esa-sceva/satcom-chunk-collection huggingface dataset:
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
## 3 Scenarios of Simulation and Metrics
### Scenarios of Simulation  
For testing the AQM algorithms, the topology used for the simulation (Sections 4.3 and 5.3) is a simple dumbbell topology (see Figure 1), where \(N\) is the number of long-lived TCP connections sharing a single bottleneck. Traffic is generated from the left side to the right side, specifically for each \(i\in\{1,2,\dots,N\}\), \(S_{i}\) and \(D_{i}\) denote the source and destination of the TCP flow \(i\). The router \(R_{1}\) on the left is where the bottleneck is actually located and the control AQM model will be installed. All other nodes, by default, have the Drop Tail queue manager installed. The edge links between the TCP sources and the router \(R_{1}\), and the router \(R_{2}\) and the TCP sinks have a capacity of \(100\,\mathrm{Mbps}\) with a mean of \(1\,\mathrm{ms}\) propagation delay. Router \(R_{1}\) is connected to \(R_{2}\) through a capacity \(C\) of \(50\,\mathrm{Mbps}\) and \(10\,\mathrm{ms}\) propagation delay. The maximum buffer size \(B\) of each router is set to \(1000\) packets of a size \(M\) of \(1000\) bytes each. The TCP transport agent will be TCP Cubic.  
The level of congestion of the simulation is set according to the number \(N\) of long-lived TCP connections sharing the bottleneck. In that sense, we distinguish two different scenarios:  
1. Constant congestion level. In this case the number \(N\) of active flows is constant throughout the simulation time, which is of \(250\,\mathrm{s}\).
2. Changing congestion level. In this case the number \(N\) of active flows varies as a function of time according to the following distribution: * Between \(0\) and \(50\,\mathrm{s}\), the number of active flows is \(N=100\). * Between \(50\) and \(100\,\mathrm{s}\), the number of active flows is \(N=200\). * Between \(100\) and \(150\,\mathrm{s}\), the number of active flows is \(N=N_{\max}\). * Between \(150\) and \(200\,\mathrm{s}\), the number of active flows is \(N=200\). * Between \(200\) and \(250\,\mathrm{s}\), the number of active flows is \(N=100\).  
The congestion level in this case will be determined by the maximum number of active flows \(N_{\max}\) in the simulation.
## 3 Result and Discussion
### Analysis of the Tensile Test Data using Weibull Distribution
The characteristics tensile strength of the Enset bundle fiber portion at EV-I and EV-IV configuration dropped by 13.36 and 32.03% and the Weibull shape parameter also reduced by 19.33% and 44.08%, respectively, in comparison to the fiber portion EV-II, indicating higher variability. It has been shown that the Weibull modulus can be used as an indicative parameter of the effects caused by the fiber portion along the length on the tensile strength of Enset fibers, more accurately describing the effects of the fiber sections upon the fiber integrity. For crosschecking the Enset fiber section along the length at four points, mechanical properties were described at four positions, EV-I, EV-II, EV-III, and EV-IV, and their variance was also assessed using one-way ANOVA. In Table 5, the means value interactions of EV-II with EV-I, EV-III with EV-I, and EV-III with EV-II are shown to be not significant at 0.05, but the other interactions in a particular fiber section EV-IV differ significantly from the other fiber sections, as demonstrated in Table 5. Similarly, the work of rupture follows the same pattern as the tenacity investigation. Overall, the comparison of Enset bundle fiber tensile strength with the overall ANOVA shows that there is no significant difference within 75 percent of the length of Enset fiber from the bottom. However, there is a significant difference between the interactions between the top sections compared to the other Enset bundle fiber sections. Figure 10: The Weibull plots of Enset fiber tenacity at each fiber portion in specific EV-IV (1125–1500). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interactions** & **Mean Diff** & **Prob** & **Sig** \\ \hline EV-II: EV-I & 109.38 & 0.13 & 0 \\ EV-III: EV-I & 48.83 & 0.76 & 0 \\ EV-III: EV-II & \(-\)60.55 & 0.62 & 0 \\ EV-IV: EV-I & \(-\)147.58 & 0.02 & 1 \\ EV-IV: EV-II & \(-\)256.96 & 3.32 \(\times\) 10\({}^{-6}\) & 1 \\ EV-IV: EV-III & \(-\)196.41 & 6.42 \(\times\) 10\({}^{-4}\) & 1 \\ \hline \hline \end{tabular} Note: Sig 1 indicates that the mean is significant at the 0.05 level and sig 0 indicates that the mean is not significant.  
\end{table} 
Table 5: One-way ANOVA tenacity comparison along the fiber sections. Despite the significance of optimization, the variability in the physical and mechanical properties of plant fibers does not limit their application. Hence, fibers are used in bundles in the manufacturing industry, and the tolerable variations are much higher than those required in the design of structural parts [9]. Overall, separating fibers based on their location in the plant is of interest in order to improve the properties of the application, particularly the effect of the upper portion of the fiber, which requires special attention.
# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Study Area  
In this study, forested areas with reported outbreaks of pine wood nematode disease were chosen as our test areas. Recognizing the importance of coastal zone protection forests as vital ecological barriers on the Shandong Peninsula, we specifically chose the coastal area in Laishan District, Yantai City, as our primary data collection location. To capture aerial imagery, we utilized an 8-rotor unmanned aerial vehicle (UAV) equipped with a GaiaSky-mini camera (DUALIX, Wuxi, China). We employed multispectral and visible light data to classify the various feature types within the surveyed region. Images showing non-vegetation areas, such as rocks, bare ground, or water bodies, as well as non-pine vegetation like broad-leaved forests, shrubs, or grasslands, were excluded. This filtering process ensured that only images of black pine trees were retained for analysis. Furthermore, we verified the accuracy of feature type identification through a combination of visual interpretation of the visible light data and on-site survey data. The GaiaSky-mini camera offers a spectral coverage range of 450-1000 nm, a spectral resolution of 3.5 nm, and a spatial resolution of 0.3 m when flown at an altitude of 100 m. The data are stored in hypercube format, enabling the capture of up to 380 bands of imagery. The main specifications of UAV and camera used in this study are summarized in Table 1.  
We utilized Pix4 DMapper 4.5.6 software for the orthorectification of UAV aerial images. Orthorectification involves selecting ground control points on the image and leveraging the inherent digital elevation model (DEM) data to simultaneously correct image tilt and projection discrepancies. Subsequently, the image is resampled into an orthophoto. After mosaicing multiple orthophotos and performing color balance adjustments, the final orthophoto image is cropped to a specific range. Ultimately, we obtained orthophoto maps  
The three study areas are located in (a) Binhai East Road, Yantai City, Shandong Province, (b) Jingliu Road, and (c) Guanzhuang Road. The geographic locations of these images on the map are in Figure 3.  
Because of the considerable geographical overlap in some captured photos, it was advantageous to omit certain images from the dataset. This helped avoid repetitive learning of analogous image features or geographical conditions during model training, ultimately improving training efficiency and reducing overall training time. The samples excluded due to these issues are designated as invalid samples. Following their elimination, 500 drone images were organized. These 500 images were then processed, converted, and calibrated to ENVI format using ArcMap v. 10.0.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 6 Conclusions  
In this paper, we presented a general interpretation for deepfake audio detection by analyzing the attribution score patterns of several post-hoc XAI methods. Based on the characteristics of the XAI methods found in image classification, we applied them to the audio domain and attempted to interpret the characteristics at a higher level to enable non-expert cognition.  
Using traditional methods used for audio-related tasks, the attribution score of the deepfake detection model was interpreted. Under a certain environment, we could find a clear distinction in formants and voiced-unvoiced speech. The experiment showed a consistent interpretation of each XAI method under various environments.  
Furthermore, by recomposing the attribution score back to audio, human cognition became easier as a supplementary recognition assist. With this assist, the interpretation teardown needed to be less exclusive. With the recomposed audio, the end-user could be provided interpretation with intuitive differences in high-frequency pitch variance and a general rhythm of the speech.  
Through the experiment, model interpretability on deepfake voice detection could be obtained and explained at a human cognitive level using existing XAI methods. As the current study has identified differences between model and human perception in detecting deepfake media, implementation of the marker without human recognition can be studied in the future to relieve public anxiety towards generative models creating fake media.  
Figure 9: Top 10% amplitude of reconstructed Deep Taylor-based attribution score from LJSpeech. (**a**) Deepfake voice. (**b**) Real voice.  
**Author Contributions:** Conceptualization, S.-Y.L. and D.-K.C.; methodology, S.-Y.L. and D.-K.C.; software, S.-Y.L.; validation, D.-K.C.; formal analysis, S.-Y.L.; investigation, D.-K.C.; resources, D.-K.C.; data curation, D.-K.C.; writing--original draft preparation, S.-Y.L.; writing--review and editing, S.-Y.L., D.-K.C. and S.-C.L.; visualization, S.-Y.L. and D.-K.C.; supervision, S.-C.L.; project administration, S.-C.L.; funding acquisition, S.-C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by (1) the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)); (2) the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2021M3E5D2A01021156); and (3) the DGIST R&D program of the Ministry of Science and ICT of Korea (22-IT-10-03).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Complexity Analysis
#### 6.1.1 Complexity of NLEs Based on Digital Back-Propagation  
Each DBP segment requires a time-to-frequency and a frequency-to-time conversion, in addition to the operations to implement both the non-linear and linear compensation stages. The number of FLOPs for each DBP steps is then:  
\[N_{step}=N_{linear}+N_{nonlinear}, \tag{2}\]  
where \(N_{linear}\) and \(N_{non\text{-}linear}\) are given by \(8N_{block}\)\(\log_{2}N_{block}\) \(-6N_{block}\) + 16 and \(18N_{block}\), respectively. On the other hand, the number of DBP steps is, assuming uniform length:  
\[N_{steps}=\ \frac{N_{spans}\times L_{span}}{\Delta d}. \tag{3}\]  
The total number of FLOPs required by the DBP is then given by  
\[\begin{array}{c}N_{DBP}=\ \frac{N_{spans}\times L_{span}}{\Delta d}\ (N_{linear}+N_{nonlinear})\\ =\ \frac{N_{spans}\times L_{span}}{\Delta d}\ (8N_{block}\ \log_{2}N_{block}\ +\ 12N_{block}\ +\ 16) \end{array} \tag{4}\]
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 1 Introduction  
Research on anomaly detection in graph networks has increasingly focused on the development of adaptive and real-time solutions capable of handling complex streaming graph data [1, 2]. Anomaly detection is an important task in both static and dynamic networks. Unlike static networks, where the topology remains constant, dynamic networks are continuously (or periodically) changing their nodes and edges [2]. Figure 1 illustrates two graph representations: (a) a static graph, \(G\), and (b) presents a snapshot of the evolving dynamic graph (\(\mathcal{G}=(V_{t},E_{t},\mathcal{T})\)). The need for real-time graph-based detection algorithms tailored to dynamic graphs is critical for addressing diverse real-world challenges. These applications span network intrusion prevention [3], fraud detection in financial transactions [4], drug discovery and motif discovery in biological networks, power grid monitoring [5], and machine learning-based vulnerability categorization [6]. The wide-ranging applicability of these methods reinforces the importance of adaptive machine learning models in tackling real-world problems and highlights the growing demand for innovative approaches to anomaly detection in dynamic graph settings.  
Existing graph-based anomaly detection methods in the literature focus more on static graphs [7; 8]. However, graph-based approaches still face significant limitations when it comes to inductive learning and adapting to the evolving nature of real-world graphs [2], where edge connections and node importance can change rapidly.  
Among the proposed methods for anomaly detection in dynamic graphs, these can be categorized into matrix factorization and tensor decomposition, probabilistic models, distance-based methods, and deep-learning graph embeddings [2]. Despite their respective successes, these techniques often face limitations in terms of speed, accuracy, adaptability to concept drift, and scalability. Detecting anomalous network behaviors in real-world critical infrastructures, such as power grids and nuclear plants, necessitates prompt responses with maximum accuracy.
## 2 Materials and Methods
### Methods
#### 2.2.4 Self-Healing Evaluation of Mortar/SAPs Specimens  
Cementitious structures can be damaged in a variety of ways, the most common being cracking. MicroCT has been utilized as a laboratory-scale method to evaluate their damage extent and interpret healing mechanisms. In the past more conventional methods, such as SEM (Scanning Electron Microscopy), have been used to evaluate crack morphology and mitigation but they can provide insight only on the specimen surface, whereas microCT can provide useful information on the bulk of the specimen and consequently evaluate internal cracks and internal self-healing [46].  
In this work, after 28 days of curing the cylindrical specimens that were previously examined via microCT were precracked under compressive load using a hydraulic press. During their compression, the load was applied in a controlled and smooth manner but the specimens were completely split in two halves in all cases. Therefore, prior to this procedure they were wrapped tightly in a polypropylene based film in order to avoid the complete separation of the two halves but instead to form a crack as shown in Figure 3. Afterwards, the specimen circumference and base were coated with an epoxy resin (a mixture of Simmast J 158 (component A) and Simmast S2 liquid primer (component B) by Sintecno in a ratio A:B 77:23) in order to secure the two halves together. The top surfaces of the cylinders were untreated, so that the formed crack could interact with healing agents (water). The epoxy resin was cured at ambient temperature for 1 day and then at 60 \({}^{\circ}\)C for 2 h.  
Figure 3: Schematic representation of mortar cylinder (**a**), specimen image from microCT CCD camera (**b**), crack inducement in cylindrical specimens via compression load enforcement (**c**).  
In order to estimate their self-healing behavior, the cracked specimens were submerged in water at ambient temperature and they were evaluated at different time slots (0 and 8 days). The self-healing efficiency of mortar/SAPs composites was quantified and visualized via microCT analysis. Prior to microCT evaluation the specimens were dried at 50 \({}^{\circ}\)C for three days and were stored in a desiccator. The cracked mortar specimens were scanned using the SkyScan 1272 desktop microCT at 25.0 \(\upmu\)m pixel resolution with 0.5 mm aluminum filter. The scanned images were reconstructed via NRecon software, while three-dimensional evaluation was conducted by CTAnalyzer software following a methodology proposed by [PERSON] et al., appropriately adjusted for cracked mortar specimens [51]. The main outcomes discussed in this work were the evolution of connectivity density and the percent object volume versus healing time. These parameters were adopted because they allow the quantitative evaluation of crack healing in 3D (not only in 2D), with respect to crack closure in terms of changes in morphology and density and are embedded in the CTAnalyzer software.
## 1 Introduction
This paper reports on the design, execution and findings of SLR aimed at systematically identifying, selecting and summarizing a comprehensive set of approaches that adopt information systems or technology to combat money laundering. For this review, 71 relevant papers were identified and rigorously reviewed, summarizing the extracted data in order to answer a set of research questions that motivated this review. The findings of this review showed that, in the selected studies, the approaches of the technological solutions adopted can be classified into 5 general categories of application domains. Of the identified categories, the detection of suspicious transactions attracted greater attention from researchers, followed by the pattern/group/anomaly money laundering detection category, which shows in which direction there is a greater tendency for anti-money laundering solutions using information technology. Regarding the support mechanisms adopted, four main categories were identified, where the application of algorithms or mathematical applications as, for example, the use of data mining or machine learning, were more adopted by researchers. From the analysis of the research questions adopted in the systematic review, it was found that most of the selected studies did not examine the potential bias of researchers and influence of results. There was also a lack of discussions about the limitations of techniques and tools reported in the reviewed studies. The two significant contributions of this study to the body of knowledge regarding the use of information technology to combat money laundering are:  
* The paper reports on the design, execution and results of a review that systematically identifies a comprehensive set of relevant studies on information technology applications in the fight against money laundering. The study was based on predefined selection criteria, rigorously analyzing and synthesizing the approaches, associated support mechanisms and reported evidence in an easily accessible format. * The paper structures and classifies the approaches and support mechanisms adopted, as well as the available evidence, using different formats that are expected to be useful to practitioners and researchers concerned. Findings can be used as an evidence-based guide to select appropriate techniques, solutions, approaches or support mechanisms based on the different activities and needs. The findings also identify issues relevant to interested researchers.
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
###### Abstract  
The accurate localization and classification of lung abnormalities from radiological images are important for clinical diagnosis and treatment strategies. However, multilabel classification, wherein medical images are interpreted to point out multiple existing or suspected pathologies, presents practical constraints. Building a highly precise classification model typically requires a huge number of images manually annotated with labels and finding masks that are expensive to acquire in practice. To address this intrinsically weakly supervised learning problem, we present the integration of different features extracted from shallow handcrafted techniques and a pretrained deep CNN model. The model consists of two main approaches: a localization approach that concentrates adaptively on the pathologically abnormal regions utilizing pretrained DenseNet-121 and a classification approach that integrates four types of local and deep features extracted respectively from SIFT, GIST, LBP, and HOG, and convolutional CNN features. We demonstrate that our approaches efficiently leverage interdependencies among target annotations and establish the state of the art classification results of 14 thoracic diseases in comparison with current reference baselines on the publicly available ChestX-ray14 dataset.  
Keywords:ChestX-ray14; multiple feature integration; shallow features; deep features; convolutional neural network; pretrained model +
Footnote †: journal: Medical Image Analysis
## 3 Methodology
### The Group Preference Model Construction  
In the previous section, we obtained the feature vector of each user by integrating the user's category feature and time feature. It was composed of the predicted value of user preference for category and user preference for time. For a group with a set of users, users in the group will have intragroup influence. Therefore, to construct the feature vector of a group, we aggregated member preferences into group preference by taking the intragroup influence of users. How to measure the intragroup influence of users is an open problem. We obtained the intragroup influence of users according to calculating the affinity of user with other members from the perspective of location. It is known that if a user has visited locations that are also visited by others, then the user will have a close relationship with others. Consequently, we calculate the probability of whether the location visited by each user in the group is visited by other users to express the current user's affinity in the group. \(\eta_{u}^{g}\) is defined in this paper to reflect the importance of the user in the group feature preference. The formula is:  
\[\eta_{u}^{g}=\frac{L_{u}\bigcap L_{u,g}}{L_{u}} \tag{7}\]  
where \(L_{u}\) represents the POI visited by user \(u\) in the group, \(L_{u,g}\) represents the set of locations visited by other users in the group except for user \(u\), and \(\eta_{u}^{g}\) denotes the probability that the locations visited by user \(u\) are visited by other users in the group.  
Among them, \(p_{u,m_{k}}\) represents the feature vector of users in the group. Here, the higher the value of \(\eta_{u}^{g}\), the greater the probability that the place visited by the user \(u\) is visited by other people in the group. That is to say, the feature preference of the user \(u\) is closer to the feature preference of the group.  
The preference of a feature \(m_{k}\) of a group is constructed by aggregating the preference for the feature of each user in the group. It is calculated as follows.  
\[p_{g,m_{k}}=\frac{\sum_{u\in g}p_{u,m_{k}}\times\eta_{u}^{g}}{\sum_{u\in g} \eta_{u}^{g}} \tag{8}\]  
Thus, splice the preference of all the features to construct the group feature vector. The construction of the group preference model is shown as follows.  
\[M_{g}=(p_{g,m_{1}},p_{g,m_{2}}...p_{g,m_{k}}) \tag{9}\]