esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### StarGAN  
StarGAN, introduced by [PERSON] et al. [13] represents a significant advancement in generative adversarial networks by enabling multi-domain image-to-image translation using a single model architecture. Unlike previous approaches that required separate networks for each domain transformation, StarGAN employs a unified framework with one generator and discriminator, where the generator translates input images to target domains specified by domain labels, while the discriminator both distinguishes real from fake images and classifies domain attributes. This unified approach dramatically improves scalability and training efficiency. Building on this foundation, [PERSON] et al. [12] successfully applied StarGAN specifically to endoscopic image augmentation for gastrointestinal disease classification. Their approach implemented the original StarGAN architecture without modifications to generate synthetic endoscopic images for data augmentation. They trained classification models on both the original dataset and an augmented dataset containing StarGAN generated images. Their research empirically demonstrated that StarGAN-based augmentation yields superior results compared to traditional data augmentation methods when applied to datasets showing various gastrointestinal conditions. Their work established that InceptionNet-V3 showed the most significant performance improvements when enhanced with StarGAN augmented training data, providing a strong foundation for applying generative approaches to medical image classification tasks where limited training data represents a persistent challenge.
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 2 Design Methodology  
### Antenna Geometry  
Figure 1a, b depict the configurations and detailed specifications of the radiating and ground planes for the proposed implanted ultra-wideband circularly polarized MIMO antenna. Two radiating patches, each with an equivalent design, are linked to a common ground plane and stimulated by coaxial probes. The probes feature inner and outer diameters measuring 0.6 mm and 0.8 mm, respectively. Figure 1c,d show that the antenna cover layer is selected as Rogers 3010 (\(\varepsilon_{r}\) = 10.2, tan\(\delta\) = 0.002) and the dielectric substrate is selected as Rogers 5880 (\(\varepsilon_{r}\) = 2.2, tan\(\delta\) = 0.002), and both of them have a thickness of 0.254 mm. The cover layer can effectively avoid mutual interference caused by direct contact between the antenna and other components of the IMD, and at the same time broaden the operating bandwidth of the antenna [22]. The low dielectric constant of the dielectric substrate reduces the propagation speed of the signal in the antenna structure, thus reducing the signal transmission delay, which is important for communication systems requiring high-speed data transmission and low latency. Since the two radiating patches are disconnected, there is no direct current (DC) leakage from one element to the other. However, the fringing field of the antenna can generate capacitive coupling, which varies with the inter-element gap. To ensure minimal coupling, a 0.4 mm gap is maintained between the radiating surfaces.  
Figure 1: _Cont._
## 2 Background and Related Work
### Methods of Detection
However, previous research has identified that with an increase in the number of layers and model depth, a loss of gradient or excessive smoothing occurs, resulting in a marked decline in \begin{table}
\begin{tabular}{c c c c} \hline
**Networks** & **Advantage** & **Disadvantage** & **Applications** \\ \hline MLP & Simple and intuitive; easy to understand and implement. & Sensitive to the structure and relationships of input data; limitations in processing sequence and graph structured data. & Used for basic classification and regression tasks with relatively simple structured data [17]. \\ \hline CNN & With translation invariance, it is suitable for processing data such as images, parameter sharing, and local connection help to reduce model parameters. & Sensitive to the size of the input data. Not suitable for modeling serial data. & Computer vision tasks such as image classification and target detection [18; 19]. \\ \hline RNN & \
\begin{tabular}{c} Capable of capturing timing information in sequence data and suitable for processing variable length input sequences. \\ \end{tabular} & Difficult to capture long-range dependencies and prone to gradient vanishing or exploding during the temporal relationships [20; 21]. \\ \hline GNN &
\begin{tabular}{c} Ideal for working with graph-structured data; capable of capturing relationships between nodes. \\ \end{tabular} & Computational efficiency issues. & Social network analysis, molecular mapping prediction, numerous correlated signal inputs, and other tasks that require consideration of graph structure relationships [22; 23; 24; 25; 26; 27; 28; 29]. \\ \end{tabular}
\end{table} 
Table 1: Comparison of different neural networks. performance [31; 32]. In order to break through the limit of the number of layers, RestNet [33] designed a residual connection in the field of image processing; this strategy has achieved good results. Inspired by the idea of residual connection, we considered adopting a sparse aggregation method in the neighbor node information aggregation link. In this context, the process involves discreetly aggregating neighbor information at each order while simultaneously elevating the output of each layer, facilitating a direct transition to the output layer within the Graph Neural Network. 3. _Hop-attention_: Incorporating an attention mechanism enables a comprehensive evaluation of interrelationships among multiple variables, and the consideration of contextual information plays a big role in natural language processing [34]. The neighbor aggregation link in each layer output, after the skip connection, directly to the final layer of the graph neighbor aggregation network, where these are inputs for the attention connection; because each input represents the node representation of a certain hop neighbor, it is called the hop-attention layer; the mechanism comprehensively considers the weight of each layer on the final result in order to highlight the most important part of the information of the neighbors of each order. The immunity of the model to noise is improved.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Evaluation Results
#### 5.3.1 Effects of the Delay Bound
As shown in Figure 4c, the average E2E delay of the five schemes linearly increases and satisfies the given delay bound. As the duty cycle interval increases, the wake-up frequency of nodes is reduced. Transmitters have to wait longer for forwarding candidates to wake up. As a result, the one-hop delay, as well as the average E2E delay, increases. For instance, as the delay bound varies from 10 s to 50 s, the average E2E delay with DDS linearly rises from 2.6 s to 13.0 s, while the average E2E delay under OGP linearly increases from 1.2 s to 6.8 s, respectively. DDS achieves a lower average E2E delay than SROD-MM and SROD-WA because the obtained duty cycle interval under DDS is lower than that under SROD-MM and SROD-MA. However, the average E2E delay from DDS is longer than that from OAPF and OGP. The reason is attributed to the fact that OAPF and OGP use a short period to transmit data packets, i.e., OAPF and OGP focus on the average E2E delay rather than considering the DSR and energy consumption.
## 4 Results and Discussion  
### Characterisation of Heat Pipe Start-Up Performance  
Figure 7 presents the effect of working fluid on the start-up performance of the MF-AHPAs in a vertical orientation. The thermal response curves show that the working fluid greatly affects the start-up performance of the MF-AHPA. As shown in Figure 7, the acetone MF-AHPA responds the fastest among all of the tested samples. After approximately 65 s, the acetone MF-AHPA nearly reached the steady state. The response times of the acetone, cyclopentane, and n-hexane MF-AHPAs are 16 s, 29 s, and 46 s, respectively. The results suggest that working fluid with a high Ga value is desirable to shorten the response time. For example, with the maximum Ga number of 621.2, the viscous force of acetone is relatively smaller and can be overcome by the gravitational force. Thus, the acetone MF-AHPA has a faster liquid returning process from the condensation section to the evaporation section. As a result, the acetone MF-AHPA responds much faster than the other two heat pipes.  
Figure 8 presents a series of infrared camera (IR) images of the three MF-AHPAs with different working fluids during the start-up process in a 15-s interval. The water bath was set at 55 \({}^{\circ}\)C with an ambient temperature of 28 \({}^{\circ}\)C. The axial temperature distribution is described by colour contours with the maximum temperature represented by a white colour. In the start-up process of the n-hexane MF-AHPA, the temperature rose very quickly in the evaporation section, while the temperature of the condensation section remained unchanged in the first 10 s. Then, the condensation section was  
Figure 6: Validation of present liquid film model by comparing it with (**a**) CFD results by [PERSON] et al. (2018), and (**b**) [PERSON]’s model.  
slowly heated up. In the acetone and cyclopentane MF-AHPAs, the temperatures of entire pipes were relatively uniform after heat was applied. The acetone and cyclopentane MF-AHPAs outperformed the n-hexane MF-AHPA in terms of temperature uniformity. This could be caused by the low latent heat of evaporation and large vapour density of n-hexane, as specified in Table 2. For example, the latent heat of n-hexane is as low as 66.3% of acetone, but the vapour density is as high as 148.1% of acetone. The low latent heat of evaporation means a smaller amount of vapour to be generated for a given working load; while a large vapour density leads to a large vapour inertia force on n-hexane. These two factors would result in high vapour flow resistance and an insufficient vapour supply consequently lowering the condensation rate.  
Figure 8: Evaluation of temperature uniformity by an infrared camera during the start-up stage: (1) n-hexane; (2) cyclopentane; and (3) acetone.  
Figure 7: Start-up performance of the MF-AHPAs with different working fluids.
## 5 Discussion  
The SEB-A method ignores the inner mechanisms of ET; rather, it only concerns itself with the final estimates and thus the principle of it is clear to understand. Besides the principle, the SEB-A approach has another advantage, which uses the exact value of air aerodynamic resistance (150 s/m). Because the air aerodynamic resistance required by the method is under the condition of no advection, its value is exact, which will avoid complex computational processing of it in practice. In previous studies, the air aerodynamic resistance was difficult to determine. In addition, the surface air temperature, \(T_{a-loc}\), needed is under the condition of no advection and can be estimated by remote sensing, which is another situation where the SEB-A method has an edge over previous methods that usually use interpolated air temperature from meteorological stations. Furthermore, the sensitivity analyses indicate that the SEB-A method is insensitive to the temperature difference between the air and the ground. This is promising because determining LST and \(T_{a-loc}\) is complex and may result in transferring errors. The insensitivity of LST \(-\)\(T_{a-loc}\) improves the applicability of the SEB-A method.  
Despite the advantages of the SEB-A method, it still carries some uncertainties. On the one hand, the approach requires plenty of ground-based observations to calculate the exotic ET driven by horizontal advection; therefore, the lack of sufficient ground-based observations would lead to inaccurate ET estimates. On the other hand, because the exotic ET is calculated by using EC observations, it is ground-based and needs to be extended from a point scale to a regional scale by the IDW. The IDW, however, interpolates according to the distance between inputted objectives with different values (see Equations (13) and (14)). As a result, the interpolation results may display in the form of circular high-value or low-value areas that do not exist in practice. Thus, the IDW may be inappropriate to handle the exotic ET. It is essential to find out the diffusion mechanism of the exotic ET and propose an appropriate way to address it.  
The SEB-A method provides a way to take into account the horizontal advection when applying the energy balance theory and gives avenues for quantifying the exotic ET produced by the horizontal advection.
## 3 Results and Discussion
### Effect of LAI on Stomatal Conductance Prediction Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Data Set**} & \multicolumn{3}{c}{\(R^{2}\)} & \multicolumn{3}{c}{**RMSE (mm/s)**} \\ \cline{3-7}  & & **uWUE** & **Perez-Priego** & **TEA** & **uWUE** & **Perez-Priego** & **TEA** \\ \hline \multirow{2}{*}{Growing} & Train & 0.7478 & 0.7691 & 0.7781 & 0.4186 & 0.1503 & 0.4278 \\  & Test & 0.5173 & 0.6106 & 0.5738 & 0.5644 & 0.2020 & 0.6077 \\ \hline \multirow{2}{*}{Overwintering} & Train & 0.5863 & 0.6680 & 0.6397 & 0.1317 & 0.0479 & 0.1832 \\  & Test & 0.3114 & 0.6444 & 0.3415 & 0.1708 & 0.4896 & 0.2963 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model accuracy of winter wheat growth period and overwintering period.
## 3 Methodology
### Discourse Graph Construction and Positional Encoding of EDUs
From the above formula for positional encoding, we can identify the following characteristics:  
Due to the very low periodic change frequency of the function, it ensures that even if the EDU sequence is very long, the position vector for each token remains unique. The characteristics of the periodic function ensure that every value in the positional vector is bounded and lies in a continuous space. This makes it easier for the model to generalize when handling positional vectors and better handle sequences whose lengths differ from the training data. The vectors represent different positions that can be obtained through linear transformation. We will prove this in the following paragraph. Figure 3 shows a sequence of EDUs of length 50, after position encoding the first 128-dimensional visualization results, it can be found that due to the nature of the sin/cos  
Figure 2: Example of two sentences with the same neighbor node but with opposite semantics. function, every value of the position vector lies in the bounded continuous space of \([-1,1]\). And with the help of the nature of trigonometric functions:  
\[ \begin{cases}\sin(\alpha+\beta)=\sin\alpha cos\beta+\cos\alpha\sin\beta\\ \cos(\alpha+\beta)=\cos\alpha cos\beta-\sin\alpha\sin\beta\end{cases}  \tag{3}\]  
we found that:  
\[ \begin{cases}\begin{aligned} PE^{(2i)}(t+k)=PE^{(2i)}(t)\times PE^{(2 i+1)}(k)+PE^{(2i+1)}(t)\times PE^{(2i)}(k)\\ PE^{(2i+1)}(t+k)=PE^{(2i+1)}(t)\times PE^{(2i+1)}(k)+PE^{(2i)}(t)\times PE^{ (2i)}(k)\end{aligned},\end{cases}  \tag{4}\]  
It can be seen that for any position \(t+k\in pos\), its position embedding can be linearly combined by the vector of position \(t\) and position \(k\), which makes the position vector that also contains the relative position information.
## 3 The Proposed Approach
### Proposed Framework
Using the knowledge of the high-frequency texture component matrix \(\mathbf{H}\) and a Frobenius norm to estimate the errorbetween the texture component and the original data, the LRR formula can be reformulated as follows:  
\[\min_{\mathbf{C}}~{}rank(\mathbf{C})+\lambda_{1}\|\mathbf{X}\mathbf{C}-\mathbf{H}\|_{F}^{2}+\lambda_ {2}\|\mathbf{X}\mathbf{C}-\mathbf{X}\|_{I} \tag{6}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are the trade-off parameters. In the iterative process, the algorithm can learn the unique representation of \(\mathbf{C}\) by integrating all aspects of the knowledge. Since the rank (\(\cdot\)) operator is a non-convex problem, a convex kernel norm is typically used to relax it [23]. As a matter of convenience, \(\mathbf{P}\) can be used to represent the data's own error term. Finally, High-Frequency Low-Rank Subspace Clustering (HFLRSC) is proposed, which is written as a minimization problem:  
\[\min_{\mathbf{C},\mathbf{P}}~{}\|\mathbf{C}\|_{*}+\lambda_{1}\|\mathbf{X}\mathbf{C}-\mathbf{H}\|_{F}^{ 2}+\lambda_{2}\|\mathbf{P}\|_{l}~{}~{}s.t.~{}~{}\mathbf{X}=\mathbf{X}\mathbf{C}+\mathbf{P} \tag{7}\]  
As previously described, \(\lambda_{1}\) and \(\lambda_{2}\) are the trade-off parameters. The nuclear norm and the Frobenius norm are denoted by \(\|\cdot\|_{*}\) and \(\|\cdot\|_{F}\), respectively. Within this framework, the \(\|\cdot\|_{F}\) function is used to estimate the distance between the self-representation term \(\mathbf{X}\mathbf{C}\) and the texture component term \(\mathbf{H}\). Subspace \(\mathbf{C}\) will progressively acquire the information stored in the texture components during the iterative procedure of minimizing the term \(\|\mathbf{X}\mathbf{C}-\mathbf{H}\|_{F}^{2}\). Because of the sparse error and low-rank term limitations, the learning results will attain a relative balance while modifying the trade-off parameters. When \(\mathbf{C}\) is obtained, the category to which each sample belongs can be revealed by the subspace structure. With \(\mathbf{C}\), the affinity matrix could be generated as follows:  
\[\mathbf{W}=\frac{|\mathbf{C}|+|\mathbf{C}^{\mathbf{T}}|}{2} \tag{8}\]  
where \(|\cdot|\) is the absolute operator. The final clustering results can be acquired using the standard Special Clustering (SC) approach by using the generated w matrix as illustrated below:  
\[\min_{\mathbf{F}}\textit{Tr}(\mathbf{F}^{\mathbf{T}}\mathbf{L}\mathbf{F})~{}~{}s.t.~{}\mathbf{F}^{ \mathbf{T}}\mathbf{D}\mathbf{F}=\mathbf{I} \tag{9}\]  
where \(\textit{Tr}(\cdot)\) denotes the trace of the matrix. \(\mathbf{F}\) denotes the cluster indicator matrix, while \(\mathbf{I}\) denotes the identity matrix. \(\mathbf{L}\) is the Laplacian matrix, and \(\mathbf{D}\) is a diagonal matrix whose elements are the sum of the \(i\)th row or \(i\)th column of \(\mathbf{W}\). Thus, Equation (9) can be effectively transformed into an eigenvalue decomposition problem, and the clustering index of the data points can be directly expressed by a matrix \(\mathbf{F}\).
## 3 Methodology
### Data Collection  
#### 3.1.1 Climatic Data  
In cooperation with King Abdullah City for Atomic and Renewable Energy, Hail University constructed a climate monitoring station on its roof, from which the set of meteorological data spanning the years 2018 through 2021 was collected. The monthly averaged statistics for the relative humidity, wind speed, and air temperature are comprehensively visualized in Figure 1. Throughout the year, the average monthly temperature ranges from 16 to 35 \({}^{\circ}\)C. The hot time is from May to October, and August is the warmest month, although January is the coldest. The relative humidity is lowest in July and highest in January and December. It also appears from the figure that the average wind speed is approximately constant from 2 to 3 m/s throughout the year. The purpose of presenting these statistics is merely to create a rough forecast. As for the data used for calculations, we relied on the data available in the programs.
## 2 Materials and Methods
### Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) Analysis  
Following a 24 h treatment with HE, total RNA was isolated from HDP cells using the RiboEx reagent (GeneAll Biotechnology, Seoul, Republic of Korea). Complementary DNA (cDNA) synthesis was performed using 1 \(\upmu\)g of total RNA and M-MLV reverse transcriptase (Thermo Fisher Scientific). Quantitative real-time PCR (RT-qPCR) was conducted with the HOT FIREPoI(r) EvaGreen(r) qPCR Mix Plus (Solis Biophysne, Tartu, Estonia) on a Step OnePlus Real-Time PCR system (Applied Biosystems, Woburn, MA, USA). The expression levels of target genes were analyzed and normalized against glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an internal reference. The primers used for the amplification of specific genes used in the analysis were as follows: _WNT5A_, 5\({}^{\prime}\)-TTGAAGCCAATTCTGTGGTGCGC-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-TGGTCCTGAATCAAGTGGCACAG T-3\({}^{\prime}\) (reverse); _ADP_, 5\({}^{\prime}\)-CAAACCGGATACAAGCAC TCC-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CGAAAGAGACCCAATAGTGTGCCAC-3\({}^{\prime}\) (reverse); _VCAN_, 5\({}^{\prime}\)-GCCAATCTATTACCAGG ACCTGAT-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-TGGCACACAGGTGCATAC GT-3\({}^{\prime}\) (reverse); _FGF_, 5\({}^{\prime}\)-ACCTGCAGACTGTTTTCCC-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GGTGTCCAC GTAGAGACG-3\({}^{\prime}\) (reverse); _VEGF_, 5\({}^{\prime}\)-GAGGGCAGAATCATCACAGAGT-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CACCAG GGTCTCGATTGGA-3\({}^{\prime}\) (reverse); _LEF_, 5\({}^{\prime}\)-CCTGTGCCCCACACA ACT-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GGTCCTCCTGTCTCTTCTC-3\({}^{\prime}\) (reverse); _IGF1_, 5\({}^{\prime}\)-CTCTTCTAC CTGGCCGTGT G-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CATACCCTGGGCTTGT-3\({}^{\prime}\) (reverse); _FGF_, 5\({}^{\prime}\)-TCTGCCA ACACAGTGTGACTCTG-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GCCACTGTCCTATTCCA TGA-3\({}^{\prime}\) (reverse); _IL-1a_, 5\({}^{\prime}\)-CTTTCGGAAACTCACGGCA-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GTGAGC TCCAGACCTACCGC-3\({}^{\prime}\) (reverse); _NOG_, 5\({}^{\prime}\)-CCAGTACCCTCCATATTCCGAGT-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CCTCTACAAAAGTTCCCCGCTCT-3\({}^{\prime}\) (reverse); and _GAPDH_, 5\({}^{\prime}\)-TCCAAAATCAA GTGGGGCGATG-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GCCAGTAGAGGCAGGATGATG-3\({}^{\prime}\) (reverse).