esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion  
### Prediction Performance  
A comprehensive set of metrics is employed to evaluate the performance of the proposed prediction model. Based on the performance evaluation results of the three tested algorithms with various smoothing factors listed in Table 3, several conclusions can be drawn. Overall, Random Forest demonstrated the best performance among the GPR and SVR algorithms. With a Smoothing Factor of approximately 0.3, Random Forest is capable of providing highly accurate predictions with a Root Mean Squared Error (RMSE) of 0.0401, Mean Absolute Error (MAE) of 0.0241, and R-squared (R\({}^{2}\)) of 0.9651. These results highlight the remarkable ability of Random Forest to predict the battery voltage and degradation values. On the other hand, GPR and SVR exhibited good performance, although not as strong as Random Forest. GPR has an RMSE ranging from 0.0445 to 0.0477, MAE from 0.0277 to 0.0309, and R2 from 0.9532 to 0.9580, whereas SVR has an RMSE ranging from 0.1354 to 0.1379, MAE from 0.0921 to 0.0923, and R2 from 0.6548 to 0.6557. Therefore, based on these results, it can be concluded that Random Forest is an excellent choice for predicting battery voltage and degradation values online, particularly with a Smoothing Factor of approximately 0.3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Smoothing Factor** & **RMSE** & **MAE** & **R-Squared** \\ \hline GPR & 0.3 & 0.0445 & 0.0277 & 0.9580 \\ SVR & 0.3 & 0.1354 & 0.0922 & 0.6557 \\ Random Forest & 0.3 & 0.0401 & 0.0241 & 0.9651 \\ GPR & 0.4 & 0.0461 & 0.0293 & 0.9557 \\ SVR & 0.4 & 0.1357 & 0.0923 & 0.6548 \\ Random Forest & 0.4 & 0.0413 & 0.0256 & 0.9634 \\ GPR & 0.5 & 0.0477 & 0.0309 & 0.9532 \\ SVR & 0.5 & 0.1379 & 0.0921 & 0.6554 \\ Random Forest & 0.5 & 0.0428 & 0.0271 & 0.9615 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance comparison with exponential smoothing.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 2 Materials and Methods
### Below-Canopy Measurements  
We used a 24 Megapixel Sony 6000 DSLR Compact 2571 camera (Regent Instruments; Quebec, QC, Canada) with a 180\({}^{\circ}\) hemispherical lens with a maximum field-of-view of 90\({}^{\circ}\) to capture hemispherical canopy imagery (Figure 1). The camera has three bands--blue, green, and NIR. To calculate NDVI, blue is used as the absorption band and NIR is used as the reflectance band. This differs from space-based NDIV approaches where the red channel is the absorption band since the blue channel is more scattered due to atmospheric Rayleigh scattering. For ground-based and UAV-based vegetation applications, the blue channel allows for superior band discrimination.  
The camera was mounted on a self-leveling tripod, with the lens at 1 m from the ground, facing-upwards, looking into the canopy. At each plot location, five images were taken during the early morning hours (before 1000), approximately, weekly from late April until late May--at center, and 10 m off center at cardinal directions (Figure 2). For April 26 images, we used the \"sunblocker,\" a small, nylon disk supplied by the manufacturer attached to a flexible rod that is used to block the sun from the lens--the \"sunblocker\" is then masked out of the image later during analysis. For the first week of June, only images taken at plot center are available. We used image sets for analysis only during periods where there was coincident UAV-based imagery.  
We calculated leaf area index (LAI), NDVI, and gap fraction for each image using WinSCANOPY (Regent Instruments; Quebec, QU, Canada) with a hemispherical image radius of 1925 px (total image size is \(6000\times 4000\) px) using a pixel color classification algorithm in WinSCANOPY that is more tolerant to variations in sky conditions, allowing the use of images with dark blue sky or partial-cloud coverage. Color-based classification palettes were established for each measurement period.  
Figure 1: Hemispherical normalized difference vegetation index (NDVI) imagery taken at plot 2 for each measurement period through the onset of the growing season. The April 26 image shows the “sunblocker”, which is masked out of the image. Variations in light environment and sky conditions are corrected for via the color-based pixel classification algorithm in WinSCANOPY (Regent Instruments; Quebec, QU, Canada).
## 2 Methodology
### System Modeling and Identification  
The transfer function method and the neural network method are used to identify the model of the generator set, respectively. The two identification models obtained in the end will be used as the prediction model inside the MPC controller and the controlled model of the simulation process.  
The neural network model architecture is depicted in Figure 2. Drawing on prior research [29], we selected an appropriate neural network structure and training parameters to optimize performance. The dataset for parameter identification was split into a training set and a validation set at a ratio of 8:2, with preprocessing steps such as normalization applied to ensure data consistency. The Adam optimization algorithm was employed for training, with a learning rate set to 0.01. Additionally, an early stopping mechanism was implemented to halt training if the validation set error did not improve for 10 consecutive epochs, preventing overfitting and enhancing model generalization.  
Figure 1: Schematic diagram of a supercritical power generation system.  
Figure 2: Schematic diagram of the BP model.  
For the transfer function identification method, the tools in the MATLAB R2024B identification toolbox are used, and the transfer function model is defined in the following form:  
\[G(s)=\frac{num(s)}{den(s)} \tag{1}\]  
where \(G(s)\) is the transfer function, representing the relationship between the system's output and input. num(s) is a molecular polynomial that represents the zero of the transfer function. den(s) is the denominator polynomial, representing the poles of the transfer function. s is the complex frequency, a variable in the Laplace transform.  
\[num(s)=b_{1}*s^{(n-1)}+b_{2}*s^{(n-2)}+\ldots+b_{n-1}*s+b_{n} \tag{2}\]  
\[den(s)=a_{1}*s^{n}+a_{2}*s^{(n-1)}+\ldots+a_{n-1}*s+a_{n} \tag{3}\]  
where a\({}_{1}\), a\({}_{2}\),... a\({}_{n}\) is the coefficient of the denominator polynomial. b\({}_{1}\), b\({}_{2}\),... b\({}_{n}\) is the coefficient of the molecular polynomial. n is the order of the denominator polynomial, representing the order of the system.
## 4 Experimental Results and Analysis
### Overview of Experimental Data  
For dynamic data, to reduce category imbalance and obtain effective ship dynamic features distribution, we select 300,000 pieces of messages from every four types of ships. Each ship should have more than 500 pieces of messages. Considering that the dynamic data should reflect ships' motion features, the messages for which the SOG is lower than 2 knots are removed. The amount of dynamic data used in the experiments is shown in Table 4,  
For static data, all static data of the four types of ships are extracted from the database. Table 6 shows the amount of data processed according to the method in Section 3.1. The number of static data messages is greater than the number of ships corresponding to the static data because the features in static messages may change sometimes (e.g., draught and position reference point), and there exists fraudulent use of MMSI.  
There are 2066 MMSI in MMSI_C (see Figure 9), and the dynamic and the static datasets are split according to the methods in Section 3.3. It should be noted that if one MMSI in MMSI_C corresponds to several \(s^{in}\), those \(s^{in}\) should be replaced by their average before the datasets are split.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### NOMA with Imperfect SIC  
The near user has a perfect and complete understanding of the signal information relating to the far user in an ideal SIC procedure. In contrast, in imperfect SIC circumstances, this information is not fully available. As a result, in this imperfect SIC, there appears to be some residual inter-user interference. The interference from the far user cannot entirely be eliminated by the near user [214]. Numerous factors, including hardware limitations, finite length codes, and channel estimation errors, might contribute to the imperfect SIC. The performance of NOMA systems and optimization problems are significantly impacted by the imperfect SIC. In particular, it is difficult to attain reliable concavity conditions for the optimization problem of ideal power allocation (i.e., without power order constraints) in multiple-carrier NOMA systems when the SIC is imperfect [215]. In comparison to the perfect SIC, the imperfect SIC has a larger outage probability and a lower ergodic capacity [216]. The SIC procedure carried out in NOMA systems with relays may result in problems that would cause an outage. Both relay nodes and destination nodes may experience the defective SIC, which can also cause an outage [217; 218].
## 3 ML for SatCom RRM: Architecture and Techniques  
### Machine Learning Implementation in SatComs Systems  
#### 3.1.1 Online or Offline Learning  
ML techniques for RRM undergo two phases, as shown in Figure 1: training and inference. Initially, the ML model experiences the training stage. The objective is to find the optimal model parameters to predict the radio resource configuration at the satellite for the system conditions. Then, the trained model is obtained and used to indicate the best payload resource allocation.  
In this regard, one of the first decisions to make when implementing ML for RRM is whether learning should be online or offline. Online learning means learning as the data comes in. Offline means that a static dataset is available. Thus, with online learning, more data are (usually) available, but there are time constraints.  
A system trade-off exists between the type of training selected. Depending on the service requirements, one option can be chosen. Suppose the most important objective is to maintain the quality of service and availability. In that case, online training can be chosen as it has the ability to better adapt to sudden and abrupt changes in the system over time because learning is constantly updated. However, it requires processing times to be invested in the training, which can delay updating the resources in the payload.  
On the other hand, if offline training is chosen, processing times are reduced since inference on a trained ML model requires a low computational cost compared to the training phase. The ML model acts as an intelligent switch that selects the payload resource configuration based on the current system requirements. Nevertheless, a large database may be required to obtain a successful training that considers multiple RRM parameters, and the model may present large errors for unexpected situations in the system.  
This presented trade-off has a strong impact on the implementation of the AI Chipset in the system as explained in Section 3.1.2.
# AR Presentation of Team Members' Performance and Inner Status to Their Leader: A Comparative Study
## 4 Simulated AR in VR Application: MATB-II-Collab
### Experimental Platform  
We developed an experimental platform where a participant performs the role of a team leader facing virtual team members labeled _operators_. The number and placement of operators in the virtual environment can be set or modified depending on the experiment conducted. The system automatically creates tasks that the team leader must assign to operators. Cubes are used to represent the tasks the team leader must allocate to operators by throwing the task cube in the corresponding basket of each operator (see Figure 3). The system drops a set of tasks onto the table in front of the team leader. The team leader either picks up the tasks from the table by hand or assigns them to the different operators. The sets of tasks are in blocks of 5 to 20 cubes every 30 s.  
Figure 3: The virtual environment used in the experiment was populated by six virtual team members. The team leader’s table is in the center of the room, and the six black buckets are where tasks are allocated. The tasks are represented as cubes on the table.  
We modelled the operators to perform tasks at different degrees of success, which is represented by the score, a decimal value between 0 and 1 inclusive given to an operator after performing a task, depending on how stressed they are; Section 4.2 fully describes this model. The operators have humanoid avatars to tackle the same occlusion and placement issues we would have in an AR workspace. The experimental platform integrates the visualization techniques presented in Section 3, providing AR-displayed information around each operator, as feedback on the operator's status. This AR display provides data to inform the team leader's choice of an operator to assign a task to, thus optimizing the team's overall success. The experiment was designed for the participants to receive tasks over time to assign to the operators to achieve the greatest team performance. The team performance was calculated by how well the operators performed the given tasks. The team score is calculated as the sum of the scores of all the tasks performed by the operators. The experimental platform enabled two factors to be manipulated: (1) the presentation of indicators (textual and graphical) and (2) whether the _simulated stress_ feedback is shown or not shown.
## 4 Applications of the Transient Regime
### Analysis of Products Using Transient Cuffp Signals  
#### 4.2.1 Agri-Food Products  
Transient CWFP/CP-CWFP signals have been used to analyze agriculture and food products as a useful alternative to CPMG. The first application of CWFP transient signals, using 90\({}^{\circ}\) pulses, was to measure the fat content in beef samples. The measurement was based on the \(\left.1\right.\mathrm{M}_{\mathrm{ss}}\left|\right./\mathrm{M}_{0}\) ratio and the univariate statistic that shows a better correlation (\(r\) = 0.9) than T\({}_{2}\) (\(r\) = \(-\)0.25) to beef fat intramuscular content [26]. The full transient signals of beef samples were also analyzed using multivariate (chemometric) analysis. The multivariate analyses of CWFP have been used to classify beef samples based on animal sex or breed [26; 40], to predict water loss during beef cooking, and fat and moisture contents [41]. More recently, we showed that CWFP-T\({}_{1}\) can be a simple method to measure fat content in beef [39]. Figure 12 shows the relaxation profile of two beef samples with 20% and 62% fat contents. The peak between 300 and 400 ms was assigned to meat and fat, and the signal at 100 ms to fat only. Therefore, samples with higher fat contents show a large 100 ms peak. The ratio of the area of these two peaks has been used to measure fat content in beef with higher precision than standard CWFP sequences [39].  
The CWFP signals have been used to study the oilseed quality in static and flowing modes. CWFP and CP-CWFP signals have been used to measure oil viscosity and fatty acid composition using multivariate statistics [42; 43].  
Figure 11: Original (gray) and denoised (black) continuous wave free precession (CWFP-\(T_{1}\)) signals using a second-order Savitzky–Golay filter with 41-data-point windows of a MnSO\({}_{4}\) aqueous solution. The signal to noise ratio (SNR) of the original and smoothed data signals are 39 and 374, respectively.  
Figure 12: T\({}_{1}\) distribution time of obtained by the inverse Laplace transform of a CWFP-T\({}_{1}\) signal acquisition of beef samples with different fat contents: (**A**) 20% and (**B**) 62%. Adapted from Publication [39], Copyright (2014), with permission from Wiley.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs  
From a chemical point of view, MNP characterization involves determining their composition, structure and surface properties. To achieve such information, different analytical techniques can be employed. In the next paragraph many of them are briefly presented.  
Usually, MNPs contain magnetic materials such as iron oxide, magnetite, maghemite, but can contain other materials like cobalt, nickel, or alloys. Such compositions are typically coated with silica, surfactants, or biocompatible materials (e.g., polymers). These coatings provide biocompatibility, functionality, and stability [104].  
The surface properties, also called reactivity, are highly dependent on the surface area. This property controls the surface charge, interactions, oxidation state and heating efficiency of MNPs [104; 105].  
Chemical characterization ensures the quality and the consistency of the MNPs in achieving the desired specifications and properties required for particular applications. As for biomedical applications, safety and biocompatibility are parameters of crucial importance. Understanding reactivity and interactions between NPs and other entities is essential for such applications.
## 5 Experimental Validation of Quadrotors
### Measurement Model  
The three types of measurement are utilized to realize the CLAT purpose in this system setup: private absolute measurement from the onboard navigation system, cooperative relative range measurement from the UWB sensors, and angle measurement relative to the target from the downward cameras.  
Although the position information from the onboard navigation system is a fusion result from multiple sensors, in this part, it is treated as a private absolute measurement and is modeled as below:  
\[\mathbf{z}_{i,k}^{a}=H_{i}^{a}\mathbf{x}_{i,k}+\mathbf{v}_{i,k}^{a} \tag{34}\]  
The measurement noise is assumed to be subject to a zero-mean Gaussian distribution, \(\mathbf{v}_{i,k}^{a}\sim\mathcal{N}(\mathbf{0},\mathbf{R}^{a})\). Obviously, the absolute measurement model is a linear model. Therefore, we can substitute the inferred Jacobian matrix (defined in Section 3.2.1) with \(H_{i}^{a}\). In this paper, we assume that only a subset of all robots has access to the onboard navigation signal.  
The cooperative relative measurement between robots \(i\) and \(j\) from UWB is a scalar distance modeled as the following nonlinear model:  
\[z_{ij,k}^{c}=\|\mathbf{x}_{i,k}-\mathbf{x}_{j,k}\|_{d}+v_{i,k}^{c}, \tag{35}\]  
The measurement noise \(v_{ij,k}^{c}\) is assumed to be subject to the zero-mean Gaussian noise \(v_{ij,k}^{c}\sim\mathcal{N}(0,\mathbf{R}_{i}^{c})\).  
The target detection measurement is the position of the target on the captured image plane, and the measurement function is defined as  
\[\mathbf{z}_{it,k}^{t}=d_{f}\frac{\bar{\mathbf{x}}_{it,k}^{(1:2)}}{\bar{ \mathbf{x}}_{it,k}^{(3)}}+\mathbf{v}_{i,k}^{t} \tag{36}\]  
where \(d_{f}\) denotes the focal length in pixels, \(\bar{\mathbf{x}}_{it,k}\) denotes the position of the target in the camera coordinate, i.e., \(\bar{\mathbf{x}}_{it,k}=\mathcal{R}(\theta_{i,k})\mathcal{R}_{c,k}(\mathbf{x}_ {t,k}-\mathbf{x}_{i,k})\). \(\mathcal{R}(\theta_{i,k})\) denotes the yaw angle rotation matrix, and  
\[\mathcal{R}(\theta_{i,k})=\begin{bmatrix}\cos(\theta_{i,k})&\sin(\theta_{i,k} )&0\\ -\sin(\theta_{i,k})&\cos(\theta_{i,k})&0\\ 0&0&1\end{bmatrix}.\]\(\mathcal{R}_{c,k}\) denotes the roll and pitch rotation matrix and is assumed to be retrieved from the quadrotor navigation system, and therefore, it is treated as a known variable. The measurement noise is \(v_{i,k}^{t}\sim\mathcal{N}(\mathbf{0},\mathbf{R}_{i}^{t})\). In this paper, target detection is carried out using kernelized correlation filters (KCFs) [36] on the image plane.
# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 3 The Multiple Criteria Decision Analysis Framework
### Definition of the Entities and How They Relate  
In a GDM problem, there is a set of decision-makers \(DM=\{dm_{1},dm_{2},\ldots,dm_{n}\}\) that (together) have to choose one alternative \(a_{i}\) from a set of alternatives \(A=\{a_{1},a_{2},\ldots,a_{m}\}\). There is also a set of criteria \(C=\left\{c_{1},c_{2},\ldots,c_{p}\right\}\), and each criterion \(c_{j}\) is related to each alternative \(a_{i}\). A decision matrix \(D\) is composed by a relation between \(A\) and \(C\). **Definition 1**. : _Let \(D\) be the decision matrix \(D=A\times C\), where:_  
* \(C\) _is the set of criteria_ \(C=\left\{c_{1},c_{2},\ldots,c_{p}\right\}\)_,_ \(p>1\)_;_
* \(A\) _is the set of alternatives_ \(A=\left\{a_{1},a_{2},\ldots,a_{m}\right\}\)_,_ \(m>1\)_._  
**Rule 1**. : \(\forall a_{i}\in A,\forall c_{j}\in C,d_{c_{i_{j_{i}}}}\in D\), each criterion \(c_{j}\in C\) is related to each alternative \(a_{i}\in A\), so \(d_{c_{i_{j_{i}}}}\) is the value of criterion \(c_{j}\) in alternative \(a_{i}\). There cannot exist an alternative with values in criteria that are not defined in the problem. **Definition 2**. : _A criterion \(c_{j}=\left\{id_{c_{j}},type_{c_{j}},\text{great}_{c_{j}}\right\}\) consists of:_  
* \(\forall c_{j}\in C,j\in\left\{1,2,\ldots,p\right\}\)_;_
* \(id_{c_{j}}\) _is the identification of a particular criterion;_
* \(type_{c_{j}}\) _is the type of a particular criterion (Numeric, Boolean or Classificatory);_
* \(great_{c_{j}}\) _is the greatness associated with the criterion (Maximization, Minimization or Subjective)._  
The example of choosing a restaurant is composed of 9 criteria, defined as follows:  
* \(distance=\left\{c_{1},Numeric,Minimization\right\}\); //minutes
* \(transportExpenses=\left\{c_{2},Numeric,Minimization\right\}\); //dollars
* \(speedOfService=\left\{c_{3},\Numeric,Minimization\right\}\); //minutes
* \(qualityOfTheFood=\left\{c_{4},Classificatory,Maximization\right\}\); //good, ok or poor
* \(mealPrice=\left\{c_{5},\Numeric,Minimization\right\}\); //dollars
* \(atmosphere=\left\{c_{6},\Bolean,Maximization\right\}\); //true or false
* \(wine=\left\{c_{7},\Bolean,Maximization\right\}\); //true or false
* \(c_{i}usineStyle=\left\{c_{8},\Classificatory,Subjective\right\}\); //thai, italian or american
* \(healthy=\left\{c_{9},\Bolean,Maximization\right\}\). //true or false