esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 2 Common Methods for Detecting Underwater Cracks in the Surface of Concrete Dams
### Digital Image Detection Method  
As a low-cost and high-efficiency detection method, digital image crack detection is widely used for underwater crack detection in concrete dams [25; 50; 51]. Digital image detection methods can be divided into optical image detection methods and sonar image detection methods based on different detection instruments and detection principles. Optical images and sonar images are shown in Figure 2. Optical image detection methods mainly use optical instruments such as cameras to obtain underwater images, and use artificial or computer techniques to identify and measure cracks in the acquired images. However, due to the presence of suspended particles in water, light is prone to refraction and scattering underwater. Water also has certain absorption and attenuation properties of light, and the quality of underwater image acquisition is often difficult to guarantee. Sonar image detection uses ultrasound to obtain high-resolution and wide-area images, which can make up for the shortcomings of optical images in muddy water environments and is an effective means to detect cracks in muddy water [52]. Digital image detection has gradually become the dominant method for underwater crack detection due to its high accuracy, low cost, wide applicability, and non-contact nature with the development of computer technology [20].  
Figure 1: Change of medium around the measuring point before and after cracking: (**a**) before; (**b**) after.  
Figure 2: Digital image: (**a**) sonar image [53] and (**b**) optical image.
# Thermodynamic Analysis of In-Cylinder Steam Assist Technology within an Internal Combustion Engine
## 3 Results and Discussion
### Effects of Injection Pressure on Cycle Performance  
Injection pressure is a significant parameter qualifying the spray, which potentially affects the in-cylinder steam evaporation rate. Additionally, it also influences the temperature of saturated water in the thermodynamic calculation. In this section, the impacts of injection pressure on the proposed cycle are studied with the aim of taking full advantage of the latent heat of water-spray evaporation.  
Figure 6 displays the relationships between in-cylinder/exhaust temperatures and steam injection mass at various injection pressures. The lower lines represent the exhaust temperature at the expansion end, while the upper lines are the in-cylinder temperatures at TDC. The injection pressure of the steam ranges from 10 to 50 MPa. As shown in Figure 6, the trends in in-cylinder and exhaust temperatures coincide with each other, and, in all cases, the gaps are too small to affect the thermodynamic characteristics. The results suggest that injection pressure has little influence on the thermodynamic state of steam. For instance, at a steam injection mass = 0.16 g/cycle, the in-cylinder temperature is 2097 K at 10 MPa injection pressure, which is 3 K less than the value for 50 MPa. The gap in temperature at the bottom dead centre between 10 MPa and 50 MPa is much smaller. The enthalpy of water steam at 20 MPa rises from 130.84 kJ/kg to 980.07 kJ/kg as injection temperature rises from 300 K to 500 K. Compared with the change caused by increasing the injection temperature, the change caused by increasing the injection pressure is negligible. Hence, increasing the steam injection pressure contributes little to thermal efficiency. The thermal efficiencies in the range of steam injection pressures considered are almost the same at different steam injection masses because this zero-dimensional model does not consider the spray process. With increases in injection pressure, the jet velocity rises significantly,  
Figure 5: Work output according to steam injection mass (P steam = 20 MPa; T steam = 450 K; P intake = 0.1 MPa; CR = 10).  
and the Weber and Reynolds numbers increase. The atomization quality of the spray is enhanced with increasing injection pressure, which shortens its evaporation time and reduces negative effects on local combustion in the combustion chamber.
## 5 Performance Evaluation
### Performance Evaluation of ICN Hierarchical Cache System
#### 5.1.2 Verifying the Accuracy of Analysis Model of ICN Hieratical Cache System  
To verify the accuracy of the model of the HCS under different caching strategies (LCD, LCE, and LCP with \(p=0.2\)), we implemented three simple ICN hieratical cache systems in a full binary tree (depth = 4). We deployed L3 RN, i.e., the GNRS, at the root node to manage the location information of all content in the topology. The L2/L1 RN, which served for the L2/L1 HEA containing the attachment nodes and the subtrees owned by the child nodes, was deployed at the second/third layer nodes of the tree. In the case of tree-like networks, the cache hit ratio of some upper level nodes could be evaluated step-by-step starting from the leaves and going up to this node through previous Equations (2) and (5)-(7). Then, the cache hit average ratio of the L1/L2 HEA could be calculated by simple summation from the hit ratio of a single node.  
Figure 6 reports the average hit ratio of the L1/L2 HEA in different caching strategies, under the two considered approximations, against simulation results. We observed that with the increase of nodes in HEA and cache size, the standard Che approximation tended to overestimate the average hit probability, essentially as a consequence of a large overestimate of the hit probability on every single node. Our optimal Che approximation considered the prediction of request behavior, which brought the analytical prediction of the average cache-hit probability very close to the simulation results. Furthermore, we could obtain a minimum error against simulation results in LCD since the optimized Che approximation we created for LCD was based on a strong assumption that excluded many unknown possibilities. We noted that the biggest gap between the approximation results of the standard Che approach and the simulation results happened in LCE. This was because LCE cached multiple same copies in the same HEA, which resulted in an increase of the number of the overestimated nodes.
###### Abstract  
Exhaled breath is a body secretion, and the sampling process of this is simple and cost effective. It can be non-invasively collected for diagnostic procedures. Variations in the chemical composition of exhaled breath resulting from gaseous exchange in the extensive capillary network of the body are proposed to be associated with pathophysiological changes. In light of the foreseeable potential of exhaled breath as a diagnostic specimen, we used gas chromatography and mass spectrometry (GC-MS) to study the chemical compounds present in exhaled breath samples from patients with Alzheimer's disease (AD), Parkinson's disease (PD), and from healthy individuals as a control group. In addition, we also designed and developed a chemical-based exhaled breath sensor system to examine the distribution pattern in the patient and control groups. The results of our study showed that several chemical compounds, such as 1-phenantherol and ethyl 3-cyano-2,3-bis (2,5,-dimethyl-3-thienylyl)-acrylate, had a higher percentage area in the AD group than in the PD and control groups. These results may indicate an association of these chemical components in exhaled breath with the progression of disease. In addition, in-house fabricated exhaled breath sensor systems, containing several types of gas sensors, showed significant differences in terms of the normalized response of the sensitivity characteristics between the patient and control groups. A subsequent clustering analysis was able to distinguish between the AD patients, PD patients, and healthy individuals using principal component analysis, Sammon's mapping, and a combination of both methods, in particular when using the exhaled breath sensor array system A consisting of eight sensors. With this in mind, the exhaled breath sensor system could provide alternative option for diagnosis and be applied as a useful, effective tool for the screening and diagnosis of AD in the near future.  
Keywords:exhaled breath; alzheimer's disease; gas sensor; exhaled breath sensor system; gas chromatography-mass spectrometry +
Footnote †: journal: Sensors
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 5 Parametric Study
### Effect of Bus Speed  
The bus speed is an important factor affecting passenger comfort. Since the common bus speed is less than 120 km/h, the human comfort analysis was carried out at speeds of 18, 36, 54, 72, 90, and 108 km/h, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weight of** & **Weighted RMS** & **Reactions** & **Annoyance** \\
**Bus/t** & **Acceleration /m-s\({}^{-2}\)** & & **Rate** \\ \hline
11.57 & 0.6998 & uncomfortable & 36.38\% \\
13.73 & 0.5032 & slightly uncomfortable & 20.94\% \\
15.89 & 0.3748 & slightly uncomfortable & 9.33\% \\
16.97 & 0.2928 & not uncomfortable & 3.27\% \\
18.05 & 0.2278 & not uncomfortable & 0.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of human comfort for different weights of the bus.  
Figure 21: The weighted RMS of human body acceleration and annoyance rate for different weights of the bus.  
Figure 20: The acceleration amplitude for different weights of the bus.  
It can be seen in Figure 22 and Table 7 that with the gradual increase of the bus speed, the acceleration trend of the human body is consistent, showing an increasing trend. Compared with 18 km/h, the vertical acceleration, pitch acceleration, and roll acceleration at 108 km/h increased by 2.69, 1.68, and 1.97 times, respectively.  
It is found in Figure 23 that as the bus speed increases, the weighted acceleration RMS of the human body gradually increases, and the annoyance rate also increases, that is, the comfort gradually decreases. The weighted acceleration RMS and the annoyance rate of the human body both show nonlinear growth.  
When the bus speed is 72 km/h, the weighted acceleration RMS of the human body is 0.6142 m/s\({}^{2}\). The weighted acceleration RMS of the human body is in the range of 0.5 to 0.8 m/s\({}^{2}\), which is \"uncomfortable\" according to the ISO 2631 evaluation standard. However, the ISO 2631 evaluation results are not as intuitive as numbers; 30.16% of passengers feel uncomfortable in the annoyance rate evaluation. The annoyance rate quantifies each interval so as to make the result more intuitive and clearer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Speed/km-h\({}^{-1}\)**} & **Weighted RMS** & **Annoyance** \\  & **Acceleration/m-s\({}^{-2}\)** & **Rate** \\ \hline
18 & 0.3186 & slightly uncomfortable & 4.90\% \\
36 & 0.3748 & slightly uncomfortable & 9.33\% \\
54 & 0.4799 & slightly uncomfortable & 18.85\% \\
72 & 0.6142 & uncomfortable & 30.16\% \\
90 & 0.8015 & extremely uncomfortable & 42.90\% \\
108 & 0.9782 & extremely uncomfortable & 52.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of human comfort for different bus speeds.  
Figure 23: The weighted RMS of human body acceleration and annoyance rate for different bus speeds.  
Figure 22: The acceleration amplitude for different bus speeds.
# Study on the Wellbore Flow for Carbon Dioxide Fracturing
## 2. Mathematic Model of Wellbore Flow
### Pressure Field Model  
The pressure drop can be derived from the momentum conservation equation of compressible fluid. On the basis of assumptions, when the wellbore flow is in steady state, the pressure drop of fluid is given by:  
\[\frac{dp}{dz}=\rho g\cos\theta-\rho v\frac{dv}{dz}-fv^{2}\frac{\rho}{2d_{i}} \tag{7}\]  
where \(p\) is the pressure of fluid, MPa; \(\rho\) is the density of fluid, kg/m\({}^{3}\); \(v\) is flow velocity, m/s; \(d_{i}\) is the inner diameter of the pipe, m;\(f\) is the friction coefficient of fluid, dimensionless.  
Thanks to the low viscosity of CO\({}_{2}\) and the large fluid mass flow rate during CO\({}_{2}\) fracturing, it is easy to reach turbulence in the wellbore. Regarding the calculation of the friction coefficient, the formula proposed by [PERSON] (2018) was chosen:  
\[\frac{1}{\sqrt{f}}=-2.34\lg\Bigg{(}\frac{\varepsilon}{17.2 de}-\frac{9.26}{\text {Re}}\lg\Bigg{(}\Bigg{(}\frac{\varepsilon}{29.3 de}\Bigg{)}^{0.95}+\bigg{(} \frac{18.35}{\text{Re}}\bigg{)}^{1.108}\Bigg{)}\Bigg{)} \tag{8}\]  
where \(\varepsilon\) is the absolute roughness, m; _de_ is equivalent diameter, m; Re is Reynolds number, dimensionless.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results
### Impact of the Modulation Coding Scheme  
Figures 7-9 show the influence of the Modulation Coding Scheme (MCS) on the duty cycles, TBS, and the number of bits per RB for 5G NR, 5G LTE, and 4G LTE.  
Figure 7: Impact of the MCS per use case on the DC for (**a**) 5G NR, (**b**) 5G LTE, and (**c**) 4G LTE. Remark that the MCS value depends on the technology and modulation used.  
Figure 8: Impact of the MCS per use case on the bits per RB for (**a**) 5G NR, (**b**) 5G LTE, and (**c**) 4G LTE. Remark that the MCS value depends on the technology and modulation used.  
Figure 9: Impact of the MCS per use case on the bits per TBS for (**a**) 5G NR, (**b**) 5G LTE, and (**c**) 4G LTE. Remark that the MCS value depends on the technology and modulation used.  
For 5G NR, the MCS value did not seem to impact the duty cycle except for the iPerf and Twitch (video upload stream) use cases (see Figure 7a). In the case of iPerf, we observed an increase in the spread of the calculated duty cycle data with increasing MCS values (min-max spread of 1% to 99% for MCS values from 20 to 28). For Twitch, the duty cycle decreased with increasing MCS values (median TBS size from 1534 bits to 4963 bits for MCS values from 11 to 28). Figure 8a shows that an increase in the MCS value resulted in an increase in the number of bits per resource block. Figure 9a shows the TBS as a function of the MCS: increasing MCS values resulted in an increase in TBS. With higher MCS values, a larger amount of data can be transmitted at the same time.  
For 4G LTE, the duty cycles also do not show a trend when assessed as a function of the MCS value (see Figure 7).
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction
### Previous Survey Works
Moreover, Figure 2 provides a classification of UAV-based communication network aspects, where the adoption of ML techniques can enhance their performance compared to conventional optimization techniques. Stemming from this taxonomy, the rest of the paper is organized as follows. In Section 2, we survey communication aspects related to the physical layer, such as channel modeling and interference management. In Section 3, we survey security and safety issues, while in Section 4, we present ML-based researches related to resource managements and, in Section 5, we survey applications related to UAVs positions. Finally, Section 6 presents open issues and research challenges in the context of ML applications in UAV-enabled communications and Section 7 concludes this article. Figure 1: Applications of the AI/ML in UAV-based communications.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 5 Conclusions  
In this paper, the in-network caching management algorithm over ICN is investigated. A RXI-based caching replacement algorithm customized for streaming delivery over ICN is proposed. We have introduced RXI to offer a unified estimation criteria of possible future request probability for cached chunks. RXI adopts both file-level and chunk-level internal request probability and is estimated according to the dynamically varied request status at each ICN router. We have evaluated the proposed algorithm in two scenarios by comparing with the LRU, LFU and TLP-TTH. The simulation results indicate that the RXI-based algorithm evicts the cached chunk with the minimum RXI in order to maintain high cache utilization, and improves the ICN network performance in several aspects. In addition, the RXI-based algorithm can also avoid additional management overhead, such as control packets transmission. Thus, the proposed algorithm can be deployed in complex network scenarios, as proved by the results. In conclusion, by taking fine-grained request probability and dynamically varied request status into consideration, the customized in-network caching management algorithm specifically for streaming delivery can further improve the ICN network performance.  
**Acknowledgments:** [PERSON] is supported by the China Scholarship Council (No.20120643003) as a Ph.D. student.  
**Author Contributions:** [PERSON] contributed to the design of the proposed scheme, the performing of the simulation and the writing of the paper. [PERSON] contributed to reformation of proposed scheme and the supervision of the work. [PERSON] reviewed and revised the paper. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Damage Localization  
Due to the fact that the SHM approach requires that a sensor grid in some form is applied to the monitored structure, where a prediction unit is assigned to each sensor, it is also possible to locate damages. To demonstrate this, a straight-forward approach is proposed, where the residuals \(\delta_{j}\) of a specific test sample \(\delta\) are interpolated inside the sensor grid area. Specifically, the _meshgrid_ function of the _numpy_ 1.26.4 python module is used to construct an evenly spaced grid of coordinates over the submodel area. These coordinates are then passed, together with the coordinates of the sensors and the corresponding transformed data values, to the _griddata_ function of the python module _scipy_ 1.12.0. This function interpolates the transformed data values at the grid coordinates, using a cubic spline.  
The edges of the submodel, used in the generation of synthetic training data, are used as borders for the interpolation. Here, a value of 0 is assigned, based on the assumption that there is no damage located well outside of the sensor grid.  
An anomaly somewhere inside the sensor grid will result in an increased residual value \(\delta_{j}\) at sensors close to the damage measuring feature \(j\), where the magnitude is proportional to the distance between damage and sensor. The coordinates corresponding to the maximum interpolation value are then considered as a damage location prediction.
###### Abstract  
In this work, the effect of doubly functionalized montmorillonite (MMT) on the structure, morphology, thermal, and tribological characteristics of the resulting polystyrene (PS) nanocomposites was investigated. The modification of the MMT was performed using a cationic surfactant and an anionic surfactant or a silane coupling agent to increase the compatibility with PS matrix. The polystyrene/organo-montmorillonite (PS/OMMT) nanocomposite particles were prepared by soap-free emulsion polymerization. The OMMT was studied using Fourier-transform infrared (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The structural and morphological changes of PS/OMMT nanocomposites were further characterized by dynamic light scattering (DLS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The thermal stability of all the PS/OMMT nanocomposites was higher than that of the pure PS. The anti-wear properties of the polyalphaolefin (PAO) were significantly improved due to the introduction of the PS/OMMT nanocomposite particles. The nanocomposites prepared by a cationic surfactant and a silane coupling agent exhibited the best thermal stability and tribological performance. Our results provide the valuable insights needed to guide the design of lubrication and friction reducing materials.  
Keywords:doubly functionalized montmorillonite; polystyrene; soap-free emulsion polymerization; thermal stability; tribological property Article
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 3 Tile-Type Transmitter Module Based on HTCC Technology
### Thermal Design  
The thermal characteristic of the module is analyzed by the finite element method. To increase the computation speed, some geometrical characteristics of the model are simplified. The thermal analysis is based on a steady-state calculation with conditions of the highest working temperature of 65 degrees at the boundary of the module.  
Under the above working conditions, the temperature distribution of each part is calculated. The detailed calculation results of temperature are shown in Table 4. The temperature distribution diagram is shown in Figure 10. The results show that under the calculated working conditions, the temperatures of all devices are lower than the first-level derating value, which verifies the good heat dissipation design of the module.
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials  
### Dataset  
The dataset utilized in this research comprises two distinct groups: the normative group (NG) and the SMS population (SMS). This corpus stands as a significant contribution to the field, as it contains recordings encompassing a diverse array of linguistic features. Its compilation followed a rigorous methodology for data collection. The target population for the recordings was defined as the SMS population within the Spanish Association of Smith-Magenis Syndrome (ASME), while the NG was sourced from the Public School \"Maria Luisa Canas\" (Ciudad Real), where tutors and teachers were duly informed about the research. An explicit exclusion criterion for constituting the NG was the presence of vocal pathology, a condition verified by the school's speech therapist and confirmed by the parents. Informed consent was obtained from all tutors and parents of underage participants involved in this research. This study upholds ethical standards and does not infringe upon the rights of minors, adhering to the principles outlined in the Declaration of Helsinki by the World Medical Association in 1964 [84]. It is noteworthy to mention that SMS has a low prevalence and is often underdiagnosed. In Spain, the average age of diagnosis for this syndrome is 6.5 years [30], and ASME currently accounts for 72 diagnosed cases.  
A rigorous methodology was followed for data collection. Each participant was instructed to sustain the vowel \"a\" for a duration of several seconds at a consistent pitch, with this protocol being iterated approximately five times per participant. Subsequently, all audio recordings underwent meticulous trimming to a standardized duration of half second, as it was determined that this temporal interval sufficed for the comprehensive extraction of all necessary features.  
In summary, a total cohort of 19 participants diagnosed with SMS and 292 individuals drawn from the normative group (NG) were enrolled in the study case. Consequently,a corpus comprising 93 audio samples from the SMS cohort and 1452 from the NG was accurately compiled.