esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Local Characteristics on PAH Concentrations  
The above results show that local sources might also impact the PAH concentrations differently depending on the location of the sampling site. Kanazawa does not have major  
Figure 3: The backward trajectory and cluster analysis of four patterns: **(a)** AD days with high PAH concentration (\(\geq\)mean PAH concentration in AD days: 0.85 ng m\({}^{-3}\) in Fukuoka and 0.43 ng m\({}^{-3}\) in Kanazawa), **(b)** AD days low PAH concentration (\(<\)mean PAH concentration in AD days), **(c)** non-AD with high PAH concentration (\(\geq\)mean PAH concentration in non-AD days: 0.76 ng m\({}^{-3}\) in Fukuoka and 0.28 ng m\({}^{-3}\) in Kanazawa), and **(d)** non-AD days with low PAH concentrations (\(<\)mean PAH concentration in non-AD days).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Fukuoka**} & \multicolumn{4}{c}{**Kanazawa**} & \multirow{2}{*}{
\begin{tabular}{c} \multirow{2}{*}{\(p\)-Value b} \\ **Pulutants** \\ \end{tabular} } \\ \cline{2-2} \cline{4-2} \  
industrial areas and is substantially influenced by automobile emissions [21]. On the other hand, Fukuoka might be affected by the nearby heavy industrial area, Kita-Kyushu [26]. As an example, from 7-9 March in 2021, the PAH concentrations decreased in Kanazawa, while they increased in Fukuoka due to the different local air mass (Figure 2).  
Besides the geology, different topographic sampling sites might affect the air pollution level. The Kanazawa sampling site was on a hill in the residential area, while the Fukuoka sampling site was in the valley bound to downtown. Valley topography can cause temperature inversion, which traps air pollutants emitted from downtown [27; 28]. These considerations can explain the difference in daily and seasonal variations of TSP and PAHs between Fukuoka and Kanazawa.
## 3 Data Processing and Kinematic Analysis
### Calculation of the Excavator's Working Area
The predicted angle of each part was calculated using Equation (24), which follows the Gaussian distribution, having a zero mean and a standard deviation of the noise in angular velocity. The maximum possible value of noise is 3\(\sigma\). \(\Delta t\) is the discrete-time increment. \(i\) is the prediction step. By using Equations (24) and (25), the \(x\) (horizontal position) of the point \(p\) (\(P_{1}\)\(\sim\)\(P_{3}\)-2) is calculated as shown in Equation (26). The predicted horizontal displacements, \(x_{pre,p,i}\) can be generated using Equation (27). The maximum radius for the predicted working area is chosen from the maximum value among the predicted displacements. Figure 6 illustrates the horizontal position of \(p\) (left subfigure) and the predicted and maximum working areas (right subfigure). One notes that the maximum working area is defined as the maximum working limit, and therefore it does not change since it can be measured by extending the three links fully. The actual working area is determined by calculating the maximum values among all \(x\) components.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Classifiers**} & \multicolumn{4}{c}{**Datasets**} \\ \cline{3-6}  & & & **Adience** & **LFW** & **Average over All Datasets** \\ \hline \multirow{3}{*}{Hand-Crafted} & HOG & SVM & 66.5\% & 66.4\% & 66.45\% \\ \cline{2-6}  & LBP & SVM & 65.0\% & 67.1\% & 66.05\% \\ \cline{2-6}  & PCA & SVM & 65.7\% & 64.5\% & 65.10\% \\ \hline \multirow{3}{*}{Deep-Learned} & CNN features & SVM & 82.3\% & 62.6\% & 72.45\% \\ \cline{2-6}  & CNN features & CNN & 88.7\% & 81.4\% & 85.05\% \\ \hline \multirow{3}{*}{Fusion} & \multirow{3}{*}{HOG-DL} & SVM & 85\% & 90.7\% & 87.85\% \\ \cline{2-6}  & CNN & 81.7\% & 69.6\% & 75.65\% \\ \cline{2-6}  & & SVM & 84.8\% & 91.3\% & 88.05\% \\ \cline{1-1} \cline{2-6}  & LBP-DL & CNN & 76.2\% & 89.5\% & 82.85\% \\ \cline{1-1} \cline{2-6}  & PCA-DL & SVM & 85.7\% & 91.1\% & 88.40\% \\ \cline{1-1} \cline{2-6}  & CNN & 65.5\% & 62.2\% & 63.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation with respect to f-score on the Adience and LFW datasets. significant whereas the result with LFW (\(p\) = 0.093) is insignificant at \(p\) < 0.05. These results suggest that CNN with deep-learned features is superior to SVM using any type of feature. These results further support the observations from earlier studies [33]. Similar trends can be observed in Tables 3 and 4, where the performances are presented with respect to the F-score and AUC, respectively. In both the tables, SVM exhibits the worst average performance with hand-crafted features. The model's average performance improves when deep-learned features are used, whereas further improvement is achieved with fused features. For CNNs, the latter features yield the worst performance as compared with the performance with deep-learned features. In addition, similar to the observations in Table 2, the CNN model performs significantly better at \(p\) < 0.05 than the best-performing SVM with fused features with \(p\) = 0.002 on the Adience dataset; however, the difference in performances between the SVM with the fused features and CNN with deep-learned features on the LFW dataset is insignificant (\(p\) = 0.123). Similar observations apply on the AUC with \(p\) = 0.00003 on the Adience dataset and \(p\) = 0.098 on the LFW dataset.
## 3 Results
A plot of predicted values vs. residuals for the best-performing LightGBM model (23_LightGBM_GF_SF_HC1) can be seen in Figure 4, and no obvious pattern in the error is present. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **Configuration** & **Type** & **RMSE** \\ \hline _Default_ & 1\_Default\_LightGBM & LightGBM & 20.88 * \\  & 2\_Default\_Xgboost & Xgboost & 21.05 \\  & 3\_Default\_CatBoost & CatBoost & 21.16 \\  & 4\_Default\_NeuralNetwork & Neural Network & 45.64 \\  & 5\_Default\_RandomForest & Random Forest & 21.56 \\ \hline _Not\_So\_Random_ & & & \\  & 6\_Xgboost\_NSR & Xgboost & 21.04 \\  & 7\_Xgboost\_NSR & Xgboost & 21.23 \\  & 10\_LightGBM\_NSR & LightGBM & 21.29 \\  & 11\_LightGBM\_NSR & LightGBM & 20.96 \\  & 14\_CatBoost\_NSR & CatBoost & 21.19 \\  & 15\_CatBoost\_NSR & CatBoost & 21.37 \\  & 18\_RandomForest\_NSR & Random Forest & 21.34 \\  & 19\_RandomForest\_NSR & Random Forest & 20.86 * \\  & 22\_NeuralNetwork\_NSR & Neural Network & 65.83 \\ \hline _Golden\_Features_ & & & \\  & 1\_Default\_LightGBM\_GF & LightGBM & 19.37 * \\  & 11\_LightGBM\_NSR\_GF & LightGBM & 19.59 \\  & 19\_RandomForest\_NSR\_GF & Random Forest & 20.86 \\ \hline _Insert\_Random\_Feature_ & & & \\  & 1\_Default\_LightGBM\_GF\_RF & LightGBM & 19.27 \\ \hline _Selected\_Features_ & & & \\  & 1\_Default\_LightGBM\_GF\_SF & LightGBM & 15.86 * \\  & 4\_Default\_NeuralNetwork\_SF & Neural Network & 22.56 \\  & 6\_Xgboost\_NSR\_SF & Xgboost & 21.04 \\  & 19\_RandomForest\_NSR\_GF\_SF & Random Forest & 20.09 \\ \hline _Hill\_Climbing\_1_ & & & \\  & 23\_LightGBM\_GF\_SF\_HC1 & LightGBM & 15.65 * \\  & 24\_LightGBM\_GF\_HC1 & LightGBM & 19.15 \\  & 25\_RandomForest\_GF\_SF\_HC1 & Random Forest & 20.09 \\  & 26\_RandomForest\_GF\_HC1 & Random Forest & 20.86 \\  & 27\_Xgboost\_SF\_HC1 & Xgboost & 21.04 \\  & 28\_Xgboost\_SF\_HC1 & Xgboost & 21.04 \\  & 29\_Xgboost\_HC1 & Xgboost & 21.05 \\  & 30\_Xgboost\_HC1 & Xgboost & 21.05 \\  & 31\_CatBoost\_HC1 & CatBoost & 21.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performance of machine learning models.
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
###### Abstract  
An adaptive neuro-fuzzy inference system (ANFIS)-based model was developed to predict the punching shear strength of flat concrete slabs without shear reinforcement. The model was developed using a database collected from 207 experiments available in the existing literature. Five key input parameters were used to build the model, which were slab effective depth, concrete strength, reinforcement ratio, yield tensile strength of reinforcement, and width of square loaded area. The output parameter of the model was punching shear strength. The results from the adaptive neural fuzzy inference model were compared to those from the simplified punching shear equations of ACI, BS-8110, Model Code 2010, Euro-Code 2, and also experimental results. The root mean square error (RMSE) and the correlation coefficient (\(R\)) were used as evaluation criteria. Parametric studies were presented using ANFIS to assess the effect of each input parameter on the punching shear strength and to compare ANFIS results to those from the equations proposed in commonly used codes. The results showed that the ANFIS model is simple and provided the most accurate predictions of the punching shear strength of two-way flat concrete slabs without shear reinforcement.  
concrete; punching shear; two-way flat slabs; ANFIS Article  
An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 4 Proposed Clustering Algorithm  
### Expected Number of Clusters  
Before the selection of the CH, it is necessary to define the expected number of clusters in the network. Using the following computation and connectivity model, we analytically estimate the expected number of clusters (\(k_{opt}\)). Let us assume there are N nodes which are distributed uniformly in area A. The nodes are stationary, and therefore, the density is constant. The transmission area of a sensor node can be assumed to be \(A_{S}=\pi r_{tx}{}^{2}\), where \(r_{tx}\) is the transmission range of the sensor node (from Equation (3)). As mentioned before, transmission range depends on node connectivity and density, and using these two assumptions, the expected number of clusters, \(E_{n}\), is computed as \(E_{n}=\frac{A}{A_{S}}\). An illustrative example is given to clarify the above assumptions: nodes n = 100, and area A = 100 m \(\times\) 100 m. Because the nodes are stationary, the node density \(\rho=\frac{n}{A}=0.01\) m\({}^{-2}\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline \hline \(E_{elec}\) & Energy required to run the transmitter or receiver \\ \hline \(\in_{fs}\) & Amplifier’s power loss for \\  & a short distance, called free space \\ \hline \(\in_{mp}\) & Amplifier’s power loss for \\  & a long distance, called multipath fading \\ \hline \(d_{non-CH~{}to~{}CH}~{}(d^{2})\) & Distance from a non-CH to a CH \\ \hline \(d_{CH~{}to~{}BS}~{}(d^{4})\) & Distance from a CH to the BS \\ \hline \(d_{0}=\sqrt{\dfrac{\in_{fs}}{\in_{mp}}}\) & Threshold transmission distance \\ \hline \(E_{DA}*\) & Data aggregation \\ \hline \hline \end{tabular}
\end{table}
Table 1: The energy model’s parameters and their descriptions.  
From Example 1, we already found that our expected transmission range is 17.12 m. The transmission area of one sensor node will be \(A_{S}=\pi{r_{tx}}^{2}=[3.1416\times(17.12)^{2}]=921\) m\({}^{2}\). Now, we must calculate the expected number of clusters by dividing the entire sensing area through the obtained transmission area:  
\[\text{Expected number of clusters }E_{n}=\frac{100\times 100}{921}\approx 10.\]
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 4 Experimental Results  
According to the proposed method, the tilting angle_\(\theta\), the yaw angle_\(\varphi\), and the spinning angle_\(\omega\) can be calculated to determine a rotor position of the PMSMs. In order to verify the RPM method for PMSMs, an experimental platform, as shown in Figure 13a, was constructed, which consisted of a PMSM and its control circuit, a LSGM and its driver circuit, a high-speed camera (Revealer, Hefei, Anhui, China) and tripod, a power supply (Tradex, Beijing, China), and a computer (Lenovo, Beijing, China). Figure 13b shows the block diagram of the control system. The block diagram of the rotor position measurement system is shown in Figure 13c.  
Figure 12: Images of three optical feature points (OFPs) for calculation of spinning angle_\(\omega\).  
Figure 13: _Cont_.  
Table 2 shows the parameters of the spherical motor driver circuit.  
In the following experiment, we set the resolution, the sampling time, and the exposure time of the high-speed camera as 1100 \(\times\) 1100, 20 ms and 5000 \(\upmu\)s, respectively. The distance between the LSGM's tip and high-speed camera lens \(l\) was 420 mm, we can measure through Equation (11) that the maximum value of \(\theta\) is \(42^{\circ}\), (the maximum tilting angle of PMSM is \(37.5^{\circ}\)). Consequently, the intrinsic parameter matrix of the camera generated by the calibration method [30] is  
\[M_{a}=\left(\begin{array}{ccc}2295.71&0&555.51&0\\ 0&2295.71&554.87&0\\ 0&0&1&0\end{array}\right) \tag{16}\]  
From Table 1, \(d_{\alpha}=d_{\beta}=0.007~{}mm\), the focal length of the camera can be computed as:  
\[f=a_{\alpha}\times d_{\alpha}=16.07~{}mm \tag{17}\]  
Measurement results obtained by the MEMS were set as the reference (analytical results), Table 3 shows the parameters of the MEMS. Although MEMS can get a precise position of PMSM in a certain time (about 10 min) after calibration, it is known that the measurement error will gradually increase after a certain time. Every measurement time was 10 s in the following experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline Working voltage & 24 V \\ Maximum input voltage & 55 V \\ Total driver unit number & 24 \\ Maximum output current & \(\pm 5\) A \\ (per every driver unit) & \(\pm 20\) mA \\ Constant-current precision & \(\pm 20\) mA \\ Maximum output frequency & \(200\) Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the spherical motor driver circuit.  
Figure 13: Experimental platform for measuring the position of the PMSM rotor: (**a**) Experimental prototype; (**b**) Block diagram of the control system; (**c**) Block diagram of rotor position measurement system, micro-electro-mechanical system(MEMS): MPU6050.
# Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor
## 2 Description of the Speed Control Algorithm
### Fuzzy Speed Controller  
The most essential element of the structure presented in Figure 2 is the fuzzy speed controller. It provides the possibility of computing the control signal using fuzzy-logic-based data processing and expert knowledge according to the dataflow presented in Figure 3.  
The inference stage is computed using the fuzzy values obtained in the fuzzification part. The \"crisp\" input signal suffers interference from the membership function representing linguistic description, called the rationale. As a result of this process, the input signals are characterized by attributed significance levels. This can be modeled by Equation (4):  
\[x_{i}=f_{i}\cdot a_{i} \tag{4}\]  
where \(a_{i}\) is the crisp value input; \(f_{i}\) is the membership function; and \(x_{i}\) is the fuzzified input. The following step is responsible for the exact inference based on both the fuzzified input signals and the knowledge base. The knowledge base is the container of membership functions and the relationships between particular variables. The calculations are based on expert knowledge, providing the conclusions according to the input rationale. The shape of the expert base may vary depending on the fuzzy model used. For the Mamdami reasoning approach, the base rule is given by a fully linguistic description, such as:  
_If_ **input** _is... and_ **input**_ _is..., then the_ **output** _is...,_  
Figure 3: Data processing in fuzzy model.  
Figure 2: Overall structure of the analyzed control system.  
where the considered variables are not given as crisp values, but rather as fuzzy labels. The next step of the fuzzy controller data processing is defuzzification, the process of translating a fuzzy conclusion into a crisp output value. Different defuzzification methods can be found in the literature [52]. The commonly used singleton method utilizes weights placed at the peak coordinates of the membership functions. In comparison to Mamdami reasoning, the Takagi-Sugeno (T-S) model applies a different structure for the inference block. The fuzzified inputs are compiled with functions, resulting in a crisp output. The T-S fuzzy system is very efficient, and hardware implementation in programable devices seems to be easier. It was applied in tests described later on in this paper. A schematic representing the fuzzy logic controller is presented in Figure 4.  
The output value of the Takagi-Sugeno type 1 fuzzy system can be described as in Equation (5):  
\[y=\frac{\sum_{i=1}^{l}x_{i}\cdot w_{i}}{\sum_{i=1}^{l}x_{i}}=\frac{\sum_{i=1}^{ l}\left(e\cdot\mathbf{f_{1}}\cdot e\cdot\mathbf{f_{2}}\right)\cdot w_{i}}{ \sum_{i=1}^{l}x_{i}} \tag{5}\]  
where \(y\) is the crisp output of the fuzzy system; \(x_{i}\) is the fuzzified input; \(f_{i}\) is the vector of membership functions; \(I\) is the number of input signals; and \(w_{i}\) is the defuzzification singleton value.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 4 Results and Discussion
### Scenario 3: Adaptive Communication Switching Between LoRa and BLE with Cluster-Based Mitigation
Collectively, these findings highlight the effectiveness of the proposed mitigation algorithm in optimizing the hybrid BLE-LoRa network's performance under jamming conditions. The results validate that by integrating intelligent switching, adaptive routing, and cluster-based coordination, the network not only achieves lower energy consumption and reduced retransmissions but also exhibits enhanced resilience against interference. This underscores the necessity of advanced network management strategies to fully harness the benefits of hybrid communication systems in real-world IoT applications where reliability and energy efficiency are critical. Figure 8: Impact of the proposed mitigation algorithm on LoRa and BLE energy consumption under jamming conditions.
## 3 Results and Discussion  
### NPs Characterization  
The morphology of the as-prepared synthesized nanoparticles was observed by field emission gun scanning electron microscopy (FEG-SEM). The nanoparticles were spherical and relatively monodispersed. The diameter distribution ranged from 90 to 150 nm. The composition of the  
Figure 1: Schematic description of the coating method: (**a**) preparation of metal-nanoparticles (NPs) doped PVA/GA coatings; (**b**) NP deposition by spin coating and (**c**) example of the appearance of the coated substrate.  
Figure 2: Summary of the laboratory raceway (**a**) freshwater raceway; (**b**) positioning of the substrates in the raceway and (**c**) optical microscopy of a culture of _Coelastella_ microalgae.  
nanoparticles was obtained by EDX analysis, confirming the absence of impurities. Furthermore, the crystalline nature of the synthesized nanoparticles was confirmed by XRD. All the results are collected in Figure A1 in Appendix A.  
Hydrodynamic diameter of each particle in suspension was investigated by DLS. Table 1 summarizes the results. The polydispersity index (PDI) was used to estimate the average uniformity of the particle suspension. It is accepted that a sample is considered monodisperse when the PDI value is less than 0.1 [48]. As observed in Table 1, the PDI of all the synthesized particles were around 0.1, therefore they can be considered monodisperse. These results agree with the sizing found by the SEM observation.
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 3 Antenna Fabrication and Discussion of Results
### Scattering Parameters  
Figure 9 shows a good agreement between the simulated and measured reflection coefficients of the wideband antenna. The results confirm that the antenna operates from 0.59 GHz to 0.95 GHz numerically and experimentally with a good matching over the wide band. Figure 10 presents the measured reflection coefficients of narrowband antennas at the corresponding voltages of the above-mentioned capacitors values. The bias voltages of 15, 10, 5, 2.5, 1, and 0 V are applied consecutively to the varactor diode for both NB antennas, resulting in the resonant frequency's reconfigurability from 0.6 GHz to 1 GHz. All the reconfigurable cases have good matching. In addition, high isolations between the WB antenna and NB antennas have been validated in Figure 11a,b, with an isolation factor of more than 15 dB in all cases. The high isolation between NB-P3 and NB-P4 has been validated in Figure 11c.  
Figure 8: Photograph of the prototype of the proposed antenna: (**a**) front view and (**b**) bottom view.  
Figure 9: Simulated and measured reflection coefficients of the wideband (sensing) antenna: (**a**) at port-1 and (**b**) at port-2.
###### Abstract  
In this work, the phytochemicalhemical composition and the biological activity of the ethanolic extracts obtained from _Ajuga reptans_ L. (_Lamiacane_) leaves and roots (growing in Lesser Poland Vivotoselugh, Poland) were compared. The phytochemicalhemical composition of the extracts were determined by the high-performance liquid chromatography with diode-array detection (HPLC-DAD) method. The dominant compounds in both extracts were verbacoside, isoverbacoside, 3_A_-dihydroxyphenylacetic acid and rosmarintic acid. The antioxidant capacity of the extracts was evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP) and 2,2'-azobis(3-ethylbenzothidine-6-sulfonate (ABTS) tests. The Folin-Ciocalteu assay was used to determine the total polyphenic content. Additionally, enzyme (tyrosinase, collagenase) inhibition tests and metal chelating ability were studied to assess the anti-aging properties of the extracts. Moreover, the _A. reptans_ extracts' capacity to absorb the whole range of ultraviolet radiation and high-energy visible (HEV) light was evaluated. The skin irritation test (SIT) EpiDerm was applied to evaluate the safety of the bugle extracts. The noteworthy point is that there is a lack of literature on the assessment of _A. reptans_ root extract activity. Our study is the only one that compares the quantitative composition and biological activity of extracts from the root and leaves of _A. reptans_. The obtained results indicated that both of the extracts exhibit high antioxidant, chelating and photoprotective activity, but the extract from _A. reptans_ roots showed a higher enzyme inhibition effect for mature skin. The _A. reptans_ root extract, similarly to the leaf extract, could be applied as potentially multi-functional, safe and sensitive cosmetic raw materials, especially in anti-aging and anti-pollution cosmetics.  
Ajuga reptans_ leaf extracts; _Ajuga reptans_ root extracts; antioxidant potential; chelating capacity; anti-collagenase; anti-tyrosine; HEV protection; photoprotection; HPLC quantification
## 4 Performance Evaluations and Results Analysis
### Results and Analysis  
#### 4.2.1 Generated Packet  
The total packet production predicted in Section 3.4 was analyzed. The average number of packets produced by each node is shown in Figure 9. We use Constant Bit Rate (CBR) and Variable Bit Rate (VBR) to compare types of traffic. The prediction model is considered to be validated since the error rate of the total packet simulation product (b) was approximately 5% on average using the total number of packets (a) derived from the proposed prediction technique and NS-2.  
The proposed traffic prediction model is compared with the real networks modeled in the ns-2 and traffic adaptive routing protocol for mobile sensor networks (H.MSN) [41]. The average number of packets produced by each node is shown in Figure 10. The proposed prediction model shows an average 3% difference with the real network in CBR simulation, and a 5% difference to that in VBR  
Figure 8: Poisson distribution for random event: (**a**) Event 10%\(\sim\)15%; and (**b**) Event 30%\(\sim\)35%.  
Figure 9: Total network traffic comparison—Nodes: (**a**) Congestion prediction model—total network traffic (**b**) NS-2—total network traffic.  
simulation. The H.MSN model shows an average 8% difference with the real-network in CBR, and a 12% difference to that in VBR simulation.