esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 3 Methodology  
In this study, we focus on the aspects of clustering points that have already been detected by a deep learning algorithm. This includes unsupervised clustering, which is the primary method for constructing clusters. We chose synthetic data points to stress-test the system because they are designed to replicate people's movements in public settings. Because crowd motions can be fairly chaotic, the goal is for the algorithm to be able to cluster a wide range of data point distributions.
## 5 Experimental Results and Analysis  
### Simulation Setup  
In WPMEC, it is assumed that the bandwidth is 1 MHz and the noise power \(\eta\) is \(-90\) dBm [8]. The energy harvesting efficiency \(\zeta\) is 0.5 [15]. The channel attenuation follows a Rayleigh distribution. The power gains of the uplink and downlink channels are \(g_{ij}=10^{-3}\rho_{ij}^{2}d_{ii}^{-a},\ h_{Ai}=10^{-3}\rho_{Ai}^{2}d_{Ai}^{-a}\), respectively, where \(\rho_{ij}^{2}\) follows a Rayleigh distribution, \(d_{ij}\) represents the distance between UN \(i\) and UN \(j\), \(d_{Ai}\) is the distance between UN \(i\) and the AP, and \(\alpha\) represents the path attenuation coefficient [13]. The average signal attenuation at a reference distance of 1 m is assumed to be 30 dB. Here, \(r\) and \(l_{i}\) are set as 0.1 and 10 kb, respectively, with \(\forall i\ \in\ \text{N}\). All the algorithms are based on C++ 20, CPLEX 12.9, TensorFlow 2.1, and Python 3.6.  
\"CT-t0\" and \"CT-t\" are used to denote the harvest energy time and total time obtained using the DOPA. The total time includes both the downlink energy harvesting time and the uplink transmission time. In addition, this section also describes a method for evaluating the delay performance when the UN directly transmits information to the AP. This method is known as the non-cooperative direct transmission method. \"DT-t0\" and \"DT-t\" are used to denote the harvest energy time and the total time obtained without UN cooperation.
# Multiplexing Multi-Scale Features Network for Salient Target Detection
## 3 Interpretation  
### Experimental Environment Settings and the Dataset  
In this paper, the Pytorch deep learning framework was selected as the experimental framework. The following hardware equipment was used in the experiment:  
Processor: Intel (R) Core (TM) i9-12900H 2.50 GHz;  
Running memory: 16 G;  
Graphics card: NVIDIA GeForce GTX 3060 Ti (6 G);  
System: Windows 11;  
Related software: Anaconda3-4.3.14, cuda-10.0.132, PyCharm, etc.  
Dataset and implementation details: DUTS-Tr was used as the training set; this dataset is a training dataset specially designed for salient target detection tasks. It is widely used in the field of salient target detection, and its data quality and annotation accuracy are recognized by the industry. DUTS-Te, HKU-IS, and PASCAL-S were used as the test sets. These test sets are widely used in the field of salient target detection, and their data quality and annotation accuracy are recognized by the industry. Therefore, using them as test sets can ensure the objectivity and accuracy of the assessment results. Considering the memory capacity of the GPU and the size of the dataset, our network was trained for 50 epochs with a mini-batch of 4. To reduce the computational complexity and avoid falling into local optimum solutions, we used a momentum SGD optimizer with a weight decay of \(5\times 10^{-4}\), an initial learning rate of \(1\times 10^{-3}\), and a momentum of 0.9.  
1. DUTS-Tr: This dataset is a part of the DUTS dataset. The DUTS-TR contains a total of 10,553 images. Currently, it is the largest and most commonly used training dataset for salient target detection. This dataset was augmented by horizontal flipping, yielding a total of 21,106 training images. The images in DUTS-Tr cover a variety of complex scenes, such as urban landscapes, indoor environments, natural scenery, etc. This diversity enables the model to cope with salient target detection tasks in different environments and conditions, improving the robustness and generalization ability of the model.
2. DUTS-Te: This dataset belongs to the DUTS-Tr DUTS dataset and consists of 5019 images. DUTS-Te contains a large number of real images with pixel-level annotations covering a variety of complex scenarios, providing rich data support for the performance of tested models in multiple scenarios.  
HKU-IS: The HKU-IS dataset is known for its high-quality annotation, with a high accuracy that contributes to the accurate evaluation of model performance.The images in this dataset have a high complexity, including multiple salient targets and complex backgrounds, which challenges the detection power of the model.
4. PASCAL-S: The PASCAL-S dataset not only provides pixel-level annotation, but also combines this with structural information to help to more comprehensively evaluate the performance of the model. This dataset covers a variety of object categories and scenarios, making its evaluation results more representative..
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Emission Spectroscopy
#### 2.3.2 Laser-Induced Breakdown Spectrometry (LIBS)  
LIBS, as a type of atomic emission spectroscopy technique, has been used to determine metal contents in gas, solid, and liquid samples [81]. In this technique, a high-power pulsed laser beam is irradiated on the sample surface, which creates high-temperature plasma containing atoms, ions, and free electrons. Upon cooling of the plasma, the atoms, ions, and electrons lose energy and the released emission lines provide spectral signatures detectable by a spectrometer for the elements present in the target material. In recent years, LIBS has been used by researchers as an attractive and promising optical technique in food and agriculture [82; 83]. LIBS has the advantages of analyzing multiple elements simultaneously, being nondestructive, offering real-time, fast, online detection, with no need for chemical reagents [84]. It is an entirely optical technique for qualitative and quantitative chemical analysis, which makes it suitable as a PAT (process analytical technology) or portable tool. Researchers have reported using LIBS in analyses of milk, bakery products, tea, vegetable oils, water, cereals, flour, potatoes, palm dates, and meat [82]. Quantitative elemental analysis of fresh foods with complex organic compositions using LIBS faces some challenges due to low signal-to-noise ratios and matrix effects [85; 86]. Furthermore, LIBS is very sensitive to any inhomogeneity in the sample material, since a small amount of target is ablated. To obtain reliable results, careful sample preparation is important, although it is a minimal process when compared to methods applied in AAS or ICP-MS analysis. In general, food products are washed, dried to a constant mass, ground, homogenized, and in some cases additional milling is required to improve particle size distribution and microhomogeneity. Often powders are pressed into pellets to prevent defocusing of the focal spot and pulse-to-pulse fluctuation during the laser irradiation [87].  
In a study by [PERSON] et al., seaweed samples were manually contaminated with 20 mg/L of As, Cd, Cr, Cu, Hg, Pb, and Zn solutions in seawater and cultivated for 15 days for metal uptake. Seaweed pellets with a diameter of 14 mm and a thickness of approximately 0.2 mm were prepared from dried samples for the subsequent acquisition of LIBS data [88]. Laser light with a short pulse and high energy was emitted from the Nd:YAG laser source for the analysis. A framework consisting of the threshold variables (TVs) method and partial least squares regression (PLSR) model was developed and combined with the LIBS technique to simultaneously analyze the seven studied heavy metal contents. These results demonstrated the effectiveness of the developed framework for quantitatively analyzing the heavy metals in seaweed.
# Solanesol Biosynthesis in Plants
## 2 Functions of Solanesol  
### Physiological Functions of Solanesol in Plants  
Terpenoids generally function as either primary or secondary metabolites within the plant body. Relatively few terpenoids function as primary metabolites, and these include sterols, carotenoids, and plant hormones, which are necessary for plant growth and physiological functions. For example, sterols composed of triterpenes are important components of the cell membrane that participate in the construction of these membranes; tetraterpenoid carotenoids are pigments that are indispensable for plant photosynthesis, as they participate in the absorption and transfer of light energy and prevent oxidative damage; plant hormones such as gibberellins, abscisic acid, brassinolide, and strigolactones are also terpenoids [8].  
Nonetheless, most terpenoids (including solanesol) are secondary metabolites. Various plants produce numerous highly-specific terpenoids that play important roles in plant-environment interactions [3,4]. Solanesol mainly exists in solanaceous plants. In tobacco, solanesol might participate in the immune response towards pathogens: in a study by [PERSON] et al., the solanesol content in resistant tobacco varieties increased by \(\geq\)7 times one week after infection by the tobacco mosaic virus (TMV), while it did not increase significantly after infection in susceptible varieties [7]. In potato, as compared to normal temperatures (22 \({}^{\circ}\)C during the day, 16 \({}^{\circ}\)C at night), moderately high temperatures (30 \({}^{\circ}\)C during the day, 20 \({}^{\circ}\)C at night) caused a more than six-fold increase in the solanesol content after one week, indicating that solanesol might play an important role in the response of potato to moderately high temperatures [1]. Hence, solanesol plays important roles in the interactions of solanaceous plants with environmental factors.
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 4 DSNE Algorithm  
### Definition of Community Similarity and Bi-EgoNet  
Given two communities \(G_{1}(U_{1},V_{1},E_{1})\) and \(G_{2}(U_{2},V_{2},E_{2})\), and \(CD_{G_{1},G_{2}}\) represents the similarity of the two communities, as shown in Formula (24):  
\[CD_{G_{1},G_{2}}=\frac{|L(u_{1})\cap L(u_{2})|+|L(v_{1})\cap L(v_{2})|}{|L(u_{1} )\cup L(u_{2})|+|L(v_{1})\cup L(v_{2})|}. \tag{24}\]  
In the DSNE (Density Sub_community and Node-pair Extraction) algorithm, we use the Bi-EgoNet [11] network model proposed by [PERSON] et al., which can detect the overlapping communities of the bipartite network from a microscopic point of view. Figure 6 shows an example of this model. As shown in Figure 6, in a bipartite network \(G(U,V,E)\), the microscopic bipartite network model Bi-EgoNet of node pair \((u_{2},v_{1})\) is composed of a central node pair \((u_{2},v_{1})\), its neighbor node pair set \(NeiPN(u_{2},v_{1})\), and the edges between these node pairs. \((u_{2},v_{1})\) is the Bi-Ego, which is the center node pair; \(NeiPN(u_{2},v_{1})\) is the Bi-Alter, which is the each neighbor node pair.  
The algorithm is divided into two stages: the first stage is to construct a sub-community for each node pair centered on each node pair; the second stage is to combine all node pair sub-communities to get the global community structure.
# Dynamic Initial Weight Assignment for MaxSAT
## 1 Introduction
Another example is the Genetic Algorithms (Genetic Algorithm (GA)s), where they globally use stored data generated in the search process's past stages. The data are then used to inform the search process better and guide the search toward more promising areas within the search space. Genetic algorithms were mainly inspired by [PERSON]'s theory of the natural selection process, where chromosomes store past data, and the data are utilized to make future relatively better decisions. Another interesting naturally inspired global-based searching method is Swarm Intelligence (SI), which recently has been shown to be efficient in handling optimization problems and real-world industrial problems such as scheduling [19; 20] and vehicle routing [21]. Local Search (LS) differs from global search metaheuristics where LS, instead of relying on global changes to be made within the search space, depends on information gathered from local neighboring areas of the search's current stage. Otherwise, the local search and global search metaheuristics target the same goal, where a solution to an SAT problem is accepted with some level of goodness rather than an optimum one. They also share the characteristics of the incompleteness of searching for a solution (where they do not fully explore the search space). MaxSAT has been studied for decades and exhaustively investigated. As a result: (1) advances in handling MaxSAT and its solving technologies and algorithms have been proposed; (2) the MaxSAT problems have been generally categorized into three primary forms: unweighted MaxSAT, Partial MaxSAT, and weighted MaxSAT. Unweighted MaxSAT is a problem that makes no distinctions about the importance or hardness of the constraint. For instance, in a CNF MaxSAT-based encoded problem, the cost of flipping a variable (changing its Boolean value from true to false or vice versa) is counted based on all clauses' exact cost functions. The other and more general form of MaxSAT is Partial MaxSAT (Partial Maximum Satisfiability (PMS)), where the clauses are divided into two types: hard clauses and soft clauses. An accepted solution to PMS must satisfy all the hard clausesand a maximum number of soft clauses. The third form of MaxSAT is Weighted Partial MaxSAT (Weighted Partial Maximum Satisfiability (WPMS)), where each soft clause has an associated unit of weight. Since the development of the well-known LS techniques, GSAT+weight, and the breakout method in 1993 [22; 23] that uses weights (penalties) to alter the search space to escape from traps (local optimum), a branch of LS known as Dynamic Local Search (Dynamic Local Search (DLS)) was developed. DLS algorithms have been among the best performing solving algorithms on randomly generated problems as per the SAT competition results (as in [[http://www.satcompetition.org](http://www.satcompetition.org)]([http://www.satcompetition.org](http://www.satcompetition.org)) (accessed on 20 March 2021)) for almost a decade.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 4 Implementation and Experimental Results
### Experimental Methods  
The execution environment was built with two Docker containers on the same network so that failure factors such as the external delay and packet loss could be intentionally reproduced in the outbound traffic of each container using the tc command. However, whereas the intended delay could be reproduced by imposing half of the burden on both for the external delay, the packet loss was a probability, and setting the same amount of load on both resulted in a squared survival rate. Therefore, in this experiment, when reproducing the packet loss, only the intended value was set, as it was on the server side and not on the client side. For the environment that was used as the base for this experiment, we formulated a construction method and disclosed it to the public ([[https://github.com/ebi-yade/network-compose](https://github.com/ebi-yade/network-compose)]([https://github.com/ebi-yade/network-compose](https://github.com/ebi-yade/network-compose)), accessed on 10 May 2022). The specific experimental procedure is detailed below.  
1. We measured the HTTP/3 adoption rate and connection establishment time using the proposed method in an environment without intentional failures.
2. External delay and packet loss were set separately, and the changes in these metrics were observed.
3. As a comparison experiment, we measured the connection establishment time in Alt-Svc. However, for a fair and clear evaluation, only the first request was repeatedly measured from the state in which no connection was established for both cases. (We did not measure whether HTTP/3 was adopted in the second and subsequent communications.)  
First, the HTTP/3 adoption rate and connection establishment time using the proposed method were measured in an environment where no intentional failures existed. Subsequently, external delay and packet loss were set separately, and changes in these indicators were observed. Thereafter, as a control experiment, we measured the connection establishment time in Alt-Svc. However, to ensure a fair and unambiguous evaluation, only the first request was measured repeatedly from the state in which no connection was established for both cases. That is, we did not determine whether or not HTTP/3 was adopted in the second and subsequent communications. In principle, 1000 consecutive trials under the same conditions (represented as a single column in the table below) were performed synchronously.
## 2 Materials and Methods
### Binary Semantic Segmentation
#### 2.1.2 Network Architectures  
The first stage of the proposed workflow involves binary semantic segmentation. A simple network architecture with fewer parameters was implemented to achieve faster inference times, which is critical for ensuring a good user experience in clinical applications. This decision was based on the need for a practical tool that can be easily adopted in routine workflows without requiring extensive computational resources. Consequently, the approach was compared with other methods that share a similar focus on simplicity and efficiency. To achieve this task, a neural network based on the well-known U-Net architecture was implemented, and its performances were compared with another encoder-decoder architecture designed for accurate binary segmentation tasks: SegNet.  
The U-Net architecture [8] is a CNN designed for semantic segmentation tasks, where the goal is to assign a label to each pixel in an input image. It has an encoder-decoder path. The encoder path consists of convolutional and pooling layers, which progressively downsample the input image. Each convolutional layer is followed by a rectified linear unit (ReLU) activation function. Max pooling operations are applied to reduce the spatial dimensions of the feature maps while increasing the receptive field. The decoder path consists of upsampling and convolutional layers, which gradually upsample the feature maps to the original input resolution. Skip connections are introduced between corresponding layers in the encoder and decoder paths to preserve spatial information. These  
Figure 1: Workflow for the deep learning-based segmentation: The process begins with acquiring data from a CT scanner, and using a U-Net architecture trained with a multi-planar approach, binary masks are generated. These masks undergo separation and selection to isolate and identify individual structures, resulting in detailed segmented models.  
connections directly link layers at the same spatial resolution in the encoder and decoder paths. By concatenating feature maps from the encoder with those in the decoder, skip connections enable the decoder to access high-resolution features from earlier stages of the network. This helps the decoder refine the segmentation masks by incorporating detailed spatial information that may have been lost during downsampling.  
SegNet has an encoder-decoder architecture with a symmetrical contracting and expanding structure. Unlike U-Net, which uses skip connections, SegNet uses pooling indices to perform upsampling. These indices store the locations of max pooling during the downsampling phase and are used to retain fine-grained details during the upsampling. SegNet was implemented as described by [PERSON] et al. [25].
## 3 Analytical Solution of Different SHS Columns, Cross-Section Sizes, Lengths, and End Constraints
### SHS Columns, Cross-Section Sizes, Lengths, and End Constraints  
All SHS cross-sections in pure compression (Table 1), were tested using the finite element method. The most important purpose was to examine the shape cross-section of the column and calculate the critical stability load (Figure 2a).  
The finite element chosen to apply in all models was SHELL281. This element has eight nodes and six degrees of freedom (DOF) per node (three translations: Ux, Uy, Uz; and three rotations: ROTx, ROTy, ROTz) with full integration, and it is appropriate for investigating thin or moderately thick shell elements, for linear, high rotation, and/or large nonlinear deformations [19; 24]. Shell finite elements were used in the steel hollow SHS cross-section columns and supported with an analytical solution. Due to the kind of parameters in this study, a mesh study was applied initially. Convergence studies were  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Fixed-Fixed**} & \multicolumn{2}{c}{**Fixed-Pinned**} & \multicolumn{2}{c}{**Pinned-Pinned**} & \multicolumn{2}{c}{**Fixed-Free**} \\
8 & \(L\) & \(N_{pl}\) & \(N_{cr}\) & \(N_{b,Rd}\) & \(N_{cr}\) & \(N_{b,Rd}\) & \(N_{cr}\) & \(N_{b,Rd}\) & \(N_{cr}\) & \(N_{b,Rd}\) \\  & **mm**  
given to reach the appropriate mesh size, where, after several trials, the minimum mesh size considered was 10 mm (Figure 2b). Figure 2c represents a local side of the updated geometry to simulate the imperfections.  
The studied boundary conditions were fixed-fixed, where all six DOF are fixed on the bottom and top columns; fixed-pinned, with the bottom column fixed with all six DOF but the top column with only three transversal restrictions (UX, UY, and UZ); pinned-pinned column, where both top and bottom column had only restraints with transversal displacements (Ux, Uy, Uz); and fixed-free, with six DOF on the bottom column and free of restrictions on the top column.
## 4 Validation of Proposed Method
### Validation of Blind Detection  
The performance of the blind detection process to detect security codes correctly was evaluated from the results of bit detection tests. The criterion for detecting a correct security code was a BER of less than or equal to 10%.  
Figure 7 shows the BER results of blind detection concerning the bit rate, where the horizontal axis represents bit rate and the vertical axis represents BER. Figure 7 shows the front-side and back-side forms. The solid and dotted lines shows results with the BER of less than and greater than 10%, respectively. As we can see, the BER increases with the bit rate increase. In addition, a decrease in BER was observed with an increase in the embedding-strength level. As a result, we confirmed that at bit rates of 4 and 8 bps, the BER was less than 10% at the embedding-strength level of \(-15\) dB, and at the bit rate of 16 bps, the BER was less than 10% at the embedding-strength level of \(-10\) dB.