esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Network Description  
The methodology presented in Section 5 has been simulated in a test network based on the New England IEEE-39 model, which provides a simplified representation of the northeastern U.S. transmission system [52]. The original IEEE-39 model consists of 39 buses, 10 generators, 19 loads, 34 transmission lines, and 12 transformers. Figure 3 presents a single-line diagram of the network. The different colors correspond to the different voltage levels: 345 kV (light blue), 230 kV (dark red), 16.5 kV (red) and 0.69 kV (blue).  
In this model, Generator G01 represents the interconnection with the external transmission system, while Generator G02 is the slack bus. Active power dispatch and controlled terminal voltage magnitudes are assigned to the remaining generators. The technical specifications for all grid components, including generators, loads, transmission lines, and transformers, are available in [53].  
The synchronous generator controllers include Automatic Voltage Regulators (AVRs), modeled as IEEE Type 1 rotating excitation systems in accordance with [54]. The governor models vary: IEEE Type G1 (steam turbine) is applied to Generators G02-G09, and IEEE Type G3 (hydro turbine) is used for Generator G10. To isolate and evaluate the performance of the POD-P controller, Power System Stabilizers (PSSs) are disabled throughout the study.  
Figure 3: Modified New England IEEE-39 power system model.  
To assess scenarios with high levels of renewable energy penetration, this study integrates four PV plants into the system to analyze their impact on power oscillation damping. Figure 3 highlights these PV plants (marked with black squares), which are distributed across the grid to avoid localized effects and ensure a realistic simulation of renewable integration. Details regarding the rated power of the PV plants and the active power setpoints across scenarios are provided in Section 6. The PV controllers and generators are modeled using the WECC WT Type 4A model from the DSPF library [44]. These PV plants are equipped with PPCs that incorporate POD-P controllers designed to modulate active power and damp oscillations, as detailed in Section 2. The POD-P controller parameters are optimized using the NBA, as explained in Section 3, and remain fixed throughout the simulations.
## 3 Proposed Approach
### Description of Dataset  
The UNSW-NB15 dataset [19], released in 2015 by the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS), is a widely recognized and utilized NIDS dataset. It generates benign network activities and premeditated attack scenarios, comprising a total of 2,540,044 network samples, with 2,218,761 (87.35%) benign and 321,283 (12.65%) attack samples. This dataset includes 49 features, including twelve derived using SQL algorithms. Recently, the NF-UNSW-NB15-v2 dataset was generated and released in 2021, based on the original UNSW-NB15 dataset. This NetFlow dataset includes 43 NetFlow-based features extracted from the pcap files of the UNSW-NB15 dataset using the nprobe feature extraction tool, with the data flows labeled appropriately. The NF-UNSW-NB15-v2 dataset contains a total of 2,390,275 flows, of which 95,053 (3.98%) are attack samples and 2,295,222 (96.02%) are benign [10]. Additionally, this dataset is classified into ten classes, comprising nine for different types of attacks and one for benign traffic. Table 3 provides a detailed overview of the different attack categories, including comprehensive descriptions of each, along with the distribution of samples across various classes within the datasets. Figure 1 depicts the architecture developed for binary and multi-class classification using the NF-UNSW-NB15-v2 dataset.  
Figure 1: Architectural design for binary classification and multi-class classification using NF-UNSW-NB15-v2 dataset.
## 4 Results
### Findings per Technical Debt Type
#### 4.3.4 Data Debt  
Definition: Liabilities stemming from incomplete, undocumented, biased, or unstable datasets used in training and evaluation.  
Problem: Competitions may provide datasets without metadata, versioning, or consistency between training and test sets, leading to training-serving skew, bias, and poor reproducibility.  
Example: A competition provides sunny-weather training data but evaluates submissions on mixed-weather test sets without documentation. Models fail dramatically, undermining the leaderboard.  
Stakeholder: Organizer  
Severity Score: 5--Critical for fairness, scientific validity, and real-world generalizability.
# Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
## 2 Materials and Methods
### Concrete Mix  
The research project was divided into successive stages: 15 LWC mixes were designed with different proportions of GEGA 2 mm, GEGA 4 mm and GAA 8 mm, then the mixes were prepared, and the rheological properties of the mixes were tested. Then, the physical properties of the LWC in terms of porosity and density of the LWC as well as compressive strength and thermal conductivity were determined. The aim of the research was to determine the dependence of the use of LWA with known porosity and density on the physical properties of the LWC. The obtained results were used to predict the LWC properties using ANN. The composition of the mixtures is presented in Table 2. The tested aggregate was not pre-wetted. The natural humidity of the aggregates was tested before making the mixtures and was, respectively: GEGA 2 mm (3.4%), GEGA 4 mm (2.9%), and GAA 8 mm (4.1%). The total amount of water in the mixes was corrected for the amount of water contained in the aggregates. A constant water/cement (w/c) ratio of 0.5 was maintained. No chemical admixtures or additives were used in preparation of LWC mixtures.
# Post-Authorship Attribution Using Regularized Deep Neural Network
## 3 Proposed Method
### Distributed Highway Network with Bidirectional Long Short-Term Memory
#### 3.2.2 Bidirectional Long Short-Term Memory  
Long short-term memory (LSTM) captures the contextual information for solitary text sequences. However, the feature representation that is obtained from the CNN character level does not contain the sequence information. The bidirectional LSTM (BLSTM) is specialized in sequential modeling and can further extract the contextual information from the feature sequences obtained in the convolutional layer. Hence, we have used it to learn the representation of long-range dependencies based on the surrounding word distributions. The encoder BLSTM was used to capture the lexical, and syntactic information from the CNN character level. The one-dimensional convolution involves a filter sliding over a sequence to account for features at different positions. Thereafter, the decoding layer operates on the encoded sentence representation with DHN. The decoder BLSTM will find the hidden structural representations for authorship attribution. The forward LSTM network computes the state \(\overset{\rightarrow}{h_{t}}\) of the past left context of the sentence \(c_{j}\), while the backward LSTM network reads the same sentence in the reverse order to produce \(\overset{\leftarrow}{h_{t}}\) given the future right context. The \(\overset{\rightarrow}{h_{t}}\) and \(\overset{\leftarrow}{h_{t}}\) outputs are concatenated to obtain the final output of the BLSTM network as follows.  
\[h_{t}=\left[\overset{\rightarrow}{h_{t}}:\overset{\leftarrow}{h_{t}}\right] \tag{6}\]  
The number of hidden layers for the input text is set to \(n\), and the result of the decoder BLSTM network can be expressed as follows.  
\[\mathbb{H}=[h_{t},h_{2},\dots,h_{m}] \tag{7}\]  
The parameter \(m\) is the length of the input post. The output of the last LSTM layer is \(\mathbb{H}\in\mathbb{R}^{m\times(2\times n)}\) with each row of the matrix \(\mathbb{H}\) representing a feature vector of the sentence generated by BLSTM. The hidden states of the time steps of BLSTM or the last layer of the time step hidden states of multilayer BLSTM are concatenated to obtain the feature representation of a post. The output of the highway network is used as input to the decoder BLSTM to learn the semantic representation of post sentences. The hidden document embedding of BLSTM is fed into a fully connected network for training and classification. The additional feature exploration was carried out to analyze the most important features for the overall performance of the RDNN method. The Shapley additive explanation (SHAP) [94] and local interpretable model-agnostic explanation (LIME) [95] have been applied in this study for interpretability.
## 3 Results and Discussion  
### Evaluation Indicators  
In this study, we use precision, recall, mean average precision at 0.5 ([EMAIL_ADDRESS]), mean average precision from 0.5 to 0.95 ([EMAIL_ADDRESS]:0.95), computational complexity (GFLOPs), and model size as evaluation metrics. Precision refers to the proportion of samples that are positive among all samples predicted to be positive. The calculation formula is as shown in Equation (4).  
\[\text{P}=\text{TP}/(\text{TP}+\text{FP}) \tag{4}\]  
TP stands for the number of original positive samples that are correctly detected as positive, FP represents the number of samples that are incorrectly identified as positive when they are negative, and FN represents the number of original positive samples that are not correctly detected. Recall is the proportion of all positive samples that are correctly detected. The calculation formula is given in Equation (5). Here, TP stands for the number of original positive samples that are correctly identified as positive, FP represents the  
Figure 5: SDCB-YOLO network.  
number of samples that are incorrectly identified as positive when they are negative, and FN represents the number of original positive samples that are not correctly detected.  
\[\text{R}=\text{TP}/(\text{TP}+\text{FN}) \tag{5}\]  
AP represents the average of the accuracy for a single category, while mAP is the mean value calculated across all categories. [EMAIL_ADDRESS] refers to the average value when the IOU threshold is set to 0.5, and [EMAIL_ADDRESS]:0.95 represents the average value when the IOU threshold ranges from 0.5 to 0.95 with a step size of 0.05. The calculation methods are shown in Equations (6) and (7). Here, N represents the number of target categories being detected.  
\[AP=\int_{0}^{1}P(R)dR \tag{6}\]  
\[mAP=\sum_{i=1}^{N}AP_{i}/N \tag{7}\]  
GFLOPs directly reflects the detection speed of the model. Model size will directly affect the feasibility of actual deployment on the device.
## 3 Proposed Energy-Efficient Vnf Placement, Traffic Routing, and User Association (Hero)  
Our proposed Heuristic for Energy-efficient VNF placement, traffic Routing, and user assOciation (HERO) aims to maximize the network energy efficiency while ensuring low UE blocking probability. HERO consists of two stages, as shown in Figure 3. In the first stage, the traffic path is selected (i.e., user association and routing is performed), while in the second stage VNF placement takes place, ensuring correct VNF ordering.  
Figure 3: Flowchart of the proposed energy-efficient VNF placement, traffic routing, and user association algorithm (HERO).  
Initially, to ensure a high UE acceptance ratio, the UEs are sorted based on their service demands, giving priority to the UEs with the most delay-intolerant services. For UEs with the same delay requirements, priority is given to the UEs with higher rate demands. Then, for each UE, a weighted graph is constructed from the service traffic source to the UE with all feasible links and their respective power consumption acting as weights. HERO calculates the \(k\) shortest-weighted paths and starts with the first, as long as it satisfies the delay and link capacity constraints, taking into account the decisions for the already examined users. Otherwise, the next path is selected until either a path that satisfies all constraints is found or there are no other paths. In the latter case, the UE is blocked and HERO proceeds with the next as long as there is one.  
After a valid path is found for the current UE, HERO proceeds to the second stage, where the UE VNFs are being placed. To that end, for each VNF, following the order of the UE SFC, a list is constructed with all the available computational nodes based on a parameter, denoted by \(H\). This parameter is equal to the sum of the normalized node centrality (closeness), the normalized node computational capabilities (\(c_{y}\)), and the node CPU utilization. The latter is equal to (a) 1 when the studied VNF can be placed in the examined node without initiating a new VNF instance, (b) 0.1 when there is enough computational capacity to host the studied VNF in the examined node, but a new VNF instance is required, and (c) 0 otherwise. Subsequently, the node with the highest \(H\) for the selected VNF is selected, as long as it has sufficient computational resources to host it. Otherwise, the node with the next highest \(H\) is selected, until either the VNF is placed or there is no other node to examine in the selected path. In the latter case, the algorithm returns to stage 1 and the next path out of the \(k\) calculated is examined. The process is repeated for the new path until either all VNFs of the UE are placed or there is no other path to study and the UE is blocked. In the case where all UE VNFs are placed, the network conditions are updated, and the algorithm proceeds to the next UE. The aforementioned steps are repeated until all UEs are examined.
# Evaluation of Adhesion and Hysteresis Friction of Rubber-Pavement System
## 3 Theory of Surface Energy  
There are two major components contributing to pavement friction: adhesion and hysteresis. The adhesion component of pavement friction is dominant at low speeds in dry conditions, while the hysteresis component is dominant at higher speeds in wet conditions [6]. The adhesion between any materials is a function of their surface free energy components, where the adhesion force is developed at the molecular level [7; 8]. The atoms at surface have lower level of bond energy compared to the atoms in bulk. As molecules in the material bulk are surrounded by other molecules from all sides, an external work must be applied to create a new surface area [7]. This work is known as the total surface free energy of the material and is denoted with Greek letter gamma (\(\gamma\)), the surface free energy is measured in units of ergs/cm\({}^{2}\) or mJ/m\({}^{2}\). The two most recognized theories to explain the surface free energy are: the two-component theory, and the acid-base theory. According to the two-component theory, the total surface free energy is caused by dispersion forces (e.g., Lifshitz-van der Waals forces) and specific forces (e.g., H-bonding). The total surface free energy is the sum of these two forces as shown in the Equation (1) [9; 10].  
\[\gamma=\gamma^{\text{Dispersive}}+\gamma^{\text{Specific}} \tag{1}\]  
According to the acid-base theory, the total surface free energy for any material has three components; nonpolar component also known as Lifshitz-van der Waals (\(\gamma^{\text{LVW}}\)), and two polar components: Lewis acid (\(\gamma^{+}\)), and Lewis base (\(\gamma^{-}\)) [11; 12]. The total surface energy can be calculated from these three components as given in Equation (2).  
\[\gamma^{\rm Total}\ =\ \gamma^{\rm LW}\ +\ 2\sqrt{\gamma^{+}\gamma^{-}} \tag{2}\]  
Several theories explain the adhesion between two materials by studying the interfacial forces between the materials [13]. The adhesion between rubber tires and pavement surface is one mechanism leading to friction and is dominant at low speeds. The adhesion between rubber and aggregate can be measured if the surface free energies of both materials are known. The adhesion between two materials (A and B) is a function of the surface free energy components of these two materials, as given in Equation (3) [7].  
\[\rm W_{AB}\ =\ 2\sqrt{\gamma_{A}^{\rm LW}\gamma_{B}^{\rm LW}}+2\sqrt{\gamma_{A}^{+} \gamma_{B}^{-}}+2\sqrt{\gamma_{A}^{-}\gamma_{B}^{+}} \tag{3}\]  
The amount of work required to separate the two materials at the interface in vacuum is referred as the adhesive bond energy (\(\rm W_{AB}\)). The adhesive bond energy between rubber sample (subscript A) and an aggregate (subscript B) can be calculated using Equation (3), while the cohesive bond energy of a single material (e.g., rubber) (\(\rm W_{AA}\)) can be calculated according to Equation (4) [7].  
\[\rm W_{AA}=\gamma_{A}^{\rm LW}+\ 2\sqrt{\gamma_{A}^{\rm LW}\gamma_{B}^{\rm LW}} \tag{4}\]
## 3 Results and Discussion
### Bottom Thickness  
The bottom thickness has an important role in noise, vibration, harshness (NVH), and corrosion [5]; however, high bottom thickness is generally accompanied with high head height and small \(t_{\text{eff}}\), which reduces the static joint strength [8; 9].  
The bottom thickness was significantly affected by all parameters more or less, and only the \(p\)-value for the pre-clamping force was higher than 0.5. Similar to the ANOVA results for the head height, the difference according to the setting force levels was most obvious. The bottom thickness was inversely proportional to the setting force (Figure 5b). As for the poor fit-up, the difference between type 1 (no gap) and type 2 (constant gap) is smaller than the difference between type 1 and the other types (types 3 to 6) with various angular gaps. The lowest bottom thickness was found at the highest pre-clamping force, no angular misalignment, and non-negative edge offset distance.  
The bottom thickness has a Pearson correlation coefficient of 0.642 for the head height, with a \(p\)-value of 0.004. This denotes that the strength of linearity between the bottom thickness and the head height is 0.642, while a Pearson correlation coefficient of 1 indicates that both parameters have a perfectly positive linearity, and there is a 0.4% risk to conclude the linearity.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking
#### 3.2.6 Point of Interest Tracking  
To create more visually appealing visualizations around POIs, it is important to track the location of the POI across consecutive frames. However, rerunning the entire POI recognition pipeline for every frame is not feasible due to the computational resources required, which would result in unacceptable delays for a live race broadcast. To overcome this challenge, an optical tracking algorithm is initialized after a POI is recognized, which follows the region of interest through subsequent frames. The tracking algorithm should ideally be faster than real-time to ensure smooth tracking while minimizing the delay in the broadcast chain.  
Several traditional and machine learning-based trackers were compared empirically to assess their tracking quality and speed. Of those, two are chosen to integrate in the methodology. Traditional techniques, such as KCF and CSRT, are simple and offer fast  
Figure 5: Example visualization of the keypoints and matches generated by the SuperGlue model. The left image is the helicopter video frame cropped to the proposed region by the saliency and SAM model, whereas the right image shows the reference. The color of the line indicates the confidence of the match with red being the strongest.  
Figure 6: Example visualization of (**a**) the predicted SAM mask before refinement and (**b**) the predicted SAM mask after refinement using the POI recognition keypoint information.  
tracking performance. Despite their simplicity, they have limitations in handling occlusion and large orientation changes. However, given that buildings are not frequently occluded and camera movement is often linear, they are a valid tracking possibility. Among the tested traditional algorithms, MedianFlow [41] demonstrates accurate, smooth, and high-speed tracking results on buildings, which is why it is included in this work. Machine learning-based techniques such as Stark and Cutie perform well when tracking objects like humans and cars but struggle with tracking buildings. In contrast, the large SAM 2, which also has tracking capabilities, demonstrates very high segmentation tracking accuracy, even when the camera rotates around the POI. Therefore, next to MedianFlow, SAM 2 is provided as an option within the pipeline. Which tracker to use can be decided based on the tracking accuracy needed and the computational resources available.  
To further improve the accuracy of the MedianFlow tracker, the largest inner bounding box of the POI mask is determined and tracked. By using the inner bounding box, the tracker can better focus on the POI itself, reducing the chances of being distracted by the background. Figure 7a shows the initial inner bounding box, whereas Figure 7b presents the tracked bounding box 20 s later.
## 7 Conclusions  
In this work, we introduced a new algorithm called the adaptive SSA as a feature selection algorithm for PQD classification. The classification system's feature extraction was based on the DWT and the classifier uses probabilistic neural network. The adaptive approaches were proposedly performed to enhance the ability of SSA for classification. It was found that the presented system indicated the best performance through 12 optimally selected features. The results showed that the highest accuracy in the classification of 13 types of signal power quality of 98.77% was achieved. Additionally, the proposed classification system can greatly operate in the noisy situation and the real dataset. The convergence rate of the system based on adaptive SSA was very high compared to the SSA, PSO, and GA. Considering the classification accuracy of previous studies reveals that the proposed classification system's accuracy falls on a high-range scale, implying that it is capable of being another efficient PQD classification system.  
Conceptualization, S.C., A.S., and P.K.; data curation, S.C.; resources, P.K.; methodology, S.C., A.S., and P.K.; software, S.C.; formal analysis, S.C., A.S., P.F., P.S., and P.K.; investigation, P.K.; validation, S.C. and P.K.; writing--Original draft preparation, S.C.; writing--  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Number of PQD Types** & **Feature Extraction Type** & **Classification Technique** & **Optimal Feature Selection Algorithm** & **Overall Accuracy (\%)** \\ \hline
[43] & 5 & DWT & LS-SVM & k-means +Apriori & 98.88 \\
[72] & 8 & Modified frequency slice wavelet & PNN & & 97.70 \\
[73] & 8 & Hilbert–Huang transform & NN & & 97.90 \\
[74] & 9 & Time-time transform & Decision tree & Ant Colony Optimization & 95.97 \\
[42] & 9 & ST & Fuzzy C-means & Adaptive PSO & 96.33 \\
[21] & 9 & ST + VMD & SVM & Sequential forward selection & 99.66 \\
[75] & 9 & VDM & Decision tree & & 98.56 \\
[44] & 10 & DWT & extreme learning & PSO & 97.60 \\
[76] & 10 & DWT + FFT & Threshold & & 90.04 \\
[46] & 13 & VMD & OVR-SVM & Permutation Entropy+ Fisher Score & 97.6 \\
[77] & 13 & hyperbolic S-transform & Decision tree & GA & 99.5 \\
[45] & 15 & VDM & Fast reduced kernel extreme learning & Fischer linear discriminant analysis & 98.82 \\ This work & 13 & WT & PNN & Adaptive SSA & 98.77 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of classification accuracy to the existing works.  
Review and editing, P.K.; supervision, P.K.; visualization, A.S., P.F., and P.S.; project administration, P.K.; funding acquisition, P.K. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Provincial Electricity Authority (PEA).  
Not applicable.  
The data of real power quality signals presented in this study are openly available in [[http://map.pqube.com](http://map.pqube.com)]([http://map.pqube.com](http://map.pqube.com)) (accessed on 24 February 2021).  
The authors declare no conflict of interest.