esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Feature Extraction Identification  
In order to identify the chatter phenomenon accurately in the 3D-EVC process, feature extraction identification, or extracting the features of the chatter symptoms from the various cutting parameters, was proposed. Firstly, three groups of the original signals are decomposed with the EMD and a series of IMFs in stable cutting, transient and chatter state, are obtained. Then, the mean square frequency (MSF) and one step self-correlation coefficient \(\rho_{1}\) of each IMF, which processes the primary energy in the 3D-EVC process, is calculated as the criteria for chatter identification. In this paper, the first six IMFs which contain mainly vibration information are selected, as shown in Figures 9-11, respectively.  
Figure 8: The quality of finished surface (**a**) Stable cutting (**b**) Transient state (**c**) Chatter state.  
Figure 9: The result of EMD in stable cutting (spindle speed is 90 rpm/min, the feed rate is 10 mm/min, and the depth of cut is 5 \(\upmu\)m).  
The values of MSF and \(\rho_{1}\) are calculated to judge the development trend of the vibration signals, and regarded as the index of the criterion for the undesired vibration in the 3D-EVC process, as listed in Table 3. As mentioned in the third section, MSF represents the weighted mean of the square value of the vibration frequency. When the undesired vibration happens suddenly, the frequency-shifting phenomenon will appear and the matter frequency gradually increases. The value of MSF also increased with the cutting state growing fluctuant. In addition, \(\rho_{1}\) also has consanguineous relationship with the variety of the energy in vibration signals. The value of \(\rho_{1}\) will reduce suddenly when the energy concentrates around the chatter frequencies, as a result of the uneven amplitude of vibration signals. In a stable cutting process, the values of MSF and \(\rho_{1}\) are 210 and 0.25, this value represents a relatively stable state in the cutting process. For the transient state, the value of MSF increases to 270, moreover, the value of \(\rho_{1}\) is reduced to 0.57. In the chatter state, the value of MSF increases to 6800, this increased value is explained by the fact that the chatter frequency has turned into a significant component in the 3D-EVC process. However, such as in the above theoretical study, the value of \(\rho_{1}\) is reduced to 0.29, as the correlation between the vibration signals had reduced. Therefore, the numerical  
Figure 11: The result of EMD in chatter state (spindle speed is 90 rpm/min, the feed rate is 10 mm/min, and the depth of cut is 20 \(\upmu\)m).  
Figure 10: The result of EMD in transient state (spindle speed is 90 rpm/min, the feed rate is 10 mm/min, and the depth of cut is 10 \(\upmu\)m).  
variation of MSF and \(\rho_{1}\) can be applied to an effective criterion, which identifies the change of cutting state in the 3D-EVC process.
## 4 Acoustic Testing and Results
### Vibrations of Acoustic Excited Polymer Films  
A more detailed characterisation of the flexible material was the aim of the investigation with isolated flexible structures. The results (only no-flow measurements possible) are shown in Figure 9. It can be assumed that the acoustic excitation of the flexible structure from one side propagates best to the other side of the facility, if the structure is deflected/vibrating. Therefore, an increase in transmission (green curves with diamonds) relates to a vibrating flexible structure. In addition, the material-inherent damping should be most effective, if the structure is vibrating.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Liner** & \(f_{low}\) **(Hz)** & \(f_{high}\) **(Hz)** & **“Dissipation Increase” to Ref.** \\ \hline
1170 A, 0.3 mm & 387 & 1490 & +31\% \\
1195 A, 0.1 mm & 421 & 1398 & +14\% \\
1195 A, 0.5 mm & 576 & 1317 & \(-\)0.3\% \\ Reference & 617 & 1355 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of damping curves (only dissipation data above \(\Delta=0.3\) is considered here).  
Figure 9: Vibration characteristics of flexible walls.  
Comparing the transmission and dissipation curves from above figures with the dissipation curves of the complete liner assembly (see Figure 8), a coincidence of effective acoustic damping and the flexible structure vibration can be observed.
# Flow Analysis of a Novel, Three-Way Cartridge Flow Control Valve
## 3 Methodology
### Discrete Model and Mesh Quality Assessment  
The discrete fluid model is presented in Figure 6. The model has one inlet surface \(P\) and two outlet surfaces, \(A\) and \(B\). The mesh comprises irregular elements, including tetrahedrons in the bulk flow and prisms at the boundaries. Tetrahedral elements are more convenient for modelling complex geometry, including throttling gaps and flow channels, than others, such as hexahedral or polyhedral. Pressure-velocity coupling was achieved using the pressure-based solver with the convergence condition \(10^{-4}\) for both mass and momentum residuals.  
Figure 5: Flow control valve \(gA\) and \(gB\) gap areas against spool position \(x_{1}\).  
The initial mesh was created in accordance with the general recommendations. Due to the complex geometry of the flow channels, forcing sudden shifts in flow direction and step changes in the cross-sectional areas, it was necessary to use the local grid refinement technique and conduct a mesh quality assessment process. Starting from the default mesh version, calculations were made, and the mesh was gradually refined in the vicinity of the flow gaps. The obtained flow rate against the number of mesh nodes and elements is presented in Table 1. The values of parameters and settings were assumed as for a real valve for nominal flow \(Q_{1}=10.0\,\mathrm{d}\mathrm{m}^{3}\,\mathrm{m}\mathrm{i}\mathrm{n}^{-1}\) and the load line pressure \(p_{2}=10.0\,\mathrm{M}\mathrm{p}\mathrm{a}\). The results show that as the mesh was refined, the obtained calculation result approached the laboratory results. However, increasing the number of elements above \(1.2\cdot 10^{6}\) does not affect the flow rate's obtained value. Hence, the \(m_{3}\) grid of \(2.15\cdot 10^{5}\) nodes and \(1.19\cdot 10^{6}\) elements was adopted for further simulations. The difference from laboratory results was less than 4 %, while the computation time was about 210 min.
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 2 Materials and Methods
### Methodology  
#### 2.2.1 Change Detection Algorithm  
The classic change detection _SSM_ index \(I_{SSM}\) is defined as [76]:  
\[I_{SSM}=\frac{SSM_{t}-SSM_{min}}{SSM_{max}-SSM_{min}}=\frac{\sigma_{VV}-\sigma_ {VVmin}}{\sigma_{VVmax}-\sigma_{VVmin}} \tag{2}\]  
where \(SSM_{t}\) is the soil moisture content at time \(t\); \(SSM_{min}\) and \(SSM_{max}\) are the minimum and maximum values of in situ soil moisture, respectively; \(\sigma_{VV}\) is the radar signal at time \(t\); and \(\sigma_{VVmin}\) and \(\sigma_{VVmax}\) are the minimum and maximum values of the radar signal time series, respectively. An index equal to 1 corresponds to the wettest context, and an index equal to 0 corresponds to the driest context.  
To convert this index \(I_{SSM}\) to volumetric soil moisture at time \(t\)\(SSM_{t}\), we introduce [77]:  
\[SSM_{t}=I_{SSM}\times(SSM_{max}-SSM_{min})+SSM_{min} \tag{3}\]
## 3 Simulation Verification
### The Effect of Groove Number on the Multipactor Threshold  
In this section, the SCLPFs loaded with the alumina sheets are employed to check how the groove number affects the multipactor threshold. As shown in Figure 7, we set various groove numbers, \(n\), on the loaded alumina sheets. The length of the grooves is 7.6 mm, the depth is 0.2 mm, and the groove ratio in each cell is 47.5%.  
The multipactor simulation results are shown in Figure 8. From Figure 8, we know that the multipactor threshold values of all the SCLPFs with grooved alumina sheets loaded show a clear improvement compared to the SCLPFs with flat alumina sheets loaded. Moreover, the multipactor threshold increases as the groove number increases. Simply, when the groove number is greater than 10, the raised magnitude of the multipactor  
Figure 5: The groove structures (**a**) with and (**b**) without margins on alumina substrates. Schematic models of grooves (**c**) with and (**d**) without margins on dielectric substrates.  
Figure 6: The simulated evolution of electron number for the SCLPFs with (**a**) grooved alumina loaded and (**b**) grooved PTFE loaded.  
Figure 7: A series of grooves with different sizes on the alumina sheet, with the same groove depth and groove density but different groove numbers.  
threshold declines as the groove number increases, showing a quasi-saturation status. The threshold stays at around 250 W when the groove number reaches 20, in this case, further increasing the groove number no longer improves the multipactor threshold.
# Automatic Fruits Freshness Classification Using CNN and Transfer Learning
## 2 Related Work
The automotive, commercial, and agricultural industries, as well as other worldwide businesses, have all made substantial use of it for object identification and picture categorization. Various image processing and deep learning methods are extensively used to extract and alter supervised or unsupervised features from several layers of non-linear data with the aim to classify objects to understand its patterns [13]. In [14], the developed method employed background subtraction modeling to handle diverse samples. They use a range of recent methods, which include decision trees, the k-NN, the LDA, and the SVM. Simulations showed that SVM performed better than a few techniques. In [15], CNN is used to detect various fruits. This work is performed on a relatively small dataset, and it produced an excellent performance by yielding 98.92% detection accuracy. In [16], researchers compared performances of multi-task learning, domain adaptation, and sample selection bias. They also carried out a detailed review of the method that are used to detect and classify various objects. In [17], a deep learning-based technique is used for the freshness classification of Hog Palm fruit. This work uses four CNN-based models, which were fine-tuned on imageNet Dataset. The Dataset was augmented and used for training and hyper parameter tuning for the purpose of grid search and k-fold cross validation the results were compared in terms of different parameters listed therein. In [18], the proposed method uses VGG16 and the CNN to extract various robust fruits features. In this work, SVM, decision trees, and logistic regression models were also compared. The authors concluded that the SVM the achieved highest 99% classification accuracy than the compared methods. In [19], fruit classification was achieved by using CNN and Softmax, which yielded 97.14% accurate classification. The aforementioned is a brief review of the recent methods that handle fruit freshness detection and classification problems. To achieve accurate results and to facilitate the humans each of these works performed experiments on standard and publicly available datasets. While a few researchers, such as [20], gathered their own dataset, which contains sixteen different species of fruits. The methods briefly described above are a nice addition to the research domain to tackle the fruits freshness problem. We believe that our work is latest addition in this domain, which aims to achieve high fruit freshness classification of different fruits. Our study indicates that machine learning algorithms are helpful to determine the freshness of perishable items, such as fruits as well vegetables. In addition, deep learning models focus to extract features. Ultimately, as we will see in the results section that testing the data on unseen data is a good indicator of the performance of the developed method. Below we detail our developed method.
# Studying Semi-TCP and Its Application in Marine Internet
## 2 Overview of Related Algorithms  
### Semi-TCP Congestion Control Algorithm Based on RTS/CTS  
According to the characteristics of the Marine Internet, the simplest and most efficient way to control congestion in the MAC layer through hop-by-hop congestion control is achieved by using the existing MAC layer protocol. In the IEEE802.11 wireless network, the RTS/CTS for the Distributed Coordination Function (DCF) access control mode has very extensive application. The following is a detailed introduction of the Semi-TCP congestion control algorithm based on RTS/CTS.
## 3 Results
### Acoustic Modeling
#### 3.3.2 Performance Metrics  
The evaluation metrics used to measure the performance of the audible event detection algorithm are shown in Table 5, which summarizes the performance metrics obtained during our tests.  
Accuracy measures the proportion of sound events correctly detected out of the total number of events detected. The algorithm achieved 92.5% accuracy, highlighting its ability to make accurate detections and minimize false positives. The recall represents the proportion of sound events correctly detected out of the total number of actual events in the data. A completeness of 89.3% was reached, indicating that the algorithm effectively detects most of the relevant sound events.  
The F1-score metric combines precision and completeness in a single value. With an F1-score of 0.907, the algorithm balances accurate detection capability and sound event coverage. The ROC curve and the area under the curve (AUC) evaluate the ability of the algorithm to distinguish between sound events and background noise at different  
\begin{table}
\begin{tabular}{c c c} \hline
**Acoustic Characteristic** & **Description** & **Ability to Identify Sound Events** \\ \hline Sound Amplitude & The sound intensity in decibels (dB). & High \\ Frequency Spectrogram & Visual representation of the frequency spectrum of & High \\ Acoustic Energy & Amount of energy contained in an audio signal. & Moderate \\ Duration of the Event & Duration time of a sound event. & Low \\ Dominant Frequency & The frequency at which sound energy is concentrated. & Moderate \\ \hline \end{tabular}
\end{table}
Table 4: Acoustic characteristics for the identification of sound events.  
\begin{table}
\begin{tabular}{c c} \hline
**Metrics** & **Valor** \\ \hline Precision & 92.5\% \\ Exhaustiveness & 89.3\% \\ F1-Score & 0.907 \\ ROC-AUC curve & 0.948 \\ \hline \end{tabular}
\end{table}
Table 5: Sound Event Detection Algorithm Performance Metrics.  
detection thresholds. The ROC-AUC curve obtained a value of 0.948, indicating a solid ability to discriminate between classes. These results conclusively demonstrate that the sound event detection algorithm is highly effective in accurately identifying relevant sound events in security and emergency environments. The combination of high accuracy rates, completeness, and a strong F1-score supports the suitability of our approach for applications in the early detection of risk situations and ensuring public safety in crowded spaces.
# Mapping with Pleiades--End-to-End Workflow
## 5 Results  
This section reports on a multitude of results based upon the previously presented methodologies. In particular, sensor modeling and optimization, digital surface model generation and digital terrain model derivation are addressed. First, the 2D and 3D geo-location accuracy is assessed for all images and stereo sets. This investigation includes a study on different parameters that are adjusted in the sensor modeling step. Second, the quality of the resulting DSMs is assessed. Here an investigation of the epipolar geometry, qualitative and quantitative evaluation of DSM accuracy w.r.t. reference LiDAR data and stereo versus multi-view stereo experiments are given and discussed. Third, DTM extraction is performed and results are compared to LiDAR reference data.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 2 Literature Review  
### Pure Local Buckling  
Our comprehensive literature review encompassed mainly experimental research programs focusing on local buckling, but also other research topics that deal with the local buckling resistance of welded box sections, such as the interaction behavior between local and overall buckling, concrete-refilled box sections or the incorporation of additional stiffeners along the plates of the section. Most of the research focuses on local buckling in thin-walled elements, comparing the results with the values obtained using EN1993-1-5 [4], with the objective of enhancing the calculation process, since the standard is limited in its applicability to materials with a yield strength below 420 MPa. For materials exceeding these values, the uncertainties are often addressed by simply adjusting the partial factors, ignoring the potential advantage of utilizing high-strength materials. The studies that present findings from experimental tests focusing exclusively on local buckling phenomena are as follows:  
* [PERSON] [1] examined a total of 48 experiments using materials with three different yield stresses. The findings indicated that the absolute magnitudes of residual tensile stresses were higher in high-strength plates compared to low-strength plates. However, the residual-stress-to-yield-stress ratio was lower for high-strength materials.
* [PERSON] and [PERSON] [3] studied 34 stub column specimens with yield strengths of 500, 700, and 960 MPa, which were tested to compare the results with the EN1993-1-5 [4] design procedure. This work was supported by finite element analysis (FEA) that considered equivalent imperfections and realistic imperfections. The work presented in [6] introduces a buckling curve proposal that addresses the discrepancy between values obtained from EN1993-1-5 [4] and those derived from experimental tests, specifically when dealing with plate slenderness exceeding 1.55. Given the similarity in methodology between their research and the approach employed in this work, the buckling curve of Schillo will be further considered in the results.
* [PERSON] et al. [5] conducted a study involving 12 specimens made from ultra-high-strength materials, with a yield strength exceeding 1100 MPa. The outcomes from these experiments were used to propose a simplification of factors designed to account for uncertainties specific to this type of material.
* [PERSON] and [PERSON] [7] used 18 specimens, which included cruciform, box, and I-sections, to investigate the effects of additional restraints along one or two edges and the residual stresses resulting from the welding process.
* In the work by [PERSON] et al. [8], four box sections made of 460 MPa steel were tested, and the results were compared with FEA models that accounted for initial imperfections. The conclusion drawn was that the residual-stress-to-yield-strength ratio holds significant importance in the design moment of thin-walled structures.
## 2 Materials and Methods
### Synthesis of graphene quantum dots (GQDs)  
We prepared GO from graphite adopting a modified Hummers' method [28; 29]. The obtained GO was used as the precursor synthesizing GQDs through a three-step, top-down method. Firstly, the proper amount of GO was refluxed with 8 M HNO\({}_{3}\) at 100 \({}^{\circ}\)C for 24 h. During the reaction, the color of solvent darkened. When the reaction was complete, the suspension was centrifuged at 2770 g for 10 min after being cooled to room temperature in order to remove the supernatant. The deposit (labelled as oxidized graphene oxide, O-GO) was collected after being thoroughly washed with deionized (DI) water and centrifuged. Secondly, the obtained O-GO was dispersed in 20 mL DI water, and then heated hydrothermally in a Teflon-lined stainless steel autoclave at 200 \({}^{\circ}\)C for 8 h. After that, the obtained suspension was centrifuged with the force of 2770 g for 10 min; a light brown supernatant and black deposit (labelled as reduced O-GO, R-O-GO) was collected. The supernatant showed green fluorescence under 365 nm UV light irradiation, indicating that GQDs were obtained in this procedure [27]. The supernatant was dialyzed over DI water in a dialysis bag with a retained molecular weight of 3500 Da for two weeks, in order to remove acid and impurity ions; the obtained solution was labelled as GQD1. The black sediment was collected, as numerous graphene nanosheets still existed in it. Therefore, the sediment was further refluxed with 15 M HNO\({}_{3}\) at 100 \({}^{\circ}\)C for 12 h\({}_{3}\) after the reaction, the color of solution changed to transparent brown. The strong yellow fluorescence under 365 nm UV light irradiation suggested that a lot of GQDs were released into the solution. After cooling to room temperature, the solution was dialyzed over DI water to remove acid and impurity ions; as a result, GQD2 were collected.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 1 Introduction
Firstly, an encoder-decoder model was built to learn the feature representation of user behavior sequences via multi-source behavior audit logs. The encoder involves embedding layers and recurrent neural network (RNN) layers, which transform the multi-source user behavior sequences into fixed-length feature vectors. In addition, a multi-output decoder was constructed to predict the entities of the next user behavior event following a user behavior event sequence. Since estimating the latent generating function of the data has better predictive performance than estimating the data themselves [11], instead of directly predicting the entities of the behavior event itself, we leverage the decoder to estimate the latent generating function of the entities of the behavior event. Subsequently, the deviation between the predicted parameter values of the generating function and the actual parameter values of the generating function is used as the loss to adjust the encoder-decoder network parameters. The proposed encoder-decoder model can obtain the initial user behavior features by capturing temporal relationships between user behavior events and between entities of user behavior events. Secondly, a deep clustering model of user behavior sequences is proposed to obtain clustering centroids and optimize the user behavior feature representation by using deep neural networks. In this model, we initialize the cluster centers through the application of k-means clustering to the initial feature vectors of user behavior event sequences obtained from the encoder. The objective function for deep clustering is defined based on intra-cluster compactness and inter-cluster separation. Additionally, the stochastic gradient descent algorithm is employed to update both the behavior sequence feature vectors and cluster centers. This iterative process enables the automatic adjustment of the user behavior feature representation suitable for the clustering task, achieving the optimal user behavior features and clustering results simultaneously. At last, based on the user behavior features and clustering results, sparse clusters and outliers that deviate from the cluster centers can be identified and detected as insider threats. The main contributions of this paper are as follows. * A new end-to-end insider threat detection method is proposed for automatically learning user behavior features from multi-source audit logs by using a deep neural network. * A new deep clustering model for user behavior sequences is proposed to optimize the user behavior features for the clustering task and improve the detection of insider threats. * Using the CERT benchmark datasets from Carnegie Mellon University, the proposed end-to-end insider threat detection method was evaluated in terms of frequently used metrics such as Recall, ROC curve, and area under the ROC curve (AUC).