esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Method
### Configuration of the Testing Environment  
The solution for performance monitoring uses Prometheus and Grafana. As Prometheus is not installed in a standard Kubernetes cluster, as it is on Openshift, the configuration has to be realized by the tester. Grafana is pre-configured in OpenShift and has a series of default dashboards that are prepared for visualizing several common metrics. For these tests, we work with the default Kubernetes service provided by IBM Cloud, which lacks the default integration with Prometheus and Grafana. As a result, we need to configure the testing environment from scratch alongside the Kube-state metrics, to have access to a broader pool of metrics [31; 32; 33]. The machine used to run the tests is a free-tier Kubernetes cluster that consists of a single worker node that has two cores and 3.84 GB of memory. As a software environment to be deployed on the cluster we chose WebGME, with a MongoDB database. WebGME is a web-based variant of Generic Modeling Environment (GME)--a modeling and metamodeling environment to create domain-specific languages and models [34]. WebGME is an improvement to GME that offers integrations with API's cloud, and collaboration facilities [35]. GME is a tool that has the features to enrich the modeling environment with new modeling paradigms, including the possibility to generate specific editors and add model interpreters. It is object-oriented, supporting integration with programming languages such as Java and C++. One of its main limitations is the lack of collaboration, a problem that is solved by WebGME, according to [PERSON] [36]. The deployment in the cloud of such an environment configured for the concerns of a group of users can offer services for metamodeling, modeling, and running model interpreters, e.g., executing simulation models. For our tests, we deployed WebGME version 2.42.0. The graphs generated by Grafana using the Prometheus data source can be saved in dashboards and can be monitored to find some issues, patterns in the functioning of an environment, or for root cause analysis. The created dashboards can be exported and reused in another environment. In these tests, the goal is to monitor namespace, memory, and CPU consumption for the modeling environment and the available resources cluster-wise. All this information can be seen in one place, on a custom Grafana Dashboard divided by rows. Every component of the dashboard is obtained by querying the Prometheus data source. Additionally, the metrics can be displayed on a very large period that may start from the last 7 days and end with the last 5 min. Table 2 depicts the Prometheus queries for the namespace monitoring.
## 4 Experiments
### Damping  
In this section, the damping property of the overall system is analyzed, which becomes relevant for modeling. For this purpose, the continuum is deflected to such an extent that the steady-state amplitude response is already greater than one. After reaching the steady-state oscillation, the driving oscillation is stopped at the zero position of the deflection and the free oscillation of the continuum is recorded. This procedure can be seen in the left graph of Figure 9. A parasitic effect, which can be seen in the small oscillation of the stopped driven oscillation, can also be observed in the left graph. However, this is not believed to have a significant effect on the following results.  
Upon this decaying oscillation, the positive and negative peaks are detected and the enveloping exponential decay curve \(A\exp(-\lambda t)\) is derived by utilizing a non-linear least squares method [28], see the right graph of Figure 9. From this, the decay coefficient \(\lambda_{\text{fit}}\) is used with the averaged angular frequency \(\langle\omega\rangle\) to obtain the Lehr's damping of the system:  
\[D=\frac{\lambda_{\text{fit}}}{\langle\omega\rangle}\,. \tag{7}\]  
The reciprocal value of this attenuation is proportional to the quality factor \(Q\) of the system:  
\[Q\propto\frac{1}{D}\,, \tag{8}\]  
Figure 8: Generalised visualization of the amplitude response over an interval of different pretensions \(p_{i}\in[0,10]\) mm. The red line in the right graph indicates the corresponding maximum amplitude responses.  
Figure 9: Damped decaying oscillation of the system. The left graph describes the excitation and the resulting decaying oscillation. The right graph illustrates the application of the non-linear least squares method and states the corresponding derived quantities. No pretensioning was applied here.  
which can be used as a central quantity to describe the energy loss of the oscillatory system [27]. Knowing the total inertia \(I\), which has already been specified in Section 2, the model-specific quantities \(d\), \(k\) from Equation (2) can be determined as follows:  
\[d =2\lambda I\,, \tag{9}\] \[k =\omega^{2}I\,. \tag{10}\]  
In Figure 10, the measured damping behavior is shown over different pretensions while a comparison is given with the prediction of the linear second-order model, see Equation (2). With increasing pretension of the tendons, an increase in the decay coefficient \(\lambda_{\text{fit}}\) can be observed. This is accompanied by a simultaneously increasing mean angular frequency \(\langle\omega\rangle\). The quotient of these quantities, [PERSON]'s damping \(D\), remains approximately constant over the studied range. Therefore, the damping of the system \(d\) is less sensitive towards a change of the compression state \(p_{i}\) of the continuum. At the same time, this means that the quality factor \(Q\), and thus, the resonance sharpness is morphologically similar. This finding merits further investigation.
## 3 Results
### Quality Assessment  
The risk of bias assessment for the included studies is summarized in Figure 2, using the Cochrane Risk of Bias tool. This assessment incorporated multiple domains, including random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessors, incomplete outcome data, selective reporting, and other biases. Funding sources and conflict of interest disclosures were also evaluated to determine the potential for other biases.  
Among the eight included studies:  
* Funding Sources:
* One study did not explicitly report its funding source.
* Three studies were funded by public or governmental organizations, including the European Union's Horizon program, the German Innovation Fund (G-BA), and the U.S. National Institutes of Health (NIH).
* Commercial entities funded three studies, including Tonal Systems Inc. (San Francisco, CA, USA), Kaia Health Software GmbH (80331 Munchen, Germany), and Snapcare Technologies Pvt. Ltd. (Delhi, India).
* Overall Risk of Bias:
* Most studies were categorized as low risk of bias overall.
* The studies by [PERSON] et al. and [PERSON] et al. were rated as high risk of bias.
* Specific Domains of Bias:
* Blinding of Participants and Personnel: All studies were rated at high risk of bias due to the nature of the interventions, as participants were necessarily aware of their treatment modality.
* Blinding of Investigators: Six studies [29; 30; 31; 32; 33; 34] were at high risk of bias in this domain.
* Allocation Concealment: Three studies [30; 33; 34] were rated as high risk of bias.
* Random Sequence Generation: The study by [PERSON] et al. [33] was at high risk of bias, as participants were assigned using an alternating method rather than true randomization.  
This quality assessment highlights key strengths and limitations of the included studies, ensuring transparency in evaluating the robustness of their findings. While most studies demonstrated methodological rigor, certain biases--particularly related to blinding and allocation concealment--require careful consideration when interpreting results.
# Automatic Fruits Freshness Classification Using CNN and Transfer Learning
## 4 Simulation Results
### Limitations  
Although our proposed method achieves promising results in the aforementioned datasets. However, for the research community below, we briefly discuss the limitations faced by our developed algorithm.  
* Since our method uses the AlexNet architecture, during the preprocessing stage our method consumes a bit more time.
* Since the AlexNet model was pre-trained on ImageNet dataset, which consists of 1000 object categories. Therefore, the model's performance and features are biased towards the visual patterns and objects that are present in the dataset. If the targeted class is significantly different from ImageNet dataset, then the pretrained feature may not generalize well, which ultimately leads to the reduced performance.
* The robustness of transfer learning depends on the similarity between the pre-training dataset and the target dataset. The AlexNet was trained on a large-scale dataset with millions of labeled images. If the target dataset is small or significantly different in terms of image content, style, or domain, then the pre-trained features may not capture the relevant patterns, which will also result in reduced classification performance.
* We observe that fruit classification remains a difficult task for a machine learning algorithm due to several reasons. For example, fruits shape, colors, and texture similarity among various fruits species. Moreover, high variations in a single fruit class that is dependent on fruit maturity phase, and the actual condition when some fruit is presented such as fruits placed inside plastic bags, sliced, or unpicked from farm. In such scenarios, our developed method might struggle to accurately classify the fruits freshness.
# Multi-Band Array Antenna Sharing a Common Aperture with Heterogeneous Array Elements
## 2 Unit-Cell Design for the Multi-Band Array Antenna
The measured reflection co-efficient of the S-band antenna is less than \(-10\) dB from 2.88 GHz to 3.77 GHz, where the bandwidth of 890 MHz is in good accordance with the simulated results, as depicted in Figure 4a. The averaged gains from 2.8 GHz to 3.8 GHz are 5.4 dBi by simulation and 5.2 dBi by measurement. X-band antennas are also measured, and the results show that all X-band antennas have a bandwidth over 1.1 GHz with a reflection less than \(-10\) dB at the target frequency of 9.5 GHz. The measured reflection co-efficient of the central X-band antenna  
Figure 1: The proposed array antenna: (**a**) isometric view; (**b**) printed dipole antenna for S-band; (**c**) E-shaped patch antenna for X-band. is less than \(-10\) dB from 8.97 GHz to 10.48 GHz, where the bandwidth is 1.51 GHz (a fractional bandwidth of 15.5%), as shown in Figure 4b. The averaged gains from 8.5 GHz to 11 GHz are 5.8 dBi by simulation and 5.2 dBi by measurement. Figure 2: Photographs of the fabricated multi-band array antenna: (**a**) front view of the S-band antenna; (**b**) back view of the S-band antenna; (**c**) top view of the X-band antenna; (**d**) isometric view of the assembled unit-cell; (**e**) bottom view of the assembled unit-cell. Figure 5 shows the two-dimensional (2D) radiation patterns of the design at 3 GHz and at 9.5 GHz. Figure 5a illustrates the S-band beam pattern, where a half-power beam width (HPBW) for measurement is 82.6\({}^{\circ}\). Meanwhile, Figure 5b presents the central X-band beam pattern, where a half-power beam width (HPBW) for measurement is 61.4\({}^{\circ}\). Figure 4: The bore-sight gains and reflection co-efficients of the proposed antenna: (**a**) S-band; (**b**) central X-band. Figure 5: 2D radiation patterns: (**a**) at 3.25 GHz; (**b**) at 9.5 GHz. Figure 3: Photographs of the test setup in an anechoic chamber for far-field measurements.
## 5 Numerical Example
### Example 1--L-Shaped Beam Problem  
In this section, an L-shaped structure is investigated, as shown in Figure 3, where the red area represents the restricted displacements. To simulate the L-shaped structure, a rectangular design domain is defined, using a fine mesh of 40 \(\times\) 40 \(\times\) 20 hexahedral finite elements with dimensions equal to 1 mm, producing a total of 32,000 finite elements, and certain elements in the domain are forced to be in the lower limits of the density values (\(\rho_{i}=10^{-9}\)) for SERA and SIMP, while SESO and ESO have their limit values of the elements (\(x_{i}=10^{-9}\)). The minimum radius is equal to 1.2 mm. Table 3 shows the optimization parameters, the number of iterations, the objective function, and the computational cost of each method for the problem of an L-shaped structure. Figure 4 shows the cross-section, quota \(z=10\) mm, of the optimal settings of the L-shaped structures presented in Figure 4. It was observed that the place where the force is applied has relatively higher values of compliance, and the methods SESO, ESO, and SIMP keep a larger amount of material close to this region. In contrast, the SERA method can remove more material. This can be explained by the fact that the method has a heuristic of inserting virtual elements in regions where compliance is high. Additionally, on the internal surface of Figure 4, at the edge where the structure forms an angle of 90\({}^{\circ}\), compliance has high values, also requiring more material in this corner region. Figure 5d shows that the SIMP model needs more material in the corner region, where the structure has bending stress, unlike the discrete models that managed to keep the structure's stiffness by removing material in this region, supporting these stresses with two bars as can be seen in Figure 5a-c.  
Figure 3: Design domain with essential and natural boundary conditions (measures in mm).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Number**} & \multirow{2}{*}{**Objective**} & \multirow{2}{*}{**Computational**} \\  & & **of Iterations** & & **Function** & **Cost (Minutes)** \\ \hline SESO & \(RR=ER=0.02\) & 100 & 65.74 & 49.72 \\ \hline ESO & \(RR=ER=0.02\) & 100 & 65.88 & 49.57 \\ \hline SERA & \(SR=1.15\), \(B=0.007\) & 100 & 64.83 & 48.39 \\  & \(PR=0.02\) & & & \\ \hline SIMP & \(p=3\) \(MOVE=0.02\) & 100 & 92.04 & 58.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results obtained for the L-shaped structure.
## 4 CLAP Correlation and Information Sharing Services
### Correlation and Cross-Correlation Process
#### 4.1.1 Definition of Security Policies  
Security policies typically address the constrains on functions and flow among the systems and how users must access them. CLAP uses these policies to filter the events associated with one or more data source agents. It is also possible to define sources or destination of the events, either by filtering the IP addresses ports, or event type. Security policies are used to define the events to be considered for correlation and to define the correlation bolts in the final step.  
The definition of security policies must define the source and destination IP addresses or range of addresses affected by the policy, the source and destination port numbers, the type of detected events and the sensor, tool or equipment (agent) responsible for the detection. A regular security policy uses the following structure: (IP_Src, IP_Dst, Port_Src, Port_Dst, Event_Type, Agent).  
Every matching policy has an associated action to be executed. The format used to define a policy consequence in CLAP consists of four parameters: (Action, SIEM, Logger, Forwarding). The Action parameter defines the automatic reactions to be executed after the generation of an alert; the SIEM parameter defines actions to be executed by the SIEM for a particular event (e.g., Set Event Priority, Logical Correlation, Cross-correlation); the logger parameter allows the generation of logs; and the Forwarding parameter allows forwarding events to another agent or entity.  
Once the policy and consequences are defined, we build the correlation rules. This is a three-fold process that requires (i) the definition of statements; (ii) the definition of directives; and (iii) the definition of correlation bolts.
## 2 Materials and Methods
### Evaluation Metrics  
In terms of model evaluation, the selected evaluation metrics in this study include RMSE, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), which are used to assess the predictive performance of the proposed model. These metrics can be mathematically represented by Equations (20)-(22):  
(1) RMSE. The smaller the RMSE value, the smaller the difference between the model's predicted values and the actual values, indicating higher prediction accuracy of the model. The RMSE equation is:  
\[\text{RMSE}(y,\hat{y})=\sqrt{\frac{1}{n}{\sum\limits_{1}^{n}}{(y_{i}-\hat{y}_ {i})}^{2}} \tag{20}\]  
(2) MAE. MAE represents the average of the absolute differences between the predicted values and the actual values, meaning that a smaller MAE value indicates a smaller prediction error and higher prediction accuracy of the model. The MAE equation is:  
\[\text{MAE}(y,\hat{y})=\frac{1}{n}{\sum\limits_{1}^{n}}{|y_{i}-\hat{y}_{i}|} \tag{21}\]  
(3) MAPE. In the MAPE evaluation criteria, the smaller the value of MAPE, the smaller the difference between the predicted and true values of the model, and the higher the predictive accuracy of the model. The MAPE equation is:  
\[\text{MAPE}(y,\hat{y})=\frac{100\%}{n}{\sum\limits_{i=1}^{n}}\frac{|y_{i}- \hat{y}_{i}|}{max(\epsilon,|y_{i}|)} \tag{22}\]  
where \(\hat{y}_{i}\) is the predicted value, \(y_{i}\) is the original true value, \(n\) is the total number of predicted values, and \(\epsilon\) is an arbitrarily small but strictly positive number in the MAPE formula to avoid uncertain results at zero.
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{_Common_ _Compound Name_} & \multirow{2}{*}{_Equation_} & \multirow{2}{*}{_R_\({}^{2}\)} & \multirow{2}{*}{_Linear_ _Range (ng)_} & \multicolumn{2}{c}{_LOD_} & \multicolumn{2}{c}{_LOQ_} \\ \cline{5-8}  & & & & & _Tube (ng)_ & _Breath_* _(ng -mL-1)_ & _Tube (ng)_ & _Breath_* _(ng-mL-1)_ \\ \hline
2-Butanone & y = 2:10\({}^{\text{v}}\)x \(-\) 2 \(\times\) 10\({}^{\text{8}}\) & 0.9764 & 16–100 & 1.7 & 3.4 & 5.7 & 11.4 \\ \hline
3-Heptanone & y = 4:10\({}^{\text{v}}\)x \(-\) 1 \(\times\) 10\({}^{\text{7}}\) & 0.9719 & 16–100 & 2.1 & 4.2 & 6.9 & 13.8 \\ \hline
1-Octine & y = 3:10\({}^{\text{v}}\)x \(-\) 2 \(\times\) 10\({}^{\text{7}}\) & 0.9789 & 30–100 & 1.8 & 3.6 & 6.2 & 12.4 \\ \hline Acetonitrile & y = 2:10\({}^{\text{v}}\)x \(-\) 1 \(\times\) 10\({}^{\text{8}}\) & 0.9889 & 35–100 & 1.1 & 2.2 & 3.6 & 7.2 \\ \hline Benzaldehyde & y = 2:10\({}^{\text{v}}\)x \(+\) 3 \(\times\) 10\({}^{\text{7}}\) & 0.9536 & 18–100 & 2.7 & 5.4 & 8.9 & 17.8 \\ \hline Butanoic acid & y = 6:10\({}^{\text{v}}\)x \(-\) 2 \(\times\) 10\({}^{\text{7}}\) & 0.9936 & 45–100 & 1.0 & 2.0 & 3.5 & 7.0 \\ \hline Butanol & y = 3:10\({}^{\text{7}}\)x \(-\) 3\(\times\)10\({}^{\text{8}}\) & 0.9952 & 16–100 & 1.0 & 2.0 & 3.2 & 6.4 \\ \hline Decane & y = 1:10\({}^{\text{v}}\)x \(-\) 1 \(\times\) 10\({}^{\text{7}}\) & 0.9948 & 28–100 & 1.2 & 2.4 & 3.9 & 7.8 \\ \hline Dichloromethane & y = 7:10\({}^{\text{v}}\)x \(-\) 2 \(\times\) 10\({}^{\text{7}}\) & 0.9883 & 20–100 & 1.3 & 2.6 & 4.3 & 8.6 \\ \hline Dimethylsulfoxide & y = 4:10\({}^{\text{v}}\)x \(-\) 3 \(\times\) 10\({}^{\text{7}}\) & 0.9780 & 22–100 & 1.7 & 3.4 & 5.8 & 11.6 \\ \hline Dodecane & y = 1:10\({}^{\text{v}}\)x \(-\) 3 \(\times\) 10\({}^{\text{6}}\) & 0.9947 & 28–100 & 1.0 & 2.4 & 3.4 & 6.8 \\ \hline Ethyl ether & y = 9:10\({}^{\text{v}}\)x \(-\) 6 \(\times\) 10\({}^{\text{6}}\) & 0.9917 & 18–100 & 1.1 & 2.2 & 3.6 & 7.2 \\ \hline Ethylenediamine & y = 3:10\({}^{\text{v}}\)x \(-\) 3 \(\times\) 10\({}^{\text{8}}\) & 0.9668 & 40–100 & 2.4 & 4.8 & 8.1 & 16.2 \\ \hline Hexanal & y = 604,904\({}^{\text{v}}\)x \(-\) 5 \(\times\) 10\({}^{\text{6}}\) & 0.9961 & 12–100 & 0.7 & 1.4 & 1.4 & 2.8 \\ \hline Hexanoic acid & y = 3:10\({}^{\text{v}}\)x \(-\) 3 \(\times\) 10\({}^{\text{7}}\) & 0.9923 & 20–100 & 1.2 & 2.4 & 4.0 & 8.0 \\ \hline Octanal & y = 102,130\({}^{\text{v}}\)x \(-\) 348,975 & 0.9860 & 23–100 & 1.4 & 2.8 & 4.8 & 9.6 \\ \hline Octanol & y = 921372\({}^{\text{2}}\)x \(-\) 6 \(\times\) 10\({}^{\text{6}}\) & 0.9844 & 25–100 & 1.5 & 3.0 & 5.1 & 10.2 \\ \hline Propanal & y = 2:10\({}^{\text{v}}\)x \(-\) 7 \(\times\) 10\({}^{\text{6}}\) & 0.9952 & 12–100 & 0.7 & 1.4 & 2.4 & 4.8 \\ \hline Propylamine & y = 1:10\({}^{\text{v}}\)x \(+\) 2 \(\times\) 10\({}^{\text{8}}\) & 0.9916 & 23–100 & 1.4 & 2.8 & 4.6 & 9.2 \\ \hline Toluene & y = 5:10\({}^{\text{7}}\)x \(-\) 3 \(\times\) 10\({}^{\text{8}}\) & 0.9835 & 27–100 & 1.6 & 3.2 & 5.4 & 10.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calibration curves equations, correlation coefficients (R\({}^{2}\)), linear ranges, LOD, and LOQ values for selected VOCs.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 4 Sea Ice Extent Mapping and Validation
### Reduction of Residual Classification Errors  
Because of the presence of strong winds or other physical mechanisms, the SVM model outputs can contain some misclassifications. For instance, some of the open seawater pixels are misclassified as sea ice, and sea ice pixels are misclassified as open seawater, as shown in Figure 7a. In this study, we employ the area growth technique (see [13]) to reduce the number of misclassifications. The area growth technique starts with a small region known to be within the ice area and expands this region until it reaches the outer edge of the ice region and cannot be expanded further. This eliminates pixels in the sea ice region misclassified as open seawater. Conversely, growth can be performed from the outer edge towards the inner region until the sea ice edge is reached. This eliminates pixels within the open seawater region that are misclassified as sea ice. Once area growth is complete, there may be some residual noise at the sea ice edge, which is where the \"indentation\" or \"squeeze\" observation error needs to be resolved using image erosion and dilation techniques [35]. In this study, 100 km per day was used as the maximum sea ice growth or ablation rate threshold between two consecutive days and produced good results in terms of eliminating misclassification for sea ice growth and ablation. The SVM model outputs following misclassification correction are shown in Figure 7b. It is clear that most of the misclassified pixels in the closed sea ice and the open sea have been corrected.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 4 Criteria Considering Stress Conditions  
Establishing internal stability discrimination methods that consider stress conditions not only accurately reflects the internal stability of soils, but also predicts the internal stability patterns, providing a reference for practical engineering. The stress conditions considered in internal stability discrimination methods mainly include confined compression, isotropic compression, and triaxial shear.