esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### DoH Flooding Attack Detection Scenario
#### 9.2.4 Description of the DoH Flooding Attack Exercise
This lesson, therefore, encourages security teams to be proactive in the search for vulnerabilities that could be exploited to enable this kind of attack, which, as this exercise has shown, could result in unfortunate consequences if not properly mitigated. In this case, the vulnerability could have been spotted by the blue team using the same script that the attacker used and could have been fixed to prevent this type of privilege escalation. Part 4: Using a Different ML Technology  
Before starting this last section, the blue team is asked to execute a script that is already created to reset the models to their initial state, thus clearing the model poisoning performed in the previous step. This step is not explicitly needed if the blue team has saved the results of their analysis on the three Random Forest models previously tested, but it will be useful if they need to use those models again for testing purposes. Starting in Phase F1, the blue team will be tasked with restarting the STA containers in the VM1 with the provided command and testing the new models that are based on Neural Networks instead of the Random Forest models that have been used so far. In the same way as the previous sections, the blue team will send traffic from VM2 in Phase F2 and will analyze the results shown in Kibana in Phase F3. The blue team is encouraged to compare the mean confidence and DoH flooding attacks detected by each model. This section is meant to introduce the blue team to a different ML technology (Neural Networks, in this case) and show them how the same problem may be resolved effectively using different tools. This is a valuable lesson, as the use of a different ML algorithm to build the detection models may provide some advantages in terms of performance, efficiency, or easier training and, thus, is an important aspect to take into account when implementing detection solutions that are based on ML technology. Finally, the blue team will be asked to provide the IPs of the attackers and the IPs of the compromised servers that they have detected.
## Appendix A.3.1 Generator Complexity
### Discriminator Complexity
#### a.3.4 Total Complexity  
The proposed model comprises two cGAN networks: one operating in the frequency domain and the other in the spatial domain. The total computational complexity accounts for the generator, discriminator, FFT, and IFFT operations in both domains. It is expressed as  
\[\text{FLOPs}_{\text{Total}}=2\cdot(\text{FLOPs}_{\text{Generator}}+\text{FLOPs}_{ \text{Discriminator}})+2\cdot\text{FLOPs}_{\text{FFT}}. \tag{12}\]  
Here, the factor of 2 reflects the presence of both frequency and spatial domain networks. This formula encapsulates the computational requirements for the entire model, providing a comprehensive measure of its complexity.
## 3 4-SSB System Design
### The Proposed New Scheme of 4-SSB with Low Complexity Equalizer for Compensating ISI
In the other hand, a novel vertical algorithm, called Vertical Bell labs layer space-timeV-BLAST [24], is used in interference cancellation (IC) to achieve a better performance than the prior algorithms in which the detector made by sphere interference cancellation (SIC) is affected by error propagation. In this article, we propose the new scheme of M-QAM 4-SSB OFDM multiple feedback which is used for interference cancellation with shadow area constraints (MF-SIC-SAC). The proposal is divided into four sub-sections in the 4-SSB scheme to enable low complexity and save energy for a wide range of applicable scenarios in 5G. The research is organized as follows:  
1. M-QAM 4-SSB uncoded using shadow equalizer and its performance in terms of bit error rate (BER) over the relevant schemes including MIMO and OFDM GI (Guard Interval). Besides, we apply the proposal in codec environment using a turbo coding scheme to verify the feasibility for modulation implementation toward 5G communications. 2. The proposed M-QAM 4-SSB over OFDM scheme using Shadow equalizer and its performance including complexity evaluation compared to previous work of M-QAM 4-SSB over OFDM using SISO equalizer. 3. Comparing the proposed scheme to the related OFDM scheme in 5G. 4. Applying the proposal into massive MIMO and demonstrating the system efficiency over equivalent systems in MIMO.
## 3 Latin America and Colombia
### Environmental  
Agricultural applications of UAS are the most common in South American countries. Using drones with remote sensors, the vegetation is being monitored to prevent pests [34]. These applications range from a more intricate examination of plants, such as analyzing their nitrogen levels [35], or estimating the height of the forage that will feed cattle [36]. Furthermore, the use of UAVs to classify and evaluate the development of the vegetable environment [37] and classify and monitor how different plantations evolve over time and different seasons [38; 39] has helped the astronomical context. In another study, for example, UAVs have been used for the real-time monitoring of crops, allowing better understanding and quicker reaction to their needs [40]. The use of UAVs as monitoring tools could complement the work of park rangers in patrol tasks, especially in remote or difficult-to-access areas [41]. Another common application of UAVs is their use in precision agriculture (PA). This sector has been threatened by climate change, and the rapid increase in population is driving production to grow by 70% by 2050 [34], as in the AURORA project (Autonomous Unmanned Robotic Airship Remote Monitoring) in Brazil [42]. Furthermore, vegetation detection has been implemented near high-voltage transmission lines using UAV and LiDAR technology [29]. Additionally, the UAV-based monitoring of pollutant gases, which creates a vertical profile, has been proposed as an alternative to typical measurement stations due to its mobility [43].  
In Colombia, some projects study PA to improve crops [44], mitigate the impacts of climate change in the country, and monitor environmental variables and lower layers of the atmosphere to prevent disasters with the help of a network, such as the air-ground integrated mobile edge network (AGMEN) [45].
## 2 Materials and Methods
### Procedures  
Data from all sources was loaded into Python (version 3.7.1). To avoid multicollinearity, a Variance Inflation Factor (VIF) analysis was conducted, with a threshold of 5, using the _statsmodels_ package. BorutaShap was applied as a feature selection technique, using the _BorutaShap_ package. Extreme Gradient Boosting, a tree-based machine learning technique, was applied for both BorutaShap and predicting game outcome (win or lose), using the _xgboost_ package (distributed by _SkLearn_). A 5-fold stratified cross-validation was used to validate the results. Cross-validation uses a large part of the data to fit the model, in this study 80%, and a small part of the data to test the model, in this study 20% [24]. Each part was thus used 4 times to fit the model and once for validation. Stratified K-fold was used to preserve balance between the frequency of each class of the dependent variable. The _StratifiedK Fold_ and _cross_val_score_ packages, distributed by _SkLearn_, were used for the cross-validation. The average classification accuracy, precision, recall and F1-score of each cross-validation fold is reported, as well as the standard deviation.  
The aim of this study was to identify the best predictive variables, therefore, it was opted to apply a tree-based machine learning, which has the advantage of high interpretability and the possibility to apply a theoretically well grounded method such as TreeExplainer to identify the strongest predictors [25]. TreeExplainer uses Shapley values to explain the global model structure, by combining local explanations of each prediction [25]. Using TreeExplainer, it is possible to determine the importance of each feature [26], on a global and local level. TreeExplainer was applied using the _sharp_ package. A graphical illustration on the workflow from the dataset to the identification of the best predictors is depicted in Figure 1.  
Figure 1: Graphical illustration on the workflow used for this study.
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 2 Related Work
### Traditional Image Segmentation Methods  
Traditional image segmentation methods rely on pixel-level features to extract regions of interest (ROIs) from images. These methods have been widely used in tasks requiring rapid segmentation or unsupervised processing. [PERSON] et al. [20] introduced a multi-threshold image segmentation method based on an improved Emperor Penguin Optimization (EPO) algorithm. This approach leverages EPO to identify optimal multi-level thresholds for color images and enhances its search capabilities using Gaussian mutation, Levy flight, and reverse learning. Similarly, [PERSON] et al. [21] proposed a two-dimensional (2D) histogram-based segmentation method, using a novel non-local means 2D histogram and an exponential Kbest gravitational search algorithm to determine optimal thresholds while redefining 2D Renyi entropy.  
Edge-detection-based segmentation techniques, which use gradient changes or other edge information, are commonly employed to delineate target boundaries. [PERSON] et al. [22] developed a graph theory-based color image segmentation algorithm. Unlike traditional pixel-based methods, this algorithm represents over-segmented regions as nodes, reducing complexity. Nodes are linked based on intensity similarity, encouraging the merging of regions with numerous shared links. This method demonstrates superior execution speed and segmentation quality compared to regularization-cut techniques. [PERSON] et al. [11] proposed an unsupervised edge detection method that calculates local standard deviation to effectively extract nuclei edges in pathological images. Additionally, [PERSON] et al. [23] presented an efficient superpixel (SP) and super voxel (SV) extraction method that improves computational efficiency by employing boundary adaptation techniques instead of processing entire regions.  
Traditional segmentation methods utilize principles of digital image processing and mathematical techniques. While they are computationally simple and fast, their ability to achieve detailed segmentation is often limited compared to more advanced approaches.
## 3 Results and Discussion
### Application of Kinetic Modelling
Figure 4: Cumulative Methane Production-Experimental; Transference, Logistic and Modified Gompertz Model for Reactors (**a**) Baggase and Rice straw(BARS), (**b**) Baggase and Wheat Straw (BAWS) (**c**) Baggase, Wheat straw and Rice straw (BAWSRS), (**d**) Wheat straw and Rice Straw (WSRS) with S/11.5. Figure 5: Cumulative Methane Production–Experimental; Transference, Logistic and Modified Gompertz Model for Reactors (**a**) Baggase and Rice straw(BARS), (**b**) Baggase and Wheat Straw (BAWS) (**c**) Baggase, Wheat straw and Rice straw (BAWSRS), (**d**) Wheat straw and Rice Straw (WSRS) with S/12.5. Considering the lag phase of all reactors in both experiments, the lowest lag phase was observed for the Reactor WSRS due to lower lignin content. The maximum methane production rate (\(Rm\)) increased with the decrease in the lag phase, and this is due to the pretreatment, which breaks the structure of lignocellulosic material and makes room for the acetogenic and methanogenic bacteria [47] The soundness of the modified Gompertz model depends on the bio-kinetic parameters, and these parameters, analyzed by Modified Gompertz Model, exemplifies a lower methane production with a longer lag phase due to an increase in substrate concentration, the more substrates concentration, the longer the hydrolysis phase would be. \(R^{2}\) values illustrate that the predicted model was fitted with the experimental values that are shown in Figures 4 and 5. LF and GM models have almost similar values, but the difference between the predicted model and experimental values was nearly equal to 10% or more than 10%; that is why GM was not considered as the best fit [48]. Based on the best fit, the reactor WSRS with a S/I ratio of 1.5 produces the highest methane production and have the lowest lag phase, whereas insignificant variation in the maximum rate of methane production was observed, the cumulative methane production was from the experiment and predicted by the model was close to the logistic function model and with the modified Gompertz model, but there was a huge variation in the data when compared with transference function model.
###### Abstract  
Automated machining feature recognition is an essential component linking computer-aided design (CAD) and computer-aided process planning (CAPP). Deep learning (DL) has recently emerged as a promising method to improve machining feature recognition. However, training DL-based recognition models typically require annotating large amounts of data, which is time-consuming and labor-intensive for researchers. Additionally, DL models struggle to achieve satisfactory results when presented with small labeled datasets. Furthermore, existing DL-based approaches require significant memory and processing time, thus hindering their real-world application. To address these challenges, this paper presents a semi-supervised learning framework that leverages both labeled and unlabeled data to learn meaningful visual representations. Specifically, self-supervised learning is utilized to extract prior knowledge from a large dataset without annotations, which is then transferred to improve downstream feature recognition tasks. Furthermore, we apply lightweight network techniques to two established feature recognizers, FeatureNet and MsvNet, to develop reduced-memory, computationally efficient models termed FeatureNetLite and MsvNetLite, respectively. To validate the effectiveness of the proposed approaches, we conducted comparative studies on the FeatureNet dataset. With only one training sample per class, MsvNetLite outperformed MsvNet by about 19%, whereas FeatureNetLite outperformed FeatureNet by approximately 20% in machining feature classification. On a common X86 CPU, MsvNetLite gained 6.68\(\times\) improvement in speed over MsvNet, and FeatureNetLite was 2.49\(\times\) faster than FeatureNet. The proposed semi-supervised learning framework shows a significant improvement in machining feature recognition on small labeled data while achieving the optimal balance between recognition accuracy and inference speed compared to other DL-based approaches.  
machine feature recognition; semi-supervised learning; small labeled sample learning; computer aided process planning (CAPP)
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 3 Vision-Based Line Marking Detection and Condition Assessment
### Image Preprocessing  
The first module is to pre-process the visual data and prepare them for the following procedures. Our targeted visual data were videotapes collected by an in-vehicle camera. The camera was mounted in the front to capture the front-view of the road pavement as the vehicle forwards. Image frames were first extracted in sequence and cropped to the preferred dimensions. Automatic image quality assessment has been proven to be useful for various image processing applications, especially for singly and multiply distorted stereoscopic images [36]. In this case, smoothing algorithms are applied to obtain the image in a format that is ready for feature extraction in the following steps. Examples include the Gaussian blur median filter, which has been proven to be efficient in eliminating \"salt-and-pepper\" noise in images. Another vital step in this module is image rectification, which involves removing the perspective effects through inverse perspective transform. Camera calibration is a prevalent method, that involves obtaining the camera's intrinsic parameters (i.e., the optical center and focal length of the camera) and extrinsic parameters (i.e., the position of the camera in the 3D scene) beforehand. Chessboard is typically utilized in most commercial applications [37]. Alternatively, feature points, which are supposed to be located on two parallel straight lines in the bird's eye view, can be used to calculate the transform matrix. The reason why it is important to generate plane images of road pavement is that line markings are parallel with each other after transformation, and therefore, this makes segmentation of each instance easier to achieve.
## 2 Related Work
### Accuracy  
Most proposals that focus on improving the accuracy of vehicular localization rely on cooperative positioning. The objective of cooperative positioning is to utilize the network localization resources by allowing neighboring nodes to work together to cooperatively improve the accuracy of their location via the periodic exchange of location information. The focus of research in this area is centered on three main themes: integrating ego measurements with measurements sent by neighbors [2, 6, 7], assessment of neighbors' location measurements by analyzing the communication message characteristics [4, 8, 9, 10], and integrating ego or neighbor measurements with map information [3]. Cooperative schemes were proposed to fuse data pertaining to multiple sensors within the vehicle together with data received from other neighbor vehicles in order to obtain relative position estimates [6]. Cameras were used as sensors that provide relative distance measurements [2], and those measurements were fused with the location information exchanged among vehicles. Some schemes incorporate only GPS information to compute inter-vehicle distances for cooperative localization [7]. Local topology information was generated using GPS and ranging sensors, and then exchanged among vehicles [4]. Recently, the characteristics of the Dedicated Short-Range Communications (DSRC) standard were used to enhance the GPS position estimates for vehicles [8]. The concept of centroid localization was expanded by assigning weights to vehicles' positions based on the Signal-to-Interference-plus-Noise Ratio (SINR) values of exchanged DSRC messages [9]. The Carrier-to-Noise (CNR) ratio was used to mitigate the errors in exchanged GPS pseudorange measurements in order to improve the accuracy of distance ranging [10]. Map matching was also used to verify the accuracy of position estimates produced by Particle filters that fuse GNSS and odometer measurements with the Time-of-Arrival of exchanged DSRC packets [3]. Each vehicle can then merge the different topologies received from neighbors in order to obtain more accurate position estimates. Augmentation systems such as EGNOSS/EDAS were used to mitigate the errors in the Global Navigation Satellite System (GNSS) absolute positioning, while car-to-car communication coupled with group map matching were used to provide relative positioning [11].
## 2 Materials and Methods
### RNA Isolation and Semi-Quantitative RT-PCR  
RNA was isolated from each of the samples and their corresponding negative controls. For this process, we used TRIzol reagent (Life Technologies, Thermo Fisher Scientific) and the Nucleo Spin(r) RNA II kit (Macherey-Nagel GmbH & Co. KG, Duren, Germany), in accordance with the user's manual. After successfully extracting the RNA, 1 \(\upmu\)g was reverse-transcribed by using random hexamer primers (Thermo Fisher Scientific) and Promega(r) M-MLV reverse transcriptase (Promega GmbH, Mannheim, Germany), according to the manufacturer's instructions. Afterwards, 1 \(\upmu\)L of cDNA was combined with GoTaq(r) DNA polymerase (Promega GmbH) and the gene-specific sense and anti-sense primers to combine for a 30 \(\upmu\)L reaction mix. Different characteristic marker genes of the three differentiation lineages were quantified using semi-quantitative RT-PCR. Lipoprotein lipase (LPL) and proliferator-activated receptor \(\gamma\) 2 (PPARG2) were used as adipogenic marker genes. Osteocalcin (OC), alkaline phosphatase (ALP), and collagen type Ia2 (COL1A2) were used as osteogenic marker genes. Sex-determining regions Y-box 9 (SOX9), aggrecan (AGN), and COL IIa1 (COL2A1) served as marker genes for the chondrogenic differentiation of cells. All marker genes mentioned above represent the highly characteristic proteins and transcription factors secreted by adult or maturing osteocytes, adipocytes, and chondrocytes. Therefore, the expression of the examined marker genes, as well as possible differences, regarding changes in relative gene expression among MSC-subpopulations, act as a clear indicator for the qualitative and quantitative multipotent differentiation capacity of MSC-like cells [27]. All primers and their specific sequences, annealing temperatures, and RT-PCR cycle numbers were already established in the laboratory and are listed in Table 3[19].  
Afterwards, electrophoresis, using 2% agarose gels (Biozym Scientific GmbH, Hessisch Oldendorf, Germany) in Tris/Borate/EDTA buffer containing 5 \(\upmu\)L per 100 mL GelRed(r) (Biotin, Fremont, CA, USA), was performed to visualise the results. For statistical analysis, the band densities for each primer pair were measured using the GelAnalyzer 2010a software, and the results were compared to the expression of the housekeeping gene eukaryotic elongation factor 1 \(\upalpha\) (EEF1A1). Changes in the relative gene expression of the respective differentiated cultures were calculated, in comparison to the respective undifferentiated negative controls, which were set as a baseline value of 1.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
###### Abstract  
With the rapid development of large-scale satellite constellations and the increasing demand for rapid communication and emergency rescue using global satellite-based Internet, there have been new requirements for efficient algorithms for inter-communication between satellites. As the constellations of low-orbit satellites become larger, the complexities of real-time inter-satellite calculation and path planning are becoming more complicated and are increasing geometrically. To address the bottlenecks in large-scale node space computing, we introduced a global space grid. Based on this grid, an efficient calculation method of spatial inter-connection between satellite constellations is proposed, according to the concept of \"storage for computing\" and the high computational efficiency of the spatial grid model. This strategy includes the following parts: (1) the introduction of the GeoSOT-3D global grid model into aerospace and the construction of the aerospace grid indexing BigTable; (2) a set of algorithms for satellite visibility analysis according to the visible grid look-up table and the secondary grid index; and (3) planning inter-satellite routing by querying the grid's inherent visibility. The idea at the basis of this method is to employ the \"space for time\" concept to convert the high-dimensional floating operations into one-dimensional matching operations by querying the inherent \"visible\" attribute of the grid. In our study, we simulated thousands of satellites, discretized their trajectories into grids, and pre-calculated the visibility between grid cells to plan the routing path for the ground data transmission. The theoretical analysis and experimental verification show that the algorithm is feasible and efficient, and it can significantly improve the computational efficiency of inter-satellite connection. We hope that the method can be used in emergency communications, disaster warning, and maritime rescue, and can contribute to the next generation of satellite internet and \"satellite-ground\" integrated networks.  
GeoSOT-3D; BigTable; satellite communication; satellite visibility; route planning +
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