esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Motion Model Selection  
Exact model, empirical model and differential flatness are the main techniques that can be employed for selecting the most appropriate motion model for a specified application. The appropriate motion model selection procedure varies depending on the problem formulation. For example, planning followed by controlling approaches does not necessarily have an exact motion model mainly due to high computational demands. In such scenarios, an empirical motion model is sufficient for planning, since a dedicated controller is utilized for controlling the quadrotor.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 4 IoT Query Dissemination Based on CDL
### CDL Geocasting  
The Group Addr field already includes functionality capable of configuring a region field as well as sensor type field. Using the region field, the network server can receive data only from the sensors which are in a specific area without assistance of GPS. In particular, since the LoRa can cover more than a few kilometers in radius, it is necessary to divide into subareas the area which is covered by a single gateway. For instance, as shown in Figure 10, if a sensor field can be divided into four subareas (A, B, C, and D), CDL enables gathering sensor data only from sensors which are in a specific area. In this case, a user (application server) requests the recent data in the particular area, then the network server sets the corresponding bitmap in the region field and broadcasts the message into the network. Only the nodes in the interest area (e.g., area A) can respond to the network server. In addition, since the region field is expressed by bitmap, data gathering from multiple areas is also possible. For example, if the region field contains '0101', it means both regions B and D. In addition, more advanced data-centric aggregation is available by utilizing a combination of sensor type field and region field. For example, the query can be as follows: only the nodes of which temperature is below 0 \({}^{\circ}\)C in the area A and D.  
Figure 10: Geocasting based on CDL.  
Figure 9: Groupcasting based on CDL.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
## 2 Modeling Methodology
### Nonlinear Soil-Structure Interaction Springs  
Soil-structure interaction (SSI) through spring elements can effectively capture the behavior of maritime structures; however, the definition of spring properties significantly influences the overall system ductility, and neglecting these effects may lead to unconservative results [49; 50]. In this study, SSI is modeled using zero-length elements at each node along the embedded portion of the pile to represent depth-varying nonlinear soil springs. Three uniaxial material types are used to capture the restraining effect of the soil:  
* PySimple1 for lateral resistance (p-y behavior);
* TzSimple1 for axial shaft friction (t-z behavior);
* QzSimple1 for end-bearing resistance (q-z behavior).  
Spring stiffness and ultimate capacities are defined based on recommendations by [46; 51; 52; 53; 54; 55]. Spring spacing is denser near the mudline and coarser at greater depths. All soil springs are connected to pile nodes via equalDOF constraints to ensure force compatibility.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 5 Simulation Results and Discussion
### Slice Creation through Machine Learning Techniques  
The proposed work creates slices for both the PI and the NSR concurrently. The performance datasets for physical infrastructure and NSR have been prepared separately due to their distinct characteristics. For the purpose of preparing the performance dataset, several key indicators are identified, including CPU Capacity, Link Capacity, Bandwidth, Jitter, Delay Rate, and Closeness Centrality [15]. The datasets are classified into three categories based on their fields: eMBB, mMTC, and uRLLC. A dataset with 1000 samples is currently being assembled. Training and testing are conducted using a variety of supervised machine learning techniques, including KNN, Naive Bayes, SVM, and Random Forest. A 9:1 training-to-testing ratio is proposed for training. Precision, Recall, and F1 Score are used to compare the efficiency of different training methods.  
The machine learning techniques are implemented using the Python library. The results for PI and NSR using various machine learning techniques are shown in Table 4. As illustrated in the table, all techniques capable of achieving greater than 90% training accuracy fall into one of three categories. When the overall percentage accuracy of the performance dataset is considered, Random Forest training gives better results than other techniques. Figure 5 illustrates how these four supervised machine learning techniques perform under different training-to-testing ratios, which is further evidence of theireffectiveness. From the figure, it is clear that the Random Forest technique has a better overall accuracy rate than other techniques.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ML Approach**} & \multicolumn{6}{c}{**90 \% of Training Set and 10 \% of Testing Set**} \\ \cline{2-10}  & \multicolumn{2}{c}{**eMBB**} & \multicolumn{2}{c}{**mMTC**} & \multicolumn{2}{c}{**uRLLC**} & \multicolumn{2}{c}{**Overall**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** & **(\%)** \\ \hline KNN & 0.93 & 0.95 & 0.97 & 0.97 & 0.92 & 0.94 & 0.94 & 0.97 & 0.96 & **95** \\ \hline Naive Bayes & 0.96 & 0.95 & 0.95 & 1.00 & 0.98 & 0.99 & 0.92 & 0.96 & 0.94 & **96.3** \\ \hline SVM & 0.96 & 0.95 & 0.95 & 1.00 & 1.00 & 1.00 & 0.95 & 0.96 & 0.95 & **97** \\ \hline Random Forest & **0.94** & **1.00** & **0.97** & **1.00** & **1.00** & **1.00** & **1.00** & **0.93** & **0.97** & **98.3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classificationand accuracy reports.  
Figure 5: Slice classification accuracy for different ML approaches.
###### Abstract  
Modern video surveillance systems mainly rely on human operators to monitor and interpret the behavior of individuals in real time, which may lead to severe delays in responding to an emergency. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video surveillance systems. This study proposes a novel technique incorporating a straightforward model for detecting sudden changes in a person's emotional state using low-resolution photos and video frames from surveillance cameras. The proposed technique includes a method of the geometric interpretation of facial areas to extract features of facial expression, the method of hyperplane classification for identifying emotional states in the feature vector space, and the principles of visual analytics and \"human in the loop\" to obtain transparent and interpretable classifiers. The experimental testing using the developed software prototype validates the scientific claims of the proposed technique. Its implementation improves the reliability of abnormal behavior detection via facial expressions by 0.91-2.20%, depending on different emotions and environmental conditions. Moreover, it decreases the error probability in identifying sudden emotional shifts by 0.23-2.21% compared to existing counterparts. Future research will aim to improve the approach quantitatively and address the limitations discussed in this paper.  
emotion recognition; facial feature extraction; video surveillance; machine learning; visual analytics; hyperplane classification
## 2 Materials and Methods
### Support Vector Machine (SVM) Classifier  
The support vector machine (SVM) technique is extensively applied for both classification and regression problems. In binary classification scenarios, such as the one addressed in this study, the goal is to determine an ideal separating hyperplane. Considering a binary classification with training data \(\{x_{i},y_{i}\},\ i=1,2,\ldots,N\), where \(x_{i}\in R^{d}\) are the feature vectors in a \(d\)-dimensional feature space and \(y_{i}\in\{-1,1\}\) are the corresponding binary labels, the formula for the decision function that defines the separating hyperplane in the case of linearly separable classes is described as  
\[f(x)=\omega\cdot\Phi(x)+b\]  
where \(\omega\in R^{d}\) represents the hyperplane's normal vector, and \(b\in R\) is the offset. In this context, \(\Phi(x)\) is the kernel function that maps the original feature space into a higher-dimensional space. The Radial Basis Function (RBF) kernel, a common choice for SVMs, allows the SVM to create non-linear decision boundaries by mapping input features into a higher-dimensional space. The SVM aims to position a hyperplane within this transformed feature space that maximizes the margin between the two categories and minimizes the classification error. The samples closest to the hyperplane are termed support vectors. This study utilizes an SVM classifier equipped with the RBF kernel for the classification tasks, marking its ability to handle non-linear relationships between features effectively.
## 3 Results and Discussion
### Porcine Muscle Tissue  
Figure 2a shows a photograph of a porcine muscle tissue treated by HIFU for 10 min, in which the HIFU caused a spot of damage with a diameter of 4 mm that could be observed clearly (indicated by the arrow). The reconstructed \(\mu_{s}^{\prime}\) images in XY and YZ plane are shown in Figure 2b. It was observed that there was a roughly 3-fold increase in scattering when the temperature reached 55.8 \({}^{\circ}\)C. The measured temperature and reconstrued \(\mu_{s}\) value around the HIFU focus point are shown in Figure 2c,d, respectively.  
Next, two-degree polynomial and two-term exponential models, as shown in Figure 3 (left), were utilized to fit to the temperature and \(\mu_{s}^{{}^{\prime}}\) data of five porcine tissue samples. Figure 3a,c shows the fitting curves (solid lines) with the polynomial and exponential models, respectively, and the average temperature-\(\mu_{s}^{{}^{\prime}}\) data (blue circles) measured from five porcine muscle tissues treated by HIFU. In Figure 3a, it is shown that there were no obvious changes in \(\mu_{s}^{{}^{\prime}}\) when the temperature was lower than 45 \({}^{\circ}\)C, which means that the heating from the HIFU radiation did not yet cause any damage to the tissue. Therefore, we only utilized the data from when the temperature was higher than 45 \({}^{\circ}\)C. Meanwhile, we also noticed that the \(\mu_{s}^{{}^{\prime}}\) increased dramatically when the temperature was  
Figure 2: (**a**) Photograph of a porcine sample treated by HIFU along the \(z\)-axis. (**b**) Recovered \(\mu_{s}^{{}^{\prime}}\) distribution in XY and YZ planes (t = 55.8 \({}^{\circ}\)C). (**c**) Temperature versus time and (**d**) recovered \(\mu_{s}^{{}^{\prime}}\) versus time of five porcine samples treated by HIFU in 10 min per sample.  
in the range of 45-54 \({}^{\circ}\)C. When the temperature was higher than 55 \({}^{\circ}\)C, the change in \(\mu_{s}^{{}^{\prime}}\) was slower. Based on these noted differences in the effects of \(\mu_{s}^{{}^{\prime}}\), the whole process was divided into two steps in the fitting with the polynomial model (1 st step: 45 to 54 \({}^{\circ}\)C, 2 nd step: 54 to 59 \({}^{\circ}\)C). In order to verify the precision of the created models, we compared the calculated temperatures (by (b) polynomial and (d) exponential methods, solid line) with those measured by thermometer (scattered points) for another five samples. Furthermore, the root mean square error (RMSE) was calculated to measure the performance of each fitting model, and results are shown in Table 1. It is shown that for the polynomial model, the average RMSE of five samples was 1.87 \({}^{\circ}\)C in the 1 st step, and it was 0.44 \({}^{\circ}\)C in the 2 nd step. For the exponential model, the RMSE was 1.14 \({}^{\circ}\)C.
## 1 Introduction
Additionally, the symbol timing recovery feature (detailed in Section 2.3) is absent in the other reviewed coherent systems or relies on a separate signal channel--a solution that is impractical for wireless communication. For the two non-coherent systems that do not use chaotic synchronization, symbol timing depends on directly observing the output correlation in the demodulator. However, in the presence of channel noise, these systems would require additional signal processing to achieve reliable symbol timing. Unfortunately, some parameters and features are not mentioned within the reviewed papers, which complicates the comparison. Most of the mentioned studies focus on integrating chaotic communication with image encryption schemes, relying on non-coherent chaotic modulation or the simplest form of coherent modulation, chaotic masking. In chaotic masking [7], the information signal is directly added to the chaotic carrier signal for obfuscation. While chaotic masking benefits from design simplicity, it suffers from low noise robustness and limited security compared to more advanced CSK modulation. CSK-based implementations, however, remain rare due to historical challenges. In analog-based systems, utilizing CSK in wireless communication has proven difficult because of the inherent sensitivity of chaotic synchronization. Achieving synchronization ideally requires the use of identical chaos generators, which is unattainable with analog components. Furthermore, CSK systems require four chaos generators, which can be a disadvantage in terms of energy consumption. Despite these challenges, we are confident that CSK holds significant potential for secure and reliable chaotic communication. By leveraging FPGA technology for implementation, identical chaos generators with fully controlled initial conditions become achievable, fundamentally transforming the possibilities for coherent chaotic systems in general and CSK-based designs in particular. This advancement paves the way for experimental exploration of CSK-based systems' potential in practical applications. As an initial step, new FPGA-based chaotic communication system designs should be proposed, developed and tested. The proposed coherent digital chaotic communication system is based on frequency-modulated antipodal chaos shift keying (FM-ACSK) modulation and shows a new approach to FPGA usage for chaotic communication design. By leveraging frequency modulation, the system enhances robustness against carrier frequency offsets and channel nonlinearities
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 6 Proposed Security Framework
### Fog Computing Layer  
This layer is made up of multiple fog nodes, which are insubstantial equipment that carry out processing, communication, and storing tools to do operations that an end device cannot perform. Not every piece of data that is gathered becomes processed on the cloud. Rather, this layer hosts all authentic analysis and latency-sensitive applications.  
Virtualization: Recognizing that configuring several devices, in the same manner, might be time-consuming, the nodes can be contained inside a Virtualized environment for a more pleasant setup for all subsequent fog nodes.  
Agricultural Sensor data monitoring: This node is going to collect real-time sensing data, analyze it, visualize it, manage devices, and store some of it locally. The user can obtain information from the fog layer while smoothly synchronizing cloud-directed data.  
Block-chain Client software: This consists of a piece of programs that is installed on the fog node. Its primary goal is to sync authentic sensitive Sensor data to the Blockchain. It is in charge of producing transactions, batching them, and sending them to the validator so that they can be aggregated into blocks and recorded to the Blockchain.  
Simulated Switch: Additionally, this node is used as a transmitting device with the support of virtual switch software, which provides NFV and SDN technologies to enhance network services by establishing the routing features for computational extension and control. Some network virtual functions include security, quality of service (QoS), and automated control. The main objective is to create switching layers for virtual machine environments while continuing to support the OpenFlow protocol [107], which allows this node to interact directly with the SDN Controller; an outstanding demonstration of such implementation is Open vSwitch, an interactive multilayered software switch.
## 3 Results and Discussion
### Experimental Conditions and Parameters
* Evaluation indicators: the average user data rate (AUDR), Jain fairness [27], max-min fairness [5,27] and user satisfaction [25] were introduced. Jain fairness was used to evaluate the impartiality of the user data rate and max-min fairness focused on the guaranteed baseline of the QoS data rate. User satisfaction [21,28] is a comprehensive evaluation indicator that consides both data rate and fairness.
# IoT Socioeconomicenvironmental Assessment Instrument: Validation Process Applying Delphi Method
## 1 Introduction
### IoT Technology, SM and CE Relationship  
The Internet of Things (IoT) technology is recognized as an enabler of Industry 4.0, facilitating the advancement of SM, enhancing sustainability performance [13; 14; 15] and circularity [16; 17].  
The literature highlights the role of IoT in SM and the CE, including workplace safety, workforce health, environmental protection and waste management [18; 19]. Some researchers argue that IoT technologies can enhance manufacturing efficiency by adhering to the \"6R\" model for natural resource conservation [8; 12], which includes reducing, reusing, recycling, recovering, redesigning and remanufacturing, or the \"10R\" approach, as it aligns with a smart environment [20]. For instance, IoT has the potential to drive sustainability while influencing the interconnected aspects of economic, environmental and social performance [21].  
IoT enables effective planning, monitoring and coordination of production or supply chain processes, fostering collaboration among organizations [22]. The integration of IoT solutions in manufacturing processes strengthens standardization and cooperation; drives service innovation; and supports intelligent automation for real-time monitoring and reporting of resource and material availability. These advancements effectively address critical challenges in SM, including inefficient workforce management, limited knowledge sharing, inadequate information exchange and material supply chain delays [10].  
The implementation of IoT in accordance with the SM concept should improve the economic performance (the company's financial performance), environmental performance (such as reducing waste, emissions and resource consumption) and social performance (related to employee and community well-being), minimizing or eliminating the negative impacts of the production operations in favor of nature, society and minimizing the tendency of some organizations for bluewashing or SDG-washing [4].  
The IoT contributes to CE by enabling component reuse, remanufacturing, resource optimization, predictive maintenance, reverse logistics and material recycling [16; 23]. In addition, the IoT enables the integration of interconnected smart devices to enhance supply chain (SC) operations [17]. The terms reverse supply chain, closed-loop supply chain, open-loop supply chain and green supply chain are used to describe the application of CE principles in redesigning SC activities from a linear to a circular model.
## 5 Analysis and Evaluation
### Theoretical Resolution Latency Analysis  
The latency of each step in the name resolution procedures of ENRS are as follows.  
Step 1. The transmission delay T\({}_{1}\) from a requestor to the i-th \(HM_{ij}\).  
Step 2. Retrieval time T\({}_{2}\) in \(HM_{ij}\)'s local DB.  
Step 3. The transmission delay T\({}_{3}\) from \(HM_{ij}\) to its peer neighbor resolver \(HM_{\bar{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\  
According to symmetry, Equation (2) can be simplified as Equation (5):  
\[\overline{T}=2(T_{1}+T_{2}+T_{3})-P_{1}*T_{2}-2P_{1}*T_{3} \tag{5}\]  
where P1 = 0, \(T_{\max}=2(T1+T2+T3)\). When P1 = 1, \(T_{\min}=2T1+T2\). According to a previous study [22], the single look-up latency of a 4 TB solid state drive (SSD) is about 15 \(\upmu\)s. Thus, T\({}_{3}\) is in the nanosecond magnitude. The end-to-end delay of the 5G control plane should be in the order of a millisecond [2], \(\frac{1\upmu\mathrm{s}}{15\upmu\mathrm{s}}\approx 67\). Thus, \(T_{2}<<T_{1},T_{3}\); \(T_{\max}\) and \(T_{\min}\) can be represented as Equation (6) and Equation (7):  
\[T_{\max}\approx 2(T_{1}+T_{3}) \tag{6}\]  
\[T_{\min}\approx 2T_{1} \tag{7}\]  
From Equation (6) and Equation (7) we can learn that the name resolution latency is subject to transmission latency \(T_{1}\), \(T_{3}\). \(T_{1}\) and \(T_{3}\) are related to the deterministic partition bound \(T_{i}\). If deterministic partition bound \(T_{i}\) is given, the uppers and collars of name resolution latency are deterministic. Therefore, deterministic name resolution latency can be achieved in ENRS.
## 3 Results and Discussion
### Kinetic Study of WPET-SiAMPy Chelating Adsorbent Nanofibres
#### 3.4.2 Kinetic Study of Reaction Formation Compound Cu-WPET-SiAMPy Complex  
The relationship between Cu\({}^{2+}\) ion concentration and reaction rate is expressed by an equation called a rate equation. The rate equation of Cu-WPET-SiAMPy complex can be written mathematically as  
\[\text{Rate}=k[\text{A}]^{\text{m}}[\text{B}]^{\text{n}} \tag{9}\]  
where \(k\) is the reaction rate constant, [A] is the concentration of Cu\({}^{2+}\), [B] is the concentration of the WPET-SiAMPy nanofibres, m and n are the order of the reaction. The determination of the reaction order is determined by finding the initial formation rate of the Cu\({}^{2+}\) ions binding to the WPET-SiAMPy chelating ligand nanofibres. The graph of the concentration versus time is obtained from the experimental data to determine the initial rate of reaction (see Table 2). The graph of the concentration versus time is obtained from the experimental data to determine the initial rate, which can be expressed as  
\[\frac{dc_{c}}{dt}\approx k_{CPy}C_{Cu} \tag{10}\]  
where the Cu\({}^{2+}\) aqueous solution concentration is represented by \(c\) calculated from:  
\[c_{Py}=\frac{q_{Py}m}{V} \tag{11}\]  
Figure 14 presents the Cu\({}^{2+}\) complexation formation rate with WPET-SiAMPy nanofibres in aqueous solution. The equilibrium uptake of the Cu\({}^{2+}\) on WPET-SiAMPy nanofibres was attained within the first minute of contact time, as seen in Figure 5. The fast Cu\({}^{2+}\) adsorption on the WPET-SiAMPy nanofibres was attributed to the high Cu\({}^{2+}\) concentration in aqueous solution around the high surface area of the WPET-SiAMPy nanofibres with a high number of active vacant adsorbent sites. The Cu\({}^{2+}\) complex formation rate (\(k\)) with WPET-SiAMPy was \(k\) = 0.0888, which was the adsorption rate constant.  
Figure 13: Average reaction rate against contact time using WPET-SiAMPy nanofibres.