esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of Thermodynamic Parameters  
To study the thermodynamics of Hg(II) adsorption, a series of adsorption tests were performed, each using 1.0 mg of 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) with 100 mL of 100 ppb Hg(II) solution at different temperatures (25, 35, or 40 \({}^{\circ}\)C). The thermodynamic feasibility and spontaneous nature of the adsorption process were assessed based on thermodynamic parameters including the standard Gibbs free energy (\(\Delta\)G\({}^{\circ}\)), enthalpy change (\(\Delta\)H\({}^{\circ}\)), and entropy change (\(\Delta\)S\({}^{\circ}\)), as determined from the following equations [54; 55; 57].  
\[\Delta\)G\({}^{\circ}=-\)RT ln \(K_{\rm d} \tag{5}\]  
\[\ln K_{\rm d}=(\Delta\)S\({}^{\circ}\)/R) - (\(\Delta\)H\({}^{\circ}\)/RT)  
\[K_{\rm d}=q_{\rm e}\)/C\({}_{\rm e} \tag{7}\]  
where \(K_{\rm d}\) is the distribution coefficient of adsorption, R is the universal gas constant (8.314 J.mol\({}^{-1}\).K\({}^{-1}\)), and T is the absolute temperature (K). \(q_{\rm e}\) and C\({}_{\rm e}\) are the mercury uptake and equilibrium concentration of mercury (mg.L\({}^{-1}\)) after adsorption, respectively (see Experimental section). Values of \(\Delta\)H\({}^{\circ}\) (\(-\)24.1 kJ mol\({}^{-1}\)) and \(\Delta\)S\({}^{\circ}\) (\(-\)72.7 J mol\({}^{-1}\) K\({}^{-1}\)) were quantified from the slope and intercept of the linear van't Hoff plot, as seen in Figure S4. The values of \(\Delta\)G\({}^{\circ}\) (\(-\)2.93 kJ mol\({}^{-1}\) at 298 K, \(-\)1.70 kJ mol\({}^{-1}\) at 308 K,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Metal Ion Concentration (ppb)**} \\ \cline{2-5}  & **Hg(II)** & **Pb(II)** & **Ni(II)** & **Cu(II)** \\ \hline Before adsorption & 1000.0 & 500.8 & 505.3 & 501.3 \\ After adsorption & 105.4 & 495.6 & 497.7 & 494.4 \\ \hline Removal efficiency & 89.5\% & 1.0\% & 1.5\% & 1.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Concentrations of Hg(II) Pb(II), Ni(II), and Cu(II) ions in a simulated wastewater before and after treatment with 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) sorbent. The concentrations of each metal ion in the treated wastewater shows the amount of residual metal contaminants after adsorption.  
and \(-\)1.27 kJ mol\({}^{-1}\) at 313 K) suggest that Hg(II) adsorption on the cysteine functionalized carbon-coated magnetite is thermodynamically feasible. The decrease in \(-\Delta\)G\({}^{\circ}\) with increasing temperature reveals that the sorption process is exothermic and more favorable at lower temperatures, similar to previous work [15; 55]. [PERSON] et al. [61] has suggested that escape of mercury ions from surface sites is more facile at higher temperature. The negative value of \(\Delta\)H\({}^{\circ}\) indicates that Hg(II) sorption is exothermic at 25-40 \({}^{\circ}\)C. The value of \(\Delta\)S\({}^{\circ}\) is also negative, suggesting a decrease in randomness at the solid/solution interface during Hg(II) sorption.
## 2 Material and Methods
### Identification and Optimum Solution Using Genetic Algorithms  
The genetic algorithm (GA) was selected, using the Matlab (2023b) function ga, to choose the optimal parameters for each fractional-order model. The parameters were selected based on the the optimization of the cost function, as detailed in [26; 27]. The GA is a heuristic optimization technique inspired by the process of natural selection, which mimics biological evolution to iteratively improve solutions to optimization problems. The algorithm tackles stochastic global optimization problems by modifying a population of potential solutions.  
The genetic algorithm was used to find the parameters of each model. Initially, it generates a random population of parameters for each of the FO models. Each set of parameters is evaluated using a fitness function, which measures the difference between the predicted impedances and the measured impedances. Through processes of selection, crossover, and mutation, a new generation of potential solutions is produced, with individuals that better fit the data being more likely to be selected as parents. The optimization process continues until the population converges towards an optimal or near-optimal solution, resulting in optimal parameter \((R_{r}^{*},L_{r}^{*},\alpha_{r}^{*},C_{r}^{*},\beta_{r}^{*})\) for the respiratory system, \((R_{e^{+}}^{*},K_{e^{+}}^{*},\beta_{e^{+}}^{*},p^{*})\) for the fatigue-induced changes and skeletal muscle damage model, and \((K_{w}^{*})\) for the Warburg model. The optimal parameters are chosen so that they best fit the experimental impedance data with minimal error [28; 29].  
To ensure the best solution for each patient, the Normalized Root Mean Square Error (NRMSE) is calculated for each optimal parameter vector generated after each GA iteration. An NRMSE of less than 0.1 indicates a good fit, with a fit value of 0 representing a perfect fit. Parameter sets that meet this condition and occur most frequently are used to initialize the model for the online procedure of the same patient. The fitting percentage is given by \(fit=100*(1-NRMSE)\). The optimization results are shown in Table 1 for the fractional-order model using (1), Table 2 for the Cole model using (4), and Table 3 for the Warburg model using (5). An illustrative example of the GA algorithm is given in Figure 3.  
The MRSE and NMRSE are calculated using Equations (8) and (9):  
\[\text{RMSE}(y,\hat{y})=\sqrt{\frac{1}{N}\sum_{i=0}^{N-1}(y_{i}-\hat{y}_{i})^{2}} \tag{8}\]  
\[\text{NRMSE}(y,\hat{y})=\frac{\text{RMSE}(y,\hat{y})}{y_{\text{max}}-y_{\text{ min}}} \tag{9}\]  
Over successive generations, the population of parameter sets converges towards an optimal or near-optimal solution until stopping conditions are met. The final parameter set is chosen based on the global best-fitting solution per patient. The fitness function (6) used in our study minimizes the error between the actual impedance measurements and those predicted by the fractional-order models (1). This approach ensures that the fractional-order models' parameters obtained provide the best fit for the experimental data with the lowest error.
## 3 Mathematical Model
### Optimization
#### 3.2.1 Genetic Algorithm (GA)  
The genetic algorithm is one of the most widely used optimization techniques. Unlike the other gradient-based methods, a GA does not require an objective function to be continuous and differentiable [37]. The gradient-based methods are not capable of handling discrete variables, and they find it difficult to locate the global minima for a multimodel objective function. The GA is an alternative way to find the optimum solution with some supremacy, especially when dealing with the complex problem of fitting many loops simultaneously.  
A GA is based on random walk methods, which require a random search to find the optimal solution. However, this method is not entirely random, as it uses information from the last iteration. The technique is categorized as a guided random search method. The GA methods start with a set of initial points instead of one point in the search area, and so they have a higher chance to find the universal minima for the problem. Genetic algorithms are similar to the biological evolution theory [38], based on the principle of survival of the fittest, where the universal population undergoes many transformations, at some point in time when the population is not changing anymore, and the best individual among the population is considered as the best solution.  
A GA imitates the genetic process in which the characteristics of the parents are transferred to the child. As shown in the flow chart of a GA presented in Figure 3, it can be divided into four parts: initialization, fitness, selection, and combination. At the beginning of the GA, a set of the initial population or a sample set is generated, and then for each input variable, their fitness value (the value of the objective function) is calculated. The lowest values are taken as the parents' for the subsequent iterations, and they will produce new offspring for the next iteration [38; 39]. For example, human genes have chromosomes, so when they make a new child, some chromosomes from the female mix with the male chromosomes and give a new set of chromosomes to their offspring, which has some properties of each parent. Similarly, in a GA, individuals with the least function value will undergo crossovers and mutations to produce better offspring than the parents. By better, we mean their function value is less than their parents' function value(s). In our present case, a single-point cross-over of 0.8 was established, and the mutation probability was taken as 0.1 to obtain the best results.  
In addition to the GA, we considered two other optimization algorithms, particle swarm optimization (PSO) and differential evolution (DE). Both algorithms are similar to the GA, where we start with a population set and try to find the least objective function value for each individual. Each algorithm is explained in the following subsections.
###### Abstract  
For requiring high communication rate and high-quality illumination, multi-color light-emitting diodes (LEDs) have been utilized in visible light communication (VLC) systems and attracted substantial research interests. It should be noted that multiple colors are not independent from each other since they are jointly limited by the chromaticity constraint. Thus, taking full consideration of the multi-color crosstalk problem and actual communication and illumination constraints, this paper formulates a power-efficient illumination control optimization design to reduce power consumption for the quadrichromatic LED (QLED) based VLC systems where signal to interference plus noise ratio (SINR) and quadrangle chromaticity tolerance region constraints should be satisfied. Simulation results illustrate that our proposed optimal power allocation strategy can significantly increase power efficiency for the VLC system compared with the uniform power allocation method. Moreover, the proposed scheme can provide optimal performance under different given correlated color temperature (CCT) values.  
v 2019; 9, 169; doi:10.3390/cryst9030169
# A Peak Absorption Filtering Method for Radiated EMI from a High-Speed PWM Fan
###### Abstract  
Axial flow fans are widely used for heat dissipation in electronic devices. Due to its frequent speed-regulation to adapt to the change in heat load, a fan can cause significant electromagnetic radiation interference. In this study, a peak absorption filtering method is proposed to address the radiation interference issue in a high-speed PWM axial flow fan. The mechanism and coupling paths of radiation interference were analyzed, and a radiation interference calculation using finite integration technique by a hybrid field-circuit model and experimental measurement were conducted to identify the winding as the main source of radiation in PWM fan. Considering the limited space inside the fan, an integrated, non-inductive filtering circuit was designed to absorb the peak voltage entering the windings and the filter parameters are determined via circuit simulation. The measurement results indicate that the filtering method can reduce overall electromagnetic interference with a maximum peak reduction of 41.9 dB, without affecting the useful signals.  
filter design; finite integration technique; interference suppression; peak radiated interference; radiated modeling; radiated source
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 6 Experimental Assessment  
This section presents an experimental performance assessment of RESEMBLE aiming to both evaluating the clock synchronization accuracy at different hop distances and assessing the maximum end-to-end delays in a mesh topology. The assessment also demonstrates the feasibility of RESEMBLE on COTS devices.  
The devices used for the experiment are the STMicroelectronics NUCLEO-L476 RG [39] microcontrollers, equipped with the BLE controller provided in the X-NUCLEO-IDB05A2 [40] expansion board. The BLE controller is compliant with the BLE v4.2 [30] specifications.  
The assessed metrics are:  
* The clock skew (\(S\)), measured as the clock difference between two generic nodes (\(N_{x},N_{y}\)) at the time \(t\), i.e., \[S(N_{x},N_{y})=ClockValN_{x}(t)-ClockValN_{y}(t)\] (3) where \(ClockValN_{x}(t)\) is the clock value of the Node \(N_{x}\) at time \(t\).
* The Maximum Absolute Clock Skew (MaxAbsSkew), calculated as \[MaxAbsSkew(N_{x},N_{y})=max(|S(N_{x},N_{y})|)\] (4)
* The end-to-end delay (\(e2 eDelay\)), measured above the Network Layer as the difference between the message generation time at the source and the message reception time at the destination. \[e2 eDelay=RxTime-GenTime\] (5)  
Figure 6: RESEMBLE software components.  
In the following, Section 6.1 describes the experiment aiming to evaluate the clock synchronization accuracy of the RESEMBLE synchronization protocol, while Section 6.2 discusses the experiment aiming to evaluate the obtained \(e2 eDelays\).
## 4 Results
### Results and Analysis
#### 4.3.3 Qualitative Analysis  
We visualized the attention matrix \(a^{t}\) and checked it whether obtains temporal information in questions. Figure 4 reveals the attention heatmap generated from a test question \"who is the current Ohio state senator?\". We can see that the attention matrix successfully obtained the temporal information (current) from the question, so we can further strengthen the question vector through the extracted information. In other words, in the question \"who is the current Ohio state senator?\", the word \"current\" implied temporal information. In the attention matrix, value of the word \"current\" is the highest, thus there is temporal information in the question, so we can strengthen the question vector in this way.  
In order to further prove that the introduction of external knowledge is effective, we reveal the predicted answer of our method and BAMnet from the t-data in Table 3. We divided the predicted answers into two categories, which are answer right and answer rank up. Answer right is where the predicted answers are the correct answer but not predicted in other methods, answer rank up is where the predicted answer becomes first place but includes other wrong answers. In the first type, without the temporal attention module, the model cannot capture the information of before, last, and now in the question, so generates the wrong answer, and our method obtains the right answer as the model finds the temporal information through the temporal attention module, it is important that there is no other wrong answer. In the second type, although it includes the correct answer, the model is without the temporal attention module, resulting in the generation of candidate answers, thus, the score of the correct answer is lower than other answers. However, when the model has a temporal attention module, the score of the correct answer is higher than other answers and ranks first. As we can see, compared with other methods without temporal attention module, our method predicts more valid answers and has better performance accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Macro F1** \\ \hline Full Model & 0.563 \\ w/o Temporal attention module & 0.556 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation results on the WebQuestions test set.  
Figure 4: Attention heatmap generated by the temporal attention module.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Experiment Results of Proposed Methods
#### 4.4.3 Loss Function Test  
The confidence loss function used by the YOLOX model is BCE, and although it is a classic and effective classification loss function, it did not balance the positive and negative samples in training. Therefore, we replaced it with Focal Loss (FL) [41] and Varifocal Loss (VFL) [39] for experiments and obtained the results shown in Table 4. As per Table 4, among the three loss functions, VFL can obtain the highest detection accuracy and reduces the inference time compared to FL. Therefore, we finally chose to use VFL as our confidence loss function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mAP\({}_{\text{50-95}}\) (\%)** & **P (M)** & **Latency (ms)** & **FPS** \\ \hline YOLOX & 73.57 & 8.94 & 13.66 & 73.2 \\ YOLOX + SE & 74.11 & 9.06 & 14.12 & 70.8 \\ YOLOX + CBAM & 73.98 & 9.15 & 15.89 & 62.9 \\ YOLOX + NAM & 74.21 & 8.95 & 15.45 & 64.7 \\ YOLOX + UISAM & 74.06 & 8.95 & 17.94 & 55.8 \\ YOLOX + ECA & **75.58** & 8.94 & 13.26 & 75.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of attention mechanism blocks on DOTA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**DOTA-v1.5**} & \multicolumn{2}{c}{**TGRS-HRRSD**} & \multicolumn{2}{c}{**RSOD**} \\ \cline{2-7}  & **mAP\({}_{\text{50-95}}\) (\%)** & **Latency (ms)** & **mAP\({}_{\text{50-95}}\) (\%)** & **Latency (ms)** & **mAP\({}_{\text{50-95}}\) (\%)** & **Latency (ms)** \\ \hline YOLOX & 73.57 & 13.66 & 66.07 & 11.34 & 77.18 & 14.86 \\ YOLOX + ECA & 75.58 & 13.26 & 69.23 & 11.21 & 78.23 & 14.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of ECA on three datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**P(M)**} & \multicolumn{2}{c}{**DOTA-v1.5**} & \multicolumn{2}{c}{**TGRS-HRRSD**} & \multicolumn{2}{c}{**RSOD**} \\ \cline{2-7}  & & **mAP\({}_{\text{50-95}}\) (\%)** & **Latency (ms)** & **mAP\({}_{\text{50-95}}\) (\%)** & **Latency (ms)** & **mAP\({}_{\text{50-95}}\) (\%)** & **Latency (ms)** \\ \hline YOLOX & 8.94 & 73.57 & 13.66 & 66.07 & 11.34 & 77.18 & 14.86 \\ YOLOX + ASFF & 14.38 & 75.62 & 14.38 & 66.92 & 13.97 & 78.01 & 18.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ASFF test results.
## 3 Proposed Method
### Spectro-Temporal Descriptors
#### 3.2.1 Higher-order Local Auto-Correlation (HLAC)  
Higher-order Local Auto-Correlation (HLAC) features are conventional local descriptors for extracting patterns in 2D patch [20]. The features had been successfully applied to a wide variety of real applications, including texture and face classification. The HLAC features is well-developed based on higher-order autocorrelation function:  
\[D_{\text{HLAC}}(\mathbf{a}_{1},\mathbf{a}_{2})=\int S(\mathbf{r})S(\mathbf{r }+\mathbf{a_{1}})S(\mathbf{r}+\mathbf{a_{2}}) \tag{2}\]  
The mask patterns of HLAC is shown in Figure 3. In dealing with audio, \(S(r)\) denotes TFRs, \(\mathbf{r}=[t_{r},f_{r}^{\prime}]^{\top}\) is reference point on time-frequency plane, \((\mathbf{a}_{1}=[t_{a1},f_{a1}^{\prime}]^{\top},\mathbf{a}_{2}=[t_{a2},f_{a2} ^{\prime}]^{\top})\) is a set of displacements. HLAC extraction is limited to \(3\times 3\) local region and there are 35 individual mask patterns extracted. We introduce a sliding window on TFRs covering 3 consecutive frames of spectrum, from where HLAC features are extracted. The window shifts one frame at a time. Since acoustic features are assumed to be highly correlated within local region, more discriminative features can to be obtained via computing HLAC.  
Figure 2: Flowchart of the proposed acoustic scene classification approach.
## 1 Introduction
[10], the authors summarized that the average thrust force in UAD was approximately 60% lower than that in CD. In higher cutting speeds in CD, the temperature was elevated dramatically, caused high tool wear owning to the abrasive phase of Cr in the workpiece. Work by [PERSON] et al. [11] revealed that UAD produced 36% lower thrust force with a chisel edge, similar torque, and 35% lower wear on the chisel edge compared with CD. In another study, [PERSON] et al. [12] concluded that UAD of carbon fiber composite and titanium alloy stacks resulted in lower tool wear and thrust forces than did CD at all cutting speeds. [PERSON] et al. [13] obtained a significant reduction in drilling forces (thrust force and torque), often in excess of 80% when compared with a dry conventional drilling (CD) process. The temperature in the process zone in UAD was higher than that in CD. A numerical investigation by [PERSON] et al. [14] indicated that low feed rates (<150 mm/min) and high cutting speeds (>600 rpm) are ideal for drilling carbon/epoxy laminates for a certain range of drill parameters. Experimental results in the work by [PERSON] et al. [15] showed a significant reduction of the cutting heat in the drilling of cortical bone. In a similar work from [PERSON] et al. [16], the authors revealed that the penetration force and torque dropped significantly in UAD, and improvement of chip removal from the drilling site was observed. In conventional drilling, [PERSON] et al. [17] recommended that the increase of drilling forces, especially the drilling torque, should be avoided because it is likely to break the drill. A modeling and experimental study by [PERSON] and [PERSON] addressed thermal expansion of the drill as the dominant effect, leading to oversized holes with diameters that increase with depth. This paper presents another approach to gain insight into the mechanism providing the benefits of UAD over CD. The reduction of torque and temperature was confirmed and then considered as a major cause of the improved machinability, elongated tool life, and increased material removal rate. Friction reduction under vibration would be the original mechanism behind all benefits of UAD and is to be further confirmed.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 5 Evaluations
### Comparison with Different Methods  
To validate the effectiveness of the proposed method, we compare its Bit Error Rate (BER) and bit rate with state-of-the-art benchmarks. The experimental configuration is fixed at spreading factor (SF = 7), bandwidth (BW = 250 kHz), and signal-to-noise ratio (SNR = 5 dB). The comparison methods include LoRa, SSK-LoRa, PSK-LoRa, and FBI-LoRa. The bit rate of SSK-LoRa is calculated as follows:  
\[R_{b}=\frac{SF+1}{2^{SF}}BW \tag{25}\]  
For PSK-LoRa, the bit rate is given by the following:  
\[R_{b}=\frac{SF+\log_{2}M}{2^{SF}}BW \tag{26}\]  
where M is the order of PSK. Based on the experimental parameters from the PSK-LoRa reference, M = 16 is selected. The bit rate for FBI-LoRa is computed as follows:  
\[R_{b}=\frac{N_{tot}}{T_{sym}} \tag{27}\]  
where \(N_{tot}\) is to calculate the number of information bits carried by each symbol, \(T_{sym}\) is the duration of one symbol. This study employs FBI-LoRa Scheme 2 for comparison. As evidenced by the results in Table 1, QR\(-\)LoRa demonstrates superior performance in both bit rate and Bit Error Rate (BER), achieving a maximum bit rate of 31.25 kbps and a minimum BER of \(10^{-5}\). Compared with LoRa, SSK-LoRa, and PSK-LoRa, QR\(-\)LoRa delivers up to a 17.58 kbps higher bit rate and up to \(5.99\times 10^{-3}\) lower BER. Implementation requires only an additional memory module for amplitude mapping storage, maintaining a hardware cost comparable to conventional LoRa systems, as shown in Table 1.