esa-sceva/satcom-chunk-collection huggingface dataset:
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 5 Experimental Results and Analysis
### The Comparison of Complexity  
The high complexity of hydroacoustic networks leads to rapid energy consumption. The routing algorithm is performed on the controller, which sends the results to the DN requesting the routing path. The complexity affects the results of the path calculation for CHs, but not the energy consumption of the UAN, which is the most significant advantage. The complexity comparison of these five algorithms is shown in Figure 15. We see that [PERSON]'s algorithm had the highest complexity and the LEACH algorithm had the lowest complexity. The complexity of the EEHC algorithm was close to [PERSON]'s algorithm. The complexity of FCO-MST and FPO-MST was slightly higher than that of LEACH. When the number of DNs was greater than 100, the complexity of FCO-MST was greater than that of FPO-MST. When the number of DNs was less than 80, the complexity of FPO-MST was greater than that of FCO-MST.  
In the LEACH algorithm, a random number \(r(0-1)\) is activated for DNs that has not yet served as CHs in each round. The threshold \(T(n)\) is set in the algorithm. If \(r<T(n)\), the node can be set as a CH and the complexity is \(O(n)\). For [PERSON]'s algorithm, it uses the ratio of CHs \(m\) to DNs \(n\) and the number of rounds to count for the selection probability of CHs with a complexity of \(O(m*n)\). The EEHC algorithm keeps the nodes as CHs at time \(t\). Since each node has an equal probability of becoming the CH, the complexity is \(O(t*n)\). The minimum spanning tree is constructed by judging the cut set in FCO-MST. For an orphan graph \(G\langle n,e\rangle\) with \(n\) nodes and \(e\) edges constructed from CHs, the complexity of constructing \(T(n,e^{\prime})\) based on the adjacency table is \(O(n+e)\). In addition, the complexity for building \(T(n,e^{\prime})\) using the non-tree arc approach in FPO-MST is \(O(e\log e)\).
## 5 Implementation and Results
### Experiment 2
#### 5.2.2 Comparison between the Proposed Models and Previous Work  
In this part, we compare the results of the previous work in the literature, which used the Firefly feature selection algorithm with SVM classifier (FA-SVM) on the PBC dataset [3] and IG filter with a deep belief network algorithm for classification (DBN-IG) on the GE dataset [27], with the four best obtained results of the proposed models, which selected the most informative subset of features.  
Figure 10: Comparison of accuracy between Experiment 1 and Experiment 2 on GE dataset.  
Figure 9: Comparison of accuracy between Experiment 1 and Experiment 2 on the PBC dataset.  
According to results for the PBC dataset from Tables 7 and 8, we observed that the four proposed models gave better accuracy of results compared with previous work [3], and the GWO-SVM model had the highest accuracy with 99.66%. Moreover, the size of the features in the proposed models was reduced to 4 by the BA-based wrapper model and 6 by the GWO-based wrapper model, rather than 10 by FA-based wrapper model [3]. Based on the results of the GE dataset from Tables 7 and 9, we observed that the GWO-SVM proposed model enhanced the accuracy to 99.34% compared with the accuracy of the previous work [27], which was 98.64%.  
To sum up, the experimental results showed the effectiveness of incorporating the optimized wrapper feature selection based on bio-inspired algorithms (GWO, FPA, BA, and ABC) into the four predictive classifiers (NB, KNN, SVM, and DT) in terms of the accuracy of ASD prediction for the PBC dataset and GE dataset.
## 3 Laboratory Experiment and Measurement
### Sensor Experiment
#### 3.1.2 Limitation of Detection (LoD) Measurement  
In the LoD measurement, the chamber was flushed by a 99.999% N\({}_{2}\) to avoid influence from residual gases. Then, the CO\({}_{2}\) concentration was increased by 10 ppm. The measurement results are shown in Figure 6. Without any algorithms for optimization, compared with the nearly invisible voltage difference between 10 ppm to 20 ppm, there is a relatively much clearer voltage difference when the CO\({}_{2}\) concentration was increased to 30 ppm. So, the LoD of the sensor was determined to be <30 ppm with a 0.8% fluctuation. It would be a rare case that the CO\({}_{2}\) concentration was reduced to 100 ppm or less in a greenhouse. This indicates an acceptable measurement LoD and detection error of the sensor node for the application in such an environment.
## 2 Segmentation Model
### Loss Function Construction
#### 2.3.1 Edge Consistency Loss  
Edge consistency loss is commonly used in image processing, computer vision, and other fields that rely on spatial structure, such as deep learning-based image reconstruction and segmentation tasks [23]. The loss is defined as follows:  
\[\text{Loss}_{\text{EC}}\ =\left\|\
abla\hat{\mathbf{y}}-\
abla\mathbf{y}\right\|_{1} \tag{10}\]  
Here, \(\hat{\mathbf{y}}\) represents the model's predicted output, while \(\mathbf{y}\) denotes the ground truth. \(\
abla\hat{\mathbf{y}}\) and \(\
abla\mathbf{y}\) refer to the gradients of the predicted and true values, respectively. These gradients capture pixel-wise variations in the image and are commonly used to detect edges or transitions between different regions. \(\left\|\cdot\right\|_{1}\) represents the L1 norm, which computes the total absolute error in edge prediction.  
Thus, the edge consistency loss penalizes discrepancies between the gradients of the predicted and ground truth values. In other words, any errors in the predicted edges of segmented objects are backpropagated to the model, enabling it to adjust its parameters and improve edge segmentation accuracy.  
The gradient operator is a key component of the edge consistency loss. In this study, we use the Sobel operator, which is implemented through convolution layers in deep learning. The corresponding convolution kernels are as follows.  
\[\text{G}_{\text{x}}=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix},\text{G}_{\text{y}}=\begin{bmatrix}-1&-2&-1\\ 0&0&0\\ 1&2&1\end{bmatrix} \tag{11}\]  
By applying convolution operations, the horizontal gradient \(\
abla_{\text{x}}\) and vertical gradient \(\
abla_{\text{y}}\) of the image can be computed separately and then combined to obtain the total gradient:  
\[\
abla\ =\sqrt{\left(\
abla_{\text{x}}\right)^{2}+\ \left(\
abla_{\text{y}} \right)^{2}} \tag{12}\]
## 3 Results and Discussion
### Requirements Evaluation
#### 3.3.3.1. _Grip Force Regulation System_  
Prostheses with a grip force above 20 N enable basic daily-living activities [14]. However, not all of these activities require the same grip force; therefore, the regulation of grip force is an important factor to be considered for achieving a functional device. The typical grip force regulation systems presented in commercially available alternatives are limited. For example, grip force regulation in the Hosmer 5 XA VO hook is achieved by placing additional bands [14]. This system increases the number of components, resulting in a complicated system for users that only achieves discrete regulation. Similarly, force regulation on the Ottobock 10A60 hook is achieved by a lever that varies the kinematics of the mechanism in two different positions, allowing only two levels of force [14].  
In contrast, the Carbon HK VO hook developed in this study was designed with a continuous force regulation system. Force is generated by a typical tubular commercial band held by friction from point C to point H (Figure 4), which has an initial distance of 52 mm at 0\({}^{\circ}\)\(\theta\). As shown in Figure 10, tension variation is achieved when the user tenses or releases the tubular band. The continuous regulation system, as one of the main characteristics of the Carbon HK VO hook, increases device functionality and provides advantages over similar devices. In addition, the great diversity of available tubular bands on the market with different levels of resistance allows the user to select one at their convenience and easily replace it when it degrades.
## 1 Introduction  
In the rapidly-growing information age, silicone rubber has seen extensive application in the electronic and electrical equipment industries due to its excellent chemical resistance, non-toxic nature, heat-resistance, freeze-resistance, and good flexibility [1]. However, silicone rubber is electrically-insulating and transparent to electromagnetic radiation. As electronic information technology has developed and the use of electronic devices has become more widespread, electromagnetic interference (EMI), which interrupts the functionality of electronic devices and severely affects human organs, has become a serious problem [2; 3; 4]. Therefore, conductive rubber composites containing carbon materials, such as carbon blacks and multi-walled carbon nanotubes, have excellent electrical conductivities and flexibility and have been widely used as EMI shielding materials [5; 6]. A typical approach is to add a single type of carbon material into a single kind of polymeric matrix to increase the shielding ability of the carbon-based polymer composites [7; 8; 9; 10]. Ionic liquids (ILs), which consist of a pair of soft cationic and anionic species, are used to improve the dispersion of carbon fillers in the polymer matrix since ILs and carbon fillers exhibit strong cation-\(\pi\) physical interactions [11; 12; 13; 14].  
In this study, carbon blacks (CBs) and carbon nanotubes (CNTs) were modified with 1-vinyl-3-ethylimidazolium bromide ILs that contained carbon double bonds. The synergistic effect of ILs modified with CBs and CNTs on the morphology and the EMI shielding properties of silicone rubber/polyolefin elastomer/carbon black-carbon nanotube-ionic liquid (SR/POE/CB-CNT-IL)composites were systematically investigated to reveal the mechanism of the enhanced EMI shielding performance.
## 2 Problem Formulation
: _Given a temporal graph \(G=(V,E)\), where \(|V|=n\) and \(E\) is a set of temporal edges \((u,v,t)\), consider a training pair \((\mathcal{P},\hat{\mathcal{T}})\), where \(\mathcal{P}=\{P^{t}_{u}\}_{u\in V,t\in\mathcal{T}}\) is a set of node embeddings for each node \(u\) at each timestamp \(t\) generated by the function \(g(G;\theta_{1})=\mathcal{P}\), and \(\hat{\mathcal{T}}=\{\hat{I}_{u}\}_{u\in V}=[s_{u},e_{u}]_{u\in V}\) is the set of observed activity intervals for each node \(u\), we define a function_  
\[f(g(G;\theta_{1});\theta_{2})=\mathcal{T} \tag{1}\]  
_for which we seek to find the parameters \(\theta_{1}\) and \(\theta_{2}\) that minimize the sum of squared differences between the observed activity intervals and the predicted ones \(\mathcal{T}=\{I_{u}\}_{u\in V}=[s_{u},e_{u}]_{u\in V}\), across all nodes and timestamps:_  
\[(\theta_{1}^{*},\theta_{2}^{*})=\underset{\theta_{1},\theta_{2}}{\arg\min} \frac{1}{n}\sum_{u\in V}\left((s_{u}-\hat{s_{u}})^{2}+(e_{u}-\hat{e_{u}})^{2}\right) \tag{2}\]  
_subject to the coverage constraint_  
\[\forall(u,v,t)\in E,\quad t\in I_{u}\;or\;t\in I_{v}.\]  
By formulating the MinTCover problem as a learning task, we aim to learn a mapping from temporal graphs to activity timelines that firstly effectively builds temporal node representation and then efficiently covers all interactions with a minimal total active duration. This approach allows us to leverage DL techniques to approximate optimal solutions.
## 3 Extensive Air Showers: The Heitler-Matthews Model
### Electromagnetic Showers  
The main features of an e.m. shower profiles can be described within the simple [PERSON]'s toy model of particle cascades [32]. Let us suppose that a particle (electron, positron or photon) with energy E\({}_{0}\) splits its energy equally into two particles after traveling a radiation length X\({}_{0}\) in air, and let this process be repeated by the secondaries (see Figure 4).  
Let \(X\) describe the depth in the atmosphere and define the depth at which the average CR starts interactions with the atmosphere to be \(X=0\) g/cm\({}^{2}\). After \(n\) radiation lengths, we obtain a particle cascade which has evolved into \(N=2^{n}\) particles of equal energy \(E=E_{0}/N\). Multiplication stops when the energies of the particles are too low for pair production or bremsstrahlung. This energy is the critical energy \(\varepsilon_{c}^{em}\) in the air (\(\approx\)80 MeV, below which the collisional energy losses are dominant).  
The maximum number of particles \(N_{max}\) is reached at this moment, when all particles have the same energy \(\varepsilon_{c}^{em}\), \(E_{0}=\varepsilon_{c}^{em}\cdot N_{max}\). The depth \(X_{max}\) at which the shower reaches the maximum size is \(X_{max}=n_{max}\cdot X_{0}\), where \(n_{max}\) is the number of radiation lengths required for the primary energy to be reduced to \(\varepsilon_{c}^{em}\).  
Since \(N_{max}=2^{n_{max}}\), we have  
\[n_{max}=\ln\left(\frac{E_{0}}{\varepsilon_{c}^{em}}\right)\cdot\frac{1}{\ln 2} \tag{2}\]  
so that  
\[X_{max}^{em}=\frac{X_{0}}{\ln 2}\cdot\ln\left(\frac{E_{0}}{\varepsilon_{c}^{em }}\right). \tag{3}\]  
Finally, it is interesting to estimate the _elongation rate_\(\Lambda\), that is, the rate of increase of \(X_{max}\) with the primary energy. From the relation (3), we have  
\[\Lambda^{em}=\frac{d\;X_{max}}{d\;\log_{10}E_{0}}=2.3\cdot X_{0}=85\;\text{g/ cm${}^{2}$}\;\;\;\;\;\text{per decade of energy}. \tag{4}\]  
This simple model predicts two basic features of e.m. shower development:  
* \(N_{max}\) increases proportional to the primary energy \(E_{0}\), \(N_{max}=\frac{E_{0}}{\varepsilon_{c}^{em}}\).
* \(X_{max}\) increases logarithmically with primary energy, at a rate of 85 g/cm\({}^{2}\) per decade of energy.
# Automatic Medical Image Segmentation with Vision Transformer
## 5 Conclusions  
Transformers are structured with inherent powerful self-attention mechanisms that enable them to model long-range dependencies effectively. This work investigates the application of transformers for general medical image segmentation tasks. To fully utilize the capabilities of transformers, we encoded robust global context by training image features as sequences. Additionally, we incorporated low-level CNN features through a hybrid design. Our approach provides an alternative to the prevailing FCN-based methods for segmentation tasks across different medical image modalities. Our transformer-based model outperforms various popular segmentation methods, demonstrating that transformers can move beyond their success in NLP to effectively capture visual relationships in medical  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**CT/ISIC2016/ISIC2018**} \\  & **Train** & **Test** \\ \hline U-Net [2] & 0.216 & 8.307 \\ Atte-U-Net [36] & 0.278 & 15.875 \\ Inf-Net [60] & 8.17 & 45.66 \\ \hline Ours & 8.86 & 65.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average training and test time comparison.  
images. The promising results highlight the potential of transformers in medical imaging applications. Rather than relying on the local self-attention mechanisms in CNNs, pure transformer encoders can better model global dependencies, which is advantageous for medical image segmentation. Overall, our model advances the field of medical image segmentation by demonstrating the applicability and advantages of transformers for encoding domain knowledge in this task.  
The key future directions involve developing unified AI-based system for integrated diagnosis, interactive segmentation to enable human-AI collaboration, multimodal fusion for leveraging complementary information from different imaging modalities, multi-task learning for joint optimization of multiple clinical tasks, clinical decision support systems, and the seamless integration of these AI solutions into clinical workflows and mobile health applications.  
Conceptualization, J.Z.; methodology, J.Z. and X.Z.; software, F.L.; validation, X.Z.; resources, H.W.; data curation, X.Z.; writing--original draft preparation, J.Z.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSFC Grant No. 61702409.  
Not applicable.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 5 Optimization Formulations
### Optimization Objective and Constraint Modification  
The optimization objective considered in this paper complicates the optimization, compared with our work in [16], since now we seek to minimize the total number of pilots used across all slots. The frame length is variable and is determined by the optimization, but the frame will repeat continually. This means we need to normalize the number of pilots used to the frame length, thus obtaining a _pilot rate_ in pilots used per slot of the frame.
## 2 Overview of Compressive Sensing Algorithm  
CS is a data acquisition technique which results in reduction of transmitted power and data by combining sampling and compression through random projections. The signal could be recovered accurately if it has a low information rate and is sparse in nature either  
Figure 1: A generalized block diagram of an IoT-based healthcare system.  
Figure 2: The CS compression scheme.  
in time or in some transform domain. The number of samples required for precise recovery is determined by the sparsity of the signal. The reconstruction will be accurate if the signal being acquired has a lesser information rate which implies the signal is sparse in any of the transform domains. The number of samples required for precise recovery of the original signal also depends on reconstruction algorithm [34; 35; 36; 37]. Moreover, CS manages noise in the measurements effectively.
# Data-Driven Convolutional Model for Digital Color Image Demosaicing
## 3 Methods
### Convolutional Neural Network Model  
Given that the Luminance and Chrominance estimations have different properties, we designed the full reconstruction network as two parallel networks: the Luminance reconstruction network and the Chrominance reconstruction network. These two networks are largely optimized for their specific task and can leverage a unique set of underlying modeling assumptions. The output of the two parallel networks are then combined in a final merging step to produce the final RGB output image.
## 5 Results and Discussion of Findings
### Comparative Analysis of Fitness and Cost Functions
The same algorithm started at 0.93 and ended at 0.92, with its peak at around 0.94 and trough at 0.86 for the BreastEW dataset. The BDMO curve was just below 0.70 on the Colon and around 0.93 on BreastEW. The BPSO and BEOSA curves were around 1.0 with the Colon dataset. Lastly, the SpectEW dataset had some interesting curves for BIEOSA, BPSO and BEOSA, with curves starting from 0.62, 0.83, and 0.90, respectively, and then stabilizing at 0.62, 0.83, and 0.89, respectively. BSNDO and BWOA consistently had curves at 0.75 and 0.80, respectively. BGWO spiked up and down, starting from 0.70 to 0.80 and having a peak at 0.81. BDMO was just below 0.80. The takeaway from these cost function evaluations is that whereas the values obtained varied across datasets, both BPSO and BEOSA always performed well, mostly yielding curves above those of the other algorithms. This implies that both algorithms demonstrated superiority compared with the other methods, though in most cases, BEOSA outperformed BPSO. The implication of these outcomes is that both the BEOSA and BIEOSA methods are relevant binary optimization algorithms with great potential for producing very good performance on heterogeneous datasets with different dimensionalities. The cost function, which evaluates how far an algorithm moves away from the fitness function value, also evaluates the robustness of the algorithm in terms of its ability to sustain a good cost function evaluation; the higher the cost function value, the better the fitness value obtained. Considering the consistently outstanding performance of both BEOSA and BIEOSA on the fitness and cost function evaluations with all datasets, we conclude that the algorithm is very suitable for solving the problem of feature selection with effective minimization and maximization of fitness and cost values, respectively. In the following subsection, we compare the number of selected features obtained for all methods and associate this with the fitness evaluation discussed in this section.
## 2 Expansion Strategies
### Ex Vivo Expansion of V\(\delta\)1 \(\gamma\delta\) T Cells  
To harness polyclonal activity of the \(\gamma\delta\) T cells, the expansion of a heterogenous population with a broad range of \(\gamma\delta\) TCRs is highly advantageous. [PERSON] et al. expanded FACS-sorted V\(\delta\)1 T cells using a cytokine cocktail and OKT-3 mAb, and demonstrated their robust cytotoxicity against acute myeloid leukemia (AML) cells without inducing antigen loss. Using high-throughput sequencing they confirmed a polyclonal TCR repertoire [40]. Though V\(\delta\)1 expansion is not simply achievable via a pharmacological activation comparable to the pAg-induced activation of V\(\gamma\)9V82 T cells, it has been shown that plant-derived mitogens such as concanavalin-A (Con-A) can expand V\(\delta\)1 T cells [42; 100]. [PERSON] et al. achieved a high yield of V\(\delta\)1 T cells using Con-A and recombinant human IL-2 and IL-4 (10 ng/mL) on freshly isolated PBMCs, although this result was attributed to the period of Con-A treatment which led to the apoptosis of V\(\delta\)2 cells [101].  
In addition, a two-step protocol for the expansion of V\(\delta\)1 T cells was established by [PERSON] et al. They used magnetically isolated \(\gamma\delta\) T cells as starting material, and expanded them for 14 days using anti-CD3 Ab in the presence of IL-1\(\beta\), IL-4, IL-21, and IFN-\(\gamma\). Subsequently, cells were transferred into fresh cell culture medium supplemented with IL-15 and IFN-\(\gamma\), followed by re-stimulation with anti-CD3 Ab and cultured for another week. This expansion protocol resulted in an accumulation of 70% V81 T cells with high expression of NK-receptors and high toxicity against chronic lymphocytic leukemia (CLL) cells [102].  
In another study, three different expansion methods including ZOL, Con-A, and CD3/28 antibodies were tested to expand \(\gamma\delta\) T cells from healthy PBMCs pre-transduction. As expected, ZOL preferentially expanded the V82 subtype (\(>\)80% purity by day 13 post-activation). Con-A expanded both V81 and V82 cells; however, the expansion was not as high as ZOL for V82, and most cultured cells remained \(\alpha\beta\) T cells by day 13. CD3/28 antibodies predominantly expanded \(\alpha\beta\) T cells as expected [42]. Table 1 shows a summarized list of in vitro studies focused on pAg and BP-based expansion of \(\gamma\delta\) T cells.