esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Fermentation Technology and Storage Time on the Quality of Salami-Type Sausages
## 2 Materials and Methods
### Determination of the Acid Value and TBARS
#### 2.5.2 TBARS Index  
The TBARS (Thiobarbituric Acid and Reactive Substances) index, indicating the degree of lipid peroxidation of the salami-type sausages, was determined by the Shahidi [31] modified method. The samples were examined on 0, 21, 42, and 63 days of cold storage. Two grams of the comminuted sausage was weighed into a centrifuge tube with an accuracy of 0.01 g; 5 mL of 10% trichloroacetic acid--THC (Avantor Performance Materials, Gliwice, Poland) was added; and the mixture was vigorously ground for 2 min. Then, 5 mL of 0.02 M thiobarbituric acid--TBA (Merck KGaA, Darmstadt, Germany) solution was added to the test tube, and the mixture was ground for a further 2 min. The next step was centrifuging the samples for 10 min at 4000 rpm. After centrifugation, the solution was filtered into a glass test tube and placed in a boiling water bath for 35 min. After this time, the test tube with a sample was cooled using cold running water. The absorbance of the obtained solutions was measured using a Camspec M501 Single Bean spectrophotometer (Spectronic Camspec Ltd., Garforth, UK) at a wavelength of 532 nm. The measurement was performed against a reagent sample (5 mL of 10% trichloroacetic acid and 5 mL of 0.02 M TBA solution). The TBARS index was expressed as the number of mg malondialdehydedehyde (MDA) contained in 1 kg of product, which was calculated according to the following equation:  
\[\text{TBARS}=\text{Abs}\times 2.34 \tag{1}\]  
where Abs--absorbance of the tested sample; 2.34--absorbance conversion factor.
## 3 Results
### Uhplc-Dad-qTOF-MS Qualitative Analysis  
UHPLC-DAD-qTOF-MS analysis revealed differences between the decoction obtained from the raw material collected from the natural environment and the raw material purchased in the pharmacy (Table 1). Chromatograms of decoctions analyzed at the same concentrations are presented below (Figure 4). At first glance, it can be seen that the SC decoction chromatogram shows higher peaks between 1 and 6 min of retention time than the chromatogram of the PH decoction. Most of these compounds belong to flavan-3-ols and procyanidins (peak numbers 7 (Procyanidin dimer), 8 (Procyanidin trimer), 10 (Procyanidin dimer), 11 (Catechin), 12 (Procyanidin dimer), 14 (Epicatechin), and 20 (Procyanidin dimer monogallate). In turn, in the PH chromatogram, we can see several very high peaks that are much lower in the SC chromatogram (peaks numbers 38 (Resveratrol-(gallosylglucose), 41 (Emodin-glucoside), 43 (Emodin-hexose-sulfate), 44 (Resveratrol), 45 (Emodin-hexose-sulfate), 46 (Torachrysone-hexoside), 47 (Emodin-hexose-sulfate), 50 (Sulfonyl torchrysone/isomer), 51 (Emodin-glucoside), 52 (Physcionin), and 55 (Sulfemodin). Most of these peaks belong to anthraquinones. A clear difference between the decoctions is also the significantly greater amount of sulfate compounds in the PH decoction than in the SC decoction: 13 compounds (peak numbers 9, 13, 15, 16, 21, 22, 24, 28, 33, 43, 47, 50, and 55) and 5 compounds (16, 18, 24, 43 (very low peak), and 50 (much lower peak)), respectively. \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**Nr. ** & **Compound** & **Rl. [min]** & **UV Max** & **mf/z** & **Error** & **Ion** & \ \begin{tabular}{c} **MS\({}^{2}\) Main-Ion** \\ **(Relative** \\ **Intensity \%)** \\ \end{tabular}  & \begin{tabular}{c} **MS\({}^{2}\) Fragments** \\ **(Relative** \\ **Intensity \%)** \\ \end{tabular}
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 5 Self-Evolving Classification Method  
To create a self-evolving classification method for uncovered 6G NTN protocol attacks, the rule and baseline detection processes are elucidated using XAI technologies. Furthermore, these methods operate synergistically to extract and learn from the fingerprint features of uncovered protocol attacks. Fingerprint features, which represent the normal behavior of protocol attacks, which have distinct trends, are represented by feature parameters extracted from protocol traffic. To extract the fingerprint features of uncovered attacks, transparent baseline detection results are obtained using XAI technologies. This transparency allows the deviations of uncovered attacks from the normal baseline behavior to be identified and their fingerprint features to be extracted. To learn from these fingerprint features, a transparent rule detection process is also obtained using XAI technologies. With this transparency, understandable and adjustable attention can be directed to specific  
Figure 6: Self-evolving anomaly detection framework with an online identity.  
features during the rule detection process. A collaborative approach involving the baseline and rule detection methods is then designed. In particular, the fingerprint features obtained from the baseline detection process are shared with the rule detection method, enabling the latter to increase the classification accuracy on the basis of this external information.  
The self-evolving classification method is designed considering a baseline detection method with explainable detection results. In particular, the fingerprint features of uncovered attacks are extracted by explaining the quantitative contributions of the protocol features to the detection results. Furthermore, rule detection with an explainable detection process is implemented; in this process, the attention to the features is explainable and can be adjusted during the detection process, as shown in Figure 7.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 2 Related Works
### KubeEdge  
KubeEdge [19] is an open-source edge computing platform that is designed to extend Kubernetes containerized orchestration capabilities to hosts at Edge. It supports networking, application deployment, and metadata synchronization between cloud and edge devices. KubeEdge architecture consists of two parts: Cloudcore and Edgecore as shown in Figure 1.  
Compared with Kubernetes, orchestration capabilities are separated from edge nodes to the cloud side. Cloudcore is the extended Kubernetes controller that controls edge nodes and pods metadata so that data can be targeted to specific edge nodes. Meanwhile, computing functionalities--pod deployments are handled by Edgecore.  
Several KubeEdge advantages make us choose it as the edge platform to deploy NDN network functions in disaster scenarios. First, Edgecore has a lightweight size. It  
Figure 1: KubeEdge architecture.  
is a node agent equivalent to kubelet [20] in Kubernetes. However, several features of kubelet are stripped out and SQLite [21] is used instead of etcd [22] to make Edgecore's binary size small and consume much less memory than kubelet. This allows this edge node agent to be installed even in resources-constraint devices when normal network infrastructure is heavily damaged by the disaster. Second, Edgecore supports device management with multiple protocols such as Bluetooth, Modbus [23], OPC-UA [24]. A custom protocol can also be defined by KubeEdge users. This feature allows us to use KubeEdge to manage NDN responder devices. Information can be exchanged between the disaster management commander at cloud and responders by utilizing KubeEdge's Mosquito publish/subscribe framework [25]. Finally, KubeEdge supports transmission reliability between cloud and edge, which is a very important feature, especially in disaster intermittent network conditions. The mechanism that enables this feature is called At-Least-Once Delivery [26] and the architecture design between Cloudcore and Edgecore that enables this feature is shown in Figure 2.  
The Kubernetes Custom Resources Definition (K8s-CRD) is used to store the resource version of the messages which are sent from cloud to edge. The message is marked as completed only when Cloudcore receiving the acknowledgment through the Message Acknowledgement Channel (MsgAckChannel) from Edgecore after successful message receipt here. In case of disconnection between cloud and edge happens, unacknowledged messages will be kept in the messages queue for resending after the connection is recovered. With this mechanism, data loss communication between cloud and edge can be prevented and recovered when the network is unstable.
## 6 Results
### Execution History and Performance Results
#### 6.3.1 Execution History for the 15-Location Problem  
Figure 4a illustrates the fitness evolution over generations, showing that GAAM-TS achieves the highest fitness values and converges more rapidly than GA-AM and Standard GA, confirming its superior optimization capability. Figure 4b presents the diversity evolution, demonstrating that GAAM-TS maintains a higher diversity level throughout the generations. This prevents premature convergence and allows broader exploration of the solution space, contributing to improved optimization. Similarly, Figure 4c tracks the evolution of total travel distance. GAAM-TS achieves the most substantial reduction in travel distance, outperforming the other variants in optimizing the sales route. This confirms its effectiveness in minimizing unnecessary travel. Lastly, Figure 4d shows the evolution of delivery time across generations. In line with the distance optimization trend, GAAM-TS significantly reduces the total travel time, ensuring faster and more efficient delivery routes compared to GA-AM and Standard GA.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 7 Evaluation and Discussion
### Proof of Concept Demonstration of Data Plane Forwarding
It is assumed that if the inner and the outer SFC header were two different network protocol layers, the outer SFC header would have been lost during packet processing. However, our implementation of the NSH header contains both an inner and an outer SFC in one NSH network layer. This means that outer and the inner SFC headers do not need to be separated when the packet processing parses the different network layers. The proof of concept demonstration verifies that the information in the SFC headers is maintained during packet processing.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 2 Related Works
### Gesture Segmentation  
Because radar echo signals are continuously collected, preprocessing is necessary to obtain information on gesture movements for the subsequent classification and recognition.  
Moving Target Indication (MTI) is a technique that employs clutter suppression methods to enhance the signal-to-noise ratio (SNR) of moving targets and separate them from stationary background clutter.  
Because of the frequency difference between the signal from moving targets and stationary clutter, the MTI filter suppresses stationary clutter by forming a stopband at direct current and integer multiples of the pulse repetition frequency while retaining the information of the moving targets in radar systems. To suppress clutter, a double delay canceller is utilized for extracting moving targets in gesture recognition.  
The transfer function of the double delay canceller can be expressed as  
\[\mathrm{H}(\mathrm{z})=\left(1-z^{-1}\right)^{2}=1-2z^{-1}+z^{-2} \tag{14}\]  
To compare the radar echo signal of a gesture before and after the MTI process, it is necessary to use the high-resolution range profile (HRRP) of the radar echo signal. Additionally, the HRRP of the gesture will be used to segment the gesture data sequence. The HRRP of the gesture before and after MTI processing is shown in Figure 6. It can be observed that after MTI processing, the echo signal of the gesture becomes more distinct, which benefits the improvement of gesture detection performance in the subsequent steps.  
Figure 6: Gesture HRRP before and after MTI. (**a**) HRRP before MTI. (**b**) HRRP after MTI.  
After obtaining the gesture HRRP, due to the presence of noise in the receiver output signal, there is still an impact on target detection. Performing constant false alarm rate (CFAR) detection on HRRP is necessary to obtain the gesture target. By using order statistics CFAR (OS-CFAR) to detect targets in the gesture HRRP, complete actions are obtained, as shown in Figure 7.  
By using OS-CFAR detection results, the start and end positions of the gesture can be determined for segmenting movements. Firstly, a sliding window is used along the time direction to detect gesture targets with a fixed step length. If the duration of the gesture's existence exceeds a threshold, the corresponding time of the gesture's occurrence is set as the start position of the gesture. If the gesture target continuously appears and disappears within the sliding window, and the duration of the gesture disappearance exceeds a threshold, the disappearance position of the gesture is set as the end position. At this point, a complete gesture action is collected. The start and end positions of a complete gesture action after segmentation are illustrated by the red lines in Figure 7.
## 4 Results of Numerical Errors and Discussion
Particle spacing \(\Delta x\) is 0.04 m, and the Courant number is 0.04. (**a**) Effects of \(\alpha_{\Pi}\) on the simulation results at \(\beta_{\Pi}=0.5\); (**b**) effects of \(\beta_{\Pi}\) on the simulation results at \(\alpha_{\Pi}=0.0\). function are slightly less than those using the Gaussian kernel function, but there is not much difference between these two types. From Figure 5a, it can be found that, for different smoothing kernel functions, adding an artificial viscosity term can reduce the numerical errors of sound pressure. The numerical errors of the SPH results with three kinds of smoothing kernel functions share a similar changing tendency with the increasing of \(\alpha\)n. They firstly decrease and then increase and reach their minimums when \(\alpha\)n locates at [0.03, 0.06]. However, as shown in Figure 5b, parameter \(\beta\)n has no obvious effects on the simulation results. The sound interference models with particle spacing \(\Delta\)x separately set to 0.04 m, 0.06 m and 0.08 m are also simulated. Figure 6 shows the numerical errors of sound pressure obtained by different sound interference models. As can be seen from Figure 6, for the same smoothing kernel function and Courant number, ignoring the effect of artificial viscosity terms, the largest numerical error of sound pressure occurs when particle spacing is 0.08 m, and the least error of sound pressure happens at a particle spacing of 0.04 m. That is, the numerical errors are heavily influenced by the particle spacing \(\Delta\)x, and the numerical errors increase with the particle spacing increasing. Similarly, it can also be found that the numerical errors of the SPH results with different particle spacings all have the same changing tendency with the increasing of \(\alpha\)n. Parameter \(\alpha\)n has obvious effects on the simulation results, while parameter \(\beta\)n does not. In addition, different sound interference models with Courant numbers separately set to 0.02, 0.04, 0.06 and 0.08 are simulated. Figure 7 shows the numerical errors of sound pressure for sound interference models with different Courant numbers. Figure 6: Numerical errors of sound pressure for sound interference models with different particle spacings. The smoothing kernel function is cubic spline, and the Courant number is 0.04. (**a**) Effects of \(\alpha\)n on the simulation results at \(\beta\)n = 0.5; (**b**) effects of \(\beta\)n on the simulation results at \(\alpha\)n = 0.0. Figure 7 indicates that a better simulation result can be found when the Courant number is 0.08. For different Courant numbers, the numerical errors of sound pressure still share the same changing tendency with parameter \(\alpha_{\Pi}\) increasing. Similarly, parameter \(\beta_{\Pi}\) has no obvious effect on the simulation results. In summary, the SPH algorithms using the cubic spline have optimum simulation results when the particle spacing \(\Delta x=0.04\) m and the Courant number is 0.08. The corresponding numerical errors could reach their minimum value when \(\alpha_{\Pi}\) belongs to the region [0.03, 0.06].
## 2 Materials and Methods
### Deforestation and Forest Degradation Detection  
To detect deforestation and forest degradation in the study sites and conservation units, land cover classes were reclassified into broader categories: Primary Forest (Terra-Firme Forest and Floodplain Forest), Secondary Forest, and Non-Forest (Agriculture/Pastureland, Development Areas, Barren Land, and Burned Areas). Water Bodies and Savanna classes were excluded from the analysis to focus on forest dynamics. Using the change detection analysis, deforestation was then calculated from the conversion of Primary Forest classes to Non-Forest classes, while degradation was quantified as the transition from Primary Forest classes to the Secondary Forest class. Annual rates of deforestation and forest degradation were calculated for the whole study area. Total areas of deforestation and forest degradation within 5 years, using the initial and final land cover maps, were calculated for conservation units and buffer zones.
## 3 Flightload Calculation Modeling
### Input and Output Data  
The complexity of aircraft flight loads is influenced by various input data. These loads undergo significant variations during different flight stages, such as takeoff, climb, cruise, descent, and landing. External factors like altitude, pressure, and wind speed also have a notable impact on flight loads. The output data, which represent the load status of different components of the airplane structure, vary according to specific flight load conditions. Therefore, it is crucial to carefully select flight load conditions [12].  
In this study, symmetric maneuver flight is used as an example to conduct regression analysis and validation. The load of axially symmetric maneuver flight is influenced by factors such as flight altitude, Mach number, longitudinal load, pitch acceleration, angle of attack, and elevator deflection. To ensure accuracy, flight altitude, Mach number, load factor, and pitch acceleration were chosen as input parameters, while maintaining a balanced angle of attack and elevator deflection based on empirical calculations and conventions.  
Table 1 shows the range of input parameters.  
To comprehensively investigate the severe load situations of various aircraft components, typical profiles of the components need to be selected, and their corresponding shear forces, bending moments, and torque moments should be calculated to characterize their load situations. In this study, the shear forces, bending moments, and torque moments of the airplane wings were selected as output parameters.  
Table 2 shows the output parameter.
## 3 Results and Discussion
### Effect of Friction Force on Axial Shortening  
Figure 13 shows the effect of the friction force on axial shortening for friction forces of 12.5, 15.5, 18.5, 21.5 and 24.5 kN. It is clear that during the friction phase between materials, axial shortening takes place. As the friction time increases (from 12.5 to 18 kN), the axial shortening of the samples increases linearly. During the upsetting phase, it can be seen that a logarithmic shortening of the material takes place when using axial forces from 18.5 to 25 kN (Figure 13b).  
For all applied forces, the maximum shortening of the samples did not exceed 4 mm at the end of the friction phase. After this time, plastic deformation occurred and a weld flash was formed. This process took a few seconds. A rapid loss of material occurred after applying the upsetting force for a welding time of 8 s. The maximum material shortening was 3.5 mm for a friction force of 12 kN. Doubling the friction force shortened the material by an additional 7 mm (Figure 13b). Moreover, from Figure 13a, it can be seen that the forging period had a minimal effect on the upset. The axial shortening is almost unchanged throughout this stage. A similar observation reported in the references [29; 30; 31].
## 4 Experimental Setup
### Performance Evaluation  
Two popular metrics are considered for the performance evaluation of the proposed method namely, classification accuracy (\(ACC\)) and Matthews correlation coefficient (\(MCC\)). Equations (8) and (9) depict the mathematical formulation of \(ACC\) and \(MCC\), respectively.  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[MCC=\frac{(TP\,*\,TN)-(FP\,*\,FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{11}\]  
where, \(TP\), \(TN\), \(FP\), and \(FN\) correspond to True Positive, True Negative, False Positive, and False Negative, respectively.  
For each feature selection method, 10-fold cross-validation is performed to calculate mean ACC and mean MCC along with respective standard deviations. Further, the classification performance of the considered feature selection methods is evaluated on four classification models, namely K-Nearest Neighbors (KNN), Naive-Bayes, Support Vector Machine (SVM), and Adaboost.  
In the KNN classifier, K is considered the square root of the data size. For the SVM classifier, RBF kernel is used with the parameters according to grid search. Finally, Naive Bayes is used as the base estimator in the Adaboost classifier. For other parameters, the default values are referred from the respective literature.