esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### GAN Model Construction
#### 3.5.2 Generator Design  
The GAN generator transforms random noise into synthetic audio samples through a series of layers, as follows:  
1. Noise input: a 600-dimensional random noise vector serves as the input.
2. Layer expansion: the generator progressively increases the feature dimensions through fully connected layers, from 256 up to 4096 dimensions.
3. Activation functions:  
* LeakyReLU is introduced after each intermediate layer to prevent vanishing gradients and ensure nonlinearity.
* Tanh is applied in the final layer to generate outputs in the range of [\(-\)1, 1], simulating real audio signals.  
The generator's design ensures high-quality audio generation while balancing computational efficiency.
# Flexural Buckling Failure of Rock Slopes: A Review of Influencing Factors and Analysis Methods
## 3 Deformation Process of Flexural Buckling Failure and Its Influencing Factors  
### Deformation Process of Flexural Buckling Failure  
According to previous studies [2; 35; 36; 37; 38], the deformation process of flexural buckling failure in rock slopes can be divided into three stages, as shown in Figure 3. In the slight bending stage (Figure 3a), under the downslope driving force, the middle and rear portions of a rock slope slide along the non-daylighting basal sliding surface and the rock layers at the front edge of the slope show slight bending due to compression. In addition, tension cracks, local crushing, and loosening can be observed in the bending segment. During the intense bending and uplift stage (Figure 3b), enhanced bending of rock layers leads to strong dilatancy of rock mass within the bending segment, with a significantly uplifted slope surface, and intense loosening and fracturing of rock mass can be found in the front edge of the rock slope. In the shearing failure and landslide stage (Figure 3c), a throughgoing sliding surface is formed due to the shearing failure of rock mass within the bending segment, and then a landslide is initiated. It is notable that [PERSON] et al. [39] believed that there is a fracturing and disintegration stage between the intense bending and uplift stage and the shearing failure and landslide stage, but from the perspective of deformation and failure characteristics, this intermediate stage can be regarded as one part of the intense bending and uplift stage.  
Figure 2: Keyword co-occurrence analysis of buckling failure.
# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 2 Materials and Methods
### Epoch Segmentation  
In order to test our hypothesis, the preprocessed signals were organized into epochs of different lengths, ranging from 0.5 s to 12 s with a step of 0.5 s, thus obtaining 24 different time window sizes. All the subsequent analyses were performed separately for these different time windows.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 2 Neural Networks Training for SAR Imagery
### Data Augmentation Methods  
Before applying data augmentation, it is essential to study the characteristics of the image and define a set of acceptable data augmentations operations that will not affect the image's logic. The SAR work principle is described in [23], with interferometry in [12] or in short in [17].  
One of the most apparent characteristics of the SAR scans is their isotropy--each scan can be viewed from any angle, as it is a projection of the electromagnetic wave measurements on the earth's surface. The position of the scan and its shape do not depend on the position of the sensor as is the case for optic cameras. Due to the application of Synthetic Aperture Radars in GEOINT (Geospatial Intelligence), they are usually attached with georeferencing and metadata. They contain the parameters and external conditions from the scanning period (mainly the radar's positions, speed, and wavelengths used).  
The scan does not clearly define the position of the airplane or satellite carrying the SAR. It can be deduced by the existence of the shadow when a high man-made object or natural objects with significant height changes are present--object such as a single tree in the field or canyon-like rock formations (more in Section 2.3).  
Thus images can be mirrored in both axes without affecting the image logic (which is not valid for most optical images) or being rotated. Additionally, since most of the SAR images contain a black frame around the image, sometimes with a smooth transition, but most often with rapid cut-off rims (Figure 1), images can be trimmed off the sides for the additional expansion of the image database. Examples of these operations are shown in Figure 2.  
Since SAR imagery is a projection of the reflected radar signal on the ground, it can be rotated by any angle and still maintain its logical integrity (there is no way to determine the \"bottom\", which is easy for most photographs). It is similar to nadir aerial photography, or orthophoto [24]. This means that rotation prediction cannot be used for pretext tasks for Self Supervised Learning. However, SAR imagery has a low Signal To Noise Ratio compared to optical photographs, which leads to low resolution (for satellite imagery, usually around 3-5 m to the highest of 25 cm/pix [25]). Rotation by an angle other than a multiple of 90\({}^{\circ}\) can lead to unwanted image blurring/aliasing.  
A similar effect occurs for zooming in or out. Only zoom-out should be performed with the natural scaling coefficient to maintain the information integrity. Examples of rotation and scaling are shown in Figure 3. The down-scaling is performed using bilinear interpolation.  
For further data augmentation, two basic methods can be used--artificial noise application over the image or amplitude/histogram operations (normalization, etc.). However, these two methods can affect the image's logic.
## 3 Experimental Results  
In this study, ECG and PPG signals collected in PhysioNet's Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC) database [21] were used to evaluate the proposed approaches. There are 45 patients (27 males and 18 females, the average age is 66.8 years) and patients have one of 13 diseases, including angina, bleeding, brain injury, cardiogenic shock, congestive heart failure/pulmonary edema, cord compression, myocardial infarction, postoperative coronary artery bypass graft, renal failure, postoperative valve, respiratory failure, trauma, and sepsis. The signals were obtained through invasive methods, and for each patient, around 11,000 segments were randomly extracted. When the high-level motion artifacts were not removed by using the proposed preprocess, these segments, which contain motion artifacts, were manually removed.  
An input signal of neural networks should contain at least two periods of ECG signals. According to the possible heart rate range, the duration of the input ECG signals is more than 4 s. Thus, the length of \(s_{i}^{e}\) and \(s_{i}^{p}\) is 1024. \(L_{f}\) and \(L_{k}\) are 512 and 256, respectively. Moreover, the length of the hamming window and the size of FFT are 512. Therefore, the size of inputs to neural networks for the time and frequency domain are 512 and 256, respectively. N-fold cross-validation (N = 10) was used to evaluate the proposed approaches by subject cross-validation and the results are detailed in the following subsections.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### Controlled-Bit-Flip (\(\Lambda_{f}(X)\)) Mixers
#### a.2.3 MinVertexCover  
**Problem:** Given \(G=(V,E)\), minimize the size of a subset \(V^{\prime}\subset V\) that covers \(V\), i.e., for every \((u,v)\in E\), \(u\in V^{\prime}\) or \(v\in V^{\prime}\).  
**Prior QAOA work:** A quantum walk generalization of QAOA is proposed for this problem in Reference [31].  
**Approximability:** APX-complete [51]. Has a \((2-\Theta(1/\sqrt{\log n}))\)-approximation [72] but cannot be approximated better than 1.3606 unless \(P=NP\)[73].  
**Domain:** Vertex covers, \(\Big{\{}\mathbf{x}:\bigwedge_{\{u,v\}\in E(\mathcal{G})}x_{u}\lor x_{v}\Big{\}}\).  
**Objective:** min \(|V^{\prime}|\).  
**Initial state:**\(|s\rangle=|1\rangle^{\otimes n}\), i.e., \(V^{\prime}=V\).  
**Mixing rule:** Swap \(v\) in or out of \(V^{\prime}\) if all of the edges incident to \(v\) are covered by \(V^{\prime}\cap\text{nbhd}(v)\).  
**Phase Separator:**\(U_{P}(\gamma)=\exp(-i\gamma\sum_{u\in V}Z_{u})\).  
**Reduction to MaxIndependentSet:** A subset \(V^{\prime}\subset V\) is a vertex cover if and only if \(V\backslash V^{\prime}\) is an independent set, so the problem of finding a minimum vertex cover is equivalent to that of finding a maximum independent set. While as approximation problems they are not equivalent [49], we can use the same mapping as for MaxIndependentSet with each \(\bar{x}_{v}\) replaced by \(x_{v}\). The resource counts are the same as for MaxIndependentSet.  
**Reduction to MinSat:** [PERSON] et al. [74] give an approximation-preserving reduction to Min-\(D_{G}\)-SAT enabling us to use the QAOA constuction given above for MinSat. The resource counts are the same as for MinSat with \(m\) variables and \(n\) clauses.
## 4 Analysis on Air Distance, Road Distance and Road Time  
As described in Figure 1, using the air distance between nodes would elicit distorted results. Using the newly created distance and time matrices in benchmark instances, we answer the following issues: the distortions that air distance creates in measuring closeness, the differences in the distance of outbound and return trips in terms of cost, and rough multipliers to transform air distances into road distances or road times.
# Forward Fall Detection Using Inertial Data and Machine Learning
## 5 Discussion
This strong performance suggests that the current study's model is highly reliable and comparesfavorably with the models discussed in reference [9], where the synthetic data-based models also showed strong but somewhat varying accuracy across different datasets. In contrast, existing studies [16; 27], which often rely on additional devices or fixed sensors to detect falls, may not be as practical for widespread use. For instance, the real-time fall detection system (FDS) proposed in other research [27] leverages smartphones and Google's 3D mapping services for location tracking, achieving an accuracy of within 9 m. While this system offers excellent location tracking in areas with high Wi-Fi density, it faces limitations in terms of additional equipment requirements and the need for fixed target environments, which can hinder its real-world applicability. The current study's use of digital simulation for training data enhances the system's safety and ease of deployment and provides a scalable solution for improving fall detection systems. Scenarios like sitting in bed and normal walking were also tested, which demonstrated the models' ability to distinguish falls from routine activities. The presented method proves the capability of improving prediction accuracy by focusing on signal processing improvements, rather than just using raw normalized acceleration data, and training with a larger, more diverse dataset by developing digital models of varying sizes, developing a more diverse set of fall scenarios, or incorporating different body types could further enhance the models' accuracy and generalizability in real-world applications.
## 3 Results and Discussion
### Visibility in Relation to Relative Humidity  
All the three stations show similar patterns with regards to change in visibility with _RH_. Visibility initially increases with _RH_ but begins to decrease once a threshold _RH_ interval is reached, which is approximately 50-55% at CB, 65-70% at LC, and 55-60% at RY respectively (Figure 5). The results shows negative effect of _RH_ on visibility. Over low _RH_ intervals, visibility and _RH_-corrected visibility (corrected for hygroscopic growth) do not show much difference, suggesting no or limited hygroscopic effect as compared to change in size distribution of dry particles. As _RH_ increases, the hygroscopic effect becomes the dominating process as seen with the increasing difference in visibility and RH-corrected visibility. Change in mean level of PM\({}_{10}\) under different _RH_ intervals shows the contrasting results (Figure 5). PM\({}_{10}\) does not show any change with _RH_ over CB while it decreases over LC and RY. This decrease in visibility with increase in _RH_ in spite of either no change or decrease in PM\({}_{10}\) clearly indicates the effect of hygroscopic growth of aerosols on visibility. However, it should also be noted that the effect of _RH_ on aerosols is sensitive to aerosols size distribution and chemical constituents [1; 47]. Mass concentration and chemical constituents of aerosols of difference size ranges under different _RH_ intervals will give better understanding of effect of aerosol characteristics on visibility relation with _RH_, which is not available for this study.  
Figure 4: (**a**) average PM\({}_{10}\) by visibility class and by station, and (**b–d**) average visibility, relative humidity (_RH_)-corrected visibility, and PM\({}_{10}\), respectively, at stations CB, LC, and RY during the weekdays (Monday–Saturday) and the weekend (Sunday plus holidays). The difference in any pair of mean values in the above results is statistically significant at the 0.05 level.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.10. PFLOTRAN  
The PFLOTRAN computer code [603; 686] solves the mass and energy conservation equations for a range of aqueous phases, supercritical carbon dioxide, black oil, and a gaseous phase in variably saturated porous media in one, two, or three dimensions. It couples two modules, PFLOW and PTRAN, which represent flow and reactive transport. The PFLOW module solves the mass conservation equations for water and carbon dioxide and an energy balance equation, while the PTRAN module solves mass conservation equations for a multi-species reaction system. The latter includes homogeneous aqueous speciation reactions, heterogeneous gaseous speciation, mineral precipitation, dissolution reactions, ion exchange, and sorption reactions. PFLOTRAN was tested to simulate the movement of LNAPL through the vadose zone [687] and to identify factors influencing the migration of methane near oil and gas wells [688].
## 5 Experimental Results
### Comparison of NewKLS_FVS with Other Algorithms
They change the current partition to another solution, \(S_{1}^{\prime},S_{2}^{\prime}\), by swapping exactly \(k\) vertices between \(S_{1}\) and \(S_{2}\). Therefore, the neighborhood of \((S_{1},S_{2})\) is the set of all the solutions obtained from \((S_{1},S_{2})\) by swapping exactly \(k\) vertices between \(S_{1}\) and \(S_{2}\). The parameter, \(k_{max}\), called the radius of the neighborhood, controls the maximum \(k\) of the procedure. The algorithms, k-opt-LS, NewKLS_FVS, VNS and SA, are carried out on several random graphs and DIMACS benchmarks for the maximum feedback vertex set. In the VNS algorithm, the value of \(K_{max}\) is set to be one and two, which are called VNS1 (if \(K_{max}=1\)) and VNS2 (if \(K_{max}=2\)), respectively. In the SA algorithm, the initial temperature is set to be \(T_{0}=1,000\), and the cooling down coefficient \(\alpha=0.9997\). Tables 3-7 show the results of these algorithms, and each is carried out for 15 runs with a CPU-time limit of 30 min. ```
1:Initialize \(\mathcal{N}_{k},k=1,2,\cdots,k_{max}\), initial solution \(X\) and stop criteria;
2:while Stop condition is not met do
3:\(k\gets 1\);
4: Generate a partition \(\mathcal{N}_{k}=(S_{1},S_{2})\) of \(V(G)\) with \(|S_{1}|=r\);
5:while\(k<k_{max}\)do
6: Generate a point, \(X^{\prime}\in\mathcal{N}_{k}(X)\) ;
7: Apply the local search method with \(X^{\prime}\) as the initial solution; denote by \(X^{\prime\prime}\) the obtained solution;
8:if\(X^{\prime\prime}\) is better than incumbent then
9:\(X\gets X^{\prime\prime}\); \(k\gets 1\);
10:else
11:\(k\gets k+1\);
12:endif
13:endwhile
14:\(r\gets r+1\)
15:endwhile
```  
**Algorithm 4** VNS(\(G,r\)) \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Instance** & **Best** & **Worst** & **Average** & **Instance** & **Best** & **Worst** & **Average** \\ \hline anna.col & 126 & 126 & 126 & miles1000.col & 20 & 20 & 20 \\ david.col & 66 & 66 & 66 & miles1500.col & 10 & 10 & 10 \\ DSJC125.1.col & 58 & 58 & 58 & mulsol.i.1.col & 121 & 121 & 121 \\ DSJC125.5.col & 15 & 15 & 15 & mulsol.i.2.col & 133 & 133 & 133 \\ DSJC125.9.col & 6 & 6 & 6 & mulsol.i.3.col & 129 & 129 & 129 \\ fpsol2.i.1.col & 366 & 364 & 365 & mulsol.i.4.col & 130 & 130 & 130 \\ fpsol2.i.2.col & 370 & 370 & 370 & mulsol.i.5.col & 131 & 131 & 131 \\ fpsol2.i.3.col & 344 & 344 & 344 & myciel3.col & 8 & 8 & 8 \\ games120.col & 47 & 46 & 46 & myciel4.col & 16 & 16 & 16 \\ homer.col & 520 & 520 & 520 & myciel6.col & 61 & 61 & 61 \\ huck.col & 52 & 52 & 52 & myciel7.col & 122 & 122 & 122 \\ inithx.i.1.col & 602 & 554 & 569 & queen5\_5.col & 7 & 7 & 7 \\ inithx.i.2.col & 549 & 548 & 548 & queen6\_6.col & 9 & 9 & 9 \\ inithx.i.3.col & 529 & 529 & 529 & queen7\_7.col & 11 & 11 & 11 \\ jean.col & 63 & 63 & 63 & queen8\_8.col & 13 & 13 & 13 \\ latin\_square\_10.col & 13 & 13 & 13 & queen8\_12.col & 16 & 16 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for NewKLS_FVS (LS, local search; FVS, feedback vertex set) and a CPU-time limit of 30 min for 15 runs.
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Modeling Dataset

Table 1: Modeling and validation stations data information. Figure 3: The geographical distribution of modeling and validation stations. 1. Considering that the observation period of different stations includes 15 min, 30 min, and 60 min, to ensure the uniformity of data format and avoid introducing errors, we choose 60 min as the sampling period to ensure data consistency. 2. In order to meet the modeling requirements of the Long-term regional ionospheric TEC prediction model, the monthly 24-h TEC mean values of each TEC observation station are calculated, and one observation station corresponds to a standardized modeling data file. After the above processing, we selected 8 stations for modeling and 6 stations for validation. Among the validation stations, three participated in modeling and three did not.
## 3 Agent-Based DDoS Detection System
### Agents  
The communication of the agents is semi-synchronous. The main information structure exchanged between agents is called path-message. A path-message is a cumulative information message that travels from agent to agent. It contains information regarding the traffic of all the devices that belong to the agents, through which the message has passed. In other words, each agent adds to path-messages, going through it, information observed at the corresponding installation and then pushes those to the next agent. The actual information the path-message contains is the total (summation of all agents traffic) out-going packets rate per destination IP address.  
Regarding the generation of path-messages, a bounded probability, named _generation probability_ is used. Generation probability is a parameter that controls active path-messages population existing in the network. The agent collects traffic information for all installed devices and adds that to the path-message. Then a destination agent is selected randomly and the message is sent to that agent. The agent that receives the message, adds traffic metrics of the corresponding installation to it and transfers it to another agent.  
To sum up, the communication of the agents is based on the approach of concatenating information measured in different agents to deduce on occurring DDoS attacks. An agent collects traffic information from related devices, merges this information with the traffic information in the received path-messages and then forwards the updated path-messages to other agents. Due to agents' limited memory, the maximum allowed length of a path-message is bounded by a constant, according to agents' population. When a path-message reaches its maximum path length it is not transferred to another agent, but it is checked and if it carries critical information indicating an attack, an alert is relayed to every other agent. In any case, it is then discarded from the network.  
At each step, the next agent can be chosen through different strategies. In this work the next agent is chosen randomly using only one constraint: a message cannot pass from the same agent more than once. Therefore, a path is strictly sustained while the message is traveling through the network.
## 4 Experimental Results and Discussion
### Subjective Contrast of Different Images  
We invite 20 researchers to participate in the experiment, and their research direction is the detection of breast lesions. They all have rich research experience. During the test, we let the 20 researchers stand in a row, and independently observe the contrast image on the display, every researcher gives a value to every image. The evaluation results are shown in Table 5.  
It shows in Table 5 that as the density rating increasing, the subjective evaluation score has a downward trend, which indicates that the density rating has an influence on the detection AD. Among the 30 images, only the assessment results of the two images \"B_3085_1.LEFT_MLO\" and \"B_3395_1.RIGHT_MLO\" are detection errors, which indicates that the method proposed in this paper can detect AD accurately.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predict Result** & \multicolumn{2}{c}{**Actual Situation**} \\ \hline  & AD & Non-AD \\ AD & TP & FP \\ Non-AD & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: The concept of confusion matrix.  
In Figure 10(a1,b1) are images with density rating 1, a2,b2 are images with density rating 2, a3,b3 are the image with density rating 3, the region within the red line is the lesion area marked by the expert, and the area within the yellow line is the detection result by the proposed method. It can be seen that the proposed method in this paper can accurately detect AD area.