esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Surface Roughness after Acid Treatment  
Surface roughness (Ra) measurements were taken before and after acid treatment to simulate the effects of acidic exposure on the materials. The results are detailed in Tables 4 and 5 and illustrated in Figure 4. FUJI IX GP FAST exhibited minimal change in surface roughness after acid treatment, with values of 0.95 \(\upmu\)m (\(\pm\)0.10 \(\upmu\)m) before treatment and 0.97 \(\upmu\)m (\(\pm\)0.09 \(\upmu\)m) after, showing no statistically significant difference (\(p\) = 0.133). On the other hand, RivaSilver showed a significant increase in roughness from 1.2 \(\upmu\)m (\(\pm\)0.12 \(\upmu\)m) to 1.6 \(\upmu\)m (\(\pm\)0.15 \(\upmu\)m) post-acid exposure (\(p\) = 0.007). SDR flow+ and Vertise Flow also demonstrated significant increases in surface roughness following acid treatment, with SDR flow+ roughness increasing from 0.85 \(\upmu\)m (\(\pm\)0.08 \(\upmu\)m) to 1.3 \(\upmu\)m (\(\pm\)0.14 \(\upmu\)m) (\(p\) = 0.001) and Vertise Flow increasing from 0.88 \(\upmu\)m (\(\pm\)0.09 \(\upmu\)m) to 1.4 \(\upmu\)m (\(\pm\)0.13 \(\upmu\)m) (\(p\) = 0.005). The SEM images in Figure 5 provide visual evidence of the surface alterations observed post-treatment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Group 1** & **Group 2** & \(p\)**-Value** \\ \hline FUJI IX GP FAST & 1 & 2 & 0.068 \\ FUJI IX GP FAST & 1 & 3 & 0.246 \\ FUJI IX GP FAST & 1 & 4 & 0.340 \\ FUJI IX GP FAST & 2 & 3 & 0.251 \\ FUJI IX GP FAST & 2 & 4 & 1.000 \\ FUJI IX GP FAST & 3 & 4 & 0.588 \\ RivaSilver & 1 & 2 & 0.007 \\ RivaSilver & 1 & 3 & 1.000 \\ RivaSilver & 1 & 4 & 0.003 \\ RivaSilver & 2 & 3 & 0.022 \\ RivaSilver & 2 & 4 & 0.050 \\ RivaSilver & 3 & 4 & 0.002 \\ SDR flow+ & 1 & 2 & 0.000 \\ SDR flow+ & 1 & 3 & 0.002 \\ SDR flow+ & 1 & 4 & 1.000 \\ SDR flow+ & 2 & 3 & 0.000 \\ SDR flow+ & 2 & 4 & 0.076 \\ SDR flow+ & 3 & 4 & 0.003 \\ Vertise Flow & 1 & 2 & 0.005 \\ Vertise Flow & 1 & 3 & 0.281 \\ Vertise Flow & 1 & 4 & 0.001 \\ Vertise Flow & 2 & 3 & 0.002 \\ Vertise Flow & 2 & 4 & 0.016 \\ Vertise Flow & 3 & 4 & 0.027 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Roughness values, highlighting statistically significant comparisons between materials after acid treatment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment Number** & **Group 1** & **Group 2** & \(p\)**-Value** \\ \hline
1 & RivaSilver & Vertise Flow & \(<\)0.001 \\
1 & SDR flow+ & Vertise Flow & \(<\)0.001 \\
2 & FUJI IX GP FAST & RivaSilver & 0.125 \\
2 & FUJI IX GP FAST & SDR flow+ & \(<\)0.001 \\
2 & RivasSilver & SDR flow+ & \(<\)0.001 \\
2 & FUJI IX GP FAST & Vertise Flow & \(<\)0.001 \\
2 & RivaSilver & Vertise Flow & \(<\)0.001 \\
2 & SDR flow+ & Vertise Flow & \(<\)0.001 \\
3 & FUJI IX GP FAST & RivaSilver & \(<\)0.001 \\
3 & FUJI IX GP FAST & SDR flow+ & \(<\)0.001 \\
3 & RivaSilver & SDR flow+ & \(<\)0.001 \\
3 & FUJI IX GP FAST & Vertise Flow & \(<\)0.001 \\
3 & RivasSilver & Vertise Flow & \(<\)0.001 \\
3 & SDR flow+ & Vertise Flow & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Figure 4: Boxplot of surface roughness of the four acid groups.
# Adaptive Beamforming Applied to OFDM Systems
## 1 Introduction
The work in [31] proposed a post-FFT beamformer based on the estimated one-tap channel coefficient associated with a pre-FFT switched-beam device. It is crucial to observe that there is no open eye condition in the time domain, pre-FFT device part, and this forces most works in the literature not to use the adaptive scheme without prior knowledge of the channel. In other words, there are no reference symbols in the OFDM time domain, and for that, it is necessary for the channel estimation to update the beamforming gains. The works presented in [19; 22; 32; 33; 34; 35] proposed the alternative method using beamforming with the Least Mean Squares (LMS) and Recursive Least Squares (RLS) adaptive algorithms in the time domain, pre-FFT, without channel estimation. This method updates beamforming gains in the pre-FFT; however, it calculates the error information in the post-FFT, and after, the error signal returns back to the pre-FFT adaptive algorithm. However, for each antenna gain updating, the reference signal (the OFDM pilots) arrives delayed in \(C\) samples (or one OFDM symbol) from the antenna input signal, and this desynchronization generates convergence problems on the adaptive algorithms. The new approaches using machine learning and optimization algorithms are applied to the adaptive beamforming [36; 37]. However, these techniques have a high computational complexity when compared with the LMS algorithm. This work presents a new implementation of the pre-FFT beamforming, which uses an adaptive spatial filter trained with the LMS algorithm with samples of the OFDM symbol in the time domain. The proposed scheme in this paper does not depend on the channel estimation and uses two operation modes, called here semi-supervised and semi-unsupervised, where the reference signal is an estimated OFDM symbol created in the frequency domain. For the semi-supervised mode, the reference signal is generated from pilot symbols in the frequency domain (or pilot carriers) distributed or in sequence. For the semi-unsupervised mode, the scheme proposed here uses the pilot symbols associated with decided data symbols (or decided data carriers) using the Decision Directed (DD) algorithm. Different from the proposals shown in [19; 22; 33; 34; 35], the error signal is calculated in the time domain, and the reference signals of the antenna input signal are not delayed.
## 4 System Overview
### Team  
Regarding the crew, we consider three principal roles: mission commander, team leaders, and team members. There can be other roles according to the mission and scenario, such as analysts, maintenance workers, communications technicians, etc. As shown in Figure 3 and described below, each role entails different functions, access to information, workplace, and available actions.  
\begin{table}
\begin{tabular}{l l l} \hline
**Missions** & **Specific Tasks** & **Generic Tasks** \\ \hline Prevention & Vegetation mapping & Mapping \\  & Fire investigation & Search, Monitoring, Tracking \\ Surveillance & Risk mapping & Mapping \\  & Fire surveillance & Surveillance, Reconnaissance \\ Extinguishing & Fire monitoring & Monitoring, Search \\  & Firefighter support & Support, Transport \\ \hline \end{tabular}
\end{table}
Table 1: List of tasks considered for firefighting missions.  
* Mission commander: They monitor and controls the mission from the base station, which does not have to be in the fire scenario. All the data collected by the drones is received in the base station and processed to obtain valuable information. Therefore, the mission commander has access to full information on the mission, including the telemetries of drones and measurements on the fire. They must use this information to manage the mission, coordinating the teams on the ground and commanding the swarm. The drone swarm is controlled through high-level commands (e.g., defining areas of interest, variables that must be measured, and required tasks) instead of through low-level orders (e.g., sending specific waypoints and actions to specific drones). This feature is one of the most remarkable strengths of robot swarms, which can self configure to accomplish tasks most accurately, efficiently, and safely. Finally, the mission commander communicates with the team leaders to deliver high-level orders for their teams, establishing the areas where they must work, the tasks that they must perform, and the resources that they can use.
* Team leader: They work in the fire scenario, preferably in a facility or vehicle to ensure communications with the base station. The task of a team leader is to coordinate the field operations of a firefighting team. For this purpose, they receive high-level orders from the mission commander (e.g., area of work and tasks to be performed) and sends low-level commands to the team (e.g., move along a path and attack some flames). In this role, local information is managed both geographically and functionally, that is, the events that happen in the work area and affect the performed tasks.
* Team members: They work in the fire scenario, executing prevention, surveillance, and extinguishing tasks. For this purpose, they can exercise their workforce or use different types of vehicles and machinery. They have access to limited local information, mainly related to the paths that must follow and the actions that must perform. The amount of information should be limited to avoid distractions, but should be enough to ensure their safety.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
### Multi-Source Affective Computing  
Multi-source emotion computing refers to the application of experiments utilizing multi-source data in emotion computing, aiming to transition from single-mode to multi-source data [20], transcending modal boundaries to achieve a higher emotion recognition rate [21; 22]. Its research framework encompasses two core themes and five key aspects (as depicted in Figure 1). The two themes are emotion recognition and emotion analysis, while the five aspects include the foundational theory of emotion, signal collection, algorithm modeling, modal fusion, and output presentation, which may overlap and interrelate. In the domain of emotion recognition, the focus is on detecting human emotional states (i.e., discrete or dimensional emotions) through visual, auditory/speech, and physiological modalities. Sentiment analysis, on the other hand, primarily centers on evaluating and extracting preferences for objects or events [23], with results typically categorized as positive, negative, or neutral [24; 25]. Among the five aspects, the first is the foundational theory of emotions. Psychologists have proposed two primary models--the discrete emotion model and the dimensional emotion model--to simulate human emotions from basic to complex states [26; 27]. The second aspect concerns the collection of emotional signals. Recent advancements have seen the use of various physiological signals, such as electroencephalogram (EEG), electromyogram, electrocardiogram, and eye movement, alongside non-physiological signals, including text, speech, facial expressions, and body movements, all of which contribute to emotion recognition, supported by corresponding datasets [28; 29; 30]. Notably, EEG signals have been shown to outperform other physiological signals in emotion recognition tasks [31; 32], while eye movement signals complement EEG data in multimodal emotion recognition scenarios [33; 34], enhancing the accuracy of multimodal emotion classification systems. The third aspect, algorithm modeling of emotion, involves leveraging machine learning, ensemble learning, and deep learning techniques to model and identify emotional signals. Common classifiers for environmental emotion recognition include Logistic Regression, Support Vector Machines (SVMs), Decision Trees, ensemble models, and neural networks [35; 36; 37]. The fourth aspect, modal fusion, integrates emotional features and fusion algorithms derived from multimodal recognition to enhance emotion classification accuracy [38]. The fifth aspect is the output presentation of emotion, which enables machines to express emotions through facial expressions, vocal intonation, body movements, and visualization platforms, following the learning of emotional signals [39], thereby advancing human-computer interaction. Therefore, sentiment analysis utilizing multi-source data plays a pivotal role [40] and has been increasingly validated in sentiment classification and event detection applications [30; 41].
## 2 Mechanism of Action  
Several mechanism of action have been suggested to describe how surfactants enhance the cellulase activity and enzymatic hydrolysis of pure cellulose or lignocellulosic biomass: (1) surfactants can extract hydrophobic degradation products from lignin and hemicellulose by forming emulsions, thereby enhancing the removal of lignin and increasing the access of feedstock's reaction sites to the cellulolytic enzyme [31,53,54]; (2) surfactants lessen irreversible, non-productive adsorption of cellulase to non-productive sites of biomass (e.g., crystalline cellulose and lignin), which allows the enzyme to be available in solution and have higher activity [27,33,48]; (3) improved electrostatic interaction between surfactant monomer or micelles and enzyme causes an enhanced enzyme activity by activating a certain amino acid in the enzyme [55,56] or reforming enzyme secondary structure, specially the \(\alpha\)-helixes [43,52]; (4) surfactants protect enzyme from thermal deactivation after extended incubation period [52,56] and denaturation by reducing the surface tension and viscosity of liquid that in turn diminishes the contact of enzyme with air-liquid interface [56]. Overall, in a solution of surfactants, enzymatic reactions occur either inside the surfactant micelle core or at the interface of the micelles or monomers and the pseudo-phase of the liquid, depending on the enzyme hydrophobicity [57]. In spite of the above hypotheses, a mechanism that can consistently explain how surfactants improve enzymatic hydrolysis has yet to be developed.
# Corrosion Assessment of Zr2.5 Nb Alloy in Ringer's Solution by Electrochemical Methods
## 3 Results and Discussions
### Evaluation of Polarization Resistance, \(R_{p}\), of the Zr2.5 Nb Alloy  
The evolution of polarization resistance, \(R_{p}\), is a distinctive technique in measuring corrosion rates, namely the loss of direct thickness in real time. In conclusion, the higher values of polarization resistance lead to a lower corrosion rate [17].  
Figure 3 shows the linear polarization \(log\)\(i\) vs. potential for the Zr2.5 Nb samples tested in [PERSON]'s solution at a scanning speed of 3 mV/s.  
Linear polarization was obtained by scanning the potential around the free potential with a small amplitude of \(\pm\)50 mV.  
From the polarization resistance diagram of the Zr2.5 Nb, the 100 values of the polarization resistance and corrosion currents can be extracted from the slope of the curve, according to the Stern-Geary equation [18; 19]:  
\[i_{corr}=\frac{B}{R_{p}} \tag{1}\]  
At the same time, the Stern-Geary parameter, \(B\) was calculated using the equation:  
\[B=\frac{b_{a}|b_{c}|}{2.303(b_{a}+|b_{c}|)} \tag{2}\]  
where \(i_{corr}\) = corrosion current density; \(R_{p}\) =polarization resistance; and \(b_{a}\) and \(b_{c}\) are the slopes of Tafel for anodic and cathodic reactions, respectively.  
From the analysis of Figure 4, a decrease in the polarization resistance is observed with the increase in the immersion time. If, after 6 h of immersion, \(R_{p}\) = 39.6 \(\pm\) 4 kohm cm\({}^{2}\), after about 37 h, \(R_{p}\) decreases to a value of 33.2 \(\pm\) 5 kohm cm\({}^{2}\). A lower value of the polarization resistance indicates a higher corrosion rate as the immersion time increases for the Zr2.5 Nb alloy immersed in Ringer's solution.  
Figure 3: Linear polarization curve of Zr2.5 Nb alloy immersed in Ringer’s solution drawn at a scanning speed of 3 mV/s.
## 5 Results and Discussion
### Detection Experiments for Various Attacks Based on Reconstruction Error  
From Figure 5, it can be observed that under the same perturbation strength, the detection rates of adversarial examples generated by BIM and PGD attacks are lower than those generated by FGSM attacks. For instance, when \(\varepsilon=0.15\), the detection rates of adversarial examples generated by BIM and PGD attacks using the reconstruction error-based detection method are 65.09% and 73.45%, respectively, which are lower than the detection rate for adversarial examples generated by FGSM attacks. Since FGSM is a one-step attack and is more direct and coarse in generating adversarial examples compared to the iterative attacks of BIM and PGD, it causes more significant disruption to the sample distribution, making it more prone to detection as adversarial examples.  
In this experiment, the reconstruction error is used to compare the differences between original samples and reconstructed samples generated by the AE, as well as between original samples and adversarial examples. The AE is an unsupervised machine learning model that aims to preserve the information of the original input as much as possible during the compression and decompression process. Therefore, if the AE is properly trained, the  
Figure 5: Detection accuracy against multiple adversarial attacks. (**a**) FGSM; (**b**) BIM; (**c**) PGD.  
generated reconstructed samples should be very close to the original samples in terms of visual appearance and features, resulting in a low reconstruction error.  
However, adversarial examples are usually designed to make subtle but crucial modifications to the original samples, deceiving the machine learning model without being noticeable to the human eye. Although adversarial examples may appear very similar to the original samples visually, these subtle changes can lead to larger errors when the AE attempts to reconstruct the adversarial examples. This is because these changes may cause the adversarial examples to not accurately map to the position of the original samples in the latent space. Therefore, the reconstruction error demonstrates the ability to differentiate between original and adversarial examples. By setting a reasonable reconstruction error threshold, we can to some extent distinguish between original and adversarial examples. In general, if the reconstruction error of a sample exceeds this threshold when compared to the original sample, we can consider that sample to be an adversarial one. This finding provides an effective method for detecting adversarial examples.
## 5 Experiment Evaluation
### Experiment Setup  
The numerical simulation is conducted for the proposed platform is compared with the existing platforms. The iFogSim simulator is used for the experiment of the proposed BFAN model. This simulator provides a real-time scenario for the smart city fog network [3]. We follow the same network setting as used in the article [35]. The tasks are the incoming traffic of web applications. The incoming workload requires two types of resources such as CPU and network.  
The physical proximity of the devices helps to deploy the FN at the distance (e.g., 3 feet, 30 m, 10 m). The served clusters act as a service point for a particular diameter of FN's spatial cluster.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Architecture** & **QoS** & **Technology** & **Access Medium** & **Latency** & **Bandwidth** & **Mobility** & **Storage** & **Delay** \\ \hline Primary & centralized & high & W/FG /GC /SG & fixed/wireless & low & medium & yes & yes & very low \\ Inter-primary & centralized & high & Zigbee/Bluetooth/WIFI & fixed/wireless & very low & high & yes & no & very low \\ Secondary & distributed & very high & SG/GC /WIFI & fixed & low & low & no & yes & low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of Blockchain and Fog-based Architecture Network (BFAN).  
Figure 3: Model diagram of proposed BFAN model.  
The multi-core processors are deployed in FN to serve the multiple requests at a time for 6 GB RAM, AMD Phenom 6 core processor. In order to gain high performance of processor at low energy, these settings ensure maximum gain in the operating frequency. Every FN has two modes of communication: primary and secondary, which are connected through the Giga Ethernet. The incoming workload has been processed at FN and consumes the electric power. We assume that each core processes the same number of tasks at the frequency of 10 Megabytes per second. We set the idle CPU power at 105 watts and maximum power at 195 watts. The delay is considered as 6, 4 and 2 ms for FN2 FN, tFNt, and t2t. Furthermore, the configuration time for wired communication is 10 ms and for wireless 0.5 ms is considered. The experiment is performed for 1000 s in simulation.
# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Introduction of Experimental Platform and Evaluation Indicators
#### 4.2.2 Introduction to the Experimental Platform  
This section intends to validate the effectiveness of the fusion classification of M-AVFA proposed in this paper. Firstly, the model architecture of the 2D-CNN was determined. Once the model CNN architecture was established, we proceeded to assess the effects of various parameters. Afterwards, we compared the audio-visual fusion recognition method using image recognition algorithm and sound recognition algorithm.  
During the training of our model, the predicted label and the actual label of the object we trained achieved an optimal fitting state. The sound data and image data we trained were randomly selected from the self-made dataset. Both the sound dataset and the image dataset were then randomly divided into the training and the verification sets, following a 9: 1 ratio. All the parameters we proposed are listed in Table 1. To achieve the optimal result, the loss value we took was just converging, hence preventing overfitting. The method  
Figure 3: Data acquisition. Figure (**e**) demonstrates the utilization of the microphone to collect the sound information corresponding to Figure (**a**) “occluded bicycles”, (**b**) “occluded buses”, (**c**) “occluded cars”, and (**d**) “occluded pedestrians” (captured by the camera),while simultaneously acquiring environmental acoustic data. These data are as follows: (**a**) bicycle sounds in outdoor environments, (**b**) bus noises in exterior settings, (**c**) vehicular traffic sounds in open-road conditions, and (**d**) human speech in indoor crowded spaces.  
proposed in this paper was written by python3.8 and the Pytorch 1.10.1 framework; it runs on Ubuntu20.04 and RTX4090 GPU.
## 3 Project Overview and Data Preprocessing
### Optimal Feature Subset Selection
#### 3.3.1 Feature Selection Based on Recursive Feature Elimination Method  
Using RF-RFECV for variable selection, the least important feature can be eliminated in each iteration, and the scores of each feature are adjusted through repeated iterations. Ultimately, the optimal feature subset is selected via cross-validation. When running RFECV, specific hyperparameters are chosen: RF is used as the base model; 10-fold cross-validation is employed; one feature is eliminated at a time (step = 1); and the minimum number of retained features is set to one.  
(1) Accuracy rates using these four feature selection methods.  
Accuracy measures how often the model is correct when making predictions. In order to evaluate the effectiveness of the RF-RFECV feature selection method, various techniques such as gradient boosting (GB), logistic regression (LR), k-nearest neighbors (KNN), support vector machines (SVCs), and RF-RFECV are used for the selection and extraction of feature variables. The experiments were conducted twice, and the average accuracy was computed to ensure result reliability. Figure 4a (sorted by accuracy) illustrates the achieved accuracy rates of these four feature selection methods.  
Figure 4: Results of optimal feature subset selection: (**a**) accuracy rates of these four feature selection methods; (**b**) classification accuracy corresponding to the number of selected feature variables; and (**c**) random forest feature importance ranking.  
(2) Research on the relationship between the number of feature variables and accuracy.  
The RF-RFECV process described earlier was utilized to generate a graphical representation depicting the relationship between classification accuracy and the number of feature variables, as depicted in Figure 4b.