esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Observations and Results  
### Global Response and Crack Pattern  
Figure 6 showed the final end state of all specimens. Two kinds of failure modes were observed. For the specimens with a rectangular section (BMF and BMFP), the cracks were either stepped cracks or diagonal cracks, and the cracks were concentrated at the top and bottom of the specimens, while few cracks appeared in the middle. The stepped cracks passed through the interface between the masonry units and mortar, while the diagonal cracks mainly appeared at the face of the masonry units.  
Figure 4: Test setup.  
Figure 5: Schematic diagram of load sequence.  
The stepped cracks were caused by a high shear stress, which exceeded the bond strength between the masonry units and mortar, while the diagonal cracks were due to the principal stress on the face being larger than the tensile strength of the masonry units. Moreover, the face of the masonry at the bottom of the wall was broken, and grouted concrete was exposed. This mainly related to the complex stress state of the repeated tension and compression on this zone due to cyclic loading. With regard to the specimens with a T-shape section (BMFT and BMFTP), the cracks were also of two kinds, stepped cracks and diagonal cracks, and the cracks developed exclusively on the surface of the masonry wall. The cracks firstly appeared at the interface between the masonry units and mortar located at the bottom of the masonry wall, and gradually developed from both ends to the middle of the masonry wall, which did not occur in the specimen with a rectangular section. A crossing crack occurred at the middle of the masonry wall, when the specimen reached its maximum capacity, and masonry face spalling occurred in that zone due to the repeated cyclic test. Additionally, only bed-joint cracks occurred at the flange, and there was no face spalling at the bottom of the flange (Figure 6e,f). This appearance can be explained by the influence of the flange, which enhanced the compression performance of the masonry wall. Furthermore, a main vertical crack occurred at the web face of the masonry wall, and its width was larger at the height of 400 mm or 800 mm than it was at a height of 600 mm, as shown in Figure 6c,d. This indicated that the crack propagation was related to the horizontal reinforcement, and the horizontal reinforcement diminished the crack width, which was arranged at the height of 600 mm.  
With regard to the prestressing technology, the failure mode and crack propagation of the specimens with the same section type were similar. However, the cracks in the specimen BMFTP were thin and densely covered, while the cracks in the specimen BMFT formed several wide cracks. This indicated that the prestressing technology had no significant effect on the failure state of the specimens but influenced the crack propagation. However, there needs to be more studies to prove this.  
Figure 6: _Cont._
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 3 The Localization Method Using Bearing-Only Measurements
### Run the RPEKF Based on Bearing-Only Measurements of Passing Ships
#### 3.2.3 Outlier Measurement Handling  
In some cases, a bearing measurement \(\mathbf{z}_{k}^{n_{k,s}}\) may not be accurate enough to update the KF estimation. Considering this aspect, a bearing measurement \(\mathbf{z}_{k}^{n_{k,s}}\) is discarded in the case where a bearing measurement \(\mathbf{z}_{k}^{n_{k,s}}\) satisfies the following condition.  
\[\mu_{k}^{T}(\mathbf{H}_{k}\mathbf{r}_{k}^{(j)-}\mathbf{H}_{k}^{T}+\mathbf{R}_{ k})^{-1}\mu_{k}>\Sigma^{2}, \tag{39}\]  
where \(\mu_{k}=\mathbf{z}_{k}^{n_{k,s}}-h^{n_{k,s}}(\mathbf{X}_{k}^{(j)-})\), and \(\Sigma>0\). Recall the measurement Equation (2). In the case where the measurement \(\mathbf{z}_{k}^{n_{k,s}}\) has a Gaussian distribution with mean 0 and covariance \(\mathbf{R}_{k}\), \(\mu_{k}\) is also Gaussian with mean 0 and covariance \(\mathbf{H}_{k}\mathbf{P}_{k}^{(j)-}\mathbf{H}_{k}^{T}+\mathbf{R}_{k}\). (39) implies that this paper does not utilize a bearing measurement, which is npt reliable considering the KF estimation. If the condition in (39) is satisfied, then we do not perform the measurement update associated to the bearing measurement.  
In (39), \(\Sigma\) is a tuning parameter. We use \(\Sigma=5\) in the simulations. We present why \(\Sigma\) is set as 5. Suppose \(\zeta\) is an observation from a normally distributed random variable, \(\mu(\zeta)\) is the mean of the distribution, and \(\sigma(\zeta)\) is its standard deviation. Then, we have  
\[Prob(\mu(\zeta)-5\sigma(\zeta)\leq\zeta\leq\mu(\zeta)+5\sigma(\zeta))\approx 0.99994. \tag{40}\]  
Here, \(Prob(\mu(\zeta)-5\sigma(\zeta)\leq\zeta\leq\mu(\zeta)+5\sigma(\zeta))\) is the probability that a sample in \(\zeta\) exists between \(\mu(\zeta)-5\sigma(\zeta)\) and \(\mu(\zeta)+5\sigma(\zeta)\). Using the property in (40), we use \(\Sigma=5\).
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 3 Results
### Wave Simulation Verification  
The significant wave height Hs (m) and wave period Tp (s) measured at the four measuring points in Figure 2 were used to verify the calculation accuracy of WWM-III. As shown in Figure 9a, the significant wave height of the qwadcp02 measuring point far from the coast reached 2.5 m, and the unsteady process was more obvious. At the qwadcp01 measuring point near the mouth of the Wanquan River in Figure 9b, the significant wave height of wind waves reached the maximum value of 1.2 m at 12 h, because it was affected by the runoff of the Wanquan River. The runoff of Wanquan River was opposite to the higher tidal current of open sea and cancelled each other out, making the wave height smaller than that of open sea area, which reflected the spatial difference of wind waves. The wave periods in Figure 9c,d show that the difference in the wave periods between these two measuring points was not significant, and both varied between 6 and 18 s. In general, the calculated results of WWM-III were in good agreement with the measured data and, thus, reflected the wave characteristics around the artificial island.
# Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map
###### Abstract  
Security of image communication is more and more important in many applications such as the transmission of military and medical images. To meet the requirement, a new color image encryption algorithm using a new one-dimension (1D) chaotic map was proposed recently, which can resist various attacks because the range of the new chaotic map is larger than that of the previous ones. In our study, the security of the new original method is analyzed and a novel attack method is proposed. It is demonstrated that the scheme is not secure under chosen-plaintext attack, by which the encrypted image can be successfully converted into the corresponding plaintext image without any error.  
**Keywords:** cryptanalysis; color image encryption; 1D chaotic maps; chosen-plaintext attack
# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 3 Method  
Urban mobility structure detection categorizes regions into distinct communities based on the travel patterns observed in the data. These communities are composed of regions with similar mobility characteristics, revealing the spatial structure and functional distribution of the city. It ought to be assumed that the study area can be divided into \(N\) regions, represented as nodes \(V\), with the mobility OD flows \(E~{}=~{}(r_{s},r,t_{s},t_{d})\) serving as the edges. The urban mobility network is represented as \(G~{}=~{}(V,E)\), where \(r_{s}\) and \(t_{d}\) are the starting and destination regions of OD flow, respectively, and \(t_{s}\) and \(t_{d}\) are the start and end times of travel. Through contrastive learning, the mobility network \(G\) maps to a low-dimensional vector space, generating the region vector \(\mathbf{X}~{}=~{}\{\mathbf{x}_{1},\ldots,\mathbf{x}_{N}\}\in R^{N\times D}\), while urban regions are grouped into communities \(\Phi~{}=~{}\{\phi_{1},\ldots,\phi_{U}\}\in R^{U\times D}\), with communities exhibiting similar internal characteristics. Next, the region embedded vector fits the region functional distribution \(Y~{}=~{}\{y_{1},\ldots,y_{s}\}\), building a complex relationship between urban mobility structures and urban functional distribution. Finally, we analyze the flow pattern, spatio-temporal pattern of travel, and functional distributions among different communities, providing multi-objective optimization decision support for planners, as shown in Figure 1.
## 2 Materials and Methods
### Railway Platform Configuration  
The determination of geometrical parameters of the railway track, with particular emphasis on the track axis, was one of the main tasks of the research project InnoSatTrack. Information on the position was provided by six GNSS receivers which recorded raw satellite data with a frequency of 20 Hz, and for one campaign, with a frequency of 100 Hz. Readings from other sensors, e.g., inclinometers, inertial units, or optical cameras, were used in procedures of reduction of the GNSS receivers' coordinates to the ground-level track axis, especially on curves and transition curves, where track superelevations occur [39].  
GNSS receivers and other sensors used in the measurement campaigns were located according to the fixed design configuration. The distance between two kingpins on the railway platform 401\(Z\) equals 7000 mm [40]. Receivers were mounted on the platform in two parallel lines with two central receivers above kingpins above the track axis. The remaining four receivers were positioned above the rails perpendicularly to the direction of the track axis. Given the standard Polish track gauge of 1435 mm and the distance between the railhead axes equal to 1500 mm, the external receivers were located with an offset of 750 mm from the central receivers. The configuration of the sample railway platform used in one of the InnoSatTrack measurement campaigns is presented in Figure 1.  
The point cloud presents six GNSS receivers marked in yellow mounted on the platform in a fixed configuration. The geometrical conditions include right angles in vertices and defined offsets between receivers. The remaining sensors used in the measurements are related spatially with receivers in one local coordinate system of the platform. In the case of the configuration presented in Figure 1, both the mobile laser scanner and the inertial unit components were located along the axis of the platform.
## 4 Results of the Statistical Analysis of DARP
### Statistical Analysis of the Energy  
The second dependent variable that is relevant to be analyzed in the wireless sensor network protocol, DARP, is focused on the energy field. A statistical study, as presented in Section 4.1, was performed for the Set-Up Energy (Energy) variable. As presented in the bibliography [5; 6], actual network protocols required energy-efficient wireless routing protocol, and frequently reconstruct the whole net system, meaning that the set-up energy is consistently consumed. Table 6 presents the statistical summary of the ANOVA being the dependent variable Set-Up Energy. There are three main factors: Reconnection Timer, ACK Timer and Link Timer with a \(p\)-value smaller than 0.05, and therefore the election among the levels of these factors have a strong relevance in the energy behavior of the DARP protocol. Figure 6: Mean values of the levels of the factor ACK Timer factor, analyzing the dependent variable Number of control messages sent during the set-up time. Figure 7: Evolution of the interaction between the main factor Reconnection Timer and ACK Timer, analyzing the dependent variable Number of control messages sent during the set-up time. \begin{table}
\begin{tabular}{c c c c} \hline
**ACK Timer(T\_ACK)** & **LS Mean** & **LS Sigma** & **Homogeneous Groups** \\ \hline T\_ACK2 & 20.5 & 0.046 & X \\ T\_ACK3 & 21.9 & 0.046 & X \\ T\_ACK1 & 22.7 & 0.045 & X \\ \hline \end{tabular}
\end{table} 
Table 5: Results of the Multiple Range Tests for the factor ACK Timer (T_ACK), using as the dependent variable the Number of control messages sent during the set-up time. Table 7 and Figure 8 present the Multiple Range Test for Reconnection Timer using as dependent variable the Set-Up Energy (mWs). As can be concluded from Table 7, there are three homogenous groups without intersection (identified using columns of X's), which means that the three different levels of the factor Reconnection Timer have all of them statistically significant effect on the dependent variable Set-Up Energy. The level T_REC1 has the best performance and therefore lowest mean value in term of required Energy, and the level T_REC3 has the worst performance with the highest mean value. It is also important to analyze the levels of the statistically significant factor ACK Timer (T_ACK). The information of the Multiple Range Test for ACK Timer using as dependent variable the Set-Up Energy (mWs) is presented in Table 8 and graphically in Figure 9, which shows the evolution of the mean values of the levels of the factor Reconnection Timer being the dependent variable Set-Up Energy (mWs).
# Experimental Study on Flexural Properties of Polyurethane-Cement Composites under Temperature Load
## 3 Synthesis Mechanism of Polyurethane Cement Composites
### Chemical Structure and Reaction Mechanism of Polyurethane
#### 3.1.2 Synthetic Reaction Mechanism of Polyurethane  
The reactive formation of polymers is mainly due to electrostatic attraction, where the electrostatic potential maxima and minima are close to each other. The electrostatic potential analysis shows that the reaction between isocyanate and polyether polyol belongs to the addition polymerization reaction of hydrogen transfer, which is caused by the mutual attraction between the hydroxyl group (-OH) and isocyanate group (-NCO). The reaction is carried out on the double bond of the more active -NCO group; the hydrogen atom on the hydroxyl group of PPG is transferred to the N atom in the isocyanate group. The remaining group is combined with the C atom to form a carbamate compound, and then chain-breaking, rotation, twisting, and replication occur to form the final polyurethane block copolymer. The polymerization process is shown in Figure 5.  
The isocyanate group is highly reactive and also reacts with the hydroxyl group (-OH) of water molecules to generate a large amount of carbon dioxide gas and substituted urea. This reaction process is shown in Figure 6.  
Figure 5: Polyurethane polymerization process.  
On the one hand, when Silicate cement contains moisture, water molecules will replace part of the polyol and react with isocyanate, resulting in an insufficient and ineffective reaction. The polyurethane cannot be wholly contacted with the cement, and the cement particles cannot be wholly bonded. On the other hand, the carbon dioxide gas generated with water will remain in the polyurethane colloid and be difficult to discharge, forming tiny holes and bulging bubbles on the surface, affecting the mechanical properties of the polyurethane cement composite. In preparing polyurethane cement composite materials, dehydration treatment is required. The method used in this study is to use a frying furnace to dry and dehydrate the silicate cement.
# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
###### Abstract
First, a graph-based image segmentation method [41] is applied to HSIs via a complementary pixel-grouping criterion, which can dynamically adjust the weight of the shape and amplitude in the measurement of pixel spectral similarity. This method can quickly and effectively obtain a set of initial regions, each of which is spectrally uniform and does not contain anomalous and background pixels simultaneously. In addition, the segmentation criterion is adaptive in capturing anomalies in all possible locations and scales without exhaustive searching. Then, the similarity of each pair of adjacent regions after the initial oversegmentation is calculated based on the spectral mean of each region and recorded in the initial similarity matrix. The measure of similarity is the linear combination of the spectral angle and the correlation coefficient between each pair of adjacent regions. Based on the similarity matrix, region merging via the greedy algorithm is imposed to merge similar regions successively and finally leave dissimilar regions. Further, anomalies are the regions that are distinguished from the background of the HSIs. Thanks to the above spectral-based selective search process, the proposed algorithm is effective and handy without assuming a statistical background distribution and constructing a background or target dictionary. The detection performance can be assessed by intuitive detection images, receiver operating characteristic curves and area under curve values. We evaluated and compared the proposed method using two widely-used real hyperspectral datasets. The results showed that the proposed method possessed higher detection accuracies and lower false alarm rate than several popular and state-of-the-art methods. The contents of the manuscript are arranged as follows. Section 2 first introduces the common measurement of spectral similarity and then presents a detailed description of the proposed Triple-S hyperspectral anomaly algorithm. The employed evaluation indicators of anomaly detection performance and the data set used in the experiment are also described in this section. Section 3 shows the experimental detection results of the proposed algorithm implemented on two real hyperspectral datasets. Section 4 introduces the parameter setting of our Triple-S and comparable algorithms. Anomaly detection performance of the proposed algorithm is also discussed qualitatively and quantitatively. The competitive detection performance of the proposed algorithm is demonstrated by comparing it with several representative methods. The summary and outlook of the algorithm and performance are provided in Section 5.
# S-Band Class-C-F Power Amplifier with 2 nd Harmonic Control at the Input
## 2 Theory of the Gate Harmonic Control
But in order to understand if such an approach, which implies a complexity in the generation of the suitable current shaping, could be helpful in providing better performance with respect to the classic class-F approach (i.e., sinusoidal input and class-AB bias), it is required to analyze the  
Figure 1: Intrinsic drain current harmonic stimulated by sinusoidal excitation. DC = Direct Current. Figure 2: Intrinsic drain current harmonics stimulated by rectangular excitation. theoretical performance attainable in the two cases; in particular, the DE and normalized output power level, as reported in Figures 3 and 4, respectively. Specifically, it can be noted that there exists a range of circulation angles, from \(\frac{2}{3}\pi\) to \(\frac{3}{4}\pi\), highlighted in both figures, where the theoretical attainable DE and power level values exceed the maximum ones achievable in class-AB with the standard approach. In order to realize a rectangular current wave at intrinsic drain node, a similar excitation must be applied at the input. Nevertheless, the use of a rectangular waveform as the driving signal is not practical, being typically sinusoidal in the signal generated by the external sources. Over-driving the active device is an available solution, taking the advantage of the saturation of the drain current. However, such an approach is unsuitable, since it brings an over-stress of the device itself and an eventual breakdown of the gate junction. Conversely, the strong non-linearity caused by the class-C biasing point could be used for waveform shaping. In particular, by exploiting the 2 nd harmonic termination at the input, it is possible to shape the gate voltage, as in the following [13]:  
\[v_{GS}(\theta,h_{2})=V_{GG}+V_{G,1}\cdot\left[\cos(\theta)-h_{2}\cdot\cos(2 \theta)\right], \tag{3}\]  
where \(V_{GG}\) is the gate biasing voltage, \(V_{G,1}\) is the fundamental voltage component, and \(h_{2}\) is the amplitude ratio between the 2 nd harmonic \(V_{G,2}/V_{G1}\) and the fundamental components. The 2 nd harmonic, which has proper phase and amplitude relationship with the fundamental signal, could realize a waveform with flatness behavior at the maximum value, while peaking the negative value. It would excite a reverse-peaked voltage wave at the gate node, as shown in Figure 5. It is clear that a positive \(h_{2}\) is needed for this purpose, and the optimum condition raises for \(h_{2}=\sqrt{2}/4\)[2]. Figure 4: Output power with respect to the useful biasing range of class-C-F PA.  
Figure 3: Useful biasing range for class-C-F power amplifier (PA) with respect to drain efficiency (DE). Both the class-C biasing condition and the proper 2 nd harmonic termination at the input will realize a rectangular-like (better trapezoidal-like) current waveform at the intrinsic drain node. According to the harmonic component analysis discussed previously, a class-F PA design strategy also becomes thus viable in class-C biasing range.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 9 Discussion  
In this section, we discuss some limitations of the experimentation and proposed domain adaptation methods, as well as some potential extensions of the methods.
## 3 Results and Discussion
### Protein Analysis
#### 3.2.2 Wine Protein Composition Evaluated by SDS-PAGE
Wines fermented with the addition of non-_Saccharomyces_ yeasts (Y4 and Y5) presented the highest content in proteins with high MW (28.8% of 74.8 KDa in the case of Y4 and 20.2% of 68.5 KDa in the case of Y5). Regarding base wines produced by Xarel.lo, we can observe that the Y1 and Y2 wines presented the highest MW percentages at 68.4 and 26.9 KDa values for Y1, and 73.2 and 29.5 KDa values for Y2. Y3 wine showed up to 70% of its proteins with MW of 25.3 KDa. However, and as happened in the CH variety, the wines with the Y4 and Y5 yeasts presented between 30% and 50% protein with MW around 30 KDa, and between 20% and 30% protein with the highest MW, 91.6 and 92.2 KDa, respectively. Therefore, it can be highlighted that the Chardonnay variety presented a greater number of bands (protein fractions) compared to the Xarel.lo variety, which could be influenced by the different quantity of proteins extracted from the grape. [PERSON] et al. [11] observed that wines elaborated with sequential inoculation with _T. delbrueckii_ and _M. pulcherrima_ had higher values of lower molecular weight (LMW, molecular weight < 60 kDa) compared to the control wines (wines without addition of non-_Saccharomyces_ yeasts), which related to the improved foam parameters observed in wines produced with non-_Saccharomyces_ yeasts. In our study, this trend was observed in XA cavas produced from base wines of Y4 (_M. pulcherrima_), which presented higher values of LMW (molecular weight <60 kDa). This fact could explain the height of foam stability (HS) value observed in Y4 (Figure 2).