esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Graph Theory and Fractal Geometry  
First, let us introduce a few concepts from graph theory. A graph \(G=(V,E)\) consists of a set V of nodes (or vertices) and a set E of edges between these nodes, where each edge is an unordered pair of nodes. The number of nodes in a graph is the size of the graph. Nodes that are connected by edges are called neighbors and the number of neighbors a node has is called a node degree. We denote with \(N(v)\) the neighbors of \(v\) and with \(d(v)=|N(v)|\) the degree of the node \(v\). Graphs are the main objects studied in graph theory.  
In practice, graphs represent some real-life objects and relations within them. Statistical properties of the graph are often also calculated, such as the clustering coefficient, which measures how much nodes tend to cluster together. For a graph \(G=(V,E)\), the local clustering coefficient of a node \(v\) is defined as  
\[C(v)\ =\ \frac{2\left|\left|\left(\ i,j\right):i,\ j\in N(v)\wedge(i,j)\in E \right.\right|}{d(v)(d(v)-1)}\]  
and the global clustering coefficient of a graph as defined by [PERSON] and [PERSON] is a network average clustering coefficient, i.e.,  
\[C\ =\ \frac{1}{n}\sum_{v\in V}C(v)\]  
Now, let us introduce a field of fractal geometry, which is a branch of geometry that studies fractals. It offers a plethora of tools for describing and predicting natural phenomena by exploiting iterated mathematical concepts, such as complex number fractals, iterated function systems, and cellular automata. Perhaps, two of the most well-known complex number fractals are Julia and Mandelbrot sets. The latter is described by the recursive formula,  
\[z_{n}\ =\ z_{n-1}^{2}+c\]  
where \(z_{0}\ =\ 0\) and \(c\ =\ x+y\cdot i\) is an initial complex number; here, \(i\) denotes the imaginary unit.  
Repeating the calculation of \(z_{n}\) and observing the values result in a fractal. In particular, the \(z_{n}\) is observed whether it remains near the origin or escapes to infinity. We say that a point \(c\) is a member of the Mandelbrot set if it remains near the origin. The process may be repeated for every point in the complex plane. Moreover, it is often graphically represented by coloring the points depending on the result. Such representations exhibit one of the main properties of fractals, namely, self-similarity.  
Another important property of fractals is their fractal dimension, which describes the capacity of the fractal pattern to fill the space where the fractal scales differently from the space it is embedded in. It is common that a fractal exhibits a non-integer fractal dimension.  
One way to calculate a fractal dimension \(D\) is by relating it to the Hurst exponent \(H\); in particular, \(D\) = \(2-H\). The Hurst exponent is defined for a time series as  
\[\mathbb{E}\bigg{[}\frac{R(n)}{S(n)}\bigg{]}\ =\ Cn^{H}\]  
where \(R(n)\) is the range of the first \(n\) cumulative deviations from the mean, \(S(n)\) is their standard deviation, \(n\) is the time span of the observation, and \(C\) is a constant.
## 1 Introduction  
Based on the aroma, Chinese liquor can be classified into fen-flavor, strong-flavor, Maotai-flavor, rice-flavor and other flavor types, which have highly different stylistic features in their aromas [1]. Fen-flavor liquor is the earliest liquor in China and is honored as the origin of all liquors. It is pure, sweet, mild, and natural with a fresh aftertaste [2]. According to the distiller's yeast used in the production process, fen-flavor liquor can be further classified into Daqu, Fuqu, and Xiaoqu types. Xiaoqu fen-flavor liquor is mainly produced in Hubei, Chongqing, and Yunnan, China. Owing to its sweet, fresh, mild, natural, and pure tastes, it is very popular among consumers in the South Central China. The unique flavor characteristics are attributed to the production techniques of saccharification before fermentation, steaming and distillation, as well as management of vinasse and raw materials [3; 4].  
In recent years, gas chromatography olfactometry (GC-O), solid-phase microextraction (SPME), and gas chromatography/mass spectrometry (GC/MS) have been widely applied to the research on the aroma compounds in liquor [5], and Chinese researchers have conducted a number of in-depth studies of the strong-flavor, Daqu ten-flavor, and Maotai-flavor liquor. For example, [PERSON] et al. have investigated the major components in Maotai-flavor liquor by liquid-liquid extraction and GC/MS and carried out quantification analysis of 48 aroma compounds; finally, 29 compounds were identified as the skeleton components, and the results were validated by aroma recombination and omission experiments [6]. [PERSON] et al. conducted a quantitative analysis of 66 flavor compounds in soft-style liquor with Head Space Solid Phase Microextraction (HS SPME) and identified the key flavor compounds such as ethyl hexanoate and 2-methyl ethyl butyrate by calculating the odor activity values (OAVs) [7]. These studies have greatly expanded the understanding of the chemical composition of Chinese liquor. Up to date, about 700 micro components have been detected in Daqu fen-flavor liquor; however, there have been few reports about the flavor compounds in Xiaoqu liquor. The flavor of Xiaoqu liquor not only greatly determines the taste and quality of the liquor itself but also plays a feedback regulatory role in the production techniques and microbial system. Therefore, it is highly necessary to further dissect the flavor of Xiaoqu liquor and the related compounds.  
This study aims to identify the volatile compounds by GC-O, stir bar sorptive extraction (SBSE), and GC-MS in Xiaoqu liquor based on the sensory quantitative description. Besides, the key aroma compounds in Xiaoqu liquor were identified by aroma extract dilution analysis (AEDA) and OAV determination. Finally, the results were validated by aroma recombination and omission experiments, and the flavor compounds in Xiaoqu liquor were fully dissected, which may provide important reference and support for improving the production techniques of Xiaoqu liquor.
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 4 Optimization Problem Solving
### Memory-Based Dynamic AFSA Under Linear Equality Constraints  
In this algorithm, user group parameters and RSMA parameters are optimized. Aiming at (7)and (8) in Section 3, this paper designs an artificial fish swarm optimization algorithm under linear equality constraints, which is realized by adding constraints to each step of the artificial fish. Let \(P=I-A^{T}\big{(}AA^{T}\big{)}^{-1}A\). For \(X_{1}\), satisfying \(AX_{1}=b\). Denote \(X_{2}=X_{1}+\alpha\cdot*P*X_{r}\), where \(\alpha\) is the non-negative proportional control factor and \(*\) represents the scalar multiplication by a vector and \(X_{r}\) is an arbitrary vector. \(AX_{2}=b+\alpha\cdot*(A*P)*X\) and because of \(A*P=0\), \(AX_{2}=b\), which satisfies the constraints of linear equality.  
MD-AFSA in Section 3.1 is further optimized to obtain a Memory-based Dynamic Fish Swarm Algorithm Under Linear Equality Constraints (LEC-MD-AFSA). LEC-MD-AFSA modifies the relevant operational behavior of the artificial fish, so that the new solution obtained still satisfies the linear equality constraints.  
Due to \(X_{1}\) and \(X_{2}\) satisfying the constraint \(AX=b\), it is easy to prove that \(AX_{3}=b\), where \(X_{3}=X_{1}+k*(X_{2}-X_{1})\), so the new solutions obtained from the Swarm and Rear-End behaviors of MD-AFSA still satisfy the constraint without correction. Similarly, the direction factor of the Memory behavior remains a linear combination of feasible solutions without correction. For the Foraging behavior, the artificial fish needs to try to find a new location within the field of view, and in order to make the found location satisfy the linear equality constraint, correct by \(X_{n}=X_{i}^{t}+visual_{i}^{t}*P*Rand[-1,1]\). For the random behavior, the correction is \(X_{i}^{t+1}=X_{i}^{t}+P*Rand[-1,1]*visual_{i}^{t}\). In the problem considered in this paper, the row vectors of matrix \(A\) are orthogonal to each other and the values in \(A\) only contain {0,1}. All elements in the vector \(b\) are 1, therefore, several random numbers in the [0,1] interval can be randomly generated, and then divide each element by the sum of all numbers to get the initial solution of the artificial fish. The original user group and RSMA parameters' optimization problems are turned into linear constraint problems in Section 3 using the augmented Lagrange function, and the solution algorithm is implemented based on MD-AFSA.  
In conclusion, this paper decomposes the problem of UAV-assisted communication rate optimization based on RSMA into three sub-problems for solution. When solving a parameter optimization sub-problem, the parameters of other sub-problems are treated as constants; finally, the original optimization problem is solved by the ternary cross iteration method.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Information Fusion in Multisensor Systems  
In multisensor systems, whether combining several monostatic BSs or using a multistatic setup, efficient data fusion is crucial and can be categorized into two approaches based on the information shared with the FC. In the first approach, individual sensors (e.g., a BSs or bistatic pair receivers) independently perform target detection and parameter estimation based on observations such as signal strength, time of arrival (ToA), angle of arrival (AoA), or time difference of arrival (TDoA) [25]. This method, known as hard fusion, minimizes network overhead but can result in lower detection and localization accuracy due to information loss and the lack of global data at individual sensors. The least square (LS) algorithm is commonly used to fuse individual estimations but requires at least three sensors for 2D target localization, increasing the risk of missed detections at one or more receivers, particularly when weak target echoes are involved.  
In the second approach, each sensor processes the reflected target echoes and transmits soft information to the FC, which then fuses this soft information from all nodes to perform detection and estimation tasks [26]. Soft fusion generally outperforms hard fusion by avoiding the loss of weak targets caused by individual sensor preprocessing. However, this comes at the cost of increased processing and signaling overhead.  
Ultimately, the choice of JSC deployment depends on application requirements such as sensing accuracy, range, and system complexity. Monostatic setups, despite the challenges of SI and full-duplex operation, are well suited for simple environments with co-located sensing, such as on-board vehicle systems. Bistatic configurations are ideal for V2V communication and sensing, while multisensor systems offer the greatest flexibility and reliability for applications demanding high precision, such as cooperative driving and collision avoidance. However, these systems rely on a network infrastructure, which, fortunately, is already in place.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 5 Conclusions  
The presented results provide evidence to conclude that the proposed combination of a GPU and HSA can be useful to accelerate NCC, and possibly other trackers. Furthermore, using HSA, together with the GPU, provides a greater acceleration than using the GPU alone. Even when HSA is not a deterministic tool, the accuracy is not negatively affected when using it.  
The experiments helped find the optimal value for the number of generations (two generations), an important configuration parameter for HSA that has an effect in speed and accuracy. Both the time complexity analysis and the experimental evidence showed the extent of the influence that this parameter has in the tracker's execution speed. Other experiments showed that the proposal is resistant to high levels of noise.  
The proposed Score-Time Efficiency criterion was found useful to make simple comparisons of different tracker proposals in terms of accuracy and speed, allowing making decisions that require the optimization of both variables. According to the analysis performed using this criterion, the best option was parallel HSA with 2 generations using NCC as fitness function.  
Comparison experiments of our best proposal against other recent trackers such as SiamMask and Struck show evidence that parallel HSA has no relevant effects on the accuracy of its tracker function (NCC), but dramatically reduces latency, almost four orders of magnitude in terms of average time per frame. This evidence exposes an opportunity area in which to focus future efforts: to employ the proposed methodology but using state of the art trackers as a starting point.
## 3 Data and Methodology
### Data Pre-Processing  
The pre-processing of the dataset was straightforward, due to the well-prepared nature of the provided data. During the analysis to identify missing, duplicate, and incorrectly formatted data, it was also discovered that dates without records were absent from the dataset. This pattern was observed across all variables. Additionally, variables D1 and E6 contained NaN (not a number) values. As these two variables were not relevant to our work, they were excluded, resulting in a clean dataset without any NaN values. Nevertheless, in practical terms, there were dates without records that were not included in the dataset, indicating data flaws. In places with missing values, the complete entry was removed, reducing the amount of data by 3%.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 1 Introduction
The contributions of this paper are as follows:  
* We firstly propose a satellite resource pool architecture oriented for regional tasks in satellite edge computing, which aims to integrate multiple resources of satellites in the SBIS into the resource pool for task service in the IoT. * We then solve the capacity planning of the resource pool using queuing theory and propose a search matching algorithm to solve the instability problem of satellite resource pools. * Through the queuing model and a hardware-in-the-loop simulation, the performances of isolated mode, cooperative mode, and pooled mode in the same scenario are theoretically and experimentally evaluated. This paper is organized as follows. Section 2 introduces current research on satellite edge computing and satellite resource pools. Section 3 introduces the system model and the architecture proposed in this paper, including resource sharing mode, capacity planning, and satellite node selection. Section 4 first introduces the experimental platform and the experimental scenario, and then analyzes and evaluates the experiments that have been carried out. Finally, Section 5 gives the conclusion of this paper, describes the limitations of this study, and offers future research directions.
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## Results
### Impact of the Observer Zenith on the Retrieval of Crop Parameters
Interannual changes in model predictability are represented by the mean standard deviation over all 9 pixels and per season. The results are listed in Table 4. Both LAI and LCC were more homogeneously predicted in 2014, whereas in 2015 residuals tended to be more variable throughout the growing season. Model inversions from nadir observations were 38% less prone to seasonal effects than those from angular observations. The pattern of the residuals for LAI and LCC do not show any relation and statistics suggest that they are independent of each other (R\({}^{2}\) = 0.10). Residuals of LAI followed a normal distribution (\(p\) > 0.05 for all OZA), but those of the LCC estimation did not (\(p\) < 0.01 for all OZA). A shift to negative residuals, i.e., an overestimation of LCC by the model, indicated a slight systematic bias. Figure 6: Spatial distribution of measured and estimated LCC (**left**) and LAI (**right**) for the two growing seasons of 2014 and 2015 under different observation angles. Retrieval of CCC proved quite successful (see Figure 8). Concentrations of LCC obtained from the second inversion run were multiplied with estimated LAI values and compared to in situ measured CCC (i.e., measured LAI \(\times\) measured LCC). PROSAIL tended to overestimate CCC with an intercept of 0.31 g cm\({}^{-2}\) for nadir spectra. The relative rRMSE = 0.37 was higher than for both, LAI and LCC. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**OZA**} & \multicolumn{2}{c}{**Season 2014**} & \multicolumn{2}{c}{**Season 2014/2015**} \\  & **LAI** & **LCC** & **LAI** & **LCC** \\
**(deg)** & **(m\({}^{2}\) m\({}^{-2}\))** & **(\(\mu\)g cm\({}^{-2}\))** & **(m\({}^{2}\) m\({}^{-2}\))** & **(\(\mu\)g cm\({}^{-2}\))** \\ \hline \(-30^{\circ}\) & 0.62 & 8.43 & 0.99 & 7.44 \\ \(0^{\circ}\) & 0.47 & 11.86 & 0.82 & 7.17 \\ +30\({}^{\circ}\) & 0.59 & 7.38 & 1.08 & 11.22 \\ \hline \end{tabular}
\end{table} 
Table 4: Mean deviations between estimated and in situ measured LAI and LCC, separated by season and angular setting (OZA). Figure 7: Visualization of the residuals, i.e., in situ measurements minus parameter estimations. For LCC, purple pixels show an underestimation of the model results, green pixels indicate overestimation. For LAI, green to blue hues show model underestimations and brown pixels model overestimations. Pastel yellow shades indicate a good model agreement with in situ observations. From forward scatter observations, however, CCC was estimated with an rRMSE of 0.33 meaning an improvement of 12%. Retrieval from backscatter spectra suffered from a weaker estimation of LAI and LCC, leading to a decrease of relative RMSE of 0.40 accordingly.
## 1 Introduction
Table 1 delineates the various techniques examined in the literature review to address ISI and nonlinearities, along with some notes related to their advantages and disadvantages. In addition, Table 2 elaborates and compares specific parameters employed in the literature review articles. To that end, in the present paper, we propose three new algorithms, i.e., the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms for equalization in a converged OFDM 5G mm-wave A-RoF system at 60 GHz. The modulation schemes being used in this system are QPSK, 16-QAM, 64-QAM, and 128-QAM in all the OFDM subcarriers. The proposed algorithms are developed in MATLAB-R2018a [33] to equalize the distorted subcarriers. In this work, we use an SSMF of length up to 120 km since DFE and MLD are more adept at managing fiber distortions and noise. DFE effectively mitigates inter-symbol interference (ISI) by leveraging past decisions, rendering it particularly beneficial in dispersive SSMF channels. Meanwhile, MLD offers near-optimal performance by evaluating all potential signal sequences and selecting the one that maximizes the probability of accurate detection,resulting in enhanced transmission distance relative to other known algorithms such as LMS and CMA. Finally, RDE performs best for QPSK and 16-QAM constellations.
## 2 Methods
### Encoder  
ISTD-DisNet retains the coding structure of SegFormer. As shown in Figure 2, the transformer module inside the encoder of SegFormer [44] uses OPE to extract features and downsample from the input image. Because the OPE module allows the slices to overlap with each other, the elements inside the slices are also connected to each other as a way to ensure the continuity and integrity of the elements. The OPE is then computed using a standard convolutional layer to spatially reshape the 2D features into 1D features. The features are then input into the efficient self-attention (ESA) layer and Mix-FFN layer for global linkage construction. To replace the positional encoding in the normal transformer, a 3 \(\times\) 3 Conv is added between the two linear layers of the feed-forward network (FFN) to spatially fuse the positional information. The linear layers in the encoder are followed by layer normalization (LN), and the activation function is a Gaussian error linear unit (GELU). The transformer block uses multiple ESA and Mix-FFN layers to deepen the network, so as to extract rich details and semantic features. Slices of different sizes allow the input image to compute self-attention in the ESA layers at each scale, an operation that allows SegFormer to obtain global information more quickly and with more pure self-attention at each scale than previous networks based on CNNs that perform self-attention computation after integrating information from all the scales. ISTD-DisNet uses SegFormer MiT-B2 as the backbone, and the main hyperparameters of MiT-B2 are shown in Table 1.  
Figure 2: Structure of ISTD-DisNet.  
Although the transformer block in the encoding part of SegFormer allows each element to obtain the connection with the other elements, its overly simple decoding layer cannot accurately restore the encoded information, and simple bilinear interpolation upsampling and splicing would also lead to a large amount of lost detail information, which is crucial for the segmentation of small cracks. For this reason, in this study, we optimized the design of the decoder, which consists of: (1) using the MAM module to sequentially infer the attention weight map along two independent dimensions (channel and space), and then adding the attention map to the input feature map for adaptive feature optimization; and (2) in the last MLP layer, the upsampled portion is replaced with a transposed convolution with learning capability to enhance the model's ability to restore details.
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Adsorption Technique
#### 3.1.1 Adsorption by Carbon-Based Adsorbents
Therefore, the changes of ionic strength involving cations or anions and pH in the solution would influence the adsorption efficiency. For example, the increase of ionic strength caused by monovalent or divalent cations (Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), etc.) might enhance PFAS removal efficiency, while the pH increase would reduce the adsorption capacity of most adsorbents [31, 32]. However, the electrostatic repulsion between anionic PFASs and a negatively charged adsorbent could be overcome by the hydrophobic effect of the C-F chain [33]. Therefore, the removal of short-chain PFASs was mainly dependent on the electrostatic effect, while long-chain PFASs mainly tended to hydrophobic effects. Adsorption mechanisms of carbon-based adsorbents for PFASs removal are shown in Figure 1.  
Figure 1: Adsorption mechanism of carbon-based adsorbents for PFAS removal.