esa-sceva/satcom-chunk-collection huggingface dataset:
# AHC: Advanced Network-Hiding Access Control Framework
## 5 Experiments and Analysis
### Extensibility Verification  
As an access control technology, AHAC provides a foundational guarantee for the principles of trustworthiness, controllability, reliability, and verifiability in data communication. Considering the diversity and complexity of data communication scenarios and environments, AHAC must also be highly extensible to meet custom requirements in different contexts. The extensibility of AHAC is reflected in the following aspects:  
Bidirectional Communication Mechanism: Unlike SPA's unidirectional Port Knocking mechanism, AHAC's bidirectional Port Knocking communication mechanism offers richer expansion capabilities for data communication connections. Data resource providers can use this mechanism to hide the real IP addresses of resources, achieving complete resource  
Figure 6: High availability comparison results with and without load balancing. Open time (knock interval) is set to 1; Server machine configuration is 4-core 8G; Loglevel = 4.  
invisibility. Additionally, this mechanism can be used for encryption key exchange and transmission of computing parameters before and during data communication, enabling secure access for privacy computing, data spaces, and other collaborative data scenarios.  
Authorization Service Provider (ASP) Interface: AHAC can pass the content of the Port Knocking request packets from the resource requesters to the authorization service provider for advanced identity authentication and permission verification. This allows for stricter and more fine-grained identity authentication and access control to handle complex scenarios and multi-level security requirements.  
Flexible Data Resource Identification: Compared to the domain name-based internet host identification method, AHAC's resource identification can be any string, including Chinese, English, and symbols. This provides a more descriptive method for data resource providers. Therefore, AHAC can include DNS resolution capabilities, offering a more secure, encrypted, and private domain name resolution service for data communication.  
Consequently, the extensibility architecture of the AHAC framework mainly includes two typical application scenarios: integration with DNS and integration with FIDO. The specific descriptions are as follows:
## 4 Experiments
### Anomaly Detection and Explanation
#### 4.3.2 Anomaly Detection Results  
To validate the effectiveness of the discovered invariant rules in the anomaly detection task for ICSs, we evaluated the invariant rules on the test set. The evaluation employed recall, precision, and F1-score as primary metrics to comprehensively measure the detection performance of different algorithms. The specific evaluation results are presented in Table 5.  
The evaluation results indicate that the DE algorithm excels in recall and F1-score due to its superior global search capability and local optimization performance, outperforming both the BA and PSO algorithm. However, in terms of precision, the PSO algorithm demonstrates slightly better performance.  
Table 6 illustrates several typical invariant rules mined using the PSO algorithm. The recall and precision values indicate the detection effectiveness of the rules for specific attack types. These rules not only exhibit high predictive accuracy but also possess excellent interpretability, facilitating rapid identification of potential anomaly patterns by experts. In practice, utilizing multiple invariant rules in conjunction can yield improved detection results.
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 1 Introduction
[PERSON] and [PERSON] [19] studied bench blasting in a Guizhou sand and gravel mine, analyzing vibration propagation and developing a regression model to predict blast effects on nearby buildings. [PERSON] and [PERSON] [20] utilized a fuzzy neural network and the TC-6850 vibrator to investigate the effects of urban underground blasting on vibration intensity. They accurately predicted attenuation and amplification across different strata and building types, emphasizing stronger vibrations in excavated areas and complex propagation patterns in high-rise buildings. [PERSON] and [PERSON] [21] analyzed the impacts of multiple blasts on tunnels using finite element simulations. They identified significant cumulative vibration damage at the arch bottom, with subsequent blasts increasing peak velocities while decreasing acceleration. The application of concrete spray reduced displacement by 50.4%, thereby improving stability in grade V rock conditions. When tunnel blasting occurs in close proximity to a bridge under construction, the incomplete formation of the bridge structure and the not fully cured concrete result in low strength and stability. Repeated blasting operations will significantly affect the bridge [22; 23]. Using the flood discharge tunnel project in Zhanma Tian, Qinglong, Guizhou, as a case study, this study employs FLAC3D 6.0 to develop a three-dimensional model to investigate the impact of cyclic blasting-induced vibrations on the safety of bridge structures under construction, specifically the bridge deck, piles, and piers [24; 25; 26].
## 3 Methodology  
### Method to Solve the Multi-Objective Optimization  
As stated in Section 2.3, three main design factors are selected as variables for the MOOP in this work. These variables are listed in Table 1, along with their lowest and maximum values. In actuality, it is challenging to address the multi-objective optimization (MOO) problem using an MCDM method. The reason for this is that there are a lot of options or potential solutions when it comes to dealing with a MOO problem. To ensure the accuracy of the parameters and avoid missing the optimization problem's solution, the three parameters in this work have limits, as shown in Table 1, and the step between the variables is 0.02. In this case, the number of options (or experimental runs) that must be determined and compared is \((9-1)/0.02\cdot(0.4-0.25)/0.02\cdot(0.4-0.25)/0.02=22.500\) (runs). Therefore, it is not viable to address the MOO problem directly using the MCDM approach due to the sheer amount of possibilities. The MAIRCA method is used to solve the MOOP in this work and find the optimal values for the three main design variables. Minimizing the gearbox volume and optimizing the gearbox efficiency are the two goals. A simulation experiment is constructed in order to address the MOOP for a TSHG with SDGSs. Furthermore, by adopting the full factorial design, the number of experiments can be increased without taking into account the budget for each experiment because this is a simulation experiment. There will be \(5^{3}=125\) experiments as a result of the three experimental variables and five levels for each variable. But out of the three variables that are listed, Table 1 shows that \(u_{1}\) has the widest distribution (ranging from 1 to 9). Consequently, there is a considerable difference between the levels of this variable (in this case, \((9-1)/4=2\)), even with five levels). The values of \(\mathsf{u}_{1}\) are typically rounded to the hundredths in practice. As a result, in order to make the computation easier and simpler, a way to lessen the vast distance between the variables of \(\mathsf{u}_{1}\) must be found. A solution to multi-objective problems is suggested in this work (Figure 3). The following are the two components of this procedure: phase 1 factors determine the best primary design by solving the multi-objective optimization issue; and phase 2 factors minimize the gap between the levels by solving the single-objective optimization problem. Furthermore, if the levels of the variable are not sufficiently close to one another or if the best solution is not appropriate for the requirement, the MAIRCA issue will be rerun using the smaller distance between two levels of the \(\mathsf{u}_{1}\) in order to solve the multi-objective problem (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Symbol** & **Lower Limit** & **Upper Limit** \\ \hline \hline \multicolumn{4}{c}{Gearbox ratio of stage 1} & \(u_{1}\) & 1 & 9 \\ \multicolumn{4}{c}{CWFW of stage 1} & \(\psi_{ba1}\) & 0.25 & 0.4 \\ \multicolumn{4}{c}{CWFW of stage 2} & \(\psi_{ba2}\) & 0.25 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 1: Input parameters.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 2 Modeling
### Disk Element  
According to the principle of equivalence of dynamic properties, the components, such as the fan, compressor, and turbine, which have substantially larger radial dimensionscompared to their axial dimensions, can be represented as a disk element. This subsection derives the dynamic differential equations of the disk element by employing the Lagrangian equations of the second type. It also obtains the mass matrix, gyroscopic matrix, and transient matrix (the matrix associated with rotational acceleration) of the disk element.  
To streamline the analysis procedure, this paper assumes the following:  
* The influence of the rotor disk deformation on the vibration characteristics of the rotor system is small and negligible.
* Due to processing or assembly errors, the mass center of the rotor does not coincide with geometry center. There is an eccentricity.
* The effect of the axial displacement on the vibration characteristics of the rotor system is small and negligible when service environments such as maneuvering flights, which change the spatial attitude of the rotor system, are not taken into consideration [25].  
After introducing the assumption that the disk is considered as a rigid body and the axial displacement is neglected, the dynamic differential equations of the disk are derived using the Lagrange equations of the second type. The main steps of the derivation are as follows:  
1. Project the rotation velocities \(\dot{\Phi},\dot{B},\dot{\Gamma}\) described by the Euler angles to the rotation coordinate system \(Oxyz\).
2. Write the rotational kinetic energy in a rotational coordinate system.
3. Write the translational kinetic and potential energies in a fixed coordinate system.
4. Write the generalized forces in a fixed coordinate system. (The main generalized forces on the disk element are gravity and eccentric forces. The geometry relation between mass center and geometry center is shown in Figure 2).
5. Substituting kinetic energy, potential energy and generalized force into the Lagrange equations yields the matrix form of dynamic equations.  
\[\mathbf{M}_{d}\ddot{q}_{d}+\Phi\mathbf{G}_{d}\mathbf{q}_{d}+\dot{\Phi}\mathbf{ N}_{d}q_{d}=\mathbf{Q}_{d} \tag{1}\]  
Figure 2: Mass eccentricity of the disk element.  
where \(\mathbf{q}\) is the displacement array: \(\mathbf{q}_{d}=\{V,W,B,\Gamma\}^{\mathrm{T}}\), \(\mathbf{M}_{d}\) is the inertia matrix of the disk, \(\mathbf{G}_{d}\) is the gyroscopic matrix, \(\mathbf{\mathrm{N}}_{d}\) is the transient matrix, and \(\mathbf{Q}_{d}\) is the generalized force on the disk:  
\[\mathbf{\mathrm{M}}_{d} =\begin{bmatrix}m_{d}&0&0&0\\ 0&m_{d}&0&0\\ 0&0&I_{d}&0\\ 0&0&0&I_{d}\end{bmatrix},\,\mathbf{\mathrm{G}}_{d}=\begin{bmatrix}0&0&0&0\\ 0&0&0&0\\ 0&0&0&I_{p}\\ 0&0&-I_{p}&0\end{bmatrix},\,\mathbf{\mathrm{N}}_{d}=\begin{bmatrix}0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&-I_{p}&0\end{bmatrix}\] \[\mathbf{Q}_{d} =\{F_{dy}+G_{dy}\quad F_{dz}\quad 0\quad 0\}^{\mathrm{T}}\]
# Machine Learning-Based Prediction and Optimisation System for Laser Shock Peening
## 1 Introduction
The major drawbacks are that the mathematical formulation of the objective function is undefined in an industrial setting and there are limitations associated with solving optimisation problems where process parameters have discretised values. The combination of Artificial intelligence (AI) techniques has been found to be very useful for prediction and optimisation problems, for instance, the application of ANNs and GA within an integrated system is reported in a range of manufacturing processes [25; 26; 27]. [PERSON] et al. [28] reported a parametric study showing the effect of laser spot size and coverage on the LSP-induced residual stresses. Such studies can also be undertaken using ANNs by generating data from artificial single-variable experiments that could provide valuable insights into understanding the relationships existing in the data. One criticism with applying ANNs is that they lack objective grounding: A complex model can fit the training data well but fail to provide acceptable generalised predictions in the unseen data. [PERSON] [29; 30] introduced the concept of using an ANN within a Bayesian framework where the scalar hyperparameters of a network are estimated using the evidence program. The inclusion of a regularization term with the weight matrix in the error function reduces the possibility of overfitting and enables marginalisation of the network predictions. In this study, we present neural network models developed within a Bayesian framework that can reliably predict the residual stress distribution induced by LSP, as well as GA models that can optimise the laser peening process parameters. This study aims to develop a data based cost-effective prediction and optimisation methodology without having to consider the complex physics involved in LSP process. Section 2 presents the LSP process data used in this work and the collated residual stress measurement data using hole drilling. Section 3 discusses the Bayesian neural networks and estimation of contribution of the input variables using [PERSON]'s algorithm [31] that takes into account the weightsin the different layers of ANN architecture. Section 4 describes the developed GA model to optimise the process parameters based on the trained neural network. In the results and discussion (Section 5), we characterise the relative importance of the process variables and investigate the variation of induced residual stresses as a function of different process parameters using parametric studies. We critically evaluate the efficacy of the developed machine learning models using different case studies and recommend strategies for the implementation of the developed system.
## 1 Introduction
[33] also employed the multi-temporal MODIS products of Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI) and Gross Primary Production (GPP) to monitor the burned area during the process of post-fire recovery. Furthermore, the spectral indices have been widely used to represent the spectral characteristics of the burned area, vegetation and other types. [PERSON] et al. [10; 20; 24; 25; 27], [PERSON] et al. [22; 34], and [PERSON] et al. [5] evaluated the capacity of spectral indices in discrimination of the burned area and unburned types based on the satellite data. These algorithms based on multi-temporal satellite data are typical and can utilize the information on changes before and after the wildfire; whereas the traditional threshold-based methods with a single satellite image need to manually extract the samples to do analysis and cannot make the best of the spectral differencesbetween land cover types. Hence, the distinguishing capacity of the spectral bands and derived spectral indices of MODIS data to detect the burned area needs to be further explored. Also, it is necessary to develop a method that can take full advantage of the spectral differences to improve the performance of burned area mapping. In this paper, we develop a flexible and applicable method for burned area mapping, a technique that can be applied using a single MODIS image. The sample sets of five typical types including burned area, vegetation, cloud, bare soil, and cloud shadow are extracted by the empirical formula method, multi-threshold method and visual interpretation method. Then, the spectral bands and derived spectral indices are evaluated to investigate the separability between the burned area and unburned area types. The optimal indicators with high distinguishing capacity for burned area are input into a back-propagation neural network (BPNN) to finally yield the burned area map.
## 1 Introduction
The performance of the entire algorithm could be influenced depending on how the features of the original signal were extracted or how many features were selected [21]. Therefore, it is possible to improve the reliability of the algorithms by performing an analysis according to the feature numbers and classifiers, as well as demonstrating a high accuracy by performing experiments with diverse sample DBs. This study proposes a new method of increasing the individual identification rate by extracting features based on a non-fiducial technique and applying window removal method in order to remove windows that are difficult to identify. The main consideration of this study is to evaluate whether the proposed window removal method is efficient for improving the identification rate according to the NSR, PTB, and QT DB, to analyze the recognition performance according to the NN, SVM, and LDA classifiers, and to evaluate whether it is robust in environments containing normal, as well as abnormal, signals.
# Privacy Goals for the Data Lifecycle
## 6 Step 3-Aligning the Privacy Goals to GDPR and the Data Lifecycle
### GDPR Principle 3: Data Minimisation  
This refers to the requirement that only data that are actually required for the specified purpose should be processed and deleted or anonymised as soon as is practicable (see principle 6). GDPR considers data minimisation as one of the overarching principles (Article 5(1)(c)), and also as part of Data Protection by Default and Design (Articles, 25(1)), 47(2)(d), and 89(1) and discussed in Recital 156 (supervision authorities to enforce). The term data minimisation refers to the requirement that only data that are actually required for the specified purpose should be processed and deleted or anonymised as soon as is practicable. Similarly, ISO 29100 has included this as privacy protection goal number 4. ENISA, LINDDUN and Schleswig-Holstein DPA consider this a a data protection principle. Schleswig-Holstein DPA suggest that processors should consider not collecting such data in the first place or, if they really do need the data, deleting the data \"as soon as possible\" after use. LINDDUN considers data minimisation a data protection goal, describing this as an element of \"content awareness\" which involves making sure the data subject is aware what is collected, what the data is used for and how it will be used. It is interesting to note the different interpretations of concepts across the frameworks analysed. For example, LINDDUN considers data minimisation part of user awareness and keeping the data subject informed which, for others, has been incorporated within the goal of transparency. Overall, most of the frameworks agree this concept is an important part and certainly, for decision making, while this may not be a privacy goal, it does need to be considered as part of all of the stages within 7-DS. For example, at data collection, businesses will need to make decisions about what data they collect, whether they really need the data, and how such data will be processed and by who.  
This means that data minimisation must be considered as part of every stage within 7-DL as consideration needs to be give to what data, and how much of it, is collected (7-DL:2), processed (7-DL:3), shared (7-DL:5 and 6),or stored (7-DL:4 and 7). This, in turn, then requires that any procedures or policies also reflect this, hence, 7-DL:1 will also need to be invoked.
## 3 Experimental Setup
### Temperature Measurement Setup  
To record the actual temperature development within the sample, temperature probes (Radial Glass NTC Thermistors, encapsulated in glass with 10 k\(\Omega\) resistance at 25 degrees Celsius and a temperature range between \(-40\) to \(+250\) degrees Celsius) were embedded into the epoxy. These were also used to trigger the above mentioned smaller measurement interval of 3 min, whenever the temperature was above 40 \({}^{\circ}\)C (313 K). An automated software triggered the temperature measurement within less than 5 s after each OBR measurement was finished. For some experiments, the probes were aligned above the OBR-fiber, as visualized in Figure 2. For the remaining experiments, the probes were put randomly into the epoxy to just determine the average temperature. A schematic of the practical implementation of this setup and the probes embedded in a fully cured epoxy is shown in Figure 2.  
Figure 1: Schematic of the experimental setup for OBR measurements (upper image). Photo of an empty 40 cm long steel mold (lower image). To visualize the position of the optical fiber, a black thread was added. It was taken out before the actual fiber was inserted.  
In the cases when the temperature sensors followed the OBR-fiber, these were placed ca. 1/2 cm above the optical fiber. The number of and distance between the probes depended on the length of the steel mold; details can be found in Table 1. The probes were placed with approximately the same distance between them.
## 5 Data Analysis and Results
### Validation and Predictions  
This study aimed to investigate the various factors that have an impact on the adoption of YouTube as a learning tool, which affirmably influences AA in a bilingual academic environment. The factors most likely connect the quality of the YouTube resource and the perceived information in achieving high-quality scores in academic environments. Consequently, intelligent approaches have to be utilized to predict the perceiving information quality from a different perspective. This study conducted several experiments using supervised ML techniques to validate the information quality learned using YouTube as a learning tool. As shown in Figure 3, the proposed model comprises four dataset models that are considered as the input to ML techniques. These models are: (1) Model 1, which has three inputs that are SC, IL, and IQ as independent variables and IU as a dependent variable, (2) Model 2, which also contains three factors that are IIQ, CIQ, and AIQ to predict IQ as a dependent variable, and (3) Model 3 and Model 4, each of which has one input and dependent variable, IU to IA and IA to AA, respectively. The experimental results are shown in Figure 4 using R\({}^{2}\) and Mean Square Error (MSE) as evaluation metrics. The R\({}^{2}\) and MSE values are displayed on the \(y\)-axis, while the models are depicted on the \(x\)-axis. The R\({}^{2}\) indicates how the independent values are anticipated to affect the dependent variable (target). The MSE measures the average difference between a model's evaluated and actual output values.  
Figure 4: The MSE results of using ML techniques on YouTube dataset.  
Figure 3: The R\({}^{2}\) results of using ML techniques on YouTube dataset.  
When compared to other ML techniques, the random forest and bagging REPTree ML models, as shown in Figure 3, give remarkable results reaching up to approximately 98%. This suggests that the tree-based models are more correlated to the target labels. Despite the strong performance of tree-based models, the other ML approaches produce reasonable outcomes of approximately 92%. These findings provide evidence about the effectiveness of ML to predict the information quality (IQ) when depending on the YouTube learning tool. Furthermore, Figure 4 ensures the effectiveness of the tree-based models that accomplish low MSE values flanked by the target and the actual values of the model.