esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment, Results & Discussion
### Optimization of Benchmark Problems
#### 4.1.2 PSA vs PSO and SCA  
The PSA is benchmarked with PSO and SCA. PSO is selected due to its reputation as the state-of-the-art metaheuristic algorithm. Additionally, like PSA, PSO also only has two mathematical equations to be updated at each iteration. Thus, both algorithms have similar computational complexity. The SCA is chosen due to its similarity with PSA as discussed before. Based on the findings from the previous section, all three algorithms are run with 50 agents and for 1000 iterations. Since the algorithms are stochastic algorithms with randomness incorporated into them, the algorithms are run 30 times. In addition to the best result found-i.e., the minimum value; the lower the value, the better the result-the maximum, mean and standard deviation of the algorithms are recorded and tabulated in Table 4, and their convergence curves are presented in Figure 4. Similar to the last section, statistical analysis is conducted using the best results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PSA**} & \multicolumn{4}{c}{**PSO**} & \multicolumn{4}{c}{**SCA**} \\ \hline  & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} \\  & & & & & & & & & & & Dev & & Dev \\ \hline f4 & 400.01 & 438.72 & 421.30 & 17.32 & 400.15 & 442.94 & 429.58 & 16.54 & 431.09 & 490.  
The results from the 13 functions found that the best solution of PSA is better than PSO and SCA in 8 functions. This is followed by PSO (= 4) and SCA (= 1). The Friedman test ranked PSA the best with the lowest average rank; this is followed by PSO and SCA. The ranks are listed in the last row of Table 4. Friedman statistic (distributed according to \(\chi^{2}\) with 2 degrees of freedom) is obtained at 14.923077 and the \(p\)-value computed by the Friedman Test is 0.000575. Hence, with a significance level of 0.05, it is found that there is significant different between the algorithms. Further analysis with Holm post hoc shows that PSA is on par with PSO and significantly better than SCA. This is indicated by the \(p\)-value and Holm value in Table 5.  
The standard deviation of PSA is less than 1 for 8 out of the 13 test functions. Low standard deviation shows the consistency of the algorithm's performance. This is also seen for the state-of-the-art PSO. On the other hand, SCA has a greater number of standard deviations bigger than 1. The same findings are observed through the small difference between the maximum and minimum solution of PSA.  
The convergence curves of PSA show that PSA converges gradually at the beginning and found its best solution faster than PSO and SCA. This pattern shows that further improvement focusing on enhancing agents' exploration is needed
## 6 Empirical Studies
### Scalability  
We noted above that the choice of \(k\) does not affect the running time of the algorithm significantly: the running time for two and tens of thousands of partitions differed less than 1% for Friendster.  
As another measure of its scalability, we run the algorithm with \(k=2\) on a series of random graphs (that are similar in nature) of varying sizes. In particular, we use RMAT graphs [46] with parameter 20, 22, 24, 26 and 28, whose node and edge count is given in Table 5. In addition, the last column gives the normalized running time of our algorithm on these graphs. Note that the size of the graph almost quadruples from one graph to the next.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(k\) & **2** & **10** & **102** \\ \hline Cut size & 11.9\% & 41.4\% & 59.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cut sizes produced by our algorithm on Friendster (ratio of cut edges to the total number of edges), where \(k\) denotes the number of clusters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(k\) & [21] (4\(-\)15\%) & Spinner (5\%) & FENNEL (10\%) & METIS (3\%) & Combination (3\%) \\ \hline
2 & 34\% & 15\% & **6.8\%** & 11.98\% & 7.43\% \\
4 & 55\% & 31\% & 29\% & 24.39\% & **18.16\%** \\
8 & 66\% & 49\% & 48\% & 35.96\% & **33.55\%** \\
16 & 76\% & 61\% & 59\% & N/A & **46.18\%** \\
32 & 80\% & 69\% & 67\% & N/A & **57.67\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison to prior work on Twitter. The main entries are the ratio of edges cut by the clustering over the total number of edges in the graph. The number of clusters is denoted by \(k\). Numbers in parentheses denote the permissible imbalance for each algorithm (Best results in each row are typeset in bold for easier spotting).
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
###### Abstract
Faced with complex data and prediction tasks, CNN-LSTM is a good prediction model, but in previous studies, people's attention has been focused on better time series prediction after LSTM, and few people have paid attention to the CNN part used for the feature extraction module. As a key equipment in a mineral processing plant, the SAG mill has a complex operating status and diverse operating data patterns. Therefore, clarifying the relationship between input parameters in the SAG mill power consumption task, effectively extracting the key features of the operating parameters, and assigning weights to each feature according to its importance to achieve accurate prediction of SAG mill power consumption is an urgent problem to be solved. In order to effectively extract the effective features of the SAG mill operating parameters and accurately predict the power consumption of the SAG mill during the grinding process, this paper proposes a hybrid prediction model that combines the channel attention convolutional network (CACN) and the long short-term memory (LSTM) to accurately predict the power consumption of the SAG mill. The main work of the paper is as follows:  
1. The Pearson correlation coefficient (PCC) is used to screen the real-time operation data collected from the production process of the SAG mill, and the key parameters that affect the power consumption of the SAG mill are selected for modeling, thereby effectively reducing data redundancy. 2. Based on the improved CNN network, high-dimensional features of different input parameters are extracted, and weights are adaptively assigned to the high-dimensional features so that the weak correlation coefficient can also play a role in the prediction of semi-autogenous grinding power consumption. Combining the advantages of CACN and LSTM, a new hybrid power consumption prediction model is proposed to achieve accurate prediction of the power consumption of the SAG mill. The effectiveness of the model is verified based on the measured data of the SAG mill in a mine in Yunnan.
## 4 Experiment and Results
### Comparative Experiments
#### 4.1.3 Comparative Experiments on ISIC2018  
On the ISIC2018 dataset, the proposed MA-DenseUNet model also demonstrates superior performance compared to several state-of-the-art segmentation networks across multiple evaluation metrics. As summarized in Table 5, the experimental results include key performance indicators such as accuracy, Dice coefficient, and mean Intersection over Union (mIoU). Specifically, the proposed method achieves an accuracy improvement of approximately 3.2% over the baseline U-Net and outperforms the SCSONet by around 1.13%. In terms of the Dice coefficient, the model surpasses U-Net by approximately 4.41%,  
Figure 15: Validation process on ISIC2017 dataset. (**a**) Curves of Acc metric; (**b**) curves of Dice metric.  
and exceeds SCSonet and ARU-Net by around 1.4% and 0.18%, respectively. For the mIoU metric, the proposed approach improves upon U-Net by about 4.15%, and yields gains of approximately 1.33% and 1.27% over SCSonet and EGE-UNet, respectively. Furthermore, as shown in Figure 16,the MA-DenseUNet model outperforms most comparative methods in other critical indicators such as sensitivity and specificity, further confirming its superior performance in skin lesion segmentation tasks.
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 3 Proposed Vector Quantizer Based on SOM
### Minimum Distance Search Circuit  
As discussed in Section 2, the distances between the input vector and all of the weight vectors must be first computed and then compared with each other to find the winner-neuron. In our design,  
Figure 3: The basic architecture of reconfigurable complete-binary-adder-tree.  
the distance calculation is fully conducted by the SDU and the RCBAT circuit, and the operation of minimum distance search is mainly executed by a winner-takes-all (WTA) circuit. The WTA circuit acts as an arbitration module which can locate the smallest SED and determine the winner-neuron.  
The block diagram of the WTA circuit is depicted in Figure 4. It consists of a shifter register with a depth of '7', three registers with load signal, one 'AND' gate, one comparator, and an additional ordinary register. The output port of the address generator for the codebook memory is connected to the input port of the shift register. The exact SED (\(DE^{2}\)) from the RCBAT circuit is fed to the WTA circuit as an input, and the signal '\(S_{SEP}\)' acts as a separate signal for different SEDs. During the winner-neuron searching phase, the intermediate minimum SED and the corresponding address are temporally stored in \(R2\) and \(R3\). The separation signal '\(S_{SEP}\)' is the same one that has been discussed in the RCBAT circuit. Once '\(S_{SEP}\)' turns into '1', the newly arrived SED '\(DE^{2}\)' is loaded in \(R1\) and then compared with the intermediate minimum SED. If '\(DE^{2}\)' is the smaller one, the comparator would output '1' and the values stored in \(R2\), as well as \(R3\), will be updated accordingly. After all of the weight vectors have been searched, the start address of the codebook memory where the current weight vector (winner-neuron) is stored will be output as an index and transmitted to the receiving terminal.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 6 Conclusions  
To defend against eavesdropping and link-flooding attacks, this paper proposes an SDN-based multi-constraint and multi-policy path hopping active defense method. Addressing the high time-complexity issue caused by using SMT constraint solving methods in path generation and the problem of overly strict or insufficient constraints in path switching space generation in current research, this study suggests using a DFS algorithm to compute all paths between two points as the path switching space. Constraints are then applied to residual bandwidth, path selection periods, and path similarity parameters during path selection to reduce the complexity of path switching space generation while expanding the space, enhancing the diversity and unpredictability of path switching, and increasing the difficulty for attackers. Moreover, existing research often employs a single triggering mechanism for path switching, mostly using fixed switching periods, reducing the unpredictability of path switching and the ineffectiveness against diverse attacks. In this regard, this paper calculates path weights based on residual bandwidth and critical nodes to adaptively switch path switching modes according to different attacks. Path switching periods are dynamically varied based on path weights, increasing the difficulty for attackers and enhancing defense effectiveness. Comparative experiments on the effectiveness and performance overhead of SDADM show significant improvements in path switching diversity and effectiveness against eavesdropping and link-flooding attacks, with a noticeable decrease in path switching space generation time compared to SMT. Although there is a slight increase in data transmission delays compared to PSM-ARM, it remains within an acceptable range. Overall, SDADM, while ensuring performance, effectively defends against attackers by enhancing path switching diversity.  
Conceptualization, H.L. and N.W.; methodology, J.S.; software, B.Z.; validation, B.Z., S.Z. and W.X.; writing--original draft preparation, B.Z.; writing--review and editing, N.W., H.W., and H.L.; visualization, S.Z.; supervision, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (No. 62106053), the Natural Science Foundation of Guangxi Province of China (No. 2024 GXNSFAA010242), the Guangxi Education Department Program (No. 2021 KY0347), the Doctoral Fund of Guangxi University of Science and Technology (No. XiaoKe Bo19Z33), and the Innovation Project of Guangxi Graduate Education (No. YCSW2023478).  
The data are not publicly available due to the sensitive nature.  
We are extremely grateful to the editors and the anonymous reviewer for their valuable comments and suggestions.  
The authors declare no conflicts of interest.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 2 Isolated Power System Modeling
### Load Modeling
#### 2.3.1 Static Load Models  
Within the static load representation, two modeling options were considered: the constant impedance and the exponential model. In both approaches, there is a voltage dependency of the load characteristics. Regarding the constant impedance load model, the active (P) and reactive (Q) power consumptions are expressed by (6) and (7), respectively, where \(P_{ref}\) and \(Q_{ref}\) correspond to the nominal active and reactive power consumptions, while \(V\) and \(V_{ref}\) are the terminal and nominal load voltages. On the other hand, in the exponential load model, active and reactive power consumptions are expressed by (8) and (9), where \(n_{p}\) and \(n_{q}\) are the exponential load model coefficients for the active and reactive power consumptions, which define the power consumption variation as a function of the load terminal voltage.  
\[P = P_{ref}\Bigg{(}\frac{V}{V_{ref}}\Bigg{)}^{2} \tag{6}\] \[Q = Q_{ref}\Bigg{(}\frac{V}{V_{ref}}\Bigg{)}^{2}\] (7) \[P = P_{ref}\Bigg{(}\frac{V}{V_{ref}}\Bigg{)}^{n_{p}}\] (8) \[Q = Q_{ref}\Bigg{(}\frac{V}{V_{ref}}\Bigg{)}^{n_{q}}. \tag{9}\]
# _Arthrospira platensis_ as a Feasible Feedstock for Bioethanol Production
## 3 Environmental Stresses for Glycogen Accumulation
### Light Intensity  
One of the most primitive needs of all photosynthetic organisms is the adaptation to the daily and seasonal changes in light intensities [59]. To photosynthetic organisms, light is not only a signal to diurnal and seasonal cycle, but also an energy source that is captured and secured when available [60]. [PERSON] et al. [61] evaluated the biomass production and glycogen accumulation of _Arthrospira platensis_ under different light intensities from 20 up to 700 umol photons m\({}^{-2}\) s\({}^{-1}\). The initial growth rates were found to be similar at 270 and 700 umol photons m\({}^{-2}\) s\({}^{-1}\), but it started to decrease after a few days for the lower irradiance. This decrease in growth rate might be attributed to self-shading of the culture. Regarding the glycogen accumulation, a similar tendency was observed at 50 and 270 umol photons m\({}^{-2}\) s\({}^{-1}\) as glycogen content increased from 18% to 40% of cell dry weight after 12 h and decreased to the initial contents after three days. However, for higher light intensity (700 umol photons m\({}^{-2}\) s\({}^{-1}\)), the amount of glycogen reached up to 50% of cell dry weight. [PERSON] et al. [59] conducted a metabolic simulation based on experimental data of the production of glycogen for further ethanol production based on _Arthrospira platensis_ biomass. They observed a positive correlation of the glycogen content with the increasing light intensity, reaching 42% DW for 250 umol photons m\({}^{2}\) s\({}^{-1}\). In 2019, [PERSON] et al. [56] performed a meta-bibliographic analysis focused on glycogen and EPS production by _Arthrospira_, showing an increase in glycogen content with light intensity. Based on the literature, glycogen accumulation at high light intensity (>200 umol photons m\({}^{-2}\) s\({}^{-1}\)) is about 45% DW (between 42 and 65% DW depending on studies), while it remains below 20% DW for lower irradiances. Additionally, as light intensity was found to be a key parameter for glycogen and EPS modulation, these authors also performed experiments at four irradiances (100, 400, 800, and 1200 umol photons m\({}^{-2}\) s\({}^{-1}\)) and calculated EPS/glycogen ratios, established respectively at 3.1, 1.79, 1.54, and 0.64. This decrease in EPS/glycogen ratio can thus suggest a redirection of the carbon flux toward glycogen while increasing the light.
## 1 Introduction
Figure 2 illustrates a simplified example of a TOP solution on a \(30\times 30\) km grid, where two UAVs plan their routes to maximize collected rewards while avoiding obstacles: (i) nodes are represented as numbered blue circles, with larger circles indicating higher rewards; (ii) the first UAV follows the yellow path, starting from node 1 (red square) and visiting nodes 7, 11, 10, 5, 9, and 12; (iii) the second UAV follows the purple path, also starting from node 1 and visiting nodes 4, 8, 6, and 12; (iii) obstacles appear as black rectangles, requiring UAVs to adjust their routes accordingly; and (iv) a red arrow in the top-right corner represents wind direction and speed (18 km/h), a key environmental factor in trajectory optimization. The main contributions of this study are described as follows: (i) we extend the classical TOP by integrating obstacle avoidance and environmental factors, including wind speed, wind direction, and payload weight, which impact UAV performance; (ii) the A* algorithm is applied to determine the shortest path between nodes, ensuring UAVs navigate complex environments while avoiding obstacles; (iii) a feedforward neural network is used to estimate UAV travel time, incorporating UAV speed, trajectory distance, wind conditions, and payload; and (iv) we analyze the impact of various parameters, including obstacle size and position, maximum nodes per route, route duration, UAV speed, wind conditions, and payload weight. Hence, the novelty of this approach lies in combining classical A*-based path-finding with a data-driven model to account for real-time environmental effects on UAV mobility, an aspect not typically addressed in the standard TOP literature. Theoretical contributions involve expanding the classical TOP framework to account for spatial constraints and dynamic variables, while the practical significance is shown in its potential applications to UAV logistics, disaster response, and environmental monitoring. The remainder of this paper is structured as follows: Section 2 reviews related work on TOP and UAV-based path planning. Section 3 presents the problem formulation and modeling approach. Section 4 details the proposed methodology, including A*-based path planning and neural network time estimation. Section 5 discusses experimental results, and Section 6 concludes with insights and future research directions. Figure 2: Example of a TOP solution with UAVs avoiding obstacles on a \(30\times 30\) km grid.
## 5 Test Results and Discussion  
### The Effect of Traffic Loading on the Unpaved Road  
The repeated plate loading test results in the sections in test series I and II (N\({}_{\text{traffic}}\) = 0, 5000) are given for the percentage deformation ratio (s/D) versus plate loading pressure in Figure 7. Figure 7 shows that deformation values are quite limited (decreased by 60% to 70%) in all sections for Test Series II (N\({}_{\text{traffic}}\) = 5000). The least deformation was observed in the rockfill section in the plate loading tests performed under both test series I and II traffic loading conditions. It shows that the rock fill section has a rigid base effect due to the high interlocking of the grains (rocks) and, thus, can spread the load to the weak ground more than the granular fill. In sections reinforced with geosynthetics, the best performance was obtained in the geocell-filled section, followed by the double and single geogrid sections. Geocells have been employed in road engineering to enhance the stability of base course aggregates and subgrade soils, thereby enhancing the overall performance of both unpaved and paved roads. Geocells have the potential to effectively enhance the bearing capacity and trafficability of a base layer while simultaneously reducing the rut depth within the aggregate layer located above the geocell [35]. Although rock fill is seen to be better in terms of settlement behavior, depending on the number of traffic cycles, it has been observed that geosynthetic sections show a performance close to the rock-filled section. It proves that geosynthetics can be an alternative to rock fill.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Series** & **Traffic Loading Condition** & **Test Number** & **Test Condition** \\ \hline \multirow{6}{*}{I} & & 1 & Granular Fill \\  & & 2 & Rockfill \\  & N\({}_{\text{traffic}}\) = 0 & 3 & Geogrid Reinforced (N\({}_{\text{g}}\) = 1) \\  & & 4 & Geogrid Reinforced (N\({}_{\text{g}}\) = 2) \\  & & 5 & Geocell Reinforced \\ \hline \multirow{6}{*}{II} & & 1 & Granular Fill \\  & & 2 & Rockfill \\ \cline{1-1}  & N\({}_{\text{traffic}}\) = 5000 & 3 & Geogrid Reinforced (N\({}_{\text{g}}\) = 1) \\ \cline{1-1}  & & 4 & Geogrid Reinforced (N\({}_{\text{g}}\) = 2) \\ \cline{1-1}  & & 5 & Geocell Reinforced \\ \hline \hline \end{tabular} N\({}_{\text{g}}\): the number of the geogrid; N\({}_{\text{traffic}}\): the number of traffic cycles.  
\end{table}
Table 3: Details of the PLT test program.
## 5 Pre-Processing Techniques
### Non-Uniform Frequency Resolution  
To improve the accuracy and effectiveness of equalization, the equalizer should take advantage of the characteristics of the room response and the human ear. At low frequencies, the room frequency response is more regular and the peaks and notches are mostly insensitive to the position in the room. The resolution of the ear is nonuniform and nonlinear, with a logarithmic dependence on frequency. At high frequencies, the ear is rather insensitive to notches of the room response and to high-frequency reverberation. Accordingly, the equalizer should provide fine resolution at low frequencies and a coarser resolution at higher frequencies. Many techniques have been developed following this strategy:  
* Complex smoothing,
* Frequency warping,
* Kautz filters and parallel IIR filters with fixed poles,
* Multirate approaches.