esa-sceva/satcom-chunk-collection huggingface dataset:
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 3 Results  
A total of 200 Type 1 and 200 Type 2 false-bottom echosounder data were selected for analysis. The actual depth of the false bottom and true bottom in the image was calculated utilizing the false-bottom calculation formula derived in the article to determine the theoretical depth. Subsequently, a comparison was made between the actual depth of the true and false bottoms, and the theoretical depth of the false bottom. The obtained results are presented as Table 1.  
The experimental results obtained by calculating the error of the false-bottom depth using the formula are clearly illustrated in the Table 1. It can be observed that the difference between the theoretical false-bottom depth and the actual false-bottom depth is minimal. When comparing the calculated false-bottom depth obtained using the formula with the actual false-bottom depth, both types exhibit data errors below 2% in over 90% of cases, with only a few instances exceeding 5% error. These findings demonstrate that Formulas (2) and (8), derived from the theory used for calculating false-bottom depth, are generally consistent with the actual results, satisfying the requirements for practical use.  
In the scenario of a third pseudo-bottom, the depth of the true bottom extends beyond the range of the echo detector, rendering the determination of the actual depth impossible. Nevertheless, valuable insights were gleaned from extensive observations of the echosounder data, resulting in the formulation of the following rules. The appearance of the third type of false bottom coincides with the gradual deepening of the true bottom, surpassing the detection range, and disappears as the true bottom gradually becomes shallower, returning to within the detection range. The theoretical conditions for the occurrence of the third type of false bottom align with empirical observations, wherein false bottoms typically originate at a depth of 0, consistent with real-world scenarios. While the calculation formula for the third depth satisfies the theoretical requirements, further verification is necessary before its implementation in practical production.  
We suggest carrying out multiple tests in the same water area. First, a researcher should measure the depth of the water area; then, they should adjust the maximum detection depth so that the maximum detection depth is less than the depth of the water area. Observing the depth of the false bottom in the image, the true-bottom depth can be inserted into the derived formula to obtain a set of false-bottom depth data. The results can be compared with the false-bottom depth obtained from the corresponding position experiment to verify the correctness of the derivation.
###### Abstract  
The purpose of this Special Issue is to bring together recent studies related in particular to landslide monitoring and data analysis. In engineering geology, geotechnical engineering and geomorphology, landslide monitoring using standard techniques is quite common. However, the rapid development of both hardware and software solutions, including miniaturization or remote sensing techniques, brings new possibilities for increasing monitoring accuracy, real-time or near-real-time data analysis and early warning.  
landslide; monitoring; modelling; susceptibility; InSAR 2021 11 10453 1  
ground-based InSAR to monitor slope deformations and verified the method on five landslides in the area of Huangdao, China.  
[PERSON] et al. [5] used object-based image analysis to map geomorphological features, and assessed the applicability of Sentinel-1 data to the fast creation of post-landslide digital elevation models. Their findings revealed that--without further post-processing--the automatically derived results need to be interpreted with care, as the automatic generation of a digital elevation model is influenced by several factors.  
[PERSON] et al. [6] propose a methodology for analyzing time-series monitoring data from a large, slow-moving San Andres landslide on El Hierro, Canaries, Spain. They used precise 3D dilatometric data and compared them with possible landslide-triggering factors (e.g., seismic, rainfall) to allow for fully automatic processing, thus decreasing the subjectivity of the analysis.  
[PERSON] et al. [7] applied different surface analysis and monitoring methods to decipher the structural controls of rock slope stability in coastal areas. They worked on the Adriatic coast of the Conero promontory in Central Italy, and their results stress the need to analyze slope stability over a long timescale, to understand the current processes.
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 3 Injection Timing  
### Single Early Injection  
Injection timing plays a significant role in the combustion process and emissions of a diesel engine, especially when the early injection strategy is utilized. As mentioned in Section 1.2, the early injection strategy can be classified into early, middle, and late injection based on the timing. On the one hand, injecting the fuel at an earlier time prolongs the ignition delay and helps to create a more homogeneous mixture. The formed lean mixture is then burned at a low temperature, resulting in low NOx emissions. On the other hand, the cylinder pressure and temperature are low under earlier injection timing, which leads to poor fuel evaporation and the wall-wetting problem. Under earlier injection conditions, the fuel-air mixture is mostly formed at the outside of the combustion chamber, and some local rich mixture regions are formed due to the wall-wetting issue. Moreover, the negative work during the compression stroke increases because of the earlier combustion event. These all deteriorate the combustion efficiency and increase the products of incomplete combustion. For HC and CO emissions, the impingement target is an important factor. For middle injection timing, spray wall impingement occurs on the piston head or the outside part of the combustion chamber. For early injection timing, the impingement occurs on the cylinder wall. The different temperature and flow motions of the piston head and cylinder wall directly affect the evaporation process of the wall film. In addition, research results show that when the impingement target is at the bowl-lip area, the fuel-air mixing can be better and low HC and CO emissions can be achieved. For soot emission, the factor of injection timing has two opposite effects. Earlier injection timing means that longer premixing allows the mixture to reach a lower equivalence ratio for low temperature, which restrains the generation of soot. Moreover, a longer time for soot oxidation is also achieved with earlier injection. On the contrary, spray wall-wetting will form some local rich regions, especially in the crevices, which promotes soot generation. Furthermore, the temperature of these wall-wetting regions is generally lower, which prevents the oxidation of soot emission. [PERSON] et al. [61] investigated the influence of injection timing on particle emissions with early fuel injection timing of low-temperature diesel combustion. The injection timing was set from 33 to 24\({}^{\circ}\) BTDC. Results showed that PM mass and particle number increased with advanced fuel injection timing; the number of particles larger than 50 nm was especially increased. This was mainly because the higher relative levels of liquid fuel deposition on the piston bowl surface formed a locally rich
# Optical Chirality Determined from Mueller Matrices
## 3 Theory  
Circular anisotropy can be quantified by the difference between refractive indices \(N_{\text{I}}\) and \(N_{\text{r}}\) for left- and right-handed circularly polarized light, respectively, according to \(\Delta N_{\text{lr}}=N_{\text{I}}-N_{\text{r}}=n_{\text{I}}-n_{\text{r}}+ \text{i}(k_{\text{I}}-k_{\text{r}})\), where \(n\) and \(k\) denote the real and imaginary part, respectively. For transmission through a sample with thickness \(d\) and at wavelength \(\lambda\), we introduce \(C=C_{\text{B}}+\text{i}C_{\text{D}}\) where \(C_{\text{B}}\) and \(C_{\text{D}}\) are defined in Table 1[11]. Samples may also have linear birefringence and linear dichroism, which are described by \(L=L_{\text{B}}+\text{i}L_{\text{D}}\) for the \(xy\) reference directions and \(L^{\prime}=L^{\prime}_{\text{B}}+\text{i}L^{\prime}_{\text{D}}\) for the \(\pm 45^{\circ}\) reference directions. \(L_{\text{B}}\), \(L_{\text{D}}\), \(L^{\prime}_{\text{B}}\), and \(L^{\prime}_{\text{D}}\) are defined in Table 1 (See Appendix A.1 for sign conventions).  
The interaction between polarized light and a sample is here modeled in the so called Stokes-Mueller formalism \(\mathbf{S}_{o}=\mathbf{MS}_{i}\), where \(\mathbf{S}_{o}\) and \(\mathbf{S}_{i}\) are Stokes vectors of the outgoing and incident light, respectively. The \(4\times 1\) matrix (the Stokes vector is formally a column matrix but is, by tradition, called a vector) \(\mathbf{S}\) carries all information about irradiance and polarization including the degree of depolarization of a light beam. The \(4\times 4\) matrix \(\mathbf{M}\) is called the Mueller matrix of the optical system. A more detailed description of the formalism can be found elsewhere [3; 12]. In this work, we use normalized Mueller matrices, which means that all elements are divided by the total transmittance, which corresponds to the first element in the first row in a measured unnormalized Mueller matrix. The notation of the elements in \(\mathbf{M}\) is given by  
\[\mathbf{M}=\left[\begin{array}{cccc}1&m_{12}&m_{13}&m_{14}\\ m_{21}&m_{22}&m_{23}&m_{24}\\ m_{31}&m_{32}&m_{33}&m_{34}\\ m_{41}&m_{42}&m_{43}&m_{44}\end{array}\right]. \tag{1}\]  
Below, we discuss how optical activity data can be extracted from MMSE-data through three routes: (I) directly from elements of a Mueller matrix if the sample only has circular anisotropy, i.e., only CB and/or CD; (II) by using differential decomposition provided that the sample is homogeneous along the optical path; and (III) dispersion-model analysis using the Tellegen constitutive relations.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 4 Discussion  
Most of the current pan-sharpening tasks are implemented based on CNN architecture. CNN can extract features containing more local details, but the global connectivity of these features is poor [42; 42]. In recent years, the Swin transformer has attracted the attention of many scholars due to its excellent performance in the field of machine vision. The Swin transformer can establish the connection between individual pixel points of an image and possesses strong global feature extraction, but the model is poor in perceiving the details of an image [43]. Therefore, we propose a Swin-MRDB pan-sharpening model, which employs CNN and the Swin transformer to extract shallow feature information and deep feature information of the image, respectively. Then, the features are fused by residual blocks to obtain a hyperspectral image with high resolution.  
In this section, we further discuss the effectiveness of the Swin-MRDB model. First, in order to demonstrate the advantages of the model, experiments are designed to compare the proposed model with the most current state-of-the-art models (PCA, GS, IHS, PNN, TFNet, SRPPNN, and MSDCNN), and three widely used datasets are selected for simulation experiments to reduce the experimental errors caused by the datasets. In order to show the effect of fusion, the fused images generated by different models are put into the text in this paper to better show the gap between the fused images and the ground truth images, and the effect is shown in Figures 6-8. Compared with traditional methods, deep learning methods can preserve spatial and spectral information better, and our proposed method is the closest to the ground truth image. In addition, to reduce the bias caused by subjective evaluation, we choose the four most commonly used image evaluation metrics (CC, SAM, RMSE, and ERGAS) to evaluate the fusion results objectively. As shown in Tables 3-5, the proposed model achieved the best results in all three datasets, with the most significant improvement in the Pavia dataset, where SAM, RMSE, and ERGAS obtained 8.4%, 13.3%, and 15.3% improvement, respectively.  
Figure 9: Effect of different numbers of residual blocks on the indicator.  
In addition to the objective and subjective evaluation of the fused images, we performed ablation experiments to evaluate the effect of different modules on the fusion results. Specifically, the CC, SAM, RMSE, and ERGAS obtained 1.5%, 9.2%, 13.7%, and 14.2% improvement, respectively, after adding the Pan image multi-scale feature extraction branch. With the addition of the Hs image multi-scale feature extraction branch, the above metrics were improved by 1.2%, 3.2%, 11.5%, and 14%, respectively.
## 3 Materials and Methods
### Location Algorithm Verification
#### 3.3.2 Comparison of Fusion Positioning Schemes  
Comparing the positioning results of the three fusion positioning schemes--WFA, WFB, and WFC, Figure 13 shows the comparison of the accuracy evaluation indexes of  
Figure 12: WFC cumulative distribution probability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Programme** & **Trilateration** & **Least-Squares** & **Centroiding** & **WFC Fusion** \\  & **Algorithm** & **Algorithm** & & **Algorithm** \\ \hline Mean value (m) & 88.40 & 72.91 & 189.65 & 39.97 \\ RMSE (m) & 53.88 & 43.45 & 89.10 & 41.63 \\ CRLB (m) & - & - & - & 13.03 \\ GDOP & 1.18 & 1.27 & 1.91 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Positioning accuracy evaluation index.  
the three positioning schemes. The figure shows the comparison results of the positioning error, RMSE, and CRLB of the three fusion positioning schemes.  
Figure 14 shows the positioning trajectory diagrams of the three fusion positioning schemes. It can be seen from the figure that the positioning trajectory of the WFC fusion positioning scheme is the closest to the real driving trajectory of the vehicle, and the positioning accuracy is the highest.  
Among the three positioning schemes in Table 9, the WFC fusion positioning scheme has the best positioning effect, the WFA fusion positioning scheme is in second place, and the WFB fusion positioning scheme has the lowest positioning accuracy. The positioning accuracy of the WFC fusion positioning scheme can reach 39.97 m, and the positioning accuracy of the WFA and WFB fusion schemes can also reach 43.32 and 48.56 m, respectively.  
Figure 14: Fusion of fixing location coordinates.  
Figure 13: Comparing fusion positioning accuracy.  
Compared with the better schemes in the WFA and WFB schemes, the WFC scheme is significantly improved in terms of mean, CRLB lower limit, GDOP, and CDP, proving the positioning superiority of the WFC scheme. Among them, the average positioning error is improved by 7.74%. The RMSE dropped by 2.92%, indicating that the error convergence of WFC is the best. The CRLB increased by 15.82%, indicating that the standard deviation of the WFC scheme is closer to the CRLB lower limit, and the positioning error is smaller. The GDOP decreased by 23.26%, indicating that the influence of the relative location between the vehicle and the base station on the positioning accuracy is reduced. In the WFC fusion positioning scheme, 67% of the positioning accuracy reached 46.31 m, and 95% of the positioning accuracy reached 122.53 m.  
In summary, the WFC fusion positioning scheme, based on ranging and non-ranging integration, has the best positioning effect. From the comparison of the mean error and RMSE, the positioning accuracy of the WFC fusion scheme is significantly improved. From the comparison of CRLB and GDOP, the variability of the positioning error of the WFC fusion scheme is the smallest, and the error distribution is the most uniform.
## 3 Factors Influencing U-WSN Design
### Architectures of U-WSN
#### 3.1.1 Sensor-Sensor Link (S-S)  
The communication between sensors is the basic one included in other links. A low energy and data rate protocol can be used. The standard IEEE 802.15.4 [37], along with the corresponding upper layers which are compliant with the ZigBee [38] protocol stack, is typically used at this link layer. Generally, the data-rate is less than 1 Mbps.
# Security Attack Behavioural Pattern Analysis for Critical Service Providers
## 3 Data Collection Method  
The data source used in this study consists of IT-incident reports that were submitted to MSB. Thus, it can be considered an unconventional study of the written data, primarily relying on sources that are document-based. Furthermore, the research was conducted using a secondary dataset consisting of written IT-incident reports that were submitted to MSB by different Swedish government agencies and organisations, following the guidelines specified in the European Union's NIS-directive. In Sweden and other countries, organisations have the choice to retain information. The Swedish legislation on public access to information and confidentiality, specifically the Public Access to Information and Secrecy Act (SFS 2009:400) and the Protective Security Act (SFS 2018:585), place limitations on the dissemination of information within public administration, including government agencies. Private companies have the option to withhold information about attacks and their consequences in order to protect their valuable assets or maintain positive relationships with stakeholders [3].  
One potential limitation of this approach is that the secondary data used as the data source were not specifically collected for the purpose of this study. The analysis is constrained by this limitation. The conclusions that can be derived are dependent upon the characteristics and quality of the data obtained from the particular format of MSB's IT-incident reports. Another constraint involves relying on reported incidents as the basisfor analysing the cyber threat landscape, which fundamentally leads to conclusions that are dependent upon the reporting choices and methods of the organisations.
## 2 GPS Attitude Baseline Determination
### Overview  
Figure 1 shows the overall process of the attitude determination with the developed direct georeferencing system. To aid the ambiguity estimation of the GPS baseline, first, an approximate attitude is estimated in a Kalman filter (Pos/IMU/Mag), using a position measurement (Pos) (in our case, an RTK GPS position), IMU readings and magnetic field observations (Mag) (Section 3). Afterwards, these attitude results serve as an input to the GPS baseline determination, which consists of the base float solution and the integer ambiguity resolution (Sections 2.2 and 2.3). If the ambiguities can be resolved successfully, the base fixed solution (Base) can also be integrated in the Kalman filter (Pos/IMU/Mag/Base).  
The whole strategy assumes the availability of position measurements and GPS observations. If they are not available (no GPS observations), the sensor integration switches to a second mode (IMU/Mag), which only estimates the attitude based on inertial sensor and magnetometer readings (Section 4).  
The coupling of the approximate information about the attitude and the GPS baseline raw data could generally also be realized in a tightly-coupled approach, but for reliability and practical reasons in the real-time programming, we decided to separate these steps here.
## 7 Practical Implications of the Presented Modeling Framework  
The outcomes of this research can be implemented in designing an operational traffic safety management system that can predict the relative short-term (e.g., next 5-15 min) crash risk for all regional roadways at the roadway segment level. For the operational purposes it is suggested to use relative crash likelihood as the measure of crash risk, instead of using the absolute crash probability values generated as model outputs. Each of the presented models calculates the probability of crash occurrence and its associated injury severity level for each road segment. These values can be clustered into multiple groups  
\begin{table}
\begin{tabular}{r c c c c c c c} \hline
**Group \#** & **Variable** & **N** & **\%** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline
1 & DrAge 1 \(<\) 25 and VehAge 2 \(<\) 5 & 1096 & 8.72 & 0.61 & 0.60 & 0.61 & 0.62 \\
2 & DrAge \(<\) 25 and 5 \(\leq\) VehAge \(<\) 10 & 627 & 4.99 & 0.58 & 0.66 & 0.55 & 0.63 \\
3 & DrAge \(<\) 25 and 10 \(\leq\) VehAge & 716 & 5.69 & 0.62 & 0.55 & 0.63 & 0.66 \\
4 & 25 \(\leq\) DrAge \(<\) 70 and VehAge \(<\) 5 & 5927 & 47.17 & 0.68 & 0.54 & 0.79 & 0.68 \\
5 & 25 \(\leq\) DrAge \(<\) 70 and 5 \(\leq\) VehAge \(<\) 10 & 1991 & 15.84 & 0.69 & 0.52 & 0.77 & 0.64 \\
6 & 25 \(\leq\) DrAge \(<\) 70 and 10 \(\leq\) VehAge & 1832 & 14.57 & 0.63 & 0.55 & 0.72 & 0.64 \\
7 & 70 \(\leq\) DrAge and VehAge \(<\) 5 & 198 & 1.58 & 0.68 & 0.42 & 0.89 & 0.66 \\
8 & 70 \(\leq\) DrAge and 5 \(\leq\) VehAge \(<\) 10 & 89 & 0.71 & 0.65 & 0.52 & 0.76 & 0.66 \\
9 & 70 \(\leq\) DrAge and 10 \(\leq\) VehAge & 90 & 0.72 & 0.62 & 0.52 & 0.67 & 0.61 \\
**Average** & & & & **0.64** & **0.54** & **0.71** & **0.64** \\ \hline \end{tabular}  
* 1: DrAge = driver age; 2: VehAge = vehicle age.  
\end{table}
Table 9: Summary statistics of crash records considering driver age and vehicle age, and results of the driver injury severity models.  
based on pre-defined thresholds that represent the relative risk of crash. To exemplify, the following values can be used to categorize the crash risk at a road segment level:  
\[\begin{array}{ll}\text{Low risk}&\text{if}&P_{i}\leq 0.3\\ \text{Moderate risk}&\text{if}&0.3<P_{i}\leq 0.6\\ \text{High risk}&\text{if}&0.6<P_{i}\leq 0.75\\ \text{Extremely high risk}&\text{if}&P_{i}>0.75\end{array}\]  
To facilitate monitoring of the crash risk across a roadway network in real-time, a map-based system can be deployed in which the road segments are colored/labeled based on the associated relative crash risks (see a sample illustration in Figure 5). This is expected to help the traffic operations management authorities to take proactive traffic management strategies such as utilizing variable speed limits, variable message signs, and coordinated warning signals to mitigate crash risks at the high-risk locations.