuzzy sets to traditional co-location pattern mining algorithms and form an FCPs mining method based on interval type-2 fuzzy sets and the traditional Join-based algorithm, as well as another FCPs mining algorithm based on interval type-2 fuzzy sets and the traditional Joinless algorithm.
## 2 Background and Motivation
### Typical SFC Environments
#### 2.2.3 Inter-and Intra-Data-Centre Networks  
SFC in the inter/intra DC environment allows for the chaining of virtualized enterprise network applications (in the case of intra-data-center) and chaining across multiple locations, or inter-cloud, in the case of inter-data-centre networking. The ability of service functions to be instantiated across multiple datacenters (inter-data-centre networking) is a key requirement for live VM migration. The SFC architecture to be implemented should be designed to dynamically migrate service functions from one VM/container to another without disrupting user service requests [46].  
Deploying and managing service function chains in an inter-data-center setting incurs inter-data-centre bandwidth cost, deployment cost, intra-data-centre cost, and vNF costs [47]. In these environments, NFs are also used for policy-based routing of cloud services and enterprise applications [38], which means that service providers can up-sell their services easily using SFC at the enterprise. This can be achieved by making network services user-programmable.
## 5 Results and Discussion
### Effect of Baffle Structure on Flow Pattern behind It  
Vortex is a unique form of fluid motion, and the size of the vortex affects the input power. In research on the stirring flow field and power consumption, an investigation into the vortex structure is of considerable significance. During the suspension of solid-phase particles, part of the energy of the impeller was dissipated by the large-scale vortices at  
Figure 11: A velocity comparison (\(y\)/\(h\) = 0.25) in the annular flow area (r/R = 0.6–0.9) between the impeller and the baffle. (**a**) The radial velocity; (**b**) the tangential velocity.  
Figure 10: A comparison of the average velocity along the radial direction (\(y\)/\(h\) = 0.25) of the baffle with different relative tooth widths (\(\lambda\)/\(h\)) when \(A\)/\(W\) = 0.333.  
the baffle. In Figures 12 and 13, the three-dimensional structure of the vortex behind the baffle is, respectively, compared in terms of different tooth heights and different tooth widths. The results show that there were large-scale vortices in the lower part of the baffle, which were weakened by the bionic baffle structure and tended to be replaced by weaker eddies. However, the change caused by relative tooth height (\(A\)/\(W\)) was weaker (Figure 12). Compared with the former, the change of the vortex in Figure 13 was more obvious. The relative tooth width (\(\lambda\)/\(h\)) decreased, that is, the number of teeth increased, so that the larger vortex at the baffle was dispersed into smaller-scale eddies, and the fluid passed through the sawtooth to generate a higher level of turbulence, which could promote material mixing.  
Figure 14 reflects the fluctuating level of pressure on the backwater side of the baffle. Between \(y\)/\(h\) = 0-0.4, the single broad-scale vortex behind the standard baffle was decomposed by the sawtooth structure. Between \(y\)/\(h\) = 0.4-1, the pressure of the standard baffle did not change, and the fluid disturbance in the middle and upper part of the modified rear baffle was improved. However, the pressure change caused by the increase of the relative tooth height (\(A\)/\(W\)) basically maintained the same fluctuation level. Notably, the smaller the relative tooth width (\(\lambda\)/\(h\)), the greater the overall pressure fluctuation. When \(\lambda\)/\(h\) = 0.028, the diverting effect of sawtooth was the most prominent. The turbulence effect of the sawtooth structure effectively broke the large-width low-pressure area at the bottom of the baffle plate and enhanced the pressure gradient, which was beneficial for improving energy utilization and stirring efficiency.  
Figure 12: The effect of turbulent kinetic energy on the 3D vortex structure behind the baffle at different tooth heights when \(\lambda\)/\(h\) = 0.104.  
Figure 13: The effect of turbulent kinetic energy on the 3D vortex structure behind the baffle at different tooth heights when \(A\)/\(W\) = 0.333.