esa-sceva/satcom-chunk-collection huggingface dataset:
# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 5 Conclusions  
In this paper, we propose a novel deep neural network sRsr8-Net for enhancing low-light images, by converting them from sRGB to raw and then back to sRGB under normal lighting conditions. During this transformation, the Sampler module extracts metadata to assist the RGB-iRGB module in better reconstructing raw images. Specifically, we introduce the RRAWFormer module to map the arrangement of pixels to better align with the R-G-G-B Bayer format. Additionally, we have also designed a new RRAW-sRGB module with ISP blocks to guide the color enhancement of low-light images. Extensive experiments in both the sRGB and raw domains demonstrate the effectiveness and robustness of the proposed method, particularly in retaining detailed image information and enhancing color consistency. Compared to traditional methods like Retinex-based techniques and Gamma correction, sRsr8-Net offers superior detail preservation and color accuracy. While deep learning methods such as Zero-DCE and EnlightenGAN also improve low-light images, they may have drawbacks like introducing artifacts or color distortion. Our sRsr8-Net addresses these issues and demonstrates excellent performance in generalization evaluation.  
However, it is important to acknowledge the limitations inherent in our approach. Firstly, while sRsr8-Net has shown excellent generalization ability, it may not be robust against some extreme low-light conditions. Secondly, the deep neural network model sRsr8-Net demands more computational resources than some traditional methods, such as Retinex. Our future work will aim to address these limitations and focus on improving our current method to handle a broad range of lighting conditions and to reduce computational costs without compromising the quality of enhancement.  
**Author Contributions:** Z.H.: Conceptualization, Methodology, Resources, Supervision; D.Z.: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing--Original Draft; L.X.: Investigation, Formal Analysis, Writing--Review and Editing. X.L.: Writing--Review and Editing; Y.L.: Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Doctoral Research Initiation Project of Wuyi University (Grant No. BSQD2304).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data that support the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 5 FTUC Algorithm  
The FTUC algorithm is divided into three operational phases as depicted in Figure 2. In the flooding tree phase, the base station broadcasts the 'build tree' message to all nodes to set up a tree type sub-network. After the tree-type network construction, all distance messages are transmitted through this sub-network and the BS calculates the global distance values. In the clustering phase, the network elects the CHs and builds the unequal clusters. In the stable phase, the network is a classical time division multiple access (TDMA) WSN, in the sense that each node measures its environment and sends its data to the BS through the CH inter-cluster route. The last two steps (i.e., clustering and stable phases) are re-iterated periodically in order to elect new CHs and to balance the energy consumption over the nodes.  
Figure 2: Timeline showing the operations of FTUC: at the first iteration, it builds a tree type sub-network. Then, periodically, it forms the clusters, and progresses to the steady state, where sensing and data transmissions are performed.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Treatment** & **Process Condition** & **Main Findings** & **Reference** \\ \hline \multirow{3}{*}{_Laborbita_ head} & Ultrasound-assisted extraction (UAF) of oil & UAE: 20 kHz, 40\% amplitude, for 5, 10 and 15 & Pretreatments with UAE improved the extraction & [177] \\  & Ultrasound-assisted extraction (UAF) of oil & min. Enzymatic hydrolysis: Probames ratio of & yield of oil, showing higher oil recoveries (67.48\% vs. 1:100 (\(\pi\)/u), 2 h, 150 rpm, 55\({}^{\circ}\)C & 58.74\% for SFE and untreated samples, respectively) & [181] \\ \hline \multirow{3}{*}{\begin{tabular}{c} Bigbed carp \\ (Hypyphthalism) cables \\ \end{tabular} } & Ultrasound-assisted extraction of gelatin & Temperature: 60, 70 and 80\({}^{\circ}\)C & Improved technological properties. highest storage modulus (5000 Pa), gelatin point (22.94 \({}^{\circ}\)C), and melting point (52.54 \({}^{\circ}\)C) & [178] \\ \hline \multirow{3}{*}{\begin{tabular}{c} Bigbed carp \\ (Hypyphthalism) cables \\ \end{tabular} } & Ultrasound-assisted extraction of gelatin & Temperature: 60 \({}^{\circ}\)C extraction time: 1, 3 and 5 h & Extraction yield: 46.67\% for ultrasound batch versus & [179] \\ \hline \multirow{3}{*}{Mackerel} & Ultrasound-assisted extraction of proteins & ISP-looketched stabilization precipitation. & Significant increase of protein recovery, recovering & [180] \\  & Ultrasound-assisted extraction of proteins & UAE: 40 kHz, 40 \% amplitude, 0.1 M NaOH, 10 min. & more than 95\%, of total protein from & [180] \\ \hline \multirow{3}{*}{Raw salmon} & Inhibition of _Listeris_ & Ultrasound of bath, 45 kHz, 200 W, 1 min & \multirow{3}{*}{Decimal reduction (log cfu) and antimicrobial effects} & [181] \\  & _mucaylegages_ & & & \\ \hline \multirow{3}{*}{Red seabream (Pagras major) fillets} & Ultrasound and thawing at 0 \({}^{\circ}\)C under vacuum (UVT) (40 kHz, 200 W, 10 \({}^{\circ}\)C) & No free water changes and improved & [182] \\  & & Ultrasound & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Cod (_Gadus mvhat_) fillets \\ \end{tabular} } & Ultrasound & \begin{tabular}{c} 2.9 W/kg: produced the highest increments in WG. \\ (1.86\%), reducing hydration time by 33\% US=pH 8.5 \\ (2) 25 kHz, 147 W/kg, 64.3 W, 20 min, 14\({}^{\circ}\)C \\ (2) 25 kHz, 29 W/kg, 153.5 W, 20 min, 14\({}^{\circ}\)C \\ \end{tabular} & \begin{tabular}{c} 12.9 W/kg: produced the highest increments in WG. \\ (1.86\%), reducing hydration time by 33\% US=pH 8.5 \\ 1-day shorter hydration time, US=pH 8.5 improved \\ microbial quality \\ \end{tabular} & [183] \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Brown crab (_Cancer programs_) \\ whole cooked \\ \end{tabular} } & Ultrasound and freeze drying at \(-\)20 \({}^{\circ}\)C, 20 kHz, 400 W, 0, 5, 10, 15, 20 min, room temperature &
\begin{tabular}{c} Allergencity decreased with increasing treatment \\ time (topomyosin reduced 76\% after 20 min of US \\ treatment) Total antioxidant capacity strengthened \\ \end{tabular} & [184] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Applications of ultrasound technology on various seafood products.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 6 Strategies for Eradication of Biofilms
### Bacteriophages  
Phage therapy is one of the promising approaches that can be applied for eradication of aggressive multidrug-resistant infections [182]. Phage therapy has many advantages over traditional antibiotic therapy: (i) multiplication at the site of infection, which means that a very low dose will multiply and increase at the infection site with minimum concentration at other tissues [182; 183; 184]; (ii) viral tropism leads to high specificity in selection of the target bacteria species and even specific strains within the same species [184; 185; 186]; (iii) bacteriophage tropism ensures safety, since bacteriophages cannot infect eukaryotic cells; (iv) phage cocktails can be used to allow for a broader spectrum of activity; and (iv) high effectiveness against multidrug-resistant bacteria because bacteriophages use different pathways than antibiotics so they are not affected by bacterial resistance mechanisms [187]. However, owing to the lack of institutional authorizations for use of phage therapy on humans, their use is currently limited to food industry and agriculture applications. Another rewarding application is the inhibition of bacterial biofilms formed on inanimate surfaces [188]. The penetration of bacteriophages through biofilm matrix can be explained due to their minute size, which allows them to cross through water channels within biofilms and replicate eradicating bacterial cells deep within the biofilm. Additionally, some phages will direct bacterial cells to release depolymerizing enzymes that degrade the polysaccharides matrix of biofilms [189]. Furthermore, bacteriophages can infect persister cells, preventing their relapse reactivation [184].  
However, some studies reported that bacteriophages are more effective in preventing biofilms rather than eradications of a well-performed one [190; 191]. This is put down to low penetrability of bacteriophages into older biofilm layers [192], which may be due to slow bacterial proliferation and growth resulting in decreased bacteriophage efficiency [187; 193]. In compliance, one study reported that early application of bacteriophages resulted in significant inhibition of _P. mirabilis_ biofilm formation for over eight days and prevented the blockade of the used urinary catheters [194], while the late application of bacteriophages onto well-developed biofilms did not cause significant eradication of the biofilm [87]. Bacteriophages are ubiquitous in nature and in the digestive tracts of humans and animals. Numerous studies have reported the isolation of many bacteriophages showing anti-biofilm activities, with some examples listed in Table 1.
# Energy Harvesting over Rician Fading Channel:
## 1 Introduction
Section 3 provides the detailed performance analysis of the system, including exact analysis and asymptotic analysis. The numerical results to validate the analysis are presented in Section 4. Finally, conclusions are drawn in Section 5.
## 5 The Concept of IoV
### Security Threats and Attacks in IoV Environments  
Attackers come in different forms. No matter how simple or sophisticated an attacker is, the attack will ultimately affect the integrity of the IoT network. Hence, to protect these networks, it is important to understand the existing threats. Attacks can be categorized into three types: active, passive, and malicious attacks. In active attacks, the attackers can either originate fake messages or alter the information of authentic messages. These attacks are more frequent and difficult to avoid but can easily be detected and are inexpensive to detect. Attackers usually carry out active attacks with the aim of modifying network operations. To prevent this kind of attack, physical security measures should be implemented [100]. The goal of passive attacks is to compromise the target node without changing the content of the transmitted messages. This type of attack is used by attackers to obtain dispersed data from the network. Since passive attacks do not disrupt network operations, they are difficult to detect, and since attackers do not participate in network communication, dataencryption can be utilized to prevent such attacks [101]. With malicious attacks, rather than attackers benefiting from the attacks, they are instead initiated with the intent of harming participating nodes. These attacks are dangerous and can cause significant damage. Malicious attackers can even transmit false safety-critical information that puts drivers, passengers, and pedestrians in danger [59; 78; 102; 103; 104]. Below is a detailed explanation of some of the attacks to be expected in an IoV environment.
# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 3 Method
### Parallel OWOD Framework with Uncertainty Mitigation
#### 3.2.3 Collaborative Open-World Classifier  
To accomplish the collaborative learning of known classes and unknown classes, we introduce a collaborative open-world classifier to integrate the output of the two detectors of our framework and eliminate the impact of learning unknown classes on the detection performance of known classes.  
Considering that the generalized objects logits predicted by the objectness-driven discovery module may overlap with the output of the known object detector with high probability, the collaborative open-world classifier first filters the generalized objects logitsby calculating an Intersection over Union(IoU) matrix \(\mathcal{P}=\left\{p_{mn}\right\}\) between the generalized object boxes of the objectness-driven discovery module \(\mathcal{B}^{G}=\left\{b_{m}^{G}\right\}\) and the known object boxes \(\mathcal{B}^{K}=\left\{b_{n}^{K}\right\}\). The calculation of matrix \(\mathcal{P}\) could be formulated as follows:  
\[p_{ij}=IoU(b_{m}^{G},b_{n}^{K}) \tag{5}\]  
where \(b_{m}^{G}\) denotes each generalized object box in \(\mathcal{B}^{U}\) and \(b_{n}^{K}\) denotes each known object box in \(\mathcal{B}^{K}\). Subsequently, any generalized bounding box is labeled as a known class if it satisfies that its IOU with any predicted bounding box of a known class exceeds a threshold \(P_{t}\). Conversely, the remaining generalized bounding boxes are categorized as unknown class targets. The filter process of the generalized object box could be presented as follows:  
\[\mathcal{B}^{U}=\left\{b_{m}^{G}|\forall n,p_{mn}\leq P_{t}\right\} \tag{6}\]  
The final output \(\mathcal{B}^{out}\) of the collaborative open-world classifier is formed by merging the filtered unknown class predicted boxes with the original known class predicted boxes:  
\[\mathcal{B}^{out}=\mathcal{B}^{U}\cup\mathcal{B}^{K} \tag{7}\]  
The objective of the collaborative open-world classifier is to achieve collaborative detection of both known and unknown class targets by extracting unknown class targets with the attribute of \"object\" from the generalized objects outputted by the objectness-driven discovery module, excluding objects belonging to the known class.
## 4 Results
### Size Measurement Experiments
The average absolute error between the measured values and the true values is less than 0.03 mm. Figure 14 presents a comparison of the measurement errors between the Canny-Zernike [54; 55] algorithm, the Hough circle algorithm [56], and the algorithm proposed in this paper when measuring the bead hole of the lock cylinder. The figure clearly demonstrates that the method suggested in this paper outperforms both the Canny-Zernike algorithm and the Hough circle method in terms of accuracy and stability. The measurements obtained using the algorithm proposed in this paper exhibit significantly less error and variability compared to those obtained using the Canny-Zernike algorithm. Furthermore, when compared with the Hough circle method, the algorithm proposed in this paper achieves higher measurement accuracy. These findings indicate that the method described in this paper offers superior stability and high accuracy for measuring metal workpieces. Overall, the results shown in Figure 14 highlight the advantages of the method proposed in this paper, demonstrating its capability to provide precise and consistent measurements of metal workpieces. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Value** & **Triangle Width** & **Triangle Length** & **Square Length** & **Nut Width** & **Nut Length** \\ \hline Real Value/(mm) & 5.06 & 5.60 & 5.30 & 6.88 & 7.76 \\ Mean Predictive Value/(mm) & 5.050 & 5.595 & 5.315 & 6.91 & 7.742 \\ Mean Absolute Error/(mm) & 0.010 & 0.005 & 0.015 & 0.026 & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Measurement of geometric dimensions of various shapes. Figure 13: The geometric dimensions of the three shapes. (**a**) Triangle; (**b**) square; (**c**) nut.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Novelty and Contribution
#### 1.3.6 LDPC-Assisted LS Estimation for Noise-Resilient Performance  
* While LDPC codes have been used for error correction [15], their integration with LS-based LIS channel estimation has not been fully explored. We introduce a joint LS-LDPC decoding framework, which significantly improves channel robustness.
* Our method enhances channel robustness by combining LS estimation with LDPC decoding, improving the performance in high-noise conditions.  
This framework significantly outperforms conventional LS estimation methods by reducing the BER while optimizing complexity and energy efficiency. Monte Carlo simulations confirm that our approach provides a practical, scalable, and high-performance solution for LIS systems.  
As a unique idea, our study introduces Hadamard-based pilot assignment with adaptive power control to improve channel estimation and reduce interference. We optimize pilot allocation and LIS antenna selection to lower the complexity while keeping the accuracy high. Additionally, our iterative LS refinement enhances estimation over multiple steps, and the first LS-LDPC integration in LIS systems improves noise resistance and stability.  
Table 1 highlights how our proposed method improves upon previous research across six key aspects of LS-based channel estimation in LIS systems.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Previous Methods** & **Our Improvement** \\ \hline \multirow{4}{*}{**Enhanced pilot-based channel estimation**} & & We introduce Hadamard-based dynamic pilot assignment, which significantly reduces interference and improves channel estimation accuracy under varying SNR conditions. \\  & Previous works rely on fixed pilot power allocation, which does not adapt to fluctuating channel conditions. & Our method dynamically adjusts pilot power based on real-time channel variations, ensuring an optimal SNR while maintaining spectral efficiency. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of our work with previous studies.
## 1 Introduction
The element should be unconditionally stable. **  
Otherwise, the element can hardly be viewed to be fully reliable and robust. **R2. The element should gain high nodal/element convergence ratio. **  
This is to guarantee that the local (element) error estimate is reasonably reliable and the initial conditions for each new time-step (element) are of sufficiently high accuracy. Numerous numerical experiments have suggested that, especially for relatively long time domains, the ratio between the convergence orders at element end nodes and within elements is best up to two, i.e.,  
\[\text{Accuracy ratio}\equiv\frac{\text{nodal convergence order}}{\text{element convergence order}}=2. \tag{1}\]  
In other words, for an element of degree \(m\) with a convergence \(O(h^{m+1})\) on the element, the nodal convergence order would best reach to \(O(h^{2m+2})\). **R3. The element should be equipped with a reliable point-wise error estimator. **  
With the requirement R2 satisfied, a super convergent solution with accuracy at least one order higher than the FE solution should be available to serve as an effective error estimator in the maximum norm. Among the above three requirements, both the conventional and condensed elements satisfy R1, only the condensed element satisfies R2, and neither satisfies R3 unless an extra higher order super-convergent solution is available or constructed, which is a severe challenge to confront. Based on the conventional element model, this paper proposes a novel approach, named the \"reduced element\" technique, which satisfies all the three requirements in a simple, direct, and straightforward way without the need for an additional separate computation to find an error estimator. Simply speaking, in this approach, suppose the adaptive analysis is aimed at an FE solution of \(m\)-degreed elements with the pointwise errors required to satisfy the user preset tolerance \(T_{j}\); we simply solve the problem by using elements of one order higher, i.e., degree \(m+1\), and then after the FE solution, we extract, on each element, the results up to degree \(m\) as the target solution with the highest ordered (degree \(m+1\)) term used as a pointwise error estimator. Since the target solution is from the elements of reduced order (degree \(m\)) extracted from the elements of full order (degree \(m+1\)), it is simply called the \"reduced element\" technique.
# An Implementation of the HDBSCAN* Clustering Algorithm
###### Abstract  
An implementation of the HDBSCAN* clustering algorithm, Tribuo Hdbscan, is presented in this work. The implementation is developed as a new feature of the Java machine learning library Tribuo. This implementation leverages concurrency and achieves better performance than the reference Java implementation. Tribuo Hdbscan provides prediction functionality, which is a novel technique to make fast predictions for unseen data points using an HDBSCAN* clustering model. Tribuo Hdbscan cluster results and performance measurements are also compared with the state-of-the-art HDBSCAN* implementation, the Python module hdbscan.  
Keywords:hdbscan; clustering; algorithm; implementation; tribuo; Java; prediction
# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 3 Algorithm Proposed to Conceal a Hole  
### Hole Detection  
Figure 3 explains the process used to detect a hole in an ERP image. As observed in Figure 2, because a hole is a region that has not been covered by the warped pictures using the stitching algorithm, the color of the hole is black, with a luminance of zero. Thus, in step 1 of Figure 3, the colored ERP image is converted to a black-and-white picture to efficiently detect the holes. In step 2, all of the non-black pixels are replaced by white pixels, as shown in Figure 3c, where the black and white pixels have gray levels of 0 and 255 for an image represented with 8 bits/pixel, respectively. This results in the candidate regions for holes. Note that not all of the pixels in the dark objects have a zero value, even though some of the pixels in the objects may have a zero value. In order to classify the black pixels as hole and non-hole regions, a contour finding algorithm [25] is applied to image Figure 3c in step 3, which provides several contours with various sizes. In image Figure 3d, the perimeter of the contour of the hole is longer than that of a non-hole region, because the non-hole region is part of the dark object and it has the shape of an isolated particle. Therefore, if the length of the contour is shorter than a threshold, the dark region is removed, as shown in image Figure 3e. Finally, the block overlapping the dark region is set to \(B_{L}^{c}\) in image Figure 3e.