esa-sceva/satcom-chunk-collection huggingface dataset:
# LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning
The LED nonlinearity, i.e., nonlinear power-current curve, can be accurately estimated by PBL regression and hence the adverse effect of LED nonlinearity can be efficiently compensated at the receiver side. Numerical simulations are performed to validate the feasibility of the proposed PBL-based LED nonlinearity estimation and compensation scheme in a 80-Mbit/s OFDM-based nonlinear VLC system and performance comparison between the proposed PBL-based scheme and the conventional TDA scheme is provided. The computational complexity of the proposed PBL-based LED nonlinearity estimation and compensation scheme is also analyzed. The rest of this paper is organized as follows. In Section 2, we first introduce the mathematical model of an OFDM-based nonlinear VLC system. Section 3 presents the principle of the proposed PBL-based LED nonlinearity estimation and compensation scheme. The simulation setup is described in Section 4 and the detailed results and discussions are provided in Section 5. Finally, Section 6 concludes the paper.
# Parameter Optimization for an Accurate Swept-Sine Identification Procedure of Nonlinear Systems
## 2 Materials and Methods  
A system for which the superposition principle is not valid in the relationship between input and output cannot be modeled through the usual methods of representing linear systems, and needs specific models. Historically, the model proposed by [PERSON] was the first among the representation models of nonlinear systems proposed in the technical literature [10; 11]. It can be interpreted as an extension of convolution, i.e., of the input-output relation representing linear systems. The Volterra model, even for minor nonlinearities, is characterized by a very large number of parameters, which makes it extremely complex to define their values that fit the representation of a specific nonlinear system to be identified.  
Block-structured models, such as the Wiener model, the Hammerstein model, and their combination, have been proposed as they imply a much lower number of parameters. The Wiener model consists of the sequence of a linear system with memory placed before the nonlinearity of the static type. The Hammerstein model is the reverse of the Wiener model. It consists of a static nonlinearity followed by a dynamic filter representing the memory of the system. The so-called \(N_{H}\)-order cascade of Hammerstein models also belong to this family, which consists of paralleling \(N_{H}\) structures, each consisting of a static nonlinearity followed by a dynamic filter. This is the structure shown in Figure 1, and it is the one to which the paper refers in the following section. The identification methods are procedures for defining the parameters of the chosen model in such a way that the model's input-output behavior is equivalent to that of the nonlinear physical system it is intended to represent.
## 2 Model Structure  
### Traffic Forecast Modeling  
The aviation sector presents unique challenges for traffic demand prediction due to its dynamic and highly variable nature. Traditional models often fail to capture the complex patterns of traffic flow across different geographic regions and time periods. To simulate the traffic forecasting environment of high-throughput satellites in the aviation sector, a model as shown in Figure 1 is proposed.  
This study primarily addresses the traffic conditions in the aviation sector by utilizing real-time air traffic data provided by external sources. The data include detailed information on flight paths, such as timestamps, geographic coordinates (latitude and longitude), and aircraft types. Aircraft operating on specific routes at any given moment are allocated to corresponding latitude and longitude grids based on their coordinates.  
Each flight data point is processed and mapped to the appropriate satellite beam coverage area using a predefined mapping file that links geographic coordinates with specific beam coverage areas. This ensures that each data point is accurately assigned to the correct beam. The traffic demand within each grid is calculated by aggregating the number of flights and scaling them according to the aircraft type and the time of day.  
Subsequently, the data for each grid are summed to compute the total traffic demand for each beam at each specific time interval. This results in a comprehensive dataset that encapsulates the total traffic demand for each beam at each moment, facilitating a further analysis and ensuring accurate and effective resource management in satellite communication.  
Figure 1: A schematic diagram of traffic forecasting in the aviation sector for high-throughput satellites.
## 2 Literature Review
### Mental Health Management Through Remote Healthcare Monitoring Systems
#### 2.1.1 Traditional Approaches and Their Limitations  
Traditional mental healthcare primarily relies on in-person consultations, which often face significant _limitations due to geographical, financial, and time constraints_. These barriers can lead to insufficient monitoring and delayed interventions, adversely affecting patient outcomes. Recent studies emphasize the need for more accessible and flexible care models to overcome these challenges [33]. In addition, traditional approaches are resource intensive, requiring specialized healthcare providers and facilities that may not be accessible to all patients. This lack highlights the need for more effective and inclusive care models.  
Another significant limitation is the lack of continuous monitoring, which makes it _difficult to detect early signs of deterioration and prompt intervention_. Technological advances are addressing this gap by providing real-time data for better patient management [34]. Access to evidence-based treatments is also limited in traditional psychological interventions. Internet and mobile-based interventions (IMIs) offer a solution by expanding access to effective treatments. Studies have shown significant benefits for therapist-guided IMIs in treating conditions, such as depression and anxiety, although self-guided interventions show mixed results [33].  
Traditional mental health services often struggle to manage demand and waiting lists due to limited resources. Innovative strategies, such as walk-in models and multidisciplinary care, are being explored, but further research is needed to confirm their effectiveness compared to traditional methods [35].  
Despite attempts to enhance traditional mental healthcare models, these approaches frequently fail to deliver timely, continuous, and accessible care to all patients. The dependence on resource-intensive, in-person consultations, combined with the lack of real-time monitoring, underscores the shortcomings of these conventional methods. Consequently, there is an increasing demand for innovative, technology-driven solutions that offer more flexible, efficient, and inclusive mental healthcare.
## 2 Development History of Traffic Prediction
### Deep Learning Methods
They proposed the traffic transformer using a transformer and GCN to model spatiotemporal correlations. Subsequently, many studies have innovated the transformer model, resulting in the so-called transformer families. In 2021, the informer [48] found wide application in traffic prediction. The informer is an advanced deep learning model for long-sequence time series prediction tasks. It enhances the traditional transformer architecture and is particularly suited for traffic prediction applications. The critical innovation of the informer lies in its Probabilistic Sparse (ProbSparse) self-attention mechanism, which selectively focuses on the most relevant time points for prediction, significantly reducing computational complexity and enabling more efficient handling of highly long sequences. Additionally, the informer employs a generative decoder design that supports one-shot prediction of long-term sequences, improving prediction efficiency and accuracy. With adaptive embedding layers, the informer can flexibly manage datasets of varying lengths while maintaining high prediction accuracy. In addition, the Propagation Delay-aware dynamic long-range transformer (PDFormer) proposed by [PERSON] et al. [49] is a transformer variant designed for traffic flow prediction in 2023. PDFormer captures dynamic spatial dependencies by employing dynamic spatial self-attention modules and graph masking techniques based on geographical and semantic proximity, and it integrates short-range and long-range spatial relationships. The model includes a feature transformation module to explicitly perceive delays and address time delay issues in the propagation of spatial information in traffic conditions. PDFormer also integrates temporal self-attention modules to capture long-term temporal dependencies. The development trajectory of time series forecasting algorithms based on deep learning is illustrated in Figure 2.
# Reinforcement Learning Based Resource Management for Network Slicing
## 5 Dynamic Resource Adjustment Algorithm
At each time slot \(t\), the agent monitors the states (i.e., the flow ratio of the slice, total resource demand, current market price ratio and time zone index) (Line 4). The possible action set \(\bar{A}\) considering the current resource state is determined for each trading interval (Line 6). An action \(a_{t}\) with the highest Q-value or a random action with a probability of \(\epsilon\) following the \(\epsilon\)-greedy policy can be taken from \(\bar{A}\) (Line 7). Based on the selected action, the environment changes the state and provides the agent with a reward (Line 8). The algorithm calculates the Q-value using the learning rate \(\alpha\) (Line 9). ```
1:Initialize \(Q(s,a)=0\), \(\forall s\in S\) and \(\forall a\in A\). 2:Initialize learning parameters \(\alpha\), \(\gamma\) and \(\epsilon\)
3:for each time slot \(t\)do
4: Monitor states \(s_{t}=[\varphi,D_{n,t},\rho,\hat{t}]\)
5:for each trading interval \(h\)do
6: Determine a possible action set \(\bar{A}\) by (10)
7: Select action \(a_{t}\) from \(\bar{A}\) using policy \(\pi\) derived from \(\epsilon\)-greedy by (13)
8: Take action \(a_{t}\) and observe \(v(s_{t},a_{t})\) and \(s_{t+1}\)
9: Update \(Q(s_{t},a_{t})\) by (15)
10:endfor
11:\(t\gets t+1\) and \(s_{t}\gets s_{t+1}\)
12:endfor
```  
**Algorithm 1** Resource adjustment algorithm using Q-learning.
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 2 Materials and Methods
### Model Development and Application
#### 2.2.1 Definition of the Services' Classification  
In this research, LIAM 2.0 classification of services [12] was improved to be more effective for spatial applications supporting policy making. The supplementary material includes the list of services, the correspondent spatial indicators' details, and the service codes (Table S1). LIAM 2.0 classification [8] was based on the Common International Classification of Ecosystem Services classification and is structured using three hierarchical levels: sections, divisions, and classes. LIAM 2.0 classification includes 53 final services, which can potentially support different and identifiable human activities, such as farming, commercial activity, housing, etc. A closer observation to the designed classification shows that services demand is restricted to three of the four demand typologies [56]: risk reduction, consumption, and direct use. This choice determines that the SBA gives benefits only to the use region located in the study area or nearby, since they are usually contiguous or relatively close, excluding from the study services having spatial relations at a broader scale. This choice helps the local policy makers' focus on the services benefiting the areas they can actually manage.
## 3 Results and Discussion  
### Confirmation of the Main Component in Each Raw Herbal Medicine  
For the selection of marker components for the quality evaluation of CSBPT, we analyzed and confirmed the main components of 12 raw herbs as follows: _prim-O_-glucosylclimifugin and 5-O-methylvisammioside from Saposhnikoviae Radix; imperatorin, isoimperatorin, oxypeucedanin, and oxypeucedanin hydrate from Angelicae Dahuricae; arctigenin and arcitinib from Forsythiae Fructus; platycodin D and platycodin D2 from Platycodonis Radix; baicalin, wogonoside, baicalein, and wogonin from Scutellariae Radix; ferulic acid from Cnidia Rhiz; pulegone from Schizonepetea Spica; gardenoside and geniposide from Gardeniae Fructus; berberine chloride, coptisine chloride, and palma- tine chloride from Coptidis Rhizoma; narirutin, naringin, hesperidin, and neohesperidin from Aurantii Fructus Immaturus; menthol and menthone from Menthae Herba; and glycyrrhizin, liquitiigenin, liquiritin apioside, and liquiritin from Glycyrrhizae Radix et Rhiz. The presence of these herbal medicines and constituents was confirmed by HPLC using a distilled water-acetonitrile mobile phase system containing formic acid (Supplementary Materials Figure S1).
## Materials and Methods
### Correlant Cherry Extraction Processes
#### 2.2.2 Ultrasound-Assisted Extraction (UAE)  
The UAE experiments were performed in an ultrasonic system (Vibra-Cell 75043, 750 W, Bioblock Scientific, Newtown, CT, USA) at constant ultrasound frequency (20 kHz). The identical amount of comelian cherry freeze-dried powder and volume of the solvent were used for all the experiments. An amount of 5 g of the powder of comelian cherry was mixed with 50 mL of a hydroalcoholic solution with a 60% ethanol concentration (\(v/v\)) and located in a 250 mL (internal diameter 4.8 cm) jacketed vessel. The temperature was maintained at a constant 40 \({}^{\circ}\)C by circulating cold water through the jacket. The ultrasonic processor was equipped with a 13 mm titanium probe that was submerged into the sample at a depth of 2 cm from the bottom of the vessel. The total delivered energy was registered, then the solid-liquid mixture was centrifuged at 5000 rpm at 9 \({}^{\circ}\)C for 20 min (Thermo Scientific SorvallTM ST16R, Hudson, New York, NY, USA), and the supernatant was collected and kept at 4 \({}^{\circ}\)C prior to analysis.  
A Box-Behnken experimental design was used, considering three experimental variables at three of levels each one: X\({}_{1}\), the amplitude (40, 60, and 80%), X\({}_{2}\), the sonication mode: pulsed/continuous (0.5, 0.75, and 1), and X\({}_{3}\), the extraction time (5, 10, and 15 min). At continuous sonication the pulses = 1, whereas the other settings indicate pulsed son ication (on/off switching times of the ultrasonic processor); as an example: a value of 0.5 means that the power discharges for 5 s and pauses for 5 s, or at the value of 0.75, which means that the power discharges for 7.5 s and pauses for 2.5 s. The main benefit of pulsed sonication over continuous sonication is a better control of the energy released to the sample, which involves a slower temperature increase [18]. The maximum amplitude of oscillation of the probe used in the experiments was 79 \(\upmu\)m, corresponding to the display setting value of 100%.  
Initially, the experimental plan consisted of 15 runs (including three repetitions at the central point of the experimental design (60% amplitude, 0.75 pulses, and 10 min) plus two additional experiments at 100% amplitude (in order to evaluate the effect of harsher working conditions). Three responses were studied, the TPC, the TFC, and the TAA content of the extracts, in order to find the combination of the experimental variables that maximizes the bioactive compound concentration recovery.
## 3 Proposed Approach
### Classical Machine Learning Models for DBMM  
#### 3.4.1 DBMM's Base Classifier Configurations and Parameters for SER  
We utilized several well-known classical machine learning models as base classifiers in this research: SVM, GNB, RF, 1D-CNN, and MLP. The specific configurations and parameters used for each model are outlined in Table 2.  
* SVM: A supervised learning algorithm that seeks to find the optimal hyperplane that maximally separates different classes in a high-dimensional feature space. In our implementation, we employ a linear kernel due to its computational efficiency and effectiveness for our specific feature set. The regularization parameter (C), a hyperparameter controlling the trade-off between maximizing the margin and minimizing classification error, is set to 1.0.
* RF: An ensemble learning method that constructs multiple decision trees during training. The final prediction is determined by aggregating the predictions of individual trees, typically by taking the mode of the classes (for classification) or the mean prediction (for regression). We utilize 150 decision trees in our RF model, a parameter chosen through empirical experimentation.
* GNB: A simple probabilistic classifier based on [PERSON] theorem with the \"naive\" assumption of feature independence. Despite this simplifying assumption, GNB often performs surprisingly well in practice, especially when the features are not strongly correlated.
* MLP: A class of feedforward artificial neural network composed of multiple layers of interconnected nodes. Our MLP model comprises three dense (fully connected) layers. The hidden layers utilize rectified linear unit (ReLU) activation functions, introducing non-linearity to model complex relationships. The output layer employs a softmax activation function to produce probability distributions over the multiple emotion classes.
* The 1D-CNN is trained for 350 epochs with a batch size of 256. The first hidden layer contains 320 neurons, while the second has 192. The output layer consists of a number of neurons equal to the number of target classes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classifier** & **Configuration/Parameters** & **Notes** \\ \hline SVM & Linear kernel & A linear kernel is often effective for \\  & Regularization parameter (C) = 1.0 & high-dimensional feature spaces in SER tasks. \\ \hline RF & 150 decision trees & The number of trees is a hyperparameter tuned for \\  & & optimal performance. \\ \hline GNB & Gaussian distribution & Assumes that features are conditionally independent \\  & & given the class, which is a simplification but often \\  & & works in practice for SER. \\ \hline MLP & 3 dense layers with ReLU activation & ReLU activation is used for non-linearity, and \\  & 320 neurons in layer 1 & softmax for multi-class probability distribution. \\  & & Softmax activation in the output layer & Epochs and batch size are hyperparameters \\  & 350 epochs, batch size = 256 & controlling training duration and update frequency. \\ \hline \end{tabular}
\end{table}
Table 2: Classifier parameters for SER.
###### Abstract
The performance and limitations of these devices for velocity estimation are not fully investigated in the specialized literature. In the current research, the objective is to fill this gap, in particular, by extending the available analysis to other constellations and using different devices. This study assesses the performance of the TDCP technique on three different devices: a Novatel GPS geodetic-grade receiver; a uBlox ZED-F9P high-sensitivity receiver; and a Xiaomi Mi8, equipped with a Broadcom BCM47755 chipset. The motivations of this study are related to the huge number of smartphones exploited by users for navigation purposes. The quality of the measurements collected by such devices is promising, and an analysis of the performance of low-cost devices exploiting advanced techniques and their comparison with higher-grade devices becomes interesting. The assessment was performed using real data collected with an ad hoc setup including the three devices mentioned above. The test duration was about one hour, and the analysis was performed only on L1 frequency. All the adopted devices were able to collect both Doppler shift and CP measurements; hence, velocity was determined with Doppler and TDCP approaches considering all the GNSSs, in single constellation mode. Moreover, further analysis involving the application of a RAIM-FDE algorithm was conducted with the aim of evaluating the contribution of an integrity module able to detect and exclude measurements affected by gross errors. The paper is organized as follows: in Section 2, the developed methodology is described, providing theoretical aspects related to Doppler-based velocity, TDCP-based velocity, RAIM-FDE, and the selected weighting strategy; Section 3 first provides the experimental setup description and the data analysis, in order to provide an overview of the test conditions, and in the second part, the results and the findings are presented; Section 4 concludes the paper.
# Improved Smart Pillow for Remote Health Care System
###### Abstract  
The outbreak of novel coronavirus (COVID-19) resulted in the clinical decision that reduced direct contact is optimal, especially for senior citizens residing in nursing homes. A smart pillow adapted for the Remote Healthcare System is presented in this paper, whose core is a Bluetooth (BT) host equipped with temperature and pressure sensors. The measurement of Core Body Temperature (CBT) from the perspective of heat transfer is first analyzed, with two proven effective methods introduced--classical Zero-Heat-Flux (ZHF) and Dual-Heat-Flux (DHF)--then finally the similarities between the Smart Pillow and ZHF are demonstrated. A pressure pad is inserted inside the pillow to detect occupancy and the specific position of the head on the pillow that meets clinical diagnostic needs. Furthermore, a real-time proactive monitoring mode is enabled for urgent warnings, which forces the pillow to keep detecting and reporting data in a defined time duration but results in rapid battery drain of the pillow. In this way, the system can detect the CBT and in-bed situation of the inhabitant without being physically present to determine critical measurements. Utility of this system can be extended to elderly people living alone in regional or remote areas, such that medical help can be dispatched as soon as possible in case of medical emergency.  
smart pillow; body temperature; pressure sensor; remote healthcare system; proactive monitoring; internet of things; society 5.0