esa-sceva/satcom-chunk-collection huggingface dataset:
# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 3 Defining Swarm Robotics
### Swarm Robotics Main Features  
From the first estimation, we can summarize the main features of Swarm Robotics. Still, there are three primary characteristics in SR appliances.  
* **Robustness**: The system must be able to perform the target task, even if some elements present failures. The most significant advantage of a distributed system with independent agents is the collective robustness, even with some individuals presenting failures [14; 27; 28; 29; 30; 31].
* **Flexibility**: The systems must be flexible. In other words, they must serve multiple purposes and tasks, even with restricted communication and perception resources [14; 28; 30; 32].
* **Scalability**: The system must be functional with a different number of elements. The addition or loss of individuals must not jeopardize the task completion [14; 27; 28; 31].  
In line with some fundamentals of Swarm Intelligence, an important topic is that SR ought to be created so that intended collective behavior arises from the local interactions among agents and between the agents and the environment. Our research also brings an additional set of features from SR applications:  
1. The robots of the swarm are comparatively small and low-cost [28; 29; 30].
2. The robots should be independent, with the potential to understand and manage in an actual environment by themselves [28; 29; 30; 32].
3. In theory, the robots should all be equal. However, if not, the robotic swarm should be similar [33].
4. The robots should be simple and cannot perform tasks separately or demonstrate unsatisfactory performance to accomplish it. This means they inevitably must work together to solve the problem [14; 27; 28; 30].
5. The swarm is decentralized, self-organized, and distributed [27; 28; 29; 30; 31; 32].
6. The norms controlling the swarm agents are commonly simple and executed individually, likewise to the collaborative behavior usually seen in nature, and capable of generating a wide collection of compound collective behaviors [14; 27; 29; 30].
## 1 Introduction  
The International Council on Systems Engineering (INCOSE) defines Model-Based System Engineering (MBSE) as the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases [1]. Today, MBSE and the SysML language have become an indispensable part of designing complex cyber-physical systems [2; 3; 4]. The reasons for their popularity are [1; 2; 3; 4]:  
* MBSE gives a standardized way of capturing and managing the system's requirements, architecture, design, and processes, as well as identifying its environment (System of Systems).
* Facilitates communication among various stakeholders by providing discipline-specific views for different purposes (e.g., requirements, logical system view, physical system view).
* Allows detecting defects early in the system development life cycle.
* Allows comparing and simulating \"As Is\" and \"To Be\" solutions.
* Can serve as a single source of truth for systems engineers and other team members.
* Allows exploring multiple solutions with minimal investment.  
The authors agree that the biggest value from MBSE activities is gained when system validation and verification are performed at the early phase of system design, especially in terms of change cost [5; 6]. In such a case, the defects could be fixed with less impact and prevented rework in later phases, thus mitigating uncertainties to cost and schedule [6]. The same principles apply in the security field: the risk identification and mitigation are the most effective and maximize the return on investment if it is integrated into the design process and utilized in the early stages [7]. There are several tools and methods that allow performing security analysis at the initial phase of systems creation (e.g., Misuse Cases, Abuse Cases, Secure-Tropos, combined harm assessment of safety and security for information systems (CHASIS)); however, they are disjointed from the systems engineering [8; 9].  
In this paper, we present an analysis of various techniques for defining the security aspect and then clarify how these techniques can be represented in Unified Modeling Language (UML) and SysML models and then further exploited with MBSE tools (such as simulation, validation, and verification). As a result, the MBSesec method is introduced, which allows cross-functional (systems and security) teams to perform security analysis in parallel to the systems engineering process at an early stage of system creation. The MBSesec method is verified by expanding the Hybrid Sport Utility Vehicle SysML model with four security mitigation phases and running an experiment where systems and security engineers evaluate its correctness, completeness, and learning time.
# Communication Manager for Hyper-Connected RPAS Environments
## 3 Communication Infrastructure Manager: Design and Deployment
### Software-Defined Networks
#### 3.4.2 SDN Controller  
An SDN controller in an SDN network takes the role of the brain of the SDN network. As we have advanced in Section 3.4.1, the rules installed in the SDN switches for traffic to be forwarded between the different ports can be standalone (directly installed in the switch) or through an SDN controller. The main advantage of using an SDN controller is that these forwarding rules can be modified when significant changes in the network that invalidate previous configurations occur (e.g., topological changes, intermittent links, channel overload).  
The controller and the virtual switches communicate via the OpenFlow protocol (control plane). We have decided to deploy the SDN controller in a virtual machine in the RPS. This controller is in the RPS because there is no space limitation in the equipment used. It is then possible to have equipment with better resources that allow more developments to be deployed. The SDN controller selected is RYU (RYU. Available: [[https://ryu-sdn.org/](https://ryu-sdn.org/)]([https://ryu-sdn.org/](https://ryu-sdn.org/)) (accessed on 9 February 2023)). RYU is an open-source SDN controller that provides software components and an Application Programming Interface (API) that facilitate the development of SDN management and control.  
We decided to use the cellular interface to communicate the SDN controller and switches. This selection is because cellular connectivity will always be available during the missions in the Labyrinth project (where the correct functioning of this scenario will be tested).  
It is important to remark that although for the control traffic the connectivity availability is one of the most relevant requirements (and that is why the cellular option is chosen), it may not be the case for other traffic data flows where, for instance, the low delay provided by Wi-Fi networks may be more interesting, or battery consumption may be more relevant (and may be higher in cellular interfaces), or the usage of public networks may be restricted (since some data/video traffic may be private/sensitive in some circumstances).
## 4 Experiments and Results
### Results Analysis
#### 4.4.2 The Effectiveness of Transfer Learning  
In order to evaluate the effect of the training strategy with transfer learning on the convergence of network training, we respectively trained a classification model based on Triplet-EfficientNet with the transfer learning method and another identical model but without the transfer learning method. Except for the different training strategies, the settings of the other hyperparameters were the same for these two models.  
The changes in accuracy and loss value in the training process are shown in Figure 12(a),b, respectively. As can be seen from Figure 12(a), the model with the transfer learning method can obtain an initial accuracy of up to 75.2%, and its final accuracy reaches 92.6% after 60 epochs. However, the initial accuracy of the model without the transfer learning method is only one-quarter of the former, and the final accuracy is only 44.2%. As can be seen from Figure 12(b), the initial loss value of the model with the transfer learning method is only 1.225, then it rapidly converges in 60 epochs, and the final loss value converges to 0.214. However, the overall loss value of the model without the transfer learning method is higher than one, and its convergence speed is much lower than that of the former.  
By comparing the changes in accuracy and loss value for the same model with different training strategies, it can be seen that the transfer learning method greatly speeds up the convergence process of the classification model and improves the overall accuracy of the model in the training process. This is because the transfer learning method pre-loaded the common model parameter information, which is for image feature extraction and obtained in the large-scale image dataset, to the trained model. Thus, the transfer learning method endows the model with strong image feature-characterization ability at the beginning of training, leading to improved performance and faster training convergence.
## 4 Experimental Environments and Findings
### Experimental Settings  
This research utilized Google Colab Pro+, equipped with a Tesla L4 GPU, to accelerate the training of deep learning models. The ConvResBiGRU-SE model, in conjunction with various standard deep learning networks (CNN, LSTM, BiLSTM, GRU, and BiGRU), was constructed utilizing a Python library with TensorFlow 2.17.1 and CUDA backends. The following Python libraries were also employed:  
* Numpy 1.26.4 and Pandas 2.2.2 for data management, including retrieval, processing, and sensor data investigation.
* Matplotlib 3.10.0 and Seaborn 0.13.2 are utilized to visualize and demonstrate data exploration and model assessment outcomes.
* Scikit-learn 1.5.2 for sampling and data generation in experiments.
* TensorFlow 2.17.1 and Keras 2.5.0 for establishment and training deep learning models.  
Analyses were conducted on the UCI-HAR and WISDM datasets to determine the most efficacious methodology. By employing the data segmentation technique in Section 3.2.3, the dataset derived from the two datasets utilized in our study was initially partitioned into 10,299 segments for UCI-HAR and 10,864 segments for WISDM. The quantity of segmented samples in each activity class is presented in Table 2.  
To mitigate the reliance of findings on specific data partitioning into training and testing subsets, we employed a stratified 5-fold cross-validation methodology as illustrated in Figure 8. This validation scheme involves partitioning the dataset into five equal folds, with each fold (20% of the data) utilized sequentially for testing, while the remaining four folds (80% of the data) serve for training. This validation technique preserves the original dataset's class label proportions in each fold.
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 4 Results
### Experimental Results  
In this paper, we recorded the running times of five groups of experiments. The results were conducted to test the impact of different data layouts in the two iterative methods of Form A and Form B and the effect of using a single block or multiple blocks in the iterative process on the GPU computational performance. Tables 3 and 4 show the running time of Form A and Form B in the adaptive Laplacian mesh smoothing for tetrahedral mesh, and Tables 5 and 6 show the speedups of Form A and Form B in the parallel adaptive Laplacian mesh smoothing over a corresponding serial version of the algorithm for tetrahedral meshes.  
All the experimental results indicated that (1) the algorithm with Form B is faster than Form A; (2) the algorithm with AoS data layout is slightly faster than SoA data layout; and (3) the running time of the algorithm that distributed multiple blocks during the GPU iteration is faster than the algorithm with a single block. In the five experiments, for a tetrahedral mesh with 100k nodes, the proposed mesh optimization algorithm can achieve a maximum acceleration of 22.620 times compared with the serial version.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{5}{c}{**Running Time (ms)**} \\ \cline{2-6}  & \multirow{2}{*}{**CPU**} & \multicolumn{2}{c}{**GPU-AoS**} & \multicolumn{2}{c}{**GPU-SoA**} \\ \cline{3-6}  & & **Single Block** & **Multiple Blocks** & **Single Block** & **Multiple Blocks** \\ \hline
5k & 1473 & 169 & 155 & 130 & 96 \\
10k & 3745 & 542 & 304 & 287 & 389 \\
59k & 8452 & 763 & 502 & 726 & 654 \\
77k & 15,825 & 1184 & 705 & 1263 & 1180 \\
109k & 36,592 & 3566 & 1618 & 4362 & 2230 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Running time of the proposed algorithm when using Form B.  
Figure 8: Two experimental models. (**a**) The entire mesh model with 59k nodes; (**b**) The partial mesh model with 59k nodes; (**c**) The entire mesh model with 77k nodes; (**d**) The partial mesh model with 77k nodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{5}{c}{**Running Time (ms)**} \\ \cline{2-6}  & \multirow{2}{*}{**CPU**} & \multicolumn{2}{c}{**GPU-AoS**} & \multicolumn{2}{c}{**GPU-SoA**} \\ \cline{3-6}  & & **Single Block** & **Multiple Blocks** & **Single Block** & **Multiple Blocks** \\ \hline
5k & 1473 & 450 & 467 & 151 & 139 \\
10k & 3745 & 537 & 535 & 775 & 331 \\
59k & 8452 & 799 & 954 & 986 & 1254 \\
77k & 15,825 & 3252 & 1675 & 3505 & 2231 \\
109k & 36,592 & 5811 & 4866 & 6718 & 4140 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running time of the proposed algorithm when using Form A.
## 3 Results
### ET Simulation Results  
Due to the limitation of the monitoring conditions, only monthly average daily ET data of SPT (2003-2004), AS (2004-2006), and CW (2004) stations were observed. In Figure 4, it can be seen that the simulation result of evapotranspiration is higher than the measured data. The r values of ET were 0.61 (SPT), 0.83 (AS), and 0.86 (CW). The RMSE and RE were 2.06 mm, \(-\)0.05 mm (SPT), 2.07 mm and \(-\)0.20 mm (AS), and 0.75 mm and 0.17 mm (CW) (Table 4).  
Figure 3: The observed O–IBIS, and RU–IBIS values of monthly soil water content at soil depths of 10 cm, 30 cm, 50 cm, and 100 cm in different regions: (**a–d**) SPT, (**e–h**) AS, (**i–l**) CW, and (**m–p**) ERDS. The red and gray lines represent the linear fit of improved and original simulations, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{Stations} & \multicolumn{3}{c}{10 cm} & \multicolumn{3}{c}{30 cm} & \multicolumn{3}{c}{50 cm} & \multicolumn{3}{c}{100 cm} \\ \cline{2-10}  & & RU–IBIS & O–IBIS & RU–IBIS & O–IBIS & RU–IBIS & O–IBIS & RU–IBIS & O–IBIS \\ \hline \multirow{5}{*}{SPT} & r & 0.71 & 0.45 & 0.88 & 0.78 & 0.91 & 0.82 & 0.92 & 0.90 \\  & R\({}^{2}\) & 0.51 & 0.20 & 0.78 & 0.62 &
## 6 Hybrid FSO Networks
[232] propose a hybrid Internet/cable television (CATV)/5G fiber-FSO integrated communication system for the first time; the SMF and FSO links are 40 km and 200 m, respectively. The system utilizes a triple-wavelength polarization multiplexing scheme; the lights in this project are modulated with 60 Gbps 50-550 MHz vestigial sideband (VSB) CATV even-channel with 4-PAM for the internet. On the other hand, 70 Gbps VSB with 16-QAM-OFDM for transmitting 5G signals to convince the services, and 4-PAM has a high capacity as its spectrum is half the spectrum of the non-return-to-zero signal [233, 234]. Moreover, 16-QAM-OFDM modulation has high spectrum efficiency, high capacity, and a robust dispersion tolerance [235]. A Hybrid Internet/CATV/5G fiber-FSO integrated system is suitable for broadband networks as it affords high-speed services such as internet and 5G mobile telecommunication. Channel performance and capacity can be enhanced by using a Tunable optical bandpass filter which is a straightforward and beneficial polarization demultiplexing mechanism. Moreover, [PERSON] et al. [236], study the performance of a hybrid 5G RF/FSO system. Data are sent over both channels independently to be combined at the receiver using MRC technique. Then, the MGF is calculated for the total SNR. ASER is presented of the proposed RF/FSO system for different modulations such as QPSK, BFSK, and BPSK using bivariate [PERSON]'s H-Function. The numerical results show that BPSK has the best performance compared to QPSK and BFSK.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 4 Evaluation
### Training Parameters  
In all models for training detection methods, for the sake of fairness, the parameters such as epoch and learning rate for each benchmark were consistent. Specifically, the parameters were set as follows: epoch was set to 50, learning rate was set to 0.01, momentum was set to 0.937, and batch size was set to 8. Additionally, various techniques, including random cropping, rotation, flipping, and other data augmentation methods, were employed to reduce the risk of overfitting. We also utilized cross validation to evaluate the performance of the models during the detection model training. The basic training framework was PyTorch, and the hardware used was NVIDIA GeForce GTX 1650 Ti.
# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 5 Conclusions  
In this paper, a cluster-based channel model has been proposed based on the RT and ML methods. Meanwhile, an improved k-means clustering has been developed to cluster the rays and the equivalent parameters, i.e., cluster delay and cluster power are also calculated. The communication performances, i.e., BER and channel capacity, have been derived and validated based on the reconstructed tower building at 1 GHz. The simulation results  
Figure 6: Vertical view of tower structure building.  
Figure 7: Channel capacity of different floors.  
have shown that the improved k-means clustering algorithm outperforms the traditional ones and the BER is in good agreement with the simulated one. The proposed model and simulation method can be used to evaluate the performance of communication system inside the tower buildings and provide theoretical basis for the layout and optimization of WSN nodes. Note that channel model and analysis method are also suitable for the multi-scatterer scene.  
Conceptualization, W.X. and X.C.; methodology, K.M.; software, W.X. and Y.L.; validation, X.C., L.Y. and S.F.; formal analysis, X.C.; investigation, W.X. and K.M.; resources, X.C.; writing--original draft preparation, W.X.; writing--review and editing, X.C. and K.M.; supervision, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by ISN State Key Laboratory fund (No. ISN22-11), and Open Foundation for Graduate Innovation of NUAA (No. KFJ)20200416).  
Not applicable.  
The authors declare no conflict of interest.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix B State-of-the-Art Anomaly Detection Models
### Depth-Based Models  
#### b.2.1 Isolation Forest (IF)  
The IF method [43] applies a depth approach to detecting anomalies. The algorithm is based on the idea that anomalies are easier to isolate and thus will be isolated closer to the root, whereas normal observations are isolated much further from the root. The algorithm uses random decision trees to separate the observations. The anomaly score is calculated as the path length to isolate the observation. As it is defined in the algorithm, the anomaly score will be closer to 1 for anomalies and \(\ll\)1 for normal observations. This allows for ranking the observations from the most abnormal observation to the most regular one. However, the choice of an explicit cutoff between these two types of instances is not obvious.
## 2 Materials and Methods
### Evaluation of Effective Area  
The experimental data of the starch-potassium iodide test, the (V)UV determination and the microbiological assay were analyzed by Matlab programs. The basic program was adjusted according to the available signal to noise ratio to enable a correlation between the determination of reactive species and the (V)UV irradiance area which were both suspected to be responsible for the biological efficacy. The basic principle of the program is a transformation into grayscale, a substraction of background noise and finally a definition of primary, secondary and peripheral zones based on intensity levels. Every zone is defined by setting thresholds above the background signal. The determination of the inhibition zone area was performed manually by counting the pixels in two dimensions, rescaling with the placed scale on each picture and calculating the area of an ellipse with both dimensions.
# Stabilization of Argan Oil Nanoemulsions Using Chitosan Extracted from Pink Shrimp Shells
###### Abstract  
This study investigates the use of chitosan, extracted from pink shrimp shells, as a stabilizer for argon oil nanoemulsions. Chitosan was obtained through demineralization, deproteination, bleaching, deacetylation, and purification, then characterized through potentiometric titration, viscometry, X-ray diffraction (XRD), scanning electron microscopy (SEM), and purity assessments. The resulting chitosan was used to stabilize nanoemulsions formulated with extra-virgin argon oil and a nonionic surfactant using the phase inversion composition (PIC) method. The stability of the nanoemulsion was assessed through light scattering, zeta potential, viscosity, and pH measurements. The extracted chitosan exhibited high purity, a deacetylation degree of 88.34%, a molecular weight of 408.809 kDa, and a crystalline index of 66.36%. Chitosan significantly improved nanoemulsion stability, maintaining droplet integrity for over six months at 40 \({}^{\circ}\)C. Higher chitosan concentrations increased droplet size, viscosity, and stability, indicating strong polymer adsorption. These findings demonstrate the potential of shrimp waste-derived chitosan as a sustainable stabilizer for argon oil nanoemulsions, promoting effective bioactive compound delivery and environmental sustainability.  
nanoemulsion; chitosan; argon oil; shrimp waste; phase inversion composition Article