esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Optimization: the CRO-SL Algorithm
### Objective Function: Antenna Simulation and Calculation  
The objective function considered (\(f(x)\)) to guide the algorithm toward optimal antenna optimization and takes into account different design requirements of the device, such as its resonant frequency and bandwidth. Specifically, in order to calculate \(f(x)\), we first take into account a discretization of the S\({}_{11}\) antenna parameter, which is calculated by simulation using the CST software, as described in the next subsection. In this case, a discretization in steps of 2 MHz is considered. To calculate \(f(x)\), several frequency bands for mobile communication systems (including LTE and 5G) has been considered. For each frequency band, the mathematical formulation of the objective function is the following:  
\[g_{f}(x)=0.8\cdot N^{-10 dB}+0.1\cdot M+0.1\cdot M^{*} \tag{1}\]where \(N^{-10\text{dB}}\) stands for the number of S\({}_{11}\) points in the observation window under \(-10\) dB, \(M=|\) mean (S\({}_{11}\))\(|\), \(M^{*}=|\)min(S\({}_{11}\))\(|\) and f stands for a given selected frequency band. The final objective function value \(f(x)\) is obtained by adding the value of \(g_{f}(x)\) for all the frequency bands f considered:  
\[f(x)=\sum_{f}g_{f}(x) \tag{2}\]  
Note that this function takes into account the antenna bandwidth for all the frequency bands considered, the resonant frequency, and the actual value of the antenna reflection coefficient. In order to make the calculation, we define a measurement window at each selected frequency band, with a resolution of 20 MHz: (1)\(f_{1}=791-870\) MHz (5G); (2)\(f_{2}=1.7-2.3\) GHz (LTE); (3)\(f_{3}=3.3-3.8\) GHz (5G). Note that these frequency bands cover the majority of communications services such as 2G/3G/4G and also LTE and 5G bands of ultimate mobile communication systems.  
CST Microwave Studio(r) from Dassault Systemes SE (France) is a well-known software package for electromagnetic analysis and simulation in the high frequency range. It is able to provide a fully automatic meshing procedure of any electromagnetic device and its simulation using different possible simulation techniques, depending on the case (transient solver, frequency domain solver, integral equation solver, etc.). The idea is to launch the antenna simulation for each solution encoded in the CRO-SL algorithm (a given antenna design). The CRO-SL has been coded in Matlab, and we are able to call the CST simulation directly from Matlab, so the hybridization of the CRO-SL and the CST software for antenna simulation is direct. Figure 2 shows this hybridization process: the CST software is launched in order to simulate each larva (antenna design) in the CRO-SL. After the antenna simulation, we calculate the value of the objective function associated with the simulated antenna using Equations (1) and (2), and this value is used in the evolution of the CRO-SL algorithm.
## 2 Materials and Methods
### Performance Evaluation  
To comprehensively assess the impact of quantitative information on the prediction of ICU patient mortality and readmission rates, this study employed the following five distinct evaluation metrics: the area under the receiver operating characteristic (AUROC) curve; precision; recall; F1 score; and accuracy.  
\[Precision=PPV=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=TPR=\frac{TP}{TP+FN} \tag{2}\]  
\[F1-score=\frac{2*Precision*Recall}{Precision+Recall} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
In predictive modeling, performance evaluation metrics are crucial for assessing the accuracy and effectiveness of a model. Four common metrics in binary classification tasks include true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). Precision measures the proportion of correct predictions among positive samples, indicating the ratio of true positive samples to all samples classified as positive. Recall, or sensitivity, reflects the proportion of actual positive samples that are correctly identified, representing the ratio of true positive samples to all actual positive samples. These metrics are integral to identification and prediction algorithms. The F1 score, which harmonizes precision and recall, provides a comprehensive measure of model performance and is often employed to evaluate the precision of classification algorithms. Another critical performance metric is accuracy, which quantifies the proportion of correct predictions across all samples, calculated as the ratio of correctly classified samples to the total number of samples in the test dataset. In diagnostic testing, the area under the receiver operating characteristic (AUROC) curve is the predominant metric for evaluating the performance of diagnostic tools. The AUROC curve graphically represents the relationship between the true positive rate (TPR) and the false positive rate (FPR) across varying classification thresholds, offering a thorough measure of classifier performance. It assigns equal importance to all instances, irrespective of the nature of the positive label. The ROC curve is constructed with FPR on the \(x\)-axis and TPR on the \(y\)-axis, illustrating the trade-off between sensitivity and specificity across different thresholds.
# Electrochemical Diagnostics of Sprayed Fiber-Reinforced Concrete Corrosion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameter.**} & \multicolumn{1}{c}{**Steel Fibers**} & \multicolumn{1}{c}{**Polymer Fibers**} & \multicolumn{1}{c}{**Basalt Fibers**} & \multicolumn{1}{c}{**Glass Fibers**} \\ \hline Density & t/mm\({}^{3}\) & 7.8 & 0.9–1.4 & 1.7–2.65 & 1.8–2.6 \\ Length & mm & 25–60 & 6–54 & 24–54 & 4–40 \\ Diameter & mm & 0.4–1.1 & 0.012–0.038 & 0.012–0.018 & 0.013–0.02 \\ Young’s modulus & GPa & 190–210 & 35–40 & 70–90 & 25–60 \\ Tensile strength & MPa & 550–1100 & 480–1320 & 700–1680 & 410–1180 \\ Break elongation & \% & – & 2.0–4.0 & 0.5–1.6 & 3.7–4.5 \\ Corrosion resistance & – & No & Yes & Yes & No \\ Fire resistance rating & \({}^{\circ}\)C & very high; & low; & very high; & good; \\  & up to 1600 & up to 160 & up to 1650 & 700 \\ Working & \({}^{\circ}\)C & –50–+300 & –20–+120 & –260–+750 & –100–+400 \\ temperature & \multirow{2}{*}{Mosh scale} & \multirow{2}{*}{9} & \multirow{2}{*}{1–3} & \multirow{2}{*}{8.5} & \multirow{2}{*}{5–7} \\ Hardness & & & & & \\ \cline{1-1} Fiber content & \% & 0.25–3.0 & 0.5–2.0 & – & – \\ Adhesion to concrete & – & very good & weak & very good & good \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of various fibers used for concrete reinforcement [18]. In highly developed countries, most construction works with shotcrete use concrete with dispersed reinforcement, usually in the form of steel fibers (so-called SFRC), due to its good parameters and relatively low price [4; 5; 8; 9]. However, in many other countries, Poland included, this composite is not particularly popular, which is difficult to comprehend. Perhaps, as pointed out by some authors [6; 18], one of the reasons is the concern that the micro-reinforcement fibers do not have adequate corrosion resistance and might trigger corrosion of the primary reinforcement. The main objective of this research was to assess the impact of randomly dispersed steel fibers in concrete on the corrosion development of the main reinforcement by means of the electrochemical galvanostatic pulse method under environmental conditions conducive to the development of steel corrosion.
# Integrated Module Antenna for Automotive UWB Application
## 2 Proposed Antenna Design
### Design of the Prototype Antenna  
This work aims to design the integrated antenna with different radiation properties in a compact UWB module substrate, taking into consideration the antenna case and actual vehicle implementation. For wide coverage and sufficient bandwidth for automotive UWB frequency band, the monopole antenna is selected to satisfy our purpose. To secure antenna design space, a quasi-circular rim-type monopole antenna which satisfies the UWB bandwidth with a miniaturized size is designed based on [20]. For rear passenger detection, however, an antenna with relatively narrower beam and greater gain than a monopole antenna is required. Therefore, the patch array is adopted due to the aforementioned requirements and available space. These antennas are designed on the different layers in order to efficiently utilize the available space of the limited module substrate.  
The basic monopole is designed with a feedline on the 4 th layer, and the ground is assigned to the 3 rd layer. The patch array is designed on the 1 st layer, and the ground is assigned to the 3 rd layer that is to be shared with the ground of the monopole. The patch array is fed by the rectangular slots on the ground through the feeding line on the 4 th  
Figure 4: Actual mounting position of the detection antenna with vehicle model.  
Figure 3: Actual mounting position of the localization antenna with vehicle model.
# Mechanism of HCB-Modified Asphalt and Dynamic Properties of Mixtures
## 1 Introduction  
Processing waste materials into asphalt modifiers can not only effectively improve the performance of asphalt and its mixtures, but also achieve sustainable utilization of waste materials [1; 2]. Waste rubber tires have increased massively and sharply with the development of the automotive industry, which has become a global source of pollution. Carbon black (CB) is one of the products of waste rubber under high temperature [3]. CB has wide application prospects in the field of materials, with fine particles, closely packed mesh chains, and a large specific surface area [4; 5]. The addition of CB can improve the aging properties, electrical conductivity, and thermal conductivity of asphalt [6; 7; 8], and it can also optimize some performance aspects of asphalt mixtures, such as resistance to cracking and rutting [9; 10; 11; 12; 13].  
However, CB has limited ability to improve asphalt performance. Some scholars have tried to modify CB or to compound CB with other modifiers. [PERSON] et al. [14] ascertained the temperature susceptibility of styrene-butadiene-styrene (SBS) modified asphalt with white carbon black. [PERSON] et al. [15] used pyrolysis carbon-black nanoparticles as a modifier and adopted aromatichydrocarbon oil as the softening agent for asphalt binders. The anti-rutting performance and low-temperature performance were improved.  
An asphalt mixture counts as a typical multiphase granular material, of which the viscoelastic dynamic response is bound by the rutting, cracking, and fatigue of asphalt pavement [16; 17; 18]. Delving into the viscoelastic response of CB-modified asphalt mixtures will be conducive to deeply understand their dynamic responses under environmental factors and vehicle load. [PERSON] [19] comparatively analyzed the effects of CB and SBS on the dynamic moduli of asphalt mixtures, but he did not explain the difference.  
Based on the above problems, this investigation hydroxylated carbon black to improve the performance of asphalt and its mixtures. A series of microscopic experiments were conducted to study the structure of HCB. The physical and rheological properties of HCB-modified asphalt were analyzed and the modification mechanism of hydroxymethyl carbon black (HCB) was revealed.
## 3 Protocol Framework
### Contention Period  
CP is assigned for transmission of remaining data to be sent to the PC from the node and for transmission of specific control packets such as new node registration or slot request for u-TP. In this period, as shown in Figure 4, all nodes and PC are performing transmission including emergency data with CSMA/CA mechanism after IFS duration since the start time of CP.  
All transmissions in CP can be classified into the remaining data, emergency data, and specific control frames such as registration and u-TP slot request. If a node or some nodes did not complete data transmission during the assigned u-TP, they can additionally continue to transmit it in this period with competition based on CSMA/CA, so this period can be alternatively used with TP. Furthermore, if a node tries to register at the given network or changes its slot, it can also try to request to the PC via this period.  
As the mechanism of TP, CP also employs Ack so all types of packet should be followed by Ack. After PC receives a request for registration or slot change, it should apply and broadcast the changed schedule after the next subframe through the Sync packet. On the contrary, PC can deny the request  
Figure 3: Transmission example of TP (Transmission Period) which composed of d-TP (downlink-TP) and u-TP (uplink-TP) in the Active subframe.  
from nodes by notifying Nack to the corresponding node if there is any reason to reject the request (e.g., overheated congestion or overflowed capacity). In this case, the node neglected by PC can try to again at the next Beacon time. Note that all transmissions should be competitively sent based on CSMA/CA regardless of any direction (downlink or uplink), and also note that PC should also participate in the competitive transmission with the nodes in CP.  
Basically, CP would be required in a few cases. Hence, it is very low frequency to be incurred. To address this situation, it is recommended to allocate CP whenever multiple frames are passed. As a result, all nodes can participate in the competition of CP when it is detected via Sync packet. If a new node failed to be registered, it should keep going on sleep state before the next CP which can be found by each Sync packet.
## 4 Proposed Efficient Monitoring System (EMS)
### Basic Concepts  
Every network node in the EMS does have a reputation value \((\gamma)\) that can be used to calculate the number of resources that nodes can exchange with the other nodes in the network (for instance, buffer needed to keep packages from others or communication time spent forwarding packages from others). At first, this value is 45; however, that may vary between 0 and 100 over time. The nodes in EMS can contact one another.  
Nodes share information regarding their system efficiency (e.g., number of relayed, dropped, and forwarded packages) at each encounter, allowing nodes to analyze one another. Such information is often received by EMS, which will preserve data on every network node's performance. The EMS thereafter changes each cooperating node's reputation value based on three separate values at each interaction opportunity. A cooperative value is given by the monitoring node \((CVM)\), a node reputation value viewed by neighbors \((RVN)\), as well as a node reputation value noticed by the node on its own \((RV1)\). The EMS comprises four main components to produce these values: a node categorization component, a neighborhood assessment subsystem, a punishment component, and a determination subsystem. The overall structure of the proposed scheme is shown in Figure 2. The notations used in this article are shown in Table 2.
## 3 Results  
### Overall Situation of Model Accuracy  
The accuracy of FAPAR\({}_{\mathrm{MOD}}\) was clearly lower than that of comparison models. The RMSE and MAPE of FAPAR\({}_{\mathrm{MOD}}\) were 0.216 and 100.7%, respectively, while the RMSE of FAPAR\({}_{\mathrm{LAI}}\) and FAPAR\({}_{\mathrm{FVC}}\) were 0.172 and 0.186, respectively, which were reduced by 20.5% and 13.9%, respectively; the MAPE were 64.8% and 48.2%, which were reduced by 35.6% and 52.1%, respectively; the RPIQ was 2.53, while that of FAPAR\({}_{\rm LAI}\) and FAPAR\({}_{\rm FVC}\) were 3.19 and 2.94, which were 26.1% and 16.2% higher, respectively. The Bias and MPE of FAPAR\({}_{\rm MOD}\) were 0.130 and 95.4%, respectively, while the Bias of FAPAR\({}_{\rm LAI}\) and FAPAR\({}_{\rm FVC}\) were 0.089 and \(-\)0.075, respectively, which were 31.9% and 42.8% lower in terms of quantity (regardless of positivity and negativity); the MPE were 59.1% and 16.8%, which were 38.0% and 82.4% lower in terms of quantity, respectively. The R\({}^{2}\) of FAPAR\({}_{\rm MOD}\) was 0.668, and the R\({}^{2}\) of FAPAR\({}_{\rm LAI}\) and FAPAR\({}_{\rm FVC}\) were 0.753 and 0.670, which were 12.7% and 0.3% higher, respectively (Figure 3).  
The accuracy of FAPAR\({}_{\rm FVC}\) is clearly superior to that of FAPAR\({}_{\rm LAI}\). The MAPE and MPE are notably better, with MAPE and MPE of 48.2% and 16.8%, respectively, for FAPAR\({}_{\rm FVC}\), compared to 64.8% and 59.1%, respectively, for FAPAR\({}_{\rm LAI}\), which is a reduction of 25.6% and 71.6%, respectively. RMSE, Bias, RPIQ, and R\({}^{2}\) were generally similar, with FAPAR\({}_{\rm FVC}\) having an 8.2% higher RMSE, a 7.8% lower RPIQ, and a 11.0% lower R\({}^{2}\) compared to FAPAR\({}_{\rm LAI}\), but a 15% lower Bias, in terms of quantity, respectively (Figure 3).  
All three models simulate FAPAR as overestimated, with FAPAR\({}_{\rm FVC}\) being the least overestimated. The MPE of FAPAR\({}_{\rm MOD}\), FAPAR\({}_{\rm LAI}\), and FAPAR\({}_{\rm FVC}\) are 95.4%, 59.1%, and 16.8%, respectively, and the MPE of FAPAR\({}_{\rm MOD}\) and FAPAR\({}_{\rm LAI}\) are 5.7 and 3.7 times the MPE of FAPAR\({}_{\rm FVC}\), respectively. However, FAPAR\({}_{\rm FVC}\) exhibits an underestimation with a Bias of \(-\)0.075 (Figure 3).
## 6 Discussion
### Limitations of the Study and Future Research  
The results of this study validate the device and demonstrate its effectiveness under the standard conditions defined for the tests used in measuring clogging potential. These conditions include specific parameters of pressure, temperature, filter medium, and type of fluid, which were strictly maintained during the trials. While the proposed device is suitable for replacing the pressurized tank under these standard conditions, it is important to recognize that this study is limited to this specific set of variables. Therefore, variations in these parameters are not considered in this study, and additional research is required to validate the device's performance, the validity of the results, and its repeatability under different operational conditions.  
Future research should focus on developing new indicators to measure clogging potential by incorporating additional variables that affect the filtration process. It is essential to consider the actual conditions of the bottling process in laboratory tests to increase the representativeness of the measurements. Adjusting test parameters, such as pressure, flow rate, and the specific characteristics of the wine used in production, would help ensure that the measurements more accurately reflect behavior under real operational conditions. Furthermore, it would be important to study how variations in measurement conditions affect the repeatability of the measurements and the values obtained. Additionally, establishing the optimal measurement conditions that minimize variation in analytical results would significantly contribute to the accuracy and reliability of the developed indicators.  
Associated with the current analytical methods and their limited capacity to provide directly interpretable values for decision-making, new lines of research could explore the development of measurement scales that are both interpretable and tied to the performance of the production process. This is particularly important given that these indicators are measured using a 25 mm diameter disc, whereas, at the industrial level, the filtration area can vary significantly from one bottling line to another. This discrepancy creates a scenario of uncertainty regarding the real-world application of these indicators for industrial planning. Addressing this gap would contribute to the development of more practical and representative tools for evaluating filtration performance in diverse industrial contexts.  
Furthermore, exploring the use of this device in filtration tests across other industries, such as bioprocessing, refineries, and other processes where studying clogging potential and using pressurized tank systems for measurement is necessary, could offer similar advantages in terms of safety, operational efficiency, and analytical precision. Expandingthe scope of application would validate the versatility of the device and contribute to technological advancements in areas where optimizing filtration processes is critical.
## 2 Tunnel Description and Geological Conditions
### Establishment of the DEM-Based Computational Model
#### 3.3.1 Change Rule of Displacement Field
The change curves of displacements at monitoring points on each monitoring section are displayed in Figure 5. It should be noted that the number of calculations in the horizontal coordinates in Figure 5a-c is not the final number of calculations corresponding to the damage of the face. The 35,000 calculations were taken to demonstrate the staged development of the displacements at each measuring point with multiple excavations of the tunnel and the variability of the displacement changes at each measuring point, which can be reflected in Figure 5d. Figure 5 revealed that as the excavation of the tunnel face advanced, different degrees of displacement response were observed at each monitoring point on different monitoring sections, and the change trends at all monitoring points were basically the same. The change trends of transverse displacement at the same monitoring position on different monitoring sections were identical. As the face gradually approached the water-rich fault, the displacement became larger with increasing growth magnitude. After the completion of the fourth excavation step, the closer the monitoring section was to the face, the greater the transverse displacement was at each monitoring point. On the same monitoring section, the displacement at monitoring point 8 (at the center of the face) was much larger than those at other monitoring points at the edge of the face. It was observed from Figure 5d  
Figure 5: Monitoring point displacement curve: (**a**) \(h=3\) m; (**b**) \(h=4\) m; (**c**) \(h=5\) m (Before 35,000 steps); (**d**) \(h=5\) m.  
that after the completion of the fifth excavation step, the displacements at measuring points on the face (monitoring section of x = 5 m) suddenly increased, with obvious face instability and water inrush. It could be considered that the water-resistant rock mass at the face underwent destabilization damage, which was consistent with the interconnected damage of the water-resistant rock mass at the face after the completion of the fifth excavation step in Figure 4.