esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams
### Image Processing of Underwater Cracks
#### 4.2.2 Underwater Crack Image Recognition
A hybrid algorithm, which uses a deep learning algorithm and a traditional algorithm to coarsely locate and accurately segment crack regions, not only overcomes the problem that local features are difficult to select when traditional algorithms are used to localize the fracture area, but also solves the time-consuming and labor-intensive problem of training image labeling when deep learning algorithms are used for accurate crack segmentation. [PERSON] et al. [83] used convolutional neural networks and the Ostu algorithm to achieve accurate segmentation of underwater fracture images, but the segmentation accuracy was insufficient when the background was complex or the fracture was small. The deep learning method has been applied to crack detection in underwater images, which improves the accuracy and efficiency of the underwater crack detection technology. Due to the low resolution and difficulty in extracting effective features from sonar images [84,85], sonar images are rarely used for underwater crack recognition and are generally used as a complement to visual images. However, the visual image detection of cracks in muddy water environments is limited. At this time, only sonar images can be used to detect cracks. [PERSON] et al. [53] proposed an underwater dam crack detection and classification method based on two-frequency sonar images. The proposed method can be applied to low-resolution sonar images. This approach has been attempted for the detection of cracks using sonar images, providing a reference for the subsequent use of sonar images to detect underwater cracks. However, the detection speed of this method cannot achieve the real-time effect due to the complex computational procedure [37]. As the final result of underwater crack detection on the dam surface, the quantification of the cracks is another key point of the underwater crack detection on the dam surface. There are now related studies on the underwater image mosaic [26] and spatial information measurement of cracks [32,76]. However, the localization accuracy and processing speed of the aforementioned methods in the crack region still have problems. Therefore, the timely and accurate quantification of underwater cracks will be the focus of future research efforts. The methods and results used in the corresponding documents are shown in Table 2.
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 3 Experimental Set-Up  
The pulse-echo ultrasonic ranging system is shown in Figure 3. The instruments used in the system are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Instrument** & **Description** \\ \hline Signal generator & Tektronix AF3000C \\ Amplifier & Homemade, amplification factor: 36 dB \\ He-Ne laser & Thorlabs, 633 nm, 5 mW \\ Photodetector & Thorlabs, APD430 A/M, 400 nm-1000 nm \\ Digital oscilloscope & Tektronix MDO3104, maximum sample rate: 5 G/s, resolution: 8 bits \\ Transducer & Goword, 1P20, 1 MHz, 20 mm \\ Transducer & Goword, 1P28, 1 MHz, 28 mm, immersion \\ Displacement platform & Thorlabs, travel: 25 mm, engraving: 0.01 mm \\ Water tank & 100 cm \(\times\) 30 cm \(\times\) 50 cm, glass \\ Sound velocity profiler (SVP) & Valeport Mini SVP, resolution: 0.001 m/s, uncertainty of sound velocity: 0.02 m/s \\ \hline \end{tabular}
\end{table}
Table 1: Instruments used in ranging system.  
Figure 2: (a) Schematic of pulse-echo ultrasonic ranging; (b) a result of the pulse-echo signal and the time-of-flight (TOF).  
The He-Ne laser emitted a continuous light into the water, assuming that the medium did not absorb it. The diffracted light was reflected by the mirror and was detected by the photodetector. Please note that diffracted light of high order (\(m>1\)) was neglected in our experiment due to the low intensity. The positions of the transducer and the laser were fixed. A system with similar equipment has been used for the characterization of pulsed ultrasound; more details are provided in the original paper [42].  
Both transducers were plane transducers with a central frequency of 1 MHz. The difference is that 1P28 is an immersion transducer with a 1/4 wavelength layer that is acoustically matched to water. The direction of ultrasound propagation is considered as the \(z\)-axis. Accordingly, the direction of the laser propagation is considered as the \(y\)-axis.  
The sound velocity was measured by a commercial SVP (Valeport Mini SVP). The principle of sound velocity measurement was by a sing-around method. The TOF was measured by the time of multiple round trips of sound waves within a known distance.  
There were five targets used in the experiment, as shown in Figure 4. The targets were mirrors whose diameters were 1 inch. All targets were fixed on the aluminum breadboard. The hole spacing was 25 mm. The transducer was fixed in the same line to ensure that the ultrasound propagated along the \(z\)-axis. We measured the distance between the reference plane and the reflectors. As the reflector could reflect ultrasound, we removed the reflectors one by one during the measurement, so that we could get the distance to the five reflectors one by one.  
Figure 4: Targets used in the experiment.  
Figure 3: Pulse-echo ultrasonic ranging system.
# Roadway Backfill Mining with Super-High-Water Material to Protect Surface Buildings: A Case Study
## 2 Method and Geological Conditions  
### Key Strata (KS) Theory  
The overburden is composed of different strata with delamination differences, thus the mining effects on each stratum are different. Hard and thick strata may have a stronger bearing capacity,supporting those weak and thin strata that only act as loads. Based on this difference, KS theory was proposed by [PERSON] [29]. According to the theory, the hard and thick stratum is named KS, and the overburden has at least one KS. If there are more than two KS, the top KS is named the primary KS (PKS), as it bears the entire load from roof to ground surface. Correspondingly, the other KS are named the sub-key strata (SKS) that control partial overburden.  
In general, the overburden is composed of strata with varied thicknesses and different lithology. In the mining process, the gob is under gravity without support and starts to bend when bed separation occurs. It also occurs between the upper hard strata and lower soft strata. The combination of roof movement can be determined by the bed separation position with theoretical analysis, and the calculation formula of the loads on the movement combination layer is given in Equation (1). If the first layer is the KS and its control reaches the \(n\)th layer, then the \((n+1)\)th layer becomes the second KS, which must meet the Equation (2).  
\[\left(q_{n}\right)_{1}=\frac{E_{1}h_{1}^{3}(\gamma_{1}h_{1}+\gamma_{2}h_{2}+ \ldots+\gamma_{n}h_{n})}{E_{1}h_{1}^{3}+E_{2}h_{2}^{3}+\ldots+E_{n}h_{n}^{3}} \tag{1}\]  
\[(q_{n+1})_{1}\textless(q_{n})_{1} \tag{2}\]  
where \((q_{n})_{1}\) is the load formed by the effect of \(n\) layers on the first SKS, Pa; \(E_{n}\) is the \(n\)th layer's elastic modulus, Pa; \(\gamma_{n}\) is the \(n\)th layer's volumetric weight, N/m\({}^{3}\); and \(h_{n}\) is the \(n\)th layer's thickness, m.  
At present, much research is based on the KS theory, such as the influence of KS position on the height of water-conducting fracture zones [30], the influence of KS structures on the mine pressure [31], and the induced effect of KS-breaking on rock burst and disasters [32; 33]. Therefore, based on the KS theory, combined with the distribution of KS position, backfill mining is used to control the overburden movement, so as to protect the surface buildings.
## 3. Results and Discussion
### Strain Sensing Information from Numerical Results  
The following numerical results are evaluated and discussed based on multi-directional strain patterns, namely longitudinal (\(\varepsilon_{x}=E_{11}\)), transverse (\(\varepsilon_{y}=E_{22}\)), and shear (\(\gamma_{xy}=E_{12}\); \(\gamma_{zz}=E_{13}\); \(\gamma_{yz}=E_{23}\)) strains, in the three quasi-static loading cases. The strain contours along the FOS cores can be regarded as the strains measured by FOSs in service. Note that the load levels chosen in Section 2.4 are for demonstration and are not necessarily based on experimental results or in-service data.
## 3 The Proposed Range-Division User Relay Selection Scheme
### Starting Inner Diameter of RDUR  
Since the path loss is a key factor in determining whether MG user can successfully receive in the first stage, it is important to notice that \(\gamma\) exists. In the circular area, which is centered on BS and with \(\gamma\) as radius, MG users are all SUs. \(\Gamma_{\mathcal{U}}\) denotes the set of distance from BS to each USU, \(\Gamma_{\mathcal{U}}=\left\{\gamma_{u}^{j}1\leq j\leq\left(1-C_{1}\right)\left( M_{1}+M_{2}\right)\right\}\). Without loss of generality, it is assumed that elements in \(\Gamma_{\mathcal{U}}\) are ranged in ascending order.  
From the analysis above, when \(\gamma\leq\gamma_{u}^{1}\), there is no need to implement UR selection scheme. Hence, RDUR scheme is conducted by USUs within the annular range \(\mathcal{A}\) with inner diameter \(DI_{in}^{\mathcal{A}}=2*\gamma_{u}^{1}\) and external diameter \(DI_{ex}^{\mathcal{A}}=2*R\). In summary, the starting inner diameter of RDUR is \(2*\gamma_{u}^{1}\).
## 2 Results
### Effect of KHS-Cnd on Protease Activity  
Since extracellular proteases secreted by _P. aeruginosa_ play important roles in infection pathogenesis, proteolytic activity was determined by qualitative and quantitative analyses. These tests were performed on the culture supernatants of _P. aeruginosa_ clinical and reference strains treated with 40 uM KHS-Cnd (except for 32P where 20 uM KHS-Cnd was used); results were compared with those of the untreated cultures after 24 h of growth.  
For qualitative analysis of proteases secreted by clinical isolates of _P. aeruginosa_, the gelatin-zymography assay was performed after SDS-PAGE separation of the supernatant samples. Gel incubation in a renaturation buffer permitted the digestion of the protease substrate, and after Coomassie staining, a clear band indicative of protease activity was detected for all samples without significant qualitative differences among the KHS-Cnd treated samples and their relative untreated counterparts (Figure 7).  
In the quantitative assay, proteolytic activity was detected using azocasein as the protease substrate [24]. After digestion by proteases, the produced protein fragments were quantitatively analyzed by measuring their absorbance, after the removal of the undigested substrate by trichloroacetic acid precipitation. Proteolytic activity was normalized to the amounts per microgram of the total proteins present in the supernatant samples. As reported in Figure 8, the results were expressed as a percentage of residual proteolytic activity of the treated cultures in comparison with that of the corresponding controls. The obtained data indicated that the treatment with KHS-Cnd did not significantly affect the protease activity in three tested strains including the reference one. Conversely, significant alterations were observed in the other three clinical strains; in particular, a partial significant  
Figure 6: Statistical analysis of projected areas related to holes and peaks from: _P. aeruginosa_ PA14 control sample (**A**); _P. aeruginosa_ PA14 treated with KHS-Cnd (**B**); _P. aeruginosa_ 27P control sample (**C**); and _P. aeruginosa_ 27P treated with KHS-Cnd (**D**). N = 50 measurements were taken for each sample. Statistical difference was determined by the software MedCalc®: \(p\) < 0.05 was considered statistically significant.  
reduction in protease activity was highlighted in the 23P strain; whereas a significant increase was observed in the supernatants of the 27P and 31P strains. The latter showed a higher protease production (64%) in the presence of KHS-Cnd compared to the control (Figure 8).
## 5 Numerical Results  
In this section, we present our numerical results by explicitly characterizing the metrics of interest as a function of time, different systems, and environmental characteristics. Specifically, we apply the time-dependent mathematical modeling outlined in Section 4 for the system model specified in Section 3. We start investigating the impact of the initial user location with respect to the BS, then proceed with the impact of micro- and macro-mobility parameters, and, finally, discuss the effect of different antenna radiation patterns. As a main performance metric, we utilize spectral efficiency. The default environmental and system parameters are provided in Table 1. When discussing the numerical results provided in different illustrations, we also provide specific parameters in the text.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Value** & **Description** \\ \hline \(h_{ll},h_{A}\) & UE and NR BS heights & 1.5 m, 4 m \\ \(P_{T}\) & BS emitted power & 23 dB \\ \(\zeta\) & path loss exponent & 2.1 \\ \(I_{M}\) & shadow fading and interference margin & 3 dBm \\ \(N_{H}\times N_{V}\) & BS antenna array configurations & \(4\times 4\), \(8\times 8\), \(15\times 15\) \\ \(N_{H}\times N_{V}\) & UE antenna array configuration & \(4\times 4\) \\ \(f_{C}\) & operational frequency & 28 GHz \\ \(N_{0}\) & thermal noise & \(-174\) dBm \\ \(\Delta\tau,\Delta y\) & displacement velocity & 0.8 m/s \\ \(\Delta\theta,\Delta\phi\) & displacement velocity around \(Ox\) and \(Oy\) & \(0\)-\(4\)°/s \\ \(L\) & compartment size & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: The default system parameters.  
Note that in this study, we consider a 28 GHz carrier frequency. However, the proposed approach is suitable for sub-6 GHz systems as long as they utilize the antenna arrays at the transmit and receive side. The only difference is the lower path loss at sub-6 GHz carrier frequencies.
## 4 Methodology
### Attention-Based Spatial-Temporal Fusion Graph Module (ASTFG)  
After accomplishing the interaction graph construction, the well-designed networks are exploited to extract the spatial-temporal dependencies as shown in Figures 3 and 4. Each AST-GCN block learns the feature of the current layer and builds a higher-level feature map on the graph through layers stacking. The AST-GCN block is capable of capturing the connected topology between the target vehicle and its surrounding agents along with traffic rules; then, the GRU block performs, encoding the relationships as well as vehicle attributes to handle the dependencies eventually. The topology in Figure 3 changes with time but the number of nodes is constant; what changes is the influence and weight between each node. So, we use an attention mechanism on the adjacency matrix to understand the impact of different nodes on the prediction accuracy. Specifically, the connected nodes are convolved in space by a spatial convolution kernel and then between different frames of the same node in time.  
Figure 4: Schematic diagram of the GRU block in temporal dependence modeling.  
Figure 3: The process of features extraction by spatial–temporal graph convolution.
## 3 Numerical Model
### Energy Demand Profiles
#### 3.2.2 Day of the Week, Season, and Weather  
The first external factor is the day of the week. The norm VDI 4655 distinguishes between Working day (W) and weekend day (S, for Sunday). In Figure 5a,b, the energy demand of these two typical days is compared, all the rest being equal. The most distinct difference is the time in which sanitary hot water is mostly needed, and at what intensity: mostly in the morning and at 12:00 during the week, whereas it is more smoothly distributed in the evening during the weekend.  
The season can have a major impact on the energy demand of a house. Therefore, three different times of the year are defined: Summer (S), Winter (W) and intermediate seasons (U, from the German _Uebergang_, intermediate). The difference between summer and winter, all the rest being equal, is shown in Figure 5c,d as an example. It should be noted that in summer, no heating is needed. The demand of electricity in winter is more distributed during the day, whereas predominantly in the first afternoon and evening in summer.  
The third environmental factor implemented in this analysis is the weather, distinguishing between sunny (H, from _Heiter_, clear) and cloudy days (B, from _Bewoelkt_, cloudy). Since the weather in summer does not influence the average temperature significantly, only \"neutral weather\" (X) is considered for this season. Although the weather impact on the energy demand distribution is of minor importance, it was included for completeness. A comparison between a sunny day and a cloudy day can be seen in Figure 5e,f. In conclusion, the three above-mentioned external factors constitute ten different representative days in a year: UWH, UWB, USH, USB, SWX, SSX, WWH, WWB, WSH, WSB.  
Figure 5: _Cont._
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
## 2 Preliminaries  
This section introduces the standard SM4 encryption algorithm as the basis of the improved SM4 algorithm proposed in this paper. A kind of DFA against SM4 algorithm is described, which is the specific model of DFA to prevent in this scheme.
# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 3 Results
### Discussions  
Data augmentation can be categorized into two types: online augmentation and offline augmentation. The key advantage of the online way is that it does not require the synthesis of additional data, thereby conserving data storage space and enhancing flexibility. Moreover, in certain cases, the model may not converge effectively when the image undergoes significant changes after data augmentation. In such scenarios, offline augmentation can be employed as a viable alternative. The benefit of offline augmentation is that the scaled data can be visualized prior to model training and image generation, enabling developers to control the impact of the scaled data. However, a drawback to this approach is that it necessitates more storage space and is less flexible. It should be noted that all the methods employed in this paper are of the offline variety. As shown in Tables 5 and 6, different image augmentation methods were used to evaluate the segmentation performance of austenite and Al-La alloys. In comparison with other methods, the geometric deformation method produces the highest benefit for both data sets. There are two potential explanations: (1) The microstructure itself is rotational invariant, and the tissue structure can be distorted and deformed as a result of the preparation process. The geometric deformation simulates the change due to rotation, flipping, distortion, etc., which is quite similar to the real situation of structural change. Thus, it has a considerable positive effect on deep learning-based image segmentation. (2) The convolution operation is not rotational-invariant, but data augmentation by geo metric deformation can compensate for this drawback by obtaining the image features that are actually appropriate for microstructure extraction.
## 3 Results and Discussion  
### Microbial Populations  
The microbial groups monitored varied throughout fermentation. Therefore, there was a significant difference between the monitored populations, depending on the microbial group evaluated and the coffee variety (Figure 1).  
Yeasts and filamentous fungi were counted in YPD. At the beginning of the fermentation, these organisms varied by approximately 4 log CFU.mL\({}^{-1}\). The final yeast population under conditions of natural fermentation depended on the fermented coffee variety (Figure 1). Coffee varieties CP and CA62 presented an average yeast population at the end of fermentation of 5.94 log CFU.mL\({}^{-1}\), while varieties A and P2 had an average yeast population of 3.9 log CFU.mL\({}^{-1}\). There was a significant difference in the yeast population when observing the fermentation of coffee varieties inoculated with yeast starter cultures (\(p<0.05\)). In general, there was an increase in the yeast population during fermentation. The yeast populations of varieties A, CA62, and CP inoculated with _S. cerevisiae_ CA11 were 109.26%, 78.33%, and 50.24% greater, respectively, than those inoculated with _K. lactis_ B10 after 48 h of fermentation. Therefore, yeast growth was affected by the coffee variety used.  
The population behavior of other evaluated microbial groups during fermentation also depended on the coffee variety. The most significant variation in LAB occurred in the P2 variety, followed by the A, CA62, and CP varieties. Similarly, significant variation in the mesophilic aerobics measured in the PCA medium was also noted (Figure 1). During coffee fermentation, aerobic bacteria have a larger microbial population than LAB, a behavior favored by the employed wet processing method [12].  
This population behavior is variable due to the substrate concentration available at the beginning of fermentation, which was different for each coffee variety. Coffee fermentation is complex and depends on a succession of microbial populations [9]. Given that the bacterial population was greater than that of yeasts in all the treatments evaluated, the permanence and competition of the starter cultures during fermentation are essential because their metabolic activity may be related to the differences in the chemical compounds evaluated in the coffee and possibly to the correlated sensory characteristics [30].