esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Prediction and Validation
#### 4.1.3 Predictions via Bayesian Neural Network (BNN)  
The BNN inhibits similar architecture of ANN but instils a difference with the setting up of the hyperparameters selection criteria that yields lowest error on testing and training sets with help of Sequential Model Based Optimization (SMBO). This study utilizes SMBO to avoid over fitting and optimize number of neurons in hidden layer, number of hidden layers, learning rate, dropout rate, and suitable activation function with least RMSE in the suggested solution space. In Figure 3, a comparative study of neurons in each hidden layer and different number of hidden layers is plotted. The black dots represent the samples taken from the defined solution space and the red star represents the point with least error among the selected sample points. In this case, the optimum number of neurons in each layer is 35 and number of hidden layers is 16. Figure 4 illustrates a comparative analysis of number of neurons and learning rate. The number of neurons is determined in Figure 3, whereas, the corresponding learning rate and dropout ratio are found to be 0.0067 and 0.30, respectively. Figure 5 shows the sample count of the different activation functions. The acquisition function in SMBO algorithm shifts towards the sample space with approximates the surrogate function with the true objective function. In this study, a large number of solution samples are taken from Rectified Linear Unit (ReLU) activation function in comparison with Sigmoid and Tanh, and it concludes that the ReLU function is the most suitable for this case study. Figure 6 compares the pressure loss prediction for the training and the test data. RMSE and MAE for BNN are found to be 5.3 kPa and 2.85% with R\({}^{2}\) = 0.99 for train data, while test data shows RMSE and MAE of 8.38 kPa and 3.7% with R\({}^{2}\) = 0.989. The prediction for train and test data is in good correlation with best fit curve. The data points form a closed cloud for train data and deviate from best fit after 250 kPa in case of test data.  
Figure 2: Artificial neural network (ANN) predicted pressure plotted with observed pressure loss: (a) training dataset and (b) test dataset.  
Figure 4: Optimized BNN learning rate plotted with number of neurons in each layer.  
Figure 5: Sample count from the different sample of the proposed solution space.  
Figure 3: Optimized Bayesian Neural Network (BNN) number of neurons in each layer plotted with number of layers.
## 3 Methods
### Network and User Processing
#### 3.2.1 Time Update, Measurement Update  
For the parameter estimation, regular Kalman filter equations are used, although no dynamical model for the parameters is assumed. Thus, the predicted state vector at epoch \(k\) is identical to the previously filtered state vector:  
\[\hat{x}_{k|k-1}=\hat{x}_{k-1|k-1}, \tag{3}\]  
with variance-covariance matrix (VCM)  
\[P_{k|k-1}=P_{k-1|k-1}+\Delta t\cdot S_{k}. \tag{4}\]  
Here, \(S_{k}\) denotes the process noise matrix of the state vector \(x\). The state vector contains all parameters of the functional model, as defined by Equation (2). At the end of this chapter, the process noise of the parameters are summarized in Table 3. There, the variance of the process noises of all parameters is defined. \(S_{k}\) is a diagonal matrix containing the variance of the process noise at the position of the parameter. It shall be noted that for parameters declared \"epoch-wise\", a process noise is chosen high enough that the predicted state estimate does not significantly influence the filtered state estimate and low enough to not cause numerical issues. Given the linearized system of the observation Equation (2),  
\[y_{k}+v_{k}=A_{k}\cdot\hat{x}_{k|k},\;y_{k}\sim\mathrm{N}(0,R_{k}), \tag{5}\]  
the filtered state vector and its VCM are computed with  
\[\hat{x}_{k|k}=\hat{x}_{k|k-1}+K_{k}\cdot\left(y_{k}-A_{k}\cdot\hat{x}_{k|k-1} \right),\;K_{K}=P_{k|k-1}\cdot A_{k}^{T}\cdot\left(A_{k}\cdot P_{k|k-1}\cdot A _{k}^{T}+R_{k}\right),\]  
\[P_{k|k}=(I-K_{k}\cdot A_{k})\cdot P_{k|k-1}\cdot\left(I-K_{k}\cdot A_{k}\cdot P _{k|k-1}\right)^{T}+K_{k}\cdot R_{k}\cdot K_{k}^{T}. \tag{6}\]  
Figure 3: Processing scheme for network and user.  
Here, \(K_{K}\) is the Kalman gain and \(P_{k|k}\) is the VCM of the filtered state vector \(\hat{x}_{k|k}\). The VCM of the observations is denoted with \(R_{k}\). Observations are assumed to be uncorrelated, and the standard deviation for code and phase observations is modeled as a function of the satellite elevation, as defined in Table 3.
## 3 Results
### Vegetation Recovery in the Vegetation Damage Area
#### 3.3.1 Differences in Vegetation Recovery by County  
The vegetation recovery was highest in Qingchuan, and full vegetation recovery accounted for 72.69% of the damaged area in Qingchuan. The proportions of fully recovered vegetation in Lixian and Pingwu were also greater than 50%. However, the vegetation in Anxian County recovered poorly; only 19.09% of the damaged area had recovered. The proportion of vegetation not recovering was highest in Dujiangyan County where it accounted for 20.84%. The proportion of vegetation not recovering was lowest in Qingchuan and accounts for only 2.17%. In summary, vegetation recovery was highest in the counties of Qingchuan, Lixian, Pingwu and Jiangyou, most of which are located in the northeast of the study area, and vegetation recovery was poorest in the counties of Anxian, Dujiangyan, Shifang and Pengzhou, most of which are located in the east. A large area of vegetation recovery (59.21%) was identified in Lixian County, which is located in the southwest of the study area, but the county also included sites where vegetation had not recovered (11.60%). The areas where vegetation had fully  
Figure 6: Vegetation recovery in 2013.  
recovered were mostly concentrated in the northeast on the boundary with Qingchuan County, and areas where vegetation had not recovered were primarily type is mainly distributed in the southwest. (Figure 6 and Table 2).
# Potential Possibilities of Using Peat, Humic Substances, and Sulfurous Waters in Cosmetology
## 4 Medicinal Waters
### Biological Activity of Sulphurus Water
#### 4.3.1 Anti-Inflammatory Activity  
The influence of various thermal mineral waters from two traditional and historical thermal resorts of Turkey (Bursa and Bolu) on a human keratinocyte (HaCaT) cell line was investigated. The water from Bursa was thermomineral, while the water from Bolu was oligomineral. HaCaT cells were incubated for 3 days with thermal waters; RNA isolation was carried out in the treated and untreated cells. Studies showed that the tested thermal waters significantly decreased the expression of IL-1\(\alpha\). For thermomineral water, the inhibition was 93%; for oligomineral water, it was 38%. Moreover, thermal waters downregulated the expression of TNF\(\alpha\) (59% thermomineral water and 23% oligomineral water) and VEGF (98% thermomineral water and 15% oligomineral water). The observed decrease in the gene expression of TNF\(\alpha\) and IL1\(\alpha\) could be interpreted as an anti-inflammatory effect of mineral waters on HaCaT cells. Additionally, the suppressed VEGF expression might be an indicator of the antiangiogenic effect on human keratinocytes (Table 5) [126].  
The studies were conducted on 50 volunteers to analyze the effect of sulfurous thermal water on the release of anti-inflammatory cytokines. Specific sulfur thermal water was used for the treatments, characterized by additional ions such as chloride, bromine, and sodium iodine. For 12 days, the research group performed one or more of the following treatments: aerosol, inhalations, micronized shower, and humage. The results indicated a greater release of the strong anti-inflammatory cytokine IL-10. In addition, higher levels of IL-10 were also observed in the saliva of patients after treatment with sulfurous thermal water, and this increase correlated positively with the activity of salivary catalase (Table 5) [127].
## 3 Materials and Methods
### Overview of Loss Functions Used
#### 3.8.2 Binary Cross-Entropy in Combination with F1-Score  
Another loss function used during the training of the model is a combination of Binary Cross-Entropy and F1-Score, defined through a \"Composite Score\". This combination is designed to focus the optimization not only on minimizing the loss but also on maximizing the F1-Score. However, it is important to clarify that Binary Cross-Entropy is the actual loss function used to update the model's weights during the optimization process. The Composite Score, on the other hand, is used as a criterion to compare and select the optimal configuration of the model, taking into account not only the minimization of BCE but also the ability to achieve a high F1-Score. During the process of selecting the best epoch of the model, based on the performance obtained on the Validation-Set, the Composite Score allows for balancing the importance of BCE and F1-Score, favoring configurations that are more effective at detecting anomalies. This combination is defined as follows:  
\[\text{Composite Score}=\alpha\cdot\text{F1-Score}-\beta\cdot\text{BCE} \tag{3}\]  
where \(\alpha\) is the parameter that balances the importance of the F1-Score and \(\beta\) is the parameter that balances the importance of Binary Cross-Entropy. These two parameters were treated as hyperparameters in order to find the best configuration that maximizes the model's performance. In conclusion, this composite metric proves to be an excellent solution in Anomaly Detection contexts, as it allows for balancing error minimization with the maximization of the model's ability to correctly identify anomalies, thus ensuring greater attention to the most critical observations.
###### Abstract  
Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat@OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat@OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet's ophiolitic zones.  
remote sensing; spectral enhancement techniques; support vector machine; mantle peridotite; chromite deposit +
Footnote †: journal: Applied Sciences  
1
## 6 Numerical Results
### Probability of Error
#### 6.1.2 Probability of Error at the Destination  
In order to analyze the overall performance of using IFP with the CF system, we consider the extension of the above model to the destination node. The aim of the destination is to obtain \(L\) original source signals. Hence, we consider \(L\) transmissions to study the performance of error at the destination. After \(L\) transmissions from source to relay, the \(L\) transmissions from relay to destination are orthogonalized in time. The channel from relay to destination is a random real valued channel. Figure 3 shows the probability of error of CF implemented with IFP. To make a comparison with the state-of-the-art, we plot the error performance of the system when Time Division Multiplexing (TDM) is used to transmit the signals from source to destination via relay. In the case of TDM, one source transmits at one time instant to the relay. The relay decodes the signals and sends it to the destination. In the next time instant, the relay sends the decoded signal to the destination, and the destination decodes the original signal from the first source. After \(L+1\) time slots, all the \(L\) signals are received at the destination. Figure 3 shows that CF with IFP shows a gain of approximately 5 dB at an error of 1% over TDM. This gain is basically due to multiple copies of the same signals received at the relay in the case of CF with IFP, which is not the case in TDM. Hence, in case the channel is in a deep fade, it results in an error in TDM, whereas such deep fading situations are overcome, due to the existence of multiple copies of the same signals in CF. Note that we have again considered fixed channel gains here, which reduces the theoretical integration of equalized noise in Equation (28) to a point-to-point error probability expression.  
Figure 2: Comparison of the probability of error at the relay for the proposed scheme with varying signal to noise ratio (SNR) for \(L=2\). The total signal power is adjusted for fair comparison with other schemes.
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 2 Design of Proposed PIDD\({}^{\bullet}\) Controller
### PIDD\({}^{2}\) Controller  
The PID with derivative filter (PIDD\({}^{2}\)) controller is a modification of the conventional PID controller that contains an extra derivative filter. In terms of pole placement, a standard form of PID for a second-order control system is as follows:  
\[C=\frac{\omega_{n}^{2}}{s^{2}+2\xi\omega_{n}s+\omega_{n}^{2}}, \tag{2}\]  
\[C_{Z}=\frac{\left(\frac{s}{\rho}+1\right)\omega_{n}^{2}}{s^{2}+2\zeta\omega_{ n}s+\omega_{n}^{2}}=\frac{s}{\rho}C+C, \tag{3}\]  
Figure 1: Block diagram of PID controller.  
where \(\omega_{n}\) is the undamped natural frequency. Here, the additional derivative filter adds another zero to the transfer function where \(s=-\rho\). The modified second-order controller's step response will be the original response with the scaled derivative version added based on the \(\rho\) value. The systems' response equation is shown below:  
\[Y_{Z}=\frac{s}{\rho}Y+Y\text{ or }y_{z}(t)=\frac{1}{\rho}\dot{y}(t)+y(t). \tag{4}\]  
As the zero located at \(s=-\rho\) moves to the left side away from the origin, i.e., \(\rho\) increases, the step response will start to equate to the original step response as \(y_{z}(t)\approx y(t)\). On the other hand, when the zero moves closer to the origin of the complex plane, i.e., \(\rho\) decreases, the effect of the derivative term becomes more apparent, causing higher overshoot and lower rise time and response time [17]. This controller design improves the phase margin, steady-state accuracy, and plant stability [9; 23; 28].  
The PIDD2 control action in the Laplace domain is as follows:  
\[C(s)=\frac{U(s)}{E(s)}=\frac{U(s)}{R(s)-Y(s)}=K_{p}+\frac{K_{i}}{s}+K_{d1}s+K_{ d2}s^{2}. \tag{5}\]  
In the above equation, \(K_{p}\), \(K_{i}\), and \(K_{d1}\) are the proportional, integral, and derivative constants. \(K_{d2}\) is the constant for the extra derivative filter, which controls the smoothing of the derivative term over time. Figure 2, which shows the block diagram of the controller and Equation (5), the PIDD2 controller adds another derivative control element to the pre-existing PID control elements where all four constants can be tuned to achieve the desired performance characteristics.  
This control mechanism is also subject to the derivative kick phenomenon inherent in the PID controller. In practical applications, this phenomenon can become a significant issue as its effect is amplified by the second derivative term, leading to an increase in the gain that amplifies the input signal to the square of the frequency. This is particularly problematic in high-frequency environments due to measurement noise, which can result in an overshoot in the control signal that causes the actuators to open and close abruptly, leading to wear and tear rapidly [17].
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 4 Stability Analysis  
The function \(\cos(\omega\Delta t)\) in Equation (6) must lie in the interval \([-1,1]\), where  
\[-1\leq\cos(\omega\Delta t)=1+0.5r^{2}q\leq 1. \tag{24}\]  
We mostly use the Nyquist wavenumber to analyze the stability [19, 24] such that  
\[k_{x}h=k_{z}h=\pi. \tag{25}\]  
By substituting Equation (25) into Equation (24), we obtain the 2D stability condition of the proposed FD scheme as follows:  
\[r\leq\left\{\sum_{m=1}^{M}a_{m}\Big{[}(-1)^{m-1}+1\Big{]}+\sum_{n=1}^{N}b_{n} \Big{[}(-1)^{2n-1}+1\Big{]}\right\}^{-1/2}. \tag{26}\]  
Then, the 2D stability factor \(s\) can be defined as  
\[s=\left\{\sum_{m=1}^{M}a_{m}\Big{[}(-1)^{m-1}+1\Big{]}+\sum_{n=1}^{N-1}b_{n} \Big{[}(-1)^{2n-1}+1\Big{]}\right\}^{-1/2}, \tag{27}\]  
where the stability factor \(s\) is related to the FD coefficients, and these FD coefficients are determined by the Courant number \(r\). Therefore, the FD scheme must satisfy the following stability condition:  
\[r\leq s. \tag{28}\]  
In the following, we analyze the stability factors \(s\) varying with the Courant number \(r\), and the results are shown in Figure 4. It can be seen that the TE-C method had the smallest stability factor, indicating that it is prone to instability. The stability factors of the TE-R and  
Figure 3: Dispersion curves of TE-CR and TE-R methods with different orders \(N\). Here, \(v=1500\) (m/s), \(h=6\) (m), \(M=8\), and propagation angle \(\alpha=\pi/16\): (**a**) TE-CR method and (**b**) TE-R method.  
DRP-R methods were slightly less than the TE-CR method but much larger than that of the TE-C method. Figure 4b shows the maximum factor \(s\) satisfying \(r\leq s\) for different orders \(M\). The maximum stability factors of the TE-R and DRP-R methods were obviously larger than that of the TE-C method, which showed an improvement in stability.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 7 Result Comparison and Performance Evaluation
### Protocol Evaluation
#### 7.2.1 Scenario 1  
In Scenario 1, the mobility varies from 0-4 m/s, and all other parameters are fixed, as shown in Table 2. Based on these parameters, several results are obtained from ERASCA and PUMA, and a comparison is made with the EERASCA. As shown in Figure 9a, when the mobility is low, then EERASCA transfers 90% packets, whereas the packet transfer rate of ERASCA and PUMA are 86% and 82% consecutively. As the mobility increases, then the PDF drops due to frequent topology changes. In high mobility, the packet drops increase; this decreases the PDF with the increase in overhead, as shown in Figure 9b. The high mobility increases the overhead and link failures, and hence the retransmission of packets increases. This retransmission of packet increases the flooding and hence decreases PUMA and EERASCA in terms of overhead. In Figure 9c, throughput decreases due to an increase in overhead.  
only handle 1 Mbps due to congestion, which limited the data transmission to 1 Mbps, which is the wastage of resources in terms of bandwidth. Hence, the throughput is 1/3 of the bandwidth. In Figure 9, EERASCA shows less performance in high mobility in terms of throughput and PDF than ERASCA and PUMA. In high mobility, unicasting and multicasting do not give promising results due to regular topology changes and link failure. On the other hand, ERASCA and PUMA show improvement in throughput and PDF but with higher overhead and his higher overhead is due to continuous flooding. The confidence interval for PDF, overhead and throughput is shown in Table 3 in the presence of mobility (0-4 m/s), which shows the mean value, highest and lowest value.
## 3 Proposed Method
### Attention Module  
#### 3.3.1 Channel Attention Module  
In order to effectively understand the scope of the object, many previous works [34; 35] adopted the average pooling method. In this paper, SSD7-FFAM uses both maximum pooling and average pooling on the feature map after feature fusion so that the detector channel focuses on the object and its contextual information. The schematic diagram of the channel attention module is shown in Figure 5a. Firstly, we perform average pooling and maximum pooling on the feature map \(F\in\mathbb{R}^{H\times W\times C}\) to obtain two \(1\times 1\times C\) channel descriptors. Secondly, they are sent to a two-layer parameter-sharing neural network. The number of neurons in the two layers is C/r and C, where r is the reduction ratio, and the activation function is ReLU. Then, the weight coefficient \(M_{c}\) is obtained by adding the two features through a Sigmoid activation function. In summary, the calculation formula of channel attention is as follows:  
\[M_{c}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))) \tag{1}\]  
Figure 4: An example of the structure of the feature fusion module used in the proposed SSD7-FFAM.  
Figure 5: Schematic diagram of channel attention module and spatial attention module. (**a**) Channel attention module, where \(\oplus\) represents summation operation. (**b**) Spatial attention module, which is given the output of the channel attention module as the input.