esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Natural Antioxidants and Their Role in Cosmetics
### Selected Plant Species with Antioxidant Activity
#### 4.11.4 Citrus Fruits  
Citrus fruits are plants from the Rutaceae family. The most popular are lemon (_Citrus limon_ L. ([PERSON])), sweet orange (_Citrus sinensis_ (L.) [PERSON]), grapefruit (_Citrus x paradisi_ L.), tangerine (_Citrus reticulata_ L.), and lime (_Citrus aurantifolia_ hort ex Tanaka). Commonly known as citrus fruits, these plants have fleshy berries in colors of yellow, green, orange, or red [218]. Citrus fruits are an extremely rich source of flavonoids, with the majority found in the extract from the pulp, seeds, and white parts of grapefruit. These are mainly flavanones, flavanols, and flavones, as well as vitamin C, which naturally occurs in the fruit. They have anti-inflammatory, antioxidant and anti-allergic effects; delay the aging processes of the body and skin; and have the ability to absorb solar radiation (UVA and UVB) [219].  
Hesperidin and ascorbic acid (present in _C. limon_) are used in antiaging cosmetics since they are antioxidant compounds. Lemon-derived products positively affect acne-prone skin that is affected by mycosis and sunburn [220].
## 3 Mathematical Model Description  
The mathematical model of the energy balance equation was analytically solved using MATLAB symbolic code. Building a steady-state heat energy balance equation described the heat transfer mode for the heating of the thermal fluid, the absorber, and the glass cover up to the surrounding area. The thermal performance of using a PTC was tested for Budapest weather conditions as a case study, where the solar radiation is time-dependent and uniformly distributed around the receiver part. Moreover, the effect of using a modified hybrid nanofluid, which consisted of Al\({}_{2}\)O\({}_{3}\) and WO\({}_{3}\) with Therminol VP1, was investigated. As such, the thermal properties of the base fluid varied with the inlet temperature while the thermal properties of the nanoparticles were constant, as presented in the following sections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LS2 Parameter [Symbols]** & **Specifications** & **Parameter [Symbols]** & **Specifications** \\ \hline Length of the PTC [L] & 7.8 m & Emittance of glass cover [\(\varepsilon_{c}\)] & 0.9 \\ Aperture width of the PTC [W\({}_{a}\)] & 5 m & Max optical efficiency [\(\eta_{opt}\)] & 74.5\% \\ Aperture area [A\({}_{a}\)] & 39 m\({}^{2}\) & Glass cover absorbance [\(\alpha c\)] & 0.02 \\ Absorber inner diameter [d\({}_{ci}\)] & 0.066 m & Glass cover transmittance [\(\tau_{c}\)] & 0.95 \\ Absorber outer diameter [d\({}_{co}\)] & 0.07 m & Absorber absorbance [\(\alpha_{r}\)] & 0.96 \\ Glass inner diameter [d\({}_{ci}\)] & 0.115 m & Concentrator reflectance [\(\rho_{c}\)] & 0.94 \\ Glass outer diameter [d\({}_{co}\)] & 0.121 m & Intercept factor [\(\gamma\)] & 0.93 \\ Emittance of the absorber [\(\varepsilon_{r}\)] & 0.2 & Concentration ratio [C] & 22.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: LS2 parabolic trough dimensions.  
Figure 1: A detailed cross-section schematic of LS2 PTC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbols** & **Case (1)** & **Case (2)** \\ \hline Radiation intensity & \(G_{b}\) & 1000 W/m\({}^{2}\) & Summer & Winter \\ Surrounding convection & h\({}_{out}\) & 10 W/m\({}^{2}\cdot\)K & 880 & 260 \\ Ambient temperature & T\({}_{amb}\) & 300 K & Variable with time \\ Inlet temperature & T\({}_{in}\) & 300 K–600 K & 500 K \\ Volume flow rate & V\({}_{flow}\) & 150 L/min & 150 L/min \\ Nanoparticle volume fractions & \(\varphi\) & 0–4\% & 0–4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Constant and variable operating conditions.
# An Improved CNN Model for Within-Project Software Defect Prediction
## 7 Related Works
### Deep Learning in Software Engineering  
Apart from software defect prediction, deep learning is also used in various software engineering domains. Generally, deep learning models are used in software maintenance [56], code clone detection [57], defect detection and localization [58; 59], and other domains.  
In software maintenance, [PERSON] et al. [56] used a RNN model to establish links between requirements, design, source code, test cases, and other artifacts, which is called trace links. Their results outperformed the state-of-the-art tracing methods including the vector space model and the latent semantic indexing. In code clone detection, [PERSON] et al. [57] proposed a deep learning-based clone detection approach. Code clone refers to copied code with or without modification, which could challenge software maintenance. They used AST tokens to represent method-level code clones and nonclones to train a classifier, and then used the classifier to detect code clones. Their methods achieved similar performance with low time cost. In defect detection and localization, [PERSON] et al. [58] utilized deep neural network for bug localization. The aim of the model was to solve lexical mismatch problem, which references that the terms used in bug report are different from the terms and code tokens used in source files. Their model achieved 70% accuracy with five suggested files. [PERSON] and [PERSON] [59] leveraged DeepBugs, which is a learning approach to detect name-based bugs. They used word embedding to form semantic features from methods, and they used learn-bug detectors instead of manually writing them. Their approach achieved high accuracy, high efficiency, and disclosed 102 programming mistakes in real-world code.  
Other software engineering domains also leverage deep learning, such as source code classification [60], runtime behavior analysis [61], feature location [62], vulnerability analysis [63], code author identification [64], and so on.
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 3 Proposed Architecture
### PCCNoC Design  
The PCCNoC architecture is based on a two-dimensional mesh topology. It is composed by routers, network interfaces, and PEs. Figure 3 shows a top-level diagram of the PCCNoC with a 4 \(\times\) 4 mesh structure. The router and network interface of PCCNoC are redesigned to support the brand new transmission mechanism.  
A router has two functions: (1) to set up transmission circuit by routing under the new routing protocol, and (2) to use the set-up circuit for data transmission.  
Inputs and outputs of each network router include five directions: East--Ein, South--Sin, West--Win, North--Nin, and a local module--Lin; East--Eout, South--Sout, West--Wout, North--Nout, and a local module--Lout, as shown in Figure 3.  
The PE in heterogeneous multi-core SoC can be:  
1. **Processor**. The writing bus of data storage (Lin) or program memory or configuration memory (Lin) are connected; the reading bus of data storage (Lout) is connected.
2. **Function module**. The writing bus of data storage (Lin) and configuration register (Lin) are connected; the reading bus of data storage (Lout) is connected.
3. **Memory**. The write port input to the data memory (Lin) is connected to the router; the read port of the data memory (Lout) is connected to the router.
4. **Interface (I/O)**. The router connects the interface (I/O), and the DMA inside the node provides subsequent data transmission.
## 1 Introduction
In order not to consume so many hardware resources at the relay sensor nodes, one possibility is to reduce the size of the visual data. Shrinking the size of the visual data could be done either by compression techniques via a single sensor node or by multimedia processing methods to eliminate the redundant parts between multiple sensor nodes. Visual data compression via a single sensor is easy, but it comes with the cost of inferior visual data quality. Multimedia processing methods require collaboration between multiple sensors to eliminate the redundant parts in their captured visual data. The collaboration among sensor nodes needs to exchange FoV information and then sophisticated multimedia processing techniques must be devised to determine the redundant parts from the exchanged FoV information. The sensor hardware capability should be considered when designing the visual data processing techniques. 5. _Sensor coverage in WVSNs_: In the WVSN, because of the relative angle or obstacles between the object and the sensor node, the camera on the sensor node might not be able to capture the desired shot even if the object is within the FoV of the sensor node. As a result some important scenes (e.g., face of the intruder or license plate of the car for security surveillance applications) might not be captured due to the limited angle of the camera or the obstacle blocking the view. This kind of occlusion problem requires more sensor nodes to be deployed. Devising methods to optimize the sensor deployment in occlusion-aware WVSNs so as to minimize the deployment cost is an interesting research problem in WVSNs. As discussed in the previous paragraph, existing WSN algorithms or schemes are not applicable to WVSNs. Visual data introduce a new dimension to enable a much wider range of applications, but it also brings new research challenges and opportunities. New algorithms and schemes should be proposed to address the sensor coverage and visual data capturing/processing/transmission issues. Figure 1: Wireless sensor networks _vs_. Wireless Visual Sensor Networks. In Section 2, we survey existing works on the development of sensor hardware and platforms for WVSN networks. In Section 3, we assess existing works on sensor coverage and sensor deployment in WVSNs. In Section 4, we study existing works on visual data capture in WVSNs. In Section 5, we examine existing works on visual data processing in WVSNs. In Section 6, we survey existing works on visual data transmission. In Section 7, a new research paradigm based on social networking with WVSNs is surveyed. In Section 8, we conclude what has been done and not done in WVSNs.
# World-Models for Bitrate Streaming
###### Abstract  
Adaptive bitrate (ABR) algorithms optimize the quality of streaming experiences for users in client-side video players, especially in unreliable or slow mobile networks. Several rule-based heuristic algorithms can achieve stable performance, but they sometimes fail to properly adapt to changing network conditions. Fluctuating bandwidth may cause algorithms to default to behavior that creates a negative experience for the user. ABR algorithms can be generated with reinforcement learning, a decision-making paradigm in which an agent learns to make optimal choices through interactions with an environment. Training reinforcement learning algorithms for bitrate streaming requires building a simulator for an agent to experience interactions quickly; training an agent in the real environment is infeasible due to the long step times in real environments. This project explores using supervised learning to construct a world-model, or a learned simulator, from recorded interactions. A reinforcement learning agent that is trained inside of the learned model, rather than a simulator, can outperform rule-based heuristics. Furthermore, agents that are trained inside the learned world-model can outperform model-free agents in low sample regimes. This work highlights the potential for world-models to quickly learn simulators, and to be used for generating optimal policies.  
e +
Footnote †: journal: Applied Sciences  
_Article_  
World-Models for Bitrate Streaming
# Low-Frequency Communication Based on Rydberg-Atom Receiver
## 3 Theory of Signal Detection
Our work demonstrates that Rydberg atoms can detect low-frequency signals with high sensitivity, a capability critical  
Figure 2: (**a**) EIT spectrum for the transition from intermediate state \(|6P_{3/2},F^{\prime}=5>\) to the Rydberg state \(|63P_{5/2}>\). The inset illustrates the three-level Rydberg EIT scheme. (**b**) EIT spectrum for the transition compared with a 100 kHz signal field. The inset depicts the 100 kHz signal field. (**c**) EIT spectrum for the transition compared with a DC field. (**d**) EIT spectrum for the transition compared with a 100 kHz signal field and a DC field. The inset depicts the 100 kHz signal field. for the development of quantum communication networks [26]. This aligns with the broader objective of integrating quantum communication with emerging technologies, such as 6G networks.
# Domination and Independent Domination in Extended Supergrid Graphs +
###### Abstract  
Supergrid graphs are derived by computing stitch paths for computerized embroidery machines. In the past, we have studied the Hamiltonian-related properties of supergrid graphs and their subclasses of graphs. In this paper, we propose a generalized graph class for supergrid graphs called extended supergrid graphs. Extended supergrid graphs include grid graphs, supergrid graphs, diagonal supergrid graphs, and triangular supergrid graphs as subclasses of graphs. In this paper, we study the problems of domination and independent domination on extended supergrid graphs. A dominating set of a graph is the subset of vertices on it, such that every vertex of the graph is in this set or adjacent to at least a vertex of this set. If any two vertices in a dominating set are not adjacent, this is called an independent dominating set. Domination and independent domination problems find a dominating set and an independent dominating set with the least number of vertices on a graph, respectively. The domination and independent domination set problems on grid graphs are known to be NP-complete, meaning that these two problems on extended supergrid graphs are also NP-complete. However, the complexities of these two problems in other subclasses of graphs remain unknown. In this paper, we first prove that these two problems on diagonal supergrid graphs are NP-complete, then, by a simple extension, we prove that these two problems on supergrid graphs and triangular supergrid graphs are also NP-complete. In addition, these two problems on rectangular supergrid graphs are known to be linearly solvable; however, the complexities of these two problems on rectangular triangular-supergrid graphs remain unknown. This paper provides tight upper bounds on the sizes of the minimum dominating and independent dominating sets for rectangular triangular-supergrid graphs.  
domination; independent domination; extended supergrid graph; supergrid graph; diagonal supergrid graph; triangular supergrid graph; rectangular triangular-supergrid graph; grid graph
## 2 Materials and Methods
### Confusion Matrix  
To further elucidate the model's performance, especially in a multi-class setting, the confusion matrix is an essential tool. A confusion matrix is a tabular representation that allows for the visualization of a model's performance. Each row of the matrix represents the instances in an actual class, while each column represents the instances in a predicted class. The diagonal cells represent the number of correct classifications for each class, while the off-diagonal cells indicate errors.  
The confusion matrix not only provides a clear visual of the performance but also showcases the calculation of various performance metrics, including recall for each class (normalized matrix diagonal). This is particularly important in multi-class classification problems, where understanding the model's performance for each class is critical. While accuracy gives a general overview of the model's performance, recall provides essential insight into the model's ability to correctly identify each class. This is crucial in scenarios where certain classes are more significant or have greater consequences associated with misclassification.  
In our study, we normalized the confusion matrices for each class so that the sum of values in each row equals 1. This normalization ensures that the diagonal elements of the matrix represent the recall for each class. By adopting this approach, the matrix not only indicates how well the model classifies each category but also allows for a direct and clear comparison of class-specific performance. The row-wise normalization simplifies the understanding of the matrix, making it more intuitive to evaluate the recall, as the values directly reflect the proportion of correctly identified instances for each class.
## 3 Channel Statistics
### Characterization of Angular Spread for Outdoor and Indoor Radio Propagation Channels  
The computed numerical results for angular constriction, angular spread, direction-of-maximum fading, and true standard deviation for all the four different cases of outdoor and indoor propagation environment at 28 GHz and 38 GHz with three measurement scenarios for each case of AoA are presented in Table 2. In case 1, quantifiers are computed for outdoor urban environment where measurements were taken at 38 GHz. Angular spread is higher when the distance between Tx and Rx is less and standard deviation gives exact measure in degrees. When separation distance is increased above 250 m, the scatterers in vicinity of Tx reduce, therefore, angular constriction is approaching to zero which shows no bias in concentration of multipaths around specific directions. In case 2, angular spread quantifiers are computed for the given AoA measurements in an indoor corridor environment at 28 GHz as well as 38 GHz for three different values of Tx separation from Rx. All these three separation distances are kept for NLoS scenarios. While analyzing the measurements, it can be seen that power of received signal has variations with respect to AoA at different spatial locations. These variations are higher for higher frequencies and this received power is also more sensitive with respect to variation in AoA. Seeing the results in Table 2, the parameter, \(\Lambda_{\theta}\), for 28 GHz and 38 GHz computed at 3 m, 5 m and 6 m is observed higher at 5 m than at 3 m for both the considered frequencies. This shows that at 5 m, the channel can be used effectively for providing rich diversity for a multiple antenna system.  
In case 3, all the quantifiers are computed for indoor office environment at 28 GHz. Three scenarios are considered for the measured AoA results. For the LoS case, the angular spread is observed as 0.9049, i.e., close to the highest value of 1. This is because, there also exists another dominant path other than the LoS path, which is from the direction exactly opposite from the LoS direction. This leads to the understanding that the receiver is receiving the signals from multiple directions with no clear bias to a single particular direction. For the same case, the angular constriction is observed to have a strong bias in two physical paths, with a dominant value of 0.6834, see Table 2. The true standard deviation follows the similar trend as of angular spread, but it provides a true physical meaning to the quantified spread by measuring it in a true scientific unit, i.e., radians/degrees. In an indoor environment, the high density of scattering objects in the close vicinity of both the communicating nodes results in a wider angular span. A shift in angular spread and true standard deviation is clearly evident in the results, with a shift from LoS to NLoS channel conditions.
## 2 Propagation Mechanism and Particle Distribution
### Raindrop Distribution  
Rain is a precipitation phenomenon where liquid droplets fall from the atmosphere to the ground. There are different types of rain, such as drizzle, continuous rain, and downpour, with raindrop radius typically ranging from 0.01 mm to 4 mm. According to meteorological standards, drizzle is defined as rainfall less than 2.5 mm/h, moderate rain ranges from 2.6 to 8.0 mm/h, downpours range from 8.1 to 15.9 mm/h, and a rainstorm is characterized by more than 16.0 mm of rainfall per hour. The attenuation of terahertz waves caused by rainfall is primarily determined by the extinction cross-section and the size distribution of raindrops. Rain attenuation can be calculated based on the size distribution of raindrop particles and Mie theory. The size distribution of raindrop particles refers to the number of raindrop particles of various sizes in rainfall, and several distribution models are commonly used, such as Joss distribution [30], M-P distribution [31], and Weibull distribution [32].  
In [PERSON]'s experiment, the rainfall was classified into three categories based on intensity: drizzle with \(R\leq 5\) mm/h, widespread with \(5\) mm/h \(<R\leq 25\) mm/h, and thunderstorm with \(R>25\) mm/h. Furthermore, each type of rainfall has a specific raindrop distribution with diverse coefficients. The Joss distribution is reported as:  
\[\left\{\begin{array}{rl}&\text{Drizzle}:n(r)=30,000\exp(-11.4R^{-0.21}r),\\ &\text{Widespread}:n(r)=7000\exp(-8.2R^{-0.21}r),\\ &\text{Thunderstorm}:n(r)=1400\exp(-6.0R^{-0.21}r),\end{array}\right. \tag{5}\]  
where \(R\) is the rainfall rate in millimeters per hour, and \(r\)_is the radius_ in millimeters.  
The M-P distribution was proposed by [PERSON] and [PERSON] based on measured data and is expressed as:  
\[n(r)=8000\exp(-8.2R^{-0.21}r)\ \mathrm{m}^{-3}\mathrm{mm}^{-1}. \tag{6}\]  
In order to improve the accuracy of raindrop size distribution estimation, [PERSON] et al. proposed a new Weibull distribution by calculating the raindrop distribution parameters through rainfall observation experiments, which is represented as:  
\[n(r)=N_{0}\frac{\eta}{\sigma}(\frac{2r}{\sigma})^{\eta-1}\exp[-(\frac{2r}{ \sigma})^{\eta}], \tag{7}\]  
where \(N_{0}=1000\ \mathrm{m}^{-3}\), \(\eta=0.95R^{-3}\), \(\sigma=0.26R^{0.42}\).
# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 3 Proposed Method
### Boundary-Aware Transformer Attention
The resulting attention output is then passed through a Feedforward Network (FFN) followed by layer normalization to ensure stability and enhance feature representation. Let \(F_{\text{skip}}\) denote the feature map originally propagated through the U-Net's skip connection. To minimize information loss and preserve local details, the BAT output is merged with \(F_{\text{skip}}\) in a residual manner, effectively combining both global contextual cues and local structural features. \[F_{BAT}=\text{LayerNorm}(\text{FFN}(\text{LayerNorm}(\text{MHSA}(Q,K,V)+F_{ skip}))) \tag{5}\]  
The feature maps refined through the BAT module are subsequently passed to the decoder of the U-Net to generate the final segmentation output. Let the output of the BAT module be denoted as \(F_{\text{BAT}}\); this serves as the final feature representation used in the decoder, effectively integrating both boundary-aware global context and localized structural information for improved segmentation performance. \[F_{decoder}=\text{Concat}(F_{BAT},F_{skip}) \tag{6}\]The decoder generates the segmentation mask based on these combined feature maps, ultimately producing outputs that clearly distinguish the boundary of the OC located within the OD. The overall architecture of the proposed BAT structure is visualized on the left side of Figure 4. The effectiveness of the BAT module is visually demonstrated on the right side of Figure 4. Before applying the BAT module, uniform activation appears around the center of the optic disc, but the boundary with the optic cup is not distinctly highlighted. After applying BAT, the boundary between the OD and OC becomes significantly clearer and more pronounced. Particularly within the ROI image, where the relative proportion of the OC and OD is larger compared to the full fundus image, it is even more evident that the BAT module substantially enhances fine local boundary refinement around the boundaries.