esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 4 Simulation and Discussion
During the chirp-BOK process of simulation, the BER trends were found to be consistent under four different channel conditions. Therefore, only two cases are listed here, \(T=30\) ns and various path gains, with results showing that chirp-BOK outperforms BPSK, 16-QAM and QPSK in troposcatter deep fading channel conditions. Although chirp-BOK modulation can have higher sensitivity, the spectral efficiency is relatively low. To settle this problem, the chirp signal spread method can be used with a typical digital modulation scheme and Orthogonal Frequency Division Multiplexing (OFDM) technique. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path-Gain-** & \(\mathbf{T=5\,ns}\) & \(\mathbf{T=10\,ns}\) & \(\mathbf{T=30\,ns}\) & \(\mathbf{T=50\,ns}\) \\ \hline \(D=8\) & 27.2 & 27.5 & 31.9 & 32.5
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 6 Discussion
### Comparison with JADE-Based and Python-Based Research
This significantly improves decision accuracy (96%) and task completion rates (95%) while ensuring real-time, structured agent communication. Similarly, traditional DRL-based systems, while powerful in learning, often lack structured interoperability mechanisms, making them less suitable for multi-agent government workflows. Our hybrid approach overcomes this limitation by combining the JADE's structured FIPA-ACL messaging protocol with DRL's adaptive learning capabilities. Furthermore, the proposed system is highly scalable, supporting up to 50+ agents while maintaining low communication latency (120 ms) and reducing bureaucratic workload by 40%. This makes it particularly well-suited for large-scale governmental operations, including permit processing, municipality event coordination, and inter-departmental resource allocation.
## 3 Experiments and Analysis
### Building Environment Experiment
The localization results and MSE curves in the figures indicate that the DCLAEKF algorithm displayed higher errors than the DCLEKF algorithm at some times but demonstrated lower errors most of the time. This was due to the complexity of the indoor environment, limited space, and the presence of various types of obstructions, such as walls, desks, chairs, doors, and windows. The sole use of the differential method, as described in Section 2.2, could lead to inaccurate NLOS detection, thereby limiting the effectiveness of adaptive algorithms. The MSEs in Table 2 indicate that the DCLAEKF algorithm achieved better  
Figure 8: MSE curves of soldiers in a building environment experiment. (**a**) MSE of soldier 1; (**b**) MSE of soldier 2; (**c**) MSE of soldier 3; (**d**) MSE of soldier 4; (**e**) MSE of soldier 5; (**f**) MSE of soldier 6.  
localization accuracy than the DCLEKF algorithm. The experimental results demonstrate that the proposed algorithm was suitable for the cooperative localization of firefighters in the building environment.
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)
#### 4.3.1 SOMA Philosophy  
A group of animals such as wolves or other predators makes for a good example. If they are looking for food, they usually cooperate and compete such that if one member of the group is successful (has found food or shelter) then the other animals of the group change their trajectories towards the most successful member. If a member of this group is more successful than the previous best one (it has found more food, etc.), then again all members change their trajectories towards the new successful member. This is repeated until all members meet around one food source. This principle from the real world is of course strongly simplified; nonetheless, it can be said that it is the competitive-cooperative behavior of intelligent agents that allows SOMA to carry out very successful searches.  
SOMA can be classified as an evolutionary algorithm, despite the fact that no new generations of individuals are created during the search based on philosophy of this algorithm. Only the positions of the individuals in the search space are changed during a generation, which is called a \"migration loop\". Individuals are generated by random according to what is called the \"specimen of the individual\" principle. However, it is possible to classify SOMA as an evolutionary algorithm, as the final result after one migration loop is equivalent to the result from one generation derived by the classic EA algorithms.  
Recently, Zelinka et al. used SOMA [81] for the design of quantum computing circuits for future quantum computers.  
One key point in SOMA is the possibility of avoiding premature convergence by means of a so-called _PRTVector_ (or Perturbation Vector), which limits the movement of particles to certain dimensions. Pluhacek et al. [82], considering that there is no conceptual barrier between DE, PSO, and SOMA, found that it is possible to noticeably improve the performance of a PSO algorithm driven by a Lozi chaotic map by implementing the _PRTVector_ mechanism from the SOMA algorithm.  
Through several examples of objective function, such as the Sphere function, Rosenbrock's function  
\[f(x)=\sum_{i=1}^{D-1}\Big{|}100(x_{i}^{2}-x_{i+1}^{2})^{2}-(1-x_{i})^{2}\Big{|}, \tag{59}\]  
noise function, Rastrigin's function (43), Schwefel's function (44), Ackley's function,  
\[f(x)=-20 exp\left(-0.2\sqrt{\frac{1}{D}\sum_{i=1}^{D}x_{i}^{2}}\right)-exp\left( \frac{1}{D}\sum_{i=1}^{D}x_{i}^{2}cos2\pi x_{1}\right)+20+e, \tag{60}\]  
etc., they highlighted that it is possible to find better solutions even faster than with the original design of PSO driven by Lozi map.
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
###### Abstract  
In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb-Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and global information. Our Spectral GNN implementation employs the Laplacian matrix to efficiently map tumor tissue connectivity by capturing the graph's global structure. Consequently, this enhances the model's precision in classifying brain tumors into distinct types: necrosis, edema, and enhancing tumor. This model underwent extensive hyper-parameter tuning to ascertain the most effective configuration for optimal segmentation performance. Our Spectral-Spatial GNN model surpasses traditional segmentation methods in accuracy for both whole tumor and sub-regions, validated by metrics such as the dice coefficient and accuracy. For the necrotic core, the Spectral-Spatial GNN model showed a 10.6% improvement over the Spatial GNN and 8% over the Spectral GNN. Enhancing tumor gains were 9.5% and 6.4%, respectively. For edema, improvements were 12.8% over the Spatial GNN and 7.3% over the Spectral GNN, highlighting its segmentation accuracy for each tumor sub-region. This superiority underscores the model's potential in improving brain tumor segmentation accuracy, precision, and computational efficiency.  
Graph Neural Network; Spectral GNN; supervoxel; segmentation +
Footnote †: journal: Medical Image Computing
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 3 The Digital Twin Platform
### Virtual Entity  
One of the key features of a DT is that its virtual entity represents the state of the physical entity at all times. This requires that models or the model parameters are adapted to changes in the physical entity. An adaption of the models in the virtual entity will, for example, be required when degradation changes the process performance, when parts have been replaced or refurbished in a maintenance intervention, or when new components were installed for modernization of the IES. The job of the DT is to keep track of these changes and manage the different versions of each model/parameter set.  
In our platform, this requirement is fulfilled by the model management in the virtual entity. It essentially runs a database of all available model instances and stores information on each model in a local knowledge graph. Information includes (amongst other factors) the URI (Uniform Resource Identifier), scope, inputs, outputs, validity ranges, accuracy and time of validity. The model management runs an MQTT endpoint over which models can be queried and afterwards processed. The models can thus be provided for specific services.  
Our DT platform allows for model integration developed in different environments. Thus, also different types of models, for example, physical, data-driven or hybrid models, with varying fidelity, can be applied for designated tasks within the DT. For details on modeling enabling technologies for DTs, see, for example [98].  
Accessing the metadata of model instances and storing them within an ontology requires standardized interfaces. Functional Mock-up Interfaces (FMIs) ([[https://fmi-standard.org/](https://fmi-standard.org/)]([https://fmi-standard.org/](https://fmi-standard.org/)), accessed on 10 June 2022) are an established standard to create relevant model instances and their input and output parameters (connections). This allows storing a model instance as a single file via corresponding Functional Mock-up Units (FMUs). By including a metadata file, which contains descriptions of the connection types, these two files provides all necessary information to describe the virtual entity.  
Figure 4 illustrates how services can access virtual entity models within the DT platform. The platform contains a model management service written in Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)), accessed on 10 June 2022), which processes these files and provides access to the FMU model via the File Transfer Protocol (FTP). In order to select a specific model, any service connected to the MQTT broker is able to query all provided models of the model management service via an MQTT request and subsequently can access them via FTP. The reason for using FTP in contrast to provide the FMU file via MQTT lies within the nature of MQTT, which is not designed for exchanging binary files, hence the choice of using the much more suitable File Transfer Protocol.
## 2 Materials and Methods  
The object of the experimental research is a human skeleton, so it is expected that only one person will be visible in the center of the frame in all videos. In addition, in case of the need for rehabilitative application, the videos choose to perform only arm exercises with the volunteer standing or sitting [26]. Since patients are different, the selection of samples requires as many characteristics of the person being filmed as possible, for example, gender, age, height, physique, or clothing.  
In order to complete the final work on one object of experimental research, it is necessary to have two videos that were filmed at the same time by two cameras perpendicular to each other. Because the videos need to be synchronized, an audio or visual cue, such as having the volunteer clap their hands or turn the lights off and on, should be inserted into the video before the exercise is performed. Ideally, videos from different angles should be shot with the same cameras, ensuring the same frame quality, image resolution, and frame rate.  
The system employed the Orbbec Gemini 2 Camera (Orbbec Inc., purchased in Kaunas, Lithuania), which features a resolution of 1920 \(\times\) 1080 at 30 frames per second for its RGB sensor. For processing and 3D human skeleton computation, the system relied on a high-performance PC equipped with an Intel i9-9900k processor, an NVIDIA RTX 4090 GPU with 24 GB of VRAM, and 64 GB of DDR4 RAM. The approach presented in this paper was developed using Python 3.11.2. Popular libraries, such as NumPy 1.25.0, SciPy 1.11.1, and PyTorch 2.0.1, were utilized for efficient implementation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model (Year)** & **Core Technique** & **Accuracy (MPJPE) (Dataset)** \\ \hline SlowFastFormer (2024) [22] & \begin{tabular}{l} Dual-branch network, varying \\ input rates \\ \end{tabular} & 38.9 mm (Human3.6M) \\ GAST-Net (2022) [23] & \begin{tabular}{l} Geometry-aware \\ spatiotemporal model \\ \end{tabular} & 39.8 mm (Human3.6M) \\ LiftFormer (2022) [24] & \begin{tabular}{l} Transformer, 2D to 3D lifting \\ Inverse Kinematics and deep \\ learning \\ \end{tabular} & 40.2 mm (Human3.6M) \\ WbrIK (2021) [25] & \begin{tabular}{l} Inverse Kinematics and deep \\ learning \\ \end{tabular} & 41.6 mm (Human3.6M) \\ Sosa-self (2023) [18] & \begin{tabular}{l} Ensemble of 3 networks \\ Graph ConvNet and \\ Transformer \\ \end{tabular} & 43.4 mm (Human3.6M) \\ Pose2 Mesh (2021) [20] & \
\begin{tabular}{l} Transformer \\ 2D pose to 3D mesh \\ conversion \\ \end{tabular} & 45.6 mm (3 DPW) \\ PoseFormer (2021) [21] &
\begin{tabular}{l} Transformer, spatial–temporal \\ attention \\ \end{tabular} & 51.4 mm (Human3.6M) \\ \hline \hline \end{tabular}
\end{table}
Table 1: 3D human pose estimation models sorted by accuracy (MPJPE).  
The selected posts were grouped by exercises, the number of attempts, human features, and the filmed perspective, i.e., front or side. The front and side combinations of the recordings were synchronized manually, with a video editing program, cutting everything from the recordings up to the set signal.
# A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN
## 3 Results and Discussion
### Evaluation of Statistical Indicators for the Entire Scene
#### 3.4.1 Average Traffic Flow of Road Sections  
The average traffic volume of a road section represents the total number of vehicles passing a certain road section per unit of time and is an important parameter reflecting the characteristics of traffic flow [31]. The calculation formula is shown below:  
\[Q=\frac{1}{n}\sum_{i=1}^{n}\frac{N_{i}}{t} \tag{16}\]  
where \(n\) represents the number of lanes, \(t\) represents the monitoring time, and \(N_{i}\) represents the total number of vehicles in the \(i\)-th lane during the monitoring time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & \multicolumn{2}{c}{**Speed Standard Deviation**} & \multicolumn{2}{c}{**Root Mean Square Acceleration**} \\ \hline
**Data** & **Gen** & **Org** & **Org** & **Gen** & **Org** \\ \hline Ours & 0.78 & 1.29 & 4.57 & 4.36 \\ DCGAN & 0.72 & 1.97 & 9.73 & 7.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Averaging post-processing results of \(S_{v}\) and \(A_{a}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Index** & \multicolumn{4}{c}{**Speed Standard Deviation**} & \multicolumn{4}{c}{**Root Mean Square Acceleration**} \\ \hline
**Data** & **Gen** & **Org** & **Org** & **Org** & **Gen** & **Org** & **Org** & **Org** & **Org** \\ \hline Ours & 0.78 & 0.98 & 2.26 & 0.8 & 1.15 & 4.57 & 4.43 & 2.47 & 2.63 & 4.52 \\ DCGAN & 0.72 & 1.97 & 2.41 & 2.71 & 1.17 & 9.73 & 4.49 & 9.15 & 6.72 & 9.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of \(S_{v}\) and \(A_{a}\).  
In this paper, statistics are generated on the average traffic flow of the original data and the generated data. The maximum and minimum values of the average traffic flow of the road under different scenarios are shown in Table 5.
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes
### Received Signal Strength (RSS)  
There are many positioning schemes based on received signal strength (RSS) considering various channel fading models including many path loss models [9; 10; 11; 12]. The distance between BSs and the user can be directly obtained from the the strengthes of received signal, so it is simple to implement. However, there are many channel fading models according to the dominance of a visible path (i.e., LoS) and the propagation environment, so RSS based positioning schemes require the accurate information on fading model.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 4 Discussion
### Effects of Ancillary Inputs
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature Size** & **Variable** & **Resolution** & **N** & **Obs.** & **Bias** & **MAE** & **RMSE** & **rRMSE** & **r** \\ \hline \multirow{4}{*}{large} & \multirow{2}{*}{H} & Sentinel-2 & 382 & 191 & \(-\)51 & 69 & 84 & 0.441 & 0.673 \\  & & Sentinel-3 & 368 & 189 & \(-\)49 & 75 & 90 & 0.475 & 0.620 \\ \cline{2-9}  & \multirow{2}{*}{\(\lambda E\)} & Sentinel-2 & 383 & 175 & 33 & 72 & 89 & 0.507 & 0.770 \\  & & Sentinel-3 & 367 & 176 & 21 & 72 & 93 & 0.525 & 0.719 \\ \cline{2-9}  & \multirow{2}{*}{Rn} & Sentinel-2 & 418 & 445 & \(-\)11 & 45 & 58 & 0.130 & 0.897 \\  & & Sentinel-3 & 391 & 445 & \(-\)13 & 45 & 60 & 0.135 & 0.884 \\ \cline{2-9}  & \multirow{2}{*}{G} & Sentinel-2 & 411 & 82 & 11 & 43 & 53 & 0.652 & 0.444 \\ \cline{2-9}  & & Sentinel-3 & 384 & 81 & 15 & 42 & 50 & 0.614 & 0.515 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(Cont\).
# A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
## 3 Proposed Framework
### Arabic Comparative Sentence Identification  
This section describes the detailed steps of the ACSI algorithm. Identification of Arabic comparative sentences was performed by searching for Arabic comparison keywords in the sentence using the manually built Arabic comparative keywords lexicon. If there is an Arabic comparison keyword in the sentence, then it is a valid Arabic comparative sentence; otherwise, it is not.  
Algorithm 1 shows the pseudo-code of the ACSI algorithm. As shown in the pseudo-code, the statement in line 1 accesses each sentence in order, followed by the statement in line 2 which splits each sentence into words using the space between words and stores it in an ordered list. The statement in line 3 accesses each word stored in the ordered list followed by the statement in line 4 which accesses each Arabic comparison keyword from the Arabic comparison keywords lexicon in order. The statements from line 5 to line 9 extract the Arabic comparative sentences where the statement in line 5 searches for an Arabic comparative keyword in the sentence and if the comparative keyword exists, the statement in line 7 records the sentence as an Arabic comparative sentence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Dataset**} & **Number of Non-Equal** & **Number of Equative** & **Number of Superlative** & **Number of Non-Gradable** & **Sentences Not Classified** \\  & **Sentences** & **Sentences** & **Sentences** & **Sentences** & **Sentences** \\ \hline Twitter & 36 & 36 & 523 & 29 & 721 \\ MSA & 24 & 6 & 29 & 3 & 8 \\ Social Media & 20 & 10 & 166 & 20 & 1 \\ \hline \end{tabular}
\end{table}
Table 4: Classification of comparative sentence types in each dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Dataset**} & **Total Number of Sentences** & **Number of Comparative Sentences** & **Number of Non-Comparative Sentences** \\ \hline Twitter(ASTD) [14] & 10,005 & 1345 & 8660 \\ MSA & 100 & 70 & 30 \\ Social Media & 501 & 217 & 284 \\ \hline \end{tabular}
\end{table}
Table 3: Number of comparative and non-comparative sentences in each dataset.  
```
Input: dataset of preprocessed sentences.

Output: dataset of comparative sentences.
```
1:for each sentence\({}_{\text{i}}\) in the dataset of preprocessed sentences do
2: New Array arr[] = split sentence\({}_{\text{i}}\) with \" \"
3:for each word\({}_{\text{j}}\) in arr[] do
4:for each keyword\({}_{\text{n}}\) in Comparative Keyword Lexicon do
5:if(word\({}_{\text{j}}\) = keyword\({}_{\text{n}}\)) then
6:sentence\({}_{\text{i}}\) is identified as a \"Comparative Sentence\"
7: insert sentence\({}_{\text{i}}\) in dataset of comparative sentences
8: break
9:endif
10:endfor
11:endfor
12:endfor ```
**Algorithm 1** Pseudo-code of the ACSI algorithm
## Materials and Methods
### The BRDF Model  
The PRI obtained from the AMSPEC observation was affected by the observation angle and physiological status. Assuming that the physiological state and lighting conditions of the plants remained unchanged every 15 min, and followed the method by [PERSON] et al. (2008a). The kernel-driven BRDF model has been widely used in ground measurement and satellite remote sensing. The semi-empirical kernel-driven model of vegetation canopy BRDF are generally comprised of a linear combination of three kernels describing isotropic, geometric, and volume scattering effects [42]. Isotropic scattering assumes that the leaves in the canopy were randomly distributed and have the characteristics of a Lambertian surface. Geometric optical scattering was due to the shape of the vegetation canopy. Volume scattering described the effect of scattering caused by the inside of the canopy [42]. The BRDF model could be decomposed into the sum of three weights of isotropic scattering, volume scattering, and geometric optical scattering. Among them, the Ross-Thick kernel was used for volume scattering, and the Li-Sparse kernel was used for geometrical optical scattering. It can be expressed as:  
\[R(\theta_{S},\theta_{V},\varnothing)=k_{0}+k_{1}F_{1}(\theta_{S},\theta_{V}, \varnothing)+k_{2}F_{2}(\theta_{S},\theta_{V},\varnothing)+\ldots+k_{n}F_{n}( \theta_{S},\theta_{V},\varnothing) \tag{2}\]  
where \(\theta_{S}\), \(\theta_{V}\), and \(\varnothing\) are the solar zenith, view zenith, and relative azimuth angles, respectively, \(F_{i}\) are a priori kernels based on either physical or empirical considerations, and \(k_{i}\) are coefficients based on the observed value [11; 43]. We used least square estimation to calculate the corresponding \(k_{i}\) for each \(F_{i}\).
###### Abstract  
Presently, security is a hot research topic due to the impact in daily information infrastructure. Machine-learning solutions have been improving classical detection practices, but detection tasks employ irregular amounts of data since the number of instances that represent one or several malicious samples can significantly vary. In highly unbalanced data, classification models regularly have high precision with respect to the majority class, while minority classes are considered noise due to the lack of information that they provide. Well-known datasets used for malware-based analyses like botnet attacks and Intrusion Detection Systems (IDS) mainly comprise logs, records, or network-traffic captures that do not provide an ideal source of evidence as a result of obtaining raw data. As an example, the numbers of abnormal and constant connections generated by either botnets or intruders within a network are considerably smaller than those from benign applications. In most cases, inadequate dataset design may lead to the downgrade of a learning algorithm, resulting in overfitting and poor classification rates. To address these problems, we propose a resampling method, the Synthetic Minority Oversampling Technique (SMOTE) with a grid-search algorithm optimization procedure. This work demonstrates classification-result improvements for botnet and IDS datasets by merging synthetically generated balanced data and tuning different supervised-learning algorithms.  
Keywords:imbalanced data; datasets; botnet detection; synthetic minority oversampling technique; machine learning; predictive models. +
Footnote †: journal: Applied Sciences