esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bounded Scheduling Method for Adaptive Gradient Methods
## 2 Background  
### Traditional Learning Rate Methods  
Learning rate is one of the most important hyper-parameters of gradient-based optimization methods, there have been many related works on it. Line search [10] is often used to find the learning rate of the full gradient. The line search method will set a large initial learning rate and try a learning rate at each iteration, if the loss function does not fall a certain distance than the current value, the learning rate will decrease proportionally and iterate again, until the learning rate satisfying the fall condition is found. Line search needs a large amount of computation and is often used when the data set is small. A line search method for SGD is also proposed [27]. This method uses random samples to do basic line search and estimates the Lipschitz constant \(L\), then deduces the theoretical optimal learning rate based on \(L\). However, the optimal learning rate, in theory, is different from that in practice and this method can not guarantee convergence.  
Barzilai-Borwen method [28; 29] is also often used to estimate the learning rate. The Barzilai-Borwen method is based on the quasi-Newton method and uses second-order derivative information to evaluate the learning rate, which requires little extra computational overhead. However, the learning rate estimated by the Barzilai-Borwen method may lead to the divergence of the training process. [PERSON] et al. proposed a method to view the whole performance of the learning trajectory as a function of the learning rate, then adapt the learning rate by performing a gradient descent on the learning rate itself [30]. Although these methods do not need to search the learning rate, their performance is not good enough compared with the manually tuned optimal learning rate. Cyclical learning rate method [11] does not need to use a certain learning rate, but makes the learning rate vary periodically in a certain range. Then super-convergence [12] is proposed to train DNNs with one cycle and a large maximum learning rate, which provides a boost in performance. Traditional learning rate methods scale the gradient uniformly in all directions, the performance of which will decrease when data sets are sparse or ill-scaled.
## 4 Results and Analysis
### Analysis of Compliance Behavior
#### 4.1.1 National Culture and Compliance  
Among many factors, national culture influences the psychology of individuals. Cultural variation affects human compliance towards ISP. In this context, [44] conducted a qualitative study based on the analytical grounded theory approach. The study's primary  
Figure 4: Research methodologies adopted in the sample.  
Figure 3: Number of publications per year.  
purpose was to identify the effects of national culture on ISB employees towards ISP. The researchers conducted 19 semi-structured interviews with participants from the US and Ireland. After summarizing the results, it was established that US employees have more adaptive behavior towards security policies and procedures than Irish employees.  
Consequently, three main findings were enlisted by the authors. First, organizations with more robust institutional controls have a higher degree of compliance with the ISP. Second, higher sociability organizations are less in line with the ISP. Finally, organizations that have more interaction between managers and employees seem to be more ISP compliant. A summary of the literature on national culture and ISPC is presented in Table 4.
## 2 Materials and Methods
### Machine Learning Algorithms  
Machine learning is a branch of artificial intelligence that demonstrates intelligent behavior by finding relationships in the training data. Machine learning algorithms differ from classical algorithms, which are composed of a specific sequence of instructions. Instead, they create a model based on analyzing the provided training data. The process of creating knowledge about a solution is called learning. The three most frequently mentioned categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning [55; 56]. Supervised learning involves identifying a function that predicts future outcomes based on the data containing both the input features and expected outcomes. Predictive models receive instructions on how and what to learn. Supervised machine learning algorithms include, among others, artificial neural networks, adaptive boosting, gradient boosting, extreme gradient boosting, K-nearest neighbors, naive Bayes, support vector machines, decision trees, and random forests. In unsupervised learning, the training set only consists of the input features and does not include labels (expected outcomes). Unsupervised machine learning algorithms look for patterns based on the provided examples. This group includes K-means, independent component analysis (ICA), and principal component analysis (PCA). Reinforcement learning, a trial-and-error method, occurs in a completely unknown, often dynamic environment. The algorithm uses feedback from its decisions and learns from its experiences by receiving a positive (reward) or negative reinforcement signal (penalty). Q-learning and SARSA are examples of reinforcement learning.  
This article demonstrates the use of selected supervised machine learning algorithms such as artificial neural network, adaptive boosting, gradient boosting, K-nearest Neighbors, and naive Bayes to assess potential rock burst hazards in underground openings of an active coal mine. All calculations were performed in the JupyterLab environment [57; 58]. All scripts were coded in Python using the available machine learning library Scikit-learn (sklearn) [59]. The optimal hyperparameters of each machine learning algorithm were determined using a grid search approach. In the vast majority of cases, the standard values proposed in the machine learning library Scikit-learn were adopted.
## 5 Discussion and Future Research  
This paper presents a field data-based framework for battery health management in FL scenarios, which not only provides an important basis for on-board battery health monitoring and prediction, but also paves the way for second-life assessment of batteries. It explores the privacy concerns of battery health management based on AI modelling.  
The FL approach enables the development of a common AI model while preserving the privacy of user data. However, gradient inversion attacks can potentially reconstruct user data from deep learning model updates, which raises privacy concerns. As shown in this paper, additional protection by differential privacy DP leads to saturation in gradient inversion attacks, i.e., the reconstructed signal maintains a certain error level depending on the applied DP protection levels.  
Future research could investigate more advanced GIA methods, such as training attack networks with reinforcement learning or using the foundation models for gradient inversion attacks. Furthermore, the attention mechanisms in transformers could be investigated as they are self-explanatory.  
Conceptualization, K.S.; methodology, K.S. and I.N.; formal analysis, K.S.; investigation, K.S. and I.N.; resources, I.N.; writing--original draft preparation, I.N.; writing--review and editing, K.S., A.N. and K.O.; project administration, K.O.; funding acquisition, K.O. All authors have read and agreed to the published version of the manuscript.  
This work is the result of activities within the \"Digitalization of Power Electronic Applications within Key Technology Value Chains\" (PowerizeD) project, which has received funding from the Chips Joint Undertaking under grant agreement No. 101096387. The Chips-JU is supported by the European Union's Horizon Europe Research and Innovation Programme, as well as by Austria, Belgium, the Czech Republic, Finland, Germany, Greece, Hungary, Italy, Latvia, the Netherlands, Spain, Sweden, and Romania.  
Not applicable.  
Figure 6: Comparison between the original signal and the recovered signal over a sequence of time steps without DP. Input volts/t IU.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
## 5 OCC User Satisfaction  
As discussed earlier, the data rate is the main concern in the OCC performance. However, users do not need to achieve a high data rate in every case. Sometimes, an excellent SINR is more significant than a high data rate. For example, if a user wants to localize its position using an LED access point, it will definitely need to achieve a high SINR and an accurate detection of LED to minimize the localization resolution. On the other hand, both SINR and data rate are important for a user who wants to make a video call. User satisfaction in OCC can be represented as the measurement of the communication effectiveness considering the service requirements for the users. It is measured from the required and the achieved data rates and SINRs for OCC users. As we discussed in the previous sections, these factors depend on the camera specifications. The user satisfaction factor in OCC can be expressed as  
\[S=P_{d}(1-P_{b})\frac{\varphi_{a}\kappa_{a}}{\varphi_{r}\kappa_{r}},\ [S\in \mathbf{R}:0\leq S\geq 1], \tag{17}\]  
where \(\varphi_{a}\) and \(\varphi_{r}\) denote the achievable and required data rates, whereas \(\kappa_{a}\) and \(\kappa_{r}\) indicate the achievable and required SINR, respectively. The expression \(P_{b}\) is the bit-error probability and \(P_{d}\) signifies the LED detection probability of the LED that depends on the communication distance and whether or not the user is inside the AOV of the camera.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 6 Analysis of Experimental Results
### Comparison of Performance  
#### 6.1.1 Comparison with Base Model  
Figure 6 shows the accuracy and F1-score of each classification algorithm for the base model using all features and the model applying each feature-selection method. Figure 7 shows the ROC curves for the base model and each feature selection method for each classification algorithm. Table 11 shows the score when using all features for each classification algorithm and the score when using the feature subset, showing good performance for each feature selection method. The multiclass classification result using all features was approximately 70.038%, which is significant, but the performance result increased with the feature selection method. In particular, the backward SFS method \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features** & **Accuracy** & **Precision** & **F1-Score** & **Recall** & **AUC** \\ \hline
5 & 97.106\% & 97.208\% & 96.997\% & 96.799\% & 98.113\% \\
10 & 97.183\% & 97.442\% & 97.065\% & 96.724\% & 98.075\% \\
55 (all) & 97.264\% & 97.609\% & 96.992\% & 96.47\% & 97.951\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multiclass classification performance using 5 or 10 features selected through the frequency of the features selected for each classification algorithm used as the regression model of the stacking algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Features Used** & **Protocol** & \multicolumn{3}{c}{**List of Features**} \\ \hline \multirow{3}{*}{5} & TCP (2) & seq\_raw & window\_size & \\  & IPv4 (2) & id & checksum & \\  & GTP (1) & tied & & \\ \hline \multirow{3}{*}{10} & TCP (4) & seq\_raw & window\_size & flags\_syn & tcp \\  & UDP (1) & checksum & & \\ \cline{1-1}  & IPv4 (3) & id & checksum & len & \\ \cline{1-1}  & GTP (2) & teid & length & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: List of features used by the stacking algorithm. performed best for the tree-based classification algorithms, DT and RF. With KNN, the forward SFS method performed best according to the evaluation metrics, excluding the accuracy. The DT, RF, and KNN algorithms, which were learned by selecting the features for effective training for each classification algorithm, increased the accuracy by 9.1%, 6.34%, and 17.67%, respectively, compared to the base model. However, the accuracy of the stacking algorithm with the feature selection method decreased by 0.083%, whereas the F1-score increased by 0.076% compared with the scores when using all features. Figure 6: Accuracy and F1-scores of each classification algorithm for the base model and the model applying each feature selection method: **(a)** DT; **(b)** RF; **(c)** KNN; **(d)** Stacking.
## 3 Results and Discussion
### Effect of Arm Posture  
Figure 10 shows the effect of arm posture on the transmissibility under nine different combinations of grip force and thrust force. From Figure 10, we can see that the effect of arm posture on the transmissibility is different, the resonance peak of the wrist part under SP1 posture is larger than that of SP2 posture, and the effect of arm posture on the resonance  
Figure 11: Effect of the thrust forces on averaged total vibration transmissibility. (**a**) SP1, g0 N, (**b**) SP1, g30 N, (**c**) SP1, g60 N, (**d**) SP2, g0 N, (**e**) SP2, g30 N, (**f**) SP2, g60 N.  
Figure 12: Effect of grip force on the total averaged vibration transmissibility. (**a**) SP1, p20 N, (**b**) SP1, p50 N, (**c**) SP1, p80 N, (**d**) SP2, p20 N, (**e**) SP2, p50 N, (**f**) SP2, p80 N.  
peak values becomes smaller as the grip force and thrust force increase. Arm posture also affects the resonance frequency of the transmissibility. In the case of the same grip force and thrust force, the resonance frequency of SP1 posture is always greater than that of SP2 posture; the figure also shows that, left of the intersection of the two transmissibility curves, the vibration transmissibility of the wrist part under the SP1 posture is less than or close to that of the SP2 posture. At the right of the intersection point, the vibration transmissibility of the wrist part under SP1 posture is greater than that of SP2 posture, and as the grip force or thrust force increases, this intersection point shifts to the right, that is, the frequency corresponding to the intersection point increases. In the case of small grip or thrust force, double resonance peaks appear in both curves, and with the increase of thrust force and grip force, the double resonance peaks in both curves disappear in succession. The study in the literature [14] shows that only the elbow angle 120\({}^{\circ}\) arm posture shows the double peak, but the SP1 posture does not express the double wave peak, which is different from the study in this paper. The difference is caused by the different grip and thrust force in a different experiment, and another possibility is that individual data averaging may cause double peaks to be inconspicuous or disappear.  
The trend of the vibration transmissibility on the wrist region is similar to the results of the literature [15; 16; 20], but the literature [16] only analyzes the characteristics of the vibration transmission of the bent arm and extended arm postures in the \(z\)-axis direction at a thrust force of 50 N and grip forces of 10 N, 30 N, and 50 N. The literature [15] studies the impedance characteristics in the \(z\)-direction on the SP1 and SP2 postures (80 N grip force and 0 N thrust force). The literature [20] studies the vibration transmission characteristics of the fingers at various points with the open hand placed on the shaker table. The experimental conditions of those studies differ significantly from those of this paper, and the effect of arm posture on the vibration transmission characteristics is not analyzed in the literature [20].
# Low-Cost Digitalization Solution through Scalable IIoT Prototypes
## 4 Case Study
### Verifying the Improvements  
The installed system enabled the collection of the data and quick data presentation to the operators. The benefits to the factory were that they can collect the data during the broaching process. In this way, they can track the number of tubes being cut, and which tubes had an issue with cutting. The detection of a blockage was present before, but it was not recorded in the data. The factory wants to continue digitizing by including environmental sensors that can measure air temperature, pressure, and humidity. One of the reasons for this is that in the last few months, it was noticed that the pressure drops in consecutive cuts during the extension process. The extension process is performed without load, so the expected behavior is that this pressure does not change significantly. However, as seen in Figure 10, the pressure gradually drops from cca 90 bar to the 70 bar. The research team suggested installing an analog temperature sensor on the broaching machine  
Figure 9: Reduction in the pressure deviation during the cut-through modification of the clamping system.  
to ascertain whether the temperature of the machine is correlated with this variation in pressure.  
The operators in the factory wanted to include environmental measurements in the next iteration of the solution. This can be achieved easily by installing an appropriate Click board from MikroElektronika, and creating another thread in the firmware that periodically reads the environmental data.  
The factory production manager could observe other anomalies in pressure data. Those anomalies were heightened pressure during several broaching operations. Finally, it was determined that operators were not adequately lubricating the broaching tool before the broaching operation. This resulted in the production manager creating an operating procedure that removed this problem in the future. An increased level of cooperation between factory workers and a research team was also observed with the additional features requested in the future. Better collaboration also provided the research team with the possibility for conducting further experiments remotely, with the operator's assistance. The ability to remotely update the firmware and control the installed system has provided this option to the research team.
## 2 Related Techniques  
### Surrogate Models  
In the last few decades, many construction methods of surrogate models have been introduced. A surrogate model involves three primary facets, which are data preprocessing, the initiation of the surrogate model, and the process for its ongoing updates and oversight. To refine the accuracy of fitness value forecasts, a variety of model formulation techniques are available. The highlighted methodologies include the polynomial regression model, Gaussian process model, radial basis function model, and several similar approaches. More information on these methodologies is provided in the subsequent discussions.
# Large-Aperture Real-Time Compensated Collimating Wavefront Error Detection Method
## 2 Real-Time Compensated Collimating Wavefront Detection Method
These three errors were rotations around the X-axis, the Y-axis, and the Z-axis, respectively. When the pentaprism had yet to rotate, the light emitted by the laser passed through the right-angle prism and was split into two beams. One beam was directly reflected back to the beam-splitting prism and received by CCD 2. The other beam was reflected by CCD 9 after being reflected twice. When the pentaprism rotated around the axis, the right-angle prism also rotated. Therefore, CCD 2 received a light beam that changed due to the rotation around the Y-axis and CCD 9 received a light beam that changed due to the rotations around the Z- and X-axes. Lastly, CCD 8 received the light beam that carried errors on the collimated wavefront. The coordinates on the object side and on the image side of an ideal pentaprism were correlated. The rotation angles of the pentaprism in the X, Y, and Z directions were unit vectors. With a small-angle approximation, during the process of scanning the entire width of the collimated beam along the linear  
Figure 2: Real-time compensation schematic diagram. 1, laser; 2, 8, 9, CCD cameras; 3, beam-splitting prism; 4, right-angle prism; 5, pentaprism; 6, guide rail; 7, lens. guide rail by the pentaprism, the rotational error on each axis was mapped on CCD 8. The rotational error changed in different directions as the direction of rotation changed. The right-angle prism also rotated as the pentaprism rotated on the linear guide rail. When the right-angle prism rotated, the position of the spot generated by the laser beam on the camera changed. The displacement of the spots on the two cameras in different directions was the displacement component of the spot on CCD 8 in different directions. Let the spot coordinates on CCD 8, CCD 2, and CCD 9 be \((x_{1},y_{1})\), \((x_{2},y_{2})\), and \((x_{3},y_{3})\), respectively. After the scanning movement of the pentaprism was completed and the compensation was determined, the spot coordinates of the collimated beam were as follows:  
\[\left\{ \begin{array}{l}x=x_{1}-x_{2}-x_{3}\\ y=y_{1}-y_{2}-y_{3}\end{array} \right.. \tag{3}\]  
When the pentaprism rotates around the Y-axis, the light spot on CCD 2 changes only in the vertical direction, and, therefore, \(x_{2}\) is zero. If the distance from the compensated spot center to the original position in the \(x\) direction is \(d\) and the distance to the imaging lens is \(f\), the slope of the collimated beam in the \(x\) direction is \(d\)/\(f\). The pentaprism scans the entire collimated beam along the linear guide rail and can capture the slope of each sub-beam of the collimated beam on a certain line in the \(x\) direction (i.e., the first derivative value of the collimated beam in that one-dimensional direction). The wavefront distribution of a collimated beam at that one-dimensional direction can be obtained by taking the integral of the slope values.
## 2 Materials and Methods
### Batch Adsorption Experiments  
#### 2.2.1 Effect of Resin Dosage  
To evaluate the effect of resin dosage on m-cresol removal efficiency, five 15 mL centrifuge tubes were prepared, each containing a different amount of resin (0.05 g, 0.15 g, 0.25 g, 0.5 g, and 1.0 g). Then, 5 mL of m-cresol solution at a high concentration (20 g L\({}^{-1}\)) was added to each tube. The tubes were sealed with parafilm to minimize evaporation and placed in a thermostatic shaker at 60 \({}^{\circ}\)C and 150 rpm for 1440 min to ensure equilibrium adsorption. After the incubation period, the suspensions were centrifuged at 8000 rpm for 10 min and filtered through 0.45 \(\upmu\)m membrane filters to remove particulate resin. The supernatants were then collected and analyzed for residual m-cresol concentration using spectrophotometry.