esa-sceva/satcom-chunk-collection huggingface dataset:
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 2 Materials and Methods
### Fixed-Bed Column Experiments  
To evaluate the performance of Al-S957 in practical applications, a fixed-bed column test was conducted by loading 7 mL of Al-S957 onto a glass column (14 mm diameter, 120 mm length). Real wastewater from a photovoltaic industrial park in Jiangsu province was passed through the column, with a peristaltic pump (Lange-YZ1515X, Baoding, China) used to maintain a steady flow rate. After adsorption saturation, Al-S957 was regenerated using 10 wt% AlCl\({}_{3}\)-6H\({}_{2}\)O solution and rinsed with deionized water until the effluent pH was neutral, preparing it for the next adsorption-regeneration cycle. For comparison, the same fixed-bed column experiments were also performed using Al-D412 and Al-D001 under identical conditions.
## 5 Results and Analysis
### Result Interpretation  
It can be observed from Table 2 that:  
* The performance of Linear Regression methods is not fit--this is because there is no linear relation between data points;
* SVM produces good results but the training time of SVM is relatively high as compared to that of Decision Tree. Additionally, SVM is highly dependent on the accuracy of the hyperplane boundary, which can give inaccurate results during changes in data;
* The time taken to process GPR is the highest, hence this method can be highly ineffective when the load forecasting is integrated with applications such as power trading. Although Neural Networks are producing promising results, when compared to the training time of Decision Tree, it is on the higher side;
* The accuracy of Ensemble is highly dependent on the combinations of the methods assumed. The fitting of the model (\(R^{2}\) ) is less as compared to Decision Trees.  
Hence, considering all the parameters, it can be concluded that Decision Trees can be an effective method for predicting short term load.
## 4 A Fuzzy PSO-Based Control Design
### Preliminaries on PSO
For each iteration \(k\), the velocity corresponding to all the particles in the swarm is updated, while the positions are determined using the following set of equations  
\[V^{i}_{k+1}=w_{k}V^{i}_{k}+R_{1}L_{1}\Big{(}X^{(i)}_{pk}-X^{i}(k)\Big{)}+R_{2}L _{2}\Big{(}X^{(i)}_{gk}-X^{i}(k)\Big{)} \tag{49}\]  
\[X^{i}(k+1)=X^{i}(k)+V^{i}(k+1) \tag{50}\]  
* \(X^{i}(k)\) represents the present candidate solution comprising the position of the \(i\)th particle during the \(k\)th iteration, while \(X^{i}(k+1)\) represents the new position of the particles;
* \(V^{i}(k)\) and \(V^{i}(k+1)\) represent the old and new velocities of the particles;\(X_{pk}^{(i)}\) specifies the optimal position, also referred to as the \"personnel best\", obtained in the past by the \(i\)th particle;
* \(X_{gk}^{(i)}\) represents the ideal position corresponding to a particle's neighbors and is called the global best;
* The \(L_{1}\) and \(L_{2}\) acceleration coefficients are generally equal and have values between 0 and 2. In order to achieve maximum acceleration \(R_{1}\) and \(R_{2}\) are random values generated at each iteration, they respect a uniform distribution;
* The number (k) of iterations is important information for the operation of PSO. Finally, \(w_{k}\) is inertia coefficient can be specified as:  
\[w_{k}=w_{\max}-\left(\frac{w_{\max}-w_{\min}}{k_{\max}}\right)\times k \tag{51}\]
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Graph-Based Homology Mapping Methods
#### 2.3.4. SibeliaZ  
SibeliaZ-LCB is suitable for detecting homologous sequences that have an evolutionary distance to the most recent common ancestor (MRCA) of at most 0.09 substitutions per site [39]. This algorithm is used to identify collinear blocks in closely related genomes based on the construction of a de Bruijn graph [39] to build collinear blocks (Figure 9). It globally  
Figure 8: Mercator orthologous gene detection between sequences A, B and C. Mercator builds a graph where each vertex represents an exon and edges between vertexes represent the alignment score between exons. The cliques formed are used to highlight the orthologous blocks between them.  
aligns the collinear blocks to generate the whole-genome alignment. Thus, SibeliaZ-LCB performs pairwise sequence alignments between the input genomes to identify regions of similarity and homology. It uses efficient alignment algorithms to detect conserved regions and infer synteny relationships; based on the alignment, it constructs locally collinear blocks (LCBs) by grouping aligned regions that exhibit consistent gene order and orientation across the genomes. LCBs are represented as blocks of homologous sequences that are likely to have originated from a common ancestor.  
On sixteen recently assembled mice genomes, SibeliaZ ran in less than 16 h on a single machine, while other tools did not run to completion for eight mice within a week. It also provided better performance in terms of time of execution compared with the Mercator tool. The complexity of SibeliaZ depends on the size of the input genomes and the chosen parameters. However, it typically has a worst-case time complexity of \(O(n^{2})\), where \(n\) is the total length of the input sequences.
## 2 Related Work
### State of the Art
One advantage of using neural networks accelerated by GPUs (Graphics Processing Units) or Deep Learning Processor Units is the parallelization of the classification process. By working with individual packets, in which no information needs to be saved between flows, it is possible to create pipelines of work that greatly accelerate the process. However, there is a challenge that must be addressed. To enable these GPUs to operate at the highest performance, batches of images ranging from 64 to 256, depending on the architecture used, are required [15]. This has the disadvantage that one must wait to receive the next packets to classify when their information is not necessary in the classification process. If real-time classification is desired, latency is to be minimized. In that case, Deep Learning Processing Units on FPGAs or other converged accelerators are required. Otherwise, image batches would need to be reduced, leading to compromised classification system bandwidth. Therefore, these accelerators are essential for real-time network packet classification. With this approach, individual packets can be classified in real time without extra latency due to the batching effect or compromising system bandwidth, leading to significant improvements in classification performance [16]. In contrast to the aforementioned challenges in the field of traffic classification using CNNs, we note that the authors of [17] noticed how flow characteristics might affect their network classifier. Specifically, their method utilizes flow information to contextualize the network traffic. By incorporating such information, they were able to leverage the power of temporal data and overcome the limitations of traditional packet classification approaches. Overall, their approach represents a significant advancement in the field, offering promising possibilities for improving the accuracy and reliability of network traffic classification.
# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 1 Introduction
The value can be computed by a cross-correlation function, but it is strongly dependent on the chosen wave propagation model and background noise [18; 19; 20]. Therefore, burst isolation and correct analysis of this AE wave have become methods to acquire useful information. In earlier studies, wavelet analysis has also been used to recognize the transient bursts in pressure signals for water pipelines [21; 22; 23]. The approaches in [22; 23] used the wavelet decomposition and empirical mode decomposition to enhance leakage detection by eliminating uncorrelated components of leak signals. Unfortunately, the wave attenuation and noise presence distort the AE signal, and thus these methods cannot provide a high enough performance, since most of the intrinsic components, which are decomposed relate to the remains of leak signals attenuated. To reduce the rate of error that occurs in the delayed time estimation and improve the localization accuracy of the algorithm, a critical issue existing in the AE fault detection system is to identify the leakage signal without providing false alarms. Previously, the constant fault alarm rate (CFAR) was used for object detection in the doppler radar system [24]. However, the modeling of bursts and impulses of AE signal in the role of objects which need to be detected by the CFAR, has not yet been studied. The objective of this paper is to identify the characteristics of this dispersive AE wave by detecting the impulse and burst events related to leakage, and to isolate the burst waveform by the Enhance Constant Faults Alarm Rate (ECFAR) algorithm. Investigating the characteristics of bursts will help to distinguish the different levels of leak signal. The remainder of this paper is outlined as follows. Section 2 introduces the data acquisition system and the recorded AE dataset, which are used to evaluate the proposed algorithm. Section 3 provides detail on pipeline burst and event detection techniques using ECFAR. Section 4 presents the evaluation result of the detection techniques, including the performance of classification of multi-level leakage. Finally, Section 5 draws conclusions and areas for future work.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 3 Label Correlation Mining Methods
### Joint Distribution Modeling Methods  
Current MLC methods typically consider learning label correlations directly from true labels. For instance, second-order and high-order strategy methods respectively consider correlations between pairs of labels and among multiple labels. [PERSON] et al. [27] argue that current second-order and high-order strategy methods fail to utilize the complete label correlations. They suggest that multi-label joint distribution modeling, which considers various types of label correlations, is a more thorough approach.  
Figure 4: SGM model framework. \(x\) denotes the text sequence, \(h\) denotes the hidden state, \(c\) denotes the context vector, \(s\) denotes the predicted hidden state, \(y\) denotes the probability distribution, MS denotes the masked Softmax layer, and GE denotes the global embedding [60].  
To address this, [PERSON] et al. [27] proposed a method based on an adversarial learning framework, aiming to enhance the similarity between the predicted label distribution and the true label distribution, thereby effectively utilizing label correlations. This framework consists of a multi-label classifier and a label discriminator. The multi-label classifier identifies multiple labels associated with an instance from the feature space, while the discriminator assesses the probability that each input label vector comes from the true label set. Through adversarial learning between the classifier and the discriminator, the joint label distribution of the predicted labels converges to the inherent joint label distribution of the true labels, ultimately improving the performance of MLL.  
Additionally, [PERSON] et al. [62] proposed a method based on a multivariate probabilistic variational autoencoder, which encodes instances and labels into latent space and uses a decoder to transform latent variables back into label space. During the decoding process, the covariance matrix is learned to model the joint distribution of the output labels using a multivariate probabilistic approach, thereby utilizing this covariance matrix to uncover correlations between labels.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 4 Deep Reinforcement Learning-Based Algorithm
### Deep Reinforcement Learning Model
#### 4.1.3 Reward Function  
The cost minimization function in this article contains multiple factors. Specifically, our goal is to reduce latency and energy consumption, as well as improve video analysis accuracy under long-term resource constraints. Therefore, the reward functions can be designed based on the optimization problem.  
We propose JVFRS-TO-RA-DQN, which contains two DQN networks. The first DQN network selects the optimal offloading and resource allocation strategy, and the second DQN network selects the appropriate video frame resolution scaling factor to ensure the maximum accuracy of video analysis and to reduce the system delay and energy consumption. A detailed description of the two reward functions is given below.  
After performing the action \(A_{m,t}\), a reward \(r_{m,t}\) will be obtained for the action \(A_{m,t}\) + 1 that the edge server chooses to perform. The reward function is generally related to an objective function, which aims to minimize the delay of the system in the context of task offloading and resource allocation. However, the aim of reinforcement learning training is to obtain the maximum long-term accumulation of rewards. Thus, the offloading reward function of the UEs at time \(t\) can be designed as  
\[\xi_{m,t}=-(\omega_{1}T_{m}+\omega_{2}E_{m}). \tag{18}\]  
For UEs, it should be penalized if the accuracy of the next state is not within the threshold after taking action \(\alpha_{m,t}\). Therefore, the resolution scaling reward function is designed as  
\[\xi_{m,t}=(\omega_{t}+\omega_{e}-1)\varphi_{m}, \tag{19}\]  
Finally, we use \(r_{m,t}=\xi_{m,t}+\zeta_{m,t}\) to represent the total reward of the system. By maximizing the long-term cumulative reward \(r_{m,t}\), an efficient joint video frame resolution, task offloading, and resource allocation strategy, which we abbreviate as JVFRS-CO-RA-DQN in this paper, can be developed to achieve the minimization of system delay and energy consumption while improving video analysis accuracy.
## 4 Results and Discussion
### Component Dynamic Amplification Factor  
This section and its results are derived from previous research [72], reproduced here to improve readability and aid reader comprehension. This section aims to analyze the acceleration of the NSC in relation to the floor acceleration. All the building models that are being examined have their story FRSs normalized based on the associated peak floor accelerations (PFAs). Figure 9 displays the soft-story FRSs, normalized based on the relevant PFAs. The CDAF is denoted by the ratio \(FRS/PFA\). In this study, the CDAFs of the building models are compared to the criteria of ASCE 7-16 [33], FEMA P-750 [34], and Eurocode 8 [71].  
Figure 7: Floor response spectra: five-story buildings.  
In medium soft-story buildings, the amplification factor ranges from 3.55 to 4.92, correlating with the NSCs' fundamental vibration period. For bottom-soft-story models, the amplification factors range from 1.37 to 4.07, while top-soft-story models have values between 2.69 and 4.79. Overall, buildings with a soft story at the middle floor experience the highest amplification in the acceleration of the NSC. Figure 9 shows that the CDAF deviates from the definitions provided in FEMA P-750, ASCE 7, and Eurocode 8. This disparity becomes more noticeable as the time periods approach the vibration periods of the building models under investigation.  
Considering the varying periods and damping ratios (\(\xi_{s}\)) of non-structural components (NSCs), it becomes crucial to assess the influence of these characteristics on seismic behavior [74]. Therefore, this study evaluates CDAFs for different \(\xi_{s}\) values (0.1%, 0.2%, 0.5%, 1%, 2%, 5%, and 10%). The CDAF spectrum for five- and ten-story models for various damping ratios (0.1%, 2%, and 10%) at the top floor (in the case of the reference structure) and soft-story floor level (in the case of other buildings) are shown in Figure 10. As predicted, lower damping ratios (\(\xi_{s}\)) corresponded to higher amplification factor values. The damping ratio of the NSCs played a more substantial role in influencing the amplification factors, particularly at the modal periods of the models. Surprisingly, the impact of \(\xi_{s}\) was  
Figure 8: Floor response spectra: ten-story buildings.  
found to be insignificant for very short and extremely long vibration periods. Therefore, this study aimed to develop a machine learning-based prediction model for the component dynamic amplification factor (CDAF) spectrum.  
Figure 9: _Cont._  
Figure 9: Values of CDAFs for (**a**) 5-story buildings and (**b**) 10-story buildings [33, 34, 71].
## 3 Results
### Joint Analysis of the Transcriptome and Metabolome Data  
To further understanding of the pathway of GSLs biosynthesis, we performed a combined transcriptomic and metabolomic analysis for each group. The correlation analysis between DEGs and DAMs showed that many genes were positively correlated with metabolites in the LML vs. LMH, SML vs. SMH, and FML vs. FMH comparisons (Figures 6A and S9A,B). Through KEGG enrichment analysis, we found that many DAMs and DEGs were enriched in the same pathway (Figures 6B and S9C,D). The above results indicated that the accumulation of these metabolites might be regulated by the corresponding genes.  
Figure 5: Differential metabolites analysis. (**A**) Upregulation and downregulation of DAMs between different groups. The yellow box represents upregulation, and the blue box represents downregulation. Heatmap of DAMs between LML and LMH (**B**), SML and SMH (**C**), FML and FMH (**D**). LML (LML1, LML2, LML3), SML (SML1, SML2, SML3) and FML (FML1, FML2, FML3) represent low RAA content pools in leaf, stalk and floret, respectively. LMH (LMH1, LMH2, LMH3), SMH (SMH1, SMH2, SMH3) and FMH (FMH1, FMH2, FMH3) represent high RAA content pools in leaf, stalk and floret, respectively. The different colors on \(Y-\)axis of (**B–D**) represent different metabolite categories.  
In the floret comparison group, we found DEGs and DAMs were mainly enriched in 25 pathways, such as the metabolic pathway, biosynthesis of secondary metabolites, 2-Oxocarboxylic acid metabolism, biosynthesis of amino acids, and GSLs biosynthesis (Figure 6B). Glucoraphanin contained significantly different accumulates between FML and FMH. We built a connection network between DEGs and DAMs of glucoraphanin biosynthesis. The result showed that glucoraphanin (MWSmce416) contained a positive correlation with eight genes, which were enriched in the GSLs biosynthesis pathway (Figure 7B). The eight genes also had a positive correlation with benzyl glucosinolate (Cmln001356), 3-Indolylmethyl glucosinolate (Lmyn002727), and a negative correlation  
Figure 6: Correlation analyses between transcriptome and metabolome in FML vs. FMH. (**A**) The nine-quadrant diagram shows the correlation of genes and compounds between FML and FMH. (**B**) KEGG enrichment analysis of DEGs (red column) and DAMs (blue column) enriched in the same pathway. FML and FMH represent low and high RAA content pools in floret, respectively.  
with l-Tyrosine (mws0250) (Figure 7B). In addition, we also built a connection network between DEGs and DAMs of glucoraphanin biosynthesis in the stalk group. The result showed that 13 genes had positive correlations, and one gene (_Bo5g017130_) had a negative correlation with glucoraphanin (MWSmce416) (Figure 7A). The above results indicated that metabolism is a complex biological process. Generally, one metabolite was regulated by multiple genes, or one gene was regulated by different metabolites.
# Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications
## 4 Numerical Results
Figure 3: Comparison of normalized throughput with respect to the number of relays for the cases with and without direct transmission. If obstacles impact heavily on the transmission, that is, the non-line-of-sight (NLOS) case, the performance degrades, as in Figure 3; this is the more interesting case to analyze because it is closer to real environments. Therefore, in the following, the results refer to the case without direct transmission for the parameter mainly related to the multicast transmission, that is, the end-to-end delivery delay. To emphasize the relevance of social relations in Figure 4, we compare our method, the Social Channel Quality Indicator (SCQI), with two alternatives. In the first, the source selects the relay by taking into account only the friendship level. In particular, it randomly selects a relay from the friends without considering the other neighbor relays. In the second comparison method, the D2D source selects the relay with the minimum delay among the source-relays-destination links only relying on the SNR report. Figure 4 shows the average end-to-end delivery delay for a multicast transmission among the source and all the destinations through the single relay selected, introduced by these three alternatives. To simplify the analysis of the results, the average delays are normalized with respect to the direct-link communication delay obtained for the case that the D2D source can communicate directly with the destinations. Figure 4 shows the importance of the knowledge of both friendship and channel conditions. Our method, which considers both of these, guarantees better performance in terms of transmission delays. Again, the direct link is only shown for reference in this figure  
Finally, Figure 6 shows the performance of our method as a function of different \(\gamma_{RD}\) values. From this figure, we can note a trend: the normalized delay increases as the \(SNR_{rd}\) value decreases and approaches the value of the direct link depending on the number of relays. For example, in the case of \(\gamma_{RD}\) equal to \(13\ dB\), we can note that the direct link is convenient for a number of relays of about seven. This trend is present in each relay-based model: when \(\gamma_{SR}\), \(\gamma_{RD}\) and \(\gamma_{SD}\) have similar values, the direct communication between the source and destinations becomes the best solution. Figure 5: Normalized delay with respect to the number of relays varying the friendship probability; \(\gamma_{RD}=13\ dB,\ \gamma_{SD}=3\ dB,\) and \(\sigma_{sd}=1\ dB.\)  
Figure 6: Normalized delay with respect to the number of relays for different \(\gamma_{RD}\) values; \(\gamma_{SD}=3\ dB,\) and \(\sigma_{sd}=1\ dB.\)