esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Evolution of Temperature Field  
The evolution of the temperature field in the inner shaft lining with age is depicted in Figure 4. The temperature evolution trend of the shaft lining with different locations and thicknesses is highly consistent. Based on the temperature change rate, the shaft lining concrete temperature curve within the first 7 d after pouring can be divided into five stages: induction, slow heating, rapid heating, rapid cooling, and slow cooling. The location with the lowest temperature in the shaft lining is always the position radially close to the center of the shaft lining. Depending on the temperature boundary of the shaft lining in the actual project, the five surfaces of the concrete test block were treated with heat insulation, whereas the inner surface at the center of the shaft was directly exposed to air without any treatment. Therefore, the concrete at the location radially close to the center of the shaft wall loses more heat during convective heat transfer between the air and the lining.  
The maximum temperature in the shaft lining with different thicknesses is shown in Figure 5. As shown in the figure, the maximum temperature gradually increases with an increase in the shaft lining thickness, and the maximum temperature in shaft lining concrete can reach 76.5 \({}^{\circ}\)C when the thickness is 1.5 m. The time to reach the maximum temperature in the shaft lining decreases with an increase in the lining thickness. The inner surface temperature of the shaft lining is usually close to the ventilation temperature in the shaft; thus, the difference in maximum temperature between the interior and the inner surface of the newly poured shaft lining concrete gradually increases with the shaft lining thickness. Therefore, the temperature stress caused by a large difference in temperature increases the cracking risk of the shaft lining during the initial pouring period, and the probability of cracking increases with the shaft lining thickness. When the shaft lining thicknesses were 1.0 and 1.2 m, the maximum temperature was observed at the intermediate temperature measurement point (Figure 4a,b). As the thickness increased to 1.5 m, the maximum temperature was observed at the outer temperature measurement point (Figure 4c), indicatingthat the location of the maximum temperature in the inner shaft lining gradually moved radially outside with an increase in the lining thickness.  
Figure 4: Evolution of temperature at different measurement points on the inner shaft lining with age: (**a**) 1.0 m thickness, (**b**) 1.2 m thickness, and (**c**) 1.5 m thickness.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 1 Introduction
Currently, there are mainly two methods for spectrum refinement in engineering: zoom-fast Fourier transform (ZoomFFT) and Chirp-Z transform [24]. Both algorithms can effectively improve the spectral resolution of the narrowband signal. Chirp-Z transform increases the number of FFT transform points by interpolating the target frequency band without expanding the data length to improve the spectral resolution of the signal [25]. ZoomFFT moves the signal spectrum to the baseband, decimates it, and outputs it for spectrum refinement [26]. For the multi-frequency harmonic components with single frequency and less severe spectral line interference, Chirp-Z can obtain higher frequency, amplitude, and phase accuracy than Zoom FFT [27]. For the dense multi-frequency harmonic components with serious interference, Chirp-Z can only amplify the frequency band but cannot eliminate the interference effect [28], while ZoomFFT can separate and correct the interference frequency components by increasing the refinement factor to obtain high-precision signal parameters [29; 30; 31]. Since OFDM belongs to the multi-carrier communication system, the underwater acoustic OFDM signal is a multi-frequency signal. To improve spectrum utilization and communication speed under extremely limited bandwidth resources [32], the spectrum lines of underwater acoustic OFDM signals are usually quite dense [33]. In addition, the underwater acoustic OFDM signal is vulnerable to the influence of underwater auditory channels, resulting in Inter Carrier Interference (ICI). In contrast, ZoomFFT is more suitable for underwater acoustic OFDM signal processing [34]. Considering the advantage of ZoomFFT, the UWA OFDM communication system based on ZoomFFT (Z-OFDM) is proposed in the paper, which uses ZoomFFT to replace FFT in the modulation and demodulation. This modulation method improves the channel capacity and SINR of the system and reduces the BER of the system. The Z-OFDM system is expected to become a new high-performance and low-complexity solution for UWA communication. The rest of the paper is organized as follows. In Section 2, the principle of ZoomFFT is reviewed. In Section 3, the design and performance of the Z-OFDM system are presented. Section 4 focuses on the computer simulation experiments of the Z-OFDM system. In Section 5, the innovations and advantages of the Z-OFDM system compared to the traditional FFT-based OFDM system are discussed based on the experimental results. Finally, the conclusion is drawn in Section 6.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 3 Results  
The ability of the ONELAB software platform to simulate both PACT (optical source) and RACT (radio frequency source) with the same tool and on the same mesh, is demonstrated using the phantoms described in the methods section. Below we first present PACT and RACT results for the homogeneous phantom embedded with two absorbers and then the results for the breast phantom with tumor.
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
## 3 Results and Discussion
### Characterization of the Ceramic Filled Resin and the Printed Structures
Considering five printed waveguides and comparing their geometric dimensions, the ceramic waveguides feature a dimensional accuracy of \(\pm\)0.05 mm in the xy-plane and \(\pm\)0.3 mm in z-direction. The epoxy resin printed waveguides feature a dimensional accuracy of \(\pm\)0.02 mm in the xy-plane and \(\pm\)0.1 mm in z-direction, respectively. The obtained parameters are, therefore, within the specifications of the used DLP-printer (xy-resolution: 0.05 mm, z-axis accuracy: 0.2 mm) [44]. Figure 5: Polished cross-sections of a ceramic (**a**,**b**) and epoxy resin printed (**c**,**d**) WR12 waveguide. Figure 6: Close-up view of the inner surfaces of the fabricated waveguides. Compared to the standard printing approach with epoxy resin (**b**), the ceramic waveguide (**a**,**c**) features a higher surface roughness (epoxy resin: \(R_{\text{q,e}}=0.53\) μm, ceramic: \(R_{\text{q,c}}=2.43\) μm). Figure 7: Close-up view of the narrow walls of the fabricated ceramic WR12 waveguide (**a**) in comparison to the standard printing approach with epoxy resin (**b**). The slot geometries are almost identical.
## 3 Results and Discussion
### VOC Flavor Profile of Flavored Sparkling Water
No studies have been identified regarding their identification in food or their fields of application. According to the safety information provided by the Good Scents Company, 3-menthene is not recommended for fragrance or flavor use [48]. From the category of alcohols, 2-hexenol, cis-verbenol, and trans-verbenol were identified in flavored sparkling water. 2-hexenol gives commercial drinks a fruity, green smell similar to wine and a fresh sweet taste similar to apples and pears. Studies published in the literature show the natural presence of 2-hexenol in vine species (Vitis rotundifolia) and \begin{table}
\begin{tabular}{c c c c c} \hline
**Peak No.** & **RT (min)** & **Compound** & **CAS No.** & **Probability (\%)** & **Flavor/Fragrance** \\ \hline
1 & 6.423 & Methyl butyrate & 623-42-7 & 71.7 & Sweet apple/pineapple \\
2 & 6.978 & Ethyl isobutyrate & 97-62-1 & 77.0 & Fruity \\
3 & 7.411 & Methyl2-methylbutyrate & 868-57-5 & 83.5 & Ethereal, fruity \\
4 & 7.893 & Ethyl butyrate & 105-54-4 & 71.4 & Pineapple \\
5 & 8.473 & cis-2-Hexenol & 928-94-9 & 72.1 & Fruity/green \\
6 & 8.773 & Ethyl 2-methylbutyrate & 7452-79-1 & 71.8 & Strawberries \\
7 & 10.032 & 2-Hexenal & 6728-26-3 & 78.1 & Green apple \\
8 & 11.606 & Ethyl hexanoate & 123-66-0 & 75.7 & Sweet apple/pineapple \\
9 & 12.417 & Propyl 3-methylbutyrate & 557-00-6 & 70.8 & Sweet apple/fruity \\
10 & 13.397 & trans-Verbenol & 1820-09-3 & 89.4 & Herbal \\
11 & 13.713 & cis-Verbenol & 1845-30-3 & 82.8 & Herbal \\
12 & 15.407 & 3-Menthene & 500-0.5 & 72.3 & - \\
13 & 15.720 & 3-Carene & 13466-78-9 & 76.1 & Piny \\
14 & 16.569 & 2-Menthene & 5256-65-5 & 79.5 & - \\
15 & 18.700 & trans-Methyl cinnamate & 1754-62-7 & 83.9 & Intense/aromatic \\ \hline \end{tabular}
\end{table} 
Table 4: VOCs identified in flavored sparkling water. yellow guava fruits (Psidium guajava), as well as in most apple species, citrus fruits, and ginger [49]. Cis-verbenol and trans-verbenol are terpene alcohols with a fresh pine smell and represent the main metabolites from the oxidative study of alpha-pinene. There is little information in the literature regarding the possibility of their use in the food industry, but they are applied in the agricultural industry due to their pheromone properties. Their presence was identified in a follow-up study of humans exposed to monoterpenes using a GC-MS/MS method by derivatization [50].
## 4 System Design
### Overview of the Deep Diver Architecture  
To explain the absolute efficiency of our implementation, we demonstrate the architecture of DeepDiver in Figure 3, which comprises several models: the complex roadblock check detector and patching binary, the hybrid fuzzing approach including AFL++ 2.60d [34], and the concolic execution, a bug validator.  
The fundamental step of DeepDiver's function is to detect roadblock checks from the target, which is the non-instrumented binary and negating the detected RCs automatically. RCs are some magic values and checksums which are not intended to prevent bugs. Negating RCs do not incur erroneous vulnerabilities. In addition, by the process of detection and negation of RCs, the fuzzer can be accessible to explore the newly blocked execution paths and the potential code defects can be discovered easily. However, there are critical roadblock checks that are crucial to the program's functionality. It will break the logic of software if we negate the CRC.  
The next step is the analysis of the target programs by the hybrid fuzzing approach including AFL++ [34] and concolic execution. We instrument the target program for AFL++ [34] fuzzer by LLVM Laf-Intel Instrumentation and _aff-clang/aff-clang-fast/aff-clang-fast++_. Concolic execution analyzes patched binary whose RCs are negated. We provide a detailed description of our hybrid fuzzer and  
Figure 3: Overall workflow of the DeepDiver.  
an explanation why we decided to use AFL++ rather than other fuzzing tools (AFL, AFLFast for example) in Section 4.3.  
The last step of our implementation is bug validator. It can determine whether the crash input that is found is a true bug or not by filtering out false positives. It helps to identify true crash inputs that can trigger the bug in the original program.
## 2 Related Research
### Data Quality Management  
To perform the quality management of data or information, many researchers have been working on merging quality management with information systems [33; 34]. Total Data Quality Management (TDQM) was proposed by [PERSON] [35]. To date, TDQM is still a relevant concept and has been improved from time to time. It has been implemented to manage different types of recent data and applications, such as social media [36], manufacturing [37], financial [38], and medical [39] data. In this research, TDQM was adapted to propose the Agent Quality Management (AQM) framework for managing agent quality in smart city environment.  
The TDQM cycle consists of four elements, which are define, measure, analyze, and improve. The first process is the define process where output quality parameters are identified based on characteristics of the collected input, identification of the resources to support the quality of the output, and requirements of the produced outputs and the system infrastructure. The second quality process in the cycle is the measuring process. In this phase, the scores for the defined quality parameters are calculated. To calculate the scores, data quality metrics need to be developed. The data quality requirements identified during the definition process are transformed into rules and measurable indicators (quality statuses or scores) that reflect the real condition (contextual information) of the data. In order for the parameter to receive a high-quality status or score (correct), certain conditions must exist, and if a condition does not exist, the parameter will receive a low status (error) score. Third, the analysis process investigates the data quality measurement results generated. The analysis can be simple or complex to suite the verification needs. The simplest analysis method is done by answering a list of assessment questions. Finally, the improvement process is the action taken after a thorough analysis is performed. Here, the data quality parameters may be refined, removed, or added to produce more accurate and reliable data quality statuses for the information product.
## 3 Results and Discussion
### Interpretation of CB Model
#### 3.6.2 Global Interpretation  
The SHAP algorithm can visualize the global interpretation of the predictive model. Hence, it is able to indicate how much each feature negatively or positively impacts the model output. In this part, the SHAP values were used to measure the input features' impacts on model output for the entire database (globally) based on CB model prediction. Figure 13 provides a rich visualization of the global interpretation. The vertical axis represents the input features, which were sorted based on their importance in prediction, and the horizontal axis represents their corresponding SHAP values. The SHAP value of each input feature for a specific specimen was plotted as a dot on the graph, which could be negative or positive. A positive SHAP value denotes that the feature positively impacted the prediction, whereas a negative SHAP value denotes that the feature negatively impacted the prediction. The colors of the dots represent the impact values of input features on the prediction, ranging from blue (low) to red (high). By analyzing color distribution (blue indicating low value and red indicating high value), it is possible to identify whether low or high values tend to push the prediction in a negative or positive direction. SHAP value has the same unit as the CB model output (kN). Based on Figure 13, features \(b\), \(f_{c}^{\prime}\), \(d\), \(h\), and \(\rho_{l}\) induced the highest positive impact on the predicted SS of RCDBs. On the other hand, features \(a/d\) and \(l_{e}/h\) negatively impacted the SS. This finding is consistent with mechanics-driven models outlined in Appendix A, which revealed that the SS of RCDBs is strongly dependent on \(f_{c}^{\prime}\) and \(b\).
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Results
#### 4.3.2 Benchmarks

Table 4: Benchmark results of the workflow queue signature verification process. The triangle-shaped points describe the verification process, whereas the circles correspond to the generation process. Calculating the Pearson correlation for both datasets gives us 0.9999 for signature generation and 0.9886 for signature verification which means that both are almost completely correlated. The two regression lines also show a constant offset between workflow queue signature generation and verification.
## 3 Results
Table 7 shows the results of the Bland-Altman analysis with a low bias (0.2500), corresponding to the mean of the differences between both techniques. Similarly, the 95% limits of agreement (9.0092988 and \(-\)8.5092988 for the upper and lower limit respectively) can be observed. The Bland-Altman plot (Figure 5) shows the difference scores of the two measurements against the mean for each subject, showing the 95% limits (upper limit of agreement \"ULA\" and lower limit of agreement \"LLA\"), which include 95% of differences between the two measurements for both methods, that resulted in a good agreement. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ABO Discrepancy Index** & \
\begin{tabular}{c} **Mean (SD)** \\ **Digital** \\ \end{tabular} &
\begin{tabular}{c} **Mean (SD)** \\ **Manual** \\ \end{tabular} & \(t\) & \(p\)**-Value** \\ \hline Total discrepancy index score & 24.61 (13.34) & 24.86 (14.14) & 0.296 & 0.769 \\ Overjet & 2.21 (2.21) & 1.75 (1.91) & \(-\)1.72 & 0.097 \\ Overbite & 0.61 (1.44) & 0.79 (1.52) & 1.544 & 0.134 \\ Anterior open bite & 1.25 (1.50) & 1.82 (2.76) & \(-\)1.384 & 0.178 \\ Lateral open bite & 1.00 (2.27) & 0.93 (1.99) & 0.570 & 0.573 \\ Crowding & 3.00 (2.09) & 2.96 (2.21) & 0.107 & 0.916 \\ Occlusal relationship & 2.14 (1.79) & 2.14 (1.71) & 0.000 & 1.000 \\ Lingual posterior crossbite & 0.75 (1.11) & 0.89 (1.25) & \(-\)2.121 & 0.063 \\ Buccal posterior crossbite & 0.00 (0.00) & 0.07 (0.37) & \(-\)1.000 & 0.326 \\ Cephalometrics & 10.29 (8.77) & 9.79 (8.92) & 0.706 & 0.486 \\ Other & 2.86 (2.83) & 3.71 (3.47) & \(-\)2.213 & 0.066 \\ \hline \hline \end{tabular} \(p\leq\) 0.05 (significant differences), SD: standard deviation.  
\end{table} 
Table 6: Student’s \(t\) test comparing the results obtained using digital and manual techniques. Figure 5: Bland-Altman plot of agreement between results obtained from discrepancy index by digital and manual technique in the 28 study patients.
# Predicting Electromagnetic Performance Under Wrinkling in Thin-Film Phased Arrays
## 3 Structural-Electromagnetic Coupling Model
### Electromechanical Coupling Model
In this paper, we adopt the mode of importing the results of unit calculation by Ansys HFSS into MATLAB for superposition calculation to predict the performance of a phased array antenna. In Equation (21), \(E(n,m)\) is the complex far-field contribution of the (\(n,m\)) patch and is indeed obtained from Equation (19). In our workflow, a full-wave HFSS simulation is first performed on a single deformed patch to evaluate \(E(n,m)\) in Equation (19). From this, the complex amplitude \(E(n,m)\) (magnitude and phase) is extracted. These per-element values are then exported to MATLAB, where Equation (21) is implemented to compute the array sum \(E_{Ant}\) and thus predict the phased array pattern under the given deformation. This two-step procedure ensures that the element-level HFSS results feed directly into the array-level MATLAB post-processing. All HFSS simulations use a single representative patch to enable rapid, order-of-magnitude estimates of gain and impedance errors; inter-element coupling is omitted because full-array simulations would incur excessive computational cost and significantly longer run times.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
###### Abstract  
The Variable Buoyancy System (VBS) is a critical device in the operation of underwater gliders that should be properly sized to achieve the required vehicle propulsion; safety within the operating range; and adequate efficiency at the nominal depth rating. The VBS budget volume depends mainly on the glider hydrodynamics and the main operating states of the vehicle. A method is proposed with analytical equations to analyze the performance of underwater gliders and to estimate the resultant velocities of the vehicle as a function of the buoyancy change and the glider angle. The method is validated to analyze the glider performance of underwater gliders and is essential to get the main design requirement for the propulsion system: the VBS budget volume. The paper presents the application of the method to obtain the VBS sizing for an academic glider; a comparison with the historical hydrodynamic data of the Slocum glider; the results of the glider performance study; and the development of the characteristic charts necessary to evaluate the performance of the vehicle and its flight parameters.  
variable buoyancy system; glider polar chart; glider performance; angle of attack; underwater gliders; specific energy consumption Article
## 2 Materials and Methods
### Experimental Instruments
#### 2.1.2 Data Acquisition System  
The IMC CRONOSflex was the data acquisition system (DAQ) used for this research project. This frameless modular DAQ allows for simultaneous recording of analog and digital signals. This system has a total of 8 channels, allowing each channel to sample data with a maximum sampling rate of 100 KS/s.
## 2 System Model
### BER Performance Analysis  
The on-off keying (OOK) modulation scheme is one of the most popular modulation formats in underwater wireless optical communication systems because of its simple  
Figure 2: Comparison of scattering phase functions.  
implementation and low cost. Therefore, we employ the OOK modulation to further evaluate the BER performance of the proposed UOWC system [25]. It is assumed that the original data sequence \(x\) is \([x_{1},x_{2},\ldots,x_{i},\ldots,x_{N}]\), \(x_{i}=0\) or \(1\). The transmission power \(P_{\text{s}}(t)\) can be expressed as [26]  
\[P_{\text{s}}(t)=\sum_{n=1}^{N}x_{n}Z(t-nT) \tag{13}\]  
where \(T\) is the time interval of the data, and \(Z(t)\) is the gate function with the width of \(T\). The receiving power \(P_{r}(t)\) is the convolution of the transmission power and the impulse response of the UOWC channel, which can be expressed as  
\[P_{r}(t)=\sum_{n=1}^{N}x_{n}Z(t-nT)*h(t-nT) \tag{14}\]  
The decision of the receiving sequence \(y=[y_{1},\,y_{2},\,\ldots,y_{i},\,\ldots,y_{N}]\) can be obtained by comparing the sample value \(y^{\prime}\) with the threshold value \(T_{thr}\), which can be expressed as [27]  
\[y^{{}^{\prime}}=\sum_{m=1}^{M}y^{{}^{\prime}}_{m}/M \tag{15}\]  
\[T_{thr}=\frac{\sigma_{0}\mu_{1}+\sigma_{1}\mu_{0}}{\sigma_{0}+\sigma_{1}} \tag{16}\]  
where \(M\) is the number of samples. \(u_{0}\) and \(u_{1}\) are the mean values of the received signals \"0\" and \"1\", and \(\sigma_{0}\) and \(\sigma_{1}\) are the variances of the received signal \"0\" and \"1\".  
The judgment rules are given by  
\[y_{i}=\begin{cases}0&y^{{}^{\prime}}_{i}<T_{thr}\\ 1&y^{{}^{\prime}}>T_{thr}\end{cases} \tag{17}\]  
Then, the BER of the UOWC system with OOK modulation \(P_{E}\) is given by  
\[P_{E}=Q(A_{n}) \tag{18}\]  
where  
\[A_{n}=\frac{u_{0}+\mu_{1}}{\sigma_{0}+\sigma_{1}} \tag{19}\]  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-t^{2}/2}dt \tag{20}\]