esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Limitation of REDPACK  
REDPACK showed better performance than any single guessing model. However, its cracking performance was also limited. The relativistic discriminator apparently selected more realistic password candidates. However, more realistic password candidate selection does not always guarantee effective password cracking. Although REDPACK compressed the number of password candidates by up to 66% in the case of three generators, it also missed some candidates that could be important to password cracking. Table 4 shows the performance loss of REDPACK. In the experiment, we included OMEN as a password candidate generator component. The inclusion of OMEN as a password generating unit worsened the cracking performance of the candidate dictionary by REDPACK. OMEN prevented the discriminator from selecting PCFG candidates. This degradation was caused by REDPACK's incorrect selections. OMEN hindered the selection of the PCFG candidates, which could potentially crack the password. Both OMEN and PCFG have similar characteristics. They both generate password candidates with high-order probabilities. To make up for the loss of these two probability based-models, we simply apply a random shuffle to the password candidate sets from both OMEN and PCFG. This simple approach cannot remove the loss of cracking performance completely.  
When models based on three different approaches (Hashcat: rule-based, PCFG: probability-based, rPassGAN: deep learning-based) were used as generators, REDPACK was at its most efficient in our experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Id** & **Hashcat** & **PCFG** & **rPassGAN** \\ \hline
1 & 388,369,672 (2) & 375,427,872 (1) & 236,202,456 (3) \\
2 & 391,559,411 (2) & 405,053,223 (1) & 203,387,366 (3) \\
3 & 348,230,515 (1) & 426,854,183 (2) & 224,915,302 (3) \\
4 & 453,200,929 (1) & 369,617,876 (2) & 177,181,195 (3) \\
5 & 533,483,250 (1) & 288,233,760 (2) & 178,282,990 (3) \\
6 & 39,213,755 (1) & 455,574,382 (3) & 505,211,863 (2) \\
7 & 19,847,837 (1) & 419,593,769 (3) & 560,558,394 (2) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of candidate of each model consisting of REDPACK candidates. Number in parenthesis denotes cracking performance rank among input sources.
## 3 Problem Statement and Proposed Solution
### Description of the LLDA-ISPCF Methodology
#### 3.3.8 Phase 8: Combination Firmware Writing  
Following the phases proposed in this methodology, the Combination Firmware writing is the last phase that precedes the acquisition itself. It is necessary to rewrite the _boot_, _recovery_, and _system_partitions. Such partitions will have their original content replaced by the files contained in the Combination Firmware previously obtained. It is advisable to make a backup of the partitions that will be overwritten before performing the operation. It is also possible to extract the contents of the file with _MD5_ extension and generate a new compressed file with the same name as the complete file, overwriting it. Doing so will be easier to use the software for writing the firmware.  
For the writing operation, both the _Box Octoplus Pro_ and the _Odin_ tools can be used. The necessary procedures for the Combination Firmware writing using the Box can be obtained in _Seeking the Truth from Mobile Evidence_[57].  
After replacing the partitions mentioned above, the device will boot normally, without asking for the password or the mechanism imposed by the user.  
The device on which the LLDA-ISPCF methodology was applied uses file-based encryption. When the device is initialized, the system accesses the data stored in its special encrypted area, protected with hardware keys. All data on the _data_ partition is inaccessible until the user provides authentication credentials, as this area is protected with keys based on the user's credentials. The user password is one of the cryptographic keys, and when it ceases to exist, only the system password, defined in the source code by the developers, remains. As there is only the default system key, no key exchange takes place, and the partition is automatically decrypted. The process is transparent, and as soon as the initialization process is completed, the system displays the home screen.  
After of initialization, the device displays a screen with essential functions, being possible to view photos, explore files, record images, among other basic operations.  
From that point on, the device is ready for data acquisition, being possible to activate the USB Debugging Mode, activate the option _Stay Active_, and also, if necessary for the acquisition, root the device. It is important to note that the firmware now installed runs the same Android version as the original firmware. Therefore, the acquisition methods must be compatible with the OS version if such a device is not _rooted_. The choice of the forensic acquisition tool is at the discretion of the specialist who will perform the procedure.
# Narrowing Linewidth of Wavelength-Swept Active Mode Locking Laser Based on Cross Gain Modulation
## 3 Conclusions  
We demonstrated a narrower linewidth wavelength-swept AML laser based on the XGM configuration. The 3-dB point of the frequency response of SOA was improved approximately two-fold in XGM compared to that of DGM. The experimental results show that the linewidth is narrowed with increasing the mode locking frequency based on XGM. Due to enhanced frequency response, the wavelength-swept laser based on XGM successfully worked at 1361.25 MHz modulation frequency and was measured as 0.113 nm, which is much narrower than that of DGM with 0.25 nm. The proposed laser also showed properties of high linearity, high SNR, and sweep rate of 100 kHz, in addition to the narrower linewidth. This study proposes a novel method that can overcome one of the disadvantages, the broad linewidth, of wavelength-swept AML lasers for practical applications.  
[PERSON] designed the experiments; [PERSON] and [PERSON] constructed the experimental setup; [PERSON] and [PERSON] confirmed the experiments; [PERSON] and [PERSON] obtained and analyzed the data; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] discussed the experimental results; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] contributed to writing and reviewing the paper. This work was supported by a grant from the Agency for Defense Development, Republic of Korea (NO. UD170076 FD). The authors declare no conflict of interest.
## 3 Experiments on Connections
### Connection Test Specim  
Four types of connections between a CFT column and bracing were designed to evaluate displacement ductility and energy dissipation capabilities. These connections, shown in Figure 4, vary in detail to account for the height of the connection area and shape of the detail. Specimens BC1 and BC2 were designed to evaluate the displacement ductility and energy dissipation of connections based on height, whereas BC3 featured specific  
Figure 3: Experimental result: (**a**) Welding fatigue failure owing to stress concentration, (**b**) stress developments in the bracing connection at the level of LVDT2.  
Figure 2: Small-scale test specimen of the proposed modular bridge pier.  
details to prevent buckling and disperse the stress concentration at welding part. BC4 incorporated a dog bone-shaped detail to induce an earlier plastic hinge at the connection detail. The steel tubes utilized for the columns and bracing had a yield strength of 315 and a tensile strength of 490 Mpa. The dimensions of the concrete in-filled and bracing steel tubes were \(\Phi\) 216.3 \(\times\) 8 \(\times\) 1640 mm and \(\Phi\) 165.2 \(\times\) 7 \(\times\) 1072 mm, respectively.  
The setup for the component test was designed to simulate the rotational behavior of the bracing by securing both ends of the CFT column, as shown in Figure 5. To identify the location of plastic hinges and determine the location of gauges, finite element analysis was conducted, as shown in Figure 6 before experiments, and it was confirmed that the plastic hinge occurred at the connection plates. Accordingly, the gauges were attached as shown in Figure 5. PS denotes the plastic strain gauge with a measurement limit of 50,000\(\varepsilon\). A and B denote the normal strain gauges attached to the braces and CFT columns, respectively. The loading program was defined considering gradual increase in drift ratio, as shown in Figure 7 and Table 1. Loading was applied at a height of 1000 mm from the center of the CFT column and maintained until bracing failure occurred. Displacement control was implemented by increasing the lateral displacement, utilizing an actuator with a capacity of 1000 kN.  
Figure 4: Test specimens for connection details (unit: mm).
# Study on the Vibration Variation of Rock Slope Based on Numerical Simulation and Fitting Analysis
## 3 Fitting Analysis of Blasting Vibration Prediction Formula Considering Elevation Value
By comparing the research results in references [7; 9; 25; 26], it can be learned that the vertical vibration velocity is more significantly influenced by the elevation than the horizontal vibration speed and that the blast vibration amplification effect increases with the rise in elevation. It can be determined based on the type of formula that the blast vibration speed decreases with the increase of horizontal distance (\(D\)) under the case of fixed height difference (\(H\)). With the depletion of energy in the blast vibration wave propagating to the distance, the vibration speed will also tend to be 0. The error rate between the measured value and the predicted value of the regression formula was analyzed to verify the accuracy of the fitting formula. The error rate results are shown in Table 2.  
As can be seen from Table 2, the maximum error rate of vibration velocity in the vertical direction was 20.42%, and the average value was 12.97%. The maximum error rate of vibration velocity in the horizontal direction was 23.98% and the average value was 14.95%. The average error rate calculated by the blast vibration prediction formula in reference [6] was 6% and 12%; the maximum error rate calculated by the blast vibration prediction formula in reference [27] was 4.8%. The error rate of the obtained blast vibration prediction formula was within an acceptable range considering the complex construction conditions at the site with uneven terrain and ubiquitous debris, and therefore it could be applied to the prediction of blast vibration at the site. The traditional [PERSON]'s formula was fitted, and the error rate was analyzed. The results are shown in Table 3.  
The prediction error rate of [PERSON]'s fitting formula was very large. The average error rate in the vertical direction reached 94.74%, and that in the horizontal direction reached 93.24%, indicating that [PERSON]'s fitting formula was not suitable for the prediction of blast \begin{table}
\begin{tabular}{c c c c} \hline
**Formula Fitting Results** & **Type of Vibration Velocity** & **Maximum Error Rate (\%)** & **Average Error Rate (\%)** \\ \hline \(V=68.03\left(\frac{\mathrm{Q}^{\frac{1}{3}}}{D}\right)^{2.14}\left(\frac{ \mathrm{Q}^{\frac{1}{3}}}{H}\right)^{-0.23}\) & Vertical & 20.42\% & 12.97\% \\ \(V=56.26\left(\frac{\mathrm{Q}^{\frac{1}{3}}}{D}\right)^{1.94}\left(\frac{ \mathrm{Q}^{\frac{1}{3}}}{H}\right)^{-0.085}\) & Horizontal & 23.98\% & 14.95\% \\ \hline \end{tabular}
\end{table} 
Table 2: The error rate statistics of the prediction formula. vibration velocity of this project. The project should use the prediction formula, which considered the elevation value.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
## 2 Theoretical Analysis
### Calculation of Buffeting Response Based on Sectional Model Vibration Test
It is only determined by the structural characteristic parameters and has nothing to do with the turbulent flow characteristics:  
\[\big{|}T_{h_{i}}(k_{1})\big{|}^{2}=|\chi(k_{1})|^{2}\big{|}H_{h_{i}}(k_{1}) \big{|}^{2} \tag{6}\]  
At this time, the buffeting response spectrum of the structure can be written as:  
\[S_{h_{i}}(y,k_{1})=\frac{\varphi_{h_{i}}^{2}(y)}{M_{h_{i}}^{2}}(2\rho UbC_{L})^ {2}|T(k_{1})|^{2}\big{|}J_{h_{i}}(k_{1})\big{|}^{2}S_{u}(k_{1}) \tag{7}\]  
Based on Equation (7), a sectional model with a reasonable aspect ratio can be selected for vibration testing to identify the integrated transfer function. Since the function hasnothing to do with the turbulent flow characteristics, it can be used to predict the buffeting response of bridge structures under any wind field by using Equations (1)-(3). Two points need to be explained:  
(1) Since the identification of the integrated transfer function is based on the measured value of the buffeting response at a certain point of the structure, when it is applied to the prediction of the buffeting response, the function identified at a point (such as the mid-span position of a long-span bridge) can only be used to predict the buffeting response of the actual structure at the same position;  
(2) Due to the inconsistency between the mode shape functions of different orders, the integrated transfer functions corresponding to the mode shapes of different orders are also different. In other words, there is a fixed integrated transfer function corresponding to the different order mode shapes of the structure. Therefore, when using the integrated transfer function to predict the buffeting response of the actual structure, it is necessary to calculate the response of the different modes corresponding to the real bridge through the integrated transfer function of different orders, after which the SRSS method can be used to calculate the total response of the structure.
# Pipe Spatter Detection and Grinding Robot
## 5 Robotic System Verification
### Verification of Spatter Removal  
It was not possible to confirm the grinding work inside the steel pipes with spatters used in the tests. Thus, an acrylic specimen of the same type was prepared to test the removal of a spatter at the entrance of the pipe.  
In Figure 16, a spatter was recognized by artificially processing an image the same shape and size as the spatter on the flat plate at the end of the acrylic pipe. Based on information such as the length and angle of the work head, it was possible to confirm that the work head removed the spatter using the grinding tool after checking the correct working distance through the proximity sensor.  
The spatter detected inside the pipe was removed, and after removal, the work head should not damage the base material of the pipe. Furthermore, the surface roughness after grinding is important. The surface roughness must be 1.5 Ra (roughness average) or less to ensure the quality required for the inner coating of the pipe in the additional post-processing.  
As shown in Figure 17, after detecting and grinding the spatter using the robot, the measurement obtained using the surface-roughness measuring equipment and the measurement after the operator removed the spatter were compared and evaluated to confirm the efficiency of the work performed by the robot.  
Using the five measurements summarized in Table 7, the average surface roughness was 0.81 Ra. This surface roughness was superior to the existing work results. This confirmed the high work efficiency of the SGR for spatter detection and grinding.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Surface Roughness** & \multicolumn{5}{c}{**Test Data**} \\ \hline  & \#1 & \#2 & \#3 & \#4 & \#5 \\  & 0.92 & 0.87 & 0.90 & 0.73 & 0.63 \\ \cline{2-6} Roughness average & \multicolumn{5}{c}{**Test Results**} \\ \cline{2-6}  & Maximum & Minimum & Average \\  & 0.63 & 0.92 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of measuring the surface roughness.  
Figure 16: Removal of spatter using the grinding tool.  
Figure 17: Measuring the surface roughness after grinding.
# Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries
## 2 Materials and Methods
### Degradation Cost
#### 2.3.3. Version 3 to Calculate Degradation Costs  
Version #3 aims to obtain the degradation caused by the use of the battery up to a specific moment in its life, using models that allow a high degree of accuracy.  
Using Equation (27), we can obtain Equation (47), which allows the evaluation of \(\epsilon\).  
\[e_{x}^{y}(CF)\ =\ \frac{C_{BOL}-C_{x,act+\Delta}^{y}}{C_{BOL}(1-\mu_{CF})}-\frac{ C_{BOL}-C_{x,act}^{y}}{C_{BOL}(1-\mu_{CF})}\ =\ \frac{C_{BOL}-C_{x,act+\Delta}^{y}}{C_{BOL}(1-\mu_{CF})}-CF _{x}\in\{cal,cyc\},y\in\{NMC,LFP\} \tag{47}\]  
where \(C_{x,act}^{y}\) and \(CF\) represent, respectively, the current capacity and the capacity fade just before the current use, and \(C_{x,act+\Delta}^{y}\) represents the capacity just after the current use if the only degradation of type \(x\) exists.  
Using Equations (13), (14), and (47) we can obtain Equations (48) and (49) for NMC cells.  
\[\epsilon_{cal}^{NMC}(CF)\ =\ \frac{C_{loss,cal}^{NMC}\big{(}T,V,t_{cal}^{NMC}(CF)+ \Delta t\big{)}}{1-\mu_{CF}}-CF, \tag{48}\]  
\[\epsilon_{cyc}^{NMC}(CF)\ =\ \frac{C_{loss,cyc}^{NMC}\big{(}V_{qt},DoD,Q_{cyc}^{ NMC}(CF)+\Delta Q\big{)}}{1-\mu_{CF}}-CF, \tag{49}\]  
Figure 6a shows \(\epsilon_{cal}^{NMC}\) as a function of the \(SoC\) for a range of cell temperatures from 25 to 50 \({}^{\circ}\)C, and Figure 6b shows \(\epsilon_{cyc}^{NMC}\) as a function of the \(DoD\) for CF values of 0.9, 0.5, and 0.1.  
Figure 5: \(\epsilon_{cal}\) and \(\epsilon_{cyc}\) for LFP cells. Version 2.  
Figure 6: \(\epsilon_{cal}\) and \(\epsilon_{cyc}\) for NMC cells. Version 3.  
Using Equations (17), (18), and (47), we can obtain Equations (50) and (51) for LFP cells.  
\[\epsilon_{cal}^{LFP}(CF)\ =\ \frac{C_{loss,cal}^{LFP}\big{(}SoC,T,t_{cal}^{LFP}(CF)+ \Delta t\big{)}}{100(1-\mu_{CF})}-CF, \tag{50}\]  
\[\epsilon_{cyc}^{LFP}(CF)\ =\ \frac{C_{loss,cyc}^{LFP}\big{(}CR,DoD,Q_{cyc}^{LFP} (CF)+\Delta Q\big{)}}{100(1-\mu_{CF})}-CF, \tag{51}\]  
Figure 7a shows \(\epsilon_{cyc}^{LFP}\) as a function of the \(SoC\) for a range of cell temperatures from 25 to 50 \({}^{\circ}\)C, and Figure 7b shows \(\epsilon_{cyc}^{LFP}\) as a function of the \(DoD\) for CR values of 0.77, 0.8, 1, and 2.
# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 3 Method
### Improving Resolution  
After training the network, a set of network parameters \(\hat{\phi}\) is obtained. The trained neural network \(\mathrm{Net}_{\hat{\phi}}\) is applied to the initially reconstructed low-resolution volume \(\widetilde{\mathbf{x}}^{\mathrm{low}}\) to obtain a final volume representation \(\widetilde{\mathbf{x}}^{\mathrm{final}}\). This process consists of several steps. If the training input has been up-sampled using method A, then the reconstruction \(\widetilde{\mathbf{x}}^{\mathrm{low}}\) must likewise be up-sampled to \(\mathbf{U}_{k}(\widetilde{\mathbf{x}}^{\mathrm{low}})\), which is \(k\) times larger in every dimension. If method B has been used, no up-sampling of the reconstruction is necessary. The resulting voxel grid is processed by the network slice by slice. These slices must be collected in slabs containing \(s\) slices above and below the input slice. This influences the output size, since the top and bottom \(s\) slices of \(\widetilde{\mathbf{x}}^{\mathrm{low}}\) do not have enough surrounding slices to be processed by the network. This is usually not a problem because \(s\) is small (\(s<10\)) and the low-resolution grid can be chosen large enough that any important features are at some distance from the top and bottom. Finally, the network can be applied to each of these slabs in turn, the result of which we collect into \(\widetilde{\mathbf{x}}^{\mathrm{final}}\). In summary, this process results in the final volume representation  
\[\widetilde{\mathbf{x}}^{\mathrm{final}} =\left[\mathrm{Net}_{2s+1,\hat{\phi}}\left(\left[U_{K}\left( \widetilde{\mathbf{x}}^{\mathrm{low}}\right)_{j}\right]_{j=i}^{i+2s}\right) \right]_{i=1}^{N_{\mathrm{final}}}\] (Method A), \[\widetilde{\mathbf{x}}^{\mathrm{final}} =\left[\mathrm{Net}_{2s+1,\hat{\phi}}\left(\left[\widetilde{ \mathbf{x}}^{\mathrm{low}}_{j}\right]_{j=i}^{i+2s}\right)\right]_{i=1}^{N_{ \mathrm{final}}}\] (Method B).  
For both methods, the size of the final grid \(\widetilde{\mathbf{x}}^{\mathrm{final}}\) is summarized in Table 1.
## 1 Introduction
Despite these advantages, it is precisely because they directly use satellite data that are only available in history, which makes it difficult to predict GPP in the future, and there are few studies applying LUE models to future GPP predictions [28]. In order to investigate the possible changes in GPP in the future using the LUE model, maintaining the advantages of the LUE model within fewer parameters, further improvements are still needed in the LUE model to facilitate its application in GPP predictions under future climate change. In this study, in order to analyze the effects of climate change on historical GPP and predict the possible changes in future GPP in the Yangtze River Basin under climate change, a new method for simulating and predicting GPP by coupling the LUE model and regression model is proposed, and then the proposed model is used to attribute and project the GPP changes in the Yangtze River Basin. The main objectives of this study are as follows: (1) to develop a new LUE-RE method for GPP simulation and projection and validate its performances, (2) to investigate the spatial-temporal changes of historical GPP and quantifying the contributions of different climate factors on historical GPP changes, and (3) to project the future GPP changes under different climate change scenarios based on Coupled Model Intercomparison Project Phase 6 (CMIP6) in the Yangtze River Basin.
# Performance of CRTS-II Ballastless Track-Bridge Structural System Rebars under Fatigue Loading Test
## 4 Finite Element Analysis Based on Material Fatigue Damage Model
### Simplified Calculation Method of Fatigue Damage  
Because the fatigue strengthening stage cannot be simulated, and in order to compare with the experimental data, the stiffness increase factor \(I_{f}\) in the steel bar at the 3 millionth loading cycle obtained from the experimental test is used to expand the strain increase under different fatigue loading cycles by \((1+I_{f})\) times, so as to compare the finite element calculation results with the experimental results. The simplified calculation method of fatigue damage adopts the equivalent static method, which is essentially an applicable method to transform the dynamic fatigue process into a separate static calculation process. According to the static constitutive model of the above materials, the stress level of each layer of the structure system during fatigue loading at each level is calculated, and the fatigue life \(N_{f}\) of each structural layer is determined according to the \(S\)-\(N\) curve. Then the different fatigue loading cycles ratio \(N/N_{f}\) are calculated respectively, and the material constitutive relationship through the above-mentioned material fatigue damage model is updated. Finally, the strain increments of the steel rebars in the ballastless track simple-supported box girder under different static loads within the fatigue test are calculated.  
Table 4 presents the fatigue life of the different layers of the ballastless track-bridge structural system under various fatigue loads.  
Figure 9: Finite element model of ballastless track–bridge structure: (**a**) Mesh of bridge–track cross-section; (**b**) longitudinal reinforcement and stirrups (one span); (**c**) sketch of finite element model of structural system.  
Combining the fatigue life of the material and the fatigue constitutive model, the damage of the different materials, as the number of fatigue loading increases, is obtained and is plotted in the following Figure 10. Due to the different stress levels of structural layers, the fatigue damage of the materials varies greatly. It can be seen from Figure 9 that the damage of CA mortar increases the fastest, reaching 37.21% at the end of the test. The damage of the steel rebar, support beam, track slab and track bed are relatively closed (between 11% and 18% at the end of the test).