esa-sceva/satcom-chunk-collection huggingface dataset:
# A Prediction Model for Additive Manufacturing of Inconel 718 Superalloy
The studies reviewed above focused on statistical models to find the exact correlation between the LPBF process parameters and the quality of parts. While there are many archived data in the literature, there is still a gap to be filled for finding a fast and reliable model to provide a strong relationship between LPBF parameters and the properties of fabricated products. Artificial neural network (ANN) is a rapid and reliable method that is broadly used for solving complex problems using various algorithm architectures. Therefore, ANN modeling can be applied as a prediction tool for finding a nonlinear relationship between input and output results for various manufacturing processes and, consequently, discovering the optimal processing parameters [50]. Some studies took advantage of ANN modeling for predicting the optimal parameters in AM processes. For example, [PERSON] et al. [51] employed an ANN model to study the influence of laser power on microstructural properties on the LPBF-fabricated parts. For this purpose, they utilized 13,200 images of the melting pool area in order to recognize crack formation and the density of the fabricated parts. [PERSON] et al. [52] also applied the ANN model to predict the surface roughness and investigate the effect of process parameters on the LPBF Ti-6 Al-4V alloy. The results showed that heat treatment and laser power were two main parameters with a high influence on surface quality. In another study, [PERSON] et al. [53] applied a combination of the ANN technique and fuzzy logic, which is called a neuro-fuzzy inference system (ANFIS), to predict the fatigue life of LPBF-based fabricated stainless steel 316L samples. The model was able to produce an accurate prediction of the fracture mechanism and fatigue life of the samples based on various process and post-processing parameters. [PERSON] et al. [54; 55] showed that ANN modeling can be used to optimize the process parameters in LPBF fabricating NiTi alloys. Laser power, hatch spacing, and scanning speed were considered as the principal parameters in this process and the prediction model successfully found the optimum set of parameters for the manufacturing process. This work aims to show the potential of the ANN model for predicting the optimal process parameters in a LPBF-processed IN718 alloy and, in particular, investigates the impact of input parameters, including laser power, hatching space, and scanning space, on sample dimension and surface quality of the fabricated parts.
# Comparative Analysis of CFRP and Steel Vibration Characteristics of Machine Tools Components
## 4 Conclusions  
In this study, a CFRP chuck adapter was designed and its natural frequency, vibration velocity, and stability were examined. The vibration velocity reduction during operation of the CFRP chuck adapter was compared with that of the other two types. The following conclusions were drawn.  
1. A composite material with excellent mechanical performance was manufactured by stacking Onyx materials and carbon fibers using a 3D printer. Appropriate vibration measurement equipment was constructed, and experimental conditions suitable for measurement were selected to measure the vibration velocity and displacement.
2. The CFRP chuck adapter had the same vibration shape as a steel chuck adapter and a CFRP with steel chuck adapter, and the natural frequencies of CFRP, steel, and CFRP with steel chuck adapters were presented with an error of 0.4%. The vibration in the stable range was measured for all three types of chuck adapters in the vibration stability test.
3. The maximum vibration velocity reduction of the CFRP chuck adapter was 64%, which proved that the vibration velocity of the CFRP chuck adapter was reduced more than the steel chuck adapter. In addition, the maximum displacement reduction of the CFRP chuck adapter was 31.1%, which proved that the displacement of the CFRP chuck adapter was reduced more than the steel chuck adapter.  
In this study, a vibration comparison analysis was performed with steel and CFRP chuck adapters. The results of this study are not limited to chuck adapters and can be applied to other parts.  
Conceptualization, M.-J.K. and D.-H.K.; methodology, M.-J.K.; software, J.-H.L.; validation, M.-J.K.; formal analysis, M.-J.K.; investigation, M.-J.K. and D.-H.K.; resources, M.-J.K.; data curation, J.-H.L.; writing--original draft preparation, M.-J.K. and D.-H.K.; visualization, M.-J.K.; writing--review and editing, D.-H.K.; supervision, D.-H.K. and C.-M.L.; project administration, S.-H.K. and C.-M.L.; funding acquisition, S.-H.K. and C.-M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A5A8083201).  
Not applicable.  
Figure 10: Performance improvement rate of the CFRP chuck adapter compared to steel in the fastening part.
# Horizontal Loading Performance of Offshore Wind Turbine Pile Foundation Based on DPP-BOTDA
## 2 Field Experiment
### Experimental System  
As shown in Figure 5, the entire experimental device was divided into three sections: test pile, horizontal load test reaction frames, and Y14 platform. The test pile had been driven into the soil according to the standard construction process. The Y14 platform was a well-built wind turbine platform. The experimental platform was fixed on the Y14 platform to ensure safety during the test.  
On the test platform, there was a power supply, a loading device, and the DPP-BOTDA test system. The loading device was mainly one 100 t jack and a movable loading beam. Behind the jack, there was a fixed beam to provide counterforce support. The loading principle was that the jack directly applied the force to the movable loading beam, and the beam transmitted the force to the test pile. The direction of force was the connection direction of FUT1 and FUT3. The force point was located on the FUT1 side and the elevation was +9.2 m. The pulses used in the DPP-BOTDA test system were 40 ns and 38 ns, resulting in a 20 cm spatial resolution. The sampling rate of data points was 5 cm. During the experiment, the load was divided into 15 stages and the load increment was 60 kN, as shown in Table 1. For each load, the load stability time was 30 min to achieve complete stability.  
Figure 4: The detailed arrangement of optical fiber. (**a**) Arrangement and naming of distributed optical fiber sensors (DOFS) in the test pile; (**b**) adhering the DOFS with epoxy resin; (**c**) installation of angle steel for protective fiber.  
Figure 5: Entire experimental device.  
At each loading, the data were repeatedly collected three times, and the average value was taken as the data value of the current load.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Machine Learning Methodology
#### 2.3.2 Algorithm Selection and Initial Screening
Kernel functions--including linear, polynomial, and RBF--transform input features into higher-dimensional spaces where linear separation becomes feasible. KNN is a nonparametric approach that determines the \(k\) nearest data instances to a test point using distance metrics, typically Euclidean distance [71]. For regression tasks, it predicts target values by averaging neighbors' values, making it conceptually simple yet effective for many applications. Gaussian Process Regression offers a Bayesian, nonparametric technique that produces a distribution over potential functions fitting the data [72]. It defines a prior over functions using a Gaussian process, then updates with observed data to create the posterior distribution. The covariance function (kernel) characterizes relationships between data points, while the mean function is often set to zero. AdaBoost combines multiple weak classifiers to form a strong classifier [73]. It iteratively focuses on instances misclassified by previous classifiers by adjusting their weights. This adaptive approach emphasizes challenging cases, continuing until reaching a specified number of weak classifiers or achieving an acceptable error rate. This diverse algorithm portfolio was designed to thoroughly explore the modeling capabilities across fundamentally different mathematical frameworks [74], ensuring that the most appropriate approach for this specific prediction task could be identified. Based on the initial screening results, the three best-performing algorithms were selected for comprehensive hyperparameter optimization and rigorous evaluation through nested cross-validation, as detailed in subsequent sections.
# Maximizing the Capacity of Edge Networks with Multicasting
## 3 System Architecture  
### Network Model  
This paper mainly studies the effect of multicasting on the capacity of edge caching networks. \(n\) SBSs are deployed in the network with a Poisson distribution intensity of \(\lambda_{e}\), and these nodes function as relays for multicast transmission. Each node has \(\lambda_{u}\pi R^{2}\) users in the coverage range, where \(\lambda_{u}\) is the user distribution density and \(R\) denotes the communication range of edge servers. In addition, the entire network contains \(m\) independent content, and each SBS stores \(S\). As shown in Figure 1, when multiple users covered by several SBSs request the same content simultaneously, the SBS caching the content is selected to transmit data to adjacent SBSs using multicasting, and distant SBSs obtain the requested content via the relay (red lines in the figure); in addition, if none of SBSs cache the content, the first SBS receiving the request forwards users' demands to the BS. The BS then multicasts the content to the requesting users (black lines in the figure).
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 4 Fault Prediction Results and Discussion
### Fault Prediction Results and Discussion of Vibration Signals
#### 4.1.3 The Results of the Combined Forecasting Model  
In this paper, the prediction results of IMGM (1,9) and QGA_LSSVM are used as the inputs of QGA_LSSVM combiner and the actual kurtosis values of the vibration signal are used as the output. The comparison of predicted results between the single forecasting models and combined model are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \(\gamma\) & \(\sigma^{2}\) \\ \hline QGA\_LSSVM & 100 & 99.9658 \\ GA\_LSSVM & 55.8399 & 58.4184 \\ Grid Search\_LSSVM & 89.5265 & 28.8621 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The optimized parameters for the vibration signal.  
Figure 5: Comparison of the combined forecasting model and the single forecasting models for vibration signal. (**a**) The prediction result from the 46 th points to the 60 th points; (**b**) the absolute value of relative error.  
In Figure 5a, the fluctuation range of IMGM (1,9) around the actual value is the largest. The prediction results of QGA_LSSVM and the combined forecasting model are closer to the actual value. Figure 5b shows that the absolute value of relative error of IMGM (1,9) is significantly large. From the 46 th predicted points to the 51 th predicted points, the absolute value of relative error of the combined forecasting model is less than that of QGA_LSSVM. After the 53 th predicted point, they have similar absolute value of relative error. Therefore, the combined forecasting model has a smaller absolute value of relative error than the two single forecasting models. It can also be shown from Table 9 that, for the training test, the forecasting evaluation indexes of the combined forecasting model are near to those of QGA_LSSVM and better than those of IMGM (1,9); but for the testing set, the indexes of the combined forecasting model are the best in Table 9. Thus, compared with the single forecasting models, the combined forecasting model improves the prediction accuracy and reduces the running time for the vibration signal.  
For vibration signal, the optimal parameters of the combiner are \([\gamma,\sigma^{2}]=[24.5174,\ 9.4170]\).
## 4 Machine Learning Techniques for Forecasting
### LSTM Neural Networks  
As a special recurrent neural network (RNN), long short-term memory (LSTM) is suitable for processing and predicting important events with relatively long intervals and delays in the time series [56; 57]. LSTM can alleviate the phenomenon of gradient disappearance in the structure of RNN [58]. As the result of a powerful representation ability, LSTM utilizes a complex nonlinear unit to construct larger deep neural networks.  
LSTM controls long and short-term memory through gates and cell states [10]. As shown in Figure 8, the neurons in LSTM include input gate \(i\), forget gate \(f\), cell state \(c\), output gate \(y\). Among them, three gates are calculated as follows:  
\[i_{t}=\sigma(\mathbf{W}_{i}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{i}) \tag{18}\]  
\[f_{t}=\sigma(\mathbf{W}_{f}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{f}) \tag{19}\]  
\[y_{t}=\sigma(\mathbf{W}_{y}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{y}) \tag{20}\]  
where \(\mathbf{b}_{i}\), \(\mathbf{b}_{f}\), \(\mathbf{b}_{y}\) are bias terms, \(\mathbf{W}_{i}\), \(\mathbf{W}_{f}\), \(\mathbf{W}_{y}\) are respectively the weight matrices of three gates, and \(\sigma\) is the sigmoid activation function. In Equation (18), \(\mathbf{W}_{i}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]\) indicates \(\mathbf{W}_{i1}\mathbf{h}_{t-1}+\mathbf{W}_{i2}\mathbf{x}_{t}\) where \(\mathbf{W}_{i}=(\mathbf{W}_{i1},\mathbf{W}_{i2})\). At time \(t\), the update formula of cell state is:  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\widetilde{c}_{t} \tag{21}\]  
where \(\odot\) is the Hadamard product and \(\widetilde{c}_{t}\) is the candidate cell state. Let \(\mathbf{b}_{c}\) and \(\mathbf{W}\) be respectively the bias vector and the weight matrix of the candidate cell gate. Then \(\widetilde{c}_{t}\) is computed as:  
\[\widetilde{c}_{t}=\varphi(\mathbf{W}_{c}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{c}) \tag{22}\]  
where the activation function \(\varphi\) is usually chosen as the hyperbolic tangent. At last, the hidden vector is updated:  
\[\mathbf{h}_{t}=\mathbf{y}_{t}\odot\varphi(\mathbf{c}_{t}) \tag{23}\]  
For the input information \(\mathbf{x}_{t}\), Equation (22) calculates the candidate cell state \(\widetilde{c}_{t}\) at time \(t\) by \(\mathbf{h}_{t-1}\) and \(\mathbf{x}_{t}\). Equation (21) combines the input gate and the forgetting gate to update the cell state \(c_{t}\) at time \(t\). Equation (23) calculates the hidden layer information at time \(t\). Through the combination with gate control units, the LSTM network achieves the purpose of memorizing long- and short-term information of time series data by continuously updating the cell state at each moment.  
Figure 8: Diagram of LSTM neural networks.
# The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details
###### Abstract  
Computer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image data, different problems arise in relation to the building of models suitable for various solutions. This paper investigates the influence of parameters used in the training process involved in detecting similar kinds of objects, i.e., the hyperparameters of the algorithm and the training parameters. This experimental investigation focuses on the widely used YOLOv5 algorithm and analyses the performance of different models of YOLOv5 (n, s, m, l, x). In the research, the newly collected construction details (22 categories) dataset is used. Experiments are performed using pre-trained models of the YOLOv5. A total of 185 YOLOv5 models are trained and evaluated. All models are tested on 3300 images photographed on three different backgrounds: mixed, neutral, and white. Additionally, the best-obtained models are evaluated using 150 new images, each of which has several dozen construction details and is photographed against different backgrounds. The deep analysis of different YOLOv5 models and the hyperparameters shows the influence of various parameters when analysing the object detection of similar objects. The best model was obtained when the YOLOv5l was used and the parameters are as follows: coloured images, image size--320; batch size--32; epoch number--300; layers freeze option--10; data augmentation--on; learning rate--0.001; momentum--0.95; and weight decay--0.0007. These results may be useful for various tasks in which small and similar objects are analysed.  
YOLOv5; object detection; construction details; similar objects; hyperparameters Article
## 8 A Faster Parametric Assignment Algorithm
### A Brief Review of the Gassner-Klinz Algorithm and Its Adaptation
Solve the initial standard assignment problem for the known matrix \(K\). Denote by \(I\) the obtained optimal (minimum-cost) assignment. 1.2. Calculate the average profit \(\lambda_{0}\) achieved with the obtained assignment \(I\):  
\(\lambda_{0}=\sum_{i(j,0)\in I}e_{ij}/\sum_{i(j,0)\in I}k_{ij}\). //This step is different from the corresponding initialization step in the original GKA. 1.3. Find the minimum-cost assignment \(A=A^{\prime}(\lambda_{0})\) in the graph \(G\) for \(\lambda=\lambda_{0}\). 1.4. Construct the residual graph \(N(A)\). **Step 2. Finding the nearest critical point**. //In this step, we take into account that in the objective function, the term \(b^{\prime}_{ij}=-k_{ij}\) is of the opposite sign compared to the Gassner-Klinz assignment problem. 2.1. Apply the parametric shortest path algorithm to find the nearest critical point \(\lambda_{c}\) and the critical cycle \(C\) for which \(w(C,\lambda_{c})=0\). 2.2. Calculate the cost of the assignment \(A\) at the point \(\lambda=\lambda_{c}\):  
\(w(A,\lambda_{c})=\lambda_{c}\sum_{i(j,0)\in A}k_{ij}-\sum_{i(j,0)\in A}e_{ij}\). //When we increase the values of parameter \(\lambda\) from one critical point to the next, we stop and go to Step 3.1 at the moment when we find (for the first time) a current assignment \(A\) such that \(w(A,\lambda_{c})\geq 0\). According to Proposition 4 in Section 4, this assignment \(A\) maximizes the average fleet profit per vehicle. 2.3. If \(w(A,\lambda_{c})\geq 0\), go to Step 3. 2.4. Create the new minimum-cost assignment \(A^{\prime}=(A\setminus C)\cup(C^{\prime}\setminus A)\). //\(C^{\prime}\) is a subset of arcs obtained from \(C\) by replacing all the backward arcs with the corresponding forward arcs. 2.5. Convert graph \(N(A)\) to graph \(N(A^{\prime})\); set \(N(A):=N(A^{\prime})\) and \(A:=A^{\prime}\). 2.6. Return to Step 2.1. **Step 3. Solution of the vehicle scheduling problem**  
//This step is absent in the GKA because the max-benefit UAV fleet problem was not a subject studied by [PERSON] and [PERSON]. //Let us denote by \(A^{*}\) the optimal assignment, which maximizes the average fleet profit per vehicle; \(\sum_{(i)\in A^{*}}k_{ij}\) expresses the corresponding optimal number of the required vehicles. 3.1. Set the assignment that maximizes the average fleet profit per vehicle: \(A^{*}=A\). Calculate the maximal average profit \(P^{*}=A^{*}=\sum_{(i,j)\in A^{*}}e_{ij}/\sum_{i,j\in A^{*}}k_{ij}\). 3.2. Calculate the optimal number of vehicles, which is \(\sum_{(i)\in A^{*}}k_{ij}\) to meet the given vehicle schedule. Evidently, the adapted version of GKA has the same complexity as the original GKA, and hence, the original max-benefit UAV fleet problem is solved in \(O(n^{3})\) time.
## 2 Data and Methods
### Methodology
#### 2.2.2 Stochastic Process  
This article proposes to enhance the resolution, as well as the petrophysical absolute value prediction, of the traditional EEI inversion by applying a stochastic process to the traditional EEI inversion result. The process assumes that the traditional EEI inversion result is still contained by seismic wavelet and therefore has an insufficient frequency band (low in resolution). The assumption implies that the EEI inversion result is assumed to be a convolution between EEI reflectivity and EEI wavelet. The stochastic process aims to modify the EEI reflectivity such that it has frequency as a petrophysical log frequency and, at the same time, when convolving with EEI wavelet, has a similar signature to the inverted traditional EEI. To achieve the goal, a Bayesian Monte Carlo simulation is employed to simulate the EEI reflectivity and select the best model as the inversion result. The simulation is conducted in the probability density function domain and formulated as:  
\[\text{P}\big{(}\,\text{EEI}_{\text{Pr}}\big{|}\,\text{EEI}\big{)}=\frac{\text {P}\big{(}\,\text{EEI}\,\text{EEI}_{\text{Pr}}\big{)}\times\text{P}\big{(}\, \text{EEI}_{\text{Pr}}\big{)}}{\text{P}\big{(}\,\text{EEI}\big{)}}, \tag{2}\]  
where \(\text{P}\big{(}\,\text{EEI}_{\text{Pr}}\big{|}\,\text{EEI}\big{)}\) is the posterior probability or the result of simulation which informs the best solution, \(\text{P}\big{(}\,\text{EEI}\,\text{EEI}_{\text{Pr}}\big{)}\) is the likelihood between prior and observed EEI \(\text{P}\big{(}\,\text{EEI}_{\text{Pr}}\big{)}\) is the probability of the prior model \(\text{EEI}_{\text{Pr}}\), and \(\text{P}\big{(}\,\text{EEI}\big{)}\) is the probability of the observed EEI.  
The EEI prior model is populated or perturbed by randomly modifying the two consecutive reflectors space (bed thickness in the earth layer model) of the observed EEI. The perturbation is constrained by seismic thickness criteria, i.e., maximum amplitude [17], tuning frequency [18], maximum amplitude weighted integrated energy spectra [19] and low and high-frequency models derived from petrophysical log data. A random noise of up to 20% can be added to the EEI prior model. The populated EEI prior model is then iteratively selected to be the EEI posterior model (the result of inversion) following the Markov Chain rule.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 2 Proposed Approach
### Level 1--Similarity-Based Correlation
#### 2.3.2 Event Clustering
The data mined by these rules are put into a correlation table, \(CorrTable\), which is an in-memory hash table of candidate clusters for each aggregate event that satisfies the minimum support, sup. This correlation table is passed onto the clustering algorithm which allocates the data points by picking the best-fitting cluster of the candidate clusters for each event. Event clustering groups similar events into cluster \(C\) such that each cluster has some contextual meaning using some clustering algorithm. Typically, a measure of similarity is defined which is used to decide whether two events should be put in the same cluster. The idea is that events within the same cluster should generally be given the same analytic interpretation or detection treatment. Different criteria can be used to contextualize the clusters for network events, such as clusters of source addresses, destination addresses, applications, and others. The output from this stage is a set of clusters \(C=[C_{1},C_{2},\ldots,C_{n}]\) such that each cluster \(C_{i}\in C\) has a unique semantic relevance. In an implementation of this model, the clustering is performed in a custom-developed clustering algorithm, which is shown by the pseudocode in Figure 4 below. The algorithm takes the batched-up event streams as input. Hash tables for the correlation and cluster tables are initialized on line 3. Lines 5-8 mine data from the record based on the current rule index. The hash for the current rule data is created in line 9 and used to perform a lookup on line 10. From lines 10 to 18, a new meteaveent is created if the lookup returns no match. The meteaveent is then inserted into the correlation table on line 18. If there is a match, lines 19-24 update the meteaveent. The cluster table is a subset of the correlation table for all meteaveents whose count satisfies the support. This is indicated in lines 24-28. The output of the function is the cluster table on line 29.