esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Discussion  
Using the laboratory setup, we successfully implemented a real-time online controller that uses ANN for modeling and ES for optimization. We have offered the real-time analysis and proved the convergence of the proposed controller. Additionally, we have shown that under certain conditions, the OAC-DES undoubtedly exceeds the capacities of the linear PI-controller.  
In general, we conclude from the results of the step response tests that the OAC-DES reacts to the changes of the step function comparable, or better than the linear PI-controller does. At lower velocities, it achieves lower transient peaks (overshoots). Since it can adapt to disturbance peaks, the OAC-DES also reduces them during the adaptation and over time may even eliminate them altogether. Unfortunately, problems occur due to the noisy reference current \(i_{r}\) and actual velocity \(\omega_{a}\). Thus, the OAC-DES increases steady state error, since it appears that the ANN needs constant velocity excitements to work properly.  
At higher velocities, the OAC-DES slightly under-performs the linear PI-controller. Transient peaks increase and promptness declines. However, it adapts to disturbances of the non-linear robotic mechanism, resulting in a reduction in a steady state error. This indicates that the OAC-DES has not yet adapted optimally to control plant and requires additional learning time. Indeed, we have discovered that, as long as the step reference is below \(\omega_{r}=20\) rad/s, the OAC-DES produces negligible disturbance peaks which start to rise only when the step reference exceeds \(\omega_{r}=20\) rad/s. Nevertheless, for common robotic applications, actual velocities on the secondary axis of up \(\omega_{a}=10\) rad/s are easily sufficient.  
We observed, that spring load heavily affects the linear PI-controller as follows: it becomes less and less manageable by increasing the load. Step responses might be acceptable for a wider practice only at the lowest spring constant \(k=420\) N/m. Performance of the OAC-DES, on the other hand, does not deteriorate much by increasing the spring load (not taking the reference current \(i_{r}\) saturation into account), but restlessness becomes very disturbing.  
During testing, there were two periodic occasions: periodic disturbances and periodic control cycle. Therefore, two types of adaptation appeared: (1) adaptation to the periodic disturbances, or short-term adaptation, and (2) adaptation to the periodic control cycle, or long-term adaptation.  
The first is valid for a single reference velocity, e.g., for one series \(\omega_{r}=30\) rad/s, while the second comes into play when adaptations from the previous cycle are carried over to the next one. However, the OAC-DES requires time to adapt, and the longer the adaptation time the better the step response (Figure 8). This means that, given sufficient time, the OAC-DES will control the non-linear robotic mechanism optimally.
## 3 Results
### Main Study Outcome  
The primary outcome, represented by the total score of the I-NOSE questionnaire, is illustrated in Figure 1. At baseline (T0), both study groups demonstrated similar NOSE scores, reflecting comparable severity of nasal obstruction. However, after 5 days of treatment (T1), the NOSE scores were significantly lower in the group treated with the active product compared to the placebo group. Specifically, at T1 the mean NOSE score was 69.2 (95% CI: 64.8-73.5) in the placebo group and 26.7 (95% CI: 20.5-32.8) in the active treatment group, confirming a marked improvement in nasal obstruction symptoms. Additionally, a statistically significant reduction in NOSE scores was observed within the active group when comparing T0 to T1.  
Supplementary Table S1 shows the distribution of the five individual items of the I-NOSE questionnaire across the study period. While no substantial changes were observed in the placebo group, the active group demonstrated a clear trend toward improvement, with responses shifting from \"severe problem\" and \"quite significant problem\" to \"moderate problem\", \"very mild problem\", and \"no problem\".  
Table 3 highlights the distribution of nasal obstruction severity throughout this study. In the placebo group, the proportions of subjects with moderate, severe, or extreme obstruction remained relatively unchanged between T0 and T1. Conversely, in the active group, the proportion of subjects with severe or extreme nasal obstruction decreased markedly, with a shift toward mild and moderate categories.  
\begin{table}
\begin{tabular}{c c c} \hline  & **PLACEBO** & **ACTIVE** \\  & **N = 30** & **N = 30** \\ \hline Number of respiratory infections per year & & \\ \textless{}3 & 2 (6.7\%) & 7 (23.3\%) \\ \textgreater{}6 & 24 (80\%) & 19 (63.3\%) \\ \textgreater{}6 & 4 (13.3\%) & 3 (10\%) \\
1 per month & 0 & 1 (3.3\%) \\ \hline Number of respiratory infections between & & \\ October–December and March–May & & \\ \textless{}3 & 22 (73.3\%) & 15 (50\%) \\ \textgreater{}6 & 8 (26.7\%) & 14 (46.7\%) \\
1 per month & 0 & 1 (3.3\%) \\ \hline Mean duration of each episode & & \\ \textless{}3 & 7 (23.3\%) & 9 (30\%) \\ \textgreater{}6 & 23 (76.7\%) & 19 (63.3\%) \\ \textgreater{}6 & 0 & 2 (6.7\%) \\ \hline Number of febrile episodes per year & & \\ \textless{}3 & 3 (10\%) & 6 (20\%) \\ \textgreater{}6 & 20 (66.7\%) & 19 (63.3\%) \\ \textgreater{}6 & 7 (23.3\%) & 5 (16.7\%) \\ \hline \end{tabular} Discrete variables are reported as the number and proportion of subjects with the characteristic of interest.  
\end{table}
Table 2: Frequency and duration of respiratory infections and febrile episodes at baseline.
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 3 Related Work
### Other Techniques  
Beyond conventional machine learning, hybrid and content-aware methods have been explored. [PERSON]. [25] proposed a semantic filtering model that combines creative computing and fuzzy logic. Although this approach improved contextual understanding, its dependency on manual category definition posed scalability challenges.  
Behavioral modeling using deep learning was explored in [26], where CNNs and Keras word embeddings processed both raw text and metadata. Despite operating on imbalanced datasets, their system achieved 96.8% and 94.2% accuracy in two experiments involving emails with and without headers.  
In comparative studies, [PERSON]. [27] evaluated Naive Bayes and Hidden Markov Models (HMMs) under various pre-processing configurations of NLP. HMM consistently outperformed Naive Bayes, with precisions of 84. 46% and 74. 86%, respectively.  
Furthermore, [PERSON]. [28] explored active learning for spam detection using TF-IDF weighting and multiple classifiers, including SVM and SGD. The system achieved up to 88% accuracy but required several days for manual labeling, suggesting limited real-time applicability.  
For image-based spam detection, [PERSON]. [29] proposed a SpamAssassin plugin that used multifractal analysis and SVM classification. The system processed up to 100,000 emails daily and achieved 92% detection with fewer than 1% false positives.  
Although these studies demonstrate substantial progress in phishing detection, most existing solutions are not designed to address emerging obfuscation techniques such as Zero-Font phishing. These attacks exploit font-level manipulation to bypass user scrutiny and traditional spam filters. Furthermore, many detection systems operate in silos, focusing exclusively on header analysis, email body content, or URL inspection, thus limiting their ability to detect more sophisticated and multifaceted threats. This fragmentation creates vulnerabilities that can be exploited by attackers using layered deception strategies.
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 3 Results
### Angle Selection  
To further investigate the effect of angle quantity in our algorithm, five angles and 21 angles were selected for the PSF simulation as well. The beamformed results are shown in Figure 11. As seen, fewer or more angles have more of an impact on plain CPWC results, as shown in Figure 11a-c. The performances of our algorithm in Figure 11d-f remain good and change little when increasing or decreasing the number of angles exploited for the image formation. Results shown in Figure 11b,d and 11c,e are both comparable. This indicates that our algorithm is able to reach a similar performance as the simple CPWC using only half the angles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Beamformer** & **PSF** & **Simulated Cyst** & **Point Phantom** & **Cyst Phantom** \\ \hline CPWC & 15.35 s & 3.15 s & 19.74 s & 2.57 s \\ CPWC + Hamming & 15.38 s & 3.16 s & 19.78 s & 2.58 s \\ CPWC + our SVD filter & 15.49 s & 3.28 s & 19.89 s & 2.71 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: The run time (s) of three beamformers in four experiments.  
Figure 11: Beamformed responses of the simulated PSF: (**a**) CPWC using five angles; (**b**) CPWC using 11 angles; (**c**) CPWC using 21 angles; (**d**) CPWC + SVD using five angles; (**e**) CPWC + SVD using 11 angles; and (**f**) CPWC + SVD using 21 angles. All images are shown with a dynamic range of 60 dB.  
Table 7 gives the run time for the above trials. In order to see the time for SVD filtering more clearly, we separate the total time into time for receiving dynamic focus and time for our sidelobe suppression. As Table 7 shows, run time for our SVD filter grows almost cubically along with the increase of total angles. Combined with the results shown in Figure 11, a similar imaging quality as the simple CPWC can be obtained faster by our SVD filter using fewer angles. Considering the competitive imaging performance, our algorithm is more suitable for a small angle number in real-time applications. A choice, like 11, in our experiment seems reasonable.
## 3 Malware Graph Construction
### Graph Construction with Feature Refinement  
We built a bipartite graph from malware and its features. Malware and distinct features have their own nodes. Each malware node in the malware part is connected to its extracted feature nodes in the feature part with edges.  
Before applying graph embedding methods to this graph, we first refine some feature nodes from the graph to reduce their size. Among the approximately 524 K feature nodes,we note that some feature nodes are found unusable and only increase the computation overhead, which would make the prediction inaccurate and slow. More specifically, features with a low frequency are often meaningless and insignificant for the representation of an author group; however, some might be a detail for single malware samples. These features are a limited relationship between genetically nearby samples rather than a flow of malware mutations. By removing such meaningless feature nodes, the classification time can be significantly decreased with an improvement in the accuracy.  
Unlike low frequency features, there are also some feature nodes that are commonly found throughout multiple author groups. These commonly found features may contribute little to the author group identification. Here, we used the well-known information gain (IG) and entropy (E) to evaluate the contribution of such features for classification [24]. These were calculated as follows:  
\[IG_{F}=E(S)-E_{F}(S), \tag{1}\]  
\[E(S)=-\sum_{i\in I}P_{i,S}\log P_{i,S}, \tag{2}\]  
\[P_{i,S}=\frac{\left|C_{i,S}\right|}{\left|S\right|}, \tag{3}\]  
\[E_{F}(S)=\sum_{j\in F}\frac{\left|S_{j}\right|}{\left|S\right|}\times E(S_{j}), \tag{4}\]  
where \(S\) represents the complete set of malware samples, and \(C_{(i,S)}\) represent the labeled samples in \(S\) with author group class \(i\). Furthermore, \(I\) represents a set of all author groups found in the samples, and \(S_{j}\) represents sample \(S\) with a feature value \(j\) of a feature in question \(F\). Then, \(IG_{F}\) represents the amount of entropy reduction with the feature \(F\) as a given. In Equation (1), entropy \(E\) is considered a common occurrence throughout the total author groups. This implies that the high entropy is less distinctive for the classification. In other words, features with a high information gain are informative features that should be kept. Both information gain and entropy can be used as metrics to determine the usefulness of a given feature \(A\)[24]. Between the two, we used information gain for this research because it provided better results in our evaluation.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 3 Methodology
### Implementation of the Proposed Approaches MIRA and InCROSS
#### 3.4.1 MIRA (Multi-Dimensional InSAR Research and Analysis)
The classification probability threshold was set to 0.01, meaning that only pixels with a predicted class probability above 1% were assigned to that class. All used SVM parameters have been generated using the parameter optimization tool from the EnMap-Box 2.2.1 tool [51]. In this study, SVM classification using pyramid level 1 was also performed, and the spatial differences were visually compared (Figure 7). However, no significant difference was observed regarding the locations and size  
Figure 6: (**a**) Schematic illustration of how pixel values across multiple bands define their position in the three-dimensional feature space. (**b**) The scatter plot of the n-D Visualizer displays red and green pixel clouds, which represent disparate deformation zones. Four InSAR products are represented on four axes. of the deformation (anomaly) sites. In pyramid level 0, the resolution of the original data is preserved; in pyramid level 1, analysis/classification is performed with a lower resolution version. The fact that the different pyramid level classification results obtained are not very different from each other shows that the deformation features used for classification in this study are not affected by the scale change. Another feature is that the hyper-plane model created for the SVM classification is sufficiently robust, and has enabled us to obtain similar results at both pyramid levels. In addition, the input data set was considered to have a homogeneous structure against scale change due to the lack of differences between the pyramid levels. The fact that the classification results do not change despite the differences between the pyramid levels indicates that the model is robust to scale changes, and that the features used for classification are determined from general structures. This implies that the model has good generalization ability and gives consistent results at different resolutions. As a result of these, the pyramid level is used as 0 for the SVM classification process. The support vector machine classifier was applied to the pixel samples selected from the red and green pixel clouds in the n-dimensional scatter plot generated by the n-D Visualizer module in ENVI 5.0. The ROIs defined from these pixel clouds were used as training and test data to distinguish between different deformation characteristics. The final deformation distribution map produced by the SVM was then compared to the one obtained using the InCROSS approach to evaluate the classification performance.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing  
Motor fault diagnosis based on cloud computing involves installing multiple sensors on the drive and non-drive ends of the motor shaft to collect operational data. The collected data are then directly transmitted to the host computer for data preprocessing, feature extraction, and fault diagnosis. This generates a large volume of operational data at the device end, imposing significant pressure on data transmission and storage. Furthermore, due to limitations in transmission speed, this often results in relatively large diagnostic delays, failing to meet the real-time requirements of fault diagnosis.  
To reduce data transmission volume and minimize diagnostic delays, a fault diagnosis method based on edge computing is proposed. The basic framework of this method is illustrated in Figure 1. The core of this approach is the design of an edge node that integrates signal acquisition, signal preprocessing, and fault diagnosis, while also enabling the display of diagnostic results and feature values transmitted from the edge node on the host computer.  
To reduce the computational load on the edge node, the proposed method is divided into two parts. First, model training and parameter optimization are performed on the host computer. Second, the trained model is embedded into the edge node for fault diagnosis.  
As shown in the blue section of the figure, the host computer analyzes the correlation coefficient and mean squared error of time-domain signals to determine the relevant parameters for sliding window data augmentation. Meanwhile, the CNN-DS model is trained. Once the model training is complete, the model, along with signal acquisition, signal preprocessing, and data augmentation, is embedded into the edge node, as shown in the orange section of the figure.  
Finally, the sensor signals from the main motor of the equipment are collected and transmitted to the edge node, where the trained model performs fault diagnosis.
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Siting
Nevertheless, a radar far away from a point of interest can still provide data of sufficient quality whenever the prime radar for that location is out of service. This is part of a strategy to obtain high availability of radar data in an operational setting.
## 4 Simulation Environment
### Simulation of Nonlinear Effects at PA  
The fifth stage of the main simulation loop is the simulation of the nonlinear effects at the PA. While there exists an analytical framework for nonlinear distortion power calculation [26], it is limited, e.g., it requires the statistical properties of each wireless channel. Instead, link-level simulations are used. This is performed for all RUs and all scheduled UEs in the system. We simulate a batch of random QAM symbols, which are then subjected to precoding using the vectors already computed for all scheduled UEs, RBs,and RUs. These samples are passed through the Rapp model of the PA according to (6). Based on the knowledge of both the input and output samples, one can estimate the power scaling coefficient matrix \(\mathbf{A}_{l}\) and the power of nonlinear distortion \(\sigma_{\text{dis},i,r}\) for each UE \(i\) and RB \(r\) using (8) and (9).
## 3 Results and Discussion
### The Discussion of the MRP Model
We speculate that this result may be due to two reasons: firstly, when the launch power is too large, the nonlinear power also increases significantly, and a limited number of branches can no longer effectively adapt to nonlinear damage. Secondly, as the launch power increases, the proportion of high-order nonlinear interactions in the signal also increases, and the existing model structures are not effectively adapted to this part of nonlinearity. Therefore, increasing the number of branches, \(N_{step}\), has a limited effect on improving RMSE performance. We hope to address this issue in our future work by adding branches with high-order nonlinearity. Figure 8: RMSE of the MRP and RP models under a different number of branches. To further analyze the performance of the model, we compared the results under different distances and waveform conditions, as shown in Figures 10 and 11. Here, we use a 2 km/step SSFM model as the training label and the object for calculating RMSE and EVM. Consistent with expectations, the fitting accuracy of RMSE and EVM of MRP model is obviously better than that of RP model. As the distance increases, the errors between the RP model and MRP model with SSFM tend to stabilize, that is because the fiber attenuation makes the contribution of the fiber beyond 80 km to the signals' nonlinearity nearly negligible. For filters with different roll-off factor, the RMSE and EVM of the MRP model are also significantly better than those of the RP model. Figure 12 shows the number of steps and corresponding computational complexity required to achieve the same accuracy for MRP and SSFM when the training label is generated by 600-step SSFM. The results indicate that the MRP model with 20 branches performs as well as 200-step SSFM. Since the trainable parameters introduced by the MRP model do not increase the computational complexity of the RP model. This means that, when achieving the same degree of nonlinear fitting, the number of real multiplications and real additions required by the MRP model are only 10.23% and 10.35% of those required by SSFM, respectively. Figure 10: (**a**) RMSE and (**b**) EVM of the MRP and RP models under different fiber lengths (launch power = 13 dBm, MRP branches number \(N_{step}\) = 20). Figure 9: EVM of the MRP and RP models (**a**) w/o and (**b**) w/ ASE noise under a different number of branches, \(N_{step}\) (initial SNR = 37 dB, AWGN power = 0.01 mW).
# Experimental Study of Biogas-Hydrogen Mixtures Combustion in Conventional Natural Gas Systems
## 2 Materials and Methods
### Fuel and Oxidant Properties
#### 2.3.3 Wobbe Index  
The Wobbe Index is the main indicator of the interchangeability of fuel gases. It is defined as the volumetric value of the heating value at the specified reference conditions divided by the square root of the relative density at the same specified reference measurement conditions. Therefore, two gases with different heating values and densities can produce similar heating loads if they have a similar Wobbe Index value. Figure 4 plots the Wobbe Index results, according to the theoretical method described in the standard EN ISO 6976 [44]. As expected, the Wobbe Index lowers as carbon dioxide is blended into natural gas (Figure 4a). However, for constant carbon dioxide content, the addition of hydrogen increases the Wobbe Index, though more moderately than expected. It is precisely the relative density of hydrogen that smooths the increase in the Wobbe Index. By increasing the hydrogen content by 25%, the Wobbe Index of BG50 improves by 3.4%, while BG60 and BG70 perceive a rise of 7.75% and 9.77%, respectively. Considering the typical Wobbe  
Figure 3: Heating value comparison of BG50, BG60 and BG70, depending on hydrogen content.  
Index range of natural gas, which is between 39.1 MJ/m\({}^{3}\) and 54.8 MJ/m\({}^{3}\)[45], not even BG70 reaches the minimum value. The ideal condition to replace natural gas for biogas in a conventional combustion device is to assure the same heating value and Wobbe Index. The results plotted in Figure 4a reflect that CO\({}_{2}\) addition to natural gas drastically reduces the Wobbe Index. Since the addition of hydrogen implied an increase in the calorific value of the mixture that exceeded the calorific value of natural gas, a similar trend was expected for the Wobbe Index. However, Figure 4b shows that the biogas-hydrogen mixtures considered in this study do not reach the minimum value for natural gas.
## 5 Experimental Results
### Results Using the Dataset with Pathologies
#### 5.1.4. Results for Decision Tree with Classical Feature Selection (DT-RFECV)  
Using RFECV, a total of 13 variables were selected, reaching an accuracy rate of 0.6962 and, in the best case, up to 0.7559, as shown in Table 2. Some of the variables are education, alcoholic drinks, physical exercise, and diabetes, among others. Again the most common variables are included: sex, age, and apnea. Figure 3 shows the evolution of the accuracy rate, in this case, the maximum working with 13 variables, reducing the accuracy when the number of variables increases.