esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical and Engineering Examples  
Two numerical examples and one engineering example are demonstrated to explain the effectiveness of the proposed probabilistic representation approaches for multiple types of epistemic uncertainties. The three examples were calculated using Matlab(r) R2017b on a computer with Inter i5-7400 CPU and 8 GB RAM. Numerical example 1 illustrates that the proposed methodology can deal with multiple different types of epistemic uncertainties. In numerical example 2, the proposed methodology is applied to uncertainty propagation analysis for multiple types of epistemic uncertainties, and the probabilistic uncertainty result is similar to the results of the Monte Carlo simulation (MCS) method, while the computational efficiency is improved. In the engineering example, the proposed method can determinate accurate uncertainty bounds of the total heat transfer rate with high computational efficiency.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Science and Technology Advances
#### 3.1.5 Observing Precipitation through Other Water Cycle Components  
Precipitation-oriented missions have to be combined with existing missions observing the other components of the water cycle. We will first mention ESA's Soil Moisture Ocean Salinity (SMOS, [143]) mission, NASA's Soil Moisture Active Passive (SMAP, [144]), and their follow-ons. PMW data are combined with data from scatterometers such as the Advanced SCATerometer (ASCAT) to produce global soil moisture data, such as the ESA Climate Change Initiative (CCI) soil moisture dataset [145]. Approaches have been proposed for measuring precipitation using variations of the soil moisture measured by these sensors to infer preceding rainfall amounts; the Soil Moisture to Rain (SM2 RAIN, [146]) method is based on the inversion of the hydrological water balance for estimating rainfall.  
A follow-on mission is essential for the Gravity Recovery and Climate Experiment (GRACE, [147; 148; 149]), which measured ground water levels from space for the first time. It provided data for the investigation of the effect of soil moisture on climate [150], and underscored the importance of considering water storage, along with water fluxes, to improve global models [151]. GRACE observations have also been found to be important for cold region assessments of precipitation products and the determination of proper gauge undercatch corrections [152; 153]. Time series are now available with the necessary quality assessments [154]; this is especially important, as in situ data are often sparse or inaccurate (e.g., due to gauge undercatch) in cold regions.  
Evapotranspiration is another key component that can be observed from space to produce global data sets such as the Global Land Evaporation Amsterdam Model (GLEAM, [155]) that separately estimates the different components of land evaporation, i.e., transpiration, bare-soil evaporation, interception loss, open-water evaporation, and sublimation. A recent study underlines the potentialimportance of nocturnal water loss from the surface into the atmosphere, which has been overlooked so far because of the absence of solar radiation to drive evapotranspiration [156]. The current tendency is to combine these spaceborne datasets with data from other sources, including ground based ones; the result are global gridded datasets such as the Derived Optimal Linear Combination Evapotranspiration (DOLCE, [157]) and the Linear Optimal Runoff Aggregate (LORA, [158]). On a smaller/national scale, the approach is very similar, and datasets at the catchment level are being proposed, combining all data sources, including satellite-based precipitation estimates; a notable example is the Catchment Attributes and MEeorology for Large-sample Studies-Chile (CAMELS-CL) dataset [159].
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 2 Numerical Simulation Model Verification and Fire Source Condition Setting
### Numerical Analysis Method Validation
#### 2.1.1 Fire Test and Finite Element Model
(**a**) The test model before the structural fire tests; (**b**) The test model after the structural fire tests; (**c**) Test 1; (**d**) Test 4. The simulation assumes that the cooling effect of the roof and the wall was not considered in the windless environment; the turbulent large eddy numerical simulation (LES) method was used to solve the Navier-Stokes equation for fire simulation. The initial ambient temperatures of simulations 1 and 4 were set to 15 \({}^{\circ}\)C and 24 \({}^{\circ}\)C, and the initial atmospheric pressure was 1.01 \(\times\) 10\({}^{5}\) Pa. Boundary conditions were set to open and connected to the atmosphere. The simulation time was 600 s. The simulations captured the thermal properties of various materials by fine-tuning the surface characteristics of each corresponding obstacle unit. Based on the real conditions and considering the change of thermal parameters with temperature, [PERSON] et al. [46] conducted a finite element analysis of composite steel deck concrete slabs under fire. However, in this study, in order to simplify the model and restore the numerical simulation of [PERSON] et al. [45], constant thermal parameters were used for fire simulation. A compilation of these materials' thermal properties, including density (\(\rho\)), specific heat capacity (c), and thermal conductivity (\(\lambda\)), is presented in Table 2.
## 4 Fractional-Order Controller and Fractional-Order Disturbance Observer Design
### Altitude Controller Design
We can select the virtual control from (44) and control input from (43) as follows:  
\[\alpha_{1}=-s_{11}\sigma_{1}-e_{2}-s_{12}sig^{1/2}(\sigma_{1})+\dot{x}_{1d}- \sigma_{1}, \tag{44}\]  
\[u_{1}=\frac{m_{0}}{\cos\phi\cos\theta}(-s_{21}\sigma_{2}+g-s_{22}sig^{1/2}( \sigma_{2})-s_{23}\sigma_{2}/(|\sigma_{2}|+\varepsilon_{\sigma 2})^{-1}-\omega_{2}-\hat{f}_{d2}), \tag{45}\]  
where \(s_{ij}>0\) and \(\varepsilon_{\sigma 2}>0,\ \ i=1,2,3,\ j=1,2\), are constant, and \(\hat{f}_{d2}\) denotes the estimate of \(f_{d2}\). Define the Lyapunov function as follows:  
\[V_{1}=\frac{1}{2}\sigma_{1}^{2}, \tag{46}\]  
\[V_{2}=\frac{1}{2}\sigma_{2}^{2}. \tag{47}\]Applying the fractional operator \(D^{\overline{7}_{1}}\) to the Lyapunov function in (46) and (47) and using (42), (43), (44), (45), and Lemma 1 yields in the following expression:  
\[ \begin{array}{c}D^{\overline{7}_{1}}V_{1}+D^{\overline{7}_{1}}V_{2}=\sigma_{1} D^{\overline{7}_{1}}\sigma_{1}+\sigma_{2}D^{\overline{7}_{1}}\sigma_{2}+\left| \sum\limits_{k=1}^{\infty}\frac{\Gamma(1+\overline{7}_{1})}{\Gamma(1-k+ \overline{7}_{1})\Gamma(1+k)}D^{k}\sigma_{1}D^{\overline{7}_{1}-k}\sigma_{1} \right|\\ +\left|\sum\limits_{k=1}^{\infty}\frac{\Gamma(1+\overline{7}_{1})}{\Gamma(1-k +\overline{7}_{1})\Gamma(1+k)}D^{k}\sigma_{2}D^{\overline{7}_{1}-k}\sigma_{2 }\right|\\ \leq\sigma_{1}\big{(}e_{2}-\dot{x}_{1d}+\alpha_{1}+\alpha_{1}\big{)}+\sigma_{2 }\big{(}\frac{\cos\cos\cos\theta}{\cos\theta}u_{1}-g+\omega_{2}+f_{dz}\big{)} +\mu_{1}|\sigma_{1}|^{3/2}+\mu_{2}|\sigma_{2}|^{3/2}\\ \leq-s_{11}\sigma_{1}^{2}-(s_{12}-\mu_{1})|\sigma_{1}|^{3/2}-s_{21}\sigma_{2} ^{2}-(s_{22}-\mu_{2})|\sigma_{2}|^{3/2}-s_{23}\sigma_{2}^{2}(|\sigma_{2}|+ \varepsilon_{\sigma 2})^{-1}\\ +|\sigma_{2}|\left|\widetilde{f}_{dz}\right|\\ \leq-s_{11}\sigma_{1}^{2}-s_{21}\sigma_{2}^{2}-(s_{12}-\mu_{1})|\sigma_{1}|^{ 3/2}-(s_{22}-\mu_{2})|\sigma_{2}|^{3/2}\\ -|\sigma_{2}|\left|s_{23}|\sigma_{2}|(|\sigma_{2}|+\varepsilon_{\sigma 2})^{-1}-\delta_{d2}\right) \right]\!,\end{array}  \tag{48}\]  
where \(\left|\widetilde{f}_{d2}\right|\leq\delta_{d2}\) and \(\delta_{d2}\) denotes an unknown constant. If \(s_{23}\) is selected such that \(s_{23}|\sigma_{2}|(|\sigma_{2}|+\varepsilon_{\sigma 2})^{-1}\geq\delta_{d2}\) is satisfied,  
\[D^{\overline{7}_{1}}V_{1}+D^{\overline{7}_{1}}V_{2}\leq-(s_{12}-\mu_{1})| \sigma_{1}|^{3/2}-(s_{22}-\mu_{2})|\sigma_{2}|^{3/2} \tag{49}\]
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 4 Case Studies
### IBM's AI-Powered Defect Prediction Tool Enhances Software Quality  
IBM's recent integration of an AI-powered defect prediction tool reflects its long-standing commitment to technological innovation. Trained on a decade's worth of code and defect data from large-scale projects, this tool aims to anticipate defect-prone areas, enabling proactive management throughout the software development lifecycle.  
Upon deployment, development teams within IBM's software division used the tool's real-time insights to prioritize testing and debugging efforts. The resulting improvements were significant: post-release defects decreased by 20%, while defect detection rates during development rose by 15%. Furthermore, debugging time dropped by 30%, translating to faster project timelines and lower overall costs.  
These metrics highlight the potential of AI-driven analytics to elevate software quality and developer productivity. By systematically identifying latent issues, the tool ensures more efficient allocation of resources, reinforcing IBM's competitive edge in delivering robust, high-quality software.
# Test-Retest Reliability of Kinematic Parameters of Timed Up and Go in People with Type 2 Diabetes
## 3 Results
### Peak Forces from the Heel and the Forefoot in the Timed Up and Go  
Regarding the peak forces from the heel and the forefoot, differences between test and retest were only observed for the right heel and left forefoot forces in the group without neuropathy. For the rest of the variables, the retest was not significantly different than the test (see Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Test Measurement in Newtons** & **Test** & **Retest** & \(p\)**-Value** \\ \hline \multirow{6}{*}{GENERAL (\(n=56\))} & Right forefoot forces (before reaching the mark) & 846.91 (185.68) & 841.62 (192.78) & 0.525 a  \\  & Left forefoot forces (before reaching the mark) & 827.33 (195.94) & 828.06 (187.55) & 0.562 a  \\  & Right forefoot forces (after reaching the mark) & 823.69 (181.65) & 819.28 (174.34) & 0.707 a  \\  & Left forefoot forces (after reaching the mark) & 773.50 (179.97) & 767.44 (179.11) & 0.458 a  \\  & Right heel forces (before reaching the mark) & 881.64 (201.23) & 902.94 (209.41) & 0.058 a  \\  & Left heel forces (before reaching the mark) & 934.64 (217.76) & 944.97 (217.63) & 0.098 a  \\  & Right heel forces (after reaching the mark) & 938.52 (226.32) & 940.87 (211.95) & 0.579 a  \\  & Left heel forces (after reaching the mark) & 937.35 (210.51) & 943.70 (214.06) & 0.458 a  \\ \hline \multirow{6}{*}{SEVERE (\(n=22\))} & Right forefoot forces (before reaching the mark) & 868.77 (198.23) & 867.59 (215.78) & 0.833 a  \\  & Left forefoot forces (before reaching the mark) & 879.37 (208.31) & 857.73 (204.77) & 0.291 a  \\  & Right forefoot forces (after reaching the mark) & 859.59 (204.95) & 849.56 (204.22) & 0.615 a  \\  & Left forefoot forces (after reaching the mark) & 821.39 (199.56) & 820.55 (194.15) & 0.783 a  \\  & Right heel forces (before reaching the mark) & 937.91 (209.96) & 958.33 (237.64) & 0.485 a  \\  & Left heel forces (before reaching the mark) & 994.51 (229.16) & 1004.19 (232.83) & 0.338 a  \\  & Right heel forces (after reaching the mark) & 991.13 (263.60) & 973.35 (237.58) & 0.548 a  \\  & Left heel forces (after reaching the mark) & 983.31 (226.01) & 979.73 (219.78) & 0.783 a  \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Differences between the test and retest on the peak forces from the heel and the forefoot forces in the entire sample and in participants with (a) severe neuropathy, (b) moderate neuropathy and (c) normal perception.
## 4 Results
### The Strain Energy Solutions  
In the section, we studied the performance of the ES-FEM on the strain energy for this practical problem. First, we calculated the reference solution of strain energy using the finer mesh mentioned above, which is 2.4240 N - mm. Then, the numerical results of strain energy using different methods  
Figure 11: The distribution of Von-Mises stress along the generator of the tip using different methods for the conical pick.  
Figure 10: The maximum Von-Mises stress obtained using different methods for the conical picks using the same triangular meshes.  
are presented in Table 4. The error of strain energy obtained using ES-FEM is smallest no matter which mesh is used. The according convergence curves of strain energy is plotted in Figure 12 against the degrees of freedom (DOFs). It can be found that the strain energy of ES-FEM are just a little bigger than the reference solution and quickly converge to it with the increase of DOFs. In contrast, the strain energy solution of FEM is much lower than the reference solution and it converges very slowly. It is noted that, although the ES-FEM solution fluctuates a little bit when using the coarse meshes, the value is more close to the reference solution than other methods. Besides, it is observed that the upper bounds of NS-FEM and the lower bounds of FEM still remain the same in this simulation.  
Next, in order to verify the accuracy and validity, the error norm of the strain energy of ES-FEM was calculated and compared with that of FEM, NS-FEM, and FS-FEM models. Errors of these numerical methods are analyzed using energy norms. The error norm of strain energy is calculated by:  
\[e_{\epsilon}=\left[\int\limits_{\Omega}\frac{1}{2}(\epsilon-\widetilde{ \epsilon})^{T}\mathbf{c}(\epsilon-\widetilde{\epsilon})d\Omega\right]^{1/2}= \left[\sum\limits_{i=1}^{N_{\epsilon}}\int\limits_{\Omega_{i}^{2}}\frac{1}{2} (\epsilon-\widetilde{\epsilon})^{T}\mathbf{c}(\epsilon-\widetilde{\epsilon})d \Omega\right]^{1/2} \tag{12}\]  
where \(\epsilon\) is the reference solution for the strain energy, and \(\widetilde{\epsilon}\) is the numerical solution for the strain energy using a numerical model. Table 5 lists seven sets of data calculated using different methods for seven meshes. It is shown that the error of ES-FEM is about 0.217 against 0.534 of FEM, which illustrates again that ES-FEM stands out clearly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Mesh** & **FEM** & **ES-FEM** & **FS-FEM** & **NS-FEM** & **Reference Solution** \\ \hline Mesh 1 & 5.29 & 0.32 & 3.28 & 12.8 & \\ Mesh 2 & 3.08 & 0.88 & 1.62 & 8.47 & \\ Mesh 3 & 3.07 & 0.14 & 4.51 & 6.04 & \\ Mesh 4 & 2.87 & 0.16 & 1.72 & 5.71 & 2.4240 N \(\cdot\) mm \\ Mesh 5 & 2.04 & 0.23 & 1.14 & 9.88 & \\ Mesh 6 & 1.85 & 0.23 & 1.06 & 3.96 & \\ Mesh 7 & 1.24 & 0.27 & 0.64 & 6.64 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The error of strain energy obtained using different methods compared to the reference solution (%).  
Figure 12: Convergence of the strain energy solution for the conical picks.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 3 Comparison Experiment
### Comparative Experiments with ResNet34 Models
#### 3.3.1 Single-Layer Pruning Analysis  
During single-layer pruning, strategy A excludes the pruning of residual branches, while strategy B involves pruning shortcuts and the Conv2 structure of Basic Block to maintain dimensionality consistency. In the single-layer pruning analysis, analysis was exclusively conducted to prune strategy A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ratio** & **Flop** & **Flop Ratio** & **Param** & **Param Ratio** \\ \hline
0.1 & \(3.51\times 10^{8}\) & 15.86\% & \(3.75\times 10^{7}\) & 4.58\% \\
0.2 & \(2.93\times 10^{8}\) & 29.82\% & \(3.59\times 10^{7}\) & 8.74\% \\
0.3 & \(2.41\times 10^{8}\) & 42.32\% & \(3.44\times 10^{7}\) & 12.44\% \\
0.4 & \(1.96\times 10^{8}\) & 53.20\% & \(3.31\times 10^{7}\) & 15.76\% \\
0.5 & \(1.57\times 10^{8}\) & 62.32\% & \(3.20\times 10^{7}\) & 18.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parametric quantities and floating-point computations of VGG19 model with different pruning ratios.  
Figure 14 illustrates the single-layer pruning analysis for the first three Basic Blocks in Layer1 and Layer2 of the ResNet34 model. When applying single-layer pruning to the model in Layer1, the Top1 accuracy exhibits varying degrees of decrease. Notably, Basic Block1 experiences a less pronounced decrease, with MCP-L1 showing only a 0.4% reduction at a pruning ratio of 0.4. In contrast, Basic Block2 and Basic Block3 show more significant reductions of approximately 5% and 6% with the MCP-L1 method. Both the Lasso regression and MCP regression methods exhibit less-substantial decreases under single-layer pruning, with MCP regression demonstrating a slight superiority over Lasso regression methods.  
In Layer2, none of the three methods exhibit a loss in Top1 accuracy at a given pruning ratio. This suggests redundancy in the ResNet34 model under CIFAR100 training, and pruning a single layer does not impact its performance. The Top1 accuracy also remains constant when pruning deeper Basic Blocks.
# Random Access for Underwater Acoustic Cellular Systems
## 6 Conclusions  
In this paper, two different types of RAPs (RAP 1 and RAP 2) were proposed to detect the identity of UE and estimate the time delay between a UBS and a UE at the physical layer, after showing that the conventional RAP used in LTE systems is not appropriate for underwater acoustic cellular systems. The properties of RAP 1 and RAP 2 are evaluated using analytical solution and computer simulation, and can be summarized as follows. The AF and CAF of RAP 1 have desirable properties when there is no Doppler shift. RAP 1 provides a sharp peak around the origin in the AF and a small cross-correlation level in the CAF when there is no Doppler shift. The performance degradation (detection probability) caused by an interfering UE is minimal. However, undesired properties develop in AF when there is a Doppler shift. The magnitude of AF at the correct timing is reduced and the side peaks increase when there is a Doppler shift. The detection probability of RAP 1 also decreases significantly owing to its Doppler sensitivity. However, RAP 2 is robust to Doppler shift because it is generated using a Doppler-insensitive waveform. The detection probability of RAP 2 is degraded slightly when there is a Doppler shift. However, the magnitude of AF in RAP 2 varies depending on the selection of \(\beta_{c}\). The number of possible RAPs and the cross-correlation level of CAF vary depending on the selection of \(\Delta\beta\). In this paper, analytical solutions for the upper bound of the cross-correlation level and the number of possible RAPs are provided such that they can be used in selecting the appropriate values of \(\beta_{c}\) and \(\Delta\beta\), required for the design of RAP 2. Finally, the AF and CAF analytically obtained in this paper are shown to be similar to those obtained using experimental data, except for a small difference caused by channels and noise.  
This research was a part of the project titled 'Development of Distributed Underwater Monitoring & Control Networks', funded by the Ministry of Oceans and Fisheries, Korea.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] developed the described design technique of random access preamble for underwater acoustic cellular systems. [PERSON] supervised the project.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 17: CAF comparison (experiment vs. analytical). (**a**) Experiment before normalization; (**b**) Experiment vs. Analytical.
## 2 Methodology
### Definition and Formulation of KPI
#### 2.3.2 KPIs for Sub-Function \"Maintenance Management\"  
The standard defines this sub-function as the combination of resources, disciplines, competences, and tools to define medium-term planning according to the industrial plan of the company. It is also encompasses coordination and control of activities implemented for physical assets to achieve the objectives within the existing framework and constraints.  
Expected availability based on time (\(MM_{1}\)) is defined as:  
\[MM_{1}=1-\frac{T_{u}}{T},\ \ with \tag{10}\]  
\[T_{u}=\sum_{\begin{subarray}{c}i\in I\\ j\in I_{i}\end{subarray}}d_{i,j}+\sum_{\begin{subarray}{c}i\in I\\ j\in I_{i}\end{subarray}}T_{i,j}^{c}(ts_{i,j}) \tag{11}\]  
where \(T_{u}\): is the expected downtime due to preventive and corrective interventions, and \(d_{i,j}\) is the downtime of the _jth_ execution of activity \(i\), where \((i,j)\in I\times J_{i}\).  
Description: This KPI Measures the proportion of the horizon time (\(T\)) in which the system is expected to be available, considering scheduled and unscheduled (due to failure) stoppages (\(T_{u}\)). The indicator considers the amount of time for unscheduled stoppages as the mean failure risk for the system (\(PAM_{1}\)) multiplied by the mean duration of the corrective actions for all the activities. Perfect corrective maintenance is assumed for every expected failure.  
Expected availability percentage variation (\(MM_{2}\)) is defined as:  
\[MM_{2}=\frac{MM_{1\ (current)}}{MM_{1\ (base)}} \tag{12}\]  
Description: This KPI measures the expected availability (\(MM_{1}\)) variation between a base scenario and a current scenario.  
Expected number of stoppages (\(MM_{3}\)) is defined as:  
\[MM_{3}=|\{k\in K:\rho_{k}\
eq 0\}|+\sum_{\begin{subarray}{c}i\in I\\ j\in I_{i}\end{subarray}}\ \int_{0}^{T}\lambda_{i,j}(x)\ dx \tag{13}\]  
Description: This KPI indicates the number of scheduled and unscheduled (due to failure) stoppages for the current strategy. It assumes that the number of stoppages due to failure is equal the closest integer to the mean failure probability of the system.  
Expected number of stoppages variation (\(MM_{4}\)) is defined as:  
\[MM_{4}=\frac{M_{3\ (current)}}{M_{3\ (base)}} \tag{14}\]  
Description: This KPI measures the percentage variation in the expected number of stoppages between the current strategy and the base strategy
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Network Level
#### 3.4.3 Intrusion-Detection/Prevention Systems (IDS/IPS)  
Evidence demonstrates that intrusion-detection systems (IDS) and intrusion-prevention systems (IPS) are essential for detecting and responding to network attacks [61]. An IDS is a technology used in network security to detect and identify suspicious activity [62]. When suspicious behavior or activity that violates security policies are detected, the IDS generates and sends an alert to administrators or to an integrated security information and event management (SIEM) system [63].  
There are two main types of IDSs, namely network intrusion-detection systems (NIDSs) and host intrusion-detection systems (HIDSs) [64]. NIDSs are mainly deployed at network boundaries or data-center entrances to monitor all the network traffic passing through and detect potential malicious activities or violations, whereas HIDSs are installed on specific hosts or servers to monitor the system logs, file system changes, and detect attacks or abnormal behaviors.  
Firewalls--the first line of defense against compromise--shield IoT devices from unauthorized access and malicious traffic, and by configuring the firewall, system administrators can limit the ports and services that are accessible, allowing only the necessary communications to effectively reduce the attack surface. For example, Cisco's enterprise firewall solutions empower organizations with advanced network security, including intrusion prevention and unified communications protection, safeguarding critical data and ensuring seamless communication [65].