esa-sceva/satcom-chunk-collection huggingface dataset:
# Bonding Performance of Steel Rebar Coated with Ultra-High-Performance Concrete
## 3 Results and Discussion
### Load-Slip Relationship
In the test specimens containing 0.6% and 0.8% HSF, rapid adhesion failure between the rebar and concrete interface was shown in the softening process. It can be seen that the behavior up to adhesion failure was affected by the frictional resistance caused by the strong chemical adhesion between interface 1 (concrete matrix-UHPC interface) and interface 2 (UHPC-rebar interface). It was found that the maximum load value corresponding to the adhesion failure increased and the energy dissipation ability was also improved due to the increase in fiber mixing. Figure 6 shows the results of the pullout test for rebar coated with UHPC after mixing CSF fibers in the concrete matrix. Similar to the case for HSF, the initial section shows a steep load increase accompanied by a small slip due to the strong bonding force at the interface. Increasing the mixing amount of fibers tends to gradually decrease the load after reaching the maximum load, while causing a steeper rise section. When the mixing ratio of CSF was 0.8% or more, the yield phenomenon of the rebar occurred in the maximum load section. It was judged that the yield phenomenon of the reinforcing bar occurred because the bonding ability at the interface was larger than that of the reinforcing bar itself [40]. In particular, the specimens mixed with 1% CSF showed a pullout failure phenomenon from the concrete matrix after slipping over a long section of the yield area of the reinforcing bar. Figure 7 shows the load-displacement curves of the test specimen mixed with CSSF fibers. Compared with Figure 5, the overall tendency was similar, and the yield phenomenon of the rebar was more clearly shown at 0.8% and 1.0% fiber mixing ratios due to the strong bonding force at the bonding interface. The bond strength was larger than that of the test sample containing HSF and relatively lower than that of the test sample containing CSF. Figure 5: Load–slip curves with hooked-end steel fiber. Figure 6: Load–slip curves with crimped steel fiber. Figure 7: Load–slip curves with crimped stainless steel fiber. In the pullout resistance curve of the UHPC-coated reinforcing bar, when the initial load was raised, it increased sharply with a small slip. In particular, as the mixing ratio of the fibers increased, the maximum load was reached, with a steep increase in load. At the maximum load, the load gradually decreased due to the pullout phenomenon due to the decrease in the adhesion force, but no abrupt decay was observed after the adhesion failure, as in the case of the sand-coated FRP reinforcing bar [36; 40]. It can be judged that the UHPC was strongly attached to the surface of the reinforcing bar (Figure 8), maximizing the resistance to pullout, which did not cause a sudden load reduction after bonding failure at the maximum load. In addition, it seems that the mixing of steel fibers and the increase in the amount strongly improved the pullout resistance.
# Risk-Aware Wireless Positioning in
## 3 Problem Formulation
### Risk Modeling  
In our system model, the purpose of wireless positioning is to prevent the user from entering the unsafe zone. Thus, the BSs require more accurate information of user position as the user gets closer to the unsafe zone. Obviously, the user's location accuracy is proportional to the frequency of the user's positioning signal transmission (i.e., \(f_{i}\)) because we assumed that both the user speed and the ocean current speed are constants. Moreover, the risk is inversely proportional to the location accuracy. Thus, we have the following relationship:  
\[\text{Positioning signal frequency}\uparrow\] \[\longleftrightarrow\quad\text{Location accuracy}\uparrow\] \[\longleftrightarrow\quad\text{Risk}\downarrow \tag{5}\]  
Next, we propose a risk model and optimize our positioning scheme under this risk model. There are many ways to model the risk of the user. Thus, we designed our risk model to be capable of generalizing many scenarios and assigning the proper factors. However, note that once the risk model is given, our proposed positioning scheme can be readily extended for the risk model.  
In our risk model, we consider the following relationship.  
\[\text{Positioning signal frequency}\uparrow \Longrightarrow \text{Risk}\downarrow\] \[\text{Water depth}\downarrow \Longrightarrow \text{Risk}\downarrow\]  
Then, we can define the risk at the cell \(i\) as a function of signal transmission frequency (i.e., \(f_{i}\)) as follows:  
\[R_{i}(f_{i})\triangleq\frac{k_{i}\times d_{i}^{\beta}}{f_{i}^{a}}. \tag{6}\]  
Figure 4: System model.  
In (6), \(f_{i}\) is the user positioning signal transmission frequency (i.e., the position information accuracy) at cell \(i\) and \(\alpha\) is a positive constant (i.e., \(\alpha>0\)) that represents the sensitivity of the risk to the accuracy. In this case, \(d_{i}\) is the average water depth at cell \(i\) and \(\beta\) is the sensitivity of the risk to the water depth. The factor \(k_{i}\) is the weight to reflect the effects of the other factors (e.g., ocean current speed). With the definition of (6), the overall risk function is given by  
\[R(f_{1},\ldots,f_{N}) =\sum_{i=1}^{N}\left[p_{i}\times R_{i}(f_{i})\right] \tag{7}\] \[=\sum_{i=1}^{N}\left[p_{i}\times\frac{k_{i}\times d_{i}^{\beta}}{ f_{i}^{\alpha}}\right], \tag{8}\]  
where \(N\) is the total number of cells and \(p_{i}\) is the probability that the user is in the cell \(i\). When the user's position is uniformly distributed in the safe zone, the probability \(p_{i}\) becomes the ratio of the area of cell \(i\) to the area of the safe zone, that is,  
\[p_{i}=\frac{S_{i}}{\sum_{n=1}^{N}S_{n}}. \tag{9}\]
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
###### Abstract  
Road capacity, traffic safety, and energy efficiency can be extremely improved by forming platoons with a small intra-vehicle spacing. Automated controllers obtain vehicle speed, acceleration, and position through vehicular ad hoc networks (VANETs), which allows the performance of platoon communication to make a significant impact on the stability of the platoon. To the best of our knowledge, there is not much research relating to packet delay and packet dropping rate of platoon communication based on the IEEE 802.11p broadcasting. In this paper, we introduce platoon structure model, vehicle control model, and communication model for a single platoon scenario. By utilizing Markov process and M/G/1/K queuing theory, we put forward an analytical model to assess the property of intra-vehicle communication. The analytical model is validated by simulations and the influence of communication parameters on intra-vehicle communication performance are discussed. In addition, the experimental results demonstrate that the IEEE 802.11p-based intra-vehicle communication guarantee the stability of platoon.  
platoon; intra-vehicle communication; M/G/1/K queuing theory; Markov process; performance analysis +
Footnote †: journal: Sensors
## 5 Modified Chirp Scaling and Azimuth Compression for GTLR-BiSAR System
### Two-Dimensional Spectrum Expression after Chirp Scaling Operation
\begin{split} S_{2D}(f_{\tau},f_{a};P)=exp\{&-j\frac{2 \pi f_{c}}{c}[R_{Tp}+R_{r0p}D(f_{a},V_{r})]-j\frac{2\pi}{c}[R_{Tp}+\frac{R_{r0p} }{D(f_{a\_ref},V_{r})}]f_{\tau}\\ &-j\frac{2\pi R_{r0,ref}}{cD(f_{a},V_{r\_ref})}[1-\frac{D(f_{a},V _{r})}{D(f_{a\_ref},V_{r})}]f_{\tau}-j\frac{\pi D(f_{a},V_{r})}{K_{m}D(f_{a\_ ref},V_{r})}f_{\tau}^{2}\\ &+j\frac{\pi K_{m}}{c^{2}}[1-\frac{D(f_{a\_ref},V_{r})}{D(f_{a\_ VF},V_{r})}](R_{Tp}-R_{T\_ref})^{2}\\ &+j\frac{\pi K_{m}}{c^{2}}[1-\frac{D(f_{a},V_{r})}{D(f_{a\_ref}, V_{r})}][\frac{R_{r0p}}{D(f_{a\_VF},V_{r})}-\frac{R_{r0\_ref}}{D(f_{a\_ref},V_{r})}]^{ 2}\}\end{split}  \tag{38}\]  
The six exponential terms in the above formula are explained as follows:  
* The first term is related to azimuth Doppler frequency modulation. It is a function of GEO distance \(R_{Tp}\) and minimal LEO distance \(R_{r0p}\), which will be compensated during azimuth processing. We assume this term as \(H_{uz}\);
* The second term is a linear phase in range frequency domain, it represents the peak position after range compression;
* The third term stands for the bulk RCM after chirp scaling operation. The RCM correction of the whole scene is completed by compensating this phase, recorded as \(H_{RCM}\);
* The fourth term means the range modulation after chirp scaling operation. It is a quadratic function of \(f_{\tau}\). The range compression is achieved by compensating this phase, assumed as \(H_{ran}\);
* The last two terms is an residual phase caused by chirp scaling operation, they are functions of bistatic range \(R_{bi}\) and azimuth frequency \(f_{a}\). We define this term as \(H_{RP}\). They should be removed during azimuth processing. In the traditional monostatic CS algorithm processing for stripmap workmode, there are also similar exponential phase terms to be compensated. Different from GTLR-BiSAR system operating in SS or TOPS modes, there is no azimuth aliasing problem for the former system. Therefore, we should focus the image in the azimuth frequency domain after we complete the phase compensation operations in 2D frequency domain [21].
# High-Accuracy Entanglement Detection via a Convolutional Neural Network with Noise Resistance
## 3 Results  
### Entanglement Detection without Noise  
Using the corresponding measured frequencies as input dataset, our trained model will output corresponding detection results, with labels '1' and '2' representing separable and entangled states respectively, which is the same as previously stipulated. The measurement is conducted on 100,000 randomly generated density matrices. Using the probability \(P=Tr(\rho\mathbf{O_{k}})\), the measurement process without additional interference is simulated with the Monte Carlo method and logical operation. The number of random numbers generated can be adjusted to vary the number of simulated measurement operations.  
A minimum of 15 parameters are needed to determine a two-qubit density matrix completely, known as complete measurement [1]. To optimize resource utilization and improve efficiency, we aim to manipulate incomplete measurements for reliable results. In our work, we initially employ 16 optical polarization operators with an additional one ensuring measurement completeness.  
When simulating incomplete measurement, we gradually eliminate the aforementioned complete measurement operators. Finally, for the specific random states of interest, we reduce the number of operators to 4 as listed below:  
\[\begin{split}\mathbf{O_{1}}&=|00\rangle\langle 0 0|;\\ \mathbf{O_{2}}&=|01\rangle\langle 01|;\\ \mathbf{O_{3}}&=|10\rangle\langle 10|;\\ \mathbf{O_{4}}&=|11\rangle\langle 11|.\end{split} \tag{10}\]  
The models' computation time is shown in Table 3 and the classification results without noise are shown in Figure 2. Except for the Rec-Sep, which is slightly lower than that of the other indicators when measured 1000 times, all the judgment indicators exceed 97.5%. This classification model performs well without noise.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Optimizer & Adam \\ Initial Learning Rate & 0.01 \\ Mini Batch Size & 6000 \\ Max Epochs & 800 \\ \hline \end{tabular}
\end{table}
Table 2: The hyperparameters of our model.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Classification Time** & **Training Time** \\ \hline Complete measurement & 0.003249 s & 1 min 50 s \\ Incomplete measurement & 0.002577 s & 1 min 47 s \\ \hline \end{tabular}
\end{table}
Table 3: Model’s computation time.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 3 Framework Design
### Framework Overview
#### 3.1.2 Detecting the Extended Sinusoidal Waveform at WiFi  
If the received signal is a continuous sinusoidal wave, as analyzed in Section 2.2, the corresponding phase offset value should be stable at \(-\frac{2\pi}{5}\). Then, the extended sinusoidal waveform would induce longer stable phase offset values at WiFi preamble detection. Since the duration of the extended sinusoidal waveform is much longer than the sinusoidal waveform in the original ZigBee chips, we can recognize the extended sinusoidal waveform without checking the phase offset value. To detect the extended sinusoidal waveform at WiFi, we only need to detect whether there exists the certain amount of stable phase offset value or not. For example, there exists the 6.5 \(\upmu\)s stable phase offset value; this indicates that the extended sinusoidal waveform, which is generated by the modification in Figure 9, is detected in the WiFi preamble detection. Specifically, if there are \(6.5\times 20=130\) consecutive stable phase offset samples detected, the received signal is determined to be the extended sinusoidal wave.  
We note that the 6.5 is specific to the modification in Figure 9. In general, we could modify more chips to achieve an even longer extended sinusoidal wave (We could modify 6 chips; the corresponding evaluation results are shown in Section 4.4). If we use \(L_{E}\) to denote the duration of the extended sinusoidal wave, the number of consecutive stable phase offset samples we need for detecting the extended sinusoidal wave is derived by the following equation:  
\[L_{E}\times S_{rate} \tag{2}\]  
where \(S_{rate}\) represents the WiFi sampling rate. The value of \(S_{rate}\) supported by commodity WiFi devices includes 20 M/s and 40 M/s. We have already shown that we are able to generate the extended sinusoidal waveform at CTC sender (ZigBee) side and detect the extended sinusoidal waveform at CTC receiver (WiFi) side. In the following subsection, we show our CTC modulation/demodulation approach.  
Figure 9: An example of extending the sinusoidal wave by modifying ZigBee chips. Red chips denote the modified chips.
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 1 Introduction
However, current research results cannot support the fault diagnostic technology for a gas turbine based on transient process data. The following issues still remain unsolved:  
* The accuracy of the algorithm should be increased;
* The diagnostic result based on steady-state data and dynamic data should be compared;
* The diagnostic method based on transient process data should be used to analyze field data. In this paper, a novel gas-path fault diagnostic model is proposed, by engine transient performance analysis and a system dynamic model. Similar to the conventional gas-path fault diagnostic method, the proposed method has the same basic idea that a surrogate model stands for the real engine under different working conditions and health conditions. However, due to the change of input parameters from steady-state to transient process, the three key parts of the diagnostic model, which are calculation flowchart, simulation model, and algorithm, should be modified. Integrated deviations between measurements and simulation results are chosen as the fitness function in the diagnostic problem. Then this problem will be solved by the proposed calculation flowchart and heuristic algorithm.
## 3 Results and Discussion
### Effect of Temperature on the Conversion Degree of Aluminum  
The first series of experiments was carried out to establish the dependence of the conversion degree of aluminum on the reaction temperature. The set temperature was increased with a step of 20 \({}^{\circ}\)C, and the experiments were carried out in the temperature range of 200 \({}^{\circ}\)C to 280 \({}^{\circ}\)C. The results of these experiments are presented in Table 1. The holding time in all these experiments was 10 h.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathcal{N}\) Exp. & T\({}^{\circ}\)C & **State of Water** & \multicolumn{2}{c}{**Chemical Purity of Aluminum, \%**} & \(z\), g** & \(c\), g** & **h, \%** & **V(H\({}_{2}\)), L** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{200} & \multirow{4}{*}{Steam} & 99.99 & 3.07 & 3.14 & 1.87 & 0.07 \\ \cline{3-7}  & & & 99.9 & 3.14 & 3.15 & 0.26 & 0.01 \\ \cline{3-7}  & & & 99.7 & 3.26 & 3.27 & 0.25 & 0.01 \\ \cline{3-7}  & & & 99.99 & 3.06 & 3.17 & 2.95 & 0.11 \\ \cline{3-7}  & & & 99.9 & 3.19 & 3.21 & 0.51 & 0.02 \\ \cline{3-7}  & & & 99.7 & 3.21 & 3.22 & 0.26 & 0.01 \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{220} & \multirow{4}{*}{Steam} & 99.99 & 3.11 & 3.33 & 5.80 & 0.22 \\ \cline{3-7}  & & & 99.9 & 3.10 & 3.11 & 0.26 & 0.01 \\ \cline{3-7}  & & & 99.7 & 3.18 & 3.18 & 0.00 & 0.00 \\ \cline{3-7}  & & & 99.99 & 3.09 & 3.37 & 7.43 & 0.29 \\ \cline{3-7}  & & & 99.9 & 3.06 & 3.08 & 0.54 & 0.02 \\ \cline{3-7}  & & & 99.7 & 3.16 & 3.17 & 0.26 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conversion degree of aluminum (n) in experiments at temperatures from 200 to 280 \({}^{\circ}\)C. The holding time in all experiments was 10 h.  
The results show that (Figure 3), with an increase in temperature, the oxidation of aluminum in liquid water proceeds better than that in steam. Granules with a chemical purity of 99.99% were completely oxidized at 280 \({}^{\circ}\)C in liquid water. The graph based on the results of the experiment shows that the oxidation up to 220 \({}^{\circ}\)C in liquid and in steam is the same, but above 220 \({}^{\circ}\)C, the conversion degree (\(\eta\)) curve in a liquid is accelerated in comparison with the steam curve under the same conditions. Aluminum with a chemical purity of 99.9% retains the same tendency relative to the oxidation medium, but essentially does not oxidize compared to aluminum with a chemical purity of 99.99%; the conversion degree of aluminum granules with a chemical purity of 99.7% was less than 0.5%.
# Enhancing Vibration Analysis in Hydraulic Presses: A Case Study Evaluation
## 4 Measurement Methodology  
The proposed measurement system was an independent part of the press. The system was integrated with the overall digitization system in such a way that the main PLC controller controlled the start and end moments of the measurements. The exchange of the start/stop signal (trigger) was performed using a digital signal in TTL logic (5 V). Vibration measurement was carried out from the moment the press slide started moving down until the impact/pressing moment. The average signal recording time was approximately 5 s.  
During the execution of the test measurements, an experimental selection of two parameters was performed: the signal sampling frequency and the sensor mounting location.  
The signal sampling frequency was a measure of the number of samples recorded per second, usually expressed in [kS/s] units. The sampling frequency range of the proposed measurement system was from 10 [S/s] to 200 [kS/s]. Based on the conducted studies, this parameter was set to a value of 10 [kS/s] (10 thousand samples per second). When selecting this value, consideration was given to the accuracy of the recorded data and the size of the measurement file. Ultimately, the size of one measurement file was approximately 1990 KB with a content of about 44,500 samples.  
The initial mounting location of the vibration sensor was chosen based on the modal analysis of the press. Using the developed discrete computational model of the press, the natural frequencies of the structure and the corresponding mode shapes were determined. This task was solved using a Simeenter Nastran solver (Siemens NX, Siemens, Berlin, Deutschland) with the designation SOL103 (Real Eigenvalues). According to the results obtained, it was determined that the highest vibration amplitude would occur at the crown of the press (Figure 7).  
The final mounting location of the vibration sensor was selected through experimental means. For this purpose, a series of measurements was conducted by mounting the sensor on the press column at intervals of 1 m from the base. Sample measurement points are illustrated in Figure 8. In accordance with the manufacturer's recommendations, a dedicated magnetic holder, Kistler 8452A, (Kistler Holding AG, Winterthur, Switzerland) was used for mounting the sensor.  
Sample vibration measurement results for various measurement points are presented in Figure 9.  
The initial input for selecting the place to mount the accelerometer was a modal analysis of the press. According to its results, it was assumed that the largest vibration amplitudes would appear on the press crown. When starting the practical implementation of the measurements, an attempt was made to verify the results of the modal analysis by measuring vibrations along the press column at a distance of 1 m. After conducting the tests, it turned out that the location of the sensor had no impact on the obtained vibration measurement results (Figure 9). The structure of the press was uniform and uniformly stiff throughout its  
Figure 6: Complete and configured measurement system.
## 6 Use-Case Examples
### Positioning in Industrial Settings and Cyber-Physical Systems  
When it comes to certain use cases, general process automation tasks require sub-meter accuracy, where the latency of positioning estimation is <2 s. A very different use case is inbound logistics for manufacturing, where the requirement on both horizontal and vertical accuracy should be below 20 cm with a positioning estimation latency of <1 s. Regarding this latter parameter, augmented reality applications for smart factories have much stricterlatency criteria: less than 15 ms. Although the standard discusses various other use cases as well, their requirements fall within the range of the previously listed scenarios [88].  
Industrial use cases have extraordinary potential to benefit most from the positioning capabilities of 5G and 6G networks. Position information can help optimize and automate processes in various vertical sectors, from logistics and manufacturing to mining and transportation [2]. In industrial control and factory automation, location information is of great benefit to both sides of communication: to the (mobile) terminals (or robots) to perform their tasks, and to the network to allocate and control resources and increase processing efficiency.  
The requirements of cyber-physical control applications (i.e., in factories of the future) are covered by the 3 GPP standard [88]. Here, the main categories of generic 5G-related use cases are (i) factory automation, (ii) process automation, (iii) HMIs and production IT, (iv) logistics and warehousing, and (v) monitoring and maintenance. Among them, logistics and warehousing are the prominent case for indoor positioning.  
Asset tracking is a trivial use case, not only in warehousing [93], but also in smart product manufacturing [94] and smart logistics [95]. In all of these scenarios, 5G and 6G positioning comes into play, linking location data to the status change of the tracked asset. For smart products, not only the change in physical position is logged and time-stamped, but the change in various environmental variables (e.g., temperature, vibration, acceleration during carriage, etc.) could also be noted in the asset's history.  
When it comes to service guarantees, the 5G slice for massive Machine Type Communication (mMTC) would be best suited for information exchange in these use cases [104]. As for warehousing technology, indoor logistics are operated through AGVs or forklifts, and in less advanced settings, human-operated, manual (engineless) carts. In either case, the moving logistics equipment is the one to track, and the products themselves could be linked to the equipment upon pickup (e.g., through RFID) so that their location can be tracked even while in motion [93]. Nevertheless, both indoor and outdoor logistics require large amounts of interaction with the public network.