esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Reactive Protection Concept for Industrial Automation Systems
### Security Policies, Operational Policies and Reactive Actions in Real-Life Automation Systems
#### 5.3.4 Network Segment Reactive Protection  
Security policies and reactive actions are capable of providing protection against intrusions. However, their suitability for IAS may be questioned. In order to address this concern, it is necessary to also consider the operational policies. For this, the presented concept suggests the consideration of both operational and security policies on a zone-to-zone basis. This allows for providing decentralized protection to the automation system. Another advantage of this decentralized approach is that it allows for analyzing security events from multiple sources (e.g., third-party security solutions)--hence meeting _R4_ defined in Section 3.  
Figure 3 presents the process that is carried out by the Reactive Protection System presented in this work. This process starts with the reception of a security-related event. This event may be received from a third-party component over the network. Afterwards, this event is analyzed in order to validate whether or not a network segment security policy has been violated. If a violation has occurred, then an appropriate reactive action is chosen. If the reactive action is supported by the Reactive Protection System, then it is evaluated against the segment operational policies. If the reactive action violates an operational policy, it is not executed and this result is logged. On the other hand, if the reactive action does not violate any operational policy, the reactive action is executed.  
As it has been observed, this concept ensures that no reactive action will be executed if it compromises the operation of the automation system as long as the operational policies are well-defined. Although this requires human effort for its configuration, it provides the advantage of being able to react to intrusions without human intervention--hence providing a quick response in the presence of intrusions during normal operation of the system (_R1_ defined in Section 3).  
Figure 3: Information flow of the presented concept for reactive protection.
## 2 Materials and Methods
### Evaluation Metrics  
In this study, \(\rm F_{max}\), average precision (AvgPr), average recall (AvgRc), area under the precision-recall curve (AUPR), and the Matthews correlation coefficient (MCC) were used as model evaluation metrics to measure the performance of our model and other methods for grain protein function prediction.  
\(\rm F_{max}\) is a protein-centered evaluation metric utilized in CAFA and represents the maximum value of the geometric mean of the average precision and average recall for all proteins in the test set at each threshold [28]. First, the average precision and recall were calculated using the following formula:  
\[pr_{i}(t)\ =\ \frac{\sum_{f}\ I(f\in P_{i}(t)\wedge f\in T_{i})}{\sum_{f}\ I(f \in P_{i}(t))} \tag{6}\]  
\[rc_{i}(t)=\frac{\sum_{f}\ I(f\in P_{i}(t)\wedge f\in T_{i})}{\sum_{f}\ I(f\in T _{i})} \tag{7}\]  
\[\rm AvgPr(t)\ =\ \frac{1}{m(t)}\sum_{i=1}^{m(t)}\ pr_{i}(t) \tag{8}\]  
\[\rm AvgRc(t)\ =\ \frac{1}{n}\sum_{i=1}^{n}rc_{i}(t) \tag{9}\]  
where \(f\) is the GO class, \(P_{i}(t)\) is the predicted annotation set of protein \(i\) at threshold \(t\), \(T_{i}\) is the true annotation set for protein \(i\), \(m(t)\) is the number of proteins with at least one predicted annotation at threshold \(t\), \(\rm n\) is the total number of proteins. \(I\) is an identity function that returns 1 if the condition is true and 0 otherwise. We then computed \(\rm F_{max}\) (prediction threshold \(t\in[0,1]\) and a step size of 0.01). If the predicted probability of a class is higher than or equal to \(t\), it is considered as a predicted class:  
\[\rm F_{max}=\max_{t}\left\{\frac{2\times AvgPr(t)\times AvgRc(t)}{AvgPr(t)+AvgRc (t)}\right\} \tag{10}\]  
TP, TN, FP, and FN denote the true positives, true negatives, false positives, and false negatives, respectively. AUPR and MCC are two reasonable and important evaluation metrics for highly unbalanced learning problems, providing a comprehensive measurement standard [29]. The MCC calculation formula is as follows:  
\[\rm MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{11}\]  
The higher the value of all the evaluation metrics, the better the algorithm performance. Additionally, a trade-off between precision and recall is typically observed, where increasing precision may reduce recall and vice versa.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 4 Results and Discussion
### Discussion  
Based on the results from all our models, all three models can perform NSL recognition with high accuracy. However, there are various trade-offs regarding computational efficiency for the different models. This impacts how these models could be used in real-life scenarios or for future research.  
For the KNN model, it is clearly effective and quick if the dataset is not complex or big. Because it is a very easy-to-implement model, we still believe that it has its use cases in niche situations where scalability is not needed. Another potential issue with the KNN model we have created is that it uses skin color detection for segmentation. This is a potential limitation for this model in real-world scenarios as the dataset currently mainly consists of individuals with white skin color. Therefore, the model could face problems if a person of different skin color would use it or if the lighting was different.  
The SVM model struck a great balance between accuracy and computational efficiency, making it a very versatile model for many NSL recognition tasks. We were surprised to see that the model maintained its accuracy even when the images were resized to 32 \(\times\) 32, displaying its robustness and adaptability. While the foreground/background segmentation we performed on the dataset for the SVM model might be sensitive to lightning and background, our dataset contains images with various backgrounds, and we are therefore confident that this would also work well for real-world scenarios.  
The results of the CNN model were really encouraging as it offered a high accuracy while maintaining a low prediction time. Because CNN models only become more effective as they are exposed to more data, this model could be trained further on more images to grow and expand. The fact that CNN does not require any segmentation or feature extraction makes it a very attractive model because we do not have to consider lighting, skin color, or other uncontrollable factors. Due to its robustness against noise would alsoserve as an optimal model for future researchers/contributors willing to create an even more comprehensive data set.  
While all three of the models show great results in terms of accuracy, the SVM and CNN model stands out as the absolute strongest contenders in terms of accuracy and computational efficiency in terms of predicting new unseen data. The findings from our comparative analysis of image classification models for NSL recognition offer valuable insights that contribute to further research in the field, ultimately identifying the most effective classification model for NSL recognition.  
An observation regarding the near-perfect accuracy leads to the consideration of making the dataset more challenging. Computational enrichment could be explored in future research for more rigorous testing of the model's robustness and adaptability, such as adding noise or varying image orientations.
# Greening the NetFPGA Reference Router
## 4 Frequency Control Policies for Dynamic Frequency Scaling
### Double-Threshold Policy  
The STP is an elegant scheme if the incoming traffic does not change frequently and abruptly so that the router could switch the capacity state in response to real-time traffic load without losing QoS. However, when the incoming traffic fluctuates drastically, especially in the case when input traffic is fluctuating near a preset threshold, frequently switching the capacity state could introduce extra power consumption and even extra packet loss. An effective policy should manage the router to operate at the lowest suitable frequency for as long as possible. Otherwise, extra switching energy and extra overheads during transition time may outweigh the power savings from the DFS scheme.  
To eliminate the problem of the possible oscillation of capacity changes around a threshold associated with the STP, a double-threshold policy (DTP) is proposed as shown in Figure 6. DTP uses a high threshold \(T_{jl}\) (\(1\leqslant j\leqslant 5\)) and a low threshold \(T_{jl}\) (\(1\leqslant j\leqslant 5\)) for each capacity state \(S_{j}\) (\(1\leqslant j\leqslant 5\)). In the DTP, the buffer between \(T_{jl}\) and \(T_{jl}\) could effectively reduce the possibility of the ping pong effect, so as to avoid energy waste and overheads on unnecessary capacity switching. Table 2 presents the mapping table of operating frequencies, capacity states, measured capacities and preset thresholds for DTP. For a certain capacity state \(S_{j}\), if the total number of bytes received by all receive queues is more than \(T_{(j-1)l}\) and less than \(T_{jl}\), the router will stay in the current capacity state \(S_{j}\). The FSR will change to a lower routing capacity only when the aggregated total number of bytes in all receive queues is less than \(T_{(j-1)l}\) or change to a higher routing capacity when the aggregated total number of bytes in all of the receive queue is more than \(T_{jl}\). Figure 7 presents the flowchart of the DTP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **State** & **Capacity** & \multicolumn{2}{c}{**Threshold**} \\ \cline{2-5} \(f\) **(MHz)** & \(S_{i}\) & \(C_{i}\) **(Bytes)** & \(T_{jl}\) **(85\% of \(C_{i}\)) (Bytes)** & \(T_{jl}\) **(95\% of \(C_{i}\)) (Bytes)** \\ \hline
7.8 & \(S_{1}\) & \(C_{1}:554\) K & \(T_{1l}:471\) K & \(T_{1h}:526\) K \\
15.6 & \(S_{2}\) & \(C_{2}:1098\) K & \(T_{2l}:933\) K & \(T_{2h}:1043\) K \\
31.3 & \(S_{3}\) & \(C_{3}:2143\) K & \(T_{3l}:1822\) K & \(T_{3h}:2036\) K \\
62.5 & \(S_{4}\) & \(C_{4}:4321\) K & \(T_{4l}:3673\) K & \(T_{4h}:4105\) K \\
125 & \(S_{5}\) & \(C_{5}:8620\) K & \(T_{5l}:7327\) K & \(T_{5h}:8190\) K \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency, capacity and threshold mapping table for the double-threshold policy.  
Figure 6: Thresholds of the double-threshold policy.  
Figure 7: Flowchart of the double-threshold policy.
# A Dual-Beam Leaky-Wave Antenna Based on Squarely Modulated Reactance Surface
## 4 Conclusions  
The design method of LWA utilizing SquMRS is proposed and verified in this paper. Compared with SinMRS, SquMRS exhibits the advantage of simple structure with few kinds of unit cells. A  
Figure 8: (**a**) The photograph of the fabricated antenna, (**b**) the schematic diagram of the measurement system, (**c**) modulated geometrical parameter \(b\) along the aperture, (**d**) the measured and simulated S-parameters, (**e**) the measured and simulated gain at \(8.5\) GHz.  
novel one-dimensional LWA based on SquMRS was designed to generate dual-beam radiation by superposition of individual modulation pattern for each beam. This approach can not only produce multi-beams but also control the radiation energy of each beam. Due to the coupling between the two coexisted radiation patterns, some discrepancies can be found between the simulated and theoretical results, which can be solved by optimizing the surface impedance coefficients and modulation periods from the theoretical values. Good agreement between experiment and simulation has been observed. It is much more convenient to control the tunable unit cells simultaneously when SquMRS is applied to achieve beam scanning capability at fixed frequency. Meanwhile, it also effectively reduces the insertion loss and cost by decreasing the number of tunable components.  
Conceptualization, H.Y. and K.Z.; validation, H.Y., K.Z. and X.D.; manuscript: formal analysis, K.Z.; writing--original draft preparation, H.Y.; writing--review and editing, K.Z. and Q.W.; project administration, K.Z.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No.61771172, No.61571155, No.61401122, No.61371044, No.61701141).  
The authors declare no conflict of interest.
# Personalized E-Learning Recommender System Based on Autoencoders
## 4 Methodology and Preliminaries
### Autoencoder
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Function** & **Advantages** & **Drawbacks** \\ \hline \multirow{4}{*}{Sigmoid} & -Simple to understand & -Gradient saturation [42] \\  & -Commonly utilized in & -Slow convergence-Output is \\  & shallow networks [42] & nonzero-centered \\ \hline \multirow{2}{*}{Tanh} & \multirow{2}{*}{-Output is zero-centered} & -Vanishing gradient problem could \\  & & not be solved using this function [42] \\ \hline \multirow{2}{*}{ReLU} & \multirow{2}{*}{-Faster learning} & -Fragile during training, resulting in \\  & & the death of some gradients [42] \\ \hline \multirow{4}{*}{SELU} & -Not affected by vanishing & \multirow{2}{*}{-“Internal covariate shift” problem} \\  & gradient problems-Works well & \\ \cline{1-1}  & in standard feed-forward & \\ \cline{1-1}  & neural networks (FNNs) [43] & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advantages and drawbacks of some activation functions. Based on the comparison in Table 4 and the results in Table 5, we chose \"the scaled exponential linear unit (SELU)\", which has the following function:  
\[\text{SELU}(\text{x})=\left\{ \begin{array}{ll}\text{x}&\text{if }(\text{x}>0)\\ \alpha\text{e}^{\text{x}}-\alpha\,\text{if }(\text{x}<0)\end{array} \right. \tag{3}\]  
Choosing the right optimizer for our model is important for efficient and effective learning. There are several optimization algorithms to consider. Here are a few examples: Stochastic Gradient Descent (SGD) and Adam (Adaptive Moment Estimation). In our case, we carried out some experiments comparing the performance of Adam and SGD on a dataset made by [PERSON] et al. [23]. Table 6 shows the results of this comparison. It is clearly seen that Adam achieves the best MAE and RMSE results.
## 5 Discussion
As shown in Figure 11, our model shows over-fitting during the training process. The training accuracy was rapidly increased in the first 20 epochs, and the training accuracy reached more than 0.9 on epoch 40, while the test accuracy of \"Scene_B\" is just 0.544. It is easy to see that the model was trained to perfectly fit training data rather than unseen scenes, which is an obvious signal of over-fitting. This inconsistency may be due to the fact that we input too many features in the training data, which means that the classifier trained by the network will be inclined to distribute the training data. This result is especially exacerbated by the fact that we only used very few labeled data in the training process and graph-based DNNs typically have millions of parameters, as can be seen from the over-fitting results. Another important finding was the impact of using different amounts of training data demanded in our model (both unlabeled and labeled data). The most obvious finding that emerged from the analysis is that from three unlabeled scenes, discriminative representation may be already learned in the pre-trained AE. Moreover, although segmentation results are increased or decreased by varying labeled training data in the segmentation network training stage, the magnitude of the change is very small. These results (referring to Table 9) further support the idea that our method is label-efficient in nature. This combination of findings provides some support for the conceptual premise that our proposed AE architecture may learn powerful representations from unlabeled data, and these representations may be further used in our downstream tasks. Furthermore, the results of this study indicate the effectiveness of the design choices. Figure 11: The training accuracy while using 12 features in input point cloud.
# Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
###### Abstract  
Seismic reflection utilizes sound waves transmitted into the subsurface, reflected at rock boundaries, and recorded at the surface. Interpretation of their travel times and amplitudes are the key for reconstructing various geomorphological features across geological time (e.g., reefs, dunes, and channels). Furthermore, the integration of surface geomorphology technique mapping, such as digital elevation models, with seismic geomorphology can increase land and marine feature modelling and reduce data uncertainty, as well. This paper presents an overview of seismic and surface geomorphology techniques and proposes an integrated workflow for better geological mapping, 3D surface imaging, and reconstruction. We intend to identify which techniques are more often used and which approaches are more appropriate for better output results. We noticed that an integration of surface and subsurface geomorphology techniques could be beneficial for society in landscape mapping, reservoir characterization, and city/regional planning.  
Keywords:quantitative geomorphology; seismic geomorphology; seismic reflection; 3D imaging; earth surface reconstruction; remote sensing; aerial photogrammetry; geological mapping; integrated geomorphology +
Footnote †: journal: Medical Imaging
## 2 Experimental Setup  
The laboratory system for this work has been elaborated in Figures 1 and 2. This contains a Semiconductor Optical Amplifier (SOA), from which light is emitted in both directions in the Near Infra Red (NIR) range (1541-1545 nm). To intensify its output, the principle of optical reflection [17; 39] has been created with the aid of loop mirror on one side. Thus, the output is provided into two Fiber Bragg Gratings, namely FBG\({}_{\text{i}}\) (FBG\({}_{\text{i}}\)/FBG1) and FBG\({}_{\text{o}}\) (FBG\({}_{\text{h}}\)/FBG2). These are in reference to the inner (lower wavelength) and outer (higher wavelength) cavities, respectively. These are separated with the help of optical couplers, VC\({}_{\text{i}}\) and VC\({}_{\text{o}}\), which distinguish the two wavelengths (modes) M\({}_{\text{i}}\) and M\({}_{\text{o}}\), respectively.  
Figure 1: Experimental setup of the sensor laser under investigation, with the Fibre Bragg Gratings (FBGs), Semiconductor Optical Amplifier (SOA), Fixed/Variable Coupler (FC/VC), loop mirror, isolators (for light flow in single direction), Optical Spectrum Analyzer (OSA) and Collimator (for substance) (Reprinted with permission from [17], Copyright 2021 IEEE).  
Figure 2: Electrical output of the laser system, consisting of Optical Receiver (PIN-PhotoDiode (PD), Bias-Tee and Low Noise Amplifier (LNA)), Voltmeter and Electrical Measurement System (Electrical Spectrum Analyzer (ESA) or Oscilloscope).  
The SOA is supplied with injection current from the Laser Diode Controller (LDC), which also controls the temperature [40; 41]. The collimator is the sample holder where the substance of interest can be placed. Isolators ensure unidirectional flow of optical light, for instance into the Optical Spectrum Analyzer (OSA), for the sake of measurement purpose [10; 42; 43; 44].  
The photodiode collects the light from the laser diode sensor. The electrical signal output from the photodiode contains DC signal as well as AC signal. To prevent saturation of the RF amplifier, the DC signal is filtered by using high frequency bandwidth Bias Tee. The DC signal from Bias Tee is measured using Digital Multimeter (DMM) and is equal to the average photocurrent \(I_{p}\), which is proportional to the mean optical intensity. The AC, which is proportional to the intensity noise of the laser, is amplified by Low Noise Amplifier (LNA). It is displayed and measured using radio frequency Electrical Spectrum Analyzer (ESA), whose attenuator can be set at 0 dB for high sensitivity measurement. The ESA [45] has a minimum resolution bandwidth of 10 Hz and typical noise figure of 21 dB @ 3 MHz resolution bandwidth.
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 3 Experimental Procedure  
### Specimens Bonded by the Ultrasonic Bonding  
Figure 2 illustrates the schematics of the ultrasonic bonding machine and bonding tip geometry. An ultrasonic bonder with an output power of 2.4 kW and a driving frequency of 14.71 kHz was used for the bonding. The amplitude of the ultrasonic horn under no load was 53 \(\upmu\)m (peak-peak). An industrial aluminum alloy, AA6061-T6 sheet, was used for the bonded specimen. The sheet was cut into two metal sheets as shown in Figure 3. The upper sheet had a size of 50 \(\times\) 11 \(\times\) 0.5 mm. The lower sheet had a size of 50 \(\times\) 11.5 \(\times\) 0.8 mm. The lower sheet was placed on an anvil and clamped by a fixture to suppress slippage. The upper sheet was lapped on the lower sheet in such a way that the total length of the bonded specimen was 85 mm. Vibration was applied by the bonding tip of the ultrasonic horn under a normal force of 588 N. Bonding strength was controlled by changing the vibration time within 1000 ms. The bonding tip of the ultrasonic horn and the anvil had a knurled surface shown in Figure 2b. There were 13 \(\times\) 13 protrusions with a 0.8 mm pitch, 0.3 mm height, and 90\({}^{\circ}\) angle of groove. The total tip area was 10 \(\times\) 10 mm. The bonding was carried out by aligning the protrusions of the horn tip and the anvil tip with each other.  
Figure 1: Change of apparent spring constant by enlargement of bonded area.  
Figure 2: (**a**) Ultrasonic bonder; (**b**) Bonding tip geometry.  
The bonding strength was measured by a tensile shear test (Figure 4). The test was conducted at a cross-head speed of 0.1 mm/s. The maximum load during the tensile shear test was defined as the bonding strength in this study. The fractured surface was observed by a scanning electron microscope (SEM) and results were output to a digital image with a constant brightness and contrast. The image was binarized with a constant intensity threshold, then, the bonded area was estimated by measuring the total area of the white region. The details of the bonded area estimation are described in Section 4.1.
## 4 Discussion
### Implications of the Study  
The outcomes of this study have significant implications for agricultural practices, particularly in precision agriculture. Identifying specific growth stages and vegetation indices correlated with seed weight offers valuable guidance for farmers engaging in precision agriculture. By concentrating efforts such as irrigation and nutrient management during these critical periods, farmers can optimize seed yield. Additionally, the study underscores the importance of understanding the correlations between environmental factors, including weather and soil conditions, and seed weight. This knowledge empowers farmers to make informed decisions regarding irrigation, pest control, and other environmental management practices to enhance crop production.