esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Evaluation of Classification Technique Based on Computational Time  
The proposed HT-RLSTM is validated with respect to computation time, and the final outcomes are compared with various existing works like KNN, ANFIS, SVM, and ANN in order to state the efficiency of the model.  
Figure 6 illustrates the comparison analysis of the proposed method and existing works with respect to computation time. For this comparison, several datasets, such as KDD99, NSL-KDD99, CIDDS-001, and UNSW-NB15, are used. The estimation time is zero, for the time as required by the model to detect the attack. The computation time of the proposed work to process the dataset, such as KDD99, is 79 s, NSL-KDD99 is 80 s, CIDDS-001 is 73 s, and UNSW-NB15 is 68 s. The existing KNN, ANFIS, SVM, and ANN tend to achieve a high computational time ranging between 85 s to 193 s. Hence, the time complexities of the existing works are high. Thus, the proposed technique remains to be robust for an attack detection system and detects the attacks with limited time.  
Figure 5: Comparative analysis of proposed HT-RLSTM with respect to FPR, FNR, and MCC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Techniques**} & \multicolumn{3}{c}{**Performance Metrics (\%)**} \\ \cline{2-4}  & **FPR** & **FNR** & **MCC** \\ \hline
**Proposed HT-RLSTM** & 1.82 & 3.03 & 95.16 \\ \hline
**KNN** & 4.24 & 5.45 & 90.31 \\ \hline
**ANFIS** & 7.88 & 6.13 & 85.99 \\ \hline
**SVM** & 9.7 & 12.12 & 78.2 \\ \hline
**ANN** & 8.1 & 14.72 & 87.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance analysis of proposed HT-RLSTM with respect to FPR, FNR, and MCC.
# Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic
## 3 Methodology
### Artificial Chromatic Lighting  
According to studies on color incidence, emotions and behavior change. Blue light can increase attention and alertness, while warm light can induce relaxation, according to the study [9]. Exposure to specific chromatic lightings, particularly a blue light in the morning spectrum, can influence sleep-wake patterns [27]. Another study defines that warm, yellow light may evoke feelings of warmth and comfort, whereas cold light can create a more stimulating environment [28]. In addition, red light was found to potentially increase appetite, while blue light may suppress it [29]. In the system, the RGB configuration enables the adjustment of color palettes with 256 values for each RGB color [30]. A limited set of colors, known as Cyan-Magenta-Yellow-black (CMYK), is used, associated with the emotions intended to be evoked; see Table 1.  
The UNE 12464-1 [4] European technical standard recommends between 300 and 500 lumens on indoor surfaces, with a color temperature between 5000 k and 6000 k for tasks requiring reading or focusing on artificial light sources [31].  
Warm or neutral temperatures ranging between 2700 \({}^{\circ}\)K and 4000 \({}^{\circ}\)K are preferable for contemplative activities, with intensity varying between 100 and 400 lumens. A very  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**R** & **G** & **B** & **Colour** & **Emotion Evoked** \\ \hline
255 & 0 & 0 & [PERSON] & Activate \\
255 & 0 & 255 & Magenta & Activate \\
0 & 255 & 0 & [PERSON] & Neutro \\
255 & 255 & 0 & [PERSON] & Neutro \\
0 & 0 & 255 & [PERSON] & Relaxation \\
0 & 255 & 255 & Cyan & Relaxation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Action of color on behavior.  
strong lighting can induce fear and alter human behavior [32]. The system design enables power control [33] and reproduction of colors from the RGB palette at 8 bits (Figure 3).  
Lighting power is proportional to volume and distance from the source; an important index to consider is the color rendering index (CRI), which ranges from 0% to 100% considering values < 60% as subpar lighting [34]. This makes the design to include a power transistor output for easy adaptation to higher power controllers if required due to environmental conditions.  
During system development, various scenarios and control schemes were considered, including open-loop controls and advanced AI-based systems. An analysis of system capabilities, response time, and implementation efficiency concluded that neuro-fuzzy controllers are one of the most effective alternatives.
## 3 Controller Design and Stability Analysis  
As shown in Figure 5, the control system of the proposed quadrotor contains two loops: An outer loop and an inner loop. The PID control method in the outer loop is used for the position and altitude control, while the exploited RBFNN-based control approach is implemented in the inner loop for stable attitude control.
# A Sub-50 nW Implantable Body Temperature Sensor and Its Application to a Rat
## 3 Characterization of Fabricated Temperature Measurement Channel (CH_T)
In Figure 5b, the \(x\)-axis is the measured reference temperature, and the \(y\)-axis is the calibration error. The brown dotted line indicates the allowable error boundary of the thermometer according to the temperature range. This refers to the Electronic Thermometer Guide for Intermittent Patient Temperature Measurement (ASTM E1112-00 [14]) established by the American Society for Testing and Materials (ASTM). The accuracy of the thermometer should be within \(\pm\)0.1 \({}^{\circ}\)C between 37 \({}^{\circ}\)C and 39 \({}^{\circ}\)C, within \(\pm\)0.2 \({}^{\circ}\)C between 35.8 \({}^{\circ}\)C and 37 \({}^{\circ}\)C and between 39 \({}^{\circ}\)C and 41 \({}^{\circ}\)C, and within \(\pm\)0.3 \({}^{\circ}\)C between 34 \({}^{\circ}\)C and 35.8 \({}^{\circ}\)C and between 41 \({}^{\circ}\)C and 43 \({}^{\circ}\)C. In Figure 5b, the blue triangle symbol represents the linear calibration error. Between 37 \({}^{\circ}\)C and 39 \({}^{\circ}\)C, the maximum positive error is +0.99 \({}^{\circ}\)C and the maximum negative error is \(-\)0.1 \({}^{\circ}\)C, while in other ranges, the maximum positive error is 0.166 \({}^{\circ}\)C and the maximum negative error is \(-\)0.208 \({}^{\circ}\)C, which meets the accuracy specification presented in the ASTM guide (ASTM E1112-00). In Figure 5b, the green square symbol represents the third-order calibration error. Between 37 \({}^{\circ}\)C and 39 \({}^{\circ}\)C, the maximum positive error is +0.026 \({}^{\circ}\)C and the maximum negative error is \(-\)0.019 \({}^{\circ}\)C, while in other ranges, the maximum positive error is 0.026 \({}^{\circ}\)C and the maximum negative error is \(-\)0.021 \({}^{\circ}\)C, which is an improvement of about 8 times in precision. The power consumption of the fabricated CH_T at 37 \({}^{\circ}\)C was measured to be 40.9 nW. The CH_T has a BRG block operating at 1.8 V and SMP, VCO, and TDC blocks operating at  
Figure 4: Die photograph and characterization setup: (**a**) Die photograph of fabricated chip, (**b**) evaluation board, (**c**) temperature chamber test setup. BRG (bridge), SMP (sampler), VCO (voltage-controlled oscillator), TDC (time-to-digital converter), COB (chip on board), MCU (micro-controller unit), FPCB (flexible printed circuit board), PBS (phosphate-buffered saline).
## 5 Results and Analysis  
Based on the friction coefficient compensation control principle and the simulation model of the friction coefficient compensation control in the synchronizer synchronization process, the compensation control process was simulated and analyzed, and the synchronization performance index change after friction coefficient compensation was obtained. Because the friction coefficient compensation control system in the synchronization process of the synchronizer was a complex system with strong time-varying and multi-dimensional nonlinearity, the experiment was also carried out to verify the feasibility of the proposed friction coefficient compensation control strategy.
## 2 Materials and Methods
### Topical Features by PLSA Model  
The Probabilistic Latent Semantic Analysis model is a statistical model based on the Aspect Model which is a latent variable model serving the data of the co-occurrence matrix. Each hidden variable, called \(z_{k}\), refers to associated with each observation. Here, each observation refers to the appearance of a term in a document. We can derive and solve the PLSA model to get the topical features of the text.  
The probabilistic variables in the model are introduced as follows:  
\(P(d_{i})\) represents the probability of selecting document \(d_{i}\) in the document data set;  
\(P(w_{j}|z_{k})\) represents the conditional probability of a specific word  
the aspect model can be obtained based on approximate estimation of conditional probability \(P(w_{j}|d_{i})\): it can be regarded as a convex combination of K conditional classes or aspect \(P(w_{j}|z_{k})\).  
Once the model is determined, the parameters can be determined based on the maximum similarity formula. The objective function can be expressed as:  
\[L=\sum_{i=1}^{N}\sum_{j=1}^{M}n(d_{i},w_{j})\log p(d_{i},w_{j})=\sum_{i=1}^{N}n( d_{i})[\log p(d_{i})+\sum_{j=1}^{M}\frac{n(d_{i},w_{j})}{n(d_{i})}\log\sum_{k=1}^{ K}P(w_{j}|z_{k})P(z_{k}|d_{i})] \tag{6}\]  
The standard procedure for estimating the maximum similarity function in the probabilistic latent model is implemented by the expectation maximization (EM) algorithm.  
In order to understand the topical features of the text, it is essential to deduce and solve the PLSA model, so the determined probabilities \(P(w|z)\) and \(P(z|d)\) are needed, which are determined by maximizing the following log-likelihood function:  
\[L=\log P(D,W)=\sum_{d\in D}\sum_{w\in W}n(w,d)\log P(w,d) \tag{7}\]  
Maximizing the log-likelihood function is equivalent to minimizing the KL difference (relative entropy) between the empirical distribution and the parametric model. The expectation maximization (EM) algorithm is used to train the model effectively, involving the determination of the subject parameters of all files and that of the blending coefficients for each document.
## 3 Methodology
### Third Phase
#### 3.3.2 Safety Factor Assessment  
Slope stability was assessed by the 2-dimensional stability program (SLIDE), which is based on the limit equilibrium calculation method [65]. Profiles were drawn according to the geomechanical stations and detailed topography. These were analysed in a static analysis (considering only the geotechnical characteristics of the terrain and the topography) and a pseudostatic analysis (considering the seismic activity of the study area).  
We used seismic activity values established by the Ecuadorian Construction Standard (NEC-15, by its acronym in Spanish) [66]. According to [61], the study area is considered a zone of high seismic intensity, with a peak ground acceleration (seismic acceleration) of 0.40 g. However, in the pseudostatic analyses, 60% of the acceleration (i.e., 0.24 g) must be considered. Simplified Bishop methods were used to calculate the safety factor (SF). The simplified Bishop method uses the method of slices to discretise the soil mass and  
\begin{table}
\begin{tabular}{c c} \hline
**Susceptibility Level** & **Susceptibility Coefficient (SC)** & **Description** \\ \hline I & Very low susceptibility SD \(\leq\) 5.0 & Stable conditions \\ II & Low susceptibility: 5.0 \(<\) SD \(\leq\) 10.0 & Monitoring recommended \\ III & Median susceptibility: 10.0 \(<\) SD \(\leq\) 15.0 & Predominantly stable conditions \\ IV & High susceptibility: 15.0 \(<\) SD \(\leq\) 20.0 & Potentially unstable conditions \\ V & Very high susceptibility: SD \(>\) 20.0 & Unstable conditions \\ \hline \end{tabular}
\end{table}
Table 6: Detachment susceptibility classification (rock mass). Based on [21, 57, 61, 63, 64].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Weight** \\ \hline Soil characteristic (quality) (Sq) & 0.0 to 4.0 \\ Geological structure (Gs) & 0.0 to 4.0 \\ Morphometry (M) & 0.0 to 4.0 \\ Weathering rank (Wr) & 0.0 to 4.0 \\ Water presence (W) & 0.0 to 3.0 \\ Vegetable cover (Vc) & 0.0 to 3.0 \\ Seismic activity (S) & 0.0 to 4.0 \\ SC: (Sq + Gs + M + Wr + W + Vc + S) & 0.0 to 26.0 \\ \hline \end{tabular}
\end{table}
Table 5: Main parameters and weights assigned to soil in the susceptibility coefficient estimation, based on [21, 57, 61, 63, 64].  
determine the SF. This method satisfies the vertical force equilibrium for each slice and overall moment equilibrium about the centre of the circular trial surface. Since horizontal forces are not considered at each slice, the simplified Bishop method also assumes zero interslice shear forces [67, 68]. The parameters input in the software SLIDE: the number of slices was 25 with a maximum number of iterations of 50. In this analysis, water table values were not considered.  
Based on the susceptibility levels to detachment and landslide and the safety factor for rocks and soils, the obtained results were evaluated to establish the viability of the applied methodology.
## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.4 Group 4: Hybrid Model
_A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors [65]:_ [PERSON] and [PERSON] suggested optimal-hybrid model combined with Secondary Decomposition (SD), AI method and optimization algorithm for forecasting air quality index. In the proposed SD method, Wavelet Decomposition (WD) was chosen as the primary decomposition technique to generate a high-frequency detail sequence WD (D) and a low-frequency approximation sequence WD (A). Variational Mode Decomposition (VMD) improved by Sample Entropy (SE) was adopted to smooth the WD (D). LSTM with good ability of learning and time series memory were applied to make it easy to be predicted. LSSVM with the parameters optimized by the Bat Algorithm (BA) considered air pollutant factors including PM2.5, PM10, SO2, CO, NO2 and O3, which is suitable for forecasting WD (A) that retains original information of AQI series. The dataset was from 1 December 2016 to 31 December 2018 respectively collected from Beijing and Guilin located in China. RMSE, MAE, MAPE and R were selected as evaluation metrics. The results showed that the proposed method outperformed other methods. _A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine [66]:_ to accurately predict air quality index [PERSON] et al. used a novel hybrid model based on two-phase decomposition, extreme learning machine and different evolution. The two-phase decomposition was based on CEEMD and VMD, which helps to handle non-stationary features. The dataset was from 1 July 2014 to 30 June 2016 of the cities Beijing and Shanghai. To evaluate the model MAE, RMSE and MAPE were applied. The result showed that the proposed method outperformed other methods (MAE-2.53, RMSE-3.27, MAPE-5.09).
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Transfer Learning Model Performance on Qingdao Lake Data
#### 5.5.1 Comparative Study With and Without Transfer Learning
Most importantly, the MCC of the TL model is 1.0, showing a perfect correlation between predicted and true classes, whereas for the without-TL model, the MCC is about 0.1, reflecting a random prediction behavior. These results highlight the fact that fine-tuning not only improves feature extraction but also ensures that the model learns class-specific discrimination important for robust classification.
# Demand-Response Control in Smart Grids
## 6 Theoretical and Experimental Comparison between Vickrey and Dutch Auctions  
The Vickrey auction and the Dutch auction are two different types of auctions that are used in demand-response control to sell energy. The Vickrey auction is a sealed-bid auction in which bidders submit bids without knowing the bids of other participants. The highest bidder wins the item and pays the second-highest bid price. The Dutch auction is an open-outerc auction in which the price of an item is lowered until a bidder accepts it.  
In this section, we present the simulation architecture. After that, we compare Vickrey and Dutch auctions theoretically as well as experimentally to show their advantages and disadvantages.
## 3 Results and Discussion
### Optimal Dosage Range Determination
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flecculant** & **pH** & \begin{tabular}{c} **Initial Biomass** \\ **Concentration** \\ **(m\({}_{\text{BW}}\)-L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Dosage** \\ **(mg\(\cdot\)L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Final Biomass** \\ **Concentration** \\ **(m\({}_{\text{BW}}\)-L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Harvesting** \\ **Efficiency (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Aluminum \\ sulfate \\ \end{tabular} } & \multirow{4}{*}{9} & \multirow{4}{*}{335 \(\pm\) 1} & 10 & 34 \(\pm\) 4 & 89.8 \(\pm\) 0.2 \\  & & & 50 & 12 \(\pm\) 3 & 96.4 \(\pm\) 0.1 \\  & & & 100 & 13 \(\pm\) 3 & 96.2 \(\pm\) 0.1 \\  & & & 150 & 14 \(\pm\) 2 & 95.9 \(\pm\) 0.1 \\  & & & 200 & 12 \(\pm\) 1 & 96.4 \(\pm\) 0.1 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Ferric sulfate \\ \end{tabular} } & \multirow{4}{*}{9} & \multirow{4}{*}{335 \(\pm\) 2} & 10 & 10 \(\pm\) 1 & 96.9 \(\pm\) 0.2 \\  & & & 50 & 10 \(\pm\) 1 & 97.0 \(\pm\) 0.1 \\  & & & 100 & 11 \(\pm\) 2 & 96.7 \(\pm\) 0.1 \\  & & & 150 & 9 \(\pm\) 1 & 97.4 \(\pm\) 0.1 \\  & & & 200 & 8 \(\pm\) 1 & 97.7 \(\pm\) 0.1 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Chitosan \\ \end{tabular} } & \multirow{4}{*}{5} & \multirow{4}{*}{335 \(\pm\) 2} & 10 & 2 \(\pm\) 1 & 99.3 \(\pm\) 0.2 \\  & & & 50 & 5 \(\pm\) 3 & 98.4 \(\pm\) 0.1 \\  & & & 100 & 13 \(\pm\) 3 & 96.1 \(\pm\) 0.1 \\  & & & 150 & 22 \(\pm\) 8 & 93.2 \(\pm\) 0.3 \\  & & & 200 & 29 \(\pm\) 4 & 91.2 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Harvesting efficiency of aluminum sulfate, ferric sulfate, and chitosan at 10–200 mg\(\cdot\)L\({}^{-1}\). Figure 1: Appearance of the culture medium after flocculation and sedimentation with: (**a**) aluminum sulfate; (**b**) ferric sulfate; and (**c**) chitosan. Flocculant dosages from left to right: 10, 50, 100, 150, and 200 mg\(\cdot\)L\({}^{-1}\). primarily act by sweep flocculation at pH 9 needed higher dosages to achieve the highest biomass recovery. The optimal dosage ranges were selected to be 50-200 mg\(\cdot\)L\({}^{-1}\) for aluminum sulfate and 150-200 mg\(\cdot\)L\({}^{-1}\) for ferric sulfate. Chitosan performed better at lower dosages. In fact, from 50 mg\(\cdot\)L\({}^{-1}\) of chitosan, the harvesting efficiency decreased. The elevated concentration of flocculant, creating an excessive positive charge around negatively charged cells, might have impeded cell aggregation, leading to the destabilization of microalgal cells due to electrostatic repulsion [30]. This means that there was an excess of chitosan molecules in the medium that did not interact with the microalgae cells and created repulsion forces. Under the conditions studied, the optimal dosage range was 10-50 mg\(\cdot\)L\({}^{-1}\) for chitosan.
## 2 Machine Learning
### Received Signal Strength Indicator  
The strength of the received optical power RSSI is an important decision indicator in the process of hard switching of a hybrid FSO/RF system [18]. Modern FSO/RF systems switch to RF transmission mode after exceeding the critical low received optical power limit on the receiving side. This information must be distributed and synchronized on both sides of the line. The hybrid system switches back to optical mode if the value of the current RSSI is above the critical level for a certain time [19]. The operating frequencies of commercially deployed RF lines are usually around 60 GHz (unlicensed band), which means that the transmission speed of such a system is disproportionately lower than that of an optical line [20].  
However, there is an effort to make a reliable prediction of the development of the RSSI parameter, based on which it would be possible to identify the necessary communication time of the hybrid system more accurately in the RF mode. Effective reduction in the operating time of the hybrid FSO/RF system will increase the maximum use time of the high transmission speed of the FSO line, while maintaining the condition of continuous communication between the two nodes of the hybrid FSO/RF system [21]. The optical beam emitted from the FSO head is naturally attenuated by passing through the atmospheric channel, depending on the current nature of the weather conditions. The received RSSI optical power detected on the optical receiver can in fact be interpreted as a function of a set of parameters describing the nature of the weather conditions. In the present system, the parameters of temperature, humidity, and concentration of particles in the air, visibility, barometric pressure, and wind speed are measured and recorded in real time [22].
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
## 3 Design and Results  
### Vivaldi Antenna Design  
The AVA is schematically illustrated in Figure 1. It is an exponentially tapered three-flare patch antenna operating in the frequency range \(f_{AVA}=3\div 13.5\) GHz. The shapes of the flare edges allow an enhancement in compactness [3].  
In the design, the antenna optimization has been performed by numerical simulations by varying the following parameters: (i) the position of the second point of the first flare edge \(d_{1}\); (ii) the distance between the first and the second flares \(d_{2}\); (iii) the second flare width \(d_{3}\); (iv) the distance between the second and the third flares \(d_{4}\); (v) the second flare width \(d_{5}\); and (vi) the position of the second point of the third flare edge \(d_{6}\). The resulting geometrical parameters for the optimized antenna, allowing the gain maximization to parity of bandwidth, are listed in Table 1.  
The Rogers RO4350B laminate, with a dielectric permittivity of \(\varepsilon_{r}=3.66\) and a loss tangent of \(tan\delta=0.0037\), having a commercial thickness of \(t=0.762\) mm and standard copper cladding with a thickness of \(t_{c}=0.035\) mm, has been chosen.  
The simulated frequency \(-10\) dB bandwidth is about \(BW=10.5\) GHz, over the frequency range \(f_{AVA}=3\div 13.5\) GHz, and the gain is close to \(G=10\) dB for frequencies higher than \(f>7.5\) GHz.