esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 3 Results and Discussions
### Effect of Spatial Dropout and L1 Regularization  
#### 3.3.1 Quantitative Comparison  
Table 3 shows the training results of U-Net and DeepLabV3+, when several configurations of spatial dropout and L1 regularizations are implemented. Both U-Net and DeepLabV3+ trained without L1 and spatial dropout suffered from extreme overfitting, indicated by the big gap of the validation and training curve. Figure 17 shows the overfitting tendency of U-Net and DeepLabV3+ trained without L1 and spatial dropout. Both models started to overfit after epoch 10, indicated by the plateauing validation curve and the increasing training curve concurrently. The overfitting tendency of U-Net and DeepLabV3+  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mean IoU** & **F1** \\ \hline U-Net with categorical cross entropy loss & 0.4778 & 0.5819 \\ U-Net with generalized Dice loss & 0.5993 & 0.7199 \\ DeepLabV3+ with categorical cross entropy loss & 0.3821 & 0.4716 \\ DeepLabV3+ with generalized Dice loss & 0.5174 & 0.6478 \\ \hline \hline \end{tabular}
\end{table}
Table 2: U-Net and DeepLabV3+ trained using categorical cross entropy and generalized Dice loss.  
Figure 16: Comparison on the use of categorical cross entropy or generalized dice as loss function in U-Net and DeepLabV3+.  
was slightly reduced when L1 regularization was applied as shown in Figure 18. Figure 19 shows that a similar case also happened when both models used spatial dropout. Both models slightly benefitted from the dropout techniques. Finally, when both L1 and spatial dropout were implemented in U-Net and DeepLabV3+, the models generalized very well on the validation data, which is shown by the overlapping of the validation and training curves in Figure 20. The evaluation metrics were also the highest when both L1 and spatial dropout were used, as shown in Table 3. Looking at the general line trends, some models had big fluctuations during training such as in Figure 17a around epoch 69. This might happen when an outlier ended up on a relatively small batch size. Since the dataset is relatively small and complex, the occurrence of outliers on a small, trained batch becomes larger. Note that the small batch size of four was chosen due to GPU memory limitation. However, the training quickly stabilized and returned to normal. Although the improvement on the validation data is not significant, the main contribution of these regularizations is stabilizing the discrepancy between training and validation performance. This is especially important when more data will be added in the future. Note that the dataset was divided into training and validation sets first before being augmented to make sure that the validation data did not generalize on the training data. Therefore, in a small and complex dataset with proper division between
## 2 State-of-the-Art
### Model Transformation Approach
#### 2.3.2 The Emergence of Model Transformation Languages  
Model transformation is based on creating a program to transform a model from one representation to another. The approach has been implemented in a large number of languages specialized in model transformation, like QVT [11] and Triple Graph Grammars (TGGs) [21], among others. Therefore, the approach has been applied to various research studies.  
[PERSON] et al. in Reference [22] used a TGG tool to construct links of the interconnections between geometric models (CAD) and the corresponding dynamic simulation models (Modelica).  
A bidirectional transformation was then performed to build models in a CAD tool and to synchronize the changes based on a dynamic simulation model. [PERSON] et al. [23] also used TGG to formalize a model composition activity, which is used to produce a composed model that would represent an integrated view of different domain-specific views.  
Model transformation is an interesting approach used to manage interoperability between modeling languages, allowing each designer to use his or her own language and tool. However, the major drawback of this technique is that there is a rapid increase in the number of tools and design actors involved into the design process. Consequently, this approach requires considerable programming development to implement each transformation process. Moreover, this approach considers orienting relations encoded in the transformation rule. For instance, a SysML model can be transformed into a Modelica model but a change in a Modelica model cannot automatically be transformed back into a SysML model if the transformation rules consider unidirectional model transformation. Therefore, consistency between models cannot always be guaranteed.
# Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions
## 4 New Forensic Technologies
### Machine Learning (AI)  
#### 4.2.1 Machine Learning as an Investigative Tool  
Machine learning has revolutionized criminal tracking and investigations. It can be applied to investigations in various ways. Investigators can use machine learning to teach their systems how to recognize elements of crimes from sources like social media or surveillance footage [PERSON] et al. (2016).  
This method can be applied to online identification. It uses machine learning to teach the system how to recognize what criminal organization may be behind a crime by what trademarks are seen in the crime. For example, different scamming organizations have different methods of operation. As [PERSON] et al., (2015) discussed, investigators are able to detect which organizations are behind scams using machine learning techniques.  
Another use of machine learning can be in coordination with computer vision [PERSON] et al. (2015). It can be applied to the process of detecting the identity of individuals in videos or images, helping investigators determine the identity of individuals associated with crimes.  
Machine learning is most often used in terrorism and extremism cases and harassment cases. In terrorism cases, investigators are able to identify terrorists and terrorist activities by the online footprints of these organizations. For example, information acquired from a data mine of Twitter can be analyzed using machine learning to detect links within unstructured data [PERSON] et al. (2016). In harassment cases, machine learning can be used to detect text-based threats. [PERSON] et al., (2015) discussed that threats in emails can be detected using a decision-tree algorithm.  
Machine learning is also being developed in the field of digital forensics. AI can be used to analyze forensic material, such as network traffic and taught to look for patternsthat indicate malicious activity [21]. This will make investigators task of analysis faster and less costly. It can also be used to examine network forensic data, such as spam activity [22]. The analysis of this can lead to discoveries of network activity trends, which can indicate potential attacks.  
Finally, machine learning can be applied to predicting crime hotspots [23]. Machine learning can be applied to historical crime data in order to determine trends and likely future criminal activity. This can be an important tool for investigators when analyzing crime trends, which can lead to discoveries of criminal locations. It can also help investigators be one step ahead of criminals if they are able to predict likely crimes and warn potential victims to put in safeguards in place.
# Modeling of Fuzzy Systems Based on the Competitive Neural Network
## 4 Experimental Results  
This section presents the simulations and results after being applied to the proposed method: Design of Fuzzy Inference Systems based on a Competitive Neural Network denoted as CNNT1 FL. The variants of the CNNT1 FL model are applied to three classification datasets: Iris, Wine, and Wisconsin Breast Cancer Dataset (WDBC). Table 4 shows the characteristics of each dataset, such as the data number, features, and number of classes.  
In Table 4, the \"number of data\" parameter is the input data for the Competitive Neural Network, the \"features\" parameter represents the number of partitions of the input variable of the FIS, and the \"Classes\" parameter represents the FIS decision or the output variables.  
On the one hand, in the first part of CNNT1 FL, the parameters used in the Neural Network are the following:  
* Network type: Competitive Neural Network.
* Learning of Network: Unsupervised.
* Performance: MSE (Mean Square Error).
* Inputs: \(N\) (data number of the dataset).
* Outputs: \(M\) (Centroids).
* Epochs: 100.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Classes** & **Features** & **Data Number** \\ \hline Iris & 3 & 4 & 150 \\ Wine & 3 & 13 & 178 \\ WDBC & 2 & 30 & 569 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the datasets.  
On the other hand, in the second part of CNNT1 FL, we have designed two types of FISs, Mamdani and Sugeno, with the aim of analyzing and comparing the performance and improving the data classification task.  
It is very important to mention that all the features of each dataset are used to generate the model, where 80% of the data are used to design each FIS and 20% are used to test the performance of the created FIS.  
The results obtained with Mamdani FIS and Sugeno FIS are presented in the tables below containing the mean, standard deviation, best and worst result based on 31 independent runs of the proposed method for Iris, Wine, and WDBC datasets.
# Developing a Universal Mirror-mirror Laser Mapping System for Single Event Effect Research
## 3 Results
### TFIT Simulation Result  
A widely used simulation tool called TFIT was used to simulate SEEs in a radiation environment and to generate corresponding results (e.g., cross-section, FIT value, etc.). This tool was provided by iRoC Technologies company. TFIT is a fast simulation tool that can be used to predict and improve the SER and the FIT performance of cells design. TFIT allows for the reasonably accurate calculation of the  
Figure 20: (**a**) Sensitivity map for Regular_11T structure with all ’0’ stored data pattern; (**b**) sensitivity map for Regular_11T structure with ‘0/1’ pattern (laser energy: 90 pJ).  
electrical effect of particles impact to a transistor, a cell or a circuit in the design flow, at much faster speeds than traditional 3D TCAD simulations [18; 19].  
Figure 21 shows the simulation result of Regular_ 11T SRAM structure based on the 65 nm CMOS process model that has been verified by former researchers [20; 21].  
The sensitivity distribution shown by the TFIT simulation indicates that the most sensitive areas (indicated by pink & red color) were located at N1, P2 and part of A1. Compared with the actual laser mapping results, a strong consistency can be observed. There were two features that need to be noted for this simulation. First, the size of sensitive area in the actual mapping result was not exactly the same as the one from simulation. This may be due to the limitations of laser spot size and charge diffusion. However, as demonstrated, by carefully controlling the laser energy to be close to the threshold energy, the spatial resolution for a laser beam can be much less than its calculated spot size. Second, by counting the number of steps (0.18 \(\upmu\)m per step) in the mapping result, the real spatial resolution was about 0.36 \(\upmu\)m (two steps in Figure 20a). This result provides much benefit to related research and extends the boundary of SEE laser mapping technology.
## 8 Conclusions  
This article addresses the path planing of a robot in a large building with many floors. The robot needs to arrive at the goal in the building safely. The robot can utilize elevators to move from one floor to another. Using the building Voronoi graph as a topological map, the robot derives a safe path to the goal. The weight of a Voronoi edge is set, based on the travel time inside an elevator and the traversal time of a passage. The robot then finds the shortest path to the goal, based on the weighted Voronoi graph. In the case where the robot detects an object with arbitrary shapes, such as a human, while moving along a Voronoi edge, the robot avoids the object utilizing reactive control laws.  
In summary, we addressed how to make the robot follow the building Voronoi graph, while avoiding collision with map environments as well as objects with arbitrary shapes. This paper proposed a reactive collision-avoidance control, which achieves collision avoidance with map environments as well as objects with arbitrary shapes. We integrated the proposed reactive collision-avoidance control with a path-tracking control, so that the robot can follow the Voronoi graph, while still avoiding collision with static map environments or objects with arbitrary shapes.  
As far as we know, our paper is novel in the path planning and control of a robot utilizing the building Voronoi graph representing a large building structure with many elevators. Furthermore, the proposed reactive collision-avoidance control is novel in considering collision avoidance with map environments as well as (moving or static) objects with arbitrary shapes. This paper verifies the effectiveness of the proposed approaches utilizing MATLAB simulations. In the future, we will demonstrate the performance of the proposed approaches utilizing experiments with a real robot.  
The proposed control scheme can be extended to the case where multiple robots move in the building. Each robot can share the building Voronoi graph and use the graph as its topological map. A robot can be considered as a moving obstacle to another robot. A robot can share its position and velocity information with its nearby robot using local communication. Then, the proposed reactive collision-avoidance control laws can be used for avoiding collision among multiple robots.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant Number: 2022R1A2C1091682). This research was supported by the faculty research fund of Sejong University in 2023.  
Not applicable.  
The authors declare no conflict of interest.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Security and Privacy Requirements
#### 2.1.1 Short-Term Link-Ability  
Short-term link-ability is a necessary attribute in Intelligent Transportation System applications. Let us say if a car receives two or three messages within a short time span, the driver on the receiving end will be able to check the origins of the messages. By adding short-term link-ability, we guarantee that a compromised OBU cannot impersonate several vehicles and unleash a Sybil-attack [10]. Vehicles in the vehicle network often relay messages with their speed, position and acceleration. To create a trajectory of surrounding OBUs, these are the signals that are used. Meanwhile the privacy of a user regarding his/her location is not affected because location privacy of a vehicle is not impacted by the minor increment [9].
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid
### Grid monitoring
The end-user can, for example, alter their habits, and shift the demand to off-peak hours in response to dynamic pricing programs such as time of use, real time pricing, critical peak timing or to incentivize payment when grid reliability is low [45; 46; 47]. Demand response can also be an automated part of home energy management systems, where certain appliances or lighting can be turned off to reduce consumption [48]. Demand side management or demand response can be used to reduce power constraint, shift peak load, reduce distribution losses, and regulate voltage drops and avoid or postpone the need for building new power lines [29; 49]. **Home Energy Management Systems (HEMS)**  
HEMS is used to enable demand response applications. HEMS systems permit the end-users to monitor, control, and manage the power consumption. These systems are comprised of smart appliances, sensors, smart meters, and in-home displays, and include applications for example home automation, temperature zone setting, water temperature, and controlling electricity use depending on real-time pricing information, etc. Appliances and sensors connects to HEMS through sensor networks and to the utility companies AMI through the smart meter HAN interface [32; 34].
## 4 Functionalization of Electrospun Nanofiber Mats for Electromagnetic Shielding  
As mentioned before, the conductive, magnetic and also dielectric properties of nanofiber mats will influence the shielding effectiveness. Improving these properties is possible by adding corresponding blend materials or nanoparticles directly to the spinning solution, in this way creating intrinsic properties of the nanofibers, or by a post-treatment, e.g., by dip-coating, carbonization, etc. This section describes some exemplary possibilities of functionalization. Other factors are the thickness of the nanofiber mat, the fiber diameters and the porosity as well as the fiber orientation, which will also be discussed here in brief.
## 6 Presentation and Analysis of Results
### Micro-Doppler Spectrograms for Multi-Passenger Scenario Tests  
We begin by comparing the spectrograms from the Multi-Passenger Test, specifically focusing on scenarios with two versus four passengers. The corresponding micro-Doppler spectrograms for both the front and back sides are presented in Figure 9.  
Several key observations can be made from the spectrograms. On the front side of the GMC, there are 3-5 relatively straight lines that represent tonal vibrations, which are more prominent than others. These lines likely correspond to vibrations from the internal combustion engine, which typically exhibit a fundamental tone matching the combustion frequency. In a four-stroke, six-cylinder engine, there is one combustion event per cylinder for every two revolutions. For instance, if the engine operates at 600 rpm (equivalent to 10 revolutions per second), and each cylinder fires five times per second, the fundamental tone would be 30 Hz. Some low-frequency cyclic vibrations up to \(\sim\)5 Hz are noted possibly from the engine block vibration transmitted through engine mounts. Data from the rear side also shows the strong tones observed from the front side. We note the presence of higher amplitude low-frequency (compared to the front side) components, owing to the factthat the vehicle side and rear are larger flat surfaces where the front view radar beamwidth covers vehicle grill, hood, and windshield'smearing' low-frequency components.  
A comparison of radar and accelerometer spectrograms is presented in Figure 10 when viewed from the back. As observed in Figure 10, spectral lines at 30 Hz also appear in the accelerometer spectrogram, which aligns with the engine running at 600 rpm. Additional spectral lines are seen at 60 Hz and 90 Hz, suggesting harmonic behavior. In some cases, a dominant line at 40 Hz (with a harmonic at 80 Hz) appears, the cause for which is unknown at this point. These spectral lines, visible in both radar and accelerometer spectrograms, align with the frequencies expected from an idling engine.  
Occasionally, additional frequencies between 0 and 20 Hz briefly emerge. Depending on the vehicle's year and model, modern engines often include features like idle control. Even when idling, the engine periodically adjusts to stabilize. This behavior is likely caused by factors such as damping effects in the gearbox, the AC compressor, and the electronic speed control system.  
The path through which vibrations transfer across the vehicle structure is minimally influenced by passenger occupancy. This is likely due to the much larger mass of the vehicle itself. Consequently, the number of passengers inside the vehicle has little effect on the  
Figure 9: Two- and four-person multi-passenger micro-Doppler spectrograms for GMC Acadia: (**a**) Front side, 2 persons; (**b**) Front side, 4 persons; (**c**) Back side, 2 persons; (**d**) Back side, 4 persons. The horizontal red lines at 30 Hz and 60 Hz represent the fundamental tone from a typical 6-cylinder 4-stroke engine.  
overall mass, and the change in vibration amplitude observed is minimal between two and four persons.
## 2 Related Work
### Needs for Feature Selection  
In the real world, data can be noisy, incomplete, and inconsistent [10] because they are collected from various sources with different techniques. Data preprocessing on collected raw data will increase the quality of the data for further analysis. Data cleaning, reduction, integration, and transformation are commonly used data preprocessing techniques. Dimension reduction, numerosity reduction, and data compression are the methods used during the data reduction process [11; 12]. Feature selection and principal component analysis are two commonly used data reduction and dimension reduction techniques. This study aims to make the dataset more suitable for machine learning by examining the data reduction techniques and suggesting new feature selection techniques.  
Feature selection is the choosing of the optimal subsets of features that can summarize the data according to the determined criteria. Statistical tests [13], probability tests, and distance calculations, methods, and metrics are widely used in the feature selection process. Examples of these are the Fisher score, Hellinger distance [14], correlation coefficient, Cosine coefficient, Chi-square, and Tversky index [15]. When feature creation processes take place, automatic network monitoring tools are mostly used, which skip the manual work and expert domain knowledge. This causes the collected data to be noisy, incomplete, and inconsistent. Data must be clean, less noisy, and consistent before the learning process takes place [16; 17]. Selecting optimal subsets of features in current intrusion detection datasets will increase model performances. Feature selection is needed specifically for the following reasons:  
* To remove unrelated and noisy data;
* To make the data more understandable and visible;
* To prevent excessive learning and increase the performance of the model that is used;
* To reduce data cost;
* To reduce the complexity of the model that is used;
* To reduce storage requirements and computational cost.