esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Sensitivity to Tampering Experiments and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tampering Test** & \begin{tabular}{c} **Algorithm** \\ **Based on DCT** \\ \end{tabular} & \begin{tabular}{c} **Algorithm** \\ **Based on DWT** \\ \end{tabular} & \begin{tabular}{c} **Algorithm** \\ **Based on SVD** \\ \end{tabular} & \
\begin{tabular}{c} **Algorithm** \\ **in [10]** \\ \end{tabular} &
\begin{tabular}{c} **This** \\ **Algorithm** \\ \end{tabular} \\ \hline Removing the object & 40\% & 44\% & 42\% & 100\% & 100\% \\ Appending the object & 52\% & 56\% & 56\% & 100\% & 100\% \\ Changing the object & 42\% & 36\% & 38\% & 100\% & 98\% \\ Local subtle tampering & 8\% & 8\% & 6\% & 96\% & 94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of sensitivity to tampering. Figure 13: Tampering examples of removing object: (**a**) Original HRRS images, (**b**) corresponding tampered images.
# Optimization and Experimental Study of Penetration Depth of Pocket Penetrometer
## 3 Test Results and Analysis
### In Situ Test Results  
Firstly, the method in 2.5 was used to evaluate the curve credibility, and the results are shown in Figure 15a. Among the 200 sets of test curves, there are test curves with correlation ratios lower than 0.8, but the curves with correlation ratios higher than 0.90 account for the largest proportion. The reason for this phenomenon is that the site environment is complex, and the presence of holes and small stones in the soil layer may cause a deviation of the penetration curve. However, the test probe is small, and the extent of influence  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equation No.** & **Equation** & **Soil Samples** & **Correlation** & **Rmse** \\  & & **Name** & **Coefficient** & \\ \hline (1) & \(l_{L}=1.16*\exp(-0.13F)\) & Red Clay \⃝1 & 0.98 & 0.031 \\ \hline (2) & \(l_{L}=1.03*\exp(-0.14F)\) & Red Clay \⃝1 & 0.98 & 0.027 \\ \hline (3) & \(\tau=0.036*P-0.3\) & Red Clay \⃝1 & 0.99 & 0.170 \\ \hline (4) & \(\tau=0.034*P-0.8\) & Red Clay \⃝1 & 0.99 & 0.278 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of equations.  
Figure 14: Fitting of end resistance and shear strength of different red clays.  
on the soil body during penetration is also small, so the probability of the penetration process being affected by the holes, small stones, and other factors is small. In order to reduce the influence of abnormal test data on the test results and to obtain more accurate field penetration test results, this paper considers only the test curves with correlation relationships higher than 0.90 for the extraction of stable values of penetration resistance. The statistical results are shown in Figure 15b, and the value of penetration resistance is 75-84 N.
## 2 Problem Statement
### SCOS Mathematical Model  
Based on the aforementioned availability modeling and QoS metric analysis, this study constructs a bi-objective SCOS model that simultaneously considers QoS and system availability. The model aims to satisfy both the functional and non-functional requirements of users while achieving optimal overall availability of the service composition. The objective function of the model can be formally expressed as Equation (7).  
\[\left\{\begin{array}{l}Min\ f_{1}=1-QoS(CSS)\\ Min\ f_{2}=1-A(CSS)\\ F=\left[f_{1},f_{2}\right]\end{array}\right. \tag{7}\]  
where \(f_{1}\) and \(f_{2}\) represent the two optimization objectives of QoS and system availability, respectively, and \(CSS\) represents industrial software services on the platform.
## 3 Results
### Effects of Various Soil Properties on Displacements
#### 3.1.4 Friction Angles  
Figure 8 illustrates the effect of various soil friction angles on tunnel displacements induced by both normal and reverse faults. Soil friction angles of \(27^{\circ},33^{\circ}\), and \(39^{\circ}\) were labeled as F27R, F33R, and F39R in reverse faults, and F27N, F33N, and F39N in normal faults. The results show that F27R had almost 39% more displacements compared to F39R, and more fractures appeared on the surface of F27R (Figure 9). Also, the reverse fault (F27R) caused 48% more tunnel displacements compared to the normal fault when the friction angle was set at \(27^{\circ}\) (F27N). Furthermore, the closeness of the curves in normal faults at the beginning was in contrast with the reverse faults.  
The results from the physical model also revealed that when a fault occurs, soft soil in displacements, \(\frac{h}{H}\) (h stands for displacement and H stands for soil thickness), of more than 4% gets compacted first and then displaced. Moreover, soft soil makes more fractures and affects more ground surfaces than dense soil. The results also showed that in F39R and F39N, soil reaches the ground surface more easily and makes more displacements compared to soft soil. The results discussed in this section agreed with the finding by [PERSON] et al., which is that dense soil can tolerate more deformation than soft soil [39]. When  
Figure 8: The effect of friction angle on reverse and normal faults.  
Figure 9: Fractures in (**a**) F27R (**b**) F39R.  
the soil friction angle was fixed at 39\({}^{\circ}\) and the displacement of the vertical component was set at 60 mm, the results showed that normal faults caused the maximum displacements of 20 mm to the tunnel. The results showed the importance of soil friction angles and soil density, where changing either in normal or reverse faults has a high impact on tunnel displacements. The results can be explained by a laboratory test on soil stability, which showed that increasing the friction angle decreases the settlements and also increases the shear strength in soil [40,41].
# Combined Optimization of Maintenance Works and Crews in Railway Networks
## 1 Introduction
[PERSON] [8] categorized maintenance activities based on the required time of possession per work shift, emphasizing the significance of time frames in maintenance scheduling to ensure the timely restoration of infrastructure. However, [PERSON] [9] pointed out a conflict in railway infrastructure management concerning the coordination of train traffic with maintenance activities, particularly in networks with a high traffic density. Balancing both becomes crucial, and often maintenance is scheduled during periods of minimal or no train schedule, including nighttime hours with fewer passenger movements. [PERSON] et al. [10] dealt with major and significant maintenance planning, as well as renewal processes, highlighting their complexity and involving multiple stakeholders, such as the railway infrastructure manager, train operating company, traffic control, and maintenance contractors. Conversely, [PERSON] et al. [11] proposed an algorithm that could adjust the train's route and timetable to accommodate scheduled maintenance tasks while maintaining passenger service levels to as high a degree as possible. The algorithm aimed to minimize the number of cancelled train schedules, and the results indicated that minor adjustments could significantly reduce the need for cancellations. It should be noted that the frequency of actions required in maintenance, inspection, and restoration, whether predetermined or not, depends on a range of factors, with the most critical ones being the train's traffic volume, speed, and weight. Therefore, when exceeding a critical degradation value of the railway track, corrective measures should be taken immediately. In this study, we propose a model for the combined scheduling of maintenance works and crew schedules. The remainder of this study is structured as follows. Section 2 presents the relevant literature. Section 3 introduces the problem description and the mathematical formulation of our model. The implementation, the results, and the conclusions are presented in Sections 4-6, respectively.
###### Abstract  
Timely and continuous information about flood dynamics are fundamental to ensure an effective implementation of the relief and rescue operations. Satellite data provided by optical sensors onboard meteorological satellites could have great potential in this framework, offering an adequate trade-off between spatial and temporal resolution. The latest would benefit from the integration of observations coming from different satellite systems, also helping to increase the probability of finding cloud free images over the investigated region. The Robust Satellite Techniques for detecting flooded areas (RST-FLOOD) is a sensor-independent multi-temporal approach aimed at detecting flooded areas which has already been applied with good results on different polar orbiting optical sensors. In this work, it has been implemented on both the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375 m Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The flooding event affecting the Basilicata and Puglia regions (southern Italy) in December 2013 has been selected as a test case. The achieved results confirm the RST-FLOOD potential in reliably detecting, in case of small basins, flooded areas regardless of the sensor used. Flooded areas have indeed been detected with similar performance by the two sensors, allowing for their continuous and near-real time monitoring.  
flood; remote sensing; data integration; RST-FLOOD; MODIS; VIIRS; optical data +
Footnote †: journal: ApJ
## 3 Laboratory Tests
### Effects of Different Parameters on the Bending Behaviour  
#### 3.3.1 Effect of the Bond Quality  
During DSPC installation, slurry must be used to support the walls of the excavation. While the existence of mud would weaken the bond quality between the concrete and the casing, this may have a negative effect on the bending behavior of DSPCs. Therefore, it is necessary to study this factor.  
From the results mentioned above, flexural failure was observed from those tests. Figure 9a shows comparisons of the bending moment-deflection curves of the beams with different mud conditions for two types of casing thicknesses. The bending moment at the central of the beam, M, is from the theory of a simply supported beam under concentrated loads. Groups B1, B3, and B5 were designed with the same materials and geometry (the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen ID** & \begin{tabular}{c} **Diameter** \\ **(D/mm)** \\ \end{tabular} & \begin{tabular}{c} **Thickness** \\ **(t/mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Length (L/mm)** \\ \end{tabular} &
\begin{tabular}{c} **D/t** \\ **Condition** \\ \end{tabular} \\ \hline B-1 & 426 & 5 & 4000 & 85.2 & no \\ B-2 & 426 & 10 & 4000 & 42.6 & no \\ B-3 & 426 & 5 & 4000 & 85.2 & thin \\ B-4 & 426 & 10 & 4000 & 42.6 & thin \\ B-5 & 426 & 5 & 4000 & 85.2 & thick \\ B-6 & 426 & 10 & 4000 & 42.6 & thick \\ \hline \hline \end{tabularcasing thickness was 5 mm), and the difference was the mud condition. B1 had no mud between the concrete and casing during construction, whereas B2 had worse bond quality and B3 had worse bond quality than B2. Groups B2, B4, and B6 were also designed with the same method (the casing thickness was 10 mm), and the difference was also the slurry condition.  
As seen in Figure 9, the trends of the M-\(\delta\) curves under different mud conditions were similar, which indicates that the bending behaviors of those specimens with different mud conditions were similar. However, it is interesting to see that the bending capacity shown in those curves was slightly influenced by the mud condition, the specimens with dilute mud condition showed the highest bending capacity, and those with no mud and thicker condition had lower but similar capacity. This implied that the mud condition had limited influence on the moment capacity of the specimens. The bonding strength between the casing and concrete was not an important factor with regard to the moment capacity of the specimens.  
Figure 8: (**a**) Bending deformation and (**b**) cracks.
## 4 Implementation of the Proposed UniChain System
### Generating a Student Transcript  
A student node sends a request to generate a transcript to a university node. The university node sends the student's ID to its SRHC to retrieve the associated RC address. Upon receiving the RC address, the university node then sends the filename of the requested record and the student Ethereum address to the RC. The RC forwards the request to the ACC to check whether that received Ethereum address has a permission on the requested record or not. If the student node has a permission, the AAC forwards the university's encrypted symmetric key \(E_{pk_{university}}(SMK)\) to the RC. The RC in turns forwards the received key to the university node.  
The Cipher/Decipher Manager in the university node first decrypts the received symmetric key using its private key and then decrypts the access link with that symmetric key. The DB Manager of the university node follows the related access link and then retrieves the encrypted EAR from the database to create the transcript. The created transcript will be forwarded to the Cipher/Decipher Manager that in turn will sign the transcript with the private key of the university and then encrypt the signed transcript with the public key of the student. In addition, the DB Manager creates a log indicating the process of creating a transcript, hashes the log and then forwards the log to the Cipher/Decipher Manager for encryption. The encrypted log then will be forwarded to the LC. The LC adds a new entry with the received encrypted log, a \"new log\" status to indicate that the new log has not been added to the blockchain yet, and a timestamp indicating the last status update. The university node forwards the encrypted signed transcript to the student node via HTTPS. The student node can retrieve the transcript by the use of the student private key and ensure it comes from the desired university node by the use of the university public key.
## 3 Results
### Catalytic Activity Measurements
#### 3.1.2 Role of the Type of Noble Metal on CBO  
The catalytic performance of gold-based catalysts was described above (Section 3.1.1) and presented in Figure 1.  
In Figure 2a are shown the results of CBO over Pd-based catalysts. It is obvious that they perform better than gold-based ones. Pd/YCeAl exhibited superior activity. Total benzene conversion was achieved at 180 \({}^{\circ}\)C, and no products of mild oxidation were detected within 24 h test at this temperature. Pd/CeAl demonstrated also high activity with 96% conversion at 180 \({}^{\circ}\)C, achieving 100% conversion at 300 \({}^{\circ}\)C. In the presence of yttrium this temperature decreased to 180 \({}^{\circ}\)C. It is worth noting the relatively high activity of Pd/Al catalyst reaching a maximum conversion of 85% at 100 \({}^{\circ}\)C.  
With respect to the bimetallic systems, by comparing the results in Figure 2b with those in Figure 1, the most significant effect of Pd deposition over gold-based catalysts was observed in case of Pd-Au/Al. The addition of Pd in bimetallic Pd-Au systems on CeAl and YCeAl enhanced slightly the activity of gold-based catalysts. However, different from the previously observed superiority of bimetallic formulation [22; 23], in this case, their performance could be considered as a combination of the gold-based and Pd-based activity, depending on the temperature interval. In order to separate the effect of the support from the effect of the active metal formulation, in the next section, the conversion curves already shown in Figures 1 and 2 are combined in a new figure where different formulations over each support are discussed.  
Figure 2: Temperature dependence of CBO over: (**a**) monometallic Pd-based; (**b**) Pd-Au bimetallic catalysts.
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 5 Experimental Results
### Comparison with the Previous Method  
In the experiments, we compared the adopted YOLOv5s model with several representative methods including HOG [33], DPM [34], and YOLOv3 [28]. The performance of the methods is evaluated in terms of both detection accuracy and detection speed. The experimental results are shown in Table 6. The manually designed feature-based HOG and DPM are implemented using CPU; the CNN-based YOLOv3 and YOLOv5s are implemented using GPU. From the comparison of these methods, we can observe that the algorithm based on CNN is higher than the traditional algorithm by a large margin. For the aspect of detection speed, the HOG and DPM methods are inefficient during the redundancy of a sliding window strategy while the two YOLO methods can achieve real-time detection performance. Furthermore, our adopted YOLOv5s model is more than three times faster than YOLOv3 while maintaining comparable detection accuracy. The fast detection benefits from small model size. The model size of YOLOv5s is 14.3 MB, which is much smaller than the 243.7 MB size of YOLOv3. Therefore, the YOLOv5s was selected as the basic framework, which is sufficient to meet the requirements of our laser active imaging system.
## 4 Simulation Experiments
### Classification Tasks  
#### 4.3.1 Conditions  
Using the parameters in Tables 3 and 4, we repeated the evaluation 20 times. Similar to Section 4.2, first, training data points with no pre-processing were randomly ordered using a different random seed. Then, the re-ordered training data points were used for all algorithms. The classification performance was evaluated by accuracy, NMI [52], and the Adjusted Rand Index (ARI) [53]. As a statistical analysis, the Friedman test and Nemenyi post hoc analysis [54] were utilized. The Friedman test is used to test the null hypothesis that all algorithms perform equally. If the null hypothesis is rejected, the Nemenyi post hoc analysis is conducted. The Nemenyi post hoc analysis was used for all pairwise comparisons based on the ranks of results over all the evaluation metrics for all datasets. Here, the null hypothesis was rejected at the significance level of 0.05 both in the Friedman test and the Nemenyi post hoc \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**ASC**} & \multicolumn{2}{c}{**FTCAC**} & \multicolumn{2}{c}{**GSOINN+**} & \multicolumn{2}{c}{**CAEAC**} & \multicolumn{2}{c}{**CAEAC-I**} & \multicolumn{2}{c}{**CAEAC-C**} \\  & & \(\lambda\) & \(\mathbf{a_{\text{max}}}\) & \(\lambda\) & \(\mathbf{V}\) & \(\mathbf{p}\) & \(\mathbf{k}\) & \(\lambda\) & \(\mathbf{a_{\text{max}}}\) & \(\lambda\) & \(\mathbf{a_{\text{max}}}\) \\ \hline \multirow{3}{*}{Synthetic} & Aggregation & 140 & 34 & 360 & 0.90 & 0.50 & 4 & 90 & 4 & 100 & 20 & 50 & 14 \\  & Compound & 400 & 4 & 320 & 0.95 & 0.70 & 1 & 70 & 12 & 60 & 18 & 70 & 10 \\  & Hard Distribution & 20 & 10 & 100 & 0.35 & 0.05 & 7 & 90 & 6 & 100 & 18 & 30 & 8 \\  & Jain & 20 & 34 & 380 & 0.85 & 0.01 & 1 & 40 & 6 & 30 & 16 & 20 & 10 \\  & Pathbased & 240 & 6 & 240 & 0.85 & 0.05 & 2 & 60 & 14 & 60 & 6 & 70 & 2 \\ \hline \multirow{2}{*}{Real-world} & ALLAML & 20 & 14 & 160 & 0.95 & 0.70 & 2 & 70 & 10 & 80 & 4 & 80 & 10 \\  & COII20 & 240 & 38 & 100 & 0.40 & 0.50 & 2 & 90 & 12 & 90 & 12 & 100 & 4 \\  & Iris & 280 & 30 & 400 & 0.70 & 0.05 & 3 & 90 & 12 & 90 & 12 & 10 & 20 \\  & Isolet & 40 & 8 & 360 & 0.70 & 10.00 & 2 & 100 & 6 & 70 & 12 & 100 & 20 \\  & OptDigits & 360 & 10 & 320 & 0.70 & 0.70 & 2 & 90 & 16 & 90 & 4 & 90 & 20 \\  & Seeds & 400 & 14 & 320 & 0.65 & 5.00 & 7 & 90 & 18 & 90 & 2 & 80 & 2 \\  & Semenion & 340 & 12 & 260 & 0.50 & 0.01 & 2 & 80 & 10 & 80 & 18 & 80 & 16 \\  & Sonar & 160 & 4 & 360 & 0.55 & 0.10 & 1 & 100 & 4 & 60 & 10 & 60 & 8 \\  & TOX171 & 40 & 38 & N/A & 0.70 & 1 & 70 & 14 & 70 & 14 & 100 & 12 \\ \hline \hline \end{tabular} N/A indicates that the corresponding algorithm could not build a predictive model.  
\end{table} 
Table 4: Parameters specified by grid search.
# Unbridled Integons: A Matter of Host Factors
## 6 Evolutionary Success of MI: A Matter of Host Factors  
### Specific Host Factors Are Involved in the Cassette Recruitment in SCI but Not in MI  
MI and SCI are regulated by a panel of common host factors. For example, the regulation of the integrase expression by the SOS response involves the same host factors for both SCI and MI since LexA binding boxes are found in both SCI and class 1 MI P\({}_{\rm int}\) promoters. The integrase expression is therefore dependent on the RecA protein, which forms a nucleofilament triggering the SOS response. While RecA is critical for the cassette recruitment in the _V. cholerae_ SCI, RecA is not involved in the \(attII\times attC\) reaction mediated by IntI1, the integrase of the class 1 MI [56; 77]. This suggests that this RecA effect is specific to the _V. cholerae_ SCI and that, among host factors, some may be different between SCI and MI. We performed assays in _E. coli_ and _V. cholerae_ using the IntI1 integrase and demonstrated that whatever the reaction (\(attII\times attC\) or \(attC\times attC\)), the integron system works similarly in both bacterial species. This is not the case for the SCI of _V. cholerae._ Indeed, we observed that IntIA recombines 2000 fold less efficiently \(attIA\times attC\) reaction, when expressed in a heterologous host such as _E. coli_[92]. The simplest explanation for the observed low level of integration in the in vivo reconstituted system in _E. coli_ is that IntIA, unlike IntI1, requires one or more accessory proteins for the integration process, which is either absent or too divergent in _E. coli_ to sustain the IntIA-mediated integration process. We failed at recovering \(attIA\times attC\) recombination by expressing RecA\({}_{Vch}\) in _E. coli_, suggesting that the RecA\({}_{Vch}\) protein is not the missing factor in _E. coli_ impeding this reaction to efficiently occur. This is not surprising since the RecA\({}_{Ec}\) and RecA\({}_{Vch}\) present a very high level of identity (80%) and the RecA\({}_{Ec}\) is able to complement the RecA\({}_{Vch}\) in _V. cholerae_ both for its classical recombination function and for the \(attIA\times attC\) reaction. Therefore, we hypothesize that one or more specific _V. cholerae_ host factors are missing in _E. coli._ These results demonstrate that at least two host factors, RecA and another unknown factor, are involved in the \(attIA\times attC\) recombination in _V. cholerae._