esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chelating Activity of Fe\({}^{2+}\) Ion  
The chelating capacity of Fe\({}^{2+}\) was evaluated using the ferrozine method [27]. The process of metal ion chelation significantly contributes to the prevention of reactive oxygen species formation [28]. A calibration curve was prepared by utilising aqueous solutions of FeSO\({}_{4}\) to measure Fe\({}^{2+}\) ions. First, an initial FeSO\({}_{4}\) solution with an Fe\({}^{2+}\) concentration of 0.53 mmol/L was prepared. Next, a ferrozine initial solution (concentration of 3.2 mmol/L). Then 1000 \(\mu\)L of the initial FeSO\({}_{4}\) solution (final Fe concentrations of 3; 1.2; 0.6; and 0.3 mg/L) and 1000 \(\mu\)L of ferrozine were introduced into 10; 25; 50; and 100 mL volumetric flasks. The flasks were filled with distilled water, sealed snugly with a stopper, and then kept at room temperature for a duration of 10 min. The absorbance of the test solutions was measured (using the Thermo Scientific GENESYS 50 apparatus at the wavelength \(\lambda\) = 562 nm), obtaining a calibration curve for Fe\({}^{2+}\) ions y = 0.4888x + 0.0064, R\({}^{2}\) = 0.999.  
In the next stage, 1000 \(\mu\)L of the initial FeSO\({}_{4}\) solution, 100 \(\mu\)L of FPE, and 1000 \(\mu\)L of ferrozine were introduced into 10 mL volumetric flasks. The flasks were filled with distilled water, sealed snugly with a stopper, and left to incubate at room temperature for 10 min, and then the absorbance of the solutions was measured using a spectrophotometer at a wavelength of \(\lambda\) = 562 nm. Three separate tests were conducted.  
The chelating activity (\(ChA\)) of Fe\({}^{2+}\) ions was calculated using the following Formula (7):  
\[ChA=\frac{\left[(C_{Fe2+r.s.}-C_{Fe2+t.s.})\cdot V_{s}\ \right]}{V_{FPE}}.100\% \tag{7}\]  
where  
\(ChA\)--chelating activity of Fe\({}^{2+}\) [mg/L],  
\(C_{Fe2+rs.}\)--concentration of Fe\({}^{2+}\) ions in the reference sample [mg/L],  
\(C_{Fe2+ts.}\)--concentration of Fe\({}^{2+}\) ions in the tested sample [mg/L],  
\(V_{s}\)--total volume of solution introduced into volumetric flasks [L],  
\(V_{FPE}\)--volume of FPE introduced into volumetric flasks [L].  
We used the same methods to evaluate the clove bud extract for comparison. The extraction of buds was carried out using the ultrasonic method. For this purpose, 20 g of ground plant material (in the amount of 6.40%) was introduced into 300 mL of distilled water, and extraction was carried out using an ultrasonic bath at 40 kHz (for 20 min at 40 \({}^{\circ}\)C), after which the extract obtained was filtered on a pressure funnel through a Whatman paper filter (EEA03).
# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
###### Abstract
GPP performance was evaluated through point-to-point comparisons of precipitation data at different elevation bands and through SWAT-generated runoff using GPP forcing to the observed runoff data. All SWAT simulations used the CFSR \(T\) product, combined with each GPP, and measured precipitation. The three selected pilot areas in the inner Austrian Alps are similar in size, location and climate, yet differ in catchment orientation, land cover and the slope of the surrounding ridges.
# Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations
###### Abstract
With an average of root mean squares error of roughly 12 mm, the ZTD showed great agreement with IGS products. [PERSON] et al. [27] proved a great improvement in accuracy in ZTD estimation for GNSS positioning when using the ERA5 atmospheric variables. [PERSON] et al. [28] proposed a method to obtain better ZHD corrections to solve the issues with the application of the conventional approach in areas with high variations in height, and their results showed that the method can achieve an improvement in accuracy of 50% over the conventional approach. ZTD estimation by early BDS was not good as other navigation systems due to regional BDS-2 or limited BDS-3 satellites. After the complete construction of BDS-3 since the end of 2020, the fully operating global BDS-3 constellation provides greater opportunity for GNSS meteorology. In this paper, ZTD is estimated using the single- or multi-GNSS PPP technique over one year (2019) from 55 MGEX stations, particularly adding the fully operating BDS-3 constellation. Our main objective is to evaluate the performance of GNSS ZTD estimation from single- (GPS, BDS, Galileo, and GLONASS), dual-, triple-, or multi-GNSS observations. The multi-GNSS observations from 55 globally distributed stations are processed by Positioning And Navigation Data Analyst software version 1.0, which was developed originally by Wuhan University [29], and the performance of different ZTD solutions is evaluated with IGS troposphere products, radiosonde, and ERA5 hourly data [30].
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
## 4 Experimental Setup and Results
### Numerical Experiments
#### 4.2.2 Partially Additively Separable Problems  
**Definition 2**.: _A function \(f(x_{1},x_{2},\ldots,x_{N})\) is partially separable with \(m\) independent subcomponents iff: \(\underset{(x_{1},x_{2},\ldots,x_{N})}{\text{argmin}}\ f(x_{1},x_{2},\ldots,x_{N })=\left(\underset{x_{1}}{\text{argmin}}f(x_{1},\ldots),\ldots,\underset{x_{m }}{\text{argmin}}f(\ldots,x_{m})\right)\), where \(x_{1},\ldots,x_{m}\) are disjoint sub-vectors of \((x_{1},x_{2},\ldots,x_{N})\) and \(2\leq m\leq N\)._  
An example of a partially separable problem with 2 independent subcomponents is  
\[f(\overline{x})=x_{1}^{2}x_{2}^{2}+x_{3}^{2}. \tag{6}\]  
Partially Additively Separable Problems in the benchmark are \(F_{4}\)-\(F_{11}\). The average ranks for CC(m) and iCC are presented in Figure 4. As we can see, CC(4) demonstrates the best performance in comparison with all algorithms with the fixed numbers of subcomponents. iCC demonstrates the best result in comparison with all CC(m). Tables 5 and 6 have the same structure as Tables 3 and 4, respectively. iCC has the maximum number of \"win\" and the minimum number of \"loss\" points in comparison with all CC(m). Figure 5 shows convergence graphs for CC(m) and iCC.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Components
#### 2.2.4 Management and Data Link Layer  
Management and database servers are the centralized part of the healthcare system that receives and updates the patient's data collected by the sensors and can also help the physicians manage the quantity of medicine or prescribe a new one for the patient. In emergencies, the servers send alerts to physicians and the emergency response teams for appropriate actions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Frequency** & **Range** & \begin{tabular}{c} **Data Transfer** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & \
\begin{tabular}{c} **Security** \\ \end{tabular} \\ \hline Bluetooth & 2.40–2.48 GHz & 10–50 m & 1–3 Mbps & 0.01–50 W &
\begin{tabular}{c} SAFER Block \\ Cipher \\ \end{tabular} \\ \hline BLE & 2.40 GHz & 400–1000 m & 125 Kbps-2 Mbps & 50–100 micro W & AES-CCM Cipher \\ \hline ZigBee & 860 MHz–2.40 GHz & 10–100 m & 20–250 Kbps & 10–100 micro W & AES-CCM/CBC \\ \hline LoRaWAN & 433–923 MHz & 2–7 km & 27 Kbps & 50–80 micro W & AEA-CMAC \\ \hline ANT & 2.40 GHz & 30 m & 60 Kbps & 42–72 micro W & AES-CBC \\ \hline UWB & 4.3 GHz & 10 m & 1 Mbps & 5.31 micro W & CRC \\ \hline RuBee & 131 kHz & 15 m & 9.6 Kbps & 40 nano W & AES \\ \hline \hline \end{tabular}
\end{table}
Table 3: IoHT Devices Technical Details.
## 4 Simulations and Analysis
### Sea Surface Boundary
The blue, green, and red curves represent the BER performance of the modelled channel with 0.5 m, 1.29 m, and 2 m SWH, respectively. For each color, there are 20 curves. When SNR is 30 dB, the worst curves of the three colors have similar BER value with the worst curves in Figure 8a and Figure 11a, which confirms the existence of worst-case performance bound of UWA channel estimation through LS algorithm. Considering the distribution characteristic of curves in different colors, the blue, green, and red curves are mainly concentrated in the high, middle, and low BER performance part of the distribution range, respectively. Figure 15b shows the quantitative result by plotting the mean channel estimation BER curves for each value of SWH. It is obvious that the mean BER performance of stochastic channels is worse than that of channel with flat sea surface. As the SWH rises, the average performance gradually increases. When SNR is 30 dB, UWA channel with 2 m SWH is 7.95 dB better than that with 0.5 m SWH and 8.06 dB worse than that with flat sea surface. Figure 13: Normalized CIRs of channel with different sea surface boundaries in 1.29 m SWH. Sea surface boundaries of each figure: (**a**) Sea Surface 1; (**b**) Sea Surface 2; (**c**) Sea Surface 3; (**d**) Sea Surface 4. The result is mainly caused by the effect of sea surface and the worst-case performance bound. According to the analysis of Figures 12-14, sea surface can affect the time delay and amplitudes of interfering eigenrays, which leads to the variation of estimation performance. As SWH rises, the variation becomes bigger and makes channel estimation more likely to get better or worse performance while the worst-case performance bound limits the performance from getting worse. Consequently, the channels with high SWH can get better average performance of channel estimation. In addition, the possibility of negative effect of sea surface is more than the positive effect, which makes the rough surface channel get worse average performance of channel estimation than the flat surface channel.
# Dynamic Cost-Aware Routing of Web Requests
## 1 Introduction
The latter factor captures the impact that high delay can cause to users and the operator. The learning automaton makes routing decisions for each incoming request by assessing response times and energy prices at the different server locations through action selection probabilities. Then, it probabilistically forwards the request to the server that minimizes the cost. Our contributions are: (i) a learning automata-based method for routing web requests considering both the differences in energy prices across locations and the revenue loss potentially produced by high response times; (ii) a simulation study of the performance impact of learning parameters, and; (iii) an implementation of the method for Apache Traffic Server and testbed evaluation in CloudLab [6]. We have compared our results to common baseline methods: minimum cost flow algorithm and round robin algorithm. Our experimental results show that the total average cost of serving web responses could be reduced up to 33% when compared to the minimum cost flow dynamic server selection algorithm and up to 49.2% compared to the common round robin method.
## 6 Simulation Results and Discussions
### Secrecy Rate  
Figure 10a demonstrates the achievable rate of the LU versus SNR per bit for the proposed DM schemes. As might have been expected, the achievable rate of the LU for each antenna array is almost at the same level. Owing to the introduction of AN, the achievable rates are lower than the upper bound of the achievable rate using a single antenna without AN. Especially, the achievable rates of the LU decrease as the information power splitting factors decrease.  
Figure 10b,c shows the achievable rate of the Eve at two different potential positions. As expected, the achievable rates are approximate to \(0\ \mathrm{bps/Hz}\) when the Eve is not in any desired directions. Once the Eve is in one of the desired directions, the achievable rates for the Eve are just a little lower than that of the LU.  
Furthermore, Figure 10d illustrates the secrecy rate versus SNR per bit for the proposed DM schemes and the traditional single antenna array DM schemes. Whatever value the SNR per bit and the power allocation factor \(\beta_{1}\) take, the secrecy rates of the proposed DM schemes are much higher than those of traditional single antenna array transmission schemes with the same fixed total transmitting power. As expected, the secrecy rates of the proposed DM schemes are almost twice those of the traditional single antenna array schemes due to the use of multiple antenna array simultaneously. By contrast, the secrecy rates are always positive wherever the Eve is located. Therefore, the proposed DM schemes are very secure and reliable.
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
###### Abstract  
Rice husk ash is one of the most widely studied biomass ashes used in pozzolanic addition. Given its lower silica content, rice straw ash (RSA) has been explored less often, despite the fact that, according to the United Nations Food and Agriculture Organization (FAO), rice straw (RS) production is estimated at 600 million tons/year. In this work, RSA was physically and chemically characterized, and its pozzolanic properties were assessed. A controlled conditioning, burning, homogenization and grinding procedure was carried out to obtain RSA from RS. Chemical composition, insoluble residue, reactive silica, chloride content and particle size distribution were assessed for ash characterization. To determine RSA pozzolanicity, Frattini, electrical conductivity and pH measurements in an aqueous suspension of hydrated CH/RSA mixtures were obtained. Portland cement (PC) mortars with 15% and 30% RSA substitutions evaluated. The mechanical tests showed specimens with a strength activity index up to 90% and 80% with 15% and 30% RSA, respectively, after 3 days, and these values grew to 107-109% after 90 curing days.  
rice straw ash; pozzolanic; Frattini test; pH and conductivity test; thermogravimetry; mechanical strength 202111773[[https://doi.org/10.3390/app11020773](https://doi.org/10.3390/app11020773)]([https://doi.org/10.3390/app11020773](https://doi.org/10.3390/app11020773))
## 5 Experimental Evaluation
### Impact of Hyperparameters on MAE and RMSE
#### 5.6.1 Error Metrics  
In the training dataset, each user-item interaction edge (u, i) incorporates a true preference signal \(r_{u,i}^{(\text{true})}\), represented by user ratings. Following the processing through the graph convolutional network (GCN), we derive embedding vectors for users and items, denoted as \(U[u]\) and \(P[i]\), respectively. These embeddings are subsequently concatenated as \(\text{concat}(U[u],P[i])\) and input into a trained multi-layer perceptron (MLP) module to predict the preference score:  
\[r_{u,i}^{(\text{pred})}=\text{MLP}(\text{concat}(U[u],P[i])). \tag{9}\]  
To evaluate the discrepancy between the predicted and true values, MAE and RMSE are defined as follows:  
\[MAE=\frac{1}{|\mathcal{E}|}\sum_{(u,i)\in\mathcal{E}}\Bigl{|}r_{u,i}^{(\text{ pred})}-r_{u,i}^{(\text{true})}\Bigr{|}, \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{|\mathcal{E}|}\sum_{(u,i)\in\mathcal{E}}\Bigl{(}r_{u,i}^ {(\text{pred})}-r_{u,i}^{(\text{true})}\Bigr{)}^{2}}, \tag{11}\]  
where \(\mathcal{E}\) represents the set of user-item interaction pairs in the training and validation sets. The mean absolute error (MAE) metric quantifies the average magnitude of prediction deviations from ground truth values, whereas the root mean square error (RMSE) emphasizes larger prediction discrepancies by incorporating squared errors in its calculation.
## 2 Materials and Methods
### Data Analysis  
#### 2.6.1 Preprocessing  
The analysis was performed using the NIRS-SPM open-source software package implemented in MATLAB (MathWorks, Inc., Natick, MA, USA). The concentration changes in oxy-hemoglobin (HbO) for each channel were obtained using the modified Beer-Lambert law [21] with constant differential pathlength (DPF). The DPFs were set at 7.15 for the wavelength of 760 nm and 5.98 for the wavelength of 850 nm as recommended in the software guideline. Only alterations in HbO were reported as these were shown to be more reliable and sensitive to changes in cerebral blood flow [22; 23; 24]. The hemoglobin concentrations were subsequently subjected to independent component analysis (ICA) to eliminate typical motion artifacts [25; 26]. Then, a re-referencing method was applied to the NIRS signals to remove common noise [27; 28]. The hemodynamic response function (hrf) was applied for the correction of noise from the fNIRS system, and the wavelet-minimum description length (MDL)-based detrending algorithm was used for the correction of global trends due to breathing, heartbeat, or other experimental influences [29]. The block averaging, which is common in the neuroimaging field by repeating the task several times and considering the average per time-points of all repetitions, was also used to remove physiological changes that are not associated with neuronal activity [30; 31].
## 6 Transformer Disassembly Results
### Experimental Results of Internal Component Disassembly
#### 6.2.2 Analysis of Disassembly Time  
The results from the 50 experiments indicate that the system's average disassembly time is 876.6 s, showcasing a significant advantage over traditional methods. This time reduction is largely due to the automated path planning and target detection techniques employed by the system, which prove particularly efficient for handling screws and winding studs. In our experiments, the transformer we used is a 10 KV, 400 KVA oil-immersed transformer. The number of screws is between approximately 150 and 300, the length of the core welds is about 1.5 to 3 m, the length of the oil tank welds is about 5 to 8 m, and the weld length of other components is about 5 to 10 m.
## 1 Introduction
Naturally, when the MCF rubber involves dye and electrolytes to be electrolytically polymerized, the photovoltaic effect can also manifest as materialized DSSCs. The MCF rubber not only has a photovoltaic effect, but also has sensing capabilities [30; 31; 32; 33] and serves as a capacitor [32]. In addition, the brilliant peculiarity of MCF rubber is that it is largely stretchable and compressible [31]. Regarding previous studies on flexible solar cells utilizing polymers or thin films, the solar cells bend but do not stretch and are, therefore, considered solid films [23; 24]. Alternatively, the deformation of the solar cells is so small that the tensile and compressive rates are not comparatively very large. However, MCF rubber can be stretched to twice its original length or compressed to half its initial size. Therefore, wearable and portable solar cells made of MCF rubber are highly promising for use as soft materials in robots and flexible devices, especially because of their self-generated power and power supply with sensing. As a general technique that has been seen in the case of ordinary solar cells, involving a catalyst is an effective way of enhancing the high efficiency of solar cells. In water-soluble solar cells such as DSSCs, a number of different effective catalysts have been proposed, such as graphite, carbon black, SnO\({}_{2}\)[43], Pt [44], MoS\({}_{\mathrm{x}}\)[45], MnWO\({}_{4}\), ZnWO\({}_{4}\), and CuWO\({}_{4}\)[46]. TiO\({}_{2}\) is one of many useful catalysts and is effective enough to enhance the photovoltaic effect. Because of the relevant reaction of water on photovoltaics, the chemical-photovoltaic mechanism can be explained by the Honda-Fujishima effect [47]. MCF rubber involving TiO\({}_{2}\) is effective in the type of DSSCs that contain MCF in order to achieve large efficacy. However, the effect of TiO\({}_{2}\) on DSSCs with MCF remains to be clarified, and the chemical-photovoltaic mechanism involving the catalytic effect has not been dealt with. In the present study, we investigated the effect of TiO\({}_{2}\) as a catalyst on the photovoltaic effect of MCF rubber DSSCs under tension and compression. The experimental results were investigated by using a chemical-photovoltaic mechanism that induces the Honda-Fujishima effect.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 2 Theoretical Background and Proposed Method
### Convolutional Deep Brief Network and Its Improvement  
#### 2.2.1 Convolutional Restricted Boltzmann Machine  
Convolutional RBM (CRBM) is an improvement on the basis of the original RBM, and the structure of CRBM is similar to that of RBM. CRBM is an improved model composed of two random variable matrix layers, namely, the visible and hidden layers. An image with salient features of the local receptive field and weight sharing is regarded as the input layer of CRBM. The hidden layers are locally connected with visible layers, and their weights are shared by convolution.  
The CRBM model, as shown in Figure 2, comprises the view layer V, the hidden layer H, and the pooling layer P, for a total of three layers. We assume that the size of the input layer matrix is \(N_{p}\times N_{w}\), the number of groups of matrices in the hidden layer is \(k\), each group is a binary array with the size of \(N_{H}\times N_{H}\), and \(N_{H}^{2}K\) hidden layer units are present. Each group of hidden layers is associated with an \(N_{w}\times N_{w}\) size filter.  
Figure 3 shows the process of obtaining the hidden layer from the visible layer. The size of the convolution kernel is \(3\times 3\), and the hidden layer units are divided into k sub-matrices. \(W_{1},W_{2},\cdots,W_{K}\) connects the visible and hidden layers. Each hidden layer unit represents a specific feature extracted from a neighborhood unit of a visible layer. Moreover, \(b_{k}\) indicates the value of each hidden layer unit bias, and \(C\) is the bias globally shared by all visible units. We obtain the energy number of CRBM as Equation (9):  
\[E(v,h)=-\sum\
olimits_{k=1}^{k}h^{k}(w^{k}*v)-\sum\
olimits_{k=1}^{k}b_{k} \sum\
olimits_{i,j}h_{i}\hat{v}^{k}-c\sum\
olimits_{i,j}v_{ij} \tag{8}\]where * indicates convolution, \(v_{ij}\) denotes the input values of the \(i\)th visible layer unit and the \(j\)th hidden layer unit, \(h^{k}\) denotes the kth hidden layer, \(h^{k}_{ij}\) denotes the values of the \(i\)th visible layer unit and the \(j\)th unit of the \(k\)th hidden layer, and \(w^{k}\) denotes the convolution kernel of the \(k\)th hidden unit.  
As with standard RBM, the conditional probability distribution is given as follows:  
\[p\big{(}h^{k}_{j}=1|v\big{)}=\sigma((\widetilde{W}^{k}*v)_{j}+b_{k}) \tag{10}\]  
\[p(v_{i}=1|h\,)=\sigma(\big{(}W^{k}*h^{k}\big{)}_{i}+c) \tag{11}\]  
where \(\sigma=1/(1+e^{-x})\), \(\widetilde{W}^{k}_{j}\triangleq W^{k}_{N_{\alpha-j+1}}\).  
A CRBM is a single-layered network. It can be considered as the basis of a CDBN. Stacking multiple CRBMs, with the hidden layer of the previous RBM as the visible layer of the next CRBM, constitutes a CDBN. In each training, the RBM of the lowest layer is trained, one layer at a time, until the top layer.  
Figure 3: The process of obtaining hidden layers from the visible layer based on CRBM.  
Figure 2: Structure diagram of a convolutional restricted Boltzmann machine (CRBM).