esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Active Anomaly Detection with Switching Latency
## Appendix A Proof for Theorems
### The Expected Cumulative Cost of DMSC Policy
\begin{split}&\sum_{t=n}^{\infty}E\left(\varepsilon^{s(S_{m^{2}(t)}(t )-S_{m^{1}(t)}(t)-\tilde{l}_{m^{1}(t)}(t+1)-\epsilon)}\right)\cdot e^{-s(D(g|| f)-\epsilon)}\\ \leq&\sum_{t=n}^{\infty}e^{-\gamma^{\prime}t}\leq De ^{-\gamma n},\end{split}  \tag{12}\]  
which completes the proof. It should be noted that the switchings across processes occur only before \(T_{2}^{k}\) (where \(T_{2}^{k}\) is defined on the assumption that \(s=0\)), therefore the switching times at each stage follow that \(N_{s}^{k}\leq T_{2}^{k}=T_{1}^{k}+n_{2}^{k}\). Then, based on (10) and Lemma 1, the number of switchings of Case I in DMSC is upper bounded by applying ((26), Lemma 4 and Lemma 5) (where the upper bound of the switching times is established) to our model. As a conclusion, there exists a constant \(T_{s}\in(0,\infty)\) such that  
\[E(N_{s}^{k}|\text{Case I})\leq T_{s}. \tag{13}\]  
Finally, \(T_{3}\) represents the earliest time when the sum-LLRs of the process \(m^{1}(n)\) exceeds the threshold \(B\) and \(n_{3}^{k}\triangleq T_{3}^{k}-T_{2}^{k}\). Since \(E(N_{m}|H_{1})\to B/D(g||f)\) as error constraint \(\alpha\to 0\)(1), we have  
\[E(n_{3}^{k}|\text{Case I})\leq\frac{B}{D(g||f)}\text{ as }\alpha\to 0, \tag{14}\]  
where \(B\approx\log\frac{1-\alpha}{\alpha}\approx-\log\alpha\) as \(\alpha\to 0\). Note that the observation time of stage \(k\)\(N_{d}^{k}=T_{1}^{k}+n_{2}^{k}+n_{3}^{k}\). Thus, combining (10), Lemma 1 and (14):  
\[E(N_{d}^{k}|\text{Case I})\leq(1+o(1))\frac{B}{D(g||f)}\text{ as }\alpha\to 0. \tag{15}\]Thus, combining (A7), (A8), (A13) and (A15), the number of observations and switchings of DMSC policy at each stage can be bounded  
\[E(N_{d}^{k})\leq \begin{cases}(1+o(1))B/D(g||f),&\text{if Case I,}\\ (1+o(1))B/D(f||g),&\text{if Case II,}\end{cases} \] (A16)  
where \(B\approx\log\frac{1-\alpha}{\alpha}\approx-\log\alpha\) as \(\alpha\to 0\). Besides, there exists \(T_{s}\in(0,\infty)\) such that  
\[E(N_{s}^{k})\leq T_{s}.\] (A17)  
As a result, according to (4), the expected cumulative cost of DMSC policy can be upper bounded by  
\[ \begin{split}& E(C|\text{DMSC})\\ \leq&\begin{cases}c\sum_{k=1}^{L}\sum_{i=1}^{k}\left(\frac{B}{D(g||f) }+sT_{s}\right),&\text{if Case I}\\ c\sum_{k=1}^{L}\sum_{i=1}^{M-L}\left(\frac{B}{D(f||g)}+T_{s}\right),&\text{ if Case II}\\ =&\begin{cases}c\frac{L(L+1)}{2}\left(\frac{B}{D(g||f)}+sT_{s}\right),&\text{ if Case I}\\ cL(M-L)\left(\frac{B}{D(f||g)}+sT_{s}\right),&\text{if Case II}\end{cases}. \end{split} \] (A18)
## 3 Experimental Results and Discussions
### Experimental Setup
#### 3.1.1 Dataset and Feature Presentation  
The datasets (i.e., text corpora) used for the experiment included the Twitter US Airline Sentiment dataset [21] for sentimental analysis, the Conference Paper dataset [22] for topic classification, and the SMS Spam dataset [23] for spam classification, as shown in Table 3.
## 3 Results and Discussion
### Specific Mass, Hardness (Shore A), and Abrasion Resistance Analysis  
The results of the specific mass tests, hardness tests using the Shore A scale, and abrasion loss tests of the VNR/MWCNT nanocomposites with different concentrations of MWCNTs are shown in Figure 4. According to our findings, the addition of MWCNTs resulted in a linear increase in specific mass, although the difference between the pure VNR sample and the VNR/MWCNT nanocomposite with the highest filler concentration (5 phr) is only 0.03 g cm\({}^{-3}\). In addition, the hardness of the VNR/MWCNT nanocomposites increased with an increase in the number of MWCNTs added, as the dispersion of MWCNTs in the VNR matrix makes the composites more rigid. At a microscopic level, the interaction between the MWCNTs and the VNR matrix restricts the mobility of the polymer chains [63].  
Figure 2: The degree of filler dispersion in the polymeric matrices of VNR/MWCNT nanocomposites.  
Figure 4: Curves of specific mass, hardness, and abrasion loss as a function of MWCNT concentration for MWCNT/VNR nanocomposite specimens.  
Figure 3: SEM micrographs of the cryof fractured cross-sectional surface of the (**a**) NR/MWCNT1, (**b**) NR/MWCNT3, and (**c**) NR/MWCNT5 samples.  
Figure 4 illustrates the values associated with abrasion loss for VNR/MWCNT nano composites with different MWCNT fillers. During this test, the material was evaluated for its resistance to friction-induced wear. Although the VNR/MWCNT nanocomposites demonstrated good abrasion resistance, the addition of MWCNTs reduced the resistance in the nanocomposite specimens with 4 and 5 phr of MWCNTs. A possible explanation for the observed behavior can be the agglomeration of MWCNTs within the VNR/MWCNT4 and VNR/MWCNT5 resulting in tension points that facilitate the wear of the material under friction conditions.
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 3 Results and Discussion
### MC Measurement of Coals from Different Regions  
In the above-presented experiments, the sample data were mixed and then randomly divided into the test and training sets. Thus, the two datasets contained coal samples from the same region, so it was impossible to determine whether the coal origin had an impact on the model detection effect. Therefore, additional experiments based on the coal origin were carried out to test the model's predictive effect on the MC of coal samples from unknown areas further.  
These experiments also used the NCV method, but the difference was that the samples in the test dataset were from different regions compared to the training dataset. The prediction effects of the models were also evaluated by the average results of the NCV method. The test results of different models are given in Table 5, where it could be seen that the results of the same model varied greatly when the MC of coals from different regions was predicted. The maximum difference obtained by RF model was less than that of DT model, which indicated that the performance effect of integrated learning was better than that of single learning. The difference in \(R^{2}\) value was the largest for the kNN model, and it was 0.1094. However, the smallest difference in \(R^{2}\) value was achieved by the DW-kNN model, and it was 0.0533, which is significantly smaller than that of the kNN model. This shows that adding weight factors plays an important role in processing nonlinear locally correlated data.  
Figure 6: The repeated experimental results of different models, including random forest (RF), support vector regression (SVR), partial least squares (PLS), AdaBoost, XGBoost, decision tree (DT), \(k\)-nearest neighbor (kNN) and distance-weighted kNN (DW-kNN): (**a**) The results responding to the coefficient of determination (\(R^{2}\)); (**b**) The results responding to the mean absolute error (MAE); (**c**) The results responding to the root mean square error (RMSE).  
In addition, regarding the average result, the prediction effect of the DW-kNN model was the best among all the models; its \(R^{2}\), MAE, and RMSE were 0.9088, 0.2417, and 0.2993, respectively. Moreover, for coals from different regions, the \(R^{2}\) values obtained using the DW-kNN model were all above 0.875. Consequently, the coal origin can affect the MC detection performance, but the DW-kNN model can accurately extract features related to the coal MC and can reduce this influence.
## 1 Introduction
We focus on Hurst exponent estimation and Shannon entropy at different levels of disease transmission intensity, applied to malaria time series derived from simulations with a previously introduced agent-based model [3] with different (parameterized) levels of gametocytemia, as well as to different empirical malaria time series. Our objective is to properly identify malaria transmission patterns, as well as to link long-range dependence processes in malaria incidence time series to the occurrence of phase transitions in close proximity to disease elimination. We also test the importance of Hurst exponent estimation and Shannon entropy as sound predictors of the presence of long-range dependence and long memory processes in malaria transmission. In particular, we show that from simple models connecting gametocytemia levels and measures easily extractable from empirical series of malaria incidence, such as incidence levels, a Hurst exponent and entropy, we are able to predict an indicator of \"effective\" gametocytemia for regions where malaria incidence is regularly monitored. Our study uses eight different empirical series, shown in Figure 1.  
We start in Section 2 by describing the different empirical data sets analyzed in this study and the agent-model used to produce simulated scenarios of different transmissibility levels, parameterized by the gametocytemia level. Moreover, we briefly describe the basic tools--the Hurst exponent and Shannon entropy-- explaining how they are computed from series of malaria incidence. In Sections 3 and 4 we describe, qualitatively, the behavior of Hurst exponent and entropy, and the form of the autocorrelation function decay, in eight different malaria empirical time series examples. In Section 5 we derive models to fit the values of the three observables: the malaria incidence, the Hurst exponent and the entropy for different simulated scenarios, as a function of the gametocytemia level. Using this fitted expressions we then measure the average malaria incidence, Hurst exponent and entropy of the empirical data sets. Introducing these values in the fitted expressions we retrieve an estimate of the associated \"effective\" gametocytemia level, i.e., the effective transmissibility level in each empirical case. Finally, in Section 6 we discuss the limitations of our approach, as well as possible extensions to it.
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 3 Localization Solution  
The proposed solution for the visual inspection of the viaduct requires the creation of a previous 3D map using a total station. This map will be a point cloud that identifies the reference coordinate origin for the entire inspection system. To create this map, operators should ensure that the _ENU_ coordinate system is followed. This map can be reused in future inspections of the viaduct.  
The UAV system has its own localization and navigation algorithm that provides the transform \(\{T_{LD}\}\) whose origin is the take-off point \(\{L\}\). Since this location may vary, the complete system requires a second localization system that establishes the 3D transformation, \(\{T_{GL}\}\), between the initial UAV pose and the global reference system, \(\{G\}\), expressed in _ENU_ coordinates at the origin of the map created by the total station. These transforms can be visualized in Figure 6.  
Figure 6: Full transformation system.  
Therefore, the AERO-CAM platform has two parallel localization processes to perform the automatic inspection of the viaducts. The following subsections explain the details of both processes.
# An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization
## 3 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**FUNCTION** & **GEN** & **PSO** & **IPSO** & **DE** & **GWO** & **WOA** & **MEWOA** & **PROPOSED** & **KMEANS** \\ \hline ACKLEY & 6749 & 6885 & 3418 & 16,183 & 20,182 & 24,766 & 13,879 & 5818 & 3490 \\ \hline BF1 & 4007 & 4113 & 1814 & 8268 & 9120 & 9924 & 7987 & 5585 & 3023 \\ \hline BF2 & 3793 & 3747 & 1759 & 7913 & 8731 & 9597 & 6262 & 5008 & 2693 \\ \hline BF3 & 3479 & 3305 & 1689 & 6327 & 7791 & 20,117 & 5893 & 4282 & 2442 \\ \hline BRANIN & 2376 & 2522 & 1730 & 4101 & 6055 & 5939 & 3367 & 3026 & 2087 \\ \hline CAMEL & 2869 & 2908 & 1754 & 5609 & 6688 & 5917 & 4756 & 3327 & 2607 \\ \hline DIFFOWER2 & 5443 & 8657 & 2462 & 14,669 & 22,059 & 11,436 & 11,536 & 8864 & 5617 \\ \hline DIFFOWER5 & 18,552 & 24,894 & 4446 & 39,018 & 49,769 & 42,095 & 31,372 & 26,065 & 17,221 \\ \hline DIFFOWER10 & 18,801 & 32,534 & 4690 & 46,914 & 54,442 & 56,381 & 38,559 & 29,5  
At the end of every global optimization procedure, a BFGS variant of Powell [45] was utilized to improve the discovered solution and identify with certainty a local minimum. Additionally, Figure 2 presents a statistical comparison of the used optimization methods and the data provided in Table 3.  
Table 3 contains values representing objective function evaluations, with lower values indicating better performances. The goal is to minimize the number of function calls required to achieve a result. The algorithms analyzed are GENETIC, PSO, IPSO, DE, GWO, WOA, MEWOA, PROPOSED, and PROPOSED (KMEANS). The GENETIC algorithm, with a total of 261,106 evaluations, shows mixed results. For some functions, such as SHEKEL10 with its 2488 evaluations, it performs well. However, for more demanding functions, like DIFFPOWER5 and DIFFPOWER10 with 18,552 and 18,801 evaluations, respectively, it requires many calls, indicating reduced efficiency. The PSO algorithm, with 317,168 total evaluations, needs more calls than GENETIC and is generally considered less efficient. For difficult functions, such as DIFFPOWER10, it requires many evaluations, indicating reduced effectiveness. The IPSO algorithm is the most efficient, with only 145,908 evaluations required. Its low values in several functions, such as ROSENBROCK16, with 2432 calls, and TEST2N5, with 1982 calls, indicate that it requires fewer
# Changes in Selected Quality Indices in Microbially Fermented Commercial Almond and Oat Drinks
## 3 Results and Discussion
### Fatty Acid Composition Changes following the Fermentation of PB Beverages
In all fermented almond beverages, the total content of major fatty acids decreased from 85.5 g/100 g in the control sample to the lowest content of 72.5 g/100 g in a sample fermented by _Y. lipolytica_ (the highest decrease was noted for oleic acid). For the oat beverages, the changes were more differentiated, since in two samples (fermented by _L. plantarum_ PK 1.1. and _K. marxianus_), there was an increase in the major fatty acids (to ca 82 g/100 g) in relation to the control sample (ca 73 g/100 g). The total content of minor fatty acids (with contents below 1 g/100 g of lipids) varied from ca 1600 mg/100 g of lipids in an almond beverage fermented by _C. antarctica_ and oat beverage fermented by _T. casei_ to ca 3600 mg/100 g of lipids in an oat beverage fermented by _K. marxianus_. The majority of fermented samples had more of these minor fatty acids than both control samples. Among the minor fatty acids, branched, odd-chain, and cyclic fatty acids represented from 25.1% (oat control beverage) to 73.9% (almond beverage fermented by _L. bulgarcius_). For the fermented oat beverages, all variants were characterized by enhanced shares of these unique fatty acids (increase in the range of 5-1547 mg/100 g of lipids). In contrast, for the fermented almond beverages, the contents of these compounds increased in the samples fermented by LAB strains and by _C. lipolytica_. For other strains, a decrease was noted in comparison to the content determined in the control sample. In general, LABs utilize various sugars as carbon sources while yeasts can utilize both sugars and lipids. In the case of bacteria, available sugars are converted, for example, into fatty acids that are desirable by the host, which can be utilized to reduce stress under the impact of changes in the culture conditions during fermentation (temperature, pH, pressure, oxygen, nutrients, and ethanol). These specific fatty acids, such as cyclic fatty acids, modulate the bacteria membrane composition to maintain the lipid bilayer in the liquid crystalline lamellar phase and, more precisely, maintain an optimal level of fluidity within the lipid bilayer [22]. For yeasts, variation in the fatty acid contents in the fermented biomass depends on the initial lipid content in the biomass used. This was confirmed, for example, for _Y. lipolytica_ grown on a poor vs. abundant-in-fat substrate [41]. On the medium abundant-in-fat (cotton seeds with 19.6% of lipids) substrate, there was a decrease in the total fat while on poor fat medium (cassava peels with 1.2% of lipids), there was a significant increase in the final fat content. In the current study, only two yeast strains (_C. antarctica_ and _C. lipolytica_) were able to produce branched, odd-chain, or cyclic fatty acids.
## 3 Experimental Procedure
### Experimental Result
#### 3.5.3 Algorithm Comparison  
Memory and time consumption tests and robustness tests were conducted for the grading method using failure rates within a dynamically effective space and the grading method relying on traditional electrical testing. The memory and time usage of the two algorithms are shown in Table 4, and the robustness test results are shown in Figure 5.  
In actual production environments, the chip testing process may be affected by various random disturbances, such as instrument measurement errors, environmental interference, and noise during signal transmission. To validate the robustness of the proposed method under different noise levels, Gaussian noise was artificially introduced to simulate potential random disturbances. The level of noise is represented by the standard deviation \(\sigma\), ranging  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method \textbackslash{} Test Content** & **Time** & **Memory** \\ \hline Proposed Method & 0.69 s & 62.88 MiB \\ Traditional Method [15] & 1.52 s & 63.90 MiB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time and memory consumption.  
Figure 4: Performance metrics of different classes.  
Figure 5: Comparison of robustness between two methods.  
from 0 to 8, simulating different noise environments. By adding random noise to the measured values of chip testing items, the instability of testing equipment and environmental interference can be simulated.  
Figure 5 shows the grading results of the proposed method and traditional method at different noise levels. The proposed method (blue curve) shows almost no change with increasing noise levels, while the traditional method (orange curve) exhibits sensitivity to noise, indicating its susceptibility to random interference.  
In terms of time and memory consumption, the average processing time of the proposed method is 0.69 s, while the traditional method is 1.52 s, which reduces time consumption by 54.6%. The proposed method uses 62.88 MiB of memory, which is 1.6% less than traditional method. Moreover, the proposed method has good robustness, which can provide more reliable results under complex production conditions.  
In conclusion, the proposed algorithm can optimize memory usage, reduce computation and resource consumption while ensuring grading accuracy, and its good stability is suitable for production environments that require efficient processing of large-scale chip data.
## 3 Computer-Aided Analytical Modeling of Class-E Series-Tuned RFPAs
### Reactance Compensation Technique for Wide Bandwidth  
The series resonator, consisting of \(L_{\text{s}}\) and \(C_{\text{s}}\), can be replaced by \(L_{0}\) and \(C_{0}\), as defined by Equations (38) and (39):  
\[L_{0}=1.026\frac{R}{\omega_{0}} \tag{38}\]  
\[C_{0}=\frac{1}{\omega_{0}^{2}L_{0}} \tag{39}\]  
This technique, commonly used to broaden the bandwidth in Class-E amplifiers, is known as the reactance compensation technique in standard literature [22]. In this paper, it will be referred to as Topology 2.2.1 without any loss of generality. The quality factor \(Q_{\text{s}}\) can then be expressed by Equation (40).  
\[Q_{2.2.1}=1.026R \tag{40}\]  
By substituting the value of \(R\) from Equation (21) into Equation (40), \(Q_{2.2.1}\) can be expanded into Equation (41),  
\[Q_{2.2.1}=0.65151\frac{(V_{DD})^{2}}{P_{out}} \tag{41}\]  
With normalized values of \(V_{DD}=1\) and \(P_{out}=1\), \(Q_{2.2.1}=0.65151\) is obtained for this case. Since this topology is a subset of Topology 2.2, a comparison with the limits provided in Table 2 reveals that \(Q_{2.2.1}\) does not fall within these bounds, indicating a potential compromise in the amplifier's performance.  
The analysis reveals that Topology 2.2, when combined with the reactance compensation technique, achieves improvements in efficiency-bandwidth response but with certain trade-offs in performance metrics. This issue arises because the quality factor (\(Q_{2.2.1}\)) is lower than \(Q_{\text{min}}\). This discrepancy leads to a significant reduction in theoretical efficiency, further constraining the amplifier's performance.  
Despite these limitations, Topology 2.2.1 remains the most effective configuration for balancing efficiency and bandwidth. However, to address these limitations while adhering to the Class-E design equation, the addition of harmonic control circuitry is necessary, which will be discussed in the preceding section.
## 4 Conclusions  
Check dams are commonly used in grassed stormwater channels for enhancement of channel infiltration capacity by temporarily blocking water flow and providing backwater ponding. The present study considered the application of a mass-balance method on a grassed channel model to assess the effects of varying check dam height on channel infiltration capacity. The results revealed that an increase in check dam height in channel downstream increased infiltration due to an increase in water depth and decrease in flow velocity. The observed data were used for the development of non-linear regression models to derive the relationship of inflow rate with the average value of time to downstream and the time taken to reach the peak for different check dam heights. The relationship between water level at peak flow and inflow rate was modeled for different check dam heights. A non-linear regression analysis was also conducted to derive an empirical infiltration model through the adaption of a modified Kostiakov model for accurate estimation of infiltration rate in grassed channels. The developed models were employed for the estimation of infiltration rate in a grassed channel for five different check dam system design cases. The infiltration rate in grassed channels was found to vary significantly with check dam height, whereas the effect of check dam spacing on infiltration rate was found negligible. The result showed an increase in channel infiltration from 12% to 20% by raising the check dam height from 10 to 20 cm. The results indicated that instead of increasing urban stormwater channel length, infiltration in a channel can be increased by raising the height of check dams. This can reduce the requirement of land for construction of an urban stormwater channel and the construction and maintenance-related costs. The results of this study can be used for designing of check dam system in grassed stormwater channels for better reduction of runoff.  
Conceptualization, A.M.S.A.-J., B.Y., H.A.A., S.S., and Z.M.Y.; data curation, A.M.S.A.-J. and S.S.S.; formal analysis, A.M.S.A.-J., A.H.G., B.Y., S.S.S., N.A.-A., S.S., and Z.M.Y.; investigation, A.M.S.A.-J., B.Y., S.S.S., H.A.A., N.A.-A., and S.S.; methodology, A.M.S.A.-J. and S.S.S.; project administration, Z.M.Y.; software, A.M.S.A.-J. and A.H.G.; supervision, A.H.G., N.A.-A., and S.S.; validation, A.H.G., B.Y., H.A.A., N.A.-A., S.S., and Z.M.Y.; visualization, A.H.G., S.S.S., H.A.A., N.A.-A., S.S., and Z.M.Y.; writing--original draft, A.M.S.A.-J., B.Y., S.S.S., H.A.A., N.A.-A., S.S., and Z.M.Y.; writing--review and editing, A.H.G., H.A.A., N.A.-A., S.S., and Z.M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science, Technology and Innovation (MOSTI), Malaysia (grant number UPM0008770).  
The authors declare no conflict of interest.