esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 3 The Locust Search (LS-II) Method  
The Locust Search (LS-II) method refers to a metaheuristic technique obtained from the modeling of the gregarious behavior observed in swarms of locusts. The mechanisms incorporated in LS-II avoid the concentration of individuals in promising areas and improve the exploration of the search space through the redistribution of agents [48]. With the combination of both behaviors, the LS-II approach presents very effective global and local search properties. These characteristics have motivated its use in the solution of very complex problems [49]. The LS-II proceeds from the original LS (Locust Search method). Different from LS, the new LS-II incorporates new operators and mechanisms to increase its capacity to avoid the agent concentration. Such mechanisms allow a better balance between exploration and exploitation to find the global solution when it faces several local minima.  
Under LS-II, a population \(\mathbf{P}\) of \(N\) locusts symbolize a set of \(N\) candidate solutions \(\mathbf{P}=\{\mathbf{x}_{1},\dots,\mathbf{x}_{N}\}\) (where \(N\) corresponds to the whole population size). The set of elements from \(\mathbf{P}\) interacts with each other examining a \(n\)-dimensional search space. Each solution \(\mathbf{x}_{i}=[x_{i,1},\dots,x_{i,n}]\) is identified inside a constrained space \(\mathbf{U}=\{\mathbf{x}_{i}\in R^{n}|l_{d}\leq x_{i,d}\leq u_{d}\}\) (where \(l_{d}\) and \(u_{d}\) symbolize the lower and upper limits for the \(d\)-th decision variable, respectively).  
As with any other metaheuristic approach, LS-II involves an iterative model in which candidate solutions modify their location at each iteration during its execution. The position of each candidate solution is modified through the application of a set of operators modeled from the two behavioral processes observed in locust insects: solitary process and social process.
# _Applied_
## 3 Methodology
### Audio Features
* **Chroma related features (CHROMA)**: Chroma are an interesting and powerful representations in which the entire spectrum is mapped into 12 cells representing the 12 semitones (or chroma) of the musical octave. It is also called a chromagram, and can be calculated using the short-term log Fourier transform of the sound signal. Another characteristics based on chrominance are the normalized chroma energy distribution statistics, which are used to identify the similarity between different interpretations of the given music [28]. * **Spectral Contrast (SCT)**: SCT is used for extracting spectral peaks, valleys and their differences in each sub-band while MFCCs sum up the Fast Fourier Transform (FFT) amplitudes. Thus, the spectral contrast function represents the relative spectral characteristics. Spectral contrast includes more spectral information than MFCCs because MFCCs only involve average spectral information [29]. * **Tonnetz (TZ)**: The Harmonic network or Tonnetz that are shown calculate the characteristics of the tonal centroid [30]. It is a well-known planar representation of pitch relations first attributed to [PERSON] [31], and later widely used by 19 th century music theorists such as [PERSON] and [PERSON] and in recent years by Neo-Riemannnian Music theorists [31; 32; 33]. * **Mel Spectrogram (MEL)**: The non-linear transformation of the frequency scale which is based on the perception of locations is called the mel scale. This transformation calculates an energy spectrogram on the mel scale [30], so that two pairs of frequencies separated by a delta in the mel scale are perceived by humans to be equidistant [34]. For this paper, it has been decided to use the _librosa_ package to extract useful features from sound information. This Python package is chosen because of its support of ARM-based processors and to be able to use the python package scikit-learn.
## 3 Results and Discussion  
### Total Viable Counts (TVC)  
#### 3.1.1 LAB  
The total viable counts (TVC) of LAB during fermentation are shown in Table S1. The LAB viable count increased over 96 h of incubation. The F-values obtained for the variables affecting LAB showed that the length of incubation time had the most significant effect (357.99, \(p<\) 0.01). This is a known fact as with any cell growth there would be an increase in growth as time increased until the stationary phase is reached. The TVC of LAB increased from 3.34 log CFU/mL to above 5.1 log CFU/mL for all CW samples. However, the TVC of LAB produced in all experimental sets (approximately 5 log CFU/mL) are low compared to LAB fermentation using other substrates, such as fructose and lactose, as reported in other studies [30; 31; 32]. LAB are known to be fastidious microorganisms which require growth factors, such as vitamins and amino acids, and can also be affected by nitrogen concentration [33; 34; 35; 36; 37]. A recent study further showed that sugary water substrates produced less LAB compared to milk-based substrates [14].  
In general, the sucrose and glucose addition increased the TVC of LAB after 96 h of incubation. Coconut water without any sugar additions showed the least TVC of LAB (5.02 log CFU/mL, \(p<\) 0.05). This contrasts with the CWK sets containing glucose and sucrose, which increased the growth of LAB. The CWK supplemented with sucrose at 12 g/L (Set 3) produced the highest LAB TVC, 5.72 \(\pm\) 0.04 log CFU/mL (\(p<\) 0.05) after 96 h. The second-highest LAB was obtained from Set 9 (5.64 \(\pm\) 0.02 log CFU/mL, \(p<\) 0.05),which contained CW with added glucose (12 g/L) and sucrose (12 g/L) in the ratio of 1:1. The difference in TVC between Sets 3 and 9 was less than one order of magnitude, despite the additional 12 g/L glucose in the Set 9 sample. This suggests that there might be some sugar (substrate) saturation of the enzymes involved in the metabolism of sugar [31; 38]. In addition to the substrate saturation effect, the results also suggest that there might be catabolite repression occurring between the glucose and sucrose. The acidic products of fermentation could also inhibit the growth of LAB [39], as supported by a 2D score plot that showed an increase in organic acids during CWK fermentation with increasing time.
## 4 The Early Warning Model
### The Proposed Prediction Model
The root-mean-square error (\(RMSE\)) is selected as the fitness function to evaluate QPSO [16; 33]:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{23}\]  
where \(n\) is the number of samples in the test set, \(y_{i}\) is the predicted value and \(\hat{y}_{i}\) is the true value. 4. Updating the particle position and global optimal solution according to the fitness function. 5. Judging whether the convergence condition is reached. The convergence criterion of QPSO is usually that particles \"gather\" in a small range around a certain position. In this study, the following conditions must be met when converging [16]:  
\[\max\bigl{\{}\bigl{|}u_{i}(t)-u_{j}(t)\bigr{|}\bigr{\}}<\epsilon, \tag{24}\]  
where \(1\leq i,j\leq N\), and \(i\
eq j\). Moreover, \(\epsilon\) is a small positive number, the value of which is considered \(10^{-5}\) in this study. If the convergence condition is not met, the optimization is continued. When the convergence condition is reached, the optimal parameter combination can be obtained. Step 4: Construction risk early warning based on the LSSVM  
The optimal parameter combination is introduced into Equations (16)-(18), and the LSSVM model is reconstructed for calculation so that the early warning result of the construction risk level can be obtained. It should be noted that, given the regularization parameter and the Gaussian kernel function width parameter, the LSSVM can conduct risk early warning. The purpose of Step 3 is to find the optimal calculation parameters of the LSSVM. Step 5: Reliability analysis of the early warning results  
To verify the reliability of the early warning results, the Bland-Atman analysis method is used to analyze the early warning values and the measured values. The Bland-Atman analysis is realized with the assistance of SPSS 17.0 software. If the early warning result based on the proposed method is consistent with the true value within the 95% confidence interval, it indicates that the early warning result has good reliability and the early warning is over. Otherwise, Steps 3 and 4 are repeated. Summarizing the above analysis, the flowchart of the proposed model was shown in Figure 1.  
Figure 1: The flowchart of the proposed model. It should be emphasized that the proposed model was a data-driven model. Different cases mean different engineering data and different trained models. However, the proposed model, rather than trained models, is able to be applied to any type of case study.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 6 Security Analysis  
As we described in the threat model of Section 3.2, the goal of attackers in DPNs is to deliver incorrect data to the data requester and to manipulate the log records undetectably. The attacks can be categorized into the internal threats from compromised nodes within the DPNs and the post-process tampering of log records. DPTracer protects the data provisioning process against these attacks by combining data flow tracking and accountable aggregation.
## 3 Results
### Validation against In Situ Data
During the summer months for the CRO site, SIF/PAR does not capture the some of the slight increases in transpiration efficiency (i.e., 2009), which may be due to data resolution (0.5\({}^{\circ}\)). Figure 2: In situ validation. (**a**) Correlations of flux tower sites versus eartH2 Observe models (red boxplots) and SIF/PAR (black line) ordered by fraction of bare soil (grey shade, top). (**b**) Results during decaying (gold) and growing (green) periods separately (as defined by average monthly climatologies of tower GPP) for SIF/PAR and each model across flux tower sites. (**c**) Lag at which the cross-correlations for each model and SIF/PAR versus the FLUXNET towers are maximum. Negative values imply an early onset of phenological and/or physiological constraints, while positive implies a delay relative to towers. Spatial and Temporal Variability in Solar-Induced Fluorescence/Photosynthetically Active Radiation (SIF/PAR)  
Global seasonal maps of SIF/PAR follow the expected patterns of general vegetation activity in response to different phenological and physiological constraints (Figure 4a). In the Northern Hemisphere, SIF/PAR is lowest during winter (December, January February, (DJF)), suggesting a low transpiration efficiency. In wintertime LAI is lower, with sub-optimal radiation and temperatures, resulting in a reduction of stomatal conductance and carboxylation rates. The highest values (suggesting high transpiration efficiency) occur during the summer (June, July, August (JJA)), when LAI is expected to be high and the vast majority of plant types are active--granted an absence of extreme heat or water stress. In Amazonia, despite the low seasonal variability, SIF/PAR peaks in September, October November (SON). This coincides with the end of the dry season when light availability through the canopy increases, while water is typically not limiting due to the deep rooting structure of emergent species [81; 82]. Across the Sahel, SIF/PAR is lowest during DJF and highest during SON, mimicking the timing of the monsoon season. Likewise, in northern Australia, SIF/PAR is the lowest during March, April, May (MAM) and the highest during SON, in phase with the monsoonal rainfall. These spatial and seasonal patterns agree well with the exploration of SIF/PAR by [PERSON] et al. [62]. Finally, Figure 4b displays the seasonal average \(\tau\) for 2007-2014 for the ensemble of models. While the model ensemble shows markedly lower estimates in water-limited regions, overall spatial patterns agree with those of SIF/PAR, with these similarities being larger for SURFEX and ORCHIDEE--see Figure S4. Figure 3: Timing of transpiration efficiency (\(\tau\)). Bi-weekly times series of \(\tau\) from the flux tower sites US-MMS (deciduous broadleaf forest), US-Ne2 (crop) and US-SRG (grassland) compared to SIF/PAR and the earthH2 Observe models for 2007–2014. Precipitation and snow dates are masked.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 2 Non-Uniform Polar Grid Construction for PBR
\begin{table}
\begin{tabular}{l} \hline \hline
**1. Calculate angular coordinate** \\ \hline The angular coordinate set is \(\Theta=\left\{\theta_{k}|\theta_{k+1}-\theta_{k}=3\sigma_{\theta},k=0\dots N,N =\left[\frac{\theta_{max}-\theta_{0}}{3\sigma_{\theta}}\right]\right\}\). Where \([\cdot]\) is the symbol of \\ round down, and N is the mesh counts in angular dimension. \\ \hline
**2. Aiming at each angular coordinate \(\theta_{k}\) in \(\Theta\), iteratively calculate the grid division in range dimension.** \\ \hline For \(\theta_{k}\), \(k=0\dots N\) \\ Initialization: i = 0, R\({}_{i}=\tau_{0}\); \\ Iteration: \(R_{\text{Si}}=R_{i}+\sqrt{R_{i}^{2}+L^{2}+2R_{i}Lcos(\theta_{k})}\); \\ \(\rho_{1}(R_{\text{si}},\theta_{k})=\frac{R_{i}^{2}+L^{2}+2R_{i}Lcos(\theta_{i} )}{2(R_{\text{si}}+Lcos(\theta_{i}))^{2}}\), \(\rho_{2}(R_{\text{si}},\theta_{k})=\frac{(R_{i}^{2}-L^{2})Lsin(\theta_{i})}{2 (R_{\text{si}}+Lcos(\theta_{i}))^{2}}\). \\ \(R_{i+1}=R_{i}+3\sqrt{\rho_{1}^{2}(R_{\text{si}},\theta_{k})\sigma_{\text{r}}^{2 }+\rho_{2}^{2}(R_{\text{si}},\theta_{k})\sigma_{\text{o}}^{2}}\); \\ \(i=i+1\); \\ Terminate when \(R_{i}>r_{max}\). \\ \(N_{k}=i\). \(N_{k}\) is the mesh counts in range dimension for \(\theta_{k}\). \\ The range coordinate set is \(\Lambda=\{R_{i}\omega_{k}|i=0\dots N_{k},\theta_{k}\in\Theta\}\). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The polar mesh grid calculation steps. Figure 3: An example map of the proposed polar grid.
## 7 Experiments Results and Analysis
### Experiment I: Comparison of Operation Efficiency of Key Frame Detection  
In order to speed up computation and achieve real-time recommendations in a mobile environment, the differential evolution optimization algorithm was used with deep frame feature to detect key frames. Therefore, in this section, an experiment was implemented to compare the original key frame detection and deep features-based key frame detection. The relationship between iterations and distance is illustrated in Figure 10. The running time for extracting results per video is shown in Table 1. It can be seen from the experiment that the distance of both methods will increase with iterations because the differential evolution optimization algorithm tries to find a solution satisfying Equation (19) in the whole solution space. Meanwhile, the running time per video proves that deep learning-based method can speed up the process of key frame detection. Therefore, it is applicable that differential evolution algorithm is used to detect the key frame.  
Figure 10: The relationship between iterations and distance. (**a**) The original method; (**b**) our method.
## 4 Results
### Knowledge Retention Rate Improvements  
The delayed recall tests administered four weeks following the intervention served as the retention measure.  
Twenty-five students receiving AI-driven feedback kept 85% of their newly learned content whereas the other students maintained just 65% of their learning. The statistical analysis using regression showed feedback frequency and engagement levels directly impacted knowledge retention because they produced an \(R^{2}\) value of 0.82 (\(p<0.001\)).  
The scatterplot in Figure 15 illustrates the relationship between initial post-test scores and delayed recall scores, with point sizes reflecting average weekly AI feedback interactions for the experimental group (ranging from 0 to 18.2) and a uniform marker for the control group (traditional feedback). Some confusion may arise from the visual clustering: students with 5-10 AI interactions are present (e.g., moderate performers averaged 12.4 interactions, with some falling in this range), but their distribution overlaps with other ranges, making them less distinct. The chart separates the experimental and control groups to highlight AI feedback's impact, though this distinction was not explicitly labeled. Notably, students with 10 or more weekly interactions (e.g., high performers at 18.2) consistently scored above 75 on delayed recall tests, reflecting a strong positive correlation (\(R^{2}=0.82\), \(p<0.001\)) between frequent AI engagement and retention, rather than an absolute threshold. This pattern suggests that sustained interaction with adaptive feedback reinforces long-term memory, though small sample variability or ceiling effects may amplify this trend.  
Students who achieved above 25% initial learning gains based on their testing demonstrated superior retention results, which confirm that AI feedback boosts knowledge retention in students.
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 4 Existing Studies on the Performance of Solar Control Films
### Thermal and Luminous Performance Assessment of Glazing Systems with Solar Control Films
Considering an open-plan office space with dimmable lighting controls and an SCF installed on the glazing system, artificial lighting and cooling energy savings of about 21% and 7%, respectively, were obtained. Also, in the climate of the city of Hong Kong, [PERSON] et al. [49] evaluated the energy performance of different types of buildings (office, shopping center and hotel room) through a computer simulation model, considering glass and film systems previously experimentally assessed. The authors obtained annual energy savings between 77 and 90 kWh per unit area of SCF installed in the west-oriented single-glazing of an office space. Regarding the shopping center and hotel room, annual energy savings between 44 and 57 kWh per unit area of SCF were obtained. Considering the warm-summer humid continental climate (Dfb [58]), [PERSON] [57] analyzed the performance of low emissivity films applied to double-glazing. The application of SCFs made it possible to reduce the annual energy consumption of the building for heating by 6%. However, when performing a life cycle cost analysis, the authors concluded that the high price of the low-E films and the contrasting low price of district heating resulted in an extremely long payback period of 30 years.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 4 Numerical Results and Discussion
### Experimental Analysis of Hard Shoulder Opening Strategies
#### 4.2.2 Analysis of Results
In order to analyze the effect of HMMD-HSR under different penetration rates \(\rho\), in this paper, we set \(\rho\) as 0, 0.2, 0.4, 0.6, 0.8. and 1.0, respectively, and the statistics of the accumulated upstream traffic density and travelling time are shown in Table 4, while the percentage of accumulated density and accumulated travelling time reduction under HMMD-HSR are shown in Figure 14 using N-HSR as a baseline. It can be obtained from Table 4 that both the accumulated density and accumulated travel time decreased significantly with increasing permeability \(\rho\). From Figure 14, it can be seen that the degree of the enhancement effect of HMMD-HSR becomes better and better as the penetration rate increases. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accumulated Travel Time** & **Accumulated Traffic Density** \\  & **(s)** & **(veh/km)** \\ \hline \(\rho=0.0\) & \(1.1151\times 10^{7}\) & \(1.1356\times 10^{6}\) \\ \(\rho=0.2\) & \(9.3578\times 10^{6}\) & \(1.0145\times 10^{6}\) \\ \(\rho=0.4\) & \(7.5665\times 10^{6}\) & \(8.9529\times 10^{5}\) \\ \(\rho=0.6\) & \(7.3799\times 10^{6}\) & \(9.1256\times 10^{5}\) \\ \(\rho=0.8\) & \(6.5766\times 10^{6}\) & \(8.5434\times 10^{5}\) \\ \(\rho=1.0\) & \(5.5181\times 10^{6}\) & \(7.5571\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accumulated travel time and accumulated traffic density at different \(\rho\). Figure 14: Reduction in travel time and traffic density at different \(\rho\).
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(TQA\)} & \multicolumn{6}{c}{**LR (\%) in Various Codebooks**} \\ \cline{2-7}  & **CB1** & **CB2** & **CB11** & **CB12** & **CB13** & **CB21** & **CB22** \\ \hline
0.90 & 93.98 & 89.67 & 80.51 & 84.41 & 84.43 & 77.78 & 79.20 \\
0.91 & 93.71 & 89.25 & 79.90 & 83.72 & 83.89 & 77.78 & 79.00 \\
0.92 & 93.44 & 88.73 & 79.71 & 83.20 & 83.45 & 76.11 & 78.41 \\
0.93 & 93.05 & 88.19 & 78.88 & 82.94 & 82.80 & 76.11 & 77.67 \\
0.94 & 92.63 & 87.61 & 78.07 & 82.15 & 82.25 & 75.01 & 76.34 \\
0.95 & 92.18 & 86.88 & 77.18 & 81.37 & 81.47 & 74.19 & 75.50 \\
0.96 & 91.66 & 86.01 & 76.50 & 80.65 & 80.51 & 72.27 & 74.34 \\
0.97 & 90.95 & 85.01 & 75.22 & 79.74 & 79.50 & 71.02 & 73.40 \\
0.98 & 89.96 & 83.66 & 73.86 & 78.44 & 78.23 & 68.76 & 70.85 \\
0.99 & 88.39 & 81.60 & 71.55 & 76.87 & 76.57 & 67.23 & 68.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: LR percentage representation of Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(TQA\)} & \multicolumn{6}{c}{**QA (\%) in Various Codebooks**} \\ \cline{2-7}  & **CB1** & **CB2** & **CB11** & **CB12** & **CB13** & **CB21** & **CB22** \\ \hline
0.90 & 90.42 & 90.63 & 90.84 & 90.89 & 91.06 & 93.16 & 93.10 \\
0.91 & 91.52 & 91.46 & 91.96 & 92.18 & 92.12 & 93.16 & 93.27 \\
0.92 & 92.32 & 92.58 & 92.44 & 93.18 & 93.05 & 94.86 & 93.92 \\
0.93 & 93
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 4 SDN Based Architecture
### Performance Parameters  
A Mininet-based SDN Network experiment was run for TCP and UDP flows in order to analyze the behavior of Wi-Fi. Bandwidth, jitter, and flow setup delay are factors that are taken into account while observing TCP and UDP flows behavior in a wireless environment. The installation of flow rules at switches and the creation of network event notifications from the controller side are not instantaneous tasks in a typical switch-controller cycle. A large-scale network's performance is severely impacted by flow setup delays. Low flow setup latencies are therefore needed for large-scale SDN network performance.