esa-sceva/satcom-chunk-collection huggingface dataset:
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 1 Introduction
This in turn would enable 2C task delegation to the preferred edge-computing machine or server characterized by the delay associated with the transmission to this server, queuing and computing clock frequency, packet error probability associated mostly with transmission and networking, and offered computational power. The classification criteria may alter, but in the case of multi-class classification with set sensitivity levels (from noncritical to highly critical actions), any misclassification might result in a fatal classification error. Adversarial machine learning (AML) [2] is a term describing the study of the attacks on ML algorithms and the defenses against such attacks. Attackers can use a variety of tactics to target a system (see Table 1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack** & **Description** & **Example for 2C Task Classification** \\ \hline Exploratory (Inference) Attacks & See to discover the model mechanism using the training data and then imitate the model by building a surrogate model. This is realized by probing the input and the outputs. & Build a surrogate model and use it to find its decision boundaries. This can be used later to launch attacks near these decision boundaries. \\ \hline Membership Inference Attacks MIA & The adversary aims to determine if a given data sample is a member of the training data, and then the design of the attack can be more successful. & For example, the attacker aims to determine whether a service is used for clustering. This allows the attacker to launch a more sophisticated attack. \\ \hline Evasion (Adversarial) Attacks & The aim is to manipulate the input test data to fool the model into making the wrong decision. An evasion attack determines the samples that the target classifier is likely to misclassify. & Inject adversarial examples to the 2C task allocation ML model to fool the classifier. \\ \hline Spoofing Attacks & The adversary generates synthetic data samples from scratch rather than adding perturbations to the real ones. & Spoof the ML model and use it to classify the attacker data as high QoS tasks, hence overloading the dedicated VMs for high QoS tasks. \\ \hline Causative (Poisoning) Attacks & The aim is to manipulate the training process of models by injecting weaknesses, such as altering the training data of the model. Provides erroneous training data samples to reduce the reliability of the classifier. & Target cluster poisoning by injecting a new cluster, which will fool the classifier and reduce the allocation system performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attack classification in AML with 2C task classification relevance.
# Dynamic Single-Electron Transistor Modeling for High-Frequency Capacitance Characterization
## 2 Method of Modeling
### Extraction of Capacitance Components  
As shown in Figure 1, a three-terminal SET has in total 9 terminal capacitances and transcapacitances. Since the \(I_{\text{g}}\) current source is newly added to the proposed model, the gate input capacitance \(C_{\text{gg}}\), the drain-to-gate feedback capacitance \(C_{\text{gd}}\), and source-to-gate capacitance \(C_{\text{gs}}\) can be extracted from the simulation.  
The \(C_{\text{gg}}\) is evaluated by the circuit shown in Figure 2a where small sinusoidal fluctuation with amplitude \(\Delta V_{\text{gs}}\) (\(=e/C_{\text{g}}/1000\)) is added to the gate bias voltage \(V_{\text{gs}}\), and then the amplitude \(\Delta I_{\text{g}}\) of the gate current fluctuation is measured by the simulation. The \(C_{\text{gg}}\) is calculated by  
\[C_{\text{gg}}=\frac{\Delta I_{\text{g}}}{\omega\Delta V_{\text{gs}}} \tag{6}\]  
As shown in Figure 2b, the \(C_{\text{gd}}\) is measured by adding small sinusoidal fluctuation with amplitude \(\Delta V_{\text{ds}}\) (\(=e/C_{\text{$\sum$}}/1000\)) to the drain bias voltage \(V_{\text{ds}}\), and the amplitude \(\Delta I_{\text{g}}\) of the gate current fluctuation is observed. Similarly, the \(C_{\text{gd}}\) is obtained by  
\[C_{\text{gd}}=\frac{\Delta I_{\text{g}}}{\omega\Delta V_{\text{ds}}} \tag{7}\]  
Considering that the sum of \(C_{\text{gd}}\) and \(C_{\text{gs}}\) is equal to \(C_{\text{gg}}\) based on charge conservation of the \(C_{\text{gs}}\) is given by  
\[C_{\text{gs}}=C_{\text{gg}}-C_{\text{gd}} \tag{8}\]
## 3 Research Methods, Research Platform, and Data Sources
### Research Methods,
#### 3.1.2 Topic Modeling  
We employed the LDA model to uncover and describe the thematic content associated with the application of generative artificial intelligence in the field of education. Topic mining, also referred to as topic extraction, is a process that aims to map high-dimensional textual content into a lower-dimensional semantic space through specific methodologies [52]. The LDA topic model is one such tool used for topic mining, designed to extract latent thematic information from large document collections, thereby achieving the goals of topic clustering or text classification [53]. The LDA model generates results based on two probability distributions: the document-topic distribution, which reflects the degree of association between each document and various topics, with higher weights indicating stronger relevance to a specific topic; and the topic-word distribution, which reveals the internal structure of each topic, where higher probability values for specific words indicate their greater importance within the topic [54] (see Figure 3 for the detailed process).
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 3 Surface Inspection Process  
Bearing rollers have two end surfaces and a cylindrical surface. Since the cylindrical surface is the working surface of the bearing roller, a roller must be judged as unqualified if there are defects on it. If the defects occur on the outer circumference of the end surfaces, such as material lacking at the chamfer and stamp lacking, it will also affect the working surface, and the roller must also be judged as unqualified. For the defects inside the end surfaces, we can calculate the defect area to determine whether the roller is qualified.  
Because the material lacking at the chamfer, represented by Figure 2i,j above, and the stamp lacking, represented by Figure 2l, can be first detected and excluded in the inspection process described below, our detection algorithm primarily detected and analyzed four categories of defects, which were damage, scratch, corrosion and grind lacking. Details of these defect categories are shown in Figure 4 below. Defects other than those mentioned above are not discussed because of their low frequency of occurrence.  
Image acquisition was performed at a suitable working distance. For each bearing roller, a total of two images were captured on both end surfaces, and the image was cropped to a resolution of \(416\times 416\). For the cylindrical surface, of each bearing roller, two images were captured and the resolution was also \(416\times 416\) after cropping.  
We note that, although the shapes of the same defect category are different, there are similarities in features that can be extracted and classified by convolutional neural networks. In this section, we will describe in detail the method for identifying various defects on bearing rollers. The completed process pipeline is shown in Figure 5 below.  
The process consisted of the following three stages: First, contour detection. It is used to determine if the outer contour of the end surface is a standard circle and exclude the roller with a non-circular contour. Second, defect detection. It uses a multi-task learning convolutional neural network to classify and locate defects. Third, roller quality evaluation. It is used to determine whether the bearing roller is qualified according to the position, category, and area of the defect.  
Figure 4: Details of common defect categories: (**a**) Damage; (**b**) corrosion; (**c**) grind lacking; and (**d**) scratch.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 3 Case Study
### Hysteresis Feature Extraction
#### 3.3.2 Heat Load Hysteresis Feature Extraction  
This paper analyzes the PACF of the heat load data and selects the 95% confidence interval to determine the significance of the lag order. Specifically, the lag order where the partial autocorrelation coefficient is greater than or equal to 0.05 is selected as the optimal historical period of the heat load sequence, as shown in Figure 9.  
The partial autocorrelation diagram shows that the sample coefficient of the sequence falls within the random interval for the first time after a lag order of 8. However, at a lag order of 11, it deviates from the random interval again, displaying a tailing effect. Considering the characteristics of various data acquisition cycles and other factors, a lag order of 8 is selected. Consequently, the data with a collection frequency of 15 min is downsampled to a 2-h interval. The specific method involves using a 2-h data window and taking the arithmetic average of the eight data points within this window as the characteristic data for that moment. Each data point includes heating pipe network parameters and outdoor meteorological parameters, resulting in the data volume being reduced to one-eighth of the original.  
After data downsampling, the prediction period changes from 15 min to 2 h, and the downsampled heat load sequence is shown in Figure 10. This process smooths short-term fluctuations, reduces the impact of instantaneous changes such as noise, and retains significant variations. By focusing on the overall trend of the system, this method enhances the tracking and prediction accuracy of heat load fluctuations. It also meets the requirements of practical engineering applications.  
Figure 10: One-week heat load change trend diagram (acquisition cycle is 2 h).  
Figure 9: Partial autocorrelation diagram of heat load sequence.
## 4 Simulation Setup and Experimental Results
### Time Complexity
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Time Complexity of Proposed Approach**} & \multicolumn{1}{c}{**Time Complexity**} \\ \hline  & \(T(n)=at\big{(}\frac{n}{b}\big{)}+O(n)\) \\  & Here, we divide the problem into two portions of the same size. However, the algorithm is infinite. Thus: \\  & \(T(n)=2t\big{(}\frac{n}{b}\big{)}+O(n)\) \\ HAS & \(T(n)=4t\big{(}\frac{n}{b}\big{)}+n+n\) \\  & \(T(n)=4t\big{(}\frac{n}{b}\big{)}+2n\) \\  & \(T(n)=4t+2n\) \\  & \(T(n)=O(logn)\) \\  & \(T(n)=t(1)+n+n\) \\  & \(T(n)=t+n+n\) \\  & \(T(n)=t\) is ignored; therefore, we obtain \\  & \(T(n)=t+n\) \\  & \(n=k\) \& \(k=logn\) \\  & \(T(n)=t\big{(}\frac{n}{b}\big{)}+n+n\) \\ SD & \(T(n)=t(1)+n+n\) \\  & \(T(n)=t+n+n\) \\  & \(T(n)=t\) is ignored; therefore, we obtain \\  & \(T(n)=t+n\) \\  & \(n=k\) \& \(k=logn\) \\  & \(T(n)=t\big{(}\frac{n}{b}\big{)}+n+n\) \\ SD & \(T(n)=t\big{(}\frac{n}{b}\big{)}+O(n)\) \\ SD & \(T(n)=t(1)+n+n\) \\  & \(T(n)=t+n+n\) \\  & \(T(n)=t\) is ignored; therefore, we obtain \\  & \(T(n)=t+n\) \\  & \(n=k\) \& \(k=logn\) \\  & \(T(n)=t\big{(}\frac{n}{b}\big{)}+t\big{(}\frac{2n}{b}\big{)}+n+n\) \\  & \(T(n)=2t+2n\) \\  & \(T(n)=logm\) \\  & \(T(n)=O(nlog(n))\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The simulation results confirm that our proposed SDAAA approach performs better than the known secure data aggregation approaches like SD, EEHA, HAS, IIF, and RHC. We show the detailed outcomes of metrics used in our proposed SADAA approach, compared with the other secure data approaches like SD, EEHA, HAS, IIF, and RHC in Table 5 as follows:
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 2 Materials and Methods  
Sentinel-2 multispectral images were used for the learning phase of the AANN. The AANN was trained to extract the compressed representation of the image through the bottleneck layer. Then, the trained network was used in the testing phase on a dataset specifically made for change detection and composed of couples of multi-temporal images. As summarized in Figure 1, features related to the acquisitions at time t\({}_{1}\) and time t\({}_{2}\) were extracted through the encoding part of the network. The similarity between the two compressed feature vectors was analyzed by calculating the Euclidean distance (ED). The obtained results were compared to those achieved by using DWT as the compression algorithm.
## 2 Materials and Methods
### Soil Water Repellency Measurements  
Persistence of WR was assessed by a water drop penetration time (WDPT) test [18]. Similarly to [PERSON] et al. [19] or [PERSON] et al. [20], the measurements were carried out on field-moist samples (WDPT\({}_{\rm A}\)--actual water repellency) and after drying at laboratory temperature (WDPT\({}_{\rm P}\)--potential water repellency). Soils were placed in Petri dishes and three drops of distilled water (0.05 mL) placed onto the surface of soil sample using a medicinal dropper. The actual time required for complete penetration of the droplet was recorded. To reduce evaporation, the dishes were covered when testing. The average of three WDPT values was used to characterize WR of the sample.  
Figure 1: Location of three experimental sites (**T1**, **T2**, and **T3**) where 45 topsoil samples were taken. Photographs on the right display the conditions at the three sites two years after the windstorm.  
For WR severity assessment, the molarity of an ethanol droplet (MED) test was performed [18]. Standardized ethanol/water solutions were used, ranging from 0.17 mol L\({}^{-1}\) to 7.49 mol L\({}^{-1}\) (with 1 vol. % increments). Drops (0.05 mL) were applied in increasing concentration order until penetration occurred within 3 s. The corresponding molarity value was taken as representative to characterize the sample's wettability. The MED test was performed on air-dried samples only to avoid possible dilution of the ethanol solutions by the water contained in field-moist samples. To minimize the extent of disturbance, grinding and sieving procedures were avoided and water/ethanol droplets were applied on a smoothed surface of the loose soil sample after gentle removal of macroscopic plant debris.
# Testing the Effectiveness of Voxels for Structural Analysis
## 1 Introduction
### Voxel  
Voxelization is undoubtedly a quicker method than retopology for generating NURBS, but careful selection of parameters is essential, as excessive smoothing can often be applied to the model. Nevertheless, this approach appears to be the most advantageous in terms of both time efficiency and precision, as it circumvents the complications associated with the various stages involved in handling a 3D mesh and its conversion. Voxelization can be performed using specialized software such as Blender 4.1 and Meshmixer 3.5.474, which automatically generate a voxel model from the mesh, as well as the open-source library Open3D. Blender offers a straightforward process, allowing the operator to control only the number of approximated elements by specifying the resolution or detail level of the re-meshed model. This value determines the size of the voxel. The voxels are organized on the mesh to establish the new geometry; for instance, a value of 0.5 m will yield topological patches approximately 0.5 m in size. Smaller values retain finer details but result in a mesh with a significantly denser topology. Meshmixer, on the other hand, constructs a watertight solid by converting the object into a voxel representation. The process is user-friendly, with the operator able to adjust only the solid type--either fast or accurate--along with the volume accuracy using a sliding tool that provides a numerical value as the density. It is important to note that these parameters do not directly correlate to the final polygon count of the volume. Open3D offers core functionalities that include (i) 3D data structure, (ii) 3D data processing algorithms, (iii) scene reconstruction, (iv) surface alignment, and (v) 3D visualization. To date, no research has conducted a comparative analysis of volumetric models generated through various techniques and procedures to determine which method offers the highest precision and accuracy. Most related studies utilize voxelization for object detection [14; 35; 36; 37; 38; 39], particularly in the contexts of autonomous driving and feature detection for 3D point cloud segmentation. Voxel-based modeling has extensive applications in the medical sector [40; 41; 42; 43; 44]. Some experiments have explored the application of voxels in FEA, such as assessing ballistic impacts on ceramic-polymer composite panels [45], where voxel-based micro-modeling facilitated the creation of a parametric model of the composite structure. Additionally, another investigation used voxel modeling to estimate roof collapses, successfully addressing challenges in reconstructing geometries and overcoming limitations associated with the software used [46]. To enhance the accuracy of FEA, it is noted that while voxel usage decreases mesh generation time, it may compromise precision when handling curved surfaces: reference [47] proposes a homogenization method for voxel elements to address this issue.
## 3 Methods and Data
### Data Processing
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Day** & **Time** & **T** & \(\sigma\) T & **wl** & \(\sigma\) **wl** & **Slope** & **Min d** & \(\overline{\rm d}\) & **Max d** & **Min v** & \(\overline{\rm\
u}\) & **Max v** & **Q** & **vol** & **vol \(<\) 0.5** & **vol 0.5** & **vol 1–1.2** & **vol \(>\) 1.2** \\ \hline
16 & 19:30-21:35 & 5.0 & 0.056 & 14.818 & 0.006 & 0.0010 & 0.35 & 1.38 & 2.61 & 0.12 & 0.72 & 1.43 & & 4656.6 & 306.0 & 4249.8 & 88.2 & 12.6 \\
17 & 17:00-19:00 & 6.2 & 0.084 & 15.025 & 0.007 & 0.0013 & 0.30 & 1.55 & 2.83 & 0.17 & 0.88 & 1.52 & 35.28 & 5454.0 & 90.0 & 4329.0 & 937.8 & 97.2 \\
18 & 19:20-19:40 & 5.0 & 0.002 & 15.493 & 0.002 & 0.0008 & 0.59 & 1.99 & 3.40 & 0.46 & 1.10 & 1.84 & 57.81 & 5837.4 & 3.6 & 1711.8 & 2552.4 & 1569.6 \\
19 & 13:41-15:25 & 3.5 & 0.100 & 15.64 & 0.004 & 0.0006 & 0.37 & 2.12 & 3.62 & 0.17 & 1.06 & 1.82 & 66.00 & 8870.4 & 70.2 & 3013.2 & 3835.8 & 1951.2 \\
20 & 17:57-20:05 & 4.7 & 0.163 & 15.601 & 0.001 & 0.0001 & 0.33 & 2.09 & 3.67 & 0.12 & 0.82 & 1.29 & 47.63 & 8969.4 & 300.6 & 7810.2 & 851.4 & 7.2 \\
21 & 12:52-14:06 & 3.8 & 0.035 & 15.597 & 0.001 & 0.0000 & 0.38 & 2.12 & 3.63 & 0.20 & 0.72 & 1.24 & 42.16 & 8020.8 & 586.8 & 7342.2 & 90.0 & 1.8 \\
23 & 19:59-21:37 & 9.3 & 0.040 & 15.787 & 0.001 & 0.0000 & 0.39 & 2.25 & 3.90 & 0.10 & 0.69 & 1.19 & 46.42 & 10625.4 & 1087.2 & 9068.4 & 109.8 & 0.0 \\
24 & 21:17-22:25 & 10.1 & 0.019 & 15.763 & 0.000 & 0.0000 & 0.28 & 2.17 & 3.84 & 0.09 & 0.54 & 1.12 & 33.33 & 8704.8 & 3076.2 & 5626.8 & 1.8 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the 3D-interpolated point clouds for Venesärkka for each day: time of measurement, temperature (“C) mean \(T\) and standard deviation \(\sigma\)\(T\) during measurement period, water level elevation mean _wl_ (metres above sea level) and standard deviation \(\sigma\)_wl_, slope (m), minimum velocity _min v_, mean velocity \(\overline{v}\), maximum velocity _max. v_ (ms\({}^{-1}\)), min, mean and max. depths \(d\) (m), discharge \(Q\) (m\({}^{3}\)s\({}^{-1}\)), total volume of water _vol_ (m\({}^{3}\)), volume of water moving at velocities of <0.5 m\({}^{3}\), 0.5–1.0 m\({}^{3}\), 1.0–1.2 m\({}^{3}\) and >1.2 m\({}^{3}\). In order to create a realistic interpolation of the 3D flow structure in the stream, a Delaunay triangulation-based interpolation was applied to interpolate the velocity, angle, direction, surface elevation and depth values of the raw point cloud. A 3D network of triangular planes is created using the scattered raw points; the network is then sampled into a regular voxel space (a 3D matrix) of a defined resolution (here, 3 m \(\times\) 3 m \(\times\) 0.2 m). A natural neighbor interpolation is used to interpolate values of each voxel (each cell in the matrix) using the Delaunay network. All input data, as well as all the interpolations use the exact same section of the river. That is, all point clouds have the same length of thalweg; only the width of the water area changes with the stage of the flood.
## 3 Results and Discussion
### Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & \(\mathbf{RI_{exp}}\) & \(\mathbf{RI_{lit}}\) & **Mild** & **Classic** & **Acidic** \\ \hline \multicolumn{6}{c}{_Aldehydes_} \\ \hline Acetaldehyde & - & - & \(54.50\pm 1.00\)b  & 11.13 \(\pm\) 0.04 a  & 17.06 \(\pm\) 4.35 a  \\ \hline \multicolumn{6}{c}{_Ketones_} \\ \hline
2-Propanone & - & 500 & - & 15.01 \(\pm\) 2.76 a  & - \\
2,3 Butanedione & 566 & 584 & 153.79 \(\pm\) 82.62 b  & 55.76 \(\pm\) 1.00 ab  \\
2-Pentanone & 681 & 689 & - & 5.91 \(\pm\) 0.62 b  & - \\
2,3-Pentanedione & 693 & 700 & 43.67 \(\pm\) 23.67 b  & 4.98 \(\pm\) 0.30 a  & 12.68 \(\pm\) 8.97 ab  \\
3-Hydroxy-2-butanone & 720 & 721 & 13.82 \(\pm\) 6.34 ab  & 32.16 \(\pm\) 1.00 ab  & - \\
2-Heptanone & 891 & 899 & 16.16 \(\pm\) 7.47 c  & 14.06 \(\pm\) 4.83 bc  \\ Dihydro-2-methyl-3 (2H)-thiophene & 1005 & 994 & - & 16.14 \(\pm\) 1.00 a  & - \\
**Total** & & & 128.72 \(\pm\) 69.16 & 184.22 \(\pm\) 26.41 & 187.93 \(\pm\) 77.79 \\ \hline \multicolumn{6}{c}{_Terpenes_} \\ \hline \multicolumn{6}{c}{_\(\alpha\)-Pinene} & 936 & 943 & 48.83 \(\pm\) 21.77 b  & 23.54 \(\pm\) 5.47 ab  & 32.85 \(\pm\) 12.35 ab  \\
**Camphene** & 969 & 950 & - & 13.49 \(\pm\) 2.14 bc  & 15.67 \(\pm\) 5.91 c  \\
**\(\beta\)-Pinene** & 936 & 978 & 6.69 \(\pm\) 2.82 a  & - & - \\ Limonene & 1044 & 1039 & 7.56 \(\pm\) 6.24 a  & - & - \\
**Total** & & & 63.08 \(\pm\) 30.83 & 37.03 \(\pm\) 7.61 & 48.52 \(\pm\) 18.26 \\ \hline \multicolumn{6}{c}{_Total volatiles_} \\ \hline \multicolumn{6}{c}{_\(\alpha\)-Pinene} & 198.92 \(\pm\) 104.07 & 287.34 \(\pm\) 52.14 & 236.45 \(\pm\) 96.05 \\ \hline \end{tabular}
\end{table} 
Table
## 1 Introduction  
It is commonly accepted that user experience is a crucial factor that determines the intensity of use of a product, and that if a product creates negative experiences, users will not like it and will use it half-heartedly. More specifically, during challenging educational activities, students concentrate their attention on the activity at hand and do not feel very enthusiastic about using biometric equipment or, even worse, biofeedback techniques in parallel to their main activity. They usually feel that all these collateral activities divert their attention and inhibit their efforts to achieve a better performance. Thus, products that manage to create a positive user attitude can most probably overcome many potential obstacles, therefore presenting a certain advantage.  
This work aspires to provide student anxiety awareness as detected by cost-effective sensors that are as least obtrusive as possible. To this aim, the presented article tries to examine whether the biofeedback device constitutes a convenient and useful way of detecting anxiety level awareness, as established during intensive and challenging educational activities such as examinations.  
In addition, it is commonly accepted that a user's attitude towards a tool is related to his/her personality to a certain extent. Thus, this research explores the association between student personality traits (extraversion-introversion), perceived usefulness, perceived ease of use, attitude towards using and behavioral intention.  
The paper is structured as follows: in the next section, we explore related research work. Then, we present a detailed description of the operation of the biofeedback system.  
The research questions of this study are next described in detail. Furthermore, we proceed with a detailed description and the conclusions of a case study on the user evaluation of this system, based on the system usability scale (SUS) [1; 2] and the technology readiness and acceptance Model (TRAM) [3]. Finally, we present a summary of our conclusions and our views on future work.