esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction: Crack Localization Based on Pressure Wave Propagation
### Structural Health Monitoring  
To overcome this hurdle and mitigate the risk, strategies must be developed for the detection and localization of manufacturing defects in the 3D-printed components. A critical element in aeronautics non-destructive testing is the detection of the presence of a crack before it reaches a critical size. Similar important issues retarded the introduction of lightweight composite materials in aeronautics. To prevent the same difficulties with the introduction of 3D metal-printed light-weight components in aeronautics and in many other applications, it is important to invest significant research efforts into the measurement of the structural health of the component under operation conditions. The academic community has proposed several Structural Health Monitoring (SHM) techniques in the last decade [4; 5; 6]. Many of these approaches work well in laboratory conditions, but are not able to detect damage in a sufficiently effective manner and are not robust enough to be applied in practice. A high detection probability and the avoidance of false alarms are essential for a proper operation of the system, as well as background noise reduction. One major aspect that is often overlooked is the lifetime of the monitoring system itself and its robustness with respect to the operational environmental conditions [7; 8]. All these reasons show the importance of dedicating substantial research efforts to structural health monitoring methodologies to detect and localize cracks in the 3D-printed parts of tomorrow.
## 3 Results and Discussion
### Characterization of the MWCNTs
#### 3.1.2 BET Surface Analysis  
The BET and nitrogen adsorption-desorption study investigation method was utilized to quantify the pore diameter and specific surface space of the MWCNTs (Figure 2). A sample of the MWCNTs was optimized at a deposition temperature of 750 \({}^{\circ}\)C. The results of the BET surface area examination of the MWCNTs show that the pore diameter was 6.67 nm, and the whole surface area was 181.99 m\({}^{2}\) g\({}^{-1}\). The surface area of CNTs is affected by their diameter, agglomeration, surface functionalization, presence of impurities, and so on [45].
## 4 Discussion
The impact of the increased proton flux was recorded at the riometer data in both cases with a clear latitude dependence, the larger the latitude the greater the observed values. The latitude-dependent enhancement of the ionospheric absorption at higher latitudes as a result of the increased proton flux (solar proton events) has already been discussed in the literature (see, e.g., [35; 36; 37]). In the case of the 06 September event, the D-RAP model overestimated the effect of the energetic particles at every station (Figure 8) while in the other case, it over- and underestimated the caused impact depending on the stations. Furthermore, the model predicted a second peak for the absorption around 18:00 UT on 10 September which is not visible in the observed data at all (Figure 9). At this time, the model overestimated the increased absorption at almost every station (except JYV). The determined values were almost two times higher at the higher latitude stations (ABI, IVA) than the observed ones. The D-RAP global model uses a linear relationship between absorption and the square root of the integral proton flux for the proton events, based on the observation of a single high-latitude station. [PERSON] et al. [27] discussed that D-RAP often misrepresents the occurred absorption changes and it can only be considered as a qualitative indicator of absorption. Recently, [PERSON] and [PERSON] [38] suggested an optimized D-RAP modeling approach based on data observed during 94 solar proton events and reached a better agreement between the measured and modeled values. [PERSON] and [PERSON] [36] also highlighted that a single constant of proportionality derived from data of one high latitude station [39] can no longer be used to determine the ionospheric absorption. They analyzed data from lower latitude stations (up to 58\({}^{\circ}\) MLAT) to define a factor that varies as a function of magnetic latitude leading to a much-improved fit compared to the D-RAP model. Despite the large discrepancy between the riometer and other observations and the modeled values, D-RAP is the most accepted and widely used model to determine the ionospheric absorption, thus, the degradation of HF waves used for radio communication and aviation (see, e.g., [40]). Our results agree with previous papers suggesting that the D-RAP model should be significantly improved to carefully determine the ionospheric absorption changes as a result of solar flares [20; 21; 22; 23]. Nevertheless, based on the present results, the amplitude method seems to be applicable to detect the ionospheric absorption changes during flare events taking into consideration its limitations (that it can produce high values during twilight and during negative ionospheric storms when the foF2 is low, more details in [18]).
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 3 Applications and Real-World Scenarios
### Domain Adaptation in Computer Vision: Real-World Applications
#### 3.1.3 Surveillance and Security  
In the domain of surveillance and security, real-time monitoring and threat detection heavily rely on computer vision technology. Domain adaptation algorithms enable surveillance systems to dynamically adapt to changing monitoring settings, ensuring the precise and timely detection of suspicious activities and objects [315]. Models can flexibly adjust to alterations in camera angles, lighting conditions, and other environmental factors, thereby maintaining a high level of accuracy in identifying abnormal behavior and  
Figure 8: Shift in domain due to change in weather.  
potential security threats across diverse surveillance scenarios through the utilization of domain-invariant features.
# Solution-Processed Memristor Devices Using a Colloidal Quantum Dot-Polymer Composite
## 3 Results
### Operation Stability of CdSe/ZnS QD-PMMA Memristors  
To further demonstrate the stable switching oepration of CdSe/ZnS QD-PMMA memristors, the data retention and the endurance characteristics were analyzed. Particularly, the data retention and the endurance characteristics are important for memristor devices because they reflect the reliability of memory functions. Additionally, since the switching between the LRS and HRS relies on the trapping and de-trapping of electrons at the CdSe/ZnS QDs, it is important to determine any undesired degradation of the device which may result in a loss of memory state. To evaluate the data retention characteristics of CdSe/ZnS QD-PMMA memristors, the device was programmed to either the LRS or HRS state using a V\({}_{\text{bias}}\) of +3 V or \(-\)3 V, respectively, and the variation of the current level was traced with V\({}_{\text{read}}\) of +1 V. Here, V\({}_{\text{read}}\) was set to +1 V, which is lower than the typical set bias. Figure 4a shows the time-dependent current variation for LRS and HRS states up to \(-\)10 ks. As displayed, the LRS and HRS states were well-maintained without a significant change in the current level. The corresponding current switching ratio variation is shown in Figure 4b, and up to \(-\)10 ks, the switching ratio was maintained at around 10\({}^{3}\). These results indicate that the electrons trapped at the CdSe/ZnS QDs are energetically stable and the de-trapping and/or recombination of electrons rarely occurred during this period. Figure 4c shows the endurance measurement data in which the set and reset operations were repeatedly performed for up to 500 cycles. Here, the V\({}_{\text{set}}\) and V\({}_{\text{reset}}\) were +3 V and \(-\)3 V, respectively, and each V\({}_{\text{set}}\) or V\({}_{\text{reset}}\) was applied for 50 ms (V\({}_{\text{read}}\) = +1 V). As shown in Figure 4c, the device exhibited relatively stable switching between LRS and HRS with the current switching ratio maintaining at \(-\)10\({}^{3}\) (Figure 4d). We observed, however, that after around 400 cycles of set/reset switching, the device started to show occasional unstable operation with reduced switching ratios. This can be more clearly identified in Figure 4d, where the variation of the current switching ratio is displayed as a function of the cycle number. These results imply that the device may experience partial degradation, possibly owing to the change in the conduction mechanism.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.5 Telensa  
Telensa [32] is a company providing end-to-end LPWAN connectivity. It has mainly deployed smart lighting networks in cities. It uses a proprietary UNB technology operating in the sub-GHz ISM bands [33], offering a low data rate of 62.5 bps up-link and 500 bps down-link. Telensa does not seem to propose its network for an external use other than their own applications for now, but they aim to standardize their technology.
# Mini-Review: Recent Technologies of Electrode and System in the Enzymatic Biofuel Cell (EBFC)
## 2 The Enzyme, Support, and Substrate in EBFC
The essential criteria for electrode development are a robust adsorption site [14] for enzyme and mediator interaction on the support and support-substrate interaction. The result of a strong interaction between enzyme and support, mediator and support, and support and substrate reduces overpotential, and gives high durability and stability in long-term operation. The development of suitable and robust interaction on the support material is needed to reduce the problems encountered in the enzyme immobilization process. Good support material with a high surface area and low aggregation can provide many sites for enzyme immobilization. Not only that, the mesoporous support material will help mass transport to the enzyme and increase the electronic double layer. Producing mesostructured support material needs to look at controlling uniform pore structure and pore size because the random size will cause underutilization of the support materials [60]. Development of new support materials needs to explore some of these factors to improve the ability to use supports in EBFC, namely electron transfer rate resulting from enzyme-support/electrode interaction [12], enzyme thickness, enzyme loading [15], enzyme conformation, and interfacial stability of the enzyme-support/electrode [3].
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 4 Result and Discussion
### Attention Mechanism Experiment  
To verify the role of the attention mechanism, GRAD-CAM [29] is used to visualize the VGG16, VGG16 and ECA, and VGG16 and ECA-WS. A piece of ground-based cloud image is randomly selected from different categories. The results are shown in Figures 12 and 13. Network's attention to the cloud area can be increased significantly by embedding the ECA attention mechanism into the VGG16 network. However, sometimes it can only focus on part of the cloud or focus part of the attention on the sky background. The ECA-WS attention mechanism we proposed can focus attention on image areas with more inter-class differences, which improves the classification ability of the network.  
Figure 11: Variation of classification accuracy with respect to the kernel size.  
Figure 10: Experimental results of different datasets. (**a**) Accuracy of MGCD, (**b**) accuracy of NRELCD.  
Different attention mechanisms are used for comparative experiments, and the results are listed in Table 3, where backbone is combined network with improved pooling layer. CBAM module, SE module, ECA module, ECA-WI module, and ECA-WS module are introduced, respectively, based on backbone.  
In Table 3, the ECA-WS module has the best performance on improving the accuracy of network classification than other attention modules. However, the ECA-WI cannot improve the performance of the module and may even lead to a decrease.  
Figure 12: MCCD category heat maps.  
Figure 13: NRELCD category heat maps.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attention Mechanisms** & **MCCD** & **NRELCD** \\ \hline Backbone + CBAM & 88.95\% & 94.51\% \\ Backbone + SE & 89.51\% & 95.30\% \\ Backbone + ECA & 89.63\% & 95.55\% \\ Backbone + ECA-WI & 89.53\% & 95.55\% \\ Backbone + ECA-WS & 90.08\% & 95.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative result of attention mechanisms.
## 2 Related Work
### Binary-To-Binary Comparison  
When the target programs and third-party libraries are in binary format, we need to do a binary-to-binary comparison.  
As far as we know, BinShape [16] is the first method to detection reused libraries in binary files. It produces a robust signature for each library function based on heterogeneous features covering CFGs, instruction-level characteristics, statistical characteristics, and function-call graphs. And then it desin a novel data structure to store these signatures and facilitate efficient matching against the target function. LibDX [8] extracts contents in the read-only DATA segment of binaries, which are mainly composed of string constants and sometimes contain function names and import libraries. Moreover, it takes a fuzzy filename and requires information as supplementary features. OSLDetector [17] detects third-party libraries for multi-platform software in binaries. It chooses constant strings extracted from.rodata section and.data section. And it takes measures such as filtering features and building an internal clone forest for all libraries to eliminate internal strings clones. LibDB [18] can effectively and efficiently detect imported TPLs even in stripped and fused binaries. It extracts basic and coarse-grained features and function contents features. It further adopts a function call graph-based comparison method to improve the accuracy of the detection. ModX [11] is a state-of-the-art method for binary-to-binary TPL detection. It extracts syntactic features (strings literals, constant numbers) and semantic features (function call graph, functions accessing common data) to make TPL detection.  
In this paper, we design a binary-to-binary comparison to detect third-party libraries. The considerations that we collect libraries in binary format as our local feature database for two reasons. On the one hand, it is hard for many libraries downloaded from the Internet to look for their source code. Especially some libraries, such as Google-Mobile-Ads-SDK [19], are closed source libraries [8]. On the other hand, by analyzing binary files, we can get much higher-quality features to build a database. Open-source repositories have a great deal of code duplication, which is usually stored in the source code but not compiled in the binary file. Such as OpenCV and LibPNG, the source code of LibPNG is included in the OpenCV source repository but it is not compiled into OpenCV library binaries. The source code in text is not compiled into released binaries. Features extracted from these uncompiled source codes will increase the number of features, potentially leading to a low matching ratio and causing false negatives.
## 3 Results  
### Study Selection  
The search strategy yielded a total of 55 studies from electronic databases and manual searching. After removing duplication, 49 studies were selected, of which 26 were removed after filtering the title and abstract. Finally, 23 studies were assessed for eligibility by full-text review. At this stage, the studies were excluded for some reasons, such as methodological review (n = 4), articles unrelated to the topic (n = 12), and articles not involving human participants (n = 1). Finally, six reports were included in the systematic review [6; 7; 8; 20; 21; 22] (Figure 1).
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 1 Introduction
Despite the proven effectiveness of waste marble powder (WMP) as a sustainable soil stabilizer [8; 9; 10; 11], few studies have investigated the potential of ANNs to model clay soil stabilization using WMP. [PERSON] et al. [32; 33] recently conducted extensive research on sustainable soil stabilization techniques, especially for contaminated soils. However, it is still necessary to investigate sophisticated computational techniques such as ANNs for modeling stabilized soil properties. This study attempts to fill this gap in the literature by developing novel ANN models to predict the UPV and UCS of clay soils stabilized with WMP. Unlike previous studies, this research utilizes a comprehensive laboratory experimental program that includes clay soil, WMP percentages, curing times, and moisture contents. The developed ANN models are optimized using this experimental data through rigorous training, validation, and testing. A significant innovation is the comparison of the performance of the ANN model with that of existing empirical models. This allows quantifying the enhancement in prediction accuracy obtained using ANNs over conventional models. Furthermore, this is the first study to systematically evaluate the effect of individual input parameters ( curing time, WMP%, and moisture content) on the ANN-predicted UPV and UCS values through sensitivity analyses. The results shed new light on the complex relationships between soil stabilization inputs and mechanical outputs. This work will facilitate the more effective design and optimization of clay soil stabilization with WMP in engineering practices by enhancing the current understanding in this area. In general, this study demonstrates the viability of using ANNs as a sophisticated modeling tool for the mechanical properties of WMP-stabilized soils.
## 3 Control System Design
### Pre-Emptive Braking Controller  
#### 3.3.1 Safe Speed  
There exists a certain relationship between the lateral deviation of the vehicle actual displacement and the tracking displacement, as well as the vehicle speed. Given the road curvature and adhesion coefficient, it is possible to solve for the safe speed corresponding to the maximum lateral deviation.  
Assuming the vehicle runs on a curve road with curvature \(R\) at a constant speed, Equation (3) can be expressed as Equation (4). By conversion, Equations (5)-(7) can be obtained [9].  
\[\begin{array}{c}\dot{y  
where \(y_{c}\) is lateral displacement; \(\psi\) is yaw angle; \(\Delta y_{c}\) is the relative lateral displacement; \(\Delta\psi\) is the relative yaw angle.  
\[a_{11}=-\frac{{{l_{f}}^{2}C_{f}}+{l_{r}^{2}}C_{r}}{{{I_{2}}}V},a_{12}=\frac{{{l_ {f}}C_{f}}-{l_{r}}C_{r}}{{{I_{2}}}},a_{13}=\frac{{{l_{r}}C_{r}}-{l_{f}}C_{f}}{{{I _{2}}}V}\]  
\[a_{31}=\frac{{{l_{r}}C_{r}}-{l_{f}}C_{f}}{{{m}}V},a_{32}=\frac{{{C_{f}}+{C_{r}} }}{{{m}}},a_{33}=-\frac{{{C_{f}}+{C_{r}}}}{{{m}}V}\]  
\[b_{11}=\frac{{{l_{f}}C_{f}}}{{{I_{2}}}},\;b_{31}=\frac{{{C_{f}}}}{{{m}}},c_{11 }=-V,c_{12}=a_{11}V,c_{32}=-V^{2}+a_{31}V\]  
Through calculation, the relation of the safe speed can be obtained as (8).  
\[V_{r}=\sqrt{\frac{{{L_{\mu}}C_{r}}}{{{m}}{{L_{f}}}}\left[{{l_{r}}-\frac{1}{{{ l_{f}}C_{f}}-{l_{r}}C_{r}}\left\{{{l_{f}}C_{f}}{{h}}R\Delta y_{c}-\left({{l_{f}}^{2} C_{f}}+{l_{r}}^{2}C_{r}\right)\right\}}\right]} \tag{8}\]  
Assuming a maximum lateral deviation of 0.55 m, the safe speeds for the vehicle double lane change emergency avoidance maneuver on low friction (\(\mu\) = 0.3), medium friction (\(\mu\) = 0.6), and high friction (\(\mu\) = 1.0) roads are 24.7 km/h, 35 km/h, and 45 km/h, respectively.
# Simulation Analysis and Experimental Study of the Cooker Hoods of High-Rise Residential Buildings
## 3 Exterior Flow Field of Cooker Hood  
### Analysis Theory of Exterior Flow Field  
When the simulation calculation of the exterior flow field of cooker hood for a kitchen depends on the nature of the air and oil fumes, the Reynolds-averaged incompressible continuous equation, momentum equation, energy equation, species transport equation, and standard k-epsilon turbulence model form closed equations. To simulate the turbulent motion of exterior flow field of cooker hood, and the motion of oil fumes, the heat transfer, and the mixing diffusion of oil fumes and air are analyzed; the basic equations can be expressed as equations of (4)-(7) [19]  
The continuity equation:  
\[\frac{\partial(\rho u)}{\partial x}+\frac{\partial(\rho v)}{\partial y}+\frac{ \partial(\rho w)}{\partial z}=0 \tag{4}\]  
The momentum equations:  
\[\frac{\partial(\rho u)}{\partial t}+div(puu)=-\frac{\partial p}{\partial x}+ \frac{\partial\tau_{xx}}{\partial x}+\frac{\partial\tau_{yx}}{\partial y}+ \frac{\partial\tau_{zx}}{\partial z}+F_{x}\]  
\[\frac{\partial(\rho v)}{\partial t}+div(pvu)=-\frac{\partial p}{\partial y}+ \frac{\partial\tau_{xy}}{\partial x}+\frac{\partial\tau_{yy}}{\partial y}+ \frac{\partial\tau_{zy}}{\partial z}+F_{y}\]  
\[\frac{\partial(\rho w)}{\partial t}+div(pvu)=-\frac{\partial p}{\partial z}+ \frac{\partial\tau_{xz}}{\partial x}+\frac{\partial\tau_{yz}}{\partial y}+ \frac{\partial\tau_{zz}}{\partial z}+F_{z} \tag{5}\]  
The energy equation:  
Figure 4: Relationship between the air volume of fan and installation floors of cooker hood.  
Figure 5: Relationship between the back pressure of fan and installation floors of cooker hood.  
\[\frac{\partial(\rho c_{s}u)}{\partial x}+\frac{\partial(\rho c_{s}v)}{\partial y}+ \frac{\partial(\rho c_{s}w)}{\partial z}=\frac{\partial}{\partial x}\bigg{(} \frac{k}{c_{p}}\frac{\partial K}{\partial x}\bigg{)}+\frac{\partial}{\partial y }\bigg{(}\frac{k}{c_{p}}\frac{\partial K}{\partial y}\bigg{)}+\ \frac{\partial}{\partial z}\bigg{(}\frac{k}{c_{p}}\frac{ \partial K}{\partial z}\bigg{)}+dT \tag{6}\]  
The species transport equation:  
\[\frac{\partial(\rho c_{s})}{\partial t}+div(\rhouc_{s})=div(D_{s}grad(\rho c_{ s}))+S_{s} \tag{7}\]  
in which \(u\), \(v\), \(w\) are the velocity components of in the \(x\), \(y\), \(z\) directions; \(\rho\) is the density; \(p\) is the pressure of the micro-unit; \(\tau_{xx}\), \(\tau_{xy}\), \(\tau_{xz}\) are the components of the shear stress on the surface of the micro-unit due to molecular viscosity; \(F_{x}\), \(F_{y}\), \(F_{z}\) are the body force of micro-unit; \(c_{p}\) is the specific heat capacity; \(K\) is the heat transfer coefficient of fluid; \(dT\) is the temperature gradient; \(c_{s}\) is the component volume concentration; \(\rho c_{s}\) is the component mass concentration; \(D_{s}\) is the component diffusion coefficient; and \(S_{s}\) is the quality of the components produced per unit volume per unit time in the system, also known as incidence.