esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review of Model Application  
Post-evaluation, forward-dynamics simulation models of maximal effort sporting movements have been used to investigate alternative techniques [10; 19], quantify the contribution of various factors to performance [21], identify performance limiting factors [8; 13; 124], quantify the sensitivity of performance to various factors or 'rate limiters' [12; 18; 19; 20], and identify the optimum technique for an individual [12; 13]. In addition, they have been applied to facilitate a greater understanding of aspects of motor control contributing to whole-body movement [18; 130; 161; 162]. These insights into the mechanics of sports techniques would not be possible from experimental observations.  
The research questions above have all been addressed by running multiple simulations with different inputs. To identify an optimum solution for any criteria, an objective function or performance score must first be defined. This function is then maximized or minimized by varying specified model inputs within realistic pre-determined limits using optimization algorithms such as simulated annealing [163] or genetic algorithm [164]. Alternative optimization approaches such as direct collocation optimal control [165; 166; 167] have been utilized more frequently in the muscle-driven model literature and may enhance computational speed. If attempting to predict optimum performance, the objective function must represent the task objective of the modeled activity. This has been a simple one-parameter function (e.g., maximizing jump height [124]) or multiple parameter functions (e.g., minimizing joint torque and joint torque change, while maximizing success in the presence of movement variability in the upstart of gymnastics [150]). In the example of this multiple parameter functions in gymnastics, the optimizations based on minimizing joint torques diverged from an elite gymnast's movement, whereas maximizing success given inherent movement variability generated a solution close to the gymnast's movement [150]. This highlights the need for researchers to consider the true nature of task objectives when defining optimization criteria. It is likely any true optimum technique for an individual is dependent on a combination of factors, including spatial and temporal accuracy considerations, robustness to variation from internal or external sources, and musculoskeletal loading, and so these factors should be considered in future optimization cost functions.
## 1 Introduction
First, the proposed system considers the imperfect location information of users, which is more suitable for practical applications. Second, the proposed robust algorithm can effectively degrade the influence of location uncertainty on user energy acquisition. To our best knowledge, there are few studies that address the robust design for a UAV-enabled wireless-powered MEC system. In summary, the main contributions of this paper are as follows:  
* We propose a UAV-enabled wireless-powered MEC system in a post-disaster area, while the imperfect location of users is considered. To ensure users have enough power in the post-disaster area, UAV provides charging and computing services for users. * We propose a joint resource allocation and trajectory planning algorithm under known users' location to solve the strong coupling between optimization variables. * We propose a robust cutting-set method to degrade the influence of worst-case location of users on optimization. The rest of this paper is organized as follows. We describe the system model and formulate the optimization problem in Section 2. Then, we give a joint resource allocation and trajectory planning algorithm under known users' location in Section. In Section 4, we propose a robust cutting-set method. After this, the numerical results are presented in Section 5. Finally, we draw conclusions of our work in Section 6.
## 3 Results and Discussion
### Immersion Enthalpies of Activated Carbons as a Function of Phenol Concentration
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \(\mathbf{\Delta H_{imm}}\)\(\mathbf{H_{2}O}\) & \(\mathbf{\Delta H_{imm}}\)\(\mathbf{C_{6}H_{6}}\) & **Hydrophobicity Factor** \\ \cline{2-4}  & (\(\mathbf{J}\)\(\cdot\)\(\mathbf{g^{-1}}\)) & (\(\mathbf{J}\)\(\cdot\)\(\mathbf{g^{-1}}\)) & \(\frac{\mathbf{\Delta H_{imm}}\)\(\mathbf{C_{6}H_{6}}}{\mathbf{\Delta H_{imm}}\)\(\mathbf{H_{2}O}}\) \\ \hline G & \(-11.0\) & \(-60.6\) & \(5.5\) \\ GN & \(-24.7\) & \(-53.9\) & \(2.2\) \\ GNA & \(-17.9\) & \(-49.9\) & \(2.8\) \\ GP & \(-24.8\) & \(-80.6\) & \(3.3\) \\ GC\({}_{1073}\) & \(-17.7\) & \(-122.3\) & \(6.9\) \\ GC\({}_{1173}\) & \(-23.7\) & \(-103.6\) & \(4.4\) \\ GC\({}_{1273}\) & \(-21.3\) & \(-100.1\) & \(4.7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Immersion enthalpies in water and benzene, and hydrophobic factor. Figure 9: Changing the enthalpy as a function of the phenol concentration in the solution. In Table 5, it can be seen that the hydrophobic factors obtained for the activated carbons GN, GNA, and GP presented the lowest values, which confirms that the impregnation carried out increased the number of oxygenated groups on the surface. It can also be seen that the carbonisation of the activated carbon G decreases the number of oxygenated groups on the surface, which is reflected in the increase in hydrophobicity of the carbonised samples GC\({}_{1073}\), GC\({}_{1173}\), and GC\({}_{1273}\). In Figure 10, it can be seen that the samples with higher values of the constant K\({}_{1}\), which is associated with the amount of polar groups on the activated carbon surface, presented the lowest values of hydrophobic factor. This is consistent with the fact that these samples have a greater number of oxygenated groups on the surface due to the oxidation processes. The carbonised samples GC\({}_{1073}\), GC\({}_{1173}\), and GC\({}_{1273}\) presented the highest hydrophobic factors and the lowest values for K\({}_{1}\); these results indicate that the K\({}_{1}\) constant of the modified Langmuir model is appropriate for determining the presence of polar groups on the activated carbon surface. Figure 11 presents the molar enthalpies of immersion as a function of the quantity of phenol adsorbed per square meter of activated carbon surface. Figure 11 shows that as the quantity of phenol adsorbed increases, the enthalpy values per mol decrease; this behaviour can be associated with the formation of the monolayer. The results shown in this Figure are interesting, because they indicate that the greater interaction between the activated carbon and the solution is obtained for the solid GNA and GC\({}_{1073}\). These samples presented the greatest amount adsorbed of phenol, and its decline in the immersion enthalpy is higher. For the activated carbons G and GN, the values of immersion enthalpy constants are higher, indicating a lower solid-solution interaction. Figure 10: Correlation between K\({}_{1}\) of the modified Langmuir model and the hydrophobic factor.
## 3 Results and Discussion
#### 3.1.2 Reducing Sugars and Antioxidant Characteristics
Notable peaks at 1456, 1414, 1375, 1341, 1227, 1193, 1149, 1103, 1078, and 1016 cm\({}^{-1}\) represent various C-O stretching, C-H bending, and C-C skeletal vibrations typical of polysaccharides. The peak at 1149 cm\({}^{-1}\) particularly indicates C-O stretching coupled with CH\({}_{2}\) bending vibrations. In the 1000-900 cm\({}^{-1}\) region, the characteristic carbohydrate fingerprint shows symmetrical and asymmetrical C-O-C vibrations of glycosidic linkages. The blends exhibit enhanced intensity in this region compared to pure seed press cake, reflecting the higher starch content from cassava flour addition. The E3 blend (70% cassava flour) shows the most pronounced peaks in this region, particularly at 1016 cm\({}^{-1}\). The low-frequency region (900-600 cm\({}^{-1}\)) displays several peaks, including those at 857, 797, 744, 707, and 575 cm\({}^{-1}\). The peak at 707 cm\({}^{-1}\) is particularly significant, attributed to \(\rho\)(-(CH\({}_{2}\))\({}_{n}\)) rocking vibrations and C=C bending of cis double bonds, confirming the presence of unsaturated fatty acids in the seed press cake. The spectral patterns clearly demonstrate the complementary nature of the blend components. Pure _L. cylindrica_ seed press cake shows strong lipid and protein signatures, while the addition of cassava flour enhances the carbohydrate-related absorption bands. The systematic changes in peak intensities across the blends (E1, E2, E3) correlate with their composition ratios, confirming successful blending and indicating molecular-level interactions between the seed press cake proteins/lipids and cassava starch components. This FTIR analysis validates the potential of these blends as functional ingredients with balanced nutritional profiles, combining proteins, lipids, and carbohydrates. The viable cell counts of the microbial groups investigated in the Luffa seed press cake powder indicated that all microorganisms tested (TMC, coliforms, _E. coli_, yeast and mold) were below, and in accordance with, European commission regulations concerning the microbiological criteria for foodstuffs, both for food safety and process hygiene criteria. The mean of the triplicate showed the following results: 2 CFU/g for TMC and 0 CFU/g for the other microorganisms.
## 4 Results and Discussion
### Analysis of Pressure Response
In order to improve the pressure response time, the spool motion should be started earlier in order to reach force balance. This target can be reached by increasing the active force or decreasing the passive force. The spool will move earlier if the preload force and the viscous force diminished. Moreover, the control port could be more quickly opened if the spring stiffness decreases. Nevertheless, the spring was not analyzed in this paper as it is an optional product. More research will be done on the magnetic force and the viscous force in the further. Figure 10: Forces acting on the spool during 0.4–0.43 s.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 2 Characteristic Functions and Parameters of PMs versus GSs
### Scan Angles and Angular FOV  
The two (different) pairs of characteristic scan angles of each system are marked on the graphs in Figure 3:  
* For a GS, shown in Figure 1a, the total rotational angles interval is [\(\pi/2-\theta_{\text{m}}\), \(\theta_{\text{m}}\)] (Equation (9), Table 1) and the useful rotational angles interval is [\(\pi/2-\theta_{\text{a}}\), \(\theta_{\text{a}}\)] (Equation (10), Table 1), with the latter corresponding to the portion scanned with constant velocity [9,10]. Therefore, the angular FOV could be considered with either its useful value, \(2\theta_{\text{a}}-\pi/2\), or with its total one, \(2\theta_{\text{m}}-\pi/2\), Equation (11), Table 1.
* For a PM, two pairs of scan angles are important: \(\theta_{1}\) and \(\theta_{2}\), which define the useful angular FOV, as discussed in Section 2.2--Figure 2a,b; \(\theta_{\text{min}}\) and \(\theta_{\text{max}}\), which define the down-reflected and up-deflected positions of the reflected beam of a scanner (in a concept introduced by [PERSON] [2]), therefore the total possible FOV--Figure 5a. To characterize these latter beams for a PM, we introduced the construction in Figure 5a [29,32], where the reflection is considered at the beginning and end of a PM facet (withoutreferring to the vignetting produced by the finite beam diameter). From this figure, these two reflected beams are characterized by  
\[\begin{cases}\theta_{max}-\theta_{min}=2\alpha\\ sin(\theta_{min}+\alpha)=\frac{\epsilon\cos\alpha}{R}\end{cases},\]  
therefore the expressions of the two (extreme) scanning angles are given by Equation (12), Table 1. These equations also give the total angular FOV of the PM--Equation (14), Table 1. The values of the useful angles \(\theta_{1}\) and \(\theta_{2}\) are obtained by solving Equation (13), Table 1.
## 4 Experimental Datasets and Results
### Experimental Results on Salinas  
The Salinas full scene was gathered over Salinas Valley California, through AVIRIS sensor. It comprises \(512\times 217\) pixels per band and a total of 244 bands with a 3.7 m spatial resolution. It consists of vineyard fields, vegetables and bare soils and contains sixteen classes. Few water absorption bands (108-112, 154-167 and 224) are removed from the dataset before analysis. Further details about the dataset can be found at [44].  
Further details about Salinas dataset can be found at [44]. Table 3 and Figure 4 presents the accuracies for different classifiers as well as with different sample selection methods. From comparative results, one can see that fuzziness together with MLR-LORSAL classifier works better as compared to the other sample selection and classification methods. We also presents the Salinas predicted geographical maps (classification maps) in Figure 5. These geographical locations of each predicted class label validate the superiority of our proposed pipeline. Figure 5 show the complete performance assessment on experimental results with profound improvement. As shown in the figure, the classification maps generated by adopting the proposed pipeline are less noisy and more accurate.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 5 Results and Discussion
### Algorithm Simulation and Implementation Performance Analysis  
In this section, we compare the actual implementation results shown in Figure 6 and the ideal simulation results in [3,9]. In this work, we used the F1-score as the main factor to compare the implementation and the simulation results of the AIS solutions. In this implementation, the size of \(C_{Pos+Neg}\) when we ran the NPS algorithm was 60 detectors, and it was 170 detectors for the MNSA. Consequently, to present a valid quantitative analysis, we compared the results for the same number of detectors in the simulation. Moreover, since the simulation was run as one entity, we analyzed the performance of the first four system architectures, where we ran the implementation using one IoT device. Figure 8 shows the analysis of the results for both the ideal simulation and the actual implementation results for the NPS algorithm and MNSA.  
Figure 8: Comparing the actual to the ideal performance of AISs for different IoT systems.  
Figure 7: CPU average utilization for IoT devices used in the implementation of the NPS algorithm and MNSAS.  
We compared the ideal simulation results with system 1's results since it used one IoT device with the most constrained memory size. The MNSA showed a decrease of almost 12.7% from the simulation results and system 1's results. While the NPS algorithm's performance decreased by 8% when compared to the simulation results. As anticipated, both AIS solutions, the NPS algorithm and the MNSA underperformed compared to their ideal simulation results. This validates the claim that IoT security solutions should be tested in a realistic environment to demonstrate sufficient findings.
## 3 Materials and Methods
### Datasets for Training and Testing
#### 3.1.3 Montgomery CXR  
The Montgomery dataset is collected as part of the collaboration between the Montgomery County (Maryland, USA) and the Department of Health and Human Services. The dataset contains 138 frontal chest X-rays from the Montgomery County's Tuberculosis screening program. The CXR collection of this dataset is acquired using the Eureka stationary CXR machine. Eighty cases are normal and the rest are cases with TB manifestations. All the images are provided in PNG format with either \(4020\times 4892\) or \(4892\times 4020\) spatial resolution. Each image is provided with a clinical reading text file that contains the patient's age and gender as well as normal and abnormal labels. In addition, each image has a corresponding binary ground truth mask, generated by manually segmenting lung regions. The manual lung segmentation was performed under the supervision of a radiologist, following anatomical landmarks, such as the boundary of the heart, pericardium, and aortic arc. Further details of the Montgomery CXR dataset can be found in [32].
## 4 Discussion
### Limitations and Suggestions for Future Studies  
Possible limitations of the computational techniques used in this study are firstly related to the limitations of the geometrical synchronization detection algorithm. This algorithm is based on the comparison of optimal time lags in finite observation windows. The optimality of a time lag is defined by the maximal area of the embedded attractor in the delay coordinate space. In other words, the geometrical synchronization detection algorithm does identify the rate of synchronization between two (or more) time series in terms of the similarity between optimal time delays reconstructed for each individual time series. It is well understood that the objective of any optimal time delay selection method is making the components of reconstructed vectors independent as far as possible, yet not too far to keep the information about dynamic properties of the embedded time series. However, it is not possible to determine optimal time lags for a completely random time series--because then the area of the embedded attractor does not depend on the time lag [51]. In other words, the presented computational techniques would fail if one of the analyzed time series is a completely random noise. The optimal embedding algorithms still work if the signal to noise ratio is low [52]--but one should make sure that the analyzed signals are not random. Otherwise, the resulting data on the geometrical synchronization may not be reliable. However, both the HRV and geomagnetic data analyzed with the algorithm in this study are clearly non-random data sets, therefore this is not an issue in the context of this study.  
Another limitation of the study is that the activity of the various groups was not controlled during the analysis periods before and immediately after participating in the Heart Lock-Ins. In future studies this should be controlled for as in some of the groups during the pre-lock-In period the participants may have been socializing, while other groups were more introspective in preparation for the heart focused meditation. In the post-lock-in period, in some groups, the participants immediately got up and moved on to their next activity while some remained seated for a while longer. This is most likely why the findings of the HRV synchronization among the group participants during the pre and post segments were different.  
Finally, in future studies, it would be more ideal to have a method for tighter control synchronization of HRV recorders. Although steps were taken to synchronize the timing of the bodyguard recorders, there could have been up to several seconds of differences in the time synchronization. This was not likely to have affected the results as the primary analysis was done over 24-h time blocks.
## 5 Performance Evaluation
### Results and Discussion
#### 5.3.2 Impacts of Varying Average QoS Requirement per Request  
The impacts of average QoS requirement of the incoming service requests on the performance of our proposed algorithms are manifested in Figure 5. As the average QoS requirements of the incoming requests increases, the maximum allowable delay to get the first response time becomes flexible. Hence, the QoS satisfaction percentage is increased in all approaches, as depicted in Figure 5a. However, as the FFS algorithm is specially designed for QoE maximization, it implicitly provides the best result among the studied approaches.  
Figure 5: _Cont._  
Moreover, as it relaxes the time boundary of performing baseband processing, there is a large possibility to execute within a minimal time regarding its QoS requirement. As a result, the QoE also keeps rising in all graphs in Figure 5b for our proposed algorithms as well as to the studied works. In addition, as the increase of average timeline QoS requirement offers a higher number of requests processing successfully, the service provider's profit gain also increases, as in Figure 5c.  
Our in-depth look into the simulation trace file reveals that the considered simulation matrices give better performance value for our proposed system compared to the other two reviewed schemes. However, as the BBU pool has limited computational resources, the performance of the QEPRA as well as FFS and FFP decline with the increasing number of requests, which opens a new window for further developments.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 5 Test Signal Verification
### Detection Accuracy Analysis  
Table 5 presents the harmonic detection results of the proposed method along with EMD, CEEMD, and VMD algorithms. The RIME-VMD-IWT method maintains a high detection accuracy for the measured harmonic signals. Under a noise background of 26 dB, the detection error of EMD and CEEMD is significant due to mode aliasing. The proposed RIME-VMD-IWT method can not only address the mode aliasing phenomenon but also filter out a substantial amount of noise signals, enabling accurate detection of simulated harmonic signals and demonstrating good noise robustness. The average detection errors for amplitude and frequency are 6.1 \(\times\) 10\({}^{-3}\) and 2.5 \(\times\) 10\({}^{-3}\), respectively. Particularly in terms of frequency detection, the results of this method are closer to the original data. Figure 10 compares the detection accuracy between other detection methods and the method proposed in this paper. The results indicate that, in comparison to EMD, CEEMD, and VMD algorithms, the proposed RIME-VMD-IWT algorithm exhibits less susceptibility, greater adaptability, and improved accuracy in detecting harmonic parameters under actual noise interference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Denoising Method** & **SNR/dB** & **RMSE** \\ \hline WT (Hard) & 36.14 & 0.0971 \\ WT (Soft) & 37.02 & 0.0962 \\ EMD-WT (Soft) & 41.47 & 0.0918 \\ VMD-WT (Soft) & 43.71 & 0.0875 \\ RIME-VMD-IWT & 49.39 & 0.0794 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Noise reduction indexes of simulated signals.  
Figure 10: Error detection by measured signal; (**a**) amplitude detection error; (**b**) frequency detection error.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Frequency Tue** \\ **Value/Hz** \\ \end{tabular} & \begin{tabular}{c} **Frequency** \\ **Detection Value/Hz** \\ \end{tabular} & \begin{tabular}{c} **Frequency Error** \\ **Value/A** \\ \end{tabular} & \
\begin{tabular}{c} **Amplitude Tue** \\ **Detection Value/A** \\ \end{tabular} &
\begin{tabular}{c} **Amplitude Error** \\ **Detection Value/A** \\ \end{tabular} \\ \hline RIME-VMD-IWT & 50 & 50.1208 & \(2.4\times 10^{-3}\) & 100 & 99.9542 & \(4.6\times 10^{-4}\) \\ RIME-VMD-IWT & 125 & 125.4715 & \(3.8\times 10^{-3}\) & 74.813 & 75.2469 & \(5.8\times 10^{-3}\) \\  & 25 & 25.0342 & \(1.4\times 10^{-3}\) & 64.933 & 65.7142 & \(1.2\times 10^{-2}\) \\ \hline EMD & 50 & 45.7328 & \(8.6\times 10^{-2}\) & 100 & 89.7300 & \(1.0\times 10^{-1}\) \\  & 125 & 114.8753 & \(8.1\times 10^{-2}\) & 74.813 & 66.2843 & \(1.1\times 10^{-1}\) \\  & 25 & 26.9254 & \(7.7\times 10^{-2}\) & 64.933 & 71.8808 & \(1.1\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of measured signal detection.